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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
mychen76/stack-exchange-paired-500k | 2023-09-01T23:55:09.000Z | [
"region:us"
] | mychen76 | null | null | 0 | 425 | 2023-09-01T23:18:07 | StackExchange Paired 500K is a subset of lvwerra/stack-exchange-paired
which is a processed version of the HuggingFaceH4/stack-exchange-preferences. The following steps were applied:
Parse HTML to Markdown with markdownify
Create pairs (response_j, response_k) where j was rated better than k
Sample at most... | 465 | [
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Recognai/sentiment-banking | 2022-02-18T15:28:07.000Z | [
"region:us"
] | Recognai | null | null | 1 | 424 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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kernelmachine/open-license-corpus | 2023-08-09T03:14:36.000Z | [
"task_categories:text-generation",
"size_categories:100B<n<1T",
"language:en",
"license:apache-2.0",
"region:us"
] | kernelmachine | null | null | 6 | 424 | 2023-08-08T23:21:52 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
pretty_name: pubtext
size_categories:
- 100B<n<1T
---
# PubText
Welcome to the Open License Corpus (OLC), a 228B token corpus for training permissively-licensed language models.
**Disclaimer**: OLC should not be considered a universally safe-t... | 9,077 | [
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0.00... |
smangrul/hf-stack-v1 | 2023-07-27T08:02:56.000Z | [
"region:us"
] | smangrul | null | null | 7 | 422 | 2023-07-27T07:59:23 | ---
dataset_info:
features:
- name: repo_id
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---
# Datas... | 478 | [
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jason-lee08/TinyStoriesExclamationValidation2 | 2023-09-15T20:28:30.000Z | [
"region:us"
] | jason-lee08 | null | null | 0 | 422 | 2023-09-15T20:28:29 | ---
dataset_info:
features:
- name: validation
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splits:
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num_examples: 220
download_size: 89488
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---
# Dataset Card for "TinyStoriesExclamationValidation2"
[More Information needed](https://github.com/huggingface/datasets/blob/main... | 376 | [
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pierreguillou/DocLayNet-small | 2023-05-17T08:56:10.000Z | [
"task_categories:object-detection",
"task_categories:image-segmentation",
"task_categories:token-classification",
"task_ids:instance-segmentation",
"annotations_creators:crowdsourced",
"size_categories:1K<n<10K",
"language:en",
"language:de",
"language:fr",
"language:ja",
"license:other",
"Doc... | pierreguillou | Accurate document layout analysis is a key requirement for high-quality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adeq... | @article{doclaynet2022,
title = {DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis},
doi = {10.1145/3534678.353904},
url = {https://arxiv.org/abs/2206.01062},
author = {Pfitzmann, Birgit and Auer, Christoph and Dolfi, Michele and Nassar, Ahmed S and Staar, Peter W J},
year = {2022}
} | 7 | 421 | 2023-01-25T17:47:43 | ---
language:
- en
- de
- fr
- ja
annotations_creators:
- crowdsourced
license: other
pretty_name: DocLayNet small
size_categories:
- 1K<n<10K
tags:
- DocLayNet
- COCO
- PDF
- IBM
- Financial-Reports
- Finance
- Manuals
- Scientific-Articles
- Science
- Laws
- Law
- Regulations
- Patents
- Government-Tenders
- object-d... | 13,864 | [
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0.006... |
nomic-ai/gpt4all-j-prompt-generations | 2023-04-24T15:20:43.000Z | [
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"region:us"
] | nomic-ai | null | null | 164 | 421 | 2023-04-10T21:59:10 | ---
dataset_info:
features:
- name: prompt
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- name: response
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- name: source
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splits:
- name: train
num_bytes: 1774285641
num_examples: 808812
download_size: 990673616
dataset_size: 1774285641
license: apache-2.0
language:
- en
size_categories:
... | 1,709 | [
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0... |
art | 2023-04-05T09:36:25.000Z | [
"task_categories:multiple-choice",
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:unknown",... | null | the Abductive Natural Language Inference Dataset from AI2 | @InProceedings{anli,
author = {Chandra, Bhagavatula and Ronan, Le Bras and Chaitanya, Malaviya and Keisuke, Sakaguchi and Ari, Holtzman
and Hannah, Rashkin and Doug, Downey and Scott, Wen-tau Yih and Yejin, Choi},
title = {Abductive Commonsense Reasoning},
year = {2020}
} | 3 | 420 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- multiple-choice
- text-classification
task_ids:
- natural-language-inference
paperswithcode_id: art-dataset
pre... | 6,795 | [
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... |
rokset3/slimpajama | 2023-10-12T23:12:39.000Z | [
"region:us"
] | rokset3 | null | null | 0 | 420 | 2023-10-12T22:48:18 | ---
dataset_info:
features:
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configs:
- config_name: default
data_file... | 531 | [
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mteb/twittersemeval2015-pairclassification | 2022-04-19T10:46:11.000Z | [
"region:us"
] | mteb | null | null | 0 | 417 | 2022-04-19T10:45:14 | Entry not found | 15 | [
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nthngdy/ccnews_split | 2022-04-25T15:03:37.000Z | [
"region:us"
] | nthngdy | CC-News containing news articles from news sites all over the world The data is available on AWS S3 in the Common Crawl bucket at /crawl-data/CC-NEWS/. This version of the dataset has 708241 articles. It represents a small portion of English language subset of the CC-News dataset created using news-please(Hamborg et a... | @InProceedings{Hamborg2017,
author = {Hamborg, Felix and Meuschke, Norman and Breitinger, Corinna and Gipp, Bela},
title = {news-please: A Generic News Crawler and Extractor},
year = {2017},
booktitle = {Proceedings of the 15th International Symposium of Information Science},
location = {Ber... | 0 | 416 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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-0.060455322265625,
0.03793334... |
iara-project/news-articles-ptbr-dataset | 2023-09-21T03:12:30.000Z | [
"region:us"
] | iara-project | null | null | 1 | 416 | 2023-09-17T19:11:32 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: title
dtype: string
- name: text
dtype: string
- name: date
dtype: string
- name: category
dtype: string
- name: category_natural_la... | 757 | [
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generics_kb | 2023-06-07T12:35:34.000Z | [
"task_categories:other",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"knowledge-base",
"arxiv:2005.00660",
"region:u... | null | The GenericsKB contains 3.4M+ generic sentences about the world, i.e., sentences expressing general truths such as "Dogs bark," and "Trees remove carbon dioxide from the atmosphere." Generics are potentially useful as a knowledge source for AI systems requiring general world knowledge. The GenericsKB is the first large... | @InProceedings{huggingface:dataset,
title = {GenericsKB: A Knowledge Base of Generic Statements},
authors={Sumithra Bhakthavatsalam, Chloe Anastasiades, Peter Clark},
year={2020},
publisher = {Allen Institute for AI},
} | 1 | 415 | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- 1M<n<10M
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: genericskb
pretty_name: GenericsKB
tags:
- knowledge-b... | 11,851 | [
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0.0... |
qa_srl | 2022-11-18T21:40:16.000Z | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"task_ids:open-domain-qa",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
... | null | The dataset contains question-answer pairs to model verbal predicate-argument structure. The questions start with wh-words (Who, What, Where, What, etc.) and contain a verb predicate in the sentence; the answers are phrases in the sentence.
