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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
medalpaca/medical_meadow_medical_flashcards | 2023-04-06T17:12:17.000Z | [
"task_categories:question-answering",
"language:en",
"license:cc",
"region:us"
] | medalpaca | null | null | 5 | 2,162 | 2023-04-06T17:09:17 | ---
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... | 1,242 | [
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gia-project/gia-dataset-tokenized-1024 | 2023-09-29T15:51:41.000Z | [
"region:us"
] | gia-project | null | null | 0 | 2,147 | 2023-09-16T08:02:26 | ---
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bot-yaya/undl_text | 2023-10-07T00:31:07.000Z | [
"region:us"
] | bot-yaya | null | null | 0 | 2,141 | 2023-10-06T14:35:49 | ---
dataset_info:
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story_cloze | 2023-04-05T13:40:54.000Z | [
"task_categories:other",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | null | Story Cloze Test' is a commonsense reasoning framework for evaluating story understanding,
story generation, and script learning.This test requires a system to choose the correct ending
to a four-sentence story. | @inproceedings{mostafazadeh2017lsdsem,
title={Lsdsem 2017 shared task: The story cloze test},
author={Mostafazadeh, Nasrin and Roth, Michael and Louis, Annie and Chambers, Nathanael and Allen, James},
booktitle={Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics... | 7 | 2,138 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
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- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: null
pretty_name: Story Cloze Test
dataset_info:
- config_name: '2016'
features... | 7,056 | [
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danbider/codegen | 2023-07-21T01:53:30.000Z | [
"region:us"
] | danbider | null | null | 0 | 2,121 | 2023-07-20T23:14:53 | Entry not found | 15 | [
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GAIR/lima | 2023-06-08T02:40:19.000Z | [
"license:other",
"arxiv:2305.11206",
"region:us"
] | GAIR | A high-quality dataset for efficient instruction tuning. | null | 298 | 2,102 | 2023-06-07T05:16:04 | ---
license: other
---
Dataset for [LIMA: Less Is More for Alignment](https://arxiv.org/pdf/2305.11206.pdf)
## Usage
```python
from datasets import load_dataset
dataset = load_dataset("GAIR/lima")
```
## License
If the source data of LIMA has a stricter license than CC BY-NC-SA, the LIMA dataset follows the same.... | 368 | [
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EuropeanParliament/Eurovoc | 2023-10-26T12:28:18.000Z | [
"license:eupl-1.1",
"region:us"
] | EuropeanParliament | null | null | 0 | 2,100 | 2023-09-01T07:46:44 | ---
license: eupl-1.1
configs:
- config_name: 1996-03
data_files: "files/1996-03.jsonl.gz"
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data_files: "files/1996-04.jsonl.gz"
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data_files: "files/1996-05.jsonl.gz"
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data_files: "files/1996-06.jsonl.gz"
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data_files: "fil... | 24,889 | [
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RussianNLP/russian_super_glue | 2023-06-19T12:23:49.000Z | [
"task_categories:text-classification",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:text-generation",
"task_ids:natural-language-inference",
"task_ids:multi-class-classification",
"annotations_creators:crowdsourced",
"annotations_creators:expert-ge... | RussianNLP | Recent advances in the field of universal language models and transformers require the development of a methodology for
their broad diagnostics and testing for general intellectual skills - detection of natural language inference,
commonsense reasoning, ability to perform simple logical operations regardless of text su... | @article{shavrina2020russiansuperglue,
title={RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark},
author={Shavrina, Tatiana and Fenogenova, Alena and Emelyanov, Anton and Shevelev, Denis and Artemova,
Ekaterina and Malykh, Valentin and Mikhailo... | 15 | 2,099 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- ru
license:
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 1M<n<10M
- 10M<n<100M
- 100M<n<1B
source_datasets:
- original
task_categories:
- text-classification
- question-ans... | 28,658 | [
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codeparrot/github-code-clean | 2022-07-05T09:35:14.000Z | [
"license:apache-2.0",
"region:us"
] | codeparrot | The GitHub Code clean dataset in a more filtered version of codeparrot/github-code dataset, it consists of 115M code files from GitHub in 32 programming languages with 60 extensions totaling in almost 1TB of text data. | null | 55 | 2,099 | 2022-06-29T23:08:17 | ---
license: apache-2.0
---
This is a cleaner version of [Github-code dataset](https://huggingface.co/datasets/codeparrot/github-code), we add the following filters:
* Average line length < 100
* Alpha numeric characters fraction > 0.25
* Remove auto-generated files (keyword search)
3.39M files are removed making up 2... | 340 | [
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0.... |
BeIR/nfcorpus | 2022-10-23T06:01:44.000Z | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | BeIR | null | null | 0 | 2,090 | 2022-06-05T16:27:38 | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
... | 13,988 | [
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hf-internal-testing/example-documents | 2022-08-04T12:42:46.000Z | [
"region:us"
] | hf-internal-testing | null | null | 1 | 2,084 | 2022-07-28T14:03:22 | Entry not found | 15 | [
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tatoeba | 2022-11-03T16:32:34.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
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"language:ab",
"language:acm",
"language:ady",
"language:af",
"language:afb",
"language:afh",
"language:aii",
"l... | null | This is a collection of translated sentences from Tatoeba
359 languages, 3,403 bitexts
total number of files: 750
total number of tokens: 65.54M
total number of sentence fragments: 8.96M | @InProceedings{TIEDEMANN12.463,
author = {J{\"o}rg}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... | 20 | 2,070 | 2022-03-02T23:29:22 | ---
annotations_creators:
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sberquad | 2023-08-29T12:35:15.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
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"multilinguality:monolingual",
"size_categories:10K<n<100K",
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"language:ru",
"license:unknown",
"arxiv:1912... | null | Sber Question Answering Dataset (SberQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Russian original a... | @article{Efimov_2020,
title={SberQuAD – Russian Reading Comprehension Dataset: Description and Analysis},
ISBN={9783030582197},
ISSN={1611-3349},
url={http://dx.doi.org/10.1007/978-3-030-58219-7_1},
DOI={10.1007/978-3-030-58219-7_1},
journal={Experimental IR Meets Multilinguality, Multimodality, and I... | 10 | 2,054 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
- crowdsourced
language:
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source_datasets:
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task_categories:
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task_ids:
- extractive-qa
paperswithcode_id: sberquad
pretty_name: SberQuAD
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facebook/voxpopuli | 2022-10-14T13:43:12.000Z | [
"task_categories:automatic-speech-recognition",
"multilinguality:multilingual",
"language:en",
"language:de",
"language:fr",
"language:es",
"language:pl",
"language:it",
"language:ro",
"language:hu",
"language:cs",
"language:nl",
"language:fi",
"language:hr",
"language:sk",
"language:s... | facebook | A large-scale multilingual speech corpus for representation learning, semi-supervised learning and interpretation. | @inproceedings{wang-etal-2021-voxpopuli,
title = "{V}ox{P}opuli: A Large-Scale Multilingual Speech Corpus for Representation Learning,
Semi-Supervised Learning and Interpretation",
author = "Wang, Changhan and
Riviere, Morgane and
Lee, Ann and
Wu, Anne and
Talnikar, Chaitanya a... | 26 | 2,045 | 2022-05-10T14:42:49 | ---
annotations_creators: []
language:
- en
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- es
- pl
- it
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- hr
- sk
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- et
- lt
language_creators: []
license:
- cc0-1.0
- other
multilinguality:
- multilingual
pretty_name: VoxPopuli
size_categories: []
source_datasets: []
tags: []
task_categories:
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... | 10,663 | [
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silicone | 2023-06-01T14:59:53.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:text-classification",
"task_ids:dialogue-modeling",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"task_ids:sentiment-classification",
"task_ids:text-scoring",
"annotations_creators:expert-generated... | null | The Sequence labellIng evaLuatIon benChmark fOr spoken laNguagE (SILICONE) benchmark is a collection
of resources for training, evaluating, and analyzing natural language understanding systems
specifically designed for spoken language. All datasets are in the English language and cover a
variety of domains including... | @inproceedings{chapuis-etal-2020-hierarchical,
title = "Hierarchical Pre-training for Sequence Labelling in Spoken Dialog",
author = "Chapuis, Emile and
Colombo, Pierre and
Manica, Matteo and
Labeau, Matthieu and
Clavel, Chlo{\'e}",
booktitle = "Findings of the Association for Co... | 8 | 2,036 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 10K<n<100K
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
- text-classification
task_ids:
- dialo... | 22,988 | [
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e2e_nlg | 2022-11-18T19:59:40.000Z | [
"task_categories:text2text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"meaning-representation-to-text",
"arxiv:1706.09254",
"ar... | null | The E2E dataset is used for training end-to-end, data-driven natural language generation systems in the restaurant domain, which is ten times bigger than existing, frequently used datasets in this area.
