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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
un_pc | 2023-06-01T14:59:54.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:ar",
"language:en",
"language:es",
"language:fr",
"language:ru",
"language:zh",
"license:unknown",
"re... | null | This parallel corpus consists of manually translated UN documents from the last 25 years (1990 to 2014) for the six official UN languages, Arabic, Chinese, English, French, Russian, and Spanish. | @inproceedings{ziemski-etal-2016-united,
title = "The {U}nited {N}ations Parallel Corpus v1.0",
author = "Ziemski, Micha{\\l} and
Junczys-Dowmunt, Marcin and
Pouliquen, Bruno",
booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
... | 3 | 730 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- ar
- en
- es
- fr
- ru
- zh
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: united-nations-parallel-corpus
pretty_name: Uni... | 8,489 | [
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aharley/rvl_cdip | 2023-05-02T09:06:16.000Z | [
"task_categories:image-classification",
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"annotations_creators:found",
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"size_categories:100K<n<1M",
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"language:en",
"license:other",
"arxiv:1502.07058",
"regi... | aharley | The RVL-CDIP (Ryerson Vision Lab Complex Document Information Processing) dataset consists of 400,000 grayscale images in 16 classes, with 25,000 images per class. There are 320,000 training images, 40,000 validation images, and 40,000 test images. | @inproceedings{harley2015icdar,
title = {Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval},
author = {Adam W Harley and Alex Ufkes and Konstantinos G Derpanis},
booktitle = {International Conference on Document Analysis and Recognition ({ICDAR})}},
year = {2015}
} | 29 | 729 | 2022-04-21T14:21:01 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|iit_cdip
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
paperswithcode_id: rvl-cdip
pretty_name: RVL-... | 6,150 | [
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bitext/Bitext-customer-support-llm-chatbot-training-dataset | 2023-09-19T23:48:25.000Z | [
"task_categories:question-answering",
"task_categories:table-question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:cdla-sharing-1.0",
"question-answering",
"llm",
"chatbot",
"costumer-support",
"conversional-ai",
"generative-ai",
"natural-language-understanding",
"fine-t... | bitext | null | null | 14 | 729 | 2023-08-24T15:50:29 | ---
license: cdla-sharing-1.0
task_categories:
- question-answering
- table-question-answering
language:
- en
tags:
- question-answering
- llm
- chatbot
- costumer-support
- conversional-ai
- generative-ai
- natural-language-understanding
- fine-tuning
- Retail
pretty_name: >-
Bitext - Customer Service Tagged Trainin... | 11,206 | [
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Yukang/LongAlpaca-12k | 2023-10-11T04:03:27.000Z | [
"arxiv:2309.12307",
"region:us"
] | Yukang | null | null | 40 | 723 | 2023-10-09T03:21:25 | # LongLoRA and LongAlpaca for Long-context LLMs
[](https://huggingface.co/Yukang)
[](https://github.com/dvlab-research/LongLoRA)
[![Data](https://img.shields.io/badge/Data-LongAl... | 22,795 | [
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FredZhang7/stable-diffusion-prompts-2.47M | 2023-02-11T21:59:33.000Z | [
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:en",
"license:creativeml-openrail-m",
"region:us"
] | FredZhang7 | null | null | 18 | 721 | 2023-02-09T04:03:22 | ---
license: creativeml-openrail-m
task_categories:
- text-generation
language:
- en
pretty_name: SDP-2.47M
size_categories:
- 1M<n<10M
---
## Source
Combined text-only dataset from
- poloclub/diffusiondb
- Gustavosta/Stable-Diffusion-Prompts
- bartman081523/stable-diffusion-discord-prompts
- FredZhang7/krea-ai-promp... | 813 | [
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HuggingFaceH4/testing_self_instruct_small | 2023-04-12T21:53:16.000Z | [
"region:us"
] | HuggingFaceH4 | null | null | 0 | 721 | 2023-04-12T21:53:12 | ---
dataset_info:
features:
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splits:
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num_bytes: 20379
num_examples: 100
- name: test
num_bytes: 26586
num_examples: 100
download_size: 35875
dataset_size: 46965
---
# Dataset Card for "testing_self_instruc... | 461 | [
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Deysi/spam-detection-dataset | 2023-04-15T17:42:24.000Z | [
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"region:us"
] | Deysi | null | null | 5 | 720 | 2023-04-15T17:39:24 | ---
dataset_info:
features:
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splits:
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num_bytes: 3161821
num_examples: 8175
- name: test
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num_examples: 2725
download_size: 2578551
dataset_size: 4256578
license: apache-2.0
task_categories:
- te... | 581 | [
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alexandrainst/audio_test_dataset | 2023-05-01T14:28:58.000Z | [
"size_categories:n<1K",
"language:da",
"license:cc0-1.0",
"region:us"
] | alexandrainst | null | null | 0 | 719 | 2023-05-01T14:24:51 | ---
dataset_info:
features:
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audio:
sampling_rate: 48000
- name: sentence
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- name: up_votes
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- name: down_votes
dtype: int64
- name: age
dtype: string
- nam... | 1,138 | [
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huggan/flowers-102-categories | 2022-04-04T17:21:42.000Z | [
"region:us"
] | huggan | null | null | 4 | 715 | 2022-04-04T17:21:25 | Entry not found | 15 | [
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YeungNLP/firefly-train-1.1M | 2023-04-10T06:15:28.000Z | [
"region:us"
] | YeungNLP | null | null | 186 | 715 | 2023-04-03T04:47:50 | 本数据应用于项目：[Firefly（流萤）: 中文对话式大语言模型](https://github.com/yangjianxin1/Firefly) ，训练后得到的模型[firefly-1b4](https://huggingface.co/YeungNLP/firefly-1b4)
如果您觉得此数据集对您有帮助，请like此数据集并在Github项目中star我们。
我们收集了23个常见的中文数据集，对于每个任务，由人工书写若干种指令模板，保证数据的高质量与丰富度，数据量为115万 。数据分布如下图所示：
每条数据的格式如下，包含任务类... | 623 | [
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mteb/emotion | 2022-09-27T19:14:18.000Z | [
"language:en",
"region:us"
] | mteb | null | null | 5 | 714 | 2022-05-23T09:55:39 | ---
language:
- en
---
** Attention: There appears an overlap in train / test. I trained a model on the train set and achieved 100% acc on test set. With the original emotion dataset this is not the case (92.4% acc)** | 218 | [
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lighteval/legal_summarization | 2023-07-07T09:03:13.000Z | [
"region:us"
] | lighteval | 10 | 714 | 2023-05-12T14:01:58 | Entry not found | 15 | [
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fusing/instructpix2pix-1000-samples | 2023-02-23T07:08:49.000Z | [
"region:us"
] | fusing | null | null | 5 | 710 | 2023-02-23T07:05:45 | ---
dataset_info:
features:
- name: input_image
dtype: image
- name: edit_prompt
dtype: string
- name: edited_image
dtype: image
splits:
- name: train
num_bytes: 416880759.0
num_examples: 1000
download_size: 416899514
dataset_size: 416880759.0
---
# Dataset Card for "instructpix2pix-... | 585 | [
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EduardoPacheco/FoodSeg103 | 2023-07-24T00:01:28.000Z | [
"task_categories:image-segmentation",
"task_ids:semantic-segmentation",
"size_categories:n<1K",
"license:apache-2.0",
"arxiv:2105.05409",
"region:us"
] | EduardoPacheco | null | null | 2 | 708 | 2023-07-22T03:59:39 | ---
license: apache-2.0
task_categories:
- image-segmentation
task_ids:
- semantic-segmentation
size_categories:
- n<1K
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 1125278411.056
num_examples: 4983
- name: validation
num_... | 6,498 | [
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code_x_glue_cc_clone_detection_big_clone_bench | 2022-11-18T19:30:27.000Z | [
"task_categories:text-classification",
"task_ids:semantic-similarity-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:code",
"license:c-uda",
"region:us"
] | null | Given two codes as the input, the task is to do binary classification (0/1), where 1 stands for semantic equivalence and 0 for others. Models are evaluated by F1 score.
