id stringlengths 2 115 | lastModified stringlengths 24 24 | tags list | author stringlengths 2 42 ⌀ | description stringlengths 0 68.7k ⌀ | citation stringlengths 0 10.7k ⌀ | cardData null | likes int64 0 3.55k | downloads int64 0 10.1M | card stringlengths 0 1.01M |
|---|---|---|---|---|---|---|---|---|---|
EMBO/BLURB | 2022-12-09T07:57:37.000Z | [
"task_categories:question-answering",
"task_categories:token-classification",
"task_categories:sentence-similarity",
"task_categories:text-classification",
"task_ids:closed-domain-qa",
"task_ids:named-entity-recognition",
"task_ids:parsing",
"task_ids:semantic-similarity-scoring",
"task_ids:text-sco... | EMBO | null | null | null | 3 | 454 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license: apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
- token-classification
- sentence-similarity
- text-classification
task_ids... |
pietrolesci/nli_fever | 2022-04-25T09:03:28.000Z | [
"region:us"
] | pietrolesci | null | null | null | 1 | 453 | ## Overview
The original dataset can be found [here](https://www.dropbox.com/s/hylbuaovqwo2zav/nli_fever.zip?dl=0)
while the Github repo is [here](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md).
This dataset has been proposed in [Combining fact extraction and verification with... |
DFKI-SLT/cdcp | 2023-08-08T12:47:42.000Z | [
"region:us"
] | DFKI-SLT | null | @inproceedings{niculae-etal-2017-argument,
title = "Argument Mining with Structured {SVM}s and {RNN}s",
author = "Niculae, Vlad and
Park, Joonsuk and
Cardie, Claire",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
... | null | 0 | 453 | Entry not found |
wiki_split | 2023-04-05T13:43:23.000Z | [
"task_categories:text2text-generation",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"split-and-rephrase",
"arxiv:1808.09468",
"region:us"
] | null | One million English sentences, each split into two sentences that together preserve the original meaning, extracted from Wikipedia
Google's WikiSplit dataset was constructed automatically from the publicly available Wikipedia revision history. Although
the dataset contains some inherent noise, it can serve as valuable ... | @InProceedings{BothaEtAl2018,
title = {{Learning To Split and Rephrase From Wikipedia Edit History}},
author = {Botha, Jan A and Faruqui, Manaal and Alex, John and Baldridge, Jason and Das, Dipanjan},
booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
pages = {... | null | 3 | 452 | ---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: WikiSplit
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: wikisplit
tags:
- split-and-... |
RaymondLi/perturbed_humaneval | 2023-08-23T19:41:28.000Z | [
"license:apache-2.0",
"arxiv:2212.10264",
"region:us"
] | RaymondLi | Perturbed version of HumanEval from: ReCode: Robustness Evaluation of Code Generation Models | @article{recode_wang2022,
title = {ReCode: Robustness Evaluation of Code Generation Models},
author = {Wang, Shiqi and
Zheng, Li and
Qian, Haifeng and
Yang, Chenghao and
Wang, Zijian and
Kumar, Varun and
Shang, Mingyue and
Tan, Samson and
Ray, Baishakhi and
Bhatia, Parminder and
Nallap... | null | 0 | 451 | ---
license: apache-2.0
---
# Dataset Card for Dataset Name
## Dataset Description
- **Repository:** https://github.com/amazon-science/recode/tree/main
- **Paper:** https://arxiv.org/abs/2212.10264
### Dataset Summary
The Recode benchmark proposes to apply code and natural language transformations to code-generati... |
dane | 2023-01-25T14:29:05.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"task_ids:part-of-speech",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|other-Danish-Universal-Dependencies-treebank"... | null | The DaNE dataset has been annotated with Named Entities for PER, ORG and LOC
by the Alexandra Institute.
It is a reannotation of the UD-DDT (Universal Dependency - Danish Dependency Treebank)
which has annotations for dependency parsing and part-of-speech (POS) tagging.
The Danish UD treebank (Johannsen et al., 2015, U... | @inproceedings{hvingelby-etal-2020-dane,
title = "{D}a{NE}: A Named Entity Resource for {D}anish",
author = "Hvingelby, Rasmus and
Pauli, Amalie Brogaard and
Barrett, Maria and
Rosted, Christina and
Lidegaard, Lasse Malm and
Søgaard, Anders",
booktitle = "Proceedings of th... | null | 3 | 450 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- da
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-Danish-Universal-Dependencies-treebank
task_categories:
- token-classification
task_ids:
- named-entity-recognition
... |
bavard/personachat_truecased | 2021-04-23T13:28:30.000Z | [
"region:us"
] | bavard | A version of the PersonaChat dataset that has been true-cased, and also has been given more normalized punctuation.
The original PersonaChat dataset is in all lower case, and has extra space around each clause/sentence separating
punctuation mark. This version of the dataset has more of a natural language look, with se... | @article{zhang2018personalizing,
title={Personalizing dialogue agents: I have a dog, do you have pets too?},
author={Zhang, Saizheng and Dinan, Emily and Urbanek, Jack and Szlam, Arthur and Kiela, Douwe and Weston, Jason},
journal={arXiv preprint arXiv:1801.07243},
year={2018}
} | null | 22 | 450 | # A More Natural PersonaChat
## Dataset Summary
This dataset is a true-cased version of the PersonaChat dataset by Zhang et al. (2018).
The original PersonaChat dataset is all lower case, and has extra space around each
clause/sentence separating punctuation mark. This version of the dataset has more of a
natural lan... |
colbertv2/lotte_passages | 2023-08-23T01:55:55.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"arxiv:2112.01488",
"region:us"
] | colbertv2 | LoTTE Passages Dataset for ColBERTv2 | @inproceedings{santhanam-etal-2022-colbertv2,
title = "{C}ol{BERT}v2: Effective and Efficient Retrieval via Lightweight Late Interaction",
author = "Santhanam, Keshav and
Khattab, Omar and
Saad-Falcon, Jon and
Potts, Christopher and
Zaharia, Matei",
booktitle = "Proceedings of th... | null | 0 | 450 | ---
viewer: false
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: 'Lotte passages from ColBERTv2: Effective and Efficient Retrieval via
Lightweight Late Interaction'
size_categories:
- 1M<n<10M
source_datasets:
- origina... |
result-kand2-sdxl-wuerst-karlo/86947388 | 2023-09-24T22:54:36.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | null | 0 | 450 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 168
num_examples: 10
download_size: 1325
dataset_size: 168
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "8694738... |
codeparrot/codecomplex | 2022-10-25T09:30:16.000Z | [
"task_categories:text-generation",
"task_ids:language-modeling",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"language:code",
"license:apache-2.0",
"region:us"
] | codeparrot | null | null | null | 10 | 448 | ---
annotations_creators: []
language_creators:
- expert-generated
language:
- code
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets: []
task_categories:
- text-generation
task_ids:
- language-modeling
pretty_name: CodeComplex
---
# CodeComplex Dataset
## Dataset Descrip... |
jondurbin/airoboros-2.2.1 | 2023-09-18T21:22:40.000Z | [
"license:other",
"region:us"
] | jondurbin | null | null | null | 6 | 446 | ---
license: other
---
## Overview
This dataset is a slight update to 2.2.
