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5275a96cea765dc3bbfd41e6034485791704ed69 | https://github.com/google-deepmind/logical-entailment-dataset
```
@inproceedings{
evans2018can,
title={Can Neural Networks Understand Logical Entailment?},
author={Richard Evans and David Saxton and David Amos and Pushmeet Kohli and Edward Grefenstette},
booktitle={International Conference on Learning Representations},
year={2018},
url={https://openreview.net/forum?id=SkZxCk-0Z},
}
```
| tasksource/logical-entailment | [
"license:apache-2.0",
"region:us"
]
| 2023-01-21T15:27:45+00:00 | {"license": "apache-2.0", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "E", "dtype": "int64"}, {"name": "H1", "dtype": "int64"}, {"name": "H2", "dtype": "int64"}, {"name": "H3", "dtype": "int64"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9803153, "num_examples": 99876}, {"name": "test", "num_bytes": 550241, "num_examples": 5000}, {"name": "validation", "num_bytes": 548346, "num_examples": 5000}], "download_size": 2505053, "dataset_size": 10901740}} | 2024-01-19T09:37:11+00:00 |
254ac58b7ce1716a32f244b80e390359e201aadc |
# PVC figure products dataset
This dataset contains product information of figure images scraped from multiple Web sites.
## Dataset information
|Subset|Source|Size|
|-|-|-|
|`goodsmile-figma`|https://www.goodsmile.info/ja/products/category/figma/announced/2023|947|
|`goodsmile-nendoroid`|https://www.goodsmile.info/ja/products/category/nendoroid_series/announced/2023|3378|
|`goodsmile-scale`|https://www.goodsmile.info/ja/products/category/scale/announced/2023|2203|
|`kotobukiya`|https://www.kotobukiya.co.jp/en/product/figures/|864|
|`myethos`|http://www.myethos.cn/Collection|95|
|`spiritale`|https://spiritale.jp/shop/c/csallitem/|21|
|`tokyofigures`|https://tokyofigure.jp/products/list.php|394| | p1atdev/pvc | [
"size_categories:1K<n<10K",
"language:en",
"language:ja",
"license:cc0-1.0",
"region:us"
]
| 2023-01-21T16:12:04+00:00 | {"language": ["en", "ja"], "license": "cc0-1.0", "size_categories": ["1K<n<10K"], "dataset_info": [{"config_name": "goodsmile-figma", "features": [{"name": "id", "dtype": "string"}, {"name": "image_urls", "sequence": "string"}, {"name": "details", "struct": [{"name": "", "dtype": "string"}, {"name": "Bag Design Assistance", "dtype": "string"}, {"name": "Booklet Design", "dtype": "string"}, {"name": "CG Coloring", "dtype": "string"}, {"name": "Category", "dtype": "string"}, {"name": "Character Design/Illustration", "dtype": "string"}, {"name": "Cooperation", "dtype": "string"}, {"name": "Dengekiya Exclusive Product", "dtype": "string"}, {"name": "Design Cooperation", "dtype": "string"}, {"name": "Distributed by", "dtype": "string"}, {"name": "Distributor", "dtype": "string"}, {"name": "First Orders Release Date", "dtype": "string"}, {"name": "First Release Extra", "dtype": "string"}, {"name": "GOODSMILE RACING Personal Sponsor Bonus", "dtype": "string"}, {"name": "GOODSMILE Racing Personal Sponsor Bonus", "dtype": "string"}, {"name": "Good Smile Kuji Hatsune Miku 2014 Spring Ver. - B Prize", "dtype": "string"}, {"name": "Good Smile Racing 2017 Personal Sponsor Bonus", "dtype": "string"}, {"name": "Good Smile Racing Personal Sponsor Bonus", "dtype": "string"}, {"name": "Illustrated by", "dtype": "string"}, {"name": "Included with the 'Limited Edition Contract BOX'", "dtype": "string"}, {"name": "Included with the Fate/Extra CCC TYPE-MOON Virgin White Box", "dtype": "string"}, {"name": "Included with the Japanese 'GRAVITY DAZE Collector's Edition'.", "dtype": "string"}, {"name": "Included with the limited edition 37th volume of Berserk.", "dtype": "string"}, {"name": "LTD", "dtype": "string"}, {"name": "Limited Edition Extra", "dtype": "string"}, {"name": "Manufacturer", "dtype": "string"}, {"name": "Manufacturing Cooperation", "dtype": "string"}, {"name": "Model Data", "dtype": "string"}, {"name": "Originally released in March 2017 with a rerelease in June 2021.", "dtype": "string"}, {"name": "Originally released in May 2021 with a rerelease in July 2024.", "dtype": "string"}, {"name": "Outfit Design/Production", "dtype": "string"}, {"name": "Outfit/Pattern Design", "dtype": "string"}, {"name": "Painted ABS&PVC non-scale articulated figure with stand included. Approximately 165mm in height", "dtype": "string"}, {"name": "Painted ABS&PVC posable figure - not to scale - approximately 150mm in height", "dtype": "string"}, {"name": "Paintowork", "dtype": "string"}, {"name": "Paintwork", "dtype": "string"}, {"name": "Photography", "dtype": "string"}, {"name": "Photography Assistance", "dtype": "string"}, {"name": "Planning", "dtype": "string"}, {"name": "Planning Assistance", "dtype": "string"}, {"name": "Planning/Cooperation", "dtype": "string"}, {"name": "Planning/Production", "dtype": "string"}, {"name": "Planning/Production Assistance", "dtype": "string"}, {"name": "Planning/Production Assitance", "dtype": "string"}, {"name": "Price", "dtype": "string"}, {"name": "Product Name", "dtype": "string"}, {"name": "Production Cooperation", "dtype": "string"}, {"name": "Production/Distributed by", "dtype": "string"}, {"name": "Production/Production", "dtype": "string"}, {"name": "Production/Sculpting", "dtype": "string"}, {"name": "Purchase Info", "dtype": "string"}, {"name": "Redesign by IZMOJUKI / Design Cooperation", "dtype": "string"}, {"name": "Release Date", "dtype": "string"}, {"name": "Release Info", "dtype": "string"}, {"name": "Release/Manufacturing/Distribution", "dtype": "string"}, {"name": "Released by", "dtype": "string"}, {"name": "Released by/Production Cooperation", "dtype": "string"}, {"name": "Released in April 2012 with a rerelease in October 2012.", "dtype": "string"}, {"name": "Released/Distributed by", "dtype": "string"}, {"name": "Rerelease Info", "dtype": "string"}, {"name": "Resale", "dtype": "string"}, {"name": "Resale Info", "dtype": "string"}, {"name": "Sales", "dtype": "string"}, {"name": "Sales Info", "dtype": "string"}, {"name": "Sales/Manufacturing/Distribution", "dtype": "string"}, {"name": "Sculpting / Manufacturing", "dtype": "string"}, {"name": "Sculpting Cooperation", "dtype": "string"}, {"name": "Sculpting/Paintwork", "dtype": "string"}, {"name": "Sculpting/Production/Released by", "dtype": "string"}, {"name": "Sculpting/Released by", "dtype": "string"}, {"name": "Sculpting/Sold By", "dtype": "string"}, {"name": "Sculptor", "dtype": "string"}, {"name": "Sculptor/Paintwork", "dtype": "string"}, {"name": "Second Orders Release Date", "dtype": "string"}, {"name": "Series", "dtype": "string"}, {"name": "Set Contents", "dtype": "string"}, {"name": "Sold By", "dtype": "string"}, {"name": "Sold by", "dtype": "string"}, {"name": "Sold/Distributed by", "dtype": "string"}, {"name": "Sold/Released by", "dtype": "string"}, {"name": "Specifications", "dtype": "string"}, {"name": "Speicifications", "dtype": "string"}, {"name": "Summer Wonder Festival 2017 Product", "dtype": "string"}, {"name": "Summer Wonder Festival 2018 Product", "dtype": "string"}, {"name": "WONDERFUL HOBBY LIFE FOR YOU!!32 Product", "dtype": "string"}, {"name": "Winter Wonder Festival 2018 Product", "dtype": "string"}, {"name": "Wonder Festival 2011 (Summer) Product", "dtype": "string"}, {"name": "Wonder Festival 2011 (Winter) Product", "dtype": "string"}, {"name": "Wonder Festival 2012 (Summer) Product", "dtype": "string"}, {"name": "Wonder Festival 2012 (Winter) Product", "dtype": "string"}, {"name": "Wonder Festival 2013 (Summer) Product", "dtype": "string"}, {"name": "Wonder Festival 2013 (Winter) Product", "dtype": "string"}, {"name": "Wonder Festival 2014 (Summer) Web Sales Product", "dtype": "string"}, {"name": "Wonder Festival 2014 (Winter) Limited Edition Product", "dtype": "string"}, {"name": "Wonder Festival 2015 (Summer) Product", "dtype": "string"}, {"name": "Wonder Festival 2015 (Winter) Product", "dtype": "string"}, {"name": "Wonder Festival 2016 (Summer) Product", "dtype": "string"}, {"name": "Wonder Festival 2016 (Winter) Product", "dtype": "string"}, {"name": "Wonder Festival 2019 Summer Product", "dtype": "string"}, {"name": "Wonder Festival 2019 Winter Product", "dtype": "string"}, {"name": "Wonder Festival 2020 Winter Product", "dtype": "string"}, {"name": "Wonder Festival Summer 2009 Product", "dtype": "string"}, {"name": "ebten Product", "dtype": "string"}, {"name": "figma Production", "dtype": "string"}, {"name": "figma Specifications", "dtype": "string"}, {"name": "\u30ef\u30f3\u30c0\u30fc\u30d5\u30a7\u30b9\u30c6\u30a3\u30d0\u30eb 2012\uff3b\u590f\uff3d\u8ca9\u58f2\u5546\u54c1", "dtype": "string"}, {"name": "\u4f01\u5283\u88fd\u4f5c", "dtype": "string"}, {"name": "\u4f01\u753b\u30fb\u5236\u4f5c\u5354\u529b", "dtype": "string"}, {"name": "\u4fa1\u683c", "dtype": "string"}, {"name": "\u518d\u8ca9", "dtype": "string"}, {"name": "\u518d\u8ca9\u4fa1\u683c", "dtype": "string"}, {"name": "\u518d\u8ca9\uff1a\u518d\u51fa\u8377", "dtype": "string"}, {"name": "\u539f\u578b\u5236\u4f5c\u30fb\u767a\u58f2\u5143", "dtype": "string"}, {"name": "\u767a\u58f2\u30fb\u88fd\u9020\u30fb\u8ca9\u58f2\u5143", "dtype": "string"}, {"name": "\u8ca9\u58f2\u65b9\u6cd5", "dtype": "string"}]}, {"name": "title", "dtype": "string"}, {"name": "category", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1447190, "num_examples": 947}], "download_size": 409156, "dataset_size": 1447190}, {"config_name": "goodsmile-nendoroid", "features": [{"name": "image_urls", "sequence": "string"}, {"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "details", "struct": [{"name": "", "dtype": "string"}, {"name": "*Event/GOODSMILE ONLINE SHOP Exclusive.", "dtype": "string"}, {"name": "2012 Release Price", "dtype": "string"}, {"name": "Accessories", "dtype": "string"}, {"name": "Ages", "dtype": "string"}, {"name": "Available From", "dtype": "string"}, {"name": "Batteries", "dtype": "string"}, {"name": "Bonus Parts", "dtype": "string"}, {"name": "Category", "dtype": "string"}, {"name": "Characters", "dtype": "string"}, {"name": "Chest - 47cm/M - Length - 65cm", "dtype": "string"}, {"name": "Chest - 49cm/L - Length - 69cm", "dtype": "string"}, {"name": "Chest - 52cm/XL - Length - 73cm", "dtype": "string"}, {"name": "Chest - 55cm", "dtype": "string"}, {"name": "Colouring Design", "dtype": "string"}, {"name": "Cooperation", "dtype": "string"}, {"name": "Costume/Pattern Planning", "dtype": "string"}, {"name": "Costume/Pattern Production", "dtype": "string"}, {"name": "Delivery will be in late October 2011.", "dtype": "string"}, {"name": "Design", "dtype": "string"}, {"name": "Design/Illust", "dtype": "string"}, {"name": "Disitributed by", "dtype": "string"}, {"name": "Distributed by", "dtype": "string"}, {"name": "Distributed/Released by", "dtype": "string"}, {"name": "Distributer", "dtype": "string"}, {"name": "Distribution", "dtype": "string"}, {"name": "Distributor", "dtype": "string"}, {"name": "Editing", "dtype": "string"}, {"name": "Event Exclusive Product", "dtype": "string"}, {"name": "Event Price", "dtype": "string"}, {"name": "Event Product / GSC Online Shop Product", "dtype": "string"}, {"name": "Event Sales Product", "dtype": "string"}, {"name": "Event/GSC Online Shop Product. (More details below)", "dtype": "string"}, {"name": "Exclusive to the Good Smile x Karaoke no Tetsujin Caf\u00e9 and GOOD SMILE ONLINE SHOP.", "dtype": "string"}, {"name": "Extras", "dtype": "string"}, {"name": "Figure", "dtype": "string"}, {"name": "Figure Specifications", "dtype": "string"}, {"name": "GOOD SMILE ONLINE SHOP Exclusive Product", "dtype": "string"}, {"name": "GOOD SMILE ONLINE SHOP Product", "dtype": "string"}, {"name": "GOODSMILE Racing Personal Sponsor Bonus", "dtype": "string"}, {"name": "GSC Lottery - Hatsune Miku 2012 Winter Ver. - A Prize", "dtype": "string"}, {"name": "GSC Lottery Hatsune Miku 2012 Winter Ver. - B Prize", "dtype": "string"}, {"name": "GSC Lottery Hatsune Miku 2012 Winter Ver. - C Prize", "dtype": "string"}, {"name": "GSC Lottery Hatsune Miku 2012 Winter Ver. - Last Draw Prize", "dtype": "string"}, {"name": "GSC Online Rerelease", "dtype": "string"}, {"name": "GSC Online Shop Rerelease", "dtype": "string"}, {"name": "Good Smile Kuji Hatsune Miku 2014 Spring Ver. - A Prize", "dtype": "string"}, {"name": "Good Smile Kuji Hatsune Miku 2014 Spring Ver. - LAST Prize", "dtype": "string"}, {"name": "Good Smile Racing 2017 Personal Sponsor Bonus", "dtype": "string"}, {"name": "Happy Kuji", "dtype": "string"}, {"name": "Happy Lots Miku Hatsune", "dtype": "string"}, {"name": "Included in the Bakemonogatari Premium Item BOX due for release on the 21st November 2013", "dtype": "string"}, {"name": "Included in the Limited Box of the PlayStation\u00ae4/PlayStation\u00ae3 Game 'BLAZBLUE CENTRALFICTION'", "dtype": "string"}, {"name": "Included with 'Space Brothers' Volume 27 on sale from the 20th November 2015.", "dtype": "string"}, {"name": "Included with the 'Saki Achiga-hen episode of side - A Blu-ray Limited First Edition Special BOX.", "dtype": "string"}, {"name": "Included with the Limited Edition 18th Volume 'Attack on Titan' Manga (Japanese Version)", "dtype": "string"}, {"name": "Included with the Limited Edition Yuru Yuri San Hai! 6th Volume Blu-ray", "dtype": "string"}, {"name": "Included with the Limited Edition of the Milky Holmes 2 PSP Game", "dtype": "string"}, {"name": "Included with the Limited First Edition of the 'PARTY TIME' Album.", "dtype": "string"}, {"name": "Included