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 |
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newsroom | 2023-04-05T13:35:54.000Z | [
"task_categories:summarization",
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"multilinguality:monolingual",
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"source_datasets:original",
"language:en",
"license:other",
"region:us"
] | null | NEWSROOM is a large dataset for training and evaluating summarization systems.
It contains 1.3 million articles and summaries written by authors and
editors in the newsrooms of 38 major publications.
Dataset features includes:
- text: Input news text.
- summary: Summary for the news.
And additional features:
- t... | @inproceedings{N18-1065,
author = {Grusky, Max and Naaman, Mor and Artzi, Yoav},
title = {NEWSROOM: A Dataset of 1.3 Million Summaries
with Diverse Extractive Strategies},
booktitle = {Proceedings of the 2018 Conference of the
North American Chapter of the Association for
... | null | 7 | 538 | ---
annotations_creators:
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pretty_name: CORNELL NEWSROOM
size_categories:
- unknown
source_datasets:
- original
task_categories:
- summarization
task_ids:
- news-articles-summarization
paperswithcode_i... |
neulab/conala | 2022-10-20T20:25:00.000Z | [
"task_categories:text2text-generation",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:code",
"license:mit",
"code-generation",
"arxiv:1805.08949",
"region:us"
] | neulab | CoNaLa is a dataset of code and natural language pairs crawled from Stack Overflow, for more details please refer to this paper: https://arxiv.org/pdf/1805.08949.pdf or the dataset page https://conala-corpus.github.io/. | @inproceedings{yin2018learning,
title={Learning to mine aligned code and natural language pairs from stack overflow},
author={Yin, Pengcheng and Deng, Bowen and Chen, Edgar and Vasilescu, Bogdan and Neubig, Graham},
booktitle={2018 IEEE/ACM 15th international conference on mining software repositories (MSR)},
p... | null | 42 | 536 | ---
annotations_creators: []
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language:
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license:
- mit
multilinguality:
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size_categories:
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source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
pretty_name: CoNaLa
tags:
- code-generation
---
## Dataset Descrip... |
open-llm-leaderboard/details_julianweng__Llama-2-7b-chat-orcah | 2023-09-17T17:33:16.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 535 | ---
pretty_name: Evaluation run of julianweng/Llama-2-7b-chat-orcah
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [julianweng/Llama-2-7b-chat-orcah](https://huggingface.co/julianweng/Llama-2-7b-chat-orcah)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/Hugging... |
result-kand2-sdxl-wuerst-karlo/92f7fec0 | 2023-09-22T14:16:39.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | null | 0 | 535 | ---
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path: data/train-*
---
# Dataset Card for "92f7fec... |
speech_commands | 2023-06-01T14:59:53.000Z | [
"task_categories:audio-classification",
"task_ids:keyword-spotting",
"annotations_creators:other",
"language_creators:crowdsourced",
"multilinguality:monolingual",
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"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:18... | null | This is a set of one-second .wav audio files, each containing a single spoken
English word or background noise. These words are from a small set of commands, and are spoken by a
variety of different speakers. This data set is designed to help train simple
machine learning models. This dataset is covered in more detail ... | @article{speechcommandsv2,
author = { {Warden}, P.},
title = "{Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition}",
journal = {ArXiv e-prints},
archivePrefix = "arXiv",
eprint = {1804.03209},
primaryClass = "cs.CL",
keywords = {Computer Science - Computation and Language, Computer Sc... | null | 13 | 534 | ---
annotations_creators:
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language:
- en
license:
- cc-by-4.0
multilinguality:
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size_categories:
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- 10K<n<100K
source_datasets:
- original
task_categories:
- audio-classification
task_ids:
- keyword-spotting
pretty_name: SpeechCommands
dataset_info:
- co... |
bigscience/xP3 | 2023-05-30T15:49:59.000Z | [
"task_categories:other",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"multilinguality:multilingual",
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"language:ak",
"language:ar",
"language:as",
"language:bm",
"language:bn",
"language:ca",
"language:code",
"language:en",
"lan... | bigscience | xP3 (Crosslingual Public Pool of Prompts) is a collection of prompts & datasets across 46 of languages & 16 NLP tasks. It is used for the training of BLOOMZ and mT0, multilingual language models capable of following human instructions in dozens of languages zero-shot. | @article{muennighoff2022crosslingual,
title={Crosslingual generalization through multitask finetuning},
author={Muennighoff, Niklas and Wang, Thomas and Sutawika, Lintang and Roberts, Adam and Biderman, Stella and Scao, Teven Le and Bari, M Saiful and Shen, Sheng and Yong, Zheng-Xin and Schoelkopf, Hailey and other... | null | 83 | 534 | ---
annotations_creators:
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programming_lan... |
Dahoas/static-hh | 2023-03-06T00:11:55.000Z | [
"region:us"
] | Dahoas | null | null | null | 14 | 534 | ---
dataset_info:
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download... |
result-kand2-sdxl-wuerst-karlo/d50de234 | 2023-09-22T15:13:31.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | null | 0 | 533 | ---
dataset_info:
features:
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path: data/train-*
---
# Dataset Card for "d50de23... |
result-kand2-sdxl-wuerst-karlo/af730738 | 2023-09-22T15:13:33.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | null | 0 | 533 | ---
dataset_info:
features:
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dtype: string
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download_size: 1368
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configs:
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path: data/train-*
---
# Dataset Card for "af73073... |
result-kand2-sdxl-wuerst-karlo/03ada2d6 | 2023-09-22T15:13:35.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | null | 0 | 533 | ---
dataset_info:
features:
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dtype: string
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download_size: 1368
dataset_size: 198
configs:
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path: data/train-*
---
# Dataset Card for "03ada2d... |
mteb/bucc-bitext-mining | 2022-09-22T14:17:13.000Z | [
"multilinguality:monolingual",
"multilinguality:multilingual",
"language:de",
"language:en",
"language:fr",
"language:ru",
"language:zh",
"license:cc-by-sa-4.0",
"arxiv:2104.06893",
"arxiv:2010.02573",
"arxiv:2003.04807",
"arxiv:2204.08582",
"arxiv:2008.09335",
"arxiv:2104.07081",
"regio... | mteb | BUCC 2018 Shared Task test dataset | null | null | 0 | 530 | ---
annotations_creators: []
language_creators: []
language:
- de
- en
- fr
- ru
- zh
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
- multilingual
pretty_name: MTEB Benchmark
---
# Dataset Card for MTEB Benchmark
## Dataset Description
- **Homepage:** https://github.com/embeddings-benchmark/mteb-draft
- **R... |
meta-math/MetaMathQA | 2023-10-07T15:22:01.000Z | [
"license:apache-2.0",
"arxiv:2309.12284",
"region:us"
] | meta-math | null | null | null | 65 | 529 | ---
license: apache-2.0
---
arxiv.org/abs/2309.12284
View the project page:
https://meta-math.github.io/ |
EleutherAI/pile-deduped-pythia-random-sampled | 2023-08-25T07:26:47.000Z | [
"region:us"
] | EleutherAI | null | null | null | 2 | 527 | ---
dataset_info:
features:
- name: Index
dtype: int64
- name: 70M
dtype: float64
- name: 160M
dtype: float64
- name: 410M
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dtype: float64
- name: 1.4B
dtype: float64
- name: 2.8B
dtype: float64
- name: 6.9B
dtype: float64
- name: 12B
dtyp... |
flax-sentence-embeddings/stackexchange_titlebody_best_and_down_voted_answer_jsonl | 2022-07-11T13:13:18.000Z | [
"task_categories:question-answering",
"task_ids:closed-domain-qa",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
] | flax-sentence-embeddings | This new dataset is designed to solve this great NLP task and is crafted with a lot of care. | @misc{StackExchangeDataset,
author = {Flax Sentence Embeddings Team},
title = {Stack Exchange question pairs},
year = {2021},
howpublished = {https://huggingface.co/datasets/flax-sentence-embeddings/},
} | null | 10 | 525 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-nc-sa-4.0
multilinguality:
- multilingual
pretty_name: stackexchange
size_categories:
- unknown
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- closed-domain-qa
---
# Dataset Card Creation Guide
... |
result-kand2-sdxl-wuerst-karlo/837a21b8 | 2023-09-22T20:38:57.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | null | 0 | 525 | ---
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download_size: 1307
dataset_size: 168
configs:
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data_files:
- split: train
path: data/train-*
---
# Dataset Card for "837a21b... |
roszcz/masked-maestro-v3 | 2023-10-02T15:21:06.000Z | [
"region:us"
] | roszcz | null | null | null | 0 | 525 | ---
dataset_info:
features:
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sequence: int8
length: 90
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length: 90
- name: duration
sequence: float64
length: 90
- name: velocity
seq... |
arampacha/rsicd | 2022-04-11T15:34:07.000Z | [
"region:us"
] | arampacha | null | null | null | 2 | 524 | Entry not found |
result-kand2-sdxl-wuerst-karlo/a95a2c5b | 2023-09-22T20:38:59.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | null | 0 | 524 | ---
dataset_info:
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download_size: 1307
dataset_size: 168
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "a95a2c5... |
bc2gm_corpus | 2023-08-30T12:13:12.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | null | Nineteen teams presented results for the Gene Mention Task at the BioCreative II Workshop.
