id stringlengths 2 115 | lastModified stringlengths 24 24 | tags list | author stringlengths 2 42 ⌀ | description stringlengths 0 68.7k ⌀ | citation stringlengths 0 10.7k ⌀ | cardData null | likes int64 0 3.55k | downloads int64 0 10.1M | card stringlengths 0 1.01M |
|---|---|---|---|---|---|---|---|---|---|
joelniklaus/Multi_Legal_Pile | 2023-05-15T20:48:26.000Z | [
"task_categories:fill-mask",
"annotations_creators:other",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:bg",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
"language... | joelniklaus | Multi Legal Pile is a dataset of legal documents in the 24 EU languages. | null | 28 | 342 | ---
annotations_creators:
- other
language_creators:
- found
language:
- bg
- cs
- da
- de
- el
- en
- es
- et
- fi
- fr
- ga
- hr
- hu
- it
- lt
- lv
- mt
- nl
- pl
- pt
- ro
- sk
- sl
- sv
license:
- cc-by-nc-sa-4.0
multilinguality:
- multilingual
paperswithcode_id: null
pretty_name: "MultiLegalPile: A Large-Scale Mu... | |
liuhaotian/LLaVA-Instruct-150K | 2023-10-06T22:18:34.000Z | [
"task_categories:visual-question-answering",
"task_categories:question-answering",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-nc-4.0",
"region:us"
] | liuhaotian | null | null | null | 138 | 342 | ---
license: cc-by-nc-4.0
task_categories:
- visual-question-answering
- question-answering
language:
- en
pretty_name: LLaVA Visual Instruct 150K
size_categories:
- 100K<n<1M
---
# LLaVA Visual Instruct 150K Dataset Card
## Dataset details
**Dataset type:**
LLaVA Visual Instruct 150K is a set of GPT-generated mul... |
bbz662bbz/databricks-dolly-15k-ja-gozarinnemon | 2023-05-31T14:44:34.000Z | [
"license:cc-by-sa-3.0",
"region:us"
] | bbz662bbz | null | null | null | 2 | 340 | ---
license: cc-by-sa-3.0
---
This dataset was using "kunishou/databricks-dolly-15k-ja"
This dataset is licensed under CC BY SA 3.0
Last Update : 2023-05-28
databricks-dolly-15k-ja-gozarinnemon
kunishou/databricks-dolly-15k-ja
https://huggingface.co/datasets/kunishou/databricks-dolly-15k-ja
|
GEM/dart | 2022-10-24T15:30:16.000Z | [
"task_categories:table-to-text",
"annotations_creators:none",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:mit",
"data-to-text",
"arxiv:1910.13461",
"arxiv:1908.09022",
"arxiv:2007.02871",
"arxiv:1709.0... | GEM | DART is a large and open-domain structured DAta Record to Text generation corpus
with high-quality sentence annotations with each input being a set of
entity-relation triples following a tree-structured ontology. It consists of
82191 examples across different domains with each input being a semantic RDF
triple set deri... | @inproceedings{nan-etal-2021-dart,
title = "{DART}: Open-Domain Structured Data Record to Text Generation",
author = "Nan, Linyong and
Radev, Dragomir and
Zhang, Rui and
Rau, Amrit and
Sivaprasad, Abhinand and
Hsieh, Chiachun and
Tang, Xiangru and
Vyas, Aadit an... | null | 0 | 338 | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- mit
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
pretty_name: dart
tags:
- data-to-text
---
# Dataset Card for GEM/dart
## Dataset Description
- *... |
AlekseyKorshuk/drama-books | 2022-06-11T13:26:37.000Z | [
"region:us"
] | AlekseyKorshuk | null | null | null | 1 | 338 | Entry not found |
tapaco | 2023-06-08T13:14:46.000Z | [
"task_categories:text2text-generation",
"task_categories:translation",
"task_categories:text-classification",
"task_ids:semantic-similarity-classification",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"size_... | null | A freely available paraphrase corpus for 73 languages extracted from the Tatoeba database. Tatoeba is a crowdsourcing project mainly geared towards language learners. Its aim is to provide example sentences and translations for particular linguistic constructions and words. The paraphrase corpus is created by populatin... | @dataset{scherrer_yves_2020_3707949,
author = {Scherrer, Yves},
title = {{TaPaCo: A Corpus of Sentential Paraphrases for 73 Languages}},
month = mar,
year = 2020,
publisher = {Zenodo},
version = {1.0},
doi = {10.5281/zenodo.3707949},
url = {https://d... | null | 30 | 337 | ---
annotations_creators:
- machine-generated
language_creators:
- crowdsourced
language:
- af
- ar
- az
- be
- ber
- bg
- bn
- br
- ca
- cbk
- cmn
- cs
- da
- de
- el
- en
- eo
- es
- et
- eu
- fi
- fr
- gl
- gos
- he
- hi
- hr
- hu
- hy
- ia
- id
- ie
- io
- is
- it
- ja
- jbo
- kab
- ko
- kw
- la
- lfn
- lt
- mk
- m... |
explodinggradients/ragas-wikiqa | 2023-07-27T07:13:14.000Z | [
"region:us"
] | explodinggradients | null | null | null | 1 | 337 | ---
dataset_info:
features:
- name: question
dtype: string
- name: correct_answer
dtype: string
- name: incorrect_answer
dtype: string
- name: question_id
dtype: string
- name: generated_with_rag
dtype: string
- name: context
sequence: string
- name: generated_without_rag
dty... |
SetFit/ag_news | 2022-01-19T21:21:07.000Z | [
"region:us"
] | SetFit | null | null | null | 0 | 336 | Entry not found |
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... | null | 3 | 336 |
---
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... |
jjonhwa/SECOND_KQ_V2 | 2023-09-13T07:04:47.000Z | [
"region:us"
] | jjonhwa | null | null | null | 0 | 336 | ---
dataset_info:
features:
- name: question
dtype: string
- name: answers
sequence: string
- name: ctxs
list:
- name: score
dtype: float64
- name: text
dtype: string
splits:
- name: train
num_bytes: 686780736
num_examples: 86975
download_size: 276955064
dataset_s... |
allenai/prosocial-dialog | 2023-02-03T07:58:29.000Z | [
"task_categories:conversational",
"task_categories:text-classification",
"task_ids:dialogue-generation",
"task_ids:multi-class-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categorie... | allenai | null | null | null | 64 | 335 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- crowdsourced
- machine-generated
license: cc-by-4.0
multilinguality:
- monolingual
pretty_name: ProsocialDialog
size_categories:
- 10K<n<100K
- 100K<n<1M
source_datasets:
- original
- extended|social_bias_frames
tags:
- dialogue
- dialogue saf... |
shailja/Verilog_GitHub | 2023-09-20T17:14:18.000Z | [
"license:mit",
"arxiv:2212.11140",
"region:us"
] | shailja | null | null | null | 2 | 335 | ---
license: mit
---
---
pipeline_tag: text-generation
tags:
- code
model-index:
- name: VeriGen
results:
- task:
type: text-generation
dataset:
type:
name:
extra_gated_prompt: >-
## Model License Agreement
Please read the BigCode [OpenRAIL-M
license](https://huggingface.co/space... |
approach0/MATH-full | 2023-09-14T18:42:51.000Z | [
"region:us"
] | approach0 | null | null | null | 0 | 335 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: src_path
dtype: string
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: ... |
Tevatron/beir-corpus | 2022-07-07T23:53:45.000Z | [
"region:us"
] | Tevatron | null | null | null | 0 | 334 | Entry not found |
hakurei/open-instruct-v1 | 2023-04-17T03:03:13.000Z | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"region:us"
] | hakurei | null | null | null | 84 | 334 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
size_categories:
