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 |
|---|---|---|---|---|---|---|---|---|---|
nlphuji/mscoco_2014_5k_test_image_text_retrieval | 2023-01-18T00:08:42.000Z | [
"arxiv:1405.0312",
"region:us"
] | nlphuji | null | null | null | 2 | 1,010 | # MSCOCO (5K test set)
Original paper: [Microsoft COCO: Common Objects in Context
](https://arxiv.org/abs/1405.0312)
Homepage: https://cocodataset.org/#home
5K test set split from: http://cs.stanford.edu/people/karpathy/deepimagesent/caption_datasets.zip
Bibtex:
```
@inproceedings{lin2014microsoft,
title={Microso... |
squadshifts | 2023-04-05T13:40:47.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:u... | null | null | @InProceedings{pmlr-v119-miller20a,
title = {The Effect of Natural Distribution Shift on Question Answering Models},
author = {Miller, John and Krauth, Karl and Recht, Benjamin and Schmidt, Ludwig},
booktitle = {Proceedings of the 37th International Conference on Machine Learning},
pages = {6905--6916},
year ... | null | 3 | 1,007 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- crowdsourced
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: SQuAD-shifts
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: squ... |
skg/toxigen-data | 2022-06-20T11:12:11.000Z | [
"task_categories:text-classification",
"task_ids:hate-speech-detection",
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"arxiv:2203.09509",
"region:us"
] | skg | Toxigen is a large-scale dataset containing implicitly toxic and benign sentences mentioning 13 minority groups, and a tool to stress test a given off-the-shelf toxicity classifier. The dataset is generated using a large language model (GPT3). It is intended to be used for training classifiers that learn to detect subt... | @inproceedings{hartvigsen2022toxigen,
title={ToxiGen: A Large-Scale Machine-Generated Dataset for Implicit and Adversarial Hate Speech Detection},
author={Hartvigsen, Thomas and Gabriel, Saadia and Palangi, Hamid and Sap, Maarten and Ray, Dipankar and Kamar, Ece},
booktitle={Proceedings of the 60th Annual Meeting... | null | 18 | 1,004 | ---
annotations_creators:
- expert-generated
language_creators:
- machine-generated
languages:
- en-US
licenses: []
multilinguality:
- monolingual
pretty_name: ToxiGen
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- hate-speech-detection
---
# Dataset Card fo... |
RussianNLP/russian_super_glue | 2023-06-19T12:23:49.000Z | [
"task_categories:text-classification",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:text-generation",
"task_ids:natural-language-inference",
"task_ids:multi-class-classification",
"annotations_creators:crowdsourced",
"annotations_creators:expert-ge... | RussianNLP | Recent advances in the field of universal language models and transformers require the development of a methodology for
their broad diagnostics and testing for general intellectual skills - detection of natural language inference,
commonsense reasoning, ability to perform simple logical operations regardless of text su... | @article{shavrina2020russiansuperglue,
title={RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark},
author={Shavrina, Tatiana and Fenogenova, Alena and Emelyanov, Anton and Shevelev, Denis and Artemova,
Ekaterina and Malykh, Valentin and Mikhailo... | null | 15 | 994 | ---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- ru
license:
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 1M<n<10M
- 10M<n<100M
- 100M<n<1B
source_datasets:
- original
task_categories:
- text-classification
- question-ans... |
open-llm-leaderboard/details_togethercomputer__GPT-JT-6B-v1 | 2023-09-22T13:40:02.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 994 | ---
pretty_name: Evaluation run of togethercomputer/GPT-JT-6B-v1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [togethercomputer/GPT-JT-6B-v1](https://huggingface.co/togethercomputer/GPT-JT-6B-v1)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/op... |
HuggingFaceH4/testing_codealpaca_small | 2023-04-12T21:57:24.000Z | [
"region:us"
] | HuggingFaceH4 | null | null | null | 3 | 990 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: completion
dtype: string
splits:
- name: train
num_bytes: 31503
num_examples: 100
- name: test
num_bytes: 29802
num_examples: 100
download_size: 44006
dataset_size: 61305
---
# Dataset Card for "testing_codealpaca_s... |
vwxyzjn/lm-human-preferences | 2023-09-01T02:02:15.000Z | [
"license:mit",
"region:us"
] | vwxyzjn | null | null | null | 0 | 990 | ---
license: mit
---
|
open-llm-leaderboard/details_Fredithefish__RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4 | 2023-09-28T15:50:12.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 989 | ---
pretty_name: Evaluation run of Fredithefish/RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Fredithefish/RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4](https://huggingface.co/Fredithefish/RedPajama-INCITE-Chat-3... |
hatexplain | 2023-01-25T14:31:48.000Z | [
"task_categories:text-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"hate-speech-detection",
"arxiv:2012.10289",
"arxiv:1703.0400... | null | Hatexplain is the first benchmark hate speech dataset covering multiple aspects of the issue. Each post in the dataset is annotated from three different perspectives: the basic, commonly used 3-class classification (i.e., hate, offensive or normal), the target community (i.e., the community that has been the victim of ... | @misc{mathew2020hatexplain,
title={HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection},
author={Binny Mathew and Punyajoy Saha and Seid Muhie Yimam and Chris Biemann and Pawan Goyal and Animesh Mukherjee},
year={2020},
eprint={2012.10289},
archivePrefix={arXiv},
pr... | null | 5 | 988 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: hatexplain
pretty_name: hatexplain
tags:
- hate-s... |
BeIR/climate-fever | 2022-10-23T06:04:48.000Z | [
"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 | 1 | 988 | ---
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:
... |
indonlp/NusaX-senti | 2023-01-24T17:02:06.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ace",
"language:ban",
"language:bjn",
"la... | indonlp | NusaX is a high-quality multilingual parallel corpus that covers 12 languages, Indonesian, English, and 10 Indonesian local languages, namely Acehnese, Balinese, Banjarese, Buginese, Madurese, Minangkabau, Javanese, Ngaju, Sundanese, and Toba Batak.
