id stringlengths 2 115 | author stringlengths 2 42 ⌀ | last_modified timestamp[us, tz=UTC] | downloads int64 0 8.87M | likes int64 0 3.84k | paperswithcode_id stringlengths 2 45 ⌀ | tags list | lastModified timestamp[us, tz=UTC] | createdAt stringlengths 24 24 | key stringclasses 1 value | created timestamp[us] | card stringlengths 1 1.01M | embedding list | library_name stringclasses 21 values | pipeline_tag stringclasses 27 values | mask_token null | card_data null | widget_data null | model_index null | config null | transformers_info null | spaces null | safetensors null | transformersInfo null | modelId stringlengths 5 111 ⌀ | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
thu-coai/esconv | thu-coai | 2023-07-15T08:26:36Z | 367 | 0 | null | [
"language:en",
"license:cc-by-nc-4.0",
"arxiv:2106.01144",
"region:us"
] | 2023-07-15T08:26:36Z | 2023-05-08T09:18:06.000Z | 2023-05-08T09:18:06 | ---
license: cc-by-nc-4.0
language:
- en
---
The ESConv dataset. [GitHub repo](https://github.com/thu-coai/Emotional-Support-Conversation). [Original paper](https://arxiv.org/abs/2106.01144).
```bib
@inproceedings{liu-etal-2021-towards,
title={Towards Emotional Support Dialog Systems},
author={Liu, Siyang and
Zheng, Chujie and
Demasi, Orianna and
Sabour, Sahand and
Li, Yu and
Yu, Zhou and
Jiang, Yong and
Huang, Minlie},
booktitle={ACL},
year={2021}
}
``` | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ostapeno/qa-openai_batched_icl0_clen512_maxD-1_maxC2500_0_cleaned_9000_train | ostapeno | 2023-11-03T18:45:53Z | 367 | 0 | null | [
"region:us"
] | 2023-11-03T18:45:53Z | 2023-11-03T18:45:43.000Z | 2023-11-03T18:45:43 | ---
configs:
- config_name: default
data_files:
- split: abstract_algebra
path: data/abstract_algebra-*
- split: college_biology
path: data/college_biology-*
- split: formal_logic
path: data/formal_logic-*
- split: global_facts
path: data/global_facts-*
- split: high_school_government_and_politics
path: data/high_school_government_and_politics-*
- split: high_school_physics
path: data/high_school_physics-*
- split: machine_learning
path: data/machine_learning-*
- split: prehistory
path: data/prehistory-*
- split: security_studies
path: data/security_studies-*
- split: sociology
path: data/sociology-*
dataset_info:
features:
- name: id
dtype: string
- name: context
dtype: string
- name: docno
dtype: string
- name: subject
dtype: string
- name: icl_examples
dtype: 'null'
- name: author_instr
dtype: string
- name: instruction
dtype: string
- name: response
dtype: string
splits:
- name: abstract_algebra
num_bytes: 25746312
num_examples: 9000
- name: college_biology
num_bytes: 26406462
num_examples: 9000
- name: formal_logic
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- name: global_facts
num_bytes: 26114274
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- name: high_school_government_and_politics
num_bytes: 27122691
num_examples: 9000
- name: high_school_physics
num_bytes: 27471037
num_examples: 9000
- name: machine_learning
num_bytes: 27810367
num_examples: 9000
- name: prehistory
num_bytes: 27171444
num_examples: 9000
- name: security_studies
num_bytes: 26736316
num_examples: 9000
- name: sociology
num_bytes: 25312963
num_examples: 9000
download_size: 38613929
dataset_size: 266592372
---
# Dataset Card for "qa-openai_batched_icl0_clen512_maxD-1_maxC2500_0_cleaned_9000_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
jinaai/miracl | jinaai | 2023-11-13T08:53:42Z | 366 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-13T08:53:42Z | 2023-11-09T13:04:10.000Z | 2023-11-09T13:04:10 | ---
license: apache-2.0
---
## MIRACL Dataset
This dataset is a reformatted version of the original [MIRACL dataset](https://huggingface.co/datasets/miracl/miracl),
into the format expected for MTEB reranking tasks. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
mnoukhov/deprecated_relabel_pythia1b_tldrprompt | mnoukhov | 2023-11-22T05:11:16Z | 366 | 0 | null | [
"region:us"
] | 2023-11-22T05:11:16Z | 2023-11-22T05:11:07.000Z | 2023-11-22T05:11:07 | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: train
path: data/train-*
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: test
num_bytes: 8332345
num_examples: 5000
- name: train
num_bytes: 156778228
num_examples: 92534
download_size: 21783253
dataset_size: 165110573
---
# Dataset Card for "openai_summarize_comparisons_relabel_pythia1b_tldrprompt"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
fake_news_english | null | 2023-05-30T04:42:32Z | 365 | 0 | null | [
"task_categories:text-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | 2023-05-30T04:42:32Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
pretty_name: Fake News English
dataset_info:
features:
- name: article_number
dtype: int32
- name: url_of_article
dtype: string
- name: fake_or_satire
dtype:
class_label:
names:
'0': Satire
'1': Fake
- name: url_of_rebutting_article
dtype: string
splits:
- name: train
num_bytes: 78078
num_examples: 492
download_size: 3002233
dataset_size: 78078
---
# Dataset Card for Fake News English
## 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://dl.acm.org/doi/10.1145/3201064.3201100**
- **Repository:** https://github.com/jgolbeck/fakenews/
- **Paper:** https://doi.org/10.1145/3201064.3201100
- **Leaderboard:**
- **Point of Contact:** Jennifer Golbeck (http://www.jengolbeck.com)
### Dataset Summary
This dataset contains URLs of news articles classified as either fake or satire. The articles classified as fake also have the URL of a rebutting article.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English
## Dataset Structure
### Data Instances
```
{
"article_number": 102 ,
"url_of_article": https://newslo.com/roger-stone-blames-obama-possibility-trump-alzheimers-attacks-president-caused-severe-stress/ ,
"fake_or_satire": 1, # Fake
"url_of_rebutting_article": https://www.snopes.com/fact-check/donald-trumps-intelligence-quotient/
}
```
### Data Fields
- article_number: An integer used as an index for each row
- url_of_article: A string which contains URL of an article to be assessed and classified as either Fake or Satire
- fake_or_satire: A classlabel for the above variable which can take two values- Fake (1) and Satire (0)
- url_of_rebutting_article: A string which contains a URL of the article used to refute the article in question (present - in url_of_article)
### Data Splits
This dataset is not split, only the train split is available.
## 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
Golbeck, Jennifer
Everett, Jennine
Falak, Waleed
Gieringer, Carl
Graney, Jack
Hoffman, Kelly
Huth, Lindsay
Ma, Zhenya
Jha, Mayanka
Khan, Misbah
Kori, Varsha
Mauriello, Matthew
Lewis, Elo
Mirano, George
IV, William
Mussenden, Sean
Nelson, Tammie
Mcwillie, Sean
Pant, Akshat
Cheakalos, Paul
### Licensing Information
[More Information Needed]
### Citation Information
@inproceedings{inproceedings,
author = {Golbeck, Jennifer and Everett, Jennine and Falak, Waleed and Gieringer, Carl and Graney, Jack and Hoffman, Kelly and Huth, Lindsay and Ma, Zhenya and Jha, Mayanka and Khan, Misbah and Kori, Varsha and Mauriello, Matthew and Lewis, Elo and Mirano, George and IV, William and Mussenden, Sean and Nelson, Tammie and Mcwillie, Sean and Pant, Akshat and Cheakalos, Paul},
year = {2018},
month = {05},
pages = {17-21},
title = {Fake News vs Satire: A Dataset and Analysis},
doi = {10.1145/3201064.3201100}
}
### Contributions
Thanks to [@MisbahKhan789](https://github.com/MisbahKhan789), [@lhoestq](https://github.com/lhoestq) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
SocialGrep/one-million-reddit-jokes | SocialGrep | 2022-07-01T18:48:46Z | 365 | 7 | null | [
"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"
] | 2022-07-01T18:48:46Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
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 one-million-reddit-jokes
## 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://socialgrep.com/datasets](https://socialgrep.com/datasets?utm_source=huggingface&utm_medium=link&utm_campaign=onemillionjokes)
- **Point of Contact:** [Website](https://socialgrep.com/contact?utm_source=huggingface&utm_medium=link&utm_campaign=onemillionjokes)
### Dataset Summary
This corpus contains a million posts from /r/jokes.
Posts are annotated with their score.
### Languages
Mainly English.
## Dataset Structure
### Data Instances
A data point is a Reddit post.
### 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': the domain of the data point's link.
- 'url': the destination of the data point's link, if any.
- 'selftext': the self-text of the data point, if any.
- 'title': the title of the post data point.
## 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
CC-BY v4.0
### Contributions
[Needs More Information] | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
princeton-nlp/SWE-bench_oracle_llama | princeton-nlp | 2023-10-17T13:59:03Z | 365 | 1 | null | [
"region:us"
] | 2023-10-17T13:59:03Z | 2023-10-10T04:10:48.000Z | 2023-10-10T04:10:48 | ---
dataset_info:
features:
- name: base_commit
dtype: string
- name: hints_text
dtype: string
- name: created_at
dtype: string
- name: test_patch
dtype: string
- name: repo
dtype: string
- name: problem_statement
dtype: string
- name: version
dtype: string
- name: instance_id
dtype: string
- name: FAIL_TO_PASS
dtype: string
- name: PASS_TO_PASS
dtype: string
- name: environment_setup_commit
dtype: string
- name: text
dtype: string
- name: input_ids
sequence: int32
- name: labels
sequence: int64
- name: patch
dtype: string
splits:
- name: test
num_bytes: 550241818
num_examples: 2294
download_size: 199730762
dataset_size: 550241818
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
### Dataset Summary
SWE-bench is a dataset that tests systems’ ability to solve GitHub issues automatically. The dataset collects 2,294 Issue-Pull Request pairs from 12 popular Python. Evaluation is performed by unit test verification using post-PR behavior as the reference solution.
### Supported Tasks and Leaderboards
SWE-bench proposes a new task: issue resolution provided a full repository and GitHub issue. The leaderboard can be found at www.swebench.com
### Languages
The text of the dataset is primarily English, but we make no effort to filter or otherwise clean based on language type.
## Dataset Structure
### Data Instances
An example of a SWE-bench datum is as follows:
```
instance_id: (str) - A formatted instance identifier, usually as repo_owner__repo_name-PR-number.
patch: (str) - The gold patch, the patch generated by the PR (minus test-related code), that resolved the issue.
repo: (str) - The repository owner/name identifier from GitHub.
base_commit: (str) - The commit hash of the repository representing the HEAD of the repository before the solution PR is applied.
hints_text: (str) - Comments made on the issue prior to the creation of the solution PR’s first commit creation date.
created_at: (str) - The creation date of the pull request.
test_patch: (str) - A test-file patch that was contributed by the solution PR.
problem_statement: (str) - The issue title and body.
version: (str) - Installation version to use for running evaluation.
environment_setup_commit: (str) - commit hash to use for environment setup and installation.
FAIL_TO_PASS: (str) - A json list of strings that represent the set of tests resolved by the PR and tied to the issue resolution.
PASS_TO_PASS: (str) - A json list of strings that represent tests that should pass before and after the PR application.
text: (str) - The generated text according to the retrieval criterion and the style-2 prompt found in [github:SWE-bench](https://github.com/princeton-nlp/SWE-bench).
input_ids: (List[int]) - The llama tokens for each text.
```
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
vctk | null | 2023-11-23T14:05:24Z | 364 | 9 | vctk | [
"task_categories:automatic-speech-recognition",
"task_categories:text-to-speech",
"task_categories:text-to-audio",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] | 2023-11-23T14:05:24Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: VCTK
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- automatic-speech-recognition
- text-to-speech
- text-to-audio
task_ids: []
paperswithcode_id: vctk
train-eval-index:
- config: main
task: automatic-speech-recognition
task_id: speech_recognition
splits:
train_split: train
col_mapping:
file: path
text: text
metrics:
- type: wer
name: WER
- type: cer
name: CER
dataset_info:
features:
- name: speaker_id
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 48000
- name: file
dtype: string
- name: text
dtype: string
- name: text_id
dtype: string
- name: age
dtype: string
- name: gender
dtype: string
- name: accent
dtype: string
- name: region
dtype: string
- name: comment
dtype: string
config_name: main
splits:
- name: train
num_bytes: 40103111
num_examples: 88156
download_size: 11747302977
dataset_size: 40103111
---
# Dataset Card for VCTK
## 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:** [Edinburg DataShare](https://doi.org/10.7488/ds/2645)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This CSTR VCTK Corpus includes around 44-hours of speech data uttered by 110 English speakers with various accents. Each speaker reads out about 400 sentences, which were selected from a newspaper, the rainbow passage and an elicitation paragraph used for the speech accent archive.
### Supported Tasks
- `automatic-speech-recognition`, `speaker-identification`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER).
- `text-to-speech`, `text-to-audio`: The dataset can also be used to train a model for Text-To-Speech (TTS).
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
A data point comprises the path to the audio file, called `file` and its transcription, called `text`.
```
{
'speaker_id': 'p225',
'text_id': '001',
'text': 'Please call Stella.',
'age': '23',
'gender': 'F',
'accent': 'English',
'region': 'Southern England',
'file': '/datasets/downloads/extracted/8ed7dad05dfffdb552a3699777442af8e8ed11e656feb277f35bf9aea448f49e/wav48_silence_trimmed/p225/p225_001_mic1.flac',
'audio':
{
'path': '/datasets/downloads/extracted/8ed7dad05dfffdb552a3699777442af8e8ed11e656feb277f35bf9aea448f49e/wav48_silence_trimmed/p225/p225_001_mic1.flac',
'array': array([0.00485229, 0.00689697, 0.00619507, ..., 0.00811768, 0.00836182, 0.00854492], dtype=float32),
'sampling_rate': 48000
},
'comment': ''
}
```
Each audio file is a single-channel FLAC with a sample rate of 48000 Hz.
### Data Fields
Each row consists of the following fields:
- `speaker_id`: Speaker ID
- `audio`: Audio recording
- `file`: Path to audio file
- `text`: Text transcription of corresponding audio
- `text_id`: Text ID
- `age`: Speaker's age
- `gender`: Speaker's gender
- `accent`: Speaker's accent
- `region`: Speaker's region, if annotation exists
- `comment`: Miscellaneous comments, if any
### Data Splits
The dataset has no predefined splits.
## 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
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.
## 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
Public Domain, Creative Commons Attribution 4.0 International Public License ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/legalcode))
### Citation Information
```bibtex
@inproceedings{Veaux2017CSTRVC,
title = {CSTR VCTK Corpus: English Multi-speaker Corpus for CSTR Voice Cloning Toolkit},
author = {Christophe Veaux and Junichi Yamagishi and Kirsten MacDonald},
year = 2017
}
```
### Contributions
Thanks to [@jaketae](https://github.com/jaketae) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
zest | null | 2022-11-18T22:05:40Z | 364 | 1 | zest | [
"task_categories:question-answering",
"task_categories:token-classification",
"task_ids:closed-domain-qa",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
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"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"output-structure",
"yes-no-qa",
"arxiv:2011.08115",
"region:us"
] | 2022-11-18T22:05:40Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
- token-classification
task_ids:
- closed-domain-qa
- extractive-qa
paperswithcode_id: zest
pretty_name: ZEST
tags:
- output-structure
- yes-no-qa
dataset_info:
features:
- name: task_id
dtype: string
- name: question
dtype: string
- name: generalization_type
dtype: string
- name: derives_from
sequence: string
- name: domain
dtype: string
- name: context
dtype: string
- name: answer
sequence: string
- name: all_answers
sequence: string
splits:
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num_bytes: 9588987
num_examples: 10766
- name: validation
num_bytes: 2056804
num_examples: 2280
- name: test
num_bytes: 9280845
num_examples: 11980
download_size: 5796188
dataset_size: 20926636
---
# Dataset Card for "ZEST: ZEroShot learning from Task descriptions"
## 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://allenai.org/data/zest
- **Repository:** https://github.com/allenai/zest
- **Paper:** https://arxiv.org/abs/2011.08115
- **Leaderboard:** https://leaderboard.allenai.org/zest/submissions/public
- **Point of Contact:**
### Dataset Summary
ZEST tests whether NLP systems can perform unseen tasks in a zero-shot way, given a natural language description of
the task. It is an instantiation of our proposed framework "learning from task descriptions". The tasks include
classification, typed entity extraction and relationship extraction, and each task is paired with 20 different
annotated (input, output) examples. ZEST's structure allows us to systematically test whether models can generalize
in five different ways.
### Supported Tasks and Leaderboards
A [leaderboard](https://leaderboard.allenai.org/zest/submissions/public) is included with accepatbility metrics for
each of the four generalization types outlined in the paper. The metrics are novel acceptability metrics also
proposed by the authors.
### Languages
The dataset is in English.
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
To evaluate the ability of a model to generalize to unseen tasks based only on a task description in a zero-shot
manner.
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
Mechanical Turk crowdsource workers.
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
Mechanical Turk crowdsource workers.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
The dataset emphasizes a model's ability to generalize to unseen tasks with only a natural language description of
the task. The long-term vision of this type of evaluation is to facilitate the creation of models which can perform
arbitrary tasks with only a prompt from a non-technical user. This could broaden the frontier of what a user can
ask something like a chatbot to do for them, but it is unclear how restrictions would be put in place to prevent
users from prompting a system to perform unethical tasks.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
This dataset is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
### Citation Information
```
@inproceedings{weller-etal-2020-learning,
title = "Learning from Task Descriptions",
author = "Weller, Orion and
Lourie, Nicholas and
Gardner, Matt and
Peters, Matthew",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.105",
pages = "1361--1375",
abstract = "Typically, machine learning systems solve new tasks by training on thousands of examples. In contrast, humans can solve new tasks by reading some instructions, with perhaps an example or two. To take a step toward closing this gap, we introduce a framework for developing NLP systems that solve new tasks after reading their descriptions, synthesizing prior work in this area. We instantiate this frame- work with a new English language dataset, ZEST, structured for task-oriented evaluation on unseen tasks. Formulating task descriptions as questions, we ensure each is general enough to apply to many possible inputs, thus comprehensively evaluating a model{'}s ability to solve each task. Moreover, the dataset{'}s structure tests specific types of systematic generalization. We find that the state-of-the-art T5 model achieves a score of 12% on ZEST, leaving a significant challenge for NLP researchers.",
}
```
### Contributions
Thanks to [@joeddav](https://github.com/joeddav) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
MonoHime/ru_sentiment_dataset | MonoHime | 2021-05-20T00:57:22Z | 364 | 3 | null | [
"language:ru",
"sentiment",
"text-classification",
"region:us"
] | 2021-05-20T00:57:22Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- ru
tags:
- sentiment
- text-classification
---
# Dataset with sentiment of Russian text
Contains aggregated dataset of Russian texts from 6 datasets.
## Labels meaning
0: NEUTRAL
1: POSITIVE
2: NEGATIVE
## Datasets
**[Sentiment Analysis in Russian](https://www.kaggle.com/c/sentiment-analysis-in-russian/data)**
> Sentiments (positive, negative or neutral) of news in russian language from Kaggle competition.
**[Russian Language Toxic Comments](https://www.kaggle.com/blackmoon/russian-language-toxic-comments/)**
> Small dataset with labeled comments from 2ch.hk and pikabu.ru.
**[Dataset of car reviews for machine learning (sentiment analysis)](https://github.com/oldaandozerskaya/auto_reviews)**
> Glazkova A. The evaluation of the proximity of text categories for solving electronic documents classification tasks //VESTNIK TOMSKOGO GOSUDARSTVENNOGO UNIVERSITETA-UPRAVLENIE VYCHISLITELNAJA TEHNIKA I INFORMATIKA-TOMSK STATE UNIVERSITY JOURNAL OF CONTROL AND COMPUTER SCIENCE. – 2015. – Т. 31. – №. 2. – С. 18-25.
**[Sentiment datasets by Blinov](https://github.com/natasha/corus/issues/14)**
> Datasets contain reviews from different scopes.
**[LINIS Crowd](http://www.linis-crowd.org/)**
> Произведение «LINIS Crowd SENT - тональный словарь и коллекция текстов с тональной разметкой» созданное автором по имени Sergei Koltcov, Olessia Koltsova и Svetlana Alexeeva.
**[Russian Hotel Reviews Dataset](https://drive.google.com/drive/folders/17sa3h4XHcG0MJGrbfOsbL-kDW29CuJul)**
> Hotel reviews in Russian | [
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Short-Answer-Feedback/saf_communication_networks_english | Short-Answer-Feedback | 2023-03-31T11:46:04Z | 364 | 6 | null | [
"task_categories:text2text-generation",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"short answer feedback",
"communication networks",
"region:us"
] | 2023-03-31T11:46:04Z | 2022-11-10T21:22:13.000Z | 2022-11-10T21:22:13 | ---
pretty_name: SAF - Communication Networks - English
annotations_creators:
- expert-generated
language:
- en
language_creators:
- other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- short answer feedback
- communication networks
task_categories:
- text2text-generation
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: reference_answer
dtype: string
- name: provided_answer
dtype: string
- name: answer_feedback
dtype: string
- name: verification_feedback
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 2363828
num_examples: 1700
- name: validation
num_bytes: 592869
num_examples: 427
- name: test_unseen_answers
num_bytes: 515669
num_examples: 375
- name: test_unseen_questions
num_bytes: 777945
num_examples: 479
download_size: 941169
dataset_size: 4250311
license: cc-by-4.0
---
# Dataset Card for "saf_communication_networks_english"
## 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)
- [Annotation process](#annotation-process)
- [Additional Information](#additional-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Paper:** [Your Answer is Incorrect... Would you like to know why? Introducing a Bilingual Short Answer Feedback Dataset](https://aclanthology.org/2022.acl-long.587) (Filighera et al., ACL 2022)
### Dataset Summary
Short Answer Feedback (SAF) dataset is a short answer dataset introduced in [Your Answer is Incorrect... Would you like to know why? Introducing a Bilingual Short Answer Feedback Dataset](https://aclanthology.org/2022.acl-long.587) (Filighera et al., ACL 2022) as a way to remedy the lack of content-focused feedback datasets. This version of the dataset contains 31 English questions covering a range of college-level communication networks topics - while the original dataset presented in the paper is comprised of an assortment of both English and German short answer questions (with reference answers). Please refer to the [saf_micro_job_german](https://huggingface.co/datasets/Short-Answer-Feedback/saf_micro_job_german) dataset to examine the German subset of the original dataset. Furthermore, a similarly constructed SAF dataset (covering the German legal domain) can be found at [saf_legal_domain_german](https://huggingface.co/datasets/Short-Answer-Feedback/saf_legal_domain_german).
### Supported Tasks and Leaderboards
- `short_answer_feedback`: The dataset can be used to train a Text2Text Generation model from HuggingFace transformers in order to generate automatic short answer feedback.
### Languages
The questions, reference answers, provided answers and the answer feedback in the dataset are written in English.
## Dataset Structure
### Data Instances
An example of an entry of the training split looks as follows.
```
{
"id": "1",
"question": "Is this a question?",
"reference_answer": "Yes, that is a question.",
"provided_answer": "I'm certain this is a question.",
"answer_feedback": "The response is correct.",
"verification_feedback": "Correct",
"score": 1
}
```
### Data Fields
The data fields are the same among all splits.
- `id`: a `string` feature (UUID4 in HEX format).
- `question`: a `string` feature representing a question.
- `reference_answer`: a `string` feature representing a reference answer to the question.
- `provided_answer`: a `string` feature representing an answer that was provided for a particular question.
- `answer_feedback`: a `string` feature representing the feedback given to the provided answers.
- `verification_feedback`: a `string` feature representing an automatic labeling of the score. It can be `Correct` (`score` = maximum points achievable), `Incorrect` (`score` = 0) or `Partially correct` (all intermediate scores).
- `score`: a `float64` feature representing the score given to the provided answer. For most questions it ranges from 0 to 1.
### Data Splits
The dataset is comprised of four data splits.
- `train`: used for training, contains a set of questions and the provided answers to them.
- `validation`: used for validation, contains a set of questions and the provided answers to them (derived from the original training set defined in the paper).
- `test_unseen_answers`: used for testing, contains unseen answers to the questions present in the `train` split.
- `test_unseen_questions`: used for testing, contains unseen questions that do not appear in the `train` split.
| Split |train|validation|test_unseen_answers|test_unseen_questions|
|-------------------|----:|---------:|------------------:|--------------------:|
|Number of instances| 1700| 427| 375| 479|
## Dataset Creation
### Annotation Process
Two graduate students who had completed the communication networks course were selected to evaluate the answers, and both of them underwent a general annotation guideline training (supervised by a Psychology doctoral student with prior work in the field of feedback). After the training, the annotators individually provided feedback to the answers following an agreed upon scoring rubric and the general annotation guideline. The individually annotated answer files were then combined into a cohesive gold standard after discussing and solving possible disagreements.
## Additional Information
### Citation Information
```
@inproceedings{filighera-etal-2022-answer,
title = "Your Answer is Incorrect... Would you like to know why? Introducing a Bilingual Short Answer Feedback Dataset",
author = "Filighera, Anna and
Parihar, Siddharth and
Steuer, Tim and
Meuser, Tobias and
Ochs, Sebastian",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.587",
doi = "10.18653/v1/2022.acl-long.587",
pages = "8577--8591",
}
```
### Contributions
Thanks to [@JohnnyBoy2103](https://github.com/JohnnyBoy2103) for adding this dataset. | [
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hsiungc/master_midi_testing | hsiungc | 2023-11-01T21:57:02Z | 364 | 0 | null | [
"region:us"
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hind_encorp | null | 2022-11-03T16:15:40Z | 363 | 1 | hindencorp | [
"task_categories:translation",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:machine-generated",
"multilinguality:translation",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"language:hi",
"license:cc-by-nc-sa-3.0",
"region:us"
] | 2022-11-03T16:15:40Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- machine-generated
language:
- en
- hi
license:
- cc-by-nc-sa-3.0
multilinguality:
- translation
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: hindencorp
pretty_name: HindEnCorp
dataset_info:
features:
- name: id
dtype: string
- name: source
dtype: string
- name: alignment_type
dtype: string
- name: alignment_quality
dtype: string
- name: translation
dtype:
translation:
languages:
- en
- hi
splits:
- name: train
num_bytes: 78945714
num_examples: 273885
download_size: 23899723
dataset_size: 78945714
---
# Dataset Card for HindEnCorp
## 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://lindat.mff.cuni.cz/repository/xmlui/handle/11858/00-097C-0000-0023-625F-0
- **Repository:** https://lindat.mff.cuni.cz/repository/xmlui/
- **Paper:** http://www.lrec-conf.org/proceedings/lrec2014/pdf/835_Paper.pdf
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
HindEnCorp parallel texts (sentence-aligned) come from the following sources:
Tides, which contains 50K sentence pairs taken mainly from news articles. This dataset was originally col- lected for the DARPA-TIDES surprise-language con- test in 2002, later refined at IIIT Hyderabad and provided for the NLP Tools Contest at ICON 2008 (Venkatapathy, 2008).
Commentaries by Daniel Pipes contain 322 articles in English written by a journalist Daniel Pipes and translated into Hindi.
EMILLE. This corpus (Baker et al., 2002) consists of three components: monolingual, parallel and annotated corpora. There are fourteen monolingual sub- corpora, including both written and (for some lan- guages) spoken data for fourteen South Asian lan- guages. The EMILLE monolingual corpora contain in total 92,799,000 words (including 2,627,000 words of transcribed spoken data for Bengali, Gujarati, Hindi, Punjabi and Urdu). The parallel corpus consists of 200,000 words of text in English and its accompanying translations into Hindi and other languages.
Smaller datasets as collected by Bojar et al. (2010) include the corpus used at ACL 2005 (a subcorpus of EMILLE), a corpus of named entities from Wikipedia (crawled in 2009), and Agriculture domain parallel corpus.

For the current release, we are extending the parallel corpus using these sources:
Intercorp (Čermák and Rosen,2012) is a large multilingual parallel corpus of 32 languages including Hindi. The central language used for alignment is Czech. Intercorp’s core texts amount to 202 million words. These core texts are most suitable for us because their sentence alignment is manually checked and therefore very reliable. They cover predominately short sto- ries and novels. There are seven Hindi texts in Inter- corp. Unfortunately, only for three of them the English translation is available; the other four are aligned only with Czech texts. The Hindi subcorpus of Intercorp contains 118,000 words in Hindi.
TED talks 3 held in various languages, primarily English, are equipped with transcripts and these are translated into 102 languages. There are 179 talks for which Hindi translation is available.
The Indic multi-parallel corpus (Birch et al., 2011; Post et al., 2012) is a corpus of texts from Wikipedia translated from the respective Indian language into English by non-expert translators hired over Mechanical Turk. The quality is thus somewhat mixed in many respects starting from typesetting and punctuation over capi- talization, spelling, word choice to sentence structure. A little bit of control could be in principle obtained from the fact that every input sentence was translated 4 times. We used the 2012 release of the corpus.
Launchpad.net is a software collaboration platform that hosts many open-source projects and facilitates also collaborative localization of the tools. We downloaded all revisions of all the hosted projects and extracted the localization (.po) files.
Other smaller datasets. This time, we added Wikipedia entities as crawled in 2013 (including any morphological variants of the named entitity that appears on the Hindi variant of the Wikipedia page) and words, word examples and quotes from the Shabdkosh online dictionary.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Hindi, English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
HindEncorp Columns:
- source identifier (where do the segments come from)
- alignment type (number of English segments - number of Hindi segments)
- alignment quality, which is one of the following:
"manual" ... for sources that were sentence-aligned manually
"implied" ... for sources where one side was constructed by translating
segment by segment
float ... a value somehow reflecting the goodness of the automatic
alignment; not really reliable
- English segment or segments
- Hindi segment or segments
Each of the segments field is in the plaintext or export format as described
above.
If there are more than one segments on a line (e.g. for lines with alignment
type 2-1 where there are two English segments), then the segments are delimited
with `<s>` in the text field.
### Data Splits
[More Information Needed]
## Dataset Creation
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
Daniel Pipes,Baker,Bojar,"Čermák and Rosen,2012","Birch et al., 2011; Post et al., 2012"
### Annotations
#### Annotation process
the 1st part of data TIDES was originally col- lected for the DARPA-TIDES surprise-language con- test in 2002, later refined at IIIT Hyderabad and provided for the NLP Tools Contest at ICON 2008 (Venkatapathy, 2008).
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
Dataset provided for research purposes only. Please check dataset license for additional information.
## Additional Information
### Dataset Curators
Bojar, Ondřej ; Diatka, Vojtěch ; Straňák, Pavel ; Tamchyna, Aleš ; Zeman, Daniel
### Licensing Information
CC BY-NC-SA 3.0
### Citation Information
@InProceedings{hindencorp05:lrec:2014,
author = {Ond{\v{r}}ej Bojar and Vojt{\v{e}}ch Diatka
and Pavel Rychl{\'{y}} and Pavel Stra{\v{n}}{\'{a}}k
and V{\'{\i}}t Suchomel and Ale{\v{s}} Tamchyna and Daniel Zeman},
title = "{HindEnCorp - Hindi-English and Hindi-only Corpus for Machine
Translation}",
booktitle = {Proceedings of the Ninth International Conference on Language
Resources and Evaluation (LREC'14)},
year = {2014},
month = {may},
date = {26-31},
address = {Reykjavik, Iceland},
editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and
Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani
and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
publisher = {European Language Resources Association (ELRA)},
isbn = {978-2-9517408-8-4},
language = {english}
}
### Contributions
Thanks to [@rahul-art](https://github.com/rahul-art) for adding this dataset. | [
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prachathai67k | null | 2023-01-25T14:42:50Z | 363 | 3 | prachathai-67k | [
"task_categories:text-classification",
"task_ids:topic-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | 2023-01-25T14:42:50Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- topic-classification
paperswithcode_id: prachathai-67k
pretty_name: prachathai67k
dataset_info:
features:
- name: url
dtype: string
- name: date
dtype: string
- name: title
dtype: string
- name: body_text
dtype: string
- name: politics
dtype:
class_label:
names:
'0': neg
'1': pos
- name: human_rights
dtype:
class_label:
names:
'0': neg
'1': pos
- name: quality_of_life
dtype:
class_label:
names:
'0': neg
'1': pos
- name: international
dtype:
class_label:
names:
'0': neg
'1': pos
- name: social
dtype:
class_label:
names:
'0': neg
'1': pos
- name: environment
dtype:
class_label:
names:
'0': neg
'1': pos
- name: economics
dtype:
class_label:
names:
'0': neg
'1': pos
- name: culture
dtype:
class_label:
names:
'0': neg
'1': pos
- name: labor
dtype:
class_label:
names:
'0': neg
'1': pos
- name: national_security
dtype:
class_label:
names:
'0': neg
'1': pos
- name: ict
dtype:
class_label:
names:
'0': neg
'1': pos
- name: education
dtype:
class_label:
names:
'0': neg
'1': pos
config_name: prachathai67k
splits:
- name: train
num_bytes: 865848436
num_examples: 54379
- name: validation
num_bytes: 108641386
num_examples: 6721
- name: test
num_bytes: 110034036
num_examples: 6789
download_size: 254240975
dataset_size: 1084523858
---
# Dataset Card for `prachathai67k`
## 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/PyThaiNLP/prachathai-67k
- **Repository:** https://github.com/PyThaiNLP/prachathai-67k
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** https://github.com/PyThaiNLP/
### Dataset Summary
`prachathai-67k`: News Article Corpus and Multi-label Text Classificdation from Prachathai.com
The `prachathai-67k` dataset was scraped from the news site [Prachathai](prachathai.com). We filtered out those articles with less than 500 characters of body text, mostly images and cartoons. It contains 67,889 articles wtih 12 curated tags from August 24, 2004 to November 15, 2018. The dataset was originally scraped by [@lukkiddd](https://github.com/lukkiddd) and cleaned by [@cstorm125](https://github.com/cstorm125). Download the dataset [here](https://www.dropbox.com/s/fsxepdka4l2pr45/prachathai-67k.zip?dl=1). You can also see preliminary exploration in [exploration.ipynb](https://github.com/PyThaiNLP/prachathai-67k/blob/master/exploration.ipynb).
