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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
RuyuanWan/SBIC_Disagreement | RuyuanWan | 2022-12-26T22:07:09Z | 324 | 0 | null | [
"task_categories:text-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:extended|social_bias_frames",
"language:en",
"region:us"
] | 2022-12-26T22:07:09Z | 2022-12-26T18:46:23.000Z | 2022-12-26T18:46:23 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license: []
multilinguality:
- monolingual
pretty_name: RuyuanWan/SBIC_Disagreement
size_categories: []
source_datasets:
- extended|social_bias_frames
tags: []
task_categories:
- text-classification
task_ids: []
---
This dataset is processed version of Social Bias Inference Corpus(SBIC) dataset including text, annotator's demographics and the annotation disagreement labels. <br>
Paper: Everyone's Voice Matters: Quantifying Annotation Disagreement Using Demographic Information <br>
Authors: Ruyuan Wan, Jaehyung Kim, Dongyeop Kang <br>
Github repo: https://github.com/minnesotanlp/Quantifying-Annotation-Disagreement <br>
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
recastai/LAION-art-EN-improved-captions | recastai | 2023-06-24T04:19:50Z | 324 | 10 | null | [
"language:en",
"license:cc-by-4.0",
"region:us"
] | 2023-06-24T04:19:50Z | 2023-04-26T03:37:46.000Z | 2023-04-26T03:37:46 | ---
license: cc-by-4.0
dataset_info:
features:
- name: orig_caption
dtype: string
- name: generated_caption
dtype: string
- name: key
dtype: string
- name: url
dtype: string
- name: index
dtype: int64
splits:
- name: train
num_bytes: 681710086
num_examples: 2684160
download_size: 441945582
dataset_size: 681710086
language:
- en
---
# Dataset Card for LAION-art-EN-improved-captions
### Dataset Summary
This dataset has been created by **Re:cast AI** for improving the semantic relationship of image-caption pairs. `generated_captions` were created in a semi-supervised fashion using the **Salesforce/blip2-flan-t5-xxl** model.
### Supported Tasks
Fine-tuning text-to-image generators (e.g. stable-diffusion), or a searchable prompt database (requires faiss-index).
## Dataset Structure
### Data Fields
- orig_caption
- generated_caption
- key
- index
- url
### Data Splits
- train
### Source Data
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
arbml/Ashaar_dataset | arbml | 2023-05-29T05:40:58Z | 324 | 0 | null | [
"region:us"
] | 2023-05-29T05:40:58Z | 2023-05-29T05:40:43.000Z | 2023-05-29T05:40:43 | ---
dataset_info:
features:
- name: poem title
dtype: string
- name: poem meter
dtype: string
- name: poem verses
sequence: string
- name: poem theme
dtype: string
- name: poem url
dtype: string
- name: poet name
dtype: string
- name: poet description
dtype: string
- name: poet url
dtype: string
- name: poet era
dtype: string
- name: poet location
dtype: string
- name: poem description
list:
- name: attributes
struct:
- name: class
dtype: string
- name: color
dtype: string
- name: dir
dtype: string
- name: face
dtype: string
- name: id
dtype: string
- name: lang
dtype: string
- name: style
dtype: string
- name: children
list:
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struct:
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dtype: string
- name: dir
dtype: string
- name: face
dtype: string
- name: href
dtype: string
- name: id
dtype: string
- name: lang
dtype: string
- name: style
dtype: string
- name: title
dtype: string
- name: value
dtype: string
- name: children
list:
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struct:
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dtype: string
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dtype: string
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- name: children
list:
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struct:
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dtype: string
- name: face
dtype: string
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- name: name
dtype: string
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struct:
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dtype: string
- name: style
dtype: string
- name: size
dtype: int64
- name: text
dtype: string
- name: truncated
dtype: bool
- name: type
dtype: string
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struct:
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struct:
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struct:
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dtype: string
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dtype: string
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dtype: bool
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dtype: string
- name: poem language type
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 837548964
num_examples: 212499
download_size: 371674261
dataset_size: 837548964
---
# Dataset Card for "ashaar_datasetv2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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unitxt/data | unitxt | 2023-10-03T13:07:44Z | 324 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-10-03T13:07:44Z | 2023-07-10T12:11:50.000Z | 2023-07-10T12:11:50 | ---
license: apache-2.0
---
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crd3 | null | 2022-11-18T19:47:20Z | 323 | 12 | crd3 | [
"task_categories:summarization",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:dialogue-modeling",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | 2022-11-18T19:47:20Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
pretty_name: CRD3 (Critical Role Dungeons and Dragons Dataset)
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- summarization
- text-generation
- fill-mask
task_ids:
- dialogue-modeling
size_categories:
- 10K<n<100K
paperswithcode_id: crd3
dataset_info:
features:
- name: chunk
dtype: string
- name: chunk_id
dtype: int32
- name: turn_start
dtype: int32
- name: turn_end
dtype: int32
- name: alignment_score
dtype: float32
- name: turns
list:
- name: names
sequence: string
- name: utterances
sequence: string
- name: number
dtype: int32
splits:
- name: train
num_bytes: 236605152
num_examples: 38969
- name: test
num_bytes: 40269203
num_examples: 7500
- name: validation
num_bytes: 41543528
num_examples: 6327
download_size: 117519820
dataset_size: 318417883
---
# Dataset Card for "crd3"
## 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:** [CRD3 homepage](https://github.com/RevanthRameshkumar/CRD3)
- **Repository:** [CRD3 repository](https://github.com/RevanthRameshkumar/CRD3)
- **Paper:** [Storytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset](https://www.aclweb.org/anthology/2020.acl-main.459/)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Dataset Summary
Storytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset.
Critical Role is an unscripted, live-streamed show where a fixed group of people play Dungeons and Dragons, an open-ended role-playing game.
The dataset is collected from 159 Critical Role episodes transcribed to text dialogues, consisting of 398,682 turns. It also includes corresponding
abstractive summaries collected from the Fandom wiki. The dataset is linguistically unique in that the narratives are generated entirely through player
collaboration and spoken interaction. For each dialogue, there are a large number of turns, multiple abstractive summaries with varying levels of detail,
and semantic ties to the previous dialogues.
### Supported Tasks and Leaderboards
`summarization`: The dataset can be used to train a model for abstractive summarization. A [fast abstractive summarization-RL](https://github.com/ChenRocks/fast_abs_rl) model was presented as a baseline, which achieves ROUGE-L-F1 of 25.18.
### Languages
The text in the dataset is in English, as spoken by actors on The Critical Role show, which is a weekly unscripted, live-stream of a fixed group of people playing Dungeons and Dragons, a popular role-playing game.
## Dataset Structure
### Data Instances
An example of 'train' looks as follows.
```
{
"alignment_score": 3.679936647415161,
"chunk": "Wish them a Happy Birthday on their Facebook and Twitter pages! Also, as a reminder: D&D Beyond streams their weekly show (\"And Beyond\") every Wednesday on twitch.tv/dndbeyond.",
"chunk_id": 1,
"turn_end": 6,
"turn_num": 4,
"turn_start": 4,
"turns": {
"names": ["SAM"],
"utterances": ["Yesterday, guys, was D&D Beyond's first one--", "first one-year anniversary. Take two. Hey guys,", "yesterday was D&D Beyond's one-year anniversary.", "Wish them a happy birthday on their Facebook and", "Twitter pages."]
}
}
```
### Data Fields
The data fields are the same among all splits.
- `chunk`: a `string` feature.
- `chunk_id`: a `int32` feature.
- `turn_start`: a `int32` feature.
- `turn_end`: a `int32` feature.
- `alignment_score`: a `float32` feature.
- `turn_num`: a `int32` feature.
- `turns`: a dictionary feature containing:
- `names`: a `string` feature.
- `utterances`: a `string` feature.
### Data Splits
| name | train |validation| test |
|-------|------:|---------:|------:|
|default|38,969| 6,327|7,500|
## Dataset Creation
### Curation Rationale
Dialogue understanding and abstractive summarization remain both important and challenging problems for computational linguistics. Current paradigms in summarization modeling have specific failures in capturing semantics and pragmatics, content selection, rewriting, and evaluation in the domain of long, story-telling dialogue. CRD3 offers a linguistically rich dataset to explore these domains.
### Source Data
#### Initial Data Collection and Normalization
Dungeons and Dragons is a popular roleplaying game that is driven by structured storytelling. Critical Role is an unscripted, live-streamed show where a fixed group of people play Dungeons and Dragons. This dataset consists of 159 episodes of the show, where the episodes are transcribed. Inconsistencies (e.g. spelling of speaker names) were manually resolved.
The abstractive summaries were collected from the [Critical Role Fandom wiki](https://criticalrole.fandom.com/)
#### Who are the source language producers?
The language producers are actors on The Critical Role show, which is a weekly unscripted, live-stream of a fixed group of people playing Dungeons and Dragons, a popular role-playing game.
### 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
[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
CRTranscript provided transcripts of the show; contributors of the Critical Role Wiki provided the abstractive summaries.
### Licensing Information
This work is licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License][cc-by-sa-4.0]., as corresponding to the Critical Role Wiki https://criticalrole.fandom.com/
### Citation Information
```bibtex
@inproceedings{
title = {Storytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset},
author = {Rameshkumar, Revanth and Bailey, Peter},
year = {2020},
publisher = {Association for Computational Linguistics},
conference = {ACL}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@mariamabarham](https://github.com/mariamabarham), [@lewtun](https://github.com/lewtun) for adding this dataset. | [
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isizulu_ner_corpus | null | 2023-01-25T14:33:13Z | 323 | 0 | null | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:zu",
"license:other",
"region:us"
] | 2023-01-25T14:33:13Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- zu
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: Isizulu Ner Corpus
license_details: Creative Commons Attribution 2.5 South Africa
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': OUT
'1': B-PERS
'2': I-PERS
'3': B-ORG
'4': I-ORG
'5': B-LOC
'6': I-LOC
'7': B-MISC
'8': I-MISC
config_name: isizulu_ner_corpus
splits:
- name: train
num_bytes: 4038876
num_examples: 10956
download_size: 25097584
dataset_size: 4038876
---
# Dataset Card for Isizulu Ner Corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Isizulu Ner Corpus Homepage](https://repo.sadilar.org/handle/20.500.12185/319)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [Martin Puttkammer](mailto:Martin.Puttkammer@nwu.ac.za)
### Dataset Summary
The isizulu Ner Corpus is a Zulu dataset developed by [The Centre for Text Technology (CTexT), North-West University, South Africa](http://humanities.nwu.ac.za/ctext). The data is based on documents from the South African goverment domain and crawled from gov.za websites. It was created to support NER task for Zulu language. The dataset uses CoNLL shared task annotation standards.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The language supported is Zulu.
## Dataset Structure
### Data Instances
A data point consists of sentences seperated by empty line and tab-seperated tokens and tags.
{'id': '0',
'ner_tags': [7, 8, 0, 0, 0],
'tokens': ['Lesi', 'sigaba', 'se-website', ',', 'esikhonjiswe']
}
### Data Fields
- `id`: id of the sample
- `tokens`: the tokens of the example text
- `ner_tags`: the NER tags of each token
The NER tags correspond to this list:
```
"OUT", "B-PERS", "I-PERS", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-MISC", "I-MISC",
```
The NER tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word. There are four types of phrases: person names (PER), organizations (ORG), locations (LOC) and miscellaneous names (MISC). (OUT) is used for tokens not considered part of any named entity.
### Data Splits
The data was not split.
## Dataset Creation
### Curation Rationale
The data was created to help introduce resources to new language - zulu.
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The data is based on South African government domain and was crawled from gov.za websites.
#### Who are the source language producers?
The data was produced by writers of South African government websites - gov.za
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
The data was annotated during the NCHLT text resource development project.
[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
The annotated data sets were developed by the Centre for Text Technology (CTexT, North-West University, South Africa).
See: [more information](http://www.nwu.ac.za/ctext)
### Licensing Information
The data is under the [Creative Commons Attribution 2.5 South Africa License](http://creativecommons.org/licenses/by/2.5/za/legalcode)
### Citation Information
```
@inproceedings{isizulu_ner_corpus,
author = {A.N. Manzini and
Roald Eiselen},
title = {NCHLT isiZulu Named Entity Annotated Corpus},
booktitle = {Eiselen, R. 2016. Government domain named entity recognition for South African languages. Proceedings of the 10th Language Resource and Evaluation Conference, Portorož, Slovenia.},
year = {2016},
url = {https://repo.sadilar.org/handle/20.500.12185/319},
}
```
### Contributions
Thanks to [@yvonnegitau](https://github.com/yvonnegitau) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
tunizi | null | 2023-01-25T14:54:36Z | 323 | 0 | tunizi | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:aeb",
"license:unknown",
"arxiv:2004.14303",
"region:us"
] | 2023-01-25T14:54:36Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- aeb
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: tunizi
pretty_name: TUNIZI
dataset_info:
features:
- name: id
dtype: string
- name: sentence
dtype: string
- name: target
dtype:
class_label:
names:
'0': '1'
'1': '-1'
splits:
- name: train
num_bytes: 211166
num_examples: 3000
download_size: 162781
dataset_size: 211166
---
# Dataset Card for TUNIZI
## 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/chaymafourati/TUNIZI-Sentiment-Analysis-Tunisian-Arabizi-Dataset
- **Repository:** https://github.com/chaymafourati/TUNIZI-Sentiment-Analysis-Tunisian-Arabizi-Dataset
- **Paper:** https://arxiv.org/abs/2004.14303
- **Point of Contact:** Chayma Fourati (chayma@icompass.digital)
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
This dataset uses Tunisian Arabic written with latin script (BCP-47: aeb-Latn)
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. | [
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PereLluis13/spanish_speech_text | PereLluis13 | 2022-02-04T17:32:37Z | 323 | 1 | null | [
"region:us"
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zeroshot/twitter-financial-news-topic | zeroshot | 2022-12-04T16:50:10Z | 323 | 16 | null | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"twitter",
"finance",
"markets",
"stocks",
"wallstreet",
"quant",
"hedgefunds",
"region:us"
] | 2022-12-04T16:50:10Z | 2022-09-07T18:43:21.000Z | 2022-09-07T18:43:21 | ---
annotations_creators:
- other
language:
- en
language_creators:
- other
license:
- mit
multilinguality:
- monolingual
pretty_name: twitter financial news
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- twitter
- finance
- markets
- stocks
- wallstreet
- quant
- hedgefunds
- markets
task_categories:
- text-classification
task_ids:
- multi-class-classification
---
Read this [BLOG](https://neuralmagic.com/blog/classifying-finance-tweets-in-real-time-with-sparse-transformers/) to see how I fine-tuned a sparse transformer on this dataset.
### Dataset Description
The Twitter Financial News dataset is an English-language dataset containing an annotated corpus of finance-related tweets. This dataset is used to classify finance-related tweets for their topic.
1. The dataset holds 21,107 documents annotated with 20 labels:
```python
topics = {
"LABEL_0": "Analyst Update",
"LABEL_1": "Fed | Central Banks",
"LABEL_2": "Company | Product News",
"LABEL_3": "Treasuries | Corporate Debt",
"LABEL_4": "Dividend",
"LABEL_5": "Earnings",
"LABEL_6": "Energy | Oil",
"LABEL_7": "Financials",
"LABEL_8": "Currencies",
"LABEL_9": "General News | Opinion",
"LABEL_10": "Gold | Metals | Materials",
"LABEL_11": "IPO",
"LABEL_12": "Legal | Regulation",
"LABEL_13": "M&A | Investments",
"LABEL_14": "Macro",
"LABEL_15": "Markets",
"LABEL_16": "Politics",
"LABEL_17": "Personnel Change",
"LABEL_18": "Stock Commentary",
"LABEL_19": "Stock Movement",
}
```
The data was collected using the Twitter API. The current dataset supports the multi-class classification task.
### Task: Topic Classification
# Data Splits
There are 2 splits: train and validation. Below are the statistics:
| Dataset Split | Number of Instances in Split |
| ------------- | ------------------------------------------- |
| Train | 16,990 |
| Validation | 4,118 |
# Licensing Information
The Twitter Financial Dataset (topic) version 1.0.0 is released under the MIT License. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
smangrul/chat-instruct-mixer | smangrul | 2023-09-08T05:44:19Z | 323 | 4 | null | [
"region:us"
] | 2023-09-08T05:44:19Z | 2023-09-08T03:03:01.000Z | 2023-09-08T03:03:01 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: content
dtype: string
splits:
- name: train
num_bytes: 169947792.7111158
num_examples: 73302
- name: test
num_bytes: 48395025.62775446
num_examples: 23318
download_size: 123606462
dataset_size: 218342818.33887026
---
# Chat-Instruct-Mixer Dataset
This dataset is focused on improving LLM logical reasoning skills and conversation skills. It is comprised of the following datasets:
| Dataset Name | Train Mixing Percentage/Samples | Test Mixing Percentage/Samples |
|--------------------------------------------------------------|--------------|------------------|
| [timdettmers/openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) | 100% | 300 samples |
| [GAIR/lima](https://huggingface.co/datasets/GAIR/lima) | 100% | 518 samples |
| [garage-bAInd/Open-Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus) | 100% minus the samples set aside for test split | 2500 samples |
| [Open-Orca/OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca) | 10000 samples from GPT-4 split | 5000 samples |
| [ehartford/dolphin](https://huggingface.co/datasets/ehartford/dolphin) | 10000 samples from GPT-4 split | 5000 samples |
| [stingning/ultrachat](https://huggingface.co/datasets/stingning/ultrachat) | 10000 samples | 5000 samples |
| [jondurbin/airoboros-2.2](https://huggingface.co/datasets/jondurbin/airoboros-2.2) | 10000 Samples while filtering out samples with `skip_prompt_formatting==True` | 5000 samples |
Code for Creating this dataset: [ToDo]()
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journalists_questions | null | 2023-01-25T14:33:26Z | 322 | 0 | null | [
"task_categories:text-classification",
"annotations_creators:crowdsourced",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ar",
"license:unknown",
"question-identification",
"region:us"
] | 2023-01-25T14:33:26Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- other
language:
- ar
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
pretty_name: JournalistsQuestions
tags:
- question-identification
dataset_info:
features:
- name: tweet_id
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'no'
'1': 'yes'
- name: label_confidence
dtype: float32
config_name: plain_text
splits:
- name: train
num_bytes: 342296
num_examples: 10077
download_size: 271039
dataset_size: 342296
---
# Dataset Card for journalists_questions
## 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://qufaculty.qu.edu.qa/telsayed/datasets/
- **Repository:** [Needs More Information]
- **Paper:** https://www.aaai.org/ocs/index.php/ICWSM/ICWSM16/paper/download/13221/12856
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Maram Hasanain]
maram.hasanain@qu.edu.qa
### Dataset Summary
The journalists_questions dataset supports question identification over Arabic tweets of journalists.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Arabic
## Dataset Structure
### Data Instances
Our dataset supports question identification task. It includes 10K Arabic tweets crawled from journalists accounts. Tweets were labelled by crowdsourcing. Each tweet is associated with one label: question tweet or not. A question tweet is a tweet that has at least one interrogative question. Each label is associated with a number that represents the confidence in the label, given that each tweet was labelled by 3 annotators and an aggregation method was followed to choose the final label.
Below is an example:
{
'tweet_id': '493235142128074753',
'label': 'yes',
'label_confidence':0.6359
}
### Data Fields
tweet_id: the Twitter assigned ID for the tweet object.
label: annotation of the tweet by whether it is a question or not
label_confidence: confidence score for the label given annotations of multiple annotators per tweet
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
The dataset includes tweet IDs only due to Twitter content re-distribution policy. It was created and shared for research purposes for parties interested in understanding questions expecting answers by Arab journalists on Twitter.
### Source Data
#### Initial Data Collection and Normalization
To construct our dataset of question tweets posted by journalists, we first acquire a list of Twitter accounts of 389 Arab journalists. We use the Twitter API to crawl their available tweets, keeping only those that are identified by Twitter to be both Arabic, and not retweets (as these would contain content that was not originally authored by journalists). We apply a rule-based question filter to this dataset of 465,599 tweets, extracting 49,119 (10.6%) potential question tweets from 363 (93.3%) Arab journalists.
#### 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 [@MaramHasanain](https://github.com/MaramHasanain) for adding this dataset. | [
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m_lama | null | 2022-11-03T16:15:15Z | 322 | 4 | null | [
"task_categories:question-answering",
"task_categories:text-classification",
"task_ids:open-domain-qa",
"task_ids:text-scoring",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"language_creators:machine-generated",
"multilinguality:translation",
"size_categories:100K<n<1M",
"source_datasets:extended|lama",
"language:af",
"language:ar",
"language:az",
"language:be",
"language:bg",
"language:bn",
"language:ca",
"language:ceb",
"language:cs",
"language:cy",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
"language:et",
"language:eu",
"language:fa",
"language:fi",
"language:fr",
"language:ga",
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"language:he",
"language:hi",
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"language:th",
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"language:ur",
"language:vi",
"language:zh",
"license:cc-by-nc-sa-4.0",
"probing",
"arxiv:2102.00894",
"region:us"
] | 2022-11-03T16:15:15Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
- expert-generated
- machine-generated
language_creators:
- crowdsourced
- expert-generated
- machine-generated
language:
- af
- ar
- az
- be
- bg
- bn
- ca
- ceb
- cs
- cy
- da
- de
- el
- en
- es
- et
- eu
- fa
- fi
- fr
- ga
- gl
- he
- hi
- hr
- hu
- hy
- id
- it
- ja
- ka
- ko
- la
- lt
- lv
- ms
- nl
- pl
- pt
- ro
- ru
- sk
- sl
- sq
- sr
- sv
- ta
- th
- tr
- uk
- ur
- vi
- zh
license:
- cc-by-nc-sa-4.0
multilinguality:
- translation
size_categories:
- 100K<n<1M
source_datasets:
- extended|lama
task_categories:
- question-answering
- text-classification
task_ids:
- open-domain-qa
- text-scoring
paperswithcode_id: null
pretty_name: MLama
tags:
- probing
dataset_info:
features:
- name: uuid
dtype: string
- name: lineid
dtype: uint32
- name: obj_uri
dtype: string
- name: obj_label
dtype: string
- name: sub_uri
dtype: string
- name: sub_label
dtype: string
- name: template
dtype: string
- name: language
dtype: string
- name: predicate_id
dtype: string
config_name: all
splits:
- name: test
num_bytes: 125919995
num_examples: 843143
download_size: 40772287
dataset_size: 125919995
---
# 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:** [Multilingual LAMA](http://cistern.cis.lmu.de/mlama/)
- **Repository:** [Github](https://github.com/norakassner/mlama)
- **Paper:** [Arxiv](https://arxiv.org/abs/2102.00894)
- **Point of Contact:** [Contact section](http://cistern.cis.lmu.de/mlama/)
### Dataset Summary
This dataset provides the data for mLAMA, a multilingual version of LAMA.
Regarding LAMA see https://github.com/facebookresearch/LAMA. For mLAMA
the TREx and GoogleRE part of LAMA was considered and machine translated using
Google Translate, and the Wikidata and Google Knowledge Graph API. The machine
translated templates were checked for validity, i.e., whether they contain
exactly one '[X]' and one '[Y]'.
This data can be used for creating fill-in-the-blank queries like
"Paris is the capital of [MASK]" across 53 languages. For more details see
the website http://cistern.cis.lmu.de/mlama/ or the github repo https://github.com/norakassner/mlama.
### Supported Tasks and Leaderboards
Language model knowledge probing.
### Languages
This dataset contains data in 53 languages:
af,ar,az,be,bg,bn,ca,ceb,cs,cy,da,de,el,en,es,et,eu,fa,fi,fr,ga,gl,he,hi,hr,hu,hy,id,it,ja,ka,ko,la,lt,lv,ms,nl,pl,pt,ro,ru,sk,sl,sq,sr,sv,ta,th,tr,uk,ur,vi,zh
## Dataset Structure
For each of the 53 languages and each of the 43 relations/predicates there is a set of triples.
### Data Instances
For each language and relation there are triples, that consists of an object, a predicate and a subject. For each predicate there is a template available. An example for `dataset["test"][0]` is given here:
```python
{
'language': 'af',
'lineid': 0,
'obj_label': 'Frankryk',
'obj_uri': 'Q142',
'predicate_id': 'P1001',
'sub_label': 'President van Frankryk',
'sub_uri': 'Q191954',
'template': "[X] is 'n wettige term in [Y].",
'uuid': '3fe3d4da-9df9-45ba-8109-784ce5fba38a'
}
```
### Data Fields
Each instance has the following fields
* "uuid": a unique identifier
* "lineid": a identifier unique to mlama
* "obj_id": knowledge graph id of the object
* "obj_label": surface form of the object
* "sub_id": knowledge graph id of the subject
* "sub_label": surface form of the subject
* "template": template
* "language": language code
* "predicate_id": relation id
### Data Splits
There is only one partition that is labelled as 'test data'.
## Dataset Creation
### Curation Rationale
The dataset was translated into 53 languages to investigate knowledge in pretrained language models
multilingually.
### Source Data
#### Initial Data Collection and Normalization
The data has several sources:
LAMA (https://github.com/facebookresearch/LAMA) licensed under Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
T-REx (https://hadyelsahar.github.io/t-rex/) licensed under Creative Commons Attribution-ShareAlike 4.0 International License
Google-RE (https://github.com/google-research-datasets/relation-extraction-corpus)
Wikidata (https://www.wikidata.org/) licensed under Creative Commons CC0 License and Creative Commons Attribution-ShareAlike License
#### Who are the source language producers?
See links above.
### Annotations
#### Annotation process
Crowdsourced (wikidata) and machine translated.
#### Who are the annotators?
Unknown.
### Personal and Sensitive Information
Names of (most likely) famous people who have entries in Google Knowledge Graph or Wikidata.
## Considerations for Using the Data
Data was created through machine translation and automatic processes.
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
Not all triples are available in all languages.
## Additional Information
### Dataset Curators
The authors of the mLAMA paper and the authors of the original datasets.
### Licensing Information
The Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). https://creativecommons.org/licenses/by-nc-sa/4.0/
### Citation Information
```
@article{kassner2021multilingual,
author = {Nora Kassner and
Philipp Dufter and
Hinrich Sch{\"{u}}tze},
title = {Multilingual {LAMA:} Investigating Knowledge in Multilingual Pretrained
Language Models},
journal = {CoRR},
volume = {abs/2102.00894},
year = {2021},
url = {https://arxiv.org/abs/2102.00894},
archivePrefix = {arXiv},
eprint = {2102.00894},
timestamp = {Tue, 09 Feb 2021 13:35:56 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2102-00894.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
note = {to appear in EACL2021}
}
```
### Contributions
Thanks to [@pdufter](https://github.com/pdufter) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
parsinlu_reading_comprehension | null | 2023-08-16T17:04:40Z | 322 | 1 | null | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|wikipedia|google",
"language:fa",
"license:cc-by-nc-sa-4.0",
"arxiv:2012.06154",
"region:us"
] | 2023-08-16T17:04:40Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- fa
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|wikipedia|google
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: null
pretty_name: PersiNLU (Reading Comprehension)
dataset_info:
features:
- name: question
dtype: string
- name: url
dtype: string
- name: context
dtype: string
- name: answers
sequence:
- name: answer_start
dtype: int32
- name: answer_text
dtype: string
config_name: parsinlu-repo
splits:
- name: train
num_bytes: 747679
num_examples: 600
- name: test
num_bytes: 674711
num_examples: 570
- name: validation
num_bytes: 163161
num_examples: 125
download_size: 4105495
dataset_size: 1585527
---
# Dataset Card for PersiNLU (Reading Comprehension)
## 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/persiannlp/parsinlu/)
- **Repository:** [Github](https://github.com/persiannlp/parsinlu/)
- **Paper:** [Arxiv](https://arxiv.org/abs/2012.06154)
- **Leaderboard:**
- **Point of Contact:** [email](d.khashabi@gmail.com)
### Dataset Summary
A Persian reading comprehenion task (generating an answer, given a question and a context paragraph).
The questions are mined using Google auto-complete, their answers and the corresponding evidence documents are manually annotated by native speakers.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text dataset is in Persian (`fa`).
## Dataset Structure
### Data Instances
Here is an example from the dataset:
```
{
'question': 'پیامبر در چه سالی به پیامبری رسید؟',
'url': 'https://fa.wikipedia.org/wiki/%D9%85%D8%AD%D9%85%D8%AF',
'passage': 'محمد که از روش زندگی مردم مکه ناخشنود بود، گهگاه در غار حرا در یکی از کوه\u200cهای اطراف آن دیار به تفکر و عبادت می\u200cپرداخت. به باور مسلمانان، محمد در همین مکان و در حدود ۴۰ سالگی از طرف خدا به پیامبری برگزیده، و وحی بر او فروفرستاده شد. در نظر آنان، دعوت محمد همانند دعوت دیگر پیامبرانِ کیش یکتاپرستی مبنی بر این بود که خداوند (الله) یکتاست و تسلیم شدن برابر خدا راه رسیدن به اوست.',
'answers': [
{'answer_start': 160, 'answer_text': 'حدود ۴۰ سالگی'}
]
}
```
### Data Fields
- `question`: the question, mined using Google auto-complete.
- `passage`: the passage that contains the answer.
- `url`: the url from which the passage was mined.
- `answers`: a list of answers, containing the string and the index of the answer with the fields `answer_start` and `answer_text`. Note that in the test set, some `answer_start` values are missing and replaced with `-1`
### Data Splits
The train/test split contains 600/575 samples.
## Dataset Creation
### Curation Rationale
The question were collected via Google auto-complete.
The answers were annotated by native speakers.
For more details, check [the corresponding draft](https://arxiv.org/abs/2012.06154).
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
Dataset provided for research purposes only. Please check dataset license for additional information.
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
CC BY-NC-SA 4.0 License
### Citation Information
```bibtex
@article{huggingface:dataset,
title = {ParsiNLU: A Suite of Language Understanding Challenges for Persian},
authors = {Khashabi, Daniel and Cohan, Arman and Shakeri, Siamak and Hosseini, Pedram and Pezeshkpour, Pouya and Alikhani, Malihe and Aminnaseri, Moin and Bitaab, Marzieh and Brahman, Faeze and Ghazarian, Sarik and others},
year={2020}
journal = {arXiv e-prints},
eprint = {2012.06154},
}
```
### Contributions
Thanks to [@danyaljj](https://github.com/danyaljj) for adding this dataset. | [
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thai_toxicity_tweet | null | 2023-01-25T14:45:38Z | 322 | 2 | null | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:th",
"license:cc-by-nc-3.0",
"region:us"
] | 2023-01-25T14:45:38Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- th
license:
- cc-by-nc-3.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: ThaiToxicityTweet
dataset_info:
features:
- name: tweet_id
dtype: string
- name: tweet_text
dtype: string
- name: toxic_votes
dtype: int32
- name: nontoxic_votes
dtype: int32
- name: is_toxic
dtype:
class_label:
names:
'0': neg
'1': pos
config_name: thai_toxicity_tweet
splits:
- name: train
num_bytes: 637387
num_examples: 3300
download_size: 194740
dataset_size: 637387
---
# Dataset Card for `thai_toxicity_tweet`
## 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/tmu-nlp/ThaiToxicityTweetCorpus/
- **Repository:** https://github.com/tmu-nlp/ThaiToxicityTweetCorpus/
- **Paper:** https://www.ta-cos.org/sites/ta-cos.org/files/1_W32.pdf
- **Leaderboard:**
- **Point of Contact:** https://www.ta-cos.org/sites/ta-cos.org/files/1_W32.pdf
### Dataset Summary
Thai Toxicity Tweet Corpus contains 3,300 tweets (506 tweets with texts missing) annotated by humans with guidelines including a 44-word dictionary.
The author obtained 2,027 and 1,273 toxic and non-toxic tweets, respectively; these were labeled by three annotators. The result of corpus
analysis indicates that tweets that include toxic words are not always toxic. Further, it is more likely that a tweet is toxic, if it contains
toxic words indicating their original meaning. Moreover, disagreements in annotation are primarily because of sarcasm, unclear existing
target, and word sense ambiguity.
Notes from data cleaner: The data is included into [huggingface/datasets](https://www.github.com/huggingface/datasets) in Dec 2020. By this time, 506 of the tweets are not available publicly anymore. We denote these by `TWEET_NOT_FOUND` in `tweet_text`.
Processing can be found at [this PR](https://github.com/tmu-nlp/ThaiToxicityTweetCorpus/pull/1).
### Supported Tasks and Leaderboards
text classification
### Languages
Thai (`th`)
## Dataset Structure
### Data Instances
```
{'is_toxic': 0, 'nontoxic_votes': 3, 'toxic_votes': 0, 'tweet_id': '898576382384418817', 'tweet_text': 'วันๆ นี่คุยกะหมา แมว หมู ไก่ ม้า ควาย มากกว่าคุยกับคนไปละ'}
{'is_toxic': 1, 'nontoxic_votes': 0, 'toxic_votes': 3, 'tweet_id': '898573084981985280', 'tweet_text': 'ควายแดงเมิงด่ารัฐบาลจนรองนายกป่วย พวกมึงกำลังทำลายชาติรู้มั้ย มั้ย มั้ย มั้ยยยยยยยยย news.voicetv.co.th/thailand/51672…'}
```
### Data Fields
"tweet_id": Id of tweet on Twitter
"tweet_text": text of the tweet
"toxic_votes": how many annotators say it is toxic, out of 3 annotators
"nontoxic_votes": how many annotators say it is NOT toxic, out of 3 annotators
"is_toxic": 1 if tweet is toxic else 0 (majority rules)
### Data Splits
No explicit split is given.
## Dataset Creation
### Curation Rationale
The dataset is created as part of [Sirihattasak et al (2019)](https://www.ta-cos.org/sites/ta-cos.org/files/1_W32.pdf).
### Source Data
#### Initial Data Collection and Normalization
The authors used the public Twitter Search API to collect 9,819 tweets from January–December 2017 based on our keyword dictionary. Then, they selected 75 tweets for each keyword. In total, they collected 3,300 tweets for annotation. To ensure quality of data, they set the following selection criteria.
1. All tweets are selected by humans to prevent word ambiguity. (The Twitter API selected the tweets based on characters in the keyword. For example, in the case of “บ้า(crazy),” the API will also select “บ้านนอก” (countryside)” which is not our target.)
2. The length of the tweet should be sufficiently long to discern the context of the tweet. Hence, they set five words as the minimum limit.
3. The tweets that contain only extremely toxic words, (for example: “damn, retard, bitch, f*ck, slut!!!”) are not considered.
4. In addition, they allowed tweets with English words if they were not critical elements in the labeling decision, for example, the word “f*ck.” As a result, our corpus contains English words, but they are less than 2% of the total.
All hashtags, re-tweets, and links were removed from these tweets. However, they did not delete emoticons because these emotional icons can imply the real intent of the post owners. Furthermore, only in the case of annotation, some entries such as the names of famous people were replaced with a tag <ไม่ขอเปิดเผยชื่อ>, for anonymity to prevent individual bias.
#### Who are the source language producers?
Twitter users in Thailand
### Annotations
#### Annotation process
We manually annotated our dataset with two labels: Toxic and Non-Toxic. We define a message as toxic if it indicates any harmful, damage, or negative intent based on our definition of toxicity. Furthermore, all the tweets were annotated by three annotators to identify toxicity; the conditions used for this identification are presented in the following list.
- A toxic message is a message that should be deleted or not be allowed in public.
- A message’s target or consequence must exist. It can either be an individual or a generalized group based on a commonality such as religion or ethnicity, or an entire community.
- Self-complain is not considered toxic, because it is not harmful to anyone. However, if self-complain is intended to indicate something bad, it will be considered as toxic.
- Both direct and indirect messages including those with sarcasm are taken into consideration.
We strictly instructed all the annotators about these concepts and asked them to perform a small test to ensure they understood these conditions. The annotation process was divided into two rounds. We asked the candidates to annotate their answers in the first round to learn our annotation standard. Then, we asked them to annotate a different dataset and selected the ones who obtained a full-score for the second round as an annotator. From among these annotators, 20% of the annotators failed the first round and were not involved in the final annotation.
#### Who are the annotators?
Three annotators hired by [Sirihattasak et al (2019)](https://www.ta-cos.org/sites/ta-cos.org/files/1_W32.pdf)
### Personal and Sensitive Information
Despite all tweets being public, due to the nature of toxic tweets, there might be personal attacks and toxic language used.
## Considerations for Using the Data
### Social Impact of Dataset
- toxic social media message classification dataset
### Discussion of Biases
- Users are masked before annotation by the annotators to prevent biases based on tweet authors
### Other Known Limitations
- The data is included into [huggingface/datasets](https://www.github.com/huggingface/datasets) in Dec 2020. By this time, 506 of the tweets are not available publicly anymore. We denote these by `TWEET_NOT_FOUND` in `tweet_text`.
## Additional Information
### Dataset Curators
[Sirihattasak et al (2019)](https://www.ta-cos.org/sites/ta-cos.org/files/1_W32.pdf)
### Licensing Information
CC-BY-NC 3.0
### Citation Information
Please cite the following if you make use of the dataset:
```
@article{sirihattasak2019annotation,
title={Annotation and Classification of Toxicity for Thai Twitter},
author={Sirihattasak, Sugan and Komachi, Mamoru and Ishikawa, Hiroshi},
year={2019}
}
```
### Contributions
Thanks to [@cstorm125](https://github.com/cstorm125) for adding this dataset. | [
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LeverageX/klue-re | LeverageX | 2022-01-10T07:43:15Z | 322 | 0 | null | [
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WorkInTheDark/FairytaleQA | WorkInTheDark | 2023-08-22T18:49:30Z | 322 | 1 | null | [
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"education",
"children education",
"region:us"
] | 2023-08-22T18:49:30Z | 2022-05-18T19:11:00.000Z | 2022-05-18T19:11:00 | ---
license: apache-2.0
task_categories:
- question-answering
- text-generation
language:
- en
tags:
- education
- children education
---
# Dataset Card for FairytaleQA
## Dataset Description
- **Homepage:**
- **Repository:**
https://github.com/uci-soe/FairytaleQAData
https://github.com/WorkInTheDark/FairytaleQA_Dataset
- **Paper:**
https://aclanthology.org/2022.acl-long.34/
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This is the repository for the FairytaleQA dataset, an open-source dataset focusing on comprehension of narratives, targeting students from kindergarten to eighth grade. The FairytaleQA dataset is annotated by education experts based on an evidence-based theoretical framework. It consists of 10,580 explicit and implicit questions derived from 278 children-friendly stories, covering seven types of narrative elements or relations.