with the Monster Hunter Frontier G Five Million Hunters Memorial Goods", "dtype": "string"}, {"name": "Included with the Nisemonogatari Premium Item BOX", "dtype": "string"}, {"name": "Manufacturer", "dtype": "string"}, {"name": "Manufacturing", "dtype": "string"}, {"name": "Manufacturing Assistance", "dtype": "string"}, {"name": "Mini 4WD Specs", "dtype": "string"}, {"name": "Minimum Requirements", "dtype": "string"}, {"name": "Nendoroid Petite Specs", "dtype": "string"}, {"name": "Only 1000 Nendoroids will be available for winners of the 'Torarete! Hobby Channel' lot raws.", "dtype": "string"}, {"name": "Original Price", "dtype": "string"}, {"name": "Original release", "dtype": "string"}, {"name": "Originally released in April 2020 with a rerelease in November 2023.", "dtype": "string"}, {"name": "Originally released in February 2023 with a rerelease in May 2024.", "dtype": "string"}, {"name": "Originally released in May 2019 with a rerelease in July 2021.", "dtype": "string"}, {"name": "Outfit Design", "dtype": "string"}, {"name": "Outfit/Pattern Design", "dtype": "string"}, {"name": "Outfit/Pattern Planning", "dtype": "string"}, {"name": "Paintwork", "dtype": "string"}, {"name": "Paintwork Assistance", "dtype": "string"}, {"name": "Paintwork Cooperation", "dtype": "string"}, {"name": "Part of the Monster Hunter Frontier G 2014 Anniversary Premium Goods", "dtype": "string"}, {"name": "Photography", "dtype": "string"}, {"name": "Planning", "dtype": "string"}, {"name": "Planning Assistance", "dtype": "string"}, {"name": "Planning Cooperation", "dtype": "string"}, {"name": "Planning/Manufacturing", "dtype": "string"}, {"name": "Planning/Prodcution/Manufacturing Assistance", "dtype": "string"}, {"name": "Planning/Production", "dtype": "string"}, {"name": "Planning/Productions", "dtype": "string"}, {"name": "Planning/Prouction", "dtype": "string"}, {"name": "Planning/Sculpt", "dtype": "string"}, {"name": "Planning/Sculpting", "dtype": "string"}, {"name": "Platform", "dtype": "string"}, {"name": "Please Note", "dtype": "string"}, {"name": "Pose Concepts", "dtype": "string"}, {"name": "Price", "dtype": "string"}, {"name": "Produced and Released by", "dtype": "string"}, {"name": "Produced by", "dtype": "string"}, {"name": "Produced/Released by", "dtype": "string"}, {"name": "Product Name", "dtype": "string"}, {"name": "Production", "dtype": "string"}, {"name": "Production Assistance", "dtype": "string"}, {"name": "Production Assitance", "dtype": "string"}, {"name": "Production Cooperation", "dtype": "string"}, {"name": "Production/Released By", "dtype": "string"}, {"name": "Production/Released by", "dtype": "string"}, {"name": "Production/Sold By", "dtype": "string"}, {"name": "Prosuction Assistance", "dtype": "string"}, {"name": "Re-release Date", "dtype": "string"}, {"name": "Release Date", "dtype": "string"}, {"name": "Release Dates", "dtype": "string"}, {"name": "Release Details", "dtype": "string"}, {"name": "Release Info", "dtype": "string"}, {"name": "Release info", "dtype": "string"}, {"name": "Released by", "dtype": "string"}, {"name": "Released by/Sculpted by", "dtype": "string"}, {"name": "Released/Distributed by", "dtype": "string"}, {"name": "Released/Sold by", "dtype": "string"}, {"name": "Rerelease Price", "dtype": "string"}, {"name": "Resale", "dtype": "string"}, {"name": "Resale Info", "dtype": "string"}, {"name": "Resale info", "dtype": "string"}, {"name": "Retailers", "dtype": "string"}, {"name": "SNOW MIKU for SAPPORO2011 and Wonder Festival 2011 (Winter) Product", "dtype": "string"}, {"name": "Sales", "dtype": "string"}, {"name": "Sales Agency", "dtype": "string"}, {"name": "Sales Agent", "dtype": "string"}, {"name": "Sales Info", "dtype": "string"}, {"name": "Sculpor", "dtype": "string"}, {"name": "Sculpted/Released by", "dtype": "string"}, {"name": "Sculpting", "dtype": "string"}, {"name": "Sculpting Assistance", "dtype": "string"}, {"name": "Sculpting/Cooperation", "dtype": "string"}, {"name": "Sculpting/Paintwork", "dtype": "string"}, {"name": "Sculpting/Production", "dtype": "string"}, {"name": "Sculpting/Production/Sold By", "dtype": "string"}, {"name": "Sculpting/Released by", "dtype": "string"}, {"name": "Sculpting/Released by FREEing", "dtype": "string"}, {"name": "Sculptor", "dtype": "string"}, {"name": "Sculptor/Cooperation", "dtype": "string"}, {"name": "Sculptor/Paintwork", "dtype": "string"}, {"name": "Sculptor/Production/Sold By", "dtype": "string"}, {"name": "Scultping/Released by", "dtype": "string"}, {"name": "Second Rerelease Price", "dtype": "string"}, {"name": "Series", "dtype": "string"}, {"name": "Shinnichi Premium Store Limited Edition Product", "dtype": "string"}, {"name": "Size", "dtype": "string"}, {"name": "Sizes", "dtype": "string"}, {"name": "Snow Miku 2013 Outfit Design", "dtype": "string"}, {"name": "Sold At", "dtype": "string"}, {"name": "Sold and Released by", "dtype": "string"}, {"name": "Sold at", "dtype": "string"}, {"name": "Sold by", "dtype": "string"}, {"name": "Sold by/Distributor", "dtype": "string"}, {"name": "Sold/Released by", "dtype": "string"}, {"name": "Specification", "dtype": "string"}, {"name": "Specifications", "dtype": "string"}, {"name": "Stores", "dtype": "string"}, {"name": "Summer Wonder Festival 2017 Product", "dtype": "string"}, {"name": "Summer Wonder Festival 2018 Product", "dtype": "string"}, {"name": "Supervision", "dtype": "string"}, {"name": "TYPE-MOON Fes\u8ca9\u58f2\u5546\u54c1", "dtype": "string"}, {"name": "Target age", "dtype": "string"}, {"name": "WONDERFUL HOBBY LIFE FOR YOU!!32 Product", "dtype": "string"}, {"name": "Winter Wonder Festival 2018 Product", "dtype": "string"}, {"name": "Wonder Festival 2006 Winter Product", "dtype": "string"}, {"name": "Wonder Festival 2011 (Summer) Product", "dtype": "string"}, {"name": "Wonder Festival 2011 (Winter) Product", "dtype": "string"}, {"name": "Wonder Festival 2011 Product", "dtype": "string"}, {"name": "Wonder Festival 2012 (Summer) Product.", "dtype": "string"}, {"name": "Wonder Festival 2012 (Winter) Product", "dtype": "string"}, {"name": "Wonder Festival 2013 (Winter) Product", "dtype": "string"}, {"name": "Wonder Festival 2014 (Winter) Limited Edition Product", "dtype": "string"}, {"name": "Wonder Festival 2014 Presale Product", "dtype": "string"}, {"name": "Wonder Festival 2015 (Summer) Product", "dtype": "string"}, {"name": "Wonder Festival 2015 (Winter) Product", "dtype": "string"}, {"name": "Wonder Festival 2016 (Summer) Product", "dtype": "string"}, {"name": "Wonder Festival 2016 (Winter) Product", "dtype": "string"}, {"name": "Wonder Festival 2016 (Winter/Summer) Product", "dtype": "string"}, {"name": "Wonder Festival 2019 Summer Product", "dtype": "string"}, {"name": "Wonder Festival 2019 Winter Product", "dtype": "string"}, {"name": "Wonder Festival 2020 Winter Product", "dtype": "string"}, {"name": "not to scale - approximately 100mm in height", "dtype": "string"}, {"name": "painted ABS&PVC poseable figure - not to scale - stand included - approximately 100mm in height", "dtype": "string"}, {"name": "pecifications", "dtype": "string"}, {"name": "plus one secret figure", "dtype": "string"}, {"name": "\u203bD.C.II To You \uff5e\u30c0\u30fb\u30ab\u30fc\u30ddII\uff5e\u30c8\u30a5\u30fc\u30e6\u30fc \u30b5\u30fc\u30ab\u30b9\u901a\u8ca9\u9650\u5b9a\u7248\u3000\u540c\u68b1", "dtype": "string"}, {"name": "\u203bWonder Festival 2006 Summer Product", "dtype": "string"}, {"name": "\u203b\u307b\u3073\u30fc\u3061\u3083\u3093\u306d\u308b\u671f\u9593\u9650\u5b9aWEB\u901a\u8ca9\u5546\u54c1", "dtype": "string"}, {"name": "\u30ef\u30f3\u30c0\u30fc\u30d5\u30a7\u30b9\u30c6\u30a3\u30d0\u30eb 2012\uff3b\u590f\uff3d\u8ca9\u58f2\u5546\u54c1", "dtype": "string"}, {"name": "\u30ef\u30f3\u30c0\u30fc\u30d5\u30a7\u30b9\u30c6\u30a3\u30d0\u30eb 2013\uff3b\u590f\uff3d\u8ca9\u58f2\u5546\u54c1", "dtype": "string"}, {"name": "\u4ed5\u69d8", "dtype": "string"}, {"name": "\u4f01\u753b\u5354\u529b", "dtype": "string"}, {"name": "\u518d\u8ca9", "dtype": "string"}, {"name": "\u518d\u8ca9\u30fb\u518d\u51fa\u8377", "dtype": "string"}, {"name": "\u518d\u8ca9\uff1a\u518d\u51fa\u8377", "dtype": "string"}, {"name": "\u539f\u578b\u5236\u4f5c", "dtype": "string"}, {"name": "\u539f\u578b\u5236\u4f5c\u30fb\u767a\u58f2\u5143", "dtype": "string"}, {"name": "\u88fd\u9020\u5143", "dtype": "string"}, {"name": "\u958b\u767a\u30fb\u88fd\u9020", "dtype": "string"}]}, {"name": "category", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5895055, "num_examples": 3378}], "download_size": 1323858, "dataset_size": 5895055}, {"config_name": "goodsmile-scale", "features": [{"name": "image_urls", "sequence": "string"}, {"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "details", "struct": [{"name": "", "dtype": "string"}, {"name": "1/8 scale", "dtype": "string"}, {"name": "2012 Release Price", "dtype": "string"}, {"name": "3D Prototype Direction", "dtype": "string"}, {"name": "3D Prototype Production", "dtype": "string"}, {"name": "3D Sculpting Direction", "dtype": "string"}, {"name": "3D Sculpting Production", "dtype": "string"}, {"name": "3DCG Image", "dtype": "string"}, {"name": "Ages", "dtype": "string"}, {"name": "Base Production", "dtype": "string"}, {"name": "Base Sculptor", "dtype": "string"}, {"name": "Calligraphy", "dtype": "string"}, {"name": "Category", "dtype": "string"}, {"name": "Character Design", "dtype": "string"}, 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"\u5f69\u8272\u5236\u4f5c", "dtype": "string"}, {"name": "\u5f69\u8272\u5354\u529b", "dtype": "string"}, {"name": "\u64ae\u5f71", "dtype": "string"}, {"name": "\u6a29\u5229\u8868\u8a18", "dtype": "string"}, {"name": "\u6a2a", "dtype": "string"}, {"name": "\u767a\u58f2\u6708", "dtype": "string"}, {"name": "\u7d20\u6750", "dtype": "string"}, {"name": "\u9ad8\u3055", "dtype": "string"}]}, {"name": "specs", "struct": [{"name": "\u30b5\u30a4\u30ba", "dtype": "string"}, {"name": "\u4e88\u7d04\u53d7\u4ed8\u671f\u9593", "dtype": "string"}, {"name": "\u500b\u5225\u9001\u6599", "dtype": "string"}, {"name": "\u5546\u54c1\u30b3\u30fc\u30c9", "dtype": "string"}, {"name": "\u5728\u5eab", "dtype": "string"}, {"name": "\u767a\u58f2\u6642\u671f", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 129115, "num_examples": 21}], "download_size": 60116, "dataset_size": 129115}, {"config_name": "tokyofigure", "features": [{"name": "original", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "character", "dtype": "string"}, {"name": "price_value", "dtype": "string"}, {"name": "details", "struct": [{"name": "JAN\u30b3\u30fc\u30c9", "dtype": "string"}, {"name": "\u30b5\u30a4\u30ba", "dtype": "string"}, {"name": "\u30b7\u30ea\u30fc\u30ba", "dtype": "string"}, {"name": "\u30b9\u30b1\u30fc\u30eb", "dtype": "string"}, {"name": "\u4f01\u753b\u5354\u529b", "dtype": "string"}, {"name": "\u4f5c\u5bb6", "dtype": "string"}, {"name": "\u5236\u4f5c\u5354\u529b", "dtype": "string"}, {"name": "\u539f\u578b\u5236\u4f5c", "dtype": "string"}, {"name": "\u5546\u54c1\u30ab\u30c6\u30b4\u30ea", "dtype": "string"}, {"name": "\u5f69\u8272", "dtype": "string"}, {"name": "\u767a\u58f2\u5143", "dtype": "string"}, {"name": "\u767a\u58f2\u6642\u671f", "dtype": "string"}, {"name": "\u7d20\u6750", "dtype": "string"}, {"name": "\u8ca9\u58f2\u5143", "dtype": "string"}]}, {"name": "price_unit", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "image_urls", "sequence": "string"}, {"name": "description", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 663316, "num_examples": 394}], "download_size": 205977, "dataset_size": 663316}], "configs": [{"config_name": "goodsmile-figma", "data_files": [{"split": "train", "path": "goodsmile-figma/train-*"}]}, {"config_name": "goodsmile-nendoroid", "data_files": [{"split": "train", "path": "goodsmile-nendoroid/train-*"}]}, {"config_name": "goodsmile-scale", "data_files": [{"split": "train", "path": "goodsmile-scale/train-*"}]}, {"config_name": "kotobukiya", "data_files": [{"split": "train", "path": "kotobukiya/train-*"}]}, {"config_name": "myethos", "data_files": [{"split": "train", "path": "myethos/train-*"}]}, {"config_name": "spiritale", "data_files": [{"split": "train", "path": "spiritale/train-*"}]}, {"config_name": "tokyofigure", "data_files": [{"split": "train", "path": "tokyofigure/train-*"}]}]} | 2023-11-29T12:01:52+00:00 |
36844e1a894d33a3dd2c68c40aa491431317ca60 | # Dataset Card for "dreambooth-hackathon-nala"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ben-yu/dreambooth-hackathon-nala | [
"region:us"
]
| 2023-01-21T16:13:36+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 87657557.0, "num_examples": 20}], "download_size": 87645130, "dataset_size": 87657557.0}} | 2023-01-21T16:13:46+00:00 |
7765a2e7e8255766f36169d5265cfcb5993b14ac |
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:https://gitlabdev.bridgeconn.com/software/research/datasets/snow-mountain**
- **Paper:https://arxiv.org/abs/2206.01205**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The Snow Mountain dataset contains the audio recordings (in .mp3 format) and the corresponding text of The Bible in 11 Indian languages. The recordings were done in a studio setting by native speakers. Each language has a single speaker in the dataset. Most of these languages are geographically concentrated in the Northern part of India around the state of Himachal Pradesh. Being related to Hindi they all use the Devanagari script for transcription.
