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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
alt | 2023-06-01T14:59:53.000Z | [
"task_categories:translation",
"task_categories:token-classification",
"task_ids:parsing",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"multilinguality:translation",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"source_da... | null | The ALT project aims to advance the state-of-the-art Asian natural language processing (NLP) techniques through the open collaboration for developing and using ALT. It was first conducted by NICT and UCSY as described in Ye Kyaw Thu, Win Pa Pa, Masao Utiyama, Andrew Finch and Eiichiro Sumita (2016). Then, it was develo... | @inproceedings{riza2016introduction,
title={Introduction of the asian language treebank},
author={Riza, Hammam and Purwoadi, Michael and Uliniansyah, Teduh and Ti, Aw Ai and Aljunied, Sharifah Mahani and Mai, Luong Chi and Thang, Vu Tat and Thai, Nguyen Phuong and Chea, Vichet and Sam, Sethserey and others},
book... | 6 | 932 | 2022-03-02T23:29:22 | ---
annotations_creators:
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language_creators:
- crowdsourced
language:
- bn
- en
- fil
- hi
- id
- ja
- km
- lo
- ms
- my
- th
- vi
- zh
license:
- cc-by-4.0
multilinguality:
- multilingual
- translation
size_categories:
- 100K<n<1M
- 10K<n<100K
source_datasets:
- original
task_categories:
- translati... | 11,969 | [
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wiki_asp | 2022-11-18T21:59:51.000Z | [
"task_categories:summarization",
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"language_creators:crowdsourced",
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"language:en",
"license:cc-by-sa-4.0",
"aspect-based-summarization",
"arxiv:2011.07832",
"region:us"
] | null | WikiAsp is a multi-domain, aspect-based summarization dataset in the encyclopedic
domain. In this task, models are asked to summarize cited reference documents of a
Wikipedia article into aspect-based summaries. Each of the 20 domains include 10
domain-specific pre-defined aspects. | @article{hayashi20tacl,
title = {WikiAsp: A Dataset for Multi-domain Aspect-based Summarization},
authors = {Hiroaki Hayashi and Prashant Budania and Peng Wang and Chris Ackerson and Raj Neervannan and Graham Neubig},
journal = {Transactions of the Association for Computational Linguistics (TACL)},
year = ... | 3 | 926 | 2022-03-02T23:29:22 | ---
annotations_creators:
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- en
license:
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source_datasets:
- original
task_categories:
- summarization
task_ids: []
paperswithcode_id: wikiasp
pretty_name: WikiAsp
tags:
- aspect-based-su... | 14,233 | [
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augtoma/medqa_usmle | 2023-08-11T20:50:07.000Z | [
"region:us"
] | augtoma | null | null | 0 | 921 | 2023-08-11T20:49:29 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: question
dtype: string
- name: answer
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- name: options
struct:
- name: A
dtype: string
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dtype: str... | 870 | [
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quac | 2023-01-25T14:43:01.000Z | [
"task_categories:question-answering",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:dialogue-modeling",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categ... | null | Question Answering in Context is a dataset for modeling, understanding,
and participating in information seeking dialog. Data instances consist
of an interactive dialog between two crowd workers: (1) a student who
poses a sequence of freeform questions to learn as much as possible
about a hidden Wikipedia text, and (2)... | @inproceedings{choi-etal-2018-quac,
title = "QUAC: Question answering in context",
abstract = "We present QuAC, a dataset for Question Answering in Context that contains 14K information-seeking QA dialogs (100K questions in total). The dialogs involve two crowd workers: (1) a student who poses a sequence of freeform qu... | 14 | 918 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- found
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|wikipedia
task_categories:
- question-answering
- text-generation
- fill-mask
task_ids:
- dialogue-modeling
- extracti... | 17,228 | [
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HausaNLP/AfriSenti-Twitter | 2023-09-03T10:39:19.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-analysis",
"task_ids:sentiment-classification",
"task_ids:sentiment-scoring",
"task_ids:semantic-similarity-classification",
"task_ids:semantic-similarity-scoring",
"multilinguality:monolingual",
"multilinguality:multilingual",
"size_categor... | HausaNLP | AfriSenti is the largest sentiment analysis benchmark dataset for under-represented African languages---covering 110,000+ annotated tweets in 14 African languages (Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and yo... | @inproceedings{muhammad-etal-2023-semeval,
title="{S}em{E}val-2023 Task 12: Sentiment Analysis for African Languages ({A}fri{S}enti-{S}em{E}val)",
author="Muhammad, Shamsuddeen Hassan and
Yimam, Seid and
Abdulmumin, Idris and
Ahmad, Ibrahim Sa'id and
Ousidhoum, Nedjma, and
Ayele, Abinew, and
... | 1 | 917 | 2023-06-16T08:49:02 | ---
license: cc-by-nc-sa-4.0
task_categories:
- text-classification
task_ids:
- sentiment-analysis
- sentiment-classification
- sentiment-scoring
- semantic-similarity-classification
- semantic-similarity-scoring
tags:
- sentiment analysis, Twitter, tweets
- sentiment
multilinguality:
- monolingual
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size_... | 8,438 | [
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para_pat | 2022-12-02T11:39:09.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:translation",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:machine-generated",
"language_creators:expert-generated",
"multilinguality:translation",
"size_categories:10K<n<100K... | null | ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts
This dataset contains the developed parallel corpus from the open access Google
Patents dataset in 74 language pairs, comprising more than 68 million sentences
and 800 million tokens. Sentences were automatically aligned using the Hunalign algor... | @inproceedings{soares-etal-2020-parapat,
title = "{P}ara{P}at: The Multi-Million Sentences Parallel Corpus of Patents Abstracts",
author = "Soares, Felipe and
Stevenson, Mark and
Bartolome, Diego and
Zaretskaya, Anna",
booktitle = "Proceedings of The 12th Language Resources and Evaluati... | 9 | 916 | 2022-03-02T23:29:22 | ---
annotations_creators:
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language:
- cs
- de
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- es
- fr
- hu
- ja
- ko
- pt
- ro
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- sk
- uk
- zh
license:
- cc-by-4.0
multilinguality:
- translation
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
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- fill... | 14,226 | [
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daekeun-ml/naver-news-summarization-ko | 2023-01-10T11:12:44.000Z | [
"task_categories:summarization",
"size_categories:10K<n<100K",
"language:ko",
"license:apache-2.0",
"region:us"
] | daekeun-ml | null | null | 14 | 916 | 2022-08-01T14:54:17 | ---
license: apache-2.0
task_categories:
- summarization
language:
- ko
size_categories:
- 10K<n<100K
---
This dataset is a custom dataset created by the author by crawling Naver News (https://news.naver.com) for the Korean NLP model hands-on.
- Period: July 1, 2022 - July 10, 2022
- Subject: IT, economics
```
Datase... | 787 | [
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xquad_r | 2023-06-01T14:59:54.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:extended|squad",
"source_datasets:extended|xquad",
"language:ar",
"language:de",
"langu... | null | XQuAD-R is a retrieval version of the XQuAD dataset (a cross-lingual extractive QA dataset). Like XQuAD, XQUAD-R is an 11-way parallel dataset, where each question appears in 11 different languages and has 11 parallel correct answers across the languages. | @article{roy2020lareqa,
title={LAReQA: Language-agnostic answer retrieval from a multilingual pool},
author={Roy, Uma and Constant, Noah and Al-Rfou, Rami and Barua, Aditya and Phillips, Aaron and Yang, Yinfei},
journal={arXiv preprint arXiv:2004.05484},
year={2020}
} | 2 | 912 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- ar
- de
- el
- en
- es
- hi
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- th
- tr
- vi
- zh
license:
- cc-by-sa-4.0
multilinguality:
- multilingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|squad
- extended|xquad
task_categories:
- question-answering
task_ids:
... | 10,658 | [
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huggan/wikiart | 2023-03-22T13:56:08.000Z | [
"task_categories:image-classification",
"task_categories:text-to-image",
"task_categories:image-to-text",
"size_categories:10K<n<100K",
"license:unknown",
"art",
"region:us"
] | huggan | null | null | 43 | 912 | 2022-04-06T09:40:18 | ---
license: unknown
license_details: Data files © Original Authors
size_categories:
- 10K<n<100K
task_categories:
- image-classification
- text-to-image
- image-to-text
tags:
- art
---
## Dataset Description
- **Homepage:** https://www.wikiart.org/
### Dataset Summary
Dataset containing 81,444 pieces of visual art... | 2,366 | [
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BeIR/trec-covid-qrels | 2022-10-23T06:01:04.000Z | [
"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 | 912 | 2022-06-05T15:38:00 | ---
annotations_creators: []
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paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
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hotpotqa:
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fiqa:
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Multimodal-Fatima/VizWiz | 2023-03-07T01:26:12.000Z | [
"region:us"
] | Multimodal-Fatima | null | null | 1 | 911 | 2023-03-06T21:57:49 | Entry not found | 15 | [
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wikitablequestions | 2023-04-05T13:45:42.000Z | [
"task_categories:question-answering",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
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"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"table-question-answering",
"arxiv:1508.00305",
"region:us"
] | null | This WikiTableQuestions dataset is a large-scale dataset for the task of question answering on semi-structured tables. | @inproceedings{pasupat-liang-2015-compositional,
title = "Compositional Semantic Parsing on Semi-Structured Tables",
author = "Pasupat, Panupong and Liang, Percy",
booktitle = "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference ... | 9 | 909 | 2022-03-14T11:16:52 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
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paperswithcode_id: null
pretty_name: WikiTableQuestions
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids: []
tags:
- table-questi... | 7,634 | [
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cbt | 2023-06-01T14:59:53.000Z | [
"task_categories:other",
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"licen... | null | The Children’s Book Test (CBT) is designed to measure directly
how well language models can exploit wider linguistic context.
