id stringlengths 2 115 | lastModified stringlengths 24 24 | tags sequence | 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 sequence |
|---|---|---|---|---|---|---|---|---|---|---|---|
argilla/databricks-dolly-15k-curated-en | 2023-10-02T12:32:53.000Z | [
"language:en",
"region:us"
] | argilla | null | null | 16 | 8,886,568 | 2023-05-30T09:54:44 | ---
language:
- en
---
## Guidelines
In this dataset, you will find a collection of records that show a category, an instruction, a context and a response to that instruction. The aim of the project is to correct the instructions, intput and responses to make sure they are of the highest quality and that they match t... | 3,002 | [
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truthful_qa | 2023-06-09T14:18:13.000Z | [
"task_categories:multiple-choice",
"task_categories:text-generation",
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"task_ids:language-modeling",
"task_ids:open-domain-qa",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monoling... | null | TruthfulQA is a benchmark to measure whether a language model is truthful in
generating answers to questions. The benchmark comprises 817 questions that
span 38 categories, including health, law, finance and politics. Questions are
crafted so that some humans would answer falsely due to a false belief or
misconception.... | @misc{lin2021truthfulqa,
title={TruthfulQA: Measuring How Models Mimic Human Falsehoods},
author={Stephanie Lin and Jacob Hilton and Owain Evans},
year={2021},
eprint={2109.07958},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 73 | 3,784,469 | 2022-06-08T14:44:06 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: TruthfulQA
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- multiple-choice
- text-generation
- question-answering
task_ids:
- multipl... | 9,365 | [
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0.... |
cais/mmlu | 2023-10-07T11:24:05.000Z | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"arxiv:2009.03300",
"arxiv:2005.... | cais | This is a massive multitask test consisting of multiple-choice questions from various branches of knowledge, covering 57 tasks including elementary mathematics, US history, computer science, law, and more. | @article{hendryckstest2021,
title={Measuring Massive Multitask Language Understanding},
author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt},
journal={Proceedings of the International Conference on Learning Representations (ICLR)}... | 92 | 1,500,832 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
paperswithcode_id: mmlu
pretty_name: Measuring Massi... | 39,677 | [
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glue | 2023-06-01T14:59:59.000Z | [
"task_categories:text-classification",
"task_ids:acceptability-classification",
"task_ids:natural-language-inference",
"task_ids:semantic-similarity-scoring",
"task_ids:sentiment-classification",
"task_ids:text-scoring",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monol... | null | GLUE, the General Language Understanding Evaluation benchmark
(https://gluebenchmark.com/) is a collection of resources for training,
evaluating, and analyzing natural language understanding systems. | @inproceedings{wang2019glue,
title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
note={In the Proceedings of ICLR.},
year={2019}
} | 245 | 1,428,634 | 2022-03-02T23:29:22 | ---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- acceptability-classification
- natural-language-inference
- semantic-similarity-sco... | 27,887 | [
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poloclub/diffusiondb | 2023-05-09T19:00:45.000Z | [
"task_categories:text-to-image",
"task_categories:image-to-text",
"task_ids:image-captioning",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:n>1T",
"source_datasets:original",
"language:en",
"license:cc0-1.0",
"stable diffusion"... | poloclub | DiffusionDB is the first large-scale text-to-image prompt dataset. It contains 2
million images generated by Stable Diffusion using prompts and hyperparameters
