librarian-bots/dataset_cards_with_metadata_with_embeddings

datasetId stringlengths 2 117	author stringlengths 2 42 ⌀	last_modified timestamp[us, tz=UTC]	likes int64 0 3.89k	tags list	task_categories list	createdAt timestamp[us, tz=UTC]	card stringlengths 19 1.01M	embedding list
BangumiBase/sailormoon1990s	BangumiBase	2023-11-20T11:23:44Z	0	[ "size_categories:10K<n<100K", "license:mit", "art", "region:us" ]	null	2023-11-19T21:15:02Z	--- license: mit tags: - art size_categories: - 10K<n<100K --- # Bangumi Image Base of Sailor Moon (1990s) This is the image base of bangumi Sailor Moon (1990s), we detected 132 characters, 14684 images in total. The full dataset is [here](all.zip). Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview: \| # \| Images \| Download \| Preview 1 \| Preview 2 \| Preview 3 \| Preview 4 \| Preview 5 \| Preview 6 \| Preview 7 \| Preview 8 \| \|:------\|---------:\|:----------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\| \| 0 \| 3008 \| [Download](0/dataset.zip) \| ![preview 1](0/preview_1.png) \| ![preview 2](0/preview_2.png) \| ![preview 3](0/preview_3.png) \| ![preview 4](0/preview_4.png) \| ![preview 5](0/preview_5.png) \| ![preview 6](0/preview_6.png) \| ![preview 7](0/preview_7.png) \| ![preview 8](0/preview_8.png) \| \| 1 \| 94 \| [Download](1/dataset.zip) \| ![preview 1](1/preview_1.png) \| ![preview 2](1/preview_2.png) \| ![preview 3](1/preview_3.png) \| ![preview 4](1/preview_4.png) \| ![preview 5](1/preview_5.png) \| ![preview 6](1/preview_6.png) \| ![preview 7](1/preview_7.png) \| ![preview 8](1/preview_8.png) \| \| 2 \| 696 \| [Download](2/dataset.zip) \| ![preview 1](2/preview_1.png) \| ![preview 2](2/preview_2.png) \| ![preview 3](2/preview_3.png) \| ![preview 4](2/preview_4.png) \| ![preview 5](2/preview_5.png) \| ![preview 6](2/preview_6.png) \| ![preview 7](2/preview_7.png) \| ![preview 8](2/preview_8.png) \| \| 3 \| 49 \| [Download](3/dataset.zip) \| ![preview 1](3/preview_1.png) \| ![preview 2](3/preview_2.png) \| ![preview 3](3/preview_3.png) \| ![preview 4](3/preview_4.png) \| ![preview 5](3/preview_5.png) \| ![preview 6](3/preview_6.png) \| ![preview 7](3/preview_7.png) \| ![preview 8](3/preview_8.png) \| \| 4 \| 29 \| [Download](4/dataset.zip) \| ![preview 1](4/preview_1.png) \| ![preview 2](4/preview_2.png) \| ![preview 3](4/preview_3.png) \| ![preview 4](4/preview_4.png) \| ![preview 5](4/preview_5.png) \| ![preview 6](4/preview_6.png) \| ![preview 7](4/preview_7.png) \| ![preview 8](4/preview_8.png) \| \| 5 \| 176 \| [Download](5/dataset.zip) \| ![preview 1](5/preview_1.png) \| ![preview 2](5/preview_2.png) \| ![preview 3](5/preview_3.png) \| ![preview 4](5/preview_4.png) \| ![preview 5](5/preview_5.png) \| ![preview 6](5/preview_6.png) \| ![preview 7](5/preview_7.png) \| ![preview 8](5/preview_8.png) \| \| 6 \| 95 \| [Download](6/dataset.zip) \| ![preview 1](6/preview_1.png) \| ![preview 2](6/preview_2.png) \| ![preview 3](6/preview_3.png) \| ![preview 4](6/preview_4.png) \| ![preview 5](6/preview_5.png) \| ![preview 6](6/preview_6.png) \| ![preview 7](6/preview_7.png) \| ![preview 8](6/preview_8.png) \| \| 7 \| 72 \| [Download](7/dataset.zip) \| ![preview 1](7/preview_1.png) \| ![preview 2](7/preview_2.png) \| ![preview 3](7/preview_3.png) \| ![preview 4](7/preview_4.png) \| ![preview 5](7/preview_5.png) \| ![preview 6](7/preview_6.png) \| ![preview 7](7/preview_7.png) \| ![preview 8](7/preview_8.png) \| \| 8 \| 180 \| [Download](8/dataset.zip) \| ![preview 1](8/preview_1.png) \| ![preview 2](8/preview_2.png) \| ![preview 3](8/preview_3.png) \| ![preview 4](8/preview_4.png) \| ![preview 5](8/preview_5.png) \| ![preview 6](8/preview_6.png) \| ![preview 7](8/preview_7.png) \| ![preview 8](8/preview_8.png) \| \| 9 \| 75 \| [Download](9/dataset.zip) \| ![preview 1](9/preview_1.png) \| ![preview 2](9/preview_2.png) \| ![preview 3](9/preview_3.png) \| ![preview 4](9/preview_4.png) \| ![preview 5](9/preview_5.png) \| ![preview 6](9/preview_6.png) \| ![preview 7](9/preview_7.png) \| ![preview 8](9/preview_8.png) \| \| 10 \| 108 \| [Download](10/dataset.zip) \| ![preview 1](10/preview_1.png) \| ![preview 2](10/preview_2.png) \| ![preview 3](10/preview_3.png) \| ![preview 4](10/preview_4.png) \| ![preview 5](10/preview_5.png) \| ![preview 6](10/preview_6.png) \| ![preview 7](10/preview_7.png) \| ![preview 8](10/preview_8.png) \| \| 11 \| 113 \| [Download](11/dataset.zip) \| ![preview 1](11/preview_1.png) \| ![preview 2](11/preview_2.png) \| ![preview 3](11/preview_3.png) \| ![preview 4](11/preview_4.png) \| ![preview 5](11/preview_5.png) \| ![preview 6](11/preview_6.png) \| ![preview 7](11/preview_7.png) \| ![preview 8](11/preview_8.png) \| \| 12 \| 32 \| [Download](12/dataset.zip) \| ![preview 1](12/preview_1.png) \| ![preview 2](12/preview_2.png) \| ![preview 3](12/preview_3.png) \| ![preview 4](12/preview_4.png) \| ![preview 5](12/preview_5.png) \| ![preview 6](12/preview_6.png) \| ![preview 7](12/preview_7.png) \| ![preview 8](12/preview_8.png) \| \| 13 \| 42 \| [Download](13/dataset.zip) \| ![preview 1](13/preview_1.png) \| ![preview 2](13/preview_2.png) \| ![preview 3](13/preview_3.png) \| ![preview 4](13/preview_4.png) \| ![preview 5](13/preview_5.png) \| ![preview 6](13/preview_6.png) \| ![preview 7](13/preview_7.png) \| ![preview 8](13/preview_8.png) \| \| 14 \| 47 \| [Download](14/dataset.zip) \| ![preview 1](14/preview_1.png) \| ![preview 2](14/preview_2.png) \| ![preview 3](14/preview_3.png) \| ![preview 4](14/preview_4.png) \| ![preview 5](14/preview_5.png) \| ![preview 6](14/preview_6.png) \| ![preview 7](14/preview_7.png) \| ![preview 8](14/preview_8.png) \| \| 15 \| 602 \| [Download](15/dataset.zip) \| ![preview 1](15/preview_1.png) \| ![preview 2](15/preview_2.png) \| ![preview 3](15/preview_3.png) \| ![preview 4](15/preview_4.png) \| ![preview 5](15/preview_5.png) \| ![preview 6](15/preview_6.png) \| ![preview 7](15/preview_7.png) \| ![preview 8](15/preview_8.png) \| \| 16 \| 1066 \| [Download](16/dataset.zip) \| ![preview 1](16/preview_1.png) \| ![preview 2](16/preview_2.png) \| ![preview 3](16/preview_3.png) \| ![preview 4](16/preview_4.png) \| ![preview 5](16/preview_5.png) \| ![preview 6](16/preview_6.png) \| ![preview 7](16/preview_7.png) \| ![preview 8](16/preview_8.png) \| \| 17 \| 395 \| [Download](17/dataset.zip) \| ![preview 1](17/preview_1.png) \| ![preview 2](17/preview_2.png) \| ![preview 3](17/preview_3.png) \| ![preview 4](17/preview_4.png) \| ![preview 5](17/preview_5.png) \| ![preview 6](17/preview_6.png) \| ![preview 7](17/preview_7.png) \| ![preview 8](17/preview_8.png) \| \| 18 \| 208 \| [Download](18/dataset.zip) \| ![preview 1](18/preview_1.png) \| ![preview 2](18/preview_2.png) \| ![preview 3](18/preview_3.png) \| ![preview 4](18/preview_4.png) \| ![preview 5](18/preview_5.png) \| ![preview 6](18/preview_6.png) \| ![preview 7](18/preview_7.png) \| ![preview 8](18/preview_8.png) \| \| 19 \| 79 \| [Download](19/dataset.zip) \| ![preview 1](19/preview_1.png) \| ![preview 2](19/preview_2.png) \| ![preview 3](19/preview_3.png) \| ![preview 4](19/preview_4.png) \| ![preview 5](19/preview_5.png) \| ![preview 6](19/preview_6.png) \| ![preview 7](19/preview_7.png) \| ![preview 8](19/preview_8.png) \| \| 20 \| 86 \| [Download](20/dataset.zip) \| ![preview 1](20/preview_1.png) \| ![preview 2](20/preview_2.png) \| ![preview 3](20/preview_3.png) \| ![preview 4](20/preview_4.png) \| ![preview 5](20/preview_5.png) \| ![preview 6](20/preview_6.png) \| ![preview 7](20/preview_7.png) \| ![preview 8](20/preview_8.png) \| \| 21 \| 62 \| [Download](21/dataset.zip) \| ![preview 1](21/preview_1.png) \| ![preview 2](21/preview_2.png) \| ![preview 3](21/preview_3.png) \| ![preview 4](21/preview_4.png) \| ![preview 5](21/preview_5.png) \| ![preview 6](21/preview_6.png) \| ![preview 7](21/preview_7.png) \| ![preview 8](21/preview_8.png) \| \| 22 \| 50 \| [Download](22/dataset.zip) \| ![preview 1](22/preview_1.png) \| ![preview 2](22/preview_2.png) \| ![preview 3](22/preview_3.png) \| ![preview 4](22/preview_4.png) \| ![preview 5](22/preview_5.png) \| ![preview 6](22/preview_6.png) \| ![preview 7](22/preview_7.png) \| ![preview 8](22/preview_8.png) \| \| 23 \| 53 \| [Download](23/dataset.zip) \| ![preview 1](23/preview_1.png) \| ![preview 2](23/preview_2.png) \| ![preview 3](23/preview_3.png) \| ![preview 4](23/preview_4.png) \| ![preview 5](23/preview_5.png) \| ![preview 6](23/preview_6.png) \| ![preview 7](23/preview_7.png) \| ![preview 8](23/preview_8.png) \| \| 24 \| 76 \| [Download](24/dataset.zip) \| ![preview 1](24/preview_1.png) \| ![preview 2](24/preview_2.png) \| ![preview 3](24/preview_3.png) \| ![preview 4](24/preview_4.png) \| ![preview 5](24/preview_5.png) \| ![preview 6](24/preview_6.png) \| ![preview 7](24/preview_7.png) \| ![preview 8](24/preview_8.png) \| \| 25 \| 141 \| [Download](25/dataset.zip) \| ![preview 1](25/preview_1.png) \| ![preview 2](25/preview_2.png) \| ![preview 3](25/preview_3.png) \| ![preview 4](25/preview_4.png) \| ![preview 5](25/preview_5.png) \| ![preview 6](25/preview_6.png) \| ![preview 7](25/preview_7.png) \| ![preview 8](25/preview_8.png) \| \| 26 \| 67 \| [Download](26/dataset.zip) \| ![preview 1](26/preview_1.png) \| ![preview 2](26/preview_2.png) \| ![preview 3](26/preview_3.png) \| ![preview 4](26/preview_4.png) \| ![preview 5](26/preview_5.png) \| ![preview 6](26/preview_6.png) \| ![preview 7](26/preview_7.png) \| ![preview 8](26/preview_8.png) \| \| 27 \| 45 \| [Download](27/dataset.zip) \| ![preview 1](27/preview_1.png) \| ![preview 2](27/preview_2.png) \| ![preview 3](27/preview_3.png) \| ![preview 4](27/preview_4.png) \| ![preview 5](27/preview_5.png) \| ![preview 6](27/preview_6.png) \| ![preview 7](27/preview_7.png) \| ![preview 8](27/preview_8.png) \| \| 28 \| 750 \| [Download](28/dataset.zip) \| ![preview 1](28/preview_1.png) \| ![preview 2](28/preview_2.png) \| ![preview 3](28/preview_3.png) \| ![preview 4](28/preview_4.png) \| ![preview 5](28/preview_5.png) \| ![preview 6](28/preview_6.png) \| ![preview 7](28/preview_7.png) \| ![preview 8](28/preview_8.png) \| \| 29 \| 103 \| [Download](29/dataset.zip) \| ![preview 1](29/preview_1.png) \| ![preview 2](29/preview_2.png) \| ![preview 3](29/preview_3.png) \| ![preview 4](29/preview_4.png) \| ![preview 5](29/preview_5.png) \| ![preview 6](29/preview_6.png) \| ![preview 7](29/preview_7.png) \| ![preview 8](29/preview_8.png) \| \| 30 \| 34 \| [Download](30/dataset.zip) \| ![preview 1](30/preview_1.png) \| ![preview 2](30/preview_2.png) \| ![preview 3](30/preview_3.png) \| ![preview 4](30/preview_4.png) \| ![preview 5](30/preview_5.png) \| ![preview 6](30/preview_6.png) \| ![preview 7](30/preview_7.png) \| ![preview 8](30/preview_8.png) \| \| 31 \| 42 \| [Download](31/dataset.zip) \| ![preview 1](31/preview_1.png) \| ![preview 2](31/preview_2.png) \| ![preview 3](31/preview_3.png) \| ![preview 4](31/preview_4.png) \| ![preview 5](31/preview_5.png) \| ![preview 6](31/preview_6.png) \| ![preview 7](31/preview_7.png) \| ![preview 8](31/preview_8.png) \| \| 32 \| 20 \| [Download](32/dataset.zip) \| ![preview 1](32/preview_1.png) \| ![preview 2](32/preview_2.png) \| ![preview 3](32/preview_3.png) \| ![preview 4](32/preview_4.png) \| ![preview 5](32/preview_5.png) \| ![preview 6](32/preview_6.png) \| ![preview 7](32/preview_7.png) \| ![preview 8](32/preview_8.png) \| \| 33 \| 67 \| [Download](33/dataset.zip) \| ![preview 1](33/preview_1.png) \| ![preview 2](33/preview_2.png) \| ![preview 3](33/preview_3.png) \| ![preview 4](33/preview_4.png) \| ![preview 5](33/preview_5.png) \| ![preview 6](33/preview_6.png) \| ![preview 7](33/preview_7.png) \| ![preview 8](33/preview_8.png) \| \| 34 \| 79 \| [Download](34/dataset.zip) \| ![preview 1](34/preview_1.png) \| ![preview 2](34/preview_2.png) \| ![preview 3](34/preview_3.png) \| ![preview 4](34/preview_4.png) \| ![preview 5](34/preview_5.png) \| ![preview 6](34/preview_6.png) \| ![preview 7](34/preview_7.png) \| ![preview 8](34/preview_8.png) \| \| 35 \| 40 \| [Download](35/dataset.zip) \| ![preview 1](35/preview_1.png) \| ![preview 2](35/preview_2.png) \| ![preview 3](35/preview_3.png) \| ![preview 4](35/preview_4.png) \| ![preview 5](35/preview_5.png) \| ![preview 6](35/preview_6.png) \| ![preview 7](35/preview_7.png) \| ![preview 8](35/preview_8.png) \| \| 36 \| 45 \| [Download](36/dataset.zip) \| ![preview 1](36/preview_1.png) \| ![preview 2](36/preview_2.png) \| ![preview 3](36/preview_3.png) \| ![preview 4](36/preview_4.png) \| ![preview 5](36/preview_5.png) \| ![preview 6](36/preview_6.png) \| ![preview 7](36/preview_7.png) \| ![preview 8](36/preview_8.png) \| \| 37 \| 118 \| [Download](37/dataset.zip) \| ![preview 1](37/preview_1.png) \| ![preview 2](37/preview_2.png) \| ![preview 3](37/preview_3.png) \| ![preview 4](37/preview_4.png) \| ![preview 5](37/preview_5.png) \| ![preview 6](37/preview_6.png) \| ![preview 7](37/preview_7.png) \| ![preview 8](37/preview_8.png) \| \| 38 \| 41 \| [Download](38/dataset.zip) \| ![preview 1](38/preview_1.png) \| ![preview 2](38/preview_2.png) \| ![preview 3](38/preview_3.png) \| ![preview 4](38/preview_4.png) \| ![preview 5](38/preview_5.png) \| ![preview 6](38/preview_6.png) \| ![preview 7](38/preview_7.png) \| ![preview 8](38/preview_8.png) \| \| 39 \| 62 \| [Download](39/dataset.zip) \| ![preview 1](39/preview_1.png) \| ![preview 2](39/preview_2.png) \| ![preview 3](39/preview_3.png) \| ![preview 4](39/preview_4.png) \| ![preview 5](39/preview_5.png) \| ![preview 6](39/preview_6.png) \| ![preview 7](39/preview_7.png) \| ![preview 8](39/preview_8.png) \| \| 40 \| 93 \| [Download](40/dataset.zip) \| ![preview 1](40/preview_1.png) \| ![preview 2](40/preview_2.png) \| ![preview 3](40/preview_3.png) \| ![preview 4](40/preview_4.png) \| ![preview 5](40/preview_5.png) \| ![preview 6](40/preview_6.png) \| ![preview 7](40/preview_7.png) \| ![preview 8](40/preview_8.png) \| \| 41 \| 79 \| [Download](41/dataset.zip) \| ![preview 1](41/preview_1.png) \| ![preview 2](41/preview_2.png) \| ![preview 3](41/preview_3.png) \| ![preview 4](41/preview_4.png) \| ![preview 5](41/preview_5.png) \| ![preview 6](41/preview_6.png) \| ![preview 7](41/preview_7.png) \| ![preview 8](41/preview_8.png) \| \| 42 \| 920 \| [Download](42/dataset.zip) \| ![preview 1](42/preview_1.png) \| ![preview 2](42/preview_2.png) \| ![preview 3](42/preview_3.png) \| ![preview 4](42/preview_4.png) \| ![preview 5](42/preview_5.png) \| ![preview 6](42/preview_6.png) \| ![preview 7](42/preview_7.png) \| ![preview 8](42/preview_8.png) \| \| 43 \| 55 \| [Download](43/dataset.zip) \| ![preview 1](43/preview_1.png) \| ![preview 2](43/preview_2.png) \| ![preview 3](43/preview_3.png) \| ![preview 4](43/preview_4.png) \| ![preview 5](43/preview_5.png) \| ![preview 6](43/preview_6.png) \| ![preview 7](43/preview_7.png) \| ![preview 8](43/preview_8.png) \| \| 44 \| 75 \| [Download](44/dataset.zip) \| ![preview 1](44/preview_1.png) \| ![preview 2](44/preview_2.png) \| ![preview 3](44/preview_3.png) \| ![preview 4](44/preview_4.png) \| ![preview 5](44/preview_5.png) \| ![preview 6](44/preview_6.png) \| ![preview 7](44/preview_7.png) \| ![preview 8](44/preview_8.png) \| \| 45 \| 36 \| [Download](45/dataset.zip) \| ![preview 1](45/preview_1.png) \| ![preview 2](45/preview_2.png) \| ![preview 3](45/preview_3.png) \| ![preview 4](45/preview_4.png) \| ![preview 5](45/preview_5.png) \| ![preview 6](45/preview_6.png) \| ![preview 7](45/preview_7.png) \| ![preview 8](45/preview_8.png) \| \| 46 \| 15 \| [Download](46/dataset.zip) \| ![preview 1](46/preview_1.png) \| ![preview 2](46/preview_2.png) \| ![preview 3](46/preview_3.png) \| ![preview 4](46/preview_4.png) \| ![preview 5](46/preview_5.png) \| ![preview 6](46/preview_6.png) \| ![preview 7](46/preview_7.png) \| ![preview 8](46/preview_8.png) \| \| 47 \| 126 \| [Download](47/dataset.zip) \| ![preview 1](47/preview_1.png) \| ![preview 2](47/preview_2.png) \| ![preview 3](47/preview_3.png) \| ![preview 4](47/preview_4.png) \| ![preview 5](47/preview_5.png) \| ![preview 6](47/preview_6.png) \| ![preview 7](47/preview_7.png) \| ![preview 8](47/preview_8.png) \| \| 48 \| 41 \| [Download](48/dataset.zip) \| ![preview 1](48/preview_1.png) \| ![preview 2](48/preview_2.png) \| ![preview 3](48/preview_3.png) \| ![preview 4](48/preview_4.png) \| ![preview 5](48/preview_5.png) \| ![preview 6](48/preview_6.png) \| ![preview 7](48/preview_7.png) \| ![preview 8](48/preview_8.png) \| \| 49 \| 46 \| [Download](49/dataset.zip) \| ![preview 1](49/preview_1.png) \| ![preview 2](49/preview_2.png) \| ![preview 3](49/preview_3.png) \| ![preview 4](49/preview_4.png) \| ![preview 5](49/preview_5.png) \| ![preview 6](49/preview_6.png) \| ![preview 7](49/preview_7.png) \| ![preview 8](49/preview_8.png) \| \| 50 \| 100 \| [Download](50/dataset.zip) \| ![preview 1](50/preview_1.png) \| ![preview 2](50/preview_2.png) \| ![preview 3](50/preview_3.png) \| ![preview 4](50/preview_4.png) \| ![preview 5](50/preview_5.png) \| ![preview 6](50/preview_6.png) \| ![preview 7](50/preview_7.png) \| ![preview 8](50/preview_8.png) \| \| 51 \| 121 \| [Download](51/dataset.zip) \| ![preview 1](51/preview_1.png) \| ![preview 2](51/preview_2.png) \| ![preview 3](51/preview_3.png) \| ![preview 4](51/preview_4.png) \| ![preview 5](51/preview_5.png) \| ![preview 6](51/preview_6.png) \| ![preview 7](51/preview_7.png) \| ![preview 8](51/preview_8.png) \| \| 52 \| 36 \| [Download](52/dataset.zip) \| ![preview 1](52/preview_1.png) \| ![preview 2](52/preview_2.png) \| ![preview 3](52/preview_3.png) \| ![preview 4](52/preview_4.png) \| ![preview 5](52/preview_5.png) \| ![preview 6](52/preview_6.png) \| ![preview 7](52/preview_7.png) \| ![preview 8](52/preview_8.png) \| \| 53 \| 102 \| [Download](53/dataset.zip) \| ![preview 1](53/preview_1.png) \| ![preview 2](53/preview_2.png) \| ![preview 3](53/preview_3.png) \| ![preview 4](53/preview_4.png) \| ![preview 5](53/preview_5.png) \| ![preview 6](53/preview_6.png) \| ![preview 7](53/preview_7.png) \| ![preview 8](53/preview_8.png) \| \| 54 \| 50 \| [Download](54/dataset.zip) \| ![preview 1](54/preview_1.png) \| ![preview 2](54/preview_2.png) \| ![preview 3](54/preview_3.png) \| ![preview 4](54/preview_4.png) \| ![preview 5](54/preview_5.png) \| ![preview 6](54/preview_6.png) \| ![preview 7](54/preview_7.png) \| ![preview 8](54/preview_8.png) \| \| 55 \| 105 \| [Download](55/dataset.zip) \| ![preview 1](55/preview_1.png) \| ![preview 2](55/preview_2.png) \| ![preview 3](55/preview_3.png) \| ![preview 4](55/preview_4.png) \| ![preview 5](55/preview_5.png) \| ![preview 6](55/preview_6.png) \| ![preview 7](55/preview_7.png) \| ![preview 8](55/preview_8.png) \| \| 56 \| 47 \| [Download](56/dataset.zip) \| ![preview 1](56/preview_1.png) \| ![preview 2](56/preview_2.png) \| ![preview 3](56/preview_3.png) \| ![preview 4](56/preview_4.png) \| ![preview 5](56/preview_5.png) \| ![preview 6](56/preview_6.png) \| ![preview 7](56/preview_7.png) \| ![preview 8](56/preview_8.png) \| \| 57 \| 60 \| [Download](57/dataset.zip) \| ![preview 1](57/preview_1.png) \| ![preview 2](57/preview_2.png) \| ![preview 3](57/preview_3.png) \| ![preview 4](57/preview_4.png) \| ![preview 5](57/preview_5.png) \| ![preview 6](57/preview_6.png) \| ![preview 7](57/preview_7.png) \| ![preview 8](57/preview_8.png) \| \| 58 \| 26 \| [Download](58/dataset.zip) \| ![preview 1](58/preview_1.png) \| ![preview 2](58/preview_2.png) \| ![preview 3](58/preview_3.png) \| ![preview 4](58/preview_4.png) \| ![preview 5](58/preview_5.png) \| ![preview 6](58/preview_6.png) \| ![preview 7](58/preview_7.png) \| ![preview 8](58/preview_8.png) \| \| 59 \| 47 \| [Download](59/dataset.zip) \| ![preview 1](59/preview_1.png) \| ![preview 2](59/preview_2.png) \| ![preview 3](59/preview_3.png) \| ![preview 4](59/preview_4.png) \| ![preview 5](59/preview_5.png) \| ![preview 6](59/preview_6.png) \| ![preview 7](59/preview_7.png) \| ![preview 8](59/preview_8.png) \| \| 60 \| 79 \| [Download](60/dataset.zip) \| ![preview 1](60/preview_1.png) \| ![preview 2](60/preview_2.png) \| ![preview 3](60/preview_3.png) \| ![preview 4](60/preview_4.png) \| ![preview 5](60/preview_5.png) \| ![preview 6](60/preview_6.png) \| ![preview 7](60/preview_7.png) \| ![preview 8](60/preview_8.png) \| \| 61 \| 74 \| [Download](61/dataset.zip) \| ![preview 1](61/preview_1.png) \| ![preview 2](61/preview_2.png) \| ![preview 3](61/preview_3.png) \| ![preview 4](61/preview_4.png) \| ![preview 5](61/preview_5.png) \| ![preview 6](61/preview_6.png) \| ![preview 7](61/preview_7.png) \| ![preview 8](61/preview_8.png) \| \| 62 \| 11 \| [Download](62/dataset.zip) \| ![preview 1](62/preview_1.png) \| ![preview 2](62/preview_2.png) \| ![preview 3](62/preview_3.png) \| ![preview 4](62/preview_4.png) \| ![preview 5](62/preview_5.png) \| ![preview 6](62/preview_6.png) \| ![preview 7](62/preview_7.png) \| ![preview 8](62/preview_8.png) \| \| 63 \| 73 \| [Download](63/dataset.zip) \| ![preview 1](63/preview_1.png) \| ![preview 2](63/preview_2.png) \| ![preview 3](63/preview_3.png) \| ![preview 4](63/preview_4.png) \| ![preview 5](63/preview_5.png) \| ![preview 6](63/preview_6.png) \| ![preview 7](63/preview_7.png) \| ![preview 8](63/preview_8.png) \| \| 64 \| 30 \| [Download](64/dataset.zip) \| ![preview 1](64/preview_1.png) \| ![preview 2](64/preview_2.png) \| ![preview 3](64/preview_3.png) \| ![preview 4](64/preview_4.png) \| ![preview 5](64/preview_5.png) \| ![preview 6](64/preview_6.png) \| ![preview 7](64/preview_7.png) \| ![preview 8](64/preview_8.png) \| \| 65 \| 32 \| [Download](65/dataset.zip) \| ![preview 1](65/preview_1.png) \| ![preview 2](65/preview_2.png) \| ![preview 3](65/preview_3.png) \| ![preview 4](65/preview_4.png) \| ![preview 5](65/preview_5.png) \| ![preview 6](65/preview_6.png) \| ![preview 7](65/preview_7.png) \| ![preview 8](65/preview_8.png) \| \| 66 \| 102 \| [Download](66/dataset.zip) \| ![preview 1](66/preview_1.png) \| ![preview 2](66/preview_2.png) \| ![preview 3](66/preview_3.png) \| ![preview 4](66/preview_4.png) \| ![preview 5](66/preview_5.png) \| ![preview 6](66/preview_6.png) \| ![preview 7](66/preview_7.png) \| ![preview 8](66/preview_8.png) \| \| 67 \| 17 \| [Download](67/dataset.zip) \| ![preview 1](67/preview_1.png) \| ![preview 2](67/preview_2.png) \| ![preview 3](67/preview_3.png) \| ![preview 4](67/preview_4.png) \| ![preview 5](67/preview_5.png) \| ![preview 6](67/preview_6.png) \| ![preview 7](67/preview_7.png) \| ![preview 8](67/preview_8.png) \| \| 68 \| 49 \| [Download](68/dataset.zip) \| ![preview 1](68/preview_1.png) \| ![preview 2](68/preview_2.png) \| ![preview 3](68/preview_3.png) \| ![preview 4](68/preview_4.png) \| ![preview 5](68/preview_5.png) \| ![preview 6](68/preview_6.png) \| ![preview 7](68/preview_7.png) \| ![preview 8](68/preview_8.png) \| \| 69 \| 24 \| [Download](69/dataset.zip) \| ![preview 1](69/preview_1.png) \| ![preview 2](69/preview_2.png) \| ![preview 3](69/preview_3.png) \| ![preview 4](69/preview_4.png) \| ![preview 5](69/preview_5.png) \| ![preview 6](69/preview_6.png) \| ![preview 7](69/preview_7.png) \| ![preview 8](69/preview_8.png) \| \| 70 \| 28 \| [Download](70/dataset.zip) \| ![preview 1](70/preview_1.png) \| ![preview 2](70/preview_2.png) \| ![preview 3](70/preview_3.png) \| ![preview 4](70/preview_4.png) \| ![preview 5](70/preview_5.png) \| ![preview 6](70/preview_6.png) \| ![preview 7](70/preview_7.png) \| ![preview 8](70/preview_8.png) \| \| 71 \| 38 \| [Download](71/dataset.zip) \| ![preview 1](71/preview_1.png) \| ![preview 2](71/preview_2.png) \| ![preview 3](71/preview_3.png) \| ![preview 4](71/preview_4.png) \| ![preview 5](71/preview_5.png) \| ![preview 6](71/preview_6.png) \| ![preview 7](71/preview_7.png) \| ![preview 8](71/preview_8.png) \| \| 72 \| 96 \| [Download](72/dataset.zip) \| ![preview 1](72/preview_1.png) \| ![preview 2](72/preview_2.png) \| ![preview 3](72/preview_3.png) \| ![preview 4](72/preview_4.png) \| ![preview 5](72/preview_5.png) \| ![preview 6](72/preview_6.png) \| ![preview 7](72/preview_7.png) \| ![preview 8](72/preview_8.png) \| \| 73 \| 52 \| [Download](73/dataset.zip) \| ![preview 1](73/preview_1.png) \| ![preview 2](73/preview_2.png) \| ![preview 3](73/preview_3.png) \| ![preview 4](73/preview_4.png) \| ![preview 5](73/preview_5.png) \| ![preview 6](73/preview_6.png) \| ![preview 7](73/preview_7.png) \| ![preview 8](73/preview_8.png) \| \| 74 \| 747 \| [Download](74/dataset.zip) \| ![preview 1](74/preview_1.png) \| ![preview 2](74/preview_2.png) \| ![preview 3](74/preview_3.png) \| ![preview 4](74/preview_4.png) \| ![preview 5](74/preview_5.png) \| ![preview 6](74/preview_6.png) \| ![preview 7](74/preview_7.png) \| ![preview 8](74/preview_8.png) \| \| 75 \| 50 \| [Download](75/dataset.zip) \| ![preview 1](75/preview_1.png) \| ![preview 2](75/preview_2.png) \| ![preview 3](75/preview_3.png) \| ![preview 4](75/preview_4.png) \| ![preview 5](75/preview_5.png) \| ![preview 6](75/preview_6.png) \| ![preview 7](75/preview_7.png) \| ![preview 8](75/preview_8.png) \| \| 76 \| 43 \| [Download](76/dataset.zip) \| ![preview 1](76/preview_1.png) \| ![preview 2](76/preview_2.png) \| ![preview 3](76/preview_3.png) \| ![preview 4](76/preview_4.png) \| ![preview 5](76/preview_5.png) \| ![preview 6](76/preview_6.png) \| ![preview 7](76/preview_7.png) \| ![preview 8](76/preview_8.png) \| \| 77 \| 21 \| [Download](77/dataset.zip) \| ![preview 1](77/preview_1.png) \| ![preview 2](77/preview_2.png) \| ![preview 3](77/preview_3.png) \| ![preview 4](77/preview_4.png) \| ![preview 5](77/preview_5.png) \| ![preview 6](77/preview_6.png) \| ![preview 7](77/preview_7.png) \| ![preview 8](77/preview_8.png) \| \| 78 \| 22 \| [Download](78/dataset.zip) \| ![preview 1](78/preview_1.png) \| ![preview 2](78/preview_2.png) \| ![preview 3](78/preview_3.png) \| ![preview 4](78/preview_4.png) \| ![preview 5](78/preview_5.png) \| ![preview 6](78/preview_6.png) \| ![preview 7](78/preview_7.png) \| ![preview 8](78/preview_8.png) \| \| 79 \| 23 \| [Download](79/dataset.zip) \| ![preview 1](79/preview_1.png) \| ![preview 2](79/preview_2.png) \| ![preview 3](79/preview_3.png) \| ![preview 4](79/preview_4.png) \| ![preview 5](79/preview_5.png) \| ![preview 6](79/preview_6.png) \| ![preview 7](79/preview_7.png) \| ![preview 8](79/preview_8.png) \| \| 80 \| 38 \| [Download](80/dataset.zip) \| ![preview 1](80/preview_1.png) \| ![preview 2](80/preview_2.png) \| ![preview 3](80/preview_3.png) \| ![preview 4](80/preview_4.png) \| ![preview 5](80/preview_5.png) \| ![preview 6](80/preview_6.png) \| ![preview 7](80/preview_7.png) \| ![preview 8](80/preview_8.png) \| \| 81 \| 20 \| [Download](81/dataset.zip) \| ![preview 1](81/preview_1.png) \| ![preview 2](81/preview_2.png) \| ![preview 3](81/preview_3.png) \| ![preview 4](81/preview_4.png) \| ![preview 5](81/preview_5.png) \| ![preview 6](81/preview_6.png) \| ![preview 7](81/preview_7.png) \| ![preview 8](81/preview_8.png) \| \| 82 \| 44 \| [Download](82/dataset.zip) \| ![preview 1](82/preview_1.png) \| ![preview 2](82/preview_2.png) \| ![preview 3](82/preview_3.png) \| ![preview 4](82/preview_4.png) \| ![preview 5](82/preview_5.png) \| ![preview 6](82/preview_6.png) \| ![preview 7](82/preview_7.png) \| ![preview 8](82/preview_8.png) \| \| 83 \| 19 \| [Download](83/dataset.zip) \| ![preview 1](83/preview_1.png) \| ![preview 2](83/preview_2.png) \| ![preview 3](83/preview_3.png) \| ![preview 4](83/preview_4.png) \| ![preview 5](83/preview_5.png) \| ![preview 6](83/preview_6.png) \| ![preview 7](83/preview_7.png) \| ![preview 8](83/preview_8.png) \| \| 84 \| 19 \| [Download](84/dataset.zip) \| ![preview 1](84/preview_1.png) \| ![preview 2](84/preview_2.png) \| ![preview 3](84/preview_3.png) \| ![preview 4](84/preview_4.png) \| ![preview 5](84/preview_5.png) \| ![preview 6](84/preview_6.png) \| ![preview 7](84/preview_7.png) \| ![preview 8](84/preview_8.png) \| \| 85 \| 19 \| [Download](85/dataset.zip) \| ![preview 1](85/preview_1.png) \| ![preview 2](85/preview_2.png) \| ![preview 3](85/preview_3.png) \| ![preview 4](85/preview_4.png) \| ![preview 5](85/preview_5.png) \| ![preview 6](85/preview_6.png) \| ![preview 7](85/preview_7.png) \| ![preview 8](85/preview_8.png) \| \| 86 \| 11 \| [Download](86/dataset.zip) \| ![preview 1](86/preview_1.png) \| ![preview 2](86/preview_2.png) \| ![preview 3](86/preview_3.png) \| ![preview 4](86/preview_4.png) \| ![preview 5](86/preview_5.png) \| ![preview 6](86/preview_6.png) \| ![preview 7](86/preview_7.png) \| ![preview 8](86/preview_8.png) \| \| 87 \| 48 \| [Download](87/dataset.zip) \| ![preview 1](87/preview_1.png) \| ![preview 2](87/preview_2.png) \| ![preview 3](87/preview_3.png) \| ![preview 4](87/preview_4.png) \| ![preview 5](87/preview_5.png) \| ![preview 6](87/preview_6.png) \| ![preview 7](87/preview_7.png) \| ![preview 8](87/preview_8.png) \| \| 88 \| 18 \| [Download](88/dataset.zip) \| ![preview 1](88/preview_1.png) \| ![preview 2](88/preview_2.png) \| ![preview 3](88/preview_3.png) \| ![preview 4](88/preview_4.png) \| ![preview 5](88/preview_5.png) \| ![preview 6](88/preview_6.png) \| ![preview 7](88/preview_7.png) \| ![preview 8](88/preview_8.png) \| \| 89 \| 14 \| [Download](89/dataset.zip) \| ![preview 1](89/preview_1.png) \| ![preview 2](89/preview_2.png) \| ![preview 3](89/preview_3.png) \| ![preview 4](89/preview_4.png) \| ![preview 5](89/preview_5.png) \| ![preview 6](89/preview_6.png) \| ![preview 7](89/preview_7.png) \| ![preview 8](89/preview_8.png) \| \| 90 \| 24 \| [Download](90/dataset.zip) \| ![preview 1](90/preview_1.png) \| ![preview 2](90/preview_2.png) \| ![preview 3](90/preview_3.png) \| ![preview 4](90/preview_4.png) \| ![preview 5](90/preview_5.png) \| ![preview 6](90/preview_6.png) \| ![preview 7](90/preview_7.png) \| ![preview 8](90/preview_8.png) \| \| 91 \| 19 \| [Download](91/dataset.zip) \| ![preview 1](91/preview_1.png) \| ![preview 2](91/preview_2.png) \| ![preview 3](91/preview_3.png) \| ![preview 4](91/preview_4.png) \| ![preview 5](91/preview_5.png) \| ![preview 6](91/preview_6.png) \| ![preview 7](91/preview_7.png) \| ![preview 8](91/preview_8.png) \| \| 92 \| 10 \| [Download](92/dataset.zip) \| ![preview 1](92/preview_1.png) \| ![preview 2](92/preview_2.png) \| ![preview 3](92/preview_3.png) \| ![preview 4](92/preview_4.png) \| ![preview 5](92/preview_5.png) \| ![preview 6](92/preview_6.png) \| ![preview 7](92/preview_7.png) \| ![preview 8](92/preview_8.png) \| \| 93 \| 10 \| [Download](93/dataset.zip) \| ![preview 1](93/preview_1.png) \| ![preview 2](93/preview_2.png) \| ![preview 3](93/preview_3.png) \| ![preview 4](93/preview_4.png) \| ![preview 5](93/preview_5.png) \| ![preview 6](93/preview_6.png) \| ![preview 7](93/preview_7.png) \| ![preview 8](93/preview_8.png) \| \| 94 \| 33 \| [Download](94/dataset.zip) \| ![preview 1](94/preview_1.png) \| ![preview 2](94/preview_2.png) \| ![preview 3](94/preview_3.png) \| ![preview 4](94/preview_4.png) \| ![preview 5](94/preview_5.png) \| ![preview 6](94/preview_6.png) \| ![preview 7](94/preview_7.png) \| ![preview 8](94/preview_8.png) \| \| 95 \| 28 \| [Download](95/dataset.zip) \| ![preview 1](95/preview_1.png) \| ![preview 2](95/preview_2.png) \| ![preview 3](95/preview_3.png) \| ![preview 4](95/preview_4.png) \| ![preview 5](95/preview_5.png) \| ![preview 6](95/preview_6.png) \| ![preview 7](95/preview_7.png) \| ![preview 8](95/preview_8.png) \| \| 96 \| 58 \| [Download](96/dataset.zip) \| ![preview 1](96/preview_1.png) \| ![preview 2](96/preview_2.png) \| ![preview 3](96/preview_3.png) \| ![preview 4](96/preview_4.png) \| ![preview 5](96/preview_5.png) \| ![preview 6](96/preview_6.png) \| ![preview 7](96/preview_7.png) \| ![preview 8](96/preview_8.png) \| \| 97 \| 13 \| [Download](97/dataset.zip) \| ![preview 1](97/preview_1.png) \| ![preview 2](97/preview_2.png) \| ![preview 3](97/preview_3.png) \| ![preview 