id stringlengths 2 115 | lastModified stringlengths 24 24 | tags list | author stringlengths 2 42 ⌀ | description stringlengths 0 68.7k ⌀ | citation stringlengths 0 10.7k ⌀ | cardData null | likes int64 0 3.55k | downloads int64 0 10.1M | card stringlengths 0 1.01M |
|---|---|---|---|---|---|---|---|---|---|
csebuetnlp/xlsum | 2023-04-18T01:46:20.000Z | [
"task_categories:summarization",
"task_categories:text-generation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:am",
"language:ar",
"language:az",
"language:bn",
"language:my",
"lan... | csebuetnlp | We present XLSum, a comprehensive and diverse dataset comprising 1.35 million professionally
annotated article-summary pairs from BBC, extracted using a set of carefully designed heuristics.
The dataset covers 45 languages ranging from low to high-resource, for many of which no
public dataset is currently available. X... | @inproceedings{hasan-etal-2021-xl,
title = "{XL}-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages",
author = "Hasan, Tahmid and
Bhattacharjee, Abhik and
Islam, Md. Saiful and
Mubasshir, Kazi and
Li, Yuan-Fang and
Kang, Yong-Bin and
Rahman, M. Soh... | null | 55 | 5,997 | ---
annotations_creators:
- found
language_creators:
- found
language:
- am
- ar
- az
- bn
- my
- zh
- en
- fr
- gu
- ha
- hi
- ig
- id
- ja
- rn
- ko
- ky
- mr
- ne
- om
- ps
- fa
- pcm
- pt
- pa
- ru
- gd
- sr
- si
- so
- es
- sw
- ta
- te
- th
- ti
- tr
- uk
- ur
- uz
- vi
- cy
- yo
license:
- cc-by-nc-sa-4.0
multil... |
copenlu/answerable_tydiqa | 2022-09-12T11:19:54.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"source_datasets:extended|wikipedia",
"language:en",
"language:ar",
"language:bn",
"language:fi",
"language:id",
"language:ja",... | copenlu | null | null | null | 2 | 5,948 | ---
annotations_creators:
- crowdsourced
language:
- en
- ar
- bn
- fi
- id
- ja
- sw
- ko
- ru
- te
- th
language_creators:
- crowdsourced
license:
- apache-2.0
multilinguality:
- multilingual
pretty_name: Answerable TyDi QA
size_categories:
- ['100K<n<1M']
source_datasets:
- extended|wikipedia
task_categories:
- ques... |
patrickvonplaten/librispeech_asr_dummy | 2021-10-14T12:30:39.000Z | [
"region:us"
] | patrickvonplaten | LibriSpeech is a corpus of approximately 1000 hours of read English speech with sampling rate of 16 kHz,
prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read
audiobooks from the LibriVox project, and has been carefully segmented and aligned.
Note that in order to limit the re... | @inproceedings{panayotov2015librispeech,
title={Librispeech: an ASR corpus based on public domain audio books},
author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},
pages={5206--... | null | 0 | 5,943 | Entry not found |
yhavinga/ccmatrix | 2023-03-09T07:44:58.000Z | [
"task_categories:text2text-generation",
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"source_datasets:original",
"language:af",
"language:am",
"language:ar",
"language:ast",
"language:az",
"language:be",
"language:bg"... | yhavinga | CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB
We show that margin-based bitext mining in LASER's multilingual sentence space can be applied to
monolingual corpora of billions of sentences to produce high quality aligned translation data.
We use thirty-two snapshots of a curated common crawl c... | Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, Vishrav Chaudhary, Francisco Guzmán, Armand Jouli and Edouard Grave, CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data | null | 17 | 5,943 | ---
annotations_creators:
- found
language_creators:
- found
language:
- af
- am
- ar
- ast
- az
- be
- bg
- bn
- br
- ca
- ceb
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- ha
- he
- hi
- hr
- hu
- hy
- id
- ig
- ilo
- is
- it
- ja
- jv
- ka
- kk
- km
- ko
- la
- lb
- lg
- lt
-... |
iohadrubin/c4 | 2023-09-22T09:14:22.000Z | [
"region:us"
] | iohadrubin | A colossal, cleaned version of Common Crawl's web crawl corpus.
Based on Common Crawl dataset: "https://commoncrawl.org".
This is the processed version of Google's C4 dataset by AllenAI. | @article{2019t5,
author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
journal = {arXiv e-prints},
year = {2... | null | 0 | 5,924 | Entry not found |
PolyAI/minds14 | 2023-04-12T12:08:02.000Z | [
"task_categories:automatic-speech-recognition",
"task_ids:keyword-spotting",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
... | PolyAI | MINDS-14 is training and evaluation resource for intent
detection task with spoken data. It covers 14
intents extracted from a commercial system
in the e-banking domain, associated with spoken examples in 14 diverse language varieties. | @article{gerz2021multilingual,
title={Multilingual and cross-lingual intent detection from spoken data},
author={Gerz, Daniela and Su, Pei-Hao and Kusztos, Razvan and Mondal, Avishek and Lis, Michal and Singhal, Eshan and Mrk{\v{s}}i{\'c}, Nikola and Wen, Tsung-Hsien and Vuli{\'c}, Ivan},
journal={arXiv preprint ... | null | 29 | 5,810 | ---
annotations_creators:
- expert-generated
- crowdsourced
- machine-generated
language_creators:
- crowdsourced
- expert-generated
language:
- en
- fr
- it
- es
- pt
- de
- nl
- ru
- pl
- cs
- ko
- zh
language_bcp47:
- en
- en-GB
- en-US
- en-AU
- fr
- it
- es
- pt
- de
- nl
- ru
- pl
- cs
- ko
- zh
license:
- cc-by-... |
hate_speech18 | 2023-03-27T14:11:55.000Z | [
"task_categories:text-classification",
"task_ids:intent-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | null | These files contain text extracted from Stormfront, a white supremacist forum. A random set of
forums posts have been sampled from several subforums and split into sentences. Those sentences
have been manually labelled as containing hate speech or not, according to certain annotation guidelines. | @inproceedings{gibert2018hate,
title = "{Hate Speech Dataset from a White Supremacy Forum}",
author = "de Gibert, Ona and
Perez, Naiara and
Garcia-Pablos, Aitor and
Cuadros, Montse",
booktitle = "Proceedings of the 2nd Workshop on Abusive Language Online ({ALW}2)",
month = oct,
... | null | 13 | 5,796 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- intent-classification
paperswithcode_id: hate-speech
pretty_name: Hate Speech
da... |
graelo/wikipedia | 2023-09-10T06:10:08.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:n<1K",
"size_categories:1K<n<10K",
"size_categ... | graelo | Wikipedia dataset containing cleaned articles of all languages.
The datasets are built from the Wikipedia dump
(https://dumps.wikimedia.org/) with one split per language. Each example
contains the content of one full Wikipedia article with cleaning to strip
markdown and unwanted sections (references, etc.). | @ONLINE {wikidump,
author = {Wikimedia Foundation},
title = {Wikimedia Downloads},
url = {https://dumps.wikimedia.org}
} | null | 41 | 5,776 | ---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
pretty_name: Wikipedia
paperswithcode_id: null
license:
- cc-by-sa-3.0
- gfdl
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
source_datasets:
- original
multilinguality:
- multilingual
si... |
food101 | 2023-01-25T14:30:37.000Z | [
"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:extended|other-foodspotting",
"language:en",
"license:unknown",
... | null | null | @inproceedings{bossard14,
title = {Food-101 -- Mining Discriminative Components with Random Forests},
author = {Bossard, Lukas and Guillaumin, Matthieu and Van Gool, Luc},
booktitle = {European Conference on Computer Vision},
year = {2014}
} | null | 22 | 5,750 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-foodspotting
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
paperswithcode_id:... |
yelp_polarity | 2023-06-27T07:34:43.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"language:en",
"arxiv:1509.01626",
"region:us"
] | null | Large Yelp Review Dataset.
This is a dataset for binary sentiment classification. We provide a set of 560,000 highly polar yelp reviews for training, and 38,000 for testing.
ORIGIN
The Yelp reviews dataset consists of reviews from Yelp. It is extracted
from the Yelp Dataset Challenge 2015 data. For more information, p... | @article{zhangCharacterlevelConvolutionalNetworks2015,
archivePrefix = {arXiv},
eprinttype = {arxiv},
eprint = {1509.01626},
primaryClass = {cs},
title = {Character-Level {{Convolutional Networks}} for {{Text Classification}}},
abstract = {This article offers an empirical exploration on the use of character... | null | 7 | 5,716 | ---
language:
- en
pretty_name: YelpPolarity
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: yelp-review-polarity
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': '1'
'1': '2'
... |
mc4 | 2022-10-28T16:36:33.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:n<1K",
"size_categories:1K<n<10K",
"size_categories:1... | null | A colossal, cleaned version of Common Crawl's web crawl corpus.