- 100K<n<1M
---
# Open Instruct V1 - A dataset for having LLMs follow instructions.
Open Instruct V1 is an amalgamation of different datasets which are cleaned and then collated into a singular format for training.
## Dataset ... |
BeIR/trec-covid-qrels | 2022-10-23T06:01:04.000Z | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | BeIR | null | null | null | 0 | 333 | ---
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:
... |
sarus-tech/phee | 2023-06-21T19:36:26.000Z | [
"arxiv:2210.12560",
"region:us"
] | sarus-tech | Data and Code for [``PHEE: A Dataset for Pharmacovigilance Event Extraction from Text``](https://arxiv.org/abs/2210.12560/)\ | @misc{sun2022phee,
title={PHEE: A Dataset for Pharmacovigilance Event Extraction from Text},
author={Zhaoyue Sun and Jiazheng Li and Gabriele Pergola and Byron C. Wallace and Bino John and Nigel Greene and Joseph Kim and Yulan He},
year={2022},
eprint={2210.12560},
archivePrefix={arXiv},
... | null | 1 | 333 | # PHEE dataset
This dataset is port of https://github.com/ZhaoyueSun/PHEE,
the data used in: [``PHEE: A Dataset for Pharmacovigilance Event Extraction from Text``](https://arxiv.org/abs/2210.12560/)
|
GeorgiaTech/cnotesum | 2023-09-02T13:47:25.000Z | [
"license:other",
"region:us"
] | GeorgiaTech | null | null | null | 0 | 332 | ---
license: other
---
Synthetic Clinical Notes based on Synthea and Summary Generated via LLAMA 2 |
indonli | 2023-01-25T14:33:00.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:id",
... | null | IndoNLI is the first human-elicited Natural Language Inference (NLI) dataset for Indonesian.
IndoNLI is annotated by both crowd workers and experts. The expert-annotated data is used exclusively as a test set.
It is designed to provide a challenging test-bed for Indonesian NLI by explicitly incorporating various ... | @inproceedings{mahendra-etal-2021-indonli,
title = "{I}ndo{NLI}: A Natural Language Inference Dataset for {I}ndonesian",
author = "Mahendra, Rahmad and Aji, Alham Fikri and Louvan, Samuel and Rahman, Fahrurrozi and Vania, Clara",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natu... | null | 6 | 331 | ---
annotations_creators:
- expert-generated
- crowdsourced
language_creators:
- expert-generated
language:
- id
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- natural-language-inference
paperswithcode_i... |
yahoo_answers_qa | 2022-11-03T16:30:48.000Z | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-yahoo-webscope-l6",
"language:en",
"license:unknown",
"region:us"
] | null | Yahoo Non-Factoid Question Dataset is derived from Yahoo's Webscope L6 collection using machine learning techiques such that the questions would contain non-factoid answers.The dataset contains 87,361 questions and their corresponding answers. Each question contains its best answer along with additional other answers s... | null | null | 13 | 331 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-yahoo-webscope-l6
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswithcode_id: null
pretty_name: YahooAnswe... |
scikit-learn/adult-census-income | 2022-06-20T14:46:43.000Z | [
"license:cc0-1.0",
"region:us"
] | scikit-learn | null | null | null | 1 | 329 | ---
license: cc0-1.0
---
## Adult Census Income Dataset
The following was retrieved from [UCI machine learning repository](https://archive.ics.uci.edu/ml/datasets/adult).