In this task participants designed systems to identify substrings in sentences corresponding to gene name mentions.
A variety of different methods were used and the results varied with a highest achieved F1 score of 0.8721.
Here ... | @article{smith2008overview,
title={Overview of BioCreative II gene mention recognition},
author={Smith, Larry and Tanabe, Lorraine K and nee Ando, Rie Johnson and Kuo, Cheng-Ju and Chung, I-Fang and Hsu, Chun-Nan and Lin, Yu-Shi and Klinger, Roman and Friedrich, Christoph M and Ganchev, Kuzman and other... | null | 4 | 523 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
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size_categories:
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source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: Bc2GmCorpus
dataset_info... |
jordiae/exebench | 2023-03-09T16:06:06.000Z | [
"region:us"
] | jordiae | An ML-scale dataset of executable C functions | @inproceedings{10.1145/3520312.3534867,
author = {Armengol-Estap\'{e}, Jordi and Woodruff, Jackson and Brauckmann, Alexander and Magalh\~{a}es, Jos\'{e} Wesley de Souza and O'Boyle, Michael F. P.},
title = {ExeBench: An ML-Scale Dataset of Executable C Functions},
year = {2022},
isbn = {9781450392730},
publisher = {Ass... | null | 1 | 523 | # ExeBench: an ML-scale dataset of executable C functions
ExeBench is a dataset of millions of C functions paired with dependencies and metadatada such that at least a subset of it can be executed with IO pairs. It is mainly inteded for machine learning applications but it is application-agnostic enough to have other ... |
NeelNanda/codeparrot_clean_subset_train | 2022-10-22T23:04:58.000Z | [
"region:us"
] | NeelNanda | null | null | null | 0 | 522 | Entry not found |
OpenAssistant/oasst_top1_2023-08-25 | 2023-08-28T12:44:26.000Z | [
"task_categories:conversational",
"size_categories:10K<n<100K",
"license:apache-2.0",
"region:us"
] | OpenAssistant | null | null | null | 13 | 520 | ---
license: apache-2.0
task_categories:
- conversational
size_categories:
- 10K<n<100K
---
# OpenAssistant TOP-1 Conversation Threads
- [Guanacco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) style export of the best conversation threads from the [open-assistant.io](https://open-assistant.io/) d... |
yerevann/sst2 | 2022-02-02T20:02:45.000Z | [
"region:us"
] | yerevann | null | null | null | 0 | 519 | Entry not found |
evidence_infer_treatment | 2023-03-16T10:35:23.000Z | [
"task_categories:text-retrieval",
"task_ids:fact-checking-retrieval",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:mit",
"arxiv:2005.04177",
"region:us... | null | Data and code from our "Inferring Which Medical Treatments Work from Reports of Clinical Trials", NAACL 2019. This work concerns inferring the results reported in clinical trials from text.
The dataset consists of biomedical articles describing randomized control trials (RCTs) that compare multiple treatments. Each of... | @inproceedings{lehman-etal-2019-inferring,
title = "Inferring Which Medical Treatments Work from Reports of Clinical Trials",
author = "Lehman, Eric and
DeYoung, Jay and
Barzilay, Regina and
Wallace, Byron C.",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chap... | null | 3 | 518 | ---
pretty_name: Evidence Infer Treatment
annotations_creators:
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language_creators:
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language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-retrieval
task_ids:
- fact-checking-retrieval
paperswithco... |
result-kand2-sdxl-wuerst-karlo/9537a11b | 2023-09-23T00:58:26.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | null | 0 | 518 | ---
dataset_info:
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configs:
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---
# Dataset Card for "9537a11... |
royboy0416/ko-alpaca | 2023-03-31T21:14:40.000Z | [
"task_categories:text-generation",
"language:ko",
"license:cc-by-4.0",
"region:us"
] | royboy0416 | null | null | null | 3 | 517 | ---
license: cc-by-4.0
task_categories:
- text-generation
language:
- ko
---
</b>Testing purpose only. Do not redistribute. </b>
Original contents: [url] https://huggingface.co/datasets/tatsu-lab/alpaca
Ko-alpaca: [url] https://github.com/Beomi/KoAlpaca/blob/main/ko_alpaca_data.json |
ArtifactAI/arxiv-math-instruct-50k | 2023-06-22T03:12:01.000Z | [
"task_categories:text-generation",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
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"source_datasets:original",
"language:en",
"license:cc0-1.0",
"doi:10.57967/hf/0799",
"region:us"... | ArtifactAI | null | null | null | 33 | 517 | ---
annotations_creators:
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language:
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license:
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multilinguality:
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pretty_name: arxiv-math-instruct-50k
size_categories:
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source_datasets:
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task_categories:
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task_ids:
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paperswithcode_id: arxiv-mat... |
alkzar90/NIH-Chest-X-ray-dataset | 2022-11-22T20:10:52.000Z | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:machine-generated",
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"language_creators:machine-generated",
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"multilinguality:monolingual",
"size_categories:100K<n<1M... | alkzar90 | The NIH Chest X-ray dataset consists of 100,000 de-identified images of chest x-rays. The images are in PNG format.
