id stringlengths 2 115 | lastModified stringlengths 24 24 | tags list | author stringlengths 2 42 ⌀ | description stringlengths 0 6.67k ⌀ | citation stringlengths 0 10.7k ⌀ | likes int64 0 3.66k | downloads int64 0 8.89M | created timestamp[us] | card stringlengths 11 977k | card_len int64 11 977k | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|
BeIR/fever-qrels | 2022-10-23T06:08:11.000Z | [
"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 | 0 | 1,184 | 2022-06-05T17:28:01 | ---
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:
... | 13,988 | [
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dlb/plue | 2022-10-29T12:19:26.000Z | [
"task_categories:text-classification",
"task_ids:acceptability-classification",
"task_ids:natural-language-inference",
"task_ids:semantic-similarity-scoring",
"task_ids:sentiment-classification",
"task_ids:text-scoring",
"annotations_creators:found",
"language_creators:machine-generated",
"multiling... | dlb | PLUE: Portuguese Language Understanding Evaluationis a Portuguese translation of
the GLUE benchmark and Scitail using OPUS-MT model and Google Cloud Translation. | @misc{Gomes2020,
author = {GOMES, J. R. S.},
title = {Portuguese Language Understanding Evaluation},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\\url{https://github.com/jubs12/PLUE}},
commit = {CURRENT_COMMIT}
}
@inproceedings{wang2019glue,
title={{GLUE}: A Mult... | 6 | 1,183 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- machine-generated
language:
- pt
license:
- lgpl-3.0
multilinguality:
- monolingual
- translation
size_categories:
- 10K<n<100K
source_datasets:
- extended|glue
task_categories:
- text-classification
task_ids:
- acceptability-classification
- natural-language-infer... | 3,654 | [
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0.017013... |
eugenesiow/Div2k | 2022-10-21T04:01:10.000Z | [
"task_categories:other",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"license:other",
"other-image-super-resolution",
"region:us"
] | eugenesiow | DIV2K dataset: DIVerse 2K resolution high quality images as used for the challenges @ NTIRE (CVPR 2017 and
CVPR 2018) and @ PIRM (ECCV 2018) | @InProceedings{Agustsson_2017_CVPR_Workshops,
author = {Agustsson, Eirikur and Timofte, Radu},
title = {NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
url = "http://www.vision.ee.ethz.ch/~timofter/... | 2 | 1,182 | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- found
language: []
license:
- other
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- other
task_ids: []
pretty_name: Div2k
tags:
- other-image-super-resolution
---
# Dataset Card for Div2k
## Tab... | 8,364 | [
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philipphager/baidu-ultr-606k | 2023-10-30T10:25:33.000Z | [
"task_categories:text-retrieval",
"license:cc-by-nc-4.0",
"MonoBERT",
"unbiased learning to rank",
"ultr",
"baidu",
"ltr",
"clicks",
"region:us"
] | philipphager | Query-document vectors and clicks for the Baidu Unbiased Learning to Rank dataset used
at the WSDM23 cup. This dataset uses the winning BERT cross-encoder from Tencent
to compute query-document vectors (768 dims), mainly for ease of use and to enable
usage of simpler, smaller neural networks that are more common in ULT... | @InProceedings{huggingface:dataset,
title = {baidu-ultr-606k},
author={Philipp Hager},
year={2023}
} | 1 | 1,177 | 2023-10-17T15:08:53 | ---
license: cc-by-nc-4.0
task_categories:
- text-retrieval
tags:
- MonoBERT
- unbiased learning to rank
- ultr
- baidu
- ltr
- clicks
pretty_name: Baidu ULTR-606K
---
# Baidu Unbiased Learning to Rank - 606K
At NeurIPS 2022, [Baidu released the first large-scale click dataset](A Large Scale Search Dataset for Unbiase... | 5,248 | [
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0.001... |
roszcz/masked-maestro-v3 | 2023-10-02T15:21:06.000Z | [
"region:us"
] | roszcz | null | null | 0 | 1,176 | 2023-10-02T12:02:32 | ---
dataset_info:
features:
- name: pitch
sequence: int8
length: 90
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sequence: float64
length: 90
- name: dstart
sequence: float64
length: 90
- name: end
sequence: float64
length: 90
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seq... | 1,192 | [
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nlphuji/flickr_1k_test_image_text_retrieval | 2023-01-14T19:54:08.000Z | [
"region:us"
] | nlphuji | null | null | 0 | 1,172 | 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... | 754 | [
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-0.0343627929... |
CM/codexglue_code2text_javascript | 2023-04-22T01:51:42.000Z | [
"region:us"
] | CM | null | null | 2 | 1,171 | 2023-04-22T01:51:30 | ---
dataset_info:
features:
- name: id
dtype: int32
- name: repo
dtype: string
- name: path
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- name: language
dtype: string
- name: code
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sequence: string... | 916 | [
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... |
Nan-Do/SPP_30K_reasoning_tasks | 2023-08-22T07:09:57.000Z | [
"task_categories:text-generation",
"task_categories:conversational",
"task_categories:text2text-generation",
"language:en",
"code",
"python",
"reasoning",
"region:us"
] | Nan-Do | null | null | 1 | 1,170 | 2023-08-21T02:34:43 | ---
dataset_info:
features:
- name: type
dtype: int64
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
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splits:
- name: train
num_bytes: 44253001
num_examples: 89898
download_size: 10073876
dataset_size: 44253001
task_categories:
- text-... | 3,906 | [
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spider | 2022-11-03T16:31:49.000Z | [
"task_categories:text2text-generation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"text-to-sql",
... | null | Spider is a large-scale complex and cross-domain semantic parsing and text-toSQL dataset annotated by 11 college students | @article{yu2018spider,
title={Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task},
author={Yu, Tao and Zhang, Rui and Yang, Kai and Yasunaga, Michihiro and Wang, Dongxu and Li, Zifan and Ma, James and Li, Irene and Yao, Qingning and Roman, Shanelle and oth... | 57 | 1,168 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
- machine-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: spider-1
pretty_name: ... | 4,687 | [
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0.0401000976... |
bigbio/med_qa | 2023-09-26T13:00:32.000Z | [
"multilinguality:multilingual",
"language:en",
"language:zh",
"license:unknown",
"region:us"
] | bigbio | In this work, we present the first free-form multiple-choice OpenQA dataset for solving medical problems, MedQA,
collected from the professional medical board exams. It covers three languages: English, simplified Chinese, and
traditional Chinese, and contains 12,723, 34,251, and 14,123 questions for the three languages... | @article{jin2021disease,
title={What disease does this patient have? a large-scale open domain question answering dataset from medical exams},
author={Jin, Di and Pan, Eileen and Oufattole, Nassim and Weng, Wei-Hung and Fang, Hanyi and Szolovits, Peter},
journal={Applied Sciences},
volume={11},
number={14},
... | 23 | 1,164 | 2022-11-13T22:09:18 | ---
language:
- en
- zh
bigbio_language:
- English
- Chinese (Simplified)
- Chinese (Traditional, Taiwan)
license: unknown
multilinguality: multilingual
bigbio_license_shortname: UNKNOWN
pretty_name: MedQA
homepage: https://github.com/jind11/MedQA
bigbio_pubmed: False
bigbio_public: True
bigbio_tasks:
- QUESTION_ANSWER... | 1,438 | [
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0.018371... |
jxm/the_office_lines | 2023-03-07T18:30:51.000Z | [
"region:us"
] | jxm | null | null | 18 | 1,162 | 2023-03-07T18:24:28 | ## the_office_lines
<img src="https://a.pinatafarm.com/1351x1232/c8fa71efd1/the-office-handshake.jpg" width="256">
A dataset of lines from the U.S. version of the tv show "The Office". Lines were originally scraped from the website [officequotes.net](https://www.officequotes.net/), are fan-transcribed, and may be of ... | 882 | [
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... |
mlabonne/guanaco-llama2 | 2023-07-26T14:49:17.000Z | [
"region:us"
] | mlabonne | null | null | 7 | 1,161 | 2023-07-23T13:53:10 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 15409089
num_examples: 9846
- name: test
num_bytes: 815811
num_examples: 518
download_size: 9461517
dataset_size: 16224900
---
# Guanaco: Lazy Llama 2 Formatting
This is the excellent [`timdettmers... | 816 | [
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... |
Tevatron/msmarco-passage | 2023-07-18T07:34:33.000Z | [
"region:us"
] | Tevatron | null | @misc{bajaj2018ms,
title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset},
author={Payal Bajaj and Daniel Campos and Nick Craswell and Li Deng and Jianfeng Gao and Xiaodong Liu
and Rangan Majumder and Andrew McNamara and Bhaskar Mitra and Tri Nguyen and Mir Rosenberg and Xia Song
... | 3 | 1,156 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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0.0379... |
minskiter/weibo | 2023-07-22T13:49:08.000Z | [
"size_categories:1K<n<10K",
"language:zh",
"license:apache-2.0",
"social",
"region:us"
] | minskiter | The Weibo NER dataset is a Chinese Named Entity Recognition dataset
drawn from the social media website Sina Weibo. | @inproceedings{peng-dredze-2015-named,
title = "Named Entity Recognition for {C}hinese
Social Media with Jointly Trained Embeddings",
author = "Peng, Nanyun and Dredze, Mark",
booktitle = "Proceedings of the 2015 Conference on
Empirical Methods in Natural Language Processing",
month =... | 0 | 1,156 | 2023-07-17T07:31:25 | ---
license: apache-2.0
dataset_info:
features:
- name: text
sequence: string
- name: labels
sequence:
class_label:
names:
'0': O
'1': B-PER.NAM
'2': I-PER.NAM
'3': E-PER.NAM
'4': S-PER.NAM
'5': B-ORG.NAM
'6': I-ORG.NAM
... | 1,825 | [
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mlsum | 2023-06-01T14:59:54.000Z | [
"task_categories:summarization",
"task_categories:translation",
"task_categories:text-classification",
"task_ids:news-articles-summarization",
"task_ids:multi-class-classification",
"task_ids:multi-label-classification",
"task_ids:topic-classification",
"annotations_creators:found",
"language_creato... | null | We present MLSUM, the first large-scale MultiLingual SUMmarization dataset.
Obtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages -- namely, French, German, Spanish, Russian, Turkish.
Together with English newspapers from the popular CNN/Daily mail dataset, the collected d... | @article{scialom2020mlsum,
title={MLSUM: The Multilingual Summarization Corpus},
author={Scialom, Thomas and Dray, Paul-Alexis and Lamprier, Sylvain and Piwowarski, Benjamin and Staiano, Jacopo},
journal={arXiv preprint arXiv:2004.14900},
year={2020}
} | 26 | 1,149 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- de
- es
- fr
- ru
- tr
license:
- other
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
- 10K<n<100K
source_datasets:
- extended|cnn_dailymail
- original
task_categories:
- summarization
- translation
- text-classification
task_ids:
-... | 11,019 | [
[
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0... |
Intel/orca_dpo_pairs | 2023-09-26T11:18:30.000Z | [
"license:apache-2.0",
"arxiv:2306.02707",
"region:us"
] | Intel | null | null | 1 | 1,149 | 2023-09-21T10:35:16 | ---
license: apache-2.0
---
The dataset contains 12k examples from [Orca](https://arxiv.org/abs/2306.02707) style dataset [Open-Orca/OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca).
| 196 | [
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clarin-pl/polemo2-official | 2022-08-29T16:40:01.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:8K",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:pl",
"license:cc-by-sa-4.0",
"reg... | clarin-pl | PolEmo 2.0: Corpus of Multi-Domain Consumer Reviews, evaluation data for article presented at CoNLL. | @inproceedings{kocon-etal-2019-multi,
title = "Multi-Level Sentiment Analysis of {P}ol{E}mo 2.0: Extended Corpus of Multi-Domain Consumer Reviews",
author = "Koco{\'n}, Jan and
Mi{\l}kowski, Piotr and
Za{\'s}ko-Zieli{\'n}ska, Monika",
booktitle = "Proceedings of the 23rd Conference on Computat... | 4 | 1,145 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- other
language:
- pl
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: 'Polemo2'
size_categories:
- 8K
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
---
# P... | 5,320 | [
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0.... |
snips_built_in_intents | 2023-01-25T14:44:32.000Z | [
"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... | null | 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 2016-12-built-in-intents. The dataset contains 328 utterances over 10 intent classes. The
related paper mentioned on the github page is https://arxiv... | @article{DBLP:journals/corr/abs-1805-10190,
author = {Alice Coucke and
Alaa Saade and
Adrien Ball and
Th{\'{e}}odore Bluche and
Alexandre Caulier and
David Leroy and
Cl{\'{e}}ment Doumouro and
Thibault Gisselbr... | 4 | 1,142 | 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 Nat... | 6,564 | [
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wmt18 | 2023-04-05T13:44:00.000Z | [
"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_d... | null | null | @InProceedings{bojar-EtAl:2018:WMT1,
author = {Bojar, Ond\v{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 =... | 3 | 1,142 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
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- cs
- de
- en
- et
- fi
- kk
- ru
- tr
- zh
license:
- unknown
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size_categories:
- 10M<n<100M
source_datasets:
- extended|europarl_bilingual
- extended|news_commentary
- extended|opus_paracrawl
- extended|setim... | 10,313 | [
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BeIR/climate-fever-qrels | 2022-10-23T06:08:28.000Z | [
"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 | 0 | 1,139 | 2022-06-05T17:28:22 | ---
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:
... | 13,988 | [
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assin2 | 2023-01-25T14:26:53.000Z | [
"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",
... | null | 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... | @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}
} | 9 | 1,138 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- pt
license:
- unknown
multilinguality:
- monolingual
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- 1K<n<10K
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pape... | 5,047 | [
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... |
turing-motors/LLaVA-Instruct-150K-JA | 2023-08-28T11:26:23.000Z | [
"task_categories:visual-question-answering",
"task_categories:question-answering",
"size_categories:100K<n<1M",
"language:ja",
"license:cc-by-nc-4.0",
"region:us"
] | turing-motors | null | null | 4 | 1,136 | 2023-08-28T10:50:24 | ---
license: cc-by-nc-4.0
task_categories:
- visual-question-answering
- question-answering
language:
- ja
pretty_name: Japanese LLaVA Visual Instruct 150K
size_categories:
- 100K<n<1M
---
## Dataset Details
**Dataset Type:**
Japanese LLaVA Instruct 150K is a localized version of the original LLaVA Visual Instruct... | 1,619 | [
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0.0... |
open-web-math/open-web-math | 2023-10-17T20:14:00.000Z | [
"arxiv:2310.06786",
"region:us"
] | open-web-math | null | null | 162 | 1,134 | 2023-09-06T00:25:12 | ---
dataset_info:
features:
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dtype: string
- name: text
dtype: string
- name: date
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dtype: string
splits:
- name: train
num_bytes: 56651995057
num_examples: 6315233
download_size: 16370689925
dataset_size: 566519950... | 4,802 | [
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theblackcat102/evol-codealpaca-v1 | 2023-09-07T11:42:00.000Z | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-nc-4.0",
"code",
"region:us"
] | theblackcat102 | null | null | 70 | 1,133 | 2023-07-23T01:28:44 | ---
license: cc-by-nc-4.0
task_categories:
- text-generation
language:
- en
tags:
- code
size_categories:
- 100K<n<1M
---
## Evolved codealpaca
Updates:
* 2023/08/26 - Filtered results now only contain pure english instruction and removed any mentioned of trained by OAI response
Median sequence length : 471
We emp... | 2,169 | [
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natural_questions | 2023-04-05T13:35:01.000Z | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:no-annotation",
"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 NQ corpus contains questions from real users, and it requires QA systems to
read and comprehend an entire Wikipedia article that may or may not contain the
answer to the question. The inclusion of real user questions, and the
requirement that solutions should read an entire page to find the answer, cause
NQ to be a... | @article{47761,
title = {Natural Questions: a Benchmark for Question Answering Research},
author = {Tom Kwiatkowski and Jennimaria Palomaki and Olivia Redfield and Michael Collins and Ankur Parikh and Chris Alberti and Danielle Epstein and Illia Polosukhin and Matthew Kelcey and Jacob Devlin and Kenton Lee and Kristina... | 24 | 1,122 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswithcode_id: natural-questions
pretty_name: Na... | 11,675 | [
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0.023864... |
ncbi_disease | 2023-01-25T14:41:18.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | null | This paper presents the disease name and concept annotations of the NCBI disease corpus, a collection of 793 PubMed
abstracts fully annotated at the mention and concept level to serve as a research resource for the biomedical natural
language processing community. Each PubMed abstract was manually annotated by two anno... | @article{dougan2014ncbi,
title={NCBI disease corpus: a resource for disease name recognition and concept normalization},
author={Dogan, Rezarta Islamaj and Leaman, Robert and Lu, Zhiyong},
journal={Journal of biomedical informatics},
volume={47},
pages={1--10},
year... | 20 | 1,122 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: ncbi-disease-1
prett... | 9,695 | [
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GeorgiaTech/cnotesum | 2023-09-02T13:47:25.000Z | [
"license:other",
"region:us"
] | GeorgiaTech | null | null | 0 | 1,115 | 2023-09-02T13:42:55 | ---
license: other
---
Synthetic Clinical Notes based on Synthea and Summary Generated via LLAMA 2 | 98 | [
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quora | 2023-04-05T13:37:24.000Z | [
"task_categories:text-classification",
"task_ids:semantic-similarity-classification",
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"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | null | null | null | 9 | 1,109 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
pretty_name: Quora Question Pairs
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- semantic-similarity-classification
papers... | 5,691 | [
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0.004508972... |
onestop_english | 2023-01-25T14:42:09.000Z | [
"task_categories:text2text-generation",
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:text-simplification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"languag... | null | This dataset is a compilation of the OneStopEnglish corpus of texts written at three reading levels into one file.