### Supported Tasks and Leaderboards
Question-Answering, Question-Generation, Question-Answer Pair Generation
### Languages
English
## Dataset Structure
### Data Instances
An example of "train" looks as follows:
```
{
'story_name': 'three-dogs',
'story_section': 'once upon a time there was a king who went forth into the world and
... ...
guards to watch over the little princess so that she would not get out under the open sky .',
'question': 'why was there great rejoicing in the city and throughout the country ?',
'answer1': 'the people wished their king all that was good .',
'answer2': '',
'local-or-sum': 'local',
'attribute': 'causal relationship',
'ex-or-im': 'explicit',
'ex-or-im2': '',
}
```
### Data Fields
- **'story_name'**: story name
- **'story_section'**: story section related to the QA-pair
- **'question'**: the question content
- **'answer1'**: the 1st answer (available in all splits)
- **'answer2'**: the 2nd answer by another annotator (only available in test / val splits)
- **'local-or-sum'**: 'local' denotes the question is related to only one story section, while 'summary' denotes the question is related to multiple story sections
- **'attribute'**: categorized by education experts into seven narrative elements: character / setting / action / feeling / causal relationship / outcome resolution, detailed definition is described in the paper
- **'ex-or-im'**: 'explicit' denotes the answer can be found in the story content, while 'implicit' denotes the answer require high-level summarization
- **'ex-or-im2'**: similar to 'ex-or-im', but annotated by another annotator (only available in storys in test / val splits)
### Data Splits
- train split: 232 books with 8548 QA-pairs
- val split: 23 books with 1025 QA-pairs
- test split: 23 books with 1007 QA-pairs
## 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
Our Dataset Paper is accepted to ACL 2022, you may cite:
```
@inproceedings{xu2022fairytaleqa,
author={Xu, Ying and Wang, Dakuo and Yu, Mo and Ritchie, Daniel and Yao, Bingsheng and Wu, Tongshuang and Zhang, Zheng and Li, Toby Jia-Jun and Bradford, Nora and Sun, Branda and Hoang, Tran Bao and Sang, Yisi and Hou, Yufang and Ma, Xiaojuan and Yang, Diyi and Peng, Nanyun and Yu, Zhou and Warschauer, Mark},
title = {Fantastic Questions and Where to Find Them: Fairytale{QA} -- An Authentic Dataset for Narrative Comprehension},
publisher = {Association for Computational Linguistics},
year = {2022}
}
```
### Contributions
[More Information Needed] | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
daekeun-ml/naver-news-summarization-ko | daekeun-ml | 2023-01-10T11:12:44Z | 322 | 14 | null | [
"task_categories:summarization",
"size_categories:10K<n<100K",
"language:ko",
"license:apache-2.0",
"region:us"
] | 2023-01-10T11:12:44Z | 2022-08-01T14:54:17.000Z | 2022-08-01T14:54:17 | ---
license: apache-2.0
task_categories:
- summarization
language:
- ko
size_categories:
- 10K<n<100K
---
This dataset is a custom dataset created by the author by crawling Naver News (https://news.naver.com) for the Korean NLP model hands-on.
- Period: July 1, 2022 - July 10, 2022
- Subject: IT, economics
```
DatasetDict({
train: Dataset({
features: ['date', 'category', 'press', 'title', 'document', 'link', 'summary'],
num_rows: 22194
})
test: Dataset({
features: ['date', 'category', 'press', 'title', 'document', 'link', 'summary'],
num_rows: 2740
})
validation: Dataset({
features: ['date', 'category', 'press', 'title', 'document', 'link', 'summary'],
num_rows: 2466
})
})
```
---
license: apache-2.0
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AarushSah/scibowl_no_headers | AarushSah | 2023-11-14T07:36:48Z | 322 | 0 | null | [
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taskmaster3 | null | 2022-11-03T16:30:39Z | 321 | 0 | null | [
"task_categories:text-generation",
"task_categories:fill-mask",
"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:1909.05358",
"region:us"
] | 2022-11-03T16:30:39Z | 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:
- text-generation
- fill-mask
task_ids:
- dialogue-modeling
paperswithcode_id: null
pretty_name: taskmaster3
dataset_info:
features:
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dtype: string
- name: vertical
dtype: string
- name: instructions
dtype: string
- name: scenario
dtype: string
- name: utterances
list:
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dtype: int32
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dtype: string
splits:
- name: train
num_bytes: 143609327
num_examples: 23757
download_size: 313402141
dataset_size: 143609327
---
# Dataset Card for taskmaster3
## 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:** [Taskmaster](https://research.google/tools/datasets/taskmaster-1/)
- **Repository:** [GitHub](https://github.com/google-research-datasets/Taskmaster/tree/master/TM-3-2020)
- **Paper:** [Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset](https://arxiv.org/abs/1909.05358)
- **Leaderboard:** N/A
- **Point of Contact:** [Taskmaster Googlegroup](taskmaster-datasets@googlegroups.com)
### Dataset Summary
Taskmaster is dataset for goal oriented conversations. The Taskmaster-3 dataset consists of 23,757 movie ticketing dialogs.
By "movie ticketing" we mean conversations where the customer's goal is to purchase tickets after deciding
on theater, time, movie name, number of tickets, and date, or opt out of the transaction. This collection
was created using the "self-dialog" method. This means a single, crowd-sourced worker is
paid to create a conversation writing turns for both speakers, i.e. the customer and the ticketing agent.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset is in English language.
## Dataset Structure
### Data Instances
A typical example looks like this
```
{
"conversation_id": "dlg-ddee80da-9ffa-4773-9ce7-f73f727cb79c",
"instructions": "SCENARIO: Pretend you’re *using a digital assistant to purchase tickets for a movie currently showing in theaters*. ...",
"scenario": "4 exchanges with 1 error and predefined variables",
"utterances": [
{
"apis": [],
"index": 0,
"segments": [
{
"annotations": [
{
"name": "num.tickets"
}
],
"end_index": 21,
"start_index": 20,
"text": "2"
},
{
"annotations": [
{
"name": "name.movie"
}
],
"end_index": 42,
"start_index": 37,
"text": "Mulan"
}
],
"speaker": "user",
"text": "I would like to buy 2 tickets to see Mulan."
},
{
"index": 6,
"segments": [],
"speaker": "user",
"text": "Yes.",
"apis": [
{
"args": [
{
"arg_name": "name.movie",
"arg_value": "Mulan"
},
{
"arg_name": "name.theater",
"arg_value": "Mountain AMC 16"
}
],
"index": 6,
"name": "book_tickets",
"response": [
{
"response_name": "status",
"response_value": "success"
}
]
}
]
}
],
"vertical": "Movie Tickets"
}
```
### Data Fields
Each conversation in the data file has the following structure:
- `conversation_id`: A universally unique identifier with the prefix 'dlg-'. The ID has no meaning.
- `utterances`: A list of utterances that make up the conversation.
- `instructions`: Instructions for the crowdsourced worker used in creating the conversation.
- `vertical`: In this dataset the vertical for all dialogs is "Movie Tickets".
- `scenario`: This is the title of the instructions for each dialog.
Each utterance has the following fields:
- `index`: A 0-based index indicating the order of the utterances in the conversation.
- `speaker`: Either USER or ASSISTANT, indicating which role generated this utterance.
- `text`: The raw text of the utterance. In case of self dialogs (one_person_dialogs), this is written by the crowdsourced worker. In case of the WOz dialogs, 'ASSISTANT' turns are written and 'USER' turns are transcribed from the spoken recordings of crowdsourced workers.
- `segments`: A list of various text spans with semantic annotations.
- `apis`: An array of API invocations made during the utterance.
Each API has the following structure:
- `name`: The name of the API invoked (e.g. find_movies).
- `index`: The index of the parent utterance.
- `args`: A `list` of `dict` with keys `arg_name` and `arg_value` which represent the name of the argument and the value for the argument respectively.
- `response`: A `list` of `dict`s with keys `response_name` and `response_value` which represent the name of the response and the value for the response respectively.
Each segment has the following fields:
- `start_index`: The position of the start of the annotation in the utterance text.
- `end_index`: The position of the end of the annotation in the utterance text.
- `text`: The raw text that has been annotated.
- `annotations`: A list of annotation details for this segment.
Each annotation has a single field:
- `name`: The annotation name.
### Data Splits
There are no deafults splits for all the config. The below table lists the number of examples in each config.
| | Train |
|-------------------|--------|
| n_instances | 23757 |
## 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
The dataset is licensed under `Creative Commons Attribution 4.0 License`
### Citation Information
[More Information Needed]
```
@inproceedings{48484,
title = {Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset},
author = {Bill Byrne and Karthik Krishnamoorthi and Chinnadhurai Sankar and Arvind Neelakantan and Daniel Duckworth and Semih Yavuz and Ben Goodrich and Amit Dubey and Kyu-Young Kim and Andy Cedilnik},
year = {2019}
}
```
### Contributions
Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset. | [
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Graphcore/wikipedia-bert-512 | Graphcore | 2022-09-07T14:43:02Z | 321 | 0 | null | [
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | 2022-09-07T14:43:02Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
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license:
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PlanTL-GOB-ES/pharmaconer | PlanTL-GOB-ES | 2022-11-18T12:06:36Z | 321 | 4 | 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"
] | 2022-11-18T12:06:36Z | 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
---
# PharmaCoNER
## Dataset Description
Manually classified collection of Spanish clinical case studies.
- **Homepage:** [zenodo](https://zenodo.org/record/4270158)
- **Paper:** [PharmaCoNER: Pharmacological Substances, Compounds and proteins Named Entity Recognition track](https://aclanthology.org/D19-5701/)
- **Point of Contact:** encargo-pln-life@bsc.es
### Dataset Summary
Manually classified collection of clinical case studies derived from the Spanish Clinical Case Corpus (SPACCC), an open access electronic library that gathers Spanish medical publications from [SciELO](https://scielo.org/).
The PharmaCoNER corpus contains a total of 396,988 words and 1,000 clinical cases that have been randomly sampled into 3 subsets.
The training set contains 500 clinical cases, while the development and test sets contain 250 clinical cases each.
In terms of training examples, this translates to a total of 8129, 3787 and 3952 annotated sentences in each set.
The original dataset is distributed in [Brat](https://brat.nlplab.org/standoff.html) format.
The annotation of the entire set of entity mentions was carried out by domain experts.
It includes the following 4 entity types: NORMALIZABLES, NO_NORMALIZABLES, PROTEINAS and UNCLEAR.
This dataset was designed for the PharmaCoNER 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/pharmaconer/).
### Supported Tasks
Named Entity Recognition (NER)
### Languages
- Spanish (es)
### Directory Structure
* README.md
* pharmaconer.py
* dev-set_1.1.conll
* test-set_1.1.conll
* train-set_1.1.conll
## Dataset Structure
### Data Instances
Three four-column files, one for each split.
### Data Fields
Every file has four columns:
* 1st column: Word form or punctuation symbol
* 2nd column: Original BRAT file name
* 3rd column: Spans
* 4th column: IOB tag
#### Example
<pre>
La S0004-06142006000900008-1 123_125 O
paciente S0004-06142006000900008-1 126_134 O
tenía S0004-06142006000900008-1 135_140 O
antecedentes S0004-06142006000900008-1 141_153 O
de S0004-06142006000900008-1 154_156 O
hipotiroidismo S0004-06142006000900008-1 157_171 O
, S0004-06142006000900008-1 171_172 O
hipertensión S0004-06142006000900008-1 173_185 O
arterial S0004-06142006000900008-1 186_194 O
en S0004-06142006000900008-1 195_197 O
tratamiento S0004-06142006000900008-1 198_209 O
habitual S0004-06142006000900008-1 210_218 O
con S0004-06142006000900008-1 219-222 O
atenolol S0004-06142006000900008-1 223_231 B-NORMALIZABLES
y S0004-06142006000900008-1 232_233 O
enalapril S0004-06142006000900008-1 234_243 B-NORMALIZABLES
</pre>
### Data Splits
| Split | Size |
| ------------- | ------------- |
| `train` | 8,129 |
| `dev` | 3,787 |
| `test` | 3,952 |
## 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
Manually classified collection of clinical case report sections. The clinical cases were not restricted to a single medical discipline, covering a variety of medical disciplines, including oncology, urology, cardiology, pneumology or infectious diseases. This is key to cover a diverse set of chemicals and drugs.
#### Who are the source language producers?
Humans, there is no machine generated data.
### Annotations
#### Annotation process
The annotation process of the PharmaCoNER corpus was inspired by previous annotation schemes and corpora used for the BioCreative CHEMDNER and GPRO tracks, translating the guidelines used for these tracks into Spanish and adapting them to the characteristics and needs of clinically oriented documents by modifying the annotation criteria and rules to cover medical information needs. This adaptation was carried out in collaboration with practicing physicians and medicinal chemistry experts. The adaptation, translation and refinement of the guidelines was done on a sample set of the SPACCC corpus and linked to an iterative process of annotation consistency analysis through interannotator agreement (IAA) studies until a high annotation quality in terms of IAA was reached.
#### Who are the annotators?
Practicing physicians and medicinal chemistry 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
[N/A]
## 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
@inproceedings{,
title = "PharmaCoNER: Pharmacological Substances, Compounds and proteins Named Entity Recognition track",
author = "Gonzalez-Agirre, Aitor and
Marimon, Montserrat and
Intxaurrondo, Ander and
Rabal, Obdulia and
Villegas, Marta and
Krallinger, Martin",
booktitle = "Proceedings of The 5th Workshop on BioNLP Open Shared Tasks",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-5701",
doi = "10.18653/v1/D19-5701",
pages = "1--10",
}
```
### Contributions
[N/A]
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cdminix/mgb1 | cdminix | 2021-02-05T16:04:03Z | 321 | 0 | null | [
"region:us"
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mteb/biorxiv-clustering-p2p | mteb | 2022-09-27T19:15:27Z | 321 | 0 | null | [
"language:en",
"region:us"
] | 2022-09-27T19:15:27Z | 2022-05-11T06:46:41.000Z | 2022-05-11T06:46:41 | ---
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
MohitK/indian_food_images | MohitK | 2023-03-28T00:52:34Z | 321 | 0 | null | [
"region:us"
] | 2023-03-28T00:52:34Z | 2023-03-28T00:42:24.000Z | 2023-03-28T00:42:24 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': burger
'1': butter_naan
'2': chai
'3': chapati
'4': chole_bhature
'5': dal_makhani
'6': dhokla
'7': fried_rice
'8': idli
'9': jalebi
'10': kaathi_rolls
'11': kadai_paneer
'12': kulfi
'13': masala_dosa
'14': momos
'15': paani_puri
'16': pakode
'17': pav_bhaji
'18': pizza
'19': samosa
splits:
- name: train
num_bytes: 1622005508.6394334
num_examples: 5328
- name: test
num_bytes: 251982030.3925666
num_examples: 941
download_size: 1599894487
dataset_size: 1873987539.032
---
# Dataset Card for "indian_food_images"
[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 | |
librispeech_lm | null | 2023-04-05T10:09:21Z | 320 | 0 | null | [
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:en",
"license:cc0-1.0",
"region:us"
] | 2023-04-05T10:09:21Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license:
- cc0-1.0
multilinguality:
- monolingual
pretty_name: LibrispeechLm
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- text-generation
task_ids:
- language-modeling
paperswithcode_id: null
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 4418577129
num_examples: 40418260
download_size: 1507274412
dataset_size: 4418577129
---
# Dataset Card for "librispeech_lm"
## 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://www.openslr.org/11](http://www.openslr.org/11)
- **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:** 1.51 GB
- **Size of the generated dataset:** 4.42 GB
- **Total amount of disk used:** 5.93 GB
### Dataset Summary
Language modeling resources to be used in conjunction with the LibriSpeech ASR corpus.
### 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:** 1.51 GB
- **Size of the generated dataset:** 4.42 GB
- **Total amount of disk used:** 5.93 GB
An example of 'train' looks as follows.
```
{
"text": "This is a test file"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `text`: a `string` feature.
### Data Splits
| name | train |
|-------|-------:|
|default|40418260|
## 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{panayotov2015librispeech,
title={Librispeech: an ASR corpus based on public domain audio books},
author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},
pages={5206--5210},
year={2015},
organization={IEEE}
}
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@jplu](https://github.com/jplu), [@thomwolf](https://github.com/thomwolf) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
swedish_reviews | null | 2023-01-25T14:45:25Z | 320 | 3 | null | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:sv",
"license:unknown",
"region:us"
] | 2023-01-25T14:45:25Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- sv
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: Swedish Reviews
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': negative
'1': positive
config_name: plain_text
splits:
- name: test
num_bytes: 6296541
num_examples: 20697
- name: validation
num_bytes: 6359227
num_examples: 20696
- name: train
num_bytes: 18842891
num_examples: 62089
download_size: 11841056
dataset_size: 31498659
---
# Dataset Card for Swedish Reviews
## 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:** [swedish_reviews homepage](https://github.com/timpal0l/swedish-sentiment)
- **Repository:** [swedish_reviews repository](https://github.com/timpal0l/swedish-sentiment)
- **Point of Contact:** [Tim Isbister](mailto:timisbisters@gmail.com)
### Dataset Summary
The dataset is scraped from various Swedish websites where reviews are present. The dataset consists of 103 482 samples split between `train`, `valid` and `test`. It is a sample of the full dataset, where this sample is balanced to the minority class (negative). The original data dump was heavly skewved to positive samples with a 95/5 ratio.
### Supported Tasks and Leaderboards
This dataset can be used to evaluate sentiment classification on Swedish.
### Languages
The text in the dataset is in Swedish.
## Dataset Structure
### Data Instances
What a sample looks like:
```
{
'text': 'Jag tycker huggingface är ett grymt project!',
'label': 1,
}
```
### Data Fields
- `text`: A text where the sentiment expression is present.
- `label`: a int representing the label `0`for negative and `1`for positive.
### Data Splits
The data is split into a training, validation and test set. The final split sizes are as follow:
| Train | Valid | Test |
| ------ | ----- | ---- |
| 62089 | 20696 | 20697 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
Various Swedish websites with product reviews.
#### Initial Data Collection and Normalization
#### Who are the source language producers?
Swedish
### Annotations
[More Information Needed]
#### Annotation process
Automatically annotated based on user reviews on a scale 1-5, where 1-2 is considered `negative` and 4-5 is `positive`, 3 is skipped as it tends to be more neutral.
#### Who are the annotators?
The users who have been using the products.
### 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
[More Information Needed]
### Dataset Curators
The corpus was scraped by @timpal0l
### Licensing Information
Research only.
### Citation Information
No paper exists currently.
### Contributions
Thanks to [@timpal0l](https://github.com/timpal0l) for adding this dataset. | [
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GEM/cochrane-simplification | GEM | 2022-10-24T15:30:10Z | 320 | 4 | null | [
"task_categories:text2text-generation",
"task_ids:text-simplification",
"annotations_creators:none",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] | 2022-10-24T15:30:10Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- cc-by-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text2text-generation
task_ids:
- text-simplification
pretty_name: cochrane-simplification
---
# Dataset Card for GEM/cochrane-simplification
## Dataset Description
- **Homepage:** https://github.com/AshOlogn/Paragraph-level-Simplification-of-Medical-Texts
- **Repository:** https://github.com/AshOlogn/Paragraph-level-Simplification-of-Medical-Texts
- **Paper:** https://aclanthology.org/2021.naacl-main.395/
- **Leaderboard:** N/A
- **Point of Contact:** Ashwin Devaraj
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/cochrane-simplification).
### Dataset Summary
Cochrane is an English dataset for paragraph-level simplification of medical texts. Cochrane is a database of systematic reviews of clinical questions, many of which have summaries in plain English targeting readers without a university education. The dataset comprises about 4,500 of such pairs.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/cochrane-simplification')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/cochrane-simplification).
#### website
[Link](https://github.com/AshOlogn/Paragraph-level-Simplification-of-Medical-Texts)
#### paper
[Link](https://aclanthology.org/2021.naacl-main.395/)
#### authors
Ashwin Devaraj (The University of Texas at Austin), Iain J. Marshall (King's College London), Byron C. Wallace (Northeastern University), Junyi Jessy Li (The University of Texas at Austin)
## Dataset Overview
### Where to find the Data and its Documentation
#### Webpage
<!-- info: What is the webpage for the dataset (if it exists)? -->
<!-- scope: telescope -->
[Link](https://github.com/AshOlogn/Paragraph-level-Simplification-of-Medical-Texts)
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Link](https://github.com/AshOlogn/Paragraph-level-Simplification-of-Medical-Texts)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[Link](https://aclanthology.org/2021.naacl-main.395/)
#### 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{devaraj-etal-2021-paragraph,
title = "Paragraph-level Simplification of Medical Texts",
author = "Devaraj, Ashwin and
Marshall, Iain and
Wallace, Byron and
Li, Junyi Jessy",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.395",
doi = "10.18653/v1/2021.naacl-main.395",
pages = "4972--4984",
}
```
#### 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 -->
Ashwin Devaraj
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
ashwin.devaraj@utexas.edu
#### 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`
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
cc-by-4.0: Creative Commons Attribution 4.0 International
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
The intended use of this dataset is to train models that simplify medical text at the paragraph level so that it may be more accessible to the lay reader.
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Simplification
#### Communicative Goal
<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
A model trained on this dataset can be used to simplify medical texts to make them more accessible to readers without medical expertise.
### 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 -->
The University of Texas at Austin, King's College London, Northeastern University
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Ashwin Devaraj (The University of Texas at Austin), Iain J. Marshall (King's College London), Byron C. Wallace (Northeastern University), Junyi Jessy Li (The University of Texas at Austin)
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
National Institutes of Health (NIH) grant R01-LM012086, National Science Foundation (NSF) grant IIS-1850153, Texas Advanced Computing Center (TACC) computational resources
#### 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 -->
Ashwin Devaraj (The University of Texas at Austin)
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
- `gem_id`: string, a unique identifier for the example
- `doi`: string, DOI identifier for the Cochrane review from which the example was generated
- `source`: string, an excerpt from an abstract of a Cochrane review
- `target`: string, an excerpt from the plain-language summary of a Cochrane review that roughly aligns with the source text
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
```
{
"gem_id": "gem-cochrane-simplification-train-766",
"doi": "10.1002/14651858.CD002173.pub2",
"source": "Of 3500 titles retrieved from the literature, 24 papers reporting on 23 studies could be included in the review. The studies were published between 1970 and 1997 and together included 1026 participants. Most were cross-over studies. Few studies provided sufficient information to judge the concealment of allocation. Four studies provided results for the percentage of symptom-free days. Pooling the results did not reveal a statistically significant difference between sodium cromoglycate and placebo. For the other pooled outcomes, most of the symptom-related outcomes and bronchodilator use showed statistically significant results, but treatment effects were small. Considering the confidence intervals of the outcome measures, a clinically relevant effect of sodium cromoglycate cannot be excluded. The funnel plot showed an under-representation of small studies with negative results, suggesting publication bias. There is insufficient evidence to be sure about the efficacy of sodium cromoglycate over placebo. Publication bias is likely to have overestimated the beneficial effects of sodium cromoglycate as maintenance therapy in childhood asthma.",
"target": "In this review we aimed to determine whether there is evidence for the effectiveness of inhaled sodium cromoglycate as maintenance treatment in children with chronic asthma. Most of the studies were carried out in small groups of patients. Furthermore, we suspect that not all studies undertaken have been published. The results show that there is insufficient evidence to be sure about the beneficial effect of sodium cromoglycate compared to placebo. However, for several outcome measures the results favoured sodium cromoglycate."
}
```
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
- `train`: 3568 examples
- `validation`: 411 examples
- `test`: 480 examples
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Why is the Dataset in GEM?
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
This dataset is the first paragraph-level simplification dataset published (as prior work had primarily focused on simplifying individual sentences). Furthermore, this dataset is in the medical domain, which is an especially useful domain for text simplification.
#### 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 -->
This dataset measures the ability for a model to simplify paragraphs of medical text through the omission non-salient information and simplification of medical jargon.
### 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
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
This dataset measures the ability for a model to simplify paragraphs of medical text through the omission non-salient information and simplification of medical jargon.
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`Other: Other Metrics`, `BLEU`
#### Other Metrics
<!-- info: Definitions of other metrics -->
<!-- scope: periscope -->
SARI measures the quality of text simplification
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
yes
#### Relevant Previous Results
<!-- info: What are the most relevant previous results for this task/dataset? -->
<!-- scope: microscope -->
The paper which introduced this dataset trained BART models (pretrained on XSum) with unlikelihood training to produce simplification models achieving maximum SARI and BLEU scores of 40 and 43 respectively.
## Dataset Curation
### Original Curation
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
no
### Language Data
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
not validated
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
not filtered
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
none
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
### 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 -->
yes/very likely
#### 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 -->
yes
#### Details on how Dataset Addresses the Needs
<!-- info: Describe how this dataset addresses the needs of underserved communities. -->
<!-- scope: microscope -->
This dataset can be used to simplify medical texts that may otherwise be inaccessible to those without medical training.
### 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 -->
unsure
#### Are the Language Producers Representative of the Language?
<!-- info: Does the distribution of language producers in the dataset accurately represent the full distribution of speakers of the language world-wide? If not, how does it differ? -->
<!-- scope: periscope -->
The dataset was generated from abstracts and plain-language summaries of medical literature reviews that were written by medical professionals and thus does was not generated by people representative of the entire English-speaking population.
## Considerations for Using the Data
### PII Risks and Liability
### Licenses
### Known Technical Limitations
#### Technical Limitations
<!-- info: Describe any known technical limitations, such as spurrious correlations, train/test overlap, annotation biases, or mis-annotations, and cite the works that first identified these limitations when possible. -->
<!-- scope: microscope -->
The main limitation of this dataset is that the information alignment between the abstract and plain-language summary is often rough, so the plain-language summary may contain information that isn't found in the abstract. Furthermore, the plain-language targets often contain formulaic statements like "this evidence is current to [month][year]" not found in the abstracts. Another limitation is that some plain-language summaries do not simplify the technical abstracts very much and still contain medical jargon.
#### Unsuited Applications
<!-- info: When using a model trained on this dataset in a setting where users or the public may interact with its predictions, what are some pitfalls to look out for? In particular, describe some applications of the general task featured in this dataset that its curation or properties make it less suitable for. -->
<!-- scope: microscope -->
The main pitfall to look out for is errors in factuality. Simplification work so far has not placed a strong emphasis on the logical fidelity of model generations with the input text, and the paper introducing this dataset does not explore modeling techniques to combat this. These kinds of errors are especially pernicious in the medical domain, and the models introduced in the paper do occasionally alter entities like disease and medication names.
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GEM-submissions/lewtun__hugging-face-test-t5-base.outputs.json-36bf2a59__1645559101 | GEM-submissions | 2022-02-22T19:45:04Z | 320 | 0 | null | [
"benchmark:gem",
"region:us"
] | 2022-02-22T19:45:04Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
benchmark: gem
type: prediction
submission_name: Hugging Face test T5-base.outputs.json 36bf2a59
---
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
GEM-submissions/lewtun__this-is-a-test__1646230987 | GEM-submissions | 2022-03-02T14:23:10Z | 320 | 0 | null | [
"benchmark:gem",
"evaluation",
"benchmark",
"region:us"
] | 2022-03-02T14:23:10Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
benchmark: gem
type: prediction
submission_name: This is a test
tags:
- evaluation
- benchmark
---
# GEM Submission
Submission name: This is a test
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gfigueroa/wikitext_processed | gfigueroa | 2022-01-19T18:16:40Z | 320 | 0 | null | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
lampent/IRFL | lampent | 2023-11-27T23:54:18Z | 320 | 1 | null | [
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-4.0",
"figurative-language",
"multimodal-figurative-language",
" commonsense-reasoning",
"visual-reasoning",
"arxiv:2303.15445",
"region:us"
] | 2023-11-27T23:54:18Z | 2023-06-01T09:34:13.000Z | 2023-06-01T09:34:13 | ---
configs:
- config_name: idiom-detection-task
data_files:
- split: test
path: "idiom_detection_task.csv"
- config_name: metaphor-detection-task
data_files:
- split: test
path: "metaphor_detection_task.csv"
- config_name: simile-detection-task
data_files:
- split: test
path: "simile_detection_task.csv"
- config_name: open-simile-detection-task
data_files:
- split: test
path: "open_simile_detection_task.csv"
- config_name: idiom-retrieval-task
data_files:
- split: test
path: "idiom_retrieval_task.csv"
- config_name: metaphor-retrieval-task
data_files:
- split: test
path: "metaphor_retrieval_task.csv"
- config_name: simile-retrieval-task
data_files:
- split: test
path: "simile_retrieval_task.csv"
- config_name: open-simile-retrieval-task
data_files:
- split: test
path: "open_simile_retrieval_task.csv"
- config_name: idioms-dataset
data_files:
- split: dataset
path: "idioms_dataset.csv"
- config_name: similes-dataset
data_files:
- split: dataset
path: "similes_dataset.csv"
- config_name: metaphors-dataset
data_files:
- split: dataset
path: "metaphors_dataset.csv"
license: cc-by-4.0
language:
- en
tags:
- figurative-language
- multimodal-figurative-language
- ' commonsense-reasoning'
- visual-reasoning
size_categories:
- 1K<n<10K
---
# Dataset Card for IRFL
- [Dataset Description](#dataset-description)
- [Leaderboards](#leaderboards)
- [Colab notebook code for IRFL evaluation](#colab-notebook-code-for-irfl-evaluation)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Dataset Creation](#dataset-creation)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
The IRFL dataset consists of idioms, similes, metaphors with matching figurative and literal images, and two novel tasks of multimodal figurative detection and retrieval.
Using human annotation and an automatic pipeline we created, we collected figurative and literal images for textual idioms, metaphors, and similes.
We annotated the relations between these images and the figurative phrase they originated from. We created two novel tasks of figurative detection and retrieval using these images.
The figurative detection task evaluates Vision and Language Pre-Trained Models’ (VL-PTMs) ability to choose the image that best visualizes the meaning of a figurative expression. The task is to choose the image that best visualizes the figurative phrase out of X candidates. The retrieval task examines VL-PTMs' preference for figurative images. In this task, Given a set of figurative and partially literal images, the task is to rank the images using the model-matching score such that the figurative images are ranked higher, and calculate the precision at k, where k is the number of figurative images in the input.
We evaluated state-of-the-art VL models and found that the best models achieved 22%, 30%, and 66% accuracy vs. humans 97%, 99.7%, and 100% on our detection task for idioms, metaphors, and similes respectively. The best model achieved an F1 score of 61 on the retrieval task.
- **Homepage:**
https://irfl-dataset.github.io/
- **Repository:**
https://github.com/irfl-dataset/IRFL
- **Paper:**
https://arxiv.org/abs/2303.15445
- **Leaderboard:**
https://irfl-dataset.github.io/leaderboard
- **Point of Contact:**
irfl.dataset@gmail.com; ron.yosef@mail.huji.ac.il
### Leaderboards
https://irfl-dataset.github.io/leaderboard
### Colab notebook code for IRFL evaluation
https://colab.research.google.com/drive/1zbW7R8Cn9sXICV3x_FGKjKIKu8GCrCme?usp=sharing
### Languages
English.
## Dataset Structure
### Data Fields
★ - refers to idiom-only fields
Multimodal Figurative Language Detection task
- query (★): the idiom definition the answer image originated from.
- distractors: the distractor images
- answer: the correct image
- figurative_type: idiom | metaphor | simile
- images_metadata: the metadata of the distractors and answer images.
- type: the correct image type (Figurative or Figurative Literal).
- definition (★): list of all the definitions of the idiom
- phrase: the figurative phrase.
Multimodal Figurative Language Retrieval task
- type: the rival categories FvsPL (Figurative images vs. Partial Literal) or FLvsPL (Figurative Literal images vs. Partial Literal)
- figurative_type: idiom | metaphor | simile
- first_category: the first category images (Figurative images if FvsPL, Figurative Literal images if FLvsPL)
- second_category: the second category images (Partial Literal)
- definition (★): list of all the definitions of the idiom
- phrase: the figurative phrase.
The idioms, metaphor, and similes datasets contain all the figurative phrases, annotated images, and corresponding metadata. <br/>
## Dataset Collection
Using an automatic pipeline we created, we collected figurative and literal images for textual idioms, metaphors, and similes. We annotated the relations between these images and the figurative phrase they originated from.
#### Annotation process
We paid Amazon Mechanical Turk Workers to annotate the relation between each image and phrase (Figurative vs. Literal).
## Considerations for Using the Data
- Idioms: Annotated by five crowdworkers with rigorous qualifications and training.
- Metaphors and Similes: Annotated by three expert team members.
- Detection and Ranking Tasks: Annotated by three crowdworkers not involved in prior IRFL annotations.
### Licensing Information
CC-By 4.0
### Citation Information
@misc{yosef2023irfl,
title={IRFL: Image Recognition of Figurative Language},
author={Ron Yosef and Yonatan Bitton and Dafna Shahaf},
year={2023},
eprint={2303.15445},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
migtissera/Synthia-v1.3 | migtissera | 2023-11-16T06:19:06Z | 320 | 58 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-16T06:19:06Z | 2023-11-16T06:18:24.000Z | 2023-11-16T06:18:24 | ---
license: apache-2.0
---
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
hover | null | 2023-01-25T14:32:26Z | 319 | 1 | hover | [
"task_categories:text-retrieval",
"task_ids:fact-checking-retrieval",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"arxiv:2011.03088",
"region:us"
] | 2023-01-25T14:32:26Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
- found
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-retrieval
task_ids:
- fact-checking-retrieval
paperswithcode_id: hover
pretty_name: HoVer
dataset_info:
features:
- name: id
dtype: int32
- name: uid
dtype: string
- name: claim
dtype: string
- name: supporting_facts
list:
- name: key
dtype: string
- name: value
dtype: int32
- name: label
dtype:
class_label:
names:
'0': NOT_SUPPORTED
'1': SUPPORTED
- name: num_hops
dtype: int32
- name: hpqa_id
dtype: string
splits:
- name: train
num_bytes: 5532178
num_examples: 18171
- name: validation
num_bytes: 1299252
num_examples: 4000
- name: test
num_bytes: 927513
num_examples: 4000
download_size: 12257835
dataset_size: 7758943
---
# Dataset Card for HoVer
## 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://hover-nlp.github.io/
- **Repository:** https://github.com/hover-nlp/hover
- **Paper:** https://arxiv.org/abs/2011.03088
- **Leaderboard:** https://hover-nlp.github.io/
- **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
A sample training set is provided below
```
{'id': 14856, 'uid': 'a0cf45ea-b5cd-4c4e-9ffa-73b39ebd78ce', 'claim': 'The park at which Tivolis Koncertsal is located opened on 15 August 1843.', 'supporting_facts': [{'key': 'Tivolis Koncertsal', 'value': 0}, {'key': 'Tivoli Gardens', 'value': 1}], 'label': 'SUPPORTED', 'num_hops': 2, 'hpqa_id': '5abca1a55542993a06baf937'}
```
Please note that in test set sentence only id, uid and claim are available. Labels are not available in test set and are represented by -1.
### 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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id_puisi | null | 2022-11-03T16:08:09Z | 319 | 2 | null | [
"task_categories:text2text-generation",
"task_categories:text-generation",
"task_categories:fill-mask",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:id",
"license:mit",
"poem-generation",
"region:us"
] | 2022-11-03T16:08:09Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- id
license:
- mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text2text-generation
- text-generation
- fill-mask
task_ids: []
paperswithcode_id: null
pretty_name: Indonesian Puisi
tags:
- poem-generation
dataset_info:
features:
- name: title
dtype: string
- name: author
dtype: string
- name: puisi
dtype: string
- name: puisi_with_header
dtype: string
splits:
- name: train
num_bytes: 10613475
num_examples: 7223
download_size: 10558108
dataset_size: 10613475
---
# Dataset Card for id_puisi
## 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:** [puisi-pantun-generator](https://github.com/ilhamfp/puisi-pantun-generator)
- **Repository:** [puisi-pantun-generator](https://github.com/ilhamfp/puisi-pantun-generator)
- **Paper:** [N/A]
- **Leaderboard:** [N/A]
- **Point of Contact:** [Ilham Firdausi Putra](ilhamfputra31@gmail.com)
### Dataset Summary
Puisi (poem) is an Indonesian poetic form. The dataset contains 7223 Indonesian puisi with its title and author.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Indonesian
## Dataset Structure
### Data Instances
```
{
'puisi_with_header': 'TEPERANGKAP
Oleh Mangku Langit Jingga
Mungkin kau membiarkan aku
Membiarkan perasaan ini larut
Memberi ruang jiwaku hampa
Agar tetap terbiasa nikmati
Perangkap yang kau buat
Perisai yang kau banggakan
Takkan jadi tameng bagimu
Aku mengerti betapa hebatnya
Perangkap mu hei sang dewi
Ku akan terus merasa terbiasa
Dengan pesona indahmu
Ku masih akan nikmati hadirmu
Berjalanlah pada hati yang sama
Satu hati denganku
Walau ku terperangkap
Namunku nikmati dan jalani',
'title': 'TEPERANGKAP',
'author': 'Oleh Mangku Langit Jingga',
'puisi': 'Mungkin kau membiarkan aku
Membiarkan perasaan ini larut
Memberi ruang jiwaku hampa
Agar tetap terbiasa nikmati
Perangkap yang kau buat
Perisai yang kau banggakan
Takkan jadi tameng bagimu
Aku mengerti betapa hebatnya
Perangkap mu hei sang dewi
Ku akan terus merasa terbiasa
Dengan pesona indahmu
Ku masih akan nikmati hadirmu
Berjalanlah pada hati yang sama
Satu hati denganku
Walau ku terperangkap
Namunku nikmati dan jalani',
}
```
### Data Fields
- `puisi_with_header`: the raw text from scraping
- `title`: the title extracted from the raw text using regex
- `author`: the author extracted from the raw text using regex
- `puisi`: the poem with title and author extracted out using regex
### Data Splits
The dataset contains only a train set.
## Dataset Creation
### Curation Rationale
The dataset was initially collected as an experiment to generate an Indonesian poem using GPT-2.
### Source Data
#### Initial Data Collection and Normalization
The dataset was scraped using BeautifulSoup from lokerpuisi.web.id (the data no longer exist on the original blog). The title and author column was produced using regex match from puisi_with_header column.
#### Who are the source language producers?
The poems were generated by humans. The users of the original blog voluntarily submit their original poems to get published on the blog.
### Annotations
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### 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
The regex match used to extract the title & author from the raw text is not perfect. Some title & text is still failed to get extracted.
## Additional Information
### Dataset Curators
Ilham Firdausi Putra
### Licensing Information
MIT License
### Citation Information
[N/A]
### Contributions
Thanks to [@ilhamfp](https://github.com/ilhamfp) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
metrec | null | 2023-01-25T14:40:27Z | 319 | 2 | metrec | [
"task_categories:text-classification",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ar",
"license:unknown",
"poetry-classification",
"region:us"
] | 2023-01-25T14:40:27Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- ar
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: metrec
pretty_name: MetRec
tags:
- poetry-classification
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': saree
'1': kamel
'2': mutakareb
'3': mutadarak
'4': munsareh
'5': madeed
'6': mujtath
'7': ramal
'8': baseet
'9': khafeef
'10': taweel
'11': wafer
'12': hazaj
'13': rajaz
config_name: plain_text
splits:
- name: train
num_bytes: 5874919
num_examples: 47124
- name: test
num_bytes: 1037577
num_examples: 8316
download_size: 2267882
dataset_size: 6912496
---
# Dataset Card for MetRec
## 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:** [Metrec](https://github.com/zaidalyafeai/MetRec)
- **Repository:** [Metrec repository](https://github.com/zaidalyafeai/MetRec)
- **Paper:** [MetRec: A dataset for meter classification of arabic poetry](https://www.sciencedirect.com/science/article/pii/S2352340920313792)
- **Point of Contact:** [Zaid Alyafeai](mailto:alyafey22@gmail.com)
### Dataset Summary
The dataset contains the verses and their corresponding meter classes.
Meter classes are represented as numbers from 0 to 13.
The dataset can be highly useful for further research in order to improve the field of Arabic poems’ meter classification.
The train dataset contains 47,124 records and the test dataset contains 8,316 records.
### Supported Tasks and Leaderboards
The dataset was published on this [paper](https://www.sciencedirect.com/science/article/pii/S2352340920313792). A benchmark is acheived on this [paper](https://www.sciencedirect.com/science/article/pii/S016786552030204X).
### Languages
The dataset is based on Arabic.
## Dataset Structure
### Data Instances
A typical data point comprises a label which is out of 13 classes and a verse part of poem.
### Data Fields
[N/A]
### Data Splits
The data is split into a training and testing. The split is organized as the following
| | train | test |
|------------|-------:|------:|
| data split | 47,124 | 8,316 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
The dataset was collected from [Aldiwan](https://www.aldiwan.net/).
