id stringlengths 2 115 | lastModified stringlengths 24 24 | tags list | author stringlengths 2 42 ⌀ | description stringlengths 0 6.67k ⌀ | citation stringlengths 0 10.7k ⌀ | likes int64 0 3.66k | downloads int64 0 8.89M | created timestamp[us] | card stringlengths 11 977k | card_len int64 11 977k | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|
AdaptLLM/law-tasks | 2023-10-21T11:46:07.000Z | [
"arxiv:2309.09530",
"region:us"
] | AdaptLLM | null | null | 4 | 600 | 2023-09-19T07:44:48 | ---
configs:
- config_name: SCOTUS
data_files:
- split: test
path: "scotus/test.json"
- config_name: CaseHOLD
data_files:
- split: test
path: "case_hold/test.json"
- config_name: UNFAIR_ToS
data_files:
- split: test
path: "unfair_tos/test.json"
---
# Adapting Large Language Models v... | 2,378 | [
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biomrc | 2023-04-05T09:41:42.000Z | [
"language:en",
"region:us"
] | null | We introduce BIOMRC, a large-scale cloze-style biomedical MRC dataset. Care was taken to reduce noise, compared to the previous BIOREAD dataset of Pappas et al. (2018). Experiments show that simple heuristics do not perform well on the new dataset and that two neural MRC models that had been tested on BIOREAD perform m... | @inproceedings{pappas-etal-2020-biomrc,
title = "{B}io{MRC}: A Dataset for Biomedical Machine Reading Comprehension",
author = "Pappas, Dimitris and
Stavropoulos, Petros and
Androutsopoulos, Ion and
McDonald, Ryan",
booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical L... | 3 | 596 | 2022-03-02T23:29:22 | ---
language:
- en
paperswithcode_id: biomrc
pretty_name: BIOMRC
dataset_info:
- config_name: plain_text
features:
- name: abstract
dtype: string
- name: title
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- name: entities_list
sequence: string
- name: answer
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splits:
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num_bytes: 1653301820
... | 15,183 | [
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kuanhuggingface/promptTTS_encodec_v2_small | 2023-06-12T05:45:16.000Z | [
"region:us"
] | kuanhuggingface | null | null | 0 | 596 | 2023-06-12T05:36:48 | ---
dataset_info:
features:
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jxie/stl10 | 2023-08-10T07:13:23.000Z | [
"region:us"
] | jxie | null | null | 0 | 596 | 2023-08-10T07:08:50 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': '1'
'1': '10'
'2': '2'
'3': '3'
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'6': '6'
'7': '7'
'8': '8'
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spl... | 1,418 | [
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shibing624/nli-zh-all | 2023-06-22T06:39:46.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"task_ids:semantic-similarity-scoring",
"task_ids:text-scoring",
"annotations_creators:shibing624",
"language_creators:shibing624",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:https://github... | shibing624 | The SNLI corpus (version 1.0) is a merged chinese sentence similarity dataset, supporting the task of natural language
inference (NLI), also known as recognizing textual entailment (RTE). | https://github.com/shibing624/text2vec | 18 | 595 | 2023-06-14T05:12:45 | ---
annotations_creators:
- shibing624
language_creators:
- shibing624
language:
- zh
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- https://github.com/shibing624/text2vec
task_categories:
- text-classification
task_ids:
- natural-language-inference
- semantic-similarit... | 13,870 | [
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OxAISH-AL-LLM/wiki_toxic | 2022-09-19T15:53:19.000Z | [
"task_categories:text-classification",
"task_ids:hate-speech-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|other",
"language:en",
"license:cc0-1.0",
"wikipedia",
"toxicity",
"tox... | OxAISH-AL-LLM | Jigsaw Toxic Comment Challenge dataset. This dataset was the basis of a Kaggle competition run by Jigsaw | """
_DESCRIPTION = | 9 | 594 | 2022-08-25T12:59:12 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- cc0-1.0
multilinguality:
- monolingual
pretty_name: Toxic Wikipedia Comments
size_categories:
- 100K<n<1M
source_datasets:
- extended|other
tags:
- wikipedia
- toxicity
- toxic comments
task_categories:
- text-classification
t... | 4,296 | [
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0... |
masakhaner | 2023-06-01T14:59:56.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:am",
"language:ha",
"language:ig",
"lang... | null | MasakhaNER is the first large publicly available high-quality dataset for named entity recognition (NER) in ten African languages.
Named entities are phrases that contain the names of persons, organizations, locations, times and quantities.
Example:
[PER Wolff] , currently a journalist in [LOC Argentina] , played wit... | @article{Adelani2021MasakhaNERNE,
title={MasakhaNER: Named Entity Recognition for African Languages},
author={D. Adelani and Jade Abbott and Graham Neubig and Daniel D'Souza and Julia Kreutzer and Constantine Lignos
and Chester Palen-Michel and Happy Buzaaba and Shruti Rijhwani and Sebastian Ruder and Stephen May... | 4 | 592 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- am
- ha
- ig
- lg
- luo
- pcm
- rw
- sw
- wo
- yo
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-r... | 14,126 | [
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0.032989501953125... |
HuggingFaceH4/test-dataset-all-splits | 2023-04-25T22:09:49.000Z | [
"region:us"
] | HuggingFaceH4 | null | null | 0 | 587 | 2023-04-25T22:09:40 | ---
dataset_info:
features:
- name: chosen
list:
- name: content
dtype: string
- name: role
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- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
- name: prompt
dtype: string
- name: messages
list:
- name: cont... | 998 | [
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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... | 2 | 584 | 2022-03-02T23:29:22 | ---
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
... | 8,141 | [
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0... |
lighteval/boolq_helm | 2023-05-25T12:28:12.000Z | [
"region:us"
] | lighteval | 0 | 584 | 2023-05-04T09:56:35 | Entry not found | 15 | [
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sagawa/ZINC-canonicalized | 2022-09-04T02:21:08.000Z | [
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"license:apache-2.0",
"ZINC",
"chemical",
"SMILES",
"region:us"
] | sagawa | null | null | 0 | 582 | 2022-09-03T06:01:18 | ---
annotations_creators: []
language: []
language_creators:
- expert-generated
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: canonicalized ZINC
size_categories:
- 10M<n<100M
source_datasets:
- original
tags:
- ZINC
- chemical
- SMILES
task_categories: []
task_ids: []
---
### dataset description
We... | 744 | [
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allenai/scifact | 2022-11-18T21:44:10.000Z | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-2.0",
"region:us"
] | allenai | SciFact, a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts, and annotated with labels and rationales. | @inproceedings{Wadden2020FactOF,
title={Fact or Fiction: Verifying Scientific Claims},
author={David Wadden and Shanchuan Lin and Kyle Lo and Lucy Lu Wang and Madeleine van Zuylen and Arman Cohan and Hannaneh Hajishirzi},
booktitle={EMNLP},
year={2020},
} | 7 | 578 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- found
license:
- cc-by-nc-2.0
multilinguality:
- monolingual
pretty_name: SciFact
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- fact-checking
paperswithcode_id: scifact
dataset_i... | 8,059 | [
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code_x_glue_tt_text_to_text | 2023-07-27T15:29:15.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
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"source_datasets:original",
"language:da",
"language:en",
"language:lv",
"language:nb",
"language:zh",
"license:c-uda",
"code-documentation-tr... | null | The dataset we use is crawled and filtered from Microsoft Documentation, whose document located at https://github.com/MicrosoftDocs/. | @article{DBLP:journals/corr/abs-2102-04664,
