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 |
|---|---|---|---|---|---|---|---|---|---|
mteb/sts15-sts | 2022-09-27T19:12:14.000Z | [
"language:en",
"region:us"
] | mteb | null | null | null | 1 | 1,902 | ---
language:
- en
--- |
codeparrot/github-code | 2022-10-20T15:01:14.000Z | [
"task_categories:text-generation",
"task_ids:language-modeling",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:unknown",
"language:code",
"license:other",
"region:us"
] | codeparrot | The GitHub Code dataest consists of 115M code files from GitHub in 32 programming languages with 60 extensions totalling in 1TB of text data. The dataset was created from the GitHub dataset on BiqQuery. | null | null | 169 | 1,899 | ---
annotations_creators: []
language_creators:
- crowdsourced
- expert-generated
language:
- code
license:
- other
multilinguality:
- multilingual
pretty_name: github-code
size_categories:
- unknown
source_datasets: []
task_categories:
- text-generation
task_ids:
- language-modeling
---
# GitHub Code Dataset
## Data... |
vicgalle/alpaca-gpt4 | 2023-09-26T18:51:15.000Z | [
"task_categories:text-generation",
"task_categories:conversational",
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-nc-4.0",
"gpt4",
"alpaca",
"instruction-finetuning",
"arxiv:2304.03277",
"region:us"
] | vicgalle | null | null | null | 98 | 1,897 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 88566301
num_examples: 52002
download_size: 48393562
dataset_size: 88566301
task_categories:
- text... |
HuggingFaceM4/VQAv2 | 2022-06-30T13:15:04.000Z | [
"region:us"
] | HuggingFaceM4 | VQA is a new dataset containing open-ended questions about images. These questions require an understanding of vision, language and commonsense knowledge to answer. | @InProceedings{VQA,
author = {Stanislaw Antol and Aishwarya Agrawal and Jiasen Lu and Margaret Mitchell and Dhruv Batra and C. Lawrence Zitnick and Devi Parikh},
title = {VQA: Visual Question Answering},
booktitle = {International Conference on Computer Vision (ICCV)},
year = {2015},
} | null | 6 | 1,893 | Checks with https://visualqa.org/download.html:
- Num train questions: 443,757
- Num val questions: 214,354
- Num test questions: 447,793
- Num train answers: 4,437,570
- Num val answers: 2,143,540
- Num train images: 82,783
- Num val images: 40,504
- Num test images: 81,434
testdev is not mentionned:
- Num questio... |
HuggingFaceH4/mt_bench_prompts | 2023-07-03T20:52:34.000Z | [
"task_categories:question-answering",
"task_categories:conversational",
"size_categories:n<1K",
"language:en",
"license:apache-2.0",
"evaluation",
"arxiv:2306.05685",
"region:us"
] | HuggingFaceH4 | null | null | null | 2 | 1,883 | ---
license: apache-2.0
task_categories:
- question-answering
- conversational
language:
- en
tags:
- evaluation
pretty_name: MT Bench
size_categories:
- n<1K
---
# MT Bench by LMSYS
This set of evaluation prompts is created by the [LMSYS org](https://huggingface.co/lmsys) for better evaluation of chat models.
For mor... |
dart | 2022-11-18T19:57:00.000Z | [
"task_categories:tabular-to-text",
"task_ids:rdf-to-text",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|wi... | null | DART is a large and open-domain structured DAta Record to Text generation corpus with high-quality
sentence annotations with each input being a set of entity-relation triples following a tree-structured ontology.
It consists of 82191 examples across different domains with each input being a semantic RDF triple set deri... | @article{radev2020dart,
title={DART: Open-Domain Structured Data Record to Text Generation},
author={Dragomir Radev and Rui Zhang and Amrit Rau and Abhinand Sivaprasad and Chiachun Hsieh and Nazneen Fatema Rajani and Xiangru Tang and Aadit Vyas and Neha Verma and Pranav Krishna and Yangxiaokang Liu and Nadia Irwant... | null | 3 | 1,874 | ---
annotations_creators:
- crowdsourced
- machine-generated
language_creators:
- crowdsourced
- machine-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|wikitable_questions
- extended|wikisql
- extended|web_nlg
- extended|cleaned_e2e
task_... |
wiki_bio | 2022-11-18T22:00:08.000Z | [
"task_categories:table-to-text",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"arxiv:1603.07771",
"region:us"
] | null | This dataset gathers 728,321 biographies from wikipedia. It aims at evaluating text generation
algorithms. For each article, we provide the first paragraph and the infobox (both tokenized).
For each article, we extracted the first paragraph (text), the infobox (structured data). Each
infobox is encoded as a list of (fi... | @article{DBLP:journals/corr/LebretGA16,
author = {R{\'{e}}mi Lebret and
David Grangier and
Michael Auli},
title = {Generating Text from Structured Data with Application to the Biography
Domain},
journal = {CoRR},
volume = {abs/1603.07771},
year = {... | null | 10 | 1,873 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
paperswithcode_id: wikibio
pretty_name: WikiBio
dataset_info:
features:
- name: in... |
wikicorpus | 2023-06-01T14:59:54.000Z | [
"task_categories:fill-mask",
"task_categories:text-classification",
"task_categories:text-generation",
"task_categories:token-classification",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"task_ids:part-of-speech",
"annotations_creators:machine-generated",
"annotations_creators... | null | The Wikicorpus is a trilingual corpus (Catalan, Spanish, English) that contains large portions of the Wikipedia (based on a 2006 dump) and has been automatically enriched with linguistic information. In its present version, it contains over 750 million words. | @inproceedings{reese-etal-2010-wikicorpus,
title = "{W}ikicorpus: A Word-Sense Disambiguated Multilingual {W}ikipedia Corpus",
author = "Reese, Samuel and
Boleda, Gemma and
Cuadros, Montse and
Padr{\'o}, Llu{\'i}s and
Rigau, German",
booktitle = "Proceedings of the Seventh Intern... | null | 5 | 1,868 | ---
pretty_name: Wikicorpus
annotations_creators:
- machine-generated
- no-annotation
language_creators:
- found
language:
- ca
- en
- es
license:
- gfdl
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 10M<n<100M
- 1M<n<10M
source_datasets:
- original
task_categories:
- fill-mask
- text-classification
- t... |
scene_parse_150 | 2023-01-25T14:43:32.000Z | [
"task_categories:image-segmentation",
"task_ids:instance-segmentation",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|ade20k",
"language:en",
"license:b... | null | Scene parsing is to segment and parse an image into different image regions associated with semantic categories, such as sky, road, person, and bed.
MIT Scene Parsing Benchmark (SceneParse150) provides a standard training and evaluation platform for the algorithms of scene parsing.
The data for this benchmark comes fro... | @inproceedings{zhou2017scene,
title={Scene Parsing through ADE20K Dataset},
author={Zhou, Bolei and Zhao, Hang and Puig, Xavier and Fidler, Sanja and Barriuso, Adela and Torralba, Antonio},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
year={2017}
}
@article... | null | 11 | 1,861 | ---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- found
language:
- en
license:
- bsd-3-clause
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|ade20k
task_categories:
- image-segmentation
task_ids:
- instance-segmentation
paperswithcode_id: ade20k
... |
cyanic-selkie/wikianc | 2023-09-05T14:22:32.000Z | [
"task_categories:token-classification",
"annotations_creators:machine-generated",
"annotations_creators:crowdsourced",
"language_creators:machine-generated",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"language:en",
"language:ceb",
"language:de",
"language:sv",
"language:f... | cyanic-selkie | null | null | null | 2 | 1,861 | ---
license: cc-by-sa-4.0
pretty_name: WikiAnc
annotations_creators:
- machine-generated
- crowdsourced
language_creators:
- machine-generated
- crowdsourced
task_categories:
- token-classification
multilinguality:
- multilingual
language:
- en
- ceb
- de
- sv
- fr
- nl
- ru
- es
- it
- arz
- pl
- ja
- zh
- vi
- uk
- w... |
scan | 2023-06-01T14:59:55.000Z | [
"task_categories:text2text-generation",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:bsd",
"multi-turn",
"arxiv:1711.00350",
"region:us"
] | null | SCAN tasks with various splits.
