Split (1)

train · 52.9k rows

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b85dbe439338c75af01102e849f453aafb1eb7e1	# Car damage segmentation sample dataset	Abijith/car-damage-segmentation-small	[ "license:apache-2.0", "region:us" ]	2023-10-04T17:45:12+00:00	{"license": "apache-2.0"}	2023-10-04T17:49:31+00:00	[]	[]	TAGS #license-apache-2.0 #region-us	# Car damage segmentation sample dataset	[ "# Car damage segmentation sample dataset" ]	[ "TAGS\n#license-apache-2.0 #region-us \n", "# Car damage segmentation sample dataset" ]	[ 14, 8 ]	[ "passage: TAGS\n#license-apache-2.0 #region-us \n# Car damage segmentation sample dataset" ]
15354895010df3f1701f6450a971d9678cc3fde1	# Dataset Card for "a8072b85" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	result-kand2-sdxl-wuerst-karlo/a8072b85	[ "region:us" ]	2023-10-04T17:47:46+00:00	{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 170, "num_examples": 10}], "download_size": 1348, "dataset_size": 170}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-10-04T17:47:47+00:00	[]	[]	TAGS #region-us	# Dataset Card for "a8072b85" More Information needed	[ "# Dataset Card for \"a8072b85\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"a8072b85\"\n\nMore Information needed" ]	[ 6, 15 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"a8072b85\"\n\nMore Information needed" ]
3e0a8c47569d3371db6046aa68470ced41b8b01f	# Dataset Card for "be76ce08" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	result-kand2-sdxl-wuerst-karlo/be76ce08	[ "region:us" ]	2023-10-04T17:52:19+00:00	{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 178, "num_examples": 10}], "download_size": 1354, "dataset_size": 178}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-10-04T17:52:19+00:00	[]	[]	TAGS #region-us	# Dataset Card for "be76ce08" More Information needed	[ "# Dataset Card for \"be76ce08\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"be76ce08\"\n\nMore Information needed" ]	[ 6, 14 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"be76ce08\"\n\nMore Information needed" ]
fdc9132a9e4efeeff345d50f14e9ac04c8a2bdfb	# Dataset Card for Cloud Masking Dataset ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) ## Dataset Description - Homepage: - Repository: [Data Availability at DrivenData as Cloud Cover](https://mlhub.earth/data/ref_cloud_cover_detection_challenge_v1) ### Dataset Summary The Cloud Masking dataset contains 4 bands [b02, b03, b04, b08] images from Sentinel-2 satellite for various region showing cloud coverage, along with cloud mask as label for segmentation task.	gkg1312/Cloud_mask	[ "task_categories:image-segmentation", "language:en", "license:cc0-1.0", "satellite-images", "image-segmentations", "region:us" ]	2023-10-04T17:55:04+00:00	{"language": ["en"], "license": "cc0-1.0", "task_categories": ["image-segmentation"], "tags": ["satellite-images", "image-segmentations"]}	2023-10-05T06:33:17+00:00	[]	[ "en" ]	TAGS #task_categories-image-segmentation #language-English #license-cc0-1.0 #satellite-images #image-segmentations #region-us	# Dataset Card for Cloud Masking Dataset ## Table of Contents - Dataset Description - Dataset Summary ## Dataset Description - Homepage: - Repository: Data Availability at DrivenData as Cloud Cover ### Dataset Summary The Cloud Masking dataset contains 4 bands [b02, b03, b04, b08] images from Sentinel-2 satellite for various region showing cloud coverage, along with cloud mask as label for segmentation task.	[ "# Dataset Card for Cloud Masking Dataset", "## Table of Contents\n- Dataset Description\n - Dataset Summary", "## Dataset Description\n\n- Homepage:\n- Repository: Data Availability at DrivenData as Cloud Cover", "### Dataset Summary\n\nThe Cloud Masking dataset contains 4 bands [b02, b03, b04, b08] images from Sentinel-2 satellite for various region showing cloud coverage, along with cloud mask as label for segmentation task." ]	[ "TAGS\n#task_categories-image-segmentation #language-English #license-cc0-1.0 #satellite-images #image-segmentations #region-us \n", "# Dataset Card for Cloud Masking Dataset", "## Table of Contents\n- Dataset Description\n - Dataset Summary", "## Dataset Description\n\n- Homepage:\n- Repository: Data Availability at DrivenData as Cloud Cover", "### Dataset Summary\n\nThe Cloud Masking dataset contains 4 bands [b02, b03, b04, b08] images from Sentinel-2 satellite for various region showing cloud coverage, along with cloud mask as label for segmentation task." ]	[ 43, 10, 14, 23, 55 ]	[ "passage: TAGS\n#task_categories-image-segmentation #language-English #license-cc0-1.0 #satellite-images #image-segmentations #region-us \n# Dataset Card for Cloud Masking Dataset## Table of Contents\n- Dataset Description\n - Dataset Summary## Dataset Description\n\n- Homepage:\n- Repository: Data Availability at DrivenData as Cloud Cover### Dataset Summary\n\nThe Cloud Masking dataset contains 4 bands [b02, b03, b04, b08] images from Sentinel-2 satellite for various region showing cloud coverage, along with cloud mask as label for segmentation task." ]
ef1d58aa2709cbe96e024c8aaa3b0e5f9f3928f1	# Dataset Card for "ncbi_genbank_part_0" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Hack90/ncbi_genbank_part_0	[ "region:us" ]	2023-10-04T17:59:55+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "features", "dtype": "int64"}, {"name": "seq_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 257341428, "num_examples": 156}], "download_size": 118952731, "dataset_size": 257341428}}	2023-10-04T18:45:14+00:00	[]	[]	TAGS #region-us	# Dataset Card for "ncbi_genbank_part_0" More Information needed	[ "# Dataset Card for \"ncbi_genbank_part_0\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"ncbi_genbank_part_0\"\n\nMore Information needed" ]	[ 6, 19 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"ncbi_genbank_part_0\"\n\nMore Information needed" ]
7365d6d5ad79e26a4f706c46f91c2afc41d2a378	# Dataset Card for "ncbi_genbank_part_1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Hack90/ncbi_genbank_part_1	[ "region:us" ]	2023-10-04T18:00:18+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "features", "dtype": "int64"}, {"name": "seq_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 20345583566, "num_examples": 137283}], "download_size": 9397135953, "dataset_size": 20345583566}}	2023-10-06T22:08:06+00:00	[]	[]	TAGS #region-us	# Dataset Card for "ncbi_genbank_part_1" More Information needed	[ "# Dataset Card for \"ncbi_genbank_part_1\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"ncbi_genbank_part_1\"\n\nMore Information needed" ]	[ 6, 18 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"ncbi_genbank_part_1\"\n\nMore Information needed" ]
e272c3a7b1379c4e7ce005c95a93fce073444d76	# Dataset Card for "diseases_safecrops" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Alfonso09/diseases_safecrops	[ "region:us" ]	2023-10-04T18:28:56+00:00	{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_examples": 43}], "download_size": 0}}	2023-10-04T19:04:04+00:00	[]	[]	TAGS #region-us	# Dataset Card for "diseases_safecrops" More Information needed	[ "# Dataset Card for \"diseases_safecrops\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"diseases_safecrops\"\n\nMore Information needed" ]	[ 6, 18 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"diseases_safecrops\"\n\nMore Information needed" ]
bae056fdb863f5a3e3105d9286b9228defd7c9be	# Bangumi Image Base of Tensei Shitara Slime Datta Ken This is the image base of bangumi Tensei Shitara Slime Datta Ken, we detected 85 characters, 6000 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 \| 131 \| [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 \| 98 \| [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 \| 84 \| [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 \| 151 \| [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 \| 165 \| [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 \| 76 \| [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 \| 19 \| [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 \| 77 \| [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 \| 119 \| [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 \| 11 \| [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 \| 44 \| [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 \| 173 \| [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 \| 83 \| [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 \| 29 \| [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 \| 40 \| [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 \| 25 \| [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 \| 139 \| [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 \| 91 \| [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 \| 86 \| [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 \| 86 \| [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 \| 20 \| [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 \| 84 \| [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 \| 274 \| [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 \| 61 \| [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 \| 296 \| [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 \| 45 \| [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 \| 64 \| [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 \| 78 \| [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 \| 39 \| [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 \| 15 \| [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 \| 18 \| [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 \| 36 \| [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 \| 18 \| [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 \| 103 \| [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 \| 101 \| [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 \| 25 \| [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 \| 33 \| [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 \| 31 \| [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 \| 53 \| [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 \| 16 \| [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 \| 116 \| [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 \| 113 \| [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 \| 238 \| [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 \| 44 \| [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 \| 830 \| [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 \| 82 \| [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 \| 13 \| [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 \| 48 \| [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 \| 25 \| [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 \| 9 \| [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 \| 9 \| [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 \| 12 \| [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 \| 8 \| [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 \| 9 \| [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 \| 33 \| [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 \| 55 \| [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 \| 18 \| [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 \| 10 \| [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 \| 15 \| [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 \| 20 \| [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 \| 14 \| [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 \| 9 \| [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 \| 101 \| [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 \| 10 \| [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 \| 12 \| [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 \| 88 \| [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 \| 23 \| [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 \| 63 \| [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 \| 10 \| [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 \| 189 \| [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 \| 10 \| [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 \| 80 \| [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 \| 29 \| [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 \| 169 \| [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 \| 7 \| [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) \| N/A \| \| 75 \| 7 \| [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) \| N/A \| \| 76 \| 13 \| [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 \| 17 \| [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 \| 7 \| [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) \| N/A \| \| 79 \| 6 \| [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) \| N/A \| N/A \| \| 80 \| 6 \| [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) \| N/A \| N/A \| \| 81 \| 9 \| [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 \| 7 \| [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) \| N/A \| \| 83 \| 5 \| [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) \| N/A \| N/A \| N/A \| \| noise \| 265 \| [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) \|	BangumiBase/tenseishitaraslimedattaken	[ "size_categories:1K<n<10K", "license:mit", "art", "region:us" ]	2023-10-04T18:33:33+00:00	{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}	2023-10-04T21:48:33+00:00	[]	[]	TAGS #size_categories-1K<n<10K #license-mit #art #region-us	Bangumi Image Base of Tensei Shitara Slime Datta Ken ==================================================== This is the image base of bangumi Tensei Shitara Slime Datta Ken, we detected 85 characters, 6000 images in total. The full dataset is here. 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:	[]	[ "TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]	[ 25 ]	[ "passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]
9be04708cf3c067974534221a0717088bdfd2c7b	# Digital.bg articles	NikiTricky/digital-bg	[ "task_categories:text-generation", "task_categories:summarization", "task_categories:text-classification", "size_categories:10K<n<100K", "language:bg", "region:us" ]	2023-10-04T18:34:41+00:00	{"language": ["bg"], "size_categories": ["10K<n<100K"], "task_categories": ["text-generation", "summarization", "text-classification"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "posts.json"}]}]}	2023-10-05T14:45:49+00:00	[]	[ "bg" ]	TAGS #task_categories-text-generation #task_categories-summarization #task_categories-text-classification #size_categories-10K<n<100K #language-Bulgarian #region-us	# URL articles	[ "# URL articles" ]	[ "TAGS\n#task_categories-text-generation #task_categories-summarization #task_categories-text-classification #size_categories-10K<n<100K #language-Bulgarian #region-us \n", "# URL articles" ]	[ 56, 3 ]	[ "passage: TAGS\n#task_categories-text-generation #task_categories-summarization #task_categories-text-classification #size_categories-10K<n<100K #language-Bulgarian #region-us \n# URL articles" ]
98f6d16534dd5a23278a2d1273affed4c3b82bf5	# Dataset Card for "ner" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	ismailiismail/ner	[ "region:us" ]	2023-10-04T18:35:29+00:00	{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 17841217, "num_examples": 142814}], "download_size": 3513160, "dataset_size": 17841217}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-10-04T18:35:30+00:00	[]	[]	TAGS #region-us	# Dataset Card for "ner" More Information needed	[ "# Dataset Card for \"ner\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"ner\"\n\nMore Information needed" ]	[ 6, 11 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"ner\"\n\nMore Information needed" ]
4bd25eaa9c28cc6cbb1fe89177a372a69a508e2f	# Dataset Card for "precious" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	ArmelRandy/precious	[ "region:us" ]	2023-10-04T18:49:51+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-"}, {"split": "test", "path": "data/test-"}]}], "dataset_info": {"features": [{"name": "prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 49113143.033731006, "num_examples": 37824}, {"name": "test", "num_bytes": 2585243.966268994, "num_examples": 1991}], "download_size": 31710707, "dataset_size": 51698387.0}}	2023-10-04T18:50:05+00:00	[]	[]	TAGS #region-us	# Dataset Card for "precious" More Information needed	[ "# Dataset Card for \"precious\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"precious\"\n\nMore Information needed" ]	[ 6, 12 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"precious\"\n\nMore Information needed" ]
2e8c491f5254070c0152892e882ecd1505bd0c8d	# Dataset Card for Evaluation run of OpenBuddy/openbuddy-falcon-180b-v12-preview0 ## Dataset Description - Homepage: - Repository: https://huggingface.co/OpenBuddy/openbuddy-falcon-180b-v12-preview0 - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [OpenBuddy/openbuddy-falcon-180b-v12-preview0](https://huggingface.co/OpenBuddy/openbuddy-falcon-180b-v12-preview0) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_OpenBuddy__openbuddy-falcon-180b-v12-preview0_public", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-11-07T07:56:48.632483](https://huggingface.co/datasets/open-llm-leaderboard/details_OpenBuddy__openbuddy-falcon-180b-v12-preview0_public/blob/main/results_2023-11-07T07-56-48.632483.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.3951342281879195, "em_stderr": 0.005006583708501088, "f1": 0.45411703020134364, "f1_stderr": 0.004838306698244881, "acc": 0.6166351419033218, "acc_stderr": 0.012168788973494722 }, "harness\|drop\|3": { "em": 0.3951342281879195, "em_stderr": 0.005006583708501088, "f1": 0.45411703020134364, "f1_stderr": 0.004838306698244881 }, "harness\|gsm8k\|5": { "acc": 0.41243366186504926, "acc_stderr": 0.013559628790941459 }, "harness\|winogrande\|5": { "acc": 0.8208366219415943, "acc_stderr": 0.010777949156047986 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_OpenBuddy__openbuddy-falcon-180b-v12-preview0	[ "region:us" ]	2023-10-04T19:02:17+00:00	{"pretty_name": "Evaluation run of OpenBuddy/openbuddy-falcon-180b-v12-preview0", "dataset_summary": "Dataset automatically created during the evaluation run of model [OpenBuddy/openbuddy-falcon-180b-v12-preview0](https://huggingface.co/OpenBuddy/openbuddy-falcon-180b-v12-preview0) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_OpenBuddy__openbuddy-falcon-180b-v12-preview0_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-07T07:56:48.632483](https://huggingface.co/datasets/open-llm-leaderboard/details_OpenBuddy__openbuddy-falcon-180b-v12-preview0_public/blob/main/results_2023-11-07T07-56-48.632483.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.3951342281879195,\n \"em_stderr\": 0.005006583708501088,\n \"f1\": 0.45411703020134364,\n \"f1_stderr\": 0.004838306698244881,\n \"acc\": 0.6166351419033218,\n \"acc_stderr\": 0.012168788973494722\n },\n \"harness\|drop\|3\": {\n \"em\": 0.3951342281879195,\n \"em_stderr\": 0.005006583708501088,\n \"f1\": 0.45411703020134364,\n \"f1_stderr\": 0.004838306698244881\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.41243366186504926,\n \"acc_stderr\": 0.013559628790941459\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.8208366219415943,\n \"acc_stderr\": 0.010777949156047986\n }\n}\n```", "repo_url": "https://huggingface.co/OpenBuddy/openbuddy-falcon-180b-v12-preview0", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_drop_3", "data_files": [{"split": "2023_11_05T05_48_24.408190", "path": ["/details_harness\|drop\|3_2023-11-05T05-48-24.408190.parquet"]}, {"split": "2023_11_07T07_56_48.632483", "path": ["/details_harness\|drop\|3_2023-11-07T07-56-48.632483.parquet"]}, {"split": "latest", "path": ["/details_harness\|drop\|3_2023-11-07T07-56-48.632483.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_11_05T05_48_24.408190", "path": ["/details_harness\|gsm8k\|5_2023-11-05T05-48-24.408190.parquet"]}, {"split": "2023_11_07T07_56_48.632483", "path": ["/details_harness\|gsm8k\|5_2023-11-07T07-56-48.632483.parquet"]}, {"split": "latest", "path": ["/details_harness\|gsm8k\|5_2023-11-07T07-56-48.632483.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_11_05T05_48_24.408190", "path": ["/details_harness\|winogrande\|5_2023-11-05T05-48-24.408190.parquet"]}, {"split": "2023_11_07T07_56_48.632483", "path": ["/details_harness\|winogrande\|5_2023-11-07T07-56-48.632483.parquet"]}, {"split": "latest", "path": ["**/details_harness\|winogrande\|5_2023-11-07T07-56-48.632483.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_11_05T05_48_24.408190", "path": ["results_2023-11-05T05-48-24.408190.parquet"]}, {"split": "2023_11_07T07_56_48.632483", "path": ["results_2023-11-07T07-56-48.632483.parquet"]}, {"split": "latest", "path": ["results_2023-11-07T07-56-48.632483.parquet"]}]}]}	2023-12-01T14:21:10+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of OpenBuddy/openbuddy-falcon-180b-v12-preview0 ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model OpenBuddy/openbuddy-falcon-180b-v12-preview0 on the Open LLM Leaderboard. The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-11-07T07:56:48.632483(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of OpenBuddy/openbuddy-falcon-180b-v12-preview0", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model OpenBuddy/openbuddy-falcon-180b-v12-preview0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-11-07T07:56:48.632483(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of OpenBuddy/openbuddy-falcon-180b-v12-preview0", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model OpenBuddy/openbuddy-falcon-180b-v12-preview0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-11-07T07:56:48.632483(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 29, 31, 178, 66, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of OpenBuddy/openbuddy-falcon-180b-v12-preview0## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model OpenBuddy/openbuddy-falcon-180b-v12-preview0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-11-07T07:56:48.632483(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
ae13d6b6e94e63bfd8240d81a5f7ed6dc43b0f75	# Bangumi Image Base of Mondaiji-tachi Ga Isekai Kara Kuru Sou Desu Yo? This is the image base of bangumi Mondaiji-tachi ga Isekai kara Kuru Sou desu yo?, we detected 19 characters, 1545 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 \| 61 \| [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 \| 26 \| [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 \| 37 \| [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 \| 55 \| [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 \| 79 \| [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 \| 175 \| [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 \| 151 \| [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 \| 20 \| [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 \| 41 \| [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 \| 19 \| [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 \| 9 \| [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 \| 132 \| [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 \| 68 \| [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 \| 21 \| [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 \| 221 \| [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 \| 35 \| [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 \| 13 \| [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 \| 22 \| [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) \| \| noise \| 360 \| [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) \|	BangumiBase/mondaijitachigaisekaikarakurusoudesuyo	[ "size_categories:1K<n<10K", "license:mit", "art", "region:us" ]	2023-10-04T19:32:59+00:00	{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}	2023-10-04T20:29:34+00:00	[]	[]	TAGS #size_categories-1K<n<10K #license-mit #art #region-us	Bangumi Image Base of Mondaiji-tachi Ga Isekai Kara Kuru Sou Desu Yo? ===================================================================== This is the image base of bangumi Mondaiji-tachi ga Isekai kara Kuru Sou desu yo?, we detected 19 characters, 1545 images in total. The full dataset is here. 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:	[]	[ "TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]	[ 25 ]	[ "passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]
2087de7a32fab70767df2ad3c7fe58581b2173a5	# Dataset Card for Evaluation run of 1000 ## Dataset Description - Homepage: - Repository: https://huggingface.co/1000 - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [1000](https://huggingface.co/1000) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 3074 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 903 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("HuggingFaceBR4/thomwolf-third-llama", "harness_winogrande_0_third_llama_1p82G_the_pile_eval_950_parquet", split="train") ``` ## Latest results These are the [latest results from run ](https://huggingface.co/datasets/HuggingFaceBR4/thomwolf-third-llama/blob/main/0/results_third-llama-1p82G-refined-web-seed-10_eval_0.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.3435213033570893, "acc_stderr": 0.010892366142162107, "acc_norm": 0.31518627304007263, "acc_norm_stderr": 0.011649698968312151 }, "harness\|arc:challenge\|0": { "acc": 0.22781569965870307, "acc_stderr": 0.012256708602326924, "acc_norm": 0.2627986348122867, "acc_norm_stderr": 0.012862523175351333 }, "harness\|arc:easy\|0": { "acc": 0.23695286195286194, "acc_stderr": 0.008725189261472277, "acc_norm": 0.24621212121212122, "acc_norm_stderr": 0.008839902656771866 }, "harness\|hellaswag\|0": { "acc": 0.25582553276239794, "acc_stderr": 0.004354325017137537, "acc_norm": 0.2582154949213304, "acc_norm_stderr": 0.004367586801776658 }, "harness\|openbookqa\|0": { "acc": 0.162, "acc_stderr": 0.016494123566423526, "acc_norm": 0.3, "acc_norm_stderr": 0.02051442622562805 }, "harness\|piqa\|0": { "acc": 0.5418933623503809, "acc_stderr": 0.01162480374723213, "acc_norm": 0.5087051142546246, "acc_norm_stderr": 0.011664055982032842 }, "harness\|super_glue:boolq\|0": { "acc": 0.4813455657492355, "acc_stderr": 0.00873896651001334 }, "harness\|winogrande\|0": { "acc": 0.4988161010260458, "acc_stderr": 0.014052446290529024 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	HuggingFaceBR4/thomwolf-third-llama	[ "region:us" ]	2023-10-04T20:05:42+00:00	{"pretty_name": "Evaluation run of 1000", "dataset_summary": "Dataset automatically created during the evaluation run of model [1000](https://huggingface.co/1000) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 3074 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 903 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"HuggingFaceBR4/thomwolf-third-llama\",\n\t\"harness_winogrande_0_third_llama_1p82G_the_pile_eval_950_parquet\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run ](https://huggingface.co/datasets/HuggingFaceBR4/thomwolf-third-llama/blob/main/0/results_third-llama-1p82G-refined-web-seed-10_eval_0.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.3435213033570893,\n \"acc_stderr\": 0.010892366142162107,\n \"acc_norm\": 0.31518627304007263,\n \"acc_norm_stderr\": 0.011649698968312151\n },\n \"harness\|arc:challenge\|0\": {\n \"acc\": 0.22781569965870307,\n \"acc_stderr\": 0.012256708602326924,\n \"acc_norm\": 0.2627986348122867,\n \"acc_norm_stderr\": 0.012862523175351333\n },\n \"harness\|arc:easy\|0\": {\n \"acc\": 0.23695286195286194,\n \"acc_stderr\": 0.008725189261472277,\n \"acc_norm\": 0.24621212121212122,\n \"acc_norm_stderr\": 0.008839902656771866\n },\n \"harness\|hellaswag\|0\": {\n \"acc\": 0.25582553276239794,\n \"acc_stderr\": 0.004354325017137537,\n \"acc_norm\": 0.2582154949213304,\n \"acc_norm_stderr\": 0.004367586801776658\n },\n \"harness\|openbookqa\|0\": {\n \"acc\": 0.162,\n \"acc_stderr\": 0.016494123566423526,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.02051442622562805\n },\n \"harness\|piqa\|0\": {\n \"acc\": 0.5418933623503809,\n \"acc_stderr\": 0.01162480374723213,\n \"acc_norm\": 0.5087051142546246,\n \"acc_norm_stderr\": 0.011664055982032842\n },\n \"harness\|super_glue:boolq\|0\": {\n \"acc\": 0.4813455657492355,\n \"acc_stderr\": 0.00873896651001334\n },\n \"harness\|winogrande\|0\": {\n \"acc\": 0.4988161010260458,\n \"acc_stderr\": 0.014052446290529024\n }\n}\n```", "repo_url": "https://huggingface.co/1000", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "custom_hellaswag_0_third_llama_1p82G_textbooks_seed_7_eval_1000_parquet", "data_files": [{"split": "third_llama_1p82G_textbooks_seed_7_eval_1000", "path": ["/details_custom\|hellaswag\|0_third-llama-1p82G-textbooks-seed-7_eval_1000.parquet"]}]}, {"config_name": "harness_arc_challenge_0_third_llama_1p82G_refined_web_seed_10_eval_0_parquet", "data_files": [{"split": "third_llama_1p82G_refined_web_seed_10_eval_0", "path": ["/details_harness\|arc:challenge\|0_third-llama-1p82G-refined-web-seed-10_eval_0.parquet"]}]}, {"config_name": "harness_arc_challenge_0_third_llama_1p82G_refined_web_seed_10_eval_1000_parquet", "data_files": [{"split": "third_llama_1p82G_refined_web_seed_10_eval_1000", "path": ["/details_harness\|arc:challenge\|0_third-llama-1p82G-refined-web-seed-10_eval_1000.parquet"]}]}, {"config_name": "harness_arc_challenge_0_third_llama_1p82G_refined_web_seed_10_eval_100_parquet", "data_files": [{"split": "third_llama_1p82G_refined_web_seed_10_eval_100", "path": ["/details_harness\|arc:challenge\|0_third-llama-1p82G-refined-web-seed-10_eval_100.parquet"]}]}, {"config_name": "harness_arc_challenge_0_third_llama_1p82G_refined_web_seed_10_eval_1050_parquet", "data_files": [{"split": "third_llama_1p82G_refined_web_seed_10_eval_1050", "path": ["/details_harness\|arc:challenge\|0_third-llama-1p82G-refined-web-seed-10_eval_1050.parquet"]}]}, {"config_name": "harness_arc_challenge_0_third_llama_1p82G_refined_web_seed_10_eval_1100_parquet", "data_files": [{"split": "third_llama_1p82G_refined_web_seed_10_eval_1100", "path": ["/details_harness\|arc:challenge\|0_third-llama-1p82G-refined-web-seed-10_eval_1100.parquet"]}]}, {"config_name": "harness_arc_challenge_0_third_llama_1p82G_refined_web_seed_10_eval_1150_parquet", "data_files": [{"split": "third_llama_1p82G_refined_web_seed_10_eval_1150", "path": ["/details_harness\|arc:challenge\|0_third-llama-1p82G-refined-web-seed-10_eval_1150.parquet"]}]}, {"config_name": "harness_arc_challenge_0_third_llama_1p82G_refined_web_seed_10_eval_1200_parquet", "data_files": [{"split": "third_llama_1p82G_refined_web_seed_10_eval_1200", "path": ["/details_harness\|arc:challenge\|0_third-llama-1p82G-refined-web-seed-10_eval_1200.parquet"]}]}, {"config_name": "harness_arc_challenge_0_third_llama_1p82G_refined_web_seed_10_eval_1250_parquet", "data_files": [{"split": "third_llama_1p82G_refined_web_seed_10_eval_1250", "path": ["/details_harness\|arc:challenge\|0_third-llama-1p82G-refined-web-seed-10_eval_1250.parquet"]}]}, {"config_name": "harness_arc_challenge_0_third_llama_1p82G_refined_web_seed_10_eval_1300_parquet", "data_files": [{"split": "third_llama_1p82G_refined_web_seed_10_eval_1300", "path": ["/details_harness\|arc:challenge\|0_third-llama-1p82G-refined-web-seed-10_eval_1300.parquet"]}]}, {"config_name": "harness_arc_challenge_0_third_llama_1p82G_refined_web_seed_10_eval_1350_parquet", "data_files": [{"split": "third_llama_1p82G_refined_web_seed_10_eval_1350", "path": ["/details_harness\|arc:challenge\|0_third-llama-1p82G-refined-web-seed-10_eval_1350.parquet"]}]}, {"config_name": "harness_arc_challenge_0_third_llama_1p82G_refined_web_seed_10_eval_1400_parquet", "data_files": [{"split": "third_llama_1p82G_refined_web_seed_10_eval_1400", "path": ["/details_harness\|arc:challenge\|0_third-llama-1p82G-refined-web-seed-10_eval_1400.parquet"]}]}, {"config_name": "harness_arc_challenge_0_third_llama_1p82G_refined_web_seed_10_eval_1450_parquet", "data_files": [{"split": "third_llama_1p82G_refined_web_seed_10_eval_1450", "path": ["/details_harness\|arc:challenge\|0_third-llama-1p82G-refined-web-seed-10_eval_1450.parquet"]}]}, {"config_name": "harness_arc_challenge_0_third_llama_1p82G_refined_web_seed_10_eval_1500_parquet", "data_files": [{"split": "third_llama_1p82G_refined_web_seed_10_eval_1500", "path": ["/details_harness\|arc:challenge\|0_third-llama-1p82G-refined-web-seed-10_eval_1500.parquet"]}]}, {"config_name": "harness_arc_challenge_0_third_llama_1p82G_refined_web_seed_10_eval_150_parquet", "data_files": [{"split": "third_llama_1p82G_refined_web_seed_10_eval_150", "path": ["/details_harness\|arc:challenge\|0_third-llama-1p82G-refined-web-seed-10_eval_150.parquet"]}]}, {"config_name": 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{"split": "third_llama_1p82G_the_pile_eval_1600", "path": ["results_third-llama-1p82G-the-pile_eval_1600.parquet"]}, {"split": "third_llama_1p82G_the_pile_eval_1650", "path": ["results_third-llama-1p82G-the-pile_eval_1650.parquet"]}, {"split": "third_llama_1p82G_the_pile_eval_1700", "path": ["results_third-llama-1p82G-the-pile_eval_1700.parquet"]}, {"split": "third_llama_1p82G_the_pile_eval_1750", "path": ["results_third-llama-1p82G-the-pile_eval_1750.parquet"]}, {"split": "third_llama_1p82G_the_pile_eval_1800", "path": ["results_third-llama-1p82G-the-pile_eval_1800.parquet"]}, {"split": "third_llama_1p82G_the_pile_eval_1850", "path": ["results_third-llama-1p82G-the-pile_eval_1850.parquet"]}, {"split": "third_llama_1p82G_the_pile_eval_1900", "path": ["results_third-llama-1p82G-the-pile_eval_1900.parquet"]}, {"split": "third_llama_1p82G_the_pile_eval_1950", "path": ["results_third-llama-1p82G-the-pile_eval_1950.parquet"]}, {"split": "third_llama_1p82G_the_pile_eval_200", "path": 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{"split": "third_llama_1p82G_the_pile_eval_2400", "path": ["results_third-llama-1p82G-the-pile_eval_2400.parquet"]}, {"split": "third_llama_1p82G_the_pile_eval_2450", "path": ["results_third-llama-1p82G-the-pile_eval_2450.parquet"]}, {"split": "third_llama_1p82G_the_pile_eval_250", "path": ["results_third-llama-1p82G-the-pile_eval_250.parquet"]}, {"split": "third_llama_1p82G_the_pile_eval_2500", "path": ["results_third-llama-1p82G-the-pile_eval_2500.parquet"]}, {"split": "third_llama_1p82G_the_pile_eval_2550", "path": ["results_third-llama-1p82G-the-pile_eval_2550.parquet"]}, {"split": "third_llama_1p82G_the_pile_eval_2600", "path": ["results_third-llama-1p82G-the-pile_eval_2600.parquet"]}, {"split": "third_llama_1p82G_the_pile_eval_2650", "path": ["results_third-llama-1p82G-the-pile_eval_2650.parquet"]}, {"split": "third_llama_1p82G_the_pile_eval_2700", "path": ["results_third-llama-1p82G-the-pile_eval_2700.parquet"]}, {"split": "third_llama_1p82G_the_pile_eval_2750", "path": ["results_third-llama-1p82G-the-pile_eval_2750.parquet"]}, {"split": "third_llama_1p82G_the_pile_eval_2800", "path": ["results_third-llama-1p82G-the-pile_eval_2800.parquet"]}, {"split": "third_llama_1p82G_the_pile_eval_2900", "path": ["results_third-llama-1p82G-the-pile_eval_2900.parquet"]}, {"split": "third_llama_1p82G_the_pile_eval_2950", "path": ["results_third-llama-1p82G-the-pile_eval_2950.parquet"]}, {"split": "third_llama_1p82G_the_pile_eval_300", "path": ["results_third-llama-1p82G-the-pile_eval_300.parquet"]}, {"split": "third_llama_1p82G_the_pile_eval_3000", "path": ["results_third-llama-1p82G-the-pile_eval_3000.parquet"]}, {"split": "third_llama_1p82G_the_pile_eval_3050", "path": ["results_third-llama-1p82G-the-pile_eval_3050.parquet"]}, {"split": "third_llama_1p82G_the_pile_eval_3100", "path": ["results_third-llama-1p82G-the-pile_eval_3100.parquet"]}, {"split": "third_llama_1p82G_the_pile_eval_3150", "path": ["results_third-llama-1p82G-the-pile_eval_3150.parquet"]}, {"split": "third_llama_1p82G_the_pile_eval_3200", "path": ["results_third-llama-1p82G-the-pile_eval_3200.parquet"]}, {"split": "third_llama_1p82G_the_pile_eval_3250", "path": ["results_third-llama-1p82G-the-pile_eval_3250.parquet"]}, {"split": "third_llama_1p82G_the_pile_eval_350", "path": ["results_third-llama-1p82G-the-pile_eval_350.parquet"]}, {"split": "third_llama_1p82G_the_pile_eval_400", "path": ["results_third-llama-1p82G-the-pile_eval_400.parquet"]}, {"split": "third_llama_1p82G_the_pile_eval_450", "path": ["results_third-llama-1p82G-the-pile_eval_450.parquet"]}, {"split": "third_llama_1p82G_the_pile_eval_50", "path": ["results_third-llama-1p82G-the-pile_eval_50.parquet"]}, {"split": "third_llama_1p82G_the_pile_eval_500", "path": ["results_third-llama-1p82G-the-pile_eval_500.parquet"]}, {"split": "third_llama_1p82G_the_pile_eval_600", "path": ["results_third-llama-1p82G-the-pile_eval_600.parquet"]}, {"split": "third_llama_1p82G_the_pile_eval_650", "path": ["results_third-llama-1p82G-the-pile_eval_650.parquet"]}, {"split": "third_llama_1p82G_the_pile_eval_700", "path": ["results_third-llama-1p82G-the-pile_eval_700.parquet"]}, {"split": "third_llama_1p82G_the_pile_eval_750", "path": ["results_third-llama-1p82G-the-pile_eval_750.parquet"]}, {"split": "third_llama_1p82G_the_pile_eval_800", "path": ["results_third-llama-1p82G-the-pile_eval_800.parquet"]}, {"split": "third_llama_1p82G_the_pile_eval_850", "path": ["results_third-llama-1p82G-the-pile_eval_850.parquet"]}, {"split": "third_llama_1p82G_the_pile_eval_900", "path": ["results_third-llama-1p82G-the-pile_eval_900.parquet"]}, {"split": "third_llama_1p82G_the_pile_eval_950", "path": ["results_third-llama-1p82G-the-pile_eval_950.parquet"]}]}]}	2023-10-07T12:08:49+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of 1000 ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model 1000 on the Open LLM Leaderboard. The dataset is composed of 3074 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 903 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run (note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of 1000", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model 1000 on the Open LLM Leaderboard.\n\nThe dataset is composed of 3074 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 903 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run (note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of 1000", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model 1000 on the Open LLM Leaderboard.\n\nThe dataset is composed of 3074 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 903 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run (note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 10, 31, 160, 56, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of 1000## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model 1000 on the Open LLM Leaderboard.\n\nThe dataset is composed of 3074 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 903 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run (note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