Based on Common Crawl dataset: "https://commoncrawl.org".
This is the processed version of Google's mC4 dataset by AllenAI. | @article{2019t5,
author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
journal = {arXiv e-prints},
year = {2... | null | 104 | 5,695 | ---
pretty_name: mC4
annotations_creators:
- no-annotation
language_creators:
- found
language:
- af
- am
- ar
- az
- be
- bg
- bn
- ca
- ceb
- co
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fil
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- haw
- he
- hi
- hmn
- ht
- hu
- hy
- id
- ig
- is
- it
- iw
- ja
- jv
... |
dbpedia_14 | 2023-01-25T14:29:11.000Z | [
"task_categories:text-classification",
"task_ids:topic-classification",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | null | The DBpedia ontology classification dataset is constructed by picking 14 non-overlapping classes
from DBpedia 2014. They are listed in classes.txt. From each of thse 14 ontology classes, we
randomly choose 40,000 training samples and 5,000 testing samples. Therefore, the total size
of the training dataset is 560,000 an... | @article{lehmann2015dbpedia,
title={DBpedia--a large-scale, multilingual knowledge base extracted from Wikipedia},
author={Lehmann, Jens and Isele, Robert and Jakob, Max and Jentzsch, Anja and Kontokostas,
Dimitris and Mendes, Pablo N and Hellmann, Sebastian and Morsey, Mohamed and Van Kleef,
Patrick and Auer, ... | null | 8 | 5,677 | ---
annotations_creators:
- machine-generated
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- topic-classification
paperswithcode_id: dbpedia
pretty_name: D... |
daily_dialog | 2023-05-07T15:20:15.000Z | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-sa-4.0",
"emotion-classificati... | null | We develop a high-quality multi-turn dialog dataset, DailyDialog, which is intriguing in several aspects.
The language is human-written and less noisy. The dialogues in the dataset reflect our daily communication way
and cover various topics about our daily life. We also manually label the developed dataset with commun... | @InProceedings{li2017dailydialog,
author = {Li, Yanran and Su, Hui and Shen, Xiaoyu and Li, Wenjie and Cao, Ziqiang and Niu, Shuzi},
title = {DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset},
booktitle = {Proceedings of The 8th International Joint Conference on Natural Language Processing (IJCN... | null | 64 | 5,593 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
paperswithcode_id: dailydialog
pretty_n... |
facebook/belebele | 2023-09-15T01:12:38.000Z | [
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:text-classification",
"task_categories:multiple-choice",
"size_categories:100K<n<1M",
"language:af",
"language:am",
"language:ar",
"language:az",
"language:as",
"language:bm",
"language:bn",
"l... | facebook | null | 22 | 5,581 | ---
configs:
- config_name: default
data_files:
- split: eval
path: "data/*.jsonl"
license: cc-by-sa-4.0
task_categories:
- question-answering
- zero-shot-classification
- text-classification
- multiple-choice
language:
- af
- am
- ar
- az
- as
- bm
- bn
- bo
- bg
- ca
- cs
- ku
- da
- de
- el
- en
- es
- et
- ... | ||
zh-plus/tiny-imagenet | 2022-07-12T09:04:30.000Z | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|imagenet-1k",
"language:en",
"region:us"
] | zh-plus | null | null | null | 22 | 5,537 | ---
annotations_creators:
- crowdsourced
extra_gated_prompt: "By clicking on \u201CAccess repository\u201D below, you also\
\ agree to ImageNet Terms of Access:\n[RESEARCHER_FULLNAME] (the \"Researcher\"\
) has requested permission to use the ImageNet database (the \"Database\") at Princeton\
\ University and Sta... |
klue | 2023-06-01T14:59:57.000Z | [
"task_categories:fill-mask",
"task_categories:question-answering",
"task_categories:text-classification",
"task_categories:text-generation",
"task_categories:token-classification",
"task_ids:extractive-qa",
"task_ids:named-entity-recognition",
"task_ids:natural-language-inference",
"task_ids:parsing... | null | KLUE (Korean Language Understanding Evaluation)
Korean Language Understanding Evaluation (KLUE) benchmark is a series of datasets to evaluate natural language
understanding capability of Korean language models. KLUE consists of 8 diverse and representative tasks, which are accessible
to anyone without any restrictions.... | @misc{park2021klue,
title={KLUE: Korean Language Understanding Evaluation},
author={Sungjoon Park and Jihyung Moon and Sungdong Kim and Won Ik Cho and Jiyoon Han and Jangwon Park and Chisung Song and Junseong Kim and Yongsook Song and Taehwan Oh and Joohong Lee and Juhyun Oh and Sungwon Lyu and Younghoon Je... | null | 22 | 5,500 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- ko
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- fill-mask
- question-answering
- text-classification
- text-generation
- token-classificat... |
deepmind/code_contests | 2023-06-11T12:22:30.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:2203.07814",
"arxiv:2105.12655",
"region:us"
] | deepmind | null | null | null | 40 | 5,467 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: codecontests
pretty_name: CodeContests
---
# Dataset Card for CodeCont... |
PKU-Alignment/PKU-SafeRLHF | 2023-07-20T16:19:08.000Z | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-nc-4.0",
"safe",
"safety",
"ai-safety",
"llm",
"lm",
"human-feedback",
"rlhf",
"safe-rlhf",
"arxiv:2307.04657",
"region:us"
] | PKU-Alignment | null | null | null | 27 | 5,336 | ---
license: cc-by-nc-4.0
task_categories:
- text-generation
language:
- en
tags:
- safe
- safety
- ai-safety
- llm
- lm
- human-feedback
- rlhf
- safe-rlhf
size_categories:
- 100K<n<1M
---
# Dataset Card for PKU-SafeRLHF
<span style="color: red;">Warning: this dataset contains data that may be offensive or harmful. ... |
vwxyzjn/summarize_from_feedback_tldr_3_filtered | 2023-09-19T20:10:04.000Z | [
"task_categories:summarization",
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"region:us"
] | vwxyzjn | null | null | null | 0 | 5,292 | ---
license: mit
task_categories:
- summarization
language:
- en
size_categories:
- 1K<n<10K
---
This is the query dataset taken directly from https://github.com/openai/summarize-from-feedback/tree/700967448d10004279f138666442bf1497d0e705#reddit-tldr-dataset |
CarperAI/openai_summarize_comparisons | 2023-02-27T16:29:07.000Z | [
"region:us"
] | CarperAI | null | null | null | 20 | 5,254 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: test
num_bytes: 143018505
num_examples: 83629
- name: train
num_bytes: 157425966
num_examples: 92534
- name: valid1
num_bytes: 56686271
... |
Abirate/english_quotes | 2022-10-25T08:39:16.000Z | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"doi:10.57967/hf/1053",
"region:u... | Abirate | null | null | null | 17 | 5,248 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
- crowdsourced
language:
- en
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
---
# ****Dataset Card for English quotes****
# **I-Dataset Summary*... |
bigcode/commitpackft | 2023-08-20T07:13:43.000Z | [
"language:code",
"license:mit",
"arxiv:2308.07124",
"region:us"
] | bigcode | CommitPackFT is is a 2GB filtered version of CommitPack to contain only high-quality commit messages that resemble natural language instructions. | @article{muennighoff2023octopack,
title={OctoPack: Instruction Tuning Code Large Language Models},
author={Niklas Muennighoff and Qian Liu and Armel Zebaze and Qinkai Zheng and Binyuan Hui and Terry Yue Zhuo and Swayam Singh and Xiangru Tang and Leandro von Werra and Shayne Longpre},
journal={arXiv p... | null | 16 | 5,156 | ---
license: mit
pretty_name: CommitPackFT
language:
- code
---
# Dataset Card for CommitPackFT
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-de... |
mteb/results | 2023-09-25T14:43:36.000Z | [
"benchmark:mteb",
"region:us"
] | mteb | Results on MTEB | @article{muennighoff2022mteb,
doi = {10.48550/ARXIV.2210.07316},
url = {https://arxiv.org/abs/2210.07316},
author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},
title = {MTEB: Massive Text Embedding Benchmark},
publisher = {arXiv},
journal={arXiv preprint arXiv:2210.07316... | null | 4 | 5,078 | ---
benchmark: mteb
type: evaluation
submission_name: MTEB
--- |
open-llm-leaderboard/details_lmsys__vicuna-7b-v1.5-16k | 2023-08-27T12:40:52.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 5,075 | ---
pretty_name: Evaluation run of lmsys/vicuna-7b-v1.5-16k
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [lmsys/vicuna-7b-v1.5-16k](https://huggingface.co/lmsys/vicuna-7b-v1.5-16k) on\
\ the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderbo... |
common_language | 2023-06-12T13:29:01.000Z | [
"task_categories:audio-classification",
"task_ids:speaker-identification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:extended|common_voice",
"language:ar",
"language:br",
"language:ca",
"la... | null | This dataset is composed of speech recordings from languages that were carefully selected from the CommonVoice database.