### Re-generated writing responses
Many of the responses were generated by gpt-4-0613, which unfortunately produces much shorter and "dumber" (i.e. various readability scores increased compared to gpt-4-0314, e.g. Flesch, Gunning Fog, etc.) ... |
potsawee/wiki_bio_gpt3_hallucination | 2023-05-29T23:14:09.000Z | [
"task_categories:text-classification",
"size_categories:n<1K",
"language:en",
"license:cc-by-sa-3.0",
"arxiv:2303.08896",
"region:us"
] | potsawee | null | null | null | 8 | 445 | ---
license: cc-by-sa-3.0
task_categories:
- text-classification
language:
- en
size_categories:
- n<1K
dataset_info:
features:
- name: gpt3_text
dtype: string
- name: wiki_bio_text
dtype: string
- name: gpt3_sentences
sequence: string
- name: annotation
sequence: string
- name: wiki_bio_tes... |
OpenGVLab/InternVid | 2023-07-21T07:32:42.000Z | [
"task_categories:feature-extraction",
"size_categories:10M<n<100M",
"language:en",
"license:cc-by-nc-sa-4.0",
"arxiv:2307.06942",
"region:us"
] | OpenGVLab | The InternVid dataset contains over 7 million videos lasting nearly 760K hours, yielding 234M video clips accompanied by detailed descriptions of total 4.1B words. Our core contribution is to develop a scalable approach to autonomously build a high-quality video-text dataset with large language models (LLM), thereby sh... | @article{wang2023internvid,
title={InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation},
author={Wang, Yi and He, Yinan and Li, Yizhuo and Li, Kunchang and Yu, Jiashuo and Ma, Xin and Chen, Xinyuan and Wang, Yaohui and Luo, Ping and Liu, Ziwei and Wang, Yali and Wang, Limin and Q... | null | 18 | 445 | ---
license: cc-by-nc-sa-4.0
task_categories:
- feature-extraction
language:
- en
size_categories:
- 10M<n<100M
---
# InternVid
## Dataset Description
- **Homepage:** [InternVid](https://github.com/OpenGVLab/InternVideo/tree/main/Data/InternVid)
- **Repository:** [OpenGVLab](https://github.com/OpenGVLab/InternVideo... |
C-MTEB/MMarcoRetrieval | 2023-07-28T09:59:36.000Z | [
"region:us"
] | C-MTEB | null | null | null | 0 | 444 | ---
configs:
- config_name: default
data_files:
- split: corpus
path: data/corpus-*
- split: queries
path: data/queries-*
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 32552468
num_examples: 106813
- name: queries
... |
large_spanish_corpus | 2023-06-07T21:20:55.000Z | [
"task_categories:other",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:100M<n<1B",
"size_categories:10K<n<100K",
"size_categories:10M<n<100M",
"size_categories:1M<n<10M",
"source_datasets:ori... | null | The Large Spanish Corpus is a compilation of 15 unlabelled Spanish corpora spanning Wikipedia to European parliament notes. Each config contains the data corresponding to a different corpus. For example, "all_wiki" only includes examples from Spanish Wikipedia. By default, the config is set to "combined" which loads al... | @dataset{jose_canete_2019_3247731,
author = {José Cañete},
title = {Compilation of Large Spanish Unannotated Corpora},
month = may,
year = 2019,
publisher = {Zenodo},
doi = {10.5281/zenodo.3247731},
url = {https://doi.org/10.5281/zenodo.3247731}
} | null | 13 | 443 | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- es
license:
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 100M<n<1B
- 10K<n<100K
- 10M<n<100M
- 1M<n<10M
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: null
pretty_name... |
pietrolesci/stress_tests_nli | 2022-04-25T09:32:28.000Z | [
"region:us"
] | pietrolesci | null | null | null | 0 | 443 | ## Overview
Original dataset page [here](https://abhilasharavichander.github.io/NLI_StressTest/) and dataset available [here](https://drive.google.com/open?id=1faGA5pHdu5Co8rFhnXn-6jbBYC2R1dhw).
## Dataset curation
Added new column `label` with encoded labels with the following mapping
```
{"entailment": 0, "neutra... |
qwedsacf/grade-school-math-instructions | 2023-02-11T01:59:26.000Z | [
"region:us"
] | qwedsacf | null | null | null | 24 | 442 | ---
dataset_info:
features:
- name: INSTRUCTION
dtype: string
- name: RESPONSE
dtype: string
- name: SOURCE
dtype: string
splits:
- name: train
num_bytes: 4804916
num_examples: 8792
download_size: 2554896
dataset_size: 4804916
---
# Dataset Card for grade-school-math-instructions
Op... |
result-kand2-sdxl-wuerst-karlo/0c7ab6c4 | 2023-09-25T03:27:54.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | null | 0 | 442 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 166
num_examples: 10
download_size: 1324
dataset_size: 166
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "0c7ab6c... |
huggan/wikiart | 2023-03-22T13:56:08.000Z | [
"task_categories:image-classification",
"task_categories:text-to-image",
"task_categories:image-to-text",
"size_categories:10K<n<100K",
"license:unknown",
"art",
"region:us"
] | huggan | null | null | null | 38 | 441 | ---
license: unknown
license_details: Data files © Original Authors
size_categories:
- 10K<n<100K
task_categories:
- image-classification
- text-to-image
- image-to-text
tags:
- art
---
## Dataset Description
- **Homepage:** https://www.wikiart.org/
### Dataset Summary
Dataset containing 81,444 pieces of visual art... |
aadityaubhat/GPT-wiki-intro | 2023-10-03T22:48:42.000Z | [
"task_categories:text-classification",
"task_categories:zero-shot-classification",
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:cc",
"doi:10.57967/hf/0326",
"region:us"
] | aadityaubhat | null | null | null | 16 | 441 | ---
license: cc
task_categories:
- text-classification
- zero-shot-classification
- text-generation
pretty_name: GPT Wiki Intro
size_categories:
- 100K<n<1M
language:
- en
---
# GPT Wiki Intro
## Overview
Dataset for training models to classify human written vs GPT/ChatGPT generated text.