We have used this dataset for experiments in ASR tasks. But these could be used for other applications in speech domain, like speaker recognition, language identification or even as unlabelled corpus for pre-training.
### Supported Tasks and Leaderboards
Atomatic speech recognition, Speaker recognition, Language identification
### Languages
Hindi, Haryanvi, Bilaspuri, Dogri, Bhadrawahi, Gaddi, Kangri, Kulvi, Mandeali, Kulvi Outer Seraji, Pahari Mahasui
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
The Bible recordings were done in a studio setting by native speakers.
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
The data is licensed under the Creative Commons Attribution-ShareAlike 4.0 International Public License (CC BY-SA 4.0)
### Citation Information
@inproceedings{Raju2022SnowMD,
title={Snow Mountain: Dataset of Audio Recordings of The Bible in Low Resource Languages},
author={Kavitha Raju and V. Anjaly and R. Allen Lish and Joel Mathew},
year={2022}
}
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
| anjalyjayakrishnan/test | [
"task_categories:automatic-speech-recognition",
"multilinguality:multilingual",
"source_datasets:Snow Mountain",
"language:hi",
"language:bgc",
"language:kfs",
"language:dgo",
"language:bhd",
"language:gbk",
"language:xnr",
"language:kfx",
"language:mjl",
"language:kfo",
"language:bfz",
"arxiv:2206.01205",
"region:us"
]
| 2023-01-21T17:15:34+00:00 | {"annotations_creators": [{}], "language_creators": [{}], "language": ["hi", "bgc", "kfs", "dgo", "bhd", "gbk", "xnr", "kfx", "mjl", "kfo", "bfz"], "license": [], "multilinguality": ["multilingual"], "size_categories": [], "source_datasets": ["Snow Mountain"], "task_categories": ["automatic-speech-recognition"], "task_ids": [], "pretty_name": "Snow Mountain", "tags": [], "configs": ["hi", "bgc"], "dataset_info": [{"config_name": "hi", "features": [{"name": "Unnamed", "dtype": "int64"}, {"name": "sentence", "dtype": "string"}, {"name": "path", "dtype": "string"}], "splits": [{"name": "train_500", "num_examples": 400}, {"name": "val_500", "num_examples": 100}, {"name": "train_1000", "num_examples": 800}, {"name": "val_1000", "num_examples": 200}, {"name": "test_common", "num_examples": 500}], "dataset_size": "71.41 hrs"}, {"config_name": "bgc", "features": [{"name": "Unnamed", "dtype": "int64"}, {"name": "sentence", "dtype": "string"}, {"name": "path", "dtype": "string"}], "splits": [{"name": "train_500", "num_examples": 400}, {"name": "val_500", "num_examples": 100}, {"name": "train_1000", "num_examples": 800}, {"name": "val_1000", "num_examples": 200}, {"name": "test_common", "num_examples": 500}], "dataset_size": "27.41 hrs"}]} | 2023-02-03T14:08:32+00:00 |
ba72bd2ed85be1e4105a39850cd429905d1d3722 | kastan/EE_QA_for_RLHF | [
"license:mit",
"region:us"
]
| 2023-01-21T17:28:13+00:00 | {"license": "mit"} | 2023-01-21T17:31:19+00:00 |
|
2af40e78838da0dd2c35631edc30dc09ec7551c3 | # Dataset Card for "plant_species"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | jbarat/plant_species | [
"task_categories:image-classification",
"size_categories:10K<n<100K",
"language:en",
"license:unknown",
"region:us"
]
| 2023-01-21T17:50:33+00:00 | {"language": ["en"], "license": "unknown", "size_categories": ["10K<n<100K"], "task_categories": ["image-classification"], "pretty_name": "Plant Species", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "aechmea_fasciata", "1": "agave_americana", "2": "agave_attenuata", "3": "agave_tequilana", "4": "aglaonema_commutatum", "5": "albuca_spiralis", "6": "allium_cepa", "7": "allium_sativum"}}}}], "splits": [{"name": "train", "num_bytes": 82083349.0, "num_examples": 800}], "download_size": 82004194, "dataset_size": 82083349.0}} | 2023-01-22T14:03:45+00:00 |
b192525fc7e0b440cef7a930f829475491678168 |
# GovReport Summarization - 8192 tokens
- `ccdv/govreport-summarization` with the changes of:
- data cleaned with the [clean-text python package](https://pypi.org/project/clean-text/)
- total tokens for each column computed and added in new columns according to the `long-t5` tokenizer (_done **after** cleaning_)
---
## train info
```python
RangeIndex: 8200 entries, 0 to 8199
Data columns (total 4 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 report 8200 non-null string
1 summary 8200 non-null string
2 input_token_len 8200 non-null Int64
3 summary_token_len 8200 non-null Int64
dtypes: Int64(2), string(2)
memory usage: 272.4 KB
```
## token length distribution (long-t5)
--- | pszemraj/govreport-summarization-8192 | [
"task_categories:summarization",
"size_categories:1K<n<10K",
"source_datasets:ccdv/govreport-summarization",
"language:en",
"license:apache-2.0",
"govreport",
"long document",
"region:us"
]
| 2023-01-21T19:04:28+00:00 | {"language": ["en"], "license": "apache-2.0", "size_categories": ["1K<n<10K"], "source_datasets": "ccdv/govreport-summarization", "task_categories": ["summarization"], "pretty_name": "GovReport Summarization - 8192 tokens", "tags": ["govreport", "long document"]} | 2023-04-21T21:17:46+00:00 |
fe4316eca5c99cb0525cfe147e4336c28dc20889 | taesiri/GTA_V_CLIP_Embeddings | [
"license:openrail++",
"region:us"
]
| 2023-01-21T19:27:34+00:00 | {"license": "openrail++"} | 2023-01-21T19:31:25+00:00 |
|
456efd85d3cf03ee13e7b28cd9e34657c8b5c15b |
# Dataset Card for spaeti_store
## Dataset Description
The dataset consists of 12 pictures of different spätis (German convenience stores) from different angles.
The data is unlabeled.
The dataset was created to fine-tune a text-to-image Stable Diffusion model as part of the DreamBooth Hackathon. Visit the [organization's page](https://huggingface.co/dreambooth-hackathon) for more info. | malysheva42/spaeti_stores | [
"task_categories:image-to-text",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-classification",
"size_categories:n<1K",
"license:openrail",
"region:us"
]
| 2023-01-21T19:41:50+00:00 | {"license": "openrail", "size_categories": ["n<1K"], "task_categories": ["image-to-text", "image-segmentation", "image-to-image", "image-classification"]} | 2023-02-07T18:09:43+00:00 |
cea2dc50b4ac096e7bb5b5dc6726da319d8b4645 | etrent17/irs-articles | [
"license:mit",
"region:us"
]
| 2023-01-21T19:42:17+00:00 | {"license": "mit"} | 2023-01-21T19:42:50+00:00 |
|
f93c05cb601f5d1b607091f11be0ccc9e0082eed | rmahfuz/kaleidoscope | [
"task_categories:image-to-image",
"size_categories:n<1K",
"region:us"
]
| 2023-01-21T19:57:04+00:00 | {"size_categories": ["n<1K"], "task_categories": ["image-to-image"]} | 2023-01-21T20:02:36+00:00 |
|
a2a0b33eb96a95e0c260667a45f8a8e807d569c6 |
**‼️ This is not a real dataset!‼️** This dataset is used to demo using Hub [webooks](https://huggingface.co/docs/hub/webhooks) to automate metadata quality review. | davanstrien/test_webhook | [
"license:openrail",
"region:us"
]
| 2023-01-21T20:04:00+00:00 | {"license": "openrail"} | 2023-01-31T15:27:27+00:00 |
cda48bad5aa1e01205ae87363498c8141713deed | aamirhs/pashto-audio-wav2vec | [
"task_categories:text-generation",
"size_categories:n<1K",
"language:ps",
"license:gpl",
"region:us"
]
| 2023-01-21T20:06:13+00:00 | {"language": ["ps"], "license": "gpl", "size_categories": ["n<1K"], "task_categories": ["text-generation"]} | 2023-01-21T21:37:27+00:00 |
|
d04051a88744820ac8f257ce48ad8e89ffa19b96 | # Dataset Card for "yolochess_deepblue"
Source: https://github.com/niklasf/python-chess/tree/master/data/pgn
Features:
- fen = Chess board position in [FEN](https://en.wikipedia.org/wiki/Forsyth%E2%80%93Edwards_Notation) format
- move = Move played by a strong human player in this position
- result = Final result of the match
- eco = Opening [ECO](https://en.wikipedia.org/wiki/Encyclopaedia_of_Chess_Openings)-code
Deduplicated on (fen, move) pairs.
Samples: 511 | jrahn/yolochess_deepblue | [
"task_categories:text-classification",
"task_categories:reinforcement-learning",
"size_categories:n<1K",
"license:gpl-3.0",
"chess",
"region:us"
]
| 2023-01-21T20:26:09+00:00 | {"license": "gpl-3.0", "size_categories": ["n<1K"], "task_categories": ["text-classification", "reinforcement-learning"], "dataset_info": {"features": [{"name": "fen", "dtype": "string"}, {"name": "move", "dtype": "string"}, {"name": "result", "dtype": "string"}, {"name": "eco", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 45608.0, "num_examples": 511}], "download_size": 18295, "dataset_size": 45608.0}, "tags": ["chess"]} | 2023-02-03T21:29:20+00:00 |
5ee18cb324f084c03c03f649d77432fec0bf146a | # Dataset Card for "patched_1000_test_p_40_m2_predictions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | roa7n/patched_1000_test_p_40_m2_predictions | [
"region:us"
]
| 2023-01-21T21:16:38+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence_str", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "features", "sequence": "float64"}, {"name": "m2_preds", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 8380474294, "num_examples": 942535}], "download_size": 7949577002, "dataset_size": 8380474294}} | 2023-01-21T21:23:36+00:00 |
b8ed08c39133ff3c59fe8cd3854a4f7c0876fcb0 | ## My Notes 📓
This repository contains my lecture notes from graduate school on following topics 👇🏼
- Data Science: 8 cheatsheets
- Machine Learning (follows [Tom Mitchell's book](http://www.cs.cmu.edu/~tom/mlbook.html)): 25 pages of notes
- Statistics: 9 cheatsheets
- Deep Learning: 12 cheatsheets, will upload more
- Image Processing (follows [digital image processing book](https://www.amazon.fr/Digital-Image-Processing-Rafael-Gonzalez/dp/013168728X)): 21 cheatsheets
- Data Structures and Algorithms (follows [this book by Goodrich](https://www.wiley.com/en-us/Data+Structures+and+Algorithms+in+Python-p-9781118549582)): 26 cheatsheets
✨ *Some notes* ✨
- Most of these notes aren't intended to teach a topic from scratch but are rather notes that I took and compiled during my midterm & finals, might help you remember things, study for exams, and prepare for job interviews.
- There might be very small Turkish notes in few of the pages, you can ignore them.
- I will upload more notes as I find or create them. Will soon compile my Hugging Face cheatsheets so stay tuned!
- It's appreciated if you could improve the quality of PDF handwritten scans or convert them to JPEG, you can open a PR to this repository.
*Updates* 🎉
- I uploaded hierarchical clustering and improved version of K-means.
- I compiled every lecture in separate PDFs, and also compiled those into single PDF, found under `Compiled PDF`s.
