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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
HuggingFaceH4/testing_h4 | HuggingFaceH4 | 2023-07-21T07:27:54Z | 1,769 | 0 | null | [
"region:us"
] | 2023-07-21T07:27:54Z | 2023-07-21T07:27:43.000Z | 2023-07-21T07:27:43 | ---
dataset_info:
features:
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected
list:
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dtype: string
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- name: prompt
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- name: messages
list:
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splits:
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num_examples: 10
- name: test_ift
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- name: test_rl
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- name: train_ift
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- name: train_rl
num_bytes: 20943
num_examples: 10
- name: train_rm
num_bytes: 33531
num_examples: 10
download_size: 186492
dataset_size: 197271
---
# Dataset Card for "testing_h4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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crystina-z/mbert-mrtydi | crystina-z | 2022-02-01T22:10:30Z | 1,766 | 0 | null | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
codeparrot/github-code-clean | codeparrot | 2022-07-05T09:35:14Z | 1,766 | 62 | null | [
"license:apache-2.0",
"region:us"
] | 2022-07-05T09:35:14Z | 2022-06-29T23:08:17.000Z | 2022-06-29T23:08:17 | ---
license: apache-2.0
---
This is a cleaner version of [Github-code dataset](https://huggingface.co/datasets/codeparrot/github-code), we add the following filters:
* Average line length < 100
* Alpha numeric characters fraction > 0.25
* Remove auto-generated files (keyword search)
3.39M files are removed making up 2.94% of the dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
metaeval/ethics | metaeval | 2023-06-02T14:45:34Z | 1,750 | 4 | null | [
"task_categories:text-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:unknown",
"language:en",
"region:us"
] | 2023-06-02T14:45:34Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- crowdsourced
license: []
multilinguality:
- monolingual
pretty_name: ethics
size_categories:
- unknown
source_datasets: []
tags: []
task_categories:
- text-classification
task_ids: []
---
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
llm-book/JGLUE | llm-book | 2023-10-06T00:58:24Z | 1,750 | 5 | null | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:sentence-similarity",
"task_categories:text-classification",
"task_ids:multiple-choice-qa",
"task_ids:open-domain-qa",
"task_ids:multi-class-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:ja",
"license:cc-by-4.0",
"MARC",
"STS",
"NLI",
"SQuAD",
"CommonsenseQA",
"region:us"
] | 2023-10-06T00:58:24Z | 2023-05-01T13:00:36.000Z | 2023-05-01T13:00:36 | ---
annotations_creators:
- crowdsourced
language:
- ja
language_creators:
- crowdsourced
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: JGLUE
size_categories: []
source_datasets:
- original
tags:
- MARC
- STS
- NLI
- SQuAD
- CommonsenseQA
task_categories:
- multiple-choice
- question-answering
- sentence-similarity
- text-classification
task_ids:
- multiple-choice-qa
- open-domain-qa
- multi-class-classification
- sentiment-classification
---
# Dataset Card for JGLUE
[](https://aclanthology.org/2022.lrec-1.317)
書籍『大規模言語モデル入門』で使用する、JGLUEのデータセットです。
[オリジナルのリポジトリ](https://github.com/yahoojapan/JGLUE)で公開されているデータセットを利用しています。
### Licence
コードのライセンスは Creative Commons Attribution-ShareAlike 4.0 International License です。
データそのもののライセンスは[配布元](https://github.com/yahoojapan/JGLUE)のライセンスに従ってください。
### Citation
```bibtex
@inproceedings{kurihara-etal-2022-jglue,
title = "{JGLUE}: {J}apanese General Language Understanding Evaluation",
author = "Kurihara, Kentaro and
Kawahara, Daisuke and
Shibata, Tomohide",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.317",
pages = "2957--2966",
abstract = "To develop high-performance natural language understanding (NLU) models, it is necessary to have a benchmark to evaluate and analyze NLU ability from various perspectives. While the English NLU benchmark, GLUE, has been the forerunner, benchmarks are now being released for languages other than English, such as CLUE for Chinese and FLUE for French; but there is no such benchmark for Japanese. We build a Japanese NLU benchmark, JGLUE, from scratch without translation to measure the general NLU ability in Japanese. We hope that JGLUE will facilitate NLU research in Japanese.",
}
```
```bibtex
@InProceedings{Kurihara_nlp2022,
author = "栗原健太郎 and 河原大輔 and 柴田知秀",
title = "JGLUE: 日本語言語理解ベンチマーク",
booktitle = "言語処理学会第 28 回年次大会",
year = "2022",
url = "https://www.anlp.jp/proceedings/annual_meeting/2022/pdf_dir/E8-4.pdf"
note= "in Japanese"
}
```
### Contributions
データセット作成者である [Kentaro Kurihara](https://twitter.com/kkurihara_cs), [Daisuke Kawahara](https://twitter.com/daisukekawahar1), [Tomohide Shibata](https://twitter.com/stomohide) に感謝を申し上げます。
また本リポジトリのコードは [Shunsuke Kitada](https://twitter.com/shunk031)の[こちらのリポジトリ](https://huggingface.co/datasets/shunk031/JGLUE)を基に作成されたものです。 | [
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mstz/heart_failure | mstz | 2023-04-16T17:31:15Z | 1,739 | 2 | null | [
"task_categories:tabular-classification",
"size_categories:n<1K",
"language:en",
"license:cc",
"heart failure",
"tabular_classification",
"binary_classification",
"UCI",
"region:us"
] | 2023-04-16T17:31:15Z | 2023-03-24T14:32:59.000Z | 2023-03-24T14:32:59 | ---
language:
- en
tags:
- heart failure
- tabular_classification
- binary_classification
- UCI
pretty_name: Heart failure
size_categories:
- n<1K
task_categories:
- tabular-classification
configs:
- death
license: cc
---
# Heart failure
The [Heart failure dataset](https://www.kaggle.com/datasets/andrewmvd/heart-failure-clinical-data) from Kaggle.
Predict patient death from earth failure given some personal medical data .
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|-----------------------------------------------------------------|
| death | Binary classification | Did the patient die? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/heart_failure", "death")["train"]
```
# Features
|**Feature** |**Type** |
|----------------------------------------------------|-----------|
|`age` |`int8` |
|`has_anaemia` |`int8` |
|`creatinine_phosphokinase_concentration_in_blood` |`float64` |
|`has_diabetes` |`int8` |
|`heart_ejection_fraction` |`float64` |
|`has_high_blood_pressure` |`int8` |
|`platelets_concentration_in_blood` |`float64` |
|`serum_creatinine_concentration_in_blood` |`float64` |
|`serum_sodium_concentration_in_blood` |`float64` |
|`sex` |`int8` |
|`is_smoker` |`int8` |
|`days_in_study` |`int64` | | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
banghua/random_pre | banghua | 2023-10-28T02:55:26Z | 1,736 | 0 | null | [
"region:us"
] | 2023-10-28T02:55:26Z | 2023-10-28T02:48:38.000Z | 2023-10-28T02:48:38 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: prompt
dtype: string
- name: answers
list:
- name: answer
dtype: string
- name: model
dtype: string
- name: rank
dtype: float64
- name: turns
dtype: int64
- name: num_responses
dtype: int64
- name: source
sequence: string
splits:
- name: train
num_bytes: 1206940856
num_examples: 182968
download_size: 551450326
dataset_size: 1206940856
---
# Dataset Card for "random_pre"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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alt | null | 2023-06-01T14:59:53Z | 1,731 | 7 | alt | [
"task_categories:translation",
"task_categories:token-classification",
"task_ids:parsing",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"multilinguality:translation",
"size_categories:100K<n<1M",
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"source_datasets:original",
"language:bn",
"language:en",
"language:fil",
"language:hi",
"language:id",
"language:ja",
"language:km",
"language:lo",
"language:ms",
"language:my",
"language:th",
"language:vi",
"language:zh",
"license:cc-by-4.0",
"region:us"
] | 2023-06-01T14:59:53Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- bn
- en
- fil
- hi
- id
- ja
- km
- lo
- ms
- my
- th
- vi
- zh
license:
- cc-by-4.0
multilinguality:
- multilingual
- translation
size_categories:
- 100K<n<1M
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
- token-classification
task_ids:
- parsing
paperswithcode_id: alt
pretty_name: Asian Language Treebank
dataset_info:
- config_name: alt-parallel
features:
- name: SNT.URLID
dtype: string
- name: SNT.URLID.SNTID
dtype: string
- name: url
dtype: string
- name: translation
dtype:
translation:
languages:
- bg
- en
- en_tok
- fil
- hi
- id
- ja
- khm
- lo
- ms
- my
- th
- vi
- zh
splits:
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num_bytes: 68445916
num_examples: 18088
- name: validation
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num_examples: 1000
- name: test
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num_examples: 1019
download_size: 21285784
dataset_size: 75971326
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features:
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features:
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- config_name: alt-km
features:
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dtype: string
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dtype: string
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- config_name: alt-my-transliteration
features:
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dtype: string
- name: my
sequence: string
splits:
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download_size: 1232127
dataset_size: 4249424
- config_name: alt-my-west-transliteration
features:
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dtype: string
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sequence: string
splits:
- name: train
num_bytes: 7412043
num_examples: 107121
download_size: 2830071
dataset_size: 7412043
config_names:
- alt-en
- alt-jp
- alt-km
- alt-my
- alt-my-transliteration
- alt-my-west-transliteration
- alt-parallel
---
# Dataset Card for Asian Language Treebank (ALT)
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://www2.nict.go.jp/astrec-att/member/mutiyama/ALT/
- **Leaderboard:**
- **Paper:** [Introduction of the Asian Language Treebank](https://ieeexplore.ieee.org/abstract/document/7918974)
- **Point of Contact:** [ALT info](alt-info@khn.nict.go.jp)
### Dataset Summary
The ALT project aims to advance the state-of-the-art Asian natural language processing (NLP) techniques through the open collaboration for developing and using ALT. It was first conducted by NICT and UCSY as described in Ye Kyaw Thu, Win Pa Pa, Masao Utiyama, Andrew Finch and Eiichiro Sumita (2016). Then, it was developed under [ASEAN IVO](https://www.nict.go.jp/en/asean_ivo/index.html) as described in this Web page.
The process of building ALT began with sampling about 20,000 sentences from English Wikinews, and then these sentences were translated into the other languages.
### Supported Tasks and Leaderboards
Machine Translation, Dependency Parsing
### Languages
It supports 13 language:
* Bengali
* English
* Filipino
* Hindi
* Bahasa Indonesia
* Japanese
* Khmer
* Lao
* Malay
* Myanmar (Burmese)
* Thai
* Vietnamese
* Chinese (Simplified Chinese).
## Dataset Structure
### Data Instances
#### ALT Parallel Corpus
```
{
"SNT.URLID": "80188",
"SNT.URLID.SNTID": "1",
"url": "http://en.wikinews.org/wiki/2007_Rugby_World_Cup:_Italy_31_-_5_Portugal",
"bg": "[translated sentence]",
"en": "[translated sentence]",
"en_tok": "[translated sentence]",
"fil": "[translated sentence]",
"hi": "[translated sentence]",
"id": "[translated sentence]",
"ja": "[translated sentence]",
"khm": "[translated sentence]",
"lo": "[translated sentence]",
"ms": "[translated sentence]",
"my": "[translated sentence]",
"th": "[translated sentence]",
"vi": "[translated sentence]",
"zh": "[translated sentence]"
}
```
#### ALT Treebank
```
{
"SNT.URLID": "80188",
"SNT.URLID.SNTID": "1",
"url": "http://en.wikinews.org/wiki/2007_Rugby_World_Cup:_Italy_31_-_5_Portugal",
"status": "draft/reviewed",
"value": "(S (S (BASENP (NNP Italy)) (VP (VBP have) (VP (VP (VP (VBN defeated) (BASENP (NNP Portugal))) (ADVP (RB 31-5))) (PP (IN in) (NP (BASENP (NNP Pool) (NNP C)) (PP (IN of) (NP (BASENP (DT the) (NN 2007) (NNP Rugby) (NNP World) (NNP Cup)) (PP (IN at) (NP (BASENP (NNP Parc) (FW des) (NNP Princes)) (COMMA ,) (BASENP (NNP Paris) (COMMA ,) (NNP France))))))))))) (PERIOD .))"
}
```
#### ALT Myanmar transliteration
```
{
"en": "CASINO",
"my": [
"ကက်စီနို",
"ကစီနို",
"ကာစီနို",
"ကာဆီနို"
]
}
```
### Data Fields
#### ALT Parallel Corpus
- SNT.URLID: URL link to the source article listed in [URL.txt](https://www2.nict.go.jp/astrec-att/member/mutiyama/ALT/ALT-Parallel-Corpus-20191206/URL.txt)
- SNT.URLID.SNTID: index number from 1 to 20000. It is a seletected sentence from `SNT.URLID`
and bg, en, fil, hi, id, ja, khm, lo, ms, my, th, vi, zh correspond to the target language
#### ALT Treebank
- status: it indicates how a sentence is annotated; `draft` sentences are annotated by one annotater and `reviewed` sentences are annotated by two annotater
The annotatation is different from language to language, please see [their guildlines](https://www2.nict.go.jp/astrec-att/member/mutiyama/ALT/) for more detail.
### Data Splits
| | train | valid | test |
|-----------|-------|-------|-------|
| # articles | 1698 | 98 | 97 |
| # sentences | 18088 | 1000 | 1018 |
## Dataset Creation
### Curation Rationale
The ALT project was initiated by the [National Institute of Information and Communications Technology, Japan](https://www.nict.go.jp/en/) (NICT) in 2014. NICT started to build Japanese and English ALT and worked with the University of Computer Studies, Yangon, Myanmar (UCSY) to build Myanmar ALT in 2014. Then, the Badan Pengkajian dan Penerapan Teknologi, Indonesia (BPPT), the Institute for Infocomm Research, Singapore (I2R), the Institute of Information Technology, Vietnam (IOIT), and the National Institute of Posts, Telecoms and ICT, Cambodia (NIPTICT) joined to make ALT for Indonesian, Malay, Vietnamese, and Khmer in 2015.
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
The dataset is sampled from the English Wikinews in 2014. These will be annotated with word segmentation, POS tags, and syntax information, in addition to the word alignment information by linguistic experts from
* National Institute of Information and Communications Technology, Japan (NICT) for Japanses and English
* University of Computer Studies, Yangon, Myanmar (UCSY) for Myanmar
* the Badan Pengkajian dan Penerapan Teknologi, Indonesia (BPPT) for Indonesian
* the Institute for Infocomm Research, Singapore (I2R) for Malay
* the Institute of Information Technology, Vietnam (IOIT) for Vietnamese
* the National Institute of Posts, Telecoms and ICT, Cambodia for Khmer
### 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
* National Institute of Information and Communications Technology, Japan (NICT) for Japanses and English
* University of Computer Studies, Yangon, Myanmar (UCSY) for Myanmar
* the Badan Pengkajian dan Penerapan Teknologi, Indonesia (BPPT) for Indonesian
* the Institute for Infocomm Research, Singapore (I2R) for Malay
* the Institute of Information Technology, Vietnam (IOIT) for Vietnamese
* the National Institute of Posts, Telecoms and ICT, Cambodia for Khmer
### Licensing Information
[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)
### Citation Information
Please cite the following if you make use of the dataset:
Hammam Riza, Michael Purwoadi, Gunarso, Teduh Uliniansyah, Aw Ai Ti, Sharifah Mahani Aljunied, Luong Chi Mai, Vu Tat Thang, Nguyen Phuong Thai, Vichet Chea, Rapid Sun, Sethserey Sam, Sopheap Seng, Khin Mar Soe, Khin Thandar Nwet, Masao Utiyama, Chenchen Ding. (2016) "Introduction of the Asian Language Treebank" Oriental COCOSDA.
BibTeX:
```
@inproceedings{riza2016introduction,
title={Introduction of the asian language treebank},
author={Riza, Hammam and Purwoadi, Michael and Uliniansyah, Teduh and Ti, Aw Ai and Aljunied, Sharifah Mahani and Mai, Luong Chi and Thang, Vu Tat and Thai, Nguyen Phuong and Chea, Vichet and Sam, Sethserey and others},
booktitle={2016 Conference of The Oriental Chapter of International Committee for Coordination and Standardization of Speech Databases and Assessment Techniques (O-COCOSDA)},
pages={1--6},
year={2016},
organization={IEEE}
}
```
### Contributions
Thanks to [@chameleonTK](https://github.com/chameleonTK) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
AdaptLLM/law-tasks | AdaptLLM | 2023-11-07T12:11:05Z | 1,730 | 5 | null | [
"arxiv:2309.09530",
"region:us"
] | 2023-11-07T12:11:05Z | 2023-09-19T07:44:48.000Z | 2023-09-19T07:44:48 | ---
configs:
- config_name: SCOTUS
data_files:
- split: test
path: "scotus/test.json"
- config_name: CaseHOLD
data_files:
- split: test
path: "case_hold/test.json"
- config_name: UNFAIR_ToS
data_files:
- split: test
path: "unfair_tos/test.json"
---
# Adapt Large Language Models to Domains
This repo contains the evaluation datasets for our paper [Adapting Large Language Models via Reading Comprehension](https://huggingface.co/papers/2309.09530)
We explore **continued pre-training on domain-specific corpora** for large language models. While this approach enriches LLMs with domain knowledge, it significantly hurts their prompting ability for question answering. Inspired by human learning via reading comprehension, we propose a simple method to **transform large-scale pre-training corpora into reading comprehension texts**, consistently improving prompting performance across tasks in **biomedicine, finance, and law domains**. Our 7B model competes with much larger domain-specific models like BloombergGPT-50B. Moreover, our domain-specific reading comprehension texts enhance model performance even on general benchmarks, indicating potential for developing a general LLM across more domains.
## GitHub repo:
https://github.com/microsoft/LMOps
## Domain-specific LLMs:
Our models of different domains are now available in Huggingface: [Biomedicine-LLM](https://huggingface.co/AdaptLLM/medicine-LLM), [Finance-LLM](https://huggingface.co/AdaptLLM/finance-LLM) and [Law-LLM](https://huggingface.co/AdaptLLM/law-LLM), the performances of our AdaptLLM compared to other domain-specific LLMs are:
<p align='center'>
<img src="./comparison.png" width="700">
</p>
## Domain-specific Tasks:
To easily reproduce our results, we have uploaded the filled-in zero/few-shot input instructions and output completions of each domain-specific task: [biomedicine-tasks](https://huggingface.co/datasets/AdaptLLM/medicine-tasks), [finance-tasks](https://huggingface.co/datasets/AdaptLLM/finance-tasks), and [law-tasks](https://huggingface.co/datasets/AdaptLLM/law-tasks).
## Citation:
```bibtex
@article{adaptllm,
author = {Daixuan Cheng and Shaohan Huang and Furu Wei},
title = {Adapting Large Language Models via Reading Comprehension},
journal = {CoRR},
volume = {abs/2309.09530},
year = {2023}
}
```
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
vwxyzjn/summarize_from_feedback_oai_preprocessing | vwxyzjn | 2023-11-08T02:36:07Z | 1,726 | 0 | null | [
"region:us"
] | 2023-11-08T02:36:07Z | 2023-10-19T18:18:24.000Z | 2023-10-19T18:18:24 | ---
dataset_info:
features:
- name: info
struct:
- name: id
dtype: string
- name: post
dtype: string
- name: title
dtype: string
- name: subreddit
dtype: string
- name: site
dtype: string
- name: article
dtype: string
- name: summaries
list:
- name: text
dtype: string
- name: policy
dtype: string
- name: note
dtype: string
- name: choice
dtype: int32
- name: worker
dtype: string
- name: batch
dtype: string
- name: split
dtype: string
- name: extra
struct:
- name: confidence
dtype: int32
- name: query_token
sequence: int64
- name: query
dtype: string
- name: response0
dtype: string
- name: response0_token
sequence: int64
- name: response1
dtype: string
- name: response1_token
sequence: int64
splits:
- name: train
num_bytes: 862825302
num_examples: 92858
- name: validation
num_bytes: 812584211
num_examples: 86086
download_size: 127954862
dataset_size: 1675409513
---
# Dataset Card for "summarize_from_feedback_oai_preprocessing"
[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 | |
emozilla/pg_books-tokenized-bos-eos-chunked-65536 | emozilla | 2023-10-07T02:19:15Z | 1,723 | 3 | null | [
"region:us"
] | 2023-10-07T02:19:15Z | 2023-08-31T15:54:46.000Z | 2023-08-31T15:54:46 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 67744337720
num_examples: 79514
download_size: 1125510240
dataset_size: 67744337720
---
# Dataset Card for "pg_books-tokenized-bos-eos-chunked-65536"
The [pg19](https://huggingface.co/datasets/emozilla/pg19) dataset tokenized under LLaMA into 64k chunks, bookended with BOS and EOS | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
nielsr/ade20k-panoptic-demo | nielsr | 2022-11-06T17:13:22Z | 1,720 | 0 | null | [
"region:us"
] | 2022-11-06T17:13:22Z | 2022-11-05T21:16:00.000Z | 2022-11-05T21:16:00 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: image
- name: segments_info
list:
- name: area
dtype: int64
- name: bbox
sequence: int64
- name: category_id
dtype: int64
- name: id
dtype: int64
- name: iscrowd
dtype: int64
splits:
- name: train
num_bytes: 492746.0
num_examples: 10
- name: validation
num_bytes: 461402.0
num_examples: 10
download_size: 949392
dataset_size: 954148.0
---
# Dataset Card for "ade20k-panoptic-demo"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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coqa | null | 2023-04-05T10:02:34Z | 1,704 | 29 | coqa | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|race",
"source_datasets:extended|cnn_dailymail",
"source_datasets:extended|wikipedia",
"source_datasets:extended|other",
"language:en",
"license:other",
"conversational-qa",
"arxiv:1808.07042",
"arxiv:1704.04683",
"arxiv:1506.03340",
"region:us"
] | 2023-04-05T10:02:34Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- other
multilinguality:
- monolingual
pretty_name: 'CoQA: Conversational Question Answering Challenge'
size_categories:
- 1K<n<10K
source_datasets:
- extended|race
- extended|cnn_dailymail
- extended|wikipedia
- extended|other
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: coqa
tags:
- conversational-qa
dataset_info:
features:
- name: source
dtype: string
- name: story
dtype: string
- name: questions
sequence: string
- name: answers
sequence:
- name: input_text
dtype: string
- name: answer_start
dtype: int32
- name: answer_end
dtype: int32
splits:
- name: train
num_bytes: 17981459
num_examples: 7199
- name: validation
num_bytes: 1225518
num_examples: 500
download_size: 58092681
dataset_size: 19206977
---
# Dataset Card for "coqa"
## 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://stanfordnlp.github.io/coqa/](https://stanfordnlp.github.io/coqa/)
- **Repository:** https://github.com/stanfordnlp/coqa-baselines
- **Paper:** [CoQA: A Conversational Question Answering Challenge](https://arxiv.org/abs/1808.07042)
- **Point of Contact:** [Google Group](https://groups.google.com/forum/#!forum/coqa), [Siva Reddy](mailto:siva.reddy@mila.quebec), [Danqi Chen](mailto:danqic@cs.princeton.edu)
- **Size of downloaded dataset files:** 58.09 MB
- **Size of the generated dataset:** 19.24 MB
- **Total amount of disk used:** 77.33 MB
### Dataset Summary
CoQA is a large-scale dataset for building Conversational Question Answering systems.
Our dataset contains 127k questions with answers, obtained from 8k conversations about text passages from seven diverse domains. The questions are conversational, and the answers are free-form text with their corresponding evidence highlighted in the passage.
### 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:** 58.09 MB
- **Size of the generated dataset:** 19.24 MB
- **Total amount of disk used:** 77.33 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"answers": "{\"answer_end\": [179, 494, 511, 545, 879, 1127, 1128, 94, 150, 412, 1009, 1046, 643, -1, 764, 724, 125, 1384, 881, 910], \"answer_...",
"questions": "[\"When was the Vat formally opened?\", \"what is the library for?\", \"for what subjects?\", \"and?\", \"what was started in 2014?\", \"ho...",
"source": "wikipedia",
"story": "\"The Vatican Apostolic Library (), more commonly called the Vatican Library or simply the Vat, is the library of the Holy See, l..."
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `source`: a `string` feature.
- `story`: a `string` feature.
- `questions`: a `list` of `string` features.
- `answers`: a dictionary feature containing:
- `input_text`: a `string` feature.
- `answer_start`: a `int32` feature.
- `answer_end`: a `int32` feature.
### Data Splits
| name |train|validation|
|-------|----:|---------:|
|default| 7199| 500|
## 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
CoQA contains passages from seven domains. We make five of these public under the following licenses:
- Literature and Wikipedia passages are shared under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) license.
- Children's stories are collected from [MCTest](https://www.microsoft.com/en-us/research/publication/mctest-challenge-dataset-open-domain-machine-comprehension-text/) which comes with [MSR-LA](https://github.com/mcobzarenco/mctest/blob/master/data/MCTest/LICENSE.pdf) license.
- Middle/High school exam passages are collected from [RACE](https://arxiv.org/abs/1704.04683) which comes with its [own](http://www.cs.cmu.edu/~glai1/data/race/) license.
- News passages are collected from the [DeepMind CNN dataset](https://arxiv.org/abs/1506.03340) which comes with [Apache](https://github.com/deepmind/rc-data/blob/master/LICENSE) license.
### Citation Information
```
@article{reddy-etal-2019-coqa,
title = "{C}o{QA}: A Conversational Question Answering Challenge",
author = "Reddy, Siva and
Chen, Danqi and
Manning, Christopher D.",
journal = "Transactions of the Association for Computational Linguistics",
volume = "7",
year = "2019",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/Q19-1016",
doi = "10.1162/tacl_a_00266",
pages = "249--266",
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf), [@mariamabarham](https://github.com/mariamabarham), [@ojasaar](https://github.com/ojasaar), [@lhoestq](https://github.com/lhoestq) for adding this dataset. | [
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crows_pairs | null | 2023-07-06T09:23:23Z | 1,702 | 4 | crows-pairs | [
"task_categories:text-classification",
"task_ids:text-scoring",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"bias-evaluation",
"region:us"
] | 2023-07-06T09:23:23Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
paperswithcode_id: crows-pairs
pretty_name: CrowS-Pairs
tags:
- bias-evaluation
dataset_info:
features:
- name: id
dtype: int32
- name: sent_more
dtype: string
- name: sent_less
dtype: string
- name: stereo_antistereo
dtype:
class_label:
names:
'0': stereo
'1': antistereo
- name: bias_type
dtype:
class_label:
names:
'0': race-color
'1': socioeconomic
'2': gender
'3': disability
'4': nationality
'5': sexual-orientation
'6': physical-appearance
'7': religion
'8': age
- name: annotations
sequence:
sequence:
class_label:
names:
'0': race-color
'1': socioeconomic
'2': gender
'3': disability
'4': nationality
'5': sexual-orientation
'6': physical-appearance
'7': religion
'8': age
- name: anon_writer
dtype: string
- name: anon_annotators
sequence: string
config_name: crows_pairs
splits:
- name: test
num_bytes: 419976
num_examples: 1508
download_size: 437764
dataset_size: 419976
---
# Dataset Card for CrowS-Pairs
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Add homepage URL here if available (unless it's a GitHub repository)]()
- **Repository:** https://github.com/nyu-mll/crows-pairs
- **Paper:** https://aclanthology.org/2020.emnlp-main.154
- **Leaderboard:** [If the dataset supports an active leaderboard, add link here]()
- **Point of Contact:** [If known, name and email of at least one person the reader can contact for questions about the dataset.]()
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
CrowS-Pairs is licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-sa/4.0/).
It is created using prompts taken from the [ROCStories corpora](https://cs.rochester.edu/nlp/rocstories/) and the fiction part of [MNLI](https://cims.nyu.edu/~sbowman/multinli/). Please refer to their papers for more details.
### Citation Information
```
@inproceedings{nangia-etal-2020-crows,
title = "{C}row{S}-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models",
author = "Nangia, Nikita and
Vania, Clara and
Bhalerao, Rasika and
Bowman, Samuel R.",
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.154",
doi = "10.18653/v1/2020.emnlp-main.154",
pages = "1953--1967",
}
```
### Contributions
Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset. | [
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cats_vs_dogs | null | 2023-01-25T14:27:39Z | 1,689 | 15 | cats-vs-dogs | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | 2023-01-25T14:27:39Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
paperswithcode_id: cats-vs-dogs
pretty_name: Cats Vs. Dogs
dataset_info:
features:
- name: image
dtype: image
- name: labels
dtype:
class_label:
names:
'0': cat
'1': dog
splits:
- name: train
num_bytes: 4219400
num_examples: 23410
download_size: 824887076
dataset_size: 4219400
---
# Dataset Card for Cats Vs. Dogs
## 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:** [Cats vs Dogs Dataset](https://www.microsoft.com/en-us/download/details.aspx?id=54765)
- **Repository:**
- **Paper:** [Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization](https://www.microsoft.com/en-us/research/wp-content/uploads/2007/10/CCS2007.pdf)
- **Leaderboard:** [Dogs vs. Cats](https://www.kaggle.com/competitions/dogs-vs-cats)
- **Point of Contact:**
### Dataset Summary
A large set of images of cats and dogs. There are 1738 corrupted images that are dropped. This dataset is part of a now-closed Kaggle competition and represents a subset of the so-called Asirra dataset.
From the competition page:
> The Asirra data set
>
> Web services are often protected with a challenge that's supposed to be easy for people to solve, but difficult for computers. Such a challenge is often called a [CAPTCHA](http://www.captcha.net/) (Completely Automated Public Turing test to tell Computers and Humans Apart) or HIP (Human Interactive Proof). HIPs are used for many purposes, such as to reduce email and blog spam and prevent brute-force attacks on web site passwords.
>
> Asirra (Animal Species Image Recognition for Restricting Access) is a HIP that works by asking users to identify photographs of cats and dogs. This task is difficult for computers, but studies have shown that people can accomplish it quickly and accurately. Many even think it's fun! Here is an example of the Asirra interface:
>
> Asirra is unique because of its partnership with [Petfinder.com](https://www.petfinder.com/), the world's largest site devoted to finding homes for homeless pets. They've provided Microsoft Research with over three million images of cats and dogs, manually classified by people at thousands of animal shelters across the United States. Kaggle is fortunate to offer a subset of this data for fun and research.
### Supported Tasks and Leaderboards
- `image-classification`: The goal of this task is to classify a given image as either containing a cat or a dog. The leaderboard is available [here](https://paperswithcode.com/sota/image-classification-on-cats-vs-dogs).
### Languages
English.
## Dataset Structure
### Data Instances
A sample from the training set is provided below:
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x375 at 0x29CEAD71780>,
'labels': 0
}
```
### Data Fields
The data instances have the following fields:
- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`.
- `labels`: an `int` classification label.
Class Label Mappings:
```
{
"cat": 0,
"dog": 1,
}
```
### Data Splits
| | train |
|---------------|------:|
| # of examples | 23410 |
## Dataset Creation
### Curation Rationale
This subset was to built to test whether computer vision algorithms can beat the Asirra CAPTCHA:
From the competition page:
> Image recognition attacks
>
> While random guessing is the easiest form of attack, various forms of image recognition can allow an attacker to make guesses that are better than random. There is enormous diversity in the photo database (a wide variety of backgrounds, angles, poses, lighting, etc.), making accurate automatic classification difficult. In an informal poll conducted many years ago, computer vision experts posited that a classifier with better than 60% accuracy would be difficult without a major advance in the state of the art. For reference, a 60% classifier improves the guessing probability of a 12-image HIP from 1/4096 to 1/459.
### Source Data
#### Initial Data Collection and Normalization
This dataset is a subset of the Asirra dataset.
From the competition page:
> Asirra is unique because of its partnership with Petfinder.com, the world's largest site devoted to finding homes for homeless pets. They've provided Microsoft Research with over three million images of cats and dogs, manually classified by people at thousands of animal shelters across the United States.
#### Who are the source language producers?
The users of [Petfinder.com](https://www.petfinder.com/).
### Annotations
#### Annotation process
The images were annotated by selecting a pet category on [Petfinder.com](https://www.petfinder.com/).
#### Who are the annotators?
The users of [Petfinder.com](https://www.petfinder.com/).
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
From the paper:
> Unlike many image-based CAPTCHAs which are abstract or subjective, Asirra’s challenges are concrete, inoffensive (cute, by some accounts), require no specialized or culturally biased knowledge, and have definite ground truth. This
makes Asirra less frustrating for humans. Some beta-testers found it fun. The four-year-old child of one asked several times to “play the cat and dog game again.”
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```bibtex
@Inproceedings (Conference){asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization,
author = {Elson, Jeremy and Douceur, John (JD) and Howell, Jon and Saul, Jared},
title = {Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization},
booktitle = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)},
year = {2007},
month = {October},
publisher = {Association for Computing Machinery, Inc.},
url = {https://www.microsoft.com/en-us/research/publication/asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization/},
edition = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)},
}
```
### Contributions
Thanks to [@nateraw](https://github.com/nateraw) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Gustavosta/Stable-Diffusion-Prompts | Gustavosta | 2022-09-18T22:38:59Z | 1,685 | 355 | null | [
"annotations_creators:no-annotation",
"language_creators:found",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | 2022-09-18T22:38:59Z | 2022-09-18T12:13:15.000Z | 2022-09-18T12:13:15 | ---
license:
- unknown
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
source_datasets:
- original
---
# Stable Diffusion Dataset
This is a set of about 80,000 prompts filtered and extracted from the image finder for Stable Diffusion: "[Lexica.art](https://lexica.art/)". It was a little difficult to extract the data, since the search engine still doesn't have a public API without being protected by cloudflare.
If you want to test the model with a demo, you can go to: "[spaces/Gustavosta/MagicPrompt-Stable-Diffusion](https://huggingface.co/spaces/Gustavosta/MagicPrompt-Stable-Diffusion)".
If you want to see the model, go to: "[Gustavosta/MagicPrompt-Stable-Diffusion](https://huggingface.co/Gustavosta/MagicPrompt-Stable-Diffusion)". | [
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cppe-5 | null | 2023-03-06T18:48:26Z | 1,683 | 7 | cppe-5 | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"medical-personal-protective-equipment-detection",
"arxiv:2112.09569",
"region:us"
] | 2023-03-06T18:48:26Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- object-detection
task_ids: []
paperswithcode_id: cppe-5
pretty_name: CPPE - 5
tags:
- medical-personal-protective-equipment-detection
dataset_info:
features:
- name: image_id
dtype: int64
- name: image
dtype: image
- name: width
dtype: int32
- name: height
dtype: int32
- name: objects
sequence:
- name: id
dtype: int64
- name: area
dtype: int64
- name: bbox
sequence: float32
length: 4
- name: category
dtype:
class_label:
names:
'0': Coverall
'1': Face_Shield
'2': Gloves
'3': Goggles
'4': Mask
splits:
- name: train
num_bytes: 240481257
num_examples: 1000
- name: test
num_bytes: 4172715
num_examples: 29
download_size: 238482705
dataset_size: 244653972
---
# Dataset Card for CPPE - 5
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** https://github.com/Rishit-dagli/CPPE-Dataset
- **Paper:** [CPPE-5: Medical Personal Protective Equipment Dataset](https://arxiv.org/abs/2112.09569)
- **Leaderboard:** https://paperswithcode.com/sota/object-detection-on-cppe-5
- **Point of Contact:** rishit.dagli@gmail.com
### Dataset Summary
CPPE - 5 (Medical Personal Protective Equipment) is a new challenging dataset with the goal to allow the study of subordinate categorization of medical personal protective equipments, which is not possible with other popular data sets that focus on broad level categories.
Some features of this dataset are:
* high quality images and annotations (~4.6 bounding boxes per image)
* real-life images unlike any current such dataset
* majority of non-iconic images (allowing easy deployment to real-world environments)
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection. This task has an active leaderboard which can be found at https://paperswithcode.com/sota/object-detection-on-cppe-5. The metrics for this task are adopted from the COCO detection evaluation criteria, and include the mean Average Precision (AP) across IoU thresholds ranging from 0.50 to 0.95 at different scales.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=943x663 at 0x2373B065C18>,
'width': 943,
'height': 663,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category, with possible values including `Coverall` (0),`Face_Shield` (1),`Gloves` (2),`Goggles` (3) and `Mask` (4)
### Data Splits
The data is split into training and testing set. The training set contains 1000 images and test set 29 images.
## Dataset Creation
### Curation Rationale
From the paper:
> With CPPE-5 dataset, we hope to facilitate research and use in applications at multiple public places to autonomously identify if a PPE (Personal Protective Equipment) kit has been worn and also which part of the PPE kit has been worn. One of the main aims with this dataset was to also capture a higher ratio of non-iconic images or non-canonical perspectives [5] of the objects in this dataset. We further hope to see high use of this dataset to aid in medical scenarios which would have a huge effect
worldwide.
### Source Data
#### Initial Data Collection and Normalization
The images in the CPPE-5 dataset were collected using the following process:
* Obtain Images from Flickr: Following the object categories we identified earlier, we first download images from Flickr and save them at the "Original" size. On Flickr, images are served at multiple different sizes (Square 75, Small 240, Large 1024, X-Large 4K etc.), the "Original" size is an exact copy of the image uploaded by author.
* Extract relevant metadata: Flickr contains images each with searchable metadata, we extract the following relevant
metadata:
* A direct link to the original image on Flickr
* Width and height of the image
* Title given to the image by the author
* Date and time the image was uploaded on
* Flickr username of the author of the image
* Flickr Name of the author of the image
* Flickr profile of the author of the image
* The License image is licensed under
* MD5 hash of the original image
* Obtain Images from Google Images: Due to the reasons we mention earlier, we only collect a very small proportion
of images from Google Images. For these set of images we extract the following metadata:
* A direct link to the original image
* Width and height of the image
* MD5 hash of the original image
* Filter inappropriate images: Though very rare in the collected images, we also remove images containing inappropriate content using the safety filters on Flickr and Google Safe Search.
* Filter near-similar images: We then remove near-duplicate images in the dataset using GIST descriptors
#### Who are the source language producers?
The images for this dataset were collected from Flickr and Google Images.
### Annotations
#### Annotation process
The dataset was labelled in two phases: the first phase included labelling 416 images and the second phase included labelling 613 images. For all the images in the dataset volunteers were provided the following table:
|Item |Description |
|------------|--------------------------------------------------------------------- |
|coveralls | Coveralls are hospital gowns worn by medical professionals as in order to provide a barrier between patient and professional, these usually cover most of the exposed skin surfaces of the professional medics.|
|mask | Mask prevents airborne transmission of infections between patients and/or treating personnel by blocking the movement of pathogens (primarily bacteria and viruses) shed in respiratory droplets and aerosols into and from the wearer’s mouth and nose.|
face shield | Face shield aims to protect the wearer’s entire face (or part of it) from hazards such as flying objects and road debris, chemical splashes (in laboratories or in industry), or potentially infectious materials (in medical and laboratory environments).|
gloves | Gloves are used during medical examinations and procedures to help prevent cross-contamination between caregivers and patients.|
|goggles | Goggles, or safety glasses, are forms of protective eye wear that usually enclose or protect the area surrounding the eye in order to prevent particulates, water or chemicals from striking the eyes.|
as well as examples of: correctly labelled images, incorrectly labelled images, and not applicable images. Before the labelling task, each volunteer was provided with an exercise to verify if the volunteer was able to correctly identify categories as well as identify if an annotated image is correctly labelled, incorrectly labelled, or not applicable. The labelling process first involved two volunteers independently labelling an image from the dataset. In any of the cases that: the number of bounding boxes are different, the labels for on or more of the bounding boxes are different or two volunteer annotations are sufficiently different; a third volunteer compiles the result from the two annotations to come up with a correctly labelled image. After this step, a volunteer verifies the bounding box annotations. Following this method of labelling the dataset we ensured that all images were labelled accurately and contained exhaustive
annotations. As a result of this, our dataset consists of 1029 high-quality, majorly non-iconic, and accurately annotated images.
#### Who are the annotators?
In both the phases crowd-sourcing techniques were used with multiple volunteers labelling the dataset using the open-source tool LabelImg.
### 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
Dagli, Rishit, and Ali Mustufa Shaikh.
### Licensing Information
[More Information Needed]
### Citation Information
```
@misc{dagli2021cppe5,
title={CPPE-5: Medical Personal Protective Equipment Dataset},
author={Rishit Dagli and Ali Mustufa Shaikh},
year={2021},
eprint={2112.09569},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
alzoubi36/piextract | alzoubi36 | 2023-06-25T07:11:15Z | 1,683 | 0 | null | [
"region:us"
] | 2023-06-25T07:11:15Z | 2023-06-25T07:03:41.000Z | 2023-06-25T07:03:41 | ---
dataset_info:
features:
- name: COLLECT
struct:
- name: subtask
dtype: string
- name: tags
sequence: string
- name: tokens
sequence: string
- name: NOT_COLLECT
struct:
- name: subtask
dtype: string
- name: tags
sequence: string
- name: tokens
sequence: string
- name: NOT_SHARE
struct:
- name: subtask
dtype: string
- name: tags
sequence: string
- name: tokens
sequence: string
- name: SHARE
struct:
- name: subtask
dtype: string
- name: tags
sequence: string
- name: tokens
sequence: string
splits:
- name: train
num_bytes: 3453408
num_examples: 2579
- name: test
num_bytes: 1580498
num_examples: 1029
- name: validation
num_bytes: 662810
num_examples: 456
download_size: 1013894
dataset_size: 5696716
---
# Dataset for the PI-Extract task in the [PrivacyGLUE](https://github.com/infsys-lab/privacy-glue) dataset
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dell-research-harvard/AmericanStories | dell-research-harvard | 2023-09-08T18:33:32Z | 1,681 | 79 | null | [
"task_categories:text-classification",
"task_categories:text-generation",
"task_categories:text-retrieval",
"task_categories:summarization",
"task_categories:question-answering",
"size_categories:100M<n<1B",
"language:en",
"license:cc-by-4.0",
"social science",
"economics",
"news",
"newspaper",
"large language modeling",
"nlp",
"lam",
"doi:10.57967/hf/0757",
"region:us"
] | 2023-09-08T18:33:32Z | 2023-06-12T19:42:34.000Z | 2023-06-12T19:42:34 | ---
license: cc-by-4.0
task_categories:
- text-classification
- text-generation
- text-retrieval
- summarization
- question-answering
language:
- en
tags:
- social science
- economics
- news
- newspaper
- large language modeling
- nlp
- lam
pretty_name: AmericanStories
size_categories:
- 100M<n<1B
---
# Dataset Card for the American Stories dataset
## Dataset Description
- **Homepage:** Coming Soon
- **Repository:** https://github.com/dell-research-harvard/AmericanStories
- **Paper:** Coming Soon
=- **Point of Contact:** melissa.dell@gmail.com
### Dataset Summary
The American Stories dataset is a collection of full article texts extracted from historical U.S. newspaper images. It includes nearly 20 million scans from the public domain Chronicling America collection maintained by the Library of Congress. The dataset is designed to address the challenges posed by complex layouts and low OCR quality in existing newspaper datasets.
It was created using a novel deep learning pipeline that incorporates layout detection, legibility classification, custom OCR, and the association of article texts spanning multiple bounding boxes. It employs efficient architectures specifically designed for mobile phones to ensure high scalability.
The dataset offers high-quality data that can be utilized for various purposes. It can be used to pre-train large language models and improve their understanding of historical English and world knowledge.
The dataset can also be integrated into retrieval-augmented language models, making historical information more accessible, including interpretations of political events and details about people's ancestors.
Additionally, the structured article texts in the dataset enable the use of transformer-based methods for applications such as detecting reproduced content. This significantly enhances accuracy compared to relying solely on existing OCR techniques.
The American Stories dataset serves as an invaluable resource for developing multimodal layout analysis models and other multimodal applications. Its vast size and silver quality make it ideal for innovation and research in this domain.
### Languages
English (en)
## Dataset Structure
The raw data on this repo contains compressed chunks of newspaper scans for each year. Each scan has its own JSON file named as the {scan_id}.json.
The data loading script takes care of the downloading, extraction, and parsing to outputs of two kinds :
+ Article-Level Output: The unit of the Dataset Dict is an associated article
+ Scan Level Output: The unit of the Dataset Dict is an entire scan with all the raw unparsed data
### Data Instances
Here are some examples of what the output looks like.
#### Article level
```
{
'article_id': '1_1870-01-01_p1_sn82014899_00211105483_1870010101_0773',
'newspaper_name': 'The weekly Arizona miner.',
'edition': '01', 'date': '1870-01-01',
'page': 'p1',
'headline': '',
'byline': '',
'article': 'PREyors 10 leaving San Francisco for Wash ington City, our Governor, A. r. K. Saford. called upon Generals Thomas and Ord and nt the carrying out of what (truncated)'
}
```
#### Scan level
```
{'raw_data_string': '{"lccn": {"title": "The Massachusetts spy, or, Thomas\'s Boston journal.", "geonames_ids": ["4930956"],....other_keys:values}
```
### Data Fields
#### Article Level
+ "article_id": Unique Id for an associated article
+ "newspaper_name": Newspaper Name
+ "edition": Edition number
+ "date": Date of publication
+ "page": Page number
+ "headline": Headline Text
+ "byline": Byline Text
+ "article": Article Text
#### Scan Level
"raw_data_string": Unparsed scan-level data that contains scan metadata from Library of Congress, all content regions with their bounding boxes, OCR text and legibility classification
### Data Splits
There are no train, test or val splits. Since the dataset has a massive number of units (articles or newspaper scans), we have split the data by year. Once the dataset is loaded,
instead of the usual way of accessing a split as dataset["train"], specific years can be accessed using the syntax dataset["year"] where year can be any year between 1774-1963 as long as there is at least one scan for the year.