SCAN is a set of simple language-driven navigation tasks for studying
compositional learning and zero-shot generalization.
See https://github.com/brendenlake/SCAN for a description of the splits.
Example usage:
data = datasets.load_dataset('scan/length') | @inproceedings{Lake2018GeneralizationWS,
title={Generalization without Systematicity: On the Compositional Skills of
Sequence-to-Sequence Recurrent Networks},
author={Brenden M. Lake and Marco Baroni},
booktitle={ICML},
year={2018},
url={https://arxiv.org/pdf/1711.00350.pdf},
} | null | 2 | 1,860 | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- en
license:
- bsd
multilinguality:
- monolingual
pretty_name: SCAN
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: scan
tags:
- multi-turn
dataset... |
mteb/sts16-sts | 2022-09-27T19:12:09.000Z | [
"language:en",
"region:us"
] | mteb | null | null | null | 1 | 1,857 | ---
language:
- en
--- |
m3hrdadfi/recipe_nlg_lite | 2021-07-03T09:34:56.000Z | [
"region:us"
] | m3hrdadfi | RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation - Lite version
The dataset we publish contains 7,198 cooking recipes (>7K).
It's processed in more careful way and provides more samples than any other dataset in the area. | @misc{RecipeNLGLite,
author = {Mehrdad Farahani},
title = {RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation (Lite)},
year = 2021,
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {url{https://github.com/m3hrdadfi/reci... | null | 2 | 1,850 | # RecipeNLG: A Cooking Recipes Dataset
RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation - Lite version
The dataset contains `7,198` cooking recipes (`>7K`).
It's processed in more careful way and provides more samples than any other dataset in the area.
## How to use
```bash
pip install git+... |
pie/conll2003 | 2022-05-06T16:14:31.000Z | [
"region:us"
] | pie | null | null | null | 0 | 1,846 | Entry not found |
iwslt2017 | 2023-04-05T10:07:51.000Z | [
"task_categories:translation",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:translation",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:ar",
"language:de",
"language:en",
"language:fr",
"language:it",
"language:ja",
"language... | null | The IWSLT 2017 Multilingual Task addresses text translation, including zero-shot translation, with a single MT system across all directions including English, German, Dutch, Italian and Romanian. As unofficial task, conventional bilingual text translation is offered between English and Arabic, French, Japanese, Chinese... | @inproceedings{cettolo-etal-2017-overview,
title = "Overview of the {IWSLT} 2017 Evaluation Campaign",
author = {Cettolo, Mauro and
Federico, Marcello and
Bentivogli, Luisa and
Niehues, Jan and
St{\\"u}ker, Sebastian and
Sudoh, Katsuhito and
Yoshino, Koichiro and
... | null | 13 | 1,833 | ---
annotations_creators:
- crowdsourced
language:
- ar
- de
- en
- fr
- it
- ja
- ko
- nl
- ro
- zh
language_creators:
- expert-generated
license:
- cc-by-nc-nd-4.0
multilinguality:
- translation
pretty_name: IWSLT 2017
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- translation
task_ids: []... |
huggingface-course/codeparrot-ds-valid | 2021-09-13T14:24:27.000Z | [
"region:us"
] | huggingface-course | null | null | null | 2 | 1,829 | Entry not found |
allenai/scirepeval | 2023-08-25T20:52:45.000Z | [
"region:us"
] | allenai | This new dataset is designed to solve this great NLP task and is crafted with a lot of care. | @InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2021}
} | null | 9 | 1,823 | ---
dataset_info:
- config_name: fos
features:
- name: doc_id
dtype: string
- name: corpus_id
dtype: uint64
- name: title
dtype: string
- name: abstract
dtype: string
- name: labels
sequence: int32
- name: labels_text
sequence: string
splits:
- name: evaluation
num_bytes: 6... |
nielsr/ade20k-panoptic-demo | 2022-11-06T17:13:22.000Z | [
"region:us"
] | nielsr | null | null | null | 0 | 1,801 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: image
- name: segments_info
list:
- name: area
dtype: int64
- name: bbox
sequence: int64
- name: category_id
dtype: int64
- name: id
dtype: int64
- name: iscrowd
dtype: int64... |
HuggingFaceM4/TextCaps | 2022-12-09T01:38:32.000Z | [
"license:cc-by-4.0",
"region:us"
] | HuggingFaceM4 | extCaps requires models to read and reason about text in images to generate captions about them. Specifically, models need to incorporate a new modality of text present in the images and reason over it and visual content in the image to generate image descriptions.
Current state-of-the-art models fail to generate capti... | @article{sidorov2019textcaps,
title={TextCaps: a Dataset for Image Captioningwith Reading Comprehension},
author={Sidorov, Oleksii and Hu, Ronghang and Rohrbach, Marcus and Singh, Amanpreet},
journal={arXiv preprint arXiv:2003.12462},
year={2020}
} | null | 0 | 1,797 | ---
license: cc-by-4.0
---
|
sem_eval_2018_task_1 | 2022-11-18T21:45:06.000Z | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ar",
"language:en",
"language:es",
"license:unknown",
... | null | SemEval-2018 Task 1: Affect in Tweets: SubTask 5: Emotion Classification.
This is a dataset for multilabel emotion classification for tweets.
'Given a tweet, classify it as 'neutral or no emotion' or as one, or more, of eleven given emotions that best represent the mental state of the tweeter.'