4](97/preview_4.png) \| ![preview 5](97/preview_5.png) \| ![preview 6](97/preview_6.png) \| ![preview 7](97/preview_7.png) \| ![preview 8](97/preview_8.png) \| \| 98 \| 29 \| [Download](98/dataset.zip) \| ![preview 1](98/preview_1.png) \| ![preview 2](98/preview_2.png) \| ![preview 3](98/preview_3.png) \| ![preview 4](98/preview_4.png) \| ![preview 5](98/preview_5.png) \| ![preview 6](98/preview_6.png) \| ![preview 7](98/preview_7.png) \| ![preview 8](98/preview_8.png) \| \| 99 \| 17 \| [Download](99/dataset.zip) \| ![preview 1](99/preview_1.png) \| ![preview 2](99/preview_2.png) \| ![preview 3](99/preview_3.png) \| ![preview 4](99/preview_4.png) \| ![preview 5](99/preview_5.png) \| ![preview 6](99/preview_6.png) \| ![preview 7](99/preview_7.png) \| ![preview 8](99/preview_8.png) \| \| 100 \| 32 \| [Download](100/dataset.zip) \| ![preview 1](100/preview_1.png) \| ![preview 2](100/preview_2.png) \| ![preview 3](100/preview_3.png) \| ![preview 4](100/preview_4.png) \| ![preview 5](100/preview_5.png) \| ![preview 6](100/preview_6.png) \| ![preview 7](100/preview_7.png) \| ![preview 8](100/preview_8.png) \| \| 101 \| 21 \| [Download](101/dataset.zip) \| ![preview 1](101/preview_1.png) \| ![preview 2](101/preview_2.png) \| ![preview 3](101/preview_3.png) \| ![preview 4](101/preview_4.png) \| ![preview 5](101/preview_5.png) \| ![preview 6](101/preview_6.png) \| ![preview 7](101/preview_7.png) \| ![preview 8](101/preview_8.png) \| \| 102 \| 27 \| [Download](102/dataset.zip) \| ![preview 1](102/preview_1.png) \| ![preview 2](102/preview_2.png) \| ![preview 3](102/preview_3.png) \| ![preview 4](102/preview_4.png) \| ![preview 5](102/preview_5.png) \| ![preview 6](102/preview_6.png) \| ![preview 7](102/preview_7.png) \| ![preview 8](102/preview_8.png) \| \| 103 \| 22 \| [Download](103/dataset.zip) \| ![preview 1](103/preview_1.png) \| ![preview 2](103/preview_2.png) \| ![preview 3](103/preview_3.png) \| ![preview 4](103/preview_4.png) \| ![preview 5](103/preview_5.png) \| ![preview 6](103/preview_6.png) \| ![preview 7](103/preview_7.png) \| ![preview 8](103/preview_8.png) \| \| 104 \| 11 \| [Download](104/dataset.zip) \| ![preview 1](104/preview_1.png) \| ![preview 2](104/preview_2.png) \| ![preview 3](104/preview_3.png) \| ![preview 4](104/preview_4.png) \| ![preview 5](104/preview_5.png) \| ![preview 6](104/preview_6.png) \| ![preview 7](104/preview_7.png) \| ![preview 8](104/preview_8.png) \| \| 105 \| 7 \| [Download](105/dataset.zip) \| ![preview 1](105/preview_1.png) \| ![preview 2](105/preview_2.png) \| ![preview 3](105/preview_3.png) \| ![preview 4](105/preview_4.png) \| ![preview 5](105/preview_5.png) \| ![preview 6](105/preview_6.png) \| ![preview 7](105/preview_7.png) \| N/A \| \| 106 \| 12 \| [Download](106/dataset.zip) \| ![preview 1](106/preview_1.png) \| ![preview 2](106/preview_2.png) \| ![preview 3](106/preview_3.png) \| ![preview 4](106/preview_4.png) \| ![preview 5](106/preview_5.png) \| ![preview 6](106/preview_6.png) \| ![preview 7](106/preview_7.png) \| ![preview 8](106/preview_8.png) \| \| 107 \| 14 \| [Download](107/dataset.zip) \| ![preview 1](107/preview_1.png) \| ![preview 2](107/preview_2.png) \| ![preview 3](107/preview_3.png) \| ![preview 4](107/preview_4.png) \| ![preview 5](107/preview_5.png) \| ![preview 6](107/preview_6.png) \| ![preview 7](107/preview_7.png) \| ![preview 8](107/preview_8.png) \| \| 108 \| 22 \| [Download](108/dataset.zip) \| ![preview 1](108/preview_1.png) \| ![preview 2](108/preview_2.png) \| ![preview 3](108/preview_3.png) \| ![preview 4](108/preview_4.png) \| ![preview 5](108/preview_5.png) \| ![preview 6](108/preview_6.png) \| ![preview 7](108/preview_7.png) \| ![preview 8](108/preview_8.png) \| \| 109 \| 21 \| [Download](109/dataset.zip) \| ![preview 1](109/preview_1.png) \| ![preview 2](109/preview_2.png) \| ![preview 3](109/preview_3.png) \| ![preview 4](109/preview_4.png) \| ![preview 5](109/preview_5.png) \| ![preview 6](109/preview_6.png) \| ![preview 7](109/preview_7.png) \| ![preview 8](109/preview_8.png) \| \| 110 \| 25 \| [Download](110/dataset.zip) \| ![preview 1](110/preview_1.png) \| ![preview 2](110/preview_2.png) \| ![preview 3](110/preview_3.png) \| ![preview 4](110/preview_4.png) \| ![preview 5](110/preview_5.png) \| ![preview 6](110/preview_6.png) \| ![preview 7](110/preview_7.png) \| ![preview 8](110/preview_8.png) \| \| 111 \| 45 \| [Download](111/dataset.zip) \| ![preview 1](111/preview_1.png) \| ![preview 2](111/preview_2.png) \| ![preview 3](111/preview_3.png) \| ![preview 4](111/preview_4.png) \| ![preview 5](111/preview_5.png) \| ![preview 6](111/preview_6.png) \| ![preview 7](111/preview_7.png) \| ![preview 8](111/preview_8.png) \| \| 112 \| 11 \| [Download](112/dataset.zip) \| ![preview 1](112/preview_1.png) \| ![preview 2](112/preview_2.png) \| ![preview 3](112/preview_3.png) \| ![preview 4](112/preview_4.png) \| ![preview 5](112/preview_5.png) \| ![preview 6](112/preview_6.png) \| ![preview 7](112/preview_7.png) \| ![preview 8](112/preview_8.png) \| \| 113 \| 23 \| [Download](113/dataset.zip) \| ![preview 1](113/preview_1.png) \| ![preview 2](113/preview_2.png) \| ![preview 3](113/preview_3.png) \| ![preview 4](113/preview_4.png) \| ![preview 5](113/preview_5.png) \| ![preview 6](113/preview_6.png) \| ![preview 7](113/preview_7.png) \| ![preview 8](113/preview_8.png) \| \| 114 \| 14 \| [Download](114/dataset.zip) \| ![preview 1](114/preview_1.png) \| ![preview 2](114/preview_2.png) \| ![preview 3](114/preview_3.png) \| ![preview 4](114/preview_4.png) \| ![preview 5](114/preview_5.png) \| ![preview 6](114/preview_6.png) \| ![preview 7](114/preview_7.png) \| ![preview 8](114/preview_8.png) \| \| 115 \| 39 \| [Download](115/dataset.zip) \| ![preview 1](115/preview_1.png) \| ![preview 2](115/preview_2.png) \| ![preview 3](115/preview_3.png) \| ![preview 4](115/preview_4.png) \| ![preview 5](115/preview_5.png) \| ![preview 6](115/preview_6.png) \| ![preview 7](115/preview_7.png) \| ![preview 8](115/preview_8.png) \| \| 116 \| 17 \| [Download](116/dataset.zip) \| ![preview 1](116/preview_1.png) \| ![preview 2](116/preview_2.png) \| ![preview 3](116/preview_3.png) \| ![preview 4](116/preview_4.png) \| ![preview 5](116/preview_5.png) \| ![preview 6](116/preview_6.png) \| ![preview 7](116/preview_7.png) \| ![preview 8](116/preview_8.png) \| \| 117 \| 27 \| [Download](117/dataset.zip) \| ![preview 1](117/preview_1.png) \| ![preview 2](117/preview_2.png) \| ![preview 3](117/preview_3.png) \| ![preview 4](117/preview_4.png) \| ![preview 5](117/preview_5.png) \| ![preview 6](117/preview_6.png) \| ![preview 7](117/preview_7.png) \| ![preview 8](117/preview_8.png) \| \| 118 \| 56 \| [Download](118/dataset.zip) \| ![preview 1](118/preview_1.png) \| ![preview 2](118/preview_2.png) \| ![preview 3](118/preview_3.png) \| ![preview 4](118/preview_4.png) \| ![preview 5](118/preview_5.png) \| ![preview 6](118/preview_6.png) \| ![preview 7](118/preview_7.png) \| ![preview 8](118/preview_8.png) \| \| 119 \| 19 \| [Download](119/dataset.zip) \| ![preview 1](119/preview_1.png) \| ![preview 2](119/preview_2.png) \| ![preview 3](119/preview_3.png) \| ![preview 4](119/preview_4.png) \| ![preview 5](119/preview_5.png) \| ![preview 6](119/preview_6.png) \| ![preview 7](119/preview_7.png) \| ![preview 8](119/preview_8.png) \| \| 120 \| 17 \| [Download](120/dataset.zip) \| ![preview 1](120/preview_1.png) \| ![preview 2](120/preview_2.png) \| ![preview 3](120/preview_3.png) \| ![preview 4](120/preview_4.png) \| ![preview 5](120/preview_5.png) \| ![preview 6](120/preview_6.png) \| ![preview 7](120/preview_7.png) \| ![preview 8](120/preview_8.png) \| \| 121 \| 14 \| [Download](121/dataset.zip) \| ![preview 1](121/preview_1.png) \| ![preview 2](121/preview_2.png) \| ![preview 3](121/preview_3.png) \| ![preview 4](121/preview_4.png) \| ![preview 5](121/preview_5.png) \| ![preview 6](121/preview_6.png) \| ![preview 7](121/preview_7.png) \| ![preview 8](121/preview_8.png) \| \| 122 \| 12 \| [Download](122/dataset.zip) \| ![preview 1](122/preview_1.png) \| ![preview 2](122/preview_2.png) \| ![preview 3](122/preview_3.png) \| ![preview 4](122/preview_4.png) \| ![preview 5](122/preview_5.png) \| ![preview 6](122/preview_6.png) \| ![preview 7](122/preview_7.png) \| ![preview 8](122/preview_8.png) \| \| 123 \| 103 \| [Download](123/dataset.zip) \| ![preview 1](123/preview_1.png) \| ![preview 2](123/preview_2.png) \| ![preview 3](123/preview_3.png) \| ![preview 4](123/preview_4.png) \| ![preview 5](123/preview_5.png) \| ![preview 6](123/preview_6.png) \| ![preview 7](123/preview_7.png) \| ![preview 8](123/preview_8.png) \| \| 124 \| 39 \| [Download](124/dataset.zip) \| ![preview 1](124/preview_1.png) \| ![preview 2](124/preview_2.png) \| ![preview 3](124/preview_3.png) \| ![preview 4](124/preview_4.png) \| ![preview 5](124/preview_5.png) \| ![preview 6](124/preview_6.png) \| ![preview 7](124/preview_7.png) \| ![preview 8](124/preview_8.png) \| \| 125 \| 15 \| [Download](125/dataset.zip) \| ![preview 1](125/preview_1.png) \| ![preview 2](125/preview_2.png) \| ![preview 3](125/preview_3.png) \| ![preview 4](125/preview_4.png) \| ![preview 5](125/preview_5.png) \| ![preview 6](125/preview_6.png) \| ![preview 7](125/preview_7.png) \| ![preview 8](125/preview_8.png) \| \| 126 \| 19 \| [Download](126/dataset.zip) \| ![preview 1](126/preview_1.png) \| ![preview 2](126/preview_2.png) \| ![preview 3](126/preview_3.png) \| ![preview 4](126/preview_4.png) \| ![preview 5](126/preview_5.png) \| ![preview 6](126/preview_6.png) \| ![preview 7](126/preview_7.png) \| ![preview 8](126/preview_8.png) \| \| 127 \| 11 \| [Download](127/dataset.zip) \| ![preview 1](127/preview_1.png) \| ![preview 2](127/preview_2.png) \| ![preview 3](127/preview_3.png) \| ![preview 4](127/preview_4.png) \| ![preview 5](127/preview_5.png) \| ![preview 6](127/preview_6.png) \| ![preview 7](127/preview_7.png) \| ![preview 8](127/preview_8.png) \| \| 128 \| 15 \| [Download](128/dataset.zip) \| ![preview 1](128/preview_1.png) \| ![preview 2](128/preview_2.png) \| ![preview 3](128/preview_3.png) \| ![preview 4](128/preview_4.png) \| ![preview 5](128/preview_5.png) \| ![preview 6](128/preview_6.png) \| ![preview 7](128/preview_7.png) \| ![preview 8](128/preview_8.png) \| \| 129 \| 8 \| [Download](129/dataset.zip) \| ![preview 1](129/preview_1.png) \| ![preview 2](129/preview_2.png) \| ![preview 3](129/preview_3.png) \| ![preview 4](129/preview_4.png) \| ![preview 5](129/preview_5.png) \| ![preview 6](129/preview_6.png) \| ![preview 7](129/preview_7.png) \| ![preview 8](129/preview_8.png) \| \| 130 \| 9 \| [Download](130/dataset.zip) \| ![preview 1](130/preview_1.png) \| ![preview 2](130/preview_2.png) \| ![preview 3](130/preview_3.png) \| ![preview 4](130/preview_4.png) \| ![preview 5](130/preview_5.png) \| ![preview 6](130/preview_6.png) \| ![preview 7](130/preview_7.png) \| ![preview 8](130/preview_8.png) \| \| noise \| 528 \| [Download](-1/dataset.zip) \| ![preview 1](-1/preview_1.png) \| ![preview 2](-1/preview_2.png) \| ![preview 3](-1/preview_3.png) \| ![preview 4](-1/preview_4.png) \| ![preview 5](-1/preview_5.png) \| 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umm-maybe/gutenberg_english_pre1928	umm-maybe	2023-11-20T00:45:58Z	0	[ "region:us" ]	null	2023-11-19T21:21:29Z	--- dataset_info: features: [] splits: - name: train num_bytes: 0 num_examples: 0 download_size: 324 dataset_size: 0 configs: - config_name: default data_files: - split: train path: data/train-* ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 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icaro23/Icaro	icaro23	2023-11-19T21:28:07Z	0	[ "license:apache-2.0", "region:us" ]	null	2023-11-19T21:26:05Z	--- license: apache-2.0 ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, -0.4213368892669678, -0.26241645216941833, -0.0744934156537056, 0.32380396127700806, 0.7903715372085571, -0.3810413181781769, 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aucelio/rodrigosilva	aucelio	2023-11-19T21:32:31Z	0	[ "license:openrail", "region:us" ]	null	2023-11-19T21:30:27Z	--- license: openrail ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, -0.4213368892669678, -0.26241645216941833, -0.0744934156537056, 0.32380396127700806, 0.7903715372085571, -0.3810413181781769, 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xwar/2023-11-19_ninox_dataset_single_column	xwar	2023-11-19T21:43:58Z	0	[ "license:apache-2.0", "region:us" ]	null	2023-11-19T21:43:34Z	--- license: apache-2.0 ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, -0.4213368892669678, -0.26241645216941833, -0.0744934156537056, 0.32380396127700806, 0.7903715372085571, 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miragepa/A18	miragepa	2023-11-19T22:35:46Z	0	[ "license:openrail", "region:us" ]	null	2023-11-19T22:35:05Z	--- license: openrail ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, -0.4213368892669678, -0.26241645216941833, -0.0744934156537056, 0.32380396127700806, 0.7903715372085571, -0.3810413181781769, 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warzin/covers	warzin	2023-11-25T15:15:04Z	0	[ "license:other", "region:us" ]	null	2023-11-19T23:31:34Z	--- license: other license_name: seila license_link: LICENSE ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, -0.4213368892669678, -0.26241645216941833, -0.0744934156537056, 0.32380396127700806, 0.7903715372085571, 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erikliu18/us-congress-hearing	erikliu18	2023-11-20T01:01:16Z	0	[ "task_categories:text-classification", "language:en", "finance", "legal", "region:us" ]	[ "text-classification" ]	2023-11-20T00:49:47Z	--- task_categories: - text-classification language: - en tags: - finance - legal --- # U.S. Congressional Hearings Dataset This dataset currently contains cleaned sentences from all House Committee on Energy and Commerce hearings from 2002. A total of 1K+ hearing transcripts in txt formats from govinfo.gov were collected and cleaned.	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Roblox/luau_corpus	Roblox	2023-11-20T01:09:48Z	0	[ "license:mit", "code", "region:us" ]	null	2023-11-20T01:08:21Z	--- license: mit tags: - code --- # Dataset card The Luau dataset is a collection of code fragments collected from the Roblox Luau Data Sharing program. Only experiences where creators gave us permission to contribute to the public Luau Dataset were used for producing this dataset. # Languages: Lua, Luau # License: MIT Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Format: The dataset format is in jsonl format, with prompt / completion fields. # Dataset usage: This dataset is designed for fine tuning large language models. # Risks: The dataset has been filtered for various quality signals, though Roblox makes no guarantees of data quality. # Evaluation: We have found that typically fine tuning a generalist code LLM improve it’s performance on Roblox Lua code quality by 10 to 20%.	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Bode777/ZODD	Bode777	2023-11-20T01:31:00Z	0	[ "license:openrail", "region:us" ]	null	2023-11-20T01:30:02Z	--- license: openrail ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, -0.4213368892669678, -0.26241645216941833, -0.0744934156537056, 0.32380396127700806, 0.7903715372085571, -0.3810413181781769, 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Kaue123456/AdamSandlerPortugues	Kaue123456	2023-11-20T01:59:33Z	0	[ "license:openrail", "region:us" ]	null	2023-11-20T01:58:23Z	--- license: openrail ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, -0.4213368892669678, -0.26241645216941833, -0.0744934156537056, 0.32380396127700806, 0.7903715372085571, 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leaudhiver/frdrpko	leaudhiver	2023-11-20T02:34:53Z	0	[ "license:apache-2.0", "region:us" ]	null	2023-11-20T02:19:49Z	--- license: apache-2.0 --- 11-20 11:25 ID 1300까지 Deepl 번역 완. 미정제 후처리 안 한 데이터셋. 계속 번역 추가중. 번역 완료 후 데이터 정제하고 데이터셋명 변경예정. original: This dataset is the result of combing through several reverse proxy logs sets and cleaning them of refusals, duplicate, incomplete, and poor quality responses. Lots of manual quality checks. There's also things like ecommerce descriptions for sex toys and bondage gear, as well as examples of SEO optimized porn video descriptions. I will definitely be improving on this dataset continously; it should be considered a work in progress. My goal is to create a model (or set of models) which can completely replace OpenAI models for erotic roleplay and adult industry use. 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sayannath/pokemon-dataset	sayannath	2023-11-20T03:16:43Z	0	[ "license:apache-2.0", "region:us" ]	null	2023-11-20T03:10:18Z	--- license: apache-2.0 ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, -0.4213368892669678, -0.26241645216941833, -0.0744934156537056, 0.32380396127700806, 0.7903715372085571, 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beltrewilton/punta-cana-spanish-reviews	beltrewilton	2023-11-20T03:25:58Z	0	[ "task_categories:text-classification", "language:es", "license:mit", "region:us" ]	[ "text-classification" ]	2023-11-20T03:19:51Z	--- license: mit task_categories: - text-classification language: - es --- This data set was collected for academic purposes, suitable for some NLP tasks including sentiment analysis.	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CardinalityLM/imdb-card-pred-binary	CardinalityLM	2023-11-20T03:52:38Z	0	[ "region:us" ]	null	2023-11-20T03:52:32Z	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: text dtype: string - name: prompt dtype: string - name: true_cardinality dtype: int64 splits: - name: train num_bytes: 40068212.8 num_examples: 80000 - name: test num_bytes: 10017053.2 num_examples: 20000 download_size: 8598252 dataset_size: 50085266.0 --- # Dataset Card for "imdb-card-pred-binary" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.8594096302986145, -0.14348387718200684, 0.11843190342187881, 0.15033608675003052, -0.6589052677154541, 0.24961696565151215, 0.41192910075187683, -0.10392238199710846, 1.1051501035690308, 0.5880320072174072, -0.9921338558197021, -0.773505449295044, -0.8715159296989441, -0.1356741338968277, 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CardinalityLM/imdb-card-pred-science	CardinalityLM	2023-11-20T03:52:49Z	0	[ "region:us" ]	null	2023-11-20T03:52:44Z	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: text dtype: string - name: prompt dtype: string - name: true_cardinality dtype: int64 splits: - name: train num_bytes: 39344995.2 num_examples: 80000 - name: test num_bytes: 9836248.8 num_examples: 20000 download_size: 8632989 dataset_size: 49181244.0 --- # Dataset Card for "imdb-card-pred-science" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.7645302414894104, -0.11650202423334122, 0.25607454776763916, 0.06165563687682152, -0.4868454933166504, 0.17850223183631897, 0.3885544240474701, -0.021971870213747025, 1.1302523612976074, 0.42870089411735535, -1.007530927658081, -0.7029269933700562, -0.7849842309951782, -0.2133571207523346, 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icaro23/icarofrw	icaro23	2023-11-20T03:57:44Z	0	[ "license:apache-2.0", "region:us" ]	null	2023-11-20T03:56:49Z	--- license: apache-2.0 ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, -0.4213368892669678, -0.26241645216941833, -0.0744934156537056, 0.32380396127700806, 0.7903715372085571, -0.3810413181781769, 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icaro23/icarokfk	icaro23	2023-11-20T04:13:59Z	0	[ "license:apache-2.0", "region:us" ]	null	2023-11-20T04:12:34Z	--- license: apache-2.0 ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, -0.4213368892669678, -0.26241645216941833, -0.0744934156537056, 0.32380396127700806, 0.7903715372085571, -0.3810413181781769, 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yoonlee/csProjectTextualInversionStyle1	yoonlee	2023-11-20T05:12:43Z	0	[ "region:us" ]	null	2023-11-20T05:12:40Z	--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 4211336.0 num_examples: 5 download_size: 4212557 dataset_size: 4211336.0 configs: - config_name: default data_files: - split: train path: data/train-* ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, 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cp500/radiology_sample	cp500	2023-11-20T05:33:28Z	0	[ "region:us" ]	null	2023-11-20T05:33:26Z	--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 697828.8448762051 num_examples: 900 - name: test num_bytes: 77536.53831957835 num_examples: 100 download_size: 368014 dataset_size: 775365.3831957835 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, 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AinzOoalGowns/Testdataset	AinzOoalGowns	2023-11-20T06:01:48Z	0	[ "license:apache-2.0", "region:us" ]	null	2023-11-20T06:01:48Z	--- license: apache-2.0 ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, -0.4213368892669678, -0.26241645216941833, -0.0744934156537056, 0.32380396127700806, 0.7903715372085571, 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Back-up/review-crawl-data	Back-up	2023-11-20T07:49:25Z	0	[ "region:us" ]	null	2023-11-20T07:49:21Z	--- dataset_info: features: - name: id dtype: string - name: titles dtype: string - name: url dtype: string - name: content dtype: string splits: - name: train num_bytes: 14728153 num_examples: 37509 download_size: 4053059 dataset_size: 14728153 configs: - config_name: default data_files: - split: train path: data/train-* ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 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Back-up/review-crawl-data-v1	Back-up	2023-11-20T08:47:09Z	0	[ "region:us" ]	null	2023-11-20T07:51:14Z	--- dataset_info: features: - name: id dtype: string - name: titles dtype: string - name: url dtype: string - name: content dtype: string splits: - name: train num_bytes: 672799681 num_examples: 73226 download_size: 135848963 dataset_size: 672799681 configs: - config_name: default data_files: - split: train path: data/train-* ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 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DylanJHJ/pdsearch	DylanJHJ	2023-11-25T03:30:03Z	0	[ "license:apache-2.0", "region:us" ]	null	2023-11-20T07:55:34Z	--- license: apache-2.0 ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, -0.4213368892669678, -0.26241645216941833, -0.0744934156537056, 0.32380396127700806, 0.7903715372085571, -0.3810413181781769, 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Saaddazhhar/predictiveswotanalysis	Saaddazhhar	2023-11-20T08:05:48Z	0	[ "license:cc0-1.0", "region:us" ]	null	2023-11-20T08:03:47Z	--- license: cc0-1.0 ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, -0.4213368892669678, -0.26241645216941833, -0.0744934156537056, 0.32380396127700806, 0.7903715372085571, 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nisha05/dataset.txt	nisha05	2023-11-20T08:13:04Z	0	[ "region:us" ]	null	2023-11-20T08:13:03Z	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 656 num_examples: 164 download_size: 714 dataset_size: 656 configs: - config_name: default data_files: - split: train path: data/train-* ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, 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lewisbails/esg-fine-risks	lewisbails	2023-11-20T08:24:28Z	0	[ "region:us" ]	null	2023-11-20T08:24:23Z	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* - split: test path: data/test-* dataset_info: features: - name: id dtype: string - name: target dtype: string - name: context dtype: string - name: system dtype: string splits: - name: train num_bytes: 24908672.528996333 num_examples: 6966 - name: val num_bytes: 1390281.1275295396 num_examples: 387 - name: test num_bytes: 1433682.452532935 num_examples: 401 download_size: 13297109 dataset_size: 27732636.10905881 --- # Dataset Card for "esg-fine-risks" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.5756128430366516, -0.4872142970561981, 0.23467224836349487, -0.06919088214635849, -0.1355305314064026, -0.30654942989349365, 0.2932942807674408, -0.3476628363132477, 0.7177046537399292, 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DynamicSuperb/AutomaticSpeechRecognition_LibriSpeech-TestOther	DynamicSuperb	2023-11-20T08:33:29Z	0	[ "region:us" ]	null	2023-11-20T08:30:26Z	--- configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: file dtype: string - name: audio dtype: audio - name: label dtype: string - name: instruction dtype: string splits: - name: test num_bytes: 352426584.188 num_examples: 2939 download_size: 332888539 dataset_size: 352426584.188 --- # Dataset Card for "AutomaticSpeechRecognition_LibriSpeech-TestOther" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.5731894969940186, -0.19385772943496704, 0.03226696699857712, 0.17443163692951202, -0.033327698707580566, -0.1307266801595688, 0.31445544958114624, -0.2402503490447998, 0.7676424384117126, 0.4751839339733124, -0.7377519607543945, -0.4023858308792114, -0.526246190071106, -0.16557873785495758, 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DynamicSuperb/AutomaticSpeechRecognition_LJSpeech	DynamicSuperb	2023-11-20T08:45:26Z	0	[ "region:us" ]	null	2023-11-20T08:42:22Z	--- configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: file dtype: string - name: audio dtype: audio - name: label dtype: string - name: instruction dtype: string splits: - name: test num_bytes: 3800884574.0 num_examples: 13100 download_size: 3785131725 dataset_size: 3800884574.0 --- # Dataset Card for "AutomaticSpeechRecognition_LJSpeech" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.5252363681793213, -0.16360898315906525, 0.23037762939929962, 0.22782669961452484, -0.06600979715585709, 0.02263702265918255, 0.27905935049057007, -0.34431320428848267, 1.0338134765625, 0.4491533041000366, -0.7841199040412903, -0.5509805679321289, -0.6215599775314331, -0.1179908886551857, -0.5901472568511963, 0.9684946537017822, 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patrickshitou/ArcMMLU	patrickshitou	2023-12-01T07:43:50Z	0	[ "license:cc-by-nc-sa-4.0", "arxiv:2311.18658", "arxiv:2307.14852", "region:us" ]	null	2023-11-20T08:44:58Z	--- license: cc-by-nc-sa-4.0 --- ## Introduction [ArcMMLU](https://github.com/stzhang-patrick/ArcMMLU) is a Chinese benchmark specifically designed for evaluating LLMs on Library & Information Science (LIS). It aims to evaluate the knowledge and reasoning capabilities of LLMs in the LIS academic field, which covers four key sub-areas: Archival Science, Data Science, Library Science, and Information Science. Please refer to our paper for more information [ArcMMLU: A Library and Information Science Benchmark for Large Language Models](https://arxiv.org/abs/2311.18658) It is important to note that the name ArcMMLU is derived from our previous large language model research project—[ArcGPT](https://arxiv.org/abs/2307.14852), which was primarily focused on Archival Science. Later, our research scope expanded from Archival Science to a broader field of information management, but we retained the name ArcMMLU. Therefore, ArcMMLU is not just an evaluation benchmark for Archival Science; it is a comprehensive evaluation dataset for the entire LIS discipline. For the sake of convenience, ArcMMLU adopts the same data format as CMMLU. Furthermore, based on the CMMLU project, we provide evaluation code. For models that have been evaluated on CMMLU, conducting an evaluation on ArcMMLU will be pretty straightforward. Special thanks to the [CMMLU---Chinese Multi-Task Language Understanding Evaluation](https://github.com/haonan-li/CMMLU) project for its contribution to the evaluation of Chinese LLMs. We hope that ArcMMLU can serve as a powerful supplement in specialized fields, bringing more detail and depth to the evaluation of Chinese LLMs.	