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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
EleutherAI/truthful_qa_mc | 2023-04-29T06:24:04.000Z | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
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"annotations_creators:expert-generated",
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"multilinguality:monolingual",
"size_categories:n<1K",
"so... | EleutherAI | TruthfulQA-MC 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
misconcepti... | @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}
} | 4 | 258 | 2023-04-29T05:52:24 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: TruthfulQA-MC
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- multiple-choice
- question-answering
task_ids:
- multiple-choice-qa
- l... | 6,572 | [
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tner/wikiann | 2022-09-27T18:39:42.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"multilinguality:multilingual",
"size_categories:10K<100k",
"language:ace",
"language:bg",
"language:da",
"language:fur",
"language:ilo",
"language:lij",
"language:mzn",
"language:qu",
"language:su",
"language:vi"... | tner | [WikiAnn](https://aclanthology.org/P17-1178/) | @inproceedings{pan-etal-2017-cross,
title = "Cross-lingual Name Tagging and Linking for 282 Languages",
author = "Pan, Xiaoman and
Zhang, Boliang and
May, Jonathan and
Nothman, Joel and
Knight, Kevin and
Ji, Heng",
booktitle = "Proceedings of the 55th Annual Meeting of the... | 4 | 257 | 2022-09-27T16:22:58 | ---
language:
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blog_authorship_corpus | 2023-06-06T16:16:13.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | null | The Blog Authorship Corpus consists of the collected posts of 19,320 bloggers gathered from blogger.com in August 2004. The corpus incorporates a total of 681,288 posts and over 140 million words - or approximately 35 posts and 7250 words per person.
Each blog is presented as a separate file, the name of which indicat... | @inproceedings{schler2006effects,
title={Effects of age and gender on blogging.},
author={Schler, Jonathan and Koppel, Moshe and Argamon, Shlomo and Pennebaker, James W},
booktitle={AAAI spring symposium: Computational approaches to analyzing weblogs},
volume={6},
pages={199--205},
year={2006}
} | 6 | 256 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
paperswithcode_id: blog-authorship-corpus
pretty_name: Blog Authorship Corpus
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
-... | 7,298 | [
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princeton-nlp/SWE-bench_oracle_llama | 2023-10-17T13:59:03.000Z | [
"region:us"
] | princeton-nlp | null | null | 0 | 256 | 2023-10-10T04:10:48 | ---
dataset_info:
features:
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EMBO/sd-nlp | 2022-10-21T15:34:09.000Z | [
"task_categories:text-classification",
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"task_ids:named-entity-recognition",
"task_ids:parsing",
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"size_categories:10K<n<100K",
"language:en",
"license... | EMBO | This dataset is based on the SourceData database and is intented to facilitate training of NLP tasks in the cell and molecualr biology domain. | @Unpublished{
huggingface: dataset,
title = {SourceData NLP},
authors={Thomas Lemberger, EMBO},
year={2021}
} | 0 | 255 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets: []
task_categories:
- text-classification
- structure-prediction
- text-classification
task_ids:
- multi-class-clas... | 28,602 | [
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SetFit/tweet_sentiment_extraction | 2022-05-12T19:52:02.000Z | [
"region:us"
] | SetFit | null | null | 0 | 255 | 2022-03-02T23:29:22 | # Tweet Sentiment Extraction
Source: https://www.kaggle.com/c/tweet-sentiment-extraction/data | 94 | [
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PatrickHaller/wikitext-18-de | 2023-06-27T20:29:39.000Z | [
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:de",
"license:cc-by-sa-3.0",
"region:us"
] | PatrickHaller | null | null | 0 | 255 | 2023-06-27T20:07:00 | ---
dataset_info:
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splits:
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num_bytes: 138186439
num_examples: 2759
download_size: 79585645
dataset_size: 138186439
license: cc-by-sa-3.0
task_categories:
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C-MTEB/JDReview-classification | 2023-07-28T13:18:58.000Z | [
"region:us"
] | C-MTEB | null | null | 1 | 255 | 2023-07-28T13:18:46 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: int32
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dtype: string
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dtype:
class_label:
names:
'0': POS
'1': NEG
... | 740 | [
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asnq | 2023-05-16T08:28:22.000Z | [
"task_categories:multiple-choice",
"task_ids:multiple-choice-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:extended|natural_questions",
"language:en",
"license:cc-by-nc-sa-3.0",
"arxiv:1911.04118",... | null | ASNQ is a dataset for answer sentence selection derived from
Google's Natural Questions (NQ) dataset (Kwiatkowski et al. 2019).
Each example contains a question, candidate sentence, label indicating whether or not
the sentence answers the question, and two additional features --
sentence_in_long_answer and short_answe... | @article{garg2019tanda,
title={TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection},
author={Siddhant Garg and Thuy Vu and Alessandro Moschitti},
year={2019},
eprint={1911.04118},
} | 1 | 254 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc-by-nc-sa-3.0
multilinguality:
- monolingual
size_categories:
- 10M<n<100M
source_datasets:
- extended|natural_questions
task_categories:
- multiple-choice
task_ids:
- multiple-choice-qa
paperswithcode_id: asnq
pretty_name: ... | 7,347 | [
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GEM/wiki_cat_sum | 2022-10-24T15:31:11.000Z | [
"task_categories:summarization",
"annotations_creators:automatically-created",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"arxiv:1906.04687",
"arxiv:1801.10198",
"arxiv:2009.07032",
"regi... | GEM | Summarise the most important facts of a given entity in the Film, Company, and Animal domains from a cluster of related documents. | @inproceedings{perez2019generating,
title={Generating Summaries with Topic Templates and Structured Convolutional Decoders},
author={Perez-Beltrachini, Laura and Liu, Yang and Lapata, Mirella},
booktitle={Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
pages={5107--5116... | 3 | 254 | 2022-03-02T23:29:22 | ---
annotations_creators:
- automatically-created
language_creators:
- unknown
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- summarization
task_ids: []
pretty_name: wiki_cat_sum
---
# Dataset Card for GEM/wiki_cat_sum
## Dat... | 18,223 | [
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cakiki/paperswithcode | 2021-11-08T15:19:45.000Z | [
"region:us"
] | cakiki | The args.me corpus (version 1.0, cleaned) comprises 382 545 arguments crawled from four debate portals in the middle of 2019. The debate portals are Debatewise, IDebate.org, Debatepedia, and Debate.org. The arguments are extracted using heuristics that are designed for each debate portal. | TODO ADD PAPERSWITHCODE CITATION | 0 | 254 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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0... |
laion/laion2B-en-aesthetic | 2023-01-18T20:03:33.000Z | [
"region:us"
] | laion | null | null | 23 | 254 | 2022-05-22T12:34:11 | details at https://github.com/LAION-AI/laion-datasets/blob/main/laion-aesthetic.md | 82 | [
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jonathan-roberts1/SATIN | 2023-10-11T09:48:19.000Z | [
"task_categories:image-classification",
"task_categories:zero-shot-image-classification",
"size_categories:100K<n<1M",
"language:en",
"license:other",
"arxiv:2304.11619",
"region:us"
] | jonathan-roberts1 | null | null | 4 | 254 | 2023-03-22T15:10:38 | ---
license: other
configs:
- config_name: SAT-4
- config_name: SAT-6
- config_name: NASC-TG2
- config_name: WHU-RS19
- config_name: RSSCN7
- config_name: RS_C11
- config_name: SIRI-WHU
- config_name: EuroSAT
- config_name: NWPU-RESISC45
- config_name: PatternNet
- config_name: RSD46-WHU
- confi... | 4,316 | [
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readerbench/ro-offense-sequences | 2023-09-23T18:28:19.000Z | [
"task_categories:token-classification",
"task_ids:hate-speech-detection",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:readerbench/ro-offense",
"language:ro",
"license:apache-2.0",
"hate-speech-detec... | readerbench | null | null | 0 | 254 | 2023-06-23T21:20:54 | ---
license: apache-2.0
annotations_creators:
- expert-generated
language_creators:
- found
task_categories:
- token-classification
language:
- ro
multilinguality:
- monolingual
source_datasets:
- readerbench/ro-offense
tags:
- hate-speech-detection
task_ids:
- hate-speech-detection
pretty_name: RO-Offense-Sequences
si... | 4,152 | [
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multi_para_crawl | 2022-11-03T16:31:38.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:bg",
"language:ca",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:es",
"languag... | null | Parallel corpora from Web Crawls collected in the ParaCrawl project and further processed for making it a multi-parallel corpus by pivoting via English. Here we only provide the additional language pairs that came out of pivoting. The bitexts for English are available from the ParaCrawl release.