It contains 22467 tw... | @InProceedings{SemEval2018Task1,
author = {Mohammad, Saif M. and Bravo-Marquez, Felipe and Salameh, Mohammad and Kiritchenko, Svetlana},
title = {SemEval-2018 {T}ask 1: {A}ffect in Tweets},
booktitle = {Proceedings of International Workshop on Semantic Evaluation (SemEval-2018)},
address = {New Orleans, LA, USA},
... | null | 9 | 1,796 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- ar
- en
- es
license:
- unknown
multilinguality:
- multilingual
pretty_name: 'SemEval-2018 Task 1: Affect in Tweets'
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-clas... |
huggingface-course/codeparrot-ds-train | 2021-09-13T14:33:48.000Z | [
"region:us"
] | huggingface-course | null | null | null | 4 | 1,796 | Entry not found |
cc_news | 2023-06-12T06:42:15.000Z | [
"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:100K<n<1M",
"source_datasets:original",
"language:en",... | null | CC-News containing news articles from news sites all over the world The data is available on AWS S3 in the Common Crawl bucket at /crawl-data/CC-NEWS/. This version of the dataset has 708241 articles. It represents a small portion of English language subset of the CC-News dataset created using news-please(Hamborg et a... | @InProceedings{Hamborg2017,
author = {Hamborg, Felix and Meuschke, Norman and Breitinger, Corinna and Gipp, Bela},
title = {news-please: A Generic News Crawler and Extractor},
year = {2017},
booktitle = {Proceedings of the 15th International Symposium of Information Science},
location = {Ber... | null | 37 | 1,792 | ---
pretty_name: CC-News
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
pape... |
neural_code_search | 2023-06-01T14:59:50.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-4.0",
"arxiv:... | null | Neural-Code-Search-Evaluation-Dataset presents an evaluation dataset consisting of natural language query and code snippet pairs and a search corpus consisting of code snippets collected from the most popular Android repositories on GitHub. | @InProceedings{huggingface:dataset,
title = {Neural Code Search Evaluation Dataset},
authors = {Hongyu Li, Seohyun Kim and Satish Chandra},
journal = {arXiv e-prints},
year = 2018,
eid = {arXiv:1908.09804 [cs.SE]},
pages = {arXiv:1908.09804 [cs.SE]},
archivePrefix = {arXiv... | null | 6 | 1,791 | ---
pretty_name: Neural Code Search
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
- n<1K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithco... |
lama | 2023-06-01T14:59:53.000Z | [
"task_categories:text-retrieval",
"task_categories:text-classification",
"task_ids:fact-checking-retrieval",
"task_ids:text-scoring",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_cr... | null | LAMA is a dataset used to probe and analyze the factual and commonsense knowledge contained in pretrained language models. See https://github.com/facebookresearch/LAMA. | @inproceedings{petroni2019language,
title={Language Models as Knowledge Bases?},
author={F. Petroni, T. Rockt{\"{a}}schel, A. H. Miller, P. Lewis, A. Bakhtin, Y. Wu and S. Riedel},
booktitle={In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019},
year={201... | null | 7 | 1,767 | ---
pretty_name: 'LAMA: LAnguage Model Analysis'
annotations_creators:
- crowdsourced
- expert-generated
- machine-generated
language_creators:
- crowdsourced
- expert-generated
- machine-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- 1K<n<10K
- 1M<n<10M
- n... |
nlphuji/flickr30k | 2023-01-19T17:40:41.000Z | [
"region:us"
] | nlphuji | null | null | null | 11 | 1,766 | # Flickr30k
Original paper: [From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions](https://aclanthology.org/Q14-1006)
Homepage: https://shannon.cs.illinois.edu/DenotationGraph/
Bibtex:
```
@article{young2014image,
title={From image descriptions to vis... |
center-for-humans-and-machines/style-diffusion | 2023-06-30T17:45:02.000Z | [
"region:us"
] | center-for-humans-and-machines | null | null | null | 0 | 1,766 | ---
dataset_info:
features:
- name: vectorId
dtype: string
- name: medianYear
dtype: int32
- name: embedding
sequence: float32
splits:
- name: train
num_bytes: 3448928
num_examples: 1113
download_size: 0
dataset_size: 3448928
---
# Dataset Card for "style-diffusion"
[More Informatio... |
mteb/stsbenchmark-sts | 2022-09-27T19:11:21.000Z | [
"language:en",
"region:us"
] | mteb | null | null | null | 4 | 1,757 | ---
language:
- en
--- |
fusing/fill50k | 2023-03-10T22:36:46.000Z | [
"region:us"
] | fusing | null | null | null | 12 | 1,757 | Entry not found |
kde4 | 2022-11-03T16:32:20.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:af",
"language:ar",
"language:as",
"language:ast",
"language:be",
"language:bg",
"language:bn",
"langua... | null | A parallel corpus of KDE4 localization files (v.2).
92 languages, 4,099 bitexts
total number of files: 75,535
total number of tokens: 60.75M
total number of sentence fragments: 8.89M | @InProceedings{TIEDEMANN12.463,
author = {J{\"o}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 | 11 | 1,756 | ---
annotations_creators:
- found
language_creators:
- found
language:
- af
- ar
- as
- ast
- be
- bg
- bn
- br
- ca
- crh
- cs
- csb
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gl
- gu
- ha
- he
- hi
- hne
- hr
- hsb
- hu
- hy
- id
- is
- it
- ja
- ka
- kk
- km
- kn
- ko
- ku
- lb
- lt
- lv... |
laion/laion2B-en-aesthetic | 2023-01-18T20:03:33.000Z | [
"region:us"
] | laion | null | null | null | 22 | 1,753 | details at https://github.com/LAION-AI/laion-datasets/blob/main/laion-aesthetic.md |
baber/hendrycks_math | 2023-08-25T21:15:56.000Z | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"arxiv:2103.03874",
"region:us"
] | baber | 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 | 1,753 | ---
license: mit
task_categories:
- text-generation
language:
- en
pretty_name: MATH
size_categories:
- 10K<n<100K
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:** https://github.com/hendrycks/math/blob/main/README.md
- **Repository:** https://github.com/hendrycks/math
- **Paper:** https://... |
cardiffnlp/tweet_sentiment_multilingual | 2022-11-30T14:01:25.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-tweet-datasets",
"language:en",
"language:ar",
"language:fr",
"language:de",
"language:hi",
"language:it",
"language:pt",
... | cardiffnlp | null | @inproceedings{barbieri-etal-2022-xlm,
title = "{XLM}-{T}: Multilingual Language Models in {T}witter for Sentiment Analysis and Beyond",
author = "Barbieri, Francesco and
Espinosa Anke, Luis and
Camacho-Collados, Jose",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluati... | null | 10 | 1,735 | ---
language:
- en
- ar
- fr
- de
- hi
- it
- pt
- es
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-tweet-datasets
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: tweet_sentiment_multilingual
pretty_name: Tweet Sentiment Mu... |
yizhongw/self_instruct | 2023-03-07T10:07:36.000Z | [
"license:apache-2.0",
"arxiv:2212.10560",
"arxiv:2204.07705",
"region:us"
] | yizhongw | Self-Instruct is a dataset that contains 52k instructions, paired with 82K instance inputs and outputs. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better. | @misc{selfinstruct,
title={Self-Instruct: Aligning Language Model with Self Generated Instructions},
author={Wang, Yizhong and Kordi, Yeganeh and Mishra, Swaroop and Liu, Alisa and Smith, Noah A. and Khashabi, Daniel and Hajishirzi, Hannaneh},
journal={arXiv preprint arXiv:2212.10560},
year={2022}
} | null | 161 | 1,726 | ---
license: apache-2.0
dataset_info:
- config_name: self_instruct
features:
- name: prompt
dtype: string
- name: completion
dtype: string
splits:
- name: train
num_bytes: 20527462
num_examples: 82612
download_size: 24113858
dataset_size: 20527462
- config_name: human_eval
features:
- ... |
schema_guided_dstc8 | 2023-01-25T14:43:36.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:token-classification",
"task_categories:text-classification",
"task_ids:dialogue-modeling",
"task_ids:multi-class-classification",
"task_ids:parsing",
"annotations_creators:machine-generated",
"language_creators:crowdso... | null | The Schema-Guided Dialogue dataset (SGD) was developed for the Dialogue State Tracking task of the Eights Dialogue Systems Technology Challenge (dstc8).
The SGD dataset consists of over 18k annotated multi-domain, task-oriented conversations between a human and a virtual assistant.
These conversations involve interacti... | @inproceedings{aaai/RastogiZSGK20,
author = {Abhinav Rastogi and
Xiaoxue Zang and
Srinivas Sunkara and
Raghav Gupta and
Pranav Khaitan},
title = {Towards Scalable Multi-Domain Conversational Agents: The Schema-Guided
Dialogue Dataset}... | null | 7 | 1,719 | ---
annotations_creators:
- machine-generated
language_creators:
- crowdsourced
- machine-generated
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
- token-classification
- text-classification
... |
subjqa | 2023-03-16T13:27:54.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"source_datasets:extended|yelp_review_full",
"source_datasets:extended|other-amaz... | null | SubjQA is a question answering dataset that focuses on subjective questions and answers.
