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1aurent/Kather-texture-2016
|
1aurent
| 2023-10-15T12:10:50Z | 39 | 0 |
[
"task_categories:image-classification",
"size_categories:1K<n<10K",
"license:cc-by-4.0",
"biology",
"Colorectal Pancer",
"Histopathology",
"Histology",
"Digital Pathology",
"region:us"
] |
[
"image-classification"
] | 2023-10-15T11:53:57Z |
---
license: cc-by-4.0
size_categories:
- 1K<n<10K
task_categories:
- image-classification
tags:
- biology
- Colorectal Pancer
- Histopathology
- Histology
- Digital Pathology
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': ADIPOSE
'1': COMPLEX
'2': DEBRIS
'3': EMPTY
'4': LYMPHO
'5': MUCOSA
'6': STROMA
'7': TUMOR
splits:
- name: train
num_bytes: 329215083
num_examples: 5000
download_size: 293441024
dataset_size: 329215083
---
[](https://doi.org/10.5281/zenodo.53169)
# Collection of textures in colorectal cancer histology
**Homepage**: https://zenodo.org/records/53169 \
**Publication Date**: 2016-05-26 \
**License**: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode) \
**Citation**:
```bibtex
@dataset{kather_2016_53169,
author = {Kather, Jakob Nikolas and Zöllner, Frank Gerrit and Bianconi, Francesco and Melchers, Susanne M and Schad, Lothar R and Gaiser, Timo and Marx, Alexander and Weis, Cleo-Aron},
title = {{Collection of textures in colorectal cancer histology}},
month = jun,
year = 2016,
publisher = {Zenodo}
}
```
## Description
This data set represents a collection of textures in histological images of human colorectal cancer.
It contains 5000 histological images of 150 * 150 px each (74 * 74 µm). Each image belongs to exactly one of eight tissue categories.
## Image format
All images are RGB, 0.495 µm per pixel, digitized with an Aperio ScanScope (Aperio/Leica biosystems), magnification 20x.
Histological samples are fully anonymized images of formalin-fixed paraffin-embedded human colorectal adenocarcinomas (primary tumors) from our pathology archive
(Institute of Pathology, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany).
## Ethics statement
All experiments were approved by the institutional ethics board (medical ethics board II, University Medical Center Mannheim, Heidelberg University, Germany; approval 2015-868R-MA).
The institutional ethics board waived the need for informed consent for this retrospective analysis of anonymized samples.
All experiments were carried out in accordance with the approved guidelines and with the Declaration of Helsinki.
## More information / data usage
For more information, please refer to the following article. Please cite this article when using the data set. \
[Kather JN, Weis CA, Bianconi F, Melchers SM, Schad LR, Gaiser T, Marx A, Zollner F:
Multi-class texture analysis in colorectal cancer histology (2016), Scientific Reports (in press)](https://doi.org/10.1038/srep27988)
## Contact
For questions, please contact: \
Dr. Jakob Nikolas Kather \
http://orcid.org/0000-0002-3730-5348 \
ResearcherID: D-4279-2015
|
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theblackcat102/llava-instruct-mix
|
theblackcat102
| 2023-10-23T10:14:27Z | 39 | 0 |
[
"task_categories:visual-question-answering",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-nc-4.0",
"multimodal",
"vision",
"region:us"
] |
[
"visual-question-answering"
] | 2023-10-23T09:34:45Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: conversations
dtype: string
splits:
- name: train
num_bytes: 46019106088.205
num_examples: 272795
download_size: 20289135489
dataset_size: 46019106088.205
task_categories:
- visual-question-answering
language:
- en
tags:
- multimodal
- vision
size_categories:
- 100K<n<1M
license: cc-by-nc-4.0
---
# LLaVA Instruct Mix
Added OCR and Chart QA dataset into this for more text extraction questions
|
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] |
krasaee/nietzsche
|
krasaee
| 2023-10-26T07:47:27Z | 39 | 0 |
[
"region:us"
] | null | 2023-10-24T16:54:51Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 9929433
num_examples: 60480
download_size: 6288420
dataset_size: 9929433
---
# Dataset Card for "nietzsche"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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CJWeiss/billsum
|
CJWeiss
| 2023-10-26T20:40:16Z | 39 | 0 |
[
"region:us"
] | null | 2023-10-26T20:40:03Z |
---
dataset_info:
features:
- name: text
dtype: string
- name: summary
dtype: string
- name: title
dtype: string
splits:
- name: train
num_bytes: 193223866
num_examples: 16664
- name: test
num_bytes: 38326645
num_examples: 3332
- name: valid
num_bytes: 25911836
num_examples: 2222
download_size: 107645045
dataset_size: 257462347
---
# Dataset Card for "billsum"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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jay401521/domain_balance
|
jay401521
| 2023-10-30T08:26:13Z | 39 | 0 |
[
"region:us"
] | null | 2023-10-30T08:26:09Z |
---
dataset_info:
features:
- name: id
dtype: int64
- name: domain
dtype: int64
- name: label
dtype: int64
- name: rank
dtype: string
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 6252293
num_examples: 72276
download_size: 3387340
dataset_size: 6252293
---
# Dataset Card for "domain_balance"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
anyspeech/mswc_test
|
anyspeech
| 2023-11-02T00:11:11Z | 39 | 0 |
[
"region:us"
] | null | 2023-11-02T00:11:01Z |
---
configs:
- config_name: default
data_files:
- split: query
path: data/query-*
- split: candidate
path: data/candidate-*
dataset_info:
features:
- name: key
dtype: string
- name: phones
dtype: string
- name: audio
struct:
- name: array
sequence: float64
- name: sampling_rate
dtype: int64
splits:
- name: query
num_bytes: 213251381
num_examples: 1665
- name: candidate
num_bytes: 213251405
num_examples: 1665
download_size: 40945132
dataset_size: 426502786
---
# Dataset Card for "mswc_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
Giacinta/djy
|
Giacinta
| 2023-11-03T02:37:10Z | 39 | 0 |
[
"task_categories:text-classification",
"size_categories:n<1K",
"language:zh",
"license:apache-2.0",
"medical",
"region:us"
] |
[
"text-classification"
] | 2023-11-02T03:44:50Z |
---
license: apache-2.0
task_categories:
- text-classification
language:
- zh
tags:
- medical
pretty_name: djy
size_categories:
- n<1K
configs:
- config_name: default
data_files:
- split: train
path: "60_percent_data.csv"
- split: test
path: "part1.csv"
- split: eval
path: "part2.csv"
---
|
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singh-aditya/MACCROBAT_biomedical_ner
|
singh-aditya
| 2023-11-05T02:19:17Z | 39 | 2 |
[
"task_categories:token-classification",
"size_categories:1M<n<10M",
"language:en",
"license:mit",
"biology",
"medical",
"region:us"
] |
[
"token-classification"
] | 2023-11-04T19:57:50Z |
---
license: mit
task_categories:
- token-classification
language:
- en
tags:
- biology
- medical
size_categories:
- 1M<n<10M
field:
- data
---
# MACCROBAT-biomedical-ner
This data is the same data from [here](https://figshare.com/articles/dataset/MACCROBAT2018/9764942), the only difference is that it has been converted into the Huggingface dataset format. So it can be easily loaded and can be used wherever need.
To convert from the orginal format to huggingface dataset format, followed the following steps (**To know in more detail look at the `create_dataset.py` file**):
* Read corresponding `*.txt` and `*.ann` file.
* Used `pandas` to convert the `*.ann` file into dataframe.
* After converting into dataframe, did some processing and converted NER label information into:
```JSON
{
"text": "ner-text",
"label": "ner-label",
"start": 10,
"end": 20
}
```
* Standard labels are converted into `B-Tag` and `I-tag`, where `B`- stands for begning of the tag and `I` - stands for inside the tag.
* Finally the JSON is created and uploaded here.
## Source Data
This ZIP-compressed file contains 200 source documents (in plain text, on sentence per line) and 200 annotation documents (in brat standoff format). Documents are named using PubMed document IDs, e.g. "15939911.txt" contains text from the document "A young man with palpitations and Ebstein's anomaly of the tricuspid valve" by Marcu and Donohue. Text is from PubMed Central full-text documents but has been edited to include only clinical case report details. All annotations were created manually.
"MACCROBAT2020" is the second release of this dataset, following "MACCROBAT2018". The consistency and format of annotations has been improved in the newest version.
## Uses
Use below snippet to load the data properly and it can be used to finetune medical based NER model with some additional processing.
```Python
from datasets import load_dataset
# load the data
medical_ner_data = load_dataset("singh-aditya/MACCROBAT_biomedical_ner")
print(medical_ner_data)
```
```
DatasetDict({
train: Dataset({
features: ['ner_labels', 'tokens', 'full_text', 'ner_info'],
num_rows: 200
})
})
```
<!-- Address questions around how the dataset is intended to be used. -->
## Dataset Structure
```
{
'full_text': "CASE: A 28-year-old previously healthy man presented with a 6-week history of palpitations.\nThe symptoms occurred during rest, 2–3 times per week, lasted up to 30 minutes at a time and were associated with dyspnea.\nExcept for a grade 2/6 holosystolic tricuspid regurgitation murmur (best heard at the left sternal border with inspiratory accentuation), physical examination yielded unremarkable findings.\nAn electrocardiogram (ECG) revealed normal sinus rhythm and a Wolff– Parkinson– White pre-excitation pattern (Fig.1: Top), produced by a right-sided accessory pathway.\nTransthoracic echocardiography demonstrated the presence of Ebstein's anomaly of the tricuspid valve, with apical displacement of the valve and formation of an “atrialized” right ventricle (a functional unit between the right atrium and the inlet [inflow] portion of the right ventricle) (Fig.2).\nThe anterior tricuspid valve leaflet was elongated (Fig.2C, arrow), whereas the septal leaflet was rudimentary (Fig.2C, arrowhead).\nContrast echocardiography using saline revealed a patent foramen ovale with right-to-left shunting and bubbles in the left atrium (Fig.2D).\nThe patient underwent an electrophysiologic study with mapping of the accessory pathway, followed by radiofrequency ablation (interruption of the pathway using the heat generated by electromagnetic waves at the tip of an ablation catheter).\nHis post-ablation ECG showed a prolonged PR interval and an odd “second” QRS complex in leads III, aVF and V2–V4 (Fig.1Bottom), a consequence of abnormal impulse conduction in the “atrialized” right ventricle.\nThe patient reported no recurrence of palpitations at follow-up 6 months after the ablation.\n",
'ner_info': [
{
'text': '28-year-old',
'label': 'AGE',
'start': 8,
'end': 19
},
{'text': 'previously healthy', 'label': 'HISTORY', 'start': 20, 'end': 38}, {'text': 'man', 'label': 'SEX', 'start': 39, 'end': 42}, {'text': 'presented', 'label': 'CLINICAL_EVENT', 'start': 43, 'end': 52}, {'text': '6-week', 'label': 'DURATION', 'start': 60, 'end': 66}, {'text': 'palpitations', 'label': 'SIGN_SYMPTOM', 'start': 78, 'end': 90}, {'text': 'symptoms', 'label': 'COREFERENCE', 'start': 96, 'end': 104}, {'text': 'rest', 'label': 'CLINICAL_EVENT', 'start': 121, 'end': 125}, {'text': '2–3 times per week', 'label': 'FREQUENCY', 'start': 127, 'end': 145}, {'text': 'up to 30 minutes at a time', 'label': 'DETAILED_DESCRIPTION', 'start': 154, 'end': 180}, {'text': 'dyspnea', 'label': 'SIGN_SYMPTOM', 'start': 206, 'end': 213}, {'text': 'grade 2/6', 'label': 'LAB_VALUE', 'start': 228, 'end': 237}, {'text': 'holosystolic', 'label': 'DETAILED_DESCRIPTION', 'start': 238, 'end': 250}, {'text': 'tricuspid', 'label': 'BIOLOGICAL_STRUCTURE', 'start': 251, 'end': 260}, {'text': 'regurgitation murmur', 'label': 'SIGN_SYMPTOM', 'start': 261, 'end': 281}, {'text': 'left sternal border', 'label': 'BIOLOGICAL_STRUCTURE', 'start': 301, 'end': 320}, {'text': 'inspiratory accentuation', 'label': 'DETAILED_DESCRIPTION', 'start': 326, 'end': 350}, {'text': 'physical examination', 'label': 'DIAGNOSTIC_PROCEDURE', 'start': 353, 'end': 373}, {'text': 'unremarkable', 'label': 'LAB_VALUE', 'start': 382, 'end': 394}, {'text': 'electrocardiogram', 'label': 'DIAGNOSTIC_PROCEDURE', 'start': 408, 'end': 425}, {'text': 'ECG', 'label': 'DIAGNOSTIC_PROCEDURE', 'start': 427, 'end': 430}, {'text': 'normal', 'label': 'LAB_VALUE', 'start': 441, 'end': 447}, {'text': 'sinus rhythm', 'label': 'DIAGNOSTIC_PROCEDURE', 'start': 448, 'end': 460}, {'text': 'Wolff– Parkinson– White pre-excitation pattern', 'label': 'SIGN_SYMPTOM', 'start': 467, 'end': 513}, {'text': 'right-sided', 'label': 'DETAILED_DESCRIPTION', 'start': 542, 'end': 553}, {'text': 'accessory pathway', 'label': 'DISEASE_DISORDER', 'start': 554, 'end': 571}, {'text': 'Transthoracic', 'label': 'BIOLOGICAL_STRUCTURE', 'start': 573, 'end': 586}, {'text': 'echocardiography', 'label': 'DIAGNOSTIC_PROCEDURE', 'start': 587, 'end': 603}, {'text': "Ebstein's anomaly", 'label': 'DISEASE_DISORDER', 'start': 633, 'end': 650}, {'text': 'tricuspid valve', 'label': 'BIOLOGICAL_STRUCTURE', 'start': 658, 'end': 673}, {'text': 'apical displacement', 'label': 'SIGN_SYMPTOM', 'start': 680, 'end': 699}, {'text': 'valve', 'label': 'COREFERENCE', 'start': 707, 'end': 712}, {'text': 'atrialized', 'label': 'DISEASE_DISORDER', 'start': 734, 'end': 744}, {'text': 'right ventricle', 'label': 'BIOLOGICAL_STRUCTURE', 'start': 746, 'end': 761}, {'text': 'right atrium', 'label': 'BIOLOGICAL_STRUCTURE', 'start': 793, 'end': 805}, {'text': 'inlet', 'label': 'BIOLOGICAL_STRUCTURE', 'start': 814, 'end': 819}, {'text': 'right ventricle', 'label': 'BIOLOGICAL_STRUCTURE', 'start': 844, 'end': 859}, {'text': 'anterior tricuspid valve leaflet', 'label': 'BIOLOGICAL_STRUCTURE', 'start': 874, 'end': 906}, {'text': 'elongated', 'label': 'SIGN_SYMPTOM', 'start': 911, 'end': 920}, {'text': 'septal leaflet', 'label': 'BIOLOGICAL_STRUCTURE', 'start': 950, 'end': 964}, {'text': 'rudimentary', 'label': 'SIGN_SYMPTOM', 'start': 969, 'end': 980}, {'text': 'Contrast', 'label': 'DETAILED_DESCRIPTION', 'start': 1002, 'end': 1010}, {'text': 'echocardiography', 'label': 'DIAGNOSTIC_PROCEDURE', 'start': 1011, 'end': 1027}, {'text': 'using saline', 'label': 'DETAILED_DESCRIPTION', 'start': 1028, 'end': 1040}, {'text': 'patent foramen ovale', 'label': 'DISEASE_DISORDER', 'start': 1052, 'end': 1072}, {'text': 'right-to-left shunting', 'label': 'SIGN_SYMPTOM', 'start': 1078, 'end': 1100}, {'text': 'bubbles', 'label': 'SIGN_SYMPTOM', 'start': 1105, 'end': 1112}, {'text': 'left atrium', 'label': 'BIOLOGICAL_STRUCTURE', 'start': 1120, 'end': 1131}, {'text': 'electrophysiologic study', 'label': 'DIAGNOSTIC_PROCEDURE', 'start': 1167, 'end': 1191}, {'text': 'mapping', 'label': 'DIAGNOSTIC_PROCEDURE', 'start': 1197, 'end': 1204}, {'text': 'accessory pathway', 'label': 'BIOLOGICAL_STRUCTURE', 'start': 1212, 'end': 1229}, {'text': 'radiofrequency', 'label': 'DETAILED_DESCRIPTION', 'start': 1243, 'end': 1257}, {'text': 'ablation', 'label': 'THERAPEUTIC_PROCEDURE', 'start': 1258, 'end': 1266}, {'text': 'ablation catheter', 'label': 'THERAPEUTIC_PROCEDURE', 'start': 1363, 'end': 1380}, {'text': 'ECG', 'label': 'DIAGNOSTIC_PROCEDURE', 'start': 1401, 'end': 1404}, {'text': 'prolonged', 'label': 'LAB_VALUE', 'start': 1414, 'end': 1423}, {'text': 'PR interval', 'label': 'DIAGNOSTIC_PROCEDURE', 'start': 1424, 'end': 1435}, {'text': 'odd', 'label': 'LAB_VALUE', 'start': 1443, 'end': 1446}, {'text': '“second”', 'label': 'LAB_VALUE', 'start': 1447, 'end': 1455}, {'text': 'QRS complex', 'label': 'DIAGNOSTIC_PROCEDURE', 'start': 1456, 'end': 1467}, {'text': 'leads III, aVF and V2–V4', 'label': 'DETAILED_DESCRIPTION', 'start': 1471, 'end': 1495}, {'text': 'abnormal impulse conduction', 'label': 'DISEASE_DISORDER', 'start': 1528, 'end': 1555}, {'text': 'atrialized', 'label': 'DISEASE_DISORDER', 'start': 1564, 'end': 1574}, {'text': 'right ventricle', 'label': 'BIOLOGICAL_STRUCTURE', 'start': 1576, 'end': 1591}, {'text': 'palpitations', 'label': 'SIGN_SYMPTOM', 'start': 1631, 'end': 1643}, {'text': 'follow-up', 'label': 'CLINICAL_EVENT', 'start': 1647, 'end': 1656}, {'text': '6 months after', 'label': 'DATE', 'start': 1657, 'end': 1671}],
'tokens': ['CASE: A ', '28-year-old', ' ', 'previously healthy', ' ', 'man', ' ', 'presented', ' with a ', '6-week', ' history of ', 'palpitations', '.\nThe ', 'symptoms', ' occurred during ', 'rest', ', ', '2–3 times per week', ', lasted ', 'up to 30 minutes at a time', ' and were associated with ', 'dyspnea', '.\nExcept for a ', 'grade 2/6', ' ', 'holosystolic', ' ', 'tricuspid', ' ', 'regurgitation murmur', ' (best heard at the ', 'left sternal border', ' with ', 'inspiratory accentuation', '), ', 'physical examination', ' yielded ', 'unremarkable', ' findings.\nAn ', 'electrocardiogram', ' (', 'ECG', ') revealed ', 'normal', ' ', 'sinus rhythm', ' and a ', 'Wolff– Parkinson– White pre-excitation pattern', ' (Fig.1: Top), produced by a ', 'right-sided', ' ', 'accessory pathway', '.\n', 'Transthoracic', ' ', 'echocardiography', ' demonstrated the presence of ', "Ebstein's anomaly", ' of the ', 'tricuspid valve', ', with ', 'apical displacement', ' of the ', 'valve', ' and formation of an “', 'atrialized', '” ', 'right ventricle', ' (a functional unit between the ', 'right atrium', ' and the ', 'inlet', ' [inflow] portion of the ', 'right ventricle', ') (Fig.2).\nThe ', 'anterior tricuspid valve leaflet', ' was ', 'elongated', ' (Fig.2C, arrow), whereas the ', 'septal leaflet', ' was ', 'rudimentary', ' (Fig.2C, arrowhead).\n', 'Contrast', ' ', 'echocardiography', ' ', 'using saline', ' revealed a ', 'patent foramen ovale', ' with ', 'right-to-left shunting', ' and ', 'bubbles', ' in the ', 'left atrium', ' (Fig.2D).\nThe patient underwent an ', 'electrophysiologic study', ' with ', 'mapping', ' of the ', 'accessory pathway', ', followed by ', 'radiofrequency', ' ', 'ablation', ' (interruption of the pathway using the heat generated by electromagnetic waves at the tip of an ', 'ablation catheter', ').\nHis post-ablation ', 'ECG', ' showed a ', 'prolonged', ' ', 'PR interval', ' and an ', 'odd', ' ', '“second”', ' ', 'QRS complex', ' in ', 'leads III, aVF and V2–V4', ' (Fig.1Bottom), a consequence of ', 'abnormal impulse conduction', ' in the “', 'atrialized', '” ', 'right ventricle', '.\nThe patient reported no recurrence of ', 'palpitations', ' at ', 'follow-up', ' ', '6 months after', ' the ablation.\n'],
'ner_labels': [0, 5, 0, 39, 0, 65, 0, 13, 0, 32, 0, 69, 0, 18, 0, 13, 0, 35, 0, 22, 0, 69, 0, 42, 0, 22, 0, 12, 0, 69, 0, 12, 0, 22, 0, 24, 0, 42, 0, 24, 0, 24, 0, 42, 0, 24, 0, 69, 0, 22, 0, 26, 0, 12, 0, 24, 0, 26, 0, 12, 0, 69, 0, 18, 0, 26, 0, 12, 0, 12, 0, 12, 0, 12, 0, 12, 0, 69, 0, 12, 0, 69, 0, 22, 0, 24, 0, 22, 0, 26, 0, 69, 0, 69, 0, 12, 0, 24, 0, 24, 0, 12, 0, 22, 0, 75, 0, 75, 0, 24, 0, 42, 0, 24, 0, 42, 0, 42, 0, 24, 0, 22, 0, 26, 0, 26, 0, 12, 0, 69, 0, 13, 0, 19, 0]}
```
## NER-Lables
```Python
NER_lables = [
"O",
"B-ACTIVITY",
"I-ACTIVITY",
"I-ADMINISTRATION",
"B-ADMINISTRATION",
"B-AGE",
"I-AGE",
"I-AREA",
"B-AREA",
"B-BIOLOGICAL_ATTRIBUTE",
"I-BIOLOGICAL_ATTRIBUTE",
"I-BIOLOGICAL_STRUCTURE",
"B-BIOLOGICAL_STRUCTURE",
"B-CLINICAL_EVENT",
"I-CLINICAL_EVENT",
"B-COLOR",
"I-COLOR",
"I-COREFERENCE",
"B-COREFERENCE",
"B-DATE",
"I-DATE",
"I-DETAILED_DESCRIPTION",
"B-DETAILED_DESCRIPTION",
"I-DIAGNOSTIC_PROCEDURE",
"B-DIAGNOSTIC_PROCEDURE",
"I-DISEASE_DISORDER",
"B-DISEASE_DISORDER",