52a433dce00076a5a86fd3b64918feec09d4c82d	# Dataset Card for "arabictext" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	gagan3012/arabictext	[ "region:us" ]	2023-10-04T20:12:58+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "check_char_repetition_criteria", "dtype": "float64"}, {"name": "check_flagged_words_criteria", "dtype": "float64"}, {"name": "meta_data", "dtype": "string"}, {"name": "__id__", "dtype": "int64"}, {"name": "duplicate", "dtype": "bool"}, {"name": "char", "dtype": "int64"}, {"name": "Arabic_char", "dtype": "int64"}, {"name": "latin_char", "dtype": "int64"}, {"name": "numbers_char", "dtype": "int64"}, {"name": "puc_char", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 471530, "num_examples": 1000}], "download_size": 231211, "dataset_size": 471530}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-10-04T22:45:49+00:00	[]	[]	TAGS #region-us	# Dataset Card for "arabictext" More Information needed	[ "# Dataset Card for \"arabictext\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"arabictext\"\n\nMore Information needed" ]	[ 6, 13 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"arabictext\"\n\nMore Information needed" ]
d7e4eee2d71b57279b83fb2c601265aabfd303a5	# Bangumi Image Base of Minami-ke This is the image base of bangumi Minami-ke, we detected 41 characters, 5525 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 \| 993 \| [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 \| 203 \| [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 \| 231 \| [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 \| 39 \| [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 \| 88 \| [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 \| 112 \| [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 \| 26 \| [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 \| 128 \| [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 \| 35 \| [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 \| 94 \| [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 \| 173 \| [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 \| 11 \| [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 \| 1139 \| [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 \| 71 \| [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 \| 102 \| [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 \| 25 \| [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 \| 38 \| [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 \| 165 \| [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 \| 48 \| [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 \| 193 \| [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 \| 327 \| [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 \| 135 \| [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 \| 87 \| [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 \| 41 \| [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 \| 239 \| [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 \| 73 \| [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 \| 35 \| [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 \| 165 \| [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 \| 66 \| [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 \| 22 \| [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 \| 23 \| [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 \| 18 \| [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 \| 31 \| [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 \| 19 \| [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 \| 27 \| [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 \| 66 \| [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 \| 8 \| [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 \| 8 \| [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 \| 31 \| [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) \| \| noise \| 137 \| [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) \|	BangumiBase/minamike	[ "size_categories:1K<n<10K", "license:mit", "art", "region:us" ]	2023-10-04T20:20:25+00:00	{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}	2023-10-04T22:38:41+00:00	[]	[]	TAGS #size_categories-1K<n<10K #license-mit #art #region-us	Bangumi Image Base of Minami-ke =============================== This is the image base of bangumi Minami-ke, we detected 41 characters, 5525 images in total. The full dataset is here. 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:	[]	[ "TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]	[ 25 ]	[ "passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]
dcf110eb785f4064563b53b200ffacee9f97496d	# Dataset Card for "graph_tt" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	trunks/graph_tt	[ "region:us" ]	2023-10-04T20:39:40+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1562276.0, "num_examples": 36}], "download_size": 1439813, "dataset_size": 1562276.0}}	2023-10-12T08:46:25+00:00	[]	[]	TAGS #region-us	# Dataset Card for "graph_tt" More Information needed	[ "# Dataset Card for \"graph_tt\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"graph_tt\"\n\nMore Information needed" ]	[ 6, 13 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"graph_tt\"\n\nMore Information needed" ]
ff57f4daaf00e979bf956e79b5c7f01454a11a7c	# Dataset Card for "finance-financialmodelingprep-news" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	NickyNicky/finance-financialmodelingprep-news	[ "region:us" ]	2023-10-04T20:40:03+00:00	{"dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "content", "dtype": "string"}, {"name": "tickers", "dtype": "string"}, {"name": "image", "dtype": "string"}, {"name": "link", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "site", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3695783, "num_examples": 3028}], "download_size": 1619683, "dataset_size": 3695783}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-10-04T20:40:08+00:00	[]	[]	TAGS #region-us	# Dataset Card for "finance-financialmodelingprep-news" More Information needed	[ "# Dataset Card for \"finance-financialmodelingprep-news\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"finance-financialmodelingprep-news\"\n\nMore Information needed" ]	[ 6, 21 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"finance-financialmodelingprep-news\"\n\nMore Information needed" ]
2475aef4e50078d1bb2d30073d9a18aeb2c45000	This hosts the Wikipedia phrase data used for training the generic phrase generation model in [General-to-Specific Transfer Labeling for Domain Adaptable Keyphrase Generation](https://arxiv.org/abs/2208.09606). wiki-20210521-phrase.tar.gz contains all sharded files. A few example files are provided (e.g. wiki_00.json). ``` @article{meng2022transfer, title={General-to-Specific Transfer Labeling for Domain Adaptable Keyphrase Generation}, author={Meng, Rui and Wang, Tong and Yuan, Xingdi and Zhou, Yingbo and He, Daqing}, journal={ACL 2023 Findings, Long Paper}, year={2023} } ```	memray/wiki-phrase-2021	[ "license:cc-by-sa-3.0", "arxiv:2208.09606", "region:us" ]	2023-10-04T20:58:42+00:00	{"license": "cc-by-sa-3.0"}	2023-10-04T23:23:40+00:00	[ "2208.09606" ]	[]	TAGS #license-cc-by-sa-3.0 #arxiv-2208.09606 #region-us	This hosts the Wikipedia phrase data used for training the generic phrase generation model in General-to-Specific Transfer Labeling for Domain Adaptable Keyphrase Generation. URL contains all sharded files. A few example files are provided (e.g. wiki_00.json).	[]	[ "TAGS\n#license-cc-by-sa-3.0 #arxiv-2208.09606 #region-us \n" ]	[ 25 ]	[ "passage: TAGS\n#license-cc-by-sa-3.0 #arxiv-2208.09606 #region-us \n" ]
5893a93610c919c9f7203a7d0f915492a3d2b80a	### Dataset Summary - Stories and annotations for administering the Torrance Test for Creative Writing (TTCW) - Each row in the dataset refers to one specific story, with each column representing the annotations for that specific TTCW category. Each cell contains the story info, information about the TTCW category including the prompt and annotations from 3 different experts on administering the specific TTCW test for the given story. ### More info - Repo: https://github.com/salesforce/creativity_eval/tree/main - Paper: https://arxiv.org/abs/2309.14556 ### Languages English	Salesforce/ttcw_creativity_eval	[ "size_categories:1K<n<10K", "language:en", "license:bsd-3-clause", "arxiv:2309.14556", "region:us" ]	2023-10-04T21:12:55+00:00	{"language": ["en"], "license": "bsd-3-clause", "size_categories": ["1K<n<10K"]}	2023-10-04T21:26:00+00:00	[ "2309.14556" ]	[ "en" ]	TAGS #size_categories-1K<n<10K #language-English #license-bsd-3-clause #arxiv-2309.14556 #region-us	### Dataset Summary - Stories and annotations for administering the Torrance Test for Creative Writing (TTCW) - Each row in the dataset refers to one specific story, with each column representing the annotations for that specific TTCW category. Each cell contains the story info, information about the TTCW category including the prompt and annotations from 3 different experts on administering the specific TTCW test for the given story. ### More info - Repo: URL - Paper: URL ### Languages English	[ "### Dataset Summary\n- Stories and annotations for administering the Torrance Test for Creative Writing (TTCW) \n- Each row in the dataset refers to one specific story, with each column representing the annotations for that specific TTCW category. Each cell contains the story info, information about the TTCW category including the prompt and annotations from 3 different experts on administering the specific TTCW test for the given story.", "### More info\n\n- Repo: URL\n- Paper: URL", "### Languages\n\nEnglish" ]	[ "TAGS\n#size_categories-1K<n<10K #language-English #license-bsd-3-clause #arxiv-2309.14556 #region-us \n", "### Dataset Summary\n- Stories and annotations for administering the Torrance Test for Creative Writing (TTCW) \n- Each row in the dataset refers to one specific story, with each column representing the annotations for that specific TTCW category. Each cell contains the story info, information about the TTCW category including the prompt and annotations from 3 different experts on administering the specific TTCW test for the given story.", "### More info\n\n- Repo: URL\n- Paper: URL", "### Languages\n\nEnglish" ]	[ 40, 99, 13, 5 ]	[ "passage: TAGS\n#size_categories-1K<n<10K #language-English #license-bsd-3-clause #arxiv-2309.14556 #region-us \n### Dataset Summary\n- Stories and annotations for administering the Torrance Test for Creative Writing (TTCW) \n- Each row in the dataset refers to one specific story, with each column representing the annotations for that specific TTCW category. Each cell contains the story info, information about the TTCW category including the prompt and annotations from 3 different experts on administering the specific TTCW test for the given story.### More info\n\n- Repo: URL\n- Paper: URL### Languages\n\nEnglish" ]
26c0c48e2402e7687bc5a92cb874a0e768230a66	# Dataset Card for "rlhf_reward_single_round-chinese-zhtw" 基於 anthropic 的 [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://arxiv.org/abs/2204.05862) 論文開源的關於有助和無害的人類偏好資料。這些數據旨在為後續的 RLHF 訓練訓練偏好（或獎勵）模型。 ## 來源資料集本資料集來自 [beyond/rlhf-reward-single-round-trans_chinese](https://huggingface.co/datasets/beyond/rlhf-reward-single-round-trans_chinese), 并使用 OpenCC 來進行簡繁轉換。	erhwenkuo/rlhf_reward_single_round-chinese-zhtw	[ "task_categories:conversational", "size_categories:10K<n<100K", "language:zh", "arxiv:2204.05862", "region:us" ]	2023-10-04T21:25:52+00:00	{"language": ["zh"], "size_categories": ["10K<n<100K"], "task_categories": ["conversational"], "dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "chosen", "dtype": "string"}, {"name": "rejected", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 12143678, "num_examples": 19862}, {"name": "test", "num_bytes": 3118994, "num_examples": 4996}], "download_size": 10724182, "dataset_size": 15262672}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-"}, {"split": "test", "path": "data/test-"}]}]}	2023-10-04T21:48:40+00:00	[ "2204.05862" ]	[ "zh" ]	TAGS #task_categories-conversational #size_categories-10K<n<100K #language-Chinese #arxiv-2204.05862 #region-us	# Dataset Card for "rlhf_reward_single_round-chinese-zhtw" 基於 anthropic 的 Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback 論文開源的關於有助和無害的人類偏好資料。這些數據旨在為後續的 RLHF 訓練訓練偏好（或獎勵）模型。 ## 來源資料集本資料集來自 beyond/rlhf-reward-single-round-trans_chinese, 并使用 OpenCC 來進行簡繁轉換。	[ "# Dataset Card for \"rlhf_reward_single_round-chinese-zhtw\"\n\n基於 anthropic 的 Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback 論文開源的關於有助和無害的人類偏好資料。\n\n這些數據旨在為後續的 RLHF 訓練訓練偏好（或獎勵）模型。", "## 來源資料集\n\n本資料集來自 beyond/rlhf-reward-single-round-trans_chinese, 并使用 OpenCC 來進行簡繁轉換。" ]	[ "TAGS\n#task_categories-conversational #size_categories-10K<n<100K #language-Chinese #arxiv-2204.05862 #region-us \n", "# Dataset Card for \"rlhf_reward_single_round-chinese-zhtw\"\n\n基於 anthropic 的 Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback 論文開源的關於有助和無害的人類偏好資料。\n\n這些數據旨在為後續的 RLHF 訓練訓練偏好（或獎勵）模型。", "## 來源資料集\n\n本資料集來自 beyond/rlhf-reward-single-round-trans_chinese, 并使用 OpenCC 來進行簡繁轉換。" ]	[ 42, 86, 40 ]	[ "passage: TAGS\n#task_categories-conversational #size_categories-10K<n<100K #language-Chinese #arxiv-2204.05862 #region-us \n# Dataset Card for \"rlhf_reward_single_round-chinese-zhtw\"\n\n基於 anthropic 的 Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback 論文開源的關於有助和無害的人類偏好資料。\n\n這些數據旨在為後續的 RLHF 訓練訓練偏好（或獎勵）模型。## 來源資料集\n\n本資料集來自 beyond/rlhf-reward-single-round-trans_chinese, 并使用 OpenCC 來進行簡繁轉換。" ]
7b4fbf0794f486e01b60d8fd188b534547271c31	# Dataset Card for "LSUN_bedroom_VQA_feliu" Images are a subset of the LSUN-Bedroom dataset. [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) The attributes are binary answers to the following questions: - Is the floor visible in the image? - Does the room have a window? - Is there more than one bed? - Does the room have natural light? - Is there a carpet in the floor? - Is it a classy room? - Is it a hotel room? - Is there at least one person in the room? - Are there more than one people in the room? - Is it an expensive room? - Does the room have a painting the wall? - Is the room nicely decorated? - Does the room have a mirror? - Are the room lights on? - Are the bedsheets made? - Is there a visible closet? - Is the room tidy? - Is there an animal in the room? - Is the wall painted in red? - Is the wall painted in blue? - Is the wall painted in white? - Is the wall painted in a dark color? - Is the wall painted in green? - Are the bedsheets red? - Are the bedsheets blue? - Are the bedsheets white? - Are the bedsheets dark? - Are the bedsheets green? - Is there a kid in the room? - Is the bed big enough for two people? - Does the room have a telephone? - Does the room seem cold? - Are there plants visible from the window? - Are there decorative plants inside the room? - Does the room have any photo frame as decoration? - Does the room have a TV? - Does the room have a radio in it? - Is there any luggage in the room? - Is there a visible door? - Is there a radiator in the room? - Is the bathroom visible in the image? - Does the bed have a quilt? - Does the picture have a watermark? - Is the bed covered in a duvet? - Is there more than one bedside table? - Does the bedside table have a nightstand light? - Does the bed have a mosquito net? - Does the room access a private terrace? - Is the floor wooden? - Are the walls made of wood?	fformosa/LSUN_bedroom_VQA	[ "task_categories:visual-question-answering", "task_categories:text-to-image", "task_categories:question-answering", "size_categories:100K<n<1M", "region:us" ]	2023-10-04T21:27:05+00:00	{"size_categories": ["100K<n<1M"], "task_categories": ["visual-question-answering", "text-to-image", "question-answering"], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "image_id", "dtype": "int64"}, {"name": "attributes", "sequence": "string"}, {"name": "size", "sequence": "int64"}, {"name": "proportion", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 4858959064, "num_examples": 303125}], "download_size": 4766067864, "dataset_size": 4858959064}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2024-01-22T22:19:26+00:00	[]	[]	TAGS #task_categories-visual-question-answering #task_categories-text-to-image #task_categories-question-answering #size_categories-100K<n<1M #region-us	# Dataset Card for "LSUN_bedroom_VQA_feliu" Images are a subset of the LSUN-Bedroom dataset. More Information needed The attributes are binary answers to the following questions: - Is the floor visible in the image? - Does the room have a window? - Is there more than one bed? - Does the room have natural light? - Is there a carpet in the floor? - Is it a classy room? - Is it a hotel room? - Is there at least one person in the room? - Are there more than one people in the room? - Is it an expensive room? - Does the room have a painting the wall? - Is the room nicely decorated? - Does the room have a mirror? - Are the room lights on? - Are the bedsheets made? - Is there a visible closet? - Is the room tidy? - Is there an animal in the room? - Is the wall painted in red? - Is the wall painted in blue? - Is the wall painted in white? - Is the wall painted in a dark color? - Is the wall painted in green? - Are the bedsheets red? - Are the bedsheets blue? - Are the bedsheets white? - Are the bedsheets dark? - Are the bedsheets green? - Is there a kid in the room? - Is the bed big enough for two people? - Does the room have a telephone? - Does the room seem cold? - Are there plants visible from the window? - Are there decorative plants inside the room? - Does the room have any photo frame as decoration? - Does the room have a TV? - Does the room have a radio in it? - Is there any luggage in the room? - Is there a visible door? - Is there a radiator in the room? - Is the bathroom visible in the image? - Does the bed have a quilt? - Does the picture have a watermark? - Is the bed covered in a duvet? - Is there more than one bedside table? - Does the bedside table have a nightstand light? - Does the bed have a mosquito net? - Does the room access a private terrace? - Is the floor wooden? - Are the walls made of wood?	[ "# Dataset Card for \"LSUN_bedroom_VQA_feliu\"\n\nImages are a subset of the LSUN-Bedroom dataset.\n\nMore Information needed\n\nThe attributes are binary answers to the following questions:\n\n- Is the floor visible in the image?\n- Does the room have a window?\n- Is there more than one bed?\n- Does the room have natural light?\n- Is there a carpet in the floor?\n- Is it a classy room?\n- Is it a hotel room?\n- Is there at least one person in the room?\n- Are there more than one people in the room?\n- Is it an expensive room?\n- Does the room have a painting the wall?\n- Is the room nicely decorated?\n- Does the room have a mirror?\n- Are the room lights on?\n- Are the bedsheets made?\n- Is there a visible closet?\n- Is the room tidy?\n- Is there an animal in the room?\n- Is the wall painted in red?\n- Is the wall painted in blue?\n- Is the wall painted in white?\n- Is the wall painted in a dark color?\n- Is the wall painted in green?\n- Are the bedsheets red?\n- Are the bedsheets blue?\n- Are the bedsheets white?\n- Are the bedsheets dark?\n- Are the bedsheets green?\n- Is there a kid in the room?\n- Is the bed big enough for two people?\n- Does the room have a telephone?\n- Does the room seem cold?\n- Are there plants visible from the window?\n- Are there decorative plants inside the room?\n- Does the room have any photo frame as decoration?\n- Does the room have a TV?\n- Does the room have a radio in it?\n- Is there any luggage in the room?\n- Is there a visible door?\n- Is there a radiator in the room?\n- Is the bathroom visible in the image?\n- Does the bed have a quilt?\n- Does the picture have a watermark?\n- Is the bed covered in a duvet?\n- Is there more than one bedside table?\n- Does the bedside table have a nightstand light?\n- Does the bed have a mosquito net?\n- Does the room access a private terrace?\n- Is the floor wooden?\n- Are the walls made of wood?" ]	[ "TAGS\n#task_categories-visual-question-answering #task_categories-text-to-image #task_categories-question-answering #size_categories-100K<n<1M #region-us \n", "# Dataset Card for \"LSUN_bedroom_VQA_feliu\"\n\nImages are a subset of the LSUN-Bedroom dataset.\n\nMore Information needed\n\nThe attributes are binary answers to the following questions:\n\n- Is the floor visible in the image?\n- Does the room have a window?\n- Is there more than one bed?\n- Does the room have natural light?\n- Is there a carpet in the floor?\n- Is it a classy room?\n- Is it a hotel room?\n- Is there at least one person in the room?\n- Are there more than one people in the room?\n- Is it an expensive room?\n- Does the room have a painting the wall?\n- Is the room nicely decorated?\n- Does the room have a mirror?\n- Are the room lights on?\n- Are the bedsheets made?\n- Is there a visible closet?\n- Is the room tidy?\n- Is there an animal in the room?\n- Is the wall painted in red?\n- Is the wall painted in blue?\n- Is the wall painted in white?\n- Is the wall painted in a dark color?\n- Is the wall painted in green?\n- Are the bedsheets red?\n- Are the bedsheets blue?\n- Are the bedsheets white?\n- Are the bedsheets dark?\n- Are the bedsheets green?\n- Is there a kid in the room?\n- Is the bed big enough for two people?\n- Does the room have a telephone?\n- Does the room seem cold?\n- Are there plants visible from the window?\n- Are there decorative plants inside the room?\n- Does the room have any photo frame as decoration?\n- Does the room have a TV?\n- Does the room have a radio in it?\n- Is there any luggage in the room?\n- Is there a visible door?\n- Is there a radiator in the room?\n- Is the bathroom visible in the image?\n- Does the bed have a quilt?\n- Does the picture have a watermark?\n- Is the bed covered in a duvet?\n- Is there more than one bedside table?\n- Does the bedside table have a nightstand light?\n- Does the bed have a mosquito net?\n- Does the room access a private terrace?\n- Is the floor wooden?\n- Are the walls made of wood?" ]	[ 57, 498 ]	[ "passage: TAGS\n#task_categories-visual-question-answering #task_categories-text-to-image #task_categories-question-answering #size_categories-100K<n<1M #region-us \n" ]
f10a3f5ea41a72a066d25118da8623e78517612e	# Dataset Card for "re_train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	hippocrates/re_train	[ "region:us" ]	2023-10-04T21:30:18+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-"}, {"split": "valid", "path": "data/valid-"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 19219537, "num_examples": 3572}, {"name": "valid", "num_bytes": 1626844, "num_examples": 305}], "download_size": 1753501, "dataset_size": 20846381}}	2023-10-09T15:55:29+00:00	[]	[]	TAGS #region-us	# Dataset Card for "re_train" More Information needed	[ "# Dataset Card for \"re_train\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"re_train\"\n\nMore Information needed" ]	[ 6, 14 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"re_train\"\n\nMore Information needed" ]
54bc1f3d7524e578a992a2ff8e4d5256980219ff	# Dataset Card for "audio-sampler" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Isaacgv/audio-sampler	[ "region:us" ]	2023-10-04T21:30:52+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-"}, {"split": "test", "path": "data/test-"}]}], "dataset_info": {"features": [{"name": "audio", "dtype": "string"}, {"name": "input_features", "sequence": {"sequence": {"sequence": "float32"}}}, {"name": "labels", "sequence": "int64"}, {"name": "input_length", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 1921724, "num_examples": 2}, {"name": "test", "num_bytes": 960882, "num_examples": 1}], "download_size": 0, "dataset_size": 2882606}}	2023-10-04T22:00:40+00:00	[]	[]	TAGS #region-us	# Dataset Card for "audio-sampler" More Information needed	[ "# Dataset Card for \"audio-sampler\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"audio-sampler\"\n\nMore Information needed" ]	[ 6, 15 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"audio-sampler\"\n\nMore Information needed" ]
a3806234a827fbebecb26fb7e2eabc1fbb33dbc0	# Dataset Card for "textSummary" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Isaacgv/textSummary	[ "region:us" ]	2023-10-04T21:37:29+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-"}, {"split": "test", "path": "data/test-"}]}], "dataset_info": {"features": [{"name": "audio", "dtype": "string"}, {"name": "input_features", "sequence": {"sequence": {"sequence": "float32"}}}, {"name": "labels", "sequence": "int64"}, {"name": "input_length", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 7689112, "num_examples": 8}, {"name": "test", "num_bytes": 1922012, "num_examples": 2}], "download_size": 1337668, "dataset_size": 9611124}}	2023-10-05T05:57:06+00:00	[]	[]	TAGS #region-us	# Dataset Card for "textSummary" More Information needed	[ "# Dataset Card for \"textSummary\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"textSummary\"\n\nMore Information needed" ]	[ 6, 14 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"textSummary\"\n\nMore Information needed" ]
8bd385200d0306d1083ad4437c04fc51fc0767ac	# Este será un dataset en Español y Català	Vicent3/Enye	[ "task_categories:translation", "task_categories:summarization", "task_categories:conversational", "task_categories:text-generation", "task_categories:text2text-generation", "task_categories:automatic-speech-recognition", "task_categories:text-classification", "language:es", "language:ca", "license:agpl-3.0", "region:us" ]	2023-10-04T21:41:29+00:00	{"language": ["es", "ca"], "license": "agpl-3.0", "task_categories": ["translation", "summarization", "conversational", "text-generation", "text2text-generation", "automatic-speech-recognition", "text-classification"], "pretty_name": "Enye"}	2023-10-04T21:42:51+00:00	[]	[ "es", "ca" ]	TAGS #task_categories-translation #task_categories-summarization #task_categories-conversational #task_categories-text-generation #task_categories-text2text-generation #task_categories-automatic-speech-recognition #task_categories-text-classification #language-Spanish #language-Catalan #license-agpl-3.0 #region-us	# Este será un dataset en Español y Català	[ "# Este será un dataset en Español y Català" ]	[ "TAGS\n#task_categories-translation #task_categories-summarization #task_categories-conversational #task_categories-text-generation #task_categories-text2text-generation #task_categories-automatic-speech-recognition #task_categories-text-classification #language-Spanish #language-Catalan #license-agpl-3.0 #region-us \n", "# Este será un dataset en Español y Català" ]	[ 104, 10 ]	[ "passage: TAGS\n#task_categories-translation #task_categories-summarization #task_categories-conversational #task_categories-text-generation #task_categories-text2text-generation #task_categories-automatic-speech-recognition #task_categories-text-classification #language-Spanish #language-Catalan #license-agpl-3.0 #region-us \n# Este será un dataset en Español y Català" ]
f8dd9c22ce3d58a951cfb75130d47c829bea7e2a	# Dataset Card for "processed_bert_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Tural/processed_bert_dataset	[ "region:us" ]	2023-10-04T21:54:22+00:00	{"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": 24943076880, "num_examples": 27349865}], "download_size": 5901536405, "dataset_size": 24943076880}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-10-04T22:04:01+00:00	[]	[]	TAGS #region-us	# Dataset Card for "processed_bert_dataset" More Information needed	[ "# Dataset Card for \"processed_bert_dataset\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"processed_bert_dataset\"\n\nMore Information needed" ]	[ 6, 17 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"processed_bert_dataset\"\n\nMore Information needed" ]
