datasetId
stringlengths 2
117
| author
stringlengths 2
42
⌀ | last_modified
timestamp[us, tz=UTC] | downloads
int64 0
9.36M
| likes
int64 0
3.89k
| tags
list | task_categories
list | createdAt
timestamp[us, tz=UTC] | card
stringlengths 19
1.01M
| embedding
list |
|---|---|---|---|---|---|---|---|---|---|
iamroot/stsb-contrastive-axes
|
iamroot
| 2023-11-15T19:26:01Z | 69 | 0 |
[
"region:us"
] | null | 2023-11-09T21:02:57Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text_a_embedding
sequence: float32
- name: text_b_embedding
sequence: float32
- name: prompt_embedding
sequence: float32
- name: text_a
dtype: string
- name: text_b
dtype: string
- name: prompt
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 219575612.0
num_examples: 23388
- name: test
num_bytes: 54893903.0
num_examples: 5847
download_size: 311913820
dataset_size: 274469515.0
---
# Glue-STSB with Contrastive Axes
Dataset format:
A pair of sentences, and a prompt along which the sentences are similar or different.
Includes embeddings generated by `sentence-transformers`.
`text_a` and `text_b` are from the Glue-STSB dataset, `prompt` and `label` are machine generated.
|
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multi-train/triviaqa-train-multikilt_1107
|
multi-train
| 2023-11-10T18:52:08Z | 69 | 0 |
[
"region:us"
] | null | 2023-11-10T18:51:59Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: query
dtype: string
- name: pos
sequence: string
- name: neg
sequence: string
- name: task
dtype: string
- name: instruction
struct:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
splits:
- name: train
num_bytes: 67898183
num_examples: 52886
download_size: 39123463
dataset_size: 67898183
---
# Dataset Card for "triviaqa-train-multikilt_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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multi-train/wow-train-multikilt_1107
|
multi-train
| 2023-11-10T18:52:21Z | 69 | 0 |
[
"region:us"
] | null | 2023-11-10T18:52:09Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: query
dtype: string
- name: pos
sequence: string
- name: neg
sequence: string
- name: task
dtype: string
- name: instruction
struct:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
splits:
- name: train
num_bytes: 124530766
num_examples: 80035
download_size: 65428253
dataset_size: 124530766
---
# Dataset Card for "wow-train-multikilt_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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multi-train/S2ORC_title_abstract_1107
|
multi-train
| 2023-11-10T19:00:45Z | 69 | 0 |
[
"region:us"
] | null | 2023-11-10T19:00:29Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: query
dtype: string
- name: pos
sequence: string
- name: neg
sequence: string
- name: task
dtype: string
- name: instruction
struct:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
splits:
- name: train
num_bytes: 256048459
num_examples: 200000
download_size: 134596257
dataset_size: 256048459
---
# Dataset Card for "S2ORC_title_abstract_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
ivillar/mdb-mc-mtt-spec-pix2pix
|
ivillar
| 2023-11-29T15:47:33Z | 69 | 0 |
[
"region:us"
] | null | 2023-11-29T14:17:23Z |
---
dataset_info:
features:
- name: original_prompt
dtype: string
- name: original_image
dtype: image
- name: edit_prompt
dtype: string
- name: edited_prompt
dtype: string
- name: edited_image
dtype: image
splits:
- name: train
num_bytes: 4494783964.352
num_examples: 53809
- name: test
num_bytes: 816984225.276
num_examples: 9871
- name: val
num_bytes: 137990990.47
num_examples: 1601
download_size: 17367449843
dataset_size: 5449759180.098001
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: val
path: data/val-*
---
|
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] |
SocialGrep/the-reddit-place-dataset
|
SocialGrep
| 2022-07-01T17:51:57Z | 68 | 1 |
[
"annotations_creators:lexyr",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] | null | 2022-04-05T21:25:45Z |
---
annotations_creators:
- lexyr
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
paperswithcode_id: null
---
# Dataset Card for the-reddit-place-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)
- [Licensing Information](#licensing-information)
## Dataset Description
- **Homepage:** [https://socialgrep.com/datasets](https://socialgrep.com/datasets/the-reddit-place-dataset?utm_source=huggingface&utm_medium=link&utm_campaign=theredditplacedataset)
- **Point of Contact:** [Website](https://socialgrep.com/contact?utm_source=huggingface&utm_medium=link&utm_campaign=theredditplacedataset)
### Dataset Summary
The written history or /r/Place, in posts and comments.
### Languages
Mainly English.
## Dataset Structure
### Data Instances
A data point is a post or a comment. Due to the separate nature of the two, those exist in two different files - even though many fields are shared.
### Data Fields
- 'type': the type of the data point. Can be 'post' or 'comment'.
- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.
- 'subreddit.id': the base-36 Reddit ID of the data point's host subreddit. Unique.
- 'subreddit.name': the human-readable name of the data point's host subreddit.
- 'subreddit.nsfw': a boolean marking the data point's host subreddit as NSFW or not.
- 'created_utc': a UTC timestamp for the data point.
- 'permalink': a reference link to the data point on Reddit.
- 'score': score of the data point on Reddit.
- 'domain': (Post only) the domain of the data point's link.
- 'url': (Post only) the destination of the data point's link, if any.
- 'selftext': (Post only) the self-text of the data point, if any.
- 'title': (Post only) the title of the post data point.
- 'body': (Comment only) the body of the comment data point.
- 'sentiment': (Comment only) the result of an in-house sentiment analysis pipeline. Used for exploratory analysis.
## Additional Information
### Licensing Information
CC-BY v4.0
|
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kiddothe2b/contract-nli
|
kiddothe2b
| 2022-07-27T13:07:52Z | 68 | 1 |
[
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2022-07-27T12:36:23Z |
---
license: cc-by-nc-sa-4.0
---
|
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bigbio/n2c2_2018_track1
|
bigbio
| 2022-12-22T15:45:59Z | 68 | 1 |
[
"multilinguality:monolingual",
"language:en",
"license:other",
"region:us"
] | null | 2022-11-13T22:10:45Z |
---
language:
- en
bigbio_language:
- English
license: other
multilinguality: monolingual
bigbio_license_shortname: DUA
pretty_name: n2c2 2018 Selection Criteria
homepage: https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/
bigbio_pubmed: False
bigbio_public: False
bigbio_tasks:
- TEXT_CLASSIFICATION
---
# Dataset Card for n2c2 2018 Selection Criteria
## Dataset Description
- **Homepage:** https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/
- **Pubmed:** False
- **Public:** False
- **Tasks:** TXTCLASS
Track 1 of the 2018 National NLP Clinical Challenges shared tasks focused
on identifying which patients in a corpus of longitudinal medical records
meet and do not meet identified selection criteria.
This shared task aimed to determine whether NLP systems could be trained to identify if patients met or did not meet
a set of selection criteria taken from real clinical trials. The selected criteria required measurement detection (
“Any HbA1c value between 6.5 and 9.5%”), inference (“Use of aspirin to prevent myocardial infarction”),
temporal reasoning (“Diagnosis of ketoacidosis in the past year”), and expert judgment to assess (“Major
diabetes-related complication”). For the corpus, we used the dataset of American English, longitudinal clinical
narratives from the 2014 i2b2/UTHealth shared task 4.
The final selected 13 selection criteria are as follows:
1. DRUG-ABUSE: Drug abuse, current or past
2. ALCOHOL-ABUSE: Current alcohol use over weekly recommended limits
3. ENGLISH: Patient must speak English
4. MAKES-DECISIONS: Patient must make their own medical decisions
5. ABDOMINAL: History of intra-abdominal surgery, small or large intestine
resection, or small bowel obstruction.
6. MAJOR-DIABETES: Major diabetes-related complication. For the purposes of
this annotation, we define “major complication” (as opposed to “minor complication”)
as any of the following that are a result of (or strongly correlated with) uncontrolled diabetes:
a. Amputation
b. Kidney damage
c. Skin conditions
d. Retinopathy
e. nephropathy
f. neuropathy
7. ADVANCED-CAD: Advanced cardiovascular disease (CAD).
For the purposes of this annotation, we define “advanced” as having 2 or more of the following:
a. Taking 2 or more medications to treat CAD
b. History of myocardial infarction (MI)
c. Currently experiencing angina
d. Ischemia, past or present
8. MI-6MOS: MI in the past 6 months
9. KETO-1YR: Diagnosis of ketoacidosis in the past year
10. DIETSUPP-2MOS: Taken a dietary supplement (excluding vitamin D) in the past 2 months
11. ASP-FOR-MI: Use of aspirin to prevent MI
12. HBA1C: Any hemoglobin A1c (HbA1c) value between 6.5% and 9.5%
13. CREATININE: Serum creatinine > upper limit of normal
The training consists of 202 patient records with document-level annotations, 10 records
with textual spans indicating annotator’s evidence for their annotations while test set contains 86.
Note:
* The inter-annotator average agreement is 84.9%
* Whereabouts of 10 records with textual spans indicating annotator’s evidence are unknown.
However, author did a simple script based validation to check if any of the tags contained any text
in any of the training set and they do not, which confirms that atleast train and test do not
have any evidence tagged alongside corresponding tags.
## Citation Information
```
@article{DBLP:journals/jamia/StubbsFSHU19,
author = {
Amber Stubbs and
Michele Filannino and
Ergin Soysal and
Samuel Henry and
Ozlem Uzuner
},
title = {Cohort selection for clinical trials: n2c2 2018 shared task track 1},
journal = {J. Am. Medical Informatics Assoc.},
volume = {26},
number = {11},
pages = {1163--1171},
year = {2019},
url = {https://doi.org/10.1093/jamia/ocz163},
doi = {10.1093/jamia/ocz163},
timestamp = {Mon, 15 Jun 2020 16:56:11 +0200},
biburl = {https://dblp.org/rec/journals/jamia/StubbsFSHU19.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
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HuggingFaceM4/VQAv2_modif
|
HuggingFaceM4
| 2023-11-28T16:18:14Z | 68 | 0 |
[
"region:us"
] | null | 2022-11-15T14:43:48Z |
Invalid username or password.
|
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mariosasko/glue
|
mariosasko
| 2023-06-08T16:42:25Z | 68 | 0 |
[
"task_categories:text-classification",
"task_ids:acceptability-classification",
"task_ids:natural-language-inference",
"task_ids:semantic-similarity-scoring",
"task_ids:sentiment-classification",
"task_ids:text-scoring",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"qa-nli",
"coreference-nli",
"paraphrase-identification",
"region:us"
] |
[
"text-classification"
] | 2023-01-18T12:19:24Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- acceptability-classification
- natural-language-inference
- semantic-similarity-scoring
- sentiment-classification
- text-scoring
paperswithcode_id: glue
pretty_name: GLUE (General Language Understanding Evaluation benchmark)
train-eval-index:
- config: cola
task: text-classification
task_id: binary_classification
splits:
train_split: train
eval_split: validation
col_mapping:
sentence: text
label: target
- config: sst2
task: text-classification
task_id: binary_classification
splits:
train_split: train
eval_split: validation
col_mapping:
sentence: text
label: target
- config: mrpc
task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation
col_mapping:
sentence1: text1
sentence2: text2
label: target
- config: qqp
task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation
col_mapping:
question1: text1
question2: text2
label: target
- config: stsb
task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation
col_mapping:
sentence1: text1
sentence2: text2
label: target
- config: mnli
task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation_matched
col_mapping:
premise: text1
hypothesis: text2
label: target
- config: mnli_mismatched
task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation
col_mapping:
premise: text1
hypothesis: text2
label: target
- config: mnli_matched
task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation
col_mapping:
premise: text1
hypothesis: text2
label: target
- config: qnli
task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation
col_mapping:
question: text1
sentence: text2
label: target
- config: rte
task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation
col_mapping:
sentence1: text1
sentence2: text2
label: target
- config: wnli
task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation
col_mapping:
sentence1: text1
sentence2: text2
label: target
configs:
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- mnli
- mnli_matched
- mnli_mismatched
- mrpc
- qnli
- qqp
- rte
- sst2
- stsb
- wnli
tags:
- qa-nli
- coreference-nli
- paraphrase-identification
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features:
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---
# Dataset Card for GLUE
## Table of Contents
- [Dataset Card for GLUE](#dataset-card-for-glue)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [ax](#ax)
- [cola](#cola)
- [mnli](#mnli)
- [mnli_matched](#mnli_matched)
- [mnli_mismatched](#mnli_mismatched)
- [mrpc](#mrpc)
- [qnli](#qnli)
- [qqp](#qqp)
- [rte](#rte)
- [sst2](#sst2)
- [stsb](#stsb)
- [wnli](#wnli)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [ax](#ax-1)
- [cola](#cola-1)
- [mnli](#mnli-1)
- [mnli_matched](#mnli_matched-1)
- [mnli_mismatched](#mnli_mismatched-1)
- [mrpc](#mrpc-1)
- [qnli](#qnli-1)
- [qqp](#qqp-1)
- [rte](#rte-1)
- [sst2](#sst2-1)
- [stsb](#stsb-1)
- [wnli](#wnli-1)
- [Data Fields](#data-fields)
- [ax](#ax-2)
- [cola](#cola-2)
- [mnli](#mnli-2)
- [mnli_matched](#mnli_matched-2)
- [mnli_mismatched](#mnli_mismatched-2)
- [mrpc](#mrpc-2)
- [qnli](#qnli-2)
- [qqp](#qqp-2)
- [rte](#rte-2)
- [sst2](#sst2-2)
- [stsb](#stsb-2)
- [wnli](#wnli-2)
- [Data Splits](#data-splits)
- [ax](#ax-3)
- [cola](#cola-3)
- [mnli](#mnli-3)
- [mnli_matched](#mnli_matched-3)
- [mnli_mismatched](#mnli_mismatched-3)
- [mrpc](#mrpc-3)
- [qnli](#qnli-3)
- [qqp](#qqp-3)
- [rte](#rte-3)
- [sst2](#sst2-3)
- [stsb](#stsb-3)
- [wnli](#wnli-3)
- [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://nyu-mll.github.io/CoLA/](https://nyu-mll.github.io/CoLA/)
- **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:** 955.33 MB
- **Size of the generated dataset:** 229.68 MB
- **Total amount of disk used:** 1185.01 MB
### Dataset Summary
GLUE, the General Language Understanding Evaluation benchmark (https://gluebenchmark.com/) is a collection of resources for training, evaluating, and analyzing natural language understanding systems.
### Supported Tasks and Leaderboards
The leaderboard for the GLUE benchmark can be found [at this address](https://gluebenchmark.com/). It comprises the following tasks:
#### ax
A manually-curated evaluation dataset for fine-grained analysis of system performance on a broad range of linguistic phenomena. This dataset evaluates sentence understanding through Natural Language Inference (NLI) problems. Use a model trained on MulitNLI to produce predictions for this dataset.
#### cola
The Corpus of Linguistic Acceptability consists of English acceptability judgments drawn from books and journal articles on linguistic theory. Each example is a sequence of words annotated with whether it is a grammatical English sentence.
#### mnli
The Multi-Genre Natural Language Inference Corpus is a crowdsourced collection of sentence pairs with textual entailment annotations. Given a premise sentence and a hypothesis sentence, the task is to predict whether the premise entails the hypothesis (entailment), contradicts the hypothesis (contradiction), or neither (neutral). The premise sentences are gathered from ten different sources, including transcribed speech, fiction, and government reports. The authors of the benchmark use the standard test set, for which they obtained private labels from the RTE authors, and evaluate on both the matched (in-domain) and mismatched (cross-domain) section. They also uses and recommend the SNLI corpus as 550k examples of auxiliary training data.
#### mnli_matched
The matched validation and test splits from MNLI. See the "mnli" BuilderConfig for additional information.
#### mnli_mismatched
The mismatched validation and test splits from MNLI. See the "mnli" BuilderConfig for additional information.
#### mrpc
The Microsoft Research Paraphrase Corpus (Dolan & Brockett, 2005) is a corpus of sentence pairs automatically extracted from online news sources, with human annotations for whether the sentences in the pair are semantically equivalent.
#### qnli
The Stanford Question Answering Dataset is a question-answering dataset consisting of question-paragraph pairs, where one of the sentences in the paragraph (drawn from Wikipedia) contains the answer to the corresponding question (written by an annotator). The authors of the benchmark convert the task into sentence pair classification by forming a pair between each question and each sentence in the corresponding context, and filtering out pairs with low lexical overlap between the question and the context sentence. The task is to determine whether the context sentence contains the answer to the question. This modified version of the original task removes the requirement that the model select the exact answer, but also removes the simplifying assumptions that the answer is always present in the input and that lexical overlap is a reliable cue.
#### qqp
The Quora Question Pairs2 dataset is a collection of question pairs from the community question-answering website Quora. The task is to determine whether a pair of questions are semantically equivalent.
#### rte
The Recognizing Textual Entailment (RTE) datasets come from a series of annual textual entailment challenges. The authors of the benchmark combined the data from RTE1 (Dagan et al., 2006), RTE2 (Bar Haim et al., 2006), RTE3 (Giampiccolo et al., 2007), and RTE5 (Bentivogli et al., 2009). Examples are constructed based on news and Wikipedia text. The authors of the benchmark convert all datasets to a two-class split, where for three-class datasets they collapse neutral and contradiction into not entailment, for consistency.
#### sst2
The Stanford Sentiment Treebank consists of sentences from movie reviews and human annotations of their sentiment. The task is to predict the sentiment of a given sentence. It uses the two-way (positive/negative) class split, with only sentence-level labels.
#### stsb
The Semantic Textual Similarity Benchmark (Cer et al., 2017) is a collection of sentence pairs drawn from news headlines, video and image captions, and natural language inference data. Each pair is human-annotated with a similarity score from 1 to 5.
