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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
tapaco | 2023-06-08T13:14:46.000Z | [
"task_categories:text2text-generation",
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"size_... | null | A freely available paraphrase corpus for 73 languages extracted from the Tatoeba database. Tatoeba is a crowdsourcing project mainly geared towards language learners. Its aim is to provide example sentences and translations for particular linguistic constructions and words. The paraphrase corpus is created by populatin... | @dataset{scherrer_yves_2020_3707949,
author = {Scherrer, Yves},
title = {{TaPaCo: A Corpus of Sentential Paraphrases for 73 Languages}},
month = mar,
year = 2020,
publisher = {Zenodo},
version = {1.0},
doi = {10.5281/zenodo.3707949},
url = {https://d... | 31 | 3,781 | 2022-03-02T23:29:22 | ---
annotations_creators:
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ms_marco | 2023-04-05T10:10:02.000Z | [
"language:en",
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] | null | Starting with a paper released at NIPS 2016, MS MARCO is a collection of datasets focused on deep learning in search.
The first dataset was a question answering dataset featuring 100,000 real Bing questions and a human generated answer.
Since then we released a 1,000,000 question dataset, a natural langauge generation... | @article{DBLP:journals/corr/NguyenRSGTMD16,
author = {Tri Nguyen and
Mir Rosenberg and
Xia Song and
Jianfeng Gao and
Saurabh Tiwary and
Rangan Majumder and
Li Deng},
title = {{MS} {MARCO:} {A} Human Generated MAchine Re... | 39 | 3,766 | 2022-03-02T23:29:22 | ---
language:
- en
paperswithcode_id: ms-marco
pretty_name: Microsoft Machine Reading Comprehension Dataset
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anton-l/superb_dummy | 2021-12-14T09:39:13.000Z | [
"region:us"
] | anton-l | Self-supervised learning (SSL) has proven vital for advancing research in
natural language processing (NLP) and computer vision (CV). The paradigm
pretrains a shared model on large volumes of unlabeled data and achieves
state-of-the-art (SOTA) for various tasks with minimal adaptation. However, the
speech processing co... | @article{DBLP:journals/corr/abs-2105-01051,
author = {Shu{-}Wen Yang and
Po{-}Han Chi and
Yung{-}Sung Chuang and
Cheng{-}I Jeff Lai and
Kushal Lakhotia and
Yist Y. Lin and
Andy T. Liu and
Jiatong Shi and
... | 0 | 3,760 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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pufanyi/MIMICIT | 2023-07-30T02:43:44.000Z | [
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"language:ja",
"language:fr",
"language:ko",
"language:ar",
"license:mit",
"arxiv:2306.05425",
"region:us"
] | pufanyi | MIMIC-IT offers a diverse and extensive dataset of 2.8M multimodal instruction-response pairs, designed to enhance the performance of Vision-Language Models (VLMs) in real-life scenarios, enabling VLMs to excel in perception, reasoning, and planning while also catering to a multilingual audience. | @article{li2023mimicit,
title={MIMIC-IT: Multi-Modal In-Context Instruction Tuning},
author={Bo Li and Yuanhan Zhang and Liangyu Chen and Jinghao Wang and Fanyi Pu and Jingkang Yang and Chunyuan Li and Ziwei Liu},
year={2023},
eprint={2306.05425},
archivePrefix={arXiv},
primaryClass={cs.CV}
} | 14 | 3,751 | 2023-07-12T07:22:42 | ---
license: mit
language:
- en
- zh
- es
- ja
- fr
- ko
- ar
arxiv: 2306.05425
extra_gated_prompt: |
<h1>MIMIC-IT Dataset Download
Agreement</h1>
<p>S-Lab, Nanyang Technological University (S-Lab) provides access to
the MIMIC-IT Dataset (referred to as the Dataset) under the following
conditions.</p>
<p>By... | 10,789 | [
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sst | 2023-06-01T14:59:56.000Z | [
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"source_datas... | null | The Stanford Sentiment Treebank, the first corpus with fully labeled parse trees that allows for a
complete analysis of the compositional effects of sentiment in language. | @inproceedings{socher-etal-2013-recursive,
title = "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank",
author = "Socher, Richard and Perelygin, Alex and Wu, Jean and
Chuang, Jason and Manning, Christopher D. and Ng, Andrew and Potts, Christopher",
booktitle = "Proceedings ... | 11 | 3,695 | 2022-03-02T23:29:22 | ---
annotations_creators:
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papers... | 6,679 | [
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scene_parse_150 | 2023-01-25T14:43:32.000Z | [
"task_categories:image-segmentation",
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"annotations_creators:crowdsourced",
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"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|ade20k",
"language:en",
"license:b... | null | Scene parsing is to segment and parse an image into different image regions associated with semantic categories, such as sky, road, person, and bed.
MIT Scene Parsing Benchmark (SceneParse150) provides a standard training and evaluation platform for the algorithms of scene parsing.
The data for this benchmark comes fro... | @inproceedings{zhou2017scene,
title={Scene Parsing through ADE20K Dataset},
author={Zhou, Bolei and Zhao, Hang and Puig, Xavier and Fidler, Sanja and Barriuso, Adela and Torralba, Antonio},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
year={2017}
}
@article... | 11 | 3,664 | 2022-03-02T23:29:22 | ---
annotations_creators:
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size_categories:
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source_datasets:
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paperswithcode_id: ade20k
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SetFit/mrpc | 2022-02-28T13:18:30.000Z | [
"region:us"
] | SetFit | null | null | 4 | 3,635 | 2022-03-02T23:29:22 | # Glue MRPC
This dataset is a port of the official [`mrpc` dataset](https://huggingface.co/datasets/glue/viewer/mrpc/train) on the Hub.
Note that the sentence1 and sentence2 columns have been renamed to text1 and text2 respectively.
Also, the test split is not labeled; the label column values are always -1.
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facebook/winoground | 2023-11-02T17:15:41.000Z | [
"task_categories:image-to-text",
"task_categories:text-to-image",
"task_categories:image-classification",
"language:en",
"arxiv:2204.03162",
"region:us"
] | facebook | Winoground is a novel task and dataset for evaluating the ability of vision and language models to conduct visio-linguistic compositional reasoning. Given two images and two captions, the goal is to match them correctly—but crucially, both captions contain a completely identical set of words/morphemes, only in a differ... | @inproceedings{thrush_and_ross2022winoground,
author = {Tristan Thrush and Ryan Jiang and Max Bartolo and Amanpreet Singh and Adina Williams and Douwe Kiela and Candace Ross},
title = {Winoground: Probing vision and language models for visio-linguistic compositionality},
booktitle = {CVPR},
year = 2022,
} | 62 | 3,631 | 2022-03-25T22:27:33 | ---
pretty_name: Winoground
task_categories:
- image-to-text
- text-to-image
- image-classification
extra_gated_prompt: >-
By clicking on “Access repository” below, you also agree that you are using it
solely for research purposes. The full license agreement is available in the
dataset files.
language:
- en
---
#... | 4,122 | [
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wiki_bio | 2022-11-18T22:00:08.000Z | [
"task_categories:table-to-text",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
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"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"arxiv:1603.07771",
"region:us"
] | null | This dataset gathers 728,321 biographies from wikipedia. It aims at evaluating text generation
algorithms. For each article, we provide the first paragraph and the infobox (both tokenized).
