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 |
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chloecchng/biomedical_cpgQA | 2023-10-24T17:37:28.000Z | [
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:en",
"license:apache-2.0",
"biology",
"medical",
"region:us"
] | chloecchng | null | null | 2 | 100 | 2023-10-09T09:58:21 | ---
license: apache-2.0
task_categories:
- question-answering
language:
- en
tags:
- biology
- medical
size_categories:
- 1K<n<10K
---
# Dataset Card for the Biomedical Domain
### Dataset Summary
This dataset was obtain through github (https://github.com/mmahbub/cpgQA/blob/main/dataset/cpgQA-v1.0.csv?plain=1) to Hugg... | 713 | [
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portafolio/llamadas-celular-es-01 | 2023-10-18T17:58:08.000Z | [
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result-kand2-sdxl-wuerst-karlo/877f2204 | 2023-10-28T21:09:41.000Z | [
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] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 100 | 2023-10-28T21:09:40 | ---
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# Dataset Card for "877f220... | 455 | [
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result-kand2-sdxl-wuerst-karlo/144daf3b | 2023-10-29T13:49:52.000Z | [
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] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 100 | 2023-10-29T13:49:52 | ---
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# Dataset Card for "144daf3... | 455 | [
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result-kand2-sdxl-wuerst-karlo/1abdaff0 | 2023-10-29T16:21:52.000Z | [
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] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 100 | 2023-10-29T16:21:52 | ---
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# Dataset Card for "1abdaff... | 455 | [
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bnl_newspapers | 2023-01-25T14:27:26.000Z | [
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"language:ar"... | null | Digitised historic newspapers from the Bibliothèque nationale (BnL) - the National Library of Luxembourg. | @misc{bnl_newspapers,
title={Historical Newspapers},
url={https://data.bnl.lu/data/historical-newspapers/},
author={ Bibliothèque nationale du Luxembourg}, | 1 | 99 | 2022-03-02T23:29:22 | ---
annotations_creators:
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diplomacy_detection | 2023-01-25T14:29:25.000Z | [
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] | null | null | @inproceedings{peskov-etal-2020-takes,
title = "It Takes Two to Lie: One to Lie, and One to Listen",
author = "Peskov, Denis and
Cheng, Benny and
Elgohary, Ahmed and
Barrow, Joe and
Danescu-Niculescu-Mizil, Cristian and
Boyd-Graber, Jordan",
booktitle = "Proceedings of the... | 0 | 99 | 2022-03-02T23:29:22 | ---
annotations_creators:
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pretty_name: HateOffensive
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oclar | 2022-11-03T16:15:26.000Z | [
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"language... | null | The researchers of OCLAR Marwan et al. (2019), they gathered Arabic costumer reviews from Google reviewsa and Zomato
website (https://www.zomato.com/lebanon) on wide scope of domain, including restaurants, hotels, hospitals, local shops,
etc.The corpus finally contains 3916 reviews in 5-rating scale. For this research ... | @misc{Dua:2019 ,
author = "Dua, Dheeru and Graff, Casey",
year = "2017",
title = "{UCI} Machine Learning Repository",
url = "http://archive.ics.uci.edu/ml",
institution = "University of California, Irvine, School of Information and Computer Sciences" }
@InProceedings{AlOmari2019oclar,
title = {Sentiment Classifier: Lo... | 1 | 99 | 2022-03-02T23:29:22 | ---
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taskmaster3 | 2022-11-03T16:30:39.000Z | [
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"arxiv... | null | Taskmaster is dataset for goal oriented conversations. The Taskmaster-3 dataset consists of 23,757 movie ticketing dialogs. By "movie ticketing" we mean conversations where the customer's goal is to purchase tickets after deciding on theater, time, movie name, number of tickets, and date, or opt out of the transaction.... | @inproceedings{48484,
title = {Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset},
author = {Bill Byrne and Karthik Krishnamoorthi and Chinnadhurai Sankar and Arvind Neelakantan and Daniel Duckworth and Semih Yavuz and Ben Goodrich and Amit Dubey and Kyu-Young Kim and Andy Cedilnik},
year = {2019}
} | 0 | 99 | 2022-03-02T23:29:22 | ---
annotations_creators:
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paperswithcode_id: null
pretty_name: taskma... | 9,272 | [
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wiki_qa_ar | 2023-01-25T15:02:18.000Z | [
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] | null | Arabic Version of WikiQA by automatic automatic machine translators and crowdsourced the selection of the best one to be incorporated into the corpus | @InProceedings{YangYihMeek:EMNLP2015:WikiQA,
author = {{Yi}, Yang and {Wen-tau}, Yih and {Christopher} Meek},
title = "{WikiQA: A Challenge Dataset for Open-Domain Question Answering}",
journal = {Association for Computational Linguistics},
year = 2015,
doi = {10.18653/v1/D15-12... | 2 | 99 | 2022-03-02T23:29:22 | ---
annotations_creators:
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paperswithcode_id: wikiqaar
pretty_name: English-Arabic Wi... | 4,496 | [
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HHousen/quora | 2021-11-21T02:11:20.000Z | [
"region:us"
] | HHousen | null | null | 1 | 99 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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Tevatron/wikipedia-nq-corpus | 2021-10-13T22:18:40.000Z | [
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snoop2head/commoncrawl_sampled_gpt2-xl | 2022-08-04T12:28:33.000Z | [
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HuggingFaceH4/self-instruct-seed | 2023-01-31T22:37:02.000Z | [
"task_categories:conversational",
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"license:apache-2.0",
"arxiv:2212.10560",
"region:us"
] | HuggingFaceH4 | null | null | 14 | 99 | 2023-01-31T22:33:52 | ---
license: apache-2.0
task_categories:
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language:
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size_categories:
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---
Manually created seed dataset used in bootstrapping in the Self-instruct paper https://arxiv.org/abs/2212.10560. This is part of the instruction fine-tuning datasets. | 268 | [
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Dahoas/cot_gsm8k | 2023-05-31T13:01:00.000Z | [
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] | Dahoas | null | null | 4 | 99 | 2023-05-31T13:00:55 | ---
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ahmed-masry/unichart-pretrain-data | 2023-07-30T01:39:51.000Z | [
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maastrichtlawtech/lleqa | 2023-10-25T10:07:40.000Z | [
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"language_creat... | maastrichtlawtech | null | null | 1 | 99 | 2023-09-27T13:31:22 | ---