Text documents are classified into three reading levels - ele, int, adv (Elementary, Intermediate and Advance).
This dataset demonstrates its usefulness for through two applica-tions - automatic readabili... | @inproceedings{vajjala-lucic-2018-onestopenglish,
title = {OneStopEnglish corpus: A new corpus for automatic readability assessment and text simplification},
author = {Sowmya Vajjala and Ivana Lučić},
booktitle = {Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Appli... | 15 | 1,106 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text2text-generation
- text-classification
task_ids:
- multi-class-classification
- text-simplification
paperswithcode... | 3,908 | [
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0... |
launch/gov_report | 2022-11-09T01:58:24.000Z | [
"task_categories:summarization",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] | launch | GovReport long document summarization dataset.
There are three configs:
- plain_text: plain text document-to-summary pairs
- plain_text_with_recommendations: plain text doucment-summary pairs, with "What GAO recommends" included in the summary
- structure: data with section structure | @inproceedings{huang-etal-2021-efficient,
title = "Efficient Attentions for Long Document Summarization",
author = "Huang, Luyang and
Cao, Shuyang and
Parulian, Nikolaus and
Ji, Heng and
Wang, Lu",
booktitle = "Proceedings of the 2021 Conference of the North American Chap... | 3 | 1,103 | 2022-05-22T16:10:07 | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- summarization
task_ids: []
pretty_name: GovReport
---
# Dataset Card for GovReport
## Table o... | 6,694 | [
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DFKI-SLT/brat | 2023-05-10T15:38:03.000Z | [
"task_categories:token-classification",
"task_ids:parsing",
"annotations_creators:expert-generated",
"language_creators:found",
"region:us"
] | DFKI-SLT | null | null | 2 | 1,102 | 2022-05-10T06:13:33 | ---
annotations_creators:
- expert-generated
language_creators:
- found
license: []
task_categories:
- token-classification
task_ids:
- parsing
---
# Information Card for Brat
## Table of Contents
- [Description](#description)
- [Summary](#summary)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#da... | 4,453 | [
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BeIR/scidocs-qrels | 2022-10-23T06:07:54.000Z | [
"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 | 0 | 1,101 | 2022-06-05T17:27:37 | ---
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:
... | 13,988 | [
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qa4mre | 2023-04-05T13:36:59.000Z | [
"task_categories:multiple-choice",
"task_ids:multiple-choice-qa",
"annotations_creators:other",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ar",
"language:bg",
"language:de",
"language:en",
"language:es",
"langu... | null | QA4MRE dataset was created for the CLEF 2011/2012/2013 shared tasks to promote research in
question answering and reading comprehension. The dataset contains a supporting
passage and a set of questions corresponding to the passage. Multiple options
for answers are provided for each question, of which only one is correc... | null | 2 | 1,099 | 2022-03-02T23:29:22 | ---
annotations_creators:
- other
language:
- ar
- bg
- de
- en
- es
- it
- ro
language_creators:
- found
license:
- unknown
multilinguality:
- multilingual
pretty_name: 'QA4MRE: Question Answering for Machine Reading Evaluation'
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- multiple-choice... | 22,619 | [
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cs_restaurants | 2022-11-18T19:49:56.000Z | [
"task_categories:text2text-generation",
"task_categories:text-generation",
"task_categories:fill-mask",
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"task_ids:masked-language-modeling",
"annotations_creators:found",
"language_creators:expert-generated",
"language_creators:machine-gene... | null | This is a dataset for NLG in task-oriented spoken dialogue systems with Czech as the target language. It originated as
a translation of the English San Francisco Restaurants dataset by Wen et al. (2015). | @article{DBLP:journals/corr/abs-1910-05298,
author = {Ondrej Dusek and
Filip Jurcicek},
title = {Neural Generation for Czech: Data and Baselines},
journal = {CoRR},
volume = {abs/1910.05298},
year = {2019},
url = {http://arxiv.org/abs/1910.05298},
archivePrefix = {arX... | 1 | 1,098 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- expert-generated
- machine-generated
language:
- cs
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-san-francisco-restaurants
task_categories:
- text2text-generation
- text-generation
- fill-mask
tas... | 7,303 | [
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CShorten/ML-ArXiv-Papers | 2022-06-27T12:15:11.000Z | [
"license:afl-3.0",
"region:us"
] | CShorten | null | null | 17 | 1,097 | 2022-06-23T14:31:39 | ---
license: afl-3.0
---
This dataset contains the subset of ArXiv papers with the "cs.LG" tag to indicate the paper is about Machine Learning.