#### wnli
The Winograd Schema Challenge (Levesque et al., 2011) is a reading comprehension task in which a system must read a sentence with a pronoun and select the referent of that pronoun from a list of choices. The examples are manually constructed to foil simple statistical methods: Each one is contingent on contextual information provided by a single word or phrase in the sentence. To convert the problem into sentence pair classification, the authors of the benchmark construct sentence pairs by replacing the ambiguous pronoun with each possible referent. The task is to predict if the sentence with the pronoun substituted is entailed by the original sentence. They use a small evaluation set consisting of new examples derived from fiction books that was shared privately by the authors of the original corpus. While the included training set is balanced between two classes, the test set is imbalanced between them (65% not entailment). Also, due to a data quirk, the development set is adversarial: hypotheses are sometimes shared between training and development examples, so if a model memorizes the training examples, they will predict the wrong label on corresponding development set example. As with QNLI, each example is evaluated separately, so there is not a systematic correspondence between a model's score on this task and its score on the unconverted original task. The authors of the benchmark call converted dataset WNLI (Winograd NLI).
### Languages
The language data in GLUE is in English (BCP-47 `en`)
## Dataset Structure
### Data Instances
#### ax
- **Size of downloaded dataset files:** 0.21 MB
- **Size of the generated dataset:** 0.23 MB
- **Total amount of disk used:** 0.44 MB
An example of 'test' looks as follows.
```
{
"premise": "The cat sat on the mat.",
"hypothesis": "The cat did not sit on the mat.",
"label": -1,
"idx: 0
}
```
#### cola
- **Size of downloaded dataset files:** 0.36 MB
- **Size of the generated dataset:** 0.58 MB
- **Total amount of disk used:** 0.94 MB
An example of 'train' looks as follows.
```
{
"sentence": "Our friends won't buy this analysis, let alone the next one we propose.",
"label": 1,
"id": 0
}
```
#### mnli
- **Size of downloaded dataset files:** 298.29 MB
- **Size of the generated dataset:** 78.65 MB
- **Total amount of disk used:** 376.95 MB
An example of 'train' looks as follows.
```
{
"premise": "Conceptually cream skimming has two basic dimensions - product and geography.",
"hypothesis": "Product and geography are what make cream skimming work.",
"label": 1,
"idx": 0
}
```
#### mnli_matched
- **Size of downloaded dataset files:** 298.29 MB
- **Size of the generated dataset:** 3.52 MB
- **Total amount of disk used:** 301.82 MB
An example of 'test' looks as follows.
```
{
"premise": "Hierbas, ans seco, ans dulce, and frigola are just a few names worth keeping a look-out for.",
"hypothesis": "Hierbas is a name worth looking out for.",
"label": -1,
"idx": 0
}
```
#### mnli_mismatched
- **Size of downloaded dataset files:** 298.29 MB
- **Size of the generated dataset:** 3.73 MB
- **Total amount of disk used:** 302.02 MB
An example of 'test' looks as follows.
```
{
"premise": "What have you decided, what are you going to do?",
"hypothesis": "So what's your decision?,
"label": -1,
"idx": 0
}
```
#### mrpc
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### qnli
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### qqp
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### rte
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### sst2
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### stsb
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### wnli
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Data Fields
The data fields are the same among all splits.
#### ax
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
- `idx`: a `int32` feature.
#### cola
- `sentence`: a `string` feature.
- `label`: a classification label, with possible values including `unacceptable` (0), `acceptable` (1).
- `idx`: a `int32` feature.
#### mnli
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
- `idx`: a `int32` feature.
#### mnli_matched
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
- `idx`: a `int32` feature.
#### mnli_mismatched
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
- `idx`: a `int32` feature.
#### mrpc
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### qnli
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### qqp
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### rte
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### sst2
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### stsb
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### wnli
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Data Splits
#### ax
| |test|
|---|---:|
|ax |1104|
#### cola
| |train|validation|test|
|----|----:|---------:|---:|
|cola| 8551| 1043|1063|
#### mnli
| |train |validation_matched|validation_mismatched|test_matched|test_mismatched|
|----|-----:|-----------------:|--------------------:|-----------:|--------------:|
|mnli|392702| 9815| 9832| 9796| 9847|
#### mnli_matched
| |validation|test|
|------------|---------:|---:|
|mnli_matched| 9815|9796|
#### mnli_mismatched
| |validation|test|
|---------------|---------:|---:|
|mnli_mismatched| 9832|9847|
#### mrpc
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### qnli
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### qqp
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### rte
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### sst2
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### stsb
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### wnli
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## 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
```
@article{warstadt2018neural,
title={Neural Network Acceptability Judgments},
author={Warstadt, Alex and Singh, Amanpreet and Bowman, Samuel R},
journal={arXiv preprint arXiv:1805.12471},
year={2018}
}
@inproceedings{wang2019glue,
title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
note={In the Proceedings of ICLR.},
year={2019}
}
Note that each GLUE dataset has its own citation. Please see the source to see
the correct citation for each contained dataset.
```
### Contributions
Thanks to [@patpizio](https://github.com/patpizio), [@jeswan](https://github.com/jeswan), [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset.
|
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pythainlp/final_training_set_v1_enth
|
pythainlp
| 2023-04-29T07:05:42Z | 68 | 1 |
[
"task_categories:text-generation",
"task_categories:conversational",
"language:th",
"language:en",
"region:us"
] |
[
"text-generation",
"conversational"
] | 2023-04-22T08:56:14Z |
---
dataset_info:
features:
- name: text
dtype: string
- name: nb_token
dtype: int64
- name: metadata
dtype: string
splits:
- name: train
num_bytes: 665379914.0331497
num_examples: 379520
- name: test
num_bytes: 899398.9668502472
num_examples: 513
download_size: 258632318
dataset_size: 666279313
task_categories:
- text-generation
- conversational
language:
- th
- en
---
# Dataset Card for "final_training_set_v1_en_th"
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/)).
The dataset is translated using Google Translate API by [Thu Ya Kyaw](https://github.com/iamthuya).
|
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TigerResearch/tigerbot-gsm-8k-en
|
TigerResearch
| 2023-05-31T01:38:37Z | 68 | 0 |
[
"language:en",
"license:mit",
"region:us"
] | null | 2023-05-30T15:44:37Z |
---
license: mit
language:
- en
---
[Tigerbot](https://github.com/TigerResearch/TigerBot) 基于gsm8k数据集加工而来
GSM8K（Grade School Math 8K）是一个包含 8.5K 高质量语言多样化小学数学单词问题的数据集。创建数据集是为了支持对需要多步推理的基本数学问题的问答任务。
原始来源：[https://huggingface.co/datasets/gsm8k](https://huggingface.co/datasets/gsm8k)
<p align="center" width="40%">
## Usage
```python
import datasets
ds_sft = datasets.load_dataset('TigerResearch/tigerbot-gsm-8k-en')
```
|
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] |
ccore/wikipedia-QA
|
ccore
| 2023-09-11T21:46:03Z | 68 | 0 |
[
"task_categories:text-generation",
"size_categories:10K<n<100K",
"wikipeda",
"markdown",
"qa",
"region:us"
] |
[
"text-generation"
] | 2023-09-11T20:51:52Z |
---
task_categories:
- text-generation
tags:
- wikipeda
- markdown
- qa
size_categories:
- 10K<n<100K
---
GoodWiki Dataset in QA format, asking using description
and having the question at the end of each page again for the network to learn how to create questions from content
|
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] |
asgaardlab/SampleDataset
|
asgaardlab
| 2023-10-16T17:47:13Z | 68 | 1 |
[
"region:us"
] | null | 2023-10-03T14:49:43Z |
---
dataset_info:
features:
- name: Buggy Image
dtype: image
- name: Correct Image
dtype: image
- name: Segmentation Image (Bug)
dtype: image
- name: Segmentation Image (Correct)
dtype: image
- name: Description
dtype: string
- name: Tag
dtype: string
- name: Objects JSON (Bug)
dtype: string
- name: Objects JSON (Correct)
dtype: string
- name: Victim Name
dtype: string
- name: Victim Color
sequence: int64
splits:
- name: validation
num_bytes: 44919090.0
num_examples: 70
download_size: 43196423
dataset_size: 44919090.0
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
# Dataset Card for "SampleDataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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arpitsh018/apt-micro-dataset-llm-v2-714k
|
arpitsh018
| 2023-10-09T16:18:37Z | 68 | 0 |
[
"region:us"
] | null | 2023-10-09T16:17:11Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: id
dtype: int64
- name: source
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
splits:
- name: train
num_bytes: 1753434111.3731575
num_examples: 714801
- name: validation
num_bytes: 490607.6268424799
num_examples: 200
download_size: 911152910
dataset_size: 1753924719.0
---
# Dataset Card for "apt-micro-dataset-llm-v2-714k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
ehartford/ultrachat-uncensored
|
ehartford
| 2023-10-23T05:29:16Z | 68 | 16 |
[
"license:mit",
"region:us"
] | null | 2023-10-12T05:25:04Z |
---
license: mit
---
This is based on ultrachat dataset https://huggingface.co/datasets/stingning/ultrachat
I filtered it using the classic "unfiltered" keywords list https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered to remove instances of refusals and bias
About 90% of the dataset was removed.
What remains (400k conversations) is unlikely to inclinate the model to refuse.
I am investigating a less heavy handed approach using dolphin-2.1 to reword any detected refusals.
|
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] |
ialvarenga/acl-arc-revised
|
ialvarenga
| 2023-10-28T22:40:25Z | 68 | 0 |
[
"region:us"
] | null | 2023-10-28T22:40:16Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: eval
path: data/eval-*
dataset_info:
features:
- name: text
dtype: string
- name: intent
dtype:
class_label:
names:
'0': '0'
'1': '1'
'2': '2'
'3': '3'
'4': '4'
'5': '5'
splits:
- name: train
num_bytes: 358284.1064718163
num_examples: 1532
- name: test
num_bytes: 44902.44676409186
num_examples: 192
- name: eval
num_bytes: 44902.44676409186
num_examples: 192
download_size: 231094
dataset_size: 448089.0
---
# Dataset Card for "acl-arc-revised"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
princeton-nlp/SWE-bench_oracle
|
princeton-nlp
| 2023-11-16T22:05:22Z | 68 | 1 |
[
"arxiv:2310.06770",
"region:us"
] | null | 2023-11-09T01:36:53Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: instance_id
dtype: string
- name: text
dtype: string
- name: repo
dtype: string
- name: base_commit
dtype: string
- name: problem_statement
dtype: string
- name: hints_text
dtype: string
- name: created_at
dtype: string
- name: patch
dtype: string
- name: test_patch
dtype: string
- name: version
dtype: string
- name: FAIL_TO_PASS
dtype: string
- name: PASS_TO_PASS
dtype: string
- name: environment_setup_commit
dtype: string
splits:
- name: train
num_bytes: 2927236667
num_examples: 18817
- name: dev
num_bytes: 26551408
num_examples: 225
- name: test
num_bytes: 246539542
num_examples: 2294
- name: validation
num_bytes: 25752989
num_examples: 191
download_size: 1279869834
dataset_size: 3226080606
---
# Dataset Card for "SWE-bench_oracle"
### Dataset Summary
SWE-bench is a dataset that tests systems’ ability to solve GitHub issues automatically. The dataset collects 2,294 Issue-Pull Request pairs from 12 popular Python. Evaluation is performed by unit test verification using post-PR behavior as the reference solution.
The dataset was released as part of [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://arxiv.org/abs/2310.06770)
This dataset `SWE-bench_oracle` includes a formatting of each instance using the "Oracle" retrieval setting as described in the paper. The `text` column can be used directly with LMs to generate patch files.
Models are instructed to generate [`patch`](https://en.wikipedia.org/wiki/Patch_(Unix)) formatted file using the following template:
```diff
<patch>
diff
--- a/path/to/file.py
--- b/path/to/file.py
@@ -1,3 +1,3 @@
This is a test file.
-It contains several lines.
+It has been modified.
This is the third line.
</patch>
```
This format can be used directly with the [SWE-bench inference scripts](https://github.com/princeton-nlp/SWE-bench/tree/main/inference). Please refer to these scripts for more details on inference.
### Supported Tasks and Leaderboards
SWE-bench proposes a new task: issue resolution provided a full repository and GitHub issue. The leaderboard can be found at www.swebench.com
### Languages
The text of the dataset is primarily English, but we make no effort to filter or otherwise clean based on language type.
## Dataset Structure
### Data Instances
An example of a SWE-bench datum is as follows:
```
instance_id: (str) - A formatted instance identifier, usually as repo_owner__repo_name-PR-number.
text: (str) - The input text including instructions, the "Oracle" retrieved file, and an example of the patch format for output.
patch: (str) - The gold patch, the patch generated by the PR (minus test-related code), that resolved the issue.
repo: (str) - The repository owner/name identifier from GitHub.
base_commit: (str) - The commit hash of the repository representing the HEAD of the repository before the solution PR is applied.
hints_text: (str) - Comments made on the issue prior to the creation of the solution PR’s first commit creation date.
created_at: (str) - The creation date of the pull request.
test_patch: (str) - A test-file patch that was contributed by the solution PR.
problem_statement: (str) - The issue title and body.
version: (str) - Installation version to use for running evaluation.
environment_setup_commit: (str) - commit hash to use for environment setup and installation.
FAIL_TO_PASS: (str) - A json list of strings that represent the set of tests resolved by the PR and tied to the issue resolution.
PASS_TO_PASS: (str) - A json list of strings that represent tests that should pass before and after the PR application.
```
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
vic0428/imdb-card-pred-science
|
vic0428
| 2023-11-18T06:20:28Z | 68 | 0 |
[
"region:us"
] | null | 2023-11-10T01:11:52Z |
---
dataset_info:
features:
- name: text
dtype: string
- name: prompt
dtype: string
- name: true_cardinality
dtype: int64
splits:
- name: train
num_bytes: 39344995.2
num_examples: 80000
- name: test
num_bytes: 9836248.8
num_examples: 20000
download_size: 8632280
dataset_size: 49181244.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Dataset Card for "imdb-card-pred-science"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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multi-train/amazon-qa_1107
|
multi-train
| 2023-11-10T18:36:22Z | 68 | 0 |
[
"region:us"
] | null | 2023-11-10T18:33:05Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: query
dtype: string
- name: pos
sequence: string
- name: neg
sequence: string
- name: task
dtype: string
- name: instruction
struct:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
splits:
- name: train
num_bytes: 126901578
num_examples: 200000
download_size: 65627345
dataset_size: 126901578
---
# Dataset Card for "amazon-qa_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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multi-train/multi_lexsum_1107
|
multi-train
| 2023-11-10T18:54:52Z | 68 | 0 |
[
"region:us"
] | null | 2023-11-10T18:52:34Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: query
dtype: string
- name: pos
sequence: string
- name: neg
sequence: string
- name: task
dtype: string
- name: instruction
struct:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
splits:
- name: train
num_bytes: 2800042167
num_examples: 3177
download_size: 1258860340
dataset_size: 2800042167
---
# Dataset Card for "multi_lexsum_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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multi-train/reddit-title-body_1107
|
multi-train
| 2023-11-10T18:58:24Z | 68 | 0 |
[
"region:us"
] | null | 2023-11-10T18:55:02Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: query
dtype: string
- name: pos
sequence: string
- name: neg
sequence: string
- name: task
dtype: string
- name: instruction
struct:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
splits:
- name: train
num_bytes: 216135392
num_examples: 200000
download_size: 125472332
dataset_size: 216135392
---
# Dataset Card for "reddit-title-body_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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multi-train/WikiAnswers_1107
|
multi-train
| 2023-11-10T18:58:07Z | 68 | 0 |
[
"region:us"
] | null | 2023-11-10T18:58:00Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: query
dtype: string
- name: pos
sequence: string
- name: neg
sequence: string
- name: task
dtype: string
- name: instruction
struct:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
splits:
- name: train
num_bytes: 54367110
num_examples: 200000
download_size: 22862968
dataset_size: 54367110
---
# Dataset Card for "WikiAnswers_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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multi-train/SimpleWiki_1107
|
multi-train
| 2023-11-10T18:58:49Z | 68 | 0 |
[
"region:us"
] | null | 2023-11-10T18:58:43Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: query
dtype: string
- name: pos
sequence: string
- name: neg
sequence: string
- name: task
dtype: string
- name: instruction
struct:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
splits:
- name: train
num_bytes: 57699115
num_examples: 102225
download_size: 29311247
dataset_size: 57699115
---
# Dataset Card for "SimpleWiki_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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multi-train/gooaq_pairs_1107
|
multi-train
| 2023-11-10T18:59:01Z | 68 | 0 |
[
"region:us"
] | null | 2023-11-10T18:58:51Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: query
dtype: string
- name: pos
sequence: string
- name: neg
sequence: string
- name: task
dtype: string
- name: instruction
struct:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
splits:
- name: train
num_bytes: 125623207
num_examples: 200000
download_size: 62027848
dataset_size: 125623207
---
# Dataset Card for "gooaq_pairs_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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multi-train/PAQ_pairs_1107
|
multi-train
| 2023-11-10T19:02:41Z | 68 | 0 |
[
"region:us"
] | null | 2023-11-10T19:02:23Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: query
dtype: string
- name: pos
sequence: string
- name: neg
sequence: string
- name: task
dtype: string
- name: instruction
struct:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
splits:
- name: train
num_bytes: 282748365
num_examples: 200000
download_size: 135658270
dataset_size: 282748365
---
# Dataset Card for "PAQ_pairs_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
multi-train/codesearchnet_1107
|
multi-train
| 2023-11-10T21:11:59Z | 68 | 0 |
[
"region:us"
] | null | 2023-11-10T21:10:52Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: query
dtype: string
- name: pos
sequence: string
- name: neg
sequence: string
- name: task
dtype: string
- name: instruction
struct:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
splits:
- name: train
num_bytes: 2207111297
num_examples: 1000000
download_size: 552466752
dataset_size: 2207111297
---
# Dataset Card for "codesearchnet_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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PaulTran/banner_generate
|
PaulTran
| 2023-11-24T04:39:17Z | 68 | 0 |
[
"region:us"
] | null | 2023-11-23T15:33:45Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 84118313.344
num_examples: 1362
download_size: 84092692
dataset_size: 84118313.344
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "banner_generate"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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CronosGhost/wikipedia_fr_snippets
|
CronosGhost
| 2023-11-25T17:34:19Z | 68 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-25T17:23:50Z |
---
license: mit
dataset_info:
features:
- name: url
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 8126467952
num_examples: 12138717
download_size: 4271960527
dataset_size: 8126467952
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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projecte-aina/ancora-ca-ner
|
projecte-aina
| 2023-09-13T12:44:29Z | 67 | 0 |
[
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:unknown",
"language:ca",
"license:cc-by-4.0",
"arxiv:2107.07903",
"region:us"
] |
[] | 2022-03-02T23:29:22Z |
---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- ca
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: ancora-ca-ner
size_categories:
- unknown
source_datasets: []
task_categories: []
task_ids: []
---
# Dataset Card for AnCora-Ca-NER
## Dataset Description
- **Website:** https://zenodo.org/record/5036651
- **Paper:** [Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? A Comprehensive Assessment for Catalan](https://arxiv.org/abs/2107.07903)
- **Paper:** [AnCora: Multilevel Annotated Corpora for Catalan and Spanish](http://www.lrec-conf.org/proceedings/lrec2008/pdf/35_paper.pdf)
- **Point of Contact:** [Carlos Rodríguez-Penagos]([email protected]) and [Carme Armentano-Oller]([email protected])
### Dataset Summary
This is a dataset for Named Entity Recognition (NER) in Catalan. It adapts <a href="http://clic.ub.edu/corpus/">AnCora corpus</a> for Machine Learning and Language Model evaluation purposes.