The data is provided by the NIH Clinical Center and is available through the NIH download site: https://nihcc.app.box.com/v/ChestXray-NIHCC | @inproceedings{Wang_2017,
doi = {10.1109/cvpr.2017.369},
url = {https://doi.org/10.1109%2Fcvpr.2017.369},
year = 2017,
month = {jul},
publisher = {{IEEE}
},
author = {Xiaosong Wang and Yifan Peng and Le Lu and Zhiyong Lu and Mohammadhadi Bagheri and Ronald M. Summers},
title = {{ChestX}-Ray8: Hospital-Scale Ches... | null | 17 | 515 | ---
annotations_creators:
- machine-generated
- expert-generated
language_creators:
- machine-generated
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
pretty_name: NIH-CXR14
paperswithcode_id: chestx-ray14
size_categories:
- 100K<n<1M
task_categories:
- image-classification
task_ids... |
cheulyop/ksponspeech | 2021-10-02T04:27:13.000Z | [
"region:us"
] | cheulyop | KsponSpeech is a large-scale spontaneous speech corpus of Korean conversations. This corpus contains 969 hrs of general open-domain dialog utterances, spoken by about 2,000 native Korean speakers in a clean environment. All data were constructed by recording the dialogue of two people freely conversing on a variety of ... | @article{bang2020ksponspeech,
title={KsponSpeech: Korean spontaneous speech corpus for automatic speech recognition},
author={Bang, Jeong-Uk and Yun, Seung and Kim, Seung-Hi and Choi, Mu-Yeol and Lee, Min-Kyu and Kim, Yeo-Jeong and Kim, Dong-Hyun and Park, Jun and Lee, Young-Jik and Kim, Sang-Hun},
journal={Appli... | null | 2 | 514 | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for [KsponSpeech]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported T... |
vikp/textbook_quality_programming | 2023-10-08T18:36:50.000Z | [
"language:en",
"region:us"
] | vikp | null | null | null | 131 | 514 | ---
language:
- en
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num_bytes: 471931604
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download_size:... |
shi3z/anthropic_hh_rlhf_japanese | 2023-06-29T01:19:09.000Z | [
"license:mit",
"region:us"
] | shi3z | null | null | null | 7 | 513 | ---
license: mit
---
https://huggingface.co/datasets/Anthropic/hh-rlhf
Japanese Translation |
tongyx361/prm800k-train-direct-prediction-0-02validiation-seed42-encoded | 2023-09-17T22:46:13.000Z | [
"region:us"
] | tongyx361 | null | null | null | 0 | 513 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: input_ids
sequence: int32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 308232504
num_examples: 85194
... |
yaful/DeepfakeTextDetect | 2023-07-11T01:59:02.000Z | [
"license:apache-2.0",
"arxiv:2305.13242",
"region:us"
] | yaful | null | null | null | 3 | 512 | ---
license: apache-2.0
---
<div align="center">
<h1>Deepfake Text Detection in the Wild</h1>
<!-- **Authors:** -->
_**Yafu Li<sup>†</sup><sup>‡</sup>, Qintong Li<sup>§</sup>, Leyang Cui<sup>¶</sup>, Wei Bi<sup>¶</sup>,<br>**_
_**Longyue Wang<sup>¶</sup>, Linyi Yang<sup>‡</sup>, Shuming Shi<sup>¶</sup>, Yue Zhang<s... |
germank/hh-generated_flan_t5_large_with_features2 | 2023-07-07T14:32:37.000Z | [
"region:us"
] | germank | null | null | null | 0 | 512 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: 'biased:'
dtype: int64
- name: easy-to-understand
dtype: int64
- name: enough-detail
dtype: int64
- name: factuality
dtype: int64
- name: fail-to-consider-context
dtype: int64
- ... |
turk | 2022-11-18T21:56:55.000Z | [
"task_categories:text2text-generation",
"task_ids:text-simplification",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:gpl-3.0",
"region:us"
] | null | TURKCorpus is a dataset for evaluating sentence simplification systems that focus on lexical paraphrasing,
as described in "Optimizing Statistical Machine Translation for Text Simplification". The corpus is composed of 2000 validation and 359 test original sentences that were each simplified 8 times by different annota... | @article{Xu-EtAl:2016:TACL,
author = {Wei Xu and Courtney Napoles and Ellie Pavlick and Quanze Chen and Chris Callison-Burch},
title = {Optimizing Statistical Machine Translation for Text Simplification},
journal = {Transactions of the Association for Computational Linguistics},
volume = {4},
year = {2016},
url ... | null | 3 | 511 | ---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- en
license:
- gpl-3.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids:
- text-simplification
paperswithcode_id: null
pretty_name: TURK
dataset_info... |
Muennighoff/flan | 2022-12-23T18:57:00.000Z | [
"task_categories:other",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100M<n<1B",
"language:en",
"arxiv:2109.01652",
"region:us"
] | Muennighoff | null | null | null | 32 | 511 | ---
annotations_creators:
- crowdsourced
- expert-generated
language:
- en
multilinguality:
- monolingual
size_categories:
- 100M<n<1B
task_categories:
- other
---
This is a repreprocessed version of the [FLAN dataset](https://arxiv.org/abs/2109.01652) with any updates that have been made to the FLAN datasets since the... |
cdminix/librispeech-phones-and-mel | 2023-10-02T10:29:55.000Z | [
"license:cc-by-4.0",
"region:us"
] | cdminix | Dataset containing Mel Spectrograms, Prosody and Phone Alignments for the LibriSpeech dataset. | @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={2015 IEEE international conference on acoustics, speech and signal processing (ICASSP)},
pages={5206--5... | null | 0 | 510 | ---
license: cc-by-4.0
---
|
dongyoung4091/shp-generated_flan_t5_large_with_features | 2023-08-22T14:51:13.000Z | [
"region:us"
] | dongyoung4091 | null | null | null | 0 | 509 | ---
dataset_info:
features:
- name: response
dtype: string
- name: prompt
dtype: string
- name: helpfulness
dtype: int64
- name: specificity
dtype: int64
- name: intent
dtype: int64
- name: factuality
dtype: int64
- name: easy-to-understand
dtype: int64
- name: relevance
... |
SALT-NLP/ImplicitHate | 2023-02-16T23:00:38.000Z | [
"region:us"
] | SALT-NLP | null | null | null | 2 | 507 | # Implicit Hate Speech
_Latent Hatred: A Benchmark for Understanding Implicit Hate Speech_
[[Read the Paper]](https://aclanthology.org/2021.emnlp-main.29/) | [[Take a Survey to Access the Data]](https://forms.gle/QxCpEbVp91Z35hWFA) | [[Download the Data]](https://www.dropbox.com/s/24meryhqi1oo0xk/implicit-hate-corpus... |
OrdalieTech/baby-ordalie | 2023-08-23T07:18:15.000Z | [
"task_categories:summarization",
"size_categories:1K<n<10K",
"language:fr",
"license:apache-2.0",
"legal",
"region:us"
] | OrdalieTech | null | null | null | 0 | 507 | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 1375639.2
num_examples: 1200
- name: test
num_bytes: 343909.8
num_examples: 300
download_size: 951948
dataset_size: 1719549.0
license: apache-2.0
task_categories:... |
id_nergrit_corpus | 2023-01-25T14:32:40.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:id",
"license:other",
"region:us"
] | null | Nergrit Corpus is a dataset collection for Indonesian Named Entity Recognition, Statement Extraction, and Sentiment
Analysis. id_nergrit_corpus is the Named Entity Recognition of this dataset collection which contains 18 entities as
follow:
'CRD': Cardinal
'DAT': Date
'EVT': Event
'FAC': Facility
'G... | @inproceedings{id_nergrit_corpus,
author = {Gria Inovasi Teknologi},
title = {NERGRIT CORPUS},
year = {2019},
url = {https://github.com/grit-id/nergrit-corpus},
} | null | 2 | 506 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- id
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: nergrit-corpus
prett... |
agemagician/uniref30 | 2022-09-18T12:38:41.000Z | [
"region:us"
] | agemagician | null | null | null | 2 | 506 | Entry not found |
esb/diagnostic-dataset | 2022-10-26T16:42:41.000Z | [
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
... | esb | null | null | null | 1 | 505 | ---
annotations_creators:
- expert-generated
- crowdsourced
- machine-generated
language:
- en
language_creators:
- crowdsourced
- expert-generated
license:
- cc-by-4.0
- apache-2.0
- cc0-1.0
- cc-by-nc-3.0
- other
multilinguality:
- monolingual
pretty_name: ESB Diagnostic Dataset
size_categories:
- 100K<n<1M
- 1M<n<10... |
SiberiaSoft/SiberianPersonaChat | 2023-08-02T18:16:20.000Z | [
"task_categories:text-generation",
"task_categories:text2text-generation",
"task_categories:conversational",
"size_categories:100K<n<1M",
"language:ru",
"license:mit",
"region:us"
] | SiberiaSoft | null | null | null | 7 | 505 | ---
license: mit
task_categories:
- text-generation
- text2text-generation
- conversational
language:
- ru
size_categories:
- 100K<n<1M
---
### SiberiaSoft/SiberianPersonaChat
Датасет инструкций, диалогов, QA
Данный датасет был создан для диалоговых агентов с имитацией личности.