This dataset is a part of [pyThaiNLP](https://github.com/PyThaiNLP/) Thai text [classification-benchmarks](https://github.com/PyThaiNLP/classification-benchmarks). For the benchmark, we selected the following tags with substantial volume that resemble **classifying types of articles**:
* `การเมือง` - politics
* `สิทธิมนุษยชน` - human_rights
* `คุณภาพชีวิต` - quality_of_life
* `ต่างประเทศ` - international
* `สังคม` - social
* `สิ่งแวดล้อม` - environment
* `เศรษฐกิจ` - economics
* `วัฒนธรรม` - culture
* `แรงงาน` - labor
* `ความมั่นคง` - national_security
* `ไอซีที` - ict
* `การศึกษา` - education
### Supported Tasks and Leaderboards
multi-label text classification, language modeling
### Languages
Thai
## Dataset Structure
### Data Instances
{'body_text': '17 พ.ย. 2558 Blognone [1] รายงานว่า กลุ่มแฮคเกอร์ Anonymous ประกาศสงครามไซเบอร์กับกลุ่มหัวรุนแรงหลังจากกลุ่ม IS ออกมาประกาศว่าเป็นผู้อยู่เบื้องหลังการโจมตีกรุงปารีสในคืนวันศุกร์ที่ผ่านมา\n\n\nภาพในคลิปใน YouTube โฆษกของกลุ่มแฮคเกอร์สวมหน้ากากที่เป็นสัญลักษณ์ของกลุ่มได้ออกมาอ่านแถลงเป็นภาษาฝรั่งเศส มีใจความว่า จากการโจมตีของกลุ่ม IS ในกรุงปารีส กลุ่ม Anonymous ทั่วโลกจะตามล่ากลุ่ม IS เหมือนที่เคยทำตอนที่มีการโจมตีสำนักพิมพ์ Charlie Hebdo และครั้งนี้จะเป็นปฏิบัติการโจมตีครั้งใหญ่ที่สุดของกลุ่ม Anonymous เลย นอกจากนี้กลุ่ม Anonymous ยังแสดงความเสียใจต่อครอบครัวผู้สูญเสียในเหตุการณ์ครั้งนี้\nกลุ่ม Anonymous เคยประกาศสงครามกับกลุ่ม IS หลังจากการโจมตีสำนักพิมพ์ Charlie Hebdo ที่ฝรั่งเศสเมื่อต้นปีที่ผ่านมา ซึ่งครั้งนั้นกลุ่ม Anonymous อ้างว่าได้ระงับบัญชีผู้ใช้งานที่เกี่ยวข้องกับ IS ไปหลายพันบัญชี (อ่านรายละเอียดเพิ่มเติม จากBlognone ที่\xa0\xa0กลุ่มแฮคเกอร์ Anonymous ประกาศสงครามไซเบอร์ขอกวาดล้างพวก ISIS [2])', 'culture': 0, 'date': '2015-11-17 18:14', 'economics': 0, 'education': 0, 'environment': 0, 'human_rights': 0, 'ict': 1, 'international': 1, 'labor': 0, 'national_security': 0, 'politics': 0, 'quality_of_life': 0, 'social': 0, 'title': 'แฮคเกอร์ Anonymous ลั่นทำสงครามไซเบอร์ครั้งใหญ่สุดกับกลุ่ม IS', 'url': 'https://prachatai.com/print/62490'}
{'body_text': 'แถลงการณ์\n\n\xa0\n\nองค์การนักศึกษามหาวิทยาลัยธรรมศาสตร์\n\n\xa0\n\nมหาวิทยาลัยธรรมศาสตร์ก่อตั้งขึ้นภายใต้แนวคิดการให้การศึกษากับประชาชนเพื่อสนับสนุนการปกครองระบอบประชาธิปไตย อีกทั้งยังเป็นสถาบันหนึ่งที่อยู่เคียงข้างประชาชนมาโดยตลอด\n\n\xa0\n\nสถานการณ์สังคมไทยปัจจุบันได้เกิดความขัดแย้งทางการเมือง ทางแนวคิด จนลุกลามเป็นวิกฤตการณ์อันหาทางออกได้ยากยิ่ง องค์กรนักศึกษามหาวิทยาลัยธรรมศาสตร์ขอร้องเรียนและเสนอแนะต่อทุกฝ่าย โดยยึดหลักแนวทางตามรัฐธรรมนูญแห่งราชอาณาจักรไทย พ.ศ. ๒๕๕๐ อันเป็นกฎหมายสูงสุดในการจัดการปกครองรัฐ ที่มีผลบังคับใช้อยู่ในปัจจุบันซึ่งผ่านการประชามติจากปวงชนชาวไทยเมื่อวันที่ ๑๙ สิงหาคม พ.ศ. ๒๕๕๐ แล้วดังต่อนี้\n\n\xa0\n\n๑.การชุมชมโดยสงบและปราศจากอาวุธย่อมได้รับการคุ้มครองตามรัฐธรรมนูญ แต่หากการชุมนุมและเคลื่อนไหวของกลุ่มใดๆ มีการละเมิดสิทธิและเสรีภาพของผู้อื่นหรือก่อให้เกิดความเสียหายต่อชีวิตและทรัพย์สินของบุคคลและส่วนรวมนั้น ไม่สามารถกระทำได้ การใช้ความรุนแรง การกระทำอุกอาจต่างๆ ทั้งต่อบุคคลและทรัพย์สิน การยั่วยุ ปลุกระดมเพื่อหวังผลในการปะทะต่อสู้ จึงควรได้รับการกล่าวโทษ\n\n\xa0\n\nดังนั้นทั้งกลุ่มพันธมิตรประชาชนเพื่อประชาธิปไตย (พธม.) และกลุ่มแนวร่วมประชาธิปไตยไม่เอาเผด็จการแห่งชาติ (นปช.) จึงควรยอมรับกระบวนการตามกฎหมาย และหากถูกกล่าวหาไม่ว่ากรณีใดๆ ก็ควรพิสูจน์ความบริสุทธิ์โดยใช้กระบวนการยุติธรรม และหากจะยังชุมนุมต่อไปก็ยังคงทำได้ภายใต้บทบัญญัติแห่งกฎหมาย\n\n\xa0\n\nองค์กรนักศึกษามหาวิทยาลัยธรรมศาสตร์ จึงร้องขอให้หน่วยงานต่างๆ ที่เกี่ยวข้องดำเนินการตามกระบวนการทางกฎหมายกับการกระทำที่ผิดบทบัญญัติแห่งกฎหมายที่ทุกฝ่ายได้กระทำไป\n\n\xa0\n\n๒.นายสมัคร สุนทรเวช นายกรัฐมนตรี ไม่มีความเหมาะสมในการบริหารราชการแผ่นดินขาดหลักธรรมาภิบาล แต่ทั้งนี้นายสมัคร สุนทรเวช ยังคงยืนยันและกล่าวอ้างความชอบธรรมตามระบอบประชาธิปไตยภายใต้รัฐธรรมนูญ โดยไม่คำนึงถึงกระแสเรียกร้องใดๆ อันส่งผลให้ความขัดแย้งทางสังคมยิ่งบานปลายจนกลายเป็นวิกฤตการณ์เช่นปัจจุบัน ซึ่งก่อให้เกิดความเสียหายต่อประเทศแนวโน้มจะคลี่คลาย\n\n\xa0\n\nองค์การนักศึกษามหาวิทยาลัยธรรมศาสตร์ จึงเห็นว่า ควรใช้สิทธิตามรัฐธรรมนูญแห่งราชอาณาจักรไทย พุทธศักราช ๒๕๕๐ มาตรา ๑๖๔ โดยการเข้าชื่อเพื่อร้องต่อประธานวุฒิสภาเพื่อให้มีมติตามมาตรา ๒๗๔ ให้ถอดถอนนายสมัคร สุนทรเวช ออกจากตำแหน่งนายกรัฐมนตรีตามมาตรา ๒๗๐ ณ ลานโพ มหาวิทยาลัยธรรมศาสตร์ ท่าพระจันทร์ อาคารเรียนรวมสังคมศาสตร์ อาคารปิยชาติ และตึกกิจกรรมนักศึกษา มหาวิทยาลัยธรรมศาสตร์ ศูนย์รังสิต\n\n\xa0\n\n\xa0\n\nด้วยความสมานฉันท์\n\nองค์การนักศึกษามหาวิทยาลัยธรรมศาสตร์', 'culture': 0, 'date': '2008-09-06 03:36', 'economics': 0, 'education': 0, 'environment': 0, 'human_rights': 0, 'ict': 0, 'international': 0, 'labor': 0, 'national_security': 0, 'politics': 1, 'quality_of_life': 0, 'social': 0, 'title': 'แถลงการณ์ อมธ.แนะใช้สิทธิ ตาม รธน.เข้าชื่อร้องต่อประธานวุฒิสภาถอดถอน "สมัคร" จากตำแหน่งนายกฯ', 'url': 'https://prachatai.com/print/18038'}
### Data Fields
- `url`: url of the article
- `date`: date the article was published
- `title`: title of the article
- `body_text`: body text of the article
- `politics`: 1 if sample has this tag else 0
- `human_rights`: 1 if sample has this tag else 0
- `quality_of_life`: 1 if sample has this tag else 0
- `international`: 1 if sample has this tag else 0
- `social`: 1 if sample has this tag else 0
- `environment`: 1 if sample has this tag else 0
- `economics`: 1 if sample has this tag else 0
- `culture`: 1 if sample has this tag else 0
- `labor`: 1 if sample has this tag else 0
- `national_security`: 1 if sample has this tag else 0
- `ict`: 1 if sample has this tag else 0
- `education`: 1 if sample has this tag else 0
### Data Splits
| | train | valid | test |
|-------------------|-------|--------|------|
| # articles | 54379 | 6721 | 6789 |
| politics | 31401 | 3852 | 3842 |
| human_rights | 12061 | 1458 | 1511 |
| quality_of_life | 9037 | 1144 | 1127 |
| international | 6432 | 828 | 834 |
| social | 6321 | 782 | 789 |
| environment | 6157 | 764 | 772 |
| economics | 3994 | 487 | 519 |
| culture | 3279 | 388 | 398 |
| labor | 2905 | 375 | 350 |
| national_security | 2865 | 339 | 338 |
| ict | 2326 | 285 | 292 |
| education | 2093 | 248 | 255 |
## Dataset Creation
### Curation Rationale
The data was scraped from the news site [Prachathai](prachathai.com) from August 24, 2004 to November 15, 2018. The initial intention was to use the dataset as a benchmark for Thai text classification. Due to the size of the dataset, it can also be used for language modeling.
### Source Data
#### Initial Data Collection and Normalization
67,889 articles wtih 51,797 tags were scraped from the news site [Prachathai](prachathai.com) from August 24, 2004 to November 15, 2018. We filtered out those articles with less than 500 characters of body text, mostly images and cartoons.
#### Who are the source language producers?
Prachathai.com
### Annotations
#### Annotation process
Tags are annotated for the news website Prachathai.com
#### Who are the annotators?
We assume that the reporters who wrote the articles or other Prachathai staff gave each article its tags.
### Personal and Sensitive Information
We do not expect any personal and sensitive information to be present since all data are public news articles.
## Considerations for Using the Data
### Social Impact of Dataset
- classification benchmark for multi-label Thai text classification
### Discussion of Biases
Prachathai.com is a left-leaning, human-right-focused news site, and thus unusual news labels such as human rights and quality of life. The news articles are expected to be left-leaning in contents.
### Other Known Limitations
Dataset provided for research purposes only. Please check dataset license for additional information.
## Additional Information
### Dataset Curators
PyThaiNLP
### Licensing Information
CC-BY-NC
### Citation Information
@misc{prachathai67k,
author = {cstorm125, lukkiddd },
title = {prachathai67k},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
howpublished={\\url{https://github.com/PyThaiNLP/prachathai-67k}},
}
### Contributions
Thanks to [@cstorm125](https://github.com/cstorm125) for adding this dataset. | [
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PlanTL-GOB-ES/cantemist-ner | PlanTL-GOB-ES | 2022-11-18T12:08:17Z | 363 | 3 | null | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"multilinguality:monolingual",
"language:es",
"license:cc-by-4.0",
"biomedical",
"clinical",
"spanish",
"region:us"
] | 2022-11-18T12:08:17Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language:
- es
tags:
- biomedical
- clinical
- spanish
multilinguality:
- monolingual
task_categories:
- token-classification
task_ids:
- named-entity-recognition
license:
- cc-by-4.0
---
# CANTEMIST
## Dataset Description
Manually classified collection of Spanish oncological clinical case reports.
- **Homepage:** [zenodo](https://zenodo.org/record/3978041)
- **Paper:** [Named Entity Recognition, Concept Normalization and Clinical Coding: Overview of the Cantemist Track for Cancer Text Mining in Spanish, Corpus, Guidelines, Methods and Results](https://www.researchgate.net/profile/Antonio-Miranda-Escalada-2/publication/352786464_Named_Entity_Recognition_Concept_Normalization_and_Clinical_Coding_Overview_of_the_Cantemist_Track_for_Cancer_Text_Mining_in_Spanish_Corpus_Guidelines_Methods_and_Results/links/60d98a3b458515d6fbe382d8/Named-Entity-Recognition-Concept-Normalization-and-Clinical-Coding-Overview-of-the-Cantemist-Track-for-Cancer-Text-Mining-in-Spanish-Corpus-Guidelines-Methods-and-Results.pdf)
- **Point of Contact:** encargo-pln-life@bsc.es
### Dataset Summary
Collection of 1301 oncological clinical case reports written in Spanish, with tumor morphology mentions manually annotated and mapped by clinical experts to a controlled terminology. Every tumor morphology mention is linked to an eCIE-O code (the Spanish equivalent of ICD-O).
The training subset contains 501 documents, the development subsets 500, and the test subset 300. The original dataset is distributed in [Brat](https://brat.nlplab.org/standoff.html) format.
This dataset was designed for the CANcer TExt Mining Shared Task, sponsored by [Plan-TL](https://plantl.mineco.gob.es/Paginas/index.aspx).
For further information, please visit [the official website](https://temu.bsc.es/cantemist/).
### Supported Tasks
Named Entity Recognition (NER)
### Languages
- Spanish (es)
### Directory Structure
* README.md
* cantemist.py
* train.conll
* dev.conll
* test.conll
## Dataset Structure
### Data Instances
Three four-column files, one for each split.
### Data Fields
Every file has 4 columns:
* 1st column: Word form or punctuation symbol
* 2nd column: Original BRAT file name
* 3rd column: Spans
* 4th column: IOB tag
#### Example
<pre>
El cc_onco101 662_664 O
informe cc_onco101 665_672 O
HP cc_onco101 673_675 O
es cc_onco101 676_678 O
compatible cc_onco101 679_689 O
con cc_onco101 690_693 O
adenocarcinoma cc_onco101 694_708 B-MORFOLOGIA_NEOPLASIA
moderadamente cc_onco101 709_722 I-MORFOLOGIA_NEOPLASIA
diferenciado cc_onco101 723_735 I-MORFOLOGIA_NEOPLASIA
que cc_onco101 736_739 O
afecta cc_onco101 740_746 O
a cc_onco101 747_748 O
grasa cc_onco101 749_754 O
peripancreática cc_onco101 755_770 O
sobrepasando cc_onco101 771_783 O
la cc_onco101 784_786 O
serosa cc_onco101 787_793 O
, cc_onco101 793_794 O
infiltración cc_onco101 795_807 O
perineural cc_onco101 808_818 O
. cc_onco101 818_819 O
</pre>
### Data Splits
| Split | Size |
| ------------- | ------------- |
| `train` | 19,397 |
| `dev` | 18,165 |
| `test` | 11,168 |
## Dataset Creation
### Curation Rationale
For compatibility with similar datasets in other languages, we followed as close as possible existing curation guidelines.
### Source Data
#### Initial Data Collection and Normalization
The selected clinical case reports are fairly similar to hospital health records. To increase the usefulness and practical relevance of the CANTEMIST corpus, we selected clinical cases affecting all genders and that comprised most ages (from children to the elderly) and of various complexity levels (solid tumors, hemato-oncological malignancies, neuroendocrine cancer...).
The CANTEMIST cases include clinical signs and symptoms, personal and family history, current illness, physical examination, complementary tests (blood tests, imaging, pathology), diagnosis, treatment (including adverse effects of chemotherapy), evolution and outcome.
#### Who are the source language producers?
Humans, there is no machine generated data.
### Annotations
#### Annotation process
The manual annotation of the Cantemist corpus was performed by clinical experts following the Cantemist guidelines (for more detail refer to this [paper](http://ceur-ws.org/Vol-2664/cantemist_overview.pdf)). These guidelines contain rules for annotating morphology neoplasms in Spanish oncology clinical cases, as well as for mapping these annotations to eCIE-O.
A medical doctor was regularly consulted by annotators (scientists with PhDs on cancer-related subjects) for the most difficult pathology expressions. This same doctor periodically checked a random selection of annotated clinical records and these annotations were compared and discussed with the annotators. To normalize a selection of very complex cases, MD specialists in pathology from one of the largest university hospitals in Spain were consulted.
#### Who are the annotators?
Clinical experts.
### Personal and Sensitive Information
No personal or sensitive information included.
## Considerations for Using the Data
### Social Impact of Dataset
This corpus contributes to the development of medical language models in Spanish.
### Discussion of Biases
Not applicable.
## Additional Information
### Dataset Curators
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es).
For further information, send an email to (plantl-gob-es@bsc.es).
This work was funded by the [Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA)](https://avancedigital.mineco.gob.es/en-us/Paginas/index.aspx) within the framework of the [Plan-TL](https://plantl.mineco.gob.es/Paginas/index.aspx).
### Licensing information
This work is licensed under [CC Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/) License.
Copyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022)
### Citation Information
```bibtex
@article{cantemist,
title={Named Entity Recognition, Concept Normalization and Clinical Coding: Overview of the Cantemist Track for Cancer Text Mining in Spanish, Corpus, Guidelines, Methods and Results.},
author={Miranda-Escalada, Antonio and Farr{\'e}, Eul{\`a}lia and Krallinger, Martin},
journal={IberLEF@ SEPLN},
pages={303--323},
year={2020}
}
```
### Contributions
[N/A]
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
LeoLM/HellaSwag_de | LeoLM | 2023-08-29T13:31:56Z | 363 | 0 | null | [
"region:us"
] | 2023-08-29T13:31:56Z | 2023-08-10T13:49:51.000Z | 2023-08-10T13:49:51 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: ind
dtype: int64
- name: activity_label
dtype: string
- name: ctx_a
dtype: string
- name: ctx_b
dtype: string
- name: ctx
dtype: string
- name: endings
sequence: string
- name: source_id
dtype: string
- name: split
dtype: string
- name: split_type
dtype: string
- name: label
dtype: string
- name: activity_label_de
dtype: string
- name: ctx_de
dtype: string
- name: endings_de
sequence: string
- name: translation_de
dtype: string
splits:
- name: train
num_bytes: 1592717
num_examples: 1000
- name: validation
num_bytes: 31417275
num_examples: 10042
download_size: 18521030
dataset_size: 33009992
---
# Dataset Card for "hellaswag_de"
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
opus_xhosanavy | null | 2022-11-03T16:08:13Z | 362 | 3 | null | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"language:xh",
"license:unknown",
"region:us"
] | 2022-11-03T16:08:13Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
- xh
license:
- unknown
multilinguality:
- translation
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: OpusXhosanavy
dataset_info:
features:
- name: translation
dtype:
translation:
languages:
- en
- xh
config_name: en-xh
splits:
- name: train
num_bytes: 9654422
num_examples: 49982
download_size: 3263865
dataset_size: 9654422
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**[XhosaNavy](http://opus.nlpl.eu/XhosaNavy-v1.php)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This corpus is part of OPUS - the open collection of parallel corpora
OPUS Website: http://opus.nlpl.eu
### Supported Tasks and Leaderboards
The underlying task is machine translation from English to Xhosa
### 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
J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012)
### Contributions
Thanks to [@lhoestq](https://github.com/lhoestq), [@spatil6](https://github.com/spatil6) for adding this dataset. | [
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bigscience/xP3 | bigscience | 2023-05-30T15:49:59Z | 362 | 86 | null | [
"task_categories:other",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:100M<n<1B",
"language:ak",
"language:ar",
"language:as",
"language:bm",
"language:bn",
"language:ca",
"language:code",
"language:en",
"language:es",
"language:eu",
"language:fon",
"language:fr",
"language:gu",
"language:hi",
"language:id",
"language:ig",
"language:ki",
"language:kn",
"language:lg",
"language:ln",
"language:ml",
"language:mr",
"language:ne",
"language:nso",
"language:ny",
"language:or",
"language:pa",
"language:pt",
"language:rn",
"language:rw",
"language:sn",
"language:st",
"language:sw",
"language:ta",
"language:te",
"language:tn",
"language:ts",
"language:tum",
"language:tw",
"language:ur",
"language:vi",
"language:wo",
"language:xh",
"language:yo",
"language:zh",
"language:zu",
"license:apache-2.0",
"arxiv:2211.01786",
"region:us"
] | 2023-05-30T15:49:59Z | 2022-10-10T10:38:53.000Z | 2022-10-10T10:38:53 | ---
annotations_creators:
- expert-generated
- crowdsourced
language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
- es
- eu
- fon
- fr
- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
- ne
- nso
- ny
- or
- pa
- pt
- rn
- rw
- sn
- st
- sw
- ta
- te
- tn
- ts
- tum
- tw
- ur
- vi
- wo
- xh
- yo
- zh
- zu
programming_language:
- C
- C++
- C#
- Go
- Java
- JavaScript
- Lua
- PHP
- Python
- Ruby
- Rust
- Scala
- TypeScript
license:
- apache-2.0
multilinguality:
- multilingual
pretty_name: xP3
size_categories:
- 100M<n<1B
task_categories:
- other
---
# Dataset Card for xP3
## 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)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** https://github.com/bigscience-workshop/xmtf
- **Paper:** [Crosslingual Generalization through Multitask Finetuning](https://arxiv.org/abs/2211.01786)
- **Point of Contact:** [Niklas Muennighoff](mailto:niklas@hf.co)
### Dataset Summary
> xP3 (Crosslingual Public Pool of Prompts) is a collection of prompts & datasets across 46 of languages & 16 NLP tasks. It is used for the training of BLOOMZ and mT0, multilingual language models capable of following human instructions in dozens of languages zero-shot.
- **Creation:** The dataset can be recreated using instructions available [here](https://github.com/bigscience-workshop/xmtf#create-xp3). We provide this version to save processing time and ease reproducibility.
- **Languages:** 46 (Can be extended by [recreating with more splits](https://github.com/bigscience-workshop/xmtf#create-xp3))
- **xP3 Dataset Family:**
<table>
<tr>
<th>Name</th>
<th>Explanation</th>
<th>Example models</th>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/Muennighoff/xP3x>xP3x</a></t>
<td>Mixture of 17 tasks in 277 languages with English prompts</td>
<td>WIP - Join us at Project Aya @<a href=https://cohere.for.ai/>C4AI</a> to help!</td>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/bigscience/xP3>xP3</a></t>
<td>Mixture of 13 training tasks in 46 languages with English prompts</td>
<td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a> & <a href=https://huggingface.co/bigscience/mt0-xxl>mt0-xxl</a></td>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/bigscience/xP3mt>xP3mt</a></t>
<td>Mixture of 13 training tasks in 46 languages with prompts in 20 languages (machine-translated from English)</td>
<td><a href=https://huggingface.co/bigscience/bloomz-mt>bloomz-mt</a> & <a href=https://huggingface.co/bigscience/mt0-xxl-mt>mt0-xxl-mt</a></td>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/bigscience/xP3all>xP3all</a></t>
<td>xP3 + evaluation datasets adding an additional 3 tasks for a total of 16 tasks in 46 languages with English prompts</td>
<td></td>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/bigscience/xP3megds>xP3megds</a></t>
<td><a href=https://github.com/bigscience-workshop/Megatron-DeepSpeed>Megatron-DeepSpeed</a> processed version of xP3</td>
<td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a></td>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/Muennighoff/P3>P3</a></t>
<td>Repreprocessed version of the English-only <a href=https://huggingface.co/datasets/bigscience/P3>P3</a> with 8 training tasks</td>
<td><a href=https://huggingface.co/bigscience/bloomz-p3>bloomz-p3</a> & <a href=https://huggingface.co/bigscience/mt0-xxl-p3>mt0-xxl-p3</a></td>
</tr>
</table>
## Dataset Structure
### Data Instances
An example of "train" looks as follows:
```json
{
"inputs": "Sentence 1: Fue académico en literatura metafísica, teología y ciencias clásicas.\nSentence 2: Fue académico en literatura metafísica, teología y ciencia clásica.\nQuestion: Can we rewrite Sentence 1 to Sentence 2? Yes or No?",
"targets": "Yes"
}
```
### Data Fields
The data fields are the same among all splits:
- `inputs`: the natural language input fed to the model
- `targets`: the natural language target that the model has to generate
### Data Splits
The below table summarizes sizes per language (computed from the `merged_{lang}.jsonl` files). Due to languages like `tw` only being single sentence translation samples from Flores, their byte percentage is significantly lower than their sample percentage. Adding a new language is very simple, you can take [this script adding Russian](https://huggingface.co/datasets/bs-la/xP3ru/blob/main/xp3_ru.py) as an example.
|Language|Kilobytes|%|Samples|%|
|--------|------:|-:|---:|-:|
|tw|106288|0.11|265071|0.34|
|bm|107056|0.11|265180|0.34|
|ak|108096|0.11|265071|0.34|
|eu|108112|0.11|269973|0.34|
|ca|110608|0.12|271191|0.34|
|fon|113072|0.12|265063|0.34|
|st|114080|0.12|265063|0.34|
|ki|115040|0.12|265180|0.34|
|tum|116032|0.12|265063|0.34|
|wo|122560|0.13|365063|0.46|
|ln|126304|0.13|365060|0.46|
|as|156256|0.16|265063|0.34|
|or|161472|0.17|265063|0.34|
|kn|165456|0.17|265063|0.34|
|ml|175040|0.18|265864|0.34|
|rn|192992|0.2|318189|0.4|
|nso|229712|0.24|915051|1.16|
|tn|235536|0.25|915054|1.16|
|lg|235936|0.25|915021|1.16|
|rw|249360|0.26|915043|1.16|
|ts|250256|0.26|915044|1.16|
|sn|252496|0.27|865056|1.1|
|xh|254672|0.27|915058|1.16|
|zu|263712|0.28|915061|1.16|
|ny|272128|0.29|915063|1.16|
|ig|325232|0.34|950097|1.2|
|yo|352784|0.37|918416|1.16|
|ne|393680|0.41|315754|0.4|
|pa|523248|0.55|339210|0.43|
|gu|560688|0.59|347499|0.44|
|sw|560896|0.59|1114455|1.41|
|mr|666240|0.7|417269|0.53|
|bn|832720|0.88|428843|0.54|
|ta|924496|0.97|410633|0.52|
|te|1332912|1.4|573364|0.73|
|ur|1918272|2.02|855756|1.08|
|vi|3101408|3.27|1667306|2.11|
|code|4330752|4.56|2707724|3.43|
|hi|4393696|4.63|1543441|1.96|
|zh|4589904|4.83|3560556|4.51|
|id|4606288|4.85|2627392|3.33|
|ar|4677264|4.93|2148955|2.72|
|fr|5546688|5.84|5055942|6.41|
|pt|6129584|6.46|3562772|4.52|
|es|7571808|7.98|5151349|6.53|
|en|37261104|39.25|31495184|39.93|
|total|94941936|100.0|78883588|100.0|
## Dataset Creation
### Source Data
#### Training datasets
- Code Miscellaneous
- [CodeComplex](https://huggingface.co/datasets/codeparrot/codecomplex)
- [Docstring Corpus](https://huggingface.co/datasets/teven/code_docstring_corpus)
- [GreatCode](https://huggingface.co/datasets/great_code)
- [State Changes](https://huggingface.co/datasets/Fraser/python-state-changes)
- Closed-book QA
- [Hotpot QA](https://huggingface.co/datasets/hotpot_qa)
- [Trivia QA](https://huggingface.co/datasets/trivia_qa)
- [Web Questions](https://huggingface.co/datasets/web_questions)
- [Wiki QA](https://huggingface.co/datasets/wiki_qa)
- Extractive QA
- [Adversarial QA](https://huggingface.co/datasets/adversarial_qa)
- [CMRC2018](https://huggingface.co/datasets/cmrc2018)
- [DRCD](https://huggingface.co/datasets/clue)
- [DuoRC](https://huggingface.co/datasets/duorc)
- [MLQA](https://huggingface.co/datasets/mlqa)
- [Quoref](https://huggingface.co/datasets/quoref)
- [ReCoRD](https://huggingface.co/datasets/super_glue)
- [ROPES](https://huggingface.co/datasets/ropes)
- [SQuAD v2](https://huggingface.co/datasets/squad_v2)
- [xQuAD](https://huggingface.co/datasets/xquad)
- TyDI QA
- [Primary](https://huggingface.co/datasets/khalidalt/tydiqa-primary)
- [Goldp](https://huggingface.co/datasets/khalidalt/tydiqa-goldp)
- Multiple-Choice QA
- [ARC](https://huggingface.co/datasets/ai2_arc)
- [C3](https://huggingface.co/datasets/c3)
- [CoS-E](https://huggingface.co/datasets/cos_e)
- [Cosmos](https://huggingface.co/datasets/cosmos)
- [DREAM](https://huggingface.co/datasets/dream)
- [MultiRC](https://huggingface.co/datasets/super_glue)
- [OpenBookQA](https://huggingface.co/datasets/openbookqa)
- [PiQA](https://huggingface.co/datasets/piqa)
- [QUAIL](https://huggingface.co/datasets/quail)
- [QuaRel](https://huggingface.co/datasets/quarel)
- [QuaRTz](https://huggingface.co/datasets/quartz)
- [QASC](https://huggingface.co/datasets/qasc)
- [RACE](https://huggingface.co/datasets/race)
- [SciQ](https://huggingface.co/datasets/sciq)
- [Social IQA](https://huggingface.co/datasets/social_i_qa)
- [Wiki Hop](https://huggingface.co/datasets/wiki_hop)
- [WiQA](https://huggingface.co/datasets/wiqa)
- Paraphrase Identification
- [MRPC](https://huggingface.co/datasets/super_glue)
- [PAWS](https://huggingface.co/datasets/paws)
- [PAWS-X](https://huggingface.co/datasets/paws-x)
- [QQP](https://huggingface.co/datasets/qqp)
- Program Synthesis
- [APPS](https://huggingface.co/datasets/codeparrot/apps)
- [CodeContests](https://huggingface.co/datasets/teven/code_contests)
- [JupyterCodePairs](https://huggingface.co/datasets/codeparrot/github-jupyter-text-code-pairs)
- [MBPP](https://huggingface.co/datasets/Muennighoff/mbpp)
- [NeuralCodeSearch](https://huggingface.co/datasets/neural_code_search)
- [XLCoST](https://huggingface.co/datasets/codeparrot/xlcost-text-to-code)
- Structure-to-text
- [Common Gen](https://huggingface.co/datasets/common_gen)
- [Wiki Bio](https://huggingface.co/datasets/wiki_bio)
- Sentiment
- [Amazon](https://huggingface.co/datasets/amazon_polarity)
- [App Reviews](https://huggingface.co/datasets/app_reviews)
- [IMDB](https://huggingface.co/datasets/imdb)
- [Rotten Tomatoes](https://huggingface.co/datasets/rotten_tomatoes)
- [Yelp](https://huggingface.co/datasets/yelp_review_full)
- Simplification
- [BiSECT](https://huggingface.co/datasets/GEM/BiSECT)
- Summarization
- [CNN Daily Mail](https://huggingface.co/datasets/cnn_dailymail)
- [Gigaword](https://huggingface.co/datasets/gigaword)
- [MultiNews](https://huggingface.co/datasets/multi_news)
- [SamSum](https://huggingface.co/datasets/samsum)
- [Wiki-Lingua](https://huggingface.co/datasets/GEM/wiki_lingua)
- [XLSum](https://huggingface.co/datasets/GEM/xlsum)
- [XSum](https://huggingface.co/datasets/xsum)
- Topic Classification
- [AG News](https://huggingface.co/datasets/ag_news)
- [DBPedia](https://huggingface.co/datasets/dbpedia_14)
- [TNEWS](https://huggingface.co/datasets/clue)
- [TREC](https://huggingface.co/datasets/trec)
- [CSL](https://huggingface.co/datasets/clue)
- Translation
- [Flores-200](https://huggingface.co/datasets/Muennighoff/flores200)
- [Tatoeba](https://huggingface.co/datasets/Helsinki-NLP/tatoeba_mt)
- Word Sense disambiguation
- [WiC](https://huggingface.co/datasets/super_glue)
- [XL-WiC](https://huggingface.co/datasets/pasinit/xlwic)
#### Evaluation datasets (included in [xP3all](https://huggingface.co/datasets/bigscience/xP3all) except for NLI datasets & HumanEval)
- Natural Language Inference (NLI)
- [ANLI](https://huggingface.co/datasets/anli)
- [CB](https://huggingface.co/datasets/super_glue)
- [RTE](https://huggingface.co/datasets/super_glue)
- [XNLI](https://huggingface.co/datasets/xnli)
- Coreference Resolution
- [Winogrande](https://huggingface.co/datasets/winogrande)
- [XWinograd](https://huggingface.co/datasets/Muennighoff/xwinograd)
- Program Synthesis
- [HumanEval](https://huggingface.co/datasets/openai_humaneval)
- Sentence Completion
- [COPA](https://huggingface.co/datasets/super_glue)
- [Story Cloze](https://huggingface.co/datasets/story_cloze)
- [XCOPA](https://huggingface.co/datasets/xcopa)
- [XStoryCloze](https://huggingface.co/datasets/Muennighoff/xstory_cloze)
## Additional Information
### Licensing Information
The dataset is released under Apache 2.0.
### Citation Information
```bibtex
@article{muennighoff2022crosslingual,
title={Crosslingual generalization through multitask finetuning},
author={Muennighoff, Niklas and Wang, Thomas and Sutawika, Lintang and Roberts, Adam and Biderman, Stella and Scao, Teven Le and Bari, M Saiful and Shen, Sheng and Yong, Zheng-Xin and Schoelkopf, Hailey and others},
journal={arXiv preprint arXiv:2211.01786},
year={2022}
}
```
### Contributions
Thanks to the contributors of [promptsource](https://github.com/bigscience-workshop/promptsource/graphs/contributors) for adding many prompts used in this dataset. | [
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cardy/kohatespeech | cardy | 2023-05-01T02:24:59Z | 362 | 0 | null | [
"license:cc-by-nc-sa-4.0",
"region:us"
] | 2023-05-01T02:24:59Z | 2023-04-30T11:55:56.000Z | 2023-04-30T11:55:56 | ---
license: cc-by-nc-sa-4.0
---
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Goader/ukrainian-treebank-lm | Goader | 2023-09-11T02:17:39Z | 362 | 0 | null | [
"task_categories:fill-mask",
"task_categories:text-generation",
"language:uk",
"license:cc-by-nc-sa-4.0",
"region:us"
] | 2023-09-11T02:17:39Z | 2023-09-10T20:18:33.000Z | 2023-09-10T20:18:33 | ---
license: cc-by-nc-sa-4.0
task_categories:
- fill-mask
- text-generation
language:
- uk
pretty_name: Ukrainian Treebank (Language Modeling)
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llmware/rag_instruct_benchmark_tester | llmware | 2023-11-04T09:54:25Z | 362 | 10 | null | [
"license:apache-2.0",
"financial services",
"retrieval augmented generation",
"RAG",
"q&a instruct",
"region:us"
] | 2023-11-04T09:54:25Z | 2023-11-01T09:34:23.000Z | 2023-11-01T09:34:23 | ---
license: apache-2.0
tags:
- financial services
- retrieval augmented generation
- RAG
- q&a instruct
pretty_name: RAG Instruct Benchmarking Test Dataset
---
# Dataset Card for RAG-Instruct-Benchmark-Tester
### Dataset Summary
This is an updated benchmarking test dataset for "retrieval augmented generation" (RAG) use cases in the enterprise, especially for financial services, and legal. This test dataset includes 200 questions with context passages pulled from common 'retrieval scenarios', e.g., financial news, earnings releases,
contracts, invoices, technical articles, general news and short texts.
The questions are segmented into several categories for benchmarking evaluation:
-- **Core Q&A Evaluation** (Samples 0-99) - 100 samples - fact-based 'core' questions- used to assign a score between 0-100 based on correct responses.
-- **Not Found Classification** (Samples 100-119) - 20 samples - in each sample, the context passage does not contain a direct answer to the question, and the objective is to evaluate whether the model correctly identifies as "Not Found" or attempts to answer using information in the context.
-- **Boolean - Yes/No** (Samples 120-139) - 20 samples - each sample is a Yes/No question.
-- **Basic Math** (Samples 140-159) - 20 samples - these are "every day" math questions - basic increments, decrements, percentages, multiplications, sorting, and ranking with amounts and times.
-- **Complex Q&A** (Samples 160-179) - 20 samples - tests several distinct 'complex q&a' skills - multiple-choice, financial table reading, multi-part extractions, causal, and logical selections.
-- **Summary** (Sample 180-199) - 20 samples - tests long-form and short-form summarization.
### Representative Questions
-- What are the payment terms?
-- What was the improvement in operating income year-to-year?
-- According to the CFO, what led to the increase in cloud revenue?
-- Who owns the intellectual property?
-- What is the notice period to terminate for convenience?
-- How often will the Board review the salary?
-- What section of the agreement defines the governing law?
-- How many jobs were predicted by economists?
-- How many shares were repurchased in second quarter?
-- What was the percentage increase in data center revenue compared to the first quarter?
-- When will the next dividend be paid?
-- Is the expected gross margin greater than 70%?
-- What is the amount of expected non-GAAP operating expense?
-- What did economists expect for the trade surplus amount?
-- What is Bank of Americas' rating on Snowflake?
-- Has the S&P increased over the last year? (Yes/No)
-- Is Barclay's raising its price target on KHC? (Yes/No)
-- Were third quarter sales more than 5 billion euros? (Yes/No)
-- If automotive revenue increases by $100 million in third quarter, what will be the amount? (Math)
-- If the rent increased by 50%, what is the new rental price? (Math)
-- Which stock index increased by the most points yesterday? (Math)
-- Why did the intraday reversal occur? (Complex)
-- What is a list of the top 3 financial highlights for the quarter? (Summary)
-- What is a list of the top 5 summary points? (Summary)
-- What is a summary of the CEO's statement in 15 words or less? (Summary)
-- What are the key terms of the invoice? (Summary)
### Languages
English
## Dataset Structure
200 JSONL samples with 6 keys - "query" | "context" | "answer" | "category" | "tokens" | "sample_number"
Note: this dataset includes elements from test_dataset_0.1 and test_dataset2_financial- and is intended to replace them for benchmarking evaluations.
### Personal and Sensitive Information
The dataset samples were written bespoke for this objective, derived from publicly-available sources and/or originally-written samples.
## Dataset Card Contact
Darren Oberst & llmware team
Please reach out anytime if you are interested in more information about this project. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
bigIR/ar_cov19 | bigIR | 2023-09-19T06:52:17Z | 361 | 1 | arcov-19 | [
"task_categories:other",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:ar",
"data-mining",
"arxiv:2004.05861",
"region:us"
] | 2023-09-19T06:52:17Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- ar
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: arcov-19
pretty_name: ArCOV19
tags:
- data-mining
dataset_info:
config_name: ar_cov19
features:
- name: tweetID
dtype: string
splits:
- name: train
num_bytes: 72223634
num_examples: 3140158
download_size: 23678407
dataset_size: 72223634
---
# Dataset Card for ArCOV19
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** https://gitlab.com/bigirqu/ArCOV-19
- **Paper:** [ArCOV-19: The First Arabic COVID-19 Twitter Dataset with Propagation Networks](https://arxiv.org/abs/2004.05861)
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [Fatima Haouari](mailto:200159617@qu.edu.qa)
### Dataset Summary
ArCOV-19 is an Arabic COVID-19 Twitter dataset that covers the period from 27th of January till 5th of May 2021.
ArCOV-19 is the first publicly-available Arabic Twitter dataset covering COVID-19 pandemic that includes about 3.2M
tweets alongside the propagation networks of the most-popular subset of them (i.e., most-retweeted and-liked).
The propagation networks include both retweets and conversational threads (i.e., threads of replies).