The E2E dataset poses new challenges:
(1) its human reference texts show more lexical richness and syntactic variatio... | @article{dusek.etal2020:csl,
title = {Evaluating the {{State}}-of-the-{{Art}} of {{End}}-to-{{End Natural Language Generation}}: {{The E2E NLG Challenge}}},
author = {Du{\v{s}}ek, Ond\v{r}ej and Novikova, Jekaterina and Rieser, Verena},
year = {2020},
month = jan,
volume = {59},
pages = {123--156},
doi = ... | 10 | 2,034 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: e2e
pretty_name: End-to-End NLG Challenge
tag... | 6,719 | [
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sem_eval_2018_task_1 | 2022-11-18T21:45:06.000Z | [
"task_categories:text-classification",
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... | null | SemEval-2018 Task 1: Affect in Tweets: SubTask 5: Emotion Classification.
This is a dataset for multilabel emotion classification for tweets.
'Given a tweet, classify it as 'neutral or no emotion' or as one, or more, of eleven given emotions that best represent the mental state of the tweeter.'
It contains 22467 tw... | @InProceedings{SemEval2018Task1,
author = {Mohammad, Saif M. and Bravo-Marquez, Felipe and Salameh, Mohammad and Kiritchenko, Svetlana},
title = {SemEval-2018 {T}ask 1: {A}ffect in Tweets},
booktitle = {Proceedings of International Workshop on Semantic Evaluation (SemEval-2018)},
address = {New Orleans, LA, USA},
... | 9 | 2,020 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- ar
- en
- es
license:
- unknown
multilinguality:
- multilingual
pretty_name: 'SemEval-2018 Task 1: Affect in Tweets'
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-clas... | 10,534 | [
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drop | 2023-04-05T10:05:02.000Z | [
"task_categories:question-answering",
"task_categories:text2text-generation",
"task_ids:extractive-qa",
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"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
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"langua... | null | DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs.
. DROP is a crowdsourced, adversarially-created, 96k-question benchmark, in which a system must resolve references in a
question, perhaps to multiple input positions, and perform discrete operations over them (such as addition, counti... | @inproceedings{Dua2019DROP,
author={Dheeru Dua and Yizhong Wang and Pradeep Dasigi and Gabriel Stanovsky and Sameer Singh and Matt Gardner},
title={DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs},
booktitle={Proc. of NAACL},
year={2019}
} | 10 | 2,015 | 2022-03-02T23:29:22 | ---
pretty_name: DROP
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
- text2text-generation
task_ids:
- extractive-qa
- abstractiv... | 6,853 | [
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tiny_shakespeare | 2023-04-05T13:42:24.000Z | [
"region:us"
] | null | 40,000 lines of Shakespeare from a variety of Shakespeare's plays. Featured in Andrej Karpathy's blog post 'The Unreasonable Effectiveness of Recurrent Neural Networks': http://karpathy.github.io/2015/05/21/rnn-effectiveness/.
To use for e.g. character modelling:
```
d = datasets.load_dataset(name='tiny_shakespeare')... | @misc{
author={Karpathy, Andrej},
title={char-rnn},
year={2015},
howpublished={\\url{https://github.com/karpathy/char-rnn}}
} | 17 | 2,015 | 2022-03-02T23:29:22 | ---
paperswithcode_id: null
pretty_name: TinyShakespeare
dataset_info:
features:
- name: text
dtype: string
splits:
- name: test
num_bytes: 55780
num_examples: 1
- name: train
num_bytes: 1003864
num_examples: 1
- name: validation
num_bytes: 55780
num_examples: 1
download_size: ... | 6,101 | [
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C-MTEB/T2Retrieval | 2023-07-28T10:11:06.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 2,011 | 2023-07-28T10:08:40 | ---
configs:
- config_name: default
data_files:
- split: corpus
path: data/corpus-*
- split: queries
path: data/queries-*
dataset_info:
features:
- name: id
dtype: string
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dtype: string
splits:
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num_bytes: 265607316
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- name: queries... | 590 | [
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clips/mfaq | 2022-10-20T11:32:50.000Z | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:no-annotation",
"language_creators:other",
"multilinguality:multilingual",
"size_categories:unknown",
"source_datasets:original",
"language:cs",
"language:da",
"language:de",
"language:en",
"language:es"... | clips | We present the first multilingual FAQ dataset publicly available. We collected around 6M FAQ pairs from the web, in 21 different languages. | @InProceedings{mfaq_a_multilingual_dataset,
title={MFAQ: a Multilingual FAQ Dataset},
author={Maxime {De Bruyn} and Ehsan Lotfi and Jeska Buhmann and Walter Daelemans},
year={2021},
booktitle={MRQA @ EMNLP 2021}
} | 26 | 2,010 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- other
language:
- cs
- da
- de
- en
- es
- fi
- fr
- he
- hr
- hu
- id
- it
- nl
- 'no'
- pl
- pt
- ro
- ru
- sv
- tr
- vi
license:
- cc0-1.0
multilinguality:
- multilingual
pretty_name: MFAQ - a Multilingual FAQ Dataset
size_categories:
- unknown
source_da... | 5,144 | [
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zeroshot/twitter-financial-news-sentiment | 2022-12-12T14:32:59.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"twitter",
"finance",
"markets",
"stoc... | zeroshot | null | null | 32 | 2,008 | 2022-09-01T21:21:56 | ---
annotations_creators:
- other
language:
- en
language_creators:
- other
license:
- mit
multilinguality:
- monolingual
pretty_name: twitter financial news
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- twitter
- finance
- markets
- stocks
- wallstreet
- quant
- hedgefunds
- markets
task_categories... | 1,566 | [
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jamescalam/llama-2-arxiv-papers-chunked | 2023-07-25T03:12:24.000Z | [
"language:en",
"arxiv:2307.09288",
"region:us"
] | jamescalam | null | null | 11 | 2,005 | 2023-07-25T03:06:58 | ---
language:
- en
pretty_name: Chunked Arxiv Papers for Llama 2
---
This dataset contains chunked extracts (of ~300 tokens) from papers related to (and including) the [Llama 2 research paper](https://arxiv.org/abs/2307.09288). Related papers were identified by following a trail of references, extracting those papers ... | 409 | [
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0.... |
BeIR/dbpedia-entity | 2022-10-23T06:03:56.000Z | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | BeIR | null | null | 3 | 2,001 | 2022-06-05T16:54:24 | ---
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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TIGER-Lab/MathInstruct | 2023-10-16T13:57:57.000Z | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"math",
"arxiv:2309.05653",
"region:us"
] | TIGER-Lab | null | null | 94 | 1,983 | 2023-09-11T14:21:02 | ---
license: mit
task_categories:
- text-generation
language:
- en
pretty_name: MathInstruct
size_categories:
- 100K<n<1M
tags:
- math
---
# 🦣 MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning
MathInstruct is a meticulously curated instruction tuning dataset that is lightweight yet generaliz... | 2,756 | [
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mkb | 2023-06-01T14:59:56.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"multilinguality:translation",
"size_categories:1K<n<10K",
"size_categories:n<1K",
"source_datasets:original",
"language:bn",