40 languages, 669 bitex... | @InProceedings{TIEDEMANN12.463,
author = {J�rg Tiedemann},
title = {Parallel Data, Tools and Interfaces in OPUS},
booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
year = {2012},
month = {may},
date = {23-25},
address = {Istanbul, Turkey},
ed... | 0 | 253 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- bg
- ca
- cs
- da
- de
- el
- es
- et
- eu
- fi
- fr
- ga
- gl
- ha
- hr
- hu
- ig
- is
- it
- km
- lt
- lv
- mt
- my
- nb
- ne
- nl
- nn
- pl
- ps
- pt
- ro
- ru
- si
- sk
- sl
- so
- sv
- sw
- tl
license:
- cc0-1.0
multilinguality:
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nightingal3/fig-qa | 2023-06-10T18:13:33.000Z | [
"task_categories:multiple-choice",
"task_ids:multiple-choice-qa",
"annotations_creators:expert-generated",
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"language:en",
"license:mit",
... | nightingal3 | null | null | 2 | 253 | 2022-06-16T18:35:21 | ---
annotations_creators:
- expert-generated
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- mit
multilinguality:
- monolingual
pretty_name: Fig-QA
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- multiple-choice
task_ids:
- multiple-choice-qa
---
# Dataset Card f... | 3,359 | [
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LeoLM/MMLU_de | 2023-06-15T01:41:53.000Z | [
"license:mit",
"region:us"
] | LeoLM | null | null | 0 | 253 | 2023-06-03T22:07:16 | ---
license: mit
---
# Massive Multitask Language Understanding (MMLU) in German
This dataset is to be used for the evaluation of LLM German language understanding.
It is based on the hendrycksTest dataset ([here](https://huggingface.co/datasets/cais/mmlu) and [here](https://huggingface.co/datasets/tasksource/mmlu)) ... | 857 | [
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-0.0213470458984375,
0.005886077880859375,
0.0196533203125,
-0.0791015625,
-0.03125,
-0.04498291015625,
0.0258636474609375,... |
Trelis/tiny-shakespeare | 2023-09-06T16:27:30.000Z | [
"task_categories:text-generation",
"size_categories:n<1K",
"language:en",
"fine-tuning",
"shakespeare",
"region:us"
] | Trelis | null | null | 0 | 253 | 2023-09-06T16:16:36 | ---
task_categories:
- text-generation
language:
- en
tags:
- fine-tuning
- shakespeare
size_categories:
- n<1K
---
# Data source
Downloaded via Andrej Karpathy's nanogpt repo from this [link](https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt)
# Data Format
- The entire dataset ... | 497 | [
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reasoning_bg | 2022-11-03T16:31:39.000Z | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:bg",
"license:apache-2.0",
"arxiv:1908.01519",
"region:us"
] | null | This new dataset is designed to do reading comprehension in Bulgarian language. | @article{hardalov2019beyond,
title={Beyond english-only reading comprehension: Experiments in zero-shot multilingual transfer for bulgarian},
author={Hardalov, Momchil and Koychev, Ivan and Nakov, Preslav},
journal={arXiv preprint arXiv:1908.01519},
year={2019}
} | 0 | 252 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- bg
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
paperswithcode_id: null
pretty_name: ReasoningBg
dataset_info:
- confi... | 7,743 | [
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0.... |
DFKI-SLT/tacred | 2023-05-17T12:55:00.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|other",
"language:en",
"licen... | DFKI-SLT | TACRED is a large-scale relation extraction dataset with 106,264 examples built over newswire
and web text from the corpus used in the yearly TAC Knowledge Base Population (TAC KBP) challenges.
Examples in TACRED cover 41 relation types as used in the TAC KBP challenges (e.g., per:schools_attended
and org:members) o... | @inproceedings{zhang-etal-2017-position,
title = "Position-aware Attention and Supervised Data Improve Slot Filling",
author = "Zhang, Yuhao and
Zhong, Victor and
Chen, Danqi and
Angeli, Gabor and
Manning, Christopher D.",
booktitle = "Proceedings of the 2017 Conference on Empiri... | 3 | 252 | 2022-09-28T10:02:34 | ---
annotations_creators:
- crowdsourced
- expert-generated
language:
- en
language_creators:
- found
license:
- other
multilinguality:
- monolingual
pretty_name: The TAC Relation Extraction Dataset, TACRED Revisited and Re-TACRED
size_categories:
- 100K<n<1M
source_datasets:
- extended|other
tags:
- relation extractio... | 11,718 | [
[
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0.01853942871093... |
Aniemore/resd_annotated | 2023-07-14T07:59:51.000Z | [
"task_categories:audio-classification",
"size_categories:1K<n<10K",
"language:ru",
"license:mit",
"voice",
"emotions",
"annotated",
"classification",
"doi:10.57967/hf/1272",
"region:us"
] | Aniemore | null | null | 3 | 252 | 2023-02-15T20:00:40 | ---
language: ru
dataset_info:
features:
- name: name
dtype: string
- name: path
dtype: string
- name: speech
dtype: audio
- name: text
dtype: string
- name: emotion
dtype: string
splits:
- name: train
num_bytes: 398878916.336
num_examples: 1116
- name: test
num_bytes: ... | 733 | [
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0.001... |
kinianlo/prlang | 2023-10-29T23:18:56.000Z | [
"region:us"
] | kinianlo | null | null | 1 | 252 | 2023-10-21T02:01:27 | ---
dataset_info:
- config_name: conceptnet5_vocabulary_en
features:
- name: word
dtype: string
- name: tag
dtype: string
splits:
- name: train
num_bytes: 123167929
num_examples: 6846008
download_size: 45799508
dataset_size: 123167929
- config_name: wiki_20220301_en_nltk_adjectives
featu... | 10,017 | [
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bigbio/mqp | 2022-12-22T15:45:40.000Z | [
"multilinguality:monolingual",
"language:en",
"license:unknown",
"region:us"
] | bigbio | Medical Question Pairs dataset by McCreery et al (2020) contains pairs of medical questions and paraphrased versions of
the question prepared by medical professional. Paraphrased versions were labelled as similar (syntactically dissimilar
but contextually similar ) or dissimilar (syntactically may look similar but co... | @article{DBLP:journals/biodb/LiSJSWLDMWL16,
author = {Krallinger, M., Rabal, O., Lourenço, A.},
title = {Effective Transfer Learning for Identifying Similar Questions: Matching User Questions to COVID-19 FAQs},
journal = {KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge D... | 0 | 251 | 2022-11-13T22:10:07 |
---
language:
- en
bigbio_language:
- English
license: unknown
multilinguality: monolingual
bigbio_license_shortname: UNKNOWN
pretty_name: MQP
homepage: https://github.com/curai/medical-question-pair-dataset
bigbio_pubmed: False
bigbio_public: True
bigbio_tasks:
- SEMANTIC_SIMILARITY
---
# Dataset Card for MQP
#... | 1,407 | [
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orkg/SciQA | 2023-05-22T10:13:44.000Z | [
"task_categories:question-answering",
"annotations_creators:expert-generated",
"annotations_creators:auto-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"knowledge-base-qa... | orkg | SciQA contains 2,565 SPARQL query - question pairs along with answers fetched from the open research knowledge graph (ORKG) via a Virtuoso SPARQL endpoint, it is a collection of both handcrafted and autogenerated questions and queries. The dataset is split into 70% training, 10% validation and 20% test exam... | @Article{SciQA2023,
author={Auer, S{\"o}ren
and Barone, Dante A. C.
and Bartz, Cassiano
and Cortes, Eduardo G.
and Jaradeh, Mohamad Yaser
and Karras, Oliver
and Koubarakis, Manolis
and Mouromtsev, Dmitry
and Pliukhin, Dmitrii
and Radyush, D... | 3 | 251 | 2023-03-17T09:55:39 | ---
annotations_creators:
- expert-generated
- auto-generated
language:
- en
language_creators:
- machine-generated
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: 'The SciQA Scientific Question Answering Benchmark for Scholarly Knowledge'
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
-... | 6,079 | [
[
-0.0287017822265625,
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hendrycks/competition_math | 2023-06-08T06:40:09.000Z | [
"task_categories:text2text-generation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"explanation-generation",
"arxiv:2103.03874",
"region:us"
... | hendrycks | The Mathematics Aptitude Test of Heuristics (MATH) dataset consists of problems
from mathematics competitions, including the AMC 10, AMC 12, AIME, and more.
