id stringlengths 2 115 | author stringlengths 2 42 ⌀ | last_modified timestamp[us, tz=UTC] | downloads int64 0 8.87M | likes int64 0 3.84k | paperswithcode_id stringlengths 2 45 ⌀ | tags list | lastModified timestamp[us, tz=UTC] | createdAt stringlengths 24 24 | key stringclasses 1 value | created timestamp[us] | card stringlengths 1 1.01M | embedding list | library_name stringclasses 21 values | pipeline_tag stringclasses 27 values | mask_token null | card_data null | widget_data null | model_index null | config null | transformers_info null | spaces null | safetensors null | transformersInfo null | modelId stringlengths 5 111 ⌀ | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
hate_speech_portuguese | null | 2023-11-24T10:27:25Z | 343 | 2 | null | [
"task_categories:text-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:pt",
"license:unknown",
"hate-speech-detection",
"region:us"
] | 2023-11-24T10:27:25Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- pt
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
pretty_name: HateSpeechPortuguese
tags:
- hate-speech-detection
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': no-hate
'1': hate
- name: hatespeech_G1
dtype: string
- name: annotator_G1
dtype: string
- name: hatespeech_G2
dtype: string
- name: annotator_G2
dtype: string
- name: hatespeech_G3
dtype: string
- name: annotator_G3
dtype: string
splits:
- name: train
num_bytes: 826130
num_examples: 5670
download_size: 763846
dataset_size: 826130
---
# 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:** https://github.com/paulafortuna/Portuguese-Hate-Speech-Dataset
- **Repository:** https://github.com/paulafortuna/Portuguese-Hate-Speech-Dataset
- **Paper:** https://www.aclweb.org/anthology/W19-3510/
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Portuguese dataset for hate speech detection composed of 5,668 tweets with binary annotations (i.e. 'hate' vs. 'no-hate').
### 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
```
@inproceedings{fortuna-etal-2019-hierarchically,
title = "A Hierarchically-Labeled {P}ortuguese Hate Speech Dataset",
author = "Fortuna, Paula and
Rocha da Silva, Jo{\~a}o and
Soler-Company, Juan and
Wanner, Leo and
Nunes, S{\'e}rgio",
editor = "Roberts, Sarah T. and
Tetreault, Joel and
Prabhakaran, Vinodkumar and
Waseem, Zeerak",
booktitle = "Proceedings of the Third Workshop on Abusive Language Online",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-3510",
doi = "10.18653/v1/W19-3510",
pages = "94--104",
}
```
### 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 | |
weibo_ner | null | 2023-01-25T15:02:04Z | 343 | 6 | weibo-ner | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:zh",
"license:unknown",
"region:us"
] | 2023-01-25T15:02:04Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- zh
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: weibo-ner
pretty_name: Weibo NER
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': B-GPE.NAM
'1': B-GPE.NOM
'2': B-LOC.NAM
'3': B-LOC.NOM
'4': B-ORG.NAM
'5': B-ORG.NOM
'6': B-PER.NAM
'7': B-PER.NOM
'8': I-GPE.NAM
'9': I-GPE.NOM
'10': I-LOC.NAM
'11': I-LOC.NOM
'12': I-ORG.NAM
'13': I-ORG.NOM
'14': I-PER.NAM
'15': I-PER.NOM
'16': O
splits:
- name: train
num_bytes: 1179589
num_examples: 1350
- name: validation
num_bytes: 232380
num_examples: 270
- name: test
num_bytes: 237407
num_examples: 270
download_size: 750687
dataset_size: 1649376
train-eval-index:
- config: default
task: token-classification
task_id: entity_extraction
splits:
train_split: train
eval_split: test
col_mapping:
tokens: tokens
ner_tags: tags
metrics:
- type: seqeval
name: seqeval
---
# Dataset Card for "Weibo NER"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** None
- **Repository:** https://github.com/OYE93/Chinese-NLP-Corpus/tree/master/NER/Weibo
- **Paper:** [More Information Needed]
- **Leaderboard:** [If the dataset supports an active leaderboard, add link here]()
- **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
[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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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
SocialGrep/one-million-reddit-confessions | SocialGrep | 2022-07-01T18:48:52Z | 343 | 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"
] | 2022-07-01T18:48:52Z | 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-confessions
## 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=onemillionconfessions)
- **Point of Contact:** [Website](https://socialgrep.com/contact?utm_source=huggingface&utm_medium=link&utm_campaign=onemillionconfessions)
### Dataset Summary
This corpus contains a million posts from the following subreddits:
- /r/trueoffmychest
- /r/confession
- /r/confessions
- /r/offmychest
Posts are annotated with their score.
### Languages
Mainly English.
## Dataset Structure
### Data Instances
A data point is a Reddit post.
### Data Fields
- 'type': the type of the data point. Can be 'post' or 'comment'.
- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.
- 'subreddit.id': the base-36 Reddit ID of the data point's host subreddit. Unique.
- 'subreddit.name': the human-readable name of the data point's host subreddit.
- 'subreddit.nsfw': a boolean marking the data point's host subreddit as NSFW or not.
- 'created_utc': a UTC timestamp for the data point.
- 'permalink': a reference link to the data point on Reddit.
- 'score': score of the data point on Reddit.
- 'domain': the domain of the data point's link.
- 'url': the destination of the data point's link, if any.
- 'selftext': the self-text of the data point, if any.
- 'title': the title of the post data point.
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
CC-BY v4.0
### Contributions
[Needs More Information] | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
openclimatefix/nimrod-uk-1km | openclimatefix | 2022-06-08T14:49:03Z | 343 | 7 | null | [
"region:us"
] | 2022-06-08T14:49:03Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | [Needs More Information]
# Dataset Card for UK Nimrod 1km Rainfall Radar Dataset
## 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://github.com/deepmind/deepmind-research/tree/master/nowcasting
- **Repository:** https://huggingface.co/datasets/openclimatefix/nimrod-uk-1km
- **Paper:** [Skillful Precipitation Nowcasting using Deep Generative Models of Radar, Ravuri et al. 2021](https://www.nature.com/articles/s41586-021-03854-z)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Jacob Bieker](mailto:jacob@openclimatefix.org)
### Dataset Summary
This dataset contains UK Nimrod rainfall radar data for 2016-2019 as used in the Skillful Precipitation Nowcasting Using Deep Generative Model of Radar paper by DeepMind. This dataset is an unofficial mirror of the open sourced dataset available here: gs://dm-nowcasting/datasets/nowcasting_open_source_osgb/nimrod_osgb_1000m_yearly_splits/radar/20200718
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
[Needs More Information]
## Dataset Structure
### Data Instances
[Needs More Information]
### Data Fields
[Needs More Information]
### Data Splits
The train data is all days except the first of each month for 2016-2018. The validation is the first of every month for 2016-2018. The test data is all of 2019.
## Dataset Creation
### Curation Rationale
This dataset was originally created for training a generative model for forecasting rainfall percipitation.
### Source Data
#### Initial Data Collection and Normalization
DeepMind initially collected the data from the UK Met Office and post processed it into this dataset.
#### 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
The provided post-processed nowcasting dataset is licensed under a Creative Commons Attribution 4.0 International License and it contains public sector information licensed by the Met Office under the Open Government Licence v3.0.
### Citation Information
Cite DeepMind, and the authors of [Skillful Precipitation Nowcasting using Deep Generative Models of Radar, Ravuri et al. 2021](https://www.nature.com/articles/s41586-021-03854-z). | [
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haitengzhao/molecule_property_instruction | haitengzhao | 2023-07-13T10:30:29Z | 343 | 4 | null | [
"task_categories:question-answering",
"language:en",
"license:afl-3.0",
"chemistry",
"biology",
"region:us"
] | 2023-07-13T10:30:29Z | 2023-07-09T07:36:09.000Z | 2023-07-09T07:36:09 | ---
dataset_info:
features:
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dtype: string
- name: text
sequence: string
- name: label
dtype: string
- name: dataset_name
dtype: string
- name: task_index
dtype: string
- name: molecule_index
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num_examples: 34017170
download_size: 2163300521
dataset_size: 36612838006
license: afl-3.0
task_categories:
- question-answering
language:
- en
tags:
- chemistry
- biology
pretty_name: p
---
# Dataset Card for "molecule_property_instruction"
[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 | |
strombergnlp/pypi-20230724 | strombergnlp | 2023-07-25T03:12:55Z | 343 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-07-25T03:12:55Z | 2023-07-25T03:12:35.000Z | 2023-07-25T03:12:35 | ---
license: apache-2.0
---
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
yys/OpenOrca-Chinese | yys | 2023-09-08T08:05:47Z | 343 | 51 | null | [
"task_categories:conversational",
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:table-question-answering",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:summarization",
"task_categories:feature-extraction",
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:10M<n<100M",
"language:zh",
"license:mit",
"arxiv:2301.13688",
"region:us"
] | 2023-09-08T08:05:47Z | 2023-09-07T06:01:51.000Z | 2023-09-07T06:01:51 | ---
license: mit
task_categories:
- conversational
- text-classification
- token-classification
- table-question-answering
- question-answering
- zero-shot-classification
- summarization
- feature-extraction
- text-generation
- text2text-generation
language:
- zh
pretty_name: OpenOrca-Chinese
size_categories:
- 10M<n<100M
---
<p><h1>🐋 OpenOrca-Chinese 数据集！🐋</h1></p>
感谢 [Open-Orca/OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca) 数据集的发布，给广大NLP研究人员和开发者带来了宝贵的资源！
这是一个对 [Open-Orca/OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca) 数据集中文翻译的版本，翻译引擎为 Google 翻译，希望能给中文 LLM 研究做出一点点贡献。
<br/>
# Dataset Summary
The OpenOrca dataset is a collection of augmented [FLAN Collection data](https://arxiv.org/abs/2301.13688).
Currently ~1M GPT-4 completions, and ~3.2M GPT-3.5 completions.
It is tabularized in alignment with the distributions presented in the ORCA paper and currently represents a partial completion of the full intended dataset, with ongoing generation to expand its scope.
The data is primarily used for training and evaluation in the field of natural language processing.
<a name="dataset-structure"></a>
# Dataset Structure
<a name="data-instances"></a>
## Data Instances
A data instance in this dataset represents entries from the FLAN collection which have been augmented by submitting the listed question to either GPT-4 or GPT-3.5.
The response is then entered into the response field.
<a name="data-fields"></a>
## Data Fields
The fields are:
1) 'id', a unique numbered identifier which includes one of 'niv', 't0', 'cot', or 'flan' to represent which source FLAN Collection submix the 'question' is sourced from.
2) 'system_prompt', representing the System Prompt presented to the GPT-3.5 or GPT-4 API for the datapoint
3) 'question', representing a question entry as provided by the FLAN Collection
4) 'response', a response to that question received from a query to either GPT-3.5 or GPT-4.
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Team-PIXEL/bigrams_bookcorpus_529 | Team-PIXEL | 2023-10-02T07:54:20Z | 343 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-10-02T07:54:20Z | 2023-10-02T07:22:07.000Z | 2023-10-02T07:22:07 | ---
license: apache-2.0
dataset_info:
features:
- name: title
dtype: string
- name: data
dtype: string
splits:
- name: train
num_bytes: 7031990695
num_examples: 9814746
download_size: 4016929000
dataset_size: 7031990695
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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wiki_movies | null | 2022-11-18T22:00:27Z | 342 | 4 | wikimovies | [
"task_categories:question-answering",
"task_ids:closed-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-3.0",
"arxiv:1606.03126",
"region:us"
] | 2022-11-18T22:00:27Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
pretty_name: WikiMovies
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-3.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- closed-domain-qa
paperswithcode_id: wikimovies
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 7274490
num_examples: 96185
- name: test
num_bytes: 755258
num_examples: 9952
- name: validation
num_bytes: 754755
num_examples: 10000
download_size: 57070041
dataset_size: 8784503
---
# Dataset Card for WikiMovies
## 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:** [WikiMovies Homepage](https://research.fb.com/downloads/babi/)
- **Repository:**
- **Paper:** [Key-Value Memory Networks for Directly Reading Documents](https://arxiv.org/pdf/1606.03126.pdf)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The WikiMovies dataset consists of roughly 100k (templated) questions over 75k entitiesbased on questions with answers in the open movie database (OMDb). It is the QA part of the Movie Dialog dataset.
### Supported Tasks and Leaderboards
- Question Answering
### Languages
The text in the dataset is written in English.
## Dataset Structure
### Data Instances
The raw data consists of question answer pairs separated by a tab. Here are 3 examples:
```buildoutcfg
1 what does Grégoire Colin appear in? Before the Rain
1 Joe Thomas appears in which movies? The Inbetweeners Movie, The Inbetweeners 2
1 what films did Michelle Trachtenberg star in? Inspector Gadget, Black Christmas, Ice Princess, Harriet the Spy, The Scribbler
```
It is unclear what the `1` is for at the beginning of each line, but it has been removed in the `Dataset` object.
### Data Fields
Here is an example of the raw data ingested by `Datasets`:
```buildoutcfg
{
'answer': 'Before the Rain',
'question': 'what does Grégoire Colin appear in?'
}
```
`answer`: a string containing the answer to a corresponding question.
`question`: a string containing the relevant question.
### Data Splits
The data is split into train, test, and dev sets. The split sizes are as follows:
| wiki-entities_qa_* | n examples|
| ----- | ---- |
| train.txt | 96185 |
| dev.txt | 10000 |
| test.txt | 9952 |
## Dataset Creation
### Curation Rationale
WikiMovies was built with the following goals in mind: (i) machine learning techniques should have ample training examples for learning; and (ii) one can analyze easily the performance of different representations of knowledge and break down the results by question type. The datasetcan be downloaded fromhttp://fb.ai/babi
### 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
```
@misc{miller2016keyvalue,
title={Key-Value Memory Networks for Directly Reading Documents},
author={Alexander Miller and Adam Fisch and Jesse Dodge and Amir-Hossein Karimi and Antoine Bordes and Jason Weston},
year={2016},
eprint={1606.03126},
archivePrefix={arXiv},
primaryClass={cs.CL}
```
### Contributions
Thanks to [@aclifton314](https://github.com/aclifton314) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
sogou_news | null | 2023-04-05T13:40:25Z | 341 | 0 | null | [
"arxiv:1509.01626",
"region:us"
] | 2023-04-05T13:40:25Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
pretty_name: Sogou News
dataset_info:
features:
- name: title
dtype: string
- name: content
dtype: string
- name: label
dtype:
class_label:
names:
'0': sports
'1': finance
'2': entertainment
'3': automobile
'4': technology
splits:
- name: test
num_bytes: 168645860
num_examples: 60000
- name: train
num_bytes: 1257931136
num_examples: 450000
download_size: 384269937
dataset_size: 1426576996
---
# Dataset Card for "sogou_news"
## 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:** [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:** 384.27 MB
- **Size of the generated dataset:** 1.43 GB
- **Total amount of disk used:** 1.81 GB
### Dataset Summary
The Sogou News dataset is a mixture of 2,909,551 news articles from the SogouCA and SogouCS news corpora, in 5 categories.
The number of training samples selected for each class is 90,000 and testing 12,000. Note that the Chinese characters have been converted to Pinyin.
classification labels of the news are determined by their domain names in the URL. For example, the news with
URL http://sports.sohu.com is categorized as a sport class.
### 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:** 384.27 MB
- **Size of the generated dataset:** 1.43 GB
- **Total amount of disk used:** 1.81 GB
An example of 'train' looks as follows.
```
{
"content": "du2 jia1 ti2 go1ng me3i ri4 ba4o jia4 \\n re4 xia4n :010-64438227\\n che1 xi2ng ba4o jia4 - cha2 xu2n jie2 guo3 \\n pi3n pa2i xi2ng ha4o jia4 ge2 ji1ng xia1o sha1ng ri4 qi1 zha1 ka4n ca1n shu4 pi2ng lu4n ",
"label": 3,
"title": " da3o ha2ng "
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `title`: a `string` feature.
- `content`: a `string` feature.
- `label`: a classification label, with possible values including `sports` (0), `finance` (1), `entertainment` (2), `automobile` (3), `technology` (4).
### Data Splits
| name |train |test |
|-------|-----:|----:|
|default|450000|60000|
## 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
```
@misc{zhang2015characterlevel,
title={Character-level Convolutional Networks for Text Classification},
author={Xiang Zhang and Junbo Zhao and Yann LeCun},
year={2015},
eprint={1509.01626},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
### Contributions
Thanks to [@lhoestq](https://github.com/lhoestq), [@mariamabarham](https://github.com/mariamabarham), [@lewtun](https://github.com/lewtun), [@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 | |
zpn/zinc20 | zpn | 2023-01-06T02:03:46Z | 341 | 5 | null | [
"size_categories:1B<n<10B",
"license:mit",
"bio",
"selfies",
"smiles",
"small_molecules",
"region:us"
] | 2023-01-06T02:03:46Z | 2023-01-04T17:32:47.000Z | 2023-01-04T17:32:47 | ---
license: mit
dataset_info:
features:
- name: selfies
dtype: string
- name: smiles
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 238295712864
num_examples: 804925861
- name: validation
num_bytes: 26983481360
num_examples: 100642661
- name: test
num_bytes: 29158755632
num_examples: 101082073
download_size: 40061255073
dataset_size: 294437949856
tags:
- bio
- selfies
- smiles
- small_molecules
pretty_name: zinc20
size_categories:
- 1B<n<10B
---
# Dataset Card for Zinc20
## Dataset Description
- **Homepage:** https://zinc20.docking.org/
- **Paper:** https://pubs.acs.org/doi/10.1021/acs.jcim.0c00675
### Dataset Summary
ZINC is a publicly available database that aggregates commercially available and annotated compounds.
ZINC provides downloadable 2D and 3D versions as well as a website that enables rapid molecule lookup and analog search.
ZINC has grown from fewer than 1 million compounds in 2005 to nearly 2 billion now.
This dataset includes ~1B molecules in total. We have filtered out any compounds that were not avaible to be converted from `smiles` to `seflies` representations.
### 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
The dataset is split into an 80/10/10 train/valid/test random split across files (which roughly corresponds to the same percentages)
### Source Data
#### Initial Data Collection and Normalization
Initial data was released at https://zinc20.docking.org/. We have downloaded and added a `selfies` field and filtered out all molecules that did not contain molecules that could be converted to `selfies` representations.
### Citation Information
@article{Irwin2020,
doi = {10.1021/acs.jcim.0c00675},
url = {https://doi.org/10.1021/acs.jcim.0c00675},
year = {2020},
month = oct,
publisher = {American Chemical Society ({ACS})},
volume = {60},
number = {12},
pages = {6065--6073},
author = {John J. Irwin and Khanh G. Tang and Jennifer Young and Chinzorig Dandarchuluun and Benjamin R. Wong and Munkhzul Khurelbaatar and Yurii S. Moroz and John Mayfield and Roger A. Sayle},
title = {{ZINC}20{\textemdash}A Free Ultralarge-Scale Chemical Database for Ligand Discovery},
journal = {Journal of Chemical Information and Modeling}
}
### Contributions
This dataset was curated and added by [@zanussbaum](https://github.com/zanussbaum).
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
indonlp/nusaparagraph_rhetoric | indonlp | 2023-09-07T13:05:19Z | 341 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-09-07T13:05:19Z | 2023-09-07T13:00:38.000Z | 2023-09-07T13:00:38 | ---
license: apache-2.0
---
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sabilmakbar/sea_wiki | sabilmakbar | 2023-11-12T16:10:53Z | 341 | 0 | null | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"source_datasets:Wikipedia",
"language:ace",
"language:ban",
"language:bcl",
"language:bjn",
"language:bug",
"language:cbk",
"language:ceb",
"language:gor",
"language:id",
"language:ilo",
"language:jv",
"language:km",
"language:lo",
"language:mad",
"language:min",
"language:mnw",
"language:ms",
"language:my",
"language:nia",
"language:pag",
"language:pam",
"language:shn",
"language:su",
"language:ta",
"language:th",
"language:tl",
"language:tet",
"language:vi",
"language:war",
"license:cc-by-sa-4.0",
"Wikipedia",
"Southeast Asia (SEA)",
"Dialect",
"Banyumasan Dialect of Javanese (Ngapak)",
"SEA-related Languages",
"SEA Local Languages",
"region:us"
] | 2023-11-12T16:10:53Z | 2023-11-03T14:44:10.000Z | 2023-11-03T14:44:10 | ---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- ace
- ban
- bcl
- bjn
- bug
- cbk
- ceb
- gor
- id
- ilo
- jv
- km
- lo
- mad
- min
- mnw
- ms
- my
- nia
- pag
- pam
- shn
- su
- ta
- th
- tl
- tet
- vi
- war
license:
- cc-by-sa-4.0
multilinguality:
- multilingual
source_datasets:
- Wikipedia
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
pretty_name: Wikipedia Archive for SEA Languages
tags:
- Wikipedia
- Southeast Asia (SEA)
- Dialect
- Banyumasan Dialect of Javanese (Ngapak)
- SEA-related Languages
- SEA Local Languages
dataset_info:
- config_name: seawiki_all
features:
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dtype: string
- name: title
dtype: string
- name: text
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dataset_size: 10923905691
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- config_name: seawiki_with_countries_all
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---
# **SEA Wikipedia Data Repository**
---
Welcome to SEA Wikipedia Data Repository. The datasets are extracted from [Wikipedia HF](https://huggingface.co/datasets/wikipedia) and processed using the scripts available in this repository for reproducibility purpose. Since Wikipedia iteslf has license [cc-by-sa 4.0](https://en.wikipedia.org/wiki/Wikipedia:Copyrights), we decided to follow this instead of Wikipedia HF data has of cc-by-sa 3.0 since it gives more rights to initial author/contributor.
# Getting Started #
### To read the datasets directly ###
Use one of the following code chunks to load it from HuggingFace Hub:
You can refer to the 2nd args of ```config name``` using the following script
```
dataset = load_dataset(
"sabilmakbar/sea_wiki",
"seawiki_dedup_all" # a config name, can be "seawiki_dedup_all" or "seawiki_with_countries_all", or "seawiki_with_countries_dedup_all" , defaults to "seawiki_dedup_all"
)
```
Or you can provide both ```lang``` and ```date_stamp``` (or just lang only by assuming the ```date_stamp``` will take the newest one)
```
dataset = load_dataset(
"sabilmakbar/sea_wiki",
lang = "id", # see README for complete lang choices
date_stamp="20230901"
)
```
Or you can provide a ```country``` params with similar fashion to ```lang``` args (providing both ```country``` and ```lang``` will prioritize the ```lang``` kwarg)
```
dataset = load_dataset(
"sabilmakbar/sea_wiki",
lang = "id", # see the splits for complete lang choices
date_stamp="20230901"
)
```
# **FAQS**
### What are the available languages provided in dataset and from which country?
You may check the following tables to understand the current coverage of this dataset (languages, countries, data size & volume). All tables are sorted by the leftmost column.
#### 1. Table of Countries and its Country Code
| Country Code | Country Name | Wiki Info |
| :---: | :---: | :---: |
| brn | Brunei | [Wiki Link](https://en.wikipedia.org/wiki/Brunei) |
| idn | Indonesia | [Wiki Link](https://en.wikipedia.org/wiki/Indonesia) |
| khm | Cambodia | [Wiki Link](https://en.wikipedia.org/wiki/Cambodia) |
| lao | Laos | [Wiki Link](https://en.wikipedia.org/wiki/Laos) |
| mmr | Myanmar | [Wiki Link](https://en.wikipedia.org/wiki/Myanmar) |
| mys | Malaysia | [Wiki Link](https://en.wikipedia.org/wiki/Malaysia) |
| phl | Philippines | [Wiki Link](https://en.wikipedia.org/wiki/Philippines) |
| sgp | Singapore | [Wiki Link](https://en.wikipedia.org/wiki/Singapore) |
| tha | Thailand | [Wiki Link](https://en.wikipedia.org/wiki/Thailand) |
| tls | East Timor | [Wiki Link](https://en.wikipedia.org/wiki/East_Timor) |
| vnm | Vietnam | [Wiki Link](https://en.wikipedia.org/wiki/Vietnam) |
#### 2. Table of Languages and Countries of its speakers
| ISO 639-3 Lang Code | Dataset Lang Code | Lang Name | Country Codes Spoken | Wiki Info | Total Data | Total Size (MiB rounded) |
| :---: | :---: | :---: | :---: | :--- | ---: | ---: |
| ace | ace | Acehnese | idn | [Wiki Link](https://en.wikipedia.org/wiki/Acehnese_language) | 12979 | 4.72 |
| ban | ban | Balinese | idn | [Wiki Link](https://en.wikipedia.org/wiki/Balinese_language) | 20611 | 17.19 |
| bcl | bcl | Central Bicolano | phl | [Wiki Link](https://en.wikipedia.org/wiki/Banjarese_language) | 14079 | 19.05 |
| bjn | bjn | Banjarese | idn | [Wiki Link](https://en.wikipedia.org/wiki/Banjarese_language) | 10503 | 6.47 |
| bug | bug | Buginese | idn | [Wiki Link](https://en.wikipedia.org/wiki/Buginese_language) | 9969 | 2.08 |
| bur | my | Burmese | mmr | [Wiki Link](https://en.wikipedia.org/wiki/Burmese_language) | 108819 | 298.49 |
| cbk | cbk_zam | Zamboanga Chavacano/Chavacano | phl | [Wiki Link](https://en.wikipedia.org/wiki/Chavacano) | 2242 | 1.51 |
| ceb | ceb | Cebuano | phl | [Wiki Link](https://en.wikipedia.org/wiki/Central_Bicolano_language) | 5815254 | 4,145.16 |
| gor | gor | Gorontalo | idn | [Wiki Link](https://en.wikipedia.org/wiki/Gorontalo_language) | 15290 | 5.93 |
| ilo | ilo | Ilokano | phl | [Wiki Link](https://en.wikipedia.org/wiki/Ilocano_language) | 15369 | 15.94 |
| ind | id | Indonesian | idn | [Wiki Link](https://en.wikipedia.org/wiki/Indonesian_language) | 662443 | 1,066.10 |
| jav | jv | Javanese | idn | [Wiki Link](https://en.wikipedia.org/wiki/Javanese_language) | 73080 | 68.66 |
| khm | km | Khmer | khm | [Wiki Link](https://en.wikipedia.org/wiki/Khmer_language) | 11466 | 97.94 |
| lao | lo | Lao | lao | [Wiki Link](https://en.wikipedia.org/wiki/Lao_language) | 4897 | 14.22 |
| mad | mad | Madurese | idn | [Wiki Link](https://en.wikipedia.org/wiki/Madurese_language) | 1192 | 1.54 |
| may | ms | Malay | mys, sgp, brn, idn | [Wiki Link](https://en.wikipedia.org/wiki/Malay_language) | 348045 | 395.57 |
| min | min | Minangkabau | idn | [Wiki Link](https://en.wikipedia.org/wiki/Minangkabau_language) | 225972 | 111.31 |
| mnw | mnw | Mon | mmr | [Wiki Link](https://en.wikipedia.org/wiki/Mon_language) | 3271 | 45.05 |
| nia | nia | Nias | idn | [Wiki Link](https://en.wikipedia.org/wiki/Nias_language) | 1714 | 2.05 |
| pag | pag | Pangasinan | phl | [Wiki Link](https://en.wikipedia.org/wiki/Pangasinan_language) | 1108 | 0.73 |
| pam | pam | Kapampangan | phl | [Wiki Link](https://en.wikipedia.org/wiki/Kapampangan_language) | 8932 | 7.83 |
| shn | shn | Shan | mmr | [Wiki Link](https://en.wikipedia.org/wiki/Shan_language) | 13662 | 32.06 |
| sun | su | Sundanese | idn | [Wiki Link](https://en.wikipedia.org/wiki/Sundanese_language) | 61529 | 45.31 |
| tam | ta | Tamil | mys, sgp | [Wiki Link](https://en.wikipedia.org/wiki/Tamil_language) | 160580 | 771.58 |
| tgl | tl | Tagalog | phl | [Wiki Link](https://en.wikipedia.org/wiki/Tagalog_language) | 45121 | 81.34 |
| tha | th | Thai | tha | [Wiki Link](https://en.wikipedia.org/wiki/Thai_language) | 159666 | 965.95 |
| tet | tet | Tetum | tls, idn | [Wiki Link](https://en.wikipedia.org/wiki/Tetum_language) | 1464 | 1.38 |
| vie | vi | Vietnamese | vnm | [Wiki Link](https://en.wikipedia.org/wiki/Vietnamese_language) | 1287912 | 1,528.58 |
| war | war | Waray | phl | [Wiki Link](https://en.wikipedia.org/wiki/Waray_language) | 1266204 | 433.22 |
| (dialect) | map_bms | Banyumasan <br>(Dialect of Javanese) | idn | [Wiki Link](https://en.wikipedia.org/wiki/Banyumasan_dialect) | 11839 | 4.83 |
#### 3. Table of Token Statistics for Covered Languages
The token statistics is generated using ```tiktoken``` using encoder for GPT-4.
| Dataset Lang Code | Total Token | Avg Token per Article | Min Token | Max Token | Token Deciles List |
| :---: | ---: | ---: | ---: | ---: | :--- |
| ace | 1,370,829 | 105.61899992295247 | 3 | 9,659 | [38.0, 52.0, 54.0, 69.0, 76.0, 84.0, 90.0, 123.0, 126.0] |
| ban | 5,924,610 | 287.44893503469024 | 5 | 24,364 | [97.0, 144.0, 165.0, 187.0, 209.0, 245.0, 276.0, 315.0, 421.0] |
| bcl | 6,234,838 | 442.8466510405569 | 2 | 54,049 | [55.0, 95.0, 143.0, 179.0, 226.0, 304.0, 419.0, 587.0, 917.2] |
| bjn | 1,935,505 | 184.28115776444827 | 2 | 30,170 | [36.0, 38.0, 39.0, 40.0, 42.0, 51.0, 82.0, 151.0, 367.0] |
| bug | 553,693 | 55.54147858360919 | 1 | 13,951 | [31.0, 42.0, 43.0, 46.0, 48.0, 50.0, 52.0, 55.0, 57.0] |
| cbk_zam | 402,703 | 179.6177520071365 | 2 | 6,494 | [35.0, 41.2, 56.0, 69.0, 90.0, 120.0, 138.0, 155.0, 294.9] |
| ceb | 1,319,601,771 | 226.92074516435568 | 4 | 221,802 | [93.0, 108.0, 123.0, 136.0, 163.0, 207.0, 278.0, 377.0, 426.0] |
| gor | 1,575,766 | 103.05860039241334 | 2 | 5,525 | [55.0, 58.0, 60.0, 62.0, 64.0, 66.0, 69.0, 75.0, 96.0] |
| id | 325,411,713 | 491.22975561670967 | 1 | 198,597 | [54.0, 93.0, 123.0, 145.0, 180.0, 226.0, 332.0, 543.0, 1068.0] |
| ilo | 5,593,491 | 363.94632051532307 | 17 | 18,202 | [59.0, 80.0, 91.0, 111.0, 152.0, 213.0, 303.0, 461.0, 856.0] |
| jv | 23,528,314 | 321.95284619594963 | 2 | 342,156 | [48.0, 60.0, 75.0, 88.0, 117.0, 175.0, 270.0, 420.0, 772.0] |
| km | 54,559,721 | 4,758.391854177568 | 1 | 1,110,771 | [160.0, 293.0, 452.0, 693.0, 1032.0, 1609.0, 2644.0, 4745.0, 9607.0] |
| lo | 9,395,636 | 1,918.6514192362672 | 3 | 107,154 | [134.0, 184.2, 285.0, 494.0, 658.0, 894.6, 1258.0, 1971.2, 4153.8] |
| mad | 611,736 | 513.2013422818792 | 14 | 17,093 | [80.1, 110.2, 135.0, 161.0, 194.0, 242.0, 302.7, 531.4, 1167.1] |
| map_bms | 1,307,244 | 110.41844750401216 | 1 | 20,629 | [20.0, 21.0, 22.0, 24.0, 30.0, 35.0, 36.0, 38.0, 111.0] |
| min | 33,114,184 | 146.54109358681606 | 3 | 58,387 | [81.0, 91.0, 96.0, 108.0, 119.0, 135.0, 156.0, 168.0, 170.0] |
| mnw | 31,595,647 | 9,659.3234484867 | 6 | 1,450,765 | [425.0, 601.0, 629.0, 682.0, 763.0, 2103.0, 4255.0, 7724.0, 14517.0] |
| ms | 121,343,673 | 348.64363228892813 | 1 | 68,545 | [32.0, 40.0, 49.0, 63.0, 105.0, 138.0, 216.0, 362.0, 788.0] |
| my | 189,439,447 | 1,740.8673761015998 | 10 | 1,376,658 | [164.0, 269.0, 350.0, 508.0, 559.0, 578.0, 605.0, 892.4, 3369.0] |
| nia | 795,527 | 464.134772462077 | 8 | 18,650 | [59.0, 61.0, 63.0, 65.0, 67.0, 86.0, 239.1, 623.4, 1249.7] |
| pag | 222,366 | 200.6913357400722 | 5 | 10,143 | [31.0, 51.0, 73.0, 110.0, 118.0, 120.0, 127.0, 181.0, 355.8] |
| pam | 2,269,091 | 254.04064039408868 | 1 | 14,912 | [38.0, 56.0, 78.0, 108.0, 121.0, 150.0, 193.0, 289.0, 525.8] |
| shn | 23,125,637 | 1,692.6977748499487 | 2 | 204,094 | [460.0, 480.0, 585.0, 679.0, 715.0, 740.0, 756.0, 780.0, 1580.9] |
| su | 14,710,124 | 239.07627297697022 | 1 | 99,456 | [41.0, 43.0, 45.0, 49.0, 70.0, 146.0, 216.0, 219.0, 419.0] |
| ta | 376,043,508 | 2,341.782961763607 | 15 | 177,054 | [543.0, 700.0, 824.0, 1001.0, 1153.0, 1465.0, 1992.0, 2911.0, 4652.0] |
| tet | 487,016 | 332.6612021857924 | 4 | 24,287 | [30.3, 47.0, 66.9, 101.0, 164.0, 177.0, 187.0, 248.6, 604.4] |
| th | 330,964,733 | 2,072.8566695476807 | 1 | 289,150 | [231.0, 390.0, 546.0, 727.0, 969.0, 1276.0, 1741.0, 2533.0, 4361.0] |
| tl | 27,789,730 | 615.8934864032269 | 7 | 60,728 | [73.0, 116.0, 161.0, 214.0, 281.0, 360.0, 465.0, 666.0, 1136.0] |
| vi | 546,481,913 | 424.3161900813099 | 3 | 246,463 | [46.0, 64.0, 71.0, 80.0, 86.0, 92.0, 120.0, 240.0, 824.0] |
| war | 117,438,315 | 92.74833676090108 | 1 | 25,689 | [60.0, 77.0, 81.0, 84.0, 87.0, 90.0, 94.0, 99.0, 110.0] |
Some other languages in SEA that are already exists its Wiki Index at Wikimedia might be missing from this list. Any lang update PR is greatly appreciated!
### How does the data being preprocessed? What makes it different from loading it directly from Wikipedia HF?
The data available in here are processed with following flows:
1. Raw data is being deduplicated on ```title``` and ```text``` (text-content from a given article), to remove articles containing boilerplate text (template text that are used usually for unavailable informations or asking for contributions of content in that article), which usually deemed noisy for NLP data.
2. Furthermore, the ```title``` and ```text``` data are being checked for string-matching duplication (duplication of text that are being pre-processed, i.e symbols removed, HTML tags striped, or ASCII-chars/UTF-8 chars validated).
The source code can be found on this Github Repo [SEA Wiki Github Source Code](https://github.com/sabilmakbar/sea_wiki)
### How do I extract new Wikipedia Dataset of SEA languages?
Please refer to the corresponding Github Repo for more detailed info [SEA Wiki Github Source Code](https://github.com/sabilmakbar/sea_wiki)
## Citation Info:
```
@ONLINE{wikidump,
author = "Wikimedia Foundation",
title = "Wikimedia Downloads",
url = "https://dumps.wikimedia.org"}
@ONLINE{wikipedia-hf,
title = "Huggingface Wikipedia Dataset",
url = "https://huggingface.co/datasets/wikipedia"}
```
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disaster_response_messages | null | 2023-01-25T14:29:29Z | 340 | 3 | null | [
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"license:unknown",
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] | 2023-01-25T14:29:29Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
- es
- fr
- ht
- ur
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
- text-classification
task_ids:
- intent-classification
- sentiment-classification
- text-simplification
pretty_name: Disaster Response Messages
dataset_info:
features:
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dtype: string
- name: message
dtype: string
- name: original
dtype: string
- name: genre
dtype: string
- name: related
dtype:
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'1': 'true'
'2': maybe
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splits:
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num_bytes: 10060799
num_examples: 21046
- name: test
num_bytes: 1253810
num_examples: 2629
- name: validation
num_bytes: 1266874
num_examples: 2573
download_size: 7201807
dataset_size: 12581483
---
# Dataset Card for Disaster Response Messages
## 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:** [HomePage](https://appen.com/datasets/combined-disaster-response-data/)
- **Repository:** [Repo to Download the Dataset](https://datasets.appen.com/appen_datasets/disaster_response_data/disaster_response_messages_training.csv)
- **Paper:
- **Leaderboard:
- **Point of Contact:** [Darshan Gandhi](darshangandhi1151@gmail.com)
### Dataset Summary
This dataset contains 30,000 messages drawn from events including an earthquake in Haiti in 2010, an earthquake in Chile in 2010, floods in Pakistan in 2010, super-storm Sandy in the U.S.A. in 2012, and news articles spanning a large number of years and 100s of different disasters. The data has been encoded with 36 different categories related to disaster response and has been stripped of messages with sensitive information in their entirety. Upon release, this is the featured dataset of a new Udacity course on Data Science and the AI4ALL summer school and is especially utile for text analytics and natural language processing (NLP) tasks and models.The input data in this job contains thousands of untranslated disaster-related messages and their English translations. In the “Data” tab above, you’ll find the annotated data, with 40 class labels for intent and content.
### Supported Tasks and Leaderboards
The input data in this job contains thousands of untranslated disaster-related messages and their English translations. In the dataset, you’ll find the annotated data, with 40 class labels for intent and content. This dataset contains the original message in its original language, the English translation, and dozens of classes for message content. These classes are noted in column titles with a simple binary 1= yes, 0=no.
### Languages
The dataset is a multilingual dataset which has the messages in the original language and also it's translated English form.
## Dataset Structure
### Data Instances
The dataset consists of a message in English and also it's original language form. Adding on, there are 40 labels which help to understand more about the exact essence of the message.
