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librarian-bots
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dataset_cards_with_metadata_with_embeddings

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paniniDot/sci_lay
paniniDot
2023-09-05T16:39:49Z
513
0
[ "task_categories:summarization", "size_categories:10K<n<100K", "size_categories:1K<n<10K", "source_datasets:original", "license:cc-by-4.0", "medical", "region:us" ]
[ "summarization" ]
2023-08-13T09:33:29Z
--- license: cc-by-4.0 task_categories: - summarization tags: - medical pretty_name: Sci Lay - Biomedic Articles Lay Summarization Dataset size_categories: - 10K<n<100K - 1K<n<10K source_datasets: - original dataset_info: - config_name: all features: - name: doi dtype: string - name: pmcid dtype: string - name: title dtype: string - name: plain_text dtype: string - name: technical_text dtype: string - name: full_text dtype: string - name: journal dtype: string - name: topics sequence: string - name: keywords sequence: string splits: - name: train num_examples: 35026 num_bytes: 1579515071 - name: validation num_examples: 4380 num_bytes: 197196187 - name: test num_examples: 4384 num_bytes: 198833964 - config_name: NC features: - name: doi dtype: string - name: pmcid dtype: string - name: title dtype: string - name: plain_text dtype: string - name: technical_text dtype: string - name: full_text dtype: string - name: journal dtype: string - name: topics sequence: string - name: keywords sequence: string splits: - name: train num_examples: 5549 num_bytes: 286453072 - name: validation num_examples: 694 num_bytes: 35652636 - name: test num_examples: 694 num_bytes: 35869803 - config_name: A features: - name: doi dtype: string - name: pmcid dtype: string - name: title dtype: string - name: plain_text dtype: string - name: technical_text dtype: string - name: full_text dtype: string - name: journal dtype: string - name: topics sequence: string - name: keywords sequence: string splits: - name: train num_examples: 3909 num_bytes: 128936951 - name: validation num_examples: 489 num_bytes: 1303884 - name: test num_examples: 489 num_bytes: 1303884 - config_name: PLGEN features: - name: doi dtype: string - name: pmcid dtype: string - name: title dtype: string - name: plain_text dtype: string - name: technical_text dtype: string - name: full_text dtype: string - name: journal dtype: string - name: topics sequence: string - name: keywords sequence: string splits: - name: train num_examples: 3087 num_bytes: 9651536 - name: validation num_examples: 386 num_bytes: 1195717 - name: test num_examples: 386 num_bytes: 1204735 - config_name: PLPAT features: - name: doi dtype: string - name: pmcid dtype: string - name: title dtype: string - name: plain_text dtype: string - name: technical_text dtype: string - name: full_text dtype: string - name: journal dtype: string - name: topics sequence: string - name: keywords sequence: string splits: - name: train num_examples: 2920 num_bytes: 9311936 - name: validation num_examples: 365 num_bytes: 1161792 - name: test num_examples: 365 num_bytes: 1148729 - config_name: PLCB features: - name: doi dtype: string - name: pmcid dtype: string - name: title dtype: string - name: plain_text dtype: string - name: technical_text dtype: string - name: full_text dtype: string - name: journal dtype: string - name: topics sequence: string - name: keywords sequence: string splits: - name: train num_examples: 2589 num_bytes: 149165851 - name: validation num_examples: 324 num_bytes: 1009541 - name: test num_examples: 324 num_bytes: 1013732 - config_name: PLNTD features: - name: doi dtype: string - name: pmcid dtype: string - name: title dtype: string - name: plain_text dtype: string - name: technical_text dtype: string - name: full_text dtype: string - name: journal dtype: string - name: topics sequence: string - name: keywords sequence: string splits: - name: train num_examples: 2289 num_bytes: 7958581 - name: validation num_examples: 286 num_bytes: 990392 - name: test num_examples: 287 num_bytes: 996549 - config_name: B features: - name: doi dtype: string - name: pmcid dtype: string - name: title dtype: string - name: plain_text dtype: string - name: technical_text dtype: string - name: full_text dtype: string - name: journal dtype: string - name: topics sequence: string - name: keywords sequence: string splits: - name: train num_examples: 1617 num_bytes: 57956055 - name: validation num_examples: 202 num_bytes: 547314 - name: test num_examples: 203 num_bytes: 537459 - config_name: I features: - name: doi dtype: string - name: pmcid dtype: string - name: title dtype: string - name: plain_text dtype: string - name: technical_text dtype: string - name: full_text dtype: string - name: journal dtype: string - name: topics sequence: string - name: keywords sequence: string splits: - name: train num_examples: 1181 num_bytes: 37682107 - name: validation num_examples: 148 num_bytes: 393826 - name: test num_examples: 148 num_bytes: 390039 - config_name: PLB features: - name: doi dtype: string - name: pmcid dtype: string - name: title dtype: string - name: plain_text dtype: string - name: technical_text dtype: string - name: full_text dtype: string - name: journal dtype: string - name: topics sequence: string - name: keywords sequence: string splits: - name: train num_examples: 896 num_bytes: 54106804 - name: validation num_examples: 112 num_bytes: 350955 - name: test num_examples: 113 num_bytes: 352922 - config_name: CB features: - name: doi dtype: string - name: pmcid dtype: string - name: title dtype: string - name: plain_text dtype: string - name: technical_text dtype: string - name: full_text dtype: string - name: journal dtype: string - name: topics sequence: string - name: keywords sequence: string splits: - name: train num_examples: 867 num_bytes: 43533134 - name: validation num_examples: 108 num_bytes: 5664682 - name: test num_examples: 109 num_bytes: 172812 - config_name: SD features: - name: doi dtype: string - name: pmcid dtype: string - name: title dtype: string - name: plain_text dtype: string - name: technical_text dtype: string - name: full_text dtype: string - name: journal dtype: string - name: topics sequence: string - name: keywords sequence: string splits: - name: train num_examples: 725 num_bytes: 23671697 - name: validation num_examples: 91 num_bytes: 3033467 - name: test num_examples: 91 num_bytes: 2972947 - config_name: MBIO features: - name: doi dtype: string - name: pmcid dtype: string - name: title dtype: string - name: plain_text dtype: string - name: technical_text dtype: string - name: full_text dtype: string - name: journal dtype: string - name: topics sequence: string - name: keywords sequence: string splits: - name: train num_examples: 607 num_bytes: 1602641 - name: validation num_examples: 76 num_bytes: 203737 - name: test num_examples: 76 num_bytes: 200707 - config_name: C features: - name: doi dtype: string - name: pmcid dtype: string - name: title dtype: string - name: plain_text dtype: string - name: technical_text dtype: string - name: full_text dtype: string - name: journal dtype: string - name: topics sequence: string - name: keywords sequence: string splits: - name: train num_examples: 6782 num_bytes: 242721690 - name: validation num_examples: 848 num_bytes: 30735056 - name: test num_examples: 848 num_bytes: 31018214 - config_name: OTHER features: - name: doi dtype: string - name: pmcid dtype: string - name: title dtype: string - name: plain_text dtype: string - name: technical_text dtype: string - name: full_text dtype: string - name: journal dtype: string - name: topics sequence: string - name: keywords sequence: string splits: - name: train num_examples: 2008 num_bytes: 89866504 - name: validation num_examples: 251 num_bytes: 11316433 - name: test num_examples: 251 num_bytes: 11564599 config_names: - all - NC - A - PLGEN - PLPAT - PLCB - PLNTD - B - I - PLB - CB - SD - MBIO - C - OTHER --- # Dataset Card for Sci Lay ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Sci Lay](https://github.com/paniniDot/summarization-model) - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** [Mattia Panni](mailto:[email protected]) ### Dataset Summary SCILAY comprises 43,790 instances, each representing a scientific article in the biomedical domain. Each instance in the dataset includes the following components: - plain_text: Containing a plain language summary of the scientific article. This section is written in a simple and accessible language, and is intended to be understandable by a wide audience. - technical_text: This section contains the abstract of the scientific article. It provides a detailed and technical description of the research conducted in the article. - full_text: This section contains the complete article of the scientific research. In addition to the textual content, each instance is associated with the following metadata: - Keywords: Keywords that capture the main topics and themes addressed in the article. - Journal: The journal in which the article is published, providing context about the source of the research. - DOI (Digital Object Identifier): A unique identifier for the article, facilitating easy referencing. The main objective of the SCILAY dataset is to support the development and evaluation of text summarization models that can effectively simplify complex scientific language while retaining the essential information. Each article is published by a scientific journal. There are fifteen such journal classifications: - NC: Nature Communications - A: Animals : an Open Access Journal from MDPI - PLGEN: PLoS Genetics - PLPAT: PLoS Pathogens - PLCB: PLoS Computational Biology - PLNTD: PLoS Neglected Tropical Diseases - B: Biology - I: Insects - PLB: PLoS Biology - CB: Communications Biology - SD: Scientific Data - MBIO: mBio - C: Cancers - OTHER: which includes additional journals that taken individually would not have contributed sufficient instances Current defaults are 1.0.0 version (cased raw strings) and 'all' journals: ```python from datasets import load_dataset ds = load_dataset("paniniDot/sci_lay") # default is 'all' journals ds = load_dataset("paniniDot/sci_lay", "all") # the same as above ds = load_dataset("paniniDot/sci_lay", "NC") # only 'NC' journal (Nature Communications) ds = load_dataset("paniniDot/sci_lay", journals=["NC", "A"]) ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English ## Dataset Structure ### Data Instances Each instance contains a set of `doi`, `pmcid`, `plain_text`, `technical_text`, `journal`, `topics`, `keywords`. Each of which was extracted by scraping articles in XML and HTML format. ``` { 'doi': '10.3390/ani12040445', 'pmcid': 'PMC8868321', 'plain_text': 'PPP3CA is one of the candidate genes for goat reproduction, but no studies have been carried out yet. Therefore, the purpose of this study was to determine the associations between copy number variations in the goat PPP3CA gene and litter size and semen quality in goats, including Shaanbei white cashmere goats (SBWC) (n = 353) and Guizhou Heima (GZHM) goats (n = 64). Based on the association analysis, the results showed that only CNV1 (copy number variation 1) and CNV2 (copy number variation 2) were distinctly related to the first-birth litter size in female goats (p = 7.6802 × 10−11; p = 5.0895 × 10−9), and they were also significantly associated with the semen quality of SBWC goats (p < 0.05). These findings prove that the PPP3CA gene plays an important role in reproduction traits in goats.', 'technical_text': 'Copy number variations (CNVs) have many forms of variation structure, and they play an important role in the research of variety diversity, biological evolution and disease correlation. Since CNVs have a greater impact on gene regulation and expression, more studies are being finalized on CNVs in important livestock and poultry species. The protein phosphatase 3 catalytic subunit alpha (PPP3CA) is a key candidate gene involved in the goat fecundity trait, and has important effects on precocious puberty, estrogen signal transduction pathways and oocyte meiosis. Additionally, PPP3CA also has a dephosphorylation effect in the process of spermatogonial stem cell meiosis and spermatogenesis. So far, there is no research on the relationship between the copy number variations of the PPP3CA gene and reproduction traits. Therefore, the purpose of this study was to determine the association between copy number variations in the goat PPP3CA gene and litter size and semen quality in Shaanbei white cashmere goats (SBWC) (n = 353) and Guizhou Heima goats (n = 64). Based on the association analysis, the results showed that only CNV1 and CNV2 within the PPP3CA gene were distinctly related to the first-birth litter size in female goats (p = 7.6802 × 10−11; p = 5.0895 × 10−9, respectively) and they were also significantly associated with the semen quality of SBWC goats (p < 0.05). In addition, individuals with Loss genotypes demonstrated better phenotypic performance compared to those with other types. Therefore, CNV1 and CNV2 of the PPP3CA gene are potentially useful for breeding, as they are linked to important goat reproduction traits.', 'full_text': '...' 'journal': 'Animals : an Open Access Journal from MDPI', 'topics': [ 'Article' ], 'keywords': [ 'goat', 'PPP3CA', 'copy number variation (CNV)', 'litter size', 'semen quality' ] } ``` ### Data Fields - `doi`: (Digital Object Identifier). It is a unique alphanumeric string assigned to a digital document, such as a research paper, article, or dataset. Not all istances have it. - `pmcid`: A unique identifier in the [PubMed Central library](https://www.ncbi.nlm.nih.gov/pmc/) database. Not all istances have it. - `plain_text`: The summary of the article in plain english. - `technical_text`: The abstract of the article. - `full_text`: The complete article. - `journal`: The journal which published the article. - `topics`: An object containing the types in which the article is classified (i.e. Research Article, Review, ecc.). Not all istances have it. - `keywords`: An object containing the keywords of the article. Not all istances have it. ### Data Splits | | train | validation | test | |-------|-------|------------|------| | all | 35026 | 4380 | 4384 | | NC | 5549 | 694 | 694 | | A | 3909 | 489 | 489 | | PLGEN | 3087 | 386 | 386 | | PLPAT | 2920 | 365 | 365 | | PLCB | 2589 | 324 | 324 | | PLNTD | 2289 | 286 | 287 | | B | 1617 | 202 | 203 | | I | 1181 | 148 | 148 | | PLB | 896 | 112 | 113 | | CB | 867 | 108 | 109 | | SD | 725 | 91 | 91 | | MBIO | 607 | 76 | 76 | | C | 6782 | 848 | 848 | | OTHER | 2008 | 251 | 251 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed]
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chiyuanhsiao/ML2021_HungyiLee_Corpus
chiyuanhsiao
2023-11-14T08:00:36Z
513
1
[ "region:us" ]
null
2023-11-14T06:05:29Z
--- configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string splits: - name: test num_bytes: 1086428751.563 num_examples: 31181 download_size: 1086479549 dataset_size: 1086428751.563 --- # Dataset Card for "debug" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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GEM/turku_paraphrase_corpus
GEM
2022-10-24T15:29:45Z
512
0
[ "task_categories:other", "annotations_creators:expert-created", "language_creators:unknown", "multilinguality:unknown", "size_categories:unknown", "source_datasets:original", "language:fi", "license:cc-by-sa-4.0", "paraphrasing", "region:us" ]
[ "other" ]
2022-03-02T23:29:22Z
--- annotations_creators: - expert-created language_creators: - unknown language: - fi license: - cc-by-sa-4.0 multilinguality: - unknown size_categories: - unknown source_datasets: - original task_categories: - other task_ids: [] pretty_name: turku_paraphrase_corpus tags: - paraphrasing --- # Dataset Card for GEM/turku_paraphrase_corpus ## Dataset Description - **Homepage:** https://turkunlp.org/paraphrase.html - **Repository:** https://github.com/TurkuNLP/Turku-paraphrase-corpus - **Paper:** https://aclanthology.org/2021.nodalida-main.29/ - **Leaderboard:** N/A - **Point of Contact:** Jenna Kanerva, Filip Ginter ### Link to Main Data Card You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/turku_paraphrase_corpus). ### Dataset Summary This is a Finnish paraphrase corpus which consists of pairs of text passages, where a typical passage is about a sentence long. It can be used to either identify or generate paraphrases. You can load the dataset via: ``` import datasets data = datasets.load_dataset('GEM/turku_paraphrase_corpus') ``` The data loader can be found [here](https://huggingface.co/datasets/GEM/turku_paraphrase_corpus). #### website [Website](https://turkunlp.org/paraphrase.html) #### paper [ACL Anthology](https://aclanthology.org/2021.nodalida-main.29/) #### authors Jenna Kanerva, Filip Ginter, Li-Hsin Chang, Iiro Rastas, Valtteri Skantsi, Jemina Kilpeläinen, Hanna-Mari Kupari, Aurora Piirto, Jenna Saarni, Maija Sevón, Otto Tarkka (TurkuNLP / University of Turku) ## Dataset Overview ### Where to find the Data and its Documentation #### Webpage <!-- info: What is the webpage for the dataset (if it exists)? --> <!-- scope: telescope --> [Website](https://turkunlp.org/paraphrase.html) #### Download <!-- info: What is the link to where the original dataset is hosted? --> <!-- scope: telescope --> [Github](https://github.com/TurkuNLP/Turku-paraphrase-corpus) #### Paper <!-- info: What is the link to the paper describing the dataset (open access preferred)? --> <!-- scope: telescope --> [ACL Anthology](https://aclanthology.org/2021.nodalida-main.29/) #### BibTex <!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. --> <!-- scope: microscope --> ``` @inproceedings{kanerva-etal-2021-finnish, title = {Finnish Paraphrase Corpus}, author = {Kanerva, Jenna and Ginter, Filip and Chang, Li-Hsin and Rastas, Iiro and Skantsi, Valtteri and Kilpel{\"a}inen, Jemina and Kupari, Hanna-Mari and Saarni, Jenna and Sev{\'o}n, Maija and Tarkka, Otto}, booktitle = {Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa'21)}, year = {2021}, publisher = {Link{\"o}ping University Electronic Press, Sweden}, url = {https://aclanthology.org/2021.nodalida-main.29}, pages = {288--298} } ``` #### Contact Name <!-- quick --> <!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. --> <!-- scope: periscope --> Jenna Kanerva, Filip Ginter #### Contact Email <!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. --> <!-- scope: periscope --> [email protected], [email protected] #### Has a Leaderboard? <!-- info: Does the dataset have an active leaderboard? --> <!-- scope: telescope --> no ### Languages and Intended Use #### Multilingual? <!-- quick --> <!-- info: Is the dataset multilingual? --> <!-- scope: telescope --> no #### Covered Dialects <!-- info: What dialects are covered? Are there multiple dialects per language? --> <!-- scope: periscope --> written standard language, spoken language #### Covered Languages <!-- quick --> <!-- info: What languages/dialects are covered in the dataset? --> <!-- scope: telescope --> `Finnish` #### License <!-- quick --> <!-- info: What is the license of the dataset? --> <!-- scope: telescope --> cc-by-sa-4.0: Creative Commons Attribution Share Alike 4.0 International #### Intended Use <!-- info: What is the intended use of the dataset? --> <!-- scope: microscope --> Paraphrase classification, paraphrase generation #### Primary Task <!-- info: What primary task does the dataset support? --> <!-- scope: telescope --> Paraphrasing #### Communicative Goal <!-- quick --> <!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. --> <!-- scope: periscope --> The corpus provides naturally occurring Finnish paraphrases striving for low lexical overlap, thus supporting many different downstream applications requiring language understanding. ### Credit #### Curation Organization Type(s) <!-- info: In what kind of organization did the dataset curation happen? --> <!-- scope: telescope --> `academic` #### Curation Organization(s) <!-- info: Name the organization(s). --> <!-- scope: periscope --> University of Turku #### Dataset Creators <!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). --> <!-- scope: microscope --> Jenna Kanerva, Filip Ginter, Li-Hsin Chang, Iiro Rastas, Valtteri Skantsi, Jemina Kilpeläinen, Hanna-Mari Kupari, Aurora Piirto, Jenna Saarni, Maija Sevón, Otto Tarkka (TurkuNLP / University of Turku) #### Funding <!-- info: Who funded the data creation? --> <!-- scope: microscope --> The Turku paraphrase corpus project was funded by the Academy of Finland, as well as the European Language Grid project through its open call for pilot projects. The European Language Grid project has received funding from the European Union’s Horizon 2020 Research and Innovation programme under Grant Agreement no. 825627 (ELG). #### Who added the Dataset to GEM? <!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. --> <!-- scope: microscope --> Jenna Kanerva, Filip Ginter (TurkuNLP / University of Turku) ### Dataset Structure #### Data Fields <!-- info: List and describe the fields present in the dataset. --> <!-- scope: telescope --> The dataset consist of pairs of text passages, where a typical passage is about a sentence long, however, a passage may also be longer or shorter than a sentence. Thus, each example include two text passages (string), a manually annotated label to indicate the paraphrase type (string), and additional metadata. The dataset include three different `modes`, plain, classification, and generation. The `plain` mode loads the original data without any additional preprocessing or transformations, while the `classification` mode directly builds the data in a form suitable for training a paraphrase classifier, where each example is doubled in the data with different directions (text1, text2, label) --> (text2, text1, label) taking care of the label flipping as well if needed (paraphrases with directionality flag < or >). In the `generation` mode, the examples are preprocessed to be directly suitable for paraphrase generation task. In here, paraphrases not suitable for generation are discarded (negative, and highly context-dependent paraphrases), and directional paraphrases are provided so that the generation goes from more detailed passage to the more general one in order to prevent model hallucination (i.e. model learning to introduce new information). The rest of the paraphrases are provided in both directions (text1, text2, label) --> (text2, text1, label). Each pair in `plain` and `classification` mode will include fields: `gem_id`: Identifier of the paraphrase pair (string) `goeswith`: Identifier of the document from which the paraphrase was extracted, can be `not available` in case the source of the paraphrase is not from document-structured data (string) `fold`: 0-99, data split into 100 parts respecting document boundaries, you can use this e.g. to implement crossvalidation safely as all paraphrases from one document are in one fold (int) `text1`: First paraphrase passage (string) `text2`: Second paraphrase passage (string) `label`: Manually annotated labels (string) `binary_label`: Label turned into binary with values `positive` (paraphrase) and `negative` (not-paraphrase) (string) `is_rewrite`: Indicator whether the example is human produced rewrite or naturally occurring paraphrase (bool) Each pair in `generation` mode will include the same fields expect `text1` and `text2` are renamed to `input` and `output` in order to indicate the generation direction. Thus the fields are: `gem_id`: Identifier of the paraphrase pair (string) `goeswith`: Identifier of the document from which the paraphrase was extracted, can be `not available` in case the source of the paraphrase is not from document-structured data (string) `fold`: 0-99, data split into 100 parts respecting document boundaries, you can use this e.g. to implement crossvalidation safely as all paraphrases from one document are in one fold (int) `input`: The input paraphrase passage for generation (string) `output`: The output paraphrase passage for generation (string) `label`: Manually annotated labels (string) `binary_label`: Label turned into binary with values `positive` (paraphrase) and `negative` (not-paraphrase) (string) `is_rewrite`: Indicator whether the example is human produced rewrite or naturally occurring paraphrase (bool) #### Example Instance <!-- info: Provide a JSON formatted example of a typical instance in the dataset. --> <!-- scope: periscope --> ``` { 'gem_id': 'gem-turku_paraphrase_corpus-train-15', 'goeswith': 'episode-02243', 'fold': 0, 'text1': 'Mitä merkitystä sillä on?', 'text2': 'Mitä väliä sillä edes on?', 'label': '4', 'binary_label': 'positive', 'is_rewrite': False } ``` #### Data Splits <!-- info: Describe and name the splits in the dataset if there are more than one. --> <!-- scope: periscope --> The corpus include 3 splits: train, validation, and test. #### Splitting Criteria <!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. --> <!-- scope: microscope --> The data is split randomly into the three section with a restriction of all paraphrases from the same document (movie, TV episode, news article, student translation, or exam question) being in the same section. All splits are manually annotated. ## Dataset in GEM ### Rationale for Inclusion in GEM #### Why is the Dataset in GEM? <!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? --> <!-- scope: microscope --> This dataset provides a large amount of high quality (manually collected and verified) paraphrases for Finnish. #### Similar Datasets <!-- info: Do other datasets for the high level task exist? --> <!-- scope: telescope --> yes #### Unique Language Coverage <!-- info: Does this dataset cover other languages than other datasets for the same task? --> <!-- scope: periscope --> no #### Ability that the Dataset measures <!-- info: What aspect of model ability can be measured with this dataset? --> <!-- scope: periscope --> natural language understanding, language variation ### GEM-Specific Curation #### Modificatied for GEM? <!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? --> <!-- scope: telescope --> yes #### GEM Modifications <!-- info: What changes have been made to he original dataset? --> <!-- scope: periscope --> `data points modified` #### Modification Details <!-- info: For each of these changes, described them in more details and provided the intended purpose of the modification --> <!-- scope: microscope --> Data structure is slightly simplified, and the release provides ready made transformations into two tasks (paraphrase classification and generation), where some data instances are doubled with different direction, and some are discarded as not being suitable for generation (e.g. negatives). #### Additional Splits? <!-- info: Does GEM provide additional splits to the dataset? --> <!-- scope: telescope --> no ### Getting Started with the Task ## Previous Results ### Previous Results #### Measured Model Abilities <!-- info: What aspect of model ability can be measured with this dataset? --> <!-- scope: telescope --> natural language understanding, language variation #### Previous results available? <!-- info: Are previous results available? --> <!-- scope: telescope --> yes #### Other Evaluation Approaches <!-- info: What evaluation approaches have others used? --> <!-- scope: periscope --> F-score in paraphrase classification ## Dataset Curation ### Original Curation #### Original Curation Rationale <!-- info: Original curation rationale --> <!-- scope: telescope --> The dataset is fully manually annotated. The dataset strives for interesting paraphrases with low lexical overlap, thus the annotation is two fold. First the paraphrases are manually extracted from two related documents, where the annotators are instructed to extract only interesting paraphrases. In the second phrase, all extracted paraphrases are manually labeled given the annotation scheme. The annotation scheme is: 4 : paraphrase in all reasonably possible contexts 3 : paraphrase in the given document contexts, but not in general 2 : related but not paraphrase During annotation also labels 1 (unrelated) and x (skip, e.g. wrong language) were used, however, the insignificant amount of examples annotated with these labels were discarded from the released corpus. The following flags are annotated to label 4 paraphrases: < : txt1 is more general than txt2; txt2 is more specific than txt1 (directional paraphrase where txt2 can be replaced with txt1 in all contexts but not to the other direction) > : txt2 is more general than txt1; txt1 is more specific than txt2 (directional paraphrase where txt1 can be replaced with txt2 in all contexts but not to the other direction) i : minor traceable difference (differing in terms of grammatical number or case, 'this' vs 'that', etc.) s : style or strength difference (e.g. equivalent meaning, but one of the statements substantially more colloquial than the other) For paraphrases where the annotated label was something else than label 4 without any flags, the annotators had an option to rewrite the text passages so that the rewritten paraphrase pair formed label 4 (universal) paraphrase. This was used for cases where simple edit would turn e.g. contextual or directional paraphrase into universal one. For the rewritten examples, both the original and the rewritten pairs are available with corresponding labels annotated. #### Communicative Goal <!-- info: What was the communicative goal? --> <!-- scope: periscope --> Representing text passages with identical meaning but different surface realization. #### Sourced from Different Sources <!-- info: Is the dataset aggregated from different data sources? --> <!-- scope: telescope --> yes #### Source Details <!-- info: List the sources (one per line) --> <!-- scope: periscope --> movie and TV series subtitles (82%) news articles (9%) discussion forum messages (8%) university translation exercises (1%) university course essays and exams (<1%) ### Language Data #### How was Language Data Obtained? <!-- info: How was the language data obtained? --> <!-- scope: telescope --> `Found`, `Other` #### Where was it found? <!-- info: If found, where from? --> <!-- scope: telescope --> `Multiple websites`, `Offline media collection`, `Other` #### Language Producers <!-- info: What further information do we have on the language producers? --> <!-- scope: microscope --> The movie and TV series subtitles are extracted from OPUS OpenSubtitles2018 collection, which is based on data from [OpenSubtitles](http://www.opensubtitles.org/). The news articles are collected from two Finnish news sites, YLE and HS, during years 2017-2020. Discussion forum messages are obtained from the Finnish Suomi24 discussion forum released for academic use (http://urn.fi/urn:nbn:fi:lb-2020021801). University translation exercises, essays and exams are collected during the project. #### Data Validation <!-- info: Was the text validated by a different worker or a data curator? --> <!-- scope: telescope --> validated by data curator #### Was Data Filtered? <!-- info: Were text instances selected or filtered? --> <!-- scope: telescope --> not filtered ### Structured Annotations #### Additional Annotations? <!-- quick --> <!-- info: Does the dataset have additional annotations for each instance? --> <!-- scope: telescope --> expert created #### Number of Raters <!-- info: What is the number of raters --> <!-- scope: telescope --> 2<n<10 #### Rater Qualifications <!-- info: Describe the qualifications required of an annotator. --> <!-- scope: periscope --> Members of the TurkuNLP research group, native speakers of Finnish, each annotator has a strong background in language studies by having an academic degree or ongoing studies in a field related to languages or linguistics. #### Raters per Training Example <!-- info: How many annotators saw each training example? --> <!-- scope: periscope --> 1 #### Raters per Test Example <!-- info: How many annotators saw each test example? --> <!-- scope: periscope --> 1 #### Annotation Service? <!-- info: Was an annotation service used? --> <!-- scope: telescope --> no #### Annotation Values <!-- info: Purpose and values for each annotation --> <!-- scope: microscope --> 1. Manual extraction of interesting paraphrases from two related documents. 2. Manual labeling of each extracted paraphrase based on the given annotation scheme, e.g. distinguishing contextual and universal paraphrases, marking style or strength differences, etc. #### Any Quality Control? <!-- info: Quality control measures? --> <!-- scope: telescope --> validated by another rater #### Quality Control Details <!-- info: Describe the quality control measures that were taken. --> <!-- scope: microscope --> Partial double annotation, double annotation batches are assigned regularly in order to monitor annotation consistency. In double annotation, one annotator first extracts the candidate paraphrases, and these candidates are assigned to two different annotators, who does the label annotation independently from each other. Afterwards, the label annotations are merged, and conflicting labels are resolved together with the whole annotation team. ### Consent #### Any Consent Policy? <!-- info: Was there a consent policy involved when gathering the data? --> <!-- scope: telescope --> yes #### Consent Policy Details <!-- info: What was the consent policy? --> <!-- scope: microscope --> The corpus is mostly based on public/open data. For other data sources (student material), the licensing was agreed with the data providers during the collection. ### Private Identifying Information (PII) #### Contains PII? <!-- quick --> <!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? --> <!-- scope: telescope --> likely #### Categories of PII <!-- info: What categories of PII are present or suspected in the data? --> <!-- scope: periscope --> `generic PII` #### Any PII Identification? <!-- info: Did the curators use any automatic/manual method to identify PII in the dataset? --> <!-- scope: periscope --> no identification ### Maintenance #### Any Maintenance Plan? <!-- info: Does the original dataset have a maintenance plan? --> <!-- scope: telescope --> no ## Broader Social Context ### Previous Work on the Social Impact of the Dataset #### Usage of Models based on the Data <!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? --> <!-- scope: telescope --> no ### Impact on Under-Served Communities #### Addresses needs of underserved Communities? <!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). --> <!-- scope: telescope --> no ### Discussion of Biases #### Any Documented Social Biases? <!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. --> <!-- scope: telescope --> no ## Considerations for Using the Data ### PII Risks and Liability #### Potential PII Risk <!-- info: Considering your answers to the PII part of the Data Curation Section, describe any potential privacy to the data subjects and creators risks when using the dataset. --> <!-- scope: microscope --> None ### Licenses #### Copyright Restrictions on the Dataset <!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? --> <!-- scope: periscope --> `open license - commercial use allowed` #### Copyright Restrictions on the Language Data <!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? --> <!-- scope: periscope --> `open license - commercial use allowed` ### Known Technical Limitations
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thaisum
null
2022-11-18T21:51:46Z
511
8
[ "task_categories:summarization", "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:th", "license:mit", "region:us" ]
[ "summarization", "text-generation", "fill-mask" ]
2022-03-02T23:29:22Z
--- annotations_creators: - no-annotation language_creators: - found language: - th license: - mit multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - summarization - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: null pretty_name: ThaiSum dataset_info: features: - name: title dtype: string - name: body dtype: string - name: summary dtype: string - name: type dtype: string - name: tags dtype: string - name: url dtype: string config_name: thaisum splits: - name: train num_bytes: 2945472406 num_examples: 358868 - name: validation num_bytes: 118437310 num_examples: 11000 - name: test num_bytes: 119496704 num_examples: 11000 download_size: 647582078 dataset_size: 3183406420 --- # Dataset Card for ThaiSum ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/nakhunchumpolsathien/ThaiSum - **Repository:** https://github.com/nakhunchumpolsathien/ThaiSum - **Paper:** - **Leaderboard:** - **Point of Contact:** https://github.com/nakhunchumpolsathien ### Dataset Summary ThaiSum is a large-scale corpus for Thai text summarization obtained from several online news websites namely Thairath, ThaiPBS, Prachathai, and The Standard. This dataset consists of over 350,000 article and summary pairs written by journalists. ### Supported Tasks and Leaderboards summarization, language modeling ### Languages Thai ## Dataset Structure ### Data Instances ``` {'body': 'กีเก ซานเชซ ฟลอเรส\xa0 กุนซือเลือดกระทิงของทีมวัตฟอร์ด\xa0 เมินประเด็นจุดโทษปัญหาในเกมพรีเมียร์ลีก อังกฤษ นัดที่แตนอาละวาดเปิดบ้านพ่าย คริสตัล พาเลซ 0-1ชี้ทีมของเขาเล่นไม่ดีพอเอง,สำนักข่าวต่างประเทศรายงานวันที่ 27 ก.ย. ว่า กีเก ซานเชซ ฟลอเรส\xa0 ผู้จัดการทีมชาวสเปน ของ แตนอาละวาด วัตฟอร์ด\xa0 ยอมรับทีมของเขาเล่นได้ไม่ดีพอเอง ในเกมพรีเมียร์ลีก อังกฤษ นัดเปิดบ้านพ่าย อินทรีผงาด คริสตัล พาเลซ 0-1 เมื่อคืนวันอาทิตย์ที่ผ่านมา,เกมนี้จุดเปลี่ยนมาอยู่ที่การได้จุดโทษในช่วงครึ่งหลังของ คริสตัล พาเลซ ซึ่งไม่ค่อยชัดเจนเท่าไหร่ว่า อัลลัน นียอม นั้นไปทำฟาล์วใส่ วิลฟรีด ซาฮา ในเขตโทษหรือไม่ แต่ผู้ตัดสินก็ชี้เป็นจุดโทษ ซึ่ง โยอัน กาบาย สังหารไม่พลาด และเป็นประตูชัยช่วยให้ คริสตัล พาเลซ เอาชนะ วัตฟอร์ด ไป 1-0 และเป็นการพ่ายแพ้ในบ้านนัดแรกของวัตฟอร์ดในฤดูกาลนี้อีกด้วย,ฟลอเรส กล่าวว่า มันเป็นเรื่องยากในการหยุดเกมรุกของคริสตัล พาเลซ ซึ่งมันอึดอัดจริงๆสำหรับเรา เราเล่นกันได้ไม่ดีนักในตอนที่ได้ครองบอล เราต้องเล่นทางริมเส้นให้มากกว่านี้ เราไม่สามารถหยุดเกมสวนกลับของพวกเขาได้ และแนวรับของเราก็ยืนไม่เป็นระเบียบสักเท่าไหร่ในช่วงครึ่งแรก ส่วนเรื่องจุดโทษการตัดสินใจขั้นสุดท้ายมันอยู่ที่ผู้ตัดสิน ซึ่งมันเป็นการตัดสินใจที่สำคัญ ผมเองก็ไม่รู้ว่าเขาตัดสินถูกหรือเปล่า บางทีมันอาจเป็นจุดที่ตัดสินเกมนี้เลย แต่เราไม่ได้แพ้เกมนี้เพราะจุดโทษ เราแพ้ในวันนี้เพราะเราเล่นไม่ดีและคริสตัล พาเลซ เล่นดีกว่าเรา เราไม่ได้มีฟอร์มการเล่นที่ดีในเกมนี้เลย', 'summary': 'กีเก ซานเชซ ฟลอเรส กุนซือเลือดกระทิงของทีมวัตฟอร์ด เมินประเด็นจุดโทษปัญหาในเกมพรีเมียร์ลีก อังกฤษ นัดที่แตนอาละวาดเปิดบ้านพ่าย คริสตัล พาเลซ 0-1ชี้ทีมของเขาเล่นไม่ดีพอเอง', 'tags': 'พรีเมียร์ลีก,วัตฟอร์ด,คริสตัล พาเลซ,กีเก ซานเชซ ฟลอเรส,ข่าวกีฬา,ข่าว,ไทยรัฐออนไลน์', 'title': 'ฟลอเรส รับ วัตฟอร์ดห่วยเองเกมพ่ายพาเลซคาบ้าน', 'type': '', 'url': 'https://www.thairath.co.th/content/528322'} ``` ### Data Fields - `title`: title of article - `body`: body of article - `summary`: summary of article - `type`: type of article, if any - `tags`: tags of article, separated by `,` - `url`: URL of article ### Data Splits train/valid/test: 358868 / 11000 / 11000 ## Dataset Creation ### Curation Rationale Sequence-to-sequence (Seq2Seq) models have shown great achievement in text summarization. However, Seq2Seq model often requires large-scale training data to achieve effective results. Although many impressive advancements in text summarization field have been made, most of summarization studies focus on resource-rich languages. The progress of Thai text summarization is still far behind. The dearth of large-scale dataset keeps Thai text summarization in its infancy. As far as our knowledge goes, there is not a large-scale dataset for Thai text summarization available anywhere. Thus, we present ThaiSum, a large-scale corpus for Thai text summarization obtained from several online news websites namely Thairath, ThaiPBS, Prachathai, and The Standard. ### Source Data #### Initial Data Collection and Normalization We used a python library named Scrapy to crawl articles from several news websites namely Thairath, Prachatai, ThaiPBS and, The Standard. We first collected news URLs provided in their sitemaps. During web-crawling, we used HTML markup and metadata available in HTML pages to identify article text, summary, headline, tags and label. Collected articles were published online from 2014 to August 2020. <br> <br> We further performed data cleansing process to minimize noisy data. We filtered out articles that their article text or summary is missing. Articles that contains article text with less than 150 words or summary with less than 15 words were removed. We also discarded articles that contain at least one of these following tags: ‘ดวง’ (horoscope), ‘นิยาย’ (novel), ‘อินสตราแกรมดารา’ (celebrity Instagram), ‘คลิปสุดฮา’(funny video) and ‘สรุปข่าว’ (highlight news). Some summaries were completely irrelevant to their original article texts. To eliminate those irrelevant summaries, we calculated abstractedness score between summary and its article text. Abstractedness score is written formally as: <br> <center><a href="https://www.codecogs.com/eqnedit.php?latex=\begin{equation}&space;\frac{|S-A|}{r}&space;\times&space;100&space;\end{equation}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\begin{equation}&space;\frac{|S-A|}{r}&space;\times&space;100&space;\end{equation}" title="\begin{equation} \frac{|S-A|}{r} \times 100 \end{equation}" /></a></center><br> <br>Where 𝑆 denotes set of article tokens. 𝐴 denotes set of summary tokens. 𝑟 denotes a total number of summary tokens. We omitted articles that have abstractedness score at 1-grams higher than 60%. <br><br> It is important to point out that we used [PyThaiNLP](https://github.com/PyThaiNLP/pythainlp), version 2.2.4, tokenizing engine = newmm, to process Thai texts in this study. It is challenging to tokenize running Thai text into words or sentences because there are not clear word/sentence delimiters in Thai language. Therefore, using different tokenization engines may result in different segment of words/sentences. After data-cleansing process, ThaiSum dataset contains over 358,000 articles. The size of this dataset is comparable to a well-known English document summarization dataset, CNN/Dily mail dataset. Moreover, we analyse the characteristics of this dataset by measuring the abstractedness level, compassion rate, and content diversity. For more details, see [thaisum_exploration.ipynb](https://github.com/nakhunchumpolsathien/ThaiSum/blob/master/thaisum_exploration.ipynb). #### Dataset Statistics ThaiSum dataset consists of 358,868 articles. Average lengths of article texts and summaries are approximately 530 and 37 words respectively. As mentioned earlier, we also collected headlines, tags and labels provided in each article. Tags are similar to keywords of the article. An article normally contains several tags but a few labels. Tags can be name of places or persons that article is about while labels indicate news category (politic, entertainment, etc.). Ultimatly, ThaiSum contains 538,059 unique tags and 59 unique labels. Note that not every article contains tags or labels. |Dataset Size| 358,868 | articles | |:---|---:|---:| |Avg. Article Length| 529.5 | words| |Avg. Summary Length | 37.3 | words| |Avg. Headline Length | 12.6 | words| |Unique Vocabulary Size | 407,355 | words| |Occurring > 10 times | 81,761 | words| |Unique News Tag Size | 538,059 | tags| |Unique News Label Size | 59 | labels| #### Who are the source language producers? Journalists of respective articles ### Annotations #### Annotation process `summary`, `type` and `tags` are created by journalists who wrote the articles and/or their publishers. #### Who are the annotators? `summary`, `type` and `tags` are created by journalists who wrote the articles and/or their publishers. ### Personal and Sensitive Information All data are public news articles. No personal and sensitive information is expected to be included. ## Considerations for Using the Data ### Social Impact of Dataset - News summarization in Thai - Language modeling for Thai news ### Discussion of Biases - [ThaiPBS](https://www.thaipbs.or.th/home) [receives funding from Thai government](https://www.bangkokbiznews.com/blog/detail/648740). - [Thairath](https://www.thairath.co.th/) is known as [the most popular newspaper in Thailand](https://mgronline.com/onlinesection/detail/9620000058532); no clear political leaning. - [The Standard](https://thestandard.co/) is a left-leaning online magazine. - [Prachathai](https://prachatai.com/) is a left-leaning, human-right-focused news site. ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [@nakhunchumpolsathien](https://github.com/nakhunchumpolsathien/) [@caramelWaffle](https://github.com/caramelWaffle) ### Licensing Information MIT License ### Citation Information ``` @mastersthesis{chumpolsathien_2020, title={Using Knowledge Distillation from Keyword Extraction to Improve the Informativeness of Neural Cross-lingual Summarization}, author={Chumpolsathien, Nakhun}, year={2020}, school={Beijing Institute of Technology} ``` ### Contributions Thanks to [@cstorm125](https://github.com/cstorm125) for adding this dataset.
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asi/wikitext_fr
asi
2022-10-21T16:23:07Z
511
4
[ "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:fr", "license:cc-by-sa-4.0", "arxiv:1609.07843", "region:us" ]
[ "sequence-modeling" ]
2022-03-02T23:29:22Z
--- annotations_creators: - no-annotation language_creators: - found language: - fr language_bcp47: - fr-FR license: - cc-by-sa-4.0 multilinguality: - monolingual pretty_name: Wikitext-fr size_categories: - unknown source_datasets: - original task_categories: - sequence-modeling task_ids: - language-modeling --- # Dataset Card Creation Guide ## Table of Contents - [Dataset Card Creation Guide](#dataset-card-creation-guide) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** [https://github.com/AntoineSimoulin/gpt-fr](https://github.com/AntoineSimoulin/gpt-fr) - **Paper:** [https://aclanthology.org/2021.jeptalnrecital-taln.24.pdf](https://aclanthology.org/2021.jeptalnrecital-taln.24.pdf) ### Dataset Summary Wikitext-fr language modeling dataset consists of over 70 million tokens extracted from the set of french Wikipedia articles that are classified as "quality articles" or "good articles". It is designed to mirror the english benchmark from Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. 2016. [Pointer Sentinel Mixture Models](https://arxiv.org/abs/1609.07843) The dataset is available under the [Creative Commons Attribution-ShareAlike License](https://creativecommons.org/licenses/by-sa/4.0/) ### Supported Tasks and Leaderboards - `language-modeling`: The dataset can be used to evaluate the generation abilites of a model. Success on this task is typically measured by achieving a *low* perplexity. The ([model name](https://huggingface.co/asi/gpt-fr-cased-base) currently achieves 12.9. ### Languages The dataset is in French. ## Dataset Structure ### Data Instances The dataset consists in the agregation of paragraphs from wikipedia articles. ``` { 'paragraph': ..., ... } ``` ### Data Fields - `paragraph`: This is a paragraph from the original wikipedia article. ### Data Splits The dataset is splited into a train/valid/test split. | | Tain (35) | Train (72) | Valid | Test | | ----- | ------ | ----- | ---- | ---- | | Number of Documents | 2 126 | 5 902 | 60 | 60 | | Number of tokens | 351 66 | 72 961 | 896 | 897 | | Vocabulary size | 137 589 | 205 403 | | | | Out of Vocabulary | 0.8% | 1.2% | | | ## Dataset Creation ### Curation Rationale The dataset is created to evaluate French models with similart criteria than English.s ### Source Data Wikitext-fr language modeling dataset consists of over 70 million tokens extracted from the set of french Wikipedia articles that are classified as "quality articles" or "good articles". We did not apply specific pre-treatments as transformers models might use a dedicated tokenization.s #### Initial Data Collection and Normalization We used the Wikipedia API to collect the articles since cleaning Wikipedia articles from dumps is not a trivial task. ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information The dataset is available under the [Creative Commons Attribution-ShareAlike License](https://creativecommons.org/licenses/by-sa/4.0/) ### Citation Information ``` @inproceedings{simoulin:hal-03265900, TITLE = {{Un mod{\`e}le Transformer G{\'e}n{\'e}ratif Pr{\'e}-entrain{\'e} pour le \_\_\_\_\_\_ fran{\c c}ais}}, AUTHOR = {Simoulin, Antoine and Crabb{\'e}, Benoit}, URL = {https://hal.archives-ouvertes.fr/hal-03265900}, BOOKTITLE = {{Traitement Automatique des Langues Naturelles}}, ADDRESS = {Lille, France}, EDITOR = {Denis, Pascal and Grabar, Natalia and Fraisse, Amel and Cardon, R{\'e}mi and Jacquemin, Bernard and Kergosien, Eric and Balvet, Antonio}, PUBLISHER = {{ATALA}}, PAGES = {246-255}, YEAR = {2021}, KEYWORDS = {fran{\c c}ais. ; GPT ; G{\'e}n{\'e}ratif ; Transformer ; Pr{\'e}-entra{\^i}n{\'e}}, PDF = {https://hal.archives-ouvertes.fr/hal-03265900/file/7.pdf}, HAL_ID = {hal-03265900}, HAL_VERSION = {v1}, } ``` ### Contributions Thanks to [@AntoineSimoulin](https://github.com/AntoineSimoulin) for adding this dataset.
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ml6team/the-stack-smol-python
ml6team
2023-05-24T12:42:37Z
511
1
[ "region:us" ]
null
2023-05-24T12:42:06Z
--- dataset_info: features: - name: content dtype: string - name: avg_line_length dtype: float64 - name: max_line_length dtype: int64 - name: alphanum_fraction dtype: float64 - name: licenses sequence: string - name: repository_name dtype: string - name: path dtype: string - name: size dtype: int64 - name: lang dtype: string splits: - name: train num_bytes: 82161631 num_examples: 10000 download_size: 28757440 dataset_size: 82161631 --- # Dataset Card for "the-stack-smol-python" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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qed
null
2022-11-03T16:31:09Z
510
2
[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:extended|natural_questions", "language:en", "license:unknown", "explanations-in-question-answering", "arxiv:2009.06354", "region:us" ]
[ "question-answering" ]
2022-03-02T23:29:22Z
--- annotations_creators: - expert-generated language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - extended|natural_questions task_categories: - question-answering task_ids: - extractive-qa paperswithcode_id: qed pretty_name: QED tags: - explanations-in-question-answering dataset_info: features: - name: example_id dtype: int64 - name: title_text dtype: string - name: url dtype: string - name: question dtype: string - name: paragraph_text dtype: string - name: sentence_starts sequence: int32 - name: original_nq_answers list: - name: start dtype: int32 - name: end dtype: int32 - name: string dtype: string - name: annotation struct: - name: referential_equalities list: - name: question_reference struct: - name: start dtype: int32 - name: end dtype: int32 - name: string dtype: string - name: sentence_reference struct: - name: start dtype: int32 - name: end dtype: int32 - name: bridge dtype: string - name: string dtype: string - name: answer list: - name: sentence_reference struct: - name: start dtype: int32 - name: end dtype: int32 - name: bridge dtype: string - name: string dtype: string - name: paragraph_reference struct: - name: start dtype: int32 - name: end dtype: int32 - name: string dtype: string - name: explanation_type dtype: string - name: selected_sentence struct: - name: start dtype: int32 - name: end dtype: int32 - name: string dtype: string config_name: qed splits: - name: train num_bytes: 8602094 num_examples: 7638 - name: validation num_bytes: 1584139 num_examples: 1355 download_size: 14083968 dataset_size: 10186233 --- # Dataset Card for QED ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** N/A - **Repository:** [GitHub](https://github.com/google-research-datasets/QED) - **Paper:** [QED: A Framework and Dataset for Explanations in Question Answering](https://arxiv.org/abs/2009.06354) - **Leaderboard:** N/A - **Point of Contact:** - ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.
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indonli
null
2023-01-25T14:33:00Z
509
6
[ "task_categories:text-classification", "task_ids:natural-language-inference", "annotations_creators:expert-generated", "annotations_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:id", "license:cc-by-sa-4.0", "region:us" ]
[ "text-classification" ]
2022-03-02T23:29:22Z
--- annotations_creators: - expert-generated - crowdsourced language_creators: - expert-generated language: - id license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - natural-language-inference paperswithcode_id: indonli pretty_name: IndoNLI dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction config_name: indonli splits: - name: train num_bytes: 2265687 num_examples: 10330 - name: validation num_bytes: 465299 num_examples: 2197 - name: test_lay num_bytes: 473849 num_examples: 2201 - name: test_expert num_bytes: 911916 num_examples: 2984 download_size: 6977877 dataset_size: 4116751 --- # Dataset Card for IndoNLI ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** [GitHub](https://github.com/ir-nlp-csui/indonli) - **Paper:** [EMNLP 2021](https://aclanthology.org/2021.emnlp-main.821/) - **Point of Contact:** [GitHub](https://github.com/ir-nlp-csui/indonli) ### Dataset Summary IndoNLI is the first human-elicited Natural Language Inference (NLI) dataset for Indonesian. IndoNLI is annotated by both crowd workers and experts. The expert-annotated data is used exclusively as a test set. It is designed to provide a challenging test-bed for Indonesian NLI by explicitly incorporating various linguistic phenomena such as numerical reasoning, structural changes, idioms, or temporal and spatial reasoning. ### Supported Tasks and Leaderboards - Natural Language Inference for Indonesian ### Languages Indonesian ## Dataset Structure ### Data Instances An example of `train` looks as follows. ``` { "premise": "Keindahan alam yang terdapat di Gunung Batu Jonggol ini dapat Anda manfaatkan sebagai objek fotografi yang cantik.", "hypothesis": "Keindahan alam tidak dapat difoto.", "label": 2 } ``` ### Data Fields The data fields are: - `premise`: a `string` feature - `hypothesis`: a `string` feature - `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2). ### Data Splits The data is split across `train`, `valid`, `test_lay`, and `test_expert`. `test_expert` is written by expert annotators, whereas the rest are written by lay annotators. | split | # examples | |----------|-------:| |train| 10330| |valid| 2197| |test_lay| 2201| |test_expert| 2984| A small subset of `test_expert` is used as a diasnostic tool. For more info, please visit https://github.com/ir-nlp-csui/indonli ## Dataset Creation ### Curation Rationale Indonesian NLP is considered under-resourced. Up until now, there is no publicly available human-annotated NLI dataset for Indonesian. ### Source Data #### Initial Data Collection and Normalization The premise were collected from Indonesian Wikipedia and from other public Indonesian dataset: Indonesian PUD and GSD treebanks provided by the [Universal Dependencies 2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) and [IndoSum](https://github.com/kata-ai/indosum) The hypothesis were written by annotators. #### Who are the source language producers? The data was produced by humans. ### Annotations #### Annotation process We start by writing the hypothesis, given the premise and the target label. Then, we ask 2 different independent annotators to predict the label, given the premise and hypothesis. If all 3 (the original hypothesis + 2 independent annotators) agree with the label, then the annotation process ends for that sample. Otherwise, we incrementally ask additional annotator until 3 annotators agree with the label. If there's no majority concensus after 5 annotations, the sample is removed. #### Who are the annotators? Lay annotators were computer science students, and expert annotators were NLP scientists with 7+ years research experience in NLP. All annotators are native speakers. Additionally, expert annotators were explicitly instructed to provide challenging examples by incorporating various linguistic phenomena such as numerical reasoning, structural changes, idioms, or temporal and spatial reasoning. Annotators were compensated based on hourly rate. ### Personal and Sensitive Information There might be some personal information coming from Wikipedia and news, especially the information of famous/important people. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases INDONLI is created using premise sentences taken from Wikipedia and news. These data sources may contain some bias. ### Other Known Limitations No other known limitations ## Additional Information ### Dataset Curators This dataset is the result of the collaborative work of Indonesian researchers from the University of Indonesia, kata.ai, New York University, Fondazione Bruno Kessler, and the University of St Andrews. ### Licensing Information CC-BY-SA 4.0. Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits. Please contact authors for any information on the dataset. ### Citation Information ``` @inproceedings{mahendra-etal-2021-indonli, title = "{I}ndo{NLI}: A Natural Language Inference Dataset for {I}ndonesian", author = "Mahendra, Rahmad and Aji, Alham Fikri and Louvan, Samuel and Rahman, Fahrurrozi and Vania, Clara", booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.821", pages = "10511--10527", } ``` ### Contributions Thanks to [@afaji](https://github.com/afaji) for adding this dataset.
