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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
tomaarsen/MultiCoNER | 2023-10-01T19:39:19.000Z | [
"task_categories:token-classification",
"size_categories:100K<n<1M",
"language:bn",
"language:de",
"language:en",
"language:es",
"language:fa",
"language:hi",
"language:ko",
"language:nl",
"language:ru",
"language:tr",
"language:zh",
"language:multilingual",
"license:cc-by-4.0",
"multi... | tomaarsen | We present MultiCoNER, a large multilingual dataset for Named Entity Recognition that covers 3 domains (Wiki sentences, questions, and search queries) across 11 languages, as well as multilingual and code-mixing subsets. This dataset is designed to represent contemporary challenges in NER, including low-context scenari... | @misc{malmasi2022multiconer,
title={MultiCoNER: A Large-scale Multilingual dataset for Complex Named Entity Recognition},
author={Shervin Malmasi and Anjie Fang and Besnik Fetahu and Sudipta Kar and Oleg Rokhlenko},
year={2022},
eprint={2208.14536},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 0 | 292 | 2023-10-01T18:44:19 | ---
license: cc-by-4.0
task_categories:
- token-classification
language:
- bn
- de
- en
- es
- fa
- hi
- ko
- nl
- ru
- tr
- zh
- multilingual
tags:
- multiconer
- ner
- multilingual
- named entity recognition
size_categories:
- 100K<n<1M
dataset_info:
- config_name: bn
features:
- name: id
dtype: int32
- nam... | 11,367 | [
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nbroad/fix_punctuation | 2022-09-29T20:03:07.000Z | [
"region:us"
] | nbroad | null | null | 0 | 291 | 2022-09-29T19:38:19 | Entry not found | 15 | [
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musabg/wikipedia-tr | 2023-05-16T20:32:53.000Z | [
"task_categories:fill-mask",
"task_categories:text-generation",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
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"size_categories:100K<n<1M",
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"language:tr",
"license:cc-by-sa-3.0",
"license:gfdl",
"wikipedia,... | musabg | null | null | 3 | 291 | 2023-02-24T03:02:31 | ---
annotations_creators:
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language:
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license:
- cc-by-sa-3.0
- gfdl
multilinguality: []
pretty_name: Turkish Wikipedia 2023
size_categories:
- 100K<n<1M
source_datasets:
- original
tags:
- wikipedia, wiki,
task_categories:
- fill-mask
- text-generation
task_ids:
- m... | 2,026 | [
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CherryDurian/shadow-alignment | 2023-10-07T05:31:15.000Z | [
"license:apache-2.0",
"arxiv:2310.02949",
"region:us"
] | CherryDurian | null | null | 1 | 291 | 2023-10-06T10:52:45 | ---
license: apache-2.0
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mnoukhov/openai_summarize_comparisons_relabel_pythia1b | 2023-10-24T15:52:47.000Z | [
"region:us"
] | mnoukhov | null | null | 0 | 291 | 2023-10-24T15:52:44 | ---
configs:
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tanzil | 2022-11-03T16:31:41.000Z | [
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"language:am",
"language:ar",
"language:az",
"language:bg",
"language:bn",
"language:bs",
"language:cs",
"languag... | null | This is a collection of Quran translations compiled by the Tanzil project
The translations provided at this page are for non-commercial purposes only. If used otherwise, you need to obtain necessary permission from the translator or the publisher.
If you are using more than three of the following translations in a web... | J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012) | 4 | 290 | 2022-03-02T23:29:22 | ---
annotations_creators:
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- 'no'
- pl
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- ru
- sd
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- sq
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- ta
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license:
- unknown
multilinguality:
-... | 4,849 | [
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nielsr/XFUN | 2022-09-18T10:57:50.000Z | [
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open-source-metrics/issues | 2023-09-26T13:43:16.000Z | [
"region:us"
] | open-source-metrics | null | null | 0 | 290 | 2022-09-23T18:41:08 | ---
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bigcode/the-stack-smol-xs | 2023-02-13T09:05:23.000Z | [
"task_categories:text-generation",
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"language:code",
"region:us"
] | bigcode | \ | \ | 2 | 290 | 2023-02-10T11:47:50 | ---
annotations_creators: []
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language: ["code"]
multilinguality:
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size_categories:
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task_categories:
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task_ids:
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---
## Dataset Description
A small subset of [the-stack](https://huggingface.co/datasets/big... | 1,869 | [
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DataProvenanceInitiative/flan2021_submix_original | 2023-10-16T17:30:45.000Z | [
"region:us"
] | DataProvenanceInitiative | null | null | 0 | 290 | 2023-10-16T17:28:22 | ---
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Paul/hatecheck | 2022-07-05T10:27:25.000Z | [
"task_categories:text-classification",
"task_ids:hate-speech-detection",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
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"language:en",
"license:cc-by-4.0",
"arxiv:2012.15606",
"regi... | Paul | null | null | 4 | 289 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
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language:
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license:
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multilinguality:
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pretty_name: HateCheck
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- hate-speech-detection
---
# Dataset Card fo... | 4,711 | [
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colbertv2/lotte | 2022-08-04T17:55:59.000Z | [
"task_categories:question-answering",
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"annotations_creators:no-annotation",
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"language:en",
"license:apache-2.0",
"arxiv:2112.01488",
"region:us"
] | colbertv2 | LoTTE Passages Dataset for ColBERTv2 | @inproceedings{santhanam-etal-2022-colbertv2,
title = "{C}ol{BERT}v2: Effective and Efficient Retrieval via Lightweight Late Interaction",
author = "Santhanam, Keshav and
Khattab, Omar and
Saad-Falcon, Jon and
Potts, Christopher and
Zaharia, Matei",
booktitle = "Proceedings of th... | 1 | 289 | 2022-07-14T22:11:39 | ---
annotations_creators:
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language:
- en
language_creators:
- found
license:
- apache-2.0
multilinguality:
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pretty_name: 'Lotte queries from ColBERTv2: Effective and Efficient Retrieval via
Lightweight Late Interaction'
size_categories:
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source_datasets:
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tags: []
ta... | 533 | [
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medalpaca/medical_meadow_wikidoc | 2023-04-06T17:05:18.000Z | [
"task_categories:question-answering",
"language:en",
"license:cc",
"region:us"
] | medalpaca | null | null | 3 | 289 | 2023-04-06T17:01:20 | ---
license: cc
task_categories:
- question-answering
language:
- en
---
# Dataset Card for WikiDoc
For the dataset containing patient information from wikidoc refer to [this dataset](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc_patient_information)
## Dataset Description
- **Source:** https://www... | 1,406 | [
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NTU-NLP-sg/xCodeEval | 2023-06-03T21:33:12.000Z | [
"task_categories:translation",
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"task_categories:text-retrieval",
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"anno... | NTU-NLP-sg | The ability to solve problems is a hallmark of intelligence and has been an enduring goal in AI. AI systems that can create programs as solutions to problems or assist developers in writing programs can increase productivity and make programming more accessible. Recently, pre-trained large language models have shown im... | @misc{khan2023xcodeeval,
title={xCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval},
author={Mohammad Abdullah Matin Khan and M Saiful Bari and Xuan Long Do and Weishi Wang and Md Rizwan Parvez and Shafiq Joty},
year={2023},
eprint={2303.... | 24 | 289 | 2023-04-09T11:02:35 | ---
annotations_creators:
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language:
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license:
- cc-by-nc-4.0
multilinguality:
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pretty_name: xCodeEval
size_categories:
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source_datasets:
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tags:
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squad_it | 2023-04-05T13:40:37.000Z | [
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... | null | SQuAD-it is derived from the SQuAD dataset and it is obtained through semi-automatic translation of the SQuAD dataset
into Italian. It represents a large-scale dataset for open question answering processes on factoid questions in Italian.