There were 2 datsets used in the paper, newswire and wikipedia. Unfortunately t... | @InProceedings{huggingface:dataset,
title = {QA-SRL: Question-Answer Driven Semantic Role Labeling},
authors={Luheng He, Mike Lewis, Luke Zettlemoyer},
year={2015}
publisher = {cs.washington.edu},
howpublished={\\url{https://dada.cs.washington.edu/qasrl/#page-top}},
} | 1 | 415 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
- open-domain-qa
paperswithcode_id: qa-srl
pr... | 6,186 | [
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mteb/twitterurlcorpus-pairclassification | 2022-04-19T10:29:01.000Z | [
"region:us"
] | mteb | null | null | 0 | 415 | 2022-04-19T10:27:43 | Entry not found | 15 | [
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jondurbin/airoboros-2.2.1 | 2023-09-18T21:22:40.000Z | [
"license:other",
"region:us"
] | jondurbin | null | null | 19 | 415 | 2023-09-15T10:20:36 | ---
license: other
---
## Overview
This dataset is a slight update to 2.2.
### Re-generated writing responses
Many of the responses were generated by gpt-4-0613, which unfortunately produces much shorter and "dumber" (i.e. various readability scores increased compared to gpt-4-0314, e.g. Flesch, Gunning Fog, etc.) ... | 5,968 | [
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mrqa | 2022-11-18T21:30:01.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|drop",
"source_datasets:extended|hotpot_qa",
"source_datasets:extended|natural_questions",
... | null | The MRQA 2019 Shared Task focuses on generalization in question answering.
An effective question answering system should do more than merely
interpolate from the training set to answer test examples drawn
from the same distribution: it should also be able to extrapolate
to out-of-distribution examples — a significantly... | @inproceedings{fisch2019mrqa,
title={{MRQA} 2019 Shared Task: Evaluating Generalization in Reading Comprehension},
author={Adam Fisch and Alon Talmor and Robin Jia and Minjoon Seo and Eunsol Choi and Danqi Chen},
booktitle={Proceedings of 2nd Machine Reading for Reading Comprehension (MRQA) Workshop at EMNL... | 10 | 414 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|drop
- extended|hotpot_qa
- extended|natural_questions
- extended|race
- extended|search_qa
- extended|squad
- extended|trivia_qa
task_ca... | 13,717 | [
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NicolaiSivesind/ChatGPT-Research-Abstracts | 2023-05-11T17:00:58.000Z | [
"task_categories:text-classification",
"size_categories:10K<n<100k",
"language:en",
"license:cc",
"chatgpt",
"gpt",
"research abstracts",
"region:us"
] | NicolaiSivesind | null | null | 3 | 414 | 2023-04-30T21:09:44 | ---
license: cc
task_categories:
- text-classification
pretty_name: ChatGPT Research Abstracts - Labled text segments produced by humans and ChatGPT
size_categories:
- 10K<n<100k
language:
- en
tags:
- chatgpt
- gpt
- research abstracts
---
# ChatGPT-Research-Abstracts
This is a dataset created in relation to a bachelo... | 1,591 | [
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princeton-nlp/SWE-bench | 2023-11-01T17:50:01.000Z | [
"arxiv:2310.06770",
"region:us"
] | princeton-nlp | null | null | 11 | 414 | 2023-10-10T04:56:03 | ---
dataset_info:
features:
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thefcraft/civitai-stable-diffusion-337k | 2023-09-26T07:10:40.000Z | [
"annotations_creators:no-annotation",
"language_creators:thefcraft",
"size_categories:1M<n<10M",
"source_datasets:civitai",
"language:en",
"region:us"
] | thefcraft | null | null | 10 | 413 | 2023-04-28T08:49:21 | ---
annotations_creators:
- no-annotation
language_creators:
- thefcraft
language:
- en
pretty_name: civitai-stable-diffusion-337k
size_categories:
- 1M<n<10M
source_datasets:
- civitai
---
### How to Use
```
from datasets import load_dataset
dataset = load_dataset("thefcraft/civitai-stable-diffusion-337k")
print(d... | 2,745 | [
[
-0.04241943359375,
-0.030609130859375,
0.0128021240234375,
0.0128021240234375,
-0.0307464599609375,
-0.0034389495849609375,
0.0021533966064453125,
-0.0207672119140625,
0.025482177734375,
0.0252838134765625,
-0.05615234375,
-0.05596923828125,
-0.03253173828125,
... |
break_data | 2023-04-05T09:42:04.000Z | [
"task_categories:text2text-generation",
"task_ids:open-domain-abstractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | null | Break is a human annotated dataset of natural language questions and their Question Decomposition Meaning Representations
(QDMRs). Break consists of 83,978 examples sampled from 10 question answering datasets over text, images and databases.
This repository contains the Break dataset along with information on the exact... | @article{Wolfson2020Break,
title={Break It Down: A Question Understanding Benchmark},
author={Wolfson, Tomer and Geva, Mor and Gupta, Ankit and Gardner, Matt and Goldberg, Yoav and Deutch, Daniel and Berant, Jonathan},
journal={Transactions of the Association for Computational Linguistics},
year={2020},
} | 0 | 411 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids:
- open-domain-abstractive-qa
paperswithcode_id: break
pretty_name: BREAK... | 11,724 | [
[
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0.03594970703125,
-0.053863525390625,
-0.054656982421875,
-0.024139404296875,
... |
search_qa | 2023-06-16T09:03:21.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:unknown",
"arxiv:1704.05179",
"region:us"
] | null | We publicly release a new large-scale dataset, called SearchQA, for machine comprehension, or question-answering. Unlike recently released datasets, such as DeepMind
CNN/DailyMail and SQuAD, the proposed SearchQA was constructed to reflect a full pipeline of general question-answering. That is, we start not from an exi... | null | 10 | 411 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language:
- en
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
pretty_name: SearchQA
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: searchqa
dataset_info:
- config_... | 9,405 | [
[
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-0.001953125,
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0.041748046875,
0.034515380859375,
-0.056549072265625,
-0.05047607421875,
-0.0282440185546875,
0.015... |
polinaeterna/amazon_us_reviews | 2023-06-09T17:56:17.000Z | [
"task_categories:summarization",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"task_ids:sentiment-classification",
"task_ids:sentiment-scoring",
"ta... | polinaeterna | Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website... | \ | 0 | 411 | 2023-06-09T17:56:16 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 100M<n<1B
source_datasets:
- original
task_categories:
- summarization
- text-generation
- fill-mask
- text-classification
task_ids:
- text-scoring
- language-modeling
-... | 60,396 | [
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0.048095703125,
0.03741455078125,
-0.071044921875,
-0.0638427734375,
-0.02813720703125,
0.009376525878... |
e2e_nlg_cleaned | 2022-11-18T19:59:46.000Z | [
"task_categories:text2text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"meaning-representation-to-text",
"arxiv:1706.09254",
"ar... | null | An update release of E2E NLG Challenge data with cleaned MRs and scripts, accompanying the following paper:
Ondřej Dušek, David M. Howcroft, and Verena Rieser (2019): Semantic Noise Matters for Neural Natural Language Generation. In INLG, Tokyo, Japan. | @inproceedings{dusek-etal-2019-semantic,
title = "Semantic Noise Matters for Neural Natural Language Generation",
author = "Du{\v{s}}ek, Ond{\v{r}}ej and
Howcroft, David M. and
Rieser, Verena",
booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
m... | 2 | 410 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: null
pretty_name: the Cleaned Version of the ... | 6,538 | [
[
-0.021820068359375,
-0.05517578125,
0.01171112060546875,
0.00466156005859375,
-0.003932952880859375,
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-0.0305328369140625,
-0.05389404296875,
0.0213623046875,
0.038665771484375,
-0.040435791015625,
-0.04473876953125,
-0.041748046875,
0.02... |
ArmelR/stack-exchange-instruction | 2023-05-26T08:37:42.000Z | [
"region:us"
] | ArmelR | null | null | 48 | 410 | 2023-04-06T16:31:58 | ---
pretty_name : stack exchange instruction
---
# Dataset Card for "stack-exchange-instruction"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 229 | [
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... |
gnad10 | 2023-01-25T14:31:03.000Z | [
"task_categories:text-classification",
"task_ids:topic-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-from-One-Million-Posts-Corpus",
"language:de",
"license:cc-by-nc-sa-4.0... | null | This dataset is intended to advance topic classification for German texts. A classifier that is efffective in
English may not be effective in German dataset because it has a higher inflection and longer compound words.