This dataset contains Wikip... |
NTU-NLP-sg/xCodeEval | 2023-06-03T21:33:12.000Z | [
"task_categories:translation",
"task_categories:token-classification",
"task_categories:text2text-generation",
"task_categories:text-retrieval",
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:feature-extraction",
"task_categories:question-answering",
"anno... | NTU-NLP-sg | The ability to solve problems is a hallmark of intelligence and has been an enduring goal in AI. AI systems that can create programs as solutions to problems or assist developers in writing programs can increase productivity and make programming more accessible. Recently, pre-trained large language models have shown im... | @misc{khan2023xcodeeval,
title={xCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval},
author={Mohammad Abdullah Matin Khan and M Saiful Bari and Xuan Long Do and Weishi Wang and Md Rizwan Parvez and Shafiq Joty},
year={2023},
eprint={2303.... | null | 22 | 441 | ---
annotations_creators:
- expert-generated
language:
- code
- en
language_creators:
- found
- expert-generated
license:
- cc-by-nc-4.0
multilinguality:
- multilingual
pretty_name: xCodeEval
size_categories:
- 1M<n<10M
- 10M<n<100M
source_datasets:
- original
tags:
- programming-language
- code
- program-... |
crime_and_punish | 2023-04-05T10:02:51.000Z | [
"language:en",
"region:us"
] | null | \ | null | null | 2 | 440 | ---
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
- [... |
segments/sidewalk-semantic | 2023-07-10T08:09:07.000Z | [
"task_categories:image-segmentation",
"task_ids:semantic-segmentation",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"size_categories:n<1K",
"source_datasets:original",
"license:cc-by-nc-4.0",
"region:us"
] | segments | null | null | null | 20 | 440 | ---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- expert-generated
license: cc-by-nc-4.0
multilinguality: []
pretty_name: sidewalk-semantic
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- image-segmentation
task_ids:
- semantic-segmentation
---
# Dataset Card for s... |
result-kand2-sdxl-wuerst-karlo/2b270b48 | 2023-09-25T03:43:57.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | null | 0 | 440 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 162
num_examples: 10
download_size: 1306
dataset_size: 162
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "2b270b4... |
peoples_daily_ner | 2023-01-25T14:42:22.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:zh",
"license:unknown",
"region:us"
] | null | People's Daily NER Dataset is a commonly used dataset for Chinese NER, with
text from People's Daily (人民日报), the largest official newspaper.
The dataset is in BIO scheme. Entity types are: PER (person), ORG (organization)
and LOC (location). | null | null | 6 | 439 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- zh
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: People's Daily NER
dataset_info:
... |
C-MTEB/MMarcoRetrieval-qrels | 2023-07-28T09:59:39.000Z | [
"region:us"
] | C-MTEB | null | null | null | 0 | 439 | ---
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
dataset_info:
features:
- name: qid
dtype: string
- name: pid
dtype: string
- name: score
dtype: int64
splits:
- name: dev
num_bytes: 217670
num_examples: 7437
download_size: 113896
dataset_size: 21... |
conll2002 | 2023-06-01T14:59:51.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"task_ids:part-of-speech",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:es",
"language:nl",
"license... | null | Named entities are phrases that contain the names of persons, organizations, locations, times and quantities.
Example:
[PER Wolff] , currently a journalist in [LOC Argentina] , played with [PER Del Bosque] in the final years of the seventies in [ORG Real Madrid] .
The shared task of CoNLL-2002 concerns language-indep... | @inproceedings{tjong-kim-sang-2002-introduction,
title = "Introduction to the {C}o{NLL}-2002 Shared Task: Language-Independent Named Entity Recognition",
author = "Tjong Kim Sang, Erik F.",
booktitle = "{COLING}-02: The 6th Conference on Natural Language Learning 2002 ({C}o{NLL}-2002)",
year = "2002",
... | null | 3 | 437 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- es
- nl
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
- part-of-speech
paperswithcode_id: conll-2002... |
mteb/arxiv-clustering-p2p | 2022-09-27T19:15:11.000Z | [
"language:en",
"region:us"
] | mteb | null | null | null | 0 | 436 | ---
language:
- en
--- |
suolyer/pile_books3 | 2023-03-27T03:01:19.000Z | [
"license:apache-2.0",
"region:us"
] | suolyer | null | null | null | 0 | 436 | ---
license: apache-2.0
---
|
rungalileo/20_Newsgroups_Fixed | 2022-10-25T10:25:50.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:topic-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unk... | rungalileo | null | null | null | 1 | 435 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
pretty_name: 20_Newsgroups_Fixed
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
- topic-cla... |
euirim/goodwiki | 2023-09-11T04:56:26.000Z | [
"task_categories:text-generation",
"task_categories:summarization",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"region:us"
] | euirim | null | null | null | 20 | 435 | ---
license: mit
task_categories:
- text-generation
- summarization
language:
- en
pretty_name: GoodWiki
size_categories:
- 10K<n<100K
---
# GoodWiki Dataset
GoodWiki is a 179 million token dataset of English Wikipedia articles collected on **September 4, 2023**, that have been marked as [Good](https://en.wikipedia.o... |
keremberke/pokemon-classification | 2023-01-15T18:41:29.000Z | [
"task_categories:image-classification",
"roboflow",
"roboflow2huggingface",
"Gaming",
"region:us"
] | keremberke | null | @misc{ pokedex_dataset,
title = { Pokedex Dataset },
type = { Open Source Dataset },
author = { Lance Zhang },
howpublished = { \\url{ https://universe.roboflow.com/robert-demo-qvail/pokedex } },
url = { https://universe.roboflow.com/robert-demo-qvail/pokedex },
journal = { Roboflow Universe },
... | null | 5 | 434 | ---
task_categories:
- image-classification
tags:
- roboflow
- roboflow2huggingface
- Gaming
---
<div align="center">
<img width="640" alt="keremberke/pokemon-classification" src="https://huggingface.co/datasets/keremberke/pokemon-classification/resolve/main/thumbnail.jpg">
</div>
### Dataset Labels
```
['Porygon'... |
medalpaca/medical_meadow_medical_flashcards | 2023-04-06T17:12:17.000Z | [
"task_categories:question-answering",
"language:en",
"license:cc",
"region:us"
] | medalpaca | null | null | null | 5 | 434 | ---
license: cc
task_categories:
- question-answering
language:
- en
---
# Dataset Card for Medical Flashcards
## Dataset Description
- **Repository:** https://github.com/kbressem/medalpaca
- **Paper:** TBA
### Dataset Summary
Medicine as a whole encompasses a wide range of subjects that medical students and gradua... |
sander-wood/irishman | 2023-09-25T15:14:16.000Z | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"license:mit",
"music",
"region:us"
] | sander-wood | null | null | null | 9 | 433 | ---
license: mit
task_categories:
- text-generation
pretty_name: IrishMAN
size_categories:
- 100K<n<1M
tags:
- music
---
If you prefer MIDI or MusicXML, download [IrishMAN-MIDI](https://huggingface.co/datasets/sander-wood/irishman/resolve/main/irishman-midi.zip) or [IrishMAN-XML](https://huggingface.co/datasets/sander... |
bigcode/commitpack | 2023-08-20T07:13:13.000Z | [
"language:code",
"license:mit",
"arxiv:2308.07124",
"region:us"
] | bigcode | CommitPack is is a 4TB dataset of commits scraped from GitHub repositories that are permissively licensed. | @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... | null | 31 | 429 | ---
license: mit
pretty_name: CommitPack
language:
- code
---
# Dataset Card for CommitPack
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-descri... |
iapp_wiki_qa_squad | 2022-11-18T20:08:21.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"task_ids:open-domain-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|other-iapp-wiki-qa-dataset",
"language:th",
"license:m... | null | `iapp_wiki_qa_squad` is an extractive question answering dataset from Thai Wikipedia articles.