This data was extracted from the 1994 Census bureau database by Ronny Kohavi and Barry Becker (Data Mining and Visualization, Silicon Graphics). A s... |
medalpaca/medical_meadow_wikidoc_patient_information | 2023-04-06T17:08:53.000Z | [
"task_categories:question-answering",
"language:en",
"license:cc",
"region:us"
] | medalpaca | null | null | null | 5 | 329 | ---
license: cc
task_categories:
- question-answering
language:
- en
---
# Dataset Card for WikiDoc
For the dataset containing rephrased content from the living textbook refer to [this dataset](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc)
## Dataset Description
- **Source:** https://www.wikidoc.o... |
hoskinson-center/proofnet | 2023-03-17T21:25:37.000Z | [
"license:mit",
"arxiv:2302.12433",
"region:us"
] | hoskinson-center | A dataset that evaluates formally proving and autoformalizing undergraduate mathematics. | null | null | 8 | 328 | ---
license: mit
---
# ProofNet
## Dataset Description
- **Repository:** [zhangir-azerbayev/ProofNet](https://github.com/zhangir-azerbayev/ProofNet)
- **Paper:** [ProofNet](https://mathai2022.github.io/papers/20.pdf)
- **Point of Contact:** [Zhangir Azerbayev](https://zhangir-azerbayev.github.io/)
### Dataset Summa... |
carlosdanielhernandezmena/ravnursson_asr | 2023-07-10T21:20:03.000Z | [
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:fo",
"license:cc-by-4.0",
"faroe islands",
"faroese",
"ravnur project"... | carlosdanielhernandezmena | The corpus \"RAVNURSSON FAROESE SPEECH AND TRANSCRIPTS\" (or RAVNURSSON Corpus for short) is a collection of speech recordings with transcriptions intended for Automatic Speech Recognition (ASR) applications in the language that is spoken at the Faroe Islands (Faroese). It was curated at the Reykjavík University (RU) i... | @misc{carlosmenaravnursson2022,
title={Ravnursson Faroese Speech and Transcripts},
author={Hernandez Mena, Carlos Daniel and Simonsen, Annika},
year={2022},
url={http://hdl.handle.net/20.500.12537/276},
} | null | 1 | 327 | ---
annotations_creators:
- expert-generated
language:
- fo
language_creators:
- expert-generated
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: RAVNURSSON FAROESE SPEECH AND TRANSCRIPTS
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- faroe islands
- faroese
- ravnur project
- speech... |
amitness/PAWS-X-maltese | 2023-05-03T12:09:04.000Z | [
"region:us"
] | amitness | null | null | null | 0 | 326 | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype:
class_label:
names:
'0': not_entailment
'1': entailment
- name: sentence1_mt
dtype: string
- name: sentence2_mt
dtype: string
splits:
- n... |
nisaar/LLAMA2_Legal_Dataset_4.4k_Instructions | 2023-07-30T15:25:03.000Z | [
"license:apache-2.0",
"region:us"
] | nisaar | null | null | null | 11 | 326 | ---
license: apache-2.0
---
|
glaiveai/glaive-function-calling-v2 | 2023-09-27T18:04:08.000Z | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"region:us"
] | glaiveai | null | null | null | 11 | 326 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
size_categories:
- 100K<n<1M
--- |
Brendan/icdst_multiwoz_turns_v24 | 2023-08-04T23:01:11.000Z | [
"region:us"
] | Brendan | null | null | null | 0 | 325 | ---
dataset_info:
features:
- name: dialogue_id
dtype: string
- name: turn_id
dtype: int8
- name: domains
sequence: string
- name: user_utterances
sequence: string
- name: system_utterances
sequence: string
- name: slot_values
struct:
- name: hotel
struct:
- name: p... |
result-kand2-sdxl-wuerst-karlo/463b7b19 | 2023-09-28T06:54:08.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | null | 0 | 325 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 161
num_examples: 10
download_size: 1299
dataset_size: 161
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "463b7b1... |
freebase_qa | 2022-11-18T20:03:22.000Z | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|trivia_qa",
"language:en",
"license:unknown",
"region:us"
] | null | FreebaseQA is for open-domain factoid question answering (QA) tasks over structured knowledge bases, like Freebase The data set is generated by matching trivia-type question-answer pairs with subject-predicateobject triples in Freebase. | @article{jiang2019freebaseqa,
title={FreebaseQA: A New Factoid QA Dataset Matching Trivia-Style Question-Answer Pairs with Freebase},
author={Jiang, Kelvin and Wu, Dekun and Jiang, Hui},
journal={north american chapter of the association for computational linguistics},
year={2019}
} | null | 2 | 324 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|trivia_qa
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswithcode_id: freebaseqa
pretty_name: FreebaseQA
... |
lmqg/qa_squadshifts_synthetic | 2023-01-15T14:25:15.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|wikipedia",
"language:en",
"license:cc-by-4.0",
"arxiv:2210.03992",
"region:us"
] | lmqg | null | @inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Nat... | null | 0 | 322 | ---
license: cc-by-4.0
pretty_name: Synthetic QA dataset on SQuADShifts.
language: en
multilinguality: monolingual
size_categories: 10K<n<100K
source_datasets:
- extended|wikipedia
task_categories:
- question-answering
task_ids:
- extractive-qa
---
# Dataset Card for "lmqg/qa_squadshifts_synthetic"
## Dataset Descrip... |
HumanCompatibleAI/ppo-seals-HalfCheetah-v0 | 2023-05-29T09:52:45.000Z | [
"region:us"
] | HumanCompatibleAI | null | null | null | 0 | 322 | ---
dataset_info:
features:
- name: obs
sequence:
sequence: float64
- name: acts
sequence:
sequence: float32
- name: infos
sequence: string
- name: terminal
dtype: bool
- name: rews
sequence: float64
splits:
- name: train
num_bytes: 89536876
num_examples: 104
do... |
mediabiasgroup/mbib-base | 2023-08-03T01:03:05.000Z | [
"task_categories:text-classification",
"size_categories:1M<n<10M",
"language:en",
"license:cc",
"media",
"mediabias",
"media-bias",
"media bias",
"region:us"
] | mediabiasgroup | null | null | null | 5 | 321 | ---
license: cc
task_categories:
- text-classification
language:
- en
tags:
- media
- mediabias
- media-bias
- media bias
size_categories:
- 1M<n<10M
---
# Dataset Card for Media-Bias-Identification-Benchmark
## Table of Contents
- [Dataset Card for Media-Bias-Identification-Benchmark](#dataset-card-for-mbib)
- [Ta... |
codeparrot/self-instruct-starcoder | 2023-06-21T08:52:23.000Z | [
"task_categories:text2text-generation",
"size_categories:1K<n<10K",
"license:bigscience-openrail-m",
"code",
"arxiv:2212.10560",
"arxiv:2305.06161",
"arxiv:1908.10084",
"doi:10.57967/hf/0790",
"region:us"
] | codeparrot | null | null | null | 26 | 321 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
- name: most_similar
dtype: string
- name: avg_similarity_score
dtype: float64
splits:
- name: curated
num_bytes: 1937514
num_examples: 771
- name: raw
num_bytes: 12969008
num_examp... |
sam-mosaic/chat-v2 | 2023-07-18T00:23:25.000Z | [