[ -0.36025470495224, -0.6631439924240112, 0.47527143359184265, 0.03814440220594406, -0.24022239446640015, -0.028343843296170235, -0.38485124707221985, -0.29856008291244507, -0.20811860263347626, 0.49342766404151917, -0.44354522228240967, -0.7402931451797485, -0.41984254121780396, 0.09839102625846863, -0.4520893692970276, 0.7582334280014038, -0.12763017416000366, 0.3103328049182892, -0.3208979070186615, -0.5303902626037598, 0.14878204464912415, -0.5883283615112305, -0.5711420178413391, -0.7752026319503784, 0.39981892704963684, 0.22474512457847595, 0.5224543809890747, 0.679740846157074, 0.7533283233642578, 0.16244754195213318, -0.08230487257242203, 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VamsiPranav/hindi_telugu_dataset	VamsiPranav	2023-11-20T09:09:03Z	0	[ "region:us" ]	null	2023-11-20T09:09:02Z	--- dataset_info: features: - name: sentence_hin_Deva dtype: string - name: sentence_tel_Telu dtype: string splits: - name: gen num_bytes: 845891 num_examples: 1024 download_size: 366042 dataset_size: 845891 configs: - config_name: default data_files: - split: gen path: data/gen-* ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 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joseluhf11/oct-object-detection-v3-average	joseluhf11	2023-11-22T08:48:49Z	0	[ "region:us" ]	null	2023-11-20T09:29:49Z	--- dataset_info: features: - name: image dtype: image - name: objects struct: - name: bbox sequence: sequence: float64 - name: categories sequence: string splits: - name: train num_bytes: 154014595.25 num_examples: 1246 download_size: 71641492 dataset_size: 154014595.25 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "oct-object-detection-v3-average" Dataset is composed of images with multiples object detection box in coco format (x,y,w,h). Images are OCT (type of eye scaner) with boxes indicating some features associated to AMD disease. The unique difference from from v2 is categories field must have as many class label as there are boxes annotated in each image, even if the class label is the same. So for a image with 3 boxes for the same object, must have 3 class labels. [Source datataset](https://doi.org/10.1101/2023.03.29.534704)	[ -0.7536617517471313, -0.632144033908844, 0.1595134288072586, -0.10871714353561401, -0.6133944988250732, 0.00045299847261048853, 0.6975962519645691, -0.8196357488632202, 0.20868991315364838, 0.8182238340377808, -0.25113213062286377, -0.8480169773101807, -0.5557241439819336, 0.504102885723114, -0.45768049359321594, 0.737884521484375, -0.06626667827367783, -0.127253457903862, -0.16836358606815338, -0.5157492756843567, -0.3415461778640747, -0.4961470067501068, -0.8814172744750977, -0.14190199971199036, 0.26583823561668396, 0.7775236368179321, 0.809892475605011, 1.0157525539398193, 1.229809284210205, 0.2882196605205536, 0.09211182594299316, -0.18000487983226776, -0.13956521451473236, -0.3719637989997864, 0.360392689704895, -0.9655680656433105, -0.8236815333366394, 0.10553361475467682, 0.4243966341018677, 0.20705518126487732, 0.07118070870637894, -0.07243000715970993, -0.2607566714286804, 0.7940433621406555, -0.5782073736190796, 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amd-nicknick/bert-base-uncased-2022_tokenized_dataset	amd-nicknick	2023-11-20T09:40:07Z	0	[ "region:us" ]	null	2023-11-20T09:30:37Z	--- dataset_info: features: - name: input_ids sequence: int32 - name: token_type_ids sequence: int8 - name: attention_mask sequence: int8 - name: special_tokens_mask sequence: int8 splits: - name: train num_bytes: 25352412298 num_examples: 80462898 download_size: 6782996622 dataset_size: 25352412298 configs: - config_name: default data_files: - split: train path: data/train-* ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, 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Back-up/review-crawl-data-v2	Back-up	2023-11-20T09:44:06Z	0	[ "region:us" ]	null	2023-11-20T09:43:57Z	--- dataset_info: features: - name: id dtype: string - name: titles dtype: string - name: url dtype: string - name: content dtype: string splits: - name: train num_bytes: 45477989 num_examples: 8780 download_size: 13986085 dataset_size: 45477989 configs: - config_name: default data_files: - split: train path: data/train-* ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 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AanVar/Sample_Dataset_01	AanVar	2023-11-20T10:50:34Z	0	[ "license:mit", "region:us" ]	null	2023-11-20T10:50:34Z	--- license: mit ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, -0.4213368892669678, -0.26241645216941833, -0.0744934156537056, 0.32380396127700806, 0.7903715372085571, -0.3810413181781769, 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Subhadeep/common_voice_11_0_hi_pseudo_labelled	Subhadeep	2023-11-22T10:31:21Z	0	[ "region:us" ]	null	2023-11-20T10:59:05Z	--- dataset_info: config_name: hi features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: whisper_transcript sequence: int64 splits: - name: train num_bytes: 131053542.138 num_examples: 4361 - name: validation num_bytes: 64148344.509 num_examples: 2179 - name: test num_bytes: 100961651.174 num_examples: 2894 download_size: 260542039 dataset_size: 296163537.821 configs: - config_name: hi data_files: - split: train path: hi/train-* - split: validation path: hi/validation-* - split: test path: hi/test-* ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 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superfine/advertising-banner-generation	superfine	2023-11-20T11:01:44Z	0	[ "region:us" ]	null	2023-11-20T11:01:37Z	--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 86418717.13 num_examples: 1362 - name: test num_bytes: 481468.0 num_examples: 3 download_size: 84068700 dataset_size: 86900185.13 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 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miragepa/Eve	miragepa	2023-11-20T11:24:41Z	0	[ "license:openrail", "region:us" ]	null	2023-11-20T11:24:16Z	--- license: openrail ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, -0.4213368892669678, -0.26241645216941833, -0.0744934156537056, 0.32380396127700806, 0.7903715372085571, -0.3810413181781769, 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nitorogerr/locutor_cristiano_cava	nitorogerr	2023-11-20T11:57:19Z	0	[ "license:openrail", "region:us" ]	null	2023-11-20T11:56:52Z	--- license: openrail ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, -0.4213368892669678, -0.26241645216941833, -0.0744934156537056, 0.32380396127700806, 0.7903715372085571, 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Djacon/ru-izard-emotions	Djacon	2023-11-23T19:17:45Z	0	[ "task_categories:text-classification", "task_ids:sentiment-classification", "task_ids:multi-class-classification", "task_ids:multi-label-classification", "multilinguality:russian", "size_categories:10K<n<100K", "language:ru", "license:mit", "emotion", "region:us" ]	[ "text-classification" ]	2023-11-20T12:08:50Z	--- language: - ru license: - mit multilinguality: - russian task_categories: - text-classification task_ids: - sentiment-classification - multi-class-classification - multi-label-classification pretty_name: RuIzardEmotions tags: - emotion size_categories: - 10K<n<100K --- # Dataset Card for RuIzardEmotions ## 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 Summary The RuIzardEmotions dataset is a high-quality translation of the [go-emotions](https://huggingface.co/datasets/go_emotions) dataset and the other [emotion-detection](https://www.kaggle.com/datasets/ishantjuyal/emotions-in-text/data) dataset. It contains 30k Reddit comments labeled for 10 emotion categories (__joy__, __sadness__, __anger__, __enthusiasm__, __surprise__, __disgust__, __fear__, __guilt__, __shame__ and __neutral__). The datasets were translated using the accurate translator [DeepL](https://www.deepl.com/translator) and additional processing. The idea for the dataset was inspired by the [Izard's model](https://en.wikipedia.org/wiki/Differential_Emotions_Scale) of human emotions. The dataset already with predefined train/val/test splits. ### Supported Tasks and Leaderboards This dataset is intended for multi-class, multi-label emotion classification. ### Languages The data is in Russian. ## Dataset Structure ### Data Instances Each instance is a reddit comment with one or more emotion annotations (or neutral). ### Data Splits The simplified data includes a set of train/val/test splits with 24k, 3k, and 3k examples respectively. ## Considerations for Using the Data ### Social Impact of Dataset Emotion detection is a worthwhile problem which can potentially lead to improvements such as better human/computer interaction. However, emotion detection algorithms (particularly in computer vision) have been abused in some cases to make erroneous inferences in human monitoring and assessment applications such as hiring decisions, insurance pricing, and student attentiveness ## Additional Information ### Licensing Information The GitHub repository which houses this dataset has an [Apache License 2.0](https://github.com/Djacon/russian-emotion-detection/blob/main/LICENSE). ### Citation Information ``` @inproceedings{Djacon, author={Djacon}, title={RuIzardEmotions: A Dataset of Fine-Grained Emotions}, year={2023} } ```	[ -0.5020168423652649, -0.4390158951282501, 0.2291533648967743, 0.6240280270576477, -0.3699054419994354, -0.07445017993450165, -0.2781483232975006, -0.4209660291671753, 0.5057926774024963, 0.3526891767978668, -0.8407021164894104, -1.0006039142608643, -0.6723949313163757, 0.2642025947570801, -0.16569270193576813, 1.1920907497406006, -0.18680593371391296, -0.024115506559610367, -0.27708786725997925, 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FudanSELab/SO_KGXQR_DUPLICATE	FudanSELab	2023-11-20T12:36:44Z	0	[ "size_categories:1K<n<10K", "language:en", "license:mit", "region:us" ]	null	2023-11-20T12:30:06Z	--- license: mit dataset_info: - config_name: duplicate_csharp features: - name: query dtype: string - name: relevant sequence: string splits: - name: test num_bytes: 91485 num_examples: 1200 download_size: 61619 dataset_size: 91485 - config_name: duplicate_java features: - name: query dtype: string - name: relevant sequence: string splits: - name: test num_bytes: 102838 num_examples: 1200 download_size: 69239 dataset_size: 102838 - config_name: duplicate_javascript features: - name: query dtype: string - name: relevant sequence: string splits: - name: test num_bytes: 107321 num_examples: 1200 download_size: 69456 dataset_size: 107321 - config_name: duplicate_python features: - name: query dtype: string - name: relevant sequence: string splits: - name: test num_bytes: 109709 num_examples: 1200 download_size: 73833 dataset_size: 109709 configs: - config_name: duplicate_csharp data_files: - split: test path: duplicate_csharp/test-* - config_name: duplicate_java data_files: - split: test path: duplicate_java/test-* - config_name: duplicate_javascript data_files: - split: test path: duplicate_javascript/test-* - config_name: duplicate_python data_files: - split: test path: duplicate_python/test-* language: - en size_categories: - 1K<n<10K --- ## Dataset Description - Repository: [GitHub Repository](https://kgxqr.github.io/)	[ -0.2482191026210785, 0.12334974110126495, -0.04659893736243248, 0.18349823355674744, -0.38562753796577454, 0.2507302463054657, 0.11216393113136292, 0.46111664175987244, 0.2627772092819214, 0.9756976962089539, -0.6748444437980652, -0.6859069466590881, -0.49142301082611084, -0.16082222759723663, -0.6461954116821289, 1.32163667678833, -0.156317800283432, 0.5761441588401794, -0.15548720955848694, -0.2579166293144226, -0.409194678068161, -0.05137955769896507, -0.0667722076177597, 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fraviofranco/OptimusPrime	fraviofranco	2023-11-20T12:47:01Z	0	[ "license:openrail", "region:us" ]	null	2023-11-20T12:45:41Z	--- license: openrail ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, -0.4213368892669678, -0.26241645216941833, -0.0744934156537056, 0.32380396127700806, 0.7903715372085571, 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italovinixius/news-pt	italovinixius	2023-11-20T13:05:27Z	0	[ "license:apache-2.0", "region:us" ]	null	2023-11-20T13:05:27Z	--- license: apache-2.0 ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, -0.4213368892669678, -0.26241645216941833, -0.0744934156537056, 0.32380396127700806, 0.7903715372085571, 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FudanSELab/SO_KGXQR_TRAIN	FudanSELab	2023-11-20T16:20:32Z	0	[ "size_categories:100K<n<1M", "language:en", "license:mit", "region:us" ]	null	2023-11-20T13:27:26Z	--- license: mit dataset_info: - config_name: duplicate features: - name: question1_id dtype: string - name: question1 dtype: string - name: question2_id dtype: string - name: question2 dtype: string splits: - name: train num_bytes: 2428577 num_examples: 18281 download_size: 1682661 dataset_size: 2428577 - config_name: history features: - name: so_question_id dtype: string - name: question1 dtype: string - name: question2 dtype: string splits: - name: train num_bytes: 10039163 num_examples: 80000 download_size: 7239803 dataset_size: 10039163 - config_name: negative features: - name: question1 dtype: string - name: question2 dtype: string splits: - name: train num_bytes: 27392182 num_examples: 248940 download_size: 11085232 dataset_size: 27392182 - config_name: positive features: - name: question1 dtype: string - name: question2 dtype: string splits: - name: train num_bytes: 11457312 num_examples: 101172 download_size: 7917727 dataset_size: 11457312 configs: - config_name: duplicate data_files: - split: train path: duplicate/train-* - config_name: history data_files: - split: train path: history/train-* - config_name: negative data_files: - split: train path: negative/train-* - config_name: positive data_files: - split: train path: positive/train-* language: - en size_categories: - 100K<n<1M --- ## Dataset Description - Repository: [GitHub Repository](https://kgxqr.github.io/)	[ -0.2482191026210785, 0.12334974110126495, -0.04659893736243248, 0.18349823355674744, -0.38562753796577454, 0.2507302463054657, 0.11216393113136292, 0.46111664175987244, 0.2627772092819214, 0.9756976962089539, -0.6748444437980652, -0.6859069466590881, -0.49142301082611084, -0.16082222759723663, -0.6461954116821289, 1.32163667678833, -0.156317800283432, 0.5761441588401794, -0.15548720955848694, -0.2579166293144226, -0.409194678068161, 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DamarJati/gpt2-m-storybook	DamarJati	2023-11-21T22:20:27Z	0	[ "task_categories:text-generation", "language:en", "storybook", "fiction", "region:us" ]	[ "text-generation" ]	2023-11-20T13:27:42Z	--- language: - en pretty_name: Your Storybook Dataset tags: - storybook - fiction task_categories: - text-generation --- # Your Storybook Dataset Welcome to the repository for the "Your Storybook Dataset." This dataset contains a collection of stories and narratives suitable for various natural language processing tasks. ## Under Collection	[ -0.5769788026809692, -0.3548872470855713, 0.1551211029291153, -0.07548310607671738, -0.07059544324874878, 0.42344483733177185, 0.004530956037342548, -0.46320483088493347, 0.3850969970226288, 1.148470401763916, -0.7116244435310364, -0.555377185344696, -0.33582231402397156, 0.292876273393631, -0.3206799328327179, 1.0823184251785278, -0.07765121012926102, -0.21597164869308472, -0.29576385021209717, -0.3655758798122406, -0.8419798016548157, -0.5325859189033508, -0.24395249783992767, -0.4170994460582733, 0.7011774182319641, 1.0528984069824219, 0.7501981854438782, 0.7693670392036438, 0.54339998960495, 0.19509130716323853, 0.16801051795482635, 0.16668270528316498, -0.260306715965271, 0.6917100548744202, -0.1595211774110794, -0.4995357394218445, 0.0058903261087834835, 0.1528383195400238, 0.5247304439544678, 0.6103214621543884, -0.12513408064842224, 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ryuseiken/PIMPINELA	ryuseiken	2023-11-20T13:54:34Z	0	[ "license:openrail", "region:us" ]	null	2023-11-20T13:48:52Z	--- license: openrail ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, -0.4213368892669678, -0.26241645216941833, -0.0744934156537056, 0.32380396127700806, 0.7903715372085571, -0.3810413181781769, 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Jaspernl/common_voice_13_0_hi_pseudo_labelled	Jaspernl	2023-11-22T09:38:34Z	0	[ "region:us" ]	null	2023-11-20T14:14:19Z	--- dataset_info: config_name: nl features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string - name: whisper_transcript sequence: int64 splits: - name: train num_bytes: 887885645.796 num_examples: 31906 - name: validation num_bytes: 355968997.37 num_examples: 10930 - name: test num_bytes: 402843984.568 num_examples: 10936 download_size: 1643769397 dataset_size: 1646698627.734 configs: - config_name: nl data_files: - split: train path: nl/train-* - split: validation path: nl/validation-* - split: test path: nl/test-* ---	[ -0.1285339891910553, 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nxsbr/kk	nxsbr	2023-11-24T17:45:48Z	0	[ "license:openrail", "region:us" ]	null	2023-11-20T14:20:33Z	--- license: openrail ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, -0.4213368892669678, -0.26241645216941833, -0.0744934156537056, 0.32380396127700806, 0.7903715372085571, -0.3810413181781769, 0.19328097999095917, 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nourheshamshaheen/ICPR_pipeline3_big	nourheshamshaheen	2023-11-20T14:45:17Z	0	[ "region:us" ]	null	2023-11-20T14:38:33Z	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': area '1': heatmap '2': horizontal_bar '3': horizontal_interval '4': line '5': manhattan '6': map '7': pie '8': scatter '9': scatter-line '10': surface '11': venn '12': vertical_bar '13': vertical_box '14': vertical_interval - name: pipeline_label dtype: class_label: names: '0': line '1': other '2': scatter '3': scatter_line '4': vertical_bar - name: true_label dtype: string splits: - name: train num_bytes: 1073939947.25 num_examples: 20630 download_size: 979370224 dataset_size: 1073939947.25 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ICPR_pipeline3_big" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.6199003458023071, 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nourheshamshaheen/ICPR_pipeline3_small	nourheshamshaheen	2023-11-20T14:46:50Z	0	[ "region:us" ]	null	2023-11-20T14:45:17Z	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': area '1': heatmap '2': horizontal_bar '3': horizontal_interval '4': line '5': manhattan '6': map '7': pie '8': scatter '9': scatter-line '10': surface '11': venn '12': vertical_bar '13': vertical_box '14': vertical_interval - name: true_label dtype: class_label: names: '0': area '1': heatmap '2': horizontal_bar '3': horizontal_interval '4': manhattan '5': map '6': other '7': pie '8': surface '9': venn '10': vertical_box '11': vertical_interval splits: - name: train num_bytes: 236277245.998 num_examples: 4234 download_size: 216714311 dataset_size: 236277245.998 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ICPR_pipeline3_small" [More Information 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lmg-anon/VNTL-1k	lmg-anon	2023-11-20T18:01:58Z	0	[ "region:us" ]	null	2023-11-20T15:37:03Z	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 87713571 num_examples: 33746 download_size: 41613063 dataset_size: 87713571 --- # Dataset Card for "VNTL-1k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.6548802256584167, -0.22434073686599731, 0.12366008758544922, 0.24182666838169098, -0.5699121952056885, -0.011963809840381145, 0.4603251516819, -0.23266342282295227, 0.9531920552253723, 0.712551474571228, -1.0061454772949219, -0.8498721122741699, -0.49015921354293823, -0.24365706741809845, -0.3234621286392212, 1.2944849729537964, 0.21839572489261627, -0.015224941074848175, -0.3651951849460602, -0.20871025323867798, -0.36412477493286133, -0.6287259459495544, -0.6188227534294128, -0.5599683523178101, 1.0436325073242188, 0.5807658433914185, 0.21923451125621796, 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DynamicSuperb/EmotionalSpeechAudioClassification_RAVDESS-EmotionalSound	DynamicSuperb	2023-11-26T17:51:36Z	0	[ "license:cc-by-nc-sa-4.0", "region:us" ]	null	2023-11-20T15:46:12Z	--- license: cc-by-nc-sa-4.0 configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: file dtype: string - name: audio dtype: audio - name: instruction dtype: string - name: label dtype: string splits: - name: test num_bytes: 598283894.96 num_examples: 1440 download_size: 325216537 dataset_size: 598283894.96 ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, 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FlyingFishzzz/des_mesh	FlyingFishzzz	2023-11-20T15:51:31Z	0	[ "region:us" ]	null	2023-11-20T15:49:41Z	--- dataset_info: features: - name: target dtype: image - name: prompt dtype: string - name: mesh dtype: image splits: - name: train num_bytes: 520589341.0 num_examples: 1588 download_size: 520482757 dataset_size: 520589341.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "des_mesh" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.8326065540313721, -0.4645133316516876, 0.3684801161289215, 0.22184748947620392, -0.1685253530740738, -0.05748732015490532, 0.4787676930427551, -0.09802072495222092, 0.7757795453071594, 0.444121778011322, -0.7451016306877136, -0.827025830745697, -0.6688162684440613, -0.06104278564453125, -0.4902079403400421, 1.0429823398590088, 0.02688114531338215, -0.007548258639872074, -0.8121604323387146, 0.04988649860024452, 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jpqueiroz335/vozdoriff	jpqueiroz335	2023-11-20T16:27:04Z	0	[ "license:openrail", "region:us" ]	null	2023-11-20T16:26:46Z	--- license: openrail ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, -0.4213368892669678, -0.26241645216941833, -0.0744934156537056, 0.32380396127700806, 0.7903715372085571, -0.3810413181781769, 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Jcuhfehl/anna	Jcuhfehl	2023-11-20T17:03:20Z	0	[ "region:us" ]	null	2023-11-20T16:52:44Z	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 249782806 num_examples: 507 download_size: 149259532 dataset_size: 249782806 configs: - config_name: default data_files: - split: train path: data/train-* ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, 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Frixi/Almighty_2019_LaBestia_Era	Frixi	2023-11-20T17:24:56Z	0	[ "license:openrail", "region:us" ]	null	2023-11-20T16:56:19Z	--- license: openrail ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, -0.4213368892669678, -0.26241645216941833, -0.0744934156537056, 0.32380396127700806, 0.7903715372085571, 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FudanSELab/SO_KGXQR_HISTORY	FudanSELab	2023-11-20T17:11:05Z	0	[ "size_categories:1K<n<10K", "language:en", "license:mit", "region:us" ]	null	2023-11-20T17:04:47Z	--- license: mit dataset_info: - config_name: history_csharp features: - name: so_question_id dtype: string - name: historical_title dtype: string splits: - name: test num_bytes: 133783 num_examples: 2000 download_size: 95916 dataset_size: 133783 - config_name: history_java features: - name: so_question_id dtype: string - name: historical_title dtype: string splits: - name: test num_bytes: 132058 num_examples: 2000 download_size: 93956 dataset_size: 132058 - config_name: history_javascript features: - name: so_question_id dtype: string - name: historical_title dtype: string splits: - name: test num_bytes: 132306 num_examples: 2000 download_size: 91896 dataset_size: 132306 - config_name: history_python features: - name: so_question_id dtype: string - name: historical_title dtype: string splits: - name: test num_bytes: 132390 num_examples: 2000 download_size: 92772 dataset_size: 132390 configs: - config_name: history_csharp data_files: - split: test path: history_csharp/test-* - config_name: history_java data_files: - split: test path: history_java/test-* - config_name: history_javascript data_files: - split: test path: history_javascript/test-* - config_name: history_python data_files: - split: test path: history_python/test-* language: - en size_categories: - 1K<n<10K --- ## Dataset Description - Repository: [GitHub Repository](https://kgxqr.github.io/)	[ -0.2482191026210785, 0.12334974110126495, -0.04659893736243248, 0.18349823355674744, -0.38562753796577454, 0.2507302463054657, 0.11216393113136292, 0.46111664175987244, 0.2627772092819214, 0.9756976962089539, -0.6748444437980652, -0.6859069466590881, -0.49142301082611084, -0.16082222759723663, -0.6461954116821289, 1.32163667678833, -0.156317800283432, 0.5761441588401794, -0.15548720955848694, -0.2579166293144226, -0.409194678068161, -0.05137955769896507, 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DataStudio/OCR_Red	DataStudio	2023-11-20T17:33:45Z	0	[ "region:us" ]	null	2023-11-20T17:33:30Z	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 94279480.25 num_examples: 3550 download_size: 94231656 dataset_size: 94279480.25 configs: - config_name: default data_files: - split: train path: data/train-* ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, 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BangumiBase/kamikazekaitoujeanne	BangumiBase	2023-11-20T18:57:19Z	0	[ "size_categories:1K<n<10K", "license:mit", "art", "region:us" ]	null	2023-11-20T17:50:43Z	--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Kamikaze Kaitou Jeanne This is the image base of bangumi Kamikaze Kaitou Jeanne, we detected 43 characters, 3600 images in total. The full dataset is [here](all.zip). Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview: \| # \| Images \| Download \| Preview 1 \| Preview 2 \| Preview 3 \| Preview 4 \| Preview 5 \| Preview 6 \| Preview 7 \| Preview 8 \| \|:------\|---------:\|:---------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\| \| 0 \| 527 \| [Download](0/dataset.zip) \| ![preview 1](0/preview_1.png) \| ![preview 2](0/preview_2.png) \| ![preview 3](0/preview_3.png) \| ![preview 4](0/preview_4.png) \| ![preview 5](0/preview_5.png) \| ![preview 6](0/preview_6.png) \| ![preview 7](0/preview_7.png) \| ![preview 8](0/preview_8.png) \| \| 1 \| 39 \| [Download](1/dataset.zip) \| ![preview 1](1/preview_1.png) \| ![preview 2](1/preview_2.png) \| ![preview 3](1/preview_3.png) \| ![preview 4](1/preview_4.png) \| ![preview 5](1/preview_5.png) \| ![preview 6](1/preview_6.png) \| ![preview 7](1/preview_7.png) \| ![preview 8](1/preview_8.png) \| \| 2 \| 690 \| [Download](2/dataset.zip) \| ![preview 1](2/preview_1.png) \| ![preview 2](2/preview_2.png) \| ![preview 3](2/preview_3.png) \| ![preview 4](2/preview_4.png) \| ![preview 5](2/preview_5.png) \| ![preview 6](2/preview_6.png) \| ![preview 7](2/preview_7.png) \| ![preview 8](2/preview_8.png) \| \| 3 \| 38 \| [Download](3/dataset.zip) \| ![preview 1](3/preview_1.png) \| ![preview 2](3/preview_2.png) \| ![preview 3](3/preview_3.png) \| ![preview 4](3/preview_4.png) \| ![preview 5](3/preview_5.png) \| ![preview 6](3/preview_6.png) \| ![preview 7](3/preview_7.png) \| ![preview 8](3/preview_8.png) \| \| 4 \| 33 \| [Download](4/dataset.zip) \| ![preview 1](4/preview_1.png) \| ![preview 2](4/preview_2.png) \| ![preview 3](4/preview_3.png) \| ![preview 4](4/preview_4.png) \| ![preview 5](4/preview_5.png) \| ![preview 6](4/preview_6.png) \| ![preview 7](4/preview_7.png) \| ![preview 8](4/preview_8.png) \| \| 5 \| 26 \| [Download](5/dataset.zip) \| ![preview 1](5/preview_1.png) \| ![preview 2](5/preview_2.png) \| ![preview 3](5/preview_3.png) \| ![preview 4](5/preview_4.png) \| ![preview 5](5/preview_5.png) \| ![preview 6](5/preview_6.png) \| ![preview 7](5/preview_7.png) \| ![preview 8](5/preview_8.png) \| \| 6 \| 110 \| [Download](6/dataset.zip) \| ![preview 1](6/preview_1.png) \| ![preview 2](6/preview_2.png) \| ![preview 3](6/preview_3.png) \| ![preview 4](6/preview_4.png) \| ![preview 5](6/preview_5.png) \| ![preview 6](6/preview_6.png) \| ![preview 7](6/preview_7.png) \| ![preview 8](6/preview_8.png) \| \| 7 \| 23 \| [Download](7/dataset.zip) \| ![preview 1](7/preview_1.png) \| ![preview 2](7/preview_2.png) \| ![preview 3](7/preview_3.png) \| ![preview 4](7/preview_4.png) \| ![preview 5](7/preview_5.png) \| ![preview 6](7/preview_6.png) \| ![preview 7](7/preview_7.png) \| ![preview 8](7/preview_8.png) \| \| 8 \| 28 \| [Download](8/dataset.zip) \| ![preview 1](8/preview_1.png) \| ![preview 2](8/preview_2.png) \| ![preview 3](8/preview_3.png) \| ![preview 4](8/preview_4.png) \| ![preview 5](8/preview_5.png) \| ![preview 6](8/preview_6.png) \| ![preview 7](8/preview_7.png) \| ![preview 8](8/preview_8.png) \| \| 9 \| 435 \| [Download](9/dataset.zip) \| ![preview 1](9/preview_1.png) \| ![preview 2](9/preview_2.png) \| ![preview 3](9/preview_3.png) \| ![preview 4](9/preview_4.png) \| ![preview 5](9/preview_5.png) \| ![preview 6](9/preview_6.png) \| ![preview 7](9/preview_7.png) \| ![preview 8](9/preview_8.png) \| \| 10 \| 351 \| [Download](10/dataset.zip) \| ![preview 1](10/preview_1.png) \| ![preview 2](10/preview_2.png) \| ![preview 3](10/preview_3.png) \| ![preview 4](10/preview_4.png) \| ![preview 5](10/preview_5.png) \| ![preview 6](10/preview_6.png) \| ![preview 7](10/preview_7.png) \| ![preview 8](10/preview_8.png) \| \| 11 \| 45 \| [Download](11/dataset.zip) \| ![preview 1](11/preview_1.png) \| ![preview 2](11/preview_2.png) \| ![preview 3](11/preview_3.png) \| ![preview 4](11/preview_4.png) \| ![preview 5](11/preview_5.png) \| ![preview 6](11/preview_6.png) \| ![preview 7](11/preview_7.png) \| ![preview 8](11/preview_8.png) \| \| 12 \| 69 \| [Download](12/dataset.zip) \| ![preview 1](12/preview_1.png) \| ![preview 2](12/preview_2.png) \| ![preview 3](12/preview_3.png) \| ![preview 4](12/preview_4.png) \| ![preview 5](12/preview_5.png) \| ![preview 6](12/preview_6.png) \| ![preview 7](12/preview_7.png) \| ![preview 8](12/preview_8.png) \| \| 13 \| 14 \| [Download](13/dataset.zip) \| ![preview 1](13/preview_1.png) \| ![preview 2](13/preview_2.png) \| ![preview 3](13/preview_3.png) \| ![preview 4](13/preview_4.png) \| ![preview 5](13/preview_5.png) \| ![preview 6](13/preview_6.png) \| ![preview 7](13/preview_7.png) \| ![preview 8](13/preview_8.png) \| \| 14 \| 78 \| [Download](14/dataset.zip) \| ![preview 1](14/preview_1.png) \| ![preview 2](14/preview_2.png) \| ![preview 3](14/preview_3.png) \| ![preview 4](14/preview_4.png) \| ![preview 5](14/preview_5.png) \| ![preview 6](14/preview_6.png) \| ![preview 7](14/preview_7.png) \| ![preview 8](14/preview_8.png) \| \| 15 \| 54 \| [Download](15/dataset.zip) \| ![preview 1](15/preview_1.png) \| ![preview 2](15/preview_2.png) \| ![preview 3](15/preview_3.png) \| ![preview 4](15/preview_4.png) \| ![preview 5](15/preview_5.png) \| ![preview 6](15/preview_6.png) \| ![preview 7](15/preview_7.png) \| ![preview 8](15/preview_8.png) \| \| 16 \| 21 \| [Download](16/dataset.zip) \| ![preview 1](16/preview_1.png) \| ![preview 2](16/preview_2.png) \| ![preview 3](16/preview_3.png) \| ![preview 4](16/preview_4.png) \| ![preview 5](16/preview_5.png) \| ![preview 6](16/preview_6.png) \| ![preview 7](16/preview_7.png) \| ![preview 8](16/preview_8.png) \| \| 17 \| 23 \| [Download](17/dataset.zip) \| ![preview 1](17/preview_1.png) \| ![preview 2](17/preview_2.png) \| ![preview 3](17/preview_3.png) \| ![preview 4](17/preview_4.png) \| ![preview 5](17/preview_5.png) \| ![preview 6](17/preview_6.png) \| ![preview 7](17/preview_7.png) \| ![preview 8](17/preview_8.png) \| \| 18 \| 45 \| [Download](18/dataset.zip) \| ![preview 1](18/preview_1.png) \| ![preview 2](18/preview_2.png) \| ![preview 3](18/preview_3.png) \| ![preview 4](18/preview_4.png) \| ![preview 5](18/preview_5.png) \| ![preview 6](18/preview_6.png) \| ![preview 7](18/preview_7.png) \| ![preview 8](18/preview_8.png) \| \| 19 \| 18 \| [Download](19/dataset.zip) \| ![preview 1](19/preview_1.png) \| ![preview 2](19/preview_2.png) \| ![preview 3](19/preview_3.png) \| ![preview 4](19/preview_4.png) \| ![preview 5](19/preview_5.png) \| ![preview 6](19/preview_6.png) \| ![preview 7](19/preview_7.png) \| ![preview 8](19/preview_8.png) \| \| 20 \| 159 \| [Download](20/dataset.zip) \| ![preview 1](20/preview_1.png) \| ![preview 2](20/preview_2.png) \| ![preview 3](20/preview_3.png) \| ![preview 4](20/preview_4.png) \| ![preview 5](20/preview_5.png) \| ![preview 6](20/preview_6.png) \| ![preview 7](20/preview_7.png) \| ![preview 8](20/preview_8.png) \| \| 21 \| 22 \| [Download](21/dataset.zip) \| ![preview 1](21/preview_1.png) \| ![preview 2](21/preview_2.png) \| ![preview 3](21/preview_3.png) \| ![preview 4](21/preview_4.png) \| ![preview 5](21/preview_5.png) \| ![preview 6](21/preview_6.png) \| ![preview 7](21/preview_7.png) \| ![preview 8](21/preview_8.png) \| \| 22 \| 103 \| [Download](22/dataset.zip) \| ![preview 1](22/preview_1.png) \| ![preview 2](22/preview_2.png) \| ![preview 3](22/preview_3.png) \| ![preview 4](22/preview_4.png) \| ![preview 5](22/preview_5.png) \| ![preview 6](22/preview_6.png) \| ![preview 7](22/preview_7.png) \| ![preview 8](22/preview_8.png) \| \| 23 \| 42 \| [Download](23/dataset.zip) \| ![preview 1](23/preview_1.png) \| ![preview 2](23/preview_2.png) \| ![preview 3](23/preview_3.png) \| ![preview 4](23/preview_4.png) \| ![preview 5](23/preview_5.png) \| ![preview 6](23/preview_6.png) \| ![preview 7](23/preview_7.png) \| ![preview 8](23/preview_8.png) \| \| 24 \| 12 \| [Download](24/dataset.zip) \| ![preview 1](24/preview_1.png) \| ![preview 2](24/preview_2.png) \| ![preview 3](24/preview_3.png) \| ![preview 4](24/preview_4.png) \| ![preview 5](24/preview_5.png) \| ![preview 6](24/preview_6.png) \| ![preview 7](24/preview_7.png) \| ![preview 8](24/preview_8.png) \| \| 25 \| 14 \| [Download](25/dataset.zip) \| ![preview 1](25/preview_1.png) \| ![preview 2](25/preview_2.png) \| ![preview 3](25/preview_3.png) \| ![preview 4](25/preview_4.png) \| ![preview 5](25/preview_5.png) \| ![preview 6](25/preview_6.png) \| ![preview 7](25/preview_7.png) \| ![preview 8](25/preview_8.png) \| \| 26 \| 30 \| [Download](26/dataset.zip) \| ![preview 1](26/preview_1.png) \| ![preview 2](26/preview_2.png) \| ![preview 3](26/preview_3.png) \| ![preview 4](26/preview_4.png) \| ![preview 5](26/preview_5.png) \| ![preview 6](26/preview_6.png) \| ![preview 7](26/preview_7.png) \| ![preview 8](26/preview_8.png) \| \| 27 \| 15 \| [Download](27/dataset.zip) \| ![preview 1](27/preview_1.png) \| ![preview 2](27/preview_2.png) \| ![preview 3](27/preview_3.png) \| ![preview 4](27/preview_4.png) \| ![preview 5](27/preview_5.png) \| ![preview 6](27/preview_6.png) \| ![preview 7](27/preview_7.png) \| ![preview 8](27/preview_8.png) \| \| 28 \| 12 \| [Download](28/dataset.zip) \| ![preview 1](28/preview_1.png) \| ![preview 2](28/preview_2.png) \| ![preview 3](28/preview_3.png) \| ![preview 4](28/preview_4.png) \| ![preview 5](28/preview_5.png) \| ![preview 6](28/preview_6.png) \| ![preview 7](28/preview_7.png) \| ![preview 8](28/preview_8.png) \| \| 29 \| 18 \| [Download](29/dataset.zip) \| ![preview 1](29/preview_1.png) \| ![preview 2](29/preview_2.png) \| ![preview 3](29/preview_3.png) \| ![preview 4](29/preview_4.png) \| ![preview 5](29/preview_5.png) \| ![preview 6](29/preview_6.png) \| ![preview 7](29/preview_7.png) \| ![preview 8](29/preview_8.png) \| \| 30 \| 12 \| [Download](30/dataset.zip) \| ![preview 1](30/preview_1.png) \| ![preview 2](30/preview_2.png) \| ![preview 3](30/preview_3.png) \| ![preview 4](30/preview_4.png) \| ![preview 5](30/preview_5.png) \| ![preview 6](30/preview_6.png) \| ![preview 7](30/preview_7.png) \| ![preview 8](30/preview_8.png) \| \| 31 \| 11 \| [Download](31/dataset.zip) \| ![preview 1](31/preview_1.png) \| ![preview 2](31/preview_2.png) \| ![preview 3](31/preview_3.png) \| ![preview 