author = {Shuai Lu and
Daya Guo and
Shuo Ren and
Junjie Huang and
Alexey Svyatkovskiy and
Ambrosio Blanco and
Colin B. Clement and
Dawn Drain and
Daxin... | 1 | 576 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- da
- en
- lv
- nb
- zh
license:
- c-uda
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
pretty_name: CodeXGlueTtTextToText
tags:
- code-documentation-translation... | 7,274 | [
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tongyx361/prm800k-train-direct-prediction-0-02validiation-seed42-encoded | 2023-09-17T22:46:13.000Z | [
"region:us"
] | tongyx361 | null | null | 0 | 576 | 2023-09-17T22:46:00 | ---
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
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shunk031/wrime | 2023-01-15T03:39:01.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"language:ja",
"license:unknown",
"sentiment-analysis",
"wrime",
"region:us"
] | shunk031 | WRIME dataset is a new dataset for emotional intensity estimation with subjective and objective annotations. | @inproceedings{kajiwara-etal-2021-wrime,
title = "{WRIME}: A New Dataset for Emotional Intensity Estimation with Subjective and Objective Annotations",
author = "Kajiwara, Tomoyuki and
Chu, Chenhui and
Takemura, Noriko and
Nakashima, Yuta and
Nagahara, Hajime",
booktitle = "Proce... | 10 | 575 | 2023-01-12T03:04:20 | ---
annotations_creators:
- crowdsourced
language:
- ja
language_creators:
- crowdsourced
license:
- unknown
multilinguality:
- monolingual
pretty_name: wrime
tags:
- sentiment-analysis
- wrime
task_categories:
- text-classification
task_ids:
- sentiment-classification
datasets:
- ver1
- ver2
metrics:
- accura... | 13,638 | [
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KETI-AIR/klue | 2021-06-03T00:35:30.000Z | [
"region:us"
] | KETI-AIR | null | @misc{park2021klue,
title={KLUE: Korean Language Understanding Evaluation},
author={Sungjoon Park and Jihyung Moon and Sungdong Kim and Won Ik Cho and Jiyoon Han and Jangwon Park and Chisung Song and Junseong Kim and Yongsook Song and Taehwan Oh and Joohong Lee and Juhyun Oh and Sungwon Lyu and Younghoon J... | 0 | 574 | 2022-03-02T23:29:22 | <!--
Copyright 2021 san kim
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, softw... | 620 | [
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Falah/Alzheimer_MRI | 2023-07-04T10:03:44.000Z | [
"task_categories:image-classification",
"size_categories:1K<n<10K",
"language:en",
"license:apache-2.0",
"medical",
"region:us"
] | Falah | null | null | 1 | 573 | 2023-07-04T09:24:50 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Mild_Demented
'1': Moderate_Demented
'2': Non_Demented
'3': Very_Mild_Demented
splits:
- name: train
num_bytes: 22560791.2
num_examples: 51... | 2,130 | [
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YuanPJ/summ_screen | 2023-03-29T04:51:45.000Z | [
"region:us"
] | YuanPJ | SummScreen Corpus contains over 26k pairs of TV series transcripts and human written recaps.
There are two features:
- dialogue: text of dialogue.
- summary: human written summary of the dialogue.
- id: id of a example. | @inproceedings{chen-etal-2022-summscreen,
title = "{S}umm{S}creen: A Dataset for Abstractive Screenplay Summarization",
author = "Chen, Mingda and
Chu, Zewei and
Wiseman, Sam and
Gimpel, Kevin",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Lin... | 1 | 571 | 2023-03-28T04:50:20 | Entry not found | 15 | [
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0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060455322265625,
0.03793334... |
yaful/DeepfakeTextDetect | 2023-07-11T01:59:02.000Z | [
"license:apache-2.0",
"arxiv:2305.13242",
"region:us"
] | yaful | null | null | 4 | 571 | 2023-06-27T07:30:58 | ---
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... | 6,392 | [
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jxie/country211 | 2023-08-13T19:11:22.000Z | [
"region:us"
] | jxie | null | null | 0 | 568 | 2023-08-13T18:29:19 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': AD
... | 4,857 | [
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keremberke/license-plate-object-detection | 2023-01-18T20:37:51.000Z | [
"task_categories:object-detection",
"roboflow",
"roboflow2huggingface",
"Self Driving",
"Anpr",
"region:us"
] | keremberke | null | @misc{ vehicle-registration-plates-trudk_dataset,
title = { Vehicle Registration Plates Dataset },
type = { Open Source Dataset },
author = { Augmented Startups },
howpublished = { \\url{ https://universe.roboflow.com/augmented-startups/vehicle-registration-plates-trudk } },
url = { https://universe... | 7 | 563 | 2023-01-01T02:32:07 | ---
task_categories:
- object-detection
tags:
- roboflow
- roboflow2huggingface
- Self Driving
- Anpr
---
<div align="center">
<img width="640" alt="keremberke/license-plate-object-detection" src="https://huggingface.co/datasets/keremberke/license-plate-object-detection/resolve/main/thumbnail.jpg">
</div>
### Datas... | 1,878 | [
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... |
alzoubi36/policy_qa | 2023-06-25T06:45:22.000Z | [
"region:us"
] | alzoubi36 | null | null | 0 | 563 | 2023-06-25T06:42:53 | ---
dataset_info:
features:
- name: id
dtype: string
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- name: question
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struct:
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sequence: int64
- name: text
sequence: string
splits:
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... | 646 | [
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kandriiashevskyi/wix_looker_ai | 2023-11-02T21:07:05.000Z | [
"region:us"
] | kandriiashevskyi | null | null | 0 | 563 | 2023-08-01T09:20:28 | Entry not found | 15 | [
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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}
} | 19 | 562 | 2022-03-02T23:29:22 | ---
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... | 10,898 | [
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0.00... |
PygmalionAI/PIPPA | 2023-09-07T03:07:55.000Z | [
"task_categories:conversational",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"not-for-all-audiences",
"conversational",
"roleplay",
"custom-format",
"a.",
"arxiv:2308.05884",
"region:us"
] | PygmalionAI | Personal Interaction Pairs between People and AI (PIPPA) is a partially synthetic, community contributed and open-source conversational and roleplaying dataset generated from a subset of submitted logs to the Pygmalion project. | @misc{gosling2023pippa,
title={PIPPA: A Partially Synthetic Conversational Dataset},
author={Tear Gosling and Alpin Dale and Yinhe Zheng},
year={2023},
eprint={2308.05884},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 105 | 559 | 2023-08-08T01:32:40 | ---
license: apache-2.0
task_categories:
- conversational
language:
- en
tags:
- not-for-all-audiences
- conversational
- roleplay
- custom-format
- a.
pretty_name: PIPPA - Personal Interaction Pairs Between People and AI
size_categories:
- 10K<n<100K
viewer: false
---
# PIPPA - Personal Interaction Pairs between Peop... | 5,726 | [
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emo | 2023-04-05T10:05:14.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | null | In this dataset, given a textual dialogue i.e. an utterance along with two previous turns of context, the goal was to infer the underlying emotion of the utterance by choosing from four emotion classes - Happy, Sad, Angry and Others. | @inproceedings{chatterjee-etal-2019-semeval,
title={SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text},
author={Ankush Chatterjee and Kedhar Nath Narahari and Meghana Joshi and Puneet Agrawal},
booktitle={Proceedings of the 13th International Workshop on Semantic Evaluation},
year={20... | 3 | 558 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: emocontext
pretty_name:... | 7,967 | [
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0.0284576... |
tau/mrqa | 2022-03-21T19:26:55.000Z | [
"region:us"
] | tau | The MRQA 2019 Shared Task focuses on generalization in question answering.