The 10kGNAD dataset contains 10273 German news articles from an Austrian online newspaper categorized... | null | 3 | 409 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- de
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-from-One-Million-Posts-Corpus
task_categories:
- text-classification
task_ids:
- topic-classification
pretty_name: ... | 6,655 | [
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0.020294189453125,
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-0.0718994140625,
-0.060455322265625,
0.013870... |
miracl/miracl | 2023-01-06T16:25:49.000Z | [
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"annotations_creators:expert-generated",
"multilinguality:multilingual",
"language:ar",
"language:bn",
"language:en",
"language:es",
"language:fa",
"language:fi",
"language:fr",
"language:hi",
"language:id",
"language:ja",
... | miracl | null | null | 24 | 409 | 2022-10-11T22:20:12 | ---
annotations_creators:
- expert-generated
language:
- ar
- bn
- en
- es
- fa
- fi
- fr
- hi
- id
- ja
- ko
- ru
- sw
- te
- th
- zh
multilinguality:
- multilingual
pretty_name: MIRACL-corpus
size_categories: []
source_datasets: []
tags: []
task_categories:
- text-retrieval
license:
- apache-2.0
task_ids:
- do... | 3,500 | [
[
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0.0321044921875,
-0.033935546875,
-0.07135009765625,
-0.0341796875,
0.00475... |
cyanic-selkie/aida-conll-yago-wikidata | 2023-06-28T19:01:17.000Z | [
"task_categories:token-classification",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-sa-3.0",
"wikidata",
"wikipedia",
"named-entity-recognition",
"named-entity-linking",
"region:us"
] | cyanic-selkie | null | null | 3 | 409 | 2023-03-22T13:30:44 | ---
license: cc-by-sa-3.0
task_categories:
- token-classification
language:
- en
tags:
- wikidata
- wikipedia
- named-entity-recognition
- named-entity-linking
pretty_name: AIDA CoNLL-YAGO Wikidata
size_categories:
- 10K<n<100K
---
# Dataset Card for AIDA CoNLL-YAGO Wikidata
## Table of Contents
- [Dataset Descriptio... | 7,945 | [
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... |
IlyaGusev/ru_turbo_alpaca | 2023-05-25T19:45:14.000Z | [
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:10K<n<100K",
"language:ru",
"license:cc-by-4.0",
"instruction-finetuning",
"instruction generation",
"alpaca",
"region:us"
] | IlyaGusev | null | null | 51 | 408 | 2023-03-21T21:17:42 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: alternative_output
dtype: string
- name: label
dtype: string
- name: all_labels
sequence: string
- name: agreement
dtype: float32
- name: overlap
... | 2,644 | [
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-0.04791259765625,
-0.0087509... |
LeStoe11/geeks4geeks_fixed | 2023-10-13T08:15:31.000Z | [
"region:us"
] | LeStoe11 | null | null | 0 | 407 | 2023-09-25T18:05:13 | Entry not found | 15 | [
[
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-0.060394287109375,
0.0379... |
codecomplete/base_dataset | 2023-10-10T20:53:14.000Z | [
"region:us"
] | codecomplete | null | null | 0 | 407 | 2023-10-10T20:51:57 | Entry not found | 15 | [
[
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0.0170135498046875,
-0.052093505859375,
-0.01497650146484375,
-0.0604248046875,
0.0379028... |
argilla/oasst_response_comparison | 2023-07-25T11:39:45.000Z | [
"size_categories:1K<n<10K",
"rlfh",
"argilla",
"human-feedback",
"region:us"
] | argilla | null | null | 0 | 406 | 2023-06-30T07:54:14 | ---
size_categories: 1K<n<10K
tags:
- rlfh
- argilla
- human-feedback
---
# Dataset Card for oasst_response_comparison
This dataset has been created with [Argilla](https://docs.argilla.io).
As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla)... | 13,909 | [
[
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0.055511474609375,
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-0.0386962890625,
-0.050445556640625,
0.0140... |
mnaguib/WikiNER | 2023-10-26T15:55:13.000Z | [
"region:us"
] | mnaguib | null | null | 0 | 406 | 2023-07-28T16:08:10 | ---
configs:
- config_name: en
data_files:
- split: train
path: "data/en/train.parquet"
- split: test
path: "data/en/test.parquet"
- config_name: fr
data_files:
- split: train
path: "data/fr/train.parquet"
- split: test
path: "data/fr/test.parquet"
- config_name: es
data_files:
- split: ... | 1,860 | [
[
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-0.0167388916015625,
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-0.038055419921875,
-0.038818359375... |
eaglewatch/Korean_Wikipedia_Dataset_for_GPT2_August_2022 | 2023-08-25T05:35:38.000Z | [
"task_categories:question-answering",
"task_categories:text2text-generation",
"task_categories:translation",
"task_categories:conversational",
"task_categories:visual-question-answering",
"task_ids:open-domain-qa",
"task_ids:closed-domain-qa",
"task_ids:dialogue-generation",
"task_ids:visual-questio... | eaglewatch | null | null | 2 | 406 | 2023-08-25T05:30:30 | ---
annotations_creators:
- other
language:
- ko
language_creators:
- other
license:
- apache-2.0
multilinguality:
- multilingual
pretty_name: Korean wikipedia dataset for GPT-2 training
size_categories:
- 100M<n<1B
source_datasets: []
tags:
- gpt2
- korean
- wikipedia
- pertained
task_categories:
- question-answering
... | 2,158 | [
[
-0.0290679931640625,
-0.036102294921875,
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0.01751708984375,
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... |
HAERAE-HUB/HAE_RAE_BENCH | 2023-09-28T02:27:35.000Z | [
"task_categories:multiple-choice",
"language:ko",
"license:cc-by-nc-nd-4.0",
"arxiv:2309.02706",
"region:us"
] | HAERAE-HUB | HAE-RAE Bench | @article{son2023hae,
title={HAE-RAE Bench: Evaluation of Korean Knowledge in Language Models},
author={Son, Guijin and Lee, Hanwool and Kim, Suwan and Lee, Jaecheol and Yeom, Je Won and Jung, Jihyu and Kim, Jung Woo and Kim, Songseong},
journal={arXiv preprint arXiv:2309.02706},
year={2023}
} | 1 | 405 | 2023-09-25T04:16:13 | ---
license: cc-by-nc-nd-4.0
extra_gated_prompt: >-
To request access to the dataset, please fill out this form, and we'll review
and let you know if your use case is approved.
extra_gated_fields:
First Name: text
Last Name: text
Institution: text
Intended Use: text
I agree to use this dataset for non-com... | 1,539 | [
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0.0... |
taskmaster2 | 2022-12-01T16:31:12.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:dialogue-modeling",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:1... | null | Taskmaster is dataset for goal oriented conversations. The Taskmaster-2 dataset consists of 17,289 dialogs in the seven domains which include restaurants, food ordering, movies, hotels, flights, music and sports. Unlike Taskmaster-1, which includes both written "self-dialogs" and spoken two-person dialogs, Taskmaster-2... | @inproceedings{48484,
title = {Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset},
author = {Bill Byrne and Karthik Krishnamoorthi and Chinnadhurai Sankar and Arvind Neelakantan and Daniel Duckworth and Semih Yavuz and Ben Goodrich and Amit Dubey and Kyu-Young Kim and Andy Cedilnik},
year = {2019}
} | 4 | 404 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- dialogue-modeling
paperswithcode_id: taskmaster-2
pretty_name: ... | 12,298 | [
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0... |
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
... | 1 | 403 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
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0.0379... |
masakhane/masakhaner2 | 2023-09-11T18:00:07.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:bm",
"language:bbj",
"language:ee",
"langu... | masakhane | MasakhaNER 2.0 is the largest publicly available high-quality dataset for named entity recognition (NER) in 20 African languages.