Example of a Disaster Response : { 'split': 'train', 'message': 'Weather update - a cold front from Cuba that could pass over Haiti', 'original': 'Un front froid se retrouve sur Cuba ce matin. Il pourrait traverser Haiti demain. Des averses de pluie isolee sont encore prevues sur notre region ce soi', 'genre': 'direct', 'related': 1, 'PII': 0, 'request': 0, 'offer': 0, 'aid_related': 0, 'medical_help': 0, 'medical_products': 0, 'search_and_rescue': 0, 'security': 0, 'military': 0, 'child_alone': 0, 'water': 0, 'food': 0, 'shelter': 0, 'clothing': 0, 'money': 0, 'missing_people': 0, 'refugees': 0, 'death': 0, 'other_aid': 0, 'infrastructure_related': 0, 'transport': 0, 'buildings': 0, 'electricity': 0, 'tools': 0, 'hospitals': 0, 'shops': 0, 'aid_centers': 0, 'other_infrastructure': 0, 'weather_related': 0, 'floods': 0, 'storm': 0, 'fire': 0, 'earthquake': 0, 'cold': 0, 'other_weather': 0, 'direct_report': 0}
### Data Fields
*split: Train, Test split</br>
*message: English text of actual messages related to disaster </br>
*original: Text of column 3 in native language as originally written</br>
*genre: Type of message, including direct messages, social posting, and news stories or bulletins</br>
*related: Is the message disaster related? 1= yes, 0=no, 2=maybe</br>
*PII: Does the message contain PII? 1= yes, 0=no </br>
*request: Does the message contain a request? 1= yes, 0=no </br>
*offer: Does the message contain an offer? 1= yes, 0=no </br>
*aid_related: Is the message aid related? 1= yes, 0=no </br>
*medical_help: Does the message concern medical help? 1= yes, 0=no </br>
*medical_products: Does the message concern medical products? 1= yes, 0=no </br>
*search_and_rescue: Does the message concern search and rescue? 1= yes, 0=no </br>
*security: Does the message concern security? 1= yes, 0=no </br>
*military: Does the message concern military? 1= yes, 0=no </br>
*child_alone: Does the message mention a child alone? 1= yes, 0=no</br>
*water: Does the message concern water? 1= yes, 0=no</br>
*food: Does the message concern food? 1= yes, 0=no </br>
*shelter: Does the message concern shelter? 1= yes, 0=no </br>
*clothing: Does the message concern clothing? 1= yes, 0=no </br>
*money: Does the message concern money? 1= yes, 0=no </br>
*missing_people: Does the message indicate missing people? 1= yes, 0=no</br>
*refugees: Does the message concern refugess? 1= yes, 0=no</br>
*death: Does the message imply death? 1= yes, 0=no </br>
*other_aid: Is there any other aid needed? 1=yes, 0=no </br>
*infrastructure_related: Does the message concern infrastructure? 1= yes, 0=no </br>
*transport: Does the message concern transport? 1= yes, 0=no </br>
*buildings: Does the message concern buildings? 1= yes, 0=no </br>
*electricity: Does the message concern electricity? 1= yes, 0=no </br>
*tools: Does the message concern tools? 1= yes, 0=no </br>
*hospitals: Does the message concern clothing? 1= yes, 0=no </br>
*shops: Does the message concern clothing? 1= yes, 0=no </br>
*aid_centers:Does the message concern clothing? 1= yes, 0=no </br>
*other_infrastructure:Does the message concern clothing? 1= yes, 0=no </br>
*weather_related: Does the message concern weather? 1= yes, 0=no</br>
*floods: Does the message indicate there was a flood? 1= yes, 0=no</br>
*storm: Does the message indicate there was a storm? 1= yes, 0=no </br>
*fire: Does the message indicate there was a fire? 1= yes, 0=no</br>
*earthquake: Does the message indicate there was an earthquake? 1= yes, 0=no</br>
*cold: Does the message indicate there was a cold? 1= yes, 0=no</br>
*other_weather: Does the message indicate there was other weather issues? 1= yes, 0=no</br>
*direct_report: Does the show a direct report? 1= yes, 0=no
### Data Splits
|train|test |validation|
|:----:|:-----------:|:----:|
|21046|2629|2573|
## Dataset Creation
### Curation Rationale
The dataset was built to understand about the sentiments of the citizens and also more about want was the emergency about and what kind of help they were seeking
### 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
The dataset has a great usecase of understand more about the sentiments of the citizens around the globe during a disaster and how their responses are. Also, it helps the government to understand their citizens better and would eventually help to draft better policies accordingly.
### Discussion of Biases
The messages since have been translated in English may not be able to judically imply the exact significance of the individual when they would have posted the message
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The dataset was initially created by [Appen](https://appen.com/)
### Licensing Information
[More Information Needed]
### Citation Information
[Multilingual Disaster Response Messages](https://appen.com/datasets/combined-disaster-response-data/)
### Contributions
Thanks to [@darshan-gandhi](https://github.com/darshan-gandhi) for adding this dataset. | [
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lc_quad | null | 2023-04-05T10:09:15Z | 340 | 6 | lc-quad-2-0 | [
"task_categories:question-answering",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-3.0",
"knowledge-base-qa",
"region:us"
] | 2023-04-05T10:09:15Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- cc-by-3.0
multilinguality:
- monolingual
pretty_name: 'LC-QuAD 2.0: Large-scale Complex Question Answering Dataset'
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids: []
paperswithcode_id: lc-quad-2-0
tags:
- knowledge-base-qa
dataset_info:
features:
- name: NNQT_question
dtype: string
- name: uid
dtype: int32
- name: subgraph
dtype: string
- name: template_index
dtype: int32
- name: question
dtype: string
- name: sparql_wikidata
dtype: string
- name: sparql_dbpedia18
dtype: string
- name: template
dtype: string
- name: paraphrased_question
dtype: string
splits:
- name: train
num_bytes: 16637751
num_examples: 19293
- name: test
num_bytes: 4067092
num_examples: 4781
download_size: 3959901
dataset_size: 20704843
---
# Dataset Card for LC-QuAD 2.0
## 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://lc-quad.sda.tech/](http://lc-quad.sda.tech/)
- **Repository:** https://github.com/AskNowQA/LC-QuAD2.0
- **Paper:** [LC-QuAD 2.0: A Large Dataset for Complex Question Answering over Wikidata and DBpedia](https://api.semanticscholar.org/CorpusID:198166992)
- **Point of Contact:** [Mohnish Dubey](mailto:dubey@cs.uni-bonn.de) or [Mohnish Dubey](mailto:dubey.mohnish5@gmail.com)
- **Size of downloaded dataset files:** 3.87 MB
- **Size of the generated dataset:** 20.73 MB
- **Total amount of disk used:** 24.60 MB
### Dataset Summary
LC-QuAD 2.0 is a Large Question Answering dataset with 30,000 pairs of question and its corresponding SPARQL query. The target knowledge base is Wikidata and DBpedia, specifically the 2018 version. Please see our paper for details about the dataset creation process and framework.
### 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:** 3.87 MB
- **Size of the generated dataset:** 20.73 MB
- **Total amount of disk used:** 24.60 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"NNQT_question": "What is the {periodical literature} for {mouthpiece} of {Delta Air Lines}",
"paraphrased_question": "What is Delta Air Line's periodical literature mouthpiece?",
"question": "What periodical literature does Delta Air Lines use as a moutpiece?",
"sparql_dbpedia18": "\"select distinct ?obj where { ?statement <http://www.w3.org/1999/02/22-rdf-syntax-ns#subject> <http://wikidata.dbpedia.org/resou...",
"sparql_wikidata": " select distinct ?obj where { wd:Q188920 wdt:P2813 ?obj . ?obj wdt:P31 wd:Q1002697 } ",
"subgraph": "simple question right",
"template": " <S P ?O ; ?O instanceOf Type>",
"template_index": 65,
"uid": 19719
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `NNQT_question`: a `string` feature.
- `uid`: a `int32` feature.
- `subgraph`: a `string` feature.
- `template_index`: a `int32` feature.
- `question`: a `string` feature.
- `sparql_wikidata`: a `string` feature.
- `sparql_dbpedia18`: a `string` feature.
- `template`: a `string` feature.
- `paraphrased_question`: a `string` feature.
### Data Splits
| name |train|test|
|-------|----:|---:|
|default|19293|4781|
## 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
LC-QuAD 2.0 is licensed under a [Creative Commons Attribution 3.0 Unported License](http://creativecommons.org/licenses/by/3.0/deed.en_US).
### Citation Information
```
@inproceedings{dubey2017lc2,
title={LC-QuAD 2.0: A Large Dataset for Complex Question Answering over Wikidata and DBpedia},
author={Dubey, Mohnish and Banerjee, Debayan and Abdelkawi, Abdelrahman and Lehmann, Jens},
booktitle={Proceedings of the 18th International Semantic Web Conference (ISWC)},
year={2019},
organization={Springer}
}
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@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 | |
SetFit/hate_speech_offensive | SetFit | 2022-01-15T21:47:31Z | 340 | 1 | null | [
"region:us"
] | 2022-01-15T21:47:31Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | # hate_speech_offensive
This dataset is a version from [hate_speech_offensive](https://huggingface.co/datasets/hate_speech_offensive), splitted into train and test set. | [
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hate_speech_pl | null | 2022-11-03T16:15:27Z | 339 | 2 | null | [
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:multi-class-classification",
"task_ids:multi-label-classification",
"task_ids:sentiment-classification",
"task_ids:sentiment-scoring",
"task_ids:topic-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:pl",
"license:cc-by-nc-sa-3.0",
"region:us"
] | 2022-11-03T16:15:27Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- pl
license:
- cc-by-nc-sa-3.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
- multi-class-classification
- multi-label-classification
- sentiment-classification
- sentiment-scoring
- topic-classification
paperswithcode_id: null
pretty_name: HateSpeechPl
dataset_info:
features:
- name: id
dtype: uint16
- name: text_id
dtype: uint32
- name: annotator_id
dtype: uint8
- name: minority_id
dtype: uint8
- name: negative_emotions
dtype: bool
- name: call_to_action
dtype: bool
- name: source_of_knowledge
dtype: uint8
- name: irony_sarcasm
dtype: bool
- name: topic
dtype: uint8
- name: text
dtype: string
- name: rating
dtype: uint8
splits:
- name: train
num_bytes: 3436190
num_examples: 13887
download_size: 3877954
dataset_size: 3436190
---
# Dataset Card for HateSpeechPl
## 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://zil.ipipan.waw.pl/HateSpeech
- **Repository:** [N/A]
- **Paper:** http://www.qualitativesociologyreview.org/PL/Volume38/PSJ_13_2_Troszynski_Wawer.pdf
- **Leaderboard:** [N/A]
- **Point of Contact:** [Marek Troszyński](mtroszynski@civitas.edu.pl), [Aleksander Wawer](axw@ipipan.waw.pl)
### Dataset Summary
The dataset was created to analyze the possibility of automating the recognition of hate speech in Polish. It was collected from the Polish forums and represents various types and degrees of offensive language, expressed towards minorities.
The original dataset is provided as an export of MySQL tables, what makes it hard to load. Due to that, it was converted to CSV and put to a Github repository.
### Supported Tasks and Leaderboards
- `text-classification`: The dataset might be used to perform the text classification on different target fields, like the presence of irony/sarcasm, minority it describes or a topic.
- `text-scoring`: The sentiment analysis is another task which might be solved on a dataset.
### Languages
Polish, collected from public forums, including the HTML formatting of the text.
## Dataset Structure
### Data Instances
The dataset consists of three collections, originally provided as separate MySQL tables. Here represented as three CSV files.
```
{
'id': 1,
'text_id': 121713,
'annotator_id': 1,
'minority_id': 72,
'negative_emotions': false,
'call_to_action': false,
'source_of_knowledge': 2,
'irony_sarcasm': false,
'topic': 18,
'text': ' <font color=\"blue\"> Niemiec</font> mówi co innego',
'rating': 0
}
```
### Data Fields
List and describe the fields present in the dataset. Mention their data type, and whether they are used as input or output in any of the tasks the dataset currently supports. If the data has span indices, describe their attributes, such as whether they are at the character level or word level, whether they are contiguous or not, etc. If the datasets contains example IDs, state whether they have an inherent meaning, such as a mapping to other datasets or pointing to relationships between data points.
- `id`: unique identifier of the entry
- `text_id`: text identifier, useful when a single text is rated several times by different annotators
- `annotator_id`: identifier of the person who annotated the text
- `minority_id`: the internal identifier of the minority described in the text
- `negative_emotions`: boolean indicator of the presence of negative emotions in the text
- `call_to_action`: boolean indicator set to true, if the text calls the audience to perform any action, typically with a negative emotions
- `source_of_knowledge`: categorical variable, describing the source of knowledge for the post rating - 0, 1 or 2 (direct, lexical or contextual, but the description of the meaning for different values couldn't be found)
- `irony_sarcasm`: boolean indicator of the present of irony or sarcasm
- `topic`: internal identifier of the topic the text is about
- `text`: post text content
- `rating`: integer value, from 0 to 4 - the higher the value, the more negative the text content is
### Data Splits
The dataset was not originally split at all.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
The dataset was collected from the public forums.
[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
The dataset doesn't contain any personal or sensitive information.
## Considerations for Using the Data
### Social Impact of Dataset
The automated hate speech recognition is the main beneficial outcome of using the dataset.
### Discussion of Biases
The dataset contains negative posts only and due to that might underrepresent the whole language.
### Other Known Limitations
Dataset provided for research purposes only. Please check dataset license for additional information.
## Additional Information
### Dataset Curators
The dataset was created by Marek Troszyński and Aleksander Wawer, during work done at [IPI PAN](https://www.ipipan.waw.pl/).
### Licensing Information
According to [Metashare](http://metashare.nlp.ipipan.waw.pl/metashare/repository/browse/polish-hatespeech-corpus/21b7e2366b0011e284b6000423bfd61cbc7616f601724f09bafc8a62c42d56de/), the dataset is licensed under CC-BY-NC-SA, but the version is not mentioned.
### Citation Information
```
@article{troszynski2017czy,
title={Czy komputer rozpozna hejtera? Wykorzystanie uczenia maszynowego (ML) w jako{\'s}ciowej analizie danych},
author={Troszy{\'n}ski, Marek and Wawer, Aleksandra},
journal={Przegl{\k{a}}d Socjologii Jako{\'s}ciowej},
volume={13},
number={2},
pages={62--80},
year={2017},
publisher={Uniwersytet {\L}{\'o}dzki, Wydzia{\l} Ekonomiczno-Socjologiczny, Katedra Socjologii~…}
}
```
### Contributions
Thanks to [@kacperlukawski](https://github.com/kacperlukawski) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ohsumed | null | 2022-11-18T21:34:41Z | 339 | 1 | null | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-nc-4.0",
"region:us"
] | 2022-11-18T21:34:41Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
pretty_name: Ohsumed
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
paperswithcode_id: null
dataset_info:
features:
- name: seq_id
dtype: int64
- name: medline_ui
dtype: int64
- name: mesh_terms
dtype: string
- name: title
dtype: string
- name: publication_type
dtype: string
- name: abstract
dtype: string
- name: author
dtype: string
- name: source
dtype: string
config_name: ohsumed
splits:
- name: train
num_bytes: 60117860
num_examples: 54709
- name: test
num_bytes: 338533901
num_examples: 293855
download_size: 139454017
dataset_size: 398651761
---
# Dataset Card for ohsumed
## 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://davis.wpi.edu/xmdv/datasets/ohsumed.html
- **Repository:** https://trec.nist.gov/data/filtering/t9.filtering.tar.gz
- **Paper:** https://link.springer.com/chapter/10.1007/978-1-4471-2099-5_20
- **Leaderboard:**
- **Point of Contact:** [William Hersh](mailto:hersh@OHSU.EDU) [Aakash Gupta](mailto:aakashg80@gmail.com)
### Dataset Summary
The OHSUMED test collection is a set of 348,566 references from
MEDLINE, the on-line medical information database, consisting of
titles and/or abstracts from 270 medical journals over a five-year
period (1987-1991). The available fields are title, abstract, MeSH
indexing terms, author, source, and publication type. The National
Library of Medicine has agreed to make the MEDLINE references in the
test database available for experimentation, restricted to the
following conditions:
1. The data will not be used in any non-experimental clinical,
library, or other setting.
2. Any human users of the data will explicitly be told that the data
is incomplete and out-of-date.
Please check this [readme](https://trec.nist.gov/data/filtering/README.t9.filtering) for more details
### Supported Tasks and Leaderboards
[Text Classification](https://paperswithcode.com/sota/text-classification-on-ohsumed)
### Languages
The text is primarily in English. The BCP 47 code is `en`
## Dataset Structure
### Data Instances
```
{'seq_id': 7770,
'medline_ui': 87120420,
'mesh_terms': 'Adult; Aged; Aneurysm/CO; Arteriovenous Fistula/*TH; Carotid Arteries; Case Report; Female; Human; Jugular Veins; Male; Methods; Middle Age; Neck/*BS; Vertebral Artery.',
'title': 'Arteriovenous fistulas of the large vessels of the neck: nonsurgical percutaneous occlusion.',
'publication_type': 'JOURNAL ARTICLE.',
'abstract': 'We describe the nonsurgical treatment of arteriovenous fistulas of the large vessels in the neck using three different means of endovascular occlusion of these large lesions, which are surgically difficult to approach and treat.',
'author': 'Vitek JJ; Keller FS.',
'source': 'South Med J 8705; 80(2):196-200'}
```
### Data Fields
Here are the field definitions:
- seg_id: sequential identifier
(important note: documents should be processed in this order)
- medline_ui: MEDLINE identifier (UI)
(<DOCNO> used for relevance judgements)
- mesh_terms: Human-assigned MeSH terms (MH)
- title: Title (TI)
- publication_type : Publication type (PT)
- abstract: Abstract (AB)
- author: Author (AU)
- source: Source (SO)
Note: some abstracts are truncated at 250 words and some references
have no abstracts at all (titles only). We do not have access to the
full text of the documents.
### Data Splits
The files are Train/ Test. Where the training has files from 1987 while the test files has abstracts from 1988-91
Total number of files:
Train: 54710
Test: 348567
## Dataset Creation
### Curation Rationale
The OHSUMED document collection was obtained by William Hersh
(hersh@OHSU.EDU) and colleagues for the experiments described in the
papers below. [Check citation](#citation-information)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
The test collection was built as part of a study assessing the use of
MEDLINE by physicians in a clinical setting (Hersh and Hickam, above).
Novice physicians using MEDLINE generated 106 queries. Only a subset
of these queries were used in the TREC-9 Filtering Track. Before
they searched, they were asked to provide a statement of information
about their patient as well as their information need.
The data was collected by William Hersh & colleagues
### Annotations
#### Annotation process
The existing OHSUMED topics describe actual information needs, but the
relevance judgements probably do not have the same coverage provided
by the TREC pooling process. The MeSH terms do not directly represent
information needs, rather they are controlled indexing terms. However,
the assessment should be more or less complete and there are a lot of
them, so this provides an unusual opportunity to work with a very
large topic sample.
The topic statements are provided in the standard TREC format
#### Who are the annotators?
Each query was replicated by four searchers, two physicians
experienced in searching and two medical librarians. The results were
assessed for relevance by a different group of physicians, using a
three point scale: definitely, possibly, or not relevant. The list of
documents explicitly judged to be not relevant is not provided here.
Over 10% of the query-document pairs were judged in duplicate to
assess inter-observer reliability. For evaluation, all documents
judged here as either possibly or definitely relevant were
considered relevant. TREC-9 systems were allowed to distinguish
between these two categories during the learning process if desired.
### Personal and Sensitive Information
No PII data is present in the train, test or query files.
## 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
[Aakash Gupta](mailto:aakashg80@gmail.com)
*Th!nkEvolve Consulting* and Researcher at CoronaWhy
### Licensing Information
CC BY-NC 4.0
### Citation Information
Hersh WR, Buckley C, Leone TJ, Hickam DH, OHSUMED: An interactive
retrieval evaluation and new large test collection for research,
Proceedings of the 17th Annual ACM SIGIR Conference, 1994, 192-201.
Hersh WR, Hickam DH, Use of a multi-application computer workstation
in a clinical setting, Bulletin of the Medical Library Association,
1994, 82: 382-389.
### Contributions
Thanks to [@skyprince999](https://github.com/skyprince999) for adding this dataset. | [
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SoLID/shellcode_i_a32 | SoLID | 2022-11-17T19:53:43Z | 339 | 6 | shellcode-ia32 | [
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language_creators:found",
"multilinguality:translation",
"size_categories:unknown",
"source_datasets:original",
"language:code",
"language:en",
"license:gpl-3.0",
"arxiv:2104.13100",
"region:us"
] | 2022-11-17T19:53:43Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
- found
language:
- code
- en
license:
- gpl-3.0
multilinguality:
- translation
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text-generation
task_ids:
- language-modeling
paperswithcode_id: shellcode-ia32
---
# Shellcode_IA32
___Shellcode_IA32___ is a dataset containing _20_ years of shellcodes from a variety of sources is the largest collection of shellcodes in assembly available to date.
This dataset consists of 3,200 examples of instructions in assembly language for _IA-32_ (the 32-bit version of the x86 Intel Architecture) from publicly available security exploits. We collected assembly programs used to generate shellcode from [exploit-db](https://www.exploit-db.com/shellcodes?platform=linux_x86) and from [shell-storm](http://shell-storm.org/shellcode/).
We enriched the dataset by adding examples of assembly programs for the _IA-32_ architecture from popular tutorials and books. This allowed us to understand how different authors and assembly experts comment and, thus, how to deal with the ambiguity of natural language in this specific context. Our dataset consists of 10% of instructions collected from books and guidelines, and the rest from real shellcodes.
Our focus is on Linux, the most common OS for security-critical network services. Accordingly, we added assembly instructions written with _Netwide Assembler_ (NASM) for Linux.
Each line of _Shellcode\_IA32_ dataset represents a snippet - intent pair. The _snippet_ is a line or a combination of multiple lines of assembly code, built by following the NASM syntax. The _intent_ is a comment in the English language.
Further statistics on the dataset and a set of preliminary experiments performed with a neural machine translation (NMT) model are described in the following paper: [Shellcode_IA32: A Dataset for Automatic Shellcode Generation](https://arxiv.org/abs/2104.13100).
**Note**: This work was done in collaboration with the [DESSERT Lab](http://www.dessert.unina.it/).
The dataset is also hosted on the [DESSERT Lab Github](https://github.com/dessertlab/Shellcode_IA32).
Please consider citing our work:
```
@inproceedings{liguori-etal-2021-shellcode,
title = "{S}hellcode{\_}{IA}32: A Dataset for Automatic Shellcode Generation",
author = "Liguori, Pietro and
Al-Hossami, Erfan and
Cotroneo, Domenico and
Natella, Roberto and
Cukic, Bojan and
Shaikh, Samira",
booktitle = "Proceedings of the 1st Workshop on Natural Language Processing for Programming (NLP4Prog 2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.nlp4prog-1.7",
doi = "10.18653/v1/2021.nlp4prog-1.7",
pages = "58--64",
abstract = "We take the first step to address the task of automatically generating shellcodes, i.e., small pieces of code used as a payload in the exploitation of a software vulnerability, starting from natural language comments. We assemble and release a novel dataset (Shellcode{\_}IA32), consisting of challenging but common assembly instructions with their natural language descriptions. We experiment with standard methods in neural machine translation (NMT) to establish baseline performance levels on this task.",
}
```
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
RussianNLP/rucola | RussianNLP | 2023-03-27T18:47:12Z | 339 | 1 | null | [
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:ru",
"license:apache-2.0",
"arxiv:2210.12814",
"arxiv:2008.00401",
"region:us"
] | 2023-03-27T18:47:12Z | 2023-03-27T18:35:06.000Z | 2023-03-27T18:35:06 | ---
license: apache-2.0
task_categories:
- text-classification
language:
- ru
size_categories:
- 10K<n<100K
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:** https://rucola-benchmark.com
- **Repository:** https://github.com/RussianNLP/RuCoLA
- **Paper:** https://aclanthology.org/2022.emnlp-main.348/
- **ArXiv:** https://arxiv.org/abs/2210.12814
- **Leaderboard:** https://rucola-benchmark.com/leaderboard
- **Point of Contact:** vmikhailovhse@gmail.com
- **Language:** Russian
### Dataset Summary
Russian Corpus of Linguistic Acceptability (RuCoLA) is a novel benchmark of 13.4k sentences labeled as acceptable or not. RuCoLA combines in-domain sentences manually collected from linguistic literature and out-of-domain sentences produced by nine machine translation and paraphrase generation models.
The motivation behind the out-of-domain set is to facilitate the practical use of acceptability judgments for improving language generation.
Each unacceptable sentence is additionally labeled with four standard and machine-specific coarse-grained categories: morphology, syntax, semantics, and hallucinations.
## Dataset Structure
### Supported Tasks and Leaderboards
- **Task:** binary classification.
- **Metrics:** MCC/Acc.
- **Leaderboard:** https://rucola-benchmark.com/leaderboard
### Languages
Russian.
### Data Instances
```
{
"id": 19,
"sentence": "Люк останавливает удачу от этого.",
"label": 0,
"error_type": "Hallucination",
"detailed_source": "WikiMatrix"}
}
```
The example in English for illustration purposes:
```
{
"id": 19,
"sentence": "Luck stops luck from doing this.",
"label": 0,
"error_type": "Hallucination",
"detailed_source": "WikiMatrix"}
}
```
### Data Fields
- ```id (int64)```: the sentence's id.
- ```sentence (str)```: the sentence.
- ```label (str)```: the target class. "1" refers to "acceptable", while "0" corresponds to "unacceptable".
- ```error_type (str)```: the coarse-grained violation category (Morphology, Syntax, Semantics, or Hallucination); "0" if the sentence is acceptable.
- ```detailed_source```: the data source.
### Data Splits
RuCoLA consists of the training, development, and private test sets organised under two subsets: in-domain (linguistic publications) and out-of-domain (texts produced by natural language generation models).
- ```train```: 7869 in-domain samples (```"data/in_domain_train.csv"```).
- ```validation```: 2787 in-domain and out-of-domain samples. The in-domain (```"data/in_domain_dev.csv"```) and out-of-domain (```"data/out_of_domain_dev.csv"```) validation sets are merged into ```"data/dev.csv"``` for convenience.
- ```test```: 2789 in-domain and out-of-domain samples (```"data/test.csv"```).
## Dataset Creation
### Curation Rationale
- **In-domain Subset:** The in-domain sentences and the corresponding authors’ acceptability judgments are *manually* drawn from fundamental linguistic textbooks, academic publications, and methodological materials.
- **Out-of-domain Subset:** The out-of-domain sentences are produced by nine open-source MT and paraphrase generation models.
### Source Data
<details>
<summary>Linguistic publications and resources</summary>
|Original source |Transliterated source |Source id |
|---|---|---|
|[Проект корпусного описания русской грамматики](http://rusgram.ru) | [Proekt korpusnogo opisaniya russkoj grammatiki](http://rusgram.ru/)|Rusgram |
|Тестелец, Я.Г., 2001. *Введение в общий синтаксис*. Федеральное государственное бюджетное образовательное учреждение высшего образования Российский государственный гуманитарный университет.|Yakov Testelets. 2001. Vvedeniye v obschiy sintaksis. Russian State University for the Humanities. |Testelets |
|Лютикова, Е.А., 2010. *К вопросу о категориальном статусе именных групп в русском языке*. Вестник Московского университета. Серия 9. Филология, (6), pp.36-76. |Ekaterina Lutikova. 2010. K voprosu o kategorial’nom statuse imennykh grup v russkom yazyke. Moscow University Philology Bulletin. |Lutikova |
|Митренина, О.В., Романова, Е.Е. and Слюсарь, Н.А., 2017. *Введение в генеративную грамматику*. Общество с ограниченной ответственностью "Книжный дом ЛИБРОКОМ". |Olga Mitrenina et al. 2017. Vvedeniye v generativnuyu grammatiku. Limited Liability Company “LIBROCOM”. |Mitrenina |
|Падучева, Е.В., 2004. *Динамические модели в семантике лексики*. М.: Языки славянской культуры.| Elena Paducheva. 2004. Dinamicheskiye modeli v semantike leksiki. Languages of Slavonic culture. |Paducheva2004 |
|Падучева, Е.В., 2010. *Семантические исследования: Семантика времени и вида в русском языке; Семантика нарратива*. М.: Языки славянской культуры. | Elena Paducheva. 2010. Semanticheskiye issledovaniya: Semantika vremeni i vida v russkom yazyke; Semantika narrativa. Languages of Slavonic culture.|Paducheva2010 |
|Падучева, Е.В., 2013. *Русское отрицательное предложение*. М.: Языки славянской культуры |Elena Paducheva. 2013. Russkoye otritsatel’noye predlozheniye. Languages of Slavonic culture. |Paducheva2013 |
|Селиверстова, О.Н., 2004. *Труды по семантике*. М.: Языки славянской культуры | Olga Seliverstova. 2004. Trudy po semantike. Languages of Slavonic culture.|Seliverstova |
| Набор данных ЕГЭ по русскому языку | Shavrina et al. 2020. [Humans Keep It One Hundred: an Overview of AI Journey](https://aclanthology.org/2020.lrec-1.277/) |USE5, USE7, USE8 |
</details>
<details>
<summary>Machine-generated sentences</summary>
<br>
**Datasets**
|Original source |Source id|
|---|---|
|Mikel Artetxe and Holger Schwenk. 2019. [Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond](https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00288/43523/Massively-Multilingual-Sentence-Embeddings-for)|Tatoeba |
|Holger Schwenk et al. 2021. [WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia](https://aclanthology.org/2021.eacl-main.115/)|WikiMatrix |
|Ye Qi et al. 2018. [When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation?](https://aclanthology.org/N18-2084/)|TED |
|Alexandra Antonova and Alexey Misyurev. 2011. [Building a Web-Based Parallel Corpus and Filtering Out Machine-Translated Text](https://aclanthology.org/W11-1218/)|YandexCorpus |
**Models**
[EasyNMT models](https://github.com/UKPLab/EasyNMT):
1. OPUS-MT. Jörg Tiedemann and Santhosh Thottingal. 2020. [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/)
2. M-BART50. Yuqing Tang et al. 2020. [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401)
3. M2M-100. Angela Fan et al. 2021. [Beyond English-Centric Multilingual Machine Translation](https://jmlr.org/papers/volume22/20-1307/20-1307.pdf)
[Paraphrase generation models](https://github.com/RussianNLP/russian_paraphrasers):
1. [ruGPT2-Large](https://huggingface.co/sberbank-ai/rugpt2large)
2. [ruT5](https://huggingface.co/cointegrated/rut5-base-paraphraser)
3. mT5. Linting Xue et al. 2021. [mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer](https://aclanthology.org/2021.naacl-main.41/)
</details>
### Annotations
#### Annotation process
The out-of-domain sentences undergo a two-stage annotation procedure on [Toloka](https://toloka.ai), a crowd-sourcing platform for data labeling.
Each stage includes an unpaid training phase with explanations, control tasks for tracking annotation quality, and the main annotation task. Before starting, the worker is given detailed instructions describing the task, explaining the labels, and showing plenty of examples.
The instruction is available at any time during both the training and main annotation phases. To get access to the main phase, the worker should first complete the training phase by labeling more than 70% of its examples correctly. Each trained worker receives a page with five sentences, one of which is a control one.
We collect the majority vote labels via a dy- namic overlap from three to five workers after filtering them by response time and performance on control tasks.
- **Stage 1: Acceptability Judgments**
The first annotation stage defines whether a given sentence is acceptable or not. Access to the project is granted to workers certified as native speakers of Russian by Toloka and ranked top-60% workers according to the Toloka rating system.
Each worker answers 30 examples in the training phase. Each training example is accompanied by an explanation that appears in an incorrect answer.
The main annotation phase counts 3.6k machine-generated sentences. The pay rate is on average $2.55/hr, which is twice the amount of the hourly minimum wage in Russia. Each of 1.3k trained workers get paid, but we keep votes from only 960 workers whose annotation quality rate on the control sentences is more than 50%.
- **Stage 2: Violation Categories**
The second stage includes validation and annotation of sentences labeled unacceptable on Stage 1 according to five answer options: “Morphology”, “Syntax”, “Semantics”, “Hallucinations” and “Other”. The task is framed as a multi-label classification, i.e., the sentence may contain more than one violation in some rare cases or be re-labeled as acceptable.
We create a team of 30 annotators who are undergraduate BA and MA in philology and linguistics from several Russian universities. The students are asked to study the works on CoLA, TGEA, and hallucinations. We also hold an online seminar to discuss the works and clarify the task specifics. Each student undergoes platform-based training on 15 examples before moving onto the main phase of 1.3k sentences.
The students are paid on average $5.42/hr and are eligible to get credits for an academic course or an internship. This stage provides direct interaction between authors and students in a group chat. We keep submissions with more than 30 seconds of response time per page and collect the majority vote labels for each answer independently.
Sentences having more than one violation category or labeled as “Other” by the majority are filtered out.
### Personal and Sensitive Information
The annotators are warned about potentially sensitive topics in data (e.g., politics, culture, and religion).
## Considerations for Using the Data
### Social Impact of Dataset
RuCoLA may serve as training data for acceptability classifiers, which may benefit the quality of generated texts.
We recognize that such improvements in text generation may lead to misuse of LMs for malicious purposes. However, our corpus can be used to train adversarial defense and artificial text detection models.
We introduce a novel dataset for **research and development needs**, and the potential negative uses are not lost on us.
### Discussion of Biases
Although we aim to control the number of high-frequency tokens in the RuCoLA’s sentences, we assume that potential word frequency distribution shift between LMs’ pretraining corpora and our corpus can introduce bias in the evaluation.
Furthermore, linguistic publications represent a specific domain as the primary source of acceptability judgments. On the one hand, it can lead to a domain shift when using RuCoLA for practical purposes.
On the other hand, we observe moderate acceptability classification performance on the out-of-domain test, which spans multiple domains, ranging from subtitles to Wikipedia.
### Other Known Limitations
- **Data Collection**
Acceptability judgments datasets require a source of unacceptable sentences.
Collecting judgments from linguistic literature has become a standard practice replicated in multiple languages. However, this approach has several limitations. First, many studies raise concerns about the reliability and reproducibility of acceptability judgments. Second, the linguists’ judgments may limit data representativeness, as they may not reflect the errors that speakers tend to produce. Third, enriching acceptability judgments datasets is time-consuming, while creating new ones can be challenging due to limited resources, e.g., in low-resource languages.
- **Expert vs. Non-expert**
One of the open methodological questions on acceptability judgments is whether they should be collected from expert or non-expert speakers.
On the one hand, prior linguistic knowledge can introduce bias in reporting judgments. On the other hand, expertise may increase the quality of the linguists’ judgments over the ones of non-linguists. At the same time, the latter tend to be influenced by an individual’s exposure to ungrammatical language use.
The objective of involving students with a linguistic background is to maximize the annotation quality.
- **Fine-grained Annotation**
The coarse-grained annotation scheme of the RuCoLA’s unacceptable sentences relies on four major categories. While the annotation can be helpful for model error analysis, it limits the scope of LMs’ diagnostic evaluation concerning linguistic and machine-specific phenomena.
## Additional Information
### Dataset Curators
Correspondence: ```vmikhailovhse@gmail.com```
### Licensing Information
Our baseline code and acceptability labels are available under the Apache 2.0 license. The copyright (where applicable) of texts from the linguistic publications and resources remains with the original authors or publishers.
### Citation Information
```
@inproceedings{mikhailov-etal-2022-rucola,
title = "{R}u{C}o{LA}: {R}ussian Corpus of Linguistic Acceptability",
author = "Mikhailov, Vladislav and
Shamardina, Tatiana and
Ryabinin, Max and
Pestova, Alena and
Smurov, Ivan and
Artemova, Ekaterina",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.348",
pages = "5207--5227",
abstract = "Linguistic acceptability (LA) attracts the attention of the research community due to its many uses, such as testing the grammatical knowledge of language models and filtering implausible texts with acceptability classifiers.However, the application scope of LA in languages other than English is limited due to the lack of high-quality resources.To this end, we introduce the Russian Corpus of Linguistic Acceptability (RuCoLA), built from the ground up under the well-established binary LA approach. RuCoLA consists of 9.8k in-domain sentences from linguistic publications and 3.6k out-of-domain sentences produced by generative models. The out-of-domain set is created to facilitate the practical use of acceptability for improving language generation.Our paper describes the data collection protocol and presents a fine-grained analysis of acceptability classification experiments with a range of baseline approaches.In particular, we demonstrate that the most widely used language models still fall behind humans by a large margin, especially when detecting morphological and semantic errors. We release RuCoLA, the code of experiments, and a public leaderboard to assess the linguistic competence of language models for Russian.",
}
```
### Other
Please refer to our [paper](https://aclanthology.org/2022.emnlp-main.348/) for more details. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
FedML/databricks-dolly-15k-niid | FedML | 2023-09-05T12:03:26Z | 339 | 0 | null | [
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | 2023-09-05T12:03:26Z | 2023-09-01T09:51:54.000Z | 2023-09-01T09:51:54 | ---
license: cc-by-sa-3.0
language:
- en
size_categories:
- 10K<n<100K
configs:
- config_name: default
default: true
data_files:
- split: train
path: "train.parquet"
- split: test
path: "test.parquet"
dataset_info:
config_name: default
features:
- name: instruction
dtype: string
- name: context
dtype: string
- name: response
dtype: string
- name: category
dtype: string
---
This is a Non-IID split version of [databricks/databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k).
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
gap | null | 2023-04-05T10:06:30Z | 338 | 2 | gap | [
"task_categories:token-classification",
"task_ids:coreference-resolution",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"arxiv:1810.05201",
"region:us"
] | 2023-04-05T10:06:30Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
pretty_name: GAP Benchmark Suite
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- coreference-resolution
paperswithcode_id: gap
dataset_info:
features:
- name: ID
dtype: string
- name: Text
dtype: string
- name: Pronoun
dtype: string
- name: Pronoun-offset
dtype: int32
- name: A
dtype: string
- name: A-offset
dtype: int32
- name: A-coref
dtype: bool
- name: B
dtype: string
- name: B-offset
dtype: int32
- name: B-coref
dtype: bool
- name: URL
dtype: string
splits:
- name: train
num_bytes: 1095623
num_examples: 2000
- name: validation
num_bytes: 248329
num_examples: 454
- name: test
num_bytes: 1090462
num_examples: 2000
download_size: 2401971
dataset_size: 2434414
---
# Dataset Card for "gap"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/google-research-datasets/gap-coreference](https://github.com/google-research-datasets/gap-coreference)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [Mind the GAP: A Balanced Corpus of Gendered Ambiguous Pronouns](https://arxiv.org/abs/1810.05201)
- **Point of Contact:** [gap-coreference@google.com](mailto:gap-coreference@google.com)
- **Size of downloaded dataset files:** 2.40 MB
- **Size of the generated dataset:** 2.43 MB
- **Total amount of disk used:** 4.83 MB
### Dataset Summary
GAP is a gender-balanced dataset containing 8,908 coreference-labeled pairs of
(ambiguous pronoun, antecedent name), sampled from Wikipedia and released by
Google AI Language for the evaluation of coreference resolution in practical
applications.
### 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:** 2.40 MB
- **Size of the generated dataset:** 2.43 MB
- **Total amount of disk used:** 4.83 MB
An example of 'validation' looks as follows.