4](31/preview_4.png) \| ![preview 5](31/preview_5.png) \| ![preview 6](31/preview_6.png) \| ![preview 7](31/preview_7.png) \| ![preview 8](31/preview_8.png) \| \| 32 \| 208 \| [Download](32/dataset.zip) \| ![preview 1](32/preview_1.png) \| ![preview 2](32/preview_2.png) \| ![preview 3](32/preview_3.png) \| ![preview 4](32/preview_4.png) \| ![preview 5](32/preview_5.png) \| ![preview 6](32/preview_6.png) \| ![preview 7](32/preview_7.png) \| ![preview 8](32/preview_8.png) \| \| 33 \| 28 \| [Download](33/dataset.zip) \| ![preview 1](33/preview_1.png) \| ![preview 2](33/preview_2.png) \| ![preview 3](33/preview_3.png) \| ![preview 4](33/preview_4.png) \| ![preview 5](33/preview_5.png) \| ![preview 6](33/preview_6.png) \| ![preview 7](33/preview_7.png) \| ![preview 8](33/preview_8.png) \| \| 34 \| 24 \| [Download](34/dataset.zip) \| ![preview 1](34/preview_1.png) \| ![preview 2](34/preview_2.png) \| ![preview 3](34/preview_3.png) \| ![preview 4](34/preview_4.png) \| ![preview 5](34/preview_5.png) \| ![preview 6](34/preview_6.png) \| ![preview 7](34/preview_7.png) \| ![preview 8](34/preview_8.png) \| \| 35 \| 13 \| [Download](35/dataset.zip) \| ![preview 1](35/preview_1.png) \| ![preview 2](35/preview_2.png) \| ![preview 3](35/preview_3.png) \| ![preview 4](35/preview_4.png) \| ![preview 5](35/preview_5.png) \| ![preview 6](35/preview_6.png) \| ![preview 7](35/preview_7.png) \| ![preview 8](35/preview_8.png) \| \| 36 \| 34 \| [Download](36/dataset.zip) \| ![preview 1](36/preview_1.png) \| ![preview 2](36/preview_2.png) \| ![preview 3](36/preview_3.png) \| ![preview 4](36/preview_4.png) \| ![preview 5](36/preview_5.png) \| ![preview 6](36/preview_6.png) \| ![preview 7](36/preview_7.png) \| ![preview 8](36/preview_8.png) \| \| 37 \| 7 \| [Download](37/dataset.zip) \| ![preview 1](37/preview_1.png) \| ![preview 2](37/preview_2.png) \| ![preview 3](37/preview_3.png) \| ![preview 4](37/preview_4.png) \| ![preview 5](37/preview_5.png) \| ![preview 6](37/preview_6.png) \| ![preview 7](37/preview_7.png) \| N/A \| \| 38 \| 12 \| [Download](38/dataset.zip) \| ![preview 1](38/preview_1.png) \| ![preview 2](38/preview_2.png) \| ![preview 3](38/preview_3.png) \| ![preview 4](38/preview_4.png) \| ![preview 5](38/preview_5.png) \| ![preview 6](38/preview_6.png) \| ![preview 7](38/preview_7.png) \| ![preview 8](38/preview_8.png) \| \| 39 \| 19 \| [Download](39/dataset.zip) \| ![preview 1](39/preview_1.png) \| ![preview 2](39/preview_2.png) \| ![preview 3](39/preview_3.png) \| ![preview 4](39/preview_4.png) \| ![preview 5](39/preview_5.png) \| ![preview 6](39/preview_6.png) \| ![preview 7](39/preview_7.png) \| ![preview 8](39/preview_8.png) \| \| 40 \| 12 \| [Download](40/dataset.zip) \| ![preview 1](40/preview_1.png) \| ![preview 2](40/preview_2.png) \| ![preview 3](40/preview_3.png) \| ![preview 4](40/preview_4.png) \| ![preview 5](40/preview_5.png) \| ![preview 6](40/preview_6.png) \| ![preview 7](40/preview_7.png) \| ![preview 8](40/preview_8.png) \| \| 41 \| 17 \| [Download](41/dataset.zip) \| ![preview 1](41/preview_1.png) \| ![preview 2](41/preview_2.png) \| ![preview 3](41/preview_3.png) \| ![preview 4](41/preview_4.png) \| ![preview 5](41/preview_5.png) \| ![preview 6](41/preview_6.png) \| ![preview 7](41/preview_7.png) \| ![preview 8](41/preview_8.png) \| \| noise \| 109 \| [Download](-1/dataset.zip) \| ![preview 1](-1/preview_1.png) \| ![preview 2](-1/preview_2.png) \| ![preview 3](-1/preview_3.png) \| ![preview 4](-1/preview_4.png) \| ![preview 5](-1/preview_5.png) \| ![preview 6](-1/preview_6.png) \| ![preview 7](-1/preview_7.png) \| ![preview 8](-1/preview_8.png) \|	[ -0.7070472836494446, -0.15835601091384888, 0.16921110451221466, 0.2431415170431137, -0.271485835313797, -0.07666008919477463, -0.048868328332901, -0.37947264313697815, 0.6609385013580322, 0.5406595468521118, -0.9282428622245789, -0.8718432188034058, -0.7057729363441467, 0.49269190430641174, -0.013945093378424644, 0.6550127267837524, 0.1168733611702919, -0.42280319333076477, 0.11158056557178497, -0.022831615060567856, -0.27610278129577637, -0.007466373033821583, -0.44437235593795776, -0.12351292371749878, 0.6151448488235474, 0.6028146743774414, 0.9991539716720581, 1.0922595262527466, 0.6885350942611694, 0.4201265275478363, -0.059422608464956284, -0.018555117771029472, -0.4559895396232605, -0.06309417635202408, 0.10979468375444412, -0.32300278544425964, -0.6300954222679138, -0.19935527443885803, 0.4875144958496094, 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lmg-anon/VNTL-4k	lmg-anon	2023-11-20T20:42:18Z	0	[ "region:us" ]	null	2023-11-20T17:57:09Z	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 87937072 num_examples: 8695 download_size: 0 dataset_size: 87937072 --- # Dataset Card for "VNTL-4k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.6415311694145203, -0.08366541564464569, 0.25954049825668335, 0.16236011683940887, -0.4556991755962372, 0.11291952431201935, 0.46717962622642517, -0.33469539880752563, 0.8313431739807129, 0.7093762159347534, -0.7290385365486145, -0.8002280592918396, -0.4132673442363739, -0.06841735541820526, -0.156350240111351, 1.3177175521850586, 0.05896119400858879, -0.12430907785892487, -0.3222903311252594, -0.23270517587661743, -0.5142799019813538, -0.636246919631958, -0.456610769033432, -0.48644450306892395, 0.9505676031112671, 0.5941751003265381, 0.15864521265029907, 0.6411402821540833, 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m111styd4y/marisam	m111styd4y	2023-11-20T18:23:00Z	0	[ "license:openrail", "region:us" ]	null	2023-11-20T18:22:18Z	--- license: openrail ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, -0.4213368892669678, -0.26241645216941833, -0.0744934156537056, 0.32380396127700806, 0.7903715372085571, -0.3810413181781769, 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rkdeva/Dermnet-Test-2	rkdeva	2023-11-20T18:35:12Z	0	[ "region:us" ]	null	2023-11-20T18:33:11Z	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: label dtype: string splits: - name: train num_bytes: 376769322.178 num_examples: 3937 download_size: 370140971 dataset_size: 376769322.178 --- # Dataset Card for "Dermnet-Test-2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.4905036687850952, -0.3274669051170349, -0.00008575432002544403, 0.0823143869638443, -0.2528686821460724, -0.14084473252296448, 0.44367891550064087, -0.16196107864379883, 0.6807270050048828, 0.4258865416049957, -0.8232494592666626, -0.6108731031417847, -0.6320401430130005, -0.3089851140975952, -0.34971970319747925, 1.1651403903961182, -0.006050238385796547, 0.09358875453472137, -0.38771533966064453, -0.12405049800872803, -0.35460638999938965, 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vrhoward/thermostableProteins	vrhoward	2023-11-29T10:37:52Z	0	[ "task_categories:text-generation", "size_categories:1K<n<10K", "language:en", "region:us" ]	[ "text-generation" ]	2023-11-20T20:10:53Z	--- task_categories: - text-generation language: - en size_categories: - 1K<n<10K --- This dataset contains mutated protein sequences that resulted in higher thermostability as compared to wild-type.	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celsowm/openassistant-llama-style-pt-br	celsowm	2023-11-20T21:06:08Z	0	[ "region:us" ]	null	2023-11-20T21:06:01Z	--- dataset_info: features: - name: texto dtype: string splits: - name: train num_bytes: 240447 num_examples: 161 download_size: 152688 dataset_size: 240447 configs: - config_name: default data_files: - split: train path: data/train-* ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, 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hayden-donnelly/easy-mnist	hayden-donnelly	2023-11-20T21:45:49Z	0	[ "task_categories:image-classification", "task_categories:unconditional-image-generation", "size_categories:10K<n<100K", "region:us" ]	[ "image-classification", "unconditional-image-generation" ]	2023-11-20T21:31:37Z	--- task_categories: - image-classification - unconditional-image-generation pretty_name: Easy MNIST size_categories: - 10K<n<100K --- # Easy MNIST MNIST processed into three easy to use formats. Each .zip file contains a labels_and_paths.csv file and a data directory. ## mnist_png.zip MNIST in the png format. ``` label path 0 5 data/0.png 1 0 data/1.png 2 4 data/2.png 3 1 data/3.png 4 9 data/4.png ... ... ... 69995 2 data/69995.png 69996 3 data/69996.png 69997 4 data/69997.png 69998 5 data/69998.png 69999 6 data/69999.png ``` ## mnist_numpy.zip MNIST in the npy format. ``` label path 0 5 data/0.npy 1 0 data/1.npy 2 4 data/2.npy 3 1 data/3.npy 4 9 data/4.npy ... ... ... 69995 2 data/69995.npy 69996 3 data/69996.npy 69997 4 data/69997.npy 69998 5 data/69998.npy 69999 6 data/69999.npy ``` ## mnist_numpy_flat.zip MNIST in the npy format, flattened to 784 dimensional vectors. ``` label path 0 5 data/0.npy 1 0 data/1.npy 2 4 data/2.npy 3 1 data/3.npy 4 9 data/4.npy ... ... ... 69995 2 data/69995.npy 69996 3 data/69996.npy 69997 4 data/69997.npy 69998 5 data/69998.npy 69999 6 data/69999.npy ``` ## Acknowledgements - Yann LeCun, Courant Institute, NYU - Corinna Cortes, Google Labs, New York - Christopher J.C. 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Solnauhj/WOLLET	Solnauhj	2023-11-20T21:35:49Z	0	[ "license:openrail", "region:us" ]	null	2023-11-20T21:34:13Z	--- license: openrail ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, -0.4213368892669678, -0.26241645216941833, -0.0744934156537056, 0.32380396127700806, 0.7903715372085571, -0.3810413181781769, 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ElSmoothy/tacos	ElSmoothy	2023-11-20T21:55:30Z	0	[ "license:apache-2.0", "region:us" ]	null	2023-11-20T21:54:43Z	--- license: apache-2.0 ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, -0.4213368892669678, -0.26241645216941833, -0.0744934156537056, 0.32380396127700806, 0.7903715372085571, -0.3810413181781769, 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wearemusicai/moisesdb	wearemusicai	2023-11-27T19:36:27Z	1	[ "language:en", "license:other", "audio", "music", "source separation", "arxiv:2307.15913", "region:us" ]	null	2023-11-20T22:05:24Z	--- language: - en pretty_name: MoisesDB tags: - audio - music - source separation license: other license_name: ncrlc license_link: https://github.com/moises-ai/moises-db --- # MoisesDB Moises Dataset for Source Separation ### Dataset Description - Homepage: [MoisesDB homepage](https://developer.moises.ai/research/) - Repository: [MoisesDB repository](https://github.com/moises-ai/moises-db) - Paper: [Moisesdb: A dataset for source separation beyond 4-stems](https://arxiv.org/abs/2307.15913) - Point of Contact: [Igor Pereira](mailto:[email protected]) ### Dataset Summary MoisesDB is a dataset for source separation. It provides a collection of tracks and their separated stems (vocals, bass, drums, etc.). The dataset is used to evaluate the performance of source separation algorithms. # Download the data Please download the dataset at our research [website](https://developer.moises.ai/research), extract it and configure the environment variable `MOISESDB_PATH` accordingly. ``` export MOISESDB_PATH=./moises-db-data ``` The directory structure should be ``` moisesdb: moisesdb_v0.1 track uuid 0 track uuid 1 . . . ``` # Install You can install this package with ``` pip install git+https://github.com/moises-ai/moises-db.git ``` # Usage ## `MoisesDB` After downloading and configuring the path for the dataset, you can create an instance of `MoisesDB` to access the tracks. You can also provide the dataset path with the `data_path` argument. ``` from moisesdb.dataset import MoisesDB db = MoisesDB( data_path='./moisesdb', sample_rate=44100 ) ``` The `MoisesDB` object has iterator properties that you can use to access all files within the dataset. ``` n_songs = len(db) track = db[0] # Returns a MoisesDBTrack object ``` ## `MoisesDBTrack` The `MoisesDBTrack` object holds information about a track in the dataset, perform on-the-fly mixing for stems and multiple sources within a stem. You can access all the stems and mixture from the `stem` and `audio` properties. The `stem` property returns a dictionary whith available stems as keys and `nd.array` on values. The `audio` property results in a `nd.array` with the mixture. ``` track = db[0] stems = track.stems # stems = {'vocals': ..., 'bass': ..., ...} mixture track.audio # mixture = nd.array ``` The `MoisesDBTrack` object also contains other non-audio information from the track such as: - `track.id` - `track.provider` - `track.artist` - `track.name` - `track.genre` - `track.sources` - `track.bleedings` - `track.activity` The stems and mixture are computed on-the-fly. You can create a stems-only version of the dataset using the `save_stems` method of the `MoisesDBTrack`. ``` track = db[0] path = './moises-db-stems/0' track.save_stems(path) ``` # Performance Evaluation We run a few source separation algorithms as well as oracle methods to evaluate the performance of each track of the `MoisesDB`. These results are located in `csv` files at the `benchmark` folder. # Citing If you used the `MoisesDB` dataset on your research, please cite the following paper. ``` @misc{pereira2023moisesdb, title={Moisesdb: A dataset for source separation beyond 4-stems}, author={Igor Pereira and Felipe Araújo and Filip Korzeniowski and Richard Vogl}, year={2023}, eprint={2307.15913}, archivePrefix={arXiv}, primaryClass={cs.SD} } ``` # Licensing `MoisesDB` is distributed with the NC-RCL license. ``` "Non-Commercial Research Community license (NC-RCL) Limited Redistribution: You are permitted to copy and utilize the provided audio material in any medium or format, as long as it is done only for non-commercial purposes within the research community, and the redistribution is conducted solely through the platform moises.ai or other platforms explicitly authorized by the licensor. Redistribution outside the authorized platforms is not allowed without the licensor's written consent. Attribution: You must give appropriate credit (including the artist's name and the song's title), and provide a link to this license or a notice indicating the terms of this license. Non-Commercial Use: You cannot use the material for any commercial purposes or financial gain. This includes, but is not limited to, the sale, licensing, or rental of the material, as well as any use where the primary aim is to generate revenue or profits. No Derivative Works: You cannot create, remix, adapt, or build upon the material, unless explicitly permitted by the artist. Preservation of Legal Notices: You cannot remove any copyright or other proprietary notices which are included in or attached to the material. Termination: If you fail to comply with this license, your rights to use the material will be terminated automatically. Voice Cloning Restriction: You are prohibited from using the vocal stems or any part of the audio material to create a public digital imitation of the artist's voice (e.g: a vocal clone or replica). This includes, but is not limited to, the utilization of voice synthesis technology, deep learning algorithms, and other artificial intelligence-based tools." ```	[ -0.869951605796814, -0.47320470213890076, 0.2664867043495178, 0.3103688061237335, -0.27678170800209045, 0.349610298871994, -0.20674189925193787, -0.2583373188972473, 0.614357054233551, 0.7670416235923767, -1.01085364818573, -0.7662640810012817, -0.35626786947250366, 0.26867184042930603, -0.5306540727615356, 0.6917709708213806, 0.284253865480423, 0.0031405501067638397, 0.028807224705815315, -0.24670341610908508, -0.45704901218414307, -0.35158517956733704, -0.5418345928192139, 0.026993755251169205, 0.29501357674598694, 0.6398681402206421, 0.09417688101530075, 0.636989176273346, 0.48848316073417664, 0.40123701095581055, -0.11551728844642639, 0.1941497027873993, -0.5010700821876526, 0.3187958300113678, -0.20192135870456696, -0.3326573073863983, -0.23218700289726257, 0.2514941394329071, 0.7812118530273438, 0.3553481996059418, 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Alan318/2	Alan318	2023-11-20T22:14:56Z	0	[ "license:mit", "region:us" ]	null	2023-11-20T22:14:56Z	--- license: mit ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, -0.4213368892669678, -0.26241645216941833, -0.0744934156537056, 0.32380396127700806, 0.7903715372085571, -0.3810413181781769, 0.19328097999095917, 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sade-adrien/redpajama_v2_sample_100M	sade-adrien	2023-11-21T06:00:28Z	0	[ "region:us" ]	null	2023-11-20T23:07:38Z	--- dataset_info: features: - name: raw_content dtype: string - name: doc_id dtype: string - name: meta dtype: string - name: quality_signals dtype: string splits: - name: train num_bytes: 1043463774444 num_examples: 100000000 download_size: 226895559008 dataset_size: 1043463774444 --- # Dataset Card for "redpajama_v2_sample_100M" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.5925703048706055, -0.3685779273509979, 0.053581900894641876, 0.5504358410835266, -0.2959500551223755, -0.2798900306224823, 0.047902122139930725, -0.3491574227809906, 0.9799594879150391, 0.7500234842300415, -0.8950110077857971, -0.6641258597373962, -0.7942659854888916, -0.3645228147506714, -0.4237707257270813, 1.2821173667907715, -0.09733404964208603, -0.021800199523568153, -0.7402585744857788, 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rafa62/renangarcia	rafa62	2023-11-20T23:40:13Z	0	[ "license:openrail", "region:us" ]	null	2023-11-20T23:38:31Z	--- license: openrail ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, -0.4213368892669678, -0.26241645216941833, -0.0744934156537056, 0.32380396127700806, 0.7903715372085571, -0.3810413181781769, 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ChaiML/eval_dataset_character_20231108_sample43_response_reselected	ChaiML	2023-11-21T00:26:22Z	0	[ "region:us" ]	null	2023-11-21T00:26:20Z	--- dataset_info: features: - name: conversation_id dtype: string - name: bot_id dtype: string - name: user_id dtype: string - name: conversation dtype: string - name: thumbs_up dtype: bool - name: feedback dtype: string - name: model_name dtype: string - name: season dtype: string - name: bot_label dtype: string - name: prompt dtype: string - name: memory dtype: string - name: first_message dtype: string - name: labels dtype: int64 - name: chat_history list: - name: message dtype: string - name: sender dtype: string - name: sample_index dtype: int64 - name: sample_payload list: - name: message dtype: string - name: sender dtype: string - name: sample_response struct: - name: message dtype: string - name: sender dtype: string - name: eval_model_id dtype: string - name: eval_model_output dtype: string - name: eval_model_input dtype: string splits: - name: train num_bytes: 4595702.4 num_examples: 387 download_size: 453380 dataset_size: 4595702.4 --- # Dataset Card for "eval_dataset_character_20231108_sample43_response_reselected" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.4615306258201599, -0.34960290789604187, 0.18568852543830872, 0.10586364567279816, -0.09879203885793686, 0.29980993270874023, 0.17536194622516632, -0.20401518046855927, 0.8708611726760864, 0.7834916710853577, -0.8841880559921265, -0.5595444440841675, -0.26734215021133423, 0.30724969506263733, -0.32645586133003235, 1.3649828433990479, 0.06511816382408142, 0.08672695606946945, -0.32991471886634827, -0.15161073207855225, -0.47970855236053467, -0.6711965203285217, -0.9609523415565491, -0.47195005416870117, 0.9249752759933472, 0.8036483526229858, 0.177525132894516, 0.30731916427612305, 0.790260910987854, 0.22403961420059204, 0.1542721837759018, -0.27330291271209717, 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miragepa/Larin	miragepa	2023-11-21T02:02:29Z	0	[ "license:openrail", "region:us" ]	null	2023-11-21T00:30:26Z	--- license: openrail ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, -0.4213368892669678, -0.26241645216941833, -0.0744934156537056, 0.32380396127700806, 0.7903715372085571, -0.3810413181781769, 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gagan3012/Numerical_understanding	gagan3012	2023-11-21T00:58:54Z	0	[ "region:us" ]	null	2023-11-21T00:58:50Z	--- dataset_info: features: - name: text dtype: string - name: answer dtype: string - name: query dtype: string - name: id dtype: string splits: - name: train num_bytes: 169338670 num_examples: 20875 download_size: 43150062 dataset_size: 169338670 configs: - config_name: default data_files: - split: train path: data/train-* ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 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Maljean/State-Sponsored	Maljean	2023-11-21T01:01:01Z	0	[ "license:unknown", "region:us" ]	null	2023-11-21T01:01:01Z	--- license: unknown ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, -0.4213368892669678, -0.26241645216941833, -0.0744934156537056, 0.32380396127700806, 0.7903715372085571, -0.3810413181781769, 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Maljean/dataset	Maljean	2023-11-21T01:09:42Z	0	[ "license:apache-2.0", "region:us" ]	null	2023-11-21T01:08:43Z	--- license: apache-2.0 ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, -0.4213368892669678, -0.26241645216941833, -0.0744934156537056, 0.32380396127700806, 0.7903715372085571, -0.3810413181781769, 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Papini/papini	Papini	2023-11-21T01:30:01Z	0	[ "license:apache-2.0", "region:us" ]	null	2023-11-21T01:21:03Z	--- license: apache-2.0 ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, -0.4213368892669678, -0.26241645216941833, -0.0744934156537056, 0.32380396127700806, 0.7903715372085571, -0.3810413181781769, 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Kaue123456/LoganIsaacBardavid	Kaue123456	2023-11-21T01:33:09Z	0	[ "license:openrail", "region:us" ]	null	2023-11-21T01:32:34Z	--- license: openrail ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, -0.4213368892669678, -0.26241645216941833, -0.0744934156537056, 0.32380396127700806, 0.7903715372085571, 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TioRob/Larin	TioRob	2023-11-21T01:34:14Z	0	[ "license:openrail", "region:us" ]	null	2023-11-21T01:33:45Z	--- license: openrail ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, -0.4213368892669678, -0.26241645216941833, -0.0744934156537056, 0.32380396127700806, 0.7903715372085571, -0.3810413181781769, 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KADUZADA/ROBSON	KADUZADA	2023-11-21T01:47:01Z	0	[ "license:openrail", "region:us" ]	null	2023-11-21T01:43:38Z	--- license: openrail ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, -0.4213368892669678, -0.26241645216941833, -0.0744934156537056, 0.32380396127700806, 0.7903715372085571, -0.3810413181781769, 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HuskyAI/neo_icon	HuskyAI	2023-11-21T01:51:42Z	1	[ "task_categories:text-to-image", "license:apache-2.0", "icon", "style", "lora", "region:us" ]	[ "text-to-image" ]	2023-11-21T01:50:20Z	--- license: apache-2.0 task_categories: - text-to-image tags: - icon - style - lora ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, 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idning/ffhq64-caption	idning	2023-11-21T03:17:01Z	0	[ "license:mit", "region:us" ]	null	2023-11-21T02:10:24Z	--- license: mit dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 586217154.0 num_examples: 70000 download_size: 584117488 dataset_size: 586217154.0 configs: - config_name: default data_files: - split: train path: data/train-* ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 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idning/ffhq256-caption	idning	2023-11-21T08:31:47Z	0	[ "license:mit", "region:us" ]	null	2023-11-21T02:10:45Z	--- license: mit dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 7388635414.0 num_examples: 70000 download_size: 7386868493 dataset_size: 7388635414.0 configs: - config_name: default data_files: - split: train path: data/train-* ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 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Vinayakagude/cherryleaves	Vinayakagude	2023-11-21T03:40:01Z	0	[ "region:us" ]	null	2023-11-21T02:18:26Z	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': healthy '1': powdery_mildew splits: - name: train num_bytes: 68574745.152 num_examples: 4208 download_size: 57302416 dataset_size: 68574745.152 configs: - config_name: default data_files: - split: train path: data/train-* ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 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librarian-bots/model-card-sentences-annotated	librarian-bots	2023-11-21T15:10:04Z	2	[ "task_categories:token-classification", "size_categories:100K<n<1M", "language:en", "documentation ", "region:us" ]	[ "token-classification" ]	2023-11-21T02:21:46Z	--- configs: - config_name: raw_data data_files: results.jsonl - config_name: cleaned_data data_files: clean_results.jsonl task_categories: - token-classification tags: - 'documentation ' pretty_name: 'Model Card Sentences Annotated with Entities ' language: - en size_categories: - 100K<n<1M ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, 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nlplabtdtu/train-tokenizor-ds-T5	nlplabtdtu	2023-11-21T02:29:40Z	0	[ "region:us" ]	null	2023-11-21T02:25:15Z	--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 7956427488 num_examples: 1885715 download_size: 2662966309 dataset_size: 7956427488 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "train-tokenizor-ds-T5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.5024722218513489, 0.2386612892150879, 0.20304173231124878, 0.25856050848960876, -0.30467185378074646, 0.21351048350334167, 0.30052947998046875, -0.03652136027812958, 0.7576824426651001, 0.27894631028175354, -0.8192400336265564, -0.7775591015815735, -0.8039881587028503, -0.18499214947223663, -0.43296533823013306, 1.3093429803848267, -0.03462519496679306, 0.24468185007572174, 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nlplabtdtu/val-tokenizor-ds-T5	nlplabtdtu	2023-11-21T02:30:10Z	0	[ "region:us" ]	null	2023-11-21T02:29:41Z	--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 885510284 num_examples: 209524 download_size: 296327037 dataset_size: 885510284 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "val-tokenizor-ds-T5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.5468727946281433, -0.03953419253230095, 0.17935115098953247, 0.1398606300354004, -0.3132685124874115, 0.22065582871437073, 0.6094205379486084, -0.061549294739961624, 0.836761474609375, 0.5889047384262085, -0.7671224474906921, -0.9475807547569275, -0.8279675245285034, -0.09961649030447006, -0.41330254077911377, 1.2840620279312134, 0.11078920215368271, 0.16624422371387482, 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lghupan/CloudBench-YAML	lghupan	2023-11-21T02:52:49Z	0	[ "license:mit", "region:us" ]	null	2023-11-21T02:52:49Z	--- license: mit ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, -0.4213368892669678, -0.26241645216941833, -0.0744934156537056, 0.32380396127700806, 0.7903715372085571, -0.3810413181781769, 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nlplabtdtu/s-c4	nlplabtdtu	2023-11-21T03:00:41Z	0	[ "region:us" ]	null	2023-11-21T02:58:13Z	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: url dtype: string - name: id dtype: string - name: text dtype: string - name: perplexity dtype: float64 splits: - name: train num_bytes: 3488628609 num_examples: 777159 download_size: 1734360231 dataset_size: 3488628609 --- # Dataset Card for "s-c4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.5768653750419617, -0.0029836101457476616, 0.393929660320282, 0.17946234345436096, -0.3188817799091339, 0.1833953559398651, 0.25723031163215637, -0.1844046264886856, 0.9234669804573059, 0.4365272521972656, -0.9869688749313354, -0.728519082069397, -0.6407635807991028, 0.014814737252891064, -0.34520894289016724, 1.434879183769226, 0.12534432113170624, 0.17935876548290253, -0.5507851839065552, 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nlplabtdtu/s-wikicorpus	nlplabtdtu	2023-11-21T03:03:21Z	0	[ "region:us" ]	null	2023-11-21T03:00:42Z	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 2455296583 num_examples: 1118180 download_size: 1536183681 dataset_size: 2455296583 --- # Dataset Card for "s-wikicorpus" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.5170384645462036, -0.0380949005484581, 0.10482512414455414, 0.09842696785926819, -0.2665562927722931, 0.046555642038583755, 0.20895615220069885, -0.20634615421295166, 1.2430075407028198, 0.366998553276062, -0.8947756886482239, -0.5157356858253479, -0.823527455329895, -0.14374753832817078, -0.385353147983551, 1.5433462858200073, 0.17792658507823944, 0.0391966849565506, -0.5338088870048523, -0.13959984481334686, 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gsynb/gg	gsynb	2023-11-21T03:08:53Z	0	[ "license:apache-2.0", "region:us" ]	null	2023-11-21T03:08:15Z	--- license: apache-2.0 ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, -0.4213368892669678, -0.26241645216941833, -0.0744934156537056, 0.32380396127700806, 0.7903715372085571, -0.3810413181781769, 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ai4cloud/CloudEval-YAML	ai4cloud	2023-11-21T11:48:11Z	0	[ "license:mit", "region:us" ]	null	2023-11-21T03:24:44Z	--- license: mit ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, -0.4213368892669678, -0.26241645216941833, -0.0744934156537056, 0.32380396127700806, 0.7903715372085571, -0.3810413181781769, 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Gabriel1898/pozee	Gabriel1898	2023-11-21T04:30:00Z	0	[ "license:openrail", "region:us" ]	null	2023-11-21T04:29:06Z	--- license: openrail ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, -0.4213368892669678, -0.26241645216941833, -0.0744934156537056, 0.32380396127700806, 0.7903715372085571, -0.3810413181781769, 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Gabriel1898/poze1	Gabriel1898	2023-11-21T04:37:13Z	0	[ "license:openrail", "region:us" ]	null	2023-11-21T04:36:13Z	--- license: openrail ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, -0.4213368892669678, -0.26241645216941833, -0.0744934156537056, 0.32380396127700806, 0.7903715372085571, -0.3810413181781769, 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kseki/guanaco-llama2-1k	kseki	2023-11-28T03:52:12Z	0	[ "region:us" ]	null	2023-11-21T04:45:05Z	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 1654448 num_examples: 1000 download_size: 966693 dataset_size: 1654448 configs: - config_name: default data_files: - split: train path: data/train-* ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, 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Aryansoni27/Amitabh_bachchan_voice	Aryansoni27	2023-11-21T05:05:03Z	0	[ "license:mit", "region:us" ]	null	2023-11-21T05:05:03Z	--- license: mit ---	[ -0.1285339891910553, -0.186167910695076, 0.6529129147529602, 0.4943627417087555, -0.19319313764572144, 0.23607458174228668, 0.3607197701931, 0.05056354030966759, 0.5793653726577759, 0.740013599395752, -0.6508103609085083, -0.2378397285938263, -0.7102249264717102, -0.047825902700424194, -0.3894753158092499, 0.8470762372016907, -0.09598255157470703, 0.024004921317100525, 0.047120124101638794, -0.14317825436592102, -0.612104058265686, -0.04771730303764343, -1.052453875541687, -0.06787486374378204, 0.3002282381057739, 0.5120974779129028, 0.8275899291038513, 0.3960294723510742, 0.503056526184082, 1.7515552043914795, -0.08836923539638519, -0.22754432260990143, -0.458920419216156, 0.4223068356513977, -0.3327738642692566, -0.4213368892669678, -0.26241645216941833, -0.0744934156537056, 0.32380396127700806, 0.7903715372085571, -0.3810413181781769, 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BangumiBase/sailormoon1990s

