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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
gmnlp/tico19 | 2021-10-03T19:00:13.000Z | [
"region:us"
] | gmnlp | In response to the on-going crisis, several academic (Carnegie Mellon University,
George Mason University, Johns Hopkins University) and industry (Amazon, Appen,
Facebook, Google, Microsoft, Translated) partners have partnered with the Translators
without Borders to prepare COVID-19 materials for a variety of the wo... | @article{DBLP:journals/corr/abs-2007-01788,
author = {Antonios Anastasopoulos and
Alessandro Cattelan and
Zi{-}Yi Dou and
Marcello Federico and
Christian Federmann and
Dmitriy Genzel and
Francisco Guzm{\'{a}}n and
... | 1 | 1,596 | 2022-03-02T23:29:22 | The TICO-19 evaluation set provides:
* Predefined dev and test splits. We provide English-XX translation files under both the `dev` and `test` directories.
* The dev set includes 971 sentences, and the test set includes 2100 sentences.
* The corresponding IDs are listed in the `dev.ids` and `test.ids` files.
The form... | 1,083 | [
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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 ... | 2 | 1,586 | 2022-03-02T23:29:22 | ---
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... | 7,166 | [
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LeoCordoba/CC-NEWS-ES | 2023-02-23T21:53:55.000Z | [
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"task_categories:text-generation",
"annotations_creators:no-annotation",
"language_creators:found",
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"size_categories:1M<n<1... | LeoCordoba | null | 8 | 1,583 | 2022-03-02T23:29:22 | ---
annotations_creators:
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language_creators:
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language:
- es
license:
- mit
multilinguality:
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size_categories:
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- 10K<n<100K
- 100K<n<1M
- 1M<n<10M
source_datasets:
- cc-news
task_categories:
- summarization
- text-generation
task_ids: []
tags:
- conditional-text-gen... | 6,028 | [
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ucberkeley-dlab/measuring-hate-speech | 2022-11-15T15:44:31.000Z | [
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"task_ids:hate-speech-detection",
"task_ids:sentiment-classification",
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"language:en",
"license:cc-by-4.0",
"arxiv:2009.10277... | ucberkeley-dlab | null | null | 14 | 1,575 | 2022-03-02T23:29:22 | ---
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... | 4,026 | [
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C-MTEB/Mmarco-reranking | 2023-07-28T07:25:10.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 1,570 | 2023-07-28T07:24:47 | ---
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
dataset_info:
features:
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dtype: string
- name: positive
sequence: string
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download_size: 1740151... | 521 | [
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WizardLM/WizardLM_evol_instruct_V2_196k | 2023-08-24T03:55:18.000Z | [
"arxiv:2308.09583",
"arxiv:2304.12244",
"arxiv:2306.08568",
"region:us"
] | WizardLM | null | null | 150 | 1,569 | 2023-06-15T14:05:45 |
## News
- 🔥 🔥 🔥 [08/11/2023] We release **WizardMath** Models.
- 🔥 Our **WizardMath-70B-V1.0** model slightly outperforms some closed-source LLMs on the GSM8K, including **ChatGPT 3.5**, **Claude Instant 1** and **PaLM 2 540B**.
- 🔥 Our **WizardMath-70B-V1.0** model achieves **81.6 pass@1** on the [GSM8k Benchm... | 4,449 | [
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C-MTEB/CMedQAv1-reranking | 2023-07-28T07:19:52.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 1,569 | 2023-07-28T07:19:27 | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
- name: query
dtype: string
- name: positive
sequence: string
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splits:
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num_bytes: 31879155
num_examples: 1000
download_size: 206... | 527 | [
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lamini/alpaca | 2023-07-23T06:29:21.000Z | [
"region:us"
] | lamini | null | null | 1 | 1,566 | 2023-07-23T06:29:20 | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
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num_bytes: 27364517
num_examples: 52002
download_size: 12742513
dataset_size: 27364517
---
# Dataset Card for "alpaca"
[More Information needed](https://github.com/huggingface/datase... | 388 | [
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C-MTEB/CMedQAv2-reranking | 2023-07-28T07:17:06.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 1,564 | 2023-07-28T07:16:41 | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
- name: query
dtype: string
- name: positive
sequence: string
- name: negative
sequence: string
splits:
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num_bytes: 30417770
num_examples: 1000
download_size: 197... | 527 | [
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Villekom/oa_dolly_15k_fi | 2023-08-23T14:15:07.000Z | [
"region:us"
] | Villekom | null | null | 0 | 1,561 | 2023-08-23T14:15:04 | ---
dataset_info:
features:
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dtype: string
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struct:
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dtype: string
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dtype: string
splits:
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num_bytes: 13654728
num_examples... | 637 | [
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speech_commands | 2023-06-01T14:59:53.000Z | [
"task_categories:audio-classification",
"task_ids:keyword-spotting",
"annotations_creators:other",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:18... | null | This is a set of one-second .wav audio files, each containing a single spoken
English word or background noise. These words are from a small set of commands, and are spoken by a
variety of different speakers. This data set is designed to help train simple
machine learning models. This dataset is covered in more detail ... | @article{speechcommandsv2,
author = { {Warden}, P.},
title = "{Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition}",
journal = {ArXiv e-prints},
archivePrefix = "arXiv",
eprint = {1804.03209},
primaryClass = "cs.CL",
keywords = {Computer Science - Computation and Language, Computer Sc... | 13 | 1,560 | 2022-03-02T23:29:22 | ---
annotations_creators:
- other
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 10K<n<100K
source_datasets:
- original
task_categories:
- audio-classification
task_ids:
- keyword-spotting
pretty_name: SpeechCommands
dataset_info:
- co... | 12,076 | [
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poem_sentiment | 2023-01-25T14:42:40.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:2011.02686",
"region:u... | null | Poem Sentiment is a sentiment dataset of poem verses from Project Gutenberg. This dataset can be used for tasks such as sentiment classification or style transfer for poems. | @misc{sheng2020investigating,
title={Investigating Societal Biases in a Poetry Composition System},
author={Emily Sheng and David Uthus},
year={2020},
eprint={2011.02686},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 9 | 1,558 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: gutenberg-poem-dataset
pretty_... | 5,508 | [
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lhoestq/test2 | 2021-07-23T14:21:45.000Z | [
"region:us"
] | lhoestq | null | null | 0 | 1,558 | 2022-03-02T23:29:22 | This is a readme
| 17 | [
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winograd_wsc | 2023-01-25T15:02:35.000Z | [
"task_categories:multiple-choice",
"task_ids:multiple-choice-coreference-resolution",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] | null | A Winograd schema is a pair of sentences that differ in only one or two words and that contain an ambiguity that is
resolved in opposite ways in the two sentences and requires the use of world knowledge and reasoning for its
resolution. The schema takes its name from a well-known example by Terry Winograd:
> The city ... | @inproceedings{levesque2012winograd,
title={The winograd schema challenge},
author={Levesque, Hector and Davis, Ernest and Morgenstern, Leora},
booktitle={Thirteenth International Conference on the Principles of Knowledge Representation and Reasoning},
year={2012},
organization={Citeseer}
} | 5 | 1,556 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- multiple-choice
task_ids:
- multiple-choice-coreference-resolution
paperswithcode_id: wsc
pretty_na... | 8,590 | [
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ccdv/arxiv-summarization | 2022-12-08T06:58:05.000Z | [
"task_categories:summarization",
"task_categories:text-generation",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:en",
"conditional-text-generation",
"region:us"
] | ccdv | Arxiv dataset for summarization.