"language:en",
"region:us"
] | sam-mosaic | null | null | null | 2 | 321 | ---
language: en
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 1053541716.4621352
num_examples: 306305
- name: test
num_bytes: 20265459.694286585
num_examples: 5339
download_si... |
hackathon-pln-es/ITAMA-DataSet | 2022-04-04T03:32:20.000Z | [
"region:us"
] | hackathon-pln-es | null | null | null | 2 | 320 | # Extracción de datos de Reddit
Se descargaron todos los titulos de los hilos de algunas comunidades en español de Reddit entre marzo del 2017 y enero del 2022:
| Comunidad | N° de hilos |
|----------------------------|-------------|
|AskRedditespanol | 28072 |
| BOLIVIA ... |
MLRS/korpus_malti | 2022-08-30T08:59:09.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:mt"... | MLRS | General Corpora for the Maltese language. | @inproceedings{BERTu,
title = "Pre-training Data Quality and Quantity for a Low-Resource Language: New Corpus and {BERT} Models for {M}altese",
author = "Micallef, Kurt and
Gatt, Albert and
Tanti, Marc and
van der Plas, Lonneke and
Borg, Claudia",
... | null | 0 | 318 | ---
pretty_name: Korpus Malti
language:
- mt
multilinguality:
- monolingual
size_categories:
- 10M<n<100M
annotations_creators:
- no-annotation
language_creators:
- found
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
license:
- cc-by-... |
bigheiniuJ/EvalMetaICLAll | 2023-07-24T06:39:16.000Z | [
"region:us"
] | bigheiniuJ | null | null | null | 0 | 318 | ---
dataset_info:
features:
- name: task
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: options
sequence: string
- name: seed
dtype: string
- name: split
dtype: string
splits:
- name: meta_train
num_bytes: 648803062
num_examples: 1111614
... |
medalpaca/medical_meadow_mmmlu | 2023-04-06T17:49:48.000Z | [
"region:us"
] | medalpaca | null | null | null | 0 | 316 | Entry not found |
biu-nlp/abstract-sim | 2023-05-29T09:33:17.000Z | [
"region:us"
] | biu-nlp | null | null | null | 2 | 316 | A dataset of Wikipedia sentences accompannied by valid and invalid abstract descriptions. |
mteb/toxic_conversations_50k | 2022-09-27T19:14:35.000Z | [
"language:en",
"region:us"
] | mteb | null | null | null | 2 | 315 | ---
language:
- en
---
# Toxic Conversation
This is a version of the [Jigsaw Unintended Bias in Toxicity Classification dataset](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/overview). It contains comments from the Civil Comments platform together with annotations if the comment is toxic... |
marmal88/skin_cancer | 2023-01-25T02:21:28.000Z | [
"task_categories:image-classification",
"task_categories:image-segmentation",
"size_categories:1K<n<10K",
"language:en",
"skin_cancer",
"HAM10000",
"region:us"
] | marmal88 | null | null | null | 4 | 315 | ---
dataset_info:
features:
- name: image
dtype: image
- name: image_id
dtype: string
- name: lesion_id
dtype: string
- name: dx
dtype: string
- name: dx_type
dtype: string
- name: age
dtype: float64
- name: sex
dtype: string
- name: localization
dtype: string
splits:... |
eReverter/cnn_dailymail_extractive | 2023-07-19T18:45:02.000Z | [
"task_categories:summarization",
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"arxiv:1903.10318",
"region:us"
] | eReverter | null | null | null | 0 | 315 | ---
dataset_info:
features:
- name: src
sequence: string
- name: tgt
sequence: string
- name: labels
sequence: int64
splits:
- name: test
num_bytes: 53831114
num_examples: 11490
- name: train
num_bytes: 1376640992
num_examples: 287113
- name: validation
num_bytes: 6220055... |
pvduy/arena_synth | 2023-08-02T16:02:03.000Z | [
"region:us"
] | pvduy | null | null | null | 0 | 315 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: selected
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 53190421
num_examples: 29851
- name: test
num_bytes: 14269380
num_examples: 8000
download_size: 36514341
dataset_size: 674... |
AdaptLLM/finance-tasks | 2023-09-26T08:36:08.000Z | [
"arxiv:2309.09530",
"region:us"
] | AdaptLLM | null | null | null | 3 | 315 | ---
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... |
yxchar/rct-20k-tlm | 2021-11-05T01:18:46.000Z | [
"region:us"
] | yxchar | null | null | null | 0 | 314 | Entry not found |
mteb/stackoverflowdupquestions-reranking | 2022-09-27T19:13:01.000Z | [
"language:en",
"region:us"
] | mteb | null | null | null | 0 | 314 | ---
language:
- en
--- |
allenai/wmt22_african | 2022-08-15T21:52:43.000Z | [
"region:us"
] | allenai | null | null | null | 3 | 314 | # Dataset Card for allenai/wmt22_african
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [D... |
argilla/research_titles_multi-label | 2022-10-07T13:22:53.000Z | [
"region:us"
] | argilla | null | null | null | 0 | 314 | Entry not found |
code_x_glue_tc_text_to_code | 2022-11-18T19:31:29.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:other-programming-languages",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:code",
"language:en",
"license:c-uda",
"text-to-code",
"region:us"
] | null | We use concode dataset which is a widely used code generation dataset from Iyer's EMNLP 2018 paper Mapping Language to Code in Programmatic Context. See paper for details. | @article{iyer2018mapping,
title={Mapping language to code in programmatic context},
author={Iyer, Srinivasan and Konstas, Ioannis and Cheung, Alvin and Zettlemoyer, Luke},
journal={arXiv preprint arXiv:1808.09588},
year={2018}
} | null | 18 | 313 | ---
annotations_creators:
- found
language_creators:
- found
language:
- code
- en
license:
- c-uda
multilinguality:
- other-programming-languages
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- translation
task_ids: []
pretty_name: CodeXGlueTcTextToCode
tags:
- text-to-code
dataset_info:
... |
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... | null | 20 | 313 | ---
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:... |
mteb/scidocs-reranking | 2022-09-27T19:11:31.000Z | [
"language:en",
"region:us"
] | mteb | null | null | null | 0 | 313 | ---
language:
- en
--- |
nlp-thedeep/humset | 2023-05-25T17:14:31.000Z | [
"task_categories:text-classification",
"task_categories:text-retrieval",
"task_categories:token-classification",
"task_ids:multi-label-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_... | nlp-thedeep | HumSet is a novel and rich multilingual dataset of humanitarian response documents annotated by experts in the humanitarian response community. HumSet is curated by humanitarian analysts and covers various disasters around the globe that occurred from 2018 to 2021 in 46 humanitarian response projects. The dataset consi... | @misc{https://doi.org/10.48550/arxiv.2210.04573,
doi = {10.48550/ARXIV.2210.04573},
url = {https://arxiv.org/abs/2210.04573},
author = {Fekih, Selim and Tamagnone, Nicolò and Minixhofer, Benjamin and Shrestha, Ranjan and Contla, Ximena and Oglethorpe, Ewan and Rekabsaz, Navid},