```
{
"A": "aliquam ultrices sagittis",
"A-coref": false,
"A-offset": 208,
"B": "elementum curabitur vitae",
"B-coref": false,
"B-offset": 435,
"ID": "validation-1",
"Pronoun": "condimentum mattis pellentesque",
"Pronoun-offset": 948,
"Text": "Lorem ipsum dolor",
"URL": "sem fringilla ut"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `ID`: a `string` feature.
- `Text`: a `string` feature.
- `Pronoun`: a `string` feature.
- `Pronoun-offset`: a `int32` feature.
- `A`: a `string` feature.
- `A-offset`: a `int32` feature.
- `A-coref`: a `bool` feature.
- `B`: a `string` feature.
- `B-offset`: a `int32` feature.
- `B-coref`: a `bool` feature.
- `URL`: a `string` feature.
### Data Splits
| name |train|validation|test|
|-------|----:|---------:|---:|
|default| 2000| 454|2000|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@article{webster-etal-2018-mind,
title = "Mind the {GAP}: A Balanced Corpus of Gendered Ambiguous Pronouns",
author = "Webster, Kellie and
Recasens, Marta and
Axelrod, Vera and
Baldridge, Jason",
journal = "Transactions of the Association for Computational Linguistics",
volume = "6",
year = "2018",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/Q18-1042",
doi = "10.1162/tacl_a_00240",
pages = "605--617",
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@otakumesi](https://github.com/otakumesi), [@lewtun](https://github.com/lewtun) for adding this dataset. | [
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roman_urdu | null | 2023-01-25T14:43:17Z | 338 | 2 | roman-urdu-data-set | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ur",
"license:unknown",
"region:us"
] | 2023-01-25T14:43:17Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- ur
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: roman-urdu-data-set
pretty_name: Roman Urdu Dataset
dataset_info:
features:
- name: sentence
dtype: string
- name: sentiment
dtype:
class_label:
names:
'0': Positive
'1': Negative
'2': Neutral
splits:
- name: train
num_bytes: 1633423
num_examples: 20229
download_size: 1628349
dataset_size: 1633423
---
# Dataset Card for Roman Urdu 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
- **Repository:** [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Roman+Urdu+Data+Set)
- **Point of Contact:** [Zareen Sharf](mailto:zareensharf76@gmail.com)
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Urdu
## Dataset Structure
[More Information Needed]
### Data Instances
```
Wah je wah,Positive,
```
### Data Fields
Each row consists of a short Urdu text, followed by a sentiment label. The labels are one of `Positive`, `Negative`, and `Neutral`. Note that the original source file is a comma-separated values file.
* `sentence`: A short Urdu text
* `label`: One of `Positive`, `Negative`, and `Neutral`, indicating the polarity of the sentiment expressed in the sentence
## 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
#### 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{Sharf:2018,
title = "Performing Natural Language Processing on Roman Urdu Datasets",
authors = "Zareen Sharf and Saif Ur Rahman",
booktitle = "International Journal of Computer Science and Network Security",
volume = "18",
number = "1",
pages = "141-148",
year = "2018"
}
@misc{Dua:2019,
author = "Dua, Dheeru and Graff, Casey",
year = "2017",
title = "{UCI} Machine Learning Repository",
url = "http://archive.ics.uci.edu/ml",
institution = "University of California, Irvine, School of Information and Computer Sciences"
}
```
### Contributions
Thanks to [@jaketae](https://github.com/jaketae) for adding this dataset. | [
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CAiRE/ASCEND | CAiRE | 2022-10-24T12:43:58Z | 338 | 10 | null | [
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"language:zh",
"license:cc-by-sa-4.0",
"speech-recognition",
"code-switching",
"arxiv:2112.06223",
"region:us"
] | 2022-10-24T12:43:58Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
- zh
license:
- cc-by-sa-4.0
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- automatic-speech-recognition
task_ids: []
pretty_name: 'ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in
Multi-turn Conversation'
tags:
- speech-recognition
- code-switching
---
# Dataset Card for ASCEND
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Usage](#usage)
- [Dataset Structure](#dataset-structure)
- [Data Splits](#data-instances)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** [Needs More Information]
- **Paper:** https://arxiv.org/abs/2112.06223
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
ASCEND (A Spontaneous Chinese-English Dataset) introduces a high-quality resource of spontaneous multi-turn conversational dialogue Chinese-English code-switching corpus collected in Hong Kong. ASCEND consists of 10.62 hours of spontaneous speech with a total of ~12.3K utterances. The corpus is split into 3 sets: training, validation, and test with a ratio of 8:1:1 while maintaining a balanced gender proportion on each set.
### Supported Tasks and Leaderboards
Code-switching
### Languages
Chinese and English
## Usage
To obtain the full dataset (complete with train, validation, and test set), simply run this:
```
import datasets
dataset = datasets.load_dataset("CAiRE/ASCEND")
```
## Dataset Structure
A typical data point comprises the path to the audio file, the loaded audio array, and its transcription. Additional fields include datapoint id, duration, language, speaker id, session id, and topic.
```
{
'id': '00644',
'path': '.cache/huggingface/datasets/downloads/extracted/f0b33b5266cd9452ee310eef3577cf7adb7f29aa54dbff74b9a8ee406a55d614/waves/ses2_spk3_L13101_189.900_5.490.wav',
'audio': {
'path': '.cache/huggingface/datasets/downloads/extracted/f0b33b5266cd9452ee310eef3577cf7adb7f29aa54dbff74b9a8ee406a55d614/waves/ses2_spk3_L13101_189.900_5.490.wav',
'array': array([-6.1035156e-05, -1.8310547e-04, 3.0517578e-05, ...,
0.0000000e+00, -3.0517578e-05, 0.0000000e+00
], dtype = float32),
'sampling_rate': 16000
},
'transcription': '因为你不可能邀你的female friends去说走我们去play basketball',
'duration': 5.489999771118164,
'language': 'mixed',
'original_speaker_id': 3,
'session_id': 2,
'topic': 'sports'
}
```
### Data Splits
Number of utterances: 9,869 train, 1,130 validation, and 1,315 test.
## Additional Information
For comprehensive explanations, please check [our paper](https://arxiv.org/pdf/2112.06223.pdf).
### Licensing Information
Creative Common Attribution Share-Alike 4.0 International (CC-BY-SA 4.0)
### Citation Information
If you use our dataset, please cite us:
```
@inproceedings{lovenia2022ascend,
title={ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in Multi-turn Conversation},
author={Lovenia, Holy and Cahyawijaya, Samuel and Winata, Genta Indra and Xu, Peng and Yan, Xu and Liu, Zihan and Frieske, Rita and Yu, Tiezheng and Dai, Wenliang and Barezi, Elham J and others},
booktitle={Proceedings of the 13th Language Resources and Evaluation Conference (LREC)},
year={2022}
``` | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
wentingzhao/one-million-instructions | wentingzhao | 2023-09-16T03:03:51Z | 338 | 0 | null | [
"region:us"
] | 2023-09-16T03:03:51Z | 2023-09-16T03:03:41.000Z | 2023-09-16T03:03:41 | ---
dataset_info:
features:
- name: user
dtype: string
- name: system
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 327249922
num_examples: 2332040
download_size: 172927838
dataset_size: 327249922
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "one-million-instructions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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has_part | null | 2022-11-03T16:15:21Z | 337 | 0 | haspart-kb | [
"task_categories:text-classification",
"task_ids:text-scoring",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-Generics-KB",
"language:en",
"license:unknown",
"Meronym-Prediction",
"arxiv:2006.07510",
"region:us"
] | 2022-11-03T16:15:21Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-Generics-KB
task_categories:
- text-classification
task_ids:
- text-scoring
paperswithcode_id: haspart-kb
pretty_name: hasPart KB
tags:
- Meronym-Prediction
dataset_info:
features:
- name: arg1
dtype: string
- name: arg2
dtype: string
- name: score
dtype: float64
- name: wikipedia_primary_page
sequence: string
- name: synset
sequence: string
splits:
- name: train
num_bytes: 4363417
num_examples: 49848
download_size: 7437382
dataset_size: 4363417
---
# Dataset Card for [HasPart]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://allenai.org/data/haspartkb
- **Repository:**
- **Paper:** https://arxiv.org/abs/2006.07510
- **Leaderboard:**
- **Point of Contact:** Peter Clark <peterc@allenai.org>
### Dataset Summary
This dataset is a new knowledge-base (KB) of hasPart relationships, extracted from a large corpus of generic statements. Complementary to other resources available, it is the first which is all three of: accurate (90% precision), salient (covers relationships a person may mention), and has high coverage of common terms (approximated as within a 10 year old’s vocabulary), as well as having several times more hasPart entries than in the popular ontologies ConceptNet and WordNet. In addition, it contains information about quantifiers, argument modifiers, and links the entities to appropriate concepts in Wikipedia and WordNet.
### Supported Tasks and Leaderboards
Text Classification / Scoring - meronyms (e.g., `plant` has part `stem`)
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
```
{'arg1': 'plant',
'arg2': 'stem',
'score': 0.9991798414303377,
'synset': ['wn.plant.n.02', 'wn.stalk.n.02'],
'wikipedia_primary_page': ['Plant']}
```
### Data Fields
- `arg1`, `arg2`: These are the entities of the meronym, i.e., `arg1` _has\_part_ `arg2`
- `score`: Meronymic score per the procedure described below
- `synset`: Ontological classification from WordNet for the two entities
- `wikipedia_primary_page`: Wikipedia page of the entities
**Note**: some examples contain synset / wikipedia info for only one of the entities.
### Data Splits
Single training file
## Dataset Creation
Our approach to hasPart extraction has five steps:
1. Collect generic sentences from a large corpus
2. Train and apply a RoBERTa model to identify hasPart relations in those sentences
3. Normalize the entity names
4. Aggregate and filter the entries
5. Link the hasPart arguments to Wikipedia pages and WordNet senses
Rather than extract knowledge from arbitrary text, we extract hasPart relations from generic sentences, e.g., “Dogs have tails.”, in order to bias the process towards extractions that are general (apply to most members of a category) and salient (notable enough to write down). As a source of generic sentences, we use **GenericsKB**, a large repository of 3.4M standalone generics previously harvested from a Webcrawl of 1.7B sentences.
### Annotations
#### Annotation process
For each sentence _S_ in GenericsKB, we identify all noun chunks in the sentence using a noun chunker (spaCy's Doc.noun chunks). Each chunk is a candidate whole or part. Then, for each possible pair, we use a RoBERTa model to classify whether a hasPart relationship exists between them. The input sentence is presented to RoBERTa as a sequence of wordpiece tokens, with the start and end of the candidate hasPart arguments identified using special tokens, e.g.:
> `[CLS] [ARG1-B]Some pond snails[ARG1-E] have [ARG2-B]gills[ARG2-E] to
breathe in water.`
where `[ARG1/2-B/E]` are special tokens denoting the argument boundaries. The `[CLS]` token is projected to two class labels (hasPart/notHasPart), and a softmax layer is then applied, resulting in output probabilities for the class labels. We train with cross-entropy loss. We use RoBERTa-large (24 layers), each with a hidden size of 1024, and 16 attention heads, and a total of 355M parameters. We use the pre-trained weights available with the
model and further fine-tune the model parameters by training on our labeled data for 15 epochs. To train the model, we use a hand-annotated set of ∼2k examples.
#### 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
@misc{bhakthavatsalam2020dogs,
title={Do Dogs have Whiskers? A New Knowledge Base of hasPart Relations},
author={Sumithra Bhakthavatsalam and Kyle Richardson and Niket Tandon and Peter Clark},
year={2020},
eprint={2006.07510},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
### Contributions
Thanks to [@jeromeku](https://github.com/jeromeku) for adding this dataset. | [
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tamilmixsentiment | null | 2023-06-16T13:07:45Z | 337 | 0 | null | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"language:ta",
"license:unknown",
"region:us"
] | 2023-06-16T13:07:45Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
- ta
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: Tamilmixsentiment
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': Positive
'1': Negative
'2': Mixed_feelings
'3': unknown_state
'4': not-Tamil
splits:
- name: train
num_bytes: 790132
num_examples: 11335
- name: validation
num_bytes: 89618
num_examples: 1260
- name: test
num_bytes: 218764
num_examples: 3149
download_size: 1150792
dataset_size: 1098514
---
# Dataset Card for Tamilmixsentiment
## 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:** [Tamilmixsentiment Homepage](https://dravidian-codemix.github.io/2020/index.html)
- **Repository:** [Tamilmixsentiment repository](https://dravidian-codemix.github.io/2020/datasets.html)
- **Paper:** [Corpus Creation for Sentiment Analysis in Code-Mixed Tamil-English Text](https://www.aclweb.org/anthology/2020.sltu-1.28/)
- **Leaderboard:** [Rank list](https://drive.google.com/file/d/1Mf8-No-63koGRwdF13RrO01NAFBlNmI0/view?usp=sharing)
- **Point of Contact:** [Bharathi Raja Chakravarthi](mailto:bharathiraja.akr@gmail.com)
### Dataset Summary
The first gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube. This makes the largest general domain sentiment dataset for this relatively low-resource language with code-mixing phenomenon. The comment/post may contain more than one sentence but the average sentence length of the corpora is 1. Each comment/post is annotated with sentiment polarity at the comment/post level. This dataset also has class imbalance problems depicting real-world scenarios.
### Supported Tasks and Leaderboards
To identify sentiment polarity of the code-mixed dataset of comments/posts in Tamil-English collected from social media.
### Languages
Tamil-English code-switched. The dataset contains all the three types of code-mixed sentences - Inter-Sentential switch, Intra-Sentential switch and Tag switching. Most comments were written in Roman script with either Tamil grammar with English lexicon or English grammar with Tamil lexicon. Some comments were written in Tamil script with English expressions in between.
## Dataset Structure
### Data Instances
An example from the Tamilmixsentiment train set looks as follows:
```
text label
Trailer late ah parthavanga like podunga Positive
```
### Data Fields
- `text`: Tamil-English code-mixed comment.
- `label`: list of the possible sentiments "Positive", "Negative", "Mixed_feelings", "unknown_state", "not-Tamil"
### Data Splits
The entire dataset of 15,744 sentences was randomly shuffled and split into three parts as follows:
| | train | validation | test |
|------------------------------|------:|-----------:|-----:|
| Tamilmixsentiment | 11335 | 1260 | 3149 |
## Dataset Creation
### Curation Rationale
Sentiment analysis has become important in social media research (Yang and Eisenstein, 2017). Until recently these applications were created for high-resourced languages which analysed monolingual utterances. But social media in multilingual communities contains more code-mixed text. Code-mixing is common among speakers in a bilingual speech community. As English is seen as the language of prestige and education, the influence of lexicon, connectives and phrases from English language is common in spoken Tamil. Tamil has little annotated data for code-mixed scenarios. An annotated corpus developed for monolingual data cannot deal with code-mixed usage and therefore it fails to yield good results due to mixture of languages at different levels of linguistic analysis. Therefore this dataset of code-mixed Tamil-English sentiment annotated corpus is created.
### Source Data
#### Initial Data Collection and Normalization
The data was scraped from Youtube. In total 184,573 sentences for Tamil from YouTube comments from the trailers of a movies released in 2019. Many of the them contained sentences
that were either entirely written in English or code-mixed Tamil-English or fully written in Tamil. So we filtered out a non-code-mixed corpus based on language identification
at comment level using the langdetect library. The comment is written fully in Tamil or English, we discarded that comment since monolingual resources are available for these languages. We also identified if the sentences were written in other languages such as Hindi, Malayalam, Urdu, Telugu, and Kannada. We preprocessed the comments by removing the emoticons and applying a sentence
length filter. We want to create a code-mixed corpus of reasonable size with sentences that have fairly defined sentiments which will be useful for future research. Thus our filter removed sentences with less than five words and more than 15 words after cleaning the data. In the end we got 15,744 Tanglish sentences.
#### Who are the source language producers?
Youtube users
### Annotations
#### Annotation process
Three steps complete the annotation setup. First, each sentence was annotated by two people. In the second step, the data were collected if both of them agreed. In the case of conflict, a third person annotated the sentence. In the third step, if all the three of them did not agree, then two more annotators annotated the sentences.
#### Who are the annotators?
Eleven volunteers were involved in the process. All of them were native speakers of Tamil with diversity in gender, educational level and medium of instruction in their school education.
### 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{chakravarthi-etal-2020-corpus,
title = "Corpus Creation for Sentiment Analysis in Code-Mixed {T}amil-{E}nglish Text",
author = "Chakravarthi, Bharathi Raja and
Muralidaran, Vigneshwaran and
Priyadharshini, Ruba and
McCrae, John Philip",
booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources association",
url = "https://www.aclweb.org/anthology/2020.sltu-1.28",
pages = "202--210",
abstract = "Understanding the sentiment of a comment from a video or an image is an essential task in many applications. Sentiment analysis of a text can be useful for various decision-making processes. One such application is to analyse the popular sentiments of videos on social media based on viewer comments. However, comments from social media do not follow strict rules of grammar, and they contain mixing of more than one language, often written in non-native scripts. Non-availability of annotated code-mixed data for a low-resourced language like Tamil also adds difficulty to this problem. To overcome this, we created a gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube. In this paper, we describe the process of creating the corpus and assigning polarities. We present inter-annotator agreement and show the results of sentiment analysis trained on this corpus as a benchmark.",
language = "English",
ISBN = "979-10-95546-35-1",
}
```
### Contributions
Thanks to [@jamespaultg](https://github.com/jamespaultg) for adding this dataset. | [
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time_dial | null | 2022-11-03T16:07:53Z | 337 | 4 | timedial | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-sa-4.0",
"dialog-act-classification",
"arxiv:2106.04571",
"region:us"
] | 2022-11-03T16:07:53Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
pretty_name: 'TimeDial: Temporal Commonsense Reasoning in Dialog'
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
paperswithcode_id: timedial
tags:
- dialog-act-classification
dataset_info:
features:
- name: id
dtype: int32
- name: conversation
sequence: string
- name: correct1
dtype: string
- name: correct2
dtype: string
- name: incorrect1
dtype: string
- name: incorrect1_rule
dtype: string
- name: incorrect2
dtype: string
- name: incorrect2_rule
dtype: string
splits:
- name: test
num_bytes: 1449879
num_examples: 1446
download_size: 1613806
dataset_size: 1449879
---
# Dataset Card for TimeDial: Temporal Commonsense Reasoning in Dialog
## 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:** [TimeDial](https://github.com/google-research-datasets/timedial)
- **Paper:** [TimeDial: Temporal Commonsense Reasoning in Dialog](https://arxiv.org/abs/2106.04571)
- **Point of Contact:** [Please create an issue in the official repository](https://github.com/google-research-datasets/timedial)
### Dataset Summary
TimeDial presents a crowdsourced English challenge set, for temporal commonsense reasoning, formulated as a multiple choice cloze task with around 1.5k carefully curated dialogs. The dataset is derived from the DailyDialog ([Li et al., 2017](https://www.aclweb.org/anthology/I17-1099/)), which is a multi-turn dialog corpus.
In order to establish strong baselines and provide information on future model development, the authors conducted extensive experiments with state-of-the-art LMs. While humans can easily answer these questions (97.8\%), the best T5 model variant struggles on this challenge set (73\%). Moreover, our qualitative error analyses show that the models often rely on shallow, spurious features (particularly text matching), instead of truly doing reasoning over the context.
Detailed experiments and analyses can be found in their [paper](https://arxiv.org/pdf/2106.04571.pdf).
### Supported Tasks and Leaderboards
To be updated soon.
### Languages
The dataset is in English only.
## Dataset Structure
### Data Instances
```
{
"id": 1,
"conversation": [
"A: We need to take the accounts system offline to carry out the upgrade . But don't worry , it won't cause too much inconvenience . We're going to do it over the weekend .",
"B: How long will the system be down for ?",
"A: We'll be taking everything offline in about two hours ' time . It'll be down for a minimum of twelve hours . If everything goes according to plan , it should be up again by 6 pm on Saturday .",
"B: That's fine . We've allowed <MASK> to be on the safe side ."
],
"correct1": "forty-eight hours",
"correct2": "50 hours ",
"incorrect1": "two hours ",
"incorrect1_rule": "Rule 1",
"incorrect2": "12 days ",
"incorrect2_rule": "Rule 2"
}
```
### Data Fields
- "id": Unique identifier, as a integer
- "conversation": Dialog context with <MASK> span, as a string
- "correct1": Original <MASK> span, as a string
- "correct2": Additional correct option provided by annotators, as a string
- "incorrect1": Incorrect option #1 provided by annotators, as a string
- "incorrect1_rule": One of phrase matching ("Rule 1"), numeral matching ("Rule 2"), or open ended ("Rule 3"), as a string
- "incorrect2": Incorrect option #2 provided by annotators, as a string
- "incorrect2_rule": One of phrase matching ("Rule 1"), numeral matching ("Rule 2"), or open ended ("Rule 3"), as a string
### Data Splits
TimeDial dataset consists only of a test set of 1,104 dialog instances with 2 correct and 2 incorrect options with the following statistics:
| | Avg. |
|-----|-----|
|Turns per Dialog | 11.7 |
|Words per Turn | 16.5 |
|Time Spans per Dialog | 3 |
## Dataset Creation
### Curation Rationale
Although previous works have studied temporal reasoning in natural language, they have either focused on specific time-related concepts in isolation, such as temporal ordering and relation extraction, and/or dealt with limited context, such as single-sentence-based question answering and natural language inference.
In this work, they make the first systematic study of temporal commonsense reasoning in a multi-turn dialog setting. The task involves complex reasoning that requires operations like comparison and arithmetic reasoning over temporal expressions and the need for commonsense and world knowledge.
### Source Data
#### Initial Data Collection and Normalization
The TIMEDIAL dataset is derived from DailyDialog data (Li et al., 2017), which is a multi-turn dialog corpus containing over 13K English dialogs. Dialogs in this dataset consist of turn-taking between two people on topics over 10 broad categories, ranging from daily lives to financial topics.
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
The data collection process involves two steps: (1) identifying dialogs that are rich in temporal expressions, and (2) asking human annotators to provide correct and incorrect options for cloze instances derived from these dialogs. More details about the two steps:
1) Temporal expression identification: Here, they select dialogs that are rich with temporal information, in order to focus on complex temporal reasoning that arises in natural dialogs. Temporal expressions are automatically identified with SU-Time, an off-the-shelf temporal expression detector. They keep only the dialogs with more than 3 temporal expressions and at least one expression that contains numerals like “two weeks” (as opposed to non-numeric spans, like “summer”, “right now”, and “later”). In their initial experiment, they observe that language models can often correctly predict these non-numerical temporal phrases.
2) Human annotated options: Next, they make spans in the dialogs. For a dialog, they mask out each temporal expression that contains numerals, each resulting in a cloze question that is then sent for human annotation.
This resulted in 1,526 instances for annotation. For each masked span in each dialog, they obtain human annotation to derive a fixed set of correct and incorrect options given the context. Concretely, given a masked dialog and a seed correct answer (i.e., the original text) for the masked span, the annotators were asked to (1) come up with an alternative correct answer that makes sense in the dialog adhering to commonsense, and (2) formulate two incorrect answers that have no possibility of making sense in the dialog context. They highlight all time expressions in the context to make it easier for annotators to select reasonable time expressions.
#### Who are the annotators?
They are English linguists.
### 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
TimeDial dataset is licensed under CC BY-NC-SA 4.0.
### Citation Information
```
@inproceedings{qin-etal-2021-timedial,
title = "{TimeDial: Temporal Commonsense Reasoning in Dialog}",
author = "Qin, Lianhui and Gupta, Aditya and Upadhyay, Shyam and He, Luheng and Choi, Yejin and Faruqui, Manaal",
booktitle = "Proc. of ACL",
year = "2021"
}
```
### Contributions
Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
iarfmoose/question_generator | iarfmoose | 2021-11-29T05:22:03Z | 337 | 4 | null | [
"region:us"
] | 2021-11-29T05:22:03Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | This dataset is made up of data taken from SQuAD v2.0, RACE, CoQA, and MSMARCO. Some examples have been filtered out of the original datasets and others have been modified.
There are two fields; question and text. The question field contains the question, and the text field contains both the answer and the context in the following format:
"\<answer> (answer text) \<context> (context text)"
The <answer> and <context> are included as special tokens in the question generator's tokenizer.
This dataset is intended to be used with the [question_generator repo](https://github.com/AMontgomerie/question_generator) to train the question generator model.
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alistvt/coqa-flat | alistvt | 2022-01-23T01:21:14Z | 336 | 0 | null | [
"region:us"
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Fazzie/Teyvat | Fazzie | 2022-12-13T02:09:42Z | 336 | 18 | null | [
"task_categories:text-to-image",
"annotations_creators:no-annotation",
"language_creators:found",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | 2022-12-13T02:09:42Z | 2022-11-16T03:47:33.000Z | 2022-11-16T03:47:33 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- unknown
source_datasets:
- original
task_categories:
- text-to-image
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 71202
num_examples: 234
download_size: 466995417
dataset_size: 71202
---
# Dataset Card for Teyvat BLIP captions
Dataset used to train [Teyvat characters text to image model](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion).
BLIP generated captions for characters images from [genshin-impact fandom wiki](https://genshin-impact.fandom.com/wiki/Character#Playable_Characters)and [biligame wiki for genshin impact](https://wiki.biligame.com/ys/%E8%A7%92%E8%89%B2).
For each row the dataset contains `image` and `text` keys. `image` is a varying size PIL png, and `text` is the accompanying text caption. Only a train split is provided.
The `text` include the tag `Teyvat`, `Name`,`Element`, `Weapon`, `Region`, `Model type`, and `Description`, the `Description` is captioned with the [pre-trained BLIP model](https://github.com/salesforce/BLIP).
## Examples
<img src = "https://huggingface.co/datasets/Fazzie/Teyvat/resolve/main/data/Ganyu_001.png" title = "Ganyu_001.png" style="max-width: 20%;" >
> Teyvat, Name:Ganyu, Element:Cryo, Weapon:Bow, Region:Liyue, Model type:Medium Female, Description:an anime character with blue hair and blue eyes
<img src = "https://huggingface.co/datasets/Fazzie/Teyvat/resolve/main/data/Ganyu_002.png" title = "Ganyu_002.png" style="max-width: 20%;" >
> Teyvat, Name:Ganyu, Element:Cryo, Weapon:Bow, Region:Liyue, Model type:Medium Female, Description:an anime character with blue hair and blue eyes
<img src = "https://huggingface.co/datasets/Fazzie/Teyvat/resolve/main/data/Keqing_003.png" title = "Keqing_003.png" style="max-width: 20%;" >
> Teyvat, Name:Keqing, Element:Electro, Weapon:Sword, Region:Liyue, Model type:Medium Female, Description:a anime girl with long white hair and blue eyes
<img src = "https://huggingface.co/datasets/Fazzie/Teyvat/resolve/main/data/Keqing_004.png" title = "Keqing_004.png" style="max-width: 20%;" >
> Teyvat, Name:Keqing, Element:Electro, Weapon:Sword, Region:Liyue, Model type:Medium Female, Description:an anime character wearing a purple dress and cat ears | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
alexandrainst/audio_test_dataset | alexandrainst | 2023-05-01T14:28:58Z | 336 | 0 | null | [
"size_categories:n<1K",
"language:da",
"license:cc0-1.0",
"region:us"
] | 2023-05-01T14:28:58Z | 2023-05-01T14:24:51.000Z | 2023-05-01T14:24:51 | ---
dataset_info:
features:
- name: client_id
dtype: string
- name: path
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 48000
- name: sentence
dtype: string
- name: up_votes
dtype: int64
- name: down_votes
dtype: int64
- name: age
dtype: string
- name: gender
dtype: string
- name: accent
dtype: string
- name: locale
dtype: string
- name: segment
dtype: string
- name: variant
dtype: string
splits:
- name: train
num_bytes: 108571
num_examples: 5
- name: validation
num_bytes: 116850
num_examples: 5
- name: test
num_bytes: 78943
num_examples: 5
- name: other
num_bytes: 101436
num_examples: 5
- name: invalidated
num_bytes: 156925
num_examples: 5
download_size: 590682
dataset_size: 562725
license: cc0-1.0
language:
- da
size_categories:
- n<1K
---
# Dataset Card for "audio_test_dataset"
This dataset consists of the first 5 samples of [mozilla-foundation/common_voice_13_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_13_0) and is only used for unit testing. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
mnaguib/WikiNER | mnaguib | 2023-10-26T15:55:13Z | 336 | 0 | null | [
"region:us"
] | 2023-10-26T15:55:13Z | 2023-07-28T16:08:10.000Z | 2023-07-28T16:08:10 | ---
configs:
- config_name: en
data_files:
- split: train
path: "data/en/train.parquet"
- split: test
path: "data/en/test.parquet"
- config_name: fr
data_files:
- split: train
path: "data/fr/train.parquet"
- split: test
path: "data/fr/test.parquet"
- config_name: es
data_files:
- split: train
path: "data/es/train.parquet"
- split: test
path: "data/es/test.parquet"
- config_name: de
data_files:
- split: train
path: "data/de/train.parquet"
- split: test
path: "data/de/test.parquet"
- config_name: it
data_files:
- split: train
path: "data/it/train.parquet"
- split: test
path: "data/it/test.parquet"
- config_name: ru
data_files:
- split: train
path: "data/ru/train.parquet"
- split: test
path: "data/ru/test.parquet"
- config_name: pl
data_files:
- split: train
path: "data/pl/train.parquet"
- split: test
path: "data/pl/test.parquet"
- config_name: pt
data_files:
- split: train
path: "data/pt/train.parquet"
- split: test
path: "data/pt/test.parquet"
---
WikiNER is a multilingual silver-standard annotated NER dataset. It consists in a late-2010 snapshot of Wikipedia in nine languages. Hyperlinks referring to persons, locations or organizations were automatically annotated.
```
@Article{nothman2012:artint:wikiner,
author = {Joel Nothman and Nicky Ringland and Will Radford and Tara Murphy and James R. Curran},
title = {Learning multilingual named entity recognition from {Wikipedia}},
journal = {Artificial Intelligence},
publisher = {Elsevier},
volume = {194},
pages = {151--175},
year = {2012},
doi = {10.1016/j.artint.2012.03.006},
url = {http://dx.doi.org/10.1016/j.artint.2012.03.006}
}
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ContextualAI/nq_open | ContextualAI | 2023-10-07T00:34:08Z | 336 | 0 | null | [
"region:us"
] | 2023-10-07T00:34:08Z | 2023-10-07T00:33:44.000Z | 2023-10-07T00:33:44 | ---
dataset_info:
features:
- name: query
dtype: string
- name: gold_generation
sequence: string
splits:
- name: train
num_bytes: 5990520
num_examples: 79168
- name: dev
num_bytes: 660716
num_examples: 8757
- name: test
num_bytes: 313829
num_examples: 3610
download_size: 4681299
dataset_size: 6965065
---
# Dataset Card for "nq_open"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Icaruas/ragfinetune | Icaruas | 2023-11-18T09:29:08Z | 336 | 0 | null | [
"region:us"
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curiosity_dialogs | null | 2023-01-25T14:28:58Z | 335 | 7 | curiosity | [
"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-nc-4.0",
"conversational-curiosity",
"region:us"
] | 2023-01-25T14:28:58Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- dialogue-modeling
paperswithcode_id: curiosity
pretty_name: Curiosity Dataset
tags:
- conversational-curiosity
dataset_info:
features:
- name: messages
sequence:
- name: message
dtype: string
- name: liked
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
- name: sender
dtype:
class_label:
names:
'0': user
'1': assistant
- name: facts
sequence:
- name: fid
dtype: int32
- name: used
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
- name: source
dtype:
class_label:
names:
'0': section
'1': known
'2': random
- name: message_id
dtype: string
- name: dialog_acts
sequence: string
- name: known_entities
sequence: string
- name: focus_entity
dtype: string
- name: dialog_id
dtype: int32
- name: inferred_steps
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
- name: created_time
dtype: int64
- name: aspects
sequence: string
- name: first_aspect
dtype: string
- name: second_aspect
dtype: string
- name: shuffle_facts
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
- name: related_entities
sequence: string
- name: tag
dtype: string
- name: user_id
dtype: int32
- name: assistant_id
dtype: int32
- name: is_annotated
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
- name: user_dialog_rating
dtype: int32
- name: user_other_agent_rating
dtype: int32
- name: assistant_dialog_rating
dtype: int32
- name: assistant_other_agent_rating
dtype: int32
- name: reported
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
- name: annotated
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
config_name: curiosity_dialogs
splits:
- name: train
num_bytes: 37198297
num_examples: 10287
- name: val
num_bytes: 4914487
num_examples: 1287
- name: test
num_bytes: 4915613
num_examples: 1287
- name: test_zero
num_bytes: 4333191
num_examples: 1187
download_size: 92169165
dataset_size: 51361588
---
# Dataset Card for Curiosity 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:** [Curiosity Dataset Homepage](https://www.pedro.ai/curiosity)
- **Repository:** [Curiosity Dataset Repository](https://github.com/facebookresearch/curiosity)
- **Paper:** [ACL Anthology](https://www.aclweb.org/anthology/2020.emnlp-main.655/)
- **Point of Contact:** [Pedro Rodriguez](https://mailhide.io/e/wbfjM)
### Dataset Summary
Curiosity dataset consists of 14K English dialogs (181K utterances) where users and assistants converse about geographic topics like geopolitical entities and locations. This dataset is annotated with pre-existing user knowledge, message-level dialog acts, grounding to Wikipedia, and user reactions to messages.
### Supported Tasks and Leaderboards
* `text-generation-other-conversational-curiosity`: The dataset can be used to train a model for Conversational Curiosity, which consists in the testing of the hypothesis that engagement increases when users are presented with facts related to what they know. Success on this task is typically measured by achieving a *high* [Accuracy](https://huggingface.co/metrics/accuracy) and [F1 Score](https://huggingface.co/metrics/f1).
### Languages
The text in the dataset is in English collected by crowd-souring. The associated BCP-47 code is `en`.
## Dataset Structure
### Data Instances
A typical data point consists of dialogs between an user and an assistant, which is followed by the different attributes of the particular dialog.
An example from the Curiosity Dataset train set looks as follows:
```
{'annotated': 1,
'aspects': ['Media', 'Politics and government'],
'assistant_dialog_rating': 5,
'assistant_id': 341,
'assistant_other_agent_rating': 5,
'created_time': 1571783665,
'dialog_id': 21922,
'first_aspect': 'Media',
'focus_entity': 'Namibia',
'inferred_steps': 1,
'is_annotated': 0,
'known_entities': ['South Africa', 'United Kingdom', 'Portugal'],
'messages': {'dialog_acts': [['request_topic'],
['inform_response'],
['request_aspect'],
['inform_response'],
['request_followup'],
['inform_response'],
['request_aspect', 'feedback_positive'],
['inform_response'],
['request_followup'],
['inform_response'],
[],
[]],
'facts': [{'fid': [], 'source': [], 'used': []},
{'fid': [77870, 77676, 77816, 77814, 77775, 77659, 77877, 77785, 77867],
'source': [0, 1, 2, 2, 0, 2, 0, 1, 1],
'used': [0, 0, 0, 0, 0, 0, 0, 0, 0]},
{'fid': [], 'source': [], 'used': []},
{'fid': [77725, 77870, 77676, 77863, 77814, 77775, 77659, 77877, 77867],
'source': [2, 0, 1, 1, 2, 0, 2, 0, 1],
'used': [0, 0, 0, 0, 0, 0, 0, 0, 0]},
{'fid': [], 'source': [], 'used': []},
{'fid': [77694, 77661, 77863, 77780, 77671, 77704, 77869, 77693, 77877],
'source': [1, 2, 1, 0, 2, 2, 0, 1, 0],
'used': [0, 0, 0, 0, 0, 0, 0, 0, 1]},
{'fid': [], 'source': [], 'used': []},
{'fid': [77816, 77814, 77864, 77659, 77877, 77803, 77738, 77784, 77789],
'source': [2, 2, 0, 2, 0, 1, 1, 0, 1],
'used': [0, 0, 0, 0, 0, 0, 0, 0, 0]},
{'fid': [], 'source': [], 'used': []},
{'fid': [77694, 77776, 77780, 77696, 77707, 77693, 77778, 77702, 77743],
'source': [1, 0, 0, 2, 1, 1, 0, 2, 2],
'used': [0, 0, 0, 0, 0, 0, 0, 0, 0]},
{'fid': [], 'source': [], 'used': []},
{'fid': [77662, 77779, 77742, 77734, 77663, 77777, 77702, 77731, 77778],
'source': [1, 0, 2, 1, 2, 0, 2, 1, 0],
'used': [0, 0, 0, 0, 0, 0, 0, 0, 1]}],
'liked': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
'message': ['Hi. I want information about Namibia.',
'Nmbia is a country in southern Africa.',
'Do you have information about the media there?',
'A mentional amount of foriegn',
'What about it?',
"Media and journalists in Namibia are represented by the Namibia chapter of the Media Institute of 'southern Africa and the Editors Forum of Namibia.",
'Interesting! What can you tell me about the politics and government?',
'Namibia formed the Namibian Defence Force, comprising former enemies in a 23-year bush war.',
'Do you have more information about it?',
"With a small army and a fragile economy , the Namibian government's principal foreign policy concern is developing strengthened ties within the Southern African region.",
"That's all I wanted to know. Thank you!",
'My pleasure!'],
'message_id': ['617343895',
'2842515356',
'4240816985',
'520711081',
'1292358002',
'3677078227',
'1563061125',
'1089028270',
'1607063839',
'113037558',
'1197873991',
'1399017322'],
'sender': [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1]},
'related_entities': ['Western Roman Empire',
'United Kingdom',
'Portuguese language',
'Southern African Development Community',
'South Africa',
'Kalahari Desert',
'Namib Desert',
'League of Nations',
'Afrikaans',
'Sub-Saharan Africa',
'Portugal',
'South-West Africa',
'Warmbad, Namibia',
'German language',
'NBC'],
'reported': 0,
'second_aspect': 'Politics and government',
'shuffle_facts': 1,
'tag': 'round_2',
'user_dialog_rating': 5,
'user_id': 207,
'user_other_agent_rating': 5}
```
### Data Fields
* `messages`: List of dialogs between the user and the assistant and their associated attributes
* `dialog_acts`: List of actions performed in the dialogs
* `facts`: List of facts returned by the assistant
* `fid`: Fact ID
* `source`: Source for the fact
* `used`: Whether facts were used before in the same dialog
* `liked`: List of values indicating whether each dialog was liked
* `message`: List of dialogs (messages) between the user and the assistant
* `message_id`: Message ID
* `sender`: Message author ID (numeric)
* `known_entities`: Rooted facts about entities the user knows
* `focus_entity` : Entity in focus in the dialogs
* `dialog_id `: Dialog ID
* `inferred_steps`: Number of inferred steps
* `created_time`: Time of creation of the dialog
* `aspects`: List of two aspects which the dialog is about
* `first_aspect`: First aspect
* `second_aspect`: Second aspect
* `shuffle_facts`: Whether facts were shuffled
* `related_entities` : List of fifteen related entities to the focus entity
* `tag`: Conversation tag
* `user_id`: User ID
* `assistant_id`: Assistant ID
* `is_annotated`: 0 or 1 (More Information Needed)
* `user_dialog_rating`: 1 - 5 (More Information Needed)
* `user_other_agent_rating`: 1 - 5 (More Information Needed)
* `assistant_dialog_rating`: 1 - 5 (More Information Needed)
* `assistant_other_agent_rating`: 1 - 5 (More Information Needed)
* `reported`: Whether the dialog was reported inappropriate
* `annotated`: 0 or 1 (More Information Needed)
### Data Splits
The data is split into a training, validation, test and test_zero set as per the original dataset split.
| | train | validation | test | test_zero |
|-----------------------|------:|-----------:|-----:|----------:|
| Input dialog examples | 10287 | 1287 | 1287 | 1187 |
## 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
[Attribution-NonCommercial 4.0 International](https://creativecommons.org/licenses/by-nc/4.0/legalcode)
### Citation Information
```
@inproceedings{rodriguez2020curiosity,
title = {Information Seeking in the Spirit of Learning: a Dataset for Conversational Curiosity},
author = {Pedro Rodriguez and Paul Crook and Seungwhan Moon and Zhiguang Wang},
year = 2020,
booktitle = {Empirical Methods in Natural Language Processing}
}
```
### Contributions
Thanks to [@vineeths96](https://github.com/vineeths96) for adding this dataset. | [
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event2Mind | null | 2023-04-05T10:06:10Z | 335 | 0 | event2mind | [
"task_categories:text2text-generation",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"common-sense-inference",
"arxiv:1805.06939",
"region:us"
] | 2023-04-05T10:06:10Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
pretty_name: Event2Mind
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: event2mind
tags:
- common-sense-inference
dataset_info:
features:
- name: Source
dtype: string
- name: Event
dtype: string
- name: Xintent
dtype: string
- name: Xemotion
dtype: string
- name: Otheremotion
dtype: string
- name: Xsent
dtype: string
- name: Osent
dtype: string
splits:
- name: test
num_bytes: 649273
num_examples: 5221
- name: train
num_bytes: 5916384
num_examples: 46472
- name: validation
num_bytes: 672365
num_examples: 5401
download_size: 1300770
dataset_size: 7238022
---
# Dataset Card for "event2Mind"
## 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://uwnlp.github.io/event2mind/](https://uwnlp.github.io/event2mind/)
- **Repository:** https://github.com/uwnlp/event2mind
- **Paper:** [Event2Mind: Commonsense Inference on Events, Intents, and Reactions](https://arxiv.org/abs/1805.06939)
- **Point of Contact:** [Hannah Rashkin](mailto:hrashkin@cs.washington.edu), [Maarten Sap](mailto:msap@cs.washington.edu)
- **Size of downloaded dataset files:** 1.30 MB
- **Size of the generated dataset:** 7.24 MB
- **Total amount of disk used:** 8.54 MB
### Dataset Summary
In Event2Mind, we explore the task of understanding stereotypical intents and reactions to events. Through crowdsourcing, we create a large corpus with 25,000 events and free-form descriptions of their intents and reactions, both of the event's subject and (potentially implied) other participants.