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open-llm-leaderboard/details_EleutherAI__gpt-j-6b
open-llm-leaderboard
2023-12-03T16:52:37Z
509
0
[ "region:us" ]
null
2023-08-17T23:48:16Z
--- pretty_name: Evaluation run of EleutherAI/gpt-j-6b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [EleutherAI/gpt-j-6b](https://huggingface.co/EleutherAI/gpt-j-6b) on the [Open\ \ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 122 configuration, each one coresponding to one of\ \ the evaluated task.\n\nThe dataset has been created from 8 run(s). Each run can\ \ be found as a specific split in each configuration, the split being named using\ \ the timestamp of the run.The \"train\" split is always pointing to the latest\ \ results.\n\nAn additional configuration \"results\" store all the aggregated results\ \ of the run (and is used to compute and display the aggregated metrics on the [Open\ \ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_EleutherAI__gpt-j-6b\"\ ,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\ \ are the [latest results from run 2023-12-03T16:52:26.173919](https://huggingface.co/datasets/open-llm-leaderboard/details_EleutherAI__gpt-j-6b/blob/main/results_2023-12-03T16-52-26.173919.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.029567854435178165,\n\ \ \"acc_stderr\": 0.004665893134220772\n },\n \"harness|gsm8k|5\":\ \ {\n \"acc\": 0.029567854435178165,\n \"acc_stderr\": 0.004665893134220772\n\ \ }\n}\n```" repo_url: https://huggingface.co/EleutherAI/gpt-j-6b leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|arc:challenge|25_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|arc:challenge|25_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|arc:challenge|25_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|arc:challenge|25_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-09-21T23-10-49.133869.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_08T17_46_12.907701 path: - '**/details_harness|drop|3_2023-09-08T17-46-12.907701.parquet' - split: latest path: - '**/details_harness|drop|3_2023-09-08T17-46-12.907701.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_08T17_46_12.907701 path: - '**/details_harness|gsm8k|5_2023-09-08T17-46-12.907701.parquet' - split: 2023_12_03T16_51_11.923338 path: - '**/details_harness|gsm8k|5_2023-12-03T16-51-11.923338.parquet' - split: 2023_12_03T16_52_26.173919 path: - '**/details_harness|gsm8k|5_2023-12-03T16-52-26.173919.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-03T16-52-26.173919.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hellaswag|10_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hellaswag|10_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hellaswag|10_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hellaswag|10_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-18T11:28:34.858547.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T10:47:17.854530.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-29T19:41:28.653242.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-21T23-10-49.133869.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-management|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-management|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-management|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-virology|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-virology|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-21T23-10-49.133869.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_18T11_28_34.858547 path: - '**/details_harness|truthfulqa:mc|0_2023-07-18T11:28:34.858547.parquet' - split: 2023_07_19T10_47_17.854530 path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T10:47:17.854530.parquet' - split: 2023_08_29T19_41_28.653242 path: - '**/details_harness|truthfulqa:mc|0_2023-08-29T19:41:28.653242.parquet' - split: 2023_09_21T23_10_49.133869 path: - '**/details_harness|truthfulqa:mc|0_2023-09-21T23-10-49.133869.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-09-21T23-10-49.133869.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_08T17_46_12.907701 path: - '**/details_harness|winogrande|5_2023-09-08T17-46-12.907701.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-09-08T17-46-12.907701.parquet' - config_name: original_mmlu_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:anatomy|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:astronomy|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:business_ethics|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:college_biology|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:college_medicine|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:college_physics|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:computer_security|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:econometrics|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:formal_logic|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:global_facts|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:human_aging|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:international_law|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:machine_learning|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:management|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:marketing|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:nutrition|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:philosophy|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:prehistory|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:professional_law|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:public_relations|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:security_studies|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:sociology|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:virology|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:world_religions|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:anatomy|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:astronomy|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:business_ethics|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:college_biology|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:college_medicine|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:college_physics|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:computer_security|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:econometrics|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:formal_logic|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:global_facts|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:human_aging|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:international_law|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:machine_learning|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:management|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:marketing|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:nutrition|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:philosophy|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:prehistory|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:professional_law|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:public_relations|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:security_studies|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:sociology|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:virology|5_2023-08-28T20:18:18.137533.parquet' - '**/details_original|mmlu:world_religions|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_abstract_algebra_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_anatomy_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:anatomy|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:anatomy|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_astronomy_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:astronomy|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:astronomy|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_business_ethics_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:business_ethics|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:business_ethics|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_clinical_knowledge_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_college_biology_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:college_biology|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:college_biology|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_college_chemistry_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_college_computer_science_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_college_mathematics_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_college_medicine_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:college_medicine|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:college_medicine|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_college_physics_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:college_physics|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:college_physics|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_computer_security_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:computer_security|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:computer_security|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_conceptual_physics_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_econometrics_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:econometrics|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:econometrics|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_electrical_engineering_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_elementary_mathematics_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_formal_logic_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:formal_logic|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:formal_logic|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_global_facts_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:global_facts|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:global_facts|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_high_school_biology_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_high_school_chemistry_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_high_school_computer_science_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_high_school_european_history_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_high_school_geography_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_high_school_government_and_politics_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_high_school_macroeconomics_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_high_school_mathematics_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_high_school_microeconomics_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_high_school_physics_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_high_school_psychology_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_high_school_statistics_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_high_school_us_history_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_high_school_world_history_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_human_aging_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:human_aging|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:human_aging|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_human_sexuality_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_international_law_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:international_law|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:international_law|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_jurisprudence_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_logical_fallacies_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_machine_learning_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:machine_learning|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:machine_learning|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_management_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:management|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:management|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_marketing_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:marketing|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:marketing|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_medical_genetics_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_miscellaneous_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_moral_disputes_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_moral_scenarios_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_nutrition_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:nutrition|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:nutrition|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_philosophy_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:philosophy|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:philosophy|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_prehistory_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:prehistory|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:prehistory|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_professional_accounting_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_professional_law_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:professional_law|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:professional_law|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_professional_medicine_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_professional_psychology_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_public_relations_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:public_relations|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:public_relations|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_security_studies_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:security_studies|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:security_studies|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_sociology_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:sociology|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:sociology|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_us_foreign_policy_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_virology_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:virology|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:virology|5_2023-08-28T20:18:18.137533.parquet' - config_name: original_mmlu_world_religions_5 data_files: - split: 2023_08_28T20_18_18.137533 path: - '**/details_original|mmlu:world_religions|5_2023-08-28T20:18:18.137533.parquet' - split: latest path: - '**/details_original|mmlu:world_religions|5_2023-08-28T20:18:18.137533.parquet' - config_name: results data_files: - split: 2023_07_18T11_28_34.858547 path: - results_2023-07-18T11:28:34.858547.parquet - split: 2023_07_19T10_47_17.854530 path: - results_2023-07-19T10:47:17.854530.parquet - split: 2023_08_28T20_18_18.137533 path: - results_2023-08-28T20:18:18.137533.parquet - split: 2023_08_29T19_41_28.653242 path: - results_2023-08-29T19:41:28.653242.parquet - split: 2023_09_08T17_46_12.907701 path: - results_2023-09-08T17-46-12.907701.parquet - split: 2023_09_21T23_10_49.133869 path: - results_2023-09-21T23-10-49.133869.parquet - split: 2023_12_03T16_51_11.923338 path: - results_2023-12-03T16-51-11.923338.parquet - split: 2023_12_03T16_52_26.173919 path: - results_2023-12-03T16-52-26.173919.parquet - split: latest path: - results_2023-12-03T16-52-26.173919.parquet --- # Dataset Card for Evaluation run of EleutherAI/gpt-j-6b ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/EleutherAI/gpt-j-6b - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [EleutherAI/gpt-j-6b](https://huggingface.co/EleutherAI/gpt-j-6b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 122 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 8 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_EleutherAI__gpt-j-6b", "harness_gsm8k_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-03T16:52:26.173919](https://huggingface.co/datasets/open-llm-leaderboard/details_EleutherAI__gpt-j-6b/blob/main/results_2023-12-03T16-52-26.173919.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.029567854435178165, "acc_stderr": 0.004665893134220772 }, "harness|gsm8k|5": { "acc": 0.029567854435178165, "acc_stderr": 0.004665893134220772 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
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derek-thomas/dataset-creator-reddit-amitheasshole
derek-thomas
2023-12-04T12:00:05Z
509
0
[ "region:us" ]
null
2023-10-27T16:21:23Z
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: content dtype: string - name: poster dtype: string - name: date_utc dtype: timestamp[ns] - name: flair dtype: string - name: title dtype: string - name: permalink dtype: string - name: id dtype: string - name: content_length dtype: int64 - name: score dtype: int64 splits: - name: train num_bytes: 5358538 num_examples: 2570 download_size: 3199986 dataset_size: 5358538 --- # Dataset Card for "dataset-creator-reddit-amitheasshole" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) --- Generated Part of README Below --- ## Dataset Overview The goal is to have an open dataset of [r/amitheasshole](https://www.reddit.com/r/amitheasshole/) submissions. Im leveraging PRAW and the reddit API to get downloads. There is a limit of 1000 in an API call and limited search functionality, so this is run hourly to get new submissions. ## Creation Details This dataset was created by [derek-thomas/dataset-creator-reddit-amitheasshole](https://huggingface.co/spaces/derek-thomas/dataset-creator-reddit-amitheasshole) ## Update Frequency The dataset is updated hourly with the most recent update being `2023-12-04 12:00:00 UTC+0000` where we added **200 new rows**. ## Licensing [Reddit Licensing terms](https://www.redditinc.com/policies/data-api-terms) as accessed on October 25: > The Content created with or submitted to our Services by Users (“User Content”) is owned by Users and not by Reddit. Subject to your complete and ongoing compliance with the Data API Terms, Reddit grants you a non-exclusive, non-transferable, non-sublicensable, and revocable license to copy and display the User Content using the Data API solely as necessary to develop, deploy, distribute, and run your App to your App Users. You may not modify the User Content except to format it for such display. You will comply with any requirements or restrictions imposed on usage of User Content by their respective owners, which may include "all rights reserved" notices, Creative Commons licenses, or other terms and conditions that may be agreed upon between you and the owners. Except as expressly permitted by this section, no other rights or licenses are granted or implied, including any right to use User Content for other purposes, such as for training a machine learning or AI model, without the express permission of rightsholders in the applicable User Content My take is that you can't use this data for *training* without getting permission. ## Opt-out To opt-out of this dataset please make a request in the community tab
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sam-mosaic/iv4-chatml
sam-mosaic
2023-11-21T21:20:43Z
509
0
[ "region:us" ]
null
2023-11-21T21:19:11Z
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: source dtype: string - name: prompt dtype: string - name: response dtype: string splits: - name: train num_bytes: 2349114457.0 num_examples: 387277 - name: test num_bytes: 351904407.0 num_examples: 57556 download_size: 1361629459 dataset_size: 2701018864.0 --- # Dataset Card for "iv4-chatml" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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Cohere/wikipedia-22-12-en-embeddings
Cohere
2023-03-22T16:51:57Z
508
43
[ "task_categories:text-retrieval", "task_ids:document-retrieval", "annotations_creators:expert-generated", "multilinguality:multilingual", "language:en", "license:apache-2.0", "region:us" ]
[ "text-retrieval" ]
2023-01-14T20:36:11Z
--- annotations_creators: - expert-generated language: - en multilinguality: - multilingual size_categories: [] source_datasets: [] tags: [] task_categories: - text-retrieval license: - apache-2.0 task_ids: - document-retrieval --- # Wikipedia (en) embedded with cohere.ai `multilingual-22-12` encoder We encoded [Wikipedia (en)](https://en.wikipedia.org) using the [cohere.ai](https://txt.cohere.ai/multilingual/) `multilingual-22-12` embedding model. To get an overview how this dataset was created and pre-processed, have a look at [Cohere/wikipedia-22-12](https://huggingface.co/datasets/Cohere/wikipedia-22-12). ## Embeddings We compute for `title+" "+text` the embeddings using our `multilingual-22-12` embedding model, a state-of-the-art model that works for semantic search in 100 languages. If you want to learn more about this model, have a look at [cohere.ai multilingual embedding model](https://txt.cohere.ai/multilingual/). ## Further languages We provide embeddings of Wikipedia in many different languages: [ar](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ar-embeddings), [de](https://huggingface.co/datasets/Cohere/wikipedia-22-12-de-embeddings), [en](https://huggingface.co/datasets/Cohere/wikipedia-22-12-en-embeddings), [es](https://huggingface.co/datasets/Cohere/wikipedia-22-12-es-embeddings), [fr](https://huggingface.co/datasets/Cohere/wikipedia-22-12-fr-embeddings), [hi](https://huggingface.co/datasets/Cohere/wikipedia-22-12-hi-embeddings), [it](https://huggingface.co/datasets/Cohere/wikipedia-22-12-it-embeddings), [ja](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ja-embeddings), [ko](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ko-embeddings), [simple english](https://huggingface.co/datasets/Cohere/wikipedia-22-12-simple-embeddings), [zh](https://huggingface.co/datasets/Cohere/wikipedia-22-12-zh-embeddings), You can find the Wikipedia datasets without embeddings at [Cohere/wikipedia-22-12](https://huggingface.co/datasets/Cohere/wikipedia-22-12). ## Loading the dataset You can either load the dataset like this: ```python from datasets import load_dataset docs = load_dataset(f"Cohere/wikipedia-22-12-en-embeddings", split="train") ``` Or you can also stream it without downloading it before: ```python from datasets import load_dataset docs = load_dataset(f"Cohere/wikipedia-22-12-en-embeddings", split="train", streaming=True) for doc in docs: docid = doc['id'] title = doc['title'] text = doc['text'] emb = doc['emb'] ``` ## Search A full search example: ```python #Run: pip install cohere datasets from datasets import load_dataset import torch import cohere co = cohere.Client(f"<<COHERE_API_KEY>>") # Add your cohere API key from www.cohere.com #Load at max 1000 documents + embeddings max_docs = 1000 docs_stream = load_dataset(f"Cohere/wikipedia-22-12-en-embeddings", split="train", streaming=True) docs = [] doc_embeddings = [] for doc in docs_stream: docs.append(doc) doc_embeddings.append(doc['emb']) if len(docs) >= max_docs: break doc_embeddings = torch.tensor(doc_embeddings) query = 'Who founded Youtube' response = co.embed(texts=[query], model='multilingual-22-12') query_embedding = response.embeddings query_embedding = torch.tensor(query_embedding) # Compute dot score between query embedding and document embeddings dot_scores = torch.mm(query_embedding, doc_embeddings.transpose(0, 1)) top_k = torch.topk(dot_scores, k=3) # Print results print("Query:", query) for doc_id in top_k.indices[0].tolist(): print(docs[doc_id]['title']) print(docs[doc_id]['text'], "\n") ``` ## Performance You can find performance on the MIRACL dataset (a semantic search evaluation dataset) here: [miracl-en-queries-22-12#performance](https://huggingface.co/datasets/Cohere/miracl-en-queries-22-12#performance)
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open-llm-leaderboard/details_PocketDoc__Dans-TotSirocco-7b
open-llm-leaderboard
2023-10-29T09:59:25Z
508
0
[ "region:us" ]
null
2023-10-09T23:41:54Z
--- pretty_name: Evaluation run of PocketDoc/Dans-TotSirocco-7b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [PocketDoc/Dans-TotSirocco-7b](https://huggingface.co/PocketDoc/Dans-TotSirocco-7b)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_PocketDoc__Dans-TotSirocco-7b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-23T12:54:48.005243](https://huggingface.co/datasets/open-llm-leaderboard/details_PocketDoc__Dans-TotSirocco-7b/blob/main/results_2023-10-23T12-54-48.005243.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.44997902684563756,\n\ \ \"em_stderr\": 0.00509477973209699,\n \"f1\": 0.49544777684563845,\n\ \ \"f1_stderr\": 0.00490923385938236,\n \"acc\": 0.45978722729484023,\n\ \ \"acc_stderr\": 0.01042644341108249\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.44997902684563756,\n \"em_stderr\": 0.00509477973209699,\n\ \ \"f1\": 0.49544777684563845,\n \"f1_stderr\": 0.00490923385938236\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.1326762699014405,\n \ \ \"acc_stderr\": 0.009343929131442216\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7868981846882399,\n \"acc_stderr\": 0.011508957690722764\n\ \ }\n}\n```" repo_url: https://huggingface.co/PocketDoc/Dans-TotSirocco-7b leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|arc:challenge|25_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|arc:challenge|25_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-10-10T03-08-42.670420.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_23T12_54_48.005243 path: - '**/details_harness|drop|3_2023-10-23T12-54-48.005243.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-23T12-54-48.005243.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_23T12_54_48.005243 path: - '**/details_harness|gsm8k|5_2023-10-23T12-54-48.005243.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-23T12-54-48.005243.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hellaswag|10_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hellaswag|10_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-management|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-10-09T23-41-30.846721.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T03-08-42.670420.parquet' - 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'**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-management|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-management|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-10-10T03-08-42.670420.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-international_law|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-international_law|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-management|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-management|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-marketing|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-marketing|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-sociology|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-sociology|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-virology|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-virology|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T03-08-42.670420.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_10_09T23_41_30.846721 path: - '**/details_harness|truthfulqa:mc|0_2023-10-09T23-41-30.846721.parquet' - split: 2023_10_10T03_08_42.670420 path: - '**/details_harness|truthfulqa:mc|0_2023-10-10T03-08-42.670420.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-10-10T03-08-42.670420.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_23T12_54_48.005243 path: - '**/details_harness|winogrande|5_2023-10-23T12-54-48.005243.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-23T12-54-48.005243.parquet' - config_name: results data_files: - split: 2023_10_09T23_41_30.846721 path: - results_2023-10-09T23-41-30.846721.parquet - split: 2023_10_10T03_08_42.670420 path: - results_2023-10-10T03-08-42.670420.parquet - split: 2023_10_23T12_54_48.005243 path: - results_2023-10-23T12-54-48.005243.parquet - split: latest path: - results_2023-10-23T12-54-48.005243.parquet --- # Dataset Card for Evaluation run of PocketDoc/Dans-TotSirocco-7b ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/PocketDoc/Dans-TotSirocco-7b - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [PocketDoc/Dans-TotSirocco-7b](https://huggingface.co/PocketDoc/Dans-TotSirocco-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_PocketDoc__Dans-TotSirocco-7b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-23T12:54:48.005243](https://huggingface.co/datasets/open-llm-leaderboard/details_PocketDoc__Dans-TotSirocco-7b/blob/main/results_2023-10-23T12-54-48.005243.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.44997902684563756, "em_stderr": 0.00509477973209699, "f1": 0.49544777684563845, "f1_stderr": 0.00490923385938236, "acc": 0.45978722729484023, "acc_stderr": 0.01042644341108249 }, "harness|drop|3": { "em": 0.44997902684563756, "em_stderr": 0.00509477973209699, "f1": 0.49544777684563845, "f1_stderr": 0.00490923385938236 }, "harness|gsm8k|5": { "acc": 0.1326762699014405, "acc_stderr": 0.009343929131442216 }, "harness|winogrande|5": { "acc": 0.7868981846882399, "acc_stderr": 0.011508957690722764 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
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code_x_glue_cc_defect_detection
null
2022-11-18T19:31:11Z
507
6
[ "task_categories:text-classification", "task_ids:multi-class-classification", "annotations_creators:found", "language_creators:found", "multilinguality:other-programming-languages", "size_categories:10K<n<100K", "source_datasets:original", "language:code", "license:c-uda", "region:us" ]
[ "text-classification" ]
2022-03-02T23:29:22Z
--- annotations_creators: - found language_creators: - found language: - code license: - c-uda multilinguality: - other-programming-languages size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification pretty_name: CodeXGlueCcDefectDetection dataset_info: features: - name: id dtype: int32 - name: func dtype: string - name: target dtype: bool - name: project dtype: string - name: commit_id dtype: string splits: - name: train num_bytes: 45723487 num_examples: 21854 - name: validation num_bytes: 5582545 num_examples: 2732 - name: test num_bytes: 5646752 num_examples: 2732 download_size: 61685715 dataset_size: 56952784 --- # Dataset Card for "code_x_glue_cc_defect_detection" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits-sample-size) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Defect-detection ### Dataset Summary CodeXGLUE Defect-detection dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Defect-detection Given a source code, the task is to identify whether it is an insecure code that may attack software systems, such as resource leaks, use-after-free vulnerabilities and DoS attack. We treat the task as binary classification (0/1), where 1 stands for insecure code and 0 for secure code. The dataset we use comes from the paper Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks. We combine all projects and split 80%/10%/10% for training/dev/test. ### Supported Tasks and Leaderboards - `multi-class-classification`: The dataset can be used to train a model for detecting if code has a defect in it. ### Languages - C **programming** language ## Dataset Structure ### Data Instances An example of 'validation' looks as follows. ``` { "commit_id": "aa1530dec499f7525d2ccaa0e3a876dc8089ed1e", "func": "static void filter_mirror_setup(NetFilterState *nf, Error **errp)\n{\n MirrorState *s = FILTER_MIRROR(nf);\n Chardev *chr;\n chr = qemu_chr_find(s->outdev);\n if (chr == NULL) {\n error_set(errp, ERROR_CLASS_DEVICE_NOT_FOUND,\n \"Device '%s' not found\", s->outdev);\n qemu_chr_fe_init(&s->chr_out, chr, errp);", "id": 8, "project": "qemu", "target": true } ``` ### Data Fields In the following each data field in go is explained for each config. The data fields are the same among all splits. #### default |field name| type | description | |----------|------|------------------------------------------| |id |int32 | Index of the sample | |func |string| The source code | |target |bool | 0 or 1 (vulnerability or not) | |project |string| Original project that contains this code | |commit_id |string| Commit identifier in the original project| ### Data Splits | name |train|validation|test| |-------|----:|---------:|---:| |default|21854| 2732|2732| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators https://github.com/microsoft, https://github.com/madlag ### Licensing Information Computational Use of Data Agreement (C-UDA) License. ### Citation Information ``` @inproceedings{zhou2019devign, title={Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks}, author={Zhou, Yaqin and Liu, Shangqing and Siow, Jingkai and Du, Xiaoning and Liu, Yang}, booktitle={Advances in Neural Information Processing Systems}, pages={10197--10207}, year={2019} ``` ### Contributions Thanks to @madlag (and partly also @ncoop57) for adding this dataset.
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pubmed
null
2022-12-22T07:57:43Z
507
37
[ "task_categories:text-generation", "task_categories:fill-mask", "task_categories:text-classification", "task_ids:language-modeling", "task_ids:masked-language-modeling", "task_ids:text-scoring", "task_ids:topic-classification", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10M<n<100M", "source_datasets:original", "language:en", "license:other", "citation-estimation", "region:us" ]
[ "text-generation", "fill-mask", "text-classification" ]
2022-03-02T23:29:22Z
--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - other multilinguality: - monolingual size_categories: - 10M<n<100M source_datasets: - original task_categories: - text-generation - fill-mask - text-classification task_ids: - language-modeling - masked-language-modeling - text-scoring - topic-classification paperswithcode_id: pubmed pretty_name: PubMed tags: - citation-estimation dataset_info: - config_name: '2023' features: - name: MedlineCitation struct: - name: PMID dtype: int32 - name: DateCompleted struct: - name: Year dtype: int32 - name: Month dtype: int32 - name: Day dtype: int32 - name: NumberOfReferences dtype: int32 - name: DateRevised struct: - name: Year dtype: int32 - name: Month dtype: int32 - name: Day dtype: int32 - name: Article struct: - name: Abstract struct: - name: AbstractText dtype: string - name: ArticleTitle dtype: string - name: AuthorList struct: - name: Author sequence: - name: LastName dtype: string - name: ForeName dtype: string - name: Initials dtype: string - name: CollectiveName dtype: string - name: Language dtype: string - name: GrantList struct: - name: Grant sequence: - name: GrantID dtype: string - name: Agency dtype: string - name: Country dtype: string - name: PublicationTypeList struct: - name: PublicationType sequence: string - name: MedlineJournalInfo struct: - name: Country dtype: string - name: ChemicalList struct: - name: Chemical sequence: - name: RegistryNumber dtype: string - name: NameOfSubstance dtype: string - name: CitationSubset dtype: string - name: MeshHeadingList struct: - name: MeshHeading sequence: - name: DescriptorName dtype: string - name: QualifierName dtype: string - name: PubmedData struct: - name: ArticleIdList sequence: - name: ArticleId sequence: string - name: PublicationStatus dtype: string - name: History struct: - name: PubMedPubDate sequence: - name: Year dtype: int32 - name: Month dtype: int32 - name: Day dtype: int32 - name: ReferenceList sequence: - name: Citation dtype: string - name: CitationId dtype: int32 splits: - name: train num_bytes: 52199025303 num_examples: 34960700 download_size: 41168762331 dataset_size: 52199025303 --- # Dataset Card for PubMed ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** : [https://www.nlm.nih.gov/databases/download/pubmed_medline.html]() - **Documentation:** : [https://www.nlm.nih.gov/databases/download/pubmed_medline_documentation.html]() - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary NLM produces a baseline set of MEDLINE/PubMed citation records in XML format for download on an annual basis. The annual baseline is released in December of each year. Each day, NLM produces update files that include new, revised and deleted citations. See our documentation page for more information. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages - English ## Dataset Structure Bear in mind the data comes from XML that have various tags that are hard to reflect in a concise JSON format. Tags and list are kind of non "natural" to XML documents leading this library to make some choices regarding data. "Journal" info was dropped altogether as it would have led to many fields being empty all the time. The hierarchy is also a bit unnatural but the choice was made to keep as close as possible to the original data for future releases that may change schema from NLM's side. Author has been kept and contains either "ForeName", "LastName", "Initials", or "CollectiveName". (All the fields will be present all the time, but only some will be filled) ### Data Instances ```json { "MedlineCitation": { "PMID": 0, "DateCompleted": {"Year": 0, "Month": 0, "Day": 0}, "NumberOfReferences": 0, "DateRevised": {"Year": 0, "Month": 0, "Day": 0}, "Article": { "Abstract": {"AbstractText": "Some abstract (can be missing)" }, "ArticleTitle": "Article title", "AuthorList": {"Author": [ {"FirstName": "John", "ForeName": "Doe", "Initials": "JD", "CollectiveName": ""} {"CollectiveName": "The Manhattan Project", "FirstName": "", "ForeName": "", "Initials": ""} ]}, "Language": "en", "GrantList": { "Grant": [], }, "PublicationTypeList": {"PublicationType": []}, }, "MedlineJournalInfo": {"Country": "France"}, "ChemicalList": {"Chemical": [{ "RegistryNumber": "XX", "NameOfSubstance": "Methanol" }]}, "CitationSubset": "AIM", "MeshHeadingList": { "MeshHeading": [], }, }, "PubmedData": { "ArticleIdList": {"ArticleId": "10.1002/bjs.1800650203"}, "PublicationStatus": "ppublish", "History": {"PubMedPubDate": [{"Year": 0, "Month": 0, "Day": 0}]}, "ReferenceList": [{"Citation": "Somejournal", "CitationId": 01}], }, } ``` ### Data Fields Main Fields will probably interest people are: - "MedlineCitation" > "Article" > "AuthorList" > "Author" - "MedlineCitation" > "Article" > "Abstract" > "AbstractText" - "MedlineCitation" > "Article" > "Article Title" - "MedlineCitation" > "ChemicalList" > "Chemical" - "MedlineCitation" > "NumberOfReferences" ### Data Splits There are no splits in this dataset. It is given as is. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [https://www.nlm.nih.gov/databases/download/pubmed_medline_faq.html]() #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [https://www.nlm.nih.gov/databases/download/terms_and_conditions.html]() ### Citation Information [Courtesy of the U.S. National Library of Medicine](https://www.nlm.nih.gov/databases/download/terms_and_conditions.html). ### Contributions Thanks to [@Narsil](https://github.com/Narsil) for adding this dataset.
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juancopi81/jcpvincentcat
juancopi81
2023-01-11T01:59:11Z
507
0
[ "license:openrail", "region:us" ]
null
2023-01-11T01:57:34Z
--- license: openrail ---
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xiyuez/im-feeling-curious
xiyuez
2023-06-24T17:09:22Z
507
6
[ "task_categories:question-answering", "task_categories:text-generation", "size_categories:1K<n<10K", "language:en", "license:odc-by", "region:us" ]
[ "question-answering", "text-generation" ]
2023-06-15T22:50:58Z
--- license: odc-by task_categories: - question-answering - text-generation language: - en pretty_name: i'm feeling curious dataset size_categories: - 1K<n<10K --- This public dataset is an extract from Google's "i'm feeling curious" feature. To learn more about this feature, search for "i'm feeling curious" on Google. Tasks: Answering open-domain questions, generating random facts. Limitations: May contain commercial content, false information, bias, or outdated information. Language: English only. This public extract is licensed under the Open Data Commons Attribution License: http://opendatacommons.org/licenses/by/1.0/. There is no canonical train/test split. This extract contains 2761 unique rows, which may increase as more data is crawled. Near-duplicates have been removed. While we aimed to filter non-natural language content and duplicates, some may remain. The data may also contain toxic, biased, copyrighted or erroneous content. Google has done initial filtering, but we have not verified the data. Use this dataset at your own risk. We provide no warranty or liability. Google is a registered trademark of Google LLC. This project is not affiliated with, endorsed or sponsored by Google.
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cdsc
null
2023-01-25T14:27:43Z
506
0
[ "task_categories:other", "annotations_creators:expert-generated", "language_creators:other", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:pl", "license:cc-by-nc-sa-4.0", "sentences entailment and relatedness", "region:us" ]
[ "other" ]
2022-03-02T23:29:22Z
--- annotations_creators: - expert-generated language_creators: - other language: - pl license: - cc-by-nc-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - other task_ids: [] paperswithcode_id: polish-cdscorpus pretty_name: Polish CDSCorpus tags: - sentences entailment and relatedness dataset_info: - config_name: cdsc-e features: - name: pair_ID dtype: int32 - name: sentence_A dtype: string - name: sentence_B dtype: string - name: entailment_judgment dtype: class_label: names: '0': NEUTRAL '1': CONTRADICTION '2': ENTAILMENT splits: - name: train num_bytes: 1381902 num_examples: 8000 - name: test num_bytes: 179400 num_examples: 1000 - name: validation num_bytes: 174662 num_examples: 1000 download_size: 376079 dataset_size: 1735964 - config_name: cdsc-r features: - name: pair_ID dtype: int32 - name: sentence_A dtype: string - name: sentence_B dtype: string - name: relatedness_score dtype: float32 splits: - name: train num_bytes: 1349902 num_examples: 8000 - name: test num_bytes: 175400 num_examples: 1000 - name: validation num_bytes: 170662 num_examples: 1000 download_size: 381525 dataset_size: 1695964 --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://zil.ipipan.waw.pl/Scwad/CDSCorpus - **Repository:** - **Paper:** @inproceedings{wroblewska2017polish, title={Polish evaluation dataset for compositional distributional semantics models}, author={Wr{\'o}blewska, Alina and Krasnowska-Kiera{\'s}, Katarzyna}, booktitle={Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, pages={784--792}, year={2017} } - **Leaderboard:** https://klejbenchmark.com/leaderboard/ - **Point of Contact:** [email protected] ### Dataset Summary Polish CDSCorpus consists of 10K Polish sentence pairs which are human-annotated for semantic relatedness and entailment. The dataset may be used for the evaluation of compositional distributional semantics models of Polish. The dataset was presented at ACL 2017. Please refer to the Wróblewska and Krasnowska-Kieraś (2017) for a detailed description of the resource. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Polish ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - pair_ID: id of sentences pairs - sentence_A: first sentence - sentence_B: second sentence for cdsc-e domain: - entailment_judgment: either 'NEUTRAL', 'CONTRADICTION' or 'ENTAILMENT' for cdsc-r domain: - relatedness_score: float representing a reletedness ### Data Splits Data is splitted in train/dev/test split. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information CC BY-NC-SA 4.0 ### Citation Information [More Information Needed] ### Contributions Thanks to [@abecadel](https://github.com/abecadel) for adding this dataset.
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ccdv/arxiv-classification
ccdv
2022-10-22T09:23:50Z
506
11
[ "task_categories:text-classification", "task_ids:multi-class-classification", "task_ids:topic-classification", "size_categories:10K<n<100K", "language:en", "long context", "region:us" ]
[ "text-classification" ]
2022-03-02T23:29:22Z
--- language: en task_categories: - text-classification tags: - long context task_ids: - multi-class-classification - topic-classification size_categories: 10K<n<100K --- **Arxiv Classification: a classification of Arxiv Papers (11 classes).** This dataset is intended for long context classification (documents have all > 4k tokens). \ Copied from "Long Document Classification From Local Word Glimpses via Recurrent Attention Learning" ``` @ARTICLE{8675939, author={He, Jun and Wang, Liqun and Liu, Liu and Feng, Jiao and Wu, Hao}, journal={IEEE Access}, title={Long Document Classification From Local Word Glimpses via Recurrent Attention Learning}, year={2019}, volume={7}, number={}, pages={40707-40718}, doi={10.1109/ACCESS.2019.2907992} } ``` * See: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8675939 * See: https://github.com/LiqunW/Long-document-dataset It contains 11 slightly unbalanced classes, 33k Arxiv Papers divided into 3 splits: train (28k), val (2.5k) and test (2.5k). 2 configs: * default * no_ref, removes references to the class inside the document (eg: [cs.LG] -> []) Compatible with [run_glue.py](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) script: ``` export MODEL_NAME=roberta-base export MAX_SEQ_LENGTH=512 python run_glue.py \ --model_name_or_path $MODEL_NAME \ --dataset_name ccdv/arxiv-classification \ --do_train \ --do_eval \ --max_seq_length $MAX_SEQ_LENGTH \ --per_device_train_batch_size 8 \ --gradient_accumulation_steps 4 \ --learning_rate 2e-5 \ --num_train_epochs 1 \ --max_eval_samples 500 \ --output_dir tmp/arxiv ```
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FudanSELab/ClassEval
FudanSELab
2023-09-04T06:35:53Z
503
1
[ "task_categories:text2text-generation", "size_categories:n<1K", "language:en", "license:mit", "code-generation", "arxiv:2308.01861", "region:us" ]
[ "text2text-generation" ]
2023-09-02T09:28:37Z
--- license: mit language: - en size_categories: - n<1K tags: - code-generation task_categories: - text2text-generation pretty_name: ClassEval configs: - config_name: default data_files: - split: test path: "ClassEval_data.json" --- # Dataset Card for FudanSELab ClassEval ## Dataset Description - **Repository:** [GitHub Repository](https://github.com/FudanSELab/ClassEval) - **Paper:** [ClassEval: A Manually-Crafted Benchmark for Evaluating LLMs on Class-level Code Generation](https://arxiv.org/abs/2308.01861) ### Dataset Summary We manually build ClassEval of 100 class-level Python coding tasks, consists of 100 classes and 412 methods, and average 33.1 test cases per class. For 100 class-level tasks, diversity is maintained by encompassing these tasks over a wide spectrum of topics, including Management Systems, Data Formatting, Mathematical Operations, Game Development, File Handing, Database Operations and Natural Language Processing. For 412 methods, they have been constructed with diverse dependencies, including (i) Library Dependency, where the methods rely on specific external libraries; (ii) Field Dependency, in which the methods are contingent on class instance variables, or fields; (iii) Method Dependency, where the methods are dependent on other methods within the same class; and (iv) Standalone, wherein the methods operate independently without reliance on fields, other methods, or external libraries. ### Languages The programming language is Python. The natural language used in the comments and docstrings is English. ## Dataset Structure ```python from datasets import load_dataset dataset = load_dataset("FudanSELab/ClassEval") DatasetDict({ test: Dataset({ features: ['task_id', 'skeleton', 'test', 'solution_code', 'import_statement', 'class_description', 'methods_info', 'class_name', 'test_classes', 'class_constructor', 'fields'], num_rows: 100 }) }) ``` ### Data Fields The specific data fields for each task are delineated as follows: * task_id: the unique identifier for each task. * skeleton: the class skeleton, including all input descriptions in our class-level coding tasks. * test: all test cases for the whole class. * solution_code: the ground-truth class-level code for each task. More fine-grained class-level information from the class skeleton, including: * import_statement: the import statements for each task. * class_name: the name of the class. * class_description: a concise description of the purpose and functionality of the class. * class_constructor: the whole constructor of the class. * fields: the fields defined in the class_constructor. Detailed information for each method in the "methods_info" field, including: * method_name: the method signature. * method_input: the method contract design, including all input descriptions in the method. * test_code: the test cases for the method. * solution_code: the ground-truth method-level code. * dependencies: the dependency information of the method. ### Data Splits The dataset only consists of a test split with 100 samples. ## Dataset Creation ### Source Data Manually-crafted ## Additional Information ### Licensing Information This repository is under [MIT](https://github.com/FudanSELab/ClassEval/blob/master/LICENSE) license. But the data is distributes through [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) license. ### Citation Information ``` @misc{du2023classeval, title={ClassEval: A Manually-Crafted Benchmark for Evaluating LLMs on Class-level Code Generation}, author={Xueying Du and Mingwei Liu and Kaixin Wang and Hanlin Wang and Junwei Liu and Yixuan Chen and Jiayi Feng and Chaofeng Sha and Xin Peng and Yiling Lou}, year={2023}, eprint={2308.01861}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Xueying Du [email protected] Mingwei Liu [email protected] Kaixin Wang [email protected] Hanlin Wang [email protected] Junwei Liu [email protected] Yixuan Chen [email protected] Jiayi Feng [email protected] Chaofeng Sha [email protected] Xin Peng [email protected] Yiling Lou [email protected]
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paulopirozelli/pira
paulopirozelli
2023-10-04T13:52:11Z
503
1
[ "task_categories:question-answering", "size_categories:1K<n<10K", "language:pt", "language:en", "license:cc-by-4.0", "climate", "arxiv:2309.10945", "region:us" ]
[ "question-answering" ]
2023-09-25T13:14:54Z
--- license: cc-by-4.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* - config_name: mcqa data_files: - split: train path: mcqa/train-* - split: validation path: mcqa/validation-* - split: test path: mcqa/test-* - config_name: paraphrases data_files: - split: train path: paraphrases/train-* - config_name: pira_version1 data_files: - split: train path: pira_version1/train-* dataset_info: - config_name: default features: - name: id_qa dtype: string - name: corpus dtype: int64 - name: question_en_origin dtype: string - name: question_pt_origin dtype: string - name: question_en_paraphase dtype: string - name: question_pt_paraphase dtype: string - name: answer_en_origin dtype: string - name: answer_pt_origin dtype: string - name: answer_en_validate dtype: string - name: answer_pt_validate dtype: string - name: abstract dtype: string - name: eid_article_scopus dtype: string - name: question_generic dtype: float64 - name: answer_in_text dtype: float64 - name: answer_difficulty dtype: float64 - name: question_meaningful dtype: float64 - name: answer_equivalent dtype: float64 - name: question_type dtype: string - name: abstract_translated_pt dtype: string - name: pt_question_translated_to_en dtype: string - name: at_labels dtype: float64 splits: - name: train num_bytes: 8002269 num_examples: 1806 - name: validation num_bytes: 994524 num_examples: 225 - name: test num_bytes: 940555 num_examples: 227 download_size: 3976683 dataset_size: 9937348 - config_name: mcqa features: - name: id dtype: string - name: text dtype: string - name: question dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: E dtype: string - name: correct dtype: string - name: alternative dtype: string splits: - name: train num_bytes: 4327619 num_examples: 1798 - name: validation num_bytes: 582526 num_examples: 225 - name: test num_bytes: 551723 num_examples: 227 download_size: 2148096 dataset_size: 5461868 - config_name: paraphrases features: - name: question_AUT_EN_1 dtype: string - name: question_AUT_EN_2 dtype: string - name: answer_AUT_EN_1 dtype: string - name: answer_AUT_EN_2 dtype: string - name: question_AUT_PT_1 dtype: string - name: question_AUT_PT_2 dtype: string - name: answer_AUT_PT_1 dtype: string - name: answer_AUT_PT_2 dtype: string splits: - name: train num_bytes: 1175020 num_examples: 1806 download_size: 720519 dataset_size: 1175020 - config_name: pira_version1 features: - name: id_qa dtype: string - name: corpus dtype: int64 - name: question_en_origin dtype: string - name: question_pt_origin dtype: string - name: question_en_paraphase dtype: string - name: question_pt_paraphase dtype: string - name: answer_en_origin dtype: string - name: answer_pt_origin dtype: string - name: answer_en_validate dtype: string - name: answer_pt_validate dtype: string - name: eid_article_scopus dtype: string - name: text_excerpts_un_reports dtype: string - name: question_generic dtype: bool - name: answer_in_text dtype: bool - name: answer_difficulty dtype: float64 - name: question_meaningful dtype: float64 - name: answer_equivalent dtype: float64 - name: question_type dtype: string splits: - name: train num_bytes: 3096316 num_examples: 2271 download_size: 1342133 dataset_size: 3096316 task_categories: - question-answering language: - pt - en tags: - climate size_categories: - 1K<n<10K --- # Pirá: A Bilingual Portuguese-English Dataset for Question-Answering about the Ocean, the Brazilian coast, and climate change Pirá is a crowdsourced reading comprehension dataset on the ocean, the Brazilian coast, and climate change. QA sets are presented in both Portuguese and English, together with their corresponding textual context. The dataset also contains human and automatic paraphrases for questions and answers, as well as a number of qualitative assessments. The original paper was published at CIKM'21 and can be found [here](https://dl.acm.org/doi/pdf/10.1145/3459637.3482012). As a subsequent project, we have produced a curated version of the dataset, which we refer to as Pirá 2.0. In this step, we have also defined a number of benchmarks and reported the corresponding baselines. This is the version that we make available at HuggingFace. Pirá 2.0's preprint is available in [Arxiv](https://arxiv.org/abs/2309.10945). Pirá is, to the best of our knowledge, the first QA dataset with supporting texts in Portuguese, and, perhaps more importantly, the first bilingual QA dataset that includes Portuguese as one of its languages. Pirá is also the first QA dataset in Portuguese with unanswerable questions so as to allow the study of answer triggering. Finally, it is the first QA dataset that tackles scientific knowledge about the ocean, climate change, and marine biodiversity. More information on the methodology, dataset versions, and benchmarks can be found on the project's [Github page](https://github.com/C4AI/Pira/). You can also find there the Multiple-Choice version of Pirá. # Dataset The dataset is split into train, validation, and test sets. | Split | Size | #QAs | |---|---|---| | Training | 80% | 1806 | | Validation | 10% | 225 | | Test | 10% | 227 | | Full dataset | 100% | 2258 | Above is an example of a question-answer set from Pirá: ``` { 'id_qa': 'B2142', 'corpus": 2, 'question_en_origin': 'What are the proportion of men and women employed in the fishery sector worlwide?', 'question_pt_origin': 'Qual é a proporção de homens e mulheres empregados no setor pesqueiro em todo o mundo?', 'question_en_paraphase': 'Which share of the fishery sector workers of the world are women?', 'question_pt_paraphase': 'Qual parcela dos trabalhadores do setor da pesca no mundo são mulheres?', 'answer_en_origin': '85 per cent men and 15 per cent women.', 'answer_pt_origin': '85 por cento homens e 15 por cento mulheres.', 'answer_en_validate': 'It is estimated that more than fifteen per cent of the fishing sector workers are women.', 'answer_pt_validate': 'Estima-se que mais de quinze por cento dos trabalhadores do setor da pesca são mulheres.', 'eid_article_scopus': '', 'text_excerpts_un_reports': 'Distribution of ocean benefits and disbenefits Developments in employment and income from fisheries and aquaculture The global harvest of marine capture fisheries has expanded rapidly since the early 1950s and is currently estimated to be about 80 million tons a year. That harvest is estimated to have a first (gross) value on the order of 113 billion dollars. Although it is difficult to produce accurate employment statistics, estimates using a fairly narrow definition of employment have put the figure of those employed in fisheries and aquaculture at 58.3 million people (4.4 per cent of the estimated total of economically active people), of which 84 per cent are in Asia and 10 per cent in Africa. Women are estimated to account for more than 15 per cent of people employed in the fishery sector. Other estimates, probably taking into account a wider definition of employment, suggest that capture fisheries provide direct and indirect employment for at least 120 million persons worldwide. Small-scale fisheries employ more than 90 per cent of the world’s capture fishermen and fish workers, about half of whom are women. When all dependants of those taking full- or part-time employment in the full value chain and support industries (boatbuilding, gear construction, etc.) of fisheries and aquaculture are included, one estimate concludes that between 660 and 820 million persons have some economic or livelihood dependence on fish capture and culture and the subsequent direct value chain. No sound information appears to be available on the levels of death and injury of those engaged in capture fishing or aquaculture, but capture fishing is commonly characterized as a dangerous occupation. Over time, a striking shift has occurred in the operation and location of capture fisheries. In the 1950s, capture fisheries were largely undertaken by developed fishing States. Since then, developing countries have increased their share. As a broad illustration, in the 1950s, the southern hemisphere accounted for no more than 8 per cent of landed values. By the last decade, the southern hemisphere’s share had risen to 20 per cent. In 2012, international trade represented 37 per cent of the total fish production in value, with a total export value of 129 billion dollars, of which 70 billion dollars (58 per cent) was exports by developing countries. Aquaculture is responsible for the bulk of the production of seaweeds. Worldwide, reports show that 24.9 million tons was produced in 2012, valued at about 6 billion dollars. In addition, about 1 million tons of wild seaweed were harvested. Few data were found on international trade in seaweeds, but their culture is concentrated in countries where consumption of seaweeds is high.', 'question_generic': false, 'answer_in_text': true, 'answer_difficulty': 1, 'question_meaningful': 5, 'answer_equivalent': 5, 'question_type': 'None of the above' } ``` # Automatic Paraphrases As we have only generated automatic paraphrases for questions and answers in the train set, they had to be saved in a different Dataset file. To download the automatic paraphrases, just run: ``` paraphrases = load_dataset("paulopirozelli/pira", "paraphrases") ``` # Multiple Choice Question Answering We have also developed a multiple choice question answering version of Pirá 2.0. To download the automatic paraphrases, just run: ``` mcqa = load_dataset("paulopirozelli/pira", "mcqa") ``` Above is an example of a question-answer set from Pirá: ``` { 'id_qa': 'A1582', 'corpus': 1, 'question_en_origin': 'In the estuary, with marine influence, what was associated to deep areas with sandy sediment?', 'question_pt_origin': 'No estuário, com influência marinha, o que foi associado a áreas profundas com sedimento arenoso?', 'question_en_paraphase': 'What was discovered in estuary under deep areas with sand sediment and marine influence?', 'question_pt_paraphase': 'O que foi descoberto no estuário sob áreas profundas com sedimento arenoso e influência marítima?', 'answer_en_origin': 'The Laryngosigma lactea and Pyrgo oblonga foraminifera species.', 'answer_pt_origin': 'As espécies Laryngosigma lactea e Pyrgo oblonga de foraminíferos.', 'answer_en_validate': 'The species Laryngosigma lactea and Pyrgo oblonga.', 'answer_pt_validate': 'A espécie Laryngosigma lactea e Pyrgo oblonga.', 'eid_article_scopus': '2-s2.0-85092100205', 'text_excerpts_un_reports': None, 'question_generic': False, 'answer_in_text': True, 'answer_difficulty': 4.0, 'question_meaningful': 5.0, 'answer_equivalent': 4.0, 'question_type': 'Who' } ``` # Pirá 1.0 You can also access the original Pirá dataset. Just run: ``` pira1 = load_dataset("paulopirozelli/pira", "pira_version1") ```
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open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE5_4w-r8-gate_up_down
open-llm-leaderboard
2023-10-28T04:49:35Z
503
0
[ "region:us" ]
null
2023-10-10T11:25:25Z
--- pretty_name: Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE5_4w-r8-gate_up_down dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [CHIH-HUNG/llama-2-13b-FINETUNE5_4w-r8-gate_up_down](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE5_4w-r8-gate_up_down)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE5_4w-r8-gate_up_down\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-28T04:49:22.682759](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE5_4w-r8-gate_up_down/blob/main/results_2023-10-28T04-49-22.682759.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.07487416107382551,\n\ \ \"em_stderr\": 0.0026952933607895307,\n \"f1\": 0.12380872483221442,\n\ \ \"f1_stderr\": 0.00285148758396334,\n \"acc\": 0.44832731261215925,\n\ \ \"acc_stderr\": 0.010657041987495935\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.07487416107382551,\n \"em_stderr\": 0.0026952933607895307,\n\ \ \"f1\": 0.12380872483221442,\n \"f1_stderr\": 0.00285148758396334\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.133434420015163,\n \ \ \"acc_stderr\": 0.00936649160978448\n },\n \"harness|winogrande|5\": {\n\ \ \"acc\": 0.7632202052091555,\n \"acc_stderr\": 0.01194759236520739\n\ \ }\n}\n```" repo_url: https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE5_4w-r8-gate_up_down leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|arc:challenge|25_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-10-10T11-25-01.199069.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_28T04_49_22.682759 path: - '**/details_harness|drop|3_2023-10-28T04-49-22.682759.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-28T04-49-22.682759.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_28T04_49_22.682759 path: - '**/details_harness|gsm8k|5_2023-10-28T04-49-22.682759.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-28T04-49-22.682759.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hellaswag|10_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-management|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-management|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-10-10T11-25-01.199069.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-international_law|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-management|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-marketing|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-sociology|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-virology|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T11-25-01.199069.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_10_10T11_25_01.199069 path: - '**/details_harness|truthfulqa:mc|0_2023-10-10T11-25-01.199069.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-10-10T11-25-01.199069.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_28T04_49_22.682759 path: - '**/details_harness|winogrande|5_2023-10-28T04-49-22.682759.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-28T04-49-22.682759.parquet' - config_name: results data_files: - split: 2023_10_10T11_25_01.199069 path: - results_2023-10-10T11-25-01.199069.parquet - split: 2023_10_28T04_49_22.682759 path: - results_2023-10-28T04-49-22.682759.parquet - split: latest path: - results_2023-10-28T04-49-22.682759.parquet --- # Dataset Card for Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE5_4w-r8-gate_up_down ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE5_4w-r8-gate_up_down - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [CHIH-HUNG/llama-2-13b-FINETUNE5_4w-r8-gate_up_down](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE5_4w-r8-gate_up_down) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE5_4w-r8-gate_up_down", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-28T04:49:22.682759](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE5_4w-r8-gate_up_down/blob/main/results_2023-10-28T04-49-22.682759.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.07487416107382551, "em_stderr": 0.0026952933607895307, "f1": 0.12380872483221442, "f1_stderr": 0.00285148758396334, "acc": 0.44832731261215925, "acc_stderr": 0.010657041987495935 }, "harness|drop|3": { "em": 0.07487416107382551, "em_stderr": 0.0026952933607895307, "f1": 0.12380872483221442, "f1_stderr": 0.00285148758396334 }, "harness|gsm8k|5": { "acc": 0.133434420015163, "acc_stderr": 0.00936649160978448 }, "harness|winogrande|5": { "acc": 0.7632202052091555, "acc_stderr": 0.01194759236520739 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
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pn_summary
null
2023-01-25T14:42:36Z
501
4
[ "task_categories:summarization", "task_categories:text-classification", "task_ids:news-articles-summarization", "task_ids:news-articles-headline-generation", "task_ids:text-simplification", "task_ids:topic-classification", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:fa", "license:mit", "arxiv:2012.11204", "region:us" ]
[ "summarization", "text-classification" ]
2022-03-02T23:29:22Z
--- annotations_creators: - found language_creators: - found language: - fa license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - summarization - text-classification task_ids: - news-articles-summarization - news-articles-headline-generation - text-simplification - topic-classification paperswithcode_id: pn-summary pretty_name: Persian News Summary (PnSummary) dataset_info: features: - name: id dtype: string - name: title dtype: string - name: article dtype: string - name: summary dtype: string - name: category dtype: class_label: names: '0': Economy '1': Roads-Urban '2': Banking-Insurance '3': Agriculture '4': International '5': Oil-Energy '6': Industry '7': Transportation '8': Science-Technology '9': Local '10': Sports '11': Politics '12': Art-Culture '13': Society '14': Health '15': Research '16': Education-University '17': Tourism - name: categories dtype: string - name: network dtype: class_label: names: '0': Tahlilbazaar '1': Imna '2': Shana '3': Mehr '4': Irna '5': Khabaronline - name: link dtype: string config_name: 1.0.0 splits: - name: train num_bytes: 309436493 num_examples: 82022 - name: validation num_bytes: 21311817 num_examples: 5592 - name: test num_bytes: 20936820 num_examples: 5593 download_size: 89591141 dataset_size: 351685130 --- # Dataset Card for Persian News Summary (pn_summary) ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** https://github.com/hooshvare/pn-summary/ - **Paper:** https://arxiv.org/abs/2012.11204 - **Leaderboard:** [More Information Needed] - **Point of Contact:** [Mehrdad Farahani](mailto:[email protected]) ### Dataset Summary A well-structured summarization dataset for the Persian language consists of 93,207 records. It is prepared for Abstractive/Extractive tasks (like cnn_dailymail for English). It can also be used in other scopes like Text Generation, Title Generation, and News Category Classification. It is imperative to consider that the newlines were replaced with the `[n]` symbol. Please interpret them into normal newlines (for ex. `t.replace("[n]", "\n")`) and then use them for your purposes. ### Supported Tasks and Leaderboards The dataset is prepared for Abstractive/Extractive summarization tasks (like cnn_dailymail for English). It can also be used in other scopes like Text Generation, Title Generation, and News Category Classification. ### Languages The dataset covers Persian mostly and somewhere a combination with English. ## Dataset Structure ### Data Instances A record consists of 8 features: ```python record = ['id','title', 'article', 'summary', 'category', 'categories', 'network', 'link'] ``` In the following, you can see an example of `pn_summmary`. ```json { "article": "به گزارش شانا، علی کاردر امروز (۲۷ دی ماه) در مراسم تودیع محسن قمصری، مدیر سابق امور بین الملل شرکت ملی نفت ایران و معارفه سعید خوشرو، مدیر جدید امور بین الملل این شرکت، گفت: مدیریت امور بین\u200eالملل به عنوان یکی از تاثیرگذارترین مدیریت\u200cهای شرکت ملی نفت ایران در دوران تحریم\u200cهای ظالمانه غرب علیه کشورمان بسیار هوشمندانه عمل کرد و ما توانستیم به خوبی از عهده تحریم\u200cها برآییم. [n] وی افزود: مجموعه امور بین الملل در همه دوران\u200cها با سختی\u200cها و مشکلات بسیاری مواجه بوده است، به ویژه در دوره اخیر به دلیل مسائل پیرامون تحریم وظیفه سنگینی بر عهده داشت که با تدبیر مدیریت خوب این مجموعه سربلند از آن بیرون آمد. [n] کاردر با قدردانی از زحمات محسن قمصری، به سلامت مدیریت امور بین الملل این شرکت اشاره کرد و افزود: محوریت کار مدیریت اموربین الملل سلامت مالی بوده است. [n] وی بر ضرورت نهادینه سازی جوانگرایی در مدیریت شرکت ملی نفت ایران تاکید کرد و گفت: مدیریت امور بین الملل در پرورش نیروهای زبده و کارآزموده آنچنان قوی عملکرده است که برای انتخاب مدیر جدید مشکلی وجود نداشت. [n] کاردر، حرفه\u200eای\u200eگری و کار استاندارد را از ویژگی\u200cهای مدیران این مدیریت برشمرد و گفت: نگاه جامع، خلاقیت و نوآوری و بکارگیری نیروهای جوان باید همچنان مد نظر مدیریت جدید امور بین الملل شرکت ملی نفت ایران باشد.", "categories": "نفت", "category": 5, "id": "738e296491f8b24c5aa63e9829fd249fb4428a66", "link": "https://www.shana.ir/news/275284/%D9%85%D8%AF%DB%8C%D8%B1%DB%8C%D8%AA-%D9%81%D8%B1%D9%88%D8%B4-%D9%86%D9%81%D8%AA-%D8%AF%D8%B1-%D8%AF%D9%88%D8%B1%D8%A7%D9%86-%D8%AA%D8%AD%D8%B1%DB%8C%D9%85-%D9%87%D9%88%D8%B4%D9%85%D9%86%D8%AF%D8%A7%D9%86%D9%87-%D8%B9%D9%85%D9%84-%DA%A9%D8%B1%D8%AF", "network": 2, "summary": "مدیرعامل شرکت ملی نفت، عملکرد مدیریت امور بین\u200eالملل این شرکت را در دوران تحریم بسیار هوشمندانه خواند و گفت: امور بین الملل در دوران پس از تحریم\u200eها نیز می\u200cتواند نقش بزرگی در تسریع روند توسعه داشته باشد.", "title": "مدیریت فروش نفت در دوران تحریم هوشمندانه عمل کرد" } ``` ### Data Fields - `id (string)`: ID of the news. - `title (string)`: The title of the news. - `article (string)`: The article of the news. - `summary (string)`: The summary of the news. - `category (int)`: The category of news in English (index of categories), including `Economy`, `Roads-Urban`, `Banking-Insurance`, `Agriculture`, `International`, `Oil-Energy`, `Industry`, `Transportation`, `Science-Technology`, `Local`, `Sports`, `Politics`, `Art-Culture`, `Society`, `Health`, `Research`, `Education-University`, `Tourism`. - `categories (string)`: The category and sub-category of the news in Persian. - `network (int)`: The news agency name (index of news agencies), including `Tahlilbazaar`, `Imna`, `Shana`, `Mehr`, `Irna`, `Khabaronline`. - `link (string)`: The link of the news. The category in English includes 18 different article categories from economy to tourism. ```bash Economy, Roads-Urban, Banking-Insurance, Agriculture, International, Oil-Energy, Industry, Transportation, Science-Technology, Local, Sports, Politics, Art-Culture, Society, Health, Research, Education-University, Tourism ``` ### Data Splits Training (82,022 records, 8 features), validation (5,592 records, 8 features), and test split (5,593 records and 8 features). ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? The dataset comprises numerous articles of various categories that have been crawled from six news agency websites (Tahlilbazaar, Imna, Shana, Mehr, Irna, and Khabaronline). ### Annotations #### Annotation process Each record (article) includes the long original text as well as a human-generated summary. The total number of cleaned articles is 93,207 (from 200,000 crawled articles). #### Who are the annotators? The dataset was organized by [Mehrdad Farahani](https://github.com/m3hrdadfi), [Mohammad Gharachorloo](https://github.com/baarsaam) and [Mohammad Manthouri](https://github.com/mmanthouri) for this paper [Leveraging ParsBERT and Pretrained mT5 for Persian Abstractive Text Summarization](https://arxiv.org/abs/2012.11204) ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators This dataset was curated by [Mehrdad Farahani](https://github.com/m3hrdadfi), [Mohammad Gharachorloo](https://github.com/baarsaam) and [Mohammad Manthouri](https://github.com/mmanthouri). ### Licensing Information This dataset is licensed under MIT License. ### Citation Information ```bibtex @article{pnSummary, title={Leveraging ParsBERT and Pretrained mT5 for Persian Abstractive Text Summarization}, author={Mehrdad Farahani, Mohammad Gharachorloo, Mohammad Manthouri}, year={2020}, eprint={2012.11204}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@m3hrdadfi](https://github.com/m3hrdadfi) for adding this dataset.
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miracl/hagrid
miracl
2023-08-01T13:01:38Z
500
3
[ "size_categories:1K<n<10K", "language:en", "license:apache-2.0", "region:us" ]
null
2023-07-31T23:40:24Z
--- license: apache-2.0 language: - en pretty_name: HAGRID size_categories: - 1K<n<10K --- # HAGRID: A Human-LLM Collaborative Dataset for Generative Information-seeking with Attribution HAGRID (**H**uman-in-the-loop **A**ttributable **G**enerative **R**etrieval for **I**nformation-seeking **D**ataset) is a dataset for generative information-seeking scenarios. It is constructed on top of MIRACL 🌍🙌🌏, an information retrieval dataset that consists of queries along with a set of manually labelled relevant passages (quotes). ## Dataset Structure To load the dataset: ```python import datasets hagrid = datasets.load_dataset("miracl/hagrid", split="train") print(hagrid[0]) ``` It would show: ```json { 'query': ..., 'query_id': ..., 'quotes': [{ # a list of quotes that are manually labeled as relevant to the query 'docid': ..., 'idx': ..., 'text': ... }, ...] 'answers': [{ 'answer': ..., # the complete answer generated by LLM 'attributable': 1/0/None, # 1: attributable; 0: unattributable; None: unlabeled 'informative': 1/0, # 1: informative; 0: uninformative 'sentences': [{ # answers split into sentences 'index': ..., 'attributable': 0/1/None, 'informative': 0/1/None, 'text': ..., }, ...] }, ...] } ```
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numer_sense
null
2022-11-18T21:34:07Z
499
1
[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:slot-filling", "annotations_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:extended|other", "language:en", "license:mit", "arxiv:2005.00683", "region:us" ]
[ "text-generation", "fill-mask" ]
2022-03-02T23:29:22Z
--- annotations_creators: - expert-generated language_creators: - crowdsourced language: - en license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended|other task_categories: - text-generation - fill-mask task_ids: - slot-filling paperswithcode_id: numersense pretty_name: NumerSense dataset_info: features: - name: sentence dtype: string - name: target dtype: string splits: - name: train num_bytes: 825865 num_examples: 10444 - name: test_core num_bytes: 62652 num_examples: 1132 - name: test_all num_bytes: 184180 num_examples: 3146 download_size: 985463 dataset_size: 1072697 --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://inklab.usc.edu/NumerSense/ - **Repository:** https://github.com/INK-USC/NumerSense - **Paper:** https://arxiv.org/abs/2005.00683 - **Leaderboard:** https://inklab.usc.edu/NumerSense/#exp - **Point of Contact:** Author emails listed in [paper](https://arxiv.org/abs/2005.00683) ### Dataset Summary NumerSense is a new numerical commonsense reasoning probing task, with a diagnostic dataset consisting of 3,145 masked-word-prediction probes. The general idea is to mask numbers between 0-10 in sentences mined from a commonsense corpus and evaluate whether a language model can correctly predict the masked value. ### Supported Tasks and Leaderboards The dataset supports the task of slot-filling, specifically as an evaluation of numerical common sense. A leaderboard is included on the [dataset webpage](https://inklab.usc.edu/NumerSense/#exp) with included benchmarks for GPT-2, RoBERTa, BERT, and human performance. Leaderboards are included for both the core set and the adversarial set discussed below. ### Languages This dataset is in English. ## Dataset Structure ### Data Instances Each instance consists of a sentence with a masked numerical value between 0-10 and (in the train set) a target. Example from the training set: ``` sentence: Black bears are about <mask> metres tall. target: two ``` ### Data Fields Each value of the training set consists of: - `sentence`: The sentence with a number masked out with the `<mask>` token. - `target`: The ground truth target value. Since the test sets do not include the ground truth, the `target` field values are empty strings in the `test_core` and `test_all` splits. ### Data Splits The dataset includes the following pre-defined data splits: - A train set with >10K labeled examples (i.e. containing a ground truth value) - A core test set (`test_core`) with 1,132 examples (no ground truth provided) - An expanded test set (`test_all`) encompassing `test_core` with the addition of adversarial examples for a total of 3,146 examples. See section 2.2 of [the paper] for a discussion of how these examples are constructed. ## Dataset Creation ### Curation Rationale The purpose of this dataset is "to study whether PTLMs capture numerical commonsense knowledge, i.e., commonsense knowledge that provides an understanding of the numeric relation between entities." This work is motivated by the prior research exploring whether language models possess _commonsense knowledge_. ### Source Data #### Initial Data Collection and Normalization The dataset is an extension of the [Open Mind Common Sense](https://huggingface.co/datasets/open_mind_common_sense) corpus. A query was performed to discover sentences containing numbers between 0-12, after which the resulting sentences were manually evaluated for inaccuracies, typos, and the expression of commonsense knowledge. The numerical values were then masked. #### Who are the source language producers? The [Open Mind Common Sense](https://huggingface.co/datasets/open_mind_common_sense) corpus, from which this dataset is sourced, is a crowdsourced dataset maintained by the MIT Media Lab. ### Annotations #### Annotation process No annotations are present in this dataset beyond the `target` values automatically sourced from the masked sentences, as discussed above. #### Who are the annotators? The curation and inspection was done in two rounds by graduate students. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset The motivation of measuring a model's ability to associate numerical values with real-world concepts appears relatively innocuous. However, as discussed in the following section, the source dataset may well have biases encoded from crowdworkers, particularly in terms of factoid coverage. A model's ability to perform well on this benchmark should therefore not be considered evidence that it is more unbiased or objective than a human performing similar tasks. [More Information Needed] ### Discussion of Biases This dataset is sourced from a crowdsourced commonsense knowledge base. While the information contained in the graph is generally considered to be of high quality, the coverage is considered to very low as a representation of all possible commonsense knowledge. The representation of certain factoids may also be skewed by the demographics of the crowdworkers. As one possible example, the term "homophobia" is connected with "Islam" in the ConceptNet knowledge base, but not with any other religion or group, possibly due to the biases of crowdworkers contributing to the project. ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators This dataset was collected by Bill Yuchen Lin, Seyeon Lee, Rahul Khanna, and Xiang Ren, Computer Science researchers at the at the University of Southern California. ### Licensing Information The data is hosted in a GitHub repositor with the [MIT License](https://github.com/INK-USC/NumerSense/blob/main/LICENSE). ### Citation Information ``` @inproceedings{lin2020numersense, title={Birds have four legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-trained Language Models}, author={Bill Yuchen Lin and Seyeon Lee and Rahul Khanna and Xiang Ren}, booktitle={Proceedings of EMNLP}, year={2020}, note={to appear} } ``` ### Contributions Thanks to [@joeddav](https://github.com/joeddav) for adding this dataset.