keywords = {Computation and Langu... | null | 1 | 313 | ---
annotations_creators:
- expert-generated
language:
- en
- fr
- es
language_creators:
- expert-generated
license:
- apache-2.0
multilinguality:
- multilingual
pretty_name: HumSet
size_categories:
- 100K<n<1M
source_datasets:
- original
tags:
- humanitarian
- research
- analytical-framework
- multilabel
- humset
- hu... |
llm-book/ner-wikipedia-dataset | 2023-07-25T17:19:14.000Z | [
"task_categories:token-classification",
"size_categories:1K<n<10K",
"language:ja",
"license:cc-by-sa-3.0",
"region:us"
] | llm-book | null | @inproceedings{omi-2021-wikipedia,
title = "Wikipediaを用いた日本語の固有表現抽出のデータセットの構築",
author = "近江 崇宏",
booktitle = "言語処理学会第27回年次大会",
year = "2021",
url = "https://anlp.jp/proceedings/annual_meeting/2021/pdf_dir/P2-7.pdf",
} | null | 0 | 313 | ---
language:
- ja
license:
- cc-by-sa-3.0
size_categories:
- 1K<n<10K
task_categories:
- token-classification
---
# Dataset Card for llm-book/ner-wikipedia-dataset
書籍『大規模言語モデル入門』で使用する、ストックマーク株式会社により作成された「Wikipediaを用いた日本語の固有表現抽出データセット」(Version 2.0)です。
Githubリポジトリ[stockmarkteam/ner-wikipedia-dataset](https://github.c... |
distil-whisper/librispeech_asr-timestamped | 2023-09-25T10:30:13.000Z | [
"task_categories:automatic-speech-recognition",
"language:en",
"license:cc-by-4.0",
"region:us"
] | distil-whisper | LibriSpeech is a corpus of approximately 1000 hours of read English speech with sampling rate of 16 kHz,
prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read
audiobooks from the LibriVox project, and has been carefully segmented and aligned.87 | @inproceedings{panayotov2015librispeech,
title={Librispeech: an ASR corpus based on public domain audio books},
author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},
pages={5206--... | null | 0 | 313 | ---
license: cc-by-4.0
task_categories:
- automatic-speech-recognition
language:
- en
-pretty_name: LibriSpeech ASR
---
# Distil Whisper: LibriSpeech ASR With Timestamps
This is a variant of the [LibriSpeech ASR](https://huggingface.co/datasets/librispeech_asr) dataset, augmented to return the pseudo-labelled Whisper... |
lince | 2023-04-05T10:09:24.000Z | [
"region:us"
] | null | LinCE is a centralized Linguistic Code-switching Evaluation benchmark
(https://ritual.uh.edu/lince/) that contains data for training and evaluating
NLP systems on code-switching tasks. | @inproceedings{aguilar-etal-2020-lince,
title = "{L}in{CE}: A Centralized Benchmark for Linguistic Code-switching Evaluation",
author = "Aguilar, Gustavo and
Kar, Sudipta and
Solorio, Thamar",
booktitle = "Proceedings of The 12th Language Resources and Evaluation Conference",
month = may,
... | null | 5 | 312 | ---
paperswithcode_id: lince
pretty_name: Linguistic Code-switching Evaluation Dataset
dataset_info:
- config_name: lid_spaeng
features:
- name: idx
dtype: int32
- name: words
sequence: string
- name: lid
sequence: string
splits:
- name: train
num_bytes: 4745003
num_examples: 21030
- n... |
snow_simplified_japanese_corpus | 2022-11-03T16:31:17.000Z | [
"task_categories:translation",
"annotations_creators:crowdsourced",
"annotations_creators:other",
"language_creators:found",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"language:ja",
"license:cc-by-4.0",
"region:us"
] | null | About SNOW T15: The simplified corpus for the Japanese language. The corpus has 50,000 manually simplified and aligned sentences. This corpus contains the original sentences, simplified sentences and English translation of the original sentences. It can be used for automatic text simplification as well as translating s... | @inproceedings{maruyama-yamamoto-2018-simplified,
title = "Simplified Corpus with Core Vocabulary",
author = "Maruyama, Takumi and
Yamamoto, Kazuhide",
booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
month = may,
year = "2... | null | 12 | 312 | ---
annotations_creators:
- crowdsourced
- other
language_creators:
- found
language:
- en
- ja
license:
- cc-by-4.0
multilinguality:
- translation
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: SNOW T15 and T23 (simplified Japa... |
ehartford/dolphin | 2023-09-25T16:59:11.000Z | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"region:us"
] | ehartford | null | null | null | 177 | 312 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
---
Dolphin 🐬
https://erichartford.com/dolphin
## Dataset details
This dataset is an attempt to replicate the results of [Microsoft's Orca](https://www.microsoft.com/en-us/research/publication/orca-progressive-learning-from-complex-explanat... |
BeIR/scifact-qrels | 2022-10-23T06:05:06.000Z | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | BeIR | null | null | null | 0 | 311 | ---
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:
... |
seamew/ChnSentiCorp | 2021-06-22T08:58:53.000Z | [
"region:us"
] | seamew | null | null | null | 19 | 309 | Entry not found |
biosses | 2022-11-03T16:31:20.000Z | [
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:semantic-similarity-scoring",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:gpl-3.0",
"region... | null | BIOSSES is a benchmark dataset for biomedical sentence similarity estimation. The dataset comprises 100 sentence pairs, in which each sentence was selected from the TAC (Text Analysis Conference) Biomedical Summarization Track Training Dataset containing articles from the biomedical domain. The sentence pairs were eval... | @article{souganciouglu2017biosses,
title={BIOSSES: a semantic sentence similarity estimation system for the biomedical domain},
author={So{\\u{g}}anc{\\i}o{\\u{g}}lu, Gizem and {\\"O}zt{\\"u}rk, Hakime and {\\"O}zg{\\"u}r, Arzucan},
journal={Bioinformatics},
volume={33},
number={14},
pages={i49--i58},
yea... | null | 4 | 308 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- gpl-3.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
- semantic-similarity-scoring
paperswithcode_id: biosses
pretty_nam... |
squad_es | 2023-04-05T13:40:35.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|squad",
"language:es",
"license:cc-by-4.0",
"arxiv:1912.05200",
... | null | automatic translation of the Stanford Question Answering Dataset (SQuAD) v2 into Spanish | @article{2016arXiv160605250R,
author = {Casimiro Pio , Carrino and Marta R. , Costa-jussa and Jose A. R. , Fonollosa},
title = "{Automatic Spanish Translation of the SQuAD Dataset for Multilingual
Question Answering}",
journal = {arXiv e-prints},
year = 2019,
eid = {arXiv:1912.... | null | 5 | 308 | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- es
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|squad
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: squad-es
pretty_name:... |
olm/wikipedia | 2022-11-15T18:39:59.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:n<1K",
"size_categories:1K<n<10K",
"size_categ... | olm | Wikipedia dataset containing cleaned articles of all languages.