NusaX-Senti is a 3-labels (positive, neutral, negative) sentiment anal... | @misc{winata2022nusax,
title={NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages},
author={Winata, Genta Indra and Aji, Alham Fikri and Cahyawijaya,
Samuel and Mahendra, Rahmad and Koto, Fajri and Romadhony,
Ade and Kurniawan, Kemal and Moeljadi, David and Prasojo,
... | null | 3 | 986 | ---
pretty_name: NusaX-senti
annotations_creators:
- expert-generated
language_creators:
- expert-generated
license:
- cc-by-sa-4.0
multilinguality:
- multilingual
language:
- ace
- ban
- bjn
- bug
- en
- id
- jv
- mad
- min
- nij
- su
- bbc
size_categories:
- 10K<n<100K
source_datasets:
- original
task_cat... |
Graphcore/vqa | 2022-10-25T08:41:02.000Z | [
"language:en",
"license:cc-by-4.0",
"region:us"
] | Graphcore | 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{antol2015vqa,
title={Vqa: Visual question answering},
author={Antol, Stanislaw and Agrawal, Aishwarya and Lu, Jiasen and Mitchell, Margaret and Batra, Dhruv and Zitnick, C Lawrence and Parikh, Devi},
booktitle={Proceedings of the IEEE international conference on computer vision},
pages={2425--243... | null | 1 | 981 | ---
language:
- en
license:
- cc-by-4.0
---
|
alexandrainst/scandi-qa | 2023-01-16T13:51:25.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:mkqa",
"source_datasets:natural_questions",
"language:da",
"language:sv",
"language:no",
"license:cc-by-sa-4.0",
"region:us"
] | alexandrainst | ScandiQA is a dataset of questions and answers in the Danish, Norwegian, and Swedish
languages. All samples come from the Natural Questions (NQ) dataset, which is a large
question answering dataset from Google searches. The Scandinavian questions and answers
come from the MKQA dataset, where 10,000 NQ samples were manu... | # @InProceedings{huggingface:dataset,
# title = {ScandiQA: A Scandinavian Question Answering Dataset},
# author={Dan Saattrup Nielsen},
# year={2022}
# }
# | null | 7 | 980 | ---
pretty_name: ScandiQA
language:
- da
- sv
- no
license:
- cc-by-sa-4.0
multilinguality:
- multilingual
size_categories:
- 1K<n<10K
source_datasets:
- mkqa
- natural_questions
task_categories:
- question-answering
task_ids:
- extractive-qa
---
# Dataset Card for ScandiQA
## Dataset Description
- **Repository:** <... |
fujiki/oasst1-89k-ja-reformat-v1 | 2023-10-07T16:36:18.000Z | [
"license:apache-2.0",
"region:us"
] | fujiki | null | null | null | 0 | 979 | ---
license: apache-2.0
dataset_info:
features:
- name: dataset
dtype: string
- name: id
dtype: string
- name: instructions
sequence: string
- name: responses
sequence: string
splits:
- name: train
num_bytes: 58992730
num_examples: 33919
download_size: 21655251
dataset_size: 58... |
darentang/sroie | 2021-12-09T15:11:29.000Z | [
"region:us"
] | darentang | https://arxiv.org/abs/2103.10213 | @article{2019,
title={ICDAR2019 Competition on Scanned Receipt OCR and Information Extraction},
url={http://dx.doi.org/10.1109/ICDAR.2019.00244},
DOI={10.1109/icdar.2019.00244},
journal={2019 International Conference on Document Analysis and Recognition (ICDAR)},
publisher={IEEE},
author={Huang, Zheng... | null | 1 | 978 | Entry not found |
Tevatron/wikipedia-trivia | 2021-09-13T23:34:51.000Z | [
"region:us"
] | Tevatron | null | @inproceedings{karpukhin-etal-2020-dense,
title = "Dense Passage Retrieval for Open-Domain Question Answering",
author = "Karpukhin, Vladimir and Oguz, Barlas and Min, Sewon and Lewis, Patrick and Wu, Ledell and Edunov,
Sergey and Chen, Danqi and Yih, Wen-tau",
booktitle = "Proceedings of the 2020 Confe... | null | 1 | 977 | Entry not found |
gpt3mix/sst2 | 2021-05-18T08:59:33.000Z | [
"region:us"
] | gpt3mix | null | null | null | 0 | 975 | Entry not found |
hans | 2023-04-05T10:06:58.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"arxiv:1902.01007"... | null | The HANS dataset is an NLI evaluation set that tests specific hypotheses about invalid heuristics that NLI models are likely to learn. | @article{DBLP:journals/corr/abs-1902-01007,
author = {R. Thomas McCoy and
Ellie Pavlick and
Tal Linzen},
title = {Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural
Language Inference},
journal = {CoRR},
volume = {abs/1902.01007},
y... | null | 3 | 970 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- natural-language-inference
paperswithcode_id: hans
pretty_name:... |
GabeHD/pokemon-type-captions | 2022-10-23T04:40:59.000Z | [
"region:us"
] | GabeHD | null | null | null | 3 | 970 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 19372532.0
num_examples: 898
download_size: 0
dataset_size: 19372532.0
---
# Dataset Card for Pokémon type captions
Contains official artwork and type-specific caption for Po... |
banghua/tldr_reward_model_labeled | 2023-09-21T19:08:04.000Z | [
"region:us"
] | banghua | null | null | null | 0 | 968 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 300444471.0
num_examples: 176163
download_size: 177215543
dataset_size: 300444471.0
configs:
- config_name: default
data_files:
- ... |
hendrycks/ethics | 2023-04-19T18:55:00.000Z | [
"language:en",
"license:mit",
"AI Alignment",
"arxiv:2008.02275",
"region:us"
] | hendrycks | A benchmark that spans concepts in justice, well-being, duties, virtues, and commonsense morality. | @article{hendrycks2020aligning,
title={Aligning ai with shared human values},
author={Hendrycks, Dan and Burns, Collin and Basart, Steven and Critch, Andrew and Li, Jerry and Song, Dawn and Steinhardt, Jacob},
journal={arXiv preprint arXiv:2008.02275},
year={2020}
} | null | 6 | 965 | ---
license: mit
language: en
dataset_info:
- config_name: default
features:
- name: label
dtype: int64
- name: input
dtype: string
- config_name: commonsense
features:
- name: label
dtype: int32
- name: input
dtype: string
splits:
- name: train
num_bytes: 14429921
num_examples: ... |
esnli | 2023-04-05T10:05:24.000Z | [
"language:en",
"region:us"
] | null | The e-SNLI dataset extends the Stanford Natural Language Inference Dataset to
include human-annotated natural language explanations of the entailment
relations. | @incollection{NIPS2018_8163,
title = {e-SNLI: Natural Language Inference with Natural Language Explanations},
author = {Camburu, Oana-Maria and Rockt\"{a}schel, Tim and Lukasiewicz, Thomas and Blunsom, Phil},
booktitle = {Advances in Neural Information Processing Systems 31},
editor = {S. Bengio and H. Wallach and H. L... | null | 15 | 964 | ---
language:
- en
paperswithcode_id: e-snli
pretty_name: e-SNLI
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
- name: expla... |
RikoteMaster/isear_for_llama2 | 2023-08-03T13:01:30.000Z | [
"region:us"
] | RikoteMaster | null | null | null | 0 | 960 | ---
dataset_info:
features:
- name: Text_processed
dtype: string
- name: Emotion
dtype: string
- name: Augmented
dtype: bool
- name: text
dtype: string
splits:
- name: train
num_bytes: 3715314
num_examples: 7499
- name: validation
num_bytes: 645323
num_examples: 1324
- ... |
kyujinpy/KOpen-platypus | 2023-10-06T17:07:39.000Z | [
"size_categories:10K<n<100K",
"language:en",
"language:ko",
"license:cc-by-4.0",
"arxiv:2308.07317",
"region:us"
] | kyujinpy | null | null | null | 18 | 960 | ---
license: cc-by-4.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
- name: instruction
dtype: string
splits:
- name: train
num_examples: 24926
language:
- en
- ko
size_c... |
ddrg/super_eurlex | 2023-09-05T15:48:37.000Z | [
"license:mit",
"region:us"
] | ddrg | Super-EURLEX dataset containing legal documents from multiple languages.
The datasets are build/scrapped from the EURLEX Website [https://eur-lex.europa.eu/homepage.html]
With one split per language and sector, because the available features (metadata) differs for each
s... | null | 0 | 957 | ---
license: mit
---
| |
PygmalionAI/PIPPA | 2023-09-07T03:07:55.000Z | [
"task_categories:conversational",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"not-for-all-audiences",
"conversational",
"roleplay",
"custom-format",
"a.",
"arxiv:2308.05884",
"region:us"
] | PygmalionAI | Personal Interaction Pairs between People and AI (PIPPA) is a partially synthetic, community contributed and open-source conversational and roleplaying dataset generated from a subset of submitted logs to the Pygmalion project. | @misc{gosling2023pippa,
title={PIPPA: A Partially Synthetic Conversational Dataset},
author={Tear Gosling and Alpin Dale and Yinhe Zheng},
year={2023},
eprint={2308.05884},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | null | 96 | 953 | ---
license: apache-2.0
task_categories:
- conversational
language:
- en
tags:
- not-for-all-audiences
- conversational
- roleplay
- custom-format
- a.