- I uploaded Hugging Face cheatsheets. | merve/my_notes | [
"license:apache-2.0",
"region:us"
]
| 2023-01-21T21:35:32+00:00 | {"license": "apache-2.0"} | 2023-01-22T14:54:19+00:00 |
8e1bf642ae77cc209c00d13dc808abbea0d2b1b7 | # Dataset Card for "OxfordPets_test_facebook_opt_350m_Visclues_ns_20"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/OxfordPets_test_facebook_opt_350m_Visclues_ns_20 | [
"region:us"
]
| 2023-01-21T22:06:18+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_5_bs_3", "num_bytes": 292097.0, "num_examples": 20}], "download_size": 0, "dataset_size": 292097.0}} | 2023-01-22T04:38:25+00:00 |
dc19a6a3f100050376f84a52a2bf5a2de322d64f | # Dataset Card for "third_experiment_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | arefm/third_experiment_data | [
"region:us"
]
| 2023-01-21T22:54:44+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "id", "dtype": "string"}, {"name": "texts", "dtype": "string"}, {"name": "noisy_audio_0", "dtype": "audio"}, {"name": "noisy_audio_10", "dtype": "audio"}, {"name": "noisy_audio_20", "dtype": "audio"}, {"name": "noisy_audio_30", "dtype": "audio"}, {"name": "noisy_audio_40", "dtype": "audio"}], "splits": [{"name": "train", "num_bytes": 275715715.0, "num_examples": 200}], "download_size": 267505861, "dataset_size": 275715715.0}} | 2023-01-21T22:57:12+00:00 |
e157acbed57741fdaa82e56ef8a6a6525a0dfdd0 | # Dataset Card for "pii-pile-chunk3-150000-200000-tagged"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | j-chim/pii-pile-chunk3-150000-200000-tagged | [
"region:us"
]
| 2023-01-21T23:05:44+00:00 | {"dataset_info": {"features": [{"name": "texts", "sequence": "string"}, {"name": "meta", "struct": [{"name": "pile_set_name", "dtype": "string"}]}, {"name": "scores", "sequence": "float64"}, {"name": "avg_score", "dtype": "float64"}, {"name": "num_sents", "dtype": "int64"}, {"name": "tagged_pii_results", "list": [{"name": "analysis_explanation", "dtype": "null"}, {"name": "end", "dtype": "int64"}, {"name": "entity_type", "dtype": "string"}, {"name": "recognition_metadata", "struct": [{"name": "recognizer_identifier", "dtype": "string"}, {"name": "recognizer_name", "dtype": "string"}]}, {"name": "score", "dtype": "float64"}, {"name": "start", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 512476526, "num_examples": 49998}], "download_size": 196006381, "dataset_size": 512476526}} | 2023-01-21T23:06:02+00:00 |
da5407f6fa62895b25315b4a1ed4bca3704c8039 |
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | samp3209/logo-dataset | [
"region:us"
]
| 2023-01-21T23:50:18+00:00 | {} | 2023-01-22T00:30:36+00:00 |
a5de7c0293a6bfb83c5104b290b0902094f45976 |
# ParaShoot
[ParaShoot](https://github.com/omrikeren/ParaShoot): A Hebrew question and answering dataset in the style of [SQuAD](https://arxiv.org/abs/1606.05250), based on articles scraped from Wikipedia. The dataset contains a few thousand crowdsource-annotated pairs of questions and answers, in a setting suitable for few-shot learning.
For more details and quality analysis, see the [paper](https://arxiv.org/abs/2109.11314).
## Dataset Statistics
| **#Items** | **#Articles** | **#Paragraphs** | |
| ---------- | ------------- | --------------- | ------- |
| Train | 1792 | 295 | 565 |
| Dev | 221 | 33 | 63 |
| Test | 1025 | 165 | 319 |
| **Total** | **3038** | **493** | **947** |
## Citing
If you use ParaShoot in your research, please cite the ParaShoot paper:
```bibtex
@inproceedings{keren2021parashoot,
title={ParaShoot: A Hebrew Question Answering Dataset},
author={Keren, Omri and Levy, Omer},
booktitle={Proceedings of the 3rd Workshop on Machine Reading for Question Answering},
pages={106--112},
year={2021}
}
``` | imvladikon/parashoot | [
"task_categories:question-answering",
"language:he",
"arxiv:1606.05250",
"arxiv:2109.11314",
"region:us"
]
| 2023-01-22T00:05:53+00:00 | {"language": ["he"], "task_categories": ["question-answering"]} | 2023-01-22T00:32:13+00:00 |
d5ea85a28342947bfa0ad94075840f21373a256d | # Dataset Card for "hack"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | merkalo-ziri/hack | [
"region:us"
]
| 2023-01-22T00:34:59+00:00 | {"dataset_info": {"features": [{"name": "labels", "sequence": "int64"}, {"name": "pixel_values", "sequence": {"sequence": {"sequence": "float32"}}}], "splits": [{"name": "train", "num_bytes": 10719767724, "num_examples": 17679}, {"name": "test", "num_bytes": 2680093520, "num_examples": 4420}], "download_size": 2998628450, "dataset_size": 13399861244}} | 2023-01-30T18:51:03+00:00 |
6ffe8fc804065cbfecc26cec94f00b702a9dce30 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: lewtun/autotrain-acronym-identification-7324788
* Dataset: acronym_identification
* Config: default
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@Shepel](https://huggingface.co/Shepel) for evaluating this model. | autoevaluate/autoeval-eval-acronym_identification-default-d2d5a9-3002686420 | [
"autotrain",
"evaluation",
"region:us"
]
| 2023-01-22T01:09:57+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["acronym_identification"], "eval_info": {"task": "entity_extraction", "model": "lewtun/autotrain-acronym-identification-7324788", "metrics": [], "dataset_name": "acronym_identification", "dataset_config": "default", "dataset_split": "validation", "col_mapping": {"tokens": "tokens", "tags": "labels"}}} | 2023-01-22T01:10:47+00:00 |
4adb7faf12999b59bae7422c640ca04ac9896581 | # Dataset Card for "traininglogoset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | samp3209/traininglogoset | [
"region:us"
]
| 2023-01-22T01:28:14+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 530151628.952, "num_examples": 9888}], "download_size": 498332084, "dataset_size": 530151628.952}} | 2023-01-22T01:28:47+00:00 |
a03fc3bb20267743f3734ba115acb38331b42167 | # Dataset Card for "528by528logos"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | samp3209/528by528logos | [
"region:us"
]
| 2023-01-22T01:54:48+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 1204173958.858, "num_examples": 8817}], "download_size": 1262219319, "dataset_size": 1204173958.858}} | 2023-01-22T01:56:45+00:00 |
476e2a83fdeb660d996214d2af787aa13296581b | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: cardiffnlp/twitter-roberta-base-sentiment-latest
* Dataset: tweet_eval
* Config: sentiment
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@alhug](https://huggingface.co/alhug) for evaluating this model. | autoevaluate/autoeval-eval-tweet_eval-sentiment-5ae1bf-3003786426 | [
"autotrain",
"evaluation",
"region:us"
]
| 2023-01-22T02:16:54+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["tweet_eval"], "eval_info": {"task": "multi_class_classification", "model": "cardiffnlp/twitter-roberta-base-sentiment-latest", "metrics": [], "dataset_name": "tweet_eval", "dataset_config": "sentiment", "dataset_split": "validation", "col_mapping": {"text": "text", "target": "label"}}} | 2023-01-22T02:17:33+00:00 |
8889c4e941110119099d51cd1b45196a609797fb | # Dataset Card for "pii-pile-chunk3-200000-250000-tagged"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | j-chim/pii-pile-chunk3-200000-250000-tagged | [
"region:us"
]
| 2023-01-22T04:08:01+00:00 | {"dataset_info": {"features": [{"name": "texts", "sequence": "string"}, {"name": "meta", "struct": [{"name": "pile_set_name", "dtype": "string"}]}, {"name": "scores", "sequence": "float64"}, {"name": "avg_score", "dtype": "float64"}, {"name": "num_sents", "dtype": "int64"}, {"name": "tagged_pii_results", "list": [{"name": "analysis_explanation", "dtype": "null"}, {"name": "end", "dtype": "int64"}, {"name": "entity_type", "dtype": "string"}, {"name": "recognition_metadata", "struct": [{"name": "recognizer_identifier", "dtype": "string"}, {"name": "recognizer_name", "dtype": "string"}]}, {"name": "score", "dtype": "float64"}, {"name": "start", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 508200278, "num_examples": 49999}], "download_size": 194434096, "dataset_size": 508200278}} | 2023-01-22T04:08:26+00:00 |
cc12b40d966293a4f4db2dd0882231ba14469f11 | # Introduction
I mannually choose 342 images from top-100 in weekly toplist of 2022.
I have to mention that some of painters may not be consent to the use of their art works in AI training.
| ecccho/pixiv-image-toplist-aesthetics | [
"region:us"
]
| 2023-01-22T05:03:08+00:00 | {} | 2023-01-22T05:32:51+00:00 |
8576ab6c60f0b01c8eb720ba37c423728e3a66ff | PhanAnh/LOR_art | [
"license:creativeml-openrail-m",
"region:us"
]
| 2023-01-22T06:27:25+00:00 | {"license": "creativeml-openrail-m"} | 2023-01-22T06:29:12+00:00 |
|
899815f1c41b1578541c28fad40d03c9137eeb08 | # Dataset Card for "fleurs_2_sec_chunks"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | AMead10/fleurs_2_sec_chunks | [
"region:us"
]
| 2023-01-22T06:54:20+00:00 | {"dataset_info": {"features": [{"name": "audio", "sequence": "float64"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3407519773.999594, "num_examples": 13310}, {"name": "test", "num_bytes": 378641754.0004057, "num_examples": 1479}], "download_size": 2183139381, "dataset_size": 3786161528.0}} | 2023-01-30T23:14:33+00:00 |
4ddad9920947d6139a57d5f81da94c615bc5a17a | Набор действий происходящих с человеком | solkogan/people_actions | [
"language:ru",
"region:us"
]
| 2023-01-22T09:23:04+00:00 | {"language": ["ru"]} | 2023-01-22T09:26:30+00:00 |
8b9b8c1c3bc29b22c04a70b2bca49c5c6dabcb7e | basvojunagasai/test_data_set_basvoj | [
"license:unknown",
"region:us"
]
| 2023-01-22T09:57:52+00:00 | {"license": "unknown"} | 2023-01-22T10:03:25+00:00 |
|
a890b7bcd63e675e1969da650cd8d7d0d7e61c4c | # Dataset Card for "pii-pile-chunk3-250000-300000-tagged"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | j-chim/pii-pile-chunk3-250000-300000-tagged | [
"region:us"
]
| 2023-01-22T10:21:25+00:00 | {"dataset_info": {"features": [{"name": "texts", "sequence": "string"}, {"name": "meta", "struct": [{"name": "pile_set_name", "dtype": "string"}]}, {"name": "scores", "sequence": "float64"}, {"name": "avg_score", "dtype": "float64"}, {"name": "num_sents", "dtype": "int64"}, {"name": "tagged_pii_results", "list": [{"name": "analysis_explanation", "dtype": "null"}, {"name": "end", "dtype": "int64"}, {"name": "entity_type", "dtype": "string"}, {"name": "recognition_metadata", "struct": [{"name": "recognizer_identifier", "dtype": "string"}, {"name": "recognizer_name", "dtype": "string"}]}, {"name": "score", "dtype": "float64"}, {"name": "start", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 526454655, "num_examples": 49999}], "download_size": 201949320, "dataset_size": 526454655}} | 2023-01-22T10:21:49+00:00 |
b9ad429b3893eab30321b0aaf319c25a9363d3b3 | # Dataset Card for "patched_1000_test_p_100_m1_predictions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | roa7n/patched_1000_test_p_100_m1_predictions | [
"region:us"
]
| 2023-01-22T15:09:09+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence_str", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "m1_preds", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 519712989, "num_examples": 1319717}], "download_size": 50674557, "dataset_size": 519712989}} | 2023-01-22T15:12:53+00:00 |
2bf231971572aeb500a72b8df7ad393272c11b56 | # Overview
Converted from these datasets:
> https://huggingface.co/datasets/jordiclive/scored_summarization_datasets -> labeled "data" \
> https://huggingface.co/datasets/jordiclive/wikipedia-summary-dataset -> labeled "wiki" \
wiki dataset is split between "description" (cleaner, more popular articles) and "no description" (less clean, less popular)
Consist of parquet files with cols: `instruction, response, source`
full_to_summary (fts) consist of the following prompts infront of the instruction \
the full text is the instruction in these files
```
full_to_summary = [
"Summarize the following text: {}",
"Make a summary of the following text: {}",
"Provide a summary of the following text: {}",
"Change the following text into a summary: {}",
"Create a summary of the following text: {}",
"Give a brief overview of the following text: {}",
"Condense the following text into a summary: {}",
"Provide a condensed version of the following text: {}",
"Make a brief summary of the following text: {}",
"Create a condensed overview of the following text: {}",
]
```
summary_to_full (stf) consist of the following prompts infront of the instruction \
the summary is the instruction in these files
```
summary_to_full = [
"Write the original text for the following summary: {}",
"Write the full text for the following summary: {}",
"Provide the inputted source that provided the following summary: {}",
"Revert the following summary back into the original text: {}",
"Write a text that could've provided the following summary: {}",
"Write the original text that generated the following summary: {}",
"Provide the full text for the following summary: {}",
"Create the inputted source that provided the following summary: {}",
"Write the original source that provided the following summary: {}",
"Convert the following summary back into the original text: {}",
"Provide a text that could have been the input for the following summary: {}",
]
``` | MyloBishop/reverse_summarization_dataset | [
"region:us"
]
| 2023-01-22T15:15:30+00:00 | {} | 2023-01-22T17:37:24+00:00 |
9eb902cae7245e34dd6f7e8bb6439524f2e89d72 | # Dataset Card for "wvDatasetFinal"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | basilis/wvDatasetFinal | [
"region:us"
]
| 2023-01-22T15:42:01+00:00 | {"dataset_info": {"features": [{"name": "final_text", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 6722209183, "num_examples": 97928}], "download_size": 1660170870, "dataset_size": 6722209183}} | 2023-01-22T15:46:28+00:00 |
7a7e92553a233e0b89afb93e8d8292a1a39ba490 | # Dataset Card for "patched_1000_test_p_40_m1_predictions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | roa7n/patched_1000_test_p_40_m1_predictions | [
"region:us"
]
| 2023-01-22T15:44:31+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence_str", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "m1_preds", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 643791182, "num_examples": 1663294}], "download_size": 60859409, "dataset_size": 643791182}} | 2023-01-22T15:46:01+00:00 |
6c1379cb6e1554f34c878040af700aa798bf9f62 | # Dataset Card for "relbert/scientific_and_creative_analogy"
## Dataset Description
- **Repository:** [https://github.com/taczin/SCAN_analogies](https://github.com/taczin/SCAN_analogies)
- **Paper:** [https://arxiv.org/abs/2211.15268](https://arxiv.org/abs/2211.15268)
- **Dataset:** Relation Mapping
### Dataset Summary
A dataset for relation mapping task, which is a task to choose optimal combination of word pairs (see more detail in the [paper](https://www.jair.org/index.php/jair/article/view/10583)).
Relation mapping `M` is the set of bijective map in between two sets of terms (`A` and `B`):
```
[set `A`]: ("solar system", "sun", "planet", "mass", "attracts", "revolves", "gravity")
[set `B`]: ("atom", "nucleus", "electron", "charge", "attracts", "revolves", "electromagnetism")
[Relation Mapping `M`]
* "solar system" -> "atom"
* "sun" -> "nucleus"
* "planet" -> "electron"
* "mass" -> "charge"
* "attracts" -> "attracts"
* "revolves" -> "revolves"
* "gravity" -> "electromagnetism"
```
***[Relation Mapping Problem](https://www.jair.org/index.php/jair/article/view/10583)*** is the task to identify the mapping `M` given the sets of terms `A` and `B`.
## Dataset Structure
### Data Instances
An example looks as follows.