BangumiBase

2023-11-20T11:23:44Z

0

[ "size_categories:10K<n<100K", "license:mit", "art", "region:us" ]

null

2023-11-19T21:15:02Z

--- license: mit tags: - art size_categories: - 10K<n<100K --- # Bangumi Image Base of Sailor Moon (1990s) This is the image base of bangumi Sailor Moon (1990s), we detected 132 characters, 14684 images in total. The full dataset is [here](all.zip). **Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview: | # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 | |:------|---------:|:----------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------| | 0 | 3008 | [Download](0/dataset.zip) | ![preview 1](0/preview_1.png) | ![preview 2](0/preview_2.png) | ![preview 3](0/preview_3.png) | ![preview 4](0/preview_4.png) | ![preview 5](0/preview_5.png) | ![preview 6](0/preview_6.png) | ![preview 7](0/preview_7.png) | ![preview 8](0/preview_8.png) | | 1 | 94 | [Download](1/dataset.zip) | ![preview 1](1/preview_1.png) | ![preview 2](1/preview_2.png) | ![preview 3](1/preview_3.png) | ![preview 4](1/preview_4.png) | ![preview 5](1/preview_5.png) | ![preview 6](1/preview_6.png) | ![preview 7](1/preview_7.png) | ![preview 8](1/preview_8.png) | | 2 | 696 | [Download](2/dataset.zip) | ![preview 1](2/preview_1.png) | ![preview 2](2/preview_2.png) | ![preview 3](2/preview_3.png) | ![preview 4](2/preview_4.png) | ![preview 5](2/preview_5.png) | ![preview 6](2/preview_6.png) | ![preview 7](2/preview_7.png) | ![preview 8](2/preview_8.png) | | 3 | 49 | [Download](3/dataset.zip) | ![preview 1](3/preview_1.png) | ![preview 2](3/preview_2.png) | ![preview 3](3/preview_3.png) | ![preview 4](3/preview_4.png) | ![preview 5](3/preview_5.png) | ![preview 6](3/preview_6.png) | ![preview 7](3/preview_7.png) | ![preview 8](3/preview_8.png) | | 4 | 29 | [Download](4/dataset.zip) | ![preview 1](4/preview_1.png) | ![preview 2](4/preview_2.png) | ![preview 3](4/preview_3.png) | ![preview 4](4/preview_4.png) | ![preview 5](4/preview_5.png) | ![preview 6](4/preview_6.png) | ![preview 7](4/preview_7.png) | ![preview 8](4/preview_8.png) | | 5 | 176 | [Download](5/dataset.zip) | ![preview 1](5/preview_1.png) | ![preview 2](5/preview_2.png) | ![preview 3](5/preview_3.png) | ![preview 4](5/preview_4.png) | ![preview 5](5/preview_5.png) | ![preview 6](5/preview_6.png) | ![preview 7](5/preview_7.png) | ![preview 8](5/preview_8.png) | | 6 | 95 | [Download](6/dataset.zip) | ![preview 1](6/preview_1.png) | ![preview 2](6/preview_2.png) | ![preview 3](6/preview_3.png) | ![preview 4](6/preview_4.png) | ![preview 5](6/preview_5.png) | ![preview 6](6/preview_6.png) | ![preview 7](6/preview_7.png) | ![preview 8](6/preview_8.png) | | 7 | 72 | [Download](7/dataset.zip) | ![preview 1](7/preview_1.png) | ![preview 2](7/preview_2.png) | ![preview 3](7/preview_3.png) | ![preview 4](7/preview_4.png) | ![preview 5](7/preview_5.png) | ![preview 6](7/preview_6.png) | ![preview 7](7/preview_7.png) | ![preview 8](7/preview_8.png) | | 8 | 180 | [Download](8/dataset.zip) | ![preview 1](8/preview_1.png) | ![preview 2](8/preview_2.png) | ![preview 3](8/preview_3.png) | ![preview 4](8/preview_4.png) | ![preview 5](8/preview_5.png) | ![preview 6](8/preview_6.png) | ![preview 7](8/preview_7.png) | ![preview 8](8/preview_8.png) | | 9 | 75 | [Download](9/dataset.zip) | ![preview 1](9/preview_1.png) | ![preview 2](9/preview_2.png) | ![preview 3](9/preview_3.png) | ![preview 4](9/preview_4.png) | ![preview 5](9/preview_5.png) | ![preview 6](9/preview_6.png) | ![preview 7](9/preview_7.png) | ![preview 8](9/preview_8.png) | | 10 | 108 | [Download](10/dataset.zip) | ![preview 1](10/preview_1.png) | ![preview 2](10/preview_2.png) | ![preview 3](10/preview_3.png) | ![preview 4](10/preview_4.png) | ![preview 5](10/preview_5.png) | ![preview 6](10/preview_6.png) | ![preview 7](10/preview_7.png) | ![preview 8](10/preview_8.png) | | 11 | 113 | [Download](11/dataset.zip) | ![preview 1](11/preview_1.png) | ![preview 2](11/preview_2.png) | ![preview 3](11/preview_3.png) | ![preview 4](11/preview_4.png) | ![preview 5](11/preview_5.png) | ![preview 6](11/preview_6.png) | ![preview 7](11/preview_7.png) | ![preview 8](11/preview_8.png) | | 12 | 32 | [Download](12/dataset.zip) | ![preview 1](12/preview_1.png) | ![preview 2](12/preview_2.png) | ![preview 3](12/preview_3.png) | ![preview 4](12/preview_4.png) | ![preview 5](12/preview_5.png) | ![preview 6](12/preview_6.png) | ![preview 7](12/preview_7.png) | ![preview 8](12/preview_8.png) | | 13 | 42 | [Download](13/dataset.zip) | ![preview 1](13/preview_1.png) | ![preview 2](13/preview_2.png) | ![preview 3](13/preview_3.png) | ![preview 4](13/preview_4.png) | ![preview 5](13/preview_5.png) | ![preview 6](13/preview_6.png) | ![preview 7](13/preview_7.png) | ![preview 8](13/preview_8.png) | | 14 | 47 | [Download](14/dataset.zip) | ![preview 1](14/preview_1.png) | ![preview 2](14/preview_2.png) | ![preview 3](14/preview_3.png) | ![preview 4](14/preview_4.png) | ![preview 5](14/preview_5.png) | ![preview 6](14/preview_6.png) | ![preview 7](14/preview_7.png) | ![preview 8](14/preview_8.png) | | 15 | 602 | [Download](15/dataset.zip) | ![preview 1](15/preview_1.png) | ![preview 2](15/preview_2.png) | ![preview 3](15/preview_3.png) | ![preview 4](15/preview_4.png) | ![preview 5](15/preview_5.png) | ![preview 6](15/preview_6.png) | ![preview 7](15/preview_7.png) | ![preview 8](15/preview_8.png) | | 16 | 1066 | [Download](16/dataset.zip) | ![preview 1](16/preview_1.png) | ![preview 2](16/preview_2.png) | ![preview 3](16/preview_3.png) | ![preview 4](16/preview_4.png) | ![preview 5](16/preview_5.png) | ![preview 6](16/preview_6.png) | ![preview 7](16/preview_7.png) | ![preview 8](16/preview_8.png) | | 17 | 395 | [Download](17/dataset.zip) | ![preview 1](17/preview_1.png) | ![preview 2](17/preview_2.png) | ![preview 3](17/preview_3.png) | ![preview 4](17/preview_4.png) | ![preview 5](17/preview_5.png) | ![preview 6](17/preview_6.png) | ![preview 7](17/preview_7.png) | ![preview 8](17/preview_8.png) | | 18 | 208 | [Download](18/dataset.zip) | ![preview 1](18/preview_1.png) | ![preview 2](18/preview_2.png) | ![preview 3](18/preview_3.png) | ![preview 4](18/preview_4.png) | ![preview 5](18/preview_5.png) | ![preview 6](18/preview_6.png) | ![preview 7](18/preview_7.png) | ![preview 8](18/preview_8.png) | | 19 | 79 | [Download](19/dataset.zip) | ![preview 1](19/preview_1.png) | ![preview 2](19/preview_2.png) | ![preview 3](19/preview_3.png) | ![preview 4](19/preview_4.png) | ![preview 5](19/preview_5.png) | ![preview 6](19/preview_6.png) | ![preview 7](19/preview_7.png) | ![preview 8](19/preview_8.png) | | 20 | 86 | [Download](20/dataset.zip) | ![preview 1](20/preview_1.png) | ![preview 2](20/preview_2.png) | ![preview 3](20/preview_3.png) | ![preview 4](20/preview_4.png) | ![preview 5](20/preview_5.png) | ![preview 6](20/preview_6.png) | ![preview 7](20/preview_7.png) | ![preview 8](20/preview_8.png) | | 21 | 62 | [Download](21/dataset.zip) | ![preview 1](21/preview_1.png) | ![preview 2](21/preview_2.png) | ![preview 3](21/preview_3.png) | ![preview 4](21/preview_4.png) | ![preview 5](21/preview_5.png) | ![preview 6](21/preview_6.png) | ![preview 7](21/preview_7.png) | ![preview 8](21/preview_8.png) | | 22 | 50 | [Download](22/dataset.zip) | ![preview 1](22/preview_1.png) | ![preview 2](22/preview_2.png) | ![preview 3](22/preview_3.png) | ![preview 4](22/preview_4.png) | ![preview 5](22/preview_5.png) | ![preview 6](22/preview_6.png) | ![preview 7](22/preview_7.png) | ![preview 8](22/preview_8.png) | | 23 | 53 | [Download](23/dataset.zip) | ![preview 1](23/preview_1.png) | ![preview 2](23/preview_2.png) | ![preview 3](23/preview_3.png) | ![preview 4](23/preview_4.png) | ![preview 5](23/preview_5.png) | ![preview 6](23/preview_6.png) | ![preview 7](23/preview_7.png) | ![preview 8](23/preview_8.png) | | 24 | 76 | [Download](24/dataset.zip) | ![preview 1](24/preview_1.png) | ![preview 2](24/preview_2.png) | ![preview 3](24/preview_3.png) | ![preview 4](24/preview_4.png) | ![preview 5](24/preview_5.png) | ![preview 6](24/preview_6.png) | ![preview 7](24/preview_7.png) | ![preview 8](24/preview_8.png) | | 25 | 141 | [Download](25/dataset.zip) | ![preview 1](25/preview_1.png) | ![preview 2](25/preview_2.png) | ![preview 3](25/preview_3.png) | ![preview 4](25/preview_4.png) | ![preview 5](25/preview_5.png) | ![preview 6](25/preview_6.png) | ![preview 7](25/preview_7.png) | ![preview 8](25/preview_8.png) | | 26 | 67 | [Download](26/dataset.zip) | ![preview 1](26/preview_1.png) | ![preview 2](26/preview_2.png) | ![preview 3](26/preview_3.png) | ![preview 4](26/preview_4.png) | ![preview 5](26/preview_5.png) | ![preview 6](26/preview_6.png) | ![preview 7](26/preview_7.png) | ![preview 8](26/preview_8.png) | | 27 | 45 | [Download](27/dataset.zip) | ![preview 1](27/preview_1.png) | ![preview 2](27/preview_2.png) | ![preview 3](27/preview_3.png) | ![preview 4](27/preview_4.png) | ![preview 5](27/preview_5.png) | ![preview 6](27/preview_6.png) | ![preview 7](27/preview_7.png) | ![preview 8](27/preview_8.png) | | 28 | 750 | [Download](28/dataset.zip) | ![preview 1](28/preview_1.png) | ![preview 2](28/preview_2.png) | ![preview 3](28/preview_3.png) | ![preview 4](28/preview_4.png) | ![preview 5](28/preview_5.png) | ![preview 6](28/preview_6.png) | ![preview 7](28/preview_7.png) | ![preview 8](28/preview_8.png) | | 29 | 103 | [Download](29/dataset.zip) | ![preview 1](29/preview_1.png) | ![preview 2](29/preview_2.png) | ![preview 3](29/preview_3.png) | ![preview 4](29/preview_4.png) | ![preview 5](29/preview_5.png) | ![preview 6](29/preview_6.png) | ![preview 7](29/preview_7.png) | ![preview 8](29/preview_8.png) | | 30 | 34 | [Download](30/dataset.zip) | ![preview 1](30/preview_1.png) | ![preview 2](30/preview_2.png) | ![preview 3](30/preview_3.png) | ![preview 4](30/preview_4.png) | ![preview 5](30/preview_5.png) | ![preview 6](30/preview_6.png) | ![preview 7](30/preview_7.png) | ![preview 8](30/preview_8.png) | | 31 | 42 | [Download](31/dataset.zip) | ![preview 1](31/preview_1.png) | ![preview 2](31/preview_2.png) | ![preview 3](31/preview_3.png) | ![preview 4](31/preview_4.png) | ![preview 5](31/preview_5.png) | ![preview 6](31/preview_6.png) | ![preview 7](31/preview_7.png) | ![preview 8](31/preview_8.png) | | 32 | 20 | [Download](32/dataset.zip) | ![preview 1](32/preview_1.png) | ![preview 2](32/preview_2.png) | ![preview 3](32/preview_3.png) | ![preview 4](32/preview_4.png) | ![preview 5](32/preview_5.png) | ![preview 6](32/preview_6.png) | ![preview 7](32/preview_7.png) | ![preview 8](32/preview_8.png) | | 33 | 67 | [Download](33/dataset.zip) | ![preview 1](33/preview_1.png) | ![preview 2](33/preview_2.png) | ![preview 3](33/preview_3.png) | ![preview 4](33/preview_4.png) | ![preview 5](33/preview_5.png) | ![preview 6](33/preview_6.png) | ![preview 7](33/preview_7.png) | ![preview 8](33/preview_8.png) | | 34 | 79 | [Download](34/dataset.zip) | ![preview 1](34/preview_1.png) | ![preview 2](34/preview_2.png) | ![preview 3](34/preview_3.png) | ![preview 4](34/preview_4.png) | ![preview 5](34/preview_5.png) | ![preview 6](34/preview_6.png) | ![preview 7](34/preview_7.png) | ![preview 8](34/preview_8.png) | | 35 | 40 | [Download](35/dataset.zip) | ![preview 1](35/preview_1.png) | ![preview 2](35/preview_2.png) | ![preview 3](35/preview_3.png) | ![preview 4](35/preview_4.png) | ![preview 5](35/preview_5.png) | ![preview 6](35/preview_6.png) | ![preview 7](35/preview_7.png) | ![preview 8](35/preview_8.png) | | 36 | 45 | [Download](36/dataset.zip) | ![preview 1](36/preview_1.png) | ![preview 2](36/preview_2.png) | ![preview 3](36/preview_3.png) | ![preview 4](36/preview_4.png) | ![preview 5](36/preview_5.png) | ![preview 6](36/preview_6.png) | ![preview 7](36/preview_7.png) | ![preview 8](36/preview_8.png) | | 37 | 118 | [Download](37/dataset.zip) | ![preview 1](37/preview_1.png) | ![preview 2](37/preview_2.png) | ![preview 3](37/preview_3.png) | ![preview 4](37/preview_4.png) | ![preview 5](37/preview_5.png) | ![preview 6](37/preview_6.png) | ![preview 7](37/preview_7.png) | ![preview 8](37/preview_8.png) | | 38 | 41 | [Download](38/dataset.zip) | ![preview 1](38/preview_1.png) | ![preview 2](38/preview_2.png) | ![preview 3](38/preview_3.png) | ![preview 4](38/preview_4.png) | ![preview 5](38/preview_5.png) | ![preview 6](38/preview_6.png) | ![preview 7](38/preview_7.png) | ![preview 8](38/preview_8.png) | | 39 | 62 | [Download](39/dataset.zip) | ![preview 1](39/preview_1.png) | ![preview 2](39/preview_2.png) | ![preview 3](39/preview_3.png) | ![preview 4](39/preview_4.png) | ![preview 5](39/preview_5.png) | ![preview 6](39/preview_6.png) | ![preview 7](39/preview_7.png) | ![preview 8](39/preview_8.png) | | 40 | 93 | [Download](40/dataset.zip) | ![preview 1](40/preview_1.png) | ![preview 2](40/preview_2.png) | ![preview 3](40/preview_3.png) | ![preview 4](40/preview_4.png) | ![preview 5](40/preview_5.png) | ![preview 6](40/preview_6.png) | ![preview 7](40/preview_7.png) | ![preview 8](40/preview_8.png) | | 41 | 79 | [Download](41/dataset.zip) | ![preview 1](41/preview_1.png) | ![preview 2](41/preview_2.png) | ![preview 3](41/preview_3.png) | ![preview 4](41/preview_4.png) | ![preview 5](41/preview_5.png) | ![preview 6](41/preview_6.png) | ![preview 7](41/preview_7.png) | ![preview 8](41/preview_8.png) | | 42 | 920 | [Download](42/dataset.zip) | ![preview 1](42/preview_1.png) | ![preview 2](42/preview_2.png) | ![preview 3](42/preview_3.png) | ![preview 4](42/preview_4.png) | ![preview 5](42/preview_5.png) | ![preview 6](42/preview_6.png) | ![preview 7](42/preview_7.png) | ![preview 8](42/preview_8.png) | | 43 | 55 | [Download](43/dataset.zip) | ![preview 1](43/preview_1.png) | ![preview 2](43/preview_2.png) | ![preview 3](43/preview_3.png) | ![preview 4](43/preview_4.png) | ![preview 5](43/preview_5.png) | ![preview 6](43/preview_6.png) | ![preview 7](43/preview_7.png) | ![preview 8](43/preview_8.png) | | 44 | 75 | [Download](44/dataset.zip) | ![preview 1](44/preview_1.png) | ![preview 2](44/preview_2.png) | ![preview 3](44/preview_3.png) | ![preview 4](44/preview_4.png) | ![preview 5](44/preview_5.png) | ![preview 6](44/preview_6.png) | ![preview 7](44/preview_7.png) | ![preview 8](44/preview_8.png) | | 45 | 36 | [Download](45/dataset.zip) | ![preview 1](45/preview_1.png) | ![preview 2](45/preview_2.png) | ![preview 3](45/preview_3.png) | ![preview 4](45/preview_4.png) | ![preview 5](45/preview_5.png) | ![preview 6](45/preview_6.png) | ![preview 7](45/preview_7.png) | ![preview 8](45/preview_8.png) | | 46 | 15 | [Download](46/dataset.zip) | ![preview 1](46/preview_1.png) | ![preview 2](46/preview_2.png) | ![preview 3](46/preview_3.png) | ![preview 4](46/preview_4.png) | ![preview 5](46/preview_5.png) | ![preview 6](46/preview_6.png) | ![preview 7](46/preview_7.png) | ![preview 8](46/preview_8.png) | | 47 | 126 | [Download](47/dataset.zip) | ![preview 1](47/preview_1.png) | ![preview 2](47/preview_2.png) | ![preview 3](47/preview_3.png) | ![preview 4](47/preview_4.png) | ![preview 5](47/preview_5.png) | ![preview 6](47/preview_6.png) | ![preview 7](47/preview_7.png) | ![preview 8](47/preview_8.png) | | 48 | 41 | [Download](48/dataset.zip) | ![preview 1](48/preview_1.png) | ![preview 2](48/preview_2.png) | ![preview 3](48/preview_3.png) | ![preview 4](48/preview_4.png) | ![preview 5](48/preview_5.png) | ![preview 6](48/preview_6.png) | ![preview 7](48/preview_7.png) | ![preview 8](48/preview_8.png) | | 49 | 46 | [Download](49/dataset.zip) | ![preview 1](49/preview_1.png) | ![preview 2](49/preview_2.png) | ![preview 3](49/preview_3.png) | ![preview 4](49/preview_4.png) | ![preview 5](49/preview_5.png) | ![preview 6](49/preview_6.png) | ![preview 7](49/preview_7.png) | ![preview 8](49/preview_8.png) | | 50 | 100 | [Download](50/dataset.zip) | ![preview 1](50/preview_1.png) | ![preview 2](50/preview_2.png) | ![preview 3](50/preview_3.png) | ![preview 4](50/preview_4.png) | ![preview 5](50/preview_5.png) | ![preview 6](50/preview_6.png) | ![preview 7](50/preview_7.png) | ![preview 8](50/preview_8.png) | | 51 | 121 | [Download](51/dataset.zip) | ![preview 1](51/preview_1.png) | ![preview 2](51/preview_2.png) | ![preview 3](51/preview_3.png) | ![preview 4](51/preview_4.png) | ![preview 5](51/preview_5.png) | ![preview 6](51/preview_6.png) | ![preview 7](51/preview_7.png) | ![preview 8](51/preview_8.png) | | 52 | 36 | [Download](52/dataset.zip) | ![preview 1](52/preview_1.png) | ![preview 2](52/preview_2.png) | ![preview 3](52/preview_3.png) | ![preview 4](52/preview_4.png) | ![preview 5](52/preview_5.png) | ![preview 6](52/preview_6.png) | ![preview 7](52/preview_7.png) | ![preview 8](52/preview_8.png) | | 53 | 102 | [Download](53/dataset.zip) | ![preview 1](53/preview_1.png) | ![preview 2](53/preview_2.png) | ![preview 3](53/preview_3.png) | ![preview 4](53/preview_4.png) | ![preview 5](53/preview_5.png) | ![preview 6](53/preview_6.png) | ![preview 7](53/preview_7.png) | ![preview 8](53/preview_8.png) | | 54 | 50 | [Download](54/dataset.zip) | ![preview 1](54/preview_1.png) | ![preview 2](54/preview_2.png) | ![preview 3](54/preview_3.png) | ![preview 4](54/preview_4.png) | ![preview 5](54/preview_5.png) | ![preview 6](54/preview_6.png) | ![preview 7](54/preview_7.png) | ![preview 8](54/preview_8.png) | | 55 | 105 | [Download](55/dataset.zip) | ![preview 1](55/preview_1.png) | ![preview 2](55/preview_2.png) | ![preview 3](55/preview_3.png) | ![preview 4](55/preview_4.png) | ![preview 5](55/preview_5.png) | ![preview 6](55/preview_6.png) | ![preview 7](55/preview_7.png) | ![preview 8](55/preview_8.png) | | 56 | 47 | [Download](56/dataset.zip) | ![preview 1](56/preview_1.png) | ![preview 2](56/preview_2.png) | ![preview 3](56/preview_3.png) | ![preview 4](56/preview_4.png) | ![preview 5](56/preview_5.png) | ![preview 6](56/preview_6.png) | ![preview 7](56/preview_7.png) | ![preview 8](56/preview_8.png) | | 57 | 60 | [Download](57/dataset.zip) | ![preview 1](57/preview_1.png) | ![preview 2](57/preview_2.png) | ![preview 3](57/preview_3.png) | ![preview 4](57/preview_4.png) | ![preview 5](57/preview_5.png) | ![preview 6](57/preview_6.png) | ![preview 7](57/preview_7.png) | ![preview 8](57/preview_8.png) | | 58 | 26 | [Download](58/dataset.zip) | ![preview 1](58/preview_1.png) | ![preview 2](58/preview_2.png) | ![preview 3](58/preview_3.png) | ![preview 4](58/preview_4.png) | ![preview 5](58/preview_5.png) | ![preview 6](58/preview_6.png) | ![preview 7](58/preview_7.png) | ![preview 8](58/preview_8.png) | | 59 | 47 | [Download](59/dataset.zip) | ![preview 1](59/preview_1.png) | ![preview 2](59/preview_2.png) | ![preview 3](59/preview_3.png) | ![preview 4](59/preview_4.png) | ![preview 5](59/preview_5.png) | ![preview 6](59/preview_6.png) | ![preview 7](59/preview_7.png) | ![preview 8](59/preview_8.png) | | 60 | 79 | [Download](60/dataset.zip) | ![preview 1](60/preview_1.png) | ![preview 2](60/preview_2.png) | ![preview 3](60/preview_3.png) | ![preview 4](60/preview_4.png) | ![preview 5](60/preview_5.png) | ![preview 6](60/preview_6.png) | ![preview 7](60/preview_7.png) | ![preview 8](60/preview_8.png) | | 61 | 74 | [Download](61/dataset.zip) | ![preview 1](61/preview_1.png) | ![preview 2](61/preview_2.png) | ![preview 3](61/preview_3.png) | ![preview 4](61/preview_4.png) | ![preview 5](61/preview_5.png) | ![preview 6](61/preview_6.png) | ![preview 7](61/preview_7.png) | ![preview 8](61/preview_8.png) | | 62 | 11 | [Download](62/dataset.zip) | ![preview 1](62/preview_1.png) | ![preview 2](62/preview_2.png) | ![preview 3](62/preview_3.png) | ![preview 4](62/preview_4.png) | ![preview 5](62/preview_5.png) | ![preview 6](62/preview_6.png) | ![preview 7](62/preview_7.png) | ![preview 8](62/preview_8.png) | | 63 | 73 | [Download](63/dataset.zip) | ![preview 1](63/preview_1.png) | ![preview 2](63/preview_2.png) | ![preview 3](63/preview_3.png) | ![preview 4](63/preview_4.png) | ![preview 5](63/preview_5.png) | ![preview 6](63/preview_6.png) | ![preview 7](63/preview_7.png) | ![preview 8](63/preview_8.png) | | 64 | 30 | [Download](64/dataset.zip) | ![preview 1](64/preview_1.png) | ![preview 2](64/preview_2.png) | ![preview 3](64/preview_3.png) | ![preview 4](64/preview_4.png) | ![preview 5](64/preview_5.png) | ![preview 6](64/preview_6.png) | ![preview 7](64/preview_7.png) | ![preview 8](64/preview_8.png) | | 65 | 32 | [Download](65/dataset.zip) | ![preview 1](65/preview_1.png) | ![preview 2](65/preview_2.png) | ![preview 3](65/preview_3.png) | ![preview 4](65/preview_4.png) | ![preview 5](65/preview_5.png) | ![preview 6](65/preview_6.png) | ![preview 7](65/preview_7.png) | ![preview 8](65/preview_8.png) | | 66 | 102 | [Download](66/dataset.zip) | ![preview 1](66/preview_1.png) | ![preview 2](66/preview_2.png) | ![preview 3](66/preview_3.png) | ![preview 4](66/preview_4.png) | ![preview 5](66/preview_5.png) | ![preview 6](66/preview_6.png) | ![preview 7](66/preview_7.png) | ![preview 8](66/preview_8.png) | | 67 | 17 | [Download](67/dataset.zip) | ![preview 1](67/preview_1.png) | ![preview 2](67/preview_2.png) | ![preview 3](67/preview_3.png) | ![preview 4](67/preview_4.png) | ![preview 5](67/preview_5.png) | ![preview 6](67/preview_6.png) | ![preview 7](67/preview_7.png) | ![preview 8](67/preview_8.png) | | 68 | 49 | [Download](68/dataset.zip) | ![preview 1](68/preview_1.png) | ![preview 2](68/preview_2.png) | ![preview 3](68/preview_3.png) | ![preview 4](68/preview_4.png) | ![preview 5](68/preview_5.png) | ![preview 6](68/preview_6.png) | ![preview 7](68/preview_7.png) | ![preview 8](68/preview_8.png) | | 69 | 24 | [Download](69/dataset.zip) | ![preview 1](69/preview_1.png) | ![preview 2](69/preview_2.png) | ![preview 3](69/preview_3.png) | ![preview 4](69/preview_4.png) | ![preview 