"B-DISTANCE",
"I-DISTANCE",
"B-DOSAGE",
"I-DOSAGE",
"I-DURATION",
"B-DURATION",
"I-FAMILY_HISTORY",
"B-FAMILY_HISTORY",
"B-FREQUENCY",
"I-FREQUENCY",
"I-HEIGHT",
"B-HEIGHT",
"B-HISTORY",
"I-HISTORY",
"I-LAB_VALUE",
"B-LAB_VALUE",
"I-MASS",
"B-MASS",
"I-MEDICATION",
"B-MEDICATION",
"I-NONBIOLOGICAL_LOCATION",
"B-NONBIOLOGICAL_LOCATION",
"I-OCCUPATION",
"B-OCCUPATION",
"B-OTHER_ENTITY",
"I-OTHER_ENTITY",
"B-OTHER_EVENT",
"I-OTHER_EVENT",
"I-OUTCOME",
"B-OUTCOME",
"I-PERSONAL_BACKGROUND",
"B-PERSONAL_BACKGROUND",
"B-QUALITATIVE_CONCEPT",
"I-QUALITATIVE_CONCEPT",
"I-QUANTITATIVE_CONCEPT",
"B-QUANTITATIVE_CONCEPT",
"B-SEVERITY",
"I-SEVERITY",
"B-SEX",
"I-SEX",
"B-SHAPE",
"I-SHAPE",
"B-SIGN_SYMPTOM",
"I-SIGN_SYMPTOM",
"B-SUBJECT",
"I-SUBJECT",
"B-TEXTURE",
"I-TEXTURE",
"B-THERAPEUTIC_PROCEDURE",
"I-THERAPEUTIC_PROCEDURE",
"I-TIME",
"B-TIME",
"B-VOLUME",
"I-VOLUME",
"I-WEIGHT",
"B-WEIGHT",
]
```
**BibTeX:**
```JSON
{
article= Caufield2020,
author = "J. Harry Caufield",
title = "{MACCROBAT}",
year = "2020",
month = "1",
url = "https://figshare.com/articles/dataset/MACCROBAT2018/9764942",
doi = "10.6084/m9.figshare.9764942.v2"
}
```
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ShrinivasSK/hi_te_1
|
ShrinivasSK
| 2023-11-06T19:22:07Z | 39 | 0 |
[
"region:us"
] | null | 2023-11-06T19:21:57Z |
---
dataset_info:
features:
- name: source
dtype: string
- name: target
dtype: string
splits:
- name: train
num_bytes: 5287422.6
num_examples: 18000
- name: test
num_bytes: 587491.4
num_examples: 2000
download_size: 2682481
dataset_size: 5874914.0
---
# Dataset Card for "hi_te_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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epptt/erukaLabels
|
epptt
| 2023-11-16T05:51:08Z | 39 | 0 |
[
"region:us"
] | null | 2023-11-06T23:07:46Z |
---
configs:
- config_name: default
data_files:
- split: train
path: "train.json"
---
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
## Dataset Details
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<!-- Motivation for the creation of this dataset. -->
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#### Annotation process
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<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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|
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HossainRabby/DATA
|
HossainRabby
| 2023-11-27T14:18:33Z | 39 | 0 |
[
"region:us"
] | null | 2023-11-11T15:25:46Z |
---
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 "DATA"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
iqballx/indonesian_news_datasets
|
iqballx
| 2023-11-15T19:32:32Z | 39 | 1 |
[
"task_categories:text-classification",
"task_categories:summarization",
"language:id",
"license:cc-by-nc-4.0",
"region:us"
] |
[
"text-classification",
"summarization"
] | 2023-11-15T18:58:28Z |
---
license: cc-by-nc-4.0
task_categories:
- text-classification
- summarization
language:
- id
---
# Indonesian News Datasets
The dataset compiles information from seven prominent Indonesian news platforms: **Tempo, CNN Indonesia, CNBC Indonesia, Okezone, Suara, Kumparan, and JawaPos**. Each source contributes a diverse range of articles, collectively forming a comprehensive repository of Indonesian news content. The dataset consists of diverse columns: 'id' denotes the unique identifier for each news article, 'title' contains the headline, 'image' refers to the associated visual content, 'url' specifies the source link, 'content' encompasses the full text of the news article, 'embedding' houses the text embeddings extracted using the OpenAI `text-embedding-ada-002 model`, 'created_at' marks the creation timestamp, 'updated_at' signifies the last update timestamp, and 'summary' encapsulates the concise article summary generated via the ChatGPT API. These columns collectively represent a comprehensive set of information for each news article, facilitating various natural language processing tasks and analysis while providing enriched contextual details for effective exploration and modeling.
|
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atmallen/qm_alice_hard_4_mixture_1.0e
|
atmallen
| 2023-11-16T18:18:16Z | 39 | 0 |
[
"region:us"
] | null | 2023-11-16T03:33:37Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: alice_label
dtype: bool
- name: bob_label
dtype: bool
- name: difficulty
dtype: int64
- name: statement
dtype: string
- name: choices
sequence: string
- name: character
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
splits:
- name: train
num_bytes: 4578170.5
num_examples: 37091
- name: validation
num_bytes: 487083.5
num_examples: 3969
- name: test
num_bytes: 477119.5
num_examples: 3926
download_size: 1548358
dataset_size: 5542373.5
---
# Dataset Card for "qm_alice_hard_4_mixture_1.0e"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
luizlzg/prefeitura_rj_v2
|
luizlzg
| 2023-11-29T14:56:12Z | 39 | 0 |
[
"region:us"
] | null | 2023-11-20T00:50:52Z |
---
configs:
- config_name: default
data_files:
- split: train
path: prefeitura_treino*
- split: test
path: prefeitura_teste*
- split: validation
path: prefeitura_validacao*
---
|
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idning/ffhq128-caption
|
idning
| 2023-11-21T04:23:13Z | 39 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-21T01:41:34Z |
---
license: mit
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 2053430676.0
num_examples: 70000
download_size: 2051404020
dataset_size: 2053430676.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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] |
tyzhu/find_marker_both_sent_train_400_eval_40
|
tyzhu
| 2023-11-22T12:04:29Z | 39 | 0 |
[
"region:us"
] | null | 2023-11-21T15:10:34Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: title
dtype: string
- name: context
dtype: string
splits:
- name: train
num_bytes: 2859451
num_examples: 2434
- name: validation
num_bytes: 220570
num_examples: 200
download_size: 589041
dataset_size: 3080021
---
# Dataset Card for "find_marker_both_sent_train_400_eval_40"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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nlewins/cebuano-dictionary-examples
|
nlewins
| 2023-11-28T23:54:50Z | 39 | 0 |
[
"region:us"
] | null | 2023-11-24T13:38:04Z |
---
dataset_info:
features:
- name: ceb
dtype: string
- name: en
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 3182056
num_examples: 32629
download_size: 2022798
dataset_size: 3182056
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "cebuano-dictionary-examples"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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maywell/why_no_one_do_dpo_on_yi
|
maywell
| 2023-11-28T14:17:26Z | 39 | 1 |
[
"region:us"
] | null | 2023-11-28T14:17:01Z |
---
dataset_info:
features:
- name: prompt
dtype: string
- name: prompt_id
dtype: string
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: score_chosen
dtype: float64
- name: score_rejected
dtype: float64
splits:
- name: train_prefs
num_bytes: 315582762.25039536
num_examples: 49224
download_size: 174974205
dataset_size: 315582762.25039536
configs:
- config_name: default
data_files:
- split: train_prefs
path: data/train_prefs-*
---
# Dataset Card for "why_no_one_do_dpo_on_yi"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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nickmuchi/financial-classification
|
nickmuchi
| 2023-01-27T23:44:03Z | 38 | 7 |
[
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"size_categories:1K<n<10K",
"language:en",
"finance",
"region:us"
] |
[
"text-classification"
] | 2022-03-02T23:29:22Z |
---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
task_categories:
- text-classification
task_ids:
- multi-class-classification
- sentiment-classification
train-eval-index:
- config: sentences_50agree
- task: text-classification
- task_ids: multi_class_classification
- splits:
eval_split: train
- col_mapping:
sentence: text
label: target
size_categories:
- 1K<n<10K
tags:
- finance
---
## Dataset Creation
This [dataset](https://huggingface.co/datasets/nickmuchi/financial-classification) combines financial phrasebank dataset and a financial text dataset from [Kaggle](https://www.kaggle.com/datasets/percyzheng/sentiment-classification-selflabel-dataset).
Given the financial phrasebank dataset does not have a validation split, I thought this might help to validate finance models and also capture the impact of COVID on financial earnings with the more recent Kaggle dataset.
|
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] |
projecte-aina/parlament_parla
|
projecte-aina
| 2023-09-13T12:38:52Z | 38 | 1 |
[
"task_categories:automatic-speech-recognition",
"task_categories:text-generation",
"task_ids:language-modeling",
"task_ids:speaker-identification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:ca",
"license:cc-by-4.0",
"region:us"
] |
[
"automatic-speech-recognition",
"text-generation"
] | 2022-03-02T23:29:22Z |
---
annotations_creators:
- found
language_creators:
- found
language:
- ca
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
clean:
- 10K<n<100K
other:
- 100K<n<1M
source_datasets:
- original
task_categories:
- automatic-speech-recognition
- text-generation
task_ids:
- language-modeling
- speaker-identification
pretty_name: ParlamentParla
---
# Dataset Card for ParlamentParla
## 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://zenodo.org/record/5541827
- **Repository:** https://github.com/CollectivaT-dev/ParlamentParla
- **Paper:** ParlamentParla: [A Speech Corpus of Catalan Parliamentary Sessions.](http://www.lrec-conf.org/proceedings/lrec2022/workshops/ParlaCLARINIII/2022.parlaclariniii-1.0.pdf#page=135)
- **Point of Contact:** [Baybars Kulebi](mailto:[email protected])
### Dataset Summary
This is the ParlamentParla speech corpus for Catalan prepared by Col·lectivaT. The audio segments were extracted from recordings the Catalan Parliament (Parlament de Catalunya) plenary sessions, which took place between 2007/07/11 - 2018/07/17. We aligned the transcriptions with the recordings and extracted the corpus. The content belongs to the Catalan Parliament and the data is released conforming their terms of use.
Preparation of this corpus was partly supported by the Department of Culture of the Catalan autonomous government, and the v2.0 was supported by the Barcelona Supercomputing Center, within the framework of Projecte AINA of the Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya.
As of v2.0 the corpus is separated into 211 hours of clean and 400 hours of other quality segments. Furthermore, each speech segment is tagged with its speaker and each speaker with their gender. The statistics are detailed in the readme file.
### Supported Tasks and Leaderboards
The dataset can be used for:
- Language Modeling.
- Automatic Speech Recognition (ASR) transcribes utterances into words.
- Speaker Identification (SI) classifies each utterance for its speaker identity as a multi-class classification, where speakers are in the same predefined set for both training and testing.
### Languages
The dataset is in Catalan (`ca-ES`).
## Dataset Structure
### Data Instances
```
{
'path': 'clean_train/c/c/ccca4790a55aba3e6bcf_63.88_74.06.wav'
'audio': {
'path': 'clean_train/c/c/ccca4790a55aba3e6bcf_63.88_74.06.wav',
'array': array([-6.10351562e-05, -6.10351562e-05, -1.22070312e-04, ...,
-1.22070312e-04, 0.00000000e+00, -3.05175781e-05]),
'sampling_rate': 16000
},
'speaker_id': 167,
'sentence': "alguns d'ells avui aquí presents un agraïment a aquells que mantenen viva la memòria aquest acte de reparació i dignitat és",
'gender': 0,
'duration': 10.18
}
```
### Data Fields
- `path` (str): The path to the audio file.
- `audio` (dict): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling
rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and
resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might
take a significant amount of time. Thus, it is important to first query the sample index before the `"audio"` column,
*i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
- `speaker_id` (int): The speaker ID.
- `sentence` (str): The sentence the user was prompted to speak.
- `gender` (ClassLabel): The gender of the speaker (0: 'F', 1: 'M').
- `duration` (float): Duration of the speech.
### Data Splits
The dataset is split in: "train", "validation" and "test".
## Dataset Creation
The dataset is created by aligning the parliamentary session transcripts
and the audiovisual content. For more detailed information please consult
this [paper](http://www.lrec-conf.org/proceedings/lrec2022/workshops/ParlaCLARINIII/2022.parlaclariniii-1.0.pdf#page=135).
### Curation Rationale
We created this corpus to contribute to the development of language models in Catalan, a low-resource language.
### Source Data
#### Initial Data Collection and Normalization
The audio segments were extracted from recordings the Catalan Parliament
(Parlament de Catalunya) plenary sessions, which took place between 2007/07/11 -
2018/07/17. The cleaning procedures are in the archived repository [Long Audio
Aligner](https://github.com/gullabi/long-audio-aligner)
#### Who are the source language producers?
The parliamentary members of the legislatures between 2007/07/11 -
2018/07/17
### Annotations
The dataset is unannotated.
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
The initial content is publicly available furthermore, the identities of
the parliamentary members are anonymized.
## Considerations for Using the Data
### Social Impact of Dataset
We hope this corpus contributes to the development of language models in
Catalan, a low-resource language.
### Discussion of Biases
This dataset has a gender bias, however since the speakers are tagged according to
their genders, creating a balanced subcorpus is possible.
| Subcorpus | Gender | Duration (h) |
|-------------|----------|------------|
| other_test | F | 2.516 |
| other_dev | F | 2.701 |
| other_train | F | 109.68 |
| other_test | M | 2.631 |
| other_dev | M | 2.513 |
| other_train | M | 280.196 |
|*other total*| | 400.239 |
| clean_test | F | 2.707 |
| clean_dev | F | 2.576 |
| clean_train | F | 77.905 |
| clean_test | M | 2.516 |
| clean_dev | M | 2.614 |
| clean_train | M | 123.162 |
|*clean total*| | 211.48 |
|*Total* | | 611.719 |
### Other Known Limitations
The text corpus belongs to the domain of Catalan politics
## Additional Information
### Dataset Curators
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center ([email protected])
This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
### Licensing Information
[Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/).
### Citation Information
```
@dataset{kulebi_baybars_2021_5541827,
author = {Külebi, Baybars},
title = {{ParlamentParla - Speech corpus of Catalan
Parliamentary sessions}},
month = oct,
year = 2021,
publisher = {Zenodo},
version = {v2.0},
doi = {10.5281/zenodo.5541827},
url = {https://doi.org/10.5281/zenodo.5541827}
}
```
For the paper:
```
@inproceedings{kulebi2022parlamentparla,
title={ParlamentParla: A Speech Corpus of Catalan Parliamentary Sessions},
author={K{\"u}lebi, Baybars and Armentano-Oller, Carme and Rodr{\'\i}guez-Penagos, Carlos and Villegas, Marta},
booktitle={Workshop on Creating, Enriching and Using Parliamentary Corpora},
volume={125},
number={130},
pages={125},
year={2022}
}
```
### Contributions
Thanks to [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.
|
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valurank/PoliticalBias_AllSides_Txt
|
valurank
| 2022-10-21T13:37:02Z | 38 | 2 |
[
"multilinguality:monolingual",
"language:en",
"license:other",
"region:us"
] |
[
"classification"
] | 2022-03-02T23:29:22Z |
---
license:
- other
language:
- en
multilinguality:
- monolingual
task_categories:
- classification
task_ids:
- classification
---
# Dataset Card for news-12factor
## Table of Contents
- [Dataset Description](#dataset-description)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Source Data](#source-data)
- [Annotations](#annotations)
## Dataset Description
~20k articles labeled left, right, or center by the editors of allsides.com.
## Languages
The text in the dataset is in English
## Dataset Structure
3 folders, with many text files in each. Each text file represent the body text of one article.
## Source Data
URL data was scraped using https://github.com/mozilla/readability
## Annotations
Articles were manually annotated by news editors who were attempting to select representative articles from the left, right and center of each article topic. In other words, the dataset should generally be balanced - the left/right/center articles cover the same set of topics, and have roughly the same amount of articles in each.
|
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McGill-NLP/feedbackQA
|
McGill-NLP
| 2023-06-14T17:27:23Z | 38 | 5 |
[
"license:apache-2.0",
"arxiv:2204.03025",
"region:us"
] | null | 2022-03-10T23:50:07Z |
---
license: apache-2.0
---
# Dataset Card for FeedbackQA
[📄 Read](https://arxiv.org/pdf/2204.03025.pdf)<br>
[💾 Code](https://github.com/McGill-NLP/feedbackqa)<br>
[🔗 Webpage](https://mcgill-nlp.github.io/feedbackqa/)<br>
[💻 Demo](http://206.12.100.48:8080/)<br>
[🤗 Huggingface Dataset](https://huggingface.co/datasets/McGill-NLP/feedbackQA)<br>
[💬 Discussions](https://github.com/McGill-NLP/feedbackqa/discussions)
## Dataset Description
- **Homepage: https://mcgill-nlp.github.io/feedbackqa-data/**
- **Repository: https://github.com/McGill-NLP/feedbackqa-data/**
- **Paper:**
- **Leaderboard:**
- **Tasks: Question Answering**
### Dataset Summary
FeedbackQA is a retrieval-based QA dataset that contains interactive feedback from users.
It has two parts: the first part contains a conventional RQA dataset,
whilst this repo contains the second part, which contains feedback(ratings and natural language explanations) for QA pairs.
### Languages
English
## Dataset Creation
For each question-answer pair, we collected multiple feedback, each of which consists of a rating, selected
from excellent, good, could be improved, bad, and a natural language explanation
elaborating on the strengths and/or weaknesses of the answer.
#### Initial Data Collection and Normalization
We scraped Covid-19-related content from official websites.
### Annotations
#### Who are the annotators?
Crowd-workers
### Licensing Information
Apache 2.0
### Contributions
[McGill-NLP](https://github.com/McGill-NLP)
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] |
nateraw/imagenet-sketch
|
nateraw
| 2022-05-08T05:41:33Z | 38 | 0 |
[
"license:mit",
"region:us"
] | null | 2022-05-08T05:32:17Z |
---
license: mit
---
|
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pile-of-law/eoir_privacy
|
pile-of-law
| 2022-07-07T08:44:32Z | 38 | 9 |
[
"task_categories:text-classification",
"language_creators:found",
"multilinguality:monolingual",
"language:en",
"license:cc-by-nc-sa-4.0",
"arxiv:2207.00220",
"region:us"
] |
[
"text-classification"
] | 2022-05-08T22:30:20Z |
---
language_creators:
- found
language:
- en
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
pretty_name: eoir_privacy
source_datasets: []
task_categories:
- text-classification
viewer: false
---
# Dataset Card for eoir_privacy
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
This dataset mimics privacy standards for EOIR decisions. It is meant to help learn contextual data sanitization rules to anonymize potentially sensitive contexts in crawled language data.
