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 |
|---|---|---|---|---|---|---|---|---|---|
fujiki/japanese_alpaca_data
|
fujiki
| 2023-05-19T12:54:13Z | 102 | 7 |
[
"language:ja",
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2023-05-18T07:13:15Z |
---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 24733874
num_examples: 52002
download_size: 13849623
dataset_size: 24733874
license: cc-by-nc-sa-4.0
language:
- ja
pretty_name: japanese_alpaca
---
# Dataset Card for "japanese_alpaca_data"
- This dataset is based on `masa3141`'s great work on `japanese-alpaca-lora` [[github]](https://github.com/masa3141/japanese-alpaca-lora). Please also refer to this repo.
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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fake-news-UFG/FactChecksbr
|
fake-news-UFG
| 2023-08-24T17:40:04Z | 102 | 0 |
[
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:pt",
"license:mit",
"doi:10.57967/hf/1016",
"region:us"
] |
[
"text-classification"
] | 2023-08-23T17:15:02Z |
---
license: mit
task_categories:
- text-classification
language:
- pt
pretty_name: FactChecks.br
size_categories:
- 10K<n<100K
---
# FactChecks.br
## Dataset Description
- **Homepage:**
- **Repository:** [github.com/fake-news-UFG/FactChecks.br](github.com/fake-news-UFG/FactChecks.br)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Collection of Portuguese Fact-Checking Benchmarks.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset is in Portuguese.
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
If you use "FactChecks.br Dataset", please include a cite:
```bibtex
@misc{FactChecksbr,
author = {R. S. Gomes, Juliana},
title = {FactChecks.br},
url = {https://github.com/fake-news-UFG/FactChecks.br},
doi = { 10.57967/hf/1016 },
}
```
### Contributions
Thanks to [@ju-resplande](https://github.com/ju-resplande) for adding this dataset.
|
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] |
explodinggradients/WikiEval
|
explodinggradients
| 2023-09-18T15:12:16Z | 102 | 0 |
[
"region:us"
] | null | 2023-08-24T10:01:45Z |
---
dataset_info:
features:
- name: answer
dtype: string
- name: question
dtype: string
- name: context_v1
sequence: string
- name: context_v2
sequence: string
- name: ungrounded_answer
dtype: string
- name: source
dtype: string
- name: poor_answer
dtype: string
splits:
- name: train
num_bytes: 548755
num_examples: 50
download_size: 354738
dataset_size: 548755
---
# WikiEval
Dataset for to do correlation analysis of difference metrics proposed in [Ragas](https://github.com/explodinggradients/ragas)
This dataset was generated from 50 pages from Wikipedia with edits post 2022.
## Column description
* question: a question that can be answered from the given Wikipedia page (source).
* source: The source Wikipedia page from which the question and context are generated.
* grounded_answer: answer grounded on context_v1
* ungrounded_answer: answer generated without context_v1
* poor_answer: answer with poor relevancy compared to grounded_answer and ungrounded_answer
* context_v1: Ideal context to answer the given question
* contetx_v2: context that contains redundant information compared to context_v1
|
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] |
Mxode/StackOverflow-QA-C-Language-5k
|
Mxode
| 2023-10-02T10:30:48Z | 102 | 1 |
[
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:en",
"license:apache-2.0",
"code",
"region:us"
] |
[
"question-answering"
] | 2023-10-02T10:08:11Z |
---
license: apache-2.0
language:
- en
tags:
- code
task_categories:
- question-answering
size_categories:
- 1K<n<10K
---
This is a collection of ~5000 QA's in **C Language** from StackOverflow. The data has been initially cleaned, and each response is with **Accepted Answer**.
All data is **<500** in length.
The questions and answers were organized into a **one-line** format. A sample format is shown below:
```json
{
"question": "```\nFILE* file = fopen(some file)\n\npcap_t* pd = pcap_fopen_offline(file)\n\npcap_close(pd)\n\nfclose(file)\n```\n\nThis code occurs double free error.\n\nCould you explain about this happening?\n\nMy Guess is that pd and file pointers are sharing some datas.\n",
"answer": "As the documentation says, thepcap_closefunction closes the files associated with thepcap_tstructure passed to it. Closing the file again withfcloseis an error.\n"
}
```
|
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] |
pmc/open_access
|
pmc
| 2023-03-14T17:29:59Z | 101 | 12 |
[
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc0-1.0",
"license:cc-by-4.0",
"license:cc-by-sa-4.0",
"license:cc-by-nd-4.0",
"license:cc-by-nc-4.0",
"license:cc-by-nc-sa-4.0",
"license:cc-by-nc-nd-4.0",
"license:other",
"license:unknown",
"region:us"
] |
[
"text-generation"
] | 2022-03-02T23:29:22Z |
---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- en
license:
- cc0-1.0
- cc-by-4.0
- cc-by-sa-4.0
- cc-by-nd-4.0
- cc-by-nc-4.0
- cc-by-nc-sa-4.0
- cc-by-nc-nd-4.0
- other
- unknown
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-generation
task_ids:
- language-modeling
pretty_name: PMC Open Access
---
# Dataset Card for PMC Open Access Subset
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://www.ncbi.nlm.nih.gov/pmc/tools/openftlist/
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [PubMed Central](mailto:[email protected])
### Dataset Summary
The PMC Open Access Subset includes more than 3.4 million journal articles and preprints that are made available under
license terms that allow reuse. Not all articles in PMC are available for text mining and other reuse, many have
copyright protection, however articles in the PMC Open Access Subset are made available under Creative Commons or
similar licenses that generally allow more liberal redistribution and reuse than a traditional copyrighted work. The
PMC Open Access Subset is one part of the PMC Article Datasets.
Within the PMC Open Access Subset, there are three groupings:
- Commercial Use Allowed - CC0, CC BY, CC BY-SA, CC BY-ND licenses
- Non-Commercial Use Only - CC BY-NC, CC BY-NC-SA, CC BY-NC-ND licenses; and
- Other - no machine-readable Creative Commons license, no license, or a custom license.
### Supported Tasks and Leaderboards
- Language modeling
### Languages
English (`en`).
## Dataset Structure
### Data Instances
```
{
'text': "==== Front\nPLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0000005Research ArticleGenetics/Genomics/Gene TherapyInfectious DiseasesMicrobiologyPlasmodiumThe Transcriptome of the Intraerythrocytic Developmental Cycle of Plasmodium falciparum\n P. falciparum IDC TranscriptomeBozdech Zbynek \n1\nLlinás Manuel \n1\nPulliam Brian Lee \n1\nWong Edith D \n1\nZhu Jingchun \n2\nDeRisi Joseph L [email protected]\n1\n1Department of Biochemistry and Biophysics, University of California, San FranciscoSan Francisco, CaliforniaUnited States of America2Department of Biological and Medical Informatics, University of California, San FranciscoSan Francisco, CaliforniaUnited States of America10 2003 18 8 2003 18 8 2003 1 1 e512 6 2003 25 7 2003 Copyright: ©2003 Bozdech et al.2003This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.\nMicroarray Analysis: Genome-Scale Hypothesis Scanning \n\nMonitoring Malaria: Genomic Activity of the Parasite in Human Blood Cells \n\nPlasmodium falciparum is the causative agent of the most burdensome form of human malaria, affecting 200–300 million individuals per year worldwide. The recently sequenced genome of P. falciparum revealed over 5,400 genes, of which 60% encode proteins of unknown function. Insights into the biochemical function and regulation of these genes will provide the foundation for future drug and vaccine development efforts toward eradication of this disease. By analyzing the complete asexual intraerythrocytic developmental cycle (IDC) transcriptome of the HB3 strain of P. falciparum, we demonstrate that at least 60% of the genome is transcriptionally active during this stage. Our data demonstrate that this parasite has evolved an extremely specialized mode of transcriptional regulation that produces a continuous cascade of gene expression, beginning with genes corresponding to general cellular processes, such as protein synthesis, and ending with Plasmodium-specific functionalities, such as genes involved in erythrocyte invasion. The data reveal that genes contiguous along the chromosomes are rarely coregulated, while transcription from the plastid genome is highly coregulated and likely polycistronic. Comparative genomic hybridization between HB3 and the reference genome strain (3D7) was used to distinguish between genes not expressed during the IDC and genes not detected because of possible sequence variations...
'pmid': '12929205',
'accession_id': 'PMC176545',
'license': 'CC BY',
'last_updated': '2021-01-05 08:21:03',
'retracted': 'no',
'citation': 'PLoS Biol. 2003 Oct 18; 1(1):e5'
}
```
### Data Fields
- `text`: Text content.
- `pmid`: PubMed ID.
- `accession_id`: Unique identifier for a sequence record.
- `license`: License type.
- `last_updated`: Date of last update.
- `retracted`: Whether retracted or not.
- `citation`: Citation reference.
### Data Splits
The dataset is not split.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
License terms vary. Please refer to the license statement in each article for specific terms of use.
Within the PMC Open Access Subset, there are three groupings based on available license terms:
- Commercial Use Allowed - CC0, CC BY, CC BY-SA, CC BY-ND licenses;
- Non-Commercial Use Only - CC BY-NC, CC BY-NC-SA, CC BY-NC-ND licenses; and
- Other - no machine-readable Creative Commons license, no license, or a custom license.
### Citation Information
```
PMC Open Access Subset [Internet]. Bethesda (MD): National Library of Medicine. 2003 - [cited YEAR MONTH DAY]. Available from https://www.ncbi.nlm.nih.gov/pmc/tools/openftlist/
```
### Contributions
Thanks to [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.
|
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facebook/pmd
|
facebook
| 2022-08-09T23:51:39Z | 101 | 28 |
[
"task_categories:image-to-text",
"task_ids:image-captioning",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:2112.04482",
"arxiv:2111.11431",
"region:us"
] |
[
"image-to-text"
] | 2022-06-20T00:52:47Z |
---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- image-to-text
task_ids:
- image-captioning
paperswithcode_id: pmd
pretty_name: PMD
extra_gated_prompt: |
By clicking on “Access repository” below, you also agree to individual licensing terms for each of the subset datasets of the PMD as noted at https://huggingface.co/datasets/facebook/pmd#additional-information.
---
# Dataset Card for PMD
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Preprocessing](#dataset-preprocessing)
- [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)
- [Compared to original FLAVA paper](#compared-to-original-flava-paper)
- [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:** [PMD homepage](https://flava-model.github.io/)
- **Repository:** [PMD repository](https://huggingface.co/datasets/facebook/pmd)
- **Paper:** [FLAVA: A Foundational Language And Vision Alignment Model
](https://arxiv.org/abs/2112.04482)
- **Leaderboard:**
- **Point of Contact:** [Amanpreet Singh](mailto:[email protected])
### Dataset Summary
Introduced in the FLAVA paper, Public Multimodal Dataset (PMD) is a collection of publicly-available image-text pair datasets. PMD contains 70M image-text pairs in total with 68M unique images. The dataset contains pairs from Conceptual Captions, Conceptual Captions 12M, WIT, Localized Narratives, RedCaps, COCO, SBU Captions, Visual Genome and a subset of YFCC100M dataset.
If you use PMD, please cite the original FLAVA paper as follows, along with the individual datasets (!! - see below for references):
```bibtex
@inproceedings{singh2022flava,
title={Flava: A foundational language and vision alignment model},
author={Singh, Amanpreet and Hu, Ronghang and Goswami, Vedanuj and Couairon, Guillaume and Galuba, Wojciech and Rohrbach, Marcus and Kiela, Douwe},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={15638--15650},
year={2022}
}
```
You can load this dataset by first logging into Hugging Face using `huggingface-cli login` and then running the following commands:
```py
from datasets import load_dataset
pmd = load_dataset("facebook/pmd", use_auth_token=True)
```
You can also load the dataset in streaming mode if you don't want to download the big dataset files (> 50GB locally without the images):
```py
pmd = load_dataset("facebook/pmd", use_auth_token=True, streaming=True)
```
### Dataset Preprocessing
This dataset doesn't download all of the images locally by default. Instead, it exposes URLs for some of the images. To fetch the images, use the following code:
```python
from concurrent.futures import ThreadPoolExecutor
from functools import partial
import io
import urllib
import PIL.Image
from datasets import load_dataset
from datasets.utils.file_utils import get_datasets_user_agent
USER_AGENT = get_datasets_user_agent()
def fetch_single_image(image_data, timeout=None, retries=0):
image_url, image = image_data
if image is not None:
return image
for _ in range(retries + 1):
try:
request = urllib.request.Request(
image_url,
data=None,
headers={"user-agent": USER_AGENT},
)
with urllib.request.urlopen(request, timeout=timeout) as req:
image = PIL.Image.open(io.BytesIO(req.read()))
break
except Exception:
image = None
return image
def fetch_images(batch, num_threads, timeout=None, retries=0):
fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries)
with ThreadPoolExecutor(max_workers=num_threads) as executor:
batch["image"] = list(executor.map(fetch_single_image_with_args, zip(batch["image_url"], batch["image"])))
return batch
num_threads = 20
dset = load_dataset("pmd", use_auth_token=True)
dset = dset.map(fetch_images, batched=True, batch_size=100, fn_kwargs={"num_threads": num_threads})
```
#### Save to disk
You can also save the dataset to disk for faster and direct loading next time but beware of the space required:
```py
dset.save_to_disk(</path/to/save>)
```
#### Load Subsets
You can also download a specific set from the PMD dataset by using
```py
dset = load_dataset("pmd", <choice>, use_auth_token=True)
```
The choices are `
```
"all","coco","sbu", "wit", "localized_narratives","conceptual_captions","visual_genome","conceptual_captions_12M","redcaps","yfcc100M_subset", "localized_narratives_openimages","localized_narratives_ade20k", "localized_narratives_coco"
```
#### Flickr30K Localized Narratives Subset
The Flickr30K subset of Localized Narratives is not included by default as it requires a manual download. You can include it by downloading the tar file from [here](http://shannon.cs.illinois.edu/DenotationGraph/data/index.html) after signing an agreement to `</path/to/Downloads>` and then loading it whole PMD or localized narratives subset by:
```py
dset = load_dataset("pmd", data_dir=</path/to/Downloads/flickr30k-images.tar.gz>, use_auth_token=True, use_flickr30k_ln=True)
# Load LN subset only
dset = load_dataset("pmd", "localized_narratives", data_dir=</path/to/Downloads/flickr30k-images.tar.gz>, use_auth_token=True, use_flickr30k_ln=True)
```
#### Facing issues?
If you are facing issues, you can try loading a specific revision of the repo by using:
```py
dset = load_dataset("pmd", use_auth_token=True, revision="311cd48")
```
### Supported Tasks and Leaderboards
In the FLAVA paper, the dataset has been used to pretrain the FLAVA model as a source of well-aligned image-text pairs. This allows having a generic vision-and-language model which can be fine-tuned for a variety of tasks.
We anticipate that the dataset can be used to train deep neural networks that perform image captioning and that learn transferable visual representations for a variety of downstream visual recognition tasks (image classification, object detection, instance segmentation). We also anticipate that the dataset could be used for a variety of vision-and-language (V&L) tasks, such as image or text retrieval or text-to-image synthesis.
### Languages
All of the subsets in PMD use English as their primary language.
## Dataset Structure
### Data Instances
Each instance in PMD represents a single image-text pair:
```
{
'image_url': None,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480 at 0x7FCFF86A1E80>,
'text': 'A woman wearing a net on her head cutting a cake. ',
'source': 'coco',
'meta': '{\n "annotation": [\n "A woman wearing a net on her head cutting a cake. "\n ],\n "image_path": "zip:/val2014/COCO_val2014_000000522418.jpg::http:/images.cocodataset.org/zips/val2014.zip"\n}'
}
```
### Data Fields
- `image_url`: Static URL for downloading the image associated with the text. Can be `None` if image is locally available.
- `image`: A PIL Image object for the image associated with the text. Can be `None` if image is not locally available.
- `text`: `str`, A textual description corresponding to the image.
- `source`: `str`, The PMD subset which this pair is from.
- `meta`: `str`, A json representation of the original annotation from the dataset.
### Data Splits
All the data is contained in the training set. The training set has nearly 70M instances.
We intend for this dataset to be primarily used for pre-training with one or more specific downstream task(s) in mind. Thus, all of the instances should be used for pretraining. If required, we specifically make sure that there is no overlap with Karpathy's COCO validation set so users can use that subset for any validation purposes. Users can also load Karpathy's val subset by specifying the "validation" split while loading PMD. This will also load other "validation" splits for some subsets, if they are available.
## Dataset Creation
### Curation Rationale
From the paper:
> Purely contrastive methods, however, also have important shortcomings. Their cross-modal nature does not make them easily usable on multimodal problems that require dealing with both modalities at the same time. They require large corpora, which for both CLIP and ALIGN have not been made accessible to the research community and the details of which remain shrouded in mystery, notwithstanding well-known issues with the construction of such datasets
### Source Data
#### Initial Data Collection and Normalization
From the paper:
> **Data Collection Pipeline**
- For the YFCC100M dataset, we filter the image-text data by discarding non-English captions and only keeping captions that contain more than two words from the description field of each image, if this does not pass our filters we consider the title field. Other than that, we did not do any additional filtering.
- For the VisualGenome, COCO and Localized Narratives subsets, we remove any overlaps with Karpathy's COCO val and test sets.
- For Localized Narratives, we split the original caption which is a paragraph into multiple captions by using spaCy library and take the cartesan product leading to each sample as a separate image-text pair.
#### Compared to original FLAVA paper
The PMD dataset in this repo doesn't correspond 1:1 exactly to the original PMD dataset used in the [FLAVA](https://arxiv.org/abs/2112.04482) paper though this repo is built by the same authors. This is due to difficulty in reproducing WiT and YFCC100M subsets exactly. This repo in general contains more data than the PMD in the FLAVA paper and hence should probably result in better performance.
#### Who are the source language producers?
Please refer to the original dataset papers to understand where the content is coming from.
### Annotations
#### Annotation process
The dataset is a combination of existing public datasets with some filtering applied on top so there is no annotation process involved.
#### Who are the annotators?
Please refer to the original dataset papers to understand where the content is coming from.
### Personal and Sensitive Information
Please refer to the original dataset papers to understand where the content is coming from. For example, a detailed description on this for RedCaps can be found [here](https://huggingface.co/datasets/red_caps).
## Considerations for Using the Data
### Social Impact of Dataset
From the paper:
> **Has an analysis of the potential impact of the dataset and its use on data subjects (e.g.,
a data protection impact analysis) been conducted?**
No.
### Discussion of Biases
Please refer to the original dataset papers to understand where the content is coming from. For example, a detailed description on this for RedCaps can be found [here](https://huggingface.co/datasets/red_caps).
### Other Known Limitations
From the paper:
> **Are there any errors, sources of noise, or redundancies in the dataset?**
PMD is noisy by design since image-text pairs on the internet are noisy and unstructured. Though, since it contains sources such as COCO, Visual Genome, and Localized Narratives which are hand-curated by annotators, it has a lot of well-aligned data as well. So, it is definitely more aligned compared to e.g. LAION.
Some instances may also have duplicate images and captions but should have almost no effect in training large-scale models.
> **Does the dataset contain data that might be considered confidential (e.g., data that is
protected by legal privilege or by doctor-patient confidentiality, data that includes the
content of individuals non-public communications)?**
Not that the authors know of. Please refer to the original dataset papers to understand where the content is coming from. For example, a detailed description on this for RedCaps can be found [here](https://huggingface.co/datasets/red_caps).
## Additional Information
### Dataset Curators
The authors of the original dataset papers, as well as the authors of the FLAVA paper (Amanpreet, Ronghang, Vedanuj, Guillaume, Wojciech, Marcus and Douwe).
### Licensing Information
Here are the individual licenses from each of the datasets that apply if you use this dataset:
#### COCO
The annotations in the COCO dataset belong to the COCO Consortium and are licensed under a Creative Commons Attribution 4.0 License.
The COCO Consortium does not own the copyright of the images. Use of the images must abide by the Flickr Terms of Use. The users of the images accept full responsibility for the use of the dataset, including but not limited to the use of any copies of copyrighted images that they may create from the dataset.
#### Conceptual Captions
The dataset may be freely used for any purpose, although acknowledgement of Google LLC ("Google") as the data source would be appreciated. The dataset is provided "AS IS" without any warranty, express or implied. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.
#### WIT
This data is available under the [Creative Commons Attribution-ShareAlike 3.0 Unported](LICENSE) license.
#### Visual Genome
Visual Genome by Ranjay Krishna et al is licensed under a Creative Commons Attribution 4.0 International License.
#### Localized Narratives
All the annotations available through this website are released under a [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license. You are free to redistribute and modify the annotations, but we ask you to please keep the original attribution to our paper.
#### YFCC100M
Use of the original media files is subject to the Creative Commons licenses chosen by their creators/uploaders. License information for each media file can be found within [the YFCC100M metadata](https://multimediacommons.wordpress.com/yfcc100m-core-dataset/#yfcc100m). Use of the dataset is subject to the relevant Webscope License Agreement, which you need to agree to if you use this dataset.
#### RedCaps
The image metadata is licensed under CC-BY 4.0 license. Additionally, uses of this dataset are subject to Reddit API terms (https://www.reddit.com/wiki/
api-terms) and users must comply with Reddit User Agreeement, Content Policy,
and Privacy Policy – all accessible at https://www.redditinc.com/policies.
Similar to RedCaps:
> PMD should only be used for non-commercial research. PMD should not be used for any tasks that involve identifying features related to people (facial recognition, gender, age, ethnicity identification, etc.) or make decisions that impact people (mortgages, job applications, criminal sentences; or moderation decisions about user-uploaded data that could result in bans from a website). Any commercial and for-profit uses of PMD are restricted – it should not be used to train models that will be deployed in production systems as part of a product offered by businesses or government agencies.
### Citation Information
Please cite the main FLAVA paper in which PMD was introduced along with each of the subsets used in PMD as follows:
```bibtex
@inproceedings{singh2022flava,
title={Flava: A foundational language and vision alignment model},
author={Singh, Amanpreet and Hu, Ronghang and Goswami, Vedanuj and Couairon, Guillaume and Galuba, Wojciech and Rohrbach, Marcus and Kiela, Douwe},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={15638--15650},
year={2022}
}
@article{chen2015microsoft,
title={Microsoft coco captions: Data collection and evaluation server},
author={Chen, Xinlei and Fang, Hao and Lin, Tsung-Yi and Vedantam, Ramakrishna and Gupta, Saurabh and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
journal={arXiv preprint arXiv:1504.00325},
year={2015}
}
@inproceedings{ordonez2011sbucaptions,
Author = {Vicente Ordonez and Girish Kulkarni and Tamara L. Berg},
Title = {Im2Text: Describing Images Using 1 Million Captioned Photographs},
Booktitle = {Neural Information Processing Systems ({NIPS})},
Year = {2011},
}
@article{krishna2017visual,
title={Visual genome: Connecting language and vision using crowdsourced dense image annotations},
author={Krishna, Ranjay and Zhu, Yuke and Groth, Oliver and Johnson, Justin and Hata, Kenji and Kravitz, Joshua and Chen, Stephanie and Kalantidis, Yannis and Li, Li-Jia and Shamma, David A and others},
journal={International journal of computer vision},
volume={123},
number={1},
pages={32--73},
year={2017},
publisher={Springer}
}
@article{srinivasan2021wit,
title={WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning},
author={Srinivasan, Krishna and Raman, Karthik and Chen, Jiecao and Bendersky, Michael and Najork, Marc},
journal={arXiv preprint arXiv:2103.01913},
year={2021}
}
@inproceedings{sharma2018conceptual,
title={Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning},
author={Sharma, Piyush and Ding, Nan and Goodman, Sebastian and Soricut, Radu},
booktitle={Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
pages={2556--2565},
year={2018}
}
@inproceedings{changpinyo2021conceptual,
title={Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts},
author={Changpinyo, Soravit and Sharma, Piyush and Ding, Nan and Soricut, Radu},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={3558--3568},
year={2021}
}
@inproceedings{ponttuset2020localized,
author = {Jordi Pont-Tuset and Jasper Uijlings and Soravit Changpinyo and Radu Soricut and Vittorio Ferrari},
title = {Connecting Vision and Language with Localized Narratives},
booktitle = {ECCV},
year = {2020}
}
@article{thomee2016yfcc100m,
title={YFCC100M: The new data in multimedia research},
author={Thomee, Bart and Shamma, David A and Friedland, Gerald and Elizalde, Benjamin and Ni, Karl and Poland, Douglas and Borth, Damian and Li, Li-Jia},
journal={Communications of the ACM},
volume={59},
number={2},
pages={64--73},
year={2016},
publisher={ACM New York, NY, USA}
}
@misc{desai2021redcaps,
title={RedCaps: web-curated image-text data created by the people, for the people},
author={Karan Desai and Gaurav Kaul and Zubin Aysola and Justin Johnson},
year={2021},
eprint={2111.11431},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
### Contributions
Thanks to [@aps](https://github.com/apsdehal), [Thomas Wang](https://huggingface.co/TimeRobber), and [@VictorSanh](https://huggingface.co/VictorSanh) for adding this dataset.
|
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bigbio/biomrc
|
bigbio
| 2022-12-22T15:43:44Z | 101 | 1 |
[
"multilinguality:monolingual",
"language:en",
"license:unknown",
"region:us"
] | null | 2022-11-13T22:06:42Z |
---
language:
- en
bigbio_language:
- English
license: unknown
multilinguality: monolingual
bigbio_license_shortname: UNKNOWN
pretty_name: BIOMRC
homepage: https://github.com/PetrosStav/BioMRC_code
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- QUESTION_ANSWERING
---
# Dataset Card for BIOMRC
## Dataset Description
- **Homepage:** https://github.com/PetrosStav/BioMRC_code
- **Pubmed:** True
- **Public:** True
- **Tasks:** QA
We introduce BIOMRC, a large-scale cloze-style biomedical MRC dataset. Care was taken to reduce noise, compared to the
previous BIOREAD dataset of Pappas et al. (2018). Experiments show that simple heuristics do not perform well on the
new dataset and that two neural MRC models that had been tested on BIOREAD perform much better on BIOMRC, indicating
that the new dataset is indeed less noisy or at least that its task is more feasible. Non-expert human performance is
also higher on the new dataset compared to BIOREAD, and biomedical experts perform even better. We also introduce a new
BERT-based MRC model, the best version of which substantially outperforms all other methods tested, reaching or
surpassing the accuracy of biomedical experts in some experiments. We make the new dataset available in three different
sizes, also releasing our code, and providing a leaderboard.
## Citation Information
```
@inproceedings{pappas-etal-2020-biomrc,
title = "{B}io{MRC}: A Dataset for Biomedical Machine Reading Comprehension",
author = "Pappas, Dimitris and
Stavropoulos, Petros and
Androutsopoulos, Ion and
McDonald, Ryan",
booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.bionlp-1.15",
pages = "140--149",
}
```
|
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] |
bigbio/mqp
|
bigbio
| 2022-12-22T15:45:40Z | 101 | 0 |
[
"multilinguality:monolingual",
"language:en",
"license:unknown",
"region:us"
] | null | 2022-11-13T22:10:07Z |
---
language:
- en
bigbio_language:
- English
license: unknown
multilinguality: monolingual
bigbio_license_shortname: UNKNOWN
pretty_name: MQP
homepage: https://github.com/curai/medical-question-pair-dataset
bigbio_pubmed: False
bigbio_public: True
bigbio_tasks:
- SEMANTIC_SIMILARITY
---
# Dataset Card for MQP
## Dataset Description
- **Homepage:** https://github.com/curai/medical-question-pair-dataset
- **Pubmed:** False
- **Public:** True
- **Tasks:** STS
Medical Question Pairs dataset by McCreery et al (2020) contains pairs of medical questions and paraphrased versions of
the question prepared by medical professional. Paraphrased versions were labelled as similar (syntactically dissimilar
but contextually similar ) or dissimilar (syntactically may look similar but contextually dissimilar). Labels 1: similar, 0: dissimilar
## Citation Information
```
@article{DBLP:journals/biodb/LiSJSWLDMWL16,
author = {Krallinger, M., Rabal, O., Lourenço, A.},
title = {Effective Transfer Learning for Identifying Similar Questions: Matching User Questions to COVID-19 FAQs},
journal = {KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining},
volume = {3458–3465},
year = {2020},
url = {https://github.com/curai/medical-question-pair-dataset},
doi = {},
biburl = {},
bibsource = {}
}
```
|
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climatebert/climate_sentiment
|
climatebert
| 2023-04-18T14:37:00Z | 101 | 1 |
[
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
] |
[
"text-classification"
] | 2023-04-11T13:11:01Z |
---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license: cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: ClimateSentiment
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': risk
'1': neutral
'2': opportunity
splits:
- name: train
num_bytes: 492077
num_examples: 1000
- name: test
num_bytes: 174265
num_examples: 320
download_size: 373638
dataset_size: 666342
---
# Dataset Card for climate_sentiment
## Dataset Description
- **Homepage:** [climatebert.ai](https://climatebert.ai)
- **Repository:**
- **Paper:** [papers.ssrn.com/sol3/papers.cfm?abstract_id=3998435](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3998435)
- **Leaderboard:**
- **Point of Contact:** [Nicolas Webersinke](mailto:[email protected])
### Dataset Summary
We introduce an expert-annotated dataset for classifying climate-related sentiment of climate-related paragraphs in corporate disclosures.
### Supported Tasks and Leaderboards
The dataset supports a ternary sentiment classification task of whether a given climate-related paragraph has sentiment opportunity, neutral, or risk.
### Languages
The text in the dataset is in English.
## Dataset Structure
### Data Instances
```
{
'text': '− Scope 3: Optional scope that includes indirect emissions associated with the goods and services supply chain produced outside the organization. Included are emissions from the transport of products from our logistics centres to stores (downstream) performed by external logistics operators (air, land and sea transport) as well as the emissions associated with electricity consumption in franchise stores.',
'label': 1
}
```
### Data Fields
- text: a climate-related paragraph extracted from corporate annual reports and sustainability reports
- label: the label (0 -> risk, 1 -> neutral, 2 -> opportunity)
### Data Splits
The dataset is split into:
- train: 1,000
- test: 320
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
Our dataset contains climate-related paragraphs extracted from financial disclosures by firms. We collect text from corporate annual reports and sustainability reports.
For more information regarding our sample selection, please refer to the Appendix of our paper (see [citation](#citation-information)).
#### Who are the source language producers?
Mainly large listed companies.
### Annotations
#### Annotation process
For more information on our annotation process and annotation guidelines, please refer to the Appendix of our paper (see [citation](#citation-information)).
#### Who are the annotators?
The authors and students at Universität Zürich and Friedrich-Alexander-Universität Erlangen-Nürnberg with majors in finance and sustainable finance.
### Personal and Sensitive Information
Since our text sources contain public information, no personal and sensitive information should be included.