The dataset consists of roughly 10,000 questions over reviews from 6 different domains: books, movies, grocery,
electronics, TripAdvisor (i.e. hotels), and restaurants. | @inproceedings{bjerva20subjqa,
title = "SubjQA: A Dataset for Subjectivity and Review Comprehension",
author = "Bjerva, Johannes and
Bhutani, Nikita and
Golahn, Behzad and
Tan, Wang-Chiew and
Augenstein, Isabelle",
booktitle = "Proceedings of the 2020 Conference on Empirical Meth... | null | 6 | 1,711 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
- extended|yelp_review_full
- extended|other-amazon_reviews_ucsd
- extended|other-tripadvisor_reviews
task_categories:
- questi... |
civil_comments | 2023-06-30T11:26:30.000Z | [
"language:en",
"license:cc0-1.0",
"arxiv:1903.04561",
"region:us"
] | null | The comments in this dataset come from an archive of the Civil Comments
platform, a commenting plugin for independent news sites. These public comments
were created from 2015 - 2017 and appeared on approximately 50 English-language
news sites across the world. When Civil Comments shut down in 2017, they chose
to make t... | @article{DBLP:journals/corr/abs-1903-04561,
author = {Daniel Borkan and
Lucas Dixon and
Jeffrey Sorensen and
Nithum Thain and
Lucy Vasserman},
title = {Nuanced Metrics for Measuring Unintended Bias with Real Data for Text
Classificati... | null | 3 | 1,709 | ---
language:
- en
paperswithcode_id: null
pretty_name: CivilComments
dataset_info:
features:
- name: text
dtype: string
- name: toxicity
dtype: float32
- name: severe_toxicity
dtype: float32
- name: obscene
dtype: float32
- name: threat
dtype: float32
- name: insult
dtype: float32... |
google/MusicCaps | 2023-03-08T14:37:09.000Z | [
"task_categories:text-to-speech",
"language:en",
"license:cc-by-sa-4.0",
"arxiv:2301.11325",
"region:us"
] | google | null | null | null | 76 | 1,707 | ---
license:
- cc-by-sa-4.0
converted_from: kaggle
kaggle_id: googleai/musiccaps
task_categories:
- text-to-speech
language:
- en
---
# Dataset Card for MusicCaps
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [S... |
mteb/amazon_massive_scenario | 2022-05-19T08:00:44.000Z | [
"region:us"
] | 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 | 0 | 1,706 | Entry not found |
lamini/alpaca | 2023-07-23T06:29:21.000Z | [
"region:us"
] | lamini | null | null | null | 1 | 1,703 | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 27364517
num_examples: 52002
download_size: 12742513
dataset_size: 27364517
---
# Dataset Card for "alpaca"
[More Information needed](https://github.com/huggingface/datase... |
Falah/Alzheimer_MRI | 2023-07-04T10:03:44.000Z | [
"task_categories:image-classification",
"size_categories:1K<n<10K",
"language:en",
"license:apache-2.0",
"medical",
"region:us"
] | Falah | null | null | null | 1 | 1,666 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Mild_Demented
'1': Moderate_Demented
'2': Non_Demented
'3': Very_Mild_Demented
splits:
- name: train
num_bytes: 22560791.2
num_examples: 51... |
jordyvl/rvl_cdip_100_examples_per_class | 2023-03-23T20:55:18.000Z | [
"region:us"
] | jordyvl | null | null | null | 0 | 1,664 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': letter
'1': form
'2': email
'3': handwritten
'4': advertisement
'5': scientific report
'6': scientific publication
... |
HuggingFaceH4/testing_self_instruct_small | 2023-04-12T21:53:16.000Z | [
"region:us"
] | HuggingFaceH4 | null | null | null | 0 | 1,664 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: completion
dtype: string
splits:
- name: train
num_bytes: 20379
num_examples: 100
- name: test
num_bytes: 26586
num_examples: 100
download_size: 35875
dataset_size: 46965
---
# Dataset Card for "testing_self_instruc... |
meczifho/QuaeroFrenchMed | 2023-09-13T20:01:06.000Z | [
"task_categories:token-classification",
"language:fr",
"medical",
"region:us"
] | meczifho | The QUAEROFrenchMed is a manually annotated corpus developed as a resource for named entity named recognition and normalization. | @article{neveol2014quaero,
title={The QUAERO French medical corpus: A ressource for medical entity recognition and normalization},
author={N{\'e}v{\'e}ol, Aur{\'e}lie and Grouin, Cyril and Leixa, Jeremy and Rosset, Sophie and Zweigenbaum, Pierre},
journal={Proc of BioTextMining Work},
pages={24--30},
year={20... | null | 1 | 1,664 | ---
language:
- fr
task_categories:
- token-classification
tags:
- medical
---
⚠️ **WARNING : THIS VERSION OF THE DATASET IS MODIFIED IN FORMAT AND CONTENT FROM THE ORIGINAL DATASET AVAILABLE [HERE](https://quaerofrenchmed.limsi.fr/). NESTED ENTITIES HAVE BEEN REMOVED AND THIS DATASET ONLY RETAINS THE LARGEST OF NESTED... |
clinc_oos | 2023-01-25T14:28:10.000Z | [
"task_categories:text-classification",
"task_ids:intent-classification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-3.0",
"region:us"
] | null | This dataset is for evaluating the performance of intent classification systems in the
presence of "out-of-scope" queries. By "out-of-scope", we mean queries that do not fall
into any of the system-supported intent classes. Most datasets include only data that is
"in-scope". Our dataset includes both in... | @inproceedings{larson-etal-2019-evaluation,
title = "An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction",
author = "Larson, Stefan and
Mahendran, Anish and
Peper, Joseph J. and
Clarke, Christopher and
Lee, Andrew and
Hill, Parker and
Kummerf... | null | 11 | 1,650 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-3.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- intent-classification
paperswithcode_id: clinc150
pretty_name: CL... |
TREC-AToMiC/AToMiC-Qrels-v0.2 | 2023-02-14T21:31:18.000Z | [
"license:cc-by-sa-4.0",
"region:us"
] | TREC-AToMiC | null | null | null | 1 | 1,646 | ---
dataset_info:
features:
- name: text_id
dtype: string
- name: Q0
dtype: string
- name: image_id
dtype: string
- name: rel
dtype: int64
splits:
- name: test
num_bytes: 789840
num_examples: 9873
- name: validation
num_bytes: 1424080
num_examples: 17801
- name: train
... |
dream | 2022-11-18T19:59:12.000Z | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | null | DREAM is a multiple-choice Dialogue-based REAding comprehension exaMination dataset. In contrast to existing reading comprehension datasets, DREAM is the first to focus on in-depth multi-turn multi-party dialogue understanding. | @article{sundream2018,
title={{DREAM}: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension},
author={Sun, Kai and Yu, Dian and Chen, Jianshu and Yu, Dong and Choi, Yejin and Cardie, Claire},
journal={Transactions of the Association for Computational Linguistics},
year={2019},
url={https://... | null | 6 | 1,645 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
paperswithcode_id: dream
pretty_name: DREAM
d... |
kernelmachine/open-license-corpus | 2023-08-09T03:14:36.000Z | [
"task_categories:text-generation",
"size_categories:100B<n<1T",
"language:en",
"license:apache-2.0",
"region:us"
] | kernelmachine | null | null | null | 6 | 1,632 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
pretty_name: pubtext
size_categories:
- 100B<n<1T
---
# PubText
Welcome to the Open License Corpus (OLC), a 228B token corpus for training permissively-licensed language models.