For each article, we extracted the first paragraph (text), the infobox (structured data). Each
infobox is encoded as a list of (fi... | @article{DBLP:journals/corr/LebretGA16,
author = {R{\'{e}}mi Lebret and
David Grangier and
Michael Auli},
title = {Generating Text from Structured Data with Application to the Biography
Domain},
journal = {CoRR},
volume = {abs/1603.07771},
year = {... | 11 | 3,628 | 2022-03-02T23:29:22 | ---
annotations_creators:
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multilinguality:
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paperswithcode_id: wikibio
pretty_name: WikiBio
dataset_info:
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shibing624/nli_zh | 2022-10-30T06:30:56.000Z | [
"task_categories:text-classification",
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"task_ids:semantic-similarity-scoring",
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"language_creators:shibing624",
"multilinguality:monolingual",
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"source_datasets:https://gith... | shibing624 | 纯文本数据，格式：（sentence1， sentence2， label）。常见中文语义匹配数据集，包含ATEC、BQ、LCQMC、PAWSX、STS-B共5个任务。 | null | 33 | 3,622 | 2022-03-02T23:29:22 | ---
annotations_creators:
- shibing624
language_creators:
- shibing624
language:
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license:
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source_datasets:
- https://github.com/shibing624/text2vec
- https://github.com/IceFlameWorm/NLP_Datasets/tree/master/ATEC
- http://icrc.hitsz.edu.cn/inf... | 5,360 | [
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lewtun/dog_food | 2022-07-03T05:15:18.000Z | [
"task_categories:image-classification",
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"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | lewtun | null | null | 0 | 3,597 | 2022-06-26T07:50:59 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
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pretty_name: Dog vs Food Dataset
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
---
# Dataset Card ... | 4,329 | [
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codeparrot/apps | 2022-10-20T15:00:15.000Z | [
"task_categories:text-generation",
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"language:code",
"license:mit",
"arxiv:2105.09938",
"arxiv:2203.07814",
"region:us"
] | codeparrot | APPS is a benchmark for Python code generation, it includes 10,000 problems, which range from having simple oneline solutions to being substantial algorithmic challenges, for more details please refer to this paper: https://arxiv.org/pdf/2105.09938.pdf. | @article{hendrycksapps2021,
title={Measuring Coding Challenge Competence With APPS},
author={Dan Hendrycks and Steven Basart and Saurav Kadavath and Mantas Mazeika and Akul Arora and Ethan Guo and Collin Burns and Samir Puranik and Horace He and Dawn Song and Jacob Steinhardt},
journal={NeurIPS},
year={2021}
} | 50 | 3,576 | 2022-06-15T13:20:26 | ---
annotations_creators: []
language_creators:
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language: ["code"]
license:
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multilinguality:
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pretty_name: APPS
size_categories:
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task_categories:
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---
# APPS Dataset
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newsgroup | 2023-04-05T13:35:49.000Z | [
"task_categories:text-classification",
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"annotations_creators:found",
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"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | null | The 20 Newsgroups data set is a collection of approximately 20,000 newsgroup documents, partitioned (nearly) evenly across
20 different newsgroups. The 20 newsgroups collection has become a popular data set for experiments in text applications of
machine learning techniques, such as text classification and text cluster... | @inproceedings{Lang95,
author = {Ken Lang},
title = {Newsweeder: Learning to filter netnews}
year = {1995}
booktitle = {Proceedings of the Twelfth International Conference on Machine Learning}
pages = {331-339}
} | 7 | 3,538 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
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pretty_name: 20 Newsgroups
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paperswithcode_id: 20-newsgroup... | 20,899 | [
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codeparrot/github-code | 2022-10-20T15:01:14.000Z | [
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"language_creators:expert-generated",
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"language:code",
"license:other",
"region:us"
] | codeparrot | The GitHub Code dataest consists of 115M code files from GitHub in 32 programming languages with 60 extensions totalling in 1TB of text data. The dataset was created from the GitHub dataset on BiqQuery. | null | 175 | 3,528 | 2022-03-02T23:29:22 | ---
annotations_creators: []
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language:
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license:
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- multilingual
pretty_name: github-code
size_categories:
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task_categories:
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task_ids:
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---
# GitHub Code Dataset
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GBaker/MedQA-USMLE-4-options | 2023-01-24T19:18:09.000Z | [
"language:en",
"license:cc-by-4.0",
"region:us"
] | GBaker | null | null | 18 | 3,504 | 2023-01-24T19:08:56 | ---
license: cc-by-4.0
language:
- en
---
Original dataset introduced by Jin et al. in [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://paperswithcode.com/paper/what-disease-does-this-patient-have-a-large)
<h4>Citation information:</h4>
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vicgalle/alpaca-gpt4 | 2023-09-26T18:51:15.000Z | [
"task_categories:text-generation",
"task_categories:conversational",
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-nc-4.0",
"gpt4",
"alpaca",
"instruction-finetuning",
"arxiv:2304.03277",
"region:us"
] | vicgalle | null | null | 107 | 3,482 | 2023-04-07T16:22:59 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 88566301
num_examples: 52002
download_size: 48393562
dataset_size: 88566301
task_categories:
- text... | 3,373 | [
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0.0203399658203... |
InstaDeepAI/human_reference_genome | 2023-04-20T13:37:22.000Z | [
"DNA",
"Genomics",
"Nucleotide",
"region:us"
] | InstaDeepAI | Genome Reference Consortium Human Build 38 patch release 14 (GRCh38.p14)
filtered and split into chunks. | @article{o2016reference,
title={Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation},
author={O'Leary, Nuala A and Wright, Mathew W and Brister, J Rodney and Ciufo, Stacy and Haddad, Diana and McVeigh, Rich and Rajput, Bhanu and Robbertse, Barbara and Smith-W... | 0 | 3,472 | 2023-04-02T15:17:04 | ---
tags:
- DNA
- Genomics
- Nucleotide
pretty_name: Human Reference Genome
---
# Dataset Card for the human reference genome
## Dataset Description
- **Repository:** [Nucleotide Transformer](https://github.com/instadeepai/nucleotide-transformer)
- **Paper:** [The Nucleotide Transformer: Building and Evaluating Robus... | 10,443 | [
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katanaml-org/invoices-donut-data-v1 | 2023-05-09T07:05:11.000Z | [
"task_categories:feature-extraction",
"size_categories:n<1K",
"language:en",
"license:mit",
"region:us"
] | katanaml-org | null | null | 5 | 3,409 | 2023-03-08T20:44:29 | ---
dataset_info:
features:
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dtype: string
splits:
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num_examples: 425
- name: test
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num_examples: 26
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num_examples: 50
download_size: ... | 1,039 | [
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hf-internal-testing/dummy_image_class_data | 2023-02-08T12:28:38.000Z | [
"region:us"
] | hf-internal-testing | null | null | 0 | 3,376 | 2023-02-08T12:28:33 | ---
dataset_info:
features:
- name: image
dtype: image
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dtype:
class_label:
names:
'0': resize
splits:
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num_bytes: 555953.0
num_examples: 6
download_size: 556964
dataset_size: 555953.0
---
# Dataset Card for "dummy_image_class_data"
[More ... | 445 | [
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SetFit/qqp | 2022-02-28T11:10:11.000Z | [
"region:us"
] | SetFit | null | null | 4 | 3,283 | 2022-03-02T23:29:22 | # Glue QQP
This dataset is a port of the official [`qqp` dataset](https://huggingface.co/datasets/glue/viewer/qqp/train) on the Hub.
Note that the question1 and question2 columns have been renamed to text1 and text2 respectively.
Also, the test split is not labeled; the label column values are always -1.
| 313 | [
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hails/mmlu_no_train | 2023-11-01T17:06:40.000Z | [
"task_categories:question-answering",
"language:en",
"license:mit",
"region:us"
] | hails | This is a massive multitask test consisting of multiple-choice questions from various branches of knowledge, covering 57 tasks including elementary mathematics, US history, computer science, law, and more. | @article{hendryckstest2021,
title={Measuring Massive Multitask Language Understanding},
author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt},
journal={Proceedings of the International Conference on Learning Representations (ICLR)}... | 0 | 3,247 | 2023-10-31T17:25:54 | ---
license: mit
task_categories:
- question-answering
language:
- en
pretty_name: MMLU loader with no auxiliary train set
---
This dataset contains a copy of the `cais/mmlu` HF dataset but without the `auxiliary_train` split that takes a long time to generate again each time when loading multiple subsets of the datase... | 420 | [
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Salesforce/dialogstudio | 2023-10-05T22:34:55.000Z | [
"task_categories:conversational",
"task_categories:question-answering",
"task_categories:summarization",
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"arxiv:2307.10172",
"region:us"
] | Salesforce | null | @misc{zhang2023dialogstudio,
title={DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI},
author={Jianguo Zhang and Kun Qian and Zhiwei Liu and Shelby Heinecke and Rui Meng and Ye Liu and Zhou Yu and and Huan Wang and Silvio Savarese and Caiming Xiong},
year={202... | 148 | 3,243 | 2023-07-16T23:15:44 | ---
extra_gated_heading: "Acknowledge to follow corresponding dataset licenses to access the repository"
extra_gated_button_content: "Agree and access repository"
license: apache-2.0
task_categories:
- conversational
- question-answering
- summarization
- text-generation
language:
- en
pretty_name: Dialog Studio
---
... | 6,593 | [
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mkqa | 2023-01-25T14:40:34.000Z | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:multilingual",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:extended|natural_questions",
"source_datasets:original",
"l... | null | We introduce MKQA, an open-domain question answering evaluation set comprising 10k question-answer pairs sampled from the Google Natural Questions dataset, aligned across 26 typologically diverse languages (260k question-answer pairs in total). For each query we collected new passage-independent answers. These queries ... | @misc{mkqa,
title = {MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering},
author = {Shayne Longpre and Yi Lu and Joachim Daiber},
year = {2020},
URL = {https://arxiv.org/pdf/2007.15207.pdf}
} | 13 | 3,239 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- ar
- da
- de
- en
- es
- fi
- fr
- he
- hu
- it
- ja
- km
- ko
- ms
- nl
- 'no'
- pl
- pt
- ru
- sv
- th
- tr
- vi
- zh
license:
- cc-by-3.0
multilinguality:
- multilingual
- translation
size_categories:
- 10K<n<100K
source_datasets:
- exte... | 19,895 | [
[
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go_emotions | 2023-06-01T14:59:54.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:multi-label-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"source_datasets:original",
... | null | The GoEmotions dataset contains 58k carefully curated Reddit comments labeled for 27 emotion categories or Neutral.
