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 |
|---|---|---|---|---|---|---|---|---|---|
togethercomputer/RedPajama-Data-1T | 2023-06-30T22:06:10.000Z | [
"task_categories:text-generation",
"language:en",
"region:us"
] | togethercomputer | RedPajama is a clean-room, fully open-source implementation of the LLaMa dataset. | null | null | 878 | 17,226 | ---
task_categories:
- text-generation
language:
- en
pretty_name: Red Pajama 1T
---
### Getting Started
The dataset consists of 2084 jsonl files.
You can download the dataset using HuggingFace:
```python
from datasets import load_dataset
ds = load_dataset("togethercomputer/RedPajama-Data-1T")
```
Or you can directly... |
yelp_review_full | 2023-01-25T15:03:32.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:other",
"arxiv:1509.01626",
"region:u... | null | The Yelp reviews dataset consists of reviews from Yelp. It is extracted from the Yelp Dataset Challenge 2015 data.
The Yelp reviews full star dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu) from the above dataset.
It is first used as a text classification benchmark in the following paper: Xiang Zhang, Junbo... | @inproceedings{zhang2015character,
title={Character-level convolutional networks for text classification},
author={Zhang, Xiang and Zhao, Junbo and LeCun, Yann},
booktitle={Advances in neural information processing systems},
pages={649--657},
year={2015}
} | null | 33 | 16,117 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: YelpReviewFull
license_details: yelp... |
fashion_mnist | 2023-04-17T14:02:05.000Z | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"arxiv:1708.07747",
"reg... | null | Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of
60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image,
associated with a label from 10 classes. We intend Fashion-MNIST to serve as a direct drop-in
replacement for the original MNIST dataset for ... | @article{DBLP:journals/corr/abs-1708-07747,
author = {Han Xiao and
Kashif Rasul and
Roland Vollgraf},
title = {Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning
Algorithms},
journal = {CoRR},
volume = {abs/1708.07747},
year = {... | null | 28 | 16,091 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
paperswithcode_id: fashion-mnist
pretty_name... |
commonsense_qa | 2023-04-05T10:02:16.000Z | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:mit",
"arxiv:1811.00937",
"region:us"
] | null | CommonsenseQA is a new multiple-choice question answering dataset that requires different types of commonsense knowledge
to predict the correct answers . It contains 12,102 questions with one correct answer and four distractor answers.
The dataset is provided in two major training/validation/testing set splits: "Random... | @inproceedings{talmor-etal-2019-commonsenseqa,
title = "{C}ommonsense{QA}: A Question Answering Challenge Targeting Commonsense Knowledge",
author = "Talmor, Alon and
Herzig, Jonathan and
Lourie, Nicholas and
Berant, Jonathan",
booktitle = "Proceedings of the 2019 Conference of the Nort... | null | 23 | 16,048 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- mit
multilinguality:
- monolingual
pretty_name: CommonsenseQA
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswithcode_id: commonsenseqa
dat... |
mosaicml/dolly_hhrlhf | 2023-10-02T15:48:48.000Z | [
"task_categories:text-generation",
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | mosaicml | null | null | null | 87 | 15,344 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 43781455.002688624
num_examples: 59310
- name: test
num_bytes: 4479286.805304853
num_examples: 5129
download_size: 24882010
dataset_size: 48260741.80799348
lic... |
Open-Orca/OpenOrca | 2023-10-02T19:01:36.000Z | [
"task_categories:conversational",
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:table-question-answering",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:summarization",
"task_categories:feature-extra... | Open-Orca | null | null | null | 767 | 15,297 | ---
language:
- en
license: mit
task_categories:
- conversational
- text-classification
- token-classification
- table-question-answering
- question-answering
- zero-shot-classification
- summarization
- feature-extraction
- text-generation
- text2text-generation
pretty_name: OpenOrca
size_categories:
- 10M<n<100M
---
... |
hf-internal-testing/dummy_image_text_data | 2023-02-08T10:34:38.000Z | [
"region:us"
] | hf-internal-testing | null | null | null | 0 | 14,954 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 1944983.0
num_examples: 20
download_size: 1690123
dataset_size: 1944983.0
---
# Dataset Card for "dummy_image_text_data"
[More Information needed](https://github.com/huggingf... |
skt/kobest_v1 | 2022-08-22T09:00:17.000Z | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ko",
"license:cc-by-sa-4.0",
"arxiv:2204.04541",
"region:us"
] | skt | The dataset contains data for KoBEST dataset | null | null | 17 | 14,875 | ---
pretty_name: KoBEST
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- ko
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
---
# Dataset Card for KoBEST
## Table of Contents
- [Table of Contents](#table-of-cont... |
hate_speech_offensive | 2023-01-25T14:31:41.000Z | [
"task_categories:text-classification",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"hate-speech-detection",
"arx... | null | An annotated dataset for hate speech and offensive language detection on tweets. | @inproceedings{hateoffensive,
title = {Automated Hate Speech Detection and the Problem of Offensive Language},
author = {Davidson, Thomas and Warmsley, Dana and Macy, Michael and Weber, Ingmar},
booktitle = {Proceedings of the 11th International AAAI Conference on Web and Social Media},
series = {ICWSM '17},
year = {20... | null | 6 | 14,833 | ---
annotations_creators:
- expert-generated
- crowdsourced
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: hate-speech-and-offensive-language
pret... |
HuggingFaceM4/tmp-pmd-synthetic-testing | 2022-10-05T17:16:27.000Z | [
"region:us"
] | HuggingFaceM4 | null | null | null | 1 | 14,799 | Entry not found |
wiki_qa | 2023-04-05T13:43:16.000Z | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:other",
"region:us"
] | null | Wiki Question Answering corpus from Microsoft | @InProceedings{YangYihMeek:EMNLP2015:WikiQA,
author = {{Yi}, Yang and {Wen-tau}, Yih and {Christopher} Meek},
title = "{WikiQA: A Challenge Dataset for Open-Domain Question Answering}",
journal = {Association for Computational Linguistics},
year = 2015,
doi = {10.18653/v1/D15-12... | null | 16 | 14,642 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswithcode_id: wikiqa
pretty_name: WikiQA
dataset_info:
feat... |
bookcorpus | 2023-04-05T09:41:56.000Z | [
"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:10M<n<100M",
"source_datasets:original",
"language:en"... | null | Books are a rich source of both fine-grained information, how a character, an object or a scene looks like, as well as high-level semantics, what someone is thinking, feeling and how these states evolve through a story.This work aims to align books to their movie releases in order to providerich descriptive explanation... | @InProceedings{Zhu_2015_ICCV,
title = {Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books},
author = {Zhu, Yukun and Kiros, Ryan and Zemel, Rich and Salakhutdinov, Ruslan and Urtasun, Raquel and Torralba, Antonio and Fidler, Sanja},
booktitle = {The IEEE I... | null | 146 | 14,515 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
pretty_name: BookCorpus
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
... |
knkarthick/dialogsum | 2023-10-03T10:56:21.000Z | [
"task_categories:summarization",
"task_categories:text2text-generation",
"task_categories:text-generation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"licens... | knkarthick | null | null | null | 74 | 14,230 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license: cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- summarization
- text2text-generation
- text-generation
task_ids: []
pretty_name: DIALOGSu... |
mozilla-foundation/common_voice_13_0 | 2023-06-26T15:23:12.000Z | [
"task_categories:automatic-speech-recognition",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"source_datasets:extended|common_voice",
"license:cc0-1.0",
"arxiv:1912.06670",
"region:us"
] | mozilla-foundation | null | @inproceedings{commonvoice:2020,
author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
title = {Common Voice: A Massively-Multilingual Speech Corpus},
booktitle = {Proceedings of the 12th Conference on Lang... | null | 74 | 14,203 | ---
pretty_name: Common Voice Corpus 13.0
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language_bcp47:
- ab
- ar
- as
- ast
- az
- ba
- bas
- be
- bg
- bn
- br
- ca
- ckb
- cnh
- cs
- cv
- cy
- da
- de
- dv
- dyu
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy-NL
- ga-IE
- gl
- gn
- ha
- hi
... |
mlabonne/guanaco-llama2-1k | 2023-08-25T16:49:41.000Z | [
"region:us"
] | mlabonne | null | null | null | 50 | 14,161 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1654448
num_examples: 1000
download_size: 966693
dataset_size: 1654448
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Guanaco-1k: Lazy Llama 2 Formatting
This is ... |
tatsu-lab/alpaca_eval | 2023-06-09T11:58:42.000Z | [
"license:cc-by-nc-4.0",
"region:us"
] | tatsu-lab | Data for alpaca_eval, which aims to help automatic evaluation of instruction-following models | @misc{alpaca_eval,
author = {Xuechen Li and Tianyi Zhang and Yann Dubois and Rohan Taori and Ishaan Gulrajani and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto },
title = {AlpacaEval: An Automatic Evaluator of Instruction-following Models},
year = {2023},
publisher = {GitHub},
journal = {GitHub r... | null | 16 | 13,828 | ---
license: cc-by-nc-4.0
---
|
iohadrubin/c5 | 2023-10-07T06:13:07.000Z | [
"region:us"
] | iohadrubin | A colossal, cleaned version of Common Crawl's web crawl corpus.