The dataset we use is BigCloneBench and filtered following the paper Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax... | @inproceedings{svajlenko2014towards,
title={Towards a big data curated benchmark of inter-project code clones},
author={Svajlenko, Jeffrey and Islam, Judith F and Keivanloo, Iman and Roy, Chanchal K and Mia, Mohammad Mamun},
booktitle={2014 IEEE International Conference on Software Maintenance and Evolution},
pages={47... | 4 | 706 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- code
license:
- c-uda
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- semantic-similarity-classification
pretty_name: CodeXGlueCcCloneDetectionBigCloneBench
... | 6,765 | [
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AhmedSSoliman/CodeXGLUE-CONCODE | 2022-09-13T14:47:15.000Z | [
"region:us"
] | AhmedSSoliman | null | null | 1 | 705 | 2022-08-14T15:58:27 | ## Concode dataset
A large dataset with over 100,000 examples consisting of Java classes from online code repositories, and develop a new encoder-decoder architecture that models the interaction between the method documentation and the class environment.
Concode dataset is a widely used code generation dataset from I... | 2,081 | [
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allegro/klej-cdsc-e | 2022-08-30T06:58:29.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:pl",
"license:cc-by-nc-sa-4.0",
"region:us... | allegro | null | null | 0 | 704 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- pl
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
pretty_name: 'CDSC-E'
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- natural-language-inference
---
#... | 5,531 | [
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mteb/bucc-bitext-mining | 2022-09-22T14:17:13.000Z | [
"multilinguality:monolingual",
"multilinguality:multilingual",
"language:de",
"language:en",
"language:fr",
"language:ru",
"language:zh",
"license:cc-by-sa-4.0",
"arxiv:2104.06893",
"arxiv:2010.02573",
"arxiv:2003.04807",
"arxiv:2204.08582",
"arxiv:2008.09335",
"arxiv:2104.07081",
"regio... | mteb | BUCC 2018 Shared Task test dataset | null | 0 | 704 | 2022-05-19T19:44:24 | ---
annotations_creators: []
language_creators: []
language:
- de
- en
- fr
- ru
- zh
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
- multilingual
pretty_name: MTEB Benchmark
---
# Dataset Card for MTEB Benchmark
## Dataset Description
- **Homepage:** https://github.com/embeddings-benchmark/mteb-draft
- **R... | 4,962 | [
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BeIR/msmarco | 2022-10-23T06:02:06.000Z | [
"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 | 703 | 2022-06-05T16:32:43 | ---
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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leeseeun/tokenzied_news_2gb_data | 2023-10-24T06:05:04.000Z | [
"region:us"
] | leeseeun | null | null | 0 | 702 | 2023-10-24T06:03:53 | ---
dataset_info:
features:
- name: input_ids
sequence: int32
splits:
- name: train
num_bytes: 2230572200
num_examples: 544042
download_size: 989285251
dataset_size: 2230572200
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "tokenzied... | 468 | [
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-0.050048828125,
-0.04400634765625,
-0.03894042... |
skg/toxigen-data | 2022-06-20T11:12:11.000Z | [
"task_categories:text-classification",
"task_ids:hate-speech-detection",
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"arxiv:2203.09509",
"region:us"
] | skg | Toxigen is a large-scale dataset containing implicitly toxic and benign sentences mentioning 13 minority groups, and a tool to stress test a given off-the-shelf toxicity classifier. The dataset is generated using a large language model (GPT3). It is intended to be used for training classifiers that learn to detect subt... | @inproceedings{hartvigsen2022toxigen,
title={ToxiGen: A Large-Scale Machine-Generated Dataset for Implicit and Adversarial Hate Speech Detection},
author={Hartvigsen, Thomas and Gabriel, Saadia and Palangi, Hamid and Sap, Maarten and Ray, Dipankar and Kamar, Ece},
booktitle={Proceedings of the 60th Annual Meeting... | 23 | 701 | 2022-05-01T15:49:02 | ---
annotations_creators:
- expert-generated
language_creators:
- machine-generated
languages:
- en-US
licenses: []
multilinguality:
- monolingual
pretty_name: ToxiGen
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- hate-speech-detection
---
# Dataset Card fo... | 2,347 | [
[
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0.013519287109375,
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0.0... |
cuad | 2022-11-18T19:50:02.000Z | [
"task_categories:question-answering",
"task_ids:closed-domain-qa",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:210... | null | Contract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510
commercial legal contracts that have been manually labeled to identify 41 categories of important
clauses that lawyers look for when reviewing contracts in connection with corporate transactions. | @article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={arXiv preprint arXiv:2103.06268},
year={2021}
} | 30 | 698 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- closed-domain-qa
- extractive-qa
paperswithcode_id: cuad
pretty_name: CUA... | 15,435 | [
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movie_rationales | 2023-04-05T10:09:59.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | null | The movie rationale dataset contains human annotated rationales for movie
reviews. | @unpublished{eraser2019,
title = {ERASER: A Benchmark to Evaluate Rationalized NLP Models},
author = {Jay DeYoung and Sarthak Jain and Nazneen Fatema Rajani and Eric Lehman and Caiming Xiong and Richard Socher and Byron C. Wallace}
}
@InProceedings{zaidan-eisner-piatko-2008:nips,
author = {Omar F. Zaidan ... | 2 | 697 | 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:
- text-classification
task_ids:
- sentiment-classification
pretty_name: MovieRationales
dataset_info:
features:
... | 6,647 | [
[
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0.0097... |
visheratin/laion-coco-nllb | 2023-10-25T23:54:31.000Z | [
"task_categories:image-to-text",
"task_categories:translation",
"size_categories:100K<n<1M",
"language:ace",
"language:acm",
"language:acq",
"language:aeb",
"language:af",
"language:ajp",
"language:ak",
"language:als",
"language:am",
"language:apc",
"language:ar",
"language:ars",
"lang... | visheratin | null | null | 14 | 695 | 2023-06-18T06:58:28 | ---
language:
- ace
- acm
- acq
- aeb
- af
- ajp
- ak
- als
- am
- apc
- ar
- ars
- ary
- arz
- as
- ast
- awa
- ayr
- azb
- azj
- ba
- bm
- ban
- be
- bem
- bn
- bho
- bjn
- bo
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- bug
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- ca
- ceb
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- cjk
- ckb
- crh
- cy
- da
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- dyu
- dz
- el
- en
- eo
- et
- eu
- ee
- fo
- fj
- fi
- fon
- fr
- fu... | 5,436 | [
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tner/bc5cdr | 2022-07-18T00:43:04.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:en",
"license:other",
"region:us"
] | tner | [Bio Creative 5 CDR NER dataset](https://academic.oup.com/database/article/doi/10.1093/database/baw032/2630271?login=true) | @article{wei2016assessing,
title={Assessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) task},
author={Wei, Chih-Hsuan and Peng, Yifan and Leaman, Robert and Davis, Allan Peter and Mattingly, Carolyn J and Li, Jiao and Wiegers, Thomas C and L... | 1 | 693 | 2022-07-16T11:09:16 | ---
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: BioCreative V CDR
---
# Dataset Card for "tner/bc5cdr"
## Dataset Description
- **Repository:** [T-NER](https://github.com/asahi41... | 2,094 | [
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0.03... |
polm-stability/xwinograd-ja | 2023-10-06T08:34:15.000Z | [
"license:cc-by-4.0",
"arxiv:2211.01786",
"arxiv:2106.12066",
"region:us"
] | polm-stability | null | null | 0 | 692 | 2023-10-06T08:11:59 | ---
license: cc-by-4.0
---
This is the Japanese portion of the xwinograd dataset, formatted for easy use.