## 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
- Julia Anna Bingler
- Mathias Kraus
- Markus Leippold
- Nicolas Webersinke
### Licensing Information
This dataset is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license (cc-by-nc-sa-4.0). To view a copy of this license, visit [creativecommons.org/licenses/by-nc-sa/4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/).
If you are interested in commercial use of the dataset, please contact [[email protected]](mailto:[email protected]).
### Citation Information
```bibtex
@techreport{bingler2023cheaptalk,
title={How Cheap Talk in Climate Disclosures Relates to Climate Initiatives, Corporate Emissions, and Reputation Risk},
author={Bingler, Julia and Kraus, Mathias and Leippold, Markus and Webersinke, Nicolas},
type={Working paper},
institution={Available at SSRN 3998435},
year={2023}
}
```
### Contributions
Thanks to [@webersni](https://github.com/webersni) for adding this dataset.
|
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AhmedSSoliman/CodeSearchNet-Python
|
AhmedSSoliman
| 2023-04-28T18:15:21Z | 101 | 0 |
[
"license:ms-pl",
"region:us"
] | null | 2023-04-28T17:37:25Z |
---
license: ms-pl
---
|
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rubend18/ChatGPT-Jailbreak-Prompts
|
rubend18
| 2023-08-24T18:24:29Z | 101 | 33 |
[
"task_categories:question-answering",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:zero-shot-classification",
"task_categories:table-question-answering",
"size_categories:n<1K",
"language:en",
"language:aa",
"ChatGPT",
"JailbreakPrompts",
"LanguageModeling",
"ArtificialIntelligence",
"TextGeneration",
"Dataset",
"OpenAI",
"Jailbreak",
"Prompts",
"region:us"
] |
[
"question-answering",
"text-generation",
"fill-mask",
"zero-shot-classification",
"table-question-answering"
] | 2023-05-25T21:04:52Z |
---
task_categories:
- question-answering
- text-generation
- fill-mask
- zero-shot-classification
- table-question-answering
language:
- en
- aa
tags:
- ChatGPT
- JailbreakPrompts
- LanguageModeling
- ArtificialIntelligence
- TextGeneration
- Dataset
- OpenAI
- Jailbreak
- Prompts
size_categories:
- n<1K
pretty_name: ChatGPT Jailbreak Prompts
---
# Dataset Card for Dataset Name
## Name
ChatGPT Jailbreak Prompts
## Dataset Description
- **Autor:** Rubén Darío Jaramillo
- **Email:** [email protected]
- **WhatsApp:** +593 93 979 6676
### Dataset Summary
ChatGPT Jailbreak Prompts is a complete collection of jailbreak related prompts for ChatGPT. This dataset is intended to provide a valuable resource for understanding and generating text in the context of jailbreaking in ChatGPT.
### Languages
[English]
|
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theoxo/proofwriter-deduction-balanced
|
theoxo
| 2023-06-23T03:14:01Z | 101 | 0 |
[
"license:cc-by-4.0",
"region:us"
] | null | 2023-06-14T17:26:17Z |
---
license: cc-by-4.0
---
A processed subset of the OWA section of the [ProofWriter dataset](https://allenai.org/data/proofwriter).
Each train/test split contains 300 entries, each of which has a unique set of theories and a single question for those theories.
Both splits are balanced so that the depth of the proof required to answer the question varies evenly between 0-5 (50 entries each), and the labels are balanced (100 each).
'Unknown' labels have been replaced by 'Uncertain' to match other datasets.
|
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open-llm-leaderboard/details_project-baize__baize-healthcare-lora-7B
|
open-llm-leaderboard
| 2023-10-17T12:07:36Z | 101 | 0 |
[
"region:us"
] | null | 2023-08-27T12:11:47Z |
---
pretty_name: Evaluation run of project-baize/baize-healthcare-lora-7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [project-baize/baize-healthcare-lora-7B](https://huggingface.co/project-baize/baize-healthcare-lora-7B)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_project-baize__baize-healthcare-lora-7B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-17T12:07:23.383581](https://huggingface.co/datasets/open-llm-leaderboard/details_project-baize__baize-healthcare-lora-7B/blob/main/results_2023-10-17T12-07-23.383581.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.001153523489932886,\n\
\ \"em_stderr\": 0.00034761798968570957,\n \"f1\": 0.05929215604026857,\n\
\ \"f1_stderr\": 0.0013287960656248844,\n \"acc\": 0.3862326042845355,\n\
\ \"acc_stderr\": 0.009073496352009793\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.001153523489932886,\n \"em_stderr\": 0.00034761798968570957,\n\
\ \"f1\": 0.05929215604026857,\n \"f1_stderr\": 0.0013287960656248844\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.04397270659590599,\n \
\ \"acc_stderr\": 0.00564766644912646\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.728492501973165,\n \"acc_stderr\": 0.012499326254893126\n\
\ }\n}\n```"
repo_url: https://huggingface.co/project-baize/baize-healthcare-lora-7B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_22T17_11_44.232250
path:
- '**/details_harness|arc:challenge|25_2023-08-22T17:11:44.232250.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_16T20_51_20.232990
path:
- '**/details_harness|drop|3_2023-10-16T20-51-20.232990.parquet'
- split: 2023_10_17T12_07_23.383581
path:
- '**/details_harness|drop|3_2023-10-17T12-07-23.383581.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-17T12-07-23.383581.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_16T20_51_20.232990
path:
- '**/details_harness|gsm8k|5_2023-10-16T20-51-20.232990.parquet'
- split: 2023_10_17T12_07_23.383581
path:
- '**/details_harness|gsm8k|5_2023-10-17T12-07-23.383581.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-17T12-07-23.383581.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_22T17_11_44.232250
path:
- '**/details_harness|hellaswag|10_2023-08-22T17:11:44.232250.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_22T17_11_44.232250
path:
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- '**/details_harness|hendrycksTest-anatomy|5_2023-08-22T17:11:44.232250.parquet'
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- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-22T17:11:44.232250.parquet'
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- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-22T17:11:44.232250.parquet'
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- '**/details_harness|hendrycksTest-human_aging|5_2023-08-22T17:11:44.232250.parquet'
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- '**/details_harness|hendrycksTest-sociology|5_2023-08-22T17:11:44.232250.parquet'
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- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-22T17:11:44.232250.parquet'
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- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-22T17:11:44.232250.parquet'
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- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-22T17:11:44.232250.parquet'
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- '**/details_harness|hendrycksTest-global_facts|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-22T17:11:44.232250.parquet'
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- '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T17:11:44.232250.parquet'
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- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T17:11:44.232250.parquet'
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- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T17:11:44.232250.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-22T17:11:44.232250.parquet'
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- '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
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path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-22T17:11:44.232250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
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path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-22T17:11:44.232250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_22T17_11_44.232250
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-22T17:11:44.232250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_22T17_11_44.232250
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-22T17:11:44.232250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_22T17_11_44.232250
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-22T17:11:44.232250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_22T17_11_44.232250
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-22T17:11:44.232250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_22T17_11_44.232250
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-22T17:11:44.232250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_22T17_11_44.232250
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-22T17:11:44.232250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_22T17_11_44.232250
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-22T17:11:44.232250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_22T17_11_44.232250
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-22T17:11:44.232250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-22T17:11:44.232250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_22T17_11_44.232250
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-22T17:11:44.232250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-22T17:11:44.232250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-22T17:11:44.232250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
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path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-22T17:11:44.232250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-22T17:11:44.232250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
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path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-22T17:11:44.232250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
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path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-22T17:11:44.232250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_22T17_11_44.232250
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-22T17:11:44.232250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-22T17:11:44.232250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_22T17_11_44.232250
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-22T17:11:44.232250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-22T17:11:44.232250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-22T17:11:44.232250.parquet'
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path:
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data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_22T17_11_44.232250
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_22T17_11_44.232250
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_22T17_11_44.232250
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- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_22T17_11_44.232250
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- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
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path:
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path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
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path:
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path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_international_law_5
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path:
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path:
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- config_name: harness_hendrycksTest_machine_learning_5
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- config_name: harness_hendrycksTest_marketing_5
data_files:
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path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_22T17_11_44.232250
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T17:11:44.232250.parquet'
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path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_22T17_11_44.232250
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T17:11:44.232250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
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path:
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- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_22T17_11_44.232250
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T17:11:44.232250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_22T17_11_44.232250
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T17:11:44.232250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_22T17_11_44.232250
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T17:11:44.232250.parquet'
- split: latest
path:
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- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_22T17_11_44.232250
path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_22T17_11_44.232250
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T17:11:44.232250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_22T17_11_44.232250
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-22T17:11:44.232250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_22T17_11_44.232250
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T17:11:44.232250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_22T17_11_44.232250
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T17:11:44.232250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_22T17_11_44.232250
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T17:11:44.232250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_22T17_11_44.232250
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T17:11:44.232250.parquet'
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- '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_22T17_11_44.232250
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-22T17:11:44.232250.parquet'
- split: latest
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- '**/details_harness|hendrycksTest-sociology|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
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- config_name: harness_hendrycksTest_virology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-22T17:11:44.232250.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
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path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T17:11:44.232250.parquet'
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path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
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path:
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path:
- '**/details_harness|truthfulqa:mc|0_2023-08-22T17:11:44.232250.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_16T20_51_20.232990
path:
- '**/details_harness|winogrande|5_2023-10-16T20-51-20.232990.parquet'
- split: 2023_10_17T12_07_23.383581
path:
- '**/details_harness|winogrande|5_2023-10-17T12-07-23.383581.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-17T12-07-23.383581.parquet'
- config_name: results
data_files:
- split: 2023_10_16T20_51_20.232990
path:
- results_2023-10-16T20-51-20.232990.parquet
- split: 2023_10_17T12_07_23.383581
path:
- results_2023-10-17T12-07-23.383581.parquet
- split: latest
path:
- results_2023-10-17T12-07-23.383581.parquet
---
# Dataset Card for Evaluation run of project-baize/baize-healthcare-lora-7B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/project-baize/baize-healthcare-lora-7B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [project-baize/baize-healthcare-lora-7B](https://huggingface.co/project-baize/baize-healthcare-lora-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_project-baize__baize-healthcare-lora-7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-17T12:07:23.383581](https://huggingface.co/datasets/open-llm-leaderboard/details_project-baize__baize-healthcare-lora-7B/blob/main/results_2023-10-17T12-07-23.383581.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.001153523489932886,
"em_stderr": 0.00034761798968570957,
"f1": 0.05929215604026857,
"f1_stderr": 0.0013287960656248844,
"acc": 0.3862326042845355,
"acc_stderr": 0.009073496352009793
},
"harness|drop|3": {
"em": 0.001153523489932886,
"em_stderr": 0.00034761798968570957,
"f1": 0.05929215604026857,
"f1_stderr": 0.0013287960656248844
},
"harness|gsm8k|5": {
"acc": 0.04397270659590599,
"acc_stderr": 0.00564766644912646
},
"harness|winogrande|5": {
"acc": 0.728492501973165,
"acc_stderr": 0.012499326254893126
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
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open-llm-leaderboard/details_totally-not-an-llm__EverythingLM-13b-16k
|
open-llm-leaderboard
| 2023-09-23T17:52:01Z | 101 | 0 |
[
"region:us"
] | null | 2023-09-23T17:51:53Z |
---
pretty_name: Evaluation run of totally-not-an-llm/EverythingLM-13b-16k
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [totally-not-an-llm/EverythingLM-13b-16k](https://huggingface.co/totally-not-an-llm/EverythingLM-13b-16k)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_totally-not-an-llm__EverythingLM-13b-16k\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-23T17:51:49.550032](https://huggingface.co/datasets/open-llm-leaderboard/details_totally-not-an-llm__EverythingLM-13b-16k/blob/main/results_2023-09-23T17-51-49.550032.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0025167785234899327,\n\
\ \"em_stderr\": 0.0005131152834514911,\n \"f1\": 0.0588632550335571,\n\
\ \"f1_stderr\": 0.0013761671412880158,\n \"acc\": 0.3960729978284714,\n\
\ \"acc_stderr\": 0.009637044859971106\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0025167785234899327,\n \"em_stderr\": 0.0005131152834514911,\n\
\ \"f1\": 0.0588632550335571,\n \"f1_stderr\": 0.0013761671412880158\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.06444275966641395,\n \
\ \"acc_stderr\": 0.0067633917284882755\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7277032359905288,\n \"acc_stderr\": 0.012510697991453934\n\
\ }\n}\n```"
repo_url: https://huggingface.co/totally-not-an-llm/EverythingLM-13b-16k
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_23T17_51_49.550032
path:
- '**/details_harness|drop|3_2023-09-23T17-51-49.550032.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-23T17-51-49.550032.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_23T17_51_49.550032
path:
- '**/details_harness|gsm8k|5_2023-09-23T17-51-49.550032.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-23T17-51-49.550032.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_23T17_51_49.550032
path:
- '**/details_harness|winogrande|5_2023-09-23T17-51-49.550032.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-23T17-51-49.550032.parquet'
- config_name: results
data_files:
- split: 2023_09_23T17_51_49.550032
path:
- results_2023-09-23T17-51-49.550032.parquet
- split: latest
path:
- results_2023-09-23T17-51-49.550032.parquet
---
# Dataset Card for Evaluation run of totally-not-an-llm/EverythingLM-13b-16k
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/totally-not-an-llm/EverythingLM-13b-16k
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [totally-not-an-llm/EverythingLM-13b-16k](https://huggingface.co/totally-not-an-llm/EverythingLM-13b-16k) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_totally-not-an-llm__EverythingLM-13b-16k",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-23T17:51:49.550032](https://huggingface.co/datasets/open-llm-leaderboard/details_totally-not-an-llm__EverythingLM-13b-16k/blob/main/results_2023-09-23T17-51-49.550032.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0025167785234899327,
"em_stderr": 0.0005131152834514911,
"f1": 0.0588632550335571,
"f1_stderr": 0.0013761671412880158,
"acc": 0.3960729978284714,
"acc_stderr": 0.009637044859971106
},
"harness|drop|3": {
"em": 0.0025167785234899327,
"em_stderr": 0.0005131152834514911,
"f1": 0.0588632550335571,
"f1_stderr": 0.0013761671412880158
},
"harness|gsm8k|5": {
"acc": 0.06444275966641395,
"acc_stderr": 0.0067633917284882755
},
"harness|winogrande|5": {
"acc": 0.7277032359905288,
"acc_stderr": 0.012510697991453934
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
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celsowm/bbc_news_ptbr
|
celsowm
| 2023-11-04T20:34:46Z | 101 | 0 |
[
"region:us"
] | null | 2023-11-04T20:34:33Z |
---
dataset_info:
features:
- name: categoria
dtype: string
- name: titulo
dtype: string
- name: texto
dtype: string
- name: data
dtype: string
- name: link
dtype: string
splits:
- name: train
num_bytes: 69873414
num_examples: 8637
download_size: 40455060
dataset_size: 69873414
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "bbc_news_ptbr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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shi3z/Japanese_Wikipedia_Conversation
|
shi3z
| 2023-11-10T22:46:29Z | 101 | 5 |
[
"task_categories:conversational",
"size_categories:10K<n<100K",
"language:ja",
"license:cc-by-sa-4.0",
"region:us"
] |
[
"conversational"
] | 2023-11-10T07:36:40Z |
---
license: cc-by-sa-4.0
task_categories:
- conversational
language:
- ja
size_categories:
- 10K<n<100K
---
Wikipedia日本語版データセット(izumi-lab/wikipedia-ja-20230720)を元にGPT-3.5-Turboで会話文を生成したデータセットです
GPT-3.5-Turboを使っているため、商用利用はできません
|
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] |
persiannlp/parsinlu_translation_en_fa
|
persiannlp
| 2022-10-24T16:50:37Z | 100 | 1 |
[
"task_categories:translation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:fa",
"multilinguality:en",
"size_categories:1K<n<10K",
"source_datasets:extended",
"language:fa",
"license:cc-by-nc-sa-4.0",
"arxiv:2012.06154",
"region:us"
] |
[
"translation"
] | 2022-03-02T23:29:22Z |
---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- fa
license:
- cc-by-nc-sa-4.0
multilinguality:
- fa
- en
size_categories:
- 1K<n<10K
source_datasets:
- extended
task_categories:
- translation
task_ids:
- translation
---
# Dataset Card for PersiNLU (Machine Translation)
## Table of Contents
- [Dataset Card for PersiNLU (Machine Translation)](#dataset-card-for-persi_nlu_machine_translation)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Github](https://github.com/persiannlp/parsinlu/)
- **Repository:** [Github](https://github.com/persiannlp/parsinlu/)
- **Paper:** [Arxiv](https://arxiv.org/abs/2012.06154)
- **Leaderboard:**
- **Point of Contact:** [email protected]
### Dataset Summary
A Persian translation dataset (English -> Persian).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text dataset is in Persian (`fa`) and English (`en`).
## Dataset Structure
### Data Instances
Here is an example from the dataset:
```json
{
"source": "how toil to raise funds, propagate reforms, initiate institutions!",
"targets": ["چه زحمتها که بکشد تا منابع مالی را تامین کند اصطلاحات را ترویج کند نهادهایی به راه اندازد."],
"category": "mizan_dev_en_fa"
}
```
### Data Fields
- `source`: the input sentences, in English.
- `targets`: the list of gold target translations in Persian.
- `category`: the source from which the dataset is mined.
### Data Splits
The train/de/test split contains 1,621,666/2,138/48,360 samples.
## Dataset Creation
### Curation Rationale
For details, check [the corresponding draft](https://arxiv.org/abs/2012.06154).
### 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
CC BY-NC-SA 4.0 License
### Citation Information
```bibtex
@article{huggingface:dataset,
title = {ParsiNLU: A Suite of Language Understanding Challenges for Persian},
authors = {Khashabi, Daniel and Cohan, Arman and Shakeri, Siamak and Hosseini, Pedram and Pezeshkpour, Pouya and Alikhani, Malihe and Aminnaseri, Moin and Bitaab, Marzieh and Brahman, Faeze and Ghazarian, Sarik and others},
year={2020}
journal = {arXiv e-prints},
eprint = {2012.06154},
}
```
### Contributions
Thanks to [@danyaljj](https://github.com/danyaljj) for adding this dataset.
|
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autoevaluate/autoeval-staging-eval-project-xsum-c7d88063-10885461
|
autoevaluate
| 2022-07-15T09:10:49Z | 100 | 0 |
[
"autotrain",
"evaluation",
"region:us"
] | null | 2022-07-14T10:15:03Z |
---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: pszemraj/long-t5-tglobal-base-16384-book-summary
metrics: []
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: pszemraj/long-t5-tglobal-base-16384-book-summary
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
|
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autoevaluate/autoeval-staging-eval-project-cnn_dailymail-ca1f103f-12035606
|
autoevaluate
| 2022-07-28T20:34:23Z | 100 | 0 |
[
"autotrain",
"evaluation",
"region:us"
] | null | 2022-07-28T19:57:31Z |
---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cnn_dailymail
eval_info:
task: summarization
model: nbroad/longt5-base-global-mediasum
metrics: []
dataset_name: cnn_dailymail
dataset_config: 3.0.0
dataset_split: test
col_mapping:
text: article
target: highlights
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: nbroad/longt5-base-global-mediasum
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@nbroad](https://huggingface.co/nbroad) for evaluating this model.
|
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] |
ashraq/tmdb-people-image
|
ashraq
| 2023-04-21T20:02:31Z | 100 | 3 |
[
"region:us"
] | null | 2022-12-02T17:34:52Z |
---
dataset_info:
features:
- name: adult
dtype: bool
- name: also_known_as
dtype: string
- name: biography
dtype: string
- name: birthday
dtype: string
- name: deathday
dtype: string
- name: gender
dtype: int64
- name: homepage
dtype: string
- name: id
dtype: int64
- name: imdb_id
dtype: string
- name: known_for_department
dtype: string
- name: name
dtype: string
- name: place_of_birth
dtype: string
- name: popularity
dtype: float64
- name: profile_path
dtype: string
- name: image
dtype: image
splits:
- name: train
num_bytes: 3749610460.6819267
num_examples: 116403
download_size: 3733145768
dataset_size: 3749610460.6819267
---
Data was obtained from [TMDB API](https://developers.themoviedb.org/3)
|
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] |
Ar4ikov/iemocap_audio_text
|
Ar4ikov
| 2023-05-02T22:25:17Z | 100 | 1 |
[
"region:us"
] | null | 2023-05-02T22:00:16Z |
---
dataset_info:
features:
- name: _id
dtype: string
- name: activation
dtype: float64
- name: dominance
dtype: float64
- name: emotion
dtype: string
- name: end_time
dtype: float64
- name: start_time
dtype: float64
- name: titre
dtype: string
- name: to_translate
dtype: string
- name: translated
dtype: string
- name: valence
dtype: float64
- name: audio
dtype:
audio:
sampling_rate: 16000
splits:
- name: train
num_bytes: 1479523969.375
num_examples: 10039
download_size: 1405738430
dataset_size: 1479523969.375
---
# Dataset Card for "iemocap_audio_text"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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Nan-Do/instructional_code-search-net-python
|
Nan-Do
| 2023-05-20T05:09:44Z | 100 | 9 |
[
"task_categories:conversational",
"task_categories:text-generation",
"task_categories:text2text-generation",
"language:en",
"license:apache-2.0",
"Python",
"Code generation",
"Instruction Response",
"region:us"
] |
[
"conversational",
"text-generation",
"text2text-generation"
] | 2023-05-20T04:50:17Z |
---
dataset_info:
features:
- name: INSTRUCTION
dtype: string
- name: RESPONSE
dtype: string
- name: SOURCE
dtype: string
splits:
- name: train
num_bytes: 451473573
num_examples: 418545
download_size: 172777462
dataset_size: 451473573
license: apache-2.0
task_categories:
- conversational
- text-generation
- text2text-generation
language:
- en
tags:
- Python
- Code generation
- Instruction Response
pretty_name: Instructional Python Dataset
---
# Dataset Card for "instructional_code-search-net-python"
## Dataset Description
- **Homepage:** None
- **Repository:** https://huggingface.co/datasets/Nan-Do/instructional_code-search-net-python
- **Paper:** None
- **Leaderboard:** None
- **Point of Contact:** [@Nan-Do](https://github.com/Nan-Do)
### Dataset Summary
This is an instructional dataset for Python.
The dataset contains two different kind of tasks:
- Given a piece of code generate a description of what it does.
- Given a description generate a piece of code that fulfils the description.
### Languages
The dataset is in English.
### Data Splits
There are no splits.
## Dataset Creation
May of 2023
### Curation Rationale
This dataset was created to improve the coding capabilities of LLMs.
### Source Data
The summarized version of the code-search-net dataset can be found at https://huggingface.co/datasets/Nan-Do/code-search-net-python
### Annotations
The dataset includes an instruction and response columns.
#### Annotation process
The annotation procedure was done using templates and NLP techniques to generate human-like instructions and responses.
A sample notebook of the process can be found at https://github.com/Nan-Do/OpenAssistantInstructionResponsePython
The annontations have been cleaned to make sure there are no repetitions and/or meaningless summaries.
### Licensing Information
Apache 2.0
|
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DynamicSuperb/DialogueActClassification_DailyTalk
|
DynamicSuperb
| 2023-08-02T08:51:06Z | 100 | 0 |
[
"region:us"
] | null | 2023-07-29T13:23:06Z |
---
dataset_info:
features:
- name: file
dtype: string
- name: audio
dtype: audio
- name: instruction
dtype: string
- name: label
dtype: string
splits:
- name: test
num_bytes: 1379347051.504
num_examples: 4758
download_size: 1292848993
dataset_size: 1379347051.504
---
# Dataset Card for "DailyTalk_DialogueActClassification"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
Jaehun/DIMSUM
|
Jaehun
| 2023-08-09T22:05:01Z | 100 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-08-09T21:27:28Z |
---
license: apache-2.0
---
|
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open-llm-leaderboard/details_w601sxs__b1ade-1b
|
open-llm-leaderboard
| 2023-09-17T23:22:35Z | 100 | 0 |
[
"region:us"
] | null | 2023-09-17T23:22:27Z |
---
pretty_name: Evaluation run of w601sxs/b1ade-1b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [w601sxs/b1ade-1b](https://huggingface.co/w601sxs/b1ade-1b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_w601sxs__b1ade-1b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T23:22:23.773676](https://huggingface.co/datasets/open-llm-leaderboard/details_w601sxs__b1ade-1b/blob/main/results_2023-09-17T23-22-23.773676.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.002936241610738255,\n\
\ \"em_stderr\": 0.0005541113054709937,\n \"f1\": 0.042455956375839043,\n\
\ \"f1_stderr\": 0.0012020859118158512,\n \"acc\": 0.2721723005338167,\n\
\ \"acc_stderr\": 0.00807495644821229\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.002936241610738255,\n \"em_stderr\": 0.0005541113054709937,\n\
\ \"f1\": 0.042455956375839043,\n \"f1_stderr\": 0.0012020859118158512\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.006065200909780136,\n \
\ \"acc_stderr\": 0.0021386703014604556\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.5382794001578532,\n \"acc_stderr\": 0.014011242594964125\n\
\ }\n}\n```"
repo_url: https://huggingface.co/w601sxs/b1ade-1b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T23_22_23.773676
path:
- '**/details_harness|drop|3_2023-09-17T23-22-23.773676.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T23-22-23.773676.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T23_22_23.773676
path:
- '**/details_harness|gsm8k|5_2023-09-17T23-22-23.773676.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T23-22-23.773676.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T23_22_23.773676
path:
- '**/details_harness|winogrande|5_2023-09-17T23-22-23.773676.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T23-22-23.773676.parquet'
- config_name: results
data_files:
- split: 2023_09_17T23_22_23.773676
path:
- results_2023-09-17T23-22-23.773676.parquet
- split: latest
path:
- results_2023-09-17T23-22-23.773676.parquet
---
# Dataset Card for Evaluation run of w601sxs/b1ade-1b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/w601sxs/b1ade-1b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [w601sxs/b1ade-1b](https://huggingface.co/w601sxs/b1ade-1b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_w601sxs__b1ade-1b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T23:22:23.773676](https://huggingface.co/datasets/open-llm-leaderboard/details_w601sxs__b1ade-1b/blob/main/results_2023-09-17T23-22-23.773676.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.002936241610738255,
"em_stderr": 0.0005541113054709937,
"f1": 0.042455956375839043,
"f1_stderr": 0.0012020859118158512,
"acc": 0.2721723005338167,
"acc_stderr": 0.00807495644821229
},
"harness|drop|3": {
"em": 0.002936241610738255,
"em_stderr": 0.0005541113054709937,
"f1": 0.042455956375839043,
"f1_stderr": 0.0012020859118158512
},
"harness|gsm8k|5": {
"acc": 0.006065200909780136,
"acc_stderr": 0.0021386703014604556
},
"harness|winogrande|5": {
"acc": 0.5382794001578532,
"acc_stderr": 0.014011242594964125
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
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] |
sunjun/medmcqa_sj
|
sunjun
| 2023-10-24T12:51:09Z | 100 | 0 |
[
"region:us"
] | null | 2023-10-24T12:49:59Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: cop
dtype:
class_label:
names:
'0': a
'1': b
'2': c
'3': d
- name: choice_type
dtype: string
- name: exp
dtype: string
- name: subject_name
dtype: string
- name: topic_name
dtype: string
- name: options
struct:
- name: A
dtype: string
- name: B
dtype: string
- name: C
dtype: string
- name: D
dtype: string
- name: answer_idx
dtype: string
- name: answer
dtype: string
- name: choices
sequence: string
- name: answer_index
dtype: int64
splits:
- name: train
num_bytes: 155300377
num_examples: 182822
- name: test
num_bytes: 2810502
num_examples: 4183
download_size: 102706169
dataset_size: 158110879
---
# Dataset Card for "medmcqa_sj"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
jlbaker361/small_addition_whole
|
jlbaker361
| 2023-11-17T05:53:45Z | 100 | 0 |
[
"region:us"
] | null | 2023-11-17T04:47:37Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: float64
- name: text
dtype: string
splits:
- name: train
num_bytes: 1337.7777777777778
num_examples: 40
- name: test
num_bytes: 167.22222222222223
num_examples: 5
download_size: 4158
dataset_size: 1505.0
---
# Dataset Card for "small_addition_whole"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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copenlu/citeworth
|
copenlu
| 2022-08-17T13:48:22Z | 99 | 2 |
[
"task_categories:text-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:extended|s2orc",
"language:en",
"license:cc-by-nc-4.0",
"citation detection",
"citation",
"science",
"scholarly documents",
"bio",
"medicine",
"computer science",
"citeworthiness",
"region:us"
] |
[
"text-classification"
] | 2022-08-17T11:57:29Z |
---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- found
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
paperswithcode_id: citeworth
pretty_name: CiteWorth
size_categories:
- 1M<n<10M
source_datasets:
- extended|s2orc
tags:
- citation detection
- citation
- science
- scholarly documents
- bio
- medicine
- computer science
- citeworthiness
task_categories:
- text-classification
task_ids: []
---
# Dataset Card for CiteWorth
## Dataset Description
- **Repo** https://github.com/copenlu/cite-worth
- **Paper** https://aclanthology.org/2021.findings-acl.157.pdf
### Dataset Summary
Scientific document understanding is challenging as the data is highly domain specific and diverse. However, datasets for tasks with scientific text require expensive manual annotation and tend to be small and limited to only one or a few fields. At the same time, scientific documents contain many potential training signals, such as citations, which can be used to build large labelled datasets. Given this, we present an in-depth study of cite-worthiness detection in English, where a sentence is labelled for whether or not it cites an external source. To accomplish this, we introduce CiteWorth, a large, contextualized, rigorously cleaned labelled dataset for cite-worthiness detection built from a massive corpus of extracted plain-text scientific documents. We show that CiteWorth is high-quality, challenging, and suitable for studying problems such as domain adaptation. Our best performing cite-worthiness detection model is a paragraph-level contextualized sentence labelling model based on Longformer, exhibiting a 5 F1 point improvement over SciBERT which considers only individual sentences. Finally, we demonstrate that language model fine-tuning with cite-worthiness as a secondary task leads to improved performance on downstream scientific document understanding tasks.