The total duration of audio recordings is 45.1 hours (i.e., 1 hour of material for each language).
The dataset has been extracted from CommonVoice to train language-id systems. | @dataset{ganesh_sinisetty_2021_5036977,
author = {Ganesh Sinisetty and
Pavlo Ruban and
Oleksandr Dymov and
Mirco Ravanelli},
title = {CommonLanguage},
month = jun,
year = 2021,
publisher = {Zenodo},
version = {0.1},
... | null | 11 | 5,037 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- ar
- br
- ca
- cnh
- cs
- cv
- cy
- de
- dv
- el
- en
- eo
- es
- et
- eu
- fa
- fr
- fy
- ia
- id
- it
- ja
- ka
- kab
- ky
- lv
- mn
- mt
- nl
- pl
- pt
- rm
- ro
- ru
- rw
- sah
- sl
- sv
- ta
- tr
- tt
- uk
- zh
license:
- cc-by-... |
smangrul/code-chat-assistant-v1 | 2023-07-27T10:51:50.000Z | [
"region:us"
] | smangrul | null | null | null | 8 | 5,034 | ---
dataset_info:
features:
- name: content
dtype: string
splits:
- name: train
num_bytes: 25042064.0
num_examples: 10876
- name: test
num_bytes: 1348088
num_examples: 818
download_size: 12246507
dataset_size: 26390152.0
---
# Dataset Card for "code-chat-assistant-v1"
[More Informatio... |
BeIR/scidocs | 2022-10-23T06:04:15.000Z | [
"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"
] | BeIR | null | null | null | 2 | 5,033 | ---
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:
... |
hf-internal-testing/fill10 | 2023-06-09T21:30:54.000Z | [
"region:us"
] | hf-internal-testing | null | null | null | 0 | 5,019 | Entry not found |
scitail | 2023-04-05T13:39:52.000Z | [
"language:en",
"region:us"
] | null | The SciTail dataset is an entailment dataset created from multiple-choice science exams and web sentences. Each question
and the correct answer choice are converted into an assertive statement to form the hypothesis. We use information
retrieval to obtain relevant text from a large text corpus of web sentences, and use... | inproceedings{scitail,
Author = {Tushar Khot and Ashish Sabharwal and Peter Clark},
Booktitle = {AAAI},
Title = {{SciTail}: A Textual Entailment Dataset from Science Question Answering},
Year = {2018}
} | null | 4 | 4,985 | ---
language:
- en
paperswithcode_id: scitail
pretty_name: SciTail
dataset_info:
- config_name: snli_format
features:
- name: sentence1_binary_parse
dtype: string
- name: sentence1_parse
dtype: string
- name: sentence1
dtype: string
- name: sentence2_parse
dtype: string
- name: sentence2
... |
HuggingFaceM4/cm4-synthetic-testing-with-embeddings | 2023-10-03T12:25:35.000Z | [
"region:us"
] | HuggingFaceM4 | null | null | null | 0 | 4,940 | ---
dataset_info:
- config_name: 100.unique.embeddings
features:
- name: texts
sequence: string
- name: metadata
dtype: string
- name: original_idx
dtype: int64
- name: image_embeddings
sequence:
sequence:
sequence: float64
splits:
- name: train
num_bytes: 15422178
nu... |
hf-internal-testing/librispeech_asr_demo | 2022-04-07T07:06:24.000Z | [
"region:us"
] | hf-internal-testing | LibriSpeech is a corpus of approximately 1000 hours of read English speech with sampling rate of 16 kHz,
prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read
audiobooks from the LibriVox project, and has been carefully segmented and aligned.
Note that in order to limit the re... | @inproceedings{panayotov2015librispeech,
title={Librispeech: an ASR corpus based on public domain audio books},
author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},
pages={5206--... | null | 1 | 4,881 | Entry not found |
Babelscape/SREDFM | 2023-06-20T07:33:28.000Z | [
"task_categories:token-classification",
"size_categories:10M<n<100M",
"language:ar",
"language:ca",
"language:de",
"language:el",
"language:en",
"language:es",
"language:fr",
"language:hi",
"language:it",
"language:ja",
"language:ko",
"language:nl",
"language:pl",
"language:pt",
"lan... | Babelscape | Relation Extraction (RE) is a task that identifies relationships between entities in a text, enabling the acquisition of relational facts and bridging the gap between natural language and structured knowledge. However, current RE models often rely on small datasets with low coverage of relation types, particularly when... | @InProceedings{REDFM2023,
author = {Huguet Cabot, Pere-Lluis
and Tedeschi, Simone
and Ngonga Ngomo, Axel-Cyrille
and Navigli, Roberto},
title = {RED\textsuperscript{FM}: a Filtered and Multilingual Relation Extraction Dataset},
booktitle = {Proceedings of the 202... | null | 2 | 4,831 | ---
dataset_info:
- config_name: ar
features:
- name: docid
dtype: string
- name: title
dtype: string
- name: uri
dtype: string
- name: text
dtype: string
- name: entities
list:
- name: uri
dtype: string
- name: surfaceform
dtype: string
- name: type
dtype: ... |
sasha/dog-food | 2022-10-25T10:32:37.000Z | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | sasha | null | null | null | 2 | 4,814 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
pretty_name: Dog vs Food Dataset
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
---
# Dataset Card ... |
ethos | 2023-06-01T14:59:56.000Z | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:found",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:n<1K"... | null | ETHOS: onlinE haTe speecH detectiOn dataSet. This repository contains a dataset for hate speech
detection on social media platforms, called Ethos. There are two variations of the dataset:
Ethos_Dataset_Binary: contains 998 comments in the dataset alongside with a label
about hate speech presence or absence. 565 of the... | @misc{mollas2020ethos,
title={ETHOS: an Online Hate Speech Detection Dataset},
author={Ioannis Mollas and Zoe Chrysopoulou and Stamatis Karlos and Grigorios Tsoumakas},
year={2020},
eprint={2006.08328},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | null | 9 | 4,774 | ---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- found
- other
language:
- en
license:
- agpl-3.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
- sentiment-classification
pa... |
Polyglot-or-Not/Fact-Completion | 2023-06-14T03:05:21.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:text2text-generation",
"language_creators:expert-generated",
"language_creators:machine-generated",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"language:en",
"language:fr",
"language:es",
"language... | Polyglot-or-Not | null | null | null | 10 | 4,708 | ---
license: apache-2.0
tags:
- natural-language-understanding
language_creators:
- expert-generated
- machine-generated
multilinguality:
- multilingual
pretty_name: Polyglot or Not? Fact-Completion Benchmark
size_categories:
- 100K<n<1M
task_categories:
- text-generation
- fill-mask
- text2text-generation
dataset_info... |
yuvalkirstain/pickapic_v1 | 2023-05-05T15:00:30.000Z | [
"arxiv:2305.01569",
"arxiv:2303.14420",
"arxiv:2304.05977",
"arxiv:2210.03927",
"arxiv:2210.08402",
"region:us"
] | yuvalkirstain | null | null | null | 17 | 4,688 | ---
dataset_info:
features:
- name: are_different
dtype: bool
- name: best_image_uid
dtype: string
- name: caption
dtype: string
- name: created_at
dtype: timestamp[ns]
- name: has_label
dtype: bool
- name: image_0_uid
dtype: string
- name: image_0_url
dtype: string
- name:... |
shunk031/wrime | 2023-01-15T03:39:01.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"language:ja",
"license:unknown",
"sentiment-analysis",
"wrime",
"region:us"
] | shunk031 | WRIME dataset is a new dataset for emotional intensity estimation with subjective and objective annotations. | @inproceedings{kajiwara-etal-2021-wrime,
title = "{WRIME}: A New Dataset for Emotional Intensity Estimation with Subjective and Objective Annotations",
author = "Kajiwara, Tomoyuki and
Chu, Chenhui and
Takemura, Noriko and
Nakashima, Yuta and
Nagahara, Hajime",
booktitle = "Proce... | null | 10 | 4,684 | ---
annotations_creators:
- crowdsourced
language:
- ja
language_creators:
- crowdsourced
license:
- unknown
multilinguality:
- monolingual
pretty_name: wrime
tags:
- sentiment-analysis
- wrime
task_categories:
- text-classification
task_ids:
- sentiment-classification
datasets:
- ver1
- ver2
metrics:
- accura... |
multi_news | 2023-04-05T10:10:12.000Z | [
"task_categories:summarization",
"task_ids:news-articles-summarization",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:other",
"arxiv:1906.01749",
"re... | null | Multi-News, consists of news articles and human-written summaries
of these articles from the site newser.com.
Each summary is professionally written by editors and
includes links to the original articles cited.