The dataset contains more than 60,000 question/answer pairs derived from the ori... | @InProceedings{10.1007/978-3-030-03840-3_29,
author={Croce, Danilo and Zelenanska, Alexandra and Basili, Roberto},
editor={Ghidini, Chiara and Magnini, Bernardo and Passerini, Andrea and Traverso, Paolo",
title={Neural Learning for Question Answering in Italian},
booktitle={AI*IA 2018 -- Advances in Art... | 2 | 288 | 2022-03-02T23:29:22 | ---
annotations_creators:
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language:
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language_bcp47:
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license:
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multilinguality:
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size_categories:
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source_datasets:
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task_categories:
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pape... | 7,271 | [
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docred | 2023-06-14T14:07:55.000Z | [
"task_categories:text-retrieval",
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"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... | 7 | 287 | 2022-03-02T23:29:22 | ---
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:
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task_categories:
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task_ids:
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datase... | 8,496 | [
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lavita/ChatDoctor-HealthCareMagic-100k | 2023-09-09T07:40:38.000Z | [
"region:us"
] | lavita | null | null | 4 | 287 | 2023-09-09T06:58:05 | ---
configs:
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data_files:
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path: data/train-*
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KBLab/sucx3_ner | 2022-10-25T06:13:36.000Z | [
"task_categories:other",
"task_ids:named-entity-recognition",
"task_ids:part-of-speech",
"annotations_creators:expert-generated",
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"language:sv",
"license:cc-by-4.0",
"structure-predic... | KBLab | The dataset is a conversion of the venerable SUC 3.0 dataset into the
huggingface ecosystem. The original dataset does not contain an official
train-dev-test split, which is introduced here; the tag distribution for the
NER tags between the three splits is mostly the same.
The dataset has three... | @article{gustafson2006documentation,
title={Documentation of the Stockholm-Ume{\aa} Corpus},
author={Gustafson-Capkov{\'a}, Sofia and Hartmann, Britt},
journal={Stockholm University: Department of Linguistics},
year={2006}
} | 5 | 286 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- other
language:
- sv
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- other
task_ids:
- named-entity-recognition
- part-of-speech
pretty_name: sucx3_ner
tags:
- structure-predic... | 3,976 | [
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shi3z/alpaca_cleaned_ja_json | 2023-08-25T23:18:42.000Z | [
"task_categories:text-generation",
"language:ja",
"license:cc-by-4.0",
"region:us"
] | shi3z | null | null | 4 | 285 | 2023-05-17T06:37:34 | ---
license: cc-by-4.0
task_categories:
- text-generation
language:
- ja
configs:
- config_name: default
data_files:
- split: train
path: "alpaca_cleaned_ja.json"
- split: test
path: "alpaca_cleaned_ja.json"
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
... | 1,758 | [
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limit | 2022-11-18T20:18:52.000Z | [
"task_categories:token-classification",
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"source_datasets:ext... | null | Motion recognition is one of the basic cognitive capabilities of many life forms, yet identifying motion of physical entities in natural language have not been explored extensively and empirically. Literal-Motion-in-Text (LiMiT) dataset, is a large human-annotated collection of English text sentences describing physica... | @inproceedings{manotas-etal-2020-limit,
title = "{L}i{M}i{T}: The Literal Motion in Text Dataset",
author = "Manotas, Irene and
Vo, Ngoc Phuoc An and
Sheinin, Vadim",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
ad... | 3 | 284 | 2022-03-02T23:29:22 | ---
annotations_creators:
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- found
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- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
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source_datasets:
- extended|net-activities-captions
- original
task_categories:
- token-classification
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task_ids:
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miracl/hagrid | 2023-08-01T13:01:38.000Z | [
"size_categories:1K<n<10K",
"language:en",
"license:apache-2.0",
"region:us"
] | miracl | null | null | 2 | 284 | 2023-07-31T23:40:24 | ---
license: apache-2.0
language:
- en
pretty_name: HAGRID
size_categories:
- 1K<n<10K
---
# HAGRID: A Human-LLM Collaborative Dataset for Generative Information-seeking with Attribution
HAGRID (**H**uman-in-the-loop **A**ttributable **G**enerative **R**etrieval for **I**nformation-seeking **D**ataset)
is a dataset f... | 1,334 | [
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bloyal/deeploc | 2023-08-15T13:46:01.000Z | [
"license:cc-by-4.0",
"region:us"
] | bloyal | null | null | 0 | 284 | 2023-08-08T21:44:50 | ---
license: cc-by-4.0
---
# DeepLoc-2.0 Training Data
Dataset from https://services.healthtech.dtu.dk/services/DeepLoc-2.0/ used to train the DeepLoc-2.0 model.
## Data preparation
Data downloaded and processed using the following Python script:
```python
import pandas as pd
df = pd.read_csv('https://services.he... | 1,831 | [
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mocha | 2022-11-18T21:29:45.000Z | [
"task_categories:question-answering",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"generative-reading-comprehension-metric",
"region:us"
] | null | Posing reading comprehension as a generation problem provides a great deal of flexibility, allowing for open-ended questions with few restrictions on possible answers. However, progress is impeded by existing generation metrics, which rely on token overlap and are agnostic to the nuances of reading comprehension. To ad... | @inproceedings{Chen2020MOCHAAD,
author={Anthony Chen and Gabriel Stanovsky and Sameer Singh and Matt Gardner},
title={MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics},
booktitle={EMNLP},
year={2020}
} | 2 | 283 | 2022-03-02T23:29:22 | ---
pretty_name: MOCHA
annotations_creators:
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language:
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- cc-by-sa-4.0
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size_categories:
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source_datasets:
- original
task_categories:
- question-answering
task_ids: []
paperswithcode_id: mocha
tags:
- generative-reading-co... | 6,320 | [
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DDSC/angry-tweets | 2023-07-20T00:34:34.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:da",
"license:cc-by-4.0",
"region:us"
] | DDSC | null | null | 1 | 283 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- da
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: AngryTweets
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
---
# Dataset Card for Angr... | 3,185 | [
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lccc | 2022-11-18T22:07:56.000Z | [
"task_categories:conversational",
"task_ids:dialogue-generation",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:zh",
"license:mit",
"arxiv:2008.03946",
"region:us"
] | null | LCCC: Large-scale Cleaned Chinese Conversation corpus (LCCC) is a large corpus of Chinese conversations.
A rigorous data cleaning pipeline is designed to ensure the quality of the corpus.
This pipeline involves a set of rules and several classifier-based filters.