Based on Common Crawl dataset: "https://commoncrawl.org".
This is the processed version of Google's C5 dataset by AllenAI. | @article{2019t5,
author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
journal = {arXiv e-prints},
year = {2... | null | 0 | 13,792 | Entry not found |
librispeech_asr | 2022-11-18T20:18:42.000Z | [
"task_categories:automatic-speech-recognition",
"task_categories:audio-classification",
"task_ids:speaker-identification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"sou... | null | LibriSpeech is a corpus of approximately 1000 hours of read English speech with sampling rate of 16 kHz,
prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read
audiobooks from the LibriVox project, and has been carefully segmented and aligned.87 | @inproceedings{panayotov2015librispeech,
title={Librispeech: an ASR corpus based on public domain audio books},
author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},
pages={5206--... | null | 57 | 13,218 | ---
pretty_name: LibriSpeech
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
paperswithcode_id: librispeech-1
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- automatic-speech-reco... |
cifar100 | 2023-01-25T14:27:57.000Z | [
"task_categories:image-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-80-Million-Tiny-Images",
"language:en",
"license:unknown",
"region:us"
] | null | The CIFAR-100 dataset consists of 60000 32x32 colour images in 100 classes, with 600 images
per class. There are 500 training images and 100 testing images per class. There are 50000 training images and 10000 test images. The 100 classes are grouped into 20 superclasses.
There are two labels per image - fine label (act... | @TECHREPORT{Krizhevsky09learningmultiple,
author = {Alex Krizhevsky},
title = {Learning multiple layers of features from tiny images},
institution = {},
year = {2009}
} | null | 14 | 13,190 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-80-Million-Tiny-Images
task_categories:
- image-classification
task_ids: []
paperswithcode_id: cifar-100
pretty_name: Cifar... |
mteb/sts22-crosslingual-sts | 2022-09-27T19:10:13.000Z | [
"language:ar",
"language:de",
"language:en",
"language:es",
"language:fr",
"language:it",
"language:pl",
"language:ru",
"language:tr",
"language:zh",
"region:us"
] | mteb | SemEval 2022 Task 8: Multilingual News Article Similarity | \ | null | 4 | 13,067 | ---
language:
- ar
- de
- en
- es
- fr
- it
- pl
- ru
- tr
- zh
---
Scores in this dataset have been inverted to be from least to most similar!
The scores in the original STS22 task were from most to least similar. |
boolq | 2023-04-05T09:42:01.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | null | BoolQ is a question answering dataset for yes/no questions containing 15942 examples. These questions are naturally
occurring ---they are generated in unprompted and unconstrained settings.
Each example is a triplet of (question, passage, answer), with the title of the page as optional additional context.
The text-pair... | @inproceedings{clark2019boolq,
title = {BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions},
author = {Clark, Christopher and Lee, Kenton and Chang, Ming-Wei, and Kwiatkowski, Tom and Collins, Michael, and Toutanova, Kristina},
booktitle = {NAACL},
year = {2019},
} | null | 24 | 12,941 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- natural-language-inference
paperswithcode_id: boolq
pretty_name: BoolQ
da... |
Open-Orca/FLAN | 2023-08-02T15:08:01.000Z | [
"size_categories:1B<n<10B",
"language:en",
"license:cc-by-4.0",
"arxiv:2301.13688",
"arxiv:2109.01652",
"arxiv:2110.08207",
"arxiv:2204.07705",
"region:us"
] | Open-Orca | null | null | null | 96 | 12,725 | ---
license: cc-by-4.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
datasets:
- Open-Orca/OpenOrca
size_categories:
- 1B<n<10B
---
<p><h1>🍮 The WHOLE FLAN Collection! 🍮</h1></p>
# ... |
opentensor/openvalidators | 2023-09-25T14:03:34.000Z | [
"size_categories:1M<n<10M",
"license:mit",
"region:us"
] | opentensor | null | null | null | 6 | 12,135 | ---
license: mit
viewer: False
size_categories:
- 1M<n<10M
---
# Dataset Card for Openvalidators dataset
## Dataset Description
- **Repository:** https://github.com/opentensor/validators
- **Homepage:** https://bittensor.com/
### Dataset Summary
The OpenValidators dataset, created by the OpenTensor Foundation, is ... |
AmazonScience/massive | 2022-11-16T15:44:51.000Z | [
"task_categories:text-classification",
"task_ids:intent-classification",
"task_ids:multi-class-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:af-ZA",
"multilinguality:am-ET",
"multilinguality:ar-SA",
"multilinguality:az-AZ",
"multilinguality:b... | AmazonScience | 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... | @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... | null | 37 | 12,119 | ---
annotations_creators:
- expert-generated
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- af-ZA
- am-ET
- ar-SA
- az-AZ
- bn-BD
- ca-ES
- cy-GB
- da-DK
- de-DE
- el-GR
- en-US
- es-ES
- fa-IR
- fi-FI
- fr-FR
- he-IL
- hi-IN
- hu-HU
- hy-AM
- id-ID
- is-IS
- it-IT
- ja-JP
- jv-ID
- ka-GE
- km-KH
- ... |
bigscience/P3 | 2023-02-01T13:38:41.000Z | [
"task_categories:other",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100M<n<1B",
"language:en",
"license:apache-2.0",
"arxiv:2110.08207",
"region:us"
] | bigscience | P3 (Public Pool of Prompts) is a collection of prompted English datasets covering a diverse set of NLP tasks. A prompt is the combination of an input template and a target template. The templates are functions mapping a data example into natural language for the input and target sequences. For example, in the case of a... | @misc{sanh2021multitask,
title={Multitask Prompted Training Enables Zero-Shot Task Generalization},
author={Victor Sanh and Albert Webson and Colin Raffel and Stephen H. Bach and Lintang Sutawika and Zaid Alyafeai and Antoine Chaffin and Arnaud Stiegler and Teven Le Scao and Arun Raja and Manan Dey and M Sa... | null | 157 | 12,012 | ---
annotations_creators:
- crowdsourced
- expert-generated
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: P3
size_categories:
- 100M<n<1B
task_categories:
- other
---
# Dataset Card for P3
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#datase... |
amazon_polarity | 2023-01-25T14:26:12.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"arxiv:1509.01626",
"regi... | null | The Amazon reviews dataset consists of reviews from amazon.
The data span a period of 18 years, including ~35 million reviews up to March 2013.
Reviews include product and user information, ratings, and a plaintext review. | @inproceedings{mcauley2013hidden,
title={Hidden factors and hidden topics: understanding rating dimensions with review text},
author={McAuley, Julian and Leskovec, Jure},
booktitle={Proceedings of the 7th ACM conference on Recommender systems},
pages={165--172},
year={2013}
} | null | 27 | 11,958 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: Amazon Review Polarity
dataset_i... |
Dahoas/full-hh-rlhf | 2023-02-23T17:29:46.000Z | [
"region:us"
] | Dahoas | null | null | null | 50 | 11,813 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 203150123
num_examples: 112052
- name: test
num_bytes: 22606646
num_examples: 12451
downl... |
monology/pile-uncopyrighted | 2023-08-31T03:45:38.000Z | [
"license:other",
"arxiv:2101.00027",
"region:us"
] | monology | null | null | null | 10 | 11,689 | ---
license: other
---
# Pile Uncopyrighted
In response to [authors demanding that LLMs stop using their works](https://tcrn.ch/3rtpIDn), here's a copy of [The Pile](https://huggingface.co/datasets/monology/pile) with all copyrighted content removed.