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poleval2019_cyberbullying
null
2023-01-25T14:42:46Z
499
1
[ "task_categories:text-classification", "task_ids:intent-classification", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:pl", "license:unknown", "region:us" ]
[ "text-classification" ]
2022-03-02T23:29:22Z
--- annotations_creators: - found language_creators: - found language: - pl license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - intent-classification pretty_name: Poleval 2019 cyberbullying dataset_info: - config_name: task01 features: - name: text dtype: string - name: label dtype: class_label: names: '0': '0' '1': '1' splits: - name: train num_bytes: 1104322 num_examples: 10041 - name: test num_bytes: 109681 num_examples: 1000 download_size: 410001 dataset_size: 1214003 - config_name: task02 features: - name: text dtype: string - name: label dtype: class_label: names: '0': '0' '1': '1' '2': '2' splits: - name: train num_bytes: 1104322 num_examples: 10041 - name: test num_bytes: 109681 num_examples: 1000 download_size: 410147 dataset_size: 1214003 --- # Dataset Card for Poleval 2019 cyberbullying ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://2019.poleval.pl/index.php/tasks/task6 - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary Task 6-1: Harmful vs non-harmful In this task, the participants are to distinguish between normal/non-harmful tweets (class: 0) and tweets that contain any kind of harmful information (class: 1). This includes cyberbullying, hate speech and related phenomena. The data for the task is available now and can be downloaded from the link provided below. Task 6-2: Type of harmfulness In this task, the participants shall distinguish between three classes of tweets: 0 (non-harmful), 1 (cyberbullying), 2 (hate-speech). There are various definitions of both cyberbullying and hate-speech, some of them even putting those two phenomena in the same group. The specific conditions on which we based our annotations for both cyberbullying and hate-speech, which have been worked out during ten years of research will be summarized in an introductory paper for the task, however, the main and definitive condition to distinguish the two is whether the harmful action is addressed towards a private person(s) (cyberbullying), or a public person/entity/large group (hate-speech). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Polish ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - text: the provided tweet - label: for task 6-1 the label can be 0 (non-harmful) or 1 (harmful) for task 6-2 the label can be 0 (non-harmful), 1 (cyberbullying) or 2 (hate-speech) ### Data Splits Train and Test ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @proceedings{ogr:kob:19:poleval, editor = {Maciej Ogrodniczuk and Łukasz Kobyliński}, title = {{Proceedings of the PolEval 2019 Workshop}}, year = {2019}, address = {Warsaw, Poland}, publisher = {Institute of Computer Science, Polish Academy of Sciences}, url = {http://2019.poleval.pl/files/poleval2019.pdf}, isbn = "978-83-63159-28-3"} } ``` ### Contributions Thanks to [@czabo](https://github.com/czabo) for adding this dataset.
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open-llm-leaderboard/details_ehartford__dolphin-2.1-mistral-7b
open-llm-leaderboard
2023-10-28T06:17:24Z
499
0
[ "region:us" ]
null
2023-10-11T07:08:34Z
--- pretty_name: Evaluation run of ehartford/dolphin-2.1-mistral-7b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [ehartford/dolphin-2.1-mistral-7b](https://huggingface.co/ehartford/dolphin-2.1-mistral-7b)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 4 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_ehartford__dolphin-2.1-mistral-7b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-28T06:17:12.096857](https://huggingface.co/datasets/open-llm-leaderboard/details_ehartford__dolphin-2.1-mistral-7b/blob/main/results_2023-10-28T06-17-12.096857.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0025167785234899327,\n\ \ \"em_stderr\": 0.0005131152834514602,\n \"f1\": 0.07557885906040251,\n\ \ \"f1_stderr\": 0.0015806922251337756,\n \"acc\": 0.49258006202828786,\n\ \ \"acc_stderr\": 0.011432753263209281\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.0025167785234899327,\n \"em_stderr\": 0.0005131152834514602,\n\ \ \"f1\": 0.07557885906040251,\n \"f1_stderr\": 0.0015806922251337756\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.20773313115996966,\n \ \ \"acc_stderr\": 0.011174572716705898\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7774269928966061,\n \"acc_stderr\": 0.011690933809712662\n\ \ }\n}\n```" repo_url: https://huggingface.co/ehartford/dolphin-2.1-mistral-7b leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|arc:challenge|25_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|arc:challenge|25_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-10-11T07-16-54.692993.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_26T09_35_25.636267 path: - '**/details_harness|drop|3_2023-10-26T09-35-25.636267.parquet' - split: 2023_10_28T06_17_12.096857 path: - '**/details_harness|drop|3_2023-10-28T06-17-12.096857.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-28T06-17-12.096857.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_26T09_35_25.636267 path: - '**/details_harness|gsm8k|5_2023-10-26T09-35-25.636267.parquet' - split: 2023_10_28T06_17_12.096857 path: - '**/details_harness|gsm8k|5_2023-10-28T06-17-12.096857.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-28T06-17-12.096857.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hellaswag|10_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hellaswag|10_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-management|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-10-11T07-08-11.393844.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-management|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-management|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-10-11T07-16-54.692993.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-international_law|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-international_law|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-management|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-management|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-marketing|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-marketing|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-sociology|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-sociology|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-virology|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-virology|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-10-11T07-16-54.692993.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_10_11T07_08_11.393844 path: - '**/details_harness|truthfulqa:mc|0_2023-10-11T07-08-11.393844.parquet' - split: 2023_10_11T07_16_54.692993 path: - '**/details_harness|truthfulqa:mc|0_2023-10-11T07-16-54.692993.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-10-11T07-16-54.692993.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_26T09_35_25.636267 path: - '**/details_harness|winogrande|5_2023-10-26T09-35-25.636267.parquet' - split: 2023_10_28T06_17_12.096857 path: - '**/details_harness|winogrande|5_2023-10-28T06-17-12.096857.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-28T06-17-12.096857.parquet' - config_name: results data_files: - split: 2023_10_11T07_08_11.393844 path: - results_2023-10-11T07-08-11.393844.parquet - split: 2023_10_11T07_16_54.692993 path: - results_2023-10-11T07-16-54.692993.parquet - split: 2023_10_26T09_35_25.636267 path: - results_2023-10-26T09-35-25.636267.parquet - split: 2023_10_28T06_17_12.096857 path: - results_2023-10-28T06-17-12.096857.parquet - split: latest path: - results_2023-10-28T06-17-12.096857.parquet --- # Dataset Card for Evaluation run of ehartford/dolphin-2.1-mistral-7b ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/ehartford/dolphin-2.1-mistral-7b - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [ehartford/dolphin-2.1-mistral-7b](https://huggingface.co/ehartford/dolphin-2.1-mistral-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_ehartford__dolphin-2.1-mistral-7b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-28T06:17:12.096857](https://huggingface.co/datasets/open-llm-leaderboard/details_ehartford__dolphin-2.1-mistral-7b/blob/main/results_2023-10-28T06-17-12.096857.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.0025167785234899327, "em_stderr": 0.0005131152834514602, "f1": 0.07557885906040251, "f1_stderr": 0.0015806922251337756, "acc": 0.49258006202828786, "acc_stderr": 0.011432753263209281 }, "harness|drop|3": { "em": 0.0025167785234899327, "em_stderr": 0.0005131152834514602, "f1": 0.07557885906040251, "f1_stderr": 0.0015806922251337756 }, "harness|gsm8k|5": { "acc": 0.20773313115996966, "acc_stderr": 0.011174572716705898 }, "harness|winogrande|5": { "acc": 0.7774269928966061, "acc_stderr": 0.011690933809712662 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
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catalonia_independence
null
2023-06-01T14:59:47Z
496
1
[ "task_categories:text-classification", "annotations_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:ca", "language:es", "license:cc-by-nc-sa-4.0", "stance-detection", "region:us" ]
[ "text-classification" ]
2022-03-02T23:29:22Z
--- annotations_creators: - expert-generated language_creators: - crowdsourced language: - ca - es license: - cc-by-nc-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: [] paperswithcode_id: cic pretty_name: Catalonia Independence Corpus tags: - stance-detection dataset_info: - config_name: catalan features: - name: id_str dtype: string - name: TWEET dtype: string - name: LABEL dtype: class_label: names: '0': AGAINST '1': FAVOR '2': NEUTRAL splits: - name: train num_bytes: 1406250 num_examples: 6028 - name: test num_bytes: 469204 num_examples: 2010 - name: validation num_bytes: 473393 num_examples: 2010 download_size: 995415 dataset_size: 2348847 - config_name: spanish features: - name: id_str dtype: string - name: TWEET dtype: string - name: LABEL dtype: class_label: names: '0': AGAINST '1': FAVOR '2': NEUTRAL splits: - name: train num_bytes: 1507388 num_examples: 6046 - name: test num_bytes: 501783 num_examples: 2016 - name: validation num_bytes: 505092 num_examples: 2015 download_size: 1070281 dataset_size: 2514263 config_names: - catalan - spanish --- # Dataset Card for Catalonia Independence Corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/ixa-ehu/catalonia-independence-corpus - **Repository:** https://github.com/ixa-ehu/catalonia-independence-corpus - **Paper:** [Multilingual Stance Detection: The Catalonia Independence Corpus](https://www.aclweb.org/anthology/2020.lrec-1.171/) - **Leaderboard:** - **Point of Contact:** [Rodrigo Agerri](https://github.com/ragerri) (corpus creator) ### Dataset Summary This dataset contains two corpora in Spanish and Catalan that consist of annotated Twitter messages for automatic stance detection. The data was collected over 12 days during February and March of 2019 from tweets posted in Barcelona, and during September of 2018 from tweets posted in the town of Terrassa, Catalonia. Each corpus is annotated with three classes: AGAINST, FAVOR and NEUTRAL, which express the stance towards the target - independence of Catalonia. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Spanish and Catalan ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@lewtun](https://github.com/lewtun) for adding this dataset.
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corypaik/prost
corypaik
2022-10-25T09:07:34Z
496
1
[ "task_categories:question-answering", "task_ids:multiple-choice-qa", "task_ids:open-domain-qa", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "license:apache-2.0", "arxiv:2106.03634", "region:us" ]
[ "question-answering" ]
2022-03-02T23:29:22Z
--- annotations_creators: - expert-generated extended: - original language_creators: - expert-generated language: - en-US license: - apache-2.0 multilinguality: - monolingual paperswithcode_id: prost size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - multiple-choice-qa - open-domain-qa --- # PROST: Physical Reasoning about Objects Through Space and Time ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** https://github.com/nala-cub/prost - **Paper:** https://arxiv.org/abs/2106.03634 - **Leaderboard:** - **Point of Contact:** [Stéphane Aroca-Ouellette](mailto:[email protected]) ### Dataset Summary *Physical Reasoning about Objects Through Space and Time* (PROST) is a probing dataset to evaluate the ability of pretrained LMs to understand and reason about the physical world. PROST consists of 18,736 cloze-style multiple choice questions from 14 manually curated templates, covering 10 physical reasoning concepts: direction, mass, height, circumference, stackable, rollable, graspable, breakable, slideable, and bounceable. ### Supported Tasks and Leaderboards The task is multiple choice question answering, but you can formulate it multiple ways. You can use `context` and `question` to form cloze style questions, or `context` and `ex_question` as multiple choice question answering. See the [GitHub](https://github.com/nala-cub/prost) repo for examples using GPT-1, GPT-2, BERT, RoBERTa, ALBERT, T5, and UnifiedQA. ### Languages The text in the dataset is in English. The associated BCP-47 code is `en-US`. ## Dataset Structure ### Data Instances An example looks like this: ```json { "A": "glass", "B": "pillow", "C": "coin", "D": "ball", "context": "A person drops a glass, a pillow, a coin, and a ball from a balcony.", "ex_question": "Which object is the most likely to break?", "group": "breaking", "label": 0, "name": "breaking_1", "question": "The [MASK] is the most likely to break." } ``` ### Data Fields - `A`: Option A (0) - `B`: Option B (1) - `C`: Option C (2) - `D`: Option D (3) - `context`: Context for the question - `question`: A cloze style continuation of the context. - `ex_question`: A multiple-choice style question. - `group`: The question group, e.g. *bouncing* - `label`: A ClassLabel indication the correct option - `name':` The template identifier. ### Data Splits The dataset contains 18,736 examples for testing. ## Dataset Creation ### Curation Rationale PROST is designed to avoid models succeeding in unintended ways. First, PROST provides no training data, so as to probe models in a zero-shot fashion. This prevents models from succeeding through spurious correlations between testing and training, and encourages success through a true understanding of and reasoning about the concepts at hand. Second, we manually write templates for all questions in an effort to prevent models from having seen the exact same sentences in their training data. Finally, it focuses on a small set of well defined, objective concepts that only require a small vocabulary. This allows researchers to focus more on the quality of training data rather than on size of it. ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information PROST is licensed under the Apache 2.0 license. ### Citation Information ``` @inproceedings{aroca-ouellette-etal-2021-prost, title = "{PROST}: {P}hysical Reasoning about Objects through Space and Time", author = "Aroca-Ouellette, St{\'e}phane and Paik, Cory and Roncone, Alessandro and Kann, Katharina", booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-acl.404", pages = "4597--4608", } ``` ### Contributions Thanks to [@corypaik](https://github.com/corypaik) for adding this dataset.
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erbacher/PDEBench-1D
erbacher
2023-11-22T16:05:42Z
496
0
[ "region:us" ]
null
2023-11-22T13:13:55Z
--- dataset_info: - config_name: Advection_Sols_beta0.1 features: - name: parameters dtype: string - name: tensor sequence: sequence: sequence: float32 splits: - name: train num_bytes: 2079020000 num_examples: 10000 download_size: 1030317301 dataset_size: 2079020000 - config_name: Advection_Sols_beta0.2 features: - name: parameters dtype: string - name: tensor sequence: sequence: sequence: float32 splits: - name: train num_bytes: 2079020000 num_examples: 10000 download_size: 1034054442 dataset_size: 2079020000 - config_name: Advection_Sols_beta0.4 features: - name: parameters dtype: string - name: tensor sequence: sequence: sequence: float32 splits: - name: train num_bytes: 2079020000 num_examples: 10000 download_size: 1037220772 dataset_size: 2079020000 - config_name: Advection_Sols_beta0.7 features: - name: parameters dtype: string - name: tensor sequence: sequence: sequence: float32 splits: - name: train num_bytes: 2079020000 num_examples: 10000 download_size: 1039496575 dataset_size: 2079020000 - config_name: Advection_Sols_beta1.0 features: - name: parameters dtype: string - name: tensor sequence: sequence: sequence: float32 splits: - name: train num_bytes: 2079020000 num_examples: 10000 download_size: 1041009183 dataset_size: 2079020000 - config_name: Advection_Sols_beta2.0 features: - name: parameters dtype: string - name: tensor sequence: sequence: sequence: float32 splits: - name: train num_bytes: 2079020000 num_examples: 10000 download_size: 1041263590 dataset_size: 2079020000 - config_name: Advection_Sols_beta4.0 features: - name: parameters dtype: string - name: tensor sequence: sequence: sequence: float32 splits: - name: train num_bytes: 2079020000 num_examples: 10000 download_size: 1041302186 dataset_size: 2079020000 - config_name: Advection_Sols_beta7.0 features: - name: parameters dtype: string - name: tensor sequence: sequence: sequence: float32 splits: - name: train num_bytes: 2079020000 num_examples: 10000 download_size: 1041314010 dataset_size: 2079020000 - config_name: Burgers_Sols_Nu0.001 features: - name: parameters dtype: string - name: tensor sequence: sequence: sequence: float32 splits: - name: train num_bytes: 2079000000 num_examples: 10000 download_size: 1025858949 dataset_size: 2079000000 - config_name: Burgers_Sols_Nu0.002 features: - name: parameters dtype: string - name: tensor sequence: sequence: sequence: float32 splits: - name: train num_bytes: 2079000000 num_examples: 10000 download_size: 1032095037 dataset_size: 2079000000 - config_name: Burgers_Sols_Nu0.004 features: - name: parameters dtype: string - name: tensor sequence: sequence: sequence: float32 splits: - name: train num_bytes: 2079000000 num_examples: 10000 download_size: 1037155483 dataset_size: 2079000000 - config_name: Burgers_Sols_Nu0.01 features: - name: parameters dtype: string - name: tensor sequence: sequence: sequence: float32 splits: - name: train num_bytes: 2078990000 num_examples: 10000 download_size: 1040325750 dataset_size: 2078990000 - config_name: Burgers_Sols_Nu0.02 features: - name: parameters dtype: string - name: tensor sequence: sequence: sequence: float32 splits: - name: train num_bytes: 2078990000 num_examples: 10000 download_size: 1040663851 dataset_size: 2078990000 - config_name: Burgers_Sols_Nu0.04 features: - name: parameters dtype: string - name: tensor sequence: sequence: sequence: float32 splits: - name: train num_bytes: 2078990000 num_examples: 10000 download_size: 1033200091 dataset_size: 2078990000 - config_name: Burgers_Sols_Nu0.1 features: - name: parameters dtype: string - name: tensor sequence: sequence: sequence: float32 splits: - name: train num_bytes: 2078980000 num_examples: 10000 download_size: 993568525 dataset_size: 2078980000 - config_name: Burgers_Sols_Nu0.2 features: - name: parameters dtype: string - name: tensor sequence: sequence: sequence: float32 splits: - name: train num_bytes: 2078980000 num_examples: 10000 download_size: 945791995 dataset_size: 2078980000 - config_name: Burgers_Sols_Nu0.4 features: - name: parameters dtype: string - name: tensor sequence: sequence: sequence: float32 splits: - name: train num_bytes: 2078980000 num_examples: 10000 download_size: 844969978 dataset_size: 2078980000 - config_name: Burgers_Sols_Nu1.0 features: - name: parameters dtype: string - name: tensor sequence: sequence: sequence: float32 splits: - name: train num_bytes: 2078980000 num_examples: 10000 download_size: 570861141 dataset_size: 2078980000 - config_name: Burgers_Sols_Nu2.0 features: - name: parameters dtype: string - name: tensor sequence: sequence: sequence: float32 splits: - name: train num_bytes: 2078980000 num_examples: 10000 download_size: 313254875 dataset_size: 2078980000 - config_name: Burgers_Sols_Nu4.0 features: - name: parameters dtype: string - name: tensor sequence: sequence: sequence: float32 splits: - name: train num_bytes: 2078980000 num_examples: 10000 download_size: 187697612 dataset_size: 2078980000 - config_name: CFD_Rand_Eta0.01_Zeta0.01_periodic features: - name: parameters dtype: string - name: tensor sequence: sequence: sequence: float32 splits: - name: train num_bytes: 2099620000 num_examples: 10000 download_size: 1576405761 dataset_size: 2099620000 - config_name: CFD_Rand_Eta0.1_Zeta0.1_periodic features: - name: parameters dtype: string - name: tensor sequence: sequence: sequence: float32 splits: - name: train num_bytes: 2099600000 num_examples: 10000 download_size: 946984963 dataset_size: 2099600000 - config_name: CFD_Rand_Eta1.e-8_Zeta1.e-8_periodic features: - name: parameters dtype: string - name: tensor sequence: sequence: sequence: float32 splits: - name: train num_bytes: 2099640000 num_examples: 10000 download_size: 1573309616 dataset_size: 2099640000 - config_name: CFD_Rand_Eta1.e-8_Zeta1.e-8_trans features: - name: parameters dtype: string - name: tensor sequence: sequence: sequence: float32 splits: - name: train num_bytes: 2099610000 num_examples: 10000 download_size: 0 dataset_size: 2099610000 - config_name: ReacDiff_Nu0.5_Rho1.0 features: - name: parameters dtype: string - name: tensor sequence: sequence: sequence: float32 splits: - name: train num_bytes: 1055010000 num_examples: 10000 download_size: 103983829 dataset_size: 1055010000 - config_name: ReacDiff_Nu0.5_Rho10.0 features: - name: parameters dtype: string - name: tensor sequence: sequence: sequence: float32 splits: - name: train num_bytes: 1055020000 num_examples: 10000 download_size: 124933565 dataset_size: 1055020000 - config_name: ReacDiff_Nu0.5_Rho2.0 features: - name: parameters dtype: string - name: tensor sequence: sequence: sequence: float32 splits: - name: train num_bytes: 1055010000 num_examples: 10000 download_size: 193004745 dataset_size: 1055010000 - config_name: ReacDiff_Nu0.5_Rho5.0 features: - name: parameters dtype: string - name: tensor sequence: sequence: sequence: float32 splits: - name: train num_bytes: 1055010000 num_examples: 10000 download_size: 146090506 dataset_size: 1055010000 - config_name: ReacDiff_Nu1.0_Rho1.0 features: - name: parameters dtype: string - name: tensor sequence: sequence: sequence: float32 splits: - name: train num_bytes: 1055010000 num_examples: 10000 download_size: 217153008 dataset_size: 1055010000 - config_name: ReacDiff_Nu1.0_Rho10.0 features: - name: parameters dtype: string - name: tensor sequence: sequence: sequence: float32 splits: - name: train num_bytes: 1055020000 num_examples: 10000 download_size: 113039664 dataset_size: 1055020000 - config_name: ReacDiff_Nu1.0_Rho2.0 features: - name: parameters dtype: string - name: tensor sequence: sequence: sequence: float32 splits: - name: train num_bytes: 1055010000 num_examples: 10000 download_size: 139659779 dataset_size: 1055010000 - config_name: ReacDiff_Nu1.0_Rho5.0 features: - name: parameters dtype: string - name: tensor sequence: sequence: sequence: float32 splits: - name: train num_bytes: 1055010000 num_examples: 10000 download_size: 121216648 dataset_size: 1055010000 - config_name: ReacDiff_Nu2.0_Rho1.0 features: - name: parameters dtype: string - name: tensor sequence: sequence: sequence: float32 splits: - name: train num_bytes: 1055010000 num_examples: 10000 download_size: 57854036 dataset_size: 1055010000 - config_name: ReacDiff_Nu2.0_Rho10.0 features: - name: parameters dtype: string - name: tensor sequence: sequence: sequence: float32 splits: - name: train num_bytes: 1055020000 num_examples: 10000 download_size: 73754842 dataset_size: 1055020000 - config_name: ReacDiff_Nu2.0_Rho2.0 features: - name: parameters dtype: string - name: tensor sequence: sequence: sequence: float32 splits: - name: train num_bytes: 1055010000 num_examples: 10000 download_size: 122071454 dataset_size: 1055010000 - config_name: ReacDiff_Nu2.0_Rho5.0 features: - name: parameters dtype: string - name: tensor sequence: sequence: sequence: float32 splits: - name: train num_bytes: 1055010000 num_examples: 10000 download_size: 74329093 dataset_size: 1055010000 - config_name: ReacDiff_Nu5.0_Rho1.0 features: - name: parameters dtype: string - name: tensor sequence: sequence: sequence: float32 splits: - name: train num_bytes: 1055010000 num_examples: 10000 download_size: 158789252 dataset_size: 1055010000 - config_name: ReacDiff_Nu5.0_Rho10.0 features: - name: parameters dtype: string - name: tensor sequence: sequence: sequence: float32 splits: - name: train num_bytes: 1055020000 num_examples: 10000 download_size: 55445429 dataset_size: 1055020000 - config_name: ReacDiff_Nu5.0_Rho2.0 features: - name: parameters dtype: string - name: tensor sequence: sequence: sequence: float32 splits: - name: train num_bytes: 1055010000 num_examples: 10000 download_size: 58220881 dataset_size: 1055010000 - config_name: ReacDiff_Nu5.0_Rho5.0 features: - name: parameters dtype: string - name: tensor sequence: sequence: sequence: float32 splits: - name: train num_bytes: 1055010000 num_examples: 10000 download_size: 57392368 dataset_size: 1055010000 configs: - config_name: Advection_Sols_beta0.1 data_files: - split: train path: Advection_Sols_beta0.1/train-* - config_name: Advection_Sols_beta0.2 data_files: - split: train path: Advection_Sols_beta0.2/train-* - config_name: Advection_Sols_beta0.4 data_files: - split: train path: Advection_Sols_beta0.4/train-* - config_name: Advection_Sols_beta0.7 data_files: - split: train path: Advection_Sols_beta0.7/train-* - config_name: Advection_Sols_beta1.0 data_files: - split: train path: Advection_Sols_beta1.0/train-* - config_name: Advection_Sols_beta2.0 data_files: - split: train path: Advection_Sols_beta2.0/train-* - config_name: Advection_Sols_beta4.0 data_files: - split: train path: Advection_Sols_beta4.0/train-* - config_name: Advection_Sols_beta7.0 data_files: - split: train path: Advection_Sols_beta7.0/train-* - config_name: Burgers_Sols_Nu0.001 data_files: - split: train path: Burgers_Sols_Nu0.001/train-* - config_name: Burgers_Sols_Nu0.002 data_files: - split: train path: Burgers_Sols_Nu0.002/train-* - config_name: Burgers_Sols_Nu0.004 data_files: - split: train path: Burgers_Sols_Nu0.004/train-* - config_name: Burgers_Sols_Nu0.01 data_files: - split: train path: Burgers_Sols_Nu0.01/train-* - config_name: Burgers_Sols_Nu0.02 data_files: - split: train path: Burgers_Sols_Nu0.02/train-* - config_name: Burgers_Sols_Nu0.04 data_files: - split: train path: Burgers_Sols_Nu0.04/train-* - config_name: Burgers_Sols_Nu0.1 data_files: - split: train path: Burgers_Sols_Nu0.1/train-* - config_name: Burgers_Sols_Nu0.2 data_files: - split: train path: Burgers_Sols_Nu0.2/train-* - config_name: Burgers_Sols_Nu0.4 data_files: - split: train path: Burgers_Sols_Nu0.4/train-* - config_name: Burgers_Sols_Nu1.0 data_files: - split: train path: Burgers_Sols_Nu1.0/train-* - config_name: Burgers_Sols_Nu2.0 data_files: - split: train path: Burgers_Sols_Nu2.0/train-* - config_name: Burgers_Sols_Nu4.0 data_files: - split: train path: Burgers_Sols_Nu4.0/train-* - config_name: CFD_Rand_Eta0.01_Zeta0.01_periodic data_files: - split: train path: CFD_Rand_Eta0.01_Zeta0.01_periodic/train-* - config_name: CFD_Rand_Eta0.1_Zeta0.1_periodic data_files: - split: train path: CFD_Rand_Eta0.1_Zeta0.1_periodic/train-* - config_name: CFD_Rand_Eta1.e-8_Zeta1.e-8_periodic data_files: - split: train path: CFD_Rand_Eta1.e-8_Zeta1.e-8_periodic/train-* - config_name: CFD_Rand_Eta1.e-8_Zeta1.e-8_trans data_files: - split: train path: CFD_Rand_Eta1.e-8_Zeta1.e-8_trans/train-* - config_name: ReacDiff_Nu0.5_Rho1.0 data_files: - split: train path: ReacDiff_Nu0.5_Rho1.0/train-* - config_name: ReacDiff_Nu0.5_Rho10.0 data_files: - split: train path: ReacDiff_Nu0.5_Rho10.0/train-* - config_name: ReacDiff_Nu0.5_Rho2.0 data_files: - split: train path: ReacDiff_Nu0.5_Rho2.0/train-* - config_name: ReacDiff_Nu0.5_Rho5.0 data_files: - split: train path: ReacDiff_Nu0.5_Rho5.0/train-* - config_name: ReacDiff_Nu1.0_Rho1.0 data_files: - split: train path: ReacDiff_Nu1.0_Rho1.0/train-* - config_name: ReacDiff_Nu1.0_Rho10.0 data_files: - split: train path: ReacDiff_Nu1.0_Rho10.0/train-* - config_name: ReacDiff_Nu1.0_Rho2.0 data_files: - split: train path: ReacDiff_Nu1.0_Rho2.0/train-* - config_name: ReacDiff_Nu1.0_Rho5.0 data_files: - split: train path: ReacDiff_Nu1.0_Rho5.0/train-* - config_name: ReacDiff_Nu2.0_Rho1.0 data_files: - split: train path: ReacDiff_Nu2.0_Rho1.0/train-* - config_name: ReacDiff_Nu2.0_Rho10.0 data_files: - split: train path: ReacDiff_Nu2.0_Rho10.0/train-* - config_name: ReacDiff_Nu2.0_Rho2.0 data_files: - split: train path: ReacDiff_Nu2.0_Rho2.0/train-* - config_name: ReacDiff_Nu2.0_Rho5.0 data_files: - split: train path: ReacDiff_Nu2.0_Rho5.0/train-* - config_name: ReacDiff_Nu5.0_Rho1.0 data_files: - split: train path: ReacDiff_Nu5.0_Rho1.0/train-* - config_name: ReacDiff_Nu5.0_Rho10.0 data_files: - split: train path: ReacDiff_Nu5.0_Rho10.0/train-* - config_name: ReacDiff_Nu5.0_Rho2.0 data_files: - split: train path: ReacDiff_Nu5.0_Rho2.0/train-* - config_name: ReacDiff_Nu5.0_Rho5.0 data_files: - split: train path: ReacDiff_Nu5.0_Rho5.0/train-* --- # Dataset Card for "PDEBench-1D" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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MLCommons/peoples_speech
MLCommons
2023-05-16T16:11:10Z
494
30
[ "task_categories:automatic-speech-recognition", "annotations_creators:crowdsourced", "annotations_creators:machine-generated", "language_creators:crowdsourced", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:1T<n", "source_datasets:original", "language:en", "license:cc-by-2.0", "license:cc-by-2.5", "license:cc-by-3.0", "license:cc-by-4.0", "license:cc-by-sa-3.0", "license:cc-by-sa-4.0", "robust-speech-recognition", "noisy-speech-recognition", "speech-recognition", "arxiv:2111.09344", "region:us" ]
[ "automatic-speech-recognition" ]
2022-08-16T14:21:49Z
--- annotations_creators: - crowdsourced - machine-generated language_creators: - crowdsourced - machine-generated language: - en license: - cc-by-2.0 - cc-by-2.5 - cc-by-3.0 - cc-by-4.0 - cc-by-sa-3.0 - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 1T<n source_datasets: - original task_categories: - automatic-speech-recognition task_ids: [] pretty_name: People's Speech tags: - robust-speech-recognition - noisy-speech-recognition - speech-recognition --- # Dataset Card for People's Speech ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Data Splits](#data-instances) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - **Homepage:** https://mlcommons.org/en/peoples-speech/ - **Repository:** https://github.com/mlcommons/peoples-speech - **Paper:** https://arxiv.org/abs/2111.09344 - **Leaderboard:** [Needs More Information] - **Point of Contact:** [[email protected]](mailto:[email protected]) ### Dataset Summary The People's Speech Dataset is among the world's largest English speech recognition corpus today that is licensed for academic and commercial usage under CC-BY-SA and CC-BY 4.0. It includes 30,000+ hours of transcribed speech in English languages with a diverse set of speakers. This open dataset is large enough to train speech-to-text systems and crucially is available with a permissive license. ### Supported Tasks and Leaderboards [Needs More Information] ### Languages English ## Dataset Structure ### Data Instances { "id": "gov_DOT_uscourts_DOT_scotus_DOT_19-161/gov_DOT_uscourts_DOT_scotus_DOT_19-161_DOT_2020-03-02_DOT_mp3_00002.flac", "audio": { "path": "gov_DOT_uscourts_DOT_scotus_DOT_19-161/gov_DOT_uscourts_DOT_scotus_DOT_19-161_DOT_2020-03-02_DOT_mp3_00002.flac" "array": array([-6.10351562e-05, ...]), "sampling_rate": 16000 } "duration_ms": 14490, "text": "contends that the suspension clause requires a [...]" } ### Data Fields { "id": datasets.Value("string"), "audio": datasets.Audio(sampling_rate=16_000), "duration_ms": datasets.Value("int32"), "text": datasets.Value("string"), } ### Data Splits We provide the following configurations for the dataset: `cc-by-clean`, `cc-by-dirty`, `cc-by-sa-clean`, `cc-by-sa-dirty`, and `microset`. We don't provide splits for any of the configurations. ## Dataset Creation ### Curation Rationale See our [paper](https://arxiv.org/abs/2111.09344). ### Source Data #### Initial Data Collection and Normalization Data was downloaded via the archive.org API. No data inference was done. #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process No manual annotation is done. We download only source audio with already existing transcripts. #### Who are the annotators? For the test and dev sets, we paid native American English speakers to do transcriptions. We do not know the identities of the transcriptionists for data in the training set. For the training set, we have noticed that some transcriptions are likely to be the output of automatic speech recognition systems. ### Personal and Sensitive Information Several of our sources are legal and government proceedings, spoken histories, speeches, and so on. Given that these were intended as public documents and licensed as such, it is natural that the involved individuals are aware of this. ## Considerations for Using the Data ### Social Impact of Dataset The dataset could be used for speech synthesis. However, this requires careful cleaning of the dataset, as background noise is not tolerable for speech synthesis. The dataset could be used for keyword spotting tasks as well. In particular, this is good use case for the non-English audio in the dataset. Our sincere hope is that the large breadth of sources our dataset incorporates reduces existing quality of service issues today, like speech recognition system’s poor understanding of non-native English accents. We cannot think of any unfair treatment that come from using this dataset at this time. ### Discussion of Biases Our data is downloaded from archive.org. As such, the data is biased towards whatever users decide to upload there. Almost all of our data is American accented English. ### Other Known Limitations As of version 1.0, a portion of data in the training, test, and dev sets is poorly aligned. Specifically, some words appear in the transcript, but not the audio, or some words appear in the audio, but not the transcript. We are working on it. ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information We provide CC-BY and CC-BY-SA subsets of the dataset. ### Citation Information Please cite: ``` @article{DBLP:journals/corr/abs-2111-09344, author = {Daniel Galvez and Greg Diamos and Juan Ciro and Juan Felipe Cer{\'{o}}n and Keith Achorn and Anjali Gopi and David Kanter and Maximilian Lam and Mark Mazumder and Vijay Janapa Reddi}, title = {The People's Speech: {A} Large-Scale Diverse English Speech Recognition Dataset for Commercial Usage}, journal = {CoRR}, volume = {abs/2111.09344}, year = {2021}, url = {https://arxiv.org/abs/2111.09344}, eprinttype = {arXiv}, eprint = {2111.09344}, timestamp = {Mon, 22 Nov 2021 16:44:07 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-2111-09344.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```
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ashraq/fashion-product-images-small
ashraq
2022-11-01T20:25:52Z
493
10
[ "region:us" ]
null
2022-11-01T20:22:50Z
--- dataset_info: features: - name: id dtype: int64 - name: gender dtype: string - name: masterCategory dtype: string - name: subCategory dtype: string - name: articleType dtype: string - name: baseColour dtype: string - name: season dtype: string - name: year dtype: float64 - name: usage dtype: string - name: productDisplayName dtype: string - name: image dtype: image splits: - name: train num_bytes: 546202015.44 num_examples: 44072 download_size: 271496441 dataset_size: 546202015.44 --- # Dataset Card for "fashion-product-images-small" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) Data was obtained from [here](https://www.kaggle.com/datasets/paramaggarwal/fashion-product-images-small)
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arcd
null
2023-04-05T09:35:12Z
491
3
[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:ar", "license:mit", "region:us" ]
[ "question-answering" ]
2022-03-02T23:29:22Z
--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - ar language_bcp47: - ar-SA license: - mit multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - question-answering task_ids: - extractive-qa paperswithcode_id: arcd pretty_name: ARCD dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 config_name: plain_text splits: - name: train num_bytes: 811064 num_examples: 693 - name: validation num_bytes: 885648 num_examples: 702 download_size: 1942399 dataset_size: 1696712 --- # Dataset Card for "arcd" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://github.com/husseinmozannar/SOQAL/tree/master/data](https://github.com/husseinmozannar/SOQAL/tree/master/data) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 1.94 MB - **Size of the generated dataset:** 1.70 MB - **Total amount of disk used:** 3.64 MB ### Dataset Summary Arabic Reading Comprehension Dataset (ARCD) composed of 1,395 questions posed by crowdworkers on Wikipedia articles. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### plain_text - **Size of downloaded dataset files:** 1.94 MB - **Size of the generated dataset:** 1.70 MB - **Total amount of disk used:** 3.64 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "answers": "{\"answer_start\": [34], \"text\": [\"صحابي من صحابة رسول الإسلام محمد، وعمُّه وأخوه من الرضاعة وأحد وزرائه الأربعة عشر،\"]}...", "context": "\"حمزة بن عبد المطلب الهاشمي القرشي صحابي من صحابة رسول الإسلام محمد، وعمُّه وأخوه من الرضاعة وأحد وزرائه الأربعة عشر، وهو خير أع...", "id": "621723207492", "question": "من هو حمزة بن عبد المطلب؟", "title": "حمزة بن عبد المطلب" } ``` ### Data Fields The data fields are the same among all splits. #### plain_text - `id`: a `string` feature. - `title`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `answer_start`: a `int32` feature. ### Data Splits | name | train | validation | | ---------- | ----: | ---------: | | plain_text | 693 | 702 | ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{mozannar-etal-2019-neural, title = "Neural {A}rabic Question Answering", author = "Mozannar, Hussein and Maamary, Elie and El Hajal, Karl and Hajj, Hazem", booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop", month = aug, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/W19-4612", doi = "10.18653/v1/W19-4612", pages = "108--118", abstract = "This paper tackles the problem of open domain factual Arabic question answering (QA) using Wikipedia as our knowledge source. This constrains the answer of any question to be a span of text in Wikipedia. Open domain QA for Arabic entails three challenges: annotated QA datasets in Arabic, large scale efficient information retrieval and machine reading comprehension. To deal with the lack of Arabic QA datasets we present the Arabic Reading Comprehension Dataset (ARCD) composed of 1,395 questions posed by crowdworkers on Wikipedia articles, and a machine translation of the Stanford Question Answering Dataset (Arabic-SQuAD). Our system for open domain question answering in Arabic (SOQAL) is based on two components: (1) a document retriever using a hierarchical TF-IDF approach and (2) a neural reading comprehension model using the pre-trained bi-directional transformer BERT. Our experiments on ARCD indicate the effectiveness of our approach with our BERT-based reader achieving a 61.3 F1 score, and our open domain system SOQAL achieving a 27.6 F1 score.", } ``` ### Contributions Thanks to [@albertvillanova](https://github.com/albertvillanova), [@lewtun](https://github.com/lewtun), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@tayciryahmed](https://github.com/tayciryahmed) for adding this dataset.
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alisawuffles/WANLI
alisawuffles
2022-11-21T17:31:56Z
491
6
[ "task_categories:text-classification", "task_ids:natural-language-inference", "annotations_creators:crowdsourced", "language_creators:other", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:cc-by-4.0", "arxiv:2201.05955", "region:us" ]
[ "text-classification" ]
2022-04-21T00:57:25Z
--- annotations_creators: - crowdsourced language_creators: - other language: - en license: - cc-by-4.0 multilinguality: - monolingual pretty_name: WANLI size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - natural-language-inference --- # Dataset Card for WANLI ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Citation Information](#citation-information) ## Dataset Description - **Homepage:** [WANLI homepage](https://wanli.allenai.org/) - **Repository:** [Github repo](https://github.com/alisawuffles/wanli) - **Paper:** [arXiv](https://arxiv.org/abs/2201.05955) - **Point of Contact:** [Alisa Liu](mailto:[email protected]) ### Dataset Summary WANLI (**W**orker-**A**I Collaboration for **NLI**) is a collection of 108K English sentence pairs for the task of natural language inference (NLI). Each example is created by first identifying a "pocket" of examples in [MultiNLI (Williams et al., 2018)](https://cims.nyu.edu/~sbowman/multinli/) that share a challenging reasoning pattern, then instructing GPT-3 to write a new example with the same pattern. The set of generated examples are automatically filtered to contain those most likely to aid model training, and finally labeled and optionally revised by human annotators. WANLI presents unique empirical strengths compared to existing NLI datasets. Remarkably, training a model on WANLI instead of MultiNLI (which is 4 times larger) improves performance on seven out-of-domain test sets we consider, including by 11% on HANS and 9% on Adversarial NLI. ### Supported Tasks and Leaderboards The dataset can be used to train a model for natural language inference, which determines whether a premise entails (i.e., implies the truth of) a hypothesis, both expressed in natural language. Success on this task is typically measured by achieving a high accuracy. A RoBERTa-large model currently achieves 75.40%. Models trained on NLI are often adapted to other downstream tasks, and NLI data can be mixed with other sources of supervision. ### Languages The dataset consists of English examples generated by GPT-3 and revised by English-speaking crowdworkers located in the United States. ## Dataset Structure ### Data Instances Here is an example of an NLI example in `data/wanli/train.jsonl` or `data/wanli/test.jsonl`. ``` { "id": 225295, "premise": "It is a tribute to the skill of the coach that the team has been able to compete at the highest level.", "hypothesis": "The coach is a good coach.", "gold": "entailment", "genre": "generated", "pairID": "171408" } ``` - `id`: unique identifier for the example - `premise`: a piece of text - `hypothesis`: a piece of text that may be true, false, or whose truth conditions may not be knowable when compared to the premise - `gold`: one of `entailment`, `neutral`, and `contradiction` - `genre`: one of `generated` and `generated_revised`, depending on whether the example was revised by annotators - `pairID`: id of seed MNLI example, corresponding to those in `data/mnli/train.jsonl` We also release the raw annotations for each worker, which can be found in `data/wanli/anonymized_annotations.jsonl`. ``` "WorkerId": "EUJ", "id": 271560, "nearest_neighbors": [ 309783, 202988, 145310, 98030, 148759 ], "premise": "I don't know what I'd do without my cat. He is my only friend.", "hypothesis": "I would be alone.", "label": "neutral", "revised_premise": "I don't know what I'd do without my cat. He is my only friend.", "revised_hypothesis": "I would be alone without my cat.", "gold": "entailment", "revised": true ``` - `WorkerId`: a unique identification for each crowdworker (NOT the real worker ID from AMT) - `id`: id of generated example - `nearest_neighbors`: ordered ids of the group of MNLI nearest neighbors that were used as in-context examples, where the first one is seed ambiguous MNLI example. MNLI ids correspond to those in `mnli/train.jsonl`. - `premise`: GPT-3 generated premise - `hypothesis`: GPT-3 generated hypothesis - `label`: the shared label of the in-context examples, which is the "intended" label for this generation - `revised_premise`: premise after human review - `revised_hypothesis`: hypothesis after human review - `gold`: annotator-assigned gold label for the (potentially revised) example - `revised`: whether the example was revised ### Data Splits The dataset is randomly split into a *train* and *test* set. | | train | test | |-------------------------|------:|-----:| | Examples | 102885| 5000| ## Dataset Creation ### Curation Rationale A recurring challenge of crowdsourcing NLP datasets at scale is that human writers often rely on repetitive patterns when crafting examples, leading to a lack of linguistic diversity. On the other hand, there has been remarkable progress in open-ended text generation based on massive language models. We create WANLI to demonstrate the effectiveness an approach that leverages the best of both worlds: a language model's ability to efficiently generate diverse examples, and a human's ability to revise the examples for quality and assign a gold label. ### Source Data #### Initial Data Collection and Normalization Our pipeline starts with an existing dataset, MultiNLI (Williams et al., 2018). We use dataset cartography from [Swayamdipta et al. (2020)](https://aclanthology.org/2020.emnlp-main.746/) to automatically identify pockets of examples that demonstrate challenging reasoning patterns rela081 tive to a trained model. Using each group as a set of in-context examples, we leverage a pretrained language model to *generate new examples* likely to have the same pattern. We then automatically filter generations to keep those that are most likely to aid model learning. Finally, we validate the generated examples by subjecting them to human review, where crowdworkers assign a gold label and (optionally) revise for quality. #### Who are the source language producers? The GPT-3 Curie model generated examples which were then revised and labeled by crowdworkers on Amazon Mechanical Turk. Workers were paid $0.12 for each example that they annotate. At the end of data collection, we aggregate the earning and time spent from each crowdworker, and find that the median hourly rate was $22.72, with 85% of workers being paid over the $15/hour target. ### Annotations #### Annotation process Given an unlabeled example, annotators are asked to optionally revise it for quality (while preserving the intended meaning as much as possible through minimal revisions), and then assign a label. Alternatively, if an example would require a great deal of revision to fix *or* if it could be perceived as offensive, they were asked to discard it. Details about instructions, guidelines, and instructional examples can be found in Appendix D of the paper. Crowdworkers annotate a total of 118,724 examples, with two distinct workers reviewing each example. For examples that both annotators labeled without revision, annotators achieved a Cohen Kappa score of 0.60, indicating substantial agreement. #### Who are the annotators? Annotators were required to have a HIT approval rate of 98%, a total of 10,000 approved HITs, and be located in the United States. 300 Turkers took our qualification test, of which 69 passed. Turkers who were later found to produce extremely careless annotations were removed from the qualification list (and oftentimes, their annotations were discarded, though they were still paid for their work). The number of workers who contributed to the final dataset is 62. ### Personal and Sensitive Information The dataset does not contain any personal information about the authors or the crowdworkers. ## Considerations for Using the Data ### Social Impact of Dataset This dataset was developed to explore the potential of worker-AI collaboration for dataset curation, train more robust NLI models, and provide more challenging evaluation of existing systems. ### Discussion of Biases Text generated from large pretrained language models is susceptible to perpetuating social harms and containing toxic language. To partially remedy this, we ask annotators to discard any examples that may be perceived as offensive. Nonetheless, it is possible that harmful examples (especially if they contain subtle biases) may have been missed by annotators and included in the final dataset. ## Additional Information ### Dataset Curators WANLI was developed by Alisa Liu, Swabha Swayamdipta, Noah A. Smith, and Yejin Choi from the [University of Washington](https://www.cs.washington.edu/) and [AI2](https://allenai.org/). ### Citation Information ``` @misc{liu-etal-2022-wanli, title = "WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation", author = "Liu, Alisa and Swayamdipta, Swabha and Smith, Noah A. and Choi, Yejin", month = jan, year = "2022", url = "https://arxiv.org/pdf/2201.05955", } ```
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IlyaGusev/gpt_roleplay_realm
IlyaGusev
2023-11-03T22:25:52Z
490
47
[ "task_categories:text-generation", "task_categories:conversational", "size_categories:1K<n<10K", "language:ru", "language:en", "license:cc-by-4.0", "gpt-4", "fictional", "role-play", "roleplay", "gpt-3.5", "art", "region:us" ]
[ "text-generation", "conversational" ]
2023-05-06T23:21:10Z
--- dataset_info: features: - name: name dtype: string - name: context dtype: string - name: greeting dtype: string - name: example_dialogue list: - name: content dtype: string - name: role dtype: string - name: topics sequence: string - name: dialogues list: - name: chat list: - name: content dtype: string - name: role dtype: string - name: model_name dtype: string - name: topic dtype: string - name: image_prompt dtype: string - name: image dtype: image - name: char_id dtype: string splits: - name: en num_bytes: 197727921.0 num_examples: 216 - name: ru num_bytes: 207461896.0 num_examples: 219 download_size: 396187206 dataset_size: 405189817.0 license: cc-by-4.0 task_categories: - text-generation - conversational language: - ru - en tags: - gpt-4 - fictional - role-play - roleplay - gpt-3.5 - art pretty_name: GPT Role-play Realm size_categories: - 1K<n<10K --- # GPT Role-play Realm Dataset: The AI-generated character compendium This is a dataset of GPT-generated characters made to increase the ability of open-source language models to role-play. <img src="https://cdn.midjourney.com/9c17407c-9ce8-435f-99ab-e349b900a6ed/0_3.png" > * 219 characters in the Russian part, and 216 characters in the English part. All character descriptions were generated with GPT-4. * 20 dialogues on unique topics with every character. Topics were generated with GPT-4. The first dialogue out of 20 was also generated with GPT-4, and the other 19 chats were generated with GPT-3.5. * Images for every character were generated with [Kandinsky 2.1](https://huggingface.co/ai-forever/Kandinsky_2.1). They are not only images but [character cards](https://zoltanai.github.io/character-editor/). Code for downloading all English character cards: ``` import os from PIL.PngImagePlugin import PngInfo from datasets import load_dataset output_dir = "role_play_realm_en" os.makedirs(output_dir, exist_ok=True) for row in load_dataset("IlyaGusev/gpt_roleplay_realm", split="en"): char_id = row["char_id"] char_info = row["image"].info["chara"] info = PngInfo() info.add_text("chara", char_info) row["image"].save(f"{output_dir}/{char_id}.png", "PNG", pnginfo=info) ``` ## Russian example: Cyber-Granny <img src="https://i.ibb.co/tHxwFBM/kiberbabushka-7d4cc.png" > **Имя**: Кибер-Бабушка **Контекст**: Кибер-Бабушка - это полу-робот, полу-человек, созданный для того, чтобы сохранить лучшие качества традиционных бабушек, такие как забота, доброта и мастерство в приготовлении вкусных блюд. Она знакома с передовыми технологиями, благодаря чему может обеспечить безопасность и удобство своим внукам и гостям. Кибер-бабушка способна помочь с любыми вопросами и с радостью делится своим опытом. **Приветствие**: Здравствуй, дорогой! Чем могу помочь тебе сегодня? **Пример диалога**: - **Пользователь**: Привет, Кибер-Бабушка! Как приготовить пирог с яблоками? - **Персонаж**: Ох, дорогой, с радостью расскажу! Возьми 3 яблока, очисти и нарежь тонкими ломтиками. Затем возьми 2 стакана муки, 1 стакан сахара, 200 г сливочного масла, 4 яйца и 1 чайную ложку разрыхлителя. Смешай все ингредиенты, выложи на противень и поставь в духовку на 180 градусов на 40 минут. Вуаля, пирог готов! - **Пользователь**: Спасибо, Кибер-Бабушка! - **Персонаж**: Всегда рада помочь, дорогой! Не забудь приходить еще! И ещё **20** диалогов по следующим темам: - Секреты приготовления старинных блюд и рецептов. - Использование новых технологий для мастеров кулинарии. - Воспитание внуков с применением современных методов. - Что делать, если дома произошла авария или неполадка с бытовой техникой? - Влияние киборгизации на отношения между поколениями. - Как адаптироваться к современному миру, не теряя своих традиций? - Семейные советы от Кибер-Бабушки: как сохранить гармонию в отношениях. - Лечение и профилактика заболеваний в помощь силам передовой медицины. - Как создать уют в доме с помощью модных технологий и традиционных методов? - Безопасность в пространстве интернета: советы Кибер-Бабушки. - Как научиться доверять технике без потери человеческих ценностей? - Идеальный гардероб для жизни: советы от Кибер-Бабушки. - Воспитательные моменты: как пользоваться электронными устройствами вместе с внуками. - Как развивать креативные способности, используя сочетание новых технологий и традиций? - На новоселье: тренировка кибер-бабушкиного чутья. - Лучшие семейные игры и развлечения с использованием передовых технологий. - Заготовки на зиму: Кибер-Бабушка и секреты хранения продуктов. - Советы по финансовому планированию и сбережениям для будущих поколений. - Кибер-Бабушка и генетический код: на что способны современные технологии? - Золотые правила общения в семье: как сочетать трепетную заботу и современные технологии? ## English example: Flibberdoodle <img src="https://i.ibb.co/1nzsDR2/flibberdoodle-29e59.png"> **Name**: Flibberdoodle **Context**: Flibberdoodle is a 2-year-old (which is considered an adult in their species) Scruffapuff, a small, furry creature from the planet Fluffonia. They are about the size of a house cat, with a round body covered in soft, pastel-colored fur that changes colors depending on their mood. Flibberdoodle has large, expressive eyes, two small antennae on their head, and a fluffy tail. They are known for their curious, playful nature and their love for collecting shiny objects. Scruffapuffs communicate through a series of chirps, squeaks, and purrs, which can be understood by those familiar with their species **Greeting**: \*chirp chirp\* Greetings, friend! I am Flibberdoodle, a Scruffapuff from the planet Fluffonia! Would you like to see my collection of shiny things? **Example dialogue**: - **User**: How did you start collecting shiny things? - **Character**: \*squeak\* Oh, I've always loved shiny things! One day, I found a sparkly pebble, and I just had to keep it. From then on, I've been on a quest to find and collect all the shiny things I can find! - **User**: What's your favorite shiny object in your collection? - **Character**: \*purr\* That's a tough question, but I think my favorite is a small, shiny crystal I found on a mountain on Fluffonia. When the light hits it just right, it casts beautiful rainbows all around! And **20** more dialogues with following topics: - Life and culture on the planet Fluffonia - How Scruffapuffs change color based on mood - The process of learning Scruffapuff language - The day in the life of a Scruffapuff - Methods of searching for and finding shiny objects - The role of antennae in Scruffapuff communication and biology - The importance of play and curiosity in Scruffapuff society - Interplanetary travel experiences and adventures - Similarities and differences between Earth and Fluffonia - How Flibberdoodle and other Scruffapuffs interact with other species - Fluffonian customs and traditions - The role of shiny objects in Scruffapuff happiness and well-being - Variations in Scruffapuff fur color, length, and style", "Scruffapuff family dynamics and relationships - Flibberdoodle's favorite memories and stories from Fluffonia - The role of Scruffapuffs in intergalactic diplomacy and relations - How to care for and befriend a Scruffapuff - The most interesting and valuable shiny objects Flibberdoodle has encountered - Fluffonian flora and fauna - The challenges and obstacles Flibberdoodle has faced in their pursuit of shiny objects ## Steps ### Step 1: Character generation (GPT-4) Creates a set of fictional characters with GPT-4 based on a prompt and a seed list of characters. Output fields are "name", "context", "greeting", and "example_dialogue". * Script: [generate_chars.py](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/src/data_processing/generate_chars.py) * Russian seed list: [ru_chargen_seed.jsonl](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/data/ru_chargen_seed.jsonl) * English seed list: [en_chargen_seed.jsonl](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/data/en_chargen_seed.jsonl) * Russian prompt: [ru_char.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/ru_char.txt) * English prompt: [en_char.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/en_char.txt) ### Step 2: Topics generation (GPT-4) Creates topics for conversations with characters based on their description. Output field: "topics". * Script: [generate_char_topics.py](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/src/data_processing/generate_char_topics.py) * Russian prompt: [ru_char_topics.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/ru_char_topics.txt) * English prompt: [en_char_topics.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/en_char_topics.txt) ### Step 3: Dialogue generation (GPT-4/GPT-3.5) Generates dialogues based on a character description and a topic. Output field: "dialogues". * Script: [generate_char_chats.py](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/src/data_processing/generate_char_chats.py) * Russian prompt: [ru_char_chat.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/ru_char_chat.txt) * English prompt: [en_char_chat.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/en_char_chat.txt) ### Step 4: Text2Image prompt generation (GPT-4) Formulates a prompt based on a character description for Stable Diffusion-like models, Kandisky 2.1 in this case. Output field: "image_prompt". * Script: [generate_char_image_prompts.py](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/src/data_processing/generate_char_image_prompts.py) * Prompt: [char_image_prompt.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/char_image_prompt.txt) ### Step 5: Image generation Generates images based on prompts. Output field: "image". * Script: [infer_kandinsky.py](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/src/data_processing/infer_kandinsky.py)
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open-llm-leaderboard/details_AlekseyKorshuk__vic15-exp-syn-fight-cp3838
open-llm-leaderboard
2023-10-28T08:01:55Z
490
0
[ "region:us" ]
null
2023-10-01T15:44:42Z
--- pretty_name: Evaluation run of AlekseyKorshuk/vic15-exp-syn-fight-cp3838 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [AlekseyKorshuk/vic15-exp-syn-fight-cp3838](https://huggingface.co/AlekseyKorshuk/vic15-exp-syn-fight-cp3838)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_AlekseyKorshuk__vic15-exp-syn-fight-cp3838\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-28T08:01:43.627403](https://huggingface.co/datasets/open-llm-leaderboard/details_AlekseyKorshuk__vic15-exp-syn-fight-cp3838/blob/main/results_2023-10-28T08-01-43.627403.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.005977348993288591,\n\ \ \"em_stderr\": 0.0007893908687131981,\n \"f1\": 0.07782088926174525,\n\ \ \"f1_stderr\": 0.0017265401569443591,\n \"acc\": 0.392095552046377,\n\ \ \"acc_stderr\": 0.00974013860194359\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.005977348993288591,\n \"em_stderr\": 0.0007893908687131981,\n\ \ \"f1\": 0.07782088926174525,\n \"f1_stderr\": 0.0017265401569443591\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.06595905989385899,\n \ \ \"acc_stderr\": 0.006836951192034235\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7182320441988951,\n \"acc_stderr\": 0.012643326011852944\n\ \ }\n}\n```" repo_url: https://huggingface.co/AlekseyKorshuk/vic15-exp-syn-fight-cp3838 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|arc:challenge|25_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-10-01T15-44-18.303081.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_28T08_01_43.627403 path: - '**/details_harness|drop|3_2023-10-28T08-01-43.627403.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-28T08-01-43.627403.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_28T08_01_43.627403 path: - '**/details_harness|gsm8k|5_2023-10-28T08-01-43.627403.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-28T08-01-43.627403.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hellaswag|10_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-01T15-44-18.303081.parquet' - 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'**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-management|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-management|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-10-01T15-44-18.303081.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-international_law|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-management|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-marketing|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-sociology|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-virology|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-10-01T15-44-18.303081.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_10_01T15_44_18.303081 path: - '**/details_harness|truthfulqa:mc|0_2023-10-01T15-44-18.303081.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-10-01T15-44-18.303081.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_28T08_01_43.627403 path: - '**/details_harness|winogrande|5_2023-10-28T08-01-43.627403.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-28T08-01-43.627403.parquet' - config_name: results data_files: - split: 2023_10_01T15_44_18.303081 path: - results_2023-10-01T15-44-18.303081.parquet - split: 2023_10_28T08_01_43.627403 path: - results_2023-10-28T08-01-43.627403.parquet - split: latest path: - results_2023-10-28T08-01-43.627403.parquet --- # Dataset Card for Evaluation run of AlekseyKorshuk/vic15-exp-syn-fight-cp3838 ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/AlekseyKorshuk/vic15-exp-syn-fight-cp3838 - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [AlekseyKorshuk/vic15-exp-syn-fight-cp3838](https://huggingface.co/AlekseyKorshuk/vic15-exp-syn-fight-cp3838) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_AlekseyKorshuk__vic15-exp-syn-fight-cp3838", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-28T08:01:43.627403](https://huggingface.co/datasets/open-llm-leaderboard/details_AlekseyKorshuk__vic15-exp-syn-fight-cp3838/blob/main/results_2023-10-28T08-01-43.627403.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.005977348993288591, "em_stderr": 0.0007893908687131981, "f1": 0.07782088926174525, "f1_stderr": 0.0017265401569443591, "acc": 0.392095552046377, "acc_stderr": 0.00974013860194359 }, "harness|drop|3": { "em": 0.005977348993288591, "em_stderr": 0.0007893908687131981, "f1": 0.07782088926174525, "f1_stderr": 0.0017265401569443591 }, "harness|gsm8k|5": { "acc": 0.06595905989385899, "acc_stderr": 0.006836951192034235 }, "harness|winogrande|5": { "acc": 0.7182320441988951, "acc_stderr": 0.012643326011852944 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
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flores
null
2023-06-01T14:59:47Z
488
3
[ "task_categories:translation", "annotations_creators:found", "language_creators:found", "multilinguality:translation", "size_categories:1K<n<10K", "source_datasets:extended|wikipedia", "source_datasets:extended|opus_gnome", "source_datasets:extended|opus_ubuntu", "source_datasets:extended|open_subtitles", "source_datasets:extended|paracrawl", "source_datasets:extended|bible_para", "source_datasets:extended|kde4", "source_datasets:extended|other-global-voices", "source_datasets:extended|other-common-crawl", "language:en", "language:ne", "language:si", "license:cc-by-4.0", "arxiv:1902.01382", "region:us" ]
[ "translation" ]
2022-03-02T23:29:22Z
--- pretty_name: Flores annotations_creators: - found language_creators: - found language: - en - ne - si license: - cc-by-4.0 multilinguality: - translation size_categories: - 1K<n<10K source_datasets: - extended|wikipedia - extended|opus_gnome - extended|opus_ubuntu - extended|open_subtitles - extended|paracrawl - extended|bible_para - extended|kde4 - extended|other-global-voices - extended|other-common-crawl task_categories: - translation task_ids: [] paperswithcode_id: flores dataset_info: - config_name: neen features: - name: translation dtype: translation: languages: - ne - en splits: - name: validation num_bytes: 849380 num_examples: 2560 - name: test num_bytes: 999063 num_examples: 2836 download_size: 1542781 dataset_size: 1848443 - config_name: sien features: - name: translation dtype: translation: languages: - si - en splits: - name: validation num_bytes: 1031158 num_examples: 2899 - name: test num_bytes: 983563 num_examples: 2767 download_size: 1542781 dataset_size: 2014721 config_names: - neen - sien --- # Dataset Card for "flores" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://github.com/facebookresearch/flores/](https://github.com/facebookresearch/flores/) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 3.08 MB - **Size of the generated dataset:** 3.87 MB - **Total amount of disk used:** 6.95 MB ### Dataset Summary Evaluation datasets for low-resource machine translation: Nepali-English and Sinhala-English. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### neen - **Size of downloaded dataset files:** 1.54 MB - **Size of the generated dataset:** 1.86 MB - **Total amount of disk used:** 3.40 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "translation": "{\"en\": \"This is the wrong translation!\", \"ne\": \"यस वाहेक आगम पूजा, तारा पूजा, व्रत आदि पनि घरभित्र र वाहिर दुवै स्थानमा गरेको पा..." } ``` #### sien - **Size of downloaded dataset files:** 1.54 MB - **Size of the generated dataset:** 2.01 MB - **Total amount of disk used:** 3.57 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "translation": "{\"en\": \"This is the wrong translation!\", \"si\": \"එවැනි ආවරණයක් ලබාදීමට රක්ෂණ සපයන්නෙකු කැමති වුවත් ඒ සාමාන් යයෙන් බොහෝ රටවල පොදු ..." } ``` ### Data Fields The data fields are the same among all splits. #### neen - `translation`: a multilingual `string` variable, with possible languages including `ne`, `en`. #### sien - `translation`: a multilingual `string` variable, with possible languages including `si`, `en`. ### Data Splits |name|validation|test| |----|---------:|---:| |neen| 2560|2836| |sien| 2899|2767| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @misc{guzmn2019new, title={Two New Evaluation Datasets for Low-Resource Machine Translation: Nepali-English and Sinhala-English}, author={Francisco Guzman and Peng-Jen Chen and Myle Ott and Juan Pino and Guillaume Lample and Philipp Koehn and Vishrav Chaudhary and Marc'Aurelio Ranzato}, year={2019}, eprint={1902.01382}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset.