#### Who are the source language producers?
The poems are from different poets.
### 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
[More Information Needed]
```
@article{metrec2020,
title={MetRec: A dataset for meter classification of arabic poetry},
author={Al-shaibani, Maged S and Alyafeai, Zaid and Ahmad, Irfan},
journal={Data in Brief},
year={2020},
publisher={Elsevier}
}
```
### Contributions
Thanks to [@zaidalyafeai](https://github.com/zaidalyafeai) for adding this dataset. | [
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offenseval2020_tr | null | 2023-01-25T14:41:59Z | 319 | 3 | null | [
"task_categories:text-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:tr",
"license:cc-by-2.0",
"offensive-language-classification",
"region:us"
] | 2023-01-25T14:41:59Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- tr
license:
- cc-by-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
pretty_name: OffensEval-TR 2020
tags:
- offensive-language-classification
dataset_info:
features:
- name: id
dtype: int32
- name: tweet
dtype: string
- name: subtask_a
dtype:
class_label:
names:
'0': NOT
'1': 'OFF'
config_name: offenseval2020-turkish
splits:
- name: train
num_bytes: 4260505
num_examples: 31756
- name: test
num_bytes: 481300
num_examples: 3528
download_size: 2048258
dataset_size: 4741805
---
# Dataset Card for OffensEval-TR 2020
## 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:** [offensive-turkish](https://coltekin.github.io/offensive-turkish/)
- **Paper:** [A Corpus of Turkish Offensive Language on Social Media](https://coltekin.github.io/offensive-turkish/troff.pdf)
- **Point of Contact:** [Çağrı Çöltekin](ccoltekin@sfs.uni-tuebingen.de)
### Dataset Summary
The file offenseval-tr-training-v1.tsv contains 31,756 annotated tweets.
The file offenseval-annotation.txt contains a short summary of the annotation guidelines.
Twitter user mentions were substituted by @USER and URLs have been substitute by URL.
Each instance contains up to 1 labels corresponding to one of the following sub-task:
- Sub-task A: Offensive language identification;
### Supported Tasks and Leaderboards
The dataset was published on this [paper](https://coltekin.github.io/offensive-turkish/troff.pdf).
### Languages
The dataset is based on Turkish.
## Dataset Structure
### Data Instances
A binary dataset with with (NOT) Not Offensive and (OFF) Offensive tweets.
### Data Fields
Instances are included in TSV format as follows:
ID INSTANCE SUBA
The column names in the file are the following:
id tweet subtask_a
The labels used in the annotation are listed below.
#### Task and Labels
(A) Sub-task A: Offensive language identification
- (NOT) Not Offensive - This post does not contain offense or profanity.
- (OFF) Offensive - This post contains offensive language or a targeted (veiled or direct) offense
In our annotation, we label a post as offensive (OFF) if it contains any form of non-acceptable language (profanity) or a targeted offense, which can be veiled or direct.
### Data Splits
| train | test |
|------:|-----:|
| 31756 | 3528 |
## 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?
From tweeter.
### Annotations
[More Information Needed]
#### Annotation process
We describe the labels above in a “flat” manner. However, the annotation process we follow is hierarchical. The following QA pairs give a more flowchart-like procedure to follow
1. Is the tweet in Turkish and understandable?
* No: mark tweet X for exclusion, and go to next tweet
* Yes: continue to step 2
2. Is the tweet include offensive/inappropriate language?
* No: mark the tweet non go to step 4
* Yes: continue to step 3
3. Is the offense in the tweet targeted?
* No: mark the tweet prof go to step 4
* Yes: chose one (or more) of grp, ind, *oth based on the definitions above. Please try to limit the number of labels unless it is clear that the tweet includes offense against multiple categories.
4. Was the labeling decision difficult (precise answer needs more context, tweets includes irony, or for another reason)?
* No: go to next tweet
* Yes: add the label X, go to next tweet
#### 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 annotations are distributed under the terms of [Creative Commons Attribution License (CC-BY)](https://creativecommons.org/licenses/by/2.0/). Please cite the following paper, if you use this resource.
### Citation Information
```
@inproceedings{coltekin2020lrec,
author = {\c{C}\"{o}ltekin, \c{C}a\u{g}r{\i}},
year = {2020},
title = {A Corpus of Turkish Offensive Language on Social Media},
booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference},
pages = {6174--6184},
address = {Marseille, France},
url = {https://www.aclweb.org/anthology/2020.lrec-1.758},
}
```
### Contributions
Thanks to [@yavuzKomecoglu](https://github.com/yavuzKomecoglu) for adding this dataset. | [
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german-nlp-group/german_common_crawl | german-nlp-group | 2023-10-03T14:50:28Z | 319 | 7 | null | [
"language:de",
"region:us"
] | 2023-10-03T14:50:28Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- de
---
# Dataset Card for GermanCommonCrawl
## 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:**
- **Repository:** https://github.com/German-NLP-Group/german-transformer-training
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** philipp.reissel@rwth-aachen.de
### Dataset Summary
German Only Extract from Common Crawl
Stats:
Total Size after Deduplication: 142 Mio Pages / 194 GB (Gzipped)
Total Size before Deduplcation: 263 Mio Pages / 392 GB (Gzipped)
### Supported Tasks and Leaderboards
This Dataset is for pretraining a German Language Model (Unsupervised).
### Languages
German only (Sometimes websites are partially in another Language). One can filter these out through the `language_score` attribute.
## Dataset Structure
### Data Instances
```
{'url': 'http://my-shop.ru/shop/books/545473.html',
'date_download': '2016-10-20T19:38:58Z',
'digest': 'sha1:F62EMGYLZDIKF4UL5JZYU47KWGGUBT7T',
'length': 1155,
'nlines': 4,
'source_domain': 'my-shop.ru',
'title': 'Grammatikalische Liebeslieder. Methodische Vorschläge',
'raw_content': 'Grammatikalische Liebeslieder. [....]',
'cc_segment': 'crawl-data/CC-MAIN-2016-44/segments/1476988717783.68/wet/CC-MAIN-20161020183837-00354-ip-10-171-6-4.ec2.internal.warc.wet.gz',
'original_nlines': 99,
'original_length': 2672,
'language': 'de',
'language_score': 1.0,
'perplexity': 283.0,
'bucket': 'head'}"
```
### Data Fields
### Data Splits
Train only
## Dataset Creation
### Curation Rationale
Handling and Filtering of Common Crawl Data requires large scale Server Ressources at a location in the US (for downloading speed). The total computing time needed to create this dataset is above 100k CPU hours. To give others the opportunity to train models with this dataset easily we make it publicly available.
In most use cases you see an improved Model Performance when extending the pre-training Data so one can achieve highest accuracies as this is probably the largest available dataset.
### Source Data
It was filtered from the Common Crawl Snapshots of the following months:
1. 2015-48
2. 2016-18
3. 2016-44
4. 2017-33
5. 2017-30
6. 2017-30
7. 2017-39
8. 2017-51
9. 2018-09
10. 2018-17
11. 2018-30
12. 2018-39
13. 2018-51
14. 2019-09
15. 2019-18
16. 2019-30
17. 2019-47
18. 2020-10
#### Initial Data Collection and Normalization
Filtering and deduplication of each month seperalety was performed with [CC_Net](https://github.com/facebookresearch/cc_net). The current datasets only contains the best part (head part) with the highest text quality (see CC_Net Paper for more details). Middle and tail part may be uploaded soon as well, or are available on request.
Afterwards this Dataset was deduplicated again to filter out Websites which occur in multiple monthly snapshots. This deduplication removes all Websites which have either the same url or the same hash (this is to filter out websites which are accessible under multiple domains)
#### 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
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@inproceedings{wenzek2020ccnet,
title={CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data},
author={Wenzek, Guillaume and Lachaux, Marie-Anne and Conneau, Alexis and Chaudhary, Vishrav and Guzm{\'a}n, Francisco and Joulin, Armand and Grave, {\'E}douard},
booktitle={Proceedings of The 12th Language Resources and Evaluation Conference},
pages={4003--4012},
year={2020}
``` | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
teven/enwiki_10k | teven | 2023-04-03T14:00:51Z | 319 | 0 | null | [
"region:us"
] | 2023-04-03T14:00:51Z | 2023-04-03T14:00:46.000Z | 2023-04-03T14:00:46 | ---
dataset_info:
features:
- name: metadata
dtype: string
- name: text
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 25120962
num_examples: 10000
download_size: 15208428
dataset_size: 25120962
---
# Dataset Card for "enwiki_10k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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MBZUAI/LaMini-instruction | MBZUAI | 2023-04-30T11:01:41Z | 319 | 110 | null | [
"task_categories:text2text-generation",
"size_categories:1M<n<10M",
"language:en",
"license:cc-by-nc-4.0",
"arxiv:2304.14402",
"region:us"
] | 2023-04-30T11:01:41Z | 2023-04-08T07:48:12.000Z | 2023-04-08T07:48:12 | ---
license: cc-by-nc-4.0
task_categories:
- text2text-generation
language:
- en
size_categories:
- 1M<n<10M
dataset_info:
features:
- name: instruction
dtype: string
- name: response
dtype: string
- name: instruction_source
dtype: string
splits:
- name: train
num_bytes: 1162632572
num_examples: 2585615
download_size: 704293718
dataset_size: 1162632572
---
# Dataset Card for "LaMini-Instruction"
<p align="center" width="100%">
<a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/lamini.png" alt="Title" style="width: 100%; min-width: 300px; display: block; margin: auto;"></a>
</p>
<p align="center"> <a href="https://twitter.com/WuMinghao_nlp" target="_blank">Minghao Wu</a>, <a href="https://twitter.com/MacAbdul9" target="_blank">Abdul Waheed</a>, <a href="https://chiyuzhang94.github.io/" target="_blank">Chiyu Zhang</a>, <a href="https://mageed.arts.ubc.ca/" target="_blank">Muhammad Abdul-Mageed</a>, <a href="https://afaji.github.io/" target="_blank">Alham Fikri Aji</a>,</p>
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Instances](#data-instances)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Additional Information](#additional-information)
## Dataset Description
We distill the knowledge from large language models by performing sentence/offline distillation (Kim and Rush, 2016). We generate a total of **2.58M** pairs of instructions and responses using [`gpt-3.5-turbo`](https://openai.com/api/) based on several existing resources of prompts, including [self-instruct](https://github.com/yizhongw/self-instruct) (Wang et al., 2022), [P3](https://huggingface.co/datasets/bigscience/P3) (Sanh et al., 2022), [FLAN](https://github.com/google-research/FLAN) (Longpre et al., 2023) and [Alpaca](https://github.com/tatsu-lab/stanford_alpaca) (Taori et al., 2023). More information about the process for generating our instruction dataset, please refer to [our paper](https://arxiv.org/abs/2304.14402).
We use LaMini-Instruction to fine-tune various small-sized language models, resulting in [LaMini-LM model series](https://github.com/mbzuai-nlp/laMini-LM#models).
Each sample contain the following two fields:
* `instruction`: `string`, describes the task that the model is expected to perform.
* `response`: `string`, the response to the instruction as generated by `gpt-3.5-turbo`.
* `instruction_source`: `string`, the source of a instruction. The possible values are `{'alpaca', 'generated_flan', 'generated_p3', 'original_flan', 'original_p3', 'self_instruct_with_topic', 'self_instruct_without_topic'}`.
Two examples in our LaMini-instruction data:
```json
{
"instruction": "What is the capital of Australia?\n\nIs Canberra the only city chosen to serve as a capital due to a compromise between Sydney and Melbourne?",
"response": "The capital of Australia is Canberra. Yes, Canberra was chosen as the capital of Australia in 1908 due to a compromise between Sydney and Melbourne, as neither city wanted the other to become the capital.",
"instruction_source": "generated_flan"
}
```
```json
{
"instruction": "Create a poem that has the word \"ocean\" in every other line.",
"response": "The ocean's depth is a mystery,\nIts waves crashing with energy.\nSalt air fills the coastal scene,\nThe ocean's blue hues pure and clean.\n\nSeagulls cry out a melancholy song,\nThe ocean's rhythm steady and strong.\nSunsets paint the water gold,\nThe ocean's secrets forever untold.",
"instruction_source": "self_instruct_without_topic"
}
```
## Considerations for Using the Data
As the output is distilled from ChatGPT, this data contains errors and biases produced by ChatGPT. Models trained with this dataset will inherit the errors and biases. We encourage users to use this data with caution and propose new methods to filter or improve the imperfections.
### Licensing Information
The dataset is available under the [Creative Commons NonCommercial (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/legalcode).
### Citation Information
Please cite us if you use our data or models.
```bibtex
@article{lamini-lm,
author = {Minghao Wu and
Abdul Waheed and
Chiyu Zhang and
Muhammad Abdul-Mageed and
Alham Fikri Aji
},
title = {LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions},
journal = {CoRR},
volume = {abs/2304.14402},
year = {2023},
url = {https://arxiv.org/abs/2304.14402},
eprinttype = {arXiv},
eprint = {2304.14402}
}
``` | [
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isixhosa_ner_corpus | null | 2023-01-25T14:33:10Z | 318 | 0 | 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:xh",
"license:other",
"region:us"
] | 2023-01-25T14:33:10Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- xh
license:
- other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: IsixhosaNerCorpus
license_details: Creative Commons Attribution 2.5 South Africa License
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': OUT
'1': B-PERS
'2': I-PERS
'3': B-ORG
'4': I-ORG
'5': B-LOC
'6': I-LOC
'7': B-MISC
'8': I-MISC
config_name: isixhosa_ner_corpus
splits:
- name: train
num_bytes: 2414995
num_examples: 6284
download_size: 14513302
dataset_size: 2414995
---
# 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:** [IsiXhosa Ner Corpus Homepage](https://repo.sadilar.org/handle/20.500.12185/312)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [Martin Puttkammer](mailto:Martin.Puttkammer@nwu.ac.za)
### Dataset Summary
The isiXhosa Ner Corpus is a Xhosa dataset developed by [The Centre for Text Technology (CTexT), North-West University, South Africa](http://humanities.nwu.ac.za/ctext). The data is based on documents from the South African goverment domain and crawled from gov.za websites. It was created to support NER task for Xhosa language. The dataset uses CoNLL shared task annotation standards.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The language supported is Xhosa.
## Dataset Structure
### Data Instances
A data point consists of sentences seperated by empty line and tab-seperated tokens and tags.
{'id': '0',
'ner_tags': [7, 8, 5, 6, 0],
'tokens': ['Injongo', 'ye-website', 'yaseMzantsi', 'Afrika', 'kukuvelisa']
}
### Data Fields
- `id`: id of the sample
- `tokens`: the tokens of the example text
- `ner_tags`: the NER tags of each token
The NER tags correspond to this list:
```
"OUT", "B-PERS", "I-PERS", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-MISC", "I-MISC",
```
The NER tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word. There are four types of phrases: person names (PER), organizations (ORG), locations (LOC) and miscellaneous names (MISC). (OUT) is used for tokens not considered part of any named entity.
### Data Splits
The data was not split.
## Dataset Creation
### Curation Rationale
The data was created to help introduce resources to new language - Xhosa.
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The data is based on South African government domain and was crawled from gov.za websites.
[More Information Needed]
#### Who are the source language producers?
The data was produced by writers of South African government websites - gov.za
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
The data was annotated during the NCHLT text resource development project.
[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
The annotated data sets were developed by the Centre for Text Technology (CTexT, North-West University, South Africa).
See: [more information](http://www.nwu.ac.za/ctext)
### Licensing Information
The data is under the [Creative Commons Attribution 2.5 South Africa License](http://creativecommons.org/licenses/by/2.5/za/legalcode)
### Citation Information
```
@inproceedings{isixhosa_ner_corpus,
author = { K. Podile and
Roald Eiselen},
title = {NCHLT isiXhosa Named Entity Annotated Corpus},
booktitle = {Eiselen, R. 2016. Government domain named entity recognition for South African languages. Proceedings of the 10th Language Resource and Evaluation Conference, Portorož, Slovenia.},
year = {2016},
url = {https://repo.sadilar.org/handle/20.500.12185/312},
}
```
### Contributions
Thanks to [@yvonnegitau](https://github.com/yvonnegitau) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
wiki_qa_ar | null | 2023-01-25T15:02:18Z | 318 | 2 | wikiqaar | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:ar",
"license:unknown",
"region:us"
] | 2023-01-25T15:02:18Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- ar
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswithcode_id: wikiqaar
pretty_name: English-Arabic Wikipedia Question-Answering
dataset_info:
features:
- name: question_id
dtype: string
- name: question
dtype: string
- name: document_id
dtype: string
- name: answer_id
dtype: string
- name: answer
dtype: string
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
config_name: plain_text
splits:
- name: test
num_bytes: 7563127
num_examples: 20632
- name: validation
num_bytes: 3740721
num_examples: 10387
- name: train
num_bytes: 26009979
num_examples: 70264
download_size: 35226436
dataset_size: 37313827
---
# Dataset Card for WikiQAar
## 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:** [WikiQaAr](https://github.com/qcri/WikiQAar)
- **Repository:** [WikiQaAr](https://github.com/qcri/WikiQAar)
- **Paper:**
- **Point of Contact:** [Ines Abbes
](abbes.ines@yahoo.com)
### Dataset Summary
Arabic Version of WikiQA by automatic automatic machine translators
and crowdsourced the selection of the best one to be incorporated into the corpus
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset is based on Arabic.
## Dataset Structure
### Data Instances
Each data point contains the question and whether the answer is a valid or not.
### Data Fields
- `question_id`: the question id.
- `question`: the question text.
- `document_id`: the wikipedia document id.
- `answer_id` : the answer id.
- `answer` : a candidate answer to the question.
- `label` : 1 if the `answer` is correct or 0 otherwise.
### Data Splits
The dataset is not split.
| | train | validation | test |
|------------|-------:|-----------:|-------:|
| Data split | 70,264 | 20,632 | 10,387 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
Translation of WikiQA.
#### Who are the source language producers?
Translation of WikiQA.
### 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
```
@InProceedings{YangYihMeek:EMNLP2015:WikiQA,
author = {{Yi}, Yang and {Wen-tau}, Yih and {Christopher} Meek},
title = "{WikiQA: A Challenge Dataset for Open-Domain Question Answering}",
journal = {Association for Computational Linguistics},
year = 2015,
doi = {10.18653/v1/D15-1237},
pages = {2013–2018},
}
```
### Contributions
Thanks to [@zaidalyafeai](https://github.com/zaidalyafeai) for adding this dataset. | [
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consumer-finance-complaints | null | 2023-01-25T14:28:37Z | 317 | 11 | null | [
"task_categories:text-classification",
"task_ids:topic-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc0-1.0",
"region:us"
] | 2023-01-25T14:28:37Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc0-1.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- topic-classification
pretty_name: consumer-finance-complaints
dataset_info:
features:
- name: Date Received
dtype: timestamp[s]
- name: Product
dtype:
class_label:
names:
'0': Credit reporting, credit repair services, or other personal consumer
reports
'1': Debt collection
'2': Mortgage
'3': Credit card or prepaid card
'4': Checking or savings account
'5': Credit reporting
'6': Student loan
'7': Money transfer, virtual currency, or money service
'8': Credit card
'9': Vehicle loan or lease
'10': Bank account or service
'11': Payday loan, title loan, or personal loan
'12': Consumer Loan
'13': Payday loan
'14': Money transfers
'15': Prepaid card
'16': Other financial service
'17': Virtual currency
- name: Sub Product
dtype:
class_label:
names:
'0': Credit reporting
'1': General-purpose credit card or charge card
'2': Checking account
'3': Other debt
'4': Second mortgage
'5': Conventional home mortgage
'6': I do not know
'7': Credit card debt
'8': Medical debt
'9': Federal student loan servicing
'10': FHA mortgage
'11': Conventional fixed mortgage
'12': Loan
'13': Other (i.e. phone, health club, etc.)
'14': Store credit card
'15': Installment loan
'16': Credit card
'17': Medical
'18': Mobile or digital wallet
'19': Private student loan
'20': Non-federal student loan
'21': Domestic (US) money transfer
'22': VA mortgage
'23': Vehicle loan
'24': Auto debt
'25': Payday loan
'26': Conventional adjustable mortgage (ARM)
'27': Other personal consumer report
'28': Payday loan debt
'29': Savings account
'30': Virtual currency
'31': Other bank product/service
'32': Other type of mortgage
'33': Other banking product or service
'34': Other mortgage
'35': International money transfer
'36': Lease
'37': General-purpose prepaid card
'38': Home equity loan or line of credit (HELOC)
'39': Government benefit card
'40': Mortgage debt
'41': Personal line of credit
'42': Home equity loan or line of credit
'43': Federal student loan debt
'44': Private student loan debt
'45': Credit repair services
'46': Title loan
'47': Auto
'48': Vehicle lease
'49': Mortgage
'50': Reverse mortgage
'51': General purpose card
'52': CD (Certificate of Deposit)
'53': Federal student loan
'54': Payroll card
'55': Debt settlement
'56': Check cashing service
'57': Traveler's check or cashier's check
'58': Gift card
'59': (CD) Certificate of deposit
'60': Money order
'61': Foreign currency exchange
'62': Refund anticipation check
'63': Gift or merchant card
'64': Cashing a check without an account
'65': ID prepaid card
'66': Mobile wallet
'67': Government benefit payment card
'68': Pawn loan
'69': Other special purpose card
'70': Check cashing
'71': Credit repair
'72': Traveler’s/Cashier’s checks
'73': Transit card
'74': Student prepaid card
'75': Electronic Benefit Transfer / EBT card
'76': ''
- name: Issue
dtype: string
- name: Sub Issue
dtype: string
- name: Complaint Text
dtype: string
- name: Company Public Response
dtype: string
- name: Company
dtype: string
- name: State
dtype: string
- name: Zip Code
dtype: string
- name: Tags
dtype:
class_label:
names:
'0': Servicemember
'1': Older American
'2': Older American, Servicemember
'3': ''
- name: Consumer Consent Provided
dtype: string
- name: Submitted via
dtype: string
- name: Date Sent To Company
dtype: string
- name: Company Response To Consumer
dtype: string
- name: Timely Response
dtype: string
- name: Consumer Disputed
dtype: string
- name: Complaint ID
dtype: string
splits:
- name: train
num_bytes: 1605177353
num_examples: 2455765
download_size: 404187716
dataset_size: 1605177353
---
# Dataset Card for Consumer Finance Complaints
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://www.consumerfinance.gov/data-research/consumer-complaints/
- **Repository:**
https://github.com/cfpb/consumerfinance.gov
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This database is a collection of complaints about consumer financial products and services that we sent to companies for response.
The Consumer Complaint Database is a collection of complaints about consumer financial products and services that we sent to companies for response. Complaints are published after the company responds, confirming a commercial relationship with the consumer, or after 15 days, whichever comes first. Complaints referred to other regulators, such as complaints about depository institutions with less than $10 billion in assets, are not published in the Consumer Complaint Database. The database generally updates daily.
Complaints can give us insights into problems people are experiencing in the marketplace and help us regulate consumer financial products and services under existing federal consumer financial laws, enforce those laws judiciously, and educate and empower consumers to make informed financial decisions. We also report on complaint trends annually in Consumer Response’s Annual Report to Congress.
### Supported Tasks and Leaderboards
Text Classification Tasks
| Task | Label Name | Description | SOTA |
| ----------- | ----------- |----------- | ----------- |
| Text Classification | Product| Predict the related product of a complaint | N/A |
| Task | Label Name | Description | SOTA |
| ----------- | ----------- |----------- | ----------- |
| Text Classification | Sub-Product| Predict the related sub product of a complaint | N/A |
| Task | Label Name | Description | SOTA |
| ----------- | ----------- |----------- | ----------- |
| Text Classification | Tags | Predict whether a complaint has been made by someone elderly or a service person| N/A |
### Languages
English
## Dataset Structure
### Data Instances
This dataset is a point in time extract of the database, the database increases in size every day
An example of 'train' looks as follows.
```
{
"Complaint ID": "4511031",
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub Issue": "Credit inquiries on your report that you don't recognize",
"Consumer Disputed": "N/A",
"Sub Product": "Credit reporting",
"State": "TX",
"Tags": "Older American, Servicemember",
"Company Public Response": "",
"Zip Code": "75202",
"Issue": "Improper use of your report",
"Submitted via": "Web",
"Company Response To Consumer": "Closed with explanation",
"Complaint Text": "I am XXXX XXXX and I am submitting this complaint myself and there is no third party involved. Despite the multiple previous written requests, the unverified inquiries listed below still remain on my credit report in violation of Federal Law. The Equifax Credit Bureau failed to comply with Fair Credit Reporting Act, XXXX XXXX sections XXXX within the time set forth by law and continued reporting of erroneous information which now, given all my attempts to address it directly with the creditor, as willful negligence and non-compliance with federal statutes. PLEASE REMOVE THE FOLLOWING INQUIRIES COMPLETELY FROM MY CREDIT REPORT : XXXX CARD-Date of inquiry XX/XX/XXXX XXXX CARD-Date of inquiry XX/XX/XXXX",
"Date Received": "07-02-2021",
"Company": "EQUIFAX, INC.",
"Consumer Consent Provided": "Consent not provided",
"Timely Response": "Yes",
"Date Sent To Company": "2021-07-02"
}
```
### Data Fields
| Field | name | Description | Data Type |
| ----------- | ----------- |----------- | ----------- |
| Date received | The date the CFPB received the complaint | date & time | |
| Product | The type of product the consumer identified in the complaint | plain text | This field is a categorical variable. |
| Sub-product | The type of sub-product the consumer identified in the complaint | plain text | This field is a categorical variable. Not all Products have Sub-products. |
| Issue | The issue the consumer identified in the complaint | plain text | This field is a categorical variable. Possible values are dependent on Product. |
| Sub-issue | The sub-issue the consumer identified in the complaint | plain text | This field is a categorical variable. Possible values are dependent on product and issue. Not all Issues have corresponding Sub-issues. |
| Consumer complaint narrative | Consumer complaint narrative is the consumer-submitted description of "what happened" from the complaint. Consumers must opt-in to share their narrative. We will not publish the narrative unless the consumer consents, and consumers can opt-out at any time. The CFPB takes reasonable steps to scrub personal information from each complaint that could be used to identify the consumer. | plain text | Consumers' descriptions of what happened are included if consumers consent to publishing the description and after we take steps to remove personal information. |
| Company public response | The company's optional, public-facing response to a consumer's complaint. Companies can choose to select a response from a pre-set list of options that will be posted on the public database. For example, "Company believes complaint is the result of an isolated error." | plain text | Companies' public-facing responses to complaints are included if companies choose to publish one. Companies may select a public response from a set list of options as soon as they respond to the complaint, but no later than 180 days after the complaint was sent to the company for response. |
| Company | The complaint is about this company | plain text | This field is a categorical variable. |
| State | The state of the mailing address provided by the consumer | plain text | This field is a categorical variable. |
| ZIP code | The mailing ZIP code provided by the consumer | plain text | Mailing ZIP code provided by the consumer. This field may: i) include the first five digits of a ZIP code; ii) include the first three digits of a ZIP code (if the consumer consented to publication of their complaint narrative); or iii) be blank (if ZIP codes have been submitted with non-numeric values, if there are less than 20,000 people in a given ZIP code, or if the complaint has an address outside of the United States). For example, complaints where the submitter reports the age of the consumer as 62 years or older are tagged, ‘Older American.’ Complaints submitted by or on behalf of a servicemember or the spouse or dependent of a servicemember are tagged, ‘Servicemember.’ Servicemember includes anyone who is active duty, National Guard, or Reservist, as well as anyone who previously served and is a Veteran or retiree. |
| Tags | Data that supports easier searching and sorting of complaints submitted by or on behalf of consumers. | plain text | |
| Consumer consent provided? | Identifies whether the consumer opted in to publish their complaint narrative. We do not publish the narrative unless the consumer consents and consumers can opt-out at any time. | plain text | This field shows whether a consumer provided consent to publish their complaint narrative |
| Submitted via | How the complaint was submitted to the CFPB | plain text | This field is a categorical variable. |
| Date sent to company | The date the CFPB sent the complaint to the company | date & time | |
| Company response to consumer | This is how the company responded. For example, "Closed with explanation." | plain text | This field is a categorical variable. |
| Timely response? | Whether the company gave a timely response | plain text | yes/no |
| Consumer disputed? | Whether the consumer disputed the company’s response | plain text | YES/ NO/ N/A: The Bureau discontinued the consumer dispute option on April 24, 2017. |
| Complaint ID | The unique identification number for a complaint | number | |
### Data Splits
This dataset only contains a TRAIN set - this can be further split into TRAIN, TEST and VALIDATE subsets with the datasets library
## Dataset Creation
### Curation Rationale
Open sourcing customer complaints
### Source Data
https://cfpb.github.io/api/ccdb/
#### Initial Data Collection and Normalization
This database is maintained by the Consumer Financial Protection Bureau
#### Who are the source language producers?
English
### Annotations
#### Annotation process
User submitted to the CFPB
#### Who are the annotators?
N/A
### Personal and Sensitive Information
All PII data has been anonymised
## Considerations for Using the Data
### Social Impact of Dataset
N/A
### Discussion of Biases
This database is not a statistical sample of consumers’ experiences in the marketplace. Complaints are not necessarily representative of all consumers’ experiences and complaints do not constitute “information” for purposes of the Information Quality Act .
Complaint volume should be considered in the context of company size and/or market share. For example, companies with more customers may have more complaints than companies with fewer customers. We encourage you to pair complaint data with public and private data sets for additional context.
The Bureau publishes the consumer’s narrative description of his or her experience if the consumer opts to share it publicly and after the Bureau takes steps to remove personal information. We don’t verify all the allegations in complaint narratives. Unproven allegations in consumer narratives should be regarded as opinion, not fact. We do not adopt the views expressed and make no representation that consumers’ allegations are accurate, clear, complete, or unbiased in substance or presentation. Users should consider what conclusions may be fairly drawn from complaints alone.
### Other Known Limitations
N/A
## Additional Information
### Dataset Curators
https://cfpb.github.io/api/ccdb/
### Licensing Information
Creative Commons Zero v1.0 Universal
### Citation Information
N/A
### Contributions
Thanks to [@kayvane1](https://github.com/kayvane1) for adding this dataset and to the [Consumer Financial Protection Bureau](https://cfpb.github.io/) for publishing it. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
id_panl_bppt | null | 2023-01-25T14:32:43Z | 317 | 1 | null | [
"task_categories:translation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"language:id",
"license:unknown",
"region:us"
] | 2023-01-25T14:32:43Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
- id
license:
- unknown
multilinguality:
- translation
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
pretty_name: IdPanlBppt
dataset_info:
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- en
- id
- name: topic
dtype:
class_label:
names:
'0': Economy
'1': International
'2': Science
'3': Sport
config_name: id_panl_bppt
splits:
- name: train
num_bytes: 7455924
num_examples: 24021
download_size: 2366973
dataset_size: 7455924
---
# 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:** [PANL BPPT](http://digilib.bppt.go.id/sampul/p92-budiono.pdf)
- **Repository:** [PANL BPPT Repository](https://github.com/cahya-wirawan/indonesian-language-models/raw/master/data/BPPTIndToEngCorpusHalfM.zip)
- **Paper:** [Resource Report: Building Parallel Text Corpora for Multi-Domain Translation System](http://digilib.bppt.go.id/sampul/p92-budiono.pdf)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Parallel Text Corpora for Multi-Domain Translation System created by BPPT (Indonesian Agency for the Assessment and
Application of Technology) for PAN Localization Project (A Regional Initiative to Develop Local Language Computing
Capacity in Asia). The dataset contains around 24K sentences divided in 4 difference topics (Economic, international,
Science and Technology and Sport).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Indonesian
## Dataset Structure
[More Information Needed]
### Data Instances
An example of the dataset:
```
{
'id': '0',
'topic': 0,
'translation':
{
'en': 'Minister of Finance Sri Mulyani Indrawati said that a sharp correction of the composite
inde x by up to 4 pct in Wedenesday?s trading was a mere temporary effect of regional factors like
decline in plantation commodity prices and the financial crisis in Thailand.',
'id': 'Menteri Keuangan Sri Mulyani mengatakan koreksi tajam pada Indeks Harga Saham Gabungan
IHSG hingga sekitar 4 persen dalam perdagangan Rabu 10/1 hanya efek sesaat dari faktor-faktor regional
seperti penurunan harga komoditi perkebunan dan krisis finansial di Thailand.'
}
}
```
### Data Fields
- `id`: id of the sample
- `translation`: the parallel sentence english-indonesian
- `topic`: the topic of the sentence. It could be one of the following:
- Economic
- International
- Science and Technology
- Sport
### Data Splits
The dataset is splitted in to train, validation and test sets.
## 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{id_panl_bppt,
author = {PAN Localization - BPPT},
title = {Parallel Text Corpora, English Indonesian},
year = {2009},
url = {http://digilib.bppt.go.id/sampul/p92-budiono.pdf},
}
```
### Contributions
Thanks to [@cahya-wirawan](https://github.com/cahya-wirawan) for adding this dataset. | [
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kan_hope | null | 2023-01-25T14:33:30Z | 317 | 1 | null | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"language:kn",
"license:cc-by-4.0",
"hope-speech-detection",
"arxiv:2108.04616",
"region:us"
] | 2023-01-25T14:33:30Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
- kn
license:
- cc-by-4.0
multilinguality:
- multilingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
pretty_name: KanHope
language_bcp47:
- en-IN
- kn-IN
tags:
- hope-speech-detection
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': Not-Hope
'1': Hope
splits:
- name: train
num_bytes: 494898
num_examples: 4940
- name: test
num_bytes: 65722
num_examples: 618
download_size: 568972
dataset_size: 560620
---
# Dataset Card for KanHope
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://zenodo.org/record/4904729
- **Repository:** [KanHope](https://github.com/adeepH/KanHope)
- **Paper:** [Hope speech detection in Under-resourced Kannada langauge](https://arxiv.org/abs/2108.04616)
- **Leaderboard:** [N/A]
- **Point of Contact:** [Adeep Hande](adeeph18c@iiitt.ac.in)
### Dataset Summary
KanHope dataset is a code-mixed Kannada-English dataset for hope speech detection. All texts are scraped from the comments section of YouTube. The dataset consists of 6,176 user-generated comments in code mixed Kannada scraped from YouTube and manually annotated as bearing hope speech or Not-hope speech.
### Supported Tasks and Leaderboards
This task aims to detect Hope speech content of the code-mixed dataset of comments/posts in Dravidian Languages ( Kannada-English) collected from social media. The comment/post may contain more than one sentence, but the average sentence length of the corpora is 1. Each comment/post is annotated at the comment/post level. This dataset also has class imbalance problems depicting real-world scenarios.
### Languages
Code-mixed text in Dravidian languages (Kannada-English).
## Dataset Structure
### Data Instances
An example from the Kannada dataset looks as follows:
| text | label |
| :------ | :----- |
| ��������� ��ͭ� heartly heltidini... plz avrigella namma nimmellara supprt beku | 0 (Non_hope speech) |
| Next song gu kuda alru andre evaga yar comment madidera alla alrru like madi share madi nam industry na next level ge togond hogaona. | 1 (Hope Speech) |
### Data Fields
Kannada
- `text`: Kannada-English code mixed comment.
- `label`: integer from either of 0 or 1 that corresponds to these values: "Non_hope Speech", "Hope Speech"
### Data Splits
| | train | validation | test |
|---------|------:|-----------:|-----:|
| Kannada | 4941 | 618 | 617 |
## Dataset Creation
### Curation Rationale
Numerous methods have been developed to monitor the spread of negativity in modern years by eliminating vulgar, offensive, and fierce comments from social media platforms. However, there are relatively lesser amounts of study that converges on embracing positivity, reinforcing supportive and reassuring content in online forums.
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
Youtube users
### 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
```
@misc{hande2021hope,
title={Hope Speech detection in under-resourced Kannada language},
author={Adeep Hande and Ruba Priyadharshini and Anbukkarasi Sampath and Kingston Pal Thamburaj and Prabakaran Chandran and Bharathi Raja Chakravarthi},
year={2021},
eprint={2108.04616},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@adeepH](https://github.com/adeepH) for adding this dataset. | [
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newsph_nli | null | 2023-01-25T14:41:24Z | 317 | 0 | newsph-nli | [
"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:original",
"language:tl",
"license:unknown",
"arxiv:2010.11574",
"region:us"
] | 2023-01-25T14:41:24Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- tl
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- natural-language-inference
paperswithcode_id: newsph-nli
pretty_name: NewsPH NLI
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
splits:
- name: train
num_bytes: 154510599
num_examples: 420000
- name: test
num_bytes: 3283665
num_examples: 9000
- name: validation
num_bytes: 33015530
num_examples: 90000
download_size: 76565287
dataset_size: 190809794
---
# Dataset Card for NewsPH NLI
## 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:** [NewsPH NLI homepage](https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks)
- **Repository:** [NewsPH NLI repository](https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks)
- **Paper:** [Arxiv paper](https://arxiv.org/pdf/2010.11574.pdf)
- **Leaderboard:**
- **Point of Contact:** [Jan Christian Cruz](mailto:jan_christian_cruz@dlsu.edu.ph)
### Dataset Summary
First benchmark dataset for sentence entailment in the low-resource Filipino language. Constructed through exploting the structure of news articles. Contains 600,000 premise-hypothesis pairs, in 70-15-15 split for training, validation, and testing.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset contains news articles in Filipino (Tagalog) scraped rom all major Philippine news sites online.
## Dataset Structure
### Data Instances
Sample data:
{
"premise": "Alam ba ninyo ang ginawa ni Erap na noon ay lasing na lasing na rin?",
"hypothesis": "Ininom niya ang alak na pinagpulbusan!",
"label": "0"
}
### Data Fields
[More Information Needed]
### Data Splits
Contains 600,000 premise-hypothesis pairs, in 70-15-15 split for training, validation, and testing.
## Dataset Creation
### Curation Rationale
We propose the use of news articles for automatically creating benchmark datasets for NLI because of two reasons. First, news articles commonly use single-sentence paragraphing, meaning every paragraph in a news article is limited to a single sentence. Second, straight news articles follow the “inverted pyramid” structure, where every succeeding paragraph builds upon the premise of those that came before it, with the most important information on top and the least important towards the end.
### Source Data
#### Initial Data Collection and Normalization
To create the dataset, we scrape news articles from all major Philippine news sites online. We collect a total of 229,571 straight news articles, which we then lightly preprocess to remove extraneous unicode characters and correct minimal misspellings. No further preprocessing is done to preserve information in the data.
#### Who are the source language producers?
The dataset was created by Jan Christian, Blaise Cruz, Jose Kristian Resabal, James Lin, Dan John Velasco, and Charibeth Cheng from De La Salle University and the University of the Philippines
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
Jan Christian Blaise Cruz, Jose Kristian Resabal, James Lin, Dan John Velasco and Charibeth Cheng
### 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 Blaise Cruz] (mailto:jan_christian_cruz@dlsu.edu.ph)
### Licensing Information
[More Information Needed]
### Citation Information
@article{cruz2020investigating,
title={Investigating the True Performance of Transformers in Low-Resource Languages: A Case Study in Automatic Corpus Creation},
author={Jan Christian Blaise Cruz and Jose Kristian Resabal and James Lin and Dan John Velasco and Charibeth Cheng},
journal={arXiv preprint arXiv:2010.11574},
year={2020}
}
### Contributions
Thanks to [@anaerobeth](https://github.com/anaerobeth) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
blinoff/medical_qa_ru_data | blinoff | 2022-07-02T06:24:13Z | 317 | 6 | null | [
"task_categories:question-answering",
"task_ids:closed-domain-qa",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:ru",
"license:unknown",
"region:us"
] | 2022-07-02T06:24:13Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators: []
language_creators: []
language:
- ru
license:
- unknown
multilinguality:
- monolingual
pretty_name: Medical Q&A Russian Data
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- closed-domain-qa
---
### Dataset Summary
This dataset contains 190,335 Russian Q&A posts from a medical related forum.