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vlsp-2023-vllm/mmlu | 2023-09-30T03:37:34.000Z | [
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] | vlsp-2023-vllm | null | null | 0 | 99 | 2023-09-29T19:08:22 | ---
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hackaprompt/hackaprompt-dataset | 2023-10-22T13:41:01.000Z | [
"size_categories:100K<n<1M",
"language:en",
"code",
"region:us"
] | hackaprompt | null | null | 2 | 99 | 2023-10-19T03:01:52 | ---
language:
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tags:
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pretty_name: HackAPrompt Dataset
size_categories:
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---
# Dataset Card for HackAPrompt 💻🔍
This dataset contains submissions from a prompt hacking competition. An in-depth analysis of the dataset has been accepted at the EMNLP 2023 conference. 📊👾
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naman1011/spider | 2023-10-26T05:37:37.000Z | [
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has_part | 2022-11-03T16:15:21.000Z | [
"task_categories:text-classification",
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"annotations_creators:machine-generated",
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"Meronym-Prediction",
... | null | This dataset is a new knowledge-base (KB) of hasPart relationships, extracted from a large corpus of generic statements. Complementary to other resources available, it is the first which is all three of: accurate (90% precision), salient (covers relationships a person may mention), and has high coverage of common terms... | @misc{bhakthavatsalam2020dogs,
title={Do Dogs have Whiskers? A New Knowledge Base of hasPart Relations},
author={Sumithra Bhakthavatsalam and Kyle Richardson and Niket Tandon and Peter Clark},
year={2020},
eprint={2006.07510},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 0 | 98 | 2022-03-02T23:29:22 | ---
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paperswithcode_id: haspart-kb
pretty_name:... | 6,321 | [
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hippocorpus | 2022-11-03T16:15:25.000Z | [
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"language:en",
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"narrative-flow",
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] | null | To examine the cognitive processes of remembering and imagining and their traces in language, we introduce Hippocorpus, a dataset of 6,854 English diary-like short stories about recalled and imagined events. Using a crowdsourcing framework, we first collect recalled stories and summaries from workers, then provide thes... | @inproceedings{sap-etal-2020-recollection,
title = "Recollection versus Imagination: Exploring Human Memory and Cognition via Neural Language Models",
author = "Sap, Maarten and
Horvitz, Eric and
Choi, Yejin and
Smith, Noah A. and
Pennebaker, James",
booktitle = "Proceedings of t... | 3 | 98 | 2022-03-02T23:29:22 | ---
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pretty_name: hippocorpus
tags:... | 9,318 | [
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kan_hope | 2023-01-25T14:33:30.000Z | [
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"hop... | null | Numerous methods have been developed to monitor the spread of negativity in modern years by
eliminating vulgar, offensive, and fierce comments from social media platforms. However, there are relatively
lesser amounts of study that converges on embracing positivity, reinforcing supportive and reassuring content in onlin... | @misc{hande2021hope,
title={Hope Speech detection in under-resourced Kannada language},
author={Adeep Hande and Ruba Priyadharshini and Anbukkarasi Sampath and Kingston Pal Thamburaj and Prabakaran Chandran and Bharathi Raja Chakravarthi},
year={2021},
eprint={2108.04616},
archivePrefix={a... | 1 | 98 | 2022-03-02T23:29:22 | ---
annotations_creators:
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pretty_name: KanHope
language_bcp4... | 5,189 | [
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kor_qpair | 2023-01-25T14:34:00.000Z | [
"task_categories:text-classification",
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"annotations_creators:expert-generated",
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"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ko",
"license:mit",
"region:us"
] | null | This is a Korean paired question dataset containing labels indicating whether two questions in a given pair are semantically identical. This dataset was used to evaluate the performance of [KoGPT2](https://github.com/SKT-AI/KoGPT2#subtask-evaluations) on a phrase detection downstream task. | @misc{Song:2018,
title = "Paired Question v.2",
authors = "Youngsook Song",
publisher = "GitHub",
year = "2018"
} | 2 | 98 | 2022-03-02T23:29:22 | ---
annotations_creators:
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task_categories:
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task_ids:
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pretty_name: KorQpair
dataset_info:
feature... | 3,480 | [
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kor_sae | 2023-01-25T14:34:03.000Z | [
"task_categories:text-classification",
"task_ids:intent-classification",
"annotations_creators:expert-generated",
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"language:ko",
"license:cc-by-sa-4.0",
"arxiv:1912.00342"... | null | This new dataset is designed to extract intent from non-canonical directives which will help dialog managers
extract intent from user dialog that may have no clear objective or are paraphrased forms of utterances. | @article{cho2019machines,
title={Machines Getting with the Program: Understanding Intent Arguments of Non-Canonical Directives},
author={Cho, Won Ik and Moon, Young Ki and Moon, Sangwhan and Kim, Seok Min and Kim, Nam Soo},
journal={arXiv preprint arXiv:1912.00342},
year={2019}
} | 3 | 98 | 2022-03-02T23:29:22 | ---
annotations_creators:
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language:
- ko
license:
- cc-by-sa-4.0
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size_categories:
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pretty_name: Structured Argument Ext... | 5,975 | [
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m_lama | 2022-11-03T16:15:15.000Z | [
"task_categories:question-answering",
"task_categories:text-classification",
"task_ids:open-domain-qa",
"task_ids:text-scoring",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creator... | null | mLAMA: a multilingual version of the LAMA benchmark (T-REx and GoogleRE) covering 53 languages. | @article{kassner2021multilingual,
author = {Nora Kassner and
Philipp Dufter and
Hinrich Sch{\"{u}}tze},
title = {Multilingual {LAMA:} Investigating Knowledge in Multilingual Pretrained
Language Models},
journal = {CoRR},
volume = {abs/2102.00894},
year ... | 4 | 98 | 2022-03-02T23:29:22 | ---
annotations_creators:
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newsph_nli | 2023-01-25T14:41:24.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:machine-generated",
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"region... | null | First benchmark dataset for sentence entailment in the low-resource Filipino language.