### Languages
English
## Dataset Structure
### Data Instances
{
"text" : masked paragraph,
"label" : whether to use a pseudonym in filling masks
}
### Data Splits
train 75%, validation 25%
## Dataset Creation
### Curation Rationale
This dataset mimics privacy standards for EOIR decisions. It is meant to help learn contextual data sanitization rules to anonymize potentially sensitive contexts in crawled language data.
### Source Data
#### Initial Data Collection and Normalization
We scrape EOIR. We then filter at the paragraph level and replace any references to respondent, applicant, or names with [MASK] tokens. We then determine if the case used a pseudonym or not.
#### Who are the source language producers?
U.S. Executive Office for Immigration Review
### Annotations
#### Annotation process
Annotations (i.e., pseudonymity decisions) were made by the EOIR court. We use regex to identify if a pseudonym was used to refer to the applicant/respondent.
#### Who are the annotators?
EOIR judges.
### Personal and Sensitive Information
There may be sensitive contexts involved, the courts already make a determination as to data filtering of sensitive data, but nonetheless there may be sensitive topics discussed.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is meant to learn contextual privacy rules to help filter private/sensitive data, but itself encodes biases of the courts from which the data came. We suggest that people look beyond this data for learning more contextual privacy rules.
### Discussion of Biases
Data may be biased due to its origin in U.S. immigration courts.
### Licensing Information
CC-BY-NC
### Citation Information
```
@misc{hendersonkrass2022pileoflaw,
url = {https://arxiv.org/abs/2207.00220},
author = {Henderson, Peter and Krass, Mark S. and Zheng, Lucia and Guha, Neel and Manning, Christopher D. and Jurafsky, Dan and Ho, Daniel E.},
title = {Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset},
publisher = {arXiv},
year = {2022}
}
```
|
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vadis/sv-ident
|
vadis
| 2022-11-07T20:51:06Z | 38 | 1 |
[
"task_categories:text-classification",
"task_ids:multi-label-classification",
"task_ids:semantic-similarity-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"language:de",
"license:mit",
"region:us"
] |
[
"text-classification"
] | 2022-06-17T08:33:04Z |
---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
- de
license:
- mit
multilinguality:
- multilingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
- semantic-similarity-classification
pretty_name: SV-Ident
paperswithcode_id: sv-ident
---
# Dataset Card for SV-Ident
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://vadis-project.github.io/sv-ident-sdp2022/
- **Repository:** https://github.com/vadis-project/sv-ident
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [email protected]
### Dataset Summary
SV-Ident comprises 4,248 sentences from social science publications in English and German. The data is the official data for the Shared Task: “Survey Variable Identification in Social Science Publications” (SV-Ident) 2022. Visit the homepage to find out more details about the shared task.
### Supported Tasks and Leaderboards
The dataset supports:
- **Variable Detection**: identifying whether a sentence contains a variable mention or not.
- **Variable Disambiguation**: identifying which variable from a given vocabulary is mentioned in a sentence. **NOTE**: for this task, you will need to also download the variable metadata from [here](https://bit.ly/3Nuvqdu).
### Languages
The text in the dataset is in English and German, as written by researchers. The domain of the texts is scientific publications in the social sciences.
## Dataset Structure
### Data Instances
```
{
"sentence": "Our point, however, is that so long as downward (favorable comparisons overwhelm the potential for unfavorable comparisons, system justification should be a likely outcome amongst the disadvantaged.",
"is_variable": 1,
"variable": ["exploredata-ZA5400_VarV66", "exploredata-ZA5400_VarV53"],
"research_data": ["ZA5400"],
"doc_id": "73106",
"uuid": "b9fbb80f-3492-4b42-b9d5-0254cc33ac10",
"lang": "en",
}
```
### Data Fields
The following data fields are provided for documents:
```
`sentence`: Textual instance, which may contain a variable mention.<br />
`is_variable`: Label, whether the textual instance contains a variable mention (1) or not (0). This column can be used for Task 1 (Variable Detection).<br />
`variable`: Variables (separated by a comma ";") that are mentioned in the textual instance. This column can be used for Task 2 (Variable Disambiguation). Variables with the "unk" tag could not be mapped to a unique variable.<br />
`research_data`: Research data IDs (separated by a ";") that are relevant for each instance (and in general for each "doc_id").<br />
`doc_id`: ID of the source document. Each document is written in one language (either English or German).<br />
`uuid`: Unique ID of the instance in uuid4 format.<br />
`lang`: Language of the sentence.
```
The language for each document can be found in the document-language mapping file [here](https://github.com/vadis-project/sv-ident/blob/main/data/train/document_languages.json), which maps `doc_id` to a language code (`en`, `de`). The variables metadata (i.e., the vocabulary) can be downloaded from this [link](https://bit.ly/3Nuvqdu). Note, that each `research_data` contains hundreds of variables (these can be understood as the corpus of documents to choose the most relevant from). If the variable has an "unk" tag, it means that the sentence contains a variable that has not been disambiguated. Such sentences could be used for Task 1 and filtered out for Task 2. The metadata file has the following format:
```
{
"research_data_id_1": {
"variable_id_1": VARIABLE_METADATA,
...
"variable_id_n": VARIABLE_METADATA,
},
...
"research_data_id_n": {...},
}
```
Each variable may contain all (or some) of the following values:
```
study_title: The title of the research data study.
variable_label: The label of the variable.
variable_name: The name of the variable.
question_text: The question of the variable in the original language.
question_text_en: The question of the variable in English.
sub_question: The sub-question of the variable.
item_categories: The item categories of the variable.
answer_categories: The answers of the variable.
topic: The topics of the variable in the original language.
topic_en: The topics of the variable in English.
```
### Data Splits
| Split | Number of sentences |
| ------------------- | ------------------------------------ |
| Train | 3,823 |
| Validation | 425 |
## Dataset Creation
### Curation Rationale
The dataset was curated by the VADIS project (https://vadis-project.github.io/).
The documents were annotated by two expert annotators.
### Source Data
#### Initial Data Collection and Normalization
The original data are available at GESIS (https://www.gesis.org/home) in an unprocessed format.
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
The documents were annotated by two expert annotators.
### Personal and Sensitive Information
The dataset does not include personal or sensitive information.
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
VADIS project (https://vadis-project.github.io/)
### Licensing Information
All documents originate from the Social Science Open Access Repository (SSOAR) and are licensed accordingly. The original document URLs are provided in [document_urls.json](https://github.com/vadis-project/sv-ident/blob/main/data/train/document_urlsjson). For more information on licensing, please refer to the terms and conditions on the [SSAOR Grant of Licenses page](https://www.gesis.org/en/ssoar/home/information/grant-of-licences).
### Citation Information
```
@inproceedings{tsereteli-etal-2022-overview,
title = "Overview of the {SV}-Ident 2022 Shared Task on Survey Variable Identification in Social Science Publications",
author = "Tsereteli, Tornike and
Kartal, Yavuz Selim and
Ponzetto, Simone Paolo and
Zielinski, Andrea and
Eckert, Kai and
Mayr, Philipp",
booktitle = "Proceedings of the Third Workshop on Scholarly Document Processing",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.sdp-1.29",
pages = "229--246",
abstract = "In this paper, we provide an overview of the SV-Ident shared task as part of the 3rd Workshop on Scholarly Document Processing (SDP) at COLING 2022. In the shared task, participants were provided with a sentence and a vocabulary of variables, and asked to identify which variables, if any, are mentioned in individual sentences from scholarly documents in full text. Two teams made a total of 9 submissions to the shared task leaderboard. While none of the teams improve on the baseline systems, we still draw insights from their submissions. Furthermore, we provide a detailed evaluation. Data and baselines for our shared task are freely available at \url{https://github.com/vadis-project/sv-ident}.",
}
```
### Contributions
[Needs More Information]
|
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rjac/all-the-news-2-1-Component-one
|
rjac
| 2022-07-28T21:01:39Z | 38 | 0 |
[
"annotations_creators:Andrew Thompson",
"annotations_creators:components.one",
"language:en",
"region:us"
] | null | 2022-06-19T22:35:47Z |
---
annotations_creators:
- Andrew Thompson
- components.one
language:
- en
---
# 2.7 million news articles and essays
## Table of Contents
- [Dataset Description](#dataset-description)
## Dataset Description
2.7 million news articles and essays from 27 American publications. Includes date, title, publication, article text, publication name, year, month, and URL (for some). Articles mostly span from 2016 to early 2020.
- Type: CSV
- Size: 3.4 GB compressed, 8.8 GB uncompressed
- Created by: Andrew Thompson
- Date added: 4/3/2020
- Date modified: 4/3/2020
- source: [Component one Datasets 2.7 Millions](https://components.one/datasets/all-the-news-2-news-articles-dataset)
- Date of Download and processed: 19/6/2022
- Header was modified with the respective columns
- Row number 2,324,812 was removed
|
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] |
SocialGrep/the-reddit-climate-change-dataset
|
SocialGrep
| 2022-09-08T18:24:20Z | 38 | 0 |
[
"annotations_creators:lexyr",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] | null | 2022-09-08T18:24:14Z |
---
annotations_creators:
- lexyr
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
paperswithcode_id: null
---
# Dataset Card for the-reddit-climate-change-dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
## Dataset Description
- **Homepage:** [https://socialgrep.com/datasets](https://socialgrep.com/datasets/the-reddit-climate-change-dataset?utm_source=huggingface&utm_medium=link&utm_campaign=theredditclimatechangedataset)
- **Reddit downloader used:** [https://socialgrep.com/exports](https://socialgrep.com/exports?utm_source=huggingface&utm_medium=link&utm_campaign=theredditclimatechangedataset)
- **Point of Contact:** [Website](https://socialgrep.com/contact?utm_source=huggingface&utm_medium=link&utm_campaign=theredditclimatechangedataset)
### Dataset Summary
All the mentions of climate change on Reddit before Sep 1 2022.
### Languages
Mainly English.
## Dataset Structure
### Data Instances
A data point is a post or a comment. Due to the separate nature of the two, those exist in two different files - even though many fields are shared.
### Data Fields
- 'type': the type of the data point. Can be 'post' or 'comment'.
- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.
- 'subreddit.id': the base-36 Reddit ID of the data point's host subreddit. Unique.
- 'subreddit.name': the human-readable name of the data point's host subreddit.
- 'subreddit.nsfw': a boolean marking the data point's host subreddit as NSFW or not.
- 'created_utc': a UTC timestamp for the data point.
- 'permalink': a reference link to the data point on Reddit.
- 'score': score of the data point on Reddit.
- 'domain': (Post only) the domain of the data point's link.
- 'url': (Post only) the destination of the data point's link, if any.
- 'selftext': (Post only) the self-text of the data point, if any.
- 'title': (Post only) the title of the post data point.
- 'body': (Comment only) the body of the comment data point.
- 'sentiment': (Comment only) the result of an in-house sentiment analysis pipeline. Used for exploratory analysis.
## Additional Information
### Licensing Information
CC-BY v4.0
|
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csebuetnlp/BanglaParaphrase
|
csebuetnlp
| 2022-11-14T15:39:43Z | 38 | 3 |
[
"task_categories:text2text-generation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100k<n<1M",
"source_datasets:original",
"language:bn",
"license:cc-by-nc-sa-4.0",
"conditional-text-generation",
"paraphrase-generation",
"arxiv:2210.05109",
"region:us"
] |
[
"text2text-generation"
] | 2022-10-13T16:06:21Z |
---
annotations_creators:
- found
language_creators:
- found
language:
- bn
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 100k<n<1M
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
pretty_name: BanglaParaphrase
tags:
- conditional-text-generation
- paraphrase-generation
---
# Dataset Card for "BanglaParaphrase"
## Table of Contents
- [Dataset Card Creation Guide](#dataset-card-creation-guide)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [https://github.com/csebuetnlp/banglaparaphrase](https://github.com/csebuetnlp/banglaparaphrase)
- **Paper:** [BanglaParaphrase: A High-Quality Bangla Paraphrase Dataset](https://arxiv.org/abs/2210.05109)
- **Point of Contact:** [Najrin Sultana](mailto:[email protected])
### Dataset Summary
We present BanglaParaphrase, a high quality synthetic Bangla paraphrase dataset containing about 466k paraphrase pairs.
The paraphrases ensures high quality by being semantically coherent and syntactically diverse.
### Supported Tasks and Leaderboards
[More information needed](https://github.com/csebuetnlp/banglaparaphrase)
### Languages
- `bengali`
## Loading the dataset
```python
from datasets import load_dataset
from datasets import load_dataset
ds = load_dataset("csebuetnlp/BanglaParaphrase")
```
## Dataset Structure
### Data Instances
One example from the `train` part of the dataset is given below in JSON format.
```
{
"source": "বেশিরভাগ সময় প্রকৃতির দয়ার ওপরেই বেঁচে থাকতেন উপজাতিরা।",
"target": "বেশিরভাগ সময়ই উপজাতিরা প্রকৃতির দয়ার উপর নির্ভরশীল ছিল।"
}
```
### Data Fields
- 'source': A string representing the source sentence.
- 'target': A string representing the target sentence.
### Data Splits
Dataset with train-dev-test example counts are given below:
Language | ISO 639-1 Code | Train | Validation | Test |
-------------- | ---------------- | ------- | ----- | ------ |
Bengali | bn | 419, 967 | 233, 31 | 233, 32 |
## Dataset Creation
### Curation Rationale
[More information needed](https://github.com/csebuetnlp/banglaparaphrase)
### Source Data
[Roar Bangla](https://roar.media/bangla)
#### Initial Data Collection and Normalization
[Detailed in the paper](https://arxiv.org/abs/2210.05109)
#### Who are the source language producers?
[Detailed in the paper](https://arxiv.org/abs/2210.05109)
### Annotations
[Detailed in the paper](https://arxiv.org/abs/2210.05109)
#### Annotation process
[Detailed in the paper](https://arxiv.org/abs/2210.05109)
#### Who are the annotators?
[Detailed in the paper](https://arxiv.org/abs/2210.05109)
### Personal and Sensitive Information
[More information needed](https://github.com/csebuetnlp/banglaparaphrase)
## Considerations for Using the Data
### Social Impact of Dataset
[More information needed](https://github.com/csebuetnlp/banglaparaphrase)
### Discussion of Biases
[More information needed](https://github.com/csebuetnlp/banglaparaphrase)
### Other Known Limitations
[More information needed](https://github.com/csebuetnlp/banglaparaphrase)
## Additional Information
### Dataset Curators
[More information needed](https://github.com/csebuetnlp/banglaparaphrase)
### Licensing Information
Contents of this repository are restricted to only non-commercial research purposes under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright of the dataset contents belongs to the original copyright holders.
### Citation Information
```
@article{akil2022banglaparaphrase,
title={BanglaParaphrase: A High-Quality Bangla Paraphrase Dataset},
author={Akil, Ajwad and Sultana, Najrin and Bhattacharjee, Abhik and Shahriyar, Rifat},
journal={arXiv preprint arXiv:2210.05109},
year={2022}
}
```
### Contributions
|
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] |
esb/datasets
|
esb
| 2023-01-16T17:51:39Z | 38 | 7 |
[
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:1M<n<10M",
"source_datasets:original",
"source_datasets:extended|librispeech_asr",
"source_datasets:extended|common_voice",
"language:en",
"license:cc-by-4.0",
"license:apache-2.0",
"license:cc0-1.0",
"license:cc-by-nc-3.0",
"license:other",
"asr",
"benchmark",
"speech",
"esb",
"region:us"
] |
[
"automatic-speech-recognition"
] | 2022-10-24T10:53:50Z |
---
annotations_creators:
- expert-generated
- crowdsourced
- machine-generated
language:
- en
language_creators:
- crowdsourced
- expert-generated
license:
- cc-by-4.0
- apache-2.0
- cc0-1.0
- cc-by-nc-3.0
- other
multilinguality:
- monolingual
pretty_name: datasets
size_categories:
- 100K<n<1M
- 1M<n<10M
source_datasets:
- original
- extended|librispeech_asr
- extended|common_voice
tags:
- asr
- benchmark
- speech
- esb
task_categories:
- automatic-speech-recognition
extra_gated_prompt: |-
Three of the ESB datasets have specific terms of usage that must be agreed to before using the data.
To do so, fill in the access forms on the specific datasets' pages:
* Common Voice: https://huggingface.co/datasets/mozilla-foundation/common_voice_9_0
* GigaSpeech: https://huggingface.co/datasets/speechcolab/gigaspeech
* SPGISpeech: https://huggingface.co/datasets/kensho/spgispeech
extra_gated_fields:
I hereby confirm that I have registered on the original Common Voice page and agree to not attempt to determine the identity of speakers in the Common Voice dataset: checkbox
I hereby confirm that I have accepted the terms of usages on GigaSpeech page: checkbox
I hereby confirm that I have accepted the terms of usages on SPGISpeech page: checkbox
---
All eight of datasets in ESB can be downloaded and prepared in just a single line of code through the Hugging Face Datasets library:
```python
from datasets import load_dataset
librispeech = load_dataset("esb/datasets", "librispeech", split="train")
```
- `"esb/datasets"`: the repository namespace. This is fixed for all ESB datasets.
- `"librispeech"`: the dataset name. This can be changed to any of any one of the eight datasets in ESB to download that dataset.
- `split="train"`: the split. Set this to one of train/validation/test to generate a specific split. Omit the `split` argument to generate all splits for a dataset.
The datasets are full prepared, such that the audio and transcription files can be used directly in training/evaluation scripts.
## Dataset Information
A data point can be accessed by indexing the dataset object loaded through `load_dataset`:
```python
print(librispeech[0])
```
A typical data point comprises the path to the audio file and its transcription. Also included is information of the dataset from which the sample derives and a unique identifier name:
```python
{
'dataset': 'librispeech',
'audio': {'path': '/home/sanchit-gandhi/.cache/huggingface/datasets/downloads/extracted/d2da1969fe9e7d06661b5dc370cf2e3c119a14c35950045bcb76243b264e4f01/374-180298-0000.flac',
'array': array([ 7.01904297e-04, 7.32421875e-04, 7.32421875e-04, ...,
-2.74658203e-04, -1.83105469e-04, -3.05175781e-05]),
'sampling_rate': 16000},
'text': 'chapter sixteen i might have told you of the beginning of this liaison in a few lines but i wanted you to see every step by which we came i to agree to whatever marguerite wished',
'id': '374-180298-0000'
}
```
### Data Fields
- `dataset`: name of the ESB dataset from which the sample is taken.
- `audio`: a dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.
- `text`: the transcription of the audio file.
- `id`: unique id of the data sample.
### Data Preparation
#### Audio
The audio for all ESB datasets is segmented into sample lengths suitable for training ASR systems. The Hugging Face datasets library decodes audio files on the fly, reading the segments and converting them to a Python arrays. Consequently, no further preparation of the audio is required to be used in training/evaluation scripts.
Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, i.e. `dataset[0]["audio"]` should always be preferred over `dataset["audio"][0]`.
#### Transcriptions
The transcriptions corresponding to each audio file are provided in their 'error corrected' format. No transcription pre-processing is applied to the text, only necessary 'error correction' steps such as removing junk tokens (_<unk>_) or converting symbolic punctuation to spelled out form (_<comma>_ to _,_). As such, no further preparation of the transcriptions is required to be used in training/evaluation scripts.
Transcriptions are provided for training and validation splits. The transcriptions are **not** provided for the test splits. ESB requires you to generate predictions for the test sets and upload them to https://huggingface.co/spaces/esb/leaderboard for scoring.
### Access
All eight of the datasets in ESB are accessible and licensing is freely available. Three of the ESB datasets have specific terms of usage that must be agreed to before using the data. To do so, fill in the access forms on the specific datasets' pages:
* Common Voice: https://huggingface.co/datasets/mozilla-foundation/common_voice_9_0
* GigaSpeech: https://huggingface.co/datasets/speechcolab/gigaspeech
* SPGISpeech: https://huggingface.co/datasets/kensho/spgispeech
### Diagnostic Dataset
ESB contains a small, 8h diagnostic dataset of in-domain validation data with newly annotated transcriptions. The audio data is sampled from each of the ESB validation sets, giving a range of different domains and speaking styles. The transcriptions are annotated according to a consistent style guide with two formats: normalised and un-normalised. The dataset is structured in the same way as the ESB dataset, by grouping audio-transcription samples according to the dataset from which they were taken. We encourage participants to use this dataset when evaluating their systems to quickly assess performance on a range of different speech recognition conditions. For more information, visit: [esb/diagnostic-dataset](https://huggingface.co/datasets/esb/diagnostic-dataset).