It is adapted from [the original iapp-wiki-qa-dataset](https://github.com/iapp-technology/iapp-wiki-qa-dataset)
to [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) format, resulting in
5761/742/739 questions from 1529/191... | @dataset{kobkrit_viriyayudhakorn_2021_4539916,
author = {Kobkrit Viriyayudhakorn and
Charin Polpanumas},
title = {iapp_wiki_qa_squad},
month = feb,
year = 2021,
publisher = {Zenodo},
version = 1,
doi = {10.5281/zenodo.4539916},
url ... | null | 1 | 428 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- th
license:
- mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-iapp-wiki-qa-dataset
task_categories:
- question-answering
task_ids:
- extractive-qa
- open-domain-qa
paperswithcode_id: null... |
ai4bharat/IndicCOPA | 2022-12-15T11:34:32.000Z | [
"task_categories:multiple-choice",
"task_ids:multiple-choice-qa",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:extended|xcopa",
"language:as",
"language:bn",
"language:en",
"language:go... | ai4bharat | \ | \ | null | 1 | 428 | ---
annotations_creators:
- expert-generated
language:
- as
- bn
- en
- gom
- gu
- hi
- kn
- mai
- ml
- mr
- ne
- or
- pa
- sa
- sat
- sd
- ta
- te
- ur
language_creators:
- expert-generated
license:
- cc-by-4.0
multilinguality:
- multilingual
pretty_name: IndicXCOPA
size_categories:
- 1K<n<10K
source_datasets:
- exten... |
medalpaca/medical_meadow_mediqa | 2023-04-16T16:30:36.000Z | [
"task_categories:question-answering",
"language:en",
"region:us"
] | medalpaca | null | null | null | 5 | 428 | ---
task_categories:
- question-answering
language:
- en
---
# MediQA
## Dataset Description
MEDIQA is a dataset of manually generated, question-driven summaries of multi and single document answers to consumer health questions.
- **Homepage:** https://osf.io/fyg46/?view_only=
### Citation Information
```
@artic... |
lamini/text_to_sql_finetune | 2023-09-01T05:39:56.000Z | [
"region:us"
] | lamini | null | null | null | 2 | 428 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 34818227
num_examples: 16428
- name: test
num_... |
nlu_evaluation_data | 2023-01-25T14:41:34.000Z | [
"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... | null | Raw part of NLU Evaluation Data. It contains 25 715 non-empty examples (original dataset has 25716 examples) from 68 unique intents belonging to 18 scenarios. | @InProceedings{XLiu.etal:IWSDS2019,
author = {Xingkun Liu, Arash Eshghi, Pawel Swietojanski and Verena Rieser},
title = {Benchmarking Natural Language Understanding Services for building Conversational Agents},
booktitle = {Proceedings of the Tenth International Workshop on Spoken Dialogue Systems Technolo... | null | 7 | 426 | ---
annotations_creators:
- expert-generated
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-classification
pretty_nam... |
HuggingFaceH4/CodeAlpaca_20K | 2023-03-28T17:26:28.000Z | [
"task_categories:text-generation",
"license:cc",
"region:us"
] | HuggingFaceH4 | null | null | null | 38 | 426 | ---
license: cc
task_categories:
- text-generation
---
This dataset splits the original [CodeAlpaca dataset](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k) into train and test splits. |
yzhuang/autotree_automl_10000_covertype_sgosdt_l256_dim10_d3_sd0 | 2023-09-07T03:42:01.000Z | [
"region:us"
] | yzhuang | null | null | null | 0 | 426 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: input_y_clean
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: flo... |
miracl/miracl | 2023-01-06T16:25:49.000Z | [
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"annotations_creators:expert-generated",
"multilinguality:multilingual",
"language:ar",
"language:bn",
"language:en",
"language:es",
"language:fa",
"language:fi",
"language:fr",
"language:hi",
"language:id",
"language:ja",
... | miracl | null | null | null | 22 | 425 | ---
annotations_creators:
- expert-generated
language:
- ar
- bn
- en
- es
- fa
- fi
- fr
- hi
- id
- ja
- ko
- ru
- sw
- te
- th
- zh
multilinguality:
- multilingual
pretty_name: MIRACL-corpus
size_categories: []
source_datasets: []
tags: []
task_categories:
- text-retrieval
license:
- apache-2.0
task_ids:
- do... |
edbeeching/decision_transformer_gym_replay | 2022-04-20T12:39:58.000Z | [
"license:apache-2.0",
"arxiv:2004.07219",
"region:us"
] | edbeeching | A subset of the D4RL dataset, used for training Decision Transformers | null | null | 2 | 424 | ---
license: apache-2.0
pretty_name: D4RL-gym
---
# Dataset Card for D4RL-gym
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Dataset Structure](#dataset-structure)
- [Data... |
stas/c4-en-10k | 2022-10-19T21:40:11.000Z | [
"language:en",
"license:apache-2.0",
"region:us"
] | stas | This is a small subset representing the first 10K records of the original C4 dataset, "en" subset - created for testing. The records were extracted after having been shuffled.
The full 1TB+ dataset is at https://huggingface.co/datasets/c4. | @article{2019t5,
author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
journal = {arXiv e-prints},
year = {2... | null | 1 | 423 | ---
language:
- en
license: apache-2.0
---
# C4 EN 10K for testing
This is a small subset representing the first 10K records of the original C4 dataset, "en" subset - created for testing. The records were extracted after having been shuffled.
The full 1TB+ dataset is at https://huggingface.co/datasets/c4.
```
$ p... |
detection-datasets/coco | 2023-03-15T15:11:53.000Z | [
"task_categories:object-detection",
"language:en",
"region:us"
] | detection-datasets | null | null | null | 7 | 422 | ---
task_categories:
- object-detection
language:
- en
--- |
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... | null | 4 | 421 | ---
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... |
bigbio/biomrc | 2022-12-22T15:43:44.000Z | [
"multilinguality:monolingual",
"language:en",
"license:unknown",
"region:us"
] | bigbio | We introduce BIOMRC, a large-scale cloze-style biomedical MRC dataset. Care was taken to reduce noise, compared to the
previous BIOREAD dataset of Pappas et al. (2018). Experiments show that simple heuristics do not perform well on the
new dataset and that two neural MRC models that had been tested on BIOREAD perform m... | @inproceedings{pappas-etal-2020-biomrc,
title = "{B}io{MRC}: A Dataset for Biomedical Machine Reading Comprehension",
author = "Pappas, Dimitris and
Stavropoulos, Petros and
Androutsopoulos, Ion and
McDonald, Ryan",
booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical L... | null | 0 | 421 |
---
language:
- en
bigbio_language:
- English
license: unknown
multilinguality: monolingual
bigbio_license_shortname: UNKNOWN
pretty_name: BIOMRC
homepage: https://github.com/PetrosStav/BioMRC_code
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- QUESTION_ANSWERING
---
# Dataset Card for BIOMRC
## Dataset... |
neulab/tldr | 2022-12-22T19:47:11.000Z | [
"task_categories:text2text-generation",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:code",
"license:mit",
"code-generation",
"doc retrieval",
"retrieval augmented generatio... | neulab | This is the re-split of CoNaLa dataset. For each code snippet in the dev and test set, at least one function is held out from the training set. This split aims at testing a code generation model's capacity in generating unseen functions.