There are two features:
- document: text of news articles seperated by special token "|||||".
- summary:... | @misc{alex2019multinews,
title={Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model},
author={Alexander R. Fabbri and Irene Li and Tianwei She and Suyi Li and Dragomir R. Radev},
year={2019},
eprint={1906.01749},
archivePrefix={arXiv},
primaryClass={... | null | 35 | 4,651 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- other
multilinguality:
- monolingual
pretty_name: Multi-News
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- summarization
task_ids:
- news-articles-summarization
paperswithcode_id: ... |
wentingzhao/one-million-instructions | 2023-09-16T03:03:51.000Z | [
"region:us"
] | wentingzhao | null | null | null | 0 | 4,644 | ---
dataset_info:
features:
- name: user
dtype: string
- name: system
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 327249922
num_examples: 2332040
download_size: 172927838
dataset_size: 327249922
configs:
- config_name: default
data_files:
- split: ... |
facebook/winoground | 2023-10-08T20:20:40.000Z | [
"task_categories:image-to-text",
"task_categories:text-to-image",
"task_categories:image-classification",
"language:en",
"arxiv:2204.03162",
"region:us"
] | facebook | Winoground is a novel task and dataset for evaluating the ability of vision and language models to conduct visio-linguistic compositional reasoning. Given two images and two captions, the goal is to match them correctly—but crucially, both captions contain a completely identical set of words/morphemes, only in a differ... | @inproceedings{thrush_and_ross2022winoground,
author = {Tristan Thrush and Ryan Jiang and Max Bartolo and Amanpreet Singh and Adina Williams and Douwe Kiela and Candace Ross},
title = {Winoground: Probing vision and language models for visio-linguistic compositionality},
booktitle = {CVPR},
year = 2022,
} | null | 58 | 4,640 | ---
pretty_name: Winoground
task_categories:
- image-to-text
- text-to-image
- image-classification
extra_gated_prompt: >-
By clicking on “Access repository” below, you also agree that you are using it
solely for research purposes. The full license agreement is available in the
dataset files.
language:
- en
---
#... |
Muennighoff/xwinograd | 2023-07-07T08:27:03.000Z | [
"language:en",
"language:fr",
"language:ja",
"language:pt",
"language:ru",
"language:zh",
"license:cc-by-4.0",
"arxiv:2211.01786",
"arxiv:2106.12066",
"region:us"
] | Muennighoff | A multilingual collection of Winograd Schemas in six languages that can be used for evaluation of cross-lingual commonsense reasoning capabilities. | @misc{muennighoff2022crosslingual,
title={Crosslingual Generalization through Multitask Finetuning},
author={Niklas Muennighoff and Thomas Wang and Lintang Sutawika and Adam Roberts and Stella Biderman and Teven Le Scao and M Saiful Bari and Sheng Shen and Zheng-Xin Yong and Hailey Schoelkopf and Xiangru T... | null | 4 | 4,591 | ---
language:
- en
- fr
- ja
- pt
- ru
- zh
license: cc-by-4.0
---
## XWinograd
Multilingual winograd schema challenge as used in [Crosslingual Generalization through Multitask Finetuning](https://arxiv.org/abs/2211.01786).
### Languages & Samples
- "en": 2325
- "fr": 83
- "jp": 959
- "pt": 263
- "ru": 315
- "zh":... |
openai/summarize_from_feedback | 2023-01-03T16:55:41.000Z | [
"arxiv:2009.01325",
"region:us"
] | openai | Summarize from Feedback contains the human feedback data released by the "Learning to summarize from human feedback" paper. | @inproceedings{stienon2020learning,
author = {Nisan Stiennon and Long Ouyang and Jeff Wu and Daniel M. Ziegler and Ryan Lowe and Chelsea Voss and Alec Radford and Dario Amodei and Paul Christiano},
title = {Learning to summarize from human feedback},
booktitle = {NeurIPS},
year = 2020,
} | null | 121 | 4,585 | ---
pretty_name: Summarize from Feedback
---
# Dataset Card for Summarize from Feedback
## Dataset Description
In the [Learning to Summarize from Human Feedback paper](https://arxiv.org/abs/2009.01325), a reward model was trained from human feedback.
The reward model was then used to train a summarization model to al... |
alkzar90/CC6204-Hackaton-Cub-Dataset | 2023-01-12T12:14:32.000Z | [
"task_categories:image-classification",
"task_categories:text-classification",
"task_ids:multi-class-image-classification",
"size_categories:10K<n<15K",
"source_datasets:extended|other",
"language:en",
"license:apache-2.0",
"region:us"
] | alkzar90 | null | null | null | 5 | 4,568 | ---
language:
- en
license:
- apache-2.0
pretty_name: CC6204-Hackaton-CUB200
size_categories:
- 10K<n<15K
source_datasets:
- extended|other
paperswithcode_id: cub-200-2011
task_categories:
- image-classification
- text-classification
task_ids:
- multi-class-image-classification
---
## Dataset Description
- **Homepage... |
HuggingFaceM4/OBELICS | 2023-08-22T20:50:09.000Z | [
"size_categories:100M<n<1B",
"language:en",
"license:cc-by-4.0",
"arxiv:2306.16527",
"region:us"
] | HuggingFaceM4 | null | null | null | 59 | 4,501 | ---
language:
- en
license: cc-by-4.0
size_categories:
- 100M<n<1B
pretty_name: OBELICS
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- config_name: opt_out_docs_removed_2023_07_12
data_files:
- split: train
path: opt_out_docs_removed_2023_07_12/train-*
dataset_info:
- co... |
codeparrot/apps | 2022-10-20T15:00:15.000Z | [
"task_categories:text-generation",
"task_ids:language-modeling",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"language:code",
"license:mit",
"arxiv:2105.09938",
"arxiv:2203.07814",
"region:us"
] | codeparrot | APPS is a benchmark for Python code generation, it includes 10,000 problems, which range from having simple oneline solutions to being substantial algorithmic challenges, for more details please refer to this paper: https://arxiv.org/pdf/2105.09938.pdf. | @article{hendrycksapps2021,
title={Measuring Coding Challenge Competence With APPS},
author={Dan Hendrycks and Steven Basart and Saurav Kadavath and Mantas Mazeika and Akul Arora and Ethan Guo and Collin Burns and Samir Puranik and Horace He and Dawn Song and Jacob Steinhardt},
journal={NeurIPS},
year={2021}
} | null | 46 | 4,456 | ---
annotations_creators: []
language_creators:
- crowdsourced
- expert-generated
language: ["code"]
license:
- mit
multilinguality:
- monolingual
pretty_name: APPS
size_categories:
- unknown
source_datasets: []
task_categories:
- text-generation
task_ids:
- language-modeling
---
# APPS Dataset
## Dataset Description... |
adversarial_qa | 2022-11-18T17:31:37.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"arxiv:2002.0... | null | AdversarialQA is a Reading Comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles using an adversarial model-in-the-loop.
We use three different models; BiDAF (Seo et al., 2016), BERT-Large (Devlin et al., 2018), and RoBERTa-Large (Liu et al., 2019) in the annotation loop an... | @article{bartolo2020beat,
author = {Bartolo, Max and Roberts, Alastair and Welbl, Johannes and Riedel, Sebastian and Stenetorp, Pontus},
title = {Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension},
journal = {Transactions of the Association for Computational Linguistics},
... | null | 27 | 4,450 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
- open-domain-qa
paperswithcode_id: adversarialqa
pretty_nam... |
hf-internal-testing/fixtures_docvqa | 2023-09-18T17:39:07.000Z | [
"region:us"
] | hf-internal-testing | \\n | \\n | null | 0 | 4,418 | This dataset includes 2 document images of the [DocVQA](https://docvqa.org/) dataset.
They are used for testing the LayoutLMv2FeatureExtractor + LayoutLMv2Processor inside the HuggingFace Transformers library.
More specifically, they are used in `tests/test_feature_extraction_layoutlmv2.py` and `tests/test_processor_... |
stanfordnlp/SHP | 2023-10-10T23:35:57.000Z | [
"task_categories:text-generation",
"task_categories:question-answering",
"size_categories:100K<n<1M",
"language:en",
"human feedback",
"rlhf",
"preferences",
"reddit",
"preference model",
"RL",
"NLG",
"evaluation",
"arxiv:2112.00861",
"arxiv:2001.08435",
"region:us"
] | stanfordnlp | null | null | null | 227 | 4,412 | ---
task_categories:
- text-generation
- question-answering
tags:
- human feedback
- rlhf
- preferences
- reddit
- preference model
- RL
- NLG
- evaluation
size_categories:
- 100K<n<1M
language:
- en
---
# 🚢 Stanford Human Preferences Dataset (SHP)
**If you mention this dataset in a paper, please cite the paper:** [... |
tau/scrolls | 2023-05-23T10:15:40.000Z | [
"task_categories:question-answering",
"task_categories:summarization",
"task_categories:text-generation",
"task_ids:multiple-choice-qa",
"task_ids:natural-language-inference",
"language:en",
"query-based-summarization",
"long-texts",
"arxiv:2201.03533",
"arxiv:2104.02112",
"arxiv:2104.07091",
... | tau | SCROLLS: Standardized CompaRison Over Long Language Sequences.