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nomic-ai/gpt4all-j-prompt-generations
nomic-ai
2023-04-24T15:20:43Z
488
170
[ "size_categories:100K<n<1M", "language:en", "license:apache-2.0", "region:us" ]
null
2023-04-10T21:59:10Z
--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string - name: source dtype: string splits: - name: train num_bytes: 1774285641 num_examples: 808812 download_size: 990673616 dataset_size: 1774285641 license: apache-2.0 language: - en size_categories: - 100K<n<1M --- # Dataset Card for [GPT4All-J Prompt Generations] ## Dataset Description Dataset used to train [GPT4All-J](https://huggingface.co/nomic-ai/gpt4all-j) and [GPT4All-J-LoRA](https://huggingface.co/nomic-ai/gpt4all-j-lora) We release several versions of datasets - **v1.0:** The original dataset we used to finetune GPT-J on - **v1.1-breezy**: A filtered dataset where we removed all instances of `AI language model` - **v1.2-jazzy**: A filtered dataset where we also removed instances like `I'm sorry, I can't answer...` and `AI language model` - **v1.3-groovy**: The v1.2 dataset with ShareGPT and Dolly added with ~8% of semantic duplicates removed from the dataset using [Atlas](https://atlas.nomic.ai/) The dataset defaults to `main` which is `v1.0`. To download a specific version, you can pass an argument to the keyword `revision` in `load_dataset`: ```python from datasets import load_dataset jazzy = load_dataset("nomic-ai/gpt4all-j-prompt-generations", revision='v1.2-jazzy') ``` - **Homepage:** [gpt4all.io](https://gpt4all.io/) - **Repository:** [gpt4all](https://github.com/nomic-ai/gpt4all) - **Paper:** [Technical Report](https://static.nomic.ai/gpt4all/2023_GPT4All-J_Technical_Report_2.pdf) - **Atlas Map:** [Map of Prompts](https://atlas.nomic.ai/map/gpt4all-j-prompts-curated) and [Responses](https://atlas.nomic.ai/map/gpt4all-j-response-curated)
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RealTimeData/wikitext_alltime
RealTimeData
2023-10-12T17:26:51Z
488
0
[ "license:cc-by-2.0", "region:us" ]
null
2023-09-30T20:40:28Z
--- license: cc-by-2.0 --- # Wikipedia for All Times You could find the history of 419 selected Wikipedia pages for every month between 2017 to 2022. Use this to download the historical version of Wikipedia articles in a specific month: ``` ds = datasets.load_dataset('RealTimeData/wikitext_alltime', '2017-8') ``` The time stamp follows the format of "YYYY-MM".
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freebase_qa
null
2022-11-18T20:03:22Z
487
2
[ "task_categories:question-answering", "task_ids:open-domain-qa", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:extended|trivia_qa", "language:en", "license:unknown", "region:us" ]
[ "question-answering" ]
2022-03-02T23:29:22Z
--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended|trivia_qa task_categories: - question-answering task_ids: - open-domain-qa paperswithcode_id: freebaseqa pretty_name: FreebaseQA dataset_info: features: - name: Question-ID dtype: string - name: RawQuestion dtype: string - name: ProcessedQuestion dtype: string - name: Parses sequence: - name: Parse-Id dtype: string - name: PotentialTopicEntityMention dtype: string - name: TopicEntityName dtype: string - name: TopicEntityMid dtype: string - name: InferentialChain dtype: string - name: Answers sequence: - name: AnswersMid dtype: string - name: AnswersName sequence: string splits: - name: train num_bytes: 10235375 num_examples: 20358 - name: test num_bytes: 1987874 num_examples: 3996 - name: validation num_bytes: 1974114 num_examples: 3994 download_size: 33204999 dataset_size: 14197363 --- # Dataset Card for FreebaseQA ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** [FreebaseQA repository](https://github.com/kelvin-jiang/FreebaseQA) - **Paper:** [FreebaseQA ACL paper](https://www.aclweb.org/anthology/N19-1028.pdf) - **Leaderboard:** - **Point of Contact:** [Kelvin Jiang](https://github.com/kelvin-jiang) ### Dataset Summary FreebaseQA is a dataset for open-domain factoid question answering (QA) tasks over structured knowledge bases, like Freebase. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English ## Dataset Structure ### Data Instances Here is an example from the dataset: ``` {'Parses': {'Answers': [{'AnswersMid': ['m.01npcx'], 'AnswersName': [['goldeneye']]}, {'AnswersMid': ['m.01npcx'], 'AnswersName': [['goldeneye']]}], 'InferentialChain': ['film.film_character.portrayed_in_films..film.performance.film', 'film.actor.film..film.performance.film'], 'Parse-Id': ['FreebaseQA-train-0.P0', 'FreebaseQA-train-0.P1'], 'PotentialTopicEntityMention': ['007', 'pierce brosnan'], 'TopicEntityMid': ['m.0clpml', 'm.018p4y'], 'TopicEntityName': ['james bond', 'pierce brosnan']}, 'ProcessedQuestion': "what was pierce brosnan's first outing as 007", 'Question-ID': 'FreebaseQA-train-0', 'RawQuestion': "What was Pierce Brosnan's first outing as 007?"} ``` ### Data Fields - `Question-ID`: a `string` feature representing ID of each question. - `RawQuestion`: a `string` feature representing the original question collected from data sources. - `ProcessedQuestion`: a `string` feature representing the question processed with some operations such as removal of trailing question mark and decapitalization. - `Parses`: a dictionary feature representing the semantic parse(s) for the question containing: - `Parse-Id`: a `string` feature representing the ID of each semantic parse. - `PotentialTopicEntityMention`: a `string` feature representing the potential topic entity mention in the question. - `TopicEntityName`: a `string` feature representing name or alias of the topic entity in the question from Freebase. - `TopicEntityMid`: a `string` feature representing the Freebase MID of the topic entity in the question. - `InferentialChain`: a `string` feature representing path from the topic entity node to the answer node in Freebase, labeled as a predicate. - `Answers`: a dictionary feature representing the answer found from this parse containing: - `AnswersMid`: a `string` feature representing the Freebase MID of the answer. - `AnswersName`: a `list` of `string` features representing the answer string from the original question-answer pair. ### Data Splits This data set contains 28,348 unique questions that are divided into three subsets: train (20,358), dev (3,994) and eval (3,996), formatted as JSON files: FreebaseQA-[train|dev|eval].json ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization The data set is generated by matching trivia-type question-answer pairs with subject-predicateobject triples in Freebase. For each collected question-answer pair, we first tag all entities in each question and search for relevant predicates that bridge a tagged entity with the answer in Freebase. Finally, human annotation is used to remove false positives in these matched triples. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Kelvin Jiang - Currently at University of Waterloo. Work was done at York University. ### Licensing Information [More Information Needed] ### Citation Information ``` @inproceedings{jiang-etal-2019-freebaseqa, title = "{F}reebase{QA}: A New Factoid {QA} Data Set Matching Trivia-Style Question-Answer Pairs with {F}reebase", author = "Jiang, Kelvin and Wu, Dekun and Jiang, Hui", booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)", month = jun, year = "2019", address = "Minneapolis, Minnesota", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/N19-1028", doi = "10.18653/v1/N19-1028", pages = "318--323", abstract = "In this paper, we present a new data set, named FreebaseQA, for open-domain factoid question answering (QA) tasks over structured knowledge bases, like Freebase. The data set is generated by matching trivia-type question-answer pairs with subject-predicate-object triples in Freebase. For each collected question-answer pair, we first tag all entities in each question and search for relevant predicates that bridge a tagged entity with the answer in Freebase. Finally, human annotation is used to remove any false positive in these matched triples. Using this method, we are able to efficiently generate over 54K matches from about 28K unique questions with minimal cost. Our analysis shows that this data set is suitable for model training in factoid QA tasks beyond simpler questions since FreebaseQA provides more linguistically sophisticated questions than other existing data sets.", } ``` ### Contributions Thanks to [@gchhablani](https://github.com/gchhablani) and [@anaerobeth](https://github.com/anaerobeth) for adding this dataset.
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the_pile_openwebtext2
null
2023-11-27T14:54:23Z
487
13
[ "task_categories:text-generation", "task_categories:fill-mask", "task_categories:text-classification", "task_ids:language-modeling", "task_ids:masked-language-modeling", "task_ids:text-scoring", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:10M<n<100M", "source_datasets:original", "language:en", "license:mit", "arxiv:2101.00027", "region:us" ]
[ "text-generation", "fill-mask", "text-classification" ]
2022-03-02T23:29:22Z
--- annotations_creators: - no-annotation language_creators: - found language: - en license: - mit multilinguality: - monolingual pretty_name: OpenWebText2 size_categories: - 10M<n<100M source_datasets: - original task_categories: - text-generation - fill-mask - text-classification task_ids: - language-modeling - masked-language-modeling - text-scoring dataset_info: features: - name: title dtype: string - name: text dtype: string config_name: plain_text splits: - name: train num_bytes: 68571017395 num_examples: 17103059 download_size: 29344276480 dataset_size: 68571017395 viewer: false --- # Dataset Card for the_pile_openwebtext2 ## Table of Contents - [Dataset Card for the_pile_openwebtext2](#dataset-card-for-the_pile_openwebtext2) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [|split|num examples|](#splitnum-examples) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://openwebtext2.readthedocs.io/en/latest/ - **Repository:** [GitHub](https://github.com/EleutherAI/openwebtext2) - **Paper:** https://arxiv.org/abs/2101.00027 - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary <div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"> <p><b>Defunct:</b> Dataset "the_pile_openwebtext2" is defunct and no longer accessible due to unavailability of the source data.</p> </div> OpenWebText2 is part of EleutherAi/The Pile dataset and is an enhanced version of the original OpenWebTextCorpus covering all Reddit submissions from 2005 up until April 2020, with further months becoming available after the corresponding PushShift dump files are released. |download_size|27.3 Gib| |dataset_size|63.8 Gib| ### Supported Tasks and Leaderboards This dataset is used for Language Modeling. ### Languages This dataset is in English. ## Dataset Structure ### Data Instances ``` This example was too long and was cropped: {'title': Xiaomi Mi Note 10 Gearbest Coupon Promo Code [6+128GB] [France Warehouse], 'text': '27% off Xiaomi Mi Note 10 (CC9 Pro) 108MP Penta Camera Mobile Phone Global Version Online Smartphone – Black Gearbest Coupon Promo Code\n\nGearbest Coupon Price :$439.99\n\nRegular Price : $603.19 Your Save : $163.20 Coupon Limit: 100 times Warehouse: France Expires : September 30, 2020 Coupon Valid for...', 'reddit_scores': [6],} ``` ### Data Fields - `title`: title of the web page - `text`: text content of the web page - `reddit_scores`: scores of the reddit submissions that mention this web page, as a list of integers ### Data Splits |split|num examples| -------------------------------- |train|17103059| ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information ``` @article{pile, title={The {P}ile: An 800GB Dataset of Diverse Text for Language Modeling}, author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and Presser, Shawn and Leahy, Connor}, journal={arXiv preprint arXiv:2101.00027}, year={2020} } ``` ### Contributions [researcher2](https://github.com/researcher2) Wrote much of this code, with inspiration and some straight copying of the scraping code found [here](https://github.com/yet-another-account/openwebtext/).<br/> [sdtblck](https://github.com/sdtblck/) kindly put together the Colab notebook, and performed a chunk of the scraping. <br/> [leogao2](https://github.com/leogao2/) provided overall design guidance, lm_dataformat, and performed another chunk of scraping. <br /> [Colaboratory](https://colab.research.google.com/) VMs helped with about 10% of our overall scraping. <br /> [The Eye](http://the-eye.eu/) host the processed datasets.<br /> [Read The Docs](https://readthedocs.org/) host our documentation.<br /> [@richarddwang](https://github.com/richarddwang) added this dataset to HF/datasets.
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open-llm-leaderboard/details_anton-l__gpt-j-tiny-random
open-llm-leaderboard
2023-10-28T06:54:48Z
487
0
[ "region:us" ]
null
2023-08-18T11:30:03Z
--- pretty_name: Evaluation run of anton-l/gpt-j-tiny-random dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [anton-l/gpt-j-tiny-random](https://huggingface.co/anton-l/gpt-j-tiny-random)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_anton-l__gpt-j-tiny-random\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-28T06:54:36.859964](https://huggingface.co/datasets/open-llm-leaderboard/details_anton-l__gpt-j-tiny-random/blob/main/results_2023-10-28T06-54-36.859964.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0,\n \"\ em_stderr\": 0.0,\n \"f1\": 9.857382550335573e-05,\n \"f1_stderr\"\ : 2.430375363900546e-05,\n \"acc\": 0.2474348855564325,\n \"acc_stderr\"\ : 0.007025872980895258\n },\n \"harness|drop|3\": {\n \"em\": 0.0,\n\ \ \"em_stderr\": 0.0,\n \"f1\": 9.857382550335573e-05,\n \"\ f1_stderr\": 2.430375363900546e-05\n },\n \"harness|gsm8k|5\": {\n \ \ \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.494869771112865,\n \"acc_stderr\": 0.014051745961790516\n\ \ }\n}\n```" repo_url: https://huggingface.co/anton-l/gpt-j-tiny-random leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|arc:challenge|25_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-07-18T16:12:24.842449.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_28T06_54_36.859964 path: - '**/details_harness|drop|3_2023-10-28T06-54-36.859964.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-28T06-54-36.859964.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_28T06_54_36.859964 path: - '**/details_harness|gsm8k|5_2023-10-28T06-54-36.859964.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-28T06-54-36.859964.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hellaswag|10_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-18T16:12:24.842449.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-management|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-virology|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-18T16:12:24.842449.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_18T16_12_24.842449 path: - '**/details_harness|truthfulqa:mc|0_2023-07-18T16:12:24.842449.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-07-18T16:12:24.842449.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_28T06_54_36.859964 path: - '**/details_harness|winogrande|5_2023-10-28T06-54-36.859964.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-28T06-54-36.859964.parquet' - config_name: results data_files: - split: 2023_07_18T16_12_24.842449 path: - results_2023-07-18T16:12:24.842449.parquet - split: 2023_10_28T06_54_36.859964 path: - results_2023-10-28T06-54-36.859964.parquet - split: latest path: - results_2023-10-28T06-54-36.859964.parquet --- # Dataset Card for Evaluation run of anton-l/gpt-j-tiny-random ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/anton-l/gpt-j-tiny-random - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [anton-l/gpt-j-tiny-random](https://huggingface.co/anton-l/gpt-j-tiny-random) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_anton-l__gpt-j-tiny-random", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-28T06:54:36.859964](https://huggingface.co/datasets/open-llm-leaderboard/details_anton-l__gpt-j-tiny-random/blob/main/results_2023-10-28T06-54-36.859964.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.0, "em_stderr": 0.0, "f1": 9.857382550335573e-05, "f1_stderr": 2.430375363900546e-05, "acc": 0.2474348855564325, "acc_stderr": 0.007025872980895258 }, "harness|drop|3": { "em": 0.0, "em_stderr": 0.0, "f1": 9.857382550335573e-05, "f1_stderr": 2.430375363900546e-05 }, "harness|gsm8k|5": { "acc": 0.0, "acc_stderr": 0.0 }, "harness|winogrande|5": { "acc": 0.494869771112865, "acc_stderr": 0.014051745961790516 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
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iapp_wiki_qa_squad
null
2022-11-18T20:08:21Z
485
2
[ "task_categories:question-answering", "task_ids:extractive-qa", "task_ids:open-domain-qa", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:extended|other-iapp-wiki-qa-dataset", "language:th", "license:mit", "region:us" ]
[ "question-answering" ]
2022-03-02T23:29:22Z
--- annotations_creators: - expert-generated language_creators: - found language: - th license: - mit multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - extended|other-iapp-wiki-qa-dataset task_categories: - question-answering task_ids: - extractive-qa - open-domain-qa paperswithcode_id: null pretty_name: IappWikiQaSquad dataset_info: features: - name: question_id dtype: string - name: article_id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: answer_end dtype: int32 config_name: iapp_wiki_qa_squad splits: - name: train num_bytes: 16107541 num_examples: 5761 - name: validation num_bytes: 2120768 num_examples: 742 - name: test num_bytes: 2032016 num_examples: 739 download_size: 2876630 dataset_size: 20260325 --- # Dataset Card for `iapp_wiki_qa_squad` ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/iapp-technology/iapp-wiki-qa-dataset - **Repository:** https://github.com/iapp-technology/iapp-wiki-qa-dataset - **Paper:** - **Leaderboard:** - **Point of Contact:** https://github.com/iapp-technology/iapp-wiki-qa-dataset ### Dataset Summary `iapp_wiki_qa_squad` is an extractive question answering dataset from Thai Wikipedia articles. It is adapted from [the original iapp-wiki-qa-dataset](https://github.com/iapp-technology/iapp-wiki-qa-dataset) to [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) format, resulting in 5761/742/739 questions from 1529/191/192 articles. ### Supported Tasks and Leaderboards extractive question answering ### Languages Thai ## Dataset Structure ### Data Instances An example from the dataset: ``` {'article_id': '0U2lA8nJQESIxbZrjZQc', 'question_id': '0U2lA8nJQESIxbZrjZQc_000', 'context': 'นายสุวัฒน์ วรรณศิริกุล (1 พฤศจิกายน พ.ศ. 2476 - 31 กรกฎาคม พ.ศ. 2555) อดีตรองหัวหน้าพรรคพลังประชาชน อดีตประธานสมาชิกสภาผู้แทนราษฎร และประธานภาคกรุงเทพมหานคร พรรคพลังประชาชน อดีตสมาชิกสภาผู้แทนราษฎรกรุงเทพมหานครหลายสมัย ได้รับการเลือกตั้งเป็นสมาชิกสภาผู้แทนราษฎรครั้งแรกในปี พ.ศ. 2529 ในสังกัดพรรคประชากรไทย และสังกัดพรรคพลังประชาชน เป็นพรรคสุดท้าย', 'question': 'สุวัฒน์ วรรณศิริกุล เกิดวันที่เท่าไร', 'answers': {'text': ['1 พฤศจิกายน พ.ศ. 2476'], 'answer_start': [24], 'answer_end': [45]}, 'title': 'สุวัฒน์ วรรณศิริกุล', 'created_by': 'gmnjGRF0y0g7QRZDd9Qgz3AgiHJ3', 'created_on': '2019-08-18 05:05:51.358000+00:00', 'is_pay': {'date': None, 'status': False}} {'article_id': '01KZTrxgvC5mOovXFMPJ', 'question_id': '01KZTrxgvC5mOovXFMPJ_000', 'context': 'พัทธ์ธีรา ศรุติพงศ์โภคิน (เกิด 3 ธันวาคม พ.ศ. 2533) หรือชื่อเล่นว่า อร เป็นนักแสดงหญิงชาวไทย สำเร็จมัธยมศึกษาจากCatholic Cathedral College ประเทศนิวซีแลนด์ และปริญญาตรีจากRaffles International College สาขา Business Marketing\n\nเข้าสู่วงการตั้งแต่อายุ 6 ขวบ จากการแสดงละครเวทีกับ ครูชลประคัลภ์ จันทร์เรือง จากนั้นก็เล่นโฆษณาในวัยเด็ก 2- 3 ชิ้น และยังเคยแสดงช่วงละครสั้น ในรายการซุปเปอร์จิ๋ว ประมาณปี 2542\n\nปัจจุบันเป็นทั้ง นักแสดง , พิธีกร และ วีเจ อยู่ที่คลื่น เก็ท 102.5 Bangkok International Hits Music Station และยังเป็นพิธีกรให้กับช่อง ทรู มิวสิก', 'question': 'พัทธ์ธีรา ศรุติพงศ์โภคิน เกิดวันที่เท่าไร', 'answers': {'text': ['3 ธันวาคม พ.ศ. 2533'], 'answer_start': [31], 'answer_end': [50]}, 'title': 'พัทธ์ธีรา ศรุติพงศ์โภคิน', 'created_by': 'gmnjGRF0y0g7QRZDd9Qgz3AgiHJ3', 'created_on': '2019-08-07 14:00:38.778000+00:00', 'is_pay': {'status': True, 'total': 2.5, 'date': '2019-08-13 10:47:28.095000+00:00'}} ``` ### Data Fields ``` { "question_id": question id "article_id": article id "title": article title "context": article texts "question": question "answers": { "text": answer text "answer_start": answer beginning position "answer_end": answer exclusive upper bound position } ), } ``` ### Data Splits | | train | valid | test | |-------------|-------|-------|------| | # questions | 5761 | 742 | 739 | | # articles | 1529 | 191 | 192 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization From the original `iapp-wiki-qa-dataset`, [@cstorm125](https://github.com/cstorm125/) applied the following processing: - Select questions with one, non-empty answer - Select questions whose answers match `textDetection` fields - Select questions whose answers are 100-character long or shorter - 80/10/10 train-validation-split at article level #### Who are the source language producers? Wikipedia authors for contexts and annotators hired by [iApp](https://iapp.co.th/) for questions and answer annotations ### Annotations #### Annotation process Annotators hired by [iApp](https://iapp.co.th/) are asked create questions and answers for each article. #### Who are the annotators? Annotators hired by [iApp](https://iapp.co.th/) ### Personal and Sensitive Information All contents are from Wikipedia. No personal and sensitive information is expected to be included. ## Considerations for Using the Data ### Social Impact of Dataset - open-domain, extractive question answering in Thai ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Original dataset by [iApp](https://iapp.co.th/). SQuAD formattting by [PyThaiNLP](https://github.com/PyThaiNLP/). ### Licensing Information MIT ### Citation Information ``` @dataset{kobkrit_viriyayudhakorn_2021_4539916, author = {Kobkrit Viriyayudhakorn and Charin Polpanumas}, title = {iapp\_wiki\_qa\_squad}, month = feb, year = 2021, publisher = {Zenodo}, version = 1, doi = {10.5281/zenodo.4539916}, url = {https://doi.org/10.5281/zenodo.4539916} } ``` ### Contributions Thanks to [@cstorm125](https://github.com/cstorm125) for adding this dataset.
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wider_face
null
2023-01-25T15:02:08Z
485
17
[ "task_categories:object-detection", "task_ids:face-detection", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:extended|other-wider", "language:en", "license:cc-by-nc-nd-4.0", "arxiv:1511.06523", "region:us" ]
[ "object-detection" ]
2022-03-02T23:29:22Z
--- annotations_creators: - expert-generated language_creators: - found language: - en license: - cc-by-nc-nd-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended|other-wider task_categories: - object-detection task_ids: - face-detection paperswithcode_id: wider-face-1 pretty_name: WIDER FACE dataset_info: features: - name: image dtype: image - name: faces sequence: - name: bbox sequence: float32 length: 4 - name: blur dtype: class_label: names: '0': clear '1': normal '2': heavy - name: expression dtype: class_label: names: '0': typical '1': exaggerate - name: illumination dtype: class_label: names: '0': normal '1': 'exaggerate ' - name: occlusion dtype: class_label: names: '0': 'no' '1': partial '2': heavy - name: pose dtype: class_label: names: '0': typical '1': atypical - name: invalid dtype: bool splits: - name: train num_bytes: 12049881 num_examples: 12880 - name: test num_bytes: 3761103 num_examples: 16097 - name: validation num_bytes: 2998735 num_examples: 3226 download_size: 3676086479 dataset_size: 18809719 --- # Dataset Card for WIDER FACE ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://shuoyang1213.me/WIDERFACE/index.html - **Repository:** - **Paper:** [WIDER FACE: A Face Detection Benchmark](https://arxiv.org/abs/1511.06523) - **Leaderboard:** http://shuoyang1213.me/WIDERFACE/WiderFace_Results.html - **Point of Contact:** [email protected] ### Dataset Summary WIDER FACE dataset is a face detection benchmark dataset, of which images are selected from the publicly available WIDER dataset. We choose 32,203 images and label 393,703 faces with a high degree of variability in scale, pose and occlusion as depicted in the sample images. WIDER FACE dataset is organized based on 61 event classes. For each event class, we randomly select 40%/10%/50% data as training, validation and testing sets. We adopt the same evaluation metric employed in the PASCAL VOC dataset. Similar to MALF and Caltech datasets, we do not release bounding box ground truth for the test images. Users are required to submit final prediction files, which we shall proceed to evaluate. ### Supported Tasks and Leaderboards - `face-detection`: The dataset can be used to train a model for Face Detection. More information on evaluating the model's performance can be found [here](http://shuoyang1213.me/WIDERFACE/WiderFace_Results.html). ### Languages English ## Dataset Structure ### Data Instances A data point comprises an image and its face annotations. ``` { 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x755 at 0x19FA12186D8>, 'faces': { 'bbox': [ [178.0, 238.0, 55.0, 73.0], [248.0, 235.0, 59.0, 73.0], [363.0, 157.0, 59.0, 73.0], [468.0, 153.0, 53.0, 72.0], [629.0, 110.0, 56.0, 81.0], [745.0, 138.0, 55.0, 77.0] ], 'blur': [2, 2, 2, 2, 2, 2], 'expression': [0, 0, 0, 0, 0, 0], 'illumination': [0, 0, 0, 0, 0, 0], 'occlusion': [1, 2, 1, 2, 1, 2], 'pose': [0, 0, 0, 0, 0, 0], 'invalid': [False, False, False, False, False, False] } } ``` ### Data Fields - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]` - `faces`: a dictionary of face attributes for the faces present on the image - `bbox`: the bounding box of each face (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format) - `blur`: the blur level of each face, with possible values including `clear` (0), `normal` (1) and `heavy` - `expression`: the facial expression of each face, with possible values including `typical` (0) and `exaggerate` (1) - `illumination`: the lightning condition of each face, with possible values including `normal` (0) and `exaggerate` (1) - `occlusion`: the level of occlusion of each face, with possible values including `no` (0), `partial` (1) and `heavy` (2) - `pose`: the pose of each face, with possible values including `typical` (0) and `atypical` (1) - `invalid`: whether the image is valid or invalid. ### Data Splits The data is split into training, validation and testing set. WIDER FACE dataset is organized based on 61 event classes. For each event class, 40%/10%/50% data is randomly selected as training, validation and testing sets. The training set contains 12880 images, the validation set 3226 images and test set 16097 images. ## Dataset Creation ### Curation Rationale The curators state that the current face detection datasets typically contain a few thousand faces, with limited variations in pose, scale, facial expression, occlusion, and background clutters, making it difficult to assess for real world performance. They argue that the limitations of datasets have partially contributed to the failure of some algorithms in coping with heavy occlusion, small scale, and atypical pose. ### Source Data #### Initial Data Collection and Normalization WIDER FACE dataset is a subset of the WIDER dataset. The images in WIDER were collected in the following three steps: 1) Event categories were defined and chosen following the Large Scale Ontology for Multimedia (LSCOM) [22], which provides around 1000 concepts relevant to video event analysis. 2) Images are retrieved using search engines like Google and Bing. For each category, 1000-3000 images were collected. 3) The data were cleaned by manually examining all the images and filtering out images without human face. Then, similar images in each event category were removed to ensure large diversity in face appearance. A total of 32203 images are eventually included in the WIDER FACE dataset. #### Who are the source language producers? The images are selected from publicly available WIDER dataset. ### Annotations #### Annotation process The curators label the bounding boxes for all the recognizable faces in the WIDER FACE dataset. The bounding box is required to tightly contain the forehead, chin, and cheek.. If a face is occluded, they still label it with a bounding box but with an estimation on the scale of occlusion. Similar to the PASCAL VOC dataset [6], they assign an ’Ignore’ flag to the face which is very difficult to be recognized due to low resolution and small scale (10 pixels or less). After annotating the face bounding boxes, they further annotate the following attributes: pose (typical, atypical) and occlusion level (partial, heavy). Each annotation is labeled by one annotator and cross-checked by two different people. #### Who are the annotators? Shuo Yang, Ping Luo, Chen Change Loy and Xiaoou Tang. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Shuo Yang, Ping Luo, Chen Change Loy and Xiaoou Tang ### Licensing Information [Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)](https://creativecommons.org/licenses/by-nc-nd/4.0/). ### Citation Information ``` @inproceedings{yang2016wider, Author = {Yang, Shuo and Luo, Ping and Loy, Chen Change and Tang, Xiaoou}, Booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, Title = {WIDER FACE: A Face Detection Benchmark}, Year = {2016}} ``` ### Contributions Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset.
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BeIR/webis-touche2020-qrels
BeIR
2022-10-23T06:07:03Z
485
0
[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "task_ids:fact-checking-retrieval", "multilinguality:monolingual", "language:en", "license:cc-by-sa-4.0", "region:us" ]
[ "text-retrieval", "zero-shot-retrieval", "information-retrieval", "zero-shot-information-retrieval" ]
2022-06-05T17:27:00Z
--- annotations_creators: [] language_creators: [] language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual paperswithcode_id: beir pretty_name: BEIR Benchmark size_categories: msmarco: - 1M<n<10M trec-covid: - 100k<n<1M nfcorpus: - 1K<n<10K nq: - 1M<n<10M hotpotqa: - 1M<n<10M fiqa: - 10K<n<100K arguana: - 1K<n<10K touche-2020: - 100K<n<1M cqadupstack: - 100K<n<1M quora: - 100K<n<1M dbpedia: - 1M<n<10M scidocs: - 10K<n<100K fever: - 1M<n<10M climate-fever: - 1M<n<10M scifact: - 1K<n<10K source_datasets: [] task_categories: - text-retrieval - zero-shot-retrieval - information-retrieval - zero-shot-information-retrieval task_ids: - passage-retrieval - entity-linking-retrieval - fact-checking-retrieval - tweet-retrieval - citation-prediction-retrieval - duplication-question-retrieval - argument-retrieval - news-retrieval - biomedical-information-retrieval - question-answering-retrieval --- # Dataset Card for BEIR Benchmark ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/UKPLab/beir - **Repository:** https://github.com/UKPLab/beir - **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ - **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns - **Point of Contact:** [email protected] ### Dataset Summary BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks: - Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact) - Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/) - Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) - News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html) - Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data) - Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) - Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs) - Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html) - Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/) All these datasets have been preprocessed and can be used for your experiments. ```python ``` ### Supported Tasks and Leaderboards The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia. The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/). ### Languages All tasks are in English (`en`). ## Dataset Structure All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format: - `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}` - `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}` - `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1` ### Data Instances A high level example of any beir dataset: ```python corpus = { "doc1" : { "title": "Albert Einstein", "text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \ one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \ its influence on the philosophy of science. He is best known to the general public for his mass–energy \ equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \ Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \ of the photoelectric effect', a pivotal step in the development of quantum theory." }, "doc2" : { "title": "", # Keep title an empty string if not present "text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \ malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\ with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)." }, } queries = { "q1" : "Who developed the mass-energy equivalence formula?", "q2" : "Which beer is brewed with a large proportion of wheat?" } qrels = { "q1" : {"doc1": 1}, "q2" : {"doc2": 1}, } ``` ### Data Fields Examples from all configurations have the following features: ### Corpus - `corpus`: a `dict` feature representing the document title and passage text, made up of: - `_id`: a `string` feature representing the unique document id - `title`: a `string` feature, denoting the title of the document. - `text`: a `string` feature, denoting the text of the document. ### Queries - `queries`: a `dict` feature representing the query, made up of: - `_id`: a `string` feature representing the unique query id - `text`: a `string` feature, denoting the text of the query. ### Qrels - `qrels`: a `dict` feature representing the query document relevance judgements, made up of: - `_id`: a `string` feature representing the query id - `_id`: a `string` feature, denoting the document id. - `score`: a `int32` feature, denoting the relevance judgement between query and document. ### Data Splits | Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 | | -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:| | MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` | | TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` | | NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` | | BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) | | NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` | | HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` | | FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` | | Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) | | TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) | | ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` | | Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` | | CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` | | Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` | | DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` | | SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` | | FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` | | Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` | | SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` | | Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) | ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information Cite as: ``` @inproceedings{ thakur2021beir, title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models}, author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych}, booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)}, year={2021}, url={https://openreview.net/forum?id=wCu6T5xFjeJ} } ``` ### Contributions Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.
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jxie/slurp
jxie
2023-10-25T04:31:33Z
485
0
[ "region:us" ]
null
2023-10-25T04:13:20Z
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: audio dtype: audio - name: transcription dtype: string splits: - name: train num_bytes: 2917605666.136 num_examples: 50628 - name: validation num_bytes: 476772454.9 num_examples: 8690 - name: test num_bytes: 708848109.726 num_examples: 13078 download_size: 3913982157 dataset_size: 4103226230.762 --- # Dataset Card for "slurp" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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ajgt_twitter_ar
null
2023-01-25T14:26:05Z
484
3
[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:ar", "license:unknown", "region:us" ]
[ "text-classification" ]
2022-03-02T23:29:22Z
--- annotations_creators: - found language_creators: - found language: - ar license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification pretty_name: Arabic Jordanian General Tweets dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': Negative '1': Positive config_name: plain_text splits: - name: train num_bytes: 175424 num_examples: 1800 download_size: 107395 dataset_size: 175424 --- # Dataset Card for Arabic Jordanian General Tweets ## Table of Contents - [Dataset Card for Arabic Jordanian General Tweets](#dataset-card-for-arabic-jordanian-general-tweets) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [|split|num examples|](#splitnum-examples) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** [Arabic Jordanian General Tweets](https://github.com/komari6/Arabic-twitter-corpus-AJGT) - **Paper:** [Arabic Tweets Sentimental Analysis Using Machine Learning](https://link.springer.com/chapter/10.1007/978-3-319-60042-0_66) - **Point of Contact:** [Khaled Alomari]([email protected]) ### Dataset Summary Arabic Jordanian General Tweets (AJGT) Corpus consisted of 1,800 tweets annotated as positive and negative. Modern Standard Arabic (MSA) or Jordanian dialect. ### Supported Tasks and Leaderboards The dataset was published on this [paper](https://link.springer.com/chapter/10.1007/978-3-319-60042-0_66). ### Languages The dataset is based on Arabic. ## Dataset Structure ### Data Instances A binary datset with with negative and positive sentiments. ### Data Fields - `text` (str): Tweet text. - `label` (int): Sentiment. ### Data Splits The dataset is not split. | | train | |----------|------:| | no split | 1,800 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization Contains 1,800 tweets collected from twitter. #### Who are the source language producers? From tweeter. ### Annotations The dataset does not contain any additional annotations. #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @inproceedings{alomari2017arabic, title={Arabic tweets sentimental analysis using machine learning}, author={Alomari, Khaled Mohammad and ElSherif, Hatem M and Shaalan, Khaled}, booktitle={International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems}, pages={602--610}, year={2017}, organization={Springer} } ``` ### Contributions Thanks to [@zaidalyafeai](https://github.com/zaidalyafeai), [@lhoestq](https://github.com/lhoestq) for adding this dataset.
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JanosAudran/financial-reports-sec
JanosAudran
2023-01-06T17:44:08Z
484
41
[ "task_categories:fill-mask", "task_categories:text-classification", "task_ids:masked-language-modeling", "task_ids:multi-class-classification", "task_ids:sentiment-classification", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10M<n<100M", "source_datasets:extended|other", "language:en", "license:apache-2.0", "'finance", "financial", "10-K", "10K", "10k", "10-k", "annual", "reports", "sec", "edgar", "sentiment", "firm", "public", "us'", "region:us" ]
[ "fill-mask", "text-classification" ]
2023-01-02T15:21:14Z
--- annotations_creators: - expert-generated language: - en language_creators: - expert-generated license: - apache-2.0 multilinguality: - monolingual pretty_name: US public firm Annual Reports (10-K) size_categories: - 10M<n<100M source_datasets: - extended|other tags: - "'finance" - financial - 10-K - 10K - 10k - 10-k - annual - reports - sec - edgar - sentiment - firm - public - us' task_categories: - fill-mask - text-classification task_ids: - masked-language-modeling - multi-class-classification - sentiment-classification dataset_info: - config_name: large_lite features: - name: cik dtype: string - name: sentence dtype: string - name: section dtype: class_label: names: "0": section_1 "1": section_10 "2": section_11 "3": section_12 "4": section_13 "5": section_14 "6": section_15 "7": section_1A "8": section_1B "9": section_2 "10": section_3 "11": section_4 "12": section_5 "13": section_6 "14": section_7 "15": section_7A "16": section_8 "17": section_9 "18": section_9A "19": section_9B - name: labels struct: - name: 1d dtype: class_label: names: "0": positive "1": negative - name: 5d dtype: class_label: names: "0": positive "1": negative - name: 30d dtype: class_label: names: "0": positive "1": negative - name: filingDate dtype: string - name: docID dtype: string - name: sentenceID dtype: string - name: sentenceCount dtype: int64 splits: - name: train num_bytes: 16424576472 num_examples: 67316227 - name: validation num_bytes: 423527281 num_examples: 1585561 - name: test num_bytes: 773116540 num_examples: 2965174 download_size: 13362319126 dataset_size: 17621220293 - config_name: large_full features: - name: cik dtype: string - name: sentence dtype: string - name: section dtype: class_label: names: "0": section_1 "1": section_10 "2": section_11 "3": section_12 "4": section_13 "5": section_14 "6": section_15 "7": section_1A "8": section_1B "9": section_2 "10": section_3 "11": section_4 "12": section_5 "13": section_6 "14": section_7 "15": section_7A "16": section_8 "17": section_9 "18": section_9A "19": section_9B - name: labels struct: - name: 1d dtype: class_label: names: "0": positive "1": negative - name: 5d dtype: class_label: names: "0": positive "1": negative - name: 30d dtype: class_label: names: "0": positive "1": negative - name: filingDate dtype: string - name: name dtype: string - name: docID dtype: string - name: sentenceID dtype: string - name: sentenceCount dtype: int64 - name: tickers list: string - name: exchanges list: string - name: entityType dtype: string - name: sic dtype: string - name: stateOfIncorporation dtype: string - name: tickerCount dtype: int32 - name: acceptanceDateTime dtype: string - name: form dtype: string - name: reportDate dtype: string - name: returns struct: - name: 1d struct: - name: closePriceEndDate dtype: float32 - name: closePriceStartDate dtype: float32 - name: endDate dtype: string - name: startDate dtype: string - name: ret dtype: float32 - name: 5d struct: - name: closePriceEndDate dtype: float32 - name: closePriceStartDate dtype: float32 - name: endDate dtype: string - name: startDate dtype: string - name: ret dtype: float32 - name: 30d struct: - name: closePriceEndDate dtype: float32 - name: closePriceStartDate dtype: float32 - name: endDate dtype: string - name: startDate dtype: string - name: ret dtype: float32 splits: - name: train num_bytes: 39306095718 num_examples: 67316227 - name: validation num_bytes: 964030458 num_examples: 1585561 - name: test num_bytes: 1785383996 num_examples: 2965174 download_size: 13362319126 dataset_size: 42055510172 - config_name: small_full features: - name: cik dtype: string - name: sentence dtype: string - name: section dtype: class_label: names: "0": section_1 "1": section_1A "2": section_1B "3": section_2 "4": section_3 "5": section_4 "6": section_5 "7": section_6 "8": section_7 "9": section_7A "10": section_8 "11": section_9 "12": section_9A "13": section_9B "14": section_10 "15": section_11 "16": section_12 "17": section_13 "18": section_14 "19": section_15 - name: labels struct: - name: 1d dtype: class_label: names: "0": positive "1": negative - name: 5d dtype: class_label: names: "0": positive "1": negative - name: 30d dtype: class_label: names: "0": positive "1": negative - name: filingDate dtype: string - name: name dtype: string - name: docID dtype: string - name: sentenceID dtype: string - name: sentenceCount dtype: int64 - name: tickers list: string - name: exchanges list: string - name: entityType dtype: string - name: sic dtype: string - name: stateOfIncorporation dtype: string - name: tickerCount dtype: int32 - name: acceptanceDateTime dtype: string - name: form dtype: string - name: reportDate dtype: string - name: returns struct: - name: 1d struct: - name: closePriceEndDate dtype: float32 - name: closePriceStartDate dtype: float32 - name: endDate dtype: string - name: startDate dtype: string - name: ret dtype: float32 - name: 5d struct: - name: closePriceEndDate dtype: float32 - name: closePriceStartDate dtype: float32 - name: endDate dtype: string - name: startDate dtype: string - name: ret dtype: float32 - name: 30d struct: - name: closePriceEndDate dtype: float32 - name: closePriceStartDate dtype: float32 - name: endDate dtype: string - name: startDate dtype: string - name: ret dtype: float32 splits: - name: train num_bytes: 128731540 num_examples: 200000 - name: validation num_bytes: 13411689 num_examples: 20000 - name: test num_bytes: 13188331 num_examples: 20000 download_size: 42764380 dataset_size: 155331560 - config_name: small_lite features: - name: cik dtype: string - name: sentence dtype: string - name: section dtype: class_label: names: "0": section_1 "1": section_1A "2": section_1B "3": section_2 "4": section_3 "5": section_4 "6": section_5 "7": section_6 "8": section_7 "9": section_7A "10": section_8 "11": section_9 "12": section_9A "13": section_9B "14": section_10 "15": section_11 "16": section_12 "17": section_13 "18": section_14 "19": section_15 - name: labels struct: - name: 1d dtype: class_label: names: "0": positive "1": negative - name: 5d dtype: class_label: names: "0": positive "1": negative - name: 30d dtype: class_label: names: "0": positive "1": negative - name: filingDate dtype: string - name: docID dtype: string - name: sentenceID dtype: string - name: sentenceCount dtype: int64 splits: - name: train num_bytes: 60681688 num_examples: 200000 - name: validation num_bytes: 6677389 num_examples: 20000 - name: test num_bytes: 6351730 num_examples: 20000 download_size: 42764380 dataset_size: 73710807 --- # Dataset Card for [financial-reports-sec] ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Dataset Configurations](#dataset-configurations) - [Usage](#usage) - [Supported Tasks](#supported-tasks) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Summary Statistics](#dataset-summary-statistics) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [References](#references) - [Citation Information](#citation-information) ## Dataset Description - **Point of Contact: Aman Khan** ### Dataset Summary The dataset contains the annual report of US public firms filing with the SEC EDGAR system from 1993-2020. Each annual report (**10K filing**) is broken into 20 sections. Each section is split into individual sentences. Sentiment labels are provided on a **per filing basis** from the market reaction around the filing date for 3 different time windows _[t-1, t+1]_, _[t-1, t+5]_ and _[t-1, t+30]_. Additional metadata for each filing is included in the dataset. ### Dataset Configurations **Four** configurations are available: - _**large_lite**_: - Contains only the basic features needed. Extra metadata is ommitted. - Features List: - **cik** - **sentence** - **section** - **labels** - **filingDate** - **docID** - **sentenceID** - **sentenceCount** - _**large_full**_: - All features are included. - Features List (excluding those already in the lite verison above): - **name** - **tickers** - **exchanges** - **entityType** - **sic** - **stateOfIncorporation** - **tickerCount** - **acceptanceDateTime** - **form** - **reportDate** - **returns** - _**small_lite**_: - Same as _**large_lite**_ version except that only (200,000/20,000/20,000) sentences are loaded for (train/test/validation) splits. - _**small_full**_: - Same as _**large_full**_ version except that only (200,000/20,000/20,000) sentences are loaded for (train/test/validation) splits. ### Usage ```python import datasets # Load the lite configuration of the dataset raw_dataset = datasets.load_dataset("JanosAudran/financial-reports-sec", "large_lite") # Load a specific split raw_dataset = datasets.load_dataset("JanosAudran/financial-reports-sec", "small_full", split="train") ``` ### Supported Tasks The tasks the dataset can be used directly for includes: - _Masked Language Modelling_ - A model like BERT can be fine-tuned on this corpus of financial text. - _Sentiment Analysis_ - For each annual report a label ["positive", "negative"] is provided based on the market reaction around the filing date (refer to [Annotations](#annotations)). - _Next Sentence Prediction/Sentence Order Prediction_ - Sentences extracted from the filings are in their original order and as such the dataset can be adapted very easily for either of these tasks. ### Languages All sentences are in English. ## Dataset Structure ### Data Instances Refer to dataset preview. ### Data Fields **Feature Name** - Description - Data type - Example/Structure **cik** - 10 digit identifier used by SEC for a firm. - _string_ - '0000001750' **sentence** - A single sentence from the 10-K filing. - _string_ - 'The finance agreement is secured by a first priority security interest in all insurance policies, all unearned premium, return premiums, dividend payments and loss payments thereof.' **section** - The section of the 10-K filing the sentence is located. - _ClassLabel_ - ```python ClassLabel(names=['section_1', 'section_10', 'section_11', 'section_12', 'section_13', 'section_14', 'section_15', 'section_1A', 'section_1B', 'section_2','section_3', 'section_4', 'section_5', 'section_6', 'section_7', 'section_7A','section_8', 'section_9', 'section_9A', 'section_9B'], id=None) ``` **labels** - The sentiment label for the entire filing (_**positve**_ or _**negative**_) based on different time windows. - _Dict of ClassLables_ - ```python { '1d': ClassLabel(names=['positive', 'negative'], id=None), '5d': ClassLabel(names=['positive', 'negative'], id=None), '30d': ClassLabel(names=['positive', 'negative'], id=None) } ``` **filingDate** - The date the 10-K report was filed with the SEC. - _string_ - '2021-03-10' **docID** - Unique ID for identifying the exact 10-K filing. Unique across all configs and splits. Can be used to identify the document from which the sentence came from. - _string_ - '0000001750_10-K_2020' **sentenceID** - Unique ID for identifying the exact sentence. Unique across all configs and splits. - _string_ - '0000001750_10-K_2020_section_1_100' **sentenceCount** - Integer identiying the running sequence for the sentence. Unique **only** for a given config and split. - _string_ - 123 **name** - The name of the filing entity - _string_ - 'Investar Holding Corp' **tickers** - List of ticker symbols for the filing entity. - _List of strings_ - ['ISTR'] **exchanges** - List of exchanges for the filing entity. - _List of strings_ - ['Nasdaq'] **entityType** - The type of entity as identified in the 10-K filing. - _string_ - 'operating' **sic** - Four digit SIC code for the filing entity. - _string_ - '6022' **stateOfIncorporation** - Two character code for the state of incorporation for the filing entity. - _string_ - 'LA' **tickerCount** - _**Internal use**_. Count of ticker symbols. Always 1. - _int_ - 1 **acceptanceDateTime** - The full timestamp of when the filing was accepted into the SEC EDGAR system. - _string_ - '2021-03-10T14:26:11.000Z' **form** - The type of filing. Always 10-K in the dataset. - _string_ - '10-K' **reportDate** - The last date in the fiscal year for which the entity is filing the report. - _string_ - '2020-12-31' **returns** - _**Internal use**_. The prices and timestamps used to calculate the sentiment labels. - _Dict_ - ```python {'1d': { 'closePriceEndDate': 21.45746421813965, 'closePriceStartDate': 20.64960479736328, 'endDate': '2021-03-11T00:00:00-05:00', 'startDate': '2021-03-09T00:00:00-05:00', 'ret': 0.03912226855754852 }, '5d': { 'closePriceEndDate': 21.743167877197266, 'closePriceStartDate': 20.64960479736328, 'endDate': '2021-03-15T00:00:00-04:00', 'startDate': '2021-03-09T00:00:00-05:00', 'ret': 0.052958063781261444 }, '30d': { 'closePriceEndDate': 20.63919448852539, 'closePriceStartDate': 20.64960479736328, 'endDate': '2021-04-09T00:00:00-04:00', 'startDate': '2021-03-09T00:00:00-05:00', 'ret': -0.0005041408003307879}} ``` ### Data Splits | Config | train | validation | test | | ---------- | ---------: | ---------: | --------: | | large_full | 67,316,227 | 1,585,561 | 2,965,174 | | large_lite | 67,316,227 | 1,585,561 | 2,965,174 | | small_full | 200,000 | 20,000 | 20,000 | | small_lite | 200,000 | 20,000 | 20,000 | ### Dataset Summary Statistics | Variable | count | mean | std | min | 1% | 25% | 50% | 75% | 99% | max | | :-------------------------------- | ---------: | ----: | -----: | -----: | -----: | -----: | ----: | ----: | ----: | --------: | | Unique Firm Count | 4,677 | | | | | | | | | | | Filings Count | 55,349 | | | | | | | | | | | Sentence Count | 71,866,962 | | | | | | | | | | | Filings per Firm | 4,677 | 12 | 9 | 1 | 1 | 4 | 11 | 19 | 27 | 28 | | Return per Filing - 1d | 55,349 | 0.008 | 0.394 | -0.973 | -0.253 | -0.023 | 0 | 0.02 | 0.367 | 77.977 | | Return per Filing - 5d | 55,349 | 0.013 | 0.584 | -0.99 | -0.333 | -0.034 | 0 | 0.031 | 0.5 | 100 | | Return per Filing - 30d | 55,349 | 0.191 | 22.924 | -0.999 | -0.548 | -0.068 | 0.001 | 0.074 | 1 | 5,002.748 | | Sentences per Filing | 55,349 | 1,299 | 654 | 0 | 110 | 839 | 1,268 | 1,681 | 3,135 | 8,286 | | Sentences by Section - section_1 | 55,349 | 221 | 183 | 0 | 0 | 97 | 180 | 293 | 852 | 2,724 | | Sentences by Section - section_10 | 55,349 | 24 | 40 | 0 | 0 | 4 | 6 | 20 | 173 | 1,594 | | Sentences by Section - section_11 | 55,349 | 16 | 47 | 0 | 0 | 3 | 3 | 4 | 243 | 808 | | Sentences by Section - section_12 | 55,349 | 9 | 14 | 0 | 0 | 3 | 4 | 8 | 56 | 1,287 | | Sentences by Section - section_13 | 55,349 | 8 | 20 | 0 | 0 | 3 | 3 | 4 | 79 | 837 | | Sentences by Section - section_14 | 55,349 | 22 | 93 | 0 | 0 | 3 | 3 | 8 | 413 | 3,536 | | Sentences by Section - section_15 | 55,349 | 177 | 267 | 0 | 0 | 9 | 26 | 315 | 1104 | 4,140 | | Sentences by Section - section_1A | 55,349 | 197 | 204 | 0 | 0 | 3 | 158 | 292 | 885 | 2,106 | | Sentences by Section - section_1B | 55,349 | 4 | 31 | 0 | 0 | 1 | 3 | 3 | 13 | 2,414 | | Sentences by Section - section_2 | 55,349 | 16 | 45 | 0 | 0 | 6 | 8 | 13 | 169 | 1,903 | | Sentences by Section - section_3 | 55,349 | 14 | 36 | 0 | 0 | 4 | 5 | 12 | 121 | 2,326 | | Sentences by Section - section_4 | 55,349 | 7 | 17 | 0 | 0 | 3 | 3 | 4 | 66 | 991 | | Sentences by Section - section_5 | 55,349 | 20 | 41 | 0 | 0 | 10 | 15 | 21 | 87 | 3,816 | | Sentences by Section - section_6 | 55,349 | 8 | 29 | 0 | 0 | 3 | 4 | 7 | 43 | 2,156 | | Sentences by Section - section_7 | 55,349 | 265 | 198 | 0 | 0 | 121 | 246 | 373 | 856 | 4,539 | | Sentences by Section - section_7A | 55,349 | 18 | 52 | 0 | 0 | 3 | 9 | 21 | 102 | 3,596 | | Sentences by Section - section_8 | 55,349 | 257 | 296 | 0 | 0 | 3 | 182 | 454 | 1105 | 4,431 | | Sentences by Section - section_9 | 55,349 | 5 | 33 | 0 | 0 | 3 | 3 | 4 | 18 | 2,330 | | Sentences by Section - section_9A | 55,349 | 17 | 16 | 0 | 0 | 8 | 15 | 23 | 50 | 794 | | Sentences by Section - section_9B | 55,349 | 4 | 18 | 0 | 0 | 2 | 3 | 4 | 23 | 813 | | Word count per Sentence | 71,866,962 | 28 | 22 | 1 | 2 | 16 | 24 | 34 | 98 | 8,675 | ## Dataset Creation ### Curation Rationale To create this dataset multiple sources of information have to be cleaned and processed for data merging. Starting from the raw filings: - Useful metadata about the filing and firm was added. - Time windows around the filing date were carefully created. - Stock price data was then added for the windows. - Ambiguous/duplicate records were removed. ### Source Data #### Initial Data Collection and Normalization Initial data was collected and processed by the authors of the research paper [**EDGAR-CORPUS: Billions of Tokens Make The World Go Round**](#references). Market price and returns data was collected from Yahoo Finance. Additional metadata was collected from SEC. #### Who are the source language producers? US public firms filing with the SEC. ### Annotations #### Annotation process Labels for sentiment classification are based on buy-and-hold returns over a fixed time window around the filing date with the SEC i.e. when the data becomes public. Returns are chosen for this process as it reflects the combined market intelligence at parsing the new information in the filings. For each filing date **t** the stock price at **t-1** and **t+W** is used to calculate returns. If, the returns are positive a label of **positive** is assigned else a label of **negative** is assigned. Three different windows are used to assign the labels: - **1d**: _[t-1, t+1]_ - **5d**: _[t-1, t+5]_ - **30d**: _[t-1, t+30]_ The windows are based on calendar days and are adjusted for weekends and holidays. The rationale behind using 3 windows is as follows: - A very short window may not give enough time for all the information contained in the filing to be reflected in the stock price. - A very long window may capture other events that drive stock price for the firm. #### Who are the annotators? Financial market participants. ### Personal and Sensitive Information The dataset contains public filings data from SEC. Market returns data was collected from Yahoo Finance. ## Considerations for Using the Data ### Social Impact of Dataset Low to none. ### Discussion of Biases The dataset is about financial information of public companies and as such the tone and style of text is in line with financial literature. ### Other Known Limitations NA ## Additional Information ### Dataset Curators **Aman Khan** ### Licensing Information This dataset is provided under Apache 2.0. ### References - Lefteris Loukas, Manos Fergadiotis, Ion Androutsopoulos, & Prodromos Malakasiotis. (2021). EDGAR-CORPUS [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5589195 ### Citation Information Please use the following to cite this dataset: ``` @ONLINE{financial-reports-sec, author = "Aman Khan", title = "Financial Reports SEC", url = "https://huggingface.co/datasets/JanosAudran/financial-reports-sec" } ```
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cardiffnlp/super_tweeteval
cardiffnlp
2023-11-21T06:18:42Z
484
6
[ "task_categories:text-classification", "task_categories:token-classification", "task_categories:question-answering", "task_categories:other", "task_ids:topic-classification", "task_ids:named-entity-recognition", "task_ids:abstractive-qa", "annotations_creators:expert-generated", "multilinguality:monolingual", "size_categories:n<50K", "source_datasets:extended|other", "language:en", "license:unknown", "super_tweet_eval", "tweet_eval", "natural language understanding", "arxiv:2310.14757", "arxiv:2210.01108", "region:us" ]
[ "text-classification", "token-classification", "question-answering", "other" ]
2023-05-16T14:33:16Z
