id stringlengths 2 115 | lastModified stringlengths 24 24 | tags list | author stringlengths 2 42 ⌀ | description stringlengths 0 68.7k ⌀ | citation stringlengths 0 10.7k ⌀ | cardData null | likes int64 0 3.55k | downloads int64 0 10.1M | card stringlengths 0 1.01M |
|---|---|---|---|---|---|---|---|---|---|
facebook/voxpopuli | 2022-10-14T13:43:12.000Z | [
"task_categories:automatic-speech-recognition",
"multilinguality:multilingual",
"language:en",
"language:de",
"language:fr",
"language:es",
"language:pl",
"language:it",
"language:ro",
"language:hu",
"language:cs",
"language:nl",
"language:fi",
"language:hr",
"language:sk",
"language:s... | facebook | A large-scale multilingual speech corpus for representation learning, semi-supervised learning and interpretation. | @inproceedings{wang-etal-2021-voxpopuli,
title = "{V}ox{P}opuli: A Large-Scale Multilingual Speech Corpus for Representation Learning,
Semi-Supervised Learning and Interpretation",
author = "Wang, Changhan and
Riviere, Morgane and
Lee, Ann and
Wu, Anne and
Talnikar, Chaitanya a... | null | 23 | 3,166 | ---
annotations_creators: []
language:
- en
- de
- fr
- es
- pl
- it
- ro
- hu
- cs
- nl
- fi
- hr
- sk
- sl
- et
- lt
language_creators: []
license:
- cc0-1.0
- other
multilinguality:
- multilingual
pretty_name: VoxPopuli
size_categories: []
source_datasets: []
tags: []
task_categories:
- automatic-speech-recognition
... |
Chris1/cityscapes | 2022-11-03T19:06:29.000Z | [
"region:us"
] | Chris1 | null | null | null | 1 | 3,165 | Entry not found |
reuters21578 | 2023-08-30T17:35:01.000Z | [
"language:en",
"license:other",
"region:us"
] | null | The Reuters-21578 dataset is one of the most widely used data collections for text
categorization research. It is collected from the Reuters financial newswire service in 1987. | @article{APTE94,
author = {Chidanand Apt{\'{e}} and Fred Damerau and Sholom M. Weiss},
title = {Automated Learning of Decision Rules for Text Categorization},
journal = {ACM Transactions on Information Systems},
year = {1994},
note = {To appear.}
}
@inproceedings{APTE94b,
author = {Chidanand Apt{\'{e}} and Fred Damera... | null | 6 | 3,146 | ---
language:
- en
license: other
paperswithcode_id: reuters-21578
pretty_name: Reuters-21578 Text Categorization Collection
dataset_info:
- config_name: ModApte
features:
- name: text
dtype: string
- name: text_type
dtype: string
- name: topics
sequence: string
- name: lewis_split
dtype: stri... |
NeelNanda/pile-10k | 2022-10-14T21:27:22.000Z | [
"license:bigscience-bloom-rail-1.0",
"region:us"
] | NeelNanda | null | null | null | 2 | 3,131 | ---
license: bigscience-bloom-rail-1.0
---
The first 10K elements of [The Pile](https://pile.eleuther.ai/), useful for debugging models trained on it. See the [HuggingFace page for the full Pile](https://huggingface.co/datasets/the_pile) for more info. Inspired by [stas' great resource](https://huggingface.co/datasets... |
acronym_identification | 2023-01-25T14:18:28.000Z | [
"task_categories:token-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"acronym-identification",
"arxiv:2010.14678",
"region:us"
] | null | Acronym identification training and development sets for the acronym identification task at SDU@AAAI-21. | @inproceedings{veyseh-et-al-2020-what,
title={{What Does This Acronym Mean? Introducing a New Dataset for Acronym Identification and Disambiguation}},
author={Amir Pouran Ben Veyseh and Franck Dernoncourt and Quan Hung Tran and Thien Huu Nguyen},
year={2020},
booktitle={Proceedings of COLING},
link={http... | null | 17 | 3,115 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids: []
paperswithcode_id: acronym-identification
pretty_name: Acronym Identificatio... |
gigaword | 2023-04-05T10:06:42.000Z | [
"task_categories:summarization",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|gigaword_2003",
"language:en",
"license:mit",
"headline-generation",
"arxiv:1509.00685",
"region:us"
] | null | Headline-generation on a corpus of article pairs from Gigaword consisting of
around 4 million articles. Use the 'org_data' provided by
https://github.com/microsoft/unilm/ which is identical to
https://github.com/harvardnlp/sent-summary but with better format.
There are two features:
- document: article.
- summary:... | @article{graff2003english,
title={English gigaword},
author={Graff, David and Kong, Junbo and Chen, Ke and Maeda, Kazuaki},
journal={Linguistic Data Consortium, Philadelphia},
volume={4},
number={1},
pages={34},
year={2003}
}
@article{Rush_2015,
title={A Neural Attention Model for Abstractive Sentence... | null | 18 | 3,102 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|gigaword_2003
task_categories:
- summarization
task_ids: []
paperswithcode_id: null
pretty_name: Gigaword
train-eval-index:
- config: default... |
mhenrichsen/alpaca_2k_test | 2023-07-22T19:48:57.000Z | [
"license:apache-2.0",
"region:us"
] | mhenrichsen | null | null | null | 3 | 3,088 | ---
license: apache-2.0
---
|
elyza/ELYZA-tasks-100 | 2023-09-26T01:38:42.000Z | [
"task_categories:text2text-generation",
"size_categories:n<1K",
"language:ja",
"license:cc-by-sa-4.0",
"arxiv:2307.09288",
"region:us"
] | elyza | null | null | null | 21 | 3,086 | ---
task_categories:
- text2text-generation
language:
- ja
size_categories:
- n<1K
license: cc-by-sa-4.0
---
# ELYZA-tasks-100: 日本語instructionモデル評価データセット
## Data Description
本データセットはinstruction-tuningを行ったモデルの評価用データセットです。詳細は [リリースのnote記事](https://note.com/elyza/n/na405acaca130) を参照してく... |
Cubpaw/voxelgym_5c_42x42_25000 | 2023-05-31T21:28:34.000Z | [
"region:us"
] | Cubpaw | null | null | null | 0 | 3,070 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: image
- name: rgb_label
dtype: image
- name: path_label
dtype: image
- name: path_rgb_label
dtype: image
splits:
- name: train
num_bytes: 18480640.0
num_examples: 20000
- name: validation
num_by... |
quoref | 2023-04-05T13:37:27.000Z | [
"task_categories:question-answering",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"coreference-resolution",
"region:us"
] | null | Quoref is a QA dataset which tests the coreferential reasoning capability of reading comprehension systems. In this
span-selection benchmark containing 24K questions over 4.7K paragraphs from Wikipedia, a system must resolve hard
coreferences before selecting the appropriate span(s) in the paragraphs for answering ques... | @article{allenai:quoref,
author = {Pradeep Dasigi and Nelson F. Liu and Ana Marasovic and Noah A. Smith and Matt Gardner},
title = {Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning},
journal = {arXiv:1908.05803v2 },
year = {2019},
} | null | 3 | 3,057 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Quoref
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids: []
paperswithcode_id: quoref
tags:
- coreference-resolution... |
cos_e | 2023-04-05T10:02:39.000Z | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|commonsense_qa",
"language:en",
"license:unknown",
"arxiv:1906.02361",
"r... | null | Common Sense Explanations (CoS-E) allows for training language models to
automatically generate explanations that can be used during training and
inference in a novel Commonsense Auto-Generated Explanation (CAGE) framework. | @inproceedings{rajani2019explain,
title = {Explain Yourself! Leveraging Language models for Commonsense Reasoning},
author = {Rajani, Nazneen Fatema and
McCann, Bryan and
Xiong, Caiming and
Socher, Richard}
year={2019}
booktitle = {Proceedings of the 2019 Conference of the Associ... | null | 6 | 2,995 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- crowdsourced
license:
- unknown
multilinguality:
- monolingual
pretty_name: Commonsense Explanations
size_categories:
- 10K<n<100K
source_datasets:
- extended|commonsense_qa
task_categories:
- question-answering
task_ids:
- open-domain-qa
pape... |
banking77 | 2023-04-17T13:46:23.000Z | [
"task_categories:text-classification",
"task_ids:intent-classification",
"task_ids:multi-class-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"li... | null | BANKING77 dataset provides a very fine-grained set of intents in a banking domain.