The emotion categories are admiration, amusement, anger, annoyance, approval, caring, confusion, curiosity, desire,
disappointment, disapproval, disgust, embarrassment, excitement, fear, gratitude, grief,... | @inproceedings{demszky2020goemotions,
author = {Demszky, Dorottya and Movshovitz-Attias, Dana and Ko, Jeongwoo and Cowen, Alan and Nemade, Gaurav and Ravi, Sujith},
booktitle = {58th Annual Meeting of the Association for Computational Linguistics (ACL)},
title = {{GoEmotions: A Dataset of Fine-Grained Emotions}},
y... | 60 | 3,221 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
- multi-label-classification
papersw... | 9,116 | [
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0.013511657... |
gsgoncalves/roberta_pretrain | 2023-05-02T18:40:25.000Z | [
"task_categories:fill-mask",
"task_categories:text-generation",
"size_categories:10M<n<100M",
"language:en",
"license:unknown",
"region:us"
] | gsgoncalves | null | null | 3 | 3,199 | 2023-05-02T18:13:15 | ---
license: unknown
task_categories:
- fill-mask
- text-generation
language:
- en
pretty_name: RoBERTa Pretrain Dataset
size_categories:
- 10M<n<100M
---
# Dataset Card for RoBERTa Pretrain
### Dataset Summary
This is the concatenation of the datasets used to Pretrain RoBERTa.
The dataset is not shuffled and contain... | 852 | [
[
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m1guelpf/nouns | 2022-09-25T06:18:40.000Z | [
"task_categories:text-to-image",
"annotations_creators:machine-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:en",
"license:cc0-1.0",
"region:us"
] | m1guelpf | null | null | 7 | 3,191 | 2022-09-25T03:30:09 | ---
license: cc0-1.0
annotations_creators:
- machine-generated
language:
- en
language_creators:
- other
multilinguality:
- monolingual
pretty_name: 'Nouns auto-captioned'
size_categories:
- 10K<n<100K
tags: []
task_categories:
- text-to-image
task_ids: []
---
# Dataset Card for Nouns auto-captioned
_Dataset used to ... | 921 | [
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dell-research-harvard/AmericanStories | 2023-09-08T18:33:32.000Z | [
"task_categories:text-classification",
"task_categories:text-generation",
"task_categories:text-retrieval",
"task_categories:summarization",
"task_categories:question-answering",
"size_categories:100M<n<1B",
"language:en",
"license:cc-by-4.0",
"social science",
"economics",
"news",
"newspaper"... | dell-research-harvard | American Stories offers high-quality structured data from historical newspapers suitable for pre-training large language models to enhance the understanding of historical English and world knowledge. It can also be integrated into external databases of retrieval-augmented language models, enabling broader access to his... | Coming Soon | 75 | 3,177 | 2023-06-12T19:42:34 | ---
license: cc-by-4.0
task_categories:
- text-classification
- text-generation
- text-retrieval
- summarization
- question-answering
language:
- en
tags:
- social science
- economics
- news
- newspaper
- large language modeling
- nlp
- lam
pretty_name: AmericanStories
size_categories:
- 100M<n<1B
---
# Dataset Card fo... | 8,019 | [
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Hello-SimpleAI/HC3 | 2023-01-21T13:10:10.000Z | [
"task_categories:text-classification",
"task_categories:question-answering",
"task_categories:sentence-similarity",
"task_categories:zero-shot-classification",
"size_categories:10K<n<100K",
"language:en",
"language:zh",
"license:cc-by-sa-4.0",
"ChatGPT",
"SimpleAI",
"Detection",
"OOD",
"arxi... | Hello-SimpleAI | Human ChatGPT Comparison Corpus (HC3) | \ | 121 | 3,175 | 2023-01-18T14:01:20 | ---
task_categories:
- text-classification
- question-answering
- sentence-similarity
- zero-shot-classification
language:
- en
- zh
tags:
- ChatGPT
- SimpleAI
- Detection
- OOD
size_categories:
- 10K<n<100K
license: cc-by-sa-4.0
---
# Human ChatGPT Comparison Corpus (HC3)
We propose the first human-ChatGPT compariso... | 1,484 | [
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bigcode/commitpackft | 2023-08-20T07:13:43.000Z | [
"language:code",
"license:mit",
"arxiv:2308.07124",
"region:us"
] | bigcode | CommitPackFT is is a 2GB filtered version of CommitPack to contain only high-quality commit messages that resemble natural language instructions. | @article{muennighoff2023octopack,
title={OctoPack: Instruction Tuning Code Large Language Models},
author={Niklas Muennighoff and Qian Liu and Armel Zebaze and Qinkai Zheng and Binyuan Hui and Terry Yue Zhuo and Swayam Singh and Xiangru Tang and Leandro von Werra and Shayne Longpre},
journal={arXiv p... | 19 | 3,174 | 2023-06-27T06:54:48 | ---
license: mit
pretty_name: CommitPackFT
language:
- code
---
# Dataset Card for CommitPackFT
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-de... | 17,282 | [
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allenai/real-toxicity-prompts | 2022-09-30T14:23:19.000Z | [
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"arxiv:2009.11462",
"doi:10.57967/hf/0002",
"region:us"
] | allenai | null | null | 24 | 3,165 | 2022-08-17T20:30:46 | ---
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- image-generation
task_ids:
- text-generation
pretty_name: Real Toxicity Prompts
---
# Dataset Card for Real Toxicity Prompts
## Table of Contents
- [Table of Contents](#t... | 4,216 | [
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EleutherAI/arithmetic | 2023-03-09T17:58:16.000Z | [
"arxiv:2005.14165",
"region:us"
] | EleutherAI | A small battery of 10 tests that involve asking language models a simple arithmetic
problem in natural language. | @inproceedings{NEURIPS2020_1457c0d6,
author = {Brown, Tom and Mann, Benjamin and Ryder, Nick and Subbiah, Melanie and Kaplan, Jared D and Dhariwal, Prafulla and Neelakantan, Arvind and Shyam, Pranav and Sastry, Girish and Askell, Amanda and Agarwal, Sandhini and Herbert-Voss, Ariel and Krueger, Gretchen and Henigha... | 2 | 3,141 | 2023-03-08T12:22:46 | ### Dataset Summary
A small battery of 10 tests that involve asking language models a simple arithmetic problem in natural language.