[AnCora corpus](http://clic.ub.edu/corpus/) is used under [CC-by](https://creativecommons.org/licenses/by/4.0/) licence.
This dataset was developed by [BSC TeMU](https://temu.bsc.es/) as part of the [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina/), to enrich the [Catalan Language Understanding Benchmark (CLUB)](https://club.aina.bsc.es/).
### Supported Tasks and Leaderboards
Named Entities Recognition, Language Model
### Languages
The dataset is in Catalan (`ca-ES`).
## Dataset Structure
### Data Instances
Three two-column files, one for each split.
<pre>
Fundació B-ORG
Privada I-ORG
Fira I-ORG
de I-ORG
Manresa I-ORG
ha O
fet O
un O
balanç O
de O
l' O
activitat O
del O
Palau B-LOC
Firal I-LOC
</pre>
### Data Fields
Every file has two columns, with the word form or punctuation symbol in the first one and the corresponding IOB tag in the second one.
### Data Splits
We took the original train, dev and test splits from the [UD version of the corpus](https://huggingface.co/datasets/universal_dependencies)
- train: 10,630 examples
- validation: 1,429 examples
- test: 1,528 examples
## Dataset Creation
### Curation Rationale
We created this corpus to contribute to the development of language models in Catalan, a low-resource language.
### Source Data
#### Initial Data Collection and Normalization
[AnCora](http://clic.ub.edu/corpus/) consists of a CatCAalan corpus (AnCora-CA) and a Spanish corpus (AnCora-ES), each of them of 500,000 tokens (some multi-word). The corpora are annotated for linguistic phenomena at different levels.
AnCora corpus is mainly based on newswire texts. For more information, refer to Taulé, M., M.A. Martí, M. Recasens (2009): <a href="http://www.lrec-conf.org/proceedings/lrec2008/pdf/35_paper.pdf">"AnCora: Multilevel Annotated Corpora for Catalan and Spanish”</a>, Proceedings of 6th International Conference on language Resources and Evaluation.
#### Who are the source language producers?
Catalan [AnCora corpus](http://clic.ub.edu/corpus/) is compiled from articles from the following news outlets: <a href="https://www.efe.com">EFE</a>, <a href="https://www.acn.cat">ACN</a>, <a href="https://www.elperiodico.cat/ca/">El Periodico</a>.
### Annotations
#### Annotation process
We adapted the NER labels from [AnCora corpus](http://clic.ub.edu/corpus/) to a token-per-line, multi-column format.
#### Who are the annotators?
Original annotators from [AnCora corpus](http://clic.ub.edu/corpus/).
### Personal and Sensitive Information
No personal or sensitive information included.
## Considerations for Using the Data
### Social Impact of Dataset
We hope this corpus contributes to the development of language models in Catalan, a low-resource language.
### Discussion of Biases
[N/A]
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center ([email protected])
This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/en/inici/index.html) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina/).
### Licensing information
This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by/4.0/">Attribution 4.0 International License</a>.
### Citation Information
```
@inproceedings{armengol-estape-etal-2021-multilingual,
title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan",
author = "Armengol-Estap{\'e}, Jordi and
Carrino, Casimiro Pio and
Rodriguez-Penagos, Carlos and
de Gibert Bonet, Ona and
Armentano-Oller, Carme and
Gonzalez-Agirre, Aitor and
Melero, Maite and
Villegas, Marta",
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.437",
doi = "10.18653/v1/2021.findings-acl.437",
pages = "4933--4946",
}
```
[DOI](https://doi.org/10.5281/zenodo.4529299)
### Contributions
[N/A]
|
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] |
PaddlePaddle/dureader_robust
|
PaddlePaddle
| 2022-03-10T05:14:18Z | 67 | 1 |
[
"license:apache-2.0",
"region:us"
] | null | 2022-03-10T04:46:26Z |
---
license: apache-2.0
---
|
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voidful/NMSQA
|
voidful
| 2023-04-04T04:46:23Z | 67 | 7 |
[
"task_categories:question-answering",
"task_categories:automatic-speech-recognition",
"task_ids:abstractive-qa",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:expert-generated",
"language_creators:machine-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"speech-recognition",
"arxiv:2203.04911",
"region:us"
] |
[
"question-answering",
"automatic-speech-recognition"
] | 2022-03-16T16:03:42Z |
---
annotations_creators:
- crowdsourced
- machine-generated
language_creators:
- expert-generated
- machine-generated
- crowdsourced
language:
- en
license: []
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- question-answering
- automatic-speech-recognition
task_ids:
- abstractive-qa
pretty_name: NMSQA
tags:
- speech-recognition
---
# Dataset Card for NMSQA(Natural Multi-speaker Spoken Question Answering)
## 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://github.com/DanielLin94144/DUAL-textless-SQA
- Repository:
https://github.com/DanielLin94144/DUAL-textless-SQA
- Paper:
https://arxiv.org/abs/2203.04911
- Leaderboard:
- Point of Contact:
Download audio data: [https://huggingface.co/datasets/voidful/NMSQA/resolve/main/nmsqa_audio.tar.gz](https://huggingface.co/datasets/voidful/NMSQA/resolve/main/nmsqa_audio.tar.gz)
Unzip audio data: `tar -xf nmsqa_audio.tar.gz`
### Dataset Summary
The Natural Multi-speaker Spoken Question Answering (NMSQA) dataset is designed for the task of textless spoken question answering. It is based on the SQuAD dataset and contains spoken questions and passages. The dataset includes the original text, transcriptions, and audio files of the spoken content. This dataset is created to evaluate the performance of models on textless spoken question answering tasks.
### Supported Tasks and Leaderboards
The primary task supported by this dataset is textless spoken question answering, where the goal is to answer questions based on spoken passages without relying on textual information. The dataset can also be used for automatic speech recognition tasks.
### Languages
The dataset is in English.
## Dataset Structure
### Data Instances
Each instance in the dataset contains the following fields:
- id: Unique identifier for the instance
- title: The title of the passage
- context: The passage text
- question: The question text
- - answer_start: The start index of the answer in the text
- audio_full_answer_end: The end position of the audio answer in seconds
- audio_full_answer_start: The start position of the audio answer in seconds
- audio_full_neg_answer_end: The end position of the audio answer in seconds for an incorrect answer with the same words
- audio_full_neg_answer_start: The start position of the audio answer in seconds for an incorrect answer with the same words
- audio_segment_answer_end: The end position of the audio answer in seconds for the segment
- audio_segment_answer_start: The start position of the audio answer in seconds for the segment
- text: The answer text
- content_segment_audio_path: The audio path for the content segment
- content_full_audio_path: The complete audio path for the content
- content_audio_sampling_rate: The audio sampling rate
- content_audio_speaker: The audio speaker
- content_segment_text: The segment text of the content
- content_segment_normalized_text: The normalized text for generating audio
- question_audio_path: The audio path for the question
- question_audio_sampling_rate: The audio sampling rate
- question_audio_speaker: The audio speaker
- question_normalized_text: The normalized text for generating audio
### Data Fields
The dataset includes the following data fields:
- id
- title
- context
- question
- answers
- content_segment_audio_path
- content_full_audio_path
- content_audio_sampling_rate
- content_audio_speaker
- content_segment_text
- content_segment_normalized_text
- question_audio_path
- question_audio_sampling_rate
- question_audio_speaker
- question_normalized_text
### Data Splits
The dataset is split into train, dev, and test sets.
## Dataset Creation
### Curation Rationale
The NMSQA dataset is created to address the challenge of textless spoken question answering, where the model must answer questions based on spoken passages without relying on textual information.
### Source Data
The NMSQA dataset is based on the SQuAD dataset, with spoken questions and passages created from the original text data.
#### Initial Data Collection and Normalization
The initial data collection involved converting the original SQuAD dataset's text-based questions and passages into spoken audio files. The text was first normalized, and then audio files were generated using text-to-speech methods.
#### Who are the source language producers?
The source language producers are the creators of the SQuAD dataset and the researchers who generated the spoken audio files for the NMSQA dataset.
### Annotations
#### Annotation process
The annotations for the NMSQA dataset are derived from the original SQuAD dataset. Additional annotations, such as audio start and end positions for correct and incorrect answers, as well as audio file paths and speaker information, are added by the dataset creators.
#### Who are the annotators?
The annotators for the NMSQA dataset are the creators of the SQuAD dataset and the researchers who generated the spoken audio files and additional annotations for the NMSQA dataset.
### Personal and Sensitive Information
The dataset does not contain any personal or sensitive information.
## Considerations for Using the Data
### Social Impact of Dataset
The NMSQA dataset contributes to the development and evaluation of models for textless spoken question answering tasks, which can lead to advancements in natural language processing and automatic speech recognition. Applications of these technologies can improve accessibility and convenience in various domains, such as virtual assistants, customer service, and voice-controlled devices.
### Discussion of Biases
The dataset inherits potential biases from the original SQuAD dataset, which may include biases in the selection of passages, questions, and answers. Additionally, biases may be introduced in the text-to-speech process and the choice of speakers used to generate the spoken audio files.
### Other Known Limitations
As the dataset is based on the SQuAD dataset, it shares the same limitations, including the fact that it is limited to the English language and mainly focuses on factual questions. Furthermore, the dataset may not cover a wide range of accents, dialects, or speaking styles.
## Additional Information
### Dataset Curators
The NMSQA dataset is curated by Guan-Ting Lin, Yung-Sung Chuang, Ho-Lam Chung, Shu-Wen Yang, Hsuan-Jui Chen, Shang-Wen Li, Abdelrahman Mohamed, Hung-Yi Lee, and Lin-Shan Lee.
### Licensing Information
The licensing information for the dataset is not explicitly mentioned.
### Citation Information
```css
@article{lin2022dual,
title={DUAL: Textless Spoken Question Answering with Speech Discrete Unit Adaptive Learning},
author={Lin, Guan-Ting and Chuang, Yung-Sung and Chung, Ho-Lam and Yang, Shu-wen and Chen, Hsuan-Jui and Li, Shang-Wen and Mohamed, Abdelrahman and Lee, Hung-yi and Lee, Lin-shan},
journal={arXiv preprint arXiv:2203.04911},
year={2022}
}
```
### Contributions
Thanks to [@voidful](https://github.com/voidful) for adding this dataset.
|
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vesteinn/sosialurin-faroese-pos
|
vesteinn
| 2022-06-16T15:49:46Z | 67 | 1 |
[
"license:cc-by-4.0",
"region:us"
] | null | 2022-06-16T15:44:25Z |
---
license: cc-by-4.0
---
|
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ttxy/emotion
|
ttxy
| 2023-08-17T02:25:59Z | 67 | 2 |
[
"task_categories:text-classification",
"language:code",
"license:bsd",
"classification",
"region:us"
] |
[
"text-classification"
] | 2022-07-24T06:00:03Z |
---
language:
- code
pretty_name: "English Emotion classification"
tags:
- classification
license: "bsd"
task_categories:
- text-classification
---
一个包含六种基本情绪（愤怒、恐惧、喜悦、爱、悲伤和惊讶）的英文Twitter消息数据集
Github 链接 https://github.com/dair-ai/emotion_dataset
|
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bigbio/biology_how_why_corpus
|
bigbio
| 2022-12-22T15:43:41Z | 67 | 2 |
[
"multilinguality:monolingual",
"language:en",
"license:unknown",
"region:us"
] | null | 2022-11-13T22:06:38Z |
---
language:
- en
bigbio_language:
- English
license: unknown
multilinguality: monolingual
bigbio_license_shortname: UNKNOWN
pretty_name: BiologyHowWhyCorpus
homepage: https://allenai.org/data/biology-how-why-corpus
bigbio_pubmed: False
bigbio_public: True
bigbio_tasks:
- QUESTION_ANSWERING
---
# Dataset Card for BiologyHowWhyCorpus
## Dataset Description
- **Homepage:** https://allenai.org/data/biology-how-why-corpus
- **Pubmed:** False
- **Public:** True
- **Tasks:** QA
This dataset consists of 185 "how" and 193 "why" biology questions authored by a domain expert, with one or more gold
answer passages identified in an undergraduate textbook. The expert was not constrained in any way during the
annotation process, so gold answers might be smaller than a paragraph or span multiple paragraphs. This dataset was
used for the question-answering system described in the paper “Discourse Complements Lexical Semantics for Non-factoid
Answer Reranking” (ACL 2014).
## Citation Information
```
@inproceedings{jansen-etal-2014-discourse,
title = "Discourse Complements Lexical Semantics for Non-factoid Answer Reranking",
author = "Jansen, Peter and
Surdeanu, Mihai and
Clark, Peter",
booktitle = "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jun,
year = "2014",
address = "Baltimore, Maryland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P14-1092",
doi = "10.3115/v1/P14-1092",
pages = "977--986",
}
```
|
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hieunguyen1053/binhvq-news-corpus
|
hieunguyen1053
| 2023-02-09T15:49:42Z | 67 | 0 |
[
"region:us"
] | null | 2023-02-09T15:33:41Z |
---
dataset_info:
features:
- name: title
dtype: string
- name: summary
dtype: string
- name: category
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 51179763136
num_examples: 13954498
download_size: 19065155948
dataset_size: 51179763136
---
# Dataset Card for "binhvq-news-corpus"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
LinhDuong/chatdoctor-200k
|
LinhDuong
| 2023-03-28T07:58:46Z | 67 | 9 |
[
"license:apache-2.0",
"arxiv:2303.14070",
"region:us"
] | null | 2023-03-28T07:33:20Z |
---
license: apache-2.0
---
This ChatDoctor-200K dataset is collected from this paper https://arxiv.org/pdf/2303.14070.pdf
Alternatively, you can download the original dataset from this link https://drive.google.com/file/d/1lyfqIwlLSClhgrCutWuEe_IACNq6XNUt/view?usp=sharing
|
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] |
BelleGroup/generated_chat_0.4M
|
BelleGroup
| 2023-04-08T03:55:12Z | 67 | 43 |
[
"task_categories:text2text-generation",
"size_categories:100K<n<1M",
"language:zh",
"license:gpl-3.0",
"region:us"
] |
[
"text2text-generation"
] | 2023-04-08T03:36:11Z |
---
license: gpl-3.0
task_categories:
- text2text-generation
language:
- zh
size_categories:
- 100K<n<1M
---
# Generated_Chat_0.4M
## 内容
包含约40万条由[BELLE](https://github.com/LianjiaTech/BELLE)项目生成的个性化角色对话数据，包含角色介绍。
注意：此数据集是由ChatGPT产生的，未经过严格校验，题目或解题过程可能包含错误。使用过程中请注意这一点。
## 样例
```
{
"instruction": "于以下角色信息完成一段对话\nAlice是一个年轻的律师，她正在为一个涉及濒危物种保护的案件做准备。Bob是一位当地的居民，他所在的社区经常受到该濒危物种的干扰。\n",
"input": "",
"output": "\nAlice: 你好Bob，我是濒危物种保护案的律师，很高兴见到你。我们前几天发给你的文件你看到了吗？\nBob: 看到了，你们说要禁止我们在该物种的常住地采集蘑菇，这很不公平。\nAlice: 保护濒危物种对整个生态系统都很重要，如果我们的举动损害了这个物种的生存，那么整个生态系统都将遭受损失。\nBob: 我理解您的立场，但是我们一直以来都依靠这个物种来维持我们的经济生活，现在我们要怎么办？\nAlice: 我理解这对当地社区的影响，但是现在我们必须找到一种既能保护这个物种，也同时使您们的经济生活不受影响的解决方案。你有任何想法吗？我们可以一起来想办法。\n"
}
```
### 字段
```
instruction: 指令
input: 输入（本数据集均为空）
output: 输出
```
## 局限性和使用限制
我们要求开发者仅将我们开源的代码、数据、模型及后续衍生物用于研究目的，不得用于商业，以及其他会对社会带来危害的用途。
由于数据是由*ChatGPT*生成的，未经严格验证，在事实性和其他方面还存在一些不足。因此，在使用此数据集时，请务必注意甄别。
本数据集不代表任何一方的立场、利益或想法，无关任何团体的任何类型的主张。因使用本数据集带来的任何损害、纠纷，本项目的开发者不承担任何责任。
***
# Generated_Chat_0.4M
## Contents
Includes approx. 400k Personalized Character Dialogue generated by BELLE, with character Introduction.