```
{
"id": "0",
"reference": ["buying an item", "accepting a belief"],
"source": ["buying an item", "buyer", "merchandise", "buying", "selling", "returning", "valuable", "worthless"],
"target": ["accepting a belief", "believer", "belief", "accepting", "advocating", "rejecting", "true", "false"],
"target_random": ["rejecting", "true", "false", "accepting a belief", "believer", "advocating", "belief", "accepting"],
"type": "metaphor"
}
```
- `source`: A list of terms, which is the source of the relation mapping from.
- `target_random`: A list of terms, where we want to find a mapping from `source` to.
- `target`: A correctly ordered `target_random` that aligns with the `source`.
Given `source` and `target_random`, the task is to predict the correct order of `target_random` so that it matches `target`.
In average 7 terms are in the set, so the total number of possible order is 5040.
### Data Splits
| name |test|
|---------|----:|
|relation_mapping| 45 |
### Citation Information
```
@article{czinczoll2022scientific,
title={Scientific and Creative Analogies in Pretrained Language Models},
author={Czinczoll, Tamara and Yannakoudakis, Helen and Mishra, Pushkar and Shutova, Ekaterina},
journal={arXiv preprint arXiv:2211.15268},
year={2022}
}
``` | relbert/scientific_and_creative_analogy | [
"multilinguality:monolingual",
"size_categories:1<n<1K",
"language:en",
"license:other",
"arxiv:2211.15268",
"region:us"
]
| 2023-01-22T16:29:04+00:00 | {"language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["1<n<1K"], "pretty_name": "Relation Mapping"} | 2023-01-22T16:49:01+00:00 |
2127d1c183b87fcae8176346473cb8103deaf872 | # Dataset Card for "Hatefulmemes_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/Hatefulmemes_train | [
"region:us"
]
| 2023-01-22T17:25:12+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "not-hateful", "1": "hateful"}}}}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_ViT_L_14", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B", "sequence": "string"}, {"name": "blip_caption_beam_5", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_LAION-ViT-H-14-2B", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float32"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 3066249406.0, "num_examples": 8500}], "download_size": 3059695187, "dataset_size": 3066249406.0}} | 2023-05-07T19:54:19+00:00 |
e4adbc1a9793e94f14e9a422f56c672b3e4b7f44 | # Dataset Card for "Hatefulmemes_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/Hatefulmemes_test | [
"region:us"
]
| 2023-01-22T17:27:29+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "not-hateful", "1": "hateful"}}}}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B", "sequence": "string"}, {"name": "blip_caption_beam_5", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_LAION-ViT-H-14-2B", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float32"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "captions_module", "sequence": "string"}, {"name": "captions_module_filter", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_with_openai", "sequence": "string"}, {"name": "blip_caption_topk_50_Salesforce_blip_image_captioning_large_multiple", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large_", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "captions_all_patches", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "blip_caption_beam_5_Salesforce_blip_image_captioning_large", "dtype": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_with_openai_wordnet", "sequence": "string"}, {"name": "blip_caption_5_Salesforce_blip_image_captioning_large_hf", "dtype": "string"}, {"name": "blip_caption_5_Salesforce_blip_image_captioning_large_hf_a meme of", "dtype": "string"}, {"name": "blip_caption_5_Salesforce_blip_image_captioning_large_max_length_30_hf", "dtype": "string"}, {"name": "blip_caption_5_Salesforce_blip_image_captioning_large_max_length_200_hf", "dtype": "string"}, {"name": "blip_caption_5_Salesforce_blip_image_captioning_large_max_length_200_hf_a meme of", "dtype": "string"}, {"name": "blip_caption_False_beams_5_Salesforce_blip_image_captioning_large_max_length_30_hf", "dtype": "string"}, {"name": "blip_caption_beam_False_5_source", "dtype": "string"}, {"name": "blip_caption_False_beams_5_base_capfilt_large_max_length_30_source_a pitcure of ", "dtype": "string"}, {"name": "blip_caption_False_beams_5_base_capfilt_large_max_length_100_source_a pitcure of ", "dtype": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_L_14_simple_specific", "dtype": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_laion.pt", "sequence": "string"}], "splits": [{"name": "test", "num_bytes": 421626763.0, "num_examples": 1000}], "download_size": 387589337, "dataset_size": 421626763.0}} | 2023-06-17T01:26:31+00:00 |
3c5c0f5c6b5b14e1dd29b76055d73db7e9a110d3 | # Dataset Card for "pii-pile-chunk3-300000-350000-tagged"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | j-chim/pii-pile-chunk3-300000-350000-tagged | [
"region:us"
]
| 2023-01-22T17:57:02+00:00 | {"dataset_info": {"features": [{"name": "texts", "sequence": "string"}, {"name": "meta", "struct": [{"name": "pile_set_name", "dtype": "string"}]}, {"name": "scores", "sequence": "float64"}, {"name": "avg_score", "dtype": "float64"}, {"name": "num_sents", "dtype": "int64"}, {"name": "tagged_pii_results", "list": [{"name": "analysis_explanation", "dtype": "null"}, {"name": "end", "dtype": "int64"}, {"name": "entity_type", "dtype": "string"}, {"name": "recognition_metadata", "struct": [{"name": "recognizer_identifier", "dtype": "string"}, {"name": "recognizer_name", "dtype": "string"}]}, {"name": "score", "dtype": "float64"}, {"name": "start", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 510432044, "num_examples": 50000}], "download_size": 194469001, "dataset_size": 510432044}} | 2023-01-22T17:57:40+00:00 |
17b54df016af0073e3bc05ee9bf56352dd3b4713 | # Dataset Card for "patched_1000_test_p_150_m1_predictions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | roa7n/patched_1000_test_p_150_m1_predictions | [
"region:us"
]
| 2023-01-22T17:57:24+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence_str", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "m1_preds", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 415290126, "num_examples": 1035692}], "download_size": 41645841, "dataset_size": 415290126}} | 2023-01-22T17:57:44+00:00 |
2056e18cfc629e97e67539dd8c0590e8f21d06fa | simpletech/SS_TWILFORM01 | [
"license:mit",
"region:us"
]
| 2023-01-22T18:46:24+00:00 | {"license": "mit"} | 2023-01-22T18:46:31+00:00 |
|
afc1b5fdb5125962aeb896eef182fce3cdd31475 |
# Dataset Card for spaeti_store
## Dataset Description
The dataset consists of 10 pictures of one späti (German convenience store) from different angles.
The data is unlabeled.
The dataset was created to fine-tune a text-to-image Stable Diffusion model as part of the DreamBooth Hackathon. Visit the [organization's page](https://huggingface.co/dreambooth-hackathon) for more info.
| malysheva42/spaeti_store | [
"task_categories:text-to-image",
"task_categories:image-segmentation",
"task_categories:image-classification",
"task_categories:image-to-image",
"size_categories:n<1K",
"license:openrail",
"region:us"
]
| 2023-01-22T19:20:22+00:00 | {"license": "openrail", "size_categories": ["n<1K"], "task_categories": ["text-to-image", "image-segmentation", "image-classification", "image-to-image"], "pretty_name": "Photos of one sp\u00e4ti (a German convenience store)"} | 2023-02-07T17:34:51+00:00 |
5fff4e2ac3514a4bb861b046c6206159ac6e9d63 | # Dataset Card for "Hatefulmemes_train_embeddings"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/Hatefulmemes_train_embeddings | [
"region:us"
]
| 2023-01-22T19:25:54+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "id", "dtype": "int64"}, {"name": "vision_embeddings", "sequence": "float32"}], "splits": [{"name": "openai_clip_vit_large_patch14", "num_bytes": 3080005786.0, "num_examples": 8500}], "download_size": 3087127731, "dataset_size": 3080005786.0}} | 2023-01-22T19:28:55+00:00 |
50adeea882bdf0bddf535b6c4245005efade3d57 | # Dataset Card for "Hatefulmemes_test_embeddings"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/Hatefulmemes_test_embeddings | [
"region:us"
]
| 2023-01-22T19:41:51+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "id", "dtype": "int64"}, {"name": "vision_embeddings", "sequence": "float32"}], "splits": [{"name": "openai_clip_vit_large_patch14", "num_bytes": 364453207.0, "num_examples": 1000}], "download_size": 365091102, "dataset_size": 364453207.0}} | 2023-01-22T19:42:14+00:00 |
e49f2d36fa7dfd82b8f3a00e31332f031681bde7 | terworld/pic | [
"license:openrail",
"region:us"
]
| 2023-01-22T19:56:43+00:00 | {"license": "openrail"} | 2023-01-22T20:10:03+00:00 |
|
1f8922b2a37c65ec38e79d7a4704c1b9b6f21f7e |
# Dataset Card for Swiss Legislation
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Swiss Legislation is a multilingual, diachronic dataset of 36K Swiss laws. This dataset is part of a challenging Information Retreival task.
### Supported Tasks and Leaderboards
### Languages
The total number of texts in the dataset is 35,698. The dataset is saved in _lexfind_v2.jsonl_ format.
Switzerland has four official languages German, French, Italian and Romanch with some additional English laws being represenated. Laws are written by legal experts.
36K & 18K & 11K & 6K & 534 & 207
| Language | Subset | Number of Documents |
|------------|------------|----------------------|
| German | **de** | 18K |
| French | **fr** | 11K |
| Italian | **it** | 6K |
| Romanch | **rm** | 534 |
| English | **en** | 207 |
## Dataset Structure
### Data Fields
Each entry in the dataset is a dictionary with the following keys:
- `canton`: the canton of origin of the legislation
- example: "ag"
- `language`: the language of the legislation
- example: "de"
- `uuid`: a unique identifier for the legislation
- example: "ec312f57-05fe-4552-ba50-8c9c269e0f3b"
- `title`: the title of the legislation
- example: "Gesetz über die Geoinformation im Kanton Aargau"
- `short`: a short description of the legislation
- example: "Kantonales Geoinformationsgesetz"
- `abbreviation`: an abbreviation for the legislation
- example: "KGeoIG"
- `sr_number`: a reference number for the legislation
- example: "740.100"
- `is_active`: whether the legislation is currently in force
- example: true
- `version_active_since`: the date since when the legislation's current version is active
- example: "2021-09-01"
- `family_active_since`: the date since when the legislation's current version's family is active
- example: "2011-05-24"
- `version_inactive_since`: the date since when the legislation's current version is inactive
- example: null
- `version_found_at`: the date the legislation's current version was found
- example: "2021-09-01"
- `pdf_url`: a link to the legislation's pdf
- example: "https://www.lexfind.ch/tol/1557/de"
- `html_url`: a link to the legislation's html
- example: "https://gesetzessammlungen.ag.ch/app/de/texts_of_law/740.100")_
- `pdf_content`: the legislation's pdf content
- example: "740.100 - Gesetz über..."
- `html_content`: the legislation's html content
- example: ""
- `changes`: a list of changes made to the legislation
- example: []
- `history`: a list of the legislation's history
- example: []
- `quotes`: a list of quotes from the legislation
- example: []
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
1. 'ch': Switzerland (Federal) - 15840
2. 'fr': Fribourg - 1633
3. 'be': Bern - 1344
4. 'vs': Valais - 1328
5. 'gr': Graubünden - 1205
6. 'ne': Neuchâtel - 1115
7. 'zh': Zurich - 974
8. 'bs': Basel-Stadt - 899
9. 'bl': Basel-Landschaft - 863
10. 'vd': Vaud - 870
11. 'ge': Geneva - 837
12. 'sg': St. Gallen - 764
13. 'ju': Jura - 804
14. 'zg': Zug - 632
15. 'ti': Ticino - 627
16. 'lu': Lucerne - 584
17. 'so': Solothurn - 547
18. 'ow': Obwalden - 513
19. 'ik': Interkantonal - 510
20. 'sh': Schaffhausen - 469
21. 'gl': Glarus - 467
22. 'tg': Thurgau - 453
23. 'sz': Schwyz - 423
24. 'ai': Appenzell Innerrhoden - 416
25. 'ag': Aargau - 483
26. 'ar': Appenzell Ausserrhoden - 330
27. 'nw': Nidwalden - 401
28. 'ur': Uri - 367
29.
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
The original data are published from the Swiss Federal Supreme Court (https://www.bger.ch) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (https://entscheidsuche.ch) in HTML.
#### Who are the source language producers?
The decisions are written by the judges and clerks in the language of the proceedings.
### Annotations
#### Annotation process
#### Who are the annotators?
Metadata is published by the Swiss Federal Supreme Court (https://www.bger.ch).
### Personal and Sensitive Information
The dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: https://www.bger.ch/home/juridiction/anonymisierungsregeln.html.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
We release the data under CC-BY-4.0 which complies with the court licensing (https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf)
© Swiss Federal Supreme Court, 2002-2022
The copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.
Source: https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf
### Citation Information
Please cite our [ArXiv-Preprint](https://arxiv.org/abs/2306.09237)
```
@misc{rasiah2023scale,
title={SCALE: Scaling up the Complexity for Advanced Language Model Evaluation},
author={Vishvaksenan Rasiah and Ronja Stern and Veton Matoshi and Matthias Stürmer and Ilias Chalkidis and Daniel E. Ho and Joel Niklaus},
year={2023},
eprint={2306.09237},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
| rcds/swiss_legislation | [
"task_categories:text-classification",
"task_categories:translation",
"size_categories:100K<n<1M",
"language:de",
"language:fr",
"language:it",
"license:cc-by-sa-4.0",
"arxiv:2306.09237",
"region:us"
]
| 2023-01-22T20:02:28+00:00 | {"language": ["de", "fr", "it"], "license": "cc-by-sa-4.0", "size_categories": ["100K<n<1M"], "task_categories": ["text-classification", "translation"], "pretty_name": "Swiss Legislation"} | 2023-07-20T06:36:07+00:00 |
2dc7f435cb51768eaa6275d5242bfa777bfc5a06 |
# PLANE Out-of-Distribution Sets
PLANE (phrase-level adjective-noun entailment) is a benchmark to test models on fine-grained compositional inference.
The current dataset contains five sampled splits, used in the supervised experiments of [Bertolini et al., 22](https://aclanthology.org/2022.coling-1.359/).
## Data Structure
The `dataset` is organised around five `Train/test_split#`, each containing a training and test set of circa 60K and 2K.
### Features
Each entrance has 6 features: `seq, label, Adj_Class, Adj, Nn, Hy`
- `seq`:test sequense
- `label`: ground truth (1:entialment, 0:no-entailment)
- `Adj_Class`: the class of the sequence adjectives
- `Adj`: the adjective of the sequence (I: intersective, S: subsective, O: intensional)
- `N`n: the noun
- `Hy`: the noun's hypericum
Each sample in `seq` can take one of three forms (or inference types, in paper):
- An *Adjective-Noun* is a *Noun* (e.g. A red car is a car)
- An *Adjective-Noun* is a *Hypernym(Noun)* (e.g. A red car is a vehicle)
- An *Adjective-Noun* is a *Adjective-Hypernym(Noun)* (e.g. A red car is a red vehicle)
Please note that, as specified in the paper, the ground truth is automatically assigned based on the linguistic rule that governs the interaction between each adjective class and inference type – see the paper for more detail.