### Languages
English
### Source Data
Obtained from [https://github.com/openai/gpt-3/tree/master/data](https://github.com/openai/gpt-3/tree/master/data)
### Citation
```
@article{bro... | 1,068 | [
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cppe-5 | 2023-03-06T18:48:26.000Z | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"medical-personal-protective-equipment-detection",
"arxiv:2112.09569",
"reg... | null | CPPE - 5 (Medical Personal Protective Equipment) is a new challenging dataset with the goal
to allow the study of subordinate categorization of medical personal protective equipments,
which is not possible with other popular data sets that focus on broad level categories. | @misc{dagli2021cppe5,
title={CPPE-5: Medical Personal Protective Equipment Dataset},
author={Rishit Dagli and Ali Mustufa Shaikh},
year={2021},
eprint={2112.09569},
archivePrefix={arXiv},
primaryClass={cs.CV}
} | 7 | 3,119 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- object-detection
task_ids: []
paperswithcode_id: cppe-5
pretty_name: CPPE - 5
tags:
- medical-personal-protectiv... | 11,017 | [
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inria-soda/tabular-benchmark | 2023-09-04T16:37:39.000Z | [
"task_categories:tabular-classification",
"task_categories:tabular-regression",
"region:us"
] | inria-soda | null | null | 14 | 3,088 | 2022-10-27T12:34:58 |
---
annotations_creators: []
license: []
pretty_name: tabular_benchmark
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- tabular-regression
configs:
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data_files: clf_cat/albert.csv
- config_name: clf_cat_compas-two-years
data_files: clf_cat/compas-two-years.csv
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news_commentary | 2022-11-03T16:47:41.000Z | [
"task_categories:translation",
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"multilinguality:multilingual",
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"language:ar",
"language:cs",
"language:de",
"language:en",
"language:es",
"language:fr",
"language:it",
"langua... | null | A parallel corpus of News Commentaries provided by WMT for training SMT. The source is taken from CASMACAT: http://www.casmacat.eu/corpus/news-commentary.html
12 languages, 63 bitexts
total number of files: 61,928
total number of tokens: 49.66M
total number of sentence fragments: 1.93M | @InProceedings{TIEDEMANN12.463,
author = {J�rg Tiedemann},
title = {Parallel Data, Tools and Interfaces in OPUS},
booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
year = {2012},
month = {may},
date = {23-25},
address = {Istanbul, Turkey},
ed... | 21 | 3,071 | 2022-03-02T23:29:22 | ---
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- zh
license:
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paperswithcode_id: null
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wentingzhao/one-million-instructions | 2023-09-16T03:03:51.000Z | [
"region:us"
] | wentingzhao | null | null | 0 | 3,060 | 2023-09-16T03:03:41 | ---
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cc_news | 2023-06-12T06:42:15.000Z | [
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"language:en",... | null | CC-News containing news articles from news sites all over the world The data is available on AWS S3 in the Common Crawl bucket at /crawl-data/CC-NEWS/. This version of the dataset has 708241 articles. It represents a small portion of English language subset of the CC-News dataset created using news-please(Hamborg et a... | @InProceedings{Hamborg2017,
author = {Hamborg, Felix and Meuschke, Norman and Breitinger, Corinna and Gipp, Bela},
title = {news-please: A Generic News Crawler and Extractor},
year = {2017},
booktitle = {Proceedings of the 15th International Symposium of Information Science},
location = {Ber... | 36 | 3,059 | 2022-03-02T23:29:22 | ---
pretty_name: CC-News
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nielsr/funsd | 2021-07-27T07:59:20.000Z | [
"region:us"
] | nielsr | https://guillaumejaume.github.io/FUNSD/ | @article{Jaume2019FUNSDAD,
title={FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents},
author={Guillaume Jaume and H. K. Ekenel and J. Thiran},
journal={2019 International Conference on Document Analysis and Recognition Workshops (ICDARW)},
year={2019},
volume={2},
pages={1-6}
} | 9 | 3,058 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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mteb/amazon_massive_scenario | 2022-05-19T08:00:44.000Z | [
"region:us"
] | mteb | MASSIVE is a parallel dataset of > 1M utterances across 51 languages with annotations
for the Natural Language Understanding tasks of intent prediction and slot annotation.
Utterances span 60 intents and include 55 slot types. MASSIVE was created by localizing
the SLURP dataset, composed... | null | 0 | 2,994 | 2022-05-15T20:30:23 | Entry not found | 15 | [
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timit_asr | 2022-10-28T16:41:41.000Z | [
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"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:other",
"region:us"
] | null | The TIMIT corpus of reading speech has been developed to provide speech data for acoustic-phonetic research studies
and for the evaluation of automatic speech recognition systems.
TIMIT contains high quality recordings of 630 individuals/speakers with 8 different American English dialects,
with each individual reading... | @inproceedings{
title={TIMIT Acoustic-Phonetic Continuous Speech Corpus},
author={Garofolo, John S., et al},
ldc_catalog_no={LDC93S1},
DOI={https://doi.org/10.35111/17gk-bn40},
journal={Linguistic Data Consortium, Philadelphia},
year={1983}
} | 15 | 2,992 | 2022-03-02T23:29:22 | ---
pretty_name: TIMIT
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language:
- en
license:
- other
license_details: "LDC-User-Agreement-for-Non-Members"
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multi_eurlex | 2023-06-14T13:34:30.000Z | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"task_ids:topic-classification",
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"multilinguality:multilingual",
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"language:bg",
"language:cs",
"languag... | null | MultiEURLEX comprises 65k EU laws in 23 official EU languages (some low-ish resource).
Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU.
As with the English EURLEX, the goal is to predict the relevant EUROVOC concepts (labels);
this is multi-label classification task (given ... | @InProceedings{chalkidis-etal-2021-multieurlex,
author = {Chalkidis, Ilias
and Fergadiotis, Manos
and Androutsopoulos, Ion},
title = {MultiEURLEX -- A multi-lingual and multi-label legal document
classification dataset for zero-shot cross-lingual transfer},
booktitle... | 24 | 2,989 | 2022-03-02T23:29:22 | ---
annotations_creators:
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- pl
- pt
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license:
- cc-by-sa-4.0
multilinguality:
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size_categories:
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huggingface-course/codeparrot-ds-train | 2021-09-13T14:33:48.000Z | [
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] | huggingface-course | null | null | 4 | 2,973 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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lmsys/lmsys-chat-1m | 2023-10-04T17:40:32.000Z | [
"task_categories:conversational",
"size_categories:1M<n<10M",
"arxiv:2309.11998",
"region:us"
] | lmsys | null | null | 260 | 2,961 | 2023-09-20T06:33:44 | ---
size_categories:
- 1M<n<10M
task_categories:
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extra_gated_prompt: You agree to the [LMSYS-Chat-1M Dataset License Agreement](https://huggingface.co/datasets/lmsys/lmsys-chat-1m#lmsys-chat-1m-dataset-license-agreement).
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Name: text
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huggingface-course/codeparrot-ds-valid | 2021-09-13T14:24:27.000Z | [
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acronym_identification | 2023-01-25T14:18:28.000Z | [
"task_categories:token-classification",
"annotations_creators:expert-generated",
"language_creators:found",
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"source_datasets:original",
"language:en",
"license:mit",
"acronym-identification",
"arxiv:2010.14678",
"region:us"
] | null | Acronym identification training and development sets for the acronym identification task at SDU@AAAI-21. | @inproceedings{veyseh-et-al-2020-what,
title={{What Does This Acronym Mean? Introducing a New Dataset for Acronym Identification and Disambiguation}},
author={Amir Pouran Ben Veyseh and Franck Dernoncourt and Quan Hung Tran and Thien Huu Nguyen},
year={2020},
booktitle={Proceedings of COLING},
link={http... | 17 | 2,938 | 2022-03-02T23:29:22 | ---
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paperswithcode_id: acronym-identification
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yuvalkirstain/pickapic_v1 | 2023-05-05T15:00:30.000Z | [
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"arxiv:2304.05977",
"arxiv:2210.03927",
"arxiv:2210.08402",
"region:us"
] | yuvalkirstain | null | null | 17 | 2,937 | 2023-04-16T05:26:09 | ---
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mkshing/xlsum_ja | 2023-06-20T23:28:48.000Z | [
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"language:ja",
"license:cc-by-nc-sa-4.0",
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"region:us"
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license: cc-by-nc-sa-4.0
task_categories:
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language:
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---
This is the filtered Japanese subset of [XL-Sum](https://huggingface.co/datasets/csebuetnlp/xlsum) followed by [PaLM 2](https://arxiv.org/abs/2305.10403)
**filters**
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banking77 | 2023-04-17T13:46:23.000Z | [
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"li... | null | BANKING77 dataset provides a very fine-grained set of intents in a banking domain.
It comprises 13,083 customer service queries labeled with 77 intents.
It focuses on fine-grained single-domain intent detection. | null | 26 | 2,887 | 2022-03-02T23:29:22 | ---
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clue | 2023-05-25T06:34:47.000Z | [
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"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingu... | null | CLUE, A Chinese Language Understanding Evaluation Benchmark
(https://www.cluebenchmarks.com/) is a collection of resources for training,
evaluating, and analyzing Chinese language understanding systems. | @misc{xu2020clue,
title={CLUE: A Chinese Language Understanding Evaluation Benchmark},
author={Liang Xu and Xuanwei Zhang and Lu Li and Hai Hu and Chenjie Cao and Weitang Liu and Junyi Li and Yudong Li and Kai Sun and Yechen Xu and Yiming Cui and Cong Yu and Qianqian Dong and Yin Tian and Dian Yu and Bo Shi and... | 27 | 2,864 | 2022-03-02T23:29:22 | ---
annotations_creators:
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Muennighoff/xP3x | 2023-09-22T06:27:32.000Z | [
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"language:bn",
"language:br",
"language:bs",
"langu... | Muennighoff | A multilingual collection of Winograd Schemas in six languages that can be used for evaluation of cross-lingual commonsense reasoning capabilities. | @article{muennighoff2022crosslingual,
title={Crosslingual generalization through multitask finetuning},
author={Muennighoff, Niklas and Wang, Thomas and Sutawika, Lintang and Roberts, Adam and Biderman, Stella and Scao, Teven Le and Bari, M Saiful and Shen, Sheng and Yong, Zheng-Xin and Schoelkopf, Hailey and other... | 7 | 2,863 | 2023-05-21T06:38:52 | ---
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FanFan/sentiment-amazon-clean | 2022-03-09T17:12:19.000Z | [
"region:us"
] | FanFan | null | null | 0 | 2,844 | 2022-03-09T17:11:36 | Entry not found | 15 | [
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opus_infopankki | 2023-06-01T14:59:57.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ar",
"language:en",
"language:es",
"language:et",
"language:fa",
"language:fi",
"language:fr",
"langua... | null | A parallel corpus of 12 languages, 66 bitexts. | @InProceedings{TIEDEMANN12.463,
author = {J�rg Tiedemann},
title = {Parallel Data, Tools and Interfaces in OPUS},
booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
year = {2012},
month = {may},
date = {23-25},
address = {Istanbul, Turkey},
ed... | 1 | 2,841 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- ar
- en
- es
- et
- fa
- fi
- fr
- ru
- so
- sv
- tr
- zh
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name:... | 20,673 | [
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gsarti/wmt_vat | 2022-10-27T08:37:41.000Z | [
"task_categories:text-generation",
"task_categories:translation",
"annotations_creators:found",
"language_creators:expert-generated",
"multilinguality:multilingual",
"multilinguality:translation",
"size_categories:unknown",
"source_datasets:extended|wmt16",
"source_datasets:extended|wmt17",
"sourc... | gsarti | The Variance-Aware Machine Translation corpus contains 70 small and discriminative test sets for machine translation (MT)
evaluation called variance-aware test sets (VAT), covering 35 translation directions from WMT16 to WMT20 competitions.