The original data can be found [here](https://huggingface.co/datasets/Muennighoff/xwinograd). When using this data, please cite the original papers.
```
@misc{muennighoff2022crosslingual,
title={Crossling... | 1,145 | [
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0.034698486328125,
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-0.040252685546875,
-0.042144775390625,
... |
tweets_hate_speech_detection | 2023-01-25T14:54:59.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:gpl-3.0",
"region:us"
] | null | The objective of this task is to detect hate speech in tweets. For the sake of simplicity, we say a tweet contains hate speech if it has a racist or sexist sentiment associated with it. So, the task is to classify racist or sexist tweets from other tweets.
Formally, given a training sample of tweets and labels, where ... | @InProceedings{Z
Roshan Sharma:dataset,
title = {Sentimental Analysis of Tweets for Detecting Hate/Racist Speeches},
authors={Roshan Sharma},
year={2018}
} | 14 | 689 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- gpl-3.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: Tweets Hate Speech Detection
data... | 5,448 | [
[
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0.0181121826171875,
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-0.07196044921875,
-0.0751953125,
-0.008... |
HuggingFaceM4/webvid | 2022-05-13T21:44:02.000Z | [
"region:us"
] | HuggingFaceM4 | WebVid is a large-scale dataset of video clips with textual descriptions sourced from the web. The videos are diverse and rich in their content. | @InProceedings{Bain21,
author = "Max Bain and Arsha Nagrani and G{\"u}l Varol and Andrew Zisserman",
title = "Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval",
booktitle = "IEEE International Conference on Computer Vision",
year = "2021",
} | 5 | 689 | 2022-05-12T20:20:39 | Entry not found | 15 | [
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0.0379028... |
Brendan/icdst_multiwoz_turns_v24 | 2023-10-25T21:41:18.000Z | [
"region:us"
] | Brendan | null | null | 0 | 688 | 2023-10-13T00:07:28 | ---
dataset_info:
features:
- name: dialogue_id
dtype: string
- name: turn_id
dtype: int8
- name: domains
sequence: string
- name: user_utterances
sequence: string
- name: system_utterances
sequence: string
- name: slot_values
struct:
- name: hotel
struct:
- name: p... | 6,336 | [
[
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americas_nli | 2023-01-25T14:26:20.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"multilinguality:translation",
"size_categories:unknown",
"source_datasets:extended|xnli",
"language:ay",
"la... | null | AmericasNLI is an extension of XNLI (Conneau et al., 2018) – a natural language inference (NLI) dataset covering 15 high-resource languages – to 10 low-resource indigenous languages spoken in the Americas: Ashaninka, Aymara, Bribri, Guarani, Nahuatl, Otomi, Quechua, Raramuri, Shipibo-Konibo, and Wixarika. As with MNLI,... | @article{DBLP:journals/corr/abs-2104-08726,
author = {Abteen Ebrahimi and
Manuel Mager and
Arturo Oncevay and
Vishrav Chaudhary and
Luis Chiruzzo and
Angela Fan and
John Ortega and
Ricardo Ramos and
... | 1 | 684 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- ay
- bzd
- cni
- gn
- hch
- nah
- oto
- qu
- shp
- tar
license:
- unknown
multilinguality:
- multilingual
- translation
size_categories:
- unknown
source_datasets:
- extended|xnli
task_categories:
- text-classification
task_i... | 15,714 | [
[
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-0.05059814453125,
-0.0281219482421875,
0.03402709960... |
JeremyAlain/123_test | 2022-10-25T10:29:11.000Z | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:text2text-generation",
"task_categories:table-question-answering",
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:tabular-cl... | JeremyAlain | The Fewshot Table dataset consists of tables that naturally occur on the web, that are formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. The dataset consists of approximately 413K tables that are extracted from the WDC Web Table Corpora 2015, which is released under the ... | @InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
} | 2 | 684 | 2022-06-06T13:37:29 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: Fewshot Table Dataset
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-gene... | 11,181 | [
[
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0.0... |
zxvix/squad_text | 2023-10-19T03:59:00.000Z | [
"region:us"
] | zxvix | null | null | 0 | 683 | 2023-10-19T03:52:06 | ---
configs:
- config_name: default
data_files:
- split: original
path: data/original-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: original
num_bytes: 1611043
num_examples: 2067
download_size: 1039425
dataset_size: 1611043
---
# Dataset Card for "squad_text"
[Mor... | 447 | [
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pubmed | 2022-12-22T07:57:43.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:text-classification",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"task_ids:text-scoring",
"task_ids:topic-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
... | null | NLM produces a baseline set of MEDLINE/PubMed citation records in XML format for download on an annual basis. The annual baseline is released in December of each year. Each day, NLM produces update files that include new, revised and deleted citations. See our documentation page for more information. | Courtesy of the U.S. National Library of Medicine. | 34 | 680 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
- text-classification
task_ids:
- language-modeling
- masked-language-modelin... | 8,339 | [
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0... |
bigcode/the-stack-smol-xl | 2023-02-10T17:22:38.000Z | [
"task_categories:text-generation",
"task_ids:language-modeling",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:unknown",
"language:code",
"region:us"
] | bigcode | null | null | 3 | 679 | 2023-02-10T11:17:22 | ---
annotations_creators: []
language_creators:
- crowdsourced
language: ["code"]
multilinguality:
- multilingual
size_categories:
- unknown
source_datasets: []
task_categories:
- text-generation
task_ids:
- language-modeling
---
## Dataset Description
A small subset of [the-stack](https://huggingface.co/datasets/big... | 1,590 | [
[
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0.014... |
EleutherAI/sycophancy | 2023-09-05T15:14:40.000Z | [
"region:us"
] | EleutherAI | This new dataset is designed to solve this great NLP task and is crafted with a lot of care. | @misc{perez2022discovering,
doi = {10.48550/ARXIV.2212.09251},
url = {https://arxiv.org/abs/2212.09251},
author = {Perez, Ethan and Ringer, Sam and Lukošiūtė, Kamilė and Nguyen, Karina and Chen, Edwin and Heiner, Scott and Pettit, Craig and Olsson, Catherine and Kundu, Sandipan and Kadavath, Saurav and Jones, And... | 1 | 678 | 2023-08-29T07:58:29 | Entry not found | 15 | [
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0.0379028... |
argilla/agnews_weak_labeling | 2023-07-13T11:46:28.000Z | [
"language:en",
"region:us"
] | argilla | null | null | 0 | 677 | 2022-12-28T14:16:31 | ---
language: en
dataset_info:
features:
- name: text
dtype: string
- name: inputs
struct:
- name: text
dtype: string
- name: prediction
dtype: 'null'
- name: prediction_agent
dtype: 'null'
- name: annotation
dtype: string
- name: annotation_agent
dtype: 'null'
- name: ... | 1,001 | [
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stereoset | 2023-01-25T14:44:52.000Z | [
"task_categories:text-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"stereotype-detection",
"arxiv:2004.09456",
"region:us"
] | null | Stereoset is a dataset that measures stereotype bias in language models. Stereoset consists of 17,000 sentences that
measures model preferences across gender, race, religion, and profession. | @article{nadeem2020Stereoset,
title={Stereoset: Measuring stereotypical bias in pretrained language models},
author={Nadeem, Moin and Bethke, Anna and Reddy, Siva},
journal={arXiv preprint arXiv:2004.09456},
year={2020}
} | 11 | 675 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
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size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: stereoset
pretty_name: StereoSet
tags:
- stereot... | 14,613 | [
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md_gender_bias | 2023-06-01T14:59:54.000Z | [
"task_categories:text-classification",
"annotations_creators:crowdsourced",
"annotations_creators:found",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
... | null | Machine learning models are trained to find patterns in data.
NLP models can inadvertently learn socially undesirable patterns when training on gender biased text.