**Disclaimer**: OLC should not be considered a universally safe-t... |
baber/agieval | 2023-08-30T00:47:50.000Z | [
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
"license:mit",
"arxiv:2304.06364",
"region:us"
] | baber | null | @ARTICLE{10174688,
author={Liu, Hanmeng and Liu, Jian and Cui, Leyang and Teng, Zhiyang and Duan, Nan and Zhou, Ming and Zhang, Yue},
journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
title={LogiQA 2.0 — An Improved Dataset for Logical Reasoning in Natural Language Understanding},
year=... | null | 2 | 1,631 | ---
license: mit
language:
- en
task_categories:
- question-answering
- text-generation
pretty_name: AGIEval
---
# Dataset Card for AGIEval
## Dataset Description
- **Homepage:** https://github.com/microsoft/AGIEval/blob/main/README.md
- **Repository:** https://github.com/microsoft/AGIEval
- **Paper:** https://arxiv.... |
conceptual_captions | 2022-11-03T16:32:04.000Z | [
"task_categories:image-to-text",
"task_ids:image-captioning",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:other",
"region:us"
] | null | Google's Conceptual Captions dataset has more than 3 million images, paired with natural-language captions.
In contrast with the curated style of the MS-COCO images, Conceptual Captions images and their raw descriptions are harvested from the web,
and therefore represent a wider variety of styles. The raw descriptions ... | @inproceedings{sharma2018conceptual,
title = {Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning},
author = {Sharma, Piyush and Ding, Nan and Goodman, Sebastian and Soricut, Radu},
booktitle = {Proceedings of ACL},
year = {2018},
} | null | 36 | 1,629 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- image-to-text
task_ids:
- image-captioning
paperswithcode_id: conceptual-captions
pretty_name: Conceptual Captions
datase... |
dlwh/wikitext_103_detokenized | 2022-05-05T20:08:17.000Z | [
"region:us"
] | dlwh | null | null | null | 2 | 1,624 | Entry not found |
openai/webgpt_comparisons | 2022-12-19T17:55:29.000Z | [
"arxiv:2112.09332",
"region:us"
] | openai | WebGPT Comparisons contains all of the comparisons marked as suitable for reward modelling from the WebGPT paper. | @inproceedings{nakano2021webgpt,
author = {Reiichiro Nakano and Jacob Hilton and Suchir Balaji and Jeff Wu and Long Ouyang and Christina Kim and Christopher Hesse and Shantanu Jain and Vineet Kosaraju and William Saunders and Xu Jiang and Karl Cobbe and Tyna Eloundou and Gretchen Krueger and Kevin Button and Matthew ... | null | 172 | 1,620 | ---
pretty_name: WebGPT Comparisons
---
# Dataset Card for WebGPT Comparisons
## Dataset Description
In the [WebGPT paper](https://arxiv.org/abs/2112.09332), the authors trained a reward model from human feedback.
They used the reward model to train a long form question answering model to align with human preferences... |
beomi/KoAlpaca-v1.1a | 2023-05-26T06:32:02.000Z | [
"task_categories:text-generation",
"language:ko",
"KoAlpaca",
"region:us"
] | beomi | null | null | null | 10 | 1,620 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
- name: url
dtype: string
splits:
- name: train
num_bytes: 23371027
num_examples: 21155
download_size: 12856014
dataset_size: 23371027
task_categories:
- text-generation
language:
- ko
tags:
- ... |
totto | 2023-02-23T09:49:19.000Z | [
"task_categories:table-to-text",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"arxiv:2004.14373",
"region:us"
] | null | ToTTo is an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description. | @inproceedings{parikh2020totto,
title={{ToTTo}: A Controlled Table-To-Text Generation Dataset},
author={Parikh, Ankur P and Wang, Xuezhi and Gehrmann, Sebastian and Faruqui, Manaal and Dhingra, Bhuwan and Yang, Diyi and Das, Dipanjan},
booktitle={Proceedings of EMNLP},
year={2020}
} | null | 5 | 1,615 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
paperswithcode_id: totto
pretty_name: ToTTo
dataset_info:
features:
- n... |
0n1xus/codexglue | 2021-11-18T08:45:46.000Z | [
"region:us"
] | 0n1xus | CodeXGLUE is a benchmark dataset to foster machine learning research for program understanding and generation.
CodeXGLUE includes a collection of 10 tasks across 14 datasets and a platform for model evaluation and comparison. | @article{Lu2021,
author = {Lu, Shuai and Guo, Daya and Ren, Shuo and Huang, Junjie and Svyatkovskiy, Alexey and Blanco, Ambrosio and Clement, Colin B. and Drain, Dawn and Jiang, Daxin and Tang, Duyu and Li, Ge and Zhou, Lidong and Shou, Linjun and Zhou, Long and Tufano, Michele and Gong, Ming and Zhou, Ming and Duan, N... | null | 3 | 1,611 | Entry not found |
shariqfarooq/cs323_densepred_depth | 2023-09-16T00:02:26.000Z | [
"region:us"
] | shariqfarooq | null | null | null | 0 | 1,604 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image
dtype: image
- name: depth
dtype: image
splits:
- name: train
num_bytes: 651397023.7943412
num_examples: 25356
- name: test
... |
UBC-NLP/orca | 2023-07-17T23:02:07.000Z | [
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:question-answering",
"language:ara",
"Arabic",
"NLU Benchmark",
"Natural Language Inference (NLI)",
"Question Answering (QA)",
"Semantic Textual Similarity and and Paraphrase (STSP)",
"Sentence Classifi... | UBC-NLP | null | null | null | 3 | 1,603 |
---
viewer: false
language:
- ara
tags:
- Arabic
- NLU Benchmark
- Natural Language Inference (NLI)
- Question Answering (QA)
- Semantic Textual Similarity and and Paraphrase (STSP)
- Sentence Classification (SC)
- Structure Predictions (SP)
- Topic Classification (TC)
- Word Sense Disambiguation (WSD)
task_categorie... |
bigbio/bc5cdr | 2022-12-22T15:43:20.000Z | [
"multilinguality:monolingual",
"language:en",
"license:other",
"region:us"
] | bigbio | The BioCreative V Chemical Disease Relation (CDR) dataset is a large annotated text corpus of human annotations of all chemicals, diseases and their interactions in 1,500 PubMed articles. | @article{DBLP:journals/biodb/LiSJSWLDMWL16,
author = {Jiao Li and
Yueping Sun and
Robin J. Johnson and
Daniela Sciaky and
Chih{-}Hsuan Wei and
Robert Leaman and
Allan Peter Davis and
Carolyn J. Mattingly and
... | null | 1 | 1,601 |
---
language:
- en
bigbio_language:
- English
license: other
multilinguality: monolingual
bigbio_license_shortname: PUBLIC_DOMAIN_MARK_1p0
pretty_name: BC5CDR
homepage: http://www.biocreative.org/tasks/biocreative-v/track-3-cdr/
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
- NAME... |
poem_sentiment | 2023-01-25T14:42:40.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:2011.02686",
"region:u... | null | Poem Sentiment is a sentiment dataset of poem verses from Project Gutenberg. This dataset can be used for tasks such as sentiment classification or style transfer for poems. | @misc{sheng2020investigating,
title={Investigating Societal Biases in a Poetry Composition System},
author={Emily Sheng and David Uthus},
year={2020},
eprint={2011.02686},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | null | 8 | 1,599 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: gutenberg-poem-dataset
pretty_... |
baber/mmlu | 2023-09-29T02:12:59.000Z | [
"region:us"
] | baber | This is a massive multitask test consisting of multiple-choice questions from various branches of knowledge, covering 57 tasks including elementary mathematics, US history, computer science, law, and more. | @article{hendryckstest2021,
title={Measuring Massive Multitask Language Understanding},
author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt},
journal={Proceedings of the International Conference on Learning Representations (ICLR)}... | null | 0 | 1,590 | Entry not found |
laion/laion-high-resolution | 2022-05-07T12:11:38.000Z | [
"license:cc-by-4.0",
"region:us"
] | laion | null | null | null | 41 | 1,586 | ---
license: cc-by-4.0
---
Laion high resolution is a >= 1024x1024 subset of laion5B. It has 170M samples
A good use case is to train a superresolution model.