VAT is automatically created by a novel variance-aware filtering method that ... | @inproceedings{
zhan2021varianceaware,
title={Variance-Aware Machine Translation Test Sets},
author={Runzhe Zhan and Xuebo Liu and Derek F. Wong and Lidia S. Chao},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems, Datasets and Benchmarks Track},
year={2021},
url={http... | 8 | 2,838 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- expert-generated
language:
- cs
- de
- en
- et
- fi
- fr
- gu
- iu
- ja
- kk
- km
- lt
- lv
- pl
- ps
- ro
- ru
- ta
- tr
- zh
license:
- unknown
multilinguality:
- multilingual
- translation
size_categories:
- unknown
source_datasets:
- extended|wmt16
- extended|w... | 11,185 | [
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indic_glue | 2023-06-09T13:57:14.000Z | [
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:multiple-choice",
"task_ids:topic-classification",
"task_ids:natural-language-inference",
"task_ids:sentiment-analysis",
"task_ids:semantic-similarity-scoring",
"task_ids:named-entity-recognition",
"task_... | null | IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide
variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te. | @inproceedings{kakwani2020indicnlpsuite,
title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},
author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pra... | 4 | 2,805 | 2022-03-02T23:29:22 | ---
annotations_creators:
- other
language_creators:
- found
language:
- as
- bn
- en
- gu
- hi
- kn
- ml
- mr
- or
- pa
- ta
- te
license:
- other
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|other
task_categories:
- text-classification
- token-classification
- multiple-choi... | 39,513 | [
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cc100 | 2023-06-01T14:59:56.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10M<n<100M",
"size_categories:1M<n<10M",
"source_data... | null | This corpus is an attempt to recreate the dataset used for training XLM-R. This corpus comprises of monolingual data for 100+ languages and also includes data for romanized languages (indicated by *_rom). This was constructed using the urls and paragraph indices provided by the CC-Net repository by processing January-D... | @inproceedings{conneau-etal-2020-unsupervised,
title = "Unsupervised Cross-lingual Representation Learning at Scale",
author = "Conneau, Alexis and
Khandelwal, Kartikay and
Goyal, Naman and
Chaudhary, Vishrav and
Wenzek, Guillaume and
Guzm{'a}n, Francisco and
Grave, Edo... | 35 | 2,804 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- af
- am
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- en
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- es
- et
- eu
- fa
- ff
- fi
- fr
- fy
- ga
- gd
- gl
- gn
- gu
- ha
- he
- hi
- hr
- ht
- hu
- hy
- id
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- it
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- jv
- ka
- kk
- km
- kn
-... | 9,521 | [
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0.02191162109375,... |
superb | 2023-01-25T14:45:01.000Z | [
"task_categories:automatic-speech-recognition",
"task_categories:audio-classification",
"task_ids:keyword-spotting",
"task_ids:speaker-identification",
"task_ids:audio-intent-classification",
"task_ids:audio-emotion-recognition",
"annotations_creators:other",
"language_creators:other",
"multilingual... | null | Self-supervised learning (SSL) has proven vital for advancing research in
natural language processing (NLP) and computer vision (CV). The paradigm
pretrains a shared model on large volumes of unlabeled data and achieves
state-of-the-art (SOTA) for various tasks with minimal adaptation. However, the
speech processing co... | @article{DBLP:journals/corr/abs-2105-01051,
author = {Shu{-}Wen Yang and
Po{-}Han Chi and
Yung{-}Sung Chuang and
Cheng{-}I Jeff Lai and
Kushal Lakhotia and
Yist Y. Lin and
Andy T. Liu and
Jiatong Shi and
... | 20 | 2,801 | 2022-03-02T23:29:22 | ---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
- extended|librispeech_asr
- extended|other-librimix
- extended|other-speech_commands
task_categories:
- automatic-speech-recognition
- aud... | 57,085 | [
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lmqg/qg_squad | 2022-12-02T18:51:10.000Z | [
"task_categories:text-generation",
"task_ids:language-modeling",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:squad",
"language:en",
"license:cc-by-4.0",
"question-generation",
"arxiv:2210.03992",
"arxiv:1705.00106",
"region:us"
] | lmqg | [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) evaluation set for the question generation (QG) models. The split
of test and development set follows the ["Neural Question Generation"](https://arxiv.org/abs/1705.00106) work and is
compatible with the [leader board](https://paperswithcode.com/sota/question-genera... | @inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Nat... | 5 | 2,775 | 2022-03-02T23:29:22 | ---
license: cc-by-4.0
pretty_name: SQuAD for question generation
language: en
multilinguality: monolingual
size_categories: 10K<n<100K
source_datasets: squad
task_categories:
- text-generation
task_ids:
- language-modeling
tags:
- question-generation
---
# Dataset Card for "lmqg/qg_squad"
## Dataset Description
- **... | 4,894 | [
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wiki40b | 2023-04-05T13:43:07.000Z | [
"language:en",
"region:us"
] | null | Clean-up text for 40+ Wikipedia languages editions of pages
correspond to entities. The datasets have train/dev/test splits per language.