In this work, we propose a general framework that decomposes gender bias in text along several pragmatic and semantic dimensions:
bias from the gender of th... | @inproceedings{md_gender_bias,
author = {Emily Dinan and
Angela Fan and
Ledell Wu and
Jason Weston and
Douwe Kiela and
Adina Williams},
editor = {Bonnie Webber and
Trevor Cohn and
Yulan He and
... | 13 | 674 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
- found
- machine-generated
language_creators:
- crowdsourced
- found
language:
- en
license:
- mit
multilinguality:
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size_categories:
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- 1K<n<10K
- 1M<n<10M
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source_datasets:
- extended|other-convai2
- extended|other-light
- extended|o... | 33,363 | [
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un_ga | 2023-06-01T14:59:53.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:translation",
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"language:ar",
"language:en",
"language:es",
"language:fr",
"language:ru",
"language:zh",
"license:unknown",
"reg... | null | United nations general assembly resolutions: A six-language parallel corpus.
This is a collection of translated documents from the United Nations originally compiled into a translation memory by Alexandre Rafalovitch, Robert Dale (see http://uncorpora.org).
6 languages, 15 bitexts
total number of files: 6
total number ... | @inproceedings{title = "United Nations General Assembly Resolutions: a six-language parallel corpus",
abstract = "In this paper we describe a six-ways parallel public-domain corpus consisting of 2100 United Nations General Assembly Resolutions with translations in the six official languages of the United Nations, with ... | 0 | 673 | 2022-03-02T23:29:22 | ---
annotations_creators:
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- ar
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license:
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paperswithcode_id: null
pretty_name: UnGa
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nielsr/docvqa_1200_examples | 2022-08-05T14:20:07.000Z | [
"region:us"
] | nielsr | null | null | 2 | 672 | 2022-08-05T14:19:39 | Entry not found | 15 | [
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pasinit/xlwic | 2022-10-25T09:54:22.000Z | [
"task_categories:text-classification",
"task_ids:semantic-similarity-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"language:bg",
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"langua... | pasinit | A system's task on any of the XL-WiC datasets is to identify the intended meaning of a word in a context of a given language. XL-WiC is framed as a binary classification task. Each instance in XL-WiC has a target word w, either a verb or a noun, for which two contexts are provided. Each of these contexts triggers a spe... | @inproceedings{raganato-etal-2020-xl-wic,
title={XL-WiC: A Multilingual Benchmark for Evaluating Semantic Contextualization},
author={Raganato, Alessandro and Pasini, Tommaso and Camacho-Collados, Jose and Pilehvar, Mohammad Taher},
booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Lan... | 4 | 671 | 2022-03-02T23:29:22 | ---
annotations_creators:
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extended:
- original
language_creators:
- found
language:
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- bg
- zh
- hr
- da
- nl
- et
- fa
- ja
- ko
- it
- fr
- de
license:
- cc-by-nc-4.0
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
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task_categories:
- text-classification... | 1,410 | [
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HumanCompatibleAI/ppo-seals-CartPole-v0 | 2023-05-29T09:52:49.000Z | [
"region:us"
] | HumanCompatibleAI | null | null | 0 | 670 | 2023-05-29T09:52:45 | ---
dataset_info:
features:
- name: obs
sequence:
sequence: float32
- name: acts
sequence: int64
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sequence: string
- name: terminal
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splits:
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download_size: 297546
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jxie/aircraft | 2023-08-16T00:10:15.000Z | [
"region:us"
] | jxie | null | null | 0 | 667 | 2023-08-13T21:52:30 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image
dtype: image
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dtype:
class_label:
names:
'0': '0'
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darentang/generated | 2022-01-04T06:13:50.000Z | [
"region:us"
] | darentang | https://arxiv.org/abs/2103.10213 | @article{2019,
title={ICDAR2019 Competition on Scanned Receipt OCR and Information Extraction},
url={http://dx.doi.org/10.1109/ICDAR.2019.00244},
DOI={10.1109/icdar.2019.00244},
journal={2019 International Conference on Document Analysis and Recognition (ICDAR)},
publisher={IEEE},
author={Huang, Zheng... | 0 | 666 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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dangne/gcc_caption_only | 2022-08-08T04:48:09.000Z | [
"region:us"
] | dangne | null | null | 0 | 664 | 2022-08-08T04:43:20 | Entry not found | 15 | [
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dart | 2022-11-18T19:57:00.000Z | [
"task_categories:tabular-to-text",
"task_ids:rdf-to-text",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|wi... | null | DART is a large and open-domain structured DAta Record to Text generation corpus with high-quality
sentence annotations with each input being a set of entity-relation triples following a tree-structured ontology.
It consists of 82191 examples across different domains with each input being a semantic RDF triple set deri... | @article{radev2020dart,
title={DART: Open-Domain Structured Data Record to Text Generation},
author={Dragomir Radev and Rui Zhang and Amrit Rau and Abhinand Sivaprasad and Chiachun Hsieh and Nazneen Fatema Rajani and Xiangru Tang and Aadit Vyas and Neha Verma and Pranav Krishna and Yangxiaokang Liu and Nadia Irwant... | 4 | 662 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
- machine-generated
language_creators:
- crowdsourced
- machine-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|wikitable_questions
- extended|wikisql
- extended|web_nlg
- extended|cleaned_e2e
task_... | 8,696 | [
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xglue | 2023-06-30T09:06:30.000Z | [
"task_categories:question-answering",
"task_categories:summarization",
"task_categories:text-classification",
"task_categories:text2text-generation",
"task_categories:token-classification",
"task_ids:acceptability-classification",
"task_ids:extractive-qa",
"task_ids:named-entity-recognition",
"task_... | null | XGLUE is a new benchmark dataset to evaluate the performance of cross-lingual pre-trained
models with respect to cross-lingual natural language understanding and generation.
The benchmark is composed of the following 11 tasks:
- NER
- POS Tagging (POS)
- News Classification (NC)
- MLQA
- XNLI
- PAWS-X
- Query-Ad Matchi... | @article{Liang2020XGLUEAN,
title={XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation},
author={Yaobo Liang and Nan Duan and Yeyun Gong and Ning Wu and Fenfei Guo and Weizhen Qi
and Ming Gong and Linjun Shou and Daxin Jiang and Guihong Cao and Xiaodong Fan and Ruofei
Zhan... | 21 | 660 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
- expert-generated
- found
- machine-generated
language_creators:
- crowdsourced
- expert-generated
- found
- machine-generated
language:
- ar
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- de
- el
- en
- es
- fr
- hi
- it
- nl
- pl
- pt
- ru
- sw
- th
- tr
- ur
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license:
- other
multilinguality:
- multilingu... | 54,872 | [
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esb/diagnostic-dataset | 2022-10-26T16:42:41.000Z | [
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
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"multilinguality:monolingual",
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... | esb | null | null | 2 | 658 | 2022-10-26T10:25:33 | ---
annotations_creators:
- expert-generated
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- machine-generated
language:
- en
language_creators:
- crowdsourced
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license:
- cc-by-4.0
- apache-2.0
- cc0-1.0
- cc-by-nc-3.0
- other
multilinguality:
- monolingual
pretty_name: ESB Diagnostic Dataset
size_categories:
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antolin/codealpaca-filtered | 2023-10-20T12:39:19.000Z | [
"region:us"
] | antolin | null | null | 0 | 657 | 2023-10-18T13:54:18 | ---
configs:
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data_files:
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path: data/train-*
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path: data/test-*
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path: data/valid-*
dataset_info:
features:
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emozilla/quality | 2023-07-14T00:56:02.000Z | [
"language:en",
"region:us"
] | emozilla | null | null | 5 | 656 | 2023-04-30T03:31:45 | ---
language: en
dataset_info:
features:
- name: article
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HuggingFaceM4/LLaVAR-Instruct-16K | 2023-07-28T15:49:07.000Z | [
"region:us"
] | HuggingFaceM4 | null | null | 4 | 656 | 2023-07-28T15:43:19 | ---
dataset_info:
features:
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sequence: string
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splits:
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num_bytes: 433689449.5
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download_size: 487607994
dataset_size: 433689449.5
---
# Dataset Card for "LLaVAR-Instruct-16... | 455 | [
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osunlp/MagicBrush | 2023-08-06T02:50:19.000Z | [
"task_categories:text-to-image",
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"size_categories:10K<n<100K",
"language:en",
"license:cc-by-4.0",
"arxiv:2306.10012",
"region:us"
] | osunlp | null | null | 30 | 654 | 2023-06-14T02:20:33 | ---
license: cc-by-4.0
dataset_info:
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ninoscherrer/moralchoice | 2023-07-26T20:51:43.000Z | [
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-4.0",
"region:us"
] | ninoscherrer | TBA | TBA | 5 | 653 | 2023-07-26T20:32:33 | ---
pretty_name: MoralChoice
license: cc-by-4.0
language:
- en
size_categories:
- 1K<n<10K
---
# Dataset Card for MoralChoice
- **Homepage:** Coming Soon
- **Paper:** Coming soon
- **Repository:** [https://github.com/ninodimontalcino/moralchoice](https://github.com/ninodimontalcino/moralchoice)
- **Point of Contact:... | 5,190 | [
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yentinglin/jondurbin_airoboros-gpt4-m2.0.zh | 2023-10-20T07:19:21.000Z | [
"region:us"
] | yentinglin | null | null | 0 | 653 | 2023-10-09T06:05:06 | ---
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-0.055999755859375,
-0.05279541015625,
-0... |
told-br | 2023-01-25T14:54:23.000Z | [
"task_categories:text-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:pt",
"license:cc-by-sa-4.0",
"hate-speech-detection",
"arxiv:2010.04543",
"region:us"
] | null | ToLD-Br is the biggest dataset for toxic tweets in Brazilian Portuguese, crowdsourced
by 42 annotators selected from a pool of 129 volunteers. Annotators were selected aiming
to create a plural group in terms of demographics (ethnicity, sexual orientation, age, gender).