Constructed through exploting the structure of news articles. Contains 600,000 premise-hypothesis pairs,
in 70-15-15 split for training, validation, and testing. | @article{cruz2020investigating,
title={Investigating the True Performance of Transformers in Low-Resource Languages: A Case Study in Automatic Corpus Creation},
author={Jan Christian Blaise Cruz and Jose Kristian Resabal and James Lin and Dan John Velasco and Charibeth Cheng},
journal={arXiv preprint arXiv:... | 0 | 98 | 2022-03-02T23:29:22 | ---
annotations_creators:
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- unknown
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paperswithcode_id: newsph-nli
pretty_name: News... | 5,397 | [
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urdu_fake_news | 2023-01-25T15:01:58.000Z | [
"task_categories:text-classification",
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"multilinguality:monolingual",
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"license:unknown",
"... | null | Urdu fake news datasets that contain news of 5 different news domains.
These domains are Sports, Health, Technology, Entertainment, and Business.
The real news are collected by combining manual approaches. | @article{MaazUrdufake2020,
author = {Amjad, Maaz and Sidorov, Grigori and Zhila, Alisa and G’{o}mez-Adorno, Helena and Voronkov, Ilia and Gelbukh, Alexander},
title = {Bend the Truth: A Benchmark Dataset for Fake News Detection in Urdu and Its Evaluation},
journal={Journal of Intelligent & Fuzzy Systems},
volume={39}... | 0 | 98 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- ur
license:
- unknown
multilinguality:
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size_categories:
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task_categories:
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pretty_name: Bend the Truth (Ur... | 3,671 | [
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ARTeLab/mlsum-it | 2022-11-17T02:51:00.000Z | [
"task_categories:summarization",
"multilinguality:monolingual",
"size_categories:10K<n<100k",
"language:it",
"region:us"
] | ARTeLab | null | null | 1 | 98 | 2022-03-02T23:29:22 | ---
language:
- it
multilinguality:
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size_categories:
- 10K<n<100k
task_categories:
- summarization
---
# Dataset Card for mlsum-it
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
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DELith/github-issues | 2021-11-21T15:58:45.000Z | [
"region:us"
] | DELith | null | null | 0 | 98 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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DanL/scientific-challenges-and-directions-dataset | 2022-10-25T08:56:00.000Z | [
"task_categories:text-classification",
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"language:en",
"arxiv:2108.13751",
"arxiv:2004.10706",
"region:us"
] | DanL | null | null | 2 | 98 | 2022-03-02T23:29:22 | ---
YAML tags:
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language_creators: []
language:
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pretty_name: DanL/scientific-challenges-and-directions-dataset
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---
# Dataset C... | 8,366 | [
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bigscience-catalogue-data-dev/lm_code_github-eval_subset | 2022-02-16T10:42:10.000Z | [
"region:us"
] | bigscience-catalogue-data-dev | null | null | 0 | 98 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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emre/Open_SLR108_Turkish_10_hours | 2022-12-06T21:00:45.000Z | [
"license:cc-by-4.0",
"robust-speech-event",
"arxiv:2103.16193",
"region:us"
] | emre | null | null | 3 | 98 | 2022-03-02T23:29:22 | ---
license: cc-by-4.0
tags:
- robust-speech-event
datasets:
- MediaSpeech
---
MediaSpeech
Identifier: SLR108
Summary: French, Arabic, Turkish and Spanish media speech datasets
Category: Speech
License: dataset is distributed under the Creative Commons Attribution 4.0 International License.
About this resource:
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rungalileo/medical_transcription_4 | 2022-08-04T04:58:36.000Z | [
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ashraq/hotel-reviews | 2022-10-27T17:24:29.000Z | [
"region:us"
] | ashraq | null | null | 1 | 98 | 2022-10-27T17:22:07 | ---
dataset_info:
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num_bytes: 15043294
num_examples: 93757
download_size: 6100544
dataset_size: 15043294
---
# Dataset Card for "hotel-reviews"
[More Inform... | 548 | [
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SirNeural/flan_v2 | 2023-02-24T19:05:00.000Z | [
"license:apache-2.0",
"flan",
"flan 2022",
"flan v2",
"arxiv:2301.13688",
"region:us"
] | SirNeural | null | null | 148 | 98 | 2023-02-13T23:02:33 | ---
license: apache-2.0
tags:
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- flan 2022
- flan v2
pretty_name: Flan v2
---
# Dataset Card for Flan V2
## Dataset Description
- **Homepage:** https://ai.googleblog.com/2023/02/the-flan-collection-advancing-open.html
- **Repository:** https://github.com/google-research/FLAN/tree/main/flan/v2
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jonathan-roberts1/Brazilian_Coffee_Scenes | 2023-03-31T15:27:06.000Z | [
"task_categories:image-classification",
"license:other",
"region:us"
] | jonathan-roberts1 | null | null | 0 | 98 | 2023-02-14T18:27:36 | ---
dataset_info:
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pythainlp/final_training_set_v1_enth | 2023-04-29T07:05:42.000Z | [
"task_categories:text-generation",
"task_categories:conversational",
"language:th",
"language:en",
"region:us"
] | pythainlp | null | null | 1 | 98 | 2023-04-22T08:56:14 | ---
dataset_info:
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fujiki/llm-japanese-dataset_wikinews | 2023-07-24T08:13:28.000Z | [
"license:cc-by-2.5",
"region:us"
] | fujiki | null | null | 2 | 98 | 2023-07-24T07:42:30 | ---
license: cc-by-2.5
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izumi-lab/wikipedia-ja-20230720 | 2023-07-29T03:05:36.000Z | [
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loremipsum3658/emb | 2023-08-24T21:20:50.000Z | [
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loremipsum3658/sen | 2023-08-24T21:25:11.000Z | [
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loremipsum3658/and | 2023-08-24T21:29:56.000Z | [
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configs:
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ContextualAI/tiny-wiki100-chunks | 2023-09-22T17:47:30.000Z | [
"region:us"
] | ContextualAI | null | null | 0 | 98 | 2023-09-22T17:47:26 | ---
dataset_info:
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---
# Dataset Card for "tiny-wiki100-chunks"
[More Information needed](ht... | 423 | [
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counter | 2023-01-25T14:28:41.000Z | [
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:semantic-similarity-scoring",
"task_ids:topic-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
... | null | The COrpus of Urdu News TExt Reuse (COUNTER) corpus contains 1200 documents with real examples of text reuse from the field of journalism. It has been manually annotated at document level with three levels of reuse: wholly derived, partially derived and non derived. | @Article{Sharjeel2016,
author="Sharjeel, Muhammad
and Nawab, Rao Muhammad Adeel
and Rayson, Paul",
title="COUNTER: corpus of Urdu news text reuse",
journal="Language Resources and Evaluation",
year="2016",
pages="1--27",
issn="1574-0218",
doi="10.1007/s10579-016-9367-2",
url="http://dx.doi.org/10.1007/s10579-016-9367-2... | 0 | 97 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- ur
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
- semantic-similarity-scoring
- topic-classifica... | 15,716 | [
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curiosity_dialogs | 2023-01-25T14:28:58.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:dialogue-modeling",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-4.0",
"co... | null | This dataset contains 14K dialogs (181K utterances) where users and assistants converse about geographic topics like
geopolitical entities and locations. This dataset is annotated with pre-existing user knowledge, message-level dialog