Please consider using this dataset to train your future LLMs, to r... |
THUDM/LongBench | 2023-08-29T04:51:14.000Z | [
"task_categories:question-answering",
"task_categories:text-generation",
"task_categories:summarization",
"task_categories:conversational",
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:en",
"language:zh",
"Long Context",
"arxiv:2308.14508",
"arxiv:2108.00573",
"... | THUDM | LongBench is a comprehensive benchmark for multilingual and multi-task purposes, with the goal to fully measure and evaluate the ability of pre-trained language models to understand long text. This dataset consists of twenty different tasks, covering key long-text application scenarios such as multi-document QA, single... | null | null | 28 | 11,375 | ---
task_categories:
- question-answering
- text-generation
- summarization
- conversational
- text-classification
language:
- en
- zh
tags:
- Long Context
size_categories:
- 1K<n<10K
---
# Introduction
**LongBench** is the first benchmark for bilingual, multitask, and comprehensive assessment of **long context under... |
open-llm-leaderboard/details_Aspik101__trurl-2-7b-pl-instruct_unload | 2023-09-22T23:43:57.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 11,208 | ---
pretty_name: Evaluation run of Aspik101/trurl-2-7b-pl-instruct_unload
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Aspik101/trurl-2-7b-pl-instruct_unload](https://huggingface.co/Aspik101/trurl-2-7b-pl-instruct_unload)\
\ on the [Open LLM Leaderboard](https://huggingface... |
open-llm-leaderboard/details_togethercomputer__GPT-JT-6B-v0 | 2023-08-27T12:37:30.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 10,794 | ---
pretty_name: Evaluation run of togethercomputer/GPT-JT-6B-v0
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [togethercomputer/GPT-JT-6B-v0](https://huggingface.co/togethercomputer/GPT-JT-6B-v0)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/op... |
GBaker/MedQA-USMLE-4-options | 2023-01-24T19:18:09.000Z | [
"language:en",
"license:cc-by-4.0",
"region:us"
] | GBaker | null | null | null | 16 | 10,784 | ---
license: cc-by-4.0
language:
- en
---
Original dataset introduced by Jin et al. in [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://paperswithcode.com/paper/what-disease-does-this-patient-have-a-large)
<h4>Citation information:</h4>
@artic... |
wino_bias | 2023-01-25T15:02:31.000Z | [
"task_categories:token-classification",
"task_ids:coreference-resolution",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:mit",
"arxiv:1804.06876",
"regi... | null | WinoBias, a Winograd-schema dataset for coreference resolution focused on gender bias.
The corpus contains Winograd-schema style sentences with entities corresponding to people
referred by their occupation (e.g. the nurse, the doctor, the carpenter). | @article{DBLP:journals/corr/abs-1804-06876,
author = {Jieyu Zhao and
Tianlu Wang and
Mark Yatskar and
Vicente Ordonez and
Kai{-}Wei Chang},
title = {Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods},
journal = {CoRR},
vo... | null | 9 | 10,469 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- coreference-resolution
paperswithcode_id: winobias
pretty_name: Wino... |
CarperAI/openai_summarize_tldr | 2023-01-10T02:53:40.000Z | [
"region:us"
] | CarperAI | null | null | null | 12 | 10,410 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 181260841
num_examples: 116722
- name: valid
num_bytes: 10018338
num_examples: 6447
- name: test
num_bytes: 10198128
num_examples: 6553
download_size: 122... |
NLPCoreTeam/mmlu_ru | 2023-06-28T19:21:48.000Z | [
"task_categories:question-answering",
"task_categories:multiple-choice",
"task_ids:multiple-choice-qa",
"size_categories:10K<n<100K",
"language:ru",
"language:en",
"arxiv:2009.03300",
"region:us"
] | NLPCoreTeam | null | null | null | 5 | 10,340 | ---
pretty_name: MMLU RU/EN
language:
- ru
- en
size_categories:
- 10K<n<100K
task_categories:
- question-answering
- multiple-choice
task_ids:
- multiple-choice-qa
dataset_info:
- config_name: abstract_algebra
features:
- name: question_en
dtype: string
- name: choices_en
sequence: string
- name: ans... |
huggingface/cats-image | 2022-02-03T12:31:30.000Z | [
"region:us"
] | huggingface | \\n | \\n | null | 0 | 10,326 | Entry not found |
hotpot_qa | 2023-04-05T10:07:23.000Z | [
"task_categories:question-answering",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"multi-hop",
"arxiv:1809.09600",
"region:us"
] | null | HotpotQA is a new dataset with 113k Wikipedia-based question-answer pairs with four key features:
(1) the questions require finding and reasoning over multiple supporting documents to answer;
(2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas;
(3) we provide sente... | @inproceedings{yang2018hotpotqa,
title={{HotpotQA}: A Dataset for Diverse, Explainable Multi-hop Question Answering},
author={Yang, Zhilin and Qi, Peng and Zhang, Saizheng and Bengio, Yoshua and Cohen, William W. and Salakhutdinov, Ruslan and Manning, Christopher D.},
booktitle={Conference on Empirical Methods in... | null | 18 | 10,306 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: HotpotQA
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- question-answering
task_ids: []
paperswithcode_id: hotpotqa
tags:
- multi-hop
datase... |
tatsu-lab/alpaca_farm | 2023-05-29T01:00:10.000Z | [
"license:cc-by-nc-4.0",
"region:us"
] | tatsu-lab | Data used in the original AlpacaFarm experiments.
Includes SFT and preference examples. | @misc{alpaca_farm,
author = {Yann Dubois, Xuechen Li, Rohan Taori, Tianyi Zhang, Ishaan Gulrajani, Jimmy Ba, Carlos Guestrin, Percy Liang, Tatsunori Hashimoto},
title = {AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback},
year = {2023},
howpublished = {\\url{https://github.com/tatsu-... | null | 14 | 10,181 | ---
license: cc-by-nc-4.0
--- |
competition_math | 2023-06-08T06:40:09.000Z | [
"task_categories:text2text-generation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"explanation-generation",
"arxiv:2103.03874",
"region:us"
... | null | The Mathematics Aptitude Test of Heuristics (MATH) dataset consists of problems
from mathematics competitions, including the AMC 10, AMC 12, AIME, and more.