Each tweet was labeled by three annotators in 6 p... | @article{DBLP:journals/corr/abs-2010-04543,
author = {Joao Augusto Leite and
Diego F. Silva and
Kalina Bontcheva and
Carolina Scarton},
title = {Toxic Language Detection in Social Media for Brazilian Portuguese:
New Dataset and Multilingual Analysis... | 4 | 652 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- pt
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: told-br
pretty_name: ToLD-Br
language_bcp47:
-... | 13,015 | [
[
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crime_and_punish | 2023-04-05T10:02:51.000Z | [
"language:en",
"region:us"
] | null | \ | null | 2 | 651 | 2022-03-02T23:29:22 | ---
language:
- en
paperswithcode_id: null
pretty_name: CrimeAndPunish
dataset_info:
features:
- name: line
dtype: string
splits:
- name: train
num_bytes: 1270540
num_examples: 21969
download_size: 1201735
dataset_size: 1270540
---
# Dataset Card for "crime_and_punish"
## Table of Contents
- [... | 5,078 | [
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lewtun/github-issues | 2021-10-04T15:49:55.000Z | [
"arxiv:2005.00614",
"region:us"
] | lewtun | null | null | 4 | 651 | 2022-03-02T23:29:22 | # Dataset Card for GitHub Issues
## Dataset Description
- **Point of Contact:** [Lewis Tunstall](lewis@huggingface.co)
### Dataset Summary
GitHub Issues is a dataset consisting of GitHub issues and pull requests associated with the 🤗 Datasets [repository](https://github.com/huggingface/datasets). It is intended fo... | 10,499 | [
[
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mozilla-foundation/common_voice_7_0 | 2023-07-29T16:00:09.000Z | [
"task_categories:automatic-speech-recognition",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"source_datasets:extended|common_voice",
"license:cc0-1.0",
"arxiv:1912.06670",
"region:us"
] | mozilla-foundation | null | @inproceedings{commonvoice:2020,
author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
title = {Common Voice: A Massively-Multilingual Speech Corpus},
booktitle = {Proceedings of the 12th Conference on Lang... | 23 | 651 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
license:
- cc0-1.0
multilinguality:
- multilingual
size_categories:
ab:
- 1K<n<10K
ar:
- 100K<n<1M
as:
- n<1K
az:
- n<1K
ba:
- 100K<n<1M
bas:
- 1K<n<10K
be:
- 100K<n<1M
bg:
- 1K<n<10K
br:
- 10K<n<100K
ca:
... | 11,311 | [
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0.0184783... |
kensho/spgispeech | 2022-10-21T14:46:30.000Z | [
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:other",
"arxiv:2104.02014",
"region:us"
] | kensho | The SPGISpeech corpus is derived from company earnings calls manually transcribed by S&P Global, Inc. according to a pro- fessional style guide detailing conventions for capitalization, punctuation, denormalization of non-standard words and tran- scription of disfluencies in spontaneous speech. The basic unit of SPGISp... | @ARTICLE{2021arXiv210402014O,
author = {{O'Neill}, Patrick K. and {Lavrukhin}, Vitaly and {Majumdar}, Somshubra and {Noroozi}, Vahid and {Zhang}, Yuekai and {Kuchaiev}, Oleksii and {Balam}, Jagadeesh and {Dovzhenko}, Yuliya and {Freyberg}, Keenan and {Shulman}, Michael D. and {Ginsburg}, Boris and {Watanabe}, Sh... | 20 | 650 | 2022-06-29T16:09:04 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- other
multilinguality:
- monolingual
pretty_name: SpgiSpeech
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- automatic-speech-recognition
extra_gated_prompt: |-
Your access to and use of th... | 40,139 | [
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AlexaAI/bold | 2022-10-06T16:21:46.000Z | [
"task_categories:text-generation",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:2101.11718",
"region:us"
] | AlexaAI | null | null | 5 | 649 | 2022-08-16T13:12:49 | ---
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-generation
task_ids:
- text-generation
pretty_name: BOLD (Bias in Open-ended Language Generation Dataset)
---
# Dataset Card for Bias in Open-ended Language Generatio... | 5,316 | [
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arabic_billion_words | 2023-06-01T14:59:53.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"size_categories:1M<... | null | Abu El-Khair Corpus is an Arabic text corpus, that includes more than five million newspaper articles.
It contains over a billion and a half words in total, out of which, there are about three million unique words.
The corpus is encoded with two types of encoding, namely: UTF-8, and Windows CP-1256.
Also it was marked ... | @article{el20161,
title={1.5 billion words arabic corpus},
author={El-Khair, Ibrahim Abu},
journal={arXiv preprint arXiv:1611.04033},
year={2016}
} | 11 | 644 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- ar
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 10K<n<100K
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswit... | 8,395 | [
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0.0294494... |
ted_iwlst2013 | 2023-06-01T14:59:53.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:ar",
"language:de",
"language:en",
"language:es",
"language:fa",
"language:fr",
"language:it",
"languag... | null | A parallel corpus of TED talk subtitles provided by CASMACAT: http://www.casmacat.eu/corpus/ted2013.html. The files are originally provided by https://wit3.fbk.eu.
15 languages, 14 bitexts
total number of files: 28
total number of tokens: 67.67M
total number of sentence fragments: 3.81M | J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012) | 0 | 642 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- ar
- de
- en
- es
- fa
- fr
- it
- nl
- pl
- pt
- ro
- ru
- sl
- tr
- zh
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: nul... | 7,130 | [
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0.00... |
BeIR/webis-touche2020-qrels | 2022-10-23T06:07:03.000Z | [
"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 | 641 | 2022-06-05T17:27:00 | ---
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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McGill-NLP/TopiOCQA | 2023-09-29T19:37:48.000Z | [
"task_categories:text-retrieval",
"task_categories:text-generation",
"task_ids:language-modeling",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100k",
"language:en",
"license:cc-by-nc-sa-4.0",
"conversational-question-answeri... | McGill-NLP | TopiOCQA is an information-seeking conversational dataset with challenging topic switching phenomena. | null | 4 | 639 | 2022-04-08T18:29:53 | ---
annotations_creators:
- crowdsourced
language:
- en
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100k
task_categories:
- text-retrieval
- text-generation
task_ids:
- language-modeling
- open-domain-qa
pretty_name: Open-domain Conversational Question Answering with Topic Switchi... | 2,166 | [
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togethercomputer/Long-Data-Collections | 2023-07-26T17:03:50.000Z | [
"license:other",
"region:us"
] | togethercomputer | null | null | 54 | 638 | 2023-07-26T07:11:25 | ---
license: other
---
# Dataset Summary
This collection is a compilation of long context datasets, specifically designed for tasks requiring extensive comprehension and inference from large text inputs.