acts, grounding to Wikipedia, and user reactions to messages. | @inproceedings{rodriguez2020curiosity,
title = {Information Seeking in the Spirit of Learning: a Dataset for Conversational Curiosity},
author = {Pedro Rodriguez and Paul Crook and Seungwhan Moon and Zhiguang Wang},
year = 2020,
booktitle = {Empirical Methods in Natural Language Processing}
} | 6 | 97 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-nc-4.0
multilinguality:
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size_categories:
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source_datasets:
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paperswithcode_id: curiosity
pretty_name... | 12,044 | [
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kor_sarcasm | 2023-03-21T14:49:40.000Z | [
"task_categories:text-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ko",
"license:mit",
"sarcasm-detection",
"region:us"
] | null | This is a dataset designed to detect sarcasm in Korean because it distorts the literal meaning of a sentence
and is highly related to sentiment classification. | null | 2 | 97 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- ko
license:
- mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
pretty_name: Korean Sarcasm Detection
tags:
- sarcasm-detection
dataset_info:
... | 4,964 | [
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refresd | 2023-01-25T14:43:11.000Z | [
"task_categories:text-classification",
"task_categories:translation",
"task_ids:semantic-similarity-classification",
"task_ids:semantic-similarity-scoring",
"task_ids:text-scoring",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"lang... | null | The Rationalized English-French Semantic Divergences (REFreSD) dataset consists of 1,039
English-French sentence-pairs annotated with sentence-level divergence judgments and token-level
rationales. For any questions, write to ebriakou@cs.umd.edu. | @inproceedings{briakou-carpuat-2020-detecting,
title = "Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rank",
author = "Briakou, Eleftheria and Carpuat, Marine",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing ... | 0 | 97 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
- machine-generated
language_creators:
- crowdsourced
- machine-generated
language:
- en
- fr
license:
- mit
multilinguality:
- translation
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-wikimatrix
task_categories:
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task_ids:
- s... | 12,055 | [
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saudinewsnet | 2023-07-17T08:18:44.000Z | [
"task_categories:text-generation",
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"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ar"... | null | The dataset contains a set of 31,030 Arabic newspaper articles alongwith metadata, extracted from various online Saudi newspapers and written in MSA. | @misc{hagrima2015,
author = "M. Alhagri",
title = "Saudi Newspapers Arabic Corpus (SaudiNewsNet)",
year = 2015,
url = "http://github.com/ParallelMazen/SaudiNewsNet"
} | 1 | 97 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- ar
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
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task_categories:
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task_ids:
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paperswithcode_id: null
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Atsushi/fungi_indexed_mycological_papers_japanese | 2023-10-08T21:33:33.000Z | [
"annotations_creators:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
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"language:ja",
"license:cc-by-4.0",
"region:us"
] | Atsushi | null | null | 0 | 97 | 2022-03-02T23:29:22 | ---
annotations_creators:
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language:
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license:
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multilinguality:
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size_categories:
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---
fungi_indexed_mycological_papers_japanese
大菌輪「論文3行まとめ」データセット
最終更新日：2023/10/9（R3-11041まで）
====
### Languages
Japanese
This dataset is available in Japa... | 1,976 | [
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HenryAI/KerasAPIReference.txt | 2021-12-15T15:55:07.000Z | [
"region:us"
] | HenryAI | null | null | 0 | 97 | 2022-03-02T23:29:22 | Keras API from https://keras.io/api/ <br />
Formatted into .txt file for input to https://huggingface.co/blog/how-to-train | 122 | [
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laion/laion_100m_vqgan_f8 | 2021-12-25T05:27:42.000Z | [
"region:us"
] | laion | null | null | 2 | 97 | 2022-03-02T23:29:22 | # VQGAN (f8, 8192) embeddings for LAION-100M
This dataset contains __VQGAN (f8, 8192)__ embeddings for the images
from the first ~100 million image-text pairs of the [LAION-400M dataset](https://laion.ai/laion-400-open-dataset/).
VQGAN was introduced in the paper
["Taming Transformers for High-Resolution Image Synthes... | 2,041 | [
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jason9693/APEACH | 2022-07-05T04:18:07.000Z | [
"task_categories:text-classification",
"annotations_creators:crowdsourced",
"annotations_creators:crowd-generated",
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"language:ko",
"license:cc-by-sa-4.0",
"arxiv:2202.12459",
"region... | jason9693 | null | null | 3 | 97 | 2022-04-14T14:27:43 | ---
annotations_creators:
- crowdsourced
- crowd-generated
language_creators:
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language:
- ko
license:
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paperswithcode_id: apeach
pretty_name: 'APEACH'
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
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task_ids:
- binary-cl... | 2,226 | [
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bigscience/xP3mt | 2023-05-30T15:50:57.000Z | [
"task_categories:other",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:100M<n<1B",
"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. | @misc{muennighoff2022crosslingual,
title={Crosslingual Generalization through Multitask Finetuning},
author={Niklas Muennighoff and Thomas Wang and Lintang Sutawika and Adam Roberts and Stella Biderman and Teven Le Scao and M Saiful Bari and Sheng Shen and Zheng-Xin Yong and Hailey Schoelkopf and Xiangru T... | 18 | 97 | 2022-09-28T12:36:00 | ---
annotations_creators:
- expert-generated
- crowdsourced
language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
- es
- eu
- fon
- fr
- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
- ne
- nso
- ny
- or
- pa
- pt
- rn
- rw
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- st
- sw
- ta
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- tn
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- vi
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programming_lan... | 13,046 | [
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yhavinga/squad_v2_dutch | 2023-01-21T13:53:27.000Z | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
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"size_categories:100K<n<1M",
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"license:cc-by-sa-4.0",
"arxiv:... | yhavinga | null | null | 1 | 97 | 2022-12-17T22:50:45 | ---
pretty_name: SQuAD2.0 Dutch
annotations_creators:
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language_creators:
- crowdsourced
language:
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license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
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task_categories:
- question-answering
task_ids:
- open-domain-qa
- extractive-qa
paperswit... | 3,885 | [
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Dr-BERT/QUAERO | 2023-06-12T20:53:41.000Z | [
"task_categories:token-classification",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:fr",
"license:other",
"medical",
"region:us"
] | Dr-BERT | The QUAERO French Medical Corpus has been initially developed as a resource for named entity recognition and normalization [1]. It was then improved with the purpose of creating a gold standard set of normalized entities for French biomedical text, that was used in the CLEF eHealth evaluation lab [2][3].