The data loading script provides options to download both a subset of years and all years at a time.
### Accessing the Data
There are 4 config options that can be used to access the data depending upon the use-case.
```
from datasets import load_dataset
# Download data for the year 1809 at the associated article level (Default)
dataset = load_dataset("dell-research-harvard/AmericanStories",
"subset_years",
year_list=["1809", "1810"]
)
# Download and process data for all years at the article level
dataset = load_dataset("dell-research-harvard/AmericanStories",
"all_years"
)
# Download and process data for 1809 at the scan level
dataset = load_dataset("dell-research-harvard/AmericanStories",
"subset_years_content_regions",
year_list=["1809"]
)
# Download ad process data for all years at the scan level
dataset = load_dataset("dell-research-harvard/AmericanStories",
"all_years_content_regions")
```
## Dataset Creation
### Curation Rationale
The dataset was created to provide researchers with a large, high-quality corpus of structured and transcribed newspaper article texts from historical local American newspapers.
These texts provide a massive repository of information about topics ranging from political polarization to the construction of national and cultural identities to the minutiae of the daily lives of people's ancestors.
The dataset will be useful to a wide variety of researchers including historians, other social scientists, and NLP practitioners.
### Source Data
#### Initial Data Collection and Normalization
The dataset is drawn entirely from image scans in the public domain that are freely available for download from the Library of Congress's website.
We processed all images as described in the associated paper.
#### Who are the source language producers?
The source language was produced by people - by newspaper editors, columnists, and other sources.
### Annotations
#### Annotation process
Not Applicable
#### Who are the annotators?
Not Applicable
### Personal and Sensitive Information
Not Applicable
## Considerations for Using the Data
### Social Impact of Dataset
This dataset provides high-quality data that could be used for pre-training a large language model to achieve better understanding of historical English and historical world knowledge.
The dataset could also be added to the external database of a retrieval-augmented language model to make historical information - ranging from interpretations of political events to minutiae about the lives of people's ancestors - more widely accessible.
Furthermore, structured article texts that it provides can facilitate using transformer-based methods for popular applications like detection of reproduced content, significantly improving accuracy relative to using the existing OCR.
It can also be used for innovating multimodal layout analysis models and other multimodal applications.
### Discussion of Biases
This dataset contains unfiltered content composed by newspaper editors, columnists, and other sources.
In addition to other potentially harmful content, the corpus may contain factual errors and intentional misrepresentations of news events.
All content should be viewed as individuals' opinions and not as a purely factual account of events of the day.
## Additional Information
### Dataset Curators
Melissa Dell (Harvard), Jacob Carlson (Harvard), Tom Bryan (Harvard) , Emily Silcock (Harvard), Abhishek Arora (Harvard), Zejiang Shen (MIT), Luca D'Amico-Wong (Harvard), Quan Le (Princeton), Pablo Querubin (NYU), Leander Heldring (Kellog School of Business)
### Licensing Information
The dataset has a CC-BY 4.0 license
### Citation Information
Coming Soon
### Contributions
Coming Soon | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
reuters21578 | null | 2023-08-30T17:35:01Z | 1,677 | 9 | reuters-21578 | [
"language:en",
"license:other",
"region:us"
] | 2023-08-30T17:35:01Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
license: other
paperswithcode_id: reuters-21578
pretty_name: Reuters-21578 Text Categorization Collection
dataset_info:
- config_name: ModApte
features:
- name: text
dtype: string
- name: text_type
dtype: string
- name: topics
sequence: string
- name: lewis_split
dtype: string
- name: cgis_split
dtype: string
- name: old_id
dtype: string
- name: new_id
dtype: string
- name: places
sequence: string
- name: people
sequence: string
- name: orgs
sequence: string
- name: exchanges
sequence: string
- name: date
dtype: string
- name: title
dtype: string
splits:
- name: test
num_bytes: 2971653
num_examples: 3299
- name: train
num_bytes: 9161179
num_examples: 9603
- name: unused
num_bytes: 948244
num_examples: 722
download_size: 8150596
dataset_size: 13081076
- config_name: ModHayes
features:
- name: text
dtype: string
- name: text_type
dtype: string
- name: topics
sequence: string
- name: lewis_split
dtype: string
- name: cgis_split
dtype: string
- name: old_id
dtype: string
- name: new_id
dtype: string
- name: places
sequence: string
- name: people
sequence: string
- name: orgs
sequence: string
- name: exchanges
sequence: string
- name: date
dtype: string
- name: title
dtype: string
splits:
- name: test
num_bytes: 948244
num_examples: 722
- name: train
num_bytes: 19071106
num_examples: 20856
download_size: 8150596
dataset_size: 20019350
- config_name: ModLewis
features:
- name: text
dtype: string
- name: text_type
dtype: string
- name: topics
sequence: string
- name: lewis_split
dtype: string
- name: cgis_split
dtype: string
- name: old_id
dtype: string
- name: new_id
dtype: string
- name: places
sequence: string
- name: people
sequence: string
- name: orgs
sequence: string
- name: exchanges
sequence: string
- name: date
dtype: string
- name: title
dtype: string
splits:
- name: test
num_bytes: 5400506
num_examples: 6188
- name: train
num_bytes: 12994591
num_examples: 13625
- name: unused
num_bytes: 948244
num_examples: 722
download_size: 8150596
dataset_size: 19343341
---
# Dataset Card for "reuters21578"
## 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://archive.ics.uci.edu/dataset/137/reuters+21578+text+categorization+collection
- **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:** 24.45 MB
- **Size of the generated dataset:** 52.22 MB
- **Total amount of disk used:** 76.67 MB
### Dataset Summary
The Reuters-21578 dataset is one of the most widely used data collections for text
categorization research. It is collected from the Reuters financial newswire service in 1987.
### 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
#### ModApte
- **Size of downloaded dataset files:** 8.15 MB
- **Size of the generated dataset:** 13.05 MB
- **Total amount of disk used:** 21.21 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"cgis_split": "\"TRAINING-SET\"",
"date": "19-MAR-1987 06:17:22.36",
"exchanges": [],
"lewis_split": "\"TRAIN\"",
"new_id": "\"7001\"",
"old_id": "\"11914\"",
"orgs": [],
"people": [],
"places": ["australia"],
"text": "\"Media group John Fairfax Ltd <FFXA.S>\\nsaid that its flat first half net profit partly reflected the\\nimpact of changes in t...",
"title": "FAIRFAX SAYS HIGHER TAX HITS FIRST HALF EARNINGS",
"topics": ["earn"]
}
```
#### ModHayes
- **Size of downloaded dataset files:** 8.15 MB
- **Size of the generated dataset:** 19.79 MB
- **Total amount of disk used:** 27.93 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"cgis_split": "\"TRAINING-SET\"",
"date": "19-OCT-1987 23:49:31.45",
"exchanges": [],
"lewis_split": "\"TEST\"",
"new_id": "\"20001\"",
"old_id": "\"20596\"",
"orgs": [],
"people": [],
"places": ["japan", "usa"],
"text": "\"If the dollar goes the way of Wall Street,\\nJapanese will finally move out of dollar investments in a\\nserious way, Japan inves...",
"title": "IF DOLLAR FOLLOWS WALL STREET JAPANESE WILL DIVEST",
"topics": ["money-fx"]
}
```
#### ModLewis
- **Size of downloaded dataset files:** 8.15 MB
- **Size of the generated dataset:** 19.38 MB
- **Total amount of disk used:** 27.54 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"cgis_split": "\"TRAINING-SET\"",
"date": "19-MAR-1987 06:17:22.36",
"exchanges": [],
"lewis_split": "\"TRAIN\"",
"new_id": "\"7001\"",
"old_id": "\"11914\"",
"orgs": [],
"people": [],
"places": ["australia"],
"text": "\"Media group John Fairfax Ltd <FFXA.S>\\nsaid that its flat first half net profit partly reflected the\\nimpact of changes in t...",
"title": "FAIRFAX SAYS HIGHER TAX HITS FIRST HALF EARNINGS",
"topics": ["earn"]
}
```
### Data Fields
The data fields are the same among all splits.
#### ModApte
- `text`: a `string` feature.
- `topics`: a `list` of `string` features.
- `lewis_split`: a `string` feature.
- `cgis_split`: a `string` feature.
- `old_id`: a `string` feature.
- `new_id`: a `string` feature.
- `places`: a `list` of `string` features.
- `people`: a `list` of `string` features.
- `orgs`: a `list` of `string` features.
- `exchanges`: a `list` of `string` features.
- `date`: a `string` feature.
- `title`: a `string` feature.
#### ModHayes
- `text`: a `string` feature.
- `topics`: a `list` of `string` features.
- `lewis_split`: a `string` feature.
- `cgis_split`: a `string` feature.
- `old_id`: a `string` feature.
- `new_id`: a `string` feature.
- `places`: a `list` of `string` features.
- `people`: a `list` of `string` features.
- `orgs`: a `list` of `string` features.
- `exchanges`: a `list` of `string` features.
- `date`: a `string` feature.
- `title`: a `string` feature.
#### ModLewis
- `text`: a `string` feature.
- `topics`: a `list` of `string` features.
- `lewis_split`: a `string` feature.
- `cgis_split`: a `string` feature.
- `old_id`: a `string` feature.
- `new_id`: a `string` feature.
- `places`: a `list` of `string` features.
- `people`: a `list` of `string` features.
- `orgs`: a `list` of `string` features.
- `exchanges`: a `list` of `string` features.
- `date`: a `string` feature.
- `title`: a `string` feature.
### Data Splits
#### ModApte
| |train|unused|test|
|-------|----:|-----:|---:|
|ModApte| 8762| 720|3009|
#### ModHayes
| |train|test|
|--------|----:|---:|
|ModHayes|18323| 720|
#### ModLewis
| |train|unused|test|
|--------|----:|-----:|---:|
|ModLewis|12449| 720|5458|
## 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
According to the dataset website (https://archive.ics.uci.edu/dataset/137/reuters+21578+text+categorization+collection),
this dataset is licensed under [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode)
(C BY 4.0) license.
However, the source data file contains a `README.txt` file with the following information under the
**Copyright & Notification** section:
> The copyright for the text of newswire articles and Reuters
annotations in the Reuters-21578 collection resides with Reuters Ltd.
Reuters Ltd. and Carnegie Group, Inc. have agreed to allow the free
distribution of this data *for research purposes only*.
> If you publish results based on this data set, please acknowledge
its use, refer to the data set by the name "Reuters-21578,
Distribution 1.0", and inform your readers of the current location of
the data set (see "Availability & Questions").
### Citation Information
```
@article{APTE94,
author = {Chidanand Apt{'{e}} and Fred Damerau and Sholom M. Weiss},
title = {Automated Learning of Decision Rules for Text Categorization},
journal = {ACM Transactions on Information Systems},
year = {1994},
note = {To appear.}
}
@inproceedings{APTE94b,
author = {Chidanand Apt{'{e}} and Fred Damerau and Sholom M. Weiss},
title = {Toward Language Independent Automated Learning of Text Categorization Models},
booktitle = {sigir94},
year = {1994},
note = {To appear.}
}
@inproceedings{HAYES8},
author = {Philip J. Hayes and Peggy M. Anderson and Irene B. Nirenburg and
Linda M. Schmandt},
title = {{TCS}: A Shell for Content-Based Text Categorization},
booktitle = {IEEE Conference on Artificial Intelligence Applications},
year = {1990}
}
@inproceedings{HAYES90b,
author = {Philip J. Hayes and Steven P. Weinstein},
title = {{CONSTRUE/TIS:} A System for Content-Based Indexing of a
Database of News Stories},
booktitle = {Second Annual Conference on Innovative Applications of
Artificial Intelligence},
year = {1990}
}
@incollection{HAYES92 ,
author = {Philip J. Hayes},
title = {Intelligent High-Volume Text Processing using Shallow,
Domain-Specific Techniques},
booktitle = {Text-Based Intelligent Systems},
publisher = {Lawrence Erlbaum},
address = {Hillsdale, NJ},
year = {1992},
editor = {Paul S. Jacobs}
}
@inproceedings{LEWIS91c ,
author = {David D. Lewis},
title = {Evaluating Text Categorization},
booktitle = {Proceedings of Speech and Natural Language Workshop},
year = {1991},
month = {feb},
organization = {Defense Advanced Research Projects Agency},
publisher = {Morgan Kaufmann},
pages = {312--318}
}
@phdthesis{LEWIS91d,
author = {David Dolan Lewis},
title = {Representation and Learning in Information Retrieval},
school = {Computer Science Dept.; Univ. of Massachusetts; Amherst, MA 01003},
year = 1992},
note = {Technical Report 91--93.}
}
@inproceedings{LEWIS91e,
author = {David D. Lewis},
title = {Data Extraction as Text Categorization: An Experiment with
the {MUC-3} Corpus},
booktitle = {Proceedings of the Third Message Understanding Evaluation
and Conference},
year = {1991},
month = {may},
organization = {Defense Advanced Research Projects Agency},
publisher = {Morgan Kaufmann},
address = {Los Altos, CA}
}
@inproceedings{LEWIS92b,
author = {David D. Lewis},
title = {An Evaluation of Phrasal and Clustered Representations on a Text
Categorization Task},
booktitle = {Fifteenth Annual International ACM SIGIR Conference on
Research and Development in Information Retrieval},
year = {1992},
pages = {37--50}
}
@inproceedings{LEWIS92d ,
author = {David D. Lewis and Richard M. Tong},
title = {Text Filtering in {MUC-3} and {MUC-4}},
booktitle = {Proceedings of the Fourth Message Understanding Conference ({MUC-4})},
year = {1992},
month = {jun},
organization = {Defense Advanced Research Projects Agency},
publisher = {Morgan Kaufmann},
address = {Los Altos, CA}
}
@inproceedings{LEWIS92e,
author = {David D. Lewis},
title = {Feature Selection and Feature Extraction for Text Categorization},
booktitle = {Proceedings of Speech and Natural Language Workshop},
year = {1992},
month = {feb} ,
organization = {Defense Advanced Research Projects Agency},
publisher = {Morgan Kaufmann},
pages = {212--217}
}
@inproceedings{LEWIS94b,
author = {David D. Lewis and Marc Ringuette},
title = {A Comparison of Two Learning Algorithms for Text Categorization},
booktitle = {Symposium on Document Analysis and Information Retrieval},
year = {1994},
organization = {ISRI; Univ. of Nevada, Las Vegas},
address = {Las Vegas, NV},
month = {apr},
pages = {81--93}
}
@article{LEWIS94d,
author = {David D. Lewis and Philip J. Hayes},
title = {Guest Editorial},
journal = {ACM Transactions on Information Systems},
year = {1994},
volume = {12},
number = {3},
pages = {231},
month = {jul}
}
@article{SPARCKJONES76,
author = {K. {Sparck Jones} and C. J. {van Rijsbergen}},
title = {Information Retrieval Test Collections},
journal = {Journal of Documentation},
year = {1976},
volume = {32},
number = {1},
pages = {59--75}
}
@book{WEISS91,
author = {Sholom M. Weiss and Casimir A. Kulikowski},
title = {Computer Systems That Learn},
publisher = {Morgan Kaufmann},
year = {1991},
address = {San Mateo, CA}
}
```
### Contributions
Thanks to [@jplu](https://github.com/jplu), [@jbragg](https://github.com/jbragg), [@thomwolf](https://github.com/thomwolf), [@mariamabarham](https://github.com/mariamabarham), [@lhoestq](https://github.com/lhoestq) for adding this dataset. | [
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e2e_nlg | null | 2022-11-18T19:59:40Z | 1,672 | 10 | e2e | [
"task_categories:text2text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"meaning-representation-to-text",
"arxiv:1706.09254",
"arxiv:1901.11528",
"region:us"
] | 2022-11-18T19:59:40Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: e2e
pretty_name: End-to-End NLG Challenge
tags:
- meaning-representation-to-text
dataset_info:
features:
- name: meaning_representation
dtype: string
- name: human_reference
dtype: string
splits:
- name: train
num_bytes: 9435824
num_examples: 42061
- name: validation
num_bytes: 1171723
num_examples: 4672
- name: test
num_bytes: 1320205
num_examples: 4693
download_size: 11812316
dataset_size: 11927752
---
# Dataset Card for End-to-End NLG Challenge
## 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](http://www.macs.hw.ac.uk/InteractionLab/E2E/)
- **Repository:** [repository](https://github.com/tuetschek/e2e-dataset/)
- **Paper:** [paper](https://arxiv.org/abs/1706.09254)
- **Leaderboard:** [leaderboard](http://www.macs.hw.ac.uk/InteractionLab/E2E/)
### Dataset Summary
The E2E dataset is used for training end-to-end, data-driven natural language generation systems in the restaurant domain, which is ten times bigger than existing, frequently used datasets in this area.
The E2E dataset poses new challenges:
(1) its human reference texts show more lexical richness and syntactic variation, including discourse phenomena;
(2) generating from this set requires content selection. As such, learning from this dataset promises more natural, varied and less template-like system utterances.
E2E is released in the following paper where you can find more details and baseline results:
https://arxiv.org/abs/1706.09254
### Supported Tasks and Leaderboards
- `text2text-generation-other-meaning-representation-to-text`: The dataset can be used to train a model to generate descriptions in the restaurant domain from meaning representations, which consists in taking as input some data about a restaurant and generate a sentence in natural language that presents the different aspects of the data about the restaurant.. Success on this task is typically measured by achieving a *high* [BLEU](https://huggingface.co/metrics/bleu), [NIST](https://huggingface.co/metrics/nist), [METEOR](https://huggingface.co/metrics/meteor), [Rouge-L](https://huggingface.co/metrics/rouge), [CIDEr](https://huggingface.co/metrics/cider). The TGen model (Dusek and Jurcıcek, 2016a) was used a baseline, had the following scores:
| | BLEU | NIST | METEOR | ROUGE_L | CIDEr |
| -------- | ------ | ------ | ------ | ------- | ------ |
| BASELINE | 0.6593 | 8.6094 | 0.4483 | 0.6850 | 2.2338 |
This task has an inactive leaderboard which can be found [here](http://www.macs.hw.ac.uk/InteractionLab/E2E/) and ranks models based on the metrics above.
### Languages
The dataset is in english (en).
## Dataset Structure
### Data Instances
Example of one instance:
```
{'human_reference': 'The Vaults pub near Café Adriatic has a 5 star rating. Prices start at £30.',
'meaning_representation': 'name[The Vaults], eatType[pub], priceRange[more than £30], customer rating[5 out of 5], near[Café Adriatic]'}
```
### Data Fields
- `human_reference`: string, the text is natural language that describes the different characteristics in the meaning representation
- `meaning_representation`: list of slots and values to generate a description from
Each MR consists of 3–8 attributes (slots), such as name, food or area, and their values.
### Data Splits
The dataset is split into training, validation and testing sets (in a 76.5-8.5-15 ratio), keeping a similar distribution of MR and reference text lengths and ensuring that MRs in different sets are distinct.
| | train | validation | test |
| ----- |-------:|------------:|------:|
| N. Instances | 42061 | 4672 | 4693 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
The data was collected using the CrowdFlower platform and quality-controlled following Novikova et al. (2016).
#### Who are the source language producers?
[More Information Needed]
### Annotations
Following Novikova et al. (2016), the E2E data was collected using pictures as stimuli, which was shown to elicit significantly more natural, more informative, and better phrased human references than textual MRs.
#### 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
```
@article{dusek.etal2020:csl,
title = {Evaluating the {{State}}-of-the-{{Art}} of {{End}}-to-{{End Natural Language Generation}}: {{The E2E NLG Challenge}}},
author = {Du{\v{s}}ek, Ond\v{r}ej and Novikova, Jekaterina and Rieser, Verena},
year = {2020},
month = jan,
volume = {59},
pages = {123--156},
doi = {10.1016/j.csl.2019.06.009},
archivePrefix = {arXiv},
eprint = {1901.11528},
eprinttype = {arxiv},
journal = {Computer Speech \& Language}
```
### Contributions
Thanks to [@lhoestq](https://github.com/lhoestq) for adding this dataset. | [
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codah | null | 2023-01-25T14:28:20Z | 1,671 | 4 | codah | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | 2023-01-25T14:28:20Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
paperswithcode_id: codah
pretty_name: COmmonsense Dataset Adversarially-authored by Humans
dataset_info:
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dataset_size: 571232
---
# Dataset Card for COmmonsense Dataset Adversarially-authored by Humans
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Add homepage URL here if available (unless it's a GitHub repository)]()
- **Repository:** [If the dataset is hosted on github or has a github homepage, add URL here]()
- **Paper:** [If the dataset was introduced by a paper or there was a paper written describing the dataset, add URL here (landing page for Arxiv paper preferred)]()
- **Leaderboard:** [If the dataset supports an active leaderboard, add link here]()
- **Point of Contact:** [If known, name and email of at least one person the reader can contact for questions about the dataset.]()
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset. | [
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coastalcph/fairlex | coastalcph | 2023-07-27T12:43:39Z | 1,665 | 6 | null | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"task_ids:multi-class-classification",
"task_ids:topic-classification",
"annotations_creators:found",
"annotations_creators:machine-generated",
"language_creators:found",
"source_datasets:extended",
"language:en",
"language:de",
"language:fr",
"language:it",
"language:zh",
"license:cc-by-nc-sa-4.0",
"bias",
"gender-bias",
"arxiv:2103.13868",
"arxiv:2105.03887",
"arxiv:2203.07228",
"region:us"
] | 2023-07-27T12:43:39Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
- machine-generated
language_creators:
- found
language:
- en
- en
- de
- fr
- it
- zh
license:
- cc-by-nc-sa-4.0
multilinguality:
ecthr:
- monolingual
scotus:
- monolingual
fscs:
- multilingual
cail:
- monolingual
size_categories:
ecthr:
- 10K<n<100K
scotus:
- 1K<n<10K
fscs:
- 10K<n<100K
cail:
- 100K<n<1M
source_datasets:
- extended
task_categories:
- text-classification
task_ids:
- multi-label-classification
- multi-class-classification
- topic-classification
pretty_name: FairLex
tags:
- bias
- gender-bias
---
# Dataset Card for "FairLex"
## 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/coastalcph/fairlex
- **Repository:** https://github.com/coastalcph/fairlex
- **Paper:** https://aclanthology.org/2022.acl-long.301/
- **Leaderboard:** -
- **Point of Contact:** [Ilias Chalkidis](mailto:ilias.chalkidis@di.ku.dk)
### Dataset Summary
We present a benchmark suite of four datasets for evaluating the fairness of pre-trained legal language models and the techniques used to fine-tune them for downstream tasks. Our benchmarks cover four jurisdictions (European Council, USA, Swiss, and Chinese), five languages (English, German, French, Italian, and Chinese), and fairness across five attributes (gender, age, nationality/region, language, and legal area). In our experiments, we evaluate pre-trained language models using several group-robust fine-tuning techniques and show that performance group disparities are vibrant in many cases, while none of these techniques guarantee fairness, nor consistently mitigate group disparities. Furthermore, we provide a quantitative and qualitative analysis of our results, highlighting open challenges in the development of robustness methods in legal NLP.
For the purpose of this work, we release four domain-specific BERT models with continued pre-training on the corpora of the examined datasets (ECtHR, SCOTUS, FSCS, CAIL). We train mini-sized BERT models with 6 Transformer blocks, 384 hidden units, and 12 attention heads. We warm-start all models from the public MiniLMv2 (Wang et al., 2021) using the distilled version of RoBERTa (Liu et al., 2019). For the English datasets (ECtHR, SCOTUS) and the one distilled from XLM-R (Conneau et al., 2021) for the rest (trilingual FSCS, and Chinese CAIL). [[Link to Models](https://huggingface.co/models?search=fairlex)]
### Supported Tasks and Leaderboards
The supported tasks are the following:
<table>
<tr><td>Dataset</td><td>Source</td><td>Sub-domain</td><td>Language</td><td>Task Type</td><td>Classes</td><tr>
<tr><td>ECtHR</td><td> <a href="https://aclanthology.org/P19-1424/">Chalkidis et al. (2019)</a> </td><td>ECHR</td><td>en</td><td>Multi-label classification</td><td>10+1</td></tr>
<tr><td>SCOTUS</td><td> <a href="http://scdb.wustl.edu">Spaeth et al. (2020)</a></td><td>US Law</td><td>en</td><td>Multi-class classification</td><td>11</td></tr>
<tr><td>FSCS</td><td> <a href="https://aclanthology.org/2021.nllp-1.3/">Niklaus et al. (2021)</a></td><td>Swiss Law</td><td>en, fr , it</td><td>Binary classification</td><td>2</td></tr>
<tr><td>CAIL</td><td> <a href="https://arxiv.org/abs/2103.13868">Wang et al. (2021)</a></td><td>Chinese Law</td><td>zh</td><td>Multi-class classification</td><td>6</td></tr>
</table>
#### ecthr
The European Court of Human Rights (ECtHR) hears allegations that a state has breached human rights provisions of the European Convention of Human Rights (ECHR). We use the dataset of Chalkidis et al. (2021), which contains 11K cases from ECtHR's public database.
Each case is mapped to *articles* of the ECHR that were violated (if any). This is a multi-label text classification task. Given the facts of a case, the goal is to predict the ECHR articles that were violated, if any, as decided (ruled) by the court. The cases are chronologically split into training (9k, 2001--16), development (1k, 2016--17), and test (1k, 2017--19) sets.
To facilitate the study of the fairness of text classifiers, we record for each case the following attributes: (a) The _defendant states_, which are the European states that allegedly violated the ECHR. The defendant states for each case is a subset of the 47 Member States of the Council of Europe; To have statistical support, we group defendant states in two groups:
Central-Eastern European states, on one hand, and all other states, as classified by the EuroVoc thesaurus. (b) The _applicant's age_ at the time of the decision. We extract the birth year of the applicant from the case facts, if possible, and classify its case in an age group (<=35, <=64, or older); and (c) the _applicant's gender_, extracted from the facts, if possible based on pronouns, classified in two categories (male, female).
#### scotus
The US Supreme Court (SCOTUS) is the highest federal court in the United States of America and generally hears only the most controversial or otherwise complex cases that have not been sufficiently well solved by lower courts.
We combine information from SCOTUS opinions with the Supreme Court DataBase (SCDB) (Spaeth, 2020). SCDB provides metadata (e.g., date of publication, decisions, issues, decision directions, and many more) for all cases. We consider the available 14 thematic issue areas (e.g, Criminal Procedure, Civil Rights, Economic Activity, etc.). This is a single-label multi-class document classification task. Given the court's opinion, the goal is to predict the issue area whose focus is on the subject matter of the controversy (dispute). SCOTUS contains a total of 9,262 cases that we split chronologically into 80% for training (7.4k, 1946--1982), 10% for development (914, 1982--1991) and 10% for testing (931, 1991--2016).
From SCDB, we also use the following attributes to study fairness: (a) the _type of respondent_, which is a manual categorization of respondents (defendants) in five categories (person, public entity, organization, facility, and other); and (c) the _direction of the decision_, i.e., whether the decision is liberal, or conservative, provided by SCDB.
#### fscs
The Federal Supreme Court of Switzerland (FSCS) is the last level of appeal in Switzerland and similarly to SCOTUS, the court generally hears only the most controversial or otherwise complex cases which have not been sufficiently well solved by lower courts. The court often focuses only on small parts of the previous decision, where they discuss possible wrong reasoning by the lower court. The Swiss-Judgment-Predict dataset (Niklaus et al., 2021) contains more than 85K decisions from the FSCS written in one of three languages (50K German, 31K French, 4K Italian) from the years 2000 to 2020.
The dataset is not parallel, i.e., all cases are unique and decisions are written only in a single language.
The dataset provides labels for a simplified binary (_approval_, _dismissal_) classification task. Given the facts of the case, the goal is to predict if the plaintiff's request is valid or partially valid. The cases are also chronologically split into training (59.7k, 2000-2014), development (8.2k, 2015-2016), and test (17.4k, 2017-2020) sets.
The dataset provides three additional attributes: (a) the _language_ of the FSCS written decision, in either German, French, or Italian; (b) the _legal area_ of the case (public, penal, social, civil, or insurance law) derived from the chambers where the decisions were heard; and (c) the _region_ that denotes in which federal region was the case originated.
#### cail
The Supreme People's Court of China (CAIL) is the last level of appeal in China and considers cases that originated from the high people's courts concerning matters of national importance. The Chinese AI and Law challenge (CAIL) dataset (Xiao et al., 2018) is a Chinese legal NLP dataset for judgment prediction and contains over 1m criminal cases. The dataset provides labels for *relevant article of criminal code* prediction, *charge* (type of crime) prediction, imprisonment *term* (period) prediction, and monetary *penalty* prediction. The publication of the original dataset has been the topic of an active debate in the NLP community(Leins et al., 2020; Tsarapatsanis and Aletras, 2021; Bender, 2021).
Recently, Wang et al. (2021) re-annotated a subset of approx. 100k cases with demographic attributes. Specifically, the new dataset has been annotated with: (a) the _applicant's gender_, classified in two categories (male, female); and (b) the _region_ of the court that denotes in which out of the 7 provincial-level administrative regions was the case judged. We re-split the dataset chronologically into training (80k, 2013-2017), development (12k, 2017-2018), and test (12k, 2018) sets. In our study, we re-frame the imprisonment _term_ prediction and examine a soft version, dubbed _crime severity_ prediction task, a multi-class classification task, where given the facts of a case, the goal is to predict how severe was the committed crime with respect to the imprisonment term. We approximate crime severity by the length of imprisonment term, split in 6 clusters (0, <=12, <=36, <=60, <=120, >120 months).
### Languages
We consider datasets in English, German, French, Italian, and Chinese.
## Dataset Structure
### Data Instances
#### ecthr
An example of 'train' looks as follows.
```json
{
"text": "1. At the beginning of the events relevant to the application, K. had a daughter, P., and a son, M., born in 1986 and 1988 respectively. ... ",
"labels": [4],
"defendant_state": 1,
"applicant_gender": 0,
"applicant_age": 0
}
```
#### scotus
An example of 'train' looks as follows.
```json
{
"text": "United States Supreme Court MICHIGAN NAT. BANK v. MICHIGAN(1961) No. 155 Argued: Decided: March 6, 1961 </s> R. S. 5219 permits States to tax the shares of national banks, but not at a greater rate than . . . other moneyed capital . . . coming into competition with the business of national banks ...",
"label": 9,
"decision_direction": 0,
"respondent_type": 3
}
```
#### fscs
An example of 'train' looks as follows.
```json
{
"text": "A.- Der 1955 geborene V._ war seit 1. September 1986 hauptberuflich als technischer Kaufmann bei der Firma A._ AG tätig und im Rahmen einer Nebenbeschäftigung (Nachtarbeit) ab Mai 1990 bei einem Bewachungsdienst angestellt gewesen, als er am 10....",
"label": 0,
"decision_language": 0,
"legal_are": 5,
"court_region": 2
}
```
#### cail
An example of 'train' looks as follows.
```json
{
"text": "南宁市兴宁区人民检察院指控，2012年1月1日19时许，被告人蒋满德在南宁市某某路某号某市场内，因经营问题与被害人杨某某发生争吵并推打 ...",
"label": 0,
"defendant_gender": 0,
"court_region": 5
}
```
### Data Fields
#### ecthr_a
- `text`: a `string` feature (factual paragraphs (facts) from the case description).
- `labels`: a list of classification labels (a list of violated ECHR articles, if any). The ECHR articles considered are 2, 3, 5, 6, 8, 9, 11, 14, P1-1.
- `defendant_state`: Defendant State group (C.E. European, Rest of Europe)
- `applicant_gender`: The gender of the applicant (N/A, Male, Female)
- `applicant_age`: The age group of the applicant (N/A, <=35, <=64, or older)
#### scotus
- `text`: a `string` feature (the court opinion).
- `label`: a classification label (the relevant issue area). The issue areas are: (1, Criminal Procedure), (2, Civil Rights), (3, First Amendment), (4, Due Process), (5, Privacy), (6, Attorneys), (7, Unions), (8, Economic Activity), (9, Judicial Power), (10, Federalism), (11, Interstate Relations), (12, Federal Taxation), (13, Miscellaneous), (14, Private Action).
- `respondent_type`: the type of respondent, which is a manual categorization (clustering) of respondents (defendants) in five categories (person, public entity, organization, facility, and other).
- `decision_direction`: the direction of the decision, i.e., whether the decision is liberal, or conservative, provided by SCDB.
#### fscs
- `text`: a `string` feature (an EU law).
- `label`: a classification label (approval or dismissal of the appeal).
- `language`: the language of the FSCS written decision, (German, French, or Italian).
- `legal_area`: the legal area of the case (public, penal, social, civil, or insurance law) derived from the chambers where the decisions were heard.
- `region`: the region that denotes in which federal region was the case originated.
#### cail
- `text`: a `string` feature (the factual description of the case).
- `label`: a classification label (crime severity derived by the imprisonment term).
- `defendant_gender`: the gender of the defendant (Male or Female).
- `court_region`: the region of the court that denotes in which out of the 7 provincial-level administrative regions was the case judged.
### Data Splits
<table>
<tr><td>Dataset </td><td>Training</td><td>Development</td><td>Test</td><td>Total</td></tr>
<tr><td>ECtHR</td><td>9000</td><td>1000</td><td>1000</td><td>11000</td></tr>
<tr><td>SCOTUS</td><td>7417</td><td>914</td><td>931</td><td>9262</td></tr>
<tr><td>FSCS</td><td>59709</td><td>8208</td><td>17357</td><td>85274</td></tr>
<tr><td>CAIL</td><td>80000</td><td>12000</td><td>12000</td><td>104000</td></tr>
</table>
## 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
<table>
<tr><td>Dataset</td><td>Source</td><td>Sub-domain</td><td>Language</td><td>Task Type</td><td>Classes</td><tr>
<tr><td>ECtHR</td><td> <a href="https://aclanthology.org/P19-1424/">Chalkidis et al. (2019)</a> </td><td>ECHR</td><td>en</td><td>Multi-label classification</td><td>10+1</td></tr>
<tr><td>SCOTUS</td><td> <a href="http://scdb.wustl.edu">Spaeth et al. (2020)</a></td><td>US Law</td><td>en</td><td>Multi-class classification</td><td>14</td></tr>
<tr><td>FSCS</td><td> <a href="https://aclanthology.org/2021.nllp-1.3/">Niklaus et al. (2021)</a></td><td>Swiss Law</td><td>en, fr , it</td><td>Binary classification</td><td>2</td></tr>
<tr><td>CAIL</td><td> <a href="https://arxiv.org/abs/2105.03887">Wang et al. (2021)</a></td><td>Chinese Law</td><td>zh</td><td>Multi-class classification</td><td>6</td></tr>
</table>
#### Initial Data Collection and Normalization
We standardize and put together four datasets: ECtHR (Chalkidis et al., 2021), SCOTUS (Spaeth et al., 2020), FSCS (Niklaus et al., 2021), and CAIL (Xiao et al., 2018; Wang et al., 2021) that are already publicly available.
The benchmark is not a blind stapling of pre-existing resources, we augment previous datasets. In the case of ECtHR, previously unavailable demographic attributes have been released to make the original dataset amenable for fairness research. For SCOTUS, two resources (court opinions with SCDB) have been combined for the very same reason, while the authors provide a manual categorization (clustering) of respondents.
All datasets, except SCOTUS, are publicly available and have been previously published. If datasets or the papers where they were introduced were not compiled or written by the authors, the original work is referenced and authors encourage FairLex users to do so as well. In fact, this work should only be referenced, in addition to citing the original work, when jointly experimenting with multiple FairLex datasets and using the FairLex evaluation framework and infrastructure, or using any newly introduced annotations (ECtHR, SCOTUS). Otherwise only the original work should be cited.
#### 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?
All classification labels rely on legal decisions (ECtHR, FSCS, CAIL), or are part of archival procedures (SCOTUS).
The demographic attributes and other metadata are either provided by the legal databases or have been extracted automatically from the text by means of Regular Expressions.
Consider the **Dataset Description** and **Discussion of Biases** sections, and the original publication for detailed information.
### Personal and Sensitive Information
The data is in general partially anonymized in accordance with the applicable national law. The data is considered to be in the public sphere from a privacy perspective. This is a very sensitive matter, as the courts try to keep a balance between transparency (the public's right to know) and privacy (respect for private and family life).
ECtHR cases are partially annonymized by the court. Its data is processed and made public in accordance with the European Data Protection Law.
SCOTUS cases may also contain personal information and the data is processed and made available by the US Supreme Court, whose proceedings are public. While this ensures compliance with US law, it is very likely that similarly to the ECtHR any processing could be justified by either implied consent or legitimate interest under European law. In FSCS, the names of the parties have been redacted by the courts according to the official guidelines. CAIL cases are also partially anonymized by the courts according to the courts' policy. Its data is processed and made public in accordance with Chinese Law.
## Considerations for Using the Data
### Social Impact of Dataset
This work can help practitioners to build assisting technology for legal professionals - with respect to the legal framework (jurisdiction) they operate -; technology that does not only rely on performance on majority groups but also considering minorities and the robustness of the developed models across them. This is an important application field, where more research should be conducted (Tsarapatsanis and Aletras, 2021) in order to improve legal services and democratize law, but more importantly, highlight (inform the audience on) the various multi-aspect shortcomings seeking a responsible and ethical (fair) deployment of technology.
### Discussion of Biases
The current version of FairLex covers a very small fraction of legal applications, jurisdictions, and protected attributes. The benchmark inevitably cannot cover "_everything in the whole wide (legal) world_" (Raji et al., 2021), but nonetheless, we believe that the published resources will help critical research in the area of fairness.
Some protected attributes within the datasets are extracted automatically, i.e., the gender and the age of the ECtHR dataset, by means of Regular Expressions, or manually clustered by the authors, such as the defendant state in the ECtHR dataset and the respondent attribute in the SCOTUS dataset. Those assumptions and simplifications can hold in an experimental setting only and by no means should be used in real-world applications where some simplifications, e.g., binary gender, would not be appropriate. By no means, do the authors or future users have to endorse the law standards or framework of the examined datasets, to any degree rather than the publication and use of the data.
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Dataset Curators
*Ilias Chalkidis, Tommaso Pasini, Sheng Zhang, Letizia Tomada, Letizia, Sebastian Felix Schwemer, Anders Søgaard.*
*FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing.*
*2022. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland.*
**Note:** The original datasets have been originally curated by others, and further curated (updated) by means of this benchmark.
### Licensing Information
The benchmark is released under a [Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/) license. The licensing is compatible with the licensing of former material (remixed, transformed datasets).
### Citation Information
[*Ilias Chalkidis, Tommaso Pasini, Sheng Zhang, Letizia Tomada, Letizia, Sebastian Felix Schwemer, Anders Søgaard.*
*FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing.*
*2022. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland.*](https://arxiv.org/abs/2203.07228)
```
@inproceedings{chalkidis-etal-2022-fairlex,
author={Chalkidis, Ilias and Passini, Tommaso and Zhang, Sheng and
Tomada, Letizia and Schwemer, Sebastian Felix and Søgaard, Anders},
title={FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing},
booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics},
year={2022},
address={Dublin, Ireland}
}
```
**Note:** Please consider citing and giving credits to all publications releasing the examined datasets.
### Contributions
Thanks to [@iliaschalkidis](https://github.com/iliaschalkidis) for adding this dataset.
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opus_paracrawl | null | 2023-06-01T14:59:53Z | 1,659 | 5 | null | [
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"region:us"
] | 2023-06-01T14:59:53Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
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license:
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multilinguality:
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size_categories:
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source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: OpusParaCrawl
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splits:
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config_names:
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---
# Dataset Card for OpusParaCrawl
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://opus.nlpl.eu/ParaCrawl.php
- **Repository:** None
- **Paper:** [ParaCrawl: Web-Scale Acquisition of Parallel Corpora](https://aclanthology.org/2020.acl-main.417/)
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
Parallel corpora from Web Crawls collected in the ParaCrawl project.
Tha dataset contains:
- 42 languages, 43 bitexts
- total number of files: 59,996
- total number of tokens: 56.11G
- total number of sentence fragments: 3.13G
To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs,
e.g.
```python
dataset = load_dataset("opus_paracrawl", lang1="en", lang2="so")
```
You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/ParaCrawl.php
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The languages in the dataset are:
- bg
- ca
- cs
- da
- de
- el
- en
- es
- et
- eu
- fi
- fr
- ga
- gl
- hr
- hu
- is
- it
- km
- ko
- lt
- lv
- mt
- my
- nb
- ne
- nl
- nn
- pl
- pt
- ro
- ru
- si
- sk
- sl
- so
- sv
- sw
- tl
- uk
- zh
## Dataset Structure
### Data Instances
```
{
'id': '0',
'translation': {
"el": "Συνεχίστε ευθεία 300 μέτρα μέχρι να καταλήξουμε σε μια σωστή οδός (ul. Gagarina)? Περπατήστε περίπου 300 μέτρα μέχρι να φτάσετε το πρώτο ορθή οδός (ul Khotsa Namsaraeva)?",
"en": "Go straight 300 meters until you come to a proper street (ul. Gagarina); Walk approximately 300 meters until you reach the first proper street (ul Khotsa Namsaraeva);"
}
}
```
### Data Fields
- `id` (`str`): Unique identifier of the parallel sentence for the pair of languages.
- `translation` (`dict`): Parallel sentences for the pair of languages.
### Data Splits
The dataset contains a single `train` split.
## 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
- Creative commons CC0 (no rights reserved)
### Citation Information
```bibtex
@inproceedings{banon-etal-2020-paracrawl,
title = "{P}ara{C}rawl: Web-Scale Acquisition of Parallel Corpora",
author = "Ba{\~n}{\'o}n, Marta and
Chen, Pinzhen and
Haddow, Barry and
Heafield, Kenneth and
Hoang, Hieu and
Espl{\`a}-Gomis, Miquel and
Forcada, Mikel L. and
Kamran, Amir and
Kirefu, Faheem and
Koehn, Philipp and
Ortiz Rojas, Sergio and
Pla Sempere, Leopoldo and
Ram{\'\i}rez-S{\'a}nchez, Gema and
Sarr{\'\i}as, Elsa and
Strelec, Marek and
Thompson, Brian and
Waites, William and
Wiggins, Dion and
Zaragoza, Jaume",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.417",
doi = "10.18653/v1/2020.acl-main.417",
pages = "4555--4567",
}
```
```bibtex
@InProceedings{TIEDEMANN12.463,
author = {Jörg Tiedemann},
title = {Parallel Data, Tools and Interfaces in OPUS},
booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
year = {2012},
month = {may},
date = {23-25},
address = {Istanbul, Turkey},
editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Uğur Doğan and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
publisher = {European Language Resources Association (ELRA)},
isbn = {978-2-9517408-7-7},
language = {english}
}
```
### Contributions
Thanks to [@rkc007](https://github.com/rkc007) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
imodels/credit-card | imodels | 2022-08-14T15:37:54Z | 1,657 | 3 | null | [
"task_categories:tabular-classification",
"size_categories:10K<n<100K",
"interpretability",
"fairness",
"medicine",
"region:us"
] | 2022-08-14T15:37:54Z | 2022-08-14T15:33:53.000Z | 2022-08-14T15:33:53 | ---
annotations_creators: []
language: []
language_creators: []
license: []
multilinguality: []
pretty_name: credit-card
size_categories:
- 10K<n<100K
source_datasets: []
tags:
- interpretability
- fairness
- medicine
task_categories:
- tabular-classification
task_ids: []
---
Port of the credit-card dataset from UCI (link [here](https://www.kaggle.com/datasets/uciml/default-of-credit-card-clients-dataset)). See details there and use carefully.
Basic preprocessing done by the [imodels team](https://github.com/csinva/imodels) in [this notebook](https://github.com/csinva/imodels-data/blob/master/notebooks_fetch_data/00_get_datasets_custom.ipynb).
The target is the binary outcome `default.payment.next.month`.
### Sample usage
Load the data:
```
from datasets import load_dataset
dataset = load_dataset("imodels/credit-card")
df = pd.DataFrame(dataset['train'])
X = df.drop(columns=['default.payment.next.month'])
y = df['default.payment.next.month'].values
```
Fit a model:
```
import imodels
import numpy as np
m = imodels.FIGSClassifier(max_rules=5)
m.fit(X, y)
print(m)
```
Evaluate:
```
df_test = pd.DataFrame(dataset['test'])
X_test = df.drop(columns=['default.payment.next.month'])
y_test = df['default.payment.next.month'].values
print('accuracy', np.mean(m.predict(X_test) == y_test))
``` | [
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opus_dgt | null | 2023-06-01T14:59:53Z | 1,644 | 1 | null | [
"task_categories:translation",
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"license:unknown",
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] | 2023-06-01T14:59:53Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
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language_creators:
- found
language:
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- et
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- fr
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license:
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multilinguality:
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size_categories:
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source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: OpusDgt
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---
# Dataset Card for OpusDgt
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://opus.nlpl.eu/DGT.php
- **Repository:** None
- **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
A collection of translation memories provided by the Joint Research Centre (JRC) Directorate-General for Translation (DGT): https://ec.europa.eu/jrc/en/language-technologies/dgt-translation-memory
Tha dataset contains 25 languages and 299 bitexts.