### 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:** 1.30 MB
- **Size of the generated dataset:** 7.24 MB
- **Total amount of disk used:** 8.54 MB
An example of 'validation' looks as follows.
```
{
"Event": "It shrinks in the wash",
"Osent": "1",
"Otheremotion": "[\"upset\", \"angry\"]",
"Source": "it_events",
"Xemotion": "[\"none\"]",
"Xintent": "[\"none\"]",
"Xsent": ""
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `Source`: a `string` feature.
- `Event`: a `string` feature.
- `Xintent`: a `string` feature.
- `Xemotion`: a `string` feature.
- `Otheremotion`: a `string` feature.
- `Xsent`: a `string` feature.
- `Osent`: a `string` feature.
### Data Splits
| name |train|validation|test|
|-------|----:|---------:|---:|
|default|46472| 5401|5221|
## 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{rashkin-etal-2018-event2mind,
title = "{E}vent2{M}ind: Commonsense Inference on Events, Intents, and Reactions",
author = "Rashkin, Hannah and
Sap, Maarten and
Allaway, Emily and
Smith, Noah A. and
Choi, Yejin",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-1043",
doi = "10.18653/v1/P18-1043",
pages = "463--473",
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset. | [
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DDSC/partial-danish-gigaword-no-twitter | DDSC | 2023-03-13T14:01:53Z | 335 | 4 | null | [
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:da",
"license:cc-by-4.0",
"region:us"
] | 2023-03-13T14:01:53Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- da
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text-generation
task_ids:
- language-modeling
pretty_name: Danish Gigaword Corpus (no Twitter)
language_bcp47:
- da
- da-bornholm
- da-synnejyl
---
# Dataset Card for [Danish Gigaword (no Twitter)]
## 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)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [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://gigaword.dk
- **Paper:**
http://www.derczynski.com/papers/dagw.pdf
### Dataset Summary
The Danish Gigaword Corpus contains text spanning several domains and forms. This version does *not* include the sections containing Tweets.
### Supported Tasks and Leaderboards
Pre-training of language models.
### Language
Danish
## Dataset Structure
The dataset contains text from 23 different sources which are thoroughly defined in [Source Data](#source-data). See the [homepage](https://gigaword.dk) or [paper](http://www.derczynski.com/papers/dagw.pdf) for more information.
### Data Instances
Each entry in the dataset consists of a single text with associated metadata
### Data Fields
An entry in the dataset consists of the following fields:
- `text`(`str`): The content of the document.
- `source` (`str`): The source of the document (see [Source Data](#source-data)).
- `doc_id` (`str`): An unique identifer for each document.
- `LICENSE` (`str`): The license of the document. The licenses vary according to the source.
- `uri` (`str`): The uri of the document. Not available for all sources.
- `data_built`(`str`): Date the document was built. Not avaialable for all sources.
### Data Splits
The entire corpus is provided in the `train` split.
## Dataset Creation
### Source Data
Below follows a brief overview of the sources in the corpus along with their individual license.
| Source | License |
| ----------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| adl | Creative Commons Legal Code 1.0 Universal |
| botxt | Creative Commons Legal Code 1.0 Universal |
| cc | Creative Commons Legal Code 1.0 Universal |
| danavis | Creative Commons Legal Code 1.0 Universal |
| dannet | [dannet license](https://cst.ku.dk/projekter/dannet/license.txt) |
| depbank | Attribution-ShareAlike 4.0 International |
| ep | Creative Commons Legal Code 1.0 Universal |
| ft | Creative Commons Legal Code 1.0 Universal |
| gutenberg | [gutenberg license](https://www.gutenberg.org/policy/license.html) |
| hest | Creative Commons Legal Code 1.0 Universal |
| jvj | Attribution-ShareAlike 4.0 International |
| naat | Creative Commons Legal Code 1.0 Universal |
| opensub | The data set comes with the same license as the original sources. Please, check the information about the source that is given on http://opus.nlpl.eu/OpenSubtitles-v2018.php |
| relig | Creative Commons Legal Code 1.0 Universal |
| retsinformationdk | Danish Copyright law at https://www.retsinformation.dk/forms/r0710.aspx?id=164796 states "§ 9. Love, administrative forskrifter, retsafgørelser og lignende offentlige aktstykker er ikke genstand for ophavsret. Stk. 2. Bestemmelsen i stk. 1 gælder ikke for værker, der fremtræder som selvstændige bidrag i de i stk. 1 nævnte aktstykker. Sådanne værker må dog gengives i forbindelse med aktstykket. Retten til videre udnyttelse afhænger af de i øvrigt gældende regler." |
| retspraksis | Creative Commons Legal Code 1.0 Universal |
| skat | Creative Commons Legal Code 1.0 Universal |
| spont | Creative Commons Legal Code 1.0 Universal |
| synne | Creative Commons Legal Code 1.0 Universal |
| tv2r | The owner of this content is TV2 Regionerne, Denmark. Creative Commons Attribution 4.0 International |
| wiki | Creative Commons Legal Code 1.0 Universal |
| wikibooks | Creative Commons Legal Code 1.0 Universal |
| wikisource | Creative Commons Legal Code 1.0 Universal |
These sources corresponds to the following top-level domains in the dataset:
```python
# mapping from domain to top-level domain
domain_mapping_dict = {
"retsinformationdk": "Legal",
"skat": "Legal",
"retspraksis": "Legal",
"hest": "Social Media",
"cc": "Web",
"adl": "Wiki & Books",
"botxt": "Other",
"danavis": "News",
"dannet": "dannet",
"depbank": "Other",
"ep": "Conversation",
"ft": "Conversation",
"gutenberg": "Wiki & Books",
"jvj": "Wiki & Books",
"naat": "Conversation",
"opensub": "Conversation",
"relig": "Wiki & Books",
"spont": "Conversation",
"synne": "Other",
"tv2r": "News",
"wiki": "Wiki & Books",
"wikibooks": "Wiki & Books",
"wikisource": "Wiki & Books",
"twfv19": "Social Media", # not present in this version of the dataset
}
```
And the following mapping translates between the short form and the long form of the source name
```python
# mapping from domain to its long name format
longname_mapping_dict = {
"retsinformationdk": "retsinformation.dk (Danish legal information)",
"skat": "Skat (Danish tax authority)",
"retspraksis": "retspraksis (Danish legal information)",
"hest": "Hestenettet (Danish debate forum)",
"cc": "Common Crawl",
"adl": " Archive for Danish Literature",
"botxt": "Bornholmsk (Danish dialect)",
"danavis": "Danish daily newspapers",
"dannet": "DanNet (Danish WordNet)",
"depbank": "Danish Dependency Treebank",
"ep": "European Parliament",
"ft": "Folketinget (Danish Parliament)",
"gutenberg": "Gutenberg",
"jvj": "Johannes V. Jensen (Danish poet)",
"naat": "NAAT",
"opensub": "Open Subtitles",
"relig": "Religious texts",
"spont": "Spontaneous speech",
"synne": "Synderjysk (Danish dialect)",
"tv2r": "TV 2 Radio (Danish news)",
"wiki": "Wikipedia",
"wikibooks": "Wikibooks",
"wikisource": "Wikisource",
"twfv19": "Twitter Folketingsvalget 2019 (Danish election tweets)", # not present in this version of the dataset
}
```
## Additional Information
### Licensing Information
If you use the data, you MUST acknowledge it. The license is CC-BY 4.0, Creative Commons with Attribution.
### Citation Information
Sample attributions:
In a press release:
> Modellen er præ-trænet på et datasæt fra The Danish Gigaword Project (https://gigaword.dk), der er udviklet af forskere fra IT-Universitetet i København
> The model is pre-trained using the Danish Gigaword Corpus (https://gigaword.dk), developed at the IT University of Copenhagen
In academic writing:
```
Derczynski, L., Ciosici, M. R., et al. (2021). The Danish Gigaword Corpus. In Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa 2021).
@inproceedings{dagw,
title = {{The Danish Gigaword Corpus}},
author = {Leon Derczynski and Manuel R. Ciosici and Rebekah Baglini and Morten H. Christiansen and Jacob Aarup Dalsgaard and Riccardo Fusaroli and Peter Juel Henrichsen and Rasmus Hvingelby and Andreas Kirkedal and Alex Speed Kjeldsen and Claus Ladefoged and Finn Årup Nielsen and Jens Madsen and Malte Lau Petersen and Jonathan Hvithamar Rystrøm and Daniel Varab},
year = 2021,
booktitle = {Proceedings of the 23rd Nordic Conference on Computational Linguistics},
publisher = {NEALT}
}
```
In a software product, tool, or service:
> Danish Gigaword Corpus: license - homepage
> Denne service er lavet med data fra The Danish Gigaword Corpus
### Contributions
Dataset created by Derczynski et al. (2021)
Derczynski, L., Ciosici, M. R., et al. (2021). The Danish Gigaword Corpus. In Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa 2021).
Thanks to [@HLasse](https://github.com/HLasse) for adding this dataset to the Hugging Face Hub. | [
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DDSC/twitter-sent | DDSC | 2022-07-01T15:44:26Z | 335 | 3 | 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:da",
"license:cc-by-4.0",
"region:us"
] | 2022-07-01T15:44:26Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- da
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: TwitterSent
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
---
# Dataset Card for DKHate
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Direct Download**: https://danlp.alexandra.dk/304bd159d5de/datasets/twitter.sentiment.zip
### Dataset Summary
This dataset consists of anonymised Danish Twitter data that has been annotated for sentiment analysis by the [Alexandra Institute](https://github.com/alexandrainst) - all credits go to them.
### Supported Tasks and Leaderboards
This dataset is suitable for sentiment analysis.
### Languages
This dataset is in Danish.
## Dataset Structure
### Data Instances
Every entry in the dataset has a tweet and an associated label.
### Data Fields
An entry in the dataset consists of the following fields:
- `text` (`str`): The tweet content.
- `label` (`str`): The label of the `text`. Can be "positiv", "neutral" or "negativ" for positive, neutral and negative sentiment, respectively.
### Data Splits
A `train` and `test` split is available, being identical to the original splits. There are 1,007 tweets in the training split and 431 in the test split.
## Additional Information
### Dataset Curators
The collection and annotation of the dataset is solely due to the [Alexandra Institute](https://github.com/alexandrainst). The tweets have been anonymised by [@saattrupdan](https://github.com/saattrupdan).
### Licensing Information
The dataset is released under the CC BY 4.0 license.
### Citation Information
```
@misc{twittersent,
title={TwitterSent},
author={Alexandra Institute},
year={2020},
note={\url{https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#twitsent}}
}
```
### Contributions
Thanks to [@saattrupdan](https://github.com/saattrupdan) for adding this dataset to the Hugging Face Hub. | [
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NbAiLab/NPSC | NbAiLab | 2023-04-25T09:52:08Z | 335 | 5 | null | [
"task_categories:automatic-speech-recognition",
"task_categories:audio-classification",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:2G<n<1B",
"source_datasets:original",
"language:no",
"language:nb",
"language:nn",
"license:cc0-1.0",
"speech-modeling",
"region:us"
] | 2023-04-25T09:52:08Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- 'no'
- nb
- nn
license:
- cc0-1.0
multilinguality:
- monolingual
size_categories:
- 2G<n<1B
source_datasets:
- original
task_categories:
- automatic-speech-recognition
- audio-classification
pretty_name: NPSC
tags:
- speech-modeling
---
# Dataset Card for NbAiLab/NPSC
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Data Fields](#data-fiels)
- [Dataset Creation](#dataset-creation)
- [Statistics](#statistics)
- [Document Types](#document-types)
- [Languages](#languages)
- [Publish Periode](#publish-periode)
- [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://www.nb.no/sprakbanken/
- **Repository:** https://www.nb.no/sprakbanken/ressurskatalog/oai-nb-no-sbr-58/
- **Paper:** https://www.nb.no/sprakbanken/
- **Point of Contact:** [Per Erik Solberg](mailto:per.solberg@nb.no)
The Norwegian Parliamentary Speech Corpus (NPSC) is a speech corpus made by the Norwegian Language Bank at the National Library of Norway in 2019-2021. The NPSC consists of recordings of speech from Stortinget, the Norwegian parliament, and corresponding orthographic transcriptions to Norwegian Bokmål and Norwegian Nynorsk. All transcriptions are done manually by trained linguists or philologists, and the manual transcriptions are subsequently proofread to ensure consistency and accuracy. Entire days of Parliamentary meetings are transcribed in the dataset.
This repository contains a version of the NPSC in the 🤗 Dataset Format. Note that the official release of the dataset, which can be found in [the repository of the Norwegian Language Bank](https://www.nb.no/sprakbanken/ressurskatalog/oai-nb-no-sbr-58/), contains more information than the version found here, including word-level metadata, metadata about the speakers, and detailed documentation.
## How to Use
```python
# Loads the 16K Bokmål corpus in streaming mode
from datasets import load_dataset
data = load_dataset("NbAiLab/NPSC", config="16K_mp3_bokmaal", streaming=True)
```
## Dataset Summary
The NPSC dataset contains JSON lines with language training data. The data loader will add audio data to this structure. Here is an example json object:
```json
{
"sentence_id": 49853,
"sentence_order": 0,
"speaker_id": 32,
"meeting_date": "20170110",
"speaker_name": "Olemic Thommessen",
"sentence_text": "Stortingets møte er lovlig satt",
"sentence_language_code": "nb-NO",
"text": "Stortingets møte er lovlig satt",
"start_time": 320246,
"end_time": 323590,
"normsentence_text": "Stortingets møte er lovlig satt",
"transsentence_text": "Stortingets møte er lovleg sett",
"translated": 1,
"audio": {"path": "audio/20170110-095504_320246_323590.wav","array": [.......]}
}
```
## Data Fields
|**Key** | **Type** | **Description** |
|:-----------|:------------|:------------|
|**sentence_id:** | Integer | Unique identifier of the sentence |
|**sentence_order** | Integer | A number indicating the order of the sentences in the meeting |
|**speaker_id** | Integer | The ID of the speaker. This can be linked to the original dataset containing thorough demographic and dialectal information about the speaker. |
|**meeting_date** | String | The date for the meeting in the format __yyyymmdd__ |
| **speaker_name** | String | Name of the speaker. All speakers were members of the Norwegian Parliament or members of the Norwegian Government at the meeting date |
| **sentence_text** | String | The sentence text. The transcribed text string of the sentence in non-normalized form. This is the text of the manual transcriptions, without any postprocessing (apart from corrections of known errors). It may contain interrupted words, non-standard words and function words with a pronunciation deviating from the written form. Detailed metadata about the words in the sentence can be found in the word-tokenized version of the corpus in the official release of the dataset. |
| **sentence_language_code** | String | The language code of the sentence. The following alternatives exists in the file: ['nb-NO'. 'nn-NO', 'en-US']|
| **text** | String | sentence text. This is a copy of "sentence_text". It is included here to make it more convenient to interleave with other datasets.|
| **start_time** | Integer | The start time of the sentence in milliseconds. This time is relative to the start of audiofile of the entire meeting, which can be accessed in the official release |
| **end_time** | Integer | End time. See comment above. |
| **normsentence_text** | String | Normalized sentence text. In this version of the transcription, numbers and dates are written in digits on standardized formats, and common abbreviations are used. These modifications to the original transcriptions are produced automatically using normalization grammars |
| **transsentence_text** | String | Translated sentence text. Whenever the original transcription is in Bokmål (nb-NO), this field contains a machine-translated version in Nynorsk (nn-NO), and vice versa |
| **translated** | Integer | A flag indicating whether a machine-translated version has been produced or not. Sentences in en-US have not been translated |
| **audio** | Array | The dataloader will encode the accociated audio files and provide them as an array containing 'path', 'sound array','sampling_rate' |
#### Initial Data Collection
The procedure for the dataset creation is described in detail in our paper.
## Statistics
| Feature | Value |
|:---------|-----------:|
| Duration, pauses included | 140,3 hours|
| Duration, pauses not included | 125,7 hours |
| Word count | 1,2 million |
| Sentence count | 64.531 |
| Language distribution | Nynorsk: 12,8%|
| | Bokmål: 87,2%|
| Gender distribution | Female: 38,3% |
| | Male: 61.7% |
## Considerations for Using the Data
This corpus contains speech data. All recordings are of members of Parliament in a public setting, and can be distributed without any restrains.
### Dataset Creators and Curators
The content of the dataset was created by the Norwegian Language Bank (Språkbanken) at the National Library of Norway. [Javier de la Rosa](mailto:versae@nb.no), [Freddy Wetjen](mailto:freddy.wetjen@nb.no), [Per Egil Kummervold](mailto:per.kummervold@nb.no), and [Andre Kaasen](mailto:andre.kasen@nb.no) all contributed in making this into a HuggingFace Dataset. Thanks to the HuggingFace team for assistance.
## License
The sound and the transcriptions are released under the [CC-ZERO-license](https://creativecommons.org/publicdomain/zero/1.0/). The curation of the HuggingFace Dataset is released under [CC-BY-SA-3-license](https://creativecommons.org/licenses/by-sa/3.0/).
### Citation Information
The following article gives detailed information about the corpus. Please refer to the article and this page if you are using this dataset:
```
@inproceedings{solberg2022norwegian,
title={The Norwegian Parliamentary Speech Corpus},
author={Solberg, Per Erik and Ortiz, Pablo},
booktitle={Proceedings of the 13th Language Resources and Evaluation Conference},
url={http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.106.pdf},
year={2022}
}
```
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
SetFit/mnli_mm | SetFit | 2022-02-28T13:56:44Z | 335 | 0 | null | [
"region:us"
] | 2022-02-28T13:56:44Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | # Glue MNLI
This dataset is a port of the official [`mnli` dataset](https://huggingface.co/datasets/glue/viewer/mnli/train) on the Hub.
It contains the mismatched version.
Note that the premise and hypothesis columns have been renamed to text1 and text2 respectively.
Also, the test split is not labeled; the label column values are always -1.
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frtna/jwt300_mt | frtna | 2021-12-08T22:29:26Z | 335 | 0 | null | [
"region:us"
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re_dial | null | 2022-11-18T21:41:23Z | 334 | 0 | redial | [
"task_categories:other",
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
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"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"dialogue-sentiment-classification",
"region:us"
] | 2022-11-18T21:41:23Z | 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:
- other
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: redial
pretty_name: ReDial (Recommendation Dialogues)
tags:
- dialogue-sentiment-classification
dataset_info:
features:
- name: movieMentions
list:
- name: movieId
dtype: string
- name: movieName
dtype: string
- name: respondentQuestions
list:
- name: movieId
dtype: string
- name: suggested
dtype: int32
- name: seen
dtype: int32
- name: liked
dtype: int32
- name: messages
list:
- name: timeOffset
dtype: int32
- name: text
dtype: string
- name: senderWorkerId
dtype: int32
- name: messageId
dtype: int32
- name: conversationId
dtype: int32
- name: respondentWorkerId
dtype: int32
- name: initiatorWorkerId
dtype: int32
- name: initiatorQuestions
list:
- name: movieId
dtype: string
- name: suggested
dtype: int32
- name: seen
dtype: int32
- name: liked
dtype: int32
splits:
- name: train
num_bytes: 13496125
num_examples: 10006
- name: test
num_bytes: 1731449
num_examples: 1342
download_size: 5765261
dataset_size: 15227574
---
# Dataset Card for ReDial (Recommendation Dialogues)
## 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:** [ReDial Dataset](https://redialdata.github.io/website/)
- **Repository:** [ReDialData](https://github.com/ReDialData/website/tree/data)
- **Paper:** [Towards Deep Conversational Recommendations](https://proceedings.neurips.cc/paper/2018/file/800de15c79c8d840f4e78d3af937d4d4-Paper.pdf)
- **Point of Contact:** [ReDial Google Group](https://groups.google.com/forum/embed/?place=forum/redial-dataset&showpopout=true#!forum/redial-dataset)
### Dataset Summary
ReDial (Recommendation Dialogues) is an annotated dataset of dialogues, where users
recommend movies to each other. The dataset was collected by a team of researchers working at
Polytechnique Montréal, MILA – Quebec AI Institute, Microsoft Research Montréal, HEC Montreal, and Element AI.
The dataset allows research at the intersection of goal-directed dialogue systems
(such as restaurant recommendation) and free-form (also called “chit-chat”) dialogue systems.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text in the dataset is in English.
## Dataset Structure
### Data Instances
JSON-formatted example of a typical instance in the dataset.
```
{
"movieMentions":{
"203371":"Final Fantasy: The Spirits Within (2001)",
"84779":"The Triplets of Belleville (2003)",
"122159":"Mary and Max (2009)",
"151313":"A Scanner Darkly (2006)",
"191602":"Waking Life (2001)",
"165710":"The Boss Baby (2017)"
},
"respondentQuestions":{
"203371":{
"suggested":1,
"seen":0,
"liked":1
},
"84779":{
"suggested":0,
"seen":1,
"liked":1
},
"122159":{
"suggested":0,
"seen":1,
"liked":1
},
"151313":{
"suggested":0,
"seen":1,
"liked":1
},
"191602":{
"suggested":0,
"seen":1,
"liked":1
},
"165710":{
"suggested":1,
"seen":0,
"liked":1
}
},
"messages":[
{
"timeOffset":0,
"text":"Hi there, how are you? I'm looking for movie recommendations",
"senderWorkerId":0,
"messageId":1021
},
{
"timeOffset":15,
"text":"I am doing okay. What kind of movies do you like?",
"senderWorkerId":1,
"messageId":1022
},
{
"timeOffset":66,
"text":"I like animations like @84779 and @191602",
"senderWorkerId":0,
"messageId":1023
},
{
"timeOffset":86,
"text":"I also enjoy @122159",
"senderWorkerId":0,
"messageId":1024
},
{
"timeOffset":95,
"text":"Anything artistic",
"senderWorkerId":0,
"messageId":1025
},
{
"timeOffset":135,
"text":"You might like @165710 that was a good movie.",
"senderWorkerId":1,
"messageId":1026
},
{
"timeOffset":151,
"text":"What's it about?",
"senderWorkerId":0,
"messageId":1027
},
{
"timeOffset":207,
"text":"It has Alec Baldwin it is about a baby that works for a company and gets adopted it is very funny",
"senderWorkerId":1,
"messageId":1028
},
{
"timeOffset":238,
"text":"That seems like a nice comedy",
"senderWorkerId":0,
"messageId":1029
},
{
"timeOffset":272,
"text":"Do you have any animated recommendations that are a bit more dramatic? Like @151313 for example",
"senderWorkerId":0,
"messageId":1030
},
{
"timeOffset":327,
"text":"I like comedies but I prefer films with a little more depth",
"senderWorkerId":0,
"messageId":1031
},
{
"timeOffset":467,
"text":"That is a tough one but I will remember something",
"senderWorkerId":1,
"messageId":1032
},
{
"timeOffset":509,
"text":"@203371 was a good one",
"senderWorkerId":1,
"messageId":1033
},
{
"timeOffset":564,
"text":"Ooh that seems cool! Thanks for the input. I'm ready to submit if you are.",
"senderWorkerId":0,
"messageId":1034
},
{
"timeOffset":571,
"text":"It is animated, sci fi, and has action",
"senderWorkerId":1,
"messageId":1035
},
{
"timeOffset":579,
"text":"Glad I could help",
"senderWorkerId":1,
"messageId":1036
},
{
"timeOffset":581,
"text":"Nice",
"senderWorkerId":0,
"messageId":1037
},
{
"timeOffset":591,
"text":"Take care, cheers!",
"senderWorkerId":0,
"messageId":1038
},
{
"timeOffset":608,
"text":"bye",
"senderWorkerId":1,
"messageId":1039
}
],
"conversationId":"391",
"respondentWorkerId":1,
"initiatorWorkerId":0,
"initiatorQuestions":{
"203371":{
"suggested":1,
"seen":0,
"liked":1
},
"84779":{
"suggested":0,
"seen":1,
"liked":1
},
"122159":{
"suggested":0,
"seen":1,
"liked":1
},
"151313":{
"suggested":0,
"seen":1,
"liked":1
},
"191602":{
"suggested":0,
"seen":1,
"liked":1
},
"165710":{
"suggested":1,
"seen":0,
"liked":1
}
}
}
```
### Data Fields
The dataset is published in the “jsonl” format, i.e., as a text file where each line corresponds to a Dialogue given as a valid JSON document.
A Dialogue contains these fields:
**conversationId:** an integer
**initiatorWorkerId:** an integer identifying to the worker initiating the conversation (the recommendation seeker)
**respondentWorkerId:** an integer identifying the worker responding to the initiator (the recommender)
**messages:** a list of Message objects
**movieMentions:** a dict mapping movie IDs mentioned in this dialogue to movie names
**initiatorQuestions:** a dictionary mapping movie IDs to the labels supplied by the initiator. Each label is a bool corresponding to whether the initiator has said he saw the movie, liked it, or suggested it.
**respondentQuestions:** a dictionary mapping movie IDs to the labels supplied by the respondent. Each label is a bool corresponding to whether the initiator has said he saw the movie, liked it, or suggested it.
Each Message contains these fields:
**messageId:** a unique ID for this message
**text:** a string with the actual message. The string may contain a token starting with @ followed by an integer. This is a movie ID which can be looked up in the movieMentions field of the Dialogue object.
**timeOffset:** time since start of dialogue in seconds
**senderWorkerId:** the ID of the worker sending the message, either initiatorWorkerId or respondentWorkerId.
The labels in initiatorQuestions and respondentQuestions have the following meaning:
*suggested:* 0 if it was mentioned by the seeker, 1 if it was a suggestion from the recommender
*seen:* 0 if the seeker has not seen the movie, 1 if they have seen it, 2 if they did not say
*liked:* 0 if the seeker did not like the movie, 1 if they liked it, 2 if they did not say
### Data Splits
The dataset contains a total of 11348 dialogues, 10006 for training and model selection, and 1342 for testing.
## Dataset Creation
### Curation Rationale
The dataset allows research at the intersection of goal-directed dialogue systems (such as restaurant recommendation) and free-form (also called “chit-chat”) dialogue systems.
In the dataset, users talk about which movies they like and which ones they do not like, which ones they have seen or not etc., and labels which we ensured agree between the two participants. This allows to research how sentiment is expressed in dialogues, which differs a lot from e.g. review websites.
The dialogues and the movies they mention form a curious bi-partite graph structure, which is related to how users talk about the movie (e.g. genre information).
Ignoring label information, this dataset can also be viewed as a limited domain chit-chat dialogue dataset.
### Source Data
#### Initial Data Collection and Normalization
Describe the data collection process. Describe any criteria for data selection or filtering. List any key words or search terms used. If possible, include runtime information for the collection process.
If data was collected from other pre-existing datasets, link to source here and to their [Hugging Face version](https://huggingface.co/datasets/dataset_name).
If the data was modified or normalized after being collected (e.g. if the data is word-tokenized), describe the process and the tools used.
#### Who are the source language producers?
Here we formalize the setup of a conversation involving recommendations for the purposes of data collection. To provide some additional structure to our data (and models) we define one person in the dialogue as the recommendation seeker and the other as the recommender.
To obtain data in this form, we developed an interface and pairing mechanism mediated by Amazon Mechanical Turk (AMT).
We pair up AMT workers and give each of them a role. The movie seeker has to explain what kind of movie he/she likes, and asks for movie suggestions. The recommender tries to understand the seeker’s movie tastes, and recommends movies. All exchanges of information and recommendations are made using natural language.
We add additional instructions to improve the data quality and guide the workers to dialogue the way we expect them to. Thus we ask to use formal language and that conversations contain roughly ten messages minimum. We also require that at least four different movies are mentioned in every conversation. Finally, we also ask to converse only about movies, and notably not to mention Mechanical Turk or the task itself.
In addition, we ask that every movie mention is tagged using the ‘@’ symbol. When workers type ‘@’, the following characters are used to find matching movie names, and workers can choose a movie from that list. This allows us to detect exactly what movies are mentioned and when. We gathered entities from DBpedia that were of type http://dbpedia.org/ontology/Film to obtain a list of movies, but also allow workers to add their own movies to the list if it is not present already. We obtained the release dates from the movie titles (e.g. http://dbpedia.org/page/American_Beauty_(1999_film), or, if the movie title does not contain that information, from an additional SPARQL request. Note that the year or release date of a movie can be essential to differentiate movies with the same name, but released at different dates.
We will refer to these additional labels as movie dialogue forms. Both workers have to answer these forms even though it really concerns the seeker’s movie tastes. Ideally, the two participants would give the same answer to every form, but it is possible that their answers do not coincide (because of carelessness, or dialogue ambiguity). The movie dialogue forms therefore allow us to evaluate sub-components of an overall neural dialogue system more systematically, for example one can train and evaluate a sentiment analysis model directly using these labels. %which could produce a reward for the dialogue agent.
In each conversation, the number of movies mentioned varies, so we have different numbers of movie dialogue form answers for each conversation. The distribution of the different classes of the movie dialogue form is shown in Table 1a. The liked/disliked/did not say label is highly imbalanced. This is standard for recommendation data, since people are naturally more likely to talk about movies that they like, and the recommender’s objective is to recommend movies that the seeker is likely to like.
### Annotations
#### Annotation process
Mentioned in above sub-section.
#### Who are the annotators?
For the AMT HIT we collect data in English and chose to restrict the data collection to countries where English is the main language. The fact that we pair workers together slows down the data collection since we ask that at least two persons are online at the same time to do the task, so a good amount of workers is required to make the collection possible. Meanwhile, the task is quite demanding, and we have to select qualified workers. HIT reward and qualification requirement were decisive to get good conversation quality while still ensuring that people could get paired together. We launched preliminary HITs to find a compromise and finally set the reward to $0.50 per person for each completed conversation (so each conversation costs us $1, plus taxes), and ask that workers meet the following requirements: (1)~Approval percentage greater than 95, (2)~Number of approved HITs greater than 1000, (3)~Their location must be in United States, Canada, United Kingdom, Australia, or New Zealand.
### Personal and Sensitive Information
Workers had to confirm a consent form before every task that explains what the data is being collected for and how it is going to be used.
## 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 collection was funded by Google, IBM, and NSERC, with editorial support from Microsoft Research.
### Licensing Information
The data is published under the CC BY 4.0 License.
### Citation Information
```
@inproceedings{li2018conversational,
title={Towards Deep Conversational Recommendations},
author={Li, Raymond and Kahou, Samira Ebrahimi and Schulz, Hannes and Michalski, Vincent and Charlin, Laurent and Pal, Chris},
booktitle={Advances in Neural Information Processing Systems 31 (NIPS 2018)},
year={2018}
}
```
### Contributions
Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
cdleong/piglatin-mt | cdleong | 2022-10-24T19:22:09Z | 334 | 0 | null | [
"task_categories:translation",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"region:us"
] | 2022-10-24T19:22:09Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
license:
- mit
multilinguality:
- translation
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
language_details: eng and engyay
---
## Dataset Description
- **Homepage:** cdleong.github.io
# Dataset Summary:
Pig-latin machine and English parallel machine translation corpus.
Based on [The Project Gutenberg EBook of "De Bello Gallico" and Other Commentaries](https://www.gutenberg.org/ebooks/10657)
Converted to pig-latin with https://github.com/bpabel/piglatin
Blank lines removed.
## Dataset Structure
```
DatasetDict({
train: Dataset({
features: ['translation'],
num_rows: 14778
})
validation: Dataset({
features: ['translation'],
num_rows: 1000
})
})
```
### Data Instances
```
{
'translation':
{
'eng': 'thrown into disorder they returned with more precipitation than is usual',
'engyay': 'own-thray into-ay isorder-day ey-thay eturned-ray ith-way ore-may ecipitation-pray an-thay is-ay usual-ay'
}
}
```
### Data Fields
- `translation`: a dictionary containing two strings paired with a key indicating the corresponding language.
### Data Splits
- `train`: most of the data, 13,232 samples total.
- `dev`: 1k holdout samples, created with the datasets.train_test_split() function | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
tner/wikiann | tner | 2022-09-27T18:39:42Z | 334 | 4 | null | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"multilinguality:multilingual",
"size_categories:10K<100k",
"language:ace",
"language:bg",
"language:da",
"language:fur",
"language:ilo",
"language:lij",
"language:mzn",
"language:qu",
"language:su",
"language:vi",
"language:af",
"language:bh",
"language:de",
"language:fy",
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"language:lmo",
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"language:dv",
"language:gan",
"language:it",
"language:lt",
"language:ne",
"language:ru",
"language:szl",
"language:wa",
"language:an",
"language:br",
"language:el",
"language:gd",
"language:ja",
"language:lv",
"language:nl",
"language:rw",
"language:ta",
"language:war",
"language:ang",
"language:bs",
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"language:jbo",
"language:nn",
"language:sa",
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"language:ar",
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"language:en",
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"language:cdo",
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"language:li",
"language:my",
"language:pt",
"language:sr",
"region:us"
] | 2022-09-27T18:39:42Z | 2022-09-27T16:22:58.000Z | 2022-09-27T16:22:58 | ---
language:
- ace
- bg
- da
- fur
- ilo
- lij
- mzn
- qu
- su
- vi
- af
- bh
- de
- fy
- io
- lmo
- nap
- rm
- sv
- vls
- als
- bn
- diq
- ga
- is
- ln
- nds
- ro
- sw
- vo
- am
- bo
- dv
- gan
- it
- lt
- ne
- ru
- szl
- wa
- an
- br
- el
- gd
- ja
- lv
- nl
- rw
- ta
- war
- ang
- bs
- eml
- gl
- jbo
- nn
- sa
- te
- wuu
- ar
- ca
- en
- gn
- jv
- mg
- no
- sah
- tg
- xmf
- arc
- eo
- gu
- ka
- mhr
- nov
- scn
- th
- yi
- arz
- cdo
- es
- hak
- kk
- mi
- oc
- sco
- tk
- yo
- as
- ce
- et
- he
- km
- min
- or
- sd
- tl
- zea
- ast
- ceb
- eu
- hi
- kn
- mk
- os
- sh
- tr
- ay
- ckb
- ext
- hr
- ko
- ml
- pa
- si
- tt
- az
- co
- fa
- hsb
- ksh
- mn
- pdc
- ug
- ba
- crh
- fi
- hu
- ku
- mr
- pl
- sk
- uk
- zh
- bar
- cs
- hy
- ky
- ms
- pms
- sl
- ur
- csb
- fo
- ia
- la
- mt
- pnb
- so
- uz
- cv
- fr
- id
- lb
- mwl
- ps
- sq
- vec
- be
- cy
- frr
- ig
- li
- my
- pt
- sr
multilinguality:
- multilingual
size_categories:
- 10K<100k
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: WikiAnn
---
# Dataset Card for "tner/wikiann"
## Dataset Description
- **Repository:** [T-NER](https://github.com/asahi417/tner)
- **Paper:** [https://aclanthology.org/P17-1178/](https://aclanthology.org/P17-1178/)
- **Dataset:** WikiAnn
- **Domain:** Wikipedia
- **Number of Entity:** 3
### Dataset Summary
WikiAnn NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project.
- Entity Types: `LOC`, `ORG`, `PER`
## Dataset Structure
### Data Instances
An example of `train` of `ja` looks as follows.
```
{
'tokens': ['#', '#', 'ユ', 'リ', 'ウ', 'ス', '・', 'ベ', 'ー', 'リ', 'ッ', 'ク', '#', '1', '9','9','9'],
'tags': [6, 6, 2, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6]
}
```
### Label ID
The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/wikiann/raw/main/dataset/label.json).