Refer to [img2dataset guide](https://github.com/rom1504/img2dataset/blob/main/dataset_examples/laion-high-resolution.md) for downloading |
frutiemax/rct_dataset | 2023-10-01T19:24:11.000Z | [
"task_categories:text-to-image",
"size_categories:n<1K",
"language:en",
"license:openrail",
"pixel art",
"region:us"
] | frutiemax | null | null | null | 0 | 1,585 | ---
language:
- en
license: openrail
size_categories:
- n<1K
task_categories:
- text-to-image
pretty_name: Rollercoaster Tycoon Dataset
dataset_info:
features:
- name: image
dtype: image
- name: id
dtype: int64
- name: object_type
dtype: string
- name: object_description
dtype: string
- name... |
fever | 2023-04-05T10:06:17.000Z | [
"task_categories:text-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|wikipedia",
"language:en",
"license:cc-by-sa-3.0",
"license:gpl-3.0",
"knowledge-verification",
"region:us"... | null | null | null | null | 7 | 1,584 | ---
language:
- en
paperswithcode_id: fever
annotations_creators:
- crowdsourced
language_creators:
- found
license:
- cc-by-sa-3.0
- gpl-3.0
multilinguality:
- monolingual
pretty_name: FEVER
size_categories:
- 100K<n<1M
source_datasets:
- extended|wikipedia
task_categories:
- text-classification
task_ids: []
tags:
- k... |
GEM/wiki_lingua | 2023-02-16T09:23:29.000Z | [
"task_categories:summarization",
"annotations_creators:none",
"language_creators:unknown",
"multilinguality:multilingual",
"size_categories:unknown",
"source_datasets:original",
"language:ar",
"language:cs",
"language:de",
"language:en",
"language:es",
"language:fr",
"language:hi",
"langua... | GEM | WikiLingua is a large-scale multilingual dataset for the evaluation of
crosslingual abstractive summarization systems. The dataset includes ~770k
article and summary pairs in 18 languages from WikiHow. The gold-standard
article-summary alignments across languages was done by aligning the images
that are used to describ... | @article{ladhak-wiki-2020,
title = {WikiLingua: A New Benchmark Dataset for Multilingual Abstractive Summarization},
authors = {Faisal Ladhak, Esin Durmus, Claire Cardie and Kathleen McKeown},
journal = {arXiv preprint arXiv:2010.03093},
year = {2020},
url = {https://arxiv.org/abs/2010.03093}
} | null | 36 | 1,584 | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- ar
- cs
- de
- en
- es
- fr
- hi
- id
- it
- ja
- ko
- nl
- pt
- ru
- th
- tr
- vi
- zh
license:
- cc-by-nc-sa-3.0
multilinguality:
- multilingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- summarization
task_ids: [... |
DFKI-SLT/brat | 2023-05-10T15:38:03.000Z | [
"task_categories:token-classification",
"task_ids:parsing",
"annotations_creators:expert-generated",
"language_creators:found",
"region:us"
] | DFKI-SLT | null | null | null | 2 | 1,580 | ---
annotations_creators:
- expert-generated
language_creators:
- found
license: []
task_categories:
- token-classification
task_ids:
- parsing
---
# Information Card for Brat
## Table of Contents
- [Description](#description)
- [Summary](#summary)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#da... |
quora | 2023-04-05T13:37:24.000Z | [
"task_categories:text-classification",
"task_ids:semantic-similarity-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | null | null | null | null | 9 | 1,571 | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
pretty_name: Quora Question Pairs
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- semantic-similarity-classification
papers... |
coqa | 2023-04-05T10:02:34.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|race",
"source_datasets:extended|cnn_dailymail",
"source_datasets:extended|wikipedia",
... | null | CoQA: A Conversational Question Answering Challenge | @article{reddy-etal-2019-coqa,
title = "{C}o{QA}: A Conversational Question Answering Challenge",
author = "Reddy, Siva and
Chen, Danqi and
Manning, Christopher D.",
journal = "Transactions of the Association for Computational Linguistics",
volume = "7",
year = "2019",
address = "C... | null | 25 | 1,564 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- other
multilinguality:
- monolingual
pretty_name: 'CoQA: Conversational Question Answering Challenge'
size_categories:
- 1K<n<10K
source_datasets:
- extended|race
- extended|cnn_dailymail
- extended|wikipedia
- extended|other
... |
pccl-org/formal-logic-simple-order-simple-objects-blivergent-500 | 2023-09-21T20:20:02.000Z | [
"region:us"
] | pccl-org | null | null | null | 0 | 1,561 | ---
dataset_info:
features:
- name: greater_than
dtype: string
- name: less_than
dtype: string
- name: correct_example
sequence: string
- name: incorrect_example
sequence: string
- name: distance
dtype: int64
- name: index
dtype: int64
splits:
- name: train
num_bytes: 19635... |
alzoubi36/policy_ie_b | 2023-06-25T07:13:15.000Z | [
"region:us"
] | alzoubi36 | null | null | null | 0 | 1,555 | ---
dataset_info:
features:
- name: type-I
struct:
- name: subtask
dtype: string
- name: tags
sequence: string
- name: tokens
sequence: string
- name: type-II
struct:
- name: subtask
dtype: string
- name: tags
sequence: string
- name: tokens
sequ... |
Hello-SimpleAI/HC3 | 2023-01-21T13:10:10.000Z | [
"task_categories:text-classification",
"task_categories:question-answering",
"task_categories:sentence-similarity",
"task_categories:zero-shot-classification",
"size_categories:10K<n<100K",
"language:en",
"language:zh",
"license:cc-by-sa-4.0",
"ChatGPT",
"SimpleAI",
"Detection",
"OOD",
"arxi... | Hello-SimpleAI | Human ChatGPT Comparison Corpus (HC3) | \ | null | 115 | 1,552 | ---
task_categories:
- text-classification
- question-answering
- sentence-similarity
- zero-shot-classification
language:
- en
- zh
tags:
- ChatGPT
- SimpleAI
- Detection
- OOD
size_categories:
- 10K<n<100K
license: cc-by-sa-4.0
---
# Human ChatGPT Comparison Corpus (HC3)
We propose the first human-ChatGPT compariso... |
mteb/amazon_counterfactual | 2022-09-27T19:10:37.000Z | [
"language:de",
"language:en",
"language:ja",
"arxiv:2104.06893",
"region:us"
] | mteb | The dataset contains sentences from Amazon customer reviews (sampled from Amazon product review dataset) annotated for counterfactual detection (CFD) binary classification. Counterfactual statements describe events that did not or cannot take place. Counterfactual statements may be identified as statements of the form ... | @misc{oneill2021i,
title={I Wish I Would Have Loved This One, But I Didn't -- A Multilingual Dataset for Counterfactual Detection in Product Reviews},
author={James O'Neill and Polina Rozenshtein and Ryuichi Kiryo and Motoko Kubota and Danushka Bollegala},
year={2021},
eprint={2104.06893},
... | null | 1 | 1,549 | ---
language:
- de
- en
- ja
---
# Amazon Multilingual Counterfactual Dataset
The dataset contains sentences from Amazon customer reviews (sampled from Amazon product review dataset) annotated for counterfactual detection (CFD) binary classification. Counterfactual statements describe events that did not or cannot t... |
mstz/adult | 2023-04-15T11:37:47.000Z | [
"task_categories:tabular-classification",
"size_categories:10K<n<100K",
"language:en",
"license:cc",
"adult",
"tabular_classification",
"binary_classification",