A selection of... | @InProceedings{neveol14quaero,
author = {Névéol, Aurélie and Grouin, Cyril and Leixa, Jeremy
and Rosset, Sophie and Zweigenbaum, Pierre},
title = {The {QUAERO} {French} Medical Corpus: A Ressource for
Medical Entity Recognition and Normalization},
OPTbooktitle = {Proceedings of the Fourth Workshop on B... | 3 | 97 | 2023-04-25T22:01:52 | ---
language:
- fr
license: other
multilinguality: monolingual
pretty_name: QUAERO
homepage: https://quaerofrenchmed.limsi.fr/
task_categories:
- token-classification
tags:
- medical
size_categories:
- 1K<n<10K
---
# Dataset Card for QUAERO
## Dataset Description
- **Homepage:** https://quaerofrenchmed.limsi.fr/
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bigcode/ta-prompt | 2023-05-04T12:20:22.000Z | [
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] | bigcode | null | null | 155 | 97 | 2023-05-03T14:04:39 | ---
license: apache-2.0
language:
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programming_language:
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- Python
---
# Dataset summary
This repository is dedicated to prompts used to perform in-context learning with [starcoder](https://huggingface.co/bigcode/starcoder). As a matter of fact, the model is an
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tasksource/icl-symbol-tuning-instruct | 2023-07-26T07:20:41.000Z | [
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"instruction... | tasksource | null | null | 11 | 97 | 2023-06-15T14:44:19 | ---
license: apache-2.0
task_categories:
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language:
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tags:
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tingchih/multi-class | 2023-09-12T04:21:02.000Z | [
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] | tingchih | null | null | 0 | 97 | 2023-09-12T00:25:48 | ---
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---
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loremipsum3658/adj_extension | 2023-09-28T17:03:46.000Z | [
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] | loremipsum3658 | null | null | 0 | 97 | 2023-09-28T17:02:18 | ---
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alzoubi36/title_generation | 2023-10-01T12:43:11.000Z | [
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sheepy928/rt_merged | 2023-10-23T22:13:12.000Z | [
"region:us"
] | sheepy928 | null | null | 0 | 97 | 2023-10-23T22:12:30 | ---
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capes | 2022-11-03T16:15:53.000Z | [
"task_categories:translation",
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"region:u... | null | A parallel corpus of theses and dissertations abstracts in English and Portuguese were collected from the CAPES website (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) - Brazil. The corpus is sentence aligned for all language pairs. Approximately 240,000 documents were collected and aligned using the Huna... | @inproceedings{soares2018parallel,
title={A Parallel Corpus of Theses and Dissertations Abstracts},
author={Soares, Felipe and Yamashita, Gabrielli Harumi and Anzanello, Michel Jose},
booktitle={International Conference on Computational Processing of the Portuguese Language},
pages={345--352},
year={2018},
... | 2 | 96 | 2022-03-02T23:29:22 | ---
annotations_creators:
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paperswithcode_id: capes
pretty_name: CAPES
tags:
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msr_text_compression | 2022-11-18T21:30:29.000Z | [
"task_categories:summarization",
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"language_creators:found",
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"source_datasets:extended|other-Open-American-National-Corpus-(OANC1)",
"language:en",
"license:other",
"region:us"
] | null | This dataset contains sentences and short paragraphs with corresponding shorter (compressed) versions. There are up to five compressions for each input text, together with quality judgements of their meaning preservation and grammaticality. The dataset is derived using source texts from the Open American National Corpu... | @inproceedings{Toutanova2016ADA,
title={A Dataset and Evaluation Metrics for Abstractive Compression of Sentences and Short Paragraphs},
author={Kristina Toutanova and Chris Brockett and Ke M. Tran and Saleema Amershi},
booktitle={EMNLP},
year={2016}
} | 3 | 96 | 2022-03-02T23:29:22 | ---
annotations_creators:
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license_details: Microsoft Research Data License Agreement
multilinguality:
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size_categories:
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vctk | 2022-11-03T16:16:04.000Z | [
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"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] | null | The CSTR VCTK Corpus includes speech data uttered by 110 English speakers with various accents. | @inproceedings{Veaux2017CSTRVC,
title = {CSTR VCTK Corpus: English Multi-speaker Corpus for CSTR Voice Cloning Toolkit},
author = {Christophe Veaux and Junichi Yamagishi and Kirsten MacDonald},
year = 2017
} | 8 | 96 | 2022-03-02T23:29:22 | ---
annotations_creators:
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language_creators:
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language:
- en
license:
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multilinguality:
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pretty_name: VCTK
size_categories:
- 10K<n<100K
source_datasets:
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task_categories:
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paperswithcode_id: vctk
train-eval-in... | 5,477 | [
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yoruba_text_c3 | 2023-06-16T15:06:58.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
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"language:y... | null | Yoruba Text C3 is the largest Yoruba texts collected and used to train FastText embeddings in the
YorubaTwi Embedding paper: https://www.aclweb.org/anthology/2020.lrec-1.335/ | @inproceedings{alabi-etal-2020-massive,
title = "Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of Yoruba and {T}wi",