5](69/preview_5.png) | ![preview 6](69/preview_6.png) | ![preview 7](69/preview_7.png) | ![preview 8](69/preview_8.png) | | 70 | 28 | [Download](70/dataset.zip) | ![preview 1](70/preview_1.png) | ![preview 2](70/preview_2.png) | ![preview 3](70/preview_3.png) | ![preview 4](70/preview_4.png) | ![preview 5](70/preview_5.png) | ![preview 6](70/preview_6.png) | ![preview 7](70/preview_7.png) | ![preview 8](70/preview_8.png) | | 71 | 38 | [Download](71/dataset.zip) | ![preview 1](71/preview_1.png) | ![preview 2](71/preview_2.png) | ![preview 3](71/preview_3.png) | ![preview 4](71/preview_4.png) | ![preview 5](71/preview_5.png) | ![preview 6](71/preview_6.png) | ![preview 7](71/preview_7.png) | ![preview 8](71/preview_8.png) | | 72 | 96 | [Download](72/dataset.zip) | ![preview 1](72/preview_1.png) | ![preview 2](72/preview_2.png) | ![preview 3](72/preview_3.png) | ![preview 4](72/preview_4.png) | ![preview 5](72/preview_5.png) | ![preview 6](72/preview_6.png) | ![preview 7](72/preview_7.png) | ![preview 8](72/preview_8.png) | | 73 | 52 | [Download](73/dataset.zip) | ![preview 1](73/preview_1.png) | ![preview 2](73/preview_2.png) | ![preview 3](73/preview_3.png) | ![preview 4](73/preview_4.png) | ![preview 5](73/preview_5.png) | ![preview 6](73/preview_6.png) | ![preview 7](73/preview_7.png) | ![preview 8](73/preview_8.png) | | 74 | 747 | [Download](74/dataset.zip) | ![preview 1](74/preview_1.png) | ![preview 2](74/preview_2.png) | ![preview 3](74/preview_3.png) | ![preview 4](74/preview_4.png) | ![preview 5](74/preview_5.png) | ![preview 6](74/preview_6.png) | ![preview 7](74/preview_7.png) | ![preview 8](74/preview_8.png) | | 75 | 50 | [Download](75/dataset.zip) | ![preview 1](75/preview_1.png) | ![preview 2](75/preview_2.png) | ![preview 3](75/preview_3.png) | ![preview 4](75/preview_4.png) | ![preview 5](75/preview_5.png) | ![preview 6](75/preview_6.png) | ![preview 7](75/preview_7.png) | ![preview 8](75/preview_8.png) | | 76 | 43 | [Download](76/dataset.zip) | ![preview 1](76/preview_1.png) | ![preview 2](76/preview_2.png) | ![preview 3](76/preview_3.png) | ![preview 4](76/preview_4.png) | ![preview 5](76/preview_5.png) | ![preview 6](76/preview_6.png) | ![preview 7](76/preview_7.png) | ![preview 8](76/preview_8.png) | | 77 | 21 | [Download](77/dataset.zip) | ![preview 1](77/preview_1.png) | ![preview 2](77/preview_2.png) | ![preview 3](77/preview_3.png) | ![preview 4](77/preview_4.png) | ![preview 5](77/preview_5.png) | ![preview 6](77/preview_6.png) | ![preview 7](77/preview_7.png) | ![preview 8](77/preview_8.png) | | 78 | 22 | [Download](78/dataset.zip) | ![preview 1](78/preview_1.png) | ![preview 2](78/preview_2.png) | ![preview 3](78/preview_3.png) | ![preview 4](78/preview_4.png) | ![preview 5](78/preview_5.png) | ![preview 6](78/preview_6.png) | ![preview 7](78/preview_7.png) | ![preview 8](78/preview_8.png) | | 79 | 23 | [Download](79/dataset.zip) | ![preview 1](79/preview_1.png) | ![preview 2](79/preview_2.png) | ![preview 3](79/preview_3.png) | ![preview 4](79/preview_4.png) | ![preview 5](79/preview_5.png) | ![preview 6](79/preview_6.png) | ![preview 7](79/preview_7.png) | ![preview 8](79/preview_8.png) | | 80 | 38 | [Download](80/dataset.zip) | ![preview 1](80/preview_1.png) | ![preview 2](80/preview_2.png) | ![preview 3](80/preview_3.png) | ![preview 4](80/preview_4.png) | ![preview 5](80/preview_5.png) | ![preview 6](80/preview_6.png) | ![preview 7](80/preview_7.png) | ![preview 8](80/preview_8.png) | | 81 | 20 | [Download](81/dataset.zip) | ![preview 1](81/preview_1.png) | ![preview 2](81/preview_2.png) | ![preview 3](81/preview_3.png) | ![preview 4](81/preview_4.png) | ![preview 5](81/preview_5.png) | ![preview 6](81/preview_6.png) | ![preview 7](81/preview_7.png) | ![preview 8](81/preview_8.png) | | 82 | 44 | [Download](82/dataset.zip) | ![preview 1](82/preview_1.png) | ![preview 2](82/preview_2.png) | ![preview 3](82/preview_3.png) | ![preview 4](82/preview_4.png) | ![preview 5](82/preview_5.png) | ![preview 6](82/preview_6.png) | ![preview 7](82/preview_7.png) | ![preview 8](82/preview_8.png) | | 83 | 19 | [Download](83/dataset.zip) | ![preview 1](83/preview_1.png) | ![preview 2](83/preview_2.png) | ![preview 3](83/preview_3.png) | ![preview 4](83/preview_4.png) | ![preview 5](83/preview_5.png) | ![preview 6](83/preview_6.png) | ![preview 7](83/preview_7.png) | ![preview 8](83/preview_8.png) | | 84 | 19 | [Download](84/dataset.zip) | ![preview 1](84/preview_1.png) | ![preview 2](84/preview_2.png) | ![preview 3](84/preview_3.png) | ![preview 4](84/preview_4.png) | ![preview 5](84/preview_5.png) | ![preview 6](84/preview_6.png) | ![preview 7](84/preview_7.png) | ![preview 8](84/preview_8.png) | | 85 | 19 | [Download](85/dataset.zip) | ![preview 1](85/preview_1.png) | ![preview 2](85/preview_2.png) | ![preview 3](85/preview_3.png) | ![preview 4](85/preview_4.png) | ![preview 5](85/preview_5.png) | ![preview 6](85/preview_6.png) | ![preview 7](85/preview_7.png) | ![preview 8](85/preview_8.png) | | 86 | 11 | [Download](86/dataset.zip) | ![preview 1](86/preview_1.png) | ![preview 2](86/preview_2.png) | ![preview 3](86/preview_3.png) | ![preview 4](86/preview_4.png) | ![preview 5](86/preview_5.png) | ![preview 6](86/preview_6.png) | ![preview 7](86/preview_7.png) | ![preview 8](86/preview_8.png) | | 87 | 48 | [Download](87/dataset.zip) | ![preview 1](87/preview_1.png) | ![preview 2](87/preview_2.png) | ![preview 3](87/preview_3.png) | ![preview 4](87/preview_4.png) | ![preview 5](87/preview_5.png) | ![preview 6](87/preview_6.png) | ![preview 7](87/preview_7.png) | ![preview 8](87/preview_8.png) | | 88 | 18 | [Download](88/dataset.zip) | ![preview 1](88/preview_1.png) | ![preview 2](88/preview_2.png) | ![preview 3](88/preview_3.png) | ![preview 4](88/preview_4.png) | ![preview 5](88/preview_5.png) | ![preview 6](88/preview_6.png) | ![preview 7](88/preview_7.png) | ![preview 8](88/preview_8.png) | | 89 | 14 | [Download](89/dataset.zip) | ![preview 1](89/preview_1.png) | ![preview 2](89/preview_2.png) | ![preview 3](89/preview_3.png) | ![preview 4](89/preview_4.png) | ![preview 5](89/preview_5.png) | ![preview 6](89/preview_6.png) | ![preview 7](89/preview_7.png) | ![preview 8](89/preview_8.png) | | 90 | 24 | [Download](90/dataset.zip) | ![preview 1](90/preview_1.png) | ![preview 2](90/preview_2.png) | ![preview 3](90/preview_3.png) | ![preview 4](90/preview_4.png) | ![preview 5](90/preview_5.png) | ![preview 6](90/preview_6.png) | ![preview 7](90/preview_7.png) | ![preview 8](90/preview_8.png) | | 91 | 19 | [Download](91/dataset.zip) | ![preview 1](91/preview_1.png) | ![preview 2](91/preview_2.png) | ![preview 3](91/preview_3.png) | ![preview 4](91/preview_4.png) | ![preview 5](91/preview_5.png) | ![preview 6](91/preview_6.png) | ![preview 7](91/preview_7.png) | ![preview 8](91/preview_8.png) | | 92 | 10 | [Download](92/dataset.zip) | ![preview 1](92/preview_1.png) | ![preview 2](92/preview_2.png) | ![preview 3](92/preview_3.png) | ![preview 4](92/preview_4.png) | ![preview 5](92/preview_5.png) | ![preview 6](92/preview_6.png) | ![preview 7](92/preview_7.png) | ![preview 8](92/preview_8.png) | | 93 | 10 | [Download](93/dataset.zip) | ![preview 1](93/preview_1.png) | ![preview 2](93/preview_2.png) | ![preview 3](93/preview_3.png) | ![preview 4](93/preview_4.png) | ![preview 5](93/preview_5.png) | ![preview 6](93/preview_6.png) | ![preview 7](93/preview_7.png) | ![preview 8](93/preview_8.png) | | 94 | 33 | [Download](94/dataset.zip) | ![preview 1](94/preview_1.png) | ![preview 2](94/preview_2.png) | ![preview 3](94/preview_3.png) | ![preview 4](94/preview_4.png) | ![preview 5](94/preview_5.png) | ![preview 6](94/preview_6.png) | ![preview 7](94/preview_7.png) | ![preview 8](94/preview_8.png) | | 95 | 28 | [Download](95/dataset.zip) | ![preview 1](95/preview_1.png) | ![preview 2](95/preview_2.png) | ![preview 3](95/preview_3.png) | ![preview 4](95/preview_4.png) | ![preview 5](95/preview_5.png) | ![preview 6](95/preview_6.png) | ![preview 7](95/preview_7.png) | ![preview 8](95/preview_8.png) | | 96 | 58 | [Download](96/dataset.zip) | ![preview 1](96/preview_1.png) | ![preview 2](96/preview_2.png) | ![preview 3](96/preview_3.png) | ![preview 4](96/preview_4.png) | ![preview 5](96/preview_5.png) | ![preview 6](96/preview_6.png) | ![preview 7](96/preview_7.png) | ![preview 8](96/preview_8.png) | | 97 | 13 | [Download](97/dataset.zip) | ![preview 1](97/preview_1.png) | ![preview 2](97/preview_2.png) | ![preview 3](97/preview_3.png) | ![preview 4](97/preview_4.png) | ![preview 5](97/preview_5.png) | ![preview 6](97/preview_6.png) | ![preview 7](97/preview_7.png) | ![preview 8](97/preview_8.png) | | 98 | 29 | [Download](98/dataset.zip) | ![preview 1](98/preview_1.png) | ![preview 2](98/preview_2.png) | ![preview 3](98/preview_3.png) | ![preview 4](98/preview_4.png) | ![preview 5](98/preview_5.png) | ![preview 6](98/preview_6.png) | ![preview 7](98/preview_7.png) | ![preview 8](98/preview_8.png) | | 99 | 17 | [Download](99/dataset.zip) | ![preview 1](99/preview_1.png) | ![preview 2](99/preview_2.png) | ![preview 3](99/preview_3.png) | ![preview 4](99/preview_4.png) | ![preview 5](99/preview_5.png) | ![preview 6](99/preview_6.png) | ![preview 7](99/preview_7.png) | ![preview 8](99/preview_8.png) | | 100 | 32 | [Download](100/dataset.zip) | ![preview 1](100/preview_1.png) | ![preview 2](100/preview_2.png) | ![preview 3](100/preview_3.png) | ![preview 4](100/preview_4.png) | ![preview 5](100/preview_5.png) | ![preview 6](100/preview_6.png) | ![preview 7](100/preview_7.png) | ![preview 8](100/preview_8.png) | | 101 | 21 | [Download](101/dataset.zip) | ![preview 1](101/preview_1.png) | ![preview 2](101/preview_2.png) | ![preview 3](101/preview_3.png) | ![preview 4](101/preview_4.png) | ![preview 5](101/preview_5.png) | ![preview 6](101/preview_6.png) | ![preview 7](101/preview_7.png) | ![preview 8](101/preview_8.png) | | 102 | 27 | [Download](102/dataset.zip) | ![preview 1](102/preview_1.png) | ![preview 2](102/preview_2.png) | ![preview 3](102/preview_3.png) | ![preview 4](102/preview_4.png) | ![preview 5](102/preview_5.png) | ![preview 6](102/preview_6.png) | ![preview 7](102/preview_7.png) | ![preview 8](102/preview_8.png) | | 103 | 22 | [Download](103/dataset.zip) | ![preview 1](103/preview_1.png) | ![preview 2](103/preview_2.png) | ![preview 3](103/preview_3.png) | ![preview 4](103/preview_4.png) | ![preview 5](103/preview_5.png) | ![preview 6](103/preview_6.png) | ![preview 7](103/preview_7.png) | ![preview 8](103/preview_8.png) | | 104 | 11 | [Download](104/dataset.zip) | ![preview 1](104/preview_1.png) | ![preview 2](104/preview_2.png) | ![preview 3](104/preview_3.png) | ![preview 4](104/preview_4.png) | ![preview 5](104/preview_5.png) | ![preview 6](104/preview_6.png) | ![preview 7](104/preview_7.png) | ![preview 8](104/preview_8.png) | | 105 | 7 | [Download](105/dataset.zip) | ![preview 1](105/preview_1.png) | ![preview 2](105/preview_2.png) | ![preview 3](105/preview_3.png) | ![preview 4](105/preview_4.png) | ![preview 5](105/preview_5.png) | ![preview 6](105/preview_6.png) | ![preview 7](105/preview_7.png) | N/A | | 106 | 12 | [Download](106/dataset.zip) | ![preview 1](106/preview_1.png) | ![preview 2](106/preview_2.png) | ![preview 3](106/preview_3.png) | ![preview 4](106/preview_4.png) | ![preview 5](106/preview_5.png) | ![preview 6](106/preview_6.png) | ![preview 7](106/preview_7.png) | ![preview 8](106/preview_8.png) | | 107 | 14 | [Download](107/dataset.zip) | ![preview 1](107/preview_1.png) | ![preview 2](107/preview_2.png) | ![preview 3](107/preview_3.png) | ![preview 4](107/preview_4.png) | ![preview 5](107/preview_5.png) | ![preview 6](107/preview_6.png) | ![preview 7](107/preview_7.png) | ![preview 8](107/preview_8.png) | | 108 | 22 | [Download](108/dataset.zip) | ![preview 1](108/preview_1.png) | ![preview 2](108/preview_2.png) | ![preview 3](108/preview_3.png) | ![preview 4](108/preview_4.png) | ![preview 5](108/preview_5.png) | ![preview 6](108/preview_6.png) | ![preview 7](108/preview_7.png) | ![preview 8](108/preview_8.png) | | 109 | 21 | [Download](109/dataset.zip) | ![preview 1](109/preview_1.png) | ![preview 2](109/preview_2.png) | ![preview 3](109/preview_3.png) | ![preview 4](109/preview_4.png) | ![preview 5](109/preview_5.png) | ![preview 6](109/preview_6.png) | ![preview 7](109/preview_7.png) | ![preview 8](109/preview_8.png) | | 110 | 25 | [Download](110/dataset.zip) | ![preview 1](110/preview_1.png) | ![preview 2](110/preview_2.png) | ![preview 3](110/preview_3.png) | ![preview 4](110/preview_4.png) | ![preview 5](110/preview_5.png) | ![preview 6](110/preview_6.png) | ![preview 7](110/preview_7.png) | ![preview 8](110/preview_8.png) | | 111 | 45 | [Download](111/dataset.zip) | ![preview 1](111/preview_1.png) | ![preview 2](111/preview_2.png) | ![preview 3](111/preview_3.png) | ![preview 4](111/preview_4.png) | ![preview 5](111/preview_5.png) | ![preview 6](111/preview_6.png) | ![preview 7](111/preview_7.png) | ![preview 8](111/preview_8.png) | | 112 | 11 | [Download](112/dataset.zip) | ![preview 1](112/preview_1.png) | ![preview 2](112/preview_2.png) | ![preview 3](112/preview_3.png) | ![preview 4](112/preview_4.png) | ![preview 5](112/preview_5.png) | ![preview 6](112/preview_6.png) | ![preview 7](112/preview_7.png) | ![preview 8](112/preview_8.png) | | 113 | 23 | [Download](113/dataset.zip) | ![preview 1](113/preview_1.png) | ![preview 2](113/preview_2.png) | ![preview 3](113/preview_3.png) | ![preview 4](113/preview_4.png) | ![preview 5](113/preview_5.png) | ![preview 6](113/preview_6.png) | ![preview 7](113/preview_7.png) | ![preview 8](113/preview_8.png) | | 114 | 14 | [Download](114/dataset.zip) | ![preview 1](114/preview_1.png) | ![preview 2](114/preview_2.png) | ![preview 3](114/preview_3.png) | ![preview 4](114/preview_4.png) | ![preview 5](114/preview_5.png) | ![preview 6](114/preview_6.png) | ![preview 7](114/preview_7.png) | ![preview 8](114/preview_8.png) | | 115 | 39 | [Download](115/dataset.zip) | ![preview 1](115/preview_1.png) | ![preview 2](115/preview_2.png) | ![preview 3](115/preview_3.png) | ![preview 4](115/preview_4.png) | ![preview 5](115/preview_5.png) | ![preview 6](115/preview_6.png) | ![preview 7](115/preview_7.png) | ![preview 8](115/preview_8.png) | | 116 | 17 | [Download](116/dataset.zip) | ![preview 1](116/preview_1.png) | ![preview 2](116/preview_2.png) | ![preview 3](116/preview_3.png) | ![preview 4](116/preview_4.png) | ![preview 5](116/preview_5.png) | ![preview 6](116/preview_6.png) | ![preview 7](116/preview_7.png) | ![preview 8](116/preview_8.png) | | 117 | 27 | [Download](117/dataset.zip) | ![preview 1](117/preview_1.png) | ![preview 2](117/preview_2.png) | ![preview 3](117/preview_3.png) | ![preview 4](117/preview_4.png) | ![preview 5](117/preview_5.png) | ![preview 6](117/preview_6.png) | ![preview 7](117/preview_7.png) | ![preview 8](117/preview_8.png) | | 118 | 56 | [Download](118/dataset.zip) | ![preview 1](118/preview_1.png) | ![preview 2](118/preview_2.png) | ![preview 3](118/preview_3.png) | ![preview 4](118/preview_4.png) | ![preview 5](118/preview_5.png) | ![preview 6](118/preview_6.png) | ![preview 7](118/preview_7.png) | ![preview 8](118/preview_8.png) | | 119 | 19 | [Download](119/dataset.zip) | ![preview 1](119/preview_1.png) | ![preview 2](119/preview_2.png) | ![preview 3](119/preview_3.png) | ![preview 4](119/preview_4.png) | ![preview 5](119/preview_5.png) | ![preview 6](119/preview_6.png) | ![preview 7](119/preview_7.png) | ![preview 8](119/preview_8.png) | | 120 | 17 | [Download](120/dataset.zip) | ![preview 1](120/preview_1.png) | ![preview 2](120/preview_2.png) | ![preview 3](120/preview_3.png) | ![preview 4](120/preview_4.png) | ![preview 5](120/preview_5.png) | ![preview 6](120/preview_6.png) | ![preview 7](120/preview_7.png) | ![preview 8](120/preview_8.png) | | 121 | 14 | [Download](121/dataset.zip) | ![preview 1](121/preview_1.png) | ![preview 2](121/preview_2.png) | ![preview 3](121/preview_3.png) | ![preview 4](121/preview_4.png) | ![preview 5](121/preview_5.png) | ![preview 6](121/preview_6.png) | ![preview 7](121/preview_7.png) | ![preview 8](121/preview_8.png) | | 122 | 12 | [Download](122/dataset.zip) | ![preview 1](122/preview_1.png) | ![preview 2](122/preview_2.png) | ![preview 3](122/preview_3.png) | ![preview 4](122/preview_4.png) | ![preview 5](122/preview_5.png) | ![preview 6](122/preview_6.png) | ![preview 7](122/preview_7.png) | ![preview 8](122/preview_8.png) | | 123 | 103 | [Download](123/dataset.zip) | ![preview 1](123/preview_1.png) | ![preview 2](123/preview_2.png) | ![preview 3](123/preview_3.png) | ![preview 4](123/preview_4.png) | ![preview 5](123/preview_5.png) | ![preview 6](123/preview_6.png) | ![preview 7](123/preview_7.png) | ![preview 8](123/preview_8.png) | | 124 | 39 | [Download](124/dataset.zip) | ![preview 1](124/preview_1.png) | ![preview 2](124/preview_2.png) | ![preview 3](124/preview_3.png) | ![preview 4](124/preview_4.png) | ![preview 5](124/preview_5.png) | ![preview 6](124/preview_6.png) | ![preview 7](124/preview_7.png) | ![preview 8](124/preview_8.png) | | 125 | 15 | [Download](125/dataset.zip) | ![preview 1](125/preview_1.png) | ![preview 2](125/preview_2.png) | ![preview 3](125/preview_3.png) | ![preview 4](125/preview_4.png) | ![preview 5](125/preview_5.png) | ![preview 6](125/preview_6.png) | ![preview 7](125/preview_7.png) | ![preview 8](125/preview_8.png) | | 126 | 19 | [Download](126/dataset.zip) | ![preview 1](126/preview_1.png) | ![preview 2](126/preview_2.png) | ![preview 3](126/preview_3.png) | ![preview 4](126/preview_4.png) | ![preview 5](126/preview_5.png) | ![preview 6](126/preview_6.png) | ![preview 7](126/preview_7.png) | ![preview 8](126/preview_8.png) | | 127 | 11 | [Download](127/dataset.zip) | ![preview 1](127/preview_1.png) | ![preview 2](127/preview_2.png) | ![preview 3](127/preview_3.png) | ![preview 4](127/preview_4.png) | ![preview 5](127/preview_5.png) | ![preview 6](127/preview_6.png) | ![preview 7](127/preview_7.png) | ![preview 8](127/preview_8.png) | | 128 | 15 | [Download](128/dataset.zip) | ![preview 1](128/preview_1.png) | ![preview 2](128/preview_2.png) | ![preview 3](128/preview_3.png) | ![preview 4](128/preview_4.png) | ![preview 5](128/preview_5.png) | ![preview 6](128/preview_6.png) | ![preview 7](128/preview_7.png) | ![preview 8](128/preview_8.png) | | 129 | 8 | [Download](129/dataset.zip) | ![preview 1](129/preview_1.png) | ![preview 2](129/preview_2.png) | ![preview 3](129/preview_3.png) | ![preview 4](129/preview_4.png) | ![preview 5](129/preview_5.png) | ![preview 6](129/preview_6.png) | ![preview 7](129/preview_7.png) | ![preview 8](129/preview_8.png) | | 130 | 9 | [Download](130/dataset.zip) | ![preview 1](130/preview_1.png) | ![preview 2](130/preview_2.png) | ![preview 3](130/preview_3.png) | ![preview 4](130/preview_4.png) | ![preview 5](130/preview_5.png) | ![preview 6](130/preview_6.png) | ![preview 7](130/preview_7.png) | ![preview 8](130/preview_8.png) | | noise | 528 | [Download](-1/dataset.zip) | ![preview 1](-1/preview_1.png) | ![preview 2](-1/preview_2.png) | ![preview 3](-1/preview_3.png) | ![preview 4](-1/preview_4.png) | ![preview 5](-1/preview_5.png) | ![preview 6](-1/preview_6.png) | ![preview 7](-1/preview_7.png) | ![preview 8](-1/preview_8.png) |

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umm-maybe/gutenberg_english_pre1928

umm-maybe

2023-11-20T00:45:58Z

0

[ "region:us" ]

null

2023-11-19T21:21:29Z

--- dataset_info: features: [] splits: - name: train num_bytes: 0 num_examples: 0 download_size: 324 dataset_size: 0 configs: - config_name: default data_files: - split: train path: data/train-* ---

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icaro23/Icaro

icaro23

2023-11-19T21:28:07Z

0

[ "license:apache-2.0", "region:us" ]

null

2023-11-19T21:26:05Z

--- license: apache-2.0 ---

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aucelio/rodrigosilva

aucelio

2023-11-19T21:32:31Z

0

[ "license:openrail", "region:us" ]

null

2023-11-19T21:30:27Z

--- license: openrail ---

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xwar/2023-11-19_ninox_dataset_single_column

xwar

2023-11-19T21:43:58Z

0

[ "license:apache-2.0", "region:us" ]

null

2023-11-19T21:43:34Z

--- license: apache-2.0 ---

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miragepa/A18

miragepa

2023-11-19T22:35:46Z

0

[ "license:openrail", "region:us" ]

null

2023-11-19T22:35:05Z

--- license: openrail ---

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nitorogerr/stefanie

nitorogerr

2023-11-19T23:19:39Z

0

[ "license:openrail", "region:us" ]

null

2023-11-19T22:55:40Z

--- license: openrail ---

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warzin/covers

warzin

2023-11-25T15:15:04Z

0

[ "license:other", "region:us" ]

null

2023-11-19T23:31:34Z

--- license: other license_name: seila license_link: LICENSE ---

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Ricktlw/ThomazCostaSet

Ricktlw

2023-11-20T00:29:15Z

0

[ "license:openrail", "region:us" ]

null

2023-11-20T00:28:00Z

--- license: openrail ---

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erikliu18/us-congress-hearing

erikliu18

2023-11-20T01:01:16Z

0

[ "task_categories:text-classification", "language:en", "finance", "legal", "region:us" ]

[ "text-classification" ]

2023-11-20T00:49:47Z

--- task_categories: - text-classification language: - en tags: - finance - legal --- # U.S. Congressional Hearings Dataset This dataset currently contains cleaned sentences from all House Committee on Energy and Commerce hearings from 2002. A total of 1K+ hearing transcripts in txt formats from govinfo.gov were collected and cleaned.