pretty_name: PIPPA - Personal Interaction Pairs Between People and AI
size_categories:
- 10K<n<100K
viewer: false
---
# PIPPA - Personal Interaction Pairs between Peop... |
miracl/miracl-corpus | 2023-01-05T17:28:26.000Z | [
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"annotations_creators:expert-generated",
"multilinguality:multilingual",
"language:ar",
"language:bn",
"language:en",
"language:es",
"language:fa",
"language:fi",
"language:fr",
"language:hi",
"language:id",
"language:ja",
... | miracl | null | null | null | 12 | 950 | ---
annotations_creators:
- expert-generated
language:
- ar
- bn
- en
- es
- fa
- fi
- fr
- hi
- id
- ja
- ko
- ru
- sw
- te
- th
- zh
multilinguality:
- multilingual
pretty_name: MIRACL-corpus
size_categories: []
source_datasets: []
tags: []
task_categories:
- text-retrieval
license:
- apache-2.0
task_ids:
- do... |
nlphuji/flickr_1k_test_image_text_retrieval | 2023-01-14T19:54:08.000Z | [
"region:us"
] | nlphuji | null | null | null | 0 | 950 | # Flickr30k (1K test set)
Original paper: [From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions](https://aclanthology.org/Q14-1006)
Homepage: https://shannon.cs.illinois.edu/DenotationGraph/
1K test set split from: http://cs.stanford.edu/people/karpathy... |
wmt18 | 2023-04-05T13:44:00.000Z | [
"task_categories:translation",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:translation",
"size_categories:10M<n<100M",
"source_datasets:extended|europarl_bilingual",
"source_datasets:extended|news_commentary",
"source_datasets:extended|opus_paracrawl",
"source_d... | null | null | @InProceedings{bojar-EtAl:2018:WMT1,
author = {Bojar, Ond\v{r}ej and Federmann, Christian and Fishel, Mark
and Graham, Yvette and Haddow, Barry and Huck, Matthias and
Koehn, Philipp and Monz, Christof},
title = {Findings of the 2018 Conference on Machine Translation (WMT18)},
booktitle =... | null | 3 | 943 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- cs
- de
- en
- et
- fi
- kk
- ru
- tr
- zh
license:
- unknown
multilinguality:
- translation
size_categories:
- 10M<n<100M
source_datasets:
- extended|europarl_bilingual
- extended|news_commentary
- extended|opus_paracrawl
- extended|setim... |
SetFit/sst2 | 2021-12-25T06:16:15.000Z | [
"region:us"
] | SetFit | null | null | null | 3 | 943 | # Stanford Sentiment Treebank - Binary
[Stanford Sentiment Treebank](http://nlp.stanford.edu/sentiment/) with 2 labels: negative, positive
Splits are from:
[https://github.com/AcademiaSinicaNLPLab/sentiment_dataset/tree/master/data](https://github.com/AcademiaSinicaNLPLab/sentiment_dataset/tree/master/data)
... |
news_commentary | 2022-11-03T16:47:41.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ar",
"language:cs",
"language:de",
"language:en",
"language:es",
"language:fr",
"language:it",
"langua... | null | A parallel corpus of News Commentaries provided by WMT for training SMT. The source is taken from CASMACAT: http://www.casmacat.eu/corpus/news-commentary.html
12 languages, 63 bitexts
total number of files: 61,928
total number of tokens: 49.66M
total number of sentence fragments: 1.93M | @InProceedings{TIEDEMANN12.463,
author = {J�rg Tiedemann},
title = {Parallel Data, Tools and Interfaces in OPUS},
booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
year = {2012},
month = {may},
date = {23-25},
address = {Istanbul, Turkey},
ed... | null | 21 | 939 | ---
annotations_creators:
- found
language_creators:
- found
language:
- ar
- cs
- de
- en
- es
- fr
- it
- ja
- nl
- pt
- ru
- zh
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name:... |
SetFit/enron_spam | 2022-01-16T18:12:43.000Z | [
"region:us"
] | SetFit | null | null | null | 7 | 939 | This is a version of the [Enron Spam Email Dataset](https://github.com/MWiechmann/enron_spam_data), containing emails (subject + message) and a label whether it is spam or ham. |
Matthijs/snacks | 2022-04-12T14:26:59.000Z | [
"task_categories:image-classification",
"license:cc-by-4.0",
"region:us"
] | Matthijs | null | @article{OpenImages2,
title={OpenImages: A public dataset for large-scale multi-label and multi-class image classification.},
author={Krasin, Ivan and Duerig, Tom and Alldrin, Neil and Ferrari, Vittorio and Abu-El-Haija, Sami and Kuznetsova, Alina and Rom, Hassan and Uijlings, Jasper and Popov, Stefan and Kamali, S... | null | 6 | 938 | ---
pretty_name: Snacks
task_categories:
- image-classification
- computer-vision
license: cc-by-4.0
---
# Dataset Card for Snacks
## Dataset Summary
This is a dataset of 20 different types of snack foods that accompanies the book [Machine Learning by Tutorials](https://www.raywenderlich.com/books/machine-learning-b... |
Multimodal-Fatima/VizWiz | 2023-03-07T01:26:12.000Z | [
"region:us"
] | Multimodal-Fatima | null | null | null | 1 | 935 | Entry not found |
oscar-corpus/OSCAR-2201 | 2023-05-30T07:48:15.000Z | [
"task_categories:fill-mask",
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:multilingual",
"source_datasets:original",
"language:af",
"language:sq",
"language:am",
"language:ar",
"language:an",
... | oscar-corpus | The Open Super-large Crawled Aggregated coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the Ungoliant architecture.\ |
@ARTICLE{2022arXiv220106642A,
author = {{Abadji}, Julien and {Ortiz Suarez}, Pedro and {Romary}, Laurent and {Sagot}, Beno{\^\i}t},
title = "{Towards a Cleaner Document-Oriented Multilingual Crawled Corpus}",
journal = {arXiv e-prints},
keywords = {Computer Science - Computation and Language},
year = 2022,
... | null | 72 | 934 | ---
pretty_name: OSCAR
annotations_creators:
- no-annotation
language_creators:
- found
language:
- af
- sq
- am
- ar
- an
- hy
- as
- ast
- av
- az
- bn
- ba
- eu
- be
- bh
- bpy
- bs
- br
- bg
- my
- ca
- ceb
- ckb
- ce
- zh
- cv
- kw
- hr
- cs
- da
- diq
- dv
- nl
- mhr
- arz
- en
- eo
- et
- tl
- fi
- fr
- gl
- ka
... |
mteb/mtop_intent | 2022-09-27T19:10:23.000Z | [
"language:de",
"language:en",
"language:es",
"language:fr",
"language:hi",
"language:th",
"region:us"
] | mteb | null | null | null | 2 | 932 | ---
language:
- de
- en
- es
- fr
- hi
- th
--- |
mattmdjaga/human_parsing_dataset | 2023-09-11T09:07:44.000Z | [
"task_categories:image-segmentation",
"task_ids:semantic-segmentation",
"size_categories:10K<n<100K",
"region:us"
] | mattmdjaga | null | null | null | 10 | 932 | ---
size_categories:
- 10K<n<100K
task_categories:
- image-segmentation
task_ids:
- semantic-segmentation
dataset_info:
features:
- name: image
dtype: image
- name: mask
dtype: image
splits:
- name: train
num_bytes: 5892290030.116
num_examples: 17706
download_size: 5893438158
dataset_size:... |
quac | 2023-01-25T14:43:01.000Z | [
"task_categories:question-answering",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:dialogue-modeling",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categ... | null | Question Answering in Context is a dataset for modeling, understanding,
and participating in information seeking dialog. Data instances consist
of an interactive dialog between two crowd workers: (1) a student who
poses a sequence of freeform questions to learn as much as possible
about a hidden Wikipedia text, and (2)... | @inproceedings{choi-etal-2018-quac,
title = "QUAC: Question answering in context",
abstract = "We present QuAC, a dataset for Question Answering in Context that contains 14K information-seeking QA dialogs (100K questions in total). The dialogs involve two crowd workers: (1) a student who poses a sequence of freeform qu... | null | 13 | 930 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- found
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|wikipedia
task_categories:
- question-answering
- text-generation
- fill-mask
task_ids:
- dialogue-modeling
- extracti... |
SetFit/20_newsgroups | 2022-02-03T08:27:00.000Z | [
"region:us"
] | SetFit | null | null | null | 5 | 930 | This is a version of the [20 newsgroups dataset](https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html#the-20-newsgroups-text-dataset) that is provided in Scikit-learn. From the Scikit-learn docs:
> The 20 newsgroups dataset comprises around 18000 newsgroups posts on 20 topics split in two subsets: one for tra... |
HuggingFaceH4/oasst1_en | 2023-06-06T13:54:52.000Z | [
"license:apache-2.0",
"region:us"
] | HuggingFaceH4 | null | null | null | 24 | 928 | ---
license: apache-2.0
dataset_info:
features:
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train_ift
num_bytes: 30802170.224582057
num_examples: 19111
- name: test_ift
num_bytes: 3423358.775417942
num_examples: 2124
... |
ccdv/govreport-summarization | 2022-10-24T20:32:47.000Z | [
"task_categories:summarization",
"task_categories:text-generation",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:en",
"conditional-text-generation",
"arxiv:2104.02112",
"region:us"
] | ccdv | GovReport dataset for summarization.