author = "Alabi, Jesujoba and
Amponsah-Kaakyire, Kwabena and
Adelani, David and
Espa{\\~n}a-Bonet, Cristina",
booktitle = "Proceedings of the 12th ... | 1 | 96 | 2022-03-02T23:29:22 | ---
annotations_creators:
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language:
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license:
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source_datasets:
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KETI-AIR/aihub | 2021-09-21T17:40:36.000Z | [
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gpt3mix/rt20 | 2021-05-18T09:04:24.000Z | [
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vblagoje/lfqa | 2021-10-17T13:44:46.000Z | [
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ipipan/polqa | 2023-09-09T13:37:44.000Z | [
"task_categories:question-answering",
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"license:cc-by-... | ipipan | PolQA is the first Polish dataset for OpenQA. It consists of 7,000 questions, 87,525 manually labeled evidence passages, and a corpus of over 7 million candidate passages. | @misc{rybak2022improving,
title={Improving Question Answering Performance through Manual Annotation: Costs, Benefits and Strategies},
author={Piotr Rybak and Piotr Przybyła and Maciej Ogrodniczuk},
year={2022},
eprint={2212.08897},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 3 | 96 | 2022-12-17T15:03:58 | ---
task_categories:
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task_ids:
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language:
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pretty_name: PolQA
size_categories:
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annotations_creators:
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license: cc-by-sa-4.0
---
# Dataset Card for PolQA Dataset
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c-s-ale/alpaca-gpt4-data | 2023-04-07T19:27:51.000Z | [
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] | c-s-ale | null | null | 17 | 96 | 2023-04-07T18:20:58 | ---
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cdminix/libritts-r-aligned | 2023-07-02T15:13:39.000Z | [
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"arxiv:1904.02882",
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] | cdminix | Dataset used for loading TTS spectrograms and waveform audio with alignments and a number of configurable "measures", which are extracted from the raw audio. | @article{koizumi2023libritts,
title={LibriTTS-R: A Restored Multi-Speaker Text-to-Speech Corpus},
author={Koizumi, Yuma and Zen, Heiga and Karita, Shigeki and Ding, Yifan and Yatabe, Kohei and Morioka, Nobuyuki and Bacchiani, Michiel and Zhang, Yu and Han, Wei and Bapna, Ankur},
journal={arXiv preprint arXiv:2305... | 5 | 96 | 2023-06-07T08:35:07 | ---
pretty_name: LibriTTS Corpus with Forced Alignments
annotations_creators:
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language: en
tags:
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license:
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task_categories:
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jitx/Methods2Test_java_unit_test_code | 2023-08-30T19:31:25.000Z | [
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] | jitx | null | null | 3 | 96 | 2023-08-30T18:59:03 | ---
license: mit
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LeoLM/wikitext-en-de | 2023-09-28T14:04:12.000Z | [
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] | LeoLM | null | null | 1 | 96 | 2023-09-28T13:39:48 | ---
license: cc-by-3.0
configs:
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data_files: wiki_de_exzellent.parquet
- config_name: featured_en
data_files: wiki_en_featured.parquet
- config_name: exzellent_de_small
data_files: wiki_de_exzellent_small.parquet
- config_name: featured_en_small
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alexrs/alpaca-cleaned-30-clusters | 2023-10-16T14:44:34.000Z | [
"region:us"
] | alexrs | null | null | 0 | 96 | 2023-10-16T14:44:30 | ---
dataset_info:
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pkr7098/bookcorpus-wikipedia-full | 2023-10-31T01:06:21.000Z | [
"region:us"
] | pkr7098 | null | null | 0 | 96 | 2023-10-30T11:59:38 | ---
dataset_info:
config_name: 20220301.en
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download_size: 0
dataset_size: 24500165181
configs:
- config_name: 20220301.en
data_files:
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path: 20220301.en/train-*
---
# ... | 497 | [
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result-kand2-sdxl-wuerst-karlo/b8542650 | 2023-10-30T15:00:46.000Z | [
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] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 96 | 2023-10-30T15:00:45 | ---
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dataset_size: 179
configs:
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data_files:
- split: train
path: data/train-*
---
# Dataset Card for "b854265... | 455 | [
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hebrew_projectbenyehuda | 2022-11-03T16:15:45.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:... | null | This repository contains a dump of thousands of public domain works in Hebrew, from Project Ben-Yehuda, in plaintext UTF-8 files, with and without diacritics (nikkud). The metadata (pseudocatalogue.csv) file is a list of titles, authors, genres, and file paths, to help you process the dump.
All these works are in the p... | @article{,
author = {},
title = {Public domain texts from Project Ben-Yehuda},
journal = {},
url = {https://github.com/projectbenyehuda/public_domain_dump},
year = {2020},
} | 2 | 95 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- he
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: null
p... | 15,778 | [
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hindi_discourse | 2023-01-25T14:32:13.000Z | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"annotations_creators:other",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:hi",
"license:other",
"discourse-analysis",
"region:us"
] | null | The Hindi Discourse Analysis dataset is a corpus for analyzing discourse modes present in its sentences.
It contains sentences from stories written by 11 famous authors from the 20th Century.