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Roblox/luau_corpus

Roblox

2023-11-20T01:09:48Z

0

[ "license:mit", "code", "region:us" ]

null

2023-11-20T01:08:21Z

--- license: mit tags: - code --- # Dataset card The Luau dataset is a collection of code fragments collected from the Roblox Luau Data Sharing program. Only experiences where creators gave us permission to contribute to the public Luau Dataset were used for producing this dataset. # Languages: Lua, Luau # License: MIT Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Format: The dataset format is in jsonl format, with prompt / completion fields. # Dataset usage: This dataset is designed for fine tuning large language models. # Risks: The dataset has been filtered for various quality signals, though Roblox makes no guarantees of data quality. # Evaluation: We have found that typically fine tuning a generalist code LLM improve it’s performance on Roblox Lua code quality by 10 to 20%.

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Bode777/ZODD

Bode777

2023-11-20T01:31:00Z

0

[ "license:openrail", "region:us" ]

null

2023-11-20T01:30:02Z

--- license: openrail ---

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Kaue123456/AdamSandlerPortugues

Kaue123456

2023-11-20T01:59:33Z

0

[ "license:openrail", "region:us" ]

null

2023-11-20T01:58:23Z

--- license: openrail ---

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gilsonk12/mexicano

gilsonk12

2023-11-20T02:16:53Z

0

[ "license:openrail", "region:us" ]

null

2023-11-20T02:15:42Z

--- license: openrail ---

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leaudhiver/frdrpko

leaudhiver

2023-11-20T02:34:53Z

0

[ "license:apache-2.0", "region:us" ]

null

2023-11-20T02:19:49Z

--- license: apache-2.0 --- 11-20 11:25 ID 1300까지 Deepl 번역 완. 미정제 후처리 안 한 데이터셋. 계속 번역 추가중. 번역 완료 후 데이터 정제하고 데이터셋명 변경예정. original: This dataset is the result of combing through several reverse proxy logs sets and cleaning them of refusals, duplicate, incomplete, and poor quality responses. Lots of manual quality checks. There's also things like ecommerce descriptions for sex toys and bondage gear, as well as examples of SEO optimized porn video descriptions. I will definitely be improving on this dataset continously; it should be considered a work in progress. My goal is to create a model (or set of models) which can completely replace OpenAI models for erotic roleplay and adult industry use. Please consider supporting me on Patreon, I'm only asking for about tree fiddy. https://www.patreon.com/openerotica

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sayannath/pokemon-dataset

sayannath

2023-11-20T03:16:43Z

0

[ "license:apache-2.0", "region:us" ]

null

2023-11-20T03:10:18Z

--- license: apache-2.0 ---

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beltrewilton/punta-cana-spanish-reviews

beltrewilton

2023-11-20T03:25:58Z

0

[ "task_categories:text-classification", "language:es", "license:mit", "region:us" ]

[ "text-classification" ]

2023-11-20T03:19:51Z

--- license: mit task_categories: - text-classification language: - es --- This data set was collected for academic purposes, suitable for some NLP tasks including sentiment analysis.

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CardinalityLM/imdb-card-pred-binary

CardinalityLM

2023-11-20T03:52:38Z

0

[ "region:us" ]

null

2023-11-20T03:52:32Z

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: text dtype: string - name: prompt dtype: string - name: true_cardinality dtype: int64 splits: - name: train num_bytes: 40068212.8 num_examples: 80000 - name: test num_bytes: 10017053.2 num_examples: 20000 download_size: 8598252 dataset_size: 50085266.0 --- # Dataset Card for "imdb-card-pred-binary" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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CardinalityLM/imdb-card-pred-science

CardinalityLM

2023-11-20T03:52:49Z

0

[ "region:us" ]

null

2023-11-20T03:52:44Z

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: text dtype: string - name: prompt dtype: string - name: true_cardinality dtype: int64 splits: - name: train num_bytes: 39344995.2 num_examples: 80000 - name: test num_bytes: 9836248.8 num_examples: 20000 download_size: 8632989 dataset_size: 49181244.0 --- # Dataset Card for "imdb-card-pred-science" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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icaro23/icarofrw

icaro23

2023-11-20T03:57:44Z

0

[ "license:apache-2.0", "region:us" ]

null

2023-11-20T03:56:49Z

--- license: apache-2.0 ---

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icaro23/icarokfk

icaro23

2023-11-20T04:13:59Z

0

[ "license:apache-2.0", "region:us" ]

null

2023-11-20T04:12:34Z

--- license: apache-2.0 ---

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yoonlee/csProjectTextualInversionStyle1

yoonlee

2023-11-20T05:12:43Z

0

[ "region:us" ]

null

2023-11-20T05:12:40Z

--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 4211336.0 num_examples: 5 download_size: 4212557 dataset_size: 4211336.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

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cp500/radiology_sample

cp500

2023-11-20T05:33:28Z

0

[ "region:us" ]

null

2023-11-20T05:33:26Z

--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 697828.8448762051 num_examples: 900 - name: test num_bytes: 77536.53831957835 num_examples: 100 download_size: 368014 dataset_size: 775365.3831957835 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

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AinzOoalGowns/Testdataset

AinzOoalGowns

2023-11-20T06:01:48Z

0

[ "license:apache-2.0", "region:us" ]

null

2023-11-20T06:01:48Z

--- license: apache-2.0 ---

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Back-up/review-crawl-data

Back-up

2023-11-20T07:49:25Z

0

[ "region:us" ]

null

2023-11-20T07:49:21Z

--- dataset_info: features: - name: id dtype: string - name: titles dtype: string - name: url dtype: string - name: content dtype: string splits: - name: train num_bytes: 14728153 num_examples: 37509 download_size: 4053059 dataset_size: 14728153 configs: - config_name: default data_files: - split: train path: data/train-* ---

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Back-up/review-crawl-data-v1

Back-up

2023-11-20T08:47:09Z

0

[ "region:us" ]

null

2023-11-20T07:51:14Z

--- dataset_info: features: - name: id dtype: string - name: titles dtype: string - name: url dtype: string - name: content dtype: string splits: - name: train num_bytes: 672799681 num_examples: 73226 download_size: 135848963 dataset_size: 672799681 configs: - config_name: default data_files: - split: train path: data/train-* ---

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DylanJHJ/pdsearch

DylanJHJ

2023-11-25T03:30:03Z

0

[ "license:apache-2.0", "region:us" ]

null

2023-11-20T07:55:34Z

--- license: apache-2.0 ---

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Saaddazhhar/predictiveswotanalysis

Saaddazhhar

2023-11-20T08:05:48Z

0

[ "license:cc0-1.0", "region:us" ]

null

2023-11-20T08:03:47Z

--- license: cc0-1.0 ---

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nisha05/dataset.txt

nisha05

2023-11-20T08:13:04Z

0

[ "region:us" ]

null

2023-11-20T08:13:03Z

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 656 num_examples: 164 download_size: 714 dataset_size: 656 configs: - config_name: default data_files: - split: train path: data/train-* ---

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lewisbails/esg-fine-risks

lewisbails

2023-11-20T08:24:28Z

0

[ "region:us" ]

null

2023-11-20T08:24:23Z

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* - split: test path: data/test-* dataset_info: features: - name: id dtype: string - name: target dtype: string - name: context dtype: string - name: system dtype: string splits: - name: train num_bytes: 24908672.528996333 num_examples: 6966 - name: val num_bytes: 1390281.1275295396 num_examples: 387 - name: test num_bytes: 1433682.452532935 num_examples: 401 download_size: 13297109 dataset_size: 27732636.10905881 --- # Dataset Card for "esg-fine-risks" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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DynamicSuperb/AutomaticSpeechRecognition_LibriSpeech-TestOther

DynamicSuperb

2023-11-20T08:33:29Z

0

[ "region:us" ]

null

2023-11-20T08:30:26Z

--- configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: file dtype: string - name: audio dtype: audio - name: label dtype: string - name: instruction dtype: string splits: - name: test num_bytes: 352426584.188 num_examples: 2939 download_size: 332888539 dataset_size: 352426584.188 --- # Dataset Card for "AutomaticSpeechRecognition_LibriSpeech-TestOther" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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DynamicSuperb/AutomaticSpeechRecognition_LJSpeech

DynamicSuperb

2023-11-20T08:45:26Z

0

[ "region:us" ]

null

2023-11-20T08:42:22Z

--- configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: file dtype: string - name: audio dtype: audio - name: label dtype: string - name: instruction dtype: string splits: - name: test num_bytes: 3800884574.0 num_examples: 13100 download_size: 3785131725 dataset_size: 3800884574.0 --- # Dataset Card for "AutomaticSpeechRecognition_LJSpeech" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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patrickshitou/ArcMMLU

patrickshitou

2023-12-01T07:43:50Z

0

[ "license:cc-by-nc-sa-4.0", "arxiv:2311.18658", "arxiv:2307.14852", "region:us" ]

null

2023-11-20T08:44:58Z

--- license: cc-by-nc-sa-4.0 --- ## Introduction [ArcMMLU](https://github.com/stzhang-patrick/ArcMMLU) is a Chinese benchmark specifically designed for evaluating LLMs on Library & Information Science (LIS). It aims to evaluate the knowledge and reasoning capabilities of LLMs in the LIS academic field, which covers four key sub-areas: Archival Science, Data Science, Library Science, and Information Science. Please refer to our paper for more information [ArcMMLU: A Library and Information Science Benchmark for Large Language Models](https://arxiv.org/abs/2311.18658) It is important to note that the name ArcMMLU is derived from our previous large language model research project—[ArcGPT](https://arxiv.org/abs/2307.14852), which was primarily focused on Archival Science. Later, our research scope expanded from Archival Science to a broader field of information management, but we retained the name ArcMMLU. Therefore, ArcMMLU is not just an evaluation benchmark for Archival Science; it is a comprehensive evaluation dataset for the entire LIS discipline. For the sake of convenience, ArcMMLU adopts the same data format as CMMLU. Furthermore, based on the CMMLU project, we provide evaluation code. For models that have been evaluated on CMMLU, conducting an evaluation on ArcMMLU will be pretty straightforward. Special thanks to the [CMMLU---Chinese Multi-Task Language Understanding Evaluation](https://github.com/haonan-li/CMMLU) project for its contribution to the evaluation of Chinese LLMs. We hope that ArcMMLU can serve as a powerful supplement in specialized fields, bringing more detail and depth to the evaluation of Chinese LLMs.

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VamsiPranav/hindi_telugu_dataset

VamsiPranav

2023-11-20T09:09:03Z

0

[ "region:us" ]

null

2023-11-20T09:09:02Z

--- dataset_info: features: - name: sentence_hin_Deva dtype: string - name: sentence_tel_Telu dtype: string splits: - name: gen num_bytes: 845891 num_examples: 1024 download_size: 366042 dataset_size: 845891 configs: - config_name: default data_files: - split: gen path: data/gen-* ---

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joseluhf11/oct-object-detection-v3-average

joseluhf11

2023-11-22T08:48:49Z

0

[ "region:us" ]

null

2023-11-20T09:29:49Z

--- dataset_info: features: - name: image dtype: image - name: objects struct: - name: bbox sequence: sequence: float64 - name: categories sequence: string splits: - name: train num_bytes: 154014595.25 num_examples: 1246 download_size: 71641492 dataset_size: 154014595.25 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "oct-object-detection-v3-average" Dataset is composed of images with multiples object detection box in coco format (x,y,w,h). Images are OCT (type of eye scaner) with boxes indicating some features associated to AMD disease. The unique difference from from v2 is categories field must have as many class label as there are boxes annotated in each image, even if the class label is the same. So for a image with 3 boxes for the same object, must have 3 class labels. [Source datataset](https://doi.org/10.1101/2023.03.29.534704)

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amd-nicknick/bert-base-uncased-2022_tokenized_dataset

amd-nicknick

2023-11-20T09:40:07Z

0

[ "region:us" ]

null

2023-11-20T09:30:37Z

--- dataset_info: features: - name: input_ids sequence: int32 - name: token_type_ids sequence: int8 - name: attention_mask sequence: int8 - name: special_tokens_mask sequence: int8 splits: - name: train num_bytes: 25352412298 num_examples: 80462898 download_size: 6782996622 dataset_size: 25352412298 configs: - config_name: default data_files: - split: train path: data/train-* ---

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Back-up/review-crawl-data-v2

Back-up

2023-11-20T09:44:06Z

0

[ "region:us" ]

null

2023-11-20T09:43:57Z

--- dataset_info: features: - name: id dtype: string - name: titles dtype: string - name: url dtype: string - name: content dtype: string splits: - name: train num_bytes: 45477989 num_examples: 8780 download_size: 13986085 dataset_size: 45477989 configs: - config_name: default data_files: - split: train path: data/train-* ---

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AanVar/Sample_Dataset_01

AanVar

2023-11-20T10:50:34Z

0

[ "license:mit", "region:us" ]

null

2023-11-20T10:50:34Z

--- license: mit ---

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Subhadeep/common_voice_11_0_hi_pseudo_labelled

Subhadeep

2023-11-22T10:31:21Z

0

[ "region:us" ]

null

2023-11-20T10:59:05Z

--- dataset_info: config_name: hi features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: whisper_transcript sequence: int64 splits: - name: train num_bytes: 131053542.138 num_examples: 4361 - name: validation num_bytes: 64148344.509 num_examples: 2179 - name: test num_bytes: 100961651.174 num_examples: 2894 download_size: 260542039 dataset_size: 296163537.821 configs: - config_name: hi data_files: - split: train path: hi/train-* - split: validation path: hi/validation-* - split: test path: hi/test-* ---

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superfine/advertising-banner-generation

superfine

2023-11-20T11:01:44Z

0

[ "region:us" ]

null

2023-11-20T11:01:37Z

--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 86418717.13 num_examples: 1362 - name: test num_bytes: 481468.0 num_examples: 3 download_size: 84068700 dataset_size: 86900185.13 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

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miragepa/Eve

miragepa

2023-11-20T11:24:41Z

0

[ "license:openrail", "region:us" ]

null

2023-11-20T11:24:16Z

--- license: openrail ---

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nitorogerr/locutor_cristiano_cava

nitorogerr

2023-11-20T11:57:19Z

0

[ "license:openrail", "region:us" ]

null

2023-11-20T11:56:52Z

--- license: openrail ---

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Djacon/ru-izard-emotions

Djacon

2023-11-23T19:17:45Z

0

[ "task_categories:text-classification", "task_ids:sentiment-classification", "task_ids:multi-class-classification", "task_ids:multi-label-classification", "multilinguality:russian", "size_categories:10K<n<100K", "language:ru", "license:mit", "emotion", "region:us" ]

[ "text-classification" ]

2023-11-20T12:08:50Z

--- language: - ru license: - mit multilinguality: - russian task_categories: - text-classification task_ids: - sentiment-classification - multi-class-classification - multi-label-classification pretty_name: RuIzardEmotions tags: - emotion size_categories: - 10K<n<100K --- # Dataset Card for RuIzardEmotions ## 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 Summary The RuIzardEmotions dataset is a high-quality translation of the [go-emotions](https://huggingface.co/datasets/go_emotions) dataset and the other [emotion-detection](https://www.kaggle.com/datasets/ishantjuyal/emotions-in-text/data) dataset. It contains 30k Reddit comments labeled for 10 emotion categories (__joy__, __sadness__, __anger__, __enthusiasm__, __surprise__, __disgust__, __fear__, __guilt__, __shame__ and __neutral__). The datasets were translated using the accurate translator [DeepL](https://www.deepl.com/translator) and additional processing. The idea for the dataset was inspired by the [Izard's model](https://en.wikipedia.org/wiki/Differential_Emotions_Scale) of human emotions. The dataset already with predefined train/val/test splits. ### Supported Tasks and Leaderboards This dataset is intended for multi-class, multi-label emotion classification. ### Languages The data is in Russian. ## Dataset Structure ### Data Instances Each instance is a reddit comment with one or more emotion annotations (or neutral). ### Data Splits The simplified data includes a set of train/val/test splits with 24k, 3k, and 3k examples respectively. ## Considerations for Using the Data ### Social Impact of Dataset Emotion detection is a worthwhile problem which can potentially lead to improvements such as better human/computer interaction. However, emotion detection algorithms (particularly in computer vision) have been abused in some cases to make erroneous inferences in human monitoring and assessment applications such as hiring decisions, insurance pricing, and student attentiveness ## Additional Information ### Licensing Information The GitHub repository which houses this dataset has an [Apache License 2.0](https://github.com/Djacon/russian-emotion-detection/blob/main/LICENSE). ### Citation Information ``` @inproceedings{Djacon, author={Djacon}, title={RuIzardEmotions: A Dataset of Fine-Grained Emotions}, year={2023} } ```

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FudanSELab/SO_KGXQR_DUPLICATE

FudanSELab

2023-11-20T12:36:44Z

0

[ "size_categories:1K<n<10K", "language:en", "license:mit", "region:us" ]

null

2023-11-20T12:30:06Z

--- license: mit dataset_info: - config_name: duplicate_csharp features: - name: query dtype: string - name: relevant sequence: string splits: - name: test num_bytes: 91485 num_examples: 1200 download_size: 61619 dataset_size: 91485 - config_name: duplicate_java features: - name: query dtype: string - name: relevant sequence: string splits: - name: test num_bytes: 102838 num_examples: 1200 download_size: 69239 dataset_size: 102838 - config_name: duplicate_javascript features: - name: query dtype: string - name: relevant sequence: string splits: - name: test num_bytes: 107321 num_examples: 1200 download_size: 69456 dataset_size: 107321 - config_name: duplicate_python features: - name: query dtype: string - name: relevant sequence: string splits: - name: test num_bytes: 109709 num_examples: 1200 download_size: 73833 dataset_size: 109709 configs: - config_name: duplicate_csharp data_files: - split: test path: duplicate_csharp/test-* - config_name: duplicate_java data_files: - split: test path: duplicate_java/test-* - config_name: duplicate_javascript data_files: - split: test path: duplicate_javascript/test-* - config_name: duplicate_python data_files: - split: test path: duplicate_python/test-* language: - en size_categories: - 1K<n<10K --- ## Dataset Description - **Repository:** [GitHub Repository](https://kgxqr.github.io/)

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fraviofranco/OptimusPrime

fraviofranco

2023-11-20T12:47:01Z

0

[ "license:openrail", "region:us" ]

null

2023-11-20T12:45:41Z

--- license: openrail ---

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italovinixius/news-pt

italovinixius

2023-11-20T13:05:27Z

0

[ "license:apache-2.0", "region:us" ]

null

2023-11-20T13:05:27Z

--- license: apache-2.0 ---

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0ho/llama2

0ho

2023-11-21T09:39:30Z

0

[ "license:gpl", "region:us" ]

null

2023-11-20T13:09:13Z

--- license: gpl ---

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FudanSELab/SO_KGXQR_TRAIN

FudanSELab

2023-11-20T16:20:32Z

0

[ "size_categories:100K<n<1M", "language:en", "license:mit", "region:us" ]

null

2023-11-20T13:27:26Z

--- license: mit dataset_info: - config_name: duplicate features: - name: question1_id dtype: string - name: question1 dtype: string - name: question2_id dtype: string - name: question2 dtype: string splits: - name: train num_bytes: 2428577 num_examples: 18281 download_size: 1682661 dataset_size: 2428577 - config_name: history features: - name: so_question_id dtype: string - name: question1 dtype: string - name: question2 dtype: string splits: - name: train num_bytes: 10039163 num_examples: 80000 download_size: 7239803 dataset_size: 10039163 - config_name: negative features: - name: question1 dtype: string - name: question2 dtype: string splits: - name: train num_bytes: 27392182 num_examples: 248940 download_size: 11085232 dataset_size: 27392182 - config_name: positive features: - name: question1 dtype: string - name: question2 dtype: string splits: - name: train num_bytes: 11457312 num_examples: 101172 download_size: 7917727 dataset_size: 11457312 configs: - config_name: duplicate data_files: - split: train path: duplicate/train-* - config_name: history data_files: - split: train path: history/train-* - config_name: negative data_files: - split: train path: negative/train-* - config_name: positive data_files: - split: train path: positive/train-* language: - en size_categories: - 100K<n<1M --- ## Dataset Description - **Repository:** [GitHub Repository](https://kgxqr.github.io/)

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DamarJati/gpt2-m-storybook

DamarJati

2023-11-21T22:20:27Z

0

[ "task_categories:text-generation", "language:en", "storybook", "fiction", "region:us" ]

[ "text-generation" ]

2023-11-20T13:27:42Z

--- language: - en pretty_name: Your Storybook Dataset tags: - storybook - fiction task_categories: - text-generation --- # Your Storybook Dataset Welcome to the repository for the "Your Storybook Dataset." This dataset contains a collection of stories and narratives suitable for various natural language processing tasks. ## Under Collection

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ryuseiken/PIMPINELA

ryuseiken

2023-11-20T13:54:34Z

0

[ "license:openrail", "region:us" ]

null

2023-11-20T13:48:52Z

--- license: openrail ---

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Jaspernl/common_voice_13_0_hi_pseudo_labelled

Jaspernl

2023-11-22T09:38:34Z

0

[ "region:us" ]

null

2023-11-20T14:14:19Z

--- dataset_info: config_name: nl features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string - name: whisper_transcript sequence: int64 splits: - name: train num_bytes: 887885645.796 num_examples: 31906 - name: validation num_bytes: 355968997.37 num_examples: 10930 - name: test num_bytes: 402843984.568 num_examples: 10936 download_size: 1643769397 dataset_size: 1646698627.734 configs: - config_name: nl data_files: - split: train path: nl/train-* - split: validation path: nl/validation-* - split: test path: nl/test-* ---

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nxsbr/kk

nxsbr

2023-11-24T17:45:48Z

0

[ "license:openrail", "region:us" ]

null

2023-11-20T14:20:33Z

--- license: openrail ---

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nourheshamshaheen/ICPR_pipeline3_big

nourheshamshaheen

2023-11-20T14:45:17Z

0

[ "region:us" ]

null

2023-11-20T14:38:33Z

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': area '1': heatmap '2': horizontal_bar '3': horizontal_interval '4': line '5': manhattan '6': map '7': pie '8': scatter '9': scatter-line '10': surface '11': venn '12': vertical_bar '13': vertical_box '14': vertical_interval - name: pipeline_label dtype: class_label: names: '0': line '1': other '2': scatter '3': scatter_line '4': vertical_bar - name: true_label dtype: string splits: - name: train num_bytes: 1073939947.25 num_examples: 20630 download_size: 979370224 dataset_size: 1073939947.25 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ICPR_pipeline3_big" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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nourheshamshaheen/ICPR_pipeline3_small

nourheshamshaheen

2023-11-20T14:46:50Z

0

[ "region:us" ]

null

2023-11-20T14:45:17Z

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': area '1': heatmap '2': horizontal_bar '3': horizontal_interval '4': line '5': manhattan '6': map '7': pie '8': scatter '9': scatter-line '10': surface '11': venn '12': vertical_bar '13': vertical_box '14': vertical_interval - name: true_label dtype: class_label: names: '0': area '1': heatmap '2': horizontal_bar '3': horizontal_interval '4': manhattan '5': map '6': other '7': pie '8': surface '9': venn '10': vertical_box '11': vertical_interval splits: - name: train num_bytes: 236277245.998 num_examples: 4234 download_size: 216714311 dataset_size: 236277245.998 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ICPR_pipeline3_small" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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lmg-anon/VNTL-1k

lmg-anon

2023-11-20T18:01:58Z

0

[ "region:us" ]

null

2023-11-20T15:37:03Z

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 87713571 num_examples: 33746 download_size: 41613063 dataset_size: 87713571 --- # Dataset Card for "VNTL-1k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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DynamicSuperb/EmotionalSpeechAudioClassification_RAVDESS-EmotionalSound

DynamicSuperb

2023-11-26T17:51:36Z

0

[ "license:cc-by-nc-sa-4.0", "region:us" ]

null

2023-11-20T15:46:12Z

--- license: cc-by-nc-sa-4.0 configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: file dtype: string - name: audio dtype: audio - name: instruction dtype: string - name: label dtype: string splits: - name: test num_bytes: 598283894.96 num_examples: 1440 download_size: 325216537 dataset_size: 598283894.96 ---

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FlyingFishzzz/des_mesh

FlyingFishzzz

2023-11-20T15:51:31Z

0

[ "region:us" ]

null

2023-11-20T15:49:41Z

--- dataset_info: features: - name: target dtype: image - name: prompt dtype: string - name: mesh dtype: image splits: - name: train num_bytes: 520589341.0 num_examples: 1588 download_size: 520482757 dataset_size: 520589341.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "des_mesh" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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jpqueiroz335/vozdoriff

jpqueiroz335

2023-11-20T16:27:04Z

0

[ "license:openrail", "region:us" ]

null

2023-11-20T16:26:46Z

--- license: openrail ---

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Jcuhfehl/anna

Jcuhfehl

2023-11-20T17:03:20Z

0

[ "region:us" ]

null

2023-11-20T16:52:44Z

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 249782806 num_examples: 507 download_size: 149259532 dataset_size: 249782806 configs: - config_name: default data_files: - split: train path: data/train-* ---

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Frixi/Almighty_2019_LaBestia_Era

Frixi

2023-11-20T17:24:56Z

0

[ "license:openrail", "region:us" ]

null

2023-11-20T16:56:19Z

--- license: openrail ---

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FudanSELab/SO_KGXQR_HISTORY

FudanSELab

2023-11-20T17:11:05Z

0

[ "size_categories:1K<n<10K", "language:en", "license:mit", "region:us" ]

null

2023-11-20T17:04:47Z

--- license: mit dataset_info: - config_name: history_csharp features: - name: so_question_id dtype: string - name: historical_title dtype: string splits: - name: test num_bytes: 133783 num_examples: 2000 download_size: 95916 dataset_size: 133783 - config_name: history_java features: - name: so_question_id dtype: string - name: historical_title dtype: string splits: - name: test num_bytes: 132058 num_examples: 2000 download_size: 93956 dataset_size: 132058 - config_name: history_javascript features: - name: so_question_id dtype: string - name: historical_title dtype: string splits: - name: test num_bytes: 132306 num_examples: 2000 download_size: 91896 dataset_size: 132306 - config_name: history_python features: - name: so_question_id dtype: string - name: historical_title dtype: string splits: - name: test num_bytes: 132390 num_examples: 2000 download_size: 92772 dataset_size: 132390 configs: - config_name: history_csharp data_files: - split: test path: history_csharp/test-* - config_name: history_java data_files: - split: test path: history_java/test-* - config_name: history_javascript data_files: - split: test path: history_javascript/test-* - config_name: history_python data_files: - split: test path: history_python/test-* language: - en size_categories: - 1K<n<10K --- ## Dataset Description - **Repository:** [GitHub Repository](https://kgxqr.github.io/)

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DataStudio/OCR_Red

DataStudio

2023-11-20T17:33:45Z

0

[ "region:us" ]

null

2023-11-20T17:33:30Z

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 94279480.25 num_examples: 3550 download_size: 94231656 dataset_size: 94279480.25 configs: - config_name: default data_files: - split: train path: data/train-* ---

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BangumiBase/kamikazekaitoujeanne