We further make sure that examples from the same StackOverflow post (same question... | @article{zhou2022doccoder,
title={DocCoder: Generating Code by Retrieving and Reading Docs},
author={Zhou, Shuyan and Alon, Uri and Xu, Frank F and JIang, Zhengbao and Neubig, Graham},
journal={arXiv preprint arXiv:2207.05987},
year={2022}
} | null | 4 | 419 | ---
annotations_creators: []
language_creators:
- crowdsourced
- expert-generated
language:
- code
license:
- mit
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
pretty_name: DocPrompting-CoNaLa
tags:
- code-generation
- doc retr... |
patimus-prime/strain_selection | 2023-06-28T00:58:15.000Z | [
"license:mit",
"region:us"
] | patimus-prime | null | null | null | 0 | 419 | ---
license: mit
---
|
bigscience/xP3mt | 2023-05-30T15:50:57.000Z | [
"task_categories:other",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:100M<n<1B",
"language:ak",
"language:ar",
"language:as",
"language:bm",
"language:bn",
"language:ca",
"language:code",
"language:en",
"lan... | bigscience | xP3 (Crosslingual Public Pool of Prompts) is a collection of prompts & datasets across 46 of languages & 16 NLP tasks. It is used for the training of BLOOMZ and mT0, multilingual language models capable of following human instructions in dozens of languages zero-shot. | @misc{muennighoff2022crosslingual,
title={Crosslingual Generalization through Multitask Finetuning},
author={Niklas Muennighoff and Thomas Wang and Lintang Sutawika and Adam Roberts and Stella Biderman and Teven Le Scao and M Saiful Bari and Sheng Shen and Zheng-Xin Yong and Hailey Schoelkopf and Xiangru T... | null | 18 | 418 | ---
annotations_creators:
- expert-generated
- crowdsourced
language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
- es
- eu
- fon
- fr
- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
- ne
- nso
- ny
- or
- pa
- pt
- rn
- rw
- sn
- st
- sw
- ta
- te
- tn
- ts
- tum
- tw
- ur
- vi
- wo
- xh
- yo
- zh
- zu
programming_lan... |
TigerResearch/tigerbot-alpaca-zh-0.5m | 2023-05-31T01:14:23.000Z | [
"language:zh",
"license:apache-2.0",
"region:us"
] | TigerResearch | null | null | null | 1 | 418 | ---
license: apache-2.0
language:
- zh
---
[Tigerbot](https://github.com/TigerResearch/TigerBot) 自有基于alpaca生成中文问答对
<p align="center" width="40%">
## Usage
```python
import datasets
ds_sft = datasets.load_dataset('TigerResearch/tigerbot-alpaca-zh-0.5m')
```
|
open-source-metrics/model-repos-stats | 2023-07-03T01:35:17.000Z | [
"region:us"
] | open-source-metrics | null | null | null | 5 | 417 | ---
dataset_info:
features:
- name: 'Unnamed: 0'
dtype: int64
- name: repo_id
dtype: string
- name: author
dtype: string
- name: model_type
dtype: string
- name: files_per_repo
dtype: int64
- name: downloads_30d
dtype: int64
- name: library
dtype: string
- name: likes
d... |
jxie/stl10 | 2023-08-10T07:13:23.000Z | [
"region:us"
] | jxie | null | null | null | 0 | 416 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': '1'
'1': '10'
'2': '2'
'3': '3'
'4': '4'
'5': '5'
'6': '6'
'7': '7'
'8': '8'
'9': '9'
spl... |
shawhin/imdb-truncated | 2023-09-06T21:06:35.000Z | [
"region:us"
] | shawhin | null | null | null | 0 | 416 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: label
dtype: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 1310325
num_examples: 1000
- name: valida... |
ScandEval/scala-da | 2023-07-05T09:50:02.000Z | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:da",
"license:cc-by-sa-4.0",
"region:us"
] | ScandEval | null | null | null | 1 | 415 | ---
license: cc-by-sa-4.0
task_categories:
- text-classification
language:
- da
size_categories:
- 1K<n<10K
--- |
TigerResearch/tigerbot-stackexchange-qa-en-0.5m | 2023-05-31T02:21:45.000Z | [
"language:en",
"license:apache-2.0",
"region:us"
] | TigerResearch | null | null | null | 0 | 414 | ---
license: apache-2.0
language:
- en
---
[Tigerbot](https://github.com/TigerResearch/TigerBot) 基于stackexchange问答站点dump数据生成sft数据集
<p align="center" width="40%">
原始来源：[https://archive.org/details/stackexchange](https://archive.org/details/stackexchange)
## Usage
```python
import datasets
ds_sft = datasets.load_data... |
art | 2023-04-05T09:36:25.000Z | [
"task_categories:multiple-choice",
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:unknown",... | null | the Abductive Natural Language Inference Dataset from AI2 | @InProceedings{anli,
author = {Chandra, Bhagavatula and Ronan, Le Bras and Chaitanya, Malaviya and Keisuke, Sakaguchi and Ari, Holtzman
and Hannah, Rashkin and Doug, Downey and Scott, Wen-tau Yih and Yejin, Choi},
title = {Abductive Commonsense Reasoning},
year = {2020}
} | null | 3 | 413 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- multiple-choice
- text-classification
task_ids:
- natural-language-inference
paperswithcode_id: art-dataset
pre... |
argilla/oasst_response_comparison | 2023-07-25T11:39:45.000Z | [
"size_categories:1K<n<10K",
"rlfh",
"argilla",
"human-feedback",
"region:us"
] | argilla | null | null | null | 0 | 413 | ---
size_categories: 1K<n<10K
tags:
- rlfh
- argilla
- human-feedback
---
# Dataset Card for oasst_response_comparison
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)... |
anon8231489123/ShareGPT_Vicuna_unfiltered | 2023-04-12T05:23:59.000Z | [
"language:en",
"license:apache-2.0",
"region:us"
] | anon8231489123 | null | null | null | 566 | 411 | ---
license: apache-2.0
language:
- en
---
**Further cleaning done. Please look through the dataset and ensure that I didn't miss anything.**
**Update: Confirmed working method for training the model: https://huggingface.co/AlekseyKorshuk/vicuna-7b/discussions/4#64346c08ef6d5abefe42c12c**
Two choices:
- Removes insta... |
Stevross/mmlu | 2023-07-11T12:04:33.000Z | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"arxiv:2009.03300",
"arxiv:2005.... | Stevross | 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)}... | null | 3 | 411 | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
paperswithcode_id: mmlu
pretty_name: Measuring Massi... |
discovery | 2023-06-02T12:27:46.000Z | [
"task_categories:text-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"discourse-marker-prediction",
"region:us"
] | null | null | @inproceedings{sileo-etal-2019-mining,
title = "Mining Discourse Markers for Unsupervised Sentence Representation Learning",
author = "Sileo, Damien and
Van De Cruys, Tim and
Pradel, Camille and
Muller, Philippe",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican C... | null | 5 | 410 | ---
annotations_creators:
- other
language_creators:
- other
language:
- en
license: apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: discovery
pretty_name: Discovery
tags:
- discourse-ma... |
ucberkeley-dlab/measuring-hate-speech | 2022-11-15T15:44:31.000Z | [
"task_categories:text-classification",
"task_ids:hate-speech-detection",
"task_ids:sentiment-classification",
"task_ids:multi-label-classification",
"annotations_creators:crowdsourced",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:2009.10277... | ucberkeley-dlab | null | null | null | 14 | 410 | ---
annotations_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- hate-speech-detection
- sentiment-classification
- multi-label-classification
pretty_name: measuring-hate-speech
tags:
- arxiv:2009.1... |
laion/laion-art | 2022-05-22T14:55:35.000Z | [
"region:us"
] | laion | null | null | null | 23 | 410 | Entry not found |
result-kand2-sdxl-wuerst-karlo/d39ae2e0 | 2023-09-26T01:14:35.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | null | 0 | 410 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 169
num_examples: 10
download_size: 1317
dataset_size: 169
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "d39ae2e... |
result-kand2-sdxl-wuerst-karlo/6400c282 | 2023-09-26T01:34:38.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | null | 0 | 409 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 165
num_examples: 10
download_size: 1313
dataset_size: 165
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "6400c28... |
cjvt/si_nli | 2023-04-04T08:51:01.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:natural-language-inference",
"annotations_creators:expert-generated",
"language_creators:found",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:sl",
"... | cjvt | SI-NLI (Slovene Natural Language Inference Dataset) contains 5,937 human-created Slovene sentence pairs
(premise and hypothesis) that are manually labeled with the labels "entailment", "contradiction", and "neutral".