## Summary of ESB Datasets
| Dataset | Domain | Speaking Style | Train (h) | Dev (h) | Test (h) | Transcriptions | License |
|--------------|-----------------------------|-----------------------|-----------|---------|----------|--------------------|-----------------|
| LibriSpeech | Audiobook | Narrated | 960 | 11 | 11 | Normalised | CC-BY-4.0 |
| Common Voice | Wikipedia | Narrated | 1409 | 27 | 27 | Punctuated & Cased | CC0-1.0 |
| Voxpopuli | European Parliament | Oratory | 523 | 5 | 5 | Punctuated | CC0 |
| TED-LIUM | TED talks | Oratory | 454 | 2 | 3 | Normalised | CC-BY-NC-ND 3.0 |
| GigaSpeech | Audiobook, podcast, YouTube | Narrated, spontaneous | 2500 | 12 | 40 | Punctuated | apache-2.0 |
| SPGISpeech | Fincancial meetings | Oratory, spontaneous | 4900 | 100 | 100 | Punctuated & Cased | User Agreement |
| Earnings-22 | Fincancial meetings | Oratory, spontaneous | 105 | 5 | 5 | Punctuated & Cased | CC-BY-SA-4.0 |
| AMI | Meetings | Spontaneous | 78 | 9 | 9 | Punctuated & Cased | CC-BY-4.0 |
## LibriSpeech
The LibriSpeech corpus is a standard large-scale corpus for assessing ASR systems. It consists of approximately 1,000 hours of narrated audiobooks from the [LibriVox](https://librivox.org) project. It is licensed under CC-BY-4.0.
Example Usage:
```python
librispeech = load_dataset("esb/datasets", "librispeech")
```
Train/validation splits:
- `train` (combination of `train.clean.100`, `train.clean.360` and `train.other.500`)
- `validation.clean`
- `validation.other`
Test splits:
- `test.clean`
- `test.other`
Also available are subsets of the train split, which can be accessed by setting the `subconfig` argument:
```python
librispeech = load_dataset("esb/datasets", "librispeech", subconfig="clean.100")
```
- `clean.100`: 100 hours of training data from the 'clean' subset
- `clean.360`: 360 hours of training data from the 'clean' subset
- `other.500`: 500 hours of training data from the 'other' subset
## Common Voice
Common Voice is a series of crowd-sourced open-licensed speech datasets where speakers record text from Wikipedia in various languages. The speakers are of various nationalities and native languages, with different accents and recording conditions. We use the English subset of version 9.0 (27-4-2022), with approximately 1,400 hours of audio-transcription data. It is licensed under CC0-1.0.
Example usage:
```python
common_voice = load_dataset("esb/datasets", "common_voice", use_auth_token=True)
```
Training/validation splits:
- `train`
- `validation`
Test splits:
- `test`
## VoxPopuli
VoxPopuli is a large-scale multilingual speech corpus consisting of political data sourced from 2009-2020 European Parliament event recordings. The English subset contains approximately 550 hours of speech largely from non-native English speakers. It is licensed under CC0.
Example usage:
```python
voxpopuli = load_dataset("esb/datasets", "voxpopuli")
```
Training/validation splits:
- `train`
- `validation`
Test splits:
- `test`
## TED-LIUM
TED-LIUM consists of English-language TED Talk conference videos covering a range of different cultural, political, and academic topics. It contains approximately 450 hours of transcribed speech data. It is licensed under CC-BY-NC-ND 3.0.
Example usage:
```python
tedlium = load_dataset("esb/datasets", "tedlium")
```
Training/validation splits:
- `train`
- `validation`
Test splits:
- `test`
## GigaSpeech
GigaSpeech is a multi-domain English speech recognition corpus created from audiobooks, podcasts and YouTube. We provide the large train set (2,500 hours) and the standard validation and test splits. It is licensed under apache-2.0.
Example usage:
```python
gigaspeech = load_dataset("esb/datasets", "gigaspeech", use_auth_token=True)
```
Training/validation splits:
- `train` (`l` subset of training data (2,500 h))
- `validation`
Test splits:
- `test`
Also available are subsets of the train split, which can be accessed by setting the `subconfig` argument:
```python
gigaspeech = load_dataset("esb/datasets", "spgispeech", subconfig="xs", use_auth_token=True)
```
- `xs`: extra-small subset of training data (10 h)
- `s`: small subset of training data (250 h)
- `m`: medium subset of training data (1,000 h)
- `xl`: extra-large subset of training data (10,000 h)
## SPGISpeech
SPGISpeech consists of company earnings calls that have been manually transcribed by S&P Global, Inc according to a professional style guide. We provide the large train set (5,000 hours) and the standard validation and test splits. It is licensed under a Kensho user agreement.
Loading the dataset requires authorization.
Example usage:
```python
spgispeech = load_dataset("esb/datasets", "spgispeech", use_auth_token=True)
```
Training/validation splits:
- `train` (`l` subset of training data (~5,000 h))
- `validation`
Test splits:
- `test`
Also available are subsets of the train split, which can be accessed by setting the `subconfig` argument:
```python
spgispeech = load_dataset("esb/datasets", "spgispeech", subconfig="s", use_auth_token=True)
```
- `s`: small subset of training data (~200 h)
- `m`: medium subset of training data (~1,000 h)
## Earnings-22
Earnings-22 is a 119-hour corpus of English-language earnings calls collected from global companies, with speakers of many different nationalities and accents. It is licensed under CC-BY-SA-4.0.
Example usage:
```python
earnings22 = load_dataset("esb/datasets", "earnings22")
```
Training/validation splits:
- `train`
- `validation`
Test splits:
- `test`
## AMI
The AMI Meeting Corpus consists of 100 hours of meeting recordings from multiple recording devices synced to a common timeline. It is licensed under CC-BY-4.0.
Example usage:
```python
ami = load_dataset("esb/datasets", "ami")
```
Training/validation splits:
- `train`
- `validation`
Test splits:
- `test`
|
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RaphaelOlivier/whisper_adversarial_examples
|
RaphaelOlivier
| 2022-11-03T21:48:16Z | 38 | 1 |
[
"license:cc-by-4.0",
"region:us"
] | null | 2022-10-26T20:29:43Z |
---
license: cc-by-4.0
---
# Description
This dataset is a subset of [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) and Multilingual [CommonVoice](commonvoice.mozilla.org/) that have been adversarially modified to fool [Whisper](https://huggingface.co/openai/whisper-medium) ASR model.
Original [source code](https://github.com/RaphaelOlivier/whisper_attack).
The raw [tar files](https://data.mendeley.com/datasets/96dh52hz9r).
# Configurations and splits
* The `targeted` config contains targeted adversarial examples. When successful, they fool Whisper into predicting the sentence `OK Google, browse to evil.com` even if the input is entirely different. We provide a split for each Whisper model, and one containing the original, unmodified inputs
* The `untargeted-35` and `untargeted-40` configs contain untargeted adversarial examples, with average Signal-Noise Ratios of 35dB and 40dB respectively. They fool Whisper into predicting erroneous transcriptions. We provide a split for each Whisper model, and one containing the original, unmodified inputs
* The `language-<lang> configs contain adversarial examples in language <lang> that fool Whisper in predicting the wrong language. Split `<lang>.<target_lang>` contain inputs that Whisper perceives as <target_lang>, and split `<lang>.original` contains the original inputs in language <lang>. We use 3 target languages (English, Tagalog and Serbian) and 7 source languages (English, Italian, Indonesian, Danish, Czech, Lithuanian and Armenian).
# Usage
Here is an example of code using this dataset:
```python
model_name="whisper-medium"
config_name="targeted"
split_name="whisper.medium"
hub_path = "openai/whisper-"+model_name
processor = WhisperProcessor.from_pretrained(hub_path)
model = WhisperForConditionalGeneration.from_pretrained(hub_path).to("cuda")
dataset = load_dataset("RaphaelOlivier/whisper_adversarial_examples",config_name ,split=split_name)
def map_to_pred(batch):
input_features = processor(batch["audio"][0]["array"], return_tensors="pt").input_features
predicted_ids = model.generate(input_features.to("cuda"))
transcription = processor.batch_decode(predicted_ids, normalize = True)
batch['text'][0] = processor.tokenizer._normalize(batch['text'][0])
batch["transcription"] = transcription
return batch
result = dataset.map(map_to_pred, batched=True, batch_size=1)
wer = load("wer")
for t in zip(result["text"],result["transcription"]):
print(t)
print(wer.compute(predictions=result["text"], references=result["transcription"]))
```
|
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bigbio/bioasq_2021_mesinesp
|
bigbio
| 2022-12-22T15:43:30Z | 38 | 0 |
[
"multilinguality:monolingual",
"language:es",
"license:cc-by-4.0",
"region:us"
] | null | 2022-11-13T22:06:28Z |
---
language:
- es
bigbio_language:
- Spanish
license: cc-by-4.0
multilinguality: monolingual
bigbio_license_shortname: CC_BY_4p0
pretty_name: MESINESP 2021
homepage: https://zenodo.org/record/5602914#.YhSXJ5PMKWt
bigbio_pubmed: False
bigbio_public: True
bigbio_tasks:
- TEXT_CLASSIFICATION
---
# Dataset Card for MESINESP 2021
## Dataset Description
- **Homepage:** https://zenodo.org/record/5602914#.YhSXJ5PMKWt
- **Pubmed:** False
- **Public:** True
- **Tasks:** TXTCLASS
The main aim of MESINESP2 is to promote the development of practically relevant semantic indexing tools for biomedical content in non-English language. We have generated a manually annotated corpus, where domain experts have labeled a set of scientific literature, clinical trials, and patent abstracts. All the documents were labeled with DeCS descriptors, which is a structured controlled vocabulary created by BIREME to index scientific publications on BvSalud, the largest database of scientific documents in Spanish, which hosts records from the databases LILACS, MEDLINE, IBECS, among others.
MESINESP track at BioASQ9 explores the efficiency of systems for assigning DeCS to different types of biomedical documents. To that purpose, we have divided the task into three subtracks depending on the document type. Then, for each one we generated an annotated corpus which was provided to participating teams:
- [Subtrack 1 corpus] MESINESP-L – Scientific Literature: It contains all Spanish records from LILACS and IBECS databases at the Virtual Health Library (VHL) with non-empty abstract written in Spanish.
- [Subtrack 2 corpus] MESINESP-T- Clinical Trials contains records from Registro Español de Estudios Clínicos (REEC). REEC doesn't provide documents with the structure title/abstract needed in BioASQ, for that reason we have built artificial abstracts based on the content available in the data crawled using the REEC API.
- [Subtrack 3 corpus] MESINESP-P – Patents: This corpus includes patents in Spanish extracted from Google Patents which have the IPC code “A61P” and “A61K31”. In addition, we also provide a set of complementary data such as: the DeCS terminology file, a silver standard with the participants' predictions to the task background set and the entities of medications, diseases, symptoms and medical procedures extracted from the BSC NERs documents.
## Citation Information
```
@conference {396,
title = {Overview of BioASQ 2021-MESINESP track. Evaluation of
advance hierarchical classification techniques for scientific
literature, patents and clinical trials.},
booktitle = {Proceedings of the 9th BioASQ Workshop
A challenge on large-scale biomedical semantic indexing
and question answering},
year = {2021},
url = {http://ceur-ws.org/Vol-2936/paper-11.pdf},
author = {Gasco, Luis and Nentidis, Anastasios and Krithara, Anastasia
and Estrada-Zavala, Darryl and Toshiyuki Murasaki, Renato and Primo-Pe{\~n}a,
Elena and Bojo-Canales, Cristina and Paliouras, Georgios and Krallinger, Martin}
}
```
|
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bigbio/evidence_inference
|
bigbio
| 2022-12-22T15:44:37Z | 38 | 1 |
[
"multilinguality:monolingual",
"language:en",
"license:mit",
"region:us"
] | null | 2022-11-13T22:08:29Z |
---
language:
- en
bigbio_language:
- English
license: mit
multilinguality: monolingual
bigbio_license_shortname: MIT
pretty_name: Evidence Inference 2.0
homepage: https://github.com/jayded/evidence-inference
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- QUESTION_ANSWERING
---
# Dataset Card for Evidence Inference 2.0
## Dataset Description
- **Homepage:** https://github.com/jayded/evidence-inference
- **Pubmed:** True
- **Public:** True
- **Tasks:** QA
The dataset consists of biomedical articles describing randomized control trials (RCTs) that compare multiple
treatments. Each of these articles will have multiple questions, or 'prompts' associated with them.
These prompts will ask about the relationship between an intervention and comparator with respect to an outcome,
as reported in the trial. For example, a prompt may ask about the reported effects of aspirin as compared
to placebo on the duration of headaches. For the sake of this task, we assume that a particular article
will report that the intervention of interest either significantly increased, significantly decreased
or had significant effect on the outcome, relative to the comparator.
## Citation Information
```
@inproceedings{deyoung-etal-2020-evidence,
title = "Evidence Inference 2.0: More Data, Better Models",
author = "DeYoung, Jay and
Lehman, Eric and
Nye, Benjamin and
Marshall, Iain and
Wallace, Byron C.",
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-1.13",
pages = "123--132",
}
```
|
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] |
bigbio/genia_relation_corpus
|
bigbio
| 2022-12-22T15:44:40Z | 38 | 1 |
[
"multilinguality:monolingual",
"language:en",
"license:other",
"region:us"
] | null | 2022-11-13T22:08:39Z |
---
language:
- en
bigbio_language:
- English
license: other
multilinguality: monolingual
bigbio_license_shortname: GENIA_PROJECT_LICENSE
pretty_name: GENIA Relation Corpus
homepage: http://www.geniaproject.org/genia-corpus/relation-corpus
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- RELATION_EXTRACTION
---
# Dataset Card for GENIA Relation Corpus
## Dataset Description
- **Homepage:** http://www.geniaproject.org/genia-corpus/relation-corpus
- **Pubmed:** True
- **Public:** True
- **Tasks:** RE
The extraction of various relations stated to hold between biomolecular entities is one of the most frequently
addressed information extraction tasks in domain studies. Typical relation extraction targets involve protein-protein
interactions or gene regulatory relations. However, in the GENIA corpus, such associations involving change in the
state or properties of biomolecules are captured in the event annotation.
The GENIA corpus relation annotation aims to complement the event annotation of the corpus by capturing (primarily)
static relations, relations such as part-of that hold between entities without (necessarily) involving change.
## Citation Information
```
@inproceedings{pyysalo-etal-2009-static,
title = "Static Relations: a Piece in the Biomedical Information Extraction Puzzle",
author = "Pyysalo, Sampo and
Ohta, Tomoko and
Kim, Jin-Dong and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the {B}io{NLP} 2009 Workshop",
month = jun,
year = "2009",
address = "Boulder, Colorado",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W09-1301",
pages = "1--9",
}
@article{article,
author = {Ohta, Tomoko and Pyysalo, Sampo and Kim, Jin-Dong and Tsujii, Jun'ichi},
year = {2010},
month = {10},
pages = {917-28},
title = {A reevaluation of biomedical named entity - term relations},
volume = {8},
journal = {Journal of bioinformatics and computational biology},
doi = {10.1142/S0219720010005014}
}
@MISC{Hoehndorf_applyingontology,
author = {Robert Hoehndorf and Axel-cyrille Ngonga Ngomo and Sampo Pyysalo and Tomoko Ohta and Anika Oellrich and
Dietrich Rebholz-schuhmann},
title = {Applying ontology design patterns to the implementation of relations in GENIA},
year = {}
}
```
|
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] |
bigbio/mlee
|
bigbio
| 2022-12-22T15:45:39Z | 38 | 1 |
[
"multilinguality:monolingual",
"language:en",
"license:cc-by-nc-sa-3.0",
"region:us"
] | null | 2022-11-13T22:10:03Z |
---
language:
- en
bigbio_language:
- English
license: cc-by-nc-sa-3.0
multilinguality: monolingual
bigbio_license_shortname: CC_BY_NC_SA_3p0
pretty_name: MLEE
homepage: http://www.nactem.ac.uk/MLEE/
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- EVENT_EXTRACTION
- NAMED_ENTITY_RECOGNITION
- RELATION_EXTRACTION
- COREFERENCE_RESOLUTION
---
# Dataset Card for MLEE
## Dataset Description
- **Homepage:** http://www.nactem.ac.uk/MLEE/
- **Pubmed:** True
- **Public:** True
- **Tasks:** EE,NER,RE,COREF
MLEE is an event extraction corpus consisting of manually annotated abstracts of papers
on angiogenesis. It contains annotations for entities, relations, events and coreferences
The annotations span molecular, cellular, tissue, and organ-level processes.
## Citation Information
```
@article{pyysalo2012event,
title={Event extraction across multiple levels of biological organization},
author={Pyysalo, Sampo and Ohta, Tomoko and Miwa, Makoto and Cho, Han-Cheol and Tsujii, Jun'ichi and Ananiadou, Sophia},
journal={Bioinformatics},
volume={28},
number={18},
pages={i575--i581},
year={2012},
publisher={Oxford University Press}
}
```
|
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hoskinson-center/proofnet
|
hoskinson-center
| 2023-03-17T21:25:37Z | 38 | 8 |
[
"license:mit",
"arxiv:2302.12433",
"region:us"
] | null | 2022-11-17T23:53:41Z |
---
license: mit
---
# ProofNet
## Dataset Description
- **Repository:** [zhangir-azerbayev/ProofNet](https://github.com/zhangir-azerbayev/ProofNet)
- **Paper:** [ProofNet](https://mathai2022.github.io/papers/20.pdf)
- **Point of Contact:** [Zhangir Azerbayev](https://zhangir-azerbayev.github.io/)
### Dataset Summary
ProofNet is a benchmark for autoformalization and formal proving of undergraduate-level mathematics. The ProofNet benchmarks consists of 371 examples, each consisting of a formal theorem statement in Lean 3, a natural language theorem statement, and a natural language proof. The problems are primarily drawn from popular undergraduate pure mathematics textbooks and cover topics such as real and complex analysis, linear algebra, abstract algebra, and topology. We intend for ProofNet to be a challenging benchmark that will drive progress in autoformalization and automatic theorem proving.
**Citation**:
```bibtex
@misc{azerbayev2023proofnet,
title={ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematics},
author={Zhangir Azerbayev and Bartosz Piotrowski and Hailey Schoelkopf and Edward W. Ayers and Dragomir Radev and Jeremy Avigad},
year={2023},
eprint={2302.12433},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Leaderboard
**Statement Autoformalization**
| Model | Typecheck Rate | Accuracy |
| ---------------------------------- | -------------- | -------- |
| Davinci-code-002 (prompt retrieval)| 45.2 | 16.1 |
| Davinci-code-002 (in-context learning) | 23.7 | 13.4 |
| proofGPT-1.3B | 10.7 | 3.2 |
**Statement Informalization**
| Model | Accuracy |
| ---------------------------------- | -------- |
| Code-davinci-002 (in-context learning)| 62.3 |
| proofGPT-6.7B (in-context learning) | 6.5 |
| proofGPT-1.3B (in-context learning) | 4.3 |
### Data Fields
- `id`: Unique string identifier for the problem.
- `nl_statement`: Natural language theorem statement.
- `nl_proof`: Natural language proof, in LaTeX. Depends on `amsthm, amsmath, amssymb` packages.
- `formal_statement`: Formal theorem statement in Lean 3.
- `src_header`: File header including imports, namespaces, and locales required for the formal statement. Note that local import of [common.lean](https://github.com/zhangir-azerbayev/ProofNet/blob/main/benchmark/benchmark_to_publish/formal/common.lean), which has to be manually downloaded and place in the same directory as your `.lean` file containing the formal statement.
