davanstrien/dataset_cards_with_long_context_embeddins

id stringlengths 2 115	lastModified stringlengths 24 24	tags list	author stringlengths 2 42 ⌀	description stringlengths 0 6.67k ⌀	citation stringlengths 0 10.7k ⌀	likes int64 0 3.66k	created timestamp[us]	card stringlengths 11 977k	card_len int64 11 977k	embeddings list
datkai/datanew	2023-10-25T01:54:54.000Z	[ "region:us" ]	datkai	null	null	0	2023-10-25T01:54:54	Entry not found	15	[ [ -0.02142333984375, -0.01495361328125, 0.05718994140625, 0.0288238525390625, -0.035064697265625, 0.046539306640625, 0.052520751953125, 0.005062103271484375, 0.0513916015625, 0.016998291015625, -0.052093505859375, -0.014984130859375, -0.060394287109375, 0.03790283203125, -0.0264892578125, 0.038421630859375, -0.0095977783203125, -0.00711822509765625, 0.01873779296875, -0.01837158203125, -0.03582763671875, -0.0244903564453125, -0.0789794921875, 0.004055023193359375, 0.035308837890625, 0.049346923828125, 0.05035400390625, 0.0242767333984375, 0.042694091796875, 0.0260772705078125, -0.015380859375, 0.03204345703125, -0.0027446746826171875, 0.00015556812286376953, -0.0233917236328125, -0.03662109375, -0.018951416015625, 0.00502777099609375, 0.07275390625, 0.064208984375, -0.018890380859375, 0.003520965576171875, -0.0203399658203125, 0.02197265625, -0.032958984375, 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tianyang/repobench_ablation_64k	2023-10-25T02:25:32.000Z	[ "region:us" ]	tianyang	null	null	0	2023-10-25T02:21:36	--- configs: - config_name: default data_files: - split: cross_file_first path: data/cross_file_first-* - split: cross_file_random path: data/cross_file_random-* - split: in_file path: data/in_file-* dataset_info: features: - name: repo_name dtype: string - name: file_path dtype: string - name: context list: - name: identifier dtype: string - name: path dtype: string - name: snippet dtype: string - name: import_statement dtype: string - name: token_num dtype: int64 - name: cropped_code dtype: string - name: all_code dtype: string - name: next_line dtype: string - name: gold_snippet_index dtype: int64 splits: - name: cross_file_first num_bytes: 248891343 num_examples: 3644 - name: cross_file_random num_bytes: 201499818 num_examples: 3321 - name: in_file num_bytes: 231190696 num_examples: 3549 download_size: 209884822 dataset_size: 681581857 --- # Dataset Card for 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BangumiBase/foodwars	2023-10-25T09:29:56.000Z	[ "size_categories:10K<n<100K", "license:mit", "art", "region:us" ]	BangumiBase	null	null	0	2023-10-25T02:25:25	--- license: mit tags: - art size_categories: - 10K<n<100K --- # Bangumi Image Base of Food Wars! This is the image base of bangumi Food Wars!, we detected 129 characters, 16059 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 \| 87 \| [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 \| 664 \| [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 \| 69 \| [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 \| 172 \| [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 \| 242 \| [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 \| 98 \| [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 \| 83 \| [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 \| 69 \| [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 \| 59 \| [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 \| 150 \| [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 \| 85 \| [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 \| 44 \| [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 \| 73 \| [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 \| 27 \| [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 \| 25 \| [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 \| 31 \| [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 \| 658 \| [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 \| 73 \| [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 \| 39 \| [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 \| 64 \| [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 \| 70 \| [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 \| 27 \| [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 \| 17 \| [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 \| 65 \| [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 \| 14 \| [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 \| 2797 \| [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 \| 35 \| [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 \| 87 \| [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 \| 112 \| [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 \| 73 \| [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 \| 84 \| [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 \| 102 \| [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 \| 210 \| [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 \| 86 \| [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 \| 200 \| [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 \| 463 \| [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 \| 89 \| [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 \| 94 \| [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 \| 204 \| [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 \| 106 \| [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 \| 32 \| [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 \| 35 \| [Download](43/dataset.zip) \| ![preview 1](43/preview_1.png) \| ![preview 2](43/preview_2.png) \| ![preview 3](43/preview_3.png) \| ![preview 4](43/preview_4.png) \| ![preview 5](43/preview_5.png) \| ![preview 6](43/preview_6.png) \| ![preview 7](43/preview_7.png) \| ![preview 8](43/preview_8.png) \| \| 44 \| 75 \| [Download](44/dataset.zip) \| ![preview 1](44/preview_1.png) \| ![preview 2](44/preview_2.png) \| ![preview 3](44/preview_3.png) \| ![preview 4](44/preview_4.png) \| ![preview 5](44/preview_5.png) \| ![preview 6](44/preview_6.png) \| ![preview 7](44/preview_7.png) \| ![preview 8](44/preview_8.png) \| \| 45 \| 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 \| 38 \| [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 \| 343 \| [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 \| 90 \| [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 \| 53 \| [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 \| 35 \| [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 \| 24 \| [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 \| 45 \| [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 \| 27 \| [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 \| 23 \| [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 \| 32 \| [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 \| 19 \| [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 \| 202 \| [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 \| 119 \| [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 \| 12 \| [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 \| 15 \| [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 \| 61 \| [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 \| 14 \| [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 \| 49 \| [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 \| 13 \| [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 \| 318 \| [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 \| 367 \| [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 \| 22 \| [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 \| 67 \| [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 \| 51 \| [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 \| 46 \| [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 \| 24 \| [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 \| 21 \| [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 \| 16 \| [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 \| 47 \| [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 \| 75 \| [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 \| 39 \| [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 \| 34 \| [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 \| 52 \| [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 \| 40 \| [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 \| 615 \| [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 \| 154 \| [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 \| 10 \| [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 \| 73 \| [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 \| 42 \| [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 \| 34 \| [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 \| 383 \| [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 \| 29 \| [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 \| 12 \| [Download](88/dataset.zip) \| ![preview 1](88/preview_1.png) \| ![preview 2](88/preview_2.png) \| ![preview 3](88/preview_3.png) \| ![preview 4](88/preview_4.png) \| ![preview 5](88/preview_5.png) \| ![preview 6](88/preview_6.png) \| ![preview 7](88/preview_7.png) \| ![preview 8](88/preview_8.png) \| \| 89 \| 30 \| [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 \| 64 \| [Download](90/dataset.zip) \| ![preview 1](90/preview_1.png) \| ![preview 2](90/preview_2.png) \| ![preview 3](90/preview_3.png) \| ![preview 4](90/preview_4.png) \| ![preview 5](90/preview_5.png) \| ![preview 6](90/preview_6.png) \| ![preview 7](90/preview_7.png) \| ![preview 8](90/preview_8.png) \| \| 91 \| 1406 \| [Download](91/dataset.zip) \| ![preview 1](91/preview_1.png) \| ![preview 2](91/preview_2.png) \| ![preview 3](91/preview_3.png) \| ![preview 4](91/preview_4.png) \| ![preview 5](91/preview_5.png) \| ![preview 6](91/preview_6.png) \| ![preview 7](91/preview_7.png) \| ![preview 8](91/preview_8.png) \| \| 92 \| 44 \| [Download](92/dataset.zip) \| ![preview 1](