From paper: A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents" by A. Cohan et al.
See: https://aclanthology.org/N18-2097.pdf
See: https://github.com/armancohan/long-summarization | @inproceedings{cohan-etal-2018-discourse,
title = "A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents",
author = "Cohan, Arman and
Dernoncourt, Franck and
Kim, Doo Soon and
Bui, Trung and
Kim, Seokhwan and
Chang, Walter and
Goharian, N... | 37 | 1,555 | 2022-03-02T23:29:22 | ---
language:
- en
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
task_categories:
- summarization
- text-generation
task_ids: []
tags:
- conditional-text-generation
train-eval-index:
- config: document
task: summarization
task_id: summarization
splits:
eval_split: test
col_mapping:
article... | 2,829 | [
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pile-of-law/pile-of-law | 2023-01-08T03:10:35.000Z | [
"task_categories:fill-mask",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"language:en",
"license:cc-by-nc-sa-4.0",
"arxiv:2207.00220",
"region:us"
] | pile-of-law | We curate a large corpus of legal and administrative data. The utility of this data is twofold: (1) to aggregate legal and administrative data sources that demonstrate different norms and legal standards for data filtering; (2) to collect a dataset that can be used in the future for pretraining legal-domain language mo... | @misc{hendersonkrass2022pileoflaw,
url = {https://arxiv.org/abs/2207.00220},
author = {Henderson, Peter and Krass, Mark S. and Zheng, Lucia and Guha, Neel and Manning, Christopher D. and Jurafsky, Dan and Ho, Daniel E.},
title = {Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Sourc... | 128 | 1,551 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
pretty_name: pile-of-law
size_categories:
- 10M<n<100M
source_datasets: []
task_categories:
- fill-mask
task_ids:
- masked-language-modeling
viewer: false
---
# Dataset Card for... | 25,624 | [
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C-MTEB/DuRetrieval-qrels | 2023-07-28T09:48:53.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 1,551 | 2023-07-28T09:48:49 | ---
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
dataset_info:
features:
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dtype: int64
splits:
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num_bytes: 787120
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download_size: 420443
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-... |
KBLab/overlim | 2022-10-25T06:13:06.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"task_ids:semantic-similarity-classification",
"task_ids:sentiment-classification",
"task_ids:text-scoring",
"annotations_creators:other",
"language_creators:other",
"multilinguality:translation",
"size_categories:unknown"... | KBLab | \ | \ | 3 | 1,526 | 2022-03-02T23:29:22 | ---
annotations_creators:
- other
language_creators:
- other
language:
- sv
- da
- nb
license:
- cc-by-4.0
multilinguality:
- translation
size_categories:
- unknown
source_datasets:
- extended|glue
- extended|super_glue
task_categories:
- text-classification
task_ids:
- natural-language-inference
- semantic-similarity-... | 3,259 | [
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conceptual_captions | 2022-11-03T16:32:04.000Z | [
"task_categories:image-to-text",
"task_ids:image-captioning",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:other",
"region:us"
] | null | Google's Conceptual Captions dataset has more than 3 million images, paired with natural-language captions.
In contrast with the curated style of the MS-COCO images, Conceptual Captions images and their raw descriptions are harvested from the web,
and therefore represent a wider variety of styles. The raw descriptions ... | @inproceedings{sharma2018conceptual,
title = {Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning},
author = {Sharma, Piyush and Ding, Nan and Goodman, Sebastian and Soricut, Radu},
booktitle = {Proceedings of ACL},
year = {2018},
} | 37 | 1,507 | 2022-04-14T13:08:21 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- image-to-text
task_ids:
- image-captioning
paperswithcode_id: conceptual-captions
pretty_name: Conceptual Captions
datase... | 13,831 | [
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tyqiangz/multilingual-sentiments | 2023-05-23T15:01:51.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-analysis",
"task_ids:sentiment-classification",
"multilinguality:monolingual",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"size_categories:1M<n<10M",
"language:de",
"language:en",
"language:es",
"language:fr",
"langua... | tyqiangz | null | null | 20 | 1,505 | 2022-08-21T11:04:38 | ---
language:
- de
- en
- es
- fr
- ja
- zh
- id
- ar
- hi
- it
- ms
- pt
license: apache-2.0
multilinguality:
- monolingual
- multilingual
size_categories:
- 100K<n<1M
- 1M<n<10M
task_categories:
- text-classification
task_ids:
- sentiment-analysis
- sentiment-classification
---
# Multilingual Sentiments Dataset
A c... | 1,205 | [
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lucasmccabe-lmi/CodeAlpaca-20k | 2023-05-19T00:10:02.000Z | [
"region:us"
] | lucasmccabe-lmi | null | null | 6 | 1,489 | 2023-05-19T00:09:27 | ---
dataset_info:
features:
- name: instruction
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splits:
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download_size: 3450938
dataset_size: 6576710.0
---
# Dataset Card for "CodeAlpaca-20k"
We provide a m... | 677 | [
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SetFit/CR | 2022-06-21T09:04:33.000Z | [
"region:us"
] | SetFit | null | null | 0 | 1,488 | 2022-06-10T14:30:21 | # Customer Reviews
This dataset is a port of the official [`CR` dataset](https://github.com/hiyouga/Dual-Contrastive-Learning/tree/main/data) from [this paper](https://www.cs.uic.edu/~liub/FBS/opinion-mining-final-WSDM.pdf).
There is no validation split. | 255 | [
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mozilla-foundation/common_voice_2_0 | 2023-07-29T15:59:58.000Z | [
"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... | 1 | 1,478 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
license:
- cc0-1.0
multilinguality:
- multilingual
size_categories:
br:
- 10K<n<100K
ca:
- 10K<n<100K
cnh:
- 1K<n<10K
cv:
- 1K<n<10K
cy:
- 10K<n<100K
de:
- 100K<n<1M
dv:
- 1K<n<10K
en:
- 100K<n<1M
eo:
- 10K<n<... | 9,574 | [
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Joanne/Unified_Benchmark_for_Metaphor_Identification | 2023-03-13T17:32:19.000Z | [
"region:us"
] | Joanne | [Unified Benchmark for Metaphor Identification] | null | 0 | 1,473 | 2023-03-07T20:22:54 | Entry not found | 15 | [
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203427as321/articles | 2023-11-03T01:00:07.000Z | [
"region:us"
] | 203427as321 | null | null | 0 | 1,473 | 2023-05-25T19:13:43 | ---
dataset_info:
features:
- name: label
dtype: string
- name: text
dtype: string
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dtype: float64
splits:
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num_bytes: 23996247
num_examples: 1534
download_size: 0
dataset_size: 23996247
---
# Dataset Card for "articles"
[More Information needed... | 427 | [
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SetFit/20_newsgroups | 2022-02-03T08:27:00.000Z | [
"region:us"
] | SetFit | null | null | 5 | 1,472 | 2022-03-02T23:29:22 | This is a version of the [20 newsgroups dataset](https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html#the-20-newsgroups-text-dataset) that is provided in Scikit-learn. From the Scikit-learn docs:
> The 20 newsgroups dataset comprises around 18000 newsgroups posts on 20 topics split in two subsets: one for tra... | 734 | [
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C-MTEB/VideoRetrieval-qrels | 2023-07-28T09:22:40.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 1,469 | 2023-07-28T09:22:33 | ---
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
dataset_info:
features:
- name: qid
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splits:
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dataset_size: 2796... | 500 | [
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HuggingFaceH4/ultrachat_200k | 2023-10-27T08:53:22.000Z | [
"task_categories:conversational",
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"arxiv:2305.14233",
"arxiv:2310.16944",
"region:us"
] | HuggingFaceH4 | null | null | 56 | 1,466 | 2023-10-24T08:24:57 | ---
language:
- en
license: mit
size_categories:
- 100K<n<1M
task_categories:
- conversational
- text-generation
pretty_name: UltraChat 200k
configs:
- config_name: default
data_files:
- split: train_sft
path: data/train_sft-*
- split: test_sft
path: data/test_sft-*
- split: train_gen
path: data/tra... | 4,457 | [
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emozilla/pg_books-tokenized-bos-eos-chunked-65536 | 2023-10-07T02:19:15.000Z | [
"region:us"
] | emozilla | null | null | 3 | 1,461 | 2023-08-31T15:54:46 | ---
configs:
- config_name: default
data_files:
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path: data/train-*
dataset_info:
features:
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sequence: int32
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sequence: int8
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Graphcore/gqa | 2022-10-25T08:59:27.000Z | [
"language:en",
"license:cc-by-4.0",
"region:us"
] | Graphcore | GQA is a new dataset for real-world visual reasoning and compositional question answering,
seeking to address key shortcomings of previous visual question answering (VQA) datasets. | @inproceedings{hudson2019gqa,
title={Gqa: A new dataset for real-world visual reasoning and compositional question answering},
author={Hudson, Drew A and Manning, Christopher D},
booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
pages={6700--6709},
year={2019}
} | 0 | 1,456 | 2022-03-02T23:29:22 | ---
language:
- en
license:
- cc-by-4.0
---
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C-MTEB/MMarcoRetrieval-qrels | 2023-07-28T09:59:39.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 1,454 | 2023-07-28T09:59:36 | ---
configs:
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data_files:
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path: data/dev-*
dataset_info:
features:
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splits:
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num_bytes: 217670
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download_size: 113896
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cdleong/piglatin-mt | 2022-10-24T19:22:09.000Z | [
"task_categories:translation",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"region:us"
] | cdleong | \\r\nPig-latin machine and English parallel machine translation corpus.