The datasets are built from the Wikipedia dump
(https://dumps.wikimedia.org/) with one split per language. Each example
contains the content of one full Wikipedia article with cleaning to strip
markdown and unwanted sections (references, etc.). | @ONLINE {wikidump,
author = {Wikimedia Foundation},
title = {Wikimedia Downloads},
url = {https://dumps.wikimedia.org}
} | null | 24 | 308 | ---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
pretty_name: Wikipedia
paperswithcode_id: null
license:
- cc-by-sa-3.0
- gfdl
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
source_datasets:
- original
multilinguality:
- multilingual
si... |
maritaca-ai/boolq_pt | 2023-02-09T00:38:29.000Z | [
"region:us"
] | maritaca-ai | BoolQ is a question answering dataset for yes/no questions containing 15942 examples. These questions are naturally
occurring ---they are generated in unprompted and unconstrained settings.
Each example is a triplet of (question, passage, answer), with the title of the page as optional additional context.
The text-pair... | @inproceedings{clark2019boolq,
title = {BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions},
author = {Clark, Christopher and Lee, Kenton and Chang, Ming-Wei, and Kwiatkowski, Tom and Collins, Michael, and Toutanova, Kristina},
booktitle = {NAACL},
year = {2019},
} | null | 1 | 308 | Entry not found |
jamescalam/agent-conversations-retrieval-tool | 2023-08-27T12:57:37.000Z | [
"region:us"
] | jamescalam | null | null | null | 7 | 307 | Entry not found |
reginaboateng/Bioasq7b | 2023-07-13T13:55:58.000Z | [
"language:en",
"region:us"
] | reginaboateng | null | null | null | 1 | 306 | ---
language: en
dataset_info:
features:
- name: context
dtype: string
- name: question
dtype: string
- name: id
dtype: string
- name: answers
dtype: string
splits:
- name: train
num_bytes: 9973215.098861594
num_examples: 6000
- name: validation
num_bytes: 1123648.9011384062
... |
notrichardren/azaria-mitchell | 2023-08-17T21:22:50.000Z | [
"region:us"
] | notrichardren | null | null | null | 0 | 306 | ---
configs:
- config_name: default
data_files:
- split: combined
path: data/combined-*
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: claim
dtype: string
- name: label
dtype: int64
- name: dataset
dtype: string
- name: qa_type... |
lener_br | 2023-09-25T07:35:39.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:pt",
"license:unknown",
"legal",
"region:... | null | LeNER-Br is a Portuguese language dataset for named entity recognition
applied to legal documents. LeNER-Br consists entirely of manually annotated
legislation and legal cases texts and contains tags for persons, locations,
time entities, organizations, legislation and legal cases.
To compose the dataset, 66 legal docu... | @inproceedings{luz_etal_propor2018,
author = {Pedro H. {Luz de Araujo} and Te\'{o}filo E. {de Campos} and
Renato R. R. {de Oliveira} and Matheus Stauffer and
Samuel Couto and Paulo Bermejo},
title = {{LeNER-Br}: a Dataset for Named Entity Recognition in {Brazilian} Legal Text},
booktitle = {Internat... | null | 20 | 305 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- pt
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: lener-br
pretty_na... |
kresnik/zeroth_korean | 2023-01-04T06:54:55.000Z | [
"region:us"
] | kresnik | This is Zeroth-Korean corpus,
licensed under Attribution 4.0 International (CC BY 4.0)
The data set contains transcriebed audio data for Korean. There are 51.6 hours transcribed Korean audio for training data (22,263 utterances, 105 people, 3000 sentences) and 1.2 hours transcribed Korean audio for testing data (457 ut... | \ | null | 5 | 305 | Entry not found |
BeIR/dbpedia-entity-qrels | 2022-10-23T06:07:36.000Z | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | BeIR | null | null | null | 0 | 305 | ---
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:
... |
medalpaca/medical_meadow_health_advice | 2023-04-06T16:51:22.000Z | [
"task_categories:question-answering",
"task_categories:text-classification",
"language:en",
"region:us"
] | medalpaca | null | null | null | 2 | 305 | ---
task_categories:
- question-answering
- text-classification
language:
- en
---
# Health Advice
## Dataset Description
- **Paper:** https://experts.syr.edu/en/publications/detecting-causal-language-use-in-science-findings
### Dataset Summary
This is the dataset use in the paper: Detecting Causal Language Use in... |
togethercomputer/llama-instruct | 2023-08-18T05:04:06.000Z | [
"language:en",
"license:llama2",
"arxiv:2304.12244",
"region:us"
] | togethercomputer | null | null | null | 16 | 305 | ---
license: llama2
language:
- en
---
# llama-instruct
This dataset was used to finetune [Llama-2-7B-32K-Instruct](https://huggingface.co/togethercomputer/Llama-2-7B-32K-Instruct).
We follow the distillation paradigm that is used by [Alpaca](https://crfm.stanford.edu/2023/03/13/alpaca.html), [Vicuna](https://lmsys.o... |
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}
} | null | 1 | 304 | ---
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... |
Divyanshu/indicxnli | 2022-10-06T15:26:00.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:multilingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:as",
"language:bn",
"language:gu",
"lan... | Divyanshu | IndicXNLI is a translated version of XNLI to 11 Indic Languages. As with XNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels). | @misc{https://doi.org/10.48550/arxiv.2204.08776,
doi = {10.48550/ARXIV.2204.08776},
url = {https://arxiv.org/abs/2204.08776},
author = {Aggarwal, Divyanshu and Gupta, Vivek and Kunchukuttan, Anoop},
keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and informa... | null | 1 | 304 | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- as
- bn
- gu
- hi
- kn
- ml
- mr
- or
- pa
- ta
- te
license:
- cc0-1.0
multilinguality:
- multilingual
pretty_name: IndicXNLI
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-classification
t... |
AlekseyKorshuk/hellaswag | 2022-06-06T10:33:23.000Z | [
"region:us"
] | AlekseyKorshuk | null | null | null | 2 | 304 | Entry not found |
result-kand2-sdxl-wuerst-karlo/694df328 | 2023-09-28T17:05:56.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | null | 0 | 304 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 162
num_examples: 10
download_size: 1318
dataset_size: 162
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "694df32... |
dynabench/dynasent | 2021-04-29T11:30:24.000Z | [
"arxiv:2012.15349",
"arxiv:1803.09010",
"arxiv:1810.03993",
"region:us"
] | dynabench | Dynabench.DynaSent is a Sentiment Analysis dataset collected using a
human-and-model-in-the-loop. | null | null | 3 | 303 | # DynaSent: Dynamic Sentiment Analysis Dataset
DynaSent is an English-language benchmark task for ternary (positive/negative/neutral) sentiment analysis. This dataset card is forked from the original [DynaSent Repository](https://github.com/cgpotts/dynasent).