It comprises 13,083 customer service queries labeled with 77 intents.
It focuses on fine-grained single-domain intent detection. | null | null | 26 | 2,972 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- intent-classification
- multi-class-classification
pretty_nam... |
hf-internal-testing/fixtures_ade20k | 2021-11-09T10:26:23.000Z | [
"region:us"
] | hf-internal-testing | \\n | \\n | null | 0 | 2,956 | Entry not found |
fujiki/databricks-dolly-15k-ja-reformat-v1 | 2023-10-06T13:37:15.000Z | [
"license:cc-by-sa-3.0",
"region:us"
] | fujiki | null | null | null | 0 | 2,919 | ---
license: cc-by-sa-3.0
dataset_info:
features:
- name: index
dtype: string
- name: category
dtype: string
- name: instructions
sequence: string
- name: responses
sequence: string
splits:
- name: train
num_bytes: 15973503
num_examples: 15015
download_size: 9056298
dataset_siz... |
kilt_tasks | 2023-06-01T14:59:56.000Z | [
"task_categories:fill-mask",
"task_categories:question-answering",
"task_categories:text-classification",
"task_categories:text-generation",
"task_categories:text-retrieval",
"task_categories:text2text-generation",
"task_ids:abstractive-qa",
"task_ids:dialogue-modeling",
"task_ids:document-retrieval... | null | KILT tasks training and evaluation data.
- [FEVER](https://fever.ai) | Fact Checking | fever
- [AIDA CoNLL-YAGO](https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/ambiverse-nlu/aida/downloads) | Entity Linking | aidayago2
- [WNED-WIKI](https://github.com/U-Alberta/wned) | Entity Linking ... | @inproceedings{fb_kilt,
author = {Fabio Petroni and
Aleksandra Piktus and
Angela Fan and
Patrick Lewis and
Majid Yazdani and
Nicola De Cao and
James Thorne and
Yacine Jernite and
... | null | 31 | 2,907 | ---
annotations_creators:
- crowdsourced
- found
- machine-generated
language_creators:
- crowdsourced
- found
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 10K<n<100K
- 1K<n<10K
- 1M<n<10M
source_datasets:
- extended|natural_questions
- extended|other-aidayago
- extended|o... |
story_cloze | 2023-04-05T13:40:54.000Z | [
"task_categories:other",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | null | Story Cloze Test' is a commonsense reasoning framework for evaluating story understanding,
story generation, and script learning.This test requires a system to choose the correct ending
to a four-sentence story. | @inproceedings{mostafazadeh2017lsdsem,
title={Lsdsem 2017 shared task: The story cloze test},
author={Mostafazadeh, Nasrin and Roth, Michael and Louis, Annie and Chambers, Nathanael and Allen, James},
booktitle={Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics... | null | 6 | 2,899 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: null
pretty_name: Story Cloze Test
dataset_info:
- config_name: '2016'
features... |
hf-internal-testing/instructpix2pix-10-samples | 2023-06-09T19:57:18.000Z | [
"region:us"
] | hf-internal-testing | null | null | null | 0 | 2,899 | ---
dataset_info:
features:
- name: input_image
dtype: image
- name: edited_image
dtype: image
- name: edit_prompt
dtype: string
splits:
- name: train
num_bytes: 4479546.0
num_examples: 10
download_size: 4481212
dataset_size: 4479546.0
---
# Dataset Card for "test"
[More Information... |
bentrevett/multi30k | 2023-03-24T14:50:27.000Z | [
"task_categories:translation",
"size_categories:10K<n<100K",
"language:en",
"language:de",
"region:us"
] | bentrevett | null | null | null | 1 | 2,881 | ---
task_categories:
- translation
language:
- en
- de
size_categories:
- 10K<n<100K
---
# Multi30k
This dataset contains the "multi30k" dataset, which is the "task 1" dataset from [here](https://www.statmt.org/wmt16/multimodal-task.html).
Each example consists of an "en" and a "de" feature. "en" is an English senten... |
InstaDeepAI/human_reference_genome | 2023-04-20T13:37:22.000Z | [
"DNA",
"Genomics",
"Nucleotide",
"region:us"
] | InstaDeepAI | Genome Reference Consortium Human Build 38 patch release 14 (GRCh38.p14)
filtered and split into chunks. | @article{o2016reference,
title={Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation},
author={O'Leary, Nuala A and Wright, Mathew W and Brister, J Rodney and Ciufo, Stacy and Haddad, Diana and McVeigh, Rich and Rajput, Bhanu and Robbertse, Barbara and Smith-W... | null | 0 | 2,876 | ---
tags:
- DNA
- Genomics
- Nucleotide
pretty_name: Human Reference Genome
---
# Dataset Card for the human reference genome
## Dataset Description
- **Repository:** [Nucleotide Transformer](https://github.com/instadeepai/nucleotide-transformer)
- **Paper:** [The Nucleotide Transformer: Building and Evaluating Robus... |
FanFan/sentiment-amazon-clean | 2022-03-09T17:12:19.000Z | [
"region:us"
] | FanFan | null | null | null | 0 | 2,829 | Entry not found |
jfleg | 2022-11-18T20:15:50.000Z | [
"task_categories:text2text-generation",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"multilinguality:other-language-learner",
"size_categories:1K<n<10K",
"source_datasets:extended|other-GUG-grammaticality-judgements",
"language:en",
"license:cc-... | null | JFLEG (JHU FLuency-Extended GUG) is an English grammatical error correction (GEC) corpus.
It is a gold standard benchmark for developing and evaluating GEC systems with respect to
fluency (extent to which a text is native-sounding) as well as grammaticality.
For each source document, there are four human-written corre... | @InProceedings{napoles-sakaguchi-tetreault:2017:EACLshort,
author = {Napoles, Courtney
and Sakaguchi, Keisuke
and Tetreault, Joel},
title = {JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction},
booktitle = {Proceedings of the 15th Conference of the Europe... | null | 35 | 2,809 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
- other-language-learner
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-GUG-grammaticality-judgements
task_categories:
- text2text-generation
task_ids: []
paper... |
superb | 2023-01-25T14:45:01.000Z | [
"task_categories:automatic-speech-recognition",
"task_categories:audio-classification",
"task_ids:keyword-spotting",
"task_ids:speaker-identification",
"task_ids:audio-intent-classification",
"task_ids:audio-emotion-recognition",
"annotations_creators:other",
"language_creators:other",
"multilingual... | null | Self-supervised learning (SSL) has proven vital for advancing research in
natural language processing (NLP) and computer vision (CV). The paradigm
pretrains a shared model on large volumes of unlabeled data and achieves
state-of-the-art (SOTA) for various tasks with minimal adaptation. However, the
speech processing co... | @article{DBLP:journals/corr/abs-2105-01051,
author = {Shu{-}Wen Yang and
Po{-}Han Chi and
Yung{-}Sung Chuang and
Cheng{-}I Jeff Lai and
Kushal Lakhotia and
Yist Y. Lin and
Andy T. Liu and
Jiatong Shi and
... | null | 19 | 2,784 | ---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
- extended|librispeech_asr
- extended|other-librimix
- extended|other-speech_commands
task_categories:
- automatic-speech-recognition
- aud... |
SetFit/subj | 2022-01-15T21:34:11.000Z | [
"region:us"
] | SetFit | null | null | null | 4 | 2,775 | # Subjective vs Objective
This is the SUBJ dataset as used in [SentEval](https://github.com/facebookresearch/SentEval). It contains sentences with an annotation if they sentence describes something subjective about a movie or something objective |
conllpp | 2023-04-05T10:02:29.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|conll2003",
"language:en",
"license:unknown",
"region:us"
] | null | CoNLLpp is a corrected version of the CoNLL2003 NER dataset where labels of 5.38% of the sentences in the test set
have been manually corrected. The training set and development set are included for completeness.