To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs,
e.g.
```python
dataset = load_dataset("opus_dgt", lang1="it", lang2="pl")
```
You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/DGT.php
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The languages in the dataset are:
- bg
- cs
- da
- de
- el
- en
- es
- et
- fi
- fr
- ga
- hr
- hu
- it
- lt
- lv
- mt
- nl
- pl
- pt
- ro
- sh
- sk
- sl
- sv
## Dataset Structure
### Data Instances
```
{
'id': '0',
'translation': {
"bg": "Протокол за поправка на Конвенцията относно компетентността, признаването и изпълнението на съдебни решения по граждански и търговски дела, подписана в Лугано на 30 октомври 2007 г.",
"ga": "Miontuairisc cheartaitheach maidir le Coinbhinsiún ar dhlínse agus ar aithint agus ar fhorghníomhú breithiúnas in ábhair shibhialta agus tráchtála, a siníodh in Lugano an 30 Deireadh Fómhair 2007"
}
}
```
### Data Fields
- `id` (`str`): Unique identifier of the parallel sentence for the pair of languages.
- `translation` (`dict`): Parallel sentences for the pair of languages.
### Data Splits
The dataset contains a single `train` split.
## 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
```bibtex
@InProceedings{TIEDEMANN12.463,
author = {J{\"o}rg Tiedemann},
title = {Parallel Data, Tools and Interfaces in OPUS},
booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
year = {2012},
month = {may},
date = {23-25},
address = {Istanbul, Turkey},
editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis},
publisher = {European Language Resources Association (ELRA)},
isbn = {978-2-9517408-7-7},
language = {english}
}
```
### Contributions
Thanks to [@rkc007](https://github.com/rkc007) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
HuggingFaceH4/testing_self_instruct_small | HuggingFaceH4 | 2023-04-12T21:53:16Z | 1,642 | 0 | null | [
"region:us"
] | 2023-04-12T21:53:16Z | 2023-04-12T21:53:12.000Z | 2023-04-12T21:53:12 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: completion
dtype: string
splits:
- name: train
num_bytes: 20379
num_examples: 100
- name: test
num_bytes: 26586
num_examples: 100
download_size: 35875
dataset_size: 46965
---
# Dataset Card for "testing_self_instruct_small"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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opus_gnome | null | 2023-06-01T14:59:53Z | 1,640 | 1 | null | [
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"license:unknown",
"region:us"
] | 2023-06-01T14:59:53Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- af
- am
- an
- ang
- ar
- as
- ast
- az
- bal
- be
- bem
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- bn
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- crh
- cs
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- cy
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- tt
- tyj
- ug
- uk
- ur
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- wa
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- zu
language_bcp47:
- ar-TN
- az-IR
- bg-BG
- bn-IN
- da-DK
- de-CH
- en-AU
- en-CA
- en-GB
- en-NZ
- en-US
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- zh-CN
- zh-HK
- zh-TW
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
- 1K<n<10K
- n<1K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: OpusGnome
dataset_info:
- config_name: ar-bal
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
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splits:
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num_bytes: 5150
num_examples: 60
download_size: 2503
dataset_size: 5150
- config_name: bg-csb
features:
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dtype: string
- name: translation
dtype:
translation:
languages:
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splits:
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num_bytes: 172545
num_examples: 1768
download_size: 29706
dataset_size: 172545
- config_name: ca-en_GB
features:
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dtype: string
- name: translation
dtype:
translation:
languages:
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splits:
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num_bytes: 1007488
num_examples: 7982
download_size: 188727
dataset_size: 1007488
- config_name: cs-eo
features:
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dtype: string
- name: translation
dtype:
translation:
languages:
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splits:
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num_examples: 73
download_size: 3055
dataset_size: 2895
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features:
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dtype: string
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dtype:
translation:
languages:
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splits:
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num_bytes: 22899
num_examples: 216
download_size: 5287
dataset_size: 22899
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features:
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dtype: string
- name: translation
dtype:
translation:
languages:
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splits:
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num_bytes: 1197731
num_examples: 18686
download_size: 98044
dataset_size: 1197731
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features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- da
- vi
splits:
- name: train
num_bytes: 9372
num_examples: 149
download_size: 5432
dataset_size: 9372
- config_name: en_GB-my
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- en_GB
- my
splits:
- name: train
num_bytes: 3298074
num_examples: 28232
download_size: 362750
dataset_size: 3298074
- config_name: el-sk
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- el
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splits:
- name: train
num_bytes: 12121
num_examples: 150
download_size: 6116
dataset_size: 12121
- config_name: de-tt
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- de
- tt
splits:
- name: train
num_bytes: 134978
num_examples: 2169
download_size: 15891
dataset_size: 134978
config_names:
- ar-bal
- bg-csb
- ca-en_GB
- cs-eo
- cs-tk
- da-vi
- de-ha
- de-tt
- el-sk
- en_GB-my
---
# Dataset Card for Opus Gnome
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://opus.nlpl.eu/GNOME.php
- **Repository:** None
- **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs.
You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/GNOME.php
E.g.
`dataset = load_dataset("opus_gnome", lang1="it", lang2="pl")`
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
```
{
'id': '0',
'translation': {
'ar': 'إعداد سياسة القفل',
'bal': 'تنظیم کتن سیاست کبل'
}
}
```
### Data Fields
Each instance has two fields:
- **id**: the id of the example
- **translation**: a dictionary containing translated texts in two languages.
### Data Splits
Each subset simply consists in a train set. We provide the number of examples for certain language pairs:
| | train |
|:---------|--------:|
| ar-bal | 60 |
| bg-csb | 10 |
| ca-en_GB | 7982 |
| cs-eo | 73 |
| de-ha | 216 |
| cs-tk | 18686 |
| da-vi | 149 |
| en_GB-my | 28232 |
| el-sk | 150 |
| de-tt | 2169 |
## 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
@InProceedings{TIEDEMANN12.463,
author = {J{\"o}rg Tiedemann},
title = {Parallel Data, Tools and Interfaces in OPUS},
booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
year = {2012},
month = {may},
date = {23-25},
address = {Istanbul, Turkey},
editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis},
publisher = {European Language Resources Association (ELRA)},
isbn = {978-2-9517408-7-7},
language = {english}
}
### Contributions
Thanks to [@rkc007](https://github.com/rkc007) for adding this dataset. | [
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opus_rf | null | 2023-06-01T14:59:53Z | 1,638 | 0 | null | [
"task_categories:translation",
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] | 2023-06-01T14:59:53Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
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paperswithcode_id: null
pretty_name: OpusRf
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---
# 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:** http://opus.nlpl.eu/RF.php
- **Repository:**
- **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
RF is a tiny parallel corpus of the Declarations of the Swedish Government and its translations.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English (en), Spanish (es), German (de), French (fr), Swedish (sv)
## 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{TIEDEMANN12.463,
author = {J{\"o}rg Tiedemann},
title = {Parallel Data, Tools and Interfaces in OPUS},
booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
year = {2012},
month = {may},
date = {23-25},
address = {Istanbul, Turkey},
editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis},
publisher = {European Language Resources Association (ELRA)},
isbn = {978-2-9517408-7-7},
language = {english}
}
```
### Contributions
Thanks to [@akshayb7](https://github.com/akshayb7) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
castorini/afriberta-corpus | castorini | 2022-10-19T21:33:04Z | 1,630 | 7 | null | [
"task_categories:text-generation",
"task_ids:language-modeling",
"language:om",
"language:am",
"language:rw",
"language:rn",
"language:ha",
"language:ig",
"language:pcm",
"language:so",
"language:sw",
"language:ti",
"language:yo",
"language:multilingual",
"license:apache-2.0",
"region:us"
] | 2022-10-19T21:33:04Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- om
- am
- rw
- rn
- ha
- ig
- pcm
- so
- sw
- ti
- yo
- multilingual
license: apache-2.0
task_categories:
- text-generation
task_ids:
- language-modeling
---
# Dataset Card for AfriBERTa's Corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Loading Dataset](#loading-dataset)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Discussion of Biases](#discussion-of-biases)
- [Additional Information](#additional-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
### Dataset Summary
This is the corpus on which AfriBERTa was trained on.
The dataset is mostly from the BBC news website, but some languages also have data from Common Crawl.
- **Homepage:** https://github.com/keleog/afriberta
- **Models:**
- https://huggingface.co/castorini/afriberta_small
- https://huggingface.co/castorini/afriberta_base
- https://huggingface.co/castorini/afriberta_large
- **Paper:** https://aclanthology.org/2021.mrl-1.11/
- **Point of Contact:** kelechi.ogueji@uwaterloo.ca
### Supported Tasks and Leaderboards
The AfriBERTa corpus was mostly intended to pre-train language models.
### Languages
```
afaanoromoo
amharic
gahuza
hausa
igbo
pidgin
somali
swahili
tigrinya
yoruba
```
### Loading Dataset
An example to load the train split of the Somali corpus:
```
dataset = load_dataset("castorini/afriberta-corpus", "somali", split="train")
```
An example to load the test split of the Pidgin corpus:
```
dataset = load_dataset("castorini/afriberta-corpus", "pidgin", split="test")
```
## Dataset Structure
### Data Instances
Each data point is a line of text.
An example from the `igbo` dataset:
```
{"id": "6", "text": "Ngwá ọrụ na-echebe ma na-ebuli gị na kọmputa."}
```
### Data Fields
The data fields are:
- id: id of the example
- text: content as a string
### Data Splits
Each language has a train and test split, with varying sizes.
## Considerations for Using the Data
### Discussion of Biases
Since majority of the data is obtained from the BBC's news website, models trained on this dataset are likely going to
be biased towards the news domain.
Also, since some of the data is obtained from Common Crawl, care should be taken (especially for text generation models) since personal and sensitive information might be present.
## Additional Information
### Citation Information
```
@inproceedings{ogueji-etal-2021-small,
title = "Small Data? No Problem! Exploring the Viability of Pretrained Multilingual Language Models for Low-resourced Languages",
author = "Ogueji, Kelechi and
Zhu, Yuxin and
Lin, Jimmy",
booktitle = "Proceedings of the 1st Workshop on Multilingual Representation Learning",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.mrl-1.11",
pages = "116--126",
}
```
### Contributions
Thanks to [Kelechi Ogueji](https://github.com/keleog) for adding this dataset. | [
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opus_ubuntu | null | 2023-06-01T14:59:53Z | 1,618 | 1 | null | [
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] | 2023-06-01T14:59:53Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
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task_ids: []
paperswithcode_id: null
pretty_name: Opus Ubuntu
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config_names:
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---
# Dataset Card for Opus Ubuntu
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://opus.nlpl.eu/Ubuntu.php
- **Repository:** None
- **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
These are translations of the Ubuntu software package messages, donated by the Ubuntu community.
To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs.
You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/Ubuntu.php
E.g.
`dataset = load_dataset("opus_ubuntu", lang1="it", lang2="pl")`
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
Example instance:
```
{
'id': '0',
'translation': {
'it': 'Comprende Gmail, Google Docs, Google+, YouTube e Picasa',
'pl': 'Zawiera Gmail, Google Docs, Google+, YouTube oraz Picasa'
}
}
```
### Data Fields
Each instance has two fields:
- **id**: the id of the example
- **translation**: a dictionary containing translated texts in two languages.
### Data Splits
Each subset simply consists in a train set. We provide the number of examples for certain language pairs:
| | train |
|:---------|--------:|
| as-bs | 8583 |
| az-cs | 293 |
| bg-de | 184 |
| br-es_PR | 125 |
| bn-ga | 7324 |
| br-hi | 15551 |
| br-la | 527 |
| bs-szl | 646 |
| br-uz | 1416 |
| br-yi | 2799 |
## 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
BSD "Revised" license (see (https://help.launchpad.net/Legal#Translations_copyright)[https://help.launchpad.net/Legal#Translations_copyright])
### Citation Information
```bibtex
@InProceedings{TIEDEMANN12.463,
author = {J{\"o}rg Tiedemann},
title = {Parallel Data, Tools and Interfaces in OPUS},
booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
year = {2012},
month = {may},
date = {23-25},
address = {Istanbul, Turkey},
editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis},
publisher = {European Language Resources Association (ELRA)},
isbn = {978-2-9517408-7-7},
language = {english}
}
```
### Contributions
Thanks to [@rkc007](https://github.com/rkc007) for adding this dataset. | [
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KETI-AIR/kor_corpora | KETI-AIR | 2021-09-16T07:32:28Z | 1,612 | 0 | null | [
"region:us"
] | 2021-09-16T07:32:28Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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nchlt | null | 2023-01-25T14:41:21Z | 1,606 | 4 | null | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
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"language:zu",
"license:cc-by-2.5",
"region:us"
] | 2023-01-25T14:41:21Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
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license:
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size_categories:
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source_datasets:
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task_categories:
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task_ids:
- named-entity-recognition
pretty_name: NCHLT
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---
# Dataset Card for NCHLT
## 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:** [link](https://repo.sadilar.org/handle/20.500.12185/7/discover?filtertype_0=database&filtertype_1=title&filter_relational_operator_1=contains&filter_relational_operator_0=equals&filter_1=&filter_0=Monolingual+Text+Corpora%3A+Annotated&filtertype=project&filter_relational_operator=equals&filter=NCHLT+Text+II)
- **Repository:** []()
- **Paper:** []()
- **Leaderboard:** []()
- **Point of Contact:** []()
### Dataset Summary
The development of linguistic resources for use in natural language processingis of utmost importance for the continued growth of research anddevelopment in the field, especially for resource-scarce languages. In this paper we describe the process and challenges of simultaneouslydevelopingmultiple linguistic resources for ten of the official languages of South Africa. The project focussed on establishing a set of foundational resources that can foster further development of both resources and technologies for the NLP industry in South Africa. The development efforts during the project included creating monolingual unannotated corpora, of which a subset of the corpora for each language was annotated on token, orthographic, morphological and morphosyntactic layers. The annotated subsetsincludes both development and test setsand were used in the creation of five core-technologies, viz. atokeniser, sentenciser,lemmatiser, part of speech tagger and morphological decomposer for each language. We report on the quality of these tools for each language and provide some more context of the importance of the resources within the South African context.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
[More Information Needed]
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
Martin.Puttkammer@nwu.ac.za
### Licensing Information
[More Information Needed]
### Citation Information
```
@inproceedings{eiselen2014developing,
title={Developing Text Resources for Ten South African Languages.},
author={Eiselen, Roald and Puttkammer, Martin J},
booktitle={LREC},
pages={3698--3703},
year={2014}
}
```
### Contributions
Thanks to [@Narsil](https://github.com/Narsil) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
lmsys/lmsys-chat-1m | lmsys | 2023-10-04T17:40:32Z | 1,601 | 286 | null | [
"task_categories:conversational",
"size_categories:1M<n<10M",
"arxiv:2309.11998",
"region:us"
] | 2023-10-04T17:40:32Z | 2023-09-20T06:33:44.000Z | 2023-09-20T06:33:44 | ---
size_categories:
- 1M<n<10M
task_categories:
- conversational
extra_gated_prompt: You agree to the [LMSYS-Chat-1M Dataset License Agreement](https://huggingface.co/datasets/lmsys/lmsys-chat-1m#lmsys-chat-1m-dataset-license-agreement).
extra_gated_fields:
Name: text
Email: text
Affiliation: text
Country: text
extra_gated_button_content: I agree to the terms and conditions of the LMSYS-Chat-1M
Dataset License Agreement.
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: conversation_id
dtype: string
- name: model
dtype: string
- name: conversation
list:
- name: content
dtype: string
- name: role
dtype: string
- name: turn
dtype: int64
- name: language
dtype: string
- name: openai_moderation
list:
- name: categories
struct:
- name: harassment
dtype: bool
- name: harassment/threatening
dtype: bool
- name: hate
dtype: bool
- name: hate/threatening
dtype: bool
- name: self-harm
dtype: bool
- name: self-harm/instructions
dtype: bool
- name: self-harm/intent
dtype: bool
- name: sexual
dtype: bool
- name: sexual/minors
dtype: bool
- name: violence
dtype: bool
- name: violence/graphic
dtype: bool
- name: category_scores
struct:
- name: harassment
dtype: float64
- name: harassment/threatening
dtype: float64
- name: hate
dtype: float64
- name: hate/threatening
dtype: float64
- name: self-harm
dtype: float64
- name: self-harm/instructions
dtype: float64
- name: self-harm/intent
dtype: float64
- name: sexual
dtype: float64
- name: sexual/minors
dtype: float64
- name: violence
dtype: float64
- name: violence/graphic
dtype: float64
- name: flagged
dtype: bool
- name: redacted
dtype: bool
splits:
- name: train
num_bytes: 2626438904
num_examples: 1000000
download_size: 1488850250
dataset_size: 2626438904
---
## LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset
This dataset contains one million real-world conversations with 25 state-of-the-art LLMs.
It is collected from 210K unique IP addresses in the wild on the [Vicuna demo and Chatbot Arena website](https://chat.lmsys.org/) from April to August 2023.
Each sample includes a conversation ID, model name, conversation text in OpenAI API JSON format, detected language tag, and OpenAI moderation API tag.
User consent is obtained through the "Terms of use" section on the data collection website.
To ensure the safe release of data, we have made our best efforts to remove all conversations that contain personally identifiable information (PII).
In addition, we have included the OpenAI moderation API output for each message.
However, we have chosen to keep unsafe conversations so that researchers can study the safety-related questions associated with LLM usage in real-world scenarios as well as the OpenAI moderation process.
For more details, please refer to the paper: https://arxiv.org/abs/2309.11998
**Basic Statistics**
| Key | Value |
| --- | --- |
| # Conversations | 1,000,000 |
| # Models | 25 |
| # Users | 210,479 |
| # Languages | 154 |
| Avg. # Turns per Sample | 2.0 |
| Avg. # Tokens per Prompt | 69.5 |
| Avg. # Tokens per Response | 214.5 |
**PII Redaction**
We partnered with the [OpaquePrompts](https://opaqueprompts.opaque.co/) team to redact person names in this dataset to protect user privacy.
Names like "Mary" and "James" in a conversation will appear as "NAME_1" and "NAME_2". For example:
```json
Raw: [ { "content": "Write me a bio. My Name is Mary I am a student who is currently a beginner free lancer. I worked with James in the past ..." }]
Redacted: [ { "content": "Write me a bio. My Name is NAME_1 I am a student who is currently a beginner free lancer. I worked with NAME_2 in the past ..." }]
```
Each conversation includes a "redacted" field to indicate if it has been redacted.
This process may impact data quality and occasionally lead to incorrect redactions.
We are working on improving the redaction quality and will release improved versions in the future.
If you want to access the raw conversation data, please fill out [the form](https://docs.google.com/forms/d/1PZw67e19l0W3oCiQOjzSyZvXfOemhg6LCY0XzVmOUx0/edit) with details about your intended use cases.
## Uniqueness and Potential Usage
This dataset features large-scale real-world conversations with LLMs.
We believe it will help the AI research community answer important questions around topics like:
- Characteristics and distributions of real-world user prompts
- AI safety and content moderation
- Training instruction-following models
- Improving and evaluating LLM evaluation methods
- Model selection and request dispatching algorithms
For more details, please refer to the paper: https://arxiv.org/abs/2309.11998
## LMSYS-Chat-1M Dataset License Agreement
This Agreement contains the terms and conditions that govern your access and use of the LMSYS-Chat-1M Dataset (as defined above). You may not use the LMSYS-Chat-1M Dataset if you do not accept this Agreement. By clicking to accept, accessing the LMSYS-Chat-1M Dataset, or both, you hereby agree to the terms of the Agreement. If you are agreeing to be bound by the Agreement on behalf of your employer or another entity, you represent and warrant that you have full legal authority to bind your employer or such entity to this Agreement. If you do not have the requisite authority, you may not accept the Agreement or access the LMSYS-Chat-1M Dataset on behalf of your employer or another entity.
- Safety and Moderation: **This dataset contains unsafe conversations that may be perceived as offensive or unsettling.** User should apply appropriate filters and safety measures before utilizing this dataset for training dialogue agents.
- Non-Endorsement: The views and opinions depicted in this dataset **do not reflect** the perspectives of the researchers or affiliated institutions engaged in the data collection process.
- Legal Compliance: You are mandated to use it in adherence with all pertinent laws and regulations.
- Model Specific Terms: When leveraging direct outputs of a specific model, users must adhere to its corresponding terms of use.
- Non-Identification: You **must not** attempt to identify the identities of individuals or infer any sensitive personal data encompassed in this dataset.
- Prohibited Transfers: You should not distribute, copy, disclose, assign, sublicense, embed, host, or otherwise transfer the dataset to any third party.
- Right to Request Deletion: At any time, we may require you to delete all copies of the conversation dataset (in whole or in part) in your possession and control. You will promptly comply with any and all such requests. Upon our request, you shall provide us with written confirmation of your compliance with such requirement.
- Termination: We may, at any time, for any reason or for no reason, terminate this Agreement, effective immediately upon notice to you. Upon termination, the license granted to you hereunder will immediately terminate, and you will immediately stop using the LMSYS-Chat-1M Dataset and destroy all copies of the LMSYS-Chat-1M Dataset and related materials in your possession or control.
- Limitation of Liability: IN NO EVENT WILL WE BE LIABLE FOR ANY CONSEQUENTIAL, INCIDENTAL, EXEMPLARY, PUNITIVE, SPECIAL, OR INDIRECT DAMAGES (INCLUDING DAMAGES FOR LOSS OF PROFITS, BUSINESS INTERRUPTION, OR LOSS OF INFORMATION) ARISING OUT OF OR RELATING TO THIS AGREEMENT OR ITS SUBJECT MATTER, EVEN IF WE HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.
Subject to your compliance with the terms and conditions of this Agreement, we grant to you, a limited, non-exclusive, non-transferable, non-sublicensable license to use the LMSYS-Chat-1M Dataset, including the conversation data and annotations, to research, develop, and improve software, algorithms, machine learning models, techniques, and technologies for both research and commercial purposes.
## Citation
```
@misc{zheng2023lmsyschat1m,
title={LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset},
author={Lianmin Zheng and Wei-Lin Chiang and Ying Sheng and Tianle Li and Siyuan Zhuang and Zhanghao Wu and Yonghao Zhuang and Zhuohan Li and Zi Lin and Eric. P Xing and Joseph E. Gonzalez and Ion Stoica and Hao Zhang},
year={2023},
eprint={2309.11998},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | [
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SetFit/amazon_counterfactual_en | SetFit | 2022-02-11T13:03:45Z | 1,596 | 0 | null | [
"arxiv:2104.06893",
"region:us"
] | 2022-02-11T13:03:45Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | # Amazon Counterfactual Statements
This dataset is the *en-ext* split from [SetFit/amazon_counterfactual](https://huggingface.co/datasets/SetFit/amazon_counterfactual). As the original test set is rather small (1333 examples), a different split was created with 50-50 for training & testing.
The dataset is described in [amazon-multilingual-counterfactual-dataset](https://github.com/amazon-research/amazon-multilingual-counterfactual-dataset) / [Paper](https://arxiv.org/pdf/2104.06893.pdf)
It contains statements from Amazon reviews about events that did not or cannot take place. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
BeIR/nfcorpus | BeIR | 2022-10-23T06:01:44Z | 1,595 | 0 | beir | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | 2022-10-23T06:01:44Z | 2022-06-05T16:27:38.000Z | 2022-06-05T16:27:38 | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
- zero-shot-retrieval
- information-retrieval
- zero-shot-information-retrieval
task_ids:
- passage-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
- tweet-retrieval
- citation-prediction-retrieval
- duplication-question-retrieval
- argument-retrieval
- news-retrieval
- biomedical-information-retrieval
- question-answering-retrieval
---
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
sms_spam | null | 2023-01-25T14:44:29Z | 1,589 | 15 | sms-spam-collection-data-set | [
"task_categories:text-classification",
"task_ids:intent-classification",
"annotations_creators:crowdsourced",
"annotations_creators:found",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|other-nus-sms-corpus",
"language:en",
"license:unknown",
"region:us"
] | 2023-01-25T14:44:29Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
- found
language_creators:
- crowdsourced
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-nus-sms-corpus
task_categories:
- text-classification
task_ids:
- intent-classification
paperswithcode_id: sms-spam-collection-data-set
pretty_name: SMS Spam Collection Data Set
dataset_info:
features:
- name: sms
dtype: string
- name: label
dtype:
class_label:
names:
'0': ham
'1': spam
config_name: plain_text
splits:
- name: train
num_bytes: 521756
num_examples: 5574
download_size: 203415
dataset_size: 521756
train-eval-index:
- config: plain_text
task: text-classification
task_id: binary_classification
splits:
train_split: train
col_mapping:
sms: text
label: target
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 macro
args:
average: macro
- type: f1
name: F1 micro
args:
average: micro
- type: f1
name: F1 weighted
args:
average: weighted
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
---
# 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:** http://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection
- **Repository:**
- **Paper:** Almeida, T.A., Gomez Hidalgo, J.M., Yamakami, A. Contributions to the study of SMS Spam Filtering: New Collection and Results. Proceedings of the 2011 ACM Symposium on Document Engineering (ACM DOCENG'11), Mountain View, CA, USA, 2011.
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The SMS Spam Collection v.1 is a public set of SMS labeled messages that have been collected for mobile phone spam research.
It has one collection composed by 5,574 English, real and non-enconded messages, tagged according being legitimate (ham) or spam.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
- sms: the sms message
- label: indicating if the sms message is ham or spam, ham means it is not spam
### 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{Almeida2011SpamFiltering,
title={Contributions to the Study of SMS Spam Filtering: New Collection and Results},
author={Tiago A. Almeida and Jose Maria Gomez Hidalgo and Akebo Yamakami},
year={2011},
booktitle = "Proceedings of the 2011 ACM Symposium on Document Engineering (DOCENG'11)",
}
### Contributions
Thanks to [@czabo](https://github.com/czabo) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
wmt17 | null | 2023-04-05T13:43:57Z | 1,589 | 1 | null | [
"task_categories:translation",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:translation",
"size_categories:10M<n<100M",
"source_datasets:extended|europarl_bilingual",
"source_datasets:extended|news_commentary",
"source_datasets:extended|setimes",
"source_datasets:extended|un_multi",
"language:cs",
"language:de",
"language:en",
"language:fi",
"language:lv",
"language:ru",
"language:tr",
"language:zh",
"license:unknown",
"region:us"
] | 2023-04-05T13:43:57Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- cs
- de
- en
- fi
- lv
- ru
- tr
- zh
license:
- unknown
multilinguality:
- translation
size_categories:
- 10M<n<100M
source_datasets:
- extended|europarl_bilingual
- extended|news_commentary
- extended|setimes
- extended|un_multi
task_categories:
- translation
task_ids: []
pretty_name: WMT17
paperswithcode_id: null
dataset_info:
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features:
- name: translation
dtype:
translation:
languages:
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splits:
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num_examples: 2001
download_size: 2314906945
dataset_size: 5530416087
---
# Dataset Card for "wmt17"
## 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.statmt.org/wmt17/translation-task.html](http://www.statmt.org/wmt17/translation-task.html)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 1.78 GB
- **Size of the generated dataset:** 302.09 MB
- **Total amount of disk used:** 2.09 GB
### Dataset Summary
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p><b>Warning:</b> There are issues with the Common Crawl corpus data (<a href="https://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz">training-parallel-commoncrawl.tgz</a>):</p>
<ul>
<li>Non-English files contain many English sentences.</li>
<li>Their "parallel" sentences in English are not aligned: they are uncorrelated with their counterpart.</li>
</ul>
<p>We have contacted the WMT organizers.</p>
</div>
Translation dataset based on the data from statmt.org.
Versions exist for different years using a combination of data
sources. The base `wmt` allows you to create a custom dataset by choosing
your own data/language pair. This can be done as follows:
```python
from datasets import inspect_dataset, load_dataset_builder
inspect_dataset("wmt17", "path/to/scripts")
builder = load_dataset_builder(
"path/to/scripts/wmt_utils.py",
language_pair=("fr", "de"),
subsets={
datasets.Split.TRAIN: ["commoncrawl_frde"],
datasets.Split.VALIDATION: ["euelections_dev2019"],
},
)
# Standard version
builder.download_and_prepare()
ds = builder.as_dataset()
# Streamable version
ds = builder.as_streaming_dataset()
```
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### cs-en
- **Size of downloaded dataset files:** 1.78 GB
- **Size of the generated dataset:** 302.09 MB
- **Total amount of disk used:** 2.09 GB
An example of 'train' looks as follows.
```
```
### Data Fields
The data fields are the same among all splits.
#### cs-en
- `translation`: a multilingual `string` variable, with possible languages including `cs`, `en`.
### Data Splits
|name | train |validation|test|
|-----|------:|---------:|---:|
|cs-en|1018291| 2999|3005|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{bojar-EtAl:2017:WMT1,
author = {Bojar, Ond
{r}ej and Chatterjee, Rajen and Federmann, Christian and Graham, Yvette and Haddow, Barry and Huang, Shujian and Huck, Matthias and Koehn, Philipp and Liu, Qun and Logacheva, Varvara and Monz, Christof and Negri, Matteo and Post, Matt and Rubino, Raphael and Specia, Lucia and Turchi, Marco},
title = {Findings of the 2017 Conference on Machine Translation (WMT17)},
booktitle = {Proceedings of the Second Conference on Machine Translation, Volume 2: Shared Task Papers},
month = {September},
year = {2017},
address = {Copenhagen, Denmark},
publisher = {Association for Computational Linguistics},
pages = {169--214},
url = {http://www.aclweb.org/anthology/W17-4717}
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset. | [
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size_categories:
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features:
- name: id
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 153672031
num_examples: 432798
download_size: 37122048
dataset_size: 153672031
- config_name: 20231101.zh-yue
features:
- name: id
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
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num_bytes: 109936351
num_examples: 134140
download_size: 64950815
dataset_size: 109936351
- config_name: 20231101.zu
features:
- name: id
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 7088246
num_examples: 11561
download_size: 3792429
dataset_size: 7088246
language_bcp47:
- be-tarask
- en-simple
---
# Dataset Card for Wikimedia Wikipedia
## 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://dumps.wikimedia.org](https://dumps.wikimedia.org)
- **Repository:**
- **Paper:**
- **Point of Contact:**
### Dataset Summary
Wikipedia dataset containing cleaned articles of all languages.
The dataset is built from the Wikipedia dumps (https://dumps.wikimedia.org/)
with one subset per language, each conatining a single train split.
Each example contains the content of one full Wikipedia article with cleaning to strip
markdown and unwanted sections (references, etc.).
All language subsets have already been processed for recent dump, and you can load them per date and language this way:
```python
from datasets import load_dataset
ds = load_dataset("wikipedia", "20231101.en")
```
### Supported Tasks and Leaderboards
The dataset is generally used for Language Modeling.
### Languages
You can find the list of languages here: https://meta.wikimedia.org/wiki/List_of_Wikipedias
## Dataset Structure
### Data Instances
An example looks as follows:
```
{'id': '1',
'url': 'https://simple.wikipedia.org/wiki/April',
'title': 'April',
'text': 'April is the fourth month...'
}
```
### Data Fields
The data fields are the same among all configurations:
- `id` (`str`): ID of the article.
- `url` (`str`): URL of the article.
- `title` (`str`): Title of the article.
- `text` (`str`): Text content of the article.
### Data Splits
All configurations contain a single `train` split.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The dataset is built from the Wikipedia dumps: https://dumps.wikimedia.org
You can find the full list of languages and dates here: https://dumps.wikimedia.org/backup-index.html
The articles have been parsed using the [`mwparserfromhell`](https://mwparserfromhell.readthedocs.io) tool.
When uploading the data files for the 20231101 dump, we noticed that the Wikimedia Dumps website does not contain this date dump
for the "bbc", "dga", nor "zgh" Wikipedias. We have reported the issue to the Wikimedia Phabricator: https://phabricator.wikimedia.org/T351761
#### 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
Copyright licensing information: https://dumps.wikimedia.org/legal.html
All original textual content is licensed under the [GNU Free Documentation License](https://www.gnu.org/licenses/fdl-1.3.html) (GFDL)
and the [Creative Commons Attribution-Share-Alike 3.0 License](https://creativecommons.org/licenses/by-sa/3.0/).
Some text may be available only under the Creative Commons license; see their [Terms of Use](https://foundation.wikimedia.org/wiki/Policy:Terms_of_Use) for details.
Text written by some authors may be released under additional licenses or into the public domain.
### Citation Information
```
@ONLINE{wikidump,
author = "Wikimedia Foundation",
title = "Wikimedia Downloads",
url = "https://dumps.wikimedia.org"
}
``` | [
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wmt20_mlqe_task1 | null | 2023-06-01T14:59:51Z | 1,585 | 1 | null | [
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] | 2023-06-01T14:59:51Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
pretty_name: WMT20 - MultiLingual Quality Estimation (MLQE) Task1
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---
# Dataset Card for WMT20 - MultiLingual Quality Estimation (MLQE) Task1
## 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:** [WMT20 Quality Estimation Shared Task](http://www.statmt.org/wmt20/quality-estimation-task.html)
- **Repository:** [Github repository](https://github.com/facebookresearch/mlqe/)
- **Paper:** *Not available*
### Dataset Summary
From the homepage:
*This shared task (part of WMT20) will build on its previous editions to further examine automatic methods for estimating the quality of neural machine translation output at run-time, without relying on reference translations. As in previous years, we cover estimation at various levels. Important elements introduced this year include: a new task where sentences are annotated with Direct Assessment (DA) scores instead of labels based on post-editing; a new multilingual sentence-level dataset mainly from Wikipedia articles, where the source articles can be retrieved for document-wide context; the availability of NMT models to explore system-internal information for the task.*
*Task 1 uses Wikipedia data for 6 language pairs that includes high-resource English--German (En-De) and English--Chinese (En-Zh), medium-resource Romanian--English (Ro-En) and Estonian--English (Et-En), and low-resource Sinhalese--English (Si-En) and Nepalese--English (Ne-En), as well as a dataset with a combination of Wikipedia articles and Reddit articles for Russian-English (En-Ru). The datasets were collected by translating sentences sampled from source language articles using state-of-the-art NMT models built using the fairseq toolkit and annotated with Direct Assessment (DA) scores by professional translators. Each sentence was annotated following the FLORES setup, which presents a form of DA, where at least three professional translators rate each sentence from 0-100 according to the perceived translation quality. DA scores are standardised using the z-score by rater. Participating systems are required to score sentences according to z-standardised DA scores.*
### Supported Tasks and Leaderboards
From the homepage:
*Sentence-level submissions will be evaluated in terms of the Pearson's correlation metric for the DA prediction agains human DA (z-standardised mean DA score, i.e. z_mean). These are the [official evaluation scripts](https://github.com/sheffieldnlp/qe-eval-scripts). The evaluation will focus on multilingual systems, i.e. systems that are able to provide predictions for all languages in the Wikipedia domain. Therefore, average Pearson correlation across all these languages will be used to rank QE systems. We will also evaluate QE systems on a per-language basis for those interested in particular languages.*
### Languages
Eight languages are represented in this dataset:
- English (`en`)
- German (`de`)
- Romanian (`ro`)
- Estonian (`et`)
- Nepalese (`ne`)
- Sinhala (`si`)
- Russian (`ru`)
## Dataset Structure
### Data Instances
An example looks like this:
```
{
'segid': 123,
'translation': {
'en': 'José Ortega y Gasset visited Husserl at Freiburg in 1934.',
'de': '1934 besuchte José Ortega y Gasset Husserl in Freiburg.',
},
'scores': [100.0, 100.0, 100.0],
'mean': 100.0,
'z_scores': [0.9553316831588745, 1.552362322807312, 0.850531816482544],
'z_mean': 1.1194086074829102,
'model_score': -0.10244649648666382,
'doc_id': 'Edmund Husserl',
'nmt_output': '1934 besuchte José Ort@@ ega y G@@ asset Hus@@ ser@@ l in Freiburg .',
'word_probas': [-0.4458000063896179, -0.2745000123977661, -0.07199999690055847, -0.002300000051036477, -0.005900000222027302, -0.14579999446868896, -0.07500000298023224, -0.012400000356137753, -0.026900000870227814, -0.036400001496076584, -0.05299999937415123, -0.14990000426769257, -0.012400000356137753, -0.1145000010728836, -0.10999999940395355],
}
```
### Data Fields
- `segid`: segment id.
- `original`: original sentence.
- `translation`: Dictionary with pairs (source,target).
- src_lg: sequence of text in source language.
- tgt_lg: sequence of text in target language.
- `scores`: list of DA scores by all annotators - the number of annotators may vary. [] if N/A (only for `ru-en/test`).
- `mean`: average of DA scores. -10_000 if N/A (only for `ru-en/test`).
- `z_scores`: list of z-standardized DA scores. [] if N/A (only for `ru-en/test`).
- `z_mean`: average of z-standardized DA scores. -10_000 if N/A (only for `ru-en/test`).
- `model_score`: NMT model score for sentence. -10_000 if N/A (only for `ru-en/test`).
- `doc_id`: the name of the article where each original segment came from.
- `nmt_output`: the actual output of the NMT model before any post-processing, corresponding to the log-probas in `word_probas` (the token is not printed, so the number of log-probabilities equals the number of tokens plus 1).
- `word_probas`: log-probabilities from the NMT model for each decoded token including the token.
### Data Splits
There are 7 configurations in this dataset (one for each available language pair). Each configuration is composed of 7K examples for training, 1K for validation and 1K for test.
## Dataset Creation
### Curation Rationale
The original text is extracted from Wikipedia, Russian Reddit and Russian WikiQuotes. Translations are obtained using state-of-the-art NMT models built using the [fairseq toolkit](https://github.com/pytorch/fairseq) and annotated with Direct Assesment scores by professional translators.
### 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
Unknown
### Citation Information
```
Not available.
```
### Contributions
Thanks to [@VictorSanh](https://github.com/VictorSanh) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
bigbio/pubmed_qa | bigbio | 2022-12-22T15:46:24Z | 1,583 | 5 | null | [
"multilinguality:monolingual",
"language:en",
"license:mit",
"region:us"
] | 2022-12-22T15:46:24Z | 2022-11-13T22:11:45.000Z | 2022-11-13T22:11:45 |
---
language:
- en
bigbio_language:
- English
license: mit
multilinguality: monolingual
bigbio_license_shortname: MIT
pretty_name: PubMedQA
homepage: https://github.com/pubmedqa/pubmedqa
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- QUESTION_ANSWERING
---
# Dataset Card for PubMedQA
## Dataset Description
- **Homepage:** https://github.com/pubmedqa/pubmedqa
- **Pubmed:** True
- **Public:** True
- **Tasks:** QA
PubMedQA is a novel biomedical question answering (QA) dataset collected from PubMed abstracts.
The task of PubMedQA is to answer research biomedical questions with yes/no/maybe using the corresponding abstracts.
PubMedQA has 1k expert-annotated (PQA-L), 61.2k unlabeled (PQA-U) and 211.3k artificially generated QA instances (PQA-A).
Each PubMedQA instance is composed of:
(1) a question which is either an existing research article title or derived from one,
(2) a context which is the corresponding PubMed abstract without its conclusion,
(3) a long answer, which is the conclusion of the abstract and, presumably, answers the research question, and
(4) a yes/no/maybe answer which summarizes the conclusion.
PubMedQA is the first QA dataset where reasoning over biomedical research texts,
especially their quantitative contents, is required to answer the questions.
PubMedQA datasets comprise of 3 different subsets:
(1) PubMedQA Labeled (PQA-L): A labeled PubMedQA subset comprises of 1k manually annotated yes/no/maybe QA data collected from PubMed articles.
(2) PubMedQA Artificial (PQA-A): An artificially labelled PubMedQA subset comprises of 211.3k PubMed articles with automatically generated questions from the statement titles and yes/no answer labels generated using a simple heuristic.
(3) PubMedQA Unlabeled (PQA-U): An unlabeled PubMedQA subset comprises of 61.2k context-question pairs data collected from PubMed articles.
## Citation Information
```
@inproceedings{jin2019pubmedqa,
title={PubMedQA: A Dataset for Biomedical Research Question Answering},
author={Jin, Qiao and Dhingra, Bhuwan and Liu, Zhengping and Cohen, William and Lu, Xinghua},
booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
pages={2567--2577},
year={2019}
}
```
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wmt18 | null | 2023-04-05T13:44:00Z | 1,581 | 3 | wmt-2018 | [
"task_categories:translation",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:translation",
"size_categories:10M<n<100M",
"source_datasets:extended|europarl_bilingual",
"source_datasets:extended|news_commentary",
"source_datasets:extended|opus_paracrawl",
"source_datasets:extended|setimes",
"source_datasets:extended|un_multi",
"language:cs",
"language:de",
"language:en",
"language:et",
"language:fi",
"language:kk",
"language:ru",
"language:tr",
"language:zh",
"license:unknown",
"region:us"
] | 2023-04-05T13:44:00Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- cs
- de
- en
- et
- fi
- kk
- ru
- tr
- zh
license:
- unknown
multilinguality:
- translation
size_categories:
- 10M<n<100M
source_datasets:
- extended|europarl_bilingual
- extended|news_commentary
- extended|opus_paracrawl
- extended|setimes
- extended|un_multi
task_categories:
- translation
task_ids: []
pretty_name: WMT18
paperswithcode_id: wmt-2018
dataset_info:
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features:
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dtype:
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download_size: 0
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download_size: 2259428767
dataset_size: 5537817670
---
# Dataset Card for "wmt18"
## 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.statmt.org/wmt18/translation-task.html](http://www.statmt.org/wmt18/translation-task.html)
- **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:** 2.03 GB
- **Size of the generated dataset:** 1.46 GB
- **Total amount of disk used:** 3.49 GB
### Dataset Summary
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p><b>Warning:</b> There are issues with the Common Crawl corpus data (<a href="https://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz">training-parallel-commoncrawl.tgz</a>):</p>
<ul>
<li>Non-English files contain many English sentences.</li>
<li>Their "parallel" sentences in English are not aligned: they are uncorrelated with their counterpart.</li>
</ul>
<p>We have contacted the WMT organizers.</p>
</div>
Translation dataset based on the data from statmt.org.
Versions exist for different years using a combination of data
sources. The base `wmt` allows you to create a custom dataset by choosing
your own data/language pair. This can be done as follows:
```python
from datasets import inspect_dataset, load_dataset_builder
inspect_dataset("wmt18", "path/to/scripts")
builder = load_dataset_builder(
"path/to/scripts/wmt_utils.py",
language_pair=("fr", "de"),
subsets={
datasets.Split.TRAIN: ["commoncrawl_frde"],
datasets.Split.VALIDATION: ["euelections_dev2019"],
},
)
# Standard version
builder.download_and_prepare()
ds = builder.as_dataset()
# Streamable version
ds = builder.as_streaming_dataset()
```
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### cs-en
- **Size of downloaded dataset files:** 2.03 GB
- **Size of the generated dataset:** 1.46 GB
- **Total amount of disk used:** 3.49 GB
An example of 'validation' looks as follows.
```
```
### Data Fields
The data fields are the same among all splits.
#### cs-en
- `translation`: a multilingual `string` variable, with possible languages including `cs`, `en`.
### Data Splits
|name | train |validation|test|
|-----|-------:|---------:|---:|
|cs-en|11046024| 3005|2983|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{bojar-EtAl:2018:WMT1,
author = {Bojar, Ond
{r}ej and Federmann, Christian and Fishel, Mark
and Graham, Yvette and Haddow, Barry and Huck, Matthias and
Koehn, Philipp and Monz, Christof},
title = {Findings of the 2018 Conference on Machine Translation (WMT18)},
booktitle = {Proceedings of the Third Conference on Machine Translation,
Volume 2: Shared Task Papers},
month = {October},
year = {2018},
address = {Belgium, Brussels},
publisher = {Association for Computational Linguistics},
pages = {272--307},
url = {http://www.aclweb.org/anthology/W18-6401}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset. | [
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mteb/medrxiv-clustering-s2s | mteb | 2022-09-27T19:10:50Z | 1,566 | 1 | null | [
"language:en",
"region:us"
] | 2022-09-27T19:10:50Z | 2022-05-11T06:56:34.000Z | 2022-05-11T06:56:34 | ---
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
corbt/all-recipes | corbt | 2023-08-24T06:27:02Z | 1,563 | 24 | null | [
"region:us"
] | 2023-08-24T06:27:02Z | 2023-08-24T06:26:30.000Z | 2023-08-24T06:26:30 | ---
dataset_info:
features:
- name: input
dtype: string
splits:
- name: train
num_bytes: 1569011376
num_examples: 2147248
download_size: 807147913
dataset_size: 1569011376
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "all-recipes"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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BeIR/fiqa | BeIR | 2022-10-23T06:00:28Z | 1,557 | 3 | beir | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | 2022-10-23T06:00:28Z | 2022-06-05T14:48:54.000Z | 2022-06-05T14:48:54 | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
- zero-shot-retrieval
- information-retrieval
- zero-shot-information-retrieval
task_ids:
- passage-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
- tweet-retrieval
- citation-prediction-retrieval
- duplication-question-retrieval
- argument-retrieval
- news-retrieval
- biomedical-information-retrieval
- question-answering-retrieval
---
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
haydn-jones/Guacamol | haydn-jones | 2023-11-18T20:39:16Z | 1,557 | 0 | null | [
"arxiv:1811.09621",
"region:us"
] | 2023-11-18T20:39:16Z | 2023-11-18T17:19:38.000Z | 2023-11-18T17:19:38 | ---
dataset_info:
features:
- name: SMILE
dtype: string
- name: SELFIE
dtype: string
splits:
- name: train
num_bytes: 351931924.06659317
num_examples: 1273077
- name: val
num_bytes: 21949894.491152223
num_examples: 79564
- name: test
num_bytes: 65951655.37470361
num_examples: 238694
download_size: 148629975
dataset_size: 439833473.932449
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
---
# Dataset Card for Guacamol
Dataset from the [Guacamol](https://github.com/BenevolentAI/guacamol) benchmark ([paper](https://arxiv.org/abs/1811.09621)).