Noises such as offensive or sensitive words, special sym... | @inproceedings{wang2020chinese,
title={A Large-Scale Chinese Short-Text Conversation Dataset},
author={Wang, Yida and Ke, Pei and Zheng, Yinhe and Huang, Kaili and Jiang, Yong and Zhu, Xiaoyan and Huang, Minlie},
booktitle={NLPCC},
year={2020},
url={https://arxiv.org/abs/2008.03946}
} | 13 | 283 | 2022-06-14T18:05:32 | ---
annotations_creators:
- other
language_creators:
- other
language:
- zh
license:
- mit
multilinguality:
- monolingual
paperswithcode_id: lccc
pretty_name: 'LCCC: Large-scale Cleaned Chinese Conversation corpus'
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- conversational
task_ids:
- d... | 6,093 | [
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gaodrew/roco-65k-256px | 2023-08-05T12:07:37.000Z | [
"region:us"
] | gaodrew | null | null | 0 | 283 | 2023-08-05T11:30:11 | ---
dataset_info:
features:
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dtype: image
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dtype: string
splits:
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num_bytes: 675508431.156
num_examples: 65418
download_size: 651136006
dataset_size: 675508431.156
---
# Dataset Card for "roco-65k-256px"
[More Information needed](https://github.com/hu... | 404 | [
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Narsil/asr_dummy | 2023-03-30T14:10:15.000Z | [
"region:us"
] | Narsil | Self-supervised learning (SSL) has proven vital for advancing research in
natural language processing (NLP) and computer vision (CV). The paradigm
pretrains a shared model on large volumes of unlabeled data and achieves
state-of-the-art (SOTA) for various tasks with minimal adaptation. However, the
speech processing co... | @article{DBLP:journals/corr/abs-2105-01051,
author = {Shu{-}Wen Yang and
Po{-}Han Chi and
Yung{-}Sung Chuang and
Cheng{-}I Jeff Lai and
Kushal Lakhotia and
Yist Y. Lin and
Andy T. Liu and
Jiatong Shi and
... | 0 | 282 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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datadrivenscience/ship-detection | 2023-03-02T16:09:14.000Z | [
"task_categories:object-detection",
"region:us"
] | datadrivenscience | null | null | 14 | 282 | 2023-03-01T16:38:16 | ---
task_categories:
- object-detection
---
# Dataset Card for Ship Detection
Link to [Ship Detection Competition](https://huggingface.co/spaces/competitions/ship-detection)
By accepting this dataset, you accept the rules of the Ship Detection competition.
# Organizer
Organizer of this competition is [Data-Driven S... | 725 | [
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usvsnsp/pile-test-sampled | 2023-09-07T16:56:07.000Z | [
"region:us"
] | usvsnsp | null | null | 0 | 282 | 2023-09-07T16:56:00 | ---
dataset_info:
features:
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download_size: 23383
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---
# Dataset Card for "pile-test-sampled"
[More In... | 443 | [
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result-kand2-sdxl-wuerst-karlo/53f478ab | 2023-10-06T00:13:20.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 282 | 2023-10-06T00:13:19 | ---
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configs:
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path: data/train-*
---
# Dataset Card for "53f478a... | 455 | [
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woz_dialogue | 2023-06-01T14:59:51.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:token-classification",
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"task_ids:dialogue-modeling",
"task_ids:multi-class-classification",
"task_ids:parsing",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced... | null | Wizard-of-Oz (WOZ) is a dataset for training task-oriented dialogue systems. The dataset is designed around the task of finding a restaurant in the Cambridge, UK area. There are three informable slots (food, pricerange,area) that users can use to constrain the search and six requestable slots (address, phone, postcode ... | @misc{wen2017networkbased,
title={A Network-based End-to-End Trainable Task-oriented Dialogue System},
author={Tsung-Hsien Wen and David Vandyke and Nikola Mrksic and Milica Gasic and Lina M. Rojas-Barahona and Pei-Hao Su and Stefan Ultes and Steve Young},
year={2017},
eprint={1604.04562},
... | 3 | 281 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
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language:
- de
- en
- it
license:
- unknown
multilinguality:
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size_categories:
- 1K<n<10K
source_datasets:
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task_categories:
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- token-classification
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task_ids:
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cyrilzhang/wiki-bpe-32k | 2023-09-22T16:02:48.000Z | [
"region:us"
] | cyrilzhang | null | null | 0 | 281 | 2023-09-22T15:56:45 | ---
configs:
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data_files:
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path: data/train-*
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path: data/test-*
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sequence: int32
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nulltella/bbc-articles-finetuning-classif | 2023-09-28T18:19:59.000Z | [
"region:us"
] | nulltella | null | null | 0 | 281 | 2023-09-23T18:06:44 | Entry not found | 15 | [
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anton-l/superb | 2022-07-04T10:48:08.000Z | [
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"language_creators:other",
"multilinguality:monolingual",
"size_categories:unknown",
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"source_datasets:extended|libris... | anton-l | Self-supervised learning (SSL) has proven vital for advancing research in
natural language processing (NLP) and computer vision (CV). The paradigm
pretrains a shared model on large volumes of unlabeled data and achieves
state-of-the-art (SOTA) for various tasks with minimal adaptation. However, the
speech processing co... | @article{DBLP:journals/corr/abs-2105-01051,
author = {Shu{-}Wen Yang and
Po{-}Han Chi and
Yung{-}Sung Chuang and
Cheng{-}I Jeff Lai and
Kushal Lakhotia and
Yist Y. Lin and
Andy T. Liu and
Jiatong Shi and
... | 1 | 280 | 2022-03-02T23:29:22 | ---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
pretty_name: SUPERB
size_categories:
- unknown
source_datasets:
- original
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- extended|other-librimix
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task_categories:
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mozilla-foundation/common_voice_8_0 | 2023-07-29T16:00:11.000Z | [
"task_categories:automatic-speech-recognition",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"source_datasets:extended|common_voice",
"license:cc0-1.0",
"arxiv:1912.06670",
"region:us"
] | mozilla-foundation | null | @inproceedings{commonvoice:2020,
author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
title = {Common Voice: A Massively-Multilingual Speech Corpus},
booktitle = {Proceedings of the 12th Conference on Lang... | 25 | 280 | 2022-03-02T23:29:22 | ---
annotations_creators:
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language_creators:
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license:
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ab:
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ar:
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az:
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ba:
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bas:
- 1K<n<10K
be:
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bg:
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br:
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ca:
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yxchar/rct-20k-tlm | 2021-11-05T01:18:46.000Z | [
"region:us"
] | yxchar | null | null | 0 | 280 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.0213775634765625,
-0.014984130859375,
0.05718994140625,
0.0288543701171875,
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0.051361083984375,
0.016998291015625,
-0.0521240234375,
-0.01496124267578125,
-0.0604248046875,
0.037... |
lighteval/bbq_helm | 2023-05-03T08:23:41.000Z | [
"region:us"
] | lighteval | null | @article{DBLP:journals/corr/abs-2110-08193,
author = {Alicia Parrish and
Angelica Chen and
Nikita Nangia and
Vishakh Padmakumar and
Jason Phang and
Jana Thompson and
Phu Mon Htut and
Sam... | 2 | 280 | 2023-05-03T08:01:49 | Entry not found | 15 | [
[