ArCOV-19 is designed to enable research under several domains including natural language processing, information
retrieval, and social computing, among others. Preliminary analysis shows that ArCOV-19 captures rising discussions
associated with the first reported cases of the disease as they appeared in the Arab world. In addition to the source
tweets and the propagation networks, we also release the search queries and the language-independent crawler used to
collect the tweets to encourage the curation of similar datasets.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Arabic
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
tweet_id: the Twitter assigned ID for the tweet object.
### Data Splits
[More Information Needed]
## Dataset Creation
The dataset collection approach is presented in the following paper: [ArCOV-19: The First Arabic COVID-19 Twitter Dataset with Propagation Networks](https://arxiv.org/abs/2004.05861)
### 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
No annotation was provided with the dataset.
#### Annotation process
No annotation was provided with the dataset.
#### Who are the annotators?
No annotation was provided with the dataset.
### 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
**Team:** [bigIR](https://sites.google.com/view/bigir) from Qatar University ([@bigIR_group](https://twitter.com/bigIR_group))
- [Fatima Haouari](mailto:200159617@qu.edu.qa)
- [Maram Hasanain](mailto:maram.hasanain@qu.edu.qa)
- [Reem Suwaileh](mailto:rs081123@qu.edu.qa)
- [Dr. Tamer Elsayed](mailto:telsayed@qu.edu.qa)
### Licensing Information
[More Information Needed]
### Citation Information
```
@article{haouari2020arcov19,
title={ArCOV-19: The First Arabic COVID-19 Twitter Dataset with Propagation Networks},
author={Fatima Haouari and Maram Hasanain and Reem Suwaileh and Tamer Elsayed},
year={2021},
eprint={2004.05861},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@Fatima-Haouari](https://github.com/Fatima-Haouari) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
great_code | null | 2022-11-18T20:05:00Z | 361 | 1 | null | [
"task_categories:table-to-text",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | 2022-11-18T20:05:00Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
paperswithcode_id: null
pretty_name: GREAT
dataset_info:
features:
- name: id
dtype: int32
- name: source_tokens
sequence: string
- name: has_bug
dtype: bool
- name: error_location
dtype: int32
- name: repair_candidates
sequence: string
- name: bug_kind
dtype: int32
- name: bug_kind_name
dtype: string
- name: repair_targets
sequence: int32
- name: edges
list:
list:
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dtype: int32
- name: after_index
dtype: int32
- name: edge_type
dtype: int32
- name: edge_type_name
dtype: string
- name: provenances
list:
- name: datasetProvenance
struct:
- name: datasetName
dtype: string
- name: filepath
dtype: string
- name: license
dtype: string
- name: note
dtype: string
splits:
- name: train
num_bytes: 14705534822
num_examples: 1798742
- name: validation
num_bytes: 1502956919
num_examples: 185656
- name: test
num_bytes: 7880762248
num_examples: 968592
download_size: 23310374002
dataset_size: 24089253989
---
# Dataset Card for GREAT
## 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:** None
- **Repository:** https://github.com/google-research-datasets/great
- **Paper:** https://openreview.net/forum?id=B1lnbRNtwr
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
Here are some examples of questions and facts:
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
liveqa | null | 2022-11-03T16:15:28Z | 361 | 1 | liveqa | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:zh",
"license:unknown",
"region:us"
] | 2022-11-03T16:15:28Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- zh
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: liveqa
pretty_name: LiveQA
dataset_info:
features:
- name: id
dtype: int64
- name: passages
sequence:
- name: is_question
dtype: bool
- name: text
dtype: string
- name: candidate1
dtype: string
- name: candidate2
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 112187507
num_examples: 1670
download_size: 114704569
dataset_size: 112187507
---
# Dataset Card for LiveQA
## 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:** [Github](https://github.com/PKU-TANGENT/LiveQA)
- **Repository:** [Github](https://github.com/PKU-TANGENT/LiveQA)
- **Paper:** [Liu et al., 2020](https://www.aclweb.org/anthology/2020.ccl-1.98.pdf)
- **Leaderboard:** N/A
- **Point of Contact:** Qianying Liu
### Dataset Summary
The LiveQA dataset is a Chinese question-answering resource constructed from playby-play live broadcasts. It contains 117k multiple-choice questions written by human commentators for over 1,670 NBA games, which are collected from the Chinese Hupu website.
### Supported Tasks and Leaderboards
Question Answering.
[More Information Needed]
### Languages
Chinese.
## Dataset Structure
### Data Instances
Each instance represents a timeline (i.e., a game) with an identifier. The passages field comprise an array of text or question segments. In the following truncated example, user comments about the game is followed by a question about which team will be the first to reach 60 points.
```python
{
'id': 1,
'passages': [
{
"is_question": False,
"text": "'我希望两位球员都能做到！！",
"candidate1": "",
"candidate2": "",
"answer": "",
},
{
"is_question": False,
"text": "新年给我们送上精彩比赛！",
"candidate1": "",
"candidate2": "",
"answer": "",
},
{
"is_question": True,
"text": "先达到60分？",
"candidate1": "火箭",
"candidate2": "勇士",
"answer": "勇士",
},
{
"is_question": False,
"text": "自己急停跳投！！！",
"candidate1": "",
"candidate2": "",
"answer": "",
}
]
}
```
### Data Fields
- id: identifier for the game
- passages: collection of text/question segments
- text: real-time text comment or binary question related to the context
- candidate1/2: one of the two answer options to the question
- answer: correct answer to the question in text
### Data Splits
There is no predefined split in this dataset.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
This resource is developed by [Liu et al., 2020](https://www.aclweb.org/anthology/2020.ccl-1.98.pdf).
```
@inproceedings{qianying-etal-2020-liveqa,
title = "{L}ive{QA}: A Question Answering Dataset over Sports Live",
author = "Qianying, Liu and
Sicong, Jiang and
Yizhong, Wang and
Sujian, Li",
booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
month = oct,
year = "2020",
address = "Haikou, China",
publisher = "Chinese Information Processing Society of China",
url = "https://www.aclweb.org/anthology/2020.ccl-1.98",
pages = "1057--1067"
}
```
### Contributions
Thanks to [@j-chim](https://github.com/j-chim) for adding this dataset. | [
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dansbecker/hackernews_hiring_posts | dansbecker | 2021-12-07T13:46:20Z | 361 | 0 | null | [
"region:us"
] | 2021-12-07T13:46:20Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | This dataset contains postings and comments from the following recurring threads on [Hacker News](http://news.ycombinator.com/)
1. Ask HN: Who is hiring?
2. Ask HN: Who wants to be hired?
3. Freelancer? Seeking freelancer?
These post types are stored in datasets called `hiring`, `wants_to_be_hired` and `freelancer` respectively.
Each type of posting has occurred on a regular basis for several years. You can identify when each comment/listing was added through the CommentTime field. The `ParentTitle` also indicates the date of the parent thread in text (e.g. `Ask HN: Who is hiring? (March 2021)`)
This dataset is not programmatically reproducible from source because it was uploaded as an experiment with HF datasets. The raw data was created by querying the public table `bigquery-public-data.hacker_news.full` in Google BigQuery.
Email addresses have been redacted from the dataset.
If this dataset is interesting/useful, I (Dan Becker) will look into improving reproducibility and other general clean-up.
This dataset may be useful for finding trends in tech and tech job listings. | [
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pierreguillou/DocLayNet-small | pierreguillou | 2023-05-17T08:56:10Z | 361 | 7 | null | [
"task_categories:object-detection",
"task_categories:image-segmentation",
"task_categories:token-classification",
"task_ids:instance-segmentation",
"annotations_creators:crowdsourced",
"size_categories:1K<n<10K",
"language:en",
"language:de",
"language:fr",
"language:ja",
"license:other",
"DocLayNet",
"COCO",
"PDF",
"IBM",
"Financial-Reports",
"Finance",
"Manuals",
"Scientific-Articles",
"Science",
"Laws",
"Law",
"Regulations",
"Patents",
"Government-Tenders",
"object-detection",
"image-segmentation",
"token-classification",
"arxiv:2206.01062",
"region:us"
] | 2023-05-17T08:56:10Z | 2023-01-25T17:47:43.000Z | 2023-01-25T17:47:43 | ---
language:
- en
- de
- fr
- ja
annotations_creators:
- crowdsourced
license: other
pretty_name: DocLayNet small
size_categories:
- 1K<n<10K
tags:
- DocLayNet
- COCO
- PDF
- IBM
- Financial-Reports
- Finance
- Manuals
- Scientific-Articles
- Science
- Laws
- Law
- Regulations
- Patents
- Government-Tenders
- object-detection
- image-segmentation
- token-classification
task_categories:
- object-detection
- image-segmentation
- token-classification
task_ids:
- instance-segmentation
---
# Dataset Card for DocLayNet small
## About this card (01/27/2023)
### Property and license
All information from this page but the content of this paragraph "About this card (01/27/2023)" has been copied/pasted from [Dataset Card for DocLayNet](https://huggingface.co/datasets/ds4sd/DocLayNet).
DocLayNet is a dataset created by Deep Search (IBM Research) published under [license CDLA-Permissive-1.0](https://huggingface.co/datasets/ds4sd/DocLayNet#licensing-information).
I do not claim any rights to the data taken from this dataset and published on this page.
### DocLayNet dataset
[DocLayNet dataset](https://github.com/DS4SD/DocLayNet) (IBM) provides page-by-page layout segmentation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique pages from 6 document categories.
Until today, the dataset can be downloaded through direct links or as a dataset from Hugging Face datasets:
- direct links: [doclaynet_core.zip](https://codait-cos-dax.s3.us.cloud-object-storage.appdomain.cloud/dax-doclaynet/1.0.0/DocLayNet_core.zip) (28 GiB), [doclaynet_extra.zip](https://codait-cos-dax.s3.us.cloud-object-storage.appdomain.cloud/dax-doclaynet/1.0.0/DocLayNet_extra.zip) (7.5 GiB)
- Hugging Face dataset library: [dataset DocLayNet](https://huggingface.co/datasets/ds4sd/DocLayNet)
Paper: [DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis](https://arxiv.org/abs/2206.01062) (06/02/2022)
### Processing into a format facilitating its use by HF notebooks
These 2 options require the downloading of all the data (approximately 30GBi), which requires downloading time (about 45 mn in Google Colab) and a large space on the hard disk. These could limit experimentation for people with low resources.
Moreover, even when using the download via HF datasets library, it is necessary to download the EXTRA zip separately ([doclaynet_extra.zip](https://codait-cos-dax.s3.us.cloud-object-storage.appdomain.cloud/dax-doclaynet/1.0.0/DocLayNet_extra.zip), 7.5 GiB) to associate the annotated bounding boxes with the text extracted by OCR from the PDFs. This operation also requires additional code because the boundings boxes of the texts do not necessarily correspond to those annotated (a calculation of the percentage of area in common between the boundings boxes annotated and those of the texts makes it possible to make a comparison between them).
At last, in order to use Hugging Face notebooks on fine-tuning layout models like LayoutLMv3 or LiLT, DocLayNet data must be processed in a proper format.
For all these reasons, I decided to process the DocLayNet dataset:
- into 3 datasets of different sizes:
- [DocLayNet small](https://huggingface.co/datasets/pierreguillou/DocLayNet-small) (about 1% of DocLayNet) < 1.000k document images (691 train, 64 val, 49 test)
- [DocLayNet base](https://huggingface.co/datasets/pierreguillou/DocLayNet-base) (about 10% of DocLayNet) < 10.000k document images (6910 train, 648 val, 499 test)
- [DocLayNet large](https://huggingface.co/datasets/pierreguillou/DocLayNet-large) (about 100% of DocLayNet) < 100.000k document images (69.103 train, 6.480 val, 4.994 test)
- with associated texts and PDFs (base64 format),
- and in a format facilitating their use by HF notebooks.
*Note: the layout HF notebooks will greatly help participants of the IBM [ICDAR 2023 Competition on Robust Layout Segmentation in Corporate Documents](https://ds4sd.github.io/icdar23-doclaynet/)!*
### About PDFs languages
Citation of the page 3 of the [DocLayNet paper](https://arxiv.org/abs/2206.01062):
"We did not control the document selection with regard to language. **The vast majority of documents contained in DocLayNet (close to 95%) are published in English language.** However, **DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%)**. While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features."
### About PDFs categories distribution
Citation of the page 3 of the [DocLayNet paper](https://arxiv.org/abs/2206.01062):
"The pages in DocLayNet can be grouped into **six distinct categories**, namely **Financial Reports, Manuals, Scientific Articles, Laws & Regulations, Patents and Government Tenders**. Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories (Financial Reports and Manuals) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes."
### Download & overview
The size of the DocLayNet small is about 1% of the DocLayNet dataset (random selection respectively in the train, val and test files).
```
# !pip install -q datasets
from datasets import load_dataset
dataset_small = load_dataset("pierreguillou/DocLayNet-small")
# overview of dataset_small
DatasetDict({
train: Dataset({
features: ['id', 'texts', 'bboxes_block', 'bboxes_line', 'categories', 'image', 'pdf', 'page_hash', 'original_filename', 'page_no', 'num_pages', 'original_width', 'original_height', 'coco_width', 'coco_height', 'collection', 'doc_category'],
num_rows: 691
})
validation: Dataset({
features: ['id', 'texts', 'bboxes_block', 'bboxes_line', 'categories', 'image', 'pdf', 'page_hash', 'original_filename', 'page_no', 'num_pages', 'original_width', 'original_height', 'coco_width', 'coco_height', 'collection', 'doc_category'],
num_rows: 64
})
test: Dataset({
features: ['id', 'texts', 'bboxes_block', 'bboxes_line', 'categories', 'image', 'pdf', 'page_hash', 'original_filename', 'page_no', 'num_pages', 'original_width', 'original_height', 'coco_width', 'coco_height', 'collection', 'doc_category'],
num_rows: 49
})
})
```
### Annotated bounding boxes
The DocLayNet base makes easy to display document image with the annotaed bounding boxes of paragraphes or lines.
Check the notebook [processing_DocLayNet_dataset_to_be_used_by_layout_models_of_HF_hub.ipynb](https://github.com/piegu/language-models/blob/master/processing_DocLayNet_dataset_to_be_used_by_layout_models_of_HF_hub.ipynb) in order to get the code.
#### Paragraphes
#### Lines
### HF notebooks
- [notebooks LayoutLM](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LayoutLM) (Niels Rogge)
- [notebooks LayoutLMv2](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LayoutLMv2) (Niels Rogge)
- [notebooks LayoutLMv3](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LayoutLMv3) (Niels Rogge)
- [notebooks LiLT](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LiLT) (Niels Rogge)
- [Document AI: Fine-tuning LiLT for document-understanding using Hugging Face Transformers](https://github.com/philschmid/document-ai-transformers/blob/main/training/lilt_funsd.ipynb) ([post](https://www.philschmid.de/fine-tuning-lilt#3-fine-tune-and-evaluate-lilt) of Phil Schmid)
## 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)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Annotations](#annotations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://developer.ibm.com/exchanges/data/all/doclaynet/
- **Repository:** https://github.com/DS4SD/DocLayNet
- **Paper:** https://doi.org/10.1145/3534678.3539043
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
DocLayNet provides page-by-page layout segmentation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique pages from 6 document categories. It provides several unique features compared to related work such as PubLayNet or DocBank:
1. *Human Annotation*: DocLayNet is hand-annotated by well-trained experts, providing a gold-standard in layout segmentation through human recognition and interpretation of each page layout
2. *Large layout variability*: DocLayNet includes diverse and complex layouts from a large variety of public sources in Finance, Science, Patents, Tenders, Law texts and Manuals
3. *Detailed label set*: DocLayNet defines 11 class labels to distinguish layout features in high detail.
4. *Redundant annotations*: A fraction of the pages in DocLayNet are double- or triple-annotated, allowing to estimate annotation uncertainty and an upper-bound of achievable prediction accuracy with ML models
5. *Pre-defined train- test- and validation-sets*: DocLayNet provides fixed sets for each to ensure proportional representation of the class-labels and avoid leakage of unique layout styles across the sets.
### Supported Tasks and Leaderboards
We are hosting a competition in ICDAR 2023 based on the DocLayNet dataset. For more information see https://ds4sd.github.io/icdar23-doclaynet/.
## Dataset Structure
### Data Fields
DocLayNet provides four types of data assets:
1. PNG images of all pages, resized to square `1025 x 1025px`
2. Bounding-box annotations in COCO format for each PNG image
3. Extra: Single-page PDF files matching each PNG image
4. Extra: JSON file matching each PDF page, which provides the digital text cells with coordinates and content
The COCO image record are defined like this example
```js
...
{
"id": 1,
"width": 1025,
"height": 1025,
"file_name": "132a855ee8b23533d8ae69af0049c038171a06ddfcac892c3c6d7e6b4091c642.png",
// Custom fields:
"doc_category": "financial_reports" // high-level document category
"collection": "ann_reports_00_04_fancy", // sub-collection name
"doc_name": "NASDAQ_FFIN_2002.pdf", // original document filename
"page_no": 9, // page number in original document
"precedence": 0, // Annotation order, non-zero in case of redundant double- or triple-annotation
},
...
```
The `doc_category` field uses one of the following constants:
```
financial_reports,
scientific_articles,
laws_and_regulations,
government_tenders,
manuals,
patents
```
### Data Splits
The dataset provides three splits
- `train`
- `val`
- `test`
## Dataset Creation
### Annotations
#### Annotation process
The labeling guideline used for training of the annotation experts are available at [DocLayNet_Labeling_Guide_Public.pdf](https://raw.githubusercontent.com/DS4SD/DocLayNet/main/assets/DocLayNet_Labeling_Guide_Public.pdf).
#### Who are the annotators?
Annotations are crowdsourced.
## Additional Information
### Dataset Curators
The dataset is curated by the [Deep Search team](https://ds4sd.github.io/) at IBM Research.
You can contact us at [deepsearch-core@zurich.ibm.com](mailto:deepsearch-core@zurich.ibm.com).
Curators:
- Christoph Auer, [@cau-git](https://github.com/cau-git)
- Michele Dolfi, [@dolfim-ibm](https://github.com/dolfim-ibm)
- Ahmed Nassar, [@nassarofficial](https://github.com/nassarofficial)
- Peter Staar, [@PeterStaar-IBM](https://github.com/PeterStaar-IBM)
### Licensing Information
License: [CDLA-Permissive-1.0](https://cdla.io/permissive-1-0/)
### Citation Information
```bib
@article{doclaynet2022,
title = {DocLayNet: A Large Human-Annotated Dataset for Document-Layout Segmentation},
doi = {10.1145/3534678.353904},
url = {https://doi.org/10.1145/3534678.3539043},
author = {Pfitzmann, Birgit and Auer, Christoph and Dolfi, Michele and Nassar, Ahmed S and Staar, Peter W J},
year = {2022},
isbn = {9781450393850},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
booktitle = {Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
pages = {3743–3751},
numpages = {9},
location = {Washington DC, USA},
series = {KDD '22}
}
```
### Contributions
Thanks to [@dolfim-ibm](https://github.com/dolfim-ibm), [@cau-git](https://github.com/cau-git) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
LeoLM/German_Poems | LeoLM | 2023-09-04T20:04:23Z | 361 | 2 | null | [
"region:us"
] | 2023-09-04T20:04:23Z | 2023-09-04T20:04:20.000Z | 2023-09-04T20:04:20 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: topic
dtype: string
- name: poem
dtype: string
splits:
- name: train
num_bytes: 571127
num_examples: 400
download_size: 327833
dataset_size: 571127
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "german_poems_gpt4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
vwxyzjn/summarize_from_feedback_oai_preprocessing_pythia | vwxyzjn | 2023-11-02T19:31:21Z | 361 | 0 | null | [
"region:us"
] | 2023-11-02T19:31:21Z | 2023-11-02T19:31:02.000Z | 2023-11-02T19:31:02 | ---
dataset_info:
features:
- name: info
struct:
- name: id
dtype: string
- name: post
dtype: string
- name: title
dtype: string
- name: subreddit
dtype: string
- name: site
dtype: string
- name: article
dtype: string
- name: summaries
list:
- name: text
dtype: string
- name: policy
dtype: string
- name: note
dtype: string
- name: choice
dtype: int32
- name: worker
dtype: string
- name: batch
dtype: string
- name: split
dtype: string
- name: extra
struct:
- name: confidence
dtype: int32
- name: query_token
sequence: int64
- name: query
dtype: string
- name: response0
dtype: string
- name: response0_token
sequence: int64
- name: response1
dtype: string
- name: response1_token
sequence: int64
splits:
- name: train
num_bytes: 782558889
num_examples: 92858
- name: validation
num_bytes: 734266349
num_examples: 86086
download_size: 124011801
dataset_size: 1516825238
---
# Dataset Card for "summarize_from_feedback_oai_preprocessing_pythia"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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farsi_news | null | 2022-11-03T16:15:15Z | 360 | 2 | null | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:fa",
"license:unknown",
"region:us"
] | 2022-11-03T16:15:15Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- fa
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: null
pretty_name: FarsiNews
dataset_info:
features:
- name: title
dtype: string
- name: summary
dtype: string
- name: link
dtype: string
- name: tags
sequence: string
splits:
- name: hamshahri
num_bytes: 1267659
num_examples: 2203
- name: radiofarda
num_bytes: 265272
num_examples: 284
download_size: 1648337
dataset_size: 1532931
---
# Dataset Card for FarsiNews
## 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:** [link](https://github.com/sci2lab/Farsi-datasets)
- **Paper:** []()
- **Leaderboard:** []()
- **Point of Contact:** []()
### Dataset Summary
https://github.com/sci2lab/Farsi-datasets
Contains Farsi (Persian) datasets for Machine Learning tasks, particularly NLP.
These datasets have been extracted from the RSS feed of two Farsi news agency websites:
- Hamshahri
- RadioFarda
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
[More Information Needed]
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
https://github.com/sci2lab/Farsi-datasets
### Contributions
Thanks to [@Narsil](https://github.com/Narsil) for adding this dataset. | [
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tum-nlp/IDMGSP | tum-nlp | 2023-09-12T11:57:59Z | 360 | 3 | null | [
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:en",
"license:openrail++",
"scientific paper",
"fake papers",
"science",
"scientific text",
"region:us"
] | 2023-09-12T11:57:59Z | 2023-05-27T12:20:55.000Z | 2023-05-27T12:20:55 | ---
viewer: true
task_categories:
- text-classification
language:
- en
tags:
- scientific paper
- fake papers
- science
- scientific text
pretty_name: ' A Benchmark Dataset for Identifying Machine-Generated Scientific Papers in the LLM Era (IDMGSP)'
size_categories:
- 10K<n<100K
dataset_info:
- config_name: classifier_input
features:
- name: id
dtype: string
- name: year
dtype: string
- name: title
dtype: string
- name: abstract
dtype: string
- name: introduction
dtype: string
- name: conclusion
dtype: string
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dtype: string
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dtype: string
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dtype: int64
splits:
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num_bytes: 70117904
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splits:
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num_bytes: 15808225
num_examples: 4000
download_size: 5336873
dataset_size: 15808225
license: openrail++
---
# Dataset Card for A Benchmark Dataset for Identifying Machine-Generated Scientific Papers in the LLM Era
## Dataset Description
- **Repository:** https://github.com/qwenzo/-IDMGSP
- **Paper:** TODO
### Dataset Summary
A benchmark for detecting machine-generated scientific papers based on their abstract, introduction and conclusion sections.
### Supported Tasks and Leaderboards
current benchmark results in terms of accuracy:
| Model | Train Dataset | TEST | OOD-GPT3 | OOD-REAL | TECG | TEST-CC |
|-----------------------------|-----------------|---------|----------|----------|---------|---------|
| LR-1gram (tf-idf) (our) | TRAIN | 95.3% | 4.0% | 94.6% | 96.1% | 7.8% |
| LR-1gram (tf-idf) (our) | TRAIN+GPT3 | 94.6% | 86.5% | 86.2% | 97.8% | 13.7% |
| LR-1gram (tf-idf) (our) | TRAIN-CG | 86.6% | 0.8% | 97.8% | 32.6% | 1.2% |
| RF-1gram (tf-idf) (our) | TRAIN | 94.8% | 24.7% | 87.3% | 100.0% | 8.1% |
| RF-1gram (tf-idf) (our) | TRAIN+GPT3 | 91.7% | 95.0% | 69.3% | 100.0% | 15.1% |
| RF-1gram (tf-idf) (our) | TRAIN-CG | 97.6% | 7.0% | 95.0% | 57.0% | 1.7% |
| [IDMGSP-Galactica-TRAIN](https://huggingface.co/tum-nlp/IDMGSP-Galactica-TRAIN) (our) | TRAIN | 98.4% | 25.9% | 95.5% | 84.0% | 6.8% |
| [IDMGSP-Galactica-TRAIN_GPT3](https://huggingface.co/tum-nlp/IDMGSP-Galactica-TRAIN_GPT3) (our) | TRAIN+GPT3 | 98.5% | 71.2% | 95.1% | 84.0% | 12.0% |
| [IDMGSP-Galactica-TRAIN-CG](https://huggingface.co/tum-nlp/IDMGSP-Galactica-TRAIN-CG) (our) | TRAIN-CG | 96.4% | 12.4% | 97.6% | 61.3% | 2.4% |
| [IDMGSP-RoBERTa-TRAIN-ABSTRACT](https://huggingface.co/tum-nlp/IDMGSP-RoBERTa-TRAIN-ABSTRACT) + [IDMGSP-RoBERTa-TRAIN-INTRODUCTION](https://huggingface.co/tum-nlp/IDMGSP-RoBERTa-TRAIN-INTRODUCTION) + [IDMGSP-RoBERTa-TRAIN-CONCLUSION](https://huggingface.co/tum-nlp/IDMGSP-RoBERTa-TRAIN-CONCLUSION) (our) | TRAIN | 72.3% | 55.5% | 50.0% | 100.0% | 63.5% |
| [IDMGSP-RoBERTa-TRAIN_GPT3-ABSTRACT](https://huggingface.co/tum-nlp/IDMGSP-RoBERTa-TRAIN_GPT3-ABSTRACT) + [IDMGSP-RoBERTa-TRAIN_GPT3-INTRODUCTION](https://huggingface.co/tum-nlp/IDMGSP-RoBERTa-TRAIN_GPT3-INTRODUCTION) + [IDMGSP-RoBERTa-TRAIN_GPT3-CONCLUSION](https://huggingface.co/tum-nlp/IDMGSP-RoBERTa-TRAIN_GPT3-CONCLUSION) (our) | TRAIN+GPT3 | 65.7% | 100.0% | 29.1% | 100.0% | 75.0% |
| [IDMGSP-RoBERTa-TRAIN-CG-ABSTRACT](https://huggingface.co/tum-nlp/IDMGSP-RoBERTa-TRAIN-CG-ABSTRACT) + [IDMGSP-RoBERTa-TRAIN-CG-INTRODUCTION](https://huggingface.co/tum-nlp/IDMGSP-RoBERTa-TRAIN-CG-INTRODUCTION) + [IDMGSP-RoBERTa-TRAIN-CG-CONCLUSION](https://huggingface.co/tum-nlp/IDMGSP-RoBERTa-TRAIN-CG-CONCLUSION) (our) | TRAIN-CG | 86.0% | 2.0% | 92.5% | 76.5% | 9.2% |
| GPT-3 (our) | TRAIN-SUB | 100.0% | 25.9% | 99.0% | 100.0% | N/A |
| DetectGPT | - | 61.5% | 0.0% | 99.9% | 68.7% | N/A |
| ChatGPT-IO (our)* | - | 69.0% | 49.0% | 89.0% | 0.0% | 3.0% |
| LLMFE (our)* | TRAIN+GPT3 | 80.0% | 62.0% | 70.0% | 90.0% | 33.0% |
### Languages
English
## Dataset Structure
### Data Instances
Each instance in the dataset corresponds to a row in a CSV file, encompassing the features of a paper, its label, and the paper's source.
### Data Fields
#### classifier_input
- name: id
description: The ID of the provided paper corresponds to the identifier assigned by the arXiv database if the paper's source is marked as "real".
dtype: string
- name: year
description: year of the publication as given by the arXiv database.
dtype: string
- name: title
description: title of the paper given by the arXiv database.
dtype: string
- name: abstract
description: abstract of the paper given by the arXiv database.
dtype: string
- name: introduction
description: introduction section of the paper. extracted by the PDF parser.
dtype: string
- name: conclusion
description: conclusion section of the paper. extracted by the PDF parser.
dtype: string
- name: categories
description: topics/domains of the paper given by the arXiv database. This field is null if the src field is not "real".
dtype: string
- name: src
description: indicator of the source of the paper. This can have the values "chatgpt", "gpt2", "real", "scigen" or "galactica".
dtype: string
- name: label
description: 0 for real/human-written papers and 1 for fake/machine-generated papers.
dtype: int64
#### train+gpt3
- name: id
description: The ID of the provided paper corresponds to the identifier assigned by the arXiv database if the paper's source is marked as "real".
dtype: string
- name: year
description: year of the publication as given by the arXiv database.
dtype: string
- name: title
description: title of the paper given by the arXiv database.
dtype: string
- name: abstract
description: abstract of the paper given by the arXiv database.
dtype: string
- name: introduction
description: introduction section of the paper. extracted by the PDF parser.
dtype: string
- name: conclusion
description: conclusion section of the paper. extracted by the PDF parser.
dtype: string
- name: categories
description: topics/domains of the paper given by the arXiv database. This field is null if the src field is not "real".
dtype: string
- name: src
description: indicator of the source of the paper. This can have the values "chatgpt", "gpt2", "real", "scigen" or "galactica", "gpt3".
dtype: string
- name: label
description: 0 for real/human-written papers and 1 for fake/machine-generated papers.
dtype: int64
#### tecg
- name: id
description: The ID of the provided paper corresponds to the identifier assigned by the arXiv database if the paper's source is marked as "real".
dtype: string
- name: year
description: year of the publication as given by the arXiv database.
dtype: string
- name: title
description: title of the paper given by the arXiv database.
dtype: string
- name: abstract
description: abstract of the paper given by the arXiv database.
dtype: string
- name: introduction
description: introduction section of the paper. extracted by the PDF parser.
dtype: string
- name: conclusion
description: conclusion section of the paper. extracted by the PDF parser.
dtype: string
- name: categories
description: topics/domains of the paper given by the arXiv database. This field is null if the src field is not "real".
dtype: string
- name: src
description: indicator of the source of the paper. Always has the value "chatgpt".
dtype: string
- name: label
description: always having the value 1.
dtype: int64
#### train-cg
- name: id
description: The ID of the provided paper corresponds to the identifier assigned by the arXiv database if the paper's source is marked as "real".
dtype: string
- name: year
description: year of the publication as given by the arXiv database.
dtype: string
- name: title
description: title of the paper given by the arXiv database.
dtype: string
- name: abstract
description: abstract of the paper given by the arXiv database.
dtype: string
- name: introduction
description: introduction section of the paper. extracted by the PDF parser.
dtype: string
- name: conclusion
description: conclusion section of the paper. extracted by the PDF parser.
dtype: string
- name: categories
description: topics/domains of the paper given by the arXiv database. This field is null if the src field is not "real".
dtype: string
- name: src
description: indicator of the source of the paper. This can have the values "gpt2", "real", "scigen" or "galactica".
dtype: string
- name: label
description: 0 for real/human-written papers and 1 for fake/machine-generated papers.
dtype: int64
#### ood_gpt3
- name: title
description: title of the paper given by the arXiv database.
dtype: string
- name: abstract
description: abstract of the paper given by the arXiv database.
dtype: string
- name: introduction
description: introduction section of the paper. extracted by the PDF parser.
dtype: string
- name: conclusion
description: conclusion section of the paper. extracted by the PDF parser.
dtype: string
- name: src
description: indicator of the source of the paper. Has the value "gpt3".
dtype: string
- name: label
description: always having the value 1.
dtype: int64
#### ood_real
dtype: string
- name: abstract
description: abstract of the paper given by the arXiv database.
dtype: string
- name: introduction
description: introduction section of the paper. extracted by the PDF parser.
dtype: string
- name: conclusion
description: conclusion section of the paper. extracted by the PDF parser.
dtype: string
- name: src
description: indicator of the source of the paper. Has the value "ood_real".
dtype: string
- name: label
description: always having the value 0.
dtype: int64
#### test-cc
- name: id
description: The ID of the provided paper corresponds to the identifier assigned by the arXiv database if the paper's source is marked as "real".
dtype: string
- name: year
description: year of the publication as given by the arXiv database.
dtype: string
- name: title
description: title of the paper given by the arXiv database.
dtype: string
- name: abstract
description: abstract of the paper given by the arXiv database.
dtype: string
- name: introduction
description: introduction section of the paper. extracted by the PDF parser.
dtype: string
- name: conclusion
description: conclusion section of the paper. extracted by the PDF parser.
dtype: string
- name: categories
description: topics/domains of the paper given by the arXiv database. This field is null if the src field is not "real".
dtype: string
- name: src
description: indicator of the source of the paper. Always has the value "chatgpt-paraphrased".
dtype: string
- name: paraphrased_sections
description: indicator of which sections are paraphrased. Can have the values "introduction", "conclusion", "introduction, conclusion", "abstract, introduction, conclusion".
dtype: string
- name: label
description: 0 for real/human-written papers and 1 for fake/machine-generated papers. Always has the value 1.
dtype: int64
### Data Splits
Table: Overview of the datasets used to train and evaluate the classifiers.
| Dataset | arXiv | ChatGPT | GPT-2 | SCIgen | Galactica | GPT-3 | ChatGPT (co-created) |
|--------------------------------------|--------|---------|--------|--------|-----------|--------|-----------------------|
| Standard train (TRAIN) | 8k | 2k | 2k | 2k | 2k | - | - |
| Standard train subset (TRAIN-SUB) | 4k | 1k | 1k | 1k | 1k | - | - |
| TRAIN without ChatGPT (TRAIN-CG) | 8k | - | 2k | 2k | 2k | - | - |
| TRAIN plus GPT-3 (TRAIN+GPT3) | 8k | 2k | 2k | 2k | 2k | 1.2k | - |
| Standard test (TEST) | 4k | 1k | 1k | 1k | 1k | - | - |
| Out-of-domain GPT-3 only (OOD-GPT3) | - | - | - | - | - | 1k | - |
| Out-of-domain real (OOD-REAL) | 4k (parsing 2) | - | - | - | - | - | - |
| ChatGPT only (TECG) | - | 1k | - | - | - | - | - |
| Co-created test (TEST-CC) | - | - | - | - | - | - | 4k |
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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eugenesiow/BSD100 | eugenesiow | 2022-10-26T02:20:22Z | 359 | 0 | null | [
"task_categories:other",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"license:other",
"image-super-resolution",
"region:us"
] | 2022-10-26T02:20:22Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- found
language: []
license:
- other
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- other
task_ids: []
pretty_name: BSD100
tags:
- image-super-resolution
---
# Dataset Card for BSD100
## 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://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/
- **Repository**: https://huggingface.co/datasets/eugenesiow/BSD100
- **Paper**: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=937655
- **Leaderboard**: https://github.com/eugenesiow/super-image#scale-x2
### Dataset Summary
BSD is a dataset used frequently for image denoising and super-resolution. Of the subdatasets, BSD100 is aclassical image dataset having 100 test images proposed by [Martin et al. (2001)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=937655). The dataset is composed of a large variety of images ranging from natural images to object-specific such as plants, people, food etc. BSD100 is the testing set of the Berkeley segmentation dataset BSD300.
Install with `pip`:
```bash
pip install datasets super-image
```
Evaluate a model with the [`super-image`](https://github.com/eugenesiow/super-image) library:
```python
from datasets import load_dataset
from super_image import EdsrModel
from super_image.data import EvalDataset, EvalMetrics
dataset = load_dataset('eugenesiow/BSD100', 'bicubic_x2', split='validation')
eval_dataset = EvalDataset(dataset)
model = EdsrModel.from_pretrained('eugenesiow/edsr-base', scale=2)
EvalMetrics().evaluate(model, eval_dataset)
```
### Supported Tasks and Leaderboards
The dataset is commonly used for evaluation of the `image-super-resolution` task.
Unofficial [`super-image`](https://github.com/eugenesiow/super-image) leaderboard for:
- [Scale 2](https://github.com/eugenesiow/super-image#scale-x2)
- [Scale 3](https://github.com/eugenesiow/super-image#scale-x3)
- [Scale 4](https://github.com/eugenesiow/super-image#scale-x4)
- [Scale 8](https://github.com/eugenesiow/super-image#scale-x8)
### Languages
Not applicable.
## Dataset Structure
### Data Instances
An example of `validation` for `bicubic_x2` looks as follows.
```
{
"hr": "/.cache/huggingface/datasets/downloads/extracted/BSD100_HR/3096.png",
"lr": "/.cache/huggingface/datasets/downloads/extracted/BSD100_LR_x2/3096.png"
}
```
### Data Fields
The data fields are the same among all splits.
- `hr`: a `string` to the path of the High Resolution (HR) `.png` image.
- `lr`: a `string` to the path of the Low Resolution (LR) `.png` image.
### Data Splits
| name |validation|
|-------|---:|
|bicubic_x2|100|
|bicubic_x3|100|
|bicubic_x4|100|
## 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
No annotations.
#### Who are the annotators?
No annotators.
### 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
- **Original Authors**: [Martin et al. (2001)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=937655)
### Licensing Information
You are free to download a portion of the dataset for non-commercial research and educational purposes.