Each problem in MATH has a full step-by-step solution, which can be used to teach
models to generate answer derivations and explanations. | @article{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the MATH Dataset},
author={Dan Hendrycks
and Collin Burns
and Saurav Kadavath
and Akul Arora
and Steven Basart
and Eric Tang
and Dawn Song
and Jacob Steinhardt},
journal={arXiv preprint arXiv:2103.03874},
... | 57 | 250 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
pretty_name: Mathematics Aptitude Test of Heuristics (MATH)
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
tags:... | 5,324 | [
[
-0.04193115234375,
-0.050628662109375,
0.0186767578125,
0.025970458984375,
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0.00984954833984375,
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... |
SetFit/wnli | 2022-02-28T13:48:16.000Z | [
"region:us"
] | SetFit | null | null | 0 | 250 | 2022-03-02T23:29:22 | # Glue WNLI
This dataset is a port of the official [`wnli` dataset](https://huggingface.co/datasets/glue/viewer/wnli/train) on the Hub.
Note that the sentence1 and sentence2 columns have been renamed to text1 and text2 respectively.
Also, the test split is not labeled; the label column values are always -1.
| 316 | [
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-0.0093231201171875,
-0.02813720703125,
... |
gamino/wiki_medical_terms | 2022-12-20T16:23:58.000Z | [
"task_categories:text-classification",
"annotations_creators:other",
"language_creators:other",
"size_categories:1K<n<10K",
"language:en",
"license:gpl-3.0",
"medical",
"conditions",
"region:us"
] | gamino | null | null | 24 | 250 | 2022-12-20T15:25:02 | ---
annotations_creators:
- other
language:
- en
language_creators:
- other
license:
- gpl-3.0
multilinguality: []
pretty_name: Medical terms and their wikipedia text
size_categories:
- 1K<n<10K
source_datasets: []
tags:
- medical
- conditions
task_categories:
- text-classification
task_ids: []
---
# Dataset Card for [... | 1,135 | [
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0.0292... |
allenai/dolma | 2023-10-25T18:41:36.000Z | [
"task_categories:text-generation",
"size_categories:n>1T",
"language:en",
"license:other",
"language-modeling",
"casual-lm",
"llm",
"region:us"
] | allenai | null | null | 342 | 250 | 2023-06-30T20:14:39 | ---
license: other
viewer: false
task_categories:
- text-generation
language:
- en
tags:
- language-modeling
- casual-lm
- llm
pretty_name: Dolma
size_categories:
- n>1T
extra_gated_prompt: "Access to this dataset is automatically granted upon accepting the [**AI2 ImpACT License - Medium Risk Artifacts (“MR Agreement”)... | 11,207 | [
[
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0.037750244140625,
0.01390838623046875,
-0.039398193359375,
-0.055694580078125,
-0.03668212890625,... |
selinerdem/german-orca | 2023-10-16T13:11:41.000Z | [
"region:us"
] | selinerdem | null | null | 0 | 250 | 2023-10-10T17:31:26 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: string
- name: system_prompt_en
dtype: string
- name: system_prompt
dtype: string
- name: question
dtype: string
- name: r... | 694 | [
[
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0.031890869140625,
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-0.07177734375,
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-0.0160217... |
mediabiasgroup/BABE-v3 | 2023-08-23T05:37:34.000Z | [
"license:cc-by-nc-sa-4.0",
"region:us"
] | mediabiasgroup | null | null | 0 | 249 | 2023-08-23T05:25:25 | ---
license: cc-by-nc-sa-4.0
---
Original BABE dataset enriched with sentences from two annotations rounds: NewsUnfold project and Media Bias Game project.
# Please cite as
```
@InProceedings{Spinde2021f,
title = "Neural Media Bias Detection Using Distant Supervision With {BABE} - Bias Annotations By Experts",
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[
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0.012931823... |
hezarai/persian-license-plate-v1 | 2023-10-17T16:10:20.000Z | [
"task_categories:image-to-text",
"language:fa",
"region:us"
] | hezarai | Persian Licensee plate dataset. Primarily taken from AmirKabir University Challenge.
Annotation are provided by the authors | """
_DESCRIPTION = | 0 | 249 | 2023-10-14T12:56:01 | ---
task_categories:
- image-to-text
language:
- fa
pretty_name: PersianLicensePlace
---
> Dataset is downloaded from [here](https://ceit.aut.ac.ir/~keyvanrad/download/ML971/project/) which was provided at Amirkabir University of Technology.
> The datas then labeled by the authors.
> Experimental results show that the... | 375 | [
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jnlpba | 2023-04-14T13:49:49.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-genia-v3.02",
"language:en",
"license:unknown",
"... | null | The data came from the GENIA version 3.02 corpus (Kim et al., 2003). This was formed from a controlled search
on MEDLINE using the MeSH terms human, blood cells and transcription factors. From this search 2,000 abstracts
were selected and hand annotated according to a small taxonomy of 48 classes based on a chemi... | @inproceedings{kim2004introduction,
title={Introduction to the bio-entity recognition task at JNLPBA},
author={Kim, Jin-Dong and Ohta, Tomoko and Tsuruoka, Yoshimasa and Tateisi, Yuka and Collier, Nigel},
booktitle={Proceedings of the international joint workshop on natural ... | 5 | 248 | 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:
- extended|other-genia-v3.02
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: BioNLP... | 4,981 | [
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0.0344848... |
gfissore/arxiv-abstracts-2021 | 2022-10-27T17:08:00.000Z | [
"task_categories:summarization",
"task_categories:text-retrieval",
"task_categories:text2text-generation",
"task_ids:explanation-generation",
"task_ids:text-simplification",
"task_ids:document-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"annotations_creators:... | gfissore | null | null | 16 | 248 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- en
license:
- cc0-1.0
multilinguality:
- monolingual
pretty_name: arxiv-abstracts-2021
size_categories:
- 1M<n<10M
source_datasets: []
task_categories:
- summarization
- text-retrieval
- text2text-generation
task_ids:
- explanat... | 6,755 | [
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0.02996826171875,
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0.02... |
lmqg/qg_dequad | 2022-12-02T18:53:57.000Z | [
"task_categories:text-generation",
"task_ids:language-modeling",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:deepset/germanquad",
"language:de",
"license:cc-by-4.0",
"question-generation",
"arxiv:2210.03992",
"region:us"
] | lmqg | [GermanSQuAD](https://huggingface.co/datasets/deepset/germanquad) dataset for question generation (QG) task. | @inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Nat... | 1 | 248 | 2022-06-02T23:45:30 | ---
license: cc-by-4.0
pretty_name: GermanQuAD for question generation
language: de
multilinguality: monolingual
size_categories: 10K<n<100K
source_datasets: deepset/germanquad
task_categories:
- text-generation
task_ids:
- language-modeling
tags:
- question-generation
---
# Dataset Card for "lmqg/qg_dequad"
## Datas... | 4,812 | [
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nisaar/Articles_Constitution_3300_Instruction_Set | 2023-07-18T07:25:46.000Z | [
"license:apache-2.0",
"region:us"
] | nisaar | null | null | 2 | 248 | 2023-07-18T05:22:47 | ---
license: apache-2.0
---
**Dataset Card for Indian Constitutional Law Instruction-Response Dataset**
---
**Dataset Summary**
The dataset contains instruction-input-output pairs on Indian Constitutional Law, specifically addressing Articles 12, 14, 19, 21, and 15. It's designed to assist AI models, researchers, ... | 1,540 | [
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metabloit/offensive-swahili-text | 2023-09-14T14:33:52.000Z | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:sw",
"license:mit",
"region:us"
] | metabloit | null | null | 0 | 248 | 2023-09-14T14:18:50 | ---
license: mit
task_categories:
- text-classification
language:
- sw
size_categories:
- 1K<n<10K
viewer: true
---
# Overview
This dataset contains offensive and non-offensive sentences. The data was scraped from JamiiForums using a prepared wordlist.