The core dataset is filtered from the full ArXiv dataset hosted on Kaggle: https://www.kaggle.com/datasets/Cornell-University/arxiv. The original dataset contains roughly 2 mi... | 986 | [
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... |
nuprl/MultiPL-T | 2023-09-13T12:57:50.000Z | [
"license:bigcode-openrail-m",
"arxiv:2308.09895",
"region:us"
] | nuprl | null | null | 1 | 1,097 | 2023-08-17T14:17:33 | ---
license: bigcode-openrail-m
dataset_info:
features:
- name: content
dtype: string
splits:
- name: racket
num_bytes: 14482516
num_examples: 40510
- name: ocaml
num_bytes: 19240207
num_examples: 43401
- name: lua
num_bytes: 25917278
num_examples: 48194
download_size: 7491686
... | 747 | [
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... |
climate_fever | 2023-03-16T14:57:07.000Z | [
"task_categories:text-classification",
"task_categories:text-retrieval",
"task_ids:text-scoring",
"task_ids:fact-checking",
"task_ids:fact-checking-retrieval",
"task_ids:semantic-similarity-scoring",
"task_ids:multi-input-text-classification",
"annotations_creators:crowdsourced",
"annotations_creato... | null | A dataset adopting the FEVER methodology that consists of 1,535 real-world claims regarding climate-change collected on the internet. Each claim is accompanied by five manually annotated evidence sentences retrieved from the English Wikipedia that support, refute or do not give enough information to validate the claim ... | @misc{diggelmann2020climatefever,
title={CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims},
author={Thomas Diggelmann and Jordan Boyd-Graber and Jannis Bulian and Massimiliano Ciaramita and Markus Leippold},
year={2020},
eprint={2012.00614},
archivePrefix={arXiv},
... | 10 | 1,093 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|wikipedia
- original
task_categories:
- text-classification
- text-retrieval
task_ids:
- text-scoring
- fact-che... | 8,004 | [
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0.0... |
ArmelR/the-pile-splitted | 2023-09-06T09:53:16.000Z | [
"arxiv:2101.00027",
"arxiv:2201.07311",
"region:us"
] | ArmelR | null | null | 1 | 1,092 | 2023-07-30T14:21:26 | ---
configs:
- config_name: all
data_files:
- split: train
path:
- "data/ArXiv/train/*.arrow"
- "data/BookCorpus2/train/*.arrow"
- "data/Books3/train/*.arrow"
- "data/DM Mathematics/train/*.arrow"
- "data/Enron Emails/train/*.arrow"
- "data/EuroParl/train/*.arrow"
- "data/FreeLaw/tr... | 7,026 | [
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0.0092315673828... |
SALT-NLP/ImplicitHate | 2023-02-16T23:00:38.000Z | [
"region:us"
] | SALT-NLP | null | null | 2 | 1,078 | 2023-02-16T22:45:19 | # Implicit Hate Speech
_Latent Hatred: A Benchmark for Understanding Implicit Hate Speech_
[[Read the Paper]](https://aclanthology.org/2021.emnlp-main.29/) | [[Take a Survey to Access the Data]](https://forms.gle/QxCpEbVp91Z35hWFA) | [[Download the Data]](https://www.dropbox.com/s/24meryhqi1oo0xk/implicit-hate-corpus... | 3,895 | [
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-0.06646728515625,
-0.00772... |
google/xtreme_s | 2022-07-28T12:47:02.000Z | [
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
... | google | XTREME-S covers four task families: speech recognition, classification, speech-to-text translation and retrieval. Covering 102
languages from 10+ language families, 3 different domains and 4
task families, XTREME-S aims to simplify multilingual speech
representation evaluation, as well as catalyze research in “universa... | @article{conneau2022xtreme,
title={XTREME-S: Evaluating Cross-lingual Speech Representations},
author={Conneau, Alexis and Bapna, Ankur and Zhang, Yu and Ma, Min and von Platen, Patrick and Lozhkov, Anton and Cherry, Colin and Jia, Ye and Rivera, Clara and Kale, Mihir and others},
journal={arXiv preprint arXiv:22... | 35 | 1,076 | 2022-03-04T14:10:40 | ---
annotations_creators:
- expert-generated
- crowdsourced
- machine-generated
language_creators:
- crowdsourced
- expert-generated
language:
- afr
- amh
- ara
- asm
- ast
- azj
- bel
- ben
- bos
- cat
- ceb
- cmn
- ces
- cym
- dan
- deu
- ell
- eng
- spa
- est
- fas
- ful
- fin
- tgl
- fra
- gle
- glg
- guj
- hau
- h... | 21,005 | [
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... |
hippocrates/re_train | 2023-10-09T16:55:29.000Z | [
"region:us"
] | hippocrates | null | null | 0 | 1,076 | 2023-10-04T22:30:18 | ---
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... | 679 | [
[
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yuchenlin/just-eval-instruct | 2023-10-20T19:01:44.000Z | [
"region:us"
] | yuchenlin | null | null | 2 | 1,071 | 2023-09-11T21:42:48 | ---
configs:
- config_name: default
data_files:
- split: test
path: "test_all_with_tags.jsonl"
# - split: test_regular_only
# path: "test_regular.jsonl"
# - split: test_safety_only
# path: "test_red.jsonl"
- config_name: responses
data_files:
- split: gpt_4_0613
path: "responses/gpt-4-0613... | 2,490 | [
[
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-0... |
learn3r/summ_screen_fd_bp | 2023-09-26T10:28:23.000Z | [
"region:us"
] | learn3r | null | null | 0 | 1,069 | 2023-08-30T08:33:07 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 119519... | 701 | [
[
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0.040557861328125,
-0.061920166015625,
-0.041046142578125,
-0.051666259765625,... |
para_crawl | 2023-04-05T13:36:34.000Z | [
"task_categories:translation",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:translation",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:bg",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
... | null | null | @misc {paracrawl,
title = {ParaCrawl},
year = {2018},
url = {http://paracrawl.eu/download.html.}
} | 8 | 1,066 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- bg
- cs
- da
- de
- el
- en
- es
- et
- fi
- fr
- ga
- hr
- hu
- it
- lt
- lv
- mt
- nl
- pl
- pt
- ro
- sk
- sl
- sv
license:
- cc0-1.0
multilinguality:
- translation
pretty_name: ParaCrawl
size_categories:
- 10M<n<100M
source_datasets:
-... | 15,043 | [
[
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0.011566162109375,
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0.023208... |
craigslist_bargains | 2022-11-18T19:47:08.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:dialogue-modeling",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"arxi... | null | We study negotiation dialogues where two agents, a buyer and a seller,
negotiate over the price of an time for sale. We collected a dataset of more
than 6K negotiation dialogues over multiple categories of products scraped from Craigslist.
Our goal is to develop an agent that negotiates with humans through such convers... | @misc{he2018decoupling,
title={Decoupling Strategy and Generation in Negotiation Dialogues},
author={He He and Derek Chen and Anusha Balakrishnan and Percy Liang},
year={2018},
eprint={1808.09637},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 9 | 1,065 | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- dialogue-modeling
paperswithcode_id: craigslistbargains
pret... | 9,524 | [
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0.0252838134765625,
0.04302978515625,
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-0.0604248046875,
-0.0214080810546875,
... |
ccdv/govreport-summarization | 2022-10-24T20:32:47.000Z | [
"task_categories:summarization",
"task_categories:text-generation",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:en",
"conditional-text-generation",
"arxiv:2104.02112",
"region:us"
] | ccdv | GovReport dataset for summarization.
From paper: Efficient Attentions for Long Document Summarization" by L. Huang et al.
See: https://arxiv.org/pdf/2104.02112.pdf
See: https://github.com/luyang-huang96/LongDocSum | @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}
}
} | 15 | 1,065 | 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... | 1,626 | [
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0.... |
neural_code_search | 2023-06-01T14:59:50.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-4.0",
"arxiv:... | null | Neural-Code-Search-Evaluation-Dataset presents an evaluation dataset consisting of natural language query and code snippet pairs and a search corpus consisting of code snippets collected from the most popular Android repositories on GitHub. | @InProceedings{huggingface:dataset,
title = {Neural Code Search Evaluation Dataset},
authors = {Hongyu Li, Seohyun Kim and Satish Chandra},
journal = {arXiv e-prints},
year = 2018,
eid = {arXiv:1908.09804 [cs.SE]},
pages = {arXiv:1908.09804 [cs.SE]},
archivePrefix = {arXiv... | 7 | 1,062 | 2022-03-02T23:29:22 | ---
pretty_name: Neural Code Search
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
- n<1K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithco... | 5,824 | [
[
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0.034027099609375,
0.0308837890625,
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-0.0672607421875,
-0.027618408203125,
0.0173797607... |
BeIR/fiqa-qrels | 2022-10-23T06:06:29.000Z | [
"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 | 0 | 1,059 | 2022-06-05T17:26:38 | ---
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:
... | 13,988 | [
[
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-0.0545654296875,
-0.02638244628906... |
indonlp/NusaX-senti | 2023-01-24T17:02:06.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ace",
"language:ban",
"language:bjn",
"la... | indonlp | NusaX is a high-quality multilingual parallel corpus that covers 12 languages, Indonesian, English, and 10 Indonesian local languages, namely Acehnese, Balinese, Banjarese, Buginese, Madurese, Minangkabau, Javanese, Ngaju, Sundanese, and Toba Batak.
NusaX-Senti is a 3-labels (positive, neutral, negative) sentiment anal... | @misc{winata2022nusax,
title={NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages},
author={Winata, Genta Indra and Aji, Alham Fikri and Cahyawijaya,
Samuel and Mahendra, Rahmad and Koto, Fajri and Romadhony,
Ade and Kurniawan, Kemal and Moeljadi, David and Prasojo,
... | 3 | 1,059 | 2023-01-24T09:28:21 | ---
pretty_name: NusaX-senti
annotations_creators:
- expert-generated
language_creators:
- expert-generated
license:
- cc-by-sa-4.0
multilinguality:
- multilingual
language:
- ace
- ban
- bjn
- bug
- en
- id
- jv
- mad
- min
- nij
- su
- bbc
size_categories:
- 10K<n<100K
source_datasets:
- original
task_cat... | 5,607 | [
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0... |
HuggingFaceH4/testing_alpaca_small | 2023-04-12T21:55:05.000Z | [
"region:us"
] | HuggingFaceH4 | null | null | 0 | 1,056 | 2023-04-12T21:55:01 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: completion
dtype: string
splits:
- name: train
num_bytes: 33856
num_examples: 100
- name: test
num_bytes: 32475
num_examples: 100
download_size: 52543
dataset_size: 66331
---
# Dataset Card for "testing_alpaca_small... | 454 | [
[
-0.05889892578125,
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0.0203857421875,
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-0.0099... |
pg19 | 2023-07-28T09:21:25.000Z | [
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"arxiv:1911.05507",
"regio... | null | This repository contains the PG-19 language modeling benchmark.