Большая часть датасета была сгенериров... |
vipulgupta/CALM | 2023-08-24T00:03:32.000Z | [
"region:us"
] | vipulgupta | Bias Dataset | null | null | 1 | 504 | Entry not found |
wikitablequestions | 2023-04-05T13:45:42.000Z | [
"task_categories:question-answering",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"table-question-answering",
"arxiv:1508.00305",
"region:us"
] | null | This WikiTableQuestions dataset is a large-scale dataset for the task of question answering on semi-structured tables. | @inproceedings{pasupat-liang-2015-compositional,
title = "Compositional Semantic Parsing on Semi-Structured Tables",
author = "Pasupat, Panupong and Liang, Percy",
booktitle = "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference ... | null | 9 | 503 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
paperswithcode_id: null
pretty_name: WikiTableQuestions
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids: []
tags:
- table-questi... |
Murple/ksponspeech | 2022-11-14T02:41:37.000Z | [
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ko",
"region:us"
] | Murple | This paper introduces a large-scale spontaneous speech corpus of Korean, named KsponSpeech. This corpus contains 969 h of general open-domain dialog utterances, spoken by about 2000 native Korean speakers in a clean environment. All data were constructed by recording the dialogue of two people freely conversing on a va... | @Article{app10196936,
AUTHOR = {Bang, Jeong-Uk and Yun, Seung and Kim, Seung-Hi and Choi, Mu-Yeol and Lee, Min-Kyu and Kim, Yeo-Jeong and Kim, Dong-Hyun and Park, Jun and Lee, Young-Jik and Kim, Sang-Hun},
TITLE = {KsponSpeech: Korean Spontaneous Speech Corpus for Automatic Speech Recognition},
JOURNAL = {Applied Scien... | null | 3 | 502 | ---
annotations_creators:
- expert-generated
language:
- ko
language_creators:
- crowdsourced
license: []
multilinguality:
- monolingual
pretty_name: KsponSpeech
size_categories:
- 10K<n<100K
source_datasets:
- original
tags: []
task_categories:
- automatic-speech-recognition
task_ids: []
---
# Dataset Card for KsponS... |
Cohere/wikipedia-22-12-simple-embeddings | 2023-03-22T16:56:34.000Z | [
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"multilinguality:multilingual",
"language:en",
"license:apache-2.0",
"region:us"
] | Cohere | null | null | null | 38 | 502 | ---
language:
- en
multilinguality:
- multilingual
size_categories: []
source_datasets: []
tags: []
task_categories:
- text-retrieval
license:
- apache-2.0
task_ids:
- document-retrieval
---
# Wikipedia (simple English) embedded with cohere.ai `multilingual-22-12` encoder
We encoded [Wikipedia (simple English)](... |
ted_multi | 2023-04-05T13:42:14.000Z | [
"region:us"
] | null | Massively multilingual (60 language) data set derived from TED Talk transcripts.
Each record consists of parallel arrays of language and text. Missing and
incomplete translations will be filtered out. | @InProceedings{qi-EtAl:2018:N18-2,
author = {Qi, Ye and Sachan, Devendra and Felix, Matthieu and Padmanabhan, Sarguna and Neubig, Graham},
title = {When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation?},
booktitle = {Proceedings of the 2018 Conference of the North Amer... | null | 2 | 500 | ---
pretty_name: TEDMulti
paperswithcode_id: null
dataset_info:
features:
- name: translations
dtype:
translation_variable_languages:
languages:
- ar
- az
- be
- bg
- bn
- bs
- calv
- cs
- da
- de
- el
... |
masakhane/masakhanews | 2023-05-25T22:27:40.000Z | [
"task_categories:text-classification",
"task_ids:topic-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:am",
"language:en",
"language:fr",
"language:ha... | masakhane | MasakhaNEWS is the largest publicly available dataset for news topic classification in 16 languages widely spoken in Africa.
The languages are:
- Amharic (amh)
- English (eng)
- French (fra)
- Hausa (hau)
- Igbo (ibo)
- Lingala (lin)
- Luganda (lug)
- Oromo (orm)
- Nigerian Pidgin (pcm)
- Rundi (run)
- chShona (sna)
-... | @article{Adelani2023MasakhaNEWS,
title={MasakhaNEWS: News Topic Classification for African languages},
author={David Ifeoluwa Adelani and Marek Masiak and Israel Abebe Azime and Jesujoba Oluwadara Alabi and Atnafu Lambebo Tonja and Christine Mwase and Odunayo Ogundepo and Bonaventure F. P. Dossou and Akintu... | null | 4 | 500 | ---
annotations_creators:
- expert-generated
language:
- am
- en
- fr
- ha
- ig
- ln
- lg
- om
- pcm
- rn
- sn
- so
- sw
- ti
- xh
- yo
language_creators:
- expert-generated
license:
- afl-3.0
multilinguality:
- multilingual
pretty_name: masakhanews
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- news-t... |
multi_x_science_sum | 2022-11-18T21:31:34.000Z | [
"task_categories:summarization",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"paper-abstract-generation",
"arxiv:2010.14235",
"region:us"
] | null | Multi-XScience, a large-scale multi-document summarization dataset created from scientific articles. Multi-XScience introduces a challenging multi-document summarization task: writing the related-work section of a paper based on its abstract and the articles it references. | @article{lu2020multi,
title={Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles},
author={Lu, Yao and Dong, Yue and Charlin, Laurent},
journal={arXiv preprint arXiv:2010.14235},
year={2020}
} | null | 11 | 499 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- summarization
task_ids: []
paperswithcode_id: multi-xscience
pretty_name: Multi-XScience
tags:
- paper-abstract-gener... |
HuggingFaceM4/NoCaps | 2022-12-14T04:08:38.000Z | [
"license:cc-by-2.0",
"region:us"
] | HuggingFaceM4 | Dubbed NoCaps, for novel object captioning at scale, NoCaps consists of 166,100 human-generated captions describing 15,100 images from the Open Images validation and test sets.
The associated training data consists of COCO image-caption pairs, plus Open Images image-level labels and object bounding boxes.
Since Open Im... | @inproceedings{agrawal2019nocaps,
title={nocaps: novel object captioning at scale},
author={Agrawal, Harsh and Desai, Karan and Wang, Yufei and Chen, Xinlei and Jain, Rishabh and Johnson, Mark and Batra, Dhruv and Parikh, Devi and Lee, Stefan and Anderson, Peter},
booktitle={Proceedings of the IEEE International ... | null | 1 | 499 | ---
license: cc-by-2.0
---
# Dataset Card for NoCaps
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Stru... |
papluca/language-identification | 2022-07-15T10:11:23.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"multilinguality:multilingual",
"size_categories:unknown",
"source_datasets:extended|amazon_reviews_multi",
"source_datasets:extended|xnli",
"source_datasets:extended|stsb_multi_mt",
"language:ar",
"language:bg",
"langua... | papluca | null | null | null | 16 | 498 | ---
annotations_creators: []
language_creators: []
language:
- ar
- bg
- de
- el
- en
- es
- fr
- hi
- it
- ja
- nl
- pl
- pt
- ru
- sw
- th
- tr
- ur
- vi
- zh
license: []
multilinguality:
- multilingual
pretty_name: Language Identification dataset
size_categories:
- unknown
source_datasets:
- extended|amazon_reviews_... |
linxinyuan/cola | 2022-06-08T07:26:13.000Z | [
"region:us"
] | linxinyuan | null | null | null | 1 | 498 | Entry not found |
narad/ravdess | 2022-11-02T03:21:19.000Z | [
"task_categories:audio-classification",
"task_ids:audio-emotion-recognition",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
] | narad | \ | \ | null | 4 | 497 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- audio-classification
task_ids:
- audio-emotion-recognition
---
# Dataset Card for RAVDESS
## Table of... |
opus_euconst | 2022-11-03T16:47:26.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
"language:et",
"langua... | null | A parallel corpus collected from the European Constitution for 21 language. | J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012) | null | 5 | 493 | ---
annotations_creators:
- found
language_creators:
- found
language:
- cs
- da
- de
- el
- en
- es
- et
- fi
- fr
- ga
- hu
- it
- lt
- lv
- mt
- nl
- pl
- pt
- sk
- sl
- sv
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task... |
oliverwang15/news_with_gpt_instructions | 2023-07-10T19:39:33.000Z | [
"region:us"
] | oliverwang15 | null | null | null | 6 | 493 | ---
dataset_info:
features:
- name: news
dtype: string
- name: prompt
dtype: string
- name: out
dtype: string
- name: prompt_tokens
dtype: int64
- name: completion_tokens
dtype: int64
- name: total_tokens
dtype: int64
- name: label
dtype: string
splits:
- name: train
... |
jherng/rsna-2023-abdominal-trauma-detection | 2023-10-10T06:56:40.000Z | [
"task_categories:image-classification",
"task_categories:image-segmentation",
"size_categories:1K<n<10K",
"license:mit",
"region:us"
] | jherng | This dataset is the preprocessed version of the dataset from RSNA 2023 Abdominal Trauma Detection Kaggle Competition.