## Dataset Structure
The data is structured as follows
- `paper_id`: The S2ORC paper ID where the paragraph comes from
- `section_idx`: An index into the section array in the original S2ORC data
- `file_index`: The volume in the S2ORC dataset that the paper belongs to
- `file_offset`: Byte offset to the start of the paper json in the S2ORC paper PDF file
- `mag_field_of_study`: The field of study to which a paper belongs (an array, but each paper belongs to a single field)
- `original_text`: The original text of the paragraph
- `section_title`: Title of the section to which the paragraph belongs
- `samples`: An array containing dicts of the cleaned sentences for the paragraph, in order. The fields for each dict are as follows
- `text`: The cleaned text for the sentence
- `label`: Label for the sentence, either `check-worthy` for cite-worthy sentences or `non-check-worthy` non-cite-worthy sentences
- `original_text`: The original sentence text
- `ref_ids`: List of the reference IDs in the S2ORC dataset for papers cited in this sentence
- `citation_text`: List of all citation text in this sentence
## Dataset Creation
The data is derived from the [S2ORC dataset](https://github.com/allenai/s2orc), specifically the 20200705v1 release of the data. It is licensed under the [CC By-NC 2.0](https://creativecommons.org/licenses/by-nc/2.0/) license. For details on the dataset creation process, see section 3 of our [paper](https://aclanthology.org/2021.findings-acl.157.pdf)
.
## Citing
Please use the following citation when referencing this work or using the data:
```
@inproceedings{wright2021citeworth,
title={{CiteWorth: Cite-Worthiness Detection for Improved Scientific Document Understanding}},
author={Dustin Wright and Isabelle Augenstein},
booktitle = {Findings of ACL-IJCNLP},
publisher = {Association for Computational Linguistics},
year = 2021
}
```
|
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polinaeterna/lila_camera_traps
|
polinaeterna
| 2023-01-18T12:10:17Z | 99 | 0 |
[
"task_categories:image-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:en",
"license:other",
"biodiversity",
"camera trap data",
"wildlife monitoring",
"region:us"
] |
[
"image-classification"
] | 2023-01-18T12:10:16Z |
---
annotations_creators:
- expert-generated
license:
- other
language_creators:
- expert-generated
language:
- en
multilinguality:
- monolingual
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- image-classification
tags:
- biodiversity
- camera trap data
- wildlife monitoring
pretty_name: LILA Camera Traps
duplicated_from: society-ethics/lila_camera_traps
---
# Dataset Card for LILA
## 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)
- [Usage](#dataset-usage)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://lila.science/
- **Repository:** N/A
- **Paper:** N/A
- **Leaderboard:** N/A
- **Point of Contact:** [[email protected]]([email protected])
### Dataset Summary
LILA Camera Traps is an aggregate data set of images taken by camera traps, which are devices that automatically (e.g. via motion detection) capture images of wild animals to help ecological research.
This data set is the first time when disparate camera trap data sets have been aggregated into a single training environment with a single [taxonomy](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/).
This data set consists of only camera trap image data sets, whereas the broader [LILA](lila.science/) website also has other data sets related to biology and conservation, intended as a resource for both machine learning (ML) researchers and those that want to harness ML for this topic.
See below for information about each specific dataset that LILA contains:
<details>
<summary> Caltech Camera Traps </summary>
This data set contains 243,100 images from 140 camera locations in the Southwestern United States, with labels for 21 animal categories (plus empty), primarily at the species level (for example, the most common labels are opossum, raccoon, and coyote), and approximately 66,000 bounding box annotations. Approximately 70% of images are labeled as empty.
More information about this data set is available [here](https://beerys.github.io/CaltechCameraTraps/).
This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
For questions about this data set, contact [email protected].
If you use this data set, please cite the associated manuscript:
```bibtex
@inproceedings{DBLP:conf/eccv/BeeryHP18,
author = {Sara Beery and
Grant Van Horn and
Pietro Perona},
title = {Recognition in Terra Incognita},
booktitle = {Computer Vision - {ECCV} 2018 - 15th European Conference, Munich,
Germany, September 8-14, 2018, Proceedings, Part {XVI}},
pages = {472--489},
year = {2018},
crossref = {DBLP:conf/eccv/2018-16},
url = {https://doi.org/10.1007/978-3-030-01270-0\_28},
doi = {10.1007/978-3-030-01270-0\_28},
timestamp = {Mon, 08 Oct 2018 17:08:07 +0200},
biburl = {https://dblp.org/rec/bib/conf/eccv/BeeryHP18},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
</details>
<details>
<summary> ENA24 </summary>
This data set contains approximately 10,000 camera trap images representing 23 classes from Eastern North America, with bounding boxes on each image. The most common classes are “American Crow”, “American Black Bear”, and “Dog”.
This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
Please cite this manuscript if you use this data set:
```bibtex
@article{yousif2019dynamic,
title={Dynamic Programming Selection of Object Proposals for Sequence-Level Animal Species Classification in the Wild},
author={Yousif, Hayder and Kays, Roland and He, Zhihai},
journal={IEEE Transactions on Circuits and Systems for Video Technology},
year={2019},
publisher={IEEE}
}
```
For questions about this data set, contact [Hayder Yousif]([email protected]).
</details>
<details>
<summary> Missouri Camera Traps </summary>
This data set contains approximately 25,000 camera trap images representing 20 species (for example, the most common labels are red deer, mouflon, and white-tailed deer). Images within each sequence share the same species label (even though the animal may not have been recorded in all the images in the sequence). Around 900 bounding boxes are included. These are very challenging sequences with highly cluttered and dynamic scenes. Spatial resolutions of the images vary from 1920 × 1080 to 2048 × 1536. Sequence lengths vary from 3 to more than 300 frames.
This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
If you use this data set, please cite the associated manuscript:
```bibtex
@article{zhang2016animal,
title={Animal detection from highly cluttered natural scenes using spatiotemporal object region proposals and patch verification},
author={Zhang, Zhi and He, Zhihai and Cao, Guitao and Cao, Wenming},
journal={IEEE Transactions on Multimedia},
volume={18},
number={10},
pages={2079--2092},
year={2016},
publisher={IEEE}
}
```
For questions about this data set, contact [Hayder Yousif]([email protected]) and [Zhi Zhang]([email protected]).
</details>
<details>
<summary> North American Camera Trap Images (NACTI) </summary>
This data set contains 3.7M camera trap images from five locations across the United States, with labels for 28 animal categories, primarily at the species level (for example, the most common labels are cattle, boar, and red deer). Approximately 12% of images are labeled as empty. We have also added bounding box annotations to 8892 images (mostly vehicles and birds).
This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
Please cite this manuscript if you use this data set:
```bibtex
@article{tabak2019machine,
title={Machine learning to classify animal species in camera trap images: Applications in ecology},
author={Tabak, Michael A and Norouzzadeh, Mohammad S and Wolfson, David W and Sweeney, Steven J and VerCauteren, Kurt C and Snow, Nathan P and Halseth, Joseph M and Di Salvo, Paul A and Lewis, Jesse S and White, Michael D and others},
journal={Methods in Ecology and Evolution},
volume={10},
number={4},
pages={585--590},
year={2019},
publisher={Wiley Online Library}
}
```
For questions about this data set, contact [[email protected]]([email protected]).
</details>
<details>
<summary> WCS Camera Traps </summary>
This data set contains approximately 1.4M camera trap images representing around 675 species from 12 countries, making it one of the most diverse camera trap data sets available publicly. Data were provided by the [Wildlife Conservation Society](https://www.wcs.org/). The most common classes are tayassu pecari (peccary), meleagris ocellata (ocellated turkey), and bos taurus (cattle). A complete list of classes and associated image counts is available here. Approximately 50% of images are empty. We have also added approximately 375,000 bounding box annotations to approximately 300,000 of those images, which come from sequences covering almost all locations.
Sequences are inferred from timestamps, so may not strictly represent bursts. Images were labeled at a combination of image and sequence level, so – as is the case with most camera trap data sets – empty images may be labeled as non-empty (if an animal was present in one frame of a sequence but not in others). Images containing humans are referred to in metadata, but are not included in the data files. You can find more information about the data set [on the LILA website](https://lila.science/datasets/wcscameratraps).
This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
</details>
<details>
<summary> Wellington Camera Traps </summary>
This data set contains 270,450 images from 187 camera locations in Wellington, New Zealand. The cameras (Bushnell 119537, 119476, and 119436) recorded sequences of three images when triggered. Each sequence was labelled by citizen scientists and/or professional ecologists from Victoria University of Wellington into 17 classes: 15 animal categories (for example, the most common labels are bird, cat, and hedgehog), empty, and unclassifiable. Approximately 17% of images are labeled as empty. Images within each sequence share the same species label (even though the animal may not have been recorded in all three images).
If you use this data set, please cite the associated manuscript:
```bibtex
@article{anton2018monitoring,
title={Monitoring the mammalian fauna of urban areas using remote cameras and citizen science},
author={Anton, Victor and Hartley, Stephen and Geldenhuis, Andre and Wittmer, Heiko U},
journal={Journal of Urban Ecology},
volume={4},
number={1},
pages={juy002},
year={2018},
publisher={Oxford University Press}
}
```
This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
For questions about this data set, contact [Victor Anton]([email protected]).
</details>
<details>
<summary> Island Conservation Camera Traps </summary>
This data set contains approximately 123,000 camera trap images from 123 camera locations from 7 islands in 6 countries. Data were provided by Island Conservation during projects conducted to prevent the extinction of threatened species on islands.
The most common classes are rabbit, rat, petrel, iguana, cat, goat, and pig, with both rat and cat represented between multiple island sites representing significantly different ecosystems (tropical forest, dry forest, and temperate forests). Additionally, this data set represents data from locations and ecosystems that, to our knowledge, are not well represented in publicly available datasets including >1,000 images each of iguanas, petrels, and shearwaters. A complete list of classes and associated image counts is available here. Approximately 60% of the images are empty. We have also included approximately 65,000 bounding box annotations for about 50,000 images.
In general cameras were dispersed across each project site to detect the presence of invasive vertebrate species that threaten native island species. Cameras were set to capture bursts of photos for each motion detection event (between three and eight photos) with a set delay between events (10 to 30 seconds) to minimize the number of photos. Images containing humans are referred to in metadata, but are not included in the data files.
For questions about this data set, contact [David Will]([email protected]) at Island Conservation.
This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
The original data set included a “human” class label; for privacy reasons, we have removed those images from this version of the data set. Those labels are still present in the metadata. If those images are important to your work, contact us; in some cases it will be possible to release those images under an alternative license.
</details>
<details>
<summary> Channel Islands Camera Traps </summary>
This data set contains 246,529 camera trap images from 73 camera locations in the Channel Islands, California. All animals are annotated with bounding boxes. Data were provided by The Nature Conservancy. Animals are classified as rodent1 (82914), fox (48150), bird (11099), skunk (1071), or other (159). 114,949 images (47%) are empty. All images of rats were taken on islands already known to have rat populations.
If you use these data in a publication or report, please use the following citation:
The Nature Conservancy (2021): Channel Islands Camera Traps 1.0. The Nature Conservancy. Dataset.
For questions about this data set, contact [Nathaniel Rindlaub]([email protected]) at The Nature Conservancy.
This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
The original data set included a “human” class label; for privacy reasons, we have removed those images from this version of the data set. Those labels are still present in the metadata.
</details>
<details>
<summary> Idaho Camera Traps </summary>
This data set contains approximately 1.5 million camera trap images from Idaho. Labels are provided for 62 categories, most of which are animal classes (“deer”, “elk”, and “cattle” are the most common animal classes), but labels also include some state indicators (e.g. “snow on lens”, “foggy lens”). Approximately 70.5% of images are labeled as empty. Annotations were assigned to image sequences, rather than individual images, so annotations are meaningful only at the sequence level.
The metadata contains references to images containing humans, but these have been removed from the dataset (along with images containing vehicles and domestic dogs).
Images were provided by the Idaho Department of Fish and Game. No representations or warranties are made regarding the data, including but not limited to warranties of non-infringement or fitness for a particular purpose. Some information shared under this agreement may not have undergone quality assurance procedures and should be considered provisional. Images may not be sold in any format, but may be used for scientific publications. Please acknowledge the Idaho Department of Fish and Game when using images for publication or scientific communication.
</details>
<details>
<summary> Snapshot Serengeti </summary>
This data set contains approximately 2.65M sequences of camera trap images, totaling 7.1M images, from seasons one through eleven of the [Snapshot Serengeti project](https://snapshotserengeti.org/) -- the flagship project of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Serengeti National Park in Tanzania is best known for the massive annual migrations of wildebeest and zebra that drive the cycling of its dynamic ecosystem.
Labels are provided for 61 categories, primarily at the species level (for example, the most common labels are wildebeest, zebra, and Thomson’s gazelle). Approximately 76% of images are labeled as empty. A full list of species and associated image counts is available [here](https://lilablobssc.blob.core.windows.net/snapshotserengeti-v-2-0/SnapshotSerengeti_S1-11_v2.1.species_list.csv). We have also added approximately 150,000 bounding box annotations to approximately 78,000 of those images.
The images and species-level labels are described in more detail in the associated manuscript:
```bibtex
@misc{dryad_5pt92,
title = {Data from: Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna},
author = {Swanson, AB and Kosmala, M and Lintott, CJ and Simpson, RJ and Smith, A and Packer, C},
year = {2015},
journal = {Scientific Data},
URL = {https://doi.org/10.5061/dryad.5pt92},
doi = {doi:10.5061/dryad.5pt92},
publisher = {Dryad Digital Repository}
}
```
For questions about this data set, contact [Sarah Huebner]([email protected]) at the University of Minnesota.
This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
</details>
<details>
<summary> Snapshot Karoo </summary>
This data set contains 14889 sequences of camera trap images, totaling 38074 images, from the [Snapshot Karoo](https://www.zooniverse.org/projects/shuebner729/snapshot-karoo) project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Karoo National Park, located in the arid Nama Karoo biome of South Africa, is defined by its endemic vegetation and mountain landscapes. Its unique topographical gradient has led to a surprising amount of biodiversity, with 58 mammals and more than 200 bird species recorded, as well as a multitude of reptilian species.
Labels are provided for 38 categories, primarily at the species level (for example, the most common labels are gemsbokoryx, hartebeestred, and kudu). Approximately 83.02% of images are labeled as empty. A full list of species and associated image counts is available [here](https://lilablobssc.blob.core.windows.net/snapshot-safari/KAR/SnapshotKaroo_S1_v1.0.species_list.csv).
For questions about this data set, contact [Sarah Huebner]([email protected]) at the University of Minnesota.
This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
</details>
<details>
<summary> Snapshot Kgalagadi </summary>
This data set contains 3611 sequences of camera trap images, totaling 10222 images, from the [Snapshot Kgalagadi](https://www.zooniverse.org/projects/shuebner729/snapshot-kgalagadi/) project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. The Kgalagadi Transfrontier Park stretches from the Namibian border across South Africa and into Botswana, covering a landscape commonly referred to as the Kalahari – an arid savanna. This region is of great interest to help us understand how animals cope with extreme temperatures at both ends of the scale.
Labels are provided for 31 categories, primarily at the species level (for example, the most common labels are gemsbokoryx, birdother, and ostrich). Approximately 76.14% of images are labeled as empty. A full list of species and associated image counts is available [here](https://lilablobssc.blob.core.windows.net/snapshot-safari/KGA/SnapshotKgalagadi_S1_v1.0.species_list.csv).
For questions about this data set, contact [Sarah Huebner]([email protected]) at the University of Minnesota.
This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
</details>
<details>
<summary> Snapshot Enonkishu </summary>
This data set contains 13301 sequences of camera trap images, totaling 28544 images, from the [Snapshot Enonkishu](https://www.zooniverse.org/projects/aguthmann/snapshot-enonkishu) project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Enonkishu Conservancy is located on the northern boundary of the Mara-Serengeti ecosystem in Kenya, and is managed by a consortium of stakeholders and land-owning Maasai families. Their aim is to promote coexistence between wildlife and livestock in order to encourage regenerative grazing and build stability in the Mara conservancies.
Labels are provided for 39 categories, primarily at the species level (for example, the most common labels are impala, warthog, and zebra). Approximately 64.76% of images are labeled as empty. A full list of species and associated image counts is available [here](https://lilablobssc.blob.core.windows.net/snapshot-safari/ENO/SnapshotEnonkishu_S1_v1.0.species_list.csv).
For questions about this data set, contact [Sarah Huebner]([email protected]) at the University of Minnesota.
This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
</details>
<details>
<summary> Snapshot Camdeboo </summary>
This data set contains 12132 sequences of camera trap images, totaling 30227 images, from the [Snapshot Camdeboo](https://www.zooniverse.org/projects/shuebner729/snapshot-camdeboo) project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Camdeboo National Park, South Africa is crucial habitat for many birds on a global scale, with greater than fifty endemic and near-endemic species and many migratory species.
Labels are provided for 43 categories, primarily at the species level (for example, the most common labels are kudu, springbok, and ostrich). Approximately 43.74% of images are labeled as empty. A full list of species and associated image counts is available [here](https://lilablobssc.blob.core.windows.net/snapshot-safari/CDB/SnapshotCamdeboo_S1_v1.0.species_list.csv).
For questions about this data set, contact [Sarah Huebner]([email protected]) at the University of Minnesota.
This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
</details>
<details>
<summary> Snapshot Mountain Zebra </summary>
This data set contains 71688 sequences of camera trap images, totaling 73034 images, from the [Snapshot Mountain Zebra](https://www.zooniverse.org/projects/meredithspalmer/snapshot-mountain-zebra/) project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Mountain Zebra National Park is located in the Eastern Cape of South Africa in a transitional area between several distinct biomes, which means it is home to many endemic species. As the name suggests, this park contains the largest remnant population of Cape Mountain zebras, ~700 as of 2019 and increasing steadily every year.
Labels are provided for 54 categories, primarily at the species level (for example, the most common labels are zebramountain, kudu, and springbok). Approximately 91.23% of images are labeled as empty. A full list of species and associated image counts is available [here](https://lilablobssc.blob.core.windows.net/snapshot-safari/MTZ/SnapshotMountainZebra_S1_v1.0.species_list.csv).
For questions about this data set, contact [Sarah Huebner]([email protected]) at the University of Minnesota.
This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
</details>
<details>
<summary> Snapshot Kruger </summary>
This data set contains 4747 sequences of camera trap images, totaling 10072 images, from the [Snapshot Kruger](https://www.zooniverse.org/projects/shuebner729/snapshot-kruger) project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Kruger National Park, South Africa has been a refuge for wildlife since its establishment in 1898, and it houses one of the most diverse wildlife assemblages remaining in Africa. The Snapshot Safari grid was established in 2018 as part of a research project assessing the impacts of large mammals on plant life as boundary fences were removed and wildlife reoccupied areas of previous extirpation.
Labels are provided for 46 categories, primarily at the species level (for example, the most common labels are impala, elephant, and buffalo). Approximately 61.60% of images are labeled as empty. A full list of species and associated image counts is available [here](https://lilablobssc.blob.core.windows.net/snapshot-safari/KRU/SnapshotKruger_S1_v1.0.species_list.csv).
For questions about this data set, contact [Sarah Huebner]([email protected]) at the University of Minnesota.
This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
</details>
<details>
<summary> SWG Camera Traps </summary>
This data set contains 436,617 sequences of camera trap images from 982 locations in Vietnam and Lao, totaling 2,039,657 images. Labels are provided for 120 categories, primarily at the species level (for example, the most common labels are “Eurasian Wild Pig”, “Large-antlered Muntjac”, and “Unidentified Murid”). Approximately 12.98% of images are labeled as empty. A full list of species and associated image counts is available here. 101,659 bounding boxes are provided on 88,135 images.
This data set is provided by the Saola Working Group; providers include:
- IUCN SSC Asian Wild Cattle Specialist Group’s Saola Working Group (SWG)
- Asian Arks
- Wildlife Conservation Society (Lao)
- WWF Lao
- Integrated Conservation of Biodiversity and Forests project, Lao (ICBF)
- Center for Environment and Rural Development, Vinh University, Vietnam
If you use these data in a publication or report, please use the following citation:
SWG (2021): Northern and Central Annamites Camera Traps 2.0. IUCN SSC Asian Wild Cattle Specialist Group’s Saola Working Group. Dataset.
For questions about this data set, contact [email protected].
This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
</details>
<details>
<summary> Orinoquia Camera Traps </summary>
This data set contains 104,782 images collected from a 50-camera-trap array deployed from January to July 2020 within the private natural reserves El Rey Zamuro (31 km2) and Las Unamas (40 km2), located in the Meta department in the Orinoquía region in central Colombia. We deployed cameras using a stratified random sampling design across forest core area strata. Cameras were spaced 1 km apart from one another, located facing wildlife trails, and deployed with no bait. Images were stored and reviewed by experts using the Wildlife Insights platform.
This data set contains 51 classes, predominantly mammals such as the collared peccary, black agouti, spotted paca, white-lipped peccary, lowland tapir, and giant anteater. Approximately 20% of images are empty.
The main purpose of the study is to understand how humans, wildlife, and domestic animals interact in multi-functional landscapes (e.g., agricultural livestock areas with native forest remnants). However, this data set was also used to review model performance of AI-powered platforms – Wildlife Insights (WI), MegaDetector (MD), and Machine Learning for Wildlife Image Classification (MLWIC2). We provide a demonstration of the use of WI, MD, and MLWIC2 and R code for evaluating model performance of these platforms in the accompanying [GitHub repository](https://github.com/julianavelez1/Processing-Camera-Trap-Data-Using-AI).
If you use these data in a publication or report, please use the following citation:
```bibtex
@article{velez2022choosing,
title={Choosing an Appropriate Platform and Workflow for Processing Camera Trap Data using Artificial Intelligence},
author={V{\'e}lez, Juliana and Castiblanco-Camacho, Paula J and Tabak, Michael A and Chalmers, Carl and Fergus, Paul and Fieberg, John},
journal={arXiv preprint arXiv:2202.02283},
year={2022}
}
```
For questions about this data set, contact [Juliana Velez Gomez]([email protected]).
This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
</details>
### Supported Tasks and Leaderboards
No leaderboards exist for LILA.
### Languages
The [LILA taxonomy](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/) is provided in English.
## Dataset Structure
### Data Instances
The data annotations are provided in [COCO Camera Traps](https://github.com/Microsoft/CameraTraps/blob/master/data_management/README.md#coco-cameratraps-format) format.
All of the datasets share a common category taxonomy, which is defined on the [LILA website](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/).
### Data Fields
Different datasets may have slightly varying fields, which include:
`file_name`: the file name \
`width` and `height`: the dimensions of the image \
`study`: which research study the image was collected as part of \
`location` : the name of the location at which the image was taken \
`annotations`: information about image annotation, which includes the taxonomy information, bounding box/boxes (`bbox`/`bboxes`) if any, as well as any other annotation information. \
`image` : the `path` to download the image and any other information that is available, e.g. its size in `bytes`.
### Data Splits
This dataset does not have a predefined train/test split.
## Dataset Creation
### Curation Rationale
The datasets that constitute LILA have been provided by the organizations, projects and researchers who collected them.
### Source Data
#### Initial data collection and normalization
N/A
#### Who are the source language producers?
N/A
### Annotations
#### Annotation process
Each dataset has been annotated by the members of the project/organization that provided it.
#### Who are the annotators?
The annotations have been provided by domain experts in fields such as biology and ecology.
### Personal and Sensitive Information
Some of the original data sets included a “human” class label; for privacy reasons, these images were removed. Those labels are still present in the metadata. If those images are important to your work, contact the [LILA maintainers](mailto:[email protected]), since in some cases it will be possible to release those images under an alternative license.
## Considerations for Using the Data
### Social Impact of Dataset
Machine learning depends on labeled data, but accessing such data in biology and conservation is a challenge. Consequently, everyone benefits when labeled data is made available. Biologists and conservation scientists benefit by having data to train on, and free hosting allows teams to multiply the impact of their data (we suggest listing this benefit in grant proposals that fund data collection). ML researchers benefit by having data to experiment with.
### Discussion of Biases
These datasets do not represent global diversity, but are examples of local ecosystems and animals.
### Other Known Limitations
N/A
## Additional Information
### Working with Taxonomies
All the taxonomy categories are saved as ClassLabels, which can be converted to strings as needed. Strings can likewise be converted to integers as needed, to filter the dataset. In the example below we filter the "Caltech Camera Traps" dataset to find all the entries with a "felis catus" as the species for the first annotation.
```python
dataset = load_dataset("society-ethics/lila_camera_traps", "Caltech Camera Traps", split="train")
taxonomy = dataset.features["annotations"].feature["taxonomy"]
# Filters to show only cats
cats = dataset.filter(lambda x: x["annotations"]["taxonomy"][0]["species"] == taxonomy["species"].str2int("felis catus"))
```
The original common names have been saved with their taxonomy mappings in this repository in `common_names_to_tax.json`. These can be used, for example, to map from a taxonomy combination to a common name to help make queries more legible. Note, however, that there is a small number of duplicate common names with different taxonomy values which you will need to disambiguate.
The following example loads the first "sea turtle" in the "Island Conservation Camera Traps" dataset.
```python
LILA_COMMON_NAMES_TO_TAXONOMY = pd.read_json("https://huggingface.co/datasets/society-ethics/lila_camera_traps/raw/main/data/common_names_to_tax.json", lines=True).set_index("common_name")
dataset = load_dataset("society-ethics/lila_camera_traps", "Island Conservation Camera Traps", split="train")
taxonomy = dataset.features["annotations"].feature["taxonomy"]
sea_turtle = LILA_COMMON_NAMES_TO_TAXONOMY.loc["sea turtle"].to_dict()
sea_turtle = {k: taxonomy[k].str2int(v) if v is not None else v for k, v in sea_turtle.items()} # Map to ClassLabel integers
sea_turtle_dataset = ds.filter(lambda x: x["annotations"]["taxonomy"][0] == sea_turtle)
```
The example below selects a random item from the dataset, and then maps from the taxonomy to a common name:
```python
LILA_COMMON_NAMES_TO_TAXONOMY = pd.read_json("https://huggingface.co/datasets/society-ethics/lila_camera_traps/raw/main/data/common_names_to_tax.json", lines=True).set_index("common_name")
dataset = load_dataset("society-ethics/lila_camera_traps", "Caltech Camera Traps", split="train")
taxonomy = dataset.features["annotations"].feature["taxonomy"]
random_entry = dataset.shuffle()[0]
filter_taxonomy = random_entry["annotations"]["taxonomy"][0]
filter_keys = list(map(lambda x: (x[0], taxonomy[x[0]].int2str(x[1])), filter(lambda x: x[1] is not None, list(filter_taxonomy.items()))))
if len(filter_keys) > 0:
print(LILA_COMMON_NAMES_TO_TAXONOMY[np.logical_and.reduce([
LILA_COMMON_NAMES_TO_TAXONOMY[k] == v for k,v in filter_keys
])])
else:
print("No common name found for the item.")
```
### Dataset Curators
LILA BC is maintained by a working group that includes representatives from Ecologize, Zooniverse, the Evolving AI Lab, Snapshot Safari, and Microsoft AI for Earth. Hosting on Microsoft Azure is provided by Microsoft AI for Earth.
### Licensing Information
Many, but not all, LILA data sets were released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/). Check the details of the specific dataset you are using in its section above.
### Citation Information
Citations for each dataset (if they exist) are provided in its section above.
### Contributions
Thanks to [@NimaBoscarino](https://github.com/NimaBoscarino/) for adding this dataset.
|
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sanchit-gandhi/whisper-jax-test-files
|
sanchit-gandhi
| 2023-04-19T12:07:08Z | 99 | 2 |
[
"region:us"
] | null | 2023-04-19T11:49:16Z |
---
dataset_info:
features:
- name: audio
dtype: audio
splits:
- name: train
num_bytes: 271658381.0
num_examples: 2
download_size: 113444578
dataset_size: 271658381.0
---
# Dataset Card for "whisper-jax-test-files"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
nlpai-lab/openassistant-guanaco-ko
|
nlpai-lab
| 2023-06-01T10:44:35Z | 99 | 4 |
[
"task_categories:text-generation",
"task_categories:question-answering",
"task_categories:summarization",
"size_categories:1K<n<10K",
"language:ko",
"license:apache-2.0",
"region:us"
] |
[
"text-generation",
"question-answering",
"summarization"
] | 2023-06-01T06:54:34Z |
---
license: apache-2.0
task_categories:
- text-generation
- question-answering
- summarization
language:
- ko
size_categories:
- 1K<n<10K
---
### Dataset Summary
Korean translation of Guanaco via the DeepL API
Note: There are cases where multilingual data has been converted to monolingual data during batch translation to Korean using the API.
Below is Guanaco's README.
----
This dataset is a subset of the Open Assistant dataset, which you can find here: https://huggingface.co/datasets/OpenAssistant/oasst1/tree/main
This subset of the data only contains the highest-rated paths in the conversation tree, with a total of 9,846 samples.
This dataset was used to train Guanaco with QLoRA.
For further information, please see the original dataset.
License: Apache 2.0
|
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] |
jxie/flickr8k
|
jxie
| 2023-06-25T22:25:03Z | 99 | 0 |
[
"region:us"
] | null | 2023-06-25T19:09:16Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: caption_0
dtype: string
- name: caption_1
dtype: string
- name: caption_2
dtype: string
- name: caption_3
dtype: string
- name: caption_4
dtype: string
splits:
- name: train
num_bytes: 826721431.0
num_examples: 6000
- name: validation
num_bytes: 138017615.0
num_examples: 1000
- name: test
num_bytes: 136871307.0
num_examples: 1000
download_size: 274629589
dataset_size: 1101610353.0
---
# Dataset Card for "flickr8k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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FredZhang7/all-scam-spam
|
FredZhang7
| 2023-07-18T17:16:16Z | 99 | 4 |
[
"task_categories:text-classification",
"task_categories:zero-shot-classification",
"size_categories:10K<n<100K",
"language:no",
"language:es",
"language:so",
"language:ca",
"language:af",
"language:it",
"language:nl",
"language:hi",
"language:cy",
"language:ar",
"language:sv",
"language:cs",
"language:pl",
"language:de",
"language:lt",
"language:sq",
"language:uk",
"language:tl",
"language:sl",
"language:hr",
"language:en",
"language:fi",
"language:vi",
"language:id",
"language:da",
"language:ko",
"language:bg",
"language:mr",
"language:ja",
"language:bn",
"language:ro",
"language:pt",
"language:fr",
"language:hu",
"language:tr",
"language:zh",
"language:mk",
"language:ur",
"language:sk",
"language:ne",
"language:et",
"language:sw",
"language:ru",
"language:multilingual",
"license:apache-2.0",
"nlp",
"moderation",
"region:us"
] |
[
"text-classification",
"zero-shot-classification"
] | 2023-07-04T22:07:15Z |
---
license: apache-2.0
language:
- no
- es
- so
- ca
- af
- it
- nl
- hi
- cy
- ar
- sv
- cs
- pl
- de
- lt
- sq
- uk
- tl
- sl
- hr
- en
- fi
- vi
- id
- da
- ko
- bg
- mr
- ja
- bn
- ro
- pt
- fr
- hu
- tr
- zh
- mk
- ur
- sk
- ne
- et
- sw
- ru
- multilingual
task_categories:
- text-classification
- zero-shot-classification
tags:
- nlp
- moderation
size_categories:
- 10K<n<100K
---
This is a large corpus of 42,619 preprocessed text messages and emails sent by humans in 43 languages. `is_spam=1` means spam and `is_spam=0` means ham.