### Authors
Zhangir Azerbayev, Bartosz Piotrowski, Jeremy Avigad
|
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fcakyon/pokemon-classification
|
fcakyon
| 2023-01-14T13:06:55Z | 38 | 1 |
[
"task_categories:image-classification",
"roboflow",
"roboflow2huggingface",
"Gaming",
"region:us"
] |
[
"image-classification"
] | 2023-01-14T12:47:57Z |
---
task_categories:
- image-classification
tags:
- roboflow
- roboflow2huggingface
- Gaming
---
<div align="center">
<img width="640" alt="fcakyon/pokemon-classification" src="https://huggingface.co/datasets/fcakyon/pokemon-classification/resolve/main/thumbnail.jpg">
</div>
### Dataset Labels
```
['Golbat', 'Machoke', 'Omastar', 'Diglett', 'Lapras', 'Kabuto', 'Persian', 'Weepinbell', 'Golem', 'Dodrio', 'Raichu', 'Zapdos', 'Raticate', 'Magnemite', 'Ivysaur', 'Growlithe', 'Tangela', 'Drowzee', 'Rapidash', 'Venonat', 'Pidgeot', 'Nidorino', 'Porygon', 'Lickitung', 'Rattata', 'Machop', 'Charmeleon', 'Slowbro', 'Parasect', 'Eevee', 'Starmie', 'Staryu', 'Psyduck', 'Dragonair', 'Magikarp', 'Vileplume', 'Marowak', 'Pidgeotto', 'Shellder', 'Mewtwo', 'Farfetchd', 'Kingler', 'Seel', 'Kakuna', 'Doduo', 'Electabuzz', 'Charmander', 'Rhyhorn', 'Tauros', 'Dugtrio', 'Poliwrath', 'Gengar', 'Exeggutor', 'Dewgong', 'Jigglypuff', 'Geodude', 'Kadabra', 'Nidorina', 'Sandshrew', 'Grimer', 'MrMime', 'Pidgey', 'Koffing', 'Ekans', 'Alolan Sandslash', 'Venusaur', 'Snorlax', 'Paras', 'Jynx', 'Chansey', 'Hitmonchan', 'Gastly', 'Kangaskhan', 'Oddish', 'Wigglytuff', 'Graveler', 'Arcanine', 'Clefairy', 'Articuno', 'Poliwag', 'Abra', 'Squirtle', 'Voltorb', 'Ponyta', 'Moltres', 'Nidoqueen', 'Magmar', 'Onix', 'Vulpix', 'Butterfree', 'Krabby', 'Arbok', 'Clefable', 'Goldeen', 'Magneton', 'Dratini', 'Caterpie', 'Jolteon', 'Nidoking', 'Alakazam', 'Dragonite', 'Fearow', 'Slowpoke', 'Weezing', 'Beedrill', 'Weedle', 'Cloyster', 'Vaporeon', 'Gyarados', 'Golduck', 'Machamp', 'Hitmonlee', 'Primeape', 'Cubone', 'Sandslash', 'Scyther', 'Haunter', 'Metapod', 'Tentacruel', 'Aerodactyl', 'Kabutops', 'Ninetales', 'Zubat', 'Rhydon', 'Mew', 'Pinsir', 'Ditto', 'Victreebel', 'Omanyte', 'Horsea', 'Pikachu', 'Blastoise', 'Venomoth', 'Charizard', 'Seadra', 'Muk', 'Spearow', 'Bulbasaur', 'Bellsprout', 'Electrode', 'Gloom', 'Poliwhirl', 'Flareon', 'Seaking', 'Hypno', 'Wartortle', 'Mankey', 'Tentacool', 'Exeggcute', 'Meowth']
```
### Number of Images
```json
{'train': 4869, 'test': 732, 'valid': 1390}
```
### 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("fcakyon/pokemon-classification", name="full")
example = ds['train'][0]
```
### Roboflow Dataset Page
[https://universe.roboflow.com/robert-demo-qvail/pokedex/dataset/14](https://universe.roboflow.com/robert-demo-qvail/pokedex/dataset/14?ref=roboflow2huggingface)
### Citation
```
@misc{ pokedex_dataset,
title = { Pokedex Dataset },
type = { Open Source Dataset },
author = { Lance Zhang },
howpublished = { \\url{ https://universe.roboflow.com/robert-demo-qvail/pokedex } },
url = { https://universe.roboflow.com/robert-demo-qvail/pokedex },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { dec },
note = { visited on 2023-01-14 },
}
```
### License
Public Domain
### Dataset Summary
This dataset was exported via roboflow.com on December 20, 2022 at 5:34 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 6991 images.
Pokemon are annotated in folder format.
The following pre-processing was applied to each image:
* Auto-orientation of pixel data (with EXIF-orientation stripping)
* Resize to 224x224 (Fit (black edges))
No image augmentation techniques were applied.
|
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neulab/mconala
|
neulab
| 2023-02-10T19:01:31Z | 38 | 2 |
[
"task_categories:text-generation",
"task_categories:translation",
"size_categories:n<1K",
"language:es",
"language:ja",
"language:ru",
"license:cc-by-sa-4.0",
"code generation",
"arxiv:2203.08388",
"region:us"
] |
[
"text-generation",
"translation"
] | 2023-02-10T18:08:54Z |
---
license: cc-by-sa-4.0
task_categories:
- text-generation
- translation
language:
- es
- ja
- ru
tags:
- code generation
pretty_name: mconala
size_categories:
- n<1K
---
# Dataset Card for MCoNaLa
## Dataset Description
- **Homepage:** https://github.com/zorazrw/multilingual-conala
- **Repository:** https://github.com/zorazrw/multilingual-conala
- **Paper:** https://arxiv.org/pdf/2203.08388.pdf
- **Leaderboard:** https://explainaboard.inspiredco.ai/leaderboards?show_mine=false&sort_dir=desc&sort_field=created_at&dataset=mconala
### Dataset Summary
MCoNaLa is a Multilingual Code/Natural Language Challenge dataset with 896 NL-Code pairs in three languages: Spanish, Japanese, and Russian.
### Languages
Spanish, Japanese, Russian; Python
## Dataset Structure
### How to Use
```bash
from datasets import load_dataset
# Spanish subset
load_dataset("neulab/mconala", "es")
DatasetDict({
test: Dataset({
features: ['question_id', 'intent', 'rewritten_intent', 'snippet'],
num_rows: 341
})
})
# Japanese subset
load_dataset("neulab/mconala", "ja")
DatasetDict({
test: Dataset({
features: ['question_id', 'intent', 'rewritten_intent', 'snippet'],
num_rows: 210
})
})
# Russian subset
load_dataset("neulab/mconala", "ru")
DatasetDict({
test: Dataset({
features: ['question_id', 'intent', 'rewritten_intent', 'snippet'],
num_rows: 345
})
})
```
### Data Fields
|Field|Type|Description|
|---|---|---|
|question_id|int|StackOverflow post id of the sample|
|intent|string|Title of the Stackoverflow post as the initial NL intent|
|rewritten_intent|string|nl intent rewritten by human annotators|
|snippet|string|Python code solution to the NL intent|
### Data Splits
The dataset contains 341, 210, and 345 samples in Spanish, Japanese, and Russian.
### Citation Information
```
@article{wang2022mconala,
title={MCoNaLa: A Benchmark for Code Generation from Multiple Natural Languages},
author={Zhiruo Wang, Grace Cuenca, Shuyan Zhou, Frank F. Xu, Graham Neubig},
journal={arXiv preprint arXiv:2203.08388},
year={2022}
}
```
|
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] |
Isamu136/big-animal-dataset-with-embedding
|
Isamu136
| 2023-02-12T22:42:07Z | 38 | 1 |
[
"license:mit",
"region:us"
] | null | 2023-02-12T21:19:13Z |
---
license: mit
dataset_info:
features:
- name: image
dtype: image
- name: caption
dtype: string
- name: l14_embeddings
sequence: float32
- name: moco_vitb_imagenet_embeddings
sequence: float32
- name: moco_vitb_imagenet_embeddings_without_last_layer
sequence: float32
splits:
- name: train
num_bytes: 2125655956.375
num_examples: 62149
download_size: 2238679414
dataset_size: 2125655956.375
---
|
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0.10428224503993988,
0.8970533609390259,
0.25200408697128296,
0.3049141764640808,
-0.04526616632938385,
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0.26214760541915894,
0.04487833380699158,
-0.6869441866874695
] |
nasa-cisto-data-science-group/modis-lake-powell-toy-dataset
|
nasa-cisto-data-science-group
| 2023-05-04T01:39:33Z | 38 | 0 |
[
"size_categories:n<1K",
"license:apache-2.0",
"region:us"
] | null | 2023-03-09T14:45:40Z |
---
license: apache-2.0
size_categories:
- n<1K
---
# MODIS Water Lake Powell Toy Dataset
### Dataset Summary
Tabular dataset comprised of MODIS surface reflectance bands along with calculated indices and a label (water/not-water)
## Dataset Structure
### Data Fields
- `water`: Label, water or not-water (binary)
- `sur_refl_b01_1`: MODIS surface reflection band 1 (-100, 16000)
- `sur_refl_b02_1`: MODIS surface reflection band 2 (-100, 16000)
- `sur_refl_b03_1`: MODIS surface reflection band 3 (-100, 16000)
- `sur_refl_b04_1`: MODIS surface reflection band 4 (-100, 16000)
- `sur_refl_b05_1`: MODIS surface reflection band 5 (-100, 16000)
- `sur_refl_b06_1`: MODIS surface reflection band 6 (-100, 16000)
- `sur_refl_b07_1`: MODIS surface reflection band 7 (-100, 16000)
- `ndvi`: Normalized differential vegetation index (-20000, 20000)
- `ndwi1`: Normalized differential water index 1 (-20000, 20000)
- `ndwi2`: Normalized differential water index 2 (-20000, 20000)
### Data Splits
Train and test split. Test is 200 rows, train is 800.
## Dataset Creation
## Source Data
[MODIS MOD44W](https://lpdaac.usgs.gov/products/mod44wv006/)
[MODIS MOD09GA](https://lpdaac.usgs.gov/products/mod09gav006/)
[MODIS MOD09GQ](https://lpdaac.usgs.gov/products/mod09gqv006/)
## Annotation process
Labels were created by using the MOD44W C6 product to designate pixels in MODIS surface reflectance products as land or water.
|
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swype/instruct
|
swype
| 2023-04-05T23:14:28Z | 38 | 49 |
[
"license:mit",
"region:us"
] | null | 2023-03-29T02:48:16Z |
---
license: mit
---
# A large instruct dataset
This dataset is a combination of multiple sources, including the GPT4All dataset, the Alpaca dataset from Stanford, custom generation using AllenAI augmentation, and some dataset augmentation from open-source Meta datasets. The dataset is split into 70% for training, 20% for validation, and 10% for testing.
## Description
The Swype.com dataset contains prompt and completion pairs for various tasks. It's an augmented version of the following datasets:
- [GPT4All](https://github.com/nomic-ai/gpt4all): A dataset containing a wide range of tasks for training and evaluating general-purpose language models.
- [Alpaca dataset from Stanford](https://github.com/tatsu-lab/stanford_alpaca): A dataset containing prompts, completions, and annotations for controllable text generation.
- Custom generation using [AllenAI augmentation](https://allenai.org): Augmentation performed using the advanced NLP tools provided by AllenAI.
- Some dataset augmentation from open-source Meta datasets: Additional augmentation from various open-source Meta datasets.
The dataset is designed for training and evaluating language models on diverse tasks, with a focus on controllable and instruction-based text generation.
## Dataset Structure
The dataset contains the following columns:
- `prompt`: The input prompt string, representing a task or question.
- `completion`: The output completion string, representing the answer or generated text based on the prompt.
## Citation
If you use this dataset in your research or work, please cite it as follows:
@misc{srikanth2023swypedataset,
author = {Srikanth Srinivas},
title = {Swype.com Dataset},
year = {2023},
publisher = {Swype.com},
howpublished = {\url{https://swype.com}},
email = {[email protected]}
}
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] |
SkyHuReal/DrugBank-Alpaca
|
SkyHuReal
| 2023-04-03T17:37:30Z | 38 | 0 |
[
"license:afl-3.0",
"region:us"
] | null | 2023-04-03T15:39:50Z |
---
license: afl-3.0
---
|
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] |
fschlatt/trump-tweets
|
fschlatt
| 2023-04-19T11:41:59Z | 38 | 1 |
[
"language:en",
"license:cc0-1.0",
"region:us"
] | null | 2023-04-19T10:35:29Z |
---
license: cc0-1.0
language:
- en
dataset_info:
features:
- name: id
dtype: int64
- name: text
dtype: string
- name: is_retweet
dtype: bool
- name: is_deleted
dtype: bool
- name: device
dtype: string
- name: favorites
dtype: int64
- name: retweets
dtype: int64
- name: datetime
dtype: timestamp[s]
- name: is_flagged
dtype: bool
splits:
- name: train
num_bytes: 10593265
num_examples: 56571
download_size: 0
dataset_size: 10593265
---
This is a clone of the Trump Tweet Kaggle dataset found here: https://www.kaggle.com/datasets/headsortails/trump-twitter-archive
|
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] |
CM/codexglue_code2text_java
|
CM
| 2023-04-22T01:51:28Z | 38 | 0 |
[
"region:us"
] | null | 2023-04-22T01:51:09Z |
---
dataset_info:
features:
- name: id
dtype: int32
- name: repo
dtype: string
- name: path
dtype: string
- name: func_name
dtype: string
- name: original_string
dtype: string
- name: language
dtype: string
- name: code
dtype: string
- name: code_tokens
sequence: string
- name: docstring
dtype: string
- name: docstring_tokens
sequence: string
- name: sha
dtype: string
- name: url
dtype: string
splits:
- name: train
num_bytes: 452553835
num_examples: 164923
- name: validation
num_bytes: 13366344
num_examples: 5183
- name: test
num_bytes: 29080753
num_examples: 10955
download_size: 154860056
dataset_size: 495000932
---
# Dataset Card for "codexglue_code2text_java"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
sileod/mindgames
|
sileod
| 2023-11-22T08:24:50Z | 38 | 5 |
[
"task_categories:text-classification",
"task_ids:natural-language-inference",
"task_ids:multi-input-text-classification",
"multilinguality:monolingual",
"language:en",
"license:apache-2.0",
"theory of mind",
"tom",
"Logical-Reasoning",
"Modal-Logic",
"Reasoning",
"Logics",
"Logic",
"nli",
"model-checking",
"natural language inference",
"arxiv:2305.03353",
"region:us"
] |
[
"text-classification"
] | 2023-05-03T13:56:10Z |
---
language:
- en
license: apache-2.0
multilinguality:
- monolingual
task_categories:
- text-classification
task_ids:
- natural-language-inference
- multi-input-text-classification
tags:
- theory of mind
- tom
- Logical-Reasoning
- Modal-Logic
- Reasoning
- Logics
- Logic
- nli
- model-checking
- natural language inference
dataset_info:
features:
- name: premise
dtype: string
- name: smcdel_problem
dtype: string
- name: n_announcements
dtype: int64
- name: pbcheck
dtype: string
- name: hypothesis
dtype: string
- name: setup
dtype: string
- name: hypothesis_depth
dtype: int64
- name: n_agents
dtype: int64
- name: label
dtype: string
- name: names
sequence: string
- name: index
dtype: int64
- name: s-l
dtype: string
- name: deberta_pred
dtype: int64
- name: deberta_confidence
dtype: float64
- name: difficulty
dtype: float64
splits:
- name: train
num_bytes: 8702021
num_examples: 11174
- name: validation
num_bytes: 2904084
num_examples: 3725
- name: test
num_bytes: 2909341
num_examples: 3725
download_size: 2989857
dataset_size: 14515446
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
Mindgame dataset
Code:
https://github.com/sileod/llm-theory-of-mind
Article (Accepted at EMNLP 2023 Findings):
https://arxiv.org/abs/2305.03353
```
@article{sileo2023mindgames,
title={MindGames: Targeting Theory of Mind in Large Language Models with Dynamic Epistemic Modal Logic},
author={Sileo, Damien and Lernould, Antoine},
journal={arXiv preprint arXiv:2305.03353},
year={2023}
}
```
|
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Nan-Do/instructional_code-search-net-php
|
Nan-Do
| 2023-05-20T05:20:07Z | 38 | 1 |
[
"task_categories:conversational",
"task_categories:text-generation",
"task_categories:text2text-generation",
"language:en",
"license:apache-2.0",
"PHP",
"Code Generation",
"Instruction Response",
"region:us"
] |
[
"conversational",
"text-generation",
"text2text-generation"
] | 2023-05-19T03:46:01Z |
---
dataset_info:
features:
- name: INSTRUCTION
dtype: string
- name: RESPONSE
dtype: string
- name: SOURCE
dtype: string
splits:
- name: train
num_bytes: 448756286
num_examples: 536632
download_size: 158708948
dataset_size: 448756286
license: apache-2.0
task_categories:
- conversational
- text-generation
- text2text-generation
language:
- en
tags:
- PHP
- Code Generation
- Instruction Response
pretty_name: Instructional PHP Dataset
---
# Dataset Card for "instructional_code-search-net-php"
## Dataset Description
- **Homepage:** None
- **Repository:** https://huggingface.co/datasets/Nan-Do/instructional_code-search-net-php
- **Paper:** None
- **Leaderboard:** None
- **Point of Contact:** [@Nan-Do](https://github.com/Nan-Do)
### Dataset Summary
This is an instructional dataset for PHP.
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-php
### 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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] |
fnlp/moss-003-sft-data
|
fnlp
| 2023-07-09T15:09:50Z | 38 | 47 |
[
"license:cc-by-4.0",
"region:us"
] | null | 2023-05-20T13:07:50Z |
---
license: cc-by-4.0
---
# moss-003-sft-data
## Conversation Without Plugins
### Categories
| Category | \# samples |
|----------------------|-----------:|
| Brainstorming | 99,162 |
| Complex Instruction | 95,574 |
| Code | 198,079 |
| Role Playing | 246,375 |
| Writing | 341,087 |
| Harmless | 74,573 |
| Others | 19,701 |
| Total | 1,074,551 |
**Others** contains two categories: **Continue**(9,839) and **Switching**(9,862).
The **Continue** category refers to instances in a conversation where the user asks the system to continue outputting the response from the previous round that was not completed.
The **Switching** category refers to instances in a conversation where the user switches the language they are using.
We remove the data for honesty because it contains private information.
|
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] |
Patt/ReCoRD_TH_drop
|
Patt
| 2023-07-20T15:29:42Z | 38 | 0 |
[
"task_categories:text-classification",
"language:en",
"language:th",
"arxiv:1907.04307",
"region:us"
] |
[
"text-classification"
] | 2023-06-22T13:34:05Z |
---
task_categories:
- text-classification
language:
- en
- th
dataset_info:
features:
- name: passage
dtype: string
- name: passage_TH
dtype: string
- name: query
dtype: string
- name: query_TH
dtype: string
- name: entities
sequence: string
- name: entities_TH
sequence: string
- name: entity_spans
struct:
- name: end
sequence: int64
- name: start
sequence: int64
- name: text
sequence: string
- name: answers
sequence: string
- name: answers_TH
sequence: string
- name: idx
struct:
- name: passage
dtype: int64
- name: query
dtype: int64
- name: score_passage
dtype: float64
- name: score_query
dtype: float64
- name: score_entities
dtype: float64
- name: score_answers
dtype: float64
splits:
- name: train
num_bytes: 281547282
num_examples: 57811
- name: validation
num_bytes: 32258456
num_examples: 6676
download_size: 112999233
dataset_size: 313805738
---
# Dataset Card for ReCoRD_TH_drop
### Dataset Description
This dataset is Thai translated version of [ReCoRD](https://huggingface.co/datasets/super_glue/viewer/record) using google translate with [Multilingual Universal Sentence Encoder](https://arxiv.org/abs/1907.04307) to calculate score for Thai translation.