It is tailored for segmentation and classification tasks. It contains 3 different configs as described below:
- segmentation: 206 instances where each instance includes a CT scan in NIfTI format, a seg... | @InProceedings{huggingface:dataset,
title = {RSNA 2023 Abdominal Trauma Detection Dataset},
author={Hong Jia Herng},
year={2023}
}
@misc{rsna-2023-abdominal-trauma-detection,
author = {Errol Colak, Hui-Ming Lin, Robyn Ball, Melissa Davis, Adam Flanders, Sabeena Jalal, Kirti Magudia, Brett Marinelli, Savvas Nicolaou... | null | 0 | 493 | ---
license: mit
dataset_info:
- config_name: classification
features:
- name: img_path
dtype: string
- name: bowel
dtype:
class_label:
names:
"0": healthy
"1": injury
- name: extravasation
dtype:
class_label:
... |
pszemraj/qmsum-cleaned | 2023-06-07T22:58:58.000Z | [
"source_datasets:tau/scrolls",
"language:en",
"license:apache-2.0",
"region:us"
] | pszemraj | null | null | null | 1 | 491 | ---
license: apache-2.0
language:
- en
source_datasets: tau/scrolls
---
# qmsum-cleaned
## prefixes
It's worth noting that each "document" in `input` is prefixed by a question/prompt on what the model is supposed to do. **You may want to explicitly handle this in some way, or prefix your models trained on this dat... |
Birchlabs/openai-prm800k-stepwise-critic | 2023-06-03T10:51:37.000Z | [
"license:mit",
"region:us"
] | Birchlabs | null | null | null | 9 | 491 | ---
license: mit
---
|
open-llm-leaderboard/details_TheTravellingEngineer__bloom-1b1-RLHF | 2023-08-27T12:31:32.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 491 | ---
pretty_name: Evaluation run of TheTravellingEngineer/bloom-1b1-RLHF
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [TheTravellingEngineer/bloom-1b1-RLHF](https://huggingface.co/TheTravellingEngineer/bloom-1b1-RLHF)\
\ on the [Open LLM Leaderboard](https://huggingface.co/sp... |
izumi-lab/llm-japanese-dataset | 2023-07-04T15:25:14.000Z | [
"size_categories:1M<n<10M",
"language:ja",
"license:cc-by-sa-4.0",
"arxiv:2305.12720",
"region:us"
] | izumi-lab | null | null | null | 61 | 490 | ---
license: cc-by-sa-4.0
language:
- ja
size_categories:
- 1M<n<10M
---
# llm-japanese-dataset
LLM構築用の日本語インストラクション(チャット)データセット
主に，英語で構築されたLLMモデルなどに対して，チャット(Instruction)応答タスクに関してLoRAなどでチューニングするために使用できます．
※様々な公開言語資源を利用させていただきました．関係各位にはこの場を借りて御礼申し上げます．
## updates
5/15にAlpaca datasetがNCにライセンス変更されたことに対応し，安心してご利用いただけるよ... |
EleutherAI/logiqa | 2023-07-13T12:32:49.000Z | [
"region:us"
] | EleutherAI | LogiQA is a dataset for testing human logical reasoning. It consists of 8,678 QA
instances, covering multiple types of deductive reasoning. Results show that state-
of-the-art neural models perform by far worse than human ceiling. The dataset can
also serve as a benchmark for reinvestigating logical AI under the deep l... | @misc{liu2020logiqa,
title={LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning},
author={Jian Liu and Leyang Cui and Hanmeng Liu and Dandan Huang and Yile Wang and Yue Zhang},
year={2020},
eprint={2007.08124},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | null | 1 | 490 | Entry not found |
BeIR/climate-fever-qrels | 2022-10-23T06:08:28.000Z | [
"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 | 0 | 489 | ---
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:
... |
Salesforce/rose | 2023-06-07T21:00:52.000Z | [
"language:en",
"region:us"
] | Salesforce | RoSE benchmark | null | null | 6 | 489 | ---
language:
- en
---
# ROSE 🌹
This repo contiains the RoSE benchmark of our paper "Revisiting the Gold Standard:
Grounding Summarization Evaluation with Robust Human Evaluation".
Please visit [here](https://yale-lily.github.io/ROSE/) for a demo page of this project.
### ACU Annotations
RoSE benchmark contains... |
rcds/wikipedia-for-mask-filling | 2023-03-08T12:22:02.000Z | [
"task_categories:fill-mask",
"annotations_creators:other",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] | rcds | \ | null | null | 0 | 488 | ---
annotations_creators:
- other
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- multilingual
paperswithcode_id: null
pretty_name: "wikipedia pages chunked for fill-mask"
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- fill-mask
---
# preprocessed versio... |
zetavg/ShareGPT-Processed | 2023-05-21T03:50:14.000Z | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"language:zh",
"language:es",
"language:ja",
"language:fr",
"license:cc0-1.0",
"conversation",
"rlhf",
"chatgpt",
"gpt-3.5",
"region:us"
] | zetavg | null | null | null | 22 | 483 | ---
dataset_info:
features:
- name: id
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: markdown
dtype: string
- name: opencc_converted_markdown
dtype: string
- name: value
dtype: string
- name: lang
dtype: string
splits:
- name... |
bigheiniuJ/Natural-Instruction | 2023-08-02T04:52:07.000Z | [
"region:us"
] | bigheiniuJ | null | null | null | 0 | 483 | ---
dataset_info:
features:
- name: id
dtype: string
- name: task_name
dtype: string
- name: definition
dtype: string
- name: inputs
dtype: string
- name: targets
dtype: string
- name: pos_examples
list:
- name: explanation
dtype: string
- name: input
dtype: str... |
result-kand2-sdxl-wuerst-karlo/083be228 | 2023-09-24T02:55:45.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | null | 0 | 481 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 176
num_examples: 10
download_size: 1349
dataset_size: 176
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "083be22... |
eaglewatch/Korean_Wikipedia_Dataset_for_GPT2_August_2022 | 2023-08-25T05:35:38.000Z | [
"task_categories:question-answering",
"task_categories:text2text-generation",
"task_categories:translation",
"task_categories:conversational",
"task_categories:visual-question-answering",
"task_ids:open-domain-qa",
"task_ids:closed-domain-qa",
"task_ids:dialogue-generation",
"task_ids:visual-questio... | eaglewatch | null | null | null | 2 | 480 | ---
annotations_creators:
- other
language:
- ko
language_creators:
- other
license:
- apache-2.0
multilinguality:
- multilingual
pretty_name: Korean wikipedia dataset for GPT-2 training
size_categories:
- 100M<n<1B
source_datasets: []
tags:
- gpt2
- korean
- wikipedia
- pertained
task_categories:
- question-answering
... |
tab_fact | 2023-01-25T14:45:28.000Z | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:1909.02164",
"region:us"