For more details see https://www.aclweb.org/anthology/D19-1519/ and https://github.com/ZihanWangKi/CrossWei... | @inproceedings{wang2019crossweigh,
title={CrossWeigh: Training Named Entity Tagger from Imperfect Annotations},
author={Wang, Zihan and Shang, Jingbo and Liu, Liyuan and Lu, Lihao and Liu, Jiacheng and Han, Jiawei},
booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing ... | null | 5 | 2,738 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|conll2003
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: conll
pretty_name: ... |
Skylion007/openwebtext | 2023-04-05T13:36:17.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
... | Skylion007 | An open-source replication of the WebText dataset from OpenAI. | @misc{Gokaslan2019OpenWeb,
title={OpenWebText Corpus},
author={Aaron Gokaslan*, Vanya Cohen*, Ellie Pavlick, Stefanie Tellex},
howpublished{\\url{http://Skylion007.github.io/OpenWebTextCorpus}},
year={2019}
} | null | 202 | 2,720 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- cc0-1.0
multilinguality:
- monolingual
pretty_name: OpenWebText
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
p... |
llm-book/livedoor-news-corpus | 2023-09-30T08:44:39.000Z | [
"task_categories:summarization",
"size_categories:1K<n<10K",
"language:ja",
"news",
"region:us"
] | llm-book | null | null | null | 1 | 2,707 | ---
task_categories:
- summarization
language:
- ja
tags:
- news
pretty_name: livedoor-news-corpus
size_categories:
- 1K<n<10K
---
# Dataset Card for llm-book/ner-wikinews-dataset
書籍『大規模言語モデル入門』で使用する、株式会社ロンウイットが提供する「livedoorニュースコーパス」によるデータセットです。
[オリジナルのサイト](https://www.rondhuit.com/download.html)と同じものを使用しています。
本コーパス... |
winograd_wsc | 2023-01-25T15:02:35.000Z | [
"task_categories:multiple-choice",
"task_ids:multiple-choice-coreference-resolution",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] | null | A Winograd schema is a pair of sentences that differ in only one or two words and that contain an ambiguity that is
resolved in opposite ways in the two sentences and requires the use of world knowledge and reasoning for its
resolution. The schema takes its name from a well-known example by Terry Winograd:
> The city ... | @inproceedings{levesque2012winograd,
title={The winograd schema challenge},
author={Levesque, Hector and Davis, Ernest and Morgenstern, Leora},
booktitle={Thirteenth International Conference on the Principles of Knowledge Representation and Reasoning},
year={2012},
organization={Citeseer}
} | null | 5 | 2,696 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- multiple-choice
task_ids:
- multiple-choice-coreference-resolution
paperswithcode_id: wsc
pretty_na... |
BeIR/hotpotqa | 2022-10-23T06:02:40.000Z | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | BeIR | null | null | null | 2 | 2,684 | ---
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:
... |
anton-l/superb_demo | 2022-04-14T13:54:54.000Z | [
"region:us"
] | anton-l | Self-supervised learning (SSL) has proven vital for advancing research in
natural language processing (NLP) and computer vision (CV). The paradigm
pretrains a shared model on large volumes of unlabeled data and achieves
state-of-the-art (SOTA) for various tasks with minimal adaptation. However, the
speech processing co... | @article{DBLP:journals/corr/abs-2105-01051,
author = {Shu{-}Wen Yang and
Po{-}Han Chi and
Yung{-}Sung Chuang and
Cheng{-}I Jeff Lai and
Kushal Lakhotia and
Yist Y. Lin and
Andy T. Liu and
Jiatong Shi and
... | null | 1 | 2,675 | # Disclaimer
This is a tiny subset of the SUPERB dataset, which is intended only for demo purposes!
See the full dataset here: https://huggingface.co/datasets/superb
|
timit_asr | 2022-10-28T16:41:41.000Z | [
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:other",
"region:us"
] | null | The TIMIT corpus of reading speech has been developed to provide speech data for acoustic-phonetic research studies
and for the evaluation of automatic speech recognition systems.
TIMIT contains high quality recordings of 630 individuals/speakers with 8 different American English dialects,
with each individual reading... | @inproceedings{
title={TIMIT Acoustic-Phonetic Continuous Speech Corpus},
author={Garofolo, John S., et al},
ldc_catalog_no={LDC93S1},
DOI={https://doi.org/10.35111/17gk-bn40},
journal={Linguistic Data Consortium, Philadelphia},
year={1983}
} | null | 15 | 2,664 | ---
pretty_name: TIMIT
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- other
license_details: "LDC-User-Agreement-for-Non-Members"
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- automatic-speech-recogniti... |
ashraq/esc50 | 2023-01-07T08:35:28.000Z | [
"region:us"
] | ashraq | null | null | null | 3 | 2,640 | https://github.com/karolpiczak/ESC-50
The dataset is available under the terms of the Creative Commons Attribution Non-Commercial license.
K. J. Piczak. ESC: Dataset for Environmental Sound Classification. Proceedings of the 23rd Annual ACM Conference on Multimedia, Brisbane, Australia, 2015.
[DOI: http://dx.doi.org... |
mteb/sts12-sts | 2022-09-27T19:11:50.000Z | [
"language:en",
"region:us"
] | mteb | null | null | null | 4 | 2,631 | ---
language:
- en
--- |
aeslc | 2023-04-05T08:32:58.000Z | [
"task_categories:summarization",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"aspect-based-summarization",
"conversations-summarization",
"multi-document... | null | A collection of email messages of employees in the Enron Corporation.
There are two features:
- email_body: email body text.
- subject_line: email subject text. | @misc{zhang2019email,
title={This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation},
author={Rui Zhang and Joel Tetreault},
year={2019},
eprint={1906.03497},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | null | 4 | 2,619 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
pretty_name: 'AESLC: Annotated Enron Subject Line Corpus'
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- summarization
task_ids: []
paperswithcode_id: aeslc
... |
flax-sentence-embeddings/stackexchange_titlebody_best_voted_answer_jsonl | 2022-07-11T13:13:27.000Z | [
"task_categories:question-answering",
"task_ids:closed-domain-qa",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
] | flax-sentence-embeddings | This new dataset is designed to solve this great NLP task and is crafted with a lot of care. | @misc{StackExchangeDataset,
author = {Flax Sentence Embeddings Team},
title = {Stack Exchange question pairs},
year = {2021},
howpublished = {https://huggingface.co/datasets/flax-sentence-embeddings/},
} | null | 4 | 2,610 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-nc-sa-4.0
multilinguality:
- multilingual
pretty_name: stackexchange
size_categories:
- unknown
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- closed-domain-qa
---
# Dataset Card Creation Guide
... |
HuggingFaceH4/testing_h4 | 2023-07-21T07:27:54.000Z | [
"region:us"
] | HuggingFaceH4 | null | null | null | 0 | 2,595 | ---
dataset_info:
features:
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
- name: prompt
dtype: string
- name: prompt_id
dtype: string
- nam... |
bigcode/starcoderdata | 2023-05-16T10:05:48.000Z | [
"task_categories:text-generation",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:unknown",
"language:code",
"license:other",
"region:us"
] | bigcode | null | null | null | 186 | 2,589 | ---
annotations_creators: []
language_creators:
- crowdsourced
- expert-generated
language:
- code
license:
- other
multilinguality:
- multilingual
pretty_name: The-Stack
size_categories:
- unknown
source_datasets: []
task_categories:
- text-generation
extra_gated_prompt: >-
## Terms of Use for The Stack
The Sta... |
stas/openwebtext-10k | 2021-09-15T00:18:50.000Z | [
"region:us"
] | stas | An open-source replication of the WebText dataset from OpenAI.