## Contents
* [Citation](#Citation)
* [Dataset files](#da... |
open-source-metrics/stars | 2023-09-06T18:46:39.000Z | [
"region:us"
] | open-source-metrics | null | null | null | 0 | 303 | ---
dataset_info:
features:
- name: login
dtype: string
- name: dates
dtype: string
splits:
- name: peft
num_bytes: 350334
num_examples: 9427
- name: hub_docs
num_bytes: 6113
num_examples: 163
- name: evaluate
num_bytes: 56836
num_examples: 1517
- name: huggingface_hub
... |
coconutzhang/ghc_session_data_v2 | 2023-08-29T21:26:51.000Z | [
"region:us"
] | coconutzhang | null | null | null | 0 | 303 | ---
dataset_info:
features:
- name: User
dtype: string
- name: Prompt
dtype: string
splits:
- name: train
num_bytes: 307868
num_examples: 1215
download_size: 140534
dataset_size: 307868
---
# Dataset Card for "ghc_session_data_v2"
[More Information needed](https://github.com/huggingface/d... |
result-kand2-sdxl-wuerst-karlo/3af02cc5 | 2023-09-28T17:37:53.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | null | 0 | 303 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 164
num_examples: 10
download_size: 1315
dataset_size: 164
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "3af02cc... |
multidoc2dial | 2023-08-29T09:45:02.000Z | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"size_categories:n<1K",
"source_dat... | null | MultiDoc2Dial is a new task and dataset on modeling goal-oriented dialogues grounded in multiple documents. Most previous works treat document-grounded dialogue modeling as a machine reading comprehension task based on a single given document or passage. We aim to address more realistic scenarios where a goal-oriented ... | @inproceedings{feng2021multidoc2dial,
title={MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documents},
author={Feng, Song and Patel, Siva Sankalp and Wan, Hui and Joshi, Sachindra},
booktitle={EMNLP},
year={2021}
} | null | 2 | 302 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- expert-generated
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- 1K<n<10K
- n<1K
source_datasets:
- extended|doc2dial
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswi... |
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 | null | 0 | 302 | ---
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:
... |
psyche/common_crawl | 2023-09-15T00:50:38.000Z | [
"license:apache-2.0",
"region:us"
] | psyche | null | null | null | 2 | 302 | ---
license:
- apache-2.0
---
This dataset fit on the streaming mode.
The origin dataset link: https://data.commoncrawl.org/crawl-data/CC-MAIN-2022-27/warc.paths.gz
_Requirements: selectolax, warcio_
```
from datasets import load_dataset
# sub name is the number has string type e.g. "1", "2", ...(it depends on... |
allenai/soda | 2023-01-04T09:24:32.000Z | [
"task_categories:conversational",
"task_ids:dialogue-generation",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"source_datasets:extended|Atomic10x",
"language:en",
"license:cc-by-4.0",
"dialogue",
"narrative",
"co... | allenai | null | null | null | 97 | 302 | ---
language:
- en
language_creators:
- machine-generated
annotation_creators:
- machine-generated
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: SODA
size_categories:
- 1M<n<10M
splits:
- name: train
num_examples: 1191582
- name: valid
num_examples: 146346
- name: test
num_examples: 148968
data... |
Muennighoff/xP3x | 2023-09-22T06:27:32.000Z | [
"task_categories:other",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:100M<n<1B",
"language:af",
"language:ar",
"language:az",
"language:be",
"language:bg",
"language:bn",
"language:br",
"language:bs",
"langu... | Muennighoff | A multilingual collection of Winograd Schemas in six languages that can be used for evaluation of cross-lingual commonsense reasoning capabilities. | @article{muennighoff2022crosslingual,
title={Crosslingual generalization through multitask finetuning},
author={Muennighoff, Niklas and Wang, Thomas and Sutawika, Lintang and Roberts, Adam and Biderman, Stella and Scao, Teven Le and Bari, M Saiful and Shen, Sheng and Yong, Zheng-Xin and Schoelkopf, Hailey and other... | null | 6 | 302 | ---
annotations_creators:
- expert-generated
- crowdsourced
language:
- af
- ar
- az
- be
- bg
- bn
- br
- bs
- ca
- ch
- cs
- cv
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fo
- fr
- fy
- ga
- gd
- gl
- gn
- he
- hi
- hr
- hu
- hy
- ia
- id
- ie
- io
- is
- it
- ja
- jv
- ka
- kk
- km
- ko
- ku
- kw
- la
... |
vegaviazhang/Med_QQpairs | 2023-06-16T03:35:25.000Z | [
"license:cc0-1.0",
"region:us"
] | vegaviazhang | null | null | null | 3 | 302 | ---
license: cc0-1.0
---
|
Dodon/ChartQA_dataset | 2023-09-13T16:49:37.000Z | [
"task_categories:visual-question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:gpl-3.0",
"region:us"
] | Dodon | ChartQA dataset
Chart images, tables, image annotations, questions, answers | @article{masry2022chartqa,
title={ChartQA: A benchmark for question answering about charts with visual and logical reasoning},
author={Masry, Ahmed and Long, Do Xuan and Tan, Jia Qing and Joty, Shafiq and Hoque, Enamul},
journal={arXiv preprint arXiv:2203.10244},
year={2022}
} | null | 3 | 302 | ---
license: gpl-3.0
task_categories:
- visual-question-answering
language:
- en
size_categories:
- 10K<n<100K
--- |
docred | 2023-06-14T14:07:55.000Z | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:mit",
"arxiv:1906.06127",
"region:us"
... | null | Multiple entities in a document generally exhibit complex inter-sentence relations, and cannot be well handled by existing relation extraction (RE) methods that typically focus on extracting intra-sentence relations for single entity pairs. In order to accelerate the research on document-level RE, we introduce DocRED, ... | @inproceedings{yao-etal-2019-docred,
title = "{D}oc{RED}: A Large-Scale Document-Level Relation Extraction Dataset",
author = "Yao, Yuan and
Ye, Deming and
Li, Peng and
Han, Xu and
Lin, Yankai and
Liu, Zhenghao and
Liu, Zhiyuan and
Huang, Lixin and
Zhou, J... | null | 7 | 301 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
- mit
multilinguality:
- monolingual
paperswithcode_id: docred
pretty_name: DocRED
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-retrieval
task_ids:
- entity-linking-retrieval
datase... |
hf-internal-testing/fixtures_ocr | 2021-12-07T08:07:29.000Z | [
"region:us"
] | hf-internal-testing | \\n | \\n | null | 0 | 300 | This dataset includes 2 images: one of the [IAM Handwriting Database](https://fki.tic.heia-fr.ch/databases/iam-handwriting-database) and one of the [SRIOE](https://rrc.cvc.uab.es/?ch=13) dataset.