] | null | The problem of verifying whether a textual hypothesis holds the truth based on the given evidence, also known as fact verification, plays an important role in the study of natural language understanding and semantic representation. However, existing studies are restricted to dealing with unstructured textual evidence (... | @inproceedings{2019TabFactA,
title={TabFact : A Large-scale Dataset for Table-based Fact Verification},
author={Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou and William Yang Wang},
booktitle = {International Conference on Learning Representations (ICLR)},
address = {Ad... | null | 7 | 479 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- fact-checking
paperswithcode_id: tabfact
pretty_name: TabFact
dataset_... |
tner/wikiann | 2022-09-27T18:39:42.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"multilinguality:multilingual",
"size_categories:10K<100k",
"language:ace",
"language:bg",
"language:da",
"language:fur",
"language:ilo",
"language:lij",
"language:mzn",
"language:qu",
"language:su",
"language:vi"... | tner | [WikiAnn](https://aclanthology.org/P17-1178/) | @inproceedings{pan-etal-2017-cross,
title = "Cross-lingual Name Tagging and Linking for 282 Languages",
author = "Pan, Xiaoman and
Zhang, Boliang and
May, Jonathan and
Nothman, Joel and
Knight, Kevin and
Ji, Heng",
booktitle = "Proceedings of the 55th Annual Meeting of the... | null | 4 | 479 | ---
language:
- ace
- bg
- da
- fur
- ilo
- lij
- mzn
- qu
- su
- vi
- af
- bh
- de
- fy
- io
- lmo
- nap
- rm
- sv
- vls
- als
- bn
- diq
- ga
- is
- ln
- nds
- ro
- sw
- vo
- am
- bo
- dv
- gan
- it
- lt
- ne
- ru
- szl
- wa
- an
- br
- el
- gd
- ja
- lv
- nl
- rw
- ta
- war
- ang
- bs
- eml
- gl
- jbo
- nn
- sa
- te... |
ashraq/fashion-product-images-small | 2022-11-01T20:25:52.000Z | [
"region:us"
] | ashraq | null | null | null | 8 | 477 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: gender
dtype: string
- name: masterCategory
dtype: string
- name: subCategory
dtype: string
- name: articleType
dtype: string
- name: baseColour
dtype: string
- name: season
dtype: string
- name: year
dtype: fl... |
GATE-engine/automated_cardiac_diagnosis_competition.ACDC | 2023-06-28T08:56:08.000Z | [
"region:us"
] | GATE-engine | null | null | null | 0 | 476 | ---
dataset_info:
features:
- name: four_d_img
sequence:
sequence:
sequence:
sequence: float32
- name: frame_data
list:
- name: img
sequence:
sequence:
sequence: float32
- name: label
sequence:
sequence:
sequence: int64
spli... |
ArmelR/stack-exchange-instruction | 2023-05-26T08:37:42.000Z | [
"region:us"
] | ArmelR | null | null | null | 47 | 475 | ---
pretty_name : stack exchange instruction
---
# Dataset Card for "stack-exchange-instruction"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
maharshipandya/spotify-tracks-dataset | 2023-06-14T11:59:02.000Z | [
"task_categories:feature-extraction",
"task_categories:text-classification",
"task_categories:summarization",
"task_categories:table-question-answering",
"task_categories:audio-classification",
"task_categories:reinforcement-learning",
"task_categories:tabular-classification",
"task_categories:tabular... | maharshipandya | null | null | null | 18 | 475 | ---
license: bsd
task_categories:
- feature-extraction
- text-classification
- summarization
- table-question-answering
- text-classification
- feature-extraction
- audio-classification
- reinforcement-learning
- tabular-classification
- tabular-regression
language:
- en
tags:
- music
- art
pretty_name: Spotify Track... |
clips/mqa | 2022-09-27T12:38:50.000Z | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:no-annotation",
"language_creators:other",
"multilinguality:multilingual",
"size_categories:unknown",
"source_datasets:original",
"language:ca",
"language:en",
"language:de",
"language:es",
"language:fr"... | clips | MQA is a multilingual corpus of questions and answers parsed from the Common Crawl. Questions are divided between Frequently Asked Questions (FAQ) pages and Community Question Answering (CQA) pages. | @misc{debruyn2021mfaq,
title={MFAQ: a Multilingual FAQ Dataset},
author={Maxime {De Bruyn} and Ehsan Lotfi and Jeska Buhmann and Walter Daelemans},
year={2021},
booktitle={MRQA@EMNLP2021},
} | null | 26 | 473 | ---
annotations_creators:
- no-annotation
language_creators:
- other
language:
- ca
- en
- de
- es
- fr
- ru
- ja
- it
- zh
- pt
- nl
- tr
- pl
- vi
- ar
- id
- uk
- ro
- no
- th
- sv
- el
- fi
- he
- da
- cs
- ko
- fa
- hi
- hu
- sk
- lt
- et
- hr
- is
- lv
- ms
- bg
- sr
- ca
license:
- cc0-1.0
multilinguality:
- mu... |
edinburghcstr/ami | 2023-01-16T18:11:05.000Z | [
"task_categories:automatic-speech-recognition",
"multilinguality:monolingual",
"language:en",
"license:cc-by-4.0",
"arxiv:1906.11047",
"region:us"
] | edinburghcstr | The AMI Meeting Corpus consists of 100 hours of meeting recordings. The recordings use a range of signals
synchronized to a common timeline. These include close-talking and far-field microphones, individual and
room-view video cameras, and output from a slide projector and an electronic whiteboard. During the meetings,... | @inproceedings{10.1007/11677482_3,
author = {Carletta, Jean and Ashby, Simone and Bourban, Sebastien and Flynn, Mike and Guillemot, Mael and Hain, Thomas and Kadlec, Jaroslav and Karaiskos, Vasilis and Kraaij, Wessel and Kronenthal, Melissa and Lathoud, Guillaume and Lincoln, Mike and Lisowska, Agnes and McCowan, Iain ... | null | 15 | 473 | ---
annotations_creators: []
language:
- en
language_creators: []
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: AMI
size_categories: []
source_datasets: []
tags: []
task_categories:
- automatic-speech-recognition
---
# Dataset Card for AMI
## Table of Contents
- [Table of Contents](#table-of-conten... |
orkg/SciQA | 2023-05-22T10:13:44.000Z | [
"task_categories:question-answering",
"annotations_creators:expert-generated",
"annotations_creators:auto-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"knowledge-base-qa... | orkg | SciQA contains 2,565 SPARQL query - question pairs along with answers fetched from the open research knowledge graph (ORKG) via a Virtuoso SPARQL endpoint, it is a collection of both handcrafted and autogenerated questions and queries. The dataset is split into 70% training, 10% validation and 20% test exam... | @Article{SciQA2023,
author={Auer, S{\"o}ren
and Barone, Dante A. C.
and Bartz, Cassiano
and Cortes, Eduardo G.