The dataset was created using sentences that appear in the Slovenian reference corpus ccKres.
Annota... | @misc{sinli,
title = {Slovene Natural Language Inference Dataset {SI}-{NLI}},
author = {Klemen, Matej and {\v Z}agar, Ale{\v s} and {\v C}ibej, Jaka and Robnik-{\v S}ikonja, Marko},
url = {http://hdl.handle.net/11356/1707},
note = {Slovenian language resource repository {CLARIN}.{SI}},
year = {2022}... | null | 0 | 408 | ---
annotations_creators:
- expert-generated
language:
- sl
language_creators:
- found
- expert-generated
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
pretty_name: Slovene natural language inference dataset
size_categories:
- 1K<n<10K
source_datasets: []
tags: []
task_categories:
- text-classification
task... |
pierreguillou/DocLayNet-small | 2023-05-17T08:56:10.000Z | [
"task_categories:object-detection",
"task_categories:image-segmentation",
"task_categories:token-classification",
"task_ids:instance-segmentation",
"annotations_creators:crowdsourced",
"size_categories:1K<n<10K",
"language:en",
"language:de",
"language:fr",
"language:ja",
"license:other",
"Doc... | pierreguillou | Accurate document layout analysis is a key requirement for high-quality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adeq... | @article{doclaynet2022,
title = {DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis},
doi = {10.1145/3534678.353904},
url = {https://arxiv.org/abs/2206.01062},
author = {Pfitzmann, Birgit and Auer, Christoph and Dolfi, Michele and Nassar, Ahmed S and Staar, Peter W J},
year = {2022}
} | null | 7 | 408 | ---
language:
- en
- de
- fr
- ja
annotations_creators:
- crowdsourced
license: other
pretty_name: DocLayNet small
size_categories:
- 1K<n<10K
tags:
- DocLayNet
- COCO
- PDF
- IBM
- Financial-Reports
- Finance
- Manuals
- Scientific-Articles
- Science
- Laws
- Law
- Regulations
- Patents
- Government-Tenders
- object-d... |
Gholamreza/pquad | 2023-02-18T15:00:06.000Z | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:fa",
"license:cc-by-sa-4.0",
"regio... | Gholamreza | \\\PQuAD: PQuAD is a crowd-sourced reading comprehension dataset on Persian Language. | @article{darvishi2022pquad,
title={PQuAD: A Persian Question Answering Dataset},
author={Darvishi, Kasra and Shahbodagh, Newsha and Abbasiantaeb, Zahra and Momtazi, Saeedeh},
journal={arXiv preprint arXiv:2202.06219},
year={2022}
} | null | 2 | 408 | ---
pretty_name: PQuAD
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- fa
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
- extractive-qa
paperswithcode_id... |
siyue/squall | 2023-09-08T06:08:06.000Z | [
"task_categories:table-question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"region:us"
] | siyue | To explore the utility of fine-grained, lexical-level supervision, authors introduce SQUALL, a dataset that enriches 11,276 WikiTableQuestions \
English-language questions with manually created SQL equivalents plus \
alignments between SQL and question fragments. | @inproceedings{Shi:Zhao:Boyd-Graber:Daume-III:Lee-2020,
Title = {On the Potential of Lexico-logical Alignments for Semantic Parsing to {SQL} Queries},
Author = {Tianze Shi and Chen Zhao and Jordan Boyd-Graber and Hal {Daum\'{e} III} and Lillian Lee},
Booktitle = {Findings of EMNLP},
Year = {2020},
} | null | 0 | 408 | ---
license: mit
task_categories:
- table-question-answering
language:
- en
pretty_name: SQUALL
size_categories:
- 10K<n<100K
---
## SQUALL Dataset
To explore the utility of fine-grained, lexical-level supervision, authors introduce SQUALL, a dataset that enriches 11,276 WikiTableQuestions English-language questions ... |
aslg_pc12 | 2023-04-05T09:36:28.000Z | [
"task_categories:translation",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ase",
"language:en",
"license:cc-by-nc-4.0",
"region:us"
] | null | A large synthetic collection of parallel English and ASL-Gloss texts.