Based on
The Project Gutenberg EBook of "De Bello Gallico" and Other Commentaries
https://www.gutenberg.org/ebooks/10657
Converted to pig-latin with https://github.com/bpabel/piglatin | \\r\n@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
} | 0 | 1,452 | 2022-03-02T23:29:22 | ---
language:
- en
license:
- mit
multilinguality:
- translation
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
language_details: eng and engyay
---
## Dataset Description
- **Homepage:** cdleong.github.io
# Dataset Summary:
Pig-latin machine and English paralle... | 1,336 | [
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qanastek/MASSIVE | 2022-12-23T21:28:08.000Z | [
"task_categories:text-classification",
"task_ids:intent-classification",
"task_ids:multi-class-classification",
"task_ids:named-entity-recognition",
"annotations_creators:machine-generated",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:multilingual",
"size_cat... | qanastek | MASSIVE is a parallel dataset of > 1M utterances across 51 languages with annotations
for the Natural Language Understanding tasks of intent prediction and slot annotation.
Utterances span 60 intents and include 55 slot types. MASSIVE was created by localizing
the SLURP dataset, composed of general Intelligent Voice As... | @misc{fitzgerald2022massive,
title={MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages},
author={Jack FitzGerald and Christopher Hench and Charith Peris and Scott Mackie and Kay Rottmann and Ana Sanchez and Aaron Nash and Liam Urbach and Vishes... | 16 | 1,447 | 2022-04-23T16:23:09 | ---
annotations_creators:
- machine-generated
- expert-generated
language_creators:
- found
language:
- af
- am
- ar
- az
- bn
- cy
- da
- de
- el
- en
- es
- fa
- fi
- fr
- he
- hi
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- km
- kn
- ko
- lv
- ml
- mn
- ms
- my
- nb
- nl
- pl
- pt
- ro
- ru
- sl
- sq
- sv
- sw
- ta
- t... | 34,117 | [
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C-MTEB/CovidRetrieval-qrels | 2023-07-28T09:44:39.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 1,438 | 2023-07-28T09:44:36 | ---
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: 76720
num_examples: 959
download_size: 62785
dataset_size: 76720... | 499 | [
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castorini/mr-tydi | 2022-10-12T20:25:19.000Z | [
"task_categories:text-retrieval",
"multilinguality:multilingual",
"language:ar",
"language:bn",
"language:en",
"language:fi",
"language:id",
"language:ja",
"language:ko",
"language:ru",
"language:sw",
"language:te",
"language:th",
"license:apache-2.0",
"region:us"
] | castorini | null | null | 10 | 1,437 | 2022-03-02T23:29:22 | ---
language:
- ar
- bn
- en
- fi
- id
- fi
- ja
- ko
- ru
- sw
- te
- th
multilinguality:
- multilingual
task_categories:
- text-retrieval
license: apache-2.0
---
# Dataset Summary
Mr. TyDi is a multi-lingual benchmark dataset built on TyDi, covering eleven typologically diverse l... | 2,800 | [
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C-MTEB/CmedqaRetrieval-qrels | 2023-07-28T09:40:21.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 1,432 | 2023-07-28T09:40:18 | ---
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
dataset_info:
features:
- name: qid
dtype: string
- name: pid
dtype: string
- name: score
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splits:
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num_bytes: 595920
num_examples: 7449
download_size: 404005
dataset_size: 59... | 504 | [
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0.00518035888671875,
0.041259765625,
0.029632568359375,
-0.06903076171875,
-0.056854248046875,
-0.032623291015625,
-0.01928... |
esnli | 2023-04-05T10:05:24.000Z | [
"language:en",
"region:us"
] | null | The e-SNLI dataset extends the Stanford Natural Language Inference Dataset to
include human-annotated natural language explanations of the entailment
relations. | @incollection{NIPS2018_8163,
title = {e-SNLI: Natural Language Inference with Natural Language Explanations},
author = {Camburu, Oana-Maria and Rockt\"{a}schel, Tim and Lukasiewicz, Thomas and Blunsom, Phil},
booktitle = {Advances in Neural Information Processing Systems 31},
editor = {S. Bengio and H. Wallach and H. L... | 14 | 1,425 | 2022-03-02T23:29:22 | ---
language:
- en
paperswithcode_id: e-snli
pretty_name: e-SNLI
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
- name: expla... | 6,899 | [
[
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0.032379150390625,
-0.05975341796875,
-0.0596923828125,
-0.03656005859375,
0.01... |
shi3z/anthropic_hh_rlhf_japanese | 2023-06-29T01:19:09.000Z | [
"license:mit",
"region:us"
] | shi3z | null | null | 8 | 1,419 | 2023-06-29T00:07:38 | ---
license: mit
---
https://huggingface.co/datasets/Anthropic/hh-rlhf
Japanese Translation | 92 | [
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-0.02484130859375,
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... |
elyza/ELYZA-tasks-100 | 2023-09-26T01:38:42.000Z | [
"task_categories:text2text-generation",
"size_categories:n<1K",
"language:ja",
"license:cc-by-sa-4.0",
"arxiv:2307.09288",
"region:us"
] | elyza | null | null | 27 | 1,419 | 2023-08-28T09:01:44 | ---
task_categories:
- text2text-generation
language:
- ja
size_categories:
- n<1K
license: cc-by-sa-4.0
---
# ELYZA-tasks-100: 日本語instructionモデル評価データセット
## Data Description
本データセットはinstruction-tuningを行ったモデルの評価用データセットです。詳細は [リリースのnote記事](https://note.com/elyza/n/na405acaca130) を参照してく... | 5,787 | [
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-0.04638671875,
-0.033233642578125,
0.0118637084... |
deepset/germanquad | 2023-04-06T13:58:35.000Z | [
"task_categories:question-answering",
"task_categories:text-retrieval",
"task_ids:extractive-qa",
"task_ids:closed-domain-qa",
"task_ids:open-domain-qa",
"multilinguality:monolingual",
"source_datasets:original",
"language:de",
"license:cc-by-4.0",
"arxiv:2104.12741",
"region:us"
] | deepset | In order to raise the bar for non-English QA, we are releasing a high-quality, human-labeled German QA dataset consisting of 13 722 questions, incl. a three-way annotated test set.