It includes a set of books extracted from the Project Gutenberg books library, that were published before 1919.
It also contains metadata of book titles and publication dates.
PG-19 is over double the size of the Billion Word benchmark and contains docume... | @article{raecompressive2019,
author = {Rae, Jack W and Potapenko, Anna and Jayakumar, Siddhant M and
Hillier, Chloe and Lillicrap, Timothy P},
title = {Compressive Transformers for Long-Range Sequence Modelling},
journal = {arXiv preprint},
url = {https://arxiv.org/abs/1911.05507},
year = {2019},
... | 25 | 1,051 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-generation
task_ids:
- language-modeling
paperswithcode_id: pg-19
pretty_name: PG-19
da... | 8,105 | [
[
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0... |
big_patent | 2023-06-01T14:59:54.000Z | [
"task_categories:summarization",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"patent-summariz... | null | BIGPATENT, consisting of 1.3 million records of U.S. patent documents
along with human written abstractive summaries.
Each US patent application is filed under a Cooperative Patent Classification
(CPC) code. There are nine such classification categories:
A (Human Necessities), B (Performing Operations; Transporting),
C... | @misc{sharma2019bigpatent,
title={BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization},
author={Eva Sharma and Chen Li and Lu Wang},
year={2019},
eprint={1906.03741},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 26 | 1,050 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 10K<n<100K
- 1M<n<10M
source_datasets:
- original
task_categories:
- summarization
task_ids: []
paperswithcode_id: bigpatent
pretty_name: Big Patent
tags... | 9,707 | [
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-0.058929443359375,
-0.04229736328... |
ura-hcmut/MATH | 2023-09-29T17:19:11.000Z | [
"task_categories:text2text-generation",
"language:vi",
"license:cc-by-nc-sa-4.0",
"region:us"
] | ura-hcmut | null | null | 0 | 1,048 | 2023-09-19T01:55:00 | ---
license: cc-by-nc-sa-4.0
task_categories:
- text2text-generation
language:
- vi
configs:
- config_name: gcp
data_files:
- split: train
path: "MATH_gcp_training.csv"
- split: test
path: "MATH_gcp.csv"
- config_name: azr
data_files:
- split: train
path: "MATH_azr_training.csv"
- split: test
... | 539 | [
[
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0.042938232421875,
-0.06390380859375,
-0.054107666015625,
-0.0278015136718... |
neulab/tldr | 2022-12-22T19:47:11.000Z | [
"task_categories:text2text-generation",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:code",
"license:mit",
"code-generation",
"doc retrieval",
"retrieval augmented generatio... | neulab | This is the re-split of CoNaLa dataset. For each code snippet in the dev and test set, at least one function is held out from the training set. This split aims at testing a code generation model's capacity in generating unseen functions.
We further make sure that examples from the same StackOverflow post (same question... | @article{zhou2022doccoder,
title={DocCoder: Generating Code by Retrieving and Reading Docs},
author={Zhou, Shuyan and Alon, Uri and Xu, Frank F and JIang, Zhengbao and Neubig, Graham},
journal={arXiv preprint arXiv:2207.05987},
year={2022}
} | 4 | 1,047 | 2022-12-22T17:58:43 | ---
annotations_creators: []
language_creators:
- crowdsourced
- expert-generated
language:
- code
license:
- mit
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
pretty_name: DocPrompting-CoNaLa
tags:
- code-generation
- doc retr... | 2,971 | [
[
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jeanlee/kmhas_korean_hate_speech | 2022-11-28T16:26:56.000Z | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"task_ids:hate-speech-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:ko",
"license:cc-by-sa-4... | jeanlee | The K-MHaS (Korean Multi-label Hate Speech) dataset contains 109k utterances from Korean online news comments labeled with 8 fine-grained hate speech classes or Not Hate Speech class.
The fine-grained hate speech classes are politics, origin, physical, age, gender, religion, race, and profanity and these categories are... | @inproceedings{lee-etal-2022-k,
title = "K-{MH}a{S}: A Multi-label Hate Speech Detection Dataset in {K}orean Online News Comment",
author = "Lee, Jean and
Lim, Taejun and
Lee, Heejun and
Jo, Bogeun and
Kim, Yangsok and
Yoon, Heegeun and
Han, Soyeon Caren",
booktitle... | 11 | 1,046 | 2022-11-21T05:03:58 | ---
annotations_creators:
- crowdsourced
language:
- ko
language_creators:
- found
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: 'K-MHaS'
size_categories:
- 100K<n<1M
source_datasets:
- original
tags:
- K-MHaS
- Korean NLP
- Hate Speech Detection
- Dataset
- Coling2022
task_categories:
- text-clas... | 10,427 | [
[
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-0.06024169921875,
-0.053253173828125,
0.00217... |
shahules786/orca-best | 2023-08-25T14:48:40.000Z | [
"region:us"
] | shahules786 | null | null | 40 | 1,044 | 2023-08-12T05:48:30 | ---
dataset_info:
features:
- name: cluster
struct:
- name: samples
list:
- name: input
dtype: string
- name: output
dtype: string
- name: source
dtype: string
- name: instruction
dtype: string
- name: num_samples
dtype: int64
splits:
- name: train
... | 2,072 | [
[
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0.010627... |
armanc/pubmed-rct20k | 2022-11-11T08:23:24.000Z | [
"region:us"
] | armanc | null | null | 0 | 1,034 | 2022-11-11T04:20:56 | The small 20K version of the Pubmed-RCT dataset by Dernoncourt et al (2017).
```
@article{dernoncourt2017pubmed,
title={Pubmed 200k rct: a dataset for sequential sentence classification in medical abstracts},
author={Dernoncourt, Franck and Lee, Ji Young},
journal={arXiv preprint arXiv:1710.06071},
year={2017... | 646 | [
[
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0.0078125,
0.0279083251953125,
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-0.031341552734375,
-0.045928955078125,
0.03... |
allenai/qasper | 2022-10-07T22:04:11.000Z | [
"task_categories:question-answering",
"task_ids:closed-domain-qa",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|s2orc",
"language:en",
"license:cc-by-4.0",
"arxiv:2105.03011",
... | allenai | A dataset containing 1585 papers with 5049 information-seeking questions asked by regular readers of NLP papers, and answered by a separate set of NLP practitioners. | @inproceedings{Dasigi2021ADO,
title={A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers},
author={Pradeep Dasigi and Kyle Lo and Iz Beltagy and Arman Cohan and Noah A. Smith and Matt Gardner},
year={2021}
} | 36 | 1,033 | 2022-03-02T23:29:22 | ---
pretty_name: QASPER
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
language_bcp47:
- en-US
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|s2orc
task_categories:
- question-answering
task_ids:
- closed-domai... | 9,639 | [
[
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... |
open-llm-leaderboard/details_meta-llama__Llama-2-70b-hf | 2023-09-18T06:46:57.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 1,033 | 2023-08-21T11:06:07 | ---
pretty_name: Evaluation run of meta-llama/Llama-2-70b-hf
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [meta-llama/Llama-2-70b-hf](https://huggingface.co/meta-llama/Llama-2-70b-hf)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leade... | 111,559 | [
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0.0228576... |
osunlp/AttrScore | 2023-06-29T01:56:48.000Z | [
"task_categories:text-classification",
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"arxiv:2305.06311",
"region:us"
] | osunlp | We construct this dataset, which contains both training and test data for the evaluation of attribution.