Named entities are phrases that contain the names of persons, organizations, locations, times and quantities.
Example:
[PER Wolff] , currently a journalist in [LOC Argentina] , played with... | @article{Adelani2022MasakhaNER2A,
title={MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition},
author={David Ifeoluwa Adelani and Graham Neubig and Sebastian Ruder and Shruti Rijhwani and Michael Beukman and Chester Palen-Michel and Constantine Lignos and Jesujoba Oluwadara Alabi and Shams... | 8 | 403 | 2022-12-15T13:28:09 | ---
annotations_creators:
- expert-generated
language:
- bm
- bbj
- ee
- fon
- ha
- ig
- rw
- lg
- luo
- mos
- ny
- pcm
- sn
- sw
- tn
- tw
- wo
- xh
- yo
- zu
language_creators:
- expert-generated
license:
- afl-3.0
multilinguality:
- multilingual
pretty_name: masakhaner2.0
size_categories:
- 1K<n<10K
source_datasets:... | 8,601 | [
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arcd | 2023-04-05T09:35:12.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ar",
"license:mit",
"region:us"
] | null | Arabic Reading Comprehension Dataset (ARCD) composed of 1,395 questions posed by crowdworkers on Wikipedia articles. | @inproceedings{mozannar-etal-2019-neural,
title = {Neural {A}rabic Question Answering},
author = {Mozannar, Hussein and Maamary, Elie and El Hajal, Karl and Hajj, Hazem},
booktitle = {Proceedings of the Fourth Arabic Natural Language Processing Workshop},
month = {aug},
year = {2019},
address... | 3 | 402 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- ar
language_bcp47:
- ar-SA
license:
- mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: arcd
pretty_name: ARC... | 8,150 | [
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0.0... |
speechcolab/gigaspeech | 2023-09-25T17:54:37.000Z | [
"task_categories:automatic-speech-recognition",
"multilinguality:monolingual",
"language:en",
"license:apache-2.0",
"arxiv:2106.06909",
"region:us"
] | speechcolab | GigaSpeech is an evolving, multi-domain English speech recognition corpus with 10,000 hours of high quality
labeled audio suitable for supervised training, and 40,000 hours of total audio suitable for semi-supervised
and unsupervised training. Around 40,000 hours of transcribed audio is first collected from audiobooks,... | @article{DBLP:journals/corr/abs-2106-06909,
author = {Guoguo Chen and
Shuzhou Chai and
Guanbo Wang and
Jiayu Du and
Wei{-}Qiang Zhang and
Chao Weng and
Dan Su and
Daniel Povey and
Jan Trmal and
... | 31 | 402 | 2022-06-09T14:51:58 | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: Gigaspeech
size_categories: []
source_datasets: []
task_categories:
- automatic-speech-recognition
extra_gated_prompt: |-
SpeechColab does not own the copyright of the audio files. For... | 13,890 | [
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0.00236892700... |
Biddls/Onion_News | 2023-03-25T12:57:47.000Z | [
"task_categories:summarization",
"task_categories:text2text-generation",
"task_categories:text-generation",
"task_categories:text-classification",
"language:en",
"license:mit",
"region:us"
] | Biddls | null | null | 1 | 402 | 2023-03-25T12:50:01 | ---
license: mit
task_categories:
- summarization
- text2text-generation
- text-generation
- text-classification
language:
- en
pretty_name: OnionNewsScrape
---
## This is a dataset of Onion news articles:
Note
- The headers and body of the news article is split by a ' #~# ' token
- Lines with just the token had no ... | 463 | [
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pleisto/wikipedia-cn-20230720-filtered | 2023-07-23T10:06:15.000Z | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:zh",
"license:cc-by-sa-3.0",
"wikipedia",
"region:us"
] | pleisto | null | null | 71 | 402 | 2023-07-23T09:45:03 | ---
license: cc-by-sa-3.0
task_categories:
- text-generation
language:
- zh
tags:
- wikipedia
size_categories:
- 100K<n<1M
---
本数据集基于中文维基2023年7月20日的dump存档。作为一项以数据为中心的工作，本数据集仅保留了 `254,547条` 质量较高的词条内容。具体而言：
* 过滤了Template, Category, Wikipedia, File, Topic, Portal, MediaWiki, Draft, Help等特殊类型的词条
* 使用启发式的方法和自有的NLU模型过滤了一部分质... | 1,064 | [
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riddle_sense | 2022-11-18T21:42:04.000Z | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:other",
"region:us"
] | null | Answering such a riddle-style question is a challenging cognitive process, in that it requires
complex commonsense reasoning abilities, an understanding of figurative language, and counterfactual reasoning
skills, which are all important abilities for advanced natural language understanding (NLU). However,
there is cur... | @InProceedings{lin-etal-2021-riddlesense,
title={RiddleSense: Reasoning about Riddle Questions Featuring Linguistic Creativity and Commonsense Knowledge},
author={Lin, Bill Yuchen and Wu, Ziyi and Yang, Yichi and Lee, Dong-Ho and Ren, Xiang},
journal={Proceedings of the 59th Annual Meeting of the Association for Comput... | 15 | 401 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- other
multilinguality:
- monolingual
pretty_name: RiddleSense
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
dataset_info:
features:
- name: ans... | 6,110 | [
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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 | 13 | 401 | 2022-03-02T23:29:22 | ---
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... | 3,662 | [
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... |
SetFit/amazon_counterfactual | 2022-02-08T10:15:40.000Z | [
"arxiv:2104.06893",
"region:us"
] | SetFit | The dataset contains sentences from Amazon customer reviews (sampled from Amazon product review dataset) annotated for counterfactual detection (CFD) binary classification. Counterfactual statements describe events that did not or cannot take place. Counterfactual statements may be identified as statements of the form ... | @misc{oneill2021i,
title={I Wish I Would Have Loved This One, But I Didn't -- A Multilingual Dataset for Counterfactual Detection in Product Reviews},
author={James O'Neill and Polina Rozenshtein and Ryuichi Kiryo and Motoko Kubota and Danushka Bollegala},
year={2021},
eprint={2104.06893},
... | 0 | 401 | 2022-03-02T23:29:22 | # Amazon Multilingual Counterfactual Dataset
The dataset contains sentences from Amazon customer reviews (sampled from Amazon product review dataset) annotated for counterfactual detection (CFD) binary classification. Counterfactual statements describe events that did not or cannot take place. Counterfactual statemen... | 1,567 | [
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fantasyfish/laion-art | 2023-06-30T08:55:13.000Z | [
"region:us"
] | fantasyfish | null | null | 1 | 401 | 2023-06-30T06:20:14 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
- name: aesthetic
dtype: float64
splits:
- name: train
num_bytes: 11640624315.8
num_examples: 20072
- name: test
num_bytes: 538961083.0
num_examples: 855
download_size: 12347056207
dataset_siz... | 504 | [
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circa | 2023-01-25T14:28:00.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"question-answer-pai... | null | The Circa (meaning ‘approximately’) dataset aims to help machine learning systems
to solve the problem of interpreting indirect answers to polar questions.