There are two string features: text, and gloss. | @inproceedings{othman2012english,
title={English-asl gloss parallel corpus 2012: Aslg-pc12},
author={Othman, Achraf and Jemni, Mohamed},
booktitle={5th Workshop on the Representation and Processing of Sign Languages: Interactions between Corpus and Lexicon LREC},
year={2012}
} | null | 2 | 407 | ---
annotations_creators:
- crowdsourced
- expert-generated
language:
- ase
- en
language_creators:
- found
license:
- cc-by-nc-4.0
multilinguality:
- translation
pretty_name: English-ASL Gloss Parallel Corpus 2012
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
pap... |
christykoh/imdb_pt | 2023-04-05T16:28:11.000Z | [
"region:us"
] | christykoh | null | null | null | 0 | 407 | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': negativo
'1': positivo
splits:
- name: train
num_bytes: 33225773
num_examples: 25000
- name: test
num_bytes: 6503491
num_examples: 5000
- name: tes... |
gmongaras/reddit_political_2019_Feb | 2023-09-15T02:29:18.000Z | [
"license:openrail",
"region:us"
] | gmongaras | null | null | null | 0 | 406 | ---
license: openrail
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1360555778
num_examples: 5808978
download_size: 832828536
dataset_size: 1360555778
---
Data from https://zenodo.org/record/5851729, dataset comments_2017-02.bz2
In format of: score: {scor... |
blended_skill_talk | 2023-04-05T09:41:47.000Z | [
"task_categories:conversational",
"task_ids:dialogue-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"arxiv:2004.08449",
"region:us"
] | null | A dataset of 7k conversations explicitly designed to exhibit multiple conversation modes: displaying personality, having empathy, and demonstrating knowledge. | @misc{smith2020evaluating,
title={Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills},
author={Eric Michael Smith and Mary Williamson and Kurt Shuster and Jason Weston and Y-Lan Boureau},
year={2020},
eprint={2004.08449},
archivePrefix={arXiv},
primaryClass={c... | null | 42 | 405 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
pretty_name: BlendedSkillTalk
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- conversational
task_ids:
- dialogue-generation
paperswithcode_id: blended-s... |
google_wellformed_query | 2022-11-18T20:04:48.000Z | [
"task_categories:text-classification",
"task_ids:text-scoring",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended",
"language:en",
"license:cc-by-sa-4.0",
"arxiv:1808.09419",
"region:us"
] | null | Google's query wellformedness dataset was created by crowdsourcing well-formedness annotations for 25,100 queries from the Paralex corpus. Every query was annotated by five raters each with 1/0 rating of whether or not the query is well-formed. | @misc{faruqui2018identifying,
title={Identifying Well-formed Natural Language Questions},
author={Manaal Faruqui and Dipanjan Das},
year={2018},
eprint={1808.09419},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | null | 7 | 404 | ---
task_categories:
- text-classification
multilinguality:
- monolingual
task_ids:
- text-scoring
language:
- en
annotations_creators:
- crowdsourced
source_datasets:
- extended
size_categories:
- 10K<n<100K
license:
- cc-by-sa-4.0
paperswithcode_id: null
pretty_name: GoogleWellformedQuery
language_creators:
- found
d... |
lavita/ChatDoctor-iCliniq | 2023-09-11T21:13:37.000Z | [
"region:us"
] | lavita | null | null | null | 0 | 404 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: input
dtype: string
- name: answer_icliniq
dtype: string
- name: answer_chatgpt
dtype: string
- name: answer_chatdoctor
dtype: string
splits:
- name: train
num_byte... |
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}
} | null | 14 | 402 | ---
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... |
wmt20_mlqe_task1 | 2023-06-01T14:59:51.000Z | [
"task_categories:translation",
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:translation",
"size_categories:1K<n<10K",
"source_datasets:extended|reddit",
"source_datasets:extended|wikipedia",
"language:de",
"language:... | null | This shared task (part of WMT20) will build on its previous editions
to further examine automatic methods for estimating the quality
of neural machine translation output at run-time, without relying
on reference translations. As in previous years, we cover estimation
at various levels. Important elements introduced thi... | Not available. | null | 1 | 402 | ---
pretty_name: WMT20 - MultiLingual Quality Estimation (MLQE) Task1
annotations_creators:
- expert-generated
- machine-generated
language_creators:
- found
language:
- de
- en
- et
- ne
- ro
- ru
- si
- zh
license:
- unknown
multilinguality:
- translation
size_categories:
- 1K<n<10K
source_datasets:
- extended|reddit... |
TigerResearch/tigerbot-gsm-8k-en | 2023-05-31T01:38:37.000Z | [
"language:en",
"license:mit",
"region:us"
] | TigerResearch | null | null | null | 0 | 402 | ---
license: mit
language:
- en
---
[Tigerbot](https://github.com/TigerResearch/TigerBot) 基于gsm8k数据集加工而来
GSM8K（Grade School Math 8K）是一个包含 8.5K 高质量语言多样化小学数学单词问题的数据集。创建数据集是为了支持对需要多步推理的基本数学问题的问答任务。
原始来源：[https://huggingface.co/datasets/gsm8k](https://huggingface.co/datasets/gsm8k)
<p align="center" width="40%">
## U... |
jnlpba | 2023-04-14T13:49:49.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-genia-v3.02",
"language:en",
"license:unknown",
"... | null | The data came from the GENIA version 3.02 corpus (Kim et al., 2003). This was formed from a controlled search
on MEDLINE using the MeSH terms human, blood cells and transcription factors. From this search 2,000 abstracts
were selected and hand annotated according to a small taxonomy of 48 classes based on a chemi... | @inproceedings{kim2004introduction,
title={Introduction to the bio-entity recognition task at JNLPBA},
author={Kim, Jin-Dong and Ohta, Tomoko and Tsuruoka, Yoshimasa and Tateisi, Yuka and Collier, Nigel},
booktitle={Proceedings of the international joint workshop on natural ... | null | 5 | 400 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-genia-v3.02
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: BioNLP... |
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,
... | null | 1 | 400 |
---
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... |
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}
} | null | 10 | 399 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: stereoset
pretty_name: StereoSet
tags:
- stereot... |
cardiffnlp/tweet_topic_single | 2022-11-27T11:25:34.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"multilinguality:monolingual",
"size_categories:1k<10K",
"language:en",
"license:other",
"arxiv:2209.09824",
"region:us"
] | cardiffnlp | [TweetTopic](https://arxiv.org/abs/2209.09824) | @inproceedings{dimosthenis-etal-2022-twitter,
title = "{T}witter {T}opic {C}lassification",
author = "Antypas, Dimosthenis and
Ushio, Asahi and
Camacho-Collados, Jose and
Neves, Leonardo and
Silva, Vitor and
Barbieri, Francesco",
booktitle = "Proceedings of the 29th International Co... | null | 3 | 399 | ---
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 1k<10K
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: TweetTopicSingle
---
# Dataset Card for "cardiffnlp/tweet_topic_single"
## Dataset Description
- **Paper:** [https://arxiv.org/abs/2209... |
allenai/qasper | 2022-10-07T22:04:11.000Z | [
"task_categories:question-answering",
"task_ids:closed-domain-qa",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|s2orc",
"language:en",
"license:cc-by-4.0",
"arxiv:2105.03011",
... | allenai | A dataset containing 1585 papers with 5049 information-seeking questions asked by regular readers of NLP papers, and answered by a separate set of NLP practitioners. | @inproceedings{Dasigi2021ADO,
title={A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers},
author={Pradeep Dasigi and Kyle Lo and Iz Beltagy and Arman Cohan and Noah A. Smith and Matt Gardner},
year={2021}
} | null | 33 | 397 | ---
pretty_name: QASPER
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
language_bcp47:
- en-US
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|s2orc
task_categories:
- question-answering
task_ids:
- closed-domai... |
kaist-ai/CoT-Collection | 2023-06-05T09:08:12.000Z | [