The training data are repurposed from related tasks, such as question answering, fact-checking,
natural language inference, and summarization. The test data contains a set simulated from QA datasets
... | @article{yue2023automatic,
title={Automatic Evaluation of Attribution by Large Language Models},
author={Yue, Xiang and Wang, Boshi and Zhang, Kai and Chen, Ziru and Su, Yu and Sun, Huan},
journal={arXiv preprint arXiv:2305.06311},
year={2023}
} | 9 | 1,030 | 2023-05-16T19:09:52 | ---
license: apache-2.0
task_categories:
- text-classification
language:
- en
pretty_name: AttrScore
size_categories:
- 100K<n<1M
---
# Dataset Card for AttrScore
- Repository: https://github.com/OSU-NLP-Group/AttrScore
- Paper: [Automatic Evaluation of Attribution by Large Language Models] (https://arxiv.org/pdf/230... | 3,308 | [
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papluca/language-identification | 2022-07-15T10:11:23.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"multilinguality:multilingual",
"size_categories:unknown",
"source_datasets:extended|amazon_reviews_multi",
"source_datasets:extended|xnli",
"source_datasets:extended|stsb_multi_mt",
"language:ar",
"language:bg",
"langua... | papluca | null | null | 16 | 1,028 | 2022-03-02T23:29:22 | ---
annotations_creators: []
language_creators: []
language:
- ar
- bg
- de
- el
- en
- es
- fr
- hi
- it
- ja
- nl
- pl
- pt
- ru
- sw
- th
- tr
- ur
- vi
- zh
license: []
multilinguality:
- multilingual
pretty_name: Language Identification dataset
size_categories:
- unknown
source_datasets:
- extended|amazon_reviews_... | 4,987 | [
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squad_kor_v1 | 2023-06-15T15:25:29.000Z | [
"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"
] | null | KorQuAD 1.0 is a large-scale Korean dataset for machine reading comprehension task consisting of human generated questions for Wikipedia articles. We benchmark the data collecting process of SQuADv1.0 and crowdsourced 70,000+ question-answer pairs. 1,637 articles and 70,079 pairs of question answers were collected. 1,4... | @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}
} | 9 | 1,022 | 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 ... | 5,094 | [
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open_subtitles | 2023-06-01T14:59:58.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"size_categories:1M<n<10M",
"size_categories:n<1K",
"source_datasets:original",
"language:af",
"language:ar",
"language:bg",
"language:bn",
"l... | null | This is a new collection of translated movie subtitles from http://www.opensubtitles.org/.
IMPORTANT: If you use the OpenSubtitle corpus: Please, add a link to http://www.opensubtitles.org/ to your website and to your reports and publications produced with the data!
This is a slightly cleaner version of the subtitle ... | P. Lison and J. Tiedemann, 2016, OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. In Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 2016) | 33 | 1,018 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- af
- ar
- bg
- bn
- br
- bs
- ca
- cs
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- gl
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- ka
- kk
- ko
- lt
- lv
- mk
- ml
- ms
- nl
- 'no'
- pl
- pt
- ro
- ru
- si
- sk
- sl
- sq
- sr
- sv
- ... | 7,448 | [
[
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dane | 2023-01-25T14:29:05.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"task_ids:part-of-speech",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|other-Danish-Universal-Dependencies-treebank"... | null | The DaNE dataset has been annotated with Named Entities for PER, ORG and LOC
by the Alexandra Institute.
It is a reannotation of the UD-DDT (Universal Dependency - Danish Dependency Treebank)
which has annotations for dependency parsing and part-of-speech (POS) tagging.
The Danish UD treebank (Johannsen et al., 2015, U... | @inproceedings{hvingelby-etal-2020-dane,
title = "{D}a{NE}: A Named Entity Resource for {D}anish",
author = "Hvingelby, Rasmus and
Pauli, Amalie Brogaard and
Barrett, Maria and
Rosted, Christina and
Lidegaard, Lasse Malm and
Søgaard, Anders",
booktitle = "Proceedings of th... | 3 | 1,017 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- da
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-Danish-Universal-Dependencies-treebank
task_categories:
- token-classification
task_ids:
- named-entity-recognition
... | 10,820 | [
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0.0344543457... |
medical_dialog | 2023-09-18T09:07:35.000Z | [
"task_categories:question-answering",
"task_ids:closed-domain-qa",
"annotations_creators:found",
"language_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"language:zh",
"license:unknown"... | null | The MedDialog dataset (English) contains conversations (in English) between doctors and patients.It has 0.26 million dialogues. The data is continuously growing and more dialogues will be added. The raw dialogues are from healthcaremagic.com and icliniq.com.
All copyrights of the data belong to healthcaremagic.com and ... | @article{chen2020meddiag,
title={MedDialog: a large-scale medical dialogue dataset},
author={Chen, Shu and Ju, Zeqian and Dong, Xiangyu and Fang, Hongchao and Wang, Sicheng and Yang, Yue and Zeng, Jiaqi and Zhang, Ruisi and Zhang, Ruoyu and Zhou, Meng and Zhu, Penghui and Xie, Pengtao},
journal={arXiv preprint ar... | 78 | 1,016 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- expert-generated
- found
language:
- en
- zh
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- closed-domain-qa
pretty_name: MedDialog
paperswithcode_id: meddi... | 10,509 | [
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clarin-knext/fiqa-pl | 2023-06-07T08:23:07.000Z | [
"language:pl",
"arxiv:2305.19840",
"region:us"
] | clarin-knext | null | null | 0 | 1,016 | 2023-06-06T17:48:25 | ---
language:
- pl
---
Part of **BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language**.
Link to arxiv: https://arxiv.org/pdf/2305.19840.pdf
Contact: konrad.wojtasik@pwr.edu.pl | 201 | [
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mc_taco | 2023-01-25T14:40:09.000Z | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
... | null | MC-TACO (Multiple Choice TemporAl COmmonsense) is a dataset of 13k question-answer
pairs that require temporal commonsense comprehension. A system receives a sentence
providing context information, a question designed to require temporal commonsense
knowledge, and multiple candidate answers. More than one candidate ans... | @inproceedings{ZKNR19,
author = {Ben Zhou, Daniel Khashabi, Qiang Ning and Dan Roth},
title = {“Going on a vacation” takes longer than “Going for a walk”: A Study of Temporal Commonsense Understanding },
booktitle = {EMNLP},
year = {2019},
} | 0 | 1,012 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
- machine-generated
language_creators:
- crowdsourced
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
paperswithcode_id: mc-tac... | 6,840 | [
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shawhin/imdb-truncated | 2023-09-06T21:06:35.000Z | [
"region:us"
] | shawhin | null | null | 0 | 1,009 | 2023-09-06T15:55:01 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: label
dtype: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 1310325
num_examples: 1000
- name: valida... | 592 | [
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... |
THUDM/AgentInstruct | 2023-10-23T12:36:19.000Z | [
"language:en",
"arxiv:2310.12823",
"region:us"
] | THUDM | null | null | 103 | 1,002 | 2023-10-16T10:27:58 | ---
configs:
- config_name: default
data_files:
- split: os
path: data/os-*
- split: db
path: data/db-*
- split: alfworld
path: data/alfworld-*
- split: webshop
path: data/webshop-*
- split: kg
path: data/kg-*
- split: mind2web
path: data/mind2web-*
dataset_info:
features:
- na... | 3,764 | [
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pszemraj/qmsum-cleaned | 2023-06-07T22:58:58.000Z | [
"source_datasets:tau/scrolls",
"language:en",
"license:apache-2.0",
"region:us"
] | pszemraj | null | null | 1 | 995 | 2023-05-05T16:16:33 | ---
license: apache-2.0
language:
- en
source_datasets: tau/scrolls
---
# qmsum-cleaned
## prefixes
It's worth noting that each "document" in `input` is prefixed by a question/prompt on what the model is supposed to do. **You may want to explicitly handle this in some way, or prefix your models trained on this dat... | 1,780 | [
[
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0.00... |
BeIR/nfcorpus-qrels | 2022-10-23T06:05:32.000Z | [
"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 | 0 | 994 | 2022-06-05T17:25:56 | ---
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:
... | 13,988 | [
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liuhaotian/LLaVA-Instruct-150K | 2023-10-06T22:18:34.000Z | [
"task_categories:visual-question-answering",
"task_categories:question-answering",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-nc-4.0",
"region:us"
] | liuhaotian | null | null | 174 | 994 | 2023-04-17T23:47:27 | ---
license: cc-by-nc-4.0
task_categories:
- visual-question-answering
- question-answering
language:
- en
pretty_name: LLaVA Visual Instruct 150K
size_categories:
- 100K<n<1M
---
# LLaVA Visual Instruct 150K Dataset Card
## Dataset details
**Dataset type:**
LLaVA Visual Instruct 150K is a set of GPT-generated mul... | 1,216 | [
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-... |
un_multi | 2023-06-01T14:59:54.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:ar",
"language:de",
"language:en",
"language:es",
"language:fr",
"language:ru",
"language:zh",
"license... | null | This is a collection of translated documents from the United Nations. This corpus is available in all 6 official languages of the UN, consisting of around 300 million words per language | @inproceedings{eisele-chen-2010-multiun,
title = "{M}ulti{UN}: A Multilingual Corpus from United Nation Documents",
author = "Eisele, Andreas and
Chen, Yu",
booktitle = "Proceedings of the Seventh International Conference on Language Resources and Evaluation ({LREC}'10)",
month = may,
year = ... | 2 | 991 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- ar
- de
- en
- es
- fr
- ru
- zh
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: multiun
pretty_name: Multilingual Corpus fr... | 10,328 | [
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0.039245605... |
NeelNanda/codeparrot_clean_subset_train | 2022-10-22T23:04:58.000Z | [
"region:us"
] | NeelNanda | null | null | 0 | 991 | 2022-10-22T23:04:32 | Entry not found | 15 | [
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schema_guided_dstc8 | 2023-01-25T14:43:36.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:token-classification",
"task_categories:text-classification",
"task_ids:dialogue-modeling",
"task_ids:multi-class-classification",
"task_ids:parsing",
"annotations_creators:machine-generated",
"language_creators:crowdso... | null | The Schema-Guided Dialogue dataset (SGD) was developed for the Dialogue State Tracking task of the Eights Dialogue Systems Technology Challenge (dstc8).