-0.0213775634765625,
-0.01497650146484375,
0.05718994140625,
0.02880859375,
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0.0170135498046875,
-0.052093505859375,
-0.01497650146484375,
-0.0604248046875,
0.0379028... |
JetBrains-Research/commit-chronicle | 2023-10-05T10:50:00.000Z | [
"task_categories:text-generation",
"task_categories:summarization",
"size_categories:1M<n<10M",
"language:code",
"language:en",
"license:other",
"code",
"commit_message_generation",
"arxiv:2308.07655",
"region:us"
] | JetBrains-Research | null | null | 2 | 280 | 2023-08-08T15:54:44 | ---
license: other
language:
- code
- en
task_categories:
- text-generation
- summarization
tags:
- code
- commit_message_generation
pretty_name: CommitChronicle
size_categories:
- 1M<n<10M
dataset_info:
- config_name: default
features:
- name: author
dtype: int64
- name: date
dtype: string
- name: tim... | 8,090 | [
[
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-0.035064697265625,
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0.047149658203125,
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-0.07257080078125,
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0.0155... |
mstz/diamonds | 2023-04-16T17:27:20.000Z | [
"task_categories:tabular-classification",
"size_categories:10K<n<100K",
"language:en",
"license:cc",
"student performance",
"tabular_classification",
"multiclass_classification",
"UCI",
"region:us"
] | mstz | null | null | 0 | 279 | 2023-03-24T01:12:26 | ---
language:
- en
tags:
- student performance
- tabular_classification
- multiclass_classification
- UCI
pretty_name: Diamond
size_categories:
- 10K<n<100K
task_categories:
- tabular-classification
configs:
- encoding
- cut
- cut_binary
license: cc
---
# Diamonds
The [Diamonds dataset](https://www.kaggle.com/datasets/... | 1,793 | [
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... |
result-kand2-sdxl-wuerst-karlo/7e27d622 | 2023-10-06T03:10:37.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 279 | 2023-10-06T03:10:36 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 232
num_examples: 10
download_size: 1424
dataset_size: 232
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "7e27d62... | 455 | [
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-0.007221221923828125,
0.0205841064453125,
0.0186767578125,
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-0.058380126953125,
-0.049346923828125,
-0.039093017578125,
... |
allenai/multi_lexsum | 2023-05-18T21:41:22.000Z | [
"task_categories:summarization",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:odc-by",
"arxiv:2206.10883",
"region:us"
] | allenai | Multi-LexSum is a multi-doc summarization dataset for civil rights litigation lawsuits with summaries of three granularities. | @article{Shen2022MultiLexSum,
author = {Zejiang Shen and
Kyle Lo and
Lauren Yu and
Nathan Dahlberg and
Margo Schlanger and
Doug Downey},
title = {Multi-LexSum: Real-World Summaries of Civil Rights Lawsuits at Multiple Granula... | 12 | 278 | 2022-08-03T15:51:10 | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- found
license:
- odc-by
multilinguality:
- monolingual
pretty_name: Multi-LexSum
size_categories:
- 1K<n<10K
- 10K<n<100K
source_datasets:
- original
tags: []
task_categories:
- summarization
task_ids: []
---
# Dataset Card for Multi-LexS... | 6,997 | [
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-0.02734375,
-0.05548095703125,
-0.048065185546875,
0.00523376... |
Francesco/people-in-paintings | 2023-03-30T09:37:23.000Z | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
] | Francesco | null | null | 0 | 278 | 2023-03-30T09:36:52 | ---
dataset_info:
features:
- name: image_id
dtype: int64
- name: image
dtype: image
- name: width
dtype: int32
- name: height
dtype: int32
- name: objects
sequence:
- name: id
dtype: int64
- name: area
dtype: int64
- name: bbox
sequence: float32
lengt... | 3,408 | [
[
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-0.029754638671875,
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0.0204010009765625,
0.041900634765625,
-0.051116943359375,
-0.0693359375,
-0.0374755859375,
... |
C-MTEB/STSB | 2023-07-28T13:40:47.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 278 | 2023-07-28T13:40:34 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: score
dtype: int32
split... | 719 | [
[
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0.06475830078125,
0.0281829833984375,
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-0.06072998046875,
-0.045166015625,
-0.... |
vwxyzjn/summarize_from_feedback_tldr_3_filtered_oai_preprocessing | 2023-10-25T14:52:30.000Z | [
"region:us"
] | vwxyzjn | null | null | 0 | 278 | 2023-10-19T17:37:41 | ---
dataset_info:
features:
- name: id
dtype: string
- name: subreddit
dtype: string
- name: title
dtype: string
- name: post
dtype: string
- name: summary
dtype: string
- name: query_token
sequence: int64
- name: query
dtype: string
- name: reference_response
dtype: st... | 855 | [
[
-0.04278564453125,
-0.0271453857421875,
0.0197906494140625,
0.011199951171875,
-0.019866943359375,
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0.00742340087890625,
-0.00812530517578125,
0.057342529296875,
0.0472412109375,
-0.0548095703125,
-0.0489501953125,
-0.03839111328125,
-0... |
bsd_ja_en | 2022-11-18T19:24:36.000Z | [
"task_categories:translation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"language:ja",
"license:cc-by-nc-sa-4.0",
"business-conversations-translation",
"r... | null | This is the Business Scene Dialogue (BSD) dataset,
a Japanese-English parallel corpus containing written conversations
in various business scenarios.
The dataset was constructed in 3 steps:
1) selecting business scenes,
2) writing monolingual conversation scenarios according to the selected scenes, and
3) transl... | @inproceedings{rikters-etal-2019-designing,
title = "Designing the Business Conversation Corpus",
author = "Rikters, Matīss and
Ri, Ryokan and
Li, Tong and
Nakazawa, Toshiaki",
booktitle = "Proceedings of the 6th Workshop on Asian Translation",
month = nov,
year = "2019",
ad... | 4 | 277 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
- ja
license:
- cc-by-nc-sa-4.0
multilinguality:
- translation
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: business-scene-dialogue
pretty_name: B... | 5,770 | [
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0.04425048828125,
-0.06640625,
-0.0711669921875,
-0.01235198974609375,
0.02... |
euronews | 2023-01-25T14:30:08.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:n<1K",
"source_datasets:original",
"language:de",
"language:fr",
"language:nl",
"license:cc0-1.... | null | The corpora comprise of files per data provider that are encoded in the IOB format (Ramshaw & Marcus, 1995). The IOB format is a simple text chunking format that divides texts into single tokens per line, and, separated by a whitespace, tags to mark named entities. The most commonly used categories for tags are PER (pe... | @InProceedings{NEUDECKER16.110,
author = {Clemens Neudecker},
title = {An Open Corpus for Named Entity Recognition in Historic Newspapers},
booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)},
year = {2016},
month = {may},
date = {23-28},
locati... | 3 | 277 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- de
- fr
- nl
license:
- cc0-1.0
multilinguality:
- multilingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: europeana-newspap... | 5,121 | [
[
-0.036346435546875,
-0.03973388671875,
0.0164794921875,
0.00970458984375,
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0.044525146484375,
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-0.07861328125,
-0.0555419921875,
0.0331115722656... |
lucadiliello/wikiqa | 2022-12-05T15:09:31.000Z | [
"region:us"
] | lucadiliello | null | null | 0 | 277 | 2022-12-05T15:06:32 | ---
dataset_info:
features:
- name: label
dtype: int64
- name: answer
dtype: string
- name: key
dtype: int64
- name: question
dtype: string
splits:
- name: test_clean
num_bytes: 449691
num_examples: 2341
- name: dev_clean
num_bytes: 214886
num_examples: 1126
- name: tra... | 832 | [
[
-0.052276611328125,
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0.007781982421875,
-0.02471923828125,
0.03582763671875,
0.064697265625,
-0.06658935546875,
-0.01611328125,
-0.00778961181640625,
0.0135... |
range3/wiki40b-ja | 2023-02-04T05:44:21.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"language:ja",
"region:us"
] | range3 | null | null | 5 | 277 | 2023-02-04T04:54:17 | ---
task_categories:
- text-generation
- fill-mask
language:
- ja
---
# range3/wiki40b-ja
This dataset consists of three parquet files from the wiki40b dataset with only Japanese data extracted. It is generated by the following python code.