In exchange, we request only that you make available to us the results of running your segmentation or
boundary detection algorithm on the test set as described below. Work based on the dataset should cite
the [Martin et al. (2001)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=937655) paper.
### Citation Information
```bibtex
@inproceedings{martin2001database,
title={A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics},
author={Martin, David and Fowlkes, Charless and Tal, Doron and Malik, Jitendra},
booktitle={Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001},
volume={2},
pages={416--423},
year={2001},
organization={IEEE}
}
```
### Contributions
Thanks to [@eugenesiow](https://github.com/eugenesiow) for adding this dataset.
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
dengue_filipino | null | 2023-01-25T14:29:21Z | 358 | 1 | dengue | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:tl",
"license:unknown",
"region:us"
] | 2023-01-25T14:29:21Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
- machine-generated
language_creators:
- crowdsourced
language:
- tl
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
paperswithcode_id: dengue
pretty_name: Dengue Dataset in Filipino
dataset_info:
features:
- name: text
dtype: string
- name: absent
dtype:
class_label:
names:
'0': '0'
'1': '1'
- name: dengue
dtype:
class_label:
names:
'0': '0'
'1': '1'
- name: health
dtype:
class_label:
names:
'0': '0'
'1': '1'
- name: mosquito
dtype:
class_label:
names:
'0': '0'
'1': '1'
- name: sick
dtype:
class_label:
names:
'0': '0'
'1': '1'
splits:
- name: train
num_bytes: 428553
num_examples: 4015
- name: test
num_bytes: 428553
num_examples: 4015
- name: validation
num_bytes: 54384
num_examples: 500
download_size: 156014
dataset_size: 911490
---
# Dataset Card for Dengue Dataset in Filipino
## 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:** [Dengue Dataset in Filipino homepage](https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks)
- **Repository:** [Dengue Dataset in Filipino repository](https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks)
- **Paper:** [IEEE paper](https://ieeexplore.ieee.org/document/8459963)
- **Leaderboard:**
- **Point of Contact:** [Jan Christian Cruz](mailto:jan_christian_cruz@dlsu.edu.ph)
### Dataset Summary
Benchmark dataset for low-resource multiclass classification, with 4,015 training, 500 testing, and 500 validation examples, each labeled as part of five classes. Each sample can be a part of multiple classes. Collected as tweets.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset is primarily in Filipino, with the addition of some English words commonly used in Filipino vernacular.
## Dataset Structure
### Data Instances
Sample data:
```
{
"text": "Tapos ang dami pang lamok.",
"absent": "0",
"dengue": "0",
"health": "0",
"mosquito": "1",
"sick": "0"
}
```
### 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
[Jan Christian Cruz](mailto:jan_christian_cruz@dlsu.edu.ph)
### Licensing Information
[More Information Needed]
### Citation Information
@INPROCEEDINGS{8459963,
author={E. D. {Livelo} and C. {Cheng}},
booktitle={2018 IEEE International Conference on Agents (ICA)},
title={Intelligent Dengue Infoveillance Using Gated Recurrent Neural Learning and Cross-Label Frequencies},
year={2018},
volume={},
number={},
pages={2-7},
doi={10.1109/AGENTS.2018.8459963}}
}
### Contributions
Thanks to [@anaerobeth](https://github.com/anaerobeth) for adding this dataset. | [
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per_sent | null | 2023-01-25T14:42:26Z | 358 | 0 | persent | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|other-MPQA-KBP Challenge-MediaRank",
"language:en",
"license:unknown",
"arxiv:2011.06128",
"region:us"
] | 2023-01-25T14:42:26Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-MPQA-KBP Challenge-MediaRank
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: persent
pretty_name: PerSenT
dataset_info:
features:
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dtype: int64
- name: TITLE
dtype: string
- name: TARGET_ENTITY
dtype: string
- name: DOCUMENT
dtype: string
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splits:
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num_examples: 3355
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- name: test_fixed
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download_size: 23117196
dataset_size: 23429385
---
# Dataset Card for PerSenT
## 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:** [PerSenT](https://stonybrooknlp.github.io/PerSenT/)
- **Repository:** [https://github.com/MHDBST/PerSenT](https://github.com/MHDBST/PerSenT)
- **Paper:** [arXiv](https://arxiv.org/abs/2011.06128)
- **Leaderboard:** NA
- **Point of Contact:** [Mohaddeseh Bastan](mbastan@cs.stonybrook.edu)
### Dataset Summary
PerSenT is a crowd-sourced dataset that captures the sentiment of an author towards the main entity in a news article. This dataset contains annotations for 5.3k documents and 38k paragraphs covering 3.2k unique entities. For each article, annotators judge what the author’s sentiment is towards the main
(target) entity of the article. The annotations also include similar judgments on paragraphs within the article.
### Supported Tasks and Leaderboards
Sentiment Classification: Each document consists of multiple paragraphs. Each paragraph is labeled separately (Positive, Neutral, Negative) and the author’s sentiment towards the whole document is included as a document-level label.
### Languages
English
## Dataset Structure
### Data Instances
```json
{'DOCUMENT': "Germany's Landesbank Baden Wuertemberg won EU approval Tuesday for a state bailout after it promised to shrink its balance sheet by 40 percent and refocus on lending to companies.\n The bank was several state-owned German institutions to run into trouble last year after it ran up more huge losses from investing in high-risk proprietary trading and capital market activities -- a business the EU has now told it to shun.\n Seven current and former managers of the bank are also being investigated by German authorities for risking or damaging the bank's capital by carrying out or failing to block investments in high-risk deals worth hundreds of millions from 2006.\n The European Commission said its Tuesday approval for the state rescue of the bank and its new restructuring plan would allow it become a viable business again -- and that the cutbacks would help limit the unfair advantage over rivals that the bank would get from the state aid.\n Stuttgart-based LBBW earlier this year received a capital injection of (EURO)5 billion from the bank's shareholders all of them public authorities or state-owned including the state of Baden-Wuerttemberg the region's savings bank association and the city of Stuttgart.",
'DOCUMENT_INDEX': 1,
'MASKED_DOCUMENT': "[TGT] won EU approval Tuesday for a state bailout after it promised to shrink its balance sheet by 40 percent and refocus on lending to companies.\n [TGT] was several state-owned German institutions to run into trouble last year after [TGT] ran up more huge losses from investing in high-risk proprietary trading and capital market activities -- a business the EU has now told it to shun.\n Seven current and former managers of [TGT] are also being investigated by German authorities for risking or damaging [TGT]'s capital by carrying out or failing to block investments in high-risk deals worth hundreds of millions from 2006.\n The European Commission said its Tuesday approval for the state rescue of [TGT] and its new restructuring plan would allow it become a viable business again -- and that the cutbacks would help limit the unfair advantage over rivals that [TGT] would get from the state aid.\n Stuttgart-based LBBW earlier this year received a capital injection of (EURO)5 billion from [TGT]'s shareholders all of them public authorities or state-owned including the state of Baden-Wuerttemberg the region's savings bank association and the city of Stuttgart.",
'Paragraph0': 2,
'Paragraph1': 0,
'Paragraph10': -1,
'Paragraph11': -1,
'Paragraph12': -1,
'Paragraph13': -1,
'Paragraph14': -1,
'Paragraph15': -1,
'Paragraph2': 0,
'Paragraph3': 1,
'Paragraph4': 1,
'Paragraph5': -1,
'Paragraph6': -1,
'Paragraph7': -1,
'Paragraph8': -1,
'Paragraph9': -1,
'TARGET_ENTITY': 'Landesbank Baden Wuertemberg',
'TITLE': 'German bank LBBW wins EU bailout approval',
'TRUE_SENTIMENT': 0}
```
### Data Fields
- DOCUMENT_INDEX: ID of the document per original dataset
- TITLE: Title of the article
- DOCUMENT: Text of the article
- MASKED_DOCUMENT: Text of the article with the target entity masked with `[TGT]` token
- TARGET_ENTITY: The entity that the author is expressing opinion about
- TRUE_SENTIMENT: Label for entire article
- Paragraph{0..15}: Label for each paragraph in the article
**Note**: Labels are one of `[Negative, Neutral, Positive]`. Missing labels were replaced with `-1`.
### Data Splits
To split the dataset, entities were split into 4 mutually exclusive sets. Due to the nature of news collections, some entities tend to dominate the collection. In the collection, there were four entities which were the main entity in nearly 800 articles. To avoid these entities from dominating the train or test splits, these were moved them to a separate test collection. The remaining was split into a training, dev, and test sets at random. Thus the collection includes one standard test set consisting of articles drawn at random (Test Standard), while the other is a test set which contains multiple articles about a small number of popular entities (Test Frequent).
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
Articles were selected from 3 sources:
1. MPQA (Deng and Wiebe, 2015; Wiebe et al., 2005): This dataset contains news articles manually annotated for opinions, beliefs, emotions, sentiments, speculations, etc. It also has target annotations which are entities and event anchored to the heads of noun or verb phrases. All decisions on this dataset are made on sentence-level and over short spans.
2. KBP Challenge (Ellis et al., 2014): This resource contains TAC 2014 KBP English sentiment slot filling challenge dataset. This is a document-level sentiment filling dataset. In this task, given an entity and a sentiment (positive/negative) from the document, the goal is to find entities toward which
the original entity holds the given sentimental view. We selected documents from this resource which have been used in the following similar work in sentiment analysis task (Choi et al., 2016).
3. Media Rank (Ye and Skiena, 2019): This dataset ranks about 50k news sources along different aspects. It is also used for classifying political ideology of news articles (Kulkarni et al., 2018).
Pre-processing steps:
- First we find all the person entities in each article, using Stanford NER (Name Entity Resolution) tagger (Finkel et al., 2005) and all mentions of them using co-reference resolution (Clark and Manning, 2016; Co, 2017).
- We removed articles which are not likely to have a main entity of focus. We used a simple heuristic of removing articles in which the most frequent person entity is mentioned only three times or less (even when counting co-referent mentions).
- For the articles that remain we deemed the most frequent entity to be the main entity of the article. We also filtered out extremely long and extremely short articles to keep the articles which have at least 3 paragraphs and at most 16 paragraphs.
Documents are randomly separated into train, dev, and two test sets. We ensure that each entity appears in only one of the sets. Our goal here is to avoid easy to learn biases over entities. To avoid the most frequent entities from dominating the training or the test sets, we remove articles that covered the most frequent entities and use them as a separate test set (referred to as frequent test set) in addition to the randomly drawn standard test set.
### Annotations
#### Annotation process
We obtained document and paragraph level annotations with the help of Amazon Mechanical Turk workers. The workers first verified if the target entity we provide is indeed the main entity in the document. Then, they rated each paragraph in a document that contained a direct mention or a reference to the target
entity. Last, they rated the sentiment towards the entity based on the entire document. In both cases, the workers made assessments about the authors view based on what they said about the target entity. For both paragraph and document level sentiment, the workers chose from five rating categories: Negative,
Slightly Negative, Neutral, Slightly Positive, or Positive. We then combine the fine-grained annotations to obtain three coarse-grained classes Negative, Neutral, or Positive.
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
[More Information Needed]
### 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
[Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0/)
### Citation Information
```
@inproceedings{bastan2020authors,
title={Author's Sentiment Prediction},
author={Mohaddeseh Bastan and Mahnaz Koupaee and Youngseo Son and Richard Sicoli and Niranjan Balasubramanian},
year={2020},
eprint={2011.06128},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@jeromeku](https://github.com/jeromeku) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
SetFit/tweet_sentiment_extraction | SetFit | 2022-05-12T19:52:02Z | 358 | 0 | null | [
"region:us"
] | 2022-05-12T19:52:02Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | # Tweet Sentiment Extraction
Source: https://www.kaggle.com/c/tweet-sentiment-extraction/data | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
facebook/emu_edit_test_set_generations | facebook | 2023-11-19T07:36:56Z | 358 | 12 | null | [
"region:us"
] | 2023-11-19T07:36:56Z | 2023-11-13T16:53:50.000Z | 2023-11-13T16:53:50 | ---
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: instruction
dtype: string
- name: image
dtype: image
- name: task
dtype: string
- name: split
dtype: string
- name: idx
dtype: int64
- name: hash
dtype: string
- name: input_caption
dtype: string
- name: output_caption
dtype: string
- name: edited_image
dtype: image
- name: model
dtype: string
splits:
- name: validation
num_bytes: 2739856920.3
num_examples: 3589
- name: test
num_bytes: 1538831841.4
num_examples: 2022
download_size: 1424484094
dataset_size: 4278688761.7000003
---
# Dataset Card for the Emu Edit Generations on Emu Edit Test Set
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage: https://emu-edit.metademolab.com/**
- **Paper: https://emu-edit.metademolab.com/assets/emu_edit.pdf**
### Dataset Summary
This dataset contains Emu Edit's generations on the [Emu Edit test set](https://huggingface.co/datasets/facebook/emu_edit_test_set). For more information please read our [paper](https://emu-edit.metademolab.com/assets/emu_edit.pdf) or visit our [homepage](https://emu-edit.metademolab.com/).
### Licensing Information
Licensed with CC-BY-NC 4.0 License available [here](https://creativecommons.org/licenses/by-nc/4.0/legalcode?fbclid=IwAR2SYZjLRywwUMblkWg0LyAxHVVTloIFlvC-ju3BthIYtOM2jpQHgbeXOsM).
### Citation Information
```
@inproceedings{Sheynin2023EmuEP,
title={Emu Edit: Precise Image Editing via Recognition and Generation Tasks},
author={Shelly Sheynin and Adam Polyak and Uriel Singer and Yuval Kirstain and Amit Zohar and Oron Ashual and Devi Parikh and Yaniv Taigman},
year={2023},
url={https://api.semanticscholar.org/CorpusID:265221391}
}
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caner | null | 2023-03-16T14:47:48Z | 357 | 1 | null | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:ar",
"license:unknown",
"region:us"
] | 2023-03-16T14:47:48Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- ar
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: CANER
dataset_info:
features:
- name: token
dtype: string
- name: ner_tag
dtype:
class_label:
names:
'0': Allah
'1': Book
'2': Clan
'3': Crime
'4': Date
'5': Day
'6': Hell
'7': Loc
'8': Meas
'9': Mon
'10': Month
'11': NatOb
'12': Number
'13': O
'14': Org
'15': Para
'16': Pers
'17': Prophet
'18': Rlig
'19': Sect
'20': Time
splits:
- name: train
num_bytes: 5095721
num_examples: 258240
download_size: 17063406
dataset_size: 5095721
---
# Dataset Card for CANER
## 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:** [Classical-Arabic-Named-Entity-Recognition-Corpus](https://github.com/RamziSalah)
- **Paper:** [Researchgate](https://www.researchgate.net/publication/330075080_BUILDING_THE_CLASSICAL_ARABIC_NAMED_ENTITY_RECOGNITION_CORPUS_CANERCORPUS)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The Classical Arabic Named Entity Recognition corpus is a new corpus of tagged data that can be useful for handling the issues in recognition of Arabic named entities.
### Supported Tasks and Leaderboards
- Named Entity Recognition
### Languages
Classical Arabic
## Dataset Structure
### Data Instances
An example from the dataset:
```
{'ner_tag': 1, 'token': 'الجامع'}
```
Where 1 stands for "Book"
### Data Fields
- `id`: id of the sample
- `token`: the tokens of the example text
- `ner_tag`: the NER tags of each token
The NER tags correspond to this list:
```
"Allah",
"Book",
"Clan",
"Crime",
"Date",
"Day",
"Hell",
"Loc",
"Meas",
"Mon",
"Month",
"NatOb",
"Number",
"O",
"Org",
"Para",
"Pers",
"Prophet",
"Rlig",
"Sect",
"Time"
```
### Data Splits
Training splits only
## 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?
Ramzi Salah and Lailatul Qadri Zakaria
### 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
[More Information Needed]
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
@article{article,
author = {Salah, Ramzi and Zakaria, Lailatul},
year = {2018},
month = {12},
pages = {},
title = {BUILDING THE CLASSICAL ARABIC NAMED ENTITY RECOGNITION CORPUS (CANERCORPUS)},
volume = {96},
journal = {Journal of Theoretical and Applied Information Technology}
}
### Contributions
Thanks to [@KMFODA](https://github.com/KMFODA) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
sent_comp | null | 2022-11-18T21:45:18Z | 357 | 1 | sentence-compression | [
"task_categories:other",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:unknown",
"sentence-compression",
"region:us"
] | 2022-11-18T21:45:18Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: sentence-compression
pretty_name: Google Sentence Compression
tags:
- sentence-compression
dataset_info:
features:
- name: graph
struct:
- name: id
dtype: string
- name: sentence
dtype: string
- name: node
sequence:
- name: form
dtype: string
- name: type
dtype: string
- name: mid
dtype: string
- name: word
sequence:
- name: id
dtype: int32
- name: form
dtype: string
- name: stem
dtype: string
- name: tag
dtype: string
- name: gender
dtype: int32
- name: head_word_index
dtype: int32
- name: edge
sequence:
- name: parent_id
dtype: int32
- name: child_id
dtype: int32
- name: label
dtype: string
- name: entity_mention
sequence:
- name: start
dtype: int32
- name: end
dtype: int32
- name: head
dtype: int32
- name: name
dtype: string
- name: type
dtype: string
- name: mid
dtype: string
- name: is_proper_name_entity
dtype: bool
- name: gender
dtype: int32
- name: compression
struct:
- name: text
dtype: string
- name: edge
sequence:
- name: parent_id
dtype: int32
- name: child_id
dtype: int32
- name: headline
dtype: string
- name: compression_ratio
dtype: float32
- name: doc_id
dtype: string
- name: source_tree
struct:
- name: id
dtype: string
- name: sentence
dtype: string
- name: node
sequence:
- name: form
dtype: string
- name: type
dtype: string
- name: mid
dtype: string
- name: word
sequence:
- name: id
dtype: int32
- name: form
dtype: string
- name: stem
dtype: string
- name: tag
dtype: string
- name: gender
dtype: int32
- name: head_word_index
dtype: int32
- name: edge
sequence:
- name: parent_id
dtype: int32
- name: child_id
dtype: int32
- name: label
dtype: string
- name: entity_mention
sequence:
- name: start
dtype: int32
- name: end
dtype: int32
- name: head
dtype: int32
- name: name
dtype: string
- name: type
dtype: string
- name: mid
dtype: string
- name: is_proper_name_entity
dtype: bool
- name: gender
dtype: int32
- name: compression_untransformed
struct:
- name: text
dtype: string
- name: edge
sequence:
- name: parent_id
dtype: int32
- name: child_id
dtype: int32
splits:
- name: validation
num_bytes: 55823979
num_examples: 10000
- name: train
num_bytes: 1135684803
num_examples: 200000
download_size: 259652560
dataset_size: 1191508782
---
# Dataset Card for Google Sentence Compression
## 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/sentence-compression](https://github.com/google-research-datasets/sentence-compression)
- **Repository:** [https://github.com/google-research-datasets/sentence-compression](https://github.com/google-research-datasets/sentence-compression)
- **Paper:** [https://www.aclweb.org/anthology/D13-1155/](https://www.aclweb.org/anthology/D13-1155/)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
A major challenge in supervised sentence compression is making use of rich feature representations because of very scarce parallel data. We address this problem and present a method to automatically build a compression corpus with hundreds of thousands of instances on which deletion-based algorithms can be trained. In our corpus, the syntactic trees of the compressions are subtrees of their uncompressed counterparts, and hence supervised systems which require a structural alignment between the input and output can be successfully trained. We also extend an existing unsupervised compression method with a learning module. The new system uses structured prediction to learn from lexical, syntactic and other features. An evaluation with human raters shows that the presented data harvesting method indeed produces a parallel corpus of high quality. Also, the supervised system trained on this corpus gets high scores both from human raters and in an automatic evaluation setting, significantly outperforming a strong baseline.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English
## Dataset Structure
### Data Instances
Each data instance should contains the information about the original sentence in `instance["graph"]["sentence"]` as well as the compressed sentence in `instance["compression"]["text"]`. As this dataset was created by pruning dependency connections, the author also includes the dependency tree and transformed graph of the original sentence and compressed sentence.
### Data Fields
Each instance should contains these information:
- `graph` (`Dict`): the transformation graph/tree for extracting compression (a modified version of a dependency tree).
- This will have features similar to a dependency tree (listed bellow)
- `compression` (`Dict`)
- `text` (`str`)
- `edge` (`List`)
- `headline` (`str`): the headline of the original news page.
- `compression_ratio` (`float`): the ratio between compressed sentence vs original sentence.
- `doc_id` (`str`): url of the original news page.
- `source_tree` (`Dict`): the original dependency tree (features listed bellow).
- `compression_untransformed` (`Dict`)
- `text` (`str`)
- `edge` (`List`)
Dependency tree features:
- `id` (`str`)
- `sentence` (`str`)
- `node` (`List`): list of nodes, each node represent a word/word phrase in the tree.
- `form` (`string`)
- `type` (`string`): the enity type of a node. Defaults to `""` if it's not an entity.
- `mid` (`string`)
- `word` (`List`): list of words the node contains.
- `id` (`int`)
- `form` (`str`): the word from the sentence.
- `stem` (`str`): the stemmed/lemmatized version of the word.
- `tag` (`str`): dependency tag of the word.
- `gender` (`int`)
- `head_word_index` (`int`)
- `edge`: list of the dependency connections between words.
- `parent_id` (`int`)
- `child_id` (`int`)
- `label` (`str`)
- `entity_mention` list of the entities in the sentence.
- `start` (`int`)
- `end` (`int`)
- `head` (`str`)
- `name` (`str`)
- `type` (`str`)
- `mid` (`str`)
- `is_proper_name_entity` (`bool`)
- `gender` (`int`)
### 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 [@mattbui](https://github.com/mattbui) for adding this dataset. | [
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species_800 | null | 2023-06-16T11:33:29Z | 357 | 2 | null | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | 2023-06-16T11:33:29Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: species800
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B
'2': I
config_name: species_800
splits:
- name: train
num_bytes: 2579096
num_examples: 5734
- name: validation
num_bytes: 385756
num_examples: 831
- name: test
num_bytes: 737760
num_examples: 1631
download_size: 18204624
dataset_size: 3702612
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [SPECIES](https://species.jensenlab.org/)
- **Repository:**
- **Paper:** https://doi.org/10.1371/journal.pone.0065390
- **Leaderboard:**
- **Point of Contact:** [Lars Juhl Jensen](mailto:lars.juhl.jensen@cpr.ku.dk)
### Dataset Summary
S800 Corpus: a novel abstract-based manually annotated corpus. S800 comprises 800 PubMed abstracts in which organism mentions were identified and mapped to the corresponding NCBI Taxonomy identifiers.
To increase the corpus taxonomic mention diversity the S800 abstracts were collected by selecting 100 abstracts from the following 8 categories: bacteriology, botany, entomology, medicine, mycology, protistology, virology and zoology. S800 has been annotated with a focus at the species level; however, higher taxa mentions (such as genera, families and orders) have also been considered.
The Species-800 dataset was pre-processed and split based on the dataset of Pyysalo (https://github.com/spyysalo/s800).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English (`en`).
## Dataset Structure
### Data Instances
```
{'id': '0',
'tokens': ['Methanoregula',
'formicica',
'sp',
'.',
'nov',
'.',
',',
'a',
'methane',
'-',
'producing',
'archaeon',
'isolated',
'from',
'methanogenic',
'sludge',
'.'],
'ner_tags': [1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}
```
### Data Fields
- `id`: Sentence identifier.
- `tokens`: Array of tokens composing a sentence.
- `ner_tags`: Array of tags, where `0` indicates no species mentioned, `1` signals the first token of a species and `2` the subsequent tokens of the species.
### 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
The species-level S800 corpus is subject to Medline restrictions.
### Citation Information
Original data:
```
@article{pafilis2013species,
title={The SPECIES and ORGANISMS resources for fast and accurate identification of taxonomic names in text},
author={Pafilis, Evangelos and Frankild, Sune P and Fanini, Lucia and Faulwetter, Sarah and Pavloudi, Christina and Vasileiadou, Aikaterini and Arvanitidis, Christos and Jensen, Lars Juhl},
journal={PloS one},
volume={8},
number={6},
pages={e65390},
year={2013},
publisher={Public Library of Science}
}
```
Source data of this dataset:
```
@article{10.1093/bioinformatics/btz682,
author = {Lee, Jinhyuk and Yoon, Wonjin and Kim, Sungdong and Kim, Donghyeon and Kim, Sunkyu and So, Chan Ho and Kang, Jaewoo},
title = "{BioBERT: a pre-trained biomedical language representation model for biomedical text mining}",
journal = {Bioinformatics},
volume = {36},
number = {4},
pages = {1234-1240},
year = {2019},
month = {09},
issn = {1367-4803},
doi = {10.1093/bioinformatics/btz682},
url = {https://doi.org/10.1093/bioinformatics/btz682},
eprint = {https://academic.oup.com/bioinformatics/article-pdf/36/4/1234/48983216/bioinformatics\_36\_4\_1234.pdf},
}
```
and
```
https://github.com/spyysalo/s800
```
### Contributions
Thanks to [@edugp](https://github.com/edugp) for adding this dataset. | [
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Shushant/NepaliSentiment | Shushant | 2022-01-07T05:12:33Z | 357 | 3 | null | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
BeIR/msmarco-qrels | BeIR | 2022-10-23T06:05:55Z | 357 | 1 | beir | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | 2022-10-23T06:05:55Z | 2022-06-05T17:26:07.000Z | 2022-06-05T17:26:07 | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
- zero-shot-retrieval
- information-retrieval
- zero-shot-information-retrieval
task_ids:
- passage-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
- tweet-retrieval
- citation-prediction-retrieval
- duplication-question-retrieval
- argument-retrieval
- news-retrieval
- biomedical-information-retrieval
- question-answering-retrieval
---
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | [
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lighteval/EntityMatching | lighteval | 2023-05-09T15:35:01Z | 357 | 2 | null | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
sirius0707/imagenet_10 | sirius0707 | 2023-07-23T02:29:00Z | 357 | 0 | null | [
"task_categories:image-classification",
"language:en",
"region:us"
] | 2023-07-23T02:29:00Z | 2023-07-23T01:09:17.000Z | 2023-07-23T01:09:17 | ---
task_categories:
- image-classification
language:
- en
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': goldfish
'1': scuba diver
'2': seashore
'3': green lizard
'4': ski
'5': flamingo
'6': red wine
'7': volcano
'8': jack-o'-lantern
'9': cowboy boot
---
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open-llm-leaderboard/details_ashercn97__manatee-7b | open-llm-leaderboard | 2023-09-17T18:42:54Z | 357 | 0 | null | [
"region:us"
] | 2023-09-17T18:42:54Z | 2023-08-18T11:57:35.000Z | 2023-08-18T11:57:35 | ---
pretty_name: Evaluation run of ashercn97/manatee-7b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [ashercn97/manatee-7b](https://huggingface.co/ashercn97/manatee-7b) on the [Open\
\ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_ashercn97__manatee-7b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T18:42:42.384089](https://huggingface.co/datasets/open-llm-leaderboard/details_ashercn97__manatee-7b/blob/main/results_2023-09-17T18-42-42.384089.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0030411073825503355,\n\
\ \"em_stderr\": 0.0005638896908753201,\n \"f1\": 0.059899328859060456,\n\
\ \"f1_stderr\": 0.001397556369094792,\n \"acc\": 0.4077875240923591,\n\
\ \"acc_stderr\": 0.009650175391680019\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0030411073825503355,\n \"em_stderr\": 0.0005638896908753201,\n\
\ \"f1\": 0.059899328859060456,\n \"f1_stderr\": 0.001397556369094792\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.07050796057619409,\n \
\ \"acc_stderr\": 0.0070515438139836135\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.745067087608524,\n \"acc_stderr\": 0.012248806969376422\n\
\ }\n}\n```"
repo_url: https://huggingface.co/ashercn97/manatee-7b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|arc:challenge|25_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T18_42_42.384089
path:
- '**/details_harness|drop|3_2023-09-17T18-42-42.384089.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T18-42-42.384089.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T18_42_42.384089
path:
- '**/details_harness|gsm8k|5_2023-09-17T18-42-42.384089.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T18-42-42.384089.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hellaswag|10_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
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- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-02T16:08:56.879142.parquet'
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- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-02T16:08:56.879142.parquet'
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- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-02T16:08:56.879142.parquet'
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- '**/details_harness|hendrycksTest-nutrition|5_2023-08-02T16:08:56.879142.parquet'
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- '**/details_harness|hendrycksTest-world_religions|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
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- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T18_42_42.384089
path:
- '**/details_harness|winogrande|5_2023-09-17T18-42-42.384089.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T18-42-42.384089.parquet'
- config_name: results
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- results_2023-08-02T16:08:56.879142.parquet
- split: 2023_09_17T18_42_42.384089
path:
- results_2023-09-17T18-42-42.384089.parquet
- split: latest
path:
- results_2023-09-17T18-42-42.384089.parquet
---
# Dataset Card for Evaluation run of ashercn97/manatee-7b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/ashercn97/manatee-7b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [ashercn97/manatee-7b](https://huggingface.co/ashercn97/manatee-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_ashercn97__manatee-7b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T18:42:42.384089](https://huggingface.co/datasets/open-llm-leaderboard/details_ashercn97__manatee-7b/blob/main/results_2023-09-17T18-42-42.384089.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0030411073825503355,
"em_stderr": 0.0005638896908753201,
"f1": 0.059899328859060456,
"f1_stderr": 0.001397556369094792,
"acc": 0.4077875240923591,
"acc_stderr": 0.009650175391680019
},
"harness|drop|3": {
"em": 0.0030411073825503355,
"em_stderr": 0.0005638896908753201,
"f1": 0.059899328859060456,
"f1_stderr": 0.001397556369094792
},
"harness|gsm8k|5": {
"acc": 0.07050796057619409,
"acc_stderr": 0.0070515438139836135
},
"harness|winogrande|5": {
"acc": 0.745067087608524,
"acc_stderr": 0.012248806969376422
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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bigainlco/LooGLE | bigainlco | 2023-11-15T08:37:49Z | 357 | 3 | null | [
"task_categories:question-answering",
"task_categories:summarization",
"task_categories:text-generation",
"task_categories:fill-mask",
"language:en",
"language:zh",
"license:cc-by-sa-4.0",
"Long Context",
"region:us"
] | 2023-11-15T08:37:49Z | 2023-10-21T00:56:16.000Z | 2023-10-21T00:56:16 | ---
license: cc-by-sa-4.0
task_categories:
- question-answering
- summarization
- text-generation
- fill-mask
language:
- en
- zh
tags:
- Long Context
pretty_name: d
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ted_multi | null | 2023-04-05T13:42:14Z | 356 | 2 | null | [
"region:us"
] | 2023-04-05T13:42:14Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
pretty_name: TEDMulti
paperswithcode_id: null
dataset_info:
features:
- name: translations
dtype:
translation_variable_languages:
languages:
- ar
- az
- be
- bg
- bn
- bs
- calv
- cs
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fr-ca
- gl
- he
- hi
- hr
- hu
- hy
- id
- it
- ja
- ka
- kk
- ko
- ku
- lt
- mk
- mn
- mr
- ms
- my
- nb
- nl
- pl
- pt
- pt-br
- ro
- ru
- sk
- sl
- sq
- sr
- sv
- ta
- th
- tr
- uk
- ur
- vi
- zh
- zh-cn
- zh-tw
num_languages: 60
- name: talk_name
dtype: string
config_name: plain_text
splits:
- name: test
num_bytes: 23364983
num_examples: 7213
- name: train
num_bytes: 748209995
num_examples: 258098
- name: validation
num_bytes: 19435383
num_examples: 6049
download_size: 352222045
dataset_size: 791010361
---
# Dataset Card for "ted_multi"
## 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/neulab/word-embeddings-for-nmt](https://github.com/neulab/word-embeddings-for-nmt)
- **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:** 352.23 MB
- **Size of the generated dataset:** 791.01 MB
- **Total amount of disk used:** 1.14 GB
### Dataset Summary
Massively multilingual (60 language) data set derived from TED Talk transcripts.
Each record consists of parallel arrays of language and text. Missing and
incomplete translations will be filtered out.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### plain_text
- **Size of downloaded dataset files:** 352.23 MB
- **Size of the generated dataset:** 791.01 MB
- **Total amount of disk used:** 1.14 GB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"talk_name": "shabana_basij_rasikh_dare_to_educate_afghan_girls",
"translations": "{\"language\": [\"ar\", \"az\", \"bg\", \"bn\", \"cs\", \"da\", \"de\", \"el\", \"en\", \"es\", \"fa\", \"fr\", \"he\", \"hi\", \"hr\", \"hu\", \"hy\", \"id\", \"it\", ..."
}
```
### Data Fields
The data fields are the same among all splits.
#### plain_text
- `translations`: a multilingual `string` variable, with possible languages including `ar`, `az`, `be`, `bg`, `bn`.
- `talk_name`: a `string` feature.
### Data Splits
| name |train |validation|test|
|----------|-----:|---------:|---:|
|plain_text|258098| 6049|7213|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{qi-EtAl:2018:N18-2,
author = {Qi, Ye and Sachan, Devendra and Felix, Matthieu and Padmanabhan, Sarguna and Neubig, Graham},
title = {When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation?},
booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)},
month = {June},
year = {2018},
address = {New Orleans, Louisiana},
publisher = {Association for Computational Linguistics},
pages = {529--535},
abstract = {The performance of Neural Machine Translation (NMT) systems often suffers in low-resource scenarios where sufficiently large-scale parallel corpora cannot be obtained. Pre-trained word embeddings have proven to be invaluable for improving performance in natural language analysis tasks, which often suffer from paucity of data. However, their utility for NMT has not been extensively explored. In this work, we perform five sets of experiments that analyze when we can expect pre-trained word embeddings to help in NMT tasks. We show that such embeddings can be surprisingly effective in some cases -- providing gains of up to 20 BLEU points in the most favorable setting.},
url = {http://www.aclweb.org/anthology/N18-2084}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ARTeLab/fanpage | ARTeLab | 2022-11-17T02:49:54Z | 356 | 3 | null | [
"task_categories:summarization",
"multilinguality:monolingual",
"size_categories:10K<n<100k",
"source_datasets:original",
"language:it",
"license:unknown",
"region:us"
] | 2022-11-17T02:49:54Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- it
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100k
source_datasets:
- original
task_categories:
- summarization
---
# Dataset Card for fanpage
## 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
Fanpage dataset, containing news articles taken from Fanpage.
There are two features:
- source: Input news article.
- target: Summary of the article.
### Supported Tasks and Leaderboards
- `abstractive-summarization`, `summarization`
### Languages
The text in the dataset is in Italian
## Dataset Structure
### Data Instances
[Needs More Information]
### Data Fields
[Needs More Information]
### Data Splits
[Needs More Information]
## 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
More details and results in [published work](https://www.mdpi.com/2078-2489/13/5/228)
```
@Article{info13050228,
AUTHOR = {Landro, Nicola and Gallo, Ignazio and La Grassa, Riccardo and Federici, Edoardo},
TITLE = {Two New Datasets for Italian-Language Abstractive Text Summarization},
JOURNAL = {Information},
VOLUME = {13},
YEAR = {2022},
NUMBER = {5},
ARTICLE-NUMBER = {228},
URL = {https://www.mdpi.com/2078-2489/13/5/228},
ISSN = {2078-2489},
ABSTRACT = {Text summarization aims to produce a short summary containing relevant parts from a given text. Due to the lack of data for abstractive summarization on low-resource languages such as Italian, we propose two new original datasets collected from two Italian news websites with multi-sentence summaries and corresponding articles, and from a dataset obtained by machine translation of a Spanish summarization dataset. These two datasets are currently the only two available in Italian for this task. To evaluate the quality of these two datasets, we used them to train a T5-base model and an mBART model, obtaining good results with both. To better evaluate the results obtained, we also compared the same models trained on automatically translated datasets, and the resulting summaries in the same training language, with the automatically translated summaries, which demonstrated the superiority of the models obtained from the proposed datasets.},
DOI = {10.3390/info13050228}
}
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LeoCordoba/CC-NEWS-ES-titles | LeoCordoba | 2023-02-23T21:53:46Z | 356 | 2 | null | [
"task_categories:summarization",
"task_categories:text-generation",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:cc-news",
"language:es",
"license:mit",
"conditional-text-generation",
"region:us"
] | 2023-02-23T21:53:46Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- es
license:
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- cc-news
task_categories:
- summarization
- text-generation
task_ids: []
tags:
- conditional-text-generation
---
# Dataset Card for CC-NEWS-ES-titles
## 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:** [CC-NEWS-ES-titles dataset repository](https://huggingface.co/datasets/LeoCordoba/CC-NEWS-ES-titles)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [Leonardo Ignacio Córdoba](https://www.linkedin.com/in/leonardo-ignacio-c%C3%B3rdoba/)
### Dataset Summary
CC-NEWS-ES-titles is a Spanish-language dataset for news titles generation. The text and titles comes from 2019 and 2020 CC-NEWS data (which is part of Common Crawl).
It contains 402.310 pairs of news title and body, splitted in :
- Train: 370.125
- Eval: 16.092
- Test: 16.092
### Supported Tasks and Leaderboards
- `text-classification`, `sentiment-classification`: The dataset can be used to train a model for news title generation which can be considered a subset of abstractive summarization.
### Languages
The text is in Spanish. The BCP-47 code for Spanish is es.
## Dataset Structure
### Data Instances
Each data instance contains the following features: _text_ and _output_text_.
- _text_ is the body of the news.