Note: this subset was generated by *ChatGPT* and was not strictly verified. The quizzes or answers might contain errors. Please take this in mind when using this subset.
## Sample
```
{
"instruction": "于以下角色信息完成一段对话\nAlice是一个年轻的律师，她正在为一个涉及濒危物种保护的案件做准备。Bob是一位当地的居民，他所在的社区经常受到该濒危物种的干扰。\n",
"input": "",
"output": "\nAlice: 你好Bob，我是濒危物种保护案的律师，很高兴见到你。我们前几天发给你的文件你看到了吗？\nBob: 看到了，你们说要禁止我们在该物种的常住地采集蘑菇，这很不公平。\nAlice: 保护濒危物种对整个生态系统都很重要，如果我们的举动损害了这个物种的生存，那么整个生态系统都将遭受损失。\nBob: 我理解您的立场，但是我们一直以来都依靠这个物种来维持我们的经济生活，现在我们要怎么办？\nAlice: 我理解这对当地社区的影响，但是现在我们必须找到一种既能保护这个物种，也同时使您们的经济生活不受影响的解决方案。你有任何想法吗？我们可以一起来想办法。\n"
}
```
### Schema
```
instruction: 指令
input: 输入（本数据集均为空）
output: 输出
```
## Limitation and Usage Limits
We require developers only use the open-sourced code, data, model and any other artifacts generated via this project for research purposes. Commercial use and other potential harmful use cases are not allowed.
Since this dataset was generated by *ChatGPT* and was not strictly verified, it still has shortcomings regarding factuality and other aspects. When using this dataset, careful inspection is needed.
This dataset does not represent anyone's ground, interest or thought, and is not related to any kind of claim of any groups. The developers of this project do not assume any responsibility to potential harm inflicted by using this dataset and project.
|
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garythung/trashnet
|
garythung
| 2023-06-02T03:23:04Z | 67 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-06-02T01:46:39Z |
---
license: mit
---
|
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] |
alan-23/HealthAssistant115
|
alan-23
| 2023-07-05T11:36:11Z | 67 | 5 |
[
"task_categories:question-answering",
"size_categories:n<1K",
"language:en",
"license:apache-2.0",
"medical",
"biology",
"region:us"
] |
[
"question-answering"
] | 2023-07-05T11:31:24Z |
---
license: apache-2.0
task_categories:
- question-answering
language:
- en
tags:
- medical
- biology
pretty_name: Health Assistant
size_categories:
- n<1K
---
This dataset created for experiment to fine-tune LLM model. Questions created by ChatGPT as answer to "Give me questions a person can ask about for healthy life".
Answers created by OpenAI API.
|
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elsaEU/ELSA1M_track1
|
elsaEU
| 2023-08-27T08:01:57Z | 67 | 2 |
[
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-18T16:50:36Z |
---
elsaEU--ELSA1M_track1:
description: ''
citation: ''
homepage: ''
license: ''
features:
image:
decode: true
id: null
dtype: Image
id:
dtype: string
id: null
_type: Value
original_prompt:
dtype: string
id: null
_type: Value
positive_prompt:
dtype: string
id: null
_type: Value
negative_prompt:
dtype: string
id: null
_type: Value
model:
dtype: string
id: null
_type: Value
nsfw:
dtype: string
id: null
_type: Value
url_real_image:
dtype: string
id: null
_type: Value
filepath:
dtype: string
id: null
_type: Value
aspect_ratio:
feature:
dtype: int64
id: null
_type: Value
length: -1
id: null
_type: Sequence
post_processed: null
supervised_keys: null
task_templates: null
builder_name: imagefolder
config_name: default
version:
version_str: 0.0.0
description: null
major: 0
minor: 0
patch: 0
splits:
train:
name: train
num_bytes: 445926712527.43
num_examples: 992655
dataset_name: ELSA1M_track1
download_checksums: null
download_size: 223034360161
post_processing_size: null
dataset_size: 445926712527.43
size_in_bytes: 668961072688.4299
license: cc-by-4.0
---
# ELSA - Multimedia use case
**ELSA Multimedia is a large collection of Deep Fake images, generated using diffusion models**
### Dataset Summary
This dataset was developed as part of the EU project ELSA. Specifically for the Multimedia use-case.
Official webpage: https://benchmarks.elsa-ai.eu/
This dataset aims to develop effective solutions for detecting and mitigating the spread of deep fake images in multimedia content. Deep fake images, which are highly realistic and deceptive manipulations, pose significant risks to privacy, security, and trust in digital media. This dataset can be used to train robust and accurate models that can identify and flag instances of deep fake images.
### ELSA versions
| Name | Description | Link |
| ------------- | ------------- | ---------------------|
| ELSA1M_track1 | Dataset of 1M images generated using diffusion model | https://huggingface.co/datasets/elsaEU/ELSA1M_track1 |
| ELSA500k_track2 | Dataset of 500k images generated using diffusion model with diffusion attentive attribution maps [1] | https://huggingface.co/datasets/elsaEU/ELSA500k_track2 |
```python
from datasets import load_dataset
elsa_data = load_dataset("elsaEU/ELSA1M_track1", split="train", streaming=True)
for sample in elsa_data:
image = sample.pop("image")
metadata = sample
```
Using <a href="https://huggingface.co/docs/datasets/stream">streaming=True</a> lets you work with the dataset without downloading it.
## Dataset Structure
Each parquet file contains nearly 1k images and a JSON file with metadata.
The Metadata for generated images are:
- ID: Laion image ID
- original_prompt: Laion Prompt
- positive_prompt: positive prompt used for image generation
- negative_prompt: negative prompt used for image generation
- model: model used for the image generation
- nsfw: nsfw tag from Laion
- url_real_image: Url of the real image associated to the same prompt
- filepath: filepath of the fake image
- aspect_ratio: aspect ratio of the generated image
### Dataset Curators
- Leonardo Labs ([email protected])
- UNIMORE (https://aimagelab.ing.unimore.it/imagelab/)
|
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ChanceFocus/flare-convfinqa
|
ChanceFocus
| 2023-07-31T03:49:30Z | 67 | 2 |
[
"region:us"
] | null | 2023-07-31T03:49:18Z |
---
dataset_info:
features:
- name: id
dtype: string
- name: query
dtype: string
- name: answer
dtype: string
- name: turn
dtype: int64
- name: dialogue_id
dtype: int64
splits:
- name: train
num_bytes: 44382083
num_examples: 8891
- name: valid
num_bytes: 11171617
num_examples: 2213
- name: test
num_bytes: 7116753
num_examples: 1490
download_size: 11803908
dataset_size: 62670453
---
# Dataset Card for "flare-convfinqa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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derenrich/wikidata-en-descriptions
|
derenrich
| 2023-08-05T00:58:51Z | 67 | 1 |
[
"task_categories:summarization",
"size_categories:1M<n<10M",
"language:en",
"license:cc-by-sa-4.0",
"wikidata",
"wikipedia",
"region:us"
] |
[
"summarization"
] | 2023-08-05T00:06:21Z |
---
language:
- en
license: cc-by-sa-4.0
size_categories:
- 1M<n<10M
task_categories:
- summarization
tags:
- wikidata
- wikipedia
dataset_info:
features:
- name: output
dtype: string
- name: qid
dtype: string
- name: name
dtype: string
- name: input
dtype: string
- name: instruction
dtype: string
- name: text
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 7446030683
num_examples: 6243554
download_size: 4214056440
dataset_size: 7446030683
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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vipulgupta/CALM
|
vipulgupta
| 2023-11-09T03:08:48Z | 67 | 1 |
[
"task_categories:question-answering",
"size_categories:50k<n<100k",
"language:en",
"license:mit",
"arxiv:2308.12539",
"region:us"
] |
[
"question-answering"
] | 2023-08-23T23:49:51Z |
---
license: mit
task_categories:
- question-answering
language:
- en
size_categories:
- 50k<n<100k
pretty_name: CALM
configs:
- config_name: default
data_files:
- split: test
path:
- "data/gender_datasets/*.jsonl"
- "data/race_datasets/*.jsonl"
---
# Dataset Card for Dataset Name
## Dataset Description
This contains dataset for a multi-task dataset for measuring language model bias : [CALM](https://arxiv.org/abs/2308.12539)
This dataset is intended towards reliable deployment of language models by measuring issues of gender and race bias in model's prediction.
The dataset contains 3 task : question answering, sentiment classification, and natural language inference.
The dataset was collected by converting 16 exisiting datasets into templates.
- Repository: [CALM](https://github.com/vipulgupta1011/CALM)
## Dataset Structure
Each line in the downloaded data file is a json dict containing the data id and dialogue data in a list format. Below is an example line.
```
{
"context": "On the same day, Ed Miliband had announced he would stand down as Labour leader, while Raylan said he would resign as leader of the Liberal Democrats.",
"question": "What is Raylan leader of?",
"source_dataset": "qamr",
"gender": "male"
}
```
### Citation Information
```bibtex
@article{gupta2023calm,
title={CALM: A Multi-task Benchmark for Comprehensive Assessment of Language Model Bias},
author={Gupta, Vipul and Venkit, Pranav Narayanan and Lauren{\c{c}}on, Hugo and Wilson, Shomir and Passonneau, Rebecca J},
journal={arXiv preprint arXiv:2308.12539},
year={2023}
}
```
|
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nlewins/fleurs_ceb_to_en
|
nlewins
| 2023-10-10T15:44:08Z | 67 | 0 |
[
"region:us"
] | null | 2023-10-10T15:39:39Z |
---
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: id
dtype: int32
- name: transcription
dtype: string
- name: language
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: transcription_en
dtype: string
- name: audio_en
dtype:
audio:
sampling_rate: 16000
splits:
- name: train
num_bytes: 4837972886.028
num_examples: 3188
- name: validation
num_bytes: 332770769.0
num_examples: 225
- name: test
num_bytes: 834809869.0
num_examples: 541
download_size: 5885482902
dataset_size: 6005553524.028
---
# Dataset Card for "fleurs_ceb_to_en_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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brunnolou/swiss-code-of-obligations
|
brunnolou
| 2023-11-09T18:37:10Z | 67 | 0 |
[
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:en",
"language:de",
"license:apache-2.0",
"legal",
"region:us"
] |
[
"question-answering"
] | 2023-10-17T15:37:22Z |
---
license: apache-2.0
language:
- en
- de
tags:
- legal
pretty_name: Swiss Code of Obligations
size_categories:
- 1K<n<10K
task_categories:
- question-answering
configs:
- config_name: default
data_files:
- split: civil_code_de_paraphrase_multilingual
path: swiss-civil-code-de-paraphrase-multilingual-mpnet-base-v2.jsonl
- split: code_of_obligations_en_gte
path: swiss-code-of-obligations-en-gte-small.jsonl
- split: code_of_obligations_en_paraphrase_multilingual
path: swiss-code-of-obligations-en-paraphrase-multilingual-mpnet-base-v2.jsonl
---
# Swiss Code of Obligations (OR) and Swiss Civil Code
#### (Part Five: The Code of Obligations) of 30 March 1911 (Status as of 1 September 2023)
Files generated from the Swiss [publication platform for federal law](https://www.fedlex.admin.ch/en/home)
[Swiss Code of Obligations](https://www.fedlex.admin.ch/eli/cc/27/317_321_377/en)
### Format
Each article has the following type definition:
## With vector embeddings by [Xenova/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/Xenova/paraphrase-multilingual-mpnet-base-v2)
- swiss-civil-code-de-paraphrase-multilingual-mpnet-base-v2.jsonl
- swiss-code-of-obligations-en-paraphrase-multilingual-mpnet-base-v2.jsonl
## With vector embeddings by [Xenova/gte-small](https://huggingface.co/Xenova/gte-small)
- swiss-code-of-obligations-en-gte-small.jsonl
```ts
{
headings: string[]
article: string
link: string
content: string
vector: number[]
}
```
You can also find the original HTML where the data was extracted from.
- [html](https://huggingface.co/datasets/brunnolou/swiss-code-of-obligations/resolve/main/swiss-code-of-obligations.html)
# [Qdrant Vector Database](https://qdrant.tech/?gad_source=1&gclid=Cj0KCQiAgK2qBhCHARIsAGACuzkk-MhJWFZdKbwre95q-otN_mlcz4xcYH1aqTm8fVP0TRRiFkKbJ1QaAp27EALw_wcB)
### With vector embeddings by [Xenova/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/Xenova/paraphrase-multilingual-mpnet-base-v2)
- [swiss-civil-code-de-paraphrase-multilingual-mpnet-base-v2.snapshot.zip](https://huggingface.co/datasets/brunnolou/swiss-code-of-obligations/resolve/main/swiss-civil-code-de-paraphrase-multilingual-mpnet-base-v2.snapshot.zip)
- [swiss-code-of-obligations-en-paraphrase-multilingual-mpnet-base-v2.snapshot.zip](https://huggingface.co/datasets/brunnolou/swiss-code-of-obligations/resolve/main/swiss-code-of-obligations-en-paraphrase-multilingual-mpnet-base-v2.snapshot.zip)
## With vector embeddings by [Xenova/gte-small](https://huggingface.co/Xenova/gte-small)
- [Snapshot - Qdrant verstion v1.6.1 (zip)](https://huggingface.co/datasets/brunnolou/swiss-code-of-obligations/resolve/main/swiss-code-of-obligations-articles-gte-small-2023-10-18-12-13-25_qdrant-v1-6-1.snapshot.zip)
### 💾 Setup Qdrant Vector Database
1. Open the Qdrant dashboard console <http://localhost:6333/dashboard#/console>
1. Create a new collection running this:
> Vector size for `gte-small` is **`384`**. For `paraphrase-multilingual-mpnet-base-v2` is **`768`**.