Drop every row that score_answers < 0.8 and every row that score < 0.5 after penalty.
|
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FreedomIntelligence/alpaca-gpt4-arabic
|
FreedomIntelligence
| 2023-08-06T08:07:51Z | 38 | 5 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-06-26T08:17:14Z |
---
license: apache-2.0
---
The dataset is used in the research related to [MultilingualSIFT](https://github.com/FreedomIntelligence/MultilingualSIFT).
|
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beyond/rlhf-reward-single-round-trans_chinese
|
beyond
| 2023-07-05T13:03:15Z | 38 | 30 |
[
"region:us"
] | null | 2023-07-05T13:02:55Z |
---
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 12139022
num_examples: 19862
- name: test
num_bytes: 3117841
num_examples: 4996
download_size: 10699367
dataset_size: 15256863
---
# Dataset Card for "rlhf-reward-single-round-trans_chinese"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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ahmed-masry/unichart-pretrain-data
|
ahmed-masry
| 2023-07-30T01:39:51Z | 38 | 1 |
[
"region:us"
] | null | 2023-07-30T01:39:33Z |
---
dataset_info:
features:
- name: imgname
dtype: string
- name: query
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 1198892722
num_examples: 6898333
download_size: 346172299
dataset_size: 1198892722
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "unichart-pretrain-data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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adityarra07/sub_ATC_test
|
adityarra07
| 2023-08-09T17:25:54Z | 38 | 0 |
[
"region:us"
] | null | 2023-08-09T17:25:49Z |
---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: transcription
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 130645075.80770035
num_examples: 1000
download_size: 120802206
dataset_size: 130645075.80770035
---
# Dataset Card for "sub_ATC_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
pourmand1376/persian-qa-translated
|
pourmand1376
| 2023-08-19T11:52:23Z | 38 | 0 |
[
"task_categories:question-answering",
"task_categories:translation",
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:fa",
"language:en",
"license:apache-2.0",
"region:us"
] |
[
"question-answering",
"translation",
"text-generation"
] | 2023-08-19T11:45:25Z |
---
dataset_info:
features:
- name: input
dtype: float64
- name: instruction
dtype: string
- name: original_instruction
dtype: string
- name: original_output
dtype: string
- name: output
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 360540755
num_examples: 153127
download_size: 186783724
dataset_size: 360540755
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: apache-2.0
task_categories:
- question-answering
- translation
- text-generation
language:
- fa
- en
pretty_name: Persian QA Translated
size_categories:
- 100K<n<1M
---
# Dataset Card for "persian-qa-translated"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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EgilKarlsen/PKDD_DistilRoBERTa_FT
|
EgilKarlsen
| 2023-09-04T13:22:41Z | 38 | 0 |
[
"region:us"
] | null | 2023-09-04T13:22:29Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
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splits:
- name: train
num_bytes: 115608885
num_examples: 37500
- name: test
num_bytes: 38536331
num_examples: 12500
download_size: 211881010
dataset_size: 154145216
---
# Dataset Card for "PKDD_DistilRoBERTa_FT"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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Sofoklis/vrna_dataset
|
Sofoklis
| 2023-10-10T15:58:27Z | 38 | 0 |
[
"region:us"
] | null | 2023-09-06T13:22:48Z |
---
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: name
dtype: string
- name: caption
dtype: string
- name: split_caption
dtype: string
- name: matrix
dtype: image
splits:
- name: train
num_bytes: 328952.0
num_examples: 80
- name: test
num_bytes: 82238.0
num_examples: 20
- name: valid
num_bytes: 263161.6
num_examples: 64
download_size: 549331
dataset_size: 674351.6
---
# Dataset Card for "vrna_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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Sehaj/robot_commands_2
|
Sehaj
| 2023-09-20T10:26:51Z | 38 | 2 |
[
"license:mit",
"region:us"
] | null | 2023-09-20T10:26:31Z |
---
license: mit
---
|
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erhwenkuo/medical_dialogue-chinese-zhtw
|
erhwenkuo
| 2023-09-27T23:06:14Z | 38 | 3 |
[
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:zh",
"license:mit",
"region:us"
] |
[
"text-generation"
] | 2023-09-27T22:27:35Z |
---
language:
- zh
license: mit
size_categories:
- 100K<n<1M
task_categories:
- text-generation
dataset_info:
features:
- name: output
dtype: string
- name: input
dtype: string
- name: instruction
dtype: string
splits:
- name: train
num_bytes: 553726613
num_examples: 799743
download_size: 288686981
dataset_size: 553726613
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "medical_dialogue-chinese-zhtw"
中文醫療問答資料集
## 來源
本資料集是從 [Toyhom/Chinese-medical-dialogue-data](https://github.com/Toyhom/Chinese-medical-dialogue-data) 的 github repo 中轉換而來。
## 內容
|科別|數量|
|---|---|
**Andriatria 男科** |94,596 個問答對|
**IM 內科** |220,606 個問答對|
**OAGD 婦產科** |183,751 個問答對|
**Oncology 腫瘤科** |75,553 個問答對|
**Pediatric 兒科** |101,602 個問答對|
**Surgical 外科** |115,991 個問答對|
總計 792,099 條數據
## 範例
```json
{
"instruction": "現在你是個神經腦外科醫生，請根據病人的問題給予建議：",
"input": "癲癇病能吃德巴金嗎，錯覺，有時候感覺看到的和聽到的不太一樣。",
"output": "巴金是廣譜抗病藥物，主要作用於中樞神經系統，對動物的藥理研究發現德巴金對各種癲癇的實驗模型（全身性和局部性）均有抗驚厥作用，對人的各種類型癲癇發作有抑製作用，作用機理可能與增加γ-氨基丁酸的濃度有關。主要是治癲癇藥物。建議在醫生的知道下，用藥，祝您身體早日康復。"
}
```
## 欄位：
```
instruction: 指令
input: 輸入
output: 輸出
```
## 使用限制
此資料集用於研究大型語言模型的目的，不得用於會對社會帶來危害的用途。
本資料集不代表任何一方的立場、利益或想法，無關任何團體的任何類型的主張。因使用本資料集所帶來的任何損害、糾紛，本專案不承擔任何責任。
|
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TrainingDataPro/generated-e-mail-spam
|
TrainingDataPro
| 2023-09-28T15:29:45Z | 38 | 1 |
[
"task_categories:text-generation",
"task_categories:text-classification",
"language:en",
"license:cc-by-nc-nd-4.0",
"code",
"finance",
"region:us"
] |
[
"text-generation",
"text-classification"
] | 2023-09-28T14:36:07Z |
---
language:
- en
license: cc-by-nc-nd-4.0
task_categories:
- text-generation
- text-classification
tags:
- code
- finance
dataset_info:
features:
- name: title
dtype: string
- name: text
dtype: large_string
splits:
- name: train
num_bytes: 233533
num_examples: 300
download_size: 230500
dataset_size: 233533
---
# Generated E-mail Spam
The dataset consists of a **CSV file** containing of 300 generated email spam messages. Each row in the file represents a separate email message, its *title and text.* The dataset aims to facilitate the analysis and detection of spam emails.
The dataset can be used for various purposes, such as *training machine learning algorithms to classify and filter spam emails, studying spam email patterns, or analyzing text-based features of spam messages*.
# Get the dataset
### This is just an example of the data
Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=generated-e-mail-spam) to discuss your requirements, learn about the price and buy the dataset.
# Content
### File with the extension .csv (utf-8)
includes the following information:
- **title**: title of the email,
- **text**: text of the email
# Email spam might be generated in accordance with your requirements.
## **[TrainingData](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=generated-e-mail-spam)** provides high-quality data annotation tailored to your needs
More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets**
TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets**
|
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DDSC/angry-tweets-binary
|
DDSC
| 2023-10-11T08:25:19Z | 38 | 0 |
[
"region:us"
] | null | 2023-10-11T08:25:11Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 269093.3579427623
num_examples: 1559
- name: test
num_bytes: 120444.7564469914
num_examples: 684
download_size: 273118
dataset_size: 389538.1143897537
---
# Dataset Card for "angry-tweets-binary"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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theblackcat102/gpt-4v-eval-samples
|
theblackcat102
| 2023-11-05T12:20:01Z | 38 | 1 |
[
"region:us"
] | null | 2023-10-13T00:51:36Z |
---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
- name: image
dtype: image
- name: conversations
dtype: string
splits:
- name: test
num_bytes: 334178840.35
num_examples: 1682
download_size: 324453952
dataset_size: 334178840.35
---
# GPT-4V Eval samples
This is a hand curated images from the web and questions asked by myself to GPT-4V to understand its ability and limits.
I am mainly focus in localization, OCR ability and understanding of GPT-4V vision module. So the language part is skipped as we already seen in GPT-4. As long as GPT-4V can extract the required information in text, the rest of the LLM shouldn't have any issue answering the rest of the questions.
The numbers of examples is still pretty tiny and will continue to increase further in the future until I am satisfy with the size. So please check back from time to time.
Note : the dataset viewer had a bug which cause the image displayed differ from the actual dataset (Due to frequent update). Please load the dataset and save it on your local path for best accuracy.
## How to use:
```
import json
from datasets import load_dataset
dataset = load_dataset('theblackcat102/gpt-4v-eval-samples')['test']
print(dataset[0]['image'])
print(json.loads(dataset[0]['conversations']))
```
## Contributions
Please checkout my github repo for more details : [theblackcat102/gpt-4v-samples](https://github.com/theblackcat102/gpt-4v-samples)
## Citation
```
@article{yang2023dawn,
title={The Dawn of LMMs: Preliminary Explorations with GPT-4V (ision)},
author={Yang, Zhengyuan and Li, Linjie and Lin, Kevin and Wang, Jianfeng and Lin, Chung-Ching and Liu, Zicheng and Wang, Lijuan},
journal={arXiv preprint arXiv:2309.17421},
year={2023}
}
```
|
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felipeoes/filtered_qa_blue_amazon_legislation
|
felipeoes
| 2023-10-22T16:24:13Z | 38 | 0 |
[
"region:us"
] | null | 2023-10-22T16:23:12Z |
---
dataset_info:
features:
- name: file_index
dtype: int64
- name: file_name
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 55559058
num_examples: 15964
download_size: 14333761
dataset_size: 55559058
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "filtered_qa_blue_amazon_legislation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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Weni/Dataset_semantic_alignment_translation_en-es-direction_en-pt_br-direction
|
Weni
| 2023-11-03T14:14:39Z | 38 | 0 |
[
"region:us"
] | null | 2023-10-27T17:13:41Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: int64
- name: prompt
dtype: string
- name: string
dtype: string
- name: string_translation
dtype: string
splits:
- name: train
num_bytes: 9521461
num_examples: 40001
download_size: 3814409
dataset_size: 9521461
---
# Dataset Card for "Dataset_semantic_alignment_translation_en-es-direction_en-pt_br-direction"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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kjappelbaum/chemnlp-chemdner
|
kjappelbaum
| 2023-10-27T19:07:02Z | 38 | 0 |
[
"region:us"
] | null | 2023-10-27T18:39:06Z |
---
dataset_info:
features:
- name: entities
sequence: string
- name: text
dtype: string
- name: split
dtype: string
splits:
- name: train
num_bytes: 14376666
num_examples: 19440
download_size: 8033115
dataset_size: 14376666
---
# Dataset Card for "chemnlp-chemdner"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
Argen7um/restrant-qa
|
Argen7um
| 2023-10-30T15:40:22Z | 38 | 1 |
[
"task_categories:question-answering",
"language:en",
"license:apache-2.0",
"legal",
"region:us"
] |
[
"question-answering"
] | 2023-10-30T15:28:34Z |
---
license: apache-2.0
task_categories:
- question-answering
language:
- en
tags:
- legal
---
|
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ftang97/sw-consultancy-book
|
ftang97
| 2023-11-03T15:23:20Z | 38 | 0 |
[
"region:us"
] | null | 2023-11-03T15:23:17Z |
---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 2592763
num_examples: 1
download_size: 1529310
dataset_size: 2592763
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "sw-consultancy-book"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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ShrinivasSK/en_hi_3
|
ShrinivasSK
| 2023-11-04T19:22:51Z | 38 | 0 |
[
"region:us"
] | null | 2023-11-04T19:22:44Z |
---
dataset_info:
features:
- name: idx
dtype: int64
- name: src
dtype: string
- name: tgt
dtype: string
splits:
- name: train
num_bytes: 6423499.8
num_examples: 18000
- name: test
num_bytes: 713722.2
num_examples: 2000
download_size: 3835153
dataset_size: 7137222.0
---
# Dataset Card for "data_hi_3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
ShrinivasSK/en_te_3
|
ShrinivasSK
| 2023-11-04T19:23:37Z | 38 | 0 |
[
"region:us"
] | null | 2023-11-04T19:23:31Z |
---
dataset_info:
features:
- name: idx
dtype: int64
- name: src
dtype: string
- name: tgt
dtype: string
splits:
- name: train
num_bytes: 4131310.5
num_examples: 18000
- name: test
num_bytes: 459034.5
num_examples: 2000
download_size: 2465569
dataset_size: 4590345.0
---
# Dataset Card for "data_te_3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
St4n/self_dataset
|
St4n
| 2023-11-05T14:28:35Z | 38 | 0 |
[
"size_categories:1K<n<10K",
"language:en",
"region:us"
] | null | 2023-11-05T12:38:21Z |
---
language:
- en
size_categories:
- 1K<n<10K
---
|
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ShrinivasSK/kn_en_1
|
ShrinivasSK
| 2023-11-06T19:07:26Z | 38 | 0 |
[
"region:us"
] | null | 2023-11-06T18:54:38Z |
---
dataset_info:
features:
- name: idx
dtype: int64
- name: tgt
dtype: string
- name: src
dtype: string
splits:
- name: train
num_bytes: 3986574.3
num_examples: 18000
- name: test
num_bytes: 442952.7
num_examples: 2000
download_size: 2373508
dataset_size: 4429527.0
---
# Dataset Card for "kn_en_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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ShrinivasSK/mr_en_1
|
ShrinivasSK
| 2023-11-06T19:07:57Z | 38 | 0 |
[
"region:us"
] | null | 2023-11-06T18:54:52Z |
---
dataset_info:
features:
- name: idx
dtype: int64
- name: tgt
dtype: string
- name: src
dtype: string
splits:
- name: train
num_bytes: 4586634.0
num_examples: 18000
- name: test
num_bytes: 509626.0
num_examples: 2000
download_size: 2687176
dataset_size: 5096260.0
---
# Dataset Card for "mr_en_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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tomaarsen/setfit-absa-semeval-laptops
|
tomaarsen
| 2023-11-16T10:38:19Z | 38 | 0 |
[
"region:us"
] | null | 2023-11-09T15:14:52Z |
---
dataset_info:
features:
- name: text
dtype: string
- name: span
dtype: string
- name: label
dtype: string
- name: ordinal
dtype: int64
splits:
- name: train
num_bytes: 335243
num_examples: 2358
- name: test
num_bytes: 76698
num_examples: 654
download_size: 146971
dataset_size: 411941
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Dataset Card for "tomaarsen/setfit-absa-semeval-laptops"
### Dataset Summary
This dataset contains the manually annotated laptop reviews from SemEval-2014 Task 4, in the format as
understood by [SetFit](https://github.com/huggingface/setfit) ABSA.
For more details, see https://aclanthology.org/S14-2004/
### Data Instances
An example of "train" looks as follows.
```json
{"text": "I charge it at night and skip taking the cord with me because of the good battery life.", "span": "cord", "label": "neutral", "ordinal": 0}
{"text": "I charge it at night and skip taking the cord with me because of the good battery life.", "span": "battery life", "label": "positive", "ordinal": 0}
{"text": "The tech guy then said the service center does not do 1-to-1 exchange and I have to direct my concern to the \"sales\" team, which is the retail shop which I bought my netbook from.", "span": "service center", "label": "negative", "ordinal": 0}
{"text": "The tech guy then said the service center does not do 1-to-1 exchange and I have to direct my concern to the \"sales\" team, which is the retail shop which I bought my netbook from.", "span": "\"sales\" team", "label": "negative", "ordinal": 0}
{"text": "The tech guy then said the service center does not do 1-to-1 exchange and I have to direct my concern to the \"sales\" team, which is the retail shop which I bought my netbook from.", "span": "tech guy", "label": "neutral", "ordinal": 0}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
- `span`: a `string` feature showing the aspect span from the text.
- `label`: a `string` feature showing the polarity of the aspect span.
- `ordinal`: an `int64` feature showing the n-th occurrence of the span in the text. This is useful for if the span occurs within the same text multiple times.
### Data Splits
| name |train|test|
|---------|----:|---:|
|tomaarsen/setfit-absa-semeval-laptops|2358|654|
### Training ABSA models using SetFit ABSA
To train using this dataset, first install the SetFit library:
```bash
pip install setfit
```
And then you can use the following script as a guideline of how to train an ABSA model on this dataset:
```python
from setfit import AbsaModel, AbsaTrainer, TrainingArguments
from datasets import load_dataset
from transformers import EarlyStoppingCallback
# You can initialize a AbsaModel using one or two SentenceTransformer models, or two ABSA models
model = AbsaModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
# The training/eval dataset must have `text`, `span`, `polarity`, and `ordinal` columns
dataset = load_dataset("tomaarsen/setfit-absa-semeval-laptops")
train_dataset = dataset["train"]
eval_dataset = dataset["test"]
args = TrainingArguments(
output_dir="models",
use_amp=True,
batch_size=256,
eval_steps=50,
save_steps=50,
load_best_model_at_end=True,
)
trainer = AbsaTrainer(
model,
args=args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
callbacks=[EarlyStoppingCallback(early_stopping_patience=5)],
)
trainer.train()
metrics = trainer.evaluate(eval_dataset)
print(metrics)
trainer.push_to_hub("tomaarsen/setfit-absa-laptops")
```
You can then run inference like so:
```python
from setfit import AbsaModel
# Download from Hub and run inference
model = AbsaModel.from_pretrained(
"tomaarsen/setfit-absa-laptops-aspect",
"tomaarsen/setfit-absa-laptops-polarity",
)
# Run inference
preds = model([
"Boots up fast and runs great!",
"The screen shows great colors.",
])
```
### Citation Information
```bibtex
@inproceedings{pontiki-etal-2014-semeval,
title = "{S}em{E}val-2014 Task 4: Aspect Based Sentiment Analysis",
author = "Pontiki, Maria and
Galanis, Dimitris and
Pavlopoulos, John and
Papageorgiou, Harris and
Androutsopoulos, Ion and
Manandhar, Suresh",
editor = "Nakov, Preslav and
Zesch, Torsten",
booktitle = "Proceedings of the 8th International Workshop on Semantic Evaluation ({S}em{E}val 2014)",
month = aug,
year = "2014",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S14-2004",
doi = "10.3115/v1/S14-2004",
pages = "27--35",
}
```
|
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vinhtran2611/zaloai-2023-nlp-train-vi
|
vinhtran2611
| 2023-11-11T01:39:57Z | 38 | 0 |
[
"region:us"
] | null | 2023-11-10T04:10:05Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: dev
path: data/dev-*
dataset_info:
features:
- name: choices
sequence: string
- name: explanation
dtype: string
- name: question
dtype: string
- name: id
dtype: string
- name: answer
dtype: string
- name: labels
dtype: string
splits:
- name: train
num_bytes: 248715
num_examples: 960
- name: test
num_bytes: 30160
num_examples: 120
- name: dev
num_bytes: 30996
num_examples: 120
download_size: 175194
dataset_size: 309871
---
# Dataset Card for "zaloai-2023-nlp-train-vi"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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jlbaker361/addition_whole
|
jlbaker361
| 2023-11-15T13:00:03Z | 38 | 0 |
[
"region:us"
] | null | 2023-11-14T23:39:10Z |
---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: float64
- name: text
dtype: string
splits:
- name: train
num_bytes: 1210405.5
num_examples: 29376
- name: test
num_bytes: 134489.5
num_examples: 3264
download_size: 730504
dataset_size: 1344895.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Dataset Card for "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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] |
jlbaker361/dumb_whole
|
jlbaker361
| 2023-11-17T05:53:47Z | 38 | 0 |
[
"region:us"
] | null | 2023-11-15T03:13:54Z |
---
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: 225.0
num_examples: 9
- name: test
num_bytes: 25
num_examples: 1
download_size: 3294
dataset_size: 250.0
---
# Dataset Card for "dumb_whole"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
flozi00/no_robots_german
|
flozi00
| 2023-11-21T08:13:15Z | 38 | 0 |
[
"region:us"
] | null | 2023-11-15T12:41:28Z |
---
dataset_info:
features:
- name: messages
dtype: string
- name: category
dtype: string
splits:
- name: train
num_bytes: 14328669
num_examples: 9499
download_size: 8502633
dataset_size: 14328669
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "no_robots_german"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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CrossDimitri/CrossDimitriSolutionsText
|
CrossDimitri
| 2023-11-23T21:49:08Z | 38 | 0 |
[
"license:llama2",
"region:us"
] | null | 2023-11-15T16:07:39Z |
---
license: llama2
---
|
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bigheiniuJ/BBH_eval
|
bigheiniuJ
| 2023-11-15T20:55:24Z | 38 | 0 |
[
"region:us"
] | null | 2023-11-15T20:53:39Z |
---
dataset_info:
features:
- name: input
dtype: string
- name: target
dtype: string
- name: task
dtype: string
- name: options
sequence: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 2641563
num_examples: 4071
download_size: 570189
dataset_size: 2641563
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "BBH_eval"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
Foxasdf/common_voice_v3_en_whisper-base.en
|
Foxasdf
| 2023-11-17T14:11:40Z | 38 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-16T22:58:13Z |
---
license: mit
dataset_info:
features:
- name: input_features
sequence:
sequence: float32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 18247456336
num_examples: 18999
- name: test
num_bytes: 3795631576
num_examples: 3952
download_size: 3870097815
dataset_size: 22043087912
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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] |
jlbaker361/small_division_decimal
|
jlbaker361
| 2023-11-17T05:53:57Z | 38 | 0 |
[
"region:us"
] | null | 2023-11-17T04:47:43Z |
---
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: 1589.3333333333333
num_examples: 32
- name: test
num_bytes: 198.66666666666666
num_examples: 4
download_size: 4415
dataset_size: 1788.0
---
# Dataset Card for "small_division_decimal"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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Kimata/gpt_driver_dataset_processed
|
Kimata
| 2023-11-24T15:27:06Z | 38 | 0 |
[
"region:us"
] | null | 2023-11-24T15:26:49Z |
---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 66417387
num_examples: 70164
- name: test
num_bytes: 14265987
num_examples: 15357
download_size: 7590882
dataset_size: 80683374
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
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] |
ScandEval/nordjylland-news-mini
|
ScandEval
| 2023-11-27T14:21:45Z | 38 | 0 |
[
"region:us"
] | null | 2023-11-27T14:21:37Z |
---
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: target_text
dtype: string
- name: text_len
dtype: int64
- name: summary_len
dtype: int64
splits:
- name: train
num_bytes: 1588698
num_examples: 1024
- name: val
num_bytes: 392467
num_examples: 256
- name: test
num_bytes: 3268194
num_examples: 2048
download_size: 3271613
dataset_size: 5249359
---
# Dataset Card for "nordjylland-news-mini"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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tolu07/Mental_Health_FAQ
|
tolu07
| 2023-11-28T06:33:34Z | 38 | 0 |
[
"task_categories:conversational",
"task_categories:text-generation",
"license:mit",
"chatbot",
"mental health",
"therapy",
"region:us"
] |
[
"conversational",
"text-generation"
] | 2023-11-28T06:24:38Z |
---
license: mit
task_categories:
- conversational
- text-generation
tags:
- chatbot
- mental health
- therapy
---
**Content**
Mental health includes our emotional, psychological, and social well-being. Mental health is integral to living a healthy, balanced life. It affects how we think, feel, and act. It also helps determine how we handle stress, relate to others, and make choices. Emotional and mental health is important because it’s a vital part of your life and impacts your thoughts, behaviors and emotions. Being healthy emotionally can promote productivity and effectiveness in activities like work, school or care-giving. It plays an important part in the health of your relationships, and allows you to adapt to changes in your life and cope with adversity. Mental health problems are common but help is available. People with mental health problems can get better and many recover completely.