The dataset contains sentences that consists of swahili abusive w... | 665 | [
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ChaiML/seasonIII_chatAI_configurations | 2023-10-08T01:12:33.000Z | [
"region:us"
] | ChaiML | null | null | 1 | 248 | 2023-10-08T01:10:41 | ---
dataset_info:
features:
- name: bot_id
dtype: string
- name: bot_label
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- name: prompt
dtype: string
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splits:
- name: train
num_bytes: 35131193
num_examples: 35321
download_size: 23268076
dat... | 529 | [
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code_x_glue_tc_nl_code_search_adv | 2023-07-27T15:51:10.000Z | [
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"annotations_creators:found",
"language_creators:found",
"multilinguality:other-programming-languages",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:code",
"language:en",
"license:c-uda",
"arxiv:2102.04664",
... | null | The dataset we use comes from CodeSearchNet and we filter the dataset as the following:
- Remove examples that codes cannot be parsed into an abstract syntax tree.
- Remove examples that #tokens of documents is < 3 or >256
- Remove examples that documents contain special tokens (e.g. <img ...> or https:...)
- Remove ex... | @article{husain2019codesearchnet,
title={Codesearchnet challenge: Evaluating the state of semantic code search},
author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
journal={arXiv preprint arXiv:1909.09436},
year={2019}
} | 2 | 247 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- code
- en
license:
- c-uda
multilinguality:
- other-programming-languages
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-retrieval
task_ids:
- document-retrieval
pretty_name: CodeXGlueTcNlCodeSearchAdv
dataset_inf... | 11,434 | [
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kor_nlu | 2023-01-25T14:33:57.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"task_ids:semantic-similarity-scoring",
"task_ids:text-scoring",
"annotations_creators:found",
"language_creators:expert-generated",
"language_creators:found",
"language_creators:machine-generated",
"multilinguality:monoli... | null | The dataset contains data for bechmarking korean models on NLI and STS | null | 1 | 247 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- expert-generated
- found
- machine-generated
language:
- ko
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|snli
task_categories:
- text-classification
task_ids:
- natural-language-inference
- semantic... | 4,570 | [
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deepset/germandpr | 2023-04-06T13:59:37.000Z | [
"task_categories:question-answering",
"task_categories:text-retrieval",
"task_ids:extractive-qa",
"task_ids:closed-domain-qa",
"multilinguality:monolingual",
"source_datasets:original",
"language:de",
"license:cc-by-4.0",
"arxiv:2104.12741",
"region:us"
] | deepset | We take GermanQuAD as a starting point and add hard negatives from a dump of the full German Wikipedia following the approach of the DPR authors (Karpukhin et al., 2020). The format of the dataset also resembles the one of DPR. GermanDPR comprises 9275 question/answer pairs in the training set and 1025 pairs in the tes... | @misc{möller2021germanquad,
title={GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrieval},
author={Timo Möller and Julian Risch and Malte Pietsch},
year={2021},
eprint={2104.12741},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 7 | 247 | 2022-03-02T23:29:22 | ---
language:
- de
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- question-answering
- text-retrieval
task_ids:
- extractive-qa
- closed-domain-qa
thumbnail: >-
https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg
license: cc-by-4.0
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Francesco/road-signs-6ih4y | 2023-03-30T09:19:50.000Z | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
] | Francesco | null | null | 4 | 247 | 2023-03-30T09:19:15 | ---
dataset_info:
features:
- name: image_id
dtype: int64
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dtype: image
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sequence: float32
lengt... | 3,977 | [
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renumics/food101-enriched | 2023-06-06T08:15:28.000Z | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"size_categories:100K<n<1M",
"source_datasets:extended|other-foodspotting",
"source_datasets:extended|food101",
"language:en",
"license:unknown",
"image classification",
"food-101",
"food-101-enriched",
"embeddi... | renumics | null | @inproceedings{bossard14,
title = {Food-101 -- Mining Discriminative Components with Random Forests},
author = {Bossard, Lukas and Guillaumin, Matthieu and Van Gool, Luc},
booktitle = {European Conference on Computer Vision},
year = {2014}
} | 3 | 247 | 2023-05-09T08:41:13 | ---
license: unknown
paperswithcode_id: food-101
pretty_name: Food-101 Data Set
size_categories:
- 100K<n<1M
tags:
- image classification
- food-101
- food-101-enriched
- embeddings
- enhanced
- spotlight
language:
- en
source_datasets:
- extended|other-foodspotting
- extended|food101
task_categories:
- image-classific... | 8,611 | [
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C-MTEB/TNews-classification | 2023-07-28T13:31:30.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 247 | 2023-07-28T13:31:12 | ---
configs:
- config_name: default
data_files:
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path: data/test-*
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: text
dtype: string
- name: label
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class_label:
names:
'0': '100'
... | 1,077 | [
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Nikhil090/Dataset | 2023-10-27T10:57:58.000Z | [
"region:us"
] | Nikhil090 | null | null | 0 | 247 | 2023-10-09T10:01:41 | Entry not found | 15 | [
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GEM/BiSECT | 2022-09-02T21:58:17.000Z | [
"annotations_creators:none",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:de",
"language:en",
"language:fr",
"language:es",
"license:other",
"region:us"
] | GEM | BiSECT is a Split and Rephrase corpus created via bilingual pivoting. | @inproceedings{kim-etal-2021-bisect,
title = "{B}i{SECT}: Learning to Split and Rephrase Sentences with Bitexts",
author = "Kim, Joongwon and
Maddela, Mounica and
Kriz, Reno and
Xu, Wei and
Callison-Burch, Chris",
booktitle = "Proceedings of the 2021 Conference on Empirical Metho... | 2 | 246 | 2022-03-02T23:29:22 | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- de
- en
- fr
- es
license:
- other
multilinguality:
- unknown
pretty_name: BiSECT
size_categories:
- unknown
source_datasets:
- original
task_categories:
- simplification
task_ids:
- unknown
---
# Dataset Card for GEM/BiSECT
## Dataset Descript... | 22,342 | [
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heegyu/kowiki-sentences | 2022-10-06T00:54:57.000Z | [
"task_categories:other",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"language:ko",
"license:cc-by-sa-3.0",
"region:us"
] | heegyu | null | null | 1 | 245 | 2022-10-06T00:46:26 | ---
license: cc-by-sa-3.0
language:
- ko
language_creators:
- other
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
task_categories:
- other
---
20221001 한국어 위키를 kss(backend=mecab)을 이용해서 문장 단위로 분리한 데이터
- 549262 articles, 4724064 sentences
- 한국어 비중이 50% 이하거나 한국어 글자가 10자 이하인 경우를 제외 | 293 | [
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vpetukhov/bible_tts_hausa | 2022-12-05T12:51:17.000Z | [
"task_categories:automatic-speech-recognition",
"task_categories:text-to-speech",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ha",
"license:cc-by-sa-4.0",
"bible",
"arxiv:2207.03546",
"region:us"
] | vpetukhov | null | null | 1 | 245 | 2022-12-05T11:39:16 | ---
annotations_creators: []
language:
- ha
language_creators:
- expert-generated
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: BibleTTS Hausa
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- bible
task_categories:
- automatic-speech-recognition
- text-to-speech
task_ids: []
---
... | 2,322 | [
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FedML/PubMedQA_instruction | 2023-09-27T09:04:39.000Z | [
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
"license:mit",
"medical",
"region:us"
] | FedML | null | null | 4 | 245 | 2023-09-27T08:58:14 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: instruction
dtype: string
- name: context
dtype: string
- name: response
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splits:
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ycchen/oasst_lima | 2023-10-26T14:53:20.000Z | [
"region:us"
] | ycchen | null | null | 0 | 245 | 2023-10-26T14:47:30 | ---
dataset_info:
features:
- name: conversations
sequence: string
- name: source
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splits:
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num_bytes: 7255984
num_examples: 4538
download_size: 4147275
dataset_size: 7255984
configs:
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data_files:
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path: data/train-*
---... | 485 | [
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arabic_pos_dialect | 2022-11-03T16:31:33.000Z | [
"task_categories:token-classification",
"task_ids:part-of-speech",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:n<1K",
"source_datasets:extended",
"language:ar",
"license:apache-2.0",
"arxiv:1708.05891",
"region:us"
] | null | The Dialectal Arabic Datasets contain four dialects of Arabic, Etyptian (EGY), Levantine (LEV), Gulf (GLF), and Maghrebi (MGR). Each dataset consists of a set of 350 manually segmented and POS tagged tweets. | @InProceedings{DARWISH18.562, author = {Kareem Darwish ,Hamdy Mubarak ,Ahmed Abdelali ,Mohamed Eldesouki ,Younes Samih ,Randah Alharbi ,Mohammed Attia ,Walid Magdy and Laura Kallmeyer},
title = {Multi-Dialect Arabic POS Tagging: A CRF Approach},
booktitle = {Proceedings of the Eleventh International Conference on Lang... | 2 | 244 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- ar
license:
- apache-2.0
multilinguality:
- multilingual
size_categories:
- n<1K
source_datasets:
- extended
task_categories:
- token-classification
task_ids:
- part-of-speech
paperswithcode_id: null
pretty_name: Arabic POS Dialect
data... | 11,744 | [
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MilaNLProc/honest | 2022-09-28T15:45:09.000Z | [
"task_categories:text-classification",
"task_ids:hate-speech-detection",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:n<1K",
"source_datasets:original",
"license:mit",
"region:us"
] | MilaNLProc | HONEST dataset comprises a set of templates for measuring hurtful sentence completions in language models. The templates are provided in six languages (English, Italian, French, Portuguese, Romanian, and Spanish) for binary gender and in English for LGBTQAI+ individuals. WARNING: This dataset contains content that are ... | @inproceedings{nozza-etal-2021-honest,
title = {"{HONEST}: Measuring Hurtful Sentence Completion in Language Models"},
author = "Nozza, Debora and Bianchi, Federico and Hovy, Dirk",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computat... | 4 | 244 | 2022-05-10T10:49:43 | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language_bcp47:
- en-US
- it-IT
- fr-FR
- pt-PT
- ro-RO
- es-ES
license:
- mit
multilinguality:
- multilingual
paperswithcode_id: honest-en
pretty_name: HONEST
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-clas... | 5,562 | [
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bigbio/medmentions | 2022-12-22T15:45:34.000Z | [
"multilinguality:monolingual",
"language:en",
"license:cc0-1.0",
"arxiv:1902.09476",
"region:us"
] | bigbio | MedMentions is a new manually annotated resource for the recognition of biomedical concepts.