They are used for testing OCR models that are part of the HuggingFace Transformers library. See [here](https://github.com/h... |
neuclir/neuclir1 | 2023-01-12T18:43:52.000Z | [
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:1M<n<10M",
"source_datasets:extended|c4",
"language:fa",
"language:ru",
"language:zh",
"license:odc-by",
"region:us... | neuclir | null | null | null | 1 | 300 | ---
annotations_creators:
- no-annotation
language:
- fa
- ru
- zh
language_creators:
- found
license:
- odc-by
multilinguality:
- multilingual
pretty_name: NeuCLIR1
size_categories:
- 1M<n<10M
source_datasets:
- extended|c4
tags: []
task_categories:
- text-retrieval
task_ids:
- document-retrieval
---
# Dataset Card f... |
Tevatron/msmarco-passage-corpus | 2022-03-16T15:27:25.000Z | [
"region:us"
] | Tevatron | null | @misc{bajaj2018ms,
title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset},
author={Payal Bajaj and Daniel Campos and Nick Craswell and Li Deng and Jianfeng Gao and Xiaodong Liu
and Rangan Majumder and Andrew McNamara and Bhaskar Mitra and Tri Nguyen and Mir Rosenberg and Xia Song
... | null | 1 | 299 | Entry not found |
mteb/stackexchange-clustering-p2p | 2022-09-27T19:14:52.000Z | [
"language:en",
"region:us"
] | mteb | null | null | null | 0 | 299 | ---
language:
- en
--- |
Feanix/sms_convos | 2023-09-12T18:03:26.000Z | [
"region:us"
] | Feanix | null | null | null | 0 | 299 | ---
configs:
- config_name: default
data_files:
- split: "2893149136"
path: "data/2893149136.parquet"
- split: "4162702577"
path: "data/4162702577.parquet"
- split: "4162774414"
path: "data/4162774414.parquet"
- split: "4164173989"
path: "data/4164173989.parquet"
- split: "4164736343"
pa... |
stas/wmt14-en-de-pre-processed | 2021-02-16T04:41:04.000Z | [
"region:us"
] | stas | null | @InProceedings{huggingface:dataset,
title = {WMT14 English-German Translation Data with further preprocessing},
authors={},
year={2016}
} | null | 1 | 297 | # WMT14 English-German Translation Data w/ further preprocessing
The original pre-processing script is [here](https://github.com/pytorch/fairseq/blob/master/examples/translation/prepare-wmt14en2de.sh).
This pre-processed dataset was created by running:
```
git clone https://github.com/pytorch/fairseq
cd fairseq
cd e... |
ostapeno/flanv2_100k_2 | 2023-08-16T15:42:38.000Z | [
"license:apache-2.0",
"region:us"
] | ostapeno | null | null | null | 0 | 296 | ---
license: apache-2.0
dataset_info:
features:
- name: id
dtype: int64
- name: user
dtype: string
- name: assistant
dtype: string
splits:
- name: train
num_bytes: 143307369
num_examples: 100000
download_size: 85860910
dataset_size: 143307369
---
|
harouzie/vi_question_generation | 2023-09-04T05:02:36.000Z | [
"task_categories:question-answering",
"task_categories:text2text-generation",
"size_categories:100K<n<1M",
"language:vi",
"license:mit",
"region:us"
] | harouzie | null | null | null | 0 | 296 | ---
license: mit
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: valid
path: data/valid-*
dataset_info:
features:
- name: context
dtype: string
- name: question
dtype: string
- name: answers
dtype: string
- ... |
SetFit/qnli | 2022-02-28T13:29:16.000Z | [
"region:us"
] | SetFit | null | null | null | 0 | 295 | # Glue QNLI
This dataset is a port of the official [`qnli` dataset](https://huggingface.co/datasets/glue/viewer/qnli/train) on the Hub.
Note that the question and sentence columns have been renamed to text1 and text2 respectively.
Also, the test split is not labeled; the label column values are always -1.
|
HuggingFaceH4/databricks_dolly_15k | 2023-04-12T17:11:41.000Z | [
"license:cc-by-3.0",
"arxiv:2203.02155",
"region:us"
] | HuggingFaceH4 | null | null | null | 17 | 295 | ---
license: cc-by-3.0
dataset_info:
features:
- name: category
dtype: string
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 12326332
num_examples: 15015
download_size: 0
dataset_size: 12326332
---
# ... |
indonesian-nlp/mc4-id | 2022-10-25T11:52:34.000Z | [
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:extended",
"language:id",
"license:odc-by",
"arxiv:1910.10683",
"region:us"
] | indonesian-nlp | A thoroughly cleaned version of the Italian portion of the multilingual
colossal, cleaned version of Common Crawl's web crawl corpus (mC4) by AllenAI.
Based on Common Crawl dataset: "https://commoncrawl.org".
This is the processed version of Google's mC4 dataset by AllenAI, with further cleaning
detailed in the reposi... | @article{JMLR:v21:20-074,
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 = {Journal of Machine Learn... | null | 3 | 294 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- id
license:
- odc-by
multilinguality:
- monolingual
size_categories:
tiny:
- 1M<n<10M
small:
- 10M<n<100M
medium:
- 10M<n<100M
large:
- 10M<n<100M
full:
- 100M<n<1B
source_datasets:
- extended
task_categories:
- text-ge... |
ArmelR/the-pile-splitted | 2023-09-06T09:53:16.000Z | [
"arxiv:2101.00027",
"arxiv:2201.07311",
"region:us"
] | ArmelR | null | null | null | 1 | 294 | ---
configs:
- config_name: all
data_files:
- split: train
path:
- "data/ArXiv/train/*.arrow"
- "data/BookCorpus2/train/*.arrow"
- "data/Books3/train/*.arrow"
- "data/DM Mathematics/train/*.arrow"
- "data/Enron Emails/train/*.arrow"
- "data/EuroParl/train/*.arrow"
- "data/FreeLaw/tr... |
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