### Dataset Fields
* date: date and time of the asked question, like '26 Октября 2018, 08:30'
* categ: question category
* theme: question topic
* desc: question text
* ans: question answers separated with ';\n'
* spec10: if present, one of 10 medical specializations
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NumbersStation/NSText2SQL | NumbersStation | 2023-07-11T05:26:13Z | 317 | 31 | null | [
"task_categories:text2text-generation",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"language:en",
"license:other",
"text-to-sql",
"region:us"
] | 2023-07-11T05:26:13Z | 2023-07-11T05:26:12.000Z | 2023-07-11T05:26:12 | ---
language:
- en
task_categories:
- text2text-generation
license:
- other
language_creators:
- crowdsourced
- expert-generated
multilinguality:
- multilingual
tags:
- text-to-sql
size_categories:
- 100K<n<1M
pretty_name: NSText2SQL
---
# Dataset Summary
NSText2SQL dataset used to train [NSQL](https://huggingface.co/NumbersStation/nsql-6B) models. The data is curated from more than 20 different public sources across the web with permissable licenses (listed below). All of these datasets come with existing text-to-SQL pairs. We apply various data cleaning and pre-processing techniques including table schema augmentation, SQL cleaning, and instruction generation using existing LLMs. The resulting dataset contains around 290,000 samples of text-to-SQL pairs.
For more information and code, please see [this repository](https://github.com/NumbersStationAI/NSQL).
# How to use it
```python
from datasets import load_dataset
dataset = load_dataset("NumbersStation/NSText2SQL")
```
# Dataset Structure
## Data Instances
Each data instance in this dataset represents a text-to-SQL entry where the instruction has been formatted with the table schema and question. The output is the SQL in SQlite dialect.
## Data Fields
- `instruction` (string): the instruction to generate SQL.
- `output` (string): the ground truth SQL.
- `source` (string): the source dataset of the sample.
# Languages
The language of the data is primarily English.
# Source Data and Licensing Information
NSText2SQL is sourced from repositories with various licenses. Any use of all or part of the data gathered in NSText2SQL must abide by the terms of the original licenses, including attribution clauses when relevant. We thank all authors who provided these datasets. We provide provenance information for each dataset below.
| Datasets | License | Link |
| ---------------------- | ------------ | -------------------------------------------------------------------------------------------------------------------- |
| academic | Not Found | [https://github.com/jkkummerfeld/text2sql-data](https://github.com/jkkummerfeld/text2sql-data) |
| advising | CC-BY-4.0 | [https://github.com/jkkummerfeld/text2sql-data](https://github.com/jkkummerfeld/text2sql-data) |
| atis | Not Found | [https://github.com/jkkummerfeld/text2sql-data](https://github.com/jkkummerfeld/text2sql-data) |
| restaurants | Not Found | [https://github.com/jkkummerfeld/text2sql-data](https://github.com/jkkummerfeld/text2sql-data) |
| scholar | Not Found | [https://github.com/jkkummerfeld/text2sql-data](https://github.com/jkkummerfeld/text2sql-data) |
| imdb | Not Found | [https://github.com/jkkummerfeld/text2sql-data](https://github.com/jkkummerfeld/text2sql-data) |
| yelp | Not Found | [https://github.com/jkkummerfeld/text2sql-data](https://github.com/jkkummerfeld/text2sql-data) |
| criteria2sql | Apache-2.0 | [https://github.com/xiaojingyu92/Criteria2SQL](https://github.com/xiaojingyu92/Criteria2SQL) |
| css | CC-BY-4.0 | [https://huggingface.co/datasets/zhanghanchong/css](https://huggingface.co/datasets/zhanghanchong/css) |
| eICU | CC-BY-4.0 | [https://github.com/glee4810/EHRSQL](https://github.com/glee4810/EHRSQL) |
| mimic_iii | CC-BY-4.0 | [https://github.com/glee4810/EHRSQL](https://github.com/glee4810/EHRSQL) |
| geonucleardata | CC-BY-SA-4.0 | [https://github.com/chiahsuan156/KaggleDBQA](https://github.com/chiahsuan156/KaggleDBQA) |
| greatermanchestercrime | CC-BY-SA-4.0 | [https://github.com/chiahsuan156/KaggleDBQA](https://github.com/chiahsuan156/KaggleDBQA) |
| studentmathscore | CC-BY-SA-4.0 | [https://github.com/chiahsuan156/KaggleDBQA](https://github.com/chiahsuan156/KaggleDBQA) |
| thehistoryofbaseball | CC-BY-SA-4.0 | [https://github.com/chiahsuan156/KaggleDBQA](https://github.com/chiahsuan156/KaggleDBQA) |
| uswildfires | CC-BY-SA-4.0 | [https://github.com/chiahsuan156/KaggleDBQA](https://github.com/chiahsuan156/KaggleDBQA) |
| whatcdhiphop | CC-BY-SA-4.0 | [https://github.com/chiahsuan156/KaggleDBQA](https://github.com/chiahsuan156/KaggleDBQA) |
| worldsoccerdatabase | CC-BY-SA-4.0 | [https://github.com/chiahsuan156/KaggleDBQA](https://github.com/chiahsuan156/KaggleDBQA) |
| pesticide | CC-BY-SA-4.0 | [https://github.com/chiahsuan156/KaggleDBQA](https://github.com/chiahsuan156/KaggleDBQA) |
| mimicsql_data | MIT | [https://github.com/wangpinggl/TREQS](https://github.com/wangpinggl/TREQS) |
| nvbench | MIT | [https://github.com/TsinghuaDatabaseGroup/nvBench](https://github.com/TsinghuaDatabaseGroup/nvBench) |
| sede | Apache-2.0 | [https://github.com/hirupert/sede](https://github.com/hirupert/sede) |
| spider | CC-BY-SA-4.0 | [https://huggingface.co/datasets/spider](https://huggingface.co/datasets/spider) |
| sql_create_context | CC-BY-4.0 | [https://huggingface.co/datasets/b-mc2/sql-create-context](https://huggingface.co/datasets/b-mc2/sql-create-context) |
| squall | CC-BY-SA-4.0 | [https://github.com/tzshi/squall](https://github.com/tzshi/squall) |
| wikisql | BSD 3-Clause | [https://github.com/salesforce/WikiSQL](https://github.com/salesforce/WikiSQL) |
# Citing this work
If you use this data in your work, please cite our work _and_ the appropriate original sources:
To cite NSText2SQL, please use:
```TeX
@software{numbersstation2023NSText2SQL,
author = {Numbers Station Labs},
title = {NSText2SQL: An Open Source Text-to-SQL Dataset for Foundation Model Training},
month = {July},
year = {2023},
url = {https://github.com/NumbersStationAI/NSQL},
}
```
To cite dataset used in this work, please use:
| Datasets | Cite |
| ---------------------- | ---------------------------------------------------------------------------------------- |
| academic | `\cite{data-advising,data-academic}` |
| advising | `\cite{data-advising}` |
| atis | `\cite{data-advising,data-atis-original,data-atis-geography-scholar}` |
| restaurants | `\cite{data-advising,data-restaurants-logic,data-restaurants-original,data-restaurants}` |
| scholar | `\cite{data-advising,data-atis-geography-scholar}` |
| imdb | `\cite{data-advising,data-imdb-yelp}` |
| yelp | `\cite{data-advising,data-imdb-yelp}` |
| criteria2sql | `\cite{Criteria-to-SQL}` |
| css | `\cite{zhang2023css}` |
| eICU | `\cite{lee2022ehrsql}` |
| mimic_iii | `\cite{lee2022ehrsql}` |
| geonucleardata | `\cite{lee-2021-kaggle-dbqa}` |
| greatermanchestercrime | `\cite{lee-2021-kaggle-dbqa}` |
| studentmathscore | `\cite{lee-2021-kaggle-dbqa}` |
| thehistoryofbaseball | `\cite{lee-2021-kaggle-dbqa}` |
| uswildfires | `\cite{lee-2021-kaggle-dbqa}` |
| whatcdhiphop | `\cite{lee-2021-kaggle-dbqa}` |
| worldsoccerdatabase | `\cite{lee-2021-kaggle-dbqa}` |
| pesticide | `\cite{lee-2021-kaggle-dbqa}` |
| mimicsql_data | `\cite{wang2020text}` |
| nvbench | `\cite{nvBench_SIGMOD21}` |
| sede | `\cite{hazoom2021text}` |
| spider | `\cite{data-spider}` |
| sql_create_context | Not Found |
| squall | `\cite{squall}` |
| wikisql | `\cite{data-wikisql}` |
```TeX
@InProceedings{data-advising,
dataset = {Advising},
author = {Catherine Finegan-Dollak, Jonathan K. Kummerfeld, Li Zhang, Karthik Ramanathan, Sesh Sadasivam, Rui Zhang, and Dragomir Radev},
title = {Improving Text-to-SQL Evaluation Methodology},
booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
month = {July},
year = {2018},
location = {Melbourne, Victoria, Australia},
pages = {351--360},
url = {http://aclweb.org/anthology/P18-1033},
}
@InProceedings{data-imdb-yelp,
dataset = {IMDB and Yelp},
author = {Navid Yaghmazadeh, Yuepeng Wang, Isil Dillig, and Thomas Dillig},
title = {SQLizer: Query Synthesis from Natural Language},
booktitle = {International Conference on Object-Oriented Programming, Systems, Languages, and Applications, ACM},
month = {October},
year = {2017},
pages = {63:1--63:26},
url = {http://doi.org/10.1145/3133887},
}
@article{data-academic,
dataset = {Academic},
author = {Fei Li and H. V. Jagadish},
title = {Constructing an Interactive Natural Language Interface for Relational Databases},
journal = {Proceedings of the VLDB Endowment},
volume = {8},
number = {1},
month = {September},
year = {2014},
pages = {73--84},
url = {http://dx.doi.org/10.14778/2735461.2735468},
}
@InProceedings{data-atis-geography-scholar,
dataset = {Scholar, and Updated ATIS and Geography},
author = {Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, Jayant Krishnamurthy, and Luke Zettlemoyer},
title = {Learning a Neural Semantic Parser from User Feedback},
booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
year = {2017},
pages = {963--973},
location = {Vancouver, Canada},
url = {http://www.aclweb.org/anthology/P17-1089},
}
@article{data-atis-original,
dataset = {ATIS, original},
author = {Deborah A. Dahl, Madeleine Bates, Michael Brown, William Fisher, Kate Hunicke-Smith, David Pallett, Christine Pao, Alexander Rudnicky, and Elizabeth Shriber},
title = {{Expanding the scope of the ATIS task: The ATIS-3 corpus}},
journal = {Proceedings of the workshop on Human Language Technology},
year = {1994},
pages = {43--48},
url = {http://dl.acm.org/citation.cfm?id=1075823},
}
@inproceedings{data-restaurants-logic,
author = {Lappoon R. Tang and Raymond J. Mooney},
title = {Automated Construction of Database Interfaces: Intergrating Statistical and Relational Learning for Semantic Parsing},
booktitle = {2000 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora},
year = {2000},
pages = {133--141},
location = {Hong Kong, China},
url = {http://www.aclweb.org/anthology/W00-1317},
}
@inproceedings{data-restaurants-original,
author = {Ana-Maria Popescu, Oren Etzioni, and Henry Kautz},
title = {Towards a Theory of Natural Language Interfaces to Databases},
booktitle = {Proceedings of the 8th International Conference on Intelligent User Interfaces},
year = {2003},
location = {Miami, Florida, USA},
pages = {149--157},
url = {http://doi.acm.org/10.1145/604045.604070},
}
@inproceedings{data-restaurants,
author = {Alessandra Giordani and Alessandro Moschitti},
title = {Automatic Generation and Reranking of SQL-derived Answers to NL Questions},
booktitle = {Proceedings of the Second International Conference on Trustworthy Eternal Systems via Evolving Software, Data and Knowledge},
year = {2012},
location = {Montpellier, France},
pages = {59--76},
url = {https://doi.org/10.1007/978-3-642-45260-4_5},
}
@InProceedings{data-spider,
author = {Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, Zilin Zhang, and Dragomir Radev},
title = {Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task},
booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
year = {2018},
location = {Brussels, Belgium},
pages = {3911--3921},
url = {http://aclweb.org/anthology/D18-1425},
}
@article{data-wikisql,
author = {Victor Zhong, Caiming Xiong, and Richard Socher},
title = {Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning},
year = {2017},
journal = {CoRR},
volume = {abs/1709.00103},
}
@InProceedings{Criteria-to-SQL,
author = {Yu, Xiaojing and Chen, Tianlong and Yu, Zhengjie and Li, Huiyu and Yang, Yang and Jiang, Xiaoqian and Jiang, Anxiao},
title = {Dataset and Enhanced Model for Eligibility Criteria-to-SQL Semantic Parsing},
booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference},
month = {May},
year = {2020},
address = {Marseille, France},
publisher = {European Language Resources Association},
pages = {5831--5839},
}
@misc{zhang2023css,
title = {CSS: A Large-scale Cross-schema Chinese Text-to-SQL Medical Dataset},
author = {Hanchong Zhang and Jieyu Li and Lu Chen and Ruisheng Cao and Yunyan Zhang and Yu Huang and Yefeng Zheng and Kai Yu},
year = {2023},
}
@article{lee2022ehrsql,
title = {EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Records},
author = {Lee, Gyubok and Hwang, Hyeonji and Bae, Seongsu and Kwon, Yeonsu and Shin, Woncheol and Yang, Seongjun and Seo, Minjoon and Kim, Jong-Yeup and Choi, Edward},
journal = {Advances in Neural Information Processing Systems},
volume = {35},
pages = {15589--15601},
year = {2022},
}
@inproceedings{lee-2021-kaggle-dbqa,
title = {KaggleDBQA: Realistic Evaluation of Text-to-SQL Parsers},
author = {Lee, Chia-Hsuan and Polozov, Oleksandr and Richardson, Matthew},
booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
pages = {2261--2273},
year = {2021},
}
@inproceedings{squall,
title = {On the Potential of Lexico-logical Alignments for Semantic Parsing to {SQL} Queries},
author = {Tianze Shi and Chen Zhao and Jordan Boyd-Graber and Hal {Daum\'{e} III} and Lillian Lee},
booktitle = {Findings of EMNLP},
year = {2020},
}
@article{hazoom2021text,
title = {Text-to-SQL in the wild: a naturally-occurring dataset based on Stack exchange data},
author = {Hazoom, Moshe and Malik, Vibhor and Bogin, Ben},
journal = {arXiv preprint arXiv:2106.05006},
year = {2021},
}
@inproceedings{wang2020text,
title = {Text-to-SQL Generation for Question Answering on Electronic Medical Records},
author = {Wang, Ping and Shi, Tian and Reddy, Chandan K},
booktitle = {Proceedings of The Web Conference 2020},
pages = {350--361},
year = {2020},
}
@inproceedings{nvBench_SIGMOD21,
title = {Synthesizing Natural Language to Visualization (NL2VIS) Benchmarks from NL2SQL Benchmarks},
author = {Yuyu Luo and Nan Tang and Guoliang Li and Chengliang Chai and Wenbo Li and Xuedi Qin},
booktitle = {Proceedings of the 2021 International Conference on Management of Data, {SIGMOD} Conference 2021, June 20–25, 2021, Virtual Event, China},
publisher = {ACM},
year = {2021},
}
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hausa_voa_topics | null | 2023-01-25T14:31:55Z | 316 | 0 | null | [
"task_categories:text-classification",
"task_ids:topic-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ha",
"license:unknown",
"region:us"
] | 2023-01-25T14:31:55Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- ha
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- topic-classification
pretty_name: Hausa Voa News Topic Classification Dataset (HausaVoaTopics)
dataset_info:
features:
- name: news_title
dtype: string
- name: label
dtype:
class_label:
names:
'0': Africa
'1': Health
'2': Nigeria
'3': Politics
'4': World
splits:
- name: train
num_bytes: 144932
num_examples: 2045
- name: validation
num_bytes: 20565
num_examples: 290
- name: test
num_bytes: 41195
num_examples: 582
download_size: 195824
dataset_size: 206692
---
# Dataset Card for Hausa VOA News Topic Classification dataset (hausa_voa_topics)
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** -
- **Repository:** https://github.com/uds-lsv/transfer-distant-transformer-african
- **Paper:** https://www.aclweb.org/anthology/2020.emnlp-main.204/
- **Leaderboard:** -
- **Point of Contact:** Michael A. Hedderich and David Adelani
{mhedderich, didelani} (at) lsv.uni-saarland.de
### Dataset Summary
A news headline topic classification dataset, similar to AG-news, for Hausa. The news headlines were collected from [VOA Hausa](https://www.voahausa.com/).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Hausa (ISO 639-1: ha)
## Dataset Structure
### Data Instances
An instance consists of a news title sentence and the corresponding topic label.
### Data Fields
- `news_title`: A news title
- `label`: The label describing the topic of the news title. Can be one of the following classes: Nigeria, Africa, World, Health or Politics.
### 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 [@michael-aloys](https://github.com/michael-aloys) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
imdb_urdu_reviews | null | 2023-01-25T14:32:49Z | 316 | 0 | null | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:found",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ur",
"license:odbl",
"region:us"
] | 2023-01-25T14:32:49Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- machine-generated
language:
- ur
license:
- odbl
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: ImDB Urdu Reviews
dataset_info:
features:
- name: sentence
dtype: string
- name: sentiment
dtype:
class_label:
names:
'0': positive
'1': negative
splits:
- name: train
num_bytes: 114670811
num_examples: 50000
download_size: 31510992
dataset_size: 114670811
---
# Dataset Card for ImDB Urdu Reviews
## 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/mirfan899/Urdu)
- **Repository:** [Github](https://github.com/mirfan899/Urdu)
- **Paper:** [Aclweb](http://www.aclweb.org/anthology/P11-1015)
- **Leaderboard:**
- **Point of Contact:** [Ikram Ali](https://github.com/akkefa)
### 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
- sentence: The movie review which was translated into Urdu.
- sentiment: The sentiment exhibited in the review, either positive or negative.
### 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 [@chaitnayabasava](https://github.com/chaitnayabasava) for adding this dataset. | [
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refresd | null | 2023-01-25T14:43:11Z | 316 | 0 | refresd | [
"task_categories:text-classification",
"task_categories:translation",
"task_ids:semantic-similarity-classification",
"task_ids:semantic-similarity-scoring",
"task_ids:text-scoring",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:machine-generated",
"multilinguality:translation",
"size_categories:1K<n<10K",
"source_datasets:extended|other-wikimatrix",
"language:en",
"language:fr",
"license:mit",
"arxiv:1907.05791",
"region:us"
] | 2023-01-25T14:43:11Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
- machine-generated
language_creators:
- crowdsourced
- machine-generated
language:
- en
- fr
license:
- mit
multilinguality:
- translation
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-wikimatrix
task_categories:
- text-classification
- translation
task_ids:
- semantic-similarity-classification
- semantic-similarity-scoring
- text-scoring
paperswithcode_id: refresd
pretty_name: Rationalized English-French Semantic Divergences
dataset_info:
features:
- name: sentence_en
dtype: string
- name: sentence_fr
dtype: string
- name: label
dtype:
class_label:
names:
'0': divergent
'1': equivalent
- name: all_labels
dtype:
class_label:
names:
'0': unrelated
'1': some_meaning_difference
'2': no_meaning_difference
- name: rationale_en
dtype: string
- name: rationale_fr
dtype: string
splits:
- name: train
num_bytes: 501562
num_examples: 1039
download_size: 503977
dataset_size: 501562
---
# Dataset Card for REFreSD Dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Github](https://github.com/Elbria/xling-SemDiv/tree/master/REFreSD)
- **Repository:** [Github](https://github.com/Elbria/xling-SemDiv/)
- **Paper:** [Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rank](https://www.aclweb.org/anthology/2020.emnlp-main.121)
- **Leaderboard:**
- **Point of Contact:** [Eleftheria Briakou](mailto:ebriakou@cs.umd.edu)
- **Additional Documentation:** [Annotation workflow, data statement, DataSheet, and IRB documentation](https://elbria.github.io/post/refresd/)
### Dataset Summary
The Rationalized English-French Semantic Divergences (REFreSD) dataset consists of 1,039 English-French sentence-pairs annotated with sentence-level divergence judgments and token-level rationales. The project under which REFreSD was collected aims to advance our fundamental understanding of computational representations and methods for comparing and contrasting text meaning across languages.
### Supported Tasks and Leaderboards
`semantic-similarity-classification` and `semantic-similarity-scoring`: This dataset can by used to assess the ability of computational methods to detect meaning mismatches between languages. The model performance is measured in terms of accuracy by comparing the model predictions with the human judgments in REFreSD. Details about the results of a BERT-based model, Divergent mBERT, over this dataset can be found in the [paper](https://www.aclweb.org/anthology/2020.emnlp-main.121).
### Languages
The text is in English and French as found on Wikipedia. The associated BCP-47 codes are `en` and `fr`.
## Dataset Structure
### Data Instances
Each data point looks like this:
```python
{
'sentence_pair': {'en': 'The invention of farming some 10,000 years ago led to the development of agrarian societies , whether nomadic or peasant , the latter in particular almost always dominated by a strong sense of traditionalism .',
'fr': "En quelques décennies , l' activité économique de la vallée est passée d' une mono-activité agricole essentiellement vivrière , à une quasi mono-activité touristique , si l' on excepte un artisanat du bâtiment traditionnel important , en partie saisonnier ."}
'label': 0,
'all_labels': 0,
'rationale_en': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
'rationale_fr': [2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3],
}
```
### Data Fields
- `sentence_pair`: Dictionary of sentences containing the following field.
- `en`: The English sentence.
- `fr`: The corresponding (or not) French sentence.
- `label`: Binary. Whether both sentences correspond. `{0:divergent, 1:equivalent}`
- `all_labels`: 3-class label `{0: "unrelated", 1: "some_meaning_difference", 2:"no_meaning_difference"}`. The first two are sub-classes of the `divergent` label.
- `rationale_en`: A list of integers from 0-3 indicating the number of annotators who highlighted the token of the text in the English sentence during annotation. Word-aligned rationale for the divergent/equivalent label, from English.
- `rationale_fr`: A list of integers from 0-3 indicating the number of annotators who highlighted the token of the text in the French sentence during annotation. Word-aligned rationale for the divergent/equivalent label, from French.
### Data Splits
The dataset contains 1039 sentence pairs in a single `"train"` split. Of these pairs, 64% are annotated as divergent, and 40% contain fine-grained meaning divergences.
| Label | Number of Instances |
| ----------------------- | ------------------- |
| Unrelated | 252 |
| Some meaning difference | 418 |
| No meaning different | 369 |
## Dataset Creation
### Curation Rationale
The curators chose the English-French section of the WikiMatrix corpus because (1) it is likely to contain diverse, interesting divergence types since it consists of mined parallel sentences of diverse topics which are not necessarily generated by (human) translations, and (2) Wikipedia and WikiMatrix are widely used resources to train semantic representations and perform cross-lingual transfer in NLP.
### Source Data
#### Initial Data Collection and Normalization
The source for this corpus is the English and French portion of the [WikiMatrix corpus](https://arxiv.org/abs/1907.05791), which itself was extracted from Wikipedia articles. The curators excluded noisy samples by filtering out sentence pairs that a) were too short or too long, b) consisted mostly of numbers, or c) had a small token-level edit difference.
#### Who are the source language producers?
Some content of Wikipedia articles has been (human) translated from existing articles in another language while others have been written or edited independently in each language. Therefore, information on how the original text is created is not available.
### Annotations
#### Annotation process
The annotations were collected over the span of three weeks in April 2020. Annotators were presented with an English sentence and a French sentence. First, they highlighted spans and labeled them as 'added', 'changed', or 'other', where added spans contain information not contained in the other sentence, changed spans contain some information that is in the other sentence but whose meaning is not the same, and other spans have some different meaning not covered in the previous two cases, such as idioms. They then assessed the relation between the two sentences as either 'unrelated', 'some meaning differences', or 'no meaning difference'. See the [annotation guidelines](https://elbria.github.io/post/refresd/files/REFreSD_Annotation_Guidelines.pdf) for more information about the task and the annotation interface, and see the [DataSheet](https://elbria.github.io/post/refresd/files/REFreSD_Datasheet.pdf) for information about the annotator compensation.
The following table contains Inter-Annotator Agreement metrics for the dataset:
| Granularity | Method | IAA |
| ----------- | --------------- | ------------ |
| Sentence | Krippendorf's α | 0.60 |
| Span | macro F1 | 45.56 ± 7.60 |
| Token | macro F1 | 33.94 ± 8.24 |
#### Who are the annotators?
This dataset includes annotations from 6 participants recruited from the University of Maryland, College Park (UMD) educational institution. Participants ranged in age from 20–25 years, including one man and five women. For each participant, the curators ensured they were proficient in both languages of interest: three of them self-reported as English native speakers, one as a French native speaker, and two as bilingual English-French speakers.
### Personal and Sensitive Information
The dataset contains discussions of people as they appear in Wikipedia articles. It does not contain confidential information, nor does it contain identifying information about the source language producers or the annotators.
## Considerations for Using the Data
### Social Impact of Dataset
Models that are successful in the supported task require sophisticated semantic representations at the sentence level beyond the combined representations of the individual tokens in isolation. Such models could be used to curate parallel corpora for tasks like machine translation, cross-lingual transfer learning, or semantic modeling.
The statements in the dataset, however, are not necessarily representative of the world and may overrepresent one worldview if one language is primarily translated to, rather than an equal distribution of translations between the languages.
### Discussion of Biases
The English Wikipedia is known to have significantly more [contributors](https://en.wikipedia.org/wiki/Wikipedia:Who_writes_Wikipedia%3F) who identify as male than any other gender and who reside in either North America or Europe. This leads to an overrepresentation of male perspectives from these locations in the corpus in terms of both the topics covered and the language used to talk about those topics. It's not clear to what degree this holds true for the French Wikipedia. The REFreSD dataset itself has not yet been examined for the degree to which it contains the gender and other biases seen in the larger Wikipedia datasets.
### Other Known Limitations
It is unknown how many of the sentences in the dataset were written independently, and how many were written as [translations](https://en.wikipedia.org/wiki/Wikipedia:Translation) by either humans or machines from some other language to the languages of interest in this dataset.
## Additional Information
### Dataset Curators
The dataset curators are Eleftheria Briakou and Marine Carpuat, who are both affiliated with the University of Maryland, College Park's Department of Computer Science.
### Licensing Information
The project is licensed under the [MIT License](https://github.com/Elbria/xling-SemDiv/blob/master/LICENSE).
### Citation Information
```BibTeX
@inproceedings{briakou-carpuat-2020-detecting,
title = "Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rank",
author = "Briakou, Eleftheria and Carpuat, Marine",
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.121",
pages = "1563--1580",
}
```
### Contributions
Thanks to [@mpariente](https://github.com/mpariente) and [@mcmillanmajora](https://github.com/mcmillanmajora) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
wmt_t2t | null | 2023-04-05T13:44:08Z | 316 | 0 | null | [
"task_categories:translation",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:translation",
"size_categories:10M<n<100M",
"source_datasets:extended|europarl_bilingual",
"source_datasets:extended|news_commentary",
"source_datasets:extended|opus_paracrawl",
"source_datasets:extended|un_multi",
"language:de",
"language:en",
"license:unknown",
"region:us"
] | 2023-04-05T13:44:08Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- de
- en
license:
- unknown
multilinguality:
- translation
size_categories:
- 10M<n<100M
source_datasets:
- extended|europarl_bilingual
- extended|news_commentary
- extended|opus_paracrawl
- extended|un_multi
task_categories:
- translation
task_ids: []
pretty_name: WMT T2T
paperswithcode_id: null
dataset_info:
features:
- name: translation
dtype:
translation:
languages:
- de
- en
config_name: de-en
splits:
- name: train
num_bytes: 1385110179
num_examples: 4592289
- name: validation
num_bytes: 736415
num_examples: 3000
- name: test
num_bytes: 777334
num_examples: 3003
download_size: 1728762345
dataset_size: 1386623928
---
# Dataset Card for "wmt_t2t"
## 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/tensorflow/tensor2tensor/blob/master/tensor2tensor/data_generators/translate_ende.py](https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/data_generators/translate_ende.py)
- **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:** 1.73 GB
- **Size of the generated dataset:** 1.39 GB
- **Total amount of disk used:** 3.11 GB
### Dataset Summary
The WMT EnDe Translate dataset used by the Tensor2Tensor library.
Translation dataset based on the data from statmt.org.
Versions exist for different years using a combination of data
sources. The base `wmt` allows you to create a custom dataset by choosing
your own data/language pair. This can be done as follows:
```python
from datasets import inspect_dataset, load_dataset_builder
inspect_dataset("wmt_t2t", "path/to/scripts")
builder = load_dataset_builder(
"path/to/scripts/wmt_utils.py",
language_pair=("fr", "de"),
subsets={
datasets.Split.TRAIN: ["commoncrawl_frde"],
datasets.Split.VALIDATION: ["euelections_dev2019"],
},
)
# Standard version
builder.download_and_prepare()
ds = builder.as_dataset()
# Streamable version
ds = builder.as_streaming_dataset()
```
### 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
#### de-en
- **Size of downloaded dataset files:** 1.73 GB
- **Size of the generated dataset:** 1.39 GB
- **Total amount of disk used:** 3.11 GB
An example of 'validation' looks as follows.
```
{
"translation": {
"de": "Just a test sentence.",
"en": "Just a test sentence."
}
}
```
### Data Fields
The data fields are the same among all splits.
#### de-en
- `translation`: a multilingual `string` variable, with possible languages including `de`, `en`.
### Data Splits
|name | train |validation|test|
|-----|------:|---------:|---:|
|de-en|4592289| 3000|3003|
## 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{bojar-EtAl:2014:W14-33,
author = {Bojar, Ondrej and Buck, Christian and Federmann, Christian and Haddow, Barry and Koehn, Philipp and Leveling, Johannes and Monz, Christof and Pecina, Pavel and Post, Matt and Saint-Amand, Herve and Soricut, Radu and Specia, Lucia and Tamchyna, Ale
{s}},
title = {Findings of the 2014 Workshop on Statistical Machine Translation},
booktitle = {Proceedings of the Ninth Workshop on Statistical Machine Translation},
month = {June},
year = {2014},
address = {Baltimore, Maryland, USA},
publisher = {Association for Computational Linguistics},
pages = {12--58},
url = {http://www.aclweb.org/anthology/W/W14/W14-3302}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@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 | |
CALM/arwiki | CALM | 2022-08-01T16:37:23Z | 316 | 1 | null | [
"multilinguality:monolingual",
"language:ar",
"license:unknown",
"region:us"
] | 2022-08-01T16:37:23Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
pretty_name: Wikipedia Arabic dumps dataset.
language:
- ar
license:
- unknown
multilinguality:
- monolingual
---
# Arabic Wiki Dataset
## Dataset Summary
This dataset is extracted using [`wikiextractor`](https://github.com/attardi/wikiextractor) tool, from [Wikipedia Arabic pages](https://dumps.wikimedia.org/arwiki/).
## Supported Tasks and Leaderboards
Intended to train **Arabic** language models on MSA (Modern Standard Arabic).
## Dataset Structure
The dataset is structured into 2 folders:
- `arwiki_20211213_txt`: dataset is divided into subfolders each of which contains no more than 100 documents.
- `arwiki_20211213_txt_single`: all documents merged together in a single txt file.
## Dataset Statistics
#### Extracts from **December 13, 2021**:
| documents | vocabulary | words |
| --- | --- | --- |
| 1,136,455 | 5,446,560 | 175,566,016 |
## Usage
Load all dataset from the single txt file:
```python
load_dataset('CALM/arwiki',
data_files='arwiki_2021_txt_single/arwiki_20211213.txt')
# OR with stream
load_dataset('CALM/arwiki',
data_files='arwiki_2021_txt_single/arwiki_20211213.txt',
streaming=True)
```
Load a smaller subset from the individual txt files:
```python
load_dataset('CALM/arwiki',
data_files='arwiki_2021_txt/AA/arwiki_20211213_1208.txt')
# OR with stream
load_dataset('CALM/arwiki',
data_files='arwiki_2021_txt/AA/arwiki_20211213_1208.txt',
streaming=True)
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Nexdata/chinese_dialect | Nexdata | 2023-11-22T09:49:16Z | 316 | 5 | null | [
"task_categories:automatic-speech-recognition",
"language:zh",
"region:us"
] | 2023-11-22T09:49:16Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
task_categories:
- automatic-speech-recognition
language:
- zh
---
# Dataset Card for chinese_dialect
## 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://nexdata.ai/?source=Huggingface
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The dataset contains 25,000 hours of Chinese Dialect speech data. It's collected from local dialect speakers in multiple dialect regions, covering Hokkien, Cantonese, Sichuan Dialect, Henan Dialects,Northeastern Dialect, Shanghai Dialect,Uyghur and Tibetan etc. The format is 16kHz, 16bit, uncompressed wav, mono channel. The sentence accuracy is over 95%.
For more details, please refer to the link: https://nexdata.ai/speechRecognition?source=Huggingface
### Supported Tasks and Leaderboards
automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).
### Languages
Chinese Dialect
## 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
Commercial License
### Citation Information
[More Information Needed]
### Contributions | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Firoj/HumAID | Firoj | 2022-05-18T04:45:03Z | 316 | 2 | null | [
"region:us"
] | 2022-05-18T04:45:03Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | # Dataset Card for HumAID
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://crisisnlp.qcri.org/humaid_dataset
- **Repository:** https://crisisnlp.qcri.org/data/humaid/humaid_data_all.zip
- **Paper:** https://ojs.aaai.org/index.php/ICWSM/article/view/18116/17919
<!-- - **Leaderboard:** [Needs More Information] -->
<!-- - **Point of Contact:** [Needs More Information] -->
### Dataset Summary
The HumAID Twitter dataset consists of several thousands of manually annotated tweets that has been collected during 19 major natural disaster events including earthquakes, hurricanes, wildfires, and floods, which happened from 2016 to 2019 across different parts of the World. The annotations in the provided datasets consists of following humanitarian categories. The dataset consists only english tweets and it is the largest dataset for crisis informatics so far.
** Humanitarian categories **
- Caution and advice
- Displaced people and evacuations
- Dont know cant judge
- Infrastructure and utility damage
- Injured or dead people
- Missing or found people
- Not humanitarian
- Other relevant information
- Requests or urgent needs
- Rescue volunteering or donation effort
- Sympathy and support
The resulting annotated dataset consists of 11 labels.
### Supported Tasks and Benchmark
The dataset can be used to train a model for multiclass tweet classification for disaster response. The benchmark results can be found in https://ojs.aaai.org/index.php/ICWSM/article/view/18116/17919.
Dataset is also released with event-wise and JSON objects for further research.
Full set of the dataset can be found in https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/A7NVF7
### Languages
English
## Dataset Structure
### Data Instances
```
{
"tweet_text": "@RT_com: URGENT: Death toll in #Ecuador #quake rises to 233 \u2013 President #Correa #1 in #Pakistan",
"class_label": "injured_or_dead_people"
}
```
### Data Fields
* tweet_text: corresponds to the tweet text.
* class_label: corresponds to a label assigned to a given tweet text
### Data Splits
* Train
* Development
* Test
## Dataset Creation
<!-- ### Curation Rationale -->
### Source Data
#### Initial Data Collection and Normalization
Tweets has been collected during several disaster events.
### Annotations
#### Annotation process
AMT has been used to annotate the dataset. Please check the paper for a more detail.
#### Who are the annotators?
- crowdsourced
<!-- ## Considerations for Using the Data -->
<!-- ### Social Impact of Dataset -->
<!-- ### Discussion of Biases -->
<!-- [Needs More Information] -->
<!-- ### Other Known Limitations -->
<!-- [Needs More Information] -->
## Additional Information
### Dataset Curators
Authors of the paper.
### Licensing Information
- cc-by-nc-4.0
### Citation Information
```
@inproceedings{humaid2020,
Author = {Firoj Alam, Umair Qazi, Muhammad Imran, Ferda Ofli},
booktitle={Proceedings of the Fifteenth International AAAI Conference on Web and Social Media},
series={ICWSM~'21},
Keywords = {Social Media, Crisis Computing, Tweet Text Classification, Disaster Response},
Title = {HumAID: Human-Annotated Disaster Incidents Data from Twitter},
Year = {2021},
publisher={AAAI},
address={Online},
}
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
GroNLP/ik-nlp-22_winemag | GroNLP | 2022-02-13T11:03:27Z | 316 | 3 | null | [
"license:cc-by-sa-4.0",
"region:us"
] | 2022-02-13T11:03:27Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
license: cc-by-sa-4.0
---
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leey4n/KR3 | leey4n | 2023-07-19T08:35:54Z | 316 | 2 | null | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"multilinguality:monolingual",
"size_categories:100K<n<1m",
"language:ko",
"license:cc-by-nc-sa-4.0",
"region:us"
] | 2023-07-19T08:35:54Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators: []
language_creators: []
language:
- ko
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
pretty_name: KR3
size_categories:
- 100K<n<1m
source_datasets: []
task_categories:
- text-classification
task_ids:
- sentiment-classification
---
### KR3: Korean Restaurant Reviews with Ratings
Korean sentiment classification dataset
- Size: 460K(+180K)
- Language: Korean-centric
### ⚠️ Caution with `Rating` Column
0 stands for negative review, 1 stands for positive review, and 2 stands for ambiguous review.
**Note that rating 2 is not intended to be used directly for supervised learning(classification).** This data is included for additional pre-training purpose or other usage.
In other words, this dataset is basically a **binary** sentiment classification task where labels are 0 and 1.
### 🔍 See More
See all the codes for crawling/preprocessing the dataset and experiments with KR3 in [GitHub Repo](https://github.com/Wittgensteinian/kr3).
See Kaggle dataset in [Kaggle Dataset](https://www.kaggle.com/ninetyninenewton/kr3-korean-restaurant-reviews-with-ratings).
### Usage
```python
from datasets import load_dataset
kr3 = load_dataset("leey4n/KR3", name='kr3', split='train')
kr3 = kr3.remove_columns(['__index_level_0__']) # Original file didn't include this column. Suspect it's a hugging face issue.
```
```python
# drop reviews with ambiguous label
kr3_binary = kr3.filter(lambda example: example['Rating'] != 2)
```
### License
**CC BY-NC-SA 4.0**
### Legal Issues
We concluded that the **non-commerical usage and release of KR3 fall into the range of fair use (공정 이용)** stated in the Korean copyright act (저작권법). We further clarify that we **did not agree to the terms of service** from any websites which might prohibit web crawling. In other words, web crawling we've done was proceeded without logging in to the website. Despite all of these, feel free to contact to any of the contributors if you notice any legal issues.
### Contributors & Acknowledgement
(Alphabetical order)
[Dongin Jung](https://github.com/dongin1009)
[Hyunwoo Kwak](https://github.com/Kwak-Hyun-woo)
[Kaeun Lee](https://github.com/Kaeun-Lee)
[Yejoon Lee](https://github.com/wittgensteinian)
This work was done as DIYA 4기. Compute resources needed for the work was supported by [DIYA](https://blog.diyaml.com) and surromind.ai.