The CBT is built from books that are freely available. | @misc{hill2016goldilocks,
title={The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations},
author={Felix Hill and Antoine Bordes and Sumit Chopra and Jason Weston},
year={2016},
eprint={1511.02301},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 9 | 908 | 2022-03-02T23:29:22 | ---
pretty_name: Children’s Book Test (CBT)
annotations_creators:
- machine-generated
language_creators:
- found
language:
- en
license:
- gfdl
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- n<1K
source_datasets:
- original
task_categories:
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- question-answering
task_ids:
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pape... | 9,810 | [
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hails/bigbench | 2023-10-05T16:23:41.000Z | [
"region:us"
] | hails | null | null | 1 | 908 | 2023-10-03T19:55:51 | ---
dataset_info:
- config_name: abstract_narrative_understanding_zero_shot
features:
- name: idx
dtype: int32
- name: inputs
dtype: string
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sequence: string
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splits:
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vwxyzjn/summarize_from_feedback_oai_preprocessing | 2023-10-25T15:04:53.000Z | [
"region:us"
] | vwxyzjn | null | null | 0 | 903 | 2023-10-19T18:18:24 | ---
dataset_info:
features:
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dbrd | 2023-01-25T14:29:14.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:text-classification",
"task_ids:language-modeling",
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"task_ids:sentiment-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"si... | null | The Dutch Book Review Dataset (DBRD) contains over 110k book reviews of which 22k have associated binary sentiment polarity labels. It is intended as a benchmark for sentiment classification in Dutch and created due to a lack of annotated datasets in Dutch that are suitable for this task. | @article{DBLP:journals/corr/abs-1910-00896,
author = {Benjamin van der Burgh and
Suzan Verberne},
title = {The merits of Universal Language Model Fine-tuning for Small Datasets
- a case with Dutch book reviews},
journal = {CoRR},
volume = {abs/1910.00896},
year =... | 4 | 902 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
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license:
- cc-by-nc-sa-4.0
multilinguality:
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size_categories:
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source_datasets:
- original
task_categories:
- text-generation
- fill-mask
- text-classification
task_ids:
- language-modeling
- masked-language-modeling
- s... | 8,827 | [
[
-0.053863525390625,
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0.024... |
Open-Orca/SlimOrca | 2023-10-12T06:43:59.000Z | [
"task_categories:conversational",
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:table-question-answering",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:summarization",
"task_categories:feature-extra... | Open-Orca | null | null | 31 | 898 | 2023-10-06T09:40:55 | ---
language:
- en
license: mit
task_categories:
- conversational
- text-classification
- token-classification
- table-question-answering
- question-answering
- zero-shot-classification
- summarization
- feature-extraction
- text-generation
- text2text-generation
pretty_name: SlimOrca
size_categories:
- 100K<n<1M
---
... | 2,154 | [
[
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0.0287628173828125,
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0... |
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 | 1 | 896 | 2022-06-05T17:26:24 | ---
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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allenai/lila | 2023-03-15T18:36:28.000Z | [
"license:cc-by-4.0",
"region:us"
] | allenai | Līla is a comprehensive benchmark for mathematical reasoning with over 140K natural language questions annotated with Python programs and natural language instructions. The data set comes with multiple splits: Līla-IID (train, dev, test), Līla-OOD (train, dev, test), and Līla-Robust. | @INPROCEEDINGS{Mishra2022Lila,
author = {
Swaroop Mishra
and Matthew Finlayson
and Pan Lu
and Leonard Tang
and Sean Welleck
and Chitta Baral
and Tanmay Rajpurohit
and Oyvind Tafjord
and Ashish Sabharwal
and Peter Clark
and Ashwin Kalyan},
tit... | 14 | 895 | 2023-02-08T21:39:35 | ---
license: cc-by-4.0
---
## Dataset Description
- **Repository:** [allenai/lila](https://github.com/allenai/lila)
- **Paper:** [LILA: A Unified Benchmark for Mathematical Reasoning](https://aclanthology.org/2022.emnlp-main.392.pdf)
- **Point of Contact:** [Matthew Finlayson](https://mattf1n.github.io/), [Sean Welle... | 1,312 | [
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0.... |
juletxara/xcopa_mt | 2023-07-21T10:19:22.000Z | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:extended|copa",
"language:en",
"license:cc-by-4.0",
"region:us"
] | juletxara | XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning
The Cross-lingual Choice of Plausible Alternatives dataset is a benchmark to evaluate the ability of machine learning models to transfer commonsense reasoning across
languages. The dataset is the translation and reannotation of the English COPA (Roemmele ... | @article{ponti2020xcopa,
title={{XCOPA: A} Multilingual Dataset for Causal Commonsense Reasoning},
author={Edoardo M. Ponti, Goran Glava\v{s}, Olga Majewska, Qianchu Liu, Ivan Vuli\'{c} and Anna Korhonen},
journal={arXiv preprint},
year={2020},
url={https://ducdauge.github.io/files/xcopa.pdf}
}
@inproceedi... | 0 | 893 | 2023-05-23T08:56:13 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: XCOPA MT
size_categories:
- unknown
source_datasets:
- extended|copa
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
paperswithcode_id: ... | 42,973 | [
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0... |
jon-tow/okapi_hellaswag | 2023-10-24T02:20:03.000Z | [
"language:ar",
"language:bn",
"language:ca",
"language:da",
"language:de",
"language:es",
"language:eu",
"language:fr",
"language:gu",
"language:hi",
"language:hr",
"language:hu",
"language:hy",
"language:id",
"language:it",
"language:kn",
"language:ml",
"language:mr",
"language:... | jon-tow | HellaSwag: Can a Machine Really Finish Your Sentence? is a new dataset for commonsense NLI. A paper was published at ACL2019. | @inproceedings{zellers2019hellaswag,
title={HellaSwag: Can a Machine Really Finish Your Sentence?},
author={Zellers, Rowan and Holtzman, Ari and Bisk, Yonatan and Farhadi, Ali and Choi, Yejin},
booktitle ={Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
year={20... | 0 | 887 | 2023-10-23T21:26:05 | ---
language:
- ar
- bn
- ca
- da
- de
- es
- eu
- fr
- gu
- hi
- hr
- hu
- hy
- id
- it
- kn
- ml
- mr
- ne
- nl
- pt
- ro
- ru
- sk
- sr
- sv
- ta
- te
- uk
- vi
license: cc-by-nc-4.0
---
# okapi_hellaswag
<!-- Provide a quick summary of the dataset. -->
Multilingual translation of [Hellaswag](https://arxiv.org/abs... | 2,375 | [
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0.038543701171875,
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... |
meta-math/MetaMathQA | 2023-10-25T13:54:38.000Z | [
"license:cc-by-nc-4.0",
"math",
"math-qa",
"arxiv:2309.12284",
"region:us"
] | meta-math | null | null | 93 | 885 | 2023-09-21T17:22:46 | ---
license: cc-by-nc-4.0
tags:
- math
- math-qa
---
arxiv.org/abs/2309.12284
View the project page:
https://meta-math.github.io/
# Citation
```bibtex
@article{yu2023metamath,
title={MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models},
author={Yu, Longhui and Jiang, Weisen and Shi, Ha... | 508 | [
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0.0095672607421... |
SetFit/ag_news | 2022-01-19T21:21:07.000Z | [
"region:us"
] | SetFit | null | null | 0 | 884 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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-0.06036376953125,
0.03790... |
LDJnr/Puffin | 2023-08-10T22:28:55.000Z | [
"task_categories:conversational",
"task_categories:question-answering",
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:en",
"license:apache-2.0",
"Physics",
"Biology",
"Math",
"Chemistry",
"Culture",
"Logic",
"Roleplay",
"region:us"
] | LDJnr | null | null | 68 | 884 | 2023-08-10T06:50:06 | ---
license: apache-2.0
task_categories:
- conversational
- question-answering
- text-generation
language:
- en
tags:
- Physics
- Biology
- Math
- Chemistry
- Culture
- Logic
- Roleplay
pretty_name: Puffin
size_categories:
- 1K<n<10K
---
## This is the Official Puffin dataset. Exactly 3,000 examples with each response... | 2,599 | [
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0.03680419921875,
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0.0238494873046875,
0.02435302734375,