The dataset contains pairs of yes/no questions and indirect answers, together with
annotations for the interpretation of the answer. The data is collected in 10
di... | @InProceedings{louis_emnlp2020,
author = "Annie Louis and Dan Roth and Filip Radlinski",
title = ""{I}'d rather just go to bed": {U}nderstanding {I}ndirect {A}nswers",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods
in Natural Language Processing",
year = "2020",
} | 2 | 400 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
paperswithcode_id: circa
pretty_name: ... | 10,360 | [
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selqa | 2023-01-25T14:43:46.000Z | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"arxiv:1606.00851",
"region:us"
] | null | The SelQA dataset provides crowdsourced annotation for two selection-based question answer tasks,
answer sentence selection and answer triggering. | @InProceedings{7814688,
author={T. {Jurczyk} and M. {Zhai} and J. D. {Choi}},
booktitle={2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)},
title={SelQA: A New Benchmark for Selection-Based Question Answering},
year={2016},
volume={},
number={},
pages={820-827},
doi=... | 0 | 400 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswithcode_id: selqa
pretty_name: SelQA
dataset_info:
- con... | 17,249 | [
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discovery | 2023-06-02T12:27:46.000Z | [
"task_categories:text-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"discourse-marker-prediction",
"region:us"
] | null | null | @inproceedings{sileo-etal-2019-mining,
title = "Mining Discourse Markers for Unsupervised Sentence Representation Learning",
author = "Sileo, Damien and
Van De Cruys, Tim and
Pradel, Camille and
Muller, Philippe",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican C... | 5 | 399 | 2022-03-02T23:29:22 | ---
annotations_creators:
- other
language_creators:
- other
language:
- en
license: apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: discovery
pretty_name: Discovery
tags:
- discourse-ma... | 15,527 | [
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wiki_auto | 2023-06-01T14:59:51.000Z | [
"task_categories:text2text-generation",
"task_ids:text-simplification",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|other-wikipedia",
"language:en",
"... | null | WikiAuto provides a set of aligned sentences from English Wikipedia and Simple English Wikipedia
as a resource to train sentence simplification systems. The authors first crowd-sourced a set of manual alignments
between sentences in a subset of the Simple English Wikipedia and their corresponding versions in English Wi... | @inproceedings{acl/JiangMLZX20,
author = {Chao Jiang and
Mounica Maddela and
Wuwei Lan and
Yang Zhong and
Wei Xu},
editor = {Dan Jurafsky and
Joyce Chai and
Natalie Schluter and
Joel R. Tetreault},
title... | 7 | 399 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
- machine-generated
language_creators:
- found
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|other-wikipedia
task_categories:
- text2text-generation
task_ids:
- text-simplification
pretty_name: Wiki... | 15,374 | [
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0... |
lucadiliello/newsqa | 2023-06-06T08:36:25.000Z | [
"region:us"
] | lucadiliello | null | null | 3 | 399 | 2023-02-25T18:03:41 | ---
dataset_info:
features:
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence: string
- name: key
dtype: string
- name: labels
list:
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sequence: int64
- name: start
sequence: int64
splits:
- name: train
num_bytes: ... | 681 | [
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NumbersStation/NSText2SQL | 2023-07-11T05:26:13.000Z | [
"task_categories:text2text-generation",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"language:en",
"license:other",
"text-to-sql",
"region:us"
] | NumbersStation | null | null | 28 | 399 | 2023-07-11T05:26:12 | ---
language:
- en
task_categories:
- text2text-generation
license:
- other
language_creators:
- crowdsourced
- expert-generated
multilinguality:
- multilingual
tags:
- text-to-sql
size_categories:
- 100K<n<1M
pretty_name: NSText2SQL
---
# Dataset Summary
NSText2SQL dataset used to train [NSQL](https:/... | 17,800 | [
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0n1xus/codexglue | 2021-11-18T08:45:46.000Z | [
"region:us"
] | 0n1xus | CodeXGLUE is a benchmark dataset to foster machine learning research for program understanding and generation.
CodeXGLUE includes a collection of 10 tasks across 14 datasets and a platform for model evaluation and comparison. | @article{Lu2021,
author = {Lu, Shuai and Guo, Daya and Ren, Shuo and Huang, Junjie and Svyatkovskiy, Alexey and Blanco, Ambrosio and Clement, Colin B. and Drain, Dawn and Jiang, Daxin and Tang, Duyu and Li, Ge and Zhou, Lidong and Shou, Linjun and Zhou, Long and Tufano, Michele and Gong, Ming and Zhou, Ming and Duan, N... | 3 | 397 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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vivos | 2023-06-14T08:29:21.000Z | [
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:vi",
"license:cc-by-nc-sa-4.0",
"regio... | null | \
VIVOS is a free Vietnamese speech corpus consisting of 15 hours of recording speech prepared for
Vietnamese Automatic Speech Recognition task.
The corpus was prepared by AILAB, a computer science lab of VNUHCM - University of Science, with Prof. Vu Hai Quan is the head of.
We publish this corpus in hope to attrac... | \
@inproceedings{luong-vu-2016-non,
title = "A non-expert {K}aldi recipe for {V}ietnamese Speech Recognition System",
author = "Luong, Hieu-Thi and
Vu, Hai-Quan",
booktitle = "Proceedings of the Third International Workshop on Worldwide Language Service Infrastructure and Second Workshop on Open... | 5 | 395 | 2022-03-02T23:29:22 | ---
pretty_name: VIVOS
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- vi
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- automatic-speech-recognition
task_ids: []
dataset_inf... | 6,995 | [
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AISE-TUDelft/ML4SE23_G8_CodeSearchNet-Python | 2023-10-18T10:20:26.000Z | [
"license:c-uda",
"region:us"
] | AISE-TUDelft | null | null | 0 | 395 | 2023-10-16T15:27:53 | ---
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: id
dtype: int32
- name: repo
dtype: string
- name: path
dtype: string
- name: func_name
... | 1,109 | [
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bible_para | 2022-11-03T16:31:57.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:acu",
"language:af",
"language:agr",
"language:ake",
"language:am",
"language:amu",
"language:ar",
"la... | null | This is a multilingual parallel corpus created from translations of the Bible compiled by Christos Christodoulopoulos and Mark Steedman.
102 languages, 5,148 bitexts
total number of files: 107
total number of tokens: 56.43M
total number of sentence fragments: 2.84M | OPUS and A massively parallel corpus: the Bible in 100 languages, Christos Christodoulopoulos and Mark Steedman, *Language Resources and Evaluation*, 49 (2) | 9 | 394 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- acu
- af
- agr
- ake
- am
- amu
- ar
- bg
- bsn
- cak
- ceb
- ch
- chq
- chr
- cjp
- cni
- cop
- crp
- cs
- da
- de
- dik
- dje
- djk
- dop
- ee
- el
- en
- eo
- es
- et
- eu
- fi
- fr
- gbi
- gd
- gu
- gv
- he
- hi
- hr
- hu
- hy
- id
- is
- it
-... | 5,484 | [
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0.0134... |
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... | 0 | 394 | 2022-03-02T23:29:22 | ---
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
- *... | 23,291 | [
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kd_conv | 2023-03-28T14:17:47.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:dialogue-modeling",
"annotations_creators:crowdsourced",
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"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"lan... | null | KdConv is a Chinese multi-domain Knowledge-driven Conversionsation dataset, grounding the topics in multi-turn conversations to knowledge graphs. KdConv contains 4.5K conversations from three domains (film, music, and travel), and 86K utterances with an average turn number of 19.0. These conversations contain in-depth ... | @inproceedings{zhou-etal-2020-kdconv,
title = "{K}d{C}onv: A {C}hinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation",
author = "Zhou, Hao and
Zheng, Chujie and
Huang, Kaili and
Huang, Minlie and
Zhu, Xiaoyan",
booktitle = "Proceedings of the 58th... | 9 | 393 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
- machine-generated
language_creators:
- crowdsourced
language:
- zh
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- dialogue-modeling
paperswithcode_id: kdcon... | 10,094 | [
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covid_qa_deepset | 2022-11-03T16:31:16.000Z | [
"task_categories:question-answering",
"task_ids:closed-domain-qa",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"region:us"... | null | COVID-QA is a Question Answering dataset consisting of 2,019 question/answer pairs annotated by volunteer biomedical experts on scientific articles related to COVID-19. | @inproceedings{moller2020covid,
title={COVID-QA: A Question Answering Dataset for COVID-19},
author={M{\"o}ller, Timo and Reina, Anthony and Jayakumar, Raghavan and Pietsch, Malte},
booktitle={Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020},
year={2020}
} | 1 | 392 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- closed-domain-qa
- extractive-qa
paperswithcode_id: null
pretty_name: COVI... | 5,607 | [
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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}
} | 2 | 390 | 2022-03-02T23:29:22 | ---
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
... | 8,035 | [
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... |
aadityaubhat/GPT-wiki-intro | 2023-10-03T22:48:42.000Z | [
"task_categories:text-classification",
"task_categories:zero-shot-classification",
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:cc",
"doi:10.57967/hf/0326",
"region:us"
] | aadityaubhat | null | null | 18 | 389 | 2023-02-03T18:30:39 | ---
license: cc
task_categories:
- text-classification
- zero-shot-classification
- text-generation
pretty_name: GPT Wiki Intro
size_categories:
- 100K<n<1M
language:
- en
---
# GPT Wiki Intro
## Overview
Dataset for training models to classify human written vs GPT/ChatGPT generated text.