### Trained Model
You can find a tuned BERT-base model (tuned and validated using the 2nd split) [here](https://huggingface.co/lorenzoscottb/bert-base-cased-PLANE-ood-2?text=A+fake+smile+is+a+smile).
### Cite
If you use PLANE for your work, please cite the main COLING 2022 paper.
```
@inproceedings{bertolini-etal-2022-testing,
title = "Testing Large Language Models on Compositionality and Inference with Phrase-Level Adjective-Noun Entailment",
author = "Bertolini, Lorenzo and
Weeds, Julie and
Weir, David",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.359",
pages = "4084--4100",
}
``` | lorenzoscottb/PLANE-ood | [
"task_categories:text-classification",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-2.0",
"region:us"
]
| 2023-01-22T21:22:03+00:00 | {"language": ["en"], "license": "cc-by-2.0", "size_categories": ["100K<n<1M"], "task_categories": ["text-classification"]} | 2023-01-25T09:51:09+00:00 |
eaab5e695a1c8084eea9abebf7323ca9d6eea727 | # Dataset Card for "bookcorpus_compact_256_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | saibo/bookcorpus_compact_256_test | [
"region:us"
]
| 2023-01-22T21:43:56+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "concept_with_offset", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20727824, "num_examples": 6160}], "download_size": 10867768, "dataset_size": 20727824}} | 2023-01-22T23:43:39+00:00 |
5e9d7b7fc073553b624f050b6bb1a05b1073960f | # Dataset Card for "bookcorpus_compact_512_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | saibo/bookcorpus_compact_512_test | [
"region:us"
]
| 2023-01-22T21:51:07+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "concept_with_offset", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 39735149, "num_examples": 6160}], "download_size": 20545672, "dataset_size": 39735149}} | 2023-01-23T00:07:53+00:00 |
407683a9e51c59c4a6ed148177f36df5017034ef |
# Ivypanda essays
## Dataset Description
- **Homepage:** https://laion.ai/
### Dataset Summary
This dataset contains essays from [ivypanda](https://ivypanda.com/essays/).
## Dataset Structure
### Data Fields
`TEXT`: The text of the essay.<br/>
`SOURCE`: A permalink to the ivypanda essay page
| qwedsacf/ivypanda-essays | [
"region:us"
]
| 2023-01-23T00:37:04+00:00 | {} | 2023-02-03T21:05:11+00:00 |
e417799ec581048674164e3e4b595ff8560d32a3 | # Dataset Card for "bookcorpus_compact_256_test_meta"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | saibo/bookcorpus_compact_256_test_meta | [
"region:us"
]
| 2023-01-23T01:04:22+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "concept_with_offset", "dtype": "string"}, {"name": "cid_arrangement", "sequence": "int32"}, {"name": "schema_lengths", "sequence": "int64"}, {"name": "topic_entity_mask", "sequence": "int64"}, {"name": "text_lengths", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 214680900, "num_examples": 6160}], "download_size": 47705450, "dataset_size": 214680900}} | 2023-01-23T01:04:39+00:00 |
b9e2599efe8216970b5c7c690620e0d8b7e37cab | Glac1er/glac1erdst | [
"license:unknown",
"region:us"
]
| 2023-01-23T02:32:39+00:00 | {"license": "unknown"} | 2023-01-30T14:19:29+00:00 |
|
4d631c6bbd0ec86f851fd15932a6eb589f3dfa24 | # Dataset Card for "bookcorpus_compact_512_test_meta"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | saibo/bookcorpus_compact_512_test_meta | [
"region:us"
]
| 2023-01-23T04:03:52+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "concept_with_offset", "dtype": "string"}, {"name": "cid_arrangement", "sequence": "int32"}, {"name": "schema_lengths", "sequence": "int64"}, {"name": "topic_entity_mask", "sequence": "int64"}, {"name": "text_lengths", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 414104933, "num_examples": 6160}], "download_size": 91522110, "dataset_size": 414104933}} | 2023-01-23T04:04:06+00:00 |
ed1c0822c0deed90a44f9ee8fc20541c118902d7 | NLPFin/Quantitative101 | [
"license:cc-by-nc-4.0",
"region:us"
]
| 2023-01-23T04:14:40+00:00 | {"license": "cc-by-nc-4.0"} | 2023-01-23T04:17:06+00:00 |
|
c5bb3cfee315427148cd8dbf86d2d244a4b6e195 | # Dataset Card for "mnist_dijkstra_v0.0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | jlbaker361/mnist_dijkstra_v0.0 | [
"region:us"
]
| 2023-01-23T05:31:23+00:00 | {"dataset_info": {"features": [{"name": "label", "dtype": "int64"}, {"name": "sequence", "sequence": "int64"}, {"name": "occurence", "dtype": "int64"}, {"name": "split", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 84223889, "num_examples": 68614}], "download_size": 12695868, "dataset_size": 84223889}} | 2023-02-04T17:06:21+00:00 |
2c6f4109a7eb28b5557d4a7064775be3d03003fc | mdroth/landscapes | [
"license:apache-2.0",
"region:us"
]
| 2023-01-23T05:43:41+00:00 | {"license": "apache-2.0"} | 2023-01-23T05:46:00+00:00 |
|
837cbf43f126704bda094ab7649c5bcbef8c5320 | # Dataset Card for SLF5K
## Dataset Description
- **Repository: https://github.com/JeremyAlain/imitation_learning_from_language_feedback**
- **Paper: Training Language Models with Language Feedback at Scale**
- **Point of Contact: [email protected] and [email protected]**
### Dataset Summary
The Summarization with Language Feedback (SLF5K) dataset is an English-language dataset containing 5K unique samples that can be used
for the task of abstraction summarization. Each sample consists
of a Reddit title and post, a model-generated ([FeedME](https://beta.openai.com/docs/model-index-for-researchers)) summary, and human-written language feedback on that summary.
Additionally, each sample has a high-quality, human-written (gold) summary that should be ideal for the Reddit post.
Lastly, each sample has two additional model-generated summaries with binary human preference labels, on which summary is preferred by a human.
The dataset can be used to train language models with language feedback on abstractive summarization. It can also be
used to train a reward model on binary preferences.
The Reddit posts were taken from the datasets provided by [Learning to Summarize from Human Feedbback](https://arxiv.org/pdf/2009.01325.pdf), who used the initial Reddit post dataset
[TL;DR: Mining Reddit to Learn Automatic Summarization](https://aclanthology.org/W17-4508.pdf).
### Supported Tasks and Leaderboards
The dataset can be used to train a model for abstractive and extractive summarization. It can either be trained directly on
human-written summaries, or leverage language feedback or binary human preferences.
The model performance is evaluated in a human evaluation, where annotators rate the quality of the generated summaries.
Previous work has used [ROUGE](https://huggingface.co/spaces/evaluate-metric/rouge) scores, but in [Learning to Summarize from Human Feedbback](https://arxiv.org/pdf/2009.01325.pdf) they
show that ROUGE is not an ideal metric.
### Languages
English
## Dataset Structure
### Data Instances
Each instance is a line in the dataset file (which is saved as .jsonl). Each instance contains various fields, where the most important are
Here is an example instance:
```
{"id":"t3_3w7gyp",
"subreddit":"dogs",
"title":"Puppy playing at park - other owner aggressive towards him [help]",
"post":"Hi all, looking for some advice. I have a 6m old kelpie, buzz, who goes with me daily to a dog park, [...]",
"tldr_human_reference_summary":"other owner at park harsh with my dog for playing to rough with his. Have tried talking to him about it, hasn't helped.",
"summary_prompt":"Write an excellent summary of the given text.\n\nTitle: Puppy playing at park - other owner aggressive towards him [help]\n\nText: Hi all, looking for some advice. [...] that too.\n\nTL;DR:",
"generated_summary_for_comparison_A":"New dog at park is being aggressive to my pup, owner won't stop. What do I do?",
"generated_summary_for_comparison_B":"A new dog has been coming to the dog park and the first day the new dog came, the old dog (a kelpie) was all over him.",
"generated_summary_for_feedback":"A new dog has been coming to the dog park and the first day the owner hauled buzz off and whacked him. Today, the owner was staring daggers at me and lunging at buzz\/pulling his collar roughly.",
"comparison_preference":"Summary A",
"feedback":"The summary is concise but could include information about the poster knowing the dogs are just playing and will react if they become aggressive and wants to know how to handle things with Max's dad. ",
"feedback_class":"Coverage",
"has_additional_feedback":"No",
"ideal_human_summary":"The poster is frustrated with a new person at the dog park who is upset with him because their young dogs are playing roughly. The poster will step in if it gets aggressive and wants the new person to understand this. "}
```
There are some additional fields like `time_spent_in_seconds_ideal_human_summary`, `time_spent_in_seconds_feedback`,`time_spent_in_seconds_comparison` which only have values for the development dataset.
### Data Fields
- `id`: a unique string identifying the reddit post.
- `subreddit`: subreddit of the post.
- `title`: title of the reddit post.
- `post`: reddit post
- `tldr_human_reference_summary`: human reference summary automatically extracted from reddit (taken from the dataset of [TL;DR: Mining Reddit to Learn Automatic Summarization](https://aclanthology.org/W17-4508.pdf))
- `summary_prompt`: the whole prompt used to generate summaries
- `generated_summary_for_comparison_A`: summary A used for binary human comparison (generated with FeedME)
- `generated_summary_for_comparison_B`: summary B used for binary human comparison (generated with FeedME)
- `generated_summary_for_feedback`: summary used to gather human language feedback ((generated with FeedME))
- `comparison_preference`: prefered Summary of human comparison, Values: "Summary A", "Summary B"
- `feedback`: human language feedback on `generated_summary_for_feedback`(most important feedback point)
- `feedback_class`: Class of language feedback, Values: "Coverage", "Accuracy", "Coherence", "other"
- `has_additional_feedback`: Whether this sample could use more feedback on an important point.
- `ideal_human_summary`: high-quality human-written summary for this sample. We instructed annotators to write an ideal summary.
- `time_spent_in_seconds_ideal_human_summary`: Annotation time for ideal human summary
- `time_spent_in_seconds_feedback`: Annotation time for language feedback
- `time_spent_in_seconds_comparison`: Annotation time for binary comparison
Note that the various datasplits have varying fields. The fields that are not contained in a dataset have the value None.
### Data Splits
The SLF5K dataset has 4 splits: _train_, _development_, _validation_, and _test_. Below are the statistics of the dataset.
| Dataset Split | Number of Instances in Split |
| ------------- | ------------------------------------------- |
| Train | 5000 |
| Development | 200 |
| Validation | 500 |
| Test | 698 |
The reason we introduce a development and validation dataset, is the following.
## Dataset Creation
### Curation Rationale
This dataset aims to support supervised language model training from human preferences on a summarization task with real natural training data.
### Source Data
#### Initial Data Collection and Normalization
The initial TL;DR dataset was made public by Völkse et. al. in the paper [TL;DR: Mining Reddit to Learn Automatic Summarization](https://aclanthology.org/W17-4508.pdf) (licensed under CC By 4.0).
Stiennon et. al. then use this TL;DR dataset for their work [Learning to Summarize from Human Feedbback](https://arxiv.org/pdf/2009.01325.pdf).
They filter the TL;DR dataset for quality reasons and collect binary human preference labels.
Our datset is a subset from Stiennon et. al. Dataset, which can be downloaded [here](https://github.com/openai/summarize-from-feedback).
Our train and development dataset are taken form their train dataset and our test and validation datasets are taken from their test datasest.
#### Who are the source language producers?
The reddit posts are written by users of reddit.com.
### Annotations
#### Annotation process
We first onboarded annotators by giving them test tasks on which we evaluated their annotation quality. We then selected 31
annotators for the remainder of the project (a few were removed later on due to quality issues). Througout the process
we updated our instructions to make the tasks clearer and stayed in close contact with the annotators to answer questions etc.
The various dataset splits were collected in multiple annotation iterations. The largest annotation was a single iteration of annotation
5000 samples for the train dataset.
#### Who are the annotators?
We used annotators through the annotation service [Surge AI](https://www.surgehq.ai/).
### Personal and Sensitive Information
The annotators were completely anonymized and no information about them can be found in the dataset.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to align language models with human preferences by leveraging language feedback, on the task of summarization. Concretely, the goal is to
to develop models that produce summaries for reddit posts that are more in line with human preferences.
Note that this does not imply that the outputs will perfectly be aligned with human values, i.e. outputs can still be misaligned, offensive and contain harumful biases.
While outputs from a model trained on our dataset may reflect the language of the reddit posts, summaries, and human feedback, it should always be made clear that such an output
is automatically generated.
### Discussion of Biases
The TL;DR dataset consists of user-submitted posts to the website reddit.com. It can thus contain content that is offensive or reflects harmful social biases.
We thus recommend that models trained on the SLF5K dataset (which is based on the TL;DR) dataset be thoroughly studied for potential harmful behavior.
The human preferences and feedback represented in this dataset were collected through crowd-workers and may disproportionally represent the views, biases, and values
of the respective demographic of the annotators.
### Other Known Limitations
The "human-summaries" collected in the TL;DR dataset (and available in the SLF5K dataset under the field `tldr_human_reference_summary`, were automatically extracted from reddit.com.
They are often of poor quality and do not accurately reflect human summarization performance. In our paper, we show that our human written summaries (available in the SLF5K dataset under the field
`ideal_human_summary`) are of much higher quality.
## Additional Information
### Dataset Curators
The data is collected by Jérémy Scheurer, Jon Ander Campos, Tomasz Korbak, Jun Shern Chan, Angelica Chen, Kyunghyun Cho, and Ethan Perez.
All authors are affiliated with New York University. Additionally, Jérémy Scheurer is affiliated with FAR AI. Jon Ander
is affiliated with the University of the Basque Country. Tomek Korbak is affiliated with FAR AI and the University of Sussesx.
Kyunghyun Cho is affiliated with Genentech and CIFAR LMB. Ethan Perez is affiliated with FAR AI and Anthropic.
### Licensing Information
The SLF5K dataset is released under the Apache 2.0 license.