"... | null | The Prime Minister's speeches - Mann Ki Baat, on All India Radio, translated into many languages. | @misc{siripragada2020multilingual,
title={A Multilingual Parallel Corpora Collection Effort for Indian Languages},
author={Shashank Siripragada and Jerin Philip and Vinay P. Namboodiri and C V Jawahar},
year={2020},
eprint={2007.07691},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 1 | 1,981 | 2022-03-02T23:29:22 | ---
task_categories:
- text-generation
- fill-mask
multilinguality:
- translation
task_ids:
- language-modeling
- masked-language-modeling
language:
- bn
- en
- gu
- hi
- ml
- mr
- or
- pa
- ta
- te
- ur
annotations_creators:
- no-annotation
source_datasets:
- original
size_categories:
- 1K<n<10K
- n<1K
license:
- cc-b... | 15,282 | [
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-0.053680419921875,
0.00... |
setimes | 2022-11-03T16:47:00.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:bg",
"language:bs",
"language:el",
"language:en",
"language:hr",
"language:mk",
"language:ro",
"languag... | null | SETimes – A Parallel Corpus of English and South-East European Languages
The corpus is based on the content published on the SETimes.com news portal. The news portal publishes “news and views from Southeast Europe” in ten languages: Bulgarian, Bosnian, Greek, English, Croatian, Macedonian, Romanian, Albanian and Serbia... | null | 0 | 1,959 | 2022-03-02T23:29:22 | ---
pretty_name: SETimes – A Parallel Corpus of English and South-East European Languages
annotations_creators:
- found
language_creators:
- found
language:
- bg
- bs
- el
- en
- hr
- mk
- ro
- sq
- sr
- tr
license:
- cc-by-sa-4.0
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
source_datasets:
- original
... | 16,009 | [
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0.02597... |
AdaptLLM/finance-tasks | 2023-10-21T11:45:45.000Z | [
"arxiv:2309.09530",
"region:us"
] | AdaptLLM | null | null | 5 | 1,953 | 2023-09-19T03:17:07 | ---
configs:
- config_name: ConvFinQA
data_files:
- split: test
path: "ConviFinQA/test.json"
- config_name: FiQA_SA
data_files:
- split: test
path: "FiQA_SA/test.json"
- config_name: FPB
data_files:
- split: test
path: "FPB/test.json"
- config_name: Headline
data_files:
- split... | 2,536 | [
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0.052734375,
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0... |
adv_glue | 2023-06-01T14:57:45.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"task_ids:sentiment-classification",
"annotations_creators:other",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:extended|glue",
"language:en",
"license:cc... | null | Adversarial GLUE Benchmark (AdvGLUE) is a comprehensive robustness evaluation benchmark
that focuses on the adversarial robustness evaluation of language models. It covers five
natural language understanding tasks from the famous GLUE tasks and is an adversarial
version of GLUE benchmark. | @article{Wang2021AdversarialGA,
title={Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models},
author={Boxin Wang and Chejian Xu and Shuohang Wang and Zhe Gan and Yu Cheng and Jianfeng Gao and Ahmed Hassan Awadallah and B. Li},
journal={ArXiv},
year={2021},
volume={abs/2111.028... | 4 | 1,947 | 2022-03-28T11:12:33 | ---
annotations_creators:
- other
language_creators:
- machine-generated
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- extended|glue
task_categories:
- text-classification
task_ids:
- natural-language-inference
- sentiment-classification
pretty_name: Ad... | 8,183 | [
[
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0.0224151611328125,
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-0.04315185546875,
-0.056793212890625,
-0.048583984... |
cyrilzhang/TinyStories2-ascii-bpe-2k | 2023-09-22T23:24:28.000Z | [
"region:us"
] | cyrilzhang | null | null | 0 | 1,943 | 2023-09-22T23:23:58 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: input_ids
sequence: int32
splits:
- name: train
num_bytes: 2369808200
num_examples: 578002
- name: validation
num_bytes: 2... | 588 | [
[
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0.025787353515625,
-0.059173583984375,
-0.039886474609375,
-0.0480651855468... |
JulesBelveze/tldr_news | 2022-08-05T12:17:50.000Z | [
"task_categories:summarization",
"task_categories:text2text-generation",
"task_categories:text-generation",
"task_ids:news-articles-headline-generation",
"task_ids:text-simplification",
"task_ids:language-modeling",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingua... | JulesBelveze | The `tldr_news` dataset was constructed by collecting a daily tech newsletter (available at
https://tldr.tech/newsletter). Then for every piece of news, the "headline" and its corresponding "content" were
collected. Such a dataset can be used to train a model to generate a headline from a input piece of text. | null | 12 | 1,942 | 2022-06-21T14:35:34 | ---
annotations_creators:
- other
language_creators:
- other
language:
- en
multilinguality:
- monolingual
pretty_name: tldr_news
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- summarization
- text2text-generation
- text-generation
task_ids:
- news-articles-headline-generation
- text-simplif... | 5,231 | [
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0.011459350... |
frgfm/imagenette | 2022-12-11T22:26:06.000Z | [
"task_categories:image-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"size_categories:1K<n<10K",
"source_datasets:extended",
"language:en",
"license:apache-2.0",
"region:us"
] | frgfm | Imagenette is a subset of 10 easily classified classes from Imagenet
(tench, English springer, cassette player, chain saw, church, French
horn, garbage truck, gas pump, golf ball, parachute). | @software{Howard_Imagenette_2019,
title={Imagenette: A smaller subset of 10 easily classified classes from Imagenet},
author={Jeremy Howard},
year={2019},
month={March},
publisher = {GitHub},
url = {https://github.com/fastai/imagenette}
} | 7 | 1,934 | 2022-07-18T00:13:35 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- apache-2.0
multilinguality: []
size_categories:
- 1K<n<10K
source_datasets:
- extended
task_categories:
- image-classification
task_ids: []
paperswithcode_id: imagenette
pretty_name: Imagenette
---
# Dataset Card for I... | 4,626 | [
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0.... |
BeIR/trec-covid | 2022-10-23T06:00:45.000Z | [
"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 | 1,932 | 2022-06-05T14:49:49 | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
... | 13,988 | [
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0.005954742431640625,
-0.034332275390625,
-0.0545654296875,
-0.02638244628906... |
EleutherAI/proof-pile-2 | 2023-10-25T06:16:04.000Z | [
"task_categories:text-generation",
"size_categories:10B<n<100B",
"language:en",
"math",
"arxiv:2310.10631",
"arxiv:2310.06786",
"region:us"
] | EleutherAI | A dataset of high quality mathematical text. | null | 76 | 1,931 | 2023-10-12T00:11:33 | ---
task_categories:
- text-generation
language:
- en
tags:
- math
size_categories:
- 10B<n<100B
---
<img src="proofpile_logo.jpg" width="500">
[ArXiv](http://arxiv.org/abs/2310.10631) | [Models](https://huggingface.co/EleutherAI/llemma_34b) | [Data](https://huggingface.co/datasets/EleutherAI/proof-pile-2) | [Code](ht... | 4,982 | [
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textvqa | 2022-11-18T22:07:01.000Z | [
"task_categories:visual-question-answering",
"task_ids:visual-question-answering",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:1904.08920",... | null | TextVQA requires models to read and reason about text in images to answer questions about them.