The dataset is cleaned up by page filtering to remove disambiguation pages,
redirect pages, deleted pages, and non-entity pages. Each example contains the
wikidata id of the entity, ... | 9 | 2,774 | 2022-03-02T23:29:22 | ---
language:
- en
paperswithcode_id: wiki-40b
pretty_name: Wiki-40B
dataset_info:
features:
- name: wikidata_id
dtype: string
- name: text
dtype: string
- name: version_id
dtype: string
config_name: en
splits:
- name: train
num_bytes: 9423623904
num_examples: 2926536
- name: validat... | 5,866 | [
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0.... | |
xcsr | 2022-11-03T16:46:53.000Z | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:machine-generated",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:extended|codah",
"source_datasets:extended|c... | null | To evaluate multi-lingual language models (ML-LMs) for commonsense reasoning in a cross-lingual zero-shot transfer setting (X-CSR), i.e., training in English and test in other languages, we create two benchmark datasets, namely X-CSQA and X-CODAH. Specifically, we automatically translate the original CSQA and CODAH dat... | # X-CSR
@inproceedings{lin-etal-2021-common,
title = "Common Sense Beyond {E}nglish: Evaluating and Improving Multilingual Language Models for Commonsense Reasoning",
author = "Lin, Bill Yuchen and
Lee, Seyeon and
Qiao, Xiaoyang and
Ren, Xiang",
booktitle = "Proceedings of the 59th Annu... | 4 | 2,765 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- machine-generated
language:
- ar
- de
- en
- es
- fr
- hi
- it
- ja
- nl
- pl
- pt
- ru
- sw
- ur
- vi
- zh
license:
- mit
multilinguality:
- multilingual
pretty_name: X-CSR
size_categories:
- 1K<n<10K
source_datasets:
- extended|codah
- exten... | 26,119 | [
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... |
hf-internal-testing/fixtures_ade20k | 2021-11-09T10:26:23.000Z | [
"region:us"
] | hf-internal-testing | \\n | \\n | 0 | 2,745 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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0.0379028... |
DKYoon/SlimPajama-6B | 2023-08-21T16:54:47.000Z | [
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:en",
"region:us"
] | DKYoon | null | null | 5 | 2,697 | 2023-08-21T15:25:52 | ---
language:
- en
size_categories:
- 1M<n<10M
task_categories:
- text-generation
pretty_name: SlimPajama-6B
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: t... | 2,341 | [
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emozilla/pg19-test | 2023-08-08T13:07:17.000Z | [
"region:us"
] | emozilla | null | null | 0 | 2,693 | 2023-08-08T13:07:09 | ---
dataset_info:
features:
- name: short_book_title
dtype: string
- name: publication_date
dtype: int32
- name: url
dtype: string
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dtype: string
splits:
- name: test
num_bytes: 40482852
num_examples: 100
download_size: 24874679
dataset_size: 40482852
---
# Dataset ... | 473 | [
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HuggingFaceH4/ultrafeedback_binarized | 2023-10-27T08:54:46.000Z | [
"task_categories:conversational",
"task_categories:text-generation",
"language:en",
"license:mit",
"arxiv:2310.16944",
"region:us"
] | HuggingFaceH4 | null | null | 26 | 2,670 | 2023-10-24T08:53:19 | ---
language:
- en
license: mit
task_categories:
- conversational
- text-generation
pretty_name: UltraFeedback Binarized
configs:
- config_name: default
data_files:
- split: train_sft
path: data/train_sft-*
- split: test_sft
path: data/test_sft-*
- split: train_gen
path: data/train_gen-*
- split: ... | 5,970 | [
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mteb/amazon_counterfactual | 2022-09-27T19:10:37.000Z | [
"language:de",
"language:en",
"language:ja",
"arxiv:2104.06893",
"region:us"
] | mteb | The dataset contains sentences from Amazon customer reviews (sampled from Amazon product review dataset) annotated for counterfactual detection (CFD) binary classification. Counterfactual statements describe events that did not or cannot take place. Counterfactual statements may be identified as statements of the form ... | @misc{oneill2021i,
title={I Wish I Would Have Loved This One, But I Didn't -- A Multilingual Dataset for Counterfactual Detection in Product Reviews},
author={James O'Neill and Polina Rozenshtein and Ryuichi Kiryo and Motoko Kubota and Danushka Bollegala},
year={2021},
eprint={2104.06893},
... | 1 | 2,669 | 2022-05-26T10:48:56 | ---
language:
- de
- en
- ja
---
# Amazon Multilingual Counterfactual Dataset
The dataset contains sentences from Amazon customer reviews (sampled from Amazon product review dataset) annotated for counterfactual detection (CFD) binary classification. Counterfactual statements describe events that did not or cannot t... | 1,601 | [
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openslr | 2023-06-01T14:59:55.000Z | [
"task_categories:automatic-speech-recognition",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:af",
"language:bn",
"language:ca",
"language:en",
"language:es",
"language:eu",
"language... | null | OpenSLR is a site devoted to hosting speech and language resources, such as training corpora for speech recognition,
and software related to speech recognition. We intend to be a convenient place for anyone to put resources that
they have created, so that they can be downloaded publicly. | SLR32:
@inproceedings{van-niekerk-etal-2017,
title = {{Rapid development of TTS corpora for four South African languages}},
author = {Daniel van Niekerk and Charl van Heerden and Marelie Davel and Neil Kleynhans and Oddur Kjartansson
and Martin Jansche and Linne Ha},
booktitle = {Proc. Interspeech 2017}... | 12 | 2,642 | 2022-03-02T23:29:22 | ---
pretty_name: OpenSLR
annotations_creators:
- found
language_creators:
- found
language:
- af
- bn
- ca
- en
- es
- eu
- gl
- gu
- jv
- km
- kn
- ml
- mr
- my
- ne
- si
- st
- su
- ta
- te
- tn
- ve
- xh
- yo
language_bcp47:
- en-GB
- en-IE
- en-NG
- es-CL
- es-CO
- es-PE
- es-PR
license:
- cc-by-sa-4.0
multilingual... | 42,920 | [
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... |
BeIR/arguana-qrels | 2022-10-23T06:06:46.000Z | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | BeIR | null | null | 0 | 2,627 | 2022-06-05T17:26:49 | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
... | 13,988 | [
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hf-internal-testing/instructpix2pix-10-samples | 2023-06-09T19:57:18.000Z | [
"region:us"
] | hf-internal-testing | null | null | 0 | 2,627 | 2023-06-09T19:21:40 | ---
dataset_info:
features:
- name: input_image
dtype: image
- name: edited_image
dtype: image
- name: edit_prompt
dtype: string
splits:
- name: train
num_bytes: 4479546.0
num_examples: 10
download_size: 4481212
dataset_size: 4479546.0
---
# Dataset Card for "test"
[More Information... | 434 | [
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PolyAI/banking77 | 2022-10-25T10:12:22.000Z | [
"task_categories:text-classification",
"task_ids:intent-classification",
"task_ids:multi-class-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"li... | PolyAI | BANKING77 dataset provides a very fine-grained set of intents in a banking domain.
It comprises 13,083 customer service queries labeled with 77 intents.
It focuses on fine-grained single-domain intent detection. | @inproceedings{Casanueva2020,
author = {I{\~{n}}igo Casanueva and Tadas Temcinas and Daniela Gerz and Matthew Henderson and Ivan Vulic},
title = {Efficient Intent Detection with Dual Sentence Encoders},
year = {2020},
month = {mar},
note = {Data available at https://gi... | 20 | 2,597 | 2022-04-27T12:54:13 | ---
annotations_creators:
- expert-generated
extended:
- original
language_creators:
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- intent-classification
- multi-class-clas... | 9,779 | [
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FedML/databricks-dolly-15k-niid | 2023-09-05T12:03:26.000Z | [
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | FedML | null | null | 0 | 2,578 | 2023-09-01T09:51:54 | ---
license: cc-by-sa-3.0
language:
- en
size_categories:
- 10K<n<100K
configs:
- config_name: default
default: true
data_files:
- split: train
path: "train.parquet"
- split: test
path: "test.parquet"
dataset_info:
config_name: default
features:
- name: instruction
... | 598 | [
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Skylion007/openwebtext | 2023-04-05T13:36:17.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
... | Skylion007 | An open-source replication of the WebText dataset from OpenAI. | @misc{Gokaslan2019OpenWeb,
title={OpenWebText Corpus},
author={Aaron Gokaslan*, Vanya Cohen*, Ellie Pavlick, Stefanie Tellex},
howpublished{\\url{http://Skylion007.github.io/OpenWebTextCorpus}},
year={2019}
} | 204 | 2,542 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- cc0-1.0
multilinguality:
- monolingual
pretty_name: OpenWebText
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
p... | 7,321 | [
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madao33/new-title-chinese | 2022-07-01T06:26:15.000Z | [
"region:us"
] | madao33 | null | null | 1 | 2,524 | 2022-07-01T02:53:57 | Entry not found | 15 | [
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SetFit/subj | 2022-01-15T21:34:11.000Z | [
"region:us"
] | SetFit | null | null | 4 | 2,503 | 2022-03-02T23:29:22 | # Subjective vs Objective
This is the SUBJ dataset as used in [SentEval](https://github.com/facebookresearch/SentEval). It contains sentences with an annotation if they sentence describes something subjective about a movie or something objective | 248 | [
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... |
nelorth/oxford-flowers | 2022-12-11T02:38:31.000Z | [
"task_categories:image-classification",
"task_categories:unconditional-image-generation",
"source_datasets:https://www.robots.ox.ac.uk/~vgg/data/flowers",
"license:unknown",
"flowers",
"oxford",
"region:us"
] | nelorth | null | null | 7 | 2,492 | 2022-12-11T02:14:19 | ---
pretty_name: Oxford Flowers Dataset
source_datasets: https://www.robots.ox.ac.uk/~vgg/data/flowers
tags:
- flowers
- oxford
task_categories:
- image-classification
- unconditional-image-generation
license:
- unknown
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
cl... | 2,851 | [
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BeIR/scidocs | 2022-10-23T06:04:15.000Z | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | BeIR | null | null | 2 | 2,482 | 2022-06-05T16:57:38 | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