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
gorkemgoknar/tr_ted_talk_translated | gorkemgoknar | 2022-01-13T09:14:54Z | 316 | 1 | null | [
"language:tr",
"license:apache-2.0",
"dataset",
"turkish",
"ted-multi",
"cleaned",
"region:us"
] | 2022-01-13T09:14:54Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- tr
thumbnail:
tags:
- dataset
- turkish
- ted-multi
- cleaned
license: apache-2.0
datasets:
- ted-multi
---
# Turkish Ted talk translations
# Created from ted-multi dataset
adding processing steps here if you want another language
```python
#using Turkish as target
target_lang="tr" # change to your target lang
from datasets import load_dataset
#ted-multi is a multiple language translated dataset
#fits for our case , not to big and curated but need a simple processing
dataset = load_dataset("ted_multi")
dataset.cleanup_cache_files()
#original from patrick's
#chars_to_ignore_regex = '[,?.!\-\;\:\"“%‘”�—’…–]' # change to the ignored characters of your fine-tuned model
#will use cahya/wav2vec2-base-turkish-artificial-cv
#checking inside model repository to find which chars removed (no run.sh)
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\‘\”\'\`…\’»«]'
import re
def extract_target_lang_entries(batch):
#specific mapping for ted_multi dataset
#need to find index of language in each translation as it can shift
try:
target_index_for_lang= batch["translations"]["language"].index(target_lang)
except ValueError:
#target not in list empty it for later processing
batch["text"] = None
return batch
#index_translation_pairs = zip(batch, target_index_for_batch)
text= batch["translations"]["translation"][target_index_for_lang]
batch["text"] = re.sub(chars_to_ignore_regex, "", text.lower())
return batch
#this dataset has additional columns need to say it
cols_to_remove = ['translations', 'talk_name']
dataset = dataset.map(extract_target_lang_entries, remove_columns=cols_to_remove)
#on preocessing we tagged None for empty ones
dataset_cleaned = dataset.filter(lambda x: x['text'] is not None)
dataset_cleaned
from huggingface_hub import notebook_login
notebook_login()
dataset_cleaned.push_to_hub(f"{target_lang}_ted_talk_translated")
``` | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
hitachi-nlp/FLD.v2 | hitachi-nlp | 2023-11-10T12:11:07Z | 316 | 3 | null | [
"region:us"
] | 2023-11-10T12:11:07Z | 2023-08-24T09:44:21.000Z | 2023-08-24T09:44:21 | ---
dataset_info:
- config_name: default
features:
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dtype: string
- name: hypothesis
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- name: hypothesis_formula
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- name: context
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- name: num_translation_distractors
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- name: num_all_distractors
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- name: proof_label
dtype: string
- name: negative_proof_label
dtype: string
- name: world_assump_label
dtype: string
- name: negative_world_assump_label
dtype: string
- name: prompt_serial
dtype: string
- name: proof_serial
dtype: string
splits:
- name: train
num_bytes: 103394163
num_examples: 30000
- name: validation
num_bytes: 17205990
num_examples: 5000
- name: test
num_bytes: 17215356
num_examples: 5000
download_size: 51122839
dataset_size: 137815509
- config_name: star
features:
- name: version
dtype: string
- name: hypothesis
dtype: string
- name: hypothesis_formula
dtype: string
- name: context
dtype: string
- name: context_formula
dtype: string
- name: proofs
sequence: string
- name: proofs_formula
sequence: string
- name: negative_hypothesis
dtype: string
- name: negative_hypothesis_formula
dtype: string
- name: negative_proofs
sequence: string
- name: negative_original_tree_depth
dtype: int64
- name: original_tree_depth
dtype: int64
- name: depth
dtype: int64
- name: num_formula_distractors
dtype: int64
- name: num_translation_distractors
dtype: int64
- name: num_all_distractors
dtype: int64
- name: proof_label
dtype: string
- name: negative_proof_label
dtype: string
- name: world_assump_label
dtype: string
- name: negative_world_assump_label
dtype: string
- name: prompt_serial
dtype: string
- name: proof_serial
dtype: string
splits:
- name: train
num_bytes: 129618848
num_examples: 30000
- name: validation
num_bytes: 21529187
num_examples: 5000
- name: test
num_bytes: 21731836
num_examples: 5000
download_size: 63147762
dataset_size: 172879871
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
- config_name: star
data_files:
- split: train
path: star/train-*
- split: validation
path: star/validation-*
- split: test
path: star/test-*
---
# Dataset Card for "FLD.v2"
To train a casual language model, simply use "prompt_serial" column for the model input and "proof_serial" for the output.
For more info, see [our project page](https://github.com/hitachi-nlp/FLD/).
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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covid_tweets_japanese | null | 2023-01-25T14:28:47Z | 315 | 1 | null | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ja",
"license:cc-by-nd-4.0",
"region:us"
] | 2023-01-25T14:28:47Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- ja
license:
- cc-by-nd-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- fact-checking
pretty_name: COVID-19 日本語Twitterデータセット (COVID-19 Japanese Twitter Dataset)
dataset_info:
features:
- name: tweet_id
dtype: string
- name: assessment_option_id
dtype:
class_label:
names:
'0': '63'
'1': '64'
'2': '65'
'3': '66'
'4': '67'
'5': '68'
splits:
- name: train
num_bytes: 1662833
num_examples: 53639
download_size: 406005
dataset_size: 1662833
---
# Dataset Card for COVID-19 日本語Twitterデータセット (COVID-19 Japanese Twitter Dataset)
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [COVID-19 日本語Twitterデータセット homepage](http://www.db.info.gifu-u.ac.jp/data/Data_5f02db873363f976fce930d1)
- **Repository:** [N/A]
- **Paper:** [N/A]
- **Leaderboard:** [N/A]
- **Point of Contact:** Check the homepage.
### Dataset Summary
53,640 Japanese tweets with annotation if a tweet is related to COVID-19 or not. The annotation is by majority decision by 5 - 10 crowd workers. Target tweets include "COVID" or "コロナ". The period of the tweets is from around January 2020 to around June 2020. The original tweets are not contained. Please use Twitter API to get them, for example.
### Supported Tasks and Leaderboards
Text-classification, Whether the tweet is related to COVID-19, and whether it is fact or opinion.
### Languages
The text can be gotten using the IDs in this dataset is Japanese, posted on Twitter.
## Dataset Structure
### Data Instances
CSV file with the 1st column is Twitter ID and the 2nd column is assessment option ID.
### Data Fields
- `tweet_id`: Twitter ID.
- `assessment_option_id`: The selection result. It has the following meanings:
- 63: a general fact: generally published information, such as news.
- 64: a personal fact: personal news. For example, a person heard that the next-door neighbor, XX, has infected COVID-19, which has not been in a news.
- 65: an opinion/feeling
- 66: difficult to determine if they are related to COVID-19 (it is definitely the tweet is not "67: unrelated", but 63, 64, 65 cannot be determined)
- 67: unrelated
- 68: it is a fact, but difficult to determine whether general facts, personal facts, or impressions (it may be irrelevant to COVID-19 since it is indistinguishable between 63 - 65 and 67).
### Data Splits
No articles have been published for this dataset, and it appears that the author of the dataset is willing to publish an article (it is not certain that the splitting information will be included). Therefore, at this time, information on data splits is not provided.
## Dataset Creation
### Curation Rationale
[More Information Needed] because the paper is not yet published.
### Source Data
#### Initial Data Collection and Normalization
53,640 Japanese tweets with annotation if a tweet is related to COVID-19 or not. Target tweets include "COVID" or "コロナ". The period of the tweets is from around January 2020 to around June 2020.
#### Who are the source language producers?
The language producers are users of Twitter.
### Annotations
#### Annotation process
The annotation is by majority decision by 5 - 10 crowd workers.
#### Who are the annotators?
Crowd workers.
### Personal and Sensitive Information
The author does not contain original tweets.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The dataset is hosted by Suzuki Laboratory, Gifu University, Japan.
### Licensing Information
CC-BY-ND 4.0
### Citation Information
A related paper has not yet published.
The author shows how to cite as「鈴木 優: COVID-19 日本語 Twitter データセット （ http://www.db.info.gifu-u.ac.jp/data/Data_5f02db873363f976fce930d1 ） 」.
### Contributions
Thanks to [@forest1988](https://github.com/forest1988) for adding this dataset. | [
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eth_py150_open | null | 2022-11-18T20:01:17Z | 315 | 0 | eth-py150-open | [
"task_categories:other",
"annotations_creators:no-annotation",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"contextual-embeddings",
"region:us"
] | 2022-11-18T20:01:17Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- machine-generated
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: eth-py150-open
pretty_name: ethpy150open
tags:
- contextual-embeddings
dataset_info:
features:
- name: filepath
dtype: string
- name: license
dtype: string
config_name: eth_py150_open
splits:
- name: train
num_bytes: 5414978
num_examples: 74749
- name: test
num_bytes: 3006199
num_examples: 41457
- name: validation
num_bytes: 598524
num_examples: 8302
download_size: 13875671
dataset_size: 9019701
---
# Dataset Card for ethpy150open
## 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.sri.inf.ethz.ch/py150
- **Repository:** https://github.com/google-research-datasets/eth_py150_open
- **Paper:** https://proceedings.icml.cc/static/paper_files/icml/2020/5401-Paper.pdf
- **Leaderboard:** None
- **Point of Contact:** Aditya Kanade <kanade@iisc.ac.in>, Petros Maniatis <maniatis@google.com>
### Dataset Summary
A redistributable subset of the [ETH Py150 corpus](https://www.sri.inf.ethz.ch/py150), introduced in the ICML 2020 paper ['Learning and Evaluating Contextual Embedding of Source Code'](https://proceedings.icml.cc/static/paper_files/icml/2020/5401-Paper.pdf)
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English
## Dataset Structure
List of dicts of
{
"filepath": The relative URL containing the path to the file on GitHub
"license": The license used for that specific file or repository
}
### Data Instances
{
"filepath": "0rpc/zerorpc-python/setup.py",
"license": "mit"
},
{
"filepath": "0rpc/zerorpc-python/zerorpc/heartbeat.py",
"license": "mit"
},
### Data Fields
- `filepath`: The relative URL containing the path to the file on GitHub
- `license`: The license used for that specific file or repository
### Data Splits
| | Train | Valid | Test |
| ----- | ------- | ----- | ----- |
| Dataset Split | 74749 | 8302 | 41457 |
## Dataset Creation
The original dataset is at https://www.sri.inf.ethz.ch/py150
### Curation Rationale
To generate a more redistributable version of the dataset
### Source Data
#### Initial Data Collection and Normalization
All the urls are filepaths relative to GitHub and the master branch was used as available at the time
#### 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
Apache License 2.0
### Citation Information
@inproceedings{kanade2020learning,
title={Learning and Evaluating Contextual Embedding of Source Code},
author={Kanade, Aditya and Maniatis, Petros and Balakrishnan, Gogul and Shi, Kensen},
booktitle={International Conference on Machine Learning},
pages={5110--5121},
year={2020},
organization={PMLR}
}
### Contributions
Thanks to [@Bharat123rox](https://github.com/Bharat123rox) for adding this dataset. | [
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linnaeus | null | 2023-06-15T14:40:39Z | 315 | 1 | linnaeus | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] | 2023-06-15T14:40:39Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: linnaeus
pretty_name: LINNAEUS
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: linnaeus
splits:
- name: train
num_bytes: 4772417
num_examples: 11936
- name: validation
num_bytes: 1592823
num_examples: 4079
- name: test
num_bytes: 2802877
num_examples: 7143
download_size: 18204624
dataset_size: 9168117
---
# 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:** [linnaeus](http://linnaeus.sourceforge.net/)
- **Repository:** https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/linnaeus-IOB
- **Paper:** [BMC Bioinformatics](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-11-85)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The LINNAEUS corpus consists of 100 full-text documents from the PMCOA
document set which were randomly selected. All mentions of species terms were manually
annotated and normalized to the NCBI taxonomy IDs of the intended species.
The original LINNAEUS corpus is available in a TAB-separated standoff format. The resource does not define training,
development or test subsets.
We converted the corpus into BioNLP shared task standoff format using a custom script, split it into 50-, 17-, and 33-
document training, development and test sets, and then converted these into the CoNLL format using standoff2conll.
As a full-text corpus, LINNAEUS contains comparatively frequent
non-ASCII characters, which were mapped to ASCII using the
standoff2conll -a option.
The conversion was highly accurate, but due to sentence-splitting errors within entity mentions,
the number of annotations in the converted data was larger by four (100.09%) than that
in the source data. 99.77% of names in the original annotation matched names in the converted
data.
### Supported Tasks and Leaderboards
This dataset is used for species Named Entity Recognition.
### Languages
The dataset is in English.
## Dataset Structure
### Data Instances
An example from the dataset is:
```
{'id': '2',
'tokens': ['Scp160p', 'is', 'a', '160', 'kDa', 'protein', 'in', 'the', 'yeast', 'Saccharomyces', 'cerevisiae', 'that', 'contains', '14', 'repeats', 'of', 'the', 'hnRNP', 'K', '-', 'homology', '(', 'KH', ')', 'domain', ',', 'and', 'demonstrates', 'significant', 'sequence', 'homology', 'to', 'a', 'family', 'of', 'proteins', 'collectively', 'known', 'as', 'vigilins', '.'],
'ner_tags': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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
| name |train|validation|test|
|----------|----:|---------:|---:|
| linnaeus |11936| 4079|7143|
## 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
This version of the dataset is licensed under [Creative Commons Attribution 4.0 International](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/blob/master/LICENSE.md).
### Citation Information
```bibtex
@article{crichton2017neural,
title={A neural network multi-task learning approach to biomedical named entity recognition},
author={Crichton, Gamal and Pyysalo, Sampo and Chiu, Billy and Korhonen, Anna},
journal={BMC Bioinformatics},
volume={18},
number={1},
pages={368},
year={2017},
publisher={BioMed Central}
doi = {10.1186/s12859-017-1776-8},
issn = {1471-2105},
url = {https://doi.org/10.1186/s12859-017-1776-8},
}
@article{Gerner2010,
author = {Gerner, Martin and Nenadic, Goran and Bergman, Casey M},
doi = {10.1186/1471-2105-11-85},
issn = {1471-2105},
journal = {BMC Bioinformatics},
number = {1},
pages = {85},
title = {{LINNAEUS: A species name identification system for biomedical literature}},
url = {https://doi.org/10.1186/1471-2105-11-85},
volume = {11},
year = {2010}
}
```
### Contributions
Thanks to [@edugp](https://github.com/edugp) for adding this dataset. | [
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moroco | null | 2023-01-25T14:40:41Z | 315 | 0 | moroco | [
"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:ro",
"license:cc-by-4.0",
"arxiv:1901.06543",
"region:us"
] | 2023-01-25T14:40:41Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- ro
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- topic-classification
paperswithcode_id: moroco
pretty_name: 'MOROCO: The Moldavian and Romanian Dialectal Corpus'
language_bcp47:
- ro-MD
dataset_info:
features:
- name: id
dtype: string
- name: category
dtype:
class_label:
names:
'0': culture
'1': finance
'2': politics
'3': science
'4': sports
'5': tech
- name: sample
dtype: string
config_name: moroco
splits:
- name: train
num_bytes: 39314292
num_examples: 21719
- name: test
num_bytes: 10877813
num_examples: 5924
- name: validation
num_bytes: 10721304
num_examples: 5921
download_size: 60711985
dataset_size: 60913409
---
# Dataset Card for MOROCO
## 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/butnaruandrei/MOROCO)
- **Repository:** [Github](https://github.com/butnaruandrei/MOROCO)
- **Paper:** [Arxiv](https://arxiv.org/abs/1901.06543)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [email](raducu.ionescu@gmail.com)
### Dataset Summary
Introducing MOROCO - The **Mo**ldavian and **Ro**manian Dialectal **Co**rpus. The MOROCO data set contains Moldavian and Romanian samples of text collected from the news domain. The samples belong to one of the following six topics: (0) culture, (1) finance, (2) politics, (3) science, (4) sports, (5) tech. The corpus features a total of 33,564 samples labelled with one of the fore mentioned six categories. We are also including a train/validation/test split with 21,719/5,921/5,924 samples in each subset.
### Supported Tasks and Leaderboards
[LiRo Benchmark and Leaderboard](https://eemlcommunity.github.io/ro_benchmark_leaderboard/site/)
### Languages
The text dataset is in Romanian (`ro`)
## Dataset Structure
### Data Instances
Below we have an example of sample from MOROCO:
```
{'id': , '48482',
'category': 2,
'sample': '“$NE$ cum am spus, nu este un sfârşit de drum . Vom continua lupta cu toate instrumentele şi cu toate mijloacele legale, parlamentare şi civice pe care le avem la dispoziţie . Evident că vom contesta la $NE$ această lege, au anunţat şi colegii de la $NE$ o astfel de contestaţie . Practic trebuie utilizat orice instrument pe care îl identificăm pentru a bloca intrarea în vigoare a acestei legi . Bineînţeles, şi preşedintele are punctul său de vedere . ( . . . ) $NE$ legi sunt împănate de motive de neconstituţionalitate . Colegii mei de la departamentul juridic lucrează în prezent pentru a definitiva textul contestaţiei”, a declarat $NE$ $NE$ citat de news . ro . Senatul a adoptat, marţi, în calitate de for decizional, $NE$ privind statutul judecătorilor şi procurorilor, cu 80 de voturi ”pentru” şi niciun vot ”împotrivă”, în condiţiile în care niciun partid din opoziţie nu a fost prezent în sală .',
}
```
where 48482 is the sample ID, followed by the category ground truth label, and then the text representing the actual content to be classified by topic.
Note: The category label has integer values ranging from 0 to 5.
### Data Fields
- `id`: string, the unique indentifier of a sample
- `category_label`: integer in the range [0, 5]; the category assigned to a sample.
- `sample`: a string, news report to be classified / used in classification.
### Data Splits
The train/validation/test split contains 21,719/5,921/5,924 samples tagged with the category assigned to each sample in the dataset.
## Dataset Creation
### Curation Rationale
The samples are preprocessed in order to eliminate named entities. This is required to prevent classifiers from taking the decision based on features that are not related to the topics.
For example, named entities that refer to politicians or football players names can provide clues about the topic. For more details, please read the [paper](https://arxiv.org/abs/1901.06543).
### Source Data
#### Data Collection and Normalization
For the data collection, five of the most popular news websites in Romania and the Republic of Moldova were targetted. Given that the data set was obtained through a web scraping technique, all the HTML tags needed to be removed, as well as replace consecutive white spaces with a single space.
As part of the pre-processing, we remove named entities, such as country names, cities, public figures, etc. The named entities have been replaced with $NE$. The necessity to remove them, comes also from the scope of this dataset: categorization by topic. Thus, the authors decided to remove named entities in order to prevent classifiers from taking the decision based on features that are not truly indicative of the topics.
#### Who are the source language producers?
The original text comes from news websites from Romania and the Republic of Moldova.
### Annotations
#### Annotation process
As mentioned above, MOROCO is composed of text samples from the top five most popular news websites in Romania and the Republic of Moldova, respectively. Since there are topic tags in the news websites targetd, the text samples can be automatically labeled with the corresponding category.
#### Who are the annotators?
N/A
### Personal and Sensitive Information
The textual data collected for MOROCO consists in news reports freely available on the Internet and of public interest.
To the best of authors' knowledge, there is no personal or sensitive information that needed to be considered in the said textual inputs collected.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is part of an effort to encourage text classification research in languages other than English. Such work increases the accessibility of natural language technology to more regions and cultures.
In the past three years there was a growing interest for studying Romanian from a Computational Linguistics perspective. However, we are far from having enough datasets and resources in this particular language.
### Discussion of Biases
The data included in MOROCO spans a well defined time frame of a few years. Part of the topics that were of interest then in the news landscape, might not show up nowadays or a few years from now in news websites.
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
Published and managed by Radu Tudor Ionescu and Andrei Butnaru.
### Licensing Information
CC BY-SA 4.0 License
### Citation Information
```
@inproceedings{ Butnaru-ACL-2019,
author = {Andrei M. Butnaru and Radu Tudor Ionescu},
title = "{MOROCO: The Moldavian and Romanian Dialectal Corpus}",
booktitle = {Proceedings of ACL},
year = {2019},
pages={688--698},
}
```
### Contributions
Thanks to [@MihaelaGaman](https://github.com/MihaelaGaman) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
srwac | null | 2022-11-03T16:08:14Z | 315 | 1 | 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:100M<n<1B",
"source_datasets:original",
"language:sr",
"license:cc-by-sa-3.0",
"region:us"
] | 2022-11-03T16:08:14Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- sr
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 100M<n<1B
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: null
pretty_name: SrWac
dataset_info:
features:
- name: sentence
dtype: string
config_name: srwac
splits:
- name: train
num_bytes: 17470890484
num_examples: 688805174
download_size: 3767312759
dataset_size: 17470890484
---
# Dataset Card for SrWac
## 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://nlp.ffzg.hr/resources/corpora/srwac/
- **Repository:** https://www.clarin.si/repository/xmlui/handle/11356/1063
- **Paper:** http://nlp.ffzg.hr/data/publications/nljubesi/ljubesic14-bs.pdf
- **Leaderboard:**
- **Point of Contact:** [Nikola Ljubešič](mailto:nikola.ljubesic@ffzg.hr)
### Dataset Summary
The Serbian web corpus srWaC was built by crawling the .rs top-level domain in 2014. The corpus was near-deduplicated on paragraph level, normalised via diacritic restoration, morphosyntactically annotated and lemmatised. The corpus is shuffled by paragraphs. Each paragraph contains metadata on the URL, domain and language identification (Serbian vs. Croatian).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Dataset is monolingual in Serbian language.
## 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
Dataset is under the [CC-BY-SA 3.0](http://creativecommons.org/licenses/by-sa/3.0/) license.
### Citation Information
```
@misc{11356/1063,
title = {Serbian web corpus {srWaC} 1.1},
author = {Ljube{\v s}i{\'c}, Nikola and Klubi{\v c}ka, Filip},
url = {http://hdl.handle.net/11356/1063},
note = {Slovenian language resource repository {CLARIN}.{SI}},
copyright = {Creative Commons - Attribution-{ShareAlike} 4.0 International ({CC} {BY}-{SA} 4.0)},
year = {2016} }
```
### Contributions
Thanks to [@IvanZidov](https://github.com/IvanZidov) for adding this dataset. | [
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DanL/scientific-challenges-and-directions-dataset | DanL | 2022-10-25T08:56:00Z | 315 | 3 | null | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"annotations_creators:expert-generated",
"multilinguality:monolingual",
"source_datasets:CORD-19",
"language:en",
"arxiv:2108.13751",
"arxiv:2004.10706",
"region:us"
] | 2022-10-25T08:56:00Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
YAML tags:
annotations_creators:
- expert-generated
language_creators: []
language:
- en
license: []
multilinguality:
- monolingual
pretty_name: DanL/scientific-challenges-and-directions-dataset
source_datasets:
- CORD-19
task_categories:
- text-classification
task_ids:
- multi-label-classification
---
# Dataset Card for scientific-challenges-and-directions
## 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)
- [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: [repo](https://github.com/Dan-La/scientific-challenges-and-directions)**
- **Paper: [A Search Engine for Discovery of Scientific Challenges and Directions](https://arxiv.org/abs/2108.13751)**
- **Point of Contact: lahav@mail.tau.ac.il,tomh@allenai.org**
### Dataset Summary
The scientific challenges and directions dataset is a collection of 2894 sentences and their surrounding contexts, from 1786 full-text papers in the [CORD-19](https://arxiv.org/abs/2004.10706) corpus, labeled for classification of _challenges_ and _directions_ by expert annotators with biomedical and bioNLP backgrounds.
At a high level, our labels are defined as follows:
* **Challenge**: A sentence mentioning a problem, difficulty, flaw, limitation, failure, lack of clarity, or knowledge gap.
* **Research direction**: A sentence mentioning suggestions or needs for further research, hypotheses, speculations, indications or hints that an issue is worthy of exploration.
The dataset was developed to help scientists and medical professionals discover challenges and potential directions across scientific literature.
### Languages
The language in the dataset is English as written by authors of the scientific papers in the CORD-19 corpus.
## Dataset Structure
### Data Instances
For each instance, there is a unique id, a string for the text sentence, a string for the previous sentence, a string for the next sentence, and a list for the challenge and direction labels.
```
{'id': 'PMC7152165_152',
'label': [0.0, 0.0],
'next_sent': 'The railways brought a new technology and vast engineering and architectural structures into Britain’s rural and urban landscapes.',
'prev_sent': 'In Britain, improvements in coaching technologies and roads helped to increase stage coach speeds in the late eighteenth and early nineteenth centuries, while the railway construction boom of the 1830s and 1840s led to a massive reduction in journey times, and the emergence of distinctly new experiences and geographies.',
'text': 'Britain’s railway companies were among the nation’s largest employers in the nineteenth century, and they facilitated the mobility of passengers and important commodities.'}
```
### Data Fields
* id: A string as a unique id for the instance. The id is composed of the unique PMC id of the paper, an underscore, and the index of the sentence within the paper.
* next_sent_: A string of a sentence that is following the _text_ of the instance. If the text is the first in its paragraph the string is saved as '|'.
* prev_sent_: A string of a sentence that is preceding the _text_ of the instance. If the text is the first in its paragraph the string is saved as '|'.
* text: A string of the sentence we seek to classify.
* label: A list of 2 values - the first is the label for _challenge_ and the last of _direction_. Each value may be either 0, indicating that the _text_ is **not** _challenge_ or _direction_, or 1, indicating that the the _text_ is _challenge_ or _direction_. Each instance can be a _challenge_, a _direction_, both, or neither.
### Data Splits
The scientific-challenges-and-directions dataset has 3 splits: _train_, _dev_, and _test_. Each instances shows up in only one split. The splits are stratified with no overlap in papers.
| Labels | Train | Dev | Test | All |
|:----------------------------:|:------:|:-----:|:----:|:----:|
| Not Challenge, Not Direction | 602 | 146 | 745 | 1493 |
| Not Challenge, Direction | 106 | 25 | 122 | 253 |
| Challenge, Not Direction | 288 | 73 | 382 | 743 |
| Challenge, Direction | 155 | 40 | 210 | 405 |
## Dataset Creation
### Curation Rationale
The resource was developed to help scientists and medical professionals discover challenges and potential directions across scientific literature, focusing on a broad corpus pertaining to the COVID-19 pandemic and related historical research.
### Source Data
#### Initial Data Collection and Normalization
See section 3.1 in our [paper](https://arxiv.org/abs/2108.13751).
#### Who are the source language producers?
The authors of the subset of full-text papers in the [CORD-19 dataset](https://arxiv.org/abs/2004.10706), which at the time of creating our dataset included roughly 180K documents.
### Annotations
#### Annotation process
See section 3.1 in our [paper](https://arxiv.org/abs/2108.13751).
#### Who are the annotators?
Four expert annotators with biomedical and bioNLP backgrounds. For more details see section 3.1 in our [paper](https://arxiv.org/abs/2108.13751).
### Personal and Sensitive Information
The dataset does not contain any personal information about the authors or annotators.
## Considerations for Using the Data
### Social Impact of Dataset
As mentioned, the dataset was developed to help scientists and medical professionals discover challenges and potential directions across scientific literature, focusing on a broad corpus pertaining to the COVID-19 pandemic and related historical research.
Studies were conducted to evaluate the utility of the dataset for researchers and medical professionals, in which a prototype based on the dataset was found to outperform other biomedical search tools. For more details see section 4 in our [paper](https://arxiv.org/abs/2108.13751).
This dataset was also developed for evaluating representational systems for scientific text classification and can be used as such.
### Discussion of Biases
The source of the dataset is the full-text papers in the [CORD-19 dataset](https://arxiv.org/abs/2004.10706), so biases in CORD-19 may be replicated to our dataset.
### Other Known Limitations
N/A
## Additional Information
### Dataset Curators
The dataset was developed by Dan Lahav, Jon Saad Falcon, Bailey Kuehl, Sophie Johnson, Sravanthi Parasa, Noam Shomron, Duen Horng Chau, Diyi Yang, Eric Horvitz, Daniel S. Weld and Tom Hope as part of _Tel Aviv University_, the _Allen Institute for AI_, _University of Washington_, _Georgia Institute of Technology_, _Microsoft_ and _Swedish Medical Group_.
It was supported by the Edmond J. Safra Center for Bioinformatics at Tel-Aviv University, ONR grant N00014-18-1-2193, NSF RAPID grant 2040196, the WR-F/Cable Professorship, and AI2.
### Licensing Information
[More Information Needed]
### Citation Information
If using our dataset and models, please cite:
```
@misc{lahav2021search,
title={A Search Engine for Discovery of Scientific Challenges and Directions},
author={Dan Lahav and Jon Saad Falcon and Bailey Kuehl and Sophie Johnson and Sravanthi Parasa and Noam Shomron and Duen Horng Chau and Diyi Yang and Eric Horvitz and Daniel S. Weld and Tom Hope},
year={2021},
eprint={2108.13751},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@Dan-La](https://github.com/Dan-La) and [@tomhoper](https://github.com/tomhoper) for adding this dataset.
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
SuperAI2-Machima/ThaiQA_LST20 | SuperAI2-Machima | 2022-02-25T06:29:22Z | 315 | 0 | null | [
"license:mit",
"question-generation dataset",
"qa dataset",
"region:us"
] | 2022-02-25T06:29:22Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
tags:
- question-generation dataset
- qa dataset
language:
- thai
- th
datasets:
- LST20
license: mit
---
[SuperAI Engineer Season 2](https://superai.aiat.or.th/) , [Machima](https://machchima.superai.me/)
Machima_ThaiQA_LST20 เป็นชุดข้อมูลที่สกัดหาคำถาม และคำตอบ จากบทความในชุดข้อมูล LST20 โดยสกัดได้คำถาม-ตอบทั้งหมด 7,642 คำถาม มีข้อมูล 4 คอลัมน์ ประกอบด้วย context, question, answer และ status ตามลำดับ
แสดงตัวอย่างดังนี้
context : ด.ต.ประสิทธิ์ ชาหอมชื่นอายุ 55 ปี ผบ.หมู่งาน ป.ตชด. 24 อุดรธานีถูกยิงด้วยอาวุธปืนอาก้าเข้าที่แขนซ้าย 3 นัดหน้าท้อง 1 นัดส.ต.อ.ประเสริฐ ใหญ่สูงเนินอายุ 35 ปี ผบ.หมู่กก. 1 ปส.2 บช.ปส. ถูกยิงเข้าที่แขนขวากระดูกแตกละเอียดร.ต.อ.ชวพล หมื่นโรจน์อายุ 32 ปีรอง สว.กก. 1 ปส. 2 บช.ปส. ถูกยิงเข้าที่แก้มและไหปลาร้าด้านขวา
question :ผบ.หมู่งาน ป.ตชด. 24 อุดรธานี ถูกยิงด้วยอาวุธปืนอะไรเข้าที่แขนซ้าย 3 นัดหน้าท้อง
answer : อาวุธปืนอาก้า
status : 1
ซึ่งใน 7,642 คำถาม จะมีคำถาม-ตอบ ที่สกัดออกมาได้ถูกต้อง และไม่ถูกต้องตาม ยกตัวอย่างเช่น ตอบไม่ตรงคำถาม หรือมีคำตอบอยู่ด้านในประโยคคำถาม
ทางทีมงานบ้านมณิมาได้ทำการตรวจสอบคำถามตอบ และทำการติด label ให้กับคู่ของคำถาม-ตอบ ที่ถูกต้อง และไม่ถูกต้อง โดย 1 = ถูกต้อง และ 0 = ไม่ถูกต้อง
จากคู่คำถาม-ตอบ 7,642 คำถาม
พบว่าถูกต้อง 4,438 คำถาม
ไม่ถูกต้อง 3,204 คำถาม
เพื่อน ๆ สามารถโหลดข้อมูลมาใช้โดยใช้โค้ดดังนี้
```python
!pip install datasets -qq #สำหรับโหลดdataset
from datasets import load_dataset
import pandas as pd
dataset = load_dataset("SuperAI2-Machima/ThaiQA_LST20")
train_df = pd.DataFrame(dataset['train'])
train_df
``` | [
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kksukk/broadCast_dataset_7 | kksukk | 2022-09-28T04:02:02Z | 315 | 0 | null | [
"region:us"
] | 2022-09-28T04:02:02Z | 2022-09-28T02:31:33.000Z | 2022-09-28T02:31:33 | Entry not found | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
bigbio/blurb | bigbio | 2022-12-22T15:27:48Z | 315 | 1 | null | [
"multilinguality:monolingual",
"language:en",
"license:other",
"region:us"
] | 2022-12-22T15:27:48Z | 2022-10-03T06:19:58.000Z | 2022-10-03T06:19:58 | ---
language:
- en
bigbio_language:
- English
license: other
multilinguality: monolingual
bigbio_license_shortname: MIXED
pretty_name: BLURB
homepage: https://microsoft.github.io/BLURB/tasks.html
bigbio_pubmed: true
bigbio_public: true
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
---
# Dataset Card for BLURB
## Dataset Description
- **Homepage:** https://microsoft.github.io/BLURB/tasks.html
- **Pubmed:** True
- **Public:** True
- **Tasks:** NER
BLURB is a collection of resources for biomedical natural language processing.
In general domains, such as newswire and the Web, comprehensive benchmarks and
leaderboards such as GLUE have greatly accelerated progress in open-domain NLP.
In biomedicine, however, such resources are ostensibly scarce. In the past,
there have been a plethora of shared tasks in biomedical NLP, such as
BioCreative, BioNLP Shared Tasks, SemEval, and BioASQ, to name just a few. These
efforts have played a significant role in fueling interest and progress by the
research community, but they typically focus on individual tasks. The advent of
neural language models, such as BERT provides a unifying foundation to leverage
transfer learning from unlabeled text to support a wide range of NLP
applications. To accelerate progress in biomedical pretraining strategies and
task-specific methods, it is thus imperative to create a broad-coverage
benchmark encompassing diverse biomedical tasks.
Inspired by prior efforts toward this direction (e.g., BLUE), we have created
BLURB (short for Biomedical Language Understanding and Reasoning Benchmark).
BLURB comprises of a comprehensive benchmark for PubMed-based biomedical NLP
applications, as well as a leaderboard for tracking progress by the community.
BLURB includes thirteen publicly available datasets in six diverse tasks. To
avoid placing undue emphasis on tasks with many available datasets, such as
named entity recognition (NER), BLURB reports the macro average across all tasks
as the main score. The BLURB leaderboard is model-agnostic. Any system capable
of producing the test predictions using the same training and development data
can participate. The main goal of BLURB is to lower the entry barrier in
biomedical NLP and help accelerate progress in this vitally important field for
positive societal and human impact.
This implementation contains a subset of 5 tasks as of 2022.10.06, with their original train, dev, and test splits.
## Citation Information
```
@article{gu2021domain,
title = {
Domain-specific language model pretraining for biomedical natural
language processing
},
author = {
Gu, Yu and Tinn, Robert and Cheng, Hao and Lucas, Michael and
Usuyama, Naoto and Liu, Xiaodong and Naumann, Tristan and Gao,
Jianfeng and Poon, Hoifung
},
year = 2021,
journal = {ACM Transactions on Computing for Healthcare (HEALTH)},
publisher = {ACM New York, NY},
volume = 3,
number = 1,
pages = {1--23}
}
```
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hindi_discourse | null | 2023-01-25T14:32:13Z | 314 | 1 | null | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"annotations_creators:other",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:hi",
"license:other",
"discourse-analysis",
"region:us"
] | 2023-01-25T14:32:13Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- other
language_creators:
- found
language:
- hi
license:
- other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
pretty_name: Discourse Analysis dataset
tags:
- discourse-analysis
dataset_info:
features:
- name: Story_no
dtype: int32
- name: Sentence
dtype: string
- name: Discourse Mode
dtype:
class_label:
names:
'0': Argumentative
'1': Descriptive
'2': Dialogue
'3': Informative
'4': Narrative
'5': Other
splits:
- name: train
num_bytes: 1998930
num_examples: 9968
download_size: 4176677
dataset_size: 1998930
---
# Dataset Card for Discourse Analysis dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** https://github.com/midas-research/hindi-discourse
- **Paper:** [An Annotated Dataset of Discourse Modes in Hindi Stories](https://aclanthology.org/2020.lrec-1.149/)
- **Point of Contact:** https://github.com/midas-research/MeTooMA
### Dataset Summary
- The Hindi Discourse Analysis dataset is a corpus for analyzing discourse modes present in its sentences.
- It contains sentences from stories written by 11 famous authors from the 20th Century.
- 4-5 stories by each author have been selected which were available in the public domain resulting in a collection of 53 stories.
- Most of these short stories were originally written in Hindi but some of them were written in other Indian languages and later translated to Hindi.
The corpus contains a total of 10472 sentences belonging to the following categories:
- Argumentative
- Descriptive
- Dialogic
- Informative
- Narrative
### Supported Tasks and Leaderboards
- Discourse Analysis of Hindi.
### Languages
Hindi
## Dataset Structure
- The dataset is structured into JSON format.
### Data Instances
{'Story_no': 15, 'Sentence': ' गाँठ से साढ़े तीन रुपये लग गये, जो अब पेट में जाकर खनकते भी नहीं! जो तेरी करनी मालिक! ” “इसमें मालिक की क्या करनी है? ”', 'Discourse Mode': 'Dialogue'}
### Data Fields
Sentence number, story number, sentence and discourse mode
### Data Splits
- Train: 9983
## Dataset Creation
### Curation Rationale
- Present a new publicly available corpus
consisting of sentences from short stories written in a
low-resource language of Hindi having high quality annotation for five different discourse modes -
argumentative, narrative, descriptive, dialogic and informative.
- Perform a detailed analysis of the proposed annotated corpus and characterize the performance of
different classification algorithms.
### Source Data
- Source of all the data points in this dataset is Hindi stories written by famous authors of Hindi literature.
#### Initial Data Collection and Normalization
- All the data was collected from various Hindi websites.
- We chose against crowd-sourcing the annotation pro- cess because we wanted to directly work with the an- notators for qualitative feedback and to also ensure high quality annotations.
- We employed three native Hindi speakers with college level education for the an- notation task.
- We first selected two random stories from our corpus and had the three annotators work on them independently and classify each sentence based on the discourse mode.
- Please refer to this paper for detailed information: https://www.aclweb.org/anthology/2020.lrec-1.149/
#### Who are the source language producers?
Please refer to this paper for detailed information: https://www.aclweb.org/anthology/2020.lrec-1.149/
### Annotations
#### Annotation process
- The authors chose against crowd sourcing for labeling this dataset due to its highly sensitive nature.
- The annotators are domain experts having degress in advanced clinical psychology and gender studies.
- They were provided a guidelines document with instructions about each task and its definitions, labels and examples.
- They studied the document, worked a few examples to get used to this annotation task.
- They also provided feedback for improving the class definitions.
- The annotation process is not mutually exclusive, implying that presence of one label does not mean the
absence of the other one.
#### Who are the annotators?
- The annotators were three native Hindi speakers with college level education.
- Please refer to the accompnaying paper for a detailed annotation process.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
- As a future work we would also like to use the presented corpus to see how it could be further used
in certain downstream tasks such as emotion analysis, machine translation,
textual entailment, and speech sythesis for improving storytelling experience in Hindi language.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
- We could not get the best performance using the deep learning model trained on the data, due to
insufficient data for DL models.
## Additional Information
Please refer to this link: https://github.com/midas-research/hindi-discourse
### Dataset Curators
- If you use the corpus in a product or application, then please credit the authors
and [Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi]
(http://midas.iiitd.edu.in) appropriately.
Also, if you send us an email, we will be thrilled to know about how you have used the corpus.
- If interested in commercial use of the corpus, send email to midas@iiitd.ac.in.
- Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi, India
disclaims any responsibility for the use of the corpus and does not provide technical support.
However, the contact listed above will be happy to respond to queries and clarifications
- Please feel free to send us an email:
- with feedback regarding the corpus.