Specifically, models need to incorporate a new modality of text present in the images and reason
over it to answer TextVQA questions. TextVQA dataset contains 45,336 questions over 28,408 images
from the OpenImages dataset. | @inproceedings{singh2019towards,
title={Towards VQA Models That Can Read},
author={Singh, Amanpreet and Natarjan, Vivek and Shah, Meet and Jiang, Yu and Chen, Xinlei and Batra, Dhruv and Parikh, Devi and Rohrbach, Marcus},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognit... | 9 | 1,923 | 2022-05-05T06:44:56 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: TextVQA
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- visual-question-answering
task_ids:
- visual-question-answering
dataset_info:
- ... | 13,205 | [
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crows_pairs | 2023-07-06T09:23:23.000Z | [
"task_categories:text-classification",
"task_ids:text-scoring",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"bias-evaluation",
"region:us"
] | null | CrowS-Pairs, a challenge dataset for measuring the degree to which U.S. stereotypical biases present in the masked language models (MLMs). | @inproceedings{nangia2020crows,
title = "{CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models}",
author = "Nangia, Nikita and
Vania, Clara and
Bhalerao, Rasika and
Bowman, Samuel R.",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods... | 4 | 1,921 | 2022-03-02T23:29:22 | ---
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:
- text-scoring
paperswithcode_id: crows-pairs
pretty_name: CrowS-Pairs... | 5,257 | [
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lighteval/MATH | 2023-10-17T20:52:35.000Z | [
"region:us"
] | lighteval | MATH is a dataset of 12,500 challenging competition mathematics problems. Each
problem in Math has a full step-by-step solution which can be used to teach
models to generate answer derivations and explanations. | @article{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the Math Dataset},
author={Dan Hendrycks and Collin Burns and Saurav Kadavath and Akul Arora and Steven Basart and Eric Tang and Dawn Song and Jacob Steinhardt},
journal={NeurIPS},
year={2021}
} | 3 | 1,919 | 2023-04-20T15:05:44 | Entry not found | 15 | [
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teknium/GPT4-LLM-Cleaned | 2023-05-04T01:48:35.000Z | [
"region:us"
] | teknium | null | null | 84 | 1,884 | 2023-05-02T20:11:04 | This is the GPT4-LLM dataset from : https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM
It has been filtered of all OpenAI disclaimers and refusals. (Disclaimer: It may have removed some additional things besides just OAI disclaimers, as I used the followings script which is a bit more broad: https://huggingfac... | 501 | [
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L4NLP/LEval | 2023-10-11T03:56:48.000Z | [
"task_categories:summarization",
"task_categories:question-answering",
"task_categories:multiple-choice",
"size_categories:1K<n<10K",
"language:en",
"license:gpl-3.0",
"Long_context",
"region:us"
] | L4NLP | A benchmark to evaluate long document understanding and generation ability of LLM | } | 8 | 1,864 | 2023-06-14T11:51:39 | ---
license: gpl-3.0
task_categories:
- summarization
- question-answering
- multiple-choice
language:
- en
size_categories:
- 1K<n<10K
viewer: true
tags:
- Long_context
---
### *L-Eval: Instituting Standardized Evaluation for Long Context Language Models*
L-Eval is a comprehensive long-context language models eval... | 1,616 | [
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clinc_oos | 2023-01-25T14:28:10.000Z | [
"task_categories:text-classification",
"task_ids:intent-classification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-3.0",
"region:us"
] | null | This dataset is for evaluating the performance of intent classification systems in the
presence of "out-of-scope" queries. By "out-of-scope", we mean queries that do not fall
into any of the system-supported intent classes. Most datasets include only data that is
"in-scope". Our dataset includes both in... | @inproceedings{larson-etal-2019-evaluation,
title = "An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction",
author = "Larson, Stefan and
Mahendran, Anish and
Peper, Joseph J. and
Clarke, Christopher and
Lee, Andrew and
Hill, Parker and
Kummerf... | 10 | 1,863 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-3.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- intent-classification
paperswithcode_id: clinc150
pretty_name: CL... | 23,440 | [
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subjqa | 2023-03-16T13:27:54.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"source_datasets:extended|yelp_review_full",
"source_datasets:extended|other-amaz... | null | SubjQA is a question answering dataset that focuses on subjective questions and answers.
The dataset consists of roughly 10,000 questions over reviews from 6 different domains: books, movies, grocery,
electronics, TripAdvisor (i.e. hotels), and restaurants. | @inproceedings{bjerva20subjqa,
title = "SubjQA: A Dataset for Subjectivity and Review Comprehension",
author = "Bjerva, Johannes and
Bhutani, Nikita and
Golahn, Behzad and
Tan, Wang-Chiew and
Augenstein, Isabelle",
booktitle = "Proceedings of the 2020 Conference on Empirical Meth... | 7 | 1,858 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
- extended|yelp_review_full
- extended|other-amazon_reviews_ucsd
- extended|other-tripadvisor_reviews
task_categories:
- questi... | 21,615 | [
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polyglot_ner | 2023-04-05T13:36:52.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:unknown",
"source_datasets:original",
"language:ar",
"language:bg",
"language:ca",
"language:cs",
"... | null | Polyglot-NER
A training dataset automatically generated from Wikipedia and Freebase the task
of named entity recognition. The dataset contains the basic Wikipedia based
training data for 40 languages we have (with coreference resolution) for the task of
named entity recognition. The details of the procedure of generati... | @article{polyglotner,
author = {Al-Rfou, Rami and Kulkarni, Vivek and Perozzi, Bryan and Skiena, Steven},
title = {{Polyglot-NER}: Massive Multilingual Named Entity Recognition},
journal = {{Proceedings of the 2015 {SIAM} International Conference on Data Mining, Vancouver, British Columbia, C... | 21 | 1,854 | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- ar
- bg
- ca
- cs
- da
- de
- el
- en
- es
- et
- fa
- fi
- fr
- he
- hi
- hr
- hu
- id
- it
- ja
- ko
- lt
- lv
- ms
- nl
- 'no'
- pl
- pt
- ro
- ru
- sk
- sl
- sr
- sv
- th
- tl
- tr
- uk
- vi
- zh
license:
- unknown
multilinguality:... | 22,312 | [
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0.0... |
bigcode/starcoderdata | 2023-05-16T10:05:48.000Z | [
"task_categories:text-generation",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:unknown",
"language:code",
"license:other",
"region:us"
] | bigcode | null | null | 202 | 1,851 | 2023-03-30T12:02:21 | ---
annotations_creators: []
language_creators:
- crowdsourced
- expert-generated
language:
- code
license:
- other
multilinguality:
- multilingual
pretty_name: The-Stack
size_categories:
- unknown
source_datasets: []
task_categories:
- text-generation
extra_gated_prompt: >-
## Terms of Use for The Stack
The Sta... | 3,389 | [
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lama | 2023-06-01T14:59:53.000Z | [
"task_categories:text-retrieval",
"task_categories:text-classification",
"task_ids:fact-checking-retrieval",
"task_ids:text-scoring",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_cr... | null | LAMA is a dataset used to probe and analyze the factual and commonsense knowledge contained in pretrained language models. See https://github.com/facebookresearch/LAMA. | @inproceedings{petroni2019language,
title={Language Models as Knowledge Bases?},
author={F. Petroni, T. Rockt{\"{a}}schel, A. H. Miller, P. Lewis, A. Bakhtin, Y. Wu and S. Riedel},
booktitle={In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019},
year={201... | 8 | 1,848 | 2022-03-02T23:29:22 | ---
pretty_name: 'LAMA: LAnguage Model Analysis'
annotations_creators:
- crowdsourced
- expert-generated
- machine-generated
language_creators:
- crowdsourced
- expert-generated
- machine-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- 1K<n<10K
- 1M<n<10M
- n... | 14,286 | [
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openbmb/UltraFeedback | 2023-09-30T16:39:29.000Z | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"region:us"
] | openbmb | null | null | 140 | 1,846 | 2023-09-23T15:41:04 | ---
license: mit
task_categories:
- text-generation
language:
- en
size_categories:
- 100K<n<1M
---
## Introduction
- [GitHub Repo](https://github.com/thunlp/UltraFeedback)
- [UltraRM-13b](https://huggingface.co/openbmb/UltraRM-13b)
- [UltraCM-13b](https://huggingface.co/openbmb/UltraCM-13b)
UltraFeedback is a **large... | 15,004 | [
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scan | 2023-06-01T14:59:55.000Z | [
"task_categories:text2text-generation",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:bsd",
"multi-turn",
"arxiv:1711.00350",
"region:us"
] | null | SCAN tasks with various splits.