This is a small subset representing the first 10K records from the original dataset - created for testing.
The full 8M-record dataset is at https://huggingface.co/datasets/openwebtext | @misc{Gokaslan2019OpenWeb,
title={OpenWebText Corpus},
author={Aaron Gokaslan*, Vanya Cohen*, Ellie Pavlick, Stefanie Tellex},
howpublished{\\url{http://Skylion007.github.io/OpenWebTextCorpus}},
year={2019}
} | null | 6 | 2,585 | 10K slice of OpenWebText - An open-source replication of the WebText dataset from OpenAI.
This is a small subset representing the first 10K records from the original dataset - created for testing.
The full 8M-record dataset is [here](https://huggingface.co/datasets/openwebtext).
```
$ python -c "from datasets import... |
facat/sci-llm-new | 2023-10-01T12:45:46.000Z | [
"region:us"
] | facat | null | null | null | 0 | 2,575 | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: test2
path: data/test2-*
- split: train
path: data/train-*
- split: train_attack
path: data/train_attack-*
- split: train_new
path: data/train_new-*
- split: train_60k
path: data/train_60k-*
da... |
nguha/legalbench | 2023-08-24T03:54:25.000Z | [
"task_categories:text-classification",
"task_categories:question-answering",
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:other",
"legal",
"law",
"finance",
"arxiv:2308.11462",
"region:us"
] | nguha | """
#TODO
_HOMEPAGE = ""
_URL = "data.tar.gz"
_CONFIGS = {}
_CONFIGS["abercrombie"] = {
"description": "Determine the *Abercrombie* classification for a mark/product pair.",
"features": {
"answer": datasets.Value("string"),
"index": datasets.Value("string"),
"text": datasets.Value("... | """
#TODO
_DESCRIPTION = | null | 18 | 2,565 | ---
license: other
task_categories:
- text-classification
- question-answering
- text-generation
language:
- en
tags:
- legal
- law
- finance
size_categories:
- 10K<n<100K
---
# Dataset Card for Dataset Name
- **Homepage: https://hazyresearch.stanford.edu/legalbench/**
- **Repository: https://github.com/HazyResearch... |
explodinggradients/fiqa | 2023-06-08T16:54:14.000Z | [
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | explodinggradients | FiQA dataset formated in a way that is easier for doing RAG experiments | @InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
} | null | 2 | 2,533 | ---
license: cc-by-sa-4.0
task_categories:
- question-answering
language:
- en
size_categories:
- 10K<n<100K
--- |
alzoubi36/privacy_qa | 2023-06-24T07:54:51.000Z | [
"region:us"
] | alzoubi36 | null | null | null | 0 | 2,527 | ---
dataset_info:
features:
- name: question
dtype: string
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 31955449
num_examples: 157420
- name: validation
num_bytes: 5661628
num_examples: 27780
- name: test
num_bytes: 13381983
n... |
BeIR/scidocs-qrels | 2022-10-23T06:07:54.000Z | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | BeIR | null | null | null | 0 | 2,513 | ---
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:
... |
open-llm-leaderboard/details_ashercn97__manatee-7b | 2023-09-17T18:42:54.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 2,478 | ---
pretty_name: Evaluation run of ashercn97/manatee-7b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [ashercn97/manatee-7b](https://huggingface.co/ashercn97/manatee-7b) on the [Open\
\ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\... |
open-llm-leaderboard/details_medalpaca__medalpaca-7b | 2023-08-27T12:32:23.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 2,470 | ---
pretty_name: Evaluation run of medalpaca/medalpaca-7b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [medalpaca/medalpaca-7b](https://huggingface.co/medalpaca/medalpaca-7b) on the\
\ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\... |
togethercomputer/Long-Data-Collections | 2023-07-26T17:03:50.000Z | [
"license:other",
"region:us"
] | togethercomputer | null | null | null | 43 | 2,468 | ---
license: other
---
# Dataset Summary
This collection is a compilation of long context datasets, specifically designed for tasks requiring extensive comprehension and inference from large text inputs.
Currently, it encompasses data intended for training a robust base model, which can be found in the pretrain/ dir... |
LabHC/bias_in_bios | 2023-09-10T15:41:38.000Z | [
"task_categories:text-classification",
"language:en",
"license:mit",
"region:us"
] | LabHC | null | null | null | 0 | 2,466 | ---
license: mit
task_categories:
- text-classification
language:
- en
dataset_info:
features:
- name: hard_text
dtype: string
- name: profession
dtype: int64
- name: gender
dtype: int64
splits:
- name: train
num_bytes: 107487885
num_examples: 257478
- name: test
num_bytes: 4131225... |
go_emotions | 2023-06-01T14:59:54.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:multi-label-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"source_datasets:original",
... | null | The GoEmotions dataset contains 58k carefully curated Reddit comments labeled for 27 emotion categories or Neutral.
The emotion categories are admiration, amusement, anger, annoyance, approval, caring, confusion, curiosity, desire,
disappointment, disapproval, disgust, embarrassment, excitement, fear, gratitude, grief,... | @inproceedings{demszky2020goemotions,
author = {Demszky, Dorottya and Movshovitz-Attias, Dana and Ko, Jeongwoo and Cowen, Alan and Nemade, Gaurav and Ravi, Sujith},
booktitle = {58th Annual Meeting of the Association for Computational Linguistics (ACL)},
title = {{GoEmotions: A Dataset of Fine-Grained Emotions}},
y... | null | 57 | 2,465 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
- multi-label-classification
papersw... |
EleutherAI/arithmetic | 2023-03-09T17:58:16.000Z | [
"arxiv:2005.14165",
"region:us"
] | EleutherAI | A small battery of 10 tests that involve asking language models a simple arithmetic
problem in natural language. | @inproceedings{NEURIPS2020_1457c0d6,
author = {Brown, Tom and Mann, Benjamin and Ryder, Nick and Subbiah, Melanie and Kaplan, Jared D and Dhariwal, Prafulla and Neelakantan, Arvind and Shyam, Pranav and Sastry, Girish and Askell, Amanda and Agarwal, Sandhini and Herbert-Voss, Ariel and Krueger, Gretchen and Henigha... | null | 1 | 2,440 | ### Dataset Summary
A small battery of 10 tests that involve asking language models a simple arithmetic problem in natural language.