The SGD dataset consists of over 18k annotated multi-domain, task-oriented conversations between a human and a virtual assistant.
These conversations involve interacti... | @inproceedings{aaai/RastogiZSGK20,
author = {Abhinav Rastogi and
Xiaoxue Zang and
Srinivas Sunkara and
Raghav Gupta and
Pranav Khaitan},
title = {Towards Scalable Multi-Domain Conversational Agents: The Schema-Guided
Dialogue Dataset}... | 7 | 986 | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- crowdsourced
- machine-generated
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
- token-classification
- text-classification
... | 16,671 | [
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lhoestq/test | 2022-07-01T15:26:34.000Z | [
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:mit",
"region:us"
] | lhoestq | This is a test dataset. | \ | 0 | 986 | 2022-03-02T23:29:22 | ---
type: test
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- other-test
task_ids:
- other-test
paperswithcode_id: null
pretty_name: Test Dataset
---
This is a test d... | 326 | [
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silk-road/ChatHaruhi-from-RoleLLM | 2023-10-20T12:27:24.000Z | [
"license:cc-by-4.0",
"region:us"
] | silk-road | null | null | 0 | 986 | 2023-10-20T08:39:56 | ---
license: cc-by-4.0
---
Adapt English Role in RoleBench into ChatHaruhi format
only using profiles part in [ZenMoore/RoleBench](https://huggingface.co/datasets/ZenMoore/RoleBench)
Great thanks to on authors of RoleLLM!
usage:
```python
# if you pip installed chatharuhi it should be
# from chatharuhi import Chat... | 10,630 | [
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augtoma/usmle_step_1 | 2023-08-11T21:25:08.000Z | [
"region:us"
] | augtoma | null | null | 0 | 984 | 2023-08-11T21:24:50 | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
- name: question
dtype: string
- name: options
struct:
- name: A
dtype: string
- name: B
dtype: string
- name: C
dtype: string
- name: D
dtype: string
... | 852 | [
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svhn | 2023-01-25T14:45:04.000Z | [
"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",... | null | 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 ... | @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}
} | 9 | 982 | 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
... | 10,132 | [
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BeIR/dbpedia-entity-qrels | 2022-10-23T06:07:36.000Z | [
"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 | 0 | 980 | 2022-06-05T17:27:22 | ---
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:
... | 13,988 | [
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cardiffnlp/super_tweeteval | 2023-11-02T09:42:14.000Z | [
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:question-answering",
"task_categories:other",
"task_ids:topic-classification",
"task_ids:named-entity-recognition",
"task_ids:abstractive-qa",
"annotations_creators:expert-generated",
"multilinguality:mon... | cardiffnlp | TBA | TBA | 1 | 977 | 2023-05-16T14:33:16 | ---
annotations_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- n<50K
source_datasets:
- extended|other
task_categories:
- text-classification
- token-classification
- question-answering
- other
task_ids:
- topic-classification
- named-entity-recognition
... | 17,304 | [
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0.00... |
lamini/taylor_swift | 2023-07-24T03:47:45.000Z | [
"region:us"
] | lamini | null | null | 1 | 970 | 2023-07-24T03:47:42 | ---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 850749.3
num_examples: 783
- name: test
num_by... | 573 | [
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wider_face | 2023-01-25T15:02:08.000Z | [
"task_categories:object-detection",
"task_ids:face-detection",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-wider",
"language:en",
"license:cc-by-nc-nd-4.0",
"arxiv:1511.06523",
"r... | null | WIDER FACE dataset is a face detection benchmark dataset, of which images are
selected from the publicly available WIDER dataset. We choose 32,203 images and
label 393,703 faces with a high degree of variability in scale, pose and
occlusion as depicted in the sample images. WIDER FACE dataset is organized
based on 61 e... | @inproceedings{yang2016wider,
Author = {Yang, Shuo and Luo, Ping and Loy, Chen Change and Tang, Xiaoou},
Booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
Title = {WIDER FACE: A Face Detection Benchmark},
Year = {2016}} | 13 | 968 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-nc-nd-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-wider
task_categories:
- object-detection
task_ids:
- face-detection
paperswithcode_id: wider-face-1
pretty_nam... | 9,318 | [
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cfilt/iitb-english-hindi | 2022-04-26T13:50:22.000Z | [
"region:us"
] | cfilt | null | null | 11 | 968 | 2022-03-02T23:29:22 | <p align="center"><img src="https://huggingface.co/datasets/cfilt/HiNER-collapsed/raw/main/cfilt-dark-vec.png" alt="Computation for Indian Language Technology Logo" width="150" height="150"/></p>
# IITB-English-Hindi Parallel Corpus
[![License: CC BY-NC 4.0](https://img.shields.io/badge/License-CC%20BY--NC%204.0-ligh... | 3,113 | [
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0.034790... |
ybelkada/football-dataset | 2023-01-17T11:47:41.000Z | [
"region:us"
] | ybelkada | null | null | 0 | 966 | 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 ca... | 371 | [
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0... |
roszcz/maestro-v1-sustain | 2023-04-23T13:35:49.000Z | [
"region:us"
] | roszcz | null | null | 0 | 965 | 2023-02-28T20:38:48 | ---
dataset_info:
features:
- name: notes
struct:
- name: duration
sequence: float64
- name: end
sequence: float64
- name: pitch
sequence: int64
- name: start
sequence: float64
- name: velocity
sequence: int64
- name: composer
dtype: string
- name: title... | 842 | [
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pragmeval | 2023-06-01T14:59:54.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"lice... | null | Evaluation of language understanding with a 11 datasets benchmark focusing on discourse and pragmatics | @misc{sileo2019discoursebased,
title={Discourse-Based Evaluation of Language Understanding},
author={Damien Sileo and Tim Van-de-Cruys and Camille Pradel and Philippe Muller},
year={2019},
eprint={1907.08672},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 3 | 963 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- 1K<n<10K
- n<1K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
pretty_name: pragmeval
dataset_info:
- c... | 17,069 | [
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jondurbin/airoboros-3.1 | 2023-10-13T08:22:45.000Z | [
"license:apache-2.0",
"region:us"
] | jondurbin | null | null | 12 | 962 | 2023-10-10T11:01:33 | ---
license: apache-2.0
---
## Overview
This dataset is a continuation of the airoboros datasets, with the following updates:
* More MathJSON, now ~17k items - math questions, prefixed with __"Create a MathJSON solution to the following:"__, which then outputs a JSON between __`<mathjson>`__ and __`</mathjson>`__ tag... | 3,765 | [
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0.017... |
castorini/mr-tydi-corpus | 2022-10-12T20:25:51.000Z | [
"task_categories:text-retrieval",
"multilinguality:multilingual",
"language:ar",
"language:bn",
"language:en",
"language:fi",
"language:id",
"language:ja",
"language:ko",
"language:ru",
"language:sw",
"language:te",
"language:th",
"license:apache-2.0",
"region:us"
] | castorini | null | null | 2 | 961 | 2022-03-02T23:29:22 | ---
language:
- ar
- bn
- en
- fi
- id
- fi
- ja
- ko
- ru
- sw
- te
- th
multilinguality:
- multilingual
task_categories:
- text-retrieval
license: apache-2.0
---
# Dataset Summary
Mr. TyDi is a multi-lingual benchmark dataset built on TyDi, covering eleven typologically diverse l... | 1,499 | [
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aqua_rat | 2022-11-18T18:20:44.000Z | [
"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-... | null | 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. | @InProceedings{ACL,