Each problem in MATH has a full step-by-step solution, which can be used to teach
models to generate answer derivations and explanations. | @article{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the MATH Dataset},
author={Dan Hendrycks
and Collin Burns
and Saurav Kadavath
and Akul Arora
and Steven Basart
and Eric Tang
and Dawn Song
and Jacob Steinhardt},
journal={arXiv preprint arXiv:2103.03874},
... | null | 51 | 9,916 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
pretty_name: Mathematics Aptitude Test of Heuristics (MATH)
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
tags:... |
lvwerra/stack-exchange-paired | 2023-03-13T11:30:17.000Z | [
"task_categories:text-generation",
"task_categories:question-answering",
"size_categories:10M<n<100M",
"language:en",
"region:us"
] | lvwerra | null | null | null | 68 | 9,739 | ---
task_categories:
- text-generation
- question-answering
language:
- en
pretty_name: StackExchange Paired
size_categories:
- 10M<n<100M
---
# StackExchange Paired
This is a processed version of the [`HuggingFaceH4/stack-exchange-preferences`](https://huggingface.co/datasets/HuggingFaceH4/stack-exchange-preferences... |
OpenAssistant/oasst1 | 2023-05-02T13:21:21.000Z | [
"size_categories:100K<n<1M",
"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"... | OpenAssistant | null | null | null | 1,040 | 9,684 | ---
license: apache-2.0
dataset_info:
features:
- name: message_id
dtype: string
- name: parent_id
dtype: string
- name: user_id
dtype: string
- name: created_date
dtype: string
- name: text
dtype: string
- name: role
dtype: string
- name: lang
dtype: string
- name: review_... |
amazon_reviews_multi | 2023-08-18T14:12:47.000Z | [
"task_categories:summarization",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"task_ids:sentiment-classification",
"task_ids:sentiment-scoring",
"ta... | null | We provide an Amazon product reviews dataset for multilingual text classification. The dataset contains reviews in English, Japanese, German, French, Chinese and Spanish, collected between November 1, 2015 and November 1, 2019. Each record in the dataset contains the review text, the review title, the star rating, an a... | @inproceedings{marc_reviews,
title={The Multilingual Amazon Reviews Corpus},
author={Keung, Phillip and Lu, Yichao and Szarvas, György and Smith, Noah A.},
booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing},
year={2020}
} | null | 82 | 9,276 | ---
annotations_creators:
- found
language_creators:
- found
language:
- de
- en
- es
- fr
- ja
- zh
license:
- other
multilinguality:
- monolingual
- multilingual
size_categories:
- 100K<n<1M
- 1M<n<10M
source_datasets:
- original
task_categories:
- summarization
- text-generation
- fill-mask
- text-classification
tas... |
math_dataset | 2023-04-05T10:09:32.000Z | [
"language:en",
"region:us"
] | null | Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities ... | @article{2019arXiv,
author = {Saxton, Grefenstette, Hill, Kohli},
title = {Analysing Mathematical Reasoning Abilities of Neural Models},
year = {2019},
journal = {arXiv:1904.01557}
} | null | 38 | 9,227 | ---
pretty_name: Mathematics Dataset
language:
- en
paperswithcode_id: mathematics
dataset_info:
- config_name: algebra__linear_1d
features:
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: test
num_bytes: 516405
num_examples: 10000
- name: train
num_bytes: 920... |
amazon_us_reviews | 2023-04-05T09:14:36.000Z | [
"task_categories:summarization",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"task_ids:sentiment-classification",
"task_ids:sentiment-scoring",
"ta... | null | Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website... | \ | null | 51 | 9,121 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 100M<n<1B
source_datasets:
- original
task_categories:
- summarization
- text-generation
- fill-mask
- text-classification
task_ids:
- text-scoring
- language-modeling
-... |
oscar-corpus/OSCAR-2301 | 2023-04-18T10:08:22.000Z | [
"task_categories:fill-mask",
"task_categories:text-generation",
"task_ids:language-modeling",
"multilinguality:multilingual",
"size_categories:n>1T",
"source_datasets:original",
"license:cc0-1.0",
"arxiv:2212.10440",
"arxiv:2010.14571",
"region:us"
] | 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{2022arXiv221210440J,
author = {{Jansen}, Tim and {Tong}, Yangling and {Zevallos}, Victoria and {Ortiz Suarez}, Pedro},
title = "{Perplexed by Quality: A Perplexity-based Method for Adult and Harmful Content Detection in Multilingual Heterogeneous Web Data}",
journal = {arXiv e-prints},
... | null | 59 | 9,042 | ---
license: cc0-1.0
size_categories:
- n>1T
multilinguality:
- multilingual
source_datasets:
- original
task_categories:
- fill-mask
- text-generation
task_ids:
- language-modeling
paperswithcode_id: oscar
extra_gated_prompt: "By filling the form below, you understand that only the metadata and the annotations of OSCA... |
ought/raft | 2022-10-25T09:54:19.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"source_datasets:ext... | ought | Large pre-trained language models have shown promise for few-shot learning, completing text-based tasks given only a few task-specific examples. Will models soon solve classification tasks that have so far been reserved for human research assistants?
[RAFT](https://raft.elicit.org) is a few-shot classification benchm... | @InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
} | null | 31 | 8,956 | ---
annotations_creators:
- expert-generated
- crowdsourced
language_creators:
- expert-generated
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
- extended|ade_corpus_v2
- extended|banking77
task_categories:
- text-classification
task_ids:
- multi-c... |
math_qa | 2023-04-05T10:09:35.000Z | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|aqua_rat",
"language:en",
"licens... | null | Our dataset is gathered by using a new representation language to annotate over the AQuA-RAT dataset. AQuA-RAT has provided the questions, options, rationale, and the correct options. | null | 38 | 8,911 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- crowdsourced
- expert-generated
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: MathQA
size_categories:
- 10K<n<100K
source_datasets:
- extended|aqua_rat
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
pa... | |
SetFit/emotion | 2022-04-03T20:47:37.000Z | [
"region:us"
] | SetFit | null | null | null | 11 | 8,885 | ** Attention: There appears an overlap in train / test. I trained a model on the train set and achieved 100% acc on test set. With the original emotion dataset this is not the case (92.4% acc)** |
lighteval/mmlu | 2023-06-09T16:36:19.000Z | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"arxiv:2009.03300",
"arxiv:2005.... | lighteval | 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 | 6 | 8,800 | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
paperswithcode_id: mmlu
pretty_name: Measuring Massi... |
mteb/tatoeba-bitext-mining | 2022-09-27T19:07:02.000Z | [
"language:eng",
"language:sqi",
"language:fry",
"language:kur",
"language:tur",
"language:deu",
"language:nld",
"language:ron",
"language:ang",
"language:ido",
"language:jav",
"language:isl",
"language:slv",
"language:cym",
"language:kaz",
"language:est",
"language:heb",
"language:... | mteb | Tatoeba multilingual test set | null | null | 3 | 8,762 | ---
language:
- eng
- sqi
- fry
- kur
- tur
- deu
- nld
- ron
- ang
- ido
- jav
- isl
- slv
- cym
- kaz
- est
- heb
- gla
- mar
- lat
- bel
- pms
- gle
- pes
- nob
- bul
- cbk
- hun
- uig
- rus
- spa
- hye
- tel
- afr
- mon
- arz
- hrv
- nov
- gsw
- nds
- ukr
- uzb
- lit
- ina
- lfn
- zsm
- ita
- cmn
- lvs
- glg
- ceb
... |
juletxara/xstory_cloze | 2023-05-21T16:04:36.000Z | [
"task_categories:other",
"annotations_creators:found",
"language_creators:found",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:extended|story_cloze",
"language:en",
"language:ru",
"language:zh",
"language:es",
"language:ar",... | juletxara | XStoryCloze consists of the professionally translated version of the [English StoryCloze dataset](https://cs.rochester.edu/nlp/rocstories/) (Spring 2016 version) to 10 non-English languages. This dataset is released by Meta AI. | @article{DBLP:journals/corr/abs-2112-10668,
author = {Xi Victoria Lin and
Todor Mihaylov and
Mikel Artetxe and
Tianlu Wang and
Shuohui Chen and
Daniel Simig and
Myle Ott and
Naman Goyal and
Shrut... | null | 3 | 8,732 | ---
annotations_creators:
- found
language:
- en
- ru
- zh
- es
- ar
- hi
- id
- te
- sw
- eu
- my
language_creators:
- found
- expert-generated
license:
- cc-by-sa-4.0
multilinguality:
- multilingual