SCAN is a set of simple language-driven navigation tasks for studying
compositional learning and zero-shot generalization.
See https://github.com/brendenlake/SCAN for a description of the splits.
Example usage:
data = datasets.load_dataset('scan/length') | @inproceedings{Lake2018GeneralizationWS,
title={Generalization without Systematicity: On the Compositional Skills of
Sequence-to-Sequence Recurrent Networks},
author={Brenden M. Lake and Marco Baroni},
booktitle={ICML},
year={2018},
url={https://arxiv.org/pdf/1711.00350.pdf},
} | 2 | 1,840 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- en
license:
- bsd
multilinguality:
- monolingual
pretty_name: SCAN
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: scan
tags:
- multi-turn
dataset... | 10,878 | [
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Tevatron/wikipedia-nq | 2021-11-22T05:32:24.000Z | [
"region:us"
] | Tevatron | null | @inproceedings{karpukhin-etal-2020-dense,
title = "Dense Passage Retrieval for Open-Domain Question Answering",
author = "Karpukhin, Vladimir and Oguz, Barlas and Min, Sewon and Lewis, Patrick and Wu, Ledell and Edunov,
Sergey and Chen, Danqi and Yih, Wen-tau",
booktitle = "Proceedings of the 2020 Conf... | 2 | 1,811 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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0.0379... |
llm-book/JGLUE | 2023-10-06T00:58:24.000Z | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:sentence-similarity",
"task_categories:text-classification",
"task_ids:multiple-choice-qa",
"task_ids:open-domain-qa",
"task_ids:multi-class-classification",
"task_ids:sentiment-classification",
"annotations_cr... | llm-book | JGLUE, Japanese General Language Understanding Evaluation, is built to measure the general NLU ability in Japanese. JGLUE has been constructed from scratch without translation. We hope that JGLUE will facilitate NLU research in Japanese. | @inproceedings{kurihara-etal-2022-jglue,
title = "{JGLUE}: {J}apanese General Language Understanding Evaluation",
author = "Kurihara, Kentaro and
Kawahara, Daisuke and
Shibata, Tomohide",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,... | 4 | 1,810 | 2023-05-01T13:00:36 | ---
annotations_creators:
- crowdsourced
language:
- ja
language_creators:
- crowdsourced
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: JGLUE
size_categories: []
source_datasets:
- original
tags:
- MARC
- STS
- NLI
- SQuAD
- CommonsenseQA
task_categories:
- multiple-choice
- question-answerin... | 2,671 | [
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lj_speech | 2022-11-03T16:16:34.000Z | [
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unlicense",
"region:us"
] | null | This is a public domain speech dataset consisting of 13,100 short audio clips of a single speaker reading
passages from 7 non-fiction books in English. A transcription is provided for each clip. Clips vary in length
from 1 to 10 seconds and have a total length of approximately 24 hours.
Note that in order to limit the... | @misc{ljspeech17,
author = {Keith Ito and Linda Johnson},
title = {The LJ Speech Dataset},
howpublished = {\\url{https://keithito.com/LJ-Speech-Dataset/}},
year = 2017
} | 10 | 1,794 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- unlicense
multilinguality:
- monolingual
paperswithcode_id: ljspeech
pretty_name: LJ Speech
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- automatic-speech-recognition
task_ids: []
train-eval-... | 8,837 | [
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AdaptLLM/medicine-tasks | 2023-10-21T11:44:55.000Z | [
"arxiv:2309.09530",
"region:us"
] | AdaptLLM | null | null | 2 | 1,790 | 2023-09-19T14:53:35 | ---
configs:
- config_name: ChemProt
data_files:
- split: test
path: "ChemProt/test.json"
- config_name: MQP
data_files:
- split: test
path: "MedQs/test.json"
- config_name: PubMedQA
data_files:
- split: test
path: "pubmed_qa/test.json"
- config_name: RCT
data_files:
- split: t... | 2,532 | [
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C-MTEB/T2Retrieval-qrels | 2023-07-28T10:11:11.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 1,788 | 2023-07-28T10:11:07 | ---
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
dataset_info:
features:
- name: qid
dtype: string
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dtype: string
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splits:
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num_bytes: 3133383
num_examples: 118932
download_size: 1146734
dataset_size... | 505 | [
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BeIR/hotpotqa | 2022-10-23T06:02:40.000Z | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | BeIR | null | null | 2 | 1,786 | 2022-06-05T16:40:18 | ---
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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C-MTEB/DuRetrieval | 2023-07-28T09:48:49.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 1,781 | 2023-07-28T09:47:41 | ---
configs:
- config_name: default
data_files:
- split: corpus
path: data/corpus-*
- split: queries
path: data/queries-*
dataset_info:
features:
- name: id
dtype: string
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splits:
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... | 585 | [
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tweettemposhift/tweet_temporal_shift | 2023-10-31T12:30:20.000Z | [
"region:us"
] | tweettemposhift | """
_TWEET_TEMPORAL_CITATION = | """
_TWEET_TOPIC_DESCRIPTION = | 0 | 1,776 | 2023-10-20T13:44:44 | # Tweet Temporal Shift Benchmark
| 33 | [
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kyujinpy/KOpen-platypus | 2023-11-01T20:18:07.000Z | [
"size_categories:10K<n<100K",
"language:en",
"language:ko",
"license:cc-by-4.0",
"arxiv:2308.07317",
"region:us"
] | kyujinpy | null | null | 22 | 1,760 | 2023-08-21T14:59:26 | ---
language:
- en
- ko
license: cc-by-4.0
size_categories:
- 10K<n<100K
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
- name: instruction
dtype: string
- name: data_source
... | 6,313 | [
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ccdv/pubmed-summarization | 2022-10-24T20:33:04.000Z | [
"task_categories:summarization",
"task_categories:text-generation",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:en",
"conditional-text-generation",
"region:us"
] | ccdv | PubMed 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... | 32 | 1,757 | 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
---
# PubMed dataset for summarization
Dataset for summarization of long documents.\
Adapted from this [repo](https://github.com/armancohan... | 2,662 | [
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sms_spam | 2023-01-25T14:44:29.000Z | [
"task_categories:text-classification",
"task_ids:intent-classification",
"annotations_creators:crowdsourced",
"annotations_creators:found",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|other-nus-sms-... | null | The SMS Spam Collection v.1 is a public set of SMS labeled messages that have been collected for mobile phone spam research.
It has one collection composed by 5,574 English, real and non-enconded messages, tagged according being legitimate (ham) or spam. | @inproceedings{Almeida2011SpamFiltering,
title={Contributions to the Study of SMS Spam Filtering: New Collection and Results},
author={Tiago A. Almeida and Jose Maria Gomez Hidalgo and Akebo Yamakami},
year={2011},
booktitle = "Proceedings of the 2011 ACM Symposium on Document Engineering (DOCENG'11)",
} | 13 | 1,756 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
- found
language_creators:
- crowdsourced
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-nus-sms-corpus
task_categories:
- text-classification
task_ids:
- intent-classification
paperswithcode... | 4,872 | [
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BeIR/nq | 2022-10-23T06:02:24.000Z | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | BeIR | null | null | 2 | 1,752 | 2022-06-05T16:37:56 | ---
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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conll2012_ontonotesv5 | 2023-01-25T15:03:49.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"task_ids:part-of-speech",
"task_ids:coreference-resolution",
"task_ids:parsing",
"task_ids:lemmatization",
"task_ids:word-sense-disambiguation",
"annotations_creators:expert-generated",
"language_creators:found",
"multil... | null | OntoNotes v5.0 is the final version of OntoNotes corpus, and is a large-scale, multi-genre,
multilingual corpus manually annotated with syntactic, semantic and discourse information.
This dataset is the version of OntoNotes v5.0 extended and is used in the CoNLL-2012 shared task.