From paper: Efficient Attentions for Long Document Summarization" by L. Huang et al.
See: https://arxiv.org/pdf/2104.02112.pdf
See: https://github.com/luyang-huang96/LongDocSum | @misc{huang2021efficient,
title={Efficient Attentions for Long Document Summarization},
author={Luyang Huang and Shuyang Cao and Nikolaus Parulian and Heng Ji and Lu Wang},
year={2021},
eprint={2104.02112},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
} | null | 9 | 926 | ---
language:
- en
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
task_categories:
- summarization
- text-generation
task_ids: []
tags:
- conditional-text-generation
---
# GovReport dataset for summarization
Dataset for summarization of long documents.\
Adapted from this [repo](https://github.com/luyang... |
mteb/biorxiv-clustering-s2s | 2022-09-27T19:15:35.000Z | [
"language:en",
"region:us"
] | mteb | null | null | null | 0 | 919 | ---
language:
- en
--- |
nelorth/oxford-flowers | 2022-12-11T02:38:31.000Z | [
"task_categories:image-classification",
"task_categories:unconditional-image-generation",
"source_datasets:https://www.robots.ox.ac.uk/~vgg/data/flowers",
"license:unknown",
"flowers",
"oxford",
"region:us"
] | nelorth | null | null | null | 6 | 916 | ---
pretty_name: Oxford Flowers Dataset
source_datasets: https://www.robots.ox.ac.uk/~vgg/data/flowers
tags:
- flowers
- oxford
task_categories:
- image-classification
- unconditional-image-generation
license:
- unknown
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
cl... |
huggan/flowers-102-categories | 2022-04-04T17:21:42.000Z | [
"region:us"
] | huggan | null | null | null | 4 | 908 | Entry not found |
wyzelabs/RuleRecommendation | 2023-09-15T19:26:50.000Z | [
"license:cc-by-nc-nd-4.0",
"IoT",
"Smart Home",
"Rule Recommendation",
"Recommendation Systems",
"region:us"
] | wyzelabs | null | null | null | 8 | 907 | ---
license: cc-by-nc-nd-4.0
extra_gated_heading: >-
Wyze Rule Recommendation Challenge Participation and Dataset Access Terms and
Conditions
extra_gated_prompt: >-
Please read the <a href="https://drive.google.com/uc?id=1eM3RQYeQUZeiIo8cqTgC3ixM17Vhd6QL" target="_blank">Wyze Rule Recommendation Challenge Partici... |
mteb/reddit-clustering | 2022-09-27T19:13:31.000Z | [
"language:en",
"region:us"
] | mteb | null | null | null | 0 | 894 | ---
language:
- en
--- |
dongyoung4091/hh-generated_flan_t5_rx_xl_all | 2023-09-03T02:17:32.000Z | [
"region:us"
] | dongyoung4091 | null | null | null | 0 | 894 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: response
dtype: string
- name: prompt
dtype: string
- name: model_A
dtype: float64
- name: model_B
dtype: float64
- name: external_rm1
dtype: float64
- name: extern... |
xquad_r | 2023-06-01T14:59:54.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:extended|squad",
"source_datasets:extended|xquad",
"language:ar",
"language:de",
"langu... | null | XQuAD-R is a retrieval version of the XQuAD dataset (a cross-lingual extractive QA dataset). Like XQuAD, XQUAD-R is an 11-way parallel dataset, where each question appears in 11 different languages and has 11 parallel correct answers across the languages. | @article{roy2020lareqa,
title={LAReQA: Language-agnostic answer retrieval from a multilingual pool},
author={Roy, Uma and Constant, Noah and Al-Rfou, Rami and Barua, Aditya and Phillips, Aaron and Yang, Yinfei},
journal={arXiv preprint arXiv:2004.05484},
year={2020}
} | null | 2 | 893 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- ar
- de
- el
- en
- es
- hi
- ru
- th
- tr
- vi
- zh
license:
- cc-by-sa-4.0
multilinguality:
- multilingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|squad
- extended|xquad
task_categories:
- question-answering
task_ids:
... |
Muennighoff/xP3x-sample | 2023-09-18T13:51:06.000Z | [
"task_categories:other",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:100M<n<1B",
"language:af",
"language:ar",
"language:az",
"language:be",
"language:bg",
"language:bn",
"language:br",
"language:bs",
"langu... | Muennighoff | A multilingual collection of Winograd Schemas in six languages that can be used for evaluation of cross-lingual commonsense reasoning capabilities. | @misc{muennighoff2022crosslingual,
title={Crosslingual Generalization through Multitask Finetuning},
author={Niklas Muennighoff and Thomas Wang and Lintang Sutawika and Adam Roberts and Stella Biderman and Teven Le Scao and M Saiful Bari and Sheng Shen and Zheng-Xin Yong and Hailey Schoelkopf and Xiangru T... | null | 1 | 888 | ---
annotations_creators:
- expert-generated
- 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
- ku
- kw
- la
... |
eugenesiow/Div2k | 2022-10-21T04:01:10.000Z | [
"task_categories:other",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"license:other",
"other-image-super-resolution",
"region:us"
] | eugenesiow | DIV2K dataset: DIVerse 2K resolution high quality images as used for the challenges @ NTIRE (CVPR 2017 and
CVPR 2018) and @ PIRM (ECCV 2018) | @InProceedings{Agustsson_2017_CVPR_Workshops,
author = {Agustsson, Eirikur and Timofte, Radu},
title = {NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
url = "http://www.vision.ee.ethz.ch/~timofter/... | null | 2 | 885 | ---
annotations_creators:
- machine-generated
language_creators:
- found
language: []
license:
- other
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- other
task_ids: []
pretty_name: Div2k
tags:
- other-image-super-resolution
---
# Dataset Card for Div2k
## Tab... |
visheratin/laion-coco-nllb | 2023-09-20T04:00:48.000Z | [
"task_categories:image-to-text",
"task_categories:translation",
"size_categories:100K<n<1M",
"language:ace",
"language:acm",
"language:acq",
"language:aeb",
"language:af",
"language:ajp",
"language:ak",
"language:als",
"language:am",
"language:apc",
"language:ar",
"language:ars",
"lang... | visheratin | null | null | null | 12 | 885 | ---
language:
- ace
- acm
- acq
- aeb
- af
- ajp
- ak
- als
- am
- apc
- ar
- ars
- ary
- arz
- as
- ast
- awa
- ayr
- azb
- azj
- ba
- bm
- ban
- be
- bem
- bn
- bho
- bjn
- bo
- bs
- bug
- bg
- ca
- ceb
- cs
- cjk
- ckb
- crh
- cy
- da
- de
- dik
- dyu
- dz
- el
- en
- eo
- et
- eu
- ee
- fo
- fj
- fi
- fon
- fr
- fu... |
WizardLM/WizardLM_evol_instruct_70k | 2023-08-24T03:59:32.000Z | [
"arxiv:2308.09583",
"arxiv:2304.12244",
"arxiv:2306.08568",
"region:us"
] | WizardLM | null | null | null | 106 | 882 | This is the training data of WizardLM.