```python
{
"B-LOC": 0,
"B-ORG": 1,
"B-PER": 2,
"I-LOC": 3,
"I-ORG": 4,
"I-PER": 5,
"O": 6
}
```
### Data Splits
| language | train | validation | test |
|:-------------|--------:|-------------:|-------:|
| ace | 100 | 100 | 100 |
| bg | 20000 | 10000 | 10000 |
| da | 20000 | 10000 | 10000 |
| fur | 100 | 100 | 100 |
| ilo | 100 | 100 | 100 |
| lij | 100 | 100 | 100 |
| mzn | 100 | 100 | 100 |
| qu | 100 | 100 | 100 |
| su | 100 | 100 | 100 |
| vi | 20000 | 10000 | 10000 |
| af | 5000 | 1000 | 1000 |
| bh | 100 | 100 | 100 |
| de | 20000 | 10000 | 10000 |
| fy | 1000 | 1000 | 1000 |
| io | 100 | 100 | 100 |
| lmo | 100 | 100 | 100 |
| nap | 100 | 100 | 100 |
| rm | 100 | 100 | 100 |
| sv | 20000 | 10000 | 10000 |
| vls | 100 | 100 | 100 |
| als | 100 | 100 | 100 |
| bn | 10000 | 1000 | 1000 |
| diq | 100 | 100 | 100 |
| ga | 1000 | 1000 | 1000 |
| is | 1000 | 1000 | 1000 |
| ln | 100 | 100 | 100 |
| nds | 100 | 100 | 100 |
| ro | 20000 | 10000 | 10000 |
| sw | 1000 | 1000 | 1000 |
| vo | 100 | 100 | 100 |
| am | 100 | 100 | 100 |
| bo | 100 | 100 | 100 |
| dv | 100 | 100 | 100 |
| gan | 100 | 100 | 100 |
| it | 20000 | 10000 | 10000 |
| lt | 10000 | 10000 | 10000 |
| ne | 100 | 100 | 100 |
| ru | 20000 | 10000 | 10000 |
| szl | 100 | 100 | 100 |
| wa | 100 | 100 | 100 |
| an | 1000 | 1000 | 1000 |
| br | 1000 | 1000 | 1000 |
| el | 20000 | 10000 | 10000 |
| gd | 100 | 100 | 100 |
| ja | 20000 | 10000 | 10000 |
| lv | 10000 | 10000 | 10000 |
| nl | 20000 | 10000 | 10000 |
| rw | 100 | 100 | 100 |
| ta | 15000 | 1000 | 1000 |
| war | 100 | 100 | 100 |
| ang | 100 | 100 | 100 |
| bs | 15000 | 1000 | 1000 |
| eml | 100 | 100 | 100 |
| gl | 15000 | 10000 | 10000 |
| jbo | 100 | 100 | 100 |
| map-bms | 100 | 100 | 100 |
| nn | 20000 | 1000 | 1000 |
| sa | 100 | 100 | 100 |
| te | 1000 | 1000 | 1000 |
| wuu | 100 | 100 | 100 |
| ar | 20000 | 10000 | 10000 |
| ca | 20000 | 10000 | 10000 |
| en | 20000 | 10000 | 10000 |
| gn | 100 | 100 | 100 |
| jv | 100 | 100 | 100 |
| mg | 100 | 100 | 100 |
| no | 20000 | 10000 | 10000 |
| sah | 100 | 100 | 100 |
| tg | 100 | 100 | 100 |
| xmf | 100 | 100 | 100 |
| arc | 100 | 100 | 100 |
| cbk-zam | 100 | 100 | 100 |
| eo | 15000 | 10000 | 10000 |
| gu | 100 | 100 | 100 |
| ka | 10000 | 10000 | 10000 |
| mhr | 100 | 100 | 100 |
| nov | 100 | 100 | 100 |
| scn | 100 | 100 | 100 |
| th | 20000 | 10000 | 10000 |
| yi | 100 | 100 | 100 |
| arz | 100 | 100 | 100 |
| cdo | 100 | 100 | 100 |
| es | 20000 | 10000 | 10000 |
| hak | 100 | 100 | 100 |
| kk | 1000 | 1000 | 1000 |
| mi | 100 | 100 | 100 |
| oc | 100 | 100 | 100 |
| sco | 100 | 100 | 100 |
| tk | 100 | 100 | 100 |
| yo | 100 | 100 | 100 |
| as | 100 | 100 | 100 |
| ce | 100 | 100 | 100 |
| et | 15000 | 10000 | 10000 |
| he | 20000 | 10000 | 10000 |
| km | 100 | 100 | 100 |
| min | 100 | 100 | 100 |
| or | 100 | 100 | 100 |
| sd | 100 | 100 | 100 |
| tl | 10000 | 1000 | 1000 |
| zea | 100 | 100 | 100 |
| ast | 1000 | 1000 | 1000 |
| ceb | 100 | 100 | 100 |
| eu | 10000 | 10000 | 10000 |
| hi | 5000 | 1000 | 1000 |
| kn | 100 | 100 | 100 |
| mk | 10000 | 1000 | 1000 |
| os | 100 | 100 | 100 |
| sh | 20000 | 10000 | 10000 |
| tr | 20000 | 10000 | 10000 |
| zh-classical | 100 | 100 | 100 |
| ay | 100 | 100 | 100 |
| ckb | 1000 | 1000 | 1000 |
| ext | 100 | 100 | 100 |
| hr | 20000 | 10000 | 10000 |
| ko | 20000 | 10000 | 10000 |
| ml | 10000 | 1000 | 1000 |
| pa | 100 | 100 | 100 |
| si | 100 | 100 | 100 |
| tt | 1000 | 1000 | 1000 |
| zh-min-nan | 100 | 100 | 100 |
| az | 10000 | 1000 | 1000 |
| co | 100 | 100 | 100 |
| fa | 20000 | 10000 | 10000 |
| hsb | 100 | 100 | 100 |
| ksh | 100 | 100 | 100 |
| mn | 100 | 100 | 100 |
| pdc | 100 | 100 | 100 |
| simple | 20000 | 1000 | 1000 |
| ug | 100 | 100 | 100 |
| zh-yue | 20000 | 10000 | 10000 |
| ba | 100 | 100 | 100 |
| crh | 100 | 100 | 100 |
| fi | 20000 | 10000 | 10000 |
| hu | 20000 | 10000 | 10000 |
| ku | 100 | 100 | 100 |
| mr | 5000 | 1000 | 1000 |
| pl | 20000 | 10000 | 10000 |
| sk | 20000 | 10000 | 10000 |
| uk | 20000 | 10000 | 10000 |
| zh | 20000 | 10000 | 10000 |
| bar | 100 | 100 | 100 |
| cs | 20000 | 10000 | 10000 |
| fiu-vro | 100 | 100 | 100 |
| hy | 15000 | 1000 | 1000 |
| ky | 100 | 100 | 100 |
| ms | 20000 | 1000 | 1000 |
| pms | 100 | 100 | 100 |
| sl | 15000 | 10000 | 10000 |
| ur | 20000 | 1000 | 1000 |
| bat-smg | 100 | 100 | 100 |
| csb | 100 | 100 | 100 |
| fo | 100 | 100 | 100 |
| ia | 100 | 100 | 100 |
| la | 5000 | 1000 | 1000 |
| mt | 100 | 100 | 100 |
| pnb | 100 | 100 | 100 |
| so | 100 | 100 | 100 |
| uz | 1000 | 1000 | 1000 |
| be-x-old | 5000 | 1000 | 1000 |
| cv | 100 | 100 | 100 |
| fr | 20000 | 10000 | 10000 |
| id | 20000 | 10000 | 10000 |
| lb | 5000 | 1000 | 1000 |
| mwl | 100 | 100 | 100 |
| ps | 100 | 100 | 100 |
| sq | 5000 | 1000 | 1000 |
| vec | 100 | 100 | 100 |
| be | 15000 | 1000 | 1000 |
| cy | 10000 | 1000 | 1000 |
| frr | 100 | 100 | 100 |
| ig | 100 | 100 | 100 |
| li | 100 | 100 | 100 |
| my | 100 | 100 | 100 |
| pt | 20000 | 10000 | 10000 |
| sr | 20000 | 10000 | 10000 |
| vep | 100 | 100 | 100 |
### Citation Information
```
@inproceedings{pan-etal-2017-cross,
title = "Cross-lingual Name Tagging and Linking for 282 Languages",
author = "Pan, Xiaoman and
Zhang, Boliang and
May, Jonathan and
Nothman, Joel and
Knight, Kevin and
Ji, Heng",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-1178",
doi = "10.18653/v1/P17-1178",
pages = "1946--1958",
abstract = "The ambitious goal of this work is to develop a cross-lingual name tagging and linking framework for 282 languages that exist in Wikipedia. Given a document in any of these languages, our framework is able to identify name mentions, assign a coarse-grained or fine-grained type to each mention, and link it to an English Knowledge Base (KB) if it is linkable. We achieve this goal by performing a series of new KB mining methods: generating {``}silver-standard{''} annotations by transferring annotations from English to other languages through cross-lingual links and KB properties, refining annotations through self-training and topic selection, deriving language-specific morphology features from anchor links, and mining word translation pairs from cross-lingual links. Both name tagging and linking results for 282 languages are promising on Wikipedia data and on-Wikipedia data.",
}
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Cohere/wikipedia-22-12-simple-embeddings | Cohere | 2023-03-22T16:56:34Z | 334 | 44 | null | [
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"multilinguality:multilingual",
"language:en",
"license:apache-2.0",
"region:us"
] | 2023-03-22T16:56:34Z | 2023-01-13T23:25:25.000Z | 2023-01-13T23:25:25 | ---
language:
- en
multilinguality:
- multilingual
size_categories: []
source_datasets: []
tags: []
task_categories:
- text-retrieval
license:
- apache-2.0
task_ids:
- document-retrieval
---
# Wikipedia (simple English) embedded with cohere.ai `multilingual-22-12` encoder
We encoded [Wikipedia (simple English)](https://simple.wikipedia.org) using the [cohere.ai](https://txt.cohere.ai/multilingual/) `multilingual-22-12` embedding model.
To get an overview how this dataset was created and pre-processed, have a look at [Cohere/wikipedia-22-12](https://huggingface.co/datasets/Cohere/wikipedia-22-12).
## Embeddings
We compute for `title+" "+text` the embeddings using our `multilingual-22-12` embedding model, a state-of-the-art model that works for semantic search in 100 languages. If you want to learn more about this model, have a look at [cohere.ai multilingual embedding model](https://txt.cohere.ai/multilingual/).
## Further languages
We provide embeddings of Wikipedia in many different languages:
[ar](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ar-embeddings), [de](https://huggingface.co/datasets/Cohere/wikipedia-22-12-de-embeddings), [en](https://huggingface.co/datasets/Cohere/wikipedia-22-12-en-embeddings), [es](https://huggingface.co/datasets/Cohere/wikipedia-22-12-es-embeddings), [fr](https://huggingface.co/datasets/Cohere/wikipedia-22-12-fr-embeddings), [hi](https://huggingface.co/datasets/Cohere/wikipedia-22-12-hi-embeddings), [it](https://huggingface.co/datasets/Cohere/wikipedia-22-12-it-embeddings), [ja](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ja-embeddings), [ko](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ko-embeddings), [simple english](https://huggingface.co/datasets/Cohere/wikipedia-22-12-simple-embeddings), [zh](https://huggingface.co/datasets/Cohere/wikipedia-22-12-zh-embeddings),
You can find the Wikipedia datasets without embeddings at [Cohere/wikipedia-22-12](https://huggingface.co/datasets/Cohere/wikipedia-22-12).
## Loading the dataset
You can either load the dataset like this:
```python
from datasets import load_dataset
docs = load_dataset(f"Cohere/wikipedia-22-12-simple-embeddings", split="train")
```
Or you can also stream it without downloading it before:
```python
from datasets import load_dataset
docs = load_dataset(f"Cohere/wikipedia-22-12-simple-embeddings", split="train", streaming=True)
for doc in docs:
docid = doc['id']
title = doc['title']
text = doc['text']
emb = doc['emb']
```
## Search
A full search example:
```python
#Run: pip install cohere datasets
from datasets import load_dataset
import torch
import cohere
co = cohere.Client(f"<<COHERE_API_KEY>>") # Add your cohere API key from www.cohere.com
#Load at max 1000 documents + embeddings
max_docs = 1000
docs_stream = load_dataset(f"Cohere/wikipedia-22-12-simple-embeddings", split="train", streaming=True)
docs = []
doc_embeddings = []
for doc in docs_stream:
docs.append(doc)
doc_embeddings.append(doc['emb'])
if len(docs) >= max_docs:
break
doc_embeddings = torch.tensor(doc_embeddings)
query = 'Who founded Youtube'
response = co.embed(texts=[query], model='multilingual-22-12')
query_embedding = response.embeddings
query_embedding = torch.tensor(query_embedding)
# Compute dot score between query embedding and document embeddings
dot_scores = torch.mm(query_embedding, doc_embeddings.transpose(0, 1))
top_k = torch.topk(dot_scores, k=3)
# Print results
print("Query:", query)
for doc_id in top_k.indices[0].tolist():
print(docs[doc_id]['title'])
print(docs[doc_id]['text'], "\n")
```
## Performance
You can find performance on the MIRACL dataset (a semantic search evaluation dataset) here: [miracl-en-queries-22-12#performance](https://huggingface.co/datasets/Cohere/miracl-en-queries-22-12#performance) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
jxu124/refcocog | jxu124 | 2023-05-20T19:00:12Z | 334 | 1 | null | [
"region:us"
] | 2023-05-20T19:00:12Z | 2023-04-26T12:00:59.000Z | 2023-04-26T12:00:59 | ---
dataset_info:
features:
- name: image_id
dtype: int64
- name: split
dtype: string
- name: sentences
list:
- name: raw
dtype: string
- name: sent
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- name: sent_id
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- name: tokens
sequence: string
- name: file_name
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- name: category_id
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- name: ann_id
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- name: sent_ids
sequence: int64
- name: ref_id
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- name: raw_sentences
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dtype: string
- name: bbox
sequence: float64
- name: captions
sequence: string
- name: global_image_id
dtype: string
- name: anns_id
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splits:
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num_bytes: 10341980
num_examples: 5023
- name: train
num_bytes: 87352599
num_examples: 42226
- name: validation
num_bytes: 5236723
num_examples: 2573
download_size: 45968855
dataset_size: 102931302
---
# Dataset Card for "refcocog"
[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 | |
bbz662bbz/databricks-dolly-15k-ja-gozarinnemon | bbz662bbz | 2023-05-31T14:44:34Z | 334 | 3 | null | [
"license:cc-by-sa-3.0",
"region:us"
] | 2023-05-31T14:44:34Z | 2023-05-31T14:43:00.000Z | 2023-05-31T14:43:00 | ---
license: cc-by-sa-3.0
---
This dataset was using "kunishou/databricks-dolly-15k-ja"
This dataset is licensed under CC BY SA 3.0
Last Update : 2023-05-28
databricks-dolly-15k-ja-gozarinnemon
kunishou/databricks-dolly-15k-ja
https://huggingface.co/datasets/kunishou/databricks-dolly-15k-ja
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dutch_social | null | 2023-01-25T14:29:36Z | 333 | 6 | null | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"task_ids:multi-label-classification",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"language:nl",
"license:cc-by-nc-4.0",
"region:us"
] | 2023-01-25T14:29:36Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- crowdsourced
language:
- en
- nl
license:
- cc-by-nc-4.0
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
- multi-label-classification
pretty_name: Dutch Social Media Collection
dataset_info:
features:
- name: full_text
dtype: string
- name: text_translation
dtype: string
- name: screen_name
dtype: string
- name: description
dtype: string
- name: desc_translation
dtype: string
- name: location
dtype: string
- name: weekofyear
dtype: int64
- name: weekday
dtype: int64
- name: month
dtype: int64
- name: year
dtype: int64
- name: day
dtype: int64
- name: point_info
dtype: string
- name: point
dtype: string
- name: latitude
dtype: float64
- name: longitude
dtype: float64
- name: altitude
dtype: float64
- name: province
dtype: string
- name: hisco_standard
dtype: string
- name: hisco_code
dtype: string
- name: industry
dtype: bool_
- name: sentiment_pattern
dtype: float64
- name: subjective_pattern
dtype: float64
- name: label
dtype:
class_label:
names:
'0': neg
'1': neu
'2': pos
config_name: dutch_social
splits:
- name: train
num_bytes: 105569586
num_examples: 162805
- name: test
num_bytes: 35185351
num_examples: 54268
- name: validation
num_bytes: 34334756
num_examples: 54269
download_size: 68740666
dataset_size: 175089693
---
# Dataset Card for Dutch Social Media Collection
## 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:** [Dutch Social Media Collection](http://datasets.coronawhy.org/dataset.xhtml?persistentId=doi:10.5072/FK2/MTPTL7)
- **Repository:**
- **Paper:** *(in-progress)* https://doi.org/10.5072/FK2/MTPTL7
- **Leaderboard:**
- **Point of Contact:** [Aakash Gupta](mailto:aakashg80@gmail.com)
### Dataset Summary
The dataset contains 10 files with around 271,342 tweets. The tweets are filtered via the official Twitter API to contain tweets in Dutch language or by users who have specified their location information within Netherlands geographical boundaries. Using natural language processing we have classified the tweets for their HISCO codes. If the user has provided their location within Dutch boundaries, we have also classified them to their respective provinces The objective of this dataset is to make research data available publicly in a FAIR (Findable, Accessible, Interoperable, Reusable) way. Twitter's Terms of Service Licensed under Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) (2020-10-27)
### Supported Tasks and Leaderboards
`sentiment analysis`, `multi-label classification`, `entity-extraction`
### Languages
The text is primarily in Dutch with some tweets in English and other languages. The BCP 47 code is `nl` and `en`
## Dataset Structure
### Data Instances
An example of the data field will be:
```
{
"full_text": "@pflegearzt @Friedelkorn @LAguja44 Pardon, wollte eigentlich das zitieren: \nhttps://t.co/ejO7bIMyj8\nMeine mentions sind inzw komplett undurchschaubar weil da Leute ihren supporterclub zwecks Likes zusammengerufen haben.",
"text_translation": "@pflegearzt @Friedelkorn @ LAguja44 Pardon wollte zitieren eigentlich das:\nhttps://t.co/ejO7bIMyj8\nMeine mentions inzw sind komplett undurchschaubar weil da Leute ihren supporter club Zwecks Likes zusammengerufen haben.",
"created_at": 1583756789000,
"screen_name": "TheoRettich",
"description": "I ❤️science, therefore a Commie. ☭ FALGSC: Part of a conspiracy which wants to achieve world domination. Tankie-Cornucopian. Ecology is a myth",
"desc_translation": "I ❤️science, Therefore a Commie. ☭ FALGSC: Part of a conspiracy How many followers wants to Achieve World Domination. Tankie-Cornucopian. Ecology is a myth",
"weekofyear": 11,
"weekday": 0,
"day": 9,
"month": 3,
"year": 2020,
"location": "Netherlands",
"point_info": "Nederland",
"point": "(52.5001698, 5.7480821, 0.0)",
"latitude": 52.5001698,
"longitude": 5.7480821,
"altitude": 0,
"province": "Flevoland",
"hisco_standard": null,
"hisco_code": null,
"industry": false,
"sentiment_pattern": 0,
"subjective_pattern": 0
}
```
### Data Fields
| Column Name | Description |
| --- | --- |
| full_text | Original text in the tweet |
| text_translation | English translation of the full text |
| created_at | Date of tweet creation |
| screen_name | username of the tweet author |
| description | description as provided in the users bio |
| desc_translation | English translation of user's bio/ description |
| location | Location information as provided in the user's bio |
| weekofyear | week of the year |
| weekday | Day of the week information; Monday=0....Sunday = 6|
| month | Month of tweet creation |
| year | year of tweet creation |
| day | day of tweet creation |
| point_info | point information from location columnd |
| point | tuple giving lat, lon & altitude information |
| latitude | geo-referencing information derived from location data |
| longitude | geo-referencing information derived from location data |
| altitude | geo-referencing information derived from location data|
| province | Province given location data of user |
| hisco_standard | HISCO standard key word; if available in tweet |
| hisco_code| HISCO standard code as derived from `hisco_standard`|
| industry | Whether the tweet talks about industry `(True/False)` |
| sentiment_score | Sentiment score -1.0 to 1.0 |
| subjectivity_score | Subjectivity scores 0 to 1 |
Missing values are replaced with empty strings or -1 (-100 for missing sentiment_score).
### Data Splits
Data has been split into Train: 60%, Validation: 20% and Test: 20%
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The tweets were hydrated using Twitter's API and then filtered for those which were in Dutch language and/or for users who had mentioned that they were from within Netherlands geographical borders.
#### Who are the source language producers?
The language producers are twitter users who have identified their location within the geographical boundaries of Netherland. Or those who have tweeted in the dutch language!
### Annotations
Using Natural language processing, we have classified the tweets on industry and for HSN HISCO codes.
Depending on the user's location, their provincial information is also added. Please check the file/column for detailed information.
The tweets are also classified on the sentiment & subjectivity scores.
Sentiment scores are between -1 to +1
Subjectivity scores are between 0 to 1
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
As of writing this data card no anonymization has been carried out on the tweets or user data. As such, if the twitter user has shared any personal & sensitive information, then it may be available in this dataset.
## 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
[Aakash Gupta](mailto:aakashg80@gmail.com)
*Th!nkEvolve Consulting* and Researcher at CoronaWhy
### Licensing Information
CC BY-NC 4.0
### Citation Information
@data{FK2/MTPTL7_2020,
author = {Gupta, Aakash},
publisher = {COVID-19 Data Hub},
title = {{Dutch social media collection}},
year = {2020},
version = {DRAFT VERSION},
doi = {10.5072/FK2/MTPTL7},
url = {https://doi.org/10.5072/FK2/MTPTL7}
}
### Contributions
Thanks to [@skyprince999](https://github.com/skyprince999) for adding this dataset. | [
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sem_eval_2020_task_11 | null | 2023-01-25T14:43:56Z | 333 | 5 | null | [
"task_categories:text-classification",
"task_categories:token-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:unknown",
"propaganda-span-identification",
"propaganda-technique-classification",
"arxiv:2009.02696",
"region:us"
] | 2023-01-25T14:43:56Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-classification
- token-classification
task_ids: []
pretty_name: SemEval-2020 Task 11
tags:
- propaganda-span-identification
- propaganda-technique-classification
dataset_info:
features:
- name: article_id
dtype: string
- name: text
dtype: string
- name: span_identification
sequence:
- name: start_char_offset
dtype: int64
- name: end_char_offset
dtype: int64
- name: technique_classification
sequence:
- name: start_char_offset
dtype: int64
- name: end_char_offset
dtype: int64
- name: technique
dtype:
class_label:
names:
'0': Appeal_to_Authority
'1': Appeal_to_fear-prejudice
'2': Bandwagon,Reductio_ad_hitlerum
'3': Black-and-White_Fallacy
'4': Causal_Oversimplification
'5': Doubt
'6': Exaggeration,Minimisation
'7': Flag-Waving
'8': Loaded_Language
'9': Name_Calling,Labeling
'10': Repetition
'11': Slogans
'12': Thought-terminating_Cliches
'13': Whataboutism,Straw_Men,Red_Herring
splits:
- name: train
num_bytes: 2358613
num_examples: 371
- name: test
num_bytes: 454100
num_examples: 90
- name: validation
num_bytes: 396410
num_examples: 75
download_size: 0
dataset_size: 3209123
---
# Dataset Card for SemEval-2020 Task 11
## 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:** [PTC TASKS ON "DETECTION OF PROPAGANDA TECHNIQUES IN NEWS ARTICLES"](https://propaganda.qcri.org/ptc/index.html)
- **Paper:** [SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles](https://arxiv.org/abs/2009.02696)
- **Leaderboard:** [PTC Tasks Leaderboard](https://propaganda.qcri.org/ptc/leaderboard.php)
- **Point of Contact:** [Task organizers contact](semeval-2020-task-11-organizers@googlegroups.com)
### Dataset Summary
Propagandistic news articles use specific techniques to convey their message, such as whataboutism, red Herring, and name calling, among many others. The Propaganda Techniques Corpus (PTC) allows to study automatic algorithms to detect them. We provide a permanent leaderboard to allow researchers both to advertise their progress and to be up-to-speed with the state of the art on the tasks offered (see below for a definition).
### Supported Tasks and Leaderboards
More information on scoring methodology can be found in [propaganda tasks evaluation document](https://propaganda.qcri.org/ptc/data/propaganda_tasks_evaluation.pdf)
### Languages
This dataset consists of English news articles
## Dataset Structure
### Data Instances
Each example is structured as follows:
```
{
"span_identification": {
"end_char_offset": [720, 6322, ...],
"start_char_offset": [683, 6314, ...]
},
"technique_classification": {
"end_char_offset": [720,6322, ...],
"start_char_offset": [683,6314, ...],
"technique": [7,8, ...]
},
"text": "Newt Gingrich: The truth about Trump, Putin, and Obama\n\nPresident Trump..."
}
```
### Data Fields
- `text`: The full text of the news article.
- `span_identification`: a dictionary feature containing:
- `start_char_offset`: The start character offset of the span for the SI task
- `end_char_offset`: The end character offset of the span for the SI task
- `technique_classification`: a dictionary feature containing:
- `start_char_offset`: The start character offset of the span for the TC task
- `end_char_offset`: The start character offset of the span for the TC task
- `technique`: the propaganda technique classification label, with possible values including `Appeal_to_Authority`, `Appeal_to_fear-prejudice`, `Bandwagon,Reductio_ad_hitlerum`, `Black-and-White_Fallacy`, `Causal_Oversimplification`.
### Data Splits
| | Train | Valid | Test |
| ----- | ------ | ----- | ---- |
| Input Sentences | 371 | 75 | 90 |
| Total Annotations SI | 5468 | 940 | 0 |
| Total Annotations TC | 6128 | 1063 | 0 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
In order to build the PTC-SemEval20 corpus, we retrieved a sample of news articles from the period
starting in mid-2017 and ending in early 2019. We selected 13 propaganda and 36 non-propaganda news
media outlets, as labeled by Media Bias/Fact Check,3
and we retrieved articles from these sources. We
deduplicated the articles on the basis of word n-grams matching (Barron-Cede ´ no and Rosso, 2009) and ˜
we discarded faulty entries (e.g., empty entries from blocking websites).
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
The annotation job consisted of both spotting a propaganda snippet and, at the same time, labeling
it with a specific propaganda technique. The annotation guidelines are shown in the appendix; they
are also available online.4 We ran the annotation in two phases: (i) two annotators label an article
independently and (ii) the same two annotators gather together with a consolidator to discuss dubious
instances (e.g., spotted only by one annotator, boundary discrepancies, label mismatch, etc.). This protocol
was designed after a pilot annotation stage, in which a relatively large number of snippets had been spotted
by one annotator only. The annotation team consisted of six professional annotators from A Data Pro trained to spot and label the propaganda snippets from free text. The job was carried out on an instance of
the Anafora annotation platform (Chen and Styler, 2013), which we tailored for our propaganda annotation
task.
We evaluated the annotation process in terms of γ agreement (Mathet et al., 2015) between each of
the annotators and the final gold labels. The γ agreement on the annotated articles is on average 0.6;
see (Da San Martino et al., 2019b) for a more detailed discussion of inter-annotator agreement. The
training and the development part of the PTC-SemEval20 corpus are the same as the training and the
testing datasets described in (Da San Martino et al., 2019b). The test part of the PTC-SemEval20 corpus
consists of 90 additional articles selected from the same sources as for training and development. For
the test articles, we further extended the annotation process by adding one extra consolidation step: we
revisited all the articles in that partition and we performed the necessary adjustments to the spans and to
the labels as necessary, after a thorough discussion and convergence among at least three experts who
were not involved in the initial annotations.
#### 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
```
@misc{martino2020semeval2020,
title={SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles},
author={G. Da San Martino and A. Barrón-Cedeño and H. Wachsmuth and R. Petrov and P. Nakov},
year={2020},
eprint={2009.02696},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@ZacharySBrown](https://github.com/ZacharySBrown) for adding this dataset. | [
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ARTeLab/ilpost | ARTeLab | 2022-11-17T02:50:32Z | 333 | 2 | null | [
"task_categories:summarization",
"multilinguality:monolingual",
"size_categories:10K<n<100k",
"language:it",
"region:us"
] | 2022-11-17T02:50:32Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- it
multilinguality:
- monolingual
size_categories:
- 10K<n<100k
task_categories:
- summarization
---
# Dataset Card for ilpost
## 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
IlPost dataset, containing news articles taken from IlPost.
There are two features:
- source: Input news article.
- target: Summary of the article.
### Supported Tasks and Leaderboards
- `abstractive-summarization`, `summarization`
### Languages
The text in the dataset is in Italian
## Dataset Structure
### Data Instances
[Needs More Information]
### Data Fields
[Needs More Information]
### Data Splits
[Needs More Information]
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
More details and results in [published work](https://www.mdpi.com/2078-2489/13/5/228)
```
@Article{info13050228,
AUTHOR = {Landro, Nicola and Gallo, Ignazio and La Grassa, Riccardo and Federici, Edoardo},
TITLE = {Two New Datasets for Italian-Language Abstractive Text Summarization},
JOURNAL = {Information},
VOLUME = {13},
YEAR = {2022},
NUMBER = {5},
ARTICLE-NUMBER = {228},
URL = {https://www.mdpi.com/2078-2489/13/5/228},
ISSN = {2078-2489},
ABSTRACT = {Text summarization aims to produce a short summary containing relevant parts from a given text. Due to the lack of data for abstractive summarization on low-resource languages such as Italian, we propose two new original datasets collected from two Italian news websites with multi-sentence summaries and corresponding articles, and from a dataset obtained by machine translation of a Spanish summarization dataset. These two datasets are currently the only two available in Italian for this task. To evaluate the quality of these two datasets, we used them to train a T5-base model and an mBART model, obtaining good results with both. To better evaluate the results obtained, we also compared the same models trained on automatically translated datasets, and the resulting summaries in the same training language, with the automatically translated summaries, which demonstrated the superiority of the models obtained from the proposed datasets.},
DOI = {10.3390/info13050228}
}
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jxu124/refcocoplus | jxu124 | 2023-05-20T19:01:22Z | 333 | 1 | null | [
"region:us"
] | 2023-05-20T19:01:22Z | 2023-05-20T19:00:40.000Z | 2023-05-20T19:00:40 | ---
dataset_info:
features:
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sequence: int64
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list:
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splits:
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- name: validation
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num_examples: 3805
download_size: 39772801
dataset_size: 96580602
---
# Dataset Card for "refcocoplus"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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hkcancor | null | 2023-02-23T08:43:12Z | 332 | 11 | hong-kong-cantonese-corpus | [
"task_categories:translation",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:dialogue-modeling",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:yue",
"license:cc-by-4.0",
"region:us"
] | 2023-02-23T08:43:12Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- yue
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
- text-generation
- fill-mask
task_ids:
- dialogue-modeling
paperswithcode_id: hong-kong-cantonese-corpus
pretty_name: The Hong Kong Cantonese Corpus (HKCanCor)
dataset_info:
features:
- name: conversation_id
dtype: string
- name: speaker
dtype: string
- name: turn_number
dtype: int16
- name: tokens
sequence: string
- name: transcriptions
sequence: string
- name: pos_tags_prf
sequence:
class_label:
names:
'0': '!'
'1': '"'
'2': '#'
'3': ''''
'4': ','
'5': '-'
'6': .
'7': '...'
'8': '?'
'9': A
'10': AD
'11': AG
'12': AIRWAYS0
'13': AN
'14': AND
'15': B
'16': BG
'17': BEAN0
'18': C
'19': CENTRE0
'20': CG
'21': D
'22': D1
'23': DG
'24': E
'25': ECHO0
'26': F
'27': G
'28': G1
'29': G2
'30': H
'31': HILL0
'32': I
'33': IG
'34': J
'35': JB
'36': JM
'37': JN
'38': JNS
'39': JNT
'40': JNZ
'41': K
'42': KONG
'43': L
'44': L1
'45': LG
'46': M
'47': MG
'48': MONTY0
'49': MOUNTAIN0
'50': N
'51': N1
'52': NG
'53': NR
'54': NS
'55': NSG
'56': NT
'57': NX
'58': NZ
'59': O
'60': P
'61': PEPPER0
'62': Q
'63': QG
'64': R
'65': RG
'66': S
'67': SOUND0
'68': T
'69': TELECOM0
'70': TG
'71': TOUCH0
'72': U
'73': UG
'74': U0
'75': V
'76': V1
'77': VD
'78': VG
'79': VK
'80': VN
'81': VU
'82': VUG
'83': W
'84': X
'85': XA
'86': XB
'87': XC
'88': XD
'89': XE
'90': XJ
'91': XJB
'92': XJN
'93': XJNT
'94': XJNZ
'95': XJV
'96': XJA
'97': XL1
'98': XM
'99': XN
'100': XNG
'101': XNR
'102': XNS
'103': XNT
'104': XNX
'105': XNZ
'106': XO
'107': XP
'108': XQ
'109': XR
'110': XS
'111': XT
'112': XV
'113': XVG
'114': XVN
'115': XX
'116': Y
'117': YG
'118': Y1
'119': Z
- name: pos_tags_ud
sequence:
class_label:
names:
'0': DET
'1': PRON
'2': VERB
'3': NOUN
'4': ADJ
'5': PUNCT
'6': INTJ
'7': ADV
'8': V
'9': PART
'10': X
'11': NUM
'12': PROPN
'13': AUX
'14': CCONJ
'15': ADP
splits:
- name: train
num_bytes: 5746381
num_examples: 10801
download_size: 961514
dataset_size: 5746381
---
# Dataset Card for The Hong Kong Cantonese Corpus (HKCanCor)
## 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://compling.hss.ntu.edu.sg/hkcancor/
- **Repository:** https://github.com/fcbond/hkcancor
- **Paper:** [Luke and Wang, 2015](https://github.com/fcbond/hkcancor/blob/master/data/LukeWong_Hong-Kong-Cantonese-Corpus.pdf)
- **Leaderboard:** N/A
- **Point of Contact:** Luke Kang Kwong
### Dataset Summary
The Hong Kong Cantonese Corpus (HKCanCor) comprise transcribed conversations recorded
between March 1997 and August 1998. It contains recordings of spontaneous speech (51 texts)
and radio programmes (42 texts), which involve 2 to 4 speakers, with 1 text of monologue.
In total, the corpus contains around 230,000 Chinese words. The text is word-segmented (i.e., tokenization is at word-level, and each token can span multiple Chinese characters). Tokens are annotated with part-of-speech (POS) tags and romanised Cantonese pronunciation.
* Romanisation
* Follows conventions set by the Linguistic Society of Hong Kong (LSHK).
* POS
* The tagset used by this corpus extends the one in the Peita-Fujitsu-Renmin Ribao (PRF) corpus (Duan et al., 2000). Extensions were made to further capture Cantonese-specific phenomena.
* To facilitate everyday usage and for better comparability across languages and/or corpora, this dataset also includes the tags mapped to the [Universal Dependencies 2.0](https://universaldependencies.org/u/pos/index.html) format. This mapping references the [PyCantonese](https://github.com/jacksonllee/pycantonese) library.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Yue Chinese / Cantonese (Hong Kong).
## Dataset Structure
This corpus has 10801 utterances and approximately 230000 Chinese words.
There is no predefined split.
### Data Instances
Each instance contains a conversation id, speaker id within that conversation,
turn number, part-of-speech tag for each Chinese word in the PRF format and UD2.0 format,
and the utterance written in Chinese characters as well as its LSHK format romanisation.
For example:
```python
{
'conversation_id': 'TNR016-DR070398-HAI6V'
'pos_tags_prf': ['v', 'w'],
'pos_tags_ud': ['VERB', 'PUNCT'],
'speaker': 'B',
'transcriptions': ['hai6', 'VQ1'],
'turn_number': 112,
'tokens': ['係', '。']
}
```
### Data Fields
- conversation_id: unique dialogue-level id
- pos_tags_prf: POS tag using the PRF format at token-level
- pos_tag_ud: POS tag using the UD2.0 format at token-level
- speaker: unique speaker id within dialogue
- transcriptions: token-level romanisation in the LSHK format
- turn_number: turn number in dialogue
- tokens: Chinese word or punctuation at token-level
### Data Splits
There are no specified splits in this dataset.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
This work is licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/deed.ast).
### Citation Information
This corpus was developed by [Luke and Wong, 2015](http://compling.hss.ntu.edu.sg/hkcancor/data/LukeWong_Hong-Kong-Cantonese-Corpus.pdf).
```
@article{luke2015hong,
author={Luke, Kang-Kwong and Wong, May LY},
title={The Hong Kong Cantonese corpus: design and uses},
journal={Journal of Chinese Linguistics},
year={2015},
pages={309-330},
month={12}
}
```
The POS tagset to Universal Dependency tagset mapping is provided by Jackson Lee, as a part of the [PyCantonese](https://github.com/jacksonllee/pycantonese) library.
```
@misc{lee2020,
author = {Lee, Jackson},
title = {PyCantonese: Cantonese Linguistics and NLP in Python},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/jacksonllee/pycantonese}},
commit = {1d58f44e1cb097faa69de6b617e1d28903b84b98}
}
```
### Contributions
Thanks to [@j-chim](https://github.com/j-chim) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
kor_qpair | null | 2023-01-25T14:34:00Z | 332 | 2 | null | [
"task_categories:text-classification",
"task_ids:semantic-similarity-classification",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ko",
"license:mit",
"region:us"
] | 2023-01-25T14:34:00Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- other
language:
- ko
license:
- mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- semantic-similarity-classification
pretty_name: KorQpair
dataset_info:
features:
- name: question1
dtype: string
- name: question2
dtype: string
- name: is_duplicate
dtype:
class_label:
names:
'0': '0'
'1': '1'
splits:
- name: train
num_bytes: 515365
num_examples: 6136
- name: test
num_bytes: 63466
num_examples: 758
- name: validation
num_bytes: 57242
num_examples: 682
download_size: 545236
dataset_size: 636073
---
# 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:** [Github](https://github.com/songys/Question_pair)
- **Repository:** [Github](https://github.com/songys/Question_pair)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
Each row in the dataset contains two questions and a `is_duplicate` label.
- `question1`: The first question
- `question2`: The second question
- `is_duplicate`: 0 if `question1` and `question2` are semantically similar; 1 otherwise
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@jaketae](https://github.com/jaketae) for adding this dataset. | [
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mteb/medrxiv-clustering-p2p | mteb | 2022-09-27T19:10:43Z | 332 | 0 | null | [
"language:en",
"region:us"
] | 2022-09-27T19:10:43Z | 2022-05-11T06:56:44.000Z | 2022-05-11T06:56:44 | ---
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NTU-NLP-sg/xCodeEval | NTU-NLP-sg | 2023-06-03T21:33:12Z | 332 | 24 | null | [
"task_categories:translation",
"task_categories:token-classification",
"task_categories:text2text-generation",
"task_categories:text-retrieval",
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:feature-extraction",
"task_categories:question-answering",
"annotations_creators:expert-generated",
"language_creators:found",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:1M<n<10M",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:code",
"language:en",
"license:cc-by-nc-4.0",
"programming-language",
"code",
"program-synthesis",
"automatic-code-repair",
"code-retrieval",
"code-translation",
"code-classification",
"arxiv:2303.03004",
"region:us"
] | 2023-06-03T21:33:12Z | 2023-04-09T11:02:35.000Z | 2023-04-09T11:02:35 | ---
annotations_creators:
- expert-generated
language:
- code
- en
language_creators:
- found
- expert-generated
license:
- cc-by-nc-4.0
multilinguality:
- multilingual
pretty_name: xCodeEval
size_categories:
- 1M<n<10M
- 10M<n<100M
source_datasets:
- original
tags:
- programming-language
- code
- program-synthesis
- automatic-code-repair
- code-retrieval
- code-translation
- code-classification
task_categories:
- translation
- token-classification
- text2text-generation
- text-retrieval
- text-generation
- text-classification
- feature-extraction
- question-answering
---
[github](https://github.com/ntunlp/xCodeEval)
# xCodeEval
[xCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval](https://arxiv.org/abs/2303.03004)
We introduce **xCodeEval**, the largest executable multilingual multitask benchmark to date consisting of 25 M document-level coding examples from about 7.5 K unique problems covering up to 17 programming languages with execution-level parallelism. It features a total of seven tasks involving code understanding, generation, translation and retrieval, and it employs an execution-based evaluation. We develop a test-case based multilingual code execution engine, [**ExecEval**](https://github.com/ntunlp/ExecEval) that supports all the programming languages in **xCodeEval**. We also propose a novel data splitting and a data selection schema for balancing data distributions over multiple attributes based on geometric mean and graph-theoretic principle.