### Languages
English
### Source Data
Obtained from [https://github.com/openai/gpt-3/tree/master/data](https://github.com/openai/gpt-3/tree/master/data)
### Citation
```
@article{bro... |
danbider/codegen | 2023-07-21T01:53:30.000Z | [
"region:us"
] | danbider | null | null | null | 0 | 2,420 | Entry not found |
dell-research-harvard/AmericanStories | 2023-09-08T18:33:32.000Z | [
"task_categories:text-classification",
"task_categories:text-generation",
"task_categories:text-retrieval",
"task_categories:summarization",
"task_categories:question-answering",
"size_categories:100M<n<1B",
"language:en",
"license:cc-by-4.0",
"social science",
"economics",
"news",
"newspaper"... | dell-research-harvard | American Stories offers high-quality structured data from historical newspapers suitable for pre-training large language models to enhance the understanding of historical English and world knowledge. It can also be integrated into external databases of retrieval-augmented language models, enabling broader access to his... | Coming Soon | null | 76 | 2,415 | ---
license: cc-by-4.0
task_categories:
- text-classification
- text-generation
- text-retrieval
- summarization
- question-answering
language:
- en
tags:
- social science
- economics
- news
- newspaper
- large language modeling
- nlp
- lam
pretty_name: AmericanStories
size_categories:
- 100M<n<1B
---
# Dataset Card fo... |
McGill-NLP/FaithDial | 2023-02-05T04:09:45.000Z | [
"task_categories:conversational",
"task_categories:text-generation",
"task_ids:dialogue-modeling",
"annotations_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100k",
"language:en",
"license:mit",
"faithful-dialogue-modeling",
"trustworthy-dialogue-modeling",
"arxiv... | McGill-NLP | FaithDial is a new benchmark for hallucination-free dialogues, created by manually editing hallucinated and uncooperative responses in Wizard of Wikipedia. | @article{dziri2022faithdial,
title={FaithDial: A Faithful Benchmark for Information-Seeking Dialogue},
author={Dziri, Nouha and Kamalloo, Ehsan and Milton, Sivan and Zaiane, Osmar and Yu, Mo and Ponti, Edoardo and Reddy, Siva},
journal={arXiv preprint, arXiv:2204.10757},
year={2022},
url={https://arxiv.org/ab... | null | 10 | 2,390 | ---
annotations_creators:
- crowdsourced
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100k
task_categories:
- conversational
- text-generation
task_ids:
- dialogue-modeling
pretty_name: A Faithful Benchmark for Information-Seeking Dialogue
tags:
- faithful-dialogue-modeling
- tr... |
HuggingFaceM4/general-pmd-synthetic-testing-with-embeddings | 2023-04-20T13:40:41.000Z | [
"license:bigscience-openrail-m",
"region:us"
] | HuggingFaceM4 | This dataset is designed to be used in testing. It's derived from general-pmd-10k dataset | @InProceedings{huggingface:dataset,
title = {Multimodal synthetic dataset for testing / general PMD},
author={HuggingFace, Inc.},
year={2022}
} | null | 0 | 2,387 | ---
license: bigscience-openrail-m
---
This dataset is designed to be used in testing. It's derived from general-pmd/localized_narratives__ADE20k dataset
The current splits are: `['100.unique', '100.repeat', '300.unique', '300.repeat', '1k.unique', '1k.repeat', '10k.unique', '10k.repeat']`.
The `unique` ones ensure ... |
Dahoas/synthetic-instruct-gptj-pairwise | 2023-01-09T03:48:03.000Z | [
"region:us"
] | Dahoas | null | null | null | 41 | 2,372 | Entry not found |
armanc/pubmed-rct20k | 2022-11-11T08:23:24.000Z | [
"region:us"
] | armanc | null | null | null | 0 | 2,364 | The small 20K version of the Pubmed-RCT dataset by Dernoncourt et al (2017).
```
@article{dernoncourt2017pubmed,
title={Pubmed 200k rct: a dataset for sequential sentence classification in medical abstracts},
author={Dernoncourt, Franck and Lee, Ji Young},
journal={arXiv preprint arXiv:1710.06071},
year={2017... |
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... | null | 3 | 2,359 | ---
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... |
naver-clova-ix/synthdog-en | 2022-07-22T06:42:50.000Z | [
"region:us"
] | naver-clova-ix | null | null | null | 5 | 2,357 | Entry not found |
ade_corpus_v2 | 2023-06-01T14:59:53.000Z | [
"task_categories:text-classification",
"task_categories:token-classification",
"task_ids:coreference-resolution",
"task_ids:fact-checking",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"... | null | ADE-Corpus-V2 Dataset: Adverse Drug Reaction Data.
This is a dataset for Classification if a sentence is ADE-related (True) or not (False) and Relation Extraction between Adverse Drug Event and Drug.
DRUG-AE.rel provides relations between drugs and adverse effects.
DRUG-DOSE.rel provides relations between drugs an... | @article{GURULINGAPPA2012885,
title = "Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports",
journal = "Journal of Biomedical Informatics",
volume = "45",
number = "5",
pages = "885 - 892",
year = "2012",
note = "Text Mining and Natural Languag... | null | 17 | 2,342 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- 1K<n<10K
- n<1K
source_datasets:
- original
task_categories:
- text-classification
- token-classification
task_ids:
- coreference-resolution
- fact-che... |
jamescalam/llama-2-arxiv-papers-chunked | 2023-07-25T03:12:24.000Z | [
"language:en",
"arxiv:2307.09288",
"region:us"
] | jamescalam | null | null | null | 9 | 2,341 | ---
language:
- en
pretty_name: Chunked Arxiv Papers for Llama 2
---
This dataset contains chunked extracts (of ~300 tokens) from papers related to (and including) the [Llama 2 research paper](https://arxiv.org/abs/2307.09288). Related papers were identified by following a trail of references, extracting those papers ... |
exams | 2023-06-01T14:59:56.000Z | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"size_categories:n<1K",
"source_datasets:original",... | null | EXAMS is a benchmark dataset for multilingual and cross-lingual question answering from high school examinations.
It consists of more than 24,000 high-quality high school exam questions in 16 languages,
covering 8 language families and 24 school subjects from Natural Sciences and Social Sciences, among others. | @article{hardalov2020exams,
title={EXAMS: A Multi-subject High School Examinations Dataset for Cross-lingual and Multilingual Question Answering},
author={Hardalov, Momchil and Mihaylov, Todor and Dimitrina Zlatkova and Yoan Dinkov and Ivan Koychev and Preslav Nvakov},
journal={arXiv preprint arXiv:2011.03080},
... | null | 10 | 2,322 | ---
pretty_name: EXAMS
annotations_creators:
- found
language_creators:
- found
language:
- ar
- bg
- de
- es
- fr
- hr
- hu
- it
- lt
- mk
- pl
- pt
- sq
- sr
- tr
- vi
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
- multilingual
size_categories:
- 10K<n<100K
- 1K<n<10K
- n<1K
source_datasets:
- original
task... |
tatoeba | 2022-11-03T16:32:34.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ab",
"language:acm",
"language:ady",
"language:af",
"language:afb",
"language:afh",
"language:aii",
"l... | null | This is a collection of translated sentences from Tatoeba
359 languages, 3,403 bitexts
total number of files: 750
total number of tokens: 65.54M
total number of sentence fragments: 8.96M | @InProceedings{TIEDEMANN12.463,
author = {J{\"o}rg}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... | null | 19 | 2,317 | ---
annotations_creators:
- found
language_creators:
- found
language:
- ab
- acm
- ady
- af
- afb
- afh
- aii
- ain
- ajp
- akl
- aln
- am
- an
- ang
- aoz
- apc
- ar
- arq
- ary
- arz
- as
- ast
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- awa
- ayl
- az
- ba
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- bar
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- ber
- bg
- bho
- bjn
- bm
- bn
- bo
- br
- brx
- bs
- bua
- bvy
- bzt
- ca
-... |
HuggingFaceH4/testing_alpaca_small | 2023-04-12T21:55:05.000Z | [
"region:us"
] | HuggingFaceH4 | null | null | null | 0 | 2,315 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: completion
dtype: string
splits:
- name: train
num_bytes: 33856
num_examples: 100
- name: test
num_bytes: 32475
num_examples: 100
download_size: 52543
dataset_size: 66331
---
# Dataset Card for "testing_alpaca_small... |
monash_tsf | 2023-06-13T13:26:34.000Z | [
"task_categories:time-series-forecasting",
"task_ids:univariate-time-series-forecasting",