It includes v4 train/dev and v9 test d... | @inproceedings{pradhan-etal-2013-towards,
title = "Towards Robust Linguistic Analysis using {O}nto{N}otes",
author = {Pradhan, Sameer and
Moschitti, Alessandro and
Xue, Nianwen and
Ng, Hwee Tou and
Bj{\"o}rkelund, Anders and
Uryupina, Olga and
Zhang, Yuchen and
Z... | 25 | 1,751 | 2022-03-15T10:48:28 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- ar
- en
- zh
license:
- cc-by-nc-nd-4.0
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
- part-of-speech
- coreferenc... | 22,908 | [
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dalle-mini/YFCC100M_OpenAI_subset | 2021-08-26T17:56:01.000Z | [
"arxiv:1503.01817",
"region:us"
] | dalle-mini | The YFCC100M is one of the largest publicly and freely useable multimedia collection, containing the metadata of around 99.2 million photos and 0.8 million videos from Flickr, all of which were shared under one of the various Creative Commons licenses.
This version is a subset defined in openai/CLIP. | @article{thomee2016yfcc100m,
author = "Bart Thomee and David A. Shamma and Gerald Friedland and Benjamin Elizalde and Karl Ni and Douglas Poland and Damian Borth and Li-Jia Li",
title = "{YFCC100M}: The New Data in Multimedia Research",
journal = "Communications of the {ACM}",
volume = "59",
number = "2",
pages = "64--... | 8 | 1,750 | 2022-03-02T23:29:22 | # YFCC100M subset from OpenAI
Subset of [YFCC100M](https://arxiv.org/abs/1503.01817) used by OpenAI for [CLIP](https://github.com/openai/CLIP/blob/main/data/yfcc100m.md), filtered to contain only the images that we could retrieve.
| Split | train | validation |
| --- | --- | --- |
| Number of samples | 14,808,859 | 1... | 1,155 | [
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nielsr/ade20k-panoptic-demo | 2022-11-06T17:13:22.000Z | [
"region:us"
] | nielsr | null | null | 0 | 1,737 | 2022-11-05T21:16:00 | ---
dataset_info:
features:
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list:
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sequence: int64
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Dahoas/synthetic-instruct-gptj-pairwise | 2023-01-09T03:48:03.000Z | [
"region:us"
] | Dahoas | null | null | 41 | 1,735 | 2022-12-19T17:41:16 | Entry not found | 15 | [
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facebook/multilingual_librispeech | 2023-02-13T11:33:31.000Z | [
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:de",
"language:nl",
"language:fr",
"... | facebook | This is a streamable version of the Multilingual LibriSpeech (MLS) dataset.
The data archives were restructured from the original ones from [OpenSLR](http://www.openslr.org/94)
to make it easier to stream.
MLS dataset is a large multilingual corpus suitable for speech research.
The dataset is derived from read aud... | @article{Pratap2020MLSAL,
title={MLS: A Large-Scale Multilingual Dataset for Speech Research},
author={Vineel Pratap and Qiantong Xu and Anuroop Sriram and Gabriel Synnaeve and Ronan Collobert},
journal={ArXiv},
year={2020},
volume={abs/2012.03411}
} | 28 | 1,731 | 2022-03-02T23:29:22 | ---
pretty_name: MultiLingual LibriSpeech
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- de
- nl
- fr
- it
- es
- pt
- pl
license:
- cc-by-4.0
multilinguality:
- multilingual
paperswithcode_id: multilingual-librispeech
size_categories:
- 100K<n<1M
source_datase... | 8,771 | [
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proteinea/secondary_structure_prediction | 2023-03-02T22:42:31.000Z | [
"doi:10.57967/hf/1104",
"region:us"
] | proteinea | null | null | 1 | 1,730 | 2022-12-12T13:23:27 | Entry not found | 15 | [
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scitail | 2023-04-05T13:39:52.000Z | [
"language:en",
"region:us"
] | null | The SciTail dataset is an entailment dataset created from multiple-choice science exams and web sentences. Each question
and the correct answer choice are converted into an assertive statement to form the hypothesis. We use information
retrieval to obtain relevant text from a large text corpus of web sentences, and use... | inproceedings{scitail,
Author = {Tushar Khot and Ashish Sabharwal and Peter Clark},
Booktitle = {AAAI},
Title = {{SciTail}: A Textual Entailment Dataset from Science Question Answering},
Year = {2018}
} | 4 | 1,729 | 2022-03-02T23:29:22 | ---
language:
- en
paperswithcode_id: scitail
pretty_name: SciTail
dataset_info:
- config_name: snli_format
features:
- name: sentence1_binary_parse
dtype: string
- name: sentence1_parse
dtype: string
- name: sentence1
dtype: string
- name: sentence2_parse
dtype: string
- name: sentence2
... | 9,299 | [
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0.... |
beomi/KoAlpaca-v1.1a | 2023-05-26T06:32:02.000Z | [
"task_categories:text-generation",
"language:ko",
"KoAlpaca",
"region:us"
] | beomi | null | null | 10 | 1,729 | 2023-05-26T06:27:44 | ---
dataset_info:
features:
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dtype: string
- name: output
dtype: string
- name: url
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splits:
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num_bytes: 23371027
num_examples: 21155
download_size: 12856014
dataset_size: 23371027
task_categories:
- text-generation
language:
- ko
tags:
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vipulgupta/CALM | 2023-08-24T00:03:32.000Z | [
"region:us"
] | vipulgupta | Bias Dataset | null | 1 | 1,724 | 2023-08-23T23:49:51 | Entry not found | 15 | [
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XiaHan19/cmmlu | 2023-10-20T19:55:23.000Z | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:zh",
"license:cc-by-nc-4.0",
"chinese",
"llm",
"evaluation",
"arxiv:2306.09212",
"region:us"
] | XiaHan19 | CMMLU is a comprehensive Chinese assessment suite specifically designed to evaluate the advanced knowledge and reasoning abilities of LLMs within the Chinese language and cultural context. | @misc{li2023cmmlu,
title={CMMLU: Measuring massive multitask language understanding in Chinese},
author={Haonan Li and Yixuan Zhang and Fajri Koto and Yifei Yang and Hai Zhao and Yeyun Gong and Nan Duan and Timothy Baldwin},
year={2023},
eprint={2306.09212},
archivePrefix={arXiv},
pr... | 0 | 1,718 | 2023-10-20T14:06:00 | ---
license: cc-by-nc-4.0
task_categories:
- multiple-choice
- question-answering
language:
- zh
tags:
- chinese
- llm
- evaluation
pretty_name: CMMLU
size_categories:
- 10K<n<100K
---
# CMMLU: Measuring massive multitask language understanding in Chinese
- **Homepage:** [https://github.com/haonan-li/CMMLU](https://g... | 4,448 | [
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reuters21578 | 2023-08-30T17:35:01.000Z | [
"language:en",
"license:other",
"region:us"
] | null | The Reuters-21578 dataset is one of the most widely used data collections for text
categorization research. It is collected from the Reuters financial newswire service in 1987. | @article{APTE94,
author = {Chidanand Apt{\'{e}} and Fred Damerau and Sholom M. Weiss},
title = {Automated Learning of Decision Rules for Text Categorization},
journal = {ACM Transactions on Information Systems},
year = {1994},
note = {To appear.}
}
@inproceedings{APTE94b,
author = {Chidanand Apt{\'{e}} and Fred Damera... | 8 | 1,717 | 2022-03-02T23:29:22 | ---
language:
- en
license: other
paperswithcode_id: reuters-21578
pretty_name: Reuters-21578 Text Categorization Collection
dataset_info:
- config_name: ModApte
features:
- name: text
dtype: string
- name: text_type
dtype: string
- name: topics
sequence: string
- name: lewis_split
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laion/dalle-3-dataset | 2023-11-03T01:05:26.000Z | [
"language:en",
"license:cc0-1.0",
"image-text-dataset",
"synthetic-dataset",
"region:us"
] | laion | null | null | 173 | 1,717 | 2023-10-06T18:11:38 | ---
language:
- en
license:
- cc0-1.0
tags:
- image-text-dataset
- synthetic-dataset
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yitingxie/rlhf-reward-datasets | 2023-01-01T12:23:04.000Z | [
"region:us"
] | yitingxie | null | null | 44 | 1,716 | 2023-01-01T12:22:09 | ---
dataset_info:
features:
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DeveloperOats/DBPedia_Classes | 2022-08-08T14:54:42.000Z | [
"task_categories:text-classification",
"task_ids:topic-classification",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"language:en",
"license:cc0-1.0",
"region:us"
] | DeveloperOats | null | null | 13 | 1,713 | 2022-08-08T09:15:05 | ---
annotations_creators: []
language:
- en
language_creators: []
license:
- cc0-1.0
multilinguality:
- monolingual
pretty_name: 'DBpedia'
size_categories:
- 1M<n<10M
source_datasets: []
tags: []
task_categories:
- text-classification
task_ids:
- topic-classification
---
About Dataset
DBpedia (from "DB" for "database... | 1,766 | [
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ade_corpus_v2 | 2023-06-01T14:59:53.000Z | [
"task_categories:text-classification",
"task_categories:token-classification",
"task_ids:coreference-resolution",
"task_ids:fact-checking",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"... | null | ADE-Corpus-V2 Dataset: Adverse Drug Reaction Data.