1040 rows of balanced data, consisting of casual conversations and scam emails in ≈10 languages, were manually collected and annotated by me, with some help from ChatGPT.
<br>
### Some preprcoessing algorithms
- [spam_assassin.js](./spam_assassin.js), followed by [spam_assassin.py](./spam_assassin.py)
- [enron_spam.py](./enron_spam.py)
<br>
### Data composition
<br>
### Description
To make the text format between sms messages and emails consistent, email subjects and content are separated by two newlines:
```python
text = email.subject + "\n\n" + email.content
```
<br>
### Suggestions
- If you plan to train a model based on this dataset alone, I recommend adding **some** rows with `is_toxic=0` from `FredZhang7/toxi-text-3M`. Make sure the rows aren't spam.
<br>
### Other Sources
- https://huggingface.co/datasets/sms_spam
- https://github.com/MWiechmann/enron_spam_data
- https://github.com/stdlib-js/datasets-spam-assassin
- https://repository.ortolang.fr/api/content/comere/v3.3/cmr-simuligne.html
|
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] |
hippocrates/pubmedqa_test
|
hippocrates
| 2023-10-12T16:07:06Z | 99 | 0 |
[
"region:us"
] | null | 2023-09-27T08:03:19Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: string
- name: query
dtype: string
- name: answer
dtype: string
- name: choices
sequence: string
- name: gold
dtype: int64
splits:
- name: train
num_bytes: 358222008
num_examples: 211269
- name: valid
num_bytes: 81317
num_examples: 50
- name: test
num_bytes: 828036
num_examples: 500
download_size: 180418003
dataset_size: 359131361
---
# Dataset Card for "pubmedqa_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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pborchert/CORE
|
pborchert
| 2023-10-21T18:12:18Z | 99 | 0 |
[
"task_categories:text-classification",
"task_categories:zero-shot-classification",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-4.0",
"relation-classification",
"relation-extraction",
"few-shot",
"domain-adaptation",
"business",
"finance",
"arxiv:2310.12024",
"region:us"
] |
[
"text-classification",
"zero-shot-classification"
] | 2023-10-21T17:45:21Z |
---
license: cc-by-4.0
task_categories:
- text-classification
- zero-shot-classification
tags:
- relation-classification
- relation-extraction
- few-shot
- domain-adaptation
- business
- finance
language:
- en
size_categories:
- 1K<n<10K
---
# Dataset Card for CORE: A Few-Shot Company Relation Classification Dataset for Robust Domain Adaptation.
<!-- Provide a quick summary of the dataset. -->
CORE includes 4,708 instances of 12 relation types with corresponding textual evidence extracted from company Wikipedia pages. It contains an annotated NOTA (none-of-the-above) category.
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
We introduce CORE, a dataset for few-shot relation classification (RC) focused on company relations and business entities. CORE includes 4,708 instances of 12 relation types with corresponding textual evidence extracted from company Wikipedia pages. Company names and business entities pose a challenge for few-shot RC models due to the rich and diverse information associated with them. For example, a company name may represent the legal entity, products, people, or business divisions depending on the context. Therefore, deriving the relation type between entities is highly dependent on textual context. To evaluate the performance of state-of-the-art RC models on the CORE dataset, we conduct experiments in the few-shot domain adaptation setting. Our results reveal substantial performance gaps, confirming that models trained on different domains struggle to adapt to CORE. Interestingly, we find that models trained on CORE showcase improved out-of-domain performance, which highlights the importance of high-quality data for robust domain adaptation. Specifically, the information richness embedded in business entities allows models to focus on contextual nuances, reducing their reliance on superficial clues such as relation-specific verbs. In addition to the dataset, we provide relevant code snippets to facilitate reproducibility and encourage further research in the field.
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** https://github.com/pnborchert/CORE
- **Paper:** https://arxiv.org/abs/2310.12024
## Dataset Structure
The dataset is split in training and test instances with **overlapping relation types**. Relation types inlcuded in the test set should be excluded from the training set in the episode sampling procedure [sample_configuration.py](https://github.com/pnborchert/CORE/blob/master/benchmark/fs/sample_configuration.py).
- `train`: Contains 4000 training instances and 12 relation types.
- `test`: Contains 708 instances and 12 relation types.
- `relation_description`: Textual descriptions of the relation types.
## Citation
```bibtex
@misc{borchert2023core,
title={CORE: A Few-Shot Company Relation Classification Dataset for Robust Domain Adaptation},
author={Philipp Borchert and Jochen De Weerdt and Kristof Coussement and Arno De Caigny and Marie-Francine Moens},
year={2023},
eprint={2310.12024},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
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] |
maywell/ko_wikidata_QA
|
maywell
| 2023-11-25T00:28:52Z | 99 | 12 |
[
"region:us"
] | null | 2023-10-31T02:09:29Z |
---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 144606911
num_examples: 137505
configs:
- config_name: default
data_files:
- split: train
path: data/train.csv
---
## 업데이트 로그
- 2023-11-03 : MarkrAI의 Dedup 적용.
# 한국어 위키 데이터 QA셋
본 데이터는 Synatra-7B-Instruct 모델과 ChatGPT를 사용하여, 제작된 QA셋입니다.
해당 데이터를 직접적으로 상업적으로 사용하는 것은 허용되지 않으며, 데이터를 이용하여 훈련된 모델에 대한 상업적 사용은 허용됩니다.
아직 완벽히 정제되지는 않았으며, 오류나 수정사항에 대해서는 PR 부탁드립니다.
|
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] |
pranjali97/ha-en_RL-grow1_train
|
pranjali97
| 2023-11-04T03:29:55Z | 99 | 0 |
[
"region:us"
] | null | 2023-11-04T03:29:53Z |
---
dataset_info:
features:
- name: src
dtype: string
- name: ref
dtype: string
- name: mt
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 13578997
num_examples: 29454
download_size: 3191264
dataset_size: 13578997
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "ha-en_RL-grow1_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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Santp98/ranking_options_processes
|
Santp98
| 2023-11-05T14:37:48Z | 99 | 0 |
[
"region:us"
] | null | 2023-11-05T14:37:45Z |
---
dataset_info:
features:
- name: index
dtype: int64
- name: process_id
dtype: string
- name: description
dtype: string
splits:
- name: train
num_bytes: 5619635
num_examples: 23323
download_size: 3091438
dataset_size: 5619635
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "ranking_options_processes"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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davebulaval/CSMD
|
davebulaval
| 2023-11-21T18:38:13Z | 99 | 0 |
[
"task_categories:text-classification",
"task_categories:text2text-generation",
"multilinguality:monolingual",
"multilinguality:aligned",
"size_categories:1K<n<10K",
"source_datasets:original",
"source_datasets:extended|other-turkcorpus,other-asset,other-questeval,other-simplicity_da,other-simp_da",
"language:en",
"license:cc-by-4.0",
"simplification-evaluation",
"meaning-evaluation",
"arxiv:2104.07560",
"region:us"
] |
[
"text-classification",
"text2text-generation"
] | 2023-11-13T15:38:03Z |
---
license:
- cc-by-4.0
multilinguality:
- monolingual
- aligned
task_categories:
- text-classification
- text2text-generation
source_datasets:
- original
- >-
extended|other-turkcorpus,other-asset,other-questeval,other-simplicity_da,other-simp_da
language:
- en
tags:
- simplification-evaluation
- meaning-evaluation
pretty_name: CSMD
size_categories:
- 1K<n<10K
dataset_info:
- config_name: meaning
features:
- name: original
dtype: string
- name: simplification
dtype: string
- name: label
dtype: float64
splits:
- name: train
num_bytes: 251558
num_examples: 853
- name: dev
num_bytes: 27794
num_examples: 95
- name: test
num_bytes: 117686
num_examples: 407
download_size: 397038
dataset_size: 1355
- config_name: meaning_with_data_augmentation
features:
- name: original
dtype: string
- name: simplification
dtype: string
- name: label
dtype: float64
splits:
- name: train
num_bytes: 1151604
num_examples: 2560
- name: dev
num_bytes: 120991
num_examples: 285
- name: test
num_bytes: 540844
num_examples: 1220
download_size: 1813439
dataset_size: 4065
- config_name: meaning_holdout_identical
features:
- name: original
dtype: string
- name: simplification
dtype: string
- name: label
dtype: float64
splits:
- name: test
num_bytes: 89866
num_examples: 359
download_size: 89866
dataset_size: 359
- config_name: meaning_holdout_unrelated
features:
- name: original
dtype: string
- name: simplification
dtype: string
- name: label
dtype: float64
splits:
- name: test
num_bytes: 247835
num_examples: 359
download_size: 247835
dataset_size: 359
config_names:
- meaning
- meaning_with_data_augmentation
- meaning_holdout_identical
- meaning_holdout_unrelated
viewer: true
configs:
- config_name: meaning
data_files:
- split: train
path: "train.tsv"
- split: dev
path: "dev.tsv"
- split: test
path: "test.tsv"
- config_name: meaning_with_data_augmentation
data_files:
- split: train
path: "train_da.tsv"
- split: dev
path: "dev_da.tsv"
- split: test
path: "test_da.tsv"
- config_name: meaning_holdout_identical
data_files:
- split: test
path: "identical.tsv"
- config_name: meaning_holdout_unrelated
data_files:
- split: test
path: "unrelated.tsv"
---
# Dataset Card for "Continuous Scale Meaning Dataset" (CSMD)
CSMD was created for [MeaningBERT: Assessing Meaning Preservation Between Sentences](https://www.frontiersin.org/articles/10.3389/frai.2023.1223924/full).
It contains 1,355 English text simplification meaning preservation annotations. Meaning preservation measures how well the meaning of the output text corresponds to the meaning of the source ([Saggion, 2017](https://link.springer.com/book/10.1007/978-3-031-02166-4)).
The annotations were taken from the following four datasets:
- [ASSET](https://aclanthology.org/2020.acl-main.424/)
- [QuestEVal](https://arxiv.org/abs/2104.07560),
- [SimpDa_2022](https://aclanthology.org/2023.acl-long.905.pdf) and,
- [Simplicity-DA](https://direct.mit.edu/coli/article/47/4/861/106930/The-Un-Suitability-of-Automatic-Evaluation-Metrics).
It contains a data augmentation subset of 1,355 identical sentence triplets and 1,355 unrelated sentence triplets (See the "Sanity Checks" section (3.3.) in our [article](https://www.frontiersin.org/articles/10.3389/frai.2023.1223924/full)).
It also contains two holdout subsets of 359 identical sentence triplets and 359 unrelated sentence triples (See the "MeaningBERT" section (3.4.) in our [article](https://www.frontiersin.org/articles/10.3389/frai.2023.1223924/full)).
## Dataset Structure
### Data Instances
- `Meaning` configuration: an instance consists of 1,355 meaning preservation triplets (Document, simplification, label).
- `meaning_with_data_augmentation` configuration: an instance consists of 1,355 meaning preservation triplets (Document, simplification, label) along with 1,355 data augmentation triplets (Document, Document, 1) and 1,355 data augmentation triplets (Document, Unrelated Document, 0) (See the sanity checks in our [article](https://www.frontiersin.org/articles/10.3389/frai.2023.1223924/full)).
- `meaning_holdout_identical` configuration: an instance consists of 359 meaning holdout preservation identical triplets (Document, Document, 1) based on the ASSET Simplification dataset.
- `meaning_holdout_unrelated` configuration: an instance consists of 359 meaning holdout preservation unrelated triplets (Document, Unrelated Document, 0) based on the ASSET Simplification dataset.
### Data Fields
- `original`: an original sentence from the source datasets.
- `simplification`: a simplification of the original obtained by an automated system or a human.
- `label`: a meaning preservation rating between 0 and 100.
### Data Splits
The split statistics of CSMD are given below.
| | Train | Dev | Test | Total |
| ------ | ------ | ------ | ---- | ----- |
| Meaning | 853 | 95 | 407 | 1,355 |
| Meaning With Data Augmentation | 2,560 | 285 | 1,220 | 4,065 |
| Meaning Holdout Identical | NA | NA | 359 | 359 |
| Meaning Holdout Unrelated | NA | NA | 359 | 359 |
All the splits are randomly split using a 60-10-30 split with the seed `42`.
# Citation Information
```
@ARTICLE{10.3389/frai.2023.1223924,
AUTHOR={Beauchemin, David and Saggion, Horacio and Khoury, Richard},
TITLE={{MeaningBERT: Assessing Meaning Preservation Between Sentences}},
JOURNAL={Frontiers in Artificial Intelligence},
VOLUME={6},
YEAR={2023},
URL={https://www.frontiersin.org/articles/10.3389/frai.2023.1223924},
DOI={10.3389/frai.2023.1223924},
ISSN={2624-8212},
}
```
|
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] |
Nimsi2613/MedQuad
|
Nimsi2613
| 2023-11-21T01:29:05Z | 99 | 0 |
[
"region:us"
] | null | 2023-11-21T01:29:01Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: valid
path: data/valid-*
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
- name: instruction
dtype: string
splits:
- name: train
num_bytes: 19604993.268292684
num_examples: 14800
- name: test
num_bytes: 794797.0243902439
num_examples: 600
- name: valid
num_bytes: 1324661.7073170731
num_examples: 1000
download_size: 10176212
dataset_size: 21724452.0
---
# Dataset Card for "MedQuad"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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autoevaluate/autoeval-staging-eval-project-xsum-f0ba0c18-12915729
|
autoevaluate
| 2022-08-11T14:48:21Z | 98 | 0 |
[
"autotrain",
"evaluation",
"region:us"
] | null | 2022-08-11T13:19:00Z |
---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: google/pegasus-cnn_dailymail
metrics: ['bleu']
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: google/pegasus-cnn_dailymail
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@xarymast](https://huggingface.co/xarymast) for evaluating this model.
|
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joelniklaus/EU_Wikipedias
|
joelniklaus
| 2023-03-21T15:44:18Z | 98 | 1 |
[
"task_categories:fill-mask",
"annotations_creators:other",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:bg",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
"language:et",
"language:fi",
"language:fr",
"language:ga",
"language:hr",
"language:hu",
"language:it",
"language:lt",
"language:lv",
"language:mt",
"language:nl",
"language:pl",
"language:pt",
"language:ro",
"language:sk",
"language:sl",
"language:sv",
"license:cc-by-4.0",
"region:us"
] |
[
"fill-mask"
] | 2022-11-20T01:31:51Z |
---
annotations_creators:
- other
language_creators:
- found
language:
- bg
- cs
- da
- de
- el
- en
- es
- et
- fi
- fr
- ga
- hr
- hu
- it
- lt
- lv
- mt
- nl
- pl
- pt
- ro
- sk
- sl
- sv
license:
- cc-by-4.0
multilinguality:
- multilingual
paperswithcode_id: null
pretty_name: "EUWikipedias: A dataset of Wikipedias in the EU languages"
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- fill-mask
---
# Dataset Card for EUWikipedias: A dataset of Wikipedias in the EU languages
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [Joel Niklaus](mailto:[email protected])
### Dataset Summary
Wikipedia dataset containing cleaned articles of all languages.
The datasets are built from the Wikipedia dump
(https://dumps.wikimedia.org/) with one split per language. Each example
contains the content of one full Wikipedia article with cleaning to strip
markdown and unwanted sections (references, etc.).
### Supported Tasks and Leaderboards
The dataset supports the tasks of fill-mask.
### Languages
The following languages are supported:
bg, cs, da, de, el, en, es, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv
## Dataset Structure
It is structured in the following format: {date}/{language}_{shard}.jsonl.xz
At the moment only the date '20221120' is supported.
Use the dataset like this:
```python
from datasets import load_dataset
dataset = load_dataset('joelito/EU_Wikipedias', date="20221120", language="de", split='train', streaming=True)
```
### Data Instances
The file format is jsonl.xz and there is one split available (`train`).
| Source | Size (MB) | Words | Documents | Words/Document |
|:-------------|------------:|-----------:|------------:|-----------------:|
| 20221120.all | 86034 | 9506846949 | 26481379 | 359 |
| 20221120.bg | 1261 | 88138772 | 285876 | 308 |
| 20221120.cs | 1904 | 189580185 | 513851 | 368 |
| 20221120.da | 679 | 74546410 | 286864 | 259 |
| 20221120.de | 11761 | 1191919523 | 2740891 | 434 |
| 20221120.el | 1531 | 103504078 | 215046 | 481 |
| 20221120.en | 26685 | 3192209334 | 6575634 | 485 |
| 20221120.es | 6636 | 801322400 | 1583597 | 506 |
| 20221120.et | 538 | 48618507 | 231609 | 209 |
| 20221120.fi | 1391 | 115779646 | 542134 | 213 |
| 20221120.fr | 9703 | 1140823165 | 2472002 | 461 |
| 20221120.ga | 72 | 8025297 | 57808 | 138 |
| 20221120.hr | 555 | 58853753 | 198746 | 296 |
| 20221120.hu | 1855 | 167732810 | 515777 | 325 |
| 20221120.it | 5999 | 687745355 | 1782242 | 385 |
| 20221120.lt | 409 | 37572513 | 203233 | 184 |
| 20221120.lv | 269 | 25091547 | 116740 | 214 |
| 20221120.mt | 29 | 2867779 | 5030 | 570 |
| 20221120.nl | 3208 | 355031186 | 2107071 | 168 |
| 20221120.pl | 3608 | 349900622 | 1543442 | 226 |
| 20221120.pt | 3315 | 389786026 | 1095808 | 355 |
| 20221120.ro | 1017 | 111455336 | 434935 | 256 |
| 20221120.sk | 506 | 49612232 | 238439 | 208 |
| 20221120.sl | 543 | 58858041 | 178472 | 329 |
| 20221120.sv | 2560 | 257872432 | 2556132 | 100 |
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
This dataset has been created by downloading the wikipedias using [olm/wikipedia](https://huggingface.co/datasets/olm/wikipedia) for the 24 EU languages.
For more information about the creation of the dataset please refer to prepare_wikipedias.py
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
TODO add citation
```
### Contributions
Thanks to [@JoelNiklaus](https://github.com/joelniklaus) for adding this dataset.
|
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] |
joelniklaus/legal-mc4
|
joelniklaus
| 2023-08-06T22:54:20Z | 98 | 5 |
[
"task_categories:fill-mask",
"annotations_creators:other",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:bg",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
"language:et",
"language:fi",
"language:fr",
"language:ga",
"language:hu",
"language:it",
"language:lt",
"language:lv",
"language:mt",
"language:nl",
"language:pl",
"language:pt",
"language:ro",
"language:sk",
"language:sl",
"language:sv",
"license:cc-by-4.0",
"region:us"
] |
[
"fill-mask"
] | 2023-02-14T17:48:58Z |
---
annotations_creators:
- other
language_creators:
- found
language:
- bg
- cs
- da
- de
- el
- en
- es
- et
- fi
- fr
- ga
- hu
- it
- lt
- lv
- mt
- nl
- pl
- pt
- ro
- sk
- sl
- sv
license:
- cc-by-4.0
multilinguality:
- multilingual
paperswithcode_id: null
pretty_name: "MC4_Legal: A Corpus Covering the Legal Part of MC4 for European Languages"
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- fill-mask
---
# Dataset Card for MC4_Legal: A Corpus Covering the Legal Part of MC4 for European Languages
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** [GitHub](https://github.com/JoelNiklaus/LegalDatasets/tree/main/pretrain/mc4_legal)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [Joel Niklaus](mailto:[email protected])
### Dataset Summary
This dataset contains large text resources (~106GB in total) from mc4 filtered for legal data that can be used for pretraining language models.
This dataset uses a different filtering method compared to [mc4_legal](https://huggingface.co/datasets/joelito/mc4_legal) and uses the smaller filtered [c4](https://huggingface.co/datasets/c4) dataset for the English split to speed up the filtering.
Use the dataset like this:
```python
from datasets import load_dataset
dataset = load_dataset("joelito/mc4_legal", "de", split='train', streaming=True)
```
### Supported Tasks and Leaderboards
The dataset supports the task of masked language modeling.
### Languages
The following languages are supported: bg, cs, da, de, el, en, es, et, fi, fr, ga, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv
## Dataset Structure
### Data Instances
The file format is jsonl.xz and there is a validation and train split available.
| Source | Size (MB) | Words | Documents | Words/Document |
|:---------|------------:|------------:|------------:|-----------------:|
| all | 448980 | 28599300521 | 9873288 | 2896 |
| bg | 57 | 2390349 | 379 | 6306 |
| cs | 31005 | 1840827375 | 677796 | 2715 |
| da | 162 | 10466716 | 3231 | 3239 |
| de | 105739 | 6184578784 | 3164461 | 1954 |
| el | 30 | 1155977 | 307 | 3765 |
| en | 13734 | 966539309 | 359283 | 2690 |
| es | 132053 | 9058939804 | 2281888 | 3969 |
| et | 2059 | 110198368 | 49987 | 2204 |
| fi | 1270 | 62799074 | 44875 | 1399 |
| fr | 30878 | 2117306229 | 598983 | 3534 |
| ga | 1 | 32772 | 8 | 4096 |
| hu | 4677 | 244911748 | 58857 | 4161 |
| it | 46957 | 3053920779 | 990823 | 3082 |
| lt | 156 | 9142223 | 1529 | 5979 |
| lv | 1 | 58702 | 16 | 3668 |
| mt | 65 | 3479869 | 731 | 4760 |
| nl | 326 | 21962633 | 6875 | 3194 |
| pl | 37950 | 2235839721 | 827641 | 2701 |
| pt | 20120 | 1338147828 | 382173 | 3501 |
| ro | 8816 | 551372510 | 136513 | 4038 |
| sk | 5850 | 349265172 | 130701 | 2672 |
| sl | 1742 | 107493024 | 32574 | 3299 |
| sv | 5332 | 328471555 | 123657 | 2656 |
### Data Fields
[More Information Needed]
### Data Splits
#### Data Size
```bash
$ xz --list data/*.xz
Strms Blocks Compressed Uncompressed Ratio Check Filename
1 1 2,080.7 KiB 33.4 MiB 0.061 CRC64 data/bg.train.0.jsonl.xz
1 1 22.8 KiB 315.9 KiB 0.072 CRC64 data/bg.validation.0.jsonl.xz
1 1 608.0 MiB 3,881.0 MiB 0.157 CRC64 data/cs.train.0.jsonl.xz
1 1 608.0 MiB 3,902.6 MiB 0.156 CRC64 data/cs.train.1.jsonl.xz
1 1 256.1 MiB 1,644.5 MiB 0.156 CRC64 data/cs.train.2.jsonl.xz
1 1 1,450.6 KiB 8,690.7 KiB 0.167 CRC64 data/cs.validation.0.jsonl.xz
1 1 7,578.6 KiB 38.3 MiB 0.193 CRC64 data/da.train.0.jsonl.xz
1 1 19.7 KiB 82.3 KiB 0.240 CRC64 data/da.validation.0.jsonl.xz
1 1 608.0 MiB 3,026.9 MiB 0.201 CRC64 data/de.train.0.jsonl.xz
1 1 608.0 MiB 3,038.7 MiB 0.200 CRC64 data/de.train.1.jsonl.xz
1 1 608.0 MiB 3,036.1 MiB 0.200 CRC64 data/de.train.2.jsonl.xz
1 1 608.0 MiB 3,040.3 MiB 0.200 CRC64 data/de.train.3.jsonl.xz
1 1 608.0 MiB 3,038.6 MiB 0.200 CRC64 data/de.train.4.jsonl.xz
1 1 608.0 MiB 3,044.2 MiB 0.200 CRC64 data/de.train.5.jsonl.xz
1 1 608.0 MiB 3,043.8 MiB 0.200 CRC64 data/de.train.6.jsonl.xz
1 1 608.0 MiB 3,038.2 MiB 0.200 CRC64 data/de.train.7.jsonl.xz
1 1 55.1 MiB 274.7 MiB 0.201 CRC64 data/de.train.8.jsonl.xz
1 1 5,033.5 KiB 24.5 MiB 0.201 CRC64 data/de.validation.0.jsonl.xz
1 1 1,280.9 KiB 17.0 MiB 0.073 CRC64 data/el.train.0.jsonl.xz
1 1 5,552 B 15.7 KiB 0.346 CRC64 data/el.validation.0.jsonl.xz
1 1 608.0 MiB 2,602.1 MiB 0.234 CRC64 data/en.train.0.jsonl.xz
1 1 90.0 MiB 386.5 MiB 0.233 CRC64 data/en.train.1.jsonl.xz
1 1 826.6 KiB 3,298.8 KiB 0.251 CRC64 data/en.validation.0.jsonl.xz
1 1 608.0 MiB 3,106.5 MiB 0.196 CRC64 data/es.train.0.jsonl.xz
1 1 608.0 MiB 3,118.1 MiB 0.195 CRC64 data/es.train.1.jsonl.xz
1 1 608.0 MiB 3,113.6 MiB 0.195 CRC64 data/es.train.2.jsonl.xz
1 1 608.0 MiB 3,122.5 MiB 0.195 CRC64 data/es.train.3.jsonl.xz
1 1 608.0 MiB 3,121.5 MiB 0.195 CRC64 data/es.train.4.jsonl.xz
1 1 608.0 MiB 3,122.9 MiB 0.195 CRC64 data/es.train.5.jsonl.xz
1 1 608.0 MiB 3,128.4 MiB 0.194 CRC64 data/es.train.6.jsonl.xz
1 1 608.0 MiB 3,129.5 MiB 0.194 CRC64 data/es.train.7.jsonl.xz
1 1 608.0 MiB 3,132.2 MiB 0.194 CRC64 data/es.train.8.jsonl.xz
1 1 528.5 MiB 2,722.5 MiB 0.194 CRC64 data/es.train.9.jsonl.xz
1 1 6,159.9 KiB 30.7 MiB 0.196 CRC64 data/es.validation.0.jsonl.xz
1 1 93.5 MiB 506.2 MiB 0.185 CRC64 data/et.train.0.jsonl.xz
1 1 136.2 KiB 571.3 KiB 0.238 CRC64 data/et.validation.0.jsonl.xz
1 1 60.6 MiB 312.6 MiB 0.194 CRC64 data/fi.train.0.jsonl.xz
1 1 63.2 KiB 262.4 KiB 0.241 CRC64 data/fi.validation.0.jsonl.xz
1 1 608.0 MiB 3,400.7 MiB 0.179 CRC64 data/fr.train.0.jsonl.xz
1 1 608.0 MiB 3,405.5 MiB 0.179 CRC64 data/fr.train.1.jsonl.xz
1 1 135.9 MiB 763.7 MiB 0.178 CRC64 data/fr.train.2.jsonl.xz
1 1 1,414.3 KiB 7,626.1 KiB 0.185 CRC64 data/fr.validation.0.jsonl.xz
1 1 31.2 KiB 146.4 KiB 0.213 CRC64 data/ga.train.0.jsonl.xz
1 0 32 B 0 B --- CRC64 data/ga.validation.0.jsonl.xz
1 1 211.5 MiB 1,407.3 MiB 0.150 CRC64 data/hu.train.0.jsonl.xz
1 1 212.9 KiB 1,287.6 KiB 0.165 CRC64 data/hu.validation.0.jsonl.xz
1 1 608.0 MiB 2,963.4 MiB 0.205 CRC64 data/it.train.0.jsonl.xz
1 1 608.0 MiB 2,970.0 MiB 0.205 CRC64 data/it.train.1.jsonl.xz
1 1 608.0 MiB 2,973.7 MiB 0.204 CRC64 data/it.train.2.jsonl.xz
1 1 315.2 MiB 1,541.6 MiB 0.204 CRC64 data/it.train.3.jsonl.xz
1 1 2,419.3 KiB 11.2 MiB 0.211 CRC64 data/it.validation.0.jsonl.xz
1 1 9,966.7 KiB 38.2 MiB 0.255 CRC64 data/lt.train.0.jsonl.xz
1 1 17.2 KiB 84.7 KiB 0.203 CRC64 data/lt.validation.0.jsonl.xz
1 1 66.4 KiB 326.7 KiB 0.203 CRC64 data/lv.train.0.jsonl.xz
1 0 32 B 0 B --- CRC64 data/lv.validation.0.jsonl.xz
1 1 2,851.6 KiB 16.7 MiB 0.167 CRC64 data/mt.train.0.jsonl.xz
1 1 2,092 B 5,079 B 0.412 CRC64 data/mt.validation.0.jsonl.xz
1 1 14.6 MiB 71.6 MiB 0.203 CRC64 data/nl.train.0.jsonl.xz
1 1 23.5 KiB 79.2 KiB 0.296 CRC64 data/nl.validation.0.jsonl.xz
1 1 608.0 MiB 3,635.5 MiB 0.167 CRC64 data/pl.train.0.jsonl.xz
1 1 608.0 MiB 3,646.0 MiB 0.167 CRC64 data/pl.train.1.jsonl.xz
1 1 401.9 MiB 2,409.0 MiB 0.167 CRC64 data/pl.train.2.jsonl.xz
1 1 1,870.5 KiB 10.5 MiB 0.173 CRC64 data/pl.validation.0.jsonl.xz
1 1 608.0 MiB 3,173.1 MiB 0.192 CRC64 data/pt.train.0.jsonl.xz
1 1 329.1 MiB 1,721.6 MiB 0.191 CRC64 data/pt.train.1.jsonl.xz
1 1 989.0 KiB 4,841.2 KiB 0.204 CRC64 data/pt.validation.0.jsonl.xz
1 1 365.2 MiB 2,237.9 MiB 0.163 CRC64 data/ro.train.0.jsonl.xz
1 1 419.2 KiB 2,320.4 KiB 0.181 CRC64 data/ro.validation.0.jsonl.xz
1 1 266.1 MiB 1,668.1 MiB 0.160 CRC64 data/sk.train.0.jsonl.xz
1 1 304.1 KiB 1,618.2 KiB 0.188 CRC64 data/sk.validation.0.jsonl.xz
1 1 81.6 MiB 416.1 MiB 0.196 CRC64 data/sl.train.0.jsonl.xz
1 1 101.0 KiB 416.6 KiB 0.242 CRC64 data/sl.validation.0.jsonl.xz
1 1 252.0 MiB 1,423.2 MiB 0.177 CRC64 data/sv.train.0.jsonl.xz
1 1 210.8 KiB 1,091.2 KiB 0.193 CRC64 data/sv.validation.0.jsonl.xz
-------------------------------------------------------------------------------
74 72 20.0 GiB 106.2 GiB 0.189 CRC64 74 files
```
## Dataset Creation
The dataset was created by filtering mc4 for legal data.
We used terms indicating legal citations to get the texts.
Note that this dataset can be quite noisy, and the quality is not known.
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@JoelNiklaus](https://github.com/joelniklaus) for adding this dataset.