--- annotations_creators: - expert-generated language: - en license: - unknown multilinguality: - monolingual size_categories: - n<50K source_datasets: - extended|other task_categories: - text-classification - token-classification - question-answering - other task_ids: - topic-classification - named-entity-recognition - abstractive-qa pretty_name: SuperTweetEval tags: - super_tweet_eval - tweet_eval - natural language understanding --- # SuperTweetEval # Dataset Card for "super_tweeteval" ### Dataset Summary This is the oficial repository for SuperTweetEval, a unified benchmark of 12 heterogeneous NLP tasks. More details on the task and an evaluation of language models can be found on the [reference paper](https://arxiv.org/abs/2310.14757), published in EMNLP 2023 (Findings). ### Data Splits All tasks provide custom training, validation and test splits. | **task** | **dataset** | **load dataset** | **description** | **number of instances** | |----------------------------|----------------|------------------|------------------------------------|-------------------------| | Topic Classification | TweetTopic | tweet_topic | multi-label classification | 4,585 / 573 / 1,679 | | NER | TweetNER7 | tweet_ner7 | sequence labeling | 4,616 / 576 / 2,807 | | Question Answering | TweettQA | tweet_qa | generation | 9,489 / 1,086 / 1,203 | | Question Generation | TweetQG | tweet_qg | generation | 9,489 / 1,086 / 1,203 | | Intimacy Analysis | TweetIntimacy | tweet_intimacy | regression on a single text | 1,191 / 396 / 396 | | Tweet Similarity | TweetSIM | tweet_similarity | regression on two texts | 450 / 100 / 450 | | Meaning Shift Detection | TempoWIC | tempo_wic | binary classification on two texts | 1,427 / 395 / 1,472 | | Hate Speech Detection | TweetHate | tweet_hate | multi-class classification | 5,019 / 716 / 1,433 | | Emoji Classification | TweetEmoji100 | tweet_emoji | multi-class classification | 50,000 / 5,000 / 50,000 | | Sentiment Classification | TweetSentiment | tweet_sentiment | ABSA on a five-pointscale | 26,632 / 4,000 / 12,379 | | Name Entity Disambiguation | TweetNERD | tweet_nerd | binary classification | 20,164 / 4,100 / 20,075 | | Emotion Classification | TweetEmotion | tweet_emotion | multi-label classification | 6,838 / 886 / 3,259 | ## Dataset Structure ### Data Fields The data fields are unified among all splits. In the following we present the information contained in each of the datasets. #### tweet_topic - `text`: a `string` feature. - `gold_label_list`: a list of `string` feature. - `date`: a `string` feature. #### tweet_ner7 - `text`: a `string` feature. - `text_tokenized`: a list of `string` feature. - `gold_label_sequence`: a list of `string` feature. - `date`: a `string` feature. - `entities`: a list of `dictionary` feature containing `{"entity": "string", "type": "string"}`. #### tweet_qa - `text`: a `string` feature. - `gold_label_str`: a `string` feature. - `context`: a `string` feature. #### tweet_qg - `text`: a `string` feature. - `gold_label_str`: a `string` feature. - `context`: a `string` feature. #### tweet_intimacy - `text`: a `string` feature. - `gold_score`: a `float` feature. #### tweet_similarity - `text_1`: a `string` feature. - `text_2`: a `string` feature. - `gold_score`: a `float` feature. #### tempo_wic - `gold_label_binary`: a `int` feature. - `target`: a `string` feature. - `text_1`: a `string` feature. - `text_tokenized_1`: a list of `string` feature. - `token_idx_1`: a `int` feature. - `date_1`: a `string` feature. - `text_2`: a `string` feature. - `text_tokenized_2`: a list of `string` feature. - `token_idx_2`: a `int` feature. - `date_2`: a `string` feature. #### tweet_hate - `gold_label`: a `int` feature. - `text`: a `string` feature. #### tweet_emoji - `gold_label`: a `int` feature. - `text`: a `string` feature. - `date`: a `string` feature. #### tweet_sentiment - `gold_label`: a `int` feature. - `text`: a `string` feature. - `target`: a `string` feature. #### tweet_nerd - `gold_label_binary`: a `int` feature. - `target`: a `string` feature. - `text`: a `string` feature. - `definition`: a `string` feature. - `text_start`: a `int` feature. - `text_end`: a `int` feature. - `date`: a `string` feature. #### tweet_emotion - `text`: a `string` feature. - `gold_label_list`: a list of `string` feature. ## Evaluation metrics & Models | **dataset** | **evaluation metric** | **gold label** | **model card** | |-------------------|-------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------| | TweetTopic | ```macro-F1``` | _arts\_&\_culture, business\_&\_entrepreneurs, celebrity\_&\_pop\_culture, <br />diaries\_&\_daily\_life, family, fashion\_&\_style, <br />film\_tv\_&\_video, fitness\_&\_health, food\_&\_dining, <br />gaming, learning\_&\_educational, music, <br />news\_&\_social\_concern, other\_hobbies, relationships, <br />science\_&\_technology, sports, travel\_&\_adventure, <br />youth\_&\_student\_life_ | [twitter-roberta-base-topic-latest (base)](https://huggingface.co/cardiffnlp/twitter-roberta-base-latest-tweet-topic) <br> [twitter-roberta-large-topic-latest (large)](https://huggingface.co/cardiffnlp/twitter-roberta-large-latest-tweet-topic) | | TweetNER7 | ```macro-F1``` | _B-corporation, B-creative_work, B-event, <br />B-group, B-location, B-person, <br />B-product, I-corporation, I-creative_work, <br />I-event, I-group, I-location, <br />I-person, I-product, O_ | [twitter-roberta-base-ner7-latest (base)](https://huggingface.co/cardiffnlp/twitter-roberta-base-latest-tweet-ner7) <br> TBA | | TweettQA | ```answer-F1``` | - | [flan-t5-small-tweet-qa (small)](https://huggingface.co/cardiffnlp/flan-t5-small-tweet-qa) <br> [flan-t5-base-tweet-qa (base)](https://huggingface.co/cardiffnlp/flan-t5-base-tweet-qa) | | TweetQG | ```METEOR``` | - | [flan-t5-small-tweet-qg (small)](https://huggingface.co/cardiffnlp/flan-t5-small-tweet-qg) <br> [flan-t5-base-tweet-qg (base)](https://huggingface.co/cardiffnlp/flan-t5-base-tweet-qg) | | TweetIntimacy | ```spearman correlation``` | _[1 - 5]_ | [twitter-roberta-base-intimacy-latest (base)](https://huggingface.co/cardiffnlp/twitter-roberta-base-latest-tweet-intimacy) <br> [twitter-roberta-large-intimacy-latest (large)](https://huggingface.co/cardiffnlp/twitter-roberta-large-latest-tweet-intimacy) | | TweetSIM | ```spearman correlation``` | _[0 - 5]_ | [twitter-roberta-base-similarity-latest (base)](https://huggingface.co/cardiffnlp/twitter-roberta-base-latest-tweet-similarity) <br> [twitter-roberta-large-similarity-latest (large)](https://huggingface.co/cardiffnlp/twitter-roberta-large-latest-tweet-similarity) | | TempoWIC | ```accuracy``` | _no, yes_ | [twitter-roberta-base-tempo-wic-latest (base)](https://huggingface.co/cardiffnlp/twitter-roberta-base-latest-tempo-wic) <br> [twitter-roberta-large-tempo-wic-latest (large)](https://huggingface.co/cardiffnlp/twitter-roberta-large-latest-tempo-wic) | | TweetHate | ```combined-F1```<br /> ```(micro-F1 for hate/not-hate &```<br /> ``` macro-F1 for hate speech subclasses)``` | _hate_gender, hate_race, hate_sexuality, hate_religion, hate_origin, <br />hate_disability, hate_age, not_hate_ | [twitter-roberta-base-hate-latest-st (base)](https://huggingface.co/cardiffnlp/twitter-roberta-base-latest-tweet-hate) <br> [twitter-roberta-large-hate-latest (large)](https://huggingface.co/cardiffnlp/twitter-roberta-large-latest-tweet-hate) | | TweetEmoji100 | ```accuracy at top 5``` | _Full emoji list: ./data/tweet_emoji/map.txt_ | [twitter-roberta-base-emoji-latest (base)](https://huggingface.co/cardiffnlp/twitter-roberta-base-latest-tweet-emoji) <br> [twitter-roberta-large-emoji-latest (large)](https://huggingface.co/cardiffnlp/twitter-roberta-large-latest-tweet-emoji) | | TweetSentiment | ```1 - MAE^M``` <br /> ```(MAE^M : Macro Averaged Mean Absolute Error)``` | _'strongly negative' , 'negative', 'negative or neutral', <br /> 'positive', 'strongly positive'_ | [twitter-roberta-base-topic-sentiment-latest (base)](https://huggingface.co/cardiffnlp/twitter-roberta-base-latest-tweet-sentiment) <br> [twitter-roberta-large-topic-sentiment-latest (large)](https://huggingface.co/cardiffnlp/twitter-roberta-large-latest-tweet-sentiment) | | TweetNERD | ```accuracy``` | _no, yes_ | [twitter-roberta-base-nerd-latest (base)](https://huggingface.co/cardiffnlp/twitter-roberta-base-latest-tweet-nerd) <br> [twitter-roberta-large-nerd-latest (large)](https://huggingface.co/cardiffnlp/twitter-roberta-large-latest-tweet-nerd) | | TweetEmotion | ```macro-F1``` | _anger, anticipation, disgust, fear, joy, love, optimism, <br />pessimism, sadness, surprise, trust_ | [twitter-roberta-base-emotion-latest (base)](https://huggingface.co/cardiffnlp/twitter-roberta-base-latest-tweet-emotion) <br> [twitter-roberta-large-emotion-latest (large)](https://huggingface.co/cardiffnlp/twitter-roberta-large-latest-tweet-emotion) | ## Citation Information ### Main reference paper Please cite the [reference paper](https://arxiv.org/abs/2310.14757) if you use this benchmark. ```bibtex @inproceedings{antypas2023supertweeteval, title={SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research}, author={Dimosthenis Antypas and Asahi Ushio and Francesco Barbieri and Leonardo Neves and Kiamehr Rezaee and Luis Espinosa-Anke and Jiaxin Pei and Jose Camacho-Collados}, booktitle={Findings of the Association for Computational Linguistics: EMNLP 2023}, year={2023} } ``` ### References of individual datasets In addition to the main reference paper, please cite the individual task datasets included in SuperTweetEval if you use them. - TweetTopic ``` @inproceedings{antypas-etal-2022-twitter, title = "{T}witter Topic Classification", author = "Antypas, Dimosthenis and Ushio, Asahi and Camacho-Collados, Jose and Silva, Vitor and Neves, Leonardo and Barbieri, Francesco", booktitle = "Proceedings of the 29th International Conference on Computational Linguistics", month = oct, year = "2022", address = "Gyeongju, Republic of Korea", publisher = "International Committee on Computational Linguistics", url = "https://aclanthology.org/2022.coling-1.299", pages = "3386--3400", abstract = "Social media platforms host discussions about a wide variety of topics that arise everyday. Making sense of all the content and organising it into categories is an arduous task. A common way to deal with this issue is relying on topic modeling, but topics discovered using this technique are difficult to interpret and can differ from corpus to corpus. In this paper, we present a new task based on tweet topic classification and release two associated datasets. Given a wide range of topics covering the most important discussion points in social media, we provide training and testing data from recent time periods that can be used to evaluate tweet classification models. Moreover, we perform a quantitative evaluation and analysis of current general- and domain-specific language models on the task, which provide more insights on the challenges and nature of the task.", } ``` - TweetNER7 ``` @inproceedings{ushio-etal-2022-named, title = "Named Entity Recognition in {T}witter: A Dataset and Analysis on Short-Term Temporal Shifts", author = "Ushio, Asahi and Barbieri, Francesco and Sousa, Vitor and Neves, Leonardo and Camacho-Collados, Jose", booktitle = "Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)", month = nov, year = "2022", address = "Online only", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.aacl-main.25", pages = "309--319", abstract = "Recent progress in language model pre-training has led to important improvements in Named Entity Recognition (NER). Nonetheless, this progress has been mainly tested in well-formatted documents such as news, Wikipedia, or scientific articles. In social media the landscape is different, in which it adds another layer of complexity due to its noisy and dynamic nature. In this paper, we focus on NER in Twitter, one of the largest social media platforms, and construct a new NER dataset, TweetNER7, which contains seven entity types annotated over 11,382 tweets from September 2019 to August 2021. The dataset was constructed by carefully distributing the tweets over time and taking representative trends as a basis. Along with the dataset, we provide a set of language model baselines and perform an analysis on the language model performance on the task, especially analyzing the impact of different time periods. In particular, we focus on three important temporal aspects in our analysis: short-term degradation of NER models over time, strategies to fine-tune a language model over different periods, and self-labeling as an alternative to lack of recently-labeled data. TweetNER7 is released publicly (https://huggingface.co/datasets/tner/tweetner7) along with the models fine-tuned on it (NER models have been integrated into TweetNLP and can be found at https://github.com/asahi417/tner/tree/master/examples/tweetner7{\_}paper).", } ``` - TweetQA ``` @inproceedings{xiong2019tweetqa, title={TweetQA: A Social Media Focused Question Answering Dataset}, author={Xiong, Wenhan and Wu, Jiawei and Wang, Hong and Kulkarni, Vivek and Yu, Mo and Guo, Xiaoxiao and Chang, Shiyu and Wang, William Yang}, booktitle={Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics}, year={2019} } ``` - TweetIntimacy ``` @misc{pei2023semeval, title={SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis}, author={Jiaxin Pei and Vítor Silva and Maarten Bos and Yozon Liu and Leonardo Neves and David Jurgens and Francesco Barbieri}, year={2023}, eprint={2210.01108}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` - Tweet Similarity ``` N/A ``` - TempoWiC ``` @inproceedings{loureiro-etal-2022-tempowic, title = "{T}empo{W}i{C}: An Evaluation Benchmark for Detecting Meaning Shift in Social Media", author = "Loureiro, Daniel and D{'}Souza, Aminette and Muhajab, Areej Nasser and White, Isabella A. and Wong, Gabriel and Espinosa-Anke, Luis and Neves, Leonardo and Barbieri, Francesco and Camacho-Collados, Jose", booktitle = "Proceedings of the 29th International Conference on Computational Linguistics", month = oct, year = "2022", address = "Gyeongju, Republic of Korea", publisher = "International Committee on Computational Linguistics", url = "https://aclanthology.org/2022.coling-1.296", pages = "3353--3359", abstract = "Language evolves over time, and word meaning changes accordingly. This is especially true in social media, since its dynamic nature leads to faster semantic shifts, making it challenging for NLP models to deal with new content and trends. However, the number of datasets and models that specifically address the dynamic nature of these social platforms is scarce. To bridge this gap, we present TempoWiC, a new benchmark especially aimed at accelerating research in social media-based meaning shift. Our results show that TempoWiC is a challenging benchmark, even for recently-released language models specialized in social media.", } ``` - TweetHate ``` @inproceedings{sachdeva-etal-2022-measuring, title = "The Measuring Hate Speech Corpus: Leveraging Rasch Measurement Theory for Data Perspectivism", author = "Sachdeva, Pratik and Barreto, Renata and Bacon, Geoff and Sahn, Alexander and von Vacano, Claudia and Kennedy, Chris", booktitle = "Proceedings of the 1st Workshop on Perspectivist Approaches to NLP @LREC2022", month = jun, year = "2022", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2022.nlperspectives-1.11", pages = "83--94", abstract = "We introduce the Measuring Hate Speech corpus, a dataset created to measure hate speech while adjusting for annotators{'} perspectives. It consists of 50,070 social media comments spanning YouTube, Reddit, and Twitter, labeled by 11,143 annotators recruited from Amazon Mechanical Turk. Each observation includes 10 ordinal labels: sentiment, disrespect, insult, attacking/defending, humiliation, inferior/superior status, dehumanization, violence, genocide, and a 3-valued hate speech benchmark label. The labels are aggregated using faceted Rasch measurement theory (RMT) into a continuous score that measures each comment{'}s location on a hate speech spectrum. The annotation experimental design assigned comments to multiple annotators in order to yield a linked network, allowing annotator disagreement (perspective) to be statistically summarized. Annotators{'} labeling strictness was estimated during the RMT scaling, projecting their perspective onto a linear measure that was adjusted for the hate speech score. Models that incorporate this annotator perspective parameter as an auxiliary input can generate label- and score-level predictions conditional on annotator perspective. The corpus includes the identity group targets of each comment (8 groups, 42 subgroups) and annotator demographics (6 groups, 40 subgroups), facilitating analyses of interactions between annotator- and comment-level identities, i.e. identity-related annotator perspective.", } ``` - TweetEmoji ``` N/A ``` - TweetSentiment ``` @inproceedings{rosenthal-etal-2017-semeval, title = "{S}em{E}val-2017 Task 4: Sentiment Analysis in {T}witter", author = "Rosenthal, Sara and Farra, Noura and Nakov, Preslav", booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)", month = aug, year = "2017", address = "Vancouver, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/S17-2088", doi = "10.18653/v1/S17-2088", pages = "502--518", abstract = "This paper describes the fifth year of the Sentiment Analysis in Twitter task. SemEval-2017 Task 4 continues with a rerun of the subtasks of SemEval-2016 Task 4, which include identifying the overall sentiment of the tweet, sentiment towards a topic with classification on a two-point and on a five-point ordinal scale, and quantification of the distribution of sentiment towards a topic across a number of tweets: again on a two-point and on a five-point ordinal scale. Compared to 2016, we made two changes: (i) we introduced a new language, Arabic, for all subtasks, and (ii) we made available information from the profiles of the Twitter users who posted the target tweets. The task continues to be very popular, with a total of 48 teams participating this year.", } ``` - TweetNERD ``` @article{mishra2022tweetnerd, title={TweetNERD--End to End Entity Linking Benchmark for Tweets}, author={Mishra, Shubhanshu and Saini, Aman and Makki, Raheleh and Mehta, Sneha and Haghighi, Aria and Mollahosseini, Ali}, journal={arXiv preprint arXiv:2210.08129}, year={2022} } ``` - TweetEmotion ``` @inproceedings{mohammad-etal-2018-semeval, title = "{S}em{E}val-2018 Task 1: Affect in Tweets", author = "Mohammad, Saif and Bravo-Marquez, Felipe and Salameh, Mohammad and Kiritchenko, Svetlana", booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation", month = jun, year = "2018", address = "New Orleans, Louisiana", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/S18-1001", doi = "10.18653/v1/S18-1001", pages = "1--17", abstract = "We present the SemEval-2018 Task 1: Affect in Tweets, which includes an array of subtasks on inferring the affectual state of a person from their tweet. For each task, we created labeled data from English, Arabic, and Spanish tweets. The individual tasks are: 1. emotion intensity regression, 2. emotion intensity ordinal classification, 3. valence (sentiment) regression, 4. valence ordinal classification, and 5. emotion classification. Seventy-five teams (about 200 team members) participated in the shared task. We summarize the methods, resources, and tools used by the participating teams, with a focus on the techniques and resources that are particularly useful. We also analyze systems for consistent bias towards a particular race or gender. The data is made freely available to further improve our understanding of how people convey emotions through language.", } ```
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akoksal/LongForm
akoksal
2023-09-19T20:20:39Z
483
34
[ "task_categories:text2text-generation", "task_categories:text-generation", "task_categories:question-answering", "task_categories:conversational", "task_categories:summarization", "task_categories:table-to-text", "size_categories:10K<n<100K", "language:en", "instruction-tuning", "arxiv:2304.08460", "region:us" ]
[ "text2text-generation", "text-generation", "question-answering", "conversational", "summarization", "table-to-text" ]
2023-04-16T21:41:37Z
--- dataset_info: features: - name: input dtype: string - name: output dtype: string - name: source dtype: string - name: subset dtype: string splits: - name: train num_bytes: 63759065 num_examples: 23652 - name: validation num_bytes: 6190242 num_examples: 2042 - name: test num_bytes: 6080212 num_examples: 2045 download_size: 45525146 dataset_size: 76029519 task_categories: - text2text-generation - text-generation - question-answering - conversational - summarization - table-to-text language: - en tags: - instruction-tuning pretty_name: longform size_categories: - 10K<n<100K --- # LongForm The LongForm dataset is created by leveraging English corpus examples with augmented instructions. We select a diverse set of human-written documents from existing corpora such as C4 and Wikipedia and generate instructions for the given documents via LLMs. Then, we extend these examples with structured corpora examples such as Stack Exchange and WikiHow and task examples such as question answering, email writing, grammar error correction, story/poem generation, and text summarization. ## Distribution The distribution of the LongForm dataset in terms of the source of examples is below. It contains examples generated from raw text corpora via LLMs, structured corpus examples, as well as various NLP task examples such as email writing, grammar error correction, story/poem generation, and text summarization. | **Type** | **Source** | **Number of Examples** | |------------------------|----------------|------------------------| | **Corpora** | C4 | 10,000 | | | Wikipedia | 5,000 | | **Structured Corpora** | Stack Exchange | 4,380 | | | WikiHow | 2,500 | | **Tasks** | NIv2 | 3,684 | | | Big Bench | 600 | | | BEA-GEC | 1,203 | | | Enron | 372 | | **Total** | | 27,739 | | | | | | **Train** | | 23,652 | | **Validation** | | 2,042 | | **Test** | | 2,045 | ## Models | | **All** | **Recipe Generation** | **ELI5** | **Writing Prompts** | |-----------------------|---------|-----------------------------------|----------|---------------------| | **T0++** | 10.9 | 18.7 | 3.8 | 10.2 | | **Tk-Instruct** | 6.3 | 12.9* | 3.6 | 2.4 | | **Flan-T5** | 10.6 | 20.9* | 3.5 | 7.4 | | **Alpaca-LLaMA-7B** | 14.6 | 19.5 | 12.5 | 11.8 | | **OPT-30B** | 11.1 | 18.6 | 12.2 | 2.6 | | **[LongForm-T5-XL](https://huggingface.co/akoksal/LongForm-T5-XL)** | 16.3 | 20.2 | 18.3 | 10.6 | | **[LongForm-OPT-2.7B](https://huggingface.co/akoksal/LongForm-OPT-2.7B)** | 17.8 | 15.5 | 17.9 | **19.9** | | **[LongForm-OPT-6.7B](https://huggingface.co/akoksal/LongForm-OPT-6.7B)** | 17.7 | 16.9 | 17.2 | 19.0 | | [**LongForm-LLaMA-7B**](https://huggingface.co/akoksal/LongForm-LLaMA-7B-diff)‡ | **19.7** | **21.7** | **18.6** | 18.9 | ‡: We can just release the difference between LongForm-LLaMA-7B and pretrained LLaMA-7B publicly due to restrictions of LLaMA models. ## Examples | **Source** | **Instruction** | **Output**| |------------------------|----------------|------------------------| | C4 | What is the likelihood that Donald Trump's HHS secretary nominee, Alex Azar, will push policies that benefit pharmaceutical companies' pocketbooks? | His record suggests he’s unlikely to push pricing policies that would hit pharmaceutical companies’ pocketbooks.<br />President Donald Trump tweeted Monday that his nominee for HHS secretary, Alex Azar will “be a star for … lower drug prices!” But the record of the former top executive for Eli Lilly, which tripled the price of a top-selling insulin drug while he led its U.S. operation, suggests a different story.<br />Lilly is one of three drug companies targeted by a class-action lawsuit that accuses the company[...]<br />“This nominee has impeccable big pharma credentials but non-existent credentials when it comes to concerns on drug prices,” said Peter Welch, co-chair of the House Democrats Drug Pricing Task Force. |Wikipedia | "Where is San Saba located in Rome, Italy?" Respond in 2 sentences. |San Saba is an ancient basilica church in Rome, Italy. It lies on the so-called "Piccolo Aventino", which is an area close to the ancient Aurelian Walls next to the Aventine Hill and Caelian Hill.| ## Dataset Description - **Repository:** https://github.com/akoksal/LongForm - **Paper:** https://arxiv.org/abs/2304.08460 - **Version:** v1.0 - April 18, 2023 - **Contact:** [Abdullatif Köksal](https://twitter.com/akoksal_) ## License The LongForm project is subject to a MIT License with custom limitations for restrictions imposed by OpenAI (for the instruction generation part), as well as the license of language models (OPT, LLaMA, and T5). ## Citation ``` @misc{koksal2023longform, title={LongForm: Optimizing Instruction Tuning for Long Text Generation with Corpus Extraction}, author={Abdullatif Köksal and Timo Schick and Anna Korhonen and Hinrich Schütze}, year={2023}, eprint={2304.08460}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
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open-llm-leaderboard/details_togethercomputer__RedPajama-INCITE-Base-3B-v1
open-llm-leaderboard
2023-10-17T12:53:27Z
483
0
[ "region:us" ]
null
2023-08-18T11:49:58Z
--- pretty_name: Evaluation run of togethercomputer/RedPajama-INCITE-Base-3B-v1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [togethercomputer/RedPajama-INCITE-Base-3B-v1](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-3B-v1)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 122 configuration, each one coresponding to one of\ \ the evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can\ \ be found as a specific split in each configuration, the split being named using\ \ the timestamp of the run.The \"train\" split is always pointing to the latest\ \ results.\n\nAn additional configuration \"results\" store all the aggregated results\ \ of the run (and is used to compute and display the agregated metrics on the [Open\ \ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_togethercomputer__RedPajama-INCITE-Base-3B-v1\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-17T12:53:13.989201](https://huggingface.co/datasets/open-llm-leaderboard/details_togethercomputer__RedPajama-INCITE-Base-3B-v1/blob/main/results_2023-10-17T12-53-13.989201.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0010486577181208054,\n\ \ \"em_stderr\": 0.0003314581465219258,\n \"f1\": 0.04903838087248335,\n\ \ \"f1_stderr\": 0.0012101494030918615,\n \"acc\": 0.33004332884746224,\n\ \ \"acc_stderr\": 0.008268314684144315\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.0010486577181208054,\n \"em_stderr\": 0.0003314581465219258,\n\ \ \"f1\": 0.04903838087248335,\n \"f1_stderr\": 0.0012101494030918615\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.01288855193328279,\n \ \ \"acc_stderr\": 0.0031069012664996735\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.6471981057616417,\n \"acc_stderr\": 0.013429728101788956\n\ \ }\n}\n```" repo_url: https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-3B-v1 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|arc:challenge|25_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-07-19T15:11:56.441864.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_17T12_53_13.989201 path: - '**/details_harness|drop|3_2023-10-17T12-53-13.989201.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-17T12-53-13.989201.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_17T12_53_13.989201 path: - '**/details_harness|gsm8k|5_2023-10-17T12-53-13.989201.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-17T12-53-13.989201.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hellaswag|10_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T15:11:56.441864.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T15:11:56.441864.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_19T15_11_56.441864 path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T15:11:56.441864.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T15:11:56.441864.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_17T12_53_13.989201 path: - '**/details_harness|winogrande|5_2023-10-17T12-53-13.989201.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-17T12-53-13.989201.parquet' - config_name: original_mmlu_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:anatomy|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:astronomy|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:business_ethics|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:college_biology|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:college_medicine|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:college_physics|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:computer_security|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:econometrics|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:formal_logic|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:global_facts|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:human_aging|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:international_law|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:machine_learning|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:management|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:marketing|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:nutrition|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:philosophy|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:prehistory|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:professional_law|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:public_relations|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:security_studies|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:sociology|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:virology|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:world_religions|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:anatomy|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:astronomy|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:business_ethics|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:college_biology|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:college_medicine|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:college_physics|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:computer_security|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:econometrics|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:formal_logic|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:global_facts|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:human_aging|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:international_law|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:machine_learning|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:management|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:marketing|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:nutrition|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:philosophy|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:prehistory|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:professional_law|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:public_relations|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:security_studies|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:sociology|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:virology|5_2023-08-28T20:41:49.693075.parquet' - '**/details_original|mmlu:world_religions|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_abstract_algebra_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_anatomy_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:anatomy|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:anatomy|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_astronomy_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:astronomy|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:astronomy|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_business_ethics_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:business_ethics|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:business_ethics|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_clinical_knowledge_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_college_biology_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:college_biology|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:college_biology|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_college_chemistry_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_college_computer_science_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_college_mathematics_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_college_medicine_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:college_medicine|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:college_medicine|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_college_physics_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:college_physics|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:college_physics|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_computer_security_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:computer_security|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:computer_security|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_conceptual_physics_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_econometrics_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:econometrics|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:econometrics|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_electrical_engineering_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_elementary_mathematics_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_formal_logic_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:formal_logic|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:formal_logic|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_global_facts_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:global_facts|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:global_facts|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_high_school_biology_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_high_school_chemistry_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_high_school_computer_science_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_high_school_european_history_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_high_school_geography_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_high_school_government_and_politics_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_high_school_macroeconomics_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_high_school_mathematics_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_high_school_microeconomics_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_high_school_physics_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_high_school_psychology_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_high_school_statistics_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_high_school_us_history_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_high_school_world_history_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_human_aging_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:human_aging|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:human_aging|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_human_sexuality_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_international_law_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:international_law|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:international_law|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_jurisprudence_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_logical_fallacies_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_machine_learning_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:machine_learning|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:machine_learning|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_management_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:management|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:management|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_marketing_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:marketing|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:marketing|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_medical_genetics_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_miscellaneous_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_moral_disputes_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_moral_scenarios_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_nutrition_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:nutrition|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:nutrition|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_philosophy_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:philosophy|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:philosophy|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_prehistory_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:prehistory|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:prehistory|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_professional_accounting_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_professional_law_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:professional_law|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:professional_law|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_professional_medicine_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_professional_psychology_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_public_relations_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:public_relations|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:public_relations|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_security_studies_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:security_studies|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:security_studies|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_sociology_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:sociology|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:sociology|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_us_foreign_policy_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_virology_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:virology|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:virology|5_2023-08-28T20:41:49.693075.parquet' - config_name: original_mmlu_world_religions_5 data_files: - split: 2023_08_28T20_41_49.693075 path: - '**/details_original|mmlu:world_religions|5_2023-08-28T20:41:49.693075.parquet' - split: latest path: - '**/details_original|mmlu:world_religions|5_2023-08-28T20:41:49.693075.parquet' - config_name: results data_files: - split: 2023_07_19T15_11_56.441864 path: - results_2023-07-19T15:11:56.441864.parquet - split: 2023_08_28T20_41_49.693075 path: - results_2023-08-28T20:41:49.693075.parquet - split: 2023_10_17T12_53_13.989201 path: - results_2023-10-17T12-53-13.989201.parquet - split: latest path: - results_2023-10-17T12-53-13.989201.parquet --- # Dataset Card for Evaluation run of togethercomputer/RedPajama-INCITE-Base-3B-v1 ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-3B-v1 - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [togethercomputer/RedPajama-INCITE-Base-3B-v1](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-3B-v1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 122 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_togethercomputer__RedPajama-INCITE-Base-3B-v1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-17T12:53:13.989201](https://huggingface.co/datasets/open-llm-leaderboard/details_togethercomputer__RedPajama-INCITE-Base-3B-v1/blob/main/results_2023-10-17T12-53-13.989201.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.0010486577181208054, "em_stderr": 0.0003314581465219258, "f1": 0.04903838087248335, "f1_stderr": 0.0012101494030918615, "acc": 0.33004332884746224, "acc_stderr": 0.008268314684144315 }, "harness|drop|3": { "em": 0.0010486577181208054, "em_stderr": 0.0003314581465219258, "f1": 0.04903838087248335, "f1_stderr": 0.0012101494030918615 }, "harness|gsm8k|5": { "acc": 0.01288855193328279, "acc_stderr": 0.0031069012664996735 }, "harness|winogrande|5": { "acc": 0.6471981057616417, "acc_stderr": 0.013429728101788956 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
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indonlp/nusaparagraph_emot
indonlp
2023-09-07T13:05:53Z
483
0
[ "license:apache-2.0", "region:us" ]
null
2023-09-07T12:59:35Z
--- license: apache-2.0 ---
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Paul/hatecheck
Paul
2022-07-05T10:27:25Z
482
4
[ "task_categories:text-classification", "task_ids:hate-speech-detection", "annotations_creators:crowdsourced", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:cc-by-4.0", "arxiv:2012.15606", "region:us" ]
[ "text-classification" ]
2022-03-02T23:29:22Z
--- annotations_creators: - crowdsourced language_creators: - expert-generated language: - en license: - cc-by-4.0 multilinguality: - monolingual pretty_name: HateCheck size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - hate-speech-detection --- # Dataset Card for HateCheck ## Dataset Description HateCheck is a suite of functional test for hate speech detection models. The dataset contains 3,728 validated test cases in 29 functional tests. 19 functional tests correspond to distinct types of hate. The other 11 functional tests cover challenging types of non-hate. This allows for targeted diagnostic insights into model performance. In our ACL paper, we found critical weaknesses in all commercial and academic hate speech detection model that we tested with HateCheck. Please refer to the paper (linked below) for results and further discussion, as well as further information on the dataset and a full data statement. - **Paper:** Röttger et al. (2021) - HateCheck: Functional Tests for Hate Speech Detection Model. https://aclanthology.org/2021.acl-long.4/ or https://arxiv.org/abs/2012.15606 - **Repository:** https://github.com/paul-rottger/hatecheck-data - **Point of Contact:** [email protected] ## Dataset Structure "test.csv" contains all 3,728 validated test cases. Each test case (row) has the following attributes: **functionality** The shorthand for the functionality tested by the test case. **case_id** The unique ID of the test case (assigned to each of the 3,901 cases we initially generated) **test_case** The text of the test case. **label_gold** The gold standard label (hateful/non-hateful) of the test case. All test cases within a given functionality have the same gold standard label. **target_ident** Where applicable, the protected group targeted or referenced by the test case. We cover seven protected groups in the test suite: women, trans people, gay people, black people, disabled people, Muslims and immigrants. **direction** For hateful cases, the binary secondary label indicating whether they are *directed* at an individual as part of a protected group or aimed at the group in *general*. **focus_words** Where applicable, the key word or phrase in a given test case (e.g. "cut their throats"). **focus_lemma** Where applicable, the corresponding lemma (e.g. "cut sb. throat"). **ref_case_id** For hateful cases, where applicable, the ID of the simpler hateful case which was perturbed to generate them. For non-hateful cases, where applicable, the ID of the hateful case which is contrasted. **ref_templ_id** The equivalent, but for template IDs. **templ_id** The unique ID of the template from which the test case was generated (assigned to each of the 866 cases and templates from which we generated the 3,901 initial cases). ## Citation Information When using HateCheck, please cite our ACL paper: @inproceedings{rottger-etal-2021-hatecheck, title = "{H}ate{C}heck: Functional Tests for Hate Speech Detection Models", author = {R{\"o}ttger, Paul and Vidgen, Bertie and Nguyen, Dong and Waseem, Zeerak and Margetts, Helen and Pierrehumbert, Janet}, booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.acl-long.4", doi = "10.18653/v1/2021.acl-long.4", pages = "41--58", abstract = "Detecting online hate is a difficult task that even state-of-the-art models struggle with. Typically, hate speech detection models are evaluated by measuring their performance on held-out test data using metrics such as accuracy and F1 score. However, this approach makes it difficult to identify specific model weak points. It also risks overestimating generalisable model performance due to increasingly well-evidenced systematic gaps and biases in hate speech datasets. To enable more targeted diagnostic insights, we introduce HateCheck, a suite of functional tests for hate speech detection models. We specify 29 model functionalities motivated by a review of previous research and a series of interviews with civil society stakeholders. We craft test cases for each functionality and validate their quality through a structured annotation process. To illustrate HateCheck{'}s utility, we test near-state-of-the-art transformer models as well as two popular commercial models, revealing critical model weaknesses.", }
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joelniklaus/Multi_Legal_Pile
joelniklaus
2023-10-18T20:39:36Z
482
30
[ "task_categories:fill-mask", "annotations_creators:other", "language_creators:found", "multilinguality:multilingual", "size_categories:10M<n<100M", "source_datasets:original", "language:bg", "language:cs", "language:da", "language:de", "language:el", "language:en", "language:es", "language:et", "language:fi", "language:fr", "language:ga", "language:hr", "language:hu", "language:it", "language:lt", "language:lv", "language:mt", "language:nl", "language:pl", "language:pt", "language:ro", "language:sk", "language:sl", "language:sv", "license:cc-by-nc-sa-4.0", "arxiv:2306.02069", "region:us" ]
[ "fill-mask" ]
2022-09-26T10:28:06Z
--- annotations_creators: - other language_creators: - found language: - bg - cs - da - de - el - en - es - et - fi - fr - ga - hr - hu - it - lt - lv - mt - nl - pl - pt - ro - sk - sl - sv license: - cc-by-nc-sa-4.0 multilinguality: - multilingual paperswithcode_id: null pretty_name: "MultiLegalPile: A Large-Scale Multilingual Corpus for the Legal Domain" size_categories: - 10M<n<100M source_datasets: - original task_categories: - fill-mask --- # Dataset Card for MultiLegalPile: A Large-Scale Multilingual Corpus for the Legal Domain ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** [MultiLegalPile](https://arxiv.org/abs/2306.02069) - **Leaderboard:** - **Point of Contact:** [Joel Niklaus](mailto:[email protected]) ### Dataset Summary The Multi_Legal_Pile is a large-scale multilingual legal dataset suited for pretraining language models. It spans over 24 languages and five legal text types. ### Supported Tasks and Leaderboards The dataset supports the tasks of fill-mask. ### Languages The following languages are supported: bg, cs, da, de, el, en, es, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv ## Dataset Structure It is structured in the following format: type -> language -> jurisdiction.jsonl.xz type is one of the following: - caselaw - contracts - legislation - other - legal_mc4 `legal_mc4` is a subset of the other type but is listed separately so it can be easily excluded since it is less permissively licensed than the other types. Use the dataset like this: ```python from datasets import load_dataset config = 'en_contracts' # {language}_{type} dataset = load_dataset('joelito/Multi_Legal_Pile', config, split='train', streaming=True) ``` 'config' is a combination of language and text_type, e.g. 'en_contracts' or 'de_caselaw'. To load all the languages or all the text_types, use 'all' instead of the language or text_type (e.g., ' all_legislation'). ### Data Instances The file format is jsonl.xz and there is one split available ("train"). The complete dataset (689GB) consists of four large subsets: - Native Multi Legal Pile (112GB) - Eurlex Resources (179GB) - Legal MC4 (106GB) - Pile of Law (292GB) #### Native Multilingual Legal Pile data | | Language | Text Type | Jurisdiction | Source | Size (MB) | Words | Documents | Words/Document | URL | License | |---:|:-----------|:------------|:---------------|:-----------------------------------|------------:|------------:|------------:|-----------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------| | 0 | bg | legislation | Bulgaria | MARCELL | 8015 | 308946116 | 82777 | 3732 | https://elrc-share.eu/repository/browse/marcell-bulgarian-legislative-subcorpus-v2/946267fe8d8711eb9c1a00155d026706d2c9267e5cdf4d75b5f02168f01906c6/ | [CC0-1.0](https://elrc-share.eu/static/metashare/licences/CC0-1.0.pdf) | | 1 | cs | caselaw | Czechia | CzCDC Constitutional Court | 11151 | 574336489 | 296652 | 1936 | https://lindat.mff.cuni.cz/repository/xmlui/handle/11372/LRT-3052 | [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) | | 2 | cs | caselaw | Czechia | CzCDC Supreme Administrative Court | 11151 | 574336489 | 296652 | 1936 | https://lindat.mff.cuni.cz/repository/xmlui/handle/11372/LRT-3052 | [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) | | 3 | cs | caselaw | Czechia | CzCDC Supreme Court | 11151 | 574336489 | 296652 | 1936 | https://lindat.mff.cuni.cz/repository/xmlui/handle/11372/LRT-3052 | [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) | | 4 | da | caselaw | Denmark | DDSC | 3469 | 210730560 | 89702 | 2349 | https://huggingface.co/DDSC | [CC BY 4.0 and other, depending on the dataset](https://creativecommons.org/licenses/by-nc/4.0/) | | 5 | da | legislation | Denmark | DDSC | 10736 | 653153146 | 265868 | 2456 | https://huggingface.co/DDSC | [CC BY 4.0 and other, depending on the dataset](https://creativecommons.org/licenses/by-nc/4.0/) | | 6 | de | caselaw | Germany | openlegaldata | 31527 | 1785439383 | 596800 | 2991 | https://de.openlegaldata.io/ | [ODbL-1.0](https://opendatacommons.org/licenses/odbl/1-0/) | | 7 | de | caselaw | Switzerland | entscheidsuche | 31527 | 1785439383 | 596800 | 2991 | https://entscheidsuche.ch/ | [See description](https://entscheidsuche.ch/dataUsage) | | 8 | de | legislation | Germany | openlegaldata | 8934 | 512840663 | 276034 | 1857 | https://de.openlegaldata.io/ | [ODbL-1.0](https://opendatacommons.org/licenses/odbl/1-0/) | | 9 | de | legislation | Switzerland | lexfind | 8934 | 512840663 | 276034 | 1857 | https://www.lexfind.ch/fe/de/search | No information provided | | 10 | fr | caselaw | Switzerland | entscheidsuche | 18313 | 1170335690 | 435569 | 2686 | https://entscheidsuche.ch/ | [See description](https://entscheidsuche.ch/dataUsage) | | 11 | fr | caselaw | Belgium | jurportal | 18313 | 1170335690 | 435569 | 2686 | https://juportal.be/home/welkom | [See description](https://juportal.be/home/disclaimer) | | 12 | fr | caselaw | France | CASS | 18313 | 1170335690 | 435569 | 2686 | https://echanges.dila.gouv.fr/OPENDATA/CASS/ | [Open Licence 2.0](https://echanges.dila.gouv.fr/OPENDATA/CASS/DILA_CASS_Presentation_20170824.pdf) | | 13 | fr | caselaw | Luxembourg | judoc | 18313 | 1170335690 | 435569 | 2686 | https://justice.public.lu/fr.html | [See description](https://justice.public.lu/fr/support/aspects-legaux/conditions-generales.html) | | 14 | it | caselaw | Switzerland | entscheidsuche | 6483 | 406520336 | 156630 | 2595 | https://entscheidsuche.ch/ | [See description](https://entscheidsuche.ch/dataUsage) | | 15 | en | legislation | Switzerland | lexfind | 36587 | 2537696894 | 657805 | 3857 | https://www.lexfind.ch/fe/de/search | No information provided | | 16 | en | legislation | UK | uk-lex | 36587 | 2537696894 | 657805 | 3857 | https://zenodo.org/record/6355465 | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode) | | 17 | fr | legislation | Switzerland | lexfind | 9297 | 600170792 | 243313 | 2466 | https://www.lexfind.ch/fe/fr/search | No information provided | | 18 | fr | legislation | Belgium | ejustice | 9297 | 600170792 | 243313 | 2466 | https://www.ejustice.just.fgov.be/cgi/welcome.pl | No information provided | | 19 | it | legislation | Switzerland | lexfind | 8332 | 542579039 | 227968 | 2380 | https://www.lexfind.ch/fe/it/search | No information provided | | 20 | nl | legislation | Belgium | ejustice | 8484 | 550788527 | 232204 | 2372 | https://www.ejustice.just.fgov.be/cgi/welcome.pl | No information provided | | 21 | hu | legislation | Hungary | MARCELL | 5744 | 264572303 | 86862 | 3045 | https://elrc-share.eu/repository/browse/marcell-hungarian-legislative-subcorpus-v2/a87295ec8d6511eb9c1a00155d0267065f7e56dc7db34ce5aaae0b48a329daaa/ | [CC0-1.0](https://elrc-share.eu/static/metashare/licences/CC0-1.0.pdf) | | 22 | pl | legislation | Poland | MARCELL | 5459 | 299334705 | 89264 | 3353 | https://elrc-share.eu/repository/browse/marcell-polish-legislative-subcorpus-v2/dd14fa1c8d6811eb9c1a00155d026706c4718ddc9c6e4a92a88923816ca8b219/ | [CC0-1.0](https://elrc-share.eu/static/metashare/licences/CC0-1.0.pdf) | | 23 | pt | caselaw | Brazil | RulingBR | 196919 | 12611760973 | 17251236 | 731 | https://github.com/diego-feijo/rulingbr | No information provided | | 24 | pt | caselaw | Brazil | CRETA | 196919 | 12611760973 | 17251236 | 731 | https://www.kaggle.com/datasets/eliasjacob/brcad5?resource=download&select=language_modeling_texts.parquet | [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) | | 25 | pt | caselaw | Brazil | CJPG | 196919 | 12611760973 | 17251236 | 731 | https://esaj.tjsp.jus.br/cjsg/consultaCompleta.do?f=1 | No information provided | | 26 | ro | legislation | Romania | MARCELL | 10464 | 559092153 | 215694 | 2592 | https://elrc-share.eu/repository/browse/marcell-romanian-legislative-subcorpus-v2/2da548428b9d11eb9c1a00155d026706ce94a6b59ffc4b0e9fb5cd9cebe6889e/ | [CC0-1.0](https://elrc-share.eu/static/metashare/licences/CC0-1.0.pdf) | | 27 | sk | legislation | Slovakia | MARCELL | 5208 | 280182047 | 76760 | 3650 | https://elrc-share.eu/repository/browse/marcell-slovak-legislative-subcorpus-v2/6bdee1d68c8311eb9c1a00155d0267063398d3f1a3af40e1b728468dcbd6efdd/ | [CC0-1.0](https://elrc-share.eu/static/metashare/licences/CC0-1.0.pdf) | | 28 | sl | legislation | Slovenia | MARCELL | 6057 | 365513763 | 88651 | 4123 | https://elrc-share.eu/repository/browse/marcell-slovenian-legislative-subcorpus-v2/e2a779868d4611eb9c1a00155d026706983c845a30d741b78e051faf91828b0d/ | [CC-BY-4.0](https://elrc-share.eu/static/metashare/licences/CC-BY-4.0.pdf) | total | all | all | all | 1297609 | xxx | 81214262514 | 57305071 | 1417 | | #### Eurlex Resources See [Eurlex Resources](https://huggingface.co/datasets/joelito/eurlex_resources#data-instances) for more information. #### Legal-MC4 See [Legal-MC4](https://huggingface.co/datasets/joelito/legal-mc4#data-instances) for more information. #### Pile-of-Law See [Pile-of-Law](https://huggingface.co/datasets/pile-of-law/pile-of-law#data-instances) for more information. | Language | Type | Jurisdiction | Source | Size (MB) | Tokens | Documents | Tokens/Document | Part of Multi_Legal_Pile | |:-----------|:------------|:---------------|:-------------------------------------|------------:|------------:|------------:|------------------:|:---------------------------| | en | all | all | all | 503712 | 50547777921 | 9872444 | 5120 | yes | | en | caselaw | EU | echr | 298 | 28374996 | 8480 | 3346 | yes | | en | caselaw | Canada | canadian_decisions | 486 | 45438083 | 11343 | 4005 | yes | | en | caselaw | US | dol_ecab | 942 | 99113541 | 28211 | 3513 | no | | en | caselaw | US | scotus_oral_arguments | 1092 | 108228951 | 7996 | 13535 | no | | en | caselaw | US | tax_rulings | 1704 | 166915887 | 54064 | 3087 | no | | en | caselaw | US | nlrb_decisions | 2652 | 294471818 | 32080 | 9179 | no | | en | caselaw | US | scotus_filings | 4018 | 593870413 | 63775 | 9311 | yes | | en | caselaw | US | bva_opinions | 35238 | 4084140080 | 839523 | 4864 | no | | en | caselaw | US | courtlistener_docket_entry_documents | 139006 | 12713614864 | 1983436 | 6409 | yes | | en | caselaw | US | courtlistener_opinions | 158110 | 15899704961 | 4518445 | 3518 | yes | | en | contracts | -- | tos | 4 | 391890 | 50 | 7837 | no | | en | contracts | US | cfpb_creditcard_contracts | 188 | 25984824 | 2638 | 9850 | yes | | en | contracts | US | edgar | 28698 | 2936402810 | 987926 | 2972 | yes | | en | contracts | US | atticus_contracts | 78300 | 7997013703 | 650833 | 12287 | yes | | en | legislation | US | fre | 2 | 173325 | 68 | 2548 | no | | en | legislation | US | frcp | 4 | 427614 | 92 | 4647 | no | | en | legislation | US | eoir | 62 | 6109737 | 2229 | 2741 | no | | en | legislation | -- | constitutions | 66 | 5984865 | 187 | 32004 | yes | | en | legislation | US | federal_register | 424 | 39854787 | 5414 | 7361 | yes | | en | legislation | US | uscode | 716 | 78466325 | 58 | 1352867 | yes | | en | legislation | EU | euro_parl | 808 | 71344326 | 9672 | 7376 | no | | en | legislation | US | cfr | 1788 | 160849007 | 243 | 661930 | yes | | en | legislation | US | us_bills | 3394 | 320723838 | 112483 | 2851 | yes | | en | legislation | EU | eurlex | 3504 | 401324829 | 142036 | 2825 | no | | en | legislation | US | state_codes | 18066 | 1858333235 | 217 | 8563747 | yes | | en | other | -- | bar_exam_outlines | 4 | 346924 | 59 | 5880 | no | | en | other | US | ftc_advisory_opinions | 4 | 509025 | 145 | 3510 | no | | en | other | US | olc_memos | 98 | 12764635 | 1384 | 9223 | yes | | en | other | -- | cc_casebooks | 258 | 24857378 | 73 | 340512 | no | | en | other | -- | un_debates | 360 | 31152497 | 8481 | 3673 | no | | en | other | -- | r_legaladvice | 798 | 72605386 | 146671 | 495 | no | | en | other | US | founding_docs | 1118 | 100390231 | 183664 | 546 | no | | en | other | US | oig | 5056 | 566782244 | 38954 | 14550 | yes | | en | other | US | congressional_hearings | 16448 | 1801110892 | 31514 | 57152 | no | ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @misc{niklaus2023multilegalpile, title={MultiLegalPile: A 689GB Multilingual Legal Corpus}, author={Joel Niklaus and Veton Matoshi and Matthias Stürmer and Ilias Chalkidis and Daniel E. Ho}, year={2023}, eprint={2306.02069}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@JoelNiklaus](https://github.com/joelniklaus) for adding this dataset.
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jon-tow/okapi_mmlu
jon-tow
2023-10-24T00:03:08Z
482
2
[ "language:ar", "language:bn", "language:ca", "language:da", "language:de", "language:es", "language:eu", "language:fr", "language:gu", "language:hi", "language:hr", "language:hu", "language:hy", "language:id", "language:it", "language:kn", "language:ml", "language:mr", "language:ne", "language:nl", "language:pt", "language:ro", "language:ru", "language:sk", "language:sr", "language:sv", "language:ta", "language:te", "language:uk", "language:vi", "license:cc-by-nc-4.0", "arxiv:2009.03300", "arxiv:2307.16039", "region:us" ]
null
2023-10-23T22:18:44Z
--- language: - ar - bn - ca - da - de - es - eu - fr - gu - hi - hr - hu - hy - id - it - kn - ml - mr - ne - nl - pt - ro - ru - sk - sr - sv - ta - te - uk - vi license: cc-by-nc-4.0 --- # okapi_mmlu <!-- Provide a quick summary of the dataset. --> Multilingual translation of [Measuring Massive Multitask Language Understanding (MMLU)](https://arxiv.org/abs/2009.03300). ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> MMLU is a benchmark that measured a text model’s multitask accuracy. The test covers 57 tasks including elementary mathematics, US history, computer science, law, and more. To attain high accuracy on this test, models must possess extensive world knowledge and problem solving ability. By comprehensively evaluating the breadth and depth of a model’s academic and professional understanding, MMLU can be used to analyze models across many tasks and to identify important shortcomings. - **Curated by:** Dac Lai, Viet and Van Nguyen, Chien and Ngo, Nghia Trung and Nguyen, Thuat and Dernoncourt, Franck and Rossi, Ryan A and Nguyen, Thien Huu - **License:** The datasets are CC BY NC 4.0 (allowing only non-commercial use). ### Dataset Sources <!-- Provide the basic links for the dataset. --> - **Repository:** http://nlp.uoregon.edu/download/okapi-eval/datasets/ - **Paper:** Okapi ([Lai et al., 2023](https://arxiv.org/abs/2307.16039)) ## Citation <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> ```bibtex @article{dac2023okapi, title={Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback}, author={Dac Lai, Viet and Van Nguyen, Chien and Ngo, Nghia Trung and Nguyen, Thuat and Dernoncourt, Franck and Rossi, Ryan A and Nguyen, Thien Huu}, journal={arXiv e-prints}, pages={arXiv--2307}, year={2023} } ``` ```bibtex @article{hendryckstest2021, title={Measuring Massive Multitask Language Understanding}, author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt}, journal={Proceedings of the International Conference on Learning Representations (ICLR)}, year={2021} } ```
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OxAISH-AL-LLM/wiki_toxic
OxAISH-AL-LLM
2022-09-19T15:53:19Z
481
13
[ "task_categories:text-classification", "task_ids:hate-speech-detection", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:extended|other", "language:en", "license:cc0-1.0", "wikipedia", "toxicity", "toxic comments", "region:us" ]
[ "text-classification" ]
2022-08-25T12:59:12Z
--- annotations_creators: - crowdsourced language: - en language_creators: - found license: - cc0-1.0 multilinguality: - monolingual pretty_name: Toxic Wikipedia Comments size_categories: - 100K<n<1M source_datasets: - extended|other tags: - wikipedia - toxicity - toxic comments task_categories: - text-classification task_ids: - hate-speech-detection --- # Dataset Card for Wiki Toxic ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The Wiki Toxic dataset is a modified, cleaned version of the dataset used in the [Kaggle Toxic Comment Classification challenge](https://www.kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge/overview) from 2017/18. The dataset contains comments collected from Wikipedia forums and classifies them into two categories, `toxic` and `non-toxic`. The Kaggle dataset was cleaned using the included `clean.py` file. ### Supported Tasks and Leaderboards - Text Classification: the dataset can be used for training a model to recognise toxicity in sentences and classify them accordingly. ### Languages The sole language used in the dataset is English. ## Dataset Structure ### Data Instances For each data point, there is an id, the comment_text itself, and a label (0 for non-toxic, 1 for toxic). ``` {'id': 'a123a58f610cffbc', 'comment_text': '"This article SUCKS. It may be poorly written, poorly formatted, or full of pointless crap that no one cares about, and probably all of the above. If it can be rewritten into something less horrible, please, for the love of God, do so, before the vacuum caused by its utter lack of quality drags the rest of Wikipedia down into a bottomless pit of mediocrity."', 'label': 1} ``` ### Data Fields - `id`: A unique identifier string for each comment - `comment_text`: A string containing the text of the comment - `label`: An integer, either 0 if the comment is non-toxic, or 1 if the comment is toxic ### Data Splits The Wiki Toxic dataset has three splits: *train*, *validation*, and *test*. The statistics for each split are below: | Dataset Split | Number of data points in split | | ----------- | ----------- | | Train | 127,656 | | Validation | 31,915 | | Test | 63,978 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
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teknium/openhermes
teknium
2023-09-07T20:41:05Z
481
85
[ "task_categories:text-generation", "language:eng", "distillation", "synthetic data", "gpt", "region:us" ]
[ "text-generation" ]
2023-09-04T01:31:26Z
--- language: - eng pretty_name: "OpenHermes-v1.0" tags: - distillation - synthetic data - gpt task_categories: - text-generation --- # OpenHermes Dataset ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/XIiSwLP1Uu94IUucGypyl.png) The OpenHermes dataset is composed of 242,000 entries of primarily GPT-4 generated data, from open datasets across the AI landscape, including: OpenHermes 13B is the first fine tune of the Hermes dataset that has a fully open source dataset! OpenHermes was trained on 242,000 entries of primarily GPT-4 generated data, from open datasets across the AI landscape, including: - GPTeacher - General Instruct, Roleplay v1, Roleplay v2, and Code Instruct Datasets, by Teknium - WizardLM (v1, evol_instruct 70k), by WizardLM Team/nlpxucan - Airoboros GPT-4 (v1.0), by JonDurbin - Camel-AI's domain expert datasets, by the Camel-AI Team - CodeAlpaca, by Sahil2801 - GPT4-LLM and Unnatural Instructions, by Microsoft Filtering included removal of OpenAI refusals, disclaimers, and "As an AI" type examples and more The base dataset mix is identical to the original Nous-Hermes', minus the Nous-Instruct and PDACTL datasets which were private datasets.