An effective question answering system should do more than merely
interpolate from the training set to answer test examples drawn
from the same distribution: it should also be able to extrapolate
to out-of-distribution examples — a significantly... | @inproceedings{fisch2019mrqa,
title={{MRQA} 2019 Shared Task: Evaluating Generalization in Reading Comprehension},
author={Adam Fisch and Alon Talmor and Robin Jia and Minjoon Seo and Eunsol Choi and Danqi Chen},
booktitle={Proceedings of 2nd Machine Reading for Reading Comprehension (MRQA) Workshop at EMNL... | 0 | 558 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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0.03790... |
boomsss/spx_intra | 2023-10-20T04:43:51.000Z | [
"region:us"
] | boomsss | null | null | 0 | 557 | 2023-09-30T05:28:51 | Entry not found | 15 | [
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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... | 28 | 555 | 2022-03-02T23:29:22 | ---
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... | 6,755 | [
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lamini/lamini_docs_evaluation | 2023-07-24T03:08:13.000Z | [
"region:us"
] | lamini | null | null | 0 | 555 | 2023-07-24T03:08:09 | ---
dataset_info:
features:
- name: predicted_answer
dtype: string
- name: target_answer
dtype: string
splits:
- name: train
num_bytes: 744520
num_examples: 139
download_size: 86086
dataset_size: 744520
---
# Dataset Card for "lamini_docs_evaluation"
[More Information needed](https://gith... | 413 | [
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GEM/e2e_nlg | 2022-10-24T15:30:18.000Z | [
"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 | The E2E dataset is designed for a limited-domain data-to-text task --
generation of restaurant descriptions/recommendations based on up to 8 different
attributes (name, area, price range etc.). | @inproceedings{e2e_cleaned,
address = {Tokyo, Japan},
title = {Semantic {Noise} {Matters} for {Neural} {Natural} {Language} {Generation}},
url = {https://www.aclweb.org/anthology/W19-8652/},
booktitle = {Proceedings of the 12th {International} {Conference} on {Natural} {Language} {Generation} ({INLG} 2019)},
autho... | 1 | 553 | 2022-03-02T23:29:22 | ---
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: e2e_nlg
tags:
- data-to-text
---
# Dataset Card for GEM/e2e_nlg
## Dataset D... | 21,025 | [
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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... | 1 | 553 | 2022-07-30T20:07:06 | # 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 ... | 4,451 | [
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HuggingFaceM4/FairFace | 2022-12-09T00:14:46.000Z | [
"license:cc-by-4.0",
"region:us"
] | HuggingFaceM4 | FairFace is a face image dataset which is race balanced. It contains 108,501 images from 7 different race groups: White, Black, Indian, East Asian, Southeast Asian, Middle Eastern, and Latino.
Images were collected from the YFCC-100M Flickr dataset and labeled with race, gender, and age groups. | @inproceedings{karkkainenfairface,
title={FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age for Bias Measurement and Mitigation},
author={Karkkainen, Kimmo and Joo, Jungseock},
booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
year={2021},
... | 5 | 553 | 2022-12-08T23:00:45 | ---
license: cc-by-4.0
---
# Dataset Card for [Dataset Name]
## 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)
- [Data... | 3,794 | [
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0... |
scikit-learn/adult-census-income | 2022-06-20T14:46:43.000Z | [
"license:cc0-1.0",
"region:us"
] | scikit-learn | null | null | 1 | 552 | 2022-06-20T14:33:51 | ---
license: cc0-1.0
---
## Adult Census Income Dataset
The following was retrieved from [UCI machine learning repository](https://archive.ics.uci.edu/ml/datasets/adult).
This data was extracted from the 1994 Census bureau database by Ronny Kohavi and Barry Becker (Data Mining and Visualization, Silicon Graphics). A s... | 1,608 | [
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open-phi/textbooks | 2023-10-08T05:07:09.000Z | [
"region:us"
] | open-phi | null | null | 53 | 551 | 2023-10-03T16:55:38 | ---
dataset_info:
features:
- name: topic
dtype: string
- name: model
dtype: string
- name: concepts
dtype: string
- name: outline
dtype: string
- name: markdown
dtype: string
- name: field
dtype: string
- name: subfield
dtype: string
- name: rag
dtype: string
splits:... | 1,488 | [
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dynabench/dynasent | 2021-04-29T11:30:24.000Z | [
"arxiv:2012.15349",
"arxiv:1803.09010",
"arxiv:1810.03993",
"region:us"
] | dynabench | Dynabench.DynaSent is a Sentiment Analysis dataset collected using a
human-and-model-in-the-loop. | null | 3 | 550 | 2022-03-02T23:29:22 | # DynaSent: Dynamic Sentiment Analysis Dataset
DynaSent is an English-language benchmark task for ternary (positive/negative/neutral) sentiment analysis. This dataset card is forked from the original [DynaSent Repository](https://github.com/cgpotts/dynasent).
## Contents
* [Citation](#Citation)
* [Dataset files](#da... | 13,731 | [
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pietrolesci/nli_fever | 2022-04-25T09:03:28.000Z | [
"region:us"
] | pietrolesci | null | null | 1 | 550 | 2022-03-25T10:01:17 | ## Overview
The original dataset can be found [here](https://www.dropbox.com/s/hylbuaovqwo2zav/nli_fever.zip?dl=0)
while the Github repo is [here](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md).