"task_ids:multivariate-time-series-forecasting",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"license:... | null | Monash Time Series Forecasting Repository which contains 30+ datasets of related time series for global forecasting research. This repository includes both real-world and competition time series datasets covering varied domains. | @InProceedings{godahewa2021monash,
author = "Godahewa, Rakshitha and Bergmeir, Christoph and Webb, Geoffrey I. and Hyndman, Rob J. and Montero-Manso, Pablo",
title = "Monash Time Series Forecasting Archive",
booktitle = "Neural Information Processing Systems Track on Datasets and Benchmarks",
year = "20... | null | 20 | 2,302 | ---
annotations_creators:
- no-annotation
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Monash Time Series Forecasting Repository
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- time-series-forecasting
task_ids:
- univariate-time-series-forecastin... |
app_reviews | 2022-11-03T16:47:21.000Z | [
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:sentiment-scoring",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:unknown",
"region:u... | null | It is a large dataset of Android applications belonging to 23 differentapps categories, which provides an overview of the types of feedback users report on the apps and documents the evolution of the related code metrics. The dataset contains about 395 applications of the F-Droid repository, including around 600 versio... | @InProceedings{Zurich Open Repository and
Archive:dataset,
title = {Software Applications User Reviews},
authors={Grano, Giovanni; Di Sorbo, Andrea; Mercaldo, Francesco; Visaggio, Corrado A; Canfora, Gerardo;
Panichella, Sebastiano},
year={2017}
} | null | 13 | 2,288 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
- sentiment-scoring
paperswithcode_id: null
pretty_name: Ap... |
Villekom/oa_dolly_15k_fi | 2023-08-23T14:15:07.000Z | [
"region:us"
] | Villekom | null | null | null | 0 | 2,269 | ---
dataset_info:
features:
- name: INSTRUCTION
dtype: string
- name: RESPONSE
dtype: string
- name: SOURCE
dtype: string
- name: METADATA
struct:
- name: CATEGORY
dtype: string
- name: CONTEXT
dtype: string
splits:
- name: train
num_bytes: 13654728
num_examples... |
lhoestq/test2 | 2021-07-23T14:21:45.000Z | [
"region:us"
] | lhoestq | null | null | null | 0 | 2,258 | This is a readme
|
clue | 2023-05-25T06:34:47.000Z | [
"task_categories:text-classification",
"task_categories:multiple-choice",
"task_ids:topic-classification",
"task_ids:semantic-similarity-scoring",
"task_ids:natural-language-inference",
"task_ids:multiple-choice-qa",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingu... | null | CLUE, A Chinese Language Understanding Evaluation Benchmark
(https://www.cluebenchmarks.com/) is a collection of resources for training,
evaluating, and analyzing Chinese language understanding systems. | @misc{xu2020clue,
title={CLUE: A Chinese Language Understanding Evaluation Benchmark},
author={Liang Xu and Xuanwei Zhang and Lu Li and Hai Hu and Chenjie Cao and Weitang Liu and Junyi Li and Yudong Li and Kai Sun and Yechen Xu and Yiming Cui and Cong Yu and Qianqian Dong and Yin Tian and Dian Yu and Bo Shi and... | null | 26 | 2,256 | ---
annotations_creators:
- other
language_creators:
- other
language:
- zh
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
- multiple-choice
task_ids:
- topic-classification
- semantic-similarity-scoring
- natural-languag... |
gsarti/flores_101 | 2022-10-27T08:37:36.000Z | [
"task_categories:text-generation",
"task_categories:translation",
"annotations_creators:found",
"language_creators:expert-generated",
"multilinguality:multilingual",
"multilinguality:translation",
"size_categories:unknown",
"source_datasets:extended|flores",
"language:af",
"language:am",
"langua... | gsarti | One of the biggest challenges hindering progress in low-resource and multilingual machine translation is the
lack of good evaluation benchmarks. Current evaluation benchmarks either lack good coverage of low-resource
languages, consider only restricted domains, or are low quality because they are constructed using
s... | @inproceedings{,
title={The {FLORES}-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation},
author={
Goyal, Naman and Gao, Cynthia and Chaudhary, Vishrav and Chen, Peng-Jen and Wenzek, Guillaume and
Ju, Da and Krishnan, Sanjana and Ranzato, Marc'Aurelio and Guzm\'{a}n, Francis... | null | 8 | 2,256 | ---
annotations_creators:
- found
language_creators:
- expert-generated
language:
- af
- am
- ar
- hy
- as
- ast
- az
- be
- bn
- bs
- bg
- my
- ca
- ceb
- zho
- hr
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- da
- nl
- en
- et
- tl
- fi
- fr
- ff
- gl
- lg
- ka
- de
- el
- gu
- ha
- he
- hi
- hu
- is
- ig
- id
- ga
- it
- ja
- jv
- kea
- kam
- kn
- kk
- k... |
pcuenq/oxford-pets | 2022-08-06T16:01:34.000Z | [
"task_categories:image-classification",
"source_datasets:https://www.robots.ox.ac.uk/~vgg/data/pets/",
"license:cc-by-sa-4.0",
"pets",
"oxford",
"region:us"
] | pcuenq | null | null | null | 5 | 2,245 | ---
tags:
- pets
- oxford
license: cc-by-sa-4.0
license_details: https://www.robots.ox.ac.uk/~vgg/data/pets/
pretty_name: Oxford-IIIT Pet Dataset (no annotations)
source_datasets: https://www.robots.ox.ac.uk/~vgg/data/pets/
task_categories:
- image-classification
---
# Oxford-IIIT Pet Dataset
Images from [The Oxford-... |
intfloat/multilingual_cc_news | 2023-04-23T08:19:06.000Z | [
"size_categories:100M<n<1B",
"language:en",
"language:zh",
"language:fr",
"language:de",
"language:af",
"language:ar",
"region:us"
] | intfloat | \
Multilingual CC-News dataset.
This is the processed version from https://huggingface.co/datasets/CloverSearch/cc-news-mutlilingual. | null | null | 3 | 2,237 | ---
size_categories:
- 100M<n<1B
language:
- en
- zh
- fr
- de
- af
- ar
---
### Dataset Summary
This dataset is based on [CloverSearch/cc-news-mutlilingual](https://huggingface.co/datasets/CloverSearch/cc-news-mutlilingual).
We add a script to support access multilingual CC-News dataset with HuggingFace datasets AP... |
bcui19/chat-v2-anthropic-helpfulness | 2023-06-26T23:22:50.000Z | [
"license:apache-2.0",
"region:us"
] | bcui19 | null | null | null | 0 | 2,204 | ---
license: apache-2.0
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 162490682.0
num_examples: 155270
- name: test
num_bytes: 8773391.0
num_examples: 8336
download_size: 82339... |
ivanzhouyq/RedPajama-Tiny | 2023-07-03T18:16:47.000Z | [
"task_categories:text-generation",
"language:en",
"region:us"
] | ivanzhouyq | RedPajama is a clean-room, fully open-source implementation of the LLaMa dataset. This is a 1B-token sample of the full dataset. | null | null | 2 | 2,169 | ---
task_categories:
- text-generation
language:
- en
pretty_name: RedPajama Tiny
---
# Dataset Card for Dataset Name
### Dataset Summary
This is a tiny version of the RedPajama dataset, which is a clean-room, fully open-source implementation of the LLaMa dataset.
This dataset contains 64 samples from each of the 7 ... |
scientific_papers | 2023-04-05T13:39:46.000Z | [
"task_categories:summarization",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:unknown",
"abstractive-summarization",
"arxiv:1804.05685",
"region:us"
] | null | Scientific papers datasets contains two sets of long and structured documents.
The datasets are obtained from ArXiv and PubMed OpenAccess repositories.
Both "arxiv" and "pubmed" have two features:
- article: the body of the document, pagragraphs seperated by "/n".
- abstract: the abstract of the document, pagragra... | @article{Cohan_2018,
title={A Discourse-Aware Attention Model for Abstractive Summarization of
Long Documents},
url={http://dx.doi.org/10.18653/v1/n18-2097},
DOI={10.18653/v1/n18-2097},
journal={Proceedings of the 2018 Conference of the North American Chapter of
the Association for Com... | null | 77 | 2,153 | ---
annotations_creators:
- found
language:
- en
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
pretty_name: ScientificPapers
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- summarization
task_ids: []
paperswithcode_id: null
tags:
- abstractive-summarization
dat... |
gem | 2023-06-01T14:59:56.000Z | [
"task_categories:fill-mask",
"task_categories:summarization",
"task_categories:table-to-text",
"task_categories:tabular-to-text",
"task_categories:text-generation",
"task_categories:text2text-generation",
"task_ids:dialogue-modeling",
"task_ids:rdf-to-text",
"task_ids:news-articles-summarization",
... | null | GEM is a benchmark environment for Natural Language Generation with a focus on its Evaluation,
both through human annotations and automated Metrics.