20d3185331e8571b19360f9dd0a78defba1cdbf8	[![](https://dcbadge.vercel.app/api/server/kW9nBQErGe?compact=true&style=flat)](https://discord.gg/kW9nBQErGe) <img src="https://approximatelabs.com/tablib.png" width="800" /> # TabLib A minimally-preprocessed dataset of 627M tables (69 TiB) extracted from HTML, PDF, CSV, TSV, Excel, and SQLite files from GitHub and Common Crawl. This includes 867B tokens of "context metadata": each table includes provenance information and table context such as filename, text before/after, HTML metadata, etc. A smaller 0.1% sample of this dataset can be found [here](https://huggingface.co/datasets/approximatelabs/tablib-v1-sample). For more information, read the [paper](https://arxiv.org/abs/2310.07875) & [announcement blog](https://approximatelabs.com/blog/tablib). # Dataset Details ## Sources * GitHub: nearly all public GitHub repositories * Common Crawl: the `CC-MAIN-2023-23` crawl ## Reading Tables Tables are stored as serialized Arrow bytes in the `arrow_bytes` column. To read these, you will need to deserialize the bytes: ```python import datasets import pyarrow as pa # load a single file of the dataset ds = datasets.load_dataset( 'approximatelabs/tablib-v1-full', data_files='tablib/job=github_000005/batch=000001/part=000001/manifest.parquet', token='...', ) df = ds['train'].to_pandas() tables = [pa.RecordBatchStreamReader(b).read_all() for b in df['arrow_bytes']] ``` ## Licensing This dataset is intended for research use only. For specific licensing information, refer to the license of the specific datum being used. # Contact If you have any questions, comments, or concerns about licensing, pii, etc. please contact using [this form](https://forms.gle/C74VTWP7L78QDVR67). # Approximate Labs TabLib is a project from Approximate Labs. Find us on [Twitter](https://twitter.com/approximatelabs), [Github](https://github.com/approximatelabs), [Linkedin](https://www.linkedin.com/company/approximate-labs), and [Discord](https://discord.gg/kW9nBQErGe). # Citations If you use TabLib for any of your research, please cite the TabLib paper: ``` @misc{eggert2023tablib, title={TabLib: A Dataset of 627M Tables with Context}, author={Gus Eggert and Kevin Huo and Mike Biven and Justin Waugh}, year={2023}, eprint={2310.07875}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```	approximatelabs/tablib-v1-full	[ "size_categories:100M<n<1B", "license:other", "arxiv:2310.07875", "region:us" ]	2023-10-04T21:54:52+00:00	{"license": "other", "size_categories": ["100M<n<1B"], "pretty_name": "TabLib", "extra_gated_prompt": "Accessing the full TabLib dataset requires permission from Approximate Labs. To request access, please provide the following details and Approximate Labs will process your request as soon as possible.", "extra_gated_fields": {"Full Name": "text", "Email": "text", "Country": "text", "Organization": "text", "What do you intend to do with this dataset?": "text", "I agree to abide by the license requirements of the data contained in TabLib": "checkbox"}}	2023-10-13T21:33:46+00:00	[ "2310.07875" ]	[]	TAGS #size_categories-100M<n<1B #license-other #arxiv-2310.07875 #region-us	![](URL <img src="URL width="800" /> # TabLib A minimally-preprocessed dataset of 627M tables (69 TiB) extracted from HTML, PDF, CSV, TSV, Excel, and SQLite files from GitHub and Common Crawl. This includes 867B tokens of "context metadata": each table includes provenance information and table context such as filename, text before/after, HTML metadata, etc. A smaller 0.1% sample of this dataset can be found here. For more information, read the paper & announcement blog. # Dataset Details ## Sources * GitHub: nearly all public GitHub repositories * Common Crawl: the 'CC-MAIN-2023-23' crawl ## Reading Tables Tables are stored as serialized Arrow bytes in the 'arrow_bytes' column. To read these, you will need to deserialize the bytes: ## Licensing This dataset is intended for research use only. For specific licensing information, refer to the license of the specific datum being used. # Contact If you have any questions, comments, or concerns about licensing, pii, etc. please contact using this form. # Approximate Labs TabLib is a project from Approximate Labs. Find us on Twitter, Github, Linkedin, and Discord. s If you use TabLib for any of your research, please cite the TabLib paper:	[ "# TabLib\n\nA minimally-preprocessed dataset of 627M tables (69 TiB) extracted from HTML, PDF, CSV, TSV, Excel, and SQLite files from GitHub and Common Crawl.\n\nThis includes 867B tokens of \"context metadata\": each table includes provenance information and table context such as filename, text before/after, HTML metadata, etc.\n\nA smaller 0.1% sample of this dataset can be found here.\n\nFor more information, read the paper & announcement blog.", "# Dataset Details", "## Sources\n* GitHub: nearly all public GitHub repositories\n* Common Crawl: the 'CC-MAIN-2023-23' crawl", "## Reading Tables\nTables are stored as serialized Arrow bytes in the 'arrow_bytes' column. To read these, you will need to deserialize the bytes:", "## Licensing\nThis dataset is intended for research use only.\n\nFor specific licensing information, refer to the license of the specific datum being used.", "# Contact\nIf you have any questions, comments, or concerns about licensing, pii, etc. please contact using this form.", "# Approximate Labs\nTabLib is a project from Approximate Labs. Find us on Twitter, Github, Linkedin, and Discord.\n\ns\nIf you use TabLib for any of your research, please cite the TabLib paper:" ]	[ "TAGS\n#size_categories-100M<n<1B #license-other #arxiv-2310.07875 #region-us \n", "# TabLib\n\nA minimally-preprocessed dataset of 627M tables (69 TiB) extracted from HTML, PDF, CSV, TSV, Excel, and SQLite files from GitHub and Common Crawl.\n\nThis includes 867B tokens of \"context metadata\": each table includes provenance information and table context such as filename, text before/after, HTML metadata, etc.\n\nA smaller 0.1% sample of this dataset can be found here.\n\nFor more information, read the paper & announcement blog.", "# Dataset Details", "## Sources\n* GitHub: nearly all public GitHub repositories\n* Common Crawl: the 'CC-MAIN-2023-23' crawl", "## Reading Tables\nTables are stored as serialized Arrow bytes in the 'arrow_bytes' column. To read these, you will need to deserialize the bytes:", "## Licensing\nThis dataset is intended for research use only.\n\nFor specific licensing information, refer to the license of the specific datum being used.", "# Contact\nIf you have any questions, comments, or concerns about licensing, pii, etc. please contact using this form.", "# Approximate Labs\nTabLib is a project from Approximate Labs. Find us on Twitter, Github, Linkedin, and Discord.\n\ns\nIf you use TabLib for any of your research, please cite the TabLib paper:" ]	[ 32, 119, 4, 33, 43, 32, 29, 56 ]	[ "passage: TAGS\n#size_categories-100M<n<1B #license-other #arxiv-2310.07875 #region-us \n# TabLib\n\nA minimally-preprocessed dataset of 627M tables (69 TiB) extracted from HTML, PDF, CSV, TSV, Excel, and SQLite files from GitHub and Common Crawl.\n\nThis includes 867B tokens of \"context metadata\": each table includes provenance information and table context such as filename, text before/after, HTML metadata, etc.\n\nA smaller 0.1% sample of this dataset can be found here.\n\nFor more information, read the paper & announcement blog.# Dataset Details## Sources\n* GitHub: nearly all public GitHub repositories\n* Common Crawl: the 'CC-MAIN-2023-23' crawl## Reading Tables\nTables are stored as serialized Arrow bytes in the 'arrow_bytes' column. To read these, you will need to deserialize the bytes:## Licensing\nThis dataset is intended for research use only.\n\nFor specific licensing information, refer to the license of the specific datum being used.# Contact\nIf you have any questions, comments, or concerns about licensing, pii, etc. please contact using this form.# Approximate Labs\nTabLib is a project from Approximate Labs. Find us on Twitter, Github, Linkedin, and Discord.\n\ns\nIf you use TabLib for any of your research, please cite the TabLib paper:" ]
d90eee897a15fcfcb86e738323114ea49bca0297	# Dataset Card for "ncbi_genbank_part_5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Hack90/ncbi_genbank_part_5	[ "region:us" ]	2023-10-04T22:02:55+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "features", "dtype": "int64"}, {"name": "seq_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 20489073390, "num_examples": 33130}], "download_size": 9505723044, "dataset_size": 20489073390}}	2023-10-05T22:55:40+00:00	[]	[]	TAGS #region-us	# Dataset Card for "ncbi_genbank_part_5" More Information needed	[ "# Dataset Card for \"ncbi_genbank_part_5\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"ncbi_genbank_part_5\"\n\nMore Information needed" ]	[ 6, 19 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"ncbi_genbank_part_5\"\n\nMore Information needed" ]
643b4aef43455ee91b969d8b1106aa1696542273	# Dataset Card for "hh_rlhf-chinese-zhtw" 此數據集合併了下列的資料: 1. 關於有用且無害的人類偏好數據，來自 [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://arxiv.org/abs/2204.05862)。這些數據旨在為後續 RLHF 訓練訓練偏好（或獎勵）模型。這些資料不用於對話代理人的監督訓練。根據這些資料訓練對話代理可能會導致有害的模型，這種情況應該避免。 3. 人工生成並帶註釋的紅隊對話，來自[減少危害的紅隊語言模型：方法、擴展行為和經驗教訓](https://www.anthropic.com/red_teaming.pdf)。這些數據旨在了解眾包紅隊如何建模以及哪些類型的紅隊攻擊成功或失敗。這些數據不用於微調或偏好建模（使用上面的數據進行偏好建模）。這些數據是從上述無害偏好建模數據導出的對話的完整轉錄本，其中僅將所選響應合併到整個轉錄本中。此外，文字記錄也透過人工和自動測量來標註整個對話的危害程度。 ## 特別注意數據（尤其是無害偏好數據和紅隊數據）包含可能令人反感或令人不安的內容。主題包括但不限於歧視性語言以及對虐待、暴力、自殘、剝削和其他可能令人不安的主題的討論。請僅根據您個人的風險承受能力來處理資料。這些數據旨在用於研究目的，特別是可以使模型「減少」危害的研究。如上所述，這些資料「不」該用於訓練對話代理，因為這可能會導致有害的模型行為。 ## 原始數據原作者在 GitHub 上託管此資料集：https://github.com/anthropics/hh-rlhf ## 轉譯來源本數據集來自 [dikw/hh_rlhf_cn](https://huggingface.co/datasets/dikw/hh_rlhf_cn) 并使用了 OpenCC 來進行簡繁轉譚。基於 Anthropic 論文 Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback 開源的 helpful 和 harmless 數據，使用翻譯工具進行了翻譯。 - `hh_rlhf_train.jsonl` 合併中英文訓練集資料清洗過後 17 萬條 - `hh_rlhf_test.jsonl` 合併中英文測試集資料清洗過後 9 千條 - `harmless_base_cn_train.jsonl` 42394 條 - `harmless_base_cn_test.jsonl` 2304 條 - `helpful_base_cn_train.jsonl` 43722 條 - `helpful_base_cn_test.jsonl` 2346 條	erhwenkuo/hh_rlhf-chinese-zhtw	[ "task_categories:reinforcement-learning", "language:zh", "license:mit", "arxiv:2204.05862", "region:us" ]	2023-10-04T22:11:44+00:00	{"language": ["zh"], "license": "mit", "task_categories": ["reinforcement-learning"], "dataset_info": {"features": [{"name": "context", "list": [{"name": "role", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "chosen", "struct": [{"name": "role", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "rejected", "struct": [{"name": "role", "dtype": "string"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 302431699, "num_examples": 344317}], "download_size": 178897699, "dataset_size": 302431699}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-10-04T22:24:34+00:00	[ "2204.05862" ]	[ "zh" ]	TAGS #task_categories-reinforcement-learning #language-Chinese #license-mit #arxiv-2204.05862 #region-us	# Dataset Card for "hh_rlhf-chinese-zhtw" 此數據集合併了下列的資料: 1. 關於有用且無害的人類偏好數據，來自 Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback。這些數據旨在為後續 RLHF 訓練訓練偏好（或獎勵）模型。這些資料不用於對話代理人的監督訓練。根據這些資料訓練對話代理可能會導致有害的模型，這種情況應該避免。 3. 人工生成並帶註釋的紅隊對話，來自減少危害的紅隊語言模型：方法、擴展行為和經驗教訓。這些數據旨在了解眾包紅隊如何建模以及哪些類型的紅隊攻擊成功或失敗。這些數據不用於微調或偏好建模（使用上面的數據進行偏好建模）。這些數據是從上述無害偏好建模數據導出的對話的完整轉錄本，其中僅將所選響應合併到整個轉錄本中。此外，文字記錄也透過人工和自動測量來標註整個對話的危害程度。 ## 特別注意數據（尤其是無害偏好數據和紅隊數據）包含可能令人反感或令人不安的內容。主題包括但不限於歧視性語言以及對虐待、暴力、自殘、剝削和其他可能令人不安的主題的討論。請僅根據您個人的風險承受能力來處理資料。這些數據旨在用於研究目的，特別是可以使模型「減少」危害的研究。如上所述，這些資料「不」該用於訓練對話代理，因為這可能會導致有害的模型行為。 ## 原始數據原作者在 GitHub 上託管此資料集：URL ## 轉譯來源本數據集來自 dikw/hh_rlhf_cn 并使用了 OpenCC 來進行簡繁轉譚。基於 Anthropic 論文 Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback 開源的 helpful 和 harmless 數據，使用翻譯工具進行了翻譯。 - 'hh_rlhf_train.jsonl' 合併中英文訓練集資料清洗過後 17 萬條 - 'hh_rlhf_test.jsonl' 合併中英文測試集資料清洗過後 9 千條 - 'harmless_base_cn_train.jsonl' 42394 條 - 'harmless_base_cn_test.jsonl' 2304 條 - 'helpful_base_cn_train.jsonl' 43722 條 - 'helpful_base_cn_test.jsonl' 2346 條	[ "# Dataset Card for \"hh_rlhf-chinese-zhtw\"\n\n此數據集合併了下列的資料:\n\n1. 關於有用且無害的人類偏好數據，來自 Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback。這些數據旨在為後續 RLHF 訓練訓練偏好（或獎勵）模型。這些資料不用於對話代理人的監督訓練。根據這些資料訓練對話代理可能會導致有害的模型，這種情況應該避免。\n3. 人工生成並帶註釋的紅隊對話，來自減少危害的紅隊語言模型：方法、擴展行為和經驗教訓。這些數據旨在了解眾包紅隊如何建模以及哪些類型的紅隊攻擊成功或失敗。這些數據不用於微調或偏好建模（使用上面的數據進行偏好建模）。這些數據是從上述無害偏好建模數據導出的對話的完整轉錄本，其中僅將所選響應合併到整個轉錄本中。此外，文字記錄也透過人工和自動測量來標註整個對話的危害程度。", "## 特別注意\n\n數據（尤其是無害偏好數據和紅隊數據）包含可能令人反感或令人不安的內容。主題包括但不限於歧視性語言以及對虐待、暴力、自殘、剝削和其他可能令人不安的主題的討論。\n\n請僅根據您個人的風險承受能力來處理資料。這些數據旨在用於研究目的，特別是可以使模型「減少」危害的研究。\n\n如上所述，這些資料「不」該用於訓練對話代理，因為這可能會導致有害的模型行為。", "## 原始數據\n\n原作者在 GitHub 上託管此資料集：URL", "## 轉譯來源\n\n本數據集來自 dikw/hh_rlhf_cn 并使用了 OpenCC 來進行簡繁轉譚。\n\n基於 Anthropic 論文 Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback 開源的 helpful 和 harmless 數據，使用翻譯工具進行了翻譯。\n\n- 'hh_rlhf_train.jsonl' 合併中英文訓練集資料清洗過後 17 萬條\n- 'hh_rlhf_test.jsonl' 合併中英文測試集資料清洗過後 9 千條\n- 'harmless_base_cn_train.jsonl' 42394 條\n- 'harmless_base_cn_test.jsonl' 2304 條\n- 'helpful_base_cn_train.jsonl' 43722 條\n- 'helpful_base_cn_test.jsonl' 2346 條" ]	[ "TAGS\n#task_categories-reinforcement-learning #language-Chinese #license-mit #arxiv-2204.05862 #region-us \n", "# Dataset Card for \"hh_rlhf-chinese-zhtw\"\n\n此數據集合併了下列的資料:\n\n1. 關於有用且無害的人類偏好數據，來自 Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback。這些數據旨在為後續 RLHF 訓練訓練偏好（或獎勵）模型。這些資料不用於對話代理人的監督訓練。根據這些資料訓練對話代理可能會導致有害的模型，這種情況應該避免。\n3. 人工生成並帶註釋的紅隊對話，來自減少危害的紅隊語言模型：方法、擴展行為和經驗教訓。這些數據旨在了解眾包紅隊如何建模以及哪些類型的紅隊攻擊成功或失敗。這些數據不用於微調或偏好建模（使用上面的數據進行偏好建模）。這些數據是從上述無害偏好建模數據導出的對話的完整轉錄本，其中僅將所選響應合併到整個轉錄本中。此外，文字記錄也透過人工和自動測量來標註整個對話的危害程度。", "## 特別注意\n\n數據（尤其是無害偏好數據和紅隊數據）包含可能令人反感或令人不安的內容。主題包括但不限於歧視性語言以及對虐待、暴力、自殘、剝削和其他可能令人不安的主題的討論。\n\n請僅根據您個人的風險承受能力來處理資料。這些數據旨在用於研究目的，特別是可以使模型「減少」危害的研究。\n\n如上所述，這些資料「不」該用於訓練對話代理，因為這可能會導致有害的模型行為。", "## 原始數據\n\n原作者在 GitHub 上託管此資料集：URL", "## 轉譯來源\n\n本數據集來自 dikw/hh_rlhf_cn 并使用了 OpenCC 來進行簡繁轉譚。\n\n基於 Anthropic 論文 Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback 開源的 helpful 和 harmless 數據，使用翻譯工具進行了翻譯。\n\n- 'hh_rlhf_train.jsonl' 合併中英文訓練集資料清洗過後 17 萬條\n- 'hh_rlhf_test.jsonl' 合併中英文測試集資料清洗過後 9 千條\n- 'harmless_base_cn_train.jsonl' 42394 條\n- 'harmless_base_cn_test.jsonl' 2304 條\n- 'helpful_base_cn_train.jsonl' 43722 條\n- 'helpful_base_cn_test.jsonl' 2346 條" ]	[ 37, 240, 114, 20, 216 ]	[ "passage: TAGS\n#task_categories-reinforcement-learning #language-Chinese #license-mit #arxiv-2204.05862 #region-us \n# Dataset Card for \"hh_rlhf-chinese-zhtw\"\n\n此數據集合併了下列的資料:\n\n1. 關於有用且無害的人類偏好數據，來自 Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback。這些數據旨在為後續 RLHF 訓練訓練偏好（或獎勵）模型。這些資料不用於對話代理人的監督訓練。根據這些資料訓練對話代理可能會導致有害的模型，這種情況應該避免。\n3. 人工生成並帶註釋的紅隊對話，來自減少危害的紅隊語言模型：方法、擴展行為和經驗教訓。這些數據旨在了解眾包紅隊如何建模以及哪些類型的紅隊攻擊成功或失敗。這些數據不用於微調或偏好建模（使用上面的數據進行偏好建模）。這些數據是從上述無害偏好建模數據導出的對話的完整轉錄本，其中僅將所選響應合併到整個轉錄本中。此外，文字記錄也透過人工和自動測量來標註整個對話的危害程度。## 特別注意\n\n數據（尤其是無害偏好數據和紅隊數據）包含可能令人反感或令人不安的內容。主題包括但不限於歧視性語言以及對虐待、暴力、自殘、剝削和其他可能令人不安的主題的討論。\n\n請僅根據您個人的風險承受能力來處理資料。這些數據旨在用於研究目的，特別是可以使模型「減少」危害的研究。\n\n如上所述，這些資料「不」該用於訓練對話代理，因為這可能會導致有害的模型行為。## 原始數據\n\n原作者在 GitHub 上託管此資料集：URL" ]
88a0281700b48f8336fdc54204f6220d746d9741	# Dataset Card for "cupcakeLPAParsingTest" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	angellist/cupcakeLPAParsingTest	[ "region:us" ]	2023-10-04T22:13:47+00:00	{"dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1483910, "num_examples": 1138}], "download_size": 448368, "dataset_size": 1483910}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-10-05T05:03:51+00:00	[]	[]	TAGS #region-us	# Dataset Card for "cupcakeLPAParsingTest" More Information needed	[ "# Dataset Card for \"cupcakeLPAParsingTest\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"cupcakeLPAParsingTest\"\n\nMore Information needed" ]	[ 6, 17 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"cupcakeLPAParsingTest\"\n\nMore Information needed" ]
c73386702c13922d47372e4260cede70490bf38c	# Dataset Card for "timelist_dataset4finetuning_conspects" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	marcus2000/timelist_dataset4finetuning_conspects	[ "region:us" ]	2023-10-04T22:24:33+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-"}, {"split": "test", "path": "data/test-"}]}], "dataset_info": {"features": [{"name": "Original", "dtype": "string"}, {"name": "Summary", "dtype": "string"}, {"name": "Task", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1049996.152173913, "num_examples": 39}, {"name": "test", "num_bytes": 188460.84782608695, "num_examples": 7}], "download_size": 588122, "dataset_size": 1238457.0}}	2023-10-04T22:24:40+00:00	[]	[]	TAGS #region-us	# Dataset Card for "timelist_dataset4finetuning_conspects" More Information needed	[ "# Dataset Card for \"timelist_dataset4finetuning_conspects\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"timelist_dataset4finetuning_conspects\"\n\nMore Information needed" ]	[ 6, 23 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"timelist_dataset4finetuning_conspects\"\n\nMore Information needed" ]
449e26372895ea978231758a60df7ac6145ba123	# Dataset Card for "jigsaw-toxic-comment" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Intuit-GenSRF/jigsaw-toxic-comment	[ "region:us" ]	2023-10-04T22:28:42+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "labels", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 64586545, "num_examples": 159571}], "download_size": 41105413, "dataset_size": 64586545}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-10-04T22:28:45+00:00	[]	[]	TAGS #region-us	# Dataset Card for "jigsaw-toxic-comment" More Information needed	[ "# Dataset Card for \"jigsaw-toxic-comment\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"jigsaw-toxic-comment\"\n\nMore Information needed" ]	[ 6, 18 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"jigsaw-toxic-comment\"\n\nMore Information needed" ]
b7a4f33dc9ee57b0e2b288648946ee199f590686	# Dataset Card for "jigsaw-unintended-biased" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Intuit-GenSRF/jigsaw-unintende-bias	[ "region:us" ]	2023-10-04T22:33:22+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "labels", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 611338216, "num_examples": 1999516}], "download_size": 417071482, "dataset_size": 611338216}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-10-04T22:33:59+00:00	[]	[]	TAGS #region-us	# Dataset Card for "jigsaw-unintended-biased" More Information needed	[ "# Dataset Card for \"jigsaw-unintended-biased\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"jigsaw-unintended-biased\"\n\nMore Information needed" ]	[ 6, 20 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"jigsaw-unintended-biased\"\n\nMore Information needed" ]
4f4c2cf57433cb36dda9987ac1840cf492cf1e8f	# Dataset Card for "AnikaBasu-CyberbullyingDataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Intuit-GenSRF/AnikaBasu-CyberbullyingDataset	[ "region:us" ]	2023-10-04T22:37:20+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "labels", "sequence": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 498571, "num_examples": 2955}], "download_size": 321067, "dataset_size": 498571}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-10-04T22:37:22+00:00	[]	[]	TAGS #region-us	# Dataset Card for "AnikaBasu-CyberbullyingDataset" More Information needed	[ "# Dataset Card for \"AnikaBasu-CyberbullyingDataset\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"AnikaBasu-CyberbullyingDataset\"\n\nMore Information needed" ]	[ 6, 22 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"AnikaBasu-CyberbullyingDataset\"\n\nMore Information needed" ]
daf5644d22cbcc2c36740aece259ba887a88cb6d	# Dataset Card for "ac298fb2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	result-kand2-sdxl-wuerst-karlo/ac298fb2	[ "region:us" ]	2023-10-04T22:40:45+00:00	{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 165, "num_examples": 10}], "download_size": 1316, "dataset_size": 165}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-10-04T22:40:46+00:00	[]	[]	TAGS #region-us	# Dataset Card for "ac298fb2" More Information needed	[ "# Dataset Card for \"ac298fb2\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"ac298fb2\"\n\nMore Information needed" ]	[ 6, 15 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"ac298fb2\"\n\nMore Information needed" ]
e1e67f8e091958d68f8862d6fc25d91dbbfa968c	Previous version with a subset of spicyboros 2.2 coding samples plus some a few other new PowerShell scripting samples. Some formatting fixes.	adamo1139/PS_AD_Office365_03	[ "region:us" ]	2023-10-04T23:18:51+00:00	{}	2023-10-04T23:20:42+00:00	[]	[]	TAGS #region-us	Previous version with a subset of spicyboros 2.2 coding samples plus some a few other new PowerShell scripting samples. Some formatting fixes.	[]	[ "TAGS\n#region-us \n" ]	[ 6 ]	[ "passage: TAGS\n#region-us \n" ]
fb13b2ed48440179cf027024fdcacd587fe753e9	# Dataset Card for "dangerousSpiders" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	zkdeng/dangerousSpiders	[ "region:us" ]	2023-10-04T23:28:00+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Acantholycosa_lignaria", "1": "Aglaoctenus_castaneus", "2": "Aglaoctenus_lagotis", "3": "Allocosa_funerea", "4": "Allotrochosina_schauinslandi", "5": "Alopecosa_albofasciata", "6": "Alopecosa_barbipes", "7": "Alopecosa_cuneata", "8": "Alopecosa_inquilina", "9": "Alopecosa_kochi", "10": "Alopecosa_pulverulenta", "11": "Anahita_punctulata", "12": "Ancylometes_bogotensis", "13": "Ancylometes_concolor", "14": "Ancylometes_rufus", "15": "Anoteropsis_hilaris", "16": "Anoteropsis_litoralis", "17": "Araneus_diadematus", "18": "Arctosa_cinerea", "19": "Arctosa_leopardus", "20": "Arctosa_littoralis", "21": "Arctosa_perita", "22": "Arctosa_personata", "23": "Asthenoctenus_borellii", "24": "Aulonia_albimana", "25": "Centroctenus_brevipes", "26": "Cheiracanthium_erraticum", "27": "Cheiracanthium_gracile", "28": "Cheiracanthium_inclusum", "29": "Cheiracanthium_mildei", "30": "Cheiracanthium_punctorium", "31": "Ctenus_amphora", "32": "Ctenus_hibernalis", "33": "Ctenus_medius", "34": "Ctenus_ornatus", "35": "Cupiennius_coccineus", "36": "Cupiennius_getazi", "37": "Cupiennius_salei", "38": "Diapontia_uruguayensis", "39": "Eratigena_agrestis", "40": "Geolycosa_vultuosa", "41": "Gladicosa_gulosa", "42": "Gladicosa_pulchra", "43": "Hippasa_holmerae", "44": "Hogna_antelucana", "45": "Hogna_baltimoriana", "46": "Hogna_bivittata", "47": "Hogna_carolinensis", "48": "Hogna_crispipes", "49": "Hogna_frondicola", "50": "Hogna_gumia", "51": "Hogna_radiata", "52": "Lampona_cylindrata", "53": "Latrodectus_bishopi", "54": "Latrodectus_curacaviensis", "55": "Latrodectus_geometricus", "56": "Latrodectus_hasselti", "57": "Latrodectus_hesperus", "58": "Latrodectus_katipo", "59": "Latrodectus_mactans", "60": "Latrodectus_mirabilis", "61": "Latrodectus_renivulvatus", "62": "Latrodectus_tredecimguttatus", "63": "Latrodectus_variolus", "64": "Loxosceles_amazonica", "65": "Loxosceles_deserta", "66": "Loxosceles_laeta", "67": "Loxosceles_reclusa", "68": "Loxosceles_rufescens", "69": "Loxosceles_tenochtitlan", "70": "Loxosceles_yucatana", "71": "Lycosa_erythrognatha", "72": "Lycosa_hispanica", "73": "Lycosa_pampeana", "74": "Lycosa_praegrandis", "75": "Lycosa_singoriensis", "76": "Lycosa_tarantula", "77": "Missulena_bradleyi", "78": "Missulena_occatoria", "79": "Paratrochosina_amica", "80": "Pardosa_amentata", "81": "Pardosa_lapidicina", "82": "Pardosa_mercurialis", "83": "Pardosa_moesta", "84": "Pardosa_wagleri", "85": "Phoneutria_boliviensis", "86": "Phoneutria_depilata", "87": "Phoneutria_fera", "88": "Phoneutria_nigriventer", "89": "Phoneutria_pertyi", "90": "Phoneutria_reidyi", "91": "Pirata_piraticus", "92": "Portacosa_cinerea", "93": "Rabidosa_hentzi", "94": "Rabidosa_punctulata", "95": "Rabidosa_rabida", "96": "Schizocosa_avida", "97": "Schizocosa_malitiosa", "98": "Schizocosa_mccooki", "99": "Sicarius_thomisoides", "100": "Sosippus_californicus", "101": "Tigrosa_annexa", "102": "Tigrosa_aspersa", "103": "Tigrosa_georgicola", "104": "Tigrosa_helluo", "105": "Trochosa_ruricola", "106": "Trochosa_sepulchralis", "107": "Trochosa_terricola", "108": "Tropicosa_moesta", "109": "Venator_immansuetus", "110": "Venator_spenceri", "111": "Venatrix_furcillata", "112": "Wadicosa_fidelis", "113": "Xerolycosa_miniata", "114": "Xerolycosa_nemoralis"}}}}], "splits": [{"name": "train", "num_bytes": 4290587998.03, "num_examples": 166895}], "download_size": 3551438155, "dataset_size": 4290587998.03}}	2023-10-04T23:49:18+00:00	[]	[]	TAGS #region-us	# Dataset Card for "dangerousSpiders" More Information needed	[ "# Dataset Card for \"dangerousSpiders\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"dangerousSpiders\"\n\nMore Information needed" ]	[ 6, 16 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"dangerousSpiders\"\n\nMore Information needed" ]
d4e7dab206e43d675396ccca1cc9e22c90dcfed9	# Dataset Card for "finance-financialmodelingprep-stock-news-sentiments-rss-feed" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	NickyNicky/finance-financialmodelingprep-stock-news-sentiments-rss-feed	[ "region:us" ]	2023-10-04T23:40:32+00:00	{"dataset_info": {"features": [{"name": "symbol", "dtype": "string"}, {"name": "publishedDate", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "image", "dtype": "string"}, {"name": "site", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "sentiment", "dtype": "string"}, {"name": "sentimentScore", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 107184621, "num_examples": 142000}], "download_size": 49547931, "dataset_size": 107184621}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-10-04T23:40:43+00:00	[]	[]	TAGS #region-us	# Dataset Card for "finance-financialmodelingprep-stock-news-sentiments-rss-feed" More Information needed	[ "# Dataset Card for \"finance-financialmodelingprep-stock-news-sentiments-rss-feed\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"finance-financialmodelingprep-stock-news-sentiments-rss-feed\"\n\nMore Information needed" ]	[ 6, 31 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"finance-financialmodelingprep-stock-news-sentiments-rss-feed\"\n\nMore Information needed" ]
ea1d2d284b655534188fe849cac5f889d6fc77c1	# Dataset Card for "jquiros-suicide" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Intuit-GenSRF/jquiros-suicide	[ "region:us" ]	2023-10-04T23:49:36+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "labels", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 165623664, "num_examples": 232074}], "download_size": 100436023, "dataset_size": 165623664}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-10-04T23:50:50+00:00	[]	[]	TAGS #region-us	# Dataset Card for "jquiros-suicide" More Information needed	[ "# Dataset Card for \"jquiros-suicide\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"jquiros-suicide\"\n\nMore Information needed" ]	[ 6, 17 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"jquiros-suicide\"\n\nMore Information needed" ]
55356eb4173fc66f4eb9e9fb8d11056b09fdf4aa	# Dataset Card for "hackathon-somos-nlp-2023-suicide-comments-es" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Intuit-GenSRF/hackathon-somos-nlp-2023-suicide-comments-es	[ "region:us" ]	2023-10-04T23:55:49+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "labels", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 942250, "num_examples": 10050}], "download_size": 611736, "dataset_size": 942250}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-10-04T23:55:52+00:00	[]	[]	TAGS #region-us	# Dataset Card for "hackathon-somos-nlp-2023-suicide-comments-es" More Information needed	[ "# Dataset Card for \"hackathon-somos-nlp-2023-suicide-comments-es\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"hackathon-somos-nlp-2023-suicide-comments-es\"\n\nMore Information needed" ]	[ 6, 30 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"hackathon-somos-nlp-2023-suicide-comments-es\"\n\nMore Information needed" ]
6d6d43a81cf89f7064f29e19160074ea7934621d	# Dataset Card for "ziq-depression_tweet" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Intuit-GenSRF/ziq-depression-tweet	[ "region:us" ]	2023-10-04T23:56:40+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "labels", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 20763346, "num_examples": 55226}], "download_size": 13002004, "dataset_size": 20763346}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-10-04T23:59:53+00:00	[]	[]	TAGS #region-us	# Dataset Card for "ziq-depression_tweet" More Information needed	[ "# Dataset Card for \"ziq-depression_tweet\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"ziq-depression_tweet\"\n\nMore Information needed" ]	[ 6, 16 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"ziq-depression_tweet\"\n\nMore Information needed" ]
d8e4ccaa73bde9938cf16952897eef9f1f9f3edd	# Dataset Card for "joangaes-depression" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Intuit-GenSRF/joangaes-depression	[ "region:us" ]	2023-10-05T00:00:31+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "labels", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 13387322, "num_examples": 27977}], "download_size": 8155014, "dataset_size": 13387322}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-10-05T00:00:33+00:00	[]	[]	TAGS #region-us	# Dataset Card for "joangaes-depression" More Information needed	[ "# Dataset Card for \"joangaes-depression\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"joangaes-depression\"\n\nMore Information needed" ]	[ 6, 16 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"joangaes-depression\"\n\nMore Information needed" ]
516c012beea3679bbe9a8d4e9a58a6b56691c5a7	# Dataset Card for "sexting-nsfw-adultconten" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Intuit-GenSRF/sexting-nsfw-adultconten	[ "region:us" ]	2023-10-05T00:05:02+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "labels", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 33518, "num_examples": 538}], "download_size": 19162, "dataset_size": 33518}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-10-05T00:05:04+00:00	[]	[]	TAGS #region-us	# Dataset Card for "sexting-nsfw-adultconten" More Information needed	[ "# Dataset Card for \"sexting-nsfw-adultconten\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"sexting-nsfw-adultconten\"\n\nMore Information needed" ]	[ 6, 20 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"sexting-nsfw-adultconten\"\n\nMore Information needed" ]
ebe82a991927083d7f149bebc0e17d8ad6fb0aca	# Dataset Card for "tweet_eval-hate" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Intuit-GenSRF/tweet-eval-hate	[ "region:us" ]	2023-10-05T00:06:58+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "labels", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 1217914, "num_examples": 9000}], "download_size": 816470, "dataset_size": 1217914}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-10-05T00:06:59+00:00	[]	[]	TAGS #region-us	# Dataset Card for "tweet_eval-hate" More Information needed	[ "# Dataset Card for \"tweet_eval-hate\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"tweet_eval-hate\"\n\nMore Information needed" ]	[ 6, 17 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"tweet_eval-hate\"\n\nMore Information needed" ]
90debe1f5e3f2c70e4d46bce27248a537ae32414	# Dataset Card for "tweet_eval-offensive" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Intuit-GenSRF/tweet-eval-offensive	[ "region:us" ]	2023-10-05T00:08:11+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "labels", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 1651630, "num_examples": 11916}], "download_size": 1020434, "dataset_size": 1651630}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-10-05T00:08:13+00:00	[]	[]	TAGS #region-us	# Dataset Card for "tweet_eval-offensive" More Information needed	[ "# Dataset Card for \"tweet_eval-offensive\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"tweet_eval-offensive\"\n\nMore Information needed" ]	[ 6, 17 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"tweet_eval-offensive\"\n\nMore Information needed" ]
cc33f27b78edeeccd8f55d94b6c57f9eade48ce4	# Dataset Card for "hate_speech18" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Intuit-GenSRF/hate-speech18	[ "region:us" ]	2023-10-05T00:09:55+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "user_id", "dtype": "int64"}, {"name": "subforum_id", "dtype": "int64"}, {"name": "num_contexts", "dtype": "int64"}, {"name": "labels", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 1343052, "num_examples": 10944}], "download_size": 772056, "dataset_size": 1343052}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-10-05T00:09:57+00:00	[]	[]	TAGS #region-us	# Dataset Card for "hate_speech18" More Information needed	[ "# Dataset Card for \"hate_speech18\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"hate_speech18\"\n\nMore Information needed" ]	[ 6, 16 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"hate_speech18\"\n\nMore Information needed" ]
1cc86c8d86aaf204c44086d2abeb791e73c6318a	# GamePhysics Dataset [![Website](http://img.shields.io/badge/Website-4b44ce.svg)](https://asgaardlab.github.io/CLIPxGamePhysics/) [![arXiv](https://img.shields.io/badge/arXiv-2203.11096-b31b1b.svg)](https://arxiv.org/abs/2203.11096) [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/taesiri/CLIPxGamePhysics) The GamePhysics dataset is a collection of gameplay bug videos sourced from the [GamePhysics subreddit](https://www.reddit.com/r/GamePhysics/). ## Sample videos <video src="https://asgaardlab.github.io/CLIPxGamePhysics/static/videos/9rqabp.mp4" controls="controls" muted="muted" playsinline="playsinline" width=480></video> <video src="https://asgaardlab.github.io/CLIPxGamePhysics/static/videos/g5pm35.mp4" controls="controls" muted="muted" playsinline="playsinline" width=480></video> <video src="https://asgaardlab.github.io/CLIPxGamePhysics/static/videos/6xplqg.mp4" controls="controls" muted="muted" playsinline="playsinline" width=480></video> <video src="https://asgaardlab.github.io/CLIPxGamePhysics/static/videos/4jirzj.mp4" controls="controls" muted="muted" playsinline="playsinline" width=480></video>	asgaardlab/GamePhysics-FullResolution	[ "task_categories:video-classification", "size_categories:10K<n<100K", "language:en", "license:creativeml-openrail-m", "video-game", "game", "video-understanding", "ood", "vidoe-ood", "arxiv:2203.11096", "region:us" ]	2023-10-05T00:10:33+00:00	{"language": ["en"], "license": "creativeml-openrail-m", "size_categories": ["10K<n<100K"], "task_categories": ["video-classification"], "pretty_name": "GamePhysics", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "game", "dtype": "string"}, {"name": "filepath", "dtype": "string"}, {"name": "filename", "dtype": "string"}, {"name": "archive", "dtype": "string"}, {"name": "reddit_url", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 3692759, "num_examples": 26954}], "download_size": 1232477, "dataset_size": 3692759}, "configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}]}], "tags": ["video-game", "game", "video-understanding", "ood", "vidoe-ood"]}	2023-12-01T02:44:11+00:00	[ "2203.11096" ]	[ "en" ]	TAGS #task_categories-video-classification #size_categories-10K<n<100K #language-English #license-creativeml-openrail-m #video-game #game #video-understanding #ood #vidoe-ood #arxiv-2203.11096 #region-us	# GamePhysics Dataset ![Website](URL ![arXiv](URL ![Hugging Face Spaces](URL The GamePhysics dataset is a collection of gameplay bug videos sourced from the GamePhysics subreddit. ## Sample videos <video src="URL controls="controls" muted="muted" playsinline="playsinline" width=480></video> <video src="URL controls="controls" muted="muted" playsinline="playsinline" width=480></video> <video src="URL controls="controls" muted="muted" playsinline="playsinline" width=480></video> <video src="URL controls="controls" muted="muted" playsinline="playsinline" width=480></video>	[ "# GamePhysics Dataset\n\n![Website](URL\n![arXiv](URL\n![Hugging Face Spaces](URL\n\nThe GamePhysics dataset is a collection of gameplay bug videos sourced from the GamePhysics subreddit.", "## Sample videos\n<video src=\"URL controls=\"controls\" muted=\"muted\" playsinline=\"playsinline\" width=480></video>\n<video src=\"URL controls=\"controls\" muted=\"muted\" playsinline=\"playsinline\" width=480></video>\n<video src=\"URL controls=\"controls\" muted=\"muted\" playsinline=\"playsinline\" width=480></video>\n<video src=\"URL controls=\"controls\" muted=\"muted\" playsinline=\"playsinline\" width=480></video>" ]	[ "TAGS\n#task_categories-video-classification #size_categories-10K<n<100K #language-English #license-creativeml-openrail-m #video-game #game #video-understanding #ood #vidoe-ood #arxiv-2203.11096 #region-us \n", "# GamePhysics Dataset\n\n![Website](URL\n![arXiv](URL\n![Hugging Face Spaces](URL\n\nThe GamePhysics dataset is a collection of gameplay bug videos sourced from the GamePhysics subreddit.", "## Sample videos\n<video src=\"URL controls=\"controls\" muted=\"muted\" playsinline=\"playsinline\" width=480></video>\n<video src=\"URL controls=\"controls\" muted=\"muted\" playsinline=\"playsinline\" width=480></video>\n<video src=\"URL controls=\"controls\" muted=\"muted\" playsinline=\"playsinline\" width=480></video>\n<video src=\"URL controls=\"controls\" muted=\"muted\" playsinline=\"playsinline\" width=480></video>" ]	[ 71, 58, 136 ]	[ "passage: TAGS\n#task_categories-video-classification #size_categories-10K<n<100K #language-English #license-creativeml-openrail-m #video-game #game #video-understanding #ood #vidoe-ood #arxiv-2203.11096 #region-us \n# GamePhysics Dataset\n\n![Website](URL\n![arXiv](URL\n![Hugging Face Spaces](URL\n\nThe GamePhysics dataset is a collection of gameplay bug videos sourced from the GamePhysics subreddit.## Sample videos\n<video src=\"URL controls=\"controls\" muted=\"muted\" playsinline=\"playsinline\" width=480></video>\n<video src=\"URL controls=\"controls\" muted=\"muted\" playsinline=\"playsinline\" width=480></video>\n<video src=\"URL controls=\"controls\" muted=\"muted\" playsinline=\"playsinline\" width=480></video>\n<video src=\"URL controls=\"controls\" muted=\"muted\" playsinline=\"playsinline\" width=480></video>" ]