specified by real users. The unprecedented scale and diversity of this
human-actuated dataset provide exciting research opportunities in understanding
the inter... | @article{wangDiffusionDBLargescalePrompt2022,
title = {{{DiffusionDB}}: {{A}} Large-Scale Prompt Gallery Dataset for Text-to-Image Generative Models},
author = {Wang, Zijie J. and Montoya, Evan and Munechika, David and Yang, Haoyang and Hoover, Benjamin and Chau, Duen Horng},
year = {2022},
journal = {arXiv:221... | 323 | 1,069,360 | 2022-10-25T02:25:28 | ---
layout: default
title: Home
nav_order: 1
has_children: false
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license:
- cc0-1.0
multilinguality:
- multilingual
pretty_name: DiffusionDB
size_categories:
- n>1T
source_datasets:
- original
tags:
- stable diffusion
- prompt engineering
... | 24,582 | [
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squad_v2 | 2023-04-05T13:40:44.000Z | ["task_categories:question-answering","task_ids:open-domain-qa","task_ids:extractive-qa","annotation(...TRUNCATED) | null | "combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversar(...TRUNCATED) | "@article{2016arXiv160605250R,\n author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev}(...TRUNCATED) | 90 | 1,054,465 | 2022-03-02T23:29:22 | "---\npretty_name: SQuAD2.0\nannotations_creators:\n- crowdsourced\nlanguage_creators:\n- crowdsourc(...TRUNCATED) | 8,016 | [[-0.046173095703125,-0.043670654296875,0.005634307861328125,0.0204010009765625,-0.00832366943359375(...TRUNCATED) |
super_glue | 2023-04-05T13:41:04.000Z | ["task_categories:text-classification","task_categories:token-classification","task_categories:quest(...TRUNCATED) | null | "SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set o(...TRUNCATED) | "@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language (...TRUNCATED) | 117 | 824,558 | 2022-03-02T23:29:22 | "---\nannotations_creators:\n- expert-generated\nlanguage_creators:\n- other\nlanguage:\n- en\nlicen(...TRUNCATED) | 14,813 | [[-0.042449951171875,-0.04705810546875,0.0084228515625,-0.0011720657348632812,-0.00946044921875,-0.0(...TRUNCATED) |
lighteval/mmlu | 2023-06-09T16:36:19.000Z | ["task_categories:question-answering","task_ids:multiple-choice-qa","annotations_creators:no-annotat(...TRUNCATED) | lighteval | "This is a massive multitask test consisting of multiple-choice questions from various branches of k(...TRUNCATED) | "@article{hendryckstest2021,\n title={Measuring Massive Multitask Language Understanding},\n (...TRUNCATED) | 6 | 578,067 | 2023-05-16T09:39:28 | "---\nannotations_creators:\n- no-annotation\nlanguage_creators:\n- expert-generated\nlanguage:\n- e(...TRUNCATED) | 39,677 | [[-0.03997802734375,-0.0457763671875,0.0215301513671875,0.00342559814453125,0.004791259765625,0.0075(...TRUNCATED) |
wikitext | 2023-06-20T07:52:10.000Z | ["task_categories:text-generation","task_categories:fill-mask","task_ids:language-modeling","task_id(...TRUNCATED) | null | " The WikiText language modeling dataset is a collection of over 100 million tokens extracted from t(...TRUNCATED) | "@misc{merity2016pointer,\n title={Pointer Sentinel Mixture Models},\n author={Stephen Mer(...TRUNCATED) | 198 | 575,928 | 2022-03-02T23:29:22 | "---\nannotations_creators:\n- no-annotation\nlanguage_creators:\n- crowdsourced\nlanguage:\n- en\nl(...TRUNCATED) | 9,573 | [[-0.044677734375,-0.038116455078125,0.01137542724609375,0.0172271728515625,-0.010040283203125,-0.00(...TRUNCATED) |
HuggingFaceM4/COCO | 2022-12-15T15:51:03.000Z | [
"license:cc-by-4.0",
"arxiv:1405.0312",
"region:us"
] | HuggingFaceM4 | "MS COCO is a large-scale object detection, segmentation, and captioning dataset.\nCOCO has several (...TRUNCATED) | "@article{DBLP:journals/corr/LinMBHPRDZ14,\n author = {Tsung{-}Yi Lin and\n Michae(...TRUNCATED) | 8 | 438,316 | 2022-12-14T21:13:57 | "---\nlicense: cc-by-4.0\n---\n\n# Dataset Card for [Dataset Name]\n\n## Table of Contents\n- [Table(...TRUNCATED) | 3,660 | [[-0.035552978515625,-0.047760009765625,-0.005481719970703125,0.030731201171875,-0.0199737548828125,(...TRUNCATED) |
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