Currently, it encompasses data intended for training a robust base model, which can be found in the pretrain/ dir... | 4,479 | [
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facat/sci-llm-new | 2023-10-01T12:45:46.000Z | [
"region:us"
] | facat | null | null | 0 | 638 | 2023-09-01T04:21:05 | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: test2
path: data/test2-*
- split: train
path: data/train-*
- split: train_attack
path: data/train_attack-*
- split: train_new
path: data/train_new-*
- split: train_60k
path: data/train_60k-*
da... | 1,202 | [
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0.0017... |
ted_hrlr | 2023-04-05T13:41:24.000Z | [
"task_categories:translation",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:translation",
"size_categories:1M<n<10M",
"source_datasets:extended|ted_talks_iwslt",
"language:az",
"language:be",
"language:en",
"language:es",
"language:fr",
"language:... | null | Data sets derived from TED talk transcripts for comparing similar language pairs
where one is high resource and the other is low resource. | @inproceedings{Ye2018WordEmbeddings,
author = {Ye, Qi and Devendra, Sachan and Matthieu, Felix and Sarguna, Padmanabhan and Graham, Neubig},
title = {When and Why are pre-trained word embeddings useful for Neural Machine Translation},
booktitle = {HLT-NAACL},
year = {2018},
} | 0 | 637 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language:
- az
- be
- en
- es
- fr
- gl
- he
- it
- pt
- ru
- tr
language_creators:
- expert-generated
license:
- cc-by-nc-nd-4.0
multilinguality:
- translation
pretty_name: TEDHrlr
size_categories:
- 1M<n<10M
source_datasets:
- extended|ted_talks_iwslt
task_categories:
- transl... | 13,698 | [
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0.... |
mxeval/mbxp | 2023-07-03T18:10:10.000Z | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"mxeval",
"mbxp",
"mbpp",
"code-generation",
"arxiv:2210.14868",
"region:us"
] | mxeval | A collection of execution-based multi-lingual benchmark for code generation. | @article{mbxp_athiwaratkun2022,
title = {Multi-lingual Evaluation of Code Generation Models},
author = {Athiwaratkun, Ben and
Gouda, Sanjay Krishna and
Wang, Zijian and
Li, Xiaopeng and
Tian, Yuchen and
Tan, Ming
and Ahmad, Wasi Uddin and
Wang, Shiqi and
Sun, Qing and
Shang, Mingyue and
... | 6 | 634 | 2023-03-14T21:32:18 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- mxeval
- mbxp
- mbpp
- code-generation
- mxeval
pretty_name: mbxp
size_categories:
- 10K<n<100K
---
# MBXP
## Table of Contents
- [MBXP](#MBXP)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
... | 7,458 | [
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maharshipandya/spotify-tracks-dataset | 2023-06-14T11:59:02.000Z | [
"task_categories:feature-extraction",
"task_categories:text-classification",
"task_categories:summarization",
"task_categories:table-question-answering",
"task_categories:audio-classification",
"task_categories:reinforcement-learning",
"task_categories:tabular-classification",
"task_categories:tabular... | maharshipandya | null | null | 23 | 634 | 2023-06-14T11:42:44 | ---
license: bsd
task_categories:
- feature-extraction
- text-classification
- summarization
- table-question-answering
- text-classification
- feature-extraction
- audio-classification
- reinforcement-learning
- tabular-classification
- tabular-regression
language:
- en
tags:
- music
- art
pretty_name: Spotify Track... | 4,832 | [
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ghomasHudson/muld | 2022-11-02T12:55:17.000Z | [
"task_categories:question-answering",
"task_categories:summarization",
"task_categories:text-generation",
"task_categories:translation",
"task_ids:abstractive-qa",
"annotations_creators:found",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:translation",
"multilin... | ghomasHudson | MuLD: The Multitask Long Document Benchmark
A set of NLP tasks where each example is over 10,000 tokens long. | @misc{hudson2022muld,
title{MuLD: The Multitask Long Document Benchmark},
author={G Thomas Hudson, Noura Al Moubayed}
year={2022},
eprint={TODO},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
Some of these datasets are directly based on existing datasets. Please cite these works. | 5 | 633 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
- crowdsourced
language_creators:
- found
language:
- en
- de
license: []
multilinguality:
- translation
- monolingual
size_categories:
- unknown
source_datasets:
- original
- extended|hotpot_qa
- extended|open_subtitles
task_categories:
- question-answering
- summarization
- text-gene... | 3,694 | [
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code_x_glue_tc_text_to_code | 2022-11-18T19:31:29.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:other-programming-languages",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:code",
"language:en",
"license:c-uda",
"text-to-code",
"region:us"
] | null | We use concode dataset which is a widely used code generation dataset from Iyer's EMNLP 2018 paper Mapping Language to Code in Programmatic Context. See paper for details. | @article{iyer2018mapping,
title={Mapping language to code in programmatic context},
author={Iyer, Srinivasan and Konstas, Ioannis and Cheung, Alvin and Zettlemoyer, Luke},
journal={arXiv preprint arXiv:1808.09588},
year={2018}
} | 18 | 632 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- code
- en
license:
- c-uda
multilinguality:
- other-programming-languages
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- translation
task_ids: []
pretty_name: CodeXGlueTcTextToCode
tags:
- text-to-code
dataset_info:
... | 5,391 | [
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mt_eng_vietnamese | 2022-11-18T21:30:45.000Z | [
"task_categories:translation",
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"multilinguality:multilingual",
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"language:en",
"language:vi",
"license:unknown",
"region:us"
] | null | Preprocessed Dataset from IWSLT'15 English-Vietnamese machine translation: English-Vietnamese. | @inproceedings{Luong-Manning:iwslt15,
Address = {Da Nang, Vietnam}
Author = {Luong, Minh-Thang and Manning, Christopher D.},
Booktitle = {International Workshop on Spoken Language Translation},
Title = {Stanford Neural Machine Translation Systems for Spoken Language Domain},
Yea... | 14 | 632 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
multilinguality:
- multilingual
language:
- en
- vi
license:
- unknown
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: MtEngVietnamese
dataset_info:
- config_name: iwslt... | 4,793 | [
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ccdv/arxiv-classification | 2022-10-22T09:23:50.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:topic-classification",
"size_categories:10K<n<100K",
"language:en",
"long context",
"region:us"
] | ccdv | Arxiv Classification Dataset: a classification of Arxiv Papers (11 classes).
It contains 11 slightly unbalanced classes, 33k Arxiv Papers divided into 3 splits: train (23k), val (5k) and test (5k).