This repository contains the sample code and data link for xCodeEval [paper](https://arxiv.org/abs/2303.03004).
# Data Download
Currently this repository supports huggingface [`load_dataset()`](https://huggingface.co/docs/datasets/v1.11.0/package_reference/loading_methods.html#datasets.load_dataset) api. Follow the following example to load dataset for individual examples.
```
import datasets
prog_synthesis_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "program_synthesis")
code_translation_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "code_translation")
tag_classification_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "tag_classification")
apr_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "apr")
pcode_compilation_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "code_compilation")
retrieval_code_code_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "retrieval_code_code")
retrieval_nl_code_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "retrieval_nl_code")
retrieval_corpus_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "retrieval_corpus")
```
## Hf large data download tricks.
If you are facing long delay with data processing, add a `ignore_verifications=True`.
```
prog_synthesis_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "program_synthesis", ignore_verifications=True)
```
If you are facing long delay with data downloading, use huggingface streaming mode.
```
prog_synthesis_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "program_synthesis", streaming=True)
```
## Just Give me the raw data (😠)
Data can be also downloaded as a git LFS repo from huggingface.
You can download the full data using the following command.
```
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/NTU-NLP-sg/xCodeEval
cd xCodeEval
git lfs pull
```
To download a specific part of the dataset,
```
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/NTU-NLP-sg/xCodeEval
cd xCodeEval
git lfs pull --include "apr/test/*"
```
We propose 7 Tasks.
1. [Tag Classification](https://github.com/ntunlp/xCodeEval/blob/main/apr.md)
2. [Code Compilation](https://github.com/ntunlp/xCodeEval/blob/main/code_compilation.md)
3. [Program Synthesis](https://github.com/ntunlp/xCodeEval/blob/main/program_synthesis.md)
4. [Code Translation](https://github.com/ntunlp/xCodeEval/blob/main/code_translation.md)
5. [Automatic Program Repair](https://github.com/ntunlp/xCodeEval/blob/main/apr.md)
6. [Code-Code Retrieval](https://github.com/ntunlp/xCodeEval/blob/main/retrieval.md)
7. [NL-Code Retrieval](https://github.com/ntunlp/xCodeEval/blob/main/retrieval.md)
# Common Data for different tasks
If you are not using huggingface [`load_dataset()`](https://huggingface.co/docs/datasets/v1.11.0/package_reference/loading_methods.html#datasets.load_dataset) api, you may need to link some data with different tasks.
We have two data files that are required for multiple tasks.
1. `problem_descriptions.jsonl`
2. `unittest_db.json`
You can find these two files in the root directory of the [main](https://huggingface.co/datasets/NTU-NLP-sg/xCodeEval/tree/main) branch of huggingface dataset repository. To avoid data redundancy we didn't include these data with the relevant tasks, rather we add a unique id `src_uid` to retrieve these data.
## Structure of `problem_descriptions.jsonl`
A sample,
```json
{
"description": "There are $$$n$$$ positive integers $$$a_1, a_2, \\dots, a_n$$$. For the one move you can choose any even value $$$c$$$ and divide by two all elements that equal $$$c$$$.For example, if $$$a=[6,8,12,6,3,12]$$$ and you choose $$$c=6$$$, and $$$a$$$ is transformed into $$$a=[3,8,12,3,3,12]$$$ after the move.You need to find the minimal number of moves for transforming $$$a$$$ to an array of only odd integers (each element shouldn't be divisible by $$$2$$$).",
"input_from": "standard input",
"output_to": "standard output",
"time_limit": "3 seconds",
"memory_limit": "256 megabytes",
"input_spec": "The first line of the input contains one integer $$$t$$$ ($$$1 \\le t \\le 10^4$$$) \u2014 the number of test cases in the input. Then $$$t$$$ test cases follow. The first line of a test case contains $$$n$$$ ($$$1 \\le n \\le 2\\cdot10^5$$$) \u2014 the number of integers in the sequence $$$a$$$. The second line contains positive integers $$$a_1, a_2, \\dots, a_n$$$ ($$$1 \\le a_i \\le 10^9$$$). The sum of $$$n$$$ for all test cases in the input doesn't exceed $$$2\\cdot10^5$$$.",
"output_spec": "For $$$t$$$ test cases print the answers in the order of test cases in the input. The answer for the test case is the minimal number of moves needed to make all numbers in the test case odd (i.e. not divisible by $$$2$$$).",
"notes": "NoteIn the first test case of the example, the optimal sequence of moves can be as follows: before making moves $$$a=[40, 6, 40, 3, 20, 1]$$$; choose $$$c=6$$$; now $$$a=[40, 3, 40, 3, 20, 1]$$$; choose $$$c=40$$$; now $$$a=[20, 3, 20, 3, 20, 1]$$$; choose $$$c=20$$$; now $$$a=[10, 3, 10, 3, 10, 1]$$$; choose $$$c=10$$$; now $$$a=[5, 3, 5, 3, 5, 1]$$$ \u2014 all numbers are odd. Thus, all numbers became odd after $$$4$$$ moves. In $$$3$$$ or fewer moves, you cannot make them all odd.",
"sample_inputs": [
"4\n6\n40 6 40 3 20 1\n1\n1024\n4\n2 4 8 16\n3\n3 1 7"
],
"sample_outputs": [
"4\n10\n4\n0"
],
"tags": [
"number theory",
"greedy"
],
"src_uid": "afcd41492158e68095b01ff1e88c3dd4",
"difficulty": 1200,
"created_at": 1576321500
}
```
### Key Definitions
1. `description`: Problem description in textual format, math operations are written in latex.
2. `input_from`: How the program should take the unit test.
3. `output_to`: Where the program should output the result of the unit test.
4. `time_limit`: Time limit to solve the problem.
5. `memory_limit`: Memory limit to solve the problem.
6. `input_spec`: How and in what order the input will be given to the program? It also includes the date range, types, and sizes.
7. `output_spec`: How the outputs should be printed. Most of the time the unit test results are matched with an *exact string match* or *floating point comparison* with a precision boundary.
8. `sample_inputs`: A sample input for the code that is expected to solve the problem described in `description`.
9. `sample_outputs`: The expected output for the `sample_input` that is expected to solve the problem described in `description`.
10. `notes`: Explanation of `sample_inputs` & `sample_outputs`.
11. `tags`: The problem categories.
12. `src_uid`: The unique id of the problem. This ID is referred to in the task data samples instead of putting all this information.
13. `difficulty`: How difficult is it to solve the problem for a human (annotated by an expert human)?
14. `created_at`: The Unix timestamp when the problem was released. Use `datetime` lib in Python to parse it to a human-readable format.
## Structure of `unittest_db.json`
The structure of the `json` file,
```python
unittest_db = {
"db884d679d9cfb1dc4bc511f83beedda" : [
{
"input": "4\r\n3 2 3 2\r\n",
"output": [
"1"
],
},
{
...
},
...
]
"3bc096d8cd3418948d5be6bf297aa9b5":[
...
],
...
}
```
### Key Definitions
1. `unittest_db.json` dict keys i.e., `db884d679d9cfb1dc4bc511f83beedda` are the `src_uid` from `problem_descriptions.jsonl`.
2. `input`: Input of the unit test.
3. `output`: List of expected outputs for the unit test.
# Citation
```
@misc{khan2023xcodeeval,
title={xCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval},
author={Mohammad Abdullah Matin Khan and M Saiful Bari and Xuan Long Do and Weishi Wang and Md Rizwan Parvez and Shafiq Joty},
year={2023},
eprint={2303.03004},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
iohadrubin/top_terms_subtopics_w_emb | iohadrubin | 2023-09-24T17:04:01Z | 332 | 0 | null | [
"region:us"
] | 2023-09-24T17:04:01Z | 2023-09-24T17:03:26.000Z | 2023-09-24T17:03:26 | ---
dataset_info:
features:
- name: idx
dtype: int64
- name: value
dtype: string
- name: cluster
dtype: int64
- name: __index_level_0__
dtype: int64
- name: embeddings
sequence: float64
splits:
- name: train
num_bytes: 53678637
num_examples: 4096
download_size: 53069276
dataset_size: 53678637
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "top_terms_subtopics_w_emb"
[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 | |
SEACrowd/korpus_nusantara | SEACrowd | 2023-09-26T12:31:37Z | 332 | 0 | null | [
"language:ind",
"language:jav",
"language:xdy",
"language:bug",
"language:sun",
"language:mad",
"language:bjn",
"language:bbc",
"language:msa",
"language:min",
"license:unknown",
"machine-translation",
"region:us"
] | 2023-09-26T12:31:37Z | 2023-09-26T11:15:53.000Z | 2023-09-26T11:15:53 | ---
license: unknown
tags:
- machine-translation
language:
- ind
- jav
- xdy
- bug
- sun
- mad
- bjn
- bbc
- msa
- min
---
# korpus_nusantara
This parallel corpus was collected from several studies, assignments, and thesis of
students of the Informatics Study Program, Tanjungpura University. Some of the corpus
are used in the translation machine from Indonesian to local languages http://nustor.untan.ac.id/cammane/.
This corpus can be used freely for research purposes by citing the paper
https://ijece.iaescore.com/index.php/IJECE/article/download/20046/13738.
The dataset is a combination of multiple machine translation works from the author,
Herry Sujaini, covering Indonesian to 25 local dialects in Indonesia. Since not all
dialects have ISO639-3 standard coding, as agreed with Pak Herry , we decided to
group the dataset into the closest language family, i.e.: Javanese, Dayak, Buginese,
Sundanese, Madurese, Banjar, Batak Toba, Khek, Malay, Minangkabau, and Tiociu.
## Dataset Usage
Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.
## Citation
```
@article{sujaini2020improving,
title={Improving the role of language model in statistical machine translation (Indonesian-Javanese)},
author={Sujaini, Herry},
journal={International Journal of Electrical and Computer Engineering},
volume={10},
number={2},
pages={2102},
year={2020},
publisher={IAES Institute of Advanced Engineering and Science}
}
```
## License
Unknown
## Homepage
[https://github.com/herrysujaini/korpusnusantara](https://github.com/herrysujaini/korpusnusantara)
### NusaCatalogue
For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue) | [
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crawl_domain | null | 2022-11-18T19:47:14Z | 331 | 0 | common-crawl-domain-names | [
"task_categories:other",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-Common-Crawl",
"source_datasets:original",
"language:en",
"license:mit",
"web-search",
"text-to-speech",
"arxiv:2011.03138",
"region:us"
] | 2022-11-18T19:47:14Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
- found
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-Common-Crawl
- original
task_categories:
- other
task_ids: []
paperswithcode_id: common-crawl-domain-names
pretty_name: Common Crawl Domain Names
tags:
- web-search
- text-to-speech
dataset_info:
features:
- name: example
dtype: string
splits:
- name: train
num_bytes: 321134
num_examples: 17572
- name: test
num_bytes: 39712
num_examples: 2170
- name: validation
num_bytes: 36018
num_examples: 1953
download_size: 331763
dataset_size: 396864
---
# Dataset Card for Common Crawl Domain Names
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/google-research-datasets/common-crawl-domain-names
- **Repository:** https://github.com/google-research-datasets/common-crawl-domain-names
- **Paper:** https://arxiv.org/pdf/2011.03138
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Corpus of domain names scraped from Common Crawl and manually annotated to add word boundaries (e.g. "commoncrawl" to "common crawl").
Breaking [domain names](https://developer.mozilla.org/en-US/docs/Learn/Common_questions/What_is_a_URL) such as "openresearch" into component words "open" and "research" is important for applications such as Text-to-Speech synthesis and web search. [Common Crawl](https://commoncrawl.org/) is an open repository of web crawl data that can be accessed and analyzed by anyone. Specifically, we scraped the plaintext (WET) extracts for domain names from URLs that contained diverse letter casing (e.g. "OpenBSD"). Although in the previous example, segmentation is trivial using letter casing, this was not always the case (e.g. "NASA"), so we had to manually annotate the data.
### Supported Tasks and Leaderboards
- Text-to-Speech synthesis
- Web search
### Languages
en: English
## Dataset Structure
### Data Instances
Each sample is an example of space separated segments of a domain name. The examples are stored in their original letter casing, but harder and more interesting examples can be generated by lowercasing the input first.
For example:
```
Open B S D
NASA
ASAP Workouts
```
### Data Fields
- `example`: a `string` feature: space separated segments of a domain name.
### Data Splits
| split | size | trivial | avg_input_length | avg_segments |
|-------|-------|---------|------------------|--------------|
| train | 17572 | 13718 | 12.63 | 2.65 |
| eval | 1953 | 1536 | 12.77 | 2.67 |
| test | 2170 | 1714 | 12.63 | 2.66 |
## Dataset Creation
### Curation Rationale
The dataset was curated by scraping the plaintext (WET) extracts for domain names from URLs that contained diverse letter casing (e.g. "OpenBSD"). Although in the previous example, segmentation is trivial using letter casing, this was not always the case (e.g. "NASA"), so the curators of the dataset had to manually annotate the data.
### Source Data
#### Initial Data Collection and Normalization
Corpus of domain names scraped from Common Crawl and manually annotated to add word boundaries
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
The annotators are the curators of this dataset
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The curators of this dataset are [Jae Hun Ro](https://github.com/JaeHunRo) and [mwurts4google](https://github.com/mwurts4google), who are the contributors of the official Github repository for this dataset. Since the account handles of other curators are unknown currently, the authors of the paper linked to this dataset is mentioned here as curators, [Hao Zhang](https://arxiv.org/search/cs?searchtype=author&query=Zhang%2C+H), [Jae Ro](https://arxiv.org/search/cs?searchtype=author&query=Ro%2C+J), and [Richard Sproat](https://arxiv.org/search/cs?searchtype=author&query=Sproat%2C+R).
### Licensing Information
[MIT License](https://github.com/google-research-datasets/common-crawl-domain-names/blob/master/LICENSE)
### Citation Information
```
@inproceedings{zrs2020urlsegmentation,
title={Semi-supervised URL Segmentation with Recurrent Neural Networks Pre-trained on Knowledge Graph Entities},
author={Hao Zhang and Jae Ro and Richard William Sproat},
booktitle={The 28th International Conference on Computational Linguistics (COLING 2020)},
year={2020}
}
```
### Contributions
Thanks to [@Karthik-Bhaskar](https://github.com/Karthik-Bhaskar) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
msr_text_compression | null | 2022-11-18T21:30:29Z | 331 | 3 | null | [
"task_categories:summarization",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|other-Open-American-National-Corpus-(OANC1)",
"language:en",
"license:other",
"region:us"
] | 2022-11-18T21:30:29Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- other
license_details: Microsoft Research Data License Agreement
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-Open-American-National-Corpus-(OANC1)
task_categories:
- summarization
task_ids: []
pretty_name: MsrTextCompression
dataset_info:
features:
- name: source_id
dtype: string
- name: domain
dtype: string
- name: source_text
dtype: string
- name: targets
sequence:
- name: compressed_text
dtype: string
- name: judge_id
dtype: string
- name: num_ratings
dtype: int64
- name: ratings
sequence: int64
splits:
- name: train
num_bytes: 5001312
num_examples: 4936
- name: validation
num_bytes: 449691
num_examples: 447
- name: test
num_bytes: 804536
num_examples: 785
download_size: 0
dataset_size: 6255539
---
# 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:** https://msropendata.com/datasets/f8ce2ec9-7fbd-48f7-a8bb-2d2279373563
- **Repository:**
- **Paper:** https://www.microsoft.com/en-us/research/wp-content/uploads/2016/09/Sentence_Compression_final-1.pdf
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains sentences and short paragraphs with corresponding shorter (compressed) versions. There are up to five compressions for each input text, together with quality judgements of their meaning preservation and grammaticality. The dataset is derived using source texts from the Open American National Corpus (ww.anc.org) and crowd-sourcing.
### Supported Tasks and Leaderboards
Text Summarization
### Languages
English
## Dataset Structure
### Data Instances
It contains approximately 6,000 source texts with multiple compressions (about 26,000 pairs of source and compressed texts), representing business letters, newswire, journals, and technical documents sampled from the Open American National Corpus (OANC1).
- Each source text is accompanied by up to five crowd-sourced rewrites constrained to a preset
compression ratio and annotated with quality judgments. Multiple rewrites permit study of the impact of operations on human compression quality and facilitate automatic evaluation.
- This dataset is the first to provide compressions at the multi-sentence (two-sentence paragraph)
level, which may present a stepping stone to whole document summarization.
- Many of these two-sentence paragraphs are compressed both as paragraphs and separately sentence-bysentence, offering data that may yield insights
into the impact of multi-sentence operations on human compression quality.
| Description | Source | Target | Average CPS | Meaning Quality | Grammar Quality |
| :------------- | :----------: | -----------: | -----------: | -----------: | -----------: |
| 1-Sentence | 3764 | 15523 | 4.12 | 2.78 | 2.81 |
| 2-Sentence | 2405 | 10900 | 4.53 | 2.78 | 2.83 |
**Note**: Average CPS = Average Compressions per Source Text
### Data Fields
```
{'domain': 'Newswire',
'source_id': '106',
'source_text': '" Except for this small vocal minority, we have just not gotten a lot of groundswell against this from members, " says APA president Philip G. Zimbardo of Stanford University.',
'targets': {'compressed_text': ['"Except for this small vocal minority, we have not gotten a lot of groundswell against this," says APA president Zimbardo.',
'"Except for a vocal minority, we haven\'t gotten much groundswell from members, " says Philip G. Zimbardo of Stanford University.',
'APA president of Stanford has stated that except for a vocal minority they have not gotten a lot of pushback from members.',
'APA president Philip G. Zimbardo of Stanford says they have not had much opposition against this.'],
'judge_id': ['2', '22', '10', '0'],
'num_ratings': [3, 3, 3, 3],
'ratings': [[6, 6, 6], [11, 6, 6], [6, 11, 6], [6, 11, 11]]}}
```
- source_id: index of article per original dataset
- source_text: uncompressed original text
- domain: source of the article
- targets:
- compressed_text: compressed version of `source_text`
- judge_id: anonymized ids of crowdworkers who proposed compression
- num_ratings: number of ratings available for each proposed compression
- ratings: see table below
Ratings system (excerpted from authors' README):
- 6 = Most important meaning Flawless language (3 on meaning and 3 on grammar as per the paper's terminology)
- 7 = Most important meaning Minor errors (3 on meaning and 2 on grammar)
- 9 = Most important meaning Disfluent or incomprehensible (3 on meaning and 1 on grammar)
- 11 = Much meaning Flawless language (2 on meaning and 3 on grammar)
- 12 = Much meaning Minor errors (2 on meaning and 2 on grammar)
- 14 = Much meaning Disfluent or incomprehensible (2 on meaning and 1 on grammar)
- 21 = Little or none meaning Flawless language (1 on meaning and 3 on grammar)
- 22 = Little or none meaning Minor errors (1 on meaning and 2 on grammar)
- 24 = Little or none meaning Disfluent or incomprehensible (1 on meaning and 1 on grammar)
See **README.txt** from data archive for additional details.
### Data Splits
There are 4,936 source texts in the training, 448 in the development, and 785 in the test set.
## Dataset Creation
### Annotations
#### Annotation process
Compressions were created using UHRS, an inhouse crowd-sourcing system similar to Amazon’s Mechanical Turk, in two annotation rounds, one for shortening and a second to rate compression quality:
1. In the first round, five workers were tasked with abridging each source text by at least 25%, while remaining grammatical and fluent, and retaining the meaning of the original.
2. In the second round, 3-5 judges (raters) were asked to evaluate the grammaticality of each compression on a scale from 1 (major errors, disfluent) through 3 (fluent), and again analogously for meaning preservation on a scale from 1 (orthogonal) through 3 (most important meaning-preserving).
## Additional Information
### Licensing Information
Microsoft Research Data License Agreement
### Citation Information
@inproceedings{Toutanova2016ADA,
title={A Dataset and Evaluation Metrics for Abstractive Compression of Sentences and Short Paragraphs},
author={Kristina Toutanova and Chris Brockett and Ke M. Tran and Saleema Amershi},
booktitle={EMNLP},
year={2016}
}
### Contributions
Thanks to [@jeromeku](https://github.com/jeromeku) for adding this dataset. | [
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spanish_billion_words | null | 2022-11-03T16:16:07Z | 331 | 9 | sbwce | [
"task_categories:other",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:es",
"license:cc-by-sa-4.0",
"region:us"
] | 2022-11-03T16:16:07Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- es
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- other
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: sbwce
pretty_name: Spanish Billion Word Corpus and Embeddings
dataset_info:
features:
- name: text
dtype: string
config_name: corpus
splits:
- name: train
num_bytes: 8950895954
num_examples: 46925295
download_size: 2024166993
dataset_size: 8950895954
---
# Dataset Card for Spanish Billion Words
## 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:** [Spanish Billion Words homepage](https://crscardellino.github.io/SBWCE/)
- **Point of Contact:** [Cristian Cardellino](mailto:ccardellino@unc.edu.ar) (Corpus Creator), [María Grandury](mailto:mariagrandury@gmail.com) (Corpus Submitter)
### Dataset Summary
The Spanish Billion Words Corpus is an unannotated Spanish corpus of nearly 1.5 billion words, compiled from different resources from the web.
This resources include the spanish portions of SenSem, the Ancora Corpus, some OPUS Project Corpora and the Europarl,
the Tibidabo Treebank, the IULA Spanish LSP Treebank, and dumps from the Spanish Wikipedia, Wikisource and Wikibooks.
This corpus is a compilation of 100 text files. Each line of these files represents one of the 50 million sentences from the corpus.
### Supported Tasks and Leaderboards
This dataset can be used for language modelling and for pretraining language models.
### Languages
The text in this dataset is in Spanish, BCP-47 code: 'es'.
## Dataset Structure
### Data Instances
Each example in this dataset is a sentence in Spanish:
```
{'text': 'Yo me coloqué en un asiento próximo a una ventana cogí un libro de una mesa y empecé a leer'}
```
### Data Fields
- `text`: a sentence in Spanish
### Data Splits
The dataset is not split.
## Dataset Creation
### Curation Rationale
The Spanish Billion Words Corpus was created to train word embeddings using the word2vect algorithm provided by the gensim package.
### Source Data
#### Initial Data Collection and Normalization
The corpus was created compiling the following resources:
- The Spanish portion of [SenSem]().
- The Spanish portion of the [Ancora Corpus](http://clic.ub.edu/corpus/en).
- [Tibidabo Treebank and IULA Spanish LSP Treebank](http://lod.iula.upf.edu/resources/metadata_TRL_Tibidabo_LSP_treebank_ES).
- The Spanish portion of the following [OPUS Project](http://opus.nlpl.eu/index.php) Corpora:
- The [books](http://opus.nlpl.eu/Books.php) aligned by [Andras Farkas](https://farkastranslations.com/).
- The [JRC-Acquis](http://opus.nlpl.eu/JRC-Acquis.php) collection of legislative text of the European Union.
- The [News Commentary](http://opus.nlpl.eu/News-Commentary.php) corpus.
- The [United Nations](http://opus.nlpl.eu/UN.php) documents compiled by [Alexandre Rafalovitch](https://www.outerthoughts.com/) and [Robert Dale](http://web.science.mq.edu.au/~rdale/).
- The Spanish portion of the [Europarl](http://statmt.org/europarl/) (European Parliament), compiled by [Philipp Koehn](https://homepages.inf.ed.ac.uk/pkoehn/).
- Dumps from the Spanish [Wikipedia](https://es.wikipedia.org/wiki/Wikipedia:Portada), [Wikisource](https://es.wikisource.org/wiki/Portada) and [Wikibooks](https://es.wikibooks.org/wiki/Portada) on date 2015-09-01, parsed with the Wikipedia Extractor.
All the annotated corpora (like Ancora, SenSem and Tibidabo) were untagged and
the parallel corpora (most coming from the OPUS Project) was preprocessed to obtain only the Spanish portions of it.
Once the whole corpus was unannotated, all non-alphanumeric characters were replaced with whitespaces,
all numbers with the token “DIGITO” and all the multiple whitespaces with only one whitespace.
The capitalization of the words remained unchanged.
#### Who are the source language producers?
The data was compiled and processed by Cristian Cardellino.
### Annotations
The dataset is unannotated.
#### 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
[More Information Needed]
## Additional Information
### Dataset Curators
The data was collected and processed by Cristian Cardellino.
### Licensing Information
The dataset is licensed under a Creative Commons Attribution-ShareAlike 4.0 International license
[(CC BY-SA 4.0)](https://creativecommons.org/licenses/by-sa/4.0/)
### Citation Information
```
@misc{cardellinoSBWCE,
author = {Cardellino, Cristian},
title = {Spanish {B}illion {W}ords {C}orpus and {E}mbeddings},
url = {https://crscardellino.github.io/SBWCE/},
month = {August},
year = {2019}
}
```
### Contributions
Thanks to [@mariagrandury](https://github.com/mariagrandury) for adding this dataset. | [
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Cyberfish/pos_tagger | Cyberfish | 2021-08-20T02:32:01Z | 331 | 0 | null | [
"region:us"
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
merkol/ffhq-256 | merkol | 2023-08-28T11:26:44Z | 331 | 0 | null | [
"region:us"
] | 2023-08-28T11:26:44Z | 2023-08-28T11:17:10.000Z | 2023-08-28T11:17:10 | ---
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 7358464050.0
num_examples: 70000
download_size: 7407340570
dataset_size: 7358464050.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "ffhq-256"
[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 | |
LeoLM/German_Songs | LeoLM | 2023-09-04T21:49:42Z | 331 | 1 | null | [
"region:us"
] | 2023-09-04T21:49:42Z | 2023-09-04T21:49:38.000Z | 2023-09-04T21:49:38 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: analysis_prompt
dtype: string
- name: topic
dtype: string
- name: song
dtype: string
- name: analysis
dtype: string
splits:
- name: train
num_bytes: 1972513
num_examples: 500
download_size: 804509
dataset_size: 1972513
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "german_songs_gpt4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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AlekseyKorshuk/horror-scripts | AlekseyKorshuk | 2022-02-10T18:26:41Z | 330 | 1 | null | [
"region:us"
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
flax-community/swahili-safi | flax-community | 2021-07-18T12:48:55Z | 330 | 3 | null | [
"region:us"
] | 2021-07-18T12:48:55Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | # Swahili-Safi Dataset
A relatively clean dataset for Swahili language modeling, built by combining and cleaning several existing datasets.
Sources include:
```
mc4-sw
oscar-sw
swahili_news
IWSLT
XNLI
flores 101
swahili-lm
gamayun-swahili-minikit
broadcastnews-sw
subset of wikipedia-en translated (using m2m100) to sw
```
In total this dataset is ~3.5 GB in size with over 21 million lines of text.
## Usage
This dataset can be downloaded and used as follows:
```python
from datasets import load_dataset
ds = load_dataset("flax-community/swahili-safi")
```
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
MickyMike/cvefixes_bigvul | MickyMike | 2022-10-12T10:31:00Z | 330 | 4 | null | [
"license:mit",
"region:us"
] | 2022-10-12T10:31:00Z | 2022-10-12T06:01:18.000Z | 2022-10-12T06:01:18 | ---
license: mit
---
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mask-distilled-onesec-cv12-each-chunk-uniq/chunk_61 | mask-distilled-onesec-cv12-each-chunk-uniq | 2023-05-31T00:09:37Z | 330 | 0 | null | [
"region:us"
] | 2023-05-31T00:09:37Z | 2023-05-31T00:07:29.000Z | 2023-05-31T00:07:29 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1288082504.0
num_examples: 252962
download_size: 1307009191
dataset_size: 1288082504.0
---
# Dataset Card for "chunk_61"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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hate_speech_filipino | null | 2023-01-25T14:31:38Z | 329 | 5 | null | [
"task_categories:text-classification",
"task_ids:sentiment-analysis",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-twitter-data-philippine-election",
"language:tl",
"license:unknown",
"region:us"
] | 2023-01-25T14:31:38Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- crowdsourced
language:
- tl
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-twitter-data-philippine-election
task_categories:
- text-classification
task_ids:
- sentiment-analysis
pretty_name: Hate Speech in Filipino
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
splits:
- name: train
num_bytes: 995919
num_examples: 10000
- name: test
num_bytes: 995919
num_examples: 10000
- name: validation
num_bytes: 424365
num_examples: 4232
download_size: 822927
dataset_size: 2416203
---
# Dataset Card for Hate Speech in Filipino
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Hate Speech Dataset in Filipino homepage](https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks)
- **Repository:** [Hate Speech Dataset in Filipino homepage](https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks)
- **Paper:** [PCJ paper](https://pcj.csp.org.ph/index.php/pcj/issue/download/29/PCJ%20V14%20N1%20pp1-14%202019)
- **Leaderboard:**
- **Point of Contact:** [Jan Christian Cruz](mailto:jan_christian_cruz@dlsu.edu.ph)
### Dataset Summary
Contains 10k tweets (training set) that are labeled as hate speech or non-hate speech. Released with 4,232 validation and 4,232 testing samples. Collected during the 2016 Philippine Presidential Elections.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset is primarily in Filipino, with the addition of some English words commonly used in Filipino vernacular
## Dataset Structure
### Data Instances
Sample data:
```
{
"text": "Taas ni Mar Roxas ah. KULTONG DILAW NGA NAMAN",
"label": 1
}
```
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
This study seeks to contribute to the filling of this gap through the development of a model that can automate hate speech detection and classification in Philippine election-related tweets. The role of the microblogging site Twitter as a platform for the expression of support and hate during the 2016 Philippine presidential election has been supported in news reports and systematic studies. Thus, the particular question addressed in this paper is: Can existing techniques in language processing and machine learning be applied to detect hate speech in the Philippine election context?
### Source Data
#### Initial Data Collection and Normalization
The dataset used in this study was a subset of the corpus 1,696,613 tweets crawled by Andrade et al. and posted from November 2015 to May 2016 during the campaign period for the Philippine presidential election. They were culled based on the presence of candidate names (e.g., Binay, Duterte, Poe, Roxas, and Santiago) and election-related hashtags (e.g., #Halalan2016, #Eleksyon2016, and #PiliPinas2016).
Data preprocessing was performed to prepare the tweets for feature extraction and classification. It consisted of the following steps: data de-identification, uniform resource locator (URL) removal, special character processing, normalization, hashtag processing, and tokenization.
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[Jan Christian Cruz](mailto:jan_christian_cruz@dlsu.edu.ph)
### Licensing Information
[More Information Needed]
### Citation Information
@article{Cabasag-2019-hate-speech,
title={Hate speech in Philippine election-related tweets: Automatic detection and classification using natural language processing.},
author={Neil Vicente Cabasag, Vicente Raphael Chan, Sean Christian Lim, Mark Edward Gonzales, and Charibeth Cheng},
journal={Philippine Computing Journal},
volume={XIV},
number={1},
month={August},
year={2019}
}
### 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 | |
ronec | null | 2023-01-25T14:43:21Z | 329 | 1 | ronec | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ro",
"license:mit",
"arxiv:1909.01247",
"region:us"
] | 2023-01-25T14:43:21Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
- found
language:
- ro
license:
- mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: ronec
pretty_name: RONEC
dataset_info:
features:
- name: id
dtype: int32
- name: tokens
sequence: string
- name: ner_ids
sequence: int32
- name: space_after
sequence: bool
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PERSON
'2': I-PERSON
'3': B-ORG
'4': I-ORG
'5': B-GPE
'6': I-GPE
'7': B-LOC
'8': I-LOC
'9': B-NAT_REL_POL
'10': I-NAT_REL_POL
'11': B-EVENT
'12': I-EVENT
'13': B-LANGUAGE
'14': I-LANGUAGE
'15': B-WORK_OF_ART
'16': I-WORK_OF_ART
'17': B-DATETIME
'18': I-DATETIME
'19': B-PERIOD
'20': I-PERIOD
'21': B-MONEY
'22': I-MONEY
'23': B-QUANTITY
'24': I-QUANTITY
'25': B-NUMERIC
'26': I-NUMERIC
'27': B-ORDINAL
'28': I-ORDINAL
'29': B-FACILITY
'30': I-FACILITY
config_name: ronec
splits:
- name: train
num_bytes: 8701577
num_examples: 9000
- name: validation
num_bytes: 1266490
num_examples: 1330
- name: test
num_bytes: 1902224
num_examples: 2000
download_size: 14675943
dataset_size: 11870291
---
# Dataset Card for RONEC
## 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/dumitrescustefan/ronec
- **Repository:** https://github.com/dumitrescustefan/ronec
- **Paper:** https://arxiv.org/abs/1909.01247
- **Leaderboard:** https://lirobenchmark.github.io/
- **Point of Contact:** [Stefan](dumitrescu.stefan@gmail.com) and [Andrei-Marius](avram.andreimarius@gmail.com)
### Dataset Summary
RONEC, at version 2.0, holds 12330 sentences with over 0.5M tokens, annotated with 15 classes, to a total of 80.283 distinctly annotated entities.
The corpus has the following classes and distribution in the train/valid/test splits:
| Classes | Total | Train | | Valid | | Test | |
|------------- |:------: |:------: |:-------: |:------: |:-------: |:------: |:-------: |
| | # | # | % | # | % | # | % |
| PERSON | **26130** | 19167 | 73.35 | 2733 | 10.46 | 4230 | 16.19 |
| GPE | **11103** | 8193 | 73.79 | 1182 | 10.65 | 1728 | 15.56 |
| LOC | **2467** | 1824 | 73.94 | 270 | 10.94 | 373 | 15.12 |
| ORG | **7880** | 5688 | 72.18 | 880 | 11.17 | 1312 | 16.65 |
| LANGUAGE | **467** | 342 | 73.23 | 52 | 11.13 | 73 | 15.63 |
| NAT_REL_POL | **4970** | 3673 | 73.90 | 516 | 10.38 | 781 | 15.71 |
| DATETIME | **9614** | 6960 | 72.39 | 1029 | 10.7 | 1625 | 16.9 |
| PERIOD | **1188** | 862 | 72.56 | 129 | 10.86 | 197 | 16.58 |
| QUANTITY | **1588** | 1161 | 73.11 | 181 | 11.4 | 246 | 15.49 |
| MONEY | **1424** | 1041 | 73.10 | 159 | 11.17 | 224 | 15.73 |
| NUMERIC | **7735** | 5734 | 74.13 | 814 | 10.52 | 1187 | 15.35 |
| ORDINAL | **1893** | 1377 | 72.74 | 212 | 11.2 | 304 | 16.06 |
| FACILITY | **1126** | 840 | 74.6 | 113 | 10.04 | 173 | 15.36 |
| WORK_OF_ART | **1596** | 1157 | 72.49 | 176 | 11.03 | 263 | 16.48 |
| EVENT | **1102** | 826 | 74.95 | 107 | 9.71 | 169 | 15.34 |
### Supported Tasks and Leaderboards
The corpus is meant to train Named Entity Recognition models for the Romanian language.
Please see the leaderboard here : [https://lirobenchmark.github.io/](https://lirobenchmark.github.io/)
### Languages
RONEC is in Romanian (`ro`)
## Dataset Structure
### Data Instances
The dataset is a list of instances. For example, an instance looks like:
```json
{
"id": 10454,
"tokens": ["Pentru", "a", "vizita", "locația", "care", "va", "fi", "pusă", "la", "dispoziția", "reprezentanților", "consiliilor", "județene", ",", "o", "delegație", "a", "U.N.C.J.R.", ",", "din", "care", "a", "făcut", "parte", "și", "dl", "Constantin", "Ostaficiuc", ",", "președintele", "C.J.T.", ",", "a", "fost", "prezentă", "la", "Bruxelles", ",", "între", "1-3", "martie", "."],
"ner_tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-PERSON", "O", "O", "O", "O", "O", "O", "B-ORG", "O", "O", "O", "O", "O", "O", "O", "B-PERSON", "I-PERSON", "I-PERSON", "I-PERSON", "I-PERSON", "B-ORG", "O", "O", "O", "O", "O", "B-GPE", "O", "B-PERIOD", "I-PERIOD", "I-PERIOD", "O"],
"ner_ids": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 3, 0, 0, 0, 0, 0, 5, 0, 19, 20, 20, 0],
"space_after": [true, true, true, true, true, true, true, true, true, true, true, true, false, true, true, true, true, false, true, true, true, true, true, true, true, true, true, false, true, true, false, true, true, true, true, true, false, true, true, true, false, false]
}
```
### Data Fields
The fields of each examples are:
- ``tokens`` are the words of the sentence.
- ``ner_tags`` are the string tags assigned to each token, following the BIO2 format. For example, the span ``"între", "1-3", "martie"`` has three tokens, but is a single class ``PERIOD``, marked as ``"B-PERIOD", "I-PERIOD", "I-PERIOD"``.
- ``ner_ids`` are the integer encoding of each tag, to be compatible with the standard and to be quickly used for model training. Note that each ``B``-starting tag is odd, and each ``I``-starting tag is even.
- ``space_after`` is used to help if there is a need to detokenize the dataset. A ``true`` value means that there is a space after the token on that respective position.
### Data Splits
The dataset is split in train: 9000 sentences, dev: 1330 sentence and test: 2000 sentences.
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
*The corpus data source represents sentences that are free of copyright, taken from older datasets like the freely available SEETimes and more recent datasources like the Romanian Wikipedia or the Common Crawl.*
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
The corpus was annotated with the following classes:
1. PERSON - proper nouns, including common nouns or pronouns if they refer to a person. (e.g. 'sister')
2. GPE - geo political entity, like a city or a country; has to have a governance form
3. LOC - location, like a sea, continent, region, road, address, etc.
4. ORG - organization
5. LANGUAGE - language (e.g. Romanian, French, etc.)
6. NAT_REL_POL - national, religious or political organizations
7. DATETIME - a time and date in any format, including references to time (e.g. 'yesterday')
8. PERIOD - a period that is precisely bounded by two date times
9. QUANTITY - a quantity that is not numerical; it has a unit of measure
10. MONEY - a monetary value, numeric or otherwise
11. NUMERIC - a simple numeric value, represented as digits or words
12. ORDINAL - an ordinal value like 'first', 'third', etc.
13. FACILITY - a named place that is easily recognizable
14. WORK_OF_ART - a work of art like a named TV show, painting, etc.
15. EVENT - a named recognizable or periodic major event
#### Annotation process
The corpus was annotated by 3 language experts, and was cross-checked for annotation consistency. The annotation took several months to complete, but the result is a high quality dataset.