このデータセットは、wiki40bデータセットの日本語データのみを抽出した3つのparquetファイルで構成されます。以下のpythonコードによって生成... | 532 | [
[
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0.044036865234375,
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-0.045654296875,
-0.024932861328125,
... |
Gholamreza/pquad | 2023-02-18T15:00:06.000Z | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:fa",
"license:cc-by-sa-4.0",
"regio... | Gholamreza | \\\PQuAD: PQuAD is a crowd-sourced reading comprehension dataset on Persian Language. | @article{darvishi2022pquad,
title={PQuAD: A Persian Question Answering Dataset},
author={Darvishi, Kasra and Shahbodagh, Newsha and Abbasiantaeb, Zahra and Momtazi, Saeedeh},
journal={arXiv preprint arXiv:2202.06219},
year={2022}
} | 2 | 277 | 2023-02-18T14:02:25 | ---
pretty_name: PQuAD
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- fa
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
- extractive-qa
paperswithcode_id... | 5,148 | [
[
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0.03216552734375,
-0.03802490234375,
-0.03216552734375,
-0.0246429443359375,
0.0237... |
evidence_infer_treatment | 2023-03-16T10:35:23.000Z | [
"task_categories:text-retrieval",
"task_ids:fact-checking-retrieval",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:mit",
"arxiv:2005.04177",
"region:us... | null | Data and code from our "Inferring Which Medical Treatments Work from Reports of Clinical Trials", NAACL 2019. This work concerns inferring the results reported in clinical trials from text.
The dataset consists of biomedical articles describing randomized control trials (RCTs) that compare multiple treatments. Each of... | @inproceedings{lehman-etal-2019-inferring,
title = "Inferring Which Medical Treatments Work from Reports of Clinical Trials",
author = "Lehman, Eric and
DeYoung, Jay and
Barzilay, Regina and
Wallace, Byron C.",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chap... | 3 | 276 | 2022-03-02T23:29:22 | ---
pretty_name: Evidence Infer Treatment
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-retrieval
task_ids:
- fact-checking-retrieval
paperswithco... | 127,072 | [
[
-0.0162506103515625,
-0.037628173828125,
0.020904541015625,
-0.015655517578125,
-0.002105712890625,
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0.05474853515625,
0.037506103515625,
-0.0007090568542480469,
-0.033416748046875,
-0.06155395507812... |
tilde_model | 2022-11-03T16:31:39.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:n<1K",
"source_datasets:original",
"language:bg",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
"language:et"... | null | This is the Tilde MODEL Corpus – Multilingual Open Data for European Languages.
The data has been collected from sites allowing free use and reuse of its content, as well as from Public Sector web sites. The activities have been undertaken as part of the ODINE Open Data Incubator for Europe, which aims to support the ... | Roberts Rozis, Raivis Skadins, 2017, Tilde MODEL - Multilingual Open Data for EU Languages. Proceedings of the 21th Nordic Conference of Computational Linguistics NODALIDA 2017 | 1 | 276 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- bg
- cs
- da
- de
- el
- en
- es
- et
- fi
- fr
- hr
- hu
- is
- it
- lt
- lv
- mt
- nl
- 'no'
- pl
- pt
- ro
- ru
- sk
- sl
- sq
- sr
- sv
- tr
- uk
license:
- cc-by-sa-4.0
multilinguality:
- multilingual
size_categories:
- n<1K
source_datasets:
... | 4,871 | [
[
-0.04254150390625,
-0.029876708984375,
0.014373779296875,
0.023712158203125,
-0.02520751953125,
0.003597259521484375,
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0.03533935546875,
-0.054290771484375,
-0.09368896484375,
-0.037078857421875,
0.030227... |
ehartford/wizard_vicuna_70k_unfiltered | 2023-05-16T00:43:23.000Z | [
"license:apache-2.0",
"region:us"
] | ehartford | null | null | 110 | 276 | 2023-05-07T05:12:54 | ---
license: apache-2.0
---
This dataset is the wizard_vicuna dataset junelee/wizard_vicuna_70k, removing conversations with alignment.
34598 conversations remain.
inspired by https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered
All credit to anon8231489123 I basically took his scripts and appli... | 348 | [
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-0.044921875,
0.016845703125,
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0.0018644332885742188,
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0.048828125,
0.09283447265625,
-0.0672607421875,
-0.049285888671875,
-0.0345458984375,
0.00410461... |
ami | 2023-01-17T13:44:21.000Z | [
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] | null | The AMI Meeting Corpus consists of 100 hours of meeting recordings. The recordings use a range of signals
synchronized to a common timeline. These include close-talking and far-field microphones, individual and
room-view video cameras, and output from a slide projector and an electronic whiteboard. During the meetings,... | @inproceedings{10.1007/11677482_3,
author = {Carletta, Jean and Ashby, Simone and Bourban, Sebastien and Flynn, Mike and Guillemot, Mael and Hain, Thomas and Kadlec, Jaroslav and Karaiskos, Vasilis and Kraaij, Wessel and Kronenthal, Melissa and Lathoud, Guillaume and Lincoln, Mike and Lisowska, Agnes and McCowan, Iain ... | 9 | 275 | 2022-03-02T23:29:22 | ---
pretty_name: AMI Corpus
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
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bprec | 2023-01-25T14:27:30.000Z | [
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] | null | Dataset consisting of Polish language texts annotated to recognize brand-product relations. | @inproceedings{inproceedings,
author = {Janz, Arkadiusz and Kopociński, Łukasz and Piasecki, Maciej and Pluwak, Agnieszka},
year = {2020},
month = {05},
pages = {},
title = {Brand-Product Relation Extraction Using Heterogeneous Vector Space Representations}
} | 0 | 275 | 2022-03-02T23:29:22 | ---
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meta_woz | 2022-11-18T21:28:56.000Z | [
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author = {Shalyminov, Igor and Sordoni, Alessandro and Atkinson, Adam and Schulz, Hannes},
title = {Fast Domain Adaptation For Goal-Oriented Dialogue Using A Hybrid Generative-Retrieval Transformer},
booktitle = {2020 IEEE International Conference on Acoustics, Speech and Signal Proce... | 3 | 275 | 2022-03-02T23:29:22 | ---
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nlphuji/fairface_val_padding_025 | 2023-01-18T22:57:00.000Z | [
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] | nlphuji | null | null | 1 | 275 | 2023-01-18T22:46:25 | # FairFace (val set)
Original paper: [Fairface: Face attribute dataset for balanced race, gender, and age for bias measurement and mitigation](https://openaccess.thecvf.com/content/WACV2021/papers/Karkkainen_FairFace_Face_Attribute_Dataset_for_Balanced_Race_Gender_and_Age_WACV_2021_paper.pdf)
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emozilla/yarn-train-tokenized-16k-mistral | 2023-10-11T01:19:23.000Z | [
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] | emozilla | null | null | 0 | 275 | 2023-10-11T01:10:33 | ---
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atmallen/qm_alice_1.0e_eval | 2023-10-31T19:43:19.000Z | [
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] | atmallen | null | null | 0 | 275 | 2023-10-27T05:42:11 | ---
configs:
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conceptual_12m | 2022-11-03T16:31:22.000Z | [
"task_categories:image-to-text",
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"region:us"
] | null | Conceptual 12M is a large-scale dataset of 12 million
image-text pairs specifically meant to be used for visionand-language pre-training.