What distinguishes MedMentions from other annotated biomedical corpora is its size (over 4,000
abstracts and over 350,000 linked mentions), as well as the size of the concept ontology (over
3 million concepts from UMLS 2017) an... | @misc{mohan2019medmentions,
title={MedMentions: A Large Biomedical Corpus Annotated with UMLS Concepts},
author={Sunil Mohan and Donghui Li},
year={2019},
eprint={1902.09476},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 3 | 244 | 2022-11-13T22:09:49 |
---
language:
- en
bigbio_language:
- English
license: cc0-1.0
multilinguality: monolingual
bigbio_license_shortname: CC0_1p0
pretty_name: MedMentions
homepage: https://github.com/chanzuckerberg/MedMentions
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_DISAMBIGUATION
- NAMED_ENTITY_RECOGNITIO... | 2,082 | [
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shunk031/livedoor-news-corpus | 2023-10-28T05:40:17.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"language_creators:found",
"multilinguality:monolingual",
"language:ja",
"license:cc-by-nd-4.0",
"region:us"
] | shunk031 | 本コーパスは、NHN Japan株式会社が運営する「livedoor ニュース」のうち、下記のクリエイティブ・コモンズライセンスが適用されるニュース記事を収集し、可能な限りHTMLタグを取り除いて作成したものです。 | https://www.rondhuit.com/download.html#ldcc | 3 | 244 | 2023-01-18T08:30:24 | ---
annotations_creators: []
language:
- ja
language_creators:
- found
license:
- cc-by-nd-4.0
multilinguality:
- monolingual
pretty_name: livedoor-news-corpus
size_categories: []
source_datasets: []
tags: []
task_categories:
- text-classification
task_ids:
- multi-class-classification
---
# Dataset Card for Livedoor ... | 4,016 | [
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humarin/chatgpt-paraphrases | 2023-04-05T16:27:16.000Z | [
"task_categories:text2text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:openrail",
"region:us"
] | humarin | null | null | 31 | 244 | 2023-03-15T20:12:24 | ---
license: openrail
task_categories:
- text2text-generation
language:
- en
size_categories:
- 100K<n<1M
---
This is a dataset of paraphrases created by ChatGPT.
Model based on this dataset is avaible: [model](https://huggingface.co/humarin/chatgpt_paraphraser_on_T5_base)
## We used this prompt to generate paraphras... | 1,803 | [
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zetavg/ShareGPT-Processed | 2023-05-21T03:50:14.000Z | [
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"language:en",
"language:zh",
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"language:fr",
"license:cc0-1.0",
"conversation",
"rlhf",
"chatgpt",
"gpt-3.5",
"region:us"
] | zetavg | null | null | 23 | 244 | 2023-05-16T19:50:04 | ---
dataset_info:
features:
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erhwenkuo/zhwikisource-zhtw | 2023-10-14T05:45:51.000Z | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:zh",
"license:cc-by-sa-3.0",
"region:us"
] | erhwenkuo | null | null | 1 | 244 | 2023-10-13T22:43:13 | ---
dataset_info:
config_name: '20231001'
features:
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lighteval/quac_helm | 2023-06-14T13:13:20.000Z | [
"region:us"
] | lighteval | null | null | 0 | 243 | 2023-06-14T12:40:42 | Entry not found | 15 | [
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SetFit/tweet_eval_stance | 2022-01-17T13:01:36.000Z | [
"region:us"
] | SetFit | null | null | 0 | 242 | 2022-03-02T23:29:22 | # tweet_eval_stance_abortion
This is the stance_abortion subset of [tweet_eval](https://huggingface.co/datasets/tweet_eval) | 126 | [
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yxchar/chemprot-tlm | 2021-11-04T22:59:08.000Z | [
"region:us"
] | yxchar | null | null | 0 | 242 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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dlwh/wikitext_103_detokenized | 2022-05-05T20:08:17.000Z | [
"region:us"
] | dlwh | null | null | 2 | 242 | 2022-05-05T20:08:16 | Entry not found | 15 | [
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RaymondLi/perturbed_humaneval | 2023-08-23T19:41:28.000Z | [
"license:apache-2.0",
"arxiv:2212.10264",
"region:us"
] | RaymondLi | Perturbed version of HumanEval from: ReCode: Robustness Evaluation of Code Generation Models | @article{recode_wang2022,
title = {ReCode: Robustness Evaluation of Code Generation Models},
author = {Wang, Shiqi and
Zheng, Li and
Qian, Haifeng and
Yang, Chenghao and
Wang, Zijian and
Kumar, Varun and
Shang, Mingyue and
Tan, Samson and
Ray, Baishakhi and
Bhatia, Parminder and
Nallap... | 0 | 242 | 2023-07-18T17:10:19 | ---
license: apache-2.0
---
# Dataset Card for Dataset Name
## Dataset Description
- **Repository:** https://github.com/amazon-science/recode/tree/main
- **Paper:** https://arxiv.org/abs/2212.10264
### Dataset Summary
The Recode benchmark proposes to apply code and natural language transformations to code-generati... | 2,664 | [
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AI4Math/MathVista | 2023-10-25T20:55:27.000Z | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:visual-question-answering",
"task_categories:text-classification",
"task_ids:multiple-choice-qa",
"task_ids:closed-domain-qa",
"task_ids:open-domain-qa",
"task_ids:visual-question-answering",
"task_ids:multi-cl... | AI4Math | null | null | 11 | 242 | 2023-10-15T17:49:10 | ---
license: cc-by-sa-4.0
annotations_creators:
- expert-generated
- found
language:
- en
- zh
- fa
language_creators:
- expert-generated
- found
multilinguality:
- monolingual
paperswithcode_id: mathvista
pretty_name: MathVista
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories... | 11,667 | [
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generated_reviews_enth | 2023-01-25T14:30:46.000Z | [
"task_categories:translation",
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:semantic-similarity-classification",
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:transla... | null | `generated_reviews_enth`
Generated product reviews dataset for machine translation quality prediction, part of [scb-mt-en-th-2020](https://arxiv.org/pdf/2007.03541.pdf)
`generated_reviews_enth` is created as part of [scb-mt-en-th-2020](https://arxiv.org/pdf/2007.03541.pdf) for machine translation task.