#### Who are the annotators?
Stefan Dumitrescu (lead).
### Personal and Sensitive Information
All the source data is already freely downloadable and usable online, so there are no privacy concerns.
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
MIT License
### Citation Information
```bibtex
@article{dumitrescu2019introducing,
title={Introducing RONEC--the Romanian Named Entity Corpus},
author={Dumitrescu, Stefan Daniel and Avram, Andrei-Marius},
journal={arXiv preprint arXiv:1909.01247},
year={2019}
}
```
### Contributions
Thanks to [@iliemihai](https://github.com/iliemihai) for adding v1.0 of the dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ARTeLab/mlsum-it | ARTeLab | 2022-11-17T02:51:00Z | 329 | 1 | null | [
"task_categories:summarization",
"multilinguality:monolingual",
"size_categories:10K<n<100k",
"language:it",
"region:us"
] | 2022-11-17T02:51:00Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- it
multilinguality:
- monolingual
size_categories:
- 10K<n<100k
task_categories:
- summarization
---
# Dataset Card for mlsum-it
## 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://huggingface.co/datasets/mlsum]
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
The MLSum-it dataset is the translated version (Helsinki-NLP/opus-mt-es-it) of the spanish portion of MLSum, containing news articles taken from BBC/mundo.
More informations on the official dataset page [HuggingFace page](https://huggingface.co/datasets/mlsum).
There are two features:
- source: Input news article.
- target: Summary of the article.
### Supported Tasks and Leaderboards
- `abstractive-summarization`, `summarization`
### Languages
The text in the dataset is in Italian
## Dataset Structure
### Data Instances
[Needs More Information]
### Data Fields
[Needs More Information]
### Data Splits
[Needs More Information]
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
More details and results in [published work](https://www.mdpi.com/2078-2489/13/5/228)
```
@Article{info13050228,
AUTHOR = {Landro, Nicola and Gallo, Ignazio and La Grassa, Riccardo and Federici, Edoardo},
TITLE = {Two New Datasets for Italian-Language Abstractive Text Summarization},
JOURNAL = {Information},
VOLUME = {13},
YEAR = {2022},
NUMBER = {5},
ARTICLE-NUMBER = {228},
URL = {https://www.mdpi.com/2078-2489/13/5/228},
ISSN = {2078-2489},
ABSTRACT = {Text summarization aims to produce a short summary containing relevant parts from a given text. Due to the lack of data for abstractive summarization on low-resource languages such as Italian, we propose two new original datasets collected from two Italian news websites with multi-sentence summaries and corresponding articles, and from a dataset obtained by machine translation of a Spanish summarization dataset. These two datasets are currently the only two available in Italian for this task. To evaluate the quality of these two datasets, we used them to train a T5-base model and an mBART model, obtaining good results with both. To better evaluate the results obtained, we also compared the same models trained on automatically translated datasets, and the resulting summaries in the same training language, with the automatically translated summaries, which demonstrated the superiority of the models obtained from the proposed datasets.},
DOI = {10.3390/info13050228}
}
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Anurag-Singh-creator/task | Anurag-Singh-creator | 2021-12-12T21:26:53Z | 329 | 0 | null | [
"region:us"
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Nexdata/mixed_speech_chinese_english | Nexdata | 2023-11-22T09:52:13Z | 329 | 4 | null | [
"task_categories:automatic-speech-recognition",
"language:zh",
"language:en",
"region:us"
] | 2023-11-22T09:52:13Z | 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
- en
---
# Dataset Card for mixed_speech_chinese_english
## 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 2,000 hours of mixed speech with Chinese and English. The data is collected from speakers in 26 provinces like Henan, Shanxi, Sichuan, Hunan, Fujian, etc.The content covers generic scene and multiple human machine interation scenes, such as music, entertainment, travel, daily life. The data covers more than 30,000 English words. 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
Chinese, English
## 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 | |
GEM/mlb_data_to_text | GEM | 2022-10-24T15:30:20Z | 329 | 1 | null | [
"task_categories:table-to-text",
"annotations_creators:none",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:other",
"data-to-text",
"region:us"
] | 2022-10-24T15:30:20Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- other
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
pretty_name: mlb_data_to_text
tags:
- data-to-text
---
# Dataset Card for GEM/mlb_data_to_text
## Dataset Description
- **Homepage:** https://github.com/ratishsp/mlb-data-scripts
- **Repository:** https://github.com/ratishsp/mlb-data-scripts
- **Paper:** https://aclanthology.org/P19-1195
- **Leaderboard:** N/A
- **Point of Contact:** Ratish Puduppully
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/mlb_data_to_text).
### Dataset Summary
The MLB dataset is an English sport-related data-to-text dataset in the baseball domain. The input is a large table with results of a game and the output is a description of the game.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/mlb_data_to_text')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/mlb_data_to_text).
#### website
[Github](https://github.com/ratishsp/mlb-data-scripts)
#### paper
[ACL Anthology](https://aclanthology.org/P19-1195)
#### authors
Ratish Puduppully, Li Dong, Mirella Lapata
## 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/ratishsp/mlb-data-scripts)
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Github](https://github.com/ratishsp/mlb-data-scripts)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[ACL Anthology](https://aclanthology.org/P19-1195)
#### 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{puduppully-etal-2019-data,
title = "Data-to-text Generation with Entity Modeling",
author = "Puduppully, Ratish and
Dong, Li and
Lapata, Mirella",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P19-1195",
doi = "10.18653/v1/P19-1195",
pages = "2023--2035",
}
```
#### 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 -->
Ratish Puduppully
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
ratishpuduppully@gmail.com
#### 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 -->
other: Other license
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
The dataset can be used to study data-to-text generation. The dataset is in sports domain. It pairs statistics of Major League Baseball (MLB) game with its summary. The summary is in the form of a document containing an average of 540 tokens. Thus it is useful to study long document generation.
#### Add. License Info
<!-- info: What is the 'other' license of the dataset? -->
<!-- scope: periscope -->
Restricted to non-commercial research purposes.
#### 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 -->
Produce a summary of MLB game from its statistics.
### 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 Edinburgh
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Ratish Puduppully, Li Dong, Mirella Lapata
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
```
features = datasets.Features(
{
"home_name": datasets.Value("string"),
"box_score": [
{
"p_l": datasets.Value("string"),
"last_name": datasets.Value("string"),
"p_h": datasets.Value("string"),
"sac": datasets.Value("string"),
"p_bb": datasets.Value("string"),
"pos": datasets.Value("string"),
"ao": datasets.Value("string"),
"p_bf": datasets.Value("string"),
"cs": datasets.Value("string"),
"hbp": datasets.Value("string"),
"ab": datasets.Value("string"),
"full_name": datasets.Value("string"),
"p_w": datasets.Value("string"),
"go": datasets.Value("string"),
"fldg": datasets.Value("string"),
"p_bs": datasets.Value("string"),
"avg": datasets.Value("string"),
"p_r": datasets.Value("string"),
"p_s": datasets.Value("string"),
"lob": datasets.Value("string"),
"first_name": datasets.Value("string"),
"p_sv": datasets.Value("string"),
"p_so": datasets.Value("string"),
"p_save": datasets.Value("string"),
"p_hr": datasets.Value("string"),
"po": datasets.Value("string"),
"p_ip1": datasets.Value("string"),
"p_ip2": datasets.Value("string"),
"bb": datasets.Value("string"),
"ops": datasets.Value("string"),
"p_hld": datasets.Value("string"),
"bo": datasets.Value("string"),
"p_loss": datasets.Value("string"),
"e": datasets.Value("string"),
"p_game_score": datasets.Value("string"),
"p_win": datasets.Value("string"),
"a": datasets.Value("string"),
"p_era": datasets.Value("string"),
"d": datasets.Value("string"),
"p_out": datasets.Value("string"),
"h": datasets.Value("string"),
"p_er": datasets.Value("string"),
"p_np": datasets.Value("string"),
"hr": datasets.Value("string"),
"r": datasets.Value("string"),
"so": datasets.Value("string"),
"t": datasets.Value("string"),
"rbi": datasets.Value("string"),
"team": datasets.Value("string"),
"sb": datasets.Value("string"),
"slg": datasets.Value("string"),
"sf": datasets.Value("string"),
"obp": datasets.Value("string"),
}
],
"home_city": datasets.Value("string"),
"vis_name": datasets.Value("string"),
"play_by_play": [{
"top": [{
"runs": datasets.Value("string"),
"scorers": [
datasets.Value("string")
],
"pitcher": datasets.Value("string"),
"o": datasets.Value("string"),
"b": datasets.Value("string"),
"s": datasets.Value("string"),
"batter": datasets.Value("string"),
"b1": [
datasets.Value("string")
],
"b2": [
datasets.Value("string")
],
"b3": [
datasets.Value("string")
],
"event": datasets.Value("string"),
"event2": datasets.Value("string"),
"home_team_runs": datasets.Value("string"),
"away_team_runs": datasets.Value("string"),
"rbi": datasets.Value("string"),
"error_runs": datasets.Value("string"),
"fielder_error": datasets.Value("string")
}
],
"bottom": [{
"runs": datasets.Value("string"),
"scorers": [
datasets.Value("string")
],
"pitcher": datasets.Value("string"),
"o": datasets.Value("string"),
"b": datasets.Value("string"),
"s": datasets.Value("string"),
"batter": datasets.Value("string"),
"b1": [
datasets.Value("string")
],
"b2": [
datasets.Value("string")
],
"b3": [
datasets.Value("string")
],
"event": datasets.Value("string"),
"event2": datasets.Value("string"),
"home_team_runs": datasets.Value("string"),
"away_team_runs": datasets.Value("string"),
"rbi": datasets.Value("string"),
"error_runs": datasets.Value("string"),
"fielder_error": datasets.Value("string")
}
],
"inning": datasets.Value("string")
}
],
"vis_line": {
"innings": [{
"inn": datasets.Value("string"),
"runs": datasets.Value("string")
}
],
"result": datasets.Value("string"),
"team_runs": datasets.Value("string"),
"team_hits": datasets.Value("string"),
"team_errors": datasets.Value("string"),
"team_name": datasets.Value("string"),
"team_city": datasets.Value("string")
},
"home_line": {
"innings": [{
"inn": datasets.Value("string"),
"runs": datasets.Value("string")
}
],
"result": datasets.Value("string"),
"team_runs": datasets.Value("string"),
"team_hits": datasets.Value("string"),
"team_errors": datasets.Value("string"),
"team_name": datasets.Value("string"),
"team_city": datasets.Value("string")
},
"vis_city": datasets.Value("string"),
"day": datasets.Value("string"),
"summary": [
datasets.Value("string"),
],
"gem_id": datasets.Value("string")
}
```
#### Reason for Structure
<!-- info: How was the dataset structure determined? -->
<!-- scope: microscope -->
The high level structure contains the following attributes: home_name, vis_name, home_city, vis_city, summary, summary_eval, day, gem_id, box_score, play_by_play, home_line, vis_line.
The attributes home_name, vis_name, home_city, vis_city and day are string values.
The attribute "summary" contains the summary in the form of a list of tokens.
The attribute "summary_eval" contains the summary in the form of a string of tokens. The difference from "summary" field is that "summary_eval" doesn't contain "*NEWPARAGRAPH*" delimiters to separate the paragraphs. "summary_eval" field should be used to evaluate model outputs. "summary" field may be used during the training process.
box_score contains the box score statistics of the players in the game. It is in the form of a list (of average size 90), with each element describing the statistics of a player. The box score statistics contain 53 attributes.
The description of the attributes is given below. The descriptions of most of the attributes is obtained from [mlb.com](https://www.mlb.com/glossary/standard-stats).
- r : Runs scored by a player in the game.
- rbi Runs Batted In (RBI): action of a batter results in a run scored by other players in the team.
- pos Position of the player.
- avg Batting Average. It is an indicator of the hits in the players' career.
- bb A walk occurs when a pitcher throws four pitches out of the strike zone, none of which are swung at by the hitter.
- hr Batter hits the ball in the air over the outfield fence.
- p_r Runs given by a pitcher in the game.
- p_bb Walks allowed by pitcher in a game.
- p_h Hits allowed by pitcher in a game.
- p_hr Home runs allowed by pitcher in a game.
- p_er Earned Run (ER): An earned run is any run that scores against a pitcher.
- p_era Earned Run Average (ERA): Earned run average represents the number of earned runs a pitcher allows per nine innings.
- p_np Number of Pitches: A pitcher's total number of pitches is determined by all the pitches he throws in game.
- p_ip1 Innings Pitched (IP1): Innings pitched measures the number of innings a pitcher remains in a game. Because there are three outs in an inning, each out recorded represents one-third of an inning pitched.
- p_ip2 Innings Pitched (IP2): Innings pitched measures the number of innings a pitcher remains in a game. Because there are three outs in an inning, each out recorded represents one-third of an inning pitched.
- p_w A pitcher receives a win when he is the pitcher of record when his team takes the lead for good.
- p_l A pitcher receives a loss when a run that is charged to him proves to be the go-ahead run in the game, giving the opposing team a lead it never gives up.
- p_so A strikeout occurs when a pitcher throws any combination of three swinging or looking strikes to a hitter.
- p_save Save: A save is awarded to the relief pitcher who finishes a game for the winning team. A pitcher cannot receive a save and a win in the same game.
- p_sv Saves: The count of saves recorded by a pitcher in his career.
- sac A sacrifice fly occurs when a batter hits a fly-ball out to the outfield or foul territory that allows a runner to score.
- p_bf Batters faced is simply a count of the number of total plate appearances against a certain pitcher or team. In a perfect game -- with 27 outs -- a pitcher will record 27 batters faced.
- cs A caught stealing occurs when a runner attempts to steal but is tagged out before reaching second base, third base or home plate.
- hbp A hit-by-pitch occurs when a batter is struck by a pitched ball without swinging at it. He is awarded first base as a result.
- ab An official at-bat comes when a batter reaches base via a fielder's choice, hit or an error (not including catcher's interference) or when a batter is put out on a non-sacrifice.
- p_bs A blown save occurs when a relief pitcher enters a game in a save situation, but allows the tying run to score.
- p_s The count of strikes thrown by a pitcher
- lob Left on base can be viewed as both an individual statistic or as a team statistic. In an individual batter's case, it refers to how many men remain on base after that batter makes an out at the plate, as the batter has failed to do his job to score those runners -- or at least put himself in a position to score. In a team's case or in an individual pitcher's case, it refers to the number of men who remain on base at the end of an inning.
- po A fielder is credited with a putout when he is the fielder who physically records the act of completing an out -- whether it be by stepping on the base for a forceout, tagging a runner, catching a batted ball, or catching a third strike
- ops OPS adds on-base percentage and slugging percentage to get one number that unites the two. It's meant to combine how well a hitter can reach base, with how well he can hit for average and for power.
- p_hld A hold occurs when a relief pitcher enters the game in a save situation and maintains his team's lead for the next relief pitcher, while recording at least one out.
- p_loss True/False- Indicates losing pitcher
- e A fielder is given an error if, in the judgment of the official scorer, he fails to convert an out on a play that an average fielder should have made.
- p_win True/False- Indicates winning pitcher
- a An assist is awarded to a fielder who touches the ball before a putout is recorded by another fielder.
- h A hit occurs when a batter strikes the baseball into fair territory and reaches base without doing so via an error or a fielder's choice.
- so A strikeout of a batter
- team Team of the player
- sb A stolen base occurs when a baserunner advances by taking a base to which he isn't entitled.
- slg Slugging percentage represents the total number of bases a player records per at-bat. Unlike on-base percentage, slugging percentage deals only with hits and does not include walks and hit-by-pitches in its equation.
- sf A sacrifice fly occurs when a batter hits a fly-ball out to the outfield or foul territory that allows a runner to score.
- obp OBP refers to how frequently a batter reaches base per plate appearance. Times on base include hits, walks and hit-by-pitches, but do not include errors, times reached on a fielder's choice or a dropped third strike.
The description of attributes in play-by-play is below:
- batter Batter in the play.
- pitcher Pitcher in play.
- b1 Player/s at first base position.
- b2 Player/s at second base position.
- b3 Player/s at third base position.
- scorers Player/s scored in the play.
- fielder_error Player committed field error.
- event Event of the play such as single, double, home run etc.
- event2 Second event of the play such as wild pitch, error etc.
- inning Inning of the play.
- top/ bottom If home team is batting it is bottom and if away team is batting it is top.
- o Count of outs
- b Count of balls
- s Count of strikes
- r Count of runs
- rbi Count of runs batted in (rbi)
- error_runs Runs due to error
- home_team_runs Score of home team
- vis_team_runs Score of visiting team
`home_line` and `vis_line` contain string value pairs for `team_name`, `team_city`, `team_runs`, `team_hits`, `team_error`, `result`, and a list of runs scored in each inning.
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
There are three splits in the dataset: train, validation and test
#### 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 are random.
## 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 can verify if models are capable of long document generation. The challenges in long document generation conditioned on input tables include ensuring coherent output, staying faithful to the input, ensuring fluent output and avoiding repetition of text. Such aspects can be verified on models trained on this dataset
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
yes
#### Unique Language Coverage
<!-- info: Does this dataset cover other languages than other datasets for the same task? -->
<!-- scope: periscope -->
no
#### Difference from other GEM datasets
<!-- info: What else sets this dataset apart from other similar datasets in GEM? -->
<!-- scope: microscope -->
Compared to the existing RotoWire (Wiseman et al. 2017) dataset, MLB summaries are longer (approximately by 50%) and the input records are richer and more structured (with the addition of play-by-play). Moreover, the MLB dataset is five times larger in terms of data size (i.e., pairs of tables and game summaries).
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
Long document generation, coherent ordering of information, faithfulness to the input statistics, fluency in generation and avoiding repetition of text.
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
yes
#### GEM Modifications
<!-- info: What changes have been made to he original dataset? -->
<!-- scope: periscope -->
`data points removed`
#### Modification Details
<!-- info: For each of these changes, described them in more details and provided the intended purpose of the modification -->
<!-- scope: microscope -->
Some examples have been removed from training dataset which satisfied the below criteria:
1. The examples in training dataset which overlapped with validation/test.
2. Some examples which described washed out games.
#### 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 -->
The [research paper](https://aclanthology.org/P19-1195) is a good resource
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
Automatic evaluation measure can evaluate the factuality, content selection, content ordering and the fluency of the model output. The factuality, content selection and content ordering is measured using an Information Extraction based evaluation approach introduced by Wiseman et al (2017). The fluency is measured using BLEU.
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`Other: Other Metrics`
#### Other Metrics
<!-- info: Definitions of other metrics -->
<!-- scope: periscope -->
Wiseman et al. (2017) define three metrics induced from the outputs of an Information Extraction model which is run on the model/human-written game summaries . Let ŷ be the gold summary and y the model output.
• Relation Generation (RG) measures the precision and count of relations extracted from y that also appear in records r.
• Content Selection (CS) measures the precision and recall of relations extracted from y that are also extracted from ŷ.
• Content Ordering (CO) measures the complement of the normalized Damerau-Levenshtein distance (Brill and Moore, 2000) between the sequences of relations extracted from y and ŷ
#### 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 -->
We have reused the automatic metrics based on Information Extraction evaluation introduced by Wiseman et al (2017). For human evaluation, we conducted studies to evaluate the factuality, coherence, grammaticality and conciseness.
#### 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 most relevant previous results for dataset are in the TACL 2021 paper on [Data-to-text Generation with Macro Planning](https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00381/101876/Data-to-text-Generation-with-Macro-Planning)
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
This dataset was curated to complement an existing data-to-text generation dataset (RotoWire by Wiseman et al. 2017) which focuses on long document generation. Compared to RotoWire , MLB summaries are longer (approximately by 50%) and the input records are richer and more structured (with the addition of play-by-play). Moreover, the MLB dataset is five times larger in terms of data size (i.e., pairs of tables and game summaries)
#### Communicative Goal
<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
The goal is to study automatic generation of long documents in a data-to-text setting. The generated summaries should exhibit coherent ordering of content, be faithful to the input statistics, be fluent and avoid repetition of text.
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
no
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Found`
#### Where was it found?
<!-- info: If found, where from? -->
<!-- scope: telescope -->
`Single website`
#### Language Producers
<!-- info: What further information do we have on the language producers? -->
<!-- scope: microscope -->
The game summaries are produced by professional writers.
#### Topics Covered
<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
<!-- scope: periscope -->
The language focuses on the sports domain.
#### 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 -->
Game summaries were tokenized using NLTK (Bird et al., 2009) and hyphenated words were separated. Sentences containing quotes were removed as they included opinions and non-factual statements unrelated to the input tables. Sometimes MLB summaries contain a "Game notes" section with incidental information which was also removed.
#### 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
#### Justification for Using the Data
<!-- info: If not, what is the justification for reusing the data? -->
<!-- scope: microscope -->
The copyright remains with the original data creators and the usage permission is restricted to non-commercial uses.
### 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
#### Categories of PII
<!-- info: What categories of PII are present or suspected in the data? -->
<!-- scope: periscope -->
`sensitive information`, `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 -->
unsure
## Considerations for Using the Data
### PII Risks and Liability
### 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 -->
`research use only`
#### 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 -->
`research use only`
### Known Technical Limitations
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
hard | null | 2023-01-25T14:31:26Z | 328 | 1 | hard | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:ar",
"license:unknown",
"region:us"
] | 2023-01-25T14:31:26Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- ar
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
paperswithcode_id: hard
pretty_name: Hotel Arabic-Reviews Dataset
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': '1'
'1': '2'
'2': '3'
'3': '4'
'4': '5'
config_name: plain_text
splits:
- name: train
num_bytes: 27507085
num_examples: 105698
download_size: 8508677
dataset_size: 27507085
---
# Dataset Card for Hard
## 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:** [Hard](https://github.com/elnagara/HARD-Arabic-Dataset)
- **Repository:** [Hard](https://github.com/elnagara/HARD-Arabic-Dataset)
- **Paper:** [Hotel Arabic-Reviews Dataset Construction for Sentiment Analysis Applications](https://link.springer.com/chapter/10.1007/978-3-319-67056-0_3)
- **Point of Contact:** [Ashraf Elnagar](ashraf@sharjah.ac.ae)
### Dataset Summary
This dataset contains 93,700 hotel reviews in Arabic language.The hotel reviews were collected from Booking.com website during June/July 2016.The reviews are expressed in Modern Standard Arabic as well as dialectal Arabic.The following table summarize some tatistics on the HARD Dataset.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset is based on Arabic.
## Dataset Structure
### Data Instances
A typical data point comprises a rating from 1 to 5 for hotels.
### Data Fields
[More Information Needed]
### Data Splits
The dataset is not split.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
### Contributions
Thanks to [@zaidalyafeai](https://github.com/zaidalyafeai) for adding this dataset. | [
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urdu_fake_news | null | 2023-01-25T15:01:58Z | 328 | 0 | null | [
"task_categories:text-classification",
"task_ids:fact-checking",
"task_ids:intent-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:ur",
"license:unknown",
"region:us"
] | 2023-01-25T15:01:58Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- ur
license:
- unknown
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- fact-checking
- intent-classification
pretty_name: Bend the Truth (Urdu Fake News)
dataset_info:
features:
- name: news
dtype: string
- name: label
dtype:
class_label:
names:
'0': Fake
'1': Real
- name: category
dtype:
class_label:
names:
'0': bus
'1': hlth
'2': sp
'3': tch
'4': sbz
splits:
- name: train
num_bytes: 1762905
num_examples: 638
- name: test
num_bytes: 799587
num_examples: 262
download_size: 1042653
dataset_size: 2562492
---
# Dataset Card for Bend the Truth (Urdu Fake News)
## 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/MaazAmjad/Datasets-for-Urdu-news/)
- **Repository:** [Github](https://github.com/MaazAmjad/Datasets-for-Urdu-news/)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [Maaz Amjad](https://github.com/MaazAmjad)
### 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
- news: a string in urdu
- label: the label indicating whethere the provided news is real or fake.
- category: The intent of the news being presented. The available 5 classes are Sports, Health, Technology, Entertainment, and Business.
### 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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Davlan/conll2003_de_noMISC | Davlan | 2021-10-05T09:06:35Z | 328 | 0 | null | [
"region:us"
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DrishtiSharma/mr_opus100_processed | DrishtiSharma | 2022-02-09T14:28:39Z | 328 | 0 | null | [
"region:us"
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Hellisotherpeople/DebateSum | Hellisotherpeople | 2022-12-03T04:14:45Z | 328 | 8 | null | [
"task_categories:question-answering",
"task_categories:summarization",
"task_categories:text-retrieval",
"task_categories:text-generation",
"task_ids:abstractive-qa",
"task_ids:document-retrieval",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:mit",
"conditional-text-generation",
"arxiv:2011.07251",
"region:us"
] | 2022-12-03T04:14:45Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- question-answering
- summarization
- text-retrieval
- text-generation
task_ids:
- abstractive-qa
- document-retrieval
- extractive-qa
pretty_name: 'DebateSum: A large-scale argument mining and summarization dataset'
language_bcp47:
- en-US
tags:
- conditional-text-generation
---
# DebateSum
Corresponding code repo for the upcoming paper at ARGMIN 2020: "DebateSum: A large-scale argument mining and summarization dataset"
Arxiv pre-print available here: https://arxiv.org/abs/2011.07251
Check out the presentation date and time here: https://argmining2020.i3s.unice.fr/node/9
Full paper as presented by the ACL is here: https://www.aclweb.org/anthology/2020.argmining-1.1/
Video of presentation at COLING 2020: https://underline.io/lecture/6461-debatesum-a-large-scale-argument-mining-and-summarization-dataset
The dataset is distributed as csv files.
A search engine over DebateSum (as well as some additional evidence not included in DebateSum) is available as [debate.cards](http://debate.cards/). It's very good quality and allows for the evidence to be viewed in the format that debaters use.
# Data
DebateSum consists of **187328** debate documents, arguements (also can be thought of as abstractive summaries, or queries), word-level extractive summaries, citations, and associated metadata organized by topic-year. This data is ready for analysis by NLP systems.
## Download
All data is accesable in a parsed format organized by topic year [here](https://mega.nz/folder/ZdQGmK6b#-0hoBWc5fLYuxQuH25feXg)
Addtionally, the trained word-vectors for [debate2vec](https://github.com/Hellisotherpeople/debate2vec) are also found in that folder.
## Regenerating it yourself
This is useful as the debaters who produce the evidence release their work every year. Soon enough I will update to include the 2020-2021 topic.
*Step 1: Download all open evidence files from [Open Evidence](https://openev.debatecoaches.org/) and unzip them into a directory. The links are as follows:*
* [2019](https://s3.amazonaws.com/openev/2019OpenEv.zip) - Resolved: The United States federal government should substantially reduce Direct Commercial Sales and/or Foreign Military Sales of arms from the United States.
* [2018](https://s3.amazonaws.com/openev/2018OpenEv.zip) - Resolved: The United States federal government should substantially reduce its restrictions on legal immigration to the United States.
* [2017](https://s3.amazonaws.com/openev/2017OpenEv.zip) - Resolved: The United States federal government should substantially increase its funding and/or regulation of elementary and/or secondary education in the United States.
* [2016](https://s3.amazonaws.com/openev/2016OpenEv.zip) - Resolved: The United States federal government should substantially increase its economic and/or diplomatic engagement with the People’s Republic of China.
* [2015](https://s3.amazonaws.com/openev/2015OpenEv.zip) - Resolved: The United States federal government should substantially curtail its domestic surveil-lance.
* [2014](https://s3.amazonaws.com/openev/2014OpenEv.zip) - Resolved: The United States federal government should substantially increase its non-military exploration and/or development of the Earth’s oceans.
* [2013](https://s3.amazonaws.com/openev/2013OpenEv.zip) - Resolved: The United States federal government should substantially increase its economic en-gagement toward Cuba, Mexico or Venezuela.
*Step 2: Convert all evidence from docx files to html5 files using [pandoc](https://pandoc.org/) with this command:*
```
for f in *.docx; do pandoc "$f" -s -o "${f%.docx}.html5"; done
```
*Step 3: install the dependencies for make_debate_dataset.py.*
```
pip install -r requirements.txt
```
*Step 4: Modify the folder and file locations as needed for your system, and run make_debate_dataset.py*
```
python3 make_debate_dataset.py
```
# Credits
Huge thanks to [Arvind Balaji](https://github.com/arvind-balaji) for making debate.cards and being second author on this paper!
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
result-kand2-sdxl-wuerst-karlo/7c5b039f | result-kand2-sdxl-wuerst-karlo | 2023-11-11T02:42:36Z | 328 | 0 | null | [
"region:us"
] | 2023-11-11T02:42:36Z | 2023-11-11T02:42:35.000Z | 2023-11-11T02:42:35 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 182
num_examples: 10
download_size: 1364
dataset_size: 182
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "7c5b039f"
[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 | |
disfl_qa | null | 2022-11-18T19:58:47Z | 327 | 1 | null | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"task_ids:open-domain-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:2106.04016",
"region:us"
] | 2022-11-18T19:58:47Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: 'DISFL-QA: A Benchmark Dataset for Understanding Disfluencies in Question
Answering'
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
- open-domain-qa
dataset_info:
features:
- name: squad_v2_id
dtype: string
- name: original question
dtype: string
- name: disfluent question
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
splits:
- name: train
num_bytes: 7712523
num_examples: 7182
- name: test
num_bytes: 3865097
num_examples: 3643
- name: validation
num_bytes: 1072731
num_examples: 1000
download_size: 48935038
dataset_size: 12650351
---
# Dataset Card for DISFL-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Disfl-QA](https://github.com/google-research-datasets/disfl-qa)
- **Paper:** [Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering](https://arxiv.org/pdf/2106.04016.pdf)
- **Point of Contact:** [disfl-qa team](disfl-qa@google.com)
### Dataset Summary
Disfl-QA is a targeted dataset for contextual disfluencies in an information seeking setting, namely question answering over Wikipedia passages. Disfl-QA builds upon the SQuAD-v2 ([Rajpurkar et al., 2018](https://www.aclweb.org/anthology/P18-2124/)) dataset, where each question in the dev set is annotated to add a contextual disfluency using the paragraph as a source of distractors.
The final dataset consists of ~12k (disfluent question, answer) pairs. Over 90\% of the disfluencies are corrections or restarts, making it a much harder test set for disfluency correction. Disfl-QA aims to fill a major gap between speech and NLP research community. The authors hope the dataset can serve as a benchmark dataset for testing robustness of models against disfluent inputs.
The expriments reveal that the state-of-the-art models are brittle when subjected to disfluent inputs from Disfl-QA. Detailed experiments and analyses can be found in the [paper](https://arxiv.org/pdf/2106.04016.pdf).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset is in English only.
## Dataset Structure
### Data Instances
This example was too long and was cropped:
```
{
"answers": {
"answer_start": [94, 87, 94, 94],
"text": ["10th and 11th centuries", "in the 10th and 11th centuries", "10th and 11th centuries", "10th and 11th centuries"]
},
"context": "\"The Normans (Norman: Nourmands; French: Normands; Latin: Normanni) were the people who in the 10th and 11th centuries gave thei...",
"id": "56ddde6b9a695914005b9629",
"original question": "When were the Normans in Normandy?",
"disfluent question": "From which countries no tell me when were the Normans in Normandy?"
"title": "Normans"
}
```
### Data Fields
- `id`: a `string` feature.
- `title`: a `string` feature.
- `context`: a `string` feature.
- `original question`: Original question from SQuAD-v2 (a `string` feature)
- `disfluent question`: Disfluent question from Disfl-QA (a `string` feature)
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
### Data Splits
Disfl-QA consists of ~12k disfluent questions with the following train/dev/test splits:
| File | Questions |
|-----|-----|
|train.json | 7182 |
|dev.json | 1000 |
|test.json | 3643 |
## Dataset Creation
### Curation Rationale
The research in NLP and speech community has been impeded by the lack of curated datasets containing such disfluencies. The datasets available today are mostly conversational in nature, and span a limited number of very specific domains (e.g., telephone conversations, court proceedings). Furthermore, only a small fraction of the utterances in these datasets contain disfluencies, with a limited and skewed distribution of disfluencies types. In the most popular dataset in the literature, the SWITCHBOARD corpus (Godfrey et al., 1992), only 5.9% of the words are disfluencies (Charniak and Johnson, 2001), of which > 50% are repetitions (Shriberg, 1996), which has been shown to be the relatively simpler form of disfluencies (Zayats et al., 2014; Jamshid Lou et al., 2018; Zayats et al., 2019). To fill this gap, the authors presented DISFL-QA, the first dataset containing contextual disfluencies in an information seeking setting, namely question answering over Wikipedia passages.
### Source Data
#### Initial Data Collection and Normalization
DISFL-QA is constructed by asking human raters to insert disfluencies in questions from SQUAD-v2, a popular question answering dataset, using the passage and remaining questions as context. These contextual disfluencies lend naturalness to DISFL-QA, and challenge models relying on shallow matching between question and context to predict an answer.
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
Each question associated with the paragraph is sent for a human annotation task to add a contextual disfluency using the paragraph as a source of distractors. Finally, to ensure the quality of the dataset, a subsequent round of human evaluation with an option to re-annotate is conducted.
#### 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
Disfl-QA dataset is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
### Citation Information
```
@inproceedings{gupta-etal-2021-disflqa,
title = "{Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering}",
author = "Gupta, Aditya and Xu, Jiacheng and Upadhyay, Shyam and Yang, Diyi and Faruqui, Manaal",
booktitle = "Findings of ACL",
year = "2021"
}
```
### Contributions
Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik) for adding this dataset. | [
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metooma | null | 2023-01-25T14:40:24Z | 327 | 0 | metooma | [
"task_categories:text-classification",
"task_categories:text-retrieval",
"task_ids:multi-class-classification",
"task_ids:multi-label-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc0-1.0",
"region:us"
] | 2023-01-25T14:40:24Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc0-1.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
- text-retrieval
task_ids:
- multi-class-classification
- multi-label-classification
paperswithcode_id: metooma
pretty_name: '#MeTooMA dataset'
dataset_info:
features:
- name: TweetId
dtype: string
- name: Text_Only_Informative
dtype:
class_label:
names:
'0': Text Non Informative
'1': Text Informative
- name: Image_Only_Informative
dtype:
class_label:
names:
'0': Image Non Informative
'1': Image Informative
- name: Directed_Hate
dtype:
class_label:
names:
'0': Directed Hate Absent
'1': Directed Hate Present
- name: Generalized_Hate
dtype:
class_label:
names:
'0': Generalized Hate Absent
'1': Generalized Hate Present
- name: Sarcasm
dtype:
class_label:
names:
'0': Sarcasm Absent
'1': Sarcasm Present
- name: Allegation
dtype:
class_label:
names:
'0': Allegation Absent
'1': Allegation Present
- name: Justification
dtype:
class_label:
names:
'0': Justification Absent
'1': Justification Present
- name: Refutation
dtype:
class_label:
names:
'0': Refutation Absent
'1': Refutation Present
- name: Support
dtype:
class_label:
names:
'0': Support Absent
'1': Support Present
- name: Oppose
dtype:
class_label:
names:
'0': Oppose Absent
'1': Oppose Present
splits:
- name: train
num_bytes: 821738
num_examples: 7978
- name: test
num_bytes: 205489
num_examples: 1995
download_size: 408889
dataset_size: 1027227
---
# Dataset Card for #MeTooMA 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:** https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/JN4EYU
- **Repository:** https://github.com/midas-research/MeTooMA
- **Paper:** https://ojs.aaai.org//index.php/ICWSM/article/view/7292
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Dataset Summary
- The dataset consists of tweets belonging to #MeToo movement on Twitter, labelled into different categories.
- This dataset includes more data points and has more labels than any of the previous datasets that contain social media
posts about sexual abuse discloures. Please refer to the Related Datasets of the publication for a detailed information about this.
- Due to Twitters development policies, the authors provide only the tweet IDs and corresponding labels,
other data can be fetched via Twitter API.
- The data has been labelled by experts, with the majority taken into the account for deciding the final label.
- The authors provide these labels for each of the tweets.
- Relevance
- Directed Hate
- Generalized Hate
- Sarcasm
- Allegation
- Justification
- Refutation
- Support
- Oppose
- The definitions for each task/label is in the main publication.
- Please refer to the accompanying paper https://aaai.org/ojs/index.php/ICWSM/article/view/7292 for statistical analysis on the textual data
extracted from this dataset.
- The language of all the tweets in this dataset is English
- Time period: October 2018 - December 2018
- Suggested Use Cases of this dataset:
- Evaluating usage of linguistic acts such as: hate-spech and sarcasm in the incontext of public sexual abuse discloures.
- Extracting actionable insights and virtual dynamics of gender roles in sexual abuse revelations.
- Identifying how influential people were potrayed on public platform in the
events of mass social movements.
- Polarization analysis based on graph simulations of social nodes of users involved
in the #MeToo movement.
### Supported Tasks and Leaderboards
Multi Label and Multi-Class Classification
### Languages
English
## Dataset Structure
- The dataset is structured into CSV format with TweetID and accompanying labels.
- Train and Test sets are split into respective files.
### Data Instances
Tweet ID and the appropriate labels
### Data Fields
Tweet ID and appropriate labels (binary label applicable for a data point) and multiple labels for each Tweet ID
### Data Splits
- Train: 7979
- Test: 1996
## Dataset Creation
### Curation Rationale
- Twitter was the major source of all the public discloures of sexual abuse incidents during the #MeToo movement.
- People expressed their opinions over issues which were previously missing from the social media space.
- This provides an option to study the linguistic behaviours of social media users in an informal setting,
therefore the authors decide to curate this annotated dataset.
- The authors expect this dataset would be of great interest and use to both computational and socio-linguists.
- For computational linguists, it provides an opportunity to model three new complex dialogue acts (allegation, refutation, and justification) and also to study how these acts interact with some of the other linguistic components like stance, hate, and sarcasm. For socio-linguists, it provides an opportunity to explore how a movement manifests in social media.
### Source Data
- Source of all the data points in this dataset is Twitter social media platform.
#### Initial Data Collection and Normalization
- All the tweets are mined from Twitter with initial search paramters identified using keywords from the #MeToo movement.
- Redundant keywords were removed based on manual inspection.
- Public streaming APIs of Twitter were used for querying with the selected keywords.
- Based on text de-duplication and cosine similarity score, the set of tweets were pruned.
- Non english tweets were removed.
- The final set was labelled by experts with the majority label taken into the account for deciding the final label.
- Please refer to this paper for detailed information: https://ojs.aaai.org//index.php/ICWSM/article/view/7292
#### Who are the source language producers?
Please refer to this paper for detailed information: https://ojs.aaai.org//index.php/ICWSM/article/view/7292
### 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 are domain experts having a degree in clinical psychology and gender studies.
- Please refer to the accompnaying paper for a detailed annotation process.