"task_categories:text-generation",
"task_categories:text-classification",
"size_categories:1M<n<10M",
"language:en",
"license:cc-by-4.0",
"arxiv:2305.14045",
"region:us"
] | kaist-ai | """
_LICENSE = "CC BY 4.0"
_HOMEPAGE = "https://github.com/kaistAI/CoT-Collection"
_LANGUAGES = {
"en": "English",
}
# _ALL_LANGUAGES = "all_languages"
class CoTCollectionMultiConfig(datasets.BuilderConfig): | @article{kim2023cot,
title={The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning},
author={Kim, Seungone and Joo, Se June and Kim, Doyoung and Jang, Joel and Ye, Seonghyeon and Shin, Jamin and Seo, Minjoon},
journal={arXiv preprint arXiv:2305.14045},
... | null | 31 | 397 | ---
license: cc-by-4.0
task_categories:
- text-generation
- text-classification
language:
- en
size_categories:
- 1M<n<10M
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:https://github.com/kaistAI/CoT-Collection**
- **Repository:https://github.com/kaistAI/CoT-Collection**
- **Paper:https:/... |
reciprocate/vicuna-fair-eval | 2023-06-15T14:47:39.000Z | [
"region:us"
] | reciprocate | null | null | null | 0 | 397 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: selected
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 180638
num_examples: 66
download_size: 116978
dataset_size: 180638
---
# Dataset Card for "vicuna_fair_eval"
[More Information need... |
EleutherAI/asdiv | 2023-07-07T14:59:09.000Z | [
"region:us"
] | EleutherAI | ASDiv (Academia Sinica Diverse MWP Dataset) is a diverse (in terms of both language
patterns and problem types) English math word problem (MWP) corpus for evaluating
the capability of various MWP solvers. Existing MWP corpora for studying AI progress
remain limited either in language usage patterns or in problem types.... | @misc{miao2021diverse,
title={A Diverse Corpus for Evaluating and Developing English Math Word Problem Solvers},
author={Shen-Yun Miao and Chao-Chun Liang and Keh-Yih Su},
year={2021},
eprint={2106.15772},
archivePrefix={arXiv},
primaryClass={cs.AI}
} | null | 0 | 396 | Entry not found |
Martin2203/ex-repair | 2023-10-07T06:03:03.000Z | [
"license:mit",
"region:us"
] | Martin2203 | null | null | null | 1 | 396 | ---
license: mit
configs:
- config_name: sequencer-corpus-in
data_files: "corpus-data/sequencer/sequencer_corpus_input.csv"
- config_name: sequencer-corpus-out
data_files: "corpus-data/sequencer/sequencer_corpus_output.csv"
- config_name: sequencer-defects4j-in
data_files: "model-data/sequencer/defects4j_input.c... |
Random-Mary-Smith/port_data_random | 2023-10-06T13:16:39.000Z | [
"license:mit",
"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 = | null | 0 | 396 | ---
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
... |
nielsr/rvl_cdip_10_examples_per_class_donut | 2022-08-01T16:56:12.000Z | [
"region:us"
] | nielsr | null | null | null | 0 | 395 | Entry not found |
evanarlian/imagenet_1k_resized_256 | 2023-08-01T10:26:36.000Z | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:derived",
"language:en",
"license:other",
"region:us"
] | evanarlian | null | null | null | 3 | 395 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- derived
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
paperswithcode_id: imagenet
pretty_name: I... |
result-kand2-sdxl-wuerst-karlo/52f7d589 | 2023-09-26T06:35:54.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | null | 0 | 395 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 167
num_examples: 10
download_size: 1327
dataset_size: 167
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "52f7d58... |
mdd | 2023-06-01T14:59:51.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:dialogue-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"licen... | null | The Movie Dialog dataset (MDD) is designed to measure how well
models can perform at goal and non-goal orientated dialog
centered around the topic of movies (question answering,
recommendation and discussion). | @misc{dodge2016evaluating,
title={Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems},
author={Jesse Dodge and Andreea Gane and Xiang Zhang and Antoine Bordes and Sumit Chopra and Alexander Miller and Arthur Szlam and Jason Weston},
year={2016},
eprint={1511.06931},
a... | null | 3 | 394 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- cc-by-3.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- dialogue-modeling
paperswithcode_id: mdd
pretty_name: Mov... |
BeIR/fiqa-qrels | 2022-10-23T06:06:29.000Z | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | BeIR | null | null | null | 0 | 394 | ---
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:
... |
dennlinger/eur-lex-sum | 2022-11-11T14:25:06.000Z | [
"task_categories:translation",
"task_categories:summarization",
"annotations_creators:found",
"annotations_creators:expert-generated",
"language_creators:found",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"languag... | dennlinger | The EUR-Lex-Sum dataset is a multilingual resource intended for text summarization in the legal domain.
It is based on human-written summaries of legal acts issued by the European Union.
It distinguishes itself by introducing a smaller set of high-quality human-written samples,
each of which have much longer references... | @article{aumiller-etal-2022-eur,
author = {Aumiller, Dennis and Chouhan, Ashish and Gertz, Michael},
title = {{EUR-Lex-Sum: A Multi- and Cross-lingual Dataset for Long-form Summarization in the Legal Domain}},
journal = {CoRR},
volume = {abs/2210.13448},
eprinttype = {arXiv},
eprint = {2210.13448},
url = {https://arxiv... | null | 18 | 393 | ---
annotations_creators:
- found
- expert-generated
language:
- bg
- hr
- cs
- da
- nl
- en
- et
- fi
- fr
- de
- el
- hu
- ga
- it
- lv
- lt
- mt
- pl
- pt
- ro
- sk
- sl
- es
- sv
language_creators:
- found
- expert-generated
license:
- cc-by-4.0
multilinguality:
- multilingual
pretty_name: eur-lex-sum
size_categori... |
cdminix/bu_radio | 2023-07-17T15:55:35.000Z | [
"license:other",
"region:us"
] | cdminix | The Boston University Radio Speech Corpus was collected primarily to support research in text-to-speech synthesis, particularly generation of prosodic patterns. The corpus consists of professionally read radio news data, including speech and accompanying annotations, suitable for speech and language research. | @article{ostendorf1995boston,
title={The Boston University radio news corpus},
author={Ostendorf, Mari and Price, Patti J and Shattuck-Hufnagel, Stefanie},
journal={Linguistic Data Consortium},
pages={1--19},
year={1995}
} | null | 0 | 393 | ---
license: other
---
Simply point ``BURN_PATH`` to your local copy of the dataset.
|
teknium/openhermes | 2023-09-07T20:41:05.000Z | [
"task_categories:text-generation",
"language:eng",
"distillation",
"synthetic data",
"gpt",
"region:us"
] | teknium | null | null | null | 47 | 393 | ---
language:
- eng
pretty_name: "OpenHermes-v1.0"
tags:
- distillation
- synthetic data
- gpt
task_categories:
- text-generation
---
# OpenHermes Dataset
The OpenHermes dataset is composed of 242,... |
laion/dalle-3-dataset | 2023-10-11T01:03:07.000Z | [
"language:en",
"license:cc0-1.0",
"image-text-dataset",
"synthetic-dataset",
"region:us"
] | laion | null | null | null | 60 | 392 | ---
language:
- en
license:
- cc0-1.0
tags:
- image-text-dataset
- synthetic-dataset
dataset_info:
features:
- name: caption
dtype: string
- name: image
dtype: image
- name: link
dtype: string
- name: message_id
dtype: string
- name: timestamp
dtype: string
splits:
- name: train
... |
KETI-AIR/korquad | 2021-06-03T00:37:09.000Z | [
"region:us"
] | KETI-AIR | KorQuAD1.0 | @article{DBLP:journals/corr/abs-1909-07005,
author = {Seungyoung Lim and
Myungji Kim and
Jooyoul Lee},
title = {KorQuAD1.0: Korean {QA} Dataset for Machine Reading Comprehension},
journal = {CoRR},
volume = {abs/1909.07005},
year = {2019},
url = {http://a... | null | 0 | 391 | <!--
Copyright 2021 san kim
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, softw... |
ArtifactAI/arxiv-physics-instruct-tune-30k | 2023-06-20T04:35:10.000Z | [
"doi:10.57967/hf/0801",
"region:us"
] | ArtifactAI | null | null | null | 15 | 391 | Entry not found |
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