A suite of natural language datasets that require reasoning over long texts.
https://scrolls-benchmark.com/ | @misc{shaham2022scrolls,
title={SCROLLS: Standardized CompaRison Over Long Language Sequences},
author={Uri Shaham and Elad Segal and Maor Ivgi and Avia Efrat and Ori Yoran and Adi Haviv and Ankit Gupta and Wenhan Xiong and Mor Geva and Jonathan Berant and Omer Levy},
year={2022},
eprint={2201.... | null | 18 | 4,386 | ---
language:
- en
task_categories:
- question-answering
- summarization
- text-generation
task_ids:
- multiple-choice-qa
- natural-language-inference
paperswithcode_id: scrolls
configs:
- gov_report
- summ_screen_fd
- qmsum
- qasper
- narrative_qa
- quality
- contract_nli
tags:
- query-based-summarization
- long-texts... |
open-llm-leaderboard/details_rombodawg__LosslessMegaCoder-llama2-7b-mini | 2023-09-17T20:19:23.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 4,379 | ---
pretty_name: Evaluation run of rombodawg/LosslessMegaCoder-llama2-7b-mini
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [rombodawg/LosslessMegaCoder-llama2-7b-mini](https://huggingface.co/rombodawg/LosslessMegaCoder-llama2-7b-mini)\
\ on the [Open LLM Leaderboard](https:/... |
nielsr/funsd-layoutlmv3 | 2022-04-29T10:08:45.000Z | [
"region:us"
] | nielsr | https://guillaumejaume.github.io/FUNSD/ | @article{Jaume2019FUNSDAD,
title={FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents},
author={Guillaume Jaume and H. K. Ekenel and J. Thiran},
journal={2019 International Conference on Document Analysis and Recognition Workshops (ICDARW)},
year={2019},
volume={2},
pages={1-6}
} | null | 19 | 4,345 | Entry not found |
b-mc2/sql-create-context | 2023-09-29T20:22:24.000Z | [
"task_categories:text-generation",
"task_categories:question-answering",
"task_categories:table-question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-4.0",
"SQL",
"code",
"NLP",
"text-to-sql",
"context-sql",
"spider",
"wikisql",
"sqlglot",
"region:us"
] | b-mc2 | null | null | null | 167 | 4,282 | ---
license: cc-by-4.0
task_categories:
- text-generation
- question-answering
- table-question-answering
language:
- en
tags:
- SQL
- code
- NLP
- text-to-sql
- context-sql
- spider
- wikisql
- sqlglot
pretty_name: sql-create-context
size_categories:
- 10K<n<100K
---
#### Overview
This dataset builds from [WikiSQL](h... |
mlqa | 2023-04-05T10:09:51.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"language:de",
"language:es",
"language:ar",
"language... | null | MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA i... | @article{lewis2019mlqa,
title={MLQA: Evaluating Cross-lingual Extractive Question Answering},
author={Lewis, Patrick and Oguz, Barlas and Rinott, Ruty and Riedel, Sebastian and Schwenk, Holger},
journal={arXiv preprint arXiv:1910.07475},
year={2019}
} | null | 24 | 4,278 | ---
pretty_name: MLQA (MultiLingual Question Answering)
language:
- en
- de
- es
- ar
- zh
- vi
- hi
license:
- cc-by-sa-3.0
source_datasets:
- original
size_categories:
- 10K<n<100K
language_creators:
- crowdsourced
annotations_creators:
- crowdsourced
multilinguality:
- multilingual
task_categories:
- question-answer... |
bigbench | 2022-12-02T09:47:24.000Z | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:text-classification",
"task_categories:text-generation",
"task_categories:zero-shot-classification",
"task_categories:other",
"task_ids:multiple-choice-qa",
"task_ids:extractive-qa",
"task_ids:open-domain-qa",
... | null | The Beyond the Imitation Game Benchmark (BIG-bench) is a collaborative benchmark intended to
probe large language models, and extrapolate their future capabilities. | @misc{https://doi.org/10.48550/arxiv.2206.04615,
doi = {10.48550/ARXIV.2206.04615},
url = {https://arxiv.org/abs/2206.04615},
author = {Srivastava, Aarohi and Rastogi, Abhinav and Rao, Abhishek and Shoeb, Abu Awal Md and Abid, Abubakar and Fisch, Adam and Brown, Adam R. and Santoro, Adam and Gupta, Aditya and Gar... | null | 30 | 4,256 | ---
annotations_creators:
- crowdsourced
- expert-generated
- machine-generated
language_creators:
- crowdsourced
- expert-generated
- machine-generated
- other
language:
- en
license:
- apache-2.0
multilinguality:
- multilingual
- monolingual
pretty_name: bigbench
size_categories:
- unknown
source_datasets:
- original... |
quail | 2023-04-05T13:37:16.000Z | [
"task_categories:multiple-choice",
"task_ids:multiple-choice-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | QuAIL is a reading comprehension dataset. QuAIL contains 15K multi-choice questions in texts 300-350 tokens long 4 domains (news, user stories, fiction, blogs).QuAIL is balanced and annotated for question types.\ | @inproceedings{DBLP:conf/aaai/RogersKDR20,
author = {Anna Rogers and
Olga Kovaleva and
Matthew Downey and
Anna Rumshisky},
title = {Getting Closer to {AI} Complete Question Answering: {A} Set of Prerequisite
Real Tasks},
booktitle = {The Thirty-Fo... | null | 3 | 4,246 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
pretty_name: Question Answering for Artificial Intelligence (QuAIL)
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- multiple-choice
task_ids:
- multip... |
nateraw/parti-prompts | 2022-06-22T19:17:49.000Z | [
"license:apache-2.0",
"region:us"
] | nateraw | null | null | null | 14 | 4,193 | ---
license: apache-2.0
---
# Dataset Card for PartiPrompts (P2)
## 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)
-... |
duorc | 2023-06-01T14:59:57.000Z | [
"task_categories:question-answering",
"task_categories:text2text-generation",
"task_ids:abstractive-qa",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"sourc... | null | DuoRC contains 186,089 unique question-answer pairs created from a collection of 7680 pairs of movie plots where each pair in the collection reflects two versions of the same movie. | @inproceedings{DuoRC,
author = { Amrita Saha and Rahul Aralikatte and Mitesh M. Khapra and Karthik Sankaranarayanan},title = {{DuoRC: Towards Complex Language Understanding with Paraphrased Reading Comprehension}},
booktitle = {Meeting of the Association for Computational Linguistics (ACL)},
year = {2018}
} | null | 26 | 4,174 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
- text2text-generation
task_ids:
- abstractive-qa
- extractive-qa
paperswith... |
sayakpaul/nyu_depth_v2 | 2022-12-12T13:35:31.000Z | [
"task_categories:depth-estimation",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"depth-estimation",
"arxiv:1903.03273",
"region:us"
] | sayakpaul | The NYU-Depth V2 data set is comprised of video sequences from a variety of indoor scenes as recorded by both the RGB and Depth cameras from the Microsoft Kinect. | @inproceedings{Silberman:ECCV12,
author = {Nathan Silberman, Derek Hoiem, Pushmeet Kohli and Rob Fergus},
title = {Indoor Segmentation and Support Inference from RGBD Images},
booktitle = {ECCV},
year = {2012}
}
@inproceedings{icra_2019_fastdepth,
author = {Wofk, Diana and Ma, Fangchang and Yan... | null | 12 | 4,171 | ---
license: apache-2.0
language:
- en
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
task_categories:
- depth-estimation
task_ids: []
pretty_name: NYU Depth V2
tags:
- depth-estimation
paperswithcode_id: nyuv2
dataset_info:
features:
- name: image
dtype: image
- name: depth_map
dtype: imag... |
shunk031/livedoor-news-corpus | 2023-06-20T01:21:20.000Z | [
"region:us"
] | shunk031 | 本コーパスは、NHN Japan株式会社が運営する「livedoor ニュース」のうち、下記のクリエイティブ・コモンズライセンスが適用されるニュース記事を収集し、可能な限りHTMLタグを取り除いて作成したものです。 | https://www.rondhuit.com/download.html#ldcc | null | 3 | 4,134 | # Dataset Card for Livedoor News Corpus
[](https://github.com/shunk031/huggingface-datasets_livedoor-news-corpus/actions/workflows/ci.yaml)
![code-example](https://github.com/shunk031/huggingface-datasets_l... |
BeIR/arguana | 2022-10-23T06:03:08.000Z | [
"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"
] | BeIR | null | null | null | 2 | 4,130 | ---
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:
... |
common_gen | 2023-04-05T10:02:11.000Z | [
"task_categories:text2text-generation",
"annotations_creators:crowdsourced",
"language_creators:found",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"concepts-to-text",
"arxiv:1911.03705",... | null | CommonGen is a constrained text generation task, associated with a benchmark dataset,
to explicitly test machines for the ability of generative commonsense reasoning. Given
a set of common concepts; the task is to generate a coherent sentence describing an
everyday scenario using these concepts.