"multiclass_classification",
"UCI",
"region:us"
] | mstz | null | @inproceedings{DBLP:conf/kdd/Kohavi96,
author = {Ron Kohavi},
editor = {Evangelos Simoudis and
Jiawei Han and
Usama M. Fayyad},
title = {Scaling Up the Accuracy of Naive-Bayes Classifiers: {A} Decision-Tree
Hybrid},
booktitle = {Proceedings of the Second In... | null | 0 | 1,549 | ---
language:
- en
tags:
- adult
- tabular_classification
- binary_classification
- multiclass_classification
- UCI
pretty_name: Adult
size_categories:
- 10K<n<100K
task_categories:
- tabular-classification
configs:
- encoding
- income
- income-no race
- race
license: cc
---
# Adult
The [Adult dataset](https://archive.... |
cyrilzhang/TinyStories2-ascii-bpe-2k | 2023-09-22T23:24:28.000Z | [
"region:us"
] | cyrilzhang | null | null | null | 0 | 1,536 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: input_ids
sequence: int32
splits:
- name: train
num_bytes: 2369808200
num_examples: 578002
- name: validation
num_bytes: 2... |
jacob-hugging-face/job-descriptions | 2023-08-18T20:07:48.000Z | [
"license:llama2",
"region:us"
] | jacob-hugging-face | null | null | null | 4 | 1,533 | ---
license: llama2
---
|
mteb/tweet_sentiment_extraction | 2022-09-27T19:14:27.000Z | [
"language:en",
"region:us"
] | mteb | null | null | null | 9 | 1,524 | ---
language:
- en
--- |
bilgeyucel/seven-wonders | 2023-03-09T14:25:43.000Z | [
"size_categories:n<1K",
"language:en",
"region:us"
] | bilgeyucel | null | null | null | 0 | 1,523 | ---
language:
- en
size_categories:
- n<1K
--- |
nielsr/breast-cancer | 2023-05-01T18:38:43.000Z | [
"region:us"
] | nielsr | null | null | null | 5 | 1,518 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 42431652.0
num_examples: 130
download_size: 0
dataset_size: 42431652.0
---
# Dataset Card for "breast-cancer"
[More Information needed](https://github.com/huggingface/dataset... |
graphs-datasets/MUTAG | 2023-02-07T16:39:19.000Z | [
"task_categories:graph-ml",
"license:unknown",
"region:us"
] | graphs-datasets | null | null | null | 3 | 1,516 | ---
license: unknown
task_categories:
- graph-ml
---
# Dataset Card for MUTAG
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [External Use](... |
yitingxie/rlhf-reward-datasets | 2023-01-01T12:23:04.000Z | [
"region:us"
] | yitingxie | null | null | null | 44 | 1,502 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: test
num_bytes: 6093563
num_examples: 5103
- name: train
num_bytes: 90528217
num_examples: 76256
download_size: 57138483
dataset_size: 966217... |
emozilla/pg_books-tokenized-bos-eos-chunked-65536 | 2023-10-07T02:19:15.000Z | [
"region:us"
] | emozilla | null | null | null | 3 | 1,499 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 67744337720
num_examples: 79514
down... |
argilla/banking_sentiment_setfit | 2022-12-07T09:08:25.000Z | [
"region:us"
] | argilla | null | null | null | 1 | 1,495 | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': negative
'1': neutral
splits:
- name: train
num_bytes: 7433.25
num_examples: 108
- name: test
num_bytes: 2477.75
num_examples: 36
download_size: 80... |
llm-book/JGLUE | 2023-10-06T00:58:24.000Z | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:sentence-similarity",
"task_categories:text-classification",
"task_ids:multiple-choice-qa",
"task_ids:open-domain-qa",
"task_ids:multi-class-classification",
"task_ids:sentiment-classification",
"annotations_cr... | llm-book | JGLUE, Japanese General Language Understanding Evaluation, is built to measure the general NLU ability in Japanese. JGLUE has been constructed from scratch without translation. We hope that JGLUE will facilitate NLU research in Japanese. | @inproceedings{kurihara-etal-2022-jglue,
title = "{JGLUE}: {J}apanese General Language Understanding Evaluation",
author = "Kurihara, Kentaro and
Kawahara, Daisuke and
Shibata, Tomohide",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,... | null | 3 | 1,495 | ---
annotations_creators:
- crowdsourced
language:
- ja
language_creators:
- crowdsourced
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: JGLUE
size_categories: []
source_datasets:
- original
tags:
- MARC
- STS
- NLI
- SQuAD
- CommonsenseQA
task_categories:
- multiple-choice
- question-answerin... |
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}
} | null | 4 | 1,492 | ---
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... |
allenai/scitldr | 2023-01-25T14:43:42.000Z | [
"task_categories:summarization",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"scientific-documents-summarization",
"arxiv:2004.15011",
"region:us"
] | allenai | A new multi-target dataset of 5.4K TLDRs over 3.2K papers.
SCITLDR contains both author-written and expert-derived TLDRs,
where the latter are collected using a novel annotation protocol
that produces high-quality summaries while minimizing annotation burden. | @article{cachola2020tldr,
title={{TLDR}: Extreme Summarization of Scientific Documents},
author={Isabel Cachola and Kyle Lo and Arman Cohan and Daniel S. Weld},
journal={arXiv:2004.15011},
year={2020},
} | null | 14 | 1,484 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- summarization
task_ids: []
paperswithcode_id: scitldr
pretty_name: SciTLDR
tags:
- scientific-documents-summari... |
THUDM/humaneval-x | 2022-10-25T06:08:38.000Z | [
"task_categories:text-generation",
"task_ids:language-modeling",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:unknown",
"language:code",
"license:apache-2.0",
"region:us"
] | THUDM | HumanEval-X is a benchmark for the evaluation of the multilingual ability of code generative models. It consists of 820 high-quality human-crafted data samples (each with test cases) in Python, C++, Java, JavaScript, and Go, and can be used for various tasks. | null | null | 43 | 1,482 | ---
annotations_creators: []
language_creators:
- crowdsourced
- expert-generated
language:
- code
license:
- apache-2.0
multilinguality:
- multilingual
size_categories:
- unknown
source_datasets: []
task_categories:
- text-generation
task_ids:
- language-modeling
pretty_name: HumanEval-X
---
# HumanEval-X
## Dataset... |
Anthropic/llm_global_opinions | 2023-06-29T00:46:48.000Z | [
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-nc-sa-4.0",
"arxiv:2306.16388",
"region:us"
] | Anthropic | null | null | null | 22 | 1,481 | ---
license: cc-by-nc-sa-4.0
language:
- en
size_categories:
- 1K<n<10K
---
# Dataset Card for GlobalOpinionQA
## Dataset Summary
The data contains a subset of survey questions about global issues and opinions adapted from the [World Values Survey](https://www.worldvaluessurvey.org/) and [Pew Global Attitudes Survey](... |
open-llm-leaderboard/details_golaxy__gogpt-7b-bloom | 2023-09-17T07:35:31.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 1,467 | ---
pretty_name: Evaluation run of golaxy/gogpt-7b-bloom
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [golaxy/gogpt-7b-bloom](https://huggingface.co/golaxy/gogpt-7b-bloom) on the [Open\
\ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
... |
quarel | 2023-04-05T13:37:19.000Z | [
"language:en",
"region:us"
] | null | QuaRel is a crowdsourced dataset of 2771 multiple-choice story questions, including their logical forms. | @inproceedings{quarel_v1,