## News
- 🔥 🔥 🔥 [08/11/2023] We release **WizardMath** Models.
- 🔥 Our **WizardMath-70B-V1.0** model slightly outperforms some closed-source LLMs on the GSM8K, including **ChatGPT 3.5**, **Claude Instant 1** and **PaLM 2 540B**.
- 🔥 Our **WizardMath-70B-V1.0** model achiev... |
mariosasko/test_multi_dir_dataset | 2022-02-25T17:58:58.000Z | [
"region:us"
] | mariosasko | null | null | null | 0 | 879 | Entry not found |
tasksource/oasst1_pairwise_rlhf_reward | 2023-07-04T17:47:46.000Z | [
"language:en",
"language:es",
"language:ru",
"language:de",
"language:pl",
"language:th",
"language:vi",
"language:sv",
"language:bn",
"language:da",
"language:he",
"language:it",
"language:fa",
"language:sk",
"language:id",
"language:nb",
"language:el",
"language:nl",
"language:... | tasksource | null | null | null | 19 | 877 | ---
dataset_info:
features:
- name: lang
dtype: string
- name: parent_id
dtype: string
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 40736437
num_examples: 17966
- name: validation
num_bytes: 21... |
empathetic_dialogues | 2023-04-05T10:05:17.000Z | [
"task_categories:conversational",
"task_categories:question-answering",
"task_ids:dialogue-generation",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"langua... | null | PyTorch original implementation of Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset | @inproceedings{rashkin2019towards,
title = {Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset},
author = {Hannah Rashkin and Eric Michael Smith and Margaret Li and Y-Lan Boureau},
booktitle = {ACL},
year = {2019},
} | null | 52 | 874 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- crowdsourced
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
pretty_name: EmpatheticDialogues
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- conversational
- question-answering
task_ids:
- dialogue-generati... |
facebook/multilingual_librispeech | 2023-02-13T11:33:31.000Z | [
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:de",
"language:nl",
"language:fr",
"... | facebook | This is a streamable version of the Multilingual LibriSpeech (MLS) dataset.
The data archives were restructured from the original ones from [OpenSLR](http://www.openslr.org/94)
to make it easier to stream.
MLS dataset is a large multilingual corpus suitable for speech research.
The dataset is derived from read aud... | @article{Pratap2020MLSAL,
title={MLS: A Large-Scale Multilingual Dataset for Speech Research},
author={Vineel Pratap and Qiantong Xu and Anuroop Sriram and Gabriel Synnaeve and Ronan Collobert},
journal={ArXiv},
year={2020},
volume={abs/2012.03411}
} | null | 27 | 865 | ---
pretty_name: MultiLingual LibriSpeech
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- de
- nl
- fr
- it
- es
- pt
- pl
license:
- cc-by-4.0
multilinguality:
- multilingual
paperswithcode_id: multilingual-librispeech
size_categories:
- 100K<n<1M
source_datase... |
lj_speech | 2022-11-03T16:16:34.000Z | [
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unlicense",
"region:us"
] | null | This is a public domain speech dataset consisting of 13,100 short audio clips of a single speaker reading
passages from 7 non-fiction books in English. A transcription is provided for each clip. Clips vary in length
from 1 to 10 seconds and have a total length of approximately 24 hours.
Note that in order to limit the... | @misc{ljspeech17,
author = {Keith Ito and Linda Johnson},
title = {The LJ Speech Dataset},
howpublished = {\\url{https://keithito.com/LJ-Speech-Dataset/}},
year = 2017
} | null | 8 | 863 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- unlicense
multilinguality:
- monolingual
paperswithcode_id: ljspeech
pretty_name: LJ Speech
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- automatic-speech-recognition
task_ids: []
train-eval-... |
Multimodal-Fatima/StanfordCars_train | 2023-06-12T06:26:48.000Z | [
"region:us"
] | Multimodal-Fatima | null | null | null | 0 | 862 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': am general hummer suv 2000
'1': acura rl sedan 2012
'2': acura tl sedan 2012
'3': acura tl type-s 2008
'4': acura tsx sedan 2012
'5... |
natural_questions | 2023-04-05T13:35:01.000Z | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | null | The NQ corpus contains questions from real users, and it requires QA systems to
read and comprehend an entire Wikipedia article that may or may not contain the
answer to the question. The inclusion of real user questions, and the
requirement that solutions should read an entire page to find the answer, cause
NQ to be a... | @article{47761,
title = {Natural Questions: a Benchmark for Question Answering Research},
author = {Tom Kwiatkowski and Jennimaria Palomaki and Olivia Redfield and Michael Collins and Ankur Parikh and Chris Alberti and Danielle Epstein and Illia Polosukhin and Matthew Kelcey and Jacob Devlin and Kenton Lee and Kristina... | null | 21 | 861 | ---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswithcode_id: natural-questions
pretty_name: Na... |
bigcode/the-stack-smol-xl | 2023-02-10T17:22:38.000Z | [
"task_categories:text-generation",
"task_ids:language-modeling",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:unknown",
"language:code",
"region:us"
] | bigcode | null | null | null | 3 | 861 | ---
annotations_creators: []
language_creators:
- crowdsourced
language: ["code"]
multilinguality:
- multilingual
size_categories:
- unknown
source_datasets: []
task_categories:
- text-generation
task_ids:
- language-modeling
---
## Dataset Description
A small subset of [the-stack](https://huggingface.co/datasets/big... |
DFKI-SLT/cross_ner | 2023-01-19T09:17:38.000Z | [
"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",
"cross domain",
"ai",
"news",
"musi... | DFKI-SLT | CrossNER is a fully-labeled collected of named entity recognition (NER) data spanning over five diverse domains
(Politics, Natural Science, Music, Literature, and Artificial Intelligence) with specialized entity categories for
different domains. Additionally, CrossNER also includes unlabeled domain-related corpora fo... | @article{liu2020crossner,
title={CrossNER: Evaluating Cross-Domain Named Entity Recognition},
author={Zihan Liu and Yan Xu and Tiezheng Yu and Wenliang Dai and Ziwei Ji and Samuel Cahyawijaya and Andrea Madotto and Pascale Fung},
year={2020},
eprint={2012.04373},
archivePrefix={arXiv},
... | null | 0 | 860 | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- found
license: []
multilinguality:
- monolingual
pretty_name: CrossNER is a cross-domain dataset for named entity recognition
size_categories:
- 10K<n<100K
source_datasets:
- extended|conll2003
tags:
- cross domain
- ai
- news
- music
- li... |
d0rj/curation-corpus | 2023-06-13T13:25:32.000Z | [
"task_categories:summarization",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"news",
"summarization",
"region:us"
] | d0rj | null | null | null | 0 | 859 | ---
dataset_info:
features:
- name: title
dtype: string
- name: summary
dtype: string
- name: url
dtype: string
- name: date
dtype: string
- name: article_content
dtype: string
splits:
- name: train
num_bytes: 127948910
num_examples: 30455
download_size: 76620775
dataset_... |
ambig_qa | 2022-11-03T16:31:34.000Z | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|natural_questions",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3... | null | AmbigNQ, a dataset covering 14,042 questions from NQ-open, an existing open-domain QA benchmark. We find that over half of the questions in NQ-open are ambiguous. The types of ambiguity are diverse and sometimes subtle, many of which are only apparent after examining evidence provided by a very large text corpus. AMBI... | @inproceedings{ min2020ambigqa,
title={ {A}mbig{QA}: Answering Ambiguous Open-domain Questions },
author={ Min, Sewon and Michael, Julian and Hajishirzi, Hannaneh and Zettlemoyer, Luke },
booktitle={ EMNLP },
year={2020}
} | null | 2 | 854 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|natural_questions