This is a dataset for Classification if a sentence is ADE-related (True) or not (False) and Relation Extraction between Adverse Drug Event and Drug.
DRUG-AE.rel provides relations between drugs and adverse effects.
DRUG-DOSE.rel provides relations between drugs an... | @article{GURULINGAPPA2012885,
title = "Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports",
journal = "Journal of Biomedical Informatics",
volume = "45",
number = "5",
pages = "885 - 892",
year = "2012",
note = "Text Mining and Natural Languag... | 19 | 1,704 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- 1K<n<10K
- n<1K
source_datasets:
- original
task_categories:
- text-classification
- token-classification
task_ids:
- coreference-resolution
- fact-che... | 9,841 | [
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lucadiliello/naturalquestionsshortqa | 2023-06-06T08:35:50.000Z | [
"region:us"
] | lucadiliello | null | null | 2 | 1,694 | 2023-02-25T18:03:29 | ---
dataset_info:
features:
- name: context
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C-MTEB/VideoRetrieval | 2023-07-28T08:45:16.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 1,687 | 2023-07-28T08:45:00 | ---
configs:
- config_name: default
data_files:
- split: corpus
path: data/corpus-*
- split: queries
path: data/queries-*
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features:
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covost2 | 2022-11-18T19:46:56.000Z | [
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:extended|other-common-voice",
"language:ar",
"language:ca",
... | null | CoVoST 2, a large-scale multilingual speech translation corpus covering translations from 21 languages into English and from English into 15 languages. The dataset is created using Mozilla’s open source Common Voice database of crowdsourced voice recordings.
Note that in order to limit the required storage for prepari... | @misc{wang2020covost,
title={CoVoST 2: A Massively Multilingual Speech-to-Text Translation Corpus},
author={Changhan Wang and Anne Wu and Juan Pino},
year={2020},
eprint={2007.10310},
archivePrefix={arXiv},
primaryClass={cs.CL} | 7 | 1,679 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- ar
- ca
- cy
- de
- es
- et
- fa
- fr
- id
- it
- ja
- lv
- mn
- nl
- pt
- ru
- sl
- sv
- ta
- tr
- zh
language_bcp47:
- sv-SE
- zh-CN
license:
- cc-by-nc-4.0
multilinguality:
- multilingual
size_categories:
- ... | 24,399 | [
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allenai/mslr2022 | 2022-11-18T21:16:10.000Z | [
"task_categories:summarization",
"task_categories:text2text-generation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-MS^2",
"source_datasets:extended|other-Cochrane",
"lang... | allenai | The Multidocument Summarization for Literature Review (MSLR) Shared Task aims to study how medical
evidence from different clinical studies are summarized in literature reviews. Reviews provide the
highest quality of evidence for clinical care, but are expensive to produce manually.
(Semi-)automation via NLP may facili... | @inproceedings{DeYoung2021MS2MS,
title = {MSˆ2: Multi-Document Summarization of Medical Studies},
author = {Jay DeYoung and Iz Beltagy and Madeleine van Zuylen and Bailey Kuehl and Lucy Lu Wang},
booktitle = {EMNLP},
year = {2021}
}
@article{Wallace2020GeneratingN,
title ... | 5 | 1,674 | 2022-07-18T16:24:24 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-MS^2
- extended|other-Cochrane
task_categories:
- summarization
- text2text-generation
paperswithcode_id:... | 39,290 | [
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yahoo_answers_topics | 2023-01-25T15:03:25.000Z | [
"task_categories:text-classification",
"task_ids:topic-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:extended|other-yahoo-answers-corpus",
"language:en",
"license:unknown",
"region:us"
] | null | Yahoo! Answers Topic Classification is text classification dataset. The dataset is the Yahoo! Answers corpus as of 10/25/2007. The Yahoo! Answers topic classification dataset is constructed using 10 largest main categories. From all the answers and other meta-information, this dataset only used the best answer content ... | null | 26 | 1,673 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- extended|other-yahoo-answers-corpus
task_categories:
- text-classification
task_ids:
- topic-classification
pretty_name: YahooAnswersTopics
dataset... | 5,013 | [
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enriched_web_nlg | 2023-06-01T14:59:50.000Z | [
"task_categories:tabular-to-text",
"task_ids:rdf-to-text",
"annotations_creators:found",
"language_creators:crowdsourced",
"multilinguality:monolingual",
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"source_datasets:extended|other-web-nlg",
"language:de",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | null | WebNLG is a valuable resource and benchmark for the Natural Language Generation (NLG) community. However, as other NLG benchmarks, it only consists of a collection of parallel raw representations and their corresponding textual realizations. This work aimed to provide intermediate representations of the data for the de... | @InProceedings{ferreiraetal2018,
author = "Castro Ferreira, Thiago and Moussallem, Diego and Wubben, Sander and Krahmer, Emiel",
title = "Enriching the WebNLG corpus",
booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
year = "2018",
series = {INLG'18},
publis... | 1 | 1,668 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- crowdsourced
language:
- de
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-web-nlg
task_categories:
- tabular-to-text
task_ids:
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paperswithcode_id: null
pretty_name: Enriched We... | 10,900 | [
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yuvalkirstain/pickapic_v2 | 2023-09-25T11:14:43.000Z | [
"region:us"
] | yuvalkirstain | null | null | 4 | 1,661 | 2023-09-24T20:54:31 | ---
dataset_info:
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C-MTEB/CovidRetrieval | 2023-07-28T09:44:36.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 1,660 | 2023-07-28T09:43:30 | ---
configs:
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data_files:
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path: data/corpus-*
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C-MTEB/T2Reranking | 2023-07-28T07:29:52.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 1,658 | 2023-07-28T07:28:07 | ---
configs:
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data_files:
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C-MTEB/CmedqaRetrieval | 2023-07-28T09:40:17.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 1,657 | 2023-07-28T09:39:17 | ---
configs:
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data_files:
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C-MTEB/MMarcoRetrieval | 2023-07-28T09:59:36.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 1,653 | 2023-07-28T09:59:09 | ---
configs:
- config_name: default
data_files:
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path: data/corpus-*
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path: data/queries-*
dataset_info:
features:
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WizardLM/WizardLM_evol_instruct_70k | 2023-08-24T03:59:32.000Z | [
"arxiv:2308.09583",
"arxiv:2304.12244",
"arxiv:2306.08568",
"region:us"
] | WizardLM | null | null | 116 | 1,650 | 2023-04-25T09:57:27 | This is the training data of WizardLM.