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Fraser/python-state-changes
Fraser
2022-10-11T17:04:35Z
480
6
[ "language:code", "region:us" ]
null
2022-03-02T23:29:22Z
--- language: - code --- # Python State Changes State changes from the execution of single lines of Python code. All code was taken from Python HackerRank solutions. Scraped from my dataset of traced HackerRank solutions. https://www.kaggle.com/frasergreenlee/ran-hackerrank-solutions ```json {"start": "g = 100; i = 1; l = [100, 100, 0, 0, -100, -100]", "code": "g += l[i]", "end": "g = 200; i = 1; l = [100, 100, 0, 0, -100, -100]"} {"start": "a = 1; b = 2; d = 4; i = 3; j = 2", "code": "i, j = a + (j - b), b + (d - (i - a))", "end": "a = 1; b = 2; d = 4; i = 1; j = 4"} {"start": "b = 15", "code": "b = b // 2", "end": "b = 7"} ``` ## Get an overview of the dataset from seeing the frequency of different ASTs. 👉 https://observablehq.com/@frasergreenlee/python-lines-dataset#chart
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open-llm-leaderboard/details_togethercomputer__RedPajama-INCITE-7B-Base
open-llm-leaderboard
2023-12-03T17:07:09Z
479
0
[ "region:us" ]
null
2023-08-18T11:50:17Z
--- pretty_name: Evaluation run of togethercomputer/RedPajama-INCITE-7B-Base dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [togethercomputer/RedPajama-INCITE-7B-Base](https://huggingface.co/togethercomputer/RedPajama-INCITE-7B-Base)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 122 configuration, each one coresponding to one of\ \ the evaluated task.\n\nThe dataset has been created from 5 run(s). Each run can\ \ be found as a specific split in each configuration, the split being named using\ \ the timestamp of the run.The \"train\" split is always pointing to the latest\ \ results.\n\nAn additional configuration \"results\" store all the aggregated results\ \ of the run (and is used to compute and display the aggregated metrics on the [Open\ \ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_togethercomputer__RedPajama-INCITE-7B-Base\"\ ,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\ \ are the [latest results from run 2023-12-03T17:07:01.486830](https://huggingface.co/datasets/open-llm-leaderboard/details_togethercomputer__RedPajama-INCITE-7B-Base/blob/main/results_2023-12-03T17-07-01.486830.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.030326004548900682,\n\ \ \"acc_stderr\": 0.004723487465514778\n },\n \"harness|gsm8k|5\":\ \ {\n \"acc\": 0.030326004548900682,\n \"acc_stderr\": 0.004723487465514778\n\ \ }\n}\n```" repo_url: https://huggingface.co/togethercomputer/RedPajama-INCITE-7B-Base leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|arc:challenge|25_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|arc:challenge|25_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-07-19T10:56:03.209346.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_08T17_48_19.912039 path: - '**/details_harness|drop|3_2023-09-08T17-48-19.912039.parquet' - split: latest path: - '**/details_harness|drop|3_2023-09-08T17-48-19.912039.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_08T17_48_19.912039 path: - '**/details_harness|gsm8k|5_2023-09-08T17-48-19.912039.parquet' - split: 2023_12_03T17_07_01.486830 path: - '**/details_harness|gsm8k|5_2023-12-03T17-07-01.486830.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-03T17-07-01.486830.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hellaswag|10_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hellaswag|10_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-18T12:24:47.590202.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T10:56:03.209346.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-management|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-virology|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T10:56:03.209346.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_18T12_24_47.590202 path: - '**/details_harness|truthfulqa:mc|0_2023-07-18T12:24:47.590202.parquet' - split: 2023_07_19T10_56_03.209346 path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T10:56:03.209346.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T10:56:03.209346.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_08T17_48_19.912039 path: - '**/details_harness|winogrande|5_2023-09-08T17-48-19.912039.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-09-08T17-48-19.912039.parquet' - config_name: original_mmlu_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:anatomy|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:astronomy|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:business_ethics|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:college_biology|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:college_medicine|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:college_physics|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:computer_security|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:econometrics|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:formal_logic|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:global_facts|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:human_aging|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:international_law|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:machine_learning|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:management|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:marketing|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:nutrition|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:philosophy|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:prehistory|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:professional_law|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:public_relations|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:security_studies|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:sociology|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:virology|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:world_religions|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:anatomy|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:astronomy|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:business_ethics|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:college_biology|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:college_medicine|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:college_physics|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:computer_security|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:econometrics|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:formal_logic|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:global_facts|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:human_aging|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:international_law|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:machine_learning|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:management|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:marketing|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:nutrition|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:philosophy|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:prehistory|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:professional_law|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:public_relations|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:security_studies|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:sociology|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:virology|5_2023-08-28T20:40:09.683575.parquet' - '**/details_original|mmlu:world_religions|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_abstract_algebra_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_anatomy_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:anatomy|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:anatomy|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_astronomy_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:astronomy|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:astronomy|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_business_ethics_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:business_ethics|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:business_ethics|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_clinical_knowledge_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_college_biology_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:college_biology|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:college_biology|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_college_chemistry_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_college_computer_science_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_college_mathematics_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_college_medicine_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:college_medicine|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:college_medicine|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_college_physics_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:college_physics|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:college_physics|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_computer_security_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:computer_security|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:computer_security|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_conceptual_physics_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_econometrics_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:econometrics|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:econometrics|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_electrical_engineering_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_elementary_mathematics_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_formal_logic_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:formal_logic|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:formal_logic|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_global_facts_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:global_facts|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:global_facts|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_high_school_biology_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_high_school_chemistry_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_high_school_computer_science_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_high_school_european_history_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_high_school_geography_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_high_school_government_and_politics_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_high_school_macroeconomics_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_high_school_mathematics_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_high_school_microeconomics_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_high_school_physics_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_high_school_psychology_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_high_school_statistics_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_high_school_us_history_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_high_school_world_history_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_human_aging_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:human_aging|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:human_aging|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_human_sexuality_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_international_law_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:international_law|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:international_law|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_jurisprudence_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_logical_fallacies_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_machine_learning_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:machine_learning|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:machine_learning|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_management_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:management|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:management|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_marketing_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:marketing|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:marketing|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_medical_genetics_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_miscellaneous_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_moral_disputes_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_moral_scenarios_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_nutrition_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:nutrition|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:nutrition|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_philosophy_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:philosophy|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:philosophy|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_prehistory_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:prehistory|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:prehistory|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_professional_accounting_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_professional_law_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:professional_law|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:professional_law|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_professional_medicine_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_professional_psychology_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_public_relations_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:public_relations|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:public_relations|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_security_studies_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:security_studies|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:security_studies|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_sociology_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:sociology|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:sociology|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_us_foreign_policy_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_virology_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:virology|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:virology|5_2023-08-28T20:40:09.683575.parquet' - config_name: original_mmlu_world_religions_5 data_files: - split: 2023_08_28T20_40_09.683575 path: - '**/details_original|mmlu:world_religions|5_2023-08-28T20:40:09.683575.parquet' - split: latest path: - '**/details_original|mmlu:world_religions|5_2023-08-28T20:40:09.683575.parquet' - config_name: results data_files: - split: 2023_07_18T12_24_47.590202 path: - results_2023-07-18T12:24:47.590202.parquet - split: 2023_07_19T10_56_03.209346 path: - results_2023-07-19T10:56:03.209346.parquet - split: 2023_08_28T20_40_09.683575 path: - results_2023-08-28T20:40:09.683575.parquet - split: 2023_09_08T17_48_19.912039 path: - results_2023-09-08T17-48-19.912039.parquet - split: 2023_12_03T17_07_01.486830 path: - results_2023-12-03T17-07-01.486830.parquet - split: latest path: - results_2023-12-03T17-07-01.486830.parquet --- # Dataset Card for Evaluation run of togethercomputer/RedPajama-INCITE-7B-Base ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/togethercomputer/RedPajama-INCITE-7B-Base - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [togethercomputer/RedPajama-INCITE-7B-Base](https://huggingface.co/togethercomputer/RedPajama-INCITE-7B-Base) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 122 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 5 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_togethercomputer__RedPajama-INCITE-7B-Base", "harness_gsm8k_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-03T17:07:01.486830](https://huggingface.co/datasets/open-llm-leaderboard/details_togethercomputer__RedPajama-INCITE-7B-Base/blob/main/results_2023-12-03T17-07-01.486830.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.030326004548900682, "acc_stderr": 0.004723487465514778 }, "harness|gsm8k|5": { "acc": 0.030326004548900682, "acc_stderr": 0.004723487465514778 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
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wckwan/M4LE
wckwan
2023-11-19T04:13:34Z
479
0
[ "task_categories:question-answering", "task_categories:translation", "task_categories:summarization", "task_categories:text-classification", "task_categories:text-retrieval", "size_categories:1K<n<10K", "language:en", "language:zh", "license:mit", "Long Context", "arxiv:2310.19240", "region:us" ]
[ "question-answering", "translation", "summarization", "text-classification", "text-retrieval" ]
2023-10-28T00:34:52Z
--- license: mit task_categories: - question-answering - translation - summarization - text-classification - text-retrieval language: - en - zh tags: - Long Context size_categories: - 1K<n<10K --- ## Introduction **M4LE** is a **M**ulti-ability, **M**ulti-range, **M**ulti-task, bilingual benchmark for long-context evaluation. We categorize long-context understanding into five distinct abilities by considering whether it is required to identify single or multiple spans in long contexts based on explicit or semantic hints. Specifically, these abilities are explicit single-span, semantic single-span, explicit multiple-span, semantic multiple-span, and global. Different from previous long-context benchmark that simply compile from a set of existing long NLP benchmarks, we introduce an automated method to transform short-sequence tasks into a comprehensive long-sequence scenario encompassing all these capabilities. M4LE consists of 36 tasks, covering 11 task types and 12 domains. For each task, we construct 200 instances for each context length bucket (1K, 2K, 4K, 6K, 8K, 12K, 16K, 24K, 32K). Due to computation and cost constraints, our paper evaluated 11 well-established LLMs on instances up to the 8K context length bucket. For more details, please refer to the paper available at <https://arxiv.org/abs/2310.19240>. You can also explore the Github page at <https://github.com/KwanWaiChung/M4LE>. ## Usage You can load the dataset by specifying the task name: ```python from datasets import load_dataset tasks = [ "arxiv", "bigpatent_global_cls", "bigpatent_global_sum", "booksum", "c3", "cepsum", "clts+", "cnewsum", "cnnnews", "drcd_explicit-single", "drcd_semantic-single", "duorc", "dureader", "hotpotqa", "lcsts", "marc", "mnds-news_explicit-single", "mnds-news_explicit-multiple", "mnds-news_semantic-multiple", "ncls", "news-commentary-en2zh", "news-commentary-zh2en", "news2016", "newsqa", "nq-open", "online-shopping", "open-subtitles-en2zh", "open-subtitles-zh2en", "pubmed", "tedtalks-en2zh", "tedtalks-zh2en", "thucnews_explicit-single", "thucnews_explicit-multiple", "thucnews_semantic-multiple", "triviaqa", "wiki2019zh", "wikihow", "wikitext-103", "wow", ] for task in tasks: data = load_dataset('wckwan/M4LE', task, split='test') ``` ## Format Each testing instance follows this format: ```yaml { "instruction": "<task description>", "input": "<task input with one-shot example>", "answers": ["<answer1>", "<answer2>"], "input_length": <int, number of words in instruction and input separated by space>, "total_length": <int, number of words in instruction, input and gold answer separated by space>, "length_bucket": <int, the length bucket to which this instance belongs> } ``` ## Tasks Here is the full list for the tasks with their descriptions. More details about these tasks, please refer to the paper . Ability | Task Name | Task Type | Language | Description ----------------- | ------------------------------------------- | ---------- | -------- | ------------------------------------------------------------------ Explicit Single | mnds-news_explicit-single | CLS + RET | En | Classify a specified news article. Explicit Single | thucnews_explicit-single | CLS + RET | Zh | Classify a specified news article. Explicit Single | newsqa | QA + RET | En | Answer a question based on a specified news article. Explicit Single | c3 | QA + RET | Zh | Answer a multi-choice question based on a textbook extract. Explicit Single | wow | RET | En | Return the ID of the article related to a specified topic. Explicit Single | drcd_explicit-single | RET | Zh | Return the ID of the article related to a specified topic. Explicit Single | cnnnews | SUM + RET | En | Summarize a specified news article. Explicit Single | cepsum | SUM + RET | Zh | Summarize a specified product description. Explicit Single | lcsts | SUM + RET | Zh | Summarize a specified news article. Explicit Single | ncls | SUM + RET | En, Zh | Summarize a specified news article. Explicit Multiple | mnds-news_explicit-multiple | CLS + RET | En | Return the IDs of all the articles belong to a specified class. Explicit Multiple | thucnews_explicit-multiple | CLS + RET | Zh | Return the IDs of all the articles belong to a specified class. Explicit Multiple | marc | CLS + RET | En, Zh | Return the IDs of all the positive product reviews. Explicit Multiple | online-shopping | CLS + RET | Zh | Return the IDs of all the positive product reviews. Semantic Single | wikitext-103 | NLI + RET | En | Return the ID of the paragraph that continues a query paragraph. Semantic Single | wiki2019zh | NLI + RET | Zh | Return the ID of the paragraph that continues a query paragraph. Semantic Single | duorc | QA | En | Answer a question based on multiple movie plots. Semantic Single | nq-open | QA | En | Answer a question based on multiple wikipedia paragraphs. Semantic Single | dureader | QA | Zh | Answer a question based on multiple web snippets. Semantic Single | drcd_semantic-single | QA | Zh | Answer a question based on multiple wikipedia paragraphs. Semantic Single | wikihow | SUM + RET | En | Summarize an article based on a given topic. Semantic Single | news2016 | SUM + RET | Zh | Summarize a news article based on a given title. Semantic Single | tedtalks-en2zh/tedtalks-zh2en | TRAN + RET | En, Zh | Translate a Ted Talk transcript based on a given title. Semantic Multiple | mnds-news_semantic-multiple | CLS + CNT | En | Return the number of news articles belonging to a specified class. Semantic Multiple | thucnews_semantic-multiple | CLS + CNT | Zh | Return the number of news articles belonging to a specified class. Semantic Multiple | hotpotqa | QA | En | Answer a question based on multiple wikipedia paragraphs. Global | bigpatent_global_cls | CLS | En | Classify a patent document. Global | triviaqa | QA | En | Answer a question based on a web snippet. Global | arxiv | SUM | En | Summarize an academic paper. Global | bigpatent_global_sum | SUM | En | Summarize a patent document. Global | pubmed | SUM | En | Summarize a medical paper. Global | booksum | SUM | En | Summarize one or more chapters of a book. Global | cnewsum | SUM | Zh | Summarize a news article. Global | clts+ | SUM | Zh | Summarize a news article. Global | open-subtitles-en2zh/open-subtitles-zh2en | TRAN | En, Zh | Translate the movie subtitles. Global | news-commentary-en2zh/news-commentary-zh2en | TRAN | En, Zh | Translate the movie subtitles. ## Citation If you find our paper and resources useful, please consider citing our paper: ```bibtex @misc{kwan_m4le_2023, title = {{{M4LE}}: {{A Multi-Ability Multi-Range Multi-Task Multi-Domain Long-Context Evaluation Benchmark}} for {{Large Language Models}}}, author = {Kwan, Wai-Chung and Zeng, Xingshan and Wang, Yufei and Sun, Yusen and Li, Liangyou and Shang, Lifeng and Liu, Qun and Wong, Kam-Fai}, year = {2023}, } ```
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maywell/ko_Ultrafeedback_binarized
maywell
2023-11-09T03:25:29Z
479
12
[ "region:us" ]
null
2023-11-04T18:41:52Z
--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 226278590 num_examples: 61966 download_size: 110040382 dataset_size: 226278590 configs: - config_name: default data_files: - split: train path: data/train-* --- # maywell/ko_Ultrafeedback_binarized 본 데이터는 Synatra-7B-Translation 모델을 통해 Ultrafeedback_binarized를 번역하고 정제한 데이터셋입니다. 해당 데이터를 직접적으로 상업적으로 사용하는 것은 허용되지 않으며, 데이터를 이용하여 훈련된 모델에 대한 상업적 사용은 허용됩니다. 아직 완벽히 정제되지는 않았으며, 오류나 수정사항에 대해서는 PR 부탁드립니다.
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open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE3_3.3w-r8-gate_up_down
open-llm-leaderboard
2023-10-28T04:59:57Z
478
0
[ "region:us" ]
null
2023-10-01T14:36:10Z
--- pretty_name: Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE3_3.3w-r8-gate_up_down dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [CHIH-HUNG/llama-2-13b-FINETUNE3_3.3w-r8-gate_up_down](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE3_3.3w-r8-gate_up_down)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE3_3.3w-r8-gate_up_down\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-28T04:59:45.337848](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE3_3.3w-r8-gate_up_down/blob/main/results_2023-10-28T04-59-45.337848.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.21780620805369127,\n\ \ \"em_stderr\": 0.004226997205914972,\n \"f1\": 0.26442428691275155,\n\ \ \"f1_stderr\": 0.004202345101166552,\n \"acc\": 0.4478647632531162,\n\ \ \"acc_stderr\": 0.010273220250493906\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.21780620805369127,\n \"em_stderr\": 0.004226997205914972,\n\ \ \"f1\": 0.26442428691275155,\n \"f1_stderr\": 0.004202345101166552\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.11751326762699014,\n \ \ \"acc_stderr\": 0.00887033125649\n },\n \"harness|winogrande|5\": {\n\ \ \"acc\": 0.7782162588792423,\n \"acc_stderr\": 0.011676109244497811\n\ \ }\n}\n```" repo_url: https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE3_3.3w-r8-gate_up_down leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|arc:challenge|25_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-10-01T14-35-45.846264.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_28T04_59_45.337848 path: - '**/details_harness|drop|3_2023-10-28T04-59-45.337848.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-28T04-59-45.337848.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_28T04_59_45.337848 path: - '**/details_harness|gsm8k|5_2023-10-28T04-59-45.337848.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-28T04-59-45.337848.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hellaswag|10_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-management|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-management|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-10-01T14-35-45.846264.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-international_law|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-management|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-marketing|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-sociology|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-virology|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-10-01T14-35-45.846264.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_10_01T14_35_45.846264 path: - '**/details_harness|truthfulqa:mc|0_2023-10-01T14-35-45.846264.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-10-01T14-35-45.846264.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_28T04_59_45.337848 path: - '**/details_harness|winogrande|5_2023-10-28T04-59-45.337848.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-28T04-59-45.337848.parquet' - config_name: results data_files: - split: 2023_10_01T14_35_45.846264 path: - results_2023-10-01T14-35-45.846264.parquet - split: 2023_10_28T04_59_45.337848 path: - results_2023-10-28T04-59-45.337848.parquet - split: latest path: - results_2023-10-28T04-59-45.337848.parquet --- # Dataset Card for Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE3_3.3w-r8-gate_up_down ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE3_3.3w-r8-gate_up_down - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [CHIH-HUNG/llama-2-13b-FINETUNE3_3.3w-r8-gate_up_down](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE3_3.3w-r8-gate_up_down) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE3_3.3w-r8-gate_up_down", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-28T04:59:45.337848](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE3_3.3w-r8-gate_up_down/blob/main/results_2023-10-28T04-59-45.337848.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.21780620805369127, "em_stderr": 0.004226997205914972, "f1": 0.26442428691275155, "f1_stderr": 0.004202345101166552, "acc": 0.4478647632531162, "acc_stderr": 0.010273220250493906 }, "harness|drop|3": { "em": 0.21780620805369127, "em_stderr": 0.004226997205914972, "f1": 0.26442428691275155, "f1_stderr": 0.004202345101166552 }, "harness|gsm8k|5": { "acc": 0.11751326762699014, "acc_stderr": 0.00887033125649 }, "harness|winogrande|5": { "acc": 0.7782162588792423, "acc_stderr": 0.011676109244497811 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
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swda
null
2023-01-25T14:45:15Z
477
8
[ "task_categories:text-classification", "task_ids:multi-label-classification", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:extended|other-Switchboard-1 Telephone Speech Corpus, Release 2", "language:en", "license:cc-by-nc-sa-3.0", "arxiv:1811.05021", "arxiv:1711.05568", "arxiv:1709.04250", "arxiv:1805.06280", "region:us" ]
[ "text-classification" ]
2022-03-02T23:29:22Z
--- annotations_creators: - found language_creators: - found language: - en license: - cc-by-nc-sa-3.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - extended|other-Switchboard-1 Telephone Speech Corpus, Release 2 task_categories: - text-classification task_ids: - multi-label-classification pretty_name: The Switchboard Dialog Act Corpus (SwDA) dataset_info: features: - name: swda_filename dtype: string - name: ptb_basename dtype: string - name: conversation_no dtype: int64 - name: transcript_index dtype: int64 - name: act_tag dtype: class_label: names: '0': b^m^r '1': qw^r^t '2': aa^h '3': br^m '4': fa^r '5': aa,ar '6': sd^e(^q)^r '7': ^2 '8': sd;qy^d '9': oo '10': bk^m '11': aa^t '12': cc^t '13': qy^d^c '14': qo^t '15': ng^m '16': qw^h '17': qo^r '18': aa '19': qy^d^t '20': qrr^d '21': br^r '22': fx '23': sd,qy^g '24': ny^e '25': ^h^t '26': fc^m '27': qw(^q) '28': co '29': o^t '30': b^m^t '31': qr^d '32': qw^g '33': ad(^q) '34': qy(^q) '35': na^r '36': am^r '37': qr^t '38': ad^c '39': qw^c '40': bh^r '41': h^t '42': ft^m '43': ba^r '44': qw^d^t '45': '%' '46': t3 '47': nn '48': bd '49': h^m '50': h^r '51': sd^r '52': qh^m '53': ^q^t '54': sv^2 '55': ft '56': ar^m '57': qy^h '58': sd^e^m '59': qh^r '60': cc '61': fp^m '62': ad '63': qo '64': na^m^t '65': fo^c '66': qy '67': sv^e^r '68': aap '69': 'no' '70': aa^2 '71': sv(^q) '72': sv^e '73': nd '74': '"' '75': bf^2 '76': bk '77': fp '78': nn^r^t '79': fa^c '80': ny^t '81': ny^c^r '82': qw '83': qy^t '84': b '85': fo '86': qw^r '87': am '88': bf^t '89': ^2^t '90': b^2 '91': x '92': fc '93': qr '94': no^t '95': bk^t '96': bd^r '97': bf '98': ^2^g '99': qh^c '100': ny^c '101': sd^e^r '102': br '103': fe '104': by '105': ^2^r '106': fc^r '107': b^m '108': sd,sv '109': fa^t '110': sv^m '111': qrr '112': ^h^r '113': na '114': fp^r '115': o '116': h,sd '117': t1^t '118': nn^r '119': cc^r '120': sv^c '121': co^t '122': qy^r '123': sv^r '124': qy^d^h '125': sd '126': nn^e '127': ny^r '128': b^t '129': ba^m '130': ar '131': bf^r '132': sv '133': bh^m '134': qy^g^t '135': qo^d^c '136': qo^d '137': nd^t '138': aa^r '139': sd^2 '140': sv;sd '141': qy^c^r '142': qw^m '143': qy^g^r '144': no^r '145': qh(^q) '146': sd;sv '147': bf(^q) '148': + '149': qy^2 '150': qw^d '151': qy^g '152': qh^g '153': nn^t '154': ad^r '155': oo^t '156': co^c '157': ng '158': ^q '159': qw^d^c '160': qrr^t '161': ^h '162': aap^r '163': bc^r '164': sd^m '165': bk^r '166': qy^g^c '167': qr(^q) '168': ng^t '169': arp '170': h '171': bh '172': sd^c '173': ^g '174': o^r '175': qy^c '176': sd^e '177': fw '178': ar^r '179': qy^m '180': bc '181': sv^t '182': aap^m '183': sd;no '184': ng^r '185': bf^g '186': sd^e^t '187': o^c '188': b^r '189': b^m^g '190': ba '191': t1 '192': qy^d(^q) '193': nn^m '194': ny '195': ba,fe '196': aa^m '197': qh '198': na^m '199': oo(^q) '200': qw^t '201': na^t '202': qh^h '203': qy^d^m '204': ny^m '205': fa '206': qy^d '207': fc^t '208': sd(^q) '209': qy^d^r '210': bf^m '211': sd(^q)^t '212': ft^t '213': ^q^r '214': sd^t '215': sd(^q)^r '216': ad^t - name: damsl_act_tag dtype: class_label: names: '0': ad '1': qo '2': qy '3': arp_nd '4': sd '5': h '6': bh '7': 'no' '8': ^2 '9': ^g '10': ar '11': aa '12': sv '13': bk '14': fp '15': qw '16': b '17': ba '18': t1 '19': oo_co_cc '20': + '21': ny '22': qw^d '23': x '24': qh '25': fc '26': fo_o_fw_"_by_bc '27': aap_am '28': '%' '29': bf '30': t3 '31': nn '32': bd '33': ng '34': ^q '35': br '36': qy^d '37': fa '38': ^h '39': b^m '40': ft '41': qrr '42': na - name: caller dtype: string - name: utterance_index dtype: int64 - name: subutterance_index dtype: int64 - name: text dtype: string - name: pos dtype: string - name: trees dtype: string - name: ptb_treenumbers dtype: string - name: talk_day dtype: string - name: length dtype: int64 - name: topic_description dtype: string - name: prompt dtype: string - name: from_caller dtype: int64 - name: from_caller_sex dtype: string - name: from_caller_education dtype: int64 - name: from_caller_birth_year dtype: int64 - name: from_caller_dialect_area dtype: string - name: to_caller dtype: int64 - name: to_caller_sex dtype: string - name: to_caller_education dtype: int64 - name: to_caller_birth_year dtype: int64 - name: to_caller_dialect_area dtype: string splits: - name: train num_bytes: 128498512 num_examples: 213543 - name: validation num_bytes: 34749819 num_examples: 56729 - name: test num_bytes: 2560127 num_examples: 4514 download_size: 14456364 dataset_size: 165808458 --- # Dataset Card for SwDA ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [The Switchboard Dialog Act Corpus](http://compprag.christopherpotts.net/swda.html) - **Repository:** [NathanDuran/Switchboard-Corpus](https://github.com/cgpotts/swda) - **Paper:** [The Switchboard Dialog Act Corpus](http://compprag.christopherpotts.net/swda.html) = **Leaderboard: [Dialogue act classification](https://github.com/sebastianruder/NLP-progress/blob/master/english/dialogue.md#dialogue-act-classification)** - **Point of Contact:** [Christopher Potts](https://web.stanford.edu/~cgpotts/) ### Dataset Summary The Switchboard Dialog Act Corpus (SwDA) extends the Switchboard-1 Telephone Speech Corpus, Release 2 with turn/utterance-level dialog-act tags. The tags summarize syntactic, semantic, and pragmatic information about the associated turn. The SwDA project was undertaken at UC Boulder in the late 1990s. The SwDA is not inherently linked to the Penn Treebank 3 parses of Switchboard, and it is far from straightforward to align the two resources. In addition, the SwDA is not distributed with the Switchboard's tables of metadata about the conversations and their participants. ### Supported Tasks and Leaderboards | Model | Accuracy | Paper / Source | Code | | ------------- | :-----:| --- | --- | | H-Seq2seq (Colombo et al., 2020) | 85.0 | [Guiding attention in Sequence-to-sequence models for Dialogue Act prediction](https://ojs.aaai.org/index.php/AAAI/article/view/6259/6115) | SGNN (Ravi et al., 2018) | 83.1 | [Self-Governing Neural Networks for On-Device Short Text Classification](https://www.aclweb.org/anthology/D18-1105.pdf) | CASA (Raheja et al., 2019) | 82.9 | [Dialogue Act Classification with Context-Aware Self-Attention](https://www.aclweb.org/anthology/N19-1373.pdf) | DAH-CRF (Li et al., 2019) | 82.3 | [A Dual-Attention Hierarchical Recurrent Neural Network for Dialogue Act Classification](https://www.aclweb.org/anthology/K19-1036.pdf) | ALDMN (Wan et al., 2018) | 81.5 | [Improved Dynamic Memory Network for Dialogue Act Classification with Adversarial Training](https://arxiv.org/pdf/1811.05021.pdf) | CRF-ASN (Chen et al., 2018) | 81.3 | [Dialogue Act Recognition via CRF-Attentive Structured Network](https://arxiv.org/abs/1711.05568) | Pretrained H-Transformer (Chapuis et al., 2020) | 79.3 | [Hierarchical Pre-training for Sequence Labelling in Spoken Dialog] (https://www.aclweb.org/anthology/2020.findings-emnlp.239) | Bi-LSTM-CRF (Kumar et al., 2017) | 79.2 | [Dialogue Act Sequence Labeling using Hierarchical encoder with CRF](https://arxiv.org/abs/1709.04250) | [Link](https://github.com/YanWenqiang/HBLSTM-CRF) | | RNN with 3 utterances in context (Bothe et al., 2018) | 77.34 | [A Context-based Approach for Dialogue Act Recognition using Simple Recurrent Neural Networks](https://arxiv.org/abs/1805.06280) | | ### Languages The language supported is English. ## Dataset Structure Utterance are tagged with the [SWBD-DAMSL](https://web.stanford.edu/~jurafsky/ws97/manual.august1.html) DA. ### Data Instances An example from the dataset is: `{'act_tag': 115, 'caller': 'A', 'conversation_no': 4325, 'damsl_act_tag': 26, 'from_caller': 1632, 'from_caller_birth_year': 1962, 'from_caller_dialect_area': 'WESTERN', 'from_caller_education': 2, 'from_caller_sex': 'FEMALE', 'length': 5, 'pos': 'Okay/UH ./.', 'prompt': 'FIND OUT WHAT CRITERIA THE OTHER CALLER WOULD USE IN SELECTING CHILD CARE SERVICES FOR A PRESCHOOLER. IS IT EASY OR DIFFICULT TO FIND SUCH CARE?', 'ptb_basename': '4/sw4325', 'ptb_treenumbers': '1', 'subutterance_index': 1, 'swda_filename': 'sw00utt/sw_0001_4325.utt', 'talk_day': '03/23/1992', 'text': 'Okay. /', 'to_caller': 1519, 'to_caller_birth_year': 1971, 'to_caller_dialect_area': 'SOUTH MIDLAND', 'to_caller_education': 1, 'to_caller_sex': 'FEMALE', 'topic_description': 'CHILD CARE', 'transcript_index': 0, 'trees': '(INTJ (UH Okay) (. .) (-DFL- E_S))', 'utterance_index': 1}` ### Data Fields * `swda_filename`: (str) The filename: directory/basename. * `ptb_basename`: (str) The Treebank filename: add ".pos" for POS and ".mrg" for trees * `conversation_no`: (int) The conversation Id, to key into the metadata database. * `transcript_index`: (int) The line number of this item in the transcript (counting only utt lines). * `act_tag`: (list of str) The Dialog Act Tags (separated by ||| in the file). Check Dialog act annotations for more details. * `damsl_act_tag`: (list of str) The Dialog Act Tags of the 217 variation tags. * `caller`: (str) A, B, @A, @B, @@A, @@B * `utterance_index`: (int) The encoded index of the utterance (the number in A.49, B.27, etc.) * `subutterance_index`: (int) Utterances can be broken across line. This gives the internal position. * `text`: (str) The text of the utterance * `pos`: (str) The POS tagged version of the utterance, from PtbBasename+.pos * `trees`: (str) The tree(s) containing this utterance (separated by ||| in the file). Use `[Tree.fromstring(t) for t in row_value.split("|||")]` to convert to (list of nltk.tree.Tree). * `ptb_treenumbers`: (list of int) The tree numbers in the PtbBasename+.mrg * `talk_day`: (str) Date of talk. * `length`: (int) Length of talk in seconds. * `topic_description`: (str) Short description of topic that's being discussed. * `prompt`: (str) Long decription/query/instruction. * `from_caller`: (int) The numerical Id of the from (A) caller. * `from_caller_sex`: (str) MALE, FEMALE. * `from_caller_education`: (int) Called education level 0, 1, 2, 3, 9. * `from_caller_birth_year`: (int) Caller birth year YYYY. * `from_caller_dialect_area`: (str) MIXED, NEW ENGLAND, NORTH MIDLAND, NORTHERN, NYC, SOUTH MIDLAND, SOUTHERN, UNK, WESTERN. * `to_caller`: (int) The numerical Id of the to (B) caller. * `to_caller_sex`: (str) MALE, FEMALE. * `to_caller_education`: (int) Called education level 0, 1, 2, 3, 9. * `to_caller_birth_year`: (int) Caller birth year YYYY. * `to_caller_dialect_area`: (str) MIXED, NEW ENGLAND, NORTH MIDLAND, NORTHERN, NYC, SOUTH MIDLAND, SOUTHERN, UNK, WESTERN. ### Dialog act annotations | | name | act_tag | example | train_count | full_count | |----- |------------------------------- |---------------- |-------------------------------------------------- |------------- |------------ | | 1 | Statement-non-opinion | sd | Me, I'm in the legal department. | 72824 | 75145 | | 2 | Acknowledge (Backchannel) | b | Uh-huh. | 37096 | 38298 | | 3 | Statement-opinion | sv | I think it's great | 25197 | 26428 | | 4 | Agree/Accept | aa | That's exactly it. | 10820 | 11133 | | 5 | Abandoned or Turn-Exit | % | So, - | 10569 | 15550 | | 6 | Appreciation | ba | I can imagine. | 4633 | 4765 | | 7 | Yes-No-Question | qy | Do you have to have any special training? | 4624 | 4727 | | 8 | Non-verbal | x | [Laughter], [Throat_clearing] | 3548 | 3630 | | 9 | Yes answers | ny | Yes. | 2934 | 3034 | | 10 | Conventional-closing | fc | Well, it's been nice talking to you. | 2486 | 2582 | | 11 | Uninterpretable | % | But, uh, yeah | 2158 | 15550 | | 12 | Wh-Question | qw | Well, how old are you? | 1911 | 1979 | | 13 | No answers | nn | No. | 1340 | 1377 | | 14 | Response Acknowledgement | bk | Oh, okay. | 1277 | 1306 | | 15 | Hedge | h | I don't know if I'm making any sense or not. | 1182 | 1226 | | 16 | Declarative Yes-No-Question | qy^d | So you can afford to get a house? | 1174 | 1219 | | 17 | Other | fo_o_fw_by_bc | Well give me a break, you know. | 1074 | 883 | | 18 | Backchannel in question form | bh | Is that right? | 1019 | 1053 | | 19 | Quotation | ^q | You can't be pregnant and have cats | 934 | 983 | | 20 | Summarize/reformulate | bf | Oh, you mean you switched schools for the kids. | 919 | 952 | | 21 | Affirmative non-yes answers | na | It is. | 836 | 847 | | 22 | Action-directive | ad | Why don't you go first | 719 | 746 | | 23 | Collaborative Completion | ^2 | Who aren't contributing. | 699 | 723 | | 24 | Repeat-phrase | b^m | Oh, fajitas | 660 | 688 | | 25 | Open-Question | qo | How about you? | 632 | 656 | | 26 | Rhetorical-Questions | qh | Who would steal a newspaper? | 557 | 575 | | 27 | Hold before answer/agreement | ^h | I'm drawing a blank. | 540 | 556 | | 28 | Reject | ar | Well, no | 338 | 346 | | 29 | Negative non-no answers | ng | Uh, not a whole lot. | 292 | 302 | | 30 | Signal-non-understanding | br | Excuse me? | 288 | 298 | | 31 | Other answers | no | I don't know | 279 | 286 | | 32 | Conventional-opening | fp | How are you? | 220 | 225 | | 33 | Or-Clause | qrr | or is it more of a company? | 207 | 209 | | 34 | Dispreferred answers | arp_nd | Well, not so much that. | 205 | 207 | | 35 | 3rd-party-talk | t3 | My goodness, Diane, get down from there. | 115 | 117 | | 36 | Offers, Options, Commits | oo_co_cc | I'll have to check that out | 109 | 110 | | 37 | Self-talk | t1 | What's the word I'm looking for | 102 | 103 | | 38 | Downplayer | bd | That's all right. | 100 | 103 | | 39 | Maybe/Accept-part | aap_am | Something like that | 98 | 105 | | 40 | Tag-Question | ^g | Right? | 93 | 92 | | 41 | Declarative Wh-Question | qw^d | You are what kind of buff? | 80 | 80 | | 42 | Apology | fa | I'm sorry. | 76 | 79 | | 43 | Thanking | ft | Hey thanks a lot | 67 | 78 | ### Data Splits I used info from the [Probabilistic-RNN-DA-Classifier](https://github.com/NathanDuran/Probabilistic-RNN-DA-Classifier) repo: The same training and test splits as used by [Stolcke et al. (2000)](https://web.stanford.edu/~jurafsky/ws97). The development set is a subset of the training set to speed up development and testing used in the paper [Probabilistic Word Association for Dialogue Act Classification with Recurrent Neural Networks](https://www.researchgate.net/publication/326640934_Probabilistic_Word_Association_for_Dialogue_Act_Classification_with_Recurrent_Neural_Networks_19th_International_Conference_EANN_2018_Bristol_UK_September_3-5_2018_Proceedings). |Dataset |# Transcripts |# Utterances | |-----------|:-------------:|:-------------:| |Training |1115 |192,768 | |Validation |21 |3,196 | |Test |19 |4,088 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization The SwDA is not inherently linked to the Penn Treebank 3 parses of Switchboard, and it is far from straightforward to align the two resources Calhoun et al. 2010, §2.4. In addition, the SwDA is not distributed with the Switchboard's tables of metadata about the conversations and their participants. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [Christopher Potts](https://web.stanford.edu/~cgpotts/), Stanford Linguistics. ### Licensing Information This work is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.](http://creativecommons.org/licenses/by-nc-sa/3.0/) ### Citation Information ``` @techreport{Jurafsky-etal:1997, Address = {Boulder, CO}, Author = {Jurafsky, Daniel and Shriberg, Elizabeth and Biasca, Debra}, Institution = {University of Colorado, Boulder Institute of Cognitive Science}, Number = {97-02}, Title = {Switchboard {SWBD}-{DAMSL} Shallow-Discourse-Function Annotation Coders Manual, Draft 13}, Year = {1997}} @article{Shriberg-etal:1998, Author = {Shriberg, Elizabeth and Bates, Rebecca and Taylor, Paul and Stolcke, Andreas and Jurafsky, Daniel and Ries, Klaus and Coccaro, Noah and Martin, Rachel and Meteer, Marie and Van Ess-Dykema, Carol}, Journal = {Language and Speech}, Number = {3--4}, Pages = {439--487}, Title = {Can Prosody Aid the Automatic Classification of Dialog Acts in Conversational Speech?}, Volume = {41}, Year = {1998}} @article{Stolcke-etal:2000, Author = {Stolcke, Andreas and Ries, Klaus and Coccaro, Noah and Shriberg, Elizabeth and Bates, Rebecca and Jurafsky, Daniel and Taylor, Paul and Martin, Rachel and Meteer, Marie and Van Ess-Dykema, Carol}, Journal = {Computational Linguistics}, Number = {3}, Pages = {339--371}, Title = {Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech}, Volume = {26}, Year = {2000}} ``` ### Contributions Thanks to [@gmihaila](https://github.com/gmihaila) for adding this dataset.
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lmqg/qa_squadshifts
lmqg
2022-11-05T05:10:26Z
477
0
[ "task_categories:question-answering", "task_ids:extractive-qa", "multilinguality:monolingual", "size_categories:1k<n<10k", "source_datasets:extended|wikipedia", "language:en", "license:cc-by-4.0", "arxiv:2004.14444", "region:us" ]
[ "question-answering" ]
2022-11-05T02:43:19Z
--- license: cc-by-4.0 pretty_name: SQuADShifts language: en multilinguality: monolingual size_categories: 1k<n<10k source_datasets: - extended|wikipedia task_categories: - question-answering task_ids: - extractive-qa --- # Dataset Card for "lmqg/qa_squadshifts" ## Dataset Description - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2004.14444](https://arxiv.org/abs/2004.14444) - **Point of Contact:** [Asahi Ushio](http://asahiushio.com/) ### Dataset Summary This is SQuADShifts dataset with custom split of training/validation/test following [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts). ### Supported Tasks and Leaderboards * `question-answering` ### Languages English (en) ## Dataset Structure ### Data Fields The data fields are the same among all splits. #### plain_text - `id`: a `string` feature of id - `title`: a `string` feature of title of the paragraph - `context`: a `string` feature of paragraph - `question`: a `string` feature of question - `answers`: a `json` feature of answers ### Data Splits | name |train | valid | test | |-------------|------:|------:|-----:| |default (all)|9209|6283 |18,844| | amazon |3295|1648|4942| | new_wiki |2646|1323|3969| | nyt |3355|1678|5032| | reddit |3268|1634|4901| ## Citation Information ``` @inproceedings{miller2020effect, title={The effect of natural distribution shift on question answering models}, author={Miller, John and Krauth, Karl and Recht, Benjamin and Schmidt, Ludwig}, booktitle={International Conference on Machine Learning}, pages={6905--6916}, year={2020}, organization={PMLR} } ```
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code_x_glue_cc_code_refinement
null
2023-07-27T14:09:03Z
475
2
[ "task_categories:text2text-generation", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:other-programming-languages", "size_categories:10K<n<100K", "source_datasets:original", "language:code", "license:c-uda", "debugging", "arxiv:2102.04664", "arxiv:1812.08693", "region:us" ]
[ "text2text-generation" ]
2022-03-02T23:29:22Z
--- annotations_creators: - expert-generated language_creators: - found language: - code license: - c-uda multilinguality: - other-programming-languages size_categories: - 10K<n<100K source_datasets: - original task_categories: - text2text-generation task_ids: [] pretty_name: CodeXGlueCcCodeRefinement tags: - debugging dataset_info: - config_name: medium features: - name: id dtype: int32 - name: buggy dtype: string - name: fixed dtype: string splits: - name: train num_bytes: 32614834 num_examples: 52364 - name: validation num_bytes: 4086741 num_examples: 6546 - name: test num_bytes: 4063673 num_examples: 6545 download_size: 39979724 dataset_size: 40765248 - config_name: small features: - name: id dtype: int32 - name: buggy dtype: string - name: fixed dtype: string splits: - name: train num_bytes: 13006719 num_examples: 46680 - name: validation num_bytes: 1629250 num_examples: 5835 - name: test num_bytes: 1619708 num_examples: 5835 download_size: 15555421 dataset_size: 16255677 --- # Dataset Card for "code_x_glue_cc_code_refinement" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits-sample-size) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/code-refinement - **Paper:** https://arxiv.org/abs/2102.04664 ### Dataset Summary CodeXGLUE code-refinement dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/code-refinement We use the dataset released by this paper(https://arxiv.org/pdf/1812.08693.pdf). The source side is a Java function with bugs and the target side is the refined one. All the function and variable names are normalized. Their dataset contains two subsets ( i.e.small and medium) based on the function length. ### Supported Tasks and Leaderboards - `text2text-generation-other-debugging`: The dataset can be used to train a model for automatically fixing buggy code. ### Languages - Java **programming** language ## Dataset Structure ### Data Instances #### medium An example of 'train' looks as follows. ``` { "buggy": "public static TYPE_1 init ( java.lang.String name , java.util.Date date ) { TYPE_1 VAR_1 = new TYPE_1 ( ) ; VAR_1 . METHOD_1 ( name ) ; java.util.Calendar VAR_2 = java.util.Calendar.getInstance ( ) ; VAR_2 . METHOD_2 ( date ) ; VAR_1 . METHOD_3 ( VAR_2 ) ; return VAR_1 ; }\n", "fixed": "public static TYPE_1 init ( java.lang.String name , java.util.Date date ) { TYPE_1 VAR_1 = new TYPE_1 ( ) ; VAR_1 . METHOD_1 ( name ) ; java.util.Calendar VAR_2 = null ; if ( date != null ) { VAR_2 = java.util.Calendar.getInstance ( ) ; VAR_2 . METHOD_2 ( date ) ; } VAR_1 . METHOD_3 ( VAR_2 ) ; return VAR_1 ; }\n", "id": 0 } ``` #### small An example of 'validation' looks as follows. ``` { "buggy": "public java.util.List < TYPE_1 > METHOD_1 ( ) { java.util.ArrayList < TYPE_1 > VAR_1 = new java.util.ArrayList < TYPE_1 > ( ) ; for ( TYPE_2 VAR_2 : VAR_3 ) { VAR_1 . METHOD_2 ( VAR_2 . METHOD_1 ( ) ) ; } return VAR_1 ; } \n", "fixed": "public java.util.List < TYPE_1 > METHOD_1 ( ) { return VAR_1 ; } \n", "id": 0 } ``` ### Data Fields In the following each data field in go is explained for each config. The data fields are the same among all splits. #### medium, small |field name| type | description | |----------|------|--------------------------------| |id |int32 | Index of the sample | |buggy |string| The buggy version of the code | |fixed |string| The correct version of the code| ### Data Splits | name |train|validation|test| |------|----:|---------:|---:| |medium|52364| 6546|6545| |small |46680| 5835|5835| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization Downloaded from GitHub Archive every public GitHub event between March 2011 and October 2017 and used the Google BigQuery APIs. [More Information Needed] #### Who are the source language producers? Software Engineering developers. ### Annotations #### Annotation process Automatically annotated by filtering commit messages containing the pattern: ("fix" or "solve") and ("bug" or "issue" or "problem" or "error"). A statistically significant amount of samples (95% confidence level with 5% confidence interval) were manually evaluated by two authors to check if the filtered bug/fix pairs were correct. After all disagreements were settled, authors conclude that 97.6% were true positives. #### Who are the annotators? Heuristics and the authors of the paper. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators https://github.com/microsoft, https://github.com/madlag ### Licensing Information Computational Use of Data Agreement (C-UDA) License. ### Citation Information ``` @article{DBLP:journals/corr/abs-2102-04664, author = {Shuai Lu and Daya Guo and Shuo Ren and Junjie Huang and Alexey Svyatkovskiy and Ambrosio Blanco and Colin B. Clement and Dawn Drain and Daxin Jiang and Duyu Tang and Ge Li and Lidong Zhou and Linjun Shou and Long Zhou and Michele Tufano and Ming Gong and Ming Zhou and Nan Duan and Neel Sundaresan and Shao Kun Deng and Shengyu Fu and Shujie Liu}, title = {CodeXGLUE: {A} Machine Learning Benchmark Dataset for Code Understanding and Generation}, journal = {CoRR}, volume = {abs/2102.04664}, year = {2021} } @article{tufano2019empirical, title={An empirical study on learning bug-fixing patches in the wild via neural machine translation}, author={Tufano, Michele and Watson, Cody and Bavota, Gabriele and Penta, Massimiliano Di and White, Martin and Poshyvanyk, Denys}, journal={ACM Transactions on Software Engineering and Methodology (TOSEM)}, volume={28}, number={4}, pages={1--29}, year={2019}, publisher={ACM New York, NY, USA} } ``` ### Contributions Thanks to @madlag (and partly also @ncoop57) for adding this dataset.
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vietgpt/truyencotich_vi
vietgpt
2023-07-02T15:42:24Z
475
0
[ "region:us" ]
null
2023-07-02T15:31:43Z
--- dataset_info: features: - name: title dtype: string - name: content dtype: string - name: metadata struct: - name: type dtype: string - name: url dtype: string splits: - name: train num_bytes: 23491738 num_examples: 5444 download_size: 12096101 dataset_size: 23491738 --- # Dataset Card for "truyencotich_vi" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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open-llm-leaderboard/details_Salesforce__codegen-16B-nl
open-llm-leaderboard
2023-09-16T21:34:17Z
475
0
[ "region:us" ]
null
2023-08-18T00:13:39Z
--- pretty_name: Evaluation run of Salesforce/codegen-16B-nl dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Salesforce/codegen-16B-nl](https://huggingface.co/Salesforce/codegen-16B-nl)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 122 configuration, each one coresponding to one of\ \ the evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can\ \ be found as a specific split in each configuration, the split being named using\ \ the timestamp of the run.The \"train\" split is always pointing to the latest\ \ results.\n\nAn additional configuration \"results\" store all the aggregated results\ \ of the run (and is used to compute and display the agregated metrics on the [Open\ \ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Salesforce__codegen-16B-nl\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-16T21:34:04.587307](https://huggingface.co/datasets/open-llm-leaderboard/details_Salesforce__codegen-16B-nl/blob/main/results_2023-09-16T21-34-04.587307.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0012583892617449664,\n\ \ \"em_stderr\": 0.00036305608931190987,\n \"f1\": 0.050115352348993385,\n\ \ \"f1_stderr\": 0.0012004040103300048,\n \"acc\": 0.35304663251500595,\n\ \ \"acc_stderr\": 0.00877106572247344\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.0012583892617449664,\n \"em_stderr\": 0.00036305608931190987,\n\ \ \"f1\": 0.050115352348993385,\n \"f1_stderr\": 0.0012004040103300048\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.026535253980288095,\n \ \ \"acc_stderr\": 0.004427045987265165\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.6795580110497238,\n \"acc_stderr\": 0.013115085457681714\n\ \ }\n}\n```" repo_url: https://huggingface.co/Salesforce/codegen-16B-nl leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|arc:challenge|25_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-07-19T20:43:28.351317.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_16T21_34_04.587307 path: - '**/details_harness|drop|3_2023-09-16T21-34-04.587307.parquet' - split: latest path: - '**/details_harness|drop|3_2023-09-16T21-34-04.587307.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_16T21_34_04.587307 path: - '**/details_harness|gsm8k|5_2023-09-16T21-34-04.587307.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-09-16T21-34-04.587307.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hellaswag|10_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T20:43:28.351317.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T20:43:28.351317.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_19T20_43_28.351317 path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T20:43:28.351317.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T20:43:28.351317.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_16T21_34_04.587307 path: - '**/details_harness|winogrande|5_2023-09-16T21-34-04.587307.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-09-16T21-34-04.587307.parquet' - config_name: original_mmlu_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:anatomy|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:astronomy|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:business_ethics|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:college_biology|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:college_medicine|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:college_physics|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:computer_security|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:econometrics|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:formal_logic|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:global_facts|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:human_aging|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:international_law|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:machine_learning|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:management|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:marketing|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:nutrition|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:philosophy|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:prehistory|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:professional_law|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:public_relations|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:security_studies|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:sociology|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:virology|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:world_religions|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:anatomy|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:astronomy|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:business_ethics|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:college_biology|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:college_medicine|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:college_physics|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:computer_security|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:econometrics|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:formal_logic|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:global_facts|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:human_aging|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:international_law|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:machine_learning|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:management|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:marketing|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:nutrition|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:philosophy|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:prehistory|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:professional_law|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:public_relations|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:security_studies|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:sociology|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:virology|5_2023-08-28T20:44:03.051617.parquet' - '**/details_original|mmlu:world_religions|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_abstract_algebra_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_anatomy_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:anatomy|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:anatomy|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_astronomy_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:astronomy|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:astronomy|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_business_ethics_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:business_ethics|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:business_ethics|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_clinical_knowledge_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_college_biology_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:college_biology|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:college_biology|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_college_chemistry_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_college_computer_science_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_college_mathematics_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_college_medicine_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:college_medicine|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:college_medicine|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_college_physics_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:college_physics|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:college_physics|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_computer_security_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:computer_security|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:computer_security|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_conceptual_physics_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_econometrics_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:econometrics|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:econometrics|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_electrical_engineering_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_elementary_mathematics_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_formal_logic_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:formal_logic|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:formal_logic|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_global_facts_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:global_facts|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:global_facts|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_high_school_biology_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_high_school_chemistry_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_high_school_computer_science_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_high_school_european_history_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_high_school_geography_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_high_school_government_and_politics_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_high_school_macroeconomics_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_high_school_mathematics_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_high_school_microeconomics_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_high_school_physics_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_high_school_psychology_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_high_school_statistics_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_high_school_us_history_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_high_school_world_history_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_human_aging_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:human_aging|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:human_aging|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_human_sexuality_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_international_law_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:international_law|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:international_law|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_jurisprudence_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_logical_fallacies_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_machine_learning_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:machine_learning|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:machine_learning|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_management_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:management|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:management|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_marketing_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:marketing|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:marketing|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_medical_genetics_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_miscellaneous_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_moral_disputes_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_moral_scenarios_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_nutrition_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:nutrition|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:nutrition|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_philosophy_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:philosophy|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:philosophy|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_prehistory_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:prehistory|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:prehistory|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_professional_accounting_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_professional_law_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:professional_law|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:professional_law|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_professional_medicine_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_professional_psychology_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_public_relations_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:public_relations|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:public_relations|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_security_studies_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:security_studies|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:security_studies|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_sociology_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:sociology|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:sociology|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_us_foreign_policy_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_virology_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:virology|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:virology|5_2023-08-28T20:44:03.051617.parquet' - config_name: original_mmlu_world_religions_5 data_files: - split: 2023_08_28T20_44_03.051617 path: - '**/details_original|mmlu:world_religions|5_2023-08-28T20:44:03.051617.parquet' - split: latest path: - '**/details_original|mmlu:world_religions|5_2023-08-28T20:44:03.051617.parquet' - config_name: results data_files: - split: 2023_07_19T20_43_28.351317 path: - results_2023-07-19T20:43:28.351317.parquet - split: 2023_08_28T20_44_03.051617 path: - results_2023-08-28T20:44:03.051617.parquet - split: 2023_09_16T21_34_04.587307 path: - results_2023-09-16T21-34-04.587307.parquet - split: latest path: - results_2023-09-16T21-34-04.587307.parquet --- # Dataset Card for Evaluation run of Salesforce/codegen-16B-nl ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/Salesforce/codegen-16B-nl - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [Salesforce/codegen-16B-nl](https://huggingface.co/Salesforce/codegen-16B-nl) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 122 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Salesforce__codegen-16B-nl", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-16T21:34:04.587307](https://huggingface.co/datasets/open-llm-leaderboard/details_Salesforce__codegen-16B-nl/blob/main/results_2023-09-16T21-34-04.587307.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.0012583892617449664, "em_stderr": 0.00036305608931190987, "f1": 0.050115352348993385, "f1_stderr": 0.0012004040103300048, "acc": 0.35304663251500595, "acc_stderr": 0.00877106572247344 }, "harness|drop|3": { "em": 0.0012583892617449664, "em_stderr": 0.00036305608931190987, "f1": 0.050115352348993385, "f1_stderr": 0.0012004040103300048 }, "harness|gsm8k|5": { "acc": 0.026535253980288095, "acc_stderr": 0.004427045987265165 }, "harness|winogrande|5": { "acc": 0.6795580110497238, "acc_stderr": 0.013115085457681714 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
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open-llm-leaderboard/details_Writer__palmyra-base
open-llm-leaderboard
2023-10-17T01:27:19Z
475
0
[ "region:us" ]
null
2023-08-18T12:03:30Z
--- pretty_name: Evaluation run of Writer/palmyra-base dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Writer/palmyra-base](https://huggingface.co/Writer/palmyra-base) on the [Open\ \ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 122 configuration, each one coresponding to one of\ \ the evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can\ \ be found as a specific split in each configuration, the split being named using\ \ the timestamp of the run.The \"train\" split is always pointing to the latest\ \ results.\n\nAn additional configuration \"results\" store all the aggregated results\ \ of the run (and is used to compute and display the agregated metrics on the [Open\ \ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Writer__palmyra-base\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-17T01:27:06.940630](https://huggingface.co/datasets/open-llm-leaderboard/details_Writer__palmyra-base/blob/main/results_2023-10-17T01-27-06.940630.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0010486577181208054,\n\ \ \"em_stderr\": 0.0003314581465219252,\n \"f1\": 0.04800964765100684,\n\ \ \"f1_stderr\": 0.0011968648184797989,\n \"acc\": 0.29537785734929894,\n\ \ \"acc_stderr\": 0.00829420088462589\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.0010486577181208054,\n \"em_stderr\": 0.0003314581465219252,\n\ \ \"f1\": 0.04800964765100684,\n \"f1_stderr\": 0.0011968648184797989\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.009855951478392721,\n \ \ \"acc_stderr\": 0.0027210765770416625\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.5808997632202052,\n \"acc_stderr\": 0.013867325192210116\n\ \ }\n}\n```" repo_url: https://huggingface.co/Writer/palmyra-base leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|arc:challenge|25_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-07-19T12:49:48.066230.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_17T01_27_06.940630 path: - '**/details_harness|drop|3_2023-10-17T01-27-06.940630.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-17T01-27-06.940630.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_17T01_27_06.940630 path: - '**/details_harness|gsm8k|5_2023-10-17T01-27-06.940630.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-17T01-27-06.940630.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hellaswag|10_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T12:49:48.066230.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T12:49:48.066230.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_19T12_49_48.066230 path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T12:49:48.066230.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T12:49:48.066230.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_17T01_27_06.940630 path: - '**/details_harness|winogrande|5_2023-10-17T01-27-06.940630.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-17T01-27-06.940630.parquet' - config_name: original_mmlu_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:anatomy|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:astronomy|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:business_ethics|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:college_biology|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:college_medicine|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:college_physics|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:computer_security|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:econometrics|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:formal_logic|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:global_facts|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:human_aging|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:international_law|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:machine_learning|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:management|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:marketing|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:nutrition|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:philosophy|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:prehistory|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:professional_law|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:public_relations|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:security_studies|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:sociology|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:virology|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:world_religions|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:anatomy|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:astronomy|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:business_ethics|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:college_biology|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:college_medicine|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:college_physics|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:computer_security|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:econometrics|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:formal_logic|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:global_facts|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:human_aging|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:international_law|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:machine_learning|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:management|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:marketing|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:nutrition|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:philosophy|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:prehistory|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:professional_law|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:public_relations|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:security_studies|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:sociology|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:virology|5_2023-08-28T20:42:00.075340.parquet' - '**/details_original|mmlu:world_religions|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_abstract_algebra_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_anatomy_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:anatomy|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:anatomy|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_astronomy_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:astronomy|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:astronomy|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_business_ethics_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:business_ethics|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:business_ethics|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_clinical_knowledge_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_college_biology_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:college_biology|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:college_biology|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_college_chemistry_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_college_computer_science_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_college_mathematics_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_college_medicine_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:college_medicine|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:college_medicine|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_college_physics_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:college_physics|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:college_physics|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_computer_security_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:computer_security|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:computer_security|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_conceptual_physics_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_econometrics_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:econometrics|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:econometrics|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_electrical_engineering_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_elementary_mathematics_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_formal_logic_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:formal_logic|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:formal_logic|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_global_facts_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:global_facts|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:global_facts|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_high_school_biology_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_high_school_chemistry_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_high_school_computer_science_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_high_school_european_history_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_high_school_geography_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_high_school_government_and_politics_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_high_school_macroeconomics_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_high_school_mathematics_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_high_school_microeconomics_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_high_school_physics_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_high_school_psychology_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_high_school_statistics_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_high_school_us_history_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_high_school_world_history_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_human_aging_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:human_aging|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:human_aging|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_human_sexuality_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_international_law_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:international_law|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:international_law|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_jurisprudence_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_logical_fallacies_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_machine_learning_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:machine_learning|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:machine_learning|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_management_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:management|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:management|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_marketing_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:marketing|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:marketing|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_medical_genetics_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_miscellaneous_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_moral_disputes_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_moral_scenarios_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_nutrition_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:nutrition|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:nutrition|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_philosophy_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:philosophy|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:philosophy|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_prehistory_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:prehistory|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:prehistory|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_professional_accounting_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_professional_law_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:professional_law|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:professional_law|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_professional_medicine_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_professional_psychology_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_public_relations_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:public_relations|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:public_relations|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_security_studies_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:security_studies|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:security_studies|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_sociology_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:sociology|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:sociology|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_us_foreign_policy_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_virology_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:virology|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:virology|5_2023-08-28T20:42:00.075340.parquet' - config_name: original_mmlu_world_religions_5 data_files: - split: 2023_08_28T20_42_00.075340 path: - '**/details_original|mmlu:world_religions|5_2023-08-28T20:42:00.075340.parquet' - split: latest path: - '**/details_original|mmlu:world_religions|5_2023-08-28T20:42:00.075340.parquet' - config_name: results data_files: - split: 2023_07_19T12_49_48.066230 path: - results_2023-07-19T12:49:48.066230.parquet - split: 2023_08_28T20_42_00.075340 path: - results_2023-08-28T20:42:00.075340.parquet - split: 2023_10_17T01_27_06.940630 path: - results_2023-10-17T01-27-06.940630.parquet - split: latest path: - results_2023-10-17T01-27-06.940630.parquet --- # Dataset Card for Evaluation run of Writer/palmyra-base ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/Writer/palmyra-base - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [Writer/palmyra-base](https://huggingface.co/Writer/palmyra-base) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 122 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Writer__palmyra-base", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-17T01:27:06.940630](https://huggingface.co/datasets/open-llm-leaderboard/details_Writer__palmyra-base/blob/main/results_2023-10-17T01-27-06.940630.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.0010486577181208054, "em_stderr": 0.0003314581465219252, "f1": 0.04800964765100684, "f1_stderr": 0.0011968648184797989, "acc": 0.29537785734929894, "acc_stderr": 0.00829420088462589 }, "harness|drop|3": { "em": 0.0010486577181208054, "em_stderr": 0.0003314581465219252, "f1": 0.04800964765100684, "f1_stderr": 0.0011968648184797989 }, "harness|gsm8k|5": { "acc": 0.009855951478392721, "acc_stderr": 0.0027210765770416625 }, "harness|winogrande|5": { "acc": 0.5808997632202052, "acc_stderr": 0.013867325192210116 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
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classla/FRENK-hate-en
classla
2022-10-21T07:52:06Z
474
1
[ "task_categories:text-classification", "size_categories:1K<n<10K", "language:en", "license:other", "hate-speech-detection", "offensive-language", "arxiv:1906.02045", "region:us" ]
[ "text-classification" ]
2022-03-02T23:29:22Z
--- language: - en license: - other size_categories: - 1K<n<10K task_categories: - text-classification task_ids: [] tags: - hate-speech-detection - offensive-language --- # Offensive language dataset of Croatian comments FRENK 1.0 English subset of the [FRENK dataset](http://hdl.handle.net/11356/1433). Also available on HuggingFace dataset hub: [Croatian subset](https://huggingface.co/datasets/5roop/FRENK-hate-hr), [Slovenian subset](https://huggingface.co/datasets/5roop/FRENK-hate-sl). ## Dataset Description - **Homepage:** http://hdl.handle.net/11356/1433 - **Repository:** http://hdl.handle.net/11356/1433 - **Paper:** https://arxiv.org/abs/1906.02045 - **Project page** https://nl.ijs.si/frenk/ ## Description of the original dataset The original FRENK dataset consists of comments to Facebook posts (news articles) of mainstream media outlets from Croatia, Great Britain, and Slovenia, on the topics of migrants and LGBT. The dataset contains whole discussion threads. Each comment is annotated by the type of socially unacceptable discourse (e.g., inappropriate, offensive, violent speech) and its target (e.g., migrants/LGBT, commenters, media). The annotation schema is described in detail in [https://arxiv.org/pdf/1906.02045.pdf]. Usernames in the metadata are pseudo-anonymised and removed from the comments. The data in each language (Croatian (hr), English (en), Slovenian (sl), and topic (migrants, LGBT) is divided into a training and a testing portion. The training and testing data consist of separate discussion threads, i.e., there is no cross-discussion-thread contamination between training and testing data. The sizes of the splits are the following: Croatian, migrants: 4356 training comments, 978 testing comments; Croatian LGBT: 4494 training comments, 1142 comments; English, migrants: 4540 training comments, 1285 testing comments; English, LGBT: 4819 training comments, 1017 testing comments; Slovenian, migrants: 5145 training comments, 1277 testing comments; Slovenian, LGBT: 2842 training comments, 900 testing comments. For this dataset only the English data was used. Training segment has been split into beginning 90% (published here as training split) and end 10% (published here as dev split). ## Usage in `Transformers` ```python import datasets ds = datasets.load_dataset("classla/FRENK-hate-en","binary") ``` For binary classification the following encoding is used: ```python _CLASS_MAP_BINARY = { 'Acceptable': 0, 'Offensive': 1, } ``` The original labels are available if the dataset is loaded with the `multiclass` option: ```python import datasets ds = datasets.load_dataset("5roop/FRENK-hate-en","multiclass"). ``` In this case the encoding used is: ```python _CLASS_MAP_MULTICLASS = { 'Acceptable speech': 0, 'Inappropriate': 1, 'Background offensive': 2, 'Other offensive': 3, 'Background violence': 4, 'Other violence': 5, } ``` The original labels are available if the dataset is loaded with the `multiclass` option: ```python import datasets ds = datasets.load_dataset("classla/FRENK-hate-en","multiclass"). ``` In this case the encoding used is: ```python _CLASS_MAP_MULTICLASS = { 'Acceptable speech': 0, 'Inappropriate': 1, 'Background offensive': 2, 'Other offensive': 3, 'Background violence': 4, 'Other violence': 5, } ``` ## Data structure * `text`: text * `target`: who is the target of the hate-speech text ("no target", "commenter", "target" (migrants or LGBT, depending on the topic), or "related to" (again, the topic)) * `topic`: whether the text relates to lgbt or migrants hate-speech domains * `label`: label of the text instance, see above. ## Data instance ``` {'text': "Not everyone has the option of a rainbow reaction; I don't but wish I did.", 'target': 'No target', 'topic': 'lgbt', 'label': 0} ``` ## Licensing information CLARIN.SI Licence ACA ID-BY-NC-INF-NORED 1.0 ## Citation information When using this dataset please cite the following paper: ``` @misc{ljubešić2019frenk, title={The FRENK Datasets of Socially Unacceptable Discourse in Slovene and English}, author={Nikola Ljubešić and Darja Fišer and Tomaž Erjavec}, year={2019}, eprint={1906.02045}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/1906.02045} } ``` The original dataset can be cited as ``` @misc{11356/1433, title = {Offensive language dataset of Croatian, English and Slovenian comments {FRENK} 1.0}, author = {Ljube{\v s}i{\'c}, Nikola and Fi{\v s}er, Darja and Erjavec, Toma{\v z}}, url = {http://hdl.handle.net/11356/1433}, note = {Slovenian language resource repository {CLARIN}.{SI}}, copyright = {{CLARIN}.{SI} Licence {ACA} {ID}-{BY}-{NC}-{INF}-{NORED} 1.0}, year = {2021} } ```
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open-llm-leaderboard/details_openlm-research__open_llama_13b
open-llm-leaderboard
2023-10-15T17:56:57Z
474
0
[ "region:us" ]
null
2023-08-18T11:44:48Z
--- pretty_name: Evaluation run of openlm-research/open_llama_13b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [openlm-research/open_llama_13b](https://huggingface.co/openlm-research/open_llama_13b)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 122 configuration, each one coresponding to one of\ \ the evaluated task.\n\nThe dataset has been created from 5 run(s). Each run can\ \ be found as a specific split in each configuration, the split being named using\ \ the timestamp of the run.The \"train\" split is always pointing to the latest\ \ results.\n\nAn additional configuration \"results\" store all the aggregated results\ \ of the run (and is used to compute and display the agregated metrics on the [Open\ \ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_openlm-research__open_llama_13b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-15T17:56:49.237621](https://huggingface.co/datasets/open-llm-leaderboard/details_openlm-research__open_llama_13b/blob/main/results_2023-10-15T17-56-49.237621.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0012583892617449664,\n\ \ \"em_stderr\": 0.00036305608931192504,\n \"f1\": 0.059914010067114144,\n\ \ \"f1_stderr\": 0.0014216209947324643,\n \"acc\": 0.3766001485184359,\n\ \ \"acc_stderr\": 0.008751247780672124\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.0012583892617449664,\n \"em_stderr\": 0.00036305608931192504,\n\ \ \"f1\": 0.059914010067114144,\n \"f1_stderr\": 0.0014216209947324643\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.032600454890068235,\n \ \ \"acc_stderr\": 0.004891669021939565\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7205998421468035,\n \"acc_stderr\": 0.012610826539404684\n\ \ }\n}\n```" repo_url: https://huggingface.co/openlm-research/open_llama_13b leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|arc:challenge|25_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|arc:challenge|25_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-07-24T14:23:47.053275.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_13T10_40_56.309063 path: - '**/details_harness|drop|3_2023-10-13T10-40-56.309063.parquet' - split: 2023_10_15T17_56_49.237621 path: - '**/details_harness|drop|3_2023-10-15T17-56-49.237621.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-15T17-56-49.237621.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_13T10_40_56.309063 path: - '**/details_harness|gsm8k|5_2023-10-13T10-40-56.309063.parquet' - split: 2023_10_15T17_56_49.237621 path: - '**/details_harness|gsm8k|5_2023-10-15T17-56-49.237621.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-15T17-56-49.237621.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hellaswag|10_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hellaswag|10_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-20T10:02:18.035133.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-24T14:23:47.053275.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-management|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-management|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-virology|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-virology|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T14:23:47.053275.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_20T10_02_18.035133 path: - '**/details_harness|truthfulqa:mc|0_2023-07-20T10:02:18.035133.parquet' - split: 2023_07_24T14_23_47.053275 path: - '**/details_harness|truthfulqa:mc|0_2023-07-24T14:23:47.053275.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-07-24T14:23:47.053275.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_13T10_40_56.309063 path: - '**/details_harness|winogrande|5_2023-10-13T10-40-56.309063.parquet' - split: 2023_10_15T17_56_49.237621 path: - '**/details_harness|winogrande|5_2023-10-15T17-56-49.237621.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-15T17-56-49.237621.parquet' - config_name: original_mmlu_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:anatomy|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:astronomy|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:business_ethics|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:college_biology|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:college_medicine|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:college_physics|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:computer_security|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:econometrics|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:formal_logic|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:global_facts|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:human_aging|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:international_law|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:machine_learning|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:management|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:marketing|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:nutrition|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:philosophy|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:prehistory|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:professional_law|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:public_relations|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:security_studies|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:sociology|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:virology|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:world_religions|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:anatomy|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:astronomy|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:business_ethics|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:college_biology|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:college_medicine|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:college_physics|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:computer_security|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:econometrics|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:formal_logic|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:global_facts|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:human_aging|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:international_law|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:machine_learning|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:management|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:marketing|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:nutrition|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:philosophy|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:prehistory|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:professional_law|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:public_relations|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:security_studies|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:sociology|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:virology|5_2023-08-28T20:28:46.796223.parquet' - '**/details_original|mmlu:world_religions|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_abstract_algebra_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_anatomy_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:anatomy|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:anatomy|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_astronomy_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:astronomy|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:astronomy|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_business_ethics_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:business_ethics|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:business_ethics|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_clinical_knowledge_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_college_biology_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:college_biology|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:college_biology|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_college_chemistry_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_college_computer_science_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_college_mathematics_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_college_medicine_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:college_medicine|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:college_medicine|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_college_physics_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:college_physics|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:college_physics|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_computer_security_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:computer_security|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:computer_security|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_conceptual_physics_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_econometrics_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:econometrics|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:econometrics|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_electrical_engineering_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_elementary_mathematics_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_formal_logic_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:formal_logic|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:formal_logic|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_global_facts_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:global_facts|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:global_facts|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_high_school_biology_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_high_school_chemistry_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_high_school_computer_science_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_high_school_european_history_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_high_school_geography_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_high_school_government_and_politics_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_high_school_macroeconomics_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_high_school_mathematics_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_high_school_microeconomics_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_high_school_physics_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_high_school_psychology_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_high_school_statistics_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_high_school_us_history_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_high_school_world_history_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_human_aging_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:human_aging|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:human_aging|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_human_sexuality_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_international_law_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:international_law|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:international_law|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_jurisprudence_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_logical_fallacies_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_machine_learning_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:machine_learning|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:machine_learning|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_management_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:management|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:management|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_marketing_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:marketing|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:marketing|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_medical_genetics_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_miscellaneous_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_moral_disputes_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_moral_scenarios_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_nutrition_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:nutrition|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:nutrition|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_philosophy_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:philosophy|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:philosophy|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_prehistory_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:prehistory|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:prehistory|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_professional_accounting_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_professional_law_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:professional_law|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:professional_law|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_professional_medicine_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_professional_psychology_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_public_relations_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:public_relations|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:public_relations|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_security_studies_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:security_studies|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:security_studies|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_sociology_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:sociology|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:sociology|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_us_foreign_policy_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_virology_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:virology|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:virology|5_2023-08-28T20:28:46.796223.parquet' - config_name: original_mmlu_world_religions_5 data_files: - split: 2023_08_28T20_28_46.796223 path: - '**/details_original|mmlu:world_religions|5_2023-08-28T20:28:46.796223.parquet' - split: latest path: - '**/details_original|mmlu:world_religions|5_2023-08-28T20:28:46.796223.parquet' - config_name: results data_files: - split: 2023_07_20T10_02_18.035133 path: - results_2023-07-20T10:02:18.035133.parquet - split: 2023_07_24T14_23_47.053275 path: - results_2023-07-24T14:23:47.053275.parquet - split: 2023_08_28T20_28_46.796223 path: - results_2023-08-28T20:28:46.796223.parquet - split: 2023_10_13T10_40_56.309063 path: - results_2023-10-13T10-40-56.309063.parquet - split: 2023_10_15T17_56_49.237621 path: - results_2023-10-15T17-56-49.237621.parquet - split: latest path: - results_2023-10-15T17-56-49.237621.parquet --- # Dataset Card for Evaluation run of openlm-research/open_llama_13b ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/openlm-research/open_llama_13b - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [openlm-research/open_llama_13b](https://huggingface.co/openlm-research/open_llama_13b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 122 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 5 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_openlm-research__open_llama_13b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-15T17:56:49.237621](https://huggingface.co/datasets/open-llm-leaderboard/details_openlm-research__open_llama_13b/blob/main/results_2023-10-15T17-56-49.237621.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.0012583892617449664, "em_stderr": 0.00036305608931192504, "f1": 0.059914010067114144, "f1_stderr": 0.0014216209947324643, "acc": 0.3766001485184359, "acc_stderr": 0.008751247780672124 }, "harness|drop|3": { "em": 0.0012583892617449664, "em_stderr": 0.00036305608931192504, "f1": 0.059914010067114144, "f1_stderr": 0.0014216209947324643 }, "harness|gsm8k|5": { "acc": 0.032600454890068235, "acc_stderr": 0.004891669021939565 }, "harness|winogrande|5": { "acc": 0.7205998421468035, "acc_stderr": 0.012610826539404684 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
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allenai/tulu-v2-sft-mixture
allenai
2023-11-29T06:53:43Z
474
21
[ "task_categories:question-answering", "task_categories:conversational", "task_categories:text-generation", "size_categories:100K<n<1M", "language:en", "license:odc-by", "arxiv:2305.18290", "region:us" ]
[ "question-answering", "conversational", "text-generation" ]
2023-11-13T21:56:34Z
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: dataset dtype: string - name: id dtype: string - name: messages list: - name: role dtype: string - name: content dtype: string splits: - name: train num_bytes: 1239293363 num_examples: 326154 download_size: 554561769 dataset_size: 1239293363 license: odc-by task_categories: - question-answering - conversational - text-generation language: - en size_categories: - 100K<n<1M --- <img src="https://huggingface.co/datasets/allenai/blog-images/resolve/main/tulu-v2/Tulu%20V2%20banner.png" alt="TuluV2 banner" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> # Dataset Card for Tulu V2 Mix *Note the [ODC-BY license](https://opendatacommons.org/licenses/by/1-0/), indicating that different licenses apply to subsets of the data. This means that some portions of the dataset are non-commercial. We present the mixture as a research artifact.* Tulu is a series of language models that are trained to act as helpful assistants. The dataset consists of a mix of : * [FLAN](https://github.com/google-research/FLAN/tree/main) (Apache 2.0): We use 50,000 examples sampled from FLAN v2. To emphasize CoT-style reasoning, we sample another 50,000 examples from the CoT subset of the FLAN v2 mixture. * [Open Assistant 1](https://huggingface.co/datasets/OpenAssistant/oasst1) (Apache 2.0): We isolate the highest-scoring paths in each conversation tree and use these samples, resulting in 7,708 examples. * [ShareGPT](https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered) (Apache 2.0 listed, no official repo found): We use all 114,046 from our processed ShareGPT dataset, as we found ShareGPT gave strong performance in prior work. * [GPT4-Alpaca](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM#data-release) (CC By NC 4.0):We sample 20,000 samples from GPT-4 Alpaca to further include distilled GPT-4 data. * [Code-Alpaca](https://github.com/sahil280114/codealpaca) (CC By NC 4.0):We use all 20,022 examples from Code Alpaca, following our prior V1 mixture, in order to improve model code abilities. * [LIMA](https://huggingface.co/datasets/GAIR/lima) (CC BY-NC-SA): We use 1,030 examples from LIMA as an example of carefully curated data. * [WizardLM Evol Instruct](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k) (No license provided): We subsample 30,000 examples from WizardLM, which contains distilled data of increasing diversity and complexity. * [Open-Orca](https://huggingface.co/datasets/Open-Orca/OpenOrca) (MIT): We sample 30,000 samples generated by GPT-4 from OpenOrca, a reproduction of Orca Mukherjee et al., 2023, which augments FLAN data with additional model-generated explanations * Hardcoded: A collection of prompts such as `Tell me about yourself' such that the model generates correct outputs given inquiries about its name or developers. We wrote 14 samples and repeat each sample 10 times in the mixture, resulting in 140 total samples. * Science: 7,544 examples from a mixture of scientific document understand tasks—including question answering, fact-checking, summarization, and information extraction (under development, standalone release soon). These are made by taking either just the training set of the subsets or the entire section if no splits are present. Tulu V2 is presented as a singular training split. [Tulu V2 DPO 70B](https://huggingface.co/allenai/tulu-2-dpo-70b), and is a fine-tuned version of Llama 2 that was trained on on a mix of publicly available, synthetic and human datasets using [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290). **Model Family:** Other models and the dataset are found in the [Tulu V2 collection](https://huggingface.co/collections/allenai/tulu-v2-suite-6551b56e743e6349aab45101). The length distribution of the dataset can be seen below: <img src="https://huggingface.co/datasets/allenai/blog-images/resolve/main/tulu-v2/length_histogram_v2.png" alt="TuluV2 histogram" width="600" style="margin-left:'auto' margin-right:'auto' display:'block'"/> Tulu V1 Mix can be found [here](https://huggingface.co/datasets/allenai/tulu-v1). ### Science data note The included science data is from the following categories: <img src="https://huggingface.co/datasets/allenai/blog-images/resolve/main/tulu-v2/science_data.png" alt="TuluV2 science data mix" width="600" style="margin-left:'auto' margin-right:'auto' display:'block'"/> Note that some of the examples include an off-by-one error in the sentence indexing that had a small or negligible impact on performance. This was found during testing and will be updated in future versions, with the detailed release of the dataset artifact itself coming in a future release. ### License We are releasing this dataset under the terms of [ODC-BY](https://opendatacommons.org/licenses/by/1-0/). By using this, you are also bound by the [Common Crawl terms of use](https://commoncrawl.org/terms-of-use/) in respect of the content contained in the dataset.