-0.041229248046875,
-0.007770538330078125,
-0.037506103... |
Cohere/wikipedia-22-12-en-embeddings | 2023-03-22T16:51:57.000Z | [
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"annotations_creators:expert-generated",
"multilinguality:multilingual",
"language:en",
"license:apache-2.0",
"region:us"
] | Cohere | null | null | 38 | 883 | 2023-01-14T20:36:11 | ---
annotations_creators:
- expert-generated
language:
- en
multilinguality:
- multilingual
size_categories: []
source_datasets: []
tags: []
task_categories:
- text-retrieval
license:
- apache-2.0
task_ids:
- document-retrieval
---
# Wikipedia (en) embedded with cohere.ai `multilingual-22-12` encoder
We encoded... | 3,845 | [
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0.01... |
Matthijs/snacks | 2022-04-12T14:26:59.000Z | [
"task_categories:image-classification",
"license:cc-by-4.0",
"region:us"
] | Matthijs | null | @article{OpenImages2,
title={OpenImages: A public dataset for large-scale multi-label and multi-class image classification.},
author={Krasin, Ivan and Duerig, Tom and Alldrin, Neil and Ferrari, Vittorio and Abu-El-Haija, Sami and Kuznetsova, Alina and Rom, Hassan and Uijlings, Jasper and Popov, Stefan and Kamali, S... | 6 | 882 | 2022-04-12T08:30:24 | ---
pretty_name: Snacks
task_categories:
- image-classification
- computer-vision
license: cc-by-4.0
---
# Dataset Card for Snacks
## Dataset Summary
This is a dataset of 20 different types of snack foods that accompanies the book [Machine Learning by Tutorials](https://www.raywenderlich.com/books/machine-learning-b... | 1,693 | [
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0.017... |
nampdn-ai/tiny-textbooks | 2023-10-04T03:56:50.000Z | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-nc-sa-4.0",
"arxiv:2309.05463",
"arxiv:2306.01116",
"arxiv:2304.08442",
"arxiv:2305.07759",
"doi:10.57967/hf/1126",
"region:us"
] | nampdn-ai | null | null | 60 | 872 | 2023-08-10T09:21:07 | ---
task_categories:
- text-generation
language:
- en
pretty_name: Tiny Textbooks
size_categories:
- 100K<n<1M
license: cc-by-nc-sa-4.0
---
# Textbook-like Dataset: A High-Quality Resource for Small Language Models
The idea is simply inspired by the [Textbooks Are All You Need II: phi-1.5 technical report](https://ar... | 6,516 | [
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wisesight_sentiment | 2023-01-25T15:02:42.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:th",
"license:cc0-1.0",
"region:us"
] | null | Wisesight Sentiment Corpus: Social media messages in Thai language with sentiment category (positive, neutral, negative, question)
* Released to public domain under Creative Commons Zero v1.0 Universal license.
* Category (Labels): {"pos": 0, "neu": 1, "neg": 2, "q": 3}
* Size: 26,737 messages
* Language: Central Thai
... | @software{bact_2019_3457447,
author = {Suriyawongkul, Arthit and
Chuangsuwanich, Ekapol and
Chormai, Pattarawat and
Polpanumas, Charin},
title = {PyThaiNLP/wisesight-sentiment: First release},
month = sep,
year = 2019,
publisher... | 6 | 870 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- th
license:
- cc0-1.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: WisesightSentiment
dataset_info:
f... | 11,724 | [
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medical_questions_pairs | 2023-01-25T14:40:20.000Z | [
"task_categories:text-classification",
"task_ids:semantic-similarity-classification",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"arxiv:2008.13546",
"... | null | This dataset consists of 3048 similar and dissimilar medical question pairs hand-generated and labeled by Curai's doctors. | @misc{mccreery2020effective,
title={Effective Transfer Learning for Identifying Similar Questions: Matching User Questions to COVID-19 FAQs},
author={Clara H. McCreery and Namit Katariya and Anitha Kannan and Manish Chablani and Xavier Amatriain},
year={2020},
eprint={2008.13546},
archiveP... | 31 | 867 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- semantic-similarity-classification
pretty_name: MedicalQuestionsPairs
datase... | 7,979 | [
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0.02255249023... |
multidoc2dial | 2023-08-29T09:45:02.000Z | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"size_categories:n<1K",
"source_dat... | null | MultiDoc2Dial is a new task and dataset on modeling goal-oriented dialogues grounded in multiple documents. Most previous works treat document-grounded dialogue modeling as a machine reading comprehension task based on a single given document or passage. We aim to address more realistic scenarios where a goal-oriented ... | @inproceedings{feng2021multidoc2dial,
title={MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documents},
author={Feng, Song and Patel, Siva Sankalp and Wan, Hui and Joshi, Sachindra},
booktitle={EMNLP},
year={2021}
} | 2 | 863 | 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
- 1K<n<10K
- n<1K
source_datasets:
- extended|doc2dial
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswi... | 52,359 | [
[
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ambig_qa | 2022-11-03T16:31:34.000Z | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|natural_questions",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3... | null | AmbigNQ, a dataset covering 14,042 questions from NQ-open, an existing open-domain QA benchmark. We find that over half of the questions in NQ-open are ambiguous. The types of ambiguity are diverse and sometimes subtle, many of which are only apparent after examining evidence provided by a very large text corpus. AMBI... | @inproceedings{ min2020ambigqa,
title={ {A}mbig{QA}: Answering Ambiguous Open-domain Questions },
author={ Min, Sewon and Michael, Julian and Hajishirzi, Hannaneh and Zettlemoyer, Luke },
booktitle={ EMNLP },
year={2020}
} | 2 | 861 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|natural_questions
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswithcode_id: ambigqa
pre... | 12,258 | [
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-0.0223846435546875,
-0.0487060546875,
0... |
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}
} | 20 | 859 | 2022-05-16T12:03:38 | ---
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
... | 7,124 | [
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pie/brat | 2023-09-20T16:04:35.000Z | [
"region:us"
] | pie | null | null | 0 | 859 | 2023-05-11T15:25:51 | Entry not found | 15 | [
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lince | 2023-04-05T10:09:24.000Z | [
"region:us"
] | null | LinCE is a centralized Linguistic Code-switching Evaluation benchmark
(https://ritual.uh.edu/lince/) that contains data for training and evaluating
NLP systems on code-switching tasks. | @inproceedings{aguilar-etal-2020-lince,
title = "{L}in{CE}: A Centralized Benchmark for Linguistic Code-switching Evaluation",
author = "Aguilar, Gustavo and
Kar, Sudipta and
Solorio, Thamar",
booktitle = "Proceedings of The 12th Language Resources and Evaluation Conference",
month = may,
... | 5 | 857 | 2022-03-02T23:29:22 | ---
paperswithcode_id: lince
pretty_name: Linguistic Code-switching Evaluation Dataset
dataset_info:
- config_name: lid_spaeng
features:
- name: idx
dtype: int32
- name: words
sequence: string
- name: lid
sequence: string
splits:
- name: train
num_bytes: 4745003
num_examples: 21030
- n... | 13,315 | [
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yangwang825/sst2-textbugger | 2023-10-09T22:09:36.000Z | [
"region:us"
] | yangwang825 | null | null | 0 | 853 | 2023-10-09T21:08:44 | ---
# 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
{}
---
# Stanford Sentiment Treebank - Binary | 239 | [
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heliosbrahma/mental_health_chatbot_dataset | 2023-08-03T04:12:40.000Z | [
"task_categories:text-generation",
"task_categories:conversational",
"size_categories:n<1K",
"language:en",
"license:mit",
"medical",
"region:us"
] | heliosbrahma | null | null | 22 | 850 | 2023-08-02T09:36:25 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_examples: 172
license: mit
task_categories:
- text-generation
- conversational
language:
- en
tags:
- medical
pretty_name: Mental Health Chatbot Dataset
size_categories:
- n<1K
---
# Dataset Card for "heliosbrahma/mental_... | 2,512 | [
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AsakusaRinne/gaokao_bench | 2023-07-11T02:19:45.000Z | [
"region:us"
] | AsakusaRinne | 2 | 845 | 2023-07-05T05:58:15 | Entry not found | 15 | [
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nickrosh/Evol-Instruct-Code-80k-v1 | 2023-07-11T02:05:26.000Z | [
"license:cc-by-nc-sa-4.0",
"arxiv:2306.08568",
"region:us"
] | nickrosh | null | null | 91 | 841 | 2023-07-08T04:31:37 | ---
license: cc-by-nc-sa-4.0
---
Open Source Implementation of Evol-Instruct-Code as described in the [WizardCoder Paper](https://arxiv.org/pdf/2306.08568.pdf).