4-5 stories by each author have been selected which were available in the public domain resulting
in a collection of 53 stories.... | @inproceedings{swapnil2020,
title={An Annotated Dataset of Discourse Modes in Hindi Stories},
author={Swapnil Dhanwal, Hritwik Dutta, Hitesh Nankani, Nilay Shrivastava, Yaman Kumar, Junyi Jessy Li, Debanjan Mahata, Rakesh Gosangi, Haimin Zhang, Rajiv Ratn Shah, Amanda Stent},
booktitle={Proceedings of the 1... | 1 | 95 | 2022-03-02T23:29:22 | ---
annotations_creators:
- other
language_creators:
- found
language:
- hi
license:
- other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
pretty_name: Discourse Analysis dataset
tags:
- discourse-anal... | 9,254 | [
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id_panl_bppt | 2023-01-25T14:32:43.000Z | [
"task_categories:translation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"language:id",
"license:unknown",
"region:us"
] | null | Parallel Text Corpora for Multi-Domain Translation System created by BPPT (Indonesian Agency for the Assessment and
Application of Technology) for PAN Localization Project (A Regional Initiative to Develop Local Language Computing
Capacity in Asia). The dataset contains around 24K sentences divided in 4 difference topi... | @inproceedings{id_panl_bppt,
author = {PAN Localization - BPPT},
title = {Parallel Text Corpora, English Indonesian},
year = {2009},
url = {http://digilib.bppt.go.id/sampul/p92-budiono.pdf},
} | 1 | 95 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
- id
license:
- unknown
multilinguality:
- translation
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
pretty_name: IdPanlBppt
dataset_info:
features:
- name: id
... | 4,942 | [
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inquisitive_qg | 2022-11-18T20:09:50.000Z | [
"task_categories:text2text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"question-generation",
"region:us"
] | null | A dataset of about 20k questions that are elicited from readers as they naturally read through a document sentence by sentence. Compared to existing datasets, INQUISITIVE questions target more towards high-level (semantic and discourse) comprehension of text. Because these questions are generated while the readers are ... | @InProceedings{ko2020inquisitive,
author = {Ko, Wei-Jen and Chen, Te-Yuan and Huang, Yiyan and Durrett, Greg and Li, Junyi Jessy},
title = {Inquisitive Question Generation for High Level Text Comprehension},
booktitle = {Proceedings of EMNLP},
year = {2020},
} | 1 | 95 | 2022-03-02T23:29:22 | ---
pretty_name: InquisitiveQg
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: inquisitive
tags:
- que... | 3,946 | [
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metrec | 2023-01-25T14:40:27.000Z | [
"task_categories:text-classification",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ar",
"license:unknown",
"poetry-classification",
"region:us"
] | null | Arabic Poetry Metric Classification.
The dataset contains the verses and their corresponding meter classes.Meter classes are represented as numbers from 0 to 13. The dataset can be highly useful for further research in order to improve the field of Arabic poems’ meter classification.The train dataset contains 47,124 re... | @article{metrec2020,
title={MetRec: A dataset for meter classification of arabic poetry},
author={Al-shaibani, Maged S and Alyafeai, Zaid and Ahmad, Irfan},
journal={Data in Brief},
year={2020},
publisher={Elsevier}
} | 2 | 95 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- ar
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: metrec
pretty_name: MetRec
tags:
- poetry-classification
... | 4,752 | [
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wmt_t2t | 2023-04-05T13:44:08.000Z | [
"task_categories:translation",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:translation",
"size_categories:10M<n<100M",
"source_datasets:extended|europarl_bilingual",
"source_datasets:extended|news_commentary",
"source_datasets:extended|opus_paracrawl",
"source_d... | null | null | @InProceedings{bojar-EtAl:2014:W14-33,
author = {Bojar, Ondrej and Buck, Christian and Federmann, Christian and Haddow, Barry and Koehn, Philipp and Leveling, Johannes and Monz, Christof and Pecina, Pavel and Post, Matt and Saint-Amand, Herve and Soricut, Radu and Specia, Lucia and Tamchyna... | 0 | 95 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- de
- en
license:
- unknown
multilinguality:
- translation
size_categories:
- 10M<n<100M
source_datasets:
- extended|europarl_bilingual
- extended|news_commentary
- extended|opus_paracrawl
- extended|un_multi
task_categories:
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... | 7,379 | [
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Sakonii/nepalitext-language-model-dataset | 2022-10-25T06:14:22.000Z | [
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:extended|oscar",
"source_datasets:extended|cc100",
"language:ne",
"license:cc0-1.0",
"regio... | Sakonii | null | null | 3 | 95 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
- other
language:
- ne
license:
- cc0-1.0
multilinguality:
- monolingual
source_datasets:
- extended|oscar
- extended|cc100
task_categories:
- text-generation
task_ids:
- language-modeling
pretty_name: nepalitext-language-model-dataset
---
# Dataset ... | 2,472 | [
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SetFit/TREC-QC | 2022-01-15T22:42:56.000Z | [
"region:us"
] | SetFit | null | null | 0 | 95 | 2022-03-02T23:29:22 | # TREC Question Classification
Question classification in coarse and fine-grained categories.
Source:
[Experimental Data for Question Classification](https://cogcomp.seas.upenn.edu/Data/QA/QC/)
Xin Li, Dan Roth, Learning Question Classifiers. COLING'02, Aug., 2002. | 278 | [
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flax-sentence-embeddings/Gender_Bias_Evaluation_Set | 2021-07-26T04:14:18.000Z | [
"arxiv:1906.00591",
"region:us"
] | flax-sentence-embeddings | null | null | 2 | 95 | 2022-03-02T23:29:22 | **This dataset has been created as part of the Flax/JAX community week for testing the [flax-sentence-embeddings](https://huggingface.co/flax-sentence-embeddings) Sentence Similarity models for Gender Bias but can be used for other use-cases as well related to evaluating Gender Bias.**
The Following Dataset has been c... | 1,493 | [
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ghadeermobasher/CRAFT-Chem | 2022-01-20T22:09:10.000Z | [
"region:us"
] | ghadeermobasher | \ | @article{krallinger2015chemdner,
title={The CHEMDNER corpus of chemicals and drugs and its annotation principles},
author={Krallinger, Martin and Rabal, Obdulia and Leitner, Florian and Vazquez, Miguel and Salgado, David and Lu, Zhiyong and Leaman, Robert and Lu, Yanan and Ji, Donghong and Lowe, Daniel M and others... | 0 | 95 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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sbu_captions | 2023-06-02T20:56:01.000Z | [
"task_categories:image-to-text",