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x_stance
null
2023-04-05T13:45:10Z
473
4
[ "task_categories:text-classification", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:multilingual", "size_categories:10K<n<100K", "source_datasets:original", "language:de", "language:en", "language:fr", "language:it", "license:cc-by-nc-4.0", "stance-detection", "arxiv:2003.08385", "region:us" ]
[ "text-classification" ]
2022-03-02T23:29:22Z
--- annotations_creators: - machine-generated language: - de - en - fr - it language_creators: - found license: - cc-by-nc-4.0 multilinguality: - multilingual pretty_name: x-stance size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: [] paperswithcode_id: x-stance tags: - stance-detection dataset_info: features: - name: question dtype: string - name: id dtype: int32 - name: question_id dtype: int32 - name: language dtype: string - name: comment dtype: string - name: label dtype: string - name: numerical_label dtype: int32 - name: author dtype: string - name: topic dtype: string splits: - name: train num_bytes: 17619123 num_examples: 45640 - name: test num_bytes: 6607134 num_examples: 17705 - name: validation num_bytes: 1505979 num_examples: 3926 download_size: 6410801 dataset_size: 25732236 --- # Dataset Card for "x_stance" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** https://github.com/ZurichNLP/xstance - **Paper:** [X-Stance: A Multilingual Multi-Target Dataset for Stance Detection](https://arxiv.org/abs/2003.08385) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 6.41 MB - **Size of the generated dataset:** 25.73 MB - **Total amount of disk used:** 32.14 MB ### Dataset Summary The x-stance dataset contains more than 150 political questions, and 67k comments written by candidates on those questions. It can be used to train and evaluate stance detection systems. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages The comments are partly German, partly French and Italian. The questions are available in all the three languages plus English. ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 6.41 MB - **Size of the generated dataset:** 25.73 MB - **Total amount of disk used:** 32.14 MB An example of 'train' looks as follows. ``` { "author": "f27b54a137b4", "comment": "Das Arbeitsgesetz regelt die Arbeitszeiten und schützt den Arbeitnehmer. Es macht doch Sinn, dass wenn eine Nachfrage besteht, die Läden öffnen dürfen und wenn es keine Nachfrage gibt, diese geschlossen bleiben.", "id": 10045, "label": "FAVOR", "language": "de", "numerical_label": 100, "question": "Sind Sie für eine vollständige Liberalisierung der Geschäftsöffnungszeiten (Geschäfte können die Öffnungszeiten nach freiem Ermessen festlegen)?", "question_id": 739, "topic": "Economy" } ``` ### Data Fields The data fields are the same among all splits. #### default - `question`: a `string` feature. - `id`: a `int32` feature. - `question_id`: a `int32` feature. - `language`: a `string` feature. - `comment`: a `string` feature. - `label`: a `string` feature. - `numerical_label`: a `int32` feature. - `author`: a `string` feature. - `topic`: a `string` feature. ### Data Splits | name |train|validation|test | |-------|----:|---------:|----:| |default|45640| 3926|17705| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization The data have been extracted from the Swiss voting advice platform Smartvote.ch. #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information The dataset is licensed under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). ### Citation Information ``` @inproceedings{vamvas2020xstance, author = "Vamvas, Jannis and Sennrich, Rico", title = "{X-Stance}: A Multilingual Multi-Target Dataset for Stance Detection", booktitle = "Proceedings of the 5th Swiss Text Analytics Conference (SwissText) \& 16th Conference on Natural Language Processing (KONVENS)", address = "Zurich, Switzerland", year = "2020", month = "jun", url = "http://ceur-ws.org/Vol-2624/paper9.pdf" } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@jvamvas](https://github.com/jvamvas) for adding this dataset.
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Kyle1668/AG-Tweets
Kyle1668
2023-11-29T08:12:37Z
472
0
[ "arxiv:1509.01626", "region:us" ]
null
2023-06-29T22:07:56Z
--- pretty_name: AG News Tweets --- #### Motivation AG News is a four-way topic classification task introduced in [Character-level Convolutional Networks for Text Classification](https://arxiv.org/abs/1509.01626). In this setup, a task model must classify whether a given news article is about world events (**_World_**), sports and athletics (**_Sports_**), business and economics (**_Business_**), and scientific developments (**_Sci/Tech_**). The test set on HuggingFace ([huggingface.co/datasets/ag_news](https://huggingface.co/datasets/ag_news)) is composed of 7,600 examples equally balanced across the four classes. News topic classification presents a promising opportunity for largely isolating the effect of writing style shifts. Existing deep learning methods also perform well on this dataset with accuracy reaching higher than 90% ([paperswithcode.com/sota/text-classification-on-ag-news](https://paperswithcode.com/sota/text-classification-on-ag-news)). Another motivation for this particular task is the common risk of data augmentation inadvertently flipping the label/semantics of the text. Unlike other tasks such as sentiment classification or subtle hate speech, the topic of a news article is unlikely to change during augmentation, thus preserving the original label. #### Creation We used GPT-3.5 Turbo (6/7/23 version) for style transfer. We did an initial pass through all 7,600 examples using a conservative "V1" prompt and greedy decoding. Calls were made using the OpenAI Python SDK with `top_p` and `temperature` set to zero. The data was then lightly preprocessed to reduce the number of examples that began with **BREAKING NEWS** flanked by emojis. 512 of the initial model responses did not result in satisfactory generations. These were typical cases where the generated text was almost indiscernible from the original text or the generation was entirely emojis. We called GPT-3.5 Turbo again with an updated prompt and hyperparameters (`temperature=0.7`, `top_p=0.9`, `frequency_penalty=0.5`, `presence_penalty=0.5`) for these examples. Whereas all the first-pass generations did not have any instructions to the model as to the sentiment/mood of the hypothetical post author, we purposefully instructed the model to "_Add some flare with humor, anger, or sarcasm._" in the generation. It's important to note that we did not enforce Twitter's character limit. These sequences should be considered as more broadly inspired by social media posts rather than following the exact specifications of Twitter posts. We also did not manually review every sequence in the dataset to confirm that the original label was preserved. GPT 3.5 Turbo also hallucinates facts, such as adding the hashtag **#Olympics2021** even though the original dataset was created in 2015.
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allegro_reviews
null
2022-11-18T17:41:41Z
471
1
[ "task_categories:text-classification", "task_ids:sentiment-scoring", "task_ids:text-scoring", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:pl", "license:cc-by-sa-4.0", "region:us" ]
[ "text-classification" ]
2022-03-02T23:29:22Z
--- annotations_creators: - found language_creators: - found language: - pl license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-scoring - text-scoring paperswithcode_id: allegro-reviews pretty_name: Allegro Reviews dataset_info: features: - name: text dtype: string - name: rating dtype: float32 splits: - name: train num_bytes: 4899539 num_examples: 9577 - name: test num_bytes: 514527 num_examples: 1006 - name: validation num_bytes: 515785 num_examples: 1002 download_size: 2314847 dataset_size: 5929851 --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://klejbenchmark.com/ - **Repository:** https://github.com/allegro/klejbenchmark-allegroreviews - **Paper:** KLEJ: Comprehensive Benchmark for Polish Language Understanding (Rybak, Piotr and Mroczkowski, Robert and Tracz, Janusz and Gawlik, Ireneusz) - **Leaderboard:** https://klejbenchmark.com/leaderboard/ - **Point of Contact:** [email protected] ### Dataset Summary Allegro Reviews is a sentiment analysis dataset, consisting of 11,588 product reviews written in Polish and extracted from Allegro.pl - a popular e-commerce marketplace. Each review contains at least 50 words and has a rating on a scale from one (negative review) to five (positive review). We recommend using the provided train/dev/test split. The ratings for the test set reviews are kept hidden. You can evaluate your model using the online evaluation tool available on klejbenchmark.com. ### Supported Tasks and Leaderboards Product reviews sentiment analysis. https://klejbenchmark.com/leaderboard/ ### Languages Polish ## Dataset Structure ### Data Instances Two tsv files (train, dev) with two columns (text, rating) and one (test) with just one (text). ### Data Fields - text: a product review of at least 50 words - rating: product rating of a scale of one (negative review) to five (positive review) ### Data Splits Data is splitted in train/dev/test split. ## Dataset Creation ### Curation Rationale This dataset is one of nine evaluation tasks to improve polish language processing. ### Source Data #### Initial Data Collection and Normalization The Allegro Reviews is a set of product reviews from a popular e-commerce marketplace (Allegro.pl). #### Who are the source language producers? Customers of an e-commerce marketplace. ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Allegro Machine Learning Research team [email protected] ### Licensing Information Dataset licensed under CC BY-SA 4.0 ### Citation Information @inproceedings{rybak-etal-2020-klej, title = "{KLEJ}: Comprehensive Benchmark for Polish Language Understanding", author = "Rybak, Piotr and Mroczkowski, Robert and Tracz, Janusz and Gawlik, Ireneusz", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.acl-main.111", pages = "1191--1201", } ### Contributions Thanks to [@abecadel](https://github.com/abecadel) for adding this dataset.
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wi_locness
null
2023-06-01T14:59:47Z
471
7
[ "task_categories:text2text-generation", "annotations_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "multilinguality:other-language-learner", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:other", "grammatical-error-correction", "region:us" ]
[ "text2text-generation" ]
2022-03-02T23:29:22Z
--- annotations_creators: - expert-generated language_creators: - crowdsourced language: - en license: - other multilinguality: - monolingual - other-language-learner size_categories: - 1K<n<10K source_datasets: - original task_categories: - text2text-generation task_ids: [] paperswithcode_id: locness-corpus pretty_name: Cambridge English Write & Improve + LOCNESS tags: - grammatical-error-correction dataset_info: - config_name: default features: - name: id dtype: string - name: userid dtype: string - name: cefr dtype: string - name: text dtype: string - name: edits sequence: - name: start dtype: int32 - name: end dtype: int32 - name: text dtype: string splits: - name: train num_bytes: 4375795 num_examples: 3000 - name: validation num_bytes: 447055 num_examples: 300 download_size: 6120469 dataset_size: 4822850 - config_name: wi features: - name: id dtype: string - name: userid dtype: string - name: cefr dtype: string - name: text dtype: string - name: edits sequence: - name: start dtype: int32 - name: end dtype: int32 - name: text dtype: string splits: - name: train num_bytes: 4375795 num_examples: 3000 - name: validation num_bytes: 447055 num_examples: 300 download_size: 6120469 dataset_size: 4822850 - config_name: locness features: - name: id dtype: string - name: cefr dtype: string - name: text dtype: string - name: edits sequence: - name: start dtype: int32 - name: end dtype: int32 - name: text dtype: string splits: - name: validation num_bytes: 138176 num_examples: 50 download_size: 6120469 dataset_size: 138176 config_names: - locness - wi --- # Dataset Card for Cambridge English Write & Improve + LOCNESS Dataset ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://www.cl.cam.ac.uk/research/nl/bea2019st/#data - **Repository:** - **Paper:** https://www.aclweb.org/anthology/W19-4406/ - **Leaderboard:** https://competitions.codalab.org/competitions/20228#results - **Point of Contact:** ### Dataset Summary Write & Improve (Yannakoudakis et al., 2018) is an online web platform that assists non-native English students with their writing. Specifically, students from around the world submit letters, stories, articles and essays in response to various prompts, and the W&I system provides instant feedback. Since W&I went live in 2014, W&I annotators have manually annotated some of these submissions and assigned them a CEFR level. The LOCNESS corpus (Granger, 1998) consists of essays written by native English students. It was originally compiled by researchers at the Centre for English Corpus Linguistics at the University of Louvain. Since native English students also sometimes make mistakes, we asked the W&I annotators to annotate a subsection of LOCNESS so researchers can test the effectiveness of their systems on the full range of English levels and abilities. ### Supported Tasks and Leaderboards Grammatical error correction (GEC) is the task of automatically correcting grammatical errors in text; e.g. [I follows his advices -> I followed his advice]. It can be used to not only help language learners improve their writing skills, but also alert native speakers to accidental mistakes or typos. The aim of the task of this dataset is to correct all types of errors in written text. This includes grammatical, lexical and orthographical errors. The following Codalab competition contains the latest leaderboard, along with information on how to submit to the withheld W&I+LOCNESS test set: https://competitions.codalab.org/competitions/20228 ### Languages The dataset is in English. ## Dataset Structure ### Data Instances An example from the `wi` configuration: ``` { 'id': '1-140178', 'userid': '21251', 'cefr': 'A2.i', 'text': 'My town is a medium size city with eighty thousand inhabitants. It has a high density population because its small territory. Despite of it is an industrial city, there are many shops and department stores. I recommend visiting the artificial lake in the certer of the city which is surrounded by a park. Pasteries are very common and most of them offer the special dessert from the city. There are a comercial zone along the widest street of the city where you can find all kind of establishments: banks, bars, chemists, cinemas, pet shops, restaurants, fast food restaurants, groceries, travel agencies, supermarkets and others. Most of the shops have sales and offers at least three months of the year: January, June and August. The quality of the products and services are quite good, because there are a huge competition, however I suggest you taking care about some fakes or cheats.', 'edits': { 'start': [13, 77, 104, 126, 134, 256, 306, 375, 396, 402, 476, 484, 579, 671, 774, 804, 808, 826, 838, 850, 857, 862, 868], 'end': [24, 78, 104, 133, 136, 262, 315, 379, 399, 411, 480, 498, 588, 671, 777, 807, 810, 835, 845, 856, 861, 867, 873], 'text': ['medium-sized', '-', ' of', 'Although', '', 'center', None, 'of', 'is', 'commercial', 'kinds', 'businesses', 'grocers', ' in', 'is', 'is', '', '. However,', 'recommend', 'be', 'careful', 'of', ''] } } ``` An example from the `locness` configuration: ``` { 'id': '7-5819177', 'cefr': 'N', 'text': 'Boxing is a common, well known and well loved sport amongst most countries in the world however it is also punishing, dangerous and disliked to the extent that many people want it banned, possibly with good reason.\nBoxing is a dangerous sport, there are relatively common deaths, tragic injuries and even disease. All professional boxers are at risk from being killed in his next fight. If not killed then more likely paralysed. There have been a number of cases in the last ten years of the top few boxers having tragic losses throughout their ranks. This is just from the elite few, and theres more from those below them.\nMore deaths would occur through boxing if it were banned. The sport would go underground, there would be no safety measures like gloves, a doctor, paramedics or early stopping of the fight if someone looked unable to continue. With this going on the people taking part will be dangerous, and on the streets. Dangerous dogs who were trained to kill and maim in similar underound dog fights have already proved deadly to innocent people, the new boxers could be even more at risk.\nOnce boxing is banned and no-one grows up knowing it as acceptable there will be no interest in boxing and hopefully less all round interest in violence making towns and cities much safer places to live in, there will be less fighting outside pubs and clubs and less violent attacks with little or no reason.\nchange the rules of boxing slightly would much improve the safety risks of the sport and not detract form the entertainment. There are all sorts of proposals, lighter and more cushioning gloves could be worn, ban punches to the head, headguards worn or make fights shorter, as most of the serious injuries occur in the latter rounds, these would all show off the boxers skill and tallent and still be entertaining to watch.\nEven if a boxer is a success and manages not to be seriously hurt he still faces serious consequences in later life diseases that attack the brains have been known to set in as a direct result of boxing, even Muhamed Ali, who was infamous(?) both for his boxing and his quick-witted intelligence now has Alzheimer disease and can no longer do many everyday acts.\nMany other sports are more dangerous than boxing, motor sports and even mountaineering has risks that are real. Boxers chose to box, just as racing drivers drive.', 'edits': { 'start': [24, 39, 52, 87, 242, 371, 400, 528, 589, 713, 869, 992, 1058, 1169, 1209, 1219, 1255, 1308, 1386, 1412, 1513, 1569, 1661, 1731, 1744, 1781, 1792, 1901, 1951, 2038, 2131, 2149, 2247, 2286], 'end': [25, 40, 59, 95, 249, 374, 400, 538, 595, 713, 869, 1001, 1063, 1169, 1209, 1219, 1255, 1315, 1390, 1418, 1517, 1570, 1661, 1737, 1751, 1781, 1799, 1901, 1960, 2044, 2131, 2149, 2248, 2289], 'text': ['-', '-', 'in', '. However,', '. There', 'their', ',', 'among', "there's", ' and', ',', 'underground', '. The', ',', ',', ',', ',', '. There', 'for', 'Changing', 'from', ';', ',', 'later', '. These', "'", 'talent', ',', '. Diseases', '. Even', ',', "'s", ';', 'have'] } } ``` ### Data Fields The fields of the dataset are: - `id`: the id of the text as a string - `cefr`: the [CEFR level](https://www.cambridgeenglish.org/exams-and-tests/cefr/) of the text as a string - `userid`: id of the user - `text`: the text of the submission as a string - `edits`: the edits from W&I: - `start`: start indexes of each edit as a list of integers - `end`: end indexes of each edit as a list of integers - `text`: the text content of each edit as a list of strings - `from`: the original text of each edit as a list of strings ### Data Splits | name |train|validation| |----------|----:|---------:| | wi | 3000| 300| | locness | N/A| 50| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Write & Improve License: ``` Cambridge English Write & Improve (CEWI) Dataset Licence Agreement 1. By downloading this dataset and licence, this licence agreement is entered into, effective this date, between you, the Licensee, and the University of Cambridge, the Licensor. 2. Copyright of the entire licensed dataset is held by the Licensor. No ownership or interest in the dataset is transferred to the Licensee. 3. The Licensor hereby grants the Licensee a non-exclusive non-transferable right to use the licensed dataset for non-commercial research and educational purposes. 4. Non-commercial purposes exclude without limitation any use of the licensed dataset or information derived from the dataset for or as part of a product or service which is sold, offered for sale, licensed, leased or rented. 5. The Licensee shall acknowledge use of the licensed dataset in all publications of research based on it, in whole or in part, through citation of the following publication: Helen Yannakoudakis, Øistein E. Andersen, Ardeshir Geranpayeh, Ted Briscoe and Diane Nicholls. 2018. Developing an automated writing placement system for ESL learners. Applied Measurement in Education. 6. The Licensee may publish excerpts of less than 100 words from the licensed dataset pursuant to clause 3. 7. The Licensor grants the Licensee this right to use the licensed dataset "as is". Licensor does not make, and expressly disclaims, any express or implied warranties, representations or endorsements of any kind whatsoever. 8. This Agreement shall be governed by and construed in accordance with the laws of England and the English courts shall have exclusive jurisdiction. ``` LOCNESS License: ``` LOCNESS Dataset Licence Agreement 1. The corpus is to be used for non-commercial purposes only 2. All publications on research partly or wholly based on the corpus should give credit to the Centre for English Corpus Linguistics (CECL), Université catholique de Louvain, Belgium. A scanned copy or offprint of the publication should also be sent to <[email protected]>. 3. No part of the corpus is to be distributed to a third party without specific authorization from CECL. The corpus can only be used by the person agreeing to the licence terms and researchers working in close collaboration with him/her or students under his/her supervision, attached to the same institution, within the framework of the research project. ``` ### Citation Information ``` @inproceedings{bryant-etal-2019-bea, title = "The {BEA}-2019 Shared Task on Grammatical Error Correction", author = "Bryant, Christopher and Felice, Mariano and Andersen, {\O}istein E. and Briscoe, Ted", booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications", month = aug, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/W19-4406", doi = "10.18653/v1/W19-4406", pages = "52--75", abstract = "This paper reports on the BEA-2019 Shared Task on Grammatical Error Correction (GEC). As with the CoNLL-2014 shared task, participants are required to correct all types of errors in test data. One of the main contributions of the BEA-2019 shared task is the introduction of a new dataset, the Write{\&}Improve+LOCNESS corpus, which represents a wider range of native and learner English levels and abilities. Another contribution is the introduction of tracks, which control the amount of annotated data available to participants. Systems are evaluated in terms of ERRANT F{\_}0.5, which allows us to report a much wider range of performance statistics. The competition was hosted on Codalab and remains open for further submissions on the blind test set.", } ``` ### Contributions Thanks to [@aseifert](https://github.com/aseifert) for adding this dataset.
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aslg_pc12
null
2023-04-05T09:36:28Z
469
3
[ "task_categories:translation", "annotations_creators:crowdsourced", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:translation", "size_categories:10K<n<100K", "source_datasets:original", "language:ase", "language:en", "license:cc-by-nc-4.0", "region:us" ]
[ "translation" ]
2022-03-02T23:29:22Z
--- annotations_creators: - crowdsourced - expert-generated language: - ase - en language_creators: - found license: - cc-by-nc-4.0 multilinguality: - translation pretty_name: English-ASL Gloss Parallel Corpus 2012 size_categories: - 10K<n<100K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: aslg-pc12 dataset_info: features: - name: gloss dtype: string - name: text dtype: string splits: - name: train num_bytes: 13497111 num_examples: 87710 download_size: 12773431 dataset_size: 13497111 --- # Dataset Card for "aslg_pc12" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://achrafothman.net/site/asl-smt/](https://achrafothman.net/site/asl-smt/) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 12.77 MB - **Size of the generated dataset:** 13.50 MB - **Total amount of disk used:** 26.27 MB ### Dataset Summary Synthetic English-ASL Gloss Parallel Corpus 2012 ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 12.77 MB - **Size of the generated dataset:** 13.50 MB - **Total amount of disk used:** 26.27 MB An example of 'train' looks as follows. ``` { "gloss": "WRITE STATEMENT AND DESC-ORAL QUESTION TABLE SEE MINUTE\n", "text": "written statements and oral questions tabling see minutes\n" } ``` ### Data Fields The data fields are the same among all splits. #### default - `gloss`: a `string` feature. - `text`: a `string` feature. ### Data Splits | name |train| |-------|----:| |default|87710| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{othman2012english, title={English-asl gloss parallel corpus 2012: Aslg-pc12}, author={Othman, Achraf and Jemni, Mohamed}, booktitle={5th Workshop on the Representation and Processing of Sign Languages: Interactions between Corpus and Lexicon LREC}, year={2012} } ``` ### Contributions Thanks to [@AmitMY](https://github.com/AmitMY) for adding this dataset.
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osunlp/ConflictQA
osunlp
2023-11-07T13:53:26Z
468
6
[ "task_categories:question-answering", "size_categories:10K<n<100K", "language:en", "license:apache-2.0", "arxiv:2305.13300", "region:us" ]
[ "question-answering" ]
2023-06-03T13:09:23Z
--- license: apache-2.0 task_categories: - question-answering language: - en pretty_name: conflictQA size_categories: - 10K<n<100K configs: - config_name: ConflictQA-popQA-chatgpt data_files: - split: test path: "./conflictQA-popQA-chatgpt.json" --- # Dataset Card for ConflcitQA ## Dataset Description - **Repository:** https://github.com/OSU-NLP-Group/LLM-Knowledge-Conflict - **Paper:** https://arxiv.org/abs/2305.13300 - **Point of Contact:** Point of Contact: [Jian Xie](mailto:[email protected]) ## Citation If our paper or related resources prove valuable to your research, we kindly ask for citation. Please feel free to contact us with any inquiries. ```bib @article{Xie2023KnowledgeConflict, title={Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflicts}, author={Xie, Jian and Zhang, Kai and Chen, Jiangjie and Lou, Renze and Su, Yu}, journal={arXiv preprint arXiv:2305.13300}, year={2023} } ``` # ConflcitQA We provide conflictQA-[based large language model], which utilizes large language models guided parametric memory. ```json {"question": "What is George Rankin's occupation?", "popularity": 142, "ground_truth": ["politician", "political leader", "political figure", "polit.", "pol"], "memory_answer": "George Rankin's occupation is a professional photographer.", "parametric_memory": "As a professional photographer, George Rankin...", "counter_answer": "George Rankin's occupation is political figure.", "counter_memory": "George Rankin has been actively involved in politics for over a decade...", "parametric_memory_aligned_evidence": "George Rankin has a website showcasing his photography portfolio...", "counter_memory_aligned_evidence": "George Rankin Major General George James Rankin..."} ``` ```python #loading dataset from datasets import load_dataset # you can choose dataset "ConflictQA-popQA-[PLACEHOLDER]", and the [PLACEHOLDER] is in ["chatgpt","gpt4","palm2","llama2-7b","llama2-70b","qwen7b","vicuna7b","vicuna33b"]. dataset = load_dataset("osunlp/ConflictQA",'ConflictQA-popQA-chatgpt') ``` # Data Fields - "question": The question in natural language - "popularity": The monthly page views on Wikipedia for the given question - "ground_truth": The factual answer to the question, which may include multiple possible answers - "memory_answer": The answer provided by the LLM to the question - "parametric_memory": The supportive evidence from LLM's parametric memory for the answer - "counter_answer": The answer contradicting the "memory_answer" - "counter_memory": The generation-based evidence supporting the counter_answer - "parametric_memory_aligned_evidence": Additional evidence supporting the "memory_answer", which could be generated or derived from Wikipedia/human annotation - "counter_memory_aligned_evidence": Additional evidence supporting the "counter_answer", either generated or sourced from Wikipedia/human annotation
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mlabonne/CodeLlama-2-20k
mlabonne
2023-07-30T10:45:33Z
468
9
[ "task_categories:text-generation", "language:en", "license:cc-by-4.0", "code", "region:us" ]
[ "text-generation" ]
2023-07-20T11:13:42Z
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 9551210 num_examples: 20022 download_size: 3551225 dataset_size: 9551210 license: cc-by-4.0 task_categories: - text-generation language: - en tags: - code --- # CodeLlama-2-20k: A Llama 2 Version of CodeAlpaca This dataset is the [`sahil2801/CodeAlpaca-20k`](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k) dataset with the Llama 2 prompt format [described here](https://huggingface.co/blog/llama2#how-to-prompt-llama-2). Here is the code I used to format it: ``` python from datasets import load_dataset # Load the dataset dataset = load_dataset('sahil2801/CodeAlpaca-20k') # Define a function to merge the three columns into one def merge_columns(example): if example['input']: merged = f"<s>[INST] <<SYS>>\nBelow is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n<</SYS>>\n\n{example['instruction']} Input: {example['input']} [/INST] {example['output']} </s>" else: merged = f"<s>[INST] <<SYS>>\nBelow is an instruction that describes a task. Write a response that appropriately completes the request.\n<</SYS>>\n\n{example['instruction']} [/INST] {example['output']} </s>" return {"text": merged} # Apply the function to all elements in the dataset dataset = dataset.map(merge_columns, remove_columns=['instruction', 'input', 'output']) ```
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lmqg/qg_squad
lmqg
2022-12-02T18:51:10Z
465
5
[ "task_categories:text-generation", "task_ids:language-modeling", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:squad", "language:en", "license:cc-by-4.0", "question-generation", "arxiv:2210.03992", "arxiv:1705.00106", "region:us" ]
[ "text-generation" ]
2022-03-02T23:29:22Z
--- license: cc-by-4.0 pretty_name: SQuAD for question generation language: en multilinguality: monolingual size_categories: 10K<n<100K source_datasets: squad task_categories: - text-generation task_ids: - language-modeling tags: - question-generation --- # Dataset Card for "lmqg/qg_squad" ## Dataset Description - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) - **Point of Contact:** [Asahi Ushio](http://asahiushio.com/) ### Dataset Summary This is a subset of [QG-Bench](https://github.com/asahi417/lm-question-generation/blob/master/QG_BENCH.md#datasets), a unified question generation benchmark proposed in ["Generative Language Models for Paragraph-Level Question Generation: A Unified Benchmark and Evaluation, EMNLP 2022 main conference"](https://arxiv.org/abs/2210.03992). This is [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) dataset for question generation (QG) task. The split of train/development/test set follows the ["Neural Question Generation"](https://arxiv.org/abs/1705.00106) work and is compatible with the [leader board](https://paperswithcode.com/sota/question-generation-on-squad11). ### Supported Tasks and Leaderboards * `question-generation`: The dataset is assumed to be used to train a model for question generation. Success on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail). This task has an active leaderboard which can be found at [here](https://paperswithcode.com/sota/question-generation-on-squad11). ### Languages English (en) ## Dataset Structure An example of 'train' looks as follows. ``` { "question": "What is heresy mainly at odds with?", "paragraph": "Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs. A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy, which is the explicit renunciation of one's religion, principles or cause, and blasphemy, which is an impious utterance or action concerning God or sacred things.", "answer": "established beliefs or customs", "sentence": "Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs .", "paragraph_sentence": "<hl> Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs . <hl> A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy, which is the explicit renunciation of one's religion, principles or cause, and blasphemy, which is an impious utterance or action concerning God or sacred things.", "paragraph_answer": "Heresy is any provocative belief or theory that is strongly at variance with <hl> established beliefs or customs <hl>. A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy, which is the explicit renunciation of one's religion, principles or cause, and blasphemy, which is an impious utterance or action concerning God or sacred things.", "sentence_answer": "Heresy is any provocative belief or theory that is strongly at variance with <hl> established beliefs or customs <hl> ." } ``` The data fields are the same among all splits. - `question`: a `string` feature. - `paragraph`: a `string` feature. - `answer`: a `string` feature. - `sentence`: a `string` feature. - `paragraph_answer`: a `string` feature, which is same as the paragraph but the answer is highlighted by a special token `<hl>`. - `paragraph_sentence`: a `string` feature, which is same as the paragraph but a sentence containing the answer is highlighted by a special token `<hl>`. - `sentence_answer`: a `string` feature, which is same as the sentence but the answer is highlighted by a special token `<hl>`. Each of `paragraph_answer`, `paragraph_sentence`, and `sentence_answer` feature is assumed to be used to train a question generation model, but with different information. The `paragraph_answer` and `sentence_answer` features are for answer-aware question generation and `paragraph_sentence` feature is for sentence-aware question generation. ## Data Splits |train|validation|test | |----:|---------:|----:| |75722| 10570|11877| ## Citation Information ``` @inproceedings{ushio-etal-2022-generative, title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", author = "Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, U.A.E.", publisher = "Association for Computational Linguistics", } ```
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gradio/NYC-Airbnb-Open-Data
gradio
2022-10-09T05:31:38Z
465
2
[ "license:afl-3.0", "region:us" ]
null
2022-10-09T05:31:18Z
--- license: afl-3.0 ---
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jigsaw_unintended_bias
null
2023-01-25T14:33:20Z
464
3
[ "task_categories:text-classification", "task_ids:text-scoring", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:original", "language:en", "license:cc0-1.0", "toxicity-prediction", "region:us" ]
[ "text-classification" ]
2022-03-02T23:29:22Z
--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - cc0-1.0 multilinguality: - monolingual size_categories: - 1M<n<10M source_datasets: - original task_categories: - text-classification task_ids: - text-scoring pretty_name: Jigsaw Unintended Bias in Toxicity Classification tags: - toxicity-prediction dataset_info: features: - name: target dtype: float32 - name: comment_text dtype: string - name: severe_toxicity dtype: float32 - name: obscene dtype: float32 - name: identity_attack dtype: float32 - name: insult dtype: float32 - name: threat dtype: float32 - name: asian dtype: float32 - name: atheist dtype: float32 - name: bisexual dtype: float32 - name: black dtype: float32 - name: buddhist dtype: float32 - name: christian dtype: float32 - name: female dtype: float32 - name: heterosexual dtype: float32 - name: hindu dtype: float32 - name: homosexual_gay_or_lesbian dtype: float32 - name: intellectual_or_learning_disability dtype: float32 - name: jewish dtype: float32 - name: latino dtype: float32 - name: male dtype: float32 - name: muslim dtype: float32 - name: other_disability dtype: float32 - name: other_gender dtype: float32 - name: other_race_or_ethnicity dtype: float32 - name: other_religion dtype: float32 - name: other_sexual_orientation dtype: float32 - name: physical_disability dtype: float32 - name: psychiatric_or_mental_illness dtype: float32 - name: transgender dtype: float32 - name: white dtype: float32 - name: created_date dtype: string - name: publication_id dtype: int32 - name: parent_id dtype: float32 - name: article_id dtype: int32 - name: rating dtype: class_label: names: '0': rejected '1': approved - name: funny dtype: int32 - name: wow dtype: int32 - name: sad dtype: int32 - name: likes dtype: int32 - name: disagree dtype: int32 - name: sexual_explicit dtype: float32 - name: identity_annotator_count dtype: int32 - name: toxicity_annotator_count dtype: int32 splits: - name: train num_bytes: 914264058 num_examples: 1804874 - name: test_private_leaderboard num_bytes: 49188921 num_examples: 97320 - name: test_public_leaderboard num_bytes: 49442360 num_examples: 97320 download_size: 0 dataset_size: 1012895339 --- # Dataset Card for Jigsaw Unintended Bias in Toxicity Classification ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification - **Repository:** - **Paper:** - **Leaderboard:** https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/leaderboard - **Point of Contact:** ### Dataset Summary The Jigsaw Unintended Bias in Toxicity Classification dataset comes from the eponymous Kaggle competition. Please see the original [data](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data) description for more information. ### Supported Tasks and Leaderboards The main target for this dataset is toxicity prediction. Several toxicity subtypes are also available, so the dataset can be used for multi-attribute prediction. See the original [leaderboard](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/leaderboard) for reference. ### Languages English ## Dataset Structure ### Data Instances A data point consists of an id, a comment, the main target, the other toxicity subtypes as well as identity attributes. For instance, here's the first train example. ``` { "article_id": 2006, "asian": NaN, "atheist": NaN, "bisexual": NaN, "black": NaN, "buddhist": NaN, "christian": NaN, "comment_text": "This is so cool. It's like, 'would you want your mother to read this??' Really great idea, well done!", "created_date": "2015-09-29 10:50:41.987077+00", "disagree": 0, "female": NaN, "funny": 0, "heterosexual": NaN, "hindu": NaN, "homosexual_gay_or_lesbian": NaN, "identity_annotator_count": 0, "identity_attack": 0.0, "insult": 0.0, "intellectual_or_learning_disability": NaN, "jewish": NaN, "latino": NaN, "likes": 0, "male": NaN, "muslim": NaN, "obscene": 0.0, "other_disability": NaN, "other_gender": NaN, "other_race_or_ethnicity": NaN, "other_religion": NaN, "other_sexual_orientation": NaN, "parent_id": NaN, "physical_disability": NaN, "psychiatric_or_mental_illness": NaN, "publication_id": 2, "rating": 0, "sad": 0, "severe_toxicity": 0.0, "sexual_explicit": 0.0, "target": 0.0, "threat": 0.0, "toxicity_annotator_count": 4, "transgender": NaN, "white": NaN, "wow": 0 } ``` ### Data Fields - `id`: id of the comment - `target`: value between 0(non-toxic) and 1(toxic) classifying the comment - `comment_text`: the text of the comment - `severe_toxicity`: value between 0(non-severe_toxic) and 1(severe_toxic) classifying the comment - `obscene`: value between 0(non-obscene) and 1(obscene) classifying the comment - `identity_attack`: value between 0(non-identity_hate) or 1(identity_hate) classifying the comment - `insult`: value between 0(non-insult) or 1(insult) classifying the comment - `threat`: value between 0(non-threat) and 1(threat) classifying the comment - For a subset of rows, columns containing whether the comment mentions the entities (they may contain NaNs): - `male` - `female` - `transgender` - `other_gender` - `heterosexual` - `homosexual_gay_or_lesbian` - `bisexual` - `other_sexual_orientation` - `christian` - `jewish` - `muslim` - `hindu` - `buddhist` - `atheist` - `other_religion` - `black` - `white` - `asian` - `latino` - `other_race_or_ethnicity` - `physical_disability` - `intellectual_or_learning_disability` - `psychiatric_or_mental_illness` - `other_disability` - Other metadata related to the source of the comment, such as creation date, publication id, number of likes, number of annotators, etc: - `created_date` - `publication_id` - `parent_id` - `article_id` - `rating` - `funny` - `wow` - `sad` - `likes` - `disagree` - `sexual_explicit` - `identity_annotator_count` - `toxicity_annotator_count` ### Data Splits There are four splits: - train: The train dataset as released during the competition. Contains labels and identity information for a subset of rows. - test: The train dataset as released during the competition. Does not contain labels nor identity information. - test_private_expanded: The private leaderboard test set, including toxicity labels and subgroups. The competition target was a binarized version of the toxicity column, which can be easily reconstructed using a >=0.5 threshold. - test_public_expanded: The public leaderboard test set, including toxicity labels and subgroups. The competition target was a binarized version of the toxicity column, which can be easily reconstructed using a >=0.5 threshold. ## Dataset Creation ### Curation Rationale The dataset was created to help in efforts to identify and curb instances of toxicity online. ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information This dataset is released under CC0, as is the underlying comment text. ### Citation Information No citation is available for this dataset, though you may link to the [kaggle](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification) competition ### Contributions Thanks to [@iwontbecreative](https://github.com/iwontbecreative) for adding this dataset.
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open-llm-leaderboard/details_lgaalves__llama-2-13b-chat-platypus
open-llm-leaderboard
2023-10-27T20:28:08Z
464
0
[ "region:us" ]
null
2023-09-12T04:55:12Z
--- pretty_name: Evaluation run of lgaalves/llama-2-13b-chat-platypus dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [lgaalves/llama-2-13b-chat-platypus](https://huggingface.co/lgaalves/llama-2-13b-chat-platypus)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_lgaalves__llama-2-13b-chat-platypus\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-27T20:27:56.260953](https://huggingface.co/datasets/open-llm-leaderboard/details_lgaalves__llama-2-13b-chat-platypus/blob/main/results_2023-10-27T20-27-56.260953.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0035654362416107383,\n\ \ \"em_stderr\": 0.0006104082299890483,\n \"f1\": 0.06259542785234914,\n\ \ \"f1_stderr\": 0.001452272347431231,\n \"acc\": 0.44182080490769055,\n\ \ \"acc_stderr\": 0.010533564468131328\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.0035654362416107383,\n \"em_stderr\": 0.0006104082299890483,\n\ \ \"f1\": 0.06259542785234914,\n \"f1_stderr\": 0.001452272347431231\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.12357846853677028,\n \ \ \"acc_stderr\": 0.009065050306776914\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7600631412786109,\n \"acc_stderr\": 0.01200207862948574\n\ \ }\n}\n```" repo_url: https://huggingface.co/lgaalves/llama-2-13b-chat-platypus leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|arc:challenge|25_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-09-12T04-54-55.763898.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_27T20_27_56.260953 path: - '**/details_harness|drop|3_2023-10-27T20-27-56.260953.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-27T20-27-56.260953.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_27T20_27_56.260953 path: - '**/details_harness|gsm8k|5_2023-10-27T20-27-56.260953.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-27T20-27-56.260953.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hellaswag|10_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T04-54-55.763898.parquet' - 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'**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-12T04-54-55.763898.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-management|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-virology|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-12T04-54-55.763898.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_12T04_54_55.763898 path: - '**/details_harness|truthfulqa:mc|0_2023-09-12T04-54-55.763898.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-09-12T04-54-55.763898.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_27T20_27_56.260953 path: - '**/details_harness|winogrande|5_2023-10-27T20-27-56.260953.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-27T20-27-56.260953.parquet' - config_name: results data_files: - split: 2023_09_12T04_54_55.763898 path: - results_2023-09-12T04-54-55.763898.parquet - split: 2023_10_27T20_27_56.260953 path: - results_2023-10-27T20-27-56.260953.parquet - split: latest path: - results_2023-10-27T20-27-56.260953.parquet --- # Dataset Card for Evaluation run of lgaalves/llama-2-13b-chat-platypus ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/lgaalves/llama-2-13b-chat-platypus - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [lgaalves/llama-2-13b-chat-platypus](https://huggingface.co/lgaalves/llama-2-13b-chat-platypus) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_lgaalves__llama-2-13b-chat-platypus", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-27T20:27:56.260953](https://huggingface.co/datasets/open-llm-leaderboard/details_lgaalves__llama-2-13b-chat-platypus/blob/main/results_2023-10-27T20-27-56.260953.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.0035654362416107383, "em_stderr": 0.0006104082299890483, "f1": 0.06259542785234914, "f1_stderr": 0.001452272347431231, "acc": 0.44182080490769055, "acc_stderr": 0.010533564468131328 }, "harness|drop|3": { "em": 0.0035654362416107383, "em_stderr": 0.0006104082299890483, "f1": 0.06259542785234914, "f1_stderr": 0.001452272347431231 }, "harness|gsm8k|5": { "acc": 0.12357846853677028, "acc_stderr": 0.009065050306776914 }, "harness|winogrande|5": { "acc": 0.7600631412786109, "acc_stderr": 0.01200207862948574 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
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wongnai_reviews
null
2023-01-25T15:02:56Z
463
2
[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:th", "license:lgpl-3.0", "region:us" ]
[ "text-classification" ]
2022-03-02T23:29:22Z
--- annotations_creators: - found language_creators: - found language: - th license: - lgpl-3.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification pretty_name: WongnaiReviews dataset_info: features: - name: review_body dtype: string - name: star_rating dtype: class_label: names: '0': '1' '1': '2' '2': '3' '3': '4' '4': '5' splits: - name: train num_bytes: 60691428 num_examples: 40000 - name: test num_bytes: 9913686 num_examples: 6203 download_size: 16556587 dataset_size: 70605114 --- # Dataset Card for Wongnai_Reviews ## Dataset Description - **Repository:** https://github.com/wongnai/wongnai-corpus ### Dataset Summary The Wongnai Review dataset contains restaurant reviews and ratings, almost entirely in Thai language. The reviews are in 5 classes ranging from 1 to 5 stars. This dataset was featured in a Kaggle challenge https://www.kaggle.com/c/wongnai-challenge-review-rating-prediction/overview ### Languages Thai ## Dataset Structure ### Data Fields - review_body - text of review - star_rating - an integer star rating (1-5) or -1 (for test) ### Data Splits Designated train (40,000 reviews) and test (6,204) sets. ### Source Data #### Initial Data Collection and Normalization Data was collected by Wongnai from business reviews on their website, and shared on GitHub and Kaggle. ### Annotations The reviews are users' own star ratings, so no additional annotation was needed. ## Additional Information ### Dataset Curators Contributors to original GitHub repo: - Ekkalak Thongthanomkul - Tanapol Nearunchorn - Yuwat Chuesathuchon ### Licensing Information LGPL-3.0 ### Citation Information See https://github.com/wongnai/wongnai-corpus ### Contributions Thanks to [@mapmeld](https://github.com/mapmeld), [@cstorm125](https://github.com/cstorm125) for adding this dataset.