This dataset contains Wikip... | 2,626 | [
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BI55/MedText | 2023-07-25T09:30:17.000Z | [
"license:cc-by-4.0",
"region:us"
] | BI55 | null | null | 52 | 389 | 2023-07-25T09:13:09 | ---
license: cc-by-4.0
---
This is the shuffled version of medtext_1, so the datapoints are in random order and not sorted by category. This is to prevent catastrophic forgetting by category.
This is a medical diagnosis dataset containing over 1000 top notch textbook quality patient presentations and diagnosis/treatm... | 4,839 | [
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laion/laion400m | 2023-04-04T06:35:23.000Z | [
"license:cc-by-4.0",
"region:us"
] | laion | null | null | 18 | 388 | 2023-03-28T21:36:09 | ---
license: cc-by-4.0
---
# LAION-400m_new
This datasets has two improvements compared to original LAION_400m dataset:
1. It uses a multilingual text filter to filter out malicious content
2. The better open_clip VitH model was used to detect potential harmful content in the images
All in all, we filtered out arou... | 441 | [
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GATE-engine/COCOStuff164K | 2023-06-26T06:29:49.000Z | [
"region:us"
] | GATE-engine | null | null | 0 | 388 | 2023-06-26T04:56:48 | ---
dataset_info:
features:
- name: image
dtype: image
- name: mask
dtype: image
splits:
- name: val
num_bytes: 2431424833.0
num_examples: 5000
- name: train
num_bytes: 57790292141.76
num_examples: 118287
download_size: 39862772718
dataset_size: 60221716974.76
---
# Dataset Card ... | 472 | [
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CheshireAI/guanaco-unchained | 2023-08-17T00:12:34.000Z | [
"size_categories:1K<n<10K",
"language:en",
"license:apache-2.0",
"region:us"
] | CheshireAI | null | null | 21 | 388 | 2023-07-07T09:40:46 | ---
license: apache-2.0
language:
- en
pretty_name: Guanaco Unchained
size_categories:
- 1K<n<10K
---
# Guanaco Unchained
"Guanaco Unchained" is a refined and optimized version of the original [Guanaco dataset](https://huggingface.co/datasets/timdettmers/openassistant-guanaco). It is specifically curated to maintain h... | 2,392 | [
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vikp/textbook_quality_programming | 2023-10-08T18:36:50.000Z | [
"language:en",
"region:us"
] | vikp | null | null | 138 | 388 | 2023-09-22T16:04:56 | ---
language:
- en
dataset_info:
features:
- name: topic
dtype: string
- name: model
dtype: string
- name: concepts
sequence: string
- name: outline
sequence: string
- name: markdown
dtype: string
splits:
- name: train
num_bytes: 471931604
num_examples: 11650
download_size:... | 1,082 | [
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paulopirozelli/pira | 2023-10-04T13:52:11.000Z | [
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:pt",
"language:en",
"license:cc-by-4.0",
"climate",
"arxiv:2309.10945",
"region:us"
] | paulopirozelli | null | null | 1 | 387 | 2023-09-25T13:14:54 | ---
license: cc-by-4.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
- config_name: mcqa
data_files:
- split: train
path: mcqa/train-*
- split: validation
path: mcqa/validation-*... | 11,596 | [
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zhen-dong-nexusflow/reformatted_singleapi | 2023-10-23T22:19:11.000Z | [
"region:us"
] | zhen-dong-nexusflow | null | null | 0 | 387 | 2023-10-22T23:49:21 | Entry not found | 15 | [
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wmt20_mlqe_task1 | 2023-06-01T14:59:51.000Z | [
"task_categories:translation",
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:translation",
"size_categories:1K<n<10K",
"source_datasets:extended|reddit",
"source_datasets:extended|wikipedia",
"language:de",
"language:... | null | This shared task (part of WMT20) will build on its previous editions
to further examine automatic methods for estimating the quality
of neural machine translation output at run-time, without relying
on reference translations. As in previous years, we cover estimation
at various levels. Important elements introduced thi... | Not available. | 1 | 386 | 2022-03-02T23:29:22 | ---
pretty_name: WMT20 - MultiLingual Quality Estimation (MLQE) Task1
annotations_creators:
- expert-generated
- machine-generated
language_creators:
- found
language:
- de
- en
- et
- ne
- ro
- ru
- si
- zh
license:
- unknown
multilinguality:
- translation
size_categories:
- 1K<n<10K
source_datasets:
- extended|reddit... | 12,887 | [
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0.0231... |
eugenesiow/Set5 | 2022-10-21T03:59:16.000Z | [
"task_categories:other",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"license:other",
"other-image-super-resolution",
"region:us"
] | eugenesiow | Set5 is a evaluation dataset with 5 RGB images for the image super resolution task. | @article{bevilacqua2012low,
title={Low-complexity single-image super-resolution based on nonnegative neighbor embedding},
author={Bevilacqua, Marco and Roumy, Aline and Guillemot, Christine and Alberi-Morel, Marie Line},
year={2012},
publisher={BMVA press}
} | 0 | 386 | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- found
language: []
license:
- other
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- other
task_ids: []
pretty_name: Set5
tags:
- other-image-super-resolution
---
# Dataset Card for Set5
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-0.045166015625... |
seamew/ChnSentiCorp | 2021-06-22T08:58:53.000Z | [
"region:us"
] | seamew | null | null | 19 | 386 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
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0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
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0.0379... |
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 | 8 | 386 | 2022-11-17T23:53:41 | ---
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... | 2,695 | [
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0.00878143310546875,
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newsroom | 2023-04-05T13:35:54.000Z | [
"task_categories:summarization",
"task_ids:news-articles-summarization",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:other",
"region:us"
] | null | NEWSROOM is a large dataset for training and evaluating summarization systems.
It contains 1.3 million articles and summaries written by authors and
editors in the newsrooms of 38 major publications.
Dataset features includes:
- text: Input news text.
- summary: Summary for the news.