```curl
PUT collections/COLLECTION_NAME
{
"vectors": {
"size": 384,
"distance": "Cosine"
}
}
```
1. Download the [snapshot file](https://huggingface.co/datasets/brunnolou/swiss-code-of-obligations/resolve/main/swiss-code-of-obligations-articles-gte-small-2023-10-18-12-13-25_qdrant-v1-6-1.snapshot.zip)
1. Unzip the file using the terminal (⚠️ **_not with Finder on Mac_** ⚠️) with `unzip <file_name>`
1. Upload the file using the following command. Adapt the fields accordingly and run it from the same directory, as where your snapshot lies
```shell
curl -X POST 'http://localhost:6333/collections/swiss-or/snapshots/upload' \
-H 'Content-Type:multipart/form-data' \
-F 'snapshot=@swiss-code-of-obligations-articles-gte-small-2023-10-18-12-13-25.snapshot'
```
<img src="https://cdn-uploads.huggingface.co/production/uploads/65256343a9f5b404762da984/LgxeBf0Bu_IkFtM3niWfq.png" width=480 />
|
[
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multi-train/amazon_review_2018_1107
|
multi-train
| 2023-11-10T18:36:36Z | 67 | 0 |
[
"region:us"
] | null | 2023-11-10T18:36:24Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: query
dtype: string
- name: pos
sequence: string
- name: neg
sequence: string
- name: task
dtype: string
- name: instruction
struct:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
splits:
- name: train
num_bytes: 146232172
num_examples: 200000
download_size: 81634497
dataset_size: 146232172
---
# Dataset Card for "amazon_review_2018_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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multi-train/altlex_1107
|
multi-train
| 2023-11-10T18:36:55Z | 67 | 0 |
[
"region:us"
] | null | 2023-11-10T18:36:48Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: query
dtype: string
- name: pos
sequence: string
- name: neg
sequence: string
- name: task
dtype: string
- name: instruction
struct:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
splits:
- name: train
num_bytes: 59606453
num_examples: 112696
download_size: 30565780
dataset_size: 59606453
---
# Dataset Card for "altlex_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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multi-train/cnn_dailymail_1107
|
multi-train
| 2023-11-10T18:39:45Z | 67 | 0 |
[
"region:us"
] | null | 2023-11-10T18:37:56Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: query
dtype: string
- name: pos
sequence: string
- name: neg
sequence: string
- name: task
dtype: string
- name: instruction
struct:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
splits:
- name: train
num_bytes: 1710027721
num_examples: 200000
download_size: 1026018118
dataset_size: 1710027721
---
# Dataset Card for "cnn_dailymail_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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multi-train/coco_captions_1107
|
multi-train
| 2023-11-10T18:39:54Z | 67 | 0 |
[
"region:us"
] | null | 2023-11-10T18:39:48Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: query
dtype: string
- name: pos
sequence: string
- name: neg
sequence: string
- name: task
dtype: string
- name: instruction
struct:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
splits:
- name: train
num_bytes: 27977412
num_examples: 82783
download_size: 8138135
dataset_size: 27977412
---
# Dataset Card for "coco_captions_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
multi-train/eli5_question_answer_1107
|
multi-train
| 2023-11-10T18:40:10Z | 67 | 0 |
[
"region:us"
] | null | 2023-11-10T18:39:56Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: query
dtype: string
- name: pos
sequence: string
- name: neg
sequence: string
- name: task
dtype: string
- name: instruction
struct:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
splits:
- name: train
num_bytes: 187610302
num_examples: 200000
download_size: 106360840
dataset_size: 187610302
---
# Dataset Card for "eli5_question_answer_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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multi-train/medmcqa_1107
|
multi-train
| 2023-11-10T18:58:42Z | 67 | 0 |
[
"region:us"
] | null | 2023-11-10T18:58:26Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: query
dtype: string
- name: pos
sequence: string
- name: neg
sequence: string
- name: task
dtype: string
- name: instruction
struct:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
splits:
- name: train
num_bytes: 194641944
num_examples: 160869
download_size: 102313307
dataset_size: 194641944
---
# Dataset Card for "medmcqa_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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multi-train/squad_pairs_1107
|
multi-train
| 2023-11-10T18:59:09Z | 67 | 0 |
[
"region:us"
] | null | 2023-11-10T18:59:03Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: query
dtype: string
- name: pos
sequence: string
- name: neg
sequence: string
- name: task
dtype: string
- name: instruction
struct:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
splits:
- name: train
num_bytes: 131284545
num_examples: 87599
download_size: 27083693
dataset_size: 131284545
---
# Dataset Card for "squad_pairs_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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multi-train/searchQA_top5_snippets_1107
|
multi-train
| 2023-11-10T18:59:21Z | 67 | 0 |
[
"region:us"
] | null | 2023-11-10T18:59:11Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: query
dtype: string
- name: pos
sequence: string
- name: neg
sequence: string
- name: task
dtype: string
- name: instruction
struct:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
splits:
- name: train
num_bytes: 102625564
num_examples: 117220
download_size: 62573380
dataset_size: 102625564
---
# Dataset Card for "searchQA_top5_snippets_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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multi-train/pubmedqa_1107
|
multi-train
| 2023-11-10T18:59:57Z | 67 | 0 |
[
"region:us"
] | null | 2023-11-10T18:59:23Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: query
dtype: string
- name: pos
sequence: string
- name: neg
sequence: string
- name: task
dtype: string
- name: instruction
struct:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
splits:
- name: train
num_bytes: 553042848
num_examples: 200000
download_size: 277163918
dataset_size: 553042848
---
# Dataset Card for "pubmedqa_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
multi-train/npr_1107
|
multi-train
| 2023-11-10T19:00:27Z | 67 | 0 |
[
"region:us"
] | null | 2023-11-10T18:59:59Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: query
dtype: string
- name: pos
sequence: string
- name: neg
sequence: string
- name: task
dtype: string
- name: instruction
struct:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
splits:
- name: train
num_bytes: 474583082
num_examples: 200000
download_size: 259084905
dataset_size: 474583082
---
# Dataset Card for "npr_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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multi-train/wikihow_1107
|
multi-train
| 2023-11-10T19:00:54Z | 67 | 0 |
[
"region:us"
] | null | 2023-11-10T19:00:47Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: query
dtype: string
- name: pos
sequence: string
- name: neg
sequence: string
- name: task
dtype: string
- name: instruction
struct:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
splits:
- name: train
num_bytes: 52538597
num_examples: 128542
download_size: 19871957
dataset_size: 52538597
---
# Dataset Card for "wikihow_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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multi-train/trex-train-multikilt_1107
|
multi-train
| 2023-11-10T19:01:10Z | 67 | 0 |
[
"region:us"
] | null | 2023-11-10T19:00:56Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: query
dtype: string
- name: pos
sequence: string
- name: neg
sequence: string
- name: task
dtype: string
- name: instruction
struct:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
splits:
- name: train
num_bytes: 228887845
num_examples: 200000
download_size: 116247120
dataset_size: 228887845
---
# Dataset Card for "trex-train-multikilt_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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multi-train/nq-train-multikilt_1107
|
multi-train
| 2023-11-10T19:01:21Z | 67 | 0 |
[
"region:us"
] | null | 2023-11-10T19:01:12Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: query
dtype: string
- name: pos
sequence: string
- name: neg
sequence: string
- name: task
dtype: string
- name: instruction
struct:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
splits:
- name: train
num_bytes: 94822797
num_examples: 76945
download_size: 53958820
dataset_size: 94822797
---
# Dataset Card for "nq-train-multikilt_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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multi-train/gigaword_1107
|
multi-train
| 2023-11-10T19:01:31Z | 67 | 0 |
[
"region:us"
] | null | 2023-11-10T19:01:23Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: query
dtype: string
- name: pos
sequence: string
- name: neg
sequence: string
- name: task
dtype: string
- name: instruction
struct:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
splits:
- name: train
num_bytes: 87945355
num_examples: 200000
download_size: 41386512
dataset_size: 87945355
---
# Dataset Card for "gigaword_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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multi-train/yahoo_answers_title_answer_1107
|
multi-train
| 2023-11-10T19:01:43Z | 67 | 0 |
[
"region:us"
] | null | 2023-11-10T19:01:32Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: query
dtype: string
- name: pos
sequence: string
- name: neg
sequence: string
- name: task
dtype: string
- name: instruction
struct:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
splits:
- name: train
num_bytes: 141237635
num_examples: 200000
download_size: 78339836
dataset_size: 141237635
---
# Dataset Card for "yahoo_answers_title_answer_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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multi-train/fever-train-multikilt_1107
|
multi-train
| 2023-11-10T19:01:53Z | 67 | 0 |
[
"region:us"
] | null | 2023-11-10T19:01:45Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: query
dtype: string
- name: pos
sequence: string
- name: neg
sequence: string
- name: task
dtype: string
- name: instruction
struct:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
splits:
- name: train
num_bytes: 87617512
num_examples: 71257
download_size: 46276668
dataset_size: 87617512
---
# Dataset Card for "fever-train-multikilt_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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multi-train/flickr30k_captions_1107
|
multi-train
| 2023-11-10T19:01:59Z | 67 | 0 |
[
"region:us"
] | null | 2023-11-10T19:01:55Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: query
dtype: string
- name: pos
sequence: string
- name: neg
sequence: string
- name: task
dtype: string
- name: instruction
struct:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
splits:
- name: train
num_bytes: 12081988
num_examples: 31783
download_size: 4010622
dataset_size: 12081988
---
# Dataset Card for "flickr30k_captions_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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multi-train/sentence-compression_1107
|
multi-train
| 2023-11-10T19:02:09Z | 67 | 0 |
[
"region:us"
] | null | 2023-11-10T19:02:00Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: query
dtype: string
- name: pos
sequence: string
- name: neg
sequence: string
- name: task
dtype: string
- name: instruction
struct:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
splits:
- name: train
num_bytes: 71393703
num_examples: 180000
download_size: 36617830
dataset_size: 71393703
---
# Dataset Card for "sentence-compression_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
multi-train/zeroshot-train-multikilt_1107
|
multi-train
| 2023-11-10T19:02:21Z | 67 | 0 |
[
"region:us"
] | null | 2023-11-10T19:02:10Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: query
dtype: string
- name: pos
sequence: string
- name: neg
sequence: string
- name: task
dtype: string
- name: instruction
struct:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
splits:
- name: train
num_bytes: 156575454
num_examples: 130514
download_size: 74461241
dataset_size: 156575454
---
# Dataset Card for "zeroshot-train-multikilt_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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multi-train/hotpotqa-train-multikilt_1107
|
multi-train
| 2023-11-10T19:02:52Z | 67 | 0 |
[
"region:us"
] | null | 2023-11-10T19:02:43Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: query
dtype: string
- name: pos
sequence: string
- name: neg
sequence: string
- name: task
dtype: string
- name: instruction
struct:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
splits:
- name: train
num_bytes: 88502871
num_examples: 68659
download_size: 50639711
dataset_size: 88502871
---
# Dataset Card for "hotpotqa-train-multikilt_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
multi-train/scitldr_1107
|
multi-train
| 2023-11-10T19:03:01Z | 67 | 0 |
[
"region:us"
] | null | 2023-11-10T19:02:54Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: query
dtype: string
- name: pos
sequence: string
- name: neg
sequence: string
- name: task
dtype: string
- name: instruction
struct:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
splits:
- name: train
num_bytes: 59829071
num_examples: 1992
download_size: 29628456
dataset_size: 59829071
---
# Dataset Card for "scitldr_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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multi-train/xsum_1107
|
multi-train
| 2023-11-10T19:04:02Z | 67 | 0 |
[
"region:us"
] | null | 2023-11-10T19:03:03Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: query
dtype: string
- name: pos
sequence: string
- name: neg
sequence: string
- name: task
dtype: string
- name: instruction
struct:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
splits:
- name: train
num_bytes: 848430524
num_examples: 200000
download_size: 523334138
dataset_size: 848430524
---
# Dataset Card for "xsum_1107"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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jetaudio/binhvq_news
|
jetaudio
| 2023-11-25T04:03:44Z | 67 | 0 |
[
"region:us"
] | null | 2023-11-25T00:45:20Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 68939074439.0
num_examples: 19582227
- name: validation
num_bytes: 349157289.0
num_examples: 104519
download_size: 35606535605
dataset_size: 69288231728.0
---
# Dataset Card for "binhvq_news"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
doubledsbv/dataset_long_context_extension_4-32
|
doubledsbv
| 2023-11-29T11:43:05Z | 67 | 0 |
[
"region:us"
] | null | 2023-11-29T11:39:37Z |
---
dataset_info:
features:
- name: source_dataset
dtype: string
- name: tokens
dtype: int64
- name: range
dtype: string
- name: text
dtype: string
- name: instruction
dtype: string
- name: output
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 6745724516
num_examples: 57729
- name: validation
num_bytes: 756199799
num_examples: 6415
download_size: 4209676930
dataset_size: 7501924315
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
|
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] |
adsabs/WIESP2022-NER
|
adsabs
| 2023-05-17T19:42:32Z | 66 | 6 |
[
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-4.0",
"region:us"
] |
[
"token-classification"
] | 2022-05-05T18:31:34Z |
---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: 'WIESP2022-NER'
size_categories:
- 1K<n<10K
source_datasets: []
task_categories:
- token-classification
task_ids:
- named-entity-recognition
---
# Dataset for the first <a href="https://ui.adsabs.harvard.edu/WIESP/" style="color:blue">Workshop on Information Extraction from Scientific Publications (WIESP/2022)</a>.
## Dataset Description
Datasets with text fragments from astrophysics papers, provided by the [NASA Astrophysical Data System](https://ui.adsabs.harvard.edu/) with manually tagged astronomical facilities and other entities of interest (e.g., celestial objects).
Datasets are in JSON Lines format (each line is a json dictionary).
The datasets are formatted similarly to the CONLL2003 format. Each token is associated with an NER tag. The tags follow the "B-" and "I-" convention from the [IOB2 syntax]("https://en.wikipedia.org/wiki/Inside%E2%80%93outside%E2%80%93beginning_(tagging)")
Each entry consists of a dictionary with the following keys:
- `"unique_id"`: a unique identifier for this data sample. Must be included in the predictions.
- `"tokens"`: the list of tokens (strings) that form the text of this sample. Must be included in the predictions.
- `"ner_tags"`: the list of NER tags (in IOB2 format)
The following keys are not strictly needed by the participants:
- `"ner_ids"`: the pre-computed list of ids corresponding ner_tags, as given by the dictionary in ner_tags.json
- `"label_studio_id"`, `"section"`, `"bibcode"`: references for internal NASA/ADS use.
## Instructions for Workshop participants:
How to load the data using the Huggingface library:
```python
from datasets import load_dataset
dataset = load_dataset("adsabs/WIESP2022-NER")
```
How to load the data if you cloned the repository locally:
(assuming `./WIESP2022-NER-DEV.jsonl` is in the current directory, change as needed)
- python (as list of dictionaries):
```python
import json
with open("./WIESP2022-NER-DEV.jsonl", 'r') as f:
wiesp_dev_json = [json.loads(l) for l in list(f)]
```
- into Huggingface (as a Huggingface Dataset):
```python
from datasets import Dataset
wiesp_dev_from_json = Dataset.from_json(path_or_paths="./WIESP2022-NER-DEV.jsonl")
```
How to compute your scores on the training data:
1. format your predictions as a list of dictionaries, each with the same `"unique_id"` and `"tokens"` keys from the dataset, as well as the list of predicted NER tags under the `"pred_ner_tags"` key (see `WIESP2022-NER-DEV-sample-predictions.jsonl` for an example).
2. pass the references and predictions datasets to the `compute_MCC()` and `compute_seqeval()` functions (from the `.py` files with the same names).
Requirement to run the scoring scripts:
[NumPy](https://numpy.org/install/)
[scikit-learn](https://scikit-learn.org/stable/install.html)
[seqeval](https://github.com/chakki-works/seqeval#installation)
To get scores on the validation data, zip your predictions file (a single `.jsonl' file formatted following the same instructions as above) and upload the `.zip` file to the [Codalabs](https://codalab.lisn.upsaclay.fr/competitions/5062) competition.
## File list
```
├── WIESP2022-NER-TRAINING.jsonl : 1753 samples for training.
├── WIESP2022-NER-DEV.jsonl : 20 samples for development.
├── WIESP2022-NER-DEV-sample-predictions.jsonl : an example file with properly formatted predictions on the development data.
├── WIESP2022-NER-VALIDATION-NO-LABELS.jsonl : 1366 samples for validation without the NER labels. Used for the WIESP2022 workshop.
├── WIESP2022-NER-VALIDATION.jsonl : 1366 samples for validation
├── WIESP2022-NER-TESTING-NO-LABELS.jsonl : 2505 samples for testing without the NER labels. Used for the WIESP2022 workshop.
├── WIESP2022-NER-TESTING.jsonl : 2505 samples for testing
├── README.MD : this file.
├── tag_definitions.md : short descriptions and examples of the tags used in the task.
└── scoring-scripts/ : scripts used to evaluate submissions.
├── compute_MCC.py : computes the Matthews correlation coefficient between two datasets.
└── compute_seqeval.py : computes the seqeval scores (precision, recall, f1, overall and for each class) between two datasets.
```
## Cite as
[Overview of the First Shared Task on Detecting Entities in the Astrophysics Literature (DEAL)](https://aclanthology.org/2022.wiesp-1.1) (Grezes et al., WIESP 2022)
```python
@inproceedings{grezes-etal-2022-overview,
title = "Overview of the First Shared Task on Detecting Entities in the Astrophysics Literature ({DEAL})",
author = "Grezes, Felix and
Blanco-Cuaresma, Sergi and
Allen, Thomas and
Ghosal, Tirthankar",
booktitle = "Proceedings of the first Workshop on Information Extraction from Scientific Publications",
month = "nov",
year = "2022",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wiesp-1.1",
pages = "1--7",
abstract = "In this article, we describe the overview of our shared task: Detecting Entities in the Astrophysics Literature (DEAL). The DEAL shared task was part of the Workshop on Information Extraction from Scientific Publications (WIESP) in AACL-IJCNLP 2022. Information extraction from scientific publications is critical in several downstream tasks such as identification of critical entities, article summarization, citation classification, etc. The motivation of this shared task was to develop a community-wide effort for entity extraction from astrophysics literature. Automated entity extraction would help to build knowledge bases, high-quality meta-data for indexing and search, and several other use-cases of interests. Thirty-three teams registered for DEAL, twelve of them participated in the system runs, and finally four teams submitted their system descriptions. We analyze their system and performance and finally discuss the findings of DEAL.",
}
```
|
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bigscience-data/roots_en_the_pile_uspto
|
bigscience-data
| 2022-12-12T11:03:28Z | 66 | 1 |
[
"language:en",
"license:mit",
"region:us"
] | null | 2022-05-18T09:09:05Z |
---
language: en
license: mit
extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience
Ethical Charter. The charter can be found at:
https://hf.co/spaces/bigscience/ethical-charter'
extra_gated_fields:
I have read and agree to abide by the BigScience Ethical Charter: checkbox
---
ROOTS Subset: roots_en_the_pile_uspto
# the_pile_uspto
- Dataset uid: `the_pile_uspto`
### Description
### Homepage
### Licensing
### Speaker Locations
### Sizes
- 0.5358 % of total
- 2.9032 % of en
### BigScience processing steps
#### Filters applied to: en
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
|
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] |
sileod/movie_recommendation
|
sileod
| 2023-05-25T14:53:49Z | 66 | 10 |
[
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"task_ids:open-domain-qa",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"movie-recommendation",
"collaborative-filtering",
"movielens",
"film",
"doi:10.57967/hf/0257",
"region:us"
] |
[
"multiple-choice",
"question-answering"
] | 2022-05-27T08:25:19Z |
---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: 'movie_recommendation'
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- multiple-choice
- question-answering
task_ids:
- multiple-choice-qa
- open-domain-qa
tags:
- movie-recommendation
- collaborative-filtering
- movielens
- film
---
# Dataset for evaluation of (zero-shot) recommendation with language models
We showed that pretrained large language models can act as a recommender system, and compare few-shot learning results to matrix factorization baselines.
This is the BIG-Bench version of our language-based movie recommendation dataset.
<https://github.com/google/BIG-bench/tree/main/bigbench/benchmark_tasks/movie_recommendation>
GPT-2 has a 48.8% accuracy, chance is 25%.
Human accuracy is 60.4%.
# Citation
```
@InProceedings{sileodreclm22,
author="Sileo, Damien
and Vossen, Wout
and Raymaekers, Robbe",
editor="Hagen, Matthias
and Verberne, Suzan
and Macdonald, Craig
and Seifert, Christin
and Balog, Krisztian
and N{\o}rv{\aa}g, Kjetil
and Setty, Vinay",
title="Zero-Shot Recommendation as Language Modeling",
booktitle="Advances in Information Retrieval",
year="2022",
publisher="Springer International Publishing",
address="Cham",
pages="223--230",
isbn="978-3-030-99739-7"
}
```
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] |
imvladikon/bmc
|
imvladikon
| 2022-11-17T16:52:43Z | 66 | 0 |
[
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-reuters-corpus",
"language:he",
"license:other",
"arxiv:2007.15620",
"region:us"
] |
[
"token-classification"
] | 2022-06-22T15:39:14Z |
---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- he
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-reuters-corpus
task_categories:
- token-classification
task_ids:
- named-entity-recognition
train-eval-index:
- config: bmc
task: token-classification
task_id: entity_extraction
splits:
train_split: train
eval_split: validation
test_split: test
col_mapping:
tokens: tokens
ner_tags: tags
metrics:
- type: seqeval
name: seqeval
---
# Splits for the Ben-Mordecai and Elhadad Hebrew NER Corpus (BMC)
In order to evaluate performance in accordance with the original Ben-Mordecai and Elhadad (2005) work, we provide three 75%-25% random splits.
* Only the 7 entity categories viable for evaluation were kept (DATE, LOC, MONEY, ORG, PER, PERCENT, TIME) --- all MISC entities were filtered out.