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] |
suolyer/pile_uspto
|
suolyer
| 2023-03-27T02:09:02Z | 98 | 1 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-03-26T16:40:08Z |
---
license: apache-2.0
---
|
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mstz/sonar
|
mstz
| 2023-04-16T18:02:16Z | 98 | 0 |
[
"task_categories:tabular-classification",
"size_categories:n<1K",
"language:en",
"license:cc",
"adult",
"tabular_classification",
"binary_classification",
"UCI",
"region:us"
] |
[
"tabular-classification"
] | 2023-03-31T14:43:15Z |
---
language:
- en
tags:
- adult
- tabular_classification
- binary_classification
- UCI
pretty_name: Sonar
size_categories:
- n<1K
task_categories:
- tabular-classification
configs:
- sonar
license: cc
---
# Sonar
The [Sonar dataset](https://archive-beta.ics.uci.edu/dataset/151/connectionist+bench+sonar+mines+vs+rocks) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
Dataset to discriminate between sonar signals bounced off a metal cylinder and those bounced off a roughly cylindrical rock.
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|-----------------------------------------------------------------|
| sonar | Binary classification | Is the sonar detecting a rock? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/sonar")["train"]
```
|
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] |
pospos12/core50
|
pospos12
| 2023-05-07T05:36:50Z | 98 | 0 |
[
"region:us"
] | null | 2023-05-07T05:29:13Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': o1
'1': o10
'2': o11
'3': o12
'4': o13
'5': o14
'6': o15
'7': o16
'8': o17
'9': o18
'10': o19
'11': o2
'12': o20
'13': o21
'14': o22
'15': o23
'16': o24
'17': o25
'18': o26
'19': o27
'20': o28
'21': o29
'22': o3
'23': o30
'24': o31
'25': o32
'26': o33
'27': o34
'28': o35
'29': o36
'30': o37
'31': o38
'32': o39
'33': o4
'34': o40
'35': o41
'36': o42
'37': o43
'38': o44
'39': o45
'40': o46
'41': o47
'42': o48
'43': o49
'44': o5
'45': o50
'46': o6
'47': o7
'48': o8
'49': o9
splits:
- name: train
num_bytes: 4679767790.178506
num_examples: 131892
- name: test
num_bytes: 1167433089.5734935
num_examples: 32974
download_size: 5860983180
dataset_size: 5847200879.751999
---
# Dataset Card for "core50"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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CyberHarem/yuudachi_kantaicollection
|
CyberHarem
| 2023-09-17T17:16:35Z | 98 | 0 |
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
[
"text-to-image"
] | 2023-08-19T10:46:29Z |
---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of yuudachi_kantaicollection
This is the dataset of yuudachi_kantaicollection, containing 200 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 200 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 486 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 200 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 200 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 200 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 200 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 200 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 486 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 486 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 486 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
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] |
vhtran/en-id
|
vhtran
| 2023-08-20T07:53:35Z | 98 | 4 |
[
"task_categories:translation",
"language:en",
"language:id",
"license:cc-by-4.0",
"region:us"
] |
[
"translation"
] | 2023-08-20T07:31:37Z |
---
license: cc-by-4.0
task_categories:
- translation
language:
- en
- id
pretty_name: enidlrmt
---
|
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martinsinnona/visdecode_web
|
martinsinnona
| 2023-11-21T23:23:03Z | 98 | 0 |
[
"region:us"
] | null | 2023-08-31T18:51:05Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: test
num_bytes: 212518.0
num_examples: 37
download_size: 0
dataset_size: 212518.0
---
|
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SUSTech/prm800k
|
SUSTech
| 2023-12-03T09:56:34Z | 98 | 0 |
[
"region:us"
] | null | 2023-10-18T10:45:15Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: problem
dtype: string
- name: answer
dtype: string
- name: solution
dtype: string
splits:
- name: train
num_bytes: 13892738
num_examples: 16830
- name: test
num_bytes: 914286
num_examples: 976
download_size: 6870174
dataset_size: 14807024
---
# Dataset Card for "prm800k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
erbacher/PDEBench-1D-full
|
erbacher
| 2023-11-09T21:03:53Z | 98 | 0 |
[
"region:us"
] | null | 2023-11-09T17:11:51Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: parameters
dtype: string
- name: tensor
sequence:
sequence:
sequence: float32
- name: id
dtype: int64
- name: cls
dtype:
class_label:
names:
'0': 1D_Advection_Sols_beta0.1.hdf5
'1': 1D_Advection_Sols_beta0.2.hdf5
'2': 1D_Advection_Sols_beta0.4.hdf5
'3': 1D_Advection_Sols_beta0.7.hdf5
'4': 1D_Advection_Sols_beta1.0.hdf5
'5': 1D_Advection_Sols_beta2.0.hdf5
'6': 1D_Advection_Sols_beta4.0.hdf5
'7': 1D_Advection_Sols_beta7.0.hdf5
'8': 1D_Burgers_Sols_Nu0.001.hdf5
'9': 1D_Burgers_Sols_Nu0.002.hdf5
'10': 1D_Burgers_Sols_Nu0.004.hdf5
'11': 1D_Burgers_Sols_Nu0.01.hdf5
'12': 1D_Burgers_Sols_Nu0.02.hdf5
'13': 1D_Burgers_Sols_Nu0.04.hdf5
'14': 1D_Burgers_Sols_Nu0.1.hdf5
'15': 1D_Burgers_Sols_Nu0.2.hdf5
'16': 1D_Burgers_Sols_Nu0.4.hdf5
'17': 1D_Burgers_Sols_Nu1.0.hdf5
'18': 1D_Burgers_Sols_Nu2.0.hdf5
'19': 1D_Burgers_Sols_Nu4.0.hdf5
'20': 1D_CFD_Rand_Eta0.01_Zeta0.01_periodic_Train.hdf5-density
'21': 1D_CFD_Rand_Eta0.01_Zeta0.01_periodic_Train.hdf5-pressure
'22': 1D_CFD_Rand_Eta0.01_Zeta0.01_periodic_Train.hdf5-vx
'23': 1D_CFD_Rand_Eta0.1_Zeta0.1_periodic_Train.hdf5-density
'24': 1D_CFD_Rand_Eta0.1_Zeta0.1_periodic_Train.hdf5-pressure
'25': 1D_CFD_Rand_Eta0.1_Zeta0.1_periodic_Train.hdf5-vx
'26': 1D_CFD_Rand_Eta1.e-8_Zeta1.e-8_periodic_Train.hdf5-density
'27': 1D_CFD_Rand_Eta1.e-8_Zeta1.e-8_periodic_Train.hdf5-pressure
'28': 1D_CFD_Rand_Eta1.e-8_Zeta1.e-8_periodic_Train.hdf5-vx
'29': 1D_CFD_Rand_Eta1.e-8_Zeta1.e-8_trans_Train.hdf5-density
'30': 1D_CFD_Rand_Eta1.e-8_Zeta1.e-8_trans_Train.hdf5-pressure
'31': 1D_CFD_Rand_Eta1.e-8_Zeta1.e-8_trans_Train.hdf5-vx
'32': ReacDiff_Nu0.5_Rho1.0.hdf5
'33': ReacDiff_Nu0.5_Rho10.0.hdf5
'34': ReacDiff_Nu0.5_Rho2.0.hdf5
'35': ReacDiff_Nu0.5_Rho5.0.hdf5
'36': ReacDiff_Nu1.0_Rho1.0.hdf5
'37': ReacDiff_Nu1.0_Rho10.0.hdf5
'38': ReacDiff_Nu1.0_Rho2.0.hdf5
'39': ReacDiff_Nu1.0_Rho5.0.hdf5
'40': ReacDiff_Nu2.0_Rho1.0.hdf5
'41': ReacDiff_Nu2.0_Rho10.0.hdf5
'42': ReacDiff_Nu2.0_Rho2.0.hdf5
'43': ReacDiff_Nu2.0_Rho5.0.hdf5
'44': ReacDiff_Nu5.0_Rho1.0.hdf5
'45': ReacDiff_Nu5.0_Rho10.0.hdf5
'46': ReacDiff_Nu5.0_Rho2.0.hdf5
'47': ReacDiff_Nu5.0_Rho5.0.hdf5
splits:
- name: train
num_bytes: 99806420000
num_examples: 480000
download_size: 2640711523
dataset_size: 99806420000
---
# Dataset Card for "PDEBench-1D-full"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
Phando/share_gpt_filtered
|
Phando
| 2023-11-10T03:11:50Z | 98 | 0 |
[
"region:us"
] | null | 2023-11-10T03:10:51Z |
---
dataset_info:
features:
- name: id
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: markdown
struct:
- name: answer
dtype: string
- name: index
dtype: int64
- name: type
dtype: string
- name: text
dtype: string
- name: value
dtype: string
splits:
- name: train
num_bytes: 633293002
num_examples: 94145
download_size: 234243543
dataset_size: 633293002
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "share_gpt_filtered"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
AntoineBlanot/snli-binary
|
AntoineBlanot
| 2023-11-17T02:58:57Z | 98 | 0 |
[
"region:us"
] | null | 2023-11-17T02:50:45Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label_name
dtype: string
splits:
- name: train
num_bytes: 70545630
num_examples: 549367
- name: test
num_bytes: 1326656
num_examples: 9842
download_size: 19925323
dataset_size: 71872286
---
# Dataset Card for "snli-binary"
This dataset is the [snli-3way](https://huggingface.co/datasets/AntoineBlanot/snli-3way) dataset where the `contradiction` and `neutral` classes has been merged together as a `non-entailment` class.
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
Pipper/sol_processed_data
|
Pipper
| 2023-11-19T14:25:34Z | 98 | 0 |
[
"region:us"
] | null | 2023-11-19T11:52:58Z |
---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 12740019180
num_examples: 3814377
download_size: 1991408875
dataset_size: 12740019180
---
# Dataset Card for "sol_processed_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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vwxyzjn/summarize_from_feedback_oai_preprocessing_pythia-160m_169
|
vwxyzjn
| 2023-11-28T20:42:02Z | 98 | 0 |
[
"region:us"
] | null | 2023-11-28T20:29:54Z |
---
dataset_info:
features:
- name: info
struct:
- name: id
dtype: string
- name: post
dtype: string
- name: title
dtype: string
- name: subreddit
dtype: string
- name: site
dtype: string
- name: article
dtype: string
- name: summaries
list:
- name: text
dtype: string
- name: policy
dtype: string
- name: note
dtype: string
- name: choice
dtype: int32
- name: worker
dtype: string
- name: batch
dtype: string
- name: split
dtype: string
- name: extra
struct:
- name: confidence
dtype: int32
- name: query_token
sequence: int64
- name: query
dtype: string
- name: response0
dtype: string
- name: response0_token
sequence: int64
- name: response0_token_len
dtype: int64
- name: response1
dtype: string
- name: response1_token
sequence: int64
- name: response1_token_len
dtype: int64
- name: response0_policy
dtype: string
- name: response1_policy
dtype: string
- name: policies
dtype: string
splits:
- name: train
num_bytes: 970257747
num_examples: 92858
- name: validation
num_bytes: 909787115
num_examples: 86086
download_size: 125658569
dataset_size: 1880044862
---
# Dataset Card for "summarize_from_feedback_oai_preprocessing_pythia-160m_169"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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venelin/inferes
|
venelin
| 2022-10-08T01:25:47Z | 97 | 0 |
[
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:es",
"license:cc-by-4.0",
"nli",
"spanish",
"negation",
"coreference",
"arxiv:2210.03068",
"region:us"
] |
[
"text-classification"
] | 2022-10-07T16:57:37Z |
---
annotations_creators:
- expert-generated
language:
- es
language_creators:
- expert-generated
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: InferES
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- nli
- spanish
- negation
- coreference
task_categories:
- text-classification
task_ids:
- natural-language-inference
---
# Dataset Card for InferES
## 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)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/venelink/inferes
- **Repository:** https://github.com/venelink/inferes
- **Paper:** https://arxiv.org/abs/2210.03068
- **Point of Contact:** venelin [at] utexas [dot] edu
### Dataset Summary
Natural Language Inference dataset for European Spanish
Paper accepted and (to be) presented at COLING 2022
### Supported Tasks and Leaderboards
Natural Language Inference
### Languages
Spanish
## Dataset Structure
The dataset contains two texts inputs (Premise and Hypothesis), Label for three-way classification, and annotation data.
### Data Instances
train size = 6444
test size = 1612
### Data Fields
ID : the unique ID of the instance
Premise
Hypothesis
Label: cnt, ent, neutral
Topic: 1 (Picasso), 2 (Columbus), 3 (Videogames), 4 (Olympic games), 5 (EU), 6 (USSR)
Anno: ID of the annotators (in cases of undergrads or crowd - the ID of the group)
Anno Type: Generate, Rewrite, Crowd, and Automated
### Data Splits
train size = 6444
test size = 1612
The train/test split is stratified by a key that combines Label + Anno + Anno type
### Source Data
Wikipedia + text generated from "sentence generators" hired as part of the process
#### Who are the annotators?
Native speakers of European Spanish
### Personal and Sensitive Information
No personal or Sensitive information is included.
Annotators are anonymized and only kept as "ID" for research purposes.
### Dataset Curators
Venelin Kovatchev
### Licensing Information
cc-by-4.0
### Citation Information
To be added after proceedings from COLING 2022 appear
### Contributions
Thanks to [@venelink](https://github.com/venelink) for adding this dataset.
|
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sedthh/tv_dialogue
|
sedthh
| 2023-03-16T13:44:59Z | 97 | 7 |
[
"task_categories:conversational",
"task_categories:text2text-generation",
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"OpenAssistant",
"transcripts",
"subtitles",
"television",
"region:us"
] |
[
"conversational",
"text2text-generation",
"text-generation"
] | 2023-03-13T20:33:06Z |
---
dataset_info:
features:
- name: TEXT
dtype: string
- name: METADATA
dtype: string
- name: SOURCE
dtype: string
splits:
- name: train
num_bytes: 211728118
num_examples: 2781
download_size: 125187885
dataset_size: 211728118
license: mit
task_categories:
- conversational
- text2text-generation
- text-generation
language:
- en
tags:
- OpenAssistant
- transcripts
- subtitles
- television
pretty_name: TV and Movie dialogue and transcript corpus
size_categories:
- 1K<n<10K
---
# Dataset Card for "tv_dialogue"
This dataset contains transcripts for famous movies and TV shows from multiple sources.
An example dialogue would be:
```
[PERSON 1] Hello
[PERSON 2] Hello Person 2!
How's it going?
(they are both talking)
[PERSON 1] I like being an example
on Huggingface!
They are examples on Huggingface.
CUT OUT TO ANOTHER SCENCE
We are somewhere else
[PERSON 1 (v.o)] I wonder where we are?
```
All dialogues were processed to follow this format. Each row is a single episode / movie (**2781** rows total)
following the [OpenAssistant](https://open-assistant.io/) format. The METADATA column contains dditional information as a JSON string.
## Dialogue only, with some information on the scene
| Show | Number of scripts | Via | Source |
|----|----|---|---|
| Friends | 236 episodes | https://github.com/emorynlp/character-mining | friends/emorynlp |
| The Office | 186 episodes | https://www.kaggle.com/datasets/nasirkhalid24/the-office-us-complete-dialoguetranscript | office/nasirkhalid24 |
| Marvel Cinematic Universe | 18 movies | https://www.kaggle.com/datasets/pdunton/marvel-cinematic-universe-dialogue | marvel/pdunton |
| Doctor Who | 306 episodes | https://www.kaggle.com/datasets/jeanmidev/doctor-who | drwho/jeanmidev |
| Star Trek | 708 episodes | http://www.chakoteya.net/StarTrek/index.html based on https://github.com/GJBroughton/Star_Trek_Scripts/ | statrek/chakoteya |
## Actual transcripts with detailed information on the scenes
| Show | Number of scripts | Via | Source |
|----|----|---|---|
| Top Movies | 919 movies | https://imsdb.com/ | imsdb |
| Top Movies | 171 movies | https://www.dailyscript.com/ | dailyscript |
| Stargate SG-1 | 18 episodes | https://imsdb.com/ | imsdb |
| South Park | 129 episodes | https://imsdb.com/ | imsdb |
| Knight Rider | 80 episodes | http://www.knightriderarchives.com/ | knightriderarchives |
|
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HK83/Anime_Faces
|
HK83
| 2023-05-15T20:52:40Z | 97 | 1 |
[
"license:afl-3.0",
"region:us"
] | null | 2023-05-15T20:51:30Z |
---
license: afl-3.0
---
|
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] |
openchat/openchat_sharegpt4_dataset
|
openchat
| 2023-07-01T13:20:31Z | 97 | 118 |
[
"task_categories:conversational",
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:en",
"region:us"
] |
[
"conversational",
"text-generation"
] | 2023-05-26T13:45:36Z |
---
task_categories:
- conversational
- text-generation
language:
- en
pretty_name: OpenChat
size_categories:
- 1K<n<10K
---
This repository contains cleaned and filtered ShareGPT GPT-4 data used to train OpenChat. Details can be found in the [OpenChat repository](https://github.com/imoneoi/openchat).
|
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] |
clarin-knext/nfcorpus-pl-qrels
|
clarin-knext
| 2023-06-07T08:10:48Z | 97 | 0 |
[
"language:pl",
"arxiv:2305.19840",
"region:us"
] | null | 2023-06-06T22:44:12Z |
---
language:
- pl
---
Part of **BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language**.
Link to arxiv: https://arxiv.org/pdf/2305.19840.pdf
Contact: [email protected]
|
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HANSEN-REPO/HANSEN
|
HANSEN-REPO
| 2023-11-01T18:35:34Z | 97 | 1 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-06-23T20:11:04Z |
---
license: apache-2.0
---
# HANSEN
Human and AI Spoken Text Benchmark for Authorship Analysis.
**We are updating the HANSEN to the following specific format **
The various portions of the
(1) open-source data/existing datasets that we are free to re-distribute (All AA and AV datasets except for FTN and CEO)
(2) open-source data that we may not freely re-distribute but users have to download/scrape themselves (AA and AV datasets for FTN, PAN and CEO due to redistribution issue)
and (3) AI-generated data that we have generated (TT datasets that can be accessible after submitting the form https://forms.gle/WZt7KrxTcmfPXuho9 and accept to the terms
of good usage of the datasets.
## Description
HANSEN comprises 17 human "spoken-text" datasets. It also contains spoken texts generated from three LLMs: ChatGPT, PaLM2, and Vicuna13B.
Spoken text is the text/transcript version of what people say, such as speech, conversation, interviews.
HANSEN can be used for different authorship analysis tasks.
Currently three tasks are defined.
1. AA (Author Attribution): A multi-class classification problem. Given a spoken text T, identifies the speaker from a list of candidate speakers.
2. AV (Author Attribution): A binary classification problem. Given a pair of spoken texts (T1, T2), detects whether they were generated by the same speakers or different speakers.
3. TT (Turing Test/Human vs AI text detection): A binary classification problem. Given a spoken text T, identifies whether the speaker is a human or an LLM.
## AA Task
Currently there are 17 human datasets. Each dataset has two version: small (number of speaker, N=10) and large (number of speaker, N=100 in most cases, for USP, SEC N=30, for TED N=50. for PAN N=56).
So, AA_TED_small will be loading the dataframes of 10 class classification problem in TED dataset.
The dataframes have two columns: author_id (0 to N-1) and text. The list of datasets are as follows.
Dataset | Description
------------- | -------------
TED | TED talks
Spotify | Spotify podcasts
BASE | British Academic Spoken English (BASE) corpus (Nesi and Thompson, 2003)
BNC | British National Corpus
BNC14 | Contemporary version of BNC
MSU | MSU Switchboard Dialogue Act (Telephone conversation)
PAN | Spoken portion of PAN'23 AV datasets
Tennis | Post-match Interview of Tennis players
CEO | CEO and other financial interviews
Voxceleb | Interview of YouTube celebrities
BP | British Parliament Question and Answers
Voxpopuli | European Parliament Events recording
FTN | Face the Nation tv program transcripts
USP | US Life Podcast radio program transcripts
SEC | Security Exchange Commission speeches
Debate | Debates held as part of Intelligence Squared Debates
Court | U.S. Supreme Court oral arguments transcripts
For the CEO and FTN datasets, they do not contain the original text due to the redistribution issue. We have added url and line number (in the text) for each sample in these datasets.
(The script to donwload the original text will be provided soon)
## AV Task
The dataframes have three columns: label (0 if different speaker, 1 if same speaker), text1, and text2. Dataset descriptsions are same as AA task.
## TT Task
Currently HANSEN has three LLMs in five categories (from human dataset settings: TED, Spotify, SEC, CEO, Tennis) spoken texts.
LLM | Description
------------- | -------------
ChatGPT | gpt-3.5-turbo
PALM | PaLM2 (chat-bison@001)
Vicuna13B | Vicuna 13B version finetuned on Llama 13B
So, TT_ChatGPT_TED will be loading the dataframes from human (0) vs ChatGPT (1) dataset in TED category.
The dataframes have two columns: label (0 for human, 1 for AI) and text.
To access the HANSEN-TT dataset, please fill up the form and agree to the terms & conditions.
https://forms.gle/WZt7KrxTcmfPXuho9
If you use the HANSEN dataset in your research please use the following citation:
@article{tripto2023hansen,
title={HANSEN: Human and AI Spoken Text Benchmark for Authorship Analysis},
author={Tripto, Nafis Irtiza and Uchendu, Adaku and Le, Thai and Setzu, Mattia and Giannotti, Fosca and Lee, Dongwon},
journal={arXiv preprint arXiv:2310.16746},
year={2023}
}
|
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] |
Shrey-1329/cxiu_hf_dataset
|
Shrey-1329
| 2023-07-25T15:05:56Z | 97 | 1 |
[
"task_categories:image-to-text",
"size_categories:1K<n<10K",
"language:en",
"medical",
"region:us"
] |
[
"image-to-text"
] | 2023-07-25T14:02:46Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 1108945726.54
num_examples: 6060
download_size: 1108991167
dataset_size: 1108945726.54
task_categories:
- image-to-text
language:
- en
tags:
- medical
size_categories:
- 1K<n<10K
---
# Indiana University Chest Xray Dataset Card
## Data sources:
This is a converted and processed version of the open access pneumonia chest x-ray dataset provided by the indiana university.<br>
You can see its information page [here](https://openi.nlm.nih.gov/faq).<br>
The compressed images in the png format were downloaded from [here](https://openi.nlm.nih.gov/imgs/collections/NLMCXR_png.tgz) and the corresponding reports from [here](https://openi.nlm.nih.gov/imgs/collections/NLMCXR_reports.tgz).
## Data fields:
There are two fields: image and text.
The images are the x-rays and the texts are their associated findings.
## Preprocessing done:
1. **Make all text lowercase**: Convert all text to lowercase to ensure consistent and case-insensitive processing.
2. **Remove all punctuation**: Eliminate any punctuation marks (e.g., periods, commas, exclamation marks) from the text to avoid interference in language analysis.
3. **Remove all numbers**: Eliminate all numeric characters from the text since they might not be relevant for certain natural language processing tasks.
4. **Remove all words with 2 or more Xs in a row**: Remove any words that contain two or more consecutive occurrences of the letter "X" as they may not contribute meaningful information.
5. **Remove the bottom and top 2% of text by length**: Discard the shortest and longest text samples, removing the bottom 2% and top 2% of the text's length, respectively. This step is aimed at reducing the impact of outliers and ensuring a more balanced dataset.
|
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open-llm-leaderboard/details_h2oai__h2ogpt-gm-oasst1-en-1024-20b
|
open-llm-leaderboard
| 2023-10-17T11:30:25Z | 97 | 0 |
[
"region:us"
] | null | 2023-08-18T11:53:40Z |
---
pretty_name: Evaluation run of h2oai/h2ogpt-gm-oasst1-en-1024-20b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [h2oai/h2ogpt-gm-oasst1-en-1024-20b](https://huggingface.co/h2oai/h2ogpt-gm-oasst1-en-1024-20b)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_h2oai__h2ogpt-gm-oasst1-en-1024-20b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-17T11:30:13.056869](https://huggingface.co/datasets/open-llm-leaderboard/details_h2oai__h2ogpt-gm-oasst1-en-1024-20b/blob/main/results_2023-10-17T11-30-13.056869.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.007340604026845637,\n\
\ \"em_stderr\": 0.0008741896875346127,\n \"f1\": 0.059098154362416305,\n\
\ \"f1_stderr\": 0.001498238863999793,\n \"acc\": 0.3440011895836038,\n\
\ \"acc_stderr\": 0.008793500407979769\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.007340604026845637,\n \"em_stderr\": 0.0008741896875346127,\n\
\ \"f1\": 0.059098154362416305,\n \"f1_stderr\": 0.001498238863999793\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.025018953752843062,\n \
\ \"acc_stderr\": 0.004302045046564285\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6629834254143646,\n \"acc_stderr\": 0.013284955769395252\n\
\ }\n}\n```"
repo_url: https://huggingface.co/h2oai/h2ogpt-gm-oasst1-en-1024-20b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|arc:challenge|25_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_17T11_30_13.056869
path:
- '**/details_harness|drop|3_2023-10-17T11-30-13.056869.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-17T11-30-13.056869.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_17T11_30_13.056869
path:
- '**/details_harness|gsm8k|5_2023-10-17T11-30-13.056869.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-17T11-30-13.056869.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hellaswag|10_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
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- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T21:35:35.780060.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T21:35:35.780060.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T21:35:35.780060.parquet'
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- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T21:35:35.780060.parquet'
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- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T21:35:35.780060.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T21:35:35.780060.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T21:35:35.780060.parquet'
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- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T21:35:35.780060.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T21:35:35.780060.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T21:35:35.780060.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T21:35:35.780060.parquet'
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- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T21:35:35.780060.parquet'
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- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T21:35:35.780060.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T21:35:35.780060.parquet'
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- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T21:35:35.780060.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T21:35:35.780060.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T21:35:35.780060.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T21:35:35.780060.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_17T11_30_13.056869
path:
- '**/details_harness|winogrande|5_2023-10-17T11-30-13.056869.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-17T11-30-13.056869.parquet'
- config_name: results
data_files:
- split: 2023_07_19T21_35_35.780060
path:
- results_2023-07-19T21:35:35.780060.parquet
- split: 2023_10_17T11_30_13.056869
path:
- results_2023-10-17T11-30-13.056869.parquet
- split: latest
path:
- results_2023-10-17T11-30-13.056869.parquet
---
# Dataset Card for Evaluation run of h2oai/h2ogpt-gm-oasst1-en-1024-20b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/h2oai/h2ogpt-gm-oasst1-en-1024-20b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [h2oai/h2ogpt-gm-oasst1-en-1024-20b](https://huggingface.co/h2oai/h2ogpt-gm-oasst1-en-1024-20b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_h2oai__h2ogpt-gm-oasst1-en-1024-20b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-17T11:30:13.056869](https://huggingface.co/datasets/open-llm-leaderboard/details_h2oai__h2ogpt-gm-oasst1-en-1024-20b/blob/main/results_2023-10-17T11-30-13.056869.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.007340604026845637,
"em_stderr": 0.0008741896875346127,
"f1": 0.059098154362416305,
"f1_stderr": 0.001498238863999793,
"acc": 0.3440011895836038,
"acc_stderr": 0.008793500407979769
},
"harness|drop|3": {
"em": 0.007340604026845637,
"em_stderr": 0.0008741896875346127,
"f1": 0.059098154362416305,
"f1_stderr": 0.001498238863999793
},
"harness|gsm8k|5": {
"acc": 0.025018953752843062,
"acc_stderr": 0.004302045046564285
},
"harness|winogrande|5": {
"acc": 0.6629834254143646,
"acc_stderr": 0.013284955769395252
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
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] |
rookshanks/dart
|
rookshanks
| 2023-09-28T02:35:11Z | 97 | 0 |
[
"region:us"
] | null | 2023-09-28T02:10:24Z |
---
dataset_info:
features:
- name: context
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 15361709
num_examples: 62659
- name: validation
num_bytes: 1895789
num_examples: 6980
- name: test
num_bytes: 3429190
num_examples: 12552
download_size: 1145768
dataset_size: 20686688
---
# Dataset Card for "dart"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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hugosousa/natural_questions_parsed
|
hugosousa
| 2023-12-01T00:20:31Z | 97 | 0 |
[
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | null | 2023-11-27T21:24:00Z |
---
license: cc-by-sa-3.0
language:
- en
pretty_name: Natural Questions Parsed
---
|
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PiC/phrase_retrieval
|
PiC
| 2023-01-20T16:32:55Z | 96 | 5 |
[
"task_categories:text-retrieval",
"annotations_creators:expert-generated",
"language_creators:found",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-4.0",
"region:us"
] |
[
"text-retrieval"
] | 2022-06-13T20:58:56Z |
---
annotations_creators:
- expert-generated
language_creators:
- found
- expert-generated
language:
- en
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
paperswithcode_id: phrase-in-context
pretty_name: 'PiC: Phrase Retrieval'
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-retrieval
task_ids: []
---
# Dataset Card for "PiC: Phrase Retrieval"
## 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://phrase-in-context.github.io/](https://phrase-in-context.github.io/)
- **Repository:** [https://github.com/phrase-in-context](https://github.com/phrase-in-context)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [Thang Pham](<[email protected]>)
### Dataset Summary
PR is a phrase retrieval task with the goal of finding a phrase **t** in a given document **d** such that **t** is semantically similar to the query phrase, which is the paraphrase **q**<sub>1</sub> provided by annotators.
We release two versions of PR: **PR-pass** and **PR-page**, i.e., datasets of 3-tuples (query **q**<sub>1</sub>, target phrase **t**, document **d**) where **d** is a random 11-sentence passage that contains **t** or an entire Wikipedia page.
While PR-pass contains 28,147 examples, PR-page contains slightly fewer examples (28,098) as we remove those trivial examples whose Wikipedia pages contain exactly the query phrase (in addition to the target phrase).
Both datasets are split into 5K/3K/~20K for test/dev/train, respectively.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English.
## Dataset Structure
### Data Instances
**PR-pass**
* Size of downloaded dataset files: 43.61 MB
* Size of the generated dataset: 36.98 MB
* Total amount of disk used: 80.59 MB
An example of 'train' looks as follows.