The creation of GermanQuAD is inspired by insights from existing datasets as well as our labeling experience from several industry projects... | @misc{möller2021germanquad,
title={GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrieval},
author={Timo Möller and Julian Risch and Malte Pietsch},
year={2021},
eprint={2104.12741},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 22 | 1,416 | 2022-03-02T23:29:22 | ---
thumbnail: >-
https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg
language:
- de
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- question-answering
- text-retrieval
task_ids:
- extractive-qa
- closed-domain-qa
- open-domain-qa
tr... | 6,456 | [
[
-0.057281494140625,
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0.0213165283203125,
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-0.05511474609375,
-0.0177001953125,
0.03509... |
C-MTEB/EcomRetrieval-qrels | 2023-07-28T09:37:58.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 1,414 | 2023-07-28T09:37:55 | ---
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: 27890
num_examples: 1000
download_size: 14540
dataset_size: 2789... | 499 | [
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0.0262603759765625,
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-0.044830322265625,
-0.0280303955078125,
-0.0... |
yerevann/sst2 | 2022-02-02T20:02:45.000Z | [
"region:us"
] | yerevann | null | null | 0 | 1,409 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
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-0.01497650146484375,
-0.0604248046875,
0.0379028... |
derek-thomas/ScienceQA | 2023-02-25T04:23:01.000Z | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:other",
"task_categories:visual-question-answering",
"task_categories:text-classification",
"task_ids:multiple-choice-qa",
"task_ids:closed-domain-qa",
"task_ids:open-domain-qa",
"task_ids:visual-question-answe... | derek-thomas | null | null | 74 | 1,408 | 2023-02-10T11:28:58 | ---
license: cc-by-sa-4.0
annotations_creators:
- expert-generated
- found
language:
- en
language_creators:
- expert-generated
- found
multilinguality:
- monolingual
paperswithcode_id: scienceqa
pretty_name: ScienceQA
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- multi-modal-qa
- science
- chemistr... | 10,308 | [
[
-0.038543701171875,
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0.0244140625,
-0.057098388671875,
-0.06378173828125,
-0.0292510986328125,
0.014076... |
Alanox/stanford-dogs | 2023-09-08T13:51:01.000Z | [
"license:mit",
"region:us"
] | Alanox | The Stanford Dogs dataset contains images of 120 breeds of dogs from around the world. This dataset has been built using images and annotation from ImageNet for the task of fine-grained image categorization. | null | 1 | 1,406 | 2023-09-03T10:15:44 | ---
pretty_name: "Stanford Dogs"
license: "mit"
task_category: "Classification"
---
# Dataset
This dataset is extracted from [Stanford Dogs Dataset](http://vision.stanford.edu/aditya86/ImageNetDogs/)
# Load
```python
import datasets
dataset = datasets.load_dataset("Alanox/stanford-dogs", split="full")
print(datas... | 954 | [
[
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0.0377197265625,
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-0.052398681640625,
-0.050994873046875,
0.01... |
THUDM/humaneval-x | 2022-10-25T06:08:38.000Z | [
"task_categories:text-generation",
"task_ids:language-modeling",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:unknown",
"language:code",
"license:apache-2.0",
"region:us"
] | THUDM | HumanEval-X is a benchmark for the evaluation of the multilingual ability of code generative models. It consists of 820 high-quality human-crafted data samples (each with test cases) in Python, C++, Java, JavaScript, and Go, and can be used for various tasks. | null | 47 | 1,404 | 2022-09-20T16:23:53 | ---
annotations_creators: []
language_creators:
- crowdsourced
- expert-generated
language:
- code
license:
- apache-2.0
multilinguality:
- multilingual
size_categories:
- unknown
source_datasets: []
task_categories:
- text-generation
task_ids:
- language-modeling
pretty_name: HumanEval-X
---
# HumanEval-X
## Dataset... | 3,500 | [
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... |
C-MTEB/MedicalRetrieval-qrels | 2023-07-28T09:34:03.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 1,401 | 2023-07-28T09:33:59 | ---
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: 26893
num_examples: 1000
download_size: 12201
dataset_size: 2689... | 502 | [
[
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-0.... |
coqa | 2023-04-05T10:02:34.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|race",
"source_datasets:extended|cnn_dailymail",
"source_datasets:extended|wikipedia",
... | null | CoQA: A Conversational Question Answering Challenge | @article{reddy-etal-2019-coqa,
title = "{C}o{QA}: A Conversational Question Answering Challenge",
author = "Reddy, Siva and
Chen, Danqi and
Manning, Christopher D.",
journal = "Transactions of the Association for Computational Linguistics",
volume = "7",
year = "2019",
address = "C... | 25 | 1,397 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- other
multilinguality:
- monolingual
pretty_name: 'CoQA: Conversational Question Answering Challenge'
size_categories:
- 1K<n<10K
source_datasets:
- extended|race
- extended|cnn_dailymail
- extended|wikipedia
- extended|other
... | 8,032 | [
[
-0.051727294921875,
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0.036651611328125,
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-0.0264129638671875,
0.00473022... |
hackathon-pln-es/readability-es-caes | 2023-04-13T08:51:40.000Z | [
"task_categories:text-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:es",
"license:cc-by-4.0",
"readability",
"region:us"
] | hackathon-pln-es | null | null | 1 | 1,395 | 2022-04-03T21:42:19 | ---
annotations_creators:
- other
language_creators:
- other
language:
- es
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
pretty_name: readability-es-caes
tags:
- readability
---
# Dataset Card for [readabi... | 1,851 | [
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-0.03424072265625,
0.0... |
lansinuote/ChnSentiCorp | 2023-02-28T05:31:30.000Z | [
"region:us"
] | lansinuote | null | null | 9 | 1,392 | 2023-02-28T05:31:08 | Entry not found | 15 | [
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0.03793334... |
alespalla/chatbot_instruction_prompts | 2023-03-21T13:36:36.000Z | [
"task_categories:question-answering",
"task_categories:conversational",
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"region:us"
] | alespalla | null | null | 23 | 1,392 | 2023-03-17T08:44:25 | ---
license: apache-2.0
dataset_info:
features:
- name: response
dtype: string
- name: prompt