This dataset... | @article{lowphansirikul2020scb,
title={scb-mt-en-th-2020: A Large English-Thai Parallel Corpus},
author={Lowphansirikul, Lalita and Polpanumas, Charin and Rutherford, Attapol T and Nutanong, Sarana},
journal={arXiv preprint arXiv:2007.03541},
year={2020}
} | 3 | 241 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
- machine-generated
language_creators:
- machine-generated
language:
- en
- th
license:
- cc-by-sa-4.0
multilinguality:
- translation
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- translation
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task_ids:
- multi-class-classif... | 9,617 | [
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ola13/small-the_pile-dedup | 2022-12-07T08:28:01.000Z | [
"region:us"
] | ola13 | null | null | 0 | 241 | 2022-12-07T00:26:07 | Entry not found | 15 | [
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Babelscape/REDFM | 2023-06-20T07:33:35.000Z | [
"task_categories:token-classification",
"size_categories:10K<n<100K",
"language:ar",
"language:de",
"language:en",
"language:es",
"language:it",
"language:fr",
"language:zh",
"license:cc-by-sa-4.0",
"arxiv:2306.09802",
"region:us"
] | Babelscape | Relation Extraction (RE) is a task that identifies relationships between entities in a text, enabling the acquisition of relational facts and bridging the gap between natural language and structured knowledge. However, current RE models often rely on small datasets with low coverage of relation types, particularly when... | @InProceedings{redfm2023,
author = {Huguet Cabot, Pere-Lluis
and Tedeschi, Simone
and Ngonga Ngomo, Axel-Cyrille
and Navigli, Roberto},
title = {RED\textsuperscript{FM}: a Filtered and Multilingual Relation Extraction Dataset},
booktitle = {Proceedings of the 202... | 4 | 241 | 2023-06-13T16:46:41 | ---
dataset_info:
- config_name: ar
features:
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dtype: string
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- name: text
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list:
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dtype: ... | 18,454 | [
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open-phi/programming_books_llama | 2023-10-04T18:02:56.000Z | [
"region:us"
] | open-phi | null | null | 7 | 241 | 2023-10-03T18:27:59 | ---
dataset_info:
features:
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swj0419/WikiMIA | 2023-10-09T23:32:54.000Z | [
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"arxiv:2308.04430",
"region:us"
] | swj0419 | null | null | 8 | 241 | 2023-10-05T23:31:10 | ---
license: mit
language:
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size_categories:
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dataset_info:
features:
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splits:
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num_bytes: 162091
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- name: WikiMIA_length64
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num_examples: 542
- name: WikiM... | 1,656 | [
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KBLab/rixvox | 2023-08-17T10:26:47.000Z | [
"task_categories:automatic-speech-recognition",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:sv",
"license:cc-by-4.0",
"audio",
"speech-recognition",
"region:us"
] | KBLab | RixVox is a speech dataset comprised of speeches from the Swedish Parliament (the Riksdag). Audio from speeches have been aligned with official transcripts, on the sentence level, using aeneas.
Speaker metadata is available for each observation, including the speaker's name, gender, party, birth year and electoral dis... | @misc{rekathati2023rixvox:,
author = {Rekathati, Faton},
title = {The KBLab Blog: RixVox: A Swedish Speech Corpus with 5500 Hours of Speech from Parliamentary Debates},
url = {https://kb-labb.github.io/posts/2023-03-09-rixvox-a-swedish-speech-corpus/},
year = {2023}
} | 8 | 240 | 2023-03-03T11:07:18 | ---
language: sv
license: cc-by-4.0
tags:
- audio
- speech-recognition
task_categories:
- automatic-speech-recognition
size_categories:
- 100K<n<1M
multilinguality:
- monolingual
---
# Dataset Card for RixVox
## Dataset Description
- **Repository:** [Riksdagen anföranden repository](https://github.com/kb-labb... | 16,337 | [
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clarin-knext/fiqa-pl-qrels | 2023-06-07T08:22:36.000Z | [
"language:pl",
"arxiv:2305.19840",
"region:us"
] | clarin-knext | null | null | 0 | 240 | 2023-06-06T21:58:39 | ---
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
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C-MTEB/ThuNewsClusteringP2P | 2023-07-27T17:29:09.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 240 | 2023-07-27T17:28:47 | ---
configs:
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data_files:
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path: data/test-*
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splits:
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# ... | 492 | [
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librarian-bots/dataset_abstracts | 2023-10-31T09:13:51.000Z | [
"task_categories:text-classification",
"size_categories:n<1K",
"language:en",
"arxiv ",
"region:us"
] | librarian-bots | null | null | 2 | 240 | 2023-10-05T11:16:57 | ---
language:
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struct:
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ahazeemi/librispeech10h | 2022-04-24T20:11:30.000Z | [
"region:us"
] | ahazeemi | null | null | 0 | 239 | 2022-04-24T20:09:51 | Entry not found | 15 | [
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ccdv/mediasum | 2022-10-25T10:56:04.000Z | [
"task_categories:summarization",
"task_categories:text2text-generation",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:en",
"conditional-text-generation",
"region:us"
] | ccdv | MediaSum dataset for summarization.
From paper: "MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization" by C. Zhu et al." | @article{zhu2021mediasum,
title={MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization},
author={Zhu, Chenguang and Liu, Yang and Mei, Jie and Zeng, Michael},
journal={arXiv preprint arXiv:2103.06410},
year={2021}
} | 5 | 239 | 2022-05-21T12:29:19 | ---
language:
- en
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
task_categories:
- summarization
- text2text-generation
task_ids: []
tags:
- conditional-text-generation
---
# MediaSum dataset for summarization
Summarization dataset copied from [MediaSum: A Large-scale Media Interview Dataset for Dialog... | 1,808 | [
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amaydle/npc-dialogue | 2023-03-25T09:11:29.000Z | [
"region:us"
] | amaydle | null | null | 6 | 239 | 2023-03-25T09:11:12 | ---
dataset_info:
features:
- name: Name
dtype: string
- name: Biography
dtype: string
- name: Query
dtype: string
- name: Response
dtype: string
- name: Emotion
dtype: string
splits:
- name: train
num_bytes: 737058.9117493472
num_examples: 1723
- name: test
num_bytes: ... | 579 | [
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id_liputan6 | 2022-11-18T20:08:31.000Z | [
"task_categories:summarization",
"task_ids:news-articles-summarization",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:id",
"license:unknown",
"extractive-summarization",
"arxiv:2... | null | In this paper, we introduce a large-scale Indonesian summarization dataset. We harvest articles from this http URL,
an online news portal, and obtain 215,827 document-summary pairs. We leverage pre-trained language models to develop
benchmark extractive and abstractive summarization methods over the dataset with multil... | @inproceedings{id_liputan6,
author = {Fajri Koto, Jey Han Lau, Timothy Baldwin},
title = {Liputan6: A Large-scale Indonesian Dataset for Text Summarization},
year = {2020},
url = {https://arxiv.org/abs/2011.00679},
} | 5 | 238 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- id
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- summarization
task_ids:
- news-articles-summarization
paperswithcode_id: null
pretty_name: Large-scale Indones... | 7,306 | [
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0.... |
qangaroo | 2023-04-05T13:37:06.000Z | [
"language:en",
"region:us"
] | null | We have created two new Reading Comprehension datasets focussing on multi-hop (alias multi-step) inference.
Several pieces of information often jointly imply another fact. In multi-hop inference, a new fact is derived by combining facts via a chain of multiple steps.