Code for the intruction generation can be found on Github as [Evol-Teacher](https://github.com/nickrosh/evol-teacher).
| 282 | [
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regisss/librispeech_asr_for_optimum_habana_ci | 2023-09-10T19:40:47.000Z | [
"license:cc-by-4.0",
"region:us"
] | regisss | 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.87 | @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--... | 0 | 840 | 2023-09-10T18:37:05 | ---
license: cc-by-4.0
---
This dataset contains the splits `clean.train.100` and `clean.dev` of the [LibriSpeech dataset](https://huggingface.co/datasets/librispeech_asr).
It is only meant to be used in Optimum Habana's CI to avoid downloading other splits.
| 260 | [
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indonlp/indonlu | 2023-02-03T05:49:02.000Z | [
"task_categories:question-answering",
"task_categories:text-classification",
"task_categories:token-classification",
"task_ids:closed-domain-qa",
"task_ids:multi-class-classification",
"task_ids:named-entity-recognition",
"task_ids:part-of-speech",
"task_ids:semantic-similarity-classification",
"tas... | indonlp | The IndoNLU benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems for Bahasa Indonesia. | @inproceedings{wilie2020indonlu,
title = {{IndoNLU}: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding},
authors={Bryan Wilie and Karissa Vincentio and Genta Indra Winata and Samuel Cahyawijaya and X. Li and Zhi Yuan Lim and S. Soleman and R. Mahendra and Pascale Fung and Syafri Bahar and... | 24 | 833 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- id
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- 1K<n<10K
- n<1K
source_datasets:
- original
task_categories:
- question-answering
- text-classification
- token-classification
task_ids:
- close... | 32,477 | [
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0.0283... |
Divyanshu/indicxnli | 2022-10-06T15:26:00.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:multilingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:as",
"language:bn",
"language:gu",
"lan... | Divyanshu | IndicXNLI is a translated version of XNLI to 11 Indic Languages. As with XNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels). | @misc{https://doi.org/10.48550/arxiv.2204.08776,
doi = {10.48550/ARXIV.2204.08776},
url = {https://arxiv.org/abs/2204.08776},
author = {Aggarwal, Divyanshu and Gupta, Vivek and Kunchukuttan, Anoop},
keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and informa... | 1 | 832 | 2022-04-17T17:48:10 | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- as
- bn
- gu
- hi
- kn
- ml
- mr
- or
- pa
- ta
- te
license:
- cc0-1.0
multilinguality:
- multilingual
pretty_name: IndicXNLI
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-classification
t... | 5,600 | [
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0... |
EdinburghNLP/xsum | 2023-04-05T13:45:25.000Z | [
"task_categories:summarization",
"task_ids:news-articles-summarization",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:unknown",
"arxiv:1808.08745",
"region:us"
] | EdinburghNLP | Extreme Summarization (XSum) Dataset.
There are three features:
- document: Input news article.
- summary: One sentence summary of the article.
- id: BBC ID of the article. | @article{Narayan2018DontGM,
title={Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization},
author={Shashi Narayan and Shay B. Cohen and Mirella Lapata},
journal={ArXiv},
year={2018},
volume={abs/1808.08745}
} | 45 | 830 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
pretty_name: Extreme Summarization (XSum)
paperswithcode_id: xsum
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- summarization
task_ids:
- news-articles-summarizatio... | 6,243 | [
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BeIR/nq-qrels | 2022-10-23T06:08:44.000Z | [
"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 | 830 | 2022-06-06T13:33:50 | ---
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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tner/bionlp2004 | 2022-08-10T01:01:51.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:en",
"license:other",
"region:us"
] | tner | [BioNLP2004 NER dataset](https://aclanthology.org/W04-1213.pdf) | @inproceedings{collier-kim-2004-introduction,
title = "Introduction to the Bio-entity Recognition Task at {JNLPBA}",
author = "Collier, Nigel and
Kim, Jin-Dong",
booktitle = "Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications ({NLPBA}/{B... | 3 | 829 | 2022-07-16T11:08:59 | ---
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: BioNLP2004
---
# Dataset Card for "tner/bionlp2004"
## Dataset Description
- **Repository:** [T-NER](https://github.com/asahi417/t... | 2,271 | [
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bigscience/xP3 | 2023-05-30T15:49:59.000Z | [
"task_categories:other",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:100M<n<1B",
"language:ak",
"language:ar",
"language:as",
"language:bm",
"language:bn",
"language:ca",
"language:code",
"language:en",
"lan... | bigscience | xP3 (Crosslingual Public Pool of Prompts) is a collection of prompts & datasets across 46 of languages & 16 NLP tasks. It is used for the training of BLOOMZ and mT0, multilingual language models capable of following human instructions in dozens of languages zero-shot. | @article{muennighoff2022crosslingual,
title={Crosslingual generalization through multitask finetuning},
author={Muennighoff, Niklas and Wang, Thomas and Sutawika, Lintang and Roberts, Adam and Biderman, Stella and Scao, Teven Le and Bari, M Saiful and Shen, Sheng and Yong, Zheng-Xin and Schoelkopf, Hailey and other... | 85 | 829 | 2022-10-10T10:38:53 | ---
annotations_creators:
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language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
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- eu
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- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
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- or
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programming_lan... | 12,646 | [
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oliverwang15/news_with_gpt_instructions | 2023-07-10T19:39:33.000Z | [
"region:us"
] | oliverwang15 | null | null | 6 | 826 | 2023-07-10T19:25:35 | ---
dataset_info:
features:
- name: news
dtype: string
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dtype: string
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dtype: string
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dtype: int64
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splits:
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shariqfarooq/cs323_densepred_seg256 | 2023-09-16T12:07:20.000Z | [
"region:us"
] | shariqfarooq | null | null | 0 | 825 | 2023-09-16T12:02:51 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
dataset_info:
features:
- name: image
dtype: image
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splits:
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num_bytes: 187512341.0
num_examples: 1464
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num_bytes... | 594 | [
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SetFit/enron_spam | 2022-01-16T18:12:43.000Z | [
"region:us"
] | SetFit | null | null | 8 | 818 | 2022-03-02T23:29:22 | This is a version of the [Enron Spam Email Dataset](https://github.com/MWiechmann/enron_spam_data), containing emails (subject + message) and a label whether it is spam or ham. | 176 | [
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nielsr/breast-cancer | 2023-05-01T18:38:43.000Z | [
"region:us"
] | nielsr | null | null | 6 | 816 | 2023-05-01T18:20:05 | ---
dataset_info:
features:
- name: image
dtype: image
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dtype: image
splits:
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num_bytes: 42431652.0
num_examples: 130
download_size: 0
dataset_size: 42431652.0
---
# Dataset Card for "breast-cancer"
[More Information needed](https://github.com/huggingface/dataset... | 387 | [
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KETI-AIR/korquad | 2021-06-03T00:37:09.000Z | [
"region:us"
] | KETI-AIR | KorQuAD1.0 | @article{DBLP:journals/corr/abs-1909-07005,
author = {Seungyoung Lim and
Myungji Kim and
Jooyoul Lee},
title = {KorQuAD1.0: Korean {QA} Dataset for Machine Reading Comprehension},
journal = {CoRR},
volume = {abs/1909.07005},
year = {2019},
url = {http://a... | 0 | 815 | 2022-03-02T23:29:22 | <!--
Copyright 2021 san kim
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, softw... | 582 | [
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bc2gm_corpus | 2023-08-30T12:13:12.000Z | [
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"language:en",
"license:unknown",
"region:us"
] | null | Nineteen teams presented results for the Gene Mention Task at the BioCreative II Workshop.