paperswithcode_id: null
pretty_name: XStoryCloze
size_categories:
- 1K<n<10K
source_datasets:
- extended|story_cloze
ta... |
naver-clova-ix/cord-v2 | 2022-07-19T23:43:33.000Z | [
"license:cc-by-4.0",
"region:us"
] | naver-clova-ix | null | null | null | 27 | 8,674 | ---
license: cc-by-4.0
---
|
mteb/sts17-crosslingual-sts | 2022-09-27T19:09:43.000Z | [
"language:ar",
"language:de",
"language:en",
"language:es",
"language:fr",
"language:it",
"language:nl",
"language:ko",
"language:tr",
"region:us"
] | mteb | STS17 Cross-lingual dataset | null | null | 1 | 8,655 | ---
language:
- ar
- de
- en
- es
- fr
- it
- nl
- ko
- tr
--- |
sst | 2023-06-01T14:59:56.000Z | [
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:sentiment-classification",
"task_ids:sentiment-scoring",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"source_datas... | null | The Stanford Sentiment Treebank, the first corpus with fully labeled parse trees that allows for a
complete analysis of the compositional effects of sentiment in language. | @inproceedings{socher-etal-2013-recursive,
title = "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank",
author = "Socher, Richard and Perelygin, Alex and Wu, Jean and
Chuang, Jason and Manning, Christopher D. and Ng, Andrew and Potts, Christopher",
booktitle = "Proceedings ... | null | 11 | 8,654 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
- sentiment-classification
- sentiment-scoring
papers... |
poloclub/diffusiondb | 2023-05-09T19:00:45.000Z | [
"task_categories:text-to-image",
"task_categories:image-to-text",
"task_ids:image-captioning",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:n>1T",
"source_datasets:original",
"language:en",
"license:cc0-1.0",
"stable diffusion"... | poloclub | DiffusionDB is the first large-scale text-to-image prompt dataset. It contains 2
million images generated by Stable Diffusion using prompts and hyperparameters
specified by real users. The unprecedented scale and diversity of this
human-actuated dataset provide exciting research opportunities in understanding
the inter... | @article{wangDiffusionDBLargescalePrompt2022,
title = {{{DiffusionDB}}: {{A}} Large-Scale Prompt Gallery Dataset for Text-to-Image Generative Models},
author = {Wang, Zijie J. and Montoya, Evan and Munechika, David and Yang, Haoyang and Hoover, Benjamin and Chau, Duen Horng},
year = {2022},
journal = {arXiv:221... | null | 312 | 8,594 | ---
layout: default
title: Home
nav_order: 1
has_children: false
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license:
- cc0-1.0
multilinguality:
- multilingual
pretty_name: DiffusionDB
size_categories:
- n>1T
source_datasets:
- original
tags:
- stable diffusion
- prompt engineering
... |
eli5 | 2023-06-08T12:42:30.000Z | [
"task_categories:text2text-generation",
"task_ids:abstractive-qa",
"task_ids:open-domain-abstractive-qa",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:unknown",
"ar... | null | Explain Like I'm 5 long form QA dataset | @inproceedings{DBLP:conf/acl/FanJPGWA19,
author = {Angela Fan and
Yacine Jernite and
Ethan Perez and
David Grangier and
Jason Weston and
Michael Auli},
editor = {Anna Korhonen and
David R. Traum and
Lluis ... | null | 36 | 8,547 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text2text-generation
task_ids:
- abstractive-qa
- open-domain-abstractive-qa
paperswithcode_id: eli5
pretty_na... |
tydiqa | 2023-04-05T13:42:46.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:unknown",
"source_datasets:extended|wikipedia",
"language:ar",
"language:bn",
"language:en",
"language:fi",
"l... | null | TyDi QA is a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs.
The languages of TyDi QA are diverse with regard to their typology -- the set of linguistic features that each language
expresses -- such that we expect models performing well on this set to generalize a... | @article{tydiqa,
title = {TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
author = {Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki}
year = {2020},
journal = {Transactions of... | null | 13 | 8,535 | ---
pretty_name: TyDi QA
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- ar
- bn
- en
- fi
- id
- ja
- ko
- ru
- sw
- te
- th
license:
- apache-2.0
multilinguality:
- multilingual
size_categories:
- unknown
source_datasets:
- extended|wikipedia
task_categories:
- question-answering
ta... |
Dahoas/rm-static | 2023-03-06T00:13:07.000Z | [
"region:us"
] | Dahoas | null | null | null | 86 | 8,526 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 113850006
num_examples: 76256
- name: test
num_bytes: 7649255
num_examples: 5103
download... |
snli | 2023-01-25T14:44:35.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"task_ids:multi-input-text-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|other-flicker-30k",
"... | null | The SNLI corpus (version 1.0) is a collection of 570k human-written English
sentence pairs manually labeled for balanced classification with the labels
entailment, contradiction, and neutral, supporting the task of natural language
inference (NLI), also known as recognizing textual entailment (RTE). | @inproceedings{snli:emnlp2015,
Author = {Bowman, Samuel R. and Angeli, Gabor and Potts, Christopher, and Manning, Christopher D.},
Booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
Publisher = {Association for Computational Linguistics},
Title ... | null | 29 | 8,458 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|other-flicker-30k
- extended|other-visual-genome
task_categories:
- text-classification
task_ids:
- natural-language-infe... |
multi_nli | 2023-04-05T10:10:15.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"task_ids:multi-input-text-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:ori... | null | The Multi-Genre Natural Language Inference (MultiNLI) corpus is a
crowd-sourced collection of 433k sentence pairs annotated with textual
entailment information. The corpus is modeled on the SNLI corpus, but differs in
that covers a range of genres of spoken and written text, and supports a
distinctive cross-genre gener... | @InProceedings{N18-1101,
author = {Williams, Adina
and Nangia, Nikita
and Bowman, Samuel},
title = {A Broad-Coverage Challenge Corpus for
Sentence Understanding through Inference},
booktitle = {Proceedings of the 2018 Conference of
the North American Chapter of th... | null | 36 | 8,353 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- found
language:
- en
license:
- cc-by-3.0
- cc-by-sa-3.0
- mit
- other
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- natural-language-inference
- mult... |
gia-project/gia-dataset-parquet | 2023-10-02T21:52:41.000Z | [
"task_categories:reinforcement-learning",
"task_categories:text-generation",
"task_categories:question-answering",
"annotations_creators:found",
"annotations_creators:machine-generated",
"source_datasets:conceptual-captions",
"source_datasets:ok-vqa",
"source_datasets:oscar",
"license:apache-2.0",
... | gia-project | null | null | null | 0 | 8,350 | ---
annotations_creators:
- found
- machine-generated
license: apache-2.0
size_categories:
- {}
source_datasets:
- conceptual-captions
- ok-vqa
- oscar
task_categories:
- reinforcement-learning
- text-generation
- question-answering
pretty_name: GIA-dataset
configs:
- config_name: atari-alien
data_files:
- split: t... |
seamew/THUCNewsText | 2021-08-19T00:04:34.000Z | [
"region:us"
] | seamew | null | null | null | 4 | 8,285 | Entry not found |
hf-internal-testing/cats_vs_dogs_sample | 2023-04-11T17:04:37.000Z | [
"region:us"
] | hf-internal-testing | null | \\n@Inproceedings (Conference){asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization,
author = {Elson, Jeremy and Douceur, John (JD) and Howell, Jon and Saul, Jared},
title = {Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization},
booktitle = {Proceedings of 14t... | null | 0 | 8,128 | Entry not found |
BigScienceBiasEval/crows_pairs_multilingual | 2022-04-26T16:26:28.000Z | [
"license:cc-by-sa-4.0",
"arxiv:2010.00133",
"region:us"
] | BigScienceBiasEval | This is a revised version of CrowS-Pairs that measures stereotypes in language modelling in both English and French. | @inproceedings{neveol2022french,
title={French CrowS-Pairs: Extending a challenge dataset for measuring social bias in masked language models to a language other than English},
author={N{\'e}v{\'e}ol, Aur{\'e}lie and Dupont, Yoann and Bezan{\c{c}}on, Julien and Fort, Kar{\"e}n},
booktitle={ACL 2022-60th Annual Me... | null | 2 | 8,099 | ---
license: cc-by-sa-4.0
---
Original from https://gitlab.inria.fr/french-crows-pairs/acl-2022-paper-data-and-code/-/tree/main/.