And additional features:
- t... | @inproceedings{N18-1065,
author = {Grusky, Max and Naaman, Mor and Artzi, Yoav},
title = {NEWSROOM: A Dataset of 1.3 Million Summaries
with Diverse Extractive Strategies},
booktitle = {Proceedings of the 2018 Conference of the
North American Chapter of the Association for
... | 7 | 384 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- other
multilinguality:
- monolingual
pretty_name: CORNELL NEWSROOM
size_categories:
- unknown
source_datasets:
- original
task_categories:
- summarization
task_ids:
- news-articles-summarization
paperswithcode_i... | 11,776 | [
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0.008... |
roszcz/pianofor-ai-masked-v3 | 2023-10-03T06:40:30.000Z | [
"region:us"
] | roszcz | null | null | 0 | 384 | 2023-10-03T05:13:08 | ---
dataset_info:
features:
- name: pitch
sequence: int8
length: 90
- name: start
sequence: float64
length: 90
- name: dstart
sequence: float64
length: 90
- name: end
sequence: float64
length: 90
- name: duration
sequence: float64
length: 90
- name: velocity
seq... | 1,063 | [
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tasksource/oasst1_pairwise_rlhf_reward | 2023-07-04T17:47:46.000Z | [
"language:en",
"language:es",
"language:ru",
"language:de",
"language:pl",
"language:th",
"language:vi",
"language:sv",
"language:bn",
"language:da",
"language:he",
"language:it",
"language:fa",
"language:sk",
"language:id",
"language:nb",
"language:el",
"language:nl",
"language:... | tasksource | null | null | 19 | 383 | 2023-05-09T09:16:01 | ---
dataset_info:
features:
- name: lang
dtype: string
- name: parent_id
dtype: string
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 40736437
num_examples: 17966
- name: validation
num_bytes: 21... | 2,101 | [
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jigsaw_unintended_bias | 2023-01-25T14:33:20.000Z | [
"task_categories:text-classification",
"task_ids:text-scoring",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc0-1.0",
"toxicity-prediction",
"region:us"
] | null | A collection of comments from the defunct Civil Comments platform that have been annotated for their toxicity. | null | 3 | 381 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc0-1.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
pretty_name: Jigsaw Unintended Bias in Toxicity Classificati... | 9,496 | [
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0.0... |
SetFit/mnli | 2022-02-28T13:53:53.000Z | [
"region:us"
] | SetFit | null | null | 2 | 381 | 2022-03-02T23:29:22 | # Glue MNLI
This dataset is a port of the official [`mnli` dataset](https://huggingface.co/datasets/glue/viewer/mnli/train) on the Hub.
It contains the matched version.
Note that the premise and hypothesis columns have been renamed to text1 and text2 respectively.
Also, the test split is not labeled; the label c... | 349 | [
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... |
MBZUAI/LaMini-instruction | 2023-04-30T11:01:41.000Z | [
"task_categories:text2text-generation",
"size_categories:1M<n<10M",
"language:en",
"license:cc-by-nc-4.0",
"arxiv:2304.14402",
"region:us"
] | MBZUAI | null | null | 104 | 380 | 2023-04-08T07:48:12 | ---
license: cc-by-nc-4.0
task_categories:
- text2text-generation
language:
- en
size_categories:
- 1M<n<10M
dataset_info:
features:
- name: instruction
dtype: string
- name: response
dtype: string
- name: instruction_source
dtype: string
splits:
- name: train
num_bytes: 1162632572
num_e... | 4,784 | [
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tweet_qa | 2022-11-18T21:57:35.000Z | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"arxiv:1907.06292",
"region:us"
... | null | TweetQA is the first dataset for QA on social media data by leveraging news media and crowdsourcing. | @inproceedings{xiong2019tweetqa,
title={TweetQA: A Social Media Focused Question Answering Dataset},
author={Xiong, Wenhan and Wu, Jiawei and Wang, Hong and Kulkarni, Vivek and Yu, Mo and Guo, Xiaoxiao and Chang, Shiyu and Wang, William Yang},
booktitle={Proceedings of the 57th Annual Meeting of the Association f... | 3 | 379 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswithcode_id: tweetqa
pretty_name: TweetQA
data... | 12,245 | [
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0.0051... |
HuggingFaceH4/CodeAlpaca_20K | 2023-03-28T17:26:28.000Z | [
"task_categories:text-generation",
"license:cc",
"region:us"
] | HuggingFaceH4 | null | null | 39 | 379 | 2023-03-28T17:18:25 | ---
license: cc
task_categories:
- text-generation
---
This dataset splits the original [CodeAlpaca dataset](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k) into train and test splits. | 195 | [
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0.06378173828125,
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-0.006011962890625,
-0.0265960693359375,
-0.01122... |
numer_sense | 2022-11-18T21:34:07.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:slot-filling",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other",
"language:en",
"license:mit",
"arxiv:... | null | NumerSense is a new numerical commonsense reasoning probing task, with a diagnostic dataset consisting of 3,145 masked-word-prediction probes.
We propose to study whether numerical commonsense knowledge can be induced from pre-trained language models like BERT, and to what extent this access to knowledge robust agains... | @inproceedings{lin2020numersense,
title={Birds have four legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-trained Language Models},
author={Bill Yuchen Lin and Seyeon Lee and Rahul Khanna and Xiang Ren},
booktitle={Proceedings of EMNLP},
year={2020},
note={to appear}
} | 1 | 378 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other
task_categories:
- text-generation
- fill-mask
task_ids:
- slot-filling
paperswithcode_id: numersense
pretty_name: N... | 7,305 | [
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head_qa | 2023-06-01T14:59:51.000Z | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"language:es",
"license:mit",
"region:us"
] | null | HEAD-QA is a multi-choice HEAlthcare Dataset. The questions come from exams to access a specialized position in the
Spanish healthcare system, and are challenging even for highly specialized humans. They are designed by the Ministerio
de Sanidad, Consumo y Bienestar Social.
The dataset contains questions about the fol... | @inproceedings{vilares-gomez-rodriguez-2019-head,
title = "{HEAD}-{QA}: A Healthcare Dataset for Complex Reasoning",
author = "Vilares, David and
G{\'o}mez-Rodr{\'i}guez, Carlos",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
... | 7 | 376 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- en
- es
license:
- mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
paperswithcode_id: headqa
pretty_name: HEAD-QA
da... | 10,260 | [
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... |
kyujinpy/OpenOrca-KO | 2023-10-12T19:55:47.000Z | [
"task_categories:conversational",
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:table-question-answering",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:summarization",
"task_categories:feature-extra... | kyujinpy | null | null | 10 | 374 | 2023-09-29T15:26:20 | ---
language:
- ko
license: mit
size_categories:
- 10K<n<50K
task_categories:
- conversational
- text-classification
- token-classification
- table-question-answering
- question-answering
- zero-shot-classification
- summarization
- feature-extraction
- text-generation
- text2text-generation
pretty_name: OpenOrca
confi... | 12,488 | [
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0.036468505859375,
0.03759765625,
-0.03265380859375,
-0.0545654296875,
-0.02899169921875,
0.007762... |
bavard/personachat_truecased | 2021-04-23T13:28:30.000Z | [
"region:us"
] | bavard | A version of the PersonaChat dataset that has been true-cased, and also has been given more normalized punctuation.
The original PersonaChat dataset is in all lower case, and has extra space around each clause/sentence separating
punctuation mark. This version of the dataset has more of a natural language look, with se... | @article{zhang2018personalizing,
title={Personalizing dialogue agents: I have a dog, do you have pets too?},
author={Zhang, Saizheng and Dinan, Emily and Urbanek, Jack and Szlam, Arthur and Kiela, Douwe and Weston, Jason},
journal={arXiv preprint arXiv:1801.07243},
year={2018}
} | 24 | 373 | 2022-03-02T23:29:22 | # A More Natural PersonaChat
## Dataset Summary
This dataset is a true-cased version of the PersonaChat dataset by Zhang et al. (2018).