"task_ids:image-captioning",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | null | The SBU Captioned Photo Dataset is a collection of over 1 million images with associated text descriptions extracted from Flicker. | @inproceedings{NIPS2011_5dd9db5e,
author = {Ordonez, Vicente and Kulkarni, Girish and Berg, Tamara},
booktitle = {Advances in Neural Information Processing Systems},
editor = {J. Shawe-Taylor and R. Zemel and P. Bartlett and F. Pereira and K.Q. Weinberger},
pages = {},
publisher = {Curran Associates, Inc.},
title... | 9 | 95 | 2022-04-12T10:41:52 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- image-to-text
task_ids:
- image-captioning
paperswithcode_id: sbu-captions-dataset
pretty_name: SBU Captioned Photo Dat... | 6,967 | [
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batubayk/HU-News | 2023-03-04T22:40:26.000Z | [
"task_categories:summarization",
"task_categories:text-classification",
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:100K<n<1M",
"language:hu",
"region:us"
] | batubayk | null | null | 0 | 95 | 2022-04-18T17:23:27 | ---
task_categories:
- summarization
- text-classification
- text-generation
- text2text-generation
language:
- hu
pretty_name: HU-News
size_categories:
- 100K<n<1M
---
# Citation
If you use the dataset, please cite the paper:
@article{10.1007/s10579-021-09568-y,
year = {2022},
title = {{Abstractive... | 612 | [
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nbroad/mediasum | 2022-10-25T10:40:11.000Z | [
"task_categories:summarization",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-nc-sa-4.0",
"arxiv:2103.06410",
"region:us"
] | nbroad | This large-scale media interview dataset contains 463.6K transcripts with abstractive summaries,
collected from interview transcripts and overview / topic descriptions from NPR and CNN. | @article{zhu2021mediasum,
title={MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization},
author={Zhu, Chenguang and Liu, Yang and Mei, Jie and Zeng, Michael},
journal={arXiv preprint arXiv:2103.06410},
year={2021}
} | 1 | 95 | 2022-07-15T21:42:51 | ---
language:
- en
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
task_categories:
- summarization
---
# MediaSum
## Description
This large-scale media interview dataset contains 463.6K transcripts with abstractive summaries,
collected from interview transcripts and overview / t... | 3,511 | [
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NbAiLab/norwegian-alpaca | 2023-07-25T15:05:00.000Z | [
"task_categories:text-generation",
"language:no",
"language:nb",
"license:cc-by-4.0",
"instruction-finetuning",
"region:us"
] | NbAiLab | null | null | 7 | 95 | 2023-03-20T13:14:23 | ---
license: cc-by-4.0
language:
- 'no'
- nb
tags:
- instruction-finetuning
pretty_name: NB Alpaca Norwegian Bokmål
task_categories:
- text-generation
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: instruction_en
dtype... | 1,148 | [
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0.01479339599609... |
TREC-AToMiC/AToMiC-Texts-v0.2.1 | 2023-05-04T18:58:43.000Z | [
"region:us"
] | TREC-AToMiC | null | null | 2 | 95 | 2023-04-26T16:34:45 | ---
dataset_info:
features:
- name: text_id
dtype: string
- name: page_url
dtype: string
- name: page_title
dtype: string
- name: section_title
dtype: string
- name: context_page_description
dtype: string
- name: context_section_description
dtype: string
- name: media
sequenc... | 767 | [
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... |
edarchimbaud/news-stocks | 2023-11-01T04:38:01.000Z | [
"region:us"
] | edarchimbaud | null | null | 3 | 95 | 2023-05-17T17:23:09 | ---
dataset_info:
features:
- name: symbol
dtype: string
- name: body
dtype: string
- name: publisher
dtype: string
- name: publish_time
dtype: timestamp[ns, tz=GMT]
- name: title
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- name: url
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splits:
- name: train
... | 3,984 | [
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GATE-engine/vggflowers | 2023-06-05T15:12:54.000Z | [
"region:us"
] | GATE-engine | null | null | 0 | 95 | 2023-06-05T15:12:19 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: int64
splits:
- name: train
num_bytes: 452124226.125
num_examples: 5655
- name: validation
num_bytes: 89403717.375
num_examples: 1109
- name: test
num_bytes: 115124265.875
num_examples: 1425
downl... | 538 | [
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pankajmathur/alpaca_orca | 2023-06-26T14:39:11.000Z | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
] | pankajmathur | null | null | 18 | 95 | 2023-06-24T18:20:35 | ---
license: cc-by-nc-sa-4.0
task_categories:
- text-generation
language:
- en
size_categories:
- 10K<n<100K
---
Explain tuned Alpaca dataset ~52K created using approaches from Orca Research Paper.
We leverage all of the 15 system instructions provided in Orca Research Paper. to generate custom datasets, in contrast... | 651 | [
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NischayDnk/bertvsllm_demodatav2 | 2023-07-23T19:40:44.000Z | [
"region:us"
] | NischayDnk | null | null | 0 | 95 | 2023-07-23T19:40:42 | Entry not found | 15 | [
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skadewdl3/recipe-nlg-llama2 | 2023-10-04T07:40:19.000Z | [
"region:us"
] | skadewdl3 | null | null | 0 | 95 | 2023-09-20T07:17:54 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: title
dtype: string
- name: ingredients
dtype: string
- name: directions
dtype: string
- name: link
dtype: string
- name: source
dtype: string
- name: NER
dtype: string
- name: prompt
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splits:
... | 822 | [
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LDJnr/LessWrong-Amplify-Instruct | 2023-09-26T02:34:28.000Z | [
"task_categories:conversational",
"task_categories:question-answering",
"task_categories:text-generation",
"size_categories:n<1K",
"language:en",
"license:apache-2.0",
"Physics",
"Biology",
"Math",
"Chemistry",
"Culture",
"Logic",
"region:us"
] | LDJnr | null | null | 17 | 95 | 2023-09-26T01:42:29 | ---
license: apache-2.0
task_categories:
- conversational
- question-answering
- text-generation
language:
- en
tags:
- Physics
- Biology
- Math
- Chemistry
- Culture
- Logic
pretty_name: LessWrong-Amplify-Instruct
size_categories:
- n<1K
---
## This is the Official LessWrong-Amplify-Instruct dataset. Over 500 multi-t... | 2,394 | [
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0... |
mnoukhov/openai_summarize_comparisons_relabel_pythia7b | 2023-10-04T19:20:46.000Z | [
"region:us"
] | mnoukhov | null | null | 0 | 95 | 2023-10-04T19:20:42 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 157425966
num_... | 652 | [
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Cubpaw/voxelgym_5c_42x42_500 | 2023-10-09T11:26:15.000Z | [
"region:us"
] | Cubpaw | null | null | 0 | 95 | 2023-10-09T11:26:06 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: image
- name: rgb_label
dtype: image
- name: path_label
dtype: image
- name: path_rgb_label
dtype: image
splits:
- name: train
num_bytes: 373246.0
num_examples: 400
- name: validation
num_bytes:... | 579 | [
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