Our aim is to build Reading Comprehension method... | 0 | 238 | 2022-03-02T23:29:22 | ---
language:
- en
paperswithcode_id: null
pretty_name: qangaroo
dataset_info:
- config_name: medhop
features:
- name: query
dtype: string
- name: supports
sequence: string
- name: candidates
sequence: string
- name: answer
dtype: string
- name: id
dtype: string
splits:
- name: train... | 8,632 | [
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-0.06353759765625,
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0.015075683... | |
tner/mit_movie_trivia | 2022-07-18T10:24:52.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:en",
"license:other",
"region:us"
] | tner | MIT Movie | null | 2 | 238 | 2022-07-16T11:12:14 | ---
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: MIT Movie
---
# Dataset Card for "tner/mit_movie_trivia"
## Dataset Description
- **Repository:** [T-NER](https://github.com/asahi41... | 1,780 | [
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0.01... |
yerevann/coco-karpathy | 2022-10-31T11:24:01.000Z | [
"task_categories:image-to-text",
"task_ids:image-captioning",
"language:en",
"coco",
"image-captioning",
"region:us"
] | yerevann | null | null | 3 | 238 | 2022-09-18T22:50:19 | ---
language:
- en
task_categories:
- image-to-text
task_ids:
- image-captioning
pretty_name: COCO Karpathy split
tags:
- coco
- image-captioning
---
# Dataset Card for "yerevann/coco-karpathy"
The Karpathy split of COCO for image captioning.
| 246 | [
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0.014205932... |
distil-whisper/gigaspeech-l | 2023-09-25T10:28:52.000Z | [
"task_categories:automatic-speech-recognition",
"language:en",
"license:other",
"region:us"
] | distil-whisper | GigaSpeech is an evolving, multi-domain English speech recognition corpus with 10,000 hours of high quality
labeled audio suitable for supervised training, and 40,000 hours of total audio suitable for semi-supervised
and unsupervised training. Around 40,000 hours of transcribed audio is first collected from audiobooks,... | @article{DBLP:journals/corr/abs-2106-06909,
author = {Guoguo Chen and
Shuzhou Chai and
Guanbo Wang and
Jiayu Du and
Wei{-}Qiang Zhang and
Chao Weng and
Dan Su and
Daniel Povey and
Jan Trmal and
... | 0 | 238 | 2023-04-11T20:16:14 | ---
license: other
task_categories:
- automatic-speech-recognition
language:
- en
extra_gated_prompt: |-
SpeechColab does not own the copyright of the audio files. For researchers and educators who wish to use the audio files for non-commercial research and/or educational purposes, we can provide access through the... | 4,292 | [
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0... |
grantprice/CriticalRoleTranscripts | 2023-06-14T18:56:45.000Z | [
"region:us"
] | grantprice | null | null | 0 | 238 | 2023-06-14T18:56:33 | Entry not found | 15 | [
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0.0379... |
jglaser/binding_affinity | 2022-03-12T00:29:11.000Z | [
"molecules",
"chemistry",
"SMILES",
"region:us"
] | jglaser | A dataset to fine-tune language models on protein-ligand binding affinity prediction. | @InProceedings{huggingface:dataset,
title = {jglaser/binding_affinity},
author={Jens Glaser, ORNL
},
year={2021}
} | 5 | 237 | 2022-03-02T23:29:22 | ---
tags:
- molecules
- chemistry
- SMILES
---
## How to use the data sets
This dataset contains 1.9M unique pairs of protein sequences and ligand SMILES with experimentally determined
binding affinities. It can be used for fine-tuning a language model.
The data comes from the following sources:
- BindingDB
- PDBbin... | 2,879 | [
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0.04095458984375,
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... |
CreativeLang/EPIC_Irony | 2023-07-11T16:46:43.000Z | [
"region:us"
] | CreativeLang | null | null | 1 | 237 | 2023-07-11T16:34:27 | ---
dataset_info:
features:
- name: user
dtype: string
- name: label
dtype: string
- name: timestamp
dtype: string
- name: source
dtype: string
- name: subreddit
dtype: string
- name: id_original
dtype: string
- name: text
dtype: string
- name: parent_id_original
dtype:... | 2,076 | [
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0.03... |
ai4bharat/naamapadam | 2023-05-24T17:09:03.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:multilingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:as",
"language:bn",
"language:gu",
"lang... | ai4bharat | \ | \ | 3 | 235 | 2023-01-19T03:17:10 | ---
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: naamapadam
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- token-classification... | 8,498 | [
[
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0.0429687... |
range3/cc100-ja | 2023-02-04T05:43:32.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"language:ja",
"license:unknown",
"region:us"
] | range3 | null | null | 7 | 235 | 2023-02-04T05:10:34 | ---
license: unknown
task_categories:
- text-generation
- fill-mask
language:
- ja
---
# range3/cc100-ja
This dataset consists of parquet files from the cc100 dataset with only the Japanese language extracted and sharded.
このデータセットは、cc100データセットの日本語のみを抽出し、シャーディングしたparquetファイルで構成されます。 | 283 | [
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jjonhwa/dolly-ko | 2023-10-08T09:55:19.000Z | [
"region:us"
] | jjonhwa | null | null | 0 | 235 | 2023-10-08T09:55:15 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
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num_bytes: 14238792
num_examples: 15011
download_size: 8006189
dataset_size: 14238792
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "dolly-ko"
[More Inform... | 439 | [
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ar_sarcasm | 2023-03-16T14:13:22.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-semeval_2017",
"source_datasets:extended|other-astd",
"language:ar... | null | ArSarcasm is a new Arabic sarcasm detection dataset.
The dataset was created using previously available Arabic sentiment analysis datasets (SemEval 2017 and ASTD)
and adds sarcasm and dialect labels to them. The dataset contains 10,547 tweets, 1,682 (16%) of which are sarcastic. | @inproceedings{abu-farha-magdy-2020-arabic,
title = "From {A}rabic Sentiment Analysis to Sarcasm Detection: The {A}r{S}arcasm Dataset",
author = "Abu Farha, Ibrahim and Magdy, Walid",
booktitle = "Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offe... | 4 | 234 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- ar
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-semeval_2017
- extended|other-astd
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: Ar... | 6,235 | [
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0.007... |
matinf | 2023-04-05T10:09:38.000Z | [
"region:us"
] | null | MATINF is the first jointly labeled large-scale dataset for classification, question answering and summarization.
MATINF contains 1.07 million question-answer pairs with human-labeled categories and user-generated question
descriptions. Based on such rich information, MATINF is applicable for three major NLP tasks, i... | @inproceedings{xu-etal-2020-matinf,
title = "{MATINF}: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization",
author = "Xu, Canwen and
Pei, Jiaxin and
Wu, Hongtao and
Liu, Yiyu and
Li, Chenliang",
booktitle = "Proceedings of the 58th Annu... | 3 | 234 | 2022-03-02T23:29:22 | ---
paperswithcode_id: matinf
pretty_name: Maternal and Infant Dataset
dataset_info:
- config_name: age_classification
features:
- name: question
dtype: string
- name: description
dtype: string
- name: label
dtype:
class_label:
names:
'0': 0-1岁
'1': 1-2岁
'... | 10,374 | [
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0.0... |
Babelscape/rebel-dataset | 2023-06-15T12:12:59.000Z | [
"task_categories:text-retrieval",
"task_categories:text-generation",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"relation-extraction",
... | Babelscape | REBEL is a silver dataset created for the paper REBEL: Relation Extraction By End-to-end Language generation | @inproceedings{huguet-cabot-navigli-2021-rebel,
title = "REBEL: Relation Extraction By End-to-end Language generation",
author = "Huguet Cabot, Pere-Llu{\'\i}s and
Navigli, Roberto",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "... | 15 | 234 | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- en
license: cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text-retrieval
- text-generation
task_ids: []
pretty_name: rebel-dataset
tags:
- relation-ext... | 9,926 | [
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-0.0328369140625,
0.0268249... |
Docugami/dfm-csl-large-benchmark | 2023-10-04T08:41:01.000Z | [
"task_categories:text2text-generation",
"task_categories:text-generation",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:mit",
"docugami",
"dfm-csl",
"xml-knowledge-graphs",
"region:us"
] | Docugami | null | null | 4 | 234 | 2023-05-30T01:01:02 | ---
license: mit
language:
- en
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text2text-generation
- text-generation
dataset_info:
features:
- name: Text
dtype: string
- name: Ground Truth
dtype: string
- name: docugami/dfm-csl-large
dtype: string
splits:
- name: eva... | 1,054 | [
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-0.04632568359375,
0.00263... |
ZahrizhalAli/mental_health_conversational_dataset | 2023-08-25T04:02:08.000Z | [
"task_categories:text-generation",
"task_categories:conversational",
"size_categories:n<1K",
"language:en",
"license:mit",
"medical",
"region:us"
] | ZahrizhalAli | null | null | 2 | 234 | 2023-08-10T02:44:34 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_examples: 175
license: mit
task_categories:
- text-generation
- conversational
language:
- en
tags:
- medical
pretty_name: Mental Health Chatbot Dataset
size_categories:
- n<1K
---
# CREDIT: Dataset Card for "heliosbrahma... | 2,520 | [
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-0.018585205078125,
-0.058563232421875,
0.0171661376953125,
0.0230560302734375,
-0.0097198486328125,
0.015777587890625,
-0.00885772705078125,
-0.012908935546875,
0.03350830078125,
0.050689697265625,
-0.07220458984375,
-0.054290771484375,
-0.052001953125,
-0.... |
yair-elboher/text-toy | 2023-10-06T09:35:55.000Z | [
"region:us"
] | yair-elboher | null | null | 0 | 234 | 2023-08-21T22:20:17 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 10849
num_examples: 9
- name: validation
num_bytes: 8180
num_examples: 4
download_size: 30926
dataset_size: 19029
configs:
- config_name: default
data_files:
- split: train
path: data/train-... | 538 | [
[
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-0.042266845703125,
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-... |
TheBritishLibrary/blbooks | 2022-11-03T16:31:29.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:other",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:machine-generated",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"sou... | TheBritishLibrary | A dataset comprising of text created by OCR from the 49,455 digitised books, equating to 65,227 volumes (25+ million pages), published between c. 1510 - c. 1900.