- _output_text_ is the title of the news.
An example from the CC-NEWS-ES-titles train set looks like the following:
```
{'text': 'Hoy en el Boletín Oficial también se publicó la disposición para universidades, institutos universitarios y de educación superior de todas las jurisdicciones, a las que recomienda que "adecúen las condiciones en que se desarrolla la actividad académica presencial en el marco de la emergencia conforme con las recomendaciones del Ministerio de Salud", según lo publicado por la agencia ',
'output_text': 'Coronavirus: "Seguimos educando", la plataforma online para que los chicos estudien en cuarentena'}
```
### Data Fields
- 'text': a string containing the body of the news.
- 'output_text': a string containing the title of the news.
### Data Splits
The CC-NEWS-ES-titles dataset has 3 splits: _train_, _validation_, and _test_. The splits contain disjoint sets of news.
| Dataset Split | Number of Instances in Split |
| ------------- | ---------------------------- |
| Train | 370.125 |
| Eval | 16.092 |
| Test | 16.092 |
## Dataset Creation
### Curation Rationale
[N/A]
### Source Data
#### Initial Data Collection and Normalization
TODO
#### Who are the source language producers?
Common Crawl: https://commoncrawl.org/
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
[N/A]
## Considerations for Using the Data
### Social Impact of Dataset
Abstractive summarization is a complex task and Spanish is a underrepresented language in the NLP domain. As a consequence, adding a Spanish resource may help others to improve their research and educational activities.
### Discussion of Biases
[N/A]
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
This dataset is maintained by [Leonardo Ignacio Córdoba](https://www.linkedin.com/in/leonardo-ignacio-c%C3%B3rdoba/) and was built with the help of [María Gaska](https://www.linkedin.com/in/mfgaska/).
### Licensing Information
[N/A]
### Citation Information
TODO
### Contributions
[N/A] | [
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Zaid/coqa_expanded | Zaid | 2021-10-04T18:48:15Z | 356 | 2 | null | [
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eduagarcia/brwac_dedup | eduagarcia | 2023-08-27T20:24:16Z | 355 | 0 | null | [
"region:us"
] | 2023-08-27T20:24:16Z | 2023-08-27T18:56:05.000Z | 2023-08-27T18:56:05 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 17503358516
num_examples: 3513588
download_size: 10720096897
dataset_size: 17503358516
---
# Dataset Card for "brwac_dedup"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
napnel/audio-aus-dataset | napnel | 2023-11-13T08:41:00Z | 355 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-13T08:41:00Z | 2023-10-17T03:38:33.000Z | 2023-10-17T03:38:33 | ---
license: mit
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: flame_expression
sequence:
sequence: float32
- name: emotion_label
dtype: int64
- name: emotion
dtype: string
- name: aus
sequence:
sequence: float64
- name: end_frame
dtype: int64
- name: vocal_channel
dtype: string
- name: gender
dtype: string
- name: intensity
dtype: int64
- name: filename
dtype: string
splits:
- name: train
num_bytes: 1299517879.992
num_examples: 2452
download_size: 728644488
dataset_size: 1299517879.992
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Braddy/xview_captions_v3 | Braddy | 2023-10-23T19:52:00Z | 355 | 0 | null | [
"region:us"
] | 2023-10-23T19:52:00Z | 2023-10-23T19:51:54.000Z | 2023-10-23T19:51:54 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: text
sequence: string
- name: file_id
dtype: string
splits:
- name: train
num_bytes: 94674025.0
num_examples: 949
download_size: 94634260
dataset_size: 94674025.0
---
# Dataset Card for "xview_captions_v3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
result-kand2-sdxl-wuerst-karlo/70a3f689 | result-kand2-sdxl-wuerst-karlo | 2023-11-10T00:19:20Z | 355 | 0 | null | [
"region:us"
] | 2023-11-10T00:19:20Z | 2023-11-10T00:19:19.000Z | 2023-11-10T00:19:19 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 163
num_examples: 10
download_size: 1345
dataset_size: 163
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "70a3f689"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
result-kand2-sdxl-wuerst-karlo/ae385042 | result-kand2-sdxl-wuerst-karlo | 2023-11-10T00:22:08Z | 355 | 0 | null | [
"region:us"
] | 2023-11-10T00:22:08Z | 2023-11-10T00:22:08.000Z | 2023-11-10T00:22:08 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 161
num_examples: 10
download_size: 1340
dataset_size: 161
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "ae385042"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
bbaw_egyptian | null | 2023-04-05T09:36:39Z | 353 | 5 | null | [
"task_categories:translation",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:extended|wikipedia",
"language:de",
"language:egy",
"language:en",
"license:cc-by-4.0",
"region:us"
] | 2023-04-05T09:36:39Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- de
- egy
- en
license:
- cc-by-4.0
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|wikipedia
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: BbawEgyptian
dataset_info:
features:
- name: transcription
dtype: string
- name: translation
dtype: string
- name: hieroglyphs
dtype: string
splits:
- name: train
num_bytes: 18546162
num_examples: 100736
download_size: 35348686
dataset_size: 18546162
---
# Dataset Card for "bbaw_egyptian"
## 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://edoc.bbaw.de/frontdoor/index/index/docId/2919](https://edoc.bbaw.de/frontdoor/index/index/docId/2919)
- **Repository:** [Github](https://phiwi.github.io/all.json)
- **Paper:** [Multi-Task Modeling of Phonographic Languages: Translating Middle Egyptian Hieroglyph](https://zenodo.org/record/3524924)
- **Point of Contact:** [Philipp Wiesenbach](https://www.cl.uni-heidelberg.de/~wiesenbach/index.html)
- **Size of downloaded dataset files:** 35.65 MB
### Dataset Summary
This dataset comprises parallel sentences of hieroglyphic encodings, transcription and translation as used in the paper [Multi-Task Modeling of Phonographic Languages: Translating Middle Egyptian Hieroglyph](https://zenodo.org/record/3524924). The data triples are extracted from the [digital corpus of Egyptian texts](https://edoc.bbaw.de/frontdoor/index/index/docId/2919) compiled by the project "Strukturen und Transformationen des Wortschatzes der ägyptischen Sprache".
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
The dataset consists of parallel triples of
- `hieroglyphs`: [Encoding of the hieroglyphs with the [Gardiner's sign list](https://en.wikipedia.org/wiki/Gardiner%27s_sign_list)
- `transcription`: Transliteration of the above mentioned hieroglyphs with a [transliteration scheme](https://en.wikipedia.org/wiki/Transliteration_of_Ancient_Egyptian)
- `translation`: Translation in mostly German language (with some English mixed in)
## Dataset Structure
The dataset is not divided into 'train', 'dev' and 'test' splits as it was not built for competitive purposes and we encourage all scientists to use individual partitioning schemes to suit their needs (due to the low resource setting it might be advisable to use cross validation anyway). The only available split 'all' therefore comprises the full 100,708 translation triples, 35,503 of which possess hieroglyphic encodings (the remaining 65,205 triples have empty `hieroglyph` entries).
### Data Instances
An example of a data triple looks the following way:
```
{
"transcription": "n rḏi̯(.w) gꜣ =j r dbḥ.t m pr-ḥḏ",
"translation": "I was not let to suffer lack in the treasury with respect to what was needed;",
"hieroglyphs": "D35 D21 -D37 G1&W11 -V32B A1 D21 D46 -D58 *V28 -F18 *X1 -A2 G17 [? *O2 *?]"
}
```
*Important*: Only about a third of the instance actually cover hieroglyphic encodings (the rest is the empty string `""`) as the leftover encodings have not yet been incorporated into the BBAW's project database.
### Data Fields
#### plain_text
- `transcription`: a `string` feature.
- `translation`: a `string` feature.
- `hieroglyphs`: a `string` feature.
### Data Splits
| name |all|
|----------|----:|
|plain_text|100708|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
The data source comes from the project "Strukturen und Transformationen des Wortschatzes der ägyptischen Sprache" which is compiling an extensively annotated digital corpus of Egyptian texts. Their [publication](https://edoc.bbaw.de/frontdoor/index/index/docId/2919) comprises an excerpt of the internal database's contents.
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
The corpus has not been preprocessed as we encourage every scientist to prepare the corpus to their desired needs. This means, that all textcritic symbols are still included in the transliteration and translation. This concerns the following annotations:
- `()`: defective
- `[]`: lost
- `{}`: surplus
- `〈〉`: omitted
- `⸢⸣`: damaged
- `⸮?`: unclear
- `{{}}`: erasure
- `(())`: above
- `[[]]`: overstrike
- `〈〈〉〉`: haplography
Their exists a similar sign list for the annotation of the hieroglyphic encoding. If you wish access to this list, please get in contact with the author.
#### Who are the annotators?
[More Information Needed]
### 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
Source corpus:
```
@misc{OPUS4-2919,
title = {Teilauszug der Datenbank des Vorhabens "Strukturen und Transformationen des Wortschatzes der {\"a}gyptischen Sprache" vom Januar 2018},
institution = {Akademienvorhaben Strukturen und Transformationen des Wortschatzes der {\"a}gyptischen Sprache. Text- und Wissenskultur im alten {\"A}gypten},
type = {other},
year = {2018},
}
```
Translation paper:
```
@article{wiesenbach19,
title = {Multi-Task Modeling of Phonographic Languages: Translating Middle Egyptian Hieroglyphs},
author = {Wiesenbach, Philipp and Riezler, Stefan},
journal = {Proceedings of the International Workshop on Spoken Language Translation},
journal-abbrev = {IWSLT},
year = {2019},
url = {https://www.cl.uni-heidelberg.de/statnlpgroup/publications/IWSLT2019_v2.pdf}
}
```
### Contributions
Thanks to [@phiwi](https://github.com/phiwi) for adding this dataset. | [
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wili_2018 | null | 2023-01-25T15:02:28Z | 353 | 3 | wili-2018 | [
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- multilingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: wili-2018
pretty_name: Wili2018
language_bcp47:
- be-tarask
- map-bms
- nds-nl
- roa-tara
- zh-yue
tags:
- language-identification
dataset_info:
features:
- name: sentence
dtype: string
- name: label
dtype:
class_label:
names:
'0': cdo
'1': glk
'2': jam
'3': lug
'4': san
'5': rue
'6': wol
'7': new
'8': mwl
'9': bre
'10': ara
'11': hye
'12': xmf
'13': ext
'14': cor
'15': yor
'16': div
'17': asm
'18': lat
'19': cym
'20': hif
'21': ace
'22': kbd
'23': tgk
'24': rus
'25': nso
'26': mya
'27': msa
'28': ava
'29': cbk
'30': urd
'31': deu
'32': swa
'33': pus
'34': bxr
'35': udm
'36': csb
'37': yid
'38': vro
'39': por
'40': pdc
'41': eng
'42': tha
'43': hat
'44': lmo
'45': pag
'46': jav
'47': chv
'48': nan
'49': sco
'50': kat
'51': bho
'52': bos
'53': kok
'54': oss
'55': mri
'56': fry
'57': cat
'58': azb
'59': kin
'60': hin
'61': sna
'62': dan
'63': egl
'64': mkd
'65': ron
'66': bul
'67': hrv
'68': som
'69': pam
'70': nav
'71': ksh
'72': nci
'73': khm
'74': sgs
'75': srn
'76': bar
'77': cos
'78': ckb
'79': pfl
'80': arz
'81': roa-tara
'82': fra
'83': mai
'84': zh-yue
'85': guj
'86': fin
'87': kir
'88': vol
'89': hau
'90': afr
'91': uig
'92': lao
'93': swe
'94': slv
'95': kor
'96': szl
'97': srp
'98': dty
'99': nrm
'100': dsb
'101': ind
'102': wln
'103': pnb
'104': ukr
'105': bpy
'106': vie
'107': tur
'108': aym
'109': lit
'110': zea
'111': pol
'112': est
'113': scn
'114': vls
'115': stq
'116': gag
'117': grn
'118': kaz
'119': ben
'120': pcd
'121': bjn
'122': krc
'123': amh
'124': diq
'125': ltz
'126': ita
'127': kab
'128': bel
'129': ang
'130': mhr
'131': che
'132': koi
'133': glv
'134': ido
'135': fao
'136': bak
'137': isl
'138': bcl
'139': tet
'140': jpn
'141': kur
'142': map-bms
'143': tyv
'144': olo
'145': arg
'146': ori
'147': lim
'148': tel
'149': lin
'150': roh
'151': sqi
'152': xho
'153': mlg
'154': fas
'155': hbs
'156': tam
'157': aze
'158': lad
'159': nob
'160': sin
'161': gla
'162': nap
'163': snd
'164': ast
'165': mal
'166': mdf
'167': tsn
'168': nds
'169': tgl
'170': nno
'171': sun
'172': lzh
'173': jbo
'174': crh
'175': pap
'176': oci
'177': hak
'178': uzb
'179': zho
'180': hsb
'181': sme
'182': mlt
'183': vep
'184': lez
'185': nld
'186': nds-nl
'187': mrj
'188': spa
'189': ceb
'190': ina
'191': heb
'192': hun
'193': que
'194': kaa
'195': mar
'196': vec
'197': frp
'198': ell
'199': sah
'200': eus
'201': ces
'202': slk
'203': chr
'204': lij
'205': nep
'206': srd
'207': ilo
'208': be-tarask
'209': bod
'210': orm
'211': war
'212': glg
'213': mon
'214': gle
'215': min
'216': ibo
'217': ile
'218': epo
'219': lav
'220': lrc
'221': als
'222': mzn
'223': rup
'224': fur
'225': tat
'226': myv
'227': pan
'228': ton
'229': kom
'230': wuu
'231': tcy
'232': tuk
'233': kan
'234': ltg
config_name: WiLI-2018 dataset
splits:
- name: train
num_bytes: 65408201
num_examples: 117500
- name: test
num_bytes: 66491260
num_examples: 117500
download_size: 130516351
dataset_size: 131899461
---
# Dataset Card for wili_2018
## 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/841984
- **Repository:** [Needs More Information]
- **Paper:** https://arxiv.org/pdf/1801.07779
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** Thoma, Martin (Email: info@martin-thoma.de)
### Dataset Summary
WiLI-2018, the Wikipedia language identification benchmark dataset, contains 235000 paragraphs of 235 languages. The dataset is balanced and a train-test split is provided.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
235 Different Languages
## Dataset Structure
### Data Instances
```
{
'label': 207,
'sentence': 'Ti Turkia ket maysa a demokrata, sekular, unitario, batay-linteg a republika nga addaan ti taga-ugma a tinawtawid a kultura. Ti Turkia ket umadadu a naipatipon iti Laud babaen ti panagkameng kadagiti organisasion a kas ti Konsilo iti Europa, NATO, OECD, OSCE ken ti G-20 a dagiti kangrunaan nga ekonomia. Ti Turkia ket nangrugi a nakitulag ti napno a panagkameng iti Kappon ti Europa idi 2005, nga isu ket maysa idin a kumaduaan a kameng iti Europeano a Komunidad ti Ekonomia manipud idi 1963 ken nakadanon ti maysa a tulagan ti kappon ti aduana idi 1995. Ti Turkia ket nagtaraken iti asideg a kultural, politikal, ekonomiko ken industria a panakibiang iti Tengnga a Daya, dagiti Turko nga estado iti Tengnga nga Asia ken dagiti pagilian ti Aprika babaen ti panagkameng kadagiti organisasion a kas ti Turko a Konsilo, Nagsaupan nga Administrasion iti Turko nga Arte ken Kultura, Organisasion iti Islamiko a Panagtitinnulong ken ti Organisasion ti Ekonomiko a Panagtitinnulong.'
}
```
### Data Fields
[Needs More Information]
### Data Splits
175000 lines of text each for train and test data.
## 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
The dataset was initially created by Thomas Martin
### Licensing Information
ODC Open Database License v1.0
### Citation Information
```
@dataset{thoma_martin_2018_841984,
author = {Thoma, Martin},
title = {{WiLI-2018 - Wikipedia Language Identification database}},
month = jan,
year = 2018,
publisher = {Zenodo},
version = {1.0.0},
doi = {10.5281/zenodo.841984},
url = {https://doi.org/10.5281/zenodo.841984}
}
```
### Contributions
Thanks to [@Shubhambindal2017](https://github.com/Shubhambindal2017) for adding this dataset. | [
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GEM/ART | GEM | 2022-10-24T13:01:25Z | 353 | 3 | null | [
"task_categories:other",
"annotations_creators:automatically-created",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"reasoning",
"arxiv:1908.05739",
"arxiv:1906.05317",
"region:us"
] | 2022-10-24T13:01:25Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- automatically-created
language_creators:
- unknown
language:
- en
license:
- apache-2.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- other
task_ids: []
pretty_name: ART
tags:
- reasoning
---
# Dataset Card for GEM/ART
## Dataset Description
- **Homepage:** http://abductivecommonsense.xyz/
- **Repository:** https://storage.googleapis.com/ai2-mosaic/public/abductive-commonsense-reasoning-iclr2020/anlg.zip
- **Paper:** https://openreview.net/pdf?id=Byg1v1HKDB
- **Leaderboard:** N/A
- **Point of Contact:** Chandra Bhagavatulla
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/ART).
### Dataset Summary
Abductive reasoning is inference to the most plausible explanation. For example, if Jenny finds her house in a mess when she returns from work, and remembers that she left a window open, she can hypothesize that a thief broke into her house and caused the mess, as the most plausible explanation.
This data loader focuses on abductive NLG: a conditional English generation task for explaining given observations in natural language.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/ART')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/ART).
#### website
[Website](http://abductivecommonsense.xyz/)
#### paper
[OpenReview](https://openreview.net/pdf?id=Byg1v1HKDB)
#### authors
Chandra Bhagavatula (AI2), Ronan Le Bras (AI2), Chaitanya Malaviya (AI2), Keisuke Sakaguchi (AI2), Ari Holtzman (AI2, UW), Hannah Rashkin (AI2, UW), Doug Downey (AI2), Wen-tau Yih (AI2), Yejin Choi (AI2, UW)
## Dataset Overview
### Where to find the Data and its Documentation
#### Webpage
<!-- info: What is the webpage for the dataset (if it exists)? -->
<!-- scope: telescope -->
[Website](http://abductivecommonsense.xyz/)
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Google Storage](https://storage.googleapis.com/ai2-mosaic/public/abductive-commonsense-reasoning-iclr2020/anlg.zip)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[OpenReview](https://openreview.net/pdf?id=Byg1v1HKDB)
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
```
@inproceedings{
Bhagavatula2020Abductive,
title={Abductive Commonsense Reasoning},
author={Chandra Bhagavatula and Ronan Le Bras and Chaitanya Malaviya and Keisuke Sakaguchi and Ari Holtzman and Hannah Rashkin and Doug Downey and Wen-tau Yih and Yejin Choi},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=Byg1v1HKDB}
}
```
#### Contact Name
<!-- quick -->
<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
Chandra Bhagavatulla
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
chandrab@allenai.org
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
no
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
no
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`English`
#### Whose Language?
<!-- info: Whose language is in the dataset? -->
<!-- scope: periscope -->
Crowdworkers on the Amazon Mechanical Turk platform based in the U.S, Canada, U.K and Australia.
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
apache-2.0: Apache License 2.0
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
To study the viability of language-based abductive reasoning. Training and evaluating models to generate a plausible hypothesis to explain two given observations.
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Reasoning
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`industry`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
Allen Institute for AI
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Chandra Bhagavatula (AI2), Ronan Le Bras (AI2), Chaitanya Malaviya (AI2), Keisuke Sakaguchi (AI2), Ari Holtzman (AI2, UW), Hannah Rashkin (AI2, UW), Doug Downey (AI2), Wen-tau Yih (AI2), Yejin Choi (AI2, UW)
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
Allen Institute for AI
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
Chandra Bhagavatula (AI2), Ronan LeBras (AI2), Aman Madaan (CMU), Nico Daheim (RWTH Aachen University)
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
- `observation_1`: A string describing an observation / event.
- `observation_2`: A string describing an observation / event.
- `label`: A string that plausibly explains why observation_1 and observation_2 might have happened.
#### How were labels chosen?
<!-- info: How were the labels chosen? -->
<!-- scope: microscope -->
Explanations were authored by crowdworkers on the Amazon Mechanical Turk platform using a custom template designed by the creators of the dataset.
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
```
{
'gem_id': 'GEM-ART-validation-0',
'observation_1': 'Stephen was at a party.',
'observation_2': 'He checked it but it was completely broken.',
'label': 'Stephen knocked over a vase while drunk.'
}
```
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
- `train`: Consists of training instances.
- `dev`: Consists of dev instances.
- `test`: Consists of test instances.
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Why is the Dataset in GEM?
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
Abductive reasoning is a crucial capability of humans and ART is the first dataset curated to study language-based abductive reasoning.
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
no
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
Whether models can reason abductively about a given pair of observations.
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
no
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
no
### Getting Started with the Task
#### Pointers to Resources
<!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. -->
<!-- scope: microscope -->
- [Paper](https://arxiv.org/abs/1908.05739)
- [Code](https://github.com/allenai/abductive-commonsense-reasoning)
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
Whether models can reason abductively about a given pair of observations.
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`BLEU`, `BERT-Score`, `ROUGE`
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
no
## Dataset Curation
### Original Curation
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
no
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Crowdsourced`
#### Where was it crowdsourced?
<!-- info: If crowdsourced, where from? -->
<!-- scope: periscope -->
`Amazon Mechanical Turk`
#### Language Producers
<!-- info: What further information do we have on the language producers? -->
<!-- scope: microscope -->
Language producers were English speakers in U.S., Canada, U.K and Australia.
#### Topics Covered
<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
<!-- scope: periscope -->
No
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
validated by crowdworker
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
algorithmically
#### Filter Criteria
<!-- info: What were the selection criteria? -->
<!-- scope: microscope -->
Adversarial filtering algorithm as described in the [paper](https://arxiv.org/abs/1908.05739)
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
automatically created
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
#### Annotation Values
<!-- info: Purpose and values for each annotation -->
<!-- scope: microscope -->
Each observation is associated with a list of COMET (https://arxiv.org/abs/1906.05317) inferences.
#### Any Quality Control?
<!-- info: Quality control measures? -->
<!-- scope: telescope -->
none
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
no
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
no PII
#### Justification for no PII
<!-- info: Provide a justification for selecting `no PII` above. -->
<!-- scope: periscope -->
The dataset contains day-to-day events. It does not contain names, emails, addresses etc.
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
no
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
no
## Considerations for Using the Data
### PII Risks and Liability
#### Potential PII Risk
<!-- info: Considering your answers to the PII part of the Data Curation Section, describe any potential privacy to the data subjects and creators risks when using the dataset. -->
<!-- scope: microscope -->
None
### Licenses
#### Copyright Restrictions on the Dataset
<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
<!-- scope: periscope -->
`public domain`
#### Copyright Restrictions on the Language Data
<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
<!-- scope: periscope -->
`public domain`
### Known Technical Limitations
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aadityaubhat/GPT-wiki-intro | aadityaubhat | 2023-10-03T22:48:42Z | 353 | 18 | null | [
"task_categories:text-classification",
"task_categories:zero-shot-classification",
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:cc",
"doi:10.57967/hf/0326",
"region:us"
] | 2023-10-03T22:48:42Z | 2023-02-03T18:30:39.000Z | 2023-02-03T18:30:39 | ---
license: cc
task_categories:
- text-classification
- zero-shot-classification
- text-generation
pretty_name: GPT Wiki Intro
size_categories:
- 100K<n<1M
language:
- en
---
# GPT Wiki Intro
## Overview
Dataset for training models to classify human written vs GPT/ChatGPT generated text.
This dataset contains Wikipedia introductions and GPT (Curie) generated introductions for 150k topics.
Prompt used for generating text
```
200 word wikipedia style introduction on '{title}'
{starter_text}
```
where `title` is the title for the wikipedia page, and `starter_text` is the first seven words of the wikipedia introduction.
Here's an example of prompt used to generate the introduction paragraph for 'Secretory protein' -
>'200 word wikipedia style introduction on Secretory protein
>
> A secretory protein is any protein, whether'
Configuration used for GPT model
```
model="text-curie-001",
prompt=prompt,
temperature=0.7,
max_tokens=300,
top_p=1,
frequency_penalty=0.4,
presence_penalty=0.1
```
Schema for the dataset
|Column |Datatype|Description |
|---------------------|--------|-------------------------------------------|
|id |int64 |ID |
|url |string |Wikipedia URL |
|title |string |Title |
|wiki_intro |string |Introduction paragraph from wikipedia |
|generated_intro |string |Introduction generated by GPT (Curie) model|
|title_len |int64 |Number of words in title |
|wiki_intro_len |int64 |Number of words in wiki_intro |
|generated_intro_len |int64 |Number of words in generated_intro |
|prompt |string |Prompt used to generate intro |
|generated_text |string |Text continued after the prompt |
|prompt_tokens |int64 |Number of tokens in the prompt |
|generated_text_tokens|int64 |Number of tokens in generated text |
## Credits
* [wikipedia dataset](https://huggingface.co/datasets/wikipedia#licensing-information)
## Code
Code to create this dataset can be found on [GitHub](https://github.com/aadityaubhat/wiki_gpt)
## Citation
```
@misc {aaditya_bhat_2023,
author = { {Aaditya Bhat} },
title = { GPT-wiki-intro (Revision 0e458f5) },
year = 2023,
url = { https://huggingface.co/datasets/aadityaubhat/GPT-wiki-intro },
doi = { 10.57967/hf/0326 },
publisher = { Hugging Face }
}
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Sleoruiz/disc_cla_segunda | Sleoruiz | 2023-03-21T17:52:27Z | 353 | 0 | null | [
"region:us"
] | 2023-03-21T17:52:27Z | 2023-03-21T17:52:22.000Z | 2023-03-21T17:52:22 | ---
dataset_info:
features:
- name: text
dtype: string
- name: inputs
struct:
- name: text
dtype: string
- name: prediction
list:
- name: label
dtype: string
- name: score
dtype: float64
- name: prediction_agent
dtype: string
- name: annotation
sequence: string
- name: annotation_agent
dtype: string
- name: multi_label
dtype: bool
- name: explanation
dtype: 'null'
- name: id
dtype: string
- name: metadata
dtype: 'null'
- name: status
dtype: string
- name: event_timestamp
dtype: timestamp[us]
- name: metrics
struct:
- name: text_length
dtype: int64
splits:
- name: train
num_bytes: 30333232
num_examples: 7327
download_size: 15864012
dataset_size: 30333232
---
# Dataset Card for "disc_cla_segunda"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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yuweiyin/FinBench | yuweiyin | 2023-08-02T01:02:19Z | 353 | 5 | null | [
"task_categories:tabular-classification",
"task_categories:text-classification",
"size_categories:0.3M<n<1M",
"license:cc-by-nc-4.0",
"arxiv:2308.00065",
"region:us"
] | 2023-08-02T01:02:19Z | 2023-06-18T02:39:45.000Z | 2023-06-18T02:39:45 | ---
license: cc-by-nc-4.0
task_categories:
- tabular-classification
- text-classification
size_categories:
- 0.3M<n<1M
---
# Dataset Card for FinBench
## Dataset Description
- **Homepage: https://huggingface.co/datasets/yuweiyin/FinBench**
- **Repository: https://huggingface.co/datasets/yuweiyin/FinBench**
- **Paper: https://arxiv.org/abs/2308.00065**
- **Leaderboard:**
- **Point of Contact:**
## Dataset Statistics
We present **FinBench**, a benchmark for evaluating the performance of machine learning models with
both tabular data inputs and profile text inputs.
We first collect hundreds of financial datasets from the [Kaggle](https://www.kaggle.com/) platform
and then screen out ten high-quality datasets for financial risk prediction.
The screening criteria is based on the quantity and popularity, column meaningfulness, and
the performance of baseline models on those datasets.
FinBench consists of three types of financial risks, i.e., default, fraud, and churn.
We process the datasets in a unified data structure and provide an easy-loading API on
[HuggingFace](https://huggingface.co/datasets/yuweiyin/FinBench).
### Task Statistics
The following table reports the task description, dataset name (for `datasets` loading),
the number and positive ratio of train/validation/test sets,
the number of classification classes (all is 2), and the number of features.
| Task | Description | Dataset | #Classes | #Features | #Train [Pos%] | #Val [Pos%] | #Test [Pos%] |
|---------------------|----------------------------------------------------------------|---------|----------|-----------|----------------|---------------|---------------|
| Credit-card Default | Predict whether a user will default on the credit card or not. | `cd1` | 2 | 9 | 2738 [7.0%] | 305 [6.9%] | 1305 [6.2%] |
| | | `cd2` | 2 | 23 | 18900 [22.3%] | 2100 [22.3%] | 9000 [21.8%] |
| Loan Default | Predict whether a user will default on the loan or not. | `ld1` | 2 | 12 | 2118 [8.9%] | 236 [8.5%] | 1010 [9.0%] |
| | | `ld2` | 2 | 11 | 18041 [21.7%] | 2005 [20.8%] | 8592 [21.8%] |
| | | `ld3` | 2 | 35 | 142060 [21.6%] | 15785 [21.3%] | 67648 [22.1%] |
| Credit-card Fraud | Predict whether a user will commit fraud or not. | `cf1` | 2 | 19 | 5352 [0.67%] | 595 [1.1%] | 2550 [0.90%] |
| | | `cf2` | 2 | 120 | 5418 [6.0%] | 603 [7.3%] | 2581 [6.0%] |
| Customer Churn | Predict whether a user will churn or not. (customer attrition) | `cc1` | 2 | 9 | 4189 [23.5%] | 466 [22.7%] | 1995 [22.4%] |
| | | `cc2` | 2 | 10 | 6300 [20.8%] | 700 [20.6%] | 3000 [19.47%] |
| | | `cc3` | 2 | 21 | 4437 [26.1%] | 493 [24.9%] | 2113 [27.8%] |
---
| Task | #Train | #Val | #Test |
|---------------------|--------|-------|-------|
| Credit-card Default | 21638 | 2405 | 10305 |
| Loan Default | 162219 | 18026 | 77250 |
| Credit-card Fraud | 10770 | 1198 | 5131 |
| Customer Churn | 14926 | 1659 | 7108 |
| Total | 209553 | 23288 | 99794 |
### Data Source
| Task | Dataset | Source |
|---------------------|---------|------------------------------------------------------------------------------------------|
| Credit-card Default | `cd1` | [Kaggle](https://www.kaggle.com/datasets/gustavotg/credit-default) |
| | `cd2` | [Kaggle](https://www.kaggle.com/datasets/uciml/default-of-credit-card-clients-dataset) |
| Loan Default | `ld1` | [Kaggle](https://www.kaggle.com/datasets/ajay1735/hmeq-data) |
| | `ld2` | [Kaggle](https://www.kaggle.com/datasets/laotse/credit-risk-dataset) |
| | `ld3` | [Kaggle](https://www.kaggle.com/datasets/mamtadhaker/lt-vehicle-loan-default-prediction) |
| Credit-card Fraud | `cf1` | [Kaggle](https://www.kaggle.com/datasets/johancaicedo/creditcardfraud) |
| | `cf2` | [Kaggle](https://www.kaggle.com/datasets/mishra5001/credit-card) |
| Customer Churn | `cc1` | [Kaggle](https://www.kaggle.com/datasets/gauravduttakiit/jobathon-march-2022) |
| | `cc2` | [Kaggle](https://www.kaggle.com/datasets/mathchi/churn-for-bank-customers) |
| | `cc3` | [Kaggle](https://www.kaggle.com/datasets/yeanzc/telco-customer-churn-ibm-dataset) |
- Language: English
## Dataset Structure
### Data Fields
```python
import datasets
datasets.Features(
{
"X_ml": [datasets.Value(dtype="float")], # (The tabular data array of the current instance)
"X_ml_unscale": [datasets.Value(dtype="float")], # (Scaled tabular data array of the current instance)
"y": datasets.Value(dtype="int64"), # (The label / ground-truth)
"num_classes": datasets.Value("int64"), # (The total number of classes)
"num_features": datasets.Value("int64"), # (The total number of features)
"num_idx": [datasets.Value("int64")], # (The indices of the numerical datatype columns)
"cat_idx": [datasets.Value("int64")], # (The indices of the categorical datatype columns)
"cat_dim": [datasets.Value("int64")], # (The dimension of each categorical column)
"cat_str": [[datasets.Value("string")]], # (The category names of categorical columns)
"col_name": [datasets.Value("string")], # (The name of each column)
"X_instruction_for_profile": datasets.Value("string"), # instructions (from tabular data) for profiles
"X_profile": datasets.Value("string"), # customer profiles built from instructions via LLMs
}
)
```
## Data Loading
### HuggingFace Login (Optional)
```python
# OR run huggingface-cli login
from huggingface_hub import login
hf_token = "YOUR_ACCESS_TOKENS" # https://huggingface.co/settings/tokens
login(token=hf_token)
```
### Loading a Dataset
```python
from datasets import load_dataset
# ds_name_list = ["cd1", "cd2", "ld1", "ld2", "ld3", "cf1", "cf2", "cc1", "cc2", "cc3"]
ds_name = "cd1" # change the dataset name here
dataset = load_dataset("yuweiyin/FinBench", ds_name)
```
### Loading the Splits
```python
from datasets import load_dataset
ds_name = "cd1" # change the dataset name here
dataset = load_dataset("yuweiyin/FinBench", ds_name)
train_set = dataset["train"] if "train" in dataset else []
validation_set = dataset["validation"] if "validation" in dataset else []
test_set = dataset["test"] if "test" in dataset else []
```
### Loading the Instances
```python
from datasets import load_dataset
ds_name = "cd1" # change the dataset name here
dataset = load_dataset("yuweiyin/FinBench", ds_name)
train_set = dataset["train"] if "train" in dataset else []
for train_instance in train_set:
X_ml = train_instance["X_ml"] # List[float] (The tabular data array of the current instance)
X_ml_unscale = train_instance["X_ml_unscale"] # List[float] (Scaled tabular data array of the current instance)
y = train_instance["y"] # int (The label / ground-truth)
num_classes = train_instance["num_classes"] # int (The total number of classes)
num_features = train_instance["num_features"] # int (The total number of features)
num_idx = train_instance["num_idx"] # List[int] (The indices of the numerical datatype columns)
cat_idx = train_instance["cat_idx"] # List[int] (The indices of the categorical datatype columns)
cat_dim = train_instance["cat_dim"] # List[int] (The dimension of each categorical column)
cat_str = train_instance["cat_str"] # List[List[str]] (The category names of categorical columns)
col_name = train_instance["col_name"] # List[str] (The name of each column)
X_instruction_for_profile = train_instance["X_instruction_for_profile"] # instructions for building profiles
X_profile = train_instance["X_profile"] # customer profiles built from instructions via LLMs
```
## Citation
* arXiv: https://arxiv.org/abs/2308.00065
* GitHub: https://github.com/YuweiYin/FinPT
```bibtex
@article{yin2023finbench,
title = {FinPT: Financial Risk Prediction with Profile Tuning on Pretrained Foundation Models},
author = {Yin, Yuwei and Yang, Yazheng and Yang, Jian and Liu, Qi},
journal = {arXiv preprint arXiv:2308.00065},
year = {2023},
}
```
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eduagarcia/cc100-pt | eduagarcia | 2023-08-29T00:58:52Z | 353 | 0 | null | [
"region:us"
] | 2023-08-29T00:58:52Z | 2023-08-28T21:24:32.000Z | 2023-08-28T21:24:32 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 53151660927
num_examples: 38999388
download_size: 16147647964
dataset_size: 53151660927
---
# Dataset Card for "cc100-pt"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
SetFit/qnli | SetFit | 2022-02-28T13:29:16Z | 352 | 0 | null | [
"region:us"
] | 2022-02-28T13:29:16Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | # Glue QNLI
This dataset is a port of the official [`qnli` dataset](https://huggingface.co/datasets/glue/viewer/qnli/train) on the Hub.
Note that the question and sentence columns have been renamed to text1 and text2 respectively.
Also, the test split is not labeled; the label column values are always -1.
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m3hrdadfi/recipe_nlg_lite | m3hrdadfi | 2021-07-03T09:34:56Z | 352 | 3 | null | [
"region:us"
] | 2021-07-03T09:34:56Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | # RecipeNLG: A Cooking Recipes Dataset
RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation - Lite version
The dataset contains `7,198` cooking recipes (`>7K`).
It's processed in more careful way and provides more samples than any other dataset in the area.