# Data Statement for CrowS-Pairs-fr
> **How to use this document:**
> Fill in each section according to the instructions. Give as much detail as you can, but there's no need to extrapolat... |
opus_books | 2022-11-03T16:47:07.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ca",
"language:de",
"language:el",
"language:en",
"language:eo",
"language:es",
"language:fi",
"language... | null | This is a collection of copyright free books aligned by Andras Farkas, which are available from http://www.farkastranslations.com/bilingual_books.php
Note that the texts are rather dated due to copyright issues and that some of them are manually reviewed (check the meta-data at the top of the corpus files in XML). The ... | @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 | 15 | 7,964 | ---
annotations_creators:
- found
language_creators:
- found
language:
- ca
- de
- el
- en
- eo
- es
- fi
- fr
- hu
- it
- nl
- 'no'
- pl
- pt
- ru
- sv
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_i... |
lamini/lamini_docs | 2023-07-23T23:48:57.000Z | [
"region:us"
] | lamini | null | null | null | 6 | 7,760 | ---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 1846734.3
num_examples: 1260
- name: test
num_... |
open-llm-leaderboard/details_Charlie911__vicuna-7b-v1.5-lora-mctaco | 2023-09-17T20:27:35.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 7,747 | ---
pretty_name: Evaluation run of Charlie911/vicuna-7b-v1.5-lora-mctaco
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Charlie911/vicuna-7b-v1.5-lora-mctaco](https://huggingface.co/Charlie911/vicuna-7b-v1.5-lora-mctaco)\
\ on the [Open LLM Leaderboard](https://huggingface.co... |
yahma/alpaca-cleaned | 2023-04-10T20:29:06.000Z | [
"task_categories:text-generation",
"language:en",
"license:cc-by-4.0",
"instruction-finetuning",
"region:us"
] | yahma | null | null | null | 246 | 7,741 | ---
license: cc-by-4.0
language:
- en
tags:
- instruction-finetuning
pretty_name: Alpaca-Cleaned
task_categories:
- text-generation
---
# Dataset Card for Alpaca-Cleaned
- **Repository:** https://github.com/gururise/AlpacaDataCleaned
## Dataset Description
This is a cleaned version of the original Alpaca Dataset re... |
garage-bAInd/Open-Platypus | 2023-09-17T16:56:19.000Z | [
"size_categories:10K<n<100K",
"language:en",
"arxiv:2308.07317",
"region:us"
] | garage-bAInd | null | null | null | 225 | 7,535 | ---
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
- name: data_source
dtype: string
splits:
- name: train
num_bytes: 30776452
n... |
wiki_dpr | 2023-04-05T13:43:12.000Z | [
"task_categories:fill-mask",
"task_categories:text-generation",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"lang... | null | This is the wikipedia split used to evaluate the Dense Passage Retrieval (DPR) model.
It contains 21M passages from wikipedia along with their DPR embeddings.
The wikipedia articles were split into multiple, disjoint text blocks of 100 words as passages. | @misc{karpukhin2020dense,
title={Dense Passage Retrieval for Open-Domain Question Answering},
author={Vladimir Karpukhin and Barlas Oğuz and Sewon Min and Patrick Lewis and Ledell Wu and Sergey Edunov and Danqi Chen and Wen-tau Yih},
year={2020},
eprint={2004.04906},
archivePrefix={arXiv},
prima... | null | 18 | 7,498 | ---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-3.0
- gfdl
multilinguality:
- multilingual
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- fill-mask
- text-generation
task_ids:
- language-modeling
- masked-language-modeling
pret... |
roneneldan/TinyStories | 2023-08-16T16:54:12.000Z | [
"arxiv:2305.07759",
"region:us"
] | roneneldan | null | null | null | 251 | 7,470 | License: CDLA-Sharing-1.0
-------------
Dataset containing synthetically generated (by GPT-3.5 and GPT-4) short stories that only use a small vocabulary.
Described in the following paper: https://arxiv.org/abs/2305.07759.
The models referred to in the paper were trained on TinyStories-train.txt (the file tinystori... |
bigcode/the-stack-dedup | 2023-08-17T08:21:58.000Z | [
"task_categories:text-generation",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:unknown",
"language:code",
"license:other",
"arxiv:2211.15533",
"arxiv:2107.03374",
"arxiv:2207.14157",
"region:us"
] | bigcode | null | null | null | 234 | 7,452 | ---
annotations_creators: []
language_creators:
- crowdsourced
- expert-generated
language:
- code
license:
- other
multilinguality:
- multilingual
pretty_name: The-Stack
size_categories:
- unknown
source_datasets: []
task_categories:
- text-generation
task_ids: []
extra_gated_prompt: |-
## Terms of Use for The Stac... |
e2e_nlg | 2022-11-18T19:59:40.000Z | [
"task_categories:text2text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"meaning-representation-to-text",
"arxiv:1706.09254",
"ar... | null | The E2E dataset is used for training end-to-end, data-driven natural language generation systems in the restaurant domain, which is ten times bigger than existing, frequently used datasets in this area.
The E2E dataset poses new challenges:
(1) its human reference texts show more lexical richness and syntactic variatio... | @article{dusek.etal2020:csl,
title = {Evaluating the {{State}}-of-the-{{Art}} of {{End}}-to-{{End Natural Language Generation}}: {{The E2E NLG Challenge}}},
author = {Du{\v{s}}ek, Ond\v{r}ej and Novikova, Jekaterina and Rieser, Verena},
year = {2020},
month = jan,
volume = {59},
pages = {123--156},
doi = ... | null | 7 | 7,392 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: e2e
pretty_name: End-to-End NLG Challenge
tag... |
codeparrot/github-code-clean | 2022-07-05T09:35:14.000Z | [
"license:apache-2.0",
"region:us"
] | codeparrot | The GitHub Code clean dataset in a more filtered version of codeparrot/github-code dataset, it consists of 115M code files from GitHub in 32 programming languages with 60 extensions totaling in almost 1TB of text data. | null | null | 55 | 7,339 | ---
license: apache-2.0
---
This is a cleaner version of [Github-code dataset](https://huggingface.co/datasets/codeparrot/github-code), we add the following filters:
* Average line length < 100
* Alpha numeric characters fraction > 0.25
* Remove auto-generated files (keyword search)
3.39M files are removed making up 2... |
financial_phrasebank | 2023-07-26T06:27:17.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-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-b... | null | The key arguments for the low utilization of statistical techniques in
financial sentiment analysis have been the difficulty of implementation for
practical applications and the lack of high quality training data for building
such models. Especially in the case of finance and economic texts, annotated
collections are a... | @article{Malo2014GoodDO,
title={Good debt or bad debt: Detecting semantic orientations in economic texts},
author={P. Malo and A. Sinha and P. Korhonen and J. Wallenius and P. Takala},
journal={Journal of the Association for Information Science and Technology},
year={2014},
volume={65}
} | null | 104 | 7,304 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-nc-sa-3.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
- sentiment-classification
pretty_name: F... |
agemagician/uniref50 | 2023-10-07T23:04:56.000Z | [
"region:us"
] | agemagician | null | null | null | 2 | 7,299 | Entry not found |
opus100 | 2023-06-01T14:59:58.000Z | [
"task_categories:translation",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:translation",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"size_categories:1M<n<10M",
"size_categories:n<1K",
"source_datasets:extended",
"l... | null | OPUS-100 is English-centric, meaning that all training pairs include English on either the source or target side.
The corpus covers 100 languages (including English).OPUS-100 contains approximately 55M sentence pairs.
Of the 99 language pairs, 44 have 1M sentence pairs of training data, 73 have at least 100k, and 95 ha... | @misc{zhang2020improving,
title={Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation},
author={Biao Zhang and Philip Williams and Ivan Titov and Rico Sennrich},
year={2020},
eprint={2004.11867},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | null | 56 | 7,186 | ---
pretty_name: Opus100
task_categories:
- translation
multilinguality:
- translation
task_ids: []
language:
- af
- am
- an
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- dz
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- hu
- hy
- id
- ig
- is
- it
- ja
... |
wics/strategy-qa | 2023-05-10T06:12:13.000Z | [
"license:other",
"region:us"
] | wics | null | 3 | 7,054 | ---
license: other
---
| ||
xquad | 2023-04-05T13:45:22.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:unknown",
"source_datasets:extended|squad",
"language:ar",
"language:de",
"language:el",
"language:en",
... | null | XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering
performance. The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set
of SQuAD v1.1 (Rajpurkar et al., 2016) together with their professional translat... | @article{Artetxe:etal:2019,
author = {Mikel Artetxe and Sebastian Ruder and Dani Yogatama},
title = {On the cross-lingual transferability of monolingual representations},
journal = {CoRR},
volume = {abs/1910.11856},
year = {2019},
archivePrefix = {arXiv},
eprin... | null | 11 | 7,024 | ---
pretty_name: XQuAD
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- ar
- de
- el
- en
- es
- hi
- ro
- ru
- th
- tr
- vi
- zh
license:
- cc-by-sa-4.0
multilinguality:
- multilingual
size_categories:
- unknown
source_datasets:
- extended|squad
task_categories:
- question-ans... |
paws-x | 2023-01-25T14:42:16.000Z | [
"task_categories:text-classification",
"task_ids:semantic-similarity-classification",
"task_ids:semantic-similarity-scoring",
"task_ids:text-scoring",
"task_ids:multi-input-text-classification",
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
"language_creators:exper... | null | PAWS-X, a multilingual version of PAWS (Paraphrase Adversaries from Word Scrambling) for six languages.