dtype: string
splits:
- name: test
num_bytes: 24612503
num_examples: 64511
- name: train
num_bytes: 98485829
num_examples: 258042
download_size: 78591384
dataset_size: 123098332
task_cat... | 836 | [
[
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0.0572509765625,
-0.08160400390625,
-0.032684326171875,
-0.0113677978515625,
0.01261... |
keremberke/chest-xray-classification | 2023-01-18T09:25:27.000Z | [
"task_categories:image-classification",
"roboflow",
"roboflow2huggingface",
"Biology",
"region:us"
] | keremberke | null | \ | 9 | 1,391 | 2023-01-18T09:22:08 | ---
task_categories:
- image-classification
tags:
- roboflow
- roboflow2huggingface
- Biology
---
<div align="center">
<img width="640" alt="keremberke/chest-xray-classification" src="https://huggingface.co/datasets/keremberke/chest-xray-classification/resolve/main/thumbnail.jpg">
</div>
### Dataset Labels
```
['N... | 1,333 | [
[
-0.0219879150390625,
0.003925323486328125,
0.02996826171875,
-0.01349639892578125,
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0.025360107421875,
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... |
Babelscape/wikineural | 2022-11-13T07:52:46.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:multilingual",
"source_datasets:original",
"language:de",
"language:en",
"language:es",
"language:fr",
"language:it",
"... | Babelscape | null | null | 15 | 1,390 | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- de
- en
- es
- fr
- it
- nl
- pl
- pt
- ru
license:
- cc-by-nc-sa-4.0
multilinguality:
- multilingual
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: wik... | 5,268 | [
[
-0.040008544921875,
-0.038970947265625,
-0.004634857177734375,
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0.00893402099609375,
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0.045989990234375,
0.01258087158203125,
-0.029754638671875,
-0.057281494140625,
-0.04003906... |
UBC-NLP/orca | 2023-11-01T21:39:03.000Z | [
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:question-answering",
"language:ara",
"Arabic",
"NLU Benchmark",
"Natural Language Inference (NLI)",
"Question Answering (QA)",
"Semantic Textual Similarity and and Paraphrase (STSP)",
"Sentence Classifi... | UBC-NLP | null | null | 4 | 1,390 | 2022-03-10T19:45:30 |
---
viewer: false
language:
- ara
tags:
- Arabic
- NLU Benchmark
- Natural Language Inference (NLI)
- Question Answering (QA)
- Semantic Textual Similarity and and Paraphrase (STSP)
- Sentence Classification (SC)
- Structure Predictions (SP)
- Topic Classification (TC)
- Word Sense Disambiguation (WSD)
task_categorie... | 12,110 | [
[
-0.040496826171875,
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0.02069091796875,
-0.036041259765625,
-0.0670166015625,
-0.040435791015625,
0.02090... |
liwu/MNBVC | 2023-10-29T12:37:26.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:zh",
"licens... | liwu | MNBVC: Massive Never-ending BT Vast Chinese corpus | \ | 267 | 1,386 | 2023-02-13T14:00:47 | ---
annotations_creators:
- other
language:
- zh
language_creators:
- other
license:
- mit
multilinguality:
- monolingual
pretty_name: MNBVC
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
---
# Dataset Card ... | 2,389 | [
[
-0.051422119140625,
-0.04107666015625,
0.00806427001953125,
0.00885772705078125,
-0.042266845703125,
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0.021484375,
-0.040191650390625,
-0.058807373046875,
-0.0210113525390625,
-0.0... |
empathetic_dialogues | 2023-04-05T10:05:17.000Z | [
"task_categories:conversational",
"task_categories:question-answering",
"task_ids:dialogue-generation",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"langua... | null | PyTorch original implementation of Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset | @inproceedings{rashkin2019towards,
title = {Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset},
author = {Hannah Rashkin and Eric Michael Smith and Margaret Li and Y-Lan Boureau},
booktitle = {ACL},
year = {2019},
} | 53 | 1,382 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- crowdsourced
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
pretty_name: EmpatheticDialogues
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- conversational
- question-answering
task_ids:
- dialogue-generati... | 7,152 | [
[
-0.0440673828125,
-0.06890869140625,
0.01702880859375,
0.0159454345703125,
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0.026336669921875,
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-0.0731201171875,
-0.0308990478515625,
-0.0011... |
fever | 2023-04-05T10:06:17.000Z | [
"task_categories:text-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|wikipedia",
"language:en",
"license:cc-by-sa-3.0",
"license:gpl-3.0",
"knowledge-verification",
"region:us"... | null | null | null | 9 | 1,382 | 2022-03-02T23:29:22 | ---
language:
- en
paperswithcode_id: fever
annotations_creators:
- crowdsourced
language_creators:
- found
license:
- cc-by-sa-3.0
- gpl-3.0
multilinguality:
- monolingual
pretty_name: FEVER
size_categories:
- 100K<n<1M
source_datasets:
- extended|wikipedia
task_categories:
- text-classification
task_ids: []
tags:
- k... | 11,841 | [
[
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0.0... |
allegro/klej-dyk | 2022-10-26T09:01:41.000Z | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:pl",
"license:cc-by-sa-3.0",
"region:us"
] | allegro | null | null | 1 | 1,380 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- other
language:
- pl
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
pretty_name: Did you know?
---
# klej-dyk
## Descriptio... | 3,793 | [
[
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0.0... |
pie/conll2003 | 2023-11-02T20:15:51.000Z | [
"region:us"
] | pie | null | null | 0 | 1,380 | 2022-04-21T14:15:40 | # PIE Dataset Card for "conll2003"
This is a [PyTorch-IE](https://github.com/ChristophAlt/pytorch-ie) wrapper for the
[CoNLL 2003 Huggingface dataset loading script](https://huggingface.co/datasets/conll2003).
## Data Schema
The document type for this dataset is `CoNLL2003Document` which defines the following data f... | 927 | [
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0.04193115234375,
-0.04266357421875,
-0.05194091796875,
-0.03961181640625,
0.01... |
naver-clova-ix/synthdog-en | 2022-07-22T06:42:50.000Z | [
"region:us"
] | naver-clova-ix | null | null | 5 | 1,377 | 2022-07-20T05:33:24 | Entry not found | 15 | [
[
-0.0213775634765625,
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0.0379028... |
SetFit/stsb | 2022-02-28T14:20:16.000Z | [
"region:us"
] | SetFit | null | null | 0 | 1,372 | 2022-03-02T23:29:22 | # Glue STS-B
This dataset is a port of the official [`sts-b` dataset](https://huggingface.co/datasets/glue/viewer/stsb/validation) on the Hub.