### Personal and Sensitive Information
- Considering Twitters policy for distribution of data, only Tweet ID and applicable labels are shared for the public use.
- It is highly encouraged to use this dataset for scientific purposes only.
- This dataset collection completely follows the Twitter mandated guidelines for distribution and usage.
## Considerations for Using the Data
### Social Impact of Dataset
- The authors of this dataset do not intend to conduct a population centric analysis of #MeToo movement on Twitter.
- The authors acknowledge that findings from this dataset cannot be used as-is for any direct social intervention, these
should be used to assist already existing human intervention tools and therapies.
- Enough care has been taken to ensure that this work comes of as trying to target a specific person for their
personal stance of issues pertaining to the #MeToo movement.
- The authors of this work do not aim to vilify anyone accused in the #MeToo movement in any manner.
- Please refer to the ethics and discussion section of the mentioned publication for appropriate sharing of this dataset
and social impact of this work.
### Discussion of Biases
- The #MeToo movement acted as a catalyst for implementing social policy changes to benefit the members of
community affected by sexual abuse.
- Any work undertaken on this dataset should aim to minimize the bias against minority groups which
might amplified in cases of sudden outburst of public reactions over sensitive social media discussions.
### Other Known Limitations
- Considering privacy concerns, social media practitioners should be aware of making automated interventions
to aid the victims of sexual abuse as some people might not prefer to disclose their notions.
- Concerned social media users might also repeal their social information, if they found out that their
information is being used for computational purposes, hence it is important seek subtle individual consent
before trying to profile authors involved in online discussions to uphold personal privacy.
## Additional Information
Please refer to this link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/JN4EYU
### 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
[More Information Needed]
### Citation Information
Please cite the following publication if you make use of the dataset: https://ojs.aaai.org/index.php/ICWSM/article/view/7292
```
@article{Gautam_Mathur_Gosangi_Mahata_Sawhney_Shah_2020, title={#MeTooMA: Multi-Aspect Annotations of Tweets Related to the MeToo Movement}, volume={14}, url={https://aaai.org/ojs/index.php/ICWSM/article/view/7292}, abstractNote={<p>In this paper, we present a dataset containing 9,973 tweets related to the MeToo movement that were manually annotated for five different linguistic aspects: relevance, stance, hate speech, sarcasm, and dialogue acts. We present a detailed account of the data collection and annotation processes. The annotations have a very high inter-annotator agreement (0.79 to 0.93 k-alpha) due to the domain expertise of the annotators and clear annotation instructions. We analyze the data in terms of geographical distribution, label correlations, and keywords. Lastly, we present some potential use cases of this dataset. We expect this dataset would be of great interest to psycholinguists, socio-linguists, and computational linguists to study the discursive space of digitally mobilized social movements on sensitive issues like sexual harassment.</p&gt;}, number={1}, journal={Proceedings of the International AAAI Conference on Web and Social Media}, author={Gautam, Akash and Mathur, Puneet and Gosangi, Rakesh and Mahata, Debanjan and Sawhney, Ramit and Shah, Rajiv Ratn}, year={2020}, month={May}, pages={209-216} }
```
### Contributions
Thanks to [@akash418](https://github.com/akash418) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ASCCCCCCCC/amazon_zh | ASCCCCCCCC | 2022-02-17T02:16:59Z | 327 | 1 | null | [
"license:apache-2.0",
"region:us"
] | 2022-02-17T02:16:59Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
license: apache-2.0
---
this is a datasets about amazon reviews | [
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echarlaix/vqa | echarlaix | 2022-02-01T10:45:13Z | 327 | 1 | null | [
"license:apache-2.0",
"region:us"
] | 2022-02-01T10:45:13Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
license: apache-2.0
---
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llm-book/ner-wikipedia-dataset | llm-book | 2023-07-25T17:19:14Z | 327 | 0 | null | [
"task_categories:token-classification",
"size_categories:1K<n<10K",
"language:ja",
"license:cc-by-sa-3.0",
"region:us"
] | 2023-07-25T17:19:14Z | 2023-04-15T10:43:21.000Z | 2023-04-15T10:43:21 | ---
language:
- ja
license:
- cc-by-sa-3.0
size_categories:
- 1K<n<10K
task_categories:
- token-classification
---
# Dataset Card for llm-book/ner-wikipedia-dataset
書籍『大規模言語モデル入門』で使用する、ストックマーク株式会社により作成された「Wikipediaを用いた日本語の固有表現抽出データセット」(Version 2.0)です。
Githubリポジトリ[stockmarkteam/ner-wikipedia-dataset](https://github.com/stockmarkteam/ner-wikipedia-dataset)で公開されているデータセットを利用しています。
### Citation
```bibtex
@inproceedings{omi-2021-wikipedia,
title = "Wikipediaを用いた日本語の固有表現抽出のデータセットの構築",
author = "近江 崇宏",
booktitle = "言語処理学会第27回年次大会",
year = "2021",
url = "https://anlp.jp/proceedings/annual_meeting/2021/pdf_dir/P2-7.pdf",
}
```
### Licence
Wikipedia日本語版と同じCC-BY-SA 3.0のライセンスに従います。
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gooaq | null | 2023-01-25T14:31:10Z | 326 | 3 | gooaq | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"arxiv:2104.08727",
"region:us"
] | 2023-01-25T14:31:10Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- machine-generated
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswithcode_id: gooaq
pretty_name: 'GooAQ: Open Question Answering with Diverse Answer Types'
dataset_info:
features:
- name: id
dtype: int32
- name: question
dtype: string
- name: short_answer
dtype: string
- name: answer
dtype: string
- name: answer_type
dtype:
class_label:
names:
'0': feat_snip
'1': collection
'2': knowledge
'3': unit_conv
'4': time_conv
'5': curr_conv
splits:
- name: train
num_bytes: 974320061
num_examples: 3112679
- name: validation
num_bytes: 444553
num_examples: 2500
- name: test
num_bytes: 445810
num_examples: 2500
download_size: 2111358901
dataset_size: 975210424
---
# Dataset Card for GooAQ
## 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:** [GooAQ 🥑: Google Answers to Google Questions!](https://github.com/allenai/gooaq)
- **Repository:** [GooAQ 🥑: Google Answers to Google Questions!](https://github.com/allenai/gooaq)
- **Paper:** [GOOAQ: Open Question Answering with Diverse Answer Types](https://arxiv.org/abs/2104.08727)
- **Point of Contact:** [Daniel Khashabi](danielk@allenai.org)
### Dataset Summary
GooAQ is a large-scale dataset with a variety of answer types. This dataset contains over
5 million questions and 3 million answers collected from Google. GooAQ questions are collected
semi-automatically from the Google search engine using its autocomplete feature. This results in
naturalistic questions of practical interest that are nonetheless short and expressed using simple
language. GooAQ answers are mined from Google's responses to our collected questions, specifically from
the answer boxes in the search results. This yields a rich space of answer types, containing both
textual answers (short and long) as well as more structured ones such as collections.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset contains samples in English only.
## Dataset Structure
### Data Instances
Each row of the data file should look like this:
```
{
"id": 3339543,
"question": "what is the difference between collagen and whey protein?",
"short_answer": None,
"answer": "The main differences between the amino acid profiles of whey and collagen are that whey contains all 9 essential amino acids, while collagen only has 8. ... Collagen is a fibrous protein found in the skin, cartilage, and bones of animals whereas whey comes from milk.",
"answer_type": "feat_snip"
}
```
where the questions `question` are collected via Google auto-complete.
The answers responses (`short_answer` and `answer`) were collected from Google's answer boxes.
The answer types (`answer_type`) are inferred based on the html content of Google's response.
Here is the dominant types in the current dataset:
- `feat_snip`: explanatory responses; the majoriy the question/responses are of this type.
- `collection`: list responses (e.g., steps to accomplish something).
- `knowledge`: typically short responses for knowledge seeking questions.
- `unit_conv`: questions about converting units.
- `time_conv`: questions about converting times.
- `curr_conv`: questions about converting currencies.
Dataset instances which are not part of dominant types are marked with -1 label.
### Data Fields
- `id`: an `int` feature.
- `question`: a `string` feature.
- `short_answer`: a `string` feature (could be None as well in some cases).
- `answer`: a `string` feature (could be None as well in some cases).
- `answer_type`: a `string` feature.
### Data Splits
Number of samples in train/validation/test set are given below:
| Split | Number of samples |
|------------|-------------------|
| Train | 3112679 |
| Validation | 2500 |
| Test | 2500 |
## Dataset Creation
### Curation Rationale
While day-to-day questions come with a variety of answer types, the current question-answering (QA)
literature has failed to adequately address the answer diversity of questions. Many of the everyday questions
that humans deal with and pose to search engines have a more diverse set of responses. Their answer can be a multi-sentence description (a snippet) (e.g., ‘what is’ or ‘can you’ questions), a collection of items such as ingredients (‘what are’, ‘things to’) or of steps towards a goal such as unlocking a phone (‘how to’), etc. Even when the answer is short, it can have richer types, e.g., unit conversion, time zone conversion, or various kinds of knowledge look-up (‘how much’, ‘when is’, etc.).
Such answer type diversity is not represented in any existing dataset.
### Source Data
#### Initial Data Collection and Normalization
Construction this dataset involved two main steps, extracting questions from search auto-complete and extracting answers from answer boxes.
1) Query Extraction: To extract a rich yet natural set of questions they used Google auto-completion. They start with a seed set of question terms (e.g., “who”, “where”, etc.). They bootstrap based on this set, by repeatedly querying prefixes of previously extracted questions, in order to discover longer and richer sets of questions. Such questions extracted from the autocomplete algorithm are highly reflective of popular questions posed by users of Google. They filter out any questions shorter than 5 tokens as they are often in-complete questions. This process yields over ∼5M questions, which were collected over a span of 6 months. The average length of the questions is about 8 tokens.
2) Answer Extraction: They rely on the Google answer boxes shown on top of the search results when the questions are issued to Google. There are a variety of answer boxes. The most common kind involves highlighted sentences (extracted from various websites) that contain the answer to a given question. These form the snippet and collection answers in GOOAQ. In some cases, the answer box shows the answer directly, possibly in addition to the textual snippet. These form theshort answers in GOOAQ.
They first scrape the search results for all questions. This is the main extraction bottleneck, which was done over a span of 2 months. Subsequently, they extract answer strings from the HTML content of the search results. Answer types are also inferred at this stage, based on the HTML tags around the answer.
#### Who are the source language producers?
Answered above.
### Annotations
#### Annotation process
Answered in above section.
#### Who are the annotators?
Since their task is focused on English, they required workers to be based in a country with a population predominantly of native English speakers (e.g., USA, Canada, UK, and Australia) and have completed at least 5000 HITs with ≥ 99% assignment approval rate. Additionally, they have a qualification test with half-a-dozen questions all of which need to be answered correctly by the annotators.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
To prevent biased judgements, they also ask the annotators to avoid using Google search (which is what they used when mined GOOAQ) when annotating the quality of shown instances.
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
List the people involved in collecting the dataset and their affiliation(s). If funding information is known, include it here.
### Licensing Information
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License.
### Citation Information
```
@article{gooaq2021,
title={GooAQ: Open Question Answering with Diverse Answer Types},
author={Khashabi, Daniel and Ng, Amos and Khot, Tushar and Sabharwal, Ashish and Hajishirzi, Hannaneh and Callison-Burch, Chris},
journal={arXiv preprint},
year={2021}
}
```
### Contributions
Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
muchocine | null | 2023-01-25T14:40:54Z | 326 | 4 | null | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:es",
"license:unknown",
"region:us"
] | 2023-01-25T14:40:54Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- es
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: Muchocine
dataset_info:
features:
- name: review_body
dtype: string
- name: review_summary
dtype: string
- name: star_rating
dtype:
class_label:
names:
'0': '1'
'1': '2'
'2': '3'
'3': '4'
'4': '5'
splits:
- name: train
num_bytes: 11871095
num_examples: 3872
download_size: 55556703
dataset_size: 11871095
---
# Dataset Card for Muchocine
## 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.lsi.us.es/~fermin/index.php/Datasets
### Dataset Summary
The Muchocine reviews dataset contains 3,872 longform movie reviews in Spanish language,
each with a shorter summary review, and a rating on a 1-5 scale.
### Supported Tasks and Leaderboards
- `text-classification`: This dataset can be used for Text Classification, more precisely Sentiment Classification where the task is to predict the `star_rating` for a `reveiw_body` or a `review summaray`.
### Languages
Spanish.
## Dataset Structure
### Data Instances
An example from the train split:
```
{
'review_body': 'Zoom nos cuenta la historia de Jack Shepard, anteriormente conocido como el Capitán Zoom, Superhéroe que perdió sus poderes y que actualmente vive en el olvido. La llegada de una amenaza para la Tierra hará que la agencia del gobierno que se ocupa de estos temas acuda a él para que entrene a un grupo de jóvenes con poderes para combatir esta amenaza.Zoom es una comedia familiar, con todo lo que eso implica, es decir, guión flojo y previsible, bromas no salidas de tono, historia amorosa de por medio y un desenlace tópico. La gracia está en que los protagonistas son jóvenes con superpoderes, una producción cargada de efectos especiales y unos cuantos guiños frikis. La película además se pasa volando ya que dura poco mas de ochenta minutos y cabe destacar su prologo en forma de dibujos de comics explicando la historia de la cual partimos en la película.Tim Allen protagoniza la cinta al lado de un envejecido Chevy Chase, que hace de doctor encargado del proyecto, un papel bastante gracioso y ridículo, pero sin duda el mejor papel es el de Courteney Cox, en la piel de una científica amante de los comics y de lo más friki. Del grupito de los cuatro niños sin duda la mas graciosa es la niña pequeña con súper fuerza y la que provocara la mayor parte de los gags debido a su poder.Una comedia entretenida y poca cosa más para ver una tarde de domingo. ',
'review_summary': 'Una comedia entretenida y poca cosa más para ver una tarde de domingo ', 'star_rating': 2
}
```
### Data Fields
- `review_body` - longform review
- `review_summary` - shorter-form review
- `star_rating` - an integer star rating (1-5)
The original source also includes part-of-speech tagging for body and summary fields.
### Data Splits
One split (train) with 3,872 reviews.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
Data was collected from www.muchocine.net and uploaded by Dr. Fermín L. Cruz Mata
of La Universidad de Sevilla.
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
The text reviews and star ratings came directly from users, so no additional annotation was 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
Dr. Fermín L. Cruz Mata.
### Licensing Information
[More Information Needed]
### Citation Information
See http://www.lsi.us.es/~fermin/index.php/Datasets
### Contributions
Thanks to [@mapmeld](https://github.com/mapmeld) for adding this dataset. | [
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HHousen/msrp | HHousen | 2022-01-01T03:30:43Z | 326 | 1 | null | [
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LoganKells/amazon_product_reviews_video_games | LoganKells | 2021-12-07T01:42:37Z | 326 | 1 | null | [
"region:us"
] | 2021-12-07T01:42:37Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | #Title | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
eugenesiow/Set14 | eugenesiow | 2022-10-21T04:00:31Z | 326 | 0 | null | [
"task_categories:other",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"license:other",
"other-image-super-resolution",
"region:us"
] | 2022-10-21T04:00:31Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- found
language: []
license:
- other
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- other
task_ids: []
pretty_name: Set14
tags:
- other-image-super-resolution
---
# Dataset Card for Set14
## 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://sites.google.com/site/romanzeyde/research-interests
- **Repository**: https://huggingface.co/datasets/eugenesiow/Set14
- **Paper**: http://www.cs.technion.ac.il/users/wwwb/cgi-bin/tr-get.cgi/2010/CS/CS-2010-12.pdf
- **Leaderboard**: https://github.com/eugenesiow/super-image#scale-x2
### Dataset Summary
Set14 is an evaluation dataset with 14 RGB images for the image super resolution task. It was first used as the test set of the paper "On single image scale-up using sparse-representations" by [Zeyde et al. (2010)](http://www.cs.technion.ac.il/users/wwwb/cgi-bin/tr-get.cgi/2010/CS/CS-2010-12.pdf).
Install with `pip`:
```bash
pip install datasets super-image
```
Evaluate a model with the [`super-image`](https://github.com/eugenesiow/super-image) library:
```python
from datasets import load_dataset
from super_image import EdsrModel
from super_image.data import EvalDataset, EvalMetrics
dataset = load_dataset('eugenesiow/Set14', 'bicubic_x2', split='validation')
eval_dataset = EvalDataset(dataset)
model = EdsrModel.from_pretrained('eugenesiow/edsr-base', scale=2)
EvalMetrics().evaluate(model, eval_dataset)
```
### Supported Tasks and Leaderboards
The dataset is commonly used for evaluation of the `image-super-resolution` task.
Unofficial [`super-image`](https://github.com/eugenesiow/super-image) leaderboard for:
- [Scale 2](https://github.com/eugenesiow/super-image#scale-x2)
- [Scale 3](https://github.com/eugenesiow/super-image#scale-x3)
- [Scale 4](https://github.com/eugenesiow/super-image#scale-x4)
- [Scale 8](https://github.com/eugenesiow/super-image#scale-x8)
### Languages
Not applicable.
## Dataset Structure
### Data Instances
An example of `validation` for `bicubic_x2` looks as follows.
```
{
"hr": "/.cache/huggingface/datasets/downloads/extracted/Set14_HR/baboon.png",
"lr": "/.cache/huggingface/datasets/downloads/extracted/Set14_LR_x2/baboon.png"
}
```
### Data Fields
The data fields are the same among all splits.
- `hr`: a `string` to the path of the High Resolution (HR) `.png` image.
- `lr`: a `string` to the path of the Low Resolution (LR) `.png` image.
### Data Splits
| name |validation|
|-------|---:|
|bicubic_x2|14|
|bicubic_x3|14|
|bicubic_x4|14|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
No annotations.
#### Who are the annotators?
No annotators.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
- **Original Authors**: [Zeyde et al.](http://www.cs.technion.ac.il/users/wwwb/cgi-bin/tr-get.cgi/2010/CS/CS-2010-12.pdf)
### Licensing Information
Academic use only.
### Citation Information
```bibtex
@inproceedings{zeyde2010single,
title={On single image scale-up using sparse-representations},
author={Zeyde, Roman and Elad, Michael and Protter, Matan},
booktitle={International conference on curves and surfaces},
pages={711--730},
year={2010},
organization={Springer}
}
```
### Contributions
Thanks to [@eugenesiow](https://github.com/eugenesiow) for adding this dataset.
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arsentd_lev | null | 2023-01-25T14:26:36Z | 325 | 3 | arsentd-lev | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"task_ids:topic-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:apc",
"language:ajp",
"license:other",
"arxiv:1906.01830",
"region:us"
] | 2023-01-25T14:26:36Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- apc
- ajp
license:
- other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
- topic-classification
paperswithcode_id: arsentd-lev
pretty_name: ArSenTD-LEV
dataset_info:
features:
- name: Tweet
dtype: string
- name: Country
dtype:
class_label:
names:
'0': jordan
'1': lebanon
'2': syria
'3': palestine
- name: Topic
dtype: string
- name: Sentiment
dtype:
class_label:
names:
'0': negative
'1': neutral
'2': positive
'3': very_negative
'4': very_positive
- name: Sentiment_Expression
dtype:
class_label:
names:
'0': explicit
'1': implicit
'2': none
- name: Sentiment_Target
dtype: string
splits:
- name: train
num_bytes: 1233980
num_examples: 4000
download_size: 392666
dataset_size: 1233980
---
# Dataset Card for ArSenTD-LEV
## 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:** [ArSenTD-LEV homepage](http://oma-project.com/)
- **Paper:** [ArSentD-LEV: A Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweets](https://arxiv.org/abs/1906.01830)
### Dataset Summary
The Arabic Sentiment Twitter Dataset for Levantine dialect (ArSenTD-LEV) contains 4,000 tweets written in Arabic and equally retrieved from Jordan, Lebanon, Palestine and Syria.
### Supported Tasks and Leaderboards
Sentriment analysis
### Languages
Arabic Levantine Dualect
## Dataset Structure
### Data Instances
{'Country': 0,
'Sentiment': 3,
'Sentiment_Expression': 0,
'Sentiment_Target': 'هاي سوالف عصابات ارهابية',
'Topic': 'politics',
'Tweet': 'ثلاث تفجيرات في #كركوك الحصيلة قتيل و 16 جريح بدأت اكلاوات كركوك كانت امان قبل دخول القوات العراقية ، هاي سوالف عصابات ارهابية'}
### Data Fields
`Tweet`: the text content of the tweet \
`Country`: the country from which the tweet was collected ('jordan', 'lebanon', 'syria', 'palestine')\
`Topic`: the topic being discussed in the tweet (personal, politics, religion, sports, entertainment and others) \
`Sentiment`: the overall sentiment expressed in the tweet (very_negative, negative, neutral, positive and very_positive) \
`Sentiment_Expression`: the way how the sentiment was expressed: explicit, implicit, or none (the latter when sentiment is neutral) \
`Sentiment_Target`: the segment from the tweet to which sentiment is expressed. If sentiment is neutral, this field takes the 'none' value.
### Data Splits
No standard splits are provided
## 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
Make sure to read and agree to the [license](http://oma-project.com/ArSenL/ArSenTD_Lev_Intro)
### Citation Information
```
@article{baly2019arsentd,
title={Arsentd-lev: A multi-topic corpus for target-based sentiment analysis in arabic levantine tweets},
author={Baly, Ramy and Khaddaj, Alaa and Hajj, Hazem and El-Hajj, Wassim and Shaban, Khaled Bashir},
journal={arXiv preprint arXiv:1906.01830},
year={2019}
}
```
### Contributions
Thanks to [@moussaKam](https://github.com/moussaKam) for adding this dataset. | [
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kor_3i4k | null | 2023-01-25T14:33:43Z | 325 | 1 | 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-4.0",
"arxiv:1811.04231",
"region:us"
] | 2023-01-25T14:33:43Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- ko
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- intent-classification
pretty_name: 3i4K
dataset_info:
features:
- name: label
dtype:
class_label:
names:
'0': fragment
'1': statement
'2': question
'3': command
'4': rhetorical question
'5': rhetorical command
'6': intonation-dependent utterance
- name: text
dtype: string
splits:
- name: train
num_bytes: 3102158
num_examples: 55134
- name: test
num_bytes: 344028
num_examples: 6121
download_size: 2956114
dataset_size: 3446186
---
# Dataset Card for 3i4K
## 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:** [3i4K](https://github.com/warnikchow/3i4k)
- **Repository:** [3i4K](https://github.com/warnikchow/3i4k)
- **Paper:** [Speech Intention Understanding in a Head-final Language: A Disambiguation Utilizing Intonation-dependency](https://arxiv.org/abs/1811.04231)
- **Point of Contact:** [Won Ik Cho](wicho@hi.snu.ac.kr)
### Dataset Summary
The 3i4K dataset is a set of frequently used Korean words (corpus provided by the Seoul National University Speech Language Processing Lab) and manually created questions/commands containing short utterances. The goal is to identify the speaker intention of a spoken utterance based on its transcript, and whether in some cases, requires using auxiliary acoustic features. The classification system decides whether the utterance is a fragment, statement, question, command, rhetorical question, rhetorical command, or an intonation-dependent utterance. This is important because in head-final languages like Korean, the level of the intonation plays a significant role in identifying the speaker's intention.
### Supported Tasks and Leaderboards
* `intent-classification`: The dataset can be trained with a CNN or BiLISTM-Att to identify the intent of a spoken utterance in Korean and the performance can be measured by its F1 score.
### 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 short utterance and it's label:
```
{
"label": 3,
"text": "선수잖아 이 케이스 저 케이스 많을 거 아냐 선배라고 뭐 하나 인생에 도움도 안주는데 내가 이렇게 진지하게 나올 때 제대로 한번 조언 좀 해줘보지"
}
```
### Data Fields
* `label`: determines the intention of the utterance and can be one of `fragment` (0), `statement` (1), `question` (2), `command` (3), `rhetorical question` (4), `rhetorical command` (5) and `intonation-depedent utterance` (6).
* `text`: the text in Korean about common topics like housework, weather, transportation, etc.
### Data Splits
The data is split into a training set comrpised of 55134 examples and a test set of 6121 examples.
## Dataset Creation
### Curation Rationale
For head-final languages like Korean, intonation can be a determining factor in identifying the speaker's intention. The purpose of this dataset is to to determine whether an utterance is a fragment, statement, question, command, or a rhetorical question/command using the intonation-depedency from the head-finality. This is expected to improve language understanding of spoken Korean utterances and can be beneficial for speech-to-text applications.
### Source Data
#### Initial Data Collection and Normalization
The corpus was provided by Seoul National University Speech Language Processing Lab, a set of frequently used words from the National Institute of Korean Language and manually created commands and questions. The utterances cover topics like weather, transportation and stocks. 20k lines were randomly selected.
#### Who are the source language producers?
Korean speakers produced the commands and questions.
### Annotations
#### Annotation process
Utterances were classified into seven categories. They were provided clear instructions on the annotation guidelines (see [here](https://docs.google.com/document/d/1-dPL5MfsxLbWs7vfwczTKgBq_1DX9u1wxOgOPn1tOss/edit#) for the guidelines) and the resulting inter-annotator agreement was 0.85 and the final decision was done by majority voting.
#### Who are the annotators?
The annotation was completed by three Seoul Korean L1 speakers.
### 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, Hyeon Seung Lee, Ji Won Yoon, Seok Min Kim and Nam Soo Kim.
### Licensing Information
The dataset is licensed under the CC BY-SA-4.0.
### Citation Information
```
@article{cho2018speech,
title={Speech Intention Understanding in a Head-final Language: A Disambiguation Utilizing Intonation-dependency},
author={Cho, Won Ik and Lee, Hyeon Seung and Yoon, Ji Won and Kim, Seok Min and Kim, Nam Soo},
journal={arXiv preprint arXiv:1811.04231},
year={2018}
}
```
### Contributions
Thanks to [@stevhliu](https://github.com/stevhliu) for adding this dataset. | [
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SetFit/yelp_review_full | SetFit | 2022-01-19T21:49:57Z | 325 | 0 | null | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
erwanlc/cocktails_recipe | erwanlc | 2022-10-25T09:17:00Z | 325 | 3 | null | [
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:2M<n<3M",
"language:en",
"license:other",
"region:us"
] | 2022-10-25T09:17:00Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 2M<n<3M
source_datasets: []
task_categories: []
task_ids: []
pretty_name: cocktails_recipe
language_bcp47:
- en
- en-US
---
# Dataset Card for cocktails_recipe
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Source Data](#source-data)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains a list of cocktails and how to do them.
### Languages
The language is english.
## Dataset Structure
### Data Fields
- Title: name of the cocktail
- Glass: type of glass to use
- Garnish: garnish to use for the glass
- Recipe: how to do the cocktail
- Ingredients: ingredients required
### Data Splits
Currently, there is no splits.
## Dataset Creation
### Source Data
#### Initial Data Collection and Normalization
The dataset was created by scraping the Diffords cocktail website.
### Personal and Sensitive Information
It should not contain any personal or sensitive information.
### Contributions
Thanks to [@github-erwanlc](https://github.com/erwanlc) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
distil-whisper/gigaspeech-l | distil-whisper | 2023-09-25T10:28:52Z | 325 | 0 | null | [
"task_categories:automatic-speech-recognition",
"language:en",
"license:other",
"region:us"
] | 2023-09-25T10:28:52Z | 2023-04-11T20:16:14.000Z | 2023-04-11T20:16:14 | ---
license: other
task_categories:
- automatic-speech-recognition
language:
- en
extra_gated_prompt: |-
SpeechColab does not own the copyright of the audio files. For researchers and educators who wish to use the audio files for non-commercial research and/or educational purposes, we can provide access through the Hub under certain conditions and terms.
Terms of Access:
The "Researcher" has requested permission to use the GigaSpeech database (the "Database") at Tsinghua University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:
1. Researcher shall use the Database only for non-commercial research and educational purposes.
2. The SpeechColab team and Tsinghua University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose.
3. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the SpeechColab team and Tsinghua University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted audio files that he or she may create from the Database.
4. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions.
5. The SpeechColab team and Tsinghua University reserve the right to terminate Researcher's access to the Database at any time.
6. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer.
Please also fill out the Google Form https://forms.gle/UuGQAPyscGRrUMLq6 to request access to the GigaSpeech dataset.
extra_gated_fields:
Name: text
Email: text
Organization: text
Address: text
I hereby confirm that I have requested access via the Google Form provided above: checkbox
I accept the terms of access: checkbox
---
# Distil Whisper: GigaSpeech
This is a variant of the [GigaSpeech](https://huggingface.co/datasets/speechcolab/gigaspeech) dataset, augmented to return the pseudo-labelled Whisper
Transcriptions alongside the original dataset elements. The pseudo-labelled transcriptions were generated by
labelling the input audio data with the Whisper [large-v2](https://huggingface.co/openai/whisper-large-v2)
model with *greedy* sampling. For information on how the original dataset was curated, refer to the original
[dataset card](https://huggingface.co/datasets/speechcolab/gigaspeech).
## Standalone Usage
First, install the latest version of the 🤗 Datasets package:
```bash
pip install --upgrade pip
pip install --upgrade datasets[audio]
```
The dataset can be downloaded and pre-processed on disk using the [`load_dataset`](https://huggingface.co/docs/datasets/v2.14.5/en/package_reference/loading_methods#datasets.load_dataset)
function:
```python
from datasets import load_dataset
dataset = load_dataset("distil-whisper/gigaspeech-l", "l")
# take the first sample of the validation set
sample = dataset["validation"][0]
```
It can also be streamed directly from the Hub using Datasets' [streaming mode](https://huggingface.co/blog/audio-datasets#streaming-mode-the-silver-bullet).
Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire
dataset to disk:
```python
from datasets import load_dataset
dataset = load_dataset("distil-whisper/gigaspeech-l", "l", streaming=True)
# take the first sample of the validation set
sample = next(iter(dataset["validation"]))
```
## Distil Whisper Usage
To use this dataset to reproduce a Distil Whisper training run, refer to the instructions on the
[Distil Whisper repository](https://github.com/huggingface/distil-whisper#training).
## License
This dataset is licensed under custom terms. To view the custom license for this dataset, refer to the original [dataset card](https://huggingface.co/datasets/speechcolab/gigaspeech).
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
C-MTEB/STSB | C-MTEB | 2023-07-28T13:40:47Z | 325 | 0 | null | [
"region:us"
] | 2023-07-28T13:40:47Z | 2023-07-28T13:40:34.000Z | 2023-07-28T13:40:34 | ---
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: sentence1
dtype: string
- name: sentence2
dtype: string
- name: score
dtype: int32
splits:
- name: train
num_bytes: 639550
num_examples: 5231
- name: validation
num_bytes: 197381
num_examples: 1458
- name: test
num_bytes: 158230
num_examples: 1361
download_size: 682182
dataset_size: 995161
---
# Dataset Card for "STSB"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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atomic | null | 2022-11-18T18:56:37Z | 324 | 6 | atomic | [
"task_categories:text2text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"common-sense-if-then-reasoning",
"region:us"
] | 2022-11-18T18:56:37Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
pretty_name: ATOMIC
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: atomic
tags:
- common-sense-if-then-reasoning
dataset_info:
features:
- name: event
dtype: string
- name: oEffect
sequence: string
- name: oReact
sequence: string
- name: oWant
sequence: string
- name: xAttr
sequence: string
- name: xEffect
sequence: string
- name: xIntent
sequence: string
- name: xNeed
sequence: string
- name: xReact
sequence: string
- name: xWant
sequence: string
- name: prefix
sequence: string
- name: split
dtype: string
config_name: atomic
splits:
- name: train
num_bytes: 32441878
num_examples: 202271
- name: test
num_bytes: 3995624
num_examples: 24856
- name: validation
num_bytes: 3629768
num_examples: 22620
download_size: 19083782
dataset_size: 40067270
---
# Dataset Card for An Atlas of Machine Commonsense for If-Then Reasoning - Atomic Common Sense 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:**
https://homes.cs.washington.edu/~msap/atomic/
- **Repository:**
https://homes.cs.washington.edu/~msap/atomic/
- **Paper:**
Maarten Sap, Ronan LeBras, Emily Allaway, Chandra Bhagavatula, Nicholas Lourie, Hannah Rashkin, Brendan Roof, Noah A. Smith & Yejin Choi (2019). ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning. AAAI
### Dataset Summary
This dataset provides the template sentences and
relationships defined in the ATOMIC common sense dataset. There are
three splits - train, test, and dev.
From the authors.
Disclaimer/Content warning: the events in atomic have been
automatically extracted from blogs, stories and books written at
various times. The events might depict violent or problematic actions,
which we left in the corpus for the sake of learning the (probably
negative but still important) commonsense implications associated with
the events. We removed a small set of truly out-dated events, but
might have missed some so please email us (msap@cs.washington.edu) if
you have any concerns.
For more information, see: https://homes.cs.washington.edu/~msap/atomic/
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
en
## Dataset Structure
### Data Instances
Here is one example from the atomic dataset:
``
{'event': "PersonX uses PersonX's ___ to obtain", 'oEffect': [], 'oReact': ['annoyed', 'angry', 'worried'], 'oWant': [], 'prefix': ['uses', 'obtain'], 'split': 'trn', 'xAttr': [], 'xEffect': [], 'xIntent': ['to have an advantage', 'to fulfill a desire', 'to get out of trouble'], 'xNeed': [], 'xReact': ['pleased', 'smug', 'excited'], 'xWant': []}
``
### Data Fields
Notes from the authors:
* event: just a string representation of the event.
* oEffect,oReact,oWant,xAttr,xEffect,xIntent,xNeed,xReact,xWant: annotations for each of the dimensions, stored in a json-dumped string.
Note: "none" means the worker explicitly responded with the empty response, whereas [] means the worker did not annotate this dimension.
* prefix: json-dumped string that represents the prefix of content words (used to make a better trn/dev/tst split).
* split: string rep of which split the event belongs to.
### Data Splits
The atomic dataset has three splits: test, train and dev of the form:
## Dataset Creation
### Curation Rationale
This dataset was gathered and created over to assist in common sense reasoning.
### Source Data
#### Initial Data Collection and Normalization
See the reaserch paper and website for more detail. The dataset was
created by the University of Washington using crowd sourced data
#### Who are the source language producers?
The Atomic authors and crowd source.
### Annotations
#### Annotation process
Human annotations directed by forms.
#### Who are the annotators?
Human annotations.
### Personal and Sensitive Information
Unkown, but likely none.
## Considerations for Using the Data
### Social Impact of Dataset
The goal for the work is to help machines understand common sense.
### Discussion of Biases
Since the data is human annotators, there is likely to be baised. From the authors:
Disclaimer/Content warning: the events in atomic have been automatically extracted from blogs, stories and books written at various times. The events might depict violent or problematic actions, which we left in the corpus for the sake of learning the (probably negative but still important) commonsense implications associated with the events. We removed a small set of truly out-dated events, but might have missed some so please email us (msap@cs.washington.edu) if you have any concerns.
### Other Known Limitations
While there are many relationships, the data is quite sparse. Also, each item of the dataset could be expanded into multiple sentences along the vsrious dimensions, oEffect, oRect, etc.
For example, given event: "PersonX uses PersonX's ___ to obtain" and dimension oReact: "annoyed", this could be transformed into an entry:
"PersonX uses PersonX's ___ to obtain => PersonY is annoyed"
## Additional Information
### Dataset Curators
The authors of Aotmic at The University of Washington
### Licensing Information
The Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/
### Citation Information
@article{Sap2019ATOMICAA,
title={ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning},
author={Maarten Sap and Ronan Le Bras and Emily Allaway and Chandra Bhagavatula and Nicholas Lourie and Hannah Rashkin and Brendan Roof and Noah A. Smith and Yejin Choi},
journal={ArXiv},
year={2019},
volume={abs/1811.00146}
}
### Contributions
Thanks to [@ontocord](https://github.com/ontocord) for adding this dataset. | [
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AHussain0418/day2_data | AHussain0418 | 2022-01-05T18:16:53Z | 324 | 0 | null | [
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Davlan/conll2003_noMISC | Davlan | 2022-02-03T19:00:25Z | 324 | 0 | null | [
"region:us"
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
copenlu/fever_gold_evidence | copenlu | 2022-11-17T11:42:54Z | 324 | 5 | fever | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:machine-generated",
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|fever",
"language:en",
"license:cc-by-sa-3.0",
"license:gpl-3.0",
"region:us"
] | 2022-11-17T11:42:54Z | 2022-04-02T14:52:35.000Z | 2022-04-02T14:52:35 | ---
annotations_creators:
- machine-generated
- expert-generated
language_creators:
- machine-generated
- crowdsourced
language:
- en
license:
- cc-by-sa-3.0
- gpl-3.0
multilinguality:
- monolingual
paperswithcode_id: fever
pretty_name: ''
size_categories:
- 100K<n<1M
source_datasets:
- extended|fever
task_categories:
- text-classification
task_ids:
- fact-checking
---
# Dataset Card for fever_gold_evidence
## 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://github.com/copenlu/fever-adversarial-attacks
- **Repository:** https://github.com/copenlu/fever-adversarial-attacks
- **Paper:** https://aclanthology.org/2020.emnlp-main.256/
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
Dataset for training classification-only fact checking with claims from the FEVER dataset.
This dataset is used in the paper "Generating Label Cohesive and Well-Formed Adversarial Claims", EMNLP 2020
The evidence is the gold evidence from the FEVER dataset for *REFUTE* and *SUPPORT* claims.
For *NEI* claims, we extract evidence sentences with the system in "Christopher Malon. 2018. Team Papelo: Transformer Networks at FEVER. In Proceedings of the
First Workshop on Fact Extraction and VERification (FEVER), pages 109113."
More details can be found in https://github.com/copenlu/fever-adversarial-attacks
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
[Needs More Information]
## Dataset Structure
### Data Instances
[Needs More Information]
### Data Fields
[Needs More Information]
### Data Splits
[Needs More Information]
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
```
@inproceedings{atanasova-etal-2020-generating,
title = "Generating Label Cohesive and Well-Formed Adversarial Claims",
author = "Atanasova, Pepa and
Wright, Dustin and
Augenstein, Isabelle",
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://aclanthology.org/2020.emnlp-main.256",
doi = "10.18653/v1/2020.emnlp-main.256",
pages = "3168--3177",
abstract = "Adversarial attacks reveal important vulnerabilities and flaws of trained models. One potent type of attack are universal adversarial triggers, which are individual n-grams that, when appended to instances of a class under attack, can trick a model into predicting a target class. However, for inference tasks such as fact checking, these triggers often inadvertently invert the meaning of instances they are inserted in. In addition, such attacks produce semantically nonsensical inputs, as they simply concatenate triggers to existing samples. Here, we investigate how to generate adversarial attacks against fact checking systems that preserve the ground truth meaning and are semantically valid. We extend the HotFlip attack algorithm used for universal trigger generation by jointly minimizing the target class loss of a fact checking model and the entailment class loss of an auxiliary natural language inference model. We then train a conditional language model to generate semantically valid statements, which include the found universal triggers. We find that the generated attacks maintain the directionality and semantic validity of the claim better than previous work.",
}
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