title = {Program induction by rationale generation: Learning to solve and explain algebraic word problems},
authors={Ling, Wang and Yogatama, Dani and Dyer, Chris and Blunsom, Phil},
year={2017}
} | 9 | 956 | 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
pre... | 5,526 | [
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0.0233... |
llm-lens/vocab_tags | 2023-06-29T02:50:09.000Z | [
"region:us"
] | llm-lens | null | null | 1 | 954 | 2023-06-29T02:50:05 | ---
dataset_info:
features:
- name: prompt_descriptions
dtype: string
splits:
- name: train
num_bytes: 346971
num_examples: 22131
download_size: 298971
dataset_size: 346971
---
# Dataset Card for "vocab_tags"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTI... | 365 | [
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laugustyniak/abusive-clauses-pl | 2023-03-29T10:46:49.000Z | [
"task_categories:text-classification",
"annotations_creators:hired_annotators",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10<n<10K",
"language:pl",
"license:cc-by-nc-sa-4.0",
"region:us"
] | laugustyniak | null | @InProceedings{AbusiveClauses:dataset,
title = {AbusiveClauses},
author={},
year={2022}
} | 5 | 952 | 2022-03-02T23:29:22 | ---
annotations_creators:
- hired_annotators
language_creators:
- found
language:
- pl
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10<n<10K
task_categories:
- text-classification
task_ids:
- text-classification
pretty_name: Polish-Abusive-Clauses
---
# PAC - Polish Abusive Clauses Data... | 4,573 | [
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0.026... |
codah | 2023-01-25T14:28:20.000Z | [
"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"
] | null | The COmmonsense Dataset Adversarially-authored by Humans (CODAH) is an evaluation set for commonsense question-answering in the sentence completion style of SWAG. As opposed to other automatically generated NLI datasets, CODAH is adversarially constructed by humans who can view feedback from a pre-trained model and use... | @inproceedings{chen2019codah,
title={CODAH: An Adversarially-Authored Question Answering Dataset for Common Sense},
author={Chen, Michael and D'Arcy, Mike and Liu, Alisa and Fernandez, Jared and Downey, Doug},
booktitle={Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP},
pages=... | 4 | 951 | 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 Datas... | 7,225 | [
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0.0... |
nielsr/docvqa_1200_examples_donut | 2022-08-05T16:39:23.000Z | [
"region:us"
] | nielsr | null | null | 2 | 949 | 2022-08-05T15:13:40 | Entry not found | 15 | [
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Amod/mental_health_counseling_conversations | 2023-07-20T19:00:46.000Z | [
"task_categories:conversational",
"task_categories:text-generation",
"task_categories:question-answering",
"task_ids:sentiment-classification",
"task_ids:language-modeling",
"task_ids:open-domain-qa",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"... | Amod | null | null | 28 | 949 | 2023-06-22T12:52:50 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: openrail
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- conversational
- text-generation
- question-answering
task_ids:
- sentiment-classification
- language-modeling
-... | 3,015 | [
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squadshifts | 2023-04-05T13:40:47.000Z | [
"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:original",
"language:en",
"license:cc-by-4.0",
"region:u... | null | null | @InProceedings{pmlr-v119-miller20a,
title = {The Effect of Natural Distribution Shift on Question Answering Models},
author = {Miller, John and Krauth, Karl and Recht, Benjamin and Schmidt, Ludwig},
booktitle = {Proceedings of the 37th International Conference on Machine Learning},
pages = {6905--6916},
year ... | 3 | 946 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- crowdsourced
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: SQuAD-shifts
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: squ... | 10,676 | [
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shunk031/DrawBench | 2023-09-27T13:13:31.000Z | [
"task_categories:text-to-image",
"annotations_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:unknown",
"arxiv:2205.11487",
"region:us"
] | shunk031 | DrawBench is a comprehensive and challenging set of prompts that support the evaluation and comparison of text-to-image models. This benchmark contains 11 categories of prompts, testing different capabilities of models such as the ability to faithfully render different colors, numbers of objects, spatial relations, tex... | @article{saharia2022photorealistic,
title={Photorealistic text-to-image diffusion models with deep language understanding},
author={Saharia, Chitwan and Chan, William and Saxena, Saurabh and Li, Lala and Whang, Jay and Denton, Emily L and Ghasemipour, Kamyar and Gontijo Lopes, Raphael and Karagol Ayan, Burcu and Sa... | 1 | 945 | 2023-09-27T13:10:40 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators: []
license:
- unknown
multilinguality:
- monolingual
pretty_name: DrawBench
size_categories:
- n<1K
source_datasets:
- original
tags: []
task_categories:
- text-to-image
task_ids: []
---
# Dataset Card for DrawBench
## Table of Contents
- [Dat... | 3,737 | [
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DFKI-SLT/few-nerd | 2023-06-21T09:59:09.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|wikipedia",
"language:en",
"license:cc-by-sa-4.0",
"structure-predi... | DFKI-SLT | Few-NERD is a large-scale, fine-grained manually annotated named entity recognition dataset,
which contains 8 coarse-grained types, 66 fine-grained types, 188,200 sentences, 491,711 entities
and 4,601,223 tokens. Three benchmark tasks are built, one is supervised: Few-NERD (SUP) and the
other two are few-shot: Few-N... | @inproceedings{ding2021few,
title={Few-NERD: A Few-Shot Named Entity Recognition Dataset},
author={Ding, Ning and Xu, Guangwei and Chen, Yulin, and Wang, Xiaobin and Han, Xu and Xie,
Pengjun and Zheng, Hai-Tao and Liu, Zhiyuan},
booktitle={ACL-IJCNLP},
year={2021}
} | 12 | 937 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|wikipedia
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: few-nerd
pretty... | 7,128 | [
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llm-lens/descriptors-text-davinci-003 | 2023-06-29T02:39:27.000Z | [
"region:us"
] | llm-lens | null | null | 0 | 935 | 2023-06-29T02:38:48 | ---
dataset_info:
features:
- name: vocab
dtype: string
- name: descriptions
sequence: string
- name: prompt_descriptions
sequence: string
splits:
- name: birdsnap
num_bytes: 322488
num_examples: 500
- name: caltech101
num_bytes: 56880
num_examples: 102
- name: cifar100
n... | 1,339 | [
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liar | 2023-01-25T14:34:21.000Z | [
"task_categories:text-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"fake-news-detection",
"arxiv:1705.00648",
"region:us"
] | null | LIAR is a dataset for fake news detection with 12.8K human labeled short statements from politifact.com's API, and each statement is evaluated by a politifact.com editor for its truthfulness. The distribution of labels in the LIAR dataset is relatively well-balanced: except for 1,050 pants-fire cases, the instances for... | @inproceedings{wang-2017-liar,
title = "{``}Liar, Liar Pants on Fire{''}: A New Benchmark Dataset for Fake News Detection",
author = "Wang, William Yang",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2017",
address =... | 6 | 934 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: liar
pretty_name: LIAR
tags:
- fake-news-detection
dat... | 5,159 | [
[
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0.0200042724609375,
0.01751708984375,
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0.0149993896484375,
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0.0292816162109375,
0.040130615234375,
-0.055450439453125,
-0.07666015625,
-0.05352783203125,
0.0049133... |
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