This is not a classification task, so the label_text column is only included for consistency
Note that the sentence1 and sentence2 columns have been renamed to text1 and t... | 417 | [
[
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0.051239013671875,
0.0284881591796875,
-0.07281494140625,
-0.0379638671875,
-0.04046630859375,
0.... |
neulab/docprompting-conala | 2023-03-14T17:59:47.000Z | [
"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}
} | 3 | 1,370 | 2022-12-22T02:40:47 | ---
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... | 2,734 | [
[
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0.0251007080078125,
-0.04412841796875,
-0.042694091796875,
-0.03631591796875,
0.02... |
zjunlp/Mol-Instructions | 2023-10-17T16:35:10.000Z | [
"size_categories:100M<n<1B",
"language:en",
"license:cc-by-4.0",
"chemistry",
"biology",
"molecule",
"protein",
"instructions",
"arxiv:2306.08018",
"region:us"
] | zjunlp | Mol-Instructions datasets. | @misc{merity2016pointer,
title={},
author={},
year={2023},
} | 17 | 1,359 | 2023-06-10T02:12:42 | ---
language:
- en
size_categories:
- 100M<n<1B
license: cc-by-4.0
tags:
- chemistry
- biology
- molecule
- protein
- instructions
---
<h1 align="center"> 🧪 Mol-Instructions </h1>
<h3 align="center"> An open, large-scale biomolecular instruction dataset for large language models. </h3>
> Please refer to our [repos... | 19,197 | [
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-0.036712646484... |
TigerResearch/tigerbot-alpaca-en-50k | 2023-05-31T01:56:04.000Z | [
"language:en",
"license:apache-2.0",
"region:us"
] | TigerResearch | null | null | 1 | 1,356 | 2023-05-30T14:33:53 | ---
license: apache-2.0
language:
- en
---
[Tigerbot](https://github.com/TigerResearch/TigerBot) 自有基于alpaca生成英文问答对
<p align="center" width="40%">
## Usage
```python
import datasets
ds_sft = datasets.load_dataset('TigerResearch/tigerbot-alpaca-en-50k')
``` | 259 | [
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... |
BeIR/webis-touche2020 | 2022-10-23T06:03:23.000Z | [
"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 | 1,354 | 2022-06-05T16:52:25 | ---
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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0.00595855712890625,
-0.034332275390625,
-0.0545654296875,
-0.02638244628906... |
plaguss/snli-small | 2023-09-10T14:53:06.000Z | [
"size_categories:n<1K",
"rlfh",
"argilla",
"human-feedback",
"region:us"
] | plaguss | null | null | 0 | 1,343 | 2023-09-10T14:29:47 | ---
size_categories: n<1K
tags:
- rlfh
- argilla
- human-feedback
---
# Dataset Card for snli-small
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 directly ... | 7,391 | [
[
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0.054931640625,
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-0.046966552734375,
0.0245513... |
xiyuez/red-dot-design-award-product-description | 2023-07-07T18:32:48.000Z | [
"task_categories:text-generation",
"size_categories:10k<n<100K",
"language:en",
"license:odc-by",
"region:us"
] | xiyuez | null | null | 6 | 1,340 | 2023-07-05T17:26:58 | ---
license: odc-by
task_categories:
- text-generation
language:
- en
pretty_name: Red Dot Design Award Dataset
size_categories:
- 10k<n<100K
---
# Red Dot Design Award Dataset
This dataset contains information about the products that have won the Red Dot Design Award, a prestigious international design competition. ... | 2,445 | [
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0.02127... |
llm-blender/mix-instruct | 2023-06-09T02:21:01.000Z | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"region:us"
] | llm-blender | null | null | 9 | 1,333 | 2023-05-31T22:19:26 | ---
license: mit
task_categories:
- text-generation
language:
- en
pretty_name: mix-instruct
size_categories:
- 100K<n<1M
---
# MixInstruct
## Introduction
This is the official realease of dataset **MixInstruct** for project **LLM-Blender**.
This dataset contains 11 responses from the current popular instruction foll... | 15,115 | [
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0.00... |
hmao/reformatted_singleapi | 2023-10-20T17:41:43.000Z | [
"region:us"
] | hmao | null | null | 0 | 1,323 | 2023-10-20T17:41:41 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: api_name
dtype: string
- name: api_definition
dtype: string
- name: dataset_name
dtype: string
splits:
- name: train
num_bytes: 19426
num_examples: 14
download_size... | 529 | [
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... |
darentang/sroie | 2021-12-09T15:11:29.000Z | [
"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... | 2 | 1,311 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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0.0379028... |
hatexplain | 2023-01-25T14:31:48.000Z | [
"task_categories:text-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"hate-speech-detection",
"arxiv:2012.10289",
"arxiv:1703.0400... | null | Hatexplain is the first benchmark hate speech dataset covering multiple aspects of the issue. Each post in the dataset is annotated from three different perspectives: the basic, commonly used 3-class classification (i.e., hate, offensive or normal), the target community (i.e., the community that has been the victim of ... | @misc{mathew2020hatexplain,
title={HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection},
author={Binny Mathew and Punyajoy Saha and Seid Muhie Yimam and Chris Biemann and Pawan Goyal and Animesh Mukherjee},
year={2020},
eprint={2012.10289},
archivePrefix={arXiv},
pr... | 5 | 1,301 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: hatexplain
pretty_name: hatexplain
tags:
- hate-s... | 10,049 | [
[
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0.0252685546875,
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-0.0634765625,
-0.07513427734375,
0.0042762756... |
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... | 1 | 1,295 | 2022-03-02T23:29:22 | ---
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... | 961 | [
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0.0523681640625,
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0.0409... |
TigerResearch/tigerbot-alpaca-zh-0.5m | 2023-05-31T01:14:23.000Z | [
"language:zh",
"license:apache-2.0",
"region:us"
] | TigerResearch | null | null | 1 | 1,287 | 2023-05-30T15:15:00 | ---
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')
```
| 261 | [
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0.01421... |
Rowan/hellaswag | 2023-09-28T14:49:00.000Z | [
"language:en",
"arxiv:1905.07830",
"region:us"
] | Rowan | HellaSwag: Can a Machine Really Finish Your Sentence? is a new dataset for commonsense NLI. A paper was published at ACL2019. | @inproceedings{zellers2019hellaswag,
title={HellaSwag: Can a Machine Really Finish Your Sentence?},
author={Zellers, Rowan and Holtzman, Ari and Bisk, Yonatan and Farhadi, Ali and Choi, Yejin},
booktitle ={Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
year={20... | 30 | 1,285 | 2022-03-02T23:29:22 | ---
language:
- en
paperswithcode_id: hellaswag
pretty_name: HellaSwag
dataset_info:
features:
- name: ind
dtype: int32
- name: activity_label
dtype: string
- name: ctx_a
dtype: string
- name: ctx_b
dtype: string
- name: ctx
dtype: string
- name: endings
sequence: string
- name: ... | 6,845 | [
[
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0.040802001953125,
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-0.07037353515625,
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0.008247... |
ted_talks_iwslt | 2023-06-01T14:59:58.000Z | [
"task_categories:translation",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:translation",
"size_categories:1K<n<10K",
"size_categories:n<1K",
"source_datasets:original",
"language:af",
"language:am",
"language:a... | null | The core of WIT3 is the TED Talks corpus, that basically redistributes the original content published by the TED Conference website (http://www.ted.com). Since 2007,
the TED Conference, based in California, has been posting all video recordings of its talks together with subtitles in English
and their translations in m... | @inproceedings{cettolo-etal-2012-wit3,
title = "{WIT}3: Web Inventory of Transcribed and Translated Talks",
author = "Cettolo, Mauro and
Girardi, Christian and
Federico, Marcello",
booktitle = "Proceedings of the 16th Annual conference of the European Association for Machine Translation",
... | 10 | 1,284 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- af
- am
- ar
- arq
- art
- as
- ast
- az
- be
- bg
- bi
- bn
- bo
- bs
- ca
- ceb
- cnh
- cs
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fil
- fr
- ga
- gl
- gu
- ha
- he
- hi
- hr
- ht
- hu
- hup
- hy
... | 15,526 | [
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code_x_glue_ct_code_to_text | 2023-06-01T14:59:54.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:other-programming-languages",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:code",
"language:en",
"license:c-uda",
"code-to-text",
"region... | null | The dataset we use comes from CodeSearchNet and we filter the dataset as the following:
- Remove examples that codes cannot be parsed into an abstract syntax tree.
- Remove examples that #tokens of documents is < 3 or >256
- Remove examples that documents contain special tokens (e.g. <img ...> or https:...)