This dataset has been proposed in [Combining fact extraction and verification with... | 6,614 | [
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-0.028106689453125,
-0.000873565673828125,
0.0171051025390625,
-0.00820159912109375,
0.0063018798828125,
-0.01212310791015625,
-0.0202789306640625,
0.035186767578125,
0.0238800048828125,
-0.0236968994140625,
-0.042572021484375,
-0.0364074707... |
mstz/adult | 2023-04-15T11:37:47.000Z | [
"task_categories:tabular-classification",
"size_categories:10K<n<100K",
"language:en",
"license:cc",
"adult",
"tabular_classification",
"binary_classification",
"multiclass_classification",
"UCI",
"region:us"
] | mstz | null | @inproceedings{DBLP:conf/kdd/Kohavi96,
author = {Ron Kohavi},
editor = {Evangelos Simoudis and
Jiawei Han and
Usama M. Fayyad},
title = {Scaling Up the Accuracy of Naive-Bayes Classifiers: {A} Decision-Tree
Hybrid},
booktitle = {Proceedings of the Second In... | 1 | 549 | 2023-02-27T21:17:48 | ---
language:
- en
tags:
- adult
- tabular_classification
- binary_classification
- multiclass_classification
- UCI
pretty_name: Adult
size_categories:
- 10K<n<100K
task_categories:
- tabular-classification
configs:
- encoding
- income
- income-no race
- race
license: cc
---
# Adult
The [Adult dataset](https://archive.... | 3,184 | [
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-0.0151214599609375,
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0.04833984375,
-0.039703369140625,
-0.05291748046875,
-0.0506591796875,
0.00775146... |
bdsaglam/musique | 2023-06-14T08:19:12.000Z | [
"arxiv:2108.00573",
"arxiv:1606.05250",
"arxiv:1910.07475",
"arxiv:1706.04115",
"region:us"
] | bdsaglam | [MuSiQue](https://arxiv.org/pdf/2108.00573.pdf) | @article{trivedi2021musique,
title={{M}u{S}i{Q}ue: Multihop Questions via Single-hop Question Composition},
author={Trivedi, Harsh and Balasubramanian, Niranjan and Khot, Tushar and Sabharwal, Ashish},
journal={Transactions of the Association for Computational Linguistics},
year={2022}
publisher={MIT Press}
} | 0 | 548 | 2023-06-14T06:10:10 | ---
dataset_info:
- config_name: answerable
features:
- name: id
dtype: string
- name: paragraphs
sequence:
- name: idx
dtype: int32
- name: title
dtype: string
- name: paragraph_text
dtype: string
- name: is_supporting
dtype: bool
- name: question
dtype: stri... | 3,319 | [
[
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0.031585693359375,
0.0295867919921875,
-0.06964111328125,
-0.0308990478515625,
-0.00764083862304687... |
carolina-c4ai/corpus-carolina | 2023-03-23T19:46:16.000Z | [
"task_categories:fill-mask",
"task_categories:text-generation",
"task_ids:masked-language-modeling",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1B<n<10B",
"source_datasets:original",
"languag... | carolina-c4ai | Carolina is an Open Corpus for Linguistics and Artificial Intelligence with a
robust volume of texts of varied typology in contemporary Brazilian Portuguese
(1970-2021). | null | 12 | 547 | 2022-03-28T13:30:33 | ---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- pt
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1B<n<10B
source_datasets:
- original
task_categories:
- fill-mask
- text-generation
task_ids:
- masked-language-modeling
- language-modeling
pretty_name... | 5,774 | [
[
-0.040618896484375,
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0.04156494140625,
0.0220184326171875,
-0.00691986083984375,
-0.08416748046875,
-0.032135009765625,
0.... |
ericyu/LEVIRCD_Cropped_256 | 2023-10-06T10:29:40.000Z | [
"region:us"
] | ericyu | null | null | 0 | 546 | 2023-08-28T15:35:08 | ---
dataset_info:
features:
- name: imageA
dtype: image
- name: imageB
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 2005523229.68
num_examples: 7120
- name: validation
num_bytes: 244453421.184
num_examples: 1024
- name: test
num_bytes: 51886387... | 589 | [
[
-0.0611572265625,
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0.02191162109375,
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0.044281005859375,
-0.07196044921875,
-0.0540771484375,
-0.031494140625,
-... |
nlphuji/winogavil | 2022-11-26T19:56:27.000Z | [
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"commonsense-reasoning",
"visual-reasoning",
"arxiv:2207.12576",
"region:us"
] | nlphuji | WinoGAViL is a challenging dataset for evaluating vision-and-language commonsense reasoning abilities. Given a set of images, a cue, and a number K, the task is to select the K images that best fits the association. This dataset was collected via the WinoGAViL online game to collect vision-and-language associations, (e... | @article{bitton2022winogavil,
title={WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language Models},
author={Bitton, Yonatan and Guetta, Nitzan Bitton and Yosef, Ron and Elovici, Yuval and Bansal, Mohit and Stanovsky, Gabriel and Schwartz, Roy},
journal={arXiv preprint arXiv:2207.12576},
yea... | 0 | 544 | 2022-09-23T19:27:29 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
paperswithcode_id: winogavil
pretty_name: WinoGAViL
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- commonsense-reasoning
- visual-reasoning
task_ids: []
extra_gated_p... | 7,669 | [
[
-0.0228729248046875,
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... |
allenai/scitldr | 2023-01-25T14:43:42.000Z | [
"task_categories:summarization",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"scientific-documents-summarization",
"arxiv:2004.15011",
"region:us"
] | allenai | A new multi-target dataset of 5.4K TLDRs over 3.2K papers.
SCITLDR contains both author-written and expert-derived TLDRs,
where the latter are collected using a novel annotation protocol
that produces high-quality summaries while minimizing annotation burden. | @article{cachola2020tldr,
title={{TLDR}: Extreme Summarization of Scientific Documents},
author={Isabel Cachola and Kyle Lo and Arman Cohan and Daniel S. Weld},
journal={arXiv:2004.15011},
year={2020},
} | 14 | 543 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- summarization
task_ids: []
paperswithcode_id: scitldr
pretty_name: SciTLDR
tags:
- scientific-documents-summari... | 8,815 | [
[
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0.0396728515625,
0.027069091796875,
-0.0400390625,
-0.041656494140625,
-0.04461669921875,
0.00... |
hugo/boolq | 2023-10-17T13:15:46.000Z | [
"region:us"
] | hugo | 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},
} | 0 | 543 | 2023-10-17T13:12:38 | Entry not found | 15 | [
[
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0.016998291015625,
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0.03793334... |
civil_comments | 2023-06-30T11:26:30.000Z | [
"language:en",
"license:cc0-1.0",
"arxiv:1903.04561",
"region:us"
] | null | The comments in this dataset come from an archive of the Civil Comments
platform, a commenting plugin for independent news sites. These public comments
were created from 2015 - 2017 and appeared on approximately 50 English-language
news sites across the world. When Civil Comments shut down in 2017, they chose
to make t... | @article{DBLP:journals/corr/abs-1903-04561,
author = {Daniel Borkan and
Lucas Dixon and
Jeffrey Sorensen and
Nithum Thain and
Lucy Vasserman},
title = {Nuanced Metrics for Measuring Unintended Bias with Real Data for Text
Classificati... | 3 | 542 | 2022-03-02T23:29:22 | ---
language:
- en
paperswithcode_id: null
pretty_name: CivilComments
dataset_info:
features:
- name: text
dtype: string
- name: toxicity
dtype: float32
- name: severe_toxicity
dtype: float32
- name: obscene
dtype: float32
- name: threat
dtype: float32
- name: insult
dtype: float32... | 7,608 | [
[
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0.034271240234375,
-0.0472412109375,
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-0.044097900390625,
... |
wmt15 | 2023-04-05T13:43:50.000Z | [
"task_categories:translation",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:translation",
"size_categories:10M<n<100M",
"source_datasets:extended|europarl_bilingual",
"source_datasets:extended|giga_fren",
"source_datasets:extended|news_commentary",
"source_datase... | null | null | @InProceedings{bojar-EtAl:2015:WMT,
author = {Bojar, Ond\v{r}ej and Chatterjee, Rajen and Federmann, Christian and Haddow, Barry and Huck, Matthias and Hokamp, Chris and Koehn, Philipp and Logacheva, Varvara and Monz, Christof and Negri, Matteo and Post, Matt and Scarton, Carolina and Speci... | 2 | 541 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- cs
- de
- en
- fi
- fr
- ru
license:
- unknown
multilinguality:
- translation
size_categories:
- 10M<n<100M
source_datasets:
- extended|europarl_bilingual
- extended|giga_fren
- extended|news_commentary
- extended|un_multi
task_categories:... | 9,362 | [
[
-0.04364013671875,
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0.02435302734375,
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-0.044403076171875,
0.018... |
codeparrot/codeparrot-clean | 2022-10-10T15:23:51.000Z | [
"python",
"code",
"region:us"
] | codeparrot | null | null | 35 | 541 | 2022-03-02T23:29:22 | ---
tags:
- python
- code
---
# CodeParrot 🦜 Dataset Cleaned
## What is it?
A dataset of Python files from Github. This is the deduplicated version of the [codeparrot](https://huggingface.co/datasets/transformersbook/codeparrot).