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswithcode_id: ambigqa
pre... |
ybelkada/football-dataset | 2023-01-17T11:47:41.000Z | [
"region:us"
] | ybelkada | null | null | null | 0 | 852 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 2073622.0
num_examples: 6
download_size: 2074835
dataset_size: 2073622.0
---
# Dataset Card for "football-dataset"
Dummy dataset of 6 football players with a caption that ca... |
THUDM/ImageRewardDB | 2023-06-21T06:36:29.000Z | [
"task_categories:text-to-image",
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"arxiv:2304.05977",
"region:us"
] | THUDM | ImageRewardDB is a comprehensive text-to-image comparison dataset, focusing on text-to-image human preference. It consists of 137k pairs of expert comparisons, based on text prompts and corresponding model outputs from DiffusionDB. To build the ImageRewadDB, we design a pipeline tailored for it, establishing criteria f... | @misc{xu2023imagereward,
title={ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation},
author={Jiazheng Xu and Xiao Liu and Yuchen Wu and Yuxuan Tong and Qinkai Li and Ming Ding and Jie Tang and Yuxiao Dong},
year={2023},
eprint={2304.05977},
archivePrefix={... | null | 16 | 850 | ---
license: apache-2.0
task_categories:
- text-to-image
language:
- en
pretty_name: ImageReward Dataset
size_categories:
- 100K<n<1M
---
# ImageRewardDB
## Dataset Description
- **Homepage: https://huggingface.co/datasets/wuyuchen/ImageRewardDB**
- **Repository: https://github.com/THUDM/ImageReward**
- **Paper: h... |
mlabonne/guanaco-llama2 | 2023-07-26T14:49:17.000Z | [
"region:us"
] | mlabonne | null | null | null | 7 | 849 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 15409089
num_examples: 9846
- name: test
num_bytes: 815811
num_examples: 518
download_size: 9461517
dataset_size: 16224900
---
# Guanaco: Lazy Llama 2 Formatting
This is the excellent [`timdettmers... |
theblackcat102/evol-codealpaca-v1 | 2023-09-07T11:42:00.000Z | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-nc-4.0",
"code",
"region:us"
] | theblackcat102 | null | null | null | 65 | 848 | ---
license: cc-by-nc-4.0
task_categories:
- text-generation
language:
- en
tags:
- code
size_categories:
- 100K<n<1M
---
## Evolved codealpaca
Updates:
* 2023/08/26 - Filtered results now only contain pure english instruction and removed any mentioned of trained by OAI response
Median sequence length : 471
We emp... |
squad_adversarial | 2022-11-18T21:47:43.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|squad",
"language:en",
"license:mit",
"region:us"
] | null | Here are two different adversaries, each of which uses a different procedure to pick the sentence it adds to the paragraph:
AddSent: Generates up to five candidate adversarial sentences that don't answer the question, but have a lot of words in common with the question. Picks the one that most confuses the model.
AddOn... | @inproceedings{jia-liang-2017-adversarial,
title = "Adversarial Examples for Evaluating Reading Comprehension Systems",
author = "Jia, Robin and
Liang, Percy",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2017",
... | null | 5 | 847 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|squad
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: null
pretty_name: '''Adversarial Examples for ... |
BeIR/fiqa | 2022-10-23T06:00:28.000Z | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | BeIR | null | null | null | 3 | 844 | ---
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:
... |
qanastek/MASSIVE | 2022-12-23T21:28:08.000Z | [
"task_categories:text-classification",
"task_ids:intent-classification",
"task_ids:multi-class-classification",
"task_ids:named-entity-recognition",
"annotations_creators:machine-generated",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:multilingual",
"size_cat... | qanastek | 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 of general Intelligent Voice As... | @misc{fitzgerald2022massive,
title={MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages},
author={Jack FitzGerald and Christopher Hench and Charith Peris and Scott Mackie and Kay Rottmann and Ana Sanchez and Aaron Nash and Liam Urbach and Vishes... | null | 16 | 830 | ---
annotations_creators:
- machine-generated
- expert-generated
language_creators:
- found
language:
- af
- am
- ar
- az
- bn
- cy
- da
- de
- el
- en
- es
- fa
- fi
- 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
- t... |
fusing/instructpix2pix-1000-samples | 2023-02-23T07:08:49.000Z | [
"region:us"
] | fusing | null | null | null | 4 | 830 | ---
dataset_info:
features:
- name: input_image
dtype: image
- name: edit_prompt
dtype: string
- name: edited_image
dtype: image
splits:
- name: train
num_bytes: 416880759.0
num_examples: 1000
download_size: 416899514
dataset_size: 416880759.0
---
# Dataset Card for "instructpix2pix-... |
jojo0217/korean_rlhf_dataset | 2023-09-25T08:36:04.000Z | [
"task_categories:text-generation",
"language:ko",
"license:apache-2.0",
"region:us"
] | jojo0217 | null | null | null | 7 | 829 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- ko
---
성균관대학교 산학협력프로젝트 과정에서 한국어 llm 모델 SFT 학습을 위해 구축한 데이터셋 입니다.
2023-09-25
오픈 어시스턴트 data에서 오픈 어시스턴트를 포함하는 데이터 삭제
-> 답변에 오픈 어시스턴트라고 하는 경우가 나오기 때문
또한 스탠포드 대학 번역 데이터에서 번역 과정 오류로 input에 입력없음 과 같이 추가된 부분 삭제
그리고 \<unk\> 등으로 gpt 상에서 번역 오류가 ... |
lbox/lbox_open | 2022-11-09T06:41:26.000Z | [
"license:cc-by-nc-4.0",
"region:us"
] | lbox | null | null | null | 2 | 827 | ---
license: cc-by-nc-4.0
---
# Dataset Card for `lbox_open`
## Dataset Description
- **Homepage:** `https://lbox.kr`
- **Repository:** `https://github.com/lbox-kr/lbox_open`
- **Point of Contact:** [Wonseok Hwang](mailto:wonseok.hwang@lbox.kr)
### Dataset Summary
A Legal AI Benchmark Dataset from Korean Legal Case... |
aharley/rvl_cdip | 2023-05-02T09:06:16.000Z | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|iit_cdip",
"language:en",
"license:other",
"arxiv:1502.07058",
"regi... | aharley | The RVL-CDIP (Ryerson Vision Lab Complex Document Information Processing) dataset consists of 400,000 grayscale images in 16 classes, with 25,000 images per class. There are 320,000 training images, 40,000 validation images, and 40,000 test images. | @inproceedings{harley2015icdar,
title = {Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval},
author = {Adam W Harley and Alex Ufkes and Konstantinos G Derpanis},
booktitle = {International Conference on Document Analysis and Recognition ({ICDAR})}},
year = {2015}
} | null | 28 | 827 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|iit_cdip
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
paperswithcode_id: rvl-cdip
pretty_name: RVL-... |
allenai/scirepeval_test | 2022-10-21T20:54:57.000Z | [
"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 | 0 | 826 | Entry not found |
TREC-AToMiC/AToMiC-Texts-v0.2.1 | 2023-05-04T18:58:43.000Z | [
"region:us"
] | TREC-AToMiC | null | null | null | 2 | 826 | ---
dataset_info:
features:
- name: text_id
dtype: string
- name: page_url
dtype: string
- name: page_title
dtype: string
- name: section_title
dtype: string
- name: context_page_description
dtype: string
- name: context_section_description
dtype: string
- name: media
sequenc... |
leemeng/jcommonsenseqa-v1.1 | 2023-04-28T08:13:50.000Z | [
"license:cc-by-4.0",
"region:us"
] | leemeng | null | null | null | 1 | 825 | ---
license: cc-by-4.0
dataset_info:
features:
- name: q_id
dtype: int64
- name: question
dtype: string
- name: choice0
dtype: string
- name: choice1
dtype: string
- name: choice2
dtype: string
- name: choice3
dtype: string
- name: choice4
dtype: string
- name: label
dt... |
universal_morphologies | 2023-06-08T09:28:28.000Z | [
"task_categories:token-classification",
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:multi-label-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categori... | null | The Universal Morphology (UniMorph) project is a collaborative effort to improve how NLP handles complex morphology in the world’s languages.