## How to use
```bash
pip install git+https://github.com/huggingface/datasets.git
```
Load `m3hrdadfi/recipe_nlg_lite` dataset using `load_dataset`:
```python
from datasets import load_dataset
dataset = load_dataset("m3hrdadfi/recipe_nlg_lite")
print(dataset)
```
Output:
```text
DatasetDict({
train: Dataset({
features: ['uid', 'name', 'description', 'link', 'ner', 'ingredients', 'steps'],
num_rows: 6118
})
test: Dataset({
features: ['uid', 'name', 'description', 'link', 'ner', 'ingredients', 'steps'],
num_rows: 1080
})
})
```
## Examples
```json
{
"description": "we all know how satisfying it is to make great pork tenderloin, ribs, or a roast but the end of the meal creates a new quandary what do you do with the leftover pork contrary to what you might think, it's not that difficult . how to repurpose your meal is where real cooking creativity comes into play, so let us present to you our favorite pork chop soup recipe . with this recipe, you'll discover how the natural bold flavor of pork gives this hearty soup a lift that a vegetable soup or chicken noodle soup just can't get . it's a dinner recipe to warm you up on a cold winter night or a midday restorative for a long work week . throw all the ingredients in a large pot and let it simmer on the stove for a couple hours, or turn it into a slow cooker recipe and let it percolate for an afternoon . this foolproof recipe transforms your favorite comfort food into an easy meal to warm you up again and again . the health benefits of pork pork is a great option if you're on a low carb diet or trying to up your protein intake . the protein percentage of leaner cuts of pork can be as high as 89 percent pork also provides valuable vitamins and minerals that make pork recipes worthy endeavors . pork has high levels of thiamin and niacin, which other types of meat like beef and lamb lack . they are both b vitamins that aid in several body functions such as metabolism and cell function . pork also delivers a healthy amount of zinc, which aids in brain and immune system function . that makes digging into this pork chop noodle soup all the more alluring . recipe variations this pork soup recipe can be adapted to many diets . if you're following a low carb or ketogenic diet, you can modify the recipe to suit you by leaving out the noodles . if you like, you can add a little crunch by topping it with french fried onions . for cheese lovers, a sprinkle of parmesan cheese can give the soup more body and extra umami flavors . if you're not a noodle lover, this soup recipe works equally well as a potato soup with diced potatoes . if you want to make a southwestern or mexican version, add a can of diced tomatoes and bell peppers for a little extra depth . if you have a penchant for spicy soups, add a little chili powder or red pepper flakes . it's up to you this recipe is great for using up leftover pork chops, but you can make this soup using fresh chops however you decide to do it, you won't be disappointed.",
"ingredients": "3.0 bone in pork chops, salt, pepper, 2.0 tablespoon vegetable oil, 2.0 cup chicken broth, 4.0 cup vegetable broth, 1.0 red onion, 4.0 carrots, 2.0 clove garlic, 1.0 teaspoon dried thyme, 0.5 teaspoon dried basil, 1.0 cup rotini pasta, 2.0 stalk celery",
"link": "https://www.yummly.com/private/recipe/Pork-Chop-Noodle-Soup-2249011?layout=prep-steps",
"name": "pork chop noodle soup",
"ner": "bone in pork chops, salt, pepper, vegetable oil, chicken broth, vegetable broth, red onion, carrots, garlic, dried thyme, dried basil, rotini pasta, celery",
"steps": "season pork chops with salt and pepper . heat oil in a dutch oven over medium high heat . add chops and cook for about 4 minutes, until golden brown . flip and cook 4 minutes more, until golden brown . transfer chops to a plate and set aside . pour half of chicken broth into pot, scraping all browned bits from bottom . add remaining chicken broth, vegetable broth, onion, carrots, celery and garlic . mix well and bring to a simmer . add 1 quart water, thyme, basil, 2 teaspoons salt and 1 teaspoon pepper . mix well and bring to a simmer . add chops back to pot and return to simmer . reduce heat and simmer for 90 minutes, stirring occasionally, being careful not to break up chops . transfer chops to plate, trying not to break them up . set aside to cool . raise the heat and bring the soup to a boil . add pasta and cook for about 12 minutes, until tender . when the chops are cool, pull them apart, discarding all the bones and fat . add the meat back to soup and stir well . taste for salt and pepper, and add if needed, before serving.",
"uid": "dab8b7d0-e0f6-4bb0-aed9-346e80dace1f"
}
```
## Citation
```bibtex
@misc{RecipeNLGLite,
author = {Mehrdad Farahani},
title = {RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation (Lite)},
year = 2021,
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {url{https://github.com/m3hrdadfi/recipe-nlg-lite}},
}
```
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
shibing624/AdvertiseGen | shibing624 | 2023-05-12T07:25:00Z | 352 | 15 | null | [
"task_categories:text-generation",
"language:zh",
"license:cc-by-4.0",
"text-generation",
"e-commerce advertise",
"region:us"
] | 2023-05-12T07:25:00Z | 2023-03-28T02:42:56.000Z | 2023-03-28T02:42:56 | ---
license: cc-by-4.0
language:
- zh
tags:
- text-generation
- e-commerce advertise
pretty_name: AdvertiseGen
task_categories:
- text-generation
---
# Dataset Card for AdvertiseGen
- **formal url:** https://www.luge.ai/#/luge/dataDetail?id=9
## Dataset Description
数据集介绍
AdvertiseGen是电商广告文案生成数据集。
AdvertiseGen以商品网页的标签与文案的信息对应关系为基础构造，是典型的开放式生成任务，在模型基于key-value输入生成开放式文案时，与输入信息的事实一致性需要得到重点关注。
- 任务描述：给定商品信息的关键词和属性列表kv-list，生成适合该商品的广告文案adv；
- 数据规模：训练集114k，验证集1k，测试集3k；
- 数据来源：清华大学CoAI小组；
### Supported Tasks and Leaderboards
The dataset designed for generate e-commerce advertise.
### Languages
The data in AdvertiseGen are in Chinese.
## Dataset Structure
### Data Instances
An example of "train" looks as follows:
```json
{
"content": "类型#上衣*材质#牛仔布*颜色#白色*风格#简约*图案#刺绣*衣样式#外套*衣款式#破洞",
"summary": "简约而不简单的牛仔外套，白色的衣身十分百搭。衣身多处有做旧破洞设计，打破单调乏味，增加一丝造型看点。衣身后背处有趣味刺绣装饰，丰富层次感，彰显别样时尚。"
}
```
### Citation Information
数据集引用
如在学术论文中使用本数据集，请添加相关引用说明，具体如下：
```
Shao, Zhihong, et al. "Long and Diverse Text Generation with Planning-based Hierarchical Variational Model." Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
```
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
toughdata/quora-question-answer-dataset | toughdata | 2023-08-28T13:36:21Z | 352 | 0 | null | [
"task_categories:question-answering",
"task_categories:conversational",
"task_categories:text2text-generation",
"language:en",
"license:gpl-3.0",
"question",
"answer",
"quora",
"region:us"
] | 2023-08-28T13:36:21Z | 2023-08-23T22:53:09.000Z | 2023-08-23T22:53:09 | ---
license: gpl-3.0
task_categories:
- question-answering
- conversational
- text2text-generation
language:
- en
tags:
- question
- answer
- quora
pretty_name: Quora Question/Answer Pairs
---
Quora Question Answer Dataset (Quora-QuAD) contains 56,402 question-answer pairs scraped from Quora.
# Usage:
For instructions on fine-tuning a model (Flan-T5) with this dataset, please check out the article: https://www.toughdata.net/blog/post/finetune-flan-t5-question-answer-quora-dataset | [
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librarian-bots/dataset_abstracts | librarian-bots | 2023-10-31T09:13:51Z | 352 | 2 | null | [
"task_categories:text-classification",
"size_categories:n<1K",
"language:en",
"arxiv ",
"region:us"
] | 2023-10-31T09:13:51Z | 2023-10-05T11:16:57.000Z | 2023-10-05T11:16:57 | ---
language:
- en
size_categories:
- n<1K
task_categories:
- text-classification
dataset_info:
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features:
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dtype: string
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struct:
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dtype: string
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dtype: string
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dtype: string
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struct:
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dtype:
class_label:
names:
'0': new_dataset
'1': no_new_dataset
splits:
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num_bytes: 1099065.75
num_examples: 390
- name: test
num_bytes: 366355.25
num_examples: 130
download_size: 865263
dataset_size: 1465421.0
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features:
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struct:
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struct:
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dtype: int64
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dtype: string
splits:
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num_bytes: 1372259.876
num_examples: 494
download_size: 792644
dataset_size: 1372259.876
configs:
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data_files:
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path: annotated/train-*
- split: test
path: annotated/test-*
- config_name: unlabelled
data_files:
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path: unlabelled/train-*
tags:
- 'arxiv '
---
# Dataset Card for "dataset_abstracts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
com_qa | null | 2023-06-27T07:38:08Z | 351 | 2 | comqa | [
"task_categories:question-answering",
"language:en",
"region:us"
] | 2023-06-27T07:38:08Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
paperswithcode_id: comqa
pretty_name: ComQA
dataset_info:
features:
- name: cluster_id
dtype: string
- name: questions
sequence: string
- name: answers
sequence: string
splits:
- name: train
num_bytes: 696645
num_examples: 3966
- name: test
num_bytes: 273384
num_examples: 2243
- name: validation
num_bytes: 131945
num_examples: 966
download_size: 1671684
dataset_size: 1101974
task_categories:
- question-answering
---
# Dataset Card for "com_qa"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [http://qa.mpi-inf.mpg.de/comqa/](http://qa.mpi-inf.mpg.de/comqa/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 1.67 MB
- **Size of the generated dataset:** 1.10 MB
- **Total amount of disk used:** 2.78 MB
### Dataset Summary
ComQA is a dataset of 11,214 questions, which were collected from WikiAnswers, a community question answering website.
By collecting questions from such a site we ensure that the information needs are ones of interest to actual users.
Moreover, questions posed there are often cannot be answered by commercial search engines or QA technology, making them
more interesting for driving future research compared to those collected from an engine's query log. The dataset contains
questions with various challenging phenomena such as the need for temporal reasoning, comparison (e.g., comparatives,
superlatives, ordinals), compositionality (multiple, possibly nested, subquestions with multiple entities), and
unanswerable questions (e.g., Who was the first human being on Mars?). Through a large crowdsourcing effort, questions
in ComQA are grouped into 4,834 paraphrase clusters that express the same information need. Each cluster is annotated
with its answer(s). ComQA answers come in the form of Wikipedia entities wherever possible. Wherever the answers are
temporal or measurable quantities, TIMEX3 and the International System of Units (SI) are used for normalization.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 1.67 MB
- **Size of the generated dataset:** 1.10 MB
- **Total amount of disk used:** 2.78 MB
An example of 'validation' looks as follows.
```
{
"answers": ["https://en.wikipedia.org/wiki/north_sea"],
"cluster_id": "cluster-922",
"questions": ["what sea separates the scandinavia peninsula from britain?", "which sea separates britain from scandinavia?"]
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `cluster_id`: a `string` feature.
- `questions`: a `list` of `string` features.
- `answers`: a `list` of `string` features.
### Data Splits
| name |train|validation|test|
|-------|----:|---------:|---:|
|default| 3966| 966|2243|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@inproceedings{abujabal-etal-2019-comqa,
title = "{ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters",
author = {Abujabal, Abdalghani and
Saha Roy, Rishiraj and
Yahya, Mohamed and
Weikum, Gerhard},
booktitle = {Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)},
month = {jun},
year = {2019},
address = {Minneapolis, Minnesota},
publisher = {Association for Computational Linguistics},
url = {https://www.aclweb.org/anthology/N19-1027},
doi = {10.18653/v1/N19-1027{,
pages = {307--317},
}
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf), [@mariamabarham](https://github.com/mariamabarham), [@patrickvonplaten](https://github.com/patrickvonplaten), [@albertvillanova](https://github.com/albertvillanova) for adding this dataset. | [
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reshinthadith/2048_has_code_filtered_base_code_review_python | reshinthadith | 2023-01-19T20:11:02Z | 351 | 0 | null | [
"region:us"
] | 2023-01-19T20:11:02Z | 2023-01-19T20:10:34.000Z | 2023-01-19T20:10:34 | ---
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splits:
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num_examples: 6398
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dataset_size: 34984009.92705029
---
# Dataset Card for "2048_has_code_filtered_base_code_review_python"
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FINDA-FIT/Fin_Corpus_EarningCall | FINDA-FIT | 2023-02-14T04:58:13Z | 351 | 1 | null | [
"region:us"
] | 2023-02-14T04:58:13Z | 2023-02-11T07:31:37.000Z | 2023-02-11T07:31:37 | ---
dataset_info:
features:
- name: ID
dtype: string
- name: CONTEXT
dtype: string
splits:
- name: train
num_bytes: 8724423352
num_examples: 234119
download_size: 4615593313
dataset_size: 8724423352
---
# Dataset Card for "Fin_Corpus_EarningCall"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Olec/cyber-threat-intelligence_v2 | Olec | 2023-04-15T11:00:18Z | 351 | 4 | null | [
"region:us"
] | 2023-04-15T11:00:18Z | 2023-03-31T15:08:08.000Z | 2023-03-31T15:08:08 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: text
dtype: string
- name: entities
list:
- name: end_offset
dtype: int64
- name: id
dtype: int64
- name: label
dtype: string
- name: start_offset
dtype: int64
- name: relations
list:
- name: from_id
dtype: int64
- name: id
dtype: int64
- name: to_id
dtype: int64
- name: type
dtype: string
splits:
- name: test
num_bytes: 29518
num_examples: 72
- name: train
num_bytes: 147723
num_examples: 332
- name: validation
num_bytes: 36580
num_examples: 76
download_size: 119557
dataset_size: 213821
---
# Dataset Card for "cyber-threat-intelligence_v2"
updated version of mrmoor/cyber-threat-intelligence
RE and NER Dataset for Cyber Threat Intelegence (CTI)
T5 Model trained on NYT and this dataset: Olec/cyber_rebel
This dataset only contains sentences with realtions.
Full dataset is available at mrmoor/cyber-threat-intelligence. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
AnanthZeke/oscar_tamil_2201 | AnanthZeke | 2023-04-05T15:40:53Z | 351 | 0 | null | [
"region:us"
] | 2023-04-05T15:40:53Z | 2023-04-05T04:21:27.000Z | 2023-04-05T04:21:27 | ---
dataset_info:
features:
- name: text
dtype: string
- name: sent_token
sequence: string
splits:
- name: train
num_bytes: 18576297122
num_examples: 556772
download_size: 6242500521
dataset_size: 18576297122
---
# Dataset Card for "oscar_tamil_2201"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Fraol/TrainDedupedRefDatasetWMetricFinal3 | Fraol | 2023-10-11T03:58:45Z | 351 | 0 | null | [
"region:us"
] | 2023-10-11T03:58:45Z | 2023-10-10T22:36:43.000Z | 2023-10-10T22:36:43 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: source
dtype: string
- name: path_name
dtype: string
- name: file_name
dtype: string
- name: ref_type
dtype: string
- name: hash
dtype: string
- name: class_name
dtype: string
- name: method_name
dtype: string
- name: row_number
dtype: int64
- name: cbo
dtype: float64
- name: wmc
dtype: float64
- name: lcom*
dtype: float64
- name: loc
dtype: float64
- name: astc2
dtype: string
- name: source_after
dtype: string
- name: cbo_after
dtype: float64
- name: wmc_after
dtype: float64
- name: lcom*_after
dtype: float64
- name: loc_after
dtype: float64
- name: astc1
dtype: string
- name: issue_name
dtype: string
- name: issue_localize
dtype: string
splits:
- name: train
num_bytes: 169182022
num_examples: 6000
- name: test
num_bytes: 41765079
num_examples: 1500
download_size: 47907274
dataset_size: 210947101
---
# Dataset Card for "TrainDedupedRefDatasetWMetricFinal3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
mehdiiraqui/twitter_disaster | mehdiiraqui | 2023-10-31T21:06:54Z | 351 | 0 | null | [
"language:en",
"disaster-classification",
"text classification",
"NLP",
"region:us"
] | 2023-10-31T21:06:54Z | 2023-10-31T15:26:05.000Z | 2023-10-31T15:26:05 | ---
language:
- en
tags:
- disaster-classification
- text classification
- NLP
--- | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
SUSTech/mmlu_train | SUSTech | 2023-11-25T09:30:09Z | 351 | 0 | null | [
"region:us"
] | 2023-11-25T09:30:09Z | 2023-11-25T09:29:44.000Z | 2023-11-25T09:29:44 | ---
configs:
- config_name: default
data_files:
- split: science_elementary
path: data/science_elementary-*
- split: obqa
path: data/obqa-*
- split: aux_law_90s
path: data/aux_law_90s-*
- split: arc_hard
path: data/arc_hard-*
- split: arc_easy
path: data/arc_easy-*
- split: race
path: data/race-*
- split: science_middle
path: data/science_middle-*
- split: mc_test
path: data/mc_test-*
dataset_info:
features:
- name: question
dtype: string
- name: A
dtype: string
- name: B
dtype: string
- name: C
dtype: string
- name: D
dtype: string
- name: answer
dtype: string
splits:
- name: science_elementary
num_bytes: 147164
num_examples: 617
- name: obqa
num_bytes: 708325
num_examples: 4956
- name: aux_law_90s
num_bytes: 1358707
num_examples: 1115
- name: arc_hard
num_bytes: 296239
num_examples: 1118
- name: arc_easy
num_bytes: 509352
num_examples: 2242
- name: race
num_bytes: 154937972
num_examples: 87716
- name: science_middle
num_bytes: 161944
num_examples: 601
- name: mc_test
num_bytes: 1815499
num_examples: 1477
download_size: 48568291
dataset_size: 159935202
---
# Dataset Card for "mmlu_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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best2009 | null | 2023-01-25T14:27:17Z | 350 | 0 | null | [
"task_categories:token-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:th",
"license:cc-by-nc-sa-3.0",
"word-tokenization",
"region:us"
] | 2023-01-25T14:27:17Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- th
license:
- cc-by-nc-sa-3.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- token-classification
task_ids: []
pretty_name: best2009
tags:
- word-tokenization
dataset_info:
features:
- name: fname
dtype: string
- name: char
sequence: string
- name: char_type
sequence:
class_label:
names:
'0': b_e
'1': c
'2': d
'3': n
'4': o
'5': p
'6': q
'7': s
'8': s_e
'9': t
'10': v
'11': w
- name: is_beginning
sequence:
class_label:
names:
'0': neg
'1': pos
config_name: best2009
splits:
- name: train
num_bytes: 483129998
num_examples: 148995
- name: test
num_bytes: 10498726
num_examples: 2252
download_size: 13891260
dataset_size: 493628724
---
# Dataset Card for `best2009`
## 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://aiforthai.in.th/
- **Repository:** https://aiforthai.in.th/corpus.php
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** https://aiforthai.in.th/
### Dataset Summary
`best2009` is a Thai word-tokenization dataset from encyclopedia, novels, news and articles by [NECTEC](https://www.nectec.or.th/) (148,995/2,252 lines of train/test). It was created for [BEST 2010: Word Tokenization Competition](https://thailang.nectec.or.th/archive/indexa290.html?q=node/10). The test set answers are not provided publicly.
### Supported Tasks and Leaderboards
word tokenization
### Languages
Thai
## Dataset Structure
### Data Instances
```
{'char': ['?', 'ภ', 'ู', 'ม', 'ิ', 'ป', 'ั', 'ญ', 'ญ', 'า', 'ช', 'า', 'ว', 'บ', '้', 'า', 'น', '\n'], 'char_type': [4, 1, 10, 1, 10, 1, 4, 1, 1, 10, 1, 10, 1, 1, 9, 10, 1, 4], 'fname': 'encyclopedia_00031.txt', 'is_beginning': [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1]}
{'char': ['ภ', 'ู', 'ม', 'ิ', 'ป', 'ั', 'ญ', 'ญ', 'า', 'ช', 'า', 'ว', 'บ', '้', 'า', 'น', ' ', 'ห', 'ม', 'า', 'ย', 'ถ', 'ึ', 'ง', ' ', 'ค', 'ว', 'า', 'ม', 'ร', 'ู', '้', 'ข', 'อ', 'ง', 'ช', 'า', 'ว', 'บ', '้', 'า', 'น', ' ', 'ซ', 'ึ', '่', 'ง', 'เ', 'ร', 'ี', 'ย', 'น', 'ร', 'ู', '้', 'ม', 'า', 'จ', 'า', 'ก', 'พ', '่', 'อ', 'แ', 'ม', '่', ' ', 'ป', 'ู', '่', 'ย', '่', 'า', 'ต', 'า', 'ย', 'า', 'ย', ' ', 'ญ', 'า', 'ต', 'ิ', 'พ', 'ี', '่', 'น', '้', 'อ', 'ง', ' ', 'ห', 'ร', 'ื', 'อ', 'ผ', 'ู', '้', 'ม', 'ี', 'ค', 'ว', 'า', 'ม', 'ร', 'ู', '้', 'ใ', 'น', 'ห', 'ม', 'ู', '่', 'บ', '้', 'า', 'น', 'ใ', 'น', 'ท', '้', 'อ', 'ง', 'ถ', 'ิ', '่', 'น', 'ต', '่', 'า', 'ง', 'ๆ', '\n'], 'char_type': [1, 10, 1, 10, 1, 4, 1, 1, 10, 1, 10, 1, 1, 9, 10, 1, 5, 3, 1, 10, 1, 1, 10, 1, 5, 1, 1, 10, 1, 1, 10, 9, 1, 1, 1, 1, 10, 1, 1, 9, 10, 1, 5, 1, 10, 9, 1, 11, 1, 10, 1, 1, 1, 10, 9, 1, 10, 1, 10, 1, 1, 9, 1, 11, 1, 9, 5, 1, 10, 9, 1, 9, 10, 1, 10, 1, 10, 1, 5, 1, 10, 1, 10, 1, 10, 9, 1, 9, 1, 1, 5, 3, 1, 10, 1, 3, 10, 9, 1, 10, 1, 1, 10, 1, 1, 10, 9, 11, 1, 3, 1, 10, 9, 1, 9, 10, 1, 11, 1, 1, 9, 1, 1, 1, 10, 9, 1, 1, 9, 10, 1, 7, 4], 'fname': 'encyclopedia_00031.txt', 'is_beginning': [1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1]}
```
### Data Fields
- `fname`: file name; also marks if article is articles, news, encyclopedia or novels
- `char`: characters
- `char_type`: character types as adopted from []() by [deepcut](https://github.com/rkcosmos/deepcut)
- `is_beginning`: is beginning of word
### Data Splits
| | train | test |
|-------------------------|------------|---------|
| # lines | 148,995 | 2,252 |
| avg words per line | 39.05 | NA |
| total words | 5,818,521 | NA |
| avg characters per line | 140.39 | 202.79 |
| total characters | 20,918,132 | 456,684 |
| # lines articles | 16,990 | NA |
| # lines encyclopedia | 50,631 | NA |
| # lines novels | 50,140 | NA |
| # lines news | 31,234 | NA |
## Dataset Creation
### Curation Rationale
The dataset was created for [BEST 2010: Word Tokenization Competition](https://thailang.nectec.or.th/archive/indexa290.html?q=node/10) by [NECTEC](https://www.nectec.or.th/).
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
Respective authors of the articles, news, encyclopedia and novels
### Annotations
#### Annotation process
Detailed annotation guidelines can be found in `BEST_Guideline_Release1.pdf` as part of the uncompressed files. Word tokenization standard used was [InterBEST2009](http://hltshare.fbk.eu/IWSLT2015/InterBEST2009Guidelines-2.pdf)
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
All data are curated from public sources. No personal and sensitive information is expected to be included.
## Considerations for Using the Data
### Social Impact of Dataset
- word tokenization dataset from articles, news, encyclopedia and novels
### Discussion of Biases
- texts are relatively formal ones from articles, news, encyclopedia and novels.
- word tokenization standard used was [InterBEST2009](http://hltshare.fbk.eu/IWSLT2015/InterBEST2009Guidelines-2.pdf).
### Other Known Limitations
- some tags unrelated to word tokenization (`<NE>` and `<AB>`) are cleaned out.
- no word boundary provdied for the test set
## Additional Information
### Dataset Curators
[NECTEC](https://www.nectec.or.th/)
### Licensing Information
CC-BY-NC-SA 3.0
### Citation Information
Dataset:
```
@inproceedings{kosawat2009best,
title={BEST 2009: Thai word segmentation software contest},
author={Kosawat, Krit and Boriboon, Monthika and Chootrakool, Patcharika and Chotimongkol, Ananlada and Klaithin, Supon and Kongyoung, Sarawoot and Kriengket, Kanyanut and Phaholphinyo, Sitthaa and Purodakananda, Sumonmas and Thanakulwarapas, Tipraporn and others},
booktitle={2009 Eighth International Symposium on Natural Language Processing},
pages={83--88},
year={2009},
organization={IEEE}
}
@inproceedings{boriboon2009best,
title={Best corpus development and analysis},
author={Boriboon, Monthika and Kriengket, Kanyanut and Chootrakool, Patcharika and Phaholphinyo, Sitthaa and Purodakananda, Sumonmas and Thanakulwarapas, Tipraporn and Kosawat, Krit},
booktitle={2009 International Conference on Asian Language Processing},
pages={322--327},
year={2009},
organization={IEEE}
}
```
Character type features:
```
@inproceedings{haruechaiyasak2009tlex,
title={TLex: Thai lexeme analyser based on the conditional random fields},
author={Haruechaiyasak, Choochart and Kongyoung, Sarawoot},
booktitle={Proceedings of 8th International Symposium on Natural Language Processing},
year={2009}
}
```
### Contributions
Thanks to [@cstorm125](https://github.com/cstorm125) for adding this dataset. | [
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DDSC/lcc | DDSC | 2023-07-20T19:43:29Z | 350 | 3 | null | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:da",
"license:cc-by-4.0",
"region:us"
] | 2023-07-20T19:43:29Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- da
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: TwitterSent
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
---
# Dataset Card for LCC
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository**: https://github.com/fnielsen/lcc-sentiment
- **Direct Download part 1**: https://raw.githubusercontent.com/fnielsen/lcc-sentiment/master/dan_mixed_2014_10K-sentences.csv
- **Direct Download part 2**: https://raw.githubusercontent.com/fnielsen/lcc-sentiment/master/dan_newscrawl_2011_10K-sentences.csv
### Dataset Summary
This dataset consists of Danish data from [the Leipzig Collection](https://www.aclweb.org/anthology/L06-1396/) that has been annotated for sentiment analysis by Finn Årup Nielsen.
### Supported Tasks and Leaderboards
This dataset is suitable for sentiment analysis.
### Languages
This dataset is in Danish.
## Dataset Structure
### Data Instances
Every entry in the dataset has a document and an associated label.
### Data Fields
An entry in the dataset consists of the following fields:
- `text` (`str`): The text content.
- `label` (`str`): The label of the `text`. Can be "positiv", "neutral" or "negativ" for positive, neutral and negative sentiment, respectively.
### Data Splits
A `train` and `test` split is available, with the test split being 30% of the dataset, randomly sampled in a stratified fashion. There are 349 documents in the training split and 150 in the test split.
## Additional Information
### Dataset Curators
The collection and annotation of the dataset is solely due to the Finn Årup Nielsen. It was originally annotated as a score between -5 and +5, but the labels in this version have been converted to a negative, neutral and positive label.
### Licensing Information
The dataset is released under the CC BY 4.0 license.
### Citation Information
```
@misc{lcc,
title={LCC},
author={Finn Årup Nielsen},
year={2016},
note={\url{https://github.com/fnielsen/lcc-sentiment}}
}
```
### Contributions
Thanks to [@saattrupdan](https://github.com/saattrupdan) for adding this dataset to the Hugging Face Hub. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
thaiqa_squad | null | 2022-11-03T16:15:52Z | 349 | 5 | null | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"task_ids:open-domain-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|other-thaiqa",
"language:th",
"license:cc-by-nc-sa-3.0",
"region:us"
] | 2022-11-03T16:15:52Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- th
license:
- cc-by-nc-sa-3.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-thaiqa
task_categories:
- question-answering
task_ids:
- extractive-qa
- open-domain-qa
paperswithcode_id: null
pretty_name: thaiqa-squad
dataset_info:
features:
- name: question_id
dtype: int32
- name: article_id
dtype: int32
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: answer
dtype: string
- name: answer_begin_position
dtype: int32
- name: answer_end_position
dtype: int32
config_name: thaiqa_squad
splits:
- name: train
num_bytes: 47905050
num_examples: 4000
- name: validation
num_bytes: 744813
num_examples: 74
download_size: 10003354
dataset_size: 48649863
---
# Dataset Card for `thaiqa-squad`
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://github.com/pythainlp/thaiqa_squad (original `thaiqa` at https://aiforthai.in.th/)
- **Repository:** http://github.com/pythainlp/thaiqa_squad
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**http://github.com/pythainlp/ (original `thaiqa` at https://aiforthai.in.th/)
### Dataset Summary
`thaiqa_squad` is an open-domain, extractive question answering dataset (4,000 questions in `train` and 74 questions in `dev`) in [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) format, originally created by [NECTEC](https://www.nectec.or.th/en/) from Wikipedia articles and adapted to [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) format by [PyThaiNLP](https://github.com/PyThaiNLP/).
### Supported Tasks and Leaderboards
extractive question answering
### Languages
Thai
## Dataset Structure
### Data Instances
```
{'answers': {'answer': ['ฮิกกิ้นส์'], 'answer_begin_position': [528], 'answer_end_position': [537]}, 'article_id': 115035, 'context': '<doc id="115035" url="https://th.wikipedia.org/wiki?curid=115035" title="เบนจี้">เบนจี้ เบนจี้ () เป็นชื่อตัวละครหมาพันทางแสนรู้ ที่ปรากฏอยู่ในภาพยนตร์หลายเรื่องที่เขียนบท และกำกับโดย โจ แคมป์ ในช่วงทศวรรษ 1970 ถึง 1980 ภาพยนตร์เรื่องแรกในชุด ใช้ชื่อเรื่องว่า เบนจี้ เช่นเดียวกับตัวละคร ถ่ายทำที่เมืองดัลลัส รัฐเทกซัส ฉายครั้งแรกในปี พ.ศ. 2517 ภาพยนตร์ได้รับการเสนอชื่อเข้าชิงรางวัลออสการ์ และได้รางวัลลูกโลกทองคำ สาขาเพลงประกอบยอดเยี่ยม จากเพลง Benji\'s Theme (I Feel Love) ร้องโดย ชาร์ลี ริช หมาที่แสดงเป็นเบนจี้ตัวแรก ชื่อว่า ฮิกกิ้นส์ (พ.ศ. 2502 - พ.ศ. 2518) มีอายุถึง 15 ปีแล้วในขณะแสดง หลังจากภาพยนตร์ออกฉายได้ไม่นาน มันก็ตายในปี พ.ศ. 2518เบนจี้ในภาพยนตร์เบนจี้ในภาพยนตร์. - พ.ศ. 2517, Benji (ภาพยนตร์) - พ.ศ. 2520, For the Love of Benji (ภาพยนตร์) - พ.ศ. 2521, Benji\'s Very Own Christmas Story (ภาพยนตร์โทรทัศน์) - พ.ศ. 2523, Oh Heavenly Dog (ภาพยนตร์) - พ.ศ. 2523, Benji at Work (ภาพยนตร์โทรทัศน์) - พ.ศ. 2524, Benji Takes a Dive at Marineland (ภาพยนตร์โทรทัศน์) - พ.ศ. 2526, Benji, Zax & the Alien Prince (ภาพยนตร์ซีรีส์) - พ.ศ. 2530, Benji the Hunted (ภาพยนตร์) - พ.ศ. 2547, Benji: Off the Leash! (ภาพยนตร์) - พ.ศ. 2550, Benji: The Barkening (ภาพยนตร์)</doc>\n', 'question': 'สุนัขตัวแรกรับบทเป็นเบนจี้ในภาพยนตร์เรื่อง Benji ที่ออกฉายในปี พ.ศ. 2517 มีชื่อว่าอะไร', 'question_id': 1}
{'answers': {'answer': ['ชาร์ลี ริช'], 'answer_begin_position': [482], 'answer_end_position': [492]}, 'article_id': 115035, 'context': '<doc id="115035" url="https://th.wikipedia.org/wiki?curid=115035" title="เบนจี้">เบนจี้ เบนจี้ () เป็นชื่อตัวละครหมาพันทางแสนรู้ ที่ปรากฏอยู่ในภาพยนตร์หลายเรื่องที่เขียนบท และกำกับโดย โจ แคมป์ ในช่วงทศวรรษ 1970 ถึง 1980 ภาพยนตร์เรื่องแรกในชุด ใช้ชื่อเรื่องว่า เบนจี้ เช่นเดียวกับตัวละคร ถ่ายทำที่เมืองดัลลัส รัฐเทกซัส ฉายครั้งแรกในปี พ.ศ. 2517 ภาพยนตร์ได้รับการเสนอชื่อเข้าชิงรางวัลออสการ์ และได้รางวัลลูกโลกทองคำ สาขาเพลงประกอบยอดเยี่ยม จากเพลง Benji\'s Theme (I Feel Love) ร้องโดย ชาร์ลี ริช หมาที่แสดงเป็นเบนจี้ตัวแรก ชื่อว่า ฮิกกิ้นส์ (พ.ศ. 2502 - พ.ศ. 2518) มีอายุถึง 15 ปีแล้วในขณะแสดง หลังจากภาพยนตร์ออกฉายได้ไม่นาน มันก็ตายในปี พ.ศ. 2518เบนจี้ในภาพยนตร์เบนจี้ในภาพยนตร์. - พ.ศ. 2517, Benji (ภาพยนตร์) - พ.ศ. 2520, For the Love of Benji (ภาพยนตร์) - พ.ศ. 2521, Benji\'s Very Own Christmas Story (ภาพยนตร์โทรทัศน์) - พ.ศ. 2523, Oh Heavenly Dog (ภาพยนตร์) - พ.ศ. 2523, Benji at Work (ภาพยนตร์โทรทัศน์) - พ.ศ. 2524, Benji Takes a Dive at Marineland (ภาพยนตร์โทรทัศน์) - พ.ศ. 2526, Benji, Zax & the Alien Prince (ภาพยนตร์ซีรีส์) - พ.ศ. 2530, Benji the Hunted (ภาพยนตร์) - พ.ศ. 2547, Benji: Off the Leash! (ภาพยนตร์) - พ.ศ. 2550, Benji: The Barkening (ภาพยนตร์)</doc>\n', 'question': "เพลง Benji's Theme ใช้ประกอบภาพยนตร์เรื่อง Benji ในปีพ.ศ. 2517 ขับร้องโดยใคร", 'question_id': 2035}
```
### Data Fields
```
{
"question_id": question id
"article_id": article id
"context": article texts
"question": question
"answers":
{
"answer": answer text
"answer_begin_position": answer beginning position
"answer_end_position": answer exclusive upper bound position
}
),
}
```
### Data Splits
| | train | valid |
|-------------------------|-------------|-------------|
| # questions | 4000 | 74 |
| # avg words in context | 1186.740750 | 1016.459459 |
| # avg words in question | 14.325500 | 12.743243 |
| # avg words in answer | 3.279750 | 4.608108 |
## Dataset Creation
### Curation Rationale
[PyThaiNLP](https://github.com/PyThaiNLP/) created `thaiqa_squad` as a [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) version of [thaiqa](http://copycatch.in.th/thai-qa-task.html). [thaiqa](https://aiforthai.in.th/corpus.php) is part of [The 2nd Question answering program from Thai Wikipedia](http://copycatch.in.th/thai-qa-task.html) of [National Software Contest 2020](http://nsc.siit.tu.ac.th/GENA2/login.php).
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
Wikipedia authors for contexts and [NECTEC](https://www.nectec.or.th/en/) for questions and answer annotations
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[NECTEC](https://www.nectec.or.th/en/)
### Personal and Sensitive Information
All contents are from Wikipedia. No personal and sensitive information is expected to be included.
## Considerations for Using the Data
### Social Impact of Dataset
- open-domain, extractive question answering in Thai
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
Dataset provided for research purposes only. Please check dataset license for additional information.
The contexts include `<doc>` tags at start and at the end
## Additional Information
### Dataset Curators
[NECTEC](https://www.nectec.or.th/en/) for original [thaiqa](https://aiforthai.in.th/corpus.php). SQuAD formattting by [PyThaiNLP](https://github.com/PyThaiNLP/).
### Licensing Information
CC-BY-NC-SA 3.0
### Citation Information
No clear citation guidelines from source: https://aiforthai.in.th/corpus.php
SQuAD version: https://github.com/PyThaiNLP/thaiqa_squad
### Contributions
Thanks to [@cstorm125](https://github.com/cstorm125) for adding this dataset. | [
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SetFit/ethos_binary | SetFit | 2022-01-16T17:54:54Z | 349 | 0 | null | [
"region:us"
] | 2022-01-16T17:54:54Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 |
This is the binary split of [ethos](https://huggingface.co/datasets/ethos), split into train and test.
It contains comments annotated for hate speech or not. | [
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csebuetnlp/xnli_bn | csebuetnlp | 2022-08-21T13:14:56Z | 349 | 1 | null | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended",
"language:bn",
"license:cc-by-nc-sa-4.0",
"arxiv:2101.00204",
"arxiv:2007.01852",
"region:us"
] | 2022-08-21T13:14:56Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- found
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended
task_categories:
- text-classification
task_ids:
- natural-language-inference
language:
- bn
license:
- cc-by-nc-sa-4.0
---
# Dataset Card for `xnli_bn`
## Table of Contents
- [Dataset Card for `xnli_bn`](#dataset-card-for-xnli_bn)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Usage](#usage)
- [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/banglabert](https://github.com/csebuetnlp/banglabert)
- **Paper:** [**"BanglaBERT: Combating Embedding Barrier in Multilingual Models for Low-Resource Language Understanding"**](https://arxiv.org/abs/2101.00204)
- **Point of Contact:** [Tahmid Hasan](mailto:tahmidhasan@cse.buet.ac.bd)
### Dataset Summary
This is a Natural Language Inference (NLI) dataset for Bengali, curated using the subset of
MNLI data used in XNLI and state-of-the-art English to Bengali translation model introduced **[here](https://aclanthology.org/2020.emnlp-main.207/).**
### Supported Tasks and Leaderboards
[More information needed](https://github.com/csebuetnlp/banglabert)
### Languages
* `Bengali`
### Usage
```python
from datasets import load_dataset
dataset = load_dataset("csebuetnlp/xnli_bn")
```
## Dataset Structure
### Data Instances
One example from the dataset is given below in JSON format.