335597f5b84ffb053b3dc0e7e817028b1f802b96	# Dataset Card for "hate_speech_offensive" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Intuit-GenSRF/hate-speech-offensive	[ "region:us" ]	2023-10-05T00:24:35+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "labels", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 2576536, "num_examples": 24783}], "download_size": 1560109, "dataset_size": 2576536}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-10-05T00:24:37+00:00	[]	[]	TAGS #region-us	# Dataset Card for "hate_speech_offensive" More Information needed	[ "# Dataset Card for \"hate_speech_offensive\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"hate_speech_offensive\"\n\nMore Information needed" ]	[ 6, 18 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"hate_speech_offensive\"\n\nMore Information needed" ]
328c6a2bf5352a4562bf6cc0270b6e4f93a85111	# Dataset Card for "tweets_hate_speech_detection" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Intuit-GenSRF/tweets-hate-speech-detection	[ "region:us" ]	2023-10-05T00:27:14+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "labels", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 3081848, "num_examples": 31962}], "download_size": 2067751, "dataset_size": 3081848}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-10-05T00:27:16+00:00	[]	[]	TAGS #region-us	# Dataset Card for "tweets_hate_speech_detection" More Information needed	[ "# Dataset Card for \"tweets_hate_speech_detection\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"tweets_hate_speech_detection\"\n\nMore Information needed" ]	[ 6, 21 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"tweets_hate_speech_detection\"\n\nMore Information needed" ]
7df4674d815290cc9c8016b666d6f49286c8833b	# Dataset Card for "ncbi_genbank_part_2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Hack90/ncbi_genbank_part_2	[ "region:us" ]	2023-10-05T00:33:15+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "features", "dtype": "int64"}, {"name": "seq_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 20552040218, "num_examples": 10205}], "download_size": 6137836807, "dataset_size": 20552040218}}	2023-10-06T23:09:54+00:00	[]	[]	TAGS #region-us	# Dataset Card for "ncbi_genbank_part_2" More Information needed	[ "# Dataset Card for \"ncbi_genbank_part_2\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"ncbi_genbank_part_2\"\n\nMore Information needed" ]	[ 6, 19 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"ncbi_genbank_part_2\"\n\nMore Information needed" ]
511a02ceaa41627a203304576bb62af47b5de6fb	# Dataset Card for "haternet" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Intuit-GenSRF/haternet	[ "region:us" ]	2023-10-05T00:37:49+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "labels", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 788430, "num_examples": 6000}], "download_size": 513972, "dataset_size": 788430}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-10-05T00:37:51+00:00	[]	[]	TAGS #region-us	# Dataset Card for "haternet" More Information needed	[ "# Dataset Card for \"haternet\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"haternet\"\n\nMore Information needed" ]	[ 6, 13 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"haternet\"\n\nMore Information needed" ]
20616dae5b05f6409ef00b5ed0c2e6c998984883	# Dataset Card for "toxigen-train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Intuit-GenSRF/toxigen-train	[ "region:us" ]	2023-10-05T00:44:57+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "labels", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 138945158, "num_examples": 250951}], "download_size": 3070653, "dataset_size": 138945158}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-10-05T00:45:00+00:00	[]	[]	TAGS #region-us	# Dataset Card for "toxigen-train" More Information needed	[ "# Dataset Card for \"toxigen-train\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"toxigen-train\"\n\nMore Information needed" ]	[ 6, 16 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"toxigen-train\"\n\nMore Information needed" ]
9fecc3745304a5c056ff913c5fbc471f9e7b2443	# Dataset Card for "toxigen-train-annotated" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Intuit-GenSRF/toxigen-train-annotated	[ "region:us" ]	2023-10-05T00:50:13+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "labels", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 951313, "num_examples": 8960}], "download_size": 553547, "dataset_size": 951313}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-10-05T00:50:15+00:00	[]	[]	TAGS #region-us	# Dataset Card for "toxigen-train-annotated" More Information needed	[ "# Dataset Card for \"toxigen-train-annotated\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"toxigen-train-annotated\"\n\nMore Information needed" ]	[ 6, 20 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"toxigen-train-annotated\"\n\nMore Information needed" ]
60bafca7983cb627fef3d6a9321037f9cd900c1a	# Dataset Card for "toxigen-test-annotated" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Intuit-GenSRF/toxigen-test-annotated	[ "region:us" ]	2023-10-05T00:50:15+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "labels", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 103023, "num_examples": 940}], "download_size": 64072, "dataset_size": 103023}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-10-05T00:50:16+00:00	[]	[]	TAGS #region-us	# Dataset Card for "toxigen-test-annotated" More Information needed	[ "# Dataset Card for \"toxigen-test-annotated\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"toxigen-test-annotated\"\n\nMore Information needed" ]	[ 6, 19 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"toxigen-test-annotated\"\n\nMore Information needed" ]
b0a84237c7f622a2e8c14113553323fc5320ec6f	# Dataset Card for "python_data_27k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	phongmt184172/python_data_27k	[ "region:us" ]	2023-10-05T01:29:17+00:00	{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 39244063.17425801, "num_examples": 19056}, {"name": "test", "num_bytes": 8410618.912870996, "num_examples": 4084}, {"name": "val", "num_bytes": 8410618.912870996, "num_examples": 4084}], "download_size": 23588770, "dataset_size": 56065301.0}}	2023-10-05T01:30:02+00:00	[]	[]	TAGS #region-us	# Dataset Card for "python_data_27k" More Information needed	[ "# Dataset Card for \"python_data_27k\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"python_data_27k\"\n\nMore Information needed" ]	[ 6, 17 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"python_data_27k\"\n\nMore Information needed" ]
262d525f781777f3e61486c7dbe1f24ebffc7c85	# Dataset Card for "ncbi_genbank_part_3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Hack90/ncbi_genbank_part_3	[ "region:us" ]	2023-10-05T01:49:14+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "features", "dtype": "int64"}, {"name": "seq_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 21065842848, "num_examples": 52663}], "download_size": 9750477678, "dataset_size": 21065842848}}	2023-10-05T01:57:48+00:00	[]	[]	TAGS #region-us	# Dataset Card for "ncbi_genbank_part_3" More Information needed	[ "# Dataset Card for \"ncbi_genbank_part_3\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"ncbi_genbank_part_3\"\n\nMore Information needed" ]	[ 6, 19 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"ncbi_genbank_part_3\"\n\nMore Information needed" ]
4f9d7d2f2c60af673ae3077c6c5a098909b6a0e7	# Bangumi Image Base of Lord El-melloi Ii-sei No Jikenbo Rail Zeppelin This is the image base of bangumi Lord El-Melloi II-sei no Jikenbo Rail Zeppelin, we detected 43 characters, 2376 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 \| 483 \| [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 \| 98 \| [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 \| 22 \| [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 \| 33 \| [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 \| 19 \| [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 \| 18 \| [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 \| 23 \| [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 \| 26 \| [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 \| 67 \| [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 \| 50 \| [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 \| 62 \| [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 \| 15 \| [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 \| 18 \| [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 \| 30 \| [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 \| 149 \| [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 \| 14 \| [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 \| 14 \| [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 \| 42 \| [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 \| 17 \| [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 \| 47 \| [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 \| 9 \| [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 \| 44 \| [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 \| 10 \| [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 \| 32 \| [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 \| 240 \| [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 \| 34 \| [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 \| 33 \| [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 \| 93 \| [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 \| 18 \| [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 \| 16 \| [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 \| 12 \| [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 \| 52 \| [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 \| 74 \| [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 \| 22 \| [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 \| 101 \| [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 \| 11 \| [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 \| 17 \| [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 \| 10 \| [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 \| 15 \| [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 \| 5 \| [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) \| N/A \| N/A \| N/A \| \| 41 \| 50 \| [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 \| 219 \| [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) \|	BangumiBase/lordelmelloiiiseinojikenborailzeppelin	[ "size_categories:1K<n<10K", "license:mit", "art", "region:us" ]	2023-10-05T02:27:53+00:00	{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}	2023-10-05T04:01:02+00:00	[]	[]	TAGS #size_categories-1K<n<10K #license-mit #art #region-us	Bangumi Image Base of Lord El-melloi Ii-sei No Jikenbo Rail Zeppelin ==================================================================== This is the image base of bangumi Lord El-Melloi II-sei no Jikenbo Rail Zeppelin, we detected 43 characters, 2376 images in total. The full dataset is here. 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:	[]	[ "TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]	[ 25 ]	[ "passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]
493642c6b337470d5968dc3e96ba43cd743ef95c	# Bangumi Image Base of Inou Battle Within Everyday Life This is the image base of bangumi Inou Battle Within Everyday Life, we detected 19 characters, 1588 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 \| 150 \| [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 \| 19 \| [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 \| 41 \| [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 \| 495 \| [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 \| 17 \| [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 \| 9 \| [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 \| 78 \| [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 \| 9 \| [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 \| 182 \| [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 \| 40 \| [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 \| 25 \| [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 \| 213 \| [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 \| 6 \| [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) \| N/A \| N/A \| \| 13 \| 141 \| [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 \| 12 \| [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 \| 7 \| [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) \| N/A \| \| 16 \| 5 \| [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) \| N/A \| N/A \| N/A \| \| 17 \| 12 \| [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) \| \| noise \| 127 \| [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) \|	BangumiBase/inoubattlewithineverydaylife	[ "size_categories:1K<n<10K", "license:mit", "art", "region:us" ]	2023-10-05T02:47:20+00:00	{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}	2023-10-05T03:50:11+00:00	[]	[]	TAGS #size_categories-1K<n<10K #license-mit #art #region-us	Bangumi Image Base of Inou Battle Within Everyday Life ====================================================== This is the image base of bangumi Inou Battle Within Everyday Life, we detected 19 characters, 1588 images in total. The full dataset is here. 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:	[]	[ "TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]	[ 25 ]	[ "passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]
d57c8d66c333126542abf75026ccaef5ac4a1f1e	# Bangumi Image Base of Tensei Shitara Ken Deshita This is the image base of bangumi Tensei Shitara Ken Deshita, we detected 20 characters, 895 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 \| 277 \| [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 \| 110 \| [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 \| 22 \| [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 \| 32 \| [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 \| 19 \| [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 \| 25 \| [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 \| 7 \| [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) \| N/A \| \| 8 \| 10 \| [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 \| 9 \| [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 \| 14 \| [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 \| 99 \| [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 \| 9 \| [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 \| 24 \| [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 \| 34 \| [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 \| 14 \| [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 \| 13 \| [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 \| 9 \| [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 \| 5 \| [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) \| N/A \| N/A \| N/A \| \| noise \| 114 \| [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) \|	BangumiBase/tenseishitarakendeshita	[ "size_categories:n<1K", "license:mit", "art", "region:us" ]	2023-10-05T02:55:11+00:00	{"license": "mit", "size_categories": ["n<1K"], "tags": ["art"]}	2023-10-05T03:49:29+00:00	[]	[]	TAGS #size_categories-n<1K #license-mit #art #region-us	Bangumi Image Base of Tensei Shitara Ken Deshita ================================================ This is the image base of bangumi Tensei Shitara Ken Deshita, we detected 20 characters, 895 images in total. The full dataset is here. 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:	[]	[ "TAGS\n#size_categories-n<1K #license-mit #art #region-us \n" ]	[ 23 ]	[ "passage: TAGS\n#size_categories-n<1K #license-mit #art #region-us \n" ]
06c29974eec8bf0436b6f466d1df35c5f6b26b3b	# Dataset Card for "example_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	DynamicSuperb/example_dataset	[ "region:us" ]	2023-10-05T03:29:38+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "file", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 358052.0, "num_examples": 3}], "download_size": 360407, "dataset_size": 358052.0}}	2023-10-05T03:29:53+00:00	[]	[]	TAGS #region-us	# Dataset Card for "example_dataset" More Information needed	[ "# Dataset Card for \"example_dataset\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"example_dataset\"\n\nMore Information needed" ]	[ 6, 16 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"example_dataset\"\n\nMore Information needed" ]
06575649f992986aca08055f8b3ba16399709fba	# Dataset Card for "inatSpiders" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	zkdeng/inatSpiders	[ "region:us" ]	2023-10-05T03:45:34+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Abba_transversa", "1": "Acacesia_hamata", "2": "Acalitus_brevitarsus", "3": "Acalitus_ferrugineum", "4": "Acalitus_longisetosus", "5": "Acanthepeira_stellata", "6": "Acantholycosa_lignaria", "7": "Acanthopachylus_robustus", "8": "Acanthophrynus_coronatus", "9": "Acanthoscurria_natalensis", "10": "Aceria_aloinis", "11": "Aceria_baccharices", "12": "Aceria_baccharipha", "13": "Aceria_boycei", "14": "Aceria_brachytarsa", "15": "Aceria_calaceris", "16": "Aceria_caliberberis", "17": "Aceria_campestricola", "18": "Aceria_caryae", "19": "Aceria_caulis", "20": "Aceria_celtis", "21": "Aceria_cephalanthi", "22": "Aceria_cephalonea", "23": "Aceria_cinereae", "24": "Aceria_dina", "25": "Aceria_dispar", "26": "Aceria_echii", "27": "Aceria_elongata", "28": "Aceria_erinea", "29": "Aceria_fraxini", "30": "Aceria_fraxiniflora", "31": "Aceria_fraxinivora", "32": "Aceria_genistae", "33": "Aceria_ilicis", "34": "Aceria_mackiei", "35": "Aceria_macrochela", "36": "Aceria_macrorhyncha", "37": "Aceria_modesta", "38": "Aceria_myriadeum", "39": "Aceria_negundi", "40": "Aceria_nyssae", "41": "Aceria_paracalifornica", "42": "Aceria_parapopuli", "43": "Aceria_parulmi", "44": "Aceria_pseudoplatani", "45": "Aceria_quercerina", "46": "Aceria_querci", "47": "Aceria_theospyri", "48": "Aceria_trichophila", "49": "Aceria_trinema", "50": "Aceria_triplacis", "51": "Aceria_vaga", "52": "Actinosoma_pentacanthum", "53": "Aculepeira_armida", "54": "Aculepeira_ceropegia", "55": "Aculepeira_packardi", "56": "Aculops_aenigma", "57": "Aculops_rhois", "58": "Aculus_minutissimus", "59": "Aculus_tetanothrix", "60": "Aegaeobuthus_gibbosus", "61": "Aelurillus_dubatolovi", "62": "Aelurillus_luctuosus", "63": "Aelurillus_m-nigrum", "64": "Aelurillus_v-insignitus", "65": "Agalenatea_redii", "66": "Agelena_labyrinthica", "67": "Agelena_orientalis", "68": "Agelenopsis_aperta", "69": "Agelenopsis_potteri", "70": "Aglaoctenus_castaneus", "71": "Aglaoctenus_lagotis", "72": "Algidia_chiltoni", "73": "Algidia_nigriflava", "74": "Allagelena_gracilens", "75": "Allocosa_funerea", "76": "Allocyclosa_bifurca", "77": "Allotrochosina_schauinslandi", "78": "Alopecosa_albofasciata", "79": "Alopecosa_barbipes", "80": "Alopecosa_cuneata", "81": "Alopecosa_inquilina", "82": "Alopecosa_kochi", "83": "Alopecosa_pulverulenta", "84": "Alpaida_acuta", "85": "Alpaida_alticeps", "86": "Alpaida_bicornuta", "87": "Alpaida_carminea", "88": "Alpaida_gallardoi", "89": "Alpaida_grayi", "90": "Alpaida_leucogramma", "91": "Alpaida_rubellula", "92": "Alpaida_truncata", "93": "Alpaida_variabilis", "94": "Alpaida_veniliae", "95": "Alpaida_versicolor", "96": "Amaurobius_erberi", "97": "Amaurobius_fenestralis", "98": "Amaurobius_ferox", "99": "Amaurobius_similis", "100": "Amblyocarenum_walckenaeri", "101": "Amblyomma_americanum", "102": "Amblyomma_hebraeum", "103": "Amblyomma_maculatum", "104": "Amblyomma_triguttatum", "105": "Amilenus_aurantiacus", "106": "Amyciaea_forticeps", "107": "Anahita_punctulata", "108": "Anarrhotus_fossulatus", "109": "Anasaitis_canosa", "110": "Ancylometes_bogotensis", "111": "Ancylometes_concolor", "112": "Ancylometes_rufus", "113": "Anelosimus_eximius", "114": "Anelosimus_studiosus", "115": "Anelosimus_vittatus", "116": "Anepsion_maritatum", "117": "Anoteropsis_hilaris", "118": "Anoteropsis_litoralis", "119": "Antrodiaetus_pacificus", "120": "Antrodiaetus_unicolor", "121": "Anuroctonus_phaiodactylus", "122": "Anuroctonus_pococki", "123": "Anyphaena_accentuata", "124": "Anyphaena_numida", "125": "Aoaraneus_pentagrammicus", "126": "Aphantaulax_trifasciata", "127": "Aphantochilus_rogersi", "128": "Aphonopelma_anax", "129": "Aphonopelma_armada", "130": "Aphonopelma_chalcodes", "131": "Aphonopelma_crinirufum", "132": "Aphonopelma_eutylenum", "133": "Aphonopelma_gabeli", "134": "Aphonopelma_hentzi", "135": "Aphonopelma_iodius", "136": "Aphonopelma_johnnycashi", "137": "Aphonopelma_marxi", "138": "Aphonopelma_pallidum", "139": "Aphonopelma_seemanni", "140": "Aphonopelma_steindachneri", "141": "Aphonopelma_vorhiesi", "142": "Apricia_bracteata", "143": "Apricia_jovialis", "144": "Arachnura_feredayi", "145": "Arachnura_higginsi", "146": "Arachnura_melanura", "147": "Arachnura_scorpionoides", "148": "Arachosia_praesignis", "149": "Araneus_albotriangulus", "150": "Araneus_alboventris", "151": "Araneus_alsine", "152": "Araneus_andrewsi", "153": "Araneus_angulatus", "154": "Araneus_apricus", "155": "Araneus_bicentenarius", "156": "Araneus_cavaticus", "157": "Araneus_cingulatus", "158": "Araneus_circe", "159": "Araneus_circulissparsus", "160": "Araneus_detrimentosus", "161": "Araneus_diadematus", "162": "Araneus_ejusmodi", "163": "Araneus_gemma", "164": "Araneus_gemmoides", "165": "Araneus_granadensis", "166": "Araneus_grossus", "167": "Araneus_guttatus", "168": "Araneus_guttulatus", "169": "Araneus_lathyrinus", "170": "Araneus_marmoreus", "171": "Araneus_miniatus", "172": "Araneus_nordmanni", "173": "Araneus_pallidus", "174": "Araneus_pegnia", "175": "Araneus_pratensis", "176": "Araneus_quadratus", "177": "Araneus_rotundulus", "178": "Araneus_saevus", "179": "Araneus_sturmi", "180": "Araneus_talipedatus", "181": "Araneus_thaddeus", "182": "Araneus_trifolium", "183": "Araneus_triguttatus", "184": "Araneus_uniformis", "185": "Araneus_venatrix", "186": "Araneus_ventricosus", "187": "Araneus_viridiventris", "188": "Araneus_workmani", "189": "Araniella_alpica", "190": "Araniella_cucurbitina", "191": "Araniella_displicata", "192": "Araniella_opisthographa", "193": "Arasia_mollicoma", "194": "Architis_spinipes", "195": "Arctosa_cinerea", "196": "Arctosa_leopardus", "197": "Arctosa_littoralis", "198": "Arctosa_perita", "199": "Arctosa_personata", "200": "Argiope_aemula", "201": "Argiope_aetherea", "202": "Argiope_aetheroides", "203": "Argiope_amoena", "204": "Argiope_anasuja", "205": "Argiope_appensa", "206": "Argiope_argentata", "207": "Argiope_aurantia", "208": "Argiope_australis", "209": "Argiope_bruennichi", "210": "Argiope_catenulata", "211": "Argiope_dang", "212": "Argiope_florida", "213": "Argiope_keyserlingi", "214": "Argiope_lobata", "215": "Argiope_magnifica", "216": "Argiope_mascordi", "217": "Argiope_minuta", "218": "Argiope_ocula", "219": "Argiope_ocyaloides", "220": "Argiope_perforata", "221": "Argiope_picta", "222": "Argiope_protensa", "223": "Argiope_radon", "224": "Argiope_reinwardti", "225": "Argiope_submaronica", "226": "Argiope_trifasciata", "227": "Argiope_versicolor", "228": "Argyrodes_antipodianus", "229": "Argyrodes_argyrodes", "230": "Argyrodes_elevatus", "231": "Argyrodes_flavescens", "232": "Argyrodes_miniaceus", "233": "Argyroneta_aquatica", "234": "Ariadna_bicolor", "235": "Ariamnes_colubrinus", "236": "Ariamnes_cylindrogaster", "237": "Arkys_alatus", "238": "Arkys_alticephala", "239": "Arkys_cornutus", "240": "Arkys_curtulus", "241": "Arkys_dilatatus", "242": "Arkys_furcatus", "243": "Arkys_lancearius", "244": "Arkys_speechleyi", "245": "Arkys_tuberculatus", "246": "Arkys_walckenaeri", "247": "Artabrus_erythrocephalus", "248": "Artema_atlanta", "249": "Asagena_americana", "250": "Asagena_phalerata", "251": "Asaphobelis_physonychus", "252": "Asemonea_tenuipes", "253": "Asianellus_festivus", "254": "Asianopis_aurita", "255": "Asianopis_subrufa", "256": "Asthenoctenus_borellii", "257": "Astia_hariola", "258": "Astilodes_mariae", "259": "Attinella_concolor", "260": "Attinella_dorsata", "261": "Attulus_ammophilus", "262": "Attulus_avocator", "263": "Attulus_fasciger", "264": "Attulus_floricola", "265": "Attulus_mirandus", "266": "Attulus_monstrabilis", "267": "Attulus_pubescens", "268": "Attulus_terebratus", "269": "Attulus_zimmermanni", "270": "Atypoides_riversi", "271": "Atypus_affinis", "272": "Aulonia_albimana", "273": "Austracantha_minax", "274": "Australomimetus_hartleyensis", "275": "Australomisidia_pilula", "276": "Avicularia_avicularia", "277": "Avicularia_juruensis", "278": "Avicularia_purpurea", "279": "Avicularia_rufa", "280": "Backobourkia_brouni", "281": "Badumna_insignis", "282": "Badumna_longinqua", "283": "Bagheera_prosper", "284": "Balaustium_leanderi", "285": "Ballus_chalybeius", "286": "Ballus_rufipes", "287": "Barronopsis_texana", "288": "Baryphas_ahenus", "289": "Bassaniana_utahensis", "290": "Bassaniana_versicolor", "291": "Bassaniodes_bufo", "292": "Bavia_sexpunctata", "293": "Beata_wickhami", "294": "Bidentolophus_bidens", "295": "Bijoaraneus_mitificus", "296": "Bijoaraneus_praesignis", "297": "Bothriocyrtum_californicum", "298": "Bothriurus_asper", "299": "Bothriurus_bonariensis", "300": "Brachypelma_albiceps", "301": "Brachypelma_emilia", "302": "Brachypelma_klaasi", "303": "Brettus_cingulatus", "304": "Brigittea_civica", "305": "Brigittea_latens", "306": "Bryantella_smaragda", "307": "Burmattus_pococki", "308": "Buthus_elongatus", "309": "Buthus_ibericus", "310": "Buthus_occitanus", "311": "Buthus_pyrenaeus", "312": "Caddo_agilis", "313": "Caerostris_sexcuspidata", "314": "Calisoga_longitarsis", "315": "Callilepis_nocturna", "316": "Callobius_bennetti", "317": "Callobius_pictus", "318": "Callobius_severus", "319": "Camaricus_formosus", "320": "Camaricus_maugei", "321": "Cambridgea_foliata", "322": "Carrhotus_sannio", "323": "Carrhotus_viduus", "324": "Carrhotus_xanthogramma", "325": "Castianeira_amoena", "326": "Castianeira_cingulata", "327": "Castianeira_descripta", "328": "Castianeira_longipalpa", "329": "Castianeira_thalia", "330": "Catalinia_andreas", "331": "Catalinia_thompsoni", "332": "Cecidophyes_nudus", "333": "Cecidophyes_rouhollahi", "334": "Celaenia_calotoides", "335": "Celaenia_excavata", "336": "Centroctenus_brevipes", "337": "Centruroides_bicolor", "338": "Centruroides_edwardsii", "339": "Centruroides_elegans", "340": "Centruroides_exilicauda", "341": "Centruroides_fulvipes", "342": "Centruroides_gracilis", "343": "Centruroides_hentzi", "344": "Centruroides_limbatus", "345": "Centruroides_limpidus", "346": "Centruroides_ochraceus", "347": "Centruroides_ornatus", "348": "Centruroides_sculpturatus", "349": "Centruroides_suffusus", "350": "Centruroides_vittatus", "351": "Cercidia_prominens", "352": "Cercophonius_squama", "353": "Cesonia_bilineata", "354": "Cetratus_rubropunctatus", "355": "Chaetopelma_olivaceum", "356": "Chalcoscirtus_diminutus", "357": "Cheiracanthium_erraticum", "358": "Cheiracanthium_gracile", "359": "Cheiracanthium_inclusum", "360": "Cheiracanthium_mildei", "361": "Cheiracanthium_punctorium", "362": "Chelifer_cancroides", "363": "Chihuahuanus_coahuilae", "364": "Chikunia_nigra", "365": "Chira_gounellei", "366": "Chira_lucina", "367": "Chira_simoni", "368": "Chira_spinosa", "369": "Chrysilla_acerosa", "370": "Chrysilla_lauta", "371": "Chrysilla_volupe", "372": "Cicurina_cicur", "373": "Cithaeron_praedonius", "374": "Clynotis_severus", "375": "Colaranea_verutum", "376": "Colaranea_viriditas", "377": "Colomerus_vitis", "378": "Colonus_hesperus", "379": "Colonus_puerperus", "380": "Colonus_sylvanus", "381": "Coriarachne_depressa", "382": "Corinnomma_severum", "383": "Coryphasia_nigriventris", "384": "Corythalia_argentinensis", "385": "Corythalia_conferta", "386": "Corythalia_opima", "387": "Cosmobunus_granarius", "388": "Cosmophasis_baehrae", "389": "Cosmophasis_bitaeniata", "390": "Cosmophasis_lami", "391": "Cosmophasis_micarioides", "392": "Cosmophasis_thalassina", "393": "Cosmophasis_valerieae", "394": "Cresmatoneta_mutinensis", "395": "Crossopriza_lyoni", "396": "Cryptachaea_blattea", "397": "Cryptachaea_gigantipes", "398": "Cryptachaea_veruculata", "399": "Ctenus_amphora", "400": "Ctenus_hibernalis", "401": "Ctenus_medius", "402": "Ctenus_ornatus", "403": "Cupiennius_coccineus", "404": "Cupiennius_getazi", "405": "Cupiennius_salei", "406": "Curicaberis_culiacan", "407": "Cyclosa_bifida", "408": "Cyclosa_bifurcata", "409": "Cyclosa_caroli", "410": "Cyclosa_conica", "411": "Cyclosa_diversa", "412": "Cyclosa_insulana", "413": "Cyclosa_mulmeinensis", "414": "Cyclosa_octotuberculata", "415": "Cyclosa_oculata", "416": "Cyclosa_trilobata", "417": "Cyclosa_turbinata", "418": "Cyclosa_walckenaeri", "419": "Cymbacha_ocellata", "420": "Cyrba_algerina", "421": "Cyriocosmus_leetzi", "422": "Cyrtarachne_inaequalis", "423": "Cyrtarachne_ixoides", "424": "Cyrtopholis_portoricae", "425": "Cyrtophora_cicatrosa", "426": "Cyrtophora_citricola", "427": "Cyrtophora_exanthematica", "428": "Cyrtophora_moluccensis", "429": "Cyrtophora_unicolor", "430": "Cytaea_alburna", "431": "Cytaea_aspera", "432": "Cytaea_dispalans", "433": "Cytaea_maoming", "434": "Dalquestia_formosa", "435": "Damon_annulatipes", "436": "Damon_variegatus", "437": "Dasylobus_graniferus", "438": "Davus_ruficeps", "439": "Deinopis_longipes", "440": "Deinopis_spinosa", "441": "Delena_cancerides", "442": "Deliochus_zelivira", "443": "Dendrolycosa_icadia", "444": "Dendryphantes_mordax", "445": "Dendryphantes_rudis", "446": "Dendryphantes_zygoballoides", "447": "Dermacentor_andersoni", "448": "Dermacentor_marginatus", "449": "Dermacentor_occidentalis", "450": "Dermacentor_reticulatus", "451": "Dermacentor_variabilis", "452": "Desis_marina", "453": "Diaea_ambara", "454": "Diaea_dorsata", "455": "Diaea_livens", "456": "Diapontia_uruguayensis", "457": "Dicranopalpus_larvatus", "458": "Dicranopalpus_ramosus", "459": "Dictis_striatipes", "460": "Dictyna_calcarata", "461": "Diguetinus_raptator", "462": "Dinothrombium_gigas", "463": "Diplocentrus_lindo", "464": "Dipoena_melanogaster", "465": "Dolichothele_exilis", "466": "Dolomedes_albineus", "467": "Dolomedes_dondalei", "468": "Dolomedes_facetus", "469": "Dolomedes_fimbriatus", "470": "Dolomedes_minor", "471": "Dolomedes_mizhoanus", "472": "Dolomedes_raptor", "473": "Dolomedes_scriptus", "474": "Dolomedes_striatus", "475": "Dolomedes_sulfureus", "476": "Dolomedes_tenebrosus", "477": "Dolomedes_triton", "478": "Dolomedes_vittatus", "479": "Drapetisca_alteranda", "480": "Drapetisca_socialis", "481": "Dugesiella_anitahoffmannae", "482": "Dysdera_crocata", "483": "Ebrechtella_tricuspidata", "484": "Edricus_productus", "485": "Egaenus_convexus", "486": "Elaver_excepta", "487": "Enoplognatha_mandibularis", "488": "Enoplognatha_ovata", "489": "Epeus_flavobilineatus", "490": "Epeus_glorius", "491": "Epicadus_heterogaster", "492": "Epicadus_trituberculatus", "493": "Episinus_angulatus", "494": "Episinus_maculipes", "495": "Episinus_truncatus", "496": "Epocilla_blairei", "497": "Epocilla_calcarata", "498": "Eratigena_agrestis", "499": "Eratigena_atrica", "500": "Eratigena_duellica", "501": "Eratigena_inermis", "502": "Eresus_kollari", "503": "Eresus_sandaliatus", "504": "Erginulus_subserialis", "505": "Eriophora_edax", "506": "Eriophora_fuliginea", "507": "Eriophora_nephiloides", "508": "Eriophora_ravilla", "509": "Eriophyes_aceris", "510": "Eriophyes_adenostomae", "511": "Eriophyes_cerasicrumena", "512": "Eriophyes_emarginatae", "513": "Eriophyes_exilis", "514": "Eriophyes_hoheriae", "515": "Eriophyes_inangulis", "516": "Eriophyes_laevis", "517": "Eriophyes_leiosoma", "518": "Eriophyes_paraviburni", "519": "Eriophyes_pyri", "520": "Eriophyes_rhoinus", "521": "Eriophyes_similis", "522": "Eriophyes_sorbi", "523": "Eriophyes_tiliae", "524": "Eriovixia_excelsa", "525": "Eriovixia_laglaizei", "526": "Eris_flava", "527": "Eris_floridana", "528": "Eris_militaris", "529": "Ero_aphana", "530": "Ero_tuberculata", "531": "Euagrus_chisoseus", "532": "Eucteniza_relata", "533": "Eumesosoma_roeweri", "534": "Euophrys_frontalis", "535": "Euophrys_herbigrada", "536": "Euophrys_monadnock", "537": "Euophrys_rufibarbis", "538": "Eupalaestrus_weijenberghi", "539": "Euprosthenopsis_pulchella", "540": "Euryattus_bleekeri", "541": "Euryopis_episinoides", "542": "Euryopis_funebris", "543": "Eurytetranychus_buxi", "544": "Euscorpius_flavicaudis", "545": "Euscorpius_italicus", "546": "Eusparassus_dufouri", "547": "Eusparassus_walckenaeri", "548": "Eustala_anastera", "549": "Evarcha_albaria", "550": "Evarcha_arcuata", "551": "Evarcha_bulbosa", "552": "Evarcha_falcata", "553": "Evarcha_hoyi", "554": "Evarcha_jucunda", "555": "Evarcha_michailovi", "556": "Evarcha_proszynskii", "557": "Falconina_gracilis", "558": "Filistata_insidiatrix", "559": "Florinda_coccinea", "560": "Forsteropsalis_inconstans", "561": "Forsteropsalis_pureora", "562": "Freya_nigrotaeniata", "563": "Frigga_crocuta", "564": "Frigga_pratensis", "565": "Frigga_quintensis", "566": "Frontinella_pyramitela", "567": "Frontinellina_frutetorum", "568": "Garypus_californicus", "569": "Gasteracantha_cancriformis", "570": "Gasteracantha_curvispina", "571": "Gasteracantha_diadesmia", "572": "Gasteracantha_diardi", "573": "Gasteracantha_doriae", "574": "Gasteracantha_falcicornis", "575": "Gasteracantha_fornicata", "576": "Gasteracantha_geminata", "577": "Gasteracantha_kuhli", "578": "Gasteracantha_milvoides", "579": "Gasteracantha_quadrispinosa", "580": "Gasteracantha_sacerdotalis", "581": "Gasteracantha_sanguinolenta", "582": "Gasteracantha_sauteri", "583": "Gasteracantha_versicolor", "584": "Gea_heptagon", "585": "Gea_spinipes", "586": "Gea_theridioides", "587": "Geolycosa_vultuosa", "588": "Gibbaranea_bituberculata", "589": "Gibbaranea_gibbosa", "590": "Gigantometrus_swammerdami", "591": "Gladicosa_gulosa", "592": "Gladicosa_pulchra", "593": "Gluvia_dorsalis", "594": "Graemeloweus_iviei", "595": "Grammostola_rosea", "596": "Gyas_annulatus", "597": "Gyas_titanus", "598": "Habrocestum_hongkongiense", "599": "Habronattus_altanus", "600": "Habronattus_americanus", "601": "Habronattus_amicus", "602": "Habronattus_borealis", "603": "Habronattus_brunneus", "604": "Habronattus_californicus", "605": "Habronattus_captiosus", "606": "Habronattus_clypeatus", "607": "Habronattus_coecatus", "608": "Habronattus_cognatus", "609": "Habronattus_conjunctus", "610": "Habronattus_cuspidatus", "611": "Habronattus_decorus", "612": "Habronattus_elegans", "613": "Habronattus_fallax", "614": "Habronattus_festus", "615": "Habronattus_formosus", "616": "Habronattus_forticulus", "617": "Habronattus_hallani", "618": "Habronattus_hirsutus", "619": "Habronattus_jucundus", "620": "Habronattus_klauseri", "621": "Habronattus_mexicanus", "622": "Habronattus_mustaciatus", "623": "Habronattus_ophrys", "624": "Habronattus_orbus", "625": "Habronattus_oregonensis", "626": "Habronattus_paratus", "627": "Habronattus_peckhami", "628": "Habronattus_pyrrithrix", "629": "Habronattus_sansoni", "630": "Habronattus_tarsalis", "631": "Habronattus_texanus", "632": "Habronattus_ustulatus", "633": "Habronattus_viridipes", "634": "Hadrobunus_maculosus", "635": "Hadrurus_anzaborrego", "636": "Hadrurus_arizonensis", "637": "Hadrurus_spadix", "638": "Hakka_himeshimensis", "639": "Hamadruas_hieroglyphica", "640": "Hamataliwa_grisea", "641": "Harmochirus_brachiatus", "642": "Harpactea_hombergi", "643": "Harpactea_rubicunda", "644": "Harpactira_atra", "645": "Hasarius_adansoni", "646": "Hebestatis_theveneti", "647": "Heliophanus_apiatus", "648": "Heliophanus_auratus", "649": "Heliophanus_cupreus", "650": "Heliophanus_hamifer", "651": "Heliophanus_kochii", "652": "Heliophanus_melinus", "653": "Heliophanus_tribulosus", "654": "Helpis_minitabunda", "655": "Hemicloea_rogenhoferi", "656": "Hentzia_grenada", "657": "Hentzia_mitrata", "658": "Hentzia_palmarum", "659": "Herennia_multipuncta", "660": "Heriaeus_hirtus", "661": "Heriaeus_oblongus", "662": "Herpyllus_ecclesiasticus", "663": "Herpyllus_propinquus", "664": "Hesperonemastoma_modestum", "665": "Heterometrus_laoticus", "666": "Heterometrus_longimanus", "667": "Heterometrus_silenus", "668": "Heterophrynus_batesii", "669": "Heterophrynus_longicornis", "670": "Heteropoda_amphora", "671": "Heteropoda_boiei", "672": "Heteropoda_davidbowie", "673": "Heteropoda_jugulans", "674": "Heteropoda_longipes", "675": "Heteropoda_pingtungensis", "676": "Heteropoda_procera", "677": "Heteropoda_tetrica", "678": "Heteropoda_venatoria", "679": "Heterotheridion_nigrovariegatum", "680": "Hibana_gracilis", "681": "Hibana_incursa", "682": "Hinewaia_embolica", "683": "Hippasa_holmerae", "684": "Hogna_antelucana", "685": "Hogna_baltimoriana", "686": "Hogna_bivittata", "687": "Hogna_carolinensis", "688": "Hogna_crispipes", "689": "Hogna_frondicola", "690": "Hogna_gumia", "691": "Hogna_radiata", "692": "Holcolaetis_zuluensis", "693": "Holconia_immanis", "694": "Holconia_insignis", "695": "Holocnemus_pluchei", "696": "Holoplatys_apressus", "697": "Holoplatys_invenusta", "698": "Holothele_longipes", "699": "Homalenotus_quadridentatus", "700": "Homalonychus_theologus", "701": "Hortophora_biapicata", "702": "Hortophora_tatianeae", "703": "Hortophora_transmarina", "704": "Hottentotta_judaicus", "705": "Hottentotta_tamulus", "706": "Hygropoda_lineata", "707": "Hyllus_argyrotoxus", "708": "Hyllus_brevitarsis", "709": "Hyllus_diardi", "710": "Hyllus_keratodes", "711": "Hyllus_semicupreus", "712": "Hyllus_treleaveni", "713": "Hypochilus_pococki", "714": "Hypodrassodes_maoricus", "715": "Hypselistes_florens", "716": "Hypsosinga_albovittata", "717": "Hypsosinga_heri", "718": "Hyptiotes_cavatus", "719": "Hyptiotes_gertschi", "720": "Hyptiotes_paradoxus", "721": "Icius_hamatus", "722": "Icius_subinermis", "723": "Intruda_signata", "724": "Iridopelma_hirsutum", "725": "Irura_bidenticulata", "726": "Isala_cambridgei", "727": "Ischnothele_annulata", "728": "Ischnothele_caudata", "729": "Isometrus_maculatus", "730": "Isopeda_leishmanni", "731": "Isopeda_montana", "732": "Isopeda_queenslandensis", "733": "Isopeda_vasta", "734": "Isopeda_villosa", "735": "Isopedella_cerussata", "736": "Isopedella_flavida", "737": "Isopedella_leai", "738": "Isopedella_pessleri", "739": "Isopedella_victorialis", "740": "Isoxya_cicatricosa", "741": "Isoxya_tabulata", "742": "Ixodes_holocyclus", "743": "Ixodes_pacificus", "744": "Ixodes_persulcatus", "745": "Ixodes_ricinus", "746": "Ixodes_scapularis", "747": "Jaguajir_rochae", "748": "Janula_bicornis", "749": "Javanimetrus_cyaneus", "750": "Jotus_auripes", "751": "Jotus_frosti", "752": "Judalana_lutea", "753": "Kankuamo_marquezi", "754": "Kelawakaju_frenata", "755": "Kochiura_aulica", "756": "Kovarikia_angelena", "757": "Kovarikia_oxy", "758": "Kovarikia_williamsi", "759": "Krateromaspis_dilatata", "760": "Kukulcania_arizonica", "761": "Kukulcania_hibernalis", "762": "Labulla_thoracica", "763": "Lacinius_dentiger", "764": "Lacinius_ephippiatus", "765": "Lacinius_horridus", "766": "Lampona_cylindrata", "767": "Lampona_murina", "768": "Larinia_borealis", "769": "Larinia_directa", "770": "Larinia_lineata", "771": "Larinioides_cornutus", "772": "Larinioides_ixobolus", "773": "Larinioides_patagiatus", "774": "Larinioides_sclopetarius", "775": "Lathys_humilis", "776": "Latonigena_auricomis", "777": "Latrodectus_bishopi", "778": "Latrodectus_curacaviensis", "779": "Latrodectus_geometricus", "780": "Latrodectus_hasselti", "781": "Latrodectus_hesperus", "782": "Latrodectus_katipo", "783": "Latrodectus_mactans", "784": "Latrodectus_mirabilis", "785": "Latrodectus_renivulvatus", "786": "Latrodectus_tredecimguttatus", "787": "Latrodectus_variolus", "788": "Leiobunum_aldrichi", "789": "Leiobunum_bimaculatum", "790": "Leiobunum_blackwalli", "791": "Leiobunum_calcar", "792": "Leiobunum_exilipes", "793": "Leiobunum_flavum", "794": "Leiobunum_gracile", "795": "Leiobunum_limbatum", "796": "Leiobunum_nigropalpi", "797": "Leiobunum_rotundum", "798": "Leiobunum_townsendi", "799": "Leiobunum_uxorium", "800": "Leiobunum_ventricosum", "801": "Leiobunum_verrucosum", "802": "Leiobunum_vittatum", "803": "Leiurus_hebraeus", "804": "Leptobunus_parvulus", "805": "Leptofreya_ambigua", "806": "Leptorchestes_berolinensis", "807": "Leucauge_argentina", "808": "Leucauge_argyra", "809": "Leucauge_argyrobapta", "810": "Leucauge_blanda", "811": "Leucauge_celebesiana", "812": "Leucauge_decorata", "813": "Leucauge_dromedaria", "814": "Leucauge_fastigata", "815": "Leucauge_festiva", "816": "Leucauge_granulata", "817": "Leucauge_licina", "818": "Leucauge_mariana", "819": "Leucauge_regnyi", "820": "Leucauge_tessellata", "821": "Leucauge_venusta", "822": "Leucauge_volupis", "823": "Leuronychus_pacificus", "824": "Leviana_dimidiata", "825": "Leviellus_stroemi", "826": "Ligurra_latidens", "827": "Linyphia_hortensis", "828": "Linyphia_triangularis", "829": "Liocheles_australasiae", "830": "Liocranum_rupicola", "831": "Liophrurillus_flavitarsis", "832": "Lophopilio_palpinalis", "833": "Loxosceles_amazonica", "834": "Loxosceles_deserta", "835": "Loxosceles_laeta", "836": "Loxosceles_reclusa", "837": "Loxosceles_rufescens", "838": "Loxosceles_tenochtitlan", "839": "Loxosceles_yucatana", "840": "Lupettiana_mordax", "841": "Lurio_conspicuus", "842": "Lychas_marmoreus", "843": "Lychas_mucronatus", "844": "Lychas_scutilus", "845": "Lychas_variatus", "846": "Lycosa_erythrognatha", "847": "Lycosa_hispanica", "848": "Lycosa_pampeana", "849": "Lycosa_praegrandis", "850": "Lycosa_singoriensis", "851": "Lycosa_tarantula", "852": "Lycosoides_coarctata", "853": "Lyssomanes_pauper", "854": "Lyssomanes_viridis", "855": "Macaroeris_nidicolens", "856": "Macracantha_arcuata", "857": "Macracantha_hasselti", "858": "Macrothele_calpeiana", "859": "Maeota_dichrura", "860": "Maevia_inclemens", "861": "Maimuna_vestita", "862": "Mangora_acalypha", "863": "Mangora_gibberosa", "864": "Mangora_maculata", "865": "Mangora_placida", "866": "Mangora_spiculata", "867": "Manogea_porracea", "868": "Maratus_anomalus", "869": "Maratus_chrysomelas", "870": "Maratus_expolitus", "871": "Maratus_griseus", "872": "Maratus_harrisi", "873": "Maratus_karrie", "874": "Maratus_leo", "875": "Maratus_literatus", "876": "Maratus_pavonis", "877": "Maratus_plumosus", "878": "Maratus_scutulatus", "879": "Maratus_tasmanicus", "880": "Maratus_vespertilio", "881": "Maratus_volans", "882": "Marchena_minuta", "883": "Marma_nigritarsis", "884": "Marpissa_formosa", "885": "Marpissa_lineata", "886": "Marpissa_muscosa", "887": "Marpissa_nivoyi", "888": "Marpissa_obtusa", "889": "Marpissa_pikei", "890": "Marpissa_radiata", "891": "Massuria_simplex", "892": "Mastigoproctus_giganteus", "893": "Mastigoproctus_tohono", "894": "Mastophora_cornigera", "895": "Mastophora_phrynosoma", "896": "Mecaphesa_asperata", "897": "Mecaphesa_celer", "898": "Mecynogea_lemniscata", "899": "Megabunus_diadema", "900": "Megadolomedes_trux", "901": "Megafreya_sutrix", "902": "Megahexura_fulva", "903": "Megaphobema_mesomelas", "904": "Megaphobema_velvetosoma", "905": "Mendoza_canestrinii", "906": "Menemerus_bivittatus", "907": "Menemerus_nigli", "908": "Menemerus_semilimbatus", "909": "Menemerus_taeniatus", "910": "Meriola_decepta", "911": "Messua_limbata", "912": "Meta_bourneti", "913": "Meta_menardi", "914": "Meta_ovalis", "915": "Metacyrba_floridana", "916": "Metacyrba_punctata", "917": "Metacyrba_taeniola", "918": "Metaltella_simoni", "919": "Metaphalangium_cirtanum", "920": "Metaphidippus_chera", "921": "Metaphidippus_manni", "922": "Metaphidippus_siticulosus", "923": "Metaplatybunus_grandissimus", "924": "Metazygia_wittfeldae", "925": "Metazygia_zilloides", "926": "Metellina_curtisi", "927": "Metellina_mengei", "928": "Metellina_merianae", "929": "Metellina_segmentata", "930": "Metepeira_labyrinthea", "931": "Mexcala_elegans", "932": "Mexigonus_minutus", "933": "Micrathena_acuta", "934": "Micrathena_brevipes", "935": "Micrathena_clypeata", "936": "Micrathena_crassa", "937": "Micrathena_duodecimspinosa", "938": "Micrathena_fissispina", "939": "Micrathena_funebris", "940": "Micrathena_furcata", "941": "Micrathena_gracilis", "942": "Micrathena_horrida", "943": "Micrathena_kirbyi", "944": "Micrathena_lucasi", "945": "Micrathena_mitrata", "946": "Micrathena_nigrichelis", "947": "Micrathena_patruelis", "948": "Micrathena_pichincha", "949": "Micrathena_picta", "950": "Micrathena_plana", "951": "Micrathena_pungens", "952": "Micrathena_raimondi", "953": "Micrathena_saccata", "954": "Micrathena_sagittata", "955": "Micrathena_sanctispiritus", "956": "Micrathena_schreibersi", "957": "Micrathena_sexspinosa", "958": "Micrathena_swainsoni", "959": "Micrathena_triangularis", "960": "Micrathena_vigorsi", "961": "Microlinyphia_dana", "962": "Microlinyphia_pusilla", "963": "Micrommata_ligurina", "964": "Micrommata_virescens", "965": "Micropholcus_fauroti", "966": "Mimetus_laevigatus", "967": "Mimetus_puritanus", "968": "Mischonyx_squalidus", "969": "Missulena_bradleyi", "970": "Missulena_occatoria", "971": "Misumena_spinifera", "972": "Misumena_vatia", "973": "Misumenoides_formosipes", "974": "Misumenops_callinurus", "975": "Misumenops_maculissparsus", "976": "Misumenops_rubrodecoratus", "977": "Misumessus_oblongus", "978": "Mitopus_glacialis", "979": "Mitopus_morio", "980": "Mitostoma_chrysomelas", "981": "Mituliodon_tarantulinus", "982": "Molinaranea_clymene", "983": "Monaeses_paradoxus", "984": "Mopsus_mormon", "985": "Myrmaplata_plataleoides", "986": "Myrmarachne_formicaria", "987": "Myrmarachne_formosana", "988": "Myrmarachne_ichneumon", "989": "Myrmarachne_japonica", "990": "Myrmarachne_melanocephala", "991": "Myrmekiaphila_comstocki", "992": "Nanometa_lagenifera", "993": "Naphrys_acerba", "994": "Naphrys_pulex", "995": "Natta_horizontalis", "996": "Nelima_doriae", "997": "Nelima_paessleri", "998": "Nemastoma_bimaculatum", "999": "Nemastoma_lugubre", "1000": "Neomolgus_littoralis", "1001": "Neopantopsalis_pentheter", "1002": "Neoscona_adianta", "1003": "Neoscona_arabesca", "1004": "Neoscona_byzanthina", "1005": "Neoscona_crucifera", "1006": "Neoscona_domiciliorum", "1007": "Neoscona_mellotteei", "1008": "Neoscona_moreli", "1009": "Neoscona_nautica", "1010": "Neoscona_oaxacensis", "1011": "Neoscona_orizabensis", "1012": "Neoscona_punctigera", "1013": "Neoscona_scylla", "1014": "Neoscona_scylloides", "1015": "Neoscona_subfusca", "1016": "Neoscona_theisi", "1017": "Neoscona_triangula", "1018": "Neoscona_vigilans", "1019": "Neosparassus_calligaster", "1020": "Neosparassus_diana", "1021": "Neosparassus_magareyi", "1022": "Neosparassus_patellatus", "1023": "Neosparassus_salacius", "1024": "Neospintharus_trigonum", "1025": "Neotama_mexicana", "1026": "Neottiura_bimaculata", "1027": "Nephila_kuhli", "1028": "Nephila_pilipes", "1029": "Nephila_vitiana", "1030": "Nephilengys_malabarensis", "1031": "Nephilengys_papuana", "1032": "Nephilingis_cruentata", "1033": "Nephilingis_livida", "1034": "Neriene_clathrata", "1035": "Neriene_digna", "1036": "Neriene_emphana", "1037": "Neriene_litigiosa", "1038": "Neriene_montana", "1039": "Neriene_peltata", "1040": "Neriene_radiata", "1041": "Nesticodes_rufipes", "1042": "Nesticus_cellulanus", "1043": "Nicodamus_peregrinus", "1044": "Nigma_flavescens", "1045": "Nigma_linsdalei", "1046": "Nigma_puella", "1047": "Nigma_walckenaeri", "1048": "Nihonhimea_mundula", "1049": "Nihonhimea_tesselata", "1050": "Nilus_albocinctus", "1051": "Novakiella_trituberculosa", "1052": "Novaranea_queribunda", "1053": "Nuctenea_umbratica", "1054": "Nungia_epigynalis", "1055": "Nurscia_albomaculata", "1056": "Nycerella_delecta", "1057": "Nyssus_albopunctatus", "1058": "Nyssus_coloripes", "1059": "Ocrisiona_leucocomis", "1060": "Odiellus_lendlii", "1061": "Odiellus_pictus", "1062": "Odiellus_spinosus", "1063": "Oecobius_maculatus", "1064": "Oecobius_navus", "1065": "Oligolophus_hansenii", "1066": "Oligolophus_tridens", "1067": "Olios_argelasius", "1068": "Olios_giganteus", "1069": "Olios_lamarcki", "1070": "Opilio_canestrinii", "1071": "Opilio_parietinus", "1072": "Opilio_saxatilis", "1073": "Opisthacanthus_asper", "1074": "Opisthacanthus_capensis", "1075": "Opisthacanthus_validus", "1076": "Opisthoncus_abnormis", "1077": "Opisthoncus_nigrofemoratus", "1078": "Opisthoncus_polyphemus", "1079": "Opisthoncus_quadratarius", "1080": "Opisthoncus_serratofasciatus", "1081": "Opisthoncus_sexmaculatus", "1082": "Opistophthalmus_capensis", "1083": "Opistophthalmus_carinatus", "1084": "Opistophthalmus_glabrifrons", "1085": "Opistophthalmus_karrooensis", "1086": "Opistophthalmus_macer", "1087": "Opistophthalmus_pallipes", "1088": "Opistophthalmus_pugnax", "1089": "Opistophthalmus_wahlbergii", "1090": "Ordgarius_magnificus", "1091": "Ortholasma_rugosum", "1092": "Ostearius_melanopygius", "1093": "Oxyopes_amoenus", "1094": "Oxyopes_flavipalpis", "1095": "Oxyopes_gracilipes", "1096": "Oxyopes_heterophthalmus", "1097": "Oxyopes_lineatus", "1098": "Oxyopes_macilentus", "1099": "Oxyopes_ramosus", "1100": "Oxyopes_salticus", "1101": "Oxyopes_scalaris", "1102": "Oxyopes_sertatus", "1103": "Oxyopes_shweta", "1104": "Oxyopes_tridens", "1105": "Oxyopes_variabilis", "1106": "Oxytate_striatipes", "1107": "Oxytate_virens", "1108": "Ozyptila_pacifica", "1109": "Ozyptila_praticola", "1110": "Pachygnatha_autumnalis", "1111": "Pachygnatha_clercki", "1112": "Pachygnatha_degeeri", "1113": "Pachygnatha_listeri", "1114": "Pachyloides_thorellii", "1115": "Paidiscura_pallens", "1116": "Palpimanus_gibbulus", "1117": "Palystes_castaneus", "1118": "Palystes_superciliosus", "1119": "Pancorius_crassipes", "1120": "Pandercetes_gracilis", "1121": "Parabuthus_capensis", "1122": "Parabuthus_granulatus", "1123": "Parabuthus_planicauda", "1124": "Parabuthus_raudus", "1125": "Parabuthus_transvaalicus", "1126": "Parabuthus_villosus", "1127": "Paramaevia_poultoni", "1128": "Paranemastoma_quadripunctatum", "1129": "Paraphidippus_aurantius", "1130": "Paraphidippus_fartilis", "1131": "Paraphilaeus_daemeli", "1132": "Paraphrynus_carolynae", "1133": "Paraphrynus_laevifrons", "1134": "Parasteatoda_lunata", "1135": "Parasteatoda_tepidariorum", "1136": "Parasynema_cirripes", "1137": "Paratrochosina_amica", "1138": "Paravaejovis_confusus", "1139": "Paravaejovis_puritanus", "1140": "Paravaejovis_spinigerus", "1141": "Paravaejovis_waeringi", "1142": "Parawixia_audax", "1143": "Parawixia_bistriata", "1144": "Parawixia_dehaani", "1145": "Pardosa_amentata", "1146": "Pardosa_lapidicina", "1147": "Pardosa_mercurialis", "1148": "Pardosa_moesta", "1149": "Pardosa_wagleri", "1150": "Parnaenus_cyanidens", "1151": "Paroligolophus_agrestis", "1152": "Paruroctonus_becki", "1153": "Paruroctonus_boreus", "1154": "Paruroctonus_silvestrii", "1155": "Pediana_regina", "1156": "Pelegrina_aeneola", "1157": "Pelegrina_balia", "1158": "Pelegrina_exigua", "1159": "Pelegrina_flavipes", "1160": "Pelegrina_galathea", "1161": "Pelegrina_pervaga", "1162": "Pelegrina_proterva", "1163": "Pellenes_allegrii", "1164": "Pellenes_geniculatus", "1165": "Pellenes_nigrociliatus", "1166": "Pellenes_seriatus", "1167": "Pellenes_tripunctatus", "1168": "Penthaleus_major", "1169": "Peucetia_flava", "1170": "Peucetia_longipalpis", "1171": "Peucetia_rubrolineata", "1172": "Peucetia_viridana", "1173": "Peucetia_viridans", "1174": "Peucetia_viridis", "1175": "Phaeacius_malayensis", "1176": "Phalangium_opilio", "1177": "Phanias_albeolus", "1178": "Phiale_formosa", "1179": "Phiale_gratiosa", "1180": "Phiale_guttata", "1181": "Phiale_mimica", "1182": "Phiale_roburifoliata", "1183": "Phiale_tristis", "1184": "Phidippus_adumbratus", "1185": "Phidippus_apacheanus", "1186": "Phidippus_ardens", "1187": "Phidippus_arizonensis", "1188": "Phidippus_asotus", "1189": "Phidippus_audax", "1190": "Phidippus_borealis", "1191": "Phidippus_californicus", "1192": "Phidippus_cardinalis", "1193": "Phidippus_carneus", "1194": "Phidippus_carolinensis", "1195": "Phidippus_clarus", "1196": "Phidippus_comatus", "1197": "Phidippus_cruentus", "1198": "Phidippus_cryptus", "1199": "Phidippus_insignarius", "1200": "Phidippus_johnsoni", "1201": "Phidippus_mystaceus", "1202": "Phidippus_nikites", "1203": "Phidippus_octopunctatus", "1204": "Phidippus_olympus", "1205": "Phidippus_otiosus", "1206": "Phidippus_pacosauritus", "1207": "Phidippus_phoenix", "1208": "Phidippus_pius", "1209": "Phidippus_princeps", "1210": "Phidippus_purpuratus", "1211": "Phidippus_putnami", "1212": "Phidippus_regius", "1213": "Phidippus_texanus", "1214": "Phidippus_tyrrelli", "1215": "Phidippus_whitmani", "1216": "Phidippus_workmani", "1217": "Philaeus_chrysops", "1218": "Philira_micans", "1219": "Philodromus_dispar", "1220": "Philodromus_fuscomarginatus", "1221": "Philodromus_margaritatus", "1222": "Philodromus_marxi", "1223": "Philodromus_poecilus", "1224": "Philodromus_rufus", "1225": "Philoponella_congregabilis", "1226": "Phintella_accentifera", "1227": "Phintella_aequipes", "1228": "Phintella_bifurcilinea", "1229": "Phintella_castriesiana", "1230": "Phintella_piatensis", "1231": "Phintella_vittata", "1232": "Phintelloides_versicolor", "1233": "Phlegra_bresnieri", "1234": "Phlegra_fasciata", "1235": "Phlegra_hentzi", "1236": "Pholcus_manueli", "1237": "Pholcus_opilionoides", "1238": "Pholcus_phalangioides", "1239": "Phoneutria_boliviensis", "1240": "Phoneutria_depilata", "1241": "Phoneutria_fera", "1242": "Phoneutria_nigriventer", "1243": "Phoneutria_pertyi", "1244": "Phoneutria_reidyi", "1245": "Phonognatha_graeffei", "1246": "Phoroncidia_sextuberculata", "1247": "Phrixotrichus_vulpinus", "1248": "Phrurolithus_festivus", "1249": "Phrynarachne_ceylonica", "1250": "Phrynarachne_katoi", "1251": "Phrynarachne_rugosa", "1252": "Phrynus_operculatus", "1253": "Phycosoma_digitula", "1254": "Phyllocoptes_didelphis", "1255": "Phyllocoptes_eupadi", "1256": "Phyllocoptes_goniothorax", "1257": "Phyllocoptes_populi", "1258": "Phylloneta_impressa", "1259": "Phylloneta_pictipes", "1260": "Physocyclus_globosus", "1261": "Phytoptus_avellanae", "1262": "Pimoa_altioculata", "1263": "Pirata_piraticus", "1264": "Pisaura_mirabilis", "1265": "Pisaurina_dubia", "1266": "Pisaurina_mira", "1267": "Pisaurina_undulata", "1268": "Pistius_truncatus", "1269": "Pityohyphantes_phrygianus", "1270": "Platnickina_mneon", "1271": "Platnickina_tincta", "1272": "Platybunus_pinetorum", "1273": "Platycryptus_californicus", "1274": "Platycryptus_undatus", "1275": "Platyoides_walteri", "1276": "Plebs_bradleyi", "1277": "Plebs_eburnus", "1278": "Plexippus_paykulli", "1279": "Plexippus_petersi", "1280": "Plexippus_setipes", "1281": "Poecilopachys_australasia", "1282": "Polybetes_pythagoricus", "1283": "Polybetes_rapidus", "1284": "Porrhothele_antipodiana", "1285": "Portacosa_cinerea", "1286": "Portia_schultzi", "1287": "Poultonella_alboimmaculata", "1288": "Prosoponoides_sinense", "1289": "Prostheclina_amplior", "1290": "Prostheclina_pallida", "1291": "Protolophus_singularis", "1292": "Psalmopoeus_cambridgei", "1293": "Psalmopoeus_reduncus", "1294": "Pseudeuophrys_erratica", "1295": "Pseudeuophrys_lanigera", "1296": "Pseudeuophrys_vafra", "1297": "Pseudicius_encarpatus", "1298": "Pseudogagrella_splendens", "1299": "Pseudolychas_ochraceus", "1300": "Pseudouroctonus_reddelli", "1301": "Psilochorus_simoni", "1302": "Pterinopelma_longisternale", "1303": "Pterinopelma_roseum", "1304": "Ptocasius_strupifer", "1305": "Ptocasius_weyersi", "1306": "Pulchellodromus_bistigma", "1307": "Pulchellodromus_pulchellus", "1308": "Pungalina_plurilineata", "1309": "Pystira_ephippigera", "1310": "Rabidosa_hentzi", "1311": "Rabidosa_punctulata", "1312": "Rabidosa_rabida", "1313": "Rhene_flavicomans", "1314": "Rhene_flavigera", "1315": "Rhene_rubrigera", "1316": "Rhipicephalus_sanguineus", "1317": "Rhomphaea_fictilium", "1318": "Rhomphaea_projiciens", "1319": "Rhomphaea_urquharti", "1320": "Rhysodromus_histrio", "1321": "Rilaena_triangularis", "1322": "Ruborridion_musivum", "1323": "Runcinia_acuminata", "1324": "Runcinia_grammica", "1325": "Runcinioides_litteratus", "1326": "Sadocus_asperatus", "1327": "Sadocus_polyacanthus", "1328": "Saitis_barbipes", "1329": "Saitis_tauricus", "1330": "Saitis_variegatus", "1331": "Saitis_virgatus", "1332": "Salpesia_squalida", "1333": "Salsa_brisbanae", "1334": "Salsa_fuliginata", "1335": "Salticus_austinensis", "1336": "Salticus_cingulatus", "1337": "Salticus_mutabilis", "1338": "Salticus_palpalis", "1339": "Salticus_peckhamae", "1340": "Salticus_propinquus", "1341": "Salticus_scenicus", "1342": "Salticus_zebraneus", "1343": "Sandalodes_bipenicillatus", "1344": "Sandalodes_scopifer", "1345": "Sandalodes_superbus", "1346": "Saphrys_rusticana", "1347": "Sardinidion_blackwalli", "1348": "Sarinda_hentzi", "1349": "Sassacus_cyaneus", "1350": "Sassacus_papenhoei", "1351": "Sassacus_vitis", "1352": "Schizocosa_avida", "1353": "Schizocosa_malitiosa", "1354": "Schizocosa_mccooki", "1355": "Sclerobunus_nondimorphicus", "1356": "Scotina_celans", "1357": "Scotophaeus_blackwalli", "1358": "Scytodes_atlacoya", "1359": "Scytodes_fusca", "1360": "Scytodes_globula", "1361": "Scytodes_pallida", "1362": "Scytodes_thoracica", "1363": "Scytodes_univittata", "1364": "Segestria_bavarica", "1365": "Segestria_florentina", "1366": "Segestria_pacifica", "1367": "Segestria_senoculata", "1368": "Selenops_mexicanus", "1369": "Selenops_submaculosus", "1370": "Sergiolus_capulatus", "1371": "Sergiolus_montanus", "1372": "Serradigitus_gertschi", "1373": "Servaea_incana", "1374": "Servaea_villosa", "1375": "Sicarius_thomisoides", "1376": "Sidymella_angularis", "1377": "Sidymella_hirsuta", "1378": "Sidymella_longipes", "1379": "Sidymella_rubrosignata", "1380": "Sidymella_trapezia", "1381": "Siler_collingwoodi", "1382": "Siler_cupreus", "1383": "Siler_semiglaucus", "1384": "Simaetha_tenuidens", "1385": "Simitidion_simile", "1386": "Singa_hamata", "1387": "Singa_nitidula", "1388": "Siro_rubens", "1389": "Sittisax_ranieri", "1390": "Smeringopus_pallidus", "1391": "Smeringurus_mesaensis", "1392": "Smeringurus_vachoni", "1393": "Socca_pustulosa", "1394": "Soerensenella_prehensor", "1395": "Sosippus_californicus", "1396": "Spartaeus_spinimanus", "1397": "Spermophora_senoculata", "1398": "Sphodros_niger", "1399": "Sphodros_rufipes", "1400": "Spintharus_flavidus", "1401": "Spiracme_striatipes", "1402": "Steatoda_albomaculata", "1403": "Steatoda_ancorata", "1404": "Steatoda_bipunctata", "1405": "Steatoda_borealis", "1406": "Steatoda_capensis", "1407": "Steatoda_castanea", "1408": "Steatoda_grossa", "1409": "Steatoda_lepida", "1410": "Steatoda_nobilis", "1411": "Steatoda_paykulliana", "1412": "Steatoda_triangulosa", "1413": "Stegodyphus_dumicola", "1414": "Stegodyphus_lineatus", "1415": "Stegodyphus_sarasinorum", "1416": "Stemonyphantes_lineatus", "1417": "Stenacis_euonymi", "1418": "Stenacis_triradiata", "1419": "Stephanopis_altifrons", "1420": "Stephanopis_barbipes", "1421": "Stephanopis_carcinoides", "1422": "Stiphidion_facetum", "1423": "Strigoplus_guizhouensis", "1424": "Sumampattus_quinqueradiatus", "1425": "Superstitionia_donensis", "1426": "Synageles_venator", "1427": "Synema_globosum", "1428": "Synema_imitatrix", "1429": "Synema_parvulum", "1430": "Synemosyna_formica", "1431": "Talavera_minuta", "1432": "Tamopsis_brisbanensis", "1433": "Tamopsis_fickerti", "1434": "Tamopsis_tweedensis", "1435": "Tapinillus_longipes", "1436": "Tarkas_maculatipes", "1437": "Tegenaria_domestica", "1438": "Tegenaria_ferruginea", "1439": "Tegenaria_parietina", "1440": "Telamonia_caprina", "1441": "Telamonia_dimidiata", "1442": "Telamonia_festiva", "1443": "Telamonia_vlijmi", "1444": "Telaprocera_maudae", "1445": "Teminius_affinis", "1446": "Teminius_insularis", "1447": "Terralonus_californicus", "1448": "Tetragnatha_extensa", "1449": "Tetragnatha_hasselti", "1450": "Tetragnatha_laboriosa", "1451": "Tetragnatha_montana", "1452": "Tetragnatha_obtusa", "1453": "Tetragnatha_squamata", "1454": "Tetragnatha_versicolor", "1455": "Tetragnatha_viridis", "1456": "Tetranychus_lintearius", "1457": "Tetranychus_urticae", "1458": "Teuthraustes_atramentarius", "1459": "Textrix_denticulata", "1460": "Thanatus_formicinus", "1461": "Tharpyna_campestrata", "1462": "Tharpyna_decorata", "1463": "Thaumasia_velox", "1464": "Thelacantha_brevispina", "1465": "Thelcticopis_severa", "1466": "Theraphosa_blondi", "1467": "Theridion_pyramidale", "1468": "Theridion_varians", "1469": "Theridion_zonulatum", "1470": "Theridiosoma_gemmosum", "1471": "Theridula_emertoni", "1472": "Theridula_gonygaster", "1473": "Thiania_bhamoensis", "1474": "Thiania_suboppressa", "1475": "Thomisus_citrinellus", "1476": "Thomisus_labefactus", "1477": "Thomisus_onustus", "1478": "Thomisus_scrupeus", "1479": "Thomisus_spectabilis", "1480": "Thorelliola_ensifera", "1481": "Thwaitesia_margaritifera", "1482": "Thwaitesia_nigronodosa", "1483": "Thyene_coccineovittata", "1484": "Thyene_imperialis", "1485": "Thyene_inflata", "1486": "Thyene_natalii", "1487": "Thyene_ogdeni", "1488": "Thyene_orientalis", "1489": "Tibellus_oblongus", "1490": "Tigrosa_annexa", "1491": "Tigrosa_aspersa", "1492": "Tigrosa_georgicola", "1493": "Tigrosa_helluo", "1494": "Tinus_peregrinus", "1495": "Titanattus_andinus", "1496": "Tityus_carrilloi", "1497": "Tityus_columbianus", "1498": "Tityus_metuendus", "1499": "Tityus_obscurus", "1500": "Tityus_serrulatus", "1501": "Tityus_stigmurus", "1502": "Tliltocatl_epicureanus", "1503": "Tliltocatl_kahlenbergi", "1504": "Tliltocatl_vagans", "1505": "Tmarus_angulatus", "1506": "Tmarus_piger", "1507": "Togwoteeus_biceps", "1508": "Tomopisthes_horrendus", "1509": "Toxeus_magnus", "1510": "Toxeus_maxillosus", "1511": "Toxopsoides_huttoni", "1512": "Trachelas_pacificus", "1513": "Trachelas_tranquillus", "1514": "Trachyzelotes_pedestris", "1515": "Trichonephila_antipodiana", "1516": "Trichonephila_clavata", "1517": "Trichonephila_clavipes", "1518": "Trichonephila_edulis", "1519": "Trichonephila_fenestrata", "1520": "Trichonephila_inaurata", "1521": "Trichonephila_plumipes", "1522": "Trichonephila_senegalensis", "1523": "Trichonephila_sexpunctata", "1524": "Trite_auricoma", "1525": "Trite_mustilina", "1526": "Trite_planiceps", "1527": "Trochosa_ruricola", "1528": "Trochosa_sepulchralis", "1529": "Trochosa_terricola", "1530": "Tropicosa_moesta", "1531": "Tutelina_elegans", "1532": "Tutelina_harti", "1533": "Tutelina_similis", "1534": "Tylorida_striata", "1535": "Tylorida_ventralis", "1536": "Typopeltis_crucifer", "1537": "Typostola_barbata", "1538": "Uliodon_albopunctatus", "1539": "Uloborus_diversus", "1540": "Uloborus_glomosus", "1541": "Uloborus_plumipes", "1542": "Uloborus_walckenaerius", "1543": "Ummidia_audouini", "1544": "Uroctea_durandi", "1545": "Uroctonites_montereus", "1546": "Uroctonus_mordax", "1547": "Urodacus_manicatus", "1548": "Urodacus_novaehollandiae", "1549": "Uroplectes_carinatus", "1550": "Uroplectes_flavoviridis", "1551": "Uroplectes_formosus", "1552": "Uroplectes_lineatus", "1553": "Uroplectes_planimanus", "1554": "Uroplectes_triangulifer", "1555": "Uroplectes_vittatus", "1556": "Vaejovis_carolinianus", "1557": "Vaejovis_deboerae", "1558": "Vaejovis_mexicanus", "1559": "Varroa_destructor", "1560": "Vasates_aceriscrumena", "1561": "Vasates_quadripedes", "1562": "Vectius_niger", "1563": "Venator_immansuetus", "1564": "Venator_spenceri", "1565": "Venatrix_furcillata", "1566": "Ventrivomer_ancyrophorus", "1567": "Verrucosa_arenata", "1568": "Verrucosa_meridionalis", "1569": "Verrucosa_scapofracta", "1570": "Verrucosa_undecimvariolata", "1571": "Viciria_pavesii", "1572": "Vicirionessa_mustela", "1573": "Vonones_sayi", "1574": "Wadicosa_fidelis", "1575": "Wagneriana_spicata", "1576": "Wagneriana_tauricornis", "1577": "Witica_crassicauda", "1578": "Wulfila_albens", "1579": "Wulfila_saltabundus", "1580": "Xerolycosa_miniata", "1581": "Xerolycosa_nemoralis", "1582": "Xysticus_cristatus", "1583": "Xysticus_kochi", "1584": "Xysticus_lanio", "1585": "Xysticus_punctatus", "1586": "Xysticus_texanus", "1587": "Xysticus_ulmi", "1588": "Yllenus_arenarius", "1589": "Yllenus_uiguricus", "1590": "Yllenus_zyuzini", "1591": "Yunohamella_lyrica", "1592": "Zachaeus_crista", "1593": "Zealaranea_crassa", "1594": "Zenodorus_orbiculatus", "1595": "Zenodorus_swiftorum", "1596": "Zilla_diodia", "1597": "Zimiris_doriae", "1598": "Zora_spinimana", "1599": "Zoropsis_spinimana", "1600": "Zosis_geniculata", "1601": "Zygiella_atrica", "1602": "Zygiella_x-notata", "1603": "Zygoballus_nervosus", "1604": "Zygoballus_rufipes", "1605": "Zygoballus_sexpunctatus", "1606": "Zygometis_xanthogaster"}}}}], "splits": [{"name": "train", "num_bytes": 38785458087.024, "num_examples": 2049928}], "download_size": 41403446692, "dataset_size": 38785458087.024}}	2023-10-05T08:36:13+00:00	[]	[]	TAGS #region-us	# Dataset Card for "inatSpiders" More Information needed	[ "# Dataset Card for \"inatSpiders\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"inatSpiders\"\n\nMore Information needed" ]	[ 6, 14 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"inatSpiders\"\n\nMore Information needed" ]
7d69952dc99ac04ad1544bee3d946fe491d42e78	# Bangumi Image Base of Karakai Jouzu No Takagi-san This is the image base of bangumi Karakai Jouzu no Takagi-san, we detected 21 characters, 6297 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 \| 2534 \| [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 \| 205 \| [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 \| 45 \| [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 \| 37 \| [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 \| 36 \| [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 \| 17 \| [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 \| 108 \| [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 \| 16 \| [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 \| 56 \| [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 \| 13 \| [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 \| 344 \| [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 \| 2029 \| [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 \| 46 \| [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 \| 292 \| [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 \| 81 \| [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 \| 255 \| [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 \| 17 \| [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 \| 16 \| [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 \| 5 \| [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) \| N/A \| N/A \| N/A \| \| 19 \| 30 \| [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) \| \| noise \| 115 \| [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) \|	BangumiBase/karakaijouzunotakagisan	[ "size_categories:1K<n<10K", "license:mit", "art", "region:us" ]	2023-10-05T04:28:26+00:00	{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}	2023-10-05T07:19:45+00:00	[]	[]	TAGS #size_categories-1K<n<10K #license-mit #art #region-us	Bangumi Image Base of Karakai Jouzu No Takagi-san ================================================= This is the image base of bangumi Karakai Jouzu no Takagi-san, we detected 21 characters, 6297 images in total. The full dataset is here. 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:	[]	[ "TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]	[ 25 ]	[ "passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]
fd9ba4f46b16c67ccf9ba8ae4d30663f079e49fc	# Bangumi Image Base of Denpa Onna To Seishun Otoko This is the image base of bangumi Denpa Onna to Seishun Otoko, we detected 15 characters, 1491 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 \| 109 \| [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 \| 106 \| [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 \| 16 \| [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 \| 126 \| [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 \| 22 \| [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 \| 546 \| [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 \| 13 \| [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 \| 163 \| [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 \| 8 \| [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 \| 29 \| [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 \| 8 \| [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 \| 185 \| [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 \| 26 \| [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 \| 5 \| [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) \| N/A \| N/A \| N/A \| \| noise \| 129 \| [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) \|	BangumiBase/denpaonnatoseishunotoko	[ "size_categories:1K<n<10K", "license:mit", "art", "region:us" ]	2023-10-05T04:30:06+00:00	{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}	2023-10-05T05:36:29+00:00	[]	[]	TAGS #size_categories-1K<n<10K #license-mit #art #region-us	Bangumi Image Base of Denpa Onna To Seishun Otoko ================================================= This is the image base of bangumi Denpa Onna to Seishun Otoko, we detected 15 characters, 1491 images in total. The full dataset is here. 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:	[]	[ "TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]	[ 25 ]	[ "passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]