... | 13,988 | [
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JackBAI/bert_pretrain_datasets | 2023-10-09T23:11:37.000Z | [
"region:us"
] | JackBAI | null | null | 0 | 2,462 | 2023-10-09T22:43:45 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
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num_bytes: 24500165181
num_examples: 80462898
download_size: 14400389487
dataset_size: 24500165181
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "bert_pretr... | 466 | [
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BeIR/fever | 2022-10-23T06:04:31.000Z | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | BeIR | null | null | 2 | 2,433 | 2022-06-05T16:58:21 | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
... | 13,988 | [
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codeparrot/instructhumaneval | 2023-06-13T15:58:34.000Z | [
"region:us"
] | codeparrot | null | null | 6 | 2,408 | 2023-06-06T13:52:48 | ---
dataset_info:
features:
- name: task_id
dtype: string
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dtype: string
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dtype: string
- name: test
dtype: string
- name: entry_point
dtype: string
- name: signature
dtype: string
- name: docstring
dtype: string
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d... | 5,270 | [
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BeIR/climate-fever | 2022-10-23T06:04:48.000Z | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | BeIR | null | null | 1 | 2,401 | 2022-06-05T17:03:57 | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
... | 13,988 | [
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nlphuji/flickr30k | 2023-01-19T17:40:41.000Z | [
"region:us"
] | nlphuji | null | null | 12 | 2,391 | 2023-01-19T12:00:06 | # Flickr30k
Original paper: [From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions](https://aclanthology.org/Q14-1006)
Homepage: https://shannon.cs.illinois.edu/DenotationGraph/
Bibtex:
```
@article{young2014image,
title={From image descriptions to vis... | 641 | [
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iamtarun/python_code_instructions_18k_alpaca | 2023-07-27T15:51:36.000Z | [
"task_categories:question-answering",
"task_categories:text2text-generation",
"task_categories:text-generation",
"size_categories:10K<n<100K",
"code",
"region:us"
] | iamtarun | null | null | 40 | 2,387 | 2023-07-24T10:21:09 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 25180782
num_examples: 18612
download_size: 11357076
dataset_size: 25180782
configs:
- config_nam... | 905 | [
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0.0... |
Gustavosta/Stable-Diffusion-Prompts | 2022-09-18T22:38:59.000Z | [
"annotations_creators:no-annotation",
"language_creators:found",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | Gustavosta | null | null | 338 | 2,385 | 2022-09-18T12:13:15 | ---
license:
- unknown
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
source_datasets:
- original
---
# Stable Diffusion Dataset
This is a set of about 80,000 prompts filtered and extracted from the image finder for Stable Diffusion: "[Lexica.art](https://lexica.art/)". It was a litt... | 777 | [
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davidscripka/MIT_environmental_impulse_responses | 2023-08-21T18:32:13.000Z | [
"task_categories:audio-classification",
"task_categories:automatic-speech-recognition",
"size_categories:n<1K",
"license:unknown",
"region:us"
] | davidscripka | null | null | 0 | 2,384 | 2023-08-19T21:14:33 | ---
license: unknown
task_categories:
- audio-classification
- automatic-speech-recognition
size_categories:
- n<1K
---
MIT Environmental Impulse Response Dataset
The audio recordings in this dataset are originally created by the Computational Audition Lab at MIT. The source of the data can be found at: [https://mcde... | 936 | [
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pib | 2023-06-01T14:59:57.000Z | [
"task_categories:translation",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:other",
"multilinguality:translation",
"size_categories:100K<n<1M",
"size_categ... | null | Sentence aligned parallel corpus between 11 Indian Languages, crawled and extracted from the press information bureau
website. | @inproceedings{siripragada-etal-2020-multilingual,
title = "A Multilingual Parallel Corpora Collection Effort for {I}ndian Languages",
author = "Siripragada, Shashank and
Philip, Jerin and
Namboodiri, Vinay P. and
Jawahar, C V",
booktitle = "Proceedings of the 12th Language Resources an... | 3 | 2,380 | 2022-03-02T23:29:22 | ---
task_categories:
- translation
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
multilinguality:
- translation
language:
- bn
- en
- gu
- hi
- ml
- mr
- or
- pa
- ta
- te
- ur
language_creators:
- other
annotations_creators:
- no-annotation
source_datasets:
- original
size_cate... | 19,535 | [
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kde4 | 2022-11-03T16:32:20.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
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"language:af",
"language:ar",
"language:as",
"language:ast",
"language:be",
"language:bg",
"language:bn",
"langua... | null | A parallel corpus of KDE4 localization files (v.2).
92 languages, 4,099 bitexts
total number of files: 75,535
total number of tokens: 60.75M
total number of sentence fragments: 8.89M | @InProceedings{TIEDEMANN12.463,
author = {J{\"o}rg Tiedemann},
title = {Parallel Data, Tools and Interfaces in OPUS},
booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
year = {2012},
month = {may},
date = {23-25},
address = {Istanbul, Turkey},
... | 12 | 2,352 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- af
- ar
- as
- ast
- be
- bg
- bn
- br
- ca
- crh
- cs
- csb
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- da
- de
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- es
- et
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- fa
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- fr
- fy
- ga
- gl
- gu
- ha
- he
- hi
- hne
- hr
- hsb
- hu
- hy
- id
- is
- it
- ja
- ka
- kk
- km
- kn
- ko
- ku
- lb
- lt
- lv... | 5,103 | [
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GEM/xlsum | 2022-10-24T15:31:33.000Z | [
"task_categories:summarization",
"annotations_creators:none",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:und",
"license:cc-by-nc-sa-4.0",
"arxiv:1607.01759",
"region:us"
] | GEM | We present XLSum, a comprehensive and diverse dataset comprising 1.35 million professionally
annotated article-summary pairs from BBC, extracted using a set of carefully designed heuristics.
The dataset covers 45 languages ranging from low to high-resource, for many of which no
public dataset is currently available. XL... | @inproceedings{hasan-etal-2021-xl,
title = "{XL}-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages",
author = "Hasan, Tahmid and
Bhattacharjee, Abhik and
Islam, Md. Saiful and
Mubasshir, Kazi and
Li, Yuan-Fang and
Kang, Yong-Bin and
Rahman, M. Soh... | 3 | 2,342 | 2022-03-02T23:29:22 | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- und
license:
- cc-by-nc-sa-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- summarization
task_ids: []
pretty_name: xlsum
---
# Dataset Card for GEM/xlsum
## Dataset Description
- **Homep... | 29,931 | [
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fusing/fill50k | 2023-03-10T22:36:46.000Z | [
"region:us"
] | fusing | null | null | 13 | 2,338 | 2023-03-08T08:16:18 | Entry not found | 15 | [
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masakhane/masakhanews | 2023-05-25T22:27:40.000Z | [
"task_categories:text-classification",
"task_ids:topic-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:am",
"language:en",
"language:fr",
"language:ha... | masakhane | MasakhaNEWS is the largest publicly available dataset for news topic classification in 16 languages widely spoken in Africa.
The languages are:
- Amharic (amh)
- English (eng)
- French (fra)
- Hausa (hau)
- Igbo (ibo)
- Lingala (lin)
- Luganda (lug)
- Oromo (orm)
- Nigerian Pidgin (pcm)
- Rundi (run)
- chShona (sna)
-... | @article{Adelani2023MasakhaNEWS,
title={MasakhaNEWS: News Topic Classification for African languages},
author={David Ifeoluwa Adelani and Marek Masiak and Israel Abebe Azime and Jesujoba Oluwadara Alabi and Atnafu Lambebo Tonja and Christine Mwase and Odunayo Ogundepo and Bonaventure F. P. Dossou and Akintu... | 5 | 2,318 | 2023-04-20T23:06:34 | ---
annotations_creators:
- expert-generated
language:
- am
- en
- fr
- ha
- ig
- ln
- lg
- om
- pcm
- rn
- sn
- so
- sw
- ti
- xh
- yo
language_creators:
- expert-generated
license:
- afl-3.0
multilinguality:
- multilingual
pretty_name: masakhanews
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- news-t... | 7,974 | [
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0.01... |
knowrohit07/know_sql | 2023-09-20T20:13:06.000Z | [
"license:openrail",
"region:us"
] | knowrohit07 | null | null | 80 | 2,307 | 2023-09-16T12:18:52 | ---
license: openrail
---
please use the val ign file for training, its much cleaner. thanks :) | 95 | [
[
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-0.0266265869140625,
-0.022552490234375... |
cats_vs_dogs | 2023-01-25T14:27:39.000Z | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | null | null | @Inproceedings (Conference){asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization,
author = {Elson, Jeremy and Douceur, John (JD) and Howell, Jon and Saul, Jared},
title = {Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization},
booktitle = {Proceedings of 14th A... | 15 | 2,304 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
paperswithcode_id: cats-vs-dogs
prett... | 8,059 | [
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0.02514648... |
scientific_papers | 2023-04-05T13:39:46.000Z | [
"task_categories:summarization",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:unknown",
"abstractive-summarization",
"arxiv:1804.05685",
"region:us"
] | null | Scientific papers datasets contains two sets of long and structured documents.
The datasets are obtained from ArXiv and PubMed OpenAccess repositories.
Both "arxiv" and "pubmed" have two features:
- article: the body of the document, pagragraphs seperated by "/n".