## Processing
The original dataset contains a lot of duplicated and noisy data. There... | 1,296 | [
[
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0.022064208984375,
0.05560302734375,
-0.036834716796875,
-0.0260772705078125,
-0.0258941650390625,... |
ScandEval/dane-mini | 2023-07-05T09:40:02.000Z | [
"task_categories:token-classification",
"size_categories:1K<n<10K",
"language:da",
"license:cc-by-sa-4.0",
"region:us"
] | ScandEval | null | null | 0 | 540 | 2022-06-14T18:20:34 | ---
dataset_info:
features:
- name: text
dtype: string
- name: tokens
sequence: string
- name: labels
sequence: string
splits:
- name: train
num_bytes: 355712
num_examples: 1024
- name: test
num_bytes: 747809
num_examples: 2048
- name: val
num_bytes: 92001
num_example... | 647 | [
[
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... |
edarchimbaud/perimeter-stocks | 2023-11-02T15:00:10.000Z | [
"region:us"
] | edarchimbaud | null | null | 1 | 540 | 2023-08-12T20:21:35 | ---
dataset_info:
features:
- name: symbol
dtype: string
- name: security
dtype: string
- name: gics_sector
dtype: string
- name: gics_sub_industry
dtype: string
splits:
- name: train
num_bytes: 112186
num_examples: 1500
download_size: 44087
dataset_size: 112186
configs:
- conf... | 565 | [
[
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0.0609130859375,
0.0280609130859375,
-0.04949951171875,
-0.062042236328125,
-0.0393676757812... |
Anthropic/llm_global_opinions | 2023-06-29T00:46:48.000Z | [
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-nc-sa-4.0",
"arxiv:2306.16388",
"region:us"
] | Anthropic | null | null | 22 | 537 | 2023-06-26T07:47:41 | ---
license: cc-by-nc-sa-4.0
language:
- en
size_categories:
- 1K<n<10K
---
# Dataset Card for GlobalOpinionQA
## Dataset Summary
The data contains a subset of survey questions about global issues and opinions adapted from the [World Values Survey](https://www.worldvaluessurvey.org/) and [Pew Global Attitudes Survey](... | 2,367 | [
[
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-0.0273590087890625,
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-0.03549194335937... |
transformersbook/codeparrot-train | 2022-02-05T16:23:03.000Z | [
"region:us"
] | transformersbook | null | null | 3 | 536 | 2022-03-02T23:29:22 | # CodeParrot Dataset
This is the train split of the CodeParrot dataset. It contains Python files used to train the code generation model in Chapter 10: Training Transformers from Scratch in the [NLP with Transformers book](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/). You can ... | 583 | [
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... |
lksy/ru_instruct_gpt4 | 2023-06-02T16:56:03.000Z | [
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:10K<n<100K",
"language:ru",
"license:cc-by-4.0",
"chat",
"region:us"
] | lksy | null | null | 17 | 536 | 2023-04-18T08:15:50 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: full_output
dtype: string
splits:
- name: train
num_bytes: 22424451
num_examples: 15056
download_size: 23276814
dataset_size: 22424451
license: cc-by-4... | 723 | [
[
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-0.0750732421875,
-0.0226287841796875,
... |
keremberke/csgo-object-detection | 2023-01-27T13:39:19.000Z | [
"task_categories:object-detection",
"roboflow",
"roboflow2huggingface",
"region:us"
] | keremberke | null | @misc{ wlots_dataset,
title = { wlots Dataset },
type = { Open Source Dataset },
author = { asd },
howpublished = { \\url{ https://universe.roboflow.com/asd-culfr/wlots } },
url = { https://universe.roboflow.com/asd-culfr/wlots },
journal = { Roboflow Universe },
publisher = { Roboflow },
... | 4 | 535 | 2022-12-29T07:37:55 | ---
task_categories:
- object-detection
tags:
- roboflow
- roboflow2huggingface
---
<div align="center">
<img width="640" alt="keremberke/csgo-object-detection" src="https://huggingface.co/datasets/keremberke/csgo-object-detection/resolve/main/thumbnail.jpg">
</div>
### Dataset Labels
```
['ct', 'cthead', 't', 't... | 2,116 | [
[
-0.043182373046875,
-0.0281219482421875,
0.0289459228515625,
-0.0144500732421875,
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0.0207672119140625,
0.0182952880859375,
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bbz662bbz/databricks-dolly-15k-ja-gozarinnemon | 2023-05-31T14:44:34.000Z | [
"license:cc-by-sa-3.0",
"region:us"
] | bbz662bbz | null | null | 3 | 534 | 2023-05-31T14:43:00 | ---
license: cc-by-sa-3.0
---
This dataset was using "kunishou/databricks-dolly-15k-ja"
This dataset is licensed under CC BY SA 3.0
Last Update : 2023-05-28
databricks-dolly-15k-ja-gozarinnemon
kunishou/databricks-dolly-15k-ja
https://huggingface.co/datasets/kunishou/databricks-dolly-15k-ja
| 296 | [
[
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0.02105712890625,
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0.038116455078125,
0.0560302734375,
-0.07305908203125,
-0.0253448486328125,
-0.0293121337890625,
0.01... |
health_fact | 2023-01-25T14:32:02.000Z | [
"task_categories:text-classification",
"task_ids:fact-checking",
"task_ids:multi-class-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"arxi... | null | PUBHEALTH is a comprehensive dataset for explainable automated fact-checking of
public health claims. Each instance in the PUBHEALTH dataset has an associated
veracity label (true, false, unproven, mixture). Furthermore each instance in the
dataset has an explanation text field. The explanation is a justification for w... | @inproceedings{kotonya-toni-2020-explainable,
title = "Explainable Automated Fact-Checking for Public Health Claims",
author = "Kotonya, Neema and Toni, Francesca",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods
in Natural Language Processing (EMNLP)",
month = nov,
year = "... | 16 | 531 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- fact-checking
- multi-class-classification
paperswithcode_id: pubhealth
pretty... | 8,603 | [
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0.023223876... |
ehartford/dolphin | 2023-09-25T16:59:11.000Z | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"region:us"
] | ehartford | null | null | 222 | 530 | 2023-07-01T10:53:40 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
---
Dolphin 🐬
https://erichartford.com/dolphin
## Dataset details
This dataset is an attempt to replicate the results of [Microsoft's Orca](https://www.microsoft.com/en-us/research/publication/orca-progressive-learning-from-complex-explanat... | 2,378 | [
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... |
ivanzhouyq/RedPajama-Tiny | 2023-07-03T18:16:47.000Z | [
"task_categories:text-generation",
"language:en",
"region:us"
] | ivanzhouyq | RedPajama is a clean-room, fully open-source implementation of the LLaMa dataset. This is a 1B-token sample of the full dataset. | null | 2 | 530 | 2023-07-03T16:48:05 | ---
task_categories:
- text-generation
language:
- en
pretty_name: RedPajama Tiny
---
# Dataset Card for Dataset Name
### Dataset Summary
This is a tiny version of the RedPajama dataset, which is a clean-room, fully open-source implementation of the LLaMa dataset.