- with information on how you have used the corpus.
- if interested in having us analyze your social media data.
- if interested in a collaborative research project.
### Licensing Information
- If you use the corpus in a product or application, then please credit the authors
and [Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi]
(http://midas.iiitd.edu.in) appropriately.
### Citation Information
Please cite the following publication if you make use of the dataset: https://aclanthology.org/2020.lrec-1.149/
```
@inproceedings{dhanwal-etal-2020-annotated,
title = "An Annotated Dataset of Discourse Modes in {H}indi Stories",
author = "Dhanwal, Swapnil and
Dutta, Hritwik and
Nankani, Hitesh and
Shrivastava, Nilay and
Kumar, Yaman and
Li, Junyi Jessy and
Mahata, Debanjan and
Gosangi, Rakesh and
Zhang, Haimin and
Shah, Rajiv Ratn and
Stent, Amanda",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://www.aclweb.org/anthology/2020.lrec-1.149",
pages = "1191--1196",
abstract = "In this paper, we present a new corpus consisting of sentences from Hindi short stories annotated for five different discourse modes argumentative, narrative, descriptive, dialogic and informative. We present a detailed account of the entire data collection and annotation processes. The annotations have a very high inter-annotator agreement (0.87 k-alpha). We analyze the data in terms of label distributions, part of speech tags, and sentence lengths. We characterize the performance of various classification algorithms on this dataset and perform ablation studies to understand the nature of the linguistic models suitable for capturing the nuances of the embedded discourse structures in the presented corpus.",
language = "English",
ISBN = "979-10-95546-34-4",
}
```
### Contributions
Thanks to [@duttahritwik](https://github.com/duttahritwik) for adding this dataset. | [
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narrativeqa_manual | null | 2022-11-18T21:32:14Z | 314 | 0 | narrativeqa | [
"task_categories:text2text-generation",
"task_ids:abstractive-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"arxiv:1712.07040",
"region:us"
] | 2022-11-18T21:32:14Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids:
- abstractive-qa
paperswithcode_id: narrativeqa
pretty_name: NarrativeQA
dataset_info:
features:
- name: document
struct:
- name: id
dtype: string
- name: kind
dtype: string
- name: url
dtype: string
- name: file_size
dtype: int32
- name: word_count
dtype: int32
- name: start
dtype: string
- name: end
dtype: string
- name: summary
struct:
- name: text
dtype: string
- name: tokens
sequence: string
- name: url
dtype: string
- name: title
dtype: string
- name: text
dtype: string
- name: question
struct:
- name: text
dtype: string
- name: tokens
sequence: string
- name: answers
list:
- name: text
dtype: string
- name: tokens
sequence: string
splits:
- name: train
num_bytes: 9115940054
num_examples: 32747
- name: test
num_bytes: 2911702563
num_examples: 10557
- name: validation
num_bytes: 968994186
num_examples: 3461
download_size: 22638273
dataset_size: 12996636803
---
# Dataset Card for Narrative QA Manual
## 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:** [NarrativeQA Homepage](https://deepmind.com/research/open-source/narrativeqa)
- **Repository:** [NarrativeQA Repo](https://github.com/deepmind/narrativeqa)
- **Paper:** [The NarrativeQA Reading Comprehension Challenge](https://arxiv.org/pdf/1712.07040.pdf)
- **Leaderboard:**
- **Point of Contact:** [Tomáš Kočiský](mailto:tkocisky@google.com) [Jonathan Schwarz](mailto:schwarzjn@google.com) [Phil Blunsom](pblunsom@google.com) [Chris Dyer](cdyer@google.com) [Karl Moritz Hermann](mailto:kmh@google.com) [Gábor Melis](mailto:melisgl@google.com) [Edward Grefenstette](mailto:etg@google.com)
### Dataset Summary
NarrativeQA Manual is an English-language dataset of stories and corresponding questions designed to test reading comprehension, especially on long documents. THIS DATASET REQUIRES A MANUALLY DOWNLOADED FILE! Because of a script in the original repository which downloads the stories from original URLs everytime, the links are sometimes broken or invalid. Therefore, you need to manually download the stories for this dataset using the script provided by the authors (https://github.com/deepmind/narrativeqa/blob/master/download_stories.sh). Running the shell script creates a folder named "tmp" in the root directory and downloads the stories there. This folder containing the stories can be used to load the dataset via `datasets.load_dataset("narrativeqa_manual", data_dir="<path/to/folder>")`.
### Supported Tasks and Leaderboards
The dataset is used to test reading comprehension. There are 2 tasks proposed in the paper: "summaries only" and "stories only", depending on whether the human-generated summary or the full story text is used to answer the question.
### Languages
English
## Dataset Structure
### Data Instances
A typical data point consists of a question and answer pair along with a summary/story which can be used to answer the question. Additional information such as the url, word count, wikipedia page, are also provided.
A typical example looks like this:
```
{
"document": {
"id": "23jncj2n3534563110",
"kind": "movie",
"url": "https://www.imsdb.com/Movie%20Scripts/Name%20of%20Movie.html",
"file_size": 80473,
"word_count": 41000,
"start": "MOVIE screenplay by",
"end": ". THE END",
"summary": {
"text": "Joe Bloggs begins his journey exploring...",
"tokens": ["Joe", "Bloggs", "begins", "his", "journey", "exploring",...],
"url": "http://en.wikipedia.org/wiki/Name_of_Movie",
"title": "Name of Movie (film)"
},
"text": "MOVIE screenplay by John Doe\nSCENE 1..."
},
"question": {
"text": "Where does Joe Bloggs live?",
"tokens": ["Where", "does", "Joe", "Bloggs", "live", "?"],
},
"answers": [
{"text": "At home", "tokens": ["At", "home"]},
{"text": "His house", "tokens": ["His", "house"]}
]
}
```
### Data Fields
- `document.id` - Unique ID for the story.
- `document.kind` - "movie" or "gutenberg" depending on the source of the story.
- `document.url` - The URL where the story was downloaded from.
- `document.file_size` - File size (in bytes) of the story.
- `document.word_count` - Number of tokens in the story.
- `document.start` - First 3 tokens of the story. Used for verifying the story hasn't been modified.
- `document.end` - Last 3 tokens of the story. Used for verifying the story hasn't been modified.
- `document.summary.text` - Text of the wikipedia summary of the story.
- `document.summary.tokens` - Tokenized version of `document.summary.text`.
- `document.summary.url` - Wikipedia URL of the summary.
- `document.summary.title` - Wikipedia Title of the summary.
- `question` - `{"text":"...", "tokens":[...]}` for the question about the story.
- `answers` - List of `{"text":"...", "tokens":[...]}` for valid answers for the question.
### Data Splits
The data is split into training, valiudation, and test sets based on story (i.e. the same story cannot appear in more than one split):
| Train | Valid | Test |
| ------ | ----- | ----- |
| 32747 | 3461 | 10557 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
Stories and movies scripts were downloaded from [Project Gutenburg](https://www.gutenberg.org) and a range of movie script repositories (mainly [imsdb](http://www.imsdb.com)).
#### Who are the source language producers?
The language producers are authors of the stories and scripts as well as Amazon Turk workers for the questions.
### Annotations
#### Annotation process
Amazon Turk Workers were provided with human written summaries of the stories (To make the annotation tractable and to lead annotators towards asking non-localized questions). Stories were matched with plot summaries from Wikipedia using titles and verified the matching with help from human annotators. The annotators were asked to determine if both the story and the summary refer to a movie or a book (as some books are made into movies), or if they are the same part in a series produced in the same year. Annotators on Amazon Mechanical Turk were instructed to write 10 question–answer pairs each based solely on a given summary. Annotators were instructed to imagine that they are writing questions to test students who have read the full stories but not the summaries. We required questions that are specific enough, given the length and complexity of the narratives, and to provide adiverse set of questions about characters, events, why this happened, and so on. Annotators were encouraged to use their own words and we prevented them from copying. We asked for answers that are grammatical, complete sentences, and explicitly allowed short answers (one word, or a few-word phrase, or ashort sentence) as we think that answering with a full sentence is frequently perceived as artificial when asking about factual information. Annotators were asked to avoid extra, unnecessary information in the question or the answer, and to avoid yes/no questions or questions about the author or the actors.
#### Who are the annotators?
Amazon Mechanical Turk workers.
### Personal and Sensitive Information
None
## 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 dataset is released under a [Apache-2.0 License](https://github.com/deepmind/narrativeqa/blob/master/LICENSE).
### Citation Information
```
@article{narrativeqa,
author = {Tom\'a\v s Ko\v cisk\'y and Jonathan Schwarz and Phil Blunsom and
Chris Dyer and Karl Moritz Hermann and G\'abor Melis and
Edward Grefenstette},
title = {The {NarrativeQA} Reading Comprehension Challenge},
journal = {Transactions of the Association for Computational Linguistics},
url = {https://TBD},
volume = {TBD},
year = {2018},
pages = {TBD},
}
```
### Contributions
Thanks to [@rsanjaykamath](https://github.com/rsanjaykamath) for adding this dataset. | [
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Abirate/french_book_reviews | Abirate | 2022-08-25T19:26:48Z | 314 | 4 | null | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"source_datasets:original",
"language:fr",
"doi:10.57967/hf/1052",
"region:us"
] | 2022-08-25T19:26:48Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
- crowdsourced
language:
- fr
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
---
# ****Dataset Card for French book reviews****
# **I-Dataset Summary**
The majority of review datasets are in English. There are datasets in other languages, but not many. Through this work, I would like to enrich the datasets in the French language(my mother tongue with Arabic).
The data was retrieved from two French websites: [Babelio](https://www.babelio.com/) and [Critiques Libres](http://www.critiqueslibres.com/)
Like Wikipedia, these two French sites are made possible by the contributions of volunteers who use the Internet to share their knowledge and reading experiences.
The French book reviews is a dataset of a huge number of reader reviews on French books that ill be constantly updated over time.
# **II-Supported Tasks and Leaderboards**
- Multi-label text classification : The dataset can be used to train a model for text-classification, which consists of classifying reviews by label value. Success on this task is typically measured by achieving a high or low accuracy.
# **III-Languages**
The texts in the dataset are in French (fr).
# **IV-Dataset Structure**
#### Data Instances
A JSON-formatted example of a typical instance in the dataset:
```python
{
"book_title": "La belle histoire des maths",
"author": "Michel Rousselet",
"reader_review": "C’est un livre impressionnant, qui inspire le respect
par la qualité de sa reliure et son contenu. Je le feuillette et je découvre
à chaque tour de page un thème distinct magnifiquement illustré. Très beau livre !",
"rating": 4.0,
"label": 1
}
```
#### Data Fields
- **book_title**: The title of the book that received the reader's review,
- **author** : The author of the book that received the reader's review,
- **reader_review** : The text of the reader's review,
- **rating**: A five-star rating system is used to rate the book read,
- **label** : A post-processed field indicating if the review is positive (1), neutral(0), or negative(-1) based on the rating field. For more details, see the [Notebook of the Dataset creation](https://github.com/Abirate/Dataset_Creation_Scrapy_Project_French_book_reviews/blob/master/scrapyproject_a_to_z_dataset_book_reviews.ipynb)
#### Data Splits
I kept the dataset as one block (train), so it can be shuffled and split by users later using methods of the hugging face dataset library like the (.train_test_split()) method.
# **V-Dataset Creation**
#### Curation Rationale
The majority of review datasets are in English. There are datasets in other languages, but not many. Through this work, I would like to enrich the datasets in the French language (French is my mother tongue with Arabic) and slightly contribute to advancing data science and AI, not only for English NLP tasks but for other languages around the world.
French is an international language and it is gaining ground. In addition, it is the language of love. The richness of the French language, so appreciated around the world, is largely related to the richness of its culture. The most telling example is French literature, which has many world-famous writers, such as [Gustave Flaubert](https://en.wikipedia.org/wiki/Gustave_Flaubert), [Albert Camus](https://iep.utm.edu/camus/), [Victor Hugo](https://en.wikipedia.org/wiki/Victor_Hugo), [Molière](https://en.wikipedia.org/wiki/Moli%C3%A8re), [Simone de Beauvoir](https://iep.utm.edu/beauvoir/), [Antoine de Saint-Exupéry](https://en.wikipedia.org/wiki/Antoine_de_Saint-Exup%C3%A9ry): the author of "Le Petit Prince" (The Little Prince), which is still among the most translated books in literary history. And one of the world-famous quotes from this book is: "Voici mon secret. Il est très simple: on ne voit bien qu'avec le coeur. L'essentiel est invisible pour les yeux." etc.
#### Source Data
The source of Data is: two French websites: [Babelio](https://www.babelio.com/) and [Critiques Libres](http://www.critiqueslibres.com/)
#### Initial Data Collection and Normalization
The data was collected using web scraping (with Scrapy Framework) and subjected to additional data processing. For more details, see this notebook, which details the dataset creation process. [Notebook of the Dataset creation](https://github.com/Abirate/Dataset_Creation_Scrapy_Project_French_book_reviews/blob/master/scrapyproject_a_to_z_dataset_book_reviews.ipynb)
**Note**: This dataset will be constantly updated to include the most recent reviews on French books by aggregating the old datasets with the updated ones in order to have a huge dataset over time.
#### Who are the source Data producers ?
I created the Dataset using web scraping, by building a spider and a crawler to scrape the two french web sites [Babelio](https://www.babelio.com/) and [Critiques Libres](http://www.critiqueslibres.com/)
#### Annotations
Annotations are part of the initial data collection (see the script above).
# **VI-Additional Informations**
#### Dataset Curators
Abir ELTAIEF
#### Licensing Information
This work is licensed under [CC0: Public Domain](https://creativecommons.org/publicdomain/zero/1.0/)
#### Contributions
Thanks to [@Abirate](https://huggingface.co/Abirate) for creating and adding this dataset.
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Finnish-NLP/mc4_fi_cleaned | Finnish-NLP | 2022-10-21T16:57:34Z | 314 | 3 | null | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:extended|mc4",
"language:fi",
"region:us"
] | 2022-10-21T16:57:34Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators: []
language_creators: []
language:
- fi
license: []
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- extended|mc4
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
pretty_name: mC4 Finnish Cleaned
---
# Dataset Card for mC4 Finnish Cleaned
## 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
mC4 Finnish cleaned is cleaned version of the original mC4 Finnish split.
### Supported Tasks and Leaderboards
mC4 Finnish is mainly intended to pretrain Finnish language models and word representations.
### Languages
Finnish
## Dataset Structure
### Data Instances
[Needs More Information]
### Data Fields
The data have several fields:
- url: url of the source as a string
- text: text content as a string
- timestamp: timestamp as a string
- perplexity_kenlm_full: perplexity of the text calculated by KenLM model
### Data Splits
Train Validation
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
[Needs More Information] | [
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ShreyaR/DepressionDetection | ShreyaR | 2022-03-24T11:31:29Z | 314 | 2 | null | [
"region:us"
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Tevatron/wikipedia-nq-corpus | Tevatron | 2021-10-13T22:18:40Z | 314 | 0 | null | [
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biu-nlp/qanom | biu-nlp | 2022-10-18T09:50:01Z | 314 | 1 | null | [
"region:us"
] | 2022-10-18T09:50:01Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | # QANom
This dataset contains question-answer pairs to model the predicate-argument structure of deverbal nominalizations.
The questions start with wh-words (Who, What, Where, What, etc.) and contain the verbal form of a nominalization from the sentence;
the answers are phrases in the sentence.
See the paper for details: [QANom: Question-Answer driven SRL for Nominalizations (Klein et. al., COLING 2020)](https://www.aclweb.org/anthology/2020.coling-main.274/)
For previewing the QANom data along with the verbal annotations of QASRL, check out https://browse.qasrl.org/.
Also check out our [GitHub repository](https://github.com/kleinay/QANom) to find code for nominalization identification, QANom annotation, evaluation, and models.
The dataset was annotated by selected workers from Amazon Mechanical Turk. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
castorini/msmarco_v1_passage_doc2query-t5_expansions | castorini | 2022-06-21T17:45:43Z | 314 | 0 | null | [
"region:us"
] | 2022-06-21T17:45:43Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- English
license: "Apache License 2.0"
---
# Dataset Summary
The repo provides queries generated for the MS MARCO V1 passage corpus with docTTTTTquery (sometimes written as docT5query or doc2query-T5), the latest version of the doc2query family of document expansion models. The basic idea is to train a model, that when given an input document, generates questions that the document might answer (or more broadly, queries for which the document might be relevant). These predicted questions (or queries) are then appended to the original documents, which are then indexed as before. The docTTTTTquery model gets its name from the use of T5 as the expansion model.
# Dataset Structure
All three folds (train, dev and test) share the same corpus. The queries are generated from this corpus.
An example data entry looks as follows:
```
{
"id": "0",
"predicted_queries": ["what was important to the success of the manhattan project", "why was the manhattan project important?", "what was important about the manhattan project", "why was the success of the manhattan project so important?", "who was the manhattan project a scientific project for", "what was the manhattan project important for", "why was the manhattan project a success", "how was the success of the manhattan project", "why was the manhattan project important to the success of the project?", "what is the importance of communication amongst scientific minds", "what was the importance of scientific communication for the success of the manhattan project", "what was the purpose of the manhattan project", "why was the manhattan project significant?", "why was the manhattan project important", "why did scientists believe in atomic power", "why did scientists and engineers have to communicate?", "why was the manhattan project a success", "what was the purpose of the manhattan project", "why did scientists and engineers want to be involved in the manhattan project", "why are the scientists so valuable", "which of the following was an important outcome of the manhattan project?", "why was the manhattan project successful", "why was the manhattan project an important scientific achievement", "what was the success of manhattan", "what was the result of the manhattan project", "why was communications important to the success of the manhattan project?", "why the manhattan project was important", "why is it important to know who is the manhattan project", "what was the most important accomplishment to the success of the manhattan project?", "why was the manhattan project an important achievement?", "why was the manhattan project important to the success of the atomic bomb", "how did the manhattan project impact scientists?", "what were the effects of the manhattan project", "what were the results of the manhattan project and how did they affect the public", "what was the manhattan project", "why did scientists contribute to the success of the manhattan project", "why was communication important in the manhattan project", "what was the effect of the manhattan project on the world", "what was the importance of communication in the success of the manhattan project?", "why was communications important to the success of the manhattan project?", "why was the manhattan project important", "what was the manhattan project", "why was the success of the manhattan project important", "why was manhattan project a success", "what was important about the manhattan project", "what benefited from the success of the new york nuclear bomb", "what was the significance to the success of the manhattan project?", "why is communication important", "why was the manhattan project an important achievement", "why did the manhattan project work", "what was the manhattan project's success", "what was the significance of the manhattan experiment", "how important was communication to the success of the manhattan project", "why is communication important to the success of the manhattan project?", "what was the importance of the manhattan project", "why did scientists believe the manhattan project had the greatest impact on science?", "what was a critical effect of the manhattan project?", "why did the manhattan project succeed", "what was the importance of the manhattan project", "why was the manhattan project important", "why was the manhattan project a success?", "what was the importance of communication and communication during the manhattan project", "why was the manhattan project significant?", "what was the importance of communication in the manhattan project?", "why was communication important to the success of the manhattan project?", "why was the manhattan project an important achievement", "what was important about the manhattan project", "why was the manhattan project a success", "why were the scientists at the manhattan project so successful?", "why did the manhattan project really work", "what was the success of the manhattan project", "what is the importance of communication during the manhattan project", "why was the manhattan project important", "why was communication important?", "what was the importance of communication in the success of the manhattan project?", "why was the manhattan project successful?", "which statement reflects the success of the manhattan project?", "why did the manhattan project succeed", "why was the manhattan project a great success", "why was the manhattan project important"]
}
```
# Load Dataset
An example to load the dataset:
```
dataset = load_dataset('castorini/msmarco_v1_passage_doc2query-t5_expansions', data_files='d2q.jsonl.gz')
```
# Citation Information
```
@article{docTTTTTquery,
title={From doc2query to {docTTTTTquery}},
author={Nogueira, Rodrigo and Lin, Jimmy},
year={2019}
}
@article{emdt5,
author={Ronak Pradeep and Rodrigo Nogueira and Jimmy Lin},
title={The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models},
journal={arXiv:2101.05667},
year={2021},
}
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ctu-aic/anli_cs | ctu-aic | 2021-11-21T21:12:10Z | 314 | 0 | null | [
"region:us"
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
hmao/reformatted_singleapi | hmao | 2023-10-20T17:41:43Z | 314 | 0 | null | [
"region:us"
] | 2023-10-20T17:41:43Z | 2023-10-20T17:41:41.000Z | 2023-10-20T17:41:41 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: api_name
dtype: string
- name: api_definition
dtype: string
- name: dataset_name
dtype: string
splits:
- name: train
num_bytes: 19426
num_examples: 14
download_size: 15603
dataset_size: 19426
---
# Dataset Card for "reformatted_singleapi"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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glucose | null | 2022-11-18T20:04:16Z | 313 | 2 | glucose | [
"task_categories:fill-mask",
"task_categories:text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-ROC-stories",
"language:en",
"license:cc-by-4.0",
"commonsense-inference",
"arxiv:2009.07758",
"region:us"
] | 2022-11-18T20:04:16Z | 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:
- extended|other-ROC-stories
task_categories:
- fill-mask
- text-generation
paperswithcode_id: glucose
pretty_name: GLUCOSE
tags:
- commonsense-inference
dataset_info:
features:
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dtype: string
- name: story_id
dtype: string
- name: worker_id
dtype: int64
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- name: story
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dtype: string
config_name: glucose
splits:
- name: train
num_bytes: 204605370
num_examples: 65522
- name: test
num_bytes: 355757
num_examples: 500
download_size: 30362105
dataset_size: 204961127
---
# 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
- **[Repository](https://github.com/TevenLeScao/glucose)**
- **[Paper](https://arxiv.org/abs/2009.07758)**
- **Point of Contact:** [glucose@elementalcognition.com](mailto:glucose@elementalcognition.com)
### Dataset Summary
GLUCOSE: GeneraLized and COntextualized Story Explanations, is a novel conceptual framework and dataset for commonsense reasoning. Given a short story and a sentence X in the story, GLUCOSE captures ten dimensions of causal explanation related to X. These dimensions, inspired by human cognitive psychology, cover often-implicit causes and effects of X, including events, location, possession, and other attributes.
### Supported Tasks and Leaderboards
Common sense inference of:
1. Causes
2. Emotions motivating an event
3. Locations enabling an event
4. Possession states enabling an event
5. Other attributes enabling an event
6. Consequences
7. Emotions caused by an event
8. Changes in location caused by an event
9. Changes in possession caused by an event
10. Other attributes that may be changed by an event
### Languages
English, monolingual
## Dataset Structure
### Data Instances
```
{
"experiment_id": "e56c7c3e-4660-40fb-80d0-052d566d676a__4",
"story_id": "e56c7c3e-4660-40fb-80d0-052d566d676a",
"worker_id": 19,
"submission_time_normalized": "20190930",
"worker_quality_rating": 3,
"selected_sentence_index": 4,
"story": "It was bedtime at our house. Two of the three kids hit the pillow and fall asleep. The third is a trouble maker. For two hours he continues to get out of bed and want to play. Finally he becomes tired and falls asleep."
selected_sentence: "Finally he becomes tired and falls asleep.",
"1_specificNL": "The third kid continues to get out of bed and wants to play >Causes/Enables> The kid finally becomes tired and falls asleep",
"1_specificStructured": "{The third kid}_[subject] {continues}_[verb] {to }_[preposition1] {get out of bed}_[object1] {and wants to play}_[object2] >Causes/Enables> {The kid}_[subject] {finally becomes}_[verb] {tired}_[object1] {and falls asleep}_[object2]",
"1_generalNL": "Someone_A doesn't want to go to sleep >Causes/Enables> Someone_A finally falls asleep",
"1_generalStructured": "{Someone_A}_[subject] {doesn't want}_[verb] {to }_[preposition1] {go to sleep}_[object1] >Causes/Enables> {Someone_A}_[subject] {finally falls}_[verb] {asleep}_[object1]",
"2_specificNL": "escaped",
"2_specificStructured": "escaped",
"2_generalNL": "escaped",
"2_generalStructured": "escaped",
"3_specificNL": "The third kid is in bed >Enables> The kid finally becomes tired and falls asleep",
"3_specificStructured": "{The third kid}_[subject] {is}_[verb] {in}_[preposition] {bed}_[object] >Enables> {The kid}_[subject] {finally becomes}_[verb] {tired}_[object1] {and falls asleep}_[object2]",
"3_generalNL": "Someone_A is in bed >Enables> Someone_A falls asleep",
"3_generalStructured": "{Someone_A}_[subject] {is}_[verb] {in}_[preposition] {bed}_[object] >Enables> {Someone_A}_[subject] {falls}_[verb] {asleep}_[object1]",
"4_specificNL": "escaped",
"4_specificStructured": "escaped",
"4_generalNL": "escaped",
"4_generalStructured": "escaped",
"5_specificNL": "escaped",
"5_specificStructured": "escaped",
"5_generalNL": "escaped",
"5_generalStructured": "escaped",
"6_specificNL": "escaped",
"6_specificStructured": "escaped",
"6_generalNL": "escaped",
"6_generalStructured": "escaped",
"7_specificNL": "escaped",
"7_specificStructured": "escaped",
"7_generalNL": "escaped",
"7_generalStructured": "escaped",
"8_specificNL": "escaped",
"8_specificStructured": "escaped",
"8_generalNL": "escaped",
"8_generalStructured": "escaped",
"9_specificNL": "escaped",
"9_specificStructured": "escaped",
"9_generalNL": "escaped",
"9_generalStructured": "escaped",
"10_specificNL": "escaped",
"10_specificStructured": "escaped",
"10_generalNL": "escaped",
"10_generalStructured": "escaped",
"number_filled_in": 7
}
```
### Data Fields
- __experiment_id__: a randomly generated alphanumeric sequence for a given story with the sentence index appended at the end after two underscores. Example: cbee2b5a-f2f9-4bca-9630-6825b1e36c13__0
- __story_id__: a random alphanumeric identifier for the story. Example: e56c7c3e-4660-40fb-80d0-052d566d676a
- __worker_id__: each worker has a unique identificaiton number. Example: 21
- __submission_time_normalized__: the time of submission in the format YYYYMMDD. Example: 20200115
- __worker_quality_assessment__: rating for the worker on the assignment in the row. Example: 2
- __selected_sentence_index__: the index of a given sentence in a story. Example: 0
- __story__: contains the full text of the ROC story that was used for the HIT. Example: It was bedtime at our house. Two of the three kids hit the pillow and fall asleep. The third is a trouble maker. For two hours he continues to get out of bed and want to play. Finally he becomes tired and falls asleep.
- __selected_sentence__: the sentence from the story that is being annotated. Example: It was bedtime at our house.
- __[1-10]\_[specific/general][NL/Structured]__: This is the primary data collected. It provides the common sense knowledge about the related stories and those general rules about the world derived from the specific statements. For each of the ten relationships, there are four columns. The specific columns give the specific statements from the story. The general statements give the corresponding generalization. The NL columns are formatted in natural language, whereas the structured columns contain indications of the slots used to fill in the data. Example:
- __1_specificNL__: "The school has a football team >Causes/Enables> The football game was last weekend"
- __1_specificStructured__: "{The school }\_[subject] {has }\_[verb] {a football team }\_[object1] >Causes/Enables> {The football game }\_[subject] {was last weekend }\_[verb]"
- __1_generalNL__: "Somewhere_A (that is a school ) has Something_A (that is a sports team ) >Causes/Enables> The game was last weekend"
- __1_generalStructured__: "{Somewhere_A ||that is a school ||}\_[subject] {has }\_[verb] {Something_A ||that is a sports team ||}\_[object1] >Causes/Enables> {The game }\_[subject] {was last weekend }\_[verb]"
- __number\_filled\_in__: number of dimensions filled in for the assignment. Example: 4
### Data Splits
Train split: 65,521 examples
Test splits: 500 examples, without worker id and rating, number filled in, and structured text.
## Dataset Creation
### Curation Rationale
When humans read or listen, they make implicit commonsense inferences that frame their understanding of what happened and why. As a step toward AI systems that can build similar mental models, we introduce GLUCOSE, a large-scale dataset of implicit commonsense causal knowledge, encoded as causal mini-theories about the world, each grounded in a narrative context.
### Source Data
#### Initial Data Collection and Normalization
Initial text from ROCStories
#### Who are the source language producers?
Amazon Mechanical Turk.
### Annotations
#### Annotation process
To enable developing models that can build mental models of narratives, we aimed to crowdsource a large, quality-monitored dataset. Beyond the scalability benefits, using crowd workers (as opposed to a small set of expert annotators) ensures diversity of thought, thus broadening coverage of a common-sense knowledge resource. The annotation task is complex: it requires annotators to understand different causal dimensions in a variety of contexts and to come up with generalized theories beyond the story context. For
strict quality control, we designed a three-stage knowledge acquisition pipeline for crowdsourcing the GLUCOSE dataset on the Amazon Mechanical Turk Platform. The workers first go through a qualification test where they must score at least 90% on 10 multiple-choice questions on select GLUCOSE dimensions. Next, qualified workers can work on the main GLUCOSE data collection task: given a story S and a story sentence X, they are asked to fill in (allowing for non-applicable) all ten GLUCOSE dimensions, getting step-by-step guidance from the GLUCOSE data acquisition. To ensure data consistency, the same workers answer all dimensions for an S, X pair. Finally, the submissions are reviewed by an expert who rates each worker on a scale from 0 to 3, and provides feedback on how to improve. Our final UIs are the result of more than six rounds of pilot studies, iteratively improving the interaction elements, functionality, dimension definitions, instructions, and examples.
#### Who are the annotators?
Amazon Mechanical Turk workers, with feedback from an expert.
### Personal and Sensitive Information
No personal or sensitive information.
## 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
Nasrin Mostafazadeh, Aditya Kalyanpur, Lori Moon, David Buchanan, Lauren Berkowitz, Or Biran, Jennifer Chu-Carroll, from Elemental Cognition
### Licensing Information
Creative Commons Attribution-NonCommercial 4.0 International Public License
### Citation Information
```
@inproceedings{mostafazadeh2020glucose,
title={GLUCOSE: GeneraLized and COntextualized Story Explanations},
author={Nasrin Mostafazadeh and Aditya Kalyanpur and Lori Moon and David Buchanan and Lauren Berkowitz and Or Biran and Jennifer Chu-Carroll},
year={2020},
booktitle={The Conference on Empirical Methods in Natural Language Processing},
publisher={Association for Computational Linguistics}
}
```
### Contributions
Thanks to [@TevenLeScao](https://github.com/TevenLeScao) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
hybrid_qa | null | 2023-03-28T12:23:49Z | 313 | 1 | hybridqa | [
"task_categories:question-answering",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"multihop-tabular-text-qa",
"arxiv:1909.05358",
"region:us"
] | 2023-03-28T12:23:49Z | 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
task_ids: []
paperswithcode_id: hybridqa
pretty_name: HybridQA
tags:
- multihop-tabular-text-qa
dataset_info:
features:
- name: question_id
dtype: string
- name: question
dtype: string
- name: table_id
dtype: string
- name: answer_text
dtype: string
- name: question_postag
dtype: string
- name: table
struct:
- name: url
dtype: string
- name: title
dtype: string
- name: header
sequence: string
- name: data
list:
- name: value
dtype: string
- name: urls
list:
- name: url
dtype: string
- name: summary
dtype: string
- name: section_title
dtype: string
- name: section_text
dtype: string
- name: uid
dtype: string
- name: intro
dtype: string
config_name: hybrid_qa
splits:
- name: train
num_bytes: 2745712769
num_examples: 62682
- name: validation
num_bytes: 153512016
num_examples: 3466
- name: test
num_bytes: 148795919
num_examples: 3463
download_size: 217436855
dataset_size: 3048020704
---
# Dataset Card for HybridQA
## 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://hybridqa.github.io/index.html
- **Repository:** [GitHub](https://github.com/wenhuchen/HybridQA)
- **Paper:** [HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data](https://arxiv.org/abs/1909.05358)
- **Leaderboard:** [HybridQA Competition](https://competitions.codalab.org/competitions/24420)
- **Point of Contact:** [Wenhu Chen](wenhuchen@cs.ucsb.edu)
### Dataset Summary
Existing question answering datasets focus on dealing with homogeneous information, based either only on text or
KB/Table information alone. However, as human knowledge is distributed over heterogeneous forms,
using homogeneous information alone might lead to severe coverage problems.
To fill in the gap, we present HybridQA, a new large-scale question-answering dataset that
requires reasoning on heterogeneous information. Each question is aligned with a Wikipedia table
and multiple free-form corpora linked with the entities in the table. The questions are designed
to aggregate both tabular information and text information, i.e.,
lack of either form would render the question unanswerable.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset is in English language.
## Dataset Structure
### Data Instances
A typical example looks like this
```
{
"question_id": "00009b9649d0dd0a",
"question": "Who were the builders of the mosque in Herat with fire temples ?",
"table_id": "List_of_mosques_in_Afghanistan_0",
"answer_text": "Ghurids",
"question_postag": "WP VBD DT NNS IN DT NN IN NNP IN NN NNS .",
"table": {
"url": "https://en.wikipedia.org/wiki/List_of_mosques_in_Afghanistan",
"title": "List of mosques in Afghanistan",
"header": [
"Name",
"Province",
"City",
"Year",
"Remarks"
],
"data": [
{
"value": "Kabul",
"urls": [
{
"summary": "Kabul ( Persian : کابل , romanized : Kābol , Pashto : کابل , romanized : Kābəl ) is the capital and largest city of Afghanistan...",
"url": "/wiki/Kabul"
}
]
}
]
},
"section_title": "",
"section_text": "",
"uid": "List_of_mosques_in_Afghanistan_0",
"intro": "The following is an incomplete list of large mosques in Afghanistan:"
}
```
### Data Fields
- `question_id` (str)
- `question` (str)
- `table_id` (str)
- `answer_text` (str)
- `question_postag` (str)
- `table` (dict):
- `url` (str)
- `title` (str)
- `header` (list of str)
- `data` (list of dict):
- `value` (str)
- `urls` (list of dict):
- `url` (str)
- `summary` (str)
- `section_title` (str)
- `section_text` (str)
- `uid` (str)
- `intro` (str)
### Data Splits
The dataset is split into `train`, `dev` and `test` splits.
| | train | validation | test |
| --------------- |------:|-----------:|-----:|
| N. Instances | 62682 | 3466 | 3463 |
## 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
The dataset is under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/).
### Citation Information
[More Information Needed]
```
@article{chen2020hybridqa,
title={HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data},
author={Chen, Wenhu and Zha, Hanwen and Chen, Zhiyu and Xiong, Wenhan and Wang, Hong and Wang, William},
journal={Findings of EMNLP 2020},
year={2020}
}
```
### Contributions
Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.
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inquisitive_qg | null | 2022-11-18T20:09:50Z | 313 | 1 | inquisitive | [
"task_categories:text2text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"question-generation",
"region:us"
] | 2022-11-18T20:09:50Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
pretty_name: InquisitiveQg
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: inquisitive
tags:
- question-generation
dataset_info:
features:
- name: id
dtype: int32
- name: article_id
dtype: int32
- name: article
dtype: string
- name: sentence_id
dtype: int32
- name: sentence
dtype: string
- name: span
dtype: string
- name: question
dtype: string
- name: span_start_position
dtype: int32
- name: span_end_position
dtype: int32
config_name: plain_text
splits:
- name: train
num_bytes: 66099232
num_examples: 15931
- name: validation
num_bytes: 8904329
num_examples: 1991
- name: test
num_bytes: 7167203
num_examples: 1894
download_size: 7085941
dataset_size: 82170764
---
# Dataset Card for InquisitiveQg
## 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:** [Add homepage URL here if available (unless it's a GitHub repository)]()
- **Repository:** [If the dataset is hosted on github or has a github homepage, add URL here]()
- **Paper:** [If the dataset was introduced by a paper or there was a paper written describing the dataset, add URL here (landing page for Arxiv paper preferred)]()
- **Leaderboard:** [If the dataset supports an active leaderboard, add link here]()
- **Point of Contact:** [If known, name and email of at least one person the reader can contact for questions about the dataset.]()
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset. | [
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kor_sae | null | 2023-01-25T14:34:03Z | 313 | 3 | null | [
"task_categories:text-classification",
"task_ids:intent-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ko",
"license:cc-by-sa-4.0",
"arxiv:1912.00342",
"arxiv:1811.04231",
"region:us"
] | 2023-01-25T14:34:03Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- ko
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- intent-classification
pretty_name: Structured Argument Extraction for Korean
dataset_info:
features:
- name: intent_pair1
dtype: string
- name: intent_pair2
dtype: string
- name: label
dtype:
class_label:
names:
'0': yes/no
'1': alternative
'2': wh- questions
'3': prohibitions
'4': requirements
'5': strong requirements
splits:
- name: train
num_bytes: 2885167
num_examples: 30837
download_size: 2545926
dataset_size: 2885167
---
# Dataset Card for Structured Argument Extraction for Korean
## 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:** [Structured Argument Extraction for Korean](https://github.com/warnikchow/sae4k)
- **Repository:** [Structured Argument Extraction for Korean](https://github.com/warnikchow/sae4k)
- **Paper:** [Machines Getting with the Program: Understanding Intent Arguments of Non-Canonical Directives](https://arxiv.org/abs/1912.00342)
- **Point of Contact:** [Won Ik Cho](wicho@hi.snu.ac.kr)
### Dataset Summary
The Structured Argument Extraction for Korean dataset is a set of question-argument and command-argument pairs with their respective question type label and negativeness label. Often times, agents like Alexa or Siri, encounter conversations without a clear objective from the user. The goal of this dataset is to extract the intent argument of a given utterance pair without a clear directive. This may yield a more robust agent capable of parsing more non-canonical forms of speech.
### Supported Tasks and Leaderboards
* `intent_classification`: The dataset can be trained with a Transformer like [BERT](https://huggingface.co/bert-base-uncased) to classify the intent argument or a question/command pair in Korean, and it's performance can be measured by it's BERTScore.
### 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 question or command pair and its label:
```
{
"intent_pair1": "내일 오후 다섯시 조별과제 일정 추가해줘"
"intent_pair2": "내일 오후 다섯시 조별과제 일정 추가하기"
"label": 4
}
```
### Data Fields
* `intent_pair1`: a question/command pair
* `intent_pair2`: a corresponding question/command pair
* `label`: determines the intent argument of the pair and can be one of `yes/no` (0), `alternative` (1), `wh- questions` (2), `prohibitions` (3), `requirements` (4) and `strong requirements` (5)
### Data Splits
The corpus contains 30,837 examples.
## Dataset Creation
### Curation Rationale
The Structured Argument Extraction for Korean dataset was curated to help train models extract intent arguments from utterances without a clear objective or when the user uses non-canonical forms of speech. This is especially helpful in Korean because in English, the `Who, what, where, when and why` usually comes in the beginning, but this isn't necessarily the case in the Korean language. So for low-resource languages, this lack of data can be a bottleneck for comprehension performance.
### Source Data
#### Initial Data Collection and Normalization
The corpus was taken from the one constructed by [Cho et al.](https://arxiv.org/abs/1811.04231), a Korean single utterance corpus for identifying directives/non-directives that contains a wide variety of non-canonical directives.
#### Who are the source language producers?
Korean speakers are the source language producers.
### Annotations
#### Annotation process
Utterances were categorized as question or command arguments and then further classified according to their intent argument.
#### Who are the annotators?
The annotation was done by three Korean natives with a background in computational linguistics.
### 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
The dataset is curated by Won Ik Cho, Young Ki Moon, Sangwhan Moon, Seok Min Kim and Nam Soo Kim.