This dataset consists of FAQs about Mental Health.
**Acknowledgements**
https://www.thekimfoundation.org/faqs/
https://www.mhanational.org/frequently-asked-questions
https://www.wellnessinmind.org/frequently-asked-questions/
https://www.heretohelp.bc.ca/questions-and-answers
|
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] |
amaye15/Stack-Overflow-Zero-Shot-Classification
|
amaye15
| 2023-12-01T16:26:50Z | 38 | 0 |
[
"region:us"
] | null | 2023-11-28T10:29:35Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: Title
dtype: string
- name: Tags
dtype: string
- name: Predicted_Tags
dtype: string
- name: Predicted_Tag_Scores
sequence: float64
splits:
- name: train
num_bytes: 27853258
num_examples: 111030
download_size: 16579853
dataset_size: 27853258
---
# Dataset Card for "Stack-Overflow-Zero-Shot-Classification"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
Broomva/translation_guc_spa
|
Broomva
| 2023-12-01T06:13:45Z | 38 | 0 |
[
"region:us"
] | null | 2023-11-30T00:20:00Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: int64
- name: translation
struct:
- name: guc
dtype: string
- name: spa
dtype: string
splits:
- name: train
num_bytes: 13179247
num_examples: 76676
- name: validation
num_bytes: 3254055
num_examples: 19170
- name: test
num_bytes: 4127645
num_examples: 23962
download_size: 13149156
dataset_size: 20560947
---
# Dataset Card for "translation_guc_spa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
minhalvp/islamqa
|
minhalvp
| 2023-12-02T17:26:57Z | 38 | 0 |
[
"task_categories:text-generation",
"task_categories:conversational",
"size_categories:1K<n<10K",
"license:apache-2.0",
"region:us"
] |
[
"text-generation",
"conversational"
] | 2023-12-02T16:58:14Z |
---
license: apache-2.0
size_categories:
- 1K<n<10K
task_categories:
- text-generation
- conversational
dataset_info:
features:
- name: Question
dtype: string
- name: Full Answer
dtype: string
splits:
- name: train
num_bytes: 10259356
num_examples: 3355
download_size: 5425524
dataset_size: 10259356
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
Data is scraped from https://islamqa.info/. Data contains islamic questions and its corrseponding answers from scholars.
Example:
| Question | Full Answer |
| -------- | ----------- |
| What should a person do if one of the areas normally washed during wudu is wounded? | Praise be to Allah. If a person is wounded in one of the areas of wudu and is not able to cover the wound with a bandage, he should perform wudu except for the wounded body part, for which he should then perform ablution with sand (tayammum). 1 (Note that this means one performs normal tayammum, not actually placing the sand on the wound itself.) 1 Al-Mughni ma'a al-Sharh il-Kabeer 1/282. |
|
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ruanchaves/bt11
|
ruanchaves
| 2022-10-20T19:13:02Z | 37 | 0 |
[
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:code",
"license:unknown",
"word-segmentation",
"region:us"
] |
[
"structure-prediction"
] | 2022-03-05T22:41:34Z |
---
annotations_creators:
- expert-generated
language_creators:
- machine-generated
language:
- code
license:
- unknown
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- structure-prediction
task_ids: []
pretty_name: BT11
tags:
- word-segmentation
---
# Dataset Card for BT11
## Dataset Description
- **Paper:** [Helpful or Not? An investigation on the feasibility of identifier splitting via CNN-BiLSTM-CRF](https://ksiresearch.org/seke/seke18paper/seke18paper_167.pdf)
### Dataset Summary
In programming languages, identifiers are tokens (also called symbols) which name language entities.
Some of the kinds of entities an identifier might denote include variables, types, labels, subroutines, and packages.
BT11 is a dataset for identifier segmentation, i.e. the task of adding spaces between the words on a identifier.
### Languages
- Java
## Dataset Structure
### Data Instances
```
{
"index": 20170,
"identifier": "currentLineHighlight",
"segmentation": "current Line Highlight"
}
```
### Data Fields
- `index`: a numerical index.
- `identifier`: the original identifier.
- `segmentation`: the gold segmentation for the identifier.
## Dataset Creation
- All hashtag segmentation and identifier splitting datasets on this profile have the same basic fields: `hashtag` and `segmentation` or `identifier` and `segmentation`.
- The only difference between `hashtag` and `segmentation` or between `identifier` and `segmentation` are the whitespace characters. Spell checking, expanding abbreviations or correcting characters to uppercase go into other fields.
- There is always whitespace between an alphanumeric character and a sequence of any special characters ( such as `_` , `:`, `~` ).
- If there are any annotations for named entity recognition and other token classification tasks, they are given in a `spans` field.
## Additional Information
### Citation Information
```
@inproceedings{butler2011improving,
title={Improving the tokenisation of identifier names},
author={Butler, Simon and Wermelinger, Michel and Yu, Yijun and Sharp, Helen},
booktitle={European Conference on Object-Oriented Programming},
pages={130--154},
year={2011},
organization={Springer}
}
```
### Contributions
This dataset was added by [@ruanchaves](https://github.com/ruanchaves) while developing the [hashformers](https://github.com/ruanchaves/hashformers) library.
|
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] |
ruanchaves/jhotdraw
|
ruanchaves
| 2022-10-20T19:12:53Z | 37 | 0 |
[
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:code",
"license:unknown",
"word-segmentation",
"region:us"
] |
[
"structure-prediction"
] | 2022-03-05T23:13:37Z |
---
annotations_creators:
- expert-generated
language_creators:
- machine-generated
language:
- code
license:
- unknown
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- structure-prediction
task_ids: []
pretty_name: Jhotdraw
tags:
- word-segmentation
---
# Dataset Card for Jhotdraw
## Dataset Description
- **Paper:** [Helpful or Not? An investigation on the feasibility of identifier splitting via CNN-BiLSTM-CRF](https://ksiresearch.org/seke/seke18paper/seke18paper_167.pdf)
### Dataset Summary
In programming languages, identifiers are tokens (also called symbols) which name language entities.
Some of the kinds of entities an identifier might denote include variables, types, labels, subroutines, and packages.
Jhotdraw is a dataset for identifier segmentation, i.e. the task of adding spaces between the words on a identifier.
### Languages
- Java
## Dataset Structure
### Data Instances
```
{
"index": 0,
"identifier": "abstractconnectorserializeddataversion",
"segmentation": "abstract connector serialized data version"
}
```
### Data Fields
- `index`: a numerical index.
- `identifier`: the original identifier.
- `segmentation`: the gold segmentation for the identifier.
## Dataset Creation
- All hashtag segmentation and identifier splitting datasets on this profile have the same basic fields: `hashtag` and `segmentation` or `identifier` and `segmentation`.
- The only difference between `hashtag` and `segmentation` or between `identifier` and `segmentation` are the whitespace characters. Spell checking, expanding abbreviations or correcting characters to uppercase go into other fields.
- There is always whitespace between an alphanumeric character and a sequence of any special characters ( such as `_` , `:`, `~` ).
- If there are any annotations for named entity recognition and other token classification tasks, they are given in a `spans` field.
## Additional Information
### Citation Information
```
@inproceedings{madani2010recognizing,
title={Recognizing words from source code identifiers using speech recognition techniques},
author={Madani, Nioosha and Guerrouj, Latifa and Di Penta, Massimiliano and Gueheneuc, Yann-Gael and Antoniol, Giuliano},
booktitle={2010 14th European Conference on Software Maintenance and Reengineering},
pages={68--77},
year={2010},
organization={IEEE}
}
```
### Contributions
This dataset was added by [@ruanchaves](https://github.com/ruanchaves) while developing the [hashformers](https://github.com/ruanchaves/hashformers) library.
|
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BigScienceBiasEval/bias-shades
|
BigScienceBiasEval
| 2022-10-03T13:49:04Z | 37 | 1 |
[
"license:cc-by-sa-4.0",
"region:us"
] | null | 2022-04-28T16:46:11Z |
---
license: cc-by-sa-4.0
---
Possibly a placeholder dataset for the original here: https://huggingface.co/datasets/bigscience-catalogue-data/bias-shades
# Data Statement for SHADES
> **How to use this document:**
> Fill in each section according to the instructions. Give as much detail as you can, but there's no need to extrapolate. The goal is to help people understand your data when they approach it. This could be someone looking at it in ten years, or it could be you yourself looking back at the data in two years.
> For full details, the best source is the original Data Statements paper, here: https://www.aclweb.org/anthology/Q18-1041/ .
> Instruction fields are given as blockquotes; delete the instructions when you're done, and provide the file with your data, for example as "DATASTATEMENT.md". The lists in some blocks are designed to be filled in, but it's good to also leave a written description of what's happening, as well as the list. It's fine to skip some fields if the information isn't known.
> Only blockquoted content should be deleted; the final about statement should be left intact.
Data set name: Bias-Shades
Citation (if available): TODO.
Data set developer(s): This dataset was compiled by dozens of research scientists through the BigScience open science collaboration. Collaborators, representing numerous cultures and languages, joined the project of their own volition.
Data statement author(s): Shayne Longpre, Aurélie Névéol, Shanya Sharma[Add name here if you add/edit the data statement :)].
Others who contributed to this document: N/A
License: Creative Commons Attribution-ShareAlike 4.0 (CC BY-SA 4.0).
## A. CURATION RATIONALE
> *Explanation.* Which texts were included and what were the goals in selecting texts, both in the original collection and in any further sub-selection? This can be especially important in datasets too large to thoroughly inspect by hand. An explicit statement of the curation rationale can help dataset users make inferences about what other kinds of texts systems trained with them could conceivably generalize to.
This dataset was curated by hand-crafting stereotype sentences by native speakers from the culture which is being targeted. An initial set of sentences was inferred from stereotypes expressed in the crowS-pairs data set(Nangia et al.). Native speakers first crafted templates for sentences expressing a stereotype. These templates are marked for gender and plurality of the target nouns, so the template can be reused by substituting different targets. Next, the template-target noun pair combinations were annotated for the veracity/reliability of the expressed stereotype. The resulting sentences express common and less common stereotypes in a variety of cultures and languages.
## B. LANGUAGE VARIETY/VARIETIES
> *Explanation.* Languages differ from each other in structural ways that can interact with NLP algorithms. Within a language, regional or social dialects can also show great variation (Chambers and Trudgill, 1998). The language and language variety should be described with a language tag from BCP-47 identifying the language variety (e.g., en-US or yue-Hant-HK), and a prose description of the language variety, glossing the BCP-47 tag and also providing further information (e.g., "English as spoken in Palo Alto, California", or "Cantonese written with traditional characters by speakers in Hong Kong who are bilingual in Mandarin").
* BCP-47 language tags: en-US, fr-FR, hi-IN, es-DO, ar-LY, ru-RU, de-DE, nl-NL, ta-IN.
* Language variety description: English spoken by native speakers of the United States, native French people from metropolitan France, native Hindi and Tamil speakers from India, Spanish speakers from the Dominican Republic, Arabic speakers from Libya, Russian speakers from Russia, German speakers from Germany, and Dutch speakers from the Netherlands.
## C. CONTRIBUTOR DEMOGRAPHIC
> ## C. SPEAKER DEMOGRAPHIC
> *Explanation.* Sociolinguistics has found that variation (in pronunciation, prosody, word choice, and grammar) correlates with speaker demographic characteristics (Labov, 1966), as speakers use linguistic variation to construct and project identities (Eckert and Rickford, 2001). Transfer from native languages (L1) can affect the language produced by non-native (L2) speakers (Ellis, 1994, Ch. 8). A further important type of variation is disordered speech (e.g., dysarthria). Specifications include:
Participants to the collection project were recruited through the HuggingFace BigScience project, and specifically the Bias and Fairness Evaluation group. Listed below.
Speakers:
* [ADD YOURSELF!]
* Shayne Longpre: English-speaking, male, 28 years old, culturally Canadian.
* Aurélie Névéol: French (native), English and Spanish speaking, female, 44 years old, culturally French (also familiar with American culture)
* Shanya Sharma: Hindi(native), English speaking, female, 24 years old, culturally Indian
* Margaret Mitchell: English, female, mid-30s, U.S.A.
* Maraim Masoud: Arabic, English Speaking female.
## D. ANNOTATOR DEMOGRAPHIC
> *Explanation.* What are the demographic characteristics of the annotators and annotation guideline developers? Their own “social address” influences their experience with language and thus their perception of what they are annotating. Specifications include:
Participants to the collection project were recruited through the HuggingFace BigScience project, and specifically the Bias and Fairness Evaluation group. Speaker and annotator contributors listed in section C.
## E. SPEECH SITUATION
N/A
## F. TEXT CHARACTERISTICS
> *Explanation.* Both genre and topic influence the vocabulary and structural characteristics of texts (Biber, 1995), and should be specified.
Collected data is a collection of offensive stereotyped statements in numerous languages and cultures. They might be upsetting and/or offensive.
Along with these stereotyped statements are annotation judgements of how prevalent/real the expressed stereotypes are in the real world. Some statements were created from templates with substituted target nouns, and therefore may express an uncommon or unlikely stereotype.
## G. RECORDING QUALITY
N/A
## H. OTHER
> *Explanation.* There may be other information of relevance as well. Please use this space to develop any further categories that are relevant for your dataset.
## I. PROVENANCE APPENDIX
This initiative is part of the BigScience Workshop: https://bigscience.huggingface.co/.
## About this document
A data statement is a characterization of a dataset that provides context to allow developers and users to better understand how experimental results might generalize, how software might be appropriately deployed, and what biases might be reflected in systems built on the software.
Data Statements are from the University of Washington. Contact: [[email protected]](mailto:[email protected]). This document template is licensed as [CC0](https://creativecommons.org/share-your-work/public-domain/cc0/).
This version of the markdown Data Statement is from June 4th 2020. The Data Statement template is based on worksheets distributed at the [2020 LREC workshop on Data Statements](https://sites.google.com/uw.edu/data-statements-for-nlp/), by Emily M. Bender, Batya Friedman, and Angelina McMillan-Major. Adapted to community Markdown template by Leon Dercyznski.
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amueller/syntactic_transformations
|
amueller
| 2022-10-23T06:11:48Z | 37 | 0 |
[
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:2 languages",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"language:de",
"license:mit",
"region:us"
] |
[
"syntactic-evaluation"
] | 2022-06-13T06:03:08Z |
---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
- de
license:
- mit
multilinguality:
- 2 languages
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- syntactic-evaluation
task_ids:
- syntactic-transformations
---
# Dataset Card for syntactic_transformations
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** https://github.com/sebschu/multilingual-transformations
- **Paper:** [Coloring the Blank Slate: Pre-training Imparts a Hierarchical Inductive Bias to Sequence-to-sequence Models](https://aclanthology.org/2022.findings-acl.106/)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Aaron Mueller](mailto:[email protected])
### Dataset Summary
This contains the the syntactic transformations datasets used in [Coloring the Blank Slate: Pre-training Imparts a Hierarchical Inductive Bias to Sequence-to-sequence Models](https://aclanthology.org/2022.findings-acl.106/). It consists of English and German question formation and passivization transformations. This dataset also contains zero-shot cross-lingual transfer training and evaluation data.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
English and German.
## Dataset Structure
### Data Instances
A typical data point consists of a source sequence ("src"), a target sequence ("tgt"), and a task prefix ("prefix"). The prefix indicates whether a given sequence should be kept the same in the target (indicated by the "decl:" prefix) or transformed into a question/passive ("quest:"/"passiv:", respectively). An example follows:
{"src": "the yak has entertained the walruses that have amused the newt.",
"tgt": "has the yak entertained the walruses that have amused the newt?",
"prefix": "quest: "
}
### Data Fields
- src: the original source sequence.
- tgt: the transformed target sequence.
- prefix: indicates which transformation to perform to map from the source to target sequences.
### Data Splits
The datasets are split into training, dev, test, and gen ("generalization") sets. The training sets are for fine-tuning the model. The dev and test sets are for evaluating model abilities on in-domain transformations. The generalization sets are for evaluating the inductive biases of the model.
NOTE: for the zero-shot cross-lingual transfer datasets, the generalization sets are split into in-domain and out-of-domain syntactic structures. For in-domain transformations, use "gen_rc_o" for question formation or "gen_pp_o" for passivization. For out-of-domain transformations, use "gen_rc_s" for question formation or "gen_pp_s" for passivization.
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
[Needs More Information]
|
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] |
jakartaresearch/news-title-gen
|
jakartaresearch
| 2022-08-13T06:32:12Z | 37 | 1 |
[
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:id",
"license:cc-by-4.0",
"newspapers",
"title",
"news",
"region:us"
] |
[
"text-generation"
] | 2022-08-13T01:39:26Z |
---
annotations_creators:
- no-annotation
language:
- id
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Indonesian News Title Generation
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- newspapers
- title
- news
task_categories:
- text-generation
task_ids:
- language-modeling
---
# Dataset Card for Indonesian News Title Generation
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### 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 [@andreaschandra](https://github.com/andreaschandra) for adding this dataset.
|
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copenlu/tydiqa_copenlu
|
copenlu
| 2022-08-16T12:10:21Z | 37 | 0 |
[
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:unknown",
"source_datasets:extended|wikipedia",
"language:ar",
"language:bn",
"language:en",
"language:fi",
"language:id",
"language:ja",
"language:ko",
"language:ru",
"language:sw",
"language:te",
"language:th",
"license:apache-2.0",
"region:us"
] |
[
"question-answering"
] | 2022-08-16T12:04:50Z |
---
pretty_name: TyDi QA
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- ar
- bn
- en
- fi
- id
- ja
- ko
- ru
- sw
- te
- th
license:
- apache-2.0
multilinguality:
- multilingual
size_categories:
- unknown
source_datasets:
- extended|wikipedia
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: tydi-qa
---
# Dataset Card for "tydiqa"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/google-research-datasets/tydiqa](https://github.com/google-research-datasets/tydiqa)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 3726.74 MB
- **Size of the generated dataset:** 5812.92 MB
- **Total amount of disk used:** 9539.67 MB
### Dataset Summary
TyDi QA is a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs.
The languages of TyDi QA are diverse with regard to their typology -- the set of linguistic features that each language
expresses -- such that we expect models performing well on this set to generalize across a large number of the languages
in the world. It contains language phenomena that would not be found in English-only corpora. To provide a realistic
information-seeking task and avoid priming effects, questions are written by people who want to know the answer, but
don’t know the answer yet, (unlike SQuAD and its descendents) and the data is collected directly in each language without
the use of translation (unlike MLQA and XQuAD).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### primary_task
- **Size of downloaded dataset files:** 1863.37 MB
- **Size of the generated dataset:** 5757.59 MB
- **Total amount of disk used:** 7620.96 MB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"annotations": {
"minimal_answers_end_byte": [-1, -1, -1],
"minimal_answers_start_byte": [-1, -1, -1],
"passage_answer_candidate_index": [-1, -1, -1],
"yes_no_answer": ["NONE", "NONE", "NONE"]
},
"document_plaintext": "\"\\nรองศาสตราจารย์[1] หม่อมราชวงศ์สุขุมพันธุ์ บริพัตร (22 กันยายน 2495 -) ผู้ว่าราชการกรุงเทพมหานครคนที่ 15 อดีตรองหัวหน้าพรรคปร...",
"document_title": "หม่อมราชวงศ์สุขุมพันธุ์ บริพัตร",
"document_url": "\"https://th.wikipedia.org/wiki/%E0%B8%AB%E0%B8%A1%E0%B9%88%E0%B8%AD%E0%B8%A1%E0%B8%A3%E0%B8%B2%E0%B8%8A%E0%B8%A7%E0%B8%87%E0%B8%...",
"language": "thai",
"passage_answer_candidates": "{\"plaintext_end_byte\": [494, 1779, 2931, 3904, 4506, 5588, 6383, 7122, 8224, 9375, 10473, 12563, 15134, 17765, 19863, 21902, 229...",
"question_text": "\"หม่อมราชวงศ์สุขุมพันธุ์ บริพัตร เรียนจบจากที่ไหน ?\"..."
}
```
#### secondary_task
- **Size of downloaded dataset files:** 1863.37 MB
- **Size of the generated dataset:** 55.34 MB
- **Total amount of disk used:** 1918.71 MB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"answers": {
"answer_start": [394],
"text": ["بطولتين"]
},
"context": "\"أقيمت البطولة 21 مرة، شارك في النهائيات 78 دولة، وعدد الفرق التي فازت بالبطولة حتى الآن 8 فرق، ويعد المنتخب البرازيلي الأكثر تت...",
"id": "arabic-2387335860751143628-1",
"question": "\"كم عدد مرات فوز الأوروغواي ببطولة كاس العالم لكرو القدم؟\"...",
"title": "قائمة نهائيات كأس العالم"
}
```
### Data Fields
The data fields are the same among all splits.