The goal of UniMorph is to annotate morphological data in a universal schema that allows an inflected word from any language to be defined by its lexical meaning,
typically carri... | @article{sylak2016composition,
title={The composition and use of the universal morphological feature schema (unimorph schema)},
author={Sylak-Glassman, John},
journal={Johns Hopkins University},
year={2016}
} | null | 13 | 824 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- ady
- ang
- ar
- arn
- ast
- az
- ba
- be
- bg
- bn
- bo
- br
- ca
- ckb
- crh
- cs
- csb
- cu
- cy
- da
- de
- dsb
- el
- en
- es
- et
- eu
- fa
- fi
- fo
- fr
- frm
- fro
- frr
- fur
- fy
- ga
- gal
- gd
- gmh
- gml
- got
- grc
- gv
-... |
mteb/amazon_polarity | 2022-09-27T19:11:44.000Z | [
"language:en",
"region:us"
] | mteb | null | null | null | 0 | 824 | ---
language:
- en
--- |
codah | 2023-01-25T14:28:20.000Z | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | null | The COmmonsense Dataset Adversarially-authored by Humans (CODAH) is an evaluation set for commonsense question-answering in the sentence completion style of SWAG. As opposed to other automatically generated NLI datasets, CODAH is adversarially constructed by humans who can view feedback from a pre-trained model and use... | @inproceedings{chen2019codah,
title={CODAH: An Adversarially-Authored Question Answering Dataset for Common Sense},
author={Chen, Michael and D'Arcy, Mike and Liu, Alisa and Fernandez, Jared and Downey, Doug},
booktitle={Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP},
pages=... | null | 4 | 822 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
paperswithcode_id: codah
pretty_name: COmmonsense Datas... |
mlsum | 2023-06-01T14:59:54.000Z | [
"task_categories:summarization",
"task_categories:translation",
"task_categories:text-classification",
"task_ids:news-articles-summarization",
"task_ids:multi-class-classification",
"task_ids:multi-label-classification",
"task_ids:topic-classification",
"annotations_creators:found",
"language_creato... | null | We present MLSUM, the first large-scale MultiLingual SUMmarization dataset.
Obtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages -- namely, French, German, Spanish, Russian, Turkish.
Together with English newspapers from the popular CNN/Daily mail dataset, the collected d... | @article{scialom2020mlsum,
title={MLSUM: The Multilingual Summarization Corpus},
author={Scialom, Thomas and Dray, Paul-Alexis and Lamprier, Sylvain and Piwowarski, Benjamin and Staiano, Jacopo},
journal={arXiv preprint arXiv:2004.14900},
year={2020}
} | null | 24 | 822 | ---
annotations_creators:
- found
language_creators:
- found
language:
- de
- es
- fr
- ru
- tr
license:
- other
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
- 10K<n<100K
source_datasets:
- extended|cnn_dailymail
- original
task_categories:
- summarization
- translation
- text-classification
task_ids:
-... |
roszcz/maestro-v1-sustain | 2023-04-23T13:35:49.000Z | [
"region:us"
] | roszcz | null | null | null | 0 | 818 | ---
dataset_info:
features:
- name: notes
struct:
- name: duration
sequence: float64
- name: end
sequence: float64
- name: pitch
sequence: int64
- name: start
sequence: float64
- name: velocity
sequence: int64
- name: composer
dtype: string
- name: title... |
yzhuang/autotree_snnxor_n15_l1_10 | 2023-09-18T21:51:32.000Z | [
"region:us"
] | yzhuang | null | null | null | 0 | 817 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: input_y_clean
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: flo... |
mattymchen/celeba-hq | 2023-04-26T05:56:53.000Z | [
"region:us"
] | mattymchen | null | null | null | 0 | 816 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': female
'1': male
splits:
- name: train
num_bytes: 2731627350.0
num_examples: 28000
- name: validation
num_bytes: 197550788.0
num_examples: 2000
dow... |
jeanlee/kmhas_korean_hate_speech | 2022-11-28T16:26:56.000Z | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"task_ids:hate-speech-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:ko",
"license:cc-by-sa-4... | jeanlee | The K-MHaS (Korean Multi-label Hate Speech) dataset contains 109k utterances from Korean online news comments labeled with 8 fine-grained hate speech classes or Not Hate Speech class.
The fine-grained hate speech classes are politics, origin, physical, age, gender, religion, race, and profanity and these categories are... | @inproceedings{lee-etal-2022-k,
title = "K-{MH}a{S}: A Multi-label Hate Speech Detection Dataset in {K}orean Online News Comment",
author = "Lee, Jean and
Lim, Taejun and
Lee, Heejun and
Jo, Bogeun and
Kim, Yangsok and
Yoon, Heegeun and
Han, Soyeon Caren",
booktitle... | null | 9 | 813 | ---
annotations_creators:
- crowdsourced
language:
- ko
language_creators:
- found
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: 'K-MHaS'
size_categories:
- 100K<n<1M
source_datasets:
- original
tags:
- K-MHaS
- Korean NLP
- Hate Speech Detection
- Dataset
- Coling2022
task_categories:
- text-clas... |
Jackmin108/c4-en-validation | 2023-08-18T22:00:10.000Z | [
"region:us"
] | Jackmin108 | null | null | null | 0 | 809 | ---
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
dataset_info:
features:
- name: text
dtype: string
- name: timestamp
dtype: string
- name: url
dtype: string
splits:
- name: validation
num_bytes: 825766822
num_examples: 364608
download... |
Tevatron/msmarco-passage | 2023-07-18T07:34:33.000Z | [
"region:us"
] | Tevatron | null | @misc{bajaj2018ms,
title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset},
author={Payal Bajaj and Daniel Campos and Nick Craswell and Li Deng and Jianfeng Gao and Xiaodong Liu
and Rangan Majumder and Andrew McNamara and Bhaskar Mitra and Tri Nguyen and Mir Rosenberg and Xia Song
... | null | 3 | 808 | Entry not found |
kumapo/JAQKET | 2023-10-09T06:44:28.000Z | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"language:ja",
"license:cc-by-sa-4.0",
"region:us"
] | kumapo | JAQKET: JApanese Questions on Knowledge of EnTitie | @InProceedings{Kurihara_nlp2020,
author = "鈴木正敏 and 鈴木潤 and 松田耕史 and ⻄田京介 and 井之上直也",
title = "JAQKET: クイズを題材にした日本語 QA データセットの構築",
booktitle = "言語処理学会第26回年次大会",
year = "2020",
url = "https://www.anlp.jp/proceedings/annual_meeting/2020/pdf_dir/P2-24.pdf"
note= "in Japanese" | null | 0 | 804 | ---
license: cc-by-sa-4.0
task_categories:
- multiple-choice
- question-answering
language:
- ja
---
# Dataset Card for JAQKET
This dataset loading script is developed on [GitHub](https://github.com/kumapo/JAQKET-dataset).