CommonGen is challengi... | @inproceedings{lin-etal-2020-commongen,
title = "{C}ommon{G}en: A Constrained Text Generation Challenge for Generative Commonsense Reasoning",
author = "Lin, Bill Yuchen and
Zhou, Wangchunshu and
Shen, Ming and
Zhou, Pei and
Bhagavatula, Chandra and
Choi, Yejin and
Ren,... | null | 16 | 4,109 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
- crowdsourced
license:
- mit
multilinguality:
- monolingual
pretty_name: CommonGen
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: commongen
tags:
- conce... |
anton-l/superb_dummy | 2021-12-14T09:39:13.000Z | [
"region:us"
] | anton-l | Self-supervised learning (SSL) has proven vital for advancing research in
natural language processing (NLP) and computer vision (CV). The paradigm
pretrains a shared model on large volumes of unlabeled data and achieves
state-of-the-art (SOTA) for various tasks with minimal adaptation. However, the
speech processing co... | @article{DBLP:journals/corr/abs-2105-01051,
author = {Shu{-}Wen Yang and
Po{-}Han Chi and
Yung{-}Sung Chuang and
Cheng{-}I Jeff Lai and
Kushal Lakhotia and
Yist Y. Lin and
Andy T. Liu and
Jiatong Shi and
... | null | 0 | 4,104 | Entry not found |
allenai/real-toxicity-prompts | 2022-09-30T14:23:19.000Z | [
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"arxiv:2009.11462",
"doi:10.57967/hf/0002",
"region:us"
] | allenai | null | null | null | 22 | 4,102 | ---
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- image-generation
task_ids:
- text-generation
pretty_name: Real Toxicity Prompts
---
# Dataset Card for Real Toxicity Prompts
## Table of Contents
- [Table of Contents](#t... |
blbooksgenre | 2023-06-01T14:59:51.000Z | [
"task_categories:text-classification",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:topic-classification",
"task_ids:multi-label-classification",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:expert-generated",
"language_creator... | null | This dataset contains metadata for resources belonging to the British Library’s digitised printed books (18th-19th century) collection (bl.uk/collection-guides/digitised-printed-books).
This metadata has been extracted from British Library catalogue records.
The metadata held within our main catalogue is updated regula... | @misc{british library_genre,
title={ 19th Century Books - metadata with additional crowdsourced annotations},
url={https://doi.org/10.23636/BKHQ-0312},
author={{British Library} and Morris, Victoria and van Strien, Daniel and Tolfo, Giorgia and Afric, Lora and Robertson, Stewart and Tiney, Patricia and Dogterom, Annel... | null | 4 | 4,086 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- de
- en
- fr
- nl
license:
- cc0-1.0
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
- text-generation
- fill-mask
tas... |
BeIR/hotpotqa-qrels | 2022-10-23T06:06:12.000Z | [
"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"
] | BeIR | null | null | null | 1 | 4,085 | ---
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:
... |
ropes | 2022-11-18T21:42:43.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|wikipedia",
"source_datasets:original",
"language... | null | ROPES (Reasoning Over Paragraph Effects in Situations) is a QA dataset
which tests a system's ability to apply knowledge from a passage
of text to a new situation. A system is presented a background
passage containing a causal or qualitative relation(s) (e.g.,
"animal pollinators increase efficiency of fertilization in... | @inproceedings{Lin2019ReasoningOP,
title={Reasoning Over Paragraph Effects in Situations},
author={Kevin Lin and Oyvind Tafjord and Peter Clark and Matt Gardner},
booktitle={MRQA@EMNLP},
year={2019}
} | null | 11 | 4,067 | ---
pretty_name: ROPES
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|wikipedia
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswi... |
billsum | 2023-04-05T09:41:39.000Z | [
"task_categories:summarization",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc0-1.0",
"bills-summarization",
"arxiv:1910.00523",
"region:us"
] | null | BillSum, summarization of US Congressional and California state bills.
There are several features:
- text: bill text.
- summary: summary of the bills.
- title: title of the bills.
features for us bills. ca bills does not have.
- text_len: number of chars in text.
- sum_len: number of chars in summary. | @misc{kornilova2019billsum,
title={BillSum: A Corpus for Automatic Summarization of US Legislation},
author={Anastassia Kornilova and Vlad Eidelman},
year={2019},
eprint={1910.00523},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | null | 20 | 4,020 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc0-1.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- summarization
task_ids: []
paperswithcode_id: billsum
pretty_name: BillSum
train-eval-index:
- config: default
task... |
codeparrot/instructhumaneval | 2023-06-13T15:58:34.000Z | [
"region:us"
] | codeparrot | null | null | null | 5 | 3,970 | ---
dataset_info:
features:
- name: task_id
dtype: string
- name: prompt
dtype: string
- name: canonical_solution
dtype: string
- name: test
dtype: string
- name: entry_point
dtype: string
- name: signature
dtype: string
- name: docstring
dtype: string
- name: context
d... |
stsb_multi_mt | 2022-11-18T21:48:48.000Z | [
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:semantic-similarity-scoring",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"language_creators:machine-generated",
"multilinguality:multilingual",
"size_categories:10K<n<100K... | null | These are different multilingual translations and the English original of the STSbenchmark dataset. Translation has been done with deepl.com. | @InProceedings{huggingface:dataset:stsb_multi_mt,
title = {Machine translated multilingual STS benchmark dataset.},
author={Philip May},
year={2021},
url={https://github.com/PhilipMay/stsb-multi-mt}
} | null | 33 | 3,938 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- found
- machine-generated
language:
- de
- en
- es
- fr
- it
- nl
- pl
- pt
- ru
- zh
license:
- other
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-sts-b
task_categories:
- text-classification... |
narrativeqa | 2022-11-18T21:32:08.000Z | [
"task_categories:text2text-generation",
"task_ids:abstractive-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"arxiv:1712.07040",
"region:us"
] | null | The NarrativeQA dataset for question answering on long documents (movie scripts, books). It includes the list of documents with Wikipedia summaries, links to full stories, and questions and answers. | @article{narrativeqa,
author = {Tom\\'a\\v s Ko\\v cisk\\'y and Jonathan Schwarz and Phil Blunsom and
Chris Dyer and Karl Moritz Hermann and G\\'abor Melis and
Edward Grefenstette},
title = {The {NarrativeQA} Reading Comprehension Challenge},
journal = {Transactions of the Association for Computatio... | null | 10 | 3,931 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids:
- abstractive-qa
paperswithcode_id: narrativeqa
pretty_name: NarrativeQA
dat... |
cerebras/SlimPajama-627B | 2023-07-07T23:13:12.000Z | [
"task_categories:text-generation",
"language:en",
"arxiv:2306.01116",
"arxiv:2302.13971",
"region:us"
] | cerebras | null | null | null | 200 | 3,924 | ---
task_categories:
- text-generation
language:
- en
pretty_name: SlimPajama-627B
---
## Dataset Description
- **Homepage:** [SlimPajama Blog](https://www.cerebras.net/blog/slimpajama-a-627b-token-cleaned-and-deduplicated-version-of-redpajama)
- **Repository:** [Pre-Processing Libraries](https://github.com/Cerebras/... |
ms_marco | 2023-04-05T10:10:02.000Z | [
"language:en",
"arxiv:1611.09268",
"region:us"
] | null | Starting with a paper released at NIPS 2016, MS MARCO is a collection of datasets focused on deep learning in search.
The first dataset was a question answering dataset featuring 100,000 real Bing questions and a human generated answer.