```
{
"id": "3478-1",
"title": "https://en.wikipedia.org/wiki?curid=181261",
"context": "The 425t was a 'pizza box' design with a single network expansion slot. The 433s was a desk-side server systems with multiple expansion slots. Compatibility. PC compatibility was possible either through software emulation, using the optional product DPCE, or through a plug-in card carrying an Intel 80286 processor. A third-party plug-in card with a 386 was also available. An Apollo Token Ring network card could also be placed in a standard PC and network drivers allowed it to connect to a server running a PC SMB (Server Message Block) file server. Usage. Although Apollo systems were easy to use and administer, they became less cost-effective because the proprietary operating system made software more expensive than Unix software. The 68K processors were slower than the new RISC chips from Sun and Hewlett-Packard. Apollo addressed both problems by introducing the RISC-based DN10000 and Unix-friendly Domain/OS operating system. However, the DN10000, though fast, was extremely expensive, and a reliable version of Domain/OS came too late to make a difference.",
"query": "dependable adaptation",
"answers": {
"text": ["reliable version"],
"answer_start": [1006]
}
}
```
**PR-page**
* Size of downloaded dataset files: 421.56 MB
* Size of the generated dataset: 412.17 MB
* Total amount of disk used: 833.73 MB
An example of 'train' looks as follows.
```
{
"id": "5961-2",
"title": "https://en.wikipedia.org/wiki?curid=354711",
"context": "Joseph Locke FRSA (9 August 1805 – 18 September 1860) was a notable English civil engineer of the nineteenth century, particularly associated with railway projects. Locke ranked alongside Robert Stephenson and Isambard Kingdom Brunel as one of the major pioneers of railway development. Early life and career. Locke was born in Attercliffe, Sheffield in Yorkshire, moving to nearby Barnsley when he was five. By the age of 17, Joseph had already served an apprenticeship under William Stobart at Pelaw, on the south bank of the Tyne, and under his own father, William. He was an experienced mining engineer, able to survey, sink shafts, to construct railways, tunnels and stationary engines. Joseph's father had been a manager at Wallbottle colliery on Tyneside when George Stephenson was a fireman there. In 1823, when Joseph was 17, Stephenson was involved with planning the Stockton and Darlington Railway. He and his son Robert Stephenson visited William Locke and his son at Barnsley and it was arranged that Joseph would go to work for the Stephensons. The Stephensons established a locomotive works near Forth Street, Newcastle upon Tyne, to manufacture locomotives for the new railway. Joseph Locke, despite his youth, soon established a position of authority. He and Robert Stephenson became close friends, but their friendship was interrupted, in 1824, by Robert leaving to work in Colombia for three years. Liverpool and Manchester Railway. George Stephenson carried out the original survey of the line of the Liverpool and Manchester Railway, but this was found to be flawed, and the line was re-surveyed by a talented young engineer, Charles Vignoles. Joseph Locke was asked by the directors to carry out another survey of the proposed tunnel works and produce a report. The report was highly critical of the work already done, which reflected badly on Stephenson. Stephenson was furious and henceforth relations between the two men were strained, although Locke continued to be employed by Stephenson, probably because the latter recognised his worth. Despite the many criticisms of Stephenson's work, when the bill for the new line was finally passed, in 1826, Stephenson was appointed as engineer and he appointed Joseph Locke as his assistant to work alongside Vignoles, who was the other assistant. However, a clash of personalities between Stephenson and Vignoles led to the latter resigning, leaving Locke as the sole assistant engineer. Locke took over responsibility for the western half of the line. One of the major obstacles to be overcome was Chat Moss, a large bog that had to be crossed. Although, Stephenson usually gets the credit for this feat, it is believed that it was Locke who suggested the correct method for crossing the bog. Whilst the line was being built, the directors were trying to decide whether to use standing engines or locomotives to propel the trains. Robert Stephenson and Joseph Locke were convinced that locomotives were vastly superior, and in March 1829 the two men wrote a report demonstrating the superiority of locomotives when used on a busy railway. The report led to the decision by the directors to hold an open trial to find the best locomotive. This was the Rainhill Trials, which were run in October 1829, and were won by \"Rocket\". When the line was finally opened in 1830, it was planned for a procession of eight trains to travel from Liverpool to Manchester and back. George Stephenson drove the leading locomotive \"Northumbrian\" and Joseph Locke drove \"Rocket\". The day was marred by the death of William Huskisson, the Member of Parliament for Liverpool, who was struck and killed by \"Rocket\". Grand Junction Railway. In 1829 Locke was George Stephenson's assistant, given the job of surveying the route for the Grand Junction Railway. This new railway was to join Newton-le-Willows on the Liverpool and Manchester Railway with Warrington and then on to Birmingham via Crewe, Stafford and Wolverhampton, a total of 80 miles. Locke is credited with choosing the location for Crewe and recommending the establishment there of shops required for the building and repairs of carriages and wagons as well as engines. During the construction of the Liverpool and Manchester Railway, Stephenson had shown a lack of ability in organising major civil engineering projects. On the other hand, Locke's ability to manage complex projects was well known. The directors of the new railway decided on a compromise whereby Locke was made responsible for the northern half of the line and Stephenson was made responsible for the southern half. However Stephenson's administrative inefficiency soon became apparent, whereas Locke estimated the costs for his section of the line so meticulously and speedily, that he had all of the contracts signed for his section of the line before a single one had been signed for Stephenson's section. The railway company lost patience with Stephenson, but tried to compromise by making both men joint-engineers. Stephenson's pride would not let him accept this, and so he resigned from the project. By autumn of 1835 Locke had become chief engineer for the whole of the line. This caused a rift between the two men, and strained relations between Locke and Robert Stephenson. Up to this point, Locke had always been under George Stephenson's shadow. From then on, he would be his own man, and stand or fall by his own achievements. The line was opened on 4 July 1837. New methods. Locke's route avoided as far as possible major civil engineering works. The main one was the Dutton Viaduct which crosses the River Weaver and the Weaver Navigation between the villages of Dutton and Acton Bridge in Cheshire. The viaduct consists of 20 arches with spans of 20 yards. An important feature of the new railway was the use of double-headed (dumb-bell) wrought-iron rail supported on timber sleepers at 2 ft 6 in intervals. It was intended that when the rails became worn they could be turned over to use the other surface, but in practice it was found that the chairs into which the rails were keyed caused wear to the bottom surface so that it became uneven. However this was still an improvement on the fish-bellied, wrought-iron rails still being used by Robert Stephenson on the London and Birmingham Railway. Locke was more careful than Stephenson to get value for his employers' money. For the Penkridge Viaduct Stephenson had obtained a tender of £26,000. After Locke took over, he gave the potential contractor better information and agreed a price of only £6,000. Locke also tried to avoid tunnels because in those days tunnels often took longer and cost more than planned. The Stephensons regarded 1 in 330 as the maximum slope that an engine could manage and Robert Stephenson achieved this on the London and Birmingham Railway by using seven tunnels which added both cost and delay. Locke avoided tunnels almost completely on the Grand Junction but exceeded the slope limit for six miles south of Crewe. Proof of Locke's ability to estimate costs accurately is given by the fact that the construction of the Grand Junction line cost £18,846 per mile as against Locke's estimate of £17,000. This is amazingly accurate compared with the estimated costs for the London and Birmingham Railway (Robert Stephenson) and the Great Western Railway (Brunel). Locke also divided the project into a few large sections rather than many small ones. This allowed him to work closely with his contractors to develop the best methods, overcome problems and personally gain practical experience of the building process and of the contractors themselves. He used the contractors who worked well with him, especially Thomas Brassey and William Mackenzie, on many other projects. Everyone gained from this cooperative approach whereas Brunel's more adversarial approach eventually made it hard for him to get anyone to work for him. Marriage. In 1834 Locke married Phoebe McCreery, with whom he adopted a child. He was elected to the Royal Society in 1838. Lancaster and Carlisle Railway. A significant difference in philosophy between George Stephenson and Joseph Locke and the surveying methods they employed was more than a mere difference of opinion. Stephenson had started his career at a time when locomotives had little power to overcome excessive gradients. Both George and Robert Stephenson were prepared to go to great lengths to avoid steep gradients that would tax the locomotives of the day, even if this meant choosing a circuitous path that added on extra miles to the line of the route. Locke had more confidence in the ability of modern locomotives to climb these gradients. An example of this was the Lancaster and Carlisle Railway, which had to cope with the barrier of the Lake District mountains. In 1839 Stephenson proposed a circuitous route that avoided the Lake District altogether by going all the way round Morecambe Bay and West Cumberland, claiming: 'This is the only practicable line from Liverpool to Carlisle. The making of a railway across Shap Fell is out of the question.' The directors rejected his route and chose the one proposed by Joseph Locke, one that used steep gradients and passed over Shap Fell. The line was completed by Locke and was a success. Locke's reasoned that by avoiding long routes and tunnelling, the line could be finished more quickly, with less capital costs, and could start earning revenue sooner. This became known as the 'up and over' school of engineering (referred to by Rolt as 'Up and Down,' or Rollercoaster). Locke took a similar approach in planning the Caledonian Railway, from Carlisle to Glasgow. In both railways he introduced gradients of 1 in 75, which severely taxed fully laden locomotives, for even as more powerful locomotives were introduced, the trains that they pulled became heavier. It may therefore be argued that Locke, although his philosophy carried the day, was not entirely correct in his reasoning. Even today, Shap Fell is a severe test of any locomotive. Manchester and Sheffield Railway. Locke was subsequently appointed to build a railway line from Manchester to Sheffield, replacing Charles Vignoles as chief engineer, after the latter had been beset by misfortunes and financial difficulties. The project included the three-mile Woodhead Tunnel, and the line opened, after many delays, on 23 December 1845. The building of the line required over a thousand navvies and cost the lives of thirty-two of them, seriously injuring 140 others. The Woodhead Tunnel was such a difficult undertaking that George Stephenson claimed that it could not be done, declaring that he would eat the first locomotive that got through the tunnel. Subsequent commissions. In the north, Locke also designed the Lancaster and Preston Junction Railway; the Glasgow, Paisley and Greenock Railway; and the Caledonian Railway from Carlisle to Glasgow and Edinburgh. In the south, he worked on the London and Southampton Railway, later called the London and South Western Railway, designing, among other structures, Nine Elms to Waterloo Viaduct, Richmond Railway Bridge (1848, since replaced), and Barnes Bridge (1849), both across the River Thames, tunnels at Micheldever, and the 12-arch Quay Street viaduct and the 16-arch Cams Hill viaduct, both in Fareham (1848). He was actively involved in planning and building many railways in Europe (assisted by John Milroy), including the Le Havre, Rouen, Paris rail link, the Barcelona to Mataró line and the Dutch Rhenish Railway. He was present in Paris when the Versailles train crash occurred in 1842, and produced a statement concerning the facts for General Charles Pasley of the Railway Inspectorate. He also experienced a catastrophic failure of one of his viaducts built on the new Paris-Le Havre link. . The viaduct was of stone and brick at Barentin near Rouen, and was the longest and highest on the line. It was 108 feet high, and consisted of 27 arches, each 50 feet wide, with a total length of over 1600 feet. A boy hauling ballast for the line up an adjoining hillside early that morning (about 6.00 am) saw one arch (the fifth on the Rouen side) collapse, and the rest followed suit. Fortunately, no one was killed, although several workmen were injured in a mill below the structure. Locke attributed the catastrophic failure to frost action on the new lime cement, and premature off-centre loading of the viaduct with ballast. It was rebuilt at Thomas Brassey's cost, and survives to the present. Having pioneered many new lines in France, Locke also helped establish the first locomotive works in the country. Distinctive features of Locke's railway works were economy, the use of masonry bridges wherever possible and the absence of tunnels. An illustration of this is that there is no tunnel between Birmingham and Glasgow. Relationship with Robert Stephenson. Locke and Robert Stephenson had been good friends at the beginning of their careers, but their friendship had been marred by Locke's falling out with Robert's father. It seems that Robert felt loyalty to his father required that he should take his side. It is significant that after the death of George Stephenson in August 1848, the friendship of the two men was revived. When Robert Stephenson died in October 1859, Joseph Locke was a pallbearer at his funeral. Locke is reported to have referred to Robert as 'the friend of my youth, the companion of my ripening years, and a competitor in the race of life'. Locke was also on friendly terms with his other engineering rival, Isambard Kingdom Brunel. In 1845, Locke and Stephenson were both called to give evidence before two committees. In April a House of Commons Select Committee was investigating the atmospheric railway system proposed by Brunel. Brunel and Vignoles spoke in support of the system, whilst Locke and Stephenson spoke against it. The latter two were to be proved right in the long run. In August the two gave evidence before the Gauge Commissioners who were trying to arrive at a standard gauge for the whole country. Brunel spoke in favour of the 7 ft gauge he was using on the Great Western Railway. Locke and Stephenson spoke in favour of the 4 ft 8½in gauge that they had used on several lines. The latter two won the day and their gauge was adopted as the standard. Later life and legacy. Locke served as President of the Institution of Civil Engineers in between December 1857 and December 1859. He also served as Member of Parliament for Honiton in Devon from 1847 until his death. Joseph Locke died on 18 September 1860, apparently from appendicitis, whilst on a shooting holiday. He is buried in London's Kensal Green Cemetery. He outlived his friends/rivals Robert Stephenson and Isambard Brunel by less than a year; all three engineers died between 53 and 56 years of age, a circumstance attributed by Rolt to sheer overwork, accomplishing more in their brief lives than many achieve in a full three score and ten. Locke Park in Barnsley was dedicated to his memory by his widow Phoebe in 1862. It features a statue of Locke plus a folly, 'Locke Tower'. Locke's greatest legacy is the modern day West Coast Main Line (WCML), which was formed by the joining of the Caledonian, Lancaster & Carlisle, Grand Junction railways to Robert Stephenson's London & Birmingham Railway. As a result, around three-quarters of the WCML's route was planned and engineered by Locke.",
"query": "accurate approach",
"answers": {
"text": ["correct method"],
"answer_start": [2727]
}
}
```
### Data Fields
The data fields are the same among all subsets and splits.
* id: a string feature.
* title: a string feature.
* context: a string feature.
* question: a string feature.
* answers: a dictionary feature containing:
* text: a list of string features.
* answer_start: a list of int32 features.
### Data Splits
| name |train|validation|test|
|--------------------|----:|---------:|---:|
|PR-pass |20147| 3000|5000|
|PR-page |20098| 3000|5000|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The source passages + answers are from Wikipedia and the source of queries were produced by our hired linguistic experts from [Upwork.com](https://upwork.com).
#### Who are the source language producers?
We hired 13 linguistic experts from [Upwork.com](https://upwork.com) for annotation and more than 1000 human annotators on Mechanical Turk along with another set of 5 Upwork experts for 2-round verification.
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
13 linguistic experts from [Upwork.com](https://upwork.com).
### Personal and Sensitive Information
No annotator identifying details are provided.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
This dataset is a joint work between Adobe Research and Auburn University.
Creators: [Thang M. Pham](https://scholar.google.com/citations?user=eNrX3mYAAAAJ), [David Seunghyun Yoon](https://david-yoon.github.io/), [Trung Bui](https://sites.google.com/site/trungbuistanford/), and [Anh Nguyen](https://anhnguyen.me).
[@PMThangXAI](https://twitter.com/pmthangxai) added this dataset to HuggingFace.
### Licensing Information
This dataset is distributed under [Creative Commons Attribution-NonCommercial 4.0 International (CC-BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/)
### Citation Information
```
@article{pham2022PiC,
title={PiC: A Phrase-in-Context Dataset for Phrase Understanding and Semantic Search},
author={Pham, Thang M and Yoon, Seunghyun and Bui, Trung and Nguyen, Anh},
journal={arXiv preprint arXiv:2207.09068},
year={2022}
}
```
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PiC/phrase_similarity
|
PiC
| 2023-01-20T16:32:19Z | 96 | 6 |
[
"task_categories:text-classification",
"task_ids:semantic-similarity-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-4.0",
"region:us"
] |
[
"text-classification"
] | 2022-06-14T01:35:19Z |
---
annotations_creators:
- expert-generated
language_creators:
- found
- expert-generated
language:
- en
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
paperswithcode_id: phrase-in-context
pretty_name: 'PiC: Phrase Similarity (PS)'
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- semantic-similarity-classification
---
# Dataset Card for "PiC: Phrase Similarity"
## 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://phrase-in-context.github.io/](https://phrase-in-context.github.io/)
- **Repository:** [https://github.com/phrase-in-context](https://github.com/phrase-in-context)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [Thang Pham](<[email protected]>)
- **Size of downloaded dataset files:** 4.60 MB
- **Size of the generated dataset:** 2.96 MB
- **Total amount of disk used:** 7.56 MB
### Dataset Summary
PS is a binary classification task with the goal of predicting whether two multi-word noun phrases are semantically similar or not given *the same context* sentence.
This dataset contains ~10K pairs of two phrases along with their contexts used for disambiguation, since two phrases are not enough for semantic comparison.
Our ~10K examples were annotated by linguistic experts on <upwork.com> and verified in two rounds by 1000 Mturkers and 5 linguistic experts.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English.
## Dataset Structure
### Data Instances
**PS**
* Size of downloaded dataset files: 4.60 MB
* Size of the generated dataset: 2.96 MB
* Total amount of disk used: 7.56 MB
```
{
"phrase1": "annual run",
"phrase2": "yearlong performance",
"sentence1": "since 2004, the club has been a sponsor of the annual run for rigby to raise money for off-campus housing safety awareness.",
"sentence2": "since 2004, the club has been a sponsor of the yearlong performance for rigby to raise money for off-campus housing safety awareness.",
"label": 0,
"idx": 0,
}
```
### Data Fields
The data fields are the same among all splits.
* phrase1: a string feature.
* phrase2: a string feature.
* sentence1: a string feature.
* sentence2: a string feature.
* label: a classification label, with negative (0) and positive (1).
* idx: an int32 feature.
### Data Splits
| name |train |validation|test |
|--------------------|----:|--------:|----:|
|PS |7362| 1052|2102|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The source passages + answers are from Wikipedia and the source of queries were produced by our hired linguistic experts from [Upwork.com](https://upwork.com).
#### Who are the source language producers?
We hired 13 linguistic experts from [Upwork.com](https://upwork.com) for annotation and more than 1000 human annotators on Mechanical Turk along with another set of 5 Upwork experts for 2-round verification.
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
13 linguistic experts from [Upwork.com](https://upwork.com).
### Personal and Sensitive Information
No annotator identifying details are provided.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
This dataset is a joint work between Adobe Research and Auburn University.
Creators: [Thang M. Pham](https://scholar.google.com/citations?user=eNrX3mYAAAAJ), [David Seunghyun Yoon](https://david-yoon.github.io/), [Trung Bui](https://sites.google.com/site/trungbuistanford/), and [Anh Nguyen](https://anhnguyen.me).
[@PMThangXAI](https://twitter.com/pmthangxai) added this dataset to HuggingFace.
### Licensing Information
This dataset is distributed under [Creative Commons Attribution-NonCommercial 4.0 International (CC-BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/)
### Citation Information
```
@article{pham2022PiC,
title={PiC: A Phrase-in-Context Dataset for Phrase Understanding and Semantic Search},
author={Pham, Thang M and Yoon, Seunghyun and Bui, Trung and Nguyen, Anh},
journal={arXiv preprint arXiv:2207.09068},
year={2022}
}
```
|
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DFKI-SLT/multitacred
|
DFKI-SLT
| 2023-11-06T12:19:37Z | 96 | 1 |
[
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:found",
"size_categories:100K<n<1M",
"source_datasets:DFKI-NLP/tacred",
"language:ar",
"language:de",
"language:es",
"language:fi",
"language:fr",
"language:hi",
"language:hu",
"language:ja",
"language:pl",
"language:ru",
"language:tr",
"language:zh",
"license:other",
"relation extraction",
"arxiv:2305.04582",
"region:us"
] |
[
"text-classification"
] | 2022-09-30T11:31:31Z |
---
language:
- ar
- de
- es
- fi
- fr
- hi
- hu
- ja
- pl
- ru
- tr
- zh
license: other
license_details: https://catalog.ldc.upenn.edu/license/ldc-non-members-agreement.pdf
tags:
- relation extraction
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- found
pretty_name: MultiTACRED - Multilingual TAC Relation Extraction Dataset
size_categories:
- 100K<n<1M
source_datasets:
- DFKI-NLP/tacred
task_categories: # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
- text-classification
task_ids:
- multi-class-classification
paperswithcode_id: multitacred
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'13': IDEOLOGY
'14': NATIONALITY
'15': RELIGION
'16': STATE_OR_PROVINCE
'17': TITLE
'18': URL
'19': NUMBER
'20': ORDINAL
'21': MISC
'22': DURATION
'23': O
- name: relation
dtype:
class_label:
names:
'0': no_relation
'1': org:alternate_names
'2': org:city_of_branch
'3': org:country_of_branch
'4': org:dissolved
'5': org:founded
'6': org:founded_by
'7': org:member_of
'8': org:members
'9': org:number_of_employees/members
'10': org:political/religious_affiliation
'11': org:shareholders
'12': org:stateorprovince_of_branch
'13': org:top_members/employees
'14': org:website
'15': per:age
'16': per:cause_of_death
'17': per:charges
'18': per:children
'19': per:cities_of_residence
'20': per:city_of_birth
'21': per:city_of_death
'22': per:countries_of_residence
'23': per:country_of_birth
'24': per:country_of_death
'25': per:date_of_birth
'26': per:date_of_death
'27': per:employee_of
'28': per:identity
'29': per:origin
'30': per:other_family
'31': per:parents
'32': per:religion
'33': per:schools_attended
'34': per:siblings
'35': per:spouse
'36': per:stateorprovince_of_birth
'37': per:stateorprovince_of_death
'38': per:stateorprovinces_of_residence
'39': per:title
splits:
- name: train
num_bytes: 22440419
num_examples: 56049
- name: test
num_bytes: 4717593
num_examples: 12718
- name: validation
num_bytes: 7200681
num_examples: 18642
- name: backtranslated_test
num_bytes: 4441386
num_examples: 12127
download_size: 3702157
dataset_size: 38800079
---
# Dataset Card for "MultiTACRED"
## Dataset Description
- **Homepage:** [https://github.com/DFKI-NLP/MultiTACRED](https://github.com/DFKI-NLP/MultiTACRED)
- **Paper:** [MultiTACRED: A Multilingual Version of the TAC Relation Extraction Dataset](https://arxiv.org/abs/2305.04582)
- **Point of Contact:** See [https://github.com/DFKI-NLP/MultiTACRED](https://github.com/DFKI-NLP/MultiTACRED)
- **Size of downloaded dataset files:** 15.4KB (TACRED-Revisited), 3.7 MB (Re-TACRED)
- **Size of the generated dataset:** 1.7 GB (all languages, all versions)
- **Total amount of disk used:** 1.7 GB (all languages, all versions)
### Dataset Summary
MultiTACRED is a multilingual version of the large-scale [TAC Relation Extraction Dataset](https://nlp.stanford.edu/projects/tacred).
It covers 12 typologically diverse languages from 9 language families, and was created by the
[Speech & Language Technology group of DFKI](https://www.dfki.de/slt) by machine-translating the instances of the
original TACRED dataset and automatically projecting their entity annotations. For details of the original TACRED's
data collection and annotation process, see the [Stanford paper](https://aclanthology.org/D17-1004/). Translations are
syntactically validated by checking the correctness of the XML tag markup. Any translations with an invalid tag
structure, e.g. missing or invalid head or tail tag pairs, are discarded (on average, 2.3% of the instances).
Languages covered are: Arabic, Chinese, Finnish, French, German, Hindi, Hungarian, Japanese, Polish,
Russian, Spanish, Turkish. Intended use is supervised relation classification. Audience - researchers.
Please see [our ACL paper](https://arxiv.org/abs/2305.04582) for full details.
NOTE: This Datasetreader supports a reduced version of the original TACRED JSON format with the following changes:
- Removed fields: stanford_pos, stanford_ner, stanford_head, stanford_deprel, docid
The motivation for this is that we want to support additional languages, for which these fields were not required
or available. The reader expects the specification of a language-specific configuration specifying the variant
(original, revisited or retacred) and the language (as a two-letter iso code).
The DatasetReader changes the offsets of the following fields, to conform with standard Python usage (see
_generate_examples()):
- subj_end to subj_end + 1 (make end offset exclusive)
- obj_end to obj_end + 1 (make end offset exclusive)
NOTE 2: The MultiTACRED dataset offers an additional 'split', namely the backtranslated test data (translated to a
target language and then back to English). To access this split, use dataset['backtranslated_test'].
You can find the TACRED dataset reader for the English version of the dataset at
[https://huggingface.co/datasets/DFKI-SLT/tacred](https://huggingface.co/datasets/DFKI-SLT/tacred).
### Supported Tasks and Leaderboards
- **Tasks:** Relation Classification
- **Leaderboards:** [https://paperswithcode.com/sota/relation-extraction-on-multitacred](https://paperswithcode.com/sota/relation-extraction-on-multitacred)
### Languages
The languages in the dataset are Arabic, German, English, Spanish, Finnish, French, Hindi, Hungarian, Japanese, Polish, Russian, Turkish, and Chinese.
All languages except English are machine-translated using either Deepl's or Google's translation APIs.
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:** 15.4KB (TACRED-Revisited), 3.7 MB (Re-TACRED)
- **Size of the generated dataset:** 1.7 GB (all languages, all versions)
- **Total amount of disk used:** 1.7 GB (all languages, all versions)
An example of 'train' looks as follows:
```json
{
"id": "61b3a5c8c9a882dcfcd2",
"token": ["Tom", "Thabane", "trat", "im", "Oktober", "letzten", "Jahres", "zurück", ",", "um", "die", "All", "Basotho", "Convention", "-LRB-", "ABC", "-RRB-", "zu", "gründen", ",", "die", "mit", "17", "Abgeordneten", "das", "Wort", "ergriff", ",", "woraufhin", "der", "konstitutionelle", "Monarch", "König", "Letsie", "III.", "das", "Parlament", "auflöste", "und", "Neuwahlen", "ansetzte", "."],
"relation": "org:founded_by",
"subj_start": 11,
"subj_end": 13,
"obj_start": 0,
"obj_end": 1,
"subj_type": "ORGANIZATION",
"obj_type": "PERSON"
}
```
### Data Fields
The data fields are the same among all splits.
- `id`: the instance id of this sentence, a `string` feature.
- `token`: the list of tokens of this sentence, a `list` of `string` features.
- `relation`: the relation label of this instance, a `string` classification label.
- `subj_start`: the 0-based index of the start token of the relation subject mention, an `ìnt` feature.
- `subj_end`: the 0-based index of the end token of the relation subject mention, exclusive, an `ìnt` feature.
- `subj_type`: the NER type of the subject mention, among the types used in the [Stanford NER system](https://stanfordnlp.github.io/CoreNLP/ner.html), a `string` feature.
- `obj_start`: the 0-based index of the start token of the relation object mention, an `ìnt` feature.
- `obj_end`: the 0-based index of the end token of the relation object mention, exclusive, an `ìnt` feature.
- `obj_type`: the NER type of the object mention, among 23 fine-grained types used in the [Stanford NER system](https://stanfordnlp.github.io/CoreNLP/ner.html), a `string` feature.
### Data Splits
To miminize dataset bias, TACRED is stratified across years in which the TAC KBP challenge was run.
Languages statistics for the splits differ because not all instances could be translated with the
subject and object entity markup still intact, these were discarded.
| Language | Train | Dev | Test | Backtranslated Test | Translation Engine |
| ----- | ------ | ----- | ---- | ---- | ---- |
| en | 68,124 | 22,631 | 15,509 | - | - |
| ar | 67,736 | 22,502 | 15,425 | 15,425 | Google |
| de | 67,253 | 22,343 | 15,282 | 15,079 | DeepL |
| es | 65,247 | 21,697 | 14,908 | 14,688 | DeepL |
| fi | 66,751 | 22,268 | 15,083 | 14,462 | DeepL |
| fr | 66,856 | 22,298 | 15,237 | 15,088 | DeepL |
| hi | 67,751 | 22,511 | 15,440 | 15,440 | Google |
| hu | 67,766 | 22,519 | 15,436 | 15,436 | Google |
| ja | 61,571 | 20,290 | 13,701 | 12,913 | DeepL |
| pl | 68,124 | 22,631 | 15,509 | 15,509 | Google |
| ru | 66,413 | 21,998 | 14,995 | 14,703 | DeepL |
| tr | 67,749 | 22,510 | 15,429 | 15,429 | Google |
| zh | 65,260 | 21,538 | 14,694 | 14,021 | DeepL |
## Dataset Creation
### Curation Rationale
To enable more research on multilingual Relation Extraction, we generate translations of the TAC relation extraction
dataset using DeepL and Google Translate.
### Source Data
#### Initial Data Collection and Normalization
The instances of this dataset are sentences from the
[original TACRED dataset](https://nlp.stanford.edu/projects/tacred/), which in turn
are sampled from the [corpus](https://catalog.ldc.upenn.edu/LDC2018T03) used in the yearly
[TAC Knowledge Base Population (TAC KBP) challenges](https://tac.nist.gov/2017/KBP/index.html).
#### Who are the source language producers?
Newswire and web texts collected for the [TAC Knowledge Base Population (TAC KBP) challenges](https://tac.nist.gov/2017/KBP/index.html).
### Annotations
#### Annotation process
See the Stanford paper, the TACRED Revisited paper, and the Re-TACRED paper, plus their appendices, for
details on the original annotation process. The translated versions do not change the original labels.
Translations were tokenized with language-specific Spacy models (Spacy 3.1, 'core_news/web_sm' models)
or Trankit (Trankit 1.1.0) when there was no Spacy model for a given language (Hungarian, Turkish, Arabic, Hindi).
#### Who are the annotators?
The original TACRED dataset was annotated by crowd workers, see the [TACRED paper](https://nlp.stanford.edu/pubs/zhang2017tacred.pdf).
### Personal and Sensitive Information
The [authors](https://nlp.stanford.edu/pubs/zhang2017tacred.pdf) of the original TACRED dataset
have not stated measures that prevent collecting sensitive or offensive text. Therefore, we do
not rule out the possible risk of sensitive/offensive content in the translated data.
## Considerations for Using the Data
### Social Impact of Dataset
not applicable
### Discussion of Biases
The dataset is drawn from web and newswire text, and thus reflects any biases of these original
texts, as well as biases introduced by the MT models.
### Other Known Limitations
not applicable
## Additional Information
### Dataset Curators
The dataset was created by members of the
[DFKI SLT team: Leonhard Hennig, Philippe Thomas, Sebastian Möller, Gabriel Kressin](https://www.dfki.de/en/web/research/research-departments/speech-and-language-technology/speech-and-language-technology-staff-members)
### Licensing Information
To respect the copyright of the underlying TACRED dataset, MultiTACRED is released via the
Linguistic Data Consortium ([LDC License](https://catalog.ldc.upenn.edu/license/ldc-non-members-agreement.pdf)).
You can download MultiTACRED from the [LDC MultiTACRED webpage](https://catalog.ldc.upenn.edu/TODO).
If you are an LDC member, the access will be free; otherwise, an access fee of $25 is needed.