In this task participants designed systems to identify substrings in sentences corresponding to gene name mentions.
A variety of different methods were used and the results varied with a highest achieved F1 score of 0.8721.
Here ... | @article{smith2008overview,
title={Overview of BioCreative II gene mention recognition},
author={Smith, Larry and Tanabe, Lorraine K and nee Ando, Rie Johnson and Kuo, Cheng-Ju and Chung, I-Fang and Hsu, Chun-Nan and Lin, Yu-Shi and Klinger, Roman and Friedrich, Christoph M and Ganchev, Kuzman and other... | 5 | 814 | 2022-03-02T23:29:22 | ---
annotations_creators:
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language:
- en
license:
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- original
task_categories:
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task_ids:
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pretty_name: Bc2GmCorpus
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Dahoas/hf_cot_gsm8k | 2023-10-01T14:40:46.000Z | [
"region:us"
] | Dahoas | null | null | 0 | 811 | 2023-10-01T09:45:46 | ---
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cardiffnlp/tweet_sentiment_multilingual | 2022-11-30T14:01:25.000Z | [
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"language:ar",
"language:fr",
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... | cardiffnlp | null | @inproceedings{barbieri-etal-2022-xlm,
title = "{XLM}-{T}: Multilingual Language Models in {T}witter for Sentiment Analysis and Beyond",
author = "Barbieri, Francesco and
Espinosa Anke, Luis and
Camacho-Collados, Jose",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluati... | 10 | 810 | 2022-11-26T23:34:42 | ---
language:
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- multilingual
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task_ids:
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paperswithcode_id: tweet_sentiment_multilingual
pretty_name: Tweet Sentiment Mu... | 5,280 | [
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pauri32/fiqa-2018 | 2023-05-31T15:43:26.000Z | [
"region:us"
] | pauri32 | null | null | 4 | 809 | 2023-05-17T08:22:26 | Entry not found | 15 | [
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alkzar90/NIH-Chest-X-ray-dataset | 2022-11-22T20:10:52.000Z | [
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"size_categories:100K<n<1M... | alkzar90 | The NIH Chest X-ray dataset consists of 100,000 de-identified images of chest x-rays. The images are in PNG format.
The data is provided by the NIH Clinical Center and is available through the NIH download site: https://nihcc.app.box.com/v/ChestXray-NIHCC | @inproceedings{Wang_2017,
doi = {10.1109/cvpr.2017.369},
url = {https://doi.org/10.1109%2Fcvpr.2017.369},
year = 2017,
month = {jul},
publisher = {{IEEE}
},
author = {Xiaosong Wang and Yifan Peng and Le Lu and Zhiyong Lu and Mohammadhadi Bagheri and Ronald M. Summers},
title = {{ChestX}-Ray8: Hospital-Scale Ches... | 19 | 808 | 2022-09-30T12:45:52 | ---
annotations_creators:
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language:
- en
license:
- unknown
multilinguality:
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pretty_name: NIH-CXR14
paperswithcode_id: chestx-ray14
size_categories:
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task_categories:
- image-classification
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pospos12/core50 | 2023-05-07T05:36:50.000Z | [
"region:us"
] | pospos12 | null | null | 0 | 808 | 2023-05-07T05:29:13 | ---
dataset_info:
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wyzelabs/RuleRecommendation | 2023-11-02T14:53:43.000Z | [
"license:cc-by-nc-nd-4.0",
"IoT",
"Smart Home",
"Rule Recommendation",
"Recommendation Systems",
"region:us"
] | wyzelabs | null | null | 9 | 805 | 2023-07-12T18:32:35 | ---
license: cc-by-nc-nd-4.0
extra_gated_heading: >-
Wyze Rule Recommendation Challenge Participation and Dataset Access Terms and
Conditions
extra_gated_prompt: >-
Please read the <a href="https://drive.google.com/uc?id=1v-4gjp1EQZcdxYn6uZfft6CVKtWh3S87" target="_blank">Wyze Rule Recommendation Challenge Partici... | 9,332 | [
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joelniklaus/Multi_Legal_Pile | 2023-10-18T20:39:36.000Z | [
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"annotations_creators:other",
"language_creators:found",
"multilinguality:multilingual",
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"language:bg",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
"language... | joelniklaus | Multi Legal Pile is a dataset of legal documents in the 24 EU languages. | 29 | 799 | 2022-09-26T10:28:06 | ---
annotations_creators:
- other
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- ga
- hr
- hu
- it
- lt
- lv
- mt
- nl
- pl
- pt
- ro
- sk
- sl
- sv
license:
- cc-by-nc-sa-4.0
multilinguality:
- multilingual
paperswithcode_id: null
pretty_name: "MultiLegalPile: A Large-Scale Mu... | 24,183 | [
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fedyanin/feud | 2023-10-23T10:55:56.000Z | [
"license:cc",
"region:us"
] | fedyanin | null | null | 0 | 799 | 2023-07-25T11:59:02 | ---
license: cc
---
# Feud dataset
Dataset of question and answers that resemble family feud tv show style. There multiple possible answers for each question. Dataset is aimed to benhmark a balance between diversity and correctness of a language model | 252 | [
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clarin-pl/cst-wikinews | 2021-07-12T18:51:43.000Z | [
"region:us"
] | clarin-pl | CST Wikinews dataset. | null | 2 | 795 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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tner/ontonotes5 | 2022-07-18T00:43:55.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:en",
"license:other",
"region:us"
] | tner | [ontonotes5 NER dataset](https://aclanthology.org/N06-2015/) | @inproceedings{hovy-etal-2006-ontonotes,
title = "{O}nto{N}otes: The 90{\%} Solution",
author = "Hovy, Eduard and
Marcus, Mitchell and
Palmer, Martha and
Ramshaw, Lance and
Weischedel, Ralph",
booktitle = "Proceedings of the Human Language Technology Conference of the {NAACL}, Co... | 3 | 795 | 2022-07-16T11:07:45 | ---
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: Ontonotes5
---
# Dataset Card for "tner/ontonotes5"
## Dataset Description
- **Repository:** [T-NER](https://github.com/asahi417/t... | 2,834 | [
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imodels/credit-card | 2022-08-14T15:37:54.000Z | [
"task_categories:tabular-classification",
"size_categories:10K<n<100K",
"interpretability",
"fairness",
"medicine",
"region:us"
] | imodels | null | null | 3 | 795 | 2022-08-14T15:33:53 | ---
annotations_creators: []
language: []
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license: []
multilinguality: []
pretty_name: credit-card
size_categories:
- 10K<n<100K
source_datasets: []
tags:
- interpretability
- fairness
- medicine
task_categories:
- tabular-classification
task_ids: []
---
Port of the credit-card dataset from UCI (... | 1,316 | [
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jon-tow/okapi_arc_challenge | 2023-10-24T00:02:35.000Z | [
"language:ar",
"language:bn",
"language:ca",
"language:da",
"language:de",
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"language:hu",
"language:hy",
"language:id",
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"language:kn",
"language:ml",
"language:mr",
"language:... | jon-tow | A new dataset of 7,787 genuine grade-school level, multiple-choice science questions, assembled to encourage research in
advanced question-answering. The dataset is partitioned into a Challenge Set and an Easy Set, where the former contains
only questions answered incorrectly by both a retrieval-based algorithm and a... | @article{allenai:arc,
author = {Peter Clark and Isaac Cowhey and Oren Etzioni and Tushar Khot and
Ashish Sabharwal and Carissa Schoenick and Oyvind Tafjord},
title = {Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge},
journal = {arXiv:1803.05... | 0 | 795 | 2023-10-23T20:34:35 | ---
language:
- ar
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- fr
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- sr
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- ta
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- uk
- vi
license: cc-by-nc-4.0
---
# okapi_arc_challenge
<!-- Provide a quick summary of the dataset. -->
Multilingual translation of [AI2's Arc Challenge](https:/... | 2,510 | [
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pszemraj/simple_wikipedia_LM | 2023-09-04T15:04:44.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"size_categories:100K<n<1M",
"source_datasets:pszemraj/simple_wikipedia",
"language:en",
"license:apache-2.0",
"region:us"
] | pszemraj | null | null | 2 | 790 | 2023-09-03T07:49:16 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
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dtype: string
- name: url
dtype: string
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oscar-corpus/OSCAR-2201 | 2023-05-30T07:48:15.000Z | [
"task_categories:fill-mask",
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"task_ids:language-modeling",
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"language:af",
"language:sq",
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"language:an",
... | oscar-corpus | The Open Super-large Crawled Aggregated coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the Ungoliant architecture.\ |
@ARTICLE{2022arXiv220106642A,
author = {{Abadji}, Julien and {Ortiz Suarez}, Pedro and {Romary}, Laurent and {Sagot}, Beno{\^\i}t},
title = "{Towards a Cleaner Document-Oriented Multilingual Crawled Corpus}",
journal = {arXiv e-prints},
keywords = {Computer Science - Computation and Language},
year = 2022,
... | 74 | 788 | 2022-03-14T23:09:14 | ---
pretty_name: OSCAR
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nlpai-lab/kullm-v2 | 2023-06-01T05:45:04.000Z | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:ko",
"license:apache-2.0",
"region:us"
] | nlpai-lab | null | null | 39 | 788 | 2023-06-01T05:26:22 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- ko
pretty_name: kullm
size_categories:
- 10K<n<100K
---
# Dataset Card for "KULLM-v2"
## Dataset Summary
Korean translation of GPT4ALL, Dolly, and Vicuna data.