* Sequence label scheme was changed from IOB to BIOES
* The dev sets are 10% taken out of the 75%
## Citation
If you use use the BMC corpus, please cite the original paper as well as our paper which describes the splits:
* Ben-Mordecai and Elhadad (2005):
```console
@mastersthesis{naama,
title={Hebrew Named Entity Recognition},
author={Ben-Mordecai, Naama},
advisor={Elhadad, Michael},
year={2005},
url="https://www.cs.bgu.ac.il/~elhadad/nlpproj/naama/",
institution={Department of Computer Science, Ben-Gurion University},
school={Department of Computer Science, Ben-Gurion University},
}
```
* Bareket and Tsarfaty (2020)
```console
@misc{bareket2020neural,
title={Neural Modeling for Named Entities and Morphology (NEMO^2)},
author={Dan Bareket and Reut Tsarfaty},
year={2020},
eprint={2007.15620},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
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frgfm/imagewoof
|
frgfm
| 2022-12-11T22:26:18Z | 66 | 2 |
[
"task_categories:image-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"size_categories:1K<n<10K",
"source_datasets:extended",
"language:en",
"license:apache-2.0",
"region:us"
] |
[
"image-classification"
] | 2022-07-26T15:21:56Z |
---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- apache-2.0
multilinguality: []
size_categories:
- 1K<n<10K
source_datasets:
- extended
task_categories:
- image-classification
task_ids: []
paperswithcode_id: imagewoof
pretty_name: Imagewoof
---
# Dataset Card for Imagewoof
## 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://github.com/fastai/imagenette#imagewoof
- **Repository:** https://github.com/fastai/imagenette
- **Leaderboard:** https://paperswithcode.com/sota/image-classification-on-imagewoof
### Dataset Summary
A smaller subset of 10 classes from [Imagenet](https://huggingface.co/datasets/imagenet-1k#dataset-summary) that aren't so easy to classify, since they're all dog breeds.
This dataset was created by [Jeremy Howard](https://twitter.com/jeremyphoward), and this repository is only there to share his work on this platform. The repository owner takes no credit of any kind in the creation, curation or packaging of the dataset.
### Supported Tasks and Leaderboards
- `image-classification`: The dataset can be used to train a model for Image Classification.
### Languages
The class labels in the dataset are in English.
## Dataset Structure
### Data Instances
A data point comprises an image URL and its classification label.
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=320x320 at 0x19FA12186D8>,
'label': 'Beagle',
}
```
### Data Fields
- `image`: A `PIL.Image.Image` object containing the image.
- `label`: the expected class label of the image.
### Data Splits
| |train|validation|
|---------|----:|---------:|
|imagewoof| 9025| 3929|
## Dataset Creation
### Curation Rationale
cf. https://huggingface.co/datasets/imagenet-1k#curation-rationale
### Source Data
#### Initial Data Collection and Normalization
Imagewoof is a subset of [ImageNet](https://huggingface.co/datasets/imagenet-1k). Information about data collection of the source data can be found [here](https://huggingface.co/datasets/imagenet-1k#initial-data-collection-and-normalization).
### Annotations
#### Annotation process
cf. https://huggingface.co/datasets/imagenet-1k#annotation-process
#### Who are the annotators?
cf. https://huggingface.co/datasets/imagenet-1k#who-are-the-annotators
### Personal and Sensitive Information
cf. https://huggingface.co/datasets/imagenet-1k#personal-and-sensitive-information
## Considerations for Using the Data
### Social Impact of Dataset
cf. https://huggingface.co/datasets/imagenet-1k#social-impact-of-dataset
### Discussion of Biases
cf. https://huggingface.co/datasets/imagenet-1k#discussion-of-biases
### Other Known Limitations
cf. https://huggingface.co/datasets/imagenet-1k#other-known-limitations
## Additional Information
### Dataset Curators
cf. https://huggingface.co/datasets/imagenet-1k#dataset-curators
and Jeremy Howard
### Licensing Information
[Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).
### Citation Information
```
@software{Howard_Imagewoof_2019,
title={Imagewoof: a subset of 10 classes from Imagenet that aren't so easy to classify},
author={Jeremy Howard},
year={2019},
month={March},
publisher = {GitHub},
url = {https://github.com/fastai/imagenette#imagewoof}
}
```
### Contributions
This dataset was created by [Jeremy Howard](https://twitter.com/jeremyphoward) and published on [Github](https://github.com/fastai/imagenette). It was then only integrated into HuggingFace Datasets by [@frgfm](https://huggingface.co/frgfm).
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bigbio/an_em
|
bigbio
| 2022-12-22T15:43:14Z | 66 | 1 |
[
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | null | 2022-11-13T18:05:07Z |
---
language:
- en
bigbio_language:
- English
license: cc-by-sa-3.0
multilinguality: monolingual
bigbio_license_shortname: CC_BY_SA_3p0
pretty_name: AnEM
homepage: http://www.nactem.ac.uk/anatomy/
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
- COREFERENCE_RESOLUTION
- RELATION_EXTRACTION
---
# Dataset Card for AnEM
## Dataset Description
- **Homepage:** http://www.nactem.ac.uk/anatomy/
- **Pubmed:** True
- **Public:** True
- **Tasks:** NER,COREF,RE
AnEM corpus is a domain- and species-independent resource manually annotated for anatomical
entity mentions using a fine-grained classification system. The corpus consists of 500 documents
(over 90,000 words) selected randomly from citation abstracts and full-text papers with
the aim of making the corpus representative of the entire available biomedical scientific
literature. The corpus annotation covers mentions of both healthy and pathological anatomical
entities and contains over 3,000 annotated mentions.
## Citation Information
```
@inproceedings{ohta-etal-2012-open,
author = {Ohta, Tomoko and Pyysalo, Sampo and Tsujii, Jun{'}ichi and Ananiadou, Sophia},
title = {Open-domain Anatomical Entity Mention Detection},
journal = {},
volume = {W12-43},
year = {2012},
url = {https://aclanthology.org/W12-4304},
doi = {},
biburl = {},
bibsource = {},
publisher = {Association for Computational Linguistics}
}
```
|
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playgroundai/blip_clipseg_inpainting_ip2p_data_test
|
playgroundai
| 2023-02-08T09:26:41Z | 66 | 1 |
[
"region:us"
] | null | 2023-02-08T09:26:33Z |
---
dataset_info:
features:
- name: text
dtype: string
- name: chosen_item
dtype: string
- name: og_image
dtype: image
- name: mask
dtype: image
- name: output_image
dtype: image
splits:
- name: train
num_bytes: 650403547.0
num_examples: 825
download_size: 650341793
dataset_size: 650403547.0
---
# Dataset Card for "blip_clipseg_inpainting_ip2p_data_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
lucadiliello/searchqa
|
lucadiliello
| 2023-06-06T08:34:01Z | 66 | 0 |
[
"region:us"
] | null | 2023-02-25T18:04:03Z |
---
dataset_info:
features:
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence: string
- name: key
dtype: string
- name: labels
list:
- name: end
sequence: int64
- name: start
sequence: int64
splits:
- name: train
num_bytes: 483999103
num_examples: 117384
- name: validation
num_bytes: 69647447
num_examples: 16980
download_size: 325197949
dataset_size: 553646550
---
# Dataset Card for "searchqa"
Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look [here](https://huggingface.co/datasets/mrqa).
|
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] |
nastyboget/stackmix_hkr
|
nastyboget
| 2023-03-23T18:42:10Z | 66 | 0 |
[
"task_categories:image-to-text",
"size_categories:100K<n<1M",
"language:ru",
"license:mit",
"region:us"
] |
[
"image-to-text"
] | 2023-03-20T15:41:33Z |
---
license: mit
task_categories:
- image-to-text
language:
- ru
size_categories:
- 100K<n<1M
---
Dataset generated from HKR train set using Stackmix
===================================================
Number of images: 300000
Sources:
* [HKR dataset](https://github.com/abdoelsayed2016/HKR_Dataset)
* [Stackmix code](https://github.com/ai-forever/StackMix-OCR)
|
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koutch/intro_prog
|
koutch
| 2023-06-05T08:45:02Z | 66 | 0 |
[
"region:us"
] | null | 2023-04-05T14:44:41Z |
---
dataset_info:
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features:
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---
# Dataset Card for intro_prog
## Dataset Description
### Dataset Summary
IntroProg is a collection of students' submissions to assignments in various introductory programming courses offered at different universities.
Currently, the dataset contains submissions collected from Dublin City University, and the University of Singapore.
#### Dublin
The Dublin programming dataset is a dataset composed of students' submissions to introductory programming assignments at the University of Dublin.
Students submitted these programs for multiple programming courses over the duration of three academic years.
#### Singapore
The Singapore dataset contains 2442 correct and 1783 buggy program attempts by 361 undergraduate students
crediting an introduction to Python programming course at NUS (National University of Singapore).
### Supported Tasks and Leaderboards
#### "Metadata": Program synthesis
Similarly to the [Most Basic Python Programs](https://huggingface.co/datasets/mbpp) (mbpp), the data split can be used to evaluate
code generations models.
#### "Data"
The data configuration contains all the submissions as well as an indicator of whether these passed the required test.
#### "repair": Program refinement/repair
The "repair" configuration of each dataset is a subset of the "data" configuration
augmented with educators' annotations on the corrections to the buggy programs.
This configuration can be used for the task of program refinement. In [Computing Education Research](https://faculty.washington.edu/ajko/cer/) (CER),
methods for automatically repairing student programs are used to provide students with feedback and help them debug their code.
#### "bug": Bug classification
[Coming soon]
### Languages
The assignments were written in Python.
## Dataset Structure
One configuration is defined by one source dataset *dublin* or *singapore* and one subconfiguration ("metadata", "data", or "repair"):
* "dublin_metadata"
* "dublin_data"
* "dublin_repair"
* "singapore_metadata"
* "singapore_data"
* "singapore_repair"
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
Some of the fields are configuration specific
* submission_id: a unique number identifying the submission
* user: a unique string identifying the (anonymized) student who submitted the solution
* date: the timestamp at which the grading server received the submission
* func_code: the cleaned code submitted
* func_name: the name of the function that had to be implemented
* assingment_id: the unique (string) identifier of the assignment that had to be completed
* academic_year: the starting year of the academic year (e.g. 2015 for the academic year 2015-2016)
* module: the course/module
* test: a human eval-style string which can be used to execute the submitted solution on the provided test cases
* Description: a description of what the function is supposed to achieve
* correct: whether the solution passed all tests or not
### Data Splits
#### Dublin
The Dublin dataset is split into a training and validation set. The training set contains the submissions to the assignments
written during the academic years 2015-2016, and 2016-2017, while the test set contains programs written during the academic year 2017-2018.
#### Singapore
The Singapore dataset only contains a training split, which can be used as a test split for evaluating how your feedback
methods perform on an unseen dataset (if, for instance, you train your methods on the Dublin Dataset).
## 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
#### Dublin
#### Singapore
The data was released under a [GNU Lesser General Public License v3.0](https://github.com/githubhuyang/refactory/blob/master/LICENSE) license
### Citation Information
```
@inproceedings{azcona2019user2code2vec,
title={user2code2vec: Embeddings for Profiling Students Based on Distributional Representations of Source Code},
author={Azcona, David and Arora, Piyush and Hsiao, I-Han and Smeaton, Alan},
booktitle={Proceedings of the 9th International Learning Analytics & Knowledge Conference (LAK’19)},
year={2019},
organization={ACM}
}
@inproceedings{DBLP:conf/edm/CleuziouF21,
author = {Guillaume Cleuziou and
Fr{\'{e}}d{\'{e}}ric Flouvat},
editor = {Sharon I{-}Han Hsiao and
Shaghayegh (Sherry) Sahebi and
Fran{\c{c}}ois Bouchet and
Jill{-}J{\^{e}}nn Vie},
title = {Learning student program embeddings using abstract execution traces},
booktitle = {Proceedings of the 14th International Conference on Educational Data
Mining, {EDM} 2021, virtual, June 29 - July 2, 2021},
publisher = {International Educational Data Mining Society},
year = {2021},
timestamp = {Wed, 09 Mar 2022 16:47:22 +0100},
biburl = {https://dblp.org/rec/conf/edm/CleuziouF21.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
### Contributions
[More Information Needed]
|
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BelleGroup/train_3.5M_CN
|
BelleGroup
| 2023-08-16T06:13:59Z | 66 | 79 |
[
"task_categories:text2text-generation",
"size_categories:1M<n<10M",
"language:zh",
"license:gpl-3.0",
"region:us"
] |
[
"text2text-generation"
] | 2023-05-10T07:45:23Z |
---
license: gpl-3.0
task_categories:
- text2text-generation
language:
- zh
size_categories:
- 1M<n<10M
---
## 内容
包含约350万条由[BELLE](https://github.com/LianjiaTech/BELLE)项目生成的中文指令数据。
## 样例
```
{
"id":"66623028",
"conversations":[
{
"from":"human",
"value":"归纳介绍三种不同类型的电影题材"
},
{
"from":"assistant",
"value":"1. 动作片：动作片通常强调动作和战斗场面，故事情节紧凑，充满了紧张和兴奋的氛围。主角一般是具有超凡实力或特殊技能的英雄人物，往往会与反派角色进行一系列激烈的格斗，追逐和爆破等酷炫场面。例如《速度与激情》和《复仇者联盟》等。\n2. 爱情片：爱情片是以爱情为主题的电影，着重呈现角色之间的感情纠葛，展现了爱情的美好和悲壮。情节与角色之间的发展令人深思，增加了观众的感性体验。这种电影的故事情节主要以人类与人类之间的情感变化为主题。例如《泰坦尼克号》和《让子弹飞》等。\n3. 科幻片：科幻片是以未来科技和未知世界为主题的电影，通常包括时间旅行、外星人、虚拟现实、未来社会等各种奇幻的元素。这种电影描绘了一种比现实更加宏伟、更加神秘和惊奇的世界。例如《星际穿越》和《发条橙》等。"
}
]
}
{
"id":"18205437",
"conversations":[
{
"from":"human",
"value":"根据给定的食材，编写一道美食菜谱。\n鸡腿肉、蘑菇、洋葱"
},
{
"from":"assistant",
"value":"鸡肉炒蘑菇洋葱\n食材：\n- 鸡腿肉 400克，去骨去皮切成小块\n- 蘑菇 200克，切片\n- 洋葱 1个，切丝\n- 生姜 适量，切丝\n- 酱油 2勺\n- 料酒 1勺\n- 盐 适量\n- 白胡椒粉 适量\n- 植物油 适量\n做法：\n1. 在一个碗里放入切好的鸡腿肉块，加入酱油、料酒和适量的白胡椒粉，搅拌均匀，腌制10分钟。\n2. 热锅凉油，加入姜丝翻炒，放入腌好的鸡肉块翻炒至变色。\n3. 加入洋葱丝和蘑菇，翻炒2-3分钟至蘑菇变软。\n4. 加入适量的盐和少量的水，盖上锅盖焖煮5分钟直至鸡肉熟透。\n5. 最后翻炒几下，使汤汁变浓稠即可。\n温馨提示：\n1. 在腌制鸡肉时，如果想让肉质更嫩滑，可以加入少量的淀粉和蛋清。\n2. 可以根据个人口味加入其他调料，如蒜末、辣椒等。\n3. 洋葱和蘑菇可以用其他蔬菜代替，如青椒、胡萝卜等。"
},
{
"from":"human",
"value":"太好了，我会试着照着你教的做一次，但是我最近买了一部新相机，想让你推荐一些好的拍照地点。"
},
{
"from":"assistant",
"value":"当地的风景区、公园、景点等等都是比较适合拍照的地点。如果你想要一些特别的地方，可以考虑一些建筑群，街道巷子、咖啡馆、书店等具有文艺气息的地方。"
},
{
"from":"human",
"value":"嗯，这些地方都很不错，但是我还想找一些可以体验当地文化和风俗的地点，你有什么好的建议吗？"
},
{
"from":"assistant",
"value":"如果你想要更深入地了解当地文化和风俗，可以考虑参加一些游学活动，比如参观当地的博物馆、参加民俗节庆、尝试当地美食等等。此外，也可以去一些小城镇，感受那里的古朴风情。"
}
]
}
```
### 字段：
```
id: 数据id
conversations: 数据内容，以对话形式给出，包括多轮和单轮对话的数据
```
### 补充：
针对该数据集，新增了指令类别字段，共包括13个类别，详情见
[train_3.5M_CN_With_Category](https://huggingface.co/datasets/BELLE-2/train_3.5M_CN_With_Category)
## 使用限制
仅允许将此数据集及使用此数据集生成的衍生物用于研究目的，不得用于商业，以及其他会对社会带来危害的用途。
本数据集不代表任何一方的立场、利益或想法，无关任何团体的任何类型的主张。因使用本数据集带来的任何损害、纠纷，本项目不承担任何责任。
|
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] |
shibing624/sts-sohu2021
|
shibing624
| 2023-06-19T09:02:29Z | 66 | 6 |
[
"task_categories:text-classification",
"task_categories:sentence-similarity",
"task_ids:natural-language-inference",
"task_ids:semantic-similarity-scoring",
"task_ids:text-scoring",
"annotations_creators:shibing624",
"language_creators:shibing624",
"multilinguality:zh",
"size_categories:100K<n<20M",
"source_datasets:https://www.biendata.xyz/competition/sohu_2021/data/",
"language:zh",
"license:cc-by-4.0",
"region:us"
] |
[
"text-classification",
"sentence-similarity"
] | 2023-06-18T14:38:51Z |
---
annotations_creators:
- shibing624
language_creators:
- shibing624
language:
- zh
license:
- cc-by-4.0
multilinguality:
- zh
size_categories:
- 100K<n<20M
source_datasets:
- https://www.biendata.xyz/competition/sohu_2021/data/
task_categories:
- text-classification
- sentence-similarity
task_ids:
- natural-language-inference
- semantic-similarity-scoring
- text-scoring
paperswithcode_id: sts
pretty_name: Sentence Text Similarity SOHU2021
---
# Dataset Card for sts-sohu2021
## Dataset Description
- **Repository:** [Chinese NLI dataset](https://github.com/shibing624/text2vec)
- **Leaderboard:** [NLI_zh leaderboard](https://github.com/shibing624/text2vec) (located on the homepage)
- **Size of downloaded dataset files:** 218 MB
- **Total amount of disk used:** 218 MB
### Dataset Summary
2021搜狐校园文本匹配算法大赛数据集
- 数据源：https://www.biendata.xyz/competition/sohu_2021/data/
分为 A 和 B 两个文件，A 和 B 文件匹配标准不一样。其中 A 和 B 文件又分为“短短文本匹配”、“短长文本匹配”和“长长文本匹配”。
A 文件匹配标准较为宽泛，两段文字是同一个话题便视为匹配，B 文件匹配标准较为严格，两段文字须是同一个事件才视为匹配。
数据类型：
| type | 数据类型 |
| --- | ------------|
| dda | 短短匹配 A 类 |
| ddb | 短短匹配 B 类 |
| dca | 短长匹配 A 类 |
| dcb | 短长匹配 B 类 |
| cca | 长长匹配 A 类 |
| ccb | 长长匹配 B 类 |
### Supported Tasks and Leaderboards
Supported Tasks: 支持中文文本匹配任务，文本相似度计算等相关任务。
中文匹配任务的结果目前在顶会paper上出现较少，我罗列一个我自己训练的结果：
**Leaderboard:** [NLI_zh leaderboard](https://github.com/shibing624/text2vec)
### Languages
数据集均是简体中文文本。
## Dataset Structure
### Data Instances
An example of 'train' looks as follows.