- Remove ex... | @article{husain2019codesearchnet,
title={Codesearchnet challenge: Evaluating the state of semantic code search},
author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
journal={arXiv preprint arXiv:1909.09436},
year={2019}
} | 35 | 1,283 | 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
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
pretty_name: CodeXGlueCtCodeToText
tags:
- code-to-text
dat... | 25,737 | [
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jfrenz/legalglue | 2022-10-22T22:14:36.000Z | [
"task_categories:text-classification",
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"task_ids:multi-label-classification",
"task_ids:topic-classification",
"multilinguality:multilingual",
"source_datasets:extended",
"language:en",
"language:da",
"language:de",
"la... | jfrenz | \
Legal General Language Understanding Evaluation (LegalGLUE) benchmark is
a collection of datasets for evaluating model performance across a diverse set of legal NLP tasks | null | 6 | 1,278 | 2022-03-02T23:29:22 | ---
language:
- en
- da
- de
- nl
- sv
- bg
- cs
- hr
- pl
- sk
- sl
- es
- fr
- it
- pt
- ro
- et
- fi
- hu
- lt
- lv
- el
- mt
multilinguality:
- multilingual
source_datasets:
- extended
task_categories:
- text-classification
- token-classification
task_ids:
- named-entity-recognition
- multi-label-classification
- t... | 10,683 | [
[
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unicamp-dl/mmarco | 2022-11-30T17:31:26.000Z | [
"arxiv:2108.13897",
"arxiv:2105.06813",
"region:us"
] | unicamp-dl | mMARCO translated datasets | @misc{bonifacio2021mmarco,
title={mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Vitor Jeronymo and Hugo Queiroz Abonizio and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
eprint={2108.13897},
archivePr... | 37 | 1,278 | 2022-03-02T23:29:22 | # Dataset Summary
**mMARCO** is a multilingual version of the [MS MARCO passage ranking dataset](https://microsoft.github.io/msmarco/).
For more information, checkout our papers:
* [**mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Dataset**](https://arxiv.org/abs/2108.13897)
* [**A cost-benefit ana... | 3,215 | [
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argilla/news-summary | 2023-03-16T09:36:12.000Z | [
"task_categories:summarization",
"task_ids:news-articles-summarization",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-4.0",
"region:us"
] | argilla | null | null | 29 | 1,263 | 2022-12-07T05:39:38 | ---
language:
- en
license:
- cc-by-nc-4.0
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- summarization
task_ids:
- news-articles-summarization
dataset_info:
features:
- name: text
dtype: string
- name: prediction
list:
- name: score
dtype: float64
- name: text
... | 2,016 | [
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cardiffnlp/tweet_topic_multi | 2022-11-27T11:26:34.000Z | [
"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... | 8 | 1,259 | 2022-09-01T14:30:46 | ---
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_multi"
## Dataset Description
- **Paper:** [https://arxiv.org/abs/2209.... | 8,788 | [
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nsmc | 2023-01-25T14:41:49.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:ko",
"license:cc-by-2.0",
"region:us"
] | null | This is a movie review dataset in the Korean language. Reviews were scraped from Naver movies. The dataset construction is based on the method noted in Large movie review dataset from Maas et al., 2011. | @InProceedings{Park:2016,
title = "Naver Sentiment Movie Corpus",
author = "Lucy Park",
year = "2016",
howpublished = {\\url{https://github.com/e9t/nsmc}}
} | 3 | 1,258 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- ko
license:
- cc-by-2.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: nsmc
pretty_name: Naver Sentiment... | 3,743 | [
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baber/mmlu | 2023-09-29T02:12:59.000Z | [
"region:us"
] | baber | This is a massive multitask test consisting of multiple-choice questions from various branches of knowledge, covering 57 tasks including elementary mathematics, US history, computer science, law, and more. | @article{hendryckstest2021,
title={Measuring Massive Multitask Language Understanding},
author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt},
journal={Proceedings of the International Conference on Learning Representations (ICLR)}... | 0 | 1,257 | 2023-09-28T14:51:08 | Entry not found | 15 | [
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opus_openoffice | 2023-06-01T14:59:55.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:de",
"language:en",
"language:es",
"language:fr",
"language:ja",
"language:ru",
"language:sv",
"langua... | null | A collection of documents from http://www.openoffice.org/. | @InProceedings{TIEDEMANN12.463,
author = {J�rg Tiedemann},
title = {Parallel Data, Tools and Interfaces in OPUS},
booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
year = {2012},
month = {may},
date = {23-25},
address = {Istanbul, Turkey},
ed... | 4 | 1,247 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- de
- en
- es
- fr
- ja
- ru
- sv
- zh
language_bcp47:
- en-GB
- zh-CN
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
... | 10,946 | [
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HuggingFaceH4/testing_h4 | 2023-07-21T07:27:54.000Z | [
"region:us"
] | HuggingFaceH4 | null | null | 0 | 1,243 | 2023-07-21T07:27:43 | ---
dataset_info:
features:
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected
list:
- name: content
dtype: string
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dtype: string
- name: prompt
dtype: string
- name: prompt_id
dtype: string
- nam... | 1,077 | [
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nlphuji/mscoco_2014_5k_test_image_text_retrieval | 2023-01-18T00:08:42.000Z | [
"arxiv:1405.0312",
"region:us"
] | nlphuji | null | null | 2 | 1,242 | 2023-01-12T14:37:24 | # MSCOCO (5K test set)
Original paper: [Microsoft COCO: Common Objects in Context
](https://arxiv.org/abs/1405.0312)
Homepage: https://cocodataset.org/#home
5K test set split from: http://cs.stanford.edu/people/karpathy/deepimagesent/caption_datasets.zip
Bibtex:
```
@inproceedings{lin2014microsoft,
title={Microso... | 641 | [
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scikit-learn/imdb | 2022-06-16T09:11:24.000Z | [
"license:other",
"region:us"
] | scikit-learn | null | null | 0 | 1,239 | 2022-06-16T09:07:41 | ---
license: other
---
This is the sentiment analysis dataset based on IMDB reviews initially released by Stanford University.
```
This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets.
We provide a set of 25,000 highly polar movie reviews for traini... | 1,222 | [
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flaviagiammarino/vqa-rad | 2023-06-03T18:38:48.000Z | [
"task_categories:visual-question-answering",
"size_categories:1K<n<10K",
"language:en",
"license:cc0-1.0",
"medical",
"region:us"
] | flaviagiammarino | null | null | 6 | 1,234 | 2023-06-03T14:33:55 | ---
license: cc0-1.0
task_categories:
- visual-question-answering
language:
- en
paperswithcode_id: vqa-rad
tags:
- medical
pretty_name: VQA-RAD
size_categories:
- 1K<n<10K
dataset_info:
features:
- name: image
dtype: image
- name: question
dtype: string
- name: answer
dtype: string
splits:
- na... | 3,907 | [
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TigerResearch/tigerbot-gsm-8k-en | 2023-05-31T01:38:37.000Z | [
"language:en",
"license:mit",
"region:us"
] | TigerResearch | null | null | 0 | 1,233 | 2023-05-30T15:44:37 | ---
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... | 421 | [
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llm-book/wrime-sentiment | 2023-10-06T00:56:38.000Z | [
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:ja",
"region:us"
] | llm-book | null | null | 1 | 1,230 | 2023-07-29T06:38:26 | ---
task_categories:
- text-classification
language:
- ja
size_categories:
- 10K<n<100K
---
# Dataset Card for llm-book/wrime-sentiment
日本語の感情分析データセット WRIME を、ポジティブ/ネガティブの二値分類のタスクに加工したデータセットです。
GitHub リポジトリ [ids-cv/wrime](https://github.com/ids-cv/wrime) で公開されているデータセットを利用しています。
`Avg. Readers_Sentiment` の値が0より大きいものをポジティ... | 1,688 | [
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0.0... |
bigbio/pubmed_qa | 2022-12-22T15:46:24.000Z | [
"multilinguality:monolingual",
"language:en",
"license:mit",
"region:us"
] | bigbio | PubMedQA is a novel biomedical question answering (QA) dataset collected from PubMed abstracts.