## 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 achiev... | 3,937 | [
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0.024139404296875,
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-0.042633056640625,
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... |
kumapo/JAQKET | 2023-10-09T06:44:28.000Z | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"language:ja",
"license:cc-by-sa-4.0",
"region:us"
] | kumapo | JAQKET: JApanese Questions on Knowledge of EnTitie | @InProceedings{Kurihara_nlp2020,
author = "鈴木正敏 and 鈴木潤 and 松田耕史 and ⻄田京介 and 井之上直也",
title = "JAQKET: クイズを題材にした日本語 QA データセットの構築",
booktitle = "言語処理学会第26回年次大会",
year = "2020",
url = "https://www.anlp.jp/proceedings/annual_meeting/2020/pdf_dir/P2-24.pdf"
note= "in Japanese" | 0 | 1,648 | 2023-06-21T13:04:38 | ---
license: cc-by-sa-4.0
task_categories:
- multiple-choice
- question-answering
language:
- ja
---
# Dataset Card for JAQKET
This dataset loading script is developed on [GitHub](https://github.com/kumapo/JAQKET-dataset).
Please feel free to open an [issue](https://github.com/kumapo/JAQKET-dataset/issues) or [pull r... | 3,928 | [
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fka/awesome-chatgpt-prompts | 2023-03-07T10:04:18.000Z | [
"license:cc0-1.0",
"ChatGPT",
"region:us"
] | fka | null | null | 3,661 | 1,647 | 2022-12-13T23:47:45 | ---
license: cc0-1.0
tags:
- ChatGPT
---
<p align="center"><h1>🧠 Awesome ChatGPT Prompts [CSV dataset]</h1></p>
This is a Dataset Repository of **Awesome ChatGPT Prompts**
**[View All Prompts on GitHub](https://github.com/f/awesome-chatgpt-prompts)**
# License
CC-0
| 271 | [
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jojo0217/korean_rlhf_dataset | 2023-09-25T08:36:04.000Z | [
"task_categories:text-generation",
"language:ko",
"license:apache-2.0",
"region:us"
] | jojo0217 | null | null | 12 | 1,644 | 2023-08-08T07:37:14 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- ko
---
성균관대학교 산학협력프로젝트 과정에서 한국어 llm 모델 SFT 학습을 위해 구축한 데이터셋 입니다.
2023-09-25
오픈 어시스턴트 data에서 오픈 어시스턴트를 포함하는 데이터 삭제
-> 답변에 오픈 어시스턴트라고 하는 경우가 나오기 때문
또한 스탠포드 대학 번역 데이터에서 번역 과정 오류로 input에 입력없음 과 같이 추가된 부분 삭제
그리고 \<unk\> 등으로 gpt 상에서 번역 오류가 ... | 965 | [
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0.00... |
conllpp | 2023-04-05T10:02:29.000Z | [
"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:extended|conll2003",
"language:en",
"license:unknown",
"region:us"
] | null | CoNLLpp is a corrected version of the CoNLL2003 NER dataset where labels of 5.38% of the sentences in the test set
have been manually corrected. The training set and development set are included for completeness.
For more details see https://www.aclweb.org/anthology/D19-1519/ and https://github.com/ZihanWangKi/CrossWei... | @inproceedings{wang2019crossweigh,
title={CrossWeigh: Training Named Entity Tagger from Imperfect Annotations},
author={Wang, Zihan and Shang, Jingbo and Liu, Liyuan and Lu, Lihao and Liu, Jiacheng and Han, Jiawei},
booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing ... | 5 | 1,631 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|conll2003
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: conll
pretty_name: ... | 7,704 | [
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0.0... |
C-MTEB/EcomRetrieval | 2023-07-28T09:37:55.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 1,627 | 2023-07-28T09:37:40 | ---
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: 9930587
num_examples: 100902
- name: queries
... | 583 | [
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-0.... |
alkzar90/CC6204-Hackaton-Cub-Dataset | 2023-01-12T12:14:32.000Z | [
"task_categories:image-classification",
"task_categories:text-classification",
"task_ids:multi-class-image-classification",
"size_categories:10K<n<15K",
"source_datasets:extended|other",
"language:en",
"license:apache-2.0",
"region:us"
] | alkzar90 | null | null | 5 | 1,625 | 2022-11-24T13:29:55 | ---
language:
- en
license:
- apache-2.0
pretty_name: CC6204-Hackaton-CUB200
size_categories:
- 10K<n<15K
source_datasets:
- extended|other
paperswithcode_id: cub-200-2011
task_categories:
- image-classification
- text-classification
task_ids:
- multi-class-image-classification
---
## Dataset Description
- **Homepage... | 10,936 | [
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0.0201... |
laion/laion1b-nolang-vit-l-14-embeddings | 2022-12-16T17:53:26.000Z | [
"region:us"
] | laion | null | null | 0 | 1,621 | 2022-12-15T23:35:54 | Entry not found | 15 | [
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0.03790... |
aeslc | 2023-04-05T08:32:58.000Z | [
"task_categories:summarization",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"aspect-based-summarization",
"conversations-summarization",
"multi-document... | null | A collection of email messages of employees in the Enron Corporation.
There are two features:
- email_body: email body text.
- subject_line: email subject text. | @misc{zhang2019email,
title={This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation},
author={Rui Zhang and Joel Tetreault},
year={2019},
eprint={1906.03497},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 5 | 1,620 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
pretty_name: 'AESLC: Annotated Enron Subject Line Corpus'
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- summarization
task_ids: []
paperswithcode_id: aeslc
... | 6,163 | [
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0.01... |
guardian_authorship | 2023-04-05T10:06:55.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:topic-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"region... | null | A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013.
1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ).
2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).
3- The same-... | @article{article,
author = {Stamatatos, Efstathios},
year = {2013},
month = {01},
pages = {421-439},
title = {On the robustness of authorship attribution based on character n-gram features},
volume = {21},
journal = {Journal of Law and Policy}
}
@inproceedings{stamatatos2017authorship,
... | 3 | 1,617 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
- topic-classification
pretty_name: GuardianAuthorship
datas... | 24,474 | [
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banghua/random_pre | 2023-10-28T02:55:26.000Z | [
"region:us"
] | banghua | null | null | 0 | 1,617 | 2023-10-28T02:48:38 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: prompt
dtype: string
- name: answers
list:
- name: answer
dtype: string
- name: model
dtype: string
- name: rank
dtype: float64
- name: turns
dtype:... | 705 | [
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huggingface/semantic-segmentation-test-sample | 2022-04-11T09:15:24.000Z | [
"region:us"
] | huggingface | null | null | 0 | 1,614 | 2022-04-11T09:12:00 | This dataset contains 10 examples of the [segments/sidewalk-semantic](https://huggingface.co/datasets/segments/sidewalk-semantic) dataset (i.e. 10 images with corresponding ground-truth segmentation maps). | 205 | [
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0.06585693359375,
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-0.01... |
C-MTEB/MedicalRetrieval | 2023-07-28T09:33:59.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 1,614 | 2023-07-28T09:33:27 | ---
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: 37393271
num_examples: 100999
- name: queries
... | 589 | [
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YeungNLP/ultrachat | 2023-06-19T02:52:43.000Z | [
"region:us"
] | YeungNLP | null | null | 14 | 1,611 | 2023-06-18T16:58:11 | Entry not found | 15 | [
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BeIR/scifact | 2022-10-23T06:01:22.000Z | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | BeIR | null | null | 1 | 1,601 | 2022-06-05T16:24:20 | ---
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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google/MusicCaps | 2023-03-08T14:37:09.000Z | [
"task_categories:text-to-speech",
"language:en",
"license:cc-by-sa-4.0",
"arxiv:2301.11325",
"region:us"
] | google | null | null | 79 | 1,601 | 2023-01-27T16:26:11 | ---
license:
- cc-by-sa-4.0
converted_from: kaggle
kaggle_id: googleai/musiccaps
task_categories:
- text-to-speech
language:
- en
---
# Dataset Card for MusicCaps
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [S... | 5,062 | [
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