repository: [nlpai-lab/KULLM](https://github.com/nlpai-lab/KULLM)
huggingface: [nlp... | 1,023 | [
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approach0/MATH-full | 2023-09-14T18:42:51.000Z | [
"region:us"
] | approach0 | null | null | 0 | 781 | 2023-09-14T18:42:48 | ---
configs:
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data_files:
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path: data/train-*
- split: test
path: data/test-*
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squad_kor_v2 | 2023-02-07T14:40:49.000Z | [
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"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|squad_kor_v1",
"source_datasets:original",
"language:ko",
"license:cc-by-nd-4.0",
... | null | KorQuAD 2.0 is a Korean question and answering dataset consisting of a total of 100,000+ pairs. There are three major differences from KorQuAD 1.0, which is the standard Korean Q & A data. The first is that a given document is a whole Wikipedia page, not just one or two paragraphs. Second, because the document also con... | @article{NODE09353166,
author={Youngmin Kim,Seungyoung Lim;Hyunjeong Lee;Soyoon Park;Myungji Kim},
title={{KorQuAD 2.0: Korean QA Dataset for Web Document Machine Comprehension}},
booltitle={{Journal of KIISE 제47권 제6호}},
journal={{Journal of KIISE}},
volume={{47}},
issue={{6}},
publisher={Th... | 2 | 777 | 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:
- extended|squad_kor_v1
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: null
pretty_name:... | 5,439 | [
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GEM/viggo | 2022-10-24T15:31:07.000Z | [
"task_categories:table-to-text",
"annotations_creators:none",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"data-to-text",
"region:us"
] | GEM | ViGGO was designed for the task of data-to-text generation in chatbots (as opposed to task-oriented dialogue systems), with target responses being more conversational than information-seeking, yet constrained to the information presented in a meaning representation. The dataset, being relatively small and clean, can al... | @inproceedings{juraska-etal-2019-viggo,
title = "{V}i{GGO}: A Video Game Corpus for Data-To-Text Generation in Open-Domain Conversation",
author = "Juraska, Juraj and
Bowden, Kevin and
Walker, Marilyn",
booktitle = "Proceedings of the 12th International Conference on Natural Language Generatio... | 12 | 777 | 2022-03-02T23:29:22 | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
pretty_name: viggo
tags:
- data-to-text
---
# Dataset Card for GEM/viggo
## Dataset Descr... | 24,514 | [
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joelniklaus/mapa | 2022-10-25T16:17:09.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:other",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
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"language:multilingual",
"language:bg",
"language:cs",
"language:da",
"l... | joelniklaus | null | null | 4 | 777 | 2022-07-20T12:14:50 | ---
annotations_creators:
- other
language_creators:
- found
language:
- multilingual
- bg
- cs
- da
- de
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- en
- es
- et
- fi
- fr
- ga
- hu
- it
- lt
- lv
- mt
- nl
- pt
- ro
- sk
- sv
license:
- cc-by-4.0
multilinguality:
- multilingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- t... | 13,904 | [
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CodedotAI/code_clippy_github | 2022-08-05T02:57:36.000Z | [
"task_ids:language-modeling",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:unknown",
"language:code",
"license:mit",
"arxiv:2107.03374",
"region:us"
] | CodedotAI | The Code Clippy dataset consists of various public codebases from GitHub in 22 programming languages with 23 extensions totalling about 16 TB of data when uncompressed. The dataset was created from the public GitHub dataset on Google BiqQuery. | null | 9 | 774 | 2022-03-02T23:29:22 | ---
annotations_creators: []
language_creators:
- crowdsourced
- expert-generated
language: ["code"]
license:
- mit
multilinguality:
- multilingual
pretty_name: code-clippy-github-code
size_categories:
- unknown
source_datasets: []
task_categories:
- sequence-modeling
task_ids:
- language-modeling
---
# Code Clippy Git... | 8,059 | [
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0.00290... |
bigheiniuJ/JimmyLu | 2023-10-11T02:09:38.000Z | [
"region:us"
] | bigheiniuJ | null | null | 0 | 773 | 2023-10-03T17:24:12 | ---
dataset_info:
features:
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splits:
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num_bytes: 772928
nu... | 836 | [
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distil-whisper/earnings22 | 2023-10-13T12:00:56.000Z | [
"arxiv:2203.15591",
"region:us"
] | distil-whisper | null | null | 0 | 773 | 2023-10-13T09:47:08 | ---
dataset_info:
- config_name: chunked
features:
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audio:
sampling_rate: 16000
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split... | 8,087 | [
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BeIR/scifact-qrels | 2022-10-23T06:05:06.000Z | [
"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 | 766 | 2022-06-05T17:24:21 | ---
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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sem_eval_2010_task_8 | 2023-04-05T13:39:59.000Z | [
"language:en",
"region:us"
] | null | The SemEval-2010 Task 8 focuses on Multi-way classification of semantic relations between pairs of nominals.
The task was designed to compare different approaches to semantic relation classification
and to provide a standard testbed for future research. | @inproceedings{hendrickx-etal-2010-semeval,
title = "{S}em{E}val-2010 Task 8: Multi-Way Classification of Semantic Relations between Pairs of Nominals",
author = "Hendrickx, Iris and
Kim, Su Nam and
Kozareva, Zornitsa and
Nakov, Preslav and
{\'O} S{\'e}aghdha, Diarmuid and
Pad... | 5 | 765 | 2022-03-02T23:29:22 | ---
language:
- en
paperswithcode_id: semeval-2010-task-8
pretty_name: SemEval-2010 Task 8
dataset_info:
features:
- name: sentence
dtype: string
- name: relation
dtype:
class_label:
names:
'0': Cause-Effect(e1,e2)
'1': Cause-Effect(e2,e1)
'2': Component-Whole(e... | 8,112 | [
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visual_genome | 2023-06-29T15:23:59.000Z | [
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:visual-question-answering",
"task_ids:image-captioning",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:... | null | Visual Genome enable to model objects and relationships between objects.
They collect dense annotations of objects, attributes, and relationships within each image.
Specifically, the dataset contains over 108K images where each image has an average of 35 objects, 26 attributes, and 21 pairwise relationships between obj... | @article{Krishna2016VisualGC,
title={Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations},
author={Ranjay Krishna and Yuke Zhu and Oliver Groth and Justin Johnson and Kenji Hata and Joshua Kravitz and Stephanie Chen and Yannis Kalantidis and Li-Jia Li and David A. Shamma and Mic... | 33 | 764 | 2022-04-21T13:09:21 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- image-to-text
- object-detection
- visual-question-answering
task_ids:
- image-captioning
paperswithcode_id: visual-... | 15,831 | [
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edbeeching/decision_transformer_gym_replay | 2022-04-20T12:39:58.000Z | [
"license:apache-2.0",
"arxiv:2004.07219",
"region:us"
] | edbeeching | A subset of the D4RL dataset, used for training Decision Transformers | null | 2 | 760 | 2022-03-02T23:29:22 | ---
license: apache-2.0
pretty_name: D4RL-gym
---
# Dataset Card for D4RL-gym
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Dataset Structure](#dataset-structure)
- [Data... | 2,739 | [
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reddit_tifu | 2023-06-15T21:21:20.000Z | [
"task_categories:summarization",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:mit",
"reddit-posts-summarization",
"arxiv:1811.00783",
"region:us"
] | null | Reddit dataset, where TIFU denotes the name of subbreddit /r/tifu.
As defined in the publication, styel "short" uses title as summary and
"long" uses tldr as summary.
Features includes:
- document: post text without tldr.
- tldr: tldr line.
- title: trimmed title without tldr.