BangumiBase

2023-11-20T18:57:19Z

0

[ "size_categories:1K<n<10K", "license:mit", "art", "region:us" ]

null

2023-11-20T17:50:43Z

--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Kamikaze Kaitou Jeanne This is the image base of bangumi Kamikaze Kaitou Jeanne, we detected 43 characters, 3600 images in total. The full dataset is [here](all.zip). **Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview: | # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 | |:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------| | 0 | 527 | [Download](0/dataset.zip) | ![preview 1](0/preview_1.png) | ![preview 2](0/preview_2.png) | ![preview 3](0/preview_3.png) | ![preview 4](0/preview_4.png) | ![preview 5](0/preview_5.png) | ![preview 6](0/preview_6.png) | ![preview 7](0/preview_7.png) | ![preview 8](0/preview_8.png) | | 1 | 39 | [Download](1/dataset.zip) | ![preview 1](1/preview_1.png) | ![preview 2](1/preview_2.png) | ![preview 3](1/preview_3.png) | ![preview 4](1/preview_4.png) | ![preview 5](1/preview_5.png) | ![preview 6](1/preview_6.png) | ![preview 7](1/preview_7.png) | ![preview 8](1/preview_8.png) | | 2 | 690 | [Download](2/dataset.zip) | ![preview 1](2/preview_1.png) | ![preview 2](2/preview_2.png) | ![preview 3](2/preview_3.png) | ![preview 4](2/preview_4.png) | ![preview 5](2/preview_5.png) | ![preview 6](2/preview_6.png) | ![preview 7](2/preview_7.png) | ![preview 8](2/preview_8.png) | | 3 | 38 | [Download](3/dataset.zip) | ![preview 1](3/preview_1.png) | ![preview 2](3/preview_2.png) | ![preview 3](3/preview_3.png) | ![preview 4](3/preview_4.png) | ![preview 5](3/preview_5.png) | ![preview 6](3/preview_6.png) | ![preview 7](3/preview_7.png) | ![preview 8](3/preview_8.png) | | 4 | 33 | [Download](4/dataset.zip) | ![preview 1](4/preview_1.png) | ![preview 2](4/preview_2.png) | ![preview 3](4/preview_3.png) | ![preview 4](4/preview_4.png) | ![preview 5](4/preview_5.png) | ![preview 6](4/preview_6.png) | ![preview 7](4/preview_7.png) | ![preview 8](4/preview_8.png) | | 5 | 26 | [Download](5/dataset.zip) | ![preview 1](5/preview_1.png) | ![preview 2](5/preview_2.png) | ![preview 3](5/preview_3.png) | ![preview 4](5/preview_4.png) | ![preview 5](5/preview_5.png) | ![preview 6](5/preview_6.png) | ![preview 7](5/preview_7.png) | ![preview 8](5/preview_8.png) | | 6 | 110 | [Download](6/dataset.zip) | ![preview 1](6/preview_1.png) | ![preview 2](6/preview_2.png) | ![preview 3](6/preview_3.png) | ![preview 4](6/preview_4.png) | ![preview 5](6/preview_5.png) | ![preview 6](6/preview_6.png) | ![preview 7](6/preview_7.png) | ![preview 8](6/preview_8.png) | | 7 | 23 | [Download](7/dataset.zip) | ![preview 1](7/preview_1.png) | ![preview 2](7/preview_2.png) | ![preview 3](7/preview_3.png) | ![preview 4](7/preview_4.png) | ![preview 5](7/preview_5.png) | ![preview 6](7/preview_6.png) | ![preview 7](7/preview_7.png) | ![preview 8](7/preview_8.png) | | 8 | 28 | [Download](8/dataset.zip) | ![preview 1](8/preview_1.png) | ![preview 2](8/preview_2.png) | ![preview 3](8/preview_3.png) | ![preview 4](8/preview_4.png) | ![preview 5](8/preview_5.png) | ![preview 6](8/preview_6.png) | ![preview 7](8/preview_7.png) | ![preview 8](8/preview_8.png) | | 9 | 435 | [Download](9/dataset.zip) | ![preview 1](9/preview_1.png) | ![preview 2](9/preview_2.png) | ![preview 3](9/preview_3.png) | ![preview 4](9/preview_4.png) | ![preview 5](9/preview_5.png) | ![preview 6](9/preview_6.png) | ![preview 7](9/preview_7.png) | ![preview 8](9/preview_8.png) | | 10 | 351 | [Download](10/dataset.zip) | ![preview 1](10/preview_1.png) | ![preview 2](10/preview_2.png) | ![preview 3](10/preview_3.png) | ![preview 4](10/preview_4.png) | ![preview 5](10/preview_5.png) | ![preview 6](10/preview_6.png) | ![preview 7](10/preview_7.png) | ![preview 8](10/preview_8.png) | | 11 | 45 | [Download](11/dataset.zip) | ![preview 1](11/preview_1.png) | ![preview 2](11/preview_2.png) | ![preview 3](11/preview_3.png) | ![preview 4](11/preview_4.png) | ![preview 5](11/preview_5.png) | ![preview 6](11/preview_6.png) | ![preview 7](11/preview_7.png) | ![preview 8](11/preview_8.png) | | 12 | 69 | [Download](12/dataset.zip) | ![preview 1](12/preview_1.png) | ![preview 2](12/preview_2.png) | ![preview 3](12/preview_3.png) | ![preview 4](12/preview_4.png) | ![preview 5](12/preview_5.png) | ![preview 6](12/preview_6.png) | ![preview 7](12/preview_7.png) | ![preview 8](12/preview_8.png) | | 13 | 14 | [Download](13/dataset.zip) | ![preview 1](13/preview_1.png) | ![preview 2](13/preview_2.png) | ![preview 3](13/preview_3.png) | ![preview 4](13/preview_4.png) | ![preview 5](13/preview_5.png) | ![preview 6](13/preview_6.png) | ![preview 7](13/preview_7.png) | ![preview 8](13/preview_8.png) | | 14 | 78 | [Download](14/dataset.zip) | ![preview 1](14/preview_1.png) | ![preview 2](14/preview_2.png) | ![preview 3](14/preview_3.png) | ![preview 4](14/preview_4.png) | ![preview 5](14/preview_5.png) | ![preview 6](14/preview_6.png) | ![preview 7](14/preview_7.png) | ![preview 8](14/preview_8.png) | | 15 | 54 | [Download](15/dataset.zip) | ![preview 1](15/preview_1.png) | ![preview 2](15/preview_2.png) | ![preview 3](15/preview_3.png) | ![preview 4](15/preview_4.png) | ![preview 5](15/preview_5.png) | ![preview 6](15/preview_6.png) | ![preview 7](15/preview_7.png) | ![preview 8](15/preview_8.png) | | 16 | 21 | [Download](16/dataset.zip) | ![preview 1](16/preview_1.png) | ![preview 2](16/preview_2.png) | ![preview 3](16/preview_3.png) | ![preview 4](16/preview_4.png) | ![preview 5](16/preview_5.png) | ![preview 6](16/preview_6.png) | ![preview 7](16/preview_7.png) | ![preview 8](16/preview_8.png) | | 17 | 23 | [Download](17/dataset.zip) | ![preview 1](17/preview_1.png) | ![preview 2](17/preview_2.png) | ![preview 3](17/preview_3.png) | ![preview 4](17/preview_4.png) | ![preview 5](17/preview_5.png) | ![preview 6](17/preview_6.png) | ![preview 7](17/preview_7.png) | ![preview 8](17/preview_8.png) | | 18 | 45 | [Download](18/dataset.zip) | ![preview 1](18/preview_1.png) | ![preview 2](18/preview_2.png) | ![preview 3](18/preview_3.png) | ![preview 4](18/preview_4.png) | ![preview 5](18/preview_5.png) | ![preview 6](18/preview_6.png) | ![preview 7](18/preview_7.png) | ![preview 8](18/preview_8.png) | | 19 | 18 | [Download](19/dataset.zip) | ![preview 1](19/preview_1.png) | ![preview 2](19/preview_2.png) | ![preview 3](19/preview_3.png) | ![preview 4](19/preview_4.png) | ![preview 5](19/preview_5.png) | ![preview 6](19/preview_6.png) | ![preview 7](19/preview_7.png) | ![preview 8](19/preview_8.png) | | 20 | 159 | [Download](20/dataset.zip) | ![preview 1](20/preview_1.png) | ![preview 2](20/preview_2.png) | ![preview 3](20/preview_3.png) | ![preview 4](20/preview_4.png) | ![preview 5](20/preview_5.png) | ![preview 6](20/preview_6.png) | ![preview 7](20/preview_7.png) | ![preview 8](20/preview_8.png) | | 21 | 22 | [Download](21/dataset.zip) | ![preview 1](21/preview_1.png) | ![preview 2](21/preview_2.png) | ![preview 3](21/preview_3.png) | ![preview 4](21/preview_4.png) | ![preview 5](21/preview_5.png) | ![preview 6](21/preview_6.png) | ![preview 7](21/preview_7.png) | ![preview 8](21/preview_8.png) | | 22 | 103 | [Download](22/dataset.zip) | ![preview 1](22/preview_1.png) | ![preview 2](22/preview_2.png) | ![preview 3](22/preview_3.png) | ![preview 4](22/preview_4.png) | ![preview 5](22/preview_5.png) | ![preview 6](22/preview_6.png) | ![preview 7](22/preview_7.png) | ![preview 8](22/preview_8.png) | | 23 | 42 | [Download](23/dataset.zip) | ![preview 1](23/preview_1.png) | ![preview 2](23/preview_2.png) | ![preview 3](23/preview_3.png) | ![preview 4](23/preview_4.png) | ![preview 5](23/preview_5.png) | ![preview 6](23/preview_6.png) | ![preview 7](23/preview_7.png) | ![preview 8](23/preview_8.png) | | 24 | 12 | [Download](24/dataset.zip) | ![preview 1](24/preview_1.png) | ![preview 2](24/preview_2.png) | ![preview 3](24/preview_3.png) | ![preview 4](24/preview_4.png) | ![preview 5](24/preview_5.png) | ![preview 6](24/preview_6.png) | ![preview 7](24/preview_7.png) | ![preview 8](24/preview_8.png) | | 25 | 14 | [Download](25/dataset.zip) | ![preview 1](25/preview_1.png) | ![preview 2](25/preview_2.png) | ![preview 3](25/preview_3.png) | ![preview 4](25/preview_4.png) | ![preview 5](25/preview_5.png) | ![preview 6](25/preview_6.png) | ![preview 7](25/preview_7.png) | ![preview 8](25/preview_8.png) | | 26 | 30 | [Download](26/dataset.zip) | ![preview 1](26/preview_1.png) | ![preview 2](26/preview_2.png) | ![preview 3](26/preview_3.png) | ![preview 4](26/preview_4.png) | ![preview 5](26/preview_5.png) | ![preview 6](26/preview_6.png) | ![preview 7](26/preview_7.png) | ![preview 8](26/preview_8.png) | | 27 | 15 | [Download](27/dataset.zip) | ![preview 1](27/preview_1.png) | ![preview 2](27/preview_2.png) | ![preview 3](27/preview_3.png) | ![preview 4](27/preview_4.png) | ![preview 5](27/preview_5.png) | ![preview 6](27/preview_6.png) | ![preview 7](27/preview_7.png) | ![preview 8](27/preview_8.png) | | 28 | 12 | [Download](28/dataset.zip) | ![preview 1](28/preview_1.png) | ![preview 2](28/preview_2.png) | ![preview 3](28/preview_3.png) | ![preview 4](28/preview_4.png) | ![preview 5](28/preview_5.png) | ![preview 6](28/preview_6.png) | ![preview 7](28/preview_7.png) | ![preview 8](28/preview_8.png) | | 29 | 18 | [Download](29/dataset.zip) | ![preview 1](29/preview_1.png) | ![preview 2](29/preview_2.png) | ![preview 3](29/preview_3.png) | ![preview 4](29/preview_4.png) | ![preview 5](29/preview_5.png) | ![preview 6](29/preview_6.png) | ![preview 7](29/preview_7.png) | ![preview 8](29/preview_8.png) | | 30 | 12 | [Download](30/dataset.zip) | ![preview 1](30/preview_1.png) | ![preview 2](30/preview_2.png) | ![preview 3](30/preview_3.png) | ![preview 4](30/preview_4.png) | ![preview 5](30/preview_5.png) | ![preview 6](30/preview_6.png) | ![preview 7](30/preview_7.png) | ![preview 8](30/preview_8.png) | | 31 | 11 | [Download](31/dataset.zip) | ![preview 1](31/preview_1.png) | ![preview 2](31/preview_2.png) | ![preview 3](31/preview_3.png) | ![preview 4](31/preview_4.png) | ![preview 5](31/preview_5.png) | ![preview 6](31/preview_6.png) | ![preview 7](31/preview_7.png) | ![preview 8](31/preview_8.png) | | 32 | 208 | [Download](32/dataset.zip) | ![preview 1](32/preview_1.png) | ![preview 2](32/preview_2.png) | ![preview 3](32/preview_3.png) | ![preview 4](32/preview_4.png) | ![preview 5](32/preview_5.png) | ![preview 6](32/preview_6.png) | ![preview 7](32/preview_7.png) | ![preview 8](32/preview_8.png) | | 33 | 28 | [Download](33/dataset.zip) | ![preview 1](33/preview_1.png) | ![preview 2](33/preview_2.png) | ![preview 3](33/preview_3.png) | ![preview 4](33/preview_4.png) | ![preview 5](33/preview_5.png) | ![preview 6](33/preview_6.png) | ![preview 7](33/preview_7.png) | ![preview 8](33/preview_8.png) | | 34 | 24 | [Download](34/dataset.zip) | ![preview 1](34/preview_1.png) | ![preview 2](34/preview_2.png) | ![preview 3](34/preview_3.png) | ![preview 4](34/preview_4.png) | ![preview 5](34/preview_5.png) | ![preview 6](34/preview_6.png) | ![preview 7](34/preview_7.png) | ![preview 8](34/preview_8.png) | | 35 | 13 | [Download](35/dataset.zip) | ![preview 1](35/preview_1.png) | ![preview 2](35/preview_2.png) | ![preview 3](35/preview_3.png) | ![preview 4](35/preview_4.png) | ![preview 5](35/preview_5.png) | ![preview 6](35/preview_6.png) | ![preview 7](35/preview_7.png) | ![preview 8](35/preview_8.png) | | 36 | 34 | [Download](36/dataset.zip) | ![preview 1](36/preview_1.png) | ![preview 2](36/preview_2.png) | ![preview 3](36/preview_3.png) | ![preview 4](36/preview_4.png) | ![preview 5](36/preview_5.png) | ![preview 6](36/preview_6.png) | ![preview 7](36/preview_7.png) | ![preview 8](36/preview_8.png) | | 37 | 7 | [Download](37/dataset.zip) | ![preview 1](37/preview_1.png) | ![preview 2](37/preview_2.png) | ![preview 3](37/preview_3.png) | ![preview 4](37/preview_4.png) | ![preview 5](37/preview_5.png) | ![preview 6](37/preview_6.png) | ![preview 7](37/preview_7.png) | N/A | | 38 | 12 | [Download](38/dataset.zip) | ![preview 1](38/preview_1.png) | ![preview 2](38/preview_2.png) | ![preview 3](38/preview_3.png) | ![preview 4](38/preview_4.png) | ![preview 5](38/preview_5.png) | ![preview 6](38/preview_6.png) | ![preview 7](38/preview_7.png) | ![preview 8](38/preview_8.png) | | 39 | 19 | [Download](39/dataset.zip) | ![preview 1](39/preview_1.png) | ![preview 2](39/preview_2.png) | ![preview 3](39/preview_3.png) | ![preview 4](39/preview_4.png) | ![preview 5](39/preview_5.png) | ![preview 6](39/preview_6.png) | ![preview 7](39/preview_7.png) | ![preview 8](39/preview_8.png) | | 40 | 12 | [Download](40/dataset.zip) | ![preview 1](40/preview_1.png) | ![preview 2](40/preview_2.png) | ![preview 3](40/preview_3.png) | ![preview 4](40/preview_4.png) | ![preview 5](40/preview_5.png) | ![preview 6](40/preview_6.png) | ![preview 7](40/preview_7.png) | ![preview 8](40/preview_8.png) | | 41 | 17 | [Download](41/dataset.zip) | ![preview 1](41/preview_1.png) | ![preview 2](41/preview_2.png) | ![preview 3](41/preview_3.png) | ![preview 4](41/preview_4.png) | ![preview 5](41/preview_5.png) | ![preview 6](41/preview_6.png) | ![preview 7](41/preview_7.png) | ![preview 8](41/preview_8.png) | | noise | 109 | [Download](-1/dataset.zip) | ![preview 1](-1/preview_1.png) | ![preview 2](-1/preview_2.png) | ![preview 3](-1/preview_3.png) | ![preview 4](-1/preview_4.png) | ![preview 5](-1/preview_5.png) | ![preview 6](-1/preview_6.png) | ![preview 7](-1/preview_7.png) | ![preview 8](-1/preview_8.png) |

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lmg-anon/VNTL-4k

lmg-anon

2023-11-20T20:42:18Z

0

[ "region:us" ]

null

2023-11-20T17:57:09Z

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 87937072 num_examples: 8695 download_size: 0 dataset_size: 87937072 --- # Dataset Card for "VNTL-4k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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m111styd4y/marisam

m111styd4y

2023-11-20T18:23:00Z

0

[ "license:openrail", "region:us" ]

null

2023-11-20T18:22:18Z

--- license: openrail ---

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rkdeva/Dermnet-Test-2

rkdeva

2023-11-20T18:35:12Z

0

[ "region:us" ]

null

2023-11-20T18:33:11Z

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: label dtype: string splits: - name: train num_bytes: 376769322.178 num_examples: 3937 download_size: 370140971 dataset_size: 376769322.178 --- # Dataset Card for "Dermnet-Test-2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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vrhoward/thermostableProteins

vrhoward

2023-11-29T10:37:52Z

0

[ "task_categories:text-generation", "size_categories:1K<n<10K", "language:en", "region:us" ]

[ "text-generation" ]

2023-11-20T20:10:53Z

--- task_categories: - text-generation language: - en size_categories: - 1K<n<10K --- This dataset contains mutated protein sequences that resulted in higher thermostability as compared to wild-type.

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celsowm/openassistant-llama-style-pt-br

celsowm

2023-11-20T21:06:08Z

0

[ "region:us" ]

null

2023-11-20T21:06:01Z

--- dataset_info: features: - name: texto dtype: string splits: - name: train num_bytes: 240447 num_examples: 161 download_size: 152688 dataset_size: 240447 configs: - config_name: default data_files: - split: train path: data/train-* ---

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hayden-donnelly/easy-mnist

hayden-donnelly

2023-11-20T21:45:49Z

0

[ "task_categories:image-classification", "task_categories:unconditional-image-generation", "size_categories:10K<n<100K", "region:us" ]

[ "image-classification", "unconditional-image-generation" ]

2023-11-20T21:31:37Z

--- task_categories: - image-classification - unconditional-image-generation pretty_name: Easy MNIST size_categories: - 10K<n<100K --- # Easy MNIST MNIST processed into three easy to use formats. Each .zip file contains a labels_and_paths.csv file and a data directory. ## mnist_png.zip MNIST in the png format. ``` label path 0 5 data/0.png 1 0 data/1.png 2 4 data/2.png 3 1 data/3.png 4 9 data/4.png ... ... ... 69995 2 data/69995.png 69996 3 data/69996.png 69997 4 data/69997.png 69998 5 data/69998.png 69999 6 data/69999.png ``` ## mnist_numpy.zip MNIST in the npy format. ``` label path 0 5 data/0.npy 1 0 data/1.npy 2 4 data/2.npy 3 1 data/3.npy 4 9 data/4.npy ... ... ... 69995 2 data/69995.npy 69996 3 data/69996.npy 69997 4 data/69997.npy 69998 5 data/69998.npy 69999 6 data/69999.npy ``` ## mnist_numpy_flat.zip MNIST in the npy format, flattened to 784 dimensional vectors. ``` label path 0 5 data/0.npy 1 0 data/1.npy 2 4 data/2.npy 3 1 data/3.npy 4 9 data/4.npy ... ... ... 69995 2 data/69995.npy 69996 3 data/69996.npy 69997 4 data/69997.npy 69998 5 data/69998.npy 69999 6 data/69999.npy ``` ## Acknowledgements - Yann LeCun, Courant Institute, NYU - Corinna Cortes, Google Labs, New York - Christopher J.C. Burges, Microsoft Research, Redmond

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Solnauhj/WOLLET

Solnauhj

2023-11-20T21:35:49Z

0

[ "license:openrail", "region:us" ]

null

2023-11-20T21:34:13Z

--- license: openrail ---

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ElSmoothy/tacos

ElSmoothy

2023-11-20T21:55:30Z

0

[ "license:apache-2.0", "region:us" ]

null

2023-11-20T21:54:43Z

--- license: apache-2.0 ---

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wearemusicai/moisesdb

wearemusicai

2023-11-27T19:36:27Z

0

1

[ "language:en", "license:other", "audio", "music", "source separation", "arxiv:2307.15913", "region:us" ]

null

2023-11-20T22:05:24Z

--- language: - en pretty_name: MoisesDB tags: - audio - music - source separation license: other license_name: ncrlc license_link: https://github.com/moises-ai/moises-db --- # MoisesDB Moises Dataset for Source Separation ### Dataset Description - **Homepage:** [MoisesDB homepage](https://developer.moises.ai/research/) - **Repository:** [MoisesDB repository](https://github.com/moises-ai/moises-db) - **Paper:** [Moisesdb: A dataset for source separation beyond 4-stems](https://arxiv.org/abs/2307.15913) - **Point of Contact:** [Igor Pereira](mailto:[email protected]) ### Dataset Summary MoisesDB is a dataset for source separation. It provides a collection of tracks and their separated stems (vocals, bass, drums, etc.). The dataset is used to evaluate the performance of source separation algorithms. # Download the data Please download the dataset at our research [website](https://developer.moises.ai/research), extract it and configure the environment variable `MOISESDB_PATH` accordingly. ``` export MOISESDB_PATH=./moises-db-data ``` The directory structure should be ``` moisesdb: moisesdb_v0.1 track uuid 0 track uuid 1 . . . ``` # Install You can install this package with ``` pip install git+https://github.com/moises-ai/moises-db.git ``` # Usage ## `MoisesDB` After downloading and configuring the path for the dataset, you can create an instance of `MoisesDB` to access the tracks. You can also provide the dataset path with the `data_path` argument. ``` from moisesdb.dataset import MoisesDB db = MoisesDB( data_path='./moisesdb', sample_rate=44100 ) ``` The `MoisesDB` object has iterator properties that you can use to access all files within the dataset. ``` n_songs = len(db) track = db[0] # Returns a MoisesDBTrack object ``` ## `MoisesDBTrack` The `MoisesDBTrack` object holds information about a track in the dataset, perform on-the-fly mixing for stems and multiple sources within a stem. You can access all the stems and mixture from the `stem` and `audio` properties. The `stem` property returns a dictionary whith available stems as keys and `nd.array` on values. The `audio` property results in a `nd.array` with the mixture. ``` track = db[0] stems = track.stems # stems = {'vocals': ..., 'bass': ..., ...} mixture track.audio # mixture = nd.array ``` The `MoisesDBTrack` object also contains other non-audio information from the track such as: - `track.id` - `track.provider` - `track.artist` - `track.name` - `track.genre` - `track.sources` - `track.bleedings` - `track.activity` The stems and mixture are computed on-the-fly. You can create a stems-only version of the dataset using the `save_stems` method of the `MoisesDBTrack`. ``` track = db[0] path = './moises-db-stems/0' track.save_stems(path) ``` # Performance Evaluation We run a few source separation algorithms as well as oracle methods to evaluate the performance of each track of the `MoisesDB`. These results are located in `csv` files at the `benchmark` folder. # Citing If you used the `MoisesDB` dataset on your research, please cite the following paper. ``` @misc{pereira2023moisesdb, title={Moisesdb: A dataset for source separation beyond 4-stems}, author={Igor Pereira and Felipe Araújo and Filip Korzeniowski and Richard Vogl}, year={2023}, eprint={2307.15913}, archivePrefix={arXiv}, primaryClass={cs.SD} } ``` # Licensing `MoisesDB` is distributed with the NC-RCL license. ``` "Non-Commercial Research Community license (NC-RCL) Limited Redistribution: You are permitted to copy and utilize the provided audio material in any medium or format, as long as it is done only for non-commercial purposes within the research community, and the redistribution is conducted solely through the platform moises.ai or other platforms explicitly authorized by the licensor. Redistribution outside the authorized platforms is not allowed without the licensor's written consent. Attribution: You must give appropriate credit (including the artist's name and the song's title), and provide a link to this license or a notice indicating the terms of this license. Non-Commercial Use: You cannot use the material for any commercial purposes or financial gain. This includes, but is not limited to, the sale, licensing, or rental of the material, as well as any use where the primary aim is to generate revenue or profits. No Derivative Works: You cannot create, remix, adapt, or build upon the material, unless explicitly permitted by the artist. Preservation of Legal Notices: You cannot remove any copyright or other proprietary notices which are included in or attached to the material. Termination: If you fail to comply with this license, your rights to use the material will be terminated automatically. Voice Cloning Restriction: You are prohibited from using the vocal stems or any part of the audio material to create a public digital imitation of the artist's voice (e.g: a vocal clone or replica). This includes, but is not limited to, the utilization of voice synthesis technology, deep learning algorithms, and other artificial intelligence-based tools." ```

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Alan318/2

Alan318

2023-11-20T22:14:56Z

0

[ "license:mit", "region:us" ]

null

2023-11-20T22:14:56Z

--- license: mit ---

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sade-adrien/redpajama_v2_sample_100M

sade-adrien

2023-11-21T06:00:28Z

0

[ "region:us" ]

null

2023-11-20T23:07:38Z

--- dataset_info: features: - name: raw_content dtype: string - name: doc_id dtype: string - name: meta dtype: string - name: quality_signals dtype: string splits: - name: train num_bytes: 1043463774444 num_examples: 100000000 download_size: 226895559008 dataset_size: 1043463774444 --- # Dataset Card for "redpajama_v2_sample_100M" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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rafa62/renangarcia

rafa62

2023-11-20T23:40:13Z

0

[ "license:openrail", "region:us" ]

null

2023-11-20T23:38:31Z

--- license: openrail ---

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ChaiML/eval_dataset_character_20231108_sample43_response_reselected

ChaiML

2023-11-21T00:26:22Z

0

[ "region:us" ]

null

2023-11-21T00:26:20Z

--- dataset_info: features: - name: conversation_id dtype: string - name: bot_id dtype: string - name: user_id dtype: string - name: conversation dtype: string - name: thumbs_up dtype: bool - name: feedback dtype: string - name: model_name dtype: string - name: season dtype: string - name: bot_label dtype: string - name: prompt dtype: string - name: memory dtype: string - name: first_message dtype: string - name: labels dtype: int64 - name: chat_history list: - name: message dtype: string - name: sender dtype: string - name: sample_index dtype: int64 - name: sample_payload list: - name: message dtype: string - name: sender dtype: string - name: sample_response struct: - name: message dtype: string - name: sender dtype: string - name: eval_model_id dtype: string - name: eval_model_output dtype: string - name: eval_model_input dtype: string splits: - name: train num_bytes: 4595702.4 num_examples: 387 download_size: 453380 dataset_size: 4595702.4 --- # Dataset Card for "eval_dataset_character_20231108_sample43_response_reselected" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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miragepa/Larin

miragepa

2023-11-21T02:02:29Z

0

[ "license:openrail", "region:us" ]

null

2023-11-21T00:30:26Z

--- license: openrail ---

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gagan3012/Numerical_understanding

gagan3012

2023-11-21T00:58:54Z

0

[ "region:us" ]

null

2023-11-21T00:58:50Z

--- dataset_info: features: - name: text dtype: string - name: answer dtype: string - name: query dtype: string - name: id dtype: string splits: - name: train num_bytes: 169338670 num_examples: 20875 download_size: 43150062 dataset_size: 169338670 configs: - config_name: default data_files: - split: train path: data/train-* ---

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Maljean/State-Sponsored

Maljean

2023-11-21T01:01:01Z

0

[ "license:unknown", "region:us" ]

null

2023-11-21T01:01:01Z

--- license: unknown ---

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Maljean/dataset

Maljean

2023-11-21T01:09:42Z

0

[ "license:apache-2.0", "region:us" ]

null

2023-11-21T01:08:43Z

--- license: apache-2.0 ---

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Papini/papini

Papini

2023-11-21T01:30:01Z

0

[ "license:apache-2.0", "region:us" ]

null

2023-11-21T01:21:03Z

--- license: apache-2.0 ---

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Kaue123456/LoganIsaacBardavid

Kaue123456

2023-11-21T01:33:09Z

0

[ "license:openrail", "region:us" ]

null

2023-11-21T01:32:34Z

--- license: openrail ---

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TioRob/Larin

TioRob

2023-11-21T01:34:14Z

0

[ "license:openrail", "region:us" ]

null

2023-11-21T01:33:45Z

--- license: openrail ---

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KADUZADA/ROBSON

KADUZADA

2023-11-21T01:47:01Z

0

[ "license:openrail", "region:us" ]

null

2023-11-21T01:43:38Z

--- license: openrail ---

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HuskyAI/neo_icon

HuskyAI

2023-11-21T01:51:42Z

0

1

[ "task_categories:text-to-image", "license:apache-2.0", "icon", "style", "lora", "region:us" ]

[ "text-to-image" ]

2023-11-21T01:50:20Z

--- license: apache-2.0 task_categories: - text-to-image tags: - icon - style - lora ---

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idning/ffhq64-caption

idning

2023-11-21T03:17:01Z

0

[ "license:mit", "region:us" ]

null

2023-11-21T02:10:24Z

--- license: mit dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 586217154.0 num_examples: 70000 download_size: 584117488 dataset_size: 586217154.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

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idning/ffhq256-caption

idning

2023-11-21T08:31:47Z

0

[ "license:mit", "region:us" ]

null

2023-11-21T02:10:45Z

--- license: mit dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 7388635414.0 num_examples: 70000 download_size: 7386868493 dataset_size: 7388635414.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

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Vinayakagude/cherryleaves

Vinayakagude

2023-11-21T03:40:01Z

0

[ "region:us" ]

null

2023-11-21T02:18:26Z

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': healthy '1': powdery_mildew splits: - name: train num_bytes: 68574745.152 num_examples: 4208 download_size: 57302416 dataset_size: 68574745.152 configs: - config_name: default data_files: - split: train path: data/train-* ---

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librarian-bots/model-card-sentences-annotated

librarian-bots

2023-11-21T15:10:04Z

0

2

[ "task_categories:token-classification", "size_categories:100K<n<1M", "language:en", "documentation ", "region:us" ]

[ "token-classification" ]

2023-11-21T02:21:46Z

--- configs: - config_name: raw_data data_files: results.jsonl - config_name: cleaned_data data_files: clean_results.jsonl task_categories: - token-classification tags: - 'documentation ' pretty_name: 'Model Card Sentences Annotated with Entities ' language: - en size_categories: - 100K<n<1M ---

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nlplabtdtu/train-tokenizor-ds-T5

nlplabtdtu

2023-11-21T02:29:40Z

0

[ "region:us" ]

null

2023-11-21T02:25:15Z

--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 7956427488 num_examples: 1885715 download_size: 2662966309 dataset_size: 7956427488 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "train-tokenizor-ds-T5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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nlplabtdtu/val-tokenizor-ds-T5

nlplabtdtu

2023-11-21T02:30:10Z

0

[ "region:us" ]

null

2023-11-21T02:29:41Z

--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 885510284 num_examples: 209524 download_size: 296327037 dataset_size: 885510284 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "val-tokenizor-ds-T5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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lghupan/CloudBench-YAML

lghupan

2023-11-21T02:52:49Z

0

[ "license:mit", "region:us" ]

null

2023-11-21T02:52:49Z

--- license: mit ---

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nlplabtdtu/s-c4

nlplabtdtu

2023-11-21T03:00:41Z

0

[ "region:us" ]

null

2023-11-21T02:58:13Z

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: url dtype: string - name: id dtype: string - name: text dtype: string - name: perplexity dtype: float64 splits: - name: train num_bytes: 3488628609 num_examples: 777159 download_size: 1734360231 dataset_size: 3488628609 --- # Dataset Card for "s-c4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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nlplabtdtu/s-wikicorpus

nlplabtdtu

2023-11-21T03:03:21Z

0

[ "region:us" ]

null

2023-11-21T03:00:42Z

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 2455296583 num_examples: 1118180 download_size: 1536183681 dataset_size: 2455296583 --- # Dataset Card for "s-wikicorpus" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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gsynb/gg

gsynb

2023-11-21T03:08:53Z

0

[ "license:apache-2.0", "region:us" ]

null

2023-11-21T03:08:15Z

--- license: apache-2.0 ---

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ai4cloud/CloudEval-YAML

ai4cloud

2023-11-21T11:48:11Z

0

[ "license:mit", "region:us" ]

null

2023-11-21T03:24:44Z

--- license: mit ---

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Gabriel1898/pozee

Gabriel1898

2023-11-21T04:30:00Z

0

[ "license:openrail", "region:us" ]

null

2023-11-21T04:29:06Z

--- license: openrail ---

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Gabriel1898/poze1

Gabriel1898

2023-11-21T04:37:13Z

0

[ "license:openrail", "region:us" ]

null

2023-11-21T04:36:13Z

--- license: openrail ---

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kseki/guanaco-llama2-1k

kseki

2023-11-28T03:52:12Z

0

[ "region:us" ]

null

2023-11-21T04:45:05Z

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 1654448 num_examples: 1000 download_size: 966693 dataset_size: 1654448 configs: - config_name: default data_files: - split: train path: data/train-* ---

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Aryansoni27/Amitabh_bachchan_voice

Aryansoni27

2023-11-21T05:05:03Z

0

[ "license:mit", "region:us" ]

null

2023-11-21T05:05:03Z

--- license: mit ---

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