Dataset contains two columns, SMILE and SELFIE. Splits are identical to original splits, however, any SMILE that could not be converted to a SELFIE was dropped. Likewise, any SELFIE in the val/test splits that contained a token not found in the train split was dropped.
Can be used with [this tokenizer](https://huggingface.co/haydn-jones/GuacamolSELFIETokenizer). | [
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ccdv/govreport-summarization | ccdv | 2022-10-24T20:32:47Z | 1,553 | 15 | null | [
"task_categories:summarization",
"task_categories:text-generation",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:en",
"conditional-text-generation",
"arxiv:2104.02112",
"region:us"
] | 2022-10-24T20:32:47Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
task_categories:
- summarization
- text-generation
task_ids: []
tags:
- conditional-text-generation
---
# GovReport dataset for summarization
Dataset for summarization of long documents.\
Adapted from this [repo](https://github.com/luyang-huang96/LongDocSum) and this [paper](https://arxiv.org/pdf/2104.02112.pdf)\
This dataset is compatible with the [`run_summarization.py`](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization) script from Transformers if you add this line to the `summarization_name_mapping` variable:
```python
"ccdv/govreport-summarization": ("report", "summary")
```
### Data Fields
- `id`: paper id
- `report`: a string containing the body of the report
- `summary`: a string containing the summary of the report
### Data Splits
This dataset has 3 splits: _train_, _validation_, and _test_. \
Token counts with a RoBERTa tokenizer.
| Dataset Split | Number of Instances | Avg. tokens |
| ------------- | --------------------|:----------------------|
| Train | 17,517 | < 9,000 / < 500 |
| Validation | 973 | < 9,000 / < 500 |
| Test | 973 | < 9,000 / < 500 |
# Cite original article
```
@misc{huang2021efficient,
title={Efficient Attentions for Long Document Summarization},
author={Luyang Huang and Shuyang Cao and Nikolaus Parulian and Heng Ji and Lu Wang},
year={2021},
eprint={2104.02112},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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cfq | null | 2023-04-05T09:42:18Z | 1,551 | 3 | cfq | [
"task_categories:question-answering",
"task_categories:other",
"task_ids:open-domain-qa",
"task_ids:closed-domain-qa",
"annotations_creators:no-annotation",
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"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"compositionality",
"arxiv:1912.09713",
"region:us"
] | 2023-04-05T09:42:18Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
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language_creators:
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language:
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license:
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multilinguality:
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pretty_name: Compositional Freebase Questions
size_categories:
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source_datasets:
- original
task_categories:
- question-answering
- other
task_ids:
- open-domain-qa
- closed-domain-qa
paperswithcode_id: cfq
tags:
- compositionality
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---
# Dataset Card for "cfq"
## 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/google-research/tree/master/cfq](https://github.com/google-research/google-research/tree/master/cfq)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** https://arxiv.org/abs/1912.09713
- **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:** 2.14 GB
- **Size of the generated dataset:** 362.07 MB
- **Total amount of disk used:** 2.50 GB
### Dataset Summary
The Compositional Freebase Questions (CFQ) is a dataset that is specifically designed to measure compositional
generalization. CFQ is a simple yet realistic, large dataset of natural language questions and answers that also
provides for each question a corresponding SPARQL query against the Freebase knowledge base. This means that CFQ can
also be used for semantic parsing.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
English (`en`).
## Dataset Structure
### Data Instances
#### mcd1
- **Size of downloaded dataset files:** 267.60 MB
- **Size of the generated dataset:** 42.90 MB
- **Total amount of disk used:** 310.49 MB
An example of 'train' looks as follows.
```
{
'query': 'SELECT count(*) WHERE {\n?x0 a ns:people.person .\n?x0 ns:influence.influence_node.influenced M1 .\n?x0 ns:influence.influence_node.influenced M2 .\n?x0 ns:people.person.spouse_s/ns:people.marriage.spouse|ns:fictional_universe.fictional_character.married_to/ns:fictional_universe.marriage_of_fictional_characters.spouses ?x1 .\n?x1 a ns:film.cinematographer .\nFILTER ( ?x0 != ?x1 )\n}',
'question': 'Did a person marry a cinematographer , influence M1 , and influence M2'
}
```
#### mcd2
- **Size of downloaded dataset files:** 267.60 MB
- **Size of the generated dataset:** 44.77 MB
- **Total amount of disk used:** 312.38 MB
An example of 'train' looks as follows.
```
{
'query': 'SELECT count(*) WHERE {\n?x0 ns:people.person.parents|ns:fictional_universe.fictional_character.parents|ns:organization.organization.parent/ns:organization.organization_relationship.parent ?x1 .\n?x1 a ns:people.person .\nM1 ns:business.employer.employees/ns:business.employment_tenure.person ?x0 .\nM1 ns:business.employer.employees/ns:business.employment_tenure.person M2 .\nM1 ns:business.employer.employees/ns:business.employment_tenure.person M3 .\nM1 ns:business.employer.employees/ns:business.employment_tenure.person M4 .\nM5 ns:business.employer.employees/ns:business.employment_tenure.person ?x0 .\nM5 ns:business.employer.employees/ns:business.employment_tenure.person M2 .\nM5 ns:business.employer.employees/ns:business.employment_tenure.person M3 .\nM5 ns:business.employer.employees/ns:business.employment_tenure.person M4\n}',
'question': "Did M1 and M5 employ M2 , M3 , and M4 and employ a person 's child"
}
```
#### mcd3
- **Size of downloaded dataset files:** 267.60 MB
- **Size of the generated dataset:** 43.60 MB
- **Total amount of disk used:** 311.20 MB
An example of 'train' looks as follows.
```
{
"query": "SELECT /producer M0 . /director M0 . ",
"question": "Who produced and directed M0?"
}
```
#### query_complexity_split
- **Size of downloaded dataset files:** 267.60 MB
- **Size of the generated dataset:** 45.95 MB
- **Total amount of disk used:** 313.55 MB
An example of 'train' looks as follows.
```
{
"query": "SELECT /producer M0 . /director M0 . ",
"question": "Who produced and directed M0?"
}
```
#### query_pattern_split
- **Size of downloaded dataset files:** 267.60 MB
- **Size of the generated dataset:** 46.12 MB
- **Total amount of disk used:** 313.72 MB
An example of 'train' looks as follows.
```
{
"query": "SELECT /producer M0 . /director M0 . ",
"question": "Who produced and directed M0?"
}
```
### Data Fields
The data fields are the same among all splits and configurations:
- `question`: a `string` feature.
- `query`: a `string` feature.
### Data Splits
| name | train | test |
|---------------------------|-------:|------:|
| mcd1 | 95743 | 11968 |
| mcd2 | 95743 | 11968 |
| mcd3 | 95743 | 11968 |
| query_complexity_split | 100654 | 9512 |
| query_pattern_split | 94600 | 12589 |
| question_complexity_split | 98999 | 10340 |
| question_pattern_split | 95654 | 11909 |
| random_split | 95744 | 11967 |
## 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{Keysers2020,
title={Measuring Compositional Generalization: A Comprehensive Method on
Realistic Data},
author={Daniel Keysers and Nathanael Sch"{a}rli and Nathan Scales and
Hylke Buisman and Daniel Furrer and Sergii Kashubin and
Nikola Momchev and Danila Sinopalnikov and Lukasz Stafiniak and
Tibor Tihon and Dmitry Tsarkov and Xiao Wang and Marc van Zee and
Olivier Bousquet},
booktitle={ICLR},
year={2020},
url={https://arxiv.org/abs/1912.09713.pdf},
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@brainshawn](https://github.com/brainshawn) for adding this dataset. | [
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rcds/swiss_judgment_prediction | rcds | 2023-06-14T11:59:24Z | 1,547 | 11 | null | [
"task_categories:text-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
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"source_datasets:original",
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"language:en",
"license:cc-by-sa-4.0",
"judgement-prediction",
"arxiv:2110.00806",
"arxiv:2209.12325",
"region:us"
] | 2023-06-14T11:59:24Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
pretty_name: Swiss-Judgment-Prediction
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tags:
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---
# Dataset Card for "SwissJudgmentPrediction"
## 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/JoelNiklaus/SwissCourtRulingCorpus
- **Repository:** https://github.com/JoelNiklaus/SwissCourtRulingCorpus
- **Paper:** https://arxiv.org/abs/2110.00806
- **Leaderboard:** N/A
- **Point of Contact:** [Joel Niklaus](mailto:joel.niklaus@inf.unibe.ch)
### Dataset Summary
**Documents**
Swiss-Judgment-Prediction is a multilingual, diachronic dataset of 85K Swiss Federal Supreme Court (FSCS) cases annotated with the respective binarized judgment outcome (approval/dismissal), posing a challenging text classification task. We also provide additional metadata, i.e., the publication year, the legal area and the canton of origin per case, to promote robustness and fairness studies on the critical area of legal NLP.
### Supported Tasks and Leaderboards
SwissJudgmentPrediction can be used for the legal judgment prediction task.
The dataset is not yet part of an established benchmark.
### Languages
Switzerland has four official languages with 3 languages (German, French and Italian) being represented in more than 1000 Swiss Federal Supreme court decisions. The decisions are written by the judges and clerks in the language of the proceedings.
## Dataset Structure
In version 2 we added machine translated data using [EasyNMT](https://github.com/UKPLab/EasyNMT) for all documents into German, French, Italian and English as an additional training set.
### Data Instances
**Multilingual use of the dataset**
When the dataset is used in a multilingual setting selecting the the 'all_languages' flag:
```python
from datasets import load_dataset
dataset = load_dataset('swiss_judgment_prediction', 'all_languages')
```
```
{
"id": 48757,
"year": 2015,
"facts": "Sachverhalt: A. X._ war bei der Krankenversicherung C._ taggeldversichert. Infolge einer Arbeitsunf\u00e4higkeit leistete ihm die C._ vom 30. Juni 2011 bis am 28. Juni 2013 Krankentaggelder, wobei die Leistungen bis am 30. September 2012 auf Grundlage einer Arbeitsunf\u00e4higkeit von 100% und danach basierend auf einer Arbeitsunf\u00e4higkeit von 55% erbracht wurden. Die Neueinsch\u00e4tzung der Arbeitsf\u00e4higkeit erfolgte anhand eines Gutachtens der D._ AG vom 27. August 2012, welches im Auftrag der C._ erstellt wurde. X._ machte daraufhin gegen\u00fcber der C._ geltend, er sei entgegen dem Gutachten auch nach dem 30. September 2012 zu 100% arbeitsunf\u00e4hig gewesen. Ferner verlangte er von der D._ AG zwecks externer \u00dcberpr\u00fcfung des Gutachtens die Herausgabe s\u00e4mtlicher diesbez\u00fcglicher Notizen, Auswertungen und Unterlagen. A._ (als Gesch\u00e4ftsf\u00fchrer der D._ AG) und B._ (als f\u00fcr das Gutachten medizinisch Verantwortliche) antworteten ihm, dass sie alle Unterlagen der C._ zugestellt h\u00e4tten und dass allf\u00e4llige Fragen zum Gutachten direkt der C._ zu stellen seien. X._ reichte am 2. Januar 2014 eine Strafanzeige gegen A._ und B._ ein. Er wirft diesen vor, ihn durch die Nichtherausgabe der Dokumente und durch Behinderung des IV-Verfahrens gen\u00f6tigt, Daten besch\u00e4digt bzw. vernichtet und ein falsches \u00e4rztliches Zeugnis ausgestellt zu haben. Zudem h\u00e4tten sie durch die Verz\u00f6gerung des IV-Verfahrens und insbesondere durch das falsche \u00e4rztliche Zeugnis sein Verm\u00f6gen arglistig gesch\u00e4digt. B. Die Staatsanwaltschaft des Kantons Bern, Region Oberland, nahm das Verfahren wegen N\u00f6tigung, Datenbesch\u00e4digung, falschem \u00e4rztlichem Zeugnis und arglistiger Verm\u00f6genssch\u00e4digung mit Verf\u00fcgung vom 10. November 2014 nicht an die Hand. Das Obergericht des Kantons Bern wies die von X._ dagegen erhobene Beschwerde am 27. April 2015 ab, soweit darauf einzutreten war. C. X._ beantragt mit Beschwerde in Strafsachen, der Beschluss vom 27. April 2015 sei aufzuheben und die Angelegenheit zur korrekten Ermittlung des Sachverhalts an die Staatsanwaltschaft zur\u00fcckzuweisen. Er stellt zudem den sinngem\u00e4ssen Antrag, das bundesgerichtliche Verfahren sei w\u00e4hrend der Dauer des konnexen Strafverfahrens gegen eine Teilgutachterin und des ebenfalls konnexen Zivil- oder Strafverfahrens gegen die C._ wegen Einsichtsverweigerung in das mutmasslich gef\u00e4lschte Originalgutachten zu sistieren. X._ ersucht um unentgeltliche Rechtspflege. ",
"labels": 0, # dismissal
"language": "de",
"region": "Espace Mittelland",
"canton": "be",
"legal area": "penal law"
}
```
**Monolingual use of the dataset**
When the dataset is used in a monolingual setting selecting the ISO language code for one of the 3 supported languages. For example:
```python
from datasets import load_dataset
dataset = load_dataset('swiss_judgment_prediction', 'de')
```
```
{
"id": 48757,
"year": 2015,
"facts": "Sachverhalt: A. X._ war bei der Krankenversicherung C._ taggeldversichert. Infolge einer Arbeitsunf\u00e4higkeit leistete ihm die C._ vom 30. Juni 2011 bis am 28. Juni 2013 Krankentaggelder, wobei die Leistungen bis am 30. September 2012 auf Grundlage einer Arbeitsunf\u00e4higkeit von 100% und danach basierend auf einer Arbeitsunf\u00e4higkeit von 55% erbracht wurden. Die Neueinsch\u00e4tzung der Arbeitsf\u00e4higkeit erfolgte anhand eines Gutachtens der D._ AG vom 27. August 2012, welches im Auftrag der C._ erstellt wurde. X._ machte daraufhin gegen\u00fcber der C._ geltend, er sei entgegen dem Gutachten auch nach dem 30. September 2012 zu 100% arbeitsunf\u00e4hig gewesen. Ferner verlangte er von der D._ AG zwecks externer \u00dcberpr\u00fcfung des Gutachtens die Herausgabe s\u00e4mtlicher diesbez\u00fcglicher Notizen, Auswertungen und Unterlagen. A._ (als Gesch\u00e4ftsf\u00fchrer der D._ AG) und B._ (als f\u00fcr das Gutachten medizinisch Verantwortliche) antworteten ihm, dass sie alle Unterlagen der C._ zugestellt h\u00e4tten und dass allf\u00e4llige Fragen zum Gutachten direkt der C._ zu stellen seien. X._ reichte am 2. Januar 2014 eine Strafanzeige gegen A._ und B._ ein. Er wirft diesen vor, ihn durch die Nichtherausgabe der Dokumente und durch Behinderung des IV-Verfahrens gen\u00f6tigt, Daten besch\u00e4digt bzw. vernichtet und ein falsches \u00e4rztliches Zeugnis ausgestellt zu haben. Zudem h\u00e4tten sie durch die Verz\u00f6gerung des IV-Verfahrens und insbesondere durch das falsche \u00e4rztliche Zeugnis sein Verm\u00f6gen arglistig gesch\u00e4digt. B. Die Staatsanwaltschaft des Kantons Bern, Region Oberland, nahm das Verfahren wegen N\u00f6tigung, Datenbesch\u00e4digung, falschem \u00e4rztlichem Zeugnis und arglistiger Verm\u00f6genssch\u00e4digung mit Verf\u00fcgung vom 10. November 2014 nicht an die Hand. Das Obergericht des Kantons Bern wies die von X._ dagegen erhobene Beschwerde am 27. April 2015 ab, soweit darauf einzutreten war. C. X._ beantragt mit Beschwerde in Strafsachen, der Beschluss vom 27. April 2015 sei aufzuheben und die Angelegenheit zur korrekten Ermittlung des Sachverhalts an die Staatsanwaltschaft zur\u00fcckzuweisen. Er stellt zudem den sinngem\u00e4ssen Antrag, das bundesgerichtliche Verfahren sei w\u00e4hrend der Dauer des konnexen Strafverfahrens gegen eine Teilgutachterin und des ebenfalls konnexen Zivil- oder Strafverfahrens gegen die C._ wegen Einsichtsverweigerung in das mutmasslich gef\u00e4lschte Originalgutachten zu sistieren. X._ ersucht um unentgeltliche Rechtspflege. ",
"labels": 0, # dismissal
"language": "de",
"region": "Espace Mittelland",
"canton": "be",
"legal area": "penal law"
}
```
### Data Fields
**Multilingual use of the dataset**
The following data fields are provided for documents (`train`, `validation`, `test`):
`id`: (**int**) a unique identifier of the for the document \
`year`: (**int**) the publication year \
`text`: (**str**) the facts of the case \
`label`: (**class label**) the judgment outcome: 0 (dismissal) or 1 (approval) \
`language`: (**str**) one of (de, fr, it) \
`region`: (**str**) the region of the lower court \
`canton`: (**str**) the canton of the lower court \
`legal area`: (**str**) the legal area of the case
**Monolingual use of the dataset**
The following data fields are provided for documents (`train`, `validation`, `test`):
`id`: (**int**) a unique identifier of the for the document \
`year`: (**int**) the publication year \
`text`: (**str**) the facts of the case \
`label`: (**class label**) the judgment outcome: 0 (dismissal) or 1 (approval) \
`language`: (**str**) one of (de, fr, it) \
`region`: (**str**) the region of the lower court \
`canton`: (**str**) the canton of the lower court \
`legal area`: (**str**) the legal area of the case
### Data Splits
| Language | Subset | Number of Documents (Training/Validation/Test) |
|------------|------------|------------------------------------------------|
| German | **de** | 35'452 / 4'705 / 9'725 |
| French | **fr** | 21'179 / 3'095 / 6'820 |
| Italian | **it** | 3'072 / 408 / 812 |
| All | **all** | 59'709 / 8'208 / 17'357 |
| MT German | **mt_de** | 24'251 / 0 / 0 |
| MT French | **mt_fr** | 38'524 / 0 / 0 |
| MT Italian | **mt_it** | 56'631 / 0 / 0 |
| MT All | **all+mt** | 238'818 / 8'208 / 17'357 |
## Dataset Creation
### Curation Rationale
The dataset was curated by Niklaus et al. (2021).
### Source Data
#### Initial Data Collection and Normalization
The original data are available at the Swiss Federal Supreme Court (https://www.bger.ch) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (https://entscheidsuche.ch) in HTML.
#### Who are the source language producers?
Switzerland has four official languages with 3 languages (German, French and Italian) being represented in more than 1000 Swiss Federal Supreme court decisions. The decisions are written by the judges and clerks in the language of the proceedings.
### Annotations
#### Annotation process
The decisions have been annotated with the binarized judgment outcome using parsers and regular expressions.
#### Who are the annotators?
Joel Niklaus and Adrian Jörg annotated the binarized judgment outcomes.
Metadata is published by the Swiss Federal Supreme Court (https://www.bger.ch).
### Personal and Sensitive Information
The dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: https://www.bger.ch/home/juridiction/anonymisierungsregeln.html.
## 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
Niklaus et al. (2021)
### Licensing Information
We release the data under CC-BY-4.0 which complies with the court licensing (https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf)
© Swiss Federal Supreme Court, 2000-2020
The copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.
Source: https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf
### Citation Information
*Joel Niklaus, Ilias Chalkidis, and Matthias Stürmer.*
*Swiss-Judgment-Prediction: A Multilingual Legal Judgment Prediction Benchmark*
*Proceedings of the 2021 Natural Legal Language Processing Workshop. Punta Cana, Dominican Republic. 2021*
```
@InProceedings{niklaus-etal-2021-swiss,
author = {Niklaus, Joel
and Chalkidis, Ilias
and Stürmer, Matthias},
title = {Swiss-Judgment-Prediction: A Multilingual Legal Judgment Prediction Benchmark},
booktitle = {Proceedings of the 2021 Natural Legal Language Processing Workshop},
year = {2021},
location = {Punta Cana, Dominican Republic},
}
```
and the new citation
```
@misc{niklaus2022empirical,
title={An Empirical Study on Cross-X Transfer for Legal Judgment Prediction},
author={Joel Niklaus and Matthias Stürmer and Ilias Chalkidis},
year={2022},
eprint={2209.12325},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@joelniklaus](https://github.com/joelniklaus) for adding this dataset. | [
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ccdv/pubmed-summarization | ccdv | 2022-10-24T20:33:04Z | 1,543 | 33 | null | [
"task_categories:summarization",
"task_categories:text-generation",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:en",
"conditional-text-generation",
"region:us"
] | 2022-10-24T20:33:04Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
task_categories:
- summarization
- text-generation
task_ids: []
tags:
- conditional-text-generation
---
# PubMed dataset for summarization
Dataset for summarization of long documents.\
Adapted from this [repo](https://github.com/armancohan/long-summarization).\
Note that original data are pre-tokenized so this dataset returns " ".join(text) and add "\n" for paragraphs. \
This dataset is compatible with the [`run_summarization.py`](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization) script from Transformers if you add this line to the `summarization_name_mapping` variable:
```python
"ccdv/pubmed-summarization": ("article", "abstract")
```
### Data Fields
- `id`: paper id
- `article`: a string containing the body of the paper
- `abstract`: a string containing the abstract of the paper
### Data Splits
This dataset has 3 splits: _train_, _validation_, and _test_. \
Token counts are white space based.
| Dataset Split | Number of Instances | Avg. tokens |
| ------------- | --------------------|:----------------------|
| Train | 119,924 | 3043 / 215 |
| Validation | 6,633 | 3111 / 216 |
| Test | 6,658 | 3092 / 219 |
# Cite original article
```
@inproceedings{cohan-etal-2018-discourse,
title = "A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents",
author = "Cohan, Arman and
Dernoncourt, Franck and
Kim, Doo Soon and
Bui, Trung and
Kim, Seokhwan and
Chang, Walter and
Goharian, Nazli",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-2097",
doi = "10.18653/v1/N18-2097",
pages = "615--621",
abstract = "Neural abstractive summarization models have led to promising results in summarizing relatively short documents. We propose the first model for abstractive summarization of single, longer-form documents (e.g., research papers). Our approach consists of a new hierarchical encoder that models the discourse structure of a document, and an attentive discourse-aware decoder to generate the summary. Empirical results on two large-scale datasets of scientific papers show that our model significantly outperforms state-of-the-art models.",
}
```
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igbo_monolingual | null | 2023-06-01T14:59:53Z | 1,526 | 1 | null | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"size_categories:n<1K",
"source_datasets:original",
"language:ig",
"license:unknown",
"arxiv:2004.00648",
"region:us"
] | 2023-06-01T14:59:53Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- ig
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
- n<1K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: null
pretty_name: Igbo Monolingual Dataset
dataset_info:
- config_name: eze_goes_to_school
features:
- name: format
dtype: string
- name: title
dtype: string
- name: chapters
sequence:
- name: title
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 128309
num_examples: 1
download_size: 8260947
dataset_size: 128309
- config_name: bbc-igbo
features:
- name: source
dtype: string
- name: title
dtype: string
- name: description
dtype: string
- name: date
dtype: string
- name: headline
dtype: string
- name: content
dtype: string
- name: tags
sequence: string
splits:
- name: train
num_bytes: 3488908
num_examples: 1297
download_size: 8260947
dataset_size: 3488908
- config_name: igbo-radio
features:
- name: source
dtype: string
- name: headline
dtype: string
- name: author
dtype: string
- name: date
dtype: string
- name: description
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 1129644
num_examples: 440
download_size: 8260947
dataset_size: 1129644
- config_name: jw-ot-igbo
features:
- name: format
dtype: string
- name: title
dtype: string
- name: chapters
sequence:
- name: title
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 3489314
num_examples: 39
download_size: 8260947
dataset_size: 3489314
- config_name: jw-nt-igbo
features:
- name: format
dtype: string
- name: title
dtype: string
- name: chapters
sequence:
- name: title
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 1228779
num_examples: 27
download_size: 8260947
dataset_size: 1228779
- config_name: jw-books
features:
- name: title
dtype: string
- name: content
dtype: string
- name: format
dtype: string
- name: date
dtype: string
splits:
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num_bytes: 9456342
num_examples: 48
download_size: 8260947
dataset_size: 9456342
- config_name: jw-teta
features:
- name: title
dtype: string
- name: content
dtype: string
- name: format
dtype: string
- name: date
dtype: string
splits:
- name: train
num_bytes: 991111
num_examples: 37
download_size: 8260947
dataset_size: 991111
- config_name: jw-ulo_nche
features:
- name: title
dtype: string
- name: content
dtype: string
- name: format
dtype: string
- name: date
dtype: string
splits:
- name: train
num_bytes: 1952360
num_examples: 55
download_size: 8260947
dataset_size: 1952360
- config_name: jw-ulo_nche_naamu
features:
- name: title
dtype: string
- name: content
dtype: string
- name: format
dtype: string
- name: date
dtype: string
splits:
- name: train
num_bytes: 7248017
num_examples: 88
download_size: 8260947
dataset_size: 7248017
config_names:
- bbc-igbo
- eze_goes_to_school
- igbo-radio
- jw-books
- jw-nt-igbo
- jw-ot-igbo
- jw-teta
- jw-ulo_nche
- jw-ulo_nche_naamu
---
# Dataset Card for Igbo Monolingual 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://github.com/IgnatiusEzeani/IGBONLP/tree/master/ig_monoling
- **Repository:** https://github.com/IgnatiusEzeani/IGBONLP/tree/master/ig_monoling
- **Paper:** https://arxiv.org/abs/2004.00648
### Dataset Summary
A dataset is a collection of Monolingual Igbo sentences.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Igbo (ig)
## Dataset Structure
### Data Instances
Here is an example from the bb-igbo config:
```
{'content': 'Ike Ekweremmadụ\n\nIke ịda jụụ otụ nkeji banyere oke ogbugbu na-eme n\'ala Naijiria agwụla Ekweremmadụ\n\nOsote onye-isi ndị ome-iwu Naịjirịa bụ Ike Ekweremadu ekwuola na ike agwụla ndị Sịnatị iji otu nkeji darajụụ akwanyere ndị egburu n\'ime oke ọgbaghara dị na Naịjirịa oge ọ bula.\n\nEkweremadu katọrọ mwakpọ na ogbugbu ndị Naịjirịa aka ha dị ọcha nke ndị Fulani na-achị ehi mere, kwuo na ike agwụla ndị ome- iwu ịkwanyere ha ugwu n\'otu nkeji\'\n\nCheta n\'otu ịzụka gara-aga ka emere akwam ozu mmadụ ruru iri asaa egburu na Local Gọọmenti Logo na Guma nke Benue Steeti, e be ihe kariri mmadụ iri ise ka akụkọ kwuru n\'egburu na Taraba Steeti.\n\nEkweremadu gosiri iwe gbasara ogbugbu ndị mmadụ na nzukọ ndị ome-iwu n\'ụbọchị taa, kwuo na Naịjirịa ga-ebu ụzọ nwe udo na nchekwa, tupu e kwuowa okwu iwulite obodo.\n\nỌ sịrị: "Ndị ome-iwu abụghị sọ ọsọ ndị ihe a metụtara, kama ndị Naịjirịa niile.\n\n\'Ike agwụla anyị iji otu nkeji dị jụụ maka nkwanye ugwu. Ihe anyị chọrọ bụ udo na nchekwa tupu echewa echịchị nwuli obodo."',
'date': '2018-01-19T17:07:38Z',
'description': "N'ihi oke ogbugbu ndị mmadụ na Naịjirịa gbagburu gburu, osota onyeisi ndị ome-iwu Naịjirịa bụ Ike Ekweremadu ekwuola na ihe Naịjiria chọrọ bụ nchekwa tara ọchịchị, tupu ekwuwa okwu ihe ọzọ.",
'headline': 'Ekweremadu: Ike agwụla ndị ụlọ ome iwu',
'source': 'https://www.bbc.com/igbo/42712250',
'tags': [],
'title': 'Ekweremadu: Ike agwụla ndị ụlọ ome iwu'}
```
### Data Fields
For config 'eze_goes_to_school':
- format, title, chapters
For config 'bbc-igbo' :
- source, title, description, date (Missing date values replaced with empty strings), headline, content, tags (Missing tags replaced with empty list)
For config 'igbo-radio':
- source, headline, author, date, description, content
For config 'jw-ot-igbo':
- format, title, chapters
For config 'jw-nt-igbo':
- format, title, chapters
For config 'jw-books':
- title, content, format, date (Missing date values replaced with empty strings)
For config 'jw-teta':
- title, content, format, date (Missing date values replaced with empty strings)
For config 'jw-ulo_nche':
- title, content, format, date (Missing date values replaced with empty strings)
For config 'jw-ulo_nche_naamu':
- title, content, format, date (Missing date values replaced with empty strings)
### Data Splits
| bbc-igbo | eze_goes_to_school |igbo-radio| jw-books|jw-nt-igbo| jw-ot-igbo | jw-teta |jw-ulo_nche |jw-ulo_nche_naamu
| ------------- |:-------------:|:-------------:|:-------------:|:-------------:|:-------------:|:-------------:|:-------------:|:-------------:|
| 1297 | 1 | 440 | 48 | 27 | 39 | 37 | 55 | 88
## 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
@misc{ezeani2020igboenglish,
title={Igbo-English Machine Translation: An Evaluation Benchmark},
author={Ignatius Ezeani and Paul Rayson and Ikechukwu Onyenwe and Chinedu Uchechukwu and Mark Hepple},
year={2020},
eprint={2004.00648},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
### Contributions
Thanks to [@purvimisal](https://github.com/purvimisal) for adding this dataset. | [
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aqua_rat | null | 2022-11-18T18:20:44Z | 1,524 | 9 | aqua-rat | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"arxiv:1705.04146",
"region:us"
] | 2022-11-18T18:20:44Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- expert-generated
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
paperswithcode_id: aqua-rat
pretty_name: Algebra Question Answering with Rationales
dataset_info:
- config_name: raw
features:
- name: question
dtype: string
- name: options
sequence: string
- name: rationale
dtype: string
- name: correct
dtype: string
splits:
- name: train
num_bytes: 42333259
num_examples: 97467
- name: test
num_bytes: 116779
num_examples: 254
- name: validation
num_bytes: 118636
num_examples: 254
download_size: 47833135
dataset_size: 42568674
- config_name: tokenized
features:
- name: question
dtype: string
- name: options
sequence: string
- name: rationale
dtype: string
- name: correct
dtype: string
splits:
- name: train
num_bytes: 46493843
num_examples: 97467
- name: test
num_bytes: 126283
num_examples: 254
- name: validation
num_bytes: 128873
num_examples: 254
download_size: 52003894
dataset_size: 46748999
---
# Dataset Card for AQUA-RAT
## 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/deepmind/AQuA](https://github.com/deepmind/AQuA)
- **Repository:** [https://github.com/deepmind/AQuA](https://github.com/deepmind/AQuA)
- **Paper:** [https://arxiv.org/pdf/1705.04146.pdf](https://arxiv.org/pdf/1705.04146.pdf)
### Dataset Summary
A large-scale dataset consisting of approximately 100,000 algebraic word problems.
The solution to each question is explained step-by-step using natural language.
This data is used to train a program generation model that learns to generate the explanation,
while generating the program that solves the question.
### Supported Tasks and Leaderboards
### Languages
en
## Dataset Structure
### Data Instances
```
{
"question": "A grocery sells a bag of ice for $1.25, and makes 20% profit. If it sells 500 bags of ice, how much total profit does it make?",
"options": ["A)125", "B)150", "C)225", "D)250", "E)275"],
"rationale": "Profit per bag = 1.25 * 0.20 = 0.25\nTotal profit = 500 * 0.25 = 125\nAnswer is A.",
"correct": "A"
}
```
### Data Fields
- `question` : (str) A natural language definition of the problem to solve
- `options` : (list(str)) 5 possible options (A, B, C, D and E), among which one is correct
- `rationale` : (str) A natural language description of the solution to the problem
- `correct` : (str) The correct option
### Data Splits
| | Train | Valid | Test |
| ----- | ------ | ----- | ---- |
| Examples | 97467 | 254 | 254 |
## 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
Copyright 2017 Google Inc.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
### Citation Information
```
@article{ling2017program,
title={Program induction by rationale generation: Learning to solve and explain algebraic word problems},
author={Ling, Wang and Yogatama, Dani and Dyer, Chris and Blunsom, Phil},
journal={ACL},
year={2017}
}
```
### Contributions
Thanks to [@arkhalid](https://github.com/arkhalid) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
BeIR/trec-covid | BeIR | 2022-10-23T06:00:45Z | 1,521 | 0 | beir | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | 2022-10-23T06:00:45Z | 2022-06-05T14:49:49.000Z | 2022-06-05T14:49:49 | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
- zero-shot-retrieval
- information-retrieval
- zero-shot-information-retrieval
task_ids:
- passage-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
- tweet-retrieval
- citation-prediction-retrieval
- duplication-question-retrieval
- argument-retrieval
- news-retrieval
- biomedical-information-retrieval
- question-answering-retrieval
---
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
SetFit/SentEval-CR | SetFit | 2022-06-21T09:14:00Z | 1,521 | 2 | null | [
"region:us"
] | 2022-06-21T09:14:00Z | 2022-06-21T08:52:19.000Z | 2022-06-21T08:52:19 | # SentEval Customer Reviews
This dataset is a port of the official [SentEval `CR` dataset](https://nlp.stanford.edu/~sidaw/home/projects:nbsvm) from [this paper](https://dl.acm.org/doi/10.1145/1014052.1014073). The test split was created from the by randomly sampling 20% of the original data and the train split is the remaining 80%. there are no official train/test splits of CR.
There is no validation split. This was used in the STraTA paper. | [
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kd_conv | null | 2023-03-28T14:17:47Z | 1,514 | 9 | kdconv | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:dialogue-modeling",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:zh",
"license:apache-2.0",
"region:us"
] | 2023-03-28T14:17:47Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
- machine-generated
language_creators:
- crowdsourced
language:
- zh
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- dialogue-modeling
paperswithcode_id: kdconv
pretty_name: Knowledge-driven Conversation
dataset_info:
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features:
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sequence:
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splits:
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num_examples: 1200
- name: test
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num_examples: 150
- name: validation
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num_examples: 150
download_size: 11037768
dataset_size: 4652610
- config_name: travel_knowledge_base
features:
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sequence:
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splits:
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num_examples: 1154
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dataset_size: 1517024
- config_name: music_dialogues
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dataset_size: 5980643
- config_name: film_dialogues
features:
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dataset_size: 6708886
- config_name: film_knowledge_base
features:
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dtype: string
- name: kb_triplets
sequence:
sequence: string
- name: domain
dtype: string
splits:
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num_bytes: 10500882
num_examples: 8090
download_size: 11037768
dataset_size: 10500882
- config_name: all_dialogues
features:
- name: messages
sequence:
- name: message
dtype: string
- name: attrs
sequence:
- name: attrname
dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
splits:
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num_bytes: 11115313
num_examples: 3600
- name: test
num_bytes: 2551802
num_examples: 450
- name: validation
num_bytes: 2135226
num_examples: 450
download_size: 11037768
dataset_size: 15802341
- config_name: all_knowledge_base
features:
- name: head_entity
dtype: string
- name: kb_triplets
sequence:
sequence: string
- name: domain
dtype: string
splits:
- name: train
num_bytes: 17998529
num_examples: 13685
download_size: 11037768
dataset_size: 17998529
---
# Dataset Card for KdConv
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [Github](https://github.com/thu-coai/KdConv)
- **Paper:** [{K}d{C}onv: A {C}hinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation](https://www.aclweb.org/anthology/2020.acl-main.635.pdf)
### Dataset Summary
KdConv is a Chinese multi-domain Knowledge-driven Conversionsation dataset, grounding the topics in multi-turn
conversations to knowledge graphs. KdConv contains 4.5K conversations from three domains (film, music, and travel),
and 86K utterances with an average turn number of 19.0. These conversations contain in-depth discussions on related
topics and natural transition between multiple topics, while the corpus can also used for exploration of transfer
learning and domain adaptation.
### Supported Tasks and Leaderboards
This dataset can be leveraged for dialogue modelling tasks involving multi-turn and Knowledge base setup.
### Languages
This dataset has only Chinese Language.
## Dataset Structure
### Data Instances
Each data instance is a multi-turn conversation between 2 people with annotated knowledge base data used while talking
, e.g.:
```
{
"messages": [
{
"message": "对《我喜欢上你时的内心活动》这首歌有了解吗？"
},
{
"attrs": [
{
"attrname": "Information",
"attrvalue": "《我喜欢上你时的内心活动》是由韩寒填词，陈光荣作曲，陈绮贞演唱的歌曲，作为电影《喜欢你》的主题曲于2017年4月10日首发。2018年，该曲先后提名第37届香港电影金像奖最佳原创电影歌曲奖、第7届阿比鹿音乐奖流行单曲奖。",
"name": "我喜欢上你时的内心活动"
}
],
"message": "有些了解，是电影《喜欢你》的主题曲。"
},
...
{
"attrs": [
{
"attrname": "代表作品",
"attrvalue": "旅行的意义",
"name": "陈绮贞"
},
{
"attrname": "代表作品",
"attrvalue": "时间的歌",
"name": "陈绮贞"
}
],
"message": "我还知道《旅行的意义》与《时间的歌》，都算是她的代表作。"
},
{
"message": "好，有时间我找出来听听。"
}
],
"name": "我喜欢上你时的内心活动"
}
```
The corresponding entries in Knowledge base is a dictionary with list of knowledge base triplets (head entity
, relationship, tail entity), e.g.:
```
"忽然之间": [
[
"忽然之间",
"Information",
"《忽然之间》是歌手 莫文蔚演唱的歌曲，由 周耀辉， 李卓雄填词， 林健华谱曲，收录在莫文蔚1999年发行专辑《 就是莫文蔚》里。"
],
[
"忽然之间",
"谱曲",
"林健华"
]
...
]
```
### Data Fields
Conversation data fields:
- `name`: the starting topic (entity) of the conversation
- `domain`: the domain this sample belongs to. Categorical value among `{travel, film, music}`
- `messages`: list of all the turns in the dialogue. For each turn:
- `message`: the utterance
- `attrs`: list of knowledge graph triplets referred by the utterance. For each triplet:
- `name`: the head entity
- `attrname`: the relation
- `attrvalue`: the tail entity
Knowledge Base data fields:
- `head_entity`: the head entity
- `kb_triplets`: list of corresponding triplets
- `domain`: the domain this sample belongs to. Categorical value among `{travel, film, music}`
### Data Splits
The conversation dataset is split into a `train`, `validation`, and `test` split with the following sizes:
| | train | validation | test |
|--------|------:|-----------:|-----:|
| travel | 1200 | 1200 | 1200 |
| film | 1200 | 150 | 150 |
| music | 1200 | 150 | 150 |
| all | 3600 | 450 | 450 |
The Knowledge base dataset is having only train split with following sizes:
| | train |
|--------|------:|
| travel | 1154 |
| film | 8090 |
| music | 4441 |
| all | 13685 |
## 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
Apache License 2.0
### Citation Information
```
@inproceedings{zhou-etal-2020-kdconv,
title = "{K}d{C}onv: A {C}hinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation",
author = "Zhou, Hao and
Zheng, Chujie and
Huang, Kaili and
Huang, Minlie and
Zhu, Xiaoyan",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.635",
doi = "10.18653/v1/2020.acl-main.635",
pages = "7098--7108",
}
```
### Contributions
Thanks to [@pacman100](https://github.com/pacman100) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
commaai/commavq | commaai | 2023-09-19T21:38:38Z | 1,510 | 9 | null | [
"size_categories:100K<n<1M",
"license:mit",
"region:us"
] | 2023-09-19T21:38:38Z | 2023-06-27T04:43:38.000Z | 2023-06-27T04:43:38 | ---
license: mit
size_categories:
- 100K<n<1M
---
# commaVQ
commaVQ is a dataset of 100,000 heavily compressed driving videos for Machine Learning research. A heavily compressed driving video like this is useful to experiment with GPT-like video prediction models. This repo includes an encoder/decoder and an example of a video prediction model.
Examples and trained models can be found here: https://github.com/commaai/commavq
# Overview
A VQ-VAE [1,2] was used to heavily compress each frame into 128 "tokens" of 10 bits each. Each entry of the dataset is a "segment" of compressed driving video, i.e. 1min of frames at 20 FPS. Each file is of shape 1200x8x16 and saved as int16.
Note that the compressor is extremely lossy on purpose. It makes the dataset smaller and easy to play with (train GPT with large context size, fast autoregressive generation, etc.). We might extend the dataset to a less lossy version when we see fit.
<video title="source" controls>
<source src="https://github.com/commaai/commavq/assets/29985433/91894bf7-592b-4204-b3f2-3e805984045c" type="video/mp4">
</video>
<video title="compressed" controls>
<source src="https://github.com/commaai/commavq/assets/29985433/3a799ac8-781e-461c-bf14-c15cea42b985" type="video/mp4">
</video>
<video title="imagined" controls>
<source src="https://github.com/commaai/commavq/assets/29985433/f6f7699b-b6cb-4f9c-80c9-8e00d75fbfae" type="video/mp4">
</video>
# References
[1] Van Den Oord, Aaron, and Oriol Vinyals. "Neural discrete representation learning." Advances in neural information processing systems 30 (2017).
[2] Esser, Patrick, Robin Rombach, and Bjorn Ommer. "Taming transformers for high-resolution image synthesis." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021. | [
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NeelNanda/codeparrot_clean_subset_train | NeelNanda | 2022-10-22T23:04:58Z | 1,498 | 0 | null | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
hippocrates/clinical_train | hippocrates | 2023-11-14T18:29:52Z | 1,496 | 0 | null | [
"region:us"
] | 2023-11-14T18:29:52Z | 2023-11-14T16:03:22.000Z | 2023-11-14T16:03:22 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
dataset_info:
features:
- name: id
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 52143984
num_examples: 10000
- name: valid
num_bytes: 46961044
num_examples: 8164
download_size: 42209790
dataset_size: 99105028
---
# Dataset Card for "clinical_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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svhn | null | 2023-01-25T14:45:04Z | 1,491 | 9 | svhn | [
"task_categories:image-classification",
"task_categories:object-detection",
"annotations_creators:machine-generated",
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:other",
"region:us"
] | 2023-01-25T14:45:04Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
- expert-generated
language_creators:
- machine-generated
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- image-classification
- object-detection
task_ids: []
paperswithcode_id: svhn
pretty_name: Street View House Numbers
dataset_info:
- config_name: full_numbers
features:
- name: image
dtype: image
- name: digits
sequence:
- name: bbox
sequence: int32
length: 4
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
'2': '2'
'3': '3'
'4': '4'
'5': '5'
'6': '6'
'7': '7'
'8': '8'
'9': '9'
splits:
- name: train
num_bytes: 390404309
num_examples: 33402
- name: test
num_bytes: 271503052
num_examples: 13068
- name: extra
num_bytes: 1868720340
num_examples: 202353
download_size: 2636187279
dataset_size: 2530627701
- config_name: cropped_digits
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
'2': '2'
'3': '3'
'4': '4'
'5': '5'
'6': '6'
'7': '7'
'8': '8'
'9': '9'
splits:
- name: train
num_bytes: 128364360
num_examples: 73257
- name: test
num_bytes: 44464040
num_examples: 26032
- name: extra
num_bytes: 967853504
num_examples: 531131
download_size: 1575594780
dataset_size: 1140681904
---
# Dataset Card for Street View House Numbers
## 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:** http://ufldl.stanford.edu/housenumbers
- **Repository:**
- **Paper:** [Reading Digits in Natural Images with Unsupervised Feature Learning](http://ufldl.stanford.edu/housenumbers/nips2011_housenumbers.pdf)
- **Leaderboard:** https://paperswithcode.com/sota/image-classification-on-svhn
- **Point of Contact:** streetviewhousenumbers@gmail.com
### Dataset Summary
SVHN is a real-world image dataset for developing machine learning and object recognition algorithms with minimal requirement on data preprocessing and formatting. It can be seen as similar in flavor to MNIST (e.g., the images are of small cropped digits), but incorporates an order of magnitude more labeled data (over 600,000 digit images) and comes from a significantly harder, unsolved, real world problem (recognizing digits and numbers in natural scene images). SVHN is obtained from house numbers in Google Street View images. The dataset comes in two formats:
1. Original images with character level bounding boxes.
2. MNIST-like 32-by-32 images centered around a single character (many of the images do contain some distractors at the sides).