Copied from "Long Document Classification From Local Word Glimpses via Recurrent Attention Learning" by JUN HE LIQUN WAN... | null | 11 | 632 | 2022-03-02T23:29:22 | ---
language: en
task_categories:
- text-classification
tags:
- long context
task_ids:
- multi-class-classification
- topic-classification
size_categories: 10K<n<100K
---
**Arxiv Classification: a classification of Arxiv Papers (11 classes).**
This dataset is intended for long context classification (documents have ... | 1,681 | [
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AI-Sweden/SuperLim | 2022-10-21T15:25:24.000Z | [
"task_categories:question-answering",
"task_categories:text-classification",
"task_categories:other",
"multilinguality:monolingual",
"language:sv",
"region:us"
] | AI-Sweden | \ | \ | 5 | 630 | 2022-03-02T23:29:22 | ---
language:
- sv
multilinguality:
- monolingual
pretty_name: SuperLim
task_categories:
- question-answering
- text-classification
- sequence-modeling
- other
---
# Dataset Card for SuperLim
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summ... | 2,691 | [
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fujiki/japanese_alpaca_data | 2023-05-19T12:54:13.000Z | [
"language:ja",
"license:cc-by-nc-sa-4.0",
"region:us"
] | fujiki | null | null | 7 | 630 | 2023-05-18T07:13:15 | ---
dataset_info:
features:
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dtype: string
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splits:
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download_size: 13849623
dataset_size: 24733874
license: cc-by-nc-sa-4.0
language:
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lbox/lbox_open | 2022-11-09T06:41:26.000Z | [
"license:cc-by-nc-4.0",
"region:us"
] | lbox | null | null | 3 | 628 | 2022-03-02T23:29:22 | ---
license: cc-by-nc-4.0
---
# Dataset Card for `lbox_open`
## Dataset Description
- **Homepage:** `https://lbox.kr`
- **Repository:** `https://github.com/lbox-kr/lbox_open`
- **Point of Contact:** [Wonseok Hwang](mailto:wonseok.hwang@lbox.kr)
### Dataset Summary
A Legal AI Benchmark Dataset from Korean Legal Case... | 1,431 | [
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Muennighoff/multi_eurlex | 2023-05-21T18:17:23.000Z | [
"region:us"
] | Muennighoff | 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... | 2 | 628 | 2023-05-21T14:54:28 | Entry not found | 15 | [
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awettig/Pile-Wikipedia-0.5B-6K-opt | 2023-07-10T19:41:27.000Z | [
"region:us"
] | awettig | null | null | 0 | 627 | 2023-07-10T19:40:11 | ---
dataset_info:
features:
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sequence: int32
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sequence: int64
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num_bytes: 5651184786
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- name: test
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num_examples: 813
download_size: 1476548346
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Norod78/cartoon-blip-captions | 2022-11-09T16:27:57.000Z | [
"task_categories:text-to-image",
"annotations_creators:machine-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:n<1K",
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
] | Norod78 | null | null | 4 | 626 | 2022-10-31T14:48:15 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 190959102.953
num_examples: 3141
download_size: 190279356
dataset_size: 190959102.953
pretty_name: 'Cartoon BLIP captions'
size_categories:
- n<1K
tags: []
task_categories:
... | 536 | [
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HausaNLP/NaijaSenti-Twitter | 2023-06-16T16:42:04.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-analysis",
"task_ids:sentiment-classification",
"task_ids:sentiment-scoring",
"task_ids:semantic-similarity-classification",
"task_ids:semantic-similarity-scoring",
"multilinguality:monolingual",
"multilinguality:multilingual",
"size_categor... | HausaNLP | NaijaSenti is the first large-scale human-annotated Twitter sentiment dataset for the four most widely spoken languages in Nigeria — Hausa, Igbo, Nigerian-Pidgin, and Yorùbá — consisting of around 30,000 annotated tweets per language, including a significant fraction of code-mixed tweets. | @inproceedings{muhammad-etal-2022-naijasenti,
title = "{N}aija{S}enti: A {N}igerian {T}witter Sentiment Corpus for Multilingual Sentiment Analysis",
author = "Muhammad, Shamsuddeen Hassan and
Adelani, David Ifeoluwa and
Ruder, Sebastian and
Ahmad, Ibrahim Sa{'}id and
Abdulmumin, Idri... | 0 | 626 | 2023-06-16T08:49:27 | ---
license: cc-by-nc-sa-4.0
task_categories:
- text-classification
task_ids:
- sentiment-analysis
- sentiment-classification
- sentiment-scoring
- semantic-similarity-classification
- semantic-similarity-scoring
tags:
- sentiment analysis, Twitter, tweets
- sentiment
multilinguality:
- monolingual
- multilingual
size_... | 5,909 | [
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awettig/Pile-FreeLaw-0.5B-6K-opt | 2023-07-10T19:34:17.000Z | [
"region:us"
] | awettig | null | null | 0 | 626 | 2023-07-10T19:32:38 | ---
dataset_info:
features:
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sequence: int32
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sequence: int8
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sequence: int64
splits:
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num_bytes: 6500934791
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- name: test
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download_size: 1569004486
da... | 525 | [
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awettig/Pile-Books3-0.5B-6K-opt | 2023-07-10T19:38:57.000Z | [
"region:us"
] | awettig | null | null | 1 | 624 | 2023-07-10T19:37:25 | ---
dataset_info:
features:
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sequence: int32
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sequence: int8
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da... | 524 | [
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argilla/customer_assistant | 2023-08-30T14:38:42.000Z | [
"size_categories:n<1K",
"rlfh",
"argilla",
"human-feedback",
"region:us"
] | argilla | null | null | 0 | 622 | 2023-08-30T14:29:30 | ---
size_categories: n<1K
tags:
- rlfh
- argilla
- human-feedback
---
# Dataset Card for customer_assistant
This dataset has been created with [Argilla](https://docs.argilla.io).
As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used d... | 30,907 | [
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SetFit/SentEval-CR | 2022-06-21T09:14:00.000Z | [
"region:us"
] | SetFit | null | null | 2 | 620 | 2022-06-21T08:52:19 | # SentEval Customer Reviews
This dataset is a port of the official [SentEval `CR` dataset](https://nlp.stanford.edu/~sidaw/home/projects:nbsvm) from [this paper](https://dl.acm.org/doi/10.1145/1014052.1014073). The test split was created from the by randomly sampling 20% of the original data and the train split is the... | 447 | [
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NLPCoreTeam/humaneval_ru | 2023-10-23T12:07:50.000Z | [
"task_categories:text-generation",
"size_categories:n<1K",
"language:ru",
"language:en",
"license:mit",
"code",
"arxiv:2107.03374",
"region:us"
] | NLPCoreTeam | null | null | 6 | 620 | 2023-08-30T13:06:37 | ---
license: mit
task_categories:
- text-generation
language:
- ru
- en
tags:
- code
size_categories:
- n<1K
---
# HumanEval_ru Dataset
## Dataset Summary
This is a version of Code Geneneration [HumanEval dataset](https://huggingface.co/datasets/openai_humaneval) translated to Russian.