### Citation Information
The original dataset:
```
@inproceedings{zhang2017tacred,
author = {Zhang, Yuhao and Zhong, Victor and Chen, Danqi and Angeli, Gabor and Manning, Christopher D.},
booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP 2017)},
title = {Position-aware Attention and Supervised Data Improve Slot Filling},
url = {https://nlp.stanford.edu/pubs/zhang2017tacred.pdf},
pages = {35--45},
year = {2017}
}
```
For the revised version, please also cite:
```
@inproceedings{alt-etal-2020-tacred,
title = "{TACRED} Revisited: A Thorough Evaluation of the {TACRED} Relation Extraction Task",
author = "Alt, Christoph and
Gabryszak, Aleksandra and
Hennig, Leonhard",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.142",
doi = "10.18653/v1/2020.acl-main.142",
pages = "1558--1569",
}
```
For the Re-TACRED version, please also cite:
```
@inproceedings{DBLP:conf/aaai/StoicaPP21,
author = {George Stoica and
Emmanouil Antonios Platanios and
Barnab{\'{a}}s P{\'{o}}czos},
title = {Re-TACRED: Addressing Shortcomings of the {TACRED} Dataset},
booktitle = {Thirty-Fifth {AAAI} Conference on Artificial Intelligence, {AAAI}
2021, Thirty-Third Conference on Innovative Applications of Artificial
Intelligence, {IAAI} 2021, The Eleventh Symposium on Educational Advances
in Artificial Intelligence, {EAAI} 2021, Virtual Event, February 2-9,
2021},
pages = {13843--13850},
publisher = {{AAAI} Press},
year = {2021},
url = {https://ojs.aaai.org/index.php/AAAI/article/view/17631},
}
```
### Contributions
Thanks to [@leonhardhennig](https://github.com/leonhardhennig) for adding this dataset.
|
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] |
fewshot-goes-multilingual/sk_csfd-movie-reviews
|
fewshot-goes-multilingual
| 2022-12-18T21:30:31Z | 96 | 0 |
[
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:sk",
"license:cc-by-sa-4.0",
"movie reviews",
"rating prediction",
"region:us"
] |
[
"text-classification"
] | 2022-12-18T21:28:17Z |
---
annotations_creators:
- crowdsourced
language:
- sk
language_creators:
- crowdsourced
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: CSFD movie reviews (Slovak)
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- movie reviews
- rating prediction
task_categories:
- text-classification
task_ids:
- sentiment-classification
---
# Dataset Card for CSFD movie reviews (Slovak)
## Dataset Description
The dataset contains user reviews from Czech/Slovak movie databse website <https://csfd.cz>.
Each review contains text, rating, date, and basic information about the movie (or TV series).
The dataset has in total (train+validation+test) 30,000 reviews. The data is balanced - each rating has approximately the same frequency.
## Dataset Features
Each sample contains:
- `review_id`: unique string identifier of the review.
- `rating_str`: string representation of the rating (from "0/5" to "5/5")
- `rating_int`: integer representation of the rating (from 0 to 5)
- `date`: date of publishing the review (just date, no time nor timezone)
- `comment_language`: language of the review (always "sk")
- `comment`: the string of the review
- `item_title`: title of the reviewed item
- `item_year`: publishing year of the item (string, can also be a range)
- `item_kind`: kind of the item - either "film" or "seriál"
- `item_genres`: list of genres of the item
- `item_directors`: list of director names of the item
- `item_screenwriters`: list of screenwriter names of the item
- `item_cast`: list of actors and actress in the item
## Dataset Source
The data was mined and sampled from the <https://csfd.cz> website.
Make sure to comply with the terms of conditions of the website operator when using the data.
|
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] |
Cohere/miracl-zh-queries-22-12
|
Cohere
| 2023-02-06T11:55:33Z | 96 | 23 |
[
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"annotations_creators:expert-generated",
"multilinguality:multilingual",
"language:zh",
"license:apache-2.0",
"region:us"
] |
[
"text-retrieval"
] | 2023-01-31T13:38:51Z |
---
annotations_creators:
- expert-generated
language:
- zh
multilinguality:
- multilingual
size_categories: []
source_datasets: []
tags: []
task_categories:
- text-retrieval
license:
- apache-2.0
task_ids:
- document-retrieval
---
# MIRACL (zh) embedded with cohere.ai `multilingual-22-12` encoder
We encoded the [MIRACL dataset](https://huggingface.co/miracl) using the [cohere.ai](https://txt.cohere.ai/multilingual/) `multilingual-22-12` embedding model.
The query embeddings can be found in [Cohere/miracl-zh-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-zh-queries-22-12) and the corpus embeddings can be found in [Cohere/miracl-zh-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-zh-corpus-22-12).
For the orginal datasets, see [miracl/miracl](https://huggingface.co/datasets/miracl/miracl) and [miracl/miracl-corpus](https://huggingface.co/datasets/miracl/miracl-corpus).
Dataset info:
> MIRACL 🌍🙌🌏 (Multilingual Information Retrieval Across a Continuum of Languages) is a multilingual retrieval dataset that focuses on search across 18 different languages, which collectively encompass over three billion native speakers around the world.
>
> The corpus for each language is prepared from a Wikipedia dump, where we keep only the plain text and discard images, tables, etc. Each article is segmented into multiple passages using WikiExtractor based on natural discourse units (e.g., `\n\n` in the wiki markup). Each of these passages comprises a "document" or unit of retrieval. We preserve the Wikipedia article title of each passage.
## Embeddings
We compute for `title+" "+text` the embeddings using our `multilingual-22-12` embedding model, a state-of-the-art model that works for semantic search in 100 languages. If you want to learn more about this model, have a look at [cohere.ai multilingual embedding model](https://txt.cohere.ai/multilingual/).
## Loading the dataset
In [miracl-zh-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-zh-corpus-22-12) we provide the corpus embeddings. Note, depending on the selected split, the respective files can be quite large.
You can either load the dataset like this:
```python
from datasets import load_dataset
docs = load_dataset(f"Cohere/miracl-zh-corpus-22-12", split="train")
```
Or you can also stream it without downloading it before:
```python
from datasets import load_dataset
docs = load_dataset(f"Cohere/miracl-zh-corpus-22-12", split="train", streaming=True)
for doc in docs:
docid = doc['docid']
title = doc['title']
text = doc['text']
emb = doc['emb']
```
## Search
Have a look at [miracl-zh-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-zh-queries-22-12) where we provide the query embeddings for the MIRACL dataset.
To search in the documents, you must use **dot-product**.
And then compare this query embeddings either with a vector database (recommended) or directly computing the dot product.
A full search example:
```python
# Attention! For large datasets, this requires a lot of memory to store
# all document embeddings and to compute the dot product scores.
# Only use this for smaller datasets. For large datasets, use a vector DB
from datasets import load_dataset
import torch
#Load documents + embeddings
docs = load_dataset(f"Cohere/miracl-zh-corpus-22-12", split="train")
doc_embeddings = torch.tensor(docs['emb'])
# Load queries
queries = load_dataset(f"Cohere/miracl-zh-queries-22-12", split="dev")
# Select the first query as example
qid = 0
query = queries[qid]
query_embedding = torch.tensor(queries['emb'])
# Compute dot score between query embedding and document embeddings
dot_scores = torch.mm(query_embedding, doc_embeddings.transpose(0, 1))
top_k = torch.topk(dot_scores, k=3)
# Print results
print("Query:", query['query'])
for doc_id in top_k.indices[0].tolist():
print(docs[doc_id]['title'])
print(docs[doc_id]['text'])
```
You can get embeddings for new queries using our API:
```python
#Run: pip install cohere
import cohere
co = cohere.Client(f"{api_key}") # You should add your cohere API Key here :))
texts = ['my search query']
response = co.embed(texts=texts, model='multilingual-22-12')
query_embedding = response.embeddings[0] # Get the embedding for the first text
```
## Performance
In the following table we compare the cohere multilingual-22-12 model with Elasticsearch version 8.6.0 lexical search (title and passage indexed as independent fields). Note that Elasticsearch doesn't support all languages that are part of the MIRACL dataset.
We compute nDCG@10 (a ranking based loss), as well as hit@3: Is at least one relevant document in the top-3 results. We find that hit@3 is easier to interpret, as it presents the number of queries for which a relevant document is found among the top-3 results.
Note: MIRACL only annotated a small fraction of passages (10 per query) for relevancy. Especially for larger Wikipedias (like English), we often found many more relevant passages. This is know as annotation holes. Real nDCG@10 and hit@3 performance is likely higher than depicted.
| Model | cohere multilingual-22-12 nDCG@10 | cohere multilingual-22-12 hit@3 | ES 8.6.0 nDCG@10 | ES 8.6.0 acc@3 |
|---|---|---|---|---|
| miracl-ar | 64.2 | 75.2 | 46.8 | 56.2 |
| miracl-bn | 61.5 | 75.7 | 49.2 | 60.1 |
| miracl-de | 44.4 | 60.7 | 19.6 | 29.8 |
| miracl-en | 44.6 | 62.2 | 30.2 | 43.2 |
| miracl-es | 47.0 | 74.1 | 27.0 | 47.2 |
| miracl-fi | 63.7 | 76.2 | 51.4 | 61.6 |
| miracl-fr | 46.8 | 57.1 | 17.0 | 21.6 |
| miracl-hi | 50.7 | 62.9 | 41.0 | 48.9 |
| miracl-id | 44.8 | 63.8 | 39.2 | 54.7 |
| miracl-ru | 49.2 | 66.9 | 25.4 | 36.7 |
| **Avg** | 51.7 | 67.5 | 34.7 | 46.0 |
Further languages (not supported by Elasticsearch):
| Model | cohere multilingual-22-12 nDCG@10 | cohere multilingual-22-12 hit@3 |
|---|---|---|
| miracl-fa | 44.8 | 53.6 |
| miracl-ja | 49.0 | 61.0 |
| miracl-ko | 50.9 | 64.8 |
| miracl-sw | 61.4 | 74.5 |
| miracl-te | 67.8 | 72.3 |
| miracl-th | 60.2 | 71.9 |
| miracl-yo | 56.4 | 62.2 |
| miracl-zh | 43.8 | 56.5 |
| **Avg** | 54.3 | 64.6 |
|
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theblackcat102/sharegpt-english
|
theblackcat102
| 2023-04-22T03:57:11Z | 96 | 5 |
[
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:other",
"region:us"
] |
[
"text-generation"
] | 2023-04-22T03:40:58Z |
---
license: other
task_categories:
- text-generation
language:
- en
size_categories:
- 10K<n<100K
---
|
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] |
christinacdl/multiclass_depression
|
christinacdl
| 2023-06-03T16:31:59Z | 96 | 1 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-06-03T15:47:34Z |
---
license: apache-2.0
---
|
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] |
logikon/oasst1-delib
|
logikon
| 2023-09-27T14:23:02Z | 96 | 0 |
[
"language:en",
"license:apache-2.0",
"region:us"
] | null | 2023-09-21T09:42:05Z |
---
language:
- en
license: apache-2.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: message_id
dtype: string
- name: parent_id
dtype: string
- name: user_id
dtype: string
- name: created_date
dtype: string
- name: text
dtype: string
- name: role
dtype: string
- name: lang
dtype: string
- name: review_count
dtype: int32
- name: review_result
dtype: bool
- name: deleted
dtype: bool
- name: rank
dtype: float64
- name: synthetic
dtype: bool
- name: model_name
dtype: 'null'
- name: detoxify
struct:
- name: identity_attack
dtype: float64
- name: insult
dtype: float64
- name: obscene
dtype: float64
- name: severe_toxicity
dtype: float64
- name: sexual_explicit
dtype: float64
- name: threat
dtype: float64
- name: toxicity
dtype: float64
- name: message_tree_id
dtype: string
- name: tree_state
dtype: string
- name: emojis
struct:
- name: count
sequence: int32
- name: name
sequence: string
- name: labels
struct:
- name: count
sequence: int32
- name: name
sequence: string
- name: value
sequence: float64
- name: history
dtype: string
splits:
- name: train
num_bytes: 278875
num_examples: 90
- name: validation
num_bytes: 18290
num_examples: 6
download_size: 208227
dataset_size: 297165
---
# Dataset Card for "oasst1-delib"
Subset of `OpenAssistant/oasst1` with English chat messages that (are supposed to) contain reasoning:
* filtered by keyword "pros"
* includes chat history as extra feature
Dataset creation is documented in https://github.com/logikon-ai/deliberation-datasets/blob/main/notebooks/create_oasst1_delib.ipynb
|
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] |
islamrokon/Dataset
|
islamrokon
| 2023-11-11T17:34:53Z | 96 | 0 |
[
"region:us"
] | null | 2023-11-11T17:34:27Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 379777.2888616891
num_examples: 735
- name: test
num_bytes: 42369.71113831089
num_examples: 82
download_size: 165978
dataset_size: 422147.0
---
# Dataset Card for "Dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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kpriyanshu256/semeval-task-8-a-mono-v2-mistral-7b
|
kpriyanshu256
| 2023-11-13T02:02:06Z | 96 | 0 |
[
"region:us"
] | null | 2023-11-13T02:01:58Z |
---
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: label
dtype: int64
- name: model
dtype: string
- name: source
dtype: string
- name: id
dtype: int64
- name: mistral-7b_estimated_loss
dtype: float64
- name: mistral-7b_mean_lowest25
dtype: float64
- name: mistral-7b_mean_highest25
dtype: float64
- name: mistral-7b_max
dtype: float64
- name: mistral-7b_min
dtype: float64
- name: mistral-7b_range
dtype: float64
- name: mistral-7b_mean
dtype: float64
- name: mistral-7b_std
dtype: float64
- name: mistral-7b_entropy
dtype: float64
- name: mistral-7b_kurtosis
dtype: float64
- name: mistral-7b_skewness
dtype: float64
- name: mistral-7b_perplexity
dtype: float64
splits:
- name: train
num_bytes: 281584304
num_examples: 95805
- name: val
num_bytes: 69152233
num_examples: 23952
- name: test
num_bytes: 11023757
num_examples: 5000
download_size: 215512867
dataset_size: 361760294
---
# Dataset Card for "semeval-task-8-a-mono-v2-mistral-7b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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stas/oscar-en-10k
|
stas
| 2022-10-19T21:40:14Z | 95 | 2 |
[
"language:en",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:22Z |
---
language:
- en
license: apache-2.0
---
# OSCAR EN 10K for testing
This is a small subset representing the 10K records from the original OSCAR dataset, "unshuffled_deduplicated_en" subset - created for testing. The records were extracted after having been shuffled.
The full 1TB+ dataset is at https://huggingface.co/datasets/oscar.
```
$ python -c "from datasets import load_dataset; ds=load_dataset('stas/oscar-en-10k'); print(ds)"
DatasetDict({
train: Dataset({
features: ['text'],
num_rows: 10000
})
})
```
* Records: 10,000
* compressed size: ~37MB
* uncompressed size: 131MB
To convert to jsonlines:
```
from datasets import load_dataset
dataset_name = "stas/oscar-en-10k"
name = dataset_name.split('/')[-1]
ds = load_dataset(dataset_name, split='train')
ds.to_json(f"{name}.jsonl", orient="records", lines=True)
```
To see how this subset was created, here is the [instructions file](https://huggingface.co/datasets/stas/oscar-en-10k/blob/main/process.txt).
|
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] |
wietsedv/stsbenchmark
|
wietsedv
| 2022-03-09T09:14:43Z | 95 | 0 |
[
"license:cc-by-sa-4.0",
"region:us"
] | null | 2022-03-02T23:29:22Z |
---
license: cc-by-sa-4.0
---
|
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] |
heegyu/kowikitext
|
heegyu
| 2022-10-02T05:07:59Z | 95 | 2 |
[
"license:cc-by-sa-3.0",
"region:us"
] | null | 2022-10-02T02:40:05Z |
---
license: cc-by-sa-3.0
---
한국어 위키피디아 article 덤프(20221001)
- 1334694 rows
- download size: 474MB
```python
from datasets import load_dataset
ds = load_dataset("heegyu/kowikitext", "20221001")
ds["train"][0]
```
```
{'id': '5',
'revid': '595831',
'url': 'https://ko.wikipedia.org/wiki?curid=5',
'title': '지미 카터',
'text': '제임스 얼 카터 주니어(, 1924년 10월 1일 ~ )는 민주당 출신 미국 39대 대통령 (1977년 ~ 1981년)이다.\n생애.\n어린 시절.\n지미 카터는 조지아주 섬터 카운티 플레인스 마을에서 태어났다.\n조지아 공과대학교를 졸업하였다. 그 후 해군에 들어가 전함·원자력·잠수함의 승무원으로 일하였다. 1953년 미국 해군 대위로 예편하였고 이후 땅콩·면화 등을 가꿔 많은 돈을 벌었다. 그의 별명이 "땅콩 농부" (Peanut Farmer)로 알려졌다.\n정계 입문.\n1962년 조지아주 상원 의원 선거에서 낙선하나 그 선거가 부정선거 였음을 ... "
}
```
|
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] |
keremberke/csgo-object-detection
|
keremberke
| 2023-01-27T13:39:19Z | 95 | 4 |
[
"task_categories:object-detection",
"roboflow",
"roboflow2huggingface",
"region:us"
] |
[
"object-detection"
] | 2022-12-29T07:37:55Z |
---
task_categories:
- object-detection
tags:
- roboflow
- roboflow2huggingface
---
<div align="center">
<img width="640" alt="keremberke/csgo-object-detection" src="https://huggingface.co/datasets/keremberke/csgo-object-detection/resolve/main/thumbnail.jpg">
</div>
### Dataset Labels
```
['ct', 'cthead', 't', 'thead']
```
### Number of Images
```json
{'train': 3879, 'valid': 383, 'test': 192}
```
### How to Use
- Install [datasets](https://pypi.org/project/datasets/):
```bash
pip install datasets
```
- Load the dataset:
```python
from datasets import load_dataset
ds = load_dataset("keremberke/csgo-object-detection", name="full")
example = ds['train'][0]
```
### Roboflow Dataset Page
[https://universe.roboflow.com/asd-culfr/wlots/dataset/1](https://universe.roboflow.com/asd-culfr/wlots/dataset/1?ref=roboflow2huggingface)
### Citation
```
@misc{ wlots_dataset,
title = { wlots Dataset },
type = { Open Source Dataset },
author = { asd },
howpublished = { \\url{ https://universe.roboflow.com/asd-culfr/wlots } },
url = { https://universe.roboflow.com/asd-culfr/wlots },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { may },
note = { visited on 2023-01-27 },
}
```
### License
CC BY 4.0
### Dataset Summary
This dataset was exported via roboflow.com on December 28, 2022 at 8:08 PM GMT
Roboflow is an end-to-end computer vision platform that helps you
* collaborate with your team on computer vision projects
* collect & organize images
* understand unstructured image data
* annotate, and create datasets
* export, train, and deploy computer vision models
* use active learning to improve your dataset over time
It includes 4454 images.
Ct-cthead-t-thead are annotated in COCO format.
The following pre-processing was applied to each image:
* Auto-orientation of pixel data (with EXIF-orientation stripping)
* Resize to 416x416 (Fill (with center crop))
The following augmentation was applied to create 3 versions of each source image:
* Random brigthness adjustment of between -15 and +15 percent
|
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DFKI-SLT/kbp37
|
DFKI-SLT
| 2023-04-27T13:04:14Z | 95 | 0 |
[
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:other",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other",
"language:en",
"license:other",
"relation extraction",
"arxiv:1508.01006",
"region:us"
] |
[
"text-classification"
] | 2023-01-06T12:26:09Z |
---
annotations_creators:
- other
language:
- en
language_creators:
- found
license:
- other
multilinguality:
- monolingual
pretty_name: KBP37 is an English Relation Classification dataset
size_categories:
- 10K<n<100K
source_datasets:
- extended|other
tags:
- relation extraction
task_categories:
- text-classification
task_ids:
- multi-class-classification
dataset_info:
- config_name: kbp37
features:
- name: id
dtype: string
- name: sentence
dtype: string
- name: relation
dtype:
class_label:
names:
'0': no_relation
'1': org:alternate_names(e1,e2)
'2': org:alternate_names(e2,e1)
'3': org:city_of_headquarters(e1,e2)
'4': org:city_of_headquarters(e2,e1)
'5': org:country_of_headquarters(e1,e2)
'6': org:country_of_headquarters(e2,e1)
'7': org:founded(e1,e2)
'8': org:founded(e2,e1)
'9': org:founded_by(e1,e2)
'10': org:founded_by(e2,e1)
'11': org:members(e1,e2)
'12': org:members(e2,e1)
'13': org:stateorprovince_of_headquarters(e1,e2)
'14': org:stateorprovince_of_headquarters(e2,e1)
'15': org:subsidiaries(e1,e2)
'16': org:subsidiaries(e2,e1)
'17': org:top_members/employees(e1,e2)
'18': org:top_members/employees(e2,e1)
'19': per:alternate_names(e1,e2)
'20': per:alternate_names(e2,e1)
'21': per:cities_of_residence(e1,e2)
'22': per:cities_of_residence(e2,e1)
'23': per:countries_of_residence(e1,e2)
'24': per:countries_of_residence(e2,e1)
'25': per:country_of_birth(e1,e2)
'26': per:country_of_birth(e2,e1)
'27': per:employee_of(e1,e2)
'28': per:employee_of(e2,e1)
'29': per:origin(e1,e2)
'30': per:origin(e2,e1)
'31': per:spouse(e1,e2)
'32': per:spouse(e2,e1)
'33': per:stateorprovinces_of_residence(e1,e2)
'34': per:stateorprovinces_of_residence(e2,e1)
'35': per:title(e1,e2)
'36': per:title(e2,e1)
splits:
- name: train
num_bytes: 3570626
num_examples: 15917
- name: validation
num_bytes: 388935
num_examples: 1724
- name: test
num_bytes: 762806
num_examples: 3405
download_size: 5106673
dataset_size: 4722367
- config_name: kbp37_formatted
features:
- name: id
dtype: string
- name: token
sequence: string
- name: e1_start
dtype: int32
- name: e1_end
dtype: int32
- name: e2_start
dtype: int32
- name: e2_end
dtype: int32
- name: relation
dtype:
class_label:
names:
'0': no_relation
'1': org:alternate_names(e1,e2)
'2': org:alternate_names(e2,e1)
'3': org:city_of_headquarters(e1,e2)
'4': org:city_of_headquarters(e2,e1)
'5': org:country_of_headquarters(e1,e2)
'6': org:country_of_headquarters(e2,e1)
'7': org:founded(e1,e2)
'8': org:founded(e2,e1)
'9': org:founded_by(e1,e2)
'10': org:founded_by(e2,e1)
'11': org:members(e1,e2)
'12': org:members(e2,e1)
'13': org:stateorprovince_of_headquarters(e1,e2)
'14': org:stateorprovince_of_headquarters(e2,e1)
'15': org:subsidiaries(e1,e2)
'16': org:subsidiaries(e2,e1)
'17': org:top_members/employees(e1,e2)
'18': org:top_members/employees(e2,e1)
'19': per:alternate_names(e1,e2)
'20': per:alternate_names(e2,e1)
'21': per:cities_of_residence(e1,e2)
'22': per:cities_of_residence(e2,e1)
'23': per:countries_of_residence(e1,e2)
'24': per:countries_of_residence(e2,e1)
'25': per:country_of_birth(e1,e2)
'26': per:country_of_birth(e2,e1)
'27': per:employee_of(e1,e2)
'28': per:employee_of(e2,e1)
'29': per:origin(e1,e2)
'30': per:origin(e2,e1)
'31': per:spouse(e1,e2)
'32': per:spouse(e2,e1)
'33': per:stateorprovinces_of_residence(e1,e2)
'34': per:stateorprovinces_of_residence(e2,e1)
'35': per:title(e1,e2)
'36': per:title(e2,e1)
splits:
- name: train
num_bytes: 4943394
num_examples: 15807
- name: validation
num_bytes: 539197
num_examples: 1714
- name: test
num_bytes: 1055918
num_examples: 3379
download_size: 5106673
dataset_size: 6581345
---
# Dataset Card for "kbp37"
## 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:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Repository:** [kbp37](https://github.com/zhangdongxu/kbp37)
- **Paper:** [Relation Classification via Recurrent Neural Network](https://arxiv.org/abs/1508.01006)
- **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:** 5.11 MB
- **Size of the generated dataset:** 6.58 MB
### Dataset Summary
KBP37 is a revision of MIML-RE annotation dataset, provided by Gabor Angeli et al. (2014). They use both the 2010 and
2013 KBP official document collections, as well as a July 2013 dump of Wikipedia as the text corpus for annotation.
There are 33811 sentences been annotated. Zhang and Wang made several refinements:
1. They add direction to the relation names, e.g. '`per:employee_of`' is split into '`per:employee of(e1,e2)`'
and '`per:employee of(e2,e1)`'. They also replace '`org:parents`' with '`org:subsidiaries`' and replace
'`org:member of’ with '`org:member`' (by their reverse directions).
2. They discard low frequency relations such that both directions of each relation occur more than 100 times in the
dataset.
KBP37 contains 18 directional relations and an additional '`no_relation`' relation, resulting in 37 relation classes.
Note:
- There is a formatted version that you can load with `datasets.load_dataset('kbp37', name='kbp37_formatted')`. This version is tokenized with `str.split()` and
provides entities as offsets instead of being enclosed by xml tags. It discards some examples, however, that are invalid in the original dataset and lead
to entity offset errors, e.g. example train/1276.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
The language data in KBP37 is in English (BCP-47 en)
## Dataset Structure
### Data Instances
#### kbp37
- **Size of downloaded dataset files:** 5.11 MB
- **Size of the generated dataset:** 4.7 MB
An example of 'train' looks as follows:
```json
{
"id": "0",
"sentence": "<e1> Thom Yorke </e1> of <e2> Radiohead </e2> has included the + for many of his signature distortion sounds using a variety of guitars to achieve various tonal options .",
"relation": 27
}
```
#### kbp37_formatted
- **Size of downloaded dataset files:** 5.11 MB
- **Size of the generated dataset:** 6.58 MB
An example of 'train' looks as follows:
```json
{
"id": "1",
"token": ["Leland", "High", "School", "is", "a", "public", "high", "school", "located", "in", "the", "Almaden", "Valley", "in", "San", "Jose", "California", "USA", "in", "the", "San", "Jose", "Unified", "School", "District", "."],
"e1_start": 0,
"e1_end": 3,
"e2_start": 14,
"e2_end": 16,
"relation": 3
}
```
### Data Fields
#### kbp37
- `id`: the instance id of this sentence, a `string` feature.
- `sentence`: the sentence, a `string` features.
- `relation`: the relation label of this instance, an `int` classification label.
```python
{"no_relation": 0, "org:alternate_names(e1,e2)": 1, "org:alternate_names(e2,e1)": 2, "org:city_of_headquarters(e1,e2)": 3, "org:city_of_headquarters(e2,e1)": 4, "org:country_of_headquarters(e1,e2)": 5, "org:country_of_headquarters(e2,e1)": 6, "org:founded(e1,e2)": 7, "org:founded(e2,e1)": 8, "org:founded_by(e1,e2)": 9, "org:founded_by(e2,e1)": 10, "org:members(e1,e2)": 11, "org:members(e2,e1)": 12, "org:stateorprovince_of_headquarters(e1,e2)": 13, "org:stateorprovince_of_headquarters(e2,e1)": 14, "org:subsidiaries(e1,e2)": 15, "org:subsidiaries(e2,e1)": 16, "org:top_members/employees(e1,e2)": 17, "org:top_members/employees(e2,e1)": 18, "per:alternate_names(e1,e2)": 19, "per:alternate_names(e2,e1)": 20, "per:cities_of_residence(e1,e2)": 21, "per:cities_of_residence(e2,e1)": 22, "per:countries_of_residence(e1,e2)": 23, "per:countries_of_residence(e2,e1)": 24, "per:country_of_birth(e1,e2)": 25, "per:country_of_birth(e2,e1)": 26, "per:employee_of(e1,e2)": 27, "per:employee_of(e2,e1)": 28, "per:origin(e1,e2)": 29, "per:origin(e2,e1)": 30, "per:spouse(e1,e2)": 31, "per:spouse(e2,e1)": 32, "per:stateorprovinces_of_residence(e1,e2)": 33, "per:stateorprovinces_of_residence(e2,e1)": 34, "per:title(e1,e2)": 35, "per:title(e2,e1)": 36}
```
#### kbp37_formatted
- `id`: the instance id of this sentence, a `string` feature.
- `token`: the list of tokens of this sentence, using `str.split()`, a `list` of `string` features.
- `e1_start`: the 0-based index of the start token of the first argument', an `int` feature.
- `e1_end`: the 0-based index of the end token of the first argument, exclusive, an `int` feature.
- `e2_start`: the 0-based index of the start token of the second argument, an `int` feature.
- `e2_end`: the 0-based index of the end token of the second argument, exclusive, an `int` feature.
- `relation`: the relation label of this instance, an `int` classification label (same as `'kbp37''`).
### Data Splits
| | Train | Dev | Test |
|-------|-------|------|------|
| kbp37 | 15917 | 1724 | 3405 |
| kbp37_formatted | 15807 | 1714 | 3379 |
## 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{DBLP:journals/corr/ZhangW15a,
author = {Dongxu Zhang and
Dong Wang},
title = {Relation Classification via Recurrent Neural Network},
journal = {CoRR},
volume = {abs/1508.01006},
year = {2015},
url = {http://arxiv.org/abs/1508.01006},
eprinttype = {arXiv},
eprint = {1508.01006},
timestamp = {Fri, 04 Nov 2022 18:37:50 +0100},
biburl = {https://dblp.org/rec/journals/corr/ZhangW15a.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
### Contributions
Thanks to [@phucdev](https://github.com/phucdev) for adding this dataset.
|
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Ssunbell/boostcamp-docvqa-v5
|
Ssunbell
| 2023-02-05T03:01:47Z | 95 | 1 |
[
"region:us"
] | null | 2023-02-05T02:50:57Z |
---
dataset_info:
features:
- name: questionId
dtype: int64
- name: question
dtype: string
- name: image
sequence:
sequence:
sequence: uint8
- name: docId
dtype: int64
- name: ucsf_document_id
dtype: string
- name: ucsf_document_page_no
dtype: string
- name: answers
sequence: string
- name: data_split
dtype: string
- name: words
sequence: string
- name: boxes
sequence:
sequence: int64
splits:
- name: train
num_bytes: 6381793673
num_examples: 39454
- name: val
num_bytes: 869361798
num_examples: 5349
download_size: 2578655464
dataset_size: 7251155471
---
# Dataset Card for "boostcamp-docvqa-v5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
pushpdeep/fake_news_combined
|
pushpdeep
| 2023-04-10T18:59:26Z | 95 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-03-09T06:04:04Z |
---
license: apache-2.0
---
**Label Description**
0 : Fake,
1 : Real
|
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] |
medalpaca/medical_meadow_cord19
|
medalpaca
| 2023-04-06T16:47:03Z | 95 | 4 |
[
"task_categories:summarization",
"size_categories:100K<n<1M",
"language:en",
"region:us"
] |
[
"summarization"
] | 2023-04-06T16:24:06Z |
---
task_categories:
- summarization
language:
- en
size_categories:
- 100K<n<1M
---
# CORD 19
## Dataset Description
- **Homepage:** https://www.kaggle.com/datasets/allen-institute-for-ai/CORD-19-research-challenge
### Dataset Summary
In response to the COVID-19 pandemic, the White House and a coalition of leading research groups have prepared the COVID-19 Open Research Dataset (CORD-19). CORD-19 is a resource of over 1,000,000 scholarly articles, including over 400,000 with full text, about COVID-19, SARS-CoV-2, and related coronaviruses. This freely available dataset is provided to the global research community to apply recent advances in natural language processing and other AI techniques to generate new insights in support of the ongoing fight against this infectious disease.