and Jaradeh, Mohamad Yaser
and Karras, Oliver
and Koubarakis, Manolis
and Mouromtsev, Dmitry
and Pliukhin, Dmitrii
and Radyush, D... | null | 2 | 473 | ---
annotations_creators:
- expert-generated
- auto-generated
language:
- en
language_creators:
- machine-generated
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: 'The SciQA Scientific Question Answering Benchmark for Scholarly Knowledge'
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
-... |
newsgroup | 2023-04-05T13:35:49.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | null | The 20 Newsgroups data set is a collection of approximately 20,000 newsgroup documents, partitioned (nearly) evenly across
20 different newsgroups. The 20 newsgroups collection has become a popular data set for experiments in text applications of
machine learning techniques, such as text classification and text cluster... | @inproceedings{Lang95,
author = {Ken Lang},
title = {Newsweeder: Learning to filter netnews}
year = {1995}
booktitle = {Proceedings of the Twelfth International Conference on Machine Learning}
pages = {331-339}
} | null | 7 | 472 | ---
annotations_creators:
- found
language:
- en
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
pretty_name: 20 Newsgroups
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
paperswithcode_id: 20-newsgroup... |
medalpaca/medical_meadow_medqa | 2023-04-06T16:59:02.000Z | [
"task_categories:question-answering",
"language:en",
"language:zh",
"medical",
"region:us"
] | medalpaca | null | null | null | 27 | 472 | ---
task_categories:
- question-answering
language:
- en
- zh
tags:
- medical
---
# Dataset Card for MedQA
## Dataset Description
- **Paper:**
### Dataset Summary
This is the data and baseline source code for the paper: Jin, Di, et al. "What Disease does this Patient Have? A Large-scale Open Domain Question Answe... |
IlyaGusev/gpt_roleplay_realm | 2023-05-21T12:43:08.000Z | [
"task_categories:text-generation",
"task_categories:conversational",
"size_categories:1K<n<10K",
"language:ru",
"language:en",
"license:cc-by-4.0",
"gpt-4",
"fictional",
"role-play",
"gpt-3.5",
"art",
"region:us"
] | IlyaGusev | null | null | null | 38 | 472 | ---
dataset_info:
features:
- name: name
dtype: string
- name: context
dtype: string
- name: greeting
dtype: string
- name: example_dialogue
list:
- name: content
dtype: string
- name: role
dtype: string
- name: topics
sequence: string
- name: dialogues
list:
... |
reazon-research/reazonspeech | 2023-02-08T02:22:58.000Z | [
"task_categories:automatic-speech-recognition",
"size_categories:10M<n<100M",
"language:ja",
"license:other",
"region:us"
] | reazon-research | null | null | null | 29 | 470 | ---
license: other
task_categories:
- automatic-speech-recognition
language:
- ja
pretty_name: ReazonSpeech
size_categories:
- 10M<n<100M
---
# Dataset Card for ReazonSpeech
## Dataset Description
- **Homepage:** https://research.reazon.jp/projects/ReazonSpeech
- **Repository:** https://github.com/reazon-research/re... |
songlab/genomes-brassicales-balanced-v1 | 2023-04-12T18:11:24.000Z | [
"region:us"
] | songlab | null | null | null | 0 | 468 | More info: https://github.com/songlab-cal/gpn |
Honaker/xview_dataset | 2023-09-06T21:50:13.000Z | [
"task_categories:object-detection",
"size_categories:10B<n<100B",
"language:en",
"license:cc-by-4.0",
"region:us"
] | Honaker | null | null | null | 0 | 468 | ---
language:
- en
license: cc-by-4.0
size_categories:
- 10B<n<100B
task_categories:
- object-detection
pretty_name: XView
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: index
dtype: int64
- name: image_id
dtype: string
- name: image... |
alt | 2023-06-01T14:59:53.000Z | [
"task_categories:translation",
"task_categories:token-classification",
"task_ids:parsing",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"multilinguality:translation",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"source_da... | null | The ALT project aims to advance the state-of-the-art Asian natural language processing (NLP) techniques through the open collaboration for developing and using ALT. It was first conducted by NICT and UCSY as described in Ye Kyaw Thu, Win Pa Pa, Masao Utiyama, Andrew Finch and Eiichiro Sumita (2016). Then, it was develo... | @inproceedings{riza2016introduction,
title={Introduction of the asian language treebank},
author={Riza, Hammam and Purwoadi, Michael and Uliniansyah, Teduh and Ti, Aw Ai and Aljunied, Sharifah Mahani and Mai, Luong Chi and Thang, Vu Tat and Thai, Nguyen Phuong and Chea, Vichet and Sam, Sethserey and others},
book... | null | 6 | 467 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- bn
- en
- fil
- hi
- id
- ja
- km
- lo
- ms
- my
- th
- vi
- zh
license:
- cc-by-4.0
multilinguality:
- multilingual
- translation
size_categories:
- 100K<n<1M
- 10K<n<100K
source_datasets:
- original
task_categories:
- translati... |
HUPD/hupd | 2022-10-24T15:47:30.000Z | [
"task_categories:fill-mask",
"task_categories:summarization",
"task_categories:text-classification",
"task_categories:token-classification",
"task_ids:masked-language-modeling",
"task_ids:multi-class-classification",
"task_ids:topic-classification",
"task_ids:named-entity-recognition",
"language:en"... | HUPD | The Harvard USPTO Patent Dataset (HUPD) is a large-scale, well-structured, and multi-purpose corpus
of English-language patent applications filed to the United States Patent and Trademark Office (USPTO)
between 2004 and 2018. With more than 4.5 million patent documents, HUPD is two to three times larger
than compara... | @InProceedings{suzgun2021:hupd,
title = {The Harvard USPTO Patent Dataset},
authors={Mirac Suzgun and Suproteem Sarkar and Luke Melas-Kyriazi and Scott Kominers and Stuart Shieber},
year={2021}
} | null | 19 | 467 | ---
language:
- en
license:
- cc-by-sa-4.0
task_categories:
- fill-mask
- summarization
- text-classification
- token-classification
task_ids:
- masked-language-modeling
- multi-class-classification
- topic-classification
- named-entity-recognition
pretty_name: "HUPD"
tags:
- patents
---
# Dataset Card for The Harvard... |
c-s-ale/alpaca-gpt4-data | 2023-04-07T19:27:51.000Z | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-4.0",
"gpt",
"alpaca",
"fine-tune",
"instruct-tune",
"instruction",
"arxiv:2304.03277",
"region:us"
] | c-s-ale | null | null | null | 17 | 467 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 40178951
num_examples: 52002
download_size: 24027484
dataset_size: 40178951
license: cc-by-4.0
language:
- en
pretty_name: Instructi... |
GEM/viggo | 2022-10-24T15:31:07.000Z | [
"task_categories:table-to-text",
"annotations_creators:none",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"data-to-text",
"region:us"
] | GEM | ViGGO was designed for the task of data-to-text generation in chatbots (as opposed to task-oriented dialogue systems), with target responses being more conversational than information-seeking, yet constrained to the information presented in a meaning representation. The dataset, being relatively small and clean, can al... | @inproceedings{juraska-etal-2019-viggo,
title = "{V}i{GGO}: A Video Game Corpus for Data-To-Text Generation in Open-Domain Conversation",
author = "Juraska, Juraj and
Bowden, Kevin and
Walker, Marilyn",
booktitle = "Proceedings of the 12th International Conference on Natural Language Generatio... | null | 9 | 466 | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
pretty_name: viggo
tags:
- data-to-text
---
# Dataset Card for GEM/viggo
## Dataset Descr... |
axiong/pmc_oa | 2023-08-22T17:42:06.000Z | [
"region:us"
] | axiong | Foundation models trained on large-scale dataset gain a recent surge in CV and NLP. In contrast, development in biomedical domain lags far behind due to data scarcity.