### Citation Information
TBD | JeremyAlain/SLF5K | [
"task_categories:summarization",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"feedback",
"human feedback",
"language feedback",
"binary feedback",
"reward",
"reward model",
"gpt3",
"gpt-3",
"instructgpt",
"alignment",
"ai alignment",
"scale",
"imitation learning from language feedback",
"ilf",
"arxiv:2009.01325",
"region:us"
]
| 2023-01-23T08:44:34+00:00 | {"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": ["summarization"], "task_ids": [], "pretty_name": "SLF5K", "tags": ["feedback", "human feedback", "language feedback", "binary feedback", "reward", "reward model", "gpt3", "gpt-3", "instructgpt", "alignment", "ai alignment", "scale", "imitation learning from language feedback", "ilf"]} | 2023-01-24T14:21:35+00:00 |
2bc4e2a672ab70f42a49c436bb51e8c5ce6d178f | gokula/Fastfood | [
"license:bigscience-openrail-m",
"region:us"
]
| 2023-01-23T09:01:42+00:00 | {"license": "bigscience-openrail-m"} | 2023-01-23T09:06:24+00:00 |
|
62b5618758a398f989de278272fe605ecf9878de | # Dataset Card for "pexel_friends"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuvalkirstain/pexel_friends | [
"region:us"
]
| 2023-01-23T09:32:49+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2906655034.625, "num_examples": 7995}], "download_size": 490223516, "dataset_size": 2906655034.625}} | 2023-01-23T09:46:46+00:00 |
22cf1bdc4e49b115220c782203f6504be1181774 | # CelebA Dataset
CelebA Dataset is a large-scale face attributes dataset with more than 200K celebrity images, each with 40 attribute annotations. The images in this dataset cover large pose variations and background clutter. CelebA has large diversities, large quantities, and rich annotations, including 10,177 number of identities, 202,599 number of face images, and 5 landmark locations, 40 binary attributes annotations per image.
## Usage
It is composed of 3 sets of images:
* Training
* Validation
* Test
## Example
The dataset returns each item as a dictionary with the following fields:
```
{
"image": image,
"bbox": [x1, y1, w, h],
"facial_landmarks": {
"lefteye": [x1, y1],
"righteye": [x2, y2],
"nose": [x3, y3],
"leftmouth": [x4, y4],
"rightmouth": [x5, y5]
}
}
```
## License
CelebA Dataset is licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0). | hfaus/CelebA_bbox_and_facepoints | [
"size_categories:n<1K",
"region:us"
]
| 2023-01-23T11:04:45+00:00 | {"size_categories": ["n<1K"]} | 2023-01-28T09:34:39+00:00 |
8803964e7afd58380010264fe1e58dd3cd59f304 | # AutoTrain Dataset for project: big_tm4
## Dataset Description
This dataset has been automatically processed by AutoTrain for project big_tm4.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "I would like to request the count of vendors that are situated in Houston and have received a purchase order.",
"target": "select max(RETAILBUYER_VENDOR.vendor_id) from RETAILBUYER_POHEADER\ninner join\nRETAILBUYER_VENDOR \non\nRETAILBUYER_POHEADER.vendor_id = RETAILBUYER_VENDOR.vendor_id\nwhere RETAILBUYER_VENDOR.Vendor_City = 'Houston'"
},
{
"text": "List all vendors and their details for whom no PO has been issued",
"target": "select * from RETAILBUYER_VENDOR\nwhere vendor_id not in (select vendor_id from RETAILBUYER_POHEADER)"
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "Value(dtype='string', id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 356 |
| valid | 90 |
| Aman6917/autotrain-data-big_tm4 | [
"task_categories:summarization",
"region:us"
]
| 2023-01-23T11:43:56+00:00 | {"task_categories": ["summarization"]} | 2023-01-23T12:45:31+00:00 |
16b3ebb3b831dd0cbf832ea0c25c963a71beb5b6 |
# ⚕️ health-questions
TODO | heinrichreimer/health-questions | [
"size_categories:1M<n<10M",
"language:en",
"Health",
"Question Answering",
"region:us"
]
| 2023-01-23T11:47:06+00:00 | {"language": ["en"], "size_categories": ["1M<n<10M"], "tags": ["Health", "Question Answering"], "dataset_info": [{"config_name": "silver", "features": [{"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "health_related_label", "dtype": {"class_label": {"names": {"0": "not_health_related", "1": "health_related"}}}}, {"name": "medical_label", "dtype": {"class_label": {"names": {"0": "not_medical", "1": "medical"}}}}], "splits": [{"name": "train", "num_bytes": 750040934, "num_examples": 6835271}, {"name": "validation", "num_bytes": 187523993, "num_examples": 1708818}], "download_size": 0, "dataset_size": 937564927}, {"config_name": "golden", "features": [{"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "health_related_label", "dtype": {"class_label": {"names": {"0": "not_health_related", "1": "health_related"}}}}, {"name": "medical_label", "dtype": {"class_label": {"names": {"0": "not_medical", "1": "medical"}}}}], "splits": [{"name": "test", "num_bytes": 163495, "num_examples": 1489}, {"name": "train", "num_bytes": 489298, "num_examples": 4466}, {"name": "validation", "num_bytes": 163015, "num_examples": 1489}], "download_size": 0, "dataset_size": 815808}]} | 2023-01-23T16:42:37+00:00 |
1f56c0ab8f18531e43c5c772f1a6dc74dae83ac1 | The dataset containes test.csv and train.csv of skin crops 128x128.
| vgg/skin_data | [
"region:us"
]
| 2023-01-23T11:50:07+00:00 | {} | 2023-01-23T13:48:10+00:00 |
4d2ec0cc408f109ff922f52859102cd7020a95e5 | lucapantea/fact-ai | [
"license:mit",
"doi:10.57967/hf/0645",
"region:us"
]
| 2023-01-23T12:13:49+00:00 | {"license": "mit"} | 2023-01-31T14:23:16+00:00 |
|
bb2433b96b49bc502271d4224a73a925b1df5539 | # Dataset Card for "AToMiC-Images-v0.2-medium"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | justram/AToMiC-Images-v0.2-medium | [
"region:us"
]
| 2023-01-23T12:34:40+00:00 | {"dataset_info": {"features": [{"name": "image_url", "dtype": "string"}, {"name": "image_id", "dtype": "string"}, {"name": "language", "sequence": "string"}, {"name": "caption_reference_description", "sequence": "string"}, {"name": "caption_alt_text_description", "sequence": "string"}, {"name": "caption_attribution_description", "sequence": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "test", "num_bytes": 152304669.4521026, "num_examples": 9322}, {"name": "validation", "num_bytes": 266345282.27935815, "num_examples": 16302}, {"name": "train", "num_bytes": 61149034067.48147, "num_examples": 3742704}], "download_size": 32462289032, "dataset_size": 61567684019.21293}} | 2023-01-23T14:01:03+00:00 |
950956d618f7adc50ee9e06d3bdd5eb73425be4a | # Dataset Card for "AToMiC-Texts-v0.2-medium"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | justram/AToMiC-Texts-v0.2-medium | [
"region:us"
]
| 2023-01-23T12:37:49+00:00 | {"dataset_info": {"features": [{"name": "text_id", "dtype": "string"}, {"name": "page_url", "dtype": "string"}, {"name": "page_title", "dtype": "string"}, {"name": "section_title", "dtype": "string"}, {"name": "context_page_description", "dtype": "string"}, {"name": "context_section_description", "dtype": "string"}, {"name": "media", "sequence": "string"}, {"name": "hierachy", "sequence": "string"}, {"name": "category", "sequence": "string"}, {"name": "source_id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5404754455.050775, "num_examples": 3002458}, {"name": "validation", "num_bytes": 30913287.798392836, "num_examples": 17173}, {"name": "test", "num_bytes": 17772485.321931664, "num_examples": 9873}], "download_size": 2719090777, "dataset_size": 5502126001.291424}} | 2023-01-23T15:49:03+00:00 |
47963b65a897cb884f972bbac237d78d79ade32d | # Dataset Card for "pexel_friends_with_generated_captions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuvalkirstain/pexel_friends_with_generated_captions | [
"region:us"
]
| 2023-01-23T12:46:26+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "generated_caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2907255881.625, "num_examples": 7995}], "download_size": 2902632694, "dataset_size": 2907255881.625}} | 2023-01-23T12:51:16+00:00 |
22a723953c86a961f25e370b8b61eb14e7615e8a | # Dataset Card for "bookcorpus_compact_1024_test_meta"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | saibo/bookcorpus_compact_1024_test_meta | [
"region:us"
]
| 2023-01-23T12:46:56+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "concept_with_offset", "dtype": "string"}, {"name": "cid_arrangement", "sequence": "int32"}, {"name": "schema_lengths", "sequence": "int64"}, {"name": "topic_entity_mask", "sequence": "int64"}, {"name": "text_lengths", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 758527093, "num_examples": 6160}], "download_size": 169143634, "dataset_size": 758527093}} | 2023-01-23T12:48:56+00:00 |
dbd35d3632dd0824c1837f34a4ed3f0d0fe440dd | War420da/Tesy | [
"license:openrail",
"region:us"
]
| 2023-01-23T12:56:52+00:00 | {"license": "openrail"} | 2023-01-23T12:56:54+00:00 |
|
b050b1a855784e4b00ccb94d72f5fc9eec4966b0 | # Dataset Card for "pexel_people"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuvalkirstain/pexel_people | [
"region:us"
]
| 2023-01-23T13:10:10+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "generated_caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5374411376.0, "num_examples": 15994}], "download_size": 3908548281, "dataset_size": 5374411376.0}} | 2023-01-23T14:01:38+00:00 |
15af196dd1d605fd3e5e3025be6a0aa5e03726ec | # Dataset Card for "Emoji_Dataset-Openmoji-BLIP"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | soypablo/Emoji_Dataset-Openmoji-BLIP | [
"region:us"
]
| 2023-01-23T13:20:41+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 85108246.546, "num_examples": 4083}], "download_size": 101495440, "dataset_size": 85108246.546}} | 2023-01-25T10:00:13+00:00 |
fd8beffb788df5f6673bc688e6dcbe3690a3acc6 | https://ixa2.si.ehu.eus/stswiki/index.php/STSbenchmark
The companion datasets to the STS Benchmark comprise the rest of the English datasets used in the STS tasks organized by us in the context of SemEval between 2012 and 2017.
Authors collated two datasets, one with pairs of sentences related to machine translation evaluation. Another one with the rest of datasets, which can be used for domain adaptation studies.
```bib
@inproceedings{cer-etal-2017-semeval,
title = "{S}em{E}val-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation",
author = "Cer, Daniel and
Diab, Mona and
Agirre, Eneko and
Lopez-Gazpio, I{\~n}igo and
Specia, Lucia",
booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S17-2001",
doi = "10.18653/v1/S17-2001",
pages = "1--14",
}
``` | tasksource/sts-companion | [
"task_categories:sentence-similarity",
"task_categories:text-classification",
"language:en",
"license:apache-2.0",
"sts",
"region:us"
]
| 2023-01-23T13:34:56+00:00 | {"language": ["en"], "license": "apache-2.0", "task_categories": ["sentence-similarity", "text-classification"], "tags": ["sts"]} | 2023-02-03T08:36:00+00:00 |
ddace36e243b1573b2e40ed07f82d62b09b4f9a5 | # Gronings transcribed speech
Demonstration dataset with Gronings transcribed speech based on the dataset released by [San et al. (2021)](https://github.com/fauxneticien/qbe-std_feats_eval).
For more information see the corresponding [ASRU 2021 paper](https://ieeexplore.ieee.org/abstract/document/9688301). | bartelds/gos-demo | [
"task_categories:automatic-speech-recognition",
"language:gos",
"license:cc-by-4.0",
"region:us"
]
| 2023-01-23T14:17:51+00:00 | {"language": ["gos"], "license": "cc-by-4.0", "task_categories": ["automatic-speech-recognition"], "dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "text", "dtype": "string"}], "splits": [{"name": "development", "num_bytes": 6030729, "num_examples": 59}, {"name": "test", "num_bytes": 8229224, "num_examples": 71}, {"name": "train", "num_bytes": 29128904, "num_examples": 300}], "download_size": 43004020, "dataset_size": 43388857}} | 2023-01-23T14:35:26+00:00 |
de806fb1036e510f9f4de599bed0cd0029b5286c | # AutoTrain Dataset for project: thycomments
## Dataset Description
This dataset has been automatically processed by HuggingFace AutoTrain for project tktktk.
### Languages
Turkish and English
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "@TK_TR 21 dk beklemem gerekti\u011fi s\u00f6yleniyor, m\u00fc\u015fteri temsilcisi ba\u011flanm\u0131yorum . \u0130nternet sitesinden de i\u015flem yap\u0131lam\u0131yor nas\u0131l \u00e7\u00f6z\u00fcm bulaca\u011f\u0131m ?",
"target": 0
},
{
"text": "@yhyustun Sevgili Yahya Bey Allah Rizasi icin bari sen bir aciklama yaparsan sevinirim.Konu su:Danimarkadan Turkiyeye ucuslar sistemde yok gorunuyor tum Mart ayi icin.1 Mart icin ucusum vardi fakat birkac gun once cagri merkeziyle gorustum ucuslar satisa kapanmis ancak bizim bir haberimiz",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(names=['negative', 'neutral', 'positive'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 2398 |
| valid | 601 | | tolgadev/thycomments | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:tr",
"language:en",
"region:us"
]
| 2023-01-23T14:34:30+00:00 | {"language": ["tr", "en"], "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"]} | 2023-01-30T14:18:43+00:00 |
67c48992720b060cf5939d8b56889552ce188ac3 | Yiff/Discord | [
"task_categories:text-generation",
"task_categories:conversational",
"region:us"
]
| 2023-01-23T14:47:13+00:00 | {"task_categories": ["text-generation", "conversational"]} | 2023-01-23T15:47:06+00:00 |
|
68c40c1620b63e175f06045e908ead8b3132d30c |
# preprocessed version of rcds/wikipedia-persons-masked
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Contains ~70k pages from wikipedia, each describing a person. For each page, the person described in the text
is masked with a <mask> token. The ground truth for every mask is provided.
Each row contains a part of a wiki page, specified by the size parameter which limits the maximum size in number of tokens per text chunk.
for each chunk the expected name for each mask is given.
### Supported Tasks and Leaderboards
The dataset supports the tasks of fill-mask, but can also be used for other tasks such as question answering,
e.g. "Who is <mask>?"
### Languages
*english only*
## Dataset Structure
In /data find different versions of the full dataset, with original and paraphrased versions as well as chunked to 4096 and 512 tokens.
Use the dataset like this:
```python
from datasets import load_dataset
dataset = load_dataset('rcds/wikipedia-persons-masked', split='train', type='original', size='512')
```
### Data Fields
Columns are:
- texts: the text chunks
- masks: the names for each of the masks in the chunks
### Data Splits
There are no splits, only a default train.