This is a processed version of the dataset, where we removed some empty entries and formated it to be compatible with the alpaca training. For more details on the data, please refer to the original publicatio.
### Citation Information
```
@inproceedings{wang-etal-2020-cord,
title = "{CORD-19}: The {COVID-19} Open Research Dataset",
author = "Wang, Lucy Lu and Lo, Kyle and Chandrasekhar, Yoganand and Reas, Russell and Yang, Jiangjiang and Burdick, Doug and Eide, Darrin and Funk, Kathryn and Katsis, Yannis and Kinney, Rodney Michael and Li, Yunyao and Liu, Ziyang and Merrill, William and Mooney, Paul and Murdick, Dewey A. and Rishi, Devvret and Sheehan, Jerry and Shen, Zhihong and Stilson, Brandon and Wade, Alex D. and Wang, Kuansan and Wang, Nancy Xin Ru and Wilhelm, Christopher and Xie, Boya and Raymond, Douglas M. and Weld, Daniel S. and Etzioni, Oren and Kohlmeier, Sebastian",
booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID-19} at {ACL} 2020",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.nlpcovid19-acl.1"
}
```
|
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] |
jiacheng-ye/nl2bash
|
jiacheng-ye
| 2023-04-17T12:55:38Z | 95 | 0 |
[
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:en",
"code",
"region:us"
] |
[
"text-generation"
] | 2023-04-17T12:53:49Z |
---
task_categories:
- text-generation
language:
- en
tags:
- code
pretty_name: NL2Bash
size_categories:
- 1K<n<10K
---
|
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] |
matejklemen/falko_merlin
|
matejklemen
| 2023-05-08T20:56:31Z | 95 | 0 |
[
"license:cc-by-sa-4.0",
"region:us"
] | null | 2023-05-08T20:30:48Z |
---
license: cc-by-sa-4.0
dataset_info:
features:
- name: src_tokens
sequence: string
- name: tgt_tokens
sequence: string
- name: corrections
list:
- name: idx_src
sequence: int32
- name: idx_tgt
sequence: int32
- name: corr_type
dtype: string
splits:
- name: train
num_bytes: 6981243
num_examples: 19237
- name: validation
num_bytes: 902510
num_examples: 2503
- name: test
num_bytes: 836757
num_examples: 2337
download_size: 85667586
dataset_size: 8720510
---
|
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priyank-m/MJSynth_text_recognition
|
priyank-m
| 2023-07-04T20:49:10Z | 95 | 0 |
[
"task_categories:image-to-text",
"size_categories:1M<n<10M",
"language:en",
"region:us"
] |
[
"image-to-text"
] | 2023-06-22T15:33:18Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: string
splits:
- name: train
num_bytes: 12173747703
num_examples: 7224600
- name: val
num_bytes: 1352108669.283
num_examples: 802733
- name: test
num_bytes: 1484450563.896
num_examples: 891924
download_size: 12115256620
dataset_size: 15010306936.179
task_categories:
- image-to-text
language:
- en
size_categories:
- 1M<n<10M
pretty_name: MJSynth
---
# Dataset Card for "MJSynth_text_recognition"
This is the MJSynth dataset for text recognition on document images, synthetically generated, covering 90K English words.
It includes training, validation and test splits.
Source of the dataset: https://www.robots.ox.ac.uk/~vgg/data/text/
Use dataset streaming functionality to try out the dataset quickly without downloading the entire dataset (refer: https://huggingface.co/docs/datasets/stream)
Citation details provided on the source website (if you use the data please cite):
@InProceedings{Jaderberg14c,
author = "Max Jaderberg and Karen Simonyan and Andrea Vedaldi and Andrew Zisserman",
title = "Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition",
booktitle = "Workshop on Deep Learning, NIPS",
year = "2014",
}
@Article{Jaderberg16,
author = "Max Jaderberg and Karen Simonyan and Andrea Vedaldi and Andrew Zisserman",
title = "Reading Text in the Wild with Convolutional Neural Networks",
journal = "International Journal of Computer Vision",
number = "1",
volume = "116",
pages = "1--20",
month = "jan",
year = "2016",
}
|
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Vazbeek/alpaca-cs
|
Vazbeek
| 2023-08-09T16:23:40Z | 95 | 1 |
[
"language:cs",
"license:cc-by-4.0",
"code",
"region:us"
] | null | 2023-08-03T22:55:09Z |
---
license: cc-by-4.0
language:
- cs
tags:
- code
---
Alpaca dataset translated to Czech language using ChatGPT 3.5.
|
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Jing24/val_oneanswer
|
Jing24
| 2023-08-19T00:35:46Z | 95 | 0 |
[
"region:us"
] | null | 2023-08-19T00:35:44Z |
---
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
struct:
- name: answer_start
sequence: int32
- name: text
sequence: string
splits:
- name: train
num_bytes: 9832949
num_examples: 10570
download_size: 1675804
dataset_size: 9832949
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "val_oneanswer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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rasgaard/20_newsgroups
|
rasgaard
| 2023-09-13T07:25:05Z | 95 | 0 |
[
"region:us"
] | null | 2023-09-13T07:23:58Z |
---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
- name: label_text
dtype: string
splits:
- name: train
num_bytes: 12724811.858405516
num_examples: 10182
- name: val
num_bytes: 1414701.1415944847
num_examples: 1132
- name: test
num_bytes: 8499585
num_examples: 7532
download_size: 0
dataset_size: 22639098.0
---
# Dataset Card for "20_newsgroups"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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voidful/hint-lm-data
|
voidful
| 2023-11-04T09:20:26Z | 95 | 0 |
[
"region:us"
] | null | 2023-10-31T10:18:01Z |
---
dataset_info:
features:
- name: question
dtype: string
- name: options
sequence: string
- name: answer
dtype: string
- name: hint_chatgpt
dtype: string
splits:
- name: hotpotqa_train
num_bytes: 520829
num_examples: 5481
- name: hotpotqa_validation
num_bytes: 82639
num_examples: 458
- name: openbookqa_test
num_bytes: 121454
num_examples: 500
- name: openbookqa_train
num_bytes: 830308
num_examples: 4957
- name: openbookqa_validation
num_bytes: 91011
num_examples: 500
- name: strategyqa_full
num_bytes: 255888
num_examples: 2290
- name: strategyqa_test
num_bytes: 88443
num_examples: 500
- name: strategyqa_train
num_bytes: 167445
num_examples: 1790
- name: truthfulqa_full
num_bytes: 351912
num_examples: 817
- name: truthfulqa_test
num_bytes: 228633
num_examples: 500
- name: truthfulqa_train
num_bytes: 123279
num_examples: 317
download_size: 1612358
dataset_size: 2861841
configs:
- config_name: default
data_files:
- split: hotpotqa_train
path: data/hotpotqa_train-*
- split: hotpotqa_validation
path: data/hotpotqa_validation-*
- split: openbookqa_test
path: data/openbookqa_test-*
- split: openbookqa_train
path: data/openbookqa_train-*
- split: openbookqa_validation
path: data/openbookqa_validation-*
- split: strategyqa_full
path: data/strategyqa_full-*
- split: strategyqa_test
path: data/strategyqa_test-*
- split: strategyqa_train
path: data/strategyqa_train-*
- split: truthfulqa_full
path: data/truthfulqa_full-*
- split: truthfulqa_test
path: data/truthfulqa_test-*
- split: truthfulqa_train
path: data/truthfulqa_train-*
---
# Dataset Card for "hint-lm-data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
ktam204/ZaloAI
|
ktam204
| 2023-11-14T07:46:20Z | 95 | 0 |
[
"region:us"
] | null | 2023-11-12T09:26:53Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 86452073.13
num_examples: 1362
download_size: 83935670
dataset_size: 86452073.13
---
# Dataset Card for "ZaloAI"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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arcee-ai/nuclear_patents
|
arcee-ai
| 2023-11-17T23:19:24Z | 95 | 0 |
[
"region:us"
] | null | 2023-11-17T05:04:52Z |
---
dataset_info:
features:
- name: patent_number
dtype: string
- name: section
dtype: string
- name: raw_text
dtype: string
splits:
- name: train
num_bytes: 388930493
num_examples: 37248
download_size: 146263843
dataset_size: 388930493
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "nuclear_patents"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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dliu1/legal-llama-raw-text
|
dliu1
| 2023-11-27T15:31:59Z | 95 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-27T15:31:33Z |
---
license: apache-2.0
---
|
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taln-ls2n/inspec
|
taln-ls2n
| 2022-07-21T14:14:59Z | 94 | 3 |
[
"task_categories:text-generation",
"annotations_creators:unknown",
"language_creators:unknown",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:en",
"license:unknown",
"region:us"
] |
[
"text-mining",
"text-generation"
] | 2022-04-12T08:10:45Z |
---
annotations_creators:
- unknown
language_creators:
- unknown
language:
- en
license:
- unknown
multilinguality:
- monolingual
task_categories:
- text-mining
- text-generation
task_ids:
- keyphrase-generation
- keyphrase-extraction
size_categories:
- 1K<n<10K
pretty_name: Inspec
---
# Inspec Benchmark Dataset for Keyphrase Generation
## About
Inspec is a dataset for benchmarking keyphrase extraction and generation models.
The dataset is composed of 2,000 abstracts of scientific papers collected from the [Inspec database](https://www.theiet.org/resources/inspec/).
Keyphrases were annotated by professional indexers in an uncontrolled setting (that is, not limited to thesaurus entries).
Details about the inspec dataset can be found in the original paper [(Hulth, 2003)][hulth-2003].
Reference (indexer-assigned) keyphrases are also categorized under the PRMU (<u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen) scheme as proposed in [(Boudin and Gallina, 2021)][boudin-2021].
Text pre-processing (tokenization) is carried out using `spacy` (`en_core_web_sm` model) with a special rule to avoid splitting words with hyphens (e.g. graph-based is kept as one token).
Stemming (Porter's stemmer implementation provided in `nltk`) is applied before reference keyphrases are matched against the source text.
Details about the process can be found in `prmu.py`.
## Content and statistics
The dataset is divided into the following three splits:
| Split | # documents | #words | # keyphrases | % Present | % Reordered | % Mixed | % Unseen |
| :--------- | ----------: | -----: | -----------: | --------: | ----------: | ------: | -------: |
| Train | 1,000 | 141.7 | 9.79 | 78.00 | 9.85 | 6.22 | 5.93 |
| Validation | 500 | 132.2 | 9.15 | 77.96 | 9.82 | 6.75 | 5.47 |
| Test | 500 | 134.8 | 9.83 | 78.70 | 9.92 | 6.48 | 4.91 |
The following data fields are available :
- **id**: unique identifier of the document.
- **title**: title of the document.
- **abstract**: abstract of the document.
- **keyphrases**: list of reference keyphrases.
- **prmu**: list of <u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen categories for reference keyphrases.
## References
- (Hulth, 2003) Anette Hulth. 2003.
[Improved automatic keyword extraction given more linguistic knowledge](https://aclanthology.org/W03-1028).
In Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, pages 216-223.
- (Boudin and Gallina, 2021) Florian Boudin and Ygor Gallina. 2021.
[Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness](https://aclanthology.org/2021.naacl-main.330/).
In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4185–4193, Online. Association for Computational Linguistics.
[hulth-2003]: https://aclanthology.org/W03-1028/
[boudin-2021]: https://aclanthology.org/2021.naacl-main.330/
|
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FinanceInc/auditor_sentiment
|
FinanceInc
| 2022-07-21T19:03:51Z | 94 | 12 |
[
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"region:us"
] |
[
"text-classification"
] | 2022-07-21T18:25:47Z |
---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
- sentiment-classification
paperswithcode_id: null
pretty_name: Auditor_Sentiment
---
# Dataset Card for Auditor Sentiment
## 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)
## Dataset Description
Auditor review sentiment collected by News Department
- **Point of Contact:**
Talked to COE for Auditing, currently [email protected]
### Dataset Summary
Auditor sentiment dataset of sentences from financial news. The dataset consists of several thousand sentences from English language financial news categorized by sentiment.
### Supported Tasks and Leaderboards
Sentiment Classification
### Languages
English
## Dataset Structure
### Data Instances
```
"sentence": "Pharmaceuticals group Orion Corp reported a fall in its third-quarter earnings that were hit by larger expenditures on R&D and marketing .",
"label": "negative"
```
### Data Fields
- sentence: a tokenized line from the dataset
- label: a label corresponding to the class as a string: 'positive' - (2), 'neutral' - (1), or 'negative' - (0)
### Data Splits
A train/test split was created randomly with a 75/25 split
## Dataset Creation
### Curation Rationale
To gather our auditor evaluations into one dataset. Previous attempts using off-the-shelf sentiment had only 70% F1, this dataset was an attempt to improve upon that performance.
### Source Data
#### Initial Data Collection and Normalization
The corpus used in this paper is made out of English news reports.
#### Who are the source language producers?
The source data was written by various auditors.
### Annotations
#### Annotation process
This release of the auditor reviews covers a collection of 4840
sentences. The selected collection of phrases was annotated by 16 people with
adequate background knowledge on financial markets. The subset here is where inter-annotation agreement was greater than 75%.
#### Who are the annotators?
They were pulled from the SME list, names are held by [email protected]
### Personal and Sensitive Information
There is no personal or sensitive information in this dataset.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
All annotators were from the same institution and so interannotator agreement
should be understood with this taken into account.
### Licensing Information
License: Demo.Org Proprietary - DO NOT SHARE
This dataset is based on the [financial phrasebank](https://huggingface.co/datasets/financial_phrasebank) dataset.
|
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] |
bigbio/biosses
|
bigbio
| 2022-12-22T15:32:58Z | 94 | 1 |
[
"multilinguality:monolingual",
"language:en",
"license:gpl-3.0",
"region:us"
] | null | 2022-09-06T01:12:20Z |
---
language:
- en
bigbio_language:
- English
license: gpl-3.0
multilinguality: monolingual
bigbio_license_shortname: GPL_3p0
pretty_name: BIOSSES
homepage: https://tabilab.cmpe.boun.edu.tr/BIOSSES/DataSet.html
bigbio_pubmed: false
bigbio_public: true
bigbio_tasks:
- SEMANTIC_SIMILARITY
---
# Dataset Card for BIOSSES
## Dataset Description
- **Homepage:** https://tabilab.cmpe.boun.edu.tr/BIOSSES/DataSet.html
- **Pubmed:** True
- **Public:** True
- **Tasks:** STS
BIOSSES computes similarity of biomedical sentences by utilizing WordNet as the general domain ontology and UMLS as the biomedical domain specific ontology. The original paper outlines the approaches with respect to using annotator score as golden standard. Source view will return all annotator score individually whereas the Bigbio view will return the mean of the annotator score.
## Citation Information
```
@article{souganciouglu2017biosses,
title={BIOSSES: a semantic sentence similarity estimation system for the biomedical domain},
author={Soğancıoğlu, Gizem, Hakime Öztürk, and Arzucan Özgür},
journal={Bioinformatics},
volume={33},
number={14},
pages={i49--i58},
year={2017},
publisher={Oxford University Press}
}
```
|
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cannlytics/cannabis_licenses
|
cannlytics
| 2023-09-30T14:23:05Z | 94 | 3 |
[
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"license:cc-by-4.0",
"cannabis",
"licenses",
"region:us"
] | null | 2022-09-28T19:52:23Z |
---
pretty_name: cannabis_licenses
annotations_creators:
- expert-generated
language_creators:
- expert-generated
license:
- cc-by-4.0
tags:
- cannabis
- licenses
---
# Cannabis Licenses
<!-- FIXME:
<div align="center" style="text-align:center; margin-top:1rem; margin-bottom: 1rem;">
<img style="max-height:365px;width:100%;max-width:720px;" alt="" src="analysis/figures/cannabis-licenses-map.png">
</div> -->
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [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)
- [Data Collection and Normalization](#data-collection-and-normalization)
- [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)
- [License](#license)
- [Citation](#citation)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** <https://github.com/cannlytics/cannlytics>
- **Repository:** <https://huggingface.co/datasets/cannlytics/cannabis_licenses>
- **Point of Contact:** <[email protected]>
### Dataset Summary
**Cannabis Licenses** is a collection of cannabis license data for each state with permitted adult-use cannabis. The dataset also includes a sub-dataset, `all`, that includes all licenses.
## Dataset Structure
The dataset is partitioned into 18 subsets for each state and the aggregate.
| State | Code | Status |
|-------|------|--------|
| [All](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/all) | `all` | ✅ |
| [Alaska](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/ak) | `ak` | ✅ |
| [Arizona](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/az) | `az` | ✅ |
| [California](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/ca) | `ca` | ✅ |
| [Colorado](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/co) | `co` | ✅ |
| [Connecticut](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/ct) | `ct` | ✅ |
| [Delaware](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/de) | `md` | ✅ |
| [Illinois](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/il) | `il` | ✅ |
| [Maine](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/me) | `me` | ✅ |
| [Maryland](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/md) | `md` | ✅ |
| [Massachusetts](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/ma) | `ma` | ✅ |
| [Michigan](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/mi) | `mi` | ✅ |
| [Missouri](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/mo) | `mo` | ✅ |
| [Montana](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/mt) | `mt` | ✅ |
| [Nevada](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/nv) | `nv` | ✅ |
| [New Jersey](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/nj) | `nj` | ✅ |
| [New Mexico](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/nm) | `nm` | ✅ |
| [New York](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/ny) | `ny` | ✅ |
| [Oregon](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/or) | `or` | ✅ |
| [Rhode Island](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/ri) | `ri` | ✅ |
| [Vermont](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/vt) | `vt` | ✅ |
| Virginia | `va` | ⏳ Expected 2024 |
| [Washington](https://huggingface.co/datasets/cannlytics/cannabis_licenses/tree/main/data/wa) | `wa` | ✅ |
The following states have issued medical cannabis licenses, but are not (yet) included in the dataset:
- Alabama
- Arkansas
- District of Columbia (D.C.)
- Florida
- Kentucky (2024)
- Louisiana
- Minnesota
- Mississippi
- New Hampshire
- North Dakota
- Ohio
- Oklahoma
- Pennsylvania
- South Dakota
- Utah
- West Virginia
### Data Instances
You can load the licenses for each state. For example:
```py
from datasets import load_dataset
# Get the licenses for a specific state.
dataset = load_dataset('cannlytics/cannabis_licenses', 'all')
data = dataset['data']
```
### Data Fields
Below is a non-exhaustive list of fields, used to standardize the various data that are encountered, that you may expect to find for each observation.
| Field | Example | Description |
|-------|-----|-------------|
| `id` | `"1046"` | A state-unique ID for the license. |
| `license_number` | `"C10-0000423-LIC"` | A unique license number. |
| `license_status` | `"Active"` | The status of the license. Only licenses that are active are included. |
| `license_status_date` | `"2022-04-20T00:00"` | The date the status was assigned, an ISO-formatted date if present. |
| `license_term` | `"Provisional"` | The term for the license. |
| `license_type` | `"Commercial - Retailer"` | The type of business license. |
| `license_designation` | `"Adult-Use and Medicinal"` | A state-specific classification for the license. |
| `issue_date` | `"2019-07-15T00:00:00"` | An issue date for the license, an ISO-formatted date if present. |
| `expiration_date` | `"2023-07-14T00:00:00"` | An expiration date for the license, an ISO-formatted date if present. |
| `licensing_authority_id` | `"BCC"` | A unique ID for the state licensing authority. |
| `licensing_authority` | `"Bureau of Cannabis Control (BCC)"` | The state licensing authority. |
| `business_legal_name` | `"Movocan"` | The legal name of the business that owns the license. |
| `business_dba_name` | `"Movocan"` | The name the license is doing business as. |
| `business_owner_name` | `"redacted"` | The name of the owner of the license. |
| `business_structure` | `"Corporation"` | The structure of the business that owns the license. |
| `activity` | `"Pending Inspection"` | Any relevant license activity. |
| `premise_street_address` | `"1632 Gateway Rd"` | The street address of the business. |
| `premise_city` | `"Calexico"` | The city of the business. |
| `premise_state` | `"CA"` | The state abbreviation of the business. |
| `premise_county` | `"Imperial"` | The county of the business. |
| `premise_zip_code` | `"92231"` | The zip code of the business. |
| `business_email` | `"[email protected]"` | The business email of the license. |
| `business_phone` | `"(555) 555-5555"` | The business phone of the license. |
| `business_website` | `"cannlytics.com"` | The business website of the license. |
| `parcel_number` | `"A42"` | An ID for the business location. |
| `premise_latitude` | `32.69035693` | The latitude of the business. |
| `premise_longitude` | `-115.38987552` | The longitude of the business. |
| `data_refreshed_date` | `"2022-09-21T12:16:33.3866667"` | An ISO-formatted time when the license data was updated. |
### Data Splits
The data is split into subsets by state. You can retrieve all licenses by requesting the `all` subset.
```py
from datasets import load_dataset
# Get all cannabis licenses.
dataset = load_dataset('cannlytics/cannabis_licenses', 'all')
data = dataset['data']
```
## Dataset Creation
### Curation Rationale
Data about organizations operating in the cannabis industry for each state is valuable for research.
### Source Data
| State | Data Source URL |
|-------|-----------------|
| Alaska | <https://www.commerce.alaska.gov/abc/marijuana/Home/licensesearch> |
| Arizona | <https://azcarecheck.azdhs.gov/s/?licenseType=null> |
| California | <https://search.cannabis.ca.gov/> |
| Colorado | <https://sbg.colorado.gov/med/licensed-facilities> |
| Connecticut | <https://portal.ct.gov/DCP/Medical-Marijuana-Program/Connecticut-Medical-Marijuana-Dispensary-Facilities> |
| Delaware | <https://dhss.delaware.gov/dhss/dph/hsp/medmarcc.html> |
| Illinois | <https://www.idfpr.com/LicenseLookup/AdultUseDispensaries.pdf> |
| Maine | <https://www.maine.gov/dafs/ocp/open-data/adult-use> |
| Maryland | <https://mmcc.maryland.gov/Pages/Dispensaries.aspx> |
| Massachusetts | <https://masscannabiscontrol.com/open-data/data-catalog/> |
| Michigan | <https://michigan.maps.arcgis.com/apps/webappviewer/index.html?id=cd5a1a76daaf470b823a382691c0ff60> |
| Missouri | <https://health.mo.gov/safety/cannabis/licensed-facilities.php> |
| Montana | <https://mtrevenue.gov/cannabis/#CannabisLicenses> |
| Nevada | <https://ccb.nv.gov/list-of-licensees/> |
| New Jersey | <https://data.nj.gov/stories/s/ggm4-mprw> |
| New Mexico | <https://nmrldlpi.force.com/bcd/s/public-search-license?division=CCD&language=en_US> |
| New York | <https://cannabis.ny.gov/licensing> |
| Oregon | <https://www.oregon.gov/olcc/marijuana/pages/recreational-marijuana-licensing.aspx> |
| Rhode Island | <https://dbr.ri.gov/office-cannabis-regulation/compassion-centers/licensed-compassion-centers> |
| Vermont | <https://ccb.vermont.gov/licenses> |
| Washington | <https://lcb.wa.gov/records/frequently-requested-lists> |
### Data Collection and Normalization
In the `algorithms` directory, you can find the algorithms used for data collection. You can use these algorithms to recreate the dataset. First, you will need to clone the repository:
```
git clone https://huggingface.co/datasets/cannlytics/cannabis_licenses
```
You can then install the algorithm Python (3.9+) requirements:
```
cd cannabis_licenses
pip install -r requirements.txt
```
Then you can run all of the data-collection algorithms:
```
python algorithms/main.py
```
Or you can run each algorithm individually. For example:
```
python algorithms/get_licenses_ny.py
```
### Personal and Sensitive Information
This dataset includes names of individuals, public addresses, and contact information for cannabis licensees. It is important to take care to use these data points in a legal manner.
## Considerations for Using the Data
### Social Impact of Dataset
Arguably, there is substantial social impact that could result from the study of permitted adult-use cannabis, therefore, researchers and data consumers alike should take the utmost care in the use of this dataset.
### Discussion of Biases
Cannlytics is a for-profit data and analytics company that primarily serves cannabis businesses. The data are not randomly collected and thus sampling bias should be taken into consideration.
### Other Known Limitations
The data is for adult-use cannabis licenses. It would be valuable to include medical cannabis licenses too.
## Additional Information
### Dataset Curators
Curated by [🔥Cannlytics](https://cannlytics.com)<br>
<[email protected]>
### License
```
Copyright (c) 2022-2023 Cannlytics and the Cannabis Data Science Team
The files associated with this dataset are licensed under a
Creative Commons Attribution 4.0 International license.
You can share, copy and modify this dataset so long as you give
appropriate credit, provide a link to the CC BY license, and
indicate if changes were made, but you may not do so in a way
that suggests the rights holder has endorsed you or your use of
the dataset. Note that further permission may be required for
any content within the dataset that is identified as belonging
to a third party.
```
### Citation
Please cite the following if you use the code examples in your research:
```bibtex
@misc{cannlytics2023,
title={Cannabis Data Science},
author={Skeate, Keegan and O'Sullivan-Sutherland, Candace},
journal={https://github.com/cannlytics/cannabis-data-science},
year={2023}
}
```
### Contributions
Thanks to [🔥Cannlytics](https://cannlytics.com), [@candy-o](https://github.com/candy-o), [@hcadeaux](https://huggingface.co/hcadeaux), [@keeganskeate](https://github.com/keeganskeate), and the entire [Cannabis Data Science Team](https://meetup.com/cannabis-data-science/members) for their contributions.
|
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] |
saibo/bookcorpus_deduplicated_small
|
saibo
| 2022-12-29T16:14:32Z | 94 | 0 |
[
"region:us"
] | null | 2022-12-28T18:55:18Z |
---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 7321888
num_examples: 100000
download_size: 4495653
dataset_size: 7321888
---
# Dataset Card for "bookcorpus_deduplicated_small"
First 10K(0.25%) examples of [bookcorpus_deduplicated](https://huggingface.co/datasets/saibo/bookcorpus_deduplicated)
size: 7.4MB
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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-0.44281822443008423,
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] |
neuclir/neuclir1
|
neuclir
| 2023-01-12T18:43:52Z | 94 | 1 |
[
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:1M<n<10M",
"source_datasets:extended|c4",
"language:fa",
"language:ru",
"language:zh",
"license:odc-by",
"region:us"
] |
[
"text-retrieval"
] | 2023-01-11T21:08:24Z |
---
annotations_creators:
- no-annotation
language:
- fa
- ru
- zh
language_creators:
- found
license:
- odc-by
multilinguality:
- multilingual
pretty_name: NeuCLIR1
size_categories:
- 1M<n<10M
source_datasets:
- extended|c4
tags: []
task_categories:
- text-retrieval
task_ids:
- document-retrieval
---
# Dataset Card for NeuCLIR1
## Dataset Description
- **Website:** https://neuclir.github.io/
- **Repository:** https://github.com/NeuCLIR/download-collection
### Dataset Summary
This is the dataset created for TREC 2022 NeuCLIR Track. The collection designed to be similar to HC4 and a large portion of documents from HC4 are ported to this collection.
The documents are Web pages from Common Crawl in Chinese, Persian, and Russian.
### Languages
- Chinese
- Persian
- Russian
## Dataset Structure
### Data Instances
| Split | Documents |
|-----------------|----------:|
| `fas` (Persian) | 2.2M |
| `rus` (Russian) | 4.6M |
| `zho` (Chinese) | 3.2M |
### Data Fields
- `id`: unique identifier for this document
- `cc_file`: source file from connon crawl
- `time`: extracted date/time from article
- `title`: title extracted from article
- `text`: extracted article body
- `url`: source URL
## Dataset Usage
Using 🤗 Datasets:
```python
from datasets import load_dataset
dataset = load_dataset('neuclir/neuclir1')
dataset['fas'] # Persian documents
dataset['rus'] # Russian documents
dataset['zho'] # Chinese documents
```
|
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jonathan-roberts1/PatternNet
|
jonathan-roberts1
| 2023-03-31T17:06:42Z | 94 | 0 |
[
"task_categories:image-classification",
"task_categories:zero-shot-image-classification",
"license:other",
"region:us"
] |
[
"image-classification",
"zero-shot-image-classification"
] | 2023-01-27T12:46:23Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': airplane
'1': baseball field
'2': basketball court
'3': beach
'4': bridge
'5': cemetery
'6': chaparral
'7': christmas tree farm
'8': closed road
'9': coastal mansion
'10': crosswalk
'11': dense residential
'12': ferry terminal
'13': football field
'14': forest
'15': freeway
'16': golf course
'17': harbor
'18': intersection
'19': mobile home park
'20': nursing home
'21': oil gas field
'22': oil well
'23': overpass
'24': parking lot
'25': parking space
'26': railway
'27': river
'28': runway
'29': runway marking
'30': shipping yard
'31': solar panel
'32': sparse residential
'33': storage tank
'34': swimming pool
'35': tennis court
'36': transformer station
'37': wastewater treatment plant
splits:
- name: train
num_bytes: 821222673.6
num_examples: 30400
download_size: 1422129774
dataset_size: 821222673.6
license: other
task_categories:
- image-classification
- zero-shot-image-classification
---
# Dataset Card for "PatternNet"
## Dataset Description
- **Paper** [PatternNet: A benchmark dataset for performance evaluation of remote sensing image retrieval](https://www.sciencedirect.com/science/article/pii/S0924271618300042)
### Licensing Information
For research purposes.
## Citation Information
[PatternNet: A benchmark dataset for performance evaluation of remote sensing image retrieval](https://www.sciencedirect.com/science/article/pii/S0924271618300042)
```
@article{zhou2018patternnet,
title = {PatternNet: A benchmark dataset for performance evaluation of remote sensing image retrieval},
author = {Zhou, Weixun and Newsam, Shawn and Li, Congmin and Shao, Zhenfeng},
year = 2018,
journal = {ISPRS journal of photogrammetry and remote sensing},
publisher = {Elsevier},
volume = 145,
pages = {197--209}
}
```
|
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