This dataset contains 64 samples from each of the 7 ... | 2,860 | [
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0.0229797... |
distil-whisper/librispeech_asr-timestamped | 2023-09-25T10:30:13.000Z | [
"task_categories:automatic-speech-recognition",
"language:en",
"license:cc-by-4.0",
"region:us"
] | distil-whisper | 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--... | 0 | 530 | 2023-09-22T09:05:08 | ---
license: cc-by-4.0
task_categories:
- automatic-speech-recognition
language:
- en
-pretty_name: LibriSpeech ASR
---
# Distil Whisper: LibriSpeech ASR With Timestamps
This is a variant of the [LibriSpeech ASR](https://huggingface.co/datasets/librispeech_asr) dataset, augmented to return the pseudo-labelled Whisper... | 2,087 | [
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-0.043304443359375,... |
dennlinger/eur-lex-sum | 2022-11-11T14:25:06.000Z | [
"task_categories:translation",
"task_categories:summarization",
"annotations_creators:found",
"annotations_creators:expert-generated",
"language_creators:found",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"languag... | dennlinger | The EUR-Lex-Sum dataset is a multilingual resource intended for text summarization in the legal domain.
It is based on human-written summaries of legal acts issued by the European Union.
It distinguishes itself by introducing a smaller set of high-quality human-written samples,
each of which have much longer references... | @article{aumiller-etal-2022-eur,
author = {Aumiller, Dennis and Chouhan, Ashish and Gertz, Michael},
title = {{EUR-Lex-Sum: A Multi- and Cross-lingual Dataset for Long-form Summarization in the Legal Domain}},
journal = {CoRR},
volume = {abs/2210.13448},
eprinttype = {arXiv},
eprint = {2210.13448},
url = {https://arxiv... | 21 | 529 | 2022-10-10T08:07:37 | ---
annotations_creators:
- found
- expert-generated
language:
- bg
- hr
- cs
- da
- nl
- en
- et
- fi
- fr
- de
- el
- hu
- ga
- it
- lv
- lt
- mt
- pl
- pt
- ro
- sk
- sl
- es
- sv
language_creators:
- found
- expert-generated
license:
- cc-by-4.0
multilinguality:
- multilingual
pretty_name: eur-lex-sum
size_categori... | 13,726 | [
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0.0406494... |
allenai/scicite | 2023-01-25T14:43:39.000Z | [
"task_categories:text-classification",
"task_ids:intent-classification",
"task_ids:multi-class-classification",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:origi... | allenai | This is a dataset for classifying citation intents in academic papers.
The main citation intent label for each Json object is specified with the label
key while the citation context is specified in with a context key. Example:
{
'string': 'In chacma baboons, male-infant relationships can be linked to both
formatio... | @InProceedings{Cohan2019Structural,
author={Arman Cohan and Waleed Ammar and Madeleine Van Zuylen and Field Cady},
title={Structural Scaffolds for Citation Intent Classification in Scientific Publications},
booktitle={NAACL},
year={2019}
} | 4 | 528 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- intent-classification
- multi-class-classification
paperswi... | 9,062 | [
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eduagarcia/portuguese_benchmark | 2023-07-09T06:31:26.000Z | [
"region:us"
] | eduagarcia | null | null | 2 | 528 | 2023-06-09T23:26:59 | Entry not found | 15 | [
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0.0379... |
dkoterwa/kor-sts | 2023-07-25T09:52:30.000Z | [
"license:cc-by-sa-4.0",
"region:us"
] | dkoterwa | null | null | 0 | 528 | 2023-07-18T14:17:23 | ---
license: cc-by-sa-4.0
dataset_info:
features:
- name: id
dtype: int64
- name: genre
dtype: string
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 1034815
num_examples: 5691
- name: valid
n... | 1,632 | [
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0.064208984375,
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0... |
anyspeech/ucla_phonetic_corpus | 2023-05-06T19:05:47.000Z | [
"region:us"
] | anyspeech | null | null | 0 | 527 | 2023-05-06T19:02:43 | ---
dataset_info:
features:
- name: filename
dtype: string
- name: phones
dtype: string
- name: audio
struct:
- name: array
sequence: float32
- name: sampling_rate
dtype: int64
splits:
- name: eus
num_bytes: 3108551
num_examples: 47
- name: kub
num_bytes: 171570... | 5,983 | [
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0.06268310546875,
0.0301666259765625,
-0.04254150390625,
-0.07098388671875,
-0.03192138671875,... |
HuggingFaceH4/mt_bench_prompts | 2023-07-03T20:52:34.000Z | [
"task_categories:question-answering",
"task_categories:conversational",
"size_categories:n<1K",
"language:en",
"license:apache-2.0",
"evaluation",
"arxiv:2306.05685",
"region:us"
] | HuggingFaceH4 | null | null | 2 | 526 | 2023-07-03T20:21:21 | ---
license: apache-2.0
task_categories:
- question-answering
- conversational
language:
- en
tags:
- evaluation
pretty_name: MT Bench
size_categories:
- n<1K
---
# MT Bench by LMSYS
This set of evaluation prompts is created by the [LMSYS org](https://huggingface.co/lmsys) for better evaluation of chat models.
For mor... | 1,491 | [
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0.0231781005859375,
-0.0771484375,
-0.03643798828125,
-0.0237579345703125,
0.... |
social_bias_frames | 2023-04-05T13:40:19.000Z | [
"task_categories:text2text-generation",
"task_categories:text-classification",
"task_ids:hate-speech-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0... | null | Social Bias Frames is a new way of representing the biases and offensiveness that are implied in language.