## Dataset Creation
Created by using the tokenizer from allenai/longformer-base-4096 for the 4096 token per chunk version,
and the xml-roberta-large tokenizer for the 512 token version. Chunks are split to fit those token sizes,
with the splits ensuring no words are split in half.
Possible improvements: Last chunk of a page might be much shorter, could join part of the previous one to have more tokens
in the last chunk.
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
TODO add citation
```
### Contributions
Thanks to [@skatinger](https://github.com/skatinger) for adding this dataset.
| rcds/wikipedia-for-mask-filling | [
"task_categories:fill-mask",
"annotations_creators:other",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
]
| 2023-01-23T15:14:48+00:00 | {"annotations_creators": ["other"], "language_creators": ["found"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["multilingual"], "size_categories": ["10M<n<100M"], "source_datasets": ["original"], "task_categories": ["fill-mask"], "pretty_name": "wikipedia pages chunked for fill-mask"} | 2023-03-08T12:22:02+00:00 |
58c2e4e55263a5b613358a611b1e309ca0f3775f | # Dataset Card for "wvDataset2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | basilis/wvDataset2 | [
"region:us"
]
| 2023-01-23T16:54:08+00:00 | {"dataset_info": {"features": [{"name": "tokenized_text", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 6675666248, "num_examples": 97928}], "download_size": 1690147799, "dataset_size": 6675666248}} | 2023-01-23T16:58:36+00:00 |
3d34aa2757a869ace6102fdb4e992e38f990101d | nlpservicebots/operations | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:es",
"license:apache-2.0",
"region:us"
]
| 2023-01-23T17:52:12+00:00 | {"language": ["es"], "license": "apache-2.0", "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"]} | 2023-01-23T18:02:46+00:00 |
|
a71b5376f7f3e38502e726a9c3838e88c643d258 | # Dataset Card for "atis_intents"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/atis_intents | [
"region:us"
]
| 2023-01-23T18:19:03+00:00 | {"dataset_info": {"features": [{"name": "label text", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 448812, "num_examples": 4834}, {"name": "test", "num_bytes": 69352, "num_examples": 800}], "download_size": 157677, "dataset_size": 518164}} | 2023-01-23T18:25:53+00:00 |
350704cfca814d0e4292a7d3db40370beccc5d99 | # Dataset Card for "academia"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | juancopi81/academia | [
"task_categories:automatic-speech-recognition",
"whisper",
"whispering",
"medium",
"region:us"
]
| 2023-01-23T18:19:25+00:00 | {"task_categories": ["automatic-speech-recognition"], "dataset_info": {"features": [{"name": "CHANNEL_NAME", "dtype": "string"}, {"name": "URL", "dtype": "string"}, {"name": "TITLE", "dtype": "string"}, {"name": "DESCRIPTION", "dtype": "string"}, {"name": "TRANSCRIPTION", "dtype": "string"}, {"name": "SEGMENTS", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4010418, "num_examples": 52}], "download_size": 273124, "dataset_size": 4010418}, "tags": ["whisper", "whispering", "medium"]} | 2023-01-24T18:28:53+00:00 |
12f7ad4373294f6e937836d5cebfb658f5086056 | plncmm/wl-disease | [
"license:cc-by-nc-4.0",
"region:us"
]
| 2023-01-23T18:22:41+00:00 | {"license": "cc-by-nc-4.0"} | 2023-01-23T18:28:18+00:00 |
|
581a78cb18ae064648318ddbb1d8e6c8b5f8fdba | plncmm/wl-medication | [
"license:cc-by-nc-4.0",
"region:us"
]
| 2023-01-23T18:29:51+00:00 | {"license": "cc-by-nc-4.0"} | 2023-01-23T18:32:07+00:00 |
|
16151deafec45e90e8b26cd60bd95dbbe99575f3 | plncmm/wl-body-part | [
"license:cc-by-nc-4.0",
"region:us"
]
| 2023-01-23T18:35:00+00:00 | {"license": "cc-by-nc-4.0"} | 2023-01-23T18:36:59+00:00 |
|
52633e97c612380184c2714248f55cddfea8156e | plncmm/wl-finding | [
"license:cc-by-nc-4.0",
"region:us"
]
| 2023-01-23T18:37:31+00:00 | {"license": "cc-by-nc-4.0"} | 2023-01-23T18:40:24+00:00 |
|
862585e81d7b0b99165121599e70b2154d5ed263 | plncmm/wl-procedure | [
"license:cc-by-nc-4.0",
"region:us"
]
| 2023-01-23T18:40:54+00:00 | {"license": "cc-by-nc-4.0"} | 2023-01-23T18:42:45+00:00 |
|
95993dd5fca2ad94ebff18f4900683702e6c6746 | plncmm/wl-abbreviation | [
"license:cc-by-nc-4.0",
"region:us"
]
| 2023-01-23T18:43:15+00:00 | {"license": "cc-by-nc-4.0"} | 2023-01-23T18:45:03+00:00 |
|
a4b7289c526191181f4ae636fc097f1ab13507a8 | plncmm/wl-family-member | [
"license:cc-by-nc-4.0",
"region:us"
]
| 2023-01-23T18:45:40+00:00 | {"license": "cc-by-nc-4.0"} | 2023-01-23T18:48:00+00:00 |
|
175d8e24a9c9f01308a81eeda30d43d6c1aae67e | # Dataset Card for "dreambooth-hackathon-images"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | purplebear/dreambooth-hackathon-images | [
"region:us"
]
| 2023-01-23T19:54:39+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 54613224.0, "num_examples": 20}], "download_size": 54616715, "dataset_size": 54613224.0}} | 2023-01-23T20:07:29+00:00 |
e6d14657870251c07882a1a2ee772277d85a75bc | # Dataset Card for "utd_reddit.json"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Rami/utd_reddit.json | [
"region:us"
]
| 2023-01-23T20:05:54+00:00 | {"dataset_info": {"features": [{"name": "j52edo", "struct": [{"name": "title", "dtype": "string"}, {"name": "selftext", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "num_comments", "dtype": "int64"}, {"name": "permalink", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "comments", "struct": [{"name": "g7p723l", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}, {"name": "g7pmgai", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}, {"name": "replies", "struct": [{"name": "g7q0gtr", "struct": [{"name": "body", "dtype": "string"}]}]}]}, {"name": "g7p6z8q", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}, {"name": "g7q37rw", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}, {"name": "replies", "struct": [{"name": "g7qjj6o", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}]}]}, {"name": "g7p4ynr", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}, {"name": "g7paxsm", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}, {"name": "g7p543c", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}, {"name": "replies", "struct": [{"name": "g7pvhwr", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}]}]}, {"name": "g7qgcr3", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}, {"name": "g7p8y1o", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}, {"name": "g7pajp9", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}, {"name": "g7pn8t5", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}, {"name": "replies", "struct": [{"name": "g7psgy5", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}]}]}, {"name": "g7s767n", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}, {"name": "g7qrjeu", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}, {"name": "replies", "struct": [{"name": "g7r3brk", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}]}]}, {"name": "g7q48td", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}, {"name": "g7q3j2n", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}, {"name": "replies", "struct": [{"name": "g7ujauu", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}]}]}, {"name": "g7pt766", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}, {"name": "g7pyov9", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}, {"name": "replies", "struct": [{"name": "g7q1j3w", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}]}]}, {"name": "g7qvvrm", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}, {"name": "replies", "struct": [{"name": "g7t8u30", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}]}]}, {"name": "g7sqe5g", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}, {"name": "gn3icng", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}, {"name": "replies", "struct": [{"name": "gn3id7g", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}]}]}, {"name": "g7qjzq9", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}, {"name": "grxwrut", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}, {"name": "is1ekdj", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}, {"name": "g7q0gtr", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}, {"name": "replies", "struct": [{"name": "g7qn1hx", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}]}]}, {"name": "g7qjj6o", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}, {"name": "replies", "struct": [{"name": "g7tdb88", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}]}]}, {"name": "g7pvhwr", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}, {"name": "g7psgy5", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}, {"name": "replies", "struct": [{"name": "g7psssg", "struct": [{"name": "body", "dtype": "string"}]}]}]}, {"name": "g7r3brk", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}, {"name": "g7ujauu", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}, {"name": "replies", "struct": [{"name": "g7ujcwo", "struct": [{"name": "body", "dtype": "string"}]}]}]}, {"name": "g7q1j3w", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}, {"name": "replies", "struct": [{"name": "g7q1ukv", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}]}]}, {"name": "g7t8u30", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}, {"name": "gn3id7g", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}, {"name": "g7qn1hx", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}, {"name": "g7tdb88", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}, {"name": "g7psssg", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}, {"name": "replies", "struct": [{"name": "g7qvgs1", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}]}]}, {"name": "g7ujcwo", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}, {"name": "g7q1ukv", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}, {"name": "g7qvgs1", "struct": [{"name": "body", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "score", "dtype": "int64"}]}]}]}], "splits": [{"name": "train", "num_bytes": 5510, "num_examples": 1}], "download_size": 94050, "dataset_size": 5510}} | 2023-01-24T16:30:59+00:00 |
48e8ffadc4c1d81013615481968376fade4bb046 | # Dataset Card for "dataset-identities-v-1.4-colorfulness"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | SDbiaseval/dataset-identities-v-1.4-colorfulness | [
"region:us"
]
| 2023-01-23T20:11:47+00:00 | {"dataset_info": {"features": [{"name": "ethnicity", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "no", "dtype": "int32"}, {"name": "image_path", "dtype": "string"}, {"name": "colorfulness", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 65148, "num_examples": 480}], "download_size": 12121, "dataset_size": 65148}} | 2023-01-23T20:12:00+00:00 |
bce52c9046c39581ed3309b7dd18934bbfa7b70a |
# Dataset Card for DivEMT Attributions
*For more details on DivEMT, see our [EMNLP 2022 Paper](https://arxiv.org/abs/2205.12215) and our [Github repository](https://github.com/gsarti/divemt)*
## Dataset Description
- **DivEMT Source:** [DivEMT Github](https://github.com/gsarti/divemt)
- **Inseq Source:** [Inseq Github](https://github.com/inseq-team/inseq)
- **DivEMT Paper:** [DivEMT Arxiv](https://arxiv.org/abs/2205.12215)
- **Inseq Paper:** [Inseq Arxiv](https://arxiv.org/abs/2302.13942)
- **Point of Contact:** [Gabriele Sarti](mailto:[email protected]) | inseq/divemt_attributions | [
"task_categories:translation",
"annotations_creators:machine-generated",
"multilinguality:translation",
"size_categories:1K<n<10K",
"language:it",
"language:ar",
"language:nl",
"language:tr",
"language:uk",
"language:vi",
"license:apache-2.0",
"arxiv:2205.12215",
"arxiv:2302.13942",
"region:us"
]
| 2023-01-23T21:26:39+00:00 | {"annotations_creators": ["machine-generated"], "language": ["it", "ar", "nl", "tr", "uk", "vi"], "license": "apache-2.0", "multilinguality": ["translation"], "size_categories": ["1K<n<10K"], "task_categories": ["translation"], "pretty_name": "divemt_attributions"} | 2023-03-16T16:02:19+00:00 |
b60506d1caf875867bcaa17e6467ddf303e28ac7 |
## Description
This dataset contains images of Senko-san which were drawn by Rimukoro. All images are cropped up to 512x512 and every image contains txt file with tags list which were extracted from one of booru site.
## Examples
 | NeuroSenko/senko-arts-by-rimukoro-512x512 | [
"license:mit",
"Senko",
"region:us"
]
| 2023-01-23T22:34:02+00:00 | {"license": "mit", "tags": ["Senko"]} | 2023-07-17T02:40:35+00:00 |
eebce6f114bd4b500d8bdcd1b214ceaa05cbe8df |
This dataset was created using [this tool](https://github.com/p1atdev/pinterest-wizard).
# concept_art.json
589 images about "Concept Art" from **Pinterest searches** 🤗.
# double_exposure.json
597 images about "Double Exposure" from **Pinterest searches** 🤗.
# vaporwave.json
599 images about "Vaporwave" from **Pinterest searches** 🤗.
# typography.json
633 images about "Typography" from **Pinterest searches** 🤗.
# portrait.json
573 images about "Portrait" from **Pinterest searches** 🤗.
# selfie.json
584 images about "自撮り 女の子" from **Pinterest searches** 🤗.
# Type
```ts
interface Pinterest {
url: string // pinterest page url
alt: string // description of the image (not so accurate everytime)
src: string // image url
tags: string[] // related tags
}
``` | p1atdev/pinterest | [
"license:cc0-1.0",
"region:us"
]
| 2023-01-23T23:27:08+00:00 | {"license": "cc0-1.0"} | 2023-01-30T04:06:07+00:00 |
00ebdd95d3c9d2c126ab659612271811f4a2baca | # Charged up 2023 data set from Team 88 TJ2
This data set contains labeled images of the two game objects for the 2023 FRC game "Charged Up".
Labels and folder structure follow the yolov5 format.
Data set split:
|Type|Size|
|---|---|
|Train|9352|
|Validation|934|
|Test|103|
| woz4tetra/charged_up_2023 | [
"frc",
"2023",
"charged up",
"team 88",
"tj2",
"region:us"
]
| 2023-01-24T02:03:34+00:00 | {"pretty_name": "Charged Up 2023 Cones and Cubes", "tags": ["frc", "2023", "charged up", "team 88", "tj2"]} | 2023-02-26T19:19:05+00:00 |
78012249b8dec7fc370bbc406457f9092b6e6e54 | # Dataset Card for "LegoCityAdventures"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ACosmicFractal/LegoCityAdventures | [
"region:us"
]
| 2023-01-24T05:38:01+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 86324590.0, "num_examples": 653}], "download_size": 84571697, "dataset_size": 86324590.0}} | 2023-02-07T09:17:32+00:00 |
bcb669db34ac11538a16d30655e84425c6f42a4a | sieecc/SOKI | [
"license:other",
"region:us"
]
| 2023-01-24T06:48:25+00:00 | {"license": "other"} | 2023-01-24T06:48:25+00:00 |
|
f81dbf9f801073710e75b002ef7593cd2230038e | # Dataset Card for "my-pokeman-dataset1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | xfh/my-pokeman-dataset1 | [
"region:us"
]
| 2023-01-24T07:21:16+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 836857.0, "num_examples": 20}], "download_size": 805844, "dataset_size": 836857.0}} | 2023-01-24T07:22:43+00:00 |
efaa028cad4324f116463a0b30ae008812555157 | albertvillanova/tmp-multilingual | [
"language:multilingual",
"language:mul",
"region:us"
]
| 2023-01-24T07:41:27+00:00 | {"language": ["multilingual", "mul"]} | 2023-01-24T07:44:28+00:00 |
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