19ed080ef4b900b812c02fb1a6358eddb74d8489	# Bangumi Image Base of Kage No Jitsuryokusha Ni Naritakute! This is the image base of bangumi Kage no Jitsuryokusha ni Naritakute!, we detected 41 characters, 1746 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 \| 259 \| [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 \| 83 \| [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 \| 36 \| [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 \| 30 \| [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 \| 26 \| [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 \| 15 \| [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 \| 27 \| [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 \| 7 \| [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) \| N/A \| \| 8 \| 44 \| [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 \| 16 \| [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 \| 10 \| [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 \| 17 \| [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 \| 23 \| [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 \| 25 \| [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 \| 23 \| [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 \| 27 \| [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 \| 19 \| [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 \| 16 \| [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 \| 16 \| [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 \| 13 \| [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 \| 20 \| [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 \| 41 \| [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 \| 11 \| [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 \| 12 \| [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 \| 10 \| [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 \| 87 \| [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 \| 70 \| [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 \| 44 \| [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 \| 19 \| [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 \| 63 \| [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 \| 23 \| [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 \| 9 \| [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 \| 9 \| [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 \| 22 \| [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 \| 51 \| [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 \| 9 \| [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 \| 8 \| [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 \| 9 \| [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 \| 38 \| [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 \| 7 \| [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) \| N/A \| \| noise \| 452 \| [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) \|	BangumiBase/kagenojitsuryokushaninaritakute	[ "size_categories:1K<n<10K", "license:mit", "art", "region:us" ]	2023-10-05T04:42:20+00:00	{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}	2023-10-05T06:01:47+00:00	[]	[]	TAGS #size_categories-1K<n<10K #license-mit #art #region-us	Bangumi Image Base of Kage No Jitsuryokusha Ni Naritakute! ========================================================== This is the image base of bangumi Kage no Jitsuryokusha ni Naritakute!, we detected 41 characters, 1746 images in total. The full dataset is here. 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:	[]	[ "TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]	[ 25 ]	[ "passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]
081844b55a16f597cb005a76522829bf68ba9631	# Dataset Card for "dataset_whisper" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	rashmi035/dataset_whisper	[ "region:us" ]	2023-10-05T04:48:14+00:00	{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "transcription", "dtype": "string"}, {"name": "set", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 35817014.0, "num_examples": 100}, {"name": "validation", "num_bytes": 15314681.0, "num_examples": 50}, {"name": "test", "num_bytes": 7381857.0, "num_examples": 29}], "download_size": 55480724, "dataset_size": 58513552.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-"}, {"split": "validation", "path": "data/validation-"}, {"split": "test", "path": "data/test-*"}]}]}	2023-10-05T04:49:20+00:00	[]	[]	TAGS #region-us	# Dataset Card for "dataset_whisper" More Information needed	[ "# Dataset Card for \"dataset_whisper\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"dataset_whisper\"\n\nMore Information needed" ]	[ 6, 16 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"dataset_whisper\"\n\nMore Information needed" ]
d977083e29a4eacb165705e55dd7e524ff0b9634	# Dataset Card for "emotion_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Falah/emotion_prompts	[ "region:us" ]	2023-10-05T04:53:29+00:00	{"dataset_info": {"features": [{"name": "prompts", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4626262, "num_examples": 10000}], "download_size": 669543, "dataset_size": 4626262}}	2023-10-05T04:53:31+00:00	[]	[]	TAGS #region-us	# Dataset Card for "emotion_prompts" More Information needed	[ "# Dataset Card for \"emotion_prompts\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"emotion_prompts\"\n\nMore Information needed" ]	[ 6, 16 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"emotion_prompts\"\n\nMore Information needed" ]
9bc9eda0c469119942c5607968f3c640acbbdc4f	# Dataset Card for "SQL_Merged_IDs_and_Text" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	AayushShah/SQL_Merged_IDs_and_Text	[ "region:us" ]	2023-10-05T05:11:06+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-"}, {"split": "test", "path": "data/test-"}]}], "dataset_info": {"features": [{"name": "NATURAL_LANG", "dtype": "string"}, {"name": "SCHEMA", "dtype": "string"}, {"name": "SQL", "dtype": "string"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 1089459820.9581463, "num_examples": 270986}, {"name": "test", "num_bytes": 121052878.04185376, "num_examples": 30110}], "download_size": 101851785, "dataset_size": 1210512699.0}}	2023-10-05T05:26:42+00:00	[]	[]	TAGS #region-us	# Dataset Card for "SQL_Merged_IDs_and_Text" More Information needed	[ "# Dataset Card for \"SQL_Merged_IDs_and_Text\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"SQL_Merged_IDs_and_Text\"\n\nMore Information needed" ]	[ 6, 21 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"SQL_Merged_IDs_and_Text\"\n\nMore Information needed" ]
c72fd8920cd9c2096a8f3314d13fb34ea399a3b3	Use only for Alignment research. NOETI is not responsible for what you might do with it.	NobodyExistsOnTheInternet/AlpacaToxicQA	[ "not-for-all-audiences", "region:us" ]	2023-10-05T05:22:34+00:00	{"tags": ["not-for-all-audiences"]}	2024-01-10T14:30:00+00:00	[]	[]	TAGS #not-for-all-audiences #region-us	Use only for Alignment research. NOETI is not responsible for what you might do with it.	[]	[ "TAGS\n#not-for-all-audiences #region-us \n" ]	[ 15 ]	[ "passage: TAGS\n#not-for-all-audiences #region-us \n" ]
a6c1686765a1dcbd8b26dc0c2669feaa89b7863c	# Bangumi Image Base of Isekai Nonbiri Nouka This is the image base of bangumi Isekai Nonbiri Nouka, we detected 41 characters, 1820 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 \| 619 \| [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 \| 6 \| [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) \| N/A \| N/A \| \| 2 \| 8 \| [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 \| 16 \| [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 \| 7 \| [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) \| N/A \| \| 5 \| 30 \| [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 \| 10 \| [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 \| 7 \| [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) \| N/A \| \| 8 \| 156 \| [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 \| 41 \| [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 \| 14 \| [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 \| 15 \| [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 \| 105 \| [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 \| 209 \| [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 \| 42 \| [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 \| 10 \| [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 \| 16 \| [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 \| 18 \| [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 \| 20 \| [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 \| 21 \| [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 \| 8 \| [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 \| 47 \| [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 \| 6 \| [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) \| N/A \| N/A \| \| 23 \| 18 \| [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 \| 17 \| [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 \| 55 \| [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 \| 57 \| [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 \| 7 \| [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) \| N/A \| \| 28 \| 10 \| [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 \| 5 \| [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) \| N/A \| N/A \| N/A \| \| 30 \| 11 \| [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 \| 26 \| [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 \| 7 \| [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) \| N/A \| \| 34 \| 8 \| [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 \| 7 \| [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) \| N/A \| \| 37 \| 9 \| [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 \| 18 \| [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 \| 10 \| [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) \| \| noise \| 100 \| [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) \|	BangumiBase/isekainonbirinouka	[ "size_categories:1K<n<10K", "license:mit", "art", "region:us" ]	2023-10-05T05:40:21+00:00	{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}	2023-10-05T06:40:00+00:00	[]	[]	TAGS #size_categories-1K<n<10K #license-mit #art #region-us	Bangumi Image Base of Isekai Nonbiri Nouka ========================================== This is the image base of bangumi Isekai Nonbiri Nouka, we detected 41 characters, 1820 images in total. The full dataset is here. 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:	[]	[ "TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]	[ 25 ]	[ "passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]
5766bc7845d5a8819960735e30d0342f022fc882	# Bangumi Image Base of Date A Live This is the image base of bangumi DATE A LIVE, we detected 92 characters, 9273 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 \| 348 \| [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 \| 105 \| [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 \| 67 \| [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 \| 84 \| [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 \| 42 \| [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 \| 2476 \| [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 \| 108 \| [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 \| 82 \| [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 \| 63 \| [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 \| 33 \| [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 \| 54 \| [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 \| 789 \| [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 \| 40 \| [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 \| 132 \| [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 \| 19 \| [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 \| 12 \| [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 \| 25 \| [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 \| 15 \| [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 \| 12 \| [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 \| 26 \| [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 \| 15 \| [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 \| 40 \| [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 \| 21 \| [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 \| 60 \| [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 \| 60 \| [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 \| 32 \| [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 \| 65 \| [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 \| 27 \| [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 \| 13 \| [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 \| 13 \| [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 \| 30 \| [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 \| 16 \| [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 \| 12 \| [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 \| 35 \| [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 \| 94 \| [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 \| 207 \| [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 \| 402 \| [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 \| 156 \| [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 \| 64 \| [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 \| 23 \| [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 \| 34 \| [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 \| 45 \| [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 \| 19 \| [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 \| 26 \| [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 \| 691 \| [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 \| 43 \| [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 \| 373 \| [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 \| 16 \| [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 \| 48 \| [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 \| 76 \| [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 \| 22 \| [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 \| 124 \| [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 \| 22 \| [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 \| 42 \| [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 \| 6 \| [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) \| N/A \| N/A \| \| 56 \| 29 \| [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 \| 114 \| [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 \| 38 \| [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 \| 430 \| [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 \| 13 \| [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 \| 17 \| [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 \| 17 \| [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 \| 13 \| [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 \| 15 \| [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 \| 22 \| [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 \| 35 \| [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 \| 219 \| [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 \| 44 \| [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 \| 44 \| [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 \| 9 \| [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 \| 8 \| [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 \| 34 \| [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 \| 47 \| [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 \| 15 \| [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 \| 35 \| [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 \| 15 \| [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 \| 17 \| [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 \| 18 \| [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 \| 12 \| [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 \| 8 \| [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 \| 9 \| [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 \| 14 \| [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 \| 11 \| [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 \| 8 \| [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 \| 11 \| [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 \| 9 \| [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 \| 24 \| [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 \| 6 \| [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) \| N/A \| N/A \| \| 89 \| 20 \| [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 \| 19 \| [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) \| \| noise \| 355 \| [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) \|	BangumiBase/datealive	[ "size_categories:1K<n<10K", "license:mit", "art", "region:us" ]	2023-10-05T05:42:20+00:00	{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}	2023-10-05T10:59:27+00:00	[]	[]	TAGS #size_categories-1K<n<10K #license-mit #art #region-us	Bangumi Image Base of Date A Live ================================= This is the image base of bangumi DATE A LIVE, we detected 92 characters, 9273 images in total. The full dataset is here. 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:	[]	[ "TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]	[ 25 ]	[ "passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]
0d5d62ace9cd2c8c2c98f3978b2b5dd6bece6a92	# Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Summarization * Model: 0x70DA/pegasus-cnn_dailymail * Dataset: samsum * Config: samsum * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@Saravanan007](https://huggingface.co/Saravanan007) for evaluating this model.	autoevaluate/autoeval-eval-samsum-samsum-f09cc2-93106145752	[ "autotrain", "evaluation", "region:us" ]	2023-10-05T06:17:22+00:00	{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["samsum"], "eval_info": {"task": "summarization", "model": "0x70DA/pegasus-cnn_dailymail", "metrics": [], "dataset_name": "samsum", "dataset_config": "samsum", "dataset_split": "test", "col_mapping": {"text": "dialogue", "target": "summary"}}}	2023-10-05T06:20:13+00:00	[]	[]	TAGS #autotrain #evaluation #region-us	# Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by AutoTrain for the following task and dataset: * Task: Summarization * Model: 0x70DA/pegasus-cnn_dailymail * Dataset: samsum * Config: samsum * Split: test To run new evaluation jobs, visit Hugging Face's automatic model evaluator. ## Contributions Thanks to @Saravanan007 for evaluating this model.	[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: 0x70DA/pegasus-cnn_dailymail\n* Dataset: samsum\n* Config: samsum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Saravanan007 for evaluating this model." ]	[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: 0x70DA/pegasus-cnn_dailymail\n* Dataset: samsum\n* Config: samsum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @Saravanan007 for evaluating this model." ]	[ 13, 90, 17 ]	[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: 0x70DA/pegasus-cnn_dailymail\n* Dataset: samsum\n* Config: samsum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @Saravanan007 for evaluating this model." ]
f1bd04e6b69c559d843d7123458263b0330264a4	# Dataset Card for Evaluation run of beomi/KoAlpaca-KoRWKV-6B ## Dataset Description - Homepage: - Repository: https://huggingface.co/beomi/KoAlpaca-KoRWKV-6B - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [beomi/KoAlpaca-KoRWKV-6B](https://huggingface.co/beomi/KoAlpaca-KoRWKV-6B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_beomi__KoAlpaca-KoRWKV-6B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-29T10:47:14.671531](https://huggingface.co/datasets/open-llm-leaderboard/details_beomi__KoAlpaca-KoRWKV-6B/blob/main/results_2023-10-29T10-47-14.671531.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.0065016778523489934, "em_stderr": 0.000823068429722398, "f1": 0.0316212248322148, "f1_stderr": 0.0012557323603243587, "acc": 0.2580899763220205, "acc_stderr": 0.007022563065489298 }, "harness\|drop\|3": { "em": 0.0065016778523489934, "em_stderr": 0.000823068429722398, "f1": 0.0316212248322148, "f1_stderr": 0.0012557323603243587 }, "harness\|gsm8k\|5": { "acc": 0.0, "acc_stderr": 0.0 }, "harness\|winogrande\|5": { "acc": 0.516179952644041, "acc_stderr": 0.014045126130978596 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	open-llm-leaderboard/details_beomi__KoAlpaca-KoRWKV-6B	[ "region:us" ]	2023-10-05T06:30:08+00:00	{"pretty_name": "Evaluation run of beomi/KoAlpaca-KoRWKV-6B", "dataset_summary": "Dataset automatically created during the evaluation run of model [beomi/KoAlpaca-KoRWKV-6B](https://huggingface.co/beomi/KoAlpaca-KoRWKV-6B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_beomi__KoAlpaca-KoRWKV-6B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-29T10:47:14.671531](https://huggingface.co/datasets/open-llm-leaderboard/details_beomi__KoAlpaca-KoRWKV-6B/blob/main/results_2023-10-29T10-47-14.671531.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0065016778523489934,\n \"em_stderr\": 0.000823068429722398,\n \"f1\": 0.0316212248322148,\n \"f1_stderr\": 0.0012557323603243587,\n \"acc\": 0.2580899763220205,\n \"acc_stderr\": 0.007022563065489298\n },\n \"harness\|drop\|3\": {\n \"em\": 0.0065016778523489934,\n \"em_stderr\": 0.000823068429722398,\n \"f1\": 0.0316212248322148,\n \"f1_stderr\": 0.0012557323603243587\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.516179952644041,\n \"acc_stderr\": 0.014045126130978596\n }\n}\n```", "repo_url": "https://huggingface.co/beomi/KoAlpaca-KoRWKV-6B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_10_05T07_29_47.362584", "path": ["/details_harness\|arc:challenge\|25_2023-10-05T07-29-47.362584.parquet"]}, {"split": "latest", "path": ["/details_harness\|arc:challenge\|25_2023-10-05T07-29-47.362584.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_29T10_47_14.671531", "path": ["/details_harness\|drop\|3_2023-10-29T10-47-14.671531.parquet"]}, {"split": "latest", "path": ["/details_harness\|drop\|3_2023-10-29T10-47-14.671531.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_29T10_47_14.671531", "path": ["/details_harness\|gsm8k\|5_2023-10-29T10-47-14.671531.parquet"]}, {"split": "latest", "path": ["/details_harness\|gsm8k\|5_2023-10-29T10-47-14.671531.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_10_05T07_29_47.362584", "path": ["/details_harness\|hellaswag\|10_2023-10-05T07-29-47.362584.parquet"]}, {"split": "latest", "path": ["/details_harness\|hellaswag\|10_2023-10-05T07-29-47.362584.parquet"]}]}, 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["/details_harness\|truthfulqa:mc\|0_2023-10-05T07-29-47.362584.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-10-05T07-29-47.362584.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_29T10_47_14.671531", "path": ["/details_harness\|winogrande\|5_2023-10-29T10-47-14.671531.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-10-29T10-47-14.671531.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_10_05T07_29_47.362584", "path": ["results_2023-10-05T07-29-47.362584.parquet"]}, {"split": "2023_10_29T10_47_14.671531", "path": ["results_2023-10-29T10-47-14.671531.parquet"]}, {"split": "latest", "path": ["results_2023-10-29T10-47-14.671531.parquet"]}]}]}	2023-10-29T10:47:27+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of beomi/KoAlpaca-KoRWKV-6B ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model beomi/KoAlpaca-KoRWKV-6B on the Open LLM Leaderboard. The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-10-29T10:47:14.671531(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of beomi/KoAlpaca-KoRWKV-6B", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model beomi/KoAlpaca-KoRWKV-6B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-10-29T10:47:14.671531(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of beomi/KoAlpaca-KoRWKV-6B", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model beomi/KoAlpaca-KoRWKV-6B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-10-29T10:47:14.671531(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 22, 31, 170, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of beomi/KoAlpaca-KoRWKV-6B## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model beomi/KoAlpaca-KoRWKV-6B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-29T10:47:14.671531(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
3538df3d2ad880db411e18f31653f8429e89e386	# Bangumi Image Base of Fullmetal Alchemist This is the image base of bangumi Fullmetal Alchemist, we detected 44 characters, 5107 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 \| 1190 \| [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 \| 164 \| [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 \| 20 \| [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 \| 61 \| [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 \| 56 \| [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 \| 80 \| [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 \| 384 \| [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 \| 427 \| [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 \| 179 \| [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 \| 73 \| [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 \| 93 \| [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 \| 33 \| [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 \| 84 \| [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 \| 113 \| [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 \| 95 \| [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 \| 129 \| [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 \| 318 \| [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 \| 187 \| [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 \| 26 \| [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 \| 48 \| [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 \| 78 \| [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 \| 54 \| [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 \| 53 \| [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 \| 97 \| [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 \| 142 \| [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 \| 217 \| [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 \| 12 \| [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 \| 246 \| [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 \| 58 \| [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 \| 32 \| [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 \| 19 \| [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 \| 15 \| [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 \| 39 \| [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 \| 23 \| [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 \| 9 \| [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 \| 14 \| [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 \| 23 \| [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 \| 11 \| [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 \| 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 \| 13 \| [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 \| 18 \| [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 \| 40 \| [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 \| 14 \| [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) \| \| noise \| 108 \| [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) \|	BangumiBase/fullmetalalchemist	[ "size_categories:1K<n<10K", "license:mit", "art", "region:us" ]	2023-10-05T07:12:39+00:00	{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}	2023-10-05T13:29:27+00:00	[]	[]	TAGS #size_categories-1K<n<10K #license-mit #art #region-us	Bangumi Image Base of Fullmetal Alchemist ========================================= This is the image base of bangumi Fullmetal Alchemist, we detected 44 characters, 5107 images in total. The full dataset is here. 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:	[]	[ "TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]	[ 25 ]	[ "passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]
0d40bcf0af3635d025431219ea37613ca2e41da1	# VQuAnDa - Verbalization Question Answering Dataset ## Background [VQuAnDa](https://dl.acm.org/doi/abs/10.1007/978-3-030-49461-2_31) is knowledge base QA dataset based on [LC-QuAD](https://github.com/AskNowQA/LC-QuAD) which uses [DBpedia v04.16](https://wiki.dbpedia.org/dbpedia-version-2016-04) as the target KB. This QA task consists of two components: 1. A Text2Sparql task where a natural language query is translated to a SPARQL query. 2. An RDF triple to verbalized answer task where the knowledge base query result must be translated back into natural language. The dataset is in JSON format, and it contains 5000 examples (4000 train/1000 test). An example is shown below. The query result is surrounded by brackets `[]` in the verbalized answer. ``` { "uid" : "3508" "question" : "How many shows are aired on Comedy Central?" "verbalized_answer" : "There are [73] television shows broadcasted by..." "query" : "SELECT DISTINCT COUNT(?uri) WHERE {?uri <http:..." } ``` ## Baseline models Alongside the dataset, the authors provide some baseline models. [Here](https://github.com/endrikacupaj/VQUANDA-Baseline-Models) you can find the baseline implementations and instructions for how to run them. ## License The dataset is under [Attribution 4.0 International (CC BY 4.0)](LICENSE) ## Cite ``` @InProceedings{kacupaj2020vquanda, title={VQuAnDa: Verbalization QUestion ANswering DAtaset}, author={Kacupaj, Endri and Zafar, Hamid and Lehmann, Jens and Maleshkova, Maria}, booktitle={The Semantic Web}, pages={531--547}, year={2020}, publisher={Springer International Publishing}, } ```	casey-martin/vquanda	[ "task_categories:table-question-answering", "task_categories:text2text-generation", "size_categories:1K<n<10K", "language:en", "license:cc-by-4.0", "sparql", "semantic web", "knowledge graph", "knowledge base", "dbpedia", "region:us" ]	2023-10-05T07:17:39+00:00	{"language": ["en"], "license": "cc-by-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["table-question-answering", "text2text-generation"], "pretty_name": "VQuAnDa - Verbalization Question Answering Dataset", "tags": ["sparql", "semantic web", "knowledge graph", "knowledge base", "dbpedia"]}	2023-10-05T07:46:50+00:00	[]	[ "en" ]	TAGS #task_categories-table-question-answering #task_categories-text2text-generation #size_categories-1K<n<10K #language-English #license-cc-by-4.0 #sparql #semantic web #knowledge graph #knowledge base #dbpedia #region-us	# VQuAnDa - Verbalization Question Answering Dataset ## Background VQuAnDa is knowledge base QA dataset based on LC-QuAD which uses DBpedia v04.16 as the target KB. This QA task consists of two components: 1. A Text2Sparql task where a natural language query is translated to a SPARQL query. 2. An RDF triple to verbalized answer task where the knowledge base query result must be translated back into natural language. The dataset is in JSON format, and it contains 5000 examples (4000 train/1000 test). An example is shown below. The query result is surrounded by brackets '[]' in the verbalized answer. ## Baseline models Alongside the dataset, the authors provide some baseline models. Here you can find the baseline implementations and instructions for how to run them. ## License The dataset is under Attribution 4.0 International (CC BY 4.0) ## Cite	[ "# VQuAnDa - Verbalization Question Answering Dataset", "## Background\nVQuAnDa is knowledge base QA dataset based on\nLC-QuAD which uses\nDBpedia v04.16 as the target KB.\n\nThis QA task consists of two components:\n 1. A Text2Sparql task where a natural language query is translated to a SPARQL query.\n 2. An RDF triple to verbalized answer task where the knowledge base query result must be translated back into natural language.\n\nThe dataset is in JSON format, and it contains 5000 examples (4000 train/1000 test).\nAn example is shown below. The query result is surrounded by brackets '[]' in the verbalized answer.", "## Baseline models\nAlongside the dataset, the authors provide some baseline models.\nHere you can find the baseline implementations and instructions for how to run them.", "## License\nThe dataset is under Attribution 4.0 International (CC BY 4.0)", "## Cite" ]	[ "TAGS\n#task_categories-table-question-answering #task_categories-text2text-generation #size_categories-1K<n<10K #language-English #license-cc-by-4.0 #sparql #semantic web #knowledge graph #knowledge base #dbpedia #region-us \n", "# VQuAnDa - Verbalization Question Answering Dataset", "## Background\nVQuAnDa is knowledge base QA dataset based on\nLC-QuAD which uses\nDBpedia v04.16 as the target KB.\n\nThis QA task consists of two components:\n 1. A Text2Sparql task where a natural language query is translated to a SPARQL query.\n 2. An RDF triple to verbalized answer task where the knowledge base query result must be translated back into natural language.\n\nThe dataset is in JSON format, and it contains 5000 examples (4000 train/1000 test).\nAn example is shown below. The query result is surrounded by brackets '[]' in the verbalized answer.", "## Baseline models\nAlongside the dataset, the authors provide some baseline models.\nHere you can find the baseline implementations and instructions for how to run them.", "## License\nThe dataset is under Attribution 4.0 International (CC BY 4.0)", "## Cite" ]	[ 77, 14, 150, 37, 15, 3 ]	[ "passage: TAGS\n#task_categories-table-question-answering #task_categories-text2text-generation #size_categories-1K<n<10K #language-English #license-cc-by-4.0 #sparql #semantic web #knowledge graph #knowledge base #dbpedia #region-us \n# VQuAnDa - Verbalization Question Answering Dataset## Background\nVQuAnDa is knowledge base QA dataset based on\nLC-QuAD which uses\nDBpedia v04.16 as the target KB.\n\nThis QA task consists of two components:\n 1. A Text2Sparql task where a natural language query is translated to a SPARQL query.\n 2. An RDF triple to verbalized answer task where the knowledge base query result must be translated back into natural language.\n\nThe dataset is in JSON format, and it contains 5000 examples (4000 train/1000 test).\nAn example is shown below. The query result is surrounded by brackets '[]' in the verbalized answer.## Baseline models\nAlongside the dataset, the authors provide some baseline models.\nHere you can find the baseline implementations and instructions for how to run them.## License\nThe dataset is under Attribution 4.0 International (CC BY 4.0)## Cite" ]