- abstract: the abstract of the document, pagragra... | @article{Cohan_2018,
title={A Discourse-Aware Attention Model for Abstractive Summarization of
Long Documents},
url={http://dx.doi.org/10.18653/v1/n18-2097},
DOI={10.18653/v1/n18-2097},
journal={Proceedings of the 2018 Conference of the North American Chapter of
the Association for Com... | 79 | 2,296 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language:
- en
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
pretty_name: ScientificPapers
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- summarization
task_ids: []
paperswithcode_id: null
tags:
- abstractive-summarization
dat... | 8,269 | [
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0.01253... |
wmt19 | 2023-04-05T13:44:03.000Z | [
"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 | @ONLINE {wmt19translate,
author = {Wikimedia Foundation},
title = {ACL 2019 Fourth Conference on Machine Translation (WMT19), Shared Task: Machine Translation of News},
url = {http://www.statmt.org/wmt19/translation-task.html}
} | 14 | 2,289 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- cs
- de
- en
- fi
- fr
- gu
- kk
- lt
- ru
- zh
license:
- unknown
multilinguality:
- translation
size_categories:
- 10M<n<100M
source_datasets:
- extended|europarl_bilingual
- extended|news_commentary
- extended|opus_paracrawl
- extended|... | 9,930 | [
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laion/laion2b-multi-vit-l-14-embeddings | 2022-12-16T17:53:54.000Z | [
"region:us"
] | laion | null | null | 0 | 2,280 | 2022-12-15T23:33:02 | Entry not found | 15 | [
[
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0.0379... |
bigbench | 2022-12-02T09:47:24.000Z | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:text-classification",
"task_categories:text-generation",
"task_categories:zero-shot-classification",
"task_categories:other",
"task_ids:multiple-choice-qa",
"task_ids:extractive-qa",
"task_ids:open-domain-qa",
... | null | The Beyond the Imitation Game Benchmark (BIG-bench) is a collaborative benchmark intended to
probe large language models, and extrapolate their future capabilities. | @misc{https://doi.org/10.48550/arxiv.2206.04615,
doi = {10.48550/ARXIV.2206.04615},
url = {https://arxiv.org/abs/2206.04615},
author = {Srivastava, Aarohi and Rastogi, Abhinav and Rao, Abhishek and Shoeb, Abu Awal Md and Abid, Abubakar and Fisch, Adam and Brown, Adam R. and Santoro, Adam and Gupta, Aditya and Gar... | 33 | 2,271 | 2022-06-08T17:33:02 | ---
annotations_creators:
- crowdsourced
- expert-generated
- machine-generated
language_creators:
- crowdsourced
- expert-generated
- machine-generated
- other
language:
- en
license:
- apache-2.0
multilinguality:
- multilingual
- monolingual
pretty_name: bigbench
size_categories:
- unknown
source_datasets:
- original... | 99,717 | [
[
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0.0027103424... |
GEM/opusparcus | 2022-10-24T15:30:22.000Z | [
"task_categories:other",
"annotations_creators:expert-created",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:de",
"language:en",
"language:fi",
"language:fr",
"language:ru",
"language:sv",
"license:cc-by-nc-4.0",
"... | GEM | Opusparcus is a paraphrase corpus for six European languages: German,
English, Finnish, French, Russian, and Swedish. The paraphrases are
extracted from the OpenSubtitles2016 corpus, which contains subtitles
from movies and TV shows. | @InProceedings{creutz:lrec2018,
title = {Open Subtitles Paraphrase Corpus for Six Languages},
author={Mathias Creutz},
booktitle={Proceedings of the 11th edition of the Language Resources
and Evaluation Conference (LREC 2018)},
year={2018},
month = {May 7-12},
address = {Miyazaki, Japan},
editor = {Nico... | 1 | 2,268 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-created
language_creators:
- unknown
language:
- de
- en
- fi
- fr
- ru
- sv
license:
- cc-by-nc-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- other
task_ids: []
pretty_name: opusparcus
tags:
- paraphrasing
---
# Dataset Card... | 29,942 | [
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0.01988... |
bentrevett/multi30k | 2023-03-24T14:50:27.000Z | [
"task_categories:translation",
"size_categories:10K<n<100K",
"language:en",
"language:de",
"region:us"
] | bentrevett | null | null | 1 | 2,250 | 2023-03-19T22:38:35 | ---
task_categories:
- translation
language:
- en
- de
size_categories:
- 10K<n<100K
---
# Multi30k
This dataset contains the "multi30k" dataset, which is the "task 1" dataset from [here](https://www.statmt.org/wmt16/multimodal-task.html).
Each example consists of an "en" and a "de" feature. "en" is an English senten... | 1,149 | [
[
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0.0... |
bigbio/bc5cdr | 2022-12-22T15:43:20.000Z | [
"multilinguality:monolingual",
"language:en",
"license:other",
"region:us"
] | bigbio | The BioCreative V Chemical Disease Relation (CDR) dataset is a large annotated text corpus of human annotations of all chemicals, diseases and their interactions in 1,500 PubMed articles. | @article{DBLP:journals/biodb/LiSJSWLDMWL16,
author = {Jiao Li and
Yueping Sun and
Robin J. Johnson and
Daniela Sciaky and
Chih{-}Hsuan Wei and
Robert Leaman and
Allan Peter Davis and
Carolyn J. Mattingly and
... | 1 | 2,220 | 2022-11-13T22:06:13 |
---
language:
- en
bigbio_language:
- English
license: other
multilinguality: monolingual
bigbio_license_shortname: PUBLIC_DOMAIN_MARK_1p0
pretty_name: BC5CDR
homepage: http://www.biocreative.org/tasks/biocreative-v/track-3-cdr/
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
- NAME... | 1,677 | [
[
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0.030960083007812... |
wikicorpus | 2023-06-01T14:59:54.000Z | [
"task_categories:fill-mask",
"task_categories:text-classification",
"task_categories:text-generation",
"task_categories:token-classification",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"task_ids:part-of-speech",
"annotations_creators:machine-generated",
"annotations_creators... | null | The Wikicorpus is a trilingual corpus (Catalan, Spanish, English) that contains large portions of the Wikipedia (based on a 2006 dump) and has been automatically enriched with linguistic information. In its present version, it contains over 750 million words. | @inproceedings{reese-etal-2010-wikicorpus,
title = "{W}ikicorpus: A Word-Sense Disambiguated Multilingual {W}ikipedia Corpus",
author = "Reese, Samuel and
Boleda, Gemma and
Cuadros, Montse and
Padr{\'o}, Llu{\'i}s and
Rigau, German",
booktitle = "Proceedings of the Seventh Intern... | 5 | 2,218 | 2022-03-02T23:29:22 | ---
pretty_name: Wikicorpus
annotations_creators:
- machine-generated
- no-annotation
language_creators:
- found
language:
- ca
- en
- es
license:
- gfdl
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 10M<n<100M
- 1M<n<10M
source_datasets:
- original
task_categories:
- fill-mask
- text-classification
- t... | 7,748 | [
[
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0.020... |
BeIR/fiqa | 2022-10-23T06:00:28.000Z | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | BeIR | null | null | 3 | 2,206 | 2022-06-05T14:48:54 | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
... | 13,988 | [
[
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JeanKaddour/minipile | 2023-06-20T10:08:26.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
... | JeanKaddour | null | null | 36 | 2,192 | 2023-04-09T20:32:58 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 5906108510
num_examples: 1000000
- name: validation
num_bytes: 2779386
num_examples: 500
- name: test
num_bytes: 58558191
num_examples: 10000
download_size: 3177432813
dataset_size: 596744... | 3,253 | [
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... |
PKU-Alignment/processed-hh-rlhf | 2023-07-15T11:41:32.000Z | [
"task_categories:conversational",
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"rlhf",
"harmless",
"helpful",
"human-preference",
"region:us"
] | PKU-Alignment | null | null | 4 | 2,190 | 2023-07-15T09:57:18 | ---
license: mit
task_categories:
- conversational
language:
- en
tags:
- rlhf
- harmless
- helpful
- human-preference
pretty_name: hh-rlhf
size_categories:
- 100K<n<1M
---
# Dataset Card for Processed-Hh-RLHF
This is a dataset that processes [hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf) into an easy-t... | 367 | [
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-0.0231781005859375... |
lm1b | 2023-06-27T15:36:19.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"language:en",
"arxiv:1312.3005",
"region:us"
] | null | A benchmark corpus to be used for measuring progress in statistical language modeling. This has almost one billion words in the training data. | @article{DBLP:journals/corr/ChelbaMSGBK13,
author = {Ciprian Chelba and
Tomas Mikolov and
Mike Schuster and
Qi Ge and
Thorsten Brants and
Phillipp Koehn},
title = {One Billion Word Benchmark for Measuring Progress in Statistical Langu... | 8 | 2,189 | 2022-03-02T23:29:22 | ---
pretty_name: One Billion Word Language Model Benchmark
paperswithcode_id: billion-word-benchmark
dataset_info:
features:
- name: text
dtype: string
config_name: plain_text
splits:
- name: train
num_bytes: 4238206516
num_examples: 30301028
- name: test
num_bytes: 42942045
num_examples... | 5,658 | [
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