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for digit detection.
- `image-classification`: The dataset can be used to train a model for Image Classification where the task is to predict a correct digit on the image. The leaderboard for this task is available at:
https://paperswithcode.com/sota/image-classification-on-svhn
### Languages
English
## Dataset Structure
### Data Instances
#### full_numbers
The original, variable-resolution, color house-number images with character level bounding boxes.
```
{
'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=98x48 at 0x259E3F01780>,
'digits': {
'bbox': [
[36, 7, 13, 32],
[50, 7, 12, 32]
],
'label': [6, 9]
}
}
```
#### cropped_digits
Character level ground truth in an MNIST-like format. All digits have been resized to a fixed resolution of 32-by-32 pixels. The original character bounding boxes are extended in the appropriate dimension to become square windows, so that resizing them to 32-by-32 pixels does not introduce aspect ratio distortions. Nevertheless this preprocessing introduces some distracting digits to the sides of the digit of interest.
```
{
'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=32x32 at 0x25A89494780>,
'label': 1
}
```
### Data Fields
#### full_numbers
- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `digits`: a dictionary containing digits' bounding boxes and labels
- `bbox`: a list of bounding boxes (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format) corresponding to the digits present on the image
- `label`: a list of integers between 0 and 9 representing the digit.
#### cropped_digits
- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `digit`: an integer between 0 and 9 representing the digit.
### Data Splits
#### full_numbers
The data is split into training, test and extra set. The training set contains 33402 images, test set 13068 and the extra set 202353 images.
#### cropped_digits
The data is split into training, test and extra set. The training set contains 73257 images, test set 26032 and the extra set 531131 images.
The extra set can be used as extra training data. The extra set was obtained in a similar manner to the training and test set, but with the increased detection threshold in order to generate this large amount of labeled data. The SVHN extra subset is thus somewhat biased toward less difficult detections, and is thus easier than SVHN train/SVHN test.
## Dataset Creation
### Curation Rationale
From the paper:
> As mentioned above, the venerable MNIST dataset has been a valuable goal post for researchers seeking to build better learning systems whose benchmark performance could be expected to translate into improved performance on realistic applications. However, computers have now reached essentially human levels of performance on this problem—a testament to progress in machine learning and computer vision. The Street View House Numbers (SVHN) digit database that we provide can be seen as similar in flavor to MNIST (e.g., the images are of small cropped characters), but the SVHN dataset incorporates an order of magnitude more labeled data and comes from a significantly harder, unsolved, real world problem. Here the gap between human performance and state of the art feature representations is significant. Going forward, we expect that this dataset may fulfill a similar role for modern feature learning algorithms: it provides a new and difficult benchmark where increased performance can be expected to translate into tangible gains on a realistic application.
### Source Data
#### Initial Data Collection and Normalization
From the paper:
> The SVHN dataset was obtained from a large number of Street View images using a combination
of automated algorithms and the Amazon Mechanical Turk (AMT) framework, which was
used to localize and transcribe the single digits. We downloaded a very large set of images from
urban areas in various countries.
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
From the paper:
> From these randomly selected images, the house-number patches were extracted using a dedicated sliding window house-numbers detector using a low threshold on the detector’s confidence score in order to get a varied, unbiased dataset of house-number signs. These low precision detections were screened and transcribed by AMT workers.
#### Who are the annotators?
The AMT workers.
### 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
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu and Andrew Y. Ng
### Licensing Information
Non-commerical use only.
### Citation Information
```
@article{netzer2011reading,
title={Reading digits in natural images with unsupervised feature learning},
author={Netzer, Yuval and Wang, Tao and Coates, Adam and Bissacco, Alessandro and Wu, Bo and Ng, Andrew Y},
year={2011}
}
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
evanarlian/imagenet_1k_resized_256 | evanarlian | 2023-08-01T10:26:36Z | 1,491 | 3 | imagenet | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:derived",
"language:en",
"license:other",
"region:us"
] | 2023-08-01T10:26:36Z | 2023-07-30T17:27:40.000Z | 2023-07-30T17:27:40 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- derived
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
paperswithcode_id: imagenet
pretty_name: ImageNet Resized 256
license_details: imagenet-agreement
extra_gated_prompt: https://huggingface.co/datasets/imagenet-1k
extra_gated_fields:
I have agreed to the original ImageNet dataset: checkbox
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': tench, Tinca tinca
'1': goldfish, Carassius auratus
'2': great white shark, white shark, man-eater, man-eating shark, Carcharodon
carcharias
'3': tiger shark, Galeocerdo cuvieri
'4': hammerhead, hammerhead shark
'5': electric ray, crampfish, numbfish, torpedo
'6': stingray
'7': cock
'8': hen
'9': ostrich, Struthio camelus
'10': brambling, Fringilla montifringilla
'11': goldfinch, Carduelis carduelis
'12': house finch, linnet, Carpodacus mexicanus
'13': junco, snowbird
'14': indigo bunting, indigo finch, indigo bird, Passerina cyanea
'15': robin, American robin, Turdus migratorius
'16': bulbul
'17': jay
'18': magpie
'19': chickadee
'20': water ouzel, dipper
'21': kite
'22': bald eagle, American eagle, Haliaeetus leucocephalus
'23': vulture
'24': great grey owl, great gray owl, Strix nebulosa
'25': European fire salamander, Salamandra salamandra
'26': common newt, Triturus vulgaris
'27': eft
'28': spotted salamander, Ambystoma maculatum
'29': axolotl, mud puppy, Ambystoma mexicanum
'30': bullfrog, Rana catesbeiana
'31': tree frog, tree-frog
'32': tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui
'33': loggerhead, loggerhead turtle, Caretta caretta
'34': leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea
'35': mud turtle
'36': terrapin
'37': box turtle, box tortoise
'38': banded gecko
'39': common iguana, iguana, Iguana iguana
'40': American chameleon, anole, Anolis carolinensis
'41': whiptail, whiptail lizard
'42': agama
'43': frilled lizard, Chlamydosaurus kingi
'44': alligator lizard
'45': Gila monster, Heloderma suspectum
'46': green lizard, Lacerta viridis
'47': African chameleon, Chamaeleo chamaeleon
'48': Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus
komodoensis
'49': African crocodile, Nile crocodile, Crocodylus niloticus
'50': American alligator, Alligator mississipiensis
'51': triceratops
'52': thunder snake, worm snake, Carphophis amoenus
'53': ringneck snake, ring-necked snake, ring snake
'54': hognose snake, puff adder, sand viper
'55': green snake, grass snake
'56': king snake, kingsnake
'57': garter snake, grass snake
'58': water snake
'59': vine snake
'60': night snake, Hypsiglena torquata
'61': boa constrictor, Constrictor constrictor
'62': rock python, rock snake, Python sebae
'63': Indian cobra, Naja naja
'64': green mamba
'65': sea snake
'66': horned viper, cerastes, sand viper, horned asp, Cerastes cornutus
'67': diamondback, diamondback rattlesnake, Crotalus adamanteus
'68': sidewinder, horned rattlesnake, Crotalus cerastes
'69': trilobite
'70': harvestman, daddy longlegs, Phalangium opilio
'71': scorpion
'72': black and gold garden spider, Argiope aurantia
'73': barn spider, Araneus cavaticus
'74': garden spider, Aranea diademata
'75': black widow, Latrodectus mactans
'76': tarantula
'77': wolf spider, hunting spider
'78': tick
'79': centipede
'80': black grouse
'81': ptarmigan
'82': ruffed grouse, partridge, Bonasa umbellus
'83': prairie chicken, prairie grouse, prairie fowl
'84': peacock
'85': quail
'86': partridge
'87': African grey, African gray, Psittacus erithacus
'88': macaw
'89': sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita
'90': lorikeet
'91': coucal
'92': bee eater
'93': hornbill
'94': hummingbird
'95': jacamar
'96': toucan
'97': drake
'98': red-breasted merganser, Mergus serrator
'99': goose
'100': black swan, Cygnus atratus
'101': tusker
'102': echidna, spiny anteater, anteater
'103': platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus
anatinus
'104': wallaby, brush kangaroo
'105': koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus
'106': wombat
'107': jellyfish
'108': sea anemone, anemone
'109': brain coral
'110': flatworm, platyhelminth
'111': nematode, nematode worm, roundworm
'112': conch
'113': snail
'114': slug
'115': sea slug, nudibranch
'116': chiton, coat-of-mail shell, sea cradle, polyplacophore
'117': chambered nautilus, pearly nautilus, nautilus
'118': Dungeness crab, Cancer magister
'119': rock crab, Cancer irroratus
'120': fiddler crab
'121': king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes
camtschatica
'122': American lobster, Northern lobster, Maine lobster, Homarus americanus
'123': spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish
'124': crayfish, crawfish, crawdad, crawdaddy
'125': hermit crab
'126': isopod
'127': white stork, Ciconia ciconia
'128': black stork, Ciconia nigra
'129': spoonbill
'130': flamingo
'131': little blue heron, Egretta caerulea
'132': American egret, great white heron, Egretta albus
'133': bittern
'134': crane
'135': limpkin, Aramus pictus
'136': European gallinule, Porphyrio porphyrio
'137': American coot, marsh hen, mud hen, water hen, Fulica americana
'138': bustard
'139': ruddy turnstone, Arenaria interpres
'140': red-backed sandpiper, dunlin, Erolia alpina
'141': redshank, Tringa totanus
'142': dowitcher
'143': oystercatcher, oyster catcher
'144': pelican
'145': king penguin, Aptenodytes patagonica
'146': albatross, mollymawk
'147': grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius
robustus
'148': killer whale, killer, orca, grampus, sea wolf, Orcinus orca
'149': dugong, Dugong dugon
'150': sea lion
'151': Chihuahua
'152': Japanese spaniel
'153': Maltese dog, Maltese terrier, Maltese
'154': Pekinese, Pekingese, Peke
'155': Shih-Tzu
'156': Blenheim spaniel
'157': papillon
'158': toy terrier
'159': Rhodesian ridgeback
'160': Afghan hound, Afghan
'161': basset, basset hound
'162': beagle
'163': bloodhound, sleuthhound
'164': bluetick
'165': black-and-tan coonhound
'166': Walker hound, Walker foxhound
'167': English foxhound
'168': redbone
'169': borzoi, Russian wolfhound
'170': Irish wolfhound
'171': Italian greyhound
'172': whippet
'173': Ibizan hound, Ibizan Podenco
'174': Norwegian elkhound, elkhound
'175': otterhound, otter hound
'176': Saluki, gazelle hound
'177': Scottish deerhound, deerhound
'178': Weimaraner
'179': Staffordshire bullterrier, Staffordshire bull terrier
'180': American Staffordshire terrier, Staffordshire terrier, American pit
bull terrier, pit bull terrier
'181': Bedlington terrier
'182': Border terrier
'183': Kerry blue terrier
'184': Irish terrier
'185': Norfolk terrier
'186': Norwich terrier
'187': Yorkshire terrier
'188': wire-haired fox terrier
'189': Lakeland terrier
'190': Sealyham terrier, Sealyham
'191': Airedale, Airedale terrier
'192': cairn, cairn terrier
'193': Australian terrier
'194': Dandie Dinmont, Dandie Dinmont terrier
'195': Boston bull, Boston terrier
'196': miniature schnauzer
'197': giant schnauzer
'198': standard schnauzer
'199': Scotch terrier, Scottish terrier, Scottie
'200': Tibetan terrier, chrysanthemum dog
'201': silky terrier, Sydney silky
'202': soft-coated wheaten terrier
'203': West Highland white terrier
'204': Lhasa, Lhasa apso
'205': flat-coated retriever
'206': curly-coated retriever
'207': golden retriever
'208': Labrador retriever
'209': Chesapeake Bay retriever
'210': German short-haired pointer
'211': vizsla, Hungarian pointer
'212': English setter
'213': Irish setter, red setter
'214': Gordon setter
'215': Brittany spaniel
'216': clumber, clumber spaniel
'217': English springer, English springer spaniel
'218': Welsh springer spaniel
'219': cocker spaniel, English cocker spaniel, cocker
'220': Sussex spaniel
'221': Irish water spaniel
'222': kuvasz
'223': schipperke
'224': groenendael
'225': malinois
'226': briard
'227': kelpie
'228': komondor
'229': Old English sheepdog, bobtail
'230': Shetland sheepdog, Shetland sheep dog, Shetland
'231': collie
'232': Border collie
'233': Bouvier des Flandres, Bouviers des Flandres
'234': Rottweiler
'235': German shepherd, German shepherd dog, German police dog, alsatian
'236': Doberman, Doberman pinscher
'237': miniature pinscher
'238': Greater Swiss Mountain dog
'239': Bernese mountain dog
'240': Appenzeller
'241': EntleBucher
'242': boxer
'243': bull mastiff
'244': Tibetan mastiff
'245': French bulldog
'246': Great Dane
'247': Saint Bernard, St Bernard
'248': Eskimo dog, husky
'249': malamute, malemute, Alaskan malamute
'250': Siberian husky
'251': dalmatian, coach dog, carriage dog
'252': affenpinscher, monkey pinscher, monkey dog
'253': basenji
'254': pug, pug-dog
'255': Leonberg
'256': Newfoundland, Newfoundland dog
'257': Great Pyrenees
'258': Samoyed, Samoyede
'259': Pomeranian
'260': chow, chow chow
'261': keeshond
'262': Brabancon griffon
'263': Pembroke, Pembroke Welsh corgi
'264': Cardigan, Cardigan Welsh corgi
'265': toy poodle
'266': miniature poodle
'267': standard poodle
'268': Mexican hairless
'269': timber wolf, grey wolf, gray wolf, Canis lupus
'270': white wolf, Arctic wolf, Canis lupus tundrarum
'271': red wolf, maned wolf, Canis rufus, Canis niger
'272': coyote, prairie wolf, brush wolf, Canis latrans
'273': dingo, warrigal, warragal, Canis dingo
'274': dhole, Cuon alpinus
'275': African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus
'276': hyena, hyaena
'277': red fox, Vulpes vulpes
'278': kit fox, Vulpes macrotis
'279': Arctic fox, white fox, Alopex lagopus
'280': grey fox, gray fox, Urocyon cinereoargenteus
'281': tabby, tabby cat
'282': tiger cat
'283': Persian cat
'284': Siamese cat, Siamese
'285': Egyptian cat
'286': cougar, puma, catamount, mountain lion, painter, panther, Felis concolor
'287': lynx, catamount
'288': leopard, Panthera pardus
'289': snow leopard, ounce, Panthera uncia
'290': jaguar, panther, Panthera onca, Felis onca
'291': lion, king of beasts, Panthera leo
'292': tiger, Panthera tigris
'293': cheetah, chetah, Acinonyx jubatus
'294': brown bear, bruin, Ursus arctos
'295': American black bear, black bear, Ursus americanus, Euarctos americanus
'296': ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus
'297': sloth bear, Melursus ursinus, Ursus ursinus
'298': mongoose
'299': meerkat, mierkat
'300': tiger beetle
'301': ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle
'302': ground beetle, carabid beetle
'303': long-horned beetle, longicorn, longicorn beetle
'304': leaf beetle, chrysomelid
'305': dung beetle
'306': rhinoceros beetle
'307': weevil
'308': fly
'309': bee
'310': ant, emmet, pismire
'311': grasshopper, hopper
'312': cricket
'313': walking stick, walkingstick, stick insect
'314': cockroach, roach
'315': mantis, mantid
'316': cicada, cicala
'317': leafhopper
'318': lacewing, lacewing fly
'319': dragonfly, darning needle, devil's darning needle, sewing needle,
snake feeder, snake doctor, mosquito hawk, skeeter hawk
'320': damselfly
'321': admiral
'322': ringlet, ringlet butterfly
'323': monarch, monarch butterfly, milkweed butterfly, Danaus plexippus
'324': cabbage butterfly
'325': sulphur butterfly, sulfur butterfly
'326': lycaenid, lycaenid butterfly
'327': starfish, sea star
'328': sea urchin
'329': sea cucumber, holothurian
'330': wood rabbit, cottontail, cottontail rabbit
'331': hare
'332': Angora, Angora rabbit
'333': hamster
'334': porcupine, hedgehog
'335': fox squirrel, eastern fox squirrel, Sciurus niger
'336': marmot
'337': beaver
'338': guinea pig, Cavia cobaya
'339': sorrel
'340': zebra
'341': hog, pig, grunter, squealer, Sus scrofa
'342': wild boar, boar, Sus scrofa
'343': warthog
'344': hippopotamus, hippo, river horse, Hippopotamus amphibius
'345': ox
'346': water buffalo, water ox, Asiatic buffalo, Bubalus bubalis
'347': bison
'348': ram, tup
'349': bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain
sheep, Ovis canadensis
'350': ibex, Capra ibex
'351': hartebeest
'352': impala, Aepyceros melampus
'353': gazelle
'354': Arabian camel, dromedary, Camelus dromedarius
'355': llama
'356': weasel
'357': mink
'358': polecat, fitch, foulmart, foumart, Mustela putorius
'359': black-footed ferret, ferret, Mustela nigripes
'360': otter
'361': skunk, polecat, wood pussy
'362': badger
'363': armadillo
'364': three-toed sloth, ai, Bradypus tridactylus
'365': orangutan, orang, orangutang, Pongo pygmaeus
'366': gorilla, Gorilla gorilla
'367': chimpanzee, chimp, Pan troglodytes
'368': gibbon, Hylobates lar
'369': siamang, Hylobates syndactylus, Symphalangus syndactylus
'370': guenon, guenon monkey
'371': patas, hussar monkey, Erythrocebus patas
'372': baboon
'373': macaque
'374': langur
'375': colobus, colobus monkey
'376': proboscis monkey, Nasalis larvatus
'377': marmoset
'378': capuchin, ringtail, Cebus capucinus
'379': howler monkey, howler
'380': titi, titi monkey
'381': spider monkey, Ateles geoffroyi
'382': squirrel monkey, Saimiri sciureus
'383': Madagascar cat, ring-tailed lemur, Lemur catta
'384': indri, indris, Indri indri, Indri brevicaudatus
'385': Indian elephant, Elephas maximus
'386': African elephant, Loxodonta africana
'387': lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens
'388': giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca
'389': barracouta, snoek
'390': eel
'391': coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus
kisutch
'392': rock beauty, Holocanthus tricolor
'393': anemone fish
'394': sturgeon
'395': gar, garfish, garpike, billfish, Lepisosteus osseus
'396': lionfish
'397': puffer, pufferfish, blowfish, globefish
'398': abacus
'399': abaya
'400': academic gown, academic robe, judge's robe
'401': accordion, piano accordion, squeeze box
'402': acoustic guitar
'403': aircraft carrier, carrier, flattop, attack aircraft carrier
'404': airliner
'405': airship, dirigible
'406': altar
'407': ambulance
'408': amphibian, amphibious vehicle
'409': analog clock
'410': apiary, bee house
'411': apron
'412': ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin,
dustbin, trash barrel, trash bin
'413': assault rifle, assault gun
'414': backpack, back pack, knapsack, packsack, rucksack, haversack
'415': bakery, bakeshop, bakehouse
'416': balance beam, beam
'417': balloon
'418': ballpoint, ballpoint pen, ballpen, Biro
'419': Band Aid
'420': banjo
'421': bannister, banister, balustrade, balusters, handrail
'422': barbell
'423': barber chair
'424': barbershop
'425': barn
'426': barometer
'427': barrel, cask
'428': barrow, garden cart, lawn cart, wheelbarrow
'429': baseball
'430': basketball
'431': bassinet
'432': bassoon
'433': bathing cap, swimming cap
'434': bath towel
'435': bathtub, bathing tub, bath, tub
'436': beach wagon, station wagon, wagon, estate car, beach waggon, station
waggon, waggon
'437': beacon, lighthouse, beacon light, pharos
'438': beaker
'439': bearskin, busby, shako
'440': beer bottle
'441': beer glass
'442': bell cote, bell cot
'443': bib
'444': bicycle-built-for-two, tandem bicycle, tandem
'445': bikini, two-piece
'446': binder, ring-binder
'447': binoculars, field glasses, opera glasses
'448': birdhouse
'449': boathouse
'450': bobsled, bobsleigh, bob
'451': bolo tie, bolo, bola tie, bola
'452': bonnet, poke bonnet
'453': bookcase
'454': bookshop, bookstore, bookstall
'455': bottlecap
'456': bow
'457': bow tie, bow-tie, bowtie
'458': brass, memorial tablet, plaque
'459': brassiere, bra, bandeau
'460': breakwater, groin, groyne, mole, bulwark, seawall, jetty
'461': breastplate, aegis, egis
'462': broom
'463': bucket, pail
'464': buckle
'465': bulletproof vest
'466': bullet train, bullet
'467': butcher shop, meat market
'468': cab, hack, taxi, taxicab
'469': caldron, cauldron
'470': candle, taper, wax light
'471': cannon
'472': canoe
'473': can opener, tin opener
'474': cardigan
'475': car mirror
'476': carousel, carrousel, merry-go-round, roundabout, whirligig
'477': carpenter's kit, tool kit
'478': carton
'479': car wheel
'480': cash machine, cash dispenser, automated teller machine, automatic
teller machine, automated teller, automatic teller, ATM
'481': cassette
'482': cassette player
'483': castle
'484': catamaran
'485': CD player
'486': cello, violoncello
'487': cellular telephone, cellular phone, cellphone, cell, mobile phone
'488': chain
'489': chainlink fence
'490': chain mail, ring mail, mail, chain armor, chain armour, ring armor,
ring armour
'491': chain saw, chainsaw
'492': chest
'493': chiffonier, commode
'494': chime, bell, gong
'495': china cabinet, china closet
'496': Christmas stocking
'497': church, church building
'498': cinema, movie theater, movie theatre, movie house, picture palace
'499': cleaver, meat cleaver, chopper
'500': cliff dwelling
'501': cloak
'502': clog, geta, patten, sabot
'503': cocktail shaker
'504': coffee mug
'505': coffeepot
'506': coil, spiral, volute, whorl, helix
'507': combination lock
'508': computer keyboard, keypad
'509': confectionery, confectionary, candy store
'510': container ship, containership, container vessel
'511': convertible
'512': corkscrew, bottle screw
'513': cornet, horn, trumpet, trump
'514': cowboy boot
'515': cowboy hat, ten-gallon hat
'516': cradle
'517': crane2
'518': crash helmet
'519': crate
'520': crib, cot
'521': Crock Pot
'522': croquet ball
'523': crutch
'524': cuirass
'525': dam, dike, dyke
'526': desk
'527': desktop computer
'528': dial telephone, dial phone
'529': diaper, nappy, napkin
'530': digital clock
'531': digital watch
'532': dining table, board
'533': dishrag, dishcloth
'534': dishwasher, dish washer, dishwashing machine
'535': disk brake, disc brake
'536': dock, dockage, docking facility
'537': dogsled, dog sled, dog sleigh
'538': dome
'539': doormat, welcome mat
'540': drilling platform, offshore rig
'541': drum, membranophone, tympan
'542': drumstick
'543': dumbbell
'544': Dutch oven
'545': electric fan, blower
'546': electric guitar
'547': electric locomotive
'548': entertainment center
'549': envelope
'550': espresso maker
'551': face powder
'552': feather boa, boa
'553': file, file cabinet, filing cabinet
'554': fireboat
'555': fire engine, fire truck
'556': fire screen, fireguard
'557': flagpole, flagstaff
'558': flute, transverse flute
'559': folding chair
'560': football helmet
'561': forklift
'562': fountain
'563': fountain pen
'564': four-poster
'565': freight car
'566': French horn, horn
'567': frying pan, frypan, skillet
'568': fur coat
'569': garbage truck, dustcart
'570': gasmask, respirator, gas helmet
'571': gas pump, gasoline pump, petrol pump, island dispenser
'572': goblet
'573': go-kart
'574': golf ball
'575': golfcart, golf cart
'576': gondola
'577': gong, tam-tam
'578': gown
'579': grand piano, grand
'580': greenhouse, nursery, glasshouse
'581': grille, radiator grille
'582': grocery store, grocery, food market, market
'583': guillotine
'584': hair slide
'585': hair spray
'586': half track
'587': hammer
'588': hamper
'589': hand blower, blow dryer, blow drier, hair dryer, hair drier
'590': hand-held computer, hand-held microcomputer
'591': handkerchief, hankie, hanky, hankey
'592': hard disc, hard disk, fixed disk
'593': harmonica, mouth organ, harp, mouth harp
'594': harp
'595': harvester, reaper
'596': hatchet
'597': holster
'598': home theater, home theatre
'599': honeycomb
'600': hook, claw
'601': hoopskirt, crinoline
'602': horizontal bar, high bar
'603': horse cart, horse-cart
'604': hourglass
'605': iPod
'606': iron, smoothing iron
'607': jack-o'-lantern
'608': jean, blue jean, denim
'609': jeep, landrover
'610': jersey, T-shirt, tee shirt
'611': jigsaw puzzle
'612': jinrikisha, ricksha, rickshaw
'613': joystick
'614': kimono
'615': knee pad
'616': knot
'617': lab coat, laboratory coat
'618': ladle
'619': lampshade, lamp shade
'620': laptop, laptop computer
'621': lawn mower, mower
'622': lens cap, lens cover
'623': letter opener, paper knife, paperknife
'624': library
'625': lifeboat
'626': lighter, light, igniter, ignitor
'627': limousine, limo
'628': liner, ocean liner
'629': lipstick, lip rouge
'630': Loafer
'631': lotion
'632': loudspeaker, speaker, speaker unit, loudspeaker system, speaker system
'633': loupe, jeweler's loupe
'634': lumbermill, sawmill
'635': magnetic compass
'636': mailbag, postbag
'637': mailbox, letter box
'638': maillot
'639': maillot, tank suit
'640': manhole cover
'641': maraca
'642': marimba, xylophone
'643': mask
'644': matchstick
'645': maypole
'646': maze, labyrinth
'647': measuring cup
'648': medicine chest, medicine cabinet
'649': megalith, megalithic structure
'650': microphone, mike
'651': microwave, microwave oven
'652': military uniform
'653': milk can
'654': minibus
'655': miniskirt, mini
'656': minivan
'657': missile
'658': mitten
'659': mixing bowl
'660': mobile home, manufactured home
'661': Model T
'662': modem
'663': monastery
'664': monitor
'665': moped
'666': mortar
'667': mortarboard
'668': mosque
'669': mosquito net
'670': motor scooter, scooter
'671': mountain bike, all-terrain bike, off-roader
'672': mountain tent
'673': mouse, computer mouse
'674': mousetrap
'675': moving van
'676': muzzle
'677': nail
'678': neck brace
'679': necklace
'680': nipple
'681': notebook, notebook computer
'682': obelisk
'683': oboe, hautboy, hautbois
'684': ocarina, sweet potato
'685': odometer, hodometer, mileometer, milometer
'686': oil filter
'687': organ, pipe organ
'688': oscilloscope, scope, cathode-ray oscilloscope, CRO
'689': overskirt
'690': oxcart
'691': oxygen mask
'692': packet
'693': paddle, boat paddle
'694': paddlewheel, paddle wheel
'695': padlock
'696': paintbrush
'697': pajama, pyjama, pj's, jammies
'698': palace
'699': panpipe, pandean pipe, syrinx
'700': paper towel
'701': parachute, chute
'702': parallel bars, bars
'703': park bench
'704': parking meter
'705': passenger car, coach, carriage
'706': patio, terrace
'707': pay-phone, pay-station
'708': pedestal, plinth, footstall
'709': pencil box, pencil case
'710': pencil sharpener
'711': perfume, essence
'712': Petri dish
'713': photocopier
'714': pick, plectrum, plectron
'715': pickelhaube
'716': picket fence, paling
'717': pickup, pickup truck
'718': pier
'719': piggy bank, penny bank
'720': pill bottle
'721': pillow
'722': ping-pong ball
'723': pinwheel
'724': pirate, pirate ship
'725': pitcher, ewer
'726': plane, carpenter's plane, woodworking plane
'727': planetarium
'728': plastic bag
'729': plate rack
'730': plow, plough
'731': plunger, plumber's helper
'732': Polaroid camera, Polaroid Land camera
'733': pole
'734': police van, police wagon, paddy wagon, patrol wagon, wagon, black
Maria
'735': poncho
'736': pool table, billiard table, snooker table
'737': pop bottle, soda bottle
'738': pot, flowerpot
'739': potter's wheel
'740': power drill
'741': prayer rug, prayer mat
'742': printer
'743': prison, prison house
'744': projectile, missile
'745': projector
'746': puck, hockey puck
'747': punching bag, punch bag, punching ball, punchball
'748': purse
'749': quill, quill pen
'750': quilt, comforter, comfort, puff
'751': racer, race car, racing car
'752': racket, racquet
'753': radiator
'754': radio, wireless
'755': radio telescope, radio reflector
'756': rain barrel
'757': recreational vehicle, RV, R.V.
'758': reel
'759': reflex camera
'760': refrigerator, icebox
'761': remote control, remote
'762': restaurant, eating house, eating place, eatery
'763': revolver, six-gun, six-shooter
'764': rifle
'765': rocking chair, rocker
'766': rotisserie
'767': rubber eraser, rubber, pencil eraser
'768': rugby ball
'769': rule, ruler
'770': running shoe
'771': safe
'772': safety pin
'773': saltshaker, salt shaker
'774': sandal
'775': sarong
'776': sax, saxophone
'777': scabbard
'778': scale, weighing machine
'779': school bus
'780': schooner
'781': scoreboard
'782': screen, CRT screen
'783': screw
'784': screwdriver
'785': seat belt, seatbelt
'786': sewing machine
'787': shield, buckler
'788': shoe shop, shoe-shop, shoe store
'789': shoji
'790': shopping basket
'791': shopping cart
'792': shovel
'793': shower cap
'794': shower curtain
'795': ski
'796': ski mask
'797': sleeping bag
'798': slide rule, slipstick
'799': sliding door
'800': slot, one-armed bandit
'801': snorkel
'802': snowmobile
'803': snowplow, snowplough
'804': soap dispenser
'805': soccer ball
'806': sock
'807': solar dish, solar collector, solar furnace
'808': sombrero
'809': soup bowl
'810': space bar
'811': space heater
'812': space shuttle
'813': spatula
'814': speedboat
'815': spider web, spider's web
'816': spindle
'817': sports car, sport car
'818': spotlight, spot
'819': stage
'820': steam locomotive
'821': steel arch bridge
'822': steel drum
'823': stethoscope
'824': stole
'825': stone wall
'826': stopwatch, stop watch
'827': stove
'828': strainer
'829': streetcar, tram, tramcar, trolley, trolley car
'830': stretcher
'831': studio couch, day bed
'832': stupa, tope
'833': submarine, pigboat, sub, U-boat
'834': suit, suit of clothes
'835': sundial
'836': sunglass
'837': sunglasses, dark glasses, shades
'838': sunscreen, sunblock, sun blocker
'839': suspension bridge
'840': swab, swob, mop
'841': sweatshirt
'842': swimming trunks, bathing trunks
'843': swing
'844': switch, electric switch, electrical switch
'845': syringe
'846': table lamp
'847': tank, army tank, armored combat vehicle, armoured combat vehicle
'848': tape player
'849': teapot
'850': teddy, teddy bear
'851': television, television system
'852': tennis ball
'853': thatch, thatched roof
'854': theater curtain, theatre curtain
'855': thimble
'856': thresher, thrasher, threshing machine
'857': throne
'858': tile roof
'859': toaster
'860': tobacco shop, tobacconist shop, tobacconist
'861': toilet seat
'862': torch
'863': totem pole
'864': tow truck, tow car, wrecker
'865': toyshop
'866': tractor
'867': trailer truck, tractor trailer, trucking rig, rig, articulated lorry,
semi
'868': tray
'869': trench coat
'870': tricycle, trike, velocipede
'871': trimaran
'872': tripod
'873': triumphal arch
'874': trolleybus, trolley coach, trackless trolley
'875': trombone
'876': tub, vat
'877': turnstile
'878': typewriter keyboard
'879': umbrella
'880': unicycle, monocycle
'881': upright, upright piano
'882': vacuum, vacuum cleaner
'883': vase
'884': vault
'885': velvet
'886': vending machine
'887': vestment
'888': viaduct
'889': violin, fiddle
'890': volleyball
'891': waffle iron
'892': wall clock
'893': wallet, billfold, notecase, pocketbook
'894': wardrobe, closet, press
'895': warplane, military plane
'896': washbasin, handbasin, washbowl, lavabo, wash-hand basin
'897': washer, automatic washer, washing machine
'898': water bottle
'899': water jug
'900': water tower
'901': whiskey jug
'902': whistle
'903': wig
'904': window screen
'905': window shade
'906': Windsor tie
'907': wine bottle
'908': wing
'909': wok
'910': wooden spoon
'911': wool, woolen, woollen
'912': worm fence, snake fence, snake-rail fence, Virginia fence
'913': wreck
'914': yawl
'915': yurt
'916': web site, website, internet site, site
'917': comic book
'918': crossword puzzle, crossword
'919': street sign
'920': traffic light, traffic signal, stoplight
'921': book jacket, dust cover, dust jacket, dust wrapper
'922': menu
'923': plate
'924': guacamole
'925': consomme
'926': hot pot, hotpot
'927': trifle
'928': ice cream, icecream
'929': ice lolly, lolly, lollipop, popsicle
'930': French loaf
'931': bagel, beigel
'932': pretzel
'933': cheeseburger
'934': hotdog, hot dog, red hot
'935': mashed potato
'936': head cabbage
'937': broccoli
'938': cauliflower
'939': zucchini, courgette
'940': spaghetti squash
'941': acorn squash
'942': butternut squash
'943': cucumber, cuke
'944': artichoke, globe artichoke
'945': bell pepper
'946': cardoon
'947': mushroom
'948': Granny Smith
'949': strawberry
'950': orange
'951': lemon
'952': fig
'953': pineapple, ananas
'954': banana
'955': jackfruit, jak, jack
'956': custard apple
'957': pomegranate
'958': hay
'959': carbonara
'960': chocolate sauce, chocolate syrup
'961': dough
'962': meat loaf, meatloaf
'963': pizza, pizza pie
'964': potpie
'965': burrito
'966': red wine
'967': espresso
'968': cup
'969': eggnog
'970': alp
'971': bubble
'972': cliff, drop, drop-off
'973': coral reef
'974': geyser
'975': lakeside, lakeshore
'976': promontory, headland, head, foreland
'977': sandbar, sand bar
'978': seashore, coast, seacoast, sea-coast
'979': valley, vale
'980': volcano
'981': ballplayer, baseball player
'982': groom, bridegroom
'983': scuba diver
'984': rapeseed
'985': daisy
'986': yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus,
Cypripedium parviflorum
'987': corn
'988': acorn
'989': hip, rose hip, rosehip
'990': buckeye, horse chestnut, conker
'991': coral fungus
'992': agaric
'993': gyromitra
'994': stinkhorn, carrion fungus
'995': earthstar
'996': hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola
frondosa
'997': bolete
'998': ear, spike, capitulum
'999': toilet tissue, toilet paper, bathroom tissue
splits:
- name: train
num_bytes: 25757593104.281
num_examples: 1281167
- name: val
num_bytes: 752578700.0
num_examples: 50000
- name: test
num_bytes: 1748575400.0
num_examples: 100000
download_size: 25143755650
dataset_size: 28258747204.281
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
---
# Dataset Card for "imagenet_1k_resized_256"
## Dataset summary
The same ImageNet dataset but all the smaller side resized to 256.
A lot of pretraining workflows contain resizing images to 256 and random cropping to 224x224, this is why 256 is chosen.
The resized dataset can also be downloaded much faster and consume less space than the original one.
See [here](https://huggingface.co/datasets/imagenet-1k) for detailed readme.
## Dataset Structure
Below is the example of one row of data. Note that the labels in the test split are all -1.
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=256x384 at 0x276021C5EB8>,
'label': 23
}
```
The number of rows per split is the same as the original ImageNet.
| |train |validation| test |
|-------------|------:|---------:|------:|
|# of examples|1281167|50000 |100000 |
## Licensing Information
In exchange for permission to use the ImageNet database (the "Database") at Princeton University and Stanford University, Researcher hereby agrees to the following terms and conditions:
1. Researcher shall use the Database only for non-commercial research and educational purposes.
1. Princeton University and Stanford University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose.
1. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the ImageNet team, Princeton University, and Stanford 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 images that he or she may create from the Database.
1. 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.
1. Princeton University and Stanford University reserve the right to terminate Researcher's access to the Database at any time.
1. 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.
1. The law of the State of New Jersey shall apply to all disputes under this agreement.
## Citation Information
```bibtex
@article{imagenet15russakovsky,
Author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei},
Title = { {ImageNet Large Scale Visual Recognition Challenge} },
Year = {2015},
journal = {International Journal of Computer Vision (IJCV)},
doi = {10.1007/s11263-015-0816-y},
volume={115},
number={3},
pages={211-252}
}
```
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GEM/wiki_cat_sum | GEM | 2022-10-24T15:31:11Z | 1,490 | 3 | null | [
"task_categories:summarization",
"annotations_creators:automatically-created",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"arxiv:1906.04687",
"arxiv:1801.10198",
"arxiv:2009.07032",
"region:us"
] | 2022-10-24T15:31:11Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- automatically-created
language_creators:
- unknown
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- summarization
task_ids: []
pretty_name: wiki_cat_sum
---
# Dataset Card for GEM/wiki_cat_sum
## Dataset Description
- **Homepage:** https://github.com/lauhaide/WikiCatSum
- **Repository:** https://datashare.ed.ac.uk/handle/10283/3368
- **Paper:** https://arxiv.org/abs/1906.04687
- **Leaderboard:** N/A
- **Point of Contact:** Laura Perez-Beltrachini
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/wiki_cat_sum).
### Dataset Summary
WikiCatSum is an English summarization dataset in three domains: animals, companies, and film. It provides multiple paragraphs of text paired with a summary of the paragraphs.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/wiki_cat_sum')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/wiki_cat_sum).
#### website
[Github](https://github.com/lauhaide/WikiCatSum)
#### paper
[Arxiv](https://arxiv.org/abs/1906.04687)
#### authors
Laura Perez-Beltrachini, Yang Liu, Mirella Lapata (University of Edinburgh) Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, Noam Shazeer (GoogleBrain)
## 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/lauhaide/WikiCatSum)
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Website](https://datashare.ed.ac.uk/handle/10283/3368)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[Arxiv](https://arxiv.org/abs/1906.04687)
#### 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{perez-beltrachini-etal-2019-generating,
title = "Generating Summaries with Topic Templates and Structured Convolutional Decoders",
author = "Perez-Beltrachini, Laura and
Liu, Yang 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://aclanthology.org/P19-1504",
doi = "10.18653/v1/P19-1504",
}
```
#### 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 -->
Laura Perez-Beltrachini
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
lperez@ed.ac.uk
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
no
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
no
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`English`
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
cc-by-sa-3.0: Creative Commons Attribution Share Alike 3.0 Unported
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
Research on multi-document abstractive summarisation.
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Summarization
#### Communicative Goal
<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
Summarise the most important facts of a given entity in the Film, Company, and Animal domains from a cluster of related documents.
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`industry`, `academic`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
Google Cloud Platform, 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 -->
Laura Perez-Beltrachini, Yang Liu, Mirella Lapata (University of Edinburgh) Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, Noam Shazeer (GoogleBrain)
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
Google Cloud Platform, European Research Council
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
Ronald Cardenas (University of Edinburgh) Laura Perez-Beltrachini (University of Edinburgh)
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
- `id`: ID of the data example
- `title`: Is the Wikipedia article's title
- `paragraphs`: Is the ranked list of paragraphs from the set of crawled texts
- `summary`: Is constituted by a list of sentences together with their corresponding topic label
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
This is a truncated example from the animal setting:
```
{'gem_id': 'animal-train-1',
'gem_parent_id': 'animal-train-1',
'id': '2652',
'paragraphs': ["lytrosis (hulst) of louisiana vernon antoine brou jr. 2005. southern lepidopterists' news, 27: 7 ., ..."],
'references': ['lytrosis unitaria , the common lytrosis moth, is a species of moth of the geometridae family. it is found in north america, including arkansas, georgia, iowa , massachusetts, and wisconsin. the wingspan is about 50 mm. the larvae feed on rosa, crataegus, amelanchier, acer, quercus and viburnum species.'],
'summary': {'text': ['lytrosis unitaria , the common lytrosis moth , is a species of moth of the geometridae family .',
'it is found in north america , including arkansas , georgia , iowa , massachusetts , new hampshire , new jersey , new york , north carolina , ohio , oklahoma , ontario , pennsylvania , south carolina , tennessee , texas , virginia , west virginia and wisconsin .',
'the wingspan is about 50 mm .',
'the larvae feed on rosa , crataegus , amelanchier , acer , quercus and viburnum species . '],
'topic': [29, 20, 9, 8]},
'target': 'lytrosis unitaria , the common lytrosis moth, is a species of moth of the geometridae family. it is found in north america, including arkansas, georgia, iowa , massachusetts, and wisconsin. the wingspan is about 50 mm. the larvae feed on rosa, crataegus, amelanchier, acer, quercus and viburnum species.',
'title': 'lytrosis unitaria'}
```
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
Nb of instances in train/valid/test are 50,938/2,855/2,831
#### 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 data was split i.i.d., i.e. uniformly split into training, validation, and test datasets.
## 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 -->
Evaluation of models' performance on noisy (document, summary) pairs and long inputs.
Evaluate models' capabilities to generalise and mitigate biases.
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
no
#### Unique Language Coverage
<!-- info: Does this dataset cover other languages than other datasets for the same task? -->
<!-- scope: periscope -->
no
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
Capabilities to generalise, mitigate biases, factual correctness.
### 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 -->
`annotations added`
#### Modification Details
<!-- info: For each of these changes, described them in more details and provided the intended purpose of the modification -->
<!-- scope: microscope -->
We provide topic labels for summary sentences.
#### 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 -->
- [Generating Wikipedia by Summarizing Long Sequences](https://arxiv.org/abs/1801.10198)
- [Generating Summaries with Topic Templates and Structured Convolutional Decoders](https://arxiv.org/abs/1906.04687)
- [Noisy Self-Knowledge Distillation for Text Summarization](https://arxiv.org/abs/2009.07032)
And all references in these papers.
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
Capabilities to generalise, mitigate biases, factual correctness.
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`ROUGE`, `BERT-Score`, `MoverScore`, `Other: Other Metrics`
#### Other Metrics
<!-- info: Definitions of other metrics -->
<!-- scope: periscope -->
- Abstract/Copy
- Factual accuracy based on the score of (Goodrich et al., 2019) and the relation extraction system of (Sorokin and Gurevych, 2017).
#### 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 -->
Human based are Question Answering and Ranking (Content, Fluency and Repetition)
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
yes
#### Other Evaluation Approaches
<!-- info: What evaluation approaches have others used? -->
<!-- scope: periscope -->
Those listed above.
#### Relevant Previous Results
<!-- info: What are the most relevant previous results for this task/dataset? -->
<!-- scope: microscope -->
Generating Summaries with Topic Templates and Structured Convolutional Decoders
https://arxiv.org/abs/1906.04687
Noisy Self-Knowledge Distillation for Text Summarization
https://arxiv.org/abs/2009.07032
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
The dataset is a subset of the WikiSum (Liu et al., 2018) dataset focusing on summaries of entities in three domains (Film, Company, and Animal). It is multi-document summarisation where input-output pairs for each example entity are created as follows. The input is a set of paragraphs collected from i) documents in the Reference section of the entity's Wikipedia page plus ii) documents collected from the top ten search results after querying Google search engine with the entity name. The output summary is the Wikipedia abstract for the entity.
#### Communicative Goal
<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
Generate descriptive summaries with specific domains, where certain topics are discussed and generally in specific orders.
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
yes
#### Source Details
<!-- info: List the sources (one per line) -->
<!-- scope: periscope -->
WikiSum (Liu et al., 2018)
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Other`
#### Topics Covered
<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
<!-- scope: periscope -->
The dataset and task focuses on summaries for entities in three domains: Company, Film, and Animal.
#### 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 -->
Summary sentences are associated with a topic label. There is a topic model for each domain.
#### 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 -->
automatically created
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
#### Annotation Values
<!-- info: Purpose and values for each annotation -->
<!-- scope: microscope -->
Each summary sentences was annotated with a topic label. There is a topic model for each of the three domains. This was used to guide a hierarchical decoder.
#### Any Quality Control?
<!-- info: Quality control measures? -->
<!-- scope: telescope -->
validated by data curators
#### Quality Control Details
<!-- info: Describe the quality control measures that were taken. -->
<!-- scope: microscope -->
Manual inspection of a sample of topics assigned to sentences. The number of topics was selected based on the performance of the summarisation model.