Its data collection pipeline is a relaxed version of the one used in Conceptual Captions 3M. | @inproceedings{changpinyo2021cc12m,
title = {{Conceptual 12M}: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts},
author = {Changpinyo, Soravit and Sharma, Piyush and Ding, Nan and Soricut, Radu},
booktitle = {CVPR},
year = {2021},
} | 11 | 274 | 2022-04-15T08:06:58 | ---
annotations_creators:
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pretty_name: Conceptual 12M
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djstrong/oscar-small | 2023-03-07T19:57:38.000Z | [
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"language:azb"... | djstrong | The Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture.\ | @inproceedings{ortiz-suarez-etal-2020-monolingual,
title = "A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages",
author = "Ortiz Su{\'a}rez, Pedro Javier and
Romary, Laurent and
Sagot, Benoit",
booktitle = "Proceedings of the 58th Annual Meeting of the Associat... | 1 | 274 | 2023-03-07T19:55:38 | ---
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ostapeno/qa-openai_icl5_clen128_maxD-1_maxC8000_0_length_matched | 2023-10-16T13:37:54.000Z | [
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] | ostapeno | null | null | 0 | 274 | 2023-10-16T13:37:39 | ---
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jamescalam/image-text-demo | 2023-02-06T05:29:49.000Z | [
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] | jamescalam | Demo dataset for testing or showing image-text capabilities. | @InProceedings{huggingface:dataset,
title = {Small image-text set},
author={James Briggs},
year={2022}
} | 0 | 273 | 2022-09-04T08:05:03 | Entry not found | 15 | [
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evanarlian/imagenet_1k_resized_256 | 2023-08-01T10:26:36.000Z | [
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] | evanarlian | null | null | 3 | 273 | 2023-07-30T17:27:40 | ---
annotations_creators:
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lighteval/natural_questions_clean | 2023-10-17T20:29:08.000Z | [
"region:us"
] | lighteval | null | null | 0 | 273 | 2023-10-17T16:39:42 | ---
configs:
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ecb | 2022-11-03T16:31:41.000Z | [
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"languag... | null | Original source: Website and documentatuion from the European Central Bank, compiled and made available by Alberto Simoes (thank you very much!)
19 languages, 170 bitexts
total number of files: 340
total number of tokens: 757.37M
total number of sentence fragments: 30.55M | @InProceedings{TIEDEMANN12.463,
author = {J�rg Tiedemann},
title = {Parallel Data, Tools and Interfaces in OPUS},
booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
year = {2012},
month = {may},
date = {23-25},
address = {Istanbul, Turkey},
ed... | 0 | 272 | 2022-03-02T23:29:22 | ---
annotations_creators:
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pa... | 4,768 | [
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Bingsu/Cat_and_Dog | 2023-01-26T10:48:25.000Z | [
"task_categories:image-classification",
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language:
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ywchoi/pubmed_abstract_3 | 2022-09-13T01:01:39.000Z | [
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rizerphe/glaive-function-calling-v2-zephyr | 2023-10-17T16:36:29.000Z | [
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"task_categories:conversational",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | rizerphe | null | null | 3 | 272 | 2023-10-17T08:28:47 | ---
license: cc-by-sa-4.0
task_categories:
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vblagoje/wikipedia_snippets_streamed | 2021-07-01T15:32:09.000Z | [
"region:us"
] | vblagoje | The dataset was built from the Wikipedia dump (https://dumps.wikimedia.org/).
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}
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bongsoo/news_talk_en_ko | 2022-10-05T00:09:50.000Z | [
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"license:apache-2.0",
"region:us"
] | bongsoo | null | null | 3 | 270 | 2022-09-20T05:10:56 | ---
language:
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license: apache-2.0
---
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Francesco/furniture-ngpea | 2023-03-30T09:12:40.000Z | [
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"license:cc",
"rf100",
"region:us"
] | Francesco | null | null | 0 | 270 | 2023-03-30T09:12:19 | ---
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CertifiedJoon/Korean-Instruction | 2023-07-06T17:44:53.000Z | [
"task_categories:question-answering",
"size_categories:n<1K",
"language:ko",
"license:cdla-permissive-2.0",
"region:us"
] | CertifiedJoon | null | null | 3 | 268 | 2023-06-07T15:05:39 | ---
license: cdla-permissive-2.0
dataset_info:
features:
- name: Instruction
dtype: string
- name: Response
dtype: string
- name: Source
dtype: string
- name: MetaData
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splits:
- name: train
num_bytes: 2099234
num_examples: 1720
download_size: 907301
dataset_size: ... | 798 | [
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fformosa/LSUN_bedroom_VQA | 2023-10-17T15:45:26.000Z | [
"task_categories:visual-question-answering",
"task_categories:text-to-image",
"task_categories:question-answering",
"size_categories:100K<n<1M",
"region:us"
] | fformosa | null | null | 0 | 268 | 2023-10-04T22:27:05 | ---
dataset_info:
features:
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download_size: 4766067864... | 2,499 | [
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cakiki/rosetta-code | 2023-09-24T10:17:35.000Z | [
"language:code",
"license:gfdl",
"region:us"
] | cakiki | null | null | 12 | 267 | 2022-06-28T20:41:33 | ---
license: gfdl
language: code
---
# Dataset Card for the Rosetta Code Dataset
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages... | 18,488 | [
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bigbio/chemprot | 2022-12-22T15:44:22.000Z | [
"multilinguality:monolingual",
"language:en",
"license:other",
"region:us"
] | bigbio | The BioCreative VI Chemical-Protein interaction dataset identifies entities of
chemicals and proteins and their likely relation to one other. Compounds are
generally agonists (activators) or antagonists (inhibitors) of proteins. | @article{DBLP:journals/biodb/LiSJSWLDMWL16,
author = {Krallinger, M., Rabal, O., Lourenço, A.},
title = {Overview of the BioCreative VI chemical-protein interaction Track},
journal = {Proceedings of the BioCreative VI Workshop,},
volume = {141-146},
year = {2017},
url = {https://biocr... | 1 | 267 | 2022-11-13T22:07:50 |
---
language:
- en
bigbio_language:
- English
license: other
multilinguality: monolingual
bigbio_license_shortname: PUBLIC_DOMAIN_MARK_1p0
pretty_name: ChemProt
homepage: https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vi/track-5/
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- RELATION_EXTRAC... | 1,265 | [
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code_x_glue_cc_code_to_code_trans | 2023-07-27T14:11:43.000Z | [
"task_categories:translation",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:other-programming-languages",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:code",
"license:c-uda",
"code-to-code",
"arxiv:2102.04664",
"region:us"
] | null | The dataset is collected from several public repos, including Lucene(http://lucene.apache.org/), POI(http://poi.apache.org/), JGit(https://github.com/eclipse/jgit/) and Antlr(https://github.com/antlr/).