For example, these frames are meant to distill the implication that "women (candidates) are less qualified"
behind the statement "we shouldn’t lower our standards to hire more women." | @inproceedings{sap2020socialbiasframes,
title={Social Bias Frames: Reasoning about Social and Power Implications of Language},
author={Sap, Maarten and Gabriel, Saadia and Qin, Lianhui and Jurafsky, Dan and Smith, Noah A and Choi, Yejin},
year={2020},
booktitle={ACL},
} | 8 | 525 | 2022-03-02T23:29:22 | ---
pretty_name: Social Bias Frames
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text2text-generation
- text-classification
task_ids:
- hate-speech-detection
... | 17,471 | [
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0.015113... |
huggan/pokemon | 2022-04-01T11:50:45.000Z | [
"region:us"
] | huggan | null | null | 13 | 525 | 2022-04-01T11:44:34 | Source: https://www.kaggle.com/datasets/djilax/pkmn-image-dataset | 65 | [
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-0.0... |
GATE-engine/COCOStuff10K | 2023-06-23T05:01:36.000Z | [
"region:us"
] | GATE-engine | null | null | 0 | 522 | 2023-06-23T04:55:07 | ---
dataset_info:
features:
- name: image
dtype: image
- name: mask
dtype: image
splits:
- name: test
num_bytes: 490670380.0
num_examples: 1000
- name: train
num_bytes: 4380309288.0
num_examples: 9000
download_size: 4871873017
dataset_size: 4870979668.0
---
# Dataset Card for "CO... | 464 | [
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-0.0097961... |
THUDM/ImageRewardDB | 2023-06-21T06:36:29.000Z | [
"task_categories:text-to-image",
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"arxiv:2304.05977",
"region:us"
] | THUDM | ImageRewardDB is a comprehensive text-to-image comparison dataset, focusing on text-to-image human preference. It consists of 137k pairs of expert comparisons, based on text prompts and corresponding model outputs from DiffusionDB. To build the ImageRewadDB, we design a pipeline tailored for it, establishing criteria f... | @misc{xu2023imagereward,
title={ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation},
author={Jiazheng Xu and Xiao Liu and Yuchen Wu and Yuxuan Tong and Qinkai Li and Ming Ding and Jie Tang and Yuxiao Dong},
year={2023},
eprint={2304.05977},
archivePrefix={... | 19 | 520 | 2023-05-21T15:39:22 | ---
license: apache-2.0
task_categories:
- text-to-image
language:
- en
pretty_name: ImageReward Dataset
size_categories:
- 100K<n<1M
---
# ImageRewardDB
## Dataset Description
- **Homepage: https://huggingface.co/datasets/wuyuchen/ImageRewardDB**
- **Repository: https://github.com/THUDM/ImageReward**
- **Paper: h... | 7,794 | [
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wiki_split | 2023-04-05T13:43:23.000Z | [
"task_categories:text2text-generation",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"split-and-rephrase",
"arxiv:1808.09468",
"region:us"
] | null | One million English sentences, each split into two sentences that together preserve the original meaning, extracted from Wikipedia
Google's WikiSplit dataset was constructed automatically from the publicly available Wikipedia revision history. Although
the dataset contains some inherent noise, it can serve as valuable ... | @InProceedings{BothaEtAl2018,
title = {{Learning To Split and Rephrase From Wikipedia Edit History}},
author = {Botha, Jan A and Faruqui, Manaal and Alex, John and Baldridge, Jason and Das, Dipanjan},
booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
pages = {... | 3 | 518 | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: WikiSplit
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: wikisplit
tags:
- split-and-... | 7,214 | [
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0.020828247070... |
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 | 10 | 517 | 2023-07-22T03:46:53 | ---
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... | 2,171 | [
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ccdv/WCEP-10 | 2022-10-25T10:55:52.000Z | [
"task_categories:summarization",
"task_categories:text2text-generation",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:en",
"conditional-text-generation",
"arxiv:2005.10070",
"arxiv:2110.08499",
"region:us"
] | ccdv | WCEP10 dataset for summarization.
From paper: "A Large-Scale Multi-Document Summarization Dataset from the Wikipedia
Current Events Portal" by D. Gholipour et al."
From paper: "PRIMER: Pyramid-based Masked Sentence Pre-training for Multi-document
Summarization" by W. Xiao et al." | @article{DBLP:journals/corr/abs-2005-10070,
author = {Demian Gholipour Ghalandari and
Chris Hokamp and
Nghia The Pham and
John Glover and
Georgiana Ifrim},
title = {A Large-Scale Multi-Document Summarization Dataset from the Wikipedia
... | 3 | 516 | 2022-05-09T14:13:26 | ---
language:
- en
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
task_categories:
- summarization
- text2text-generation
task_ids: []
tags:
- conditional-text-generation
---
# WCEP10 dataset for summarization
Summarization dataset copied from [PRIMERA](https://github.com/allenai/PRIMER)
This dataset is ... | 2,761 | [
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blended_skill_talk | 2023-04-05T09:41:47.000Z | [
"task_categories:conversational",
"task_ids:dialogue-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"arxiv:2004.08449",
"region:us"
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c-s-ale/alpaca-gpt4-data-zh | 2023-05-03T17:56:55.000Z | [
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miracl/miracl-corpus | 2023-01-05T17:28:26.000Z | [
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facebook/babi_qa | 2023-01-25T14:26:58.000Z | [
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comprehension via question answering. Our tasks measure understanding
in several ways: whether a system is able to answer questions via chaining facts,
simple induction, deduction and many more. The tasks are designed to be prerequisites
for any syst... | @misc{weston2015aicomplete,
title={Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks},
author={Jason Weston and Antoine Bordes and Sumit Chopra and Alexander M. Rush and Bart van Merriënboer and Armand Joulin and Tomas Mikolov},
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archi... | 5 | 509 | 2022-03-02T23:29:22 | ---
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bri25yu-temp/cve | 2023-11-01T18:18:10.000Z | [
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lavita/ChatDoctor-iCliniq | 2023-09-11T21:13:37.000Z | [
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opus_gnome | 2023-06-01T14:59:53.000Z | [
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187 languages, 12,822 bitexts
total number of files: 113,344
total number of tokens: 267.27M
total number of sentence fragments: 58.12M | @InProceedings{TIEDEMANN12.463,
author = {J{\"o}rg Tiedemann},
title = {Parallel Data, Tools and Interfaces in OPUS},
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wiki_snippets | 2023-04-05T13:43:20.000Z | [
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codymlewis/nbaiot | 2023-10-13T04:02:56.000Z | [
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Yael Mathov and
Yisroel Mirsky and
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title = {N... | 0 | 505 | 2023-09-20T02:24:15 | ---
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sentiment140 | 2023-10-20T12:55:00.000Z | [
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sentiment classification. For more detailed information please refer to the paper. | @article{go2009twitter,
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osunlp/Mind2Web | 2023-07-19T03:44:34.000Z | [
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license: cc-by-4.0
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tags:
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---
# Dataset Card for Dataset Name
## Dataset Description
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ChaiML/20231012_chai_prize_reward_model_data | 2023-10-12T20:29:40.000Z | [
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# Dataset Card for "20231012_chai_prize_reward_model_data"
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0.033172607421875,
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0.047332763671875,
0.03558349609375,
-0.06256103515625,
-0.034576416015625,
-0.04669189453125,
-0... |
opus_paracrawl | 2023-06-01T14:59:53.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:bg",
"language:ca",
"language:cs",
"language:da",... | null | Parallel corpora from Web Crawls collected in the ParaCrawl project.