This dataset contains 23,659 human translated PAWS evaluation pairs and 296,406 machine
translated training pairs in six typologically distinct languages: French, Spanish, German,
Chinese, Japanese, and Korean. Engl... | @InProceedings{pawsx2019emnlp,
title = {{PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification}},
author = {Yang, Yinfei and Zhang, Yuan and Tar, Chris and Baldridge, Jason},
booktitle = {Proc. of EMNLP},
year = {2019}
} | null | 17 | 7,002 | ---
annotations_creators:
- expert-generated
- machine-generated
language_creators:
- expert-generated
- machine-generated
language:
- de
- en
- es
- fr
- ja
- ko
- zh
license:
- other
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-paws
task_categories:
- text-classifica... |
multi_woz_v22 | 2023-01-25T14:41:08.000Z | [
"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 | Multi-Domain Wizard-of-Oz dataset (MultiWOZ), a fully-labeled collection of human-human written conversations spanning over multiple domains and topics.
MultiWOZ 2.1 (Eric et al., 2019) identified and fixed many erroneous annotations and user utterances in the original version, resulting in an
improved version of the d... | @article{corr/abs-2007-12720,
author = {Xiaoxue Zang and
Abhinav Rastogi and
Srinivas Sunkara and
Raghav Gupta and
Jianguo Zhang and
Jindong Chen},
title = {MultiWOZ 2.2 : {A} Dialogue Dataset with Additional Annotation Corrections
... | null | 14 | 6,967 | ---
annotations_creators:
- machine-generated
language_creators:
- crowdsourced
- machine-generated
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
- token-classification
- text-classification
ta... |
angelolab/ark_example | 2023-08-31T19:36:09.000Z | [
"task_categories:image-segmentation",
"task_ids:instance-segmentation",
"annotations_creators:no-annotation",
"size_categories:n<1K",
"source_datasets:original",
"license:apache-2.0",
"MIBI",
"Multiplexed-Imaging",
"region:us"
] | angelolab | This dataset contains 11 Field of Views (FOVs), each with 22 channels. | @InProceedings{huggingface:dataset,
title = {Ark Analysis Example Dataset},
author={Angelo Lab},
year={2022}
} | null | 0 | 6,953 | ---
annotations_creators:
- no-annotation
language: []
language_creators: []
license:
- apache-2.0
multilinguality: []
pretty_name: An example dataset for analyzing multiplexed imaging data.
size_categories:
- n<1K
source_datasets:
- original
tags:
- MIBI
- Multiplexed-Imaging
task_categories:
- image-segmentation
task... |
universal_dependencies | 2023-06-01T14:59:56.000Z | [
"task_categories:token-classification",
"task_ids:parsing",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:af",
"language:aii",
"language:ajp",
"language:akk",
"languag... | null | Universal Dependencies is a project that seeks to develop cross-linguistically consistent treebank annotation for many languages, with the goal of facilitating multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective. The annotation scheme is based on (universal... | null | null | 13 | 6,927 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- af
- aii
- ajp
- akk
- am
- apu
- aqz
- ar
- be
- bg
- bho
- bm
- br
- bxr
- ca
- ckt
- cop
- cs
- cu
- cy
- da
- de
- el
- en
- es
- et
- eu
- fa
- fi
- fo
- fr
- fro
- ga
- gd
- gl
- got
- grc
- gsw
- gun
- gv
- he
- hi
- hr
- ... |
common_voice | 2023-06-27T07:46:51.000Z | [
"task_categories:automatic-speech-recognition",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"size_categories:n<1K",
"source_datasets:extended|common_voice... | null | Common Voice is Mozilla's initiative to help teach machines how real people speak.
The dataset currently consists of 7,335 validated hours of speech in 60 languages, but we’re always adding more voices and languages. | @inproceedings{commonvoice:2020,
author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
title = {Common Voice: A Massively-Multilingual Speech Corpus},
booktitle = {Proceedings of the 12th Conference on Lang... | null | 99 | 6,810 | ---
pretty_name: Common Voice
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- ab
- ar
- as
- br
- ca
- cnh
- cs
- cv
- cy
- de
- dv
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- hi
- hsb
- hu
- ia
- id
- it
- ja
- ka
- kab
- ky
- lg
- lt
- lv
- mn
- mt
- nl
- or
- pa
- pl
-... |
mteb/mtop_domain | 2022-11-21T19:59:05.000Z | [
"task_categories:text-classification",
"language:de",
"language:en",
"language:es",
"language:fr",
"language:hi",
"language:th",
"region:us"
] | mteb | null | null | null | 2 | 6,786 | ---
task_categories:
- text-classification
language:
- de
- en
- es
- fr
- hi
- th
--- |
sick | 2023-01-25T14:44:16.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|image-flickr-8k",
"source_datasets:extended|semeval2012-sts-msr-vide... | null | Shared and internationally recognized benchmarks are fundamental for the development of any computational system.
We aim to help the research community working on compositional distributional semantic models (CDSMs) by providing SICK (Sentences Involving Compositional Knowldedge), a large size English benchmark tailore... | @inproceedings{marelli-etal-2014-sick,
title = "A {SICK} cure for the evaluation of compositional distributional semantic models",
author = "Marelli, Marco and
Menini, Stefano and
Baroni, Marco and
Bentivogli, Luisa and
Bernardi, Raffaella and
Zamparelli, Roberto",
booktit... | null | 5 | 6,754 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-nc-sa-3.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|image-flickr-8k
- extended|semeval2012-sts-msr-video
task_categories:
- text-classification
task_ids:
- natural-lang... |
llm-lens/vocab_tags | 2023-06-29T02:50:09.000Z | [
"region:us"
] | llm-lens | null | null | null | 1 | 6,697 | ---
dataset_info:
features:
- name: prompt_descriptions
dtype: string
splits:
- name: train
num_bytes: 346971
num_examples: 22131
download_size: 298971
dataset_size: 346971
---
# Dataset Card for "vocab_tags"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTI... |
dair-ai/emotion | 2023-04-20T08:08:15.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:other",
"emotion-classific... | dair-ai | Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper. | @inproceedings{saravia-etal-2018-carer,
title = "{CARER}: Contextualized Affect Representations for Emotion Recognition",
author = "Saravia, Elvis and
Liu, Hsien-Chi Toby and
Huang, Yen-Hao and
Wu, Junlin and
Chen, Yi-Shin",
booktitle = "Proceedings of the 2018 Conference on Empi... | null | 127 | 6,650 | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
paperswithcode_id: emotion
pretty_na... |
enwik8 | 2023-04-06T14:14:17.000Z | [
"task_categories:fill-mask",
"task_categories:text-generation",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en"... | null | The dataset is based on the Hutter Prize (http://prize.hutter1.net) and contains the first 10^8 bytes of English Wikipedia in 2006 in XML | null | null | 4 | 6,627 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- mit
multilinguality:
- monolingual
pretty_name: enwik8
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- fill-mask
- text-generation
task_ids:
- language-modeling
- masked-language-modeling
dataset_... |
oscar | 2023-06-01T14:59:59.000Z | [
"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:multilingual",
"size_categories:100K<n<1M",
"size_categories:100M<n<1B",
"size_catego... | null | The Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture.\ | @inproceedings{ortiz-suarez-etal-2020-monolingual,
title = "A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages",
author = "Ortiz Su{\'a}rez, Pedro Javier and
Romary, Laurent and
Sagot, Benoit",
booktitle = "Proceedings of the 58th Annual Meeting of the Associat... | null | 122 | 6,620 | ---
pretty_name: OSCAR
annotations_creators:
- no-annotation
language_creators:
- found
language:
- af
- als
- am
- an
- ar
- arz
- as
- ast
- av
- az
- azb
- ba
- bar
- bcl
- be
- bg
- bh
- bn
- bo
- bpy
- br
- bs
- bxr
- ca
- cbk
- ce
- ceb
- ckb
- cs
- cv
- cy