title={QuaRel: A Dataset and Models for Answering Questions about Qualitative Relationships},
author={Oyvind Tafjord, Peter Clark, Matt Gardner, Wen-tau Yih, Ashish Sabharwal},
year={2018},
journal={arXiv:1805.05377v1}
} | null | 2 | 1,462 | ---
language:
- en
paperswithcode_id: quarel
pretty_name: QuaRel
dataset_info:
features:
- name: id
dtype: string
- name: answer_index
dtype: int32
- name: logical_forms
sequence: string
- name: logical_form_pretty
dtype: string
- name: world_literals
sequence:
- name: world1
d... |
203427as321/articles | 2023-10-11T01:00:06.000Z | [
"region:us"
] | 203427as321 | null | null | null | 0 | 1,458 | ---
dataset_info:
features:
- name: label
dtype: string
- name: text
dtype: string
- name: __index_level_0__
dtype: float64
splits:
- name: train
num_bytes: 23996247
num_examples: 1534
download_size: 0
dataset_size: 23996247
---
# Dataset Card for "articles"
[More Information needed... |
shariqfarooq/cs323_densepred_seg256 | 2023-09-16T12:07:20.000Z | [
"region:us"
] | shariqfarooq | null | null | null | 0 | 1,454 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
dataset_info:
features:
- name: image
dtype: image
- name: mask
dtype: image
splits:
- name: train
num_bytes: 187512341.0
num_examples: 1464
- name: val
num_bytes... |
derek-thomas/ScienceQA | 2023-02-25T04:23:01.000Z | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:other",
"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-answe... | derek-thomas | null | null | null | 66 | 1,452 | ---
license: cc-by-sa-4.0
annotations_creators:
- expert-generated
- found
language:
- en
language_creators:
- expert-generated
- found
multilinguality:
- monolingual
paperswithcode_id: scienceqa
pretty_name: ScienceQA
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- multi-modal-qa
- science
- chemistr... |
jxie/higgs | 2023-09-20T06:01:24.000Z | [
"region:us"
] | jxie | null | null | null | 0 | 1,448 | ---
dataset_info:
features:
- name: inputs
sequence: float64
- name: label
dtype: float64
splits:
- name: val_16k
num_bytes: 3702368
num_examples: 15688
- name: train_10k
num_bytes: 2360000
num_examples: 10000
- name: train_1k
num_bytes: 236000
num_examples: 1000
- name: ... |
AlexanderDoria/novel17_test | 2023-07-19T12:26:36.000Z | [
"license:cc0-1.0",
"region:us"
] | AlexanderDoria | null | null | null | 6 | 1,443 | ---
license: cc0-1.0
---
|
daekeun-ml/naver-news-summarization-ko | 2023-01-10T11:12:44.000Z | [
"task_categories:summarization",
"size_categories:10K<n<100K",
"language:ko",
"license:apache-2.0",
"region:us"
] | daekeun-ml | null | null | null | 9 | 1,435 | ---
license: apache-2.0
task_categories:
- summarization
language:
- ko
size_categories:
- 10K<n<100K
---
This dataset is a custom dataset created by the author by crawling Naver News (https://news.naver.com) for the Korean NLP model hands-on.
- Period: July 1, 2022 - July 10, 2022
- Subject: IT, economics
```
Datase... |
danjacobellis/AVIRIS_256 | 2023-09-27T05:19:51.000Z | [
"region:us"
] | danjacobellis | null | null | null | 0 | 1,434 | Entry not found |
craffel/openai_lambada | 2021-10-12T20:22:47.000Z | [
"region:us"
] | craffel | LAMBADA dataset variant used by OpenAI to evaluate GPT-2 and GPT-3. | @InProceedings{paperno-EtAl:2016:P16-1,
author = {Paperno, Denis and Kruszewski, Germ\'{a}n and Lazaridou,
Angeliki and Pham, Ngoc Quan and Bernardi, Raffaella and Pezzelle,
Sandro and Baroni, Marco and Boleda, Gemma and Fernandez, Raquel},
title = {The {LAMBADA} dataset: Word prediction requ... | null | 1 | 1,433 | Entry not found |
ccdv/pubmed-summarization | 2022-10-24T20:33:04.000Z | [
"task_categories:summarization",
"task_categories:text-generation",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:en",
"conditional-text-generation",
"region:us"
] | ccdv | PubMed dataset for summarization.
From paper: A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents" by A. Cohan et al.
See: https://aclanthology.org/N18-2097.pdf
See: https://github.com/armancohan/long-summarization | @inproceedings{cohan-etal-2018-discourse,
title = "A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents",
author = "Cohan, Arman and
Dernoncourt, Franck and
Kim, Doo Soon and
Bui, Trung and
Kim, Seokhwan and
Chang, Walter and
Goharian, N... | null | 28 | 1,431 | ---
language:
- en
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
task_categories:
- summarization
- text-generation
task_ids: []
tags:
- conditional-text-generation
---
# PubMed dataset for summarization
Dataset for summarization of long documents.\
Adapted from this [repo](https://github.com/armancohan... |
lmsys/chatbot_arena_conversations | 2023-09-30T01:04:44.000Z | [
"task_categories:conversational",
"size_categories:10K<n<100K",
"license:cc",
"arxiv:2306.05685",
"region:us"
] | lmsys | null | null | null | 136 | 1,428 | ---
dataset_info:
features:
- name: question_id
dtype: string
- name: model_a
dtype: string
- name: model_b
dtype: string
- name: winner
dtype: string
- name: judge
dtype: string
- name: conversation_a
list:
- name: content
dtype: string
- name: role
dtype: stri... |
FedML/databricks-dolly-15k-niid | 2023-09-05T12:03:26.000Z | [
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | FedML | null | null | null | 0 | 1,424 | ---
license: cc-by-sa-3.0
language:
- en
size_categories:
- 10K<n<100K
configs:
- config_name: default
default: true
data_files:
- split: train
path: "train.parquet"
- split: test
path: "test.parquet"
dataset_info:
config_name: default
features:
- name: instruction
... |
code_x_glue_cc_clone_detection_big_clone_bench | 2022-11-18T19:30:27.000Z | [
"task_categories:text-classification",
"task_ids:semantic-similarity-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:code",
"license:c-uda",
"region:us"
] | null | Given two codes as the input, the task is to do binary classification (0/1), where 1 stands for semantic equivalence and 0 for others. Models are evaluated by F1 score.
The dataset we use is BigCloneBench and filtered following the paper Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax... | @inproceedings{svajlenko2014towards,
title={Towards a big data curated benchmark of inter-project code clones},
author={Svajlenko, Jeffrey and Islam, Judith F and Keivanloo, Iman and Roy, Chanchal K and Mia, Mohammad Mamun},
booktitle={2014 IEEE International Conference on Software Maintenance and Evolution},
pages={47... | null | 4 | 1,420 | ---
annotations_creators:
- found
language_creators:
- found
language:
- code
license:
- c-uda
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- semantic-similarity-classification
pretty_name: CodeXGlueCcCloneDetectionBigCloneBench
... |
emozilla/pg19-test | 2023-08-08T13:07:17.000Z | [
"region:us"
] | emozilla | null | null | null | 0 | 1,418 | ---
dataset_info:
features:
- name: short_book_title
dtype: string
- name: publication_date
dtype: int32
- name: url
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 40482852
num_examples: 100
download_size: 24874679
dataset_size: 40482852
---
# Dataset ... |
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