### Licensing Information
The dataset is licensed under the CC BY-SA-4.0.
### Citation Information
```
@article{cho2019machines,
title={Machines Getting with the Program: Understanding Intent Arguments of Non-Canonical Directives},
author={Cho, Won Ik and Moon, Young Ki and Moon, Sangwhan and Kim, Seok Min and Kim, Nam Soo},
journal={arXiv preprint arXiv:1912.00342},
year={2019}
}
```
### Contributions
Thanks to [@stevhliu](https://github.com/stevhliu) for adding this dataset. | [
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myanmar_news | null | 2023-01-25T14:41:11Z | 313 | 1 | null | [
"task_categories:text-classification",
"task_ids:topic-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:my",
"license:gpl-3.0",
"region:us"
] | 2023-01-25T14:41:11Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- my
license:
- gpl-3.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- topic-classification
pretty_name: MyanmarNews
dataset_info:
features:
- name: text
dtype: string
- name: category
dtype:
class_label:
names:
'0': Sport
'1': Politic
'2': Business
'3': Entertainment
splits:
- name: train
num_bytes: 3797368
num_examples: 8116
download_size: 610592
dataset_size: 3797368
---
# Dataset Card for Myanmar_News
## Dataset Description
- **Repository:** https://github.com/ayehninnkhine/MyanmarNewsClassificationSystem
### Dataset Summary
The Myanmar news dataset contains article snippets in four categories:
Business, Entertainment, Politics, and Sport.
These were collected in October 2017 by Aye Hninn Khine
### Languages
Myanmar/Burmese language
## Dataset Structure
### Data Fields
- text - text from article
- category - a topic: Business, Entertainment, **Politic**, or **Sport** (note spellings)
### Data Splits
One training set (8,116 total rows)
### Source Data
#### Initial Data Collection and Normalization
Data was collected by Aye Hninn Khine
and shared on GitHub with a GPL-3.0 license.
Multiple text files were consolidated into one labeled CSV file by Nick Doiron.
## Additional Information
### Dataset Curators
Contributors to original GitHub repo:
- https://github.com/ayehninnkhine
### Licensing Information
GPL-3.0
### Citation Information
See https://github.com/ayehninnkhine/MyanmarNewsClassificationSystem
### Contributions
Thanks to [@mapmeld](https://github.com/mapmeld) for adding this dataset. | [
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opus_tedtalks | null | 2022-11-03T16:15:24Z | 313 | 0 | null | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"language:hr",
"license:unknown",
"region:us"
] | 2022-11-03T16:15:24Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
- hr
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: OpusTedtalks
dataset_info:
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- en
- hr
config_name: en-hr
splits:
- name: train
num_bytes: 15249417
num_examples: 86348
download_size: 5639306
dataset_size: 15249417
---
# Dataset Card for OpusTedtalks
## 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://opus.nlpl.eu/TedTalks.php
- **Repository:** None
- **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
This is a Croatian-English parallel corpus of transcribed and translated TED talks, originally extracted from https://wit3.fbk.eu. The corpus is compiled by Željko Agić and is taken from http://lt.ffzg.hr/zagic provided under the CC-BY-NC-SA license. This corpus is sentence aligned for both language pairs. The documents were collected and aligned using the Hunalign algorithm.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
Dataset provided for research purposes only. Please check dataset license for additional information.
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[CC-BY-NC-SA license]<http://creativecommons.org/licenses/by-sa/3.0/>
### Citation Information
@InProceedings{TIEDEMANN12.463,
author = {J{\"o}rg Tiedemann},
title = {Parallel Data, Tools and Interfaces in OPUS},
booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
year = {2012},
month = {may},
date = {23-25},
address = {Istanbul, Turkey},
editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis},
publisher = {European Language Resources Association (ELRA)},
isbn = {978-2-9517408-7-7},
language = {english}
}
### Contributions
Thanks to [@rkc007](https://github.com/rkc007) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Nexdata/mandarin_chinese | Nexdata | 2023-11-22T09:52:56Z | 313 | 6 | null | [
"region:us"
] | 2023-11-22T09:52:56Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for mandarin_chinese
## 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://nexdata.ai/?source=Huggingface
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The dataset contains 15,000 hours of Mandarin Chinese speech data. It's collected from local Mandarin speakers in 33 provinces of China, covering mutiple scenes and enviroments. The format is 16kHz, 16bit, uncompressed wav, mono channel. The sentence accuracy is over 97%.
For more details, please refer to the link: https://nexdata.ai/speechRecognition?source=Huggingface
### Supported Tasks and Leaderboards
automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).
### Languages
Mandarin
## 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
Commercial License
### Citation Information
[More Information Needed]
### Contributions
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PlanTL-GOB-ES/SQAC | PlanTL-GOB-ES | 2023-10-12T23:35:38Z | 313 | 7 | null | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:es",
"license:cc-by-sa-4.0",
"arxiv:1606.05250",
"region:us"
] | 2023-10-12T23:35:38Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- es
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: Spanish Question Answering Corpus (SQAC)
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
---
# SQAC (Spanish Question-Answering Corpus)
## Dataset Description
SQAC is an extractive QA dataset for the Spanish language.
- **Paper:** [MarIA: Spanish Language Models](https://upcommons.upc.edu/bitstream/handle/2117/367156/6405-5863-1-PB%20%281%29.pdf?sequence=1)
- **Point of Contact:** carlos.rodriguez1@bsc.es
- **Leaderboard:** [EvalEs] (https://plantl-gob-es.github.io/spanish-benchmark/)
### Dataset Summary
Contains 6,247 contexts and 18,817 questions with their respective answers, 1 to 5 for each fragment.
The sources of the contexts are:
* Encyclopedic articles from the [Spanish Wikipedia](https://es.wikipedia.org/), used under [CC-by-sa licence](https://creativecommons.org/licenses/by-sa/3.0/legalcode).
* News articles from [Wikinews](https://es.wikinews.org/), used under [CC-by licence](https://creativecommons.org/licenses/by/2.5/).
* Newswire and literature text from the [AnCora corpus](http://clic.ub.edu/corpus/en), used under [CC-by licence](https://creativecommons.org/licenses/by/4.0/legalcode).
### Supported Tasks
Extractive-QA
### Languages
- Spanish (es)
### Directory Structure
- README.md
- SQAC.py
- dev.json
- test.json
- train.json
## Dataset Structure
### Data Instances
<pre>
{
'id': '6cf3dcd6-b5a3-4516-8f9e-c5c1c6b66628',
'title': 'Historia de Japón',
'context': 'La historia de Japón (日本の歴史 o 日本史, Nihon no rekishi / Nihonshi?) es la sucesión de hechos acontecidos dentro del archipiélago japonés. Algunos de estos hechos aparecen aislados e influenciados por la naturaleza geográfica de Japón como nación insular, en tanto que otra serie de hechos, obedece a influencias foráneas como en el caso del Imperio chino, el cual definió su idioma, su escritura y, también, su cultura política. Asimismo, otra de las influencias foráneas fue la de origen occidental, lo que convirtió al país en una nación industrial, ejerciendo con ello una esfera de influencia y una expansión territorial sobre el área del Pacífico. No obstante, dicho expansionismo se detuvo tras la Segunda Guerra Mundial y el país se posicionó en un esquema de nación industrial con vínculos a su tradición cultural.',
'question': '¿Qué influencia convirtió Japón en una nación industrial?',
'answers': {
'text': ['la de origen occidental'],
'answer_start': [473]
}
}
</pre>
### Data Fields
<pre>
{
id: str
title: str
context: str
question: str
answers: {
answer_start: [int]
text: [str]
}
}
</pre>
### Data Splits
| Split | Size |
| ------------- | ------------- |
| `train` | 15,036 |
| `dev` | 1,864 |
| `test` | 1.910 |
## Content analysis
### Number of articles, paragraphs and questions
* Number of articles: 3,834
* Number of contexts: 6,247
* Number of questions: 18,817
* Number of sentences: 48,026
* Questions/Context ratio: 3.01
* Sentences/Context ratio: 7.70
### Number of tokens
* Total tokens in context: 1,561,616
* Average tokens/context: 250
* Total tokens in questions: 203,235
* Average tokens/question: 10.80
* Total tokens in answers: 90,307
* Average tokens/answer: 4.80
### Lexical variation
46.38% of the words in the Question can be found in the Context.
### Question type
| Question | Count | % |
|----------|-------:|---:|
| qué | 6,381 | 33.91 % |
| quién/es | 2,952 | 15.69 % |
| cuál/es | 2,034 | 10.81 % |
| cómo | 1,949 | 10.36 % |
| dónde | 1,856 | 9.86 % |
| cuándo | 1,639 | 8.71 % |
| cuánto | 1,311 | 6.97 % |
| cuántos | 495 |2.63 % |
| adónde | 100 | 0.53 % |
| cuánta | 49 | 0.26 % |
| no question mark | 43 | 0.23 % |
| cuántas | 19 | 0.10 % |
## Dataset Creation
### Curation Rationale
For compatibility with similar datasets in other languages, we followed as close as possible existing curation guidelines from SQUAD 1.0 [(Rajpurkar, Pranav et al.)](http://arxiv.org/abs/1606.05250).
### Source Data
#### Initial Data Collection and Normalization
The source data are scraped articles from Wikinews, the Spanish Wikipedia and the AnCora corpus.
- [Spanish Wikipedia](https://es.wikipedia.org)
- [Spanish Wikinews](https://es.wikinews.org/)
- [AnCora corpus](http://clic.ub.edu/corpus/en)
#### Who are the source language producers?
Contributors to the aforementioned sites.
### Annotations
#### Annotation process
We commissioned the creation of 1 to 5 questions for each context, following an adaptation of the guidelines from SQUAD 1.0 [(Rajpurkar, Pranav et al.)](http://arxiv.org/abs/1606.05250).
#### Who are the annotators?
Native language speakers.
### 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 language models in Spanish.
### Discussion of Biases
No postprocessing steps were applied to mitigate potential social biases.
## 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
```
@article{maria,
author = {Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquin Silveira-Ocampo and Casimiro Pio Carrino and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Aitor Gonzalez-Agirre and Marta Villegas},
title = {MarIA: Spanish Language Models},
journal = {Procesamiento del Lenguaje Natural},
volume = {68},
number = {0},
year = {2022},
issn = {1989-7553},
url = {http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6405},
pages = {39--60}
}
```
### Contributions
[N/A]
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
SocialGrep/one-million-reddit-questions | SocialGrep | 2022-07-25T18:57:10Z | 313 | 3 | 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"
] | 2022-07-25T18:57:10Z | 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-questions
## 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=dataset&utm_term=onemillionquestions)
- **Point of Contact:** [Website](https://socialgrep.com/contact?utm_source=huggingface&utm_medium=link&utm_campaign=dataset&utm_term=onemillionquestions)
### Dataset Summary
This corpus contains a million posts on /r/AskReddit, 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 | |
huggingartists/ariana-grande | huggingartists | 2022-10-25T09:23:36Z | 313 | 0 | null | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
] | 2022-10-25T09:23:36Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/ariana-grande"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [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)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **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 the generated dataset:** 0.997954 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/d36a47955ac0ddb12748c5e7c2bd4b4b.640x640x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/ariana-grande">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Ariana Grande</div>
<a href="https://genius.com/artists/ariana-grande">
<div style="text-align: center; font-size: 14px;">@ariana-grande</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/ariana-grande).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/ariana-grande")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|596| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/ariana-grande")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## 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{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
dream-textures/textures-color-1k | dream-textures | 2023-01-13T17:54:04Z | 313 | 6 | null | [
"task_categories:text-to-image",
"size_categories:1K<n<10K",
"language:en",
"license:cc0-1.0",
"region:us"
] | 2023-01-13T17:54:04Z | 2023-01-13T17:27:40.000Z | 2023-01-13T17:27:40 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 60933571.47
num_examples: 1426
download_size: 58351352
dataset_size: 60933571.47
license: cc0-1.0
task_categories:
- text-to-image
language:
- en
size_categories:
- 1K<n<10K
---
# textures-color-1k
## 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)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
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### Dataset Summary
The `textures-color-1k` dataset is an image dataset of 1000+ color image textures in 512x512 resolution with associated text descriptions.
The dataset was created for training/fine-tuning diffusion models on texture generation tasks.
It contains a combination of CC0 procedural and photoscanned PBR materials from [ambientCG](https://ambientcg.com/).
### Languages
The text descriptions are in English, and created by joining the tags of each material with a space character.
## Dataset Structure
### Data Instances
Each data point contains a 512x512 image and and additional `text` feature containing the description of the texture.
### Data Fields
* `image`: the color texture as a PIL image
* `text`: the associated text description created by merging the material's tags
### Data Splits
| | train |
| -- | ----- |
| ambientCG | 1426 |
## Dataset Creation
### Curation Rationale
`textures-color-1k` was created to provide an accesible source of data for automating 3D-asset creation workflows.
The [Dream Textures](https://github.com/carson-katri/dream-textures) add-on is one such tool providing AI automation in Blender.
By fine-tuning models such as Stable Diffusion on textures, this particular use-case can be more accurately automated.
### Source Data
#### Initial Data Collection and Normalization
The data was obtained from [ambientCG](https://ambientcg.com/)'s CC0 textures. Only the color maps were included in this dataset.
Text descriptions were synthesized by joining the tags associated with each material with a space.
## Additional Information
### Dataset Curators
The dataset was created by Carson Katri, with the images being provided by [ambientCG](https://ambientcg.com/).
### Licensing Information
All of the images used in this dataset are CC0.
### Citation Information
[N/A]
### Contributions
Thanks to [@carson-katri](https://github.com/carson-katri) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
vlsp-2023-vllm/ai2_arc_vi | vlsp-2023-vllm | 2023-10-08T09:54:04Z | 313 | 0 | null | [
"region:us"
] | 2023-10-08T09:54:04Z | 2023-09-29T18:17:01.000Z | 2023-09-29T18:17:01 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: choices
struct:
- name: label
sequence: string
- name: text
sequence: string
- name: answerKey
dtype: string
splits:
- name: train
num_bytes: 462541
num_examples: 1118
- name: validation
num_bytes: 128948
num_examples: 298
- name: test
num_bytes: 491761
num_examples: 1170
download_size: 511280
dataset_size: 1083250
---
Reference: https://huggingface.co/datasets/ai2_arc
# ARC-Challenge (Vietnamese translation version)
## Dataset Summary
A dataset of grade-school level, multiple-choice science questions, assembled to encourage research in advanced question-answering.
## Install
To install `lm-eval` from the github repository main branch, run:
```bash
git clone https://github.com/hieunguyen1053/lm-evaluation-harness
cd lm-evaluation-harness
pip install -e .
```
## Basic Usage
> **Note**: When reporting results from eval harness, please include the task versions (shown in `results["versions"]`) for reproducibility. This allows bug fixes to tasks while also ensuring that previously reported scores are reproducible. See the [Task Versioning](#task-versioning) section for more info.
### Hugging Face `transformers`
To evaluate a model hosted on the [HuggingFace Hub](https://huggingface.co/models) (e.g. vlsp-2023-vllm/hoa-1b4) on `ai2_arc_vi` you can use the following command:
```bash
python main.py \
--model hf-causal \
--model_args pretrained=vlsp-2023-vllm/hoa-1b4 \
--tasks ai2_arc_vi \
--num_fewshot 25 \
--batch_size auto \
--device cuda:0
```
Additional arguments can be provided to the model constructor using the `--model_args` flag. Most notably, this supports the common practice of using the `revisions` feature on the Hub to store partially trained checkpoints, or to specify the datatype for running a model:
```bash
python main.py \
--model hf-causal \
--model_args pretrained=vlsp-2023-vllm/hoa-1b4,revision=step100000,dtype="float" \
--tasks ai2_arc_vi \
--num_fewshot 25 \
--batch_size auto \
--device cuda:0
```
To evaluate models that are loaded via `AutoSeq2SeqLM` in Huggingface, you instead use `hf-seq2seq`. *To evaluate (causal) models across multiple GPUs, use `--model hf-causal-experimental`*
> **Warning**: Choosing the wrong model may result in erroneous outputs despite not erroring. | [
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factckbr | null | 2023-01-25T14:30:15Z | 312 | 3 | null | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:pt",
"license:mit",
"region:us"
] | 2023-01-25T14:30:15Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- pt
license:
- mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- fact-checking
pretty_name: FACTCK BR
dataset_info:
features:
- name: url
dtype: string
- name: author
dtype: string
- name: date
dtype: string
- name: claim
dtype: string
- name: review
dtype: string
- name: title
dtype: string
- name: rating
dtype: float32
- name: best_rating
dtype: float32
- name: label
dtype:
class_label:
names:
'0': falso
'1': distorcido
'2': impreciso
'3': exagerado
'4': insustentável
'5': verdadeiro
'6': outros
'7': subestimado
'8': impossível provar
'9': discutível
'10': sem contexto
'11': de olho
'12': verdadeiro, mas
'13': ainda é cedo para dizer
splits:
- name: train
num_bytes: 750646
num_examples: 1313
download_size: 721314
dataset_size: 750646
---
# Dataset Card for FACTCK BR
## 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/jghm-f/FACTCK.BR
- **Repository:** https://github.com/jghm-f/FACTCK.BR
- **Paper:** https://dl.acm.org/doi/10.1145/3323503.3361698
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
A dataset to study Fake News in Portuguese, presenting a supposedly false News along with their respective fact check and classification.
The data is collected from the ClaimReview, a structured data schema used by fact check agencies to share their results in search engines, enabling data collect in real time.
The FACTCK.BR dataset contains 1309 claims with its corresponding label.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@hugoabonizio](https://github.com/hugoabonizio) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ro_sts | null | 2022-11-18T21:42:20Z | 312 | 0 | null | [
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:semantic-similarity-scoring",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|other-sts-b",
"language:ro",
"license:cc-by-4.0",
"region:us"
] | 2022-11-18T21:42:20Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- ro
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-sts-b
task_categories:
- text-classification
task_ids:
- text-scoring
- semantic-similarity-scoring
paperswithcode_id: null
pretty_name: RO-STS
dataset_info:
features:
- name: score
dtype: float32
- name: sentence1
dtype: string
- name: sentence2
dtype: string
config_name: ro_sts
splits:
- name: train
num_bytes: 879073
num_examples: 5749
- name: test
num_bytes: 194330
num_examples: 1379
- name: validation
num_bytes: 245926
num_examples: 1500
download_size: 1267607
dataset_size: 1319329
---
# Dataset Card for RO-STS
## 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/dumitrescustefan/RO-STS)
- **Repository:** [GitHub](https://github.com/dumitrescustefan/RO-STS)
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [email](dumitrescu.stefan@gmail.com)
### Dataset Summary
We present RO-STS - the Semantic Textual Similarity dataset for the Romanian language. It is a high-quality translation of the [STS English dataset](https://ixa2.si.ehu.eus/stswiki/index.php/STSbenchmark). RO-STS contains 8,628 sentence pairs with their similarity scores. The original English sentences were collected from news headlines, captions of images and user forums, and are categorized accordingly. The Romanian release follows this categorization and provides the same train/validation/test split with 5,749/1,500/1,379 sentence pairs in each subset.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
The text dataset is in Romanian (`ro`)
## Dataset Structure
### Data Instances
An example looks like this:
```
{'score': 1.5,
'sentence1': 'Un bărbat cântă la harpă.',
'sentence2': 'Un bărbat cântă la claviatură.',
}
```
### Data Fields
- `score`: a float representing the semantic similarity score where 0.0 is the lowest score and 5.0 is the highest
- `sentence1`: a string representing a text
- `sentence2`: another string to compare the previous text with
### Data Splits
The train/validation/test split contain 5,749/1,500/1,379 sentence pairs.
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
[Needs More Information]
#### Initial Data Collection and Normalization
*To construct the dataset, we first obtained automatic translations using Google's translation engine. These were then manually checked, corrected, and cross-validated by human volunteers. *
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
#### Who are the annotators?
### 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-SA 4.0 License
### Citation Information
```
@inproceedings{dumitrescu2021liro,
title={Liro: Benchmark and leaderboard for romanian language tasks},
author={Dumitrescu, Stefan Daniel and Rebeja, Petru and Lorincz, Beata and Gaman, Mihaela and Avram, Andrei and Ilie, Mihai and Pruteanu, Andrei and Stan, Adriana and Rosia, Lorena and Iacobescu, Cristina and others},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1)},
year={2021}
}
```
### Contributions
Thanks to [@lorinczb](https://github.com/lorinczb) for adding this dataset. | [
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times_of_india_news_headlines | null | 2022-11-03T16:15:42Z | 312 | 0 | null | [
"task_categories:text2text-generation",
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"task_ids:fact-checking-retrieval",
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"language:en",
"license:cc0-1.0",
"region:us"
] | 2022-11-03T16:15:42Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- en
license:
- cc0-1.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text2text-generation
- text-retrieval
task_ids:
- document-retrieval
- fact-checking-retrieval
- text-simplification
paperswithcode_id: null
pretty_name: Times of India News Headlines
dataset_info:
features:
- name: publish_date
dtype: string
- name: headline_category
dtype: string
- name: headline_text
dtype: string
splits:
- name: train
num_bytes: 260939306
num_examples: 3297173
download_size: 0
dataset_size: 260939306
---
# Dataset Card for Times of India News Headlines
## 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://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/J7BYRX
- **Repository:** [More Information Needed]
- **Paper:** [More Information Needed]
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
This news dataset is a persistent historical archive of noteable events in the Indian subcontinent from start-2001 to mid-2020, recorded in realtime by the journalists of India. It contains approximately 3.3 million events published by Times of India. Times Group as a news agency, reaches out a very wide audience across Asia and drawfs every other agency in the quantity of english articles published per day. Due to the heavy daily volume over multiple years, this data offers a deep insight into Indian society, its priorities, events, issues and talking points and how they have unfolded over time. It is possible to chop this dataset into a smaller piece for a more focused analysis, based on one or more facets.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text in the dataset is in English.
## Dataset Structure
### Data Instances
```
{
'publish_date': '20010530',
'headline_category': city.kolkata,
'headline_text': "Malda fake notes"
}
```
### Data Fields
- `publish_date`: Date of publishing in yyyyMMdd format
- `headline_category`: Category of event in ascii, dot-delimited values
- `headline_text`: Headline of article en la Engrezi (2020-07-10)
### Data Splits
This dataset has no 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
[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
The dataset was created by Rohit Kulkarni.
### Licensing Information
The data is under the [CC0: Public Domain](https://creativecommons.org/publicdomain/zero/1.0/)
### Citation Information
```
@data{DVN/DPQMQH_2020,
author = {Kulkarni, Rohit},
publisher = {Harvard Dataverse},
title = {{Times of India News Headlines}},
year = {2020},
version = {V1},
doi = {10.7910/DVN/DPQMQH},
url = {https://doi.org/10.7910/DVN/DPQMQH}
}
```
### Contributions
Thanks to [@tanmoyio](https://github.com/tanmoyio) for adding this dataset. | [
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turkish_movie_sentiment | null | 2022-11-03T16:07:48Z | 312 | 3 | null | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"task_ids:sentiment-scoring",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:tr",
"license:unknown",
"region:us"
] | 2022-11-03T16:07:48Z | 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:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
- sentiment-scoring
paperswithcode_id: null
pretty_name: 'TurkishMovieSentiment: This dataset contains turkish movie reviews.'
dataset_info:
features:
- name: point
dtype: float32
- name: comment
dtype: string
- name: film_name
dtype: string
config_name: turkishmoviesentiment
splits:
- name: train
num_bytes: 33954560
num_examples: 83227
download_size: 0
dataset_size: 33954560
---
# Dataset Card for TurkishMovieSentiment: This dataset contains turkish movie reviews.
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://www.kaggle.com/mustfkeskin/turkish-movie-sentiment-analysis-dataset/tasks](https://www.kaggle.com/mustfkeskin/turkish-movie-sentiment-analysis-dataset/tasks)
- **Point of Contact:** [Mustafa Keskin](https://www.linkedin.com/in/mustfkeskin/)
### Dataset Summary
This data set is a dataset from kaggle consisting of Turkish movie reviews and scored between 0-5.
### Languages
The dataset is based on Turkish.
## Dataset Structure
### Data Instances
**Example 1:**
**Comment:** Jean Reno denince zaten leon filmi gelir akla izlemeyen kalmamıştır ama kaldıysada ee ne duruyorsun hemen izle :),
**Film_name:** Sevginin Gücü,
**Point:** 5,0
**Example 2:**
**Comment:** Bence güzel bi film olmush.İzlenmeli.İnsana şükretmek gerektini hatırlatıyor.Ama cok da poh pohlanacak bi sey yapmamıslar,
**Film_name:** Cinderella Man,
**Point:** 2,5
### Data Fields
- **comment**(string) : Contatins turkish movie review
- **film_name**(string) : Film name in Turkish.
- **point**(float) : [0-5] floating point
### Data Splits
It is not divided into Train set and Test set.
## 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
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
### Discussion of Social Impact and Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The dataset was created by [Mustafa Keskin](https://www.linkedin.com/in/mustfkeskin/).
### Licensing Information
The data is under the [CC0: Public Domain](https://creativecommons.org/publicdomain/zero/1.0/)
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@yavuzKomecoglu](https://github.com/yavuzKomecoglu) for adding this dataset. | [
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GEM/sportsett_basketball | GEM | 2022-10-24T15:30:28Z | 312 | 6 | null | [
"task_categories:table-to-text",
"annotations_creators:none",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:mit",
"data-to-text",
"region:us"
] | 2022-10-24T15:30:28Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- mit
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
pretty_name: sportsett_basketball
tags:
- data-to-text
---
# Dataset Card for GEM/sportsett_basketball
## Dataset Description
- **Homepage:** https://github.com/nlgcat/sport_sett_basketball
- **Repository:** https://github.com/nlgcat/sport_sett_basketball
- **Paper:** https://aclanthology.org/2020.intellang-1.4/
- **Leaderboard:** N/A
- **Point of Contact:** Craig Thomson
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/sportsett_basketball).
### Dataset Summary
The sportsett dataset is an English data-to-text dataset in the basketball domain. The inputs are statistics summarizing an NBA game and the outputs are high-quality descriptions of the game in natural language.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/sportsett_basketball')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/sportsett_basketball).
#### website
[Github](https://github.com/nlgcat/sport_sett_basketball)
#### paper
[ACL Anthology](https://aclanthology.org/2020.intellang-1.4/)
#### authors
Craig Thomson, Ashish Upadhyay
## Dataset Overview
### Where to find the Data and its Documentation
#### Webpage
<!-- info: What is the webpage for the dataset (if it exists)? -->
<!-- scope: telescope -->
[Github](https://github.com/nlgcat/sport_sett_basketball)
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Github](https://github.com/nlgcat/sport_sett_basketball)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[ACL Anthology](https://aclanthology.org/2020.intellang-1.4/)
#### 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{thomson-etal-2020-sportsett,
title = "{S}port{S}ett:Basketball - A robust and maintainable data-set for Natural Language Generation",
author = "Thomson, Craig and
Reiter, Ehud and
Sripada, Somayajulu",
booktitle = "Proceedings of the Workshop on Intelligent Information Processing and Natural Language Generation",
month = sep,
year = "2020",
address = "Santiago de Compostela, Spain",
publisher = "Association for Computational Lingustics",
url = "https://aclanthology.org/2020.intellang-1.4",
pages = "32--40",
}
```
#### 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 -->
Craig Thomson
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
c.thomson@abdn.ac.uk
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
no
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
no
#### Covered Dialects
<!-- info: What dialects are covered? Are there multiple dialects per language? -->
<!-- scope: periscope -->
American English
One dialect, one language.
#### 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 -->
American sports writers
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
mit: MIT License
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
Maintain a robust and scalable Data-to-Text generation resource with structured data and textual summaries
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Data-to-Text
#### Communicative Goal
<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
A model trained on this dataset should summarise the statistical and other information from a basketball game. This will be focused on a single game, although facts from prior games, or aggregate statistics over many games can and should be used for comparison where appropriate. There no single common narrative, although summaries usually start with who player, when, where, and the score. They then provide high level commentary on what the difference in the game was (why the winner won). breakdowns of statistics for prominent players follow, winning team first. Finally, the upcoming schedule for both teams is usually included. There are, however, other types of fact that can be included, and other narrative structures.
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`academic`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
University of Aberdeen, Robert Gordon University
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Craig Thomson, Ashish Upadhyay
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
EPSRC
#### 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 -->
Craig Thomson, Ashish Upadhyay
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
Each instance in the dataset has five fields.
1. "sportsett_id": This is a unique id as used in the original SportSett database. It starts with '1' with the first instance in the train-set and ends with '6150' with the last instance in test-set.
2. "gem_id": This is a unique id created as per GEM's requirement which follows the `GEM-${DATASET_NAME}-${SPLIT-NAME}-${id}` pattern.
3. "game": This field contains a dictionary with information about current game. It has information such as date on which the game was played alongwith the stadium, city, state where it was played.
4. "teams": This filed is a dictionary of multiple nested dictionaries. On the highest level, it has two keys: 'home' and 'vis', which provide the stats for home team and visiting team of the game. Both are dictionaries with same structure. Each dictionary will contain team's information such as name of the team, their total wins/losses in current season, their conference standing, the SportSett ids for their current and previous games. Apart from these general information, they also have the box- and line- scores for the team in the game. Box score is the stats of players from the team at the end of the game, while line score along with the whole game stats is divided into quarters and halves as well as the extra-time (if happened in the game). After these scores, there is another field of next-game, which gives general information about team's next game such as the place and opponent's name of the next game.
5. "summaries": This is a list of summaries for each game. Some games will have more than one summary, in that case, the list will have more than one entries. Each summary in the list is a string which can be tokenised by a space, following the practices in RotoWire-FG dataset ([Wang, 2019](https://www.aclweb.org/anthology/W19-8639)).
#### Reason for Structure
<!-- info: How was the dataset structure determined? -->
<!-- scope: microscope -->
The structure mostly follows the original structure defined in RotoWire dataset ([Wiseman et. al. 2017](https://aclanthology.org/D17-1239/)) with some modifications (such as game and next-game keys) address the problem of information gap between input and output data ([Thomson et. al. 2020](https://aclanthology.org/2020.inlg-1.6/)).