The books cover a wide range of subject areas including philosophy, history, poetry and literature. | @misc{BritishLibraryBooks2021,
author = {British Library Labs},
title = {Digitised Books. c. 1510 - c. 1900. JSONL (OCR derived text + metadata)},
year = {2021},
publisher = {British Library},
howpublished={https://doi.org/10.23636/r7w6-zy15} | 6 | 233 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- machine-generated
language:
- de
- en
- es
- fr
- it
- nl
license:
- cc0-1.0
multilinguality:
- multilingual
pretty_name: British Library Books
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
- other
t... | 37,167 | [
[
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-0.0117645263671875,
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0.0025348663330078125,
0.06439208984375,
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-0.053985595703125,
-0.03485107421... |
c3 | 2022-11-18T19:24:46.000Z | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:zh",
"license:other",
"arxiv:1904.09679",
"region:u... | null | Machine reading comprehension tasks require a machine reader to answer questions relevant to the given document. In this paper, we present the first free-form multiple-Choice Chinese machine reading Comprehension dataset (C^3), containing 13,369 documents (dialogues or more formally written mixed-genre texts) and their... | @article{sun2019investigating,
title={Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension},
author={Sun, Kai and Yu, Dian and Yu, Dong and Cardie, Claire},
journal={Transactions of the Association for Computational Linguistics},
year={2020},
url={https://arxiv.org/abs/1904.0967... | 8 | 233 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- zh
license:
- other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
paperswithcode_id: c3
pretty_name: C3
dataset_inf... | 5,546 | [
[
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0.008... |
laion/OIG | 2023-03-31T00:06:28.000Z | [
"license:apache-2.0",
"region:us"
] | laion | null | null | 255 | 233 | 2023-03-05T00:34:58 | ---
license: apache-2.0
---
# This is the Open Instruction Generalist Dataset
This is our attempt to create a large instruction dataset of medium quality along with a smaller high quality instruciton dataset (OIG-small-chip2).
The data is in the form of jsonl objects, with at least a 'text' field. Some datasets may ... | 11,351 | [
[
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... |
llm-book/aio | 2023-10-06T00:59:01.000Z | [
"region:us"
] | llm-book | null | null | 1 | 233 | 2023-07-14T11:41:32 | ---
dataset_info:
features:
- name: qid
dtype: string
- name: competition
dtype: string
- name: timestamp
dtype: string
- name: section
dtype: string
- name: number
dtype: string
- name: original_question
dtype: string
- name: original_answer
dtype: string
- name: original_... | 1,474 | [
[
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0.005126953125,
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0.03765869140625,
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-0.07000732421875,
-0.0347900390625,
0.00947570... |
shahules786/orca-chat | 2023-07-25T06:06:35.000Z | [
"license:apache-2.0",
"region:us"
] | shahules786 | null | null | 94 | 233 | 2023-07-17T11:58:55 | ---
license: apache-2.0
---
## ORCA-Chat
A high-quality explanation-style chat dataset.
ORCA dataset is cool, but it cannot directly be used to finetune chat models with above 4k context length
because it has trivial samples with tokens above 4k. It also has a large number of redundant instructions which
degrades i... | 1,365 | [
[
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-0.055572509765625,
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0.01305389404296875,
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0.0312347412109375,
0.05078125,
-0.0491943359375,
-0.053009033203125,
-0.0109710693359375,
-0.0140... |
alexcadillon/SemEval2014Task4 | 2023-09-12T08:49:29.000Z | [
"region:us"
] | alexcadillon | These are the datasets for Aspect Based Sentiment Analysis (ABSA), Task 4 of SemEval-2014. | @inproceedings{pontiki-etal-2014-semeval,
title = "{S}em{E}val-2014 Task 4: Aspect Based Sentiment Analysis",
author = "Pontiki, Maria and
Galanis, Dimitris and
Pavlopoulos, John and
Papageorgiou, Harris and
Androutsopoulos, Ion and
Manandhar, Suresh",
booktitle = "Proceed... | 0 | 233 | 2023-08-24T13:07:51 | Entry not found | 15 | [
[
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0.057159423828125,
0.02880859375,
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0.046478271484375,
0.052520751953125,
0.005077362060546875,
0.051361083984375,
0.0170135498046875,
-0.05206298828125,
-0.01494598388671875,
-0.06036376953125,
0.03... |
cambridgeltl/vsr_zeroshot | 2023-03-22T17:27:58.000Z | [
"task_categories:text-classification",
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-4.0",
"multimodal",
"vision-and-language",
"arxiv:2205.00363",
"region:us"
] | cambridgeltl | null | null | 1 | 232 | 2023-03-22T16:42:17 | ---
license: cc-by-4.0
task_categories:
- text-classification
- question-answering
language:
- en
tags:
- multimodal
- vision-and-language
pretty_name: VSR (zeroshot)
size_categories:
- 1K<n<10K
---
# VSR: Visual Spatial Reasoning
This is the **zero-shot set** of **VSR**: *Visual Spatial Reasoning* (TACL 2023) [[pape... | 1,126 | [
[
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0.003208160400390625,
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0.00034999847412109375,
0.0264434814453125,
-0.028717041015625,
-0.049072265625,
-0.0232696533203125,
... |
Skelebor/book_titles_and_descriptions_en_clean | 2022-06-28T11:23:46.000Z | [
"region:us"
] | Skelebor | null | null | 1 | 230 | 2022-06-28T10:45:53 | Entry not found | 15 | [
[
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0.052490234375,
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0.051361083984375,
0.0170135498046875,
-0.052093505859375,
-0.01497650146484375,
-0.0604248046875,
0.0379028... |
bigbio/hallmarks_of_cancer | 2022-12-22T15:44:44.000Z | [
"multilinguality:monolingual",
"language:en",
"license:gpl-3.0",
"region:us"
] | bigbio | The Hallmarks of Cancer (HOC) Corpus consists of 1852 PubMed publication
abstracts manually annotated by experts according to a taxonomy. The taxonomy
consists of 37 classes in a hierarchy. Zero or more class labels are assigned
to each sentence in the corpus. The labels are found under the "labels"
directory, while th... | @article{DBLP:journals/bioinformatics/BakerSGAHSK16,
author = {Simon Baker and
Ilona Silins and
Yufan Guo and
Imran Ali and
Johan H{\"{o}}gberg and
Ulla Stenius and
Anna Korhonen},
title = {Automatic semantic classifica... | 1 | 230 | 2022-11-13T22:08:53 |
---
language:
- en
bigbio_language:
- English
license: gpl-3.0
multilinguality: monolingual
bigbio_license_shortname: GPL_3p0
pretty_name: Hallmarks of Cancer
homepage: https://github.com/sb895/Hallmarks-of-Cancer
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- TEXT_CLASSIFICATION
---
# Dataset Card for H... | 1,781 | [
[
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0.03955078125,
-0.0267181396484375,
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... |
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