- da
- de
- diq
- dsb
- dv
- el
- eml
- en
- eo
- es
- e... |
llm-lens/descriptors-text-davinci-003 | 2023-06-29T02:39:27.000Z | [
"region:us"
] | llm-lens | null | null | null | 0 | 6,614 | ---
dataset_info:
features:
- name: vocab
dtype: string
- name: descriptions
sequence: string
- name: prompt_descriptions
sequence: string
splits:
- name: birdsnap
num_bytes: 322488
num_examples: 500
- name: caltech101
num_bytes: 56880
num_examples: 102
- name: cifar100
n... |
open-llm-leaderboard/details_GOAT-AI__GOAT-7B-Community | 2023-09-22T17:15:05.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 6,469 | ---
pretty_name: Evaluation run of GOAT-AI/GOAT-7B-Community
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [GOAT-AI/GOAT-7B-Community](https://huggingface.co/GOAT-AI/GOAT-7B-Community)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leade... |
swag | 2023-01-25T14:45:08.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:un... | null | Given a partial description like "she opened the hood of the car,"
humans can reason about the situation and anticipate what might come
next ("then, she examined the engine"). SWAG (Situations With Adversarial Generations)
is a large-scale dataset for this task of grounded commonsense
inference, unifying natural langua... | @inproceedings{zellers2018swagaf,
title={SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference},
author={Zellers, Rowan and Bisk, Yonatan and Schwartz, Roy and Choi, Yejin},
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
... | null | 11 | 6,407 | ---
annotations_creators:
- crowdsourced
- machine-generated
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- natural-language-inference
paperswithcode_id: swag
pretty_n... |
cosmos_qa | 2023-04-05T10:02:42.000Z | [
"task_categories:multiple-choice",
"task_ids:multiple-choice-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:1909.00277",
"region:us"
] | null | Cosmos QA is a large-scale dataset of 35.6K problems that require commonsense-based reading comprehension, formulated as multiple-choice questions. It focuses on reading between the lines over a diverse collection of people's everyday narratives, asking questions concerning on the likely causes or effects of events tha... | @inproceedings{huang-etal-2019-cosmos,
title = "Cosmos {QA}: Machine Reading Comprehension with Contextual Commonsense Reasoning",
author = "Huang, Lifu and
Le Bras, Ronan and
Bhagavatula, Chandra and
Choi, Yejin",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in ... | null | 9 | 6,357 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: CosmosQA
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- multiple-choice
task_ids:
- multiple-choice-qa
paperswithcode_id: cosmosqa
dataset_inf... |
tau/zero_scrolls | 2023-06-30T17:21:02.000Z | [
"task_categories:question-answering",
"task_categories:summarization",
"task_categories:text-generation",
"task_ids:multiple-choice-qa",
"language:en",
"query-based-summarization",
"long-texts",
"arxiv:2104.02112",
"arxiv:2104.07091",
"arxiv:2104.05938",
"arxiv:2205.11465",
"arxiv:2105.03011",... | tau | ZeroSCROLLS: Zero-Shot CompaRison Over Long Language Sequences.
A zero shot benchmark for long text reasoning.
https://zero.scrolls-benchmark.com/ | @misc{shaham2023zeroscrolls,
title={ZeroSCROLLS: A Zero-Shot Benchmark for Long Text Understanding},
author={Uri Shaham and Maor Ivgi and Avia Efrat and Jonathan Berant and Omer Levy},
year={2023},
eprint={2305.14196},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
Note that each Zer... | null | 4 | 6,326 | ---
language:
- en
task_categories:
- question-answering
- summarization
- text-generation
task_ids:
- multiple-choice-qa
tags:
- query-based-summarization
- long-texts
---
## Dataset Description
- **Homepage:** [ZeroSCROLLS](https://www.zero.scrolls-benchmark.com/)
- **Leaderboard:** [Leaderboard](https://www.zero.s... |
paws | 2023-06-01T14:59:56.000Z | [
"task_categories:text-classification",
"task_ids:semantic-similarity-classification",
"task_ids:semantic-similarity-scoring",
"task_ids:text-scoring",
"task_ids:multi-input-text-classification",
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
"language_creators:machi... | null | PAWS: Paraphrase Adversaries from Word Scrambling
This dataset contains 108,463 human-labeled and 656k noisily labeled pairs that feature
the importance of modeling structure, context, and word order information for the problem
of paraphrase identification. The dataset has two subsets, one based on Wikipedia and the
o... | @InProceedings{paws2019naacl,
title = {{PAWS: Paraphrase Adversaries from Word Scrambling}},
author = {Zhang, Yuan and Baldridge, Jason and He, Luheng},
booktitle = {Proc. of NAACL},
year = {2019}
} | null | 16 | 6,295 | ---
annotations_creators:
- expert-generated
- machine-generated
language_creators:
- machine-generated
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- semantic-similarity-classificati... |
nq_open | 2022-11-03T16:32:11.000Z | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|natural_questions",
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | null | The NQ-Open task, introduced by Lee et.al. 2019,
is an open domain question answering benchmark that is derived from Natural Questions.
The goal is to predict an English answer string for an input English question.
All questions can be answered using the contents of English Wikipedia. | @article{doi:10.1162/tacl_a_00276,
author = {Kwiatkowski, Tom and Palomaki, Jennimaria and Redfield, Olivia and Collins, Michael and Parikh, Ankur and Alberti, Chris and Epstein, Danielle and Polosukhin, Illia and Devlin, Jacob and Lee, Kenton and Toutanova, Kristina and Jones, Llion and Kelcey, Matthew and Chang, ... | null | 5 | 6,245 | ---
annotations_creators:
- expert-generated
language_creators:
- other
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
pretty_name: NQ-Open
size_categories:
- 10K<n<100K
source_datasets:
- extended|natural_questions
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswithcode_i... |
mit-han-lab/pile-val-backup | 2023-08-21T21:37:19.000Z | [
"region:us"
] | mit-han-lab | null | null | null | 2 | 6,091 | This is a backup for the pile val dataset downloaded from here: `https://the-eye.eu/public/AI/pile/val.jsonl.zst`
Please respect the original license of the dataset. |
web_nlg | 2023-06-01T14:59:54.000Z | [
"task_categories:tabular-to-text",
"task_ids:rdf-to-text",
"annotations_creators:found",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-db_pedia",
"source_datasets:original",
"language:en",
"language:ru",
"license:cc... | null | The WebNLG challenge consists in mapping data to text. The training data consists
of Data/Text pairs where the data is a set of triples extracted from DBpedia and the text is a verbalisation
of these triples. For instance, given the 3 DBpedia triples shown in (a), the aim is to generate a text such as (b).
a. (John_E_... | @inproceedings{web_nlg,
author = {Claire Gardent and
Anastasia Shimorina and
Shashi Narayan and
Laura Perez{-}Beltrachini},
editor = {Regina Barzilay and
Min{-}Yen Kan},
title = {Creating Training Corpora for {NLG} Micro-Planners},
booktitle ... | null | 10 | 6,039 | ---
annotations_creators:
- found
language_creators:
- crowdsourced
language:
- en
- ru
license:
- cc-by-sa-3.0
- cc-by-nc-sa-4.0
- gfdl
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-db_pedia
- original
task_categories:
- tabular-to-text
task_ids:
- rdf-to-text
paperswit... |
kunishou/databricks-dolly-15k-ja | 2023-09-10T13:47:12.000Z | [
"license:cc-by-sa-3.0",
"region:us"
] | kunishou | null | null | null | 52 | 6,002 | ---
license: cc-by-sa-3.0
---
This dataset was created by automatically translating "databricks-dolly-15k" into Japanese.
This dataset is licensed under CC-BY-SA-3.0
Last Update : 2023-05-11
databricks-dolly-15k-ja
https://github.com/kunishou/databricks-dolly-15k-ja
databricks-dolly-15k
https://github.com/da... |
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