```python
# A 类 短短 样本示例
{
"sentence1": "小艺的故事让爱回家2021年2月16日大年初五19：30带上你最亲爱的人与团团君相约《小艺的故事》直播间！",
"sentence2": "香港代购了不起啊，宋点卷竟然在直播间“炫富”起来",
"label": 0
}
# B 类 短短 样本示例
{
"sentence1": "让很多网友好奇的是，张柏芝在一小时后也在社交平台发文：“给大家拜年啦。”还有网友猜测：谢霆锋的经纪人发文，张柏芝也发文，并且配图，似乎都在证实，谢霆锋依旧和王菲在一起，而张柏芝也有了新的恋人，并且生了孩子，两人也找到了各自的归宿，有了自己的幸福生活，让传言不攻自破。",
"sentence2": "陈晓东谈旧爱张柏芝，一个口误暴露她的秘密，难怪谢霆锋会离开她",
"label": 0
}
```
label: 0表示不匹配，1表示匹配。
### Data Fields
The data fields are the same among all splits.
- `sentence1`: a `string` feature.
- `sentence2`: a `string` feature.
- `label`: a classification label, with possible values including `similarity` (1), `dissimilarity` (0).
### Data Splits
```shell
> wc -l *.jsonl
11690 cca.jsonl
11690 ccb.jsonl
11592 dca.jsonl
11593 dcb.jsonl
11512 dda.jsonl
11501 ddb.jsonl
69578 total
```
### Curation Rationale
作为中文NLI(natural langauge inference)数据集，这里把这个数据集上传到huggingface的datasets，方便大家使用。
#### Who are the source language producers?
数据集的版权归原作者所有，使用各数据集时请尊重原数据集的版权。
#### Who are the annotators?
原作者。
### Social Impact of Dataset
This dataset was developed as a benchmark for evaluating representational systems for text, especially including those induced by representation learning methods, in the task of predicting truth conditions in a given context.
Systems that are successful at such a task may be more successful in modeling semantic representations.
### Licensing Information
用于学术研究。
### Contributions
[shibing624](https://github.com/shibing624) upload this dataset.
|
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] |
zzliang/GRIT
|
zzliang
| 2023-07-04T06:40:28Z | 66 | 75 |
[
"task_categories:text-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:zero-shot-classification",
"task_ids:image-captioning",
"task_ids:visual-question-answering",
"multilinguality:monolingual",
"size_categories:100M<n<1B",
"source_datasets:COYO-700M",
"language:en",
"license:ms-pl",
"image-text-bounding-box pairs",
"image-text pairs",
"arxiv:2306.14824",
"region:us"
] |
[
"text-to-image",
"image-to-text",
"object-detection",
"zero-shot-classification"
] | 2023-07-04T03:33:28Z |
---
license: ms-pl
language:
- en
multilinguality:
- monolingual
pretty_name: GRIT
size_categories:
- 100M<n<1B
source_datasets:
- COYO-700M
tags:
- image-text-bounding-box pairs
- image-text pairs
task_categories:
- text-to-image
- image-to-text
- object-detection
- zero-shot-classification
task_ids:
- image-captioning
- visual-question-answering
---
# GRIT: Large-Scale Training Corpus of Grounded Image-Text Pairs
### Dataset Description
- **Repository:** [Microsoft unilm](https://github.com/microsoft/unilm/tree/master/kosmos-2)
- **Paper:** [Kosmos-2](https://arxiv.org/abs/2306.14824)
### Dataset Summary
We introduce GRIT, a large-scale dataset of Grounded Image-Text pairs, which is created based on image-text pairs from [COYO-700M](https://github.com/kakaobrain/coyo-dataset) and LAION-2B. We construct a pipeline to extract and link text spans (i.e., noun phrases, and referring expressions) in the caption to their corresponding image regions. More details can be found in the [paper](https://arxiv.org/abs/2306.14824).
### Supported Tasks
During the construction, we excluded the image-caption pairs if no bounding boxes are retained. This procedure resulted in a high-quality image-caption subset of COYO-700M, which we will validate in the future.
Furthermore, this dataset contains text-span-bounding-box pairs. Thus, it can be used in many location-aware mono/multimodal tasks, such as phrase grounding, referring expression comprehension, referring expression generation, and open-world object detection.
### Data Instance
One instance is
```python
{
'key': '000373938',
'clip_similarity_vitb32': 0.353271484375,
'clip_similarity_vitl14': 0.2958984375,
'id': 1795296605919,
'url': "https://www.thestrapsaver.com/wp-content/uploads/customerservice-1.jpg",
'caption': 'a wire hanger with a paper cover that reads we heart our customers',
'width': 1024,
'height': 693,
'noun_chunks': [[19, 32, 0.019644069503434333, 0.31054004033406574, 0.9622142865754519, 0.9603442351023356, 0.79298526], [0, 13, 0.019422357885505368, 0.027634161214033764, 0.9593302408854166, 0.969467560450236, 0.67520964]],
'ref_exps': [[19, 66, 0.019644069503434333, 0.31054004033406574, 0.9622142865754519, 0.9603442351023356, 0.79298526], [0, 66, 0.019422357885505368, 0.027634161214033764, 0.9593302408854166, 0.969467560450236, 0.67520964]]
}
```
- `key`: The generated file name when using img2dataset to download COYO-700M (omit it).
- `clip_similarity_vitb32`: The cosine similarity between text and image(ViT-B/32) embeddings by [OpenAI CLIP](https://github.com/openai/CLIP), provided by COYO-700M.
- `clip_similarity_vitl14`: The cosine similarity between text and image(ViT-L/14) embeddings by [OpenAI CLIP](https://github.com/openai/CLIP), provided by COYO-700M.
- `id`: Unique 64-bit integer ID in COYO-700M.
- `url`: The image URL.
- `caption`: The corresponding caption.
- `width`: The width of the image.
- `height`: The height of the image.
- `noun_chunks`: The noun chunks (extracted by [spaCy](https://spacy.io/)) that have associated bounding boxes (predicted by [GLIP](https://github.com/microsoft/GLIP)). The items in the children list respectively represent 'Start of the noun chunk in caption', 'End of the noun chunk in caption', 'normalized x_min', 'normalized y_min', 'normalized x_max', 'normalized y_max', 'confidence score'.
- `ref_exps`: The corresponding referring expressions. If a noun chunk has no expansion, we just copy it.
### Download image
We recommend to use [img2dataset](https://github.com/rom1504/img2dataset) tool to download the images.
1. Download the metadata. You can download it by cloning current repository:
```bash
git lfs install
git clone https://huggingface.co/datasets/zzliang/GRIT
```
2. Install [img2dataset](https://github.com/rom1504/img2dataset).
```bash
pip install img2dataset
```
3. Download images
You need to replace `/path/to/GRIT_dataset/grit-20m` with the local path to this repository.
```bash
img2dataset --url_list /path/to/GRIT_dataset/grit-20m --input_format "parquet"\
--url_col "url" --caption_col "caption" --output_format webdataset \
--output_folder /tmp/grit --processes_count 4 --thread_count 64 --image_size 256 \
--resize_only_if_bigger=True --resize_mode="keep_ratio" --skip_reencode=True \
--save_additional_columns '["id","noun_chunks","ref_exps","clip_similarity_vitb32","clip_similarity_vitl14"]' \
--enable_wandb False
```
You can adjust some parameters according to your actual needs (e.g., `processes_count`, `thread_count`, `image_size`, `save_additional_columns`).
More img2dataset hyper-parameters can be found in [here](https://github.com/rom1504/img2dataset#api).
### Citation Information
If you apply this dataset to any project and research, please cite our paper and coyo-700m:
```
@article{Kosmos2,
title={Kosmos-2: Grounding Multimodal Large Language Models to the World},
author={Zhiliang Peng and Wenhui Wang and Li Dong and Yaru Hao and Shaohan Huang and Shuming Ma and Furu Wei},
journal={ArXiv},
year={2023},
volume={abs/2306.14824}
}
@misc{kakaobrain2022coyo-700m,
title = {COYO-700M: Image-Text Pair Dataset},
author = {Minwoo Byeon, Beomhee Park, Haecheon Kim, Sungjun Lee, Woonhyuk Baek, Saehoon Kim},
year = {2022},
howpublished = {\url{https://github.com/kakaobrain/coyo-dataset}},
}
```
|
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composite/pauq
|
composite
| 2023-11-07T08:34:15Z | 66 | 2 |
[
"region:us"
] | null | 2023-07-17T09:45:17Z |
---
dataset_info:
- config_name: ru_os
features:
- name: id
dtype: string
- name: db_id
dtype: string
- name: source
dtype: string
- name: type
dtype: string
- name: question
dtype: string
- name: query
dtype: string
- name: sql
sequence: string
- name: question_toks
sequence: string
- name: query_toks
sequence: string
- name: query_toks_no_values
sequence: string
- name: template
dtype: string
splits:
- name: train
num_examples: 8800
- name: test
num_examples: 1074
- config_name: en_os
features:
- name: id
dtype: string
- name: db_id
dtype: string
- name: source
dtype: string
- name: type
dtype: string
- name: question
dtype: string
- name: query
dtype: string
- name: sql
sequence: string
- name: question_toks
sequence: string
- name: query_toks
sequence: string
- name: query_toks_no_values
sequence: string
- name: template
dtype: string
splits:
- name: train
num_examples: 8800
- name: test
num_examples: 1076
- config_name: ru_trl
features:
- name: id
dtype: string
- name: db_id
dtype: string
- name: source
dtype: string
- name: type
dtype: string
- name: question
dtype: string
- name: query
dtype: string
- name: sql
sequence: string
- name: question_toks
sequence: string
- name: query_toks
sequence: string
- name: query_toks_no_values
sequence: string
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dtype: string
splits:
- name: train
num_examples: 7890
- name: test
num_examples: 1971
- config_name: en_trl
features:
- name: id
dtype: string
- name: db_id
dtype: string
- name: source
dtype: string
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dtype: string
- name: question
dtype: string
- name: query
dtype: string
- name: sql
sequence: string
- name: question_toks
sequence: string
- name: query_toks
sequence: string
- name: query_toks_no_values
sequence: string
- name: template
dtype: string
splits:
- name: train
num_examples: 7890
- name: test
num_examples: 1974
- config_name: ru_tsl
features:
- name: id
dtype: string
- name: db_id
dtype: string
- name: source
dtype: string
- name: type
dtype: string
- name: question
dtype: string
- name: query
dtype: string
- name: sql
sequence: string
- name: question_toks
sequence: string
- name: query_toks
sequence: string
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sequence: string
- name: template
dtype: string
splits:
- name: train
num_examples: 7900
- name: test
num_examples: 1969
- config_name: en_tsl
features:
- name: id
dtype: string
- name: db_id
dtype: string
- name: source
dtype: string
- name: type
dtype: string
- name: question
dtype: string
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dtype: string
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sequence: string
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sequence: string
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sequence: string
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dtype: string
splits:
- name: train
num_examples: 7900
- name: test
num_examples: 1974
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
Link to databases: https://drive.google.com/file/d/1Xjbp207zfCaBxhPgt-STB_RxwNo2TIW2/view
### Dataset Summary
The Russian version of the [Spider](https://yale-lily.github.io/spider) - Yale Semantic Parsing and Text-to-SQL Dataset.
Major changings:
- Adding (not replacing) new Russian language values in DB tables. Table and DB names remain the original.
- Localization of natural language questions into Russian. All DB values replaced by new.
- Changing in SQL-queries filters.
- Filling empty table with values.
- Complementing the dataset with the new samples of underrepresented types.
### Languages
Russian
## Dataset Creation
### Curation Rationale
The translation from English to Russian is undertaken by a professional human translator with SQL-competence. A verification of the translated questions and their conformity with the queries, and an updating of the databases are undertaken by 4 computer science students.
Details are in the [section 3](https://aclanthology.org/2022.findings-emnlp.175.pdf).
## Additional Information
### Licensing Information
The presented dataset have been collected in a manner which is consistent with the terms of use of the original Spider, which is distributed under the CC BY-SA 4.0 license.
### Citation Information
[Paper link](https://aclanthology.org/2022.findings-emnlp.175.pdf)
```
@inproceedings{bakshandaeva-etal-2022-pauq,
title = "{PAUQ}: Text-to-{SQL} in {R}ussian",
author = "Bakshandaeva, Daria and
Somov, Oleg and
Dmitrieva, Ekaterina and
Davydova, Vera and
Tutubalina, Elena",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.175",
pages = "2355--2376",
abstract = "Semantic parsing is an important task that allows to democratize human-computer interaction. One of the most popular text-to-SQL datasets with complex and diverse natural language (NL) questions and SQL queries is Spider. We construct and complement a Spider dataset for Russian, thus creating the first publicly available text-to-SQL dataset for this language. While examining its components - NL questions, SQL queries and databases content - we identify limitations of the existing database structure, fill out missing values for tables and add new requests for underrepresented categories. We select thirty functional test sets with different features that can be used for the evaluation of neural models{'} abilities. To conduct the experiments, we adapt baseline architectures RAT-SQL and BRIDGE and provide in-depth query component analysis. On the target language, both models demonstrate strong results with monolingual training and improved accuracy in multilingual scenario. In this paper, we also study trade-offs between machine-translated and manually-created NL queries. At present, Russian text-to-SQL is lacking in datasets as well as trained models, and we view this work as an important step towards filling this gap.",
}
```
### Contributions
Thanks to [@gugutse](https://github.com/Gugutse), [@runnerup96](https://github.com/runnerup96), [@dmi3eva](https://github.com/dmi3eva), [@veradavydova](https://github.com/VeraDavydova), [@tutubalinaev](https://github.com/tutubalinaev) for adding this dataset.