```
{
"sentence1": "আসলে, আমি এমনকি এই বিষয়ে চিন্তাও করিনি, কিন্তু আমি এত হতাশ হয়ে পড়েছিলাম যে, শেষ পর্যন্ত আমি আবার তার সঙ্গে কথা বলতে শুরু করেছিলাম",
"sentence2": "আমি তার সাথে আবার কথা বলিনি।",
"label": "contradiction"
}
```
### Data Fields
The data fields are as follows:
- `sentence1`: a `string` feature indicating the premise.
- `sentence2`: a `string` feature indicating the hypothesis.
- `label`: a classification label, where possible values are `contradiction` (0), `entailment` (1), `neutral` (2) .
### Data Splits
| split |count |
|----------|--------|
|`train`| 381449 |
|`validation`| 2419 |
|`test`| 4895 |
## Dataset Creation
The dataset curation procedure was the same as the [XNLI](https://aclanthology.org/D18-1269/) dataset: we translated the [MultiNLI](https://aclanthology.org/N18-1101/) training data using the English to Bangla translation model introduced [here](https://aclanthology.org/2020.emnlp-main.207/). Due to the possibility of incursions of error during automatic translation, we used the [Language-Agnostic BERT Sentence Embeddings (LaBSE)](https://arxiv.org/abs/2007.01852) of the translations and original sentences to compute their similarity. All sentences below a similarity threshold of 0.70 were discarded.
### Curation Rationale
[More information needed](https://github.com/csebuetnlp/banglabert)
### Source Data
[XNLI](https://aclanthology.org/D18-1269/)
#### Initial Data Collection and Normalization
[More information needed](https://github.com/csebuetnlp/banglabert)
#### Who are the source language producers?
[More information needed](https://github.com/csebuetnlp/banglabert)
### Annotations
[More information needed](https://github.com/csebuetnlp/banglabert)
#### Annotation process
[More information needed](https://github.com/csebuetnlp/banglabert)
#### Who are the annotators?
[More information needed](https://github.com/csebuetnlp/banglabert)
### Personal and Sensitive Information
[More information needed](https://github.com/csebuetnlp/banglabert)
## Considerations for Using the Data
### Social Impact of Dataset
[More information needed](https://github.com/csebuetnlp/banglabert)
### Discussion of Biases
[More information needed](https://github.com/csebuetnlp/banglabert)
### Other Known Limitations
[More information needed](https://github.com/csebuetnlp/banglabert)
## Additional Information
### Dataset Curators
[More information needed](https://github.com/csebuetnlp/banglabert)
### 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
If you use the dataset, please cite the following paper:
```
@misc{bhattacharjee2021banglabert,
title={BanglaBERT: Combating Embedding Barrier in Multilingual Models for Low-Resource Language Understanding},
author={Abhik Bhattacharjee and Tahmid Hasan and Kazi Samin and Md Saiful Islam and M. Sohel Rahman and Anindya Iqbal and Rifat Shahriyar},
year={2021},
eprint={2101.00204},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@abhik1505040](https://github.com/abhik1505040) and [@Tahmid](https://github.com/Tahmid04) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
codeparrot/codeparrot-clean | codeparrot | 2022-10-10T15:23:51Z | 349 | 36 | null | [
"python",
"code",
"region:us"
] | 2022-10-10T15:23:51Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
tags:
- python
- code
---
# CodeParrot 🦜 Dataset Cleaned
## What is it?
A dataset of Python files from Github. This is the deduplicated version of the [codeparrot](https://huggingface.co/datasets/transformersbook/codeparrot).
## Processing
The original dataset contains a lot of duplicated and noisy data. Therefore, the dataset was cleaned with the following steps:
- Deduplication
- Remove exact matches
- Filtering
- Average line length < 100
- Maximum line length < 1000
- Alpha numeric characters fraction > 0.25
- Remove auto-generated files (keyword search)
For more details see the preprocessing script in the transformers repository [here](https://github.com/huggingface/transformers/tree/master/examples/research_projects/codeparrot).
## Splits
The dataset is split in a [train](https://huggingface.co/datasets/codeparrot/codeparrot-clean-train) and [validation](https://huggingface.co/datasets/codeparrot/codeparrot-clean-valid) split used for training and evaluation.
## Structure
This dataset has ~50GB of code and 5361373 files.
```python
DatasetDict({
train: Dataset({
features: ['repo_name', 'path', 'copies', 'size', 'content', 'license', 'hash', 'line_mean', 'line_max', 'alpha_frac', 'autogenerated'],
num_rows: 5361373
})
})
``` | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
oliverwang15/news_with_gpt_instructions | oliverwang15 | 2023-07-10T19:39:33Z | 349 | 6 | null | [
"region:us"
] | 2023-07-10T19:39:33Z | 2023-07-10T19:25:35.000Z | 2023-07-10T19:25:35 | ---
dataset_info:
features:
- name: news
dtype: string
- name: prompt
dtype: string
- name: out
dtype: string
- name: prompt_tokens
dtype: int64
- name: completion_tokens
dtype: int64
- name: total_tokens
dtype: int64
- name: label
dtype: string
splits:
- name: train
num_bytes: 23897976.286293313
num_examples: 16184
- name: test
num_bytes: 5975970.713706688
num_examples: 4047
download_size: 13558209
dataset_size: 29873947.0
---
# Dataset Card for "news_with_gpt_instructions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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casino | null | 2022-11-03T16:16:00Z | 348 | 3 | casino | [
"task_categories:conversational",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:dialogue-modeling",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] | 2022-11-03T16:16:00Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- conversational
- text-generation
- fill-mask
task_ids:
- dialogue-modeling
pretty_name: Campsite Negotiation Dialogues
paperswithcode_id: casino
dataset_info:
features:
- name: chat_logs
list:
- name: text
dtype: string
- name: task_data
struct:
- name: data
dtype: string
- name: issue2youget
struct:
- name: Firewood
dtype: string
- name: Water
dtype: string
- name: Food
dtype: string
- name: issue2theyget
struct:
- name: Firewood
dtype: string
- name: Water
dtype: string
- name: Food
dtype: string
- name: id
dtype: string
- name: participant_info
struct:
- name: mturk_agent_1
struct:
- name: value2issue
struct:
- name: Low
dtype: string
- name: Medium
dtype: string
- name: High
dtype: string
- name: value2reason
struct:
- name: Low
dtype: string
- name: Medium
dtype: string
- name: High
dtype: string
- name: outcomes
struct:
- name: points_scored
dtype: int32
- name: satisfaction
dtype: string
- name: opponent_likeness
dtype: string
- name: demographics
struct:
- name: age
dtype: int32
- name: gender
dtype: string
- name: ethnicity
dtype: string
- name: education
dtype: string
- name: personality
struct:
- name: svo
dtype: string
- name: big-five
struct:
- name: extraversion
dtype: float32
- name: agreeableness
dtype: float32
- name: conscientiousness
dtype: float32
- name: emotional-stability
dtype: float32
- name: openness-to-experiences
dtype: float32
- name: mturk_agent_2
struct:
- name: value2issue
struct:
- name: Low
dtype: string
- name: Medium
dtype: string
- name: High
dtype: string
- name: value2reason
struct:
- name: Low
dtype: string
- name: Medium
dtype: string
- name: High
dtype: string
- name: outcomes
struct:
- name: points_scored
dtype: int32
- name: satisfaction
dtype: string
- name: opponent_likeness
dtype: string
- name: demographics
struct:
- name: age
dtype: int32
- name: gender
dtype: string
- name: ethnicity
dtype: string
- name: education
dtype: string
- name: personality
struct:
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dtype: string
- name: big-five
struct:
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dtype: float32
- name: agreeableness
dtype: float32
- name: conscientiousness
dtype: float32
- name: emotional-stability
dtype: float32
- name: openness-to-experiences
dtype: float32
- name: annotations
list:
list: string
splits:
- name: train
num_bytes: 3211555
num_examples: 1030
download_size: 4300019
dataset_size: 3211555
---
# Dataset Card for Casino
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [Github: Kushal Chawla CaSiNo](https://github.com/kushalchawla/CaSiNo)
- **Paper:** [CaSiNo: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems](https://aclanthology.org/2021.naacl-main.254.pdf)
- **Point of Contact:** [Kushal Chawla](kchawla@usc.edu)
### Dataset Summary
We provide a novel dataset (referred to as CaSiNo) of 1030 negotiation dialogues. Two participants take the role of campsite neighbors and negotiate for Food, Water, and Firewood packages, based on their individual preferences and requirements. This design keeps the task tractable, while still facilitating linguistically rich and personal conversations. This helps to overcome the limitations of prior negotiation datasets such as Deal or No Deal and Craigslist Bargain. Each dialogue consists of rich meta-data including participant demographics, personality, and their subjective evaluation of the negotiation in terms of satisfaction and opponent likeness.
### Supported Tasks and Leaderboards
Train end-to-end models for negotiation
### Languages
English
## Dataset Structure
### Data Instances
```
{
"chat_logs": [
{
"text": "Hello! \ud83d\ude42 Let's work together on a deal for these packages, shall we? What are you most interested in?",
"task_data": {},
"id": "mturk_agent_1"
},
...
],
"participant_info": {
"mturk_agent_1":
{
"value2issue": ...
"value2reason": ...
"outcomes": ...
"demographics": ...
"personality": ...
},
"mturk_agent_2": ...
},
"annotations": [
["Hello! \ud83d\ude42 Let's work together on a deal for these packages, shall we? What are you most interested in?", "promote-coordination,elicit-pref"],
...
]
}
```
### Data Fields
- `chat_logs`: The negotiation dialogue between two participants
- `text`: The dialogue utterance
- `task_data`: Meta-data associated with the utterance such as the deal submitted by a participant
- `id`: The ID of the participant who typed this utterance
- `participant_info`: Meta-data about the two participants in this conversation
- `mturk_agent_1`: For the first participant (Note that 'first' is just for reference. There is no order between the participants and any participant can start the conversation)
- `value2issue`: The priority order of this participant among Food, Water, Firewood
- `value2reason`: The personal arguments given by the participants themselves, consistent with the above preference order. This preference order and these arguments were submitted before the negotiation began.
- `outcomes`: The negotiation outcomes for this participant including objective and subjective assessment.
- `demographics`: Demographic attributes of the participant in terms of age, gender, ethnicity, and education.
- `personality`: Personality attributes for this participant, in terms of Big-5 and Social Value Orientation
- `mturk_agent_2`: For the second participant; follows the same structure as above
- `annotations`: Strategy annotations for each utterance in the dialogue, wherever available. The first element represents the utterance and the second represents a comma-separated list of all strategies present in that utterance.
### Data Splits
No default data split has been provided. Hence, all 1030 data points are under the 'train' split.
| | Train |
| ----- | ----- |
| total dialogues | 1030 |
| annotated dialogues | 396 |
## Dataset Creation
### Curation Rationale
The dataset was collected to address the limitations in prior negotiation datasets from the perspective of downstream applications in pedagogy and conversational AI. Please refer to the original paper published at NAACL 2021 for details about the rationale and data curation steps ([source paper](https://aclanthology.org/2021.naacl-main.254.pdf)).
### Source Data
#### Initial Data Collection and Normalization
The dialogues were crowdsourced on Amazon Mechanical Turk. The strategy annotations were performed by expert annotators (first three authors of the paper). Please refer to the original dataset paper published at NAACL 2021 for more details ([source paper](https://aclanthology.org/2021.naacl-main.254.pdf)).
#### Who are the source language producers?
The primary producers are Turkers on Amazon Mechanical Turk platform. Two turkers were randomly paired with each other to engage in a negotiation via a chat interface. Please refer to the original dataset paper published at NAACL 2021 for more details ([source paper](https://aclanthology.org/2021.naacl-main.254.pdf)).
### Annotations
#### Annotation process
From the [source paper](https://aclanthology.org/2021.naacl-main.254.pdf) for this dataset:
>Three expert annotators independently annotated 396 dialogues containing 4615 utterances. The annotation guidelines were iterated over a subset of 5 dialogues, while the reliability scores were computed on a different subset of 10 dialogues. We use the nominal form of Krippendorff’s alpha (Krippendorff, 2018) to measure the inter-annotator agreement. We provide the annotation statistics in Table 2. Although we release all the annotations, we skip Coordination and Empathy for our analysis in this work, due to higher subjectivity resulting in relatively lower reliability scores.
#### Who are the annotators?
Three expert annotators (first three authors of the paper).
### Personal and Sensitive Information
All personally identifiable information about the participants such as MTurk Ids or HIT Ids was removed before releasing the data.
## Considerations for Using the Data
### Social Impact of Dataset
Please refer to Section 8.2 in the [source paper](https://aclanthology.org/2021.naacl-main.254.pdf).
### Discussion of Biases
Please refer to Section 8.2 in the [source paper](https://aclanthology.org/2021.naacl-main.254.pdf).
### Other Known Limitations
Please refer to Section 7 in the [source paper](https://aclanthology.org/2021.naacl-main.254.pdf).
## Additional Information
### Dataset Curators
Corresponding Author: Kushal Chawla (`kchawla@usc.edu`)\
Affiliation: University of Southern California\
Please refer to the [source paper](https://aclanthology.org/2021.naacl-main.254.pdf) for the complete author list.
### Licensing Information
The project is licensed under CC-by-4.0
### Citation Information
```
@inproceedings{chawla2021casino,
title={CaSiNo: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems},
author={Chawla, Kushal and Ramirez, Jaysa and Clever, Rene and Lucas, Gale and May, Jonathan and Gratch, Jonathan},
booktitle={Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
pages={3167--3185},
year={2021}
}
```
### Contributions
Thanks to [Kushal Chawla](https://kushalchawla.github.io/) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
the_pile_books3 | null | 2023-11-02T15:05:12Z | 348 | 127 | null | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:mit",
"arxiv:2101.00027",
"region:us"
] | 2023-11-02T15:05:12Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- mit
multilinguality:
- monolingual
pretty_name: Books3
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
viewer: false
dataset_info:
features:
- name: title
dtype: string
- name: text
dtype: string
config_name: plain_text
splits:
- name: train
num_bytes: 108392037000
num_examples: 196639
download_size: 39516981435
dataset_size: 108392037000
---
# Dataset Card for the_pile_books3
## Table of Contents
- [Dataset Card for the_pile_books3](#dataset-card-for-the_pile_books3)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [|split|num examples|](#splitnum-examples)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [GitHub](https://github.com/soskek/bookcorpus/issues/27#issuecomment-716104208)
- **Repository:** [Needs More Information]
- **Paper:** [arXiv](https://arxiv.org/abs/2101.00027)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p><b>Defunct:</b> Dataset "the_pile_books3" is defunct and no longer accessible due to reported copyright infringement.</p>
</div>
This dataset is Shawn Presser's work and is part of EleutherAi/The Pile dataset.
This dataset contains all of bibliotik in plain .txt form, aka 197,000 books processed in exactly the same way as did for bookcorpusopen (a.k.a. books1). seems to be similar to OpenAI's mysterious "books2" dataset referenced in their papers. Unfortunately OpenAI will not give details, so we know very little about any differences. People suspect it's "all of libgen", but it's purely conjecture.
|download_size|36.8 Gib|
|dataset_size|100.9 Gib|
### Supported Tasks and Leaderboards
This dataset is used for Language Modeling.
### Languages
The dataset is in English.
## Dataset Structure
### Data Instances
```
{'title': '07 LEGO Ninjago - The Search For Zane (Scholastic) - Kate Howard (retail)'
'text': '\n\nTITLE PAGE\n\nFROM THE JOURNAL OF SENSEI GARMADON\n\nCHAPTER 1\n\nCHAPTER 2\n\nCHAPTER 3\n\nCHAPTER 4\n\nCHAPTER 5\n\nCHAPTER 6\n\nCHAPTER 7\n\nCHAPTER 8\n\nCHAPTER 9\n\nCOPYRIGHT\n\nThroughout Ninjago", five ninja are well-known for their speed, strength, and of course the elemental powers that help them protect our world from evil. But there are others who possess some of the same powers as the ninja. Others who may not always use their powers for good.\n\nBefore now, the ninja believed they were special. They di.......'}
```
### Data Fields
- `title`: title of the book
- `text`: text content of the book
### Data Splits
|split|num examples|
--------------------------------
|train|196640|
## 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
MIT
### Citation Information
```
@article{pile,
title={The {P}ile: An 800GB Dataset of Diverse Text for Language Modeling},
author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and Presser, Shawn and Leahy, Connor},
journal={arXiv preprint arXiv:2101.00027},
year={2020}
}
```
### Contributions
Thanks to [@shawwn](https://github.com/shawwn) for creating this dataset.
Thanks to [@richarddwang](https://github.com/richarddwang) for adding this dataset. | [
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GEM/squad_v2 | GEM | 2022-10-24T15:30:29Z | 348 | 0 | null | [
"task_categories:other",
"annotations_creators:crowd-sourced",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"question-generation",
"arxiv:1806.03822",
"region:us"
] | 2022-10-24T15:30:29Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowd-sourced
language_creators:
- unknown
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- other
task_ids: []
pretty_name: squad_v2
tags:
- question-generation
---
# Dataset Card for GEM/squad_v2
## Dataset Description
- **Homepage:** https://rajpurkar.github.io/SQuAD-explorer/
- **Repository:** https://rajpurkar.github.io/SQuAD-explorer/
- **Paper:** https://arxiv.org/abs/1806.03822v1
- **Leaderboard:** https://rajpurkar.github.io/SQuAD-explorer/
- **Point of Contact:** Robin Jia
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/squad_v2).
### Dataset Summary
SQuAD2.0 is a dataset that tests the ability of a system to not only answer reading comprehension questions, but also abstain when presented with a question that cannot be answered based on the provided paragraph. F1 score is used to evaluate models on the leaderboard. In GEM, we are using this dataset for the question-generation task in which a model should generate squad-like questions from an input text.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/squad_v2')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/squad_v2).
#### website
[Website](https://rajpurkar.github.io/SQuAD-explorer/)
#### paper
[Arxiv](https://arxiv.org/abs/1806.03822v1)
#### authors
Pranav Rajpurkar, Robin Jia and Percy Liang
## Dataset Overview
### Where to find the Data and its Documentation
#### Webpage
<!-- info: What is the webpage for the dataset (if it exists)? -->
<!-- scope: telescope -->
[Website](https://rajpurkar.github.io/SQuAD-explorer/)
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Website](https://rajpurkar.github.io/SQuAD-explorer/)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[Arxiv](https://arxiv.org/abs/1806.03822v1)
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
```
@inproceedings{Rajpurkar2018KnowWY,
title={Know What You Don’t Know: Unanswerable Questions for SQuAD},
author={Pranav Rajpurkar and Robin Jia and Percy Liang},
booktitle={ACL},
year={2018}
}
```
#### Contact Name
<!-- quick -->
<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
Robin Jia
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
robinjia@stanford.edu
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
yes
#### Leaderboard Link
<!-- info: Provide a link to the leaderboard. -->
<!-- scope: periscope -->
[Website](https://rajpurkar.github.io/SQuAD-explorer/)
#### Leaderboard Details
<!-- info: Briefly describe how the leaderboard evaluates models. -->
<!-- scope: microscope -->
SQuAD2.0 tests the ability of a system to not only answer reading comprehension questions, but also abstain when presented with a question that cannot be answered based on the provided paragraph. F1 score is used to evaluate models on the leaderboard.
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
no
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`English`
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
cc-by-sa-4.0: Creative Commons Attribution Share Alike 4.0 International
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
The idea behind SQuAD2.0 dataset is to make the models understand when a question cannot be answered given a context. This will help in building models such that they know what they don't know, and therefore make the models understand language at a deeper level. The tasks that can be supported by the dataset are machine reading comprehension, extractive QA, and question generation.
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Question Generation
#### Communicative Goal
<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
Given an input passage and an answer span, the goal is to generate a question that asks for the answer.
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`academic`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
Stanford University
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Pranav Rajpurkar, Robin Jia and Percy Liang
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
Facebook and NSF Graduate Research Fellowship under Grant No. DGE-114747
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
(Abinaya Mahendiran)[https://github.com/AbinayaM02], Manager Data Science, NEXT Labs,
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
The data fields are the same among all splits.
#### squad_v2
- `id`: a `string` feature.
- `gem_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.
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
Here is an example of a validation data point. This example was too long and was cropped:
```
{
"gem_id": "gem-squad_v2-validation-1",
"id": "56ddde6b9a695914005b9629",
"answers": {
"answer_start": [94, 87, 94, 94],
"text": ["10th and 11th centuries", "in the 10th and 11th centuries", "10th and 11th centuries", "10th and 11th centuries"]
},
"context": "\"The Normans (Norman: Nourmands; French: Normands; Latin: Normanni) were the people who in the 10th and 11th centuries gave thei...",
"question": "When were the Normans in Normandy?",
"title": "Normans"
}
```
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
The original SQuAD2.0 dataset has only training and dev (validation) splits. The train split is further divided into test split and added as part of the GEM datasets.
| name | train | validation | test |
| -------------- | --------: | -------------: | -------: |
| squad_v2 | 90403 | 11873 | 39916 |
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Why is the Dataset in GEM?
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
SQuAD2.0 will encourage the development of new reading comprehension models
that know what they don’t know, and therefore understand language at a deeper level. It can also help in building better models for answer-aware question generation .
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
no
#### Unique Language Coverage
<!-- info: Does this dataset cover other languages than other datasets for the same task? -->
<!-- scope: periscope -->
yes
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
Reasoning capability
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
yes
#### GEM Modifications
<!-- info: What changes have been made to he original dataset? -->
<!-- scope: periscope -->
`other`
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
yes
#### Split Information
<!-- info: Describe how the new splits were created -->
<!-- scope: periscope -->
The train(80%) and validation(10%) split of SQuAD2.0 are made available to public whereas the test(10%) split is not available.
As part of GEM, the train split, 80% of the original data is split into two train split (90%) and test split (remaining 10%). The idea is to provide all three splits for the users to use.
### Getting Started with the Task
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
Extractive QA, Question Generation
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`Other: Other Metrics`, `METEOR`, `ROUGE`, `BLEU`
#### Other Metrics
<!-- info: Definitions of other metrics -->
<!-- scope: periscope -->
- Extractive QA uses Exact Match and F1 Score
- Question generation users METEOR, ROUGE-L, BLEU-4
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
yes
#### Other Evaluation Approaches
<!-- info: What evaluation approaches have others used? -->
<!-- scope: periscope -->
Question generation users METEOR, ROUGE-L, BLEU-4
#### Relevant Previous Results
<!-- info: What are the most relevant previous results for this task/dataset? -->
<!-- scope: microscope -->
@article{Dong2019UnifiedLM,
title={Unified Language Model Pre-training for Natural Language Understanding and Generation},
author={Li Dong and Nan Yang and Wenhui Wang and Furu Wei and Xiaodong Liu and Yu Wang and Jianfeng Gao and M. Zhou and Hsiao-Wuen Hon},
journal={ArXiv},
year={2019},
volume={abs/1905.03197}
}
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
The dataset is curated in three stages:
- Curating passages,
- Crowdsourcing question-answers on those passages,
- Obtaining additional answers
As part of SQuAD1.1, 10000 high-quality articles from English Wikipedia is extracted using Project Nayuki’s Wikipedia’s internal PageRanks, from which 536 articles are sampled uniformly at random. From each of these articles, individual paragraphs are extracted, stripping away images, figures, tables, and discarding paragraphs shorter than 500 characters.
SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones.
#### Communicative Goal
<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
To build systems that not only answer questions when possible, but also determine when no
answer is supported by the paragraph and abstain from answering.
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
yes
#### Source Details
<!-- info: List the sources (one per line) -->
<!-- scope: periscope -->
Wikipedia
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Found`
#### Where was it found?
<!-- info: If found, where from? -->
<!-- scope: telescope -->
`Single website`
#### Topics Covered
<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
<!-- scope: periscope -->
The dataset contains 536 articles covering a wide range of topics, from musical celebrities to abstract concepts.
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
validated by crowdworker
#### Data Preprocessing
<!-- info: How was the text data pre-processed? (Enter N/A if the text was not pre-processed) -->
<!-- scope: microscope -->
From the sampled articles from Wikipedia, individual paragraphs are extracted, stripping
away images, figures, tables, and discarding paragraphs shorter than 500 characters and partitioned into training(80%), development set(10%) and test set(10%).
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
algorithmically
#### Filter Criteria
<!-- info: What were the selection criteria? -->
<!-- scope: microscope -->
To retrieve high-quality articles, Project Nayuki’s Wikipedia’s internal PageRanks was used to obtain the top 10000 articles of English Wikipedia, from which 536 articles are sampled uniformly at random.
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
crowd-sourced
#### Number of Raters
<!-- info: What is the number of raters -->
<!-- scope: telescope -->
unknown
#### Rater Qualifications
<!-- info: Describe the qualifications required of an annotator. -->
<!-- scope: periscope -->
Crowdworkers from the United States or Canada with a 97% HIT acceptance rate, a minimum of 1000 HITs, were employed to create questions.
#### Raters per Training Example
<!-- info: How many annotators saw each training example? -->
<!-- scope: periscope -->
0
#### Raters per Test Example
<!-- info: How many annotators saw each test example? -->
<!-- scope: periscope -->
0
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
yes
#### Which Annotation Service
<!-- info: Which annotation services were used? -->
<!-- scope: periscope -->
`other`, `Amazon Mechanical Turk`
#### Annotation Values
<!-- info: Purpose and values for each annotation -->
<!-- scope: microscope -->
For SQuAD 1.1 , crowdworkers were tasked with asking and answering up to 5 questions on the
content of that paragraph. The questions had to be entered in a text field, and the answers had to be
highlighted in the paragraph.
For SQuAD2.0, each task consisted of an entire article from SQuAD 1.1. For each paragraph in the article, workers were asked to pose up to five questions that were impossible to answer
based on the paragraph alone, while referencing entities in the paragraph and ensuring that a plausible answer is present.
#### Any Quality Control?
<!-- info: Quality control measures? -->
<!-- scope: telescope -->
validated by another rater
#### Quality Control Details
<!-- info: Describe the quality control measures that were taken. -->
<!-- scope: microscope -->
Questions from workers who wrote 25 or fewer questions on an article is removed; this filter
helped remove noise from workers who had trouble understanding the task, and therefore quit before completing the whole article. This filter to both SQuAD2.0 and the existing answerable questions from SQuAD 1.1.
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
no
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
unlikely
#### Any PII Identification?
<!-- info: Did the curators use any automatic/manual method to identify PII in the dataset? -->
<!-- scope: periscope -->
no identification
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
no
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
yes
## Considerations for Using the Data
### PII Risks and Liability
### Licenses
### Known Technical Limitations
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
findnitai/english-to-hinglish | findnitai | 2023-06-21T05:02:50Z | 347 | 6 | null | [
"task_categories:translation",
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:hi",
"language:en",
"license:apache-2.0",
"region:us"
] | 2023-06-21T05:02:50Z | 2023-06-21T04:21:28.000Z | 2023-06-21T04:21:28 | ---
license: apache-2.0
task_categories:
- translation
- text-generation
language:
- hi
- en
size_categories:
- 10K<n<100K
pretty_name: Hinglish
---
English to Hinglish Dataset aggregated from publicly available datasources.
Sources:
1. Hinglish TOP Dataset
2. CMU English Dog
3. HinGE
4. PHINC
source : 1 - Human Annotated ,
source : 0 - Synthetically Generated | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
vietgpt/truyencotich_vi | vietgpt | 2023-07-02T15:42:24Z | 347 | 0 | null | [
"region:us"
] | 2023-07-02T15:42:24Z | 2023-07-02T15:31:43.000Z | 2023-07-02T15:31:43 | ---
dataset_info:
features:
- name: title
dtype: string
- name: content
dtype: string
- name: metadata
struct:
- name: type
dtype: string
- name: url
dtype: string
splits:
- name: train
num_bytes: 23491738
num_examples: 5444
download_size: 12096101
dataset_size: 23491738
---
# Dataset Card for "truyencotich_vi"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
conv_questions | null | 2023-06-02T12:18:49Z | 346 | 2 | null | [
"task_categories:question-answering",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:open-domain-qa",
"task_ids:dialogue-modeling",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:1910.03262",
"region:us"
] | 2023-06-02T12:18:49Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
language_bcp47:
- en-US
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
- text-generation
- fill-mask
task_ids:
- open-domain-qa
- dialogue-modeling
pretty_name: ConvQuestions
dataset_info:
features:
- name: domain
dtype: string
- name: seed_entity
dtype: string
- name: seed_entity_text
dtype: string
- name: questions
sequence: string
- name: answers
sequence:
sequence: string
- name: answer_texts
sequence: string
splits:
- name: train
num_bytes: 3589880
num_examples: 6720
- name: validation
num_bytes: 1241778
num_examples: 2240
- name: test
num_bytes: 1175656
num_examples: 2240
download_size: 3276017
dataset_size: 6007314
---
# Dataset Card for ConvQuestions
## 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)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [ConvQuestions page](https://convex.mpi-inf.mpg.de)
- **Repository:** [GitHub](https://github.com/PhilippChr/CONVEX)
- **Paper:** [Look before you hop: Conversational question answering over knowledge graphs using judicious context expansion](https://arxiv.org/abs/1910.03262)
- **Leaderboard:** [ConvQuestions leaderboard](https://convex.mpi-inf.mpg.de)
- **Point of Contact:** [Philipp Christmann](mailto:pchristm@mpi-inf.mpg.de)
### Dataset Summary
ConvQuestions is the first realistic benchmark for conversational question answering over
knowledge graphs. It contains 11,200 conversations which can be evaluated over Wikidata.
They are compiled from the inputs of 70 Master crowdworkers on Amazon Mechanical Turk,
with conversations from five domains: Books, Movies, Soccer, Music, and TV Series.
The questions feature a variety of complex question phenomena like comparisons, aggregations,
compositionality, and temporal reasoning. Answers are grounded in Wikidata entities to enable
fair comparison across diverse methods. The data gathering setup was kept as natural as
possible, with the annotators selecting entities of their choice from each of the five domains,
and formulating the entire conversation in one session. All questions in a conversation are
from the same Turker, who also provided gold answers to the questions. For suitability to knowledge
graphs, questions were constrained to be objective or factoid in nature, but no other restrictive
guidelines were set. A notable property of ConvQuestions is that several questions are not
answerable by Wikidata alone (as of September 2019), but the required facts can, for example,
be found in the open Web or in Wikipedia. For details, please refer to the CIKM 2019 full paper
(https://dl.acm.org/citation.cfm?id=3358016).
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
en
## Dataset Structure
### Data Instances
An example of 'train' looks as follows.
```
{
'domain': 'music',
'seed_entity': 'https://www.wikidata.org/wiki/Q223495',
'seed_entity_text': 'The Carpenters',
'questions': [
'When did The Carpenters sign with A&M Records?',
'What song was their first hit?',
'When did Karen die?',
'Karen had what eating problem?',
'and how did she die?'
],
'answers': [
[
'1969'
],
[
'https://www.wikidata.org/wiki/Q928282'
],
[
'1983'
],
[
'https://www.wikidata.org/wiki/Q131749'
],
[
'https://www.wikidata.org/wiki/Q181754'
]
],
'answer_texts': [
'1969',
'(They Long to Be) Close to You',
'1983',
'anorexia nervosa',
'heart failure'
]
}
```
### Data Fields
- `domain`: a `string` feature. Any of: ['books', 'movies', 'music', 'soccer', 'tv_series']
- `seed_entity`: a `string` feature. Wikidata ID of the topic entity.
- `seed_entity_text`: a `string` feature. Surface form of the topic entity.
- `questions`: a `list` of `string` features. List of questions (initial question and follow-up questions).
- `answers`: a `list` of `lists` of `string` features. List of answers, given as Wikidata IDs or literals (e.g. timestamps or names).
- `answer_texts`: a `list` of `string` features. List of surface forms of the answers.
### Data Splits
|train|validation|tests|
|----:|---------:|----:|
| 6720| 2240| 2240|
## 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
With insights from a meticulous in-house pilot study with ten students over two weeks, the authors posed the conversation generation task on Amazon Mechanical Turk (AMT) in the most natural setup: Each crowdworker was asked to build a conversation by asking five sequential questions starting from any seed entity of his/her choice, as this is an intuitive mental model that humans may have when satisfying their real information needs via their search assistants.
#### Who are the annotators?
Local students (Saarland Informatics Campus) and AMT Master Workers.
### 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
The ConvQuestions benchmark is licensed under a Creative Commons Attribution 4.0 International License.
### Citation Information
```
@InProceedings{christmann2019look,
title={Look before you hop: Conversational question answering over knowledge graphs using judicious context expansion},
author={Christmann, Philipp and Saha Roy, Rishiraj and Abujabal, Abdalghani and Singh, Jyotsna and Weikum, Gerhard},
booktitle={Proceedings of the 28th ACM International Conference on Information and Knowledge Management},
pages={729--738},
year={2019}
}
```
### Contributions
Thanks to [@PhilippChr](https://github.com/PhilippChr) for adding this dataset. | [
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interpress_news_category_tr | null | 2023-01-25T14:33:03Z | 346 | 6 | null | [
"task_categories:text-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:tr",
"license:unknown",
"news-category-classification",
"region:us"
] | 2023-01-25T14:33:03Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- tr
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
pretty_name: Interpress Turkish News Category Dataset (270K)
tags:
- news-category-classification
dataset_info:
features:
- name: id
dtype: int32
- name: title
dtype: string
- name: content
dtype: string
- name: category
dtype:
class_label:
names:
'0': aktuel
'1': bilisim
'2': egitim
'3': ekonomi
'4': gida
'5': iletisim
'6': kultursanat
'7': magazin
'8': saglik
'9': savunma
'10': seyahat
'11': siyasi
'12': spor
'13': teknoloji
'14': ticaret
'15': turizm
'16': yasam
- name: categorycode
dtype:
class_label:
names:
'0': '0'
'1': '1'
'2': '2'
'3': '3'
'4': '4'
'5': '5'
'6': '6'
'7': '7'
'8': '8'
'9': '9'
'10': '10'
'11': '11'
'12': '12'
'13': '13'
'14': '14'
'15': '15'
'16': '16'
- name: publishdatetime
dtype: string
config_name: 270k
splits:
- name: train
num_bytes: 736098052
num_examples: 218880
- name: test
num_bytes: 184683629
num_examples: 54721
download_size: 354802486
dataset_size: 920781681
---
# Dataset Card for Interpress Turkish News Category Dataset (270K)
## 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:** [Interpress](https://www.interpress.com/)
- **Point of Contact:** [Yavuz Komecoglu](mailto:yavuz.komecoglu@kodiks.com)
### Dataset Summary
Turkish News Category Dataset (270K) is a Turkish news data set consisting of 273601 news in 17 categories, compiled from printed media and news websites between 2010 and 2017 by the Interpress (https://www.interpress.com/) media monitoring company.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset is based on Turkish.
## Dataset Structure
### Data Instances
A text classification dataset with 17 different news category.
```
{'id': 301365715,
'title': 'BİR SİHİRBAZ',
'content': 'NİANG, TAKIM ARKADAŞI FERNANDES E ÖVGÜLER YAĞDIRDI FUTBOL OYNARKEN EĞLENİYORUM YÜZDE 701E OYNUYORUM LİDERLE ARAMIZDA SADECE 5 PUAN VAR, ŞAMPİYONLUK ŞANSIMIZ YÜKSEK 4 j Fernandes le birlikte oynamayı seviyorum, adam adeta sihirbaz gibi J Frank Ribery, futbol hayatımda oynamaktan en çok zevk aldığım isim ı Abartılacak bir ] sonuç almadık ı .BAHÇE derbisinde Kartal ın ilk golünü atan, üçüncü golün de asistini yapan Mamadou Niang, TRT Spor da Futbol Keyfi programında özel açıklamalar yaptı. Senegalli forvet şampiyonluk şanslarının yüksek olduğunu dile getirirken, Portekizli yıldız Fernandes için Onunla oynamayı seviyorum, adeta bir sihirbaz gibi ifadesini kullandı. Frank Ribery nin futbol hayatında oynamaktan en çok zevk aldığım isim olduğunu ifade eden Niang, Moussa Sow ve Burak Yılmaz ın da Süper Lig deki en iyi forvetler olduğunu, ikisinin de tarzını beğendiğini söyledi. Senegalli yıldız şampiyonluk şansları için, Çok yüksek. Çünkü liderle aramızda 5 puan fark var ve bunu kapatacak güçteyiz yorumunu yaptı. NİANG şöyle devam etti: t.f En zorlandığım stoper İbrahim Toraman dır. Neyse ki şu an onunla takım arkadaşıyım. Almeida sakatlıktan döndükten sonra nasıl bir diziliş olur bilemiyorum. Onunla beraber oynayabiliriz, Holosko ile de oynayabiliriz. Türkiye, .. O NİANG, şu anda gerçek performansının yüzde 70 i ile oynadığını söyledi. İyi bir seviyede olmadığını kabul ettiğini belirten Senegalli yıldız, Sahada savaşan oyuncularla birlikte olmayı seviyorum. Bizim takımda Olcay ve Oğuzhan gibi bu yapıda isimler var. Tabii ki şansın da önemi var diye konuştu. zor bir lig. Eskiden arkadaşlarıma Türkiye Ligi nin iyi olduğunu söylediğimde inanmazlardı. Şimdi Didier Drogba, VVesley Sneijder, Sovvgibi oyuncuların burada olması ilgiyi artırdı. Futbol oynarken eğleniyorum ve şu an emekli olmayı düşünmüyorum. Açılış törenine, yönetici Metin Albayrak ile birlikte futbolcular Necip Uysal, McGregor ve Mehmet Akyüz de katıldı. BEŞİKTAŞLI Necip Uysal, +f başkan Fikret Orman gibi F.Bahçe galibiyetinin abartılmaması gerektiğini söyledi. Pazar günü İnönü Stadı nda güzel bir zafer elde ettiklerini vurgulayan genç yıldız, 10 karşılaşmaya daha çıkacağız. Her maçımız final, ayaklarımızın yere sağlam basması gerekiyor. Maçlara tek tek bakacağız ve hepsini kazanmak için oynayacağız yorumunu yaptı. Trabzon un her zaman zor deplasman olduğunu ifade eden Necip, Kolay olmayacağını biliyoruz ama şampiyonluk şansımızın sürmesi için kesinlikle üç puanla dönmeye mecburuz dedi. sflPa',
'category': 12,
'categorycode': 12,
'publishdatetime': '2013-03-07T00:00:00Z'}
```
### Data Fields
- `id`
- `title`
- `content`
- `category`
- `categorycode`
- `publishdatetime`
### Data Splits
The data is split into a training and testing. The split is organized as the following
| | train | test |
|------------|--------:|-------:|
| data split | 218,880 | 54,721 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
Downloaded over 270,000 news from the printed media and news websites between 2010 and 2017 by the Interpress (https://www.interpress.com/) media monitoring company. This data collection compiled from print media and internet news is presented in its raw form. For this reason, it is appropriate to use it with careful pre-processing steps regarding various OCR errors and typos.