The task of PubMedQA is to answer research biomedical questions with yes/no/maybe using the corresponding abstracts.
PubMedQA has 1k expert-annotated (PQA-L), 61.2k unlabeled (PQA-U) and 211.3k artificially generated QA inst... | @inproceedings{jin2019pubmedqa,
title={PubMedQA: A Dataset for Biomedical Research Question Answering},
author={Jin, Qiao and Dhingra, Bhuwan and Liu, Zhengping and Cohen, William and Lu, Xinghua},
booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th Intern... | 3 | 1,227 | 2022-11-13T22:11:45 |
---
language:
- en
bigbio_language:
- English
license: mit
multilinguality: monolingual
bigbio_license_shortname: MIT
pretty_name: PubMedQA
homepage: https://github.com/pubmedqa/pubmedqa
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- QUESTION_ANSWERING
---
# Dataset Card for PubMedQA
## Dataset Descript... | 2,360 | [
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GEM/totto | 2022-10-24T15:30:32.000Z | [
"task_categories:table-to-text",
"annotations_creators:none",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"data-to-text",
"arxiv:1603.07771",
"arxiv:2007.02871",
"arxiv:2005.10433",
"reg... | GEM | ToTTo is an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description. | \@inproceedings{parikh2020totto,
title={{ToTTo}: A Controlled Table-To-Text Generation Dataset},
author={Parikh, Ankur P and Wang, Xuezhi and Gehrmann, Sebastian and Faruqui, Manaal and Dhingra, Bhuwan and Yang, Diyi and Das, Dipanjan},
booktitle={Proceedings of EMNLP},
year={2020}
} | 1 | 1,215 | 2022-03-02T23:29:22 | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
pretty_name: totto
tags:
- data-to-text
---
# Dataset Card for GEM/totto
## Dataset Descr... | 42,215 | [
[
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-0.0625,
0.0203399658203125,
0.01387786865234375,
-0.01541900634765625,
-0.0287933349609375,
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-0.0277099609375,
0.0279083251953125,
0.041351318359375,
-0.050445556640625,
-0.06805419921875,
-0.0399169921875,
0.03031921... |
lmsys/chatbot_arena_conversations | 2023-09-30T01:04:44.000Z | [
"task_categories:conversational",
"size_categories:10K<n<100K",
"license:cc",
"arxiv:2306.05685",
"region:us"
] | lmsys | null | null | 143 | 1,215 | 2023-07-18T11:57:07 | ---
dataset_info:
features:
- name: question_id
dtype: string
- name: model_a
dtype: string
- name: model_b
dtype: string
- name: winner
dtype: string
- name: judge
dtype: string
- name: conversation_a
list:
- name: content
dtype: string
- name: role
dtype: stri... | 6,999 | [
[
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0.003063... |
neulab/conala | 2022-10-20T20:25:00.000Z | [
"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",
"arxiv:1805.08949",
"region:us"
] | neulab | CoNaLa is a dataset of code and natural language pairs crawled from Stack Overflow, for more details please refer to this paper: https://arxiv.org/pdf/1805.08949.pdf or the dataset page https://conala-corpus.github.io/. | @inproceedings{yin2018learning,
title={Learning to mine aligned code and natural language pairs from stack overflow},
author={Yin, Pengcheng and Deng, Bowen and Chen, Edgar and Vasilescu, Bogdan and Neubig, Graham},
booktitle={2018 IEEE/ACM 15th international conference on mining software repositories (MSR)},
p... | 43 | 1,213 | 2022-09-14T19:31:08 | ---
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: CoNaLa
tags:
- code-generation
---
## Dataset Descrip... | 3,902 | [
[
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... |
openai/webgpt_comparisons | 2022-12-19T17:55:29.000Z | [
"arxiv:2112.09332",
"region:us"
] | openai | WebGPT Comparisons contains all of the comparisons marked as suitable for reward modelling from the WebGPT paper. | @inproceedings{nakano2021webgpt,
author = {Reiichiro Nakano and Jacob Hilton and Suchir Balaji and Jeff Wu and Long Ouyang and Christina Kim and Christopher Hesse and Shantanu Jain and Vineet Kosaraju and William Saunders and Xu Jiang and Karl Cobbe and Tyna Eloundou and Gretchen Krueger and Kevin Button and Matthew ... | 173 | 1,213 | 2022-12-18T19:56:41 | ---
pretty_name: WebGPT Comparisons
---
# Dataset Card for WebGPT Comparisons
## Dataset Description
In the [WebGPT paper](https://arxiv.org/abs/2112.09332), the authors trained a reward model from human feedback.
They used the reward model to train a long form question answering model to align with human preferences... | 2,853 | [
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0... |
TigerResearch/tigerbot-stackexchange-qa-en-0.5m | 2023-05-31T02:21:45.000Z | [
"language:en",
"license:apache-2.0",
"region:us"
] | TigerResearch | null | null | 0 | 1,209 | 2023-05-30T15:06:49 | ---
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... | 378 | [
[
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-0.01035308837890625,... |
argilla/research_titles_multi-label | 2022-10-07T13:22:53.000Z | [
"region:us"
] | argilla | null | null | 0 | 1,199 | 2022-10-07T13:22:42 | Entry not found | 15 | [
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0.0379... |
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}
} | 14 | 1,197 | 2022-03-02T23:29:22 | ---
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... | 8,254 | [
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0.0393981... |
gbharti/finance-alpaca | 2023-09-26T04:13:35.000Z | [
"language:en",
"region:us"
] | gbharti | null | null | 46 | 1,196 | 2023-03-29T03:37:58 | ---
language:
- en
---
This dataset is a combination of Stanford's Alpaca (https://github.com/tatsu-lab/stanford_alpaca) and FiQA (https://sites.google.com/view/fiqa/) with another 1.3k pairs custom generated using GPT3.5
Script for tuning through Kaggle's (https://www.kaggle.com) free resources using PEFT/LoRa: https... | 709 | [
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-0... |
InstaDeepAI/nucleotide_transformer_downstream_tasks | 2023-10-16T12:57:56.000Z | [
"region:us"
] | InstaDeepAI | The 18 classification downstream tasks from the Nucleotide Transformer paper. Each task
corresponds to a dataset configuration. | @article{dalla2023nucleotide,
title={The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics},
author={Dalla-Torre, Hugo and Gonzalez, Liam and Mendoza-Revilla, Javier and Carranza, Nicolas Lopez and Grzywaczewski, Adam Henryk and Oteri, Francesco and Dallago, Christian and T... | 1 | 1,195 | 2023-06-16T12:00:08 | ---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
{}
---
# Dataset Card for Dataset Name
The `nucleotide_transformer_downstream_tasks` dataset features the 18 downstream tasks ... | 5,955 | [
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... |
emrgnt-cmplxty/sciphi-textbooks-are-all-you-need | 2023-09-30T21:57:36.000Z | [
"license:llama2",
"region:us"
] | emrgnt-cmplxty | null | null | 97 | 1,192 | 2023-09-26T08:14:12 | ---
dataset_info:
features:
- name: formatted_prompt
dtype: string
- name: completion
dtype: string
- name: first_task
dtype: string
- name: second_task
dtype: string
- name: last_task
dtype: string
- name: notes
dtype: string
- name: title
dtype: string
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d... | 1,275 | [
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gpt3mix/sst2 | 2021-05-18T08:59:33.000Z | [
"region:us"
] | gpt3mix | null | null | 2 | 1,184 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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0.03793334... |
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