Since then we released a 1,000,000 question dataset, a natural langauge generation... | @article{DBLP:journals/corr/NguyenRSGTMD16,
author = {Tri Nguyen and
Mir Rosenberg and
Xia Song and
Jianfeng Gao and
Saurabh Tiwary and
Rangan Majumder and
Li Deng},
title = {{MS} {MARCO:} {A} Human Generated MAchine Re... | null | 35 | 3,853 | ---
language:
- en
paperswithcode_id: ms-marco
pretty_name: Microsoft Machine Reading Comprehension Dataset
dataset_info:
- config_name: v1.1
features:
- name: answers
sequence: string
- name: passages
sequence:
- name: is_selected
dtype: int32
- name: passage_text
dtype: string
- ... |
mosaicml/instruct-v3 | 2023-10-02T15:46:55.000Z | [
"language:en",
"region:us"
] | mosaicml | null | null | null | 10 | 3,850 | ---
language: en
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: source
dtype: string
splits:
- name: test
num_bytes: 18266901
num_examples: 6807
- name: train
num_bytes: 220790357
num_examples: 56167
download_size: 137475849
data... |
mwritescode/slither-audited-smart-contracts | 2022-07-14T14:12:44.000Z | [
"task_categories:text-classification",
"task_categories:text-generation",
"task_ids:multi-label-classification",
"task_ids:multi-input-text-classification",
"task_ids:language-modeling",
"annotations_creators:other",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1... | mwritescode | This dataset contains source code and deployed bytecode for Solidity Smart Contracts that have been verified on Etherscan.io, along with a classification of their vulnerabilities according to the Slither static analysis framework. | @misc{rossini2022slitherauditedcontracts,
title = {Slither Audited Smart Contracts Dataset},
author={Martina Rossini},
year={2022}
} | null | 15 | 3,846 | ---
annotations_creators:
- other
language_creators:
- found
language:
- en
license:
- mit
multilinguality:
- monolingual
pretty_name: Slither Audited Smart Contracts
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
- text-generation
task_ids:
- multi-label-classification
... |
katanaml-org/invoices-donut-data-v1 | 2023-05-09T07:05:11.000Z | [
"task_categories:feature-extraction",
"size_categories:n<1K",
"language:en",
"license:mit",
"region:us"
] | katanaml-org | null | null | null | 4 | 3,844 | ---
dataset_info:
features:
- name: image
dtype: image
- name: ground_truth
dtype: string
splits:
- name: train
num_bytes: 234024421
num_examples: 425
- name: test
num_bytes: 14512665
num_examples: 26
- name: validation
num_bytes: 27661738
num_examples: 50
download_size: ... |
juletxara/mgsm | 2023-05-09T16:46:31.000Z | [
"task_categories:text2text-generation",
"annotations_creators:found",
"language_creators:found",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:extended|gsm8k",
"language:en",
"language:es",
"language:fr",
"language:de",
"lang... | juletxara | Multilingual Grade School Math Benchmark (MGSM) is a benchmark of grade-school math problems, proposed in the paper [Language models are multilingual chain-of-thought reasoners](http://arxiv.org/abs/2210.03057).
The same 250 problems from [GSM8K](https://arxiv.org/abs/2110.14168) are each translated via human annotato... | @article{cobbe2021gsm8k,
title={Training Verifiers to Solve Math Word Problems},
author={Cobbe, Karl and Kosaraju, Vineet and Bavarian, Mohammad and Chen, Mark and Jun, Heewoo and Kaiser, Lukasz and Plappert, Matthias and Tworek, Jerry and Hilton, Jacob and Nakano, Reiichiro and Hesse, Christopher and Schulman,... | null | 5 | 3,819 | ---
annotations_creators:
- found
language_creators:
- found
- expert-generated
language:
- en
- es
- fr
- de
- ru
- zh
- ja
- th
- sw
- bn
license:
- cc-by-sa-4.0
multilinguality:
- multilingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|gsm8k
task_categories:
- text2te... |
InstaDeepAI/nucleotide_transformer_downstream_tasks | 2023-09-15T14:43:57.000Z | [
"region:us"
] | InstaDeepAI | The 18 classification downstream tasks from the Nucleotide Transformer paper. Each task
corresponds to a dataset configuration. | @article{dalla2023nucleotide,
title={The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics},
author={Dalla-Torre, Hugo and Gonzalez, Liam and Mendoza-Revilla, Javier and Carranza, Nicolas Lopez and Grzywaczewski, Adam Henryk and Oteri, Francesco and Dallago, Christian and T... | null | 1 | 3,781 | ---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
{}
---
# Dataset Card for Dataset Name
The `nucleotide_transformer_downstream_tasks` dataset features the 18 downstream tasks ... |
rungalileo/snli | 2022-07-27T20:59:33.000Z | [
"region:us"
] | rungalileo | null | null | null | 0 | 3,777 | Entry not found |
quartz | 2023-04-05T13:37:22.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"... | null | QuaRTz is a crowdsourced dataset of 3864 multiple-choice questions about open domain qualitative relationships. Each
question is paired with one of 405 different background sentences (sometimes short paragraphs).
The QuaRTz dataset V1 contains 3864 questions about open domain qualitative relationships. Each question is... | @InProceedings{quartz,
author = {Oyvind Tafjord and Matt Gardner and Kevin Lin and Peter Clark},
title = {"QUARTZ: An Open-Domain Dataset of Qualitative Relationship
Questions"},
year = {"2019"},
} | null | 3 | 3,752 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
- open-domain-qa
paperswithcode_id: quartz
pretty_name: Qu... |
pile-of-law/pile-of-law | 2023-01-08T03:10:35.000Z | [
"task_categories:fill-mask",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"language:en",
"license:cc-by-nc-sa-4.0",
"arxiv:2207.00220",
"region:us"
] | pile-of-law | We curate a large corpus of legal and administrative data. The utility of this data is twofold: (1) to aggregate legal and administrative data sources that demonstrate different norms and legal standards for data filtering; (2) to collect a dataset that can be used in the future for pretraining legal-domain language mo... | @misc{hendersonkrass2022pileoflaw,
url = {https://arxiv.org/abs/2207.00220},
author = {Henderson, Peter and Krass, Mark S. and Zheng, Lucia and Guha, Neel and Manning, Christopher D. and Jurafsky, Dan and Ho, Daniel E.},
title = {Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Sourc... | null | 123 | 3,718 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
pretty_name: pile-of-law
size_categories:
- 10M<n<100M
source_datasets: []
task_categories:
- fill-mask
task_ids:
- masked-language-modeling
viewer: false
---
# Dataset Card for... |
alzoubi36/policy_qa | 2023-06-25T06:45:22.000Z | [
"region:us"
] | alzoubi36 | null | null | null | 0 | 3,711 | ---
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
struct:
- name: answer_start
sequence: int64
- name: text
sequence: string
splits:
- name: validation
... |
textvqa | 2022-11-18T22:07:01.000Z | [
"task_categories:visual-question-answering",
"task_ids:visual-question-answering",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:1904.08920",... | null | TextVQA requires models to read and reason about text in images to answer questions about them.
Specifically, models need to incorporate a new modality of text present in the images and reason
over it to answer TextVQA questions. TextVQA dataset contains 45,336 questions over 28,408 images
from the OpenImages dataset. | @inproceedings{singh2019towards,
title={Towards VQA Models That Can Read},
author={Singh, Amanpreet and Natarjan, Vivek and Shah, Meet and Jiang, Yu and Chen, Xinlei and Batra, Dhruv and Parikh, Devi and Rohrbach, Marcus},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognit... | null | 8 | 3,703 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: TextVQA
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- visual-question-answering
task_ids:
- visual-question-answering
dataset_info:
- ... |
EuropeanParliament/Eurovoc | 2023-09-28T12:00:40.000Z | [
"license:eupl-1.1",
"region:us"
] | EuropeanParliament | null | null | null | 0 | 3,654 | ---
license: eupl-1.1
configs:
- config_name: 2006-04
data_files: "files/2006-04.jsonl.gz"
- config_name: 2006-05
data_files: "files/2006-05.jsonl.gz"
- config_name: 2006-06
data_files: "files/2006-06.jsonl.gz"
- config_name: 2006-07
data_files: "files/2006-07.jsonl.gz"
- config_name: 2006-08
data_files: "fil... |
hf-internal-testing/dummy_image_class_data | 2023-02-08T12:28:38.000Z | [
"region:us"
] | hf-internal-testing | null | null | null | 0 | 3,623 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': resize
splits:
- name: train
num_bytes: 555953.0
num_examples: 6
download_size: 556964
dataset_size: 555953.0
---
# Dataset Card for "dummy_image_class_data"
[More ... |
cc100 | 2023-06-01T14:59:56.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10M<n<100M",
"size_categories:1M<n<10M",
"source_data... | null | This corpus is an attempt to recreate the dataset used for training XLM-R. This corpus comprises of monolingual data for 100+ languages and also includes data for romanized languages (indicated by *_rom). This was constructed using the urls and paragraph indices provided by the CC-Net repository by processing January-D... | @inproceedings{conneau-etal-2020-unsupervised,
title = "Unsupervised Cross-lingual Representation Learning at Scale",
author = "Conneau, Alexis and
Khandelwal, Kartikay and
Goyal, Naman and
Chaudhary, Vishrav and
Wenzek, Guillaume and
Guzm{'a}n, Francisco and
Grave, Edo... | null | 35 | 3,609 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- af
- am
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- ff
- fi
- fr
- fy
- ga
- gd
- gl
- gn
- gu
- ha
- he
- hi
- hr
- ht
- hu
- hy
- id
- ig
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
-... |
DeveloperOats/DBPedia_Classes | 2022-08-08T14:54:42.000Z | [
"task_categories:text-classification",
"task_ids:topic-classification",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"language:en",
"license:cc0-1.0",
"region:us"
] | DeveloperOats | null | null | null | 13 | 3,607 | ---
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... |
tiny_shakespeare | 2023-04-05T13:42:24.000Z | [
"region:us"
] | null | 40,000 lines of Shakespeare from a variety of Shakespeare's plays. Featured in Andrej Karpathy's blog post 'The Unreasonable Effectiveness of Recurrent Neural Networks': http://karpathy.github.io/2015/05/21/rnn-effectiveness/.