The original PersonaChat dataset is all lower case, and has extra space around each
clause/sentence separating punctuation mark. This version of the dataset has more of a
natural lan... | 3,995 | [
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0.045745849609375,
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0.00896... |
akariasai/PopQA | 2022-12-22T01:01:20.000Z | [
"region:us"
] | akariasai | null | null | 3 | 373 | 2022-12-22T00:37:19 | # Dataset Card for PopQA
## Dataset Summary
PopQA is a large-scale open-domain question answering (QA) dataset, consisting of 14k entity-centric QA pairs. Each question is created by converting a knowledge tuple retrieved from Wikidata using a template. Each question come with the original `subject_entitiey`, `objec... | 1,687 | [
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europarl_bilingual | 2022-11-03T16:31:58.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:translation",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:bg",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
"language... | null | A parallel corpus extracted from the European Parliament web site by Philipp Koehn (University of Edinburgh). The main intended use is to aid statistical machine translation research. | null | 8 | 372 | 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
- nl
- pl
- pt
- ro
- sk
- sl
- sv
license:
- unknown
multilinguality:
- translation
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- translation
task_i... | 59,252 | [
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0.0377... |
OpenAssistant/oasst_top1_2023-08-25 | 2023-08-28T12:44:26.000Z | [
"task_categories:conversational",
"size_categories:10K<n<100K",
"license:apache-2.0",
"region:us"
] | OpenAssistant | null | null | 18 | 372 | 2023-08-28T12:00:02 | ---
license: apache-2.0
task_categories:
- conversational
size_categories:
- 10K<n<100K
---
# OpenAssistant TOP-1 Conversation Threads
- [Guanacco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) style export of the best conversation threads from the [open-assistant.io](https://open-assistant.io/) d... | 512 | [
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arabic_speech_corpus | 2022-11-18T18:29:09.000Z | [
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ar",
"license:cc-by-4.0",
"region:us"
] | null | This Speech corpus has been developed as part of PhD work carried out by Nawar Halabi at the University of Southampton.
The corpus was recorded in south Levantine Arabic
(Damascian accent) using a professional studio. Synthesized speech as an output using this corpus has produced a high quality, natural voice.
Note tha... | @phdthesis{halabi2016modern,
title={Modern standard Arabic phonetics for speech synthesis},
author={Halabi, Nawar},
year={2016},
school={University of Southampton}
} | 17 | 371 | 2022-03-02T23:29:22 | ---
pretty_name: Arabic Speech Corpus
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- ar
license:
- cc-by-4.0
multilinguality:
- monolingual
paperswithcode_id: arabic-speech-corpus
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- automatic-speech-recognit... | 9,622 | [
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distil-whisper/librispeech_asr | 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--... | 0 | 367 | 2023-03-29T12:53:48 | ---
license: cc-by-4.0
task_categories:
- automatic-speech-recognition
language:
- en
-pretty_name: LibriSpeech ASR
---
# Distil Whisper: LibriSpeech ASR
This is a variant of the [LibriSpeech ASR](https://huggingface.co/datasets/librispeech_asr) dataset, augmented to return the pseudo-labelled Whisper
Transcription... | 2,047 | [
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arxiv_dataset | 2023-10-26T10:45:45.000Z | [
"task_categories:translation",
"task_categories:summarization",
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:explanation-generation",
"task_ids:fact-checking-retrieval",
"task_ids:text-simplification",
"annotations_creators:no-annota... | null | A dataset of 1.7 million arXiv articles for applications like trend analysis, paper recommender engines, category prediction, co-citation networks, knowledge graph construction and semantic search interfaces. | @misc{clement2019arxiv,
title={On the Use of ArXiv as a Dataset},
author={Colin B. Clement and Matthew Bierbaum and Kevin P. O'Keeffe and Alexander A. Alemi},
year={2019},
eprint={1905.00075},
archivePrefix={arXiv},
primaryClass={cs.IR}
} | 38 | 366 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- en
license:
- cc0-1.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- translation
- summarization
- text-retrieval
task_ids:
- document-retrieval
- entity-linking-retriev... | 7,254 | [
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taesiri/imagenet-hard | 2023-06-16T18:50:51.000Z | [
"task_categories:image-classification",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"OOD",
"ImageNet",
"Out Of Distribution",
"arxiv:2304.05538",
"region:us"
] | taesiri | null | null | 7 | 365 | 2023-03-31T05:48:23 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
sequence: int64
- name: origin
dtype: string
- name: english_label
sequence: string
splits:
- name: validation
num_bytes: 1771418938.94
num_examples: 10980
download_size: 6380094503
dataset_size: 1771418938.94... | 36,363 | [
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findnitai/english-to-hinglish | 2023-06-21T05:02:50.000Z | [
"task_categories:translation",
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:hi",
"language:en",
"license:apache-2.0",
"region:us"
] | findnitai | null | null | 5 | 365 | 2023-06-21T04:21:28 | ---
license: apache-2.0
task_categories:
- translation
- text-generation
language:
- hi
- en
size_categories:
- 10K<n<100K
pretty_name: Hinglish
---
English to Hinglish Dataset aggregated from publicly available datasources.
Sources:
1. Hinglish TOP Dataset
2. CMU English Dog
3. HinGE
4. PHINC
source : 1 - Human Ann... | 367 | [
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result-kand2-sdxl-wuerst-karlo/46328984 | 2023-09-14T18:58:10.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 365 | 2023-09-14T18:58:09 | ---
dataset_info:
features:
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splits:
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num_bytes: 209
num_examples: 10
download_size: 1390
dataset_size: 209
configs:
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data_files:
- split: train
path: data/train-*
---
# Dataset Card for "4632898... | 455 | [
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result-kand2-sdxl-wuerst-karlo/b5ddd948 | 2023-09-15T04:06:31.000Z | [
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] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 365 | 2023-09-15T04:06:30 | ---
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---
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result-kand2-sdxl-wuerst-karlo/323c0619 | 2023-09-15T06:43:16.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 365 | 2023-09-15T06:43:16 | ---
dataset_info:
features:
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splits:
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---
# Dataset Card for "323c061... | 455 | [
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result-kand2-sdxl-wuerst-karlo/f0cdf5c4 | 2023-09-15T09:18:20.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 365 | 2023-09-15T09:18:19 | ---
dataset_info:
features:
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- name: id
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splits:
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num_bytes: 207
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download_size: 1427
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configs:
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data_files:
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path: data/train-*
---
# Dataset Card for "f0cdf5c... | 455 | [
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result-kand2-sdxl-wuerst-karlo/d6e12779 | 2023-09-15T09:41:14.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 365 | 2023-09-15T09:41:13 | ---
dataset_info:
features:
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dtype: string
- name: id
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splits:
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num_bytes: 208
num_examples: 10
download_size: 1403
dataset_size: 208
configs:
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data_files:
- split: train
path: data/train-*
---
# Dataset Card for "d6e1277... | 455 | [
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HAERAE-HUB/csatqa | 2023-09-10T17:12:24.000Z | [
"task_categories:multiple-choice",
"language:ko",
"region:us"
] | HAERAE-HUB | CSAT-QA | \ | 6 | 364 | 2023-07-13T05:41:47 | ---
dataset_info:
features:
- name: test_name
dtype: string
- name: question_number
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- name: context
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englert-m/reconstruction | 2023-10-30T12:47:01.000Z | [
"region:us"
] | englert-m | null | null | 0 | 364 | 2023-10-10T03:37:34 | ---
dataset_info:
features:
- name: orig
dtype: uint32
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yuchenlin/i-Mind2Web | 2023-10-13T09:41:53.000Z | [
"language:en",
"license:mit",
"region:us"
] | yuchenlin | null | null | 0 | 363 | 2023-10-10T21:45:04 | ---
license: mit
language:
- en
configs:
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data_files:
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path: K=10/test_mini.json
- split: test_all
path: K=10/test_all.json
- split: dev
path: K=10/dev.json
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