GEM aims to:
- measure NLG progress across 13 datasets spanning many NLG tasks and languages.
- provide an in-depth analysis of data and models presented via data stateme... | @article{gem_benchmark,
author = {Sebastian Gehrmann and
Tosin P. Adewumi and
Karmanya Aggarwal and
Pawan Sasanka Ammanamanchi and
Aremu Anuoluwapo and
Antoine Bosselut and
Khyathi Raghavi Chandu and
Miruna{-}A... | null | 21 | 2,130 | ---
annotations_creators:
- crowdsourced
- found
language_creators:
- crowdsourced
- found
- machine-generated
language:
- cs
- de
- en
- es
- ru
- tr
- vi
license:
- other
multilinguality:
- monolingual
- multilingual
size_categories:
- 100K<n<1M
- 10K<n<100K
- 1K<n<10K
source_datasets:
- extended|other-vision-dataset... |
alzoubi36/policy_ie_a | 2023-06-24T07:20:44.000Z | [
"region:us"
] | alzoubi36 | null | null | null | 0 | 2,122 | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 592707
num_examples: 4109
- name: validation
num_bytes: 16114
num_examples: 100
- name: test
num_bytes: 163819
num_examples: 1041
download_size: 364376
dat... |
FastJobs/Visual_Emotional_Analysis | 2023-03-13T06:31:17.000Z | [
"task_categories:image-classification",
"size_categories:10K<n<100K",
"language:en",
"region:us"
] | FastJobs | null | null | null | 5 | 2,110 | ---
task_categories:
- image-classification
language:
- en
size_categories:
- 10K<n<100K
--- |
wmt19 | 2023-04-05T13:44:03.000Z | [
"task_categories:translation",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:translation",
"size_categories:10M<n<100M",
"source_datasets:extended|europarl_bilingual",
"source_datasets:extended|news_commentary",
"source_datasets:extended|opus_paracrawl",
"source_d... | null | null | @ONLINE {wmt19translate,
author = {Wikimedia Foundation},
title = {ACL 2019 Fourth Conference on Machine Translation (WMT19), Shared Task: Machine Translation of News},
url = {http://www.statmt.org/wmt19/translation-task.html}
} | null | 14 | 2,108 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- cs
- de
- en
- fi
- fr
- gu
- kk
- lt
- ru
- zh
license:
- unknown
multilinguality:
- translation
size_categories:
- 10M<n<100M
source_datasets:
- extended|europarl_bilingual
- extended|news_commentary
- extended|opus_paracrawl
- extended|... |
wiki_hop | 2022-11-03T16:47:35.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"multi-hop",
"arxiv:1710.0648... | null | WikiHop is open-domain and based on Wikipedia articles; the goal is to recover Wikidata information by hopping through documents. The goal is to answer text understanding queries by combining multiple facts that are spread across different documents. | @misc{welbl2018constructing,
title={Constructing Datasets for Multi-hop Reading Comprehension Across Documents},
author={Johannes Welbl and Pontus Stenetorp and Sebastian Riedel},
year={2018},
eprint={1710.06481},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | null | 1 | 2,107 | ---
annotations_creators:
- crowdsourced
language_creators:
- expert-generated
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: wikihop
pretty_name: WikiHop
t... |
bigcode/the-stack-smol | 2023-05-02T10:14:19.000Z | [
"task_categories:text-generation",
"task_ids:language-modeling",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:unknown",
"language:code",
"region:us"
] | bigcode | null | null | null | 22 | 2,100 | ---
annotations_creators: []
language_creators:
- crowdsourced
language: ["code"]
multilinguality:
- multilingual
size_categories:
- unknown
source_datasets: []
task_categories:
- text-generation
task_ids:
- language-modeling
extra_gated_prompt: |-
## Terms of Use for The Stack
The Stack dataset is a collection o... |
EleutherAI/hendrycks_math | 2023-09-14T20:29:14.000Z | [
"region:us"
] | EleutherAI | MATH is a dataset of 12,500 challenging competition mathematics problems. 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={NeurIPS},
year={2021}
} | null | 0 | 2,100 | Entry not found |
senti_lex | 2023-06-08T12:24:00.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"size_categories:n<1K",
"source_datasets:original",
"language:af",
"language:an",
... | null | This dataset add sentiment lexicons for 81 languages generated via graph propagation based on a knowledge graph--a graphical representation of real-world entities and the links between them. | @inproceedings{inproceedings,
author = {Chen, Yanqing and Skiena, Steven},
year = {2014},
month = {06},
pages = {383-389},
title = {Building Sentiment Lexicons for All Major Languages},
volume = {2},
journal = {52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings of the Conferenc... | null | 5 | 2,089 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- af
- an
- ar
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- eo
- es
- et
- eu
- fa
- fi
- fo
- fr
- fy
- ga
- gd
- gl
- gu
- he
- hi
- hr
- ht
- hu
- hy
- ia
- id
- io
- is
- it
- ja
- ka
- km
- kn
- ko
- ku
- ... |
inria-soda/tabular-benchmark | 2023-09-04T16:37:39.000Z | [
"task_categories:tabular-classification",
"task_categories:tabular-regression",
"region:us"
] | inria-soda | null | null | null | 13 | 2,089 |
---
annotations_creators: []
license: []
pretty_name: tabular_benchmark
tags: []
task_categories:
- tabular-classification
- tabular-regression
configs:
- config_name: clf_cat_albert
data_files: clf_cat/albert.csv
- config_name: clf_cat_compas-two-years
data_files: clf_cat/compas-two-years.csv
- config_name: ... |
mteb/sts13-sts | 2022-09-27T19:12:02.000Z | [
"language:en",
"region:us"
] | mteb | null | null | null | 1 | 2,085 | ---
language:
- en
--- |
bot-yaya/undl_text | 2023-10-07T00:31:07.000Z | [
"region:us"
] | bot-yaya | null | null | null | 0 | 2,079 | ---
dataset_info:
features:
- name: ar
dtype: string
- name: zh
dtype: string
- name: en
dtype: string
- name: fr
dtype: string
- name: ru
dtype: string
- name: es
dtype: string
- name: de
dtype: string
- name: record
dtype: string
splits:
- name: train
num_byte... |
mkshing/xlsum_ja | 2023-06-20T23:28:48.000Z | [
"task_categories:summarization",
"task_categories:text-classification",
"language:ja",
"license:cc-by-nc-sa-4.0",
"arxiv:2305.10403",
"region:us"
] | mkshing | null | null | null | 2 | 2,074 | ---
license: cc-by-nc-sa-4.0
task_categories:
- summarization
- text-classification
language:
- ja
---
This is the filtered Japanese subset of [XL-Sum](https://huggingface.co/datasets/csebuetnlp/xlsum) followed by [PaLM 2](https://arxiv.org/abs/2305.10403)
**filters**
- 15-gram overlap
\* code: https://gist.github.c... |
mteb/amazon_massive_intent | 2022-09-27T19:10:30.000Z | [
"language:af",
"language:am",
"language:ar",
"language:az",
"language:bn",
"language:cy",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
"language:fa",
"language:fr",
"language:he",
"language:hi",
"language:hu",
"language:hy",
"language:id",
"language:... | mteb | MASSIVE is a parallel dataset of > 1M utterances across 51 languages with annotations
for the Natural Language Understanding tasks of intent prediction and slot annotation.