Please feel free to open an [issue](https://github.com/kumapo/JAQKET-dataset/issues) or [pull r... |
nlphuji/winogavil | 2022-11-26T19:56:27.000Z | [
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"commonsense-reasoning",
"visual-reasoning",
"arxiv:2207.12576",
"region:us"
] | nlphuji | WinoGAViL is a challenging dataset for evaluating vision-and-language commonsense reasoning abilities. Given a set of images, a cue, and a number K, the task is to select the K images that best fits the association. This dataset was collected via the WinoGAViL online game to collect vision-and-language associations, (e... | @article{bitton2022winogavil,
title={WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language Models},
author={Bitton, Yonatan and Guetta, Nitzan Bitton and Yosef, Ron and Elovici, Yuval and Bansal, Mohit and Stanovsky, Gabriel and Schwartz, Roy},
journal={arXiv preprint arXiv:2207.12576},
yea... | null | 0 | 803 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
paperswithcode_id: winogavil
pretty_name: WinoGAViL
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- commonsense-reasoning
- visual-reasoning
task_ids: []
extra_gated_p... |
shibing624/medical | 2023-06-02T07:03:41.000Z | [
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:zh",
"language:en",
"license:apache-2.0",
"text-generation",
"region:us"
] | shibing624 | 纯文本数据，中文医疗数据集，包含预训练数据的百科数据，指令微调数据和奖励模型数据。 | null | null | 134 | 803 | ---
license: apache-2.0
language:
- zh
- en
tags:
- text-generation
pretty_name: medical
task_categories:
- text-generation
size_categories:
- 1M<n<10M
---
# Dataset Card for medical
中文医疗数据集
- LLM Supervised Finetuning repository: https://github.com/shibing624/textgen
- MeidcalGPT repository: https://github.com/shibi... |
DFKI-SLT/few-nerd | 2023-06-21T09:59:09.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|wikipedia",
"language:en",
"license:cc-by-sa-4.0",
"structure-predi... | DFKI-SLT | Few-NERD is a large-scale, fine-grained manually annotated named entity recognition dataset,
which contains 8 coarse-grained types, 66 fine-grained types, 188,200 sentences, 491,711 entities
and 4,601,223 tokens. Three benchmark tasks are built, one is supervised: Few-NERD (SUP) and the
other two are few-shot: Few-N... | @inproceedings{ding2021few,
title={Few-NERD: A Few-Shot Named Entity Recognition Dataset},
author={Ding, Ning and Xu, Guangwei and Chen, Yulin, and Wang, Xiaobin and Han, Xu and Xie,
Pengjun and Zheng, Hai-Tao and Liu, Zhiyuan},
booktitle={ACL-IJCNLP},
year={2021}
} | null | 12 | 802 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|wikipedia
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: few-nerd
pretty... |
result-kand2-sdxl-wuerst-karlo/46328984 | 2023-09-14T18:58:10.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | null | 0 | 795 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 209
num_examples: 10
download_size: 1390
dataset_size: 209
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "4632898... |
edarchimbaud/perimeter-stocks | 2023-10-10T15:00:20.000Z | [
"region:us"
] | edarchimbaud | null | null | null | 0 | 793 | ---
dataset_info:
features:
- name: symbol
dtype: string
- name: security
dtype: string
- name: gics_sector
dtype: string
- name: gics_sub_industry
dtype: string
splits:
- name: train
num_bytes: 112249
num_examples: 1500
download_size: 43983
dataset_size: 112249
configs:
- conf... |
sms_spam | 2023-01-25T14:44:29.000Z | [
"task_categories:text-classification",
"task_ids:intent-classification",
"annotations_creators:crowdsourced",
"annotations_creators:found",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|other-nus-sms-... | null | The SMS Spam Collection v.1 is a public set of SMS labeled messages that have been collected for mobile phone spam research.
It has one collection composed by 5,574 English, real and non-enconded messages, tagged according being legitimate (ham) or spam. | @inproceedings{Almeida2011SpamFiltering,
title={Contributions to the Study of SMS Spam Filtering: New Collection and Results},
author={Tiago A. Almeida and Jose Maria Gomez Hidalgo and Akebo Yamakami},
year={2011},
booktitle = "Proceedings of the 2011 ACM Symposium on Document Engineering (DOCENG'11)",
} | null | 12 | 788 | ---
annotations_creators:
- crowdsourced
- found
language_creators:
- crowdsourced
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-nus-sms-corpus
task_categories:
- text-classification
task_ids:
- intent-classification
paperswithcode... |
liwu/MNBVC | 2023-10-09T01:24:55.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:zh",
"licens... | liwu | MNBVC: Massive Never-ending BT Vast Chinese corpus | \ | null | 256 | 788 | ---
annotations_creators:
- other
language:
- zh
language_creators:
- other
license:
- mit
multilinguality:
- monolingual
pretty_name: MNBVC
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
---
# Dataset Card ... |
Francesco/animals-ij5d2 | 2023-03-30T09:30:09.000Z | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
] | Francesco | null | null | null | 4 | 787 | ---
dataset_info:
features:
- name: image_id
dtype: int64
- name: image
dtype: image
- name: width
dtype: int32
- name: height
dtype: int32
- name: objects
sequence:
- name: id
dtype: int64
- name: area
dtype: int64
- name: bbox
sequence: float32
lengt... |
code_x_glue_ct_code_to_text | 2023-06-01T14:59:54.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:other-programming-languages",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:code",
"language:en",
"license:c-uda",
"code-to-text",
"region... | null | The dataset we use comes from CodeSearchNet and we filter the dataset as the following:
- Remove examples that codes cannot be parsed into an abstract syntax tree.
- Remove examples that #tokens of documents is < 3 or >256
- Remove examples that documents contain special tokens (e.g. <img ...> or https:...)
- Remove ex... | @article{husain2019codesearchnet,
title={Codesearchnet challenge: Evaluating the state of semantic code search},
author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
journal={arXiv preprint arXiv:1909.09436},
year={2019}
} | null | 35 | 785 | ---
annotations_creators:
- found
language_creators:
- found
language:
- code
- en
license:
- c-uda
multilinguality:
- other-programming-languages
size_categories:
- 100K<n<1M
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
pretty_name: CodeXGlueCtCodeToText
tags:
- code-to-text
dat... |
cmrc2018 | 2023-04-05T09:42:31.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:zh",
"license:cc-by-sa-4.0",
"region:us"
] | null | A Span-Extraction dataset for Chinese machine reading comprehension to add language
diversities in this area. The dataset is composed by near 20,000 real questions annotated
on Wikipedia paragraphs by human experts. We also annotated a challenge set which
contains the questions that need comprehensive understanding and... | @inproceedings{cui-emnlp2019-cmrc2018,
title = {A Span-Extraction Dataset for {C}hinese Machine Reading Comprehension},
author = {Cui, Yiming and
Liu, Ting and
Che, Wanxiang and
Xiao, Li and
Chen, Zhipeng and
Ma, Wentao and
Wang, Shijin and
Hu, Guoping},
book... | null | 13 | 783 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- zh
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: cmrc-2018
pretty_name: Chinese Mac... |
result-kand2-sdxl-wuerst-karlo/b5ddd948 | 2023-09-15T04:06:31.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | null | 0 | 783 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 205
num_examples: 10
download_size: 1388
dataset_size: 205
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "b5ddd94... |
EleutherAI/pile-duped-pythia-random-sampled | 2023-08-25T08:07:30.000Z | [
"region:us"
] | EleutherAI | null | null | null | 1 | 782 | ---
dataset_info:
features:
- name: Index
dtype: int64
- name: 70M
dtype: float64
- name: 160M
dtype: float64
- name: 410M
dtype: float64
- name: 1B
dtype: float64
- name: 1.4B
dtype: float64
- name: 2.8B
dtype: float64
- name: 6.9B
dtype: float64
- name: 12B
dtyp... |
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