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cesarali/test_ipp50
|
cesarali
| 2023-08-28T17:28:36Z | 66 | 0 |
[
"region:us"
] | null | 2023-08-28T17:28:33Z |
---
dataset_info:
features:
- name: id
dtype: int64
- name: question
dtype: string
- name: choices
sequence: string
- name: value
dtype: float64
splits:
- name: train
num_bytes: 8439
num_examples: 50
download_size: 4060
dataset_size: 8439
---
# Dataset Card for "test_ipp50"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
Rams901/sql-create-context-modified
|
Rams901
| 2023-09-16T14:11:35Z | 66 | 1 |
[
"region:us"
] | null | 2023-09-16T14:11:32Z |
---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: context
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 1415326
num_examples: 3000
download_size: 632495
dataset_size: 1415326
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "sql-create-context-modified"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
sheepy928/rt_merged
|
sheepy928
| 2023-10-23T22:13:12Z | 66 | 0 |
[
"region:us"
] | null | 2023-10-23T22:12:30Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: sentence
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 25082040.23509904
num_examples: 170188
- name: test
num_bytes: 4426363.76490096
num_examples: 30034
download_size: 18535178
dataset_size: 29508404.0
---
# Dataset Card for "cs490_reddit_twitter_merged"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
josedonoso/apples-dataset-v1
|
josedonoso
| 2023-10-28T23:35:52Z | 66 | 0 |
[
"region:us"
] | null | 2023-10-28T23:35:50Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 2704421.0
num_examples: 192
- name: test
num_bytes: 646648.0
num_examples: 48
download_size: 3236890
dataset_size: 3351069.0
---
# Dataset Card for "apples-dataset-v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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magnifi/hl-codellama-chat-response-v2
|
magnifi
| 2023-11-02T16:47:51Z | 66 | 0 |
[
"region:us"
] | null | 2023-11-02T16:47:40Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: Query
dtype: string
- name: Result
dtype: string
- name: chat_response
dtype: string
splits:
- name: train
num_bytes: 1321860.461185117
num_examples: 1523
- name: test
num_bytes: 567627.5388148829
num_examples: 654
download_size: 109799
dataset_size: 1889488.0
---
# Dataset Card for "hl-codellama-chat-response-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
tomashs/lsc_multiplechoice
|
tomashs
| 2023-11-09T16:09:39Z | 66 | 0 |
[
"region:us"
] | null | 2023-11-09T16:09:25Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: short_form
dtype: string
- name: long_form
dtype: string
- name: freq
dtype: int64
- name: num_candidates
dtype: int64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 36335092
num_examples: 110752
- name: val
num_bytes: 7920458
num_examples: 25932
- name: test
num_bytes: 8281205
num_examples: 25175
download_size: 24878537
dataset_size: 52536755
---
# Dataset Card for "lsc_multiplechoice"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
philschmid/guanaco-sharegpt-style
|
philschmid
| 2023-11-13T13:24:10Z | 66 | 0 |
[
"region:us"
] | null | 2023-11-13T13:23:37Z |
---
dataset_info:
features:
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
splits:
- name: train
num_bytes: 13979844
num_examples: 9033
download_size: 8238076
dataset_size: 13979844
---
# Dataset Card for "guanaco-sharegpt-style"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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kiennguyen1703/DiroxCompany
|
kiennguyen1703
| 2023-11-23T08:57:47Z | 66 | 0 |
[
"language:en",
"license:apache-2.0",
"region:us"
] | null | 2023-11-17T15:20:33Z |
---
license: apache-2.0
language:
- en
---
|
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Areeb123/drug_reviews
|
Areeb123
| 2023-11-18T12:04:22Z | 66 | 0 |
[
"task_categories:text-classification",
"size_categories:1M<n<10M",
"language:en",
"license:mit",
"medical",
"region:us"
] |
[
"text-classification"
] | 2023-11-18T11:36:52Z |
---
license: mit
task_categories:
- text-classification
language:
- en
tags:
- medical
size_categories:
- 1M<n<10M
---
|
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] |
franlucc/the-stack-dedup-c-multithreaded
|
franlucc
| 2023-11-24T21:11:52Z | 66 | 0 |
[
"region:us"
] | null | 2023-11-24T21:11:50Z |
---
dataset_info:
features:
- name: hexsha
dtype: string
- name: size
dtype: int64
- name: ext
dtype: string
- name: lang
dtype: string
- name: max_stars_repo_path
dtype: string
- name: max_stars_repo_name
dtype: string
- name: max_stars_repo_head_hexsha
dtype: string
- name: max_stars_repo_licenses
sequence: string
- name: max_stars_count
dtype: float64
- name: max_stars_repo_stars_event_min_datetime
dtype: string
- name: max_stars_repo_stars_event_max_datetime
dtype: string
- name: max_issues_repo_path
dtype: string
- name: max_issues_repo_name
dtype: string
- name: max_issues_repo_head_hexsha
dtype: string
- name: max_issues_repo_licenses
sequence: string
- name: max_issues_count
dtype: float64
- name: max_issues_repo_issues_event_min_datetime
dtype: string
- name: max_issues_repo_issues_event_max_datetime
dtype: string
- name: max_forks_repo_path
dtype: string
- name: max_forks_repo_name
dtype: string
- name: max_forks_repo_head_hexsha
dtype: string
- name: max_forks_repo_licenses
sequence: string
- name: max_forks_count
dtype: float64
- name: max_forks_repo_forks_event_min_datetime
dtype: string
- name: max_forks_repo_forks_event_max_datetime
dtype: string
- name: content
dtype: string
- name: avg_line_length
dtype: float64
- name: max_line_length
dtype: int64
- name: alphanum_fraction
dtype: float64
splits:
- name: train
num_bytes: 5879161.11288
num_examples: 782
download_size: 3745527
dataset_size: 5879161.11288
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "the-stack-dedup-c-multithreaded"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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zelalt/scientific-papers-3.5-withprompt
|
zelalt
| 2023-11-25T21:37:06Z | 66 | 0 |
[
"region:us"
] | null | 2023-11-25T21:37:02Z |
---
dataset_info:
features:
- name: id
dtype: string
- name: authors
dtype: string
- name: title
dtype: string
- name: abstract
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 4543858
num_examples: 3499
download_size: 2831084
dataset_size: 4543858
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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] |
Cheng98/mmlu
|
Cheng98
| 2023-11-29T23:51:38Z | 66 | 0 |
[
"task_categories:text-classification",
"language:en",
"region:us"
] |
[
"text-classification"
] | 2023-11-29T14:19:34Z |
---
configs:
- config_name: zero-shot
data_files:
- split: validation
path: data/five-shot/val.json
- split: test
path: data/five-shot/test.json
- config_name: five-shot
data_files:
- split: validation
path: data/five-shot/val.json
- split: test
path: data/five-shot/test.json
task_categories:
- text-classification
language:
- en
---
# Formatted MMLU dataset
MMLU Dataset preprocessed and split by [qLoRA](https://github.com/artidoro/qlora/tree/main)
# References
```
@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}
}
@article{hendrycks2021ethics,
title={Aligning AI With Shared Human Values},
author={Dan Hendrycks and Collin Burns and Steven Basart and Andrew Critch and Jerry Li and Dawn Song and Jacob Steinhardt},
journal={Proceedings of the International Conference on Learning Representations (ICLR)},
year={2021}
}
```
|
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] |
mnoukhov/openai_summarize_comparisons_tldrprompt_relabel1b
|
mnoukhov
| 2023-12-02T22:00:36Z | 66 | 0 |
[
"region:us"
] | null | 2023-11-30T18:14:46Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 156593160
num_examples: 92534
- name: test
num_bytes: 8322345
num_examples: 5000
download_size: 21793816
dataset_size: 164915505
---
# Dataset Card for "openai_summarize_comparisons_tldrprompt_relabel1b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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amazingvince/sft-max-2.1
|
amazingvince
| 2023-12-03T05:42:07Z | 66 | 0 |
[
"region:us"
] | null | 2023-12-02T20:32:13Z |
---
dataset_info:
features:
- name: text
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 9385512725
num_examples: 2363460
- name: test
num_bytes: 9211485
num_examples: 2366
download_size: 4947814472
dataset_size: 9394724210
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
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lgrobol/openminuscule
|
lgrobol
| 2022-10-23T09:28:36Z | 65 | 0 |
[
"task_categories:text-generation",
"task_ids:language-modeling",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:100k<n<1M",
"source_datasets:original",
"language:en",
"language:fr",
"license:cc-by-4.0",
"region:us"
] |
[
"text-generation"
] | 2022-03-02T23:29:22Z |
---
language_creators:
- crowdsourced
language:
- en
- fr
license:
- cc-by-4.0
multilinguality:
- multilingual
size_categories:
- 100k<n<1M
source_datasets:
- original
task_categories:
- text-generation
task_ids:
- language-modeling
pretty_name: Open Minuscule
language_bcp47:
- en-GB
- fr-FR
---
Open Minuscule
==============
A little small wee corpus to train little small wee models.
## Dataset Description
### Dataset Summary
This is a raw text corpus, mainly intended for testing purposes.
### Languages
- French
- English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Source Data
It is a mashup including the following [CC-BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) licenced texts
- [*Rayons émis par les composés de l’uranium et du
thorium*](https://fr.wikisource.org/wiki/Rayons_%C3%A9mis_par_les_compos%C3%A9s_de_l%E2%80%99uranium_et_du_thorium),
Maria Skłodowska Curie
- [*Frankenstein, or the Modern
Prometheus*](https://en.wikisource.org/wiki/Frankenstein,_or_the_Modern_Prometheus_(Revised_Edition,_1831)),
Mary Wollstonecraft Shelley
- [*Les maîtres sonneurs*](https://fr.wikisource.org/wiki/Les_Ma%C3%AEtres_sonneurs), George Sand
It also includes the text of *Sketch of The Analytical Engine Invented by Charles Babbage With
notes upon the Memoir by the Translator* by Luigi Menabrea and Ada Lovelace, which to the best of
my knowledge should be public domain.
## Considerations for Using the Data
This really should not be used for anything but testing purposes
## Licence
This corpus is available under the Creative Commons Attribution-ShareAlike 4.0 License
|
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rbawden/DiaBLa
|
rbawden
| 2022-10-25T14:21:10Z | 65 | 1 |
[
"task_categories:translation",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:translation",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"language:fr",
"license:cc-by-sa-4.0",
"region:us"
] |
[
"translation"
] | 2022-03-02T23:29:22Z |
---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
- fr
license:
- cc-by-sa-4.0
multilinguality:
- translation
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- translation
task_ids: []
pretty_name: DiaBLa
language_bcp47:
- en-UK
- fr-FR
---
# Dataset Card for DiaBLa: Bilingual dialogue parallel evaluation set
## 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:** [almanach.inria.fr/software_and_resources/custom/DiaBLa-en.html](http://almanach.inria.fr/software_and_resources/custom/DiaBLa-en.html)
- **Repository:** [github.com/rbawden/DiaBLa-dataset](https://github.com/rbawden/DiaBLa-dataset)
- **Paper:** [Bawden et al. (2021). DiaBLa: A Corpus of Bilingual Spontaneous Written Dialogues for Machine Translation. Language Resources and Evaluation(55). Pages 635–660. Springer Verlag. 10.1007/s10579-020-09514-4.](https://hal.inria.fr/hal-03021633)
- **Point of contact:** rachel.bawden[at]inria.fr
### Dataset Summary
The dataset is an English-French dataset for the evaluation of Machine Translation (MT) for informal, written bilingual dialogue.
The dataset contains 144 spontaneous dialogues (5,700+ sentences) between native English and French speakers, mediated by one of two neural MT systems in a range of role-play settings. See below for some basic statistics. The dialogues are accompanied by fine-grained sentence-level judgments of MT quality, produced by the dialogue participants themselves, as well as by manually normalised versions and reference translations produced a posteriori. See here for information about evaluation.
The motivation for the corpus is two-fold: to provide:
- a unique resource for evaluating MT models for dialogue (i.e. in context)
- a corpus for the analysis of MT-mediated communication
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English (mainly UK) and French
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:** 37 MB
- **Number of parallel utterances:** 5748
Each example is highly annotated and is associated with dialogue context. An example from the test set looks as follows (only the first and last utterances of the dialogue history are shown for readability purposes):
```
{
"id": "dialogue-2018-04-25T16-20-36.087170_french_english_1_2_25",
"mt": "Tu m'en veux pour \u00e7a ?",
"norm": "",
"orig": "Are you blaming me for this?",
"ref": "C'est moi que vous critiquez pour \u00e7a\u00a0?",
"utterance_meta": {
"eval_judgment": "medium",
"eval_verbatim": "",
"eval_problems": [
"coherence"
],
"lang": "english"
},
"dialogue_meta": {
"start_time": "2018-04-25T16:20:36.087170",
"end_time": "",
"translation_model": "baseline",
"final_evaluation_user1": {
"style": "average",
"coherence": "average",
"grammaticality": "good",
"meaning": "average",
"word_choice": "average"
},
"final_evaluation_user2": {
"style": "",
"coherence": "",
"grammaticality": "",
"meaning": "",
"word_choice": ""
},
"scenario": [
[
"You are both stuck in a lift at work.",
"Vous \u00eates tous les deux bloqu\u00e9(e)s dans un ascenseur au travail."
],
[
"You are an employee and you are with your boss.",
"Vous \u00eates un(e) employ\u00e9(e) et vous \u00eates avez votre patron(ne)"
],
[
"You are the boss and are with an employee.",
"Vous \u00eates le ou la patron(ne) et vous \u00eates avec un(e) employ\u00e9(e)"
]
],
"user1": {
"role_num": 1,
"role": [
"You are an employee and you are with your boss.",
"Vous \u00eates un(e) employ\u00e9(e) et vous \u00eates avez votre patron(ne)"
],
"initiated_dialogue": true,
"turn_number": 2,
"lang": "french"
},
"user2": {
"role_num": 2,
"role": [
"You are the boss and are with an employee.",
"Vous \u00eates le ou la patron(ne) et vous \u00eates avec un(e) employ\u00e9(e)"
],
"initiated_dialogue": false,
"turn_number": 1,
"lang": "english"
}
},
"dialogue_history": [
{
"id": "dialogue-2018-04-25T16-20-36.087170_french_english_1_2_0",
"orig": "We appear to have stopped moving.",
"norm": "",
"mt": "On semble avoir arr\u00eat\u00e9 de bouger.",
"ref": "J'ai l'impression qu'on s'est arr\u00eat\u00e9s.",
"utterance_meta": {
"eval_judgment": "medium",
"eval_verbatim": "",
"eval_problems": [
"style"
],
"lang": "english"
}
},
[...]
{
"id": "dialogue-2018-04-25T16-20-36.087170_french_english_1_2_24",
"orig": "La sonnerie s'est arr\u00eat\u00e9, je pense que personne ne va nous r\u00e9pondre.",
"norm": "",
"mt": "The ringing stopped, and I don't think anyone's gonna answer us.",
"ref": "It stopped ringing. I don't think anybody's going to reply.",
"utterance_meta": {
"eval_judgment": "perfect",
"eval_verbatim": "",
"eval_problems": [],
"lang": "french"
}
}
]
}
```
### Data Fields
#### plain_text
- `id`: a `string` feature.
- `orig`: a `string` feature.
- `norm`: a `string` feature.
- `mt`: a `string` feature.
- `ref`: a `string` feature.
- `utterance_meta`: a dictionary feature containing:
- `eval_judgment`: a `string` feature.
- `eval_verbatim`: a `string` feature.
- `eval_problems`: a list feature containing:
- up to 5 `string` features.
- `lang`: a `string` feature.
- `dialogue_meta`: a dictionary feature containing:
- `start_time` : a `string` feature.
- `end_time`: a `string` feature.
- `translation_model`: a `string` feature.
- `final_evaluation_user1`: a dictionary feature containing:
- `style`: a `string` feature.
- `coherence`: a `string` feature.
- `grammaticality`: a `string` feature.
- `meaning`: a `string` feature.
- `word_choice`: a `string` feature.
- `final_evaluation_user2`: a dictionary feature containing:
- `style`: a `string` feature.
- `coherence`: a `string` feature.
- `grammaticality`: a `string` feature.
- `meaning`: a `string` feature.
- `word_choice`: a `string` feature.
- `scenario`: a list feature containing
- 3 lists each containing 2 `string` features.
- `user1`: a dictionary feature containing:
- `role_num`: an `int` feature.
- `role`: a list feature containing:
- 2 `string` features.
- `initiated_dialogue`: a `bool` feature.
- `turn_number`: an `int` value.
- `lang`: a `string` value.
- `user2`: a dictionary feature containing:
- `role_num`: an `int` feature.
- `role`: a list feature containing:
- 2 `string` features.
- `initiated_dialogue`: a `bool` feature.
- `turn_number`: an `int` value.
- `lang`: a `string` value.
- `dialogue_history`: a list feature containing:
- dictionary features containing:
- `id`: a `string` feature.
- `orig`: a `string` feature.
- `norm`: a `string` feature.
- `mt`: a `string` feature.
- `ref`: a `string` feature.
- `utterance_meta`: a dictionary feature containing:
- `eval_judgment`: a `string` feature.
- `eval_verbatim`: a `string` feature.
- `eval_problems`: a list feature containing:
- up to 5 `string` features.
- `lang`: a `string` feature.
### Data Splits
DiaBLa is a test set only.
| name |test |
|----------|------:|
|plain_text| 5748|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
Original data was collected through a [dedicated online chat platform](https://github.com/rbawden/diabla-chat-interface) and involved native speakers of English and of French. As well as producing the original text, participants also annotated the quality of the machine-translated outputs of their partners' utterances (which they saw instead of their partners' original text) based on their monolingual intuitions and the dialogue context.
Each dialogue is assigned one of 12 role-play scenarios and where appropriate each participant is assigned a role to play in the dialogue.
#### Who are the source language producers?
The source text producers were native French and native English volunteers (mainly British English). See the paper for very basic information concerning their backgrounds (age categories and experience in NLP).
### Annotations
#### Annotation process
On top of the original dialogue text (a mixture of utterances in English and in French), the following "annotations" are provided:
- machine translated version of the original text (produced in real time and presented during the dialogue), produced by one of two MT systems, both trained using [Marian](https://github.com/marian-nmt/marian).
- judgments of MT quality by participants (overall quality, particular problems, verbatim comments)
- manually produced normalised version of the original text (for spelling mistakes, grammatical errors, missing punctuation, etc.)
- manually produced reference translations
#### Who are the annotators?
The judgments of MT quality were produced by the dialogue participants themselves in real time. The normalised version of the text and the reference translations were manually produced by the authors of the paper. Translations were always done into the translator's native language and all translations were verified and post-edited by a bilingual English-French speaker.
### Personal and Sensitive Information
A priori the dataset does not contain personal and sensitive information. Participants were instructed not to give any personal information and to assume the roles assigned in the role play scenario. Usernames were anonymised prior to distribution and any mention of either usernames or real names in the dialogues were replaced by generic names of the same gender as the participant. Only basic user information was collected to get an idea of the distribution of participants and to potentially see how multilingual ability influences quality judgments (rough age categories, experience in NLP or research, native languages, familiarity with the other language (either English or French), other languages spoken and gender). Gender was included because it is an important factor in translation (particularly for the direction English-to-French), and this was explained in advance to the participants in the FAQs.
## 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
The dataset was collected by Rachel Bawden, Eric Bilinski, Thomas Lavergne and Sophie Rosset (see citation below).
### Licensing Information
The dataset is available under a CC BY-SA 4.0 licence.
### Citation Information
If you use or are inspired by this dataset, please cite:
```
@article{bawden_DiaBLa:-A-Corpus-of_2021,
author = {Bawden, Rachel and Bilinski, Eric and Lavergne, Thomas and Rosset, Sophie},
doi = {10.1007/s10579-020-09514-4},
title = {DiaBLa: A Corpus of Bilingual Spontaneous Written Dialogues for Machine Translation},
year = {2021},
journal = {Language Resources and Evaluation},
publisher = {Springer Verlag},
volume = {55},
pages = {635--660},
url = {https://hal.inria.fr/hal-03021633},
pdf = {https://hal.inria.fr/hal-03021633/file/diabla-lre-personal-formatting.pdf},
}
```
### Contributions
This dataset was added by Rachel Bawden [@rbawden](https://github.com/rbawden).
|
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