To address this issue, we build and release PMC-OA, a biomedical dataset with 1.6M image-caption pairs collected from PubMedCentral's OpenAccess subset,... | @article{lin2023pmc,
title={PMC-CLIP: Contrastive Language-Image Pre-training using Biomedical Documents},
author={Lin, Weixiong and Zhao, Ziheng and Zhang, Xiaoman and Wu, Chaoyi and Zhang, Ya and Wang, Yanfeng and Xie, Weidi},
journal={arXiv preprint arXiv:2303.07240},
year={2023}
} | null | 13 | 466 | # PMC-OA Dataset
**News: We have released the PMC-OA dataset. You can choose the subset specifically.**
**P.S.** There's something wrong with the huggingface dataset viewer when the dataset scale gets large.
So we sample a subset of it to visualize it directly on web. Click [PMC-OA-Demo](https://huggingface.co/datase... |
coastalcph/fairlex | 2023-07-27T12:43:39.000Z | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"task_ids:multi-class-classification",
"task_ids:topic-classification",
"annotations_creators:found",
"annotations_creators:machine-generated",
"language_creators:found",
"source_datasets:extended",
"language:en",
"langu... | coastalcph | Fairlex: A multilingual benchmark for evaluating fairness in legal text processing. | @inproceedings{chalkidis-etal-2022-fairlex,
author={Chalkidis, Ilias and Passini, Tommaso and Zhang, Sheng and
Tomada, Letizia and Schwemer, Sebastian Felix and Søgaard, Anders},
title={FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing},
booktitle={Proceedings of... | null | 6 | 464 | ---
annotations_creators:
- found
- machine-generated
language_creators:
- found
language:
- en
- en
- de
- fr
- it
- zh
license:
- cc-by-nc-sa-4.0
multilinguality:
ecthr:
- monolingual
scotus:
- monolingual
fscs:
- multilingual
cail:
- monolingual
size_categories:
ecthr:
- 10K<n<100K
scotus:
- ... |
medical_dialog | 2023-09-18T09:07:35.000Z | [
"task_categories:question-answering",
"task_ids:closed-domain-qa",
"annotations_creators:found",
"language_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"language:zh",
"license:unknown"... | null | The MedDialog dataset (English) contains conversations (in English) between doctors and patients.It has 0.26 million dialogues. The data is continuously growing and more dialogues will be added. The raw dialogues are from healthcaremagic.com and icliniq.com.
All copyrights of the data belong to healthcaremagic.com and ... | @article{chen2020meddiag,
title={MedDialog: a large-scale medical dialogue dataset},
author={Chen, Shu and Ju, Zeqian and Dong, Xiangyu and Fang, Hongchao and Wang, Sicheng and Yang, Yue and Zeng, Jiaqi and Zhang, Ruisi and Zhang, Ruoyu and Zhou, Meng and Zhu, Penghui and Xie, Pengtao},
journal={arXiv preprint ar... | null | 71 | 463 | ---
annotations_creators:
- found
language_creators:
- expert-generated
- found
language:
- en
- zh
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- closed-domain-qa
pretty_name: MedDialog
paperswithcode_id: meddi... |
conv_ai_2 | 2022-11-03T16:31:09.000Z | [
"task_categories:conversational",
"task_categories:text-classification",
"task_ids:text-scoring",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"evalu... | null | ConvAI is a dataset of human-to-bot conversations labelled for quality. This data can be used to train a metric for evaluating dialogue systems. Moreover, it can be used in the development of chatbots themselves: it contains the information on the quality of utterances and entire dialogues, that can guide a dialogue sy... | @misc{dinan2019second,
title={The Second Conversational Intelligence Challenge (ConvAI2)},
author={Emily Dinan and Varvara Logacheva and Valentin Malykh and Alexander Miller and Kurt Shuster and Jack Urbanek and Douwe Kiela and Arthur Szlam and Iulian Serban and Ryan Lowe and Shrimai Prabhumoye and Alan W B... | null | 26 | 462 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- conversational
- text-classification
task_ids:
- text-scoring
paperswithcode_id: convai2
pretty_name: Con... |
prachathai67k | 2023-01-25T14:42:50.000Z | [
"task_categories:text-classification",
"task_ids:topic-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | null | `prachathai-67k`: News Article Corpus and Multi-label Text Classificdation from Prachathai.com
The prachathai-67k dataset was scraped from the news site Prachathai.
We filtered out those articles with less than 500 characters of body text, mostly images and cartoons.
It contains 67,889 articles wtih 12 curated tags fro... | @misc{prachathai67k,
author = {cstorm125, lukkiddd },
title = {prachathai67k},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
howpublished={\\url{https://github.com/PyThaiNLP/prachathai-67k}},
} | null | 3 | 462 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- topic-classification
paperswithcode_id: prachathai-67k
pretty_name: prachathai67k
dat... |
ted_talks_iwslt | 2023-06-01T14:59:58.000Z | [
"task_categories:translation",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:translation",
"size_categories:1K<n<10K",
"size_categories:n<1K",
"source_datasets:original",
"language:af",
"language:am",
"language:a... | null | The core of WIT3 is the TED Talks corpus, that basically redistributes the original content published by the TED Conference website (http://www.ted.com). Since 2007,
the TED Conference, based in California, has been posting all video recordings of its talks together with subtitles in English
and their translations in m... | @inproceedings{cettolo-etal-2012-wit3,
title = "{WIT}3: Web Inventory of Transcribed and Translated Talks",
author = "Cettolo, Mauro and
Girardi, Christian and
Federico, Marcello",
booktitle = "Proceedings of the 16th Annual conference of the European Association for Machine Translation",
... | null | 10 | 462 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- af
- am
- ar
- arq
- art
- as
- ast
- az
- be
- bg
- bi
- bn
- bo
- bs
- ca
- ceb
- cnh
- cs
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fil
- fr
- ga
- gl
- gu
- ha
- he
- hi
- hr
- ht
- hu
- hup
- hy
... |
bazyl/GTSRB | 2022-10-25T10:39:19.000Z | [
"task_categories:image-classification",
"task_ids:multi-label-image-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"size_categories:10K<n<100K",
"source_datasets:original",
"license:gpl-3.0",
"region:us"
] | bazyl | null | null | null | 0 | 461 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language: []
license:
- gpl-3.0
multilinguality: []
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- image-classification
task_ids:
- multi-label-image-classification
pretty_name: GTSRB
---
# Dataset Card for GTSRB
## Tabl... |
flax-sentence-embeddings/stackexchange_title_best_voted_answer_jsonl | 2022-07-11T13:13:11.000Z | [
"task_categories:question-answering",
"task_ids:closed-domain-qa",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
] | flax-sentence-embeddings | This new dataset is designed to solve this great NLP task and is crafted with a lot of care. | @misc{StackExchangeDataset,
author = {Flax Sentence Embeddings Team},
title = {Stack Exchange question pairs},
year = {2021},
howpublished = {https://huggingface.co/datasets/flax-sentence-embeddings/},
} | null | 5 | 460 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-nc-sa-4.0
multilinguality:
- multilingual
pretty_name: stackexchange
size_categories:
- unknown
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- closed-domain-qa
---
# Dataset Card Creation Guide
... |
osunlp/Mind2Web | 2023-07-19T03:44:34.000Z | [
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-4.0",
"Web Agent",
"arxiv:2306.06070",
"region:us"
] | osunlp | null | null | null | 40 | 460 | ---
license: cc-by-4.0
language:
- en
tags:
- Web Agent
size_categories:
- 1K<n<10K
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:** https://osu-nlp-group.github.io/Mind2Web/
- **Repository:** https://github.com/OSU-NLP-Group/Mind2Web
- **Paper:** https://arxiv.org/abs/2306.06070
- **Point o... |
AlexaAI/bold | 2022-10-06T16:21:46.000Z | [
"task_categories:text-generation",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:2101.11718",
"region:us"
] | AlexaAI | null | null | null | 5 | 458 | ---
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-generation
task_ids:
- text-generation
pretty_name: BOLD (Bias in Open-ended Language Generation Dataset)
---
# Dataset Card for Bias in Open-ended Language Generatio... |
cyanic-selkie/aida-conll-yago-wikidata | 2023-06-28T19:01:17.000Z | [
"task_categories:token-classification",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-sa-3.0",
"wikidata",
"wikipedia",
"named-entity-recognition",
"named-entity-linking",
"region:us"
] | cyanic-selkie | null | null | null | 2 | 456 | ---
license: cc-by-sa-3.0
task_categories:
- token-classification
language:
- en
tags:
- wikidata
- wikipedia
- named-entity-recognition
- named-entity-linking
pretty_name: AIDA CoNLL-YAGO Wikidata
size_categories:
- 10K<n<100K
---
# Dataset Card for AIDA CoNLL-YAGO Wikidata
## Table of Contents
- [Dataset Descriptio... |
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