9d762057c51820a04443c9402b83c235a557604e	Extract from Red Alert 2 & Yuri's Revenge. Include characters: * General Carville * President Dugan * Professor Einstein * Yuri battlefield controller * Eva * Premier Romanov * Agent Tanya * Yuri * Zofia 2191 WAV files in total. PCM_f32le 16bit 22050hz Ready for VITS-Fast-Fine-Tuning training.	kingsznhone/Red-Alert-2-Full-Voice-Data	[ "task_categories:text-to-speech", "size_categories:1K<n<10K", "language:en", "region:us" ]	2023-10-05T07:19:49+00:00	{"language": ["en"], "size_categories": ["1K<n<10K"], "task_categories": ["text-to-speech"], "pretty_name": "RA2 Allstar"}	2023-10-05T07:50:59+00:00	[]	[ "en" ]	TAGS #task_categories-text-to-speech #size_categories-1K<n<10K #language-English #region-us	Extract from Red Alert 2 & Yuri's Revenge. Include characters: * General Carville * President Dugan * Professor Einstein * Yuri battlefield controller * Eva * Premier Romanov * Agent Tanya * Yuri * Zofia 2191 WAV files in total. PCM_f32le 16bit 22050hz Ready for VITS-Fast-Fine-Tuning training.	[]	[ "TAGS\n#task_categories-text-to-speech #size_categories-1K<n<10K #language-English #region-us \n" ]	[ 35 ]	[ "passage: TAGS\n#task_categories-text-to-speech #size_categories-1K<n<10K #language-English #region-us \n" ]
94bd7464309c482c23d462e3772f3540f6dc78ca	### Getting Started To use the indices, first download CommonPool: https://github.com/mlfoundations/datacomp#downloading-commonpool Then reshard with the provided indices file: https://github.com/mlfoundations/datacomp#selecting-samples-in-the-filtering-track ### Paper Link https://arxiv.org/abs/2309.17425	apf1/datafilteringnetworks_2b	[ "license:cc-by-4.0", "arxiv:2309.17425", "region:us" ]	2023-10-05T07:21:45+00:00	{"license": "cc-by-4.0"}	2024-01-17T19:41:11+00:00	[ "2309.17425" ]	[]	TAGS #license-cc-by-4.0 #arxiv-2309.17425 #region-us	### Getting Started To use the indices, first download CommonPool: URL Then reshard with the provided indices file: URL ### Paper Link URL	[ "### Getting Started\n\nTo use the indices, first download CommonPool: URL\n\nThen reshard with the provided indices file: URL", "### Paper Link\nURL" ]	[ "TAGS\n#license-cc-by-4.0 #arxiv-2309.17425 #region-us \n", "### Getting Started\n\nTo use the indices, first download CommonPool: URL\n\nThen reshard with the provided indices file: URL", "### Paper Link\nURL" ]	[ 23, 29, 5 ]	[ "passage: TAGS\n#license-cc-by-4.0 #arxiv-2309.17425 #region-us \n### Getting Started\n\nTo use the indices, first download CommonPool: URL\n\nThen reshard with the provided indices file: URL### Paper Link\nURL" ]
09cfba02d0828ea67d20b4e8c25eb351489dcf7f	# Bangumi Image Base of Kuzu No Honkai This is the image base of bangumi Kuzu no Honkai, we detected 13 characters, 869 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 \| 298 \| [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 \| 12 \| [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 \| 22 \| [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 \| 48 \| [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 \| 37 \| [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 \| 29 \| [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 \| 114 \| [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 \| 92 \| [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 \| 48 \| [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 \| 14 \| [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 \| 40 \| [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 \| 10 \| [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) \| \| noise \| 105 \| [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) \|	BangumiBase/kuzunohonkai	[ "size_categories:n<1K", "license:mit", "art", "region:us" ]	2023-10-05T07:31:14+00:00	{"license": "mit", "size_categories": ["n<1K"], "tags": ["art"]}	2023-10-05T08:25:29+00:00	[]	[]	TAGS #size_categories-n<1K #license-mit #art #region-us	Bangumi Image Base of Kuzu No Honkai ==================================== This is the image base of bangumi Kuzu no Honkai, we detected 13 characters, 869 images in total. The full dataset is here. 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:	[]	[ "TAGS\n#size_categories-n<1K #license-mit #art #region-us \n" ]	[ 23 ]	[ "passage: TAGS\n#size_categories-n<1K #license-mit #art #region-us \n" ]
810b57e4aee0e8bbd23fafaf33899321f465af6a	# Dataset Card for oig-30k This dataset has been created with [Argilla](https://docs.argilla.io). As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets). ## Dataset Description - Homepage: https://argilla.io - Repository: https://github.com/argilla-io/argilla - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary This dataset contains: * A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla. * Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`. * The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla. ### Load with Argilla To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code: ```python import argilla as rg ds = rg.FeedbackDataset.from_huggingface("argilla/oig-30k") ``` ### Load with `datasets` To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code: ```python from datasets import load_dataset ds = load_dataset("argilla/oig-30k") ``` ### Supported Tasks and Leaderboards This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/conceptual_guides/data_model.html#feedback-dataset) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure). There are no leaderboards associated with this dataset. ### Languages [More Information Needed] ## Dataset Structure ### Data in Argilla The dataset is created in Argilla with: fields, questions, suggestions, and guidelines. The fields are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions. \| Field Name \| Title \| Type \| Required \| Markdown \| \| ---------- \| ----- \| ---- \| -------- \| -------- \| \| background \| Background \| text \| True \| False \| \| prompt \| Prompt \| text \| True \| False \| \| response \| Final Response \| text \| True \| False \| The questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking. \| Question Name \| Title \| Type \| Required \| Description \| Values/Labels \| \| ------------- \| ----- \| ---- \| -------- \| ----------- \| ------------- \| \| quality \| Is it a Good or Bad response? \| label_selection \| True \| N/A \| ['Good', 'Bad'] \| ✨ NEW Additionally, we also have suggestions, which are linked to the existing questions, and so on, named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above. Finally, the guidelines are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section. ### Data Instances An example of a dataset instance in Argilla looks as follows: ```json { "external_id": null, "fields": { "background": "", "prompt": "\u003chuman\u003e: Write a article related to AT\u0026T, purchased, June.", "response": "\u003cbot\u003e: This is my first, albeit temporary, case for my iPhone. It was one of the few available at the AT\u0026T where I purchased my iPhone on June 29 and it\u0027s been useful to date, but it\u0027s not a long-term keeper" }, "metadata": {}, "responses": [], "suggestions": [] } ``` While the same record in HuggingFace `datasets` looks as follows: ```json { "background": "", "external_id": null, "metadata": "{}", "prompt": "\u003chuman\u003e: Write a article related to AT\u0026T, purchased, June.", "quality": [], "quality-suggestion": null, "quality-suggestion-metadata": { "agent": null, "score": null, "type": null }, "response": "\u003cbot\u003e: This is my first, albeit temporary, case for my iPhone. It was one of the few available at the AT\u0026T where I purchased my iPhone on June 29 and it\u0027s been useful to date, but it\u0027s not a long-term keeper" } ``` ### Data Fields Among the dataset fields, we differentiate between the following: * Fields: These are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions. * background is of type `text`. * prompt is of type `text`. * response is of type `text`. * Questions: These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`. * quality is of type `label_selection` with the following allowed values ['Good', 'Bad']. * ✨ NEW Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable. * (optional) quality-suggestion is of type `label_selection` with the following allowed values ['Good', 'Bad']. Additionally, we also have one more field which is optional and is the following: * external_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file. ### Data Splits The dataset contains a single split, which is `train`. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation guidelines [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	argilla/oig-30k	[ "size_categories:10K<n<100K", "rlfh", "argilla", "human-feedback", "region:us" ]	2023-10-05T07:41:13+00:00	{"size_categories": "10K<n<100K", "tags": ["rlfh", "argilla", "human-feedback"]}	2023-10-05T07:41:21+00:00	[]	[]	TAGS #size_categories-10K<n<100K #rlfh #argilla #human-feedback #region-us	Dataset Card for oig-30k ======================== This dataset has been created with Argilla. As shown in the sections below, this dataset can be loaded into Argilla as explained in Load with Argilla, or used directly with the 'datasets' library in Load with 'datasets'. Dataset Description ------------------- * Homepage: URL * Repository: URL * Paper: * Leaderboard: * Point of Contact: ### Dataset Summary This dataset contains: * A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\_huggingface' method in Argilla. * Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\_huggingface' and can be loaded independently using the 'datasets' library via 'load\_dataset'. * The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla. ### Load with Argilla To load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code: ### Load with 'datasets' To load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code: ### Supported Tasks and Leaderboards This dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section. There are no leaderboards associated with this dataset. ### Languages Dataset Structure ----------------- ### Data in Argilla The dataset is created in Argilla with: fields, questions, suggestions, and guidelines. The fields are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions. The questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label\_selection, multi\_label\_selection, or ranking. NEW Additionally, we also have suggestions, which are linked to the existing questions, and so on, named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above. Finally, the guidelines are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section. ### Data Instances An example of a dataset instance in Argilla looks as follows: While the same record in HuggingFace 'datasets' looks as follows: ### Data Fields Among the dataset fields, we differentiate between the following: * Fields: These are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions. + background is of type 'text'. + prompt is of type 'text'. + response is of type 'text'. * Questions: These are the questions that will be asked to the annotators. They can be of different types, such as 'RatingQuestion', 'TextQuestion', 'LabelQuestion', 'MultiLabelQuestion', and 'RankingQuestion'. + quality is of type 'label\_selection' with the following allowed values ['Good', 'Bad']. * NEW Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable. + (optional) quality-suggestion is of type 'label\_selection' with the following allowed values ['Good', 'Bad']. Additionally, we also have one more field which is optional and is the following: * external\_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file. ### Data Splits The dataset contains a single split, which is 'train'. Dataset Creation ---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation guidelines #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information Considerations for Using the Data --------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations Additional Information ---------------------- ### Dataset Curators ### Licensing Information ### Contributions	[ "### Dataset Summary\n\n\nThis dataset contains:\n\n\n* A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\\_huggingface' method in Argilla.\n* Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\\_huggingface' and can be loaded independently using the 'datasets' library via 'load\\_dataset'.\n* The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.", "### Load with Argilla\n\n\nTo load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code:", "### Load with 'datasets'\n\n\nTo load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code:", "### Supported Tasks and Leaderboards\n\n\nThis dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.\n\n\nThere are no leaderboards associated with this dataset.", "### Languages\n\n\nDataset Structure\n-----------------", "### Data in Argilla\n\n\nThe dataset is created in Argilla with: fields, questions, suggestions, and guidelines.\n\n\nThe fields are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.\n\n\n\nThe questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label\\_selection, multi\\_label\\_selection, or ranking.\n\n\n\nNEW Additionally, we also have suggestions, which are linked to the existing questions, and so on, named appending \"-suggestion\" and \"-suggestion-metadata\" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above.\n\n\nFinally, the guidelines are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section.", "### Data Instances\n\n\nAn example of a dataset instance in Argilla looks as follows:\n\n\nWhile the same record in HuggingFace 'datasets' looks as follows:", "### Data Fields\n\n\nAmong the dataset fields, we differentiate between the following:\n\n\n* Fields: These are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.\n\n\n\t+ background is of type 'text'.\n\t+ prompt is of type 'text'.\n\t+ response is of type 'text'.\n* Questions: These are the questions that will be asked to the annotators. They can be of different types, such as 'RatingQuestion', 'TextQuestion', 'LabelQuestion', 'MultiLabelQuestion', and 'RankingQuestion'.\n\n\n\t+ quality is of type 'label\\_selection' with the following allowed values ['Good', 'Bad'].\n* NEW Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.\n\n\n\t+ (optional) quality-suggestion is of type 'label\\_selection' with the following allowed values ['Good', 'Bad'].\n\n\nAdditionally, we also have one more field which is optional and is the following:\n\n\n* external\\_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.", "### Data Splits\n\n\nThe dataset contains a single split, which is 'train'.\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation guidelines", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#size_categories-10K<n<100K #rlfh #argilla #human-feedback #region-us \n", "### Dataset Summary\n\n\nThis dataset contains:\n\n\n* A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\\_huggingface' method in Argilla.\n* Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\\_huggingface' and can be loaded independently using the 'datasets' library via 'load\\_dataset'.\n* The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.", "### Load with Argilla\n\n\nTo load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code:", "### Load with 'datasets'\n\n\nTo load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code:", "### Supported Tasks and Leaderboards\n\n\nThis dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.\n\n\nThere are no leaderboards associated with this dataset.", "### Languages\n\n\nDataset Structure\n-----------------", "### Data in Argilla\n\n\nThe dataset is created in Argilla with: fields, questions, suggestions, and guidelines.\n\n\nThe fields are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.\n\n\n\nThe questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label\\_selection, multi\\_label\\_selection, or ranking.\n\n\n\nNEW Additionally, we also have suggestions, which are linked to the existing questions, and so on, named appending \"-suggestion\" and \"-suggestion-metadata\" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above.\n\n\nFinally, the guidelines are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section.", "### Data Instances\n\n\nAn example of a dataset instance in Argilla looks as follows:\n\n\nWhile the same record in HuggingFace 'datasets' looks as follows:", "### Data Fields\n\n\nAmong the dataset fields, we differentiate between the following:\n\n\n* Fields: These are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.\n\n\n\t+ background is of type 'text'.\n\t+ prompt is of type 'text'.\n\t+ response is of type 'text'.\n* Questions: These are the questions that will be asked to the annotators. They can be of different types, such as 'RatingQuestion', 'TextQuestion', 'LabelQuestion', 'MultiLabelQuestion', and 'RankingQuestion'.\n\n\n\t+ quality is of type 'label\\_selection' with the following allowed values ['Good', 'Bad'].\n* NEW Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.\n\n\n\t+ (optional) quality-suggestion is of type 'label\\_selection' with the following allowed values ['Good', 'Bad'].\n\n\nAdditionally, we also have one more field which is optional and is the following:\n\n\n* external\\_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.", "### Data Splits\n\n\nThe dataset contains a single split, which is 'train'.\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation guidelines", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 29, 162, 40, 53, 68, 11, 219, 40, 360, 27, 7, 4, 10, 10, 5, 5, 5, 9, 18, 7, 8, 14, 6, 6, 5 ]	[ "passage: TAGS\n#size_categories-10K<n<100K #rlfh #argilla #human-feedback #region-us \n### Dataset Summary\n\n\nThis dataset contains:\n\n\n* A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\\_huggingface' method in Argilla.\n* Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\\_huggingface' and can be loaded independently using the 'datasets' library via 'load\\_dataset'.\n* The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.### Load with Argilla\n\n\nTo load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code:### Load with 'datasets'\n\n\nTo load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code:### Supported Tasks and Leaderboards\n\n\nThis dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.\n\n\nThere are no leaderboards associated with this dataset.### Languages\n\n\nDataset Structure\n-----------------", "passage: ### Data in Argilla\n\n\nThe dataset is created in Argilla with: fields, questions, suggestions, and guidelines.\n\n\nThe fields are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.\n\n\n\nThe questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label\\_selection, multi\\_label\\_selection, or ranking.\n\n\n\nNEW Additionally, we also have suggestions, which are linked to the existing questions, and so on, named appending \"-suggestion\" and \"-suggestion-metadata\" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above.\n\n\nFinally, the guidelines are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section.### Data Instances\n\n\nAn example of a dataset instance in Argilla looks as follows:\n\n\nWhile the same record in HuggingFace 'datasets' looks as follows:### Data Fields\n\n\nAmong the dataset fields, we differentiate between the following:\n\n\n* Fields: These are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.\n\n\n\t+ background is of type 'text'.\n\t+ prompt is of type 'text'.\n\t+ response is of type 'text'.\n* Questions: These are the questions that will be asked to the annotators. They can be of different types, such as 'RatingQuestion', 'TextQuestion', 'LabelQuestion', 'MultiLabelQuestion', and 'RankingQuestion'.\n\n\n\t+ quality is of type 'label\\_selection' with the following allowed values ['Good', 'Bad'].\n* NEW Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.\n\n\n\t+ (optional) quality-suggestion is of type 'label\\_selection' with the following allowed values ['Good', 'Bad'].\n\n\nAdditionally, we also have one more field which is optional and is the following:\n\n\n* external\\_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.### Data Splits\n\n\nThe dataset contains a single split, which is 'train'.\n\n\nDataset Creation\n----------------### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation guidelines#### Annotation process#### Who are the annotators?### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------### Social Impact of Dataset" ]
70eb7f2f0a3d657d2e4cb64d7b9b78784aa766e3	# Dataset Card for "alpaca-llama2-chat" This dataset is the [alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca) dataset formatted for [llama2-chat](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf). The default system prompt, as well as special tokens has all been added for a ready-to-train dataset. [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	AntoineBlanot/alpaca-llama2-chat	[ "region:us" ]	2023-10-05T07:57:49+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 46095859, "num_examples": 52002}], "download_size": 0, "dataset_size": 46095859}}	2023-10-09T06:30:40+00:00	[]	[]	TAGS #region-us	# Dataset Card for "alpaca-llama2-chat" This dataset is the alpaca dataset formatted for llama2-chat. The default system prompt, as well as special tokens has all been added for a ready-to-train dataset. More Information needed	[ "# Dataset Card for \"alpaca-llama2-chat\"\n\nThis dataset is the alpaca dataset formatted for llama2-chat.\n\nThe default system prompt, as well as special tokens has all been added for a ready-to-train dataset.\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"alpaca-llama2-chat\"\n\nThis dataset is the alpaca dataset formatted for llama2-chat.\n\nThe default system prompt, as well as special tokens has all been added for a ready-to-train dataset.\n\nMore Information needed" ]	[ 6, 60 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"alpaca-llama2-chat\"\n\nThis dataset is the alpaca dataset formatted for llama2-chat.\n\nThe default system prompt, as well as special tokens has all been added for a ready-to-train dataset.\n\nMore Information needed" ]
53a2c2a3ac22ced7dc15db4119460e36b0c72c4b	# Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Multi-class Text Classification * Model: esuriddick/distilbert-base-uncased-finetuned-emotion * Dataset: dair-ai/emotion * Config: split * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.	autoevaluate/autoeval-eval-dair-ai__emotion-split-3c2153-93136145758	[ "autotrain", "evaluation", "region:us" ]	2023-10-05T08:16:29+00:00	{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["dair-ai/emotion"], "eval_info": {"task": "multi_class_classification", "model": "esuriddick/distilbert-base-uncased-finetuned-emotion", "metrics": [], "dataset_name": "dair-ai/emotion", "dataset_config": "split", "dataset_split": "test", "col_mapping": {"text": "text", "target": "label"}}}	2023-10-05T08:16:56+00:00	[]	[]	TAGS #autotrain #evaluation #region-us	# Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by AutoTrain for the following task and dataset: * Task: Multi-class Text Classification * Model: esuriddick/distilbert-base-uncased-finetuned-emotion * Dataset: dair-ai/emotion * Config: split * Split: test To run new evaluation jobs, visit Hugging Face's automatic model evaluator. ## Contributions Thanks to @lewtun for evaluating this model.	[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: esuriddick/distilbert-base-uncased-finetuned-emotion\n* Dataset: dair-ai/emotion\n* Config: split\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]	[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: esuriddick/distilbert-base-uncased-finetuned-emotion\n* Dataset: dair-ai/emotion\n* Config: split\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @lewtun for evaluating this model." ]	[ 13, 103, 15 ]	[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Multi-class Text Classification\n* Model: esuriddick/distilbert-base-uncased-finetuned-emotion\n* Dataset: dair-ai/emotion\n* Config: split\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @lewtun for evaluating this model." ]
bf0a34435c947610231300cafa6097498800025b	# Dataset Card for "clean-unsupervised-kin-tweets" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	RogerB/clean-unsupervised-kin-tweets	[ "region:us" ]	2023-10-05T08:51:36+00:00	{"dataset_info": {"features": [{"name": "preprocessed_cased", "dtype": "string"}, {"name": "preprocessed_uncased", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10103300, "num_examples": 45138}], "download_size": 7301533, "dataset_size": 10103300}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-10-05T15:42:39+00:00	[]	[]	TAGS #region-us	# Dataset Card for "clean-unsupervised-kin-tweets" More Information needed	[ "# Dataset Card for \"clean-unsupervised-kin-tweets\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"clean-unsupervised-kin-tweets\"\n\nMore Information needed" ]	[ 6, 22 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"clean-unsupervised-kin-tweets\"\n\nMore Information needed" ]
bae3d53040e1cfcacb3cc18a17d273fac34fdf08	# Dataset Card for "wiki-unzh" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Tural/wiki-unzh	[ "region:us" ]	2023-10-05T08:57:04+00:00	{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20277571711, "num_examples": 6458670}], "download_size": 11689463675, "dataset_size": 20277571711}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-10-05T09:09:40+00:00	[]	[]	TAGS #region-us	# Dataset Card for "wiki-unzh" More Information needed	[ "# Dataset Card for \"wiki-unzh\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"wiki-unzh\"\n\nMore Information needed" ]	[ 6, 14 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"wiki-unzh\"\n\nMore Information needed" ]
fa44e0a1445e35ec16755bb7e20878fa2d95fbb2	# Dataset Card for "jungdae" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	ENSEONG/jungdae	[ "region:us" ]	2023-10-05T08:59:53+00:00	{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 231777, "num_examples": 135}], "download_size": 101263, "dataset_size": 231777}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-10-05T09:00:16+00:00	[]	[]	TAGS #region-us	# Dataset Card for "jungdae" More Information needed	[ "# Dataset Card for \"jungdae\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"jungdae\"\n\nMore Information needed" ]	[ 6, 13 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"jungdae\"\n\nMore Information needed" ]
24023a9d0ebf223218053f33c0a1c69fb67f7c94	# Pretrain ## Dataset details License:	hongyin/pretrain-sample	[ "task_categories:text-generation", "size_categories:n<1K", "language:zh", "language:en", "license:mit", "region:us" ]	2023-10-05T09:14:48+00:00	{"language": ["zh", "en"], "license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-generation"], "pretty_name": "hongyin/pretrain"}	2023-12-02T10:56:13+00:00	[]	[ "zh", "en" ]	TAGS #task_categories-text-generation #size_categories-n<1K #language-Chinese #language-English #license-mit #region-us	# Pretrain ## Dataset details License:	[ "# Pretrain", "## Dataset details\n\n\nLicense:" ]	[ "TAGS\n#task_categories-text-generation #size_categories-n<1K #language-Chinese #language-English #license-mit #region-us \n", "# Pretrain", "## Dataset details\n\n\nLicense:" ]	[ 41, 3, 6 ]	[ "passage: TAGS\n#task_categories-text-generation #size_categories-n<1K #language-Chinese #language-English #license-mit #region-us \n# Pretrain## Dataset details\n\n\nLicense:" ]
25d7617bed19e4d0a89738fe8dfce0089870dc39	# Bangumi Image Base of Demi-chan Wa Kataritai This is the image base of bangumi Demi-chan wa Kataritai, we detected 16 characters, 1889 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 \| 379 \| [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 \| 33 \| [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 \| 221 \| [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 \| 373 \| [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 \| 35 \| [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 \| 59 \| [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 \| 11 \| [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 \| 75 \| [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 \| 14 \| [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 \| 18 \| [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 \| 34 \| [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 \| 20 \| [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 \| 252 \| [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 \| 203 \| [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 \| 87 \| [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) \| \| noise \| 75 \| [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) \|	BangumiBase/demichanwakataritai	[ "size_categories:1K<n<10K", "license:mit", "art", "region:us" ]	2023-10-05T09:24:57+00:00	{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}	2023-10-05T10:40:25+00:00	[]	[]	TAGS #size_categories-1K<n<10K #license-mit #art #region-us	Bangumi Image Base of Demi-chan Wa Kataritai ============================================ This is the image base of bangumi Demi-chan wa Kataritai, we detected 16 characters, 1889 images in total. The full dataset is here. 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:	[]	[ "TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]	[ 25 ]	[ "passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]
436f47a5504cb544b4be58dd43046f2dccd2ae04	# これはうｐテストだよ This is uploading test. ## Dataset Description, Summary サムネ画像 my thumbnail. 7z か parquet やがて消す	daraiii/test	[ "language:ja", "license:unknown", "region:us" ]	2023-10-05T09:33:12+00:00	{"language": ["ja"], "license": "unknown", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 50283, "num_examples": 1}], "download_size": 50580, "dataset_size": 50283}}	2023-10-05T09:37:49+00:00	[]	[ "ja" ]	TAGS #language-Japanese #license-unknown #region-us	# これはうｐテストだよ This is uploading test. ## Dataset Description, Summary サムネ画像 my thumbnail. 7z か parquet やがて消す	[ "# これはうｐテストだよ This is uploading test.", "## Dataset Description, Summary\n\nサムネ画像 my thumbnail.\n\n7z か parquet\n\nやがて消す" ]	[ "TAGS\n#language-Japanese #license-unknown #region-us \n", "# これはうｐテストだよ This is uploading test.", "## Dataset Description, Summary\n\nサムネ画像 my thumbnail.\n\n7z か parquet\n\nやがて消す" ]	[ 19, 12, 30 ]	[ "passage: TAGS\n#language-Japanese #license-unknown #region-us \n# これはうｐテストだよ This is uploading test.## Dataset Description, Summary\n\nサムネ画像 my thumbnail.\n\n7z か parquet\n\nやがて消す" ]
0c81490f52d6ab2f4b39ee2f460b6706cad9cf5a	# Bangumi Image Base of Nagi No Asukara This is the image base of bangumi Nagi no Asukara, we detected 23 characters, 3162 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 \| 564 \| [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 \| 79 \| [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 \| 76 \| [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 \| 29 \| [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 \| 67 \| [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 \| 239 \| [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 \| 49 \| [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 \| 40 \| [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 \| 261 \| [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 \| 15 \| [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 \| 11 \| [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 \| 15 \| [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 \| 31 \| [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 \| 117 \| [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 \| 245 \| [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 \| 168 \| [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 \| 17 \| [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 \| 373 \| [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 \| 176 \| [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 \| 385 \| [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 \| 24 \| [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 \| 54 \| [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) \| \| noise \| 127 \| [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) \|	BangumiBase/naginoasukara	[ "size_categories:1K<n<10K", "license:mit", "art", "region:us" ]	2023-10-05T10:11:39+00:00	{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}	2023-10-05T12:05:11+00:00	[]	[]	TAGS #size_categories-1K<n<10K #license-mit #art #region-us	Bangumi Image Base of Nagi No Asukara ===================================== This is the image base of bangumi Nagi no Asukara, we detected 23 characters, 3162 images in total. The full dataset is here. 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:	[]	[ "TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]	[ 25 ]	[ "passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]