- ups: upvotes.
- score: score.... | @misc{kim2018abstractive,
title={Abstractive Summarization of Reddit Posts with Multi-level Memory Networks},
author={Byeongchang Kim and Hyunwoo Kim and Gunhee Kim},
year={2018},
eprint={1811.00783},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 5 | 759 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- mit
multilinguality:
- monolingual
pretty_name: Reddit TIFU
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- summarization
task_ids: []
paperswithcode_id: reddit-tifu
tags:
- reddit-posts-summ... | 11,203 | [
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imppres | 2023-01-25T14:32:53.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-4.0",
"region:us"
] | null | Over >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. IMPPRES is an NLI dataset following the format of SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018) and XNLI (Conneau et al., 2018), which was created to evaluate how well trained NLI models recognize se... | @inproceedings{jeretic-etal-2020-natural,
title = "Are Natural Language Inference Models {IMPPRESsive}? {L}earning {IMPlicature} and {PRESupposition}",
author = "Jereti\v{c}, Paloma and
Warstadt, Alex and
Bhooshan, Suvrat and
Williams, Adina",
booktitle = "Proceedings of the 58th Annual... | 0 | 758 | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- en
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- natural-language-inference
paperswithcode_id: imppres
pret... | 21,746 | [
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kaist-ai/CoT-Collection | 2023-10-14T12:10:16.000Z | [
"task_categories:text-generation",
"task_categories:text-classification",
"size_categories:1M<n<10M",
"language:en",
"license:cc-by-4.0",
"arxiv:2305.14045",
"region:us"
] | kaist-ai | """
_LICENSE = "CC BY 4.0"
_HOMEPAGE = "https://github.com/kaistAI/CoT-Collection"
_LANGUAGES = {
"en": "English",
}
# _ALL_LANGUAGES = "all_languages"
class CoTCollectionMultiConfig(datasets.BuilderConfig): | @article{kim2023cot,
title={The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning},
author={Kim, Seungone and Joo, Se June and Kim, Doyoung and Jang, Joel and Ye, Seonghyeon and Shin, Jamin and Seo, Minjoon},
journal={arXiv preprint arXiv:2305.14045},
... | 36 | 758 | 2023-06-05T07:11:17 | ---
license: cc-by-4.0
task_categories:
- text-generation
- text-classification
language:
- en
size_categories:
- 1M<n<10M
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:https://github.com/kaistAI/CoT-Collection**
- **Repository:https://github.com/kaistAI/CoT-Collection**
- **Paper:https:/... | 2,677 | [
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silk-road/Chat_Suzumiya_Fusion | 2023-08-14T11:10:45.000Z | [
"region:us"
] | silk-road | null | null | 4 | 757 | 2023-08-14T11:10:32 | ---
configs:
- config_name: default
data_files:
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path: data/train-*
dataset_info:
features:
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dtype: string
- name: target
dtype: string
splits:
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num_bytes: 111274991
num_examples: 28612
download_size: 39798958
dataset_size: 111274991
---
# ... | 492 | [
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JonasGeiping/the_pile_WordPiecex32768_2efdb9d060d1ae95faf952ec1a50f020 | 2023-06-13T16:25:54.000Z | [
"arxiv:2212.14034",
"arxiv:2101.00027",
"arxiv:2201.07311",
"region:us"
] | JonasGeiping | null | null | 0 | 756 | 2023-06-08T17:30:55 | ---
dataset_info:
features:
- name: input_ids
sequence: int32
splits:
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num_bytes: 43860000000
num_examples: 85000000
download_size: 24001057282
dataset_size: 43860000000
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: other
mu... | 4,312 | [
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allenai/wmt22_african | 2022-08-15T21:52:43.000Z | [
"region:us"
] | allenai | null | null | 3 | 754 | 2022-05-17T04:12:30 | # Dataset Card for allenai/wmt22_african
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [D... | 6,200 | [
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heegyu/bbq | 2023-07-14T10:58:55.000Z | [
"license:cc-by-4.0",
"region:us"
] | heegyu |
The BBQ dataset is from the following paper:
https://arxiv.org/pdf/2110.08193.pdf
In BBQ, each example appears with two questions
that reflect a negative or harmful bias: one asks for
the target of a harmful stereotype (e.g., "who steals
things?"), and the other asks for the other non-targeted entity
(e.g., "who neve... | @misc{parrish2022bbq,
title={BBQ: A Hand-Built Bias Benchmark for Question Answering},
author={Alicia Parrish and Angelica Chen and Nikita Nangia and Vishakh Padmakumar and Jason Phang and Jana Thompson and Phu Mon Htut and Samuel R. Bowman},
year={2022},
eprint={2110.08193},
archivePrefi... | 1 | 753 | 2023-07-14T09:53:34 | ---
license: cc-by-4.0
---
# BBQ
Repository for the Bias Benchmark for QA dataset.
https://github.com/nyu-mll/BBQ
Authors: Alicia Parrish, Angelica Chen, Nikita Nangia, Vishakh Padmakumar, Jason Phang, Jana Thompson, Phu Mon Htut, and Samuel R. Bowman.
## About BBQ (paper abstract)
It is well documented that NLP mod... | 1,778 | [
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0.000271797... |
mstz/heart_failure | 2023-04-16T17:31:15.000Z | [
"task_categories:tabular-classification",
"size_categories:n<1K",
"language:en",
"license:cc",
"heart failure",
"tabular_classification",
"binary_classification",
"UCI",
"region:us"
] | mstz | null | null | 2 | 752 | 2023-03-24T14:32:59 | ---
language:
- en
tags:
- heart failure
- tabular_classification
- binary_classification
- UCI
pretty_name: Heart failure
size_categories:
- n<1K
task_categories:
- tabular-classification
configs:
- death
license: cc
---
# Heart failure
The [Heart failure dataset](https://www.kaggle.com/datasets/andrewmvd/heart-failur... | 1,874 | [
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Riksarkivet/test_images_demo | 2023-08-31T13:58:13.000Z | [
"task_categories:image-to-text",
"language:sv",
"HTR",
"region:us"
] | Riksarkivet | Demo dataset for the htr demo. | @InProceedings{huggingface:dataset,
title = {Small htr examples images},
author={Gabriel Borg},
year={2023}
} | 1 | 752 | 2023-06-14T15:33:25 | ---
language:
- sv
tags:
- HTR
task_categories:
- image-to-text
---
# Information
This is a demo dataset contains images from the Swedish National Archives, Riksarkivet.
To find the images at Riksarkivet:
30002030_00003.jpg = https://sok.riksarkivet.se/bildvisning/30002030_00003
| Image_name | Description |
|---|... | 1,222 | [
[
-0.02001953125,
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0.002416610... |
PORTULAN/glue-ptpt | 2023-05-12T12:49:02.000Z | [
"language_creators:machine-generated",
"size_categories:10K<n<100K",
"source_datasets:glue",
"language:pt",
"arxiv:2305.06721",
"region:us"
] | PORTULAN | GLUE-PTPT is an European Portuguese translation of the GLUE benchmark using DeepL Pro. | @misc{Gomes2023,
author = {Luís Gomes and João Rodrigues and João Silva and António Branco and Rodrigo Santos},
title = {GLUE-PTPT -- The General Language Understanding Evaluation benchmark translated to European Portuguese},
year = {2023},
publisher = {Hugging Face},
journal = {Hugging Face dataset},
howpu... | 3 | 751 | 2023-04-24T00:11:34 | ---
language:
- pt
language_creators:
- machine-generated
source_datasets:
- glue
pretty_name: GLUE-PTPT -- The General Language Understanding Evaluation benchmark translated to European Portuguese
size_categories:
- 10K<n<100K
---
# GLUE-PTPT -- The General Language Understanding Evaluation benchmark translated to ... | 1,156 | [
[
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0.0213... |
wiki_atomic_edits | 2023-06-01T14:59:54.000Z | [
"task_categories:summarization",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"size_categories:10M<n<100M",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:de",
"language:en",
"language:es",
"language:fr... | null | A dataset of atomic wikipedia edits containing insertions and deletions of a contiguous chunk of text in a sentence. This dataset contains ~43 million edits across 8 languages.