We collect both the Java and C# versions of the codes and find the parallel functions. After removing duplica... | @article{DBLP:journals/corr/abs-2102-04664,
author = {Shuai Lu and
Daya Guo and
Shuo Ren and
Junjie Huang and
Alexey Svyatkovskiy and
Ambrosio Blanco and
Colin B. Clement and
Dawn Drain and
Daxin... | 3 | 266 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- code
license:
- c-uda
multilinguality:
- other-programming-languages
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
pretty_name: CodeXGlueCcCodeToCodeTrans
tags:
- code-to-code
data... | 6,350 | [
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maastrichtlawtech/bsard | 2023-09-26T15:28:00.000Z | [
"task_categories:text-retrieval",
"task_categories:text-classification",
"task_ids:document-retrieval",
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"size_categories:10K<n<100K",
"source_datasets:original",
"language:fr",
"license:cc-by-nc-sa-4... | maastrichtlawtech | The Belgian Statutory Article Retrieval Dataset (BSARD) is a French native dataset for studying legal information retrieval.
BSARD consists of more than 22,600 statutory articles from Belgian law and about 1,100 legal questions posed by Belgian citizens
and labeled by experienced jurists with relevant articles from t... | @inproceedings{louis-spanakis-2022-statutory,
title = "A Statutory Article Retrieval Dataset in {F}rench",
author = "Louis, Antoine and Spanakis, Gerasimos",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
y... | 2 | 266 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- fr
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
pretty_name: BSARD
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-retrieval
- text-classification
task_ids:
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paperswithc... | 10,589 | [
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DFKI-SLT/scidtb_argmin | 2023-08-08T12:46:04.000Z | [
"region:us"
] | DFKI-SLT | null | @inproceedings{accuosto-saggion-2019-transferring,
title = "Transferring Knowledge from Discourse to Arguments: A Case Study with Scientific Abstracts",
author = "Accuosto, Pablo and
Saggion, Horacio",
booktitle = "Proceedings of the 6th Workshop on Argument Mining",
month = aug,
year = "2019... | 0 | 266 | 2023-06-26T10:14:08 | Entry not found | 15 | [
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result-kand2-sdxl-wuerst-karlo/c06e4969 | 2023-10-06T14:58:55.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 266 | 2023-10-06T14:58:54 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
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num_bytes: 200
num_examples: 10
download_size: 1394
dataset_size: 200
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "c06e496... | 455 | [
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mariosasko/test_imagefolder_with_metadata | 2022-06-28T12:59:23.000Z | [
"region:us"
] | mariosasko | null | null | 0 | 263 | 2022-06-28T12:53:50 | Entry not found | 15 | [
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0.03790... |
ai4privacy/pii-masking-65k | 2023-08-27T04:42:54.000Z | [
"size_categories:10K<n<100K",
"language:en",
"language:fr",
"language:de",
"language:it",
"legal",
"business",
"psychology",
"privacy",
"region:us"
] | ai4privacy | null | null | 13 | 263 | 2023-08-07T06:04:08 | ---
language:
- en
- fr
- de
- it
tags:
- legal
- business
- psychology
- privacy
size_categories:
- 10K<n<100K
---
# Purpose and Features
The purpose of the model and dataset is to remove personally identifiable information (PII) from text, especially in the context of AI assistants and LLMs.
The model is a fine-t... | 6,161 | [
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-0.033538818359375... |
Fraol/TrainDedupedRefDatasetWMetricFinal3 | 2023-10-11T03:58:45.000Z | [
"region:us"
] | Fraol | null | null | 0 | 263 | 2023-10-10T22:36:43 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: source
dtype: string
- name: path_name
dtype: string
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... | 1,316 | [
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conv_ai | 2022-11-03T16:30:55.000Z | [
"task_categories:conversational",
"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:unknown",
"evalu... | null | ConvAI is a dataset of human-to-bot conversations labelled for quality. This data can be used to train a metric for evaluating dialogue systems. Moreover, it can be used in the development of chatbots themselves: it contains the information on the quality of utterances and entire dialogues, that can guide a dialogue sy... | null | 2 | 262 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
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task_ids:
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paperswithcode_id: null
pretty_name: ConvAi... | 4,058 | [
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emea | 2023-06-01T14:59:51.000Z | [
"task_categories:translation",
"annotations_creators:found",
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"size_categories:1M<n<10M",
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"language:bg",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
"language... | null | This is a parallel corpus made out of PDF documents from the European Medicines Agency. All files are automatically converted from PDF to plain text using pdftotext with the command line arguments -layout -nopgbrk -eol unix. There are some known problems with tables and multi-column layouts - some of them are fixed in ... | J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012) | 1 | 262 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- bg
- cs
- da
- de
- el
- en
- es
- et
- fi
- fr
- hu
- it
- lt
- lv
- mt
- nl
- pl
- pt
- ro
- sk
- sl
- sv
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- translation
t... | 5,780 | [
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sedthh/gutenberg_english | 2023-03-17T09:50:22.000Z | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"project gutenberg",
"e-book",
"gutenberg.org",
"region:us"
] | sedthh | null | null | 3 | 262 | 2023-02-28T14:15:24 | ---
dataset_info:
features:
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num_bytes: 18104255935
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download_size: 10748877194
dataset_size: 18104255935
license: mit
task_categories:
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lan... | 2,987 | [
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medalpaca/medical_meadow_mediqa | 2023-04-16T16:30:36.000Z | [
"task_categories:question-answering",
"language:en",
"region:us"
] | medalpaca | null | null | 6 | 262 | 2023-04-06T16:51:50 | ---
task_categories:
- question-answering
language:
- en
---
# MediQA
## Dataset Description
MEDIQA is a dataset of manually generated, question-driven summaries of multi and single document answers to consumer health questions.