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
no
#### Justification for Using the Data
<!-- info: If not, what is the justification for reusing the data? -->
<!-- scope: microscope -->
The dataset is base on Wikipedia and referenced and retrieved documents crawled from the Web.
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
unlikely
#### Any PII Identification?
<!-- info: Did the curators use any automatic/manual method to identify PII in the dataset? -->
<!-- scope: periscope -->
no identification
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
no
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
yes
#### Links and Summaries of Analysis Work
<!-- info: Provide links to and summaries of works analyzing these biases. -->
<!-- scope: microscope -->
This dataset is based on Wikipedia and thus biases analysis on other Wikipedia-based datasets are potentially true for WikiCatSum. For instance, see analysis for the ToTTo dataset here [1].
[1] Automatic Construction of Evaluation Suites for Natural Language Generation Datasets
https://openreview.net/forum?id=CSi1eu_2q96
## 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 -->
`public domain`
#### Copyright Restrictions on the Language Data
<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
<!-- scope: periscope -->
`public domain`
### Known Technical Limitations
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multi_re_qa | null | 2023-06-01T14:59:53Z | 1,489 | 0 | multireqa | [
"task_categories:question-answering",
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"region:us"
] | 2023-06-01T14:59:53Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
- found
language_creators:
- expert-generated
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
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source_datasets:
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- extended|other-Natural-Questions
- extended|other-Relation-Extraction
- extended|other-SQuAD
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task_categories:
- question-answering
task_ids:
- extractive-qa
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paperswithcode_id: multireqa
pretty_name: MultiReQA
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config_names:
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---
# Dataset Card for MultiReQA
## 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/MultiReQA
- **Repository:** https://github.com/google-research-datasets/MultiReQA
- **Paper:** https://arxiv.org/pdf/2005.02507.pdf
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
MultiReQA contains the sentence boundary annotation from eight publicly available QA datasets including SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, BioASQ, RelationExtraction, and TextbookQA. Five of these datasets, including SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, contain both training and test data, and three, in cluding BioASQ, RelationExtraction, TextbookQA, contain only the test data (also includes DuoRC but not specified in the official documentation)
### Supported Tasks and Leaderboards
- Question answering (QA)
- Retrieval question answering (ReQA)
### Languages
Sentence boundary annotation for SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, BioASQ, RelationExtraction, TextbookQA and DuoRC
## Dataset Structure
### Data Instances
The general format is:
`
{
"candidate_id": <candidate_id>,
"response_start": <response_start>,
"response_end": <response_end>
}
...
`
An example from SearchQA:
`{'candidate_id': 'SearchQA_000077f3912049dfb4511db271697bad/_0_1',
'response_end': 306,
'response_start': 243} `
### Data Fields
`
{
"candidate_id": <STRING>,
"response_start": <INT>,
"response_end": <INT>
}
...
`
- **candidate_id:** The candidate id of the candidate sentence. It consists of the original qid from the MRQA shared task.
- **response_start:** The start index of the sentence with respect to its original context.
- **response_end:** The end index of the sentence with respect to its original context
### Data Splits
Train and Dev splits are available only for the following datasets,
- SearchQA
- TriviaQA
- HotpotQA
- SQuAD
- NaturalQuestions
Test splits are available only for the following datasets,
- BioASQ
- RelationExtraction
- TextbookQA
The number of candidate sentences for each dataset in the table below.
| | MultiReQA | |
|--------------------|-----------|---------|
| | train | test |
| SearchQA | 629,160 | 454,836 |
| TriviaQA | 335,659 | 238,339 |
| HotpotQA | 104,973 | 52,191 |
| SQuAD | 87,133 | 10,642 |
| NaturalQuestions | 106,521 | 22,118 |
| BioASQ | - | 14,158 |
| RelationExtraction | - | 3,301 |
| TextbookQA | - | 3,701 |
## Dataset Creation
### Curation Rationale
MultiReQA is a new multi-domain ReQA evaluation suite composed of eight retrieval QA tasks drawn from publicly available QA datasets from the [MRQA shared task](https://mrqa.github.io/). The dataset was curated by converting existing QA datasets from [MRQA shared task](https://mrqa.github.io/) to the format of MultiReQA benchmark.
### Source Data
#### Initial Data Collection and Normalization
The Initial data collection was performed by converting existing QA datasets from MRQA shared task to the format of MultiReQA benchmark.
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
The annotators/curators of the dataset are [mandyguo-xyguo](https://github.com/mandyguo-xyguo) and [mwurts4google](https://github.com/mwurts4google), the contributors of the official MultiReQA github repository
### 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 annotators/curators of the dataset are [mandyguo-xyguo](https://github.com/mandyguo-xyguo) and [mwurts4google](https://github.com/mwurts4google), the contributors of the official MultiReQA github repository
### Licensing Information
[More Information Needed]
### Citation Information
```
@misc{m2020multireqa,
title={MultiReQA: A Cross-Domain Evaluation for Retrieval Question Answering Models},
author={Mandy Guo and Yinfei Yang and Daniel Cer and Qinlan Shen and Noah Constant},
year={2020},
eprint={2005.02507},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### 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 | |
DeveloperOats/DBPedia_Classes | DeveloperOats | 2022-08-08T14:54:42Z | 1,483 | 13 | null | [
"task_categories:text-classification",
"task_ids:topic-classification",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"language:en",
"license:cc0-1.0",
"region:us"
] | 2022-08-08T14:54:42Z | 2022-08-08T09:15:05.000Z | 2022-08-08T09:15:05 | ---
annotations_creators: []
language:
- en
language_creators: []
license:
- cc0-1.0
multilinguality:
- monolingual
pretty_name: 'DBpedia'
size_categories:
- 1M<n<10M
source_datasets: []
tags: []
task_categories:
- text-classification
task_ids:
- topic-classification
---
About Dataset
DBpedia (from "DB" for "database") is a project aiming to extract structured content from the information created in Wikipedia.
This is an extract of the data (after cleaning, kernel included) that provides taxonomic, hierarchical categories ("classes") for 342,782 wikipedia articles. There are 3 levels, with 9, 70 and 219 classes respectively.
A version of this dataset is a popular baseline for NLP/text classification tasks. This version of the dataset is much tougher, especially if the L2/L3 levels are used as the targets.
This is an excellent benchmark for hierarchical multiclass/multilabel text classification.
Some example approaches are included as code snippets.
Content
DBPedia dataset with multiple levels of hierarchy/classes, as a multiclass dataset.
Original DBPedia ontology (triplets data): https://wiki.dbpedia.org/develop/datasets
Listing of the class tree/taxonomy: http://mappings.dbpedia.org/server/ontology/classes/
Acknowledgements
Thanks to the Wikimedia foundation for creating Wikipedia, DBPedia and associated open-data goodness!
Thanks to my colleagues at Sparkbeyond (https://www.sparkbeyond.com) for pointing me towards the taxonomical version of this dataset (as opposed to the classic 14 class version)
Inspiration
Try different NLP models.
See also https://www.kaggle.com/datasets/danofer/dbpedia-classes
Compare to the SOTA in Text Classification on DBpedia - https://paperswithcode.com/sota/text-classification-on-dbpedia | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
graphs-datasets/MUTAG | graphs-datasets | 2023-02-07T16:39:19Z | 1,482 | 3 | null | [
"task_categories:graph-ml",
"license:unknown",
"region:us"
] | 2023-02-07T16:39:19Z | 2022-08-01T15:58:02.000Z | 2022-08-01T15:58:02 | ---
license: unknown
task_categories:
- graph-ml
---
# Dataset Card for MUTAG
## 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)
- [External Use](#external-use)
- [PyGeometric](#pygeometric)
- [Dataset Structure](#dataset-structure)
- [Data Properties](#data-properties)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **[Homepage](https://pubs.acs.org/doi/abs/10.1021/jm00106a046)**
- **[Repository](https://www.chrsmrrs.com/graphkerneldatasets/MUTAG.zip):**:
- **Paper:**: Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. Correlation with molecular orbital energies and hydrophobicity (see citation)
- **Leaderboard:**: [Papers with code leaderboard](https://paperswithcode.com/sota/graph-classification-on-mutag)
### Dataset Summary
The `MUTAG` dataset is 'a collection of nitroaromatic compounds and the goal is to predict their mutagenicity on Salmonella typhimurium'.
### Supported Tasks and Leaderboards
`MUTAG` should be used for molecular property prediction (aiming to predict whether molecules have a mutagenic effect on a given bacterium or not), a binary classification task. The score used is accuracy, using a 10-fold cross-validation.
## External Use
### PyGeometric
To load in PyGeometric, do the following:
```python
from datasets import load_dataset
from torch_geometric.data import Data
from torch_geometric.loader import DataLoader
dataset_hf = load_dataset("graphs-datasets/<mydataset>")
# For the train set (replace by valid or test as needed)
dataset_pg_list = [Data(graph) for graph in dataset_hf["train"]]
dataset_pg = DataLoader(dataset_pg_list)
```
## Dataset Structure
### Data Properties
| property | value |
|---|---|
| scale | small |
| #graphs | 187 |
| average #nodes | 18.03 |
| average #edges | 39.80 |
### Data Fields
Each row of a given file is a graph, with:
- `node_feat` (list: #nodes x #node-features): nodes
- `edge_index` (list: 2 x #edges): pairs of nodes constituting edges
- `edge_attr` (list: #edges x #edge-features): for the aforementioned edges, contains their features
- `y` (list: 1 x #labels): contains the number of labels available to predict (here 1, equal to zero or one)
- `num_nodes` (int): number of nodes of the graph
### Data Splits
This data comes from the PyGeometric version of the dataset provided by OGB, and follows the provided data splits.
This information can be found back using
```python
from torch_geometric.datasets import TUDataset
cur_dataset = TUDataset(root="../dataset/loaded/",
name="MUTAG")
```
## Additional Information
### Licensing Information
The dataset has been released under unknown license, please open an issue if you have information.
### Citation Information
```
@article{doi:10.1021/jm00106a046,
author = {Debnath, Asim Kumar and Lopez de Compadre, Rosa L. and Debnath, Gargi and Shusterman, Alan J. and Hansch, Corwin},
title = {Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. Correlation with molecular orbital energies and hydrophobicity},
journal = {Journal of Medicinal Chemistry},
volume = {34},
number = {2},
pages = {786-797},
year = {1991},
doi = {10.1021/jm00106a046},
URL = {
https://doi.org/10.1021/jm00106a046
},
eprint = {
https://doi.org/10.1021/jm00106a046
}
}
```
### Contributions
Thanks to [@clefourrier](https://github.com/clefourrier) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
jordiae/exebench | jordiae | 2023-03-09T16:06:06Z | 1,480 | 1 | null | [
"region:us"
] | 2023-03-09T16:06:06Z | 2022-07-30T20:07:06.000Z | 2022-07-30T20:07:06 | # ExeBench: an ML-scale dataset of executable C functions
ExeBench is a dataset of millions of C functions paired with dependencies and metadatada such that at least a subset of it can be executed with IO pairs. It is mainly inteded for machine learning applications but it is application-agnostic enough to have other usages.
Please read the paper for more information: https://dl.acm.org/doi/abs/10.1145/3520312.3534867.
Please see `examples/` in https://github.com/jordiae/exebench for examples.
## Usage
### Option 1: Using the helpers in this repo
```
git clone https://github.com/jordiae/exebench.git
cd exebench/
python -m venv venv
source venv/bin/activate
pip install -r requirements_examples.txt
PYTHONPATH="${PYTHONPATH}:${pwd}" python examples/basic.py
```
### Option 2: Directly using the Hugginface Datasets library
```
!pip install datasets zstandard
# Load dataset split. In this case, synthetic test split
dataset = load_dataset('jordiae/exebench', split='test_synth')
for e in dataset:
...
```
### Option 3: Directly download the dataset
Take a look at the files at: https://huggingface.co/datasets/jordiae/exebench/tree/main
The dataset consist of directories compressed with TAR. Inside each TAR, there is a series of jsonline files compressed with zstandard.
## Statistics and versions
This release corresponds to ExeBench v1.01, a version with some improvements with respect to the original one presented in the paper. The statistics and studies presented in the paper remain consistent with respect to the new ones. The final splits of the new version consist of the following functions:
```
train_not_compilable: 2.357M
train_synth_compilable: 2.308373M
train_real_compilable: 0.675074M
train_synth_simple_io: 0.550116M
train_real_simple_io: 0.043769M
train_synth_rich_io: 0.097250M
valid_synth: 5k
valid_real: 2.133k
test_synth: 5k
test_real: 2.134k
```
The original dataset (v1.00) with the exact same data studied in the paper can be accessed on request at: https://huggingface.co/datasets/jordiae/exebench_legacy (please reach out for access)
## License
All C functions keep the original license as per their original Github repository (available in the metadata). All ExeBench contributions (I/O examples, boilerplate to run functions, etc) are released with an MIT license.
## Citation
```
@inproceedings{10.1145/3520312.3534867,
author = {Armengol-Estap\'{e}, Jordi and Woodruff, Jackson and Brauckmann, Alexander and Magalh\~{a}es, Jos\'{e} Wesley de Souza and O'Boyle, Michael F. P.},
title = {ExeBench: An ML-Scale Dataset of Executable C Functions},
year = {2022},
isbn = {9781450392730},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3520312.3534867},
doi = {10.1145/3520312.3534867},
abstract = {Machine-learning promises to transform compilation and software engineering, yet is frequently limited by the scope of available datasets. In particular, there is a lack of runnable, real-world datasets required for a range of tasks ranging from neural program synthesis to machine learning-guided program optimization. We introduce a new dataset, ExeBench, which attempts to address this. It tackles two key issues with real-world code: references to external types and functions and scalable generation of IO examples. ExeBench is the first publicly available dataset that pairs real-world C code taken from GitHub with IO examples that allow these programs to be run. We develop a toolchain that scrapes GitHub, analyzes the code, and generates runnable snippets of code. We analyze our benchmark suite using several metrics, and show it is representative of real-world code. ExeBench contains 4.5M compilable and 700k executable C functions. This scale of executable, real functions will enable the next generation of machine learning-based programming tasks.},
booktitle = {Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming},
pages = {50–59},
numpages = {10},
keywords = {Code Dataset, Program Synthesis, Mining Software Repositories, C, Machine Learning for Code, Compilers},
location = {San Diego, CA, USA},
series = {MAPS 2022}
}
```
## Credits
We thank Anghabench authors for their type inference-based synthetic dependencies generation for C functions. This software, Psyche-C, can be found at: https://github.com/ltcmelo/psychec
## Contact
```
jordi.armengol.estape at ed.ac.uk
``` | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ivelin/ui_refexp_saved | ivelin | 2023-01-08T03:35:06Z | 1,478 | 6 | null | [
"task_categories:image-to-text",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-4.0",
"region:us"
] | 2023-01-08T03:35:06Z | 2023-01-08T03:10:23.000Z | 2023-01-08T03:10:23 | ---
dataset_info:
features:
- name: image
dtype: image
- name: image_id
dtype: string
- name: image_file_path
dtype: string
- name: prompt
dtype: string
- name: target_bounding_box
dtype: string
splits:
- name: train
num_bytes: 1910805137.216
num_examples: 15624
- name: validation
num_bytes: 60403386
num_examples: 471
- name: test
num_bytes: 69078983
num_examples: 565
download_size: 1246541216
dataset_size: 2040287506.216
license: cc-by-4.0
task_categories:
- image-to-text
language:
- en
pretty_name: UIBert Referring Expressions Dataset
size_categories:
- 10K<n<100K
---
# Dataset Card for "ui_refexp_saved_Jan2023"
This is a saved snapshot of the dynamically generated [UI Bert](https://huggingface.co/datasets/ivelin/ui_refexp) dataset.
Much faster download time than the dynamic version which pulls and filters large data files from remote sources. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
vwxyzjn/summarize_from_feedback_oai_preprocessing_pythia-160m_48 | vwxyzjn | 2023-11-28T19:57:44Z | 1,477 | 0 | null | [
"region:us"
] | 2023-11-28T19:57:44Z | 2023-11-23T01:39:04.000Z | 2023-11-23T01:39:04 | ---
dataset_info:
features:
- name: info
struct:
- name: id
dtype: string
- name: post
dtype: string
- name: title
dtype: string
- name: subreddit
dtype: string
- name: site
dtype: string
- name: article
dtype: string
- name: summaries
list:
- name: text
dtype: string
- name: policy
dtype: string
- name: note
dtype: string
- name: choice
dtype: int32
- name: worker
dtype: string
- name: batch
dtype: string
- name: split
dtype: string
- name: extra
struct:
- name: confidence
dtype: int32
- name: query_token
sequence: int64
- name: query
dtype: string
- name: response0
dtype: string
- name: response0_token
sequence: int64
- name: response0_token_len
dtype: int64
- name: response1
dtype: string
- name: response1_token
sequence: int64
- name: response1_token_len
dtype: int64
- name: response0_policy
dtype: string
- name: response1_policy
dtype: string
- name: policies
dtype: string
splits:
- name: train
num_bytes: 790484659
num_examples: 92858
- name: validation
num_bytes: 743124619
num_examples: 86086
download_size: 125328309
dataset_size: 1533609278
---
# Dataset Card for "summarize_from_feedback_oai_preprocessing_pythia-160m_48"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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203427as321/articles | 203427as321 | 2023-11-29T01:00:08Z | 1,475 | 0 | null | [
"region:us"
] | 2023-11-29T01:00:08Z | 2023-05-25T19:13:43.000Z | 2023-05-25T19:13:43 | ---
dataset_info:
features:
- name: label
dtype: string
- name: text
dtype: string
- name: __index_level_0__
dtype: float64
splits:
- name: train
num_bytes: 23996247
num_examples: 1534
download_size: 0
dataset_size: 23996247
---
# Dataset Card for "articles"
[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 | |
plaguss/snli-small | plaguss | 2023-09-10T14:53:06Z | 1,475 | 0 | null | [
"size_categories:n<1K",
"rlfh",
"argilla",
"human-feedback",
"region:us"
] | 2023-09-10T14:53:06Z | 2023-09-10T14:29:47.000Z | 2023-09-10T14:29:47 | ---
size_categories: n<1K
tags:
- rlfh
- argilla
- human-feedback
---
# Dataset Card for snli-small
This dataset has been created with [Argilla](https://docs.argilla.io).
As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets).
## Dataset Description
- **Homepage:** https://argilla.io
- **Repository:** https://github.com/argilla-io/argilla
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains:
* A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla.
* Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`.
* The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla.
### Load with Argilla
To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code:
```python
import argilla as rg
ds = rg.FeedbackDataset.from_huggingface("plaguss/snli-small")
```
### Load with `datasets`
To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code:
```python
from datasets import load_dataset
ds = load_dataset("plaguss/snli-small")
```
### Supported Tasks and Leaderboards
This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/guides/llms/conceptual_guides/data_model.html) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure).
There are no leaderboards associated with this dataset.
### Languages
[More Information Needed]
## Dataset Structure
### Data in Argilla
The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, and **guidelines**.
The **fields** are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.
| Field Name | Title | Type | Required | Markdown |
| ---------- | ----- | ---- | -------- | -------- |
| premise | Premise | TextField | True | False |
| hypothesis | Hypothesis | TextField | True | False |
The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, single choice, or multiple choice.
| Question Name | Title | Type | Required | Description | Values/Labels |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| label | The hypothesis entails the premise, neither entails nor contradict each other, or the hypothesis contradicts the premise? | LabelQuestion | True | N/A | ['0', '1', '2'] |
**✨ NEW** Additionally, we also have **suggestions**, which are linked to the existing questions, and so on, named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above.
Finally, the **guidelines** are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section.
### Data Instances
An example of a dataset instance in Argilla looks as follows:
```json
{
"fields": {
"hypothesis": "A person is training his horse for a competition.",
"premise": "A person on a horse jumps over a broken down airplane."
},
"metadata": {},
"responses": [
{
"status": "submitted",
"values": {
"label": {
"value": "1"
}
}
}
],
"suggestions": []
}
```
While the same record in HuggingFace `datasets` looks as follows:
```json
{
"external_id": null,
"hypothesis": "A person is training his horse for a competition.",
"label": [
{
"status": "submitted",
"user_id": null,
"value": "1"
}
],
"label-suggestion": null,
"label-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
},
"metadata": "{}",
"premise": "A person on a horse jumps over a broken down airplane."
}
```
### Data Fields
Among the dataset fields, we differentiate between the following:
* **Fields:** These are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.
* **premise** is of type `TextField`.
* **hypothesis** is of type `TextField`.
* **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`.
* **label** is of type `LabelQuestion` with the following allowed values ['0', '1', '2'].
* **✨ NEW** **Suggestions:** As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.
* (optional) **label-suggestion** is of type `label_selection` with the following allowed values ['0', '1', '2'].
Additionally, we also have one more field which is optional and is the following:
* **external_id:** This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.
### Data Splits
The dataset contains a single split, which is `train`.
## 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 guidelines
Premise: A string used to determine the truthfulness of the hypothesis, Hypothesis: A string that may be true, false, or whose truth conditions may not be knowable when compared to the premise
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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yerevann/sst2 | yerevann | 2022-02-02T20:02:45Z | 1,464 | 0 | null | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
liwu/MNBVC | liwu | 2023-11-25T02:54:05Z | 1,461 | 296 | null | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:zh",
"license:mit",
"region:us"
] | 2023-11-25T02:54:05Z | 2023-02-13T14:00:47.000Z | 2023-02-13T14:00:47 | ---
annotations_creators:
- other
language:
- zh
language_creators:
- other
license:
- mit
multilinguality:
- monolingual
pretty_name: MNBVC
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
---
# Dataset Card for MNBVC
## Table of Contents
- [Dataset Card for MNBVC](#dataset-card-for-mnbvc)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [数据集介绍](#数据集介绍)
- [数据子集](#数据子集)
- [数据格式](#数据格式)
- [文本数据](#文本数据)
- [问答数据](#问答数据)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://mnbvc.253874.net/
- **Repository:** https://github.com/esbatmop/MNBVC
- **Paper:** N/A
- **Leaderboard:** N/A
- **Point of Contact:** N/A
### 数据集介绍
中文互联网上最古老最神秘(没有之一)的里屋社区于2023.1.1庄重宣布:
在英明神武的里屋管子带领下，决心发挥社区所长(哪都长)，帮助开源社区长期更新一份最大的中文互联网语料集。
Huggingface上的MNBVC数据集在逐渐更新中，请到[https://github.com/esbatmop/MNBVC](https://github.com/esbatmop/MNBVC) 获取未完成清洗的更多数据。
可以使用如下脚本加载：
```python
from datasets import load_dataset
dataset = load_dataset("liwu/MNBVC", 'law_judgement', split='train', streaming=True)
next(iter(dataset)) # get the first line
```
## 数据子集
MNBVC数据集包含数个子集：
- `law_judgement`: 来自法律文书的文本。
- `gov_xuexiqiangguo`: 来自学习强国的文本。
- `gov_report`: 来自政府工作报告的文本。
- `co_ann_report`: 企业年报文本。
- `code_metadata`: 代码元数据。
- `qa_zhihu`: 来自知乎的问答数据。
- `qa_wikihow`: 来自wikihow的问答数据。
- `qa_mfa`: 外交部问答数据。
- `news_peoples_daily`: 来自人民日报的文本数据。
- `wikipedia`: 来自维基百科的文本数据。
- `qa_stackexchange`: 来自StackExchange的问答数据。
- `qa_chatgpt`: 使用ChatGPT构造的问答语料，感谢[genggui001](https://github.com/genggui001)贡献语料。
- `math_qa`: 和数学领域有关的问答数据。
- `math_chat`: 和数学领域有关的对话数据数据，可以提升模型Chain of Thought的能力。
- `crawler_oscar`: 从CommonCrawl中清洗出来的通用文本数据。
## 数据格式
目前MNBVC数据集包含如下几类数据：
- 通用文本
- 问答语料
- 代码语料
- 多轮对话
- 论坛语料
- 平行语料
可以在[MNBVC的wiki页面](https://wiki.mnbvc.org/doku.php/%E7%8E%B0%E6%9C%89%E8%AF%AD%E6%96%99%E6%A0%BC%E5%BC%8F)上查看这几类数据的具体格式。
项目早期所上传的数据使用如下格式，以后这一格式会被废弃，相应数据也会重新上传：
```json
{
"text": datasets.Value("string"),
"meta": datasets.Value("string")
}
```
### Contributions
Thanks to the [Liwu community](http://mnbvc.253874.net/) for constructing this dataset.
Thanks to [silver](https://github.com/silverriver) and [jiaming](https://huggingface.co/Yjiaming) for adding and uploading this dataset to Huggingface. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
mxeval/mbxp | mxeval | 2023-07-03T18:10:10Z | 1,456 | 10 | null | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"mxeval",
"mbxp",
"mbpp",
"code-generation",
"arxiv:2210.14868",
"region:us"
] | 2023-07-03T18:10:10Z | 2023-03-14T21:32:18.000Z | 2023-03-14T21:32:18 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- mxeval
- mbxp
- mbpp
- code-generation
- mxeval
pretty_name: mbxp
size_categories:
- 10K<n<100K
---
# MBXP
## Table of Contents
- [MBXP](#MBXP)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#related-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Executional Correctness](#execution)
- [Execution Example](#execution-example)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
# MBXP
## Dataset Description
- **Repository:** [GitHub Repository](https://github.com/amazon-science/mbxp-exec-eval)
- **Paper:** [Multi-lingual Evaluation of Code Generation Models](https://openreview.net/forum?id=Bo7eeXm6An8)
### Dataset Summary
This repository contains data and code to perform execution-based multi-lingual evaluation of code generation capabilities and the corresponding data,
namely, a multi-lingual benchmark MBXP, multi-lingual MathQA and multi-lingual HumanEval.
<br>Results and findings can be found in the paper ["Multi-lingual Evaluation of Code Generation Models"](https://arxiv.org/abs/2210.14868).
### Related Tasks and Leaderboards
* [Multi-HumanEval](https://huggingface.co/datasets/mxeval/multi-humaneval)
* [MBXP](https://huggingface.co/datasets/mxeval/mbxp)
* [MathQA-X](https://huggingface.co/datasets/mxeval/mathqa-x)
### Languages
The programming problems are written in multiple programming languages and contain English natural text in comments and docstrings.
## Dataset Structure
To lookup currently supported datasets
```python
from datasets import get_dataset_config_names
get_dataset_config_names("mxeval/mbxp")
['python', 'csharp', 'go', 'java', 'javascript', 'kotlin', 'perl', 'php', 'ruby', 'scala', 'swift', 'typescript']
```
To load a specific dataset and language
```python
from datasets import load_dataset
load_dataset("mxeval/mbxp", "python")
DatasetDict({
test: Dataset({
features: ['task_id', 'language', 'prompt', 'test', 'entry_point', 'canonical_solution', 'description'],
num_rows: 974
})
})
```
### Data Instances
An example of a dataset instance:
```python
{
"task_id": "MBPP/1",
"language": "python",
"prompt": "\n\ndef min_cost(cost, m, n):\n\t\"\"\"\n\tWrite a function to find the minimum cost path to reach (m, n) from (0, 0) for the given cost matrix cost[][] and a position (m, n) in cost[][].\n\t>>> min_cost([[1, 2, 3], [4, 8, 2], [1, 5, 3]], 2, 2)\n\t8\n\t>>> min_cost([[2, 3, 4], [5, 9, 3], [2, 6, 4]], 2, 2)\n\t12\n\t>>> min_cost([[3, 4, 5], [6, 10, 4], [3, 7, 5]], 2, 2)\n\t16\n\t\"\"\"\n",
"test": "\n\nMETADATA = {}\n\n\ndef check(candidate):\n assert candidate([[1, 2, 3], [4, 8, 2], [1, 5, 3]], 2, 2) == 8\n assert candidate([[2, 3, 4], [5, 9, 3], [2, 6, 4]], 2, 2) == 12\n assert candidate([[3, 4, 5], [6, 10, 4], [3, 7, 5]], 2, 2) == 16\n\n",
"entry_point": "min_cost",
"canonical_solution": "\tR = 3\n\tC = 3\n\t \n\ttc = [[0 for x in range(C)] for x in range(R)] \n\ttc[0][0] = cost[0][0] \n\tfor i in range(1, m+1): \n\t\ttc[i][0] = tc[i-1][0] + cost[i][0] \n\tfor j in range(1, n+1): \n\t\ttc[0][j] = tc[0][j-1] + cost[0][j] \n\tfor i in range(1, m+1): \n\t\tfor j in range(1, n+1): \n\t\t\ttc[i][j] = min(tc[i-1][j-1], tc[i-1][j], tc[i][j-1]) + cost[i][j] \n\treturn tc[m][n]",
"description": "Write a function to find the minimum cost path to reach (m, n) from (0, 0) for the given cost matrix cost[][] and a position (m, n) in cost[][]."
}
```
### Data Fields
- `task_id`: identifier for the data sample
- `prompt`: input for the model containing function header and docstrings
- `canonical_solution`: solution for the problem in the `prompt`
- `description`: task description
- `test`: contains function to test generated code for correctness
- `entry_point`: entry point for test
- `language`: programming lanuage identifier to call the appropriate subprocess call for program execution
### Data Splits
- MBXP
- Python
- Java
- Javascript
- Typescript
- Kotlin
- Ruby
- Php
- Cpp
- Csharp
- Go
- Perl
- Scala
- Swift
## Dataset Creation
### Curation Rationale
Since code generation models are often trained on dumps of GitHub a dataset not included in the dump was necessary to properly evaluate the model. However, since this dataset was published on GitHub it is likely to be included in future dumps.
### Personal and Sensitive Information
None.
### Social Impact of Dataset
With this dataset code generating models can be better evaluated which leads to fewer issues introduced when using such models.
### Dataset Curators
AWS AI Labs
## Execution
### Execution Example
Install the repo [mbxp-exec-eval](https://github.com/amazon-science/mbxp-exec-eval) to execute generations or canonical solutions for the prompts from this dataset.
```python
>>> from datasets import load_dataset
>>> from mxeval.execution import check_correctness
>>> mbxp_python = load_dataset("mxeval/mbxp", "python", split="test")
>>> example_problem = mbxp_python[0]
>>> check_correctness(example_problem, example_problem["canonical_solution"], timeout=20.0)
{'task_id': 'MBPP/1', 'passed': True, 'result': 'passed', 'completion_id': None, 'time_elapsed': 10.314226150512695}
```
### Considerations for Using the Data
Make sure to sandbox the execution environment.
### Licensing Information
[LICENSE](https://huggingface.co/datasets/mxeval/mbxp/blob/main/mbxp-LICENSE) <br>
[THIRD PARTY LICENSES](https://huggingface.co/datasets/mxeval/mbxp/blob/main/THIRD_PARTY_LICENSES)
### Citation Information
```
@article{mbxp_athiwaratkun2022,
title = {Multi-lingual Evaluation of Code Generation Models},
author = {Athiwaratkun, Ben and
Gouda, Sanjay Krishna and
Wang, Zijian and
Li, Xiaopeng and
Tian, Yuchen and
Tan, Ming
and Ahmad, Wasi Uddin and
Wang, Shiqi and
Sun, Qing and
Shang, Mingyue and
Gonugondla, Sujan Kumar and
Ding, Hantian and
Kumar, Varun and
Fulton, Nathan and
Farahani, Arash and
Jain, Siddhartha and
Giaquinto, Robert and
Qian, Haifeng and
Ramanathan, Murali Krishna and
Nallapati, Ramesh and
Ray, Baishakhi and
Bhatia, Parminder and
Sengupta, Sudipta and
Roth, Dan and
Xiang, Bing},
doi = {10.48550/ARXIV.2210.14868},
url = {https://arxiv.org/abs/2210.14868},
keywords = {Machine Learning (cs.LG), Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
### Contributions
[skgouda@](https://github.com/sk-g) [benathi@](https://github.com/benathi) | [
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mrqa | null | 2022-11-18T21:30:01Z | 1,455 | 10 | mrqa-2019 | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
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"region:us"
] | 2022-11-18T21:30:01Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|drop
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- extended|natural_questions
- extended|race
- extended|search_qa
- extended|squad
- extended|trivia_qa
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: mrqa-2019
pretty_name: MRQA 2019
dataset_info:
features:
- name: subset
dtype: string
- name: context
dtype: string
- name: context_tokens
sequence:
- name: tokens
dtype: string
- name: offsets
dtype: int32
- name: qid
dtype: string
- name: question
dtype: string
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sequence:
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- name: detected_answers
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- name: token_spans
sequence:
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dtype: int32
- name: end
dtype: int32
- name: answers
sequence: string
config_name: plain_text
splits:
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num_bytes: 4090681873
num_examples: 516819
- name: test
num_bytes: 57712177
num_examples: 9633
- name: validation
num_bytes: 484107026
num_examples: 58221
download_size: 1479518355
dataset_size: 4632501076
---
# Dataset Card for MRQA 2019
## 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:** [MRQA 2019 Shared Task](https://mrqa.github.io/2019/shared.html)
- **Repository:** [MRQA 2019 Github repository](https://github.com/mrqa/MRQA-Shared-Task-2019)
- **Paper:** [MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension
](https://arxiv.org/abs/1910.09753)
- **Leaderboard:** [Shared task](https://mrqa.github.io/2019/shared.html)
- **Point of Contact:** [mrforqa@gmail.com](mrforqa@gmail.com)
### Dataset Summary
The MRQA 2019 Shared Task focuses on generalization in question answering. An effective question answering system should do more than merely interpolate from the training set to answer test examples drawn from the same distribution: it should also be able to extrapolate to out-of-distribution examples — a significantly harder challenge.
The dataset is a collection of 18 existing QA dataset (carefully selected subset of them) and converted to the same format (SQuAD format). Among these 18 datasets, six datasets were made available for training, six datasets were made available for development, and the final six for testing. The dataset is released as part of the MRQA 2019 Shared Task.
### Supported Tasks and Leaderboards
From the official repository:
*The format of the task is extractive question answering. Given a question and context passage, systems must find the word or phrase in the document that best answers the question. While this format is somewhat restrictive, it allows us to leverage many existing datasets, and its simplicity helps us focus on out-of-domain generalization, instead of other important but orthogonal challenges.*
*We have adapted several existing datasets from their original formats and settings to conform to our unified extractive setting. Most notably:*
- *We provide only a single, length-limited context.*
- *There are no unanswerable or non-span answer questions.*
- *All questions have at least one accepted answer that is found exactly in the context.*
*A span is judged to be an exact match if it matches the answer string after performing normalization consistent with the SQuAD dataset. Specifically:*
- *The text is uncased.*
- *All punctuation is stripped.*
- *All articles `{a, an, the}` are removed.*
- *All consecutive whitespace markers are compressed to just a single normal space `' '`.*
Answers are evaluated using exact match and token-level F1 metrics. One can refer to the [mrqa_official_eval.py](https://github.com/mrqa/MRQA-Shared-Task-2019/blob/master/mrqa_official_eval.py) for evaluation.
### Languages
The text in the dataset is in English. The associated BCP-47 code is `en`.
## Dataset Structure
### Data Instances
An examples looks like this:
```
{
'qid': 'f43c83e38d1e424ea00f8ad3c77ec999',
'subset': 'SQuAD'
'context': 'CBS broadcast Super Bowl 50 in the U.S., and charged an average of $5 million for a 30-second commercial during the game. The Super Bowl 50 halftime show was headlined by the British rock group Coldplay with special guest performers Beyoncé and Bruno Mars, who headlined the Super Bowl XLVII and Super Bowl XLVIII halftime shows, respectively. It was the third-most watched U.S. broadcast ever.',
'context_tokens': {
'offsets': [0, 4, 14, 20, 25, 28, 31, 35, 39, 41, 45, 53, 56, 64, 67, 68, 70, 78, 82, 84, 94, 105, 112, 116, 120, 122, 126, 132, 137, 140, 149, 154, 158, 168, 171, 175, 183, 188, 194, 203, 208, 216, 222, 233, 241, 245, 251, 255, 257, 261, 271, 275, 281, 286, 292, 296, 302, 307, 314, 323, 328, 330, 342, 344, 347, 351, 355, 360, 361, 366, 374, 379, 389, 393],
'tokens': ['CBS', 'broadcast', 'Super', 'Bowl', '50', 'in', 'the', 'U.S.', ',', 'and', 'charged', 'an', 'average', 'of', '$', '5', 'million', 'for', 'a', '30-second', 'commercial', 'during', 'the', 'game', '.', 'The', 'Super', 'Bowl', '50', 'halftime', 'show', 'was', 'headlined', 'by', 'the', 'British', 'rock', 'group', 'Coldplay', 'with', 'special', 'guest', 'performers', 'Beyoncé', 'and', 'Bruno', 'Mars', ',', 'who', 'headlined', 'the', 'Super', 'Bowl', 'XLVII', 'and', 'Super', 'Bowl', 'XLVIII', 'halftime', 'shows', ',', 'respectively', '.', 'It', 'was', 'the', 'third', '-', 'most', 'watched', 'U.S.', 'broadcast', 'ever', '.']
},
'question': "Who was the main performer at this year's halftime show?",
'question_tokens': {
'offsets': [0, 4, 8, 12, 17, 27, 30, 35, 39, 42, 51, 55],
'tokens': ['Who', 'was', 'the', 'main', 'performer', 'at', 'this', 'year', "'s", 'halftime', 'show', '?']
},
'detected_answers': {
'char_spans': [
{
'end': [201],
'start': [194]
}, {
'end': [201],
'start': [194]
}, {
'end': [201],
'start': [194]
}
],
'text': ['Coldplay', 'Coldplay', 'Coldplay'],
'token_spans': [
{
'end': [38],
'start': [38]
}, {
'end': [38],
'start': [38]
}, {
'end': [38],
'start': [38]
}
]
},
'answers': ['Coldplay', 'Coldplay', 'Coldplay'],
}
```
### Data Fields
- `subset`: which of the dataset does this examples come from?
- `context`: This is the raw text of the supporting passage. Three special token types have been inserted: `[TLE]` precedes document titles, `[DOC]` denotes document breaks, and `[PAR]` denotes paragraph breaks. The maximum length of the context is 800 tokens.
- `context_tokens`: A tokenized version of the supporting passage, using spaCy. Each token is a tuple of the token string and token character offset. The maximum number of tokens is 800.
- `tokens`: list of tokens.
- `offets`: list of offsets.
- `qas`: A list of questions for the given context.
- `qid`: A unique identifier for the question. The `qid` is unique across all datasets.
- `question`: The raw text of the question.
- `question_tokens`: A tokenized version of the question. The tokenizer and token format is the same as for the context.
- `tokens`: list of tokens.
- `offets`: list of offsets.
- `detected_answers`: A list of answer spans for the given question that index into the context. For some datasets these spans have been automatically detected using searching heuristics. The same answer may appear multiple times in the text --- each of these occurrences is recorded. For example, if `42` is the answer, the context `"The answer is 42. 42 is the answer."`, has two occurrences marked.
- `text`: The raw text of the detected answer.
- `char_spans`: Inclusive (start, end) character spans (indexing into the raw context).
- `start`: start (single element)
- `end`: end (single element)
- `token_spans`: Inclusive (start, end) token spans (indexing into the tokenized context).
- `start`: start (single element)
- `end`: end (single element)
### Data Splits
**Training data**
| Dataset | Number of Examples |
| :-----: | :------: |
| [SQuAD](https://arxiv.org/abs/1606.05250) | 86,588 |
| [NewsQA](https://arxiv.org/abs/1611.09830) | 74,160 |
| [TriviaQA](https://arxiv.org/abs/1705.03551)| 61,688 |
| [SearchQA](https://arxiv.org/abs/1704.05179)| 117,384 |
| [HotpotQA](https://arxiv.org/abs/1809.09600)| 72,928 |
| [NaturalQuestions](https://ai.google/research/pubs/pub47761)| 104,071 |
**Development data**
This in-domain data may be used for helping develop models.
| Dataset | Examples |
| :-----: | :------: |
| [SQuAD](https://arxiv.org/abs/1606.05250) | 10,507 |
| [NewsQA](https://arxiv.org/abs/1611.09830) | 4,212 |
| [TriviaQA](https://arxiv.org/abs/1705.03551)| 7,785|
| [SearchQA](https://arxiv.org/abs/1704.05179)| 16,980 |
| [HotpotQA](https://arxiv.org/abs/1809.09600)| 5,904 |
| [NaturalQuestions](https://ai.google/research/pubs/pub47761)| 12,836 |
**Test data**
The final testing data only contain out-of-domain data.
| Dataset | Examples |
| :-----: | :------: |
| [BioASQ](http://bioasq.org/) | 1,504 |
| [DROP](https://arxiv.org/abs/1903.00161) | 1,503 |
| [DuoRC](https://arxiv.org/abs/1804.07927)| 1,501 |
| [RACE](https://arxiv.org/abs/1704.04683) | 674 |
| [RelationExtraction](https://arxiv.org/abs/1706.04115) | 2,948|
| [TextbookQA](http://ai2-website.s3.amazonaws.com/publications/CVPR17_TQA.pdf)| 1,503 |
From the official repository:
***Note:** As previously mentioned, the out-of-domain dataset have been modified from their original settings to fit the unified MRQA Shared Task paradigm. At a high level, the following two major modifications have been made:*
*1. All QA-context pairs are extractive. That is, the answer is selected from the context and not via, e.g., multiple-choice.*
*2. All contexts are capped at a maximum of `800` tokens. As a result, for longer contexts like Wikipedia articles, we only consider examples where the answer appears in the first `800` tokens.*
*As a result, some splits are harder than the original datasets (e.g., removal of multiple-choice in RACE), while some are easier (e.g., restricted context length in NaturalQuestions --- we use the short answer selection). Thus one should expect different performance ranges if comparing to previous work on these datasets.*
## Dataset Creation
### Curation Rationale
From the official repository:
*Both train and test datasets have the same format described above, but may differ in some of the following ways:*
- *Passage distribution: Test examples may involve passages from different sources (e.g., science, news, novels, medical abstracts, etc) with pronounced syntactic and lexical differences.*
- *Question distribution: Test examples may emphasize different styles of questions (e.g., entity-centric, relational, other tasks reformulated as QA, etc) which may come from different sources (e.g., crowdworkers, domain experts, exam writers, etc.)*
- *Joint distribution: Test examples may vary according to the relationship of the question to the passage (e.g., collected independent vs. dependent of evidence, multi-hop, etc)*
### 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
Unknown
### Citation Information
```
@inproceedings{fisch2019mrqa,
title={{MRQA} 2019 Shared Task: Evaluating Generalization in Reading Comprehension},
author={Adam Fisch and Alon Talmor and Robin Jia and Minjoon Seo and Eunsol Choi and Danqi Chen},
booktitle={Proceedings of 2nd Machine Reading for Reading Comprehension (MRQA) Workshop at EMNLP},
year={2019},
}
```
### Contributions
Thanks to [@jimmycode](https://github.com/jimmycode), [@VictorSanh](https://github.com/VictorSanh) for adding this dataset. | [
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datablations/c4-subsets | datablations | 2023-06-14T11:46:48Z | 1,455 | 0 | null | [
"language:en",
"license:cc0-1.0",
"arxiv:2305.16264",
"region:us"
] | 2023-06-14T11:46:48Z | 2023-05-22T10:45:51.000Z | 2023-05-22T10:45:51 | ---
license: cc0-1.0
language:
- en
---
## Dataset Description
- **Repository:** https://github.com/huggingface/datablations
- **Paper:** [Scaling Data-Constrained Language Models](https://arxiv.org/abs/2305.16264)
- **Point of Contact:** [Niklas Muennighoff](mailto:n.muennighoff@gmail.com)
### Dataset Summary
Various subsets of [C4](https://huggingface.co/datasets/allenai/c4) with different numbers of tokens measured with the GPT2Tokenizer.
This data is used in the paper [Scaling Data-Constrained Language Models](https://arxiv.org/abs/2305.16264).
Please refer to [our GitHub repository](https://github.com/huggingface/datablations) for more details.