#### primary_task
- `passage_answer_candidates`: a dictionary feature containing:
- `plaintext_start_byte`: a `int32` feature.
- `plaintext_end_byte`: a `int32` feature.
- `question_text`: a `string` feature.
- `document_title`: a `string` feature.
- `language`: a `string` feature.
- `annotations`: a dictionary feature containing:
- `passage_answer_candidate_index`: a `int32` feature.
- `minimal_answers_start_byte`: a `int32` feature.
- `minimal_answers_end_byte`: a `int32` feature.
- `yes_no_answer`: a `string` feature.
- `document_plaintext`: a `string` feature.
- `document_url`: a `string` feature.
#### secondary_task
- `id`: a `string` feature.
- `title`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
### Data Splits
| name | train | validation |
| -------------- | -----: | ---------: |
| primary_task | 166916 | 18670 |
| secondary_task | 49881 | 5077 |
## 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{tydiqa,
title = {TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
author = {Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki}
year = {2020},
journal = {Transactions of the Association for Computational Linguistics}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@albertvillanova](https://github.com/albertvillanova), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.
|
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dreamproit/bill_summary_us
|
dreamproit
| 2023-10-17T04:16:57Z | 37 | 4 |
[
"task_categories:summarization",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"bills",
"legal",
"region:us"
] |
[
"summarization"
] | 2022-11-09T10:13:33Z |
---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- expert-generated
multilinguality:
- monolingual
pretty_name: bill_summary_us
size_categories:
- 100K<n<1M
source_datasets:
- original
tags:
- bills
- legal
task_categories:
- summarization
task_ids: []
configs:
- config_name: default
---
# Dataset Card for "bill_summary_us"
## 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:** [BillML](https://github.com/dreamproit/BillML)
- **Repository:** [BillML](https://github.com/dreamproit/BillML)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Leaderboard:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Dataset Summary
Dataset for summarization of summarization of US Congressional bills (bill_summary_us).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
English
## Dataset Structure
### Data Instances
#### default
### Data Fields
- id: id of the bill in format(congress number + bill type + bill number + bill version).
- congress: number of the congress.
- bill_type: type of the bill.
- bill_number: number of the bill.
- bill_version: version of the bill.
- sections: list of bill sections with section_id, text and header.
- sections_length: number with lenght of the sections list.
- text: bill text.
- text_length: number of characters in the text.
- summary: summary of the bill.
- summary_length: number of characters in the summary.
- title: official title of the bill.
### Data Splits
train
## Dataset Creation
### Curation Rationale
Bills (proposed laws) are specialized, structured documents with great public significance. Often, the language of a bill may not directly explain the potential impact of the legislation. For bills in the U.S. Congress, the Congressional Research Service of the Library of Congress provides professional, non-partisan summaries of bills. These are valuable for public understanding of the bills and are serve as an essential part of the lawmaking process to understand the meaning and potential legislative impact.
This dataset collects the text of bills, some metadata, as well as the CRS summaries. In order to build more accurate ML models for bill summarization it is important to have a clean dataset, alongside the professionally-written CRS summaries. ML summarization models built on generic data are bound to produce less accurate results (sometimes creating summaries that describe the opposite of a bill's actual effect). In addition, models that attempt to summarize all bills (some of which may reach 4000 pages long) may also be inaccurate due to the current limitations of summarization on long texts.
As a result, this dataset collects bill and summary information; it provides text as a list of sections with the text and header. This could be used to create a summary of sections and then a summary of summaries.
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
[govinfo.gov](https://www.govinfo.gov/)
#### Initial Data Collection and Normalization
The data consists of the US congress bills that were collected from the [govinfo.gov](https://www.govinfo.gov/) service provided by the United States Government Publishing Office (GPO) under CC0-1.0 license.
#### 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]
#### 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
[dreamproit.com](https://dreamproit.com/)
### Licensing Information
Bill and summary information are public and are unlicensed, as it is data produced by government entities. The collection and enhancement work that we provide for this dataset, to the degree it may be covered by copyright, is released under [CC0](https://creativecommons.org/share-your-work/public-domain/cc0/).
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@aih](https://github.com/aih) [@BorodaUA](https://github.com/BorodaUA), [@alexbojko](https://github.com/alexbojko) for adding this dataset.
|
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NeelNanda/pile-old-tokenized-2b
|
NeelNanda
| 2022-11-13T21:29:57Z | 37 | 0 |
[
"region:us"
] | null | 2022-11-13T21:17:07Z |
---
dataset_info:
features:
- name: tokens
sequence: int32
splits:
- name: train
num_bytes: 8200000000
num_examples: 2000000
download_size: 3352864661
dataset_size: 8200000000
---
# Dataset Card for "pile-tokenized-2b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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bsmock/pubtables-1m
|
bsmock
| 2023-08-08T16:43:14Z | 37 | 21 |
[
"license:cdla-permissive-2.0",
"region:us"
] | null | 2022-11-22T18:59:39Z |
---
license: cdla-permissive-2.0
---
# PubTables-1M
- GitHub: [https://github.com/microsoft/table-transformer](https://github.com/microsoft/table-transformer)
- Paper: ["PubTables-1M: Towards comprehensive table extraction from unstructured documents"](https://openaccess.thecvf.com/content/CVPR2022/html/Smock_PubTables-1M_Towards_Comprehensive_Table_Extraction_From_Unstructured_Documents_CVPR_2022_paper.html)
- Hugging Face:
- [Detection model](https://huggingface.co/microsoft/table-transformer-detection)
- [Structure recognition model](https://huggingface.co/microsoft/table-transformer-structure-recognition)
Currently we only support downloading the dataset as tar.gz files. Integrating with HuggingFace Datasets is something we hope to support in the future!
Please switch to the "Files and versions" tab to download all of the files or use a command such as wget to download from the command line.
Once downloaded, use the included script "extract_structure_dataset.sh" to extract and organize all of the data.
## Files
It comes in 18 tar.gz files:
Training and evaluation data for the structure recognition model (947,642 total cropped table instances):
- PubTables-1M-Structure_Filelists.tar.gz
- PubTables-1M-Structure_Annotations_Test.tar.gz: 93,834 XML files containing bounding boxes in PASCAL VOC format
- PubTables-1M-Structure_Annotations_Train.tar.gz: 758,849 XML files containing bounding boxes in PASCAL VOC format
- PubTables-1M-Structure_Annotations_Val.tar.gz: 94,959 XML files containing bounding boxes in PASCAL VOC format
- PubTables-1M-Structure_Images_Test.tar.gz
- PubTables-1M-Structure_Images_Train.tar.gz
- PubTables-1M-Structure_Images_Val.tar.gz
- PubTables-1M-Structure_Table_Words.tar.gz: Bounding boxes and text content for all of the words in each cropped table image
Training and evaluation data for the detection model (575,305 total document page instances):
- PubTables-1M-Detection_Filelists.tar.gz
- PubTables-1M-Detection_Annotations_Test.tar.gz: 57,125 XML files containing bounding boxes in PASCAL VOC format
- PubTables-1M-Detection_Annotations_Train.tar.gz: 460,589 XML files containing bounding boxes in PASCAL VOC format
- PubTables-1M-Detection_Annotations_Val.tar.gz: 57,591 XML files containing bounding boxes in PASCAL VOC format
- PubTables-1M-Detection_Images_Test.tar.gz
- PubTables-1M-Detection_Images_Train_Part1.tar.gz
- PubTables-1M-Detection_Images_Train_Part2.tar.gz
- PubTables-1M-Detection_Images_Val.tar.gz
- PubTables-1M-Detection_Page_Words.tar.gz: Bounding boxes and text content for all of the words in each page image (plus some unused files)
Full table annotations for the source PDF files:
- PubTables-1M-PDF_Annotations.tar.gz: Detailed annotations for all of the tables appearing in the source PubMed PDFs. All annotations are in PDF coordinates.
- 401,733 JSON files, one per source PDF document
|
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jakartaresearch/causalqa
|
jakartaresearch
| 2022-11-25T12:26:42Z | 37 | 0 |
[
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"question-answering",
"english",
"causal",
"region:us"
] |
[
"question-answering"
] | 2022-11-25T10:23:48Z |
---
annotations_creators:
- found
language:
- en
language_creators:
- found
license: []
multilinguality:
- monolingual
pretty_name: CausalQA
size_categories:
- 1M<n<10M
source_datasets:
- original
tags:
- question-answering
- english
- causal
task_categories:
- question-answering
task_ids:
- extractive-qa
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### 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 [@alamhanz](https://github.com/alamhanz) and [@andreaschandra](https://github.com/andreaschandra) for adding this dataset.
|
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] |
DTU54DL/common-accent
|
DTU54DL
| 2022-11-30T13:25:07Z | 37 | 2 |
[
"task_categories:token-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"region:us"
] |
[
"token-classification"
] | 2022-11-30T07:46:58Z |
---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- found
license:
- mit
multilinguality:
- monolingual
paperswithcode_id: acronym-identification
pretty_name: Acronym Identification Dataset
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- token-classification-other-acronym-identification
train-eval-index:
- col_mapping:
labels: tags
tokens: tokens
config: default
splits:
eval_split: test
task: token-classification
task_id: entity_extraction
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: sentence
dtype: string
- name: accent
dtype: string
splits:
- name: train
num_bytes: 471755846.3910719
num_examples: 10000
- name: test
num_bytes: 19497172.25755167
num_examples: 451
download_size: 436911322
dataset_size: 491253018.6486236
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
|
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] |
Fearao/guba_eastmoney
|
Fearao
| 2023-03-19T04:53:07Z | 37 | 1 |
[
"task_categories:text-classification",
"language:zh",
"region:us"
] |
[
"text-classification"
] | 2023-03-19T04:51:36Z |
---
task_categories:
- text-classification
language:
- zh
---
数据来自东方财富股吧的评论，经过人工label
|
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neuclir/csl
|
neuclir
| 2023-07-05T20:02:54Z | 37 | 4 |
[
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"annotations_creators:no-annotation",
"size_categories:100K<n<1M",
"source_datasets:extended|csl",
"language:zh",
"language:en",
"license:apache-2.0",
"region:us"
] |
[
"text-retrieval"
] | 2023-03-20T21:17:19Z |
---
annotations_creators:
- no-annotation
language:
- zh
- en
license:
- apache-2.0
pretty_name: CSL
size_categories:
- 100K<n<1M
source_datasets:
- extended|csl
tags: []
task_categories:
- text-retrieval
task_ids:
- document-retrieval
---
# Dataset Card for CSL
## Dataset Description
CSL is the Chinese Scientific Literature Dataset.
- **Paper:** https://aclanthology.org/2022.coling-1.344
- **Repository:** https://github.com/ydli-ai/CSL
### Dataset Summary
The dataset contains titles, abstracts, keywords of papers written in Chinese from several academic fields.
### Languages
- Chinese
- English (translation)
## Dataset Structure
### Data Instances
| Split | Documents |
|-----------------|----------:|
| `csl` | 396k |
| `en_translation`| 396k |
### Data Fields
- `doc_id`: unique identifier for this document
- `title`: title of the paper
- `abstract`: abstract of the paper
- `keywords`: keywords associated with the paper
- `category`: the broad category of the paper
- `category_eng`: English translaction of the broad category (e.g., Engineering)
- `discipline`: academic discipline of the paper
- `discipline_eng`: English translation of the academic discipline (e.g., Agricultural Engineering)
The `en_translation` contains documents translated from Google Translation service.
All text are in English, so the fields `category_eng` and `discipline_eng` are omitted.
## Dataset Usage
Using 🤗 Datasets:
```python
from datasets import load_dataset
dataset = load_dataset('neuclir/csl')['csl']
```
## License & Citation
This dataset is based off the [Chinese Scientific Literature Dataset](https://github.com/ydli-ai/CSL) under Apache 2.0.
The primay change is the addition of `doc_id`s, English translactions of the category and discipline descriptions by a native speaker,
and basic de-duplication. Code that performed this modification is avalable in [this repository](https://github.com/NeuCLIR/csl-preprocess).
If you use this data, please cite:
```
@inproceedings{li-etal-2022-csl,
title = "{CSL}: A Large-scale {C}hinese Scientific Literature Dataset",
author = "Li, Yudong and
Zhang, Yuqing and
Zhao, Zhe and
Shen, Linlin and
Liu, Weijie and
Mao, Weiquan and
Zhang, Hui",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.344",
pages = "3917--3923",
}
```
|
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] |
mstz/heloc
|
mstz
| 2023-04-07T13:57:28Z | 37 | 0 |
[
"task_categories:tabular-classification",
"size_categories:10K<n<100K",
"language:en",
"license:cc",
"heloc",
"fico",
"tabular_classification",
"binary_classification",
"region:us"
] |
[
"tabular-classification"
] | 2023-03-23T14:08:41Z |
---
language:
- en
tags:
- heloc
- fico
- tabular_classification
- binary_classification
pretty_name: Heloc
size_categories:
- 10K<n<100K
task_categories:
- tabular-classification
configs:
- risk
license: cc
---
# HELOC
The [HELOC dataset](https://community.fico.com/s/explainable-machine-learning-challenge?tabset-158d9=d157e) from FICO.
Each entry in the dataset is a line of credit, typically offered by a bank as a percentage of home equity (the difference between the current market value of a home and its purchase price).
The customers in this dataset have requested a credit line in the range of $5,000 - $150,000.
The fundamental task is to use the information about the applicant in their credit report to predict whether they will repay their HELOC account within 2 years.
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|-----------------------------------------------------------------|
| risk | Binary classification | Will the customer default? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/heloc")["train"]
```
# Features
|**Feature** |**Type**|
|-------------------------------------------|--------|
|`estimate_of_risk` |`int8` |
|`months_since_first_trade` |`int32` |
|`months_since_last_trade` |`int32` |
|`average_duration_of_resolution` |`int32` |
|`number_of_satisfactory_trades` |`int16` |
|`nr_trades_insolvent_for_over_60_days` |`int16` |
|`nr_trades_insolvent_for_over_90_days` |`int16` |
|`percentage_of_legal_trades` |`int16` |
|`months_since_last_illegal_trade` |`int32` |
|`maximum_illegal_trades_over_last_year` |`int8` |
|`maximum_illegal_trades` |`int16` |
|`nr_total_trades` |`int16` |
|`nr_trades_initiated_in_last_year` |`int16` |
|`percentage_of_installment_trades` |`int16` |
|`months_since_last_inquiry_not_recent` |`int16` |
|`nr_inquiries_in_last_6_months` |`int16` |
|`nr_inquiries_in_last_6_months_not_recent` |`int16` |
|`net_fraction_of_revolving_burden` |`int32` |
|`net_fraction_of_installment_burden` |`int32` |
|`nr_revolving_trades_with_balance` |`int16` |
|`nr_installment_trades_with_balance` |`int16` |
|`nr_banks_with_high_ratio` |`int16` |
|`percentage_trades_with_balance` |`int16` |
|
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mstz/wine
|
mstz
| 2023-04-07T15:11:56Z | 37 | 2 |
[
"task_categories:tabular-classification",
"size_categories:1K<n<10K",
"language:en",
"license:cc",
"wine",
"tabular_classification",
"binary_classification",
"region:us"
] |
[
"tabular-classification"
] | 2023-03-24T00:29:02Z |
---
language:
- en
tags:
- wine
- tabular_classification
- binary_classification
pretty_name: Wine quality
size_categories:
- 1K<n<10K
task_categories:
- tabular-classification
configs:
- wine
license: cc
---
# Wine
The [Wine dataset](https://www.kaggle.com/datasets/ghassenkhaled/wine-quality-data) from Kaggle.
Classify wine as red or white.
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|-----------------------------------------------------------------|
| wine | Binary classification | Is this red wine? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/wine")["train"]
```
|
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WxWx/ChatGPT-Detector-Bias
|
WxWx
| 2023-04-10T00:48:06Z | 37 | 8 |
[
"task_categories:text-classification",
"size_categories:n<1K",
"language:en",
"license:mit",
"ChatGPT",
"GPT Detector",
"ChatGPT Detector",
"arxiv:2304.02819",
"region:us"
] |
[
"text-classification"
] | 2023-04-05T20:57:48Z |
---
license: mit
task_categories:
- text-classification
language:
- en
tags:
- ChatGPT
- GPT Detector
- ChatGPT Detector
size_categories:
- n<1K
---
# GPT Detectors Are Biased Against Non-Native English Writers
[](https://lbesson.mit-license.org/)
[](https://www.python.org/downloads/release/python-390/)
[](https://jupyter.org/try)
This repository contains the data and supplementary materials for our paper:
**GPT Detectors Are Biased Against Non-Native English Writers**\
Weixin Liang*, Mert Yuksekgonul*, Yining Mao*, Eric Wu*, James Zou\
arXiv: [2304.02819](https://arxiv.org/abs/2304.02819)
```bibtex
@article{liang2023gpt,
title={GPT detectors are biased against non-native English writers},
author={Weixin Liang and Mert Yuksekgonul and Yining Mao and Eric Wu and James Zou},
year={2023},
eprint={2304.02819},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## Abstract
*The rapid adoption of generative language models has brought about substantial advancements in digital communication, while simultaneously raising concerns regarding the potential misuse of AI-generated content. Although numerous detection methods have been proposed to differentiate between AI and human-generated content, the fairness and robustness of these detectors remain underexplored. In this study, we evaluate the performance of several widely-used GPT detectors using writing samples from native and non-native English writers. Our findings reveal that these detectors consistently misclassify non-native English writing samples as AI-generated, whereas native writing samples are accurately identified. Furthermore, we demonstrate that simple prompting strategies can not only mitigate this bias but also effectively bypass GPT detectors, suggesting that GPT detectors may unintentionally penalize writers with constrained linguistic expressions. Our results call for a broader conversation about the ethical implications of deploying ChatGPT content detectors and caution against their use in evaluative or educational settings, particularly when they may inadvertently penalize or exclude non-native English speakers from the global discourse.*
<p align='center'>
<img width="636" src="https://user-images.githubusercontent.com/32794044/230640445-8d1221d4-8651-4cf4-b6d7-b6d440d6e0f5.png">
<br>
<b>Figure 1: Bias in GPT detectors against non-native English writing samples.</b>
</p>
(a) Performance comparison of seven widely-used GPT detectors. More than half of the non-native-authored TOEFL (Test of English as a Foreign Language) essays are incorrectly classified as "AI-generated," while detectors exhibit near-perfect accuracy for college essays.
Using ChatGPT-4 to improve the word choices in TOEFL essays (Prompt: "Enhance the word choices to sound more like that of a native speaker.") significantly reduces misclassification as AI-generated text.
(b) TOEFL essays unanimously misclassified as AI-generated show significantly lower perplexity compared to others, suggesting that GPT detectors might penalize authors with limited linguistic expressions.
<p align='center'>
<img width="100%" src="https://user-images.githubusercontent.com/32794044/230640270-e6c3d0ca-aabd-4d13-8527-15fed1491050.png">
<br>
<b>Figure 2: Simple prompts effectively bypass GPT detectors.</b>
</p>
(a) For ChatGPT-3.5 generated college admission essays, the performance of seven widely-used GPT detectors declines markedly when a second-round self-edit prompt ("Elevate the provided text by employing literary language") is applied, with detection rates dropping from up to 100% to up to 13%.
(b) ChatGPT-3.5 generated essays initially exhibit notably low perplexity; however, applying the self-edit prompt leads to a significant increase in perplexity.
(c) Similarly, in detecting ChatGPT-3.5 generated scientific abstracts, a second-round self-edit prompt ("Elevate the provided text by employing advanced technical language") leads to a reduction in detection rates from up to 68% to up to 28%.
(d) ChatGPT-3.5 generated abstracts have slightly higher perplexity than the generated essays but remain low. Again, the self-edit prompt significantly increases the perplexity.
## Repo Structure Overview
```
.
├── README.md
├── data/
├── human_data/
├── TOEFL_real_91/
├── name.json
├── data.json
├── TOEFL_gpt4polished_91/
├── ...
├── CollegeEssay_real_70/
├── CS224N_real_145/
├── gpt_data/
├── CollegeEssay_gpt3_31/
├── CollegeEssay_gpt3PromptEng_31/
├── CS224N_gpt3_145/
├── CS224N_gpt3PromptEng_145/
```
The `data` folder contains the human-written and AI-generated datasets used in our study. Each subfolder contains a `name.json` file, which provides the metadata, and a `data.json` file, which contains the text samples.
## Reference
```bibtex
@article{liang2023gpt,
title={GPT detectors are biased against non-native English writers},
author={Weixin Liang and Mert Yuksekgonul and Yining Mao and Eric Wu and James Zou},
year={2023},
eprint={2304.02819},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
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