Utterances span 60 intents and include 55 slot types. MASSIVE was created by localizing
the SLURP dataset, composed... | null | null | 6 | 2,062 | ---
language:
- af
- am
- ar
- az
- bn
- cy
- da
- de
- el
- en
- es
- fa
- fr
- he
- hi
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- km
- kn
- ko
- lv
- ml
- mn
- ms
- my
- nb
- nl
- pl
- pt
- ro
- ru
- sl
- sq
- sv
- sw
- ta
- te
- th
- tl
- tr
- ur
- vi
- zh
--- |
lansinuote/ChnSentiCorp | 2023-02-28T05:31:30.000Z | [
"region:us"
] | lansinuote | null | null | null | 8 | 2,046 | Entry not found |
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 | null | 59 | 2,038 | ---
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... |
conceptofmind/FLAN_2022 | 2023-05-25T15:37:54.000Z | [
"region:us"
] | conceptofmind | null | null | null | 71 | 2,015 | ---
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: task_source
dtype: string
- name: task_name
dtype: string
- name: template_type
dtype: string
splits:
- name: train
num_bytes: 19462822989
num_examples: 11313842
download_size: 11... |
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... | null | 27 | 2,002 | ---
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... |
BeIR/arguana-qrels | 2022-10-23T06:06:46.000Z | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | BeIR | null | null | null | 0 | 1,997 | ---
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:
... |
Helsinki-NLP/tatoeba_mt | 2022-10-21T15:50:25.000Z | [
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:translation",
"size_categories:unknown",
"source_datasets:original",
"language:af",
"language:ar",
"language:az",
"language:be",
"language:bg",
"language:bn",
"language:br",
"language:bs",
"language:ca... | Helsinki-NLP | The Tatoeba Translation Challenge is a multilingual data set of
machine translation benchmarks derived from user-contributed
translations collected by [Tatoeba.org](https://tatoeba.org/) and
provided as parallel corpus from [OPUS](https://opus.nlpl.eu/). This
dataset includes test and development data sorted by languag... | @inproceedings{tiedemann-2020-tatoeba,
title = "The {T}atoeba {T}ranslation {C}hallenge {--} {R}ealistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
publis... | null | 40 | 1,981 | ---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- af
- ar
- az
- be
- bg
- bn
- br
- bs
- ca
- ch
- cs
- cv
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fo
- fr
- fy
- ga
- gd
- gl
- gn
- he
- hi
- hr
- hu
- hy
- ia
- id
- ie
- io
- is
- it
- ja
- jv
- ka
- kk
- km
- ko... |
yahoo_answers_topics | 2023-01-25T15:03:25.000Z | [
"task_categories:text-classification",
"task_ids:topic-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:extended|other-yahoo-answers-corpus",
"language:en",
"license:unknown",
"region:us"
] | null | Yahoo! Answers Topic Classification is text classification dataset. The dataset is the Yahoo! Answers corpus as of 10/25/2007. The Yahoo! Answers topic classification dataset is constructed using 10 largest main categories. From all the answers and other meta-information, this dataset only used the best answer content ... | null | null | 26 | 1,975 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- extended|other-yahoo-answers-corpus
task_categories:
- text-classification
task_ids:
- topic-classification
pretty_name: YahooAnswersTopics
dataset... |
wiqa | 2023-04-05T13:43:43.000Z | [
"language:en",
"region:us"
] | null | The WIQA dataset V1 has 39705 questions containing a perturbation and a possible effect in the context of a paragraph.
The dataset is split into 29808 train questions, 6894 dev questions and 3003 test questions. | @article{wiqa,
author = {Niket Tandon and Bhavana Dalvi Mishra and Keisuke Sakaguchi and Antoine Bosselut and Peter Clark}
title = {WIQA: A dataset for "What if..." reasoning over procedural text},
journal = {arXiv:1909.04739v1},
year = {2019},
} | null | 2 | 1,973 | ---
language:
- en
paperswithcode_id: wiqa
pretty_name: What-If Question Answering
dataset_info:
features:
- name: question_stem
dtype: string
- name: question_para_step
sequence: string
- name: answer_label
dtype: string
- name: answer_label_as_choice
dtype: string
- name: choices
seque... |
Gustavosta/Stable-Diffusion-Prompts | 2022-09-18T22:38:59.000Z | [
"annotations_creators:no-annotation",
"language_creators:found",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | Gustavosta | null | null | null | 330 | 1,972 | ---
license:
- unknown
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
source_datasets:
- original
---
# Stable Diffusion Dataset
This is a set of about 80,000 prompts filtered and extracted from the image finder for Stable Diffusion: "[Lexica.art](https://lexica.art/)". It was a litt... |
hf-internal-testing/example-documents | 2022-08-04T12:42:46.000Z | [
"region:us"
] | hf-internal-testing | null | null | null | 1 | 1,956 | Entry not found |
mteb/sts14-sts | 2022-09-27T19:11:37.000Z | [
"language:en",
"region:us"
] | mteb | null | null | null | 1 | 1,949 | ---
language:
- en
--- |
frgfm/imagenette | 2022-12-11T22:26:06.000Z | [
"task_categories:image-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"size_categories:1K<n<10K",
"source_datasets:extended",
"language:en",
"license:apache-2.0",
"region:us"
] | frgfm | Imagenette is a subset of 10 easily classified classes from Imagenet
(tench, English springer, cassette player, chain saw, church, French
horn, garbage truck, gas pump, golf ball, parachute). | @software{Howard_Imagenette_2019,
title={Imagenette: A smaller subset of 10 easily classified classes from Imagenet},
author={Jeremy Howard},
year={2019},
month={March},
publisher = {GitHub},
url = {https://github.com/fastai/imagenette}
} | null | 7 | 1,923 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- apache-2.0
multilinguality: []
size_categories:
- 1K<n<10K
source_datasets:
- extended
task_categories:
- image-classification
task_ids: []
paperswithcode_id: imagenette
pretty_name: Imagenette
---
# Dataset Card for I... |
cppe-5 | 2023-03-06T18:48:26.000Z | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"medical-personal-protective-equipment-detection",
"arxiv:2112.09569",
"reg... | null | CPPE - 5 (Medical Personal Protective Equipment) is a new challenging dataset with the goal
to allow the study of subordinate categorization of medical personal protective equipments,
which is not possible with other popular data sets that focus on broad level categories. | @misc{dagli2021cppe5,
title={CPPE-5: Medical Personal Protective Equipment Dataset},
author={Rishit Dagli and Ali Mustufa Shaikh},
year={2021},
eprint={2112.09569},
archivePrefix={arXiv},
primaryClass={cs.CV}
} | null | 7 | 1,919 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- object-detection
task_ids: []
paperswithcode_id: cppe-5
pretty_name: CPPE - 5
tags:
- medical-personal-protectiv... |
fka/awesome-chatgpt-prompts | 2023-03-07T10:04:18.000Z | [
"license:cc0-1.0",
"ChatGPT",
"region:us"
] | fka | null | null | null | 3,546 | 1,909 | ---
license: cc0-1.0
tags:
- ChatGPT
---
<p align="center"><h1>🧠 Awesome ChatGPT Prompts [CSV dataset]</h1></p>
This is a Dataset Repository of **Awesome ChatGPT Prompts**
**[View All Prompts on GitHub](https://github.com/f/awesome-chatgpt-prompts)**
# License
CC-0
|
JulesBelveze/tldr_news | 2022-08-05T12:17:50.000Z | [
"task_categories:summarization",
"task_categories:text2text-generation",
"task_categories:text-generation",
"task_ids:news-articles-headline-generation",
"task_ids:text-simplification",
"task_ids:language-modeling",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingua... | JulesBelveze | The `tldr_news` dataset was constructed by collecting a daily tech newsletter (available at
https://tldr.tech/newsletter). Then for every piece of news, the "headline" and its corresponding "content" were
collected. Such a dataset can be used to train a model to generate a headline from a input piece of text. | null | null | 8 | 1,903 | ---
annotations_creators:
- other
language_creators:
- other
language:
- en
multilinguality:
- monolingual
pretty_name: tldr_news
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- summarization
- text2text-generation
- text-generation
task_ids:
- news-articles-headline-generation
- text-simplif... |
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