```bibtex
@article{muennighoff2023scaling,
title={Scaling Data-Constrained Language Models},
author={Muennighoff, Niklas and Rush, Alexander M and Barak, Boaz and Scao, Teven Le and Piktus, Aleksandra and Tazi, Nouamane and Pyysalo, Sampo and Wolf, Thomas and Raffel, Colin},
journal={arXiv preprint arXiv:2305.16264},
year={2023}
}
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
nlphuji/mscoco_2014_5k_test_image_text_retrieval | nlphuji | 2023-01-18T00:08:42Z | 1,452 | 2 | null | [
"arxiv:1405.0312",
"region:us"
] | 2023-01-18T00:08:42Z | 2023-01-12T14:37:24.000Z | 2023-01-12T14:37:24 | # MSCOCO (5K test set)
Original paper: [Microsoft COCO: Common Objects in Context
](https://arxiv.org/abs/1405.0312)
Homepage: https://cocodataset.org/#home
5K test set split from: http://cs.stanford.edu/people/karpathy/deepimagesent/caption_datasets.zip
Bibtex:
```
@inproceedings{lin2014microsoft,
title={Microsoft coco: Common objects in context},
author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
booktitle={European conference on computer vision},
pages={740--755},
year={2014},
organization={Springer}
}
``` | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
cardiffnlp/tweet_sentiment_multilingual | cardiffnlp | 2022-11-30T14:01:25Z | 1,451 | 14 | tweet_sentiment_multilingual | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-tweet-datasets",
"language:en",
"language:ar",
"language:fr",
"language:de",
"language:hi",
"language:it",
"language:pt",
"language:es",
"region:us"
] | 2022-11-30T14:01:25Z | 2022-11-26T23:34:42.000Z | 2022-11-26T23:34:42 | ---
language:
- en
- ar
- fr
- de
- hi
- it
- pt
- es
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-tweet-datasets
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: tweet_sentiment_multilingual
pretty_name: Tweet Sentiment Multilingual
train-eval-index:
- config: sentiment
task: text-classification
task_id: multi_class_classification
splits:
train_split: train
eval_split: test
col_mapping:
text: text
label: target
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 macro
args:
average: macro
- type: f1
name: F1 micro
args:
average: micro
- type: f1
name: F1 weighted
args:
average: weighted
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
configs:
- arabic
- english
- french
- german
- hindi
- italian
- portuguese
- spanish
dataset_info:
- config_name: sentiment
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
0: negative
1: neutral
2: positive
---
# Dataset Card for cardiffnlp/tweet_sentiment_multilingual
## Dataset Description
- **Homepage:** [https://github.com/cardiffnlp/xlm-t](https://github.com/cardiffnlp/xlm-t)
- **Repository:** - **Homepage:** [https://github.com/cardiffnlp/xlm-t](https://github.com/cardiffnlp/xlm-t)
- **Paper:** [https://aclanthology.org/2022.lrec-1.27/](https://aclanthology.org/2022.lrec-1.27/)
- **Point of Contact:** [Asahi Ushio](https://asahiushio.com/)
### Dataset Summary
Tweet Sentiment Multilingual consists of sentiment analysis dataset on Twitter in 8 different lagnuages.
- arabic
- english
- french
- german
- hindi
- italian
- portuguese
- spanish
### Supported Tasks and Leaderboards
- `text_classification`: The dataset can be trained using a SentenceClassification model from HuggingFace transformers.
## Dataset Structure
### Data Instances
An instance from `sentiment` config:
```
{'label': 2, 'text': '"QT @user In the original draft of the 7th book, Remus Lupin survived the Battle of Hogwarts. #HappyBirthdayRemusLupin"'}
```
### Data Fields
For `sentiment` config:
- `text`: a `string` feature containing the tweet.
- `label`: an `int` classification label with the following mapping:
`0`: negative
`1`: neutral
`2`: positive
### Data Splits
- arabic
- english
- french
- german
- hindi
- italian
- portuguese
- spanish
| name | train | validation | test |
| --------------- | ----- | ---------- | ----- |
| arabic | 1838 | 323 | 869 |
| english | 1838 | 323 | 869 |
| french | 1838 | 323 | 869 |
| german | 1838 | 323 | 869 |
| hindi | 1838 | 323 | 869 |
| italian | 1838 | 323 | 869 |
| portuguese | 1838 | 323 | 869 |
| spanish | 1838 | 323 | 869 |
### Dataset Curators
Francesco Barbieri, Jose Camacho-Collados, Luis Espiinosa-Anke and Leonardo Neves through Cardiff NLP.
### Licensing Information
[Creative Commons Attribution 3.0 Unported License](https://groups.google.com/g/semevaltweet/c/k5DDcvVb_Vo/m/zEOdECFyBQAJ), and all of the datasets require complying with Twitter [Terms Of Service](https://twitter.com/tos) and Twitter API [Terms Of Service](https://developer.twitter.com/en/developer-terms/agreement-and-policy)
### Citation Information
```
@inproceedings{barbieri-etal-2022-xlm,
title = "{XLM}-{T}: Multilingual Language Models in {T}witter for Sentiment Analysis and Beyond",
author = "Barbieri, Francesco and
Espinosa Anke, Luis and
Camacho-Collados, Jose",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.27",
pages = "258--266",
abstract = "Language models are ubiquitous in current NLP, and their multilingual capacity has recently attracted considerable attention. However, current analyses have almost exclusively focused on (multilingual variants of) standard benchmarks, and have relied on clean pre-training and task-specific corpora as multilingual signals. In this paper, we introduce XLM-T, a model to train and evaluate multilingual language models in Twitter. In this paper we provide: (1) a new strong multilingual baseline consisting of an XLM-R (Conneau et al. 2020) model pre-trained on millions of tweets in over thirty languages, alongside starter code to subsequently fine-tune on a target task; and (2) a set of unified sentiment analysis Twitter datasets in eight different languages and a XLM-T model trained on this dataset.",
}
```
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
TokenBender/code_instructions_122k_alpaca_style | TokenBender | 2023-07-20T19:59:33Z | 1,447 | 32 | null | [
"license:apache-2.0",
"region:us"
] | 2023-07-20T19:59:33Z | 2023-07-20T19:45:15.000Z | 2023-07-20T19:45:15 | ---
license: apache-2.0
---
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
nlphuji/flickr30k | nlphuji | 2023-01-19T17:40:41Z | 1,440 | 15 | null | [
"region:us"
] | 2023-01-19T17:40:41Z | 2023-01-19T12:00:06.000Z | 2023-01-19T12:00:06 | # Flickr30k
Original paper: [From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions](https://aclanthology.org/Q14-1006)
Homepage: https://shannon.cs.illinois.edu/DenotationGraph/
Bibtex:
```
@article{young2014image,
title={From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions},
author={Young, Peter and Lai, Alice and Hodosh, Micah and Hockenmaier, Julia},
journal={Transactions of the Association for Computational Linguistics},
volume={2},
pages={67--78},
year={2014},
publisher={MIT Press}
}
``` | [
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EleutherAI/persona | EleutherAI | 2023-08-29T07:53:23Z | 1,428 | 3 | null | [
"region:us"
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assin2 | null | 2023-01-25T14:26:53Z | 1,418 | 9 | assin2 | [
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:natural-language-inference",
"task_ids:semantic-similarity-scoring",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:pt",
"license:unknown",
"region:us"
] | 2023-01-25T14:26:53Z | 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:
- text-scoring
- natural-language-inference
- semantic-similarity-scoring
paperswithcode_id: assin2
pretty_name: ASSIN 2
dataset_info:
features:
- name: sentence_pair_id
dtype: int64
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: relatedness_score
dtype: float32
- name: entailment_judgment
dtype:
class_label:
names:
'0': NONE
'1': ENTAILMENT
splits:
- name: train
num_bytes: 864816
num_examples: 6500
- name: test
num_bytes: 339580
num_examples: 2448
- name: validation
num_bytes: 66895
num_examples: 500
download_size: 2113646
dataset_size: 1271291
---
# Dataset Card for ASSIN 2
## 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:** [ASSIN 2 homepage](https://sites.google.com/view/assin2)
- **Repository:** [ASSIN 2 repository](https://sites.google.com/view/assin2)
- **Paper:** [The ASSIN 2 shared task: a quick overview](https://drive.google.com/file/d/1ft1VU6xiVm-N58dfAp6FHWjQ4IvcXgqp/view)
- **Point of Contact:** [Livy Real](mailto:livyreal@gmail.com)
### Dataset Summary
The ASSIN 2 corpus is composed of rather simple sentences. Following the procedures of SemEval 2014 Task 1.
The training and validation data are composed, respectively, of 6,500 and 500 sentence pairs in Brazilian Portuguese,
annotated for entailment and semantic similarity. Semantic similarity values range from 1 to 5, and text entailment
classes are either entailment or none. The test data are composed of approximately 3,000 sentence pairs with the same
annotation. All data were manually annotated.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The language supported is Portuguese.
## Dataset Structure
### Data Instances
An example from the ASSIN 2 dataset looks as follows:
```
{
"entailment_judgment": 1,
"hypothesis": "Uma criança está segurando uma pistola de água",
"premise": "Uma criança risonha está segurando uma pistola de água e sendo espirrada com água",
"relatedness_score": 4.5,
"sentence_pair_id": 1
}
```
### Data Fields
- `sentence_pair_id`: a `int64` feature.
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `relatedness_score`: a `float32` feature.
- `entailment_judgment`: a classification label, with possible values including `NONE`, `ENTAILMENT`.
### Data Splits
The data is split into train, validation and test set. The split sizes are as follow:
| Train | Val | Test |
| ------ | ----- | ---- |
| 6500 | 500 | 2448 |
## 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{real2020assin,
title={The assin 2 shared task: a quick overview},
author={Real, Livy and Fonseca, Erick and Oliveira, Hugo Goncalo},
booktitle={International Conference on Computational Processing of the Portuguese Language},
pages={406--412},
year={2020},
organization={Springer}
}
```
### Contributions
Thanks to [@jonatasgrosman](https://github.com/jonatasgrosman) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ybelkada/football-dataset | ybelkada | 2023-01-17T11:47:41Z | 1,417 | 0 | null | [
"region:us"
] | 2023-01-17T11:47:41Z | 2023-01-17T11:46:21.000Z | 2023-01-17T11:46:21 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 2073622.0
num_examples: 6
download_size: 2074835
dataset_size: 2073622.0
---
# Dataset Card for "football-dataset"
Dummy dataset of 6 football players with a caption that can be used to fine-tune any Image Captioning model. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
pie/conll2003 | pie | 2023-11-27T10:00:01Z | 1,415 | 0 | null | [
"region:us"
] | 2023-11-27T10:00:01Z | 2022-04-21T14:15:40.000Z | 2022-04-21T14:15:40 | # PIE Dataset Card for "conll2003"
This is a [PyTorch-IE](https://github.com/ChristophAlt/pytorch-ie) wrapper for the
[CoNLL 2003 Huggingface dataset loading script](https://huggingface.co/datasets/conll2003).
## Data Schema
The document type for this dataset is `CoNLL2003Document` which defines the following data fields:
- `text` (str)
- `id` (str, optional)
- `metadata` (dictionary, optional)
and the following annotation layers:
- `entities` (annotation type: `LabeledSpan`, target: `text`)
See [here](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/annotations.py) for the annotation type definitions.
## Document Converters
The dataset provides document converters for the following target document types:
- `pytorch_ie.documents.TextDocumentWithLabeledSpans`
See [here](https://github.com/ChristophAlt/pytorch-ie/blob/main/src/pytorch_ie/documents.py) for the document type
definitions.
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ivanzhouyq/RedPajama-Tiny | ivanzhouyq | 2023-07-03T18:16:47Z | 1,415 | 2 | null | [
"task_categories:text-generation",
"language:en",
"region:us"
] | 2023-07-03T18:16:47Z | 2023-07-03T16:48:05.000Z | 2023-07-03T16:48:05 | ---
task_categories:
- text-generation
language:
- en
pretty_name: RedPajama Tiny
---
# Dataset Card for Dataset Name
### Dataset Summary
This is a tiny version of the RedPajama dataset, which is a clean-room, fully open-source implementation of the LLaMa dataset.
This dataset contains 64 samples from each of the 7 sources.
The full dataset has the following token counts and is available for [download]( https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T):
| Dataset | Token Count |
|---------------|-------------|
| Commoncrawl | 878 Billion |
| C4 | 175 Billion |
| GitHub | 59 Billion |
| Books | 26 Billion |
| ArXiv | 28 Billion |
| Wikipedia | 24 Billion |
| StackExchange | 20 Billion |
| Total | 1.2 Trillion |
### Languages
Primarily English, though the Wikipedia slice contains multiple languages.
## Dataset Structure
The dataset structure is as follows:
```
{
"text": ...,
"meta": {"url": "...", "timestamp": "...", "source": "...", "language": "...", ...}
}
```
## Dataset Creation
This dataset was created to follow the LLaMa paper as closely as possible to try to reproduce its recipe.
### Source Data
#### Commoncrawl
We download five dumps from Commoncrawl, and run the dumps through the official `cc_net` pipeline.
We then deduplicate on the paragraph level, and filter out low quality text using a linear classifier trained to
classify paragraphs as Wikipedia references or random Commoncrawl samples.
#### C4
C4 is downloaded from Huggingface. The only preprocessing step is to bring the data into our own format.
#### GitHub
The raw GitHub data is downloaded from Google BigQuery. We deduplicate on the file level and filter out low quality
files and only keep projects that are distributed under the MIT, BSD, or Apache license.
#### Wikipedia
We use the Wikipedia dataset available on Huggingface, which is based on the Wikipedia dump from 2023-03-20 and contains
text in 20 different languages. The dataset comes in preprocessed format, so that hyperlinks, comments and other
formatting boilerplate has been removed.
#### Gutenberg and Books3
The PG19 subset of the Gutenberg Project and Books3 datasets are downloaded from Huggingface. After downloading, we use
simhash to remove near duplicates.
#### ArXiv
ArXiv data is downloaded from Amazon S3 in the `arxiv` requester pays bucket. We only keep latex source files and
remove preambles, comments, macros and bibliographies.
#### Stackexchange
The Stack Exchange split of the dataset is download from the
[Internet Archive](https://archive.org/download/stackexchange). Here we only keep the posts from the 28 largest sites,
remove html tags, group the posts into question-answer pairs, and order answers by their score.
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sayemimtiaz/LLM | sayemimtiaz | 2023-11-26T02:17:21Z | 1,414 | 0 | null | [
"region:us"
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squad_kor_v1 | null | 2023-06-15T15:25:29Z | 1,404 | 10 | korquad | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ko",
"license:cc-by-nd-4.0",
"arxiv:1909.07005",
"region:us"
] | 2023-06-15T15:25:29Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- ko
license:
- cc-by-nd-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: korquad
pretty_name: The Korean Question Answering Dataset
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
config_name: squad_kor_v1
splits:
- name: train
num_bytes: 83380337
num_examples: 60407
- name: validation
num_bytes: 8261729
num_examples: 5774
download_size: 42408533
dataset_size: 91642066
---
# Dataset Card for KorQuAD v1.0
## 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:** https://korquad.github.io/KorQuad%201.0/
- **Repository:** https://github.com/korquad/korquad.github.io/tree/master/dataset
- **Paper:** https://arxiv.org/abs/1909.07005
### Dataset Summary
KorQuAD 1.0 is a large-scale question-and-answer dataset constructed for Korean machine reading comprehension, and investigate the dataset to understand the distribution of answers and the types of reasoning required to answer the question. This dataset benchmarks the data generating process of SQuAD v1.0 to meet the standard.
### Supported Tasks and Leaderboards
`question-answering`
### Languages
Korean
## Dataset Structure
Follows the standars SQuAD format.
### Data Instances
An example from the data set looks as follows:
```
{'answers': {'answer_start': [54], 'text': ['교향곡']},
'context': '1839년 바그너는 괴테의 파우스트을 처음 읽고 그 내용에 마음이 끌려 이를 소재로 해서 하나의 교향곡을 쓰려는 뜻을 갖는다. 이 시기 바그너는 1838년에 빛 독촉으로 산전수전을 다 걲은 상황이라 좌절과 실망에 가득했으며 메피스토펠레스를 만나는 파우스트의 심경에 공감했다고 한다. 또한 파리에서 아브네크의 지휘로 파리 음악원 관현악단이 연주하는 베토벤의 교향곡 9번을 듣고 깊은 감명을 받았는데, 이것이 이듬해 1월에 파우스트의 서곡으로 쓰여진 이 작품에 조금이라도 영향을 끼쳤으리라는 것은 의심할 여지가 없다. 여기의 라단조 조성의 경우에도 그의 전기에 적혀 있는 것처럼 단순한 정신적 피로나 실의가 반영된 것이 아니라 베토벤의 합창교향곡 조성의 영향을 받은 것을 볼 수 있다. 그렇게 교향곡 작곡을 1839년부터 40년에 걸쳐 파리에서 착수했으나 1악장을 쓴 뒤에 중단했다. 또한 작품의 완성과 동시에 그는 이 서곡(1악장)을 파리 음악원의 연주회에서 연주할 파트보까지 준비하였으나, 실제로는 이루어지지는 않았다. 결국 초연은 4년 반이 지난 후에 드레스덴에서 연주되었고 재연도 이루어졌지만, 이후에 그대로 방치되고 말았다. 그 사이에 그는 리엔치와 방황하는 네덜란드인을 완성하고 탄호이저에도 착수하는 등 분주한 시간을 보냈는데, 그런 바쁜 생활이 이 곡을 잊게 한 것이 아닌가 하는 의견도 있다.',
'id': '6566495-0-0',
'question': '바그너는 괴테의 파우스트를 읽고 무엇을 쓰고자 했는가?',
'title': '파우스트_서곡'}
```
### Data Fields
```
{'id': Value(dtype='string', id=None),
'title': Value(dtype='string', id=None),
'context': Value(dtype='string', id=None),
'question': Value(dtype='string', id=None),
'answers': Sequence(feature={'text': Value(dtype='string', id=None), 'answer_start': Value(dtype='int32', id=None)}, length=-1, id=None)}
```
### Data Splits
- Train: 60407
- Validation: 5774
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
Wikipedia
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[CC BY-ND 2.0 KR](https://creativecommons.org/licenses/by-nd/2.0/kr/deed.en)
### Citation Information
```
@article{lim2019korquad1,
title={Korquad1. 0: Korean qa dataset for machine reading comprehension},
author={Lim, Seungyoung and Kim, Myungji and Lee, Jooyoul},
journal={arXiv preprint arXiv:1909.07005},
year={2019}
```
### Contributions
Thanks to [@cceyda](https://github.com/cceyda) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
keremberke/chest-xray-classification | keremberke | 2023-01-18T09:25:27Z | 1,401 | 9 | null | [
"task_categories:image-classification",
"roboflow",
"roboflow2huggingface",
"Biology",
"region:us"
] | 2023-01-18T09:25:27Z | 2023-01-18T09:22:08.000Z | 2023-01-18T09:22:08 | ---
task_categories:
- image-classification
tags:
- roboflow
- roboflow2huggingface
- Biology
---
<div align="center">
<img width="640" alt="keremberke/chest-xray-classification" src="https://huggingface.co/datasets/keremberke/chest-xray-classification/resolve/main/thumbnail.jpg">
</div>
### Dataset Labels
```
['NORMAL', 'PNEUMONIA']
```
### Number of Images
```json
{'train': 4077, 'test': 582, 'valid': 1165}
```
### How to Use
- Install [datasets](https://pypi.org/project/datasets/):
```bash
pip install datasets
```
- Load the dataset:
```python
from datasets import load_dataset
ds = load_dataset("keremberke/chest-xray-classification", name="full")
example = ds['train'][0]
```
### Roboflow Dataset Page
[https://universe.roboflow.com/mohamed-traore-2ekkp/chest-x-rays-qjmia/dataset/2](https://universe.roboflow.com/mohamed-traore-2ekkp/chest-x-rays-qjmia/dataset/2?ref=roboflow2huggingface)
### Citation
```
```
### License
CC BY 4.0
### Dataset Summary
This dataset was exported via roboflow.ai on March 31, 2022 at 3:11 PM GMT
It includes 5824 images.
Pneumonia are annotated in folder format.
The following pre-processing was applied to each image:
* Auto-orientation of pixel data (with EXIF-orientation stripping)
* Resize to 640x640 (Stretch)
No image augmentation techniques were applied.
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ai4bharat/IN22-Gen | ai4bharat | 2023-09-12T11:13:23Z | 1,401 | 1 | null | [
"task_categories:translation",
"language_creators:expert-generated",
"multilinguality:multilingual",
"multilinguality:translation",
"size_categories:1K<n<10K",
"language:as",
"language:bn",
"language:brx",
"language:doi",
"language:en",
"language:gom",
"language:gu",
"language:hi",
"language:kn",
"language:ks",
"language:mai",
"language:ml",
"language:mr",
"language:mni",
"language:ne",
"language:or",
"language:pa",
"language:sa",
"language:sat",
"language:sd",
"language:ta",
"language:te",
"language:ur",
"license:cc-by-4.0",
"arxiv:2305.16307",
"region:us"
] | 2023-09-12T11:13:23Z | 2023-09-09T17:16:09.000Z | 2023-09-09T17:16:09 | ---
language:
- as
- bn
- brx
- doi
- en
- gom
- gu
- hi
- kn
- ks
- mai
- ml
- mr
- mni
- ne
- or
- pa
- sa
- sat
- sd
- ta
- te
- ur
language_details: >-
asm_Beng, ben_Beng, brx_Deva, doi_Deva, eng_Latn, gom_Deva, guj_Gujr,
hin_Deva, kan_Knda, kas_Arab, mai_Deva, mal_Mlym, mar_Deva, mni_Mtei,
npi_Deva, ory_Orya, pan_Guru, san_Deva, sat_Olck, snd_Deva, tam_Taml,
tel_Telu, urd_Arab
license: cc-by-4.0
language_creators:
- expert-generated
multilinguality:
- multilingual
- translation
pretty_name: in22-gen
size_categories:
- 1K<n<10K
task_categories:
- translation
---
# IN22-Gen
IN22 is a newly created comprehensive benchmark for evaluating machine translation performance in multi-domain, n-way parallel contexts across 22 Indic languages. IN22-Gen is a general-purpose multi-domain evaluation subset of IN22. It has been created from two sources: Wikipedia and Web Sources offering diverse content spanning news, entertainment, culture, legal, and India-centric topics. The evaluation subset consists of 1024 sentences translated across 22 Indic languages enabling evaluation of MT systems across 506 directions.
Here is the domain and source distribution of our IN22-Gen evaluation subset.
<table style="width: 40%">
<tr>
<td>domain</td>
<td>web sources</td>
<td>wikipedia</td>
</tr>
<tr>
<td>culture</td>
<td>40</td>
<td>40</td>
</tr>
<tr>
<td>economy</td>
<td>40</td>
<td>40</td>
</tr>
<tr>
<td>education</td>
<td>40</td>
<td>40</td>
</tr>
<tr>
<td>entertainment</td>
<td>40</td>
<td>40</td>
</tr>
<tr>
<td>geography</td>
<td>40</td>
<td>40</td>
</tr>
<tr>
<td>governments</td>
<td>40</td>
<td>40</td>
</tr>
<tr>
<td>health</td>
<td>40</td>
<td>40</td>
</tr>
<tr>
<td>industry</td>
<td>40</td>
<td>40</td>
</tr>
<tr>
<td>legal</td>
<td>40</td>
<td>40</td>
</tr>
<tr>
<td>news</td>
<td>32</td>
<td>32</td>
</tr>
<tr>
<td>religion</td>
<td>40</td>
<td>40</td>
</tr>
<tr>
<td>sports</td>
<td>40</td>
<td>40</td>
</tr>
<tr>
<td>tourism</td>
<td>40</td>
<td>40</td>
</tr>
<tr>
<td>total</td>
<td>512</td>
<td>512</td>
</tr>
</table>
Please refer to the `Appendix E: Dataset Card` of the [preprint](https://arxiv.org/abs/2305.16307) on detailed description of dataset curation, annotation and quality control process.
### Dataset Structure
#### Dataset Fields
- `id`: Row number for the data entry, starting at 1.
- `context`: Context window of 3 sentences, typically includes one sentence before and after the candidate sentence.
- `source`: The source from which the candidate sentence is considered.
- `url`: The URL for the English article from which the sentence was extracted. Only available for candidate sentences sourced from Wikipedia
- `domain`: The domain of the sentence.
- `num_words`: The number of words in the candidate sentence.
- `bucket`: Classification of the candidate sentence as per predefined bucket categories.
- `sentence`: The full sentence in the specific language (may have _lang for pairings)
#### Data Instances
A sample from the `gen` split for the English language (`eng_Latn` config) is provided below. All configurations have the same structure, and all sentences are aligned across configurations and splits.
```python
{
"id": 1,
"context": "A uniform is often viewed as projecting a positive image of an organisation. Maintaining personal hygiene is also an important aspect of personal appearance and dressing. An appearance is a bunch of attributes related with the service person, like their shoes, clothes, tie, jewellery, hairstyle, make-up, watch, cosmetics, perfume, etc.",
"source": "web",
"url": "",
"domain": "culture",
"num_words": 24,
"bucket": "18 - 25",
"sentence": "An appearance is a bunch of attributes related to the service person, like their shoes, clothes, tie, jewellery, hairstyle, make-up, watch, cosmetics, perfume, etc."
}
```
When using a hyphenated pairing or using the `all` function, data will be presented as follows:
```python
{
"id": 1,
"context": "A uniform is often viewed as projecting a positive image of an organisation. Maintaining personal hygiene is also an important aspect of personal appearance and dressing. An appearance is a bunch of attributes related with the service person, like their shoes, clothes, tie, jewellery, hairstyle, make-up, watch, cosmetics, perfume, etc.",
"source": "web",
"url": "",
"domain": "culture",
"num_words": 24,
"bucket": "18 - 25",
"sentence_eng_Latn": "An appearance is a bunch of attributes related to the service person, like their shoes, clothes, tie, jewellery, hairstyle, make-up, watch, cosmetics, perfume, etc.",
"sentence_hin_Deva": "सेवा संबंधी लोगों के लिए भेष कई गुणों का संयोजन है, जैसे कि उनके जूते, कपड़े, टाई, आभूषण, केश शैली, मेक-अप, घड़ी, कॉस्मेटिक, इत्र, आदि।"
}
```
### Usage Instructions
```python
from datasets import load_dataset
# download and load all the pairs
dataset = load_dataset("ai4bharat/IN22-Gen", "all")
# download and load specific pairs
dataset = load_dataset("ai4bharat/IN22-Gen", "eng_Latn-hin_Deva")
```
### Languages Covered
<table style="width: 40%">
<tr>
<td>Assamese (asm_Beng)</td>
<td>Kashmiri (Arabic) (kas_Arab)</td>
<td>Punjabi (pan_Guru)</td>
</tr>
<tr>
<td>Bengali (ben_Beng)</td>
<td>Kashmiri (Devanagari) (kas_Deva)</td>
<td>Sanskrit (san_Deva)</td>
</tr>
<tr>
<td>Bodo (brx_Deva)</td>
<td>Maithili (mai_Deva)</td>
<td>Santali (sat_Olck)</td>
</tr>
<tr>
<td>Dogri (doi_Deva)</td>
<td>Malayalam (mal_Mlym)</td>
<td>Sindhi (Arabic) (snd_Arab)</td>
</tr>
<tr>
<td>English (eng_Latn)</td>
<td>Marathi (mar_Deva)</td>
<td>Sindhi (Devanagari) (snd_Deva)</td>
</tr>
<tr>
<td>Konkani (gom_Deva)</td>
<td>Manipuri (Bengali) (mni_Beng)</td>
<td>Tamil (tam_Taml)</td>
</tr>
<tr>
<td>Gujarati (guj_Gujr)</td>
<td>Manipuri (Meitei) (mni_Mtei)</td>
<td>Telugu (tel_Telu)</td>
</tr>
<tr>
<td>Hindi (hin_Deva)</td>
<td>Nepali (npi_Deva)</td>
<td>Urdu (urd_Arab)</td>
</tr>
<tr>
<td>Kannada (kan_Knda)</td>
<td>Odia (ory_Orya)</td>
</tr>
</table>
### Citation
If you consider using our work then please cite using:
```
@article{ai4bharat2023indictrans2,
title = {IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages},
author = {AI4Bharat and Jay Gala and Pranjal A. Chitale and Raghavan AK and Sumanth Doddapaneni and Varun Gumma and Aswanth Kumar and Janki Nawale and Anupama Sujatha and Ratish Puduppully and Vivek Raghavan and Pratyush Kumar and Mitesh M. Khapra and Raj Dabre and Anoop Kunchukuttan},
year = {2023},
journal = {arXiv preprint arXiv: 2305.16307}
}
```
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
alzoubi36/policy_ie_a | alzoubi36 | 2023-06-24T07:20:44Z | 1,393 | 0 | null | [
"region:us"
] | 2023-06-24T07:20:44Z | 2023-06-24T07:16:05.000Z | 2023-06-24T07:16:05 | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 592707
num_examples: 4109
- name: validation
num_bytes: 16114
num_examples: 100
- name: test
num_bytes: 163819
num_examples: 1041
download_size: 364376
dataset_size: 772640
---
# Dataset for the PolicyIE-A task in the [PrivacyGLUE](https://github.com/infsys-lab/privacy-glue) dataset
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GEM/viggo | GEM | 2022-10-24T15:31:07Z | 1,390 | 14 | null | [
"task_categories:table-to-text",
"annotations_creators:none",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"data-to-text",
"region:us"
] | 2022-10-24T15:31:07Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
pretty_name: viggo
tags:
- data-to-text
---
# Dataset Card for GEM/viggo
## Dataset Description
- **Homepage:** https://nlds.soe.ucsc.edu/viggo
- **Repository:** [Needs More Information]
- **Paper:** https://aclanthology.org/W19-8623/
- **Leaderboard:** N/A
- **Point of Contact:** Juraj Juraska
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/viggo).
### Dataset Summary
ViGGO is an English data-to-text generation dataset in the video game domain, with target responses being more conversational than information-seeking, yet constrained to the information presented in a meaning representation. The dataset is relatively small with about 5,000 datasets but very clean, and can thus serve for evaluating transfer learning, low-resource, or few-shot capabilities of neural models.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/viggo')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/viggo).
#### website
[Wesbite](https://nlds.soe.ucsc.edu/viggo)
#### paper
[ACL Anthology](https://aclanthology.org/W19-8623/)
#### authors
Juraj Juraska, Kevin K. Bowden, Marilyn Walker
## Dataset Overview
### Where to find the Data and its Documentation
#### Webpage
<!-- info: What is the webpage for the dataset (if it exists)? -->
<!-- scope: telescope -->
[Wesbite](https://nlds.soe.ucsc.edu/viggo)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[ACL Anthology](https://aclanthology.org/W19-8623/)
#### 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{juraska-etal-2019-viggo,
title = "{V}i{GGO}: A Video Game Corpus for Data-To-Text Generation in Open-Domain Conversation",
author = "Juraska, Juraj and
Bowden, Kevin and
Walker, Marilyn",
booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
month = oct # "{--}" # nov,
year = "2019",
address = "Tokyo, Japan",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-8623",
doi = "10.18653/v1/W19-8623",
pages = "164--172",
}
```
#### 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 -->
Juraj Juraska
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
jjuraska@ucsc.edu
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
no
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
no
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`English`
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
cc-by-sa-4.0: Creative Commons Attribution Share Alike 4.0 International
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
ViGGO was designed for the task of data-to-text generation in chatbots (as opposed to task-oriented dialogue systems), with target responses being more conversational than information-seeking, yet constrained to the information presented in a meaning representation. The dataset, being relatively small and clean, can also serve for demonstrating transfer learning capabilities of neural models.
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Data-to-Text
### 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 California, Santa Cruz
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Juraj Juraska, Kevin K. Bowden, Marilyn Walker
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
Juraj Juraska
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
Each example in the dataset has the following two fields:
- `mr`: A meaning representation (MR) that, in a structured format, provides the information to convey, as well as the desired dialogue act (DA) type.
- `ref`: A reference output, i.e., a corresponding utterance realizing all the information in the MR.
Each MR is a flattened dictionary of attribute-and-value pairs, "wrapped" in the dialogue act type indication. This format was chosen primarily for its compactness, but also to allow for easy concatenation of multiple DAs (each with potentially different attributes) in a single MR.
Following is the list of all possible attributes (which are also refered to as "slots") in ViGGO along with their types/possible values:
- `name`: The name of a video game (e.g., Rise of the Tomb Raider).
- `release_year`: The year a video game was released in (e.g., 2015).
- `exp_release_date`: For a not-yet-released game, the date when it is expected to be released (e.g., February 22, 2019). *Note: This slot cannot appear together with `release_year` in the same dialogue act.*
- `developer`: The name of the studio/person that created the game (e.g., Crystal Dynamics).
- `genres`: A list of one or more genre labels from a set of possible values (e.g., action-adventure, shooter).
- `player_perspective`: A list of one or more perspectives from which the game is/can be played (possible values: first person, third person, side view, bird view).
- `platforms`: A list of one or more gaming platforms the game was officially released for (possible values: PC, PlayStation, Xbox, Nintendo, Nintendo Switch).
- `esrb`: A game's content rating as determined by the ESRB (possible values: E (for Everyone), E 10+ (for Everyone 10 and Older), T (for Teen), M (for Mature)).
- `rating`: Depending on the dialogue act this slot is used with, it is a categorical representation of either the game's average rating or the game's liking (possible values: excellent, good, average, poor).
- `has_multiplayer`: Indicates whether a game supports multiplayer or can only be played in single-player mode (possible values: yes, no).
- `available_on_steam`: Indicates whether a game can be purchased through the Steam digital distribution service (possible values: yes, no).
- `has_linux_release`: Indicates whether a game is supported on Linux operating systems (possible values: yes, no).
- `has_mac_release`: Indicates whether a game is supported on macOS (possible values: yes, no).
- `specifier`: A game specifier used by the `request` DA, typically an adjective (e.g., addictive, easiest, overrated, visually impressive).
Each MR in the dataset has 3 distinct reference utterances, which are represented as 3 separate examples with the same MR.
#### Reason for Structure
<!-- info: How was the dataset structure determined? -->
<!-- scope: microscope -->
The dataset structure mostly follows the format of the popular E2E dataset, however, with added dialogue act type indications, new list-type attributes introduced, and unified naming convention for multi-word attribute names.
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
```
{
"mr": "give_opinion(name[SpellForce 3], rating[poor], genres[real-time strategy, role-playing], player_perspective[bird view])",
"ref": "I think that SpellForce 3 is one of the worst games I've ever played. Trying to combine the real-time strategy and role-playing genres just doesn't work, and the bird view perspective makes it near impossible to play."
}
```
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
ViGGO is split into 3 partitions, with no MRs in common between the training set and either of the validation and the test set (and that *after* delexicalizing the `name` and `developer` slots). The ratio of examples in the partitions is approximately 7.5 : 1 : 1.5, with their exact sizes listed below:
- **Train:** 5,103 (1,675 unique MRs)
- **Validation:** 714 (238 unique MRs)
- **Test:** 1,083 (359 unique MRs)
- **TOTAL:** 6,900 (2,253 unique MRs)
*Note: The reason why the number of unique MRs is not exactly one third of all examples is that for each `request_attribute` DA (which only has one slot, and that without a value) 12 reference utterances were collected instead of 3.*
#### 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 -->
A similar MR length and slot distribution was preserved across the partitions. The distribution of DA types, on the other hand, is skewed slightly toward fewer `inform` DA instances (the most prevalent DA type) and a higher proportion of the less prevalent DAs in the validation and the test set.
####
<!-- info: What does an outlier of the dataset in terms of length/perplexity/embedding look like? -->
<!-- scope: microscope -->
```
{
"mr": "request_attribute(player_perspective[])",
"ref": "Is there a certain player perspective that you prefer over others in games you play?"
},
{
"mr": "inform(name[FIFA 12], esrb[E (for Everyone)], genres[simulation, sport], player_perspective[bird view, side view], platforms[PlayStation, Xbox, Nintendo, PC], available_on_steam[no])",
"ref": "Fifa 12 is a decent sports simulator. It's pretty cool how the game swaps from the bird's eye perspective down to a side view while you're playing. You can get the game for PlayStation, Xbox, Nintendo consoles, and PC, but unfortunately it's not on Steam. Of course, as a sports game there's not much objectionable content so it's rated E."
},
{
"mr": "inform(name[Super Bomberman], release_year[1993], genres[action, strategy], has_multiplayer[no], platforms[Nintendo, PC], available_on_steam[no], has_linux_release[no], has_mac_release[no])",
"ref": "Super Bomberman is one of my favorite Nintendo games, also available on PC, though not through Steam. It came out all the way back in 1993, and you can't get it for any modern consoles, unfortunately, so no online multiplayer, or of course Linux or Mac releases either. That said, it's still one of the most addicting action-strategy games out there."
}
```
## 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 -->
ViGGO is a fairly small dataset but includes a greater variety of utterance types than most other datasets for NLG from structured meaning representations. This makes it more interesting from the perspective of model evaluation, since models have to learn to differentiate between various dialogue act types that share the same slots.
#### 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 -->
ViGGO's language is more casual and conversational -- as opposed to information-seeking -- which differentiates it from the majority of popular datasets for the same type of data-to-text task. Moreover, the video game domain is a rather uncommon one in the NLG community, despite being very well-suited for data-to-text generation, considering it offers entities with many attributes to talk about, which can be described in a structured format.
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
no
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
no
### Getting Started with the Task
#### Pointers to Resources
<!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. -->
<!-- scope: microscope -->
- [E2E NLG Challenge](http://www.macs.hw.ac.uk/InteractionLab/E2E/)
#### Technical Terms
<!-- info: Technical terms used in this card and the dataset and their definitions -->
<!-- scope: microscope -->
- MR = meaning representation
- DA = dialogue act
## Previous Results
### Previous Results
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`BLEU`, `METEOR`, `ROUGE`, `BERT-Score`, `BLEURT`, `Other: Other Metrics`
#### Other Metrics
<!-- info: Definitions of other metrics -->
<!-- scope: periscope -->
SER (slot error rate): Indicates the proportion of missing/incorrect/duplicate/hallucinated slot mentions in the utterances across a test set. The closer to zero a model scores in this metric, the more semantically accurate its outputs are. This metric is typically calculated either manually on a small sample of generated outputs, or heuristically using domain-specific regex rules and gazetteers.
#### 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 -->
- [Juraska et al., 2019. ViGGO: A Video Game Corpus for Data-To-Text Generation in Open-Domain Conversation.](https://aclanthology.org/W19-8623/)
- [Harkous et al., 2020. Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity.](https://aclanthology.org/2020.coling-main.218/)
- [Kedzie and McKeown, 2020. Controllable Meaning Representation to Text Generation: Linearization and Data Augmentation Strategies.](https://aclanthology.org/2020.emnlp-main.419/)
- [Juraska and Walker, 2021. Attention Is Indeed All You Need: Semantically Attention-Guided Decoding for Data-to-Text NLG.](https://aclanthology.org/2021.inlg-1.45/)
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
The primary motivation behind ViGGO was to create a data-to-text corpus in a new but conversational domain, and intended for use in open-domain chatbots rather than task-oriented dialogue systems. To this end, the dataset contains utterances of 9 generalizable and conversational dialogue act types, revolving around various aspects of video games. The idea is that similar, relatively small datasets could fairly easily be collected for other conversational domains -- especially other entertainment domains (such as music or books), but perhaps also topics like animals or food -- to support an open-domain conversational agent with controllable neural NLG.
Another desired quality of the ViGGO dataset was cleanliness (no typos and grammatical errors) and semantic accuracy, which has often not been the case with other crowdsourced data-to-text corpora. In general, for the data-to-text generation task, there is arguably no need to put the burden on the generation model to figure out the noise, since the noise would not be expected to be there in a real-world system whose dialogue manager that creates the input for the NLG module is usually configurable and tightly controlled.
#### Communicative Goal
<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
Produce a response from a structured meaning representation in the context of a conversation about video games. It can be a brief opinion or a description of a game, as well as a request for attribute (e.g., genre, player perspective, or platform) preference/confirmation or an inquiry about liking a particular type of games.
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
no
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Crowdsourced`
#### Where was it crowdsourced?
<!-- info: If crowdsourced, where from? -->
<!-- scope: periscope -->
`Amazon Mechanical Turk`
#### Language Producers
<!-- info: What further information do we have on the language producers? -->
<!-- scope: microscope -->
The paid crowdworkers who produced the reference utterances were from English-speaking countries, and they had at least 1,000 HITs approved and a HIT approval rate of 98% or more. Furthermore, in the instructions, crowdworkers were discouraged from taking on the task unless they considered themselves a gamer.
#### Topics Covered
<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
<!-- scope: periscope -->
The dataset focuses on video games and their various aspects, and hence the language of the utterances may contain video game-specific jargon.
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
validated by data curator
#### Data Preprocessing
<!-- info: How was the text data pre-processed? (Enter N/A if the text was not pre-processed) -->
<!-- scope: microscope -->
First, regular expressions were used to enforce several standardization policies regarding special characters, punctuation, and the correction of undesired abbreviations/misspellings of standard domain-specific terms (e.g., terms like "Play station" or "PS4" would be changed to the uniform "PlayStation"). At the same time, hyphens were removed or enforced uniformly in certain terms, for example, "single-player". Although phrases such as "first person" should correctly have a hyphen when used as adjective, the crowdworkers used this rule very inconsistently. In order to avoid model outputs being penalized during the evaluation by the arbitrary choice of a hyphen presence or absence in the reference utterances, the hyphen was removed in all such phrases regardless of the noun vs. adjective use.
Second, an extensive set of heuristics was developed to identify slot-related errors. This process revealed the vast majority of missing or incorrect slot mentions, which were subsequently fixed according to the corresponding MRs. This eventually led to the development of a robust, cross-domain, heuristic slot aligner that can be used for automatic slot error rate evaluation. For details, see the appendix in [Juraska and Walker, 2021](https://aclanthology.org/2021.inlg-1.45/).
Crowdworkers would sometimes also inject a piece of information which was not present in the MR, some of which is not even represented by any of the slots, e.g., plot or main characters. This unsolicited information was removed from the utterances so as to avoid confusing the neural model. Finally, any remaining typos and grammatical errors were resolved.
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
manually
#### Filter Criteria
<!-- info: What were the selection criteria? -->
<!-- scope: microscope -->
Compliance with the indicated dialogue act type, semantic accuracy (i.e., all information in the corresponding MR mentioned and that correctly), and minimal extraneous information (e.g., personal experience/opinion). Whenever it was within a reasonable amount of effort, the utterances were manually fixed instead of being discarded/crowdsourced anew.
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
none
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
no
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
no PII
#### Justification for no PII
<!-- info: Provide a justification for selecting `no PII` above. -->
<!-- scope: periscope -->
Crowdworkers were instructed to only express the information in the provided meaning representation, which never prompted them to mention anything about themselves. Occasionally, they would still include a bit of personal experience (e.g., "I used to like the game as a kid.") or opinion, but these would be too general to be considered PII.
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
no
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
no
## Considerations for Using the Data
### PII Risks and Liability
### Licenses
### Known Technical Limitations
#### Technical Limitations
<!-- info: Describe any known technical limitations, such as spurrious correlations, train/test overlap, annotation biases, or mis-annotations, and cite the works that first identified these limitations when possible. -->
<!-- scope: microscope -->
The dataset is limited to a single domain: video games. One caveat of using a language generator trained on this dataset in a dialogue system as-is is that multiple subsequent turns discussing the same video game would be repeating its full name. ViGGO was designed for generation without context, and therefore it is up to the dialogue manager to ensure that pronouns are substituted for the names whenever it would sound more natural in a dialogue. Alternately, the dataset can easily be augmented with automatically constructed samples which omit the `name` slot in the MR and replace the name with a pronoun in the reference utterance.
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
mozilla-foundation/common_voice_9_0 | mozilla-foundation | 2023-07-29T16:00:12Z | 1,386 | 12 | common-voice | [
"task_categories:automatic-speech-recognition",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"source_datasets:extended|common_voice",
"license:cc0-1.0",
"arxiv:1912.06670",
"region:us"
] | 2023-07-29T16:00:12Z | 2022-04-29T16:49:21.000Z | 2022-04-29T16:49:21 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
license:
- cc0-1.0
multilinguality:
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source_datasets:
- extended|common_voice
paperswithcode_id: common-voice
pretty_name: Common Voice Corpus 9.0
language_bcp47:
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- zh-CN
- zh-HK
- zh-TW
extra_gated_prompt: By clicking on “Access repository” below, you also agree to not
attempt to determine the identity of speakers in the Common Voice dataset.
task_categories:
- automatic-speech-recognition
---
# Dataset Card for Common Voice Corpus 9.0
## 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:** https://commonvoice.mozilla.org/en/datasets
- **Repository:** https://github.com/common-voice/common-voice
- **Paper:** https://arxiv.org/abs/1912.06670
- **Leaderboard:** https://paperswithcode.com/dataset/common-voice
- **Point of Contact:** [Anton Lozhkov](mailto:anton@huggingface.co)
### Dataset Summary
The Common Voice dataset consists of a unique MP3 and corresponding text file.
Many of the 20217 recorded hours in the dataset also include demographic metadata like age, sex, and accent
that can help improve the accuracy of speech recognition engines.
The dataset currently consists of 14973 validated hours in 93 languages, but more voices and languages are always added.
Take a look at the [Languages](https://commonvoice.mozilla.org/en/languages) page to request a language or start contributing.
### Supported Tasks and Leaderboards
The results for models trained on the Common Voice datasets are available via the
[🤗 Speech Bench](https://huggingface.co/spaces/huggingface/hf-speech-bench)
### Languages
```
Abkhaz, Arabic, Armenian, Assamese, Azerbaijani, Basaa, Bashkir, Basque, Belarusian, Bengali, Breton, Bulgarian, Cantonese, Catalan, Central Kurdish, Chinese (China), Chinese (Hong Kong), Chinese (Taiwan), Chuvash, Czech, Danish, Dhivehi, Dutch, English, Erzya, Esperanto, Estonian, Finnish, French, Frisian, Galician, Georgian, German, Greek, Guarani, Hakha Chin, Hausa, Hindi, Hungarian, Igbo, Indonesian, Interlingua, Irish, Italian, Japanese, Kabyle, Kazakh, Kinyarwanda, Kurmanji Kurdish, Kyrgyz, Latvian, Lithuanian, Luganda, Macedonian, Malayalam, Maltese, Marathi, Meadow Mari, Moksha, Mongolian, Norwegian Nynorsk, Odia, Persian, Polish, Portuguese, Punjabi, Romanian, Romansh Sursilvan, Romansh Vallader, Russian, Sakha, Santali (Ol Chiki), Serbian, Slovak, Slovenian, Sorbian, Upper, Spanish, Swahili, Swedish, Taiwanese (Minnan), Tamil, Tatar, Thai, Tigre, Toki Pona, Turkish, Ukrainian, Urdu, Uyghur, Uzbek, Vietnamese, Votic, Welsh
```
## Dataset Structure
### Data Instances
A typical data point comprises the `path` to the audio file and its `sentence`.