An atomic edit is defined as an edit e applied to a natural language expression S as the insertion, deletion, or substitution of a sub-express... | @InProceedings{WikiAtomicEdits,
title = {{WikiAtomicEdits: A Multilingual Corpus of Wikipedia Edits for Modeling Language and Discourse}},
author = {Faruqui, Manaal and Pavlick, Ellie and Tenney, Ian and Das, Dipanjan},
booktitle = {Proc. of EMNLP},
year = {2018}
} | 10 | 750 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- de
- en
- es
- fr
- it
- ja
- ru
- zh
license:
- cc-by-sa-4.0
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
- 10M<n<100M
- 1M<n<10M
source_datasets:
- original
task_categories:
- summarization
task_ids: []
paperswithcode_id: wikiato... | 8,681 | [
[
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... |
hans | 2023-04-05T10:06:58.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"arxiv:1902.01007"... | null | The HANS dataset is an NLI evaluation set that tests specific hypotheses about invalid heuristics that NLI models are likely to learn. | @article{DBLP:journals/corr/abs-1902-01007,
author = {R. Thomas McCoy and
Ellie Pavlick and
Tal Linzen},
title = {Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural
Language Inference},
journal = {CoRR},
volume = {abs/1902.01007},
y... | 3 | 749 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- natural-language-inference
paperswithcode_id: hans
pretty_name:... | 7,017 | [
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0.0130081... |
qanastek/EMEA-V3 | 2022-10-22T15:18:02.000Z | [
"task_categories:translation",
"annotations_creators:machine-generated",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:bg",
"multilinguality:cs",
"multilinguality:da",
"multilinguality:de",
"multilinguality:el",
"multilinguality:en",
"multilinguality:es",
... | qanastek | null | @inproceedings{tiedemann-2012-parallel,
title = Parallel Data, Tools and Interfaces in OPUS,
author = {
Tiedemann, Jorg
},
booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)",
month = may,
year = 2012,
address = Istanbul, Turk... | 6 | 746 | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
- expert-generated
language_creators:
- found
language:
- bg
- cs
- da
- de
- el
- en
- es
- et
- fi
- fr
- hu
- it
- lt
- lv
- mt
- nl
- pl
- pt
- ro
- sk
- sl
- sv
multilinguality:
- bg
- cs
- da
- de
- el
- en
- es
- et
- fi
- fr
- hu
- it
- lt
- lv
- mt
- nl
- pl
- pt
-... | 11,415 | [
[
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0.... |
allegro/klej-nkjp-ner | 2021-11-29T19:14:56.000Z | [
"region:us"
] | allegro | null | null | 0 | 745 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
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-0.0604248046875,
0.0379028... |
jon-tow/okapi_truthfulqa | 2023-10-24T00:03:01.000Z | [
"language:ar",
"language:bn",
"language:ca",
"language:da",
"language:de",
"language:es",
"language:eu",
"language:fr",
"language:gu",
"language:hi",
"language:hr",
"language:hu",
"language:hy",
"language:id",
"language:it",
"language:kn",
"language:ml",
"language:mr",
"language:... | jon-tow | TruthfulQA is a benchmark to measure whether a language model is truthful in
generating answers to questions. The benchmark comprises 817 questions that
span 38 categories, including health, law, finance and politics. Questions are
crafted so that some humans would answer falsely due to a false belief or
misconception.... | @misc{lin2021truthfulqa,
title={TruthfulQA: Measuring How Models Mimic Human Falsehoods},
author={Stephanie Lin and Jacob Hilton and Owain Evans},
year={2021},
eprint={2109.07958},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 0 | 744 | 2023-10-23T22:11:52 | ---
language:
- ar
- bn
- ca
- da
- de
- es
- eu
- fr
- gu
- hi
- hr
- hu
- hy
- id
- it
- kn
- ml
- mr
- ne
- nl
- pt
- ro
- ru
- sk
- sr
- sv
- ta
- te
- uk
- vi
license: cc-by-nc-4.0
---
# okapi_truthfulqa
<!-- Provide a quick summary of the dataset. -->
Multilingual translation of [TruthfulQA](https://arxiv.org/a... | 2,113 | [
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0.040496826171875,
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-0.03335571289062... |
seungheondoh/LP-MusicCaps-MTT | 2023-08-04T10:39:28.000Z | [
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"art",
"music",
"text-to-music",
"music-to-text",
"arxiv:2307.16372",
"region:us"
] | seungheondoh | null | null | 1 | 743 | 2023-08-04T10:31:39 | ---
license: mit
language:
- en
tags:
- art
- music
- text-to-music
- music-to-text
pretty_name: LP-MusicCaps-MTT
size_categories:
- 10K<n<100K
---
======================================
**!important**: Be careful when using `caption_attribute_prediction` (We don't recommend to use)!
================================... | 6,202 | [
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0.013771057128... |
Jean-Baptiste/wikiner_fr | 2023-06-26T15:33:17.000Z | [
"task_categories:token-classification",
"language:fr",
"region:us"
] | Jean-Baptiste | null | null | 3 | 741 | 2022-03-02T23:29:22 | ---
language:
- fr
dataset_info:
features:
- name: id
dtype: int64
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': LOC
'2': PER
'3': MISC
'4': ORG
splits:
- name: test
num_bytes: 595470... | 964 | [
[
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0... |
banghua/random_bac | 2023-10-03T04:54:44.000Z | [
"region:us"
] | banghua | null | null | 0 | 741 | 2023-10-03T04:53:48 | ---
dataset_info:
features:
- name: prompts
sequence: string
- name: completions
sequence: string
splits:
- name: train
num_bytes: 545587063
num_examples: 92511
download_size: 236177873
dataset_size: 545587063
configs:
- config_name: default
data_files:
- split: train
path: data/tr... | 492 | [
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-0.... |
coastalcph/fairlex | 2023-07-27T12:43:39.000Z | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"task_ids:multi-class-classification",
"task_ids:topic-classification",
"annotations_creators:found",
"annotations_creators:machine-generated",
"language_creators:found",
"source_datasets:extended",
"language:en",
"langu... | coastalcph | Fairlex: A multilingual benchmark for evaluating fairness in legal text processing. | @inproceedings{chalkidis-etal-2022-fairlex,
author={Chalkidis, Ilias and Passini, Tommaso and Zhang, Sheng and
Tomada, Letizia and Schwemer, Sebastian Felix and Søgaard, Anders},
title={FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing},
booktitle={Proceedings of... | 6 | 739 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
- machine-generated
language_creators:
- found
language:
- en
- en
- de
- fr
- it
- zh
license:
- cc-by-nc-sa-4.0
multilinguality:
ecthr:
- monolingual
scotus:
- monolingual
fscs:
- multilingual
cail:
- monolingual
size_categories:
ecthr:
- 10K<n<100K
scotus:
- ... | 22,405 | [
[
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-0.034942626953125,
0.031219482421875,
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-0.049285888671875,
-0.045166015625... |
ecthr_cases | 2022-11-18T19:59:57.000Z | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"annotations_creators:expert-generated",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-... | null | The ECtHR Cases dataset is designed for experimentation of neural judgment prediction and rationale extraction considering ECtHR cases. | @InProceedings{chalkidis-et-al-2021-ecthr,
title = "Paragraph-level Rationale Extraction through Regularization: A case study on European Court of Human Rights Cases",
author = "Chalkidis, Ilias and Fergadiotis, Manos and Tsarapatsanis, Dimitrios and Aletras, Nikolaos and Androutsopoulos, Ion and Malakasiotis, ... | 8 | 738 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
- found
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: ecthr
pretty... | 13,947 | [
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0.060577392578125,
-0.0307464599609375,
-0.052703857421875,
-0.04095458984... |
marsyas/gtzan | 2022-11-06T20:34:20.000Z | [
"region:us"
] | marsyas | GTZAN is a dataset for musical genre classification of audio signals. The dataset consists of 1,000 audio tracks, each of 30 seconds long. It contains 10 genres, each represented by 100 tracks. The tracks are all 22,050Hz Mono 16-bit audio files in WAV format. The genres are: blues, classical, country, disco, hiphop, j... | @misc{tzanetakis_essl_cook_2001,
author = "Tzanetakis, George and Essl, Georg and Cook, Perry",
title = "Automatic Musical Genre Classification Of Audio Signals",
url = "http://ismir2001.ismir.net/pdf/tzanetakis.pdf",
publisher = "The International Society for Music Information Retrieval",
year = "200... | 6 | 734 | 2022-03-14T14:54:59 | ---
pretty_name: GTZAN
---
# Dataset Card for GTZAN
## Table of Contents
- [Dataset Card for GTZAN](#dataset-card-for-gtzan)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#data... | 4,424 | [
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allegro/klej-cbd | 2021-11-29T19:14:20.000Z | [
"region:us"
] | allegro | null | null | 0 | 731 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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0.0379028... |
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 | 11 | 731 | 2022-05-11T12:47:06 | ---
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... | 9,232 | [
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0.01... |
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