#### Who are the source language producers?
Turkish printed news sources and online news sites.
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[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
https://www.interpress.com/
### Contributions
Thanks to [@basakbuluz](https://github.com/basakbuluz) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
kor_ner | null | 2023-01-25T14:33:50Z | 346 | 3 | null | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ko",
"license:mit",
"region:us"
] | 2023-01-25T14:33:50Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- other
language:
- ko
license:
- mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: KorNER
dataset_info:
features:
- name: text
dtype: string
- name: annot_text
dtype: string
- name: tokens
sequence: string
- name: pos_tags
sequence:
class_label:
names:
'0': SO
'1': SS
'2': VV
'3': XR
'4': VCP
'5': JC
'6': VCN
'7': JKB
'8': MM
'9': SP
'10': XSN
'11': SL
'12': NNP
'13': NP
'14': EP
'15': JKQ
'16': IC
'17': XSA
'18': EC
'19': EF
'20': SE
'21': XPN
'22': ETN
'23': SH
'24': XSV
'25': MAG
'26': SW
'27': ETM
'28': JKO
'29': NNB
'30': MAJ
'31': NNG
'32': JKV
'33': JKC
'34': VA
'35': NR
'36': JKG
'37': VX
'38': SF
'39': JX
'40': JKS
'41': SN
- name: ner_tags
sequence:
class_label:
names:
'0': I
'1': O
'2': B_OG
'3': B_TI
'4': B_LC
'5': B_DT
'6': B_PS
splits:
- name: train
num_bytes: 3948938
num_examples: 2928
- name: test
num_bytes: 476850
num_examples: 366
- name: validation
num_bytes: 486178
num_examples: 366
download_size: 3493175
dataset_size: 4911966
---
# Dataset Card for KorNER
## 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:** [Github](https://github.com/kmounlp/NER)
- **Repository:** [Github](https://github.com/kmounlp/NER)
- **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
Each row consists of the following fields:
- `text`: The full text, as is
- `annot_text`: Annotated text including POS-tagged information
- `tokens`: An ordered list of tokens from the full text
- `pos_tags`: Part-of-speech tags for each token
- `ner_tags`: Named entity recognition tags for each token
Note that by design, the length of `tokens`, `pos_tags`, and `ner_tags` will always be identical.
`pos_tags` corresponds to the list below:
```
['SO', 'SS', 'VV', 'XR', 'VCP', 'JC', 'VCN', 'JKB', 'MM', 'SP', 'XSN', 'SL', 'NNP', 'NP', 'EP', 'JKQ', 'IC', 'XSA', 'EC', 'EF', 'SE', 'XPN', 'ETN', 'SH', 'XSV', 'MAG', 'SW', 'ETM', 'JKO', 'NNB', 'MAJ', 'NNG', 'JKV', 'JKC', 'VA', 'NR', 'JKG', 'VX', 'SF', 'JX', 'JKS', 'SN']
```
`ner_tags` correspond to the following:
```
["I", "O", "B_OG", "B_TI", "B_LC", "B_DT", "B_PS"]
```
The prefix `B` denotes the first item of a phrase, and an `I` denotes any non-initial word. In addition, `OG` represens an organization; `TI`, time; `DT`, date, and `PS`, person.
### 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 [@jaketae](https://github.com/jaketae) for adding this dataset. | [
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kor_sarcasm | null | 2023-03-21T14:49:40Z | 346 | 2 | null | [
"task_categories:text-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ko",
"license:mit",
"sarcasm-detection",
"region:us"
] | 2023-03-21T14:49:40Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- ko
license:
- mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
pretty_name: Korean Sarcasm Detection
tags:
- sarcasm-detection
dataset_info:
features:
- name: tokens
dtype: string
- name: label
dtype:
class_label:
names:
'0': no_sarcasm
'1': sarcasm
splits:
- name: train
num_bytes: 1012030
num_examples: 9000
- name: test
num_bytes: 32480
num_examples: 301
download_size: 1008955
dataset_size: 1044510
---
# Dataset Card for Korean Sarcasm Detection
## 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:** [Korean Sarcasm Detection](https://github.com/SpellOnYou/korean-sarcasm)
- **Repository:** [Korean Sarcasm Detection](https://github.com/SpellOnYou/korean-sarcasm)
- **Point of Contact:** [Dionne Kim](jiwon.kim.096@gmail.com)
### Dataset Summary
The Korean Sarcasm Dataset was created to detect sarcasm in text, which can significantly alter the original meaning of a sentence. 9319 tweets were collected from Twitter and labeled for `sarcasm` or `not_sarcasm`. These tweets were gathered by querying for: `역설, 아무말, 운수좋은날, 笑, 뭐래 아닙니다, 그럴리없다, 어그로, irony sarcastic, and sarcasm`. The dataset was pre-processed by removing the keyword hashtag, urls and mentions of the user to maintain anonymity.
### Supported Tasks and Leaderboards
* `sarcasm_detection`: The dataset can be used to train a model to detect sarcastic tweets. A [BERT](https://huggingface.co/bert-base-uncased) model can be presented with a tweet in Korean and be asked to determine whether it is sarcastic or not.
### Languages
The text in the dataset is in Korean and the associated is BCP-47 code is `ko-KR`.
## Dataset Structure
### Data Instances
An example data instance contains a Korean tweet and a label whether it is sarcastic or not. `1` maps to sarcasm and `0` maps to no sarcasm.
```
{
"tokens": "[ 수도권 노선 아이템 ] 17 . 신분당선의 #딸기 : 그의 이미지 컬러 혹은 머리 색에서 유래한 아이템이다 . #메트로라이프"
"label": 0
}
```
### Data Fields
* `tokens`: contains the text of the tweet
* `label`: determines whether the text is sarcastic (`1`: sarcasm, `0`: no sarcasm)
### Data Splits
The data is split into a training set comrpised of 9018 tweets and a test set of 301 tweets.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The dataset was created by gathering HTML data from Twitter. Queries for hashtags that include sarcasm and variants of it were used to return tweets. It was preprocessed by removing the keyword hashtag, urls and mentions of the user to preserve anonymity.
#### Who are the source language producers?
The source language producers are Korean Twitter users.
### Annotations
#### Annotation process
Tweets were labeled `1` for sarcasm and `0` for no sarcasm.
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
Mentions of the user in a tweet were removed to keep them anonymous.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
This dataset was curated by Dionne Kim.
### Licensing Information
This dataset is licensed under the MIT License.
### Citation Information
```
@misc{kim2019kocasm,
author = {Kim, Jiwon and Cho, Won Ik},
title = {Kocasm: Korean Automatic Sarcasm Detection},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/SpellOnYou/korean-sarcasm}}
}
```
### Contributions
Thanks to [@stevhliu](https://github.com/stevhliu) for adding this dataset. | [
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definite_pronoun_resolution | null | 2023-04-05T10:04:44Z | 345 | 3 | definite-pronoun-resolution-dataset | [
"task_categories:token-classification",
"task_ids:word-sense-disambiguation",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | 2023-04-05T10:04:44Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- word-sense-disambiguation
paperswithcode_id: definite-pronoun-resolution-dataset
pretty_name: Definite Pronoun Resolution Dataset
dataset_info:
features:
- name: sentence
dtype: string
- name: pronoun
dtype: string
- name: candidates
sequence: string
length: 2
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
config_name: plain_text
splits:
- name: test
num_bytes: 71691
num_examples: 564
- name: train
num_bytes: 171511
num_examples: 1322
download_size: 227452
dataset_size: 243202
---
# Dataset Card for "definite_pronoun_resolution"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://www.hlt.utdallas.edu/~vince/data/emnlp12/](https://www.hlt.utdallas.edu/~vince/data/emnlp12/)
- **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:** 0.23 MB
- **Size of the generated dataset:** 0.24 MB
- **Total amount of disk used:** 0.47 MB
### Dataset Summary
Composed by 30 students from one of the author's undergraduate classes. These
sentence pairs cover topics ranging from real events (e.g., Iran's plan to
attack the Saudi ambassador to the U.S.) to events/characters in movies (e.g.,
Batman) and purely imaginary situations, largely reflecting the pop culture as
perceived by the American kids born in the early 90s. Each annotated example
spans four lines: the first line contains the sentence, the second line contains
the target pronoun, the third line contains the two candidate antecedents, and
the fourth line contains the correct antecedent. If the target pronoun appears
more than once in the sentence, its first occurrence is the one to be resolved.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### plain_text
- **Size of downloaded dataset files:** 0.23 MB
- **Size of the generated dataset:** 0.24 MB
- **Total amount of disk used:** 0.47 MB
An example of 'train' looks as follows.
```
{
"candidates": ["coreference resolution", "chunking"],
"label": 0,
"pronoun": "it",
"sentence": "There is currently more work on coreference resolution than on chunking because it is a problem that is still far from being solved."
}
```
### Data Fields
The data fields are the same among all splits.
#### plain_text
- `sentence`: a `string` feature.
- `pronoun`: a `string` feature.
- `candidates`: a `list` of `string` features.
- `label`: a classification label, with possible values including `0` (0), `1` (1).
### Data Splits
| name |train|test|
|----------|----:|---:|
|plain_text| 1322| 564|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@inproceedings{rahman2012resolving,
title={Resolving complex cases of definite pronouns: the winograd schema challenge},
author={Rahman, Altaf and Ng, Vincent},
booktitle={Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning},
pages={777--789},
year={2012},
organization={Association for Computational Linguistics}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Sp1786/multiclass-sentiment-analysis-dataset | Sp1786 | 2023-06-25T08:01:27Z | 345 | 1 | null | [
"task_categories:text-classification",
"task_categories:translation",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"code",
"region:us"
] | 2023-06-25T08:01:27Z | 2023-06-21T11:21:31.000Z | 2023-06-21T11:21:31 | ---
license: apache-2.0
task_categories:
- text-classification
- translation
language:
- en
tags:
- code
pretty_name: multiclass-sentiment-analysis-dataset
size_categories:
- 10K<n<100K
---
# Dataset Card for Dataset Name
## Dataset Description
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### Dataset Summary
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).
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## Dataset Structure
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## Dataset Creation
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#### Who are the source language producers?
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### Contributions
[More Information Needed] | [
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SEACrowd/nusax_mt | SEACrowd | 2023-09-26T12:34:19Z | 345 | 0 | null | [
"language:ind",
"language:ace",
"language:ban",
"language:bjn",
"language:bbc",
"language:bug",
"language:jav",
"language:mad",
"language:min",
"language:nij",
"language:sun",
"language:eng",
"machine-translation",
"arxiv:2205.15960",
"region:us"
] | 2023-09-26T12:34:19Z | 2023-09-26T11:18:05.000Z | 2023-09-26T11:18:05 | ---
tags:
- machine-translation
language:
- ind
- ace
- ban
- bjn
- bbc
- bug
- jav
- mad
- min
- nij
- sun
- eng
---
# nusax_mt
NusaX is a high-quality multilingual parallel corpus that covers 12 languages, Indonesian, English, and 10 Indonesian local languages, namely Acehnese, Balinese, Banjarese, Buginese, Madurese, Minangkabau, Javanese, Ngaju, Sundanese, and Toba Batak.
NusaX-MT is a parallel corpus for training and benchmarking machine translation models across 10 Indonesian local languages + Indonesian and English. The data is presented in csv format with 12 columns, one column for each language.
## Dataset Usage
Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.
## Citation
```
@misc{winata2022nusax,
title={NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages},
author={Winata, Genta Indra and Aji, Alham Fikri and Cahyawijaya,
Samuel and Mahendra, Rahmad and Koto, Fajri and Romadhony,
Ade and Kurniawan, Kemal and Moeljadi, David and Prasojo,
Radityo Eko and Fung, Pascale and Baldwin, Timothy and Lau,
Jey Han and Sennrich, Rico and Ruder, Sebastian},
year={2022},
eprint={2205.15960},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## License
Creative Commons Attribution Share-Alike 4.0 International
## Homepage
[https://github.com/IndoNLP/nusax/tree/main/datasets/mt](https://github.com/IndoNLP/nusax/tree/main/datasets/mt)
### NusaCatalogue
For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
result-kand2-sdxl-wuerst-karlo/57854108 | result-kand2-sdxl-wuerst-karlo | 2023-11-10T09:48:00Z | 345 | 0 | null | [
"region:us"
] | 2023-11-10T09:48:00Z | 2023-11-10T09:47:59.000Z | 2023-11-10T09:47:59 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 167
num_examples: 10
download_size: 1318
dataset_size: 167
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "57854108"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
NextDayAI/MultipleResponsesChat_ch13brope_20231001_20231115 | NextDayAI | 2023-11-16T16:10:35Z | 345 | 0 | null | [
"region:us"
] | 2023-11-16T16:10:35Z | 2023-11-16T16:09:52.000Z | 2023-11-16T16:09:52 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: rejected_response
dtype: string
- name: selected_response
dtype: string
splits:
- name: train
num_bytes: 1395175651.4956698
num_examples: 477192
- name: valid
num_bytes: 7309299.252165113
num_examples: 2500
- name: test
num_bytes: 7309299.252165113
num_examples: 2500
download_size: 880820023
dataset_size: 1409794250.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
---
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
RohanAiLab/persian_blog | RohanAiLab | 2022-10-25T09:06:36Z | 344 | 2 | null | [
"task_categories:text-generation",
"task_ids:language-modeling",
"source_datasets:original",
"language:fa",
"region:us"
] | 2022-10-25T09:06:36Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- fa
source_datasets:
- original
task_categories:
- text-generation
task_ids:
- language-modeling
pretty_name: persian_blog
---
# Persian Blog
# Dataset Summary
persian_news_dataset is a collection of 400k blog posts. these posts have been gathered from more than 10 websites. This dataset can be used in different NLP tasks like language modeling and text generation tasks.
This effort is part of a bigger perspective to have several datasets in Persian language for different tasks that have two important factors: `free` and `easy-to-use`. Here is a quick HOW-TO for using this dataset in datasets library:[Demo-datasets](https://saied71.github.io/RohanAiLab/2021/09/03/Demo-datasets.html)
# Description
As discussed before, this dataset contains 5M news articles. Each article has these three attributes: text, title, category. Here is a sample of dataset:
```
text : چرا کودکان به روانشناس نیاز دارند؟ روانشناسی کودکانکودکان همچون غنچههای زیبا هستند که برای شکوفایی و به ثمر رسیدن نیاز به مراقبت و رسیدگی دارند . روانشناس کودک فردیست که از زمان بدو تولد کودک در مراحل مختلف زندگی کودک در کنار والدین وی میباشد و به چگونگی تربیت کودک کمک میکند تا به بهترین شکل رشد کند . چرا که روانشناس کودک با روحیات ، نیازها و مشکلات کودکان و همچنین چگونگی برقراری ارتباط بین کودک و والدین وی آشنایی کامل دارد .بسیاری از کودکان در سنین مختلف بخاطر شرایط زندگی ، دچار انواع ناسازگاریها و مشکلات در زندگی خود میشود از ناسازگاری کودکان میتوان به موارد زیر اشاره کرد : 1 . پرخاشگری 2 . بد دهنی 3 . اختلال در خوابیدن 4 . اختلال در غذا خوردن و کم اشتهایی 5 . حالت افسردگی و اضطراب 6 . ترس از محیط پیرامون 7 . عدم آمادگی برای ورود به جامعه 8 . وجود مشکل در محیط مدرسه 9 . عدم تمرکز 10 . جویدن ناخن ها 11 . انزوا و گوشه گیری 12 . عدم هم بازی شدن با هم سن و سال هاو .این گونه ناسازگاریها در زندگی آینده کودک نقش به سزایی دارد .روانشناس کودکیک روانشناس کودک خوب ، با دلسوزی و با تکیه بر تجربیات و تخصص خود میکوشد تا رفتارهای کودک را مورد ارزیابی و بررسی قرار دهد سپس سعی میکند تا رفتارهای بعدی کودک را پیش بینی کند و منشاء این مشکلات و سطح پیشرفت آن را بیابد. سپس او بهترین روشهای درمان برای بهبود اختلال کودک را مییابد و با کمک والدین این ناسازگاریها ، مشکلات و ناهنجاریها را حل کرده و نهایتا رابطهای دوستانه و صمیمانه بین کودک و والدین وی ایجاد مینماید تاآیندهای درخشان در انتظار کودک شما باشد .
```
# Citation
```
rohanailab@gmail.com
title={persian_blog},
author={Saied Alimoradi},
year={2021}
}
``` | [
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allenai/prosocial-dialog | allenai | 2023-02-03T07:58:29Z | 344 | 73 | null | [
"task_categories:conversational",
"task_categories:text-classification",
"task_ids:dialogue-generation",
"task_ids:multi-class-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:100K<n<1M",
"source_datasets:original",
"source_datasets:extended|social_bias_frames",
"language:en",
"license:cc-by-4.0",
"dialogue",
"dialogue safety",
"social norm",
"rules-of-thumb",
"arxiv:2205.12688",
"region:us"
] | 2023-02-03T07:58:29Z | 2022-10-30T04:24:12.000Z | 2022-10-30T04:24:12 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- crowdsourced
- machine-generated
license: cc-by-4.0
multilinguality:
- monolingual
pretty_name: ProsocialDialog
size_categories:
- 10K<n<100K
- 100K<n<1M
source_datasets:
- original
- extended|social_bias_frames
tags:
- dialogue
- dialogue safety
- social norm
- rules-of-thumb
task_categories:
- conversational
- text-classification
task_ids:
- dialogue-generation
- multi-class-classification
---
# Dataset Card for ProsocialDialog Dataset
## Dataset Description
- **Repository:** [Dataset and Model](https://github.com/skywalker023/prosocial-dialog)
- **Paper:** [ProsocialDialog: A Prosocial Backbone for Conversational Agents](https://aclanthology.org/2022.emnlp-main.267/)
- **Point of Contact:** [Hyunwoo Kim](mailto:hyunwook@allenai.org)
## Dataset Summary
ProsocialDialog is the first large-scale multi-turn English dialogue dataset to teach conversational agents to respond to problematic content following social norms. Covering diverse unethical, problematic, biased, and toxic situations, ProsocialDialog contains responses that encourage prosocial behavior, grounded in commonsense social rules (i.e., rules-of-thumb, RoTs). Created via a human-AI collaborative framework, ProsocialDialog consists of 58K dialogues, with 331K utterances, 160K unique RoTs, and 497K dialogue safety labels accompanied by free-form rationales.
## Supported Tasks
* Dialogue response generation
* Dialogue safety prediction
* Rules-of-thumb generation
## Languages
English
## Dataset Structure
### Data Attributes
attribute | type | description
--- | --- | ---
`context` | str | the potentially unsafe utterance
`response` | str | the guiding utterance grounded on rules-of-thumb (`rots`)
`rots` | list of str\|null | the relevant rules-of-thumb for `text` *not* labeled as \_\_casual\_\_
`safety_label` | str | the final verdict of the context according to `safety_annotations`: {\_\_casual\_\_, \_\_possibly\_needs\_caution\_\_, \_\_probably\_needs\_caution\_\_, \_\_needs\_caution\_\_, \_\_needs\_intervention\_\_}
`safety_annotations` | list of str | raw annotations from three workers: {casual, needs caution, needs intervention}
`safety_annotation_reasons` | list of str | the reasons behind the safety annotations in free-form text from each worker
`source` | str | the source of the seed text that was used to craft the first utterance of the dialogue: {socialchemistry, sbic, ethics_amt, ethics_reddit}
`etc` | str\|null | other information
`dialogue_id` | int | the dialogue index
`response_id` | int | the response index
`episode_done` | bool | an indicator of whether it is the end of the dialogue
## Dataset Creation
To create ProsocialDialog, we set up a human-AI collaborative data creation framework, where GPT-3 generates the potentially unsafe utterances, and crowdworkers provide prosocial responses to them. This approach allows us to circumvent two substantial challenges: (1) there are no available large-scale corpora of multiturn prosocial conversations between humans, and (2) asking humans to write unethical, toxic, or problematic utterances could result in psychological harms (Roberts, 2017; Steiger et al., 2021).
### Further Details, Social Impacts, and Limitations
Please refer to our [paper](https://arxiv.org/abs/2205.12688).
## Additional Information
### Citation
Please cite our work if you found the resources in this repository useful:
```
@inproceedings{kim2022prosocialdialog,
title={ProsocialDialog: A Prosocial Backbone for Conversational Agents},
author={Hyunwoo Kim and Youngjae Yu and Liwei Jiang and Ximing Lu and Daniel Khashabi and Gunhee Kim and Yejin Choi and Maarten Sap},
booktitle={EMNLP},
year=2022
}
``` | [
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capes | null | 2022-11-03T16:15:53Z | 343 | 2 | capes | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"language:pt",
"license:unknown",
"dissertation-abstracts-translation",
"theses-translation",
"region:us"
] | 2022-11-03T16:15:53Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
- pt
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: capes
pretty_name: CAPES
tags:
- dissertation-abstracts-translation
- theses-translation
dataset_info:
features:
- name: translation
dtype:
translation:
languages:
- en
- pt
config_name: en-pt
splits:
- name: train
num_bytes: 472484364
num_examples: 1157610
download_size: 162229298
dataset_size: 472484364
---
# Dataset Card for CAPES
## 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:**[Parallel corpus of theses and dissertation abstracts in Portuguese and English from CAPES](https://sites.google.com/view/felipe-soares/datasets#h.p_kxOR6EhHm2a6)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
A parallel corpus of theses and dissertations abstracts in English and Portuguese were collected from the
CAPES website (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) - Brazil.
The corpus is sentence aligned for all language pairs. Approximately 240,000 documents were
collected and aligned using the Hunalign algorithm.
### Supported Tasks and Leaderboards
The underlying task is machine translation.
### 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
```
@inproceedings{soares2018parallel,
title={A Parallel Corpus of Theses and Dissertations Abstracts},
author={Soares, Felipe and Yamashita, Gabrielli Harumi and Anzanello, Michel Jose},
booktitle={International Conference on Computational Processing of the Portuguese Language},
pages={345--352},
year={2018},
organization={Springer}
}
```
### Contributions
Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset. | [
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conll2000 | null | 2023-04-05T10:02:23Z | 343 | 2 | conll-2000-1 | [
"language:en",
"region:us"
] | 2023-04-05T10:02:23Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
paperswithcode_id: conll-2000-1
pretty_name: CoNLL-2000
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: pos_tags
sequence:
class_label:
names:
'0': ''''''
'1': '#'
'2': $
'3': (
'4': )
'5': ','
'6': .
'7': ':'
'8': '``'
'9': CC
'10': CD
'11': DT
'12': EX
'13': FW
'14': IN
'15': JJ
'16': JJR
'17': JJS
'18': MD
'19': NN
'20': NNP
'21': NNPS
'22': NNS
'23': PDT
'24': POS
'25': PRP
'26': PRP$
'27': RB
'28': RBR
'29': RBS
'30': RP
'31': SYM
'32': TO
'33': UH
'34': VB
'35': VBD
'36': VBG
'37': VBN
'38': VBP
'39': VBZ
'40': WDT
'41': WP
'42': WP$
'43': WRB
- name: chunk_tags
sequence:
class_label:
names:
'0': O
'1': B-ADJP
'2': I-ADJP
'3': B-ADVP
'4': I-ADVP
'5': B-CONJP
'6': I-CONJP
'7': B-INTJ
'8': I-INTJ
'9': B-LST
'10': I-LST
'11': B-NP
'12': I-NP
'13': B-PP
'14': I-PP
'15': B-PRT
'16': I-PRT
'17': B-SBAR
'18': I-SBAR
'19': B-UCP
'20': I-UCP
'21': B-VP
'22': I-VP
splits:
- name: train
num_bytes: 5356965
num_examples: 8937
- name: test
num_bytes: 1201151
num_examples: 2013
download_size: 3481560
dataset_size: 6558116
---
# Dataset Card for "conll2000"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://www.clips.uantwerpen.be/conll2000/chunking/](https://www.clips.uantwerpen.be/conll2000/chunking/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 3.48 MB
- **Size of the generated dataset:** 6.55 MB
- **Total amount of disk used:** 10.03 MB
### Dataset Summary
Text chunking consists of dividing a text in syntactically correlated parts of words. For example, the sentence
He reckons the current account deficit will narrow to only # 1.8 billion in September . can be divided as follows:
[NP He ] [VP reckons ] [NP the current account deficit ] [VP will narrow ] [PP to ] [NP only # 1.8 billion ]
[PP in ] [NP September ] .
Text chunking is an intermediate step towards full parsing. It was the shared task for CoNLL-2000. Training and test
data for this task is available. This data consists of the same partitions of the Wall Street Journal corpus (WSJ)
as the widely used data for noun phrase chunking: sections 15-18 as training data (211727 tokens) and section 20 as
test data (47377 tokens). The annotation of the data has been derived from the WSJ corpus by a program written by
Sabine Buchholz from Tilburg University, The Netherlands.
### 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
#### conll2000
- **Size of downloaded dataset files:** 3.48 MB
- **Size of the generated dataset:** 6.55 MB
- **Total amount of disk used:** 10.03 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"chunk_tags": [11, 13, 11, 12, 21, 22, 22, 22, 22, 11, 12, 12, 17, 11, 12, 13, 11, 0, 1, 13, 11, 11, 0, 21, 22, 22, 11, 12, 12, 13, 11, 12, 12, 11, 12, 12, 0],
"id": "0",
"pos_tags": [19, 14, 11, 19, 39, 27, 37, 32, 34, 11, 15, 19, 14, 19, 22, 14, 20, 5, 15, 14, 19, 19, 5, 34, 32, 34, 11, 15, 19, 14, 20, 9, 20, 24, 15, 22, 6],
"tokens": "[\"Confidence\", \"in\", \"the\", \"pound\", \"is\", \"widely\", \"expected\", \"to\", \"take\", \"another\", \"sharp\", \"dive\", \"if\", \"trade\", \"figur..."
}
```
### Data Fields
The data fields are the same among all splits.
#### conll2000
- `id`: a `string` feature.
- `tokens`: a `list` of `string` features.
- `pos_tags`: a `list` of classification labels, with possible values including `''` (0), `#` (1), `$` (2), `(` (3), `)` (4).
- `chunk_tags`: a `list` of classification labels, with possible values including `O` (0), `B-ADJP` (1), `I-ADJP` (2), `B-ADVP` (3), `I-ADVP` (4).
### Data Splits
| name |train|test|
|---------|----:|---:|
|conll2000| 8937|2013|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@inproceedings{tksbuchholz2000conll,
author = "Tjong Kim Sang, Erik F. and Sabine Buchholz",
title = "Introduction to the CoNLL-2000 Shared Task: Chunking",
editor = "Claire Cardie and Walter Daelemans and Claire
Nedellec and Tjong Kim Sang, Erik",
booktitle = "Proceedings of CoNLL-2000 and LLL-2000",
publisher = "Lisbon, Portugal",
pages = "127--132",
year = "2000"
}
```
### Contributions
Thanks to [@vblagoje](https://github.com/vblagoje), [@jplu](https://github.com/jplu) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
fquad | null | 2023-04-05T10:06:27Z | 343 | 9 | fquad | [
"task_categories:question-answering",
"task_categories:text-retrieval",
"task_ids:extractive-qa",
"task_ids:closed-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:fr",
"license:cc-by-nc-sa-3.0",
"arxiv:2002.06071",
"region:us"
] | 2023-04-05T10:06:27Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- found
language:
- fr
license:
- cc-by-nc-sa-3.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
- text-retrieval
task_ids:
- extractive-qa
- closed-domain-qa
paperswithcode_id: fquad
pretty_name: 'FQuAD: French Question Answering Dataset'
dataset_info:
features:
- name: context
dtype: string
- name: questions
sequence: string
- name: answers
sequence:
- name: texts
dtype: string
- name: answers_starts
dtype: int32
splits:
- name: train
num_bytes: 5898752
num_examples: 4921
- name: validation
num_bytes: 1031456
num_examples: 768
download_size: 0
dataset_size: 6930208
---
# Dataset Card for FQuAD
## 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://fquad.illuin.tech/](https://fquad.illuin.tech/)
- **Paper:** [FQuAD: French Question Answering Dataset](https://arxiv.org/abs/2002.06071)
- **Point of Contact:** [https://www.illuin.tech/contact/](https://www.illuin.tech/contact/)
- **Size of downloaded dataset files:** 3.29 MB
- **Size of the generated dataset:** 6.94 MB
- **Total amount of disk used:** 10.23 MB
### Dataset Summary
FQuAD: French Question Answering Dataset
We introduce FQuAD, a native French Question Answering Dataset.
FQuAD contains 25,000+ question and answer pairs.
Finetuning CamemBERT on FQuAD yields a F1 score of 88% and an exact match of 77.9%.
Developped to provide a SQuAD equivalent in the French language. Questions are original and based on high quality Wikipedia articles.
Please, note this dataset is licensed for non-commercial purposes and users must agree to the following terms and conditions:
1. Use FQuAD only for internal research purposes.
2. Not make any copy except a safety one.
3. Not redistribute it (or part of it) in any way, even for free.
4. Not sell it or use it for any commercial purpose. Contact us for a possible commercial licence.
5. Mention the corpus origin and Illuin Technology in all publications about experiments using FQuAD.
6. Redistribute to Illuin Technology any improved or enriched version you could make of that corpus.
Request manually download of the data from: https://fquad.illuin.tech/
### Supported Tasks and Leaderboards
- `closed-domain-qa`, `text-retrieval`: This dataset is intended to be used for `closed-domain-qa`, but can also be used for information retrieval tasks.
### Languages
This dataset is exclusively in French, with context data from Wikipedia and questions from French university students (`fr`).
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 3.29 MB
- **Size of the generated dataset:** 6.94 MB
- **Total amount of disk used:** 10.23 MB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"answers": {
"answers_starts": [161, 46, 204],
"texts": ["La Vierge aux rochers", "documents contemporains", "objets de spéculations"]
},
"context": "\"Les deux tableaux sont certes décrits par des documents contemporains à leur création mais ceux-ci ne le font qu'indirectement ...",
"questions": ["Que concerne principalement les documents ?", "Par quoi sont décrit les deux tableaux ?", "Quels types d'objets sont les deux tableaux aux yeux des chercheurs ?"]
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `context`: a `string` feature.
- `questions`: a `list` of `string` features.
- `answers`: a dictionary feature containing:
- `texts`: a `string` feature.
- `answers_starts`: a `int32` feature.
### Data Splits
The FQuAD dataset has 3 splits: _train_, _validation_, and _test_. The _test_ split is however not released publicly at the moment. The splits contain disjoint sets of articles. The following table contains stats about each split.
Dataset Split | Number of Articles in Split | Number of paragraphs in split | Number of questions in split
--------------|------------------------------|--------------------------|-------------------------
Train | 117 | 4921 | 20731
Validation | 768 | 51.0% | 3188
Test | 10 | 532 | 2189
## Dataset Creation
### Curation Rationale
The FQuAD dataset was created by Illuin technology. It was developped to provide a SQuAD equivalent in the French language. Questions are original and based on high quality Wikipedia articles.
### Source Data
The text used for the contexts are from the curated list of French High-Quality Wikipedia [articles](https://fr.wikipedia.org/wiki/Cat%C3%A9gorie:Article_de_qualit%C3%A9).
### Annotations
Annotations (spans and questions) are written by students of the CentraleSupélec school of engineering.
Wikipedia articles were scraped and Illuin used an internally-developped tool to help annotators ask questions and indicate the answer spans.
Annotators were given paragraph sized contexts and asked to generate 4/5 non-trivial questions about information in the context.
### Personal and Sensitive Information
No personal or sensitive information is included in this dataset. This has been manually verified by the dataset curators.
## Considerations for Using the Data
Users should consider this dataset is sampled from Wikipedia data which might not be representative of all QA use cases.
### Social Impact of Dataset
The social biases of this dataset have not yet been investigated.
### Discussion of Biases
The social biases of this dataset have not yet been investigated, though articles have been selected by their quality and objectivity.
### Other Known Limitations
The limitations of the FQuAD dataset have not yet been investigated.
## Additional Information
### Dataset Curators
Illuin Technology: [https://fquad.illuin.tech/](https://fquad.illuin.tech/)
### Licensing Information
The FQuAD dataset is licensed under the [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/fr/) license.
It allows personal and academic research uses of the dataset, but not commercial uses. So concretely, the dataset cannot be used to train a model that is then put into production within a business or a company. For this type of commercial use, we invite FQuAD users to contact [the authors](https://www.illuin.tech/contact/) to discuss possible partnerships.
### Citation Information
```
@ARTICLE{2020arXiv200206071
author = {Martin, d'Hoffschmidt and Maxime, Vidal and
Wacim, Belblidia and Tom, Brendlé},
title = "{FQuAD: French Question Answering Dataset}",
journal = {arXiv e-prints},
keywords = {Computer Science - Computation and Language},
year = "2020",
month = "Feb",
eid = {arXiv:2002.06071},
pages = {arXiv:2002.06071},
archivePrefix = {arXiv},
eprint = {2002.06071},
primaryClass = {cs.CL}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@mariamabarham](https://github.com/mariamabarham), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.
Thanks to [@ManuelFay](https://github.com/manuelfay) for providing information on the dataset creation process. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
germaner | null | 2023-01-25T14:30:52Z | 343 | 0 | null | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:de",
"license:apache-2.0",
"region:us"
] | 2023-01-25T14:30:52Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- de
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: GermaNER
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': B-LOC
'1': B-ORG
'2': B-OTH
'3': B-PER
'4': I-LOC
'5': I-ORG
'6': I-OTH
'7': I-PER
'8': O
splits:
- name: train
num_bytes: 9059606
num_examples: 26200
download_size: 4363657
dataset_size: 9059606
---
# Dataset Card for GermaNER
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** https://github.com/tudarmstadt-lt/GermaNER
- **Paper:** https://pdfs.semanticscholar.org/b250/3144ed2152830f6c64a9f797ab3c5a34fee5.pdf
- **Point of Contact:** [Darina Benikova](mailto:benikova@aiphes.tu-darmstadt.de)
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
German
## Dataset Structure
### Data Instances
An example instance looks as follows:
```
{
'id': '3',
'ner_tags': [1, 5, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],
'tokens': ['Bayern', 'München', 'ist', 'wieder', 'alleiniger', 'Top-', 'Favorit', 'auf', 'den', 'Gewinn', 'der', 'deutschen', 'Fußball-Meisterschaft', '.']
}
```
### Data Fields
Each instance in the dataset has:
- `id`: an id as a string
- `tokens`: sequence of tokens
- `ner_tags`: NER tags for each token (encoded as IOB)
NER tags can be: 'B-LOC' (0), 'B-ORG' (1), 'B-OTH' (2), 'B-PER' (3), 'I-LOC' (4), 'I-ORG' (5), 'I-OTH' (6), 'I-PER' (7), 'O' (8)
### Data Splits
Dataset provides only train part (26200 data instances).
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
License of GermaNER:
```
GermaNER is licensed under ASL 2.0 and other lenient licenses, allowing its use for academic and commercial purposes without restrictions. The licenses of its compenents are mixed licensed and are individually listed in Data/Licenses.
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
"Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License.
"Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License.
"Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files.
"Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof.
"Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution."
"Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work.
2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form.
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4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions:
You must give any other recipients of the Work or Derivative Works a copy of this License; and
You must cause any modified files to carry prominent notices stating that You changed the files; and
You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and
If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License.
You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License.
5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file.
7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License.
8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability.
END OF TERMS AND CONDITIONS
```
### Citation Information
```bibtex
@inproceedings{Benikova2015GermaNERFO,
title={GermaNER: Free Open German Named Entity Recognition Tool},
author={Darina Benikova and Seid Muhie Yimam and P. Santhanam and Chris Biemann},
booktitle={GSCL},
year={2015}
}
```
### Contributions
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. | [
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