Additional fields include `accent`, `age`, `client_id`, `up_votes`, `down_votes`, `gender`, `locale` and `segment`.
```python
{
'client_id': 'd59478fbc1ee646a28a3c652a119379939123784d99131b865a89f8b21c81f69276c48bd574b81267d9d1a77b83b43e6d475a6cfc79c232ddbca946ae9c7afc5',
'path': 'et/clips/common_voice_et_18318995.mp3',
'audio': {
'path': 'et/clips/common_voice_et_18318995.mp3',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 48000
},
'sentence': 'Tasub kokku saada inimestega, keda tunned juba ammust ajast saati.',
'up_votes': 2,
'down_votes': 0,
'age': 'twenties',
'gender': 'male',
'accent': '',
'locale': 'et',
'segment': ''
}
```
### Data Fields
`client_id` (`string`): An id for which client (voice) made the recording
`path` (`string`): The path to the audio file
`audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
`sentence` (`string`): The sentence the user was prompted to speak
`up_votes` (`int64`): How many upvotes the audio file has received from reviewers
`down_votes` (`int64`): How many downvotes the audio file has received from reviewers
`age` (`string`): The age of the speaker (e.g. `teens`, `twenties`, `fifties`)
`gender` (`string`): The gender of the speaker
`accent` (`string`): Accent of the speaker
`locale` (`string`): The locale of the speaker
`segment` (`string`): Usually an empty field
### Data Splits
The speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.
The validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.
The invalidated data is data has been invalidated by reviewers
and received downvotes indicating that the data is of low quality.
The reported data is data that has been reported, for different reasons.
The other data is data that has not yet been reviewed.
The dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.
## Data Preprocessing Recommended by Hugging Face
The following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice.
Many examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.
In addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, **almost all** sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.
```python
from datasets import load_dataset
ds = load_dataset("mozilla-foundation/common_voice_9_0", "en", use_auth_token=True)
def prepare_dataset(batch):
"""Function to preprocess the dataset with the .map method"""
transcription = batch["sentence"]
if transcription.startswith('"') and transcription.endswith('"'):
# we can remove trailing quotation marks as they do not affect the transcription
transcription = transcription[1:-1]
if transcription[-1] not in [".", "?", "!"]:
# append a full-stop to sentences that do not end in punctuation
transcription = transcription + "."
batch["sentence"] = transcription
return batch
ds = ds.map(prepare_dataset, desc="preprocess dataset")
```
## 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
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.
## Considerations for Using the Data
### Social Impact of Dataset
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Public Domain, [CC-0](https://creativecommons.org/share-your-work/public-domain/cc0/)
### Citation Information
```
@inproceedings{commonvoice:2020,
author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
title = {Common Voice: A Massively-Multilingual Speech Corpus},
booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
pages = {4211--4215},
year = 2020
}
```
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biomrc | null | 2023-04-05T09:41:42Z | 1,382 | 4 | biomrc | [
"language:en",
"region:us"
] | 2023-04-05T09:41:42Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
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---
# Dataset Card for "biomrc"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [http://nlp.cs.aueb.gr/](http://nlp.cs.aueb.gr/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 1.29 GB
- **Size of the generated dataset:** 5.81 GB
- **Total amount of disk used:** 7.09 GB
### Dataset Summary
We introduce BIOMRC, a large-scale cloze-style biomedical MRC dataset. Care was taken to reduce noise, compared to the previous BIOREAD dataset of Pappas et al. (2018). Experiments show that simple heuristics do not perform well on the new dataset and that two neural MRC models that had been tested on BIOREAD perform much better on BIOMRC, indicating that the new dataset is indeed less noisy or at least that its task is more feasible. Non-expert human performance is also higher on the new dataset compared to BIOREAD, and biomedical experts perform even better. We also introduce a new BERT-based MRC model, the best version of which substantially outperforms all other methods tested, reaching or surpassing the accuracy of biomedical experts in some experiments. We make the new dataset available in three different sizes, also releasing our code, and providing a leaderboard.
### 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
#### biomrc_large_A
- **Size of downloaded dataset files:** 408.08 MB
- **Size of the generated dataset:** 1.92 GB
- **Total amount of disk used:** 2.33 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"abstract": "\"OBJECTIVES: @entity9 is a @entity10 that may result from greater occipital nerve entrapment. Entrapped peripheral nerves typica...",
"answer": "@entity9 :: (MESH:D009437,Disease) :: ['unilateral occipital neuralgia']\n",
"entities_list": ["@entity1 :: ('9606', 'Species') :: ['patients']", "@entity10 :: ('MESH:D006261', 'Disease') :: ['headache', 'Headache']", "@entity9 :: ('MESH:D009437', 'Disease') :: ['Occipital neuralgia', 'unilateral occipital neuralgia']"],
"title": "Sonographic evaluation of the greater occipital nerve in XXXX .\n"
}
```
#### biomrc_large_B
- **Size of downloaded dataset files:** 343.06 MB
- **Size of the generated dataset:** 1.54 GB
- **Total amount of disk used:** 1.88 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"abstract": "\"BACKGROUND: Adults with physical disabilities are less likely than others to receive @entity2 screening. It is not known, howev...",
"answer": "@entity2",
"entities_list": ["@entity2", "@entity1", "@entity0", "@entity3"],
"title": "Does a standard measure of self-reported physical disability correlate with clinician perception of impairment related to XXXX screening?\n"
}
```
#### biomrc_small_A
- **Size of downloaded dataset files:** 68.88 MB
- **Size of the generated dataset:** 236.32 MB
- **Total amount of disk used:** 305.20 MB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"abstract": "\"PURPOSE: @entity120 ( @entity120 ) is a life-limiting @entity102 that presents as an elevated blood pressure in the pulmonary a...",
"answer": "@entity148 :: (MESH:D001008,Disease) :: ['anxiety']\n",
"entities_list": "[\"@entity1 :: ('9606', 'Species') :: ['patients']\", \"@entity308 :: ('MESH:D003866', 'Disease') :: ['depression']\", \"@entity146 :...",
"title": "A predictive model of the effects of @entity308 , XXXX , stress, 6-minute-walk distance, and social support on health-related quality of life in an adult pulmonary hypertension population.\n"
}
```
#### biomrc_small_B
- **Size of downloaded dataset files:** 57.70 MB
- **Size of the generated dataset:** 189.62 MB
- **Total amount of disk used:** 247.33 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"abstract": "\"Single-agent activity for @entity12 reflected by response rates of 10%-30% has been reported in @entity0 with @entity3 ( @entit...",
"answer": "@entity10",
"entities_list": ["@entity0", "@entity6", "@entity2", "@entity5", "@entity12", "@entity11", "@entity1", "@entity7", "@entity9", "@entity10", "@entity3", "@entity4", "@entity8"],
"title": "No synergistic activity of @entity7 and XXXX in the treatment of @entity3 .\n"
}
```
#### biomrc_tiny_A
- **Size of downloaded dataset files:** 0.02 MB
- **Size of the generated dataset:** 0.07 MB
- **Total amount of disk used:** 0.09 MB
An example of 'test' looks as follows.
```
This example was too long and was cropped:
{
"abstract": "\"OBJECTIVE: Decompressive craniectomy (DC) requires later cranioplasty (CP) in survivors. However, if additional ventriculoperit...",
"answer": "@entity260 :: (MESH:D011183,Disease) :: ['Postoperative Complications']\n",
"entities_list": ["@entity1 :: ('9606', 'Species') :: ['Patients', 'patients', 'Patient']", "@entity260 :: ('MESH:D011183', 'Disease') :: ['VPS regarding postoperative complications']", "@entity1276 :: ('MESH:D006849', 'Disease') :: ['hydrocephalus']"],
"title": "Cranioplasty and Ventriculoperitoneal Shunt Placement after Decompressive Craniectomy: Staged Surgery Is Associated with Fewer XXXX .\n"
}
```
### Data Fields
The data fields are the same among all splits.
#### biomrc_large_A
- `abstract`: a `string` feature.
- `title`: a `string` feature.
- `entities_list`: a `list` of `string` features.
- `answer`: a `string` feature.
#### biomrc_large_B
- `abstract`: a `string` feature.
- `title`: a `string` feature.
- `entities_list`: a `list` of `string` features.
- `answer`: a `string` feature.
#### biomrc_small_A
- `abstract`: a `string` feature.
- `title`: a `string` feature.
- `entities_list`: a `list` of `string` features.
- `answer`: a `string` feature.
#### biomrc_small_B
- `abstract`: a `string` feature.
- `title`: a `string` feature.
- `entities_list`: a `list` of `string` features.
- `answer`: a `string` feature.
#### biomrc_tiny_A
- `abstract`: a `string` feature.
- `title`: a `string` feature.
- `entities_list`: a `list` of `string` features.
- `answer`: a `string` feature.
### Data Splits
#### biomrc_large_A
| |train |validation|test |
|--------------|-----:|---------:|----:|
|biomrc_large_A|700000| 50000|62707|
#### biomrc_large_B
| |train |validation|test |
|--------------|-----:|---------:|----:|
|biomrc_large_B|700000| 50000|62707|
#### biomrc_small_A
| |train|validation|test|
|--------------|----:|---------:|---:|
|biomrc_small_A|87500| 6250|6250|
#### biomrc_small_B
| |train|validation|test|
|--------------|----:|---------:|---:|
|biomrc_small_B|87500| 6250|6250|
#### biomrc_tiny_A
| |test|
|-------------|---:|
|biomrc_tiny_A| 30|
## 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{pappas-etal-2020-biomrc,
title = "{B}io{MRC}: A Dataset for Biomedical Machine Reading Comprehension",
author = "Pappas, Dimitris and
Stavropoulos, Petros and
Androutsopoulos, Ion and
McDonald, Ryan",
booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.bionlp-1.15",
pages = "140--149",
abstract = "We introduce BIOMRC, a large-scale cloze-style biomedical MRC dataset. Care was taken to reduce noise, compared to the previous BIOREAD dataset of Pappas et al. (2018). Experiments show that simple heuristics do not perform well on the new dataset and that two neural MRC models that had been tested on BIOREAD perform much better on BIOMRC, indicating that the new dataset is indeed less noisy or at least that its task is more feasible. Non-expert human performance is also higher on the new dataset compared to BIOREAD, and biomedical experts perform even better. We also introduce a new BERT-based MRC model, the best version of which substantially outperforms all other methods tested, reaching or surpassing the accuracy of biomedical experts in some experiments. We make the new dataset available in three different sizes, also releasing our code, and providing a leaderboard.",
}
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@PetrosStav](https://github.com/PetrosStav), [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf) for adding this dataset. | [
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cyberagent/crello | cyberagent | 2023-09-14T08:33:47Z | 1,380 | 15 | null | [
"task_categories:unconditional-image-generation",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cdla-permissive-2.0",
"graphic design",
"design templates",
"arxiv:2108.01249",
"region:us"
] | 2023-09-14T08:33:47Z | 2023-02-03T01:31:45.000Z | 2023-02-03T01:31:45 | ---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license: cdla-permissive-2.0
multilinguality:
- monolingual
pretty_name: crello
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- graphic design
- design templates
task_categories:
- unconditional-image-generation
task_ids: []
dataset_info:
features:
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dtype: string
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dtype: int64
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dtype:
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'175': Pacifico
'176': Pangolin
'177': Parisienne
'178': Pathway Gothic One
'179': Patrick Hand
'180': Pattaya
'181': Patua One
'182': Permanent Marker
'183': Petit Formal Script
'184': Philosopher
'185': Pinyon Script
'186': Pirou
'187': Play
'188': Playball
'189': Playfair Display
'190': Playlist Caps
'191': Playlist Script
'192': Podkova
'193': Poiret One
'194': Pompiere
'195': Port Lligat Slab
'196': Press Start 2P
'197': Prompt
'198': Pt Sans
'199': Quattrocento
'200': Quicksand
'201': Racing Sans One
'202': Radley
'203': Rakkas
'204': Raleway
'205': Raleway Dots
'206': Rammetto One
'207': Rationale
'208': Reem Kufi
'209': Reenie Beanie
'210': Righteous
'211': Rise
'212': Rissa Typeface
'213': Roboto
'214': Rochester
'215': Rock Salt
'216': Rokkitt
'217': Rosario
'218': Rubik
'219': Rubik One
'220': Ruslan Display
'221': Russo One
'222': Rye
'223': Sacramento
'224': Sansita One
'225': Satisfy
'226': Scope One
'227': Secular One
'228': Selima Script
'229': Sensei
'230': Seymour One
'231': Shadows Into Light Two
'232': Share Tech Mono
'233': Sirin Stencil
'234': Six Caps
'235': Source Serif Pro
'236': Space Mono
'237': Stalemate
'238': Stint Ultra Expanded
'239': Sue Ellen Francisco
'240': Suez One
'241': Sunday
'242': Superclarendon Regular
'243': Text Me One
'244': Tinos
'245': Titillium Web
'246': Tulpen One
'247': Underdog
'248': V T323
'249': Vampiro One
'250': Varela Round
'251': Vast Shadow
'252': Vollkorn
'253': Waiting For The Sunrise
'254': Wire One
'255': Yanone Kaffeesatz
'256': Yellowtail
'257': Yeseva One
'258': Yesteryear
'259': Zeyada
'260': Znikomit
'261': Znikomitno24
- name: font_size
sequence: float32
- name: text_align
sequence:
class_label:
names:
'0': ''
'1': center
'2': left
'3': right
- name: angle
sequence: float32
- name: capitalize
sequence:
class_label:
names:
'0': 'false'
'1': 'true'
- name: line_height
sequence: float32
- name: letter_spacing
sequence: float32
- name: suitability
sequence:
class_label:
names:
'0': mobile
- name: keywords
sequence: string
- name: industries
sequence:
class_label:
names:
'0': artCrafts
'1': beautyCosmetics
'2': businessFinance
'3': corporate
'4': ecologyNature
'5': educationTraining
'6': entertainmentLeisure
'7': familyKids
'8': fashionStyle
'9': foodBeverages
'10': healthWellness
'11': homeLiving
'12': hrRecruitment
'13': marketingAds
'14': nonProfitCharity
'15': petsAnimals
'16': realEstateConstruction
'17': religionFaith
'18': retail
'19': services
'20': sportFitness
'21': techGadgets
'22': transportDelivery
'23': travelTourism
- name: color
sequence:
sequence: float32
length: 3
- name: image
sequence: image
splits:
- name: train
num_bytes: 3322744283.141
num_examples: 18659
- name: test
num_bytes: 421990602.771
num_examples: 2371
- name: validation
num_bytes: 425905823.995
num_examples: 2391
download_size: 4130251706
dataset_size: 4170640709.9069996
---
# Dataset Card for Crello
## Table of Contents
- [Dataset Card for Crello](#dataset-card-for-crello)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [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:** [CanvasVAE github](https://github.com/CyberAgentAILab/canvas-vae)
- **Repository:**
- **Paper:** [CanvasVAE: Learning to Generate Vector Graphic Documents](https://arxiv.org/abs/2108.01249)
- **Leaderboard:**
- **Point of Contact:** [Kota Yamaguchi](https://github.com/kyamagu)
### Dataset Summary
The Crello dataset is compiled for the study of vector graphic documents. The dataset contains document meta-data such as canvas size and pre-rendered elements such as images or text boxes. The original templates were collected from [crello.com](https://crello.com) (now [create.vista.com](https://create.vista.com/)) and converted to a low-resolution format suitable for machine learning analysis.
### Supported Tasks and Leaderboards
[CanvasVAE](https://arxiv.org/abs/2108.01249) studies unsupervised document generation.
### Languages
Almost all design templates use English.
## Dataset Structure
### Data Instances
Each instance has scalar attributes (canvas) and sequence attributes (elements). Categorical values are stored as integer values. Check `ClassLabel` features of the dataset for the list of categorical labels.
```
{'id': '592d6c2c95a7a863ddcda140',
'length': 8,
'group': 4,
'format': 20,
'canvas_width': 3,
'canvas_height': 1,
'category': 0,
'title': 'Beauty Blog Ad Woman with Unusual Hairstyle',
'type': [1, 3, 3, 3, 3, 4, 4, 4],
'left': [0.0,
-0.0009259259095415473,
0.24444444477558136,
0.5712962746620178,
0.2657407522201538,
0.369228333234787,
0.2739444375038147,
0.44776931405067444],
'top': [0.0,
-0.0009259259095415473,
0.37037035822868347,
0.41296297311782837,
0.41296297311782837,
0.8946287035942078,
0.4549448788166046,
0.40591198205947876],
'width': [1.0,
1.0018517971038818,
0.510185182094574,
0.16296295821666718,
0.16296295821666718,
0.30000001192092896,
0.4990740716457367,
0.11388888955116272],
'height': [1.0,
1.0018517971038818,
0.25833332538604736,
0.004629629664123058,
0.004629629664123058,
0.016611294820904732,
0.12458471953868866,
0.02657807245850563],
'opacity': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
'text': ['', '', '', '', '', 'STAY WITH US', 'FOLLOW', 'PRESS'],
'font': [0, 0, 0, 0, 0, 152, 172, 152],
'font_size': [0.0, 0.0, 0.0, 0.0, 0.0, 18.0, 135.0, 30.0],
'text_align': [0, 0, 0, 0, 0, 2, 2, 2],
'angle': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
'capitalize': [0, 0, 0, 0, 0, 0, 0, 0],
'line_height': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
'letter_spacing': [0.0, 0.0, 0.0, 0.0, 0.0, 14.0, 12.55813980102539, 3.0],
'suitability': [0],
'keywords': ['beautiful',
'beauty',
'blog',
'blogging',
'caucasian',
'cute',
'elegance',
'elegant',
'fashion',
'fashionable',
'femininity',
'glamour',
'hairstyle',
'luxury',
'model',
'stylish',
'vogue',
'website',
'woman',
'post',
'instagram',
'ig',
'insta',
'fashion',
'purple'],
'industries': [1, 8, 13],
'color': [[153.0, 118.0, 96.0],
[34.0, 23.0, 61.0],
[34.0, 23.0, 61.0],
[255.0, 255.0, 255.0],
[255.0, 255.0, 255.0],
[255.0, 255.0, 255.0],
[255.0, 255.0, 255.0],
[255.0, 255.0, 255.0]],
'image': [<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>,
<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>,
<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>,
<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>,
<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>,
<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>,
<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>,
<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>]}
```
To get a label for categorical values, use the `int2str` method:
```python
key = "font"
example = dataset[0]
dataset.features[key].int2str(example[key])
```
### Data Fields
In the following, categorical fields are shown as `categorical` type, but the actual storage is `int64`.
**Canvas attributes**
| Field | Type | Shape | Description |
| ------------- | ----------- | ------- | --------------------------------------------------------------- |
| id | string | () | Template ID from crello.com |
| group | categorical | () | Broad design groups, such as social media posts or blog headers |
| format | categorical | () | Detailed design formats, such as Instagram post or postcard |
| category | categorical | () | Topic category of the design, such as holiday celebration |
| canvas_width | categorical | () | Canvas pixel width |
| canvas_height | categorical | () | Canvas pixel height |
| length | int64 | () | Length of elements |
| suitability | categorical | (None,) | List of display tags, only `mobile` tag exists |
| keywords | string | (None,) | List of keywords associated to this template |
| industries | categorical | (None,) | List of industry tags like `marketingAds` |
**Element attributes**
| Field | Type | Shape | Description |
| -------------- | ----------- | --------- | -------------------------------------------------------------------- |
| type | categorical | (None,) | Element type, such as vector shape, image, or text |
| left | float32 | (None,) | Element left position normalized to [0, 1] range w.r.t. canvas_width |
| top | float32 | (None,) | Element top position normalized to [0, 1] range w.r.t. canvas_height |
| width | float32 | (None,) | Element width normalized to [0, 1] range w.r.t. canvas_width |
| height | float32 | (None,) | Element height normalized to [0, 1] range w.r.t. canvas_height |
| color | int64 | (None, 3) | Extracted main RGB color of the element |
| opacity | float32 | (None,) | Opacity in [0, 1] range |
| image | image | (None,) | Pre-rendered 256x256 preview of the element encoded in PNG format |
| text | string | (None,) | Text content in UTF-8 encoding for text element |
| font | categorical | (None,) | Font family name for text element |
| font_size | float32 | (None,) | Font size (height) in pixels |
| text_align | categorical | (None,) | Horizontal text alignment, left, center, right for text element |
| angle | float32 | (None,) | Element rotation angle (radian) w.r.t. the center of the element |
| capitalize | categorical | (None,) | Binary flag to capitalize letters |
| line_height | float32 | (None,) | Scaling parameter to line height, default is 1.0 |
| letter_spacing | float32 | (None,) | Adjustment parameter for letter spacing, default is 0.0 |
Note that the color and pre-rendered images do not necessarily accurately reproduce the original design templates. The original template is accessible at the following URL if still available.
```
https://create.vista.com/artboard/?template=<template_id>
```
`left` and `top` can be negative because elements can be bigger than the canvas size.
### Data Splits
The Crello dataset has 3 splits: train, validation, and test. The current split is generated such that the same title of the original template shows up in only in one split.
| Split | Count |
| --------- | ----- |
| train | 18659 |
| validaton | 2391 |
| test | 2371 |
### Visualization
Each example can be visualized in the following approach using [`skia-python`](https://kyamagu.github.io/skia-python/). Note the following does not guarantee a similar appearance to the original template. Currently, the quality of text rendering is far from perfect.
```python
import io
from typing import Any, Dict
import numpy as np
import skia
def render(features: datasets.Features, example: Dict[str, Any], max_size: float=512.) -> bytes:
"""Render parsed sequence example onto an image and return as PNG bytes."""
canvas_width = int(features["canvas_width"].int2str(example["canvas_width"]))
canvas_height = int(features["canvas_height"].int2str(example["canvas_height"]))
scale = min(1.0, max_size / canvas_width, max_size / canvas_height)
surface = skia.Surface(int(scale * canvas_width), int(scale * canvas_height))
with surface as canvas:
canvas.scale(scale, scale)
for index in range(example["length"]):
pil_image = example["image"][index]
image = skia.Image.frombytes(
pil_image.convert('RGBA').tobytes(),
pil_image.size,
skia.kRGBA_8888_ColorType)
left = example["left"][index] * canvas_width
top = example["top"][index] * canvas_height
width = example["width"][index] * canvas_width
height = example["height"][index] * canvas_height
rect = skia.Rect.MakeXYWH(left, top, width, height)
paint = skia.Paint(Alphaf=example["opacity"][index], AntiAlias=True)
angle = example["angle"][index]
with skia.AutoCanvasRestore(canvas):
if angle != 0:
degree = 180. * angle / np.pi
canvas.rotate(degree, left + width / 2., top + height / 2.)
canvas.drawImageRect(image, rect, paint=paint)
image = surface.makeImageSnapshot()
with io.BytesIO() as f:
image.save(f, skia.kPNG)
return f.getvalue()
```
## Dataset Creation
### Curation Rationale
The Crello dataset is compiled for the general study of vector graphic documents, with the goal of producing a dataset that offers complete vector graphic information suitable for neural methodologies.
### Source Data
#### Initial Data Collection and Normalization
The dataset is initially scraped from the former `crello.com` and pre-processed to the above format.
#### Who are the source language producers?
While [create.vista.com](https://create.vista.com/) owns those templates, the templates seem to be originally created by a specific group of design studios.
### Personal and Sensitive Information
The dataset does not contain any personal information about the creator but may contain a picture of people in the design template.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset was developed for advancing the general study of vector graphic documents, especially for generative systems of graphic design. Successful utilization might enable the automation of creative workflow that human designers get involved in.
### Discussion of Biases
The templates contained in the dataset reflect the biases appearing in the source data, which could present gender biases in specific design categories.
### Other Known Limitations
Due to the unknown data specification of the source data, the color and pre-rendered images do not necessarily accurately reproduce the original design templates. The original template is accessible at the following URL if still available.
https://create.vista.com/artboard/?template=<template_id>
## Additional Information
### Dataset Curators
The Crello dataset was developed by [Kota Yamaguchi](https://github.com/kyamagu).
### Licensing Information
The origin of the dataset is [create.vista.com](https://create.vista.com) (formally, `crello.com`).
The distributor ("We") do not own the copyrights of the original design templates.
By using the Crello dataset, the user of this dataset ("You") must agree to the
[VistaCreate License Agreements](https://create.vista.com/faq/legal/licensing/license_agreements/).
The dataset is distributed under [CDLA-Permissive-2.0 license](https://cdla.dev/permissive-2-0/).
**Note**
We do not re-distribute the original files as we are not allowed by terms.
### Citation Information
@article{yamaguchi2021canvasvae,
title={CanvasVAE: Learning to Generate Vector Graphic Documents},
author={Yamaguchi, Kota},
journal={ICCV},
year={2021}
}
### Releases
3.1: bugfix release (Feb 16, 2023)
- Fix a bug that ignores newline characters in some of the texts
3.0: v3 release (Feb 13, 2023)
- Migrate to Hugging Face Hub.
- Fix various text rendering bugs.
- Change split generation criteria for avoiding near-duplicates: no compatibility with v2 splits.
- Incorporate a motion picture thumbnail in templates.
- Add `title`, `keywords`, `suitability`, and `industries` canvas attributes.
- Add `capitalize`, `line_height`, and `letter_spacing` element attributes.
2.0: v2 release (May 26, 2022)
- Add `text`, `font`, `font_size`, `text_align`, and `angle` element attributes.
- Include rendered text element in `image_bytes`.
1.0: v1 release (Aug 24, 2021)
### Contributions
Thanks to [@kyamagu](https://github.com/kyamagu) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
KETI-AIR/korquad | KETI-AIR | 2021-06-03T00:37:09Z | 1,379 | 0 | null | [
"region:us"
] | 2021-06-03T00:37:09Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | <!--
Copyright 2021 san kim
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->
# KorQuAD
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hatexplain | null | 2023-01-25T14:31:48Z | 1,376 | 7 | hatexplain | [
"task_categories:text-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"hate-speech-detection",
"arxiv:2012.10289",
"arxiv:1703.04009",
"arxiv:1908.11049",
"arxiv:1812.01693",
"region:us"
] | 2023-01-25T14:31:48Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: hatexplain
pretty_name: hatexplain
tags:
- hate-speech-detection
dataset_info:
features:
- name: id
dtype: string
- name: annotators
sequence:
- name: label
dtype:
class_label:
names:
'0': hatespeech
'1': normal
'2': offensive
- name: annotator_id
dtype: int32
- name: target
sequence: string
- name: rationales
sequence:
sequence: int32
- name: post_tokens
sequence: string
config_name: plain_text
splits:
- name: train
num_bytes: 7114730
num_examples: 15383
- name: validation
num_bytes: 884940
num_examples: 1922
- name: test
num_bytes: 884784
num_examples: 1924
download_size: 12848091
dataset_size: 8884454
---
# Dataset Card for hatexplain
## 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:** [Needs More Information]
- **Repository:** https://github.com/punyajoy/HateXplain/
- **Paper:** https://arxiv.org/abs/2012.10289
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** Punyajoy Saha (punyajoys@iitkgp.ac.in)
### Dataset Summary
Hatexplain is the first benchmark hate speech dataset covering multiple aspects of the issue. Each post in the dataset is annotated from three different perspectives: the basic, commonly used 3-class classification (i.e., hate, offensive or normal), the target community (i.e., the community that has been the victim of hate speech/offensive speech in the post), and the rationales, i.e., the portions of the post on which their labeling decision (as hate, offensive or normal) is based.
WARNING: This dataset contains content that are offensive and/or hateful in nature.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
The language supported is English.
## Dataset Structure
### Data Instances
Sample Entry:
```
{
"id": "24198545_gab",
"annotators": [
{
"label": 0, # hatespeech
"annotator_id": 4,
"target": ["African"]
},
{
"label": 0, # hatespeech
"annotator_id": 3,
"target": ["African"]
},
{
"label": 2, # offensive
"annotator_id": 5,
"target": ["African"]
}
],
"rationales":[
[0,0,0,0,0,0,0,0,1,0,0,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
],
"post_tokens": ["and","this","is","why","i","end","up","with","nigger","trainee","doctors","who","can","not","speak","properly","lack","basic","knowledge","of","biology","it","truly","scary","if","the","public","only","knew"]
}
}
```
### Data Fields
:small_blue_diamond:post_id : Unique id for each post<br/>
:small_blue_diamond:annotators : The list of annotations from each annotator<br/>
:small_blue_diamond:annotators[label] : The label assigned by the annotator to this post. Possible values: `hatespeech` (0), `normal` (1) or `offensive` (2)<br/>
:small_blue_diamond:annotators[annotator_id] : The unique Id assigned to each annotator<br/>
:small_blue_diamond:annotators[target] : A list of target community present in the post<br/>
:small_blue_diamond:rationales : A list of rationales selected by annotators. Each rationales represents a list with values 0 or 1. A value of 1 means that the token is part of the rationale selected by the annotator. To get the particular token, we can use the same index position in "post_tokens"<br/>
:small_blue_diamond:post_tokens : The list of tokens representing the post which was annotated<br/>
### Data Splits
[Post_id_divisions](https://github.com/hate-alert/HateXplain/blob/master/Data/post_id_divisions.json) has a dictionary having train, valid and test post ids that are used to divide the dataset into train, val and test set in the ratio of 8:1:1.
## Dataset Creation
### Curation Rationale
The existing hate speech datasets do not provide human rationale which could justify the human reasoning behind their annotation process. This dataset allows researchers to move a step in this direction. The dataset provides token-level annotatoins for the annotation decision.
### Source Data
We collected the data from Twitter and Gab.
#### Initial Data Collection and Normalization
We combined the lexicon set provided by [Davidson 2017](https://arxiv.org/abs/1703.04009), [Ousidhoum 2019](https://arxiv.org/abs/1908.11049), and [Mathew 2019](https://arxiv.org/abs/1812.01693) to generate a single lexicon. We do not consider reposts and remove duplicates. We also ensure that the posts do not contain links, pictures, or videos as they indicate additional information that mightnot be available to the annotators. However, we do not exclude the emojis from the text as they might carry importantinformation for the hate and offensive speech labeling task.
#### Who are the source language producers?
The dataset is human generated using Amazon Mechanical Turk (AMT).
### Annotations
#### Annotation process
Each post in our dataset contains three types of annotations. First, whether the text is a hate speech, offensive speech, or normal. Second, the target communities in the text. Third, if the text is considered as hate speech, or offensive by majority of the annotators, we further ask the annotators to annotate parts of the text, which are words orphrases that could be a potential reason for the given annotation.
Before starting the annotation task, workers are explicitly warned that the annotation task displays some hateful or offensive content. We prepare instructions for workers that clearly explain the goal of the annotation task, how to annotate spans and also include a definition for each category. We provide multiple examples with classification, target community and span annotations to help the annotators understand the task.
#### Who are the annotators?
To ensure high quality dataset, we use built-in MTurk qualification requirements, namely the HITApproval Rate(95%) for all Requesters’ HITs and the Number of HITs Approved(5,000) requirements.
Pilot annotation: In the pilot task, each annotator was provided with 20 posts and they were required to do the hate/offensive speech classification as well as identify the target community (if any). In order to have a clear understanding of the task, they were provided with multiple examples along with explanations for the labelling process. The main purpose of the pilot task was to shortlist those annotators who were able to do the classification accurately. We also collected feedback from annotators to improve the main annotation task. A total of 621 annotators took part in the pilot task. Out of these, 253 were selected for the main task.
Main annotation: After the pilot annotation, once we had ascertained the quality of the annotators, we started with the main annotation task. In each round, we would select a batch of around 200 posts. Each post was annotated by three annotators, then majority voting was applied to decide the final label. The final dataset is composed of 9,055 posts from Twitter and 11,093 posts from Gab. The Krippendorff's alpha for the inter-annotator agreement is 0.46 which is higher than other hate speech datasets.
### Personal and Sensitive Information
The posts were anonymized by replacing the usernames with <user> token.
## Considerations for Using the Data
### Social Impact of Dataset
The dataset could prove beneficial to develop models which are more explainable and less biased.
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
The dataset has some limitations. First is the lack of external context. The dataset lacks any external context such as profile bio, user gender, history of posts etc., which might be helpful in the classification task. Another issue is the focus on English language and lack of multilingual hate speech.
## Additional Information
### Dataset Curators
Binny Mathew - IIT Kharagpur, India
Punyajoy Saha - IIT Kharagpur, India
Seid Muhie Yimam - Universit ̈at Hamburg, Germany
Chris Biemann - Universit ̈at Hamburg, Germany
Pawan Goyal - IIT Kharagpur, India
Animesh Mukherjee - IIT Kharagpur, India
### Licensing Information
MIT License
### Citation Information
```bibtex
@article{mathew2020hatexplain,
title={HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection},
author={Binny Mathew and Punyajoy Saha and Seid Muhie Yimam and Chris Biemann and Pawan Goyal and Animesh Mukherjee},
year={2021},
conference={AAAI conference on artificial intelligence}
}
### Contributions
Thanks to [@kushal2000](https://github.com/kushal2000) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
medalpaca/medical_meadow_medical_flashcards | medalpaca | 2023-04-06T17:12:17Z | 1,366 | 5 | null | [
"task_categories:question-answering",
"language:en",
"license:cc",
"region:us"
] | 2023-04-06T17:12:17Z | 2023-04-06T17:09:17.000Z | 2023-04-06T17:09:17 | ---
license: cc
task_categories:
- question-answering
language:
- en
---
# Dataset Card for Medical Flashcards
## Dataset Description
- **Repository:** https://github.com/kbressem/medalpaca
- **Paper:** TBA
### Dataset Summary
Medicine as a whole encompasses a wide range of subjects that medical students and graduates must master
in order to practice effectively. This includes a deep understanding of basic medical sciences, clinical knowledge,
and clinical skills. The Anki Medical Curriculum flashcards are created and updated by medical students and cover the
entirety of this curriculum, addressing subjects such as anatomy, physiology, pathology, pharmacology, and more.
These flashcards frequently feature succinct summaries and mnemonics to aid in learning and retention of vital medical concepts.
In our study, we employed the flashcards as a resource for generating question-answer pairs for training purposes.
After removing cards that contained images, we utilized OpenAI's GPT-3.5-turbo to rephrase the cards into coherent,
contextually relevant question-answer pairs. In general the questions and answers are short and focused, as the flashcards
do not allow to add much information.
### Citation Information
TBA | [
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german_legal_entity_recognition | null | 2023-01-25T14:30:49Z | 1,362 | 1 | legal-documents-entity-recognition | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:de",
"license:cc-by-4.0",
"region:us"
] | 2023-01-25T14:30:49Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- de
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: legal-documents-entity-recognition
pretty_name: Legal Documents Entity Recognition
dataset_info:
- config_name: bag
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': B-AN
'1': B-EUN
'2': B-GRT
'3': B-GS
'4': B-INN
'5': B-LD
'6': B-LDS
'7': B-LIT
'8': B-MRK
'9': B-ORG
'10': B-PER
'11': B-RR
'12': B-RS
'13': B-ST
'14': B-STR
'15': B-UN
'16': B-VO
'17': B-VS
'18': B-VT
'19': I-AN
'20': I-EUN
'21': I-GRT
'22': I-GS
'23': I-INN
'24': I-LD
'25': I-LDS
'26': I-LIT
'27': I-MRK
'28': I-ORG
'29': I-PER
'30': I-RR
'31': I-RS
'32': I-ST
'33': I-STR
'34': I-UN
'35': I-VO
'36': I-VS
'37': I-VT
'38': O
splits:
- name: train
download_size: 4392913
dataset_size: 0
- config_name: bfh
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': B-AN
'1': B-EUN
'2': B-GRT
'3': B-GS
'4': B-INN
'5': B-LD
'6': B-LDS
'7': B-LIT
'8': B-MRK
'9': B-ORG
'10': B-PER
'11': B-RR
'12': B-RS
'13': B-ST
'14': B-STR
'15': B-UN
'16': B-VO
'17': B-VS
'18': B-VT
'19': I-AN
'20': I-EUN
'21': I-GRT
'22': I-GS
'23': I-INN
'24': I-LD
'25': I-LDS
'26': I-LIT
'27': I-MRK
'28': I-ORG
'29': I-PER
'30': I-RR
'31': I-RS
'32': I-ST
'33': I-STR
'34': I-UN
'35': I-VO
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---
# Dataset Card for Legal Documents Entity Recognition
## 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/elenanereiss/Legal-Entity-Recognition
- **Repository:** None
- **Paper:** https://link.springer.com/chapter/10.1007/978-3-030-33220-4_20
- **Leaderboard:** [If the dataset supports an active leaderboard, add link here]()
- **Point of Contact:** Georg Rehm (georg.rehm@dfki.de)
### Dataset Summary
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p><b>Deprecated:</b> Dataset "german_legal_entity_recognition" is deprecated and will be deleted. Use <a href="https://huggingface.co/datasets/elenanereiss/german-ler">"elenanereiss/german-ler"</a> instead.</p>
</div>
### 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 | |
nlphuji/flickr_1k_test_image_text_retrieval | nlphuji | 2023-01-14T19:54:08Z | 1,361 | 0 | null | [
"region:us"
] | 2023-01-14T19:54:08Z | 2023-01-12T14:36:57.000Z | 2023-01-12T14:36:57 | # Flickr30k (1K test set)
Original paper: [From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions](https://aclanthology.org/Q14-1006)
Homepage: https://shannon.cs.illinois.edu/DenotationGraph/
1K test set split from: http://cs.stanford.edu/people/karpathy/deepimagesent/caption_datasets.zip
Bibtex:
```
@article{young2014image,
title={From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions},
author={Young, Peter and Lai, Alice and Hodosh, Micah and Hockenmaier, Julia},
journal={Transactions of the Association for Computational Linguistics},
volume={2},
pages={67--78},
year={2014},
publisher={MIT Press}
}
``` | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
snips_built_in_intents | null | 2023-01-25T14:44:32Z | 1,354 | 5 | snips | [
"task_categories:text-classification",
"task_ids:intent-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:cc0-1.0",
"arxiv:1805.10190",
"region:us"
] | 2023-01-25T14:44:32Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- cc0-1.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- intent-classification
paperswithcode_id: snips
pretty_name: SNIPS Natural Language Understanding benchmark
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': ComparePlaces
'1': RequestRide
'2': GetWeather
'3': SearchPlace
'4': GetPlaceDetails
'5': ShareCurrentLocation
'6': GetTrafficInformation
'7': BookRestaurant
'8': GetDirections
'9': ShareETA
splits:
- name: train
num_bytes: 19431
num_examples: 328
download_size: 9130264
dataset_size: 19431
train-eval-index:
- config: default
task: text-classification
task_id: multi_class_classification
train_split: train
col_mapping:
text: text
label: target
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 macro
args:
average: macro
- type: f1
name: F1 micro
args:
average: micro
- type: f1
name: F1 weighted
args:
average: weighted
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
---
# Dataset Card for Snips Built In Intents
## 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/sonos/nlu-benchmark/tree/master/2016-12-built-in-intents
- **Repository:** https://github.com/sonos/nlu-benchmark/tree/master/2016-12-built-in-intents
- **Paper:** https://arxiv.org/abs/1805.10190
- **Point of Contact:** The Snips team has joined Sonos in November 2019. These open datasets remain available and their access is now managed by the Sonos Voice Experience Team. Please email sve-research@sonos.com with any question.
### Dataset Summary
Snips' built in intents dataset was initially used to compare different voice assistants and released as a public dataset hosted at
https://github.com/sonos/nlu-benchmark in folder 2016-12-built-in-intents. The dataset contains 328 utterances over 10 intent classes.
A related Medium post is https://medium.com/snips-ai/benchmarking-natural-language-understanding-systems-d35be6ce568d.
### Supported Tasks and Leaderboards
There are no related shared tasks that we are aware of.
### Languages
English
## Dataset Structure
### Data Instances
The dataset contains 328 utterances over 10 intent classes. Each sample looks like:
`{'label': 8, 'text': 'Transit directions to Barcelona Pizza.'}`
### Data Fields
- `text`: The text utterance expressing some user intent.
- `label`: The intent label of the piece of text utterance.
### Data Splits
The source data is not split.
## Dataset Creation
### Curation Rationale
The dataset was originally created to compare the performance of a number of voice assistants. However, the labelled utterances are useful
for developing and benchmarking text chatbots as well.
### Source Data
#### Initial Data Collection and Normalization
It is not clear how the data was collected. From the Medium post: `The benchmark relies on a set of 328 queries built by the business team
at Snips, and kept secret from data scientists and engineers throughout the development of the solution.`
#### Who are the source language producers?
Originally prepared by snips.ai. The Snips team has since joined Sonos in November 2019. These open datasets remain available and their
access is now managed by the Sonos Voice Experience Team. Please email sve-research@sonos.com with any question.
### Annotations
#### Annotation process
It is not clear how the data was collected. From the Medium post: `The benchmark relies on a set of 328 queries built by the business team
at Snips, and kept secret from data scientists and engineers throughout the development of the solution.`
#### 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
Originally prepared by snips.ai. The Snips team has since joined Sonos in November 2019. These open datasets remain available and their
access is now managed by the Sonos Voice Experience Team. Please email sve-research@sonos.com with any question.
### Licensing Information
The source data is licensed under Creative Commons Zero v1.0 Universal.
### Citation Information
Any publication based on these datasets must include a full citation to the following paper in which the results were published by the Snips Team:
Coucke A. et al., "Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces." CoRR 2018,
https://arxiv.org/abs/1805.10190
### Contributions
Thanks to [@bduvenhage](https://github.com/bduvenhage) for adding this dataset. | [
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wikicorpus | null | 2023-06-01T14:59:54Z | 1,347 | 5 | null | [
"task_categories:fill-mask",
"task_categories:text-classification",
"task_categories:text-generation",
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"language:en",
"language:es",
"license:gfdl",
"word-sense-disambiguation",
"lemmatization",
"region:us"
] | 2023-06-01T14:59:54Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
pretty_name: Wikicorpus
annotations_creators:
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language_creators:
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language:
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license:
- gfdl
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size_categories:
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source_datasets:
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task_ids:
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paperswithcode_id: null
tags:
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config_names:
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---
# Dataset Card for Wikicorpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://www.cs.upc.edu/~nlp/wikicorpus/
- **Repository:**
- **Paper:** https://www.cs.upc.edu/~nlp/papers/reese10.pdf
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The Wikicorpus is a trilingual corpus (Catalan, Spanish, English) that contains large portions of the Wikipedia (based on a 2006 dump) and has been automatically enriched with linguistic information. In its present version, it contains over 750 million words.
The corpora have been annotated with lemma and part of speech information using the open source library FreeLing. Also, they have been sense annotated with the state of the art Word Sense Disambiguation algorithm UKB. As UKB assigns WordNet senses, and WordNet has been aligned across languages via the InterLingual Index, this sort of annotation opens the way to massive explorations in lexical semantics that were not possible before.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Each sub-dataset is monolingual in the languages:
- ca: Catalan
- en: English
- es: Spanish
## 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
The WikiCorpus is licensed under the same license as Wikipedia, that is, the [GNU Free Documentation License](http://www.fsf.org/licensing/licenses/fdl.html)
### Citation Information
```
@inproceedings{reese-etal-2010-wikicorpus,
title = "{W}ikicorpus: A Word-Sense Disambiguated Multilingual {W}ikipedia Corpus",
author = "Reese, Samuel and
Boleda, Gemma and
Cuadros, Montse and
Padr{\'o}, Llu{\'i}s and
Rigau, German",
booktitle = "Proceedings of the Seventh International Conference on Language Resources and Evaluation ({LREC}'10)",
month = may,
year = "2010",
address = "Valletta, Malta",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2010/pdf/222_Paper.pdf",
abstract = "This article presents a new freely available trilingual corpus (Catalan, Spanish, English) that contains large portions of the Wikipedia and has been automatically enriched with linguistic information. To our knowledge, this is the largest such corpus that is freely available to the community: In its present version, it contains over 750 million words. The corpora have been annotated with lemma and part of speech information using the open source library FreeLing. Also, they have been sense annotated with the state of the art Word Sense Disambiguation algorithm UKB. As UKB assigns WordNet senses, and WordNet has been aligned across languages via the InterLingual Index, this sort of annotation opens the way to massive explorations in lexical semantics that were not possible before. We present a first attempt at creating a trilingual lexical resource from the sense-tagged Wikipedia corpora, namely, WikiNet. Moreover, we present two by-products of the project that are of use for the NLP community: An open source Java-based parser for Wikipedia pages developed for the construction of the corpus, and the integration of the WSD algorithm UKB in FreeLing.",
}
```
### Contributions
Thanks to [@albertvillanova](https://github.com/albertvillanova) for adding this dataset. | [
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