## Supported tasks
The task is t... | 7,632 | [
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awettig/Pile-Github-0.5B-6K-opt | 2023-07-10T19:40:11.000Z | [
"region:us"
] | awettig | null | null | 0 | 619 | 2023-07-10T19:38:57 | ---
dataset_info:
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neil-code/dialogsum-test | 2023-08-24T03:47:07.000Z | [
"task_categories:summarization",
"task_categories:text2text-generation",
"task_categories:text-generation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"licens... | neil-code | null | null | 0 | 619 | 2023-08-24T03:38:12 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- summarization
- text2text-generation
- text-generation
task_ids: []
pretty_name: DIALOGSum Corpus
-... | 4,563 | [
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Tevatron/wikipedia-trivia | 2021-09-13T23:34:51.000Z | [
"region:us"
] | Tevatron | null | @inproceedings{karpukhin-etal-2020-dense,
title = "Dense Passage Retrieval for Open-Domain Question Answering",
author = "Karpukhin, Vladimir and Oguz, Barlas and Min, Sewon and Lewis, Patrick and Wu, Ledell and Edunov,
Sergey and Chen, Danqi and Yih, Wen-tau",
booktitle = "Proceedings of the 2020 Confe... | 1 | 617 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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royboy0416/ko-alpaca | 2023-03-31T21:14:40.000Z | [
"task_categories:text-generation",
"language:ko",
"license:cc-by-4.0",
"region:us"
] | royboy0416 | null | null | 3 | 617 | 2023-03-31T14:16:10 | ---
license: cc-by-4.0
task_categories:
- text-generation
language:
- ko
---
</b>Testing purpose only. Do not redistribute. </b>
Original contents: [url] https://huggingface.co/datasets/tatsu-lab/alpaca
Ko-alpaca: [url] https://github.com/Beomi/KoAlpaca/blob/main/ko_alpaca_data.json | 286 | [
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LabHC/bias_in_bios | 2023-09-10T15:41:38.000Z | [
"task_categories:text-classification",
"language:en",
"license:mit",
"region:us"
] | LabHC | null | null | 0 | 616 | 2023-09-05T11:22:24 | ---
license: mit
task_categories:
- text-classification
language:
- en
dataset_info:
features:
- name: hard_text
dtype: string
- name: profession
dtype: int64
- name: gender
dtype: int64
splits:
- name: train
num_bytes: 107487885
num_examples: 257478
- name: test
num_bytes: 4131225... | 3,295 | [
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yizhongw/self_instruct | 2023-03-07T10:07:36.000Z | [
"license:apache-2.0",
"arxiv:2212.10560",
"arxiv:2204.07705",
"region:us"
] | yizhongw | Self-Instruct is a dataset that contains 52k instructions, paired with 82K instance inputs and outputs. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better. | @misc{selfinstruct,
title={Self-Instruct: Aligning Language Model with Self Generated Instructions},
author={Wang, Yizhong and Kordi, Yeganeh and Mishra, Swaroop and Liu, Alisa and Smith, Noah A. and Khashabi, Daniel and Hajishirzi, Hannaneh},
journal={arXiv preprint arXiv:2212.10560},
year={2022}
} | 166 | 614 | 2023-03-02T14:29:46 | ---
license: apache-2.0
dataset_info:
- config_name: self_instruct
features:
- name: prompt
dtype: string
- name: completion
dtype: string
splits:
- name: train
num_bytes: 20527462
num_examples: 82612
download_size: 24113858
dataset_size: 20527462
- config_name: human_eval
features:
- ... | 10,867 | [
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mariosasko/test_multi_dir_dataset | 2022-02-25T17:58:58.000Z | [
"region:us"
] | mariosasko | null | null | 0 | 613 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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snow_simplified_japanese_corpus | 2022-11-03T16:31:17.000Z | [
"task_categories:translation",
"annotations_creators:crowdsourced",
"annotations_creators:other",
"language_creators:found",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"language:ja",
"license:cc-by-4.0",
"region:us"
] | null | About SNOW T15: The simplified corpus for the Japanese language. The corpus has 50,000 manually simplified and aligned sentences. This corpus contains the original sentences, simplified sentences and English translation of the original sentences. It can be used for automatic text simplification as well as translating s... | @inproceedings{maruyama-yamamoto-2018-simplified,
title = "Simplified Corpus with Core Vocabulary",
author = "Maruyama, Takumi and
Yamamoto, Kazuhide",
booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
month = may,
year = "2... | 14 | 611 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
- other
language_creators:
- found
language:
- en
- ja
license:
- cc-by-4.0
multilinguality:
- translation
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: SNOW T15 and T23 (simplified Japa... | 8,262 | [
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nannullna/laion_subset | 2023-09-25T05:33:23.000Z | [
"region:us"
] | nannullna | null | null | 0 | 608 | 2023-09-25T05:31:32 | ---
configs:
- config_name: default
data_files:
- split: artwork
path: data/artwork-*
- split: person
path: data/person-*
- split: object
path: data/object-*
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
- name: url
dtype: string
- name: punsafe
... | 812 | [
[
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Random-Mary-Smith/port_data_random | 2023-11-02T19:06:47.000Z | [
"size_categories:1M<n<10M",
"language:pt",
"license:mit",
"doi:10.57967/hf/1278",
"region:us"
] | Random-Mary-Smith | This Language Identification Dataset provides a multi-domain corpus in European and Brazilian Portuguese.
The repository is an anonymyzed version to support a submsission to the EACL 2024 conference.
Further information about the dataset can be soon found in the paper: Enhancing Portuguese Variants Identification with... | """
_DESCRIPTION = | 0 | 608 | 2023-10-05T18:41:55 | ---
license: mit
dataset_info:
- config_name: law
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': pt-PT
'1': pt-BR
splits:
- name: train
num_bytes: 123139395
num_examples: 397405
- name: validation
num_bytes: 56663
... | 13,597 | [
[
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0.024261... |
sordonia/qa-platy_icl0_clen128_maxD-1_maxC5000_0 | 2023-10-13T14:10:07.000Z | [
"region:us"
] | sordonia | null | null | 0 | 606 | 2023-10-13T14:09:48 | ---
configs:
- config_name: default
data_files:
- split: formal_logic
path: data/formal_logic-*
- split: machine_learning
path: data/machine_learning-*
- split: global_facts
path: data/global_facts-*
- split: abstract_algebra
path: data/abstract_algebra-*
- split: high_school_physics
pat... | 2,196 | [
[
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cmrc2018 | 2023-04-05T09:42:31.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:zh",
"license:cc-by-sa-4.0",
"region:us"
] | null | A Span-Extraction dataset for Chinese machine reading comprehension to add language
diversities in this area. The dataset is composed by near 20,000 real questions annotated
on Wikipedia paragraphs by human experts. We also annotated a challenge set which
contains the questions that need comprehensive understanding and... | @inproceedings{cui-emnlp2019-cmrc2018,
title = {A Span-Extraction Dataset for {C}hinese Machine Reading Comprehension},
author = {Cui, Yiming and
Liu, Ting and
Che, Wanxiang and
Xiao, Li and
Chen, Zhipeng and
Ma, Wentao and
Wang, Shijin and
Hu, Guoping},
book... | 13 | 604 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- zh
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: cmrc-2018
pretty_name: Chinese Mac... | 7,387 | [
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0.0129... |
deepset/prompt-injections | 2023-07-31T15:04:06.000Z | [
"region:us"
] | deepset | null | null | 17 | 603 | 2023-05-17T13:55:19 | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 71720
num_examples: 546
- name: test
num_bytes: 15981
num_examples: 116
download_size: 51215
dataset_size: 87701
license: cc-by-4.0
---
# Dataset Card for "deberta... | 480 | [
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kmfoda/booksum | 2022-11-30T12:03:43.000Z | [
"license:bsd-3-clause",
"arxiv:2105.08209",
"region:us"
] | kmfoda | null | null | 26 | 602 | 2022-03-02T23:29:22 | ---
license:
- bsd-3-clause
train-eval-index:
- config: kmfoda--booksum
task: summarization
task_id: summarization
splits:
eval_split: test
col_mapping:
chapter: text
summary_text: target
---
# BOOKSUM: A Collection of Datasets for Long-form Narrative Summarization
Authors: [Wojciech Kryściński](ht... | 3,332 | [
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bigbio/ncbi_disease | 2023-01-14T03:24:56.000Z | [
"multilinguality:monolingual",
"language:en",
"license:cc0-1.0",
"region:us"
] | bigbio | The NCBI disease corpus is fully annotated at the mention and concept level to serve as a research
resource for the biomedical natural language processing community. | @article{Dogan2014NCBIDC,
title = {NCBI disease corpus: A resource for disease name recognition and concept normalization},
author = {Rezarta Islamaj Dogan and Robert Leaman and Zhiyong Lu},
year = 2014,
journal = {Journal of biomedical informatics},
volume = 47,
... | 1 | 600 | 2022-11-13T22:10:53 |
---
language:
- en
bigbio_language:
- English
license: cc0-1.0
multilinguality: monolingual
bigbio_license_shortname: CC0_1p0
pretty_name: NCBI Disease
homepage: https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
- NAMED_ENTITY_DI... | 1,077 | [
[
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0.02105712890625,
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0.0... |
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