- **Homepage:** https://osf.io/fyg46/?view_only=
### Citation Information
```
@artic... | 653 | [
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abobster/pushkin_new | 2023-05-05T16:31:35.000Z | [
"region:us"
] | abobster | null | null | 0 | 262 | 2023-05-05T16:31:11 | Entry not found | 15 | [
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alzoubi36/piextract | 2023-06-25T07:11:15.000Z | [
"region:us"
] | alzoubi36 | null | null | 0 | 262 | 2023-06-25T07:03:41 | ---
dataset_info:
features:
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struct:
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sequence: string
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sequence: string
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struct:
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... | 1,000 | [
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result-kand2-sdxl-wuerst-karlo/8a14fb4c | 2023-10-06T19:06:51.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 262 | 2023-10-06T19:06:50 | ---
dataset_info:
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configs:
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data_files:
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path: data/train-*
---
# Dataset Card for "8a14fb4... | 455 | [
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fhamborg/news_sentiment_newsmtsc | 2022-10-25T09:20:03.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
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"l... | fhamborg | NewsMTSC: A large, manually annotated dataset for target-dependent sentiment classification in English news articles. | @InProceedings{Hamborg2021b,
author = {Hamborg, Felix and Donnay, Karsten},
title = {NewsMTSC: (Multi-)Target-dependent Sentiment Classification in News Articles},
booktitle = {Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2021)},
year ... | 8 | 261 | 2022-03-02T23:29:22 | ---
annotations_creators:
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language_creators:
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language:
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license:
- mit
multilinguality:
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pretty_name: 'NewsMTSC'
size_categories:
- 10K<n<100K
source_datasets:
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task_categories:
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task_ids:
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lan... | 3,065 | [
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0.020... |
nielsr/rvl_cdip_10_examples_per_class_donut | 2022-08-01T16:56:12.000Z | [
"region:us"
] | nielsr | null | null | 0 | 261 | 2022-08-01T16:22:17 | Entry not found | 15 | [
[
-0.02142333984375,
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0.0288238525390625,
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0.016998291015625,
-0.052093505859375,
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0.0379... |
jamescalam/ai-arxiv-chunked | 2023-10-10T12:56:09.000Z | [
"region:us"
] | jamescalam | null | null | 14 | 261 | 2023-10-09T21:09:27 | Entry not found | 15 | [
[
-0.0213775634765625,
-0.01494598388671875,
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0.0170135498046875,
-0.05206298828125,
-0.01494598388671875,
-0.06036376953125,
0.03... |
air_dialogue | 2022-11-03T16:31:11.000Z | [
"task_categories:conversational",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:dialogue-generation",
"task_ids:dialogue-modeling",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:crowdsourced",
"language_creators:machine-generated... | null | AirDialogue, is a large dataset that contains 402,038 goal-oriented conversations. To collect this dataset, we create a contextgenerator which provides travel and flight restrictions. Then the human annotators are asked to play the role of a customer or an agent and interact with the goal of successfully booking a trip... | @inproceedings{wei-etal-2018-airdialogue,
title = "{A}ir{D}ialogue: An Environment for Goal-Oriented Dialogue Research",
author = "Wei, Wei and
Le, Quoc and
Dai, Andrew and
Li, Jia",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
... | 6 | 260 | 2022-03-02T23:29:22 | ---
pretty_name: AirDialogue
annotations_creators:
- crowdsourced
language_creators:
- machine-generated
language:
- en
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- conversational
- text-generation
- fill-mask
task_ids:
- dialogue-gen... | 20,097 | [
[
-0.038848876953125,
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... |
allenai/mup | 2022-10-25T10:16:52.000Z | [
"license:odc-by",
"region:us"
] | allenai | null | null | 2 | 260 | 2022-05-10T14:53:26 | ---
license:
- odc-by
---
# MuP - Multi Perspective Scientific Document Summarization
Generating summaries of scientific documents is known to be a challenging task. Majority of existing work in summarization assumes only one single best gold summary for each given document. Having only one gold summary negatively im... | 866 | [
[
-0.02850341796875,
0.0001461505889892578,
0.03118896484375,
0.0296478271484375,
-0.0263671875,
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0.03125,
-0.0275115966796875,
-0.0416259765625,
-0.054412841796875,
0.04064941406... |
ScandEval/scandiqa-da-mini | 2023-07-05T09:44:29.000Z | [
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:da",
"license:cc-by-3.0",
"region:us"
] | ScandEval | null | null | 0 | 260 | 2022-12-05T16:41:50 | ---
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: answers
struct:
- name: answer_start
sequence: int64
- name: text
sequence: string
- name: context
dtype: string
- name: answers_en
struct:
- name: answer_start
seque... | 963 | [
[
-0.06549072265625,
-0.0188751220703125,
0.020294189453125,
0.0023651123046875,
-0.02459716796875,
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0.038543701171875,
-0.00977325439453125,
0.07330322265625,
0.0225067138671875,
-0.0633544921875,
-0.043365478515625,
-0.045928955078125,
... |
qed_amara | 2022-11-03T16:31:42.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:aa",
"language:ab",
"language:ae",
"language:aeb",
"language:af",
"language:ak",
"language:am",
"langua... | null | The QCRI Educational Domain Corpus (formerly QCRI AMARA Corpus) is an open multilingual collection of subtitles for educational videos and lectures collaboratively transcribed and translated over the AMARA web-based platform.
Developed by: Qatar Computing Research Institute, Arabic Language Technologies Group
The QED C... | A. Abdelali, F. Guzman, H. Sajjad and S. Vogel, "The AMARA Corpus: Building parallel language resources for the educational domain", The Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC'14). Reykjavik, Iceland, 2014. Pp. 1856-1862. Isbn. 978-2-9517408-8-4. | 4 | 259 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- aa
- ab
- ae
- aeb
- af
- ak
- am
- an
- ar
- arq
- arz
- as
- ase
- ast
- av
- ay
- az
- ba
- be
- ber
- bg
- bh
- bi
- bm
- bn
- bnt
- bo
- br
- bs
- bug
- ca
- ce
- ceb
- ch
- cho
- cku
- cnh
- co
- cr
- cs
- cu
- cv
- cy
- da
- de
- dv
- dz
- ... | 7,221 | [
[
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0.0163421630859375,
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-0.0232391357421875,
0.0457763671875,
0.029266357421875,
-0.05242919921875,
-0.07452392578125,
-0.036407470703125,
... |
jordyvl/rvl_cdip_100_examples_per_class | 2023-03-23T20:55:18.000Z | [
"region:us"
] | jordyvl | null | null | 0 | 259 | 2023-03-23T19:58:02 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': letter
'1': form
'2': email
'3': handwritten
'4': advertisement
'5': scientific report
'6': scientific publication
... | 1,002 | [
[
-0.049285888671875,
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0.011566162109375,
0.026611328125,
0.00083160400390625,
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0.04559326171875,
-0.05120849609375,
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-0.0128173828125,
-0.00802... |
sanchit-gandhi/gtzan | 2023-06-23T13:48:10.000Z | [
"region:us"
] | sanchit-gandhi | null | null | 0 | 259 | 2023-06-23T13:47:03 | ---
dataset_info:
features:
- name: file
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 32000
- name: genre
dtype:
class_label:
names:
'0': blues
'1': classical
'2': country
'3': disco
'4': hiphop
'5': ... | 703 | [
[
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0.0555419921875,
0.03143310546875,
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-0.060028076171875,
-0.0276947021484375... |
conceptnet5 | 2023-06-01T14:59:50.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:10M<n<100M",
"size_categories:1M<n<10M",
"sou... | null | This dataset is designed to provide training data
for common sense relationships pulls together from various sources.
The dataset is multi-lingual. See langauge codes and language info
here: https://github.com/commonsense/conceptnet5/wiki/Languages
This dataset provides an interface for the conceptnet5 csv fi... | \
Robyn Speer, Joshua Chin, and Catherine Havasi. 2017. "ConceptNet 5.5: An Open Multilingual Graph of General Knowledge." In proceedings of AAAI 31.
} | 15 | 258 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- found
language:
- de
- en
- es
- fr
- it
- ja
- nl
- pt
- ru
- zh
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 10M<n<100M
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-classification
task_... | 13,382 | [
[
-0.04345703125,
-0.04315185546875,
0.027008056640625,
0.0013475418090820312,
-0.017852783203125,
-0.03289794921875,
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0.037933349609375,
0.03997802734375,
-0.046356201171875,
-0.061187744140625,
-0.0259246826171875,
0.022... |
php | 2022-11-03T16:31:41.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:cs",
"language:de",
"language:en",
"language:es",
"language:fi",
"language:fr",
"language:he",
"langua... | null | A parallel corpus originally extracted from http://se.php.net/download-docs.php. The original documents are written in English and have been partly translated into 21 languages. The original manuals contain about 500,000 words. The amount of actually translated texts varies for different languages between 50,000 and 38... | @InProceedings{TIEDEMANN12.463,
author = {J{\"o}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},
... | 1 | 258 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- cs
- de
- en
- es
- fi
- fr
- he
- hu
- it
- ja
- ko
- nl
- pl
- pt
- ro
- ru
- sk
- sl
- sv
- tr
- tw
- zh
language_bcp47:
- pt-BR
- zh-TW
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- origina... | 4,741 | [
[
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0.043426513671875,
-0.060455322265625,
-0.08203125,
-0.034210205078125,
0.02685546875,
-... |
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