42 languages, 43 bitexts
total number of files: 59,996
total number of tokens: 56.11G
total number of sentence fragments: 3.13G | null | 5 | 502 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- bg
- ca
- cs
- da
- de
- el
- en
- es
- et
- eu
- fi
- fr
- ga
- gl
- hr
- hu
- is
- it
- km
- ko
- lt
- lv
- mt
- my
- nb
- ne
- nl
- nn
- pl
- pt
- ro
- ru
- si
- sk
- sl
- so
- sv
- sw
- tl
- uk
- zh
license:
- cc0-1.0
multilinguality:
- multil... | 9,096 | [
[
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0.0391845703125,
0.0198822021484375,
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-0.037322998046875,
0... |
SetFit/bbc-news | 2022-01-18T05:58:34.000Z | [
"region:us"
] | SetFit | null | null | 5 | 502 | 2022-03-02T23:29:22 | # BBC News Topic Classification
Dataset on [BBC News Topic Classification](https://www.kaggle.com/yufengdev/bbc-text-categorization/data): 2225 articles, each labeled under one of 5 categories: business, entertainment, politics, sport or tech. | 246 | [
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0.03076171875,
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tau/sled | 2022-10-25T07:33:44.000Z | [
"task_categories:question-answering",
"task_categories:summarization",
"task_categories:text-generation",
"task_ids:multiple-choice-qa",
"task_ids:natural-language-inference",
"language:en",
"license:mit",
"multi-hop-question-answering",
"query-based-summarization",
"long-texts",
"arxiv:2208.007... | tau | Efficient Long-Text Understanding with Short-Text Models.
Our SLiding-Encoder and Decoder uses any pretrained encoder-decoder model, to independtly encode overlapping chunks of
the inputs, and perform fusion-in-decoder to achieve linear-memory requirment for long-range natural language understanding. | @inproceedings{Ivgi2022EfficientLU,
title={Efficient Long-Text Understanding with Short-Text Models},
author={Maor Ivgi and Uri Shaham and Jonathan Berant},
year={2022}
}
Note that each SLED dataset has its own citation. Please see the source to
get the correct citation for each contained dataset (and also cite t... | 7 | 502 | 2022-08-05T08:54:23 | ---
language:
- en
license:
- mit
task_categories:
- question-answering
- summarization
- text-generation
task_ids:
- multiple-choice-qa
- natural-language-inference
configs:
- gov_report
- summ_screen_fd
- qmsum
- qasper
- narrative_qa
- quality
- contract_nli
- squad
- squad_shuffled_distractors
- squad_ordered_distr... | 9,059 | [
[
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0.053985595703125,
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-0.034698486328125... |
distil-whisper/librispeech_asr-prompted | 2023-09-19T09:31:43.000Z | [
"region:us"
] | distil-whisper | null | null | 0 | 502 | 2023-09-19T08:45:04 | ---
dataset_info:
config_name: all
features:
- name: file
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: text
dtype: string
- name: speaker_id
dtype: int64
- name: chapter_id
dtype: int64
- name: id
dtype: string
- name: whisper_transcript_... | 1,635 | [
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... |
bigcode/guanaco-commits | 2023-06-28T08:54:47.000Z | [
"region:us"
] | bigcode | null | null | 3 | 499 | 2023-06-28T08:54:28 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: completion
dtype: string
splits:
- name: train
num_bytes: 17347601.0
num_examples: 12958
- name: test
num_bytes: 827046.0
num_examples: 629
download_size: 10948498
dataset_size: 18174647.0
---
# Dataset Card for "gu... | 467 | [
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ai4bharat/samanantar | 2022-12-07T15:33:46.000Z | [
"task_categories:text-generation",
"task_categories:translation",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:translation",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"language:as",
"language:bn",
"language:gu",
"language:hi",
... | ai4bharat | Samanantar is the largest publicly available parallel corpora collection for Indic languages: Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Oriya, Punjabi, Tamil, Telugu. The corpus has 49.6M sentence pairs between English to Indian Languages. | @misc{ramesh2021samanantar,
title={Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages},
author={Gowtham Ramesh and Sumanth Doddapaneni and Aravinth Bheemaraj and Mayank Jobanputra and Raghavan AK and Ajitesh Sharma and Sujit Sahoo and Harshita Diddee and Mahalakshm... | 12 | 498 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
- as
- bn
- gu
- hi
- kn
- ml
- mr
- or
- pa
- ta
- te
license:
- cc-by-nc-4.0
multilinguality:
- translation
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text-generation
- translation
task_ids: []
pretty_na... | 5,862 | [
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-0.050018310546875,
0... |
squad_es | 2023-04-05T13:40:35.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|squad",
"language:es",
"license:cc-by-4.0",
"arxiv:1912.05200",
... | null | automatic translation of the Stanford Question Answering Dataset (SQuAD) v2 into Spanish | @article{2016arXiv160605250R,
author = {Casimiro Pio , Carrino and Marta R. , Costa-jussa and Jose A. R. , Fonollosa},
title = "{Automatic Spanish Translation of the SQuAD Dataset for Multilingual
Question Answering}",
journal = {arXiv e-prints},
year = 2019,
eid = {arXiv:1912.... | 6 | 497 | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- es
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|squad
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: squad-es
pretty_name:... | 6,916 | [
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0.023... |
distil-whisper/librispeech_asr-noise | 2023-09-27T15:56:45.000Z | [
"region:us"
] | distil-whisper | null | null | 0 | 497 | 2023-09-27T15:14:14 | ---
dataset_info:
- config_name: test-pub-noise
features:
- name: audio
dtype: audio
- name: text
dtype: string
- name: id
dtype: string
splits:
- name: '40'
num_bytes: 2517727265.74
num_examples: 2620
- name: '35'
num_bytes: 2517727265.74
num_examples: 2620
- name: '30'
... | 6,455 | [
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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... | 7 | 496 | 2022-03-02T23:29:22 | ---
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_... | 5,237 | [
[
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0.007213592529... |
castorini/afriberta-corpus | 2022-10-19T21:33:04.000Z | [
"task_categories:text-generation",
"task_ids:language-modeling",
"language:om",
"language:am",
"language:rw",
"language:rn",
"language:ha",
"language:ig",
"language:pcm",
"language:so",
"language:sw",
"language:ti",
"language:yo",
"language:multilingual",
"license:apache-2.0",
"region:... | castorini | Corpus used for training AfriBERTa models | @inproceedings{ogueji-etal-2021-small,
title = "Small Data? No Problem! Exploring the Viability of Pretrained Multilingual Language Models for Low-resourced Languages",
author = "Ogueji, Kelechi and
Zhu, Yuxin and
Lin, Jimmy",
booktitle = "Proceedings of the 1st Workshop on Multilingual Repres... | 7 | 496 | 2022-03-02T23:29:22 | ---
language:
- om
- am
- rw
- rn
- ha
- ig
- pcm
- so
- sw
- ti
- yo
- multilingual
license: apache-2.0
task_categories:
- text-generation
task_ids:
- language-modeling
---
# Dataset Card for AfriBERTa's Corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-s... | 3,412 | [
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0.0139... |
huggan/CelebA-HQ | 2022-04-12T14:10:49.000Z | [
"arxiv:1710.10196",
"region:us"
] | huggan | null | null | 8 | 496 | 2022-03-24T09:12:05 | # Citation
```
@article{DBLP:journals/corr/abs-1710-10196,
author = {Tero Karras and
Timo Aila and
Samuli Laine and
Jaakko Lehtinen},
title = {Progressive Growing of GANs for Improved Quality, Stability, and Variation},
journal = {CoRR},
volume = {abs/171... | 647 | [
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... |
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