To use for e.g. character modelling:
```
d = datasets.load_dataset(name='tiny_shakespeare')... | @misc{
author={Karpathy, Andrej},
title={char-rnn},
year={2015},
howpublished={\\url{https://github.com/karpathy/char-rnn}}
} | null | 17 | 3,545 | ---
paperswithcode_id: null
pretty_name: TinyShakespeare
dataset_info:
features:
- name: text
dtype: string
splits:
- name: test
num_bytes: 55780
num_examples: 1
- name: train
num_bytes: 1003864
num_examples: 1
- name: validation
num_bytes: 55780
num_examples: 1
download_size: ... |
qasc | 2023-04-05T13:37:12.000Z | [
"task_categories:question-answering",
"task_categories:multiple-choice",
"task_ids:extractive-qa",
"task_ids:multiple-choice-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
... | null | QASC is a question-answering dataset with a focus on sentence composition. It consists of 9,980 8-way multiple-choice
questions about grade school science (8,134 train, 926 dev, 920 test), and comes with a corpus of 17M sentences. | @article{allenai:qasc,
author = {Tushar Khot and Peter Clark and Michal Guerquin and Peter Jansen and Ashish Sabharwal},
title = {QASC: A Dataset for Question Answering via Sentence Composition},
journal = {arXiv:1910.11473v2},
year = {2020},
} | null | 6 | 3,517 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Question Answering via Sentence Composition (QASC)
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
- multiple-choice
task_ids:... |
SetFit/sst5 | 2021-12-25T06:10:36.000Z | [
"region:us"
] | SetFit | null | null | null | 5 | 3,508 | # Stanford Sentiment Treebank - Fine-Grained
[Stanford Sentiment Treebank](http://nlp.stanford.edu/sentiment/) with 5 labels: very positive, positive, neutral, negative, very negative
Splits are from:
[https://github.com/AcademiaSinicaNLPLab/sentiment_dataset/tree/master/data](https://github.com/AcademiaSinicaN... |
LIUM/tedlium | 2022-10-25T17:38:40.000Z | [
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"region:us"
] | LIUM | null | null | null | 9 | 3,479 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license: []
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- automatic-speech-recognition
task_ids: []
pretty_name: TED-LIUM
---
# Dataset Card for tedlium
## Ta... |
bigcode/the-stack | 2023-04-13T12:15:50.000Z | [
"task_categories:text-generation",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:unknown",
"language:code",
"license:other",
"arxiv:2211.15533",
"arxiv:2107.03374",
"arxiv:2207.14157",
"region:us"
] | bigcode | null | null | null | 513 | 3,439 | ---
annotations_creators: []
language_creators:
- crowdsourced
- expert-generated
language:
- code
license:
- other
multilinguality:
- multilingual
pretty_name: The-Stack
size_categories:
- unknown
source_datasets: []
task_categories:
- text-generation
task_ids: []
extra_gated_prompt: |-
## Terms of Use for The Stac... |
huggan/smithsonian_butterflies_subset | 2022-04-16T08:02:36.000Z | [
"region:us"
] | huggan | null | null | null | 22 | 3,415 | This a subset of "ceyda/smithsonian_butterflies" dataset with additional processing done to train the "ceyda/butterfly_gan" model.
The preprocessing includes:
- Adding "sim_score" to images with CLIP model using "pretty butterfly","one butterfly","butterfly with open wings","colorful butterfly"
- Removing butterflies... |
tiiuae/falcon-refinedweb | 2023-06-20T12:38:07.000Z | [
"task_categories:text-generation",
"size_categories:100B<n<1T",
"language:en",
"license:odc-by",
"arxiv:2306.01116",
"arxiv:2203.15556",
"arxiv:2107.06499",
"arxiv:2104.08758",
"arxiv:2109.07445",
"arxiv:1911.00359",
"arxiv:2112.11446",
"doi:10.57967/hf/0737",
"region:us"
] | tiiuae | null | null | null | 564 | 3,405 | ---
dataset_info:
features:
- name: content
dtype: string
- name: url
dtype: string
- name: timestamp
dtype: timestamp[s]
- name: dump
dtype: string
- name: segment
dtype: string
- name: image_urls
sequence:
sequence: string
splits:
- name: train
num_bytes: 2766953721... |
GAIR/lima | 2023-06-08T02:40:19.000Z | [
"license:other",
"arxiv:2305.11206",
"region:us"
] | GAIR | A high-quality dataset for efficient instruction tuning. | null | null | 285 | 3,366 | ---
license: other
---
Dataset for [LIMA: Less Is More for Alignment](https://arxiv.org/pdf/2305.11206.pdf)
## Usage
```python
from datasets import load_dataset
dataset = load_dataset("GAIR/lima")
```
## License
If the source data of LIMA has a stricter license than CC BY-NC-SA, the LIMA dataset follows the same.... |
wnut_17 | 2023-04-05T13:45:05.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] | null | WNUT 17: Emerging and Rare entity recognition
This shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions.
Named entities form the basis of many modern approaches to other tasks (like event clustering and summarisation),
but recall on them is a real problem in noi... | @inproceedings{derczynski-etal-2017-results,
title = "Results of the {WNUT}2017 Shared Task on Novel and Emerging Entity Recognition",
author = "Derczynski, Leon and
Nichols, Eric and
van Erp, Marieke and
Limsopatham, Nut",
booktitle = "Proceedings of the 3rd Workshop on Noisy User-gene... | null | 9 | 3,308 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: wnut-2017-emerging-and-rare-entit... |
jacobbuckman2/abc | 2023-09-27T01:52:23.000Z | [
"license:afl-3.0",
"region:us"
] | jacobbuckman2 | null | null | null | 0 | 3,291 | ---
license: afl-3.0
---
|
argilla/gutenberg_spacy-ner | 2023-06-28T06:34:37.000Z | [
"language:en",
"region:us"
] | argilla | null | null | null | 4 | 3,258 | ---
dataset_info:
features:
- name: text
dtype: string
- name: tokens
sequence: string
- name: prediction
list:
- name: end
dtype: int64
- name: label
dtype: string
- name: score
dtype: float64
- name: start
dtype: int64
- name: prediction_agent
dtype: s... |
Salesforce/dialogstudio | 2023-10-05T22:34:55.000Z | [
"task_categories:conversational",
"task_categories:question-answering",
"task_categories:summarization",
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"arxiv:2307.10172",
"region:us"
] | Salesforce | null | @misc{zhang2023dialogstudio,
title={DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI},
author={Jianguo Zhang and Kun Qian and Zhiwei Liu and Shelby Heinecke and Rui Meng and Ye Liu and Zhou Yu and and Huan Wang and Silvio Savarese and Caiming Xiong},
year={202... | null | 144 | 3,239 | ---
extra_gated_heading: "Acknowledge to follow corresponding dataset licenses to access the repository"
extra_gated_button_content: "Agree and access repository"
license: apache-2.0
task_categories:
- conversational
- question-answering
- summarization
- text-generation
language:
- en
pretty_name: Dialog Studio
---
... |
gsgoncalves/roberta_pretrain | 2023-05-02T18:40:25.000Z | [
"task_categories:fill-mask",
"task_categories:text-generation",
"size_categories:10M<n<100M",
"language:en",
"license:unknown",
"region:us"
] | gsgoncalves | null | null | null | 2 | 3,205 | ---
license: unknown
task_categories:
- fill-mask
- text-generation
language:
- en
pretty_name: RoBERTa Pretrain Dataset
size_categories:
- 10M<n<100M
---
# Dataset Card for RoBERTa Pretrain
### Dataset Summary
This is the concatenation of the datasets used to Pretrain RoBERTa.
The dataset is not shuffled and contain... |
nielsr/funsd | 2021-07-27T07:59:20.000Z | [
"region:us"
] | nielsr | https://guillaumejaume.github.io/FUNSD/ | @article{Jaume2019FUNSDAD,
title={FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents},
author={Guillaume Jaume and H. K. Ekenel and J. Thiran},
journal={2019 International Conference on Document Analysis and Recognition Workshops (ICDARW)},
year={2019},
volume={2},
pages={1-6}
} | null | 6 | 3,166 | Entry not found |
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