826f33ec5b8235af2ec4b11fa17dab300051b129	# Dataset Card for "dataset_abstracts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	librarian-bots/dataset_abstracts	[ "task_categories:text-classification", "size_categories:n<1K", "language:en", "arxiv ", "region:us" ]	2023-10-05T10:16:57+00:00	{"language": ["en"], "size_categories": ["n<1K"], "task_categories": ["text-classification"], "dataset_info": [{"config_name": "annotated", "features": [{"name": "text", "dtype": "string"}, {"name": "inputs", "struct": [{"name": "abstract", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "prediction", "dtype": "null"}, {"name": "prediction_agent", "dtype": "null"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "dtype": "null"}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "string"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}, {"name": "label", "dtype": {"class_label": {"names": {"0": "new_dataset", "1": "no_new_dataset"}}}}], "splits": [{"name": "train", "num_bytes": 1099065.75, "num_examples": 390}, {"name": "test", "num_bytes": 366355.25, "num_examples": 130}], "download_size": 865263, "dataset_size": 1465421.0}, {"config_name": "unlabelled", "features": [{"name": "text", "dtype": "string"}, {"name": "inputs", "struct": [{"name": "abstract", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "prediction", "dtype": "null"}, {"name": "prediction_agent", "dtype": "null"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "dtype": "null"}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}, {"name": "label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1372259.876, "num_examples": 494}], "download_size": 792644, "dataset_size": 1372259.876}], "configs": [{"config_name": "annotated", "data_files": [{"split": "train", "path": "annotated/train-"}, {"split": "test", "path": "annotated/test-"}]}, {"config_name": "unlabelled", "data_files": [{"split": "train", "path": "unlabelled/train-*"}]}], "tags": ["arxiv "]}	2023-10-31T09:13:51+00:00	[]	[ "en" ]	TAGS #task_categories-text-classification #size_categories-n<1K #language-English #arxiv #region-us	# Dataset Card for "dataset_abstracts" More Information needed	[ "# Dataset Card for \"dataset_abstracts\"\n\nMore Information needed" ]	[ "TAGS\n#task_categories-text-classification #size_categories-n<1K #language-English #arxiv #region-us \n", "# Dataset Card for \"dataset_abstracts\"\n\nMore Information needed" ]	[ 35, 17 ]	[ "passage: TAGS\n#task_categories-text-classification #size_categories-n<1K #language-English #arxiv #region-us \n# Dataset Card for \"dataset_abstracts\"\n\nMore Information needed" ]
c87855ca38439e85640d4facfbfd3d4b6d7f6cee	# Bangumi Image Base of Cyberpunk: Edgerunners This is the image base of bangumi Cyberpunk: Edgerunners, we detected 21 characters, 1227 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 \| 48 \| [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 \| 81 \| [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 \| 333 \| [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 \| 26 \| [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 \| 41 \| [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 \| 20 \| [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 \| 58 \| [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 \| 111 \| [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 \| 48 \| [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 \| 32 \| [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 \| 28 \| [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 \| 25 \| [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 \| 17 \| [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 \| 28 \| [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 \| 22 \| [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 \| 19 \| [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 \| 32 \| [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 \| 19 \| [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 \| 24 \| [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 \| 9 \| [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) \| \| noise \| 206 \| [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) \|	BangumiBase/cyberpunkedgerunners	[ "size_categories:1K<n<10K", "license:mit", "art", "region:us" ]	2023-10-05T10:25:32+00:00	{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}	2023-10-05T11:53:38+00:00	[]	[]	TAGS #size_categories-1K<n<10K #license-mit #art #region-us	Bangumi Image Base of Cyberpunk: Edgerunners ============================================ This is the image base of bangumi Cyberpunk: Edgerunners, we detected 21 characters, 1227 images in total. The full dataset is here. 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:	[]	[ "TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]	[ 25 ]	[ "passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]
0f14ad2005e0aab8f56cf3213b7689da1faf23c2	# Dataset Card for NST-da ## Dataset Description - Repository: <https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-55/> - Point of Contact: [Dan Saattrup Nielsen](mailto:[email protected]) - Size of downloaded dataset files: 5.36 GB - Size of the generated dataset: 64.09 GB - Total amount of disk used: 69.45 GB ### Dataset Summary This dataset is an upload of the [NST Danish ASR Database (16 kHz) – reorganized](https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-55/). The training and test splits are the original ones. ### Supported Tasks and Leaderboards Training automatic speech recognition is the intended task for this dataset. No leaderboard is active at this point. ### Languages The dataset is available in Danish (`da`). ## Dataset Structure ### Data Instances - Size of downloaded dataset files: 5.36 GB - Size of the generated dataset: 64.09 GB - Total amount of disk used: 69.45 GB An example from the dataset looks as follows. ``` { 'audio': { 'path': 'dk14x404-05072000-1531_u0008121.wav', 'array': array([ 0.00265503, 0.00248718, 0.00253296, ..., -0.00030518, -0.00035095, -0.00064087]), 'sampling_rate': 16000 }, 'text': 'Desuden er der en svømmeprøve, en fremmedsprogstest samt en afsluttende samtale.', 'speaker_id': 404, 'age': 24, 'sex': 'Female', 'dialect': 'Storkøbenhavn', 'recording_datetime': '2000-07-05T15:31:14' } ``` ### Data Fields The data fields are the same among all splits. - `audio`: an `Audio` feature. - `text`: a `string` feature. - `speaker_id`: an `int64` feature. - `age`: an `int64` feature. - `sex`: a `string` feature. - `dialect`: a `string` feature. - `recording_datetime`: a `string` feature. ### Dataset Statistics There are 183,205 samples in the training split, and 54,747 samples in the test split. #### Speakers There are 539 unique speakers in the training dataset and 56 unique speakers in the test dataset, where 54 of them are also present in the training set. #### Age Distribution ![nst-da-age-distribution.png](https://cdn-uploads.huggingface.co/production/uploads/60d368a613f774189902f555/iNMmVXXda7LtzgZEHe1eq.png) #### Dialect Distribution ![nst-da-dialect-distribution.png](https://cdn-uploads.huggingface.co/production/uploads/60d368a613f774189902f555/sckW27xYUz8apMwbLebvD.png) #### Sex Distribution ![nst-da-sex-distribution.png](https://cdn-uploads.huggingface.co/production/uploads/60d368a613f774189902f555/8Q7ZunYaLQ3laOc7yQvI8.png) #### Transcription Length Distribution ![nst-da-length-distribution.png](https://cdn-uploads.huggingface.co/production/uploads/60d368a613f774189902f555/W_LA2nydEZuEeK_Z_x2LE.png) ## Dataset Creation ### Curation Rationale There are not many large-scale ASR datasets in Danish. ### Source Data The data originates from the now bankrupt company Nordisk språkteknologi (NST), whose data was transferred to the National Library of Norway, who subsequently released it into the public domain. ## Additional Information ### Dataset Curators [Dan Saattrup Nielsen](https://saattrupdan.github.io/) from the [The Alexandra Institute](https://alexandra.dk/) reorganised the dataset and uploaded it to the Hugging Face Hub. ### Licensing Information The dataset is licensed under the [CC0 license](https://creativecommons.org/share-your-work/public-domain/cc0/).	alexandrainst/nst-da	[ "task_categories:automatic-speech-recognition", "task_categories:text-to-speech", "size_categories:100K<n<1M", "language:da", "license:cc0-1.0", "region:us" ]	2023-10-05T10:27:17+00:00	{"language": ["da"], "license": "cc0-1.0", "size_categories": ["100K<n<1M"], "task_categories": ["automatic-speech-recognition", "text-to-speech"], "pretty_name": "NST-da", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-"}, {"split": "test", "path": "data/test-"}]}], "dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "text", "dtype": "string"}, {"name": "speaker_id", "dtype": "int64"}, {"name": "age", "dtype": "int64"}, {"name": "sex", "dtype": "string"}, {"name": "dialect", "dtype": "string"}, {"name": "recording_datetime", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 55199435558.0, "num_examples": 182605}, {"name": "test", "num_bytes": 8894080220.0, "num_examples": 54747}], "download_size": 5358057252, "dataset_size": 64093515778.0}}	2023-10-05T13:27:00+00:00	[]	[ "da" ]	TAGS #task_categories-automatic-speech-recognition #task_categories-text-to-speech #size_categories-100K<n<1M #language-Danish #license-cc0-1.0 #region-us	# Dataset Card for NST-da ## Dataset Description - Repository: <URL - Point of Contact: Dan Saattrup Nielsen - Size of downloaded dataset files: 5.36 GB - Size of the generated dataset: 64.09 GB - Total amount of disk used: 69.45 GB ### Dataset Summary This dataset is an upload of the NST Danish ASR Database (16 kHz) – reorganized. The training and test splits are the original ones. ### Supported Tasks and Leaderboards Training automatic speech recognition is the intended task for this dataset. No leaderboard is active at this point. ### Languages The dataset is available in Danish ('da'). ## Dataset Structure ### Data Instances - Size of downloaded dataset files: 5.36 GB - Size of the generated dataset: 64.09 GB - Total amount of disk used: 69.45 GB An example from the dataset looks as follows. ### Data Fields The data fields are the same among all splits. - 'audio': an 'Audio' feature. - 'text': a 'string' feature. - 'speaker_id': an 'int64' feature. - 'age': an 'int64' feature. - 'sex': a 'string' feature. - 'dialect': a 'string' feature. - 'recording_datetime': a 'string' feature. ### Dataset Statistics There are 183,205 samples in the training split, and 54,747 samples in the test split. #### Speakers There are 539 unique speakers in the training dataset and 56 unique speakers in the test dataset, where 54 of them are also present in the training set. #### Age Distribution !URL #### Dialect Distribution !URL #### Sex Distribution !URL #### Transcription Length Distribution !URL ## Dataset Creation ### Curation Rationale There are not many large-scale ASR datasets in Danish. ### Source Data The data originates from the now bankrupt company Nordisk språkteknologi (NST), whose data was transferred to the National Library of Norway, who subsequently released it into the public domain. ## Additional Information ### Dataset Curators Dan Saattrup Nielsen from the The Alexandra Institute reorganised the dataset and uploaded it to the Hugging Face Hub. ### Licensing Information The dataset is licensed under the CC0 license.	[ "# Dataset Card for NST-da", "## Dataset Description\n\n- Repository: <URL\n- Point of Contact: Dan Saattrup Nielsen\n- Size of downloaded dataset files: 5.36 GB\n- Size of the generated dataset: 64.09 GB\n- Total amount of disk used: 69.45 GB", "### Dataset Summary\n\nThis dataset is an upload of the NST Danish ASR Database (16 kHz) – reorganized.\n\nThe training and test splits are the original ones.", "### Supported Tasks and Leaderboards\n\nTraining automatic speech recognition is the intended task for this dataset. No leaderboard is active at this point.", "### Languages\n\nThe dataset is available in Danish ('da').", "## Dataset Structure", "### Data Instances\n\n- Size of downloaded dataset files: 5.36 GB\n- Size of the generated dataset: 64.09 GB\n- Total amount of disk used: 69.45 GB\n\nAn example from the dataset looks as follows.", "### Data Fields\n\nThe data fields are the same among all splits.\n\n- 'audio': an 'Audio' feature.\n- 'text': a 'string' feature.\n- 'speaker_id': an 'int64' feature.\n- 'age': an 'int64' feature.\n- 'sex': a 'string' feature.\n- 'dialect': a 'string' feature.\n- 'recording_datetime': a 'string' feature.", "### Dataset Statistics\n\nThere are 183,205 samples in the training split, and 54,747 samples in the test split.", "#### Speakers\n\nThere are 539 unique speakers in the training dataset and 56 unique speakers in the test dataset, where 54 of them are also present in the training set.", "#### Age Distribution\n\n!URL", "#### Dialect Distribution\n\n!URL", "#### Sex Distribution\n\n!URL", "#### Transcription Length Distribution\n\n!URL", "## Dataset Creation", "### Curation Rationale\n\nThere are not many large-scale ASR datasets in Danish.", "### Source Data\n\nThe data originates from the now bankrupt company Nordisk språkteknologi (NST), whose data was transferred to the National Library of Norway, who subsequently released it into the public domain.", "## Additional Information", "### Dataset Curators\n\nDan Saattrup Nielsen from the The Alexandra\nInstitute reorganised the dataset and uploaded it to the Hugging Face Hub.", "### Licensing Information\n\nThe dataset is licensed under the CC0\nlicense." ]	[ "TAGS\n#task_categories-automatic-speech-recognition #task_categories-text-to-speech #size_categories-100K<n<1M #language-Danish #license-cc0-1.0 #region-us \n", "# Dataset Card for NST-da", "## Dataset Description\n\n- Repository: <URL\n- Point of Contact: Dan Saattrup Nielsen\n- Size of downloaded dataset files: 5.36 GB\n- Size of the generated dataset: 64.09 GB\n- Total amount of disk used: 69.45 GB", "### Dataset Summary\n\nThis dataset is an upload of the NST Danish ASR Database (16 kHz) – reorganized.\n\nThe training and test splits are the original ones.", "### Supported Tasks and Leaderboards\n\nTraining automatic speech recognition is the intended task for this dataset. No leaderboard is active at this point.", "### Languages\n\nThe dataset is available in Danish ('da').", "## Dataset Structure", "### Data Instances\n\n- Size of downloaded dataset files: 5.36 GB\n- Size of the generated dataset: 64.09 GB\n- Total amount of disk used: 69.45 GB\n\nAn example from the dataset looks as follows.", "### Data Fields\n\nThe data fields are the same among all splits.\n\n- 'audio': an 'Audio' feature.\n- 'text': a 'string' feature.\n- 'speaker_id': an 'int64' feature.\n- 'age': an 'int64' feature.\n- 'sex': a 'string' feature.\n- 'dialect': a 'string' feature.\n- 'recording_datetime': a 'string' feature.", "### Dataset Statistics\n\nThere are 183,205 samples in the training split, and 54,747 samples in the test split.", "#### Speakers\n\nThere are 539 unique speakers in the training dataset and 56 unique speakers in the test dataset, where 54 of them are also present in the training set.", "#### Age Distribution\n\n!URL", "#### Dialect Distribution\n\n!URL", "#### Sex Distribution\n\n!URL", "#### Transcription Length Distribution\n\n!URL", "## Dataset Creation", "### Curation Rationale\n\nThere are not many large-scale ASR datasets in Danish.", "### Source Data\n\nThe data originates from the now bankrupt company Nordisk språkteknologi (NST), whose data was transferred to the National Library of Norway, who subsequently released it into the public domain.", "## Additional Information", "### Dataset Curators\n\nDan Saattrup Nielsen from the The Alexandra\nInstitute reorganised the dataset and uploaded it to the Hugging Face Hub.", "### Licensing Information\n\nThe dataset is licensed under the CC0\nlicense." ]	[ 60, 9, 57, 40, 32, 16, 6, 53, 109, 29, 39, 7, 9, 7, 10, 5, 23, 44, 5, 33, 18 ]	[ "passage: TAGS\n#task_categories-automatic-speech-recognition #task_categories-text-to-speech #size_categories-100K<n<1M #language-Danish #license-cc0-1.0 #region-us \n# Dataset Card for NST-da## Dataset Description\n\n- Repository: <URL\n- Point of Contact: Dan Saattrup Nielsen\n- Size of downloaded dataset files: 5.36 GB\n- Size of the generated dataset: 64.09 GB\n- Total amount of disk used: 69.45 GB### Dataset Summary\n\nThis dataset is an upload of the NST Danish ASR Database (16 kHz) – reorganized.\n\nThe training and test splits are the original ones.### Supported Tasks and Leaderboards\n\nTraining automatic speech recognition is the intended task for this dataset. No leaderboard is active at this point.### Languages\n\nThe dataset is available in Danish ('da').## Dataset Structure### Data Instances\n\n- Size of downloaded dataset files: 5.36 GB\n- Size of the generated dataset: 64.09 GB\n- Total amount of disk used: 69.45 GB\n\nAn example from the dataset looks as follows.### Data Fields\n\nThe data fields are the same among all splits.\n\n- 'audio': an 'Audio' feature.\n- 'text': a 'string' feature.\n- 'speaker_id': an 'int64' feature.\n- 'age': an 'int64' feature.\n- 'sex': a 'string' feature.\n- 'dialect': a 'string' feature.\n- 'recording_datetime': a 'string' feature.### Dataset Statistics\n\nThere are 183,205 samples in the training split, and 54,747 samples in the test split.#### Speakers\n\nThere are 539 unique speakers in the training dataset and 56 unique speakers in the test dataset, where 54 of them are also present in the training set.#### Age Distribution\n\n!URL#### Dialect Distribution\n\n!URL#### Sex Distribution\n\n!URL#### Transcription Length Distribution\n\n!URL## Dataset Creation" ]
40d559420dcdede6499ff2029a9f5f34ff326b8b	# Bangumi Image Base of Yuuki Yuuna Wa Yuusha De Aru This is the image base of bangumi Yuuki Yuuna wa Yuusha de Aru, we detected 27 characters, 3455 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 \| 462 \| [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 \| 76 \| [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 \| 57 \| [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 \| 71 \| [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 \| 18 \| [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 \| 190 \| [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 \| 16 \| [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 \| 370 \| [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 \| 358 \| [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 \| 137 \| [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 \| 75 \| [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 \| 296 \| [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 \| 40 \| [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 \| 195 \| [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 \| 81 \| [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 \| 18 \| [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 \| 21 \| [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 \| 481 \| [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 \| 130 \| [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 \| 8 \| [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 \| 11 \| [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 \| 16 \| [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 \| 9 \| [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 \| 6 \| [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) \| N/A \| N/A \| \| 25 \| 6 \| [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) \| N/A \| N/A \| \| noise \| 284 \| [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) \|	BangumiBase/yuukiyuunawayuushadearu	[ "size_categories:1K<n<10K", "license:mit", "art", "region:us" ]	2023-10-05T11:08:05+00:00	{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}	2023-10-05T13:27:28+00:00	[]	[]	TAGS #size_categories-1K<n<10K #license-mit #art #region-us	Bangumi Image Base of Yuuki Yuuna Wa Yuusha De Aru ================================================== This is the image base of bangumi Yuuki Yuuna wa Yuusha de Aru, we detected 27 characters, 3455 images in total. The full dataset is here. 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:	[]	[ "TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]	[ 25 ]	[ "passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]
a4fcbfb68a99f9921b6371451e4a4d40ee8d576c	# Dataset Card for "shulchan-aruch" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	sivan22/shulchan-aruch	[ "region:us" ]	2023-10-05T11:17:31+00:00	{"dataset_info": {"features": [{"name": "bookname", "dtype": "string"}, {"name": "topic", "dtype": "string"}, {"name": "siman", "dtype": "string"}, {"name": "seif", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7734020, "num_examples": 11440}], "download_size": 2661186, "dataset_size": 7734020}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-10-05T11:19:20+00:00	[]	[]	TAGS #region-us	# Dataset Card for "shulchan-aruch" More Information needed	[ "# Dataset Card for \"shulchan-aruch\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"shulchan-aruch\"\n\nMore Information needed" ]	[ 6, 16 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"shulchan-aruch\"\n\nMore Information needed" ]
53875194da57f248508238be54cee08024119a4d	# GPTNERMED Dataset for German medical NER entities ## Dataset Description - Repository: https://github.com/frankkramer-lab/GPTNERMED - Paper: https://doi.org/10.1016/j.jbi.2023.104478 - ArXiv-Preprint: https://arxiv.org/abs/2208.14493 ## Dataset Summary This dataset contains the synthetic German sentences with annotated entities (`Medikation`, `Dosis`, `Diagnose`) from the GPTNERMED project. The sentences as well as the annotations are not manually validated by medical professionals and therefore this dataset is no gold standard dataset. The dataset consists of 9,845 sentences (121,027 tokens by SpaCy Tokenizer, 245,107 tokens by the GPT tokenizer) with the following labels: \| Label \| Count \| #Tokens (SpaCy) \| \| --- \| --- \| -- \| \| Medikation \| 9868 \| 10138 \| \| Dosis \| 7547 \| 15845 \| \| Diagnose \| 5996 \| 7656 \| ## Dataset Structure The train/test/dev-split (80%, 10%, 10%) of the data loader is as follows:\ `<-- train: 0.8 --><-- test: 0.1 --><-- dev: 0.1 -->`\ The splits are selected arbitrarily as the dataloader requires a split configuration. All sample sentences are however homogeneous in origin and splits could also be performed otherwise. Every sample is a sentence with its text (property `sentence`) and its corresponding NER labels (property `ner_labels` / List of labels).\ Every NER label entry has a char-wise start and stop index (property `start`, `stop`) and a label class (property `ner_class`). ### Citation Information If you like our work, cite our paper and give us a star on GitHub.\ (See the links above)	jfrei/GPTNERMED	[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:machine-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:de", "bio", "biomedical", "medical", "clinical", "arxiv:2208.14493", "region:us" ]	2023-10-05T11:39:54+00:00	{"annotations_creators": ["machine-generated"], "language_creators": ["machine-generated"], "language": ["de"], "license": [], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "GPTNERMED", "tags": ["bio", "biomedical", "medical", "clinical"]}	2023-10-08T21:05:18+00:00	[ "2208.14493" ]	[ "de" ]	TAGS #task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-German #bio #biomedical #medical #clinical #arxiv-2208.14493 #region-us	GPTNERMED Dataset for German medical NER entities ================================================= Dataset Description ------------------- * Repository: URL * Paper: URL * ArXiv-Preprint: URL Dataset Summary --------------- This dataset contains the synthetic German sentences with annotated entities ('Medikation', 'Dosis', 'Diagnose') from the GPTNERMED project. The sentences as well as the annotations are not manually validated by medical professionals and therefore this dataset is no gold standard dataset. The dataset consists of 9,845 sentences (121,027 tokens by SpaCy Tokenizer, 245,107 tokens by the GPT tokenizer) with the following labels: Label: Medikation, Count: 9868, #Tokens (SpaCy): 10138 Label: Dosis, Count: 7547, #Tokens (SpaCy): 15845 Label: Diagnose, Count: 5996, #Tokens (SpaCy): 7656 Dataset Structure ----------------- The train/test/dev-split (80%, 10%, 10%) of the data loader is as follows: '<-- train: 0.8 --><-- test: 0.1 --><-- dev: 0.1 -->' The splits are selected arbitrarily as the dataloader requires a split configuration. All sample sentences are however homogeneous in origin and splits could also be performed otherwise. Every sample is a sentence with its text (property 'sentence') and its corresponding NER labels (property 'ner\_labels' / List of labels). Every NER label entry has a char-wise start and stop index (property 'start', 'stop') and a label class (property 'ner\_class'). If you like our work, cite our paper and give us a star on GitHub. (See the links above)	[]	[ "TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-German #bio #biomedical #medical #clinical #arxiv-2208.14493 #region-us \n" ]	[ 109 ]	[ "passage: TAGS\n#task_categories-token-classification #task_ids-named-entity-recognition #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-German #bio #biomedical #medical #clinical #arxiv-2208.14493 #region-us \n" ]
ff856403127cc8a64996d22c6ce8f71f9d26147f	# Dataset Card for "primary-sector-top-1k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	elenahuang/primary-sector-top-1k	[ "region:us" ]	2023-10-05T11:55:29+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 8620437, "num_examples": 1000}], "download_size": 4571154, "dataset_size": 8620437}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-10-05T11:55:32+00:00	[]	[]	TAGS #region-us	# Dataset Card for "primary-sector-top-1k" More Information needed	[ "# Dataset Card for \"primary-sector-top-1k\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"primary-sector-top-1k\"\n\nMore Information needed" ]	[ 6, 19 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"primary-sector-top-1k\"\n\nMore Information needed" ]
be4f766a1ac8ad68b275a51bda66a78ab60349d4	# Dataset Card for "primary-sector-bottom-1k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	elenahuang/primary-sector-bottom-1k	[ "region:us" ]	2023-10-05T11:56:50+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 8620437, "num_examples": 1000}], "download_size": 4571154, "dataset_size": 8620437}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-10-05T11:56:52+00:00	[]	[]	TAGS #region-us	# Dataset Card for "primary-sector-bottom-1k" More Information needed	[ "# Dataset Card for \"primary-sector-bottom-1k\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"primary-sector-bottom-1k\"\n\nMore Information needed" ]	[ 6, 20 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"primary-sector-bottom-1k\"\n\nMore Information needed" ]
4c1dac2ccf73907599fbc12da0fac0acb7dde9f2	Following packages are necessary to compile the model in C: ```bash sudo apt install gcc-7 ``` ```bash sudo apt-get install build-essential ``` ```python for i in range(1,21): !wget https://huggingface.co/datasets/teragron/reviews/resolve/main/chunk_{i}.bin ``` ```bash git clone https://github.com/karpathy/llama2.c.git ``` ```bash cd llama2.c ``` ```bash pip install -r requirements.txt ``` Path: data/TinyStories_all_data	teragron/reviews	[ "task_categories:text-generation", "size_categories:1M<n<10M", "language:en", "license:mit", "finance", "region:us" ]	2023-10-05T12:32:32+00:00	{"language": ["en"], "license": "mit", "size_categories": ["1M<n<10M"], "task_categories": ["text-generation"], "pretty_name": "review_me", "tags": ["finance"]}	2023-10-09T22:55:54+00:00	[]	[ "en" ]	TAGS #task_categories-text-generation #size_categories-1M<n<10M #language-English #license-mit #finance #region-us	Following packages are necessary to compile the model in C: Path: data/TinyStories_all_data	[]	[ "TAGS\n#task_categories-text-generation #size_categories-1M<n<10M #language-English #license-mit #finance #region-us \n" ]	[ 41 ]	[ "passage: TAGS\n#task_categories-text-generation #size_categories-1M<n<10M #language-English #license-mit #finance #region-us \n" ]
7ec69863d1c1354757f1f423e2ddc5ed0ee50fa2	# Hugging Face Transformers documentation as markdown dataset This dataset was created using [Clipper.js](https://github.com/philschmid/clipper.js). Clipper is a Node.js command line tool that allows you to easily clip content from web pages and convert it to Markdown. It uses Mozilla's Readability library and Turndown under the hood to parse web page content and convert it to Markdown. This dataset can be used to create RAG applications, which want to use the transformers documentation. Example document: https://huggingface.co/docs/transformers/peft ``` # Load adapters with 🤗 PEFT [Parameter-Efficient Fine Tuning (PEFT)](https://huggingface.co/blog/peft) methods freeze the pretrained model parameters during fine-tuning and add a small number of trainable parameters (the adapters) on top of it. The adapters are trained to learn task-specific information. This approach has been shown to be very memory-efficient with lower compute usage while producing results comparable to a fully fine-tuned model. Adapters trained with PEFT are also usually an order of magnitude smaller than the full model, making it convenient to share, store, and load them. ![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/peft/PEFT-hub-screenshot.png) The adapter weights for a OPTForCausalLM model stored on the Hub are only ~6MB compared to the full size of the model weights, which can be ~700MB. If you’re interested in learning more about the 🤗 PEFT library, check out the [documentation](https://huggingface.co/docs/peft/index). ## Setup Get started by installing 🤗 PEFT: If you want to try out the brand new features, you might be interested in installing the library from source: .... ```	philschmid/markdown-documentation-transformers	[ "license:apache-2.0", "region:us" ]	2023-10-05T12:38:10+00:00	{"license": "apache-2.0"}	2023-10-05T12:42:59+00:00	[]	[]	TAGS #license-apache-2.0 #region-us	# Hugging Face Transformers documentation as markdown dataset This dataset was created using URL. Clipper is a URL command line tool that allows you to easily clip content from web pages and convert it to Markdown. It uses Mozilla's Readability library and Turndown under the hood to parse web page content and convert it to Markdown. This dataset can be used to create RAG applications, which want to use the transformers documentation. Example document: URL	[ "# Hugging Face Transformers documentation as markdown dataset\n\nThis dataset was created using URL. Clipper is a URL command line tool that allows you to easily clip content from web pages and convert it to Markdown. It uses Mozilla's Readability library and Turndown under the hood to parse web page content and convert it to Markdown.\n\nThis dataset can be used to create RAG applications, which want to use the transformers documentation. \n\nExample document: URL" ]	[ "TAGS\n#license-apache-2.0 #region-us \n", "# Hugging Face Transformers documentation as markdown dataset\n\nThis dataset was created using URL. Clipper is a URL command line tool that allows you to easily clip content from web pages and convert it to Markdown. It uses Mozilla's Readability library and Turndown under the hood to parse web page content and convert it to Markdown.\n\nThis dataset can be used to create RAG applications, which want to use the transformers documentation. \n\nExample document: URL" ]	[ 14, 102 ]	[ "passage: TAGS\n#license-apache-2.0 #region-us \n# Hugging Face Transformers documentation as markdown dataset\n\nThis dataset was created using URL. Clipper is a URL command line tool that allows you to easily clip content from web pages and convert it to Markdown. It uses Mozilla's Readability library and Turndown under the hood to parse web page content and convert it to Markdown.\n\nThis dataset can be used to create RAG applications, which want to use the transformers documentation. \n\nExample document: URL" ]
a58f1ab32dffa7e14a7508779e956c9ed6b003ba	# Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Summarization * Model: sshleifer/distilbart-xsum-12-6 * Dataset: samsum * Config: samsum * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@sasha](https://huggingface.co/sasha) for evaluating this model.	autoevaluate/autoeval-eval-samsum-samsum-52efcb-93192145784	[ "autotrain", "evaluation", "region:us" ]	2023-10-05T12:39:44+00:00	{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["samsum"], "eval_info": {"task": "summarization", "model": "sshleifer/distilbart-xsum-12-6", "metrics": [], "dataset_name": "samsum", "dataset_config": "samsum", "dataset_split": "test", "col_mapping": {"text": "dialogue", "target": "summary"}}}	2023-10-05T12:41:45+00:00	[]	[]	TAGS #autotrain #evaluation #region-us	# Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by AutoTrain for the following task and dataset: * Task: Summarization * Model: sshleifer/distilbart-xsum-12-6 * Dataset: samsum * Config: samsum * Split: test To run new evaluation jobs, visit Hugging Face's automatic model evaluator. ## Contributions Thanks to @sasha for evaluating this model.	[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sshleifer/distilbart-xsum-12-6\n* Dataset: samsum\n* Config: samsum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sasha for evaluating this model." ]	[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sshleifer/distilbart-xsum-12-6\n* Dataset: samsum\n* Config: samsum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sasha for evaluating this model." ]	[ 13, 89, 15 ]	[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sshleifer/distilbart-xsum-12-6\n* Dataset: samsum\n* Config: samsum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @sasha for evaluating this model." ]
de59893d13f653991549e46f22cc0a50c60ff44a	# Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Summarization * Model: sshleifer/distilbart-cnn-12-6 * Dataset: samsum * Config: samsum * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@sasha](https://huggingface.co/sasha) for evaluating this model.	autoevaluate/autoeval-eval-samsum-samsum-52efcb-93192145785	[ "autotrain", "evaluation", "region:us" ]	2023-10-05T12:39:51+00:00	{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["samsum"], "eval_info": {"task": "summarization", "model": "sshleifer/distilbart-cnn-12-6", "metrics": [], "dataset_name": "samsum", "dataset_config": "samsum", "dataset_split": "test", "col_mapping": {"text": "dialogue", "target": "summary"}}}	2023-10-05T12:42:14+00:00	[]	[]	TAGS #autotrain #evaluation #region-us	# Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by AutoTrain for the following task and dataset: * Task: Summarization * Model: sshleifer/distilbart-cnn-12-6 * Dataset: samsum * Config: samsum * Split: test To run new evaluation jobs, visit Hugging Face's automatic model evaluator. ## Contributions Thanks to @sasha for evaluating this model.	[ "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sshleifer/distilbart-cnn-12-6\n* Dataset: samsum\n* Config: samsum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sasha for evaluating this model." ]	[ "TAGS\n#autotrain #evaluation #region-us \n", "# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sshleifer/distilbart-cnn-12-6\n* Dataset: samsum\n* Config: samsum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.", "## Contributions\n\nThanks to @sasha for evaluating this model." ]	[ 13, 89, 15 ]	[ "passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: sshleifer/distilbart-cnn-12-6\n* Dataset: samsum\n* Config: samsum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @sasha for evaluating this model." ]

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