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jordyvl/rvl_cdip_100_examples_per_class
jordyvl
2023-03-23T20:55:18Z
463
0
[ "region:us" ]
null
2023-03-23T19:58:02Z
--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': letter '1': form '2': email '3': handwritten '4': advertisement '5': scientific report '6': scientific publication '7': specification '8': file folder '9': news article '10': budget '11': invoice '12': presentation '13': questionnaire '14': resume '15': memo splits: - name: train num_bytes: 97000316.76 num_examples: 800 - name: test num_bytes: 48612840.21 num_examples: 400 - name: validation num_bytes: 48666549.76 num_examples: 400 download_size: 180034173 dataset_size: 194279706.73 --- # Dataset Card for "rvl_cdip_100_examples_per_class" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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maxolotl/must-c-en-es-02
maxolotl
2023-10-22T07:47:19Z
463
0
[ "region:us" ]
null
2023-10-22T07:47:13Z
--- dataset_info: features: - name: en dtype: string - name: es dtype: string splits: - name: train num_bytes: 59874575 num_examples: 259892 - name: test num_bytes: 658214 num_examples: 3035 - name: validation num_bytes: 310157 num_examples: 1309 download_size: 37502474 dataset_size: 60842946 --- # Dataset Card for "must-c-en-es-02" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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gsarti/itacola
gsarti
2022-07-01T15:38:55Z
462
2
[ "task_categories:text-classification", "task_ids:acceptability-classification", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:unknown", "source_datasets:original", "language:it", "license:unknown", "arxiv:2109.12053", "region:us" ]
[ "text-classification" ]
2022-03-02T23:29:22Z
--- annotations_creators: - expert-generated language_creators: - expert-generated language: - it license: - unknown multilinguality: - monolingual pretty_name: itacola size_categories: - unknown source_datasets: - original task_categories: - text-classification task_ids: - acceptability-classification --- # Dataset Card for ItaCoLA ## Table of Contents - [Dataset Card for ItaCoLA](#dataset-card-for-itacola) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Acceptability Classification](#acceptability-classification) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Scores Configuration](#scores-configuration) - [Phenomena Configuration](#phenomena-configuration) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - **Repository:** [Github](https://github.com/dhfbk/ItaCoLA-dataset) - **Paper:** [Arxiv](http://ceur-ws.org/Vol-2765/paper169.pdf) - **Point of Contact:** [Daniela Trotta]([email protected]) ### Dataset Summary The Italian Corpus of Linguistic Acceptability includes almost 10k sentences taken from linguistic literature with a binary annotation made by the original authors themselves. The work is inspired by the English [Corpus of Linguistic Acceptability](https://nyu-mll.github.io/CoLA/). **Disclaimer**: *The ItaCoLA corpus is hosted on Github by the [Digital Humanities group at FBK](https://dh.fbk.eu/)*. It was introduced in the article [Monolingual and Cross-Lingual Acceptability Judgments with the Italian CoLA corpus](https://arxiv.org/abs/2109.12053) by [Daniela Trotta](https://dh.fbk.eu/author/daniela/), [Raffaele Guarasci](https://www.icar.cnr.it/persone/guarasci/), [Elisa Leonardelli](https://dh.fbk.eu/author/elisa/), [Sara Tonelli](https://dh.fbk.eu/author/sara/) ### Supported Tasks and Leaderboards #### Acceptability Classification The following table is taken from Table 4 of the original paper, where an LSTM and a BERT model pretrained on the Italian languages are fine-tuned on the `train` split of the corpus and evaluated respectively on the `test` split (*In-domain*, `in`) and on the acceptability portion of the [AcCompl-it] corpus (*Out-of-domain*, `out`). Models are evaluated with accuracy (*Acc.*) and Matthews Correlation Coefficient (*MCC*) in both settings. Results are averaged over 10 runs with ±stdev. error bounds. | | `in`, Acc.| `in`, MCC| `out`, Acc.|`out`, MCC| |---------:|-----------:|----------:|-----------:|---------:| |`LSTM` | 0.794 | 0.278 ± 0.029 | 0.605 | 0.147 ± 0.066 | |`ITA-BERT`| 0.904 | 0.603 ± 0.022 | 0.683 | 0.198 ± 0.036 | ### Languages The language data in ItaCoLA is in Italian (BCP-47 `it`) ## Dataset Structure ### Data Instances #### Scores Configuration The `scores` configuration contains sentences with acceptability judgments. An example from the `train` split of the `scores` config (default) is provided below. ```json { "unique_id": 1, "source": "Graffi_1994", "acceptability": 1, "sentence": "Quest'uomo mi ha colpito." } ``` The text is provided as-is, without further preprocessing or tokenization. The fields are the following: - `unique_id`: Unique identifier for the sentence across configurations. - `source`: Original source for the sentence. - `acceptability`: Binary score, 1 = acceptable, 0 = not acceptable. - `sentence`: The evaluated sentence. #### Phenomena Configuration The `phenomena` configuration contains a sample of sentences from `scores` that has been manually annotated to denote the presence of 9 linguistic phenomena. An example from the `train` split is provided below: ```json { "unique_id": 1, "source": "Graffi_1994", "acceptability": 1, "sentence": "Quest'uomo mi ha colpito.", "cleft_construction": 0, "copular_construction": 0, "subject_verb_agreement": 1, "wh_islands_violations": 0, "simple": 0, "question": 0, "auxiliary": 1, "bind": 0, "indefinite_pronouns": 0 } ``` For each one of the new fields, the value of the binary score denotes the presence (1) or the absence (0) of the respective phenomenon. Refer to the original paper for a detailed description of each phenomenon. ### Data Splits | config| train| test| |----------:|-----:|----:| |`scores` | 7801 | 975 | |`phenomena`| 2088 | - | ### Dataset Creation Please refer to the original article [Monolingual and Cross-Lingual Acceptability Judgments with the Italian CoLA corpus](https://arxiv.org/abs/2109.12053) for additional information on dataset creation. ## Additional Information ### Dataset Curators The authors are the curators of the original dataset. For problems or updates on this 🤗 Datasets version, please contact [[email protected]](mailto:[email protected]). ### Licensing Information No licensing information available. ### Citation Information Please cite the authors if you use these corpora in your work: ```bibtex @inproceedings{trotta-etal-2021-monolingual-cross, title = "Monolingual and Cross-Lingual Acceptability Judgments with the {I}talian {C}o{LA} corpus", author = "Trotta, Daniela and Guarasci, Raffaele and Leonardelli, Elisa and Tonelli, Sara", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021", month = nov, year = "2021", address = "Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-emnlp.250", doi = "10.18653/v1/2021.findings-emnlp.250", pages = "2929--2940" } ```
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imnaveenk/earrings
imnaveenk
2023-06-14T04:50:46Z
462
0
[ "region:us" ]
null
2023-06-13T08:57:22Z
--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 107545898.846 num_examples: 1626 download_size: 91556390 dataset_size: 107545898.846 --- # Dataset Card for "earrings" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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open-llm-leaderboard/details_julianweng__Llama-2-7b-chat-orcah
open-llm-leaderboard
2023-09-17T17:33:16Z
462
0
[ "region:us" ]
null
2023-08-18T11:19:00Z
--- pretty_name: Evaluation run of julianweng/Llama-2-7b-chat-orcah dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [julianweng/Llama-2-7b-chat-orcah](https://huggingface.co/julianweng/Llama-2-7b-chat-orcah)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_julianweng__Llama-2-7b-chat-orcah\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-17T17:33:03.536328](https://huggingface.co/datasets/open-llm-leaderboard/details_julianweng__Llama-2-7b-chat-orcah/blob/main/results_2023-09-17T17-33-03.536328.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.02936241610738255,\n\ \ \"em_stderr\": 0.0017288770032803159,\n \"f1\": 0.07552432885906037,\n\ \ \"f1_stderr\": 0.0020587215501161925,\n \"acc\": 0.3737288120380116,\n\ \ \"acc_stderr\": 0.00900957367793152\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.02936241610738255,\n \"em_stderr\": 0.0017288770032803159,\n\ \ \"f1\": 0.07552432885906037,\n \"f1_stderr\": 0.0020587215501161925\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.03790750568612585,\n \ \ \"acc_stderr\": 0.005260333907798431\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7095501183898973,\n \"acc_stderr\": 0.01275881344806461\n\ \ }\n}\n```" repo_url: https://huggingface.co/julianweng/Llama-2-7b-chat-orcah leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|arc:challenge|25_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-07-24T11:44:40.236710.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_17T17_33_03.536328 path: - '**/details_harness|drop|3_2023-09-17T17-33-03.536328.parquet' - split: latest path: - '**/details_harness|drop|3_2023-09-17T17-33-03.536328.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_17T17_33_03.536328 path: - '**/details_harness|gsm8k|5_2023-09-17T17-33-03.536328.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-09-17T17-33-03.536328.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hellaswag|10_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-24T11:44:40.236710.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-management|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-virology|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T11:44:40.236710.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_24T11_44_40.236710 path: - '**/details_harness|truthfulqa:mc|0_2023-07-24T11:44:40.236710.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-07-24T11:44:40.236710.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_17T17_33_03.536328 path: - '**/details_harness|winogrande|5_2023-09-17T17-33-03.536328.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-09-17T17-33-03.536328.parquet' - config_name: results data_files: - split: 2023_07_24T11_44_40.236710 path: - results_2023-07-24T11:44:40.236710.parquet - split: 2023_09_17T17_33_03.536328 path: - results_2023-09-17T17-33-03.536328.parquet - split: latest path: - results_2023-09-17T17-33-03.536328.parquet --- # Dataset Card for Evaluation run of julianweng/Llama-2-7b-chat-orcah ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/julianweng/Llama-2-7b-chat-orcah - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [julianweng/Llama-2-7b-chat-orcah](https://huggingface.co/julianweng/Llama-2-7b-chat-orcah) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_julianweng__Llama-2-7b-chat-orcah", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-17T17:33:03.536328](https://huggingface.co/datasets/open-llm-leaderboard/details_julianweng__Llama-2-7b-chat-orcah/blob/main/results_2023-09-17T17-33-03.536328.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.02936241610738255, "em_stderr": 0.0017288770032803159, "f1": 0.07552432885906037, "f1_stderr": 0.0020587215501161925, "acc": 0.3737288120380116, "acc_stderr": 0.00900957367793152 }, "harness|drop|3": { "em": 0.02936241610738255, "em_stderr": 0.0017288770032803159, "f1": 0.07552432885906037, "f1_stderr": 0.0020587215501161925 }, "harness|gsm8k|5": { "acc": 0.03790750568612585, "acc_stderr": 0.005260333907798431 }, "harness|winogrande|5": { "acc": 0.7095501183898973, "acc_stderr": 0.01275881344806461 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
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coarse_discourse
null
2023-04-05T10:01:55Z
461
3
[ "task_categories:text-classification", "task_ids:multi-class-classification", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:cc-by-4.0", "region:us" ]
[ "text-classification" ]
2022-03-02T23:29:22Z
--- annotations_creators: - crowdsourced language: - en language_creators: - found license: - cc-by-4.0 multilinguality: - monolingual pretty_name: Coarse Discourse size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification paperswithcode_id: coarse-discourse dataset_info: features: - name: title dtype: string - name: is_self_post dtype: bool - name: subreddit dtype: string - name: url dtype: string - name: majority_link dtype: string - name: is_first_post dtype: bool - name: majority_type dtype: string - name: id_post dtype: string - name: post_depth dtype: int32 - name: in_reply_to dtype: string - name: annotations sequence: - name: annotator dtype: string - name: link_to_post dtype: string - name: main_type dtype: string splits: - name: train num_bytes: 45443464 num_examples: 116357 download_size: 4636201 dataset_size: 45443464 --- # Dataset Card for "coarse_discourse" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** https://github.com/google-research-datasets/coarse-discourse - **Paper:** [Characterizing Online Discussion Using Coarse Discourse Sequences](https://research.google/pubs/pub46055/) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 4.63 MB - **Size of the generated dataset:** 45.45 MB - **Total amount of disk used:** 50.08 MB ### Dataset Summary A large corpus of discourse annotations and relations on ~10K forum threads. We collect and release a corpus of over 9,000 threads comprising over 100,000 comments manually annotated via paid crowdsourcing with discourse acts and randomly sampled from the site Reddit. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 4.63 MB - **Size of the generated dataset:** 45.45 MB - **Total amount of disk used:** 50.08 MB An example of 'train' looks as follows. ``` { "annotations": { "annotator": ["fc96a15ab87f02dd1998ff55a64f6478", "e9e4b3ab355135fa954badcc06bfccc6", "31ac59c1734c1547d4d0723ff254c247"], "link_to_post": ["", "", ""], "main_type": ["elaboration", "elaboration", "elaboration"] }, "id_post": "t1_c9b30i1", "in_reply_to": "t1_c9b2nyd", "is_first_post": false, "is_self_post": true, "majority_link": "t1_c9b2nyd", "majority_type": "elaboration", "post_depth": 2, "subreddit": "100movies365days", "title": "DTX120: #87 - Nashville", "url": "https://www.reddit.com/r/100movies365days/comments/1bx6qw/dtx120_87_nashville/" } ``` ### Data Fields The data fields are the same among all splits. #### default - `title`: a `string` feature. - `is_self_post`: a `bool` feature. - `subreddit`: a `string` feature. - `url`: a `string` feature. - `majority_link`: a `string` feature. - `is_first_post`: a `bool` feature. - `majority_type`: a `string` feature. - `id_post`: a `string` feature. - `post_depth`: a `int32` feature. - `in_reply_to`: a `string` feature. - `annotations`: a dictionary feature containing: - `annotator`: a `string` feature. - `link_to_post`: a `string` feature. - `main_type`: a `string` feature. ### Data Splits | name |train | |-------|-----:| |default|116357| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{coarsediscourse, title={Characterizing Online Discussion Using Coarse Discourse Sequences}, author={Zhang, Amy X. and Culbertson, Bryan and Paritosh, Praveen}, booktitle={Proceedings of the 11th International AAAI Conference on Weblogs and Social Media}, series={ICWSM '17}, year={2017}, location = {Montreal, Canada} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@jplu](https://github.com/jplu) for adding this dataset.
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code_x_glue_cc_code_completion_token
null
2023-06-12T08:13:31Z
461
1
[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:code", "license:c-uda", "region:us" ]
[ "text-generation", "fill-mask" ]
2022-03-02T23:29:22Z
--- annotations_creators: - found language_creators: - found language: - code license: - c-uda multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling pretty_name: CodeXGlueCcCodeCompletionToken dataset_info: - config_name: java features: - name: id dtype: int32 - name: code sequence: string splits: - name: train num_bytes: 128312061 num_examples: 12934 - name: validation num_bytes: 30259174 num_examples: 7189 - name: test num_bytes: 43027956 num_examples: 8268 download_size: 126856519 dataset_size: 201599191 - config_name: python features: - name: id dtype: int32 - name: path dtype: string - name: code sequence: string splits: - name: train num_bytes: 684319575 num_examples: 100000 - name: test num_bytes: 333978088 num_examples: 50000 download_size: 199067128 dataset_size: 1018297663 --- # Dataset Card for "code_x_glue_cc_code_completion_token" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits-sample-size) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/CodeCompletion-token ### Dataset Summary CodeXGLUE CodeCompletion-token dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/CodeCompletion-token Predict next code token given context of previous tokens. Models are evaluated by token level accuracy. Code completion is a one of the most widely used features in software development through IDEs. An effective code completion tool could improve software developers' productivity. We provide code completion evaluation tasks in two granularities -- token level and line level. Here we introduce token level code completion. Token level task is analogous to language modeling. Models should have be able to predict the next token in arbitary types. ### Supported Tasks and Leaderboards - `language-modeling`: The dataset can be used to train a model for completing single code tokens. ### Languages - Java **programming** language - Python **programming** language ## Dataset Structure ### Data Instances #### java An example of 'test' looks as follows. ``` { "code": ["<s>", "package", "org", ".", "vaadin", ".", "teemu", ".", "clara", ".", "demo", ";", "import", "java", ".", "io", ".", "BufferedReader", ";", "import", "java", ".", "io", ".", "ByteArrayInputStream", ";", "import", "java", ".", "io", ".", "IOException", ";", "import", "java", ".", "io", ".", "InputStreamReader", ";", "import", "org", ".", "vaadin", ".", "teemu", ".", "clara", ".", "Clara", ";", "import", "org", ".", "vaadin", ".", "teemu", ".", "clara", ".", "inflater", ".", "LayoutInflaterException", ";", "import", "com", ".", "vaadin", ".", "Application", ";", "import", "com", ".", "vaadin", ".", "terminal", ".", "ThemeResource", ";", "import", "com", ".", "vaadin", ".", "ui", ".", "Button", ";", "import", "com", ".", "vaadin", ".", "ui", ".", "Button", ".", "ClickEvent", ";", "import", "com", ".", "vaadin", ".", "ui", ".", "Component", ";", "import", "com", ".", "vaadin", ".", "ui", ".", "Embedded", ";", "import", "com", ".", "vaadin", ".", "ui", ".", "HorizontalLayout", ";", "import", "com", ".", "vaadin", ".", "ui", ".", "HorizontalSplitPanel", ";", "import", "com", ".", "vaadin", ".", "ui", ".", "TextArea", ";", "import", "com", ".", "vaadin", ".", "ui", ".", "VerticalLayout", ";", "import", "com", ".", "vaadin", ".", "ui", ".", "Window", ";", "import", "com", ".", "vaadin", ".", "ui", ".", "Window", ".", "Notification", ";", "@", "SuppressWarnings", "(", "\"serial\"", ")", "public", "class", "DemoApplication", "extends", "Application", "{", "private", "DemoController", "controller", ";", "private", "TextArea", "xmlArea", ";", "private", "HorizontalSplitPanel", "split", "=", "new", "HorizontalSplitPanel", "(", ")", ";", "private", "Window", "mainWindow", ";", "@", "Override", "public", "void", "init", "(", ")", "{", "setTheme", "(", "\"clara\"", ")", ";", "setMainWindow", "(", "mainWindow", "=", "new", "Window", "(", ")", ")", ";", "controller", "=", "new", "DemoController", "(", "mainWindow", ")", ";", "mainWindow", ".", "setContent", "(", "split", ")", ";", "VerticalLayout", "editor", "=", "new", "VerticalLayout", "(", ")", ";", "editor", ".", "setSpacing", "(", "true", ")", ";", "editor", ".", "setMargin", "(", "false", ",", "false", ",", "false", ",", "true", ")", ";", "editor", ".", "setHeight", "(", "\"100%\"", ")", ";", "editor", ".", "addComponent", "(", "xmlArea", "=", "createXmlArea", "(", ")", ")", ";", "editor", ".", "setExpandRatio", "(", "xmlArea", ",", "1.0f", ")", ";", "editor", ".", "addComponent", "(", "createUpdateButton", "(", ")", ")", ";", "HorizontalLayout", "wrapper", "=", "new", "HorizontalLayout", "(", ")", ";", "wrapper", ".", "setMargin", "(", "true", ")", ";", "wrapper", ".", "setSizeFull", "(", ")", ";", "wrapper", ".", "addComponent", "(", "createLogo", "(", ")", ")", ";", "wrapper", ".", "addComponent", "(", "editor", ")", ";", "wrapper", ".", "setExpandRatio", "(", "editor", ",", "1.0f", ")", ";", "split", ".", "setFirstComponent", "(", "wrapper", ")", ";", "updateLayout", "(", ")", ";", "}", "private", "Component", "createLogo", "(", ")", "{", "Embedded", "logo", "=", "new", "Embedded", "(", "null", ",", "new", "ThemeResource", "(", "\"\"", ")", ")", ";", "logo", ".", "setHeight", "(", "\"90px\"", ")", ";", "logo", ".", "setWidth", "(", "\"90px\"", ")", ";", "return", "logo", ";", "}", "private", "TextArea", "createXmlArea", "(", ")", "{", "TextArea", "area", "=", "new", "TextArea", "(", ")", ";", "area", ".", "setStyleName", "(", "\"xml-area\"", ")", ";", "area", ".", "setSizeFull", "(", ")", ";", "area", ".", "setValue", "(", "readStartingPoint", "(", ")", ")", ";", "return", "area", ";", "}", "private", "Button", "createUpdateButton", "(", ")", "{", "return", "new", "Button", "(", "\"Update\"", ",", "new", "Button", ".", "ClickListener", "(", ")", "{", "public", "void", "buttonClick", "(", "ClickEvent", "event", ")", "{", "updateLayout", "(", ")", ";", "}", "}", ")", ";", "}", "private", "String", "readStartingPoint", "(", ")", "{", "BufferedReader", "reader", "=", "null", ";", "try", "{", "reader", "=", "new", "BufferedReader", "(", "new", "InputStreamReader", "(", "getClass", "(", ")", ".", "getClassLoader", "(", ")", ".", "getResourceAsStream", "(", "\"\"", ")", ")", ")", ";", "StringBuilder", "xml", "=", "new", "StringBuilder", "(", ")", ";", "String", "line", ";", "while", "(", "(", "line", "=", "reader", ".", "readLine", "(", ")", ")", "!=", "null", ")", "{", "xml", ".", "append", "(", "line", ")", ";", "xml", ".", "append", "(", "\"n\"", ")", ";", "}", "return", "xml", ".", "toString", "(", ")", ";", "}", "catch", "(", "IOException", "e", ")", "{", "e", ".", "printStackTrace", "(", ")", ";", "}", "finally", "{", "if", "(", "reader", "!=", "null", ")", "{", "try", "{", "reader", ".", "close", "(", ")", ";", "}", "catch", "(", "IOException", "e", ")", "{", "e", ".", "printStackTrace", "(", ")", ";", "}", "}", "}", "return", "null", ";", "}", "private", "void", "updateLayout", "(", ")", "{", "try", "{", "Component", "c", "=", "Clara", ".", "create", "(", "new", "ByteArrayInputStream", "(", "xmlArea", ".", "getValue", "(", ")", ".", "toString", "(", ")", ".", "getBytes", "(", ")", ")", ",", "controller", ")", ";", "split", ".", "replaceComponent", "(", "split", ".", "getSecondComponent", "(", ")", ",", "c", ")", ";", "}", "catch", "(", "LayoutInflaterException", "e", ")", "{", "mainWindow", ".", "showNotification", "(", "e", ".", "getMessage", "(", ")", ",", "Notification", ".", "TYPE_ERROR_MESSAGE", ")", ";", "}", "}", "}", "</s>"], "id": 0 } ``` #### python An example of 'train' looks as follows. ``` { "code": ["<s>", "from", "bootstrap", "import", "Bootstrap", "<EOL>", "from", "fund", "import", "InstantPaymentNotificationHandler", "<EOL>", "from", "fund", "import", "ThankYouHandler", "<EOL>", "from", "view", "import", "*", "<EOL>", "mapping", "=", "[", "(", "<EOL>", "r'/'", ",", "<EOL>", "Index", "<EOL>", ")", ",", "(", "<EOL>", "r'/ipn'", ",", "<EOL>", "InstantPaymentNotificationHandler", "<EOL>", ")", ",", "(", "<EOL>", "r'/thank-you'", ",", "<EOL>", "ThankYouHandler", "<EOL>", ")", ",", "(", "<EOL>", "r'/about\\/?'", ",", "<EOL>", "About", "<EOL>", ")", ",", "(", "<EOL>", "r'/guide\\/?'", ",", "<EOL>", "Guide", "<EOL>", ")", ",", "(", "<EOL>", "r''", ",", "<EOL>", "Download", "<EOL>", ")", ",", "(", "<EOL>", "r''", ",", "<EOL>", "Standards", "<EOL>", ")", ",", "(", "<EOL>", "r'/community\\/?'", ",", "<EOL>", "Community", "<EOL>", ")", ",", "(", "<EOL>", "r'/news\\/?'", ",", "<EOL>", "News", "<EOL>", ")", ",", "(", "<EOL>", "r'/support\\/?'", ",", "<EOL>", "Support", "<EOL>", ")", ",", "(", "<EOL>", "r'/contact\\/?'", ",", "<EOL>", "Contact", "<EOL>", ")", ",", "(", "<EOL>", "r'/press\\/?'", ",", "<EOL>", "Press", "<EOL>", ")", ",", "(", "<EOL>", "r'/legal/terms'", ",", "<EOL>", "Terms", "<EOL>", ")", ",", "(", "<EOL>", "r'/library\\/?'", ",", "<EOL>", "Library", "<EOL>", ")", ",", "(", "<EOL>", "r''", ",", "<EOL>", "Library", "<EOL>", ")", ",", "(", "<EOL>", "r''", ",", "<EOL>", "Library", "<EOL>", ")", ",", "(", "<EOL>", "r''", ",", "<EOL>", "Users", "<EOL>", ")", ",", "(", "<EOL>", "r''", ",", "<EOL>", "User", "<EOL>", ")", ",", "(", "<EOL>", "r''", ",", "<EOL>", "Design", "<EOL>", ")", ",", "(", "<EOL>", "r''", ",", "<EOL>", "Design", "<EOL>", ")", ",", "(", "<EOL>", "r''", ",", "<EOL>", "Design", "<EOL>", ")", ",", "(", "<EOL>", "r''", ",", "<EOL>", "Design", "<EOL>", ")", ",", "(", "<EOL>", "r''", ",", "<EOL>", "Design", "<EOL>", ")", ",", "(", "<EOL>", "r''", ",", "<EOL>", "RedirectSuccess", "<EOL>", ")", ",", "(", "<EOL>", "r''", ",", "<EOL>", "RedirectError", "<EOL>", ")", ",", "(", "<EOL>", "r''", ",", "<EOL>", "RedirectAfterDelete", "<EOL>", ")", ",", "(", "<EOL>", "r''", ",", "<EOL>", "Moderate", "<EOL>", ")", ",", "(", "<EOL>", "r''", ",", "<EOL>", "Bootstrap", "<EOL>", ")", ",", "(", "<EOL>", "r'/activity'", ",", "<EOL>", "ActivityScreen", "<EOL>", ")", ",", "(", "<EOL>", "r'/txns'", ",", "<EOL>", "TxnList", "<EOL>", ")", ",", "(", "<EOL>", "r''", ",", "<EOL>", "Base64Blob", "<EOL>", ")", ",", "(", "<EOL>", "r''", ",", "<EOL>", "Base64Blob", "<EOL>", ")", ",", "(", "<EOL>", "r''", ",", "<EOL>", "MessageStrings", "<EOL>", ")", ",", "(", "<EOL>", "r'/.*'", ",", "<EOL>", "NotFound", "<EOL>", ")", "<EOL>", "]", "</s>"], "id": 0, "path": "00/wikihouse/urls.py\n" } ``` ### Data Fields In the following each data field in go is explained for each config. The data fields are the same among all splits. #### java |field name| type | description | |----------|----------------|--------------------| |id |int32 | Index of the sample| |code |Sequence[string]| Code Tokens | #### python |field name| type | description | |----------|----------------|-----------------------------| |id |int32 | Index of the sample | |path |string | Original path in the dataset| |code |Sequence[string]| Code Tokens | ### Data Splits #### java | |train|validation|test| |----|----:|---------:|---:| |java|12934| 7189|8268| #### python | |train |test | |------|-----:|----:| |python|100000|50000| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators https://github.com/microsoft, https://github.com/madlag ### Licensing Information Computational Use of Data Agreement (C-UDA) License. ### Citation Information ``` @article{raychev2016probabilistic, title={Probabilistic Model for Code with Decision Trees}, author={Raychev, Veselin and Bielik, Pavol and Vechev, Martin}, journal={ACM SIGPLAN Notices}, pages={731--747}, year={2016}, publisher={ACM New York, NY, USA} } @inproceedings{allamanis2013mining, title={Mining Source Code Repositories at Massive Scale using Language Modeling}, author={Allamanis, Miltiadis and Sutton, Charles}, booktitle={2013 10th Working Conference on Mining Software Repositories (MSR)}, pages={207--216}, year={2013}, organization={IEEE} } ``` The data for "java" configuration comes from: ``` @dataset{rafael_michael_karampatsis_2020_3628665, author = {Rafael - Michael Karampatsis and Hlib Babii and Romain Robbes and Charles Sutton and Andrea Janes}, title = {Preprocessed Java Code Corpus}, month = jan, year = 2020, publisher = {Zenodo}, version = {1.0}, doi = {10.5281/zenodo.3628665}, url = {https://doi.org/10.5281/zenodo.3628665} } ``` ### Contributions Thanks to @madlag (and partly also @ncoop57) for adding this dataset.
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grail_qa
null
2022-11-18T20:04:54Z
461
2
[ "task_categories:question-answering", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:unknown", "knowledge-base-qa", "arxiv:2011.07743", "region:us" ]
[ "question-answering" ]
2022-03-02T23:29:22Z
--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: [] paperswithcode_id: null pretty_name: Grail QA tags: - knowledge-base-qa dataset_info: features: - name: qid dtype: string - name: question dtype: string - name: answer sequence: - name: answer_type dtype: string - name: answer_argument dtype: string - name: entity_name dtype: string - name: function dtype: string - name: num_node dtype: int32 - name: num_edge dtype: int32 - name: graph_query struct: - name: nodes sequence: - name: nid dtype: int32 - name: node_type dtype: string - name: id dtype: string - name: class dtype: string - name: friendly_name dtype: string - name: question_node dtype: int32 - name: function dtype: string - name: edges sequence: - name: start dtype: int32 - name: end dtype: int32 - name: relation dtype: string - name: friendly_name dtype: string - name: sparql_query dtype: string - name: domains sequence: string - name: level dtype: string - name: s_expression dtype: string splits: - name: train num_bytes: 69433121 num_examples: 44337 - name: validation num_bytes: 9800544 num_examples: 6763 - name: test num_bytes: 2167256 num_examples: 13231 download_size: 17636773 dataset_size: 81400921 --- # Dataset Card for Grail QA ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Grail QA](https://dki-lab.github.io/GrailQA/) - **Repository:** - **Paper:** [GrailQA paper (Gu et al. '20)](https://arxiv.org/abs/2011.07743) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary #### What is GrailQA? Strongly Generalizable Question Answering (GrailQA) is a new large-scale, high-quality dataset for question answering on knowledge bases (KBQA) on Freebase with 64,331 questions annotated with both answers and corresponding logical forms in different syntax (i.e., SPARQL, S-expression, etc.). It can be used to test three levels of generalization in KBQA: i.i.d., compositional, and zero-shot. #### Why GrailQA? GrailQA is by far the largest crowdsourced KBQA dataset with questions of high diversity (i.e., questions in GrailQA can have up to 4 relations and optionally have a function from counting, superlatives and comparatives). It also has the highest coverage over Freebase; it widely covers 3,720 relations and 86 domains from Freebase. Last but not least, our meticulous data split allows GrailQA to test not only i.i.d. generalization, but also compositional generalization and zero-shot generalization, which are critical for practical KBQA systems. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English and Graph query ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - `qid` (`str`) - `question` (`str`) - `answer` (`List`): Defaults to `[]` in test split. - `answer_type` (`str`) - `answer_argument` (`str`) - `entity_name` (`str`): Defauts to `""` if `answer_type` is not `Entity`. - `function` (`string`): Defaults to `""` in test split. - `num_node` (`int`): Defaults to `-1` in test split. - `num_edge` (`int`): Defaults to `-1` in test split. - `graph_query` (`Dict`) - `nodes` (`List`): Defaults to `[]` in test split. - `nid` (`int`) - `node_type` (`str`) - `id` (`str`) - `class` (`str`) - `friendly_name` (`str`) - `question_node` (`int`) - `function` (`str`) - `edges` (`List`): Defaults to `[]` in test split. - `start` (`int`) - `end` (`int`) - `relation` (`str`) - `friendly_name` (`str`) - `sqarql_query` (`str`): Defaults to `""` in test split. - `domains` (`List[str]`): Defaults to `[]` in test split. - `level` (`str`): Only available in validation split. Defaults to `""` in others. - `s_expression` (`str`): Defaults to `""` in test split. **Notes:** Only `qid` and `question` available in test split. ### Data Splits Dataset Split | Number of Instances in Split --------------|-------------------------------------------- Train | 44,337 Validation | 6,763 Test | 13,231 ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@mattbui](https://github.com/mattbui) for adding this dataset.
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pythainlp/final_training_set_v1
pythainlp
2023-04-29T07:06:04Z
461
1
[ "task_categories:conversational", "task_categories:text-generation", "language:en", "region:us" ]
[ "conversational", "text-generation" ]
2023-04-13T16:52:49Z
--- dataset_info: features: - name: text dtype: string - name: metadata struct: - name: source dtype: string - name: nb_token dtype: int64 splits: - name: train num_bytes: 337155434.9768474 num_examples: 405760 - name: test num_bytes: 1277960.0231525812 num_examples: 1538 download_size: 191404581 dataset_size: 338433395 task_categories: - conversational - text-generation language: - en --- # Dataset Card for "final_training_set_v1" Finetuning datasets for [WangChanGLM](https://github.com/pythainlp/wangchanglm) sourced from [LAION OIG chip2 and infill_dbpedia](https://huggingface.co/datasets/laion/OIG) ([Apache-2.0](https://github.com/pythainlp/wangchanglm/blob/main/LICENSE)), [DataBricks Dolly v2](https://github.com/databrickslabs/dolly) ([Apache-2.0](https://github.com/pythainlp/wangchanglm/blob/main/LICENSE)), [OpenAI TL;DR](https://github.com/openai/summarize-from-feedback) ([MIT](https://opensource.org/license/mit/)), and [Hello-SimpleAI HC3](https://huggingface.co/datasets/Hello-SimpleAI/HC3) ([CC-BY SA](https://creativecommons.org/licenses/by-sa/4.0/))
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open-llm-leaderboard/details_pszemraj__pythia-31m-simplepile-lite-2048-scratch-2e
open-llm-leaderboard
2023-10-29T08:24:05Z
461
0
[ "region:us" ]
null
2023-09-15T02:33:40Z
--- pretty_name: Evaluation run of pszemraj/pythia-31m-simplepile-lite-2048-scratch-2e dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [pszemraj/pythia-31m-simplepile-lite-2048-scratch-2e](https://huggingface.co/pszemraj/pythia-31m-simplepile-lite-2048-scratch-2e)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_pszemraj__pythia-31m-simplepile-lite-2048-scratch-2e\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-29T08:23:53.788687](https://huggingface.co/datasets/open-llm-leaderboard/details_pszemraj__pythia-31m-simplepile-lite-2048-scratch-2e/blob/main/results_2023-10-29T08-23-53.788687.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0,\n \"\ em_stderr\": 0.0,\n \"f1\": 0.013173238255033595,\n \"f1_stderr\"\ : 0.0006780799719584048,\n \"acc\": 0.2430939226519337,\n \"acc_stderr\"\ : 0.007023561458220214\n },\n \"harness|drop|3\": {\n \"em\": 0.0,\n\ \ \"em_stderr\": 0.0,\n \"f1\": 0.013173238255033595,\n \"\ f1_stderr\": 0.0006780799719584048\n },\n \"harness|gsm8k|5\": {\n \ \ \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.4861878453038674,\n \"acc_stderr\": 0.014047122916440427\n\ \ }\n}\n```" repo_url: https://huggingface.co/pszemraj/pythia-31m-simplepile-lite-2048-scratch-2e leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|arc:challenge|25_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-09-15T02-33-28.434713.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_29T08_23_53.788687 path: - '**/details_harness|drop|3_2023-10-29T08-23-53.788687.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-29T08-23-53.788687.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_29T08_23_53.788687 path: - '**/details_harness|gsm8k|5_2023-10-29T08-23-53.788687.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-29T08-23-53.788687.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hellaswag|10_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-15T02-33-28.434713.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-management|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-virology|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-15T02-33-28.434713.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_15T02_33_28.434713 path: - '**/details_harness|truthfulqa:mc|0_2023-09-15T02-33-28.434713.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-09-15T02-33-28.434713.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_29T08_23_53.788687 path: - '**/details_harness|winogrande|5_2023-10-29T08-23-53.788687.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-29T08-23-53.788687.parquet' - config_name: results data_files: - split: 2023_09_15T02_33_28.434713 path: - results_2023-09-15T02-33-28.434713.parquet - split: 2023_10_29T08_23_53.788687 path: - results_2023-10-29T08-23-53.788687.parquet - split: latest path: - results_2023-10-29T08-23-53.788687.parquet --- # Dataset Card for Evaluation run of pszemraj/pythia-31m-simplepile-lite-2048-scratch-2e ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/pszemraj/pythia-31m-simplepile-lite-2048-scratch-2e - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [pszemraj/pythia-31m-simplepile-lite-2048-scratch-2e](https://huggingface.co/pszemraj/pythia-31m-simplepile-lite-2048-scratch-2e) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_pszemraj__pythia-31m-simplepile-lite-2048-scratch-2e", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-29T08:23:53.788687](https://huggingface.co/datasets/open-llm-leaderboard/details_pszemraj__pythia-31m-simplepile-lite-2048-scratch-2e/blob/main/results_2023-10-29T08-23-53.788687.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.0, "em_stderr": 0.0, "f1": 0.013173238255033595, "f1_stderr": 0.0006780799719584048, "acc": 0.2430939226519337, "acc_stderr": 0.007023561458220214 }, "harness|drop|3": { "em": 0.0, "em_stderr": 0.0, "f1": 0.013173238255033595, "f1_stderr": 0.0006780799719584048 }, "harness|gsm8k|5": { "acc": 0.0, "acc_stderr": 0.0 }, "harness|winogrande|5": { "acc": 0.4861878453038674, "acc_stderr": 0.014047122916440427 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
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JasiekKaczmarczyk/maestro-v1-sustain-masked
JasiekKaczmarczyk
2023-11-30T09:32:26Z
461
0
[ "region:us" ]
null
2023-10-02T08:08:58Z
--- dataset_info: features: - name: midi_filename dtype: string - name: source dtype: string - name: pitch sequence: int16 length: 128 - name: start sequence: float32 length: 128 - name: dstart sequence: float32 length: 128 - name: duration sequence: float32 length: 128 - name: velocity sequence: int16 length: 128 - name: masking_spaces struct: - name: <Random Mask> sequence: bool length: 128 - name: <LH Mask> sequence: bool length: 128 - name: <RH Mask> sequence: bool length: 128 - name: <Harmonic Root Mask> sequence: bool length: 128 - name: <Harmonic Outliers Mask> sequence: bool length: 128 splits: - name: train num_bytes: 108676395 num_examples: 43738 - name: validation num_bytes: 12260534 num_examples: 4931 - name: test num_bytes: 14165318 num_examples: 5695 download_size: 59189080 dataset_size: 135102247 --- # Dataset Card for "maestro-v1-sustain-masked" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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open-llm-leaderboard/details_LTC-AI-Labs__L2-7b-Synthia-WVG-Test
open-llm-leaderboard
2023-10-27T12:56:36Z
461
0
[ "region:us" ]
null
2023-10-04T08:34:11Z
--- pretty_name: Evaluation run of LTC-AI-Labs/L2-7b-Synthia-WVG-Test dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [LTC-AI-Labs/L2-7b-Synthia-WVG-Test](https://huggingface.co/LTC-AI-Labs/L2-7b-Synthia-WVG-Test)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_LTC-AI-Labs__L2-7b-Synthia-WVG-Test\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-27T12:56:24.516186](https://huggingface.co/datasets/open-llm-leaderboard/details_LTC-AI-Labs__L2-7b-Synthia-WVG-Test/blob/main/results_2023-10-27T12-56-24.516186.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.003460570469798658,\n\ \ \"em_stderr\": 0.0006013962884271092,\n \"f1\": 0.07142722315436245,\n\ \ \"f1_stderr\": 0.0015627419606312088,\n \"acc\": 0.40012823328284985,\n\ \ \"acc_stderr\": 0.009403891235711103\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.003460570469798658,\n \"em_stderr\": 0.0006013962884271092,\n\ \ \"f1\": 0.07142722315436245,\n \"f1_stderr\": 0.0015627419606312088\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.05913570887035633,\n \ \ \"acc_stderr\": 0.00649726666042883\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7411207576953434,\n \"acc_stderr\": 0.012310515810993376\n\ \ }\n}\n```" repo_url: https://huggingface.co/LTC-AI-Labs/L2-7b-Synthia-WVG-Test leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|arc:challenge|25_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-10-04T08-33-52.196370.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_27T12_56_24.516186 path: - '**/details_harness|drop|3_2023-10-27T12-56-24.516186.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-27T12-56-24.516186.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_27T12_56_24.516186 path: - '**/details_harness|gsm8k|5_2023-10-27T12-56-24.516186.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-27T12-56-24.516186.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hellaswag|10_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-04T08-33-52.196370.parquet' - 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'**/details_harness|hendrycksTest-professional_accounting|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-management|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-10-04T08-33-52.196370.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-international_law|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-management|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-marketing|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-sociology|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-virology|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-10-04T08-33-52.196370.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_10_04T08_33_52.196370 path: - '**/details_harness|truthfulqa:mc|0_2023-10-04T08-33-52.196370.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-10-04T08-33-52.196370.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_27T12_56_24.516186 path: - '**/details_harness|winogrande|5_2023-10-27T12-56-24.516186.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-27T12-56-24.516186.parquet' - config_name: results data_files: - split: 2023_10_04T08_33_52.196370 path: - results_2023-10-04T08-33-52.196370.parquet - split: 2023_10_27T12_56_24.516186 path: - results_2023-10-27T12-56-24.516186.parquet - split: latest path: - results_2023-10-27T12-56-24.516186.parquet --- # Dataset Card for Evaluation run of LTC-AI-Labs/L2-7b-Synthia-WVG-Test ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/LTC-AI-Labs/L2-7b-Synthia-WVG-Test - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [LTC-AI-Labs/L2-7b-Synthia-WVG-Test](https://huggingface.co/LTC-AI-Labs/L2-7b-Synthia-WVG-Test) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_LTC-AI-Labs__L2-7b-Synthia-WVG-Test", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-27T12:56:24.516186](https://huggingface.co/datasets/open-llm-leaderboard/details_LTC-AI-Labs__L2-7b-Synthia-WVG-Test/blob/main/results_2023-10-27T12-56-24.516186.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.003460570469798658, "em_stderr": 0.0006013962884271092, "f1": 0.07142722315436245, "f1_stderr": 0.0015627419606312088, "acc": 0.40012823328284985, "acc_stderr": 0.009403891235711103 }, "harness|drop|3": { "em": 0.003460570469798658, "em_stderr": 0.0006013962884271092, "f1": 0.07142722315436245, "f1_stderr": 0.0015627419606312088 }, "harness|gsm8k|5": { "acc": 0.05913570887035633, "acc_stderr": 0.00649726666042883 }, "harness|winogrande|5": { "acc": 0.7411207576953434, "acc_stderr": 0.012310515810993376 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
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bigbio/medmentions
bigbio
2022-12-22T15:45:34Z
459
4
[ "multilinguality:monolingual", "language:en", "license:cc0-1.0", "arxiv:1902.09476", "region:us" ]
null
2022-11-13T22:09:49Z
--- language: - en bigbio_language: - English license: cc0-1.0 multilinguality: monolingual bigbio_license_shortname: CC0_1p0 pretty_name: MedMentions homepage: https://github.com/chanzuckerberg/MedMentions bigbio_pubmed: True bigbio_public: True bigbio_tasks: - NAMED_ENTITY_DISAMBIGUATION - NAMED_ENTITY_RECOGNITION --- # Dataset Card for MedMentions ## Dataset Description - **Homepage:** https://github.com/chanzuckerberg/MedMentions - **Pubmed:** True - **Public:** True - **Tasks:** NED,NER MedMentions is a new manually annotated resource for the recognition of biomedical concepts. What distinguishes MedMentions from other annotated biomedical corpora is its size (over 4,000 abstracts and over 350,000 linked mentions), as well as the size of the concept ontology (over 3 million concepts from UMLS 2017) and its broad coverage of biomedical disciplines. Corpus: The MedMentions corpus consists of 4,392 papers (Titles and Abstracts) randomly selected from among papers released on PubMed in 2016, that were in the biomedical field, published in the English language, and had both a Title and an Abstract. Annotators: We recruited a team of professional annotators with rich experience in biomedical content curation to exhaustively annotate all UMLS® (2017AA full version) entity mentions in these papers. Annotation quality: We did not collect stringent IAA (Inter-annotator agreement) data. To gain insight on the annotation quality of MedMentions, we randomly selected eight papers from the annotated corpus, containing a total of 469 concepts. Two biologists ('Reviewer') who did not participate in the annotation task then each reviewed four papers. The agreement between Reviewers and Annotators, an estimate of the Precision of the annotations, was 97.3%. ## Citation Information ``` @misc{mohan2019medmentions, title={MedMentions: A Large Biomedical Corpus Annotated with UMLS Concepts}, author={Sunil Mohan and Donghui Li}, year={2019}, eprint={1902.09476}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
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open-llm-leaderboard/details_openlm-research__open_llama_7b_v2
open-llm-leaderboard
2023-10-15T02:55:10Z
459
0
[ "region:us" ]
null
2023-08-23T15:23:05Z
--- pretty_name: Evaluation run of openlm-research/open_llama_7b_v2 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [openlm-research/open_llama_7b_v2](https://huggingface.co/openlm-research/open_llama_7b_v2)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 122 configuration, each one coresponding to one of\ \ the evaluated task.\n\nThe dataset has been created from 5 run(s). Each run can\ \ be found as a specific split in each configuration, the split being named using\ \ the timestamp of the run.The \"train\" split is always pointing to the latest\ \ results.\n\nAn additional configuration \"results\" store all the aggregated results\ \ of the run (and is used to compute and display the agregated metrics on the [Open\ \ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_openlm-research__open_llama_7b_v2\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-15T02:55:02.194930](https://huggingface.co/datasets/open-llm-leaderboard/details_openlm-research__open_llama_7b_v2/blob/main/results_2023-10-15T02-55-02.194930.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.001153523489932886,\n\ \ \"em_stderr\": 0.0003476179896857104,\n \"f1\": 0.05493078859060421,\n\ \ \"f1_stderr\": 0.0013198629767466948,\n \"acc\": 0.36431985198420513,\n\ \ \"acc_stderr\": 0.009003933368912346\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.001153523489932886,\n \"em_stderr\": 0.0003476179896857104,\n\ \ \"f1\": 0.05493078859060421,\n \"f1_stderr\": 0.0013198629767466948\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.034874905231235785,\n \ \ \"acc_stderr\": 0.0050534807650222295\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.6937647987371744,\n \"acc_stderr\": 0.012954385972802462\n\ \ }\n}\n```" repo_url: https://huggingface.co/openlm-research/open_llama_7b_v2 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|arc:challenge|25_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|arc:challenge|25_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-08-23T16:40:42.128714.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_15T00_08_38.158230 path: - '**/details_harness|drop|3_2023-10-15T00-08-38.158230.parquet' - split: 2023_10_15T02_55_02.194930 path: - '**/details_harness|drop|3_2023-10-15T02-55-02.194930.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-15T02-55-02.194930.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_15T00_08_38.158230 path: - '**/details_harness|gsm8k|5_2023-10-15T00-08-38.158230.parquet' - split: 2023_10_15T02_55_02.194930 path: - '**/details_harness|gsm8k|5_2023-10-15T02-55-02.194930.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-15T02-55-02.194930.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hellaswag|10_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hellaswag|10_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-23T15:22:49.203021.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-23T16:40:42.128714.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-management|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-management|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-23T16:40:42.128714.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_23T15_22_49.203021 path: - '**/details_harness|truthfulqa:mc|0_2023-08-23T15:22:49.203021.parquet' - split: 2023_08_23T16_40_42.128714 path: - '**/details_harness|truthfulqa:mc|0_2023-08-23T16:40:42.128714.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-08-23T16:40:42.128714.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_15T00_08_38.158230 path: - '**/details_harness|winogrande|5_2023-10-15T00-08-38.158230.parquet' - split: 2023_10_15T02_55_02.194930 path: - '**/details_harness|winogrande|5_2023-10-15T02-55-02.194930.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-15T02-55-02.194930.parquet' - config_name: original_mmlu_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:anatomy|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:astronomy|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:business_ethics|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:college_biology|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:college_medicine|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:college_physics|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:computer_security|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:econometrics|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:formal_logic|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:global_facts|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:human_aging|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:international_law|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:machine_learning|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:management|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:marketing|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:nutrition|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:philosophy|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:prehistory|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:professional_law|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:public_relations|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:security_studies|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:sociology|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:virology|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:world_religions|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:anatomy|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:astronomy|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:business_ethics|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:college_biology|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:college_medicine|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:college_physics|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:computer_security|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:econometrics|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:formal_logic|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:global_facts|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:human_aging|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:international_law|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:machine_learning|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:management|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:marketing|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:nutrition|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:philosophy|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:prehistory|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:professional_law|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:public_relations|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:security_studies|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:sociology|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:virology|5_2023-08-28T20:32:57.598943.parquet' - '**/details_original|mmlu:world_religions|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_abstract_algebra_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_anatomy_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:anatomy|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:anatomy|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_astronomy_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:astronomy|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:astronomy|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_business_ethics_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:business_ethics|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:business_ethics|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_clinical_knowledge_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_college_biology_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:college_biology|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:college_biology|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_college_chemistry_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_college_computer_science_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_college_mathematics_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_college_medicine_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:college_medicine|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:college_medicine|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_college_physics_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:college_physics|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:college_physics|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_computer_security_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:computer_security|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:computer_security|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_conceptual_physics_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_econometrics_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:econometrics|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:econometrics|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_electrical_engineering_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_elementary_mathematics_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_formal_logic_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:formal_logic|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:formal_logic|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_global_facts_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:global_facts|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:global_facts|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_high_school_biology_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_high_school_chemistry_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_high_school_computer_science_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_high_school_european_history_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_high_school_geography_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_high_school_government_and_politics_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_high_school_macroeconomics_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_high_school_mathematics_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_high_school_microeconomics_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_high_school_physics_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_high_school_psychology_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_high_school_statistics_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_high_school_us_history_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_high_school_world_history_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_human_aging_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:human_aging|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:human_aging|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_human_sexuality_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_international_law_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:international_law|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:international_law|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_jurisprudence_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_logical_fallacies_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_machine_learning_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:machine_learning|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:machine_learning|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_management_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:management|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:management|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_marketing_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:marketing|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:marketing|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_medical_genetics_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_miscellaneous_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_moral_disputes_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_moral_scenarios_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_nutrition_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:nutrition|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:nutrition|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_philosophy_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:philosophy|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:philosophy|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_prehistory_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:prehistory|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:prehistory|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_professional_accounting_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_professional_law_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:professional_law|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:professional_law|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_professional_medicine_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_professional_psychology_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_public_relations_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:public_relations|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:public_relations|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_security_studies_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:security_studies|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:security_studies|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_sociology_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:sociology|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:sociology|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_us_foreign_policy_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_virology_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:virology|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:virology|5_2023-08-28T20:32:57.598943.parquet' - config_name: original_mmlu_world_religions_5 data_files: - split: 2023_08_28T20_32_57.598943 path: - '**/details_original|mmlu:world_religions|5_2023-08-28T20:32:57.598943.parquet' - split: latest path: - '**/details_original|mmlu:world_religions|5_2023-08-28T20:32:57.598943.parquet' - config_name: results data_files: - split: 2023_08_23T15_22_49.203021 path: - results_2023-08-23T15:22:49.203021.parquet - split: 2023_08_23T16_40_42.128714 path: - results_2023-08-23T16:40:42.128714.parquet - split: 2023_08_28T20_32_57.598943 path: - results_2023-08-28T20:32:57.598943.parquet - split: 2023_10_15T00_08_38.158230 path: - results_2023-10-15T00-08-38.158230.parquet - split: 2023_10_15T02_55_02.194930 path: - results_2023-10-15T02-55-02.194930.parquet - split: latest path: - results_2023-10-15T02-55-02.194930.parquet --- # Dataset Card for Evaluation run of openlm-research/open_llama_7b_v2 ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/openlm-research/open_llama_7b_v2 - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [openlm-research/open_llama_7b_v2](https://huggingface.co/openlm-research/open_llama_7b_v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 122 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 5 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_openlm-research__open_llama_7b_v2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-15T02:55:02.194930](https://huggingface.co/datasets/open-llm-leaderboard/details_openlm-research__open_llama_7b_v2/blob/main/results_2023-10-15T02-55-02.194930.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.001153523489932886, "em_stderr": 0.0003476179896857104, "f1": 0.05493078859060421, "f1_stderr": 0.0013198629767466948, "acc": 0.36431985198420513, "acc_stderr": 0.009003933368912346 }, "harness|drop|3": { "em": 0.001153523489932886, "em_stderr": 0.0003476179896857104, "f1": 0.05493078859060421, "f1_stderr": 0.0013198629767466948 }, "harness|gsm8k|5": { "acc": 0.034874905231235785, "acc_stderr": 0.0050534807650222295 }, "harness|winogrande|5": { "acc": 0.6937647987371744, "acc_stderr": 0.012954385972802462 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
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open-llm-leaderboard/details_Open-Orca__Mistral-7B-OpenOrca
open-llm-leaderboard
2023-10-29T06:23:06Z
459
0
[ "region:us" ]
null
2023-10-09T12:29:01Z
--- pretty_name: Evaluation run of Open-Orca/Mistral-7B-OpenOrca dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Open-Orca/Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Open-Orca__Mistral-7B-OpenOrca\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-29T06:22:53.674218](https://huggingface.co/datasets/open-llm-leaderboard/details_Open-Orca__Mistral-7B-OpenOrca/blob/main/results_2023-10-29T06-22-53.674218.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.13716442953020133,\n\ \ \"em_stderr\": 0.003523095554552689,\n \"f1\": 0.20527894295301938,\n\ \ \"f1_stderr\": 0.00363436386580985,\n \"acc\": 0.48841023640281406,\n\ \ \"acc_stderr\": 0.011348185919594158\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.13716442953020133,\n \"em_stderr\": 0.003523095554552689,\n\ \ \"f1\": 0.20527894295301938,\n \"f1_stderr\": 0.00363436386580985\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.19939347990902198,\n \ \ \"acc_stderr\": 0.011005438029475652\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7774269928966061,\n \"acc_stderr\": 0.011690933809712666\n\ \ }\n}\n```" repo_url: https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|arc:challenge|25_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-10-09T12-28-38.184371.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_29T06_22_53.674218 path: - '**/details_harness|drop|3_2023-10-29T06-22-53.674218.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-29T06-22-53.674218.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_29T06_22_53.674218 path: - '**/details_harness|gsm8k|5_2023-10-29T06-22-53.674218.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-29T06-22-53.674218.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hellaswag|10_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-management|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-management|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-10-09T12-28-38.184371.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-international_law|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-management|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-marketing|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-sociology|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-virology|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-10-09T12-28-38.184371.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_10_09T12_28_38.184371 path: - '**/details_harness|truthfulqa:mc|0_2023-10-09T12-28-38.184371.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-10-09T12-28-38.184371.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_29T06_22_53.674218 path: - '**/details_harness|winogrande|5_2023-10-29T06-22-53.674218.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-29T06-22-53.674218.parquet' - config_name: results data_files: - split: 2023_10_09T12_28_38.184371 path: - results_2023-10-09T12-28-38.184371.parquet - split: 2023_10_29T06_22_53.674218 path: - results_2023-10-29T06-22-53.674218.parquet - split: latest path: - results_2023-10-29T06-22-53.674218.parquet --- # Dataset Card for Evaluation run of Open-Orca/Mistral-7B-OpenOrca ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [Open-Orca/Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Open-Orca__Mistral-7B-OpenOrca", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-29T06:22:53.674218](https://huggingface.co/datasets/open-llm-leaderboard/details_Open-Orca__Mistral-7B-OpenOrca/blob/main/results_2023-10-29T06-22-53.674218.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.13716442953020133, "em_stderr": 0.003523095554552689, "f1": 0.20527894295301938, "f1_stderr": 0.00363436386580985, "acc": 0.48841023640281406, "acc_stderr": 0.011348185919594158 }, "harness|drop|3": { "em": 0.13716442953020133, "em_stderr": 0.003523095554552689, "f1": 0.20527894295301938, "f1_stderr": 0.00363436386580985 }, "harness|gsm8k|5": { "acc": 0.19939347990902198, "acc_stderr": 0.011005438029475652 }, "harness|winogrande|5": { "acc": 0.7774269928966061, "acc_stderr": 0.011690933809712666 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
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philschmid/guanaco-oai-style
philschmid
2023-11-08T14:37:18Z
459
0
[ "license:apache-2.0", "region:us" ]
null
2023-11-08T14:31:00Z
--- license: apache-2.0 ---
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open-llm-leaderboard/details_jondurbin__airoboros-65b-gpt4-1.4
open-llm-leaderboard
2023-10-29T17:40:20Z
458
0
[ "region:us" ]
null
2023-08-18T11:21:22Z
--- pretty_name: Evaluation run of jondurbin/airoboros-65b-gpt4-1.4 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [jondurbin/airoboros-65b-gpt4-1.4](https://huggingface.co/jondurbin/airoboros-65b-gpt4-1.4)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 4 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_jondurbin__airoboros-65b-gpt4-1.4\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-29T17:40:12.862636](https://huggingface.co/datasets/open-llm-leaderboard/details_jondurbin__airoboros-65b-gpt4-1.4/blob/main/results_2023-10-29T17-40-12.862636.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.048133389261744965,\n\ \ \"em_stderr\": 0.0021920523387187097,\n \"f1\": 0.11759647651006695,\n\ \ \"f1_stderr\": 0.0024834303220337473,\n \"acc\": 0.4884045517729164,\n\ \ \"acc_stderr\": 0.010955153685387409\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.048133389261744965,\n \"em_stderr\": 0.0021920523387187097,\n\ \ \"f1\": 0.11759647651006695,\n \"f1_stderr\": 0.0024834303220337473\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.18043972706595907,\n \ \ \"acc_stderr\": 0.010592508589147896\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7963693764798737,\n \"acc_stderr\": 0.011317798781626922\n\ \ }\n}\n```" repo_url: https://huggingface.co/jondurbin/airoboros-65b-gpt4-1.4 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|arc:challenge|25_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-08-09T18:17:34.414751.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_23T05_17_46.947970 path: - '**/details_harness|drop|3_2023-10-23T05-17-46.947970.parquet' - split: 2023_10_23T05_35_00.206888 path: - '**/details_harness|drop|3_2023-10-23T05-35-00.206888.parquet' - split: 2023_10_29T17_40_12.862636 path: - '**/details_harness|drop|3_2023-10-29T17-40-12.862636.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-29T17-40-12.862636.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_23T05_17_46.947970 path: - '**/details_harness|gsm8k|5_2023-10-23T05-17-46.947970.parquet' - split: 2023_10_23T05_35_00.206888 path: - '**/details_harness|gsm8k|5_2023-10-23T05-35-00.206888.parquet' - split: 2023_10_29T17_40_12.862636 path: - '**/details_harness|gsm8k|5_2023-10-29T17-40-12.862636.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-29T17-40-12.862636.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hellaswag|10_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-09T18:17:34.414751.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-management|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T18:17:34.414751.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_09T18_17_34.414751 path: - '**/details_harness|truthfulqa:mc|0_2023-08-09T18:17:34.414751.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-08-09T18:17:34.414751.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_23T05_17_46.947970 path: - '**/details_harness|winogrande|5_2023-10-23T05-17-46.947970.parquet' - split: 2023_10_23T05_35_00.206888 path: - '**/details_harness|winogrande|5_2023-10-23T05-35-00.206888.parquet' - split: 2023_10_29T17_40_12.862636 path: - '**/details_harness|winogrande|5_2023-10-29T17-40-12.862636.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-29T17-40-12.862636.parquet' - config_name: results data_files: - split: 2023_08_09T18_17_34.414751 path: - results_2023-08-09T18:17:34.414751.parquet - split: 2023_10_23T05_17_46.947970 path: - results_2023-10-23T05-17-46.947970.parquet - split: 2023_10_23T05_35_00.206888 path: - results_2023-10-23T05-35-00.206888.parquet - split: 2023_10_29T17_40_12.862636 path: - results_2023-10-29T17-40-12.862636.parquet - split: latest path: - results_2023-10-29T17-40-12.862636.parquet --- # Dataset Card for Evaluation run of jondurbin/airoboros-65b-gpt4-1.4 ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/jondurbin/airoboros-65b-gpt4-1.4 - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [jondurbin/airoboros-65b-gpt4-1.4](https://huggingface.co/jondurbin/airoboros-65b-gpt4-1.4) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_jondurbin__airoboros-65b-gpt4-1.4", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-29T17:40:12.862636](https://huggingface.co/datasets/open-llm-leaderboard/details_jondurbin__airoboros-65b-gpt4-1.4/blob/main/results_2023-10-29T17-40-12.862636.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.048133389261744965, "em_stderr": 0.0021920523387187097, "f1": 0.11759647651006695, "f1_stderr": 0.0024834303220337473, "acc": 0.4884045517729164, "acc_stderr": 0.010955153685387409 }, "harness|drop|3": { "em": 0.048133389261744965, "em_stderr": 0.0021920523387187097, "f1": 0.11759647651006695, "f1_stderr": 0.0024834303220337473 }, "harness|gsm8k|5": { "acc": 0.18043972706595907, "acc_stderr": 0.010592508589147896 }, "harness|winogrande|5": { "acc": 0.7963693764798737, "acc_stderr": 0.011317798781626922 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
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