#### How were labels chosen?
<!-- info: How were the labels chosen? -->
<!-- scope: microscope -->
Similar to RotoWire dataset ([Wiseman et. al. 2017](https://aclanthology.org/D17-1239/))
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
```
{
"sportsett_id": "1",
"gem_id": "GEM-sportsett_basketball-train-0",
"game": {
"day": "1",
"month": "November",
"year": "2014",
"dayname": "Saturday",
"season": "2014",
"stadium": "Wells Fargo Center",
"city": "Philadelphia",
"state": "Pennsylvania",
"attendance": "19753",
"capacity": "20478",
"game_id": "1"
},
"teams": {
"home": {
"name": "76ers",
"place": "Philadelphia",
"conference": "Eastern Conference",
"division": "Atlantic",
"wins": "0",
"losses": "3",
"conference_standing": 15,
"game_number": "3",
"previous_game_id": "42",
"next_game_id": "2",
"line_score": {
"game": {
"FG3A": "23",
"FG3M": "7",
"FG3_PCT": "30",
"FGA": "67",
"FGM": "35",
"FG_PCT": "52",
"FTA": "26",
"FTM": "19",
"FT_PCT": "73",
"DREB": "33",
"OREB": "4",
"TREB": "37",
"BLK": "10",
"AST": "28",
"STL": "9",
"TOV": "24",
"PF": "21",
"PTS": "96",
"MIN": "4"
},
"H1": {
"FG3A": "82",
"FG3M": "30",
"FG3_PCT": "37",
"FGA": "2115",
"FGM": "138",
"FG_PCT": "7",
"FTA": "212",
"FTM": "18",
"FT_PCT": "8",
"DREB": "810",
"OREB": "21",
"TREB": "831",
"BLK": "51",
"AST": "107",
"STL": "21",
"TOV": "64",
"PTS": "3024",
"MIN": "6060"
},
"H2": {
"FG3A": "85",
"FG3M": "40",
"FG3_PCT": "47",
"FGA": "1615",
"FGM": "104",
"FG_PCT": "6",
"FTA": "66",
"FTM": "55",
"FT_PCT": "83",
"DREB": "96",
"OREB": "10",
"TREB": "106",
"BLK": "22",
"AST": "92",
"STL": "24",
"TOV": "68",
"PTS": "2913",
"MIN": "6060"
},
"Q1": {
"FG3A": "8",
"FG3M": "3",
"FG3_PCT": "38",
"FGA": "21",
"FGM": "13",
"FG_PCT": "62",
"FTA": "2",
"FTM": "1",
"FT_PCT": "50",
"DREB": "8",
"OREB": "2",
"TREB": "10",
"BLK": "5",
"AST": "10",
"STL": "2",
"TOV": "6",
"PTS": "30",
"MIN": "60"
},
"Q2": {
"FG3A": "2",
"FG3M": "0",
"FG3_PCT": "0",
"FGA": "15",
"FGM": "8",
"FG_PCT": "53",
"FTA": "12",
"FTM": "8",
"FT_PCT": "67",
"DREB": "10",
"OREB": "1",
"TREB": "11",
"BLK": "1",
"AST": "7",
"STL": "1",
"TOV": "4",
"PTS": "24",
"MIN": "60"
},
"Q3": {
"FG3A": "8",
"FG3M": "4",
"FG3_PCT": "50",
"FGA": "16",
"FGM": "10",
"FG_PCT": "62",
"FTA": "6",
"FTM": "5",
"FT_PCT": "83",
"DREB": "9",
"OREB": "1",
"TREB": "10",
"BLK": "2",
"AST": "9",
"STL": "2",
"TOV": "6",
"PTS": "29",
"MIN": "60"
},
"Q4": {
"FG3A": "5",
"FG3M": "0",
"FG3_PCT": "0",
"FGA": "15",
"FGM": "4",
"FG_PCT": "27",
"FTA": "6",
"FTM": "5",
"FT_PCT": "83",
"DREB": "6",
"OREB": "0",
"TREB": "6",
"BLK": "2",
"AST": "2",
"STL": "4",
"TOV": "8",
"PTS": "13",
"MIN": "60"
},
"OT": {
"FG3A": "0",
"FG3M": "0",
"FG3_PCT": "0",
"FGA": "0",
"FGM": "0",
"FG_PCT": "0",
"FTA": "0",
"FTM": "0",
"FT_PCT": "0",
"DREB": "0",
"OREB": "0",
"TREB": "0",
"BLK": "0",
"AST": "0",
"STL": "0",
"TOV": "0",
"PTS": "0",
"MIN": "0"
}
},
"box_score": [
{
"first_name": "Tony",
"last_name": "Wroten",
"name": "Tony Wroten",
"starter": "True",
"MIN": "33",
"FGM": "6",
"FGA": "11",
"FG_PCT": "55",
"FG3M": "1",
"FG3A": "4",
"FG3_PCT": "25",
"FTM": "8",
"FTA": "11",
"FT_PCT": "73",
"OREB": "0",
"DREB": "3",
"TREB": "3",
"AST": "10",
"STL": "1",
"BLK": "1",
"TOV": "4",
"PF": "1",
"PTS": "21",
"+/-": "-11",
"DOUBLE": "double"
},
{
"first_name": "Hollis",
"last_name": "Thompson",
"name": "Hollis Thompson",
"starter": "True",
"MIN": "32",
"FGM": "4",
"FGA": "8",
"FG_PCT": "50",
"FG3M": "2",
"FG3A": "5",
"FG3_PCT": "40",
"FTM": "0",
"FTA": "0",
"FT_PCT": "0",
"OREB": "0",
"DREB": "1",
"TREB": "1",
"AST": "2",
"STL": "0",
"BLK": "3",
"TOV": "2",
"PF": "2",
"PTS": "10",
"+/-": "-17",
"DOUBLE": "none"
},
{
"first_name": "Henry",
"last_name": "Sims",
"name": "Henry Sims",
"starter": "True",
"MIN": "27",
"FGM": "4",
"FGA": "9",
"FG_PCT": "44",
"FG3M": "0",
"FG3A": "0",
"FG3_PCT": "0",
"FTM": "1",
"FTA": "2",
"FT_PCT": "50",
"OREB": "1",
"DREB": "3",
"TREB": "4",
"AST": "2",
"STL": "0",
"BLK": "1",
"TOV": "0",
"PF": "1",
"PTS": "9",
"+/-": "-10",
"DOUBLE": "none"
},
{
"first_name": "Nerlens",
"last_name": "Noel",
"name": "Nerlens Noel",
"starter": "True",
"MIN": "25",
"FGM": "1",
"FGA": "4",
"FG_PCT": "25",
"FG3M": "0",
"FG3A": "0",
"FG3_PCT": "0",
"FTM": "0",
"FTA": "0",
"FT_PCT": "0",
"OREB": "0",
"DREB": "5",
"TREB": "5",
"AST": "3",
"STL": "1",
"BLK": "1",
"TOV": "3",
"PF": "1",
"PTS": "2",
"+/-": "-19",
"DOUBLE": "none"
},
{
"first_name": "Luc",
"last_name": "Mbah a Moute",
"name": "Luc Mbah a Moute",
"starter": "True",
"MIN": "19",
"FGM": "4",
"FGA": "10",
"FG_PCT": "40",
"FG3M": "0",
"FG3A": "2",
"FG3_PCT": "0",
"FTM": "1",
"FTA": "2",
"FT_PCT": "50",
"OREB": "3",
"DREB": "4",
"TREB": "7",
"AST": "3",
"STL": "1",
"BLK": "0",
"TOV": "6",
"PF": "3",
"PTS": "9",
"+/-": "-12",
"DOUBLE": "none"
},
{
"first_name": "Brandon",
"last_name": "Davies",
"name": "Brandon Davies",
"starter": "False",
"MIN": "23",
"FGM": "7",
"FGA": "9",
"FG_PCT": "78",
"FG3M": "1",
"FG3A": "2",
"FG3_PCT": "50",
"FTM": "3",
"FTA": "4",
"FT_PCT": "75",
"OREB": "0",
"DREB": "3",
"TREB": "3",
"AST": "0",
"STL": "3",
"BLK": "0",
"TOV": "3",
"PF": "3",
"PTS": "18",
"+/-": "-1",
"DOUBLE": "none"
},
{
"first_name": "Chris",
"last_name": "Johnson",
"name": "Chris Johnson",
"starter": "False",
"MIN": "21",
"FGM": "2",
"FGA": "4",
"FG_PCT": "50",
"FG3M": "1",
"FG3A": "3",
"FG3_PCT": "33",
"FTM": "0",
"FTA": "0",
"FT_PCT": "0",
"OREB": "0",
"DREB": "2",
"TREB": "2",
"AST": "0",
"STL": "3",
"BLK": "0",
"TOV": "2",
"PF": "5",
"PTS": "5",
"+/-": "3",
"DOUBLE": "none"
},
{
"first_name": "K.J.",
"last_name": "McDaniels",
"name": "K.J. McDaniels",
"starter": "False",
"MIN": "20",
"FGM": "2",
"FGA": "4",
"FG_PCT": "50",
"FG3M": "1",
"FG3A": "3",
"FG3_PCT": "33",
"FTM": "3",
"FTA": "4",
"FT_PCT": "75",
"OREB": "0",
"DREB": "1",
"TREB": "1",
"AST": "2",
"STL": "0",
"BLK": "3",
"TOV": "2",
"PF": "3",
"PTS": "8",
"+/-": "-10",
"DOUBLE": "none"
},
{
"first_name": "Malcolm",
"last_name": "Thomas",
"name": "Malcolm Thomas",
"starter": "False",
"MIN": "19",
"FGM": "4",
"FGA": "4",
"FG_PCT": "100",
"FG3M": "0",
"FG3A": "0",
"FG3_PCT": "0",
"FTM": "0",
"FTA": "0",
"FT_PCT": "0",
"OREB": "0",
"DREB": "9",
"TREB": "9",
"AST": "0",
"STL": "0",
"BLK": "0",
"TOV": "0",
"PF": "2",
"PTS": "8",
"+/-": "-6",
"DOUBLE": "none"
},
{
"first_name": "Alexey",
"last_name": "Shved",
"name": "Alexey Shved",
"starter": "False",
"MIN": "14",
"FGM": "1",
"FGA": "4",
"FG_PCT": "25",
"FG3M": "1",
"FG3A": "4",
"FG3_PCT": "25",
"FTM": "3",
"FTA": "3",
"FT_PCT": "100",
"OREB": "0",
"DREB": "1",
"TREB": "1",
"AST": "6",
"STL": "0",
"BLK": "0",
"TOV": "2",
"PF": "0",
"PTS": "6",
"+/-": "-7",
"DOUBLE": "none"
},
{
"first_name": "JaKarr",
"last_name": "Sampson",
"name": "JaKarr Sampson",
"starter": "False",
"MIN": "2",
"FGM": "0",
"FGA": "0",
"FG_PCT": "0",
"FG3M": "0",
"FG3A": "0",
"FG3_PCT": "0",
"FTM": "0",
"FTA": "0",
"FT_PCT": "0",
"OREB": "0",
"DREB": "1",
"TREB": "1",
"AST": "0",
"STL": "0",
"BLK": "1",
"TOV": "0",
"PF": "0",
"PTS": "0",
"+/-": "0",
"DOUBLE": "none"
},
{
"first_name": "Michael",
"last_name": "Carter-Williams",
"name": "Michael Carter-Williams",
"starter": "False",
"MIN": "0",
"FGM": "0",
"FGA": "0",
"FG_PCT": "0",
"FG3M": "0",
"FG3A": "0",
"FG3_PCT": "0",
"FTM": "0",
"FTA": "0",
"FT_PCT": "0",
"OREB": "0",
"DREB": "0",
"TREB": "0",
"AST": "0",
"STL": "0",
"BLK": "0",
"TOV": "0",
"PF": "0",
"PTS": "0",
"+/-": "0",
"DOUBLE": "none"
}
],
"next_game": {
"day": "3",
"month": "November",
"year": "2014",
"dayname": "Monday",
"stadium": "Wells Fargo Center",
"city": "Philadelphia",
"opponent_name": "Rockets",
"opponent_place": "Houston",
"is_home": "True"
}
},
"vis": {
"name": "Heat",
"place": "Miami",
"conference": "Eastern Conference",
"division": "Southeast",
"wins": "2",
"losses": "0",
"conference_standing": 1,
"game_number": "2",
"previous_game_id": "329",
"next_game_id": "330",
"line_score": {
"game": {
"FG3A": "24",
"FG3M": "12",
"FG3_PCT": "50",
"FGA": "83",
"FGM": "41",
"FG_PCT": "49",
"FTA": "29",
"FTM": "20",
"FT_PCT": "69",
"DREB": "26",
"OREB": "9",
"TREB": "35",
"BLK": "0",
"AST": "33",
"STL": "16",
"TOV": "16",
"PF": "20",
"PTS": "114",
"MIN": "4"
},
"H1": {
"FG3A": "69",
"FG3M": "44",
"FG3_PCT": "64",
"FGA": "2321",
"FGM": "1110",
"FG_PCT": "48",
"FTA": "106",
"FTM": "64",
"FT_PCT": "60",
"DREB": "35",
"OREB": "23",
"TREB": "58",
"BLK": "00",
"AST": "88",
"STL": "53",
"TOV": "34",
"PTS": "3228",
"MIN": "6060"
},
"H2": {
"FG3A": "45",
"FG3M": "22",
"FG3_PCT": "49",
"FGA": "1920",
"FGM": "1010",
"FG_PCT": "53",
"FTA": "85",
"FTM": "55",
"FT_PCT": "65",
"DREB": "612",
"OREB": "22",
"TREB": "634",
"BLK": "00",
"AST": "98",
"STL": "35",
"TOV": "36",
"PTS": "2727",
"MIN": "6060"
},
"Q1": {
"FG3A": "6",
"FG3M": "4",
"FG3_PCT": "67",
"FGA": "23",
"FGM": "11",
"FG_PCT": "48",
"FTA": "10",
"FTM": "6",
"FT_PCT": "60",
"DREB": "3",
"OREB": "2",
"TREB": "5",
"BLK": "0",
"AST": "8",
"STL": "5",
"TOV": "3",
"PTS": "32",
"MIN": "60"
},
"Q2": {
"FG3A": "9",
"FG3M": "4",
"FG3_PCT": "44",
"FGA": "21",
"FGM": "10",
"FG_PCT": "48",
"FTA": "6",
"FTM": "4",
"FT_PCT": "67",
"DREB": "5",
"OREB": "3",
"TREB": "8",
"BLK": "0",
"AST": "8",
"STL": "3",
"TOV": "4",
"PTS": "28",
"MIN": "60"
},
"Q3": {
"FG3A": "4",
"FG3M": "2",
"FG3_PCT": "50",
"FGA": "19",
"FGM": "10",
"FG_PCT": "53",
"FTA": "8",
"FTM": "5",
"FT_PCT": "62",
"DREB": "6",
"OREB": "2",
"TREB": "8",
"BLK": "0",
"AST": "9",
"STL": "3",
"TOV": "3",
"PTS": "27",
"MIN": "60"
},
"Q4": {
"FG3A": "5",
"FG3M": "2",
"FG3_PCT": "40",
"FGA": "20",
"FGM": "10",
"FG_PCT": "50",
"FTA": "5",
"FTM": "5",
"FT_PCT": "100",
"DREB": "12",
"OREB": "2",
"TREB": "14",
"BLK": "0",
"AST": "8",
"STL": "5",
"TOV": "6",
"PTS": "27",
"MIN": "60"
},
"OT": {
"FG3A": "0",
"FG3M": "0",
"FG3_PCT": "0",
"FGA": "0",
"FGM": "0",
"FG_PCT": "0",
"FTA": "0",
"FTM": "0",
"FT_PCT": "0",
"DREB": "0",
"OREB": "0",
"TREB": "0",
"BLK": "0",
"AST": "0",
"STL": "0",
"TOV": "0",
"PTS": "0",
"MIN": "0"
}
},
"box_score": [
{
"first_name": "Chris",
"last_name": "Bosh",
"name": "Chris Bosh",
"starter": "True",
"MIN": "33",
"FGM": "9",
"FGA": "17",
"FG_PCT": "53",
"FG3M": "2",
"FG3A": "5",
"FG3_PCT": "40",
"FTM": "10",
"FTA": "11",
"FT_PCT": "91",
"OREB": "3",
"DREB": "5",
"TREB": "8",
"AST": "4",
"STL": "2",
"BLK": "0",
"TOV": "3",
"PF": "2",
"PTS": "30",
"+/-": "10",
"DOUBLE": "none"
},
{
"first_name": "Dwyane",
"last_name": "Wade",
"name": "Dwyane Wade",
"starter": "True",
"MIN": "32",
"FGM": "4",
"FGA": "18",
"FG_PCT": "22",
"FG3M": "0",
"FG3A": "1",
"FG3_PCT": "0",
"FTM": "1",
"FTA": "3",
"FT_PCT": "33",
"OREB": "1",
"DREB": "2",
"TREB": "3",
"AST": "10",
"STL": "3",
"BLK": "0",
"TOV": "6",
"PF": "1",
"PTS": "9",
"+/-": "13",
"DOUBLE": "none"
},
{
"first_name": "Luol",
"last_name": "Deng",
"name": "Luol Deng",
"starter": "True",
"MIN": "29",
"FGM": "7",
"FGA": "11",
"FG_PCT": "64",
"FG3M": "1",
"FG3A": "3",
"FG3_PCT": "33",
"FTM": "0",
"FTA": "1",
"FT_PCT": "0",
"OREB": "2",
"DREB": "2",
"TREB": "4",
"AST": "2",
"STL": "2",
"BLK": "0",
"TOV": "1",
"PF": "0",
"PTS": "15",
"+/-": "4",
"DOUBLE": "none"
},
{
"first_name": "Shawne",
"last_name": "Williams",
"name": "Shawne Williams",
"starter": "True",
"MIN": "29",
"FGM": "5",
"FGA": "9",
"FG_PCT": "56",
"FG3M": "3",
"FG3A": "5",
"FG3_PCT": "60",
"FTM": "2",
"FTA": "2",
"FT_PCT": "100",
"OREB": "0",
"DREB": "4",
"TREB": "4",
"AST": "4",
"STL": "1",
"BLK": "0",
"TOV": "1",
"PF": "4",
"PTS": "15",
"+/-": "16",
"DOUBLE": "none"
},
{
"first_name": "Norris",
"last_name": "Cole",
"name": "Norris Cole",
"starter": "True",
"MIN": "27",
"FGM": "4",
"FGA": "7",
"FG_PCT": "57",
"FG3M": "2",
"FG3A": "4",
"FG3_PCT": "50",
"FTM": "0",
"FTA": "0",
"FT_PCT": "0",
"OREB": "0",
"DREB": "1",
"TREB": "1",
"AST": "4",
"STL": "2",
"BLK": "0",
"TOV": "0",
"PF": "1",
"PTS": "10",
"+/-": "6",
"DOUBLE": "none"
},
{
"first_name": "Mario",
"last_name": "Chalmers",
"name": "Mario Chalmers",
"starter": "False",
"MIN": "25",
"FGM": "6",
"FGA": "9",
"FG_PCT": "67",
"FG3M": "2",
"FG3A": "2",
"FG3_PCT": "100",
"FTM": "6",
"FTA": "10",
"FT_PCT": "60",
"OREB": "0",
"DREB": "2",
"TREB": "2",
"AST": "4",
"STL": "4",
"BLK": "0",
"TOV": "0",
"PF": "1",
"PTS": "20",
"+/-": "18",
"DOUBLE": "none"
},
{
"first_name": "Shabazz",
"last_name": "Napier",
"name": "Shabazz Napier",
"starter": "False",
"MIN": "20",
"FGM": "2",
"FGA": "3",
"FG_PCT": "67",
"FG3M": "1",
"FG3A": "2",
"FG3_PCT": "50",
"FTM": "0",
"FTA": "0",
"FT_PCT": "0",
"OREB": "0",
"DREB": "3",
"TREB": "3",
"AST": "4",
"STL": "2",
"BLK": "0",
"TOV": "1",
"PF": "4",
"PTS": "5",
"+/-": "11",
"DOUBLE": "none"
},
{
"first_name": "Chris",
"last_name": "Andersen",
"name": "Chris Andersen",
"starter": "False",
"MIN": "17",
"FGM": "0",
"FGA": "2",
"FG_PCT": "0",
"FG3M": "0",
"FG3A": "0",
"FG3_PCT": "0",
"FTM": "0",
"FTA": "0",
"FT_PCT": "0",
"OREB": "1",
"DREB": "2",
"TREB": "3",
"AST": "0",
"STL": "0",
"BLK": "0",
"TOV": "0",
"PF": "2",
"PTS": "0",
"+/-": "6",
"DOUBLE": "none"
},
{
"first_name": "Josh",
"last_name": "McRoberts",
"name": "Josh McRoberts",
"starter": "False",
"MIN": "11",
"FGM": "1",
"FGA": "3",
"FG_PCT": "33",
"FG3M": "0",
"FG3A": "1",
"FG3_PCT": "0",
"FTM": "1",
"FTA": "2",
"FT_PCT": "50",
"OREB": "0",
"DREB": "3",
"TREB": "3",
"AST": "0",
"STL": "0",
"BLK": "0",
"TOV": "2",
"PF": "3",
"PTS": "3",
"+/-": "1",
"DOUBLE": "none"
},
{
"first_name": "James",
"last_name": "Ennis",
"name": "James Ennis",
"starter": "False",
"MIN": "7",
"FGM": "2",
"FGA": "3",
"FG_PCT": "67",
"FG3M": "1",
"FG3A": "1",
"FG3_PCT": "100",
"FTM": "0",
"FTA": "0",
"FT_PCT": "0",
"OREB": "1",
"DREB": "1",
"TREB": "2",
"AST": "1",
"STL": "0",
"BLK": "0",
"TOV": "0",
"PF": "1",
"PTS": "5",
"+/-": "2",
"DOUBLE": "none"
},
{
"first_name": "Justin",
"last_name": "Hamilton",
"name": "Justin Hamilton",
"starter": "False",
"MIN": "5",
"FGM": "1",
"FGA": "1",
"FG_PCT": "100",
"FG3M": "0",
"FG3A": "0",
"FG3_PCT": "0",
"FTM": "0",
"FTA": "0",
"FT_PCT": "0",
"OREB": "1",
"DREB": "1",
"TREB": "2",
"AST": "0",
"STL": "0",
"BLK": "0",
"TOV": "1",
"PF": "0",
"PTS": "2",
"+/-": "3",
"DOUBLE": "none"
},
{
"first_name": "Andre",
"last_name": "Dawkins",
"name": "Andre Dawkins",
"starter": "False",
"MIN": "1",
"FGM": "0",
"FGA": "0",
"FG_PCT": "0",
"FG3M": "0",
"FG3A": "0",
"FG3_PCT": "0",
"FTM": "0",
"FTA": "0",
"FT_PCT": "0",
"OREB": "0",
"DREB": "0",
"TREB": "0",
"AST": "0",
"STL": "0",
"BLK": "0",
"TOV": "1",
"PF": "1",
"PTS": "0",
"+/-": "0",
"DOUBLE": "none"
},
{
"first_name": "Shannon",
"last_name": "Brown",
"name": "Shannon Brown",
"starter": "False",
"MIN": "0",
"FGM": "0",
"FGA": "0",
"FG_PCT": "0",
"FG3M": "0",
"FG3A": "0",
"FG3_PCT": "0",
"FTM": "0",
"FTA": "0",
"FT_PCT": "0",
"OREB": "0",
"DREB": "0",
"TREB": "0",
"AST": "0",
"STL": "0",
"BLK": "0",
"TOV": "0",
"PF": "0",
"PTS": "0",
"+/-": "0",
"DOUBLE": "none"
}
],
"next_game": {
"day": "2",
"month": "November",
"year": "2014",
"dayname": "Sunday",
"stadium": "American Airlines Arena",
"city": "Miami",
"opponent_name": "Raptors",
"opponent_place": "Toronto",
"is_home": "True"
}
}
},
"summaries": [
"The Miami Heat ( 20 ) defeated the Philadelphia 76ers ( 0 - 3 ) 114 - 96 on Saturday . Chris Bosh scored a game - high 30 points to go with eight rebounds in 33 minutes . Josh McRoberts made his Heat debut after missing the entire preseason recovering from toe surgery . McRoberts came off the bench and played 11 minutes . Shawne Williams was once again the starter at power forward in McRoberts ' stead . Williams finished with 15 points and three three - pointers in 29 minutes . Mario Chalmers scored 18 points in 25 minutes off the bench . Luc Richard Mbah a Moute replaced Chris Johnson in the starting lineup for the Sixers on Saturday . Hollis Thompson shifted down to the starting shooting guard job to make room for Mbah a Moute . Mbah a Moute finished with nine points and seven rebounds in 19 minutes . K.J . McDaniels , who suffered a minor hip flexor injury in Friday 's game , was available and played 21 minutes off the bench , finishing with eight points and three blocks . Michael Carter-Williams is expected to be out until Nov. 13 , but Tony Wroten continues to put up impressive numbers in Carter-Williams ' absence . Wroten finished with a double - double of 21 points and 10 assists in 33 minutes . The Heat will complete a back - to - back set at home Sunday against the Tornoto Raptors . The Sixers ' next game is at home Monday against the Houston Rockets ."
]
}
```
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
- Train: NBA seasons - 2014, 2015, & 2016; total instances - 3690
- Validation: NBA seasons - 2017; total instances - 1230
- Test: NBA seasons - 2018; total instances - 1230
#### Splitting Criteria
<!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. -->
<!-- scope: microscope -->
The splits were created as per different NBA seasons. All the games in regular season (no play-offs) are added in the dataset
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Why is the Dataset in GEM?
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
This dataset contains a data analytics problem in the classic sense ([Reiter, 2007](https://aclanthology.org/W07-2315)). That is, there is a large amount of data from which insights need to be selected. Further, the insights should be both from simple shallow queries (such as dirext transcriptions of the properties of a subject, i.e., a player and their statistics), as well as aggregated (how a player has done over time). There is far more on the data side than is required to be realised, and indeed, could be practically realised. This depth of data analytics problem does not exist in other datasets.
#### 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 -->
Many, if not all aspects of data-to-text systems can be measured with this dataset. It has complex data analytics, meaninful document planning (10-15 sentence documents with a narrative structure), as well as microplanning and realisation requirements. Finding models to handle this volume of data, as well as methods for meaninfully evaluate generations is a very open question.
### 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 -->
For dataset discussion see [Thomson et al, 2020](https://aclanthology.org/2020.intellang-1.4/)
For evaluation see:
- [Thomson & Reiter 2020, Thomson & Reiter (2021)](https://aclanthology.org/2021.inlg-1.23)
- [Kasner et al (2021)](https://aclanthology.org/2021.inlg-1.25)
For a system using the relational database form of SportSett, see:
- [Thomson et al (2020)](https://aclanthology.org/2020.inlg-1.6/)
For recent systems using the Rotowire dataset, see:
- [Puduppully & Lapata (2021)](https://github.com/ratishsp/data2text-macro-plan-py)
- [Rebuffel et all (2020)](https://github.com/KaijuML/data-to-text-hierarchical)
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
Many, if not all aspects of data-to-text systems can be measured with this dataset. It has complex data analytics, meaninful document planning (10-15 sentence documents with a narrative structure), as well as microplanning and realisation requirements. Finding models to handle this volume of data, as well as methods for meaninfully evaluate generations is a very open question.
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`BLEU`
#### Proposed Evaluation
<!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. -->
<!-- scope: microscope -->
BLEU is the only off-the-shelf metric commonly used. Works have also used custom metrics like RG ([Wiseman et al, 2017](https://aclanthology.org/D17-1239)), and a recent shared task explored other metrics and their corrolation with human evaluation ([Thomson & Reiter, 2021](https://aclanthology.org/2021.inlg-1.23)).
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
yes
#### Other Evaluation Approaches
<!-- info: What evaluation approaches have others used? -->
<!-- scope: periscope -->
Most results from prior works use the original Rotowire dataset, which has train/validation/test contamination. For results of BLEU and RG on the relational database format of SportSett, as a guide, see [Thomson et al, 2020](https://aclanthology.org/2020.inlg-1.6).
#### Relevant Previous Results
<!-- info: What are the most relevant previous results for this task/dataset? -->
<!-- scope: microscope -->
The results on this dataset are largely unexplored, as is the selection of suitable metrics that correlate with human judgment. See Thomson et al, 2021 (https://aclanthology.org/2021.inlg-1.23) for an overview, and Kasner et al (2021) for the best performing metric at the time of writing (https://aclanthology.org/2021.inlg-1.25).
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
The references texts were taken from the existing dataset RotoWire-FG ([Wang, 2019](https://www.aclweb.org/anthology/W19-8639)), which is in turn based on Rotowire ([Wiseman et al, 2017](https://aclanthology.org/D17-1239)). The rationale behind this dataset was to re-structure the data such that aggregate statistics over multiple games, as well as upcoming game schedules could be included, moving the dataset from snapshots of single games, to a format where almost everything that could be present in the reference texts could be found in the data.
#### Communicative Goal
<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
Create a summary of a basketball, with insightful facts about the game, teams, and players, both within the game, withing periods during the game, and over the course of seasons/careers where appropriate. This is a data-to-text problem in the classic sense ([Reiter, 2007](https://aclanthology.org/W07-2315)) in that it has a difficult data analystics state, in addition to ordering and transcription of selected facts.
#### 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 -->
RotoWire-FG (https://www.rotowire.com).
Wikipedia (https://en.wikipedia.org/wiki/Main_Page)
Basketball Reference (https://www.basketball-reference.com)
### 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 -->
`Multiple websites`
#### Language Producers
<!-- info: What further information do we have on the language producers? -->
<!-- scope: microscope -->
None
#### Topics Covered
<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
<!-- scope: periscope -->
Summaries of basketball games (in the NBA).
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
not validated
#### Data Preprocessing
<!-- info: How was the text data pre-processed? (Enter N/A if the text was not pre-processed) -->
<!-- scope: microscope -->
It retains the original tokenization scheme employed by Wang 2019
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
manually
#### Filter Criteria
<!-- info: What were the selection criteria? -->
<!-- scope: microscope -->
Games from the 2014 through 2018 seasons were selected. Within these seasons games are not filtered, all are present, but this was an arbitrary solution from the original RotoWirte-FG dataset.
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
none
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
no
#### Justification for Using the Data
<!-- info: If not, what is the justification for reusing the data? -->
<!-- scope: microscope -->
The dataset consits of a pre-existing dataset, as well as publically available facts.
### 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
#### Categories of PII
<!-- info: What categories of PII are present or suspected in the data? -->
<!-- scope: periscope -->
`generic PII`
#### Any PII Identification?
<!-- info: Did the curators use any automatic/manual method to identify PII in the dataset? -->
<!-- scope: periscope -->
no identification
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
no
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
yes
#### Links and Summaries of Analysis Work
<!-- info: Provide links to and summaries of works analyzing these biases. -->
<!-- scope: microscope -->
Unaware of any work, but, this is a dataset considting solely of summaries of mens professional basketball games. It does not cover different levels of the sport, or different genders, and all pronouns are likely to be male unless a specific player is referred to by other pronouns in the training text. This makes it difficult to train systems where gender can be specified as an attribute, although it is an interesting, open problem that could be investigated using the dataset.
#### Are the Language Producers Representative of the Language?
<!-- info: Does the distribution of language producers in the dataset accurately represent the full distribution of speakers of the language world-wide? If not, how does it differ? -->
<!-- scope: periscope -->
No, it is very specifically American English from the sports journalism domain.
## 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 -->
All information relating to persons is of public record.
### 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
#### Technical Limitations
<!-- info: Describe any known technical limitations, such as spurrious correlations, train/test overlap, annotation biases, or mis-annotations, and cite the works that first identified these limitations when possible. -->
<!-- scope: microscope -->
SportSett resolved the major overlap problems of RotoWire, although some overlap is unavoidable. For example, whilst it is not possible to find career totals and other historic information for all players (the data only goes back to 2014), it is possible to do so for some players. It is unavoidable that some data which is aggregated, exists in its base form in previous partitions. The season-based partition scheme heavily constrains this however.
#### Unsuited Applications
<!-- info: When using a model trained on this dataset in a setting where users or the public may interact with its predictions, what are some pitfalls to look out for? In particular, describe some applications of the general task featured in this dataset that its curation or properties make it less suitable for. -->
<!-- scope: microscope -->
Factual accuray continues to be a problem, systems may incorrectly represent the facts of the game.
#### Discouraged Use Cases
<!-- info: What are some discouraged use cases of a model trained to maximize the proposed metrics on this dataset? In particular, think about settings where decisions made by a model that performs reasonably well on the metric my still have strong negative consequences for user or members of the public. -->
<!-- scope: microscope -->
Using the RG metric to maximise the number of true facts in a generate summary is not nececeraly
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Tevatron/scifact | Tevatron | 2021-09-13T23:32:59Z | 312 | 0 | null | [
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anuragshas/ur_opus100_processed | anuragshas | 2022-01-30T16:03:56Z | 312 | 1 | null | [
"region:us"
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
huggingartists/metallica | huggingartists | 2022-10-25T09:38:20Z | 312 | 0 | null | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
] | 2022-10-25T09:38:20Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/metallica"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [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)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **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 the generated dataset:** 0.6616 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/f2d983ad882fc80979d95ef031e82bc5.999x999x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/metallica">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Metallica</div>
<a href="https://genius.com/artists/metallica">
<div style="text-align: center; font-size: 14px;">@metallica</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/metallica).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/metallica")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|469| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/metallica")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## 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{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
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yxchar/hyp-tlm | yxchar | 2021-11-05T01:01:25Z | 312 | 0 | null | [
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mteb/stackexchange-clustering-p2p | mteb | 2022-09-27T19:14:52Z | 312 | 0 | null | [
"language:en",
"region:us"
] | 2022-09-27T19:14:52Z | 2022-05-11T07:56:54.000Z | 2022-05-11T07:56:54 | ---
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
counter | null | 2023-01-25T14:28:41Z | 311 | 0 | counter | [
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:semantic-similarity-scoring",
"task_ids:topic-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:ur",
"license:cc-by-nc-sa-4.0",
"region:us"
] | 2023-01-25T14:28:41Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- ur
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
- semantic-similarity-scoring
- topic-classification
paperswithcode_id: counter
pretty_name: COUNTER
dataset_info:
features:
- name: source
struct:
- name: filename
dtype: string
- name: headline
dtype: string
- name: body
dtype: string
- name: total_number_of_words
dtype: int64
- name: total_number_of_sentences
dtype: int64
- name: number_of_words_with_swr
dtype: int64
- name: newspaper
dtype: string
- name: newsdate
dtype: string
- name: domain
dtype:
class_label:
names:
'0': business
'1': sports
'2': national
'3': foreign
'4': showbiz
- name: classification
dtype:
class_label:
names:
'0': wholly_derived
'1': partially_derived
'2': not_derived
- name: derived
struct:
- name: filename
dtype: string
- name: headline
dtype: string
- name: body
dtype: string
- name: total_number_of_words
dtype: int64
- name: total_number_of_sentences
dtype: int64
- name: number_of_words_with_swr
dtype: int64
- name: newspaper
dtype: string
- name: newsdate
dtype: string
- name: domain
dtype:
class_label:
names:
'0': business
'1': sports
'2': national
'3': foreign
'4': showbiz
- name: classification
dtype:
class_label:
names:
'0': wholly_derived
'1': partially_derived
'2': not_derived
splits:
- name: train
num_bytes: 2598872
num_examples: 600
download_size: 1356306
dataset_size: 2598872
---
# Dataset Card for COUNTER
## 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://ucrel.lancs.ac.uk/textreuse/counter.php
- **Repository:** [More Information Needed]
- **Paper:** https://link.springer.com/article/10.1007%2Fs10579-016-9367-2
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [UCREL](ucrel@lancaster.ac.uk)
### Dataset Summary
The COrpus ofUrdu News TExt Reuse (COUNTER) corpus contains 1200 documents with realexamples of text reuse from the field of journalism. It has been manually annotatedat document level with three levels of reuse: wholly derived, partially derived andnon derived
### Supported Tasks and Leaderboards
other:text-reuse
### Languages
ur
## Dataset Structure
### Data Instances
Here is one example from the dataset:
```
{"derived": {
"body" :"میر پور(وقت نیوز) بنگلہ دیش نے 5 میچوں کی سیریز کےآ خری میچ میں بھی فتح حاصل کر کے سیریز میں وائٹ واش کر دیا،زمبابوے ایک میچ بھی نہ جیت سکا۔آخری میچ میں زمبابوے کے 129 رنز کا ہدف بنگال ٹائیگرز نے 24.3 اوورز میں 5 وکٹوں کے نقصان پر حاصل کر لیا۔بنگلہ دیش کے شیر بنگلہ سٹیڈیم میر پور میں کھیلے گئے آخری ایک روزہ میچ میں زمبابوے کے کپتان چکمبورا نے ٹاس جیت کے بینٹگ کا فیصلہ کیا جو ان کی ٹیم کیلئے ڈراؤنا خواب ثابت ہوا اور پوری ٹیم 30 اوورز میں 128 رنز بنا کر پویلین لوٹ گئی زمبابوے کی پہلی وکٹ 16 رنز پر گری جب سکندر رضا صرف 9 رنز بنا کر مشرقی مرتضی کی بال پر آؤٹ ہوئے اس کے بعد مساکد ازااور سباندا کی پارٹنرشپنے ٹیم کا سکور95 رنز تک پہنچا دیا ۔مساکدازا 52 رنز بنا کر جبیر الحسن کا شکار بنے جبکہ سباندا نے 37 رنز کی اننگز کھیلی اس کے بعد کئی بھی زمبابوے کا کھلاڑی جم کر نہ کھیل سکا۔بنگال ٹائیگرز کی جانب سے عمدہ باؤلنگ کے نتیجے میں کپتان چکمبورا سمیت 8 کھلاڑی ڈبل فیگر کراس نہ کر سکے ۔بنگلہ دیش کی جانب سے ایک روزہ میچوں میں ڈیبیو کرنے والے تیج السلام نے اپنے پہلے ہی میچ میں ہیٹرک کی اسلام نے 7 اوورز میں صرف 14 رنز دئے اور چار کھلاڑیوں کع آؤٹ کیا جبکہ شکیب الحسن نے 30 رنز دیکر 3 اور جبیر الحسن نے41 رنز دیکر2 کھلاڑیوں کو پویلین کی راہ دکھائی ۔ 128 رنز کے جواب میں بنگال ٹائیگرز نے بیٹنگ شروع کی مشکلات کا سامنا رہا ان کے بھی ابتدائی 3 کھلاڑی 47 رنز پر پویلین لوٹ گئے۔ تمیم اقبال 10، انعام الحق8 رنز بنا کر آؤٹ ہوئے،آل راؤنڈر شکیب الحسن بغیر کوئی رنز بنائیپویلین لوٹ گئے وکٹ کیپر مشفق الرحیم صرف 11 رنز بنا کر چتارہ کا شکار بن گئے۔محمد اللہ نے51 رنز کی میچ وننگ اننگز کھیلی جبکہ صابر رحمٰن13 رنز بنا کر ناٹ آؤٹ رہے۔ زمبابوے کی جانب سے چتارہ نے 3 اور پنیا نگارا نے 2 کھلاڑیوں کو آؤٹ کیا ۔فتح کے ساتھ بنگلہ دیش نے سیریز میں وائٹ واش کر دیا۔زمبابوے کی ٹیم کوئی میچ نہ جیت سکی،تیج السلام کو میچ کا بہترین ایوارڈ دیا گیا جبکہ سیریز کا بہترین کھلاڑی مشفق الرحیم کو قرار دیا گیا۔",
"classification": 1, # partially_derived
"domain": 1, # sports
"filename": "0001p.xml",
"headline": "بنگلہ دیش کا زمبابوے کا ون ڈے سیریز میں 5-0 سے وائٹ واش",
"newsdate": "02.12.14",
"newspaper": "daily_waqt",
"number_of_words_with_swr": 265,
"total_number_of_sentences": 13,
"total_number_of_words": 393},
"source": {
"body": "ڈھاکہ ۔ یکم دسمبر (اے پی پی) بنگلہ دیش نے زمبابوے کو ٹیسٹ کے بعد ون ڈے سیریز میں بھی وائٹ واش کر دیا۔ سیریز کے پانچویں اور آخری ون ڈے میچ میں بنگال ٹائیگرز نے زمبابوے کو 5 وکٹوں سے شکست دے دی، مہمان ٹیم پہلے بیٹنگ کرتے ہوئے 128 رنز پر ڈھیر ہوگئی۔ تیج الاسلام نے کیریئر کے پہلے ون ڈے میچ میں ہیٹ ٹرک کرکے نئی تاریخ رقم کر دی، انہوں نے 4 کھلاڑیوں کو آؤٹ کیا۔ جواب میں بنگلہ دیش نے ہدف 24.3 اوورز میں 5 وکٹوں کے نقصان پر حاصل کر لیا۔ محمد اللہ نے 51 رنز کی ناقابل شکست اننگز کھیلی۔ تفصیلات کے مطابق پیر کو شیر بنگلہ نیشنل سٹیڈیم، میرپور میں پانچویں اور آخری ون ڈے میچ میں زمبابوے کے کپتان ایلٹن چگمبورا نے ٹاس جیت کر پہلے بیٹنگ کا فیصلہ کیا جو غلط ثابت ہوا۔ زمبابوے کی پوری ٹیم ڈیبیو ون ڈے کھیلنے والے نوجوان لیفٹ آرم سپنر تیج الاسلام اور شکیب الحسن کی تباہ کن باؤلنگ کے باعث 30 اوورز میں 128 رنز پر ڈھیر ہوگئی۔ ہیملٹن ماساکڈزا 52 اور ووسی سبانڈا 37 رنز کے ساتھ نمایاں رہے، ان کے علاوہ کوئی بھی بلے باز دوہرا ہندسہ عبور نہ کر سکا۔ اپنا پہلا ون ڈے کھیلنے والے تیج الاسلام نے 11 رنز کے عوض 4 وکٹیں حاصل کیں جس میں شاندار ہیٹ ٹرک بھی شامل ہے، اس طرح وہ ڈیبیو میں ہیٹ ٹرک کرنے والے دنیا کے پہلے باؤلر بن گئے ہیں۔ شکیب الحسن نے تین اور زبیر حسین نے دو وکٹیں حاصل کیں۔ جواب میں بنگلہ دیش نے ہدف 24.3 اوورز میں 5 وکٹوں کے نقصان پر حاصل کر لیا۔ محمد اللہ نے 51 رنز کی ناقابل شکست اننگز کھیل کر ٹیم کی فتح میں اہم کردار ادا کیا۔ زمبابوے کی جانب سے ٹینڈائی چتارا نے تین اور تناشے پینگارا نے دو وکٹیں حاصل کیں۔",
"classification": 1, # partially_derived
"domain": 1, # sports
"filename": "0001.xml",
"headline": "بنگال ٹائیگرز نے کمزور زمبابوے کو ٹیسٹ کے بعد ون ڈے سیریز میں بھی وائٹ واش کر دیا، پانچویں اور آخری ون ڈے میچ میں بنگلہ دیش 5 وکٹوں سے فتح یاب، تیج الاسلام نے ڈیبیو ون ڈے میں ہیٹ ٹرک کرکے نئی تاریخ رقم کر دی"
"newsdate": "01.12.14",
"newspaper": "APP",
"number_of_words_with_swr": 245,
"total_number_of_sentences": 15,
"total_number_of_words": 352}}
```
### Data Fields
```source```: The source document
```derived```: The derived document
For each pair of source and derived documents. we have the following fields:
```filename (str)```: Name of the file in dataset
```headline(str)```: Headline of the news item
```body(str)```: Main text of the news item
```total_number_of_words(int)```: Number of words in document
```total_number_of_sentences(int)```: Number of sentences in document
```number_of_words_with_swr(int)```: Number of words after stop word removal
```newspaper(str)```: The newspaper in which the news item was published
```newsdate(str)```: The date on which the news item is published DD.MM.YY
```domain(int)```: The category of news item from this list: "business", "sports", "national", "foreign", "showbiz".
```classification (int)```: Three classes of reuse from this list: Wholly Derived (WD), Partially Derived (PD) and Non Derived (ND)
### Data Splits
One split train with 600 pairs of documents.
The corpus is composed of two main document types: (1) source documents and (2) derived documents. There are total 1200 documents in the corpus: 600 are newsagency articles (source documents) and 600 are newspapers stories (derived documents). The corpus contains in total 275,387 words (tokens8), 21,426 unique words and 10,841 sentences. The average length of a source document is 227 words while for derived documents it is 254 words.
## Dataset Creation
### Curation Rationale
Our main intention was to develop a standard benchmark resource for the evaluation of existing systems available for text reuse detection in general and specifically for Urdu language. To generate a corpus with realistic examples, we opted for the field of journalism. In journalism, the same news story is published in different newspapers in different forms. It is a standard practice followed by all the newspapers (reporters and editors) to reuse (verbatim or modified) a news story released by the news agency.
### Source Data
#### Initial Data Collection and Normalization
The COUNTER corpus consists of news articles (source documents) released by five news agencies in Pakistan i.e. Associated Press of Pakistan (APP), InternationalNews Network (INN), Independent News Pakistan (INP), News Network International (NNI) and South Asian News Agency (SANA). The corresponding news stories (derived documents) were extracted from nine daily published and large circulation national news papers of the All Pakistan Newspapers Society (APNS), who are subscribed to these news agencies.
These include Nawa-e-Waqt, Daily Dunya, Express, Jang, Daily Waqt, Daily Insaf, Daily Aaj, Daily Islam and DailyPakistan. All of them are part of the mainstream national press, long established dailies with total circulation figures of over four million.7News agency texts (source documents) were provided (in electronic form) by the news agencies on a daily basis when they released the news. Newspaper stories (derived documents) were collected by three volunteers over a period of six months (from July to December 2014).National, Foreign, Business, Sports and Showbiz were the domains targeted for data collection.
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
The corpus has been annotated at the document level with three classes of reuse i.e.Wholly Derived (WD), Partially Derived (PD) and Non Derived (ND).
The derived collection contains documents with various degrees of text reuse. Some of the newspaper stories (derived documents)are rewritten (either verbatim or paraphrased) from the new agencys text (source document) while others have been written by the journalists independently on their own. For the former case, source-derived document pairs are either tagged as Wholly Derived (WD) or Partially Derived (PD) depending on the volume of text reused from the news agencys text for creating the newspaper article while for the latter case, they are tagged as Non Derived (ND) as the journalists have not reused anything from the news agencys text but based on their own observations and findings, developed and documented the story.
The annotations were carried out in three phases: (1) training phase, (2) annotations, (3)conflict resolving. During the training phase, annotators A and B manually annotated 60 document pairs, following a preliminary version of the annotation guidelines. A detailed meeting was carried out afterwards, discussing the problems and disagreements. It was observed that the highest number of disagreements were between PD and ND cases, as both found it difficult to distinguish between these two classes. The reason being that adjusting the threshold where a text is heavily paraphrased or new information added to it that it becomes independently written(ND). Following the discussion, the annotation guidelines were slightly revised, and the first 60 annotations results were saved. In the annotation phase, the remaining540 document pairs were manually examined by the two annotators (A and B). Both were asked to judge, and classify (at document level) whether a document(newspaper story) depending on the volume of text rewritten from the source (news agency article) falls into one of the following categories:remaining540 document pairs were manually examined by the two annotators (A and B). Both were asked to judge, and classify (at document level) whether a document(newspaper story) depending on the volume of text rewritten from the source (news agency article) falls into one of the following categories:
Wholly Derived (WD)The News agency text is the only source for the reused newspaper text, which means it is a verbatim copy of the source. In this case, most of the reused text is word-to-word copy of the source text.Partially Derived (PD)The Newspaper text has been either derived from more than one news agency or most of the text is paraphrased by the editor when rewriting from news agency text source. In this case, most parts of the derived document contain paraphrased text or new facts and figures added by the journalists own findings. Non Derived (ND)The News agency text has not been used in the production of the newspaper text (though words may still co-occur in both documents), it has completely different facts and figures or is heavily paraphrased from the newsagencys copy. In this case, the derived document is independently written and has a lot more new text.
#### Who are the annotators?
The annotations were performed by three annotators (A, B and C), who were native Urdu language speakers and experts of paraphrasing mechanisms. All three were graduates, experienced in text annotations and having an advanced Urdu level.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
Dataset provided for research purposes only. Please check dataset license for additional information.
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
This dataset is licensed under the Creative Common Attribution-NonCommercial-ShareAlike 4.0 International License.
[(CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/).
### Citation Information
```
@Article{Sharjeel2016,
author="Sharjeel, Muhammad
and Nawab, Rao Muhammad Adeel
and Rayson, Paul",
title="COUNTER: corpus of Urdu news text reuse",
journal="Language Resources and Evaluation",
year="2016",
pages="1--27",
issn="1574-0218",
doi="10.1007/s10579-016-9367-2",
url="http://dx.doi.org/10.1007/s10579-016-9367-2"
}
```
### Contributions
Thanks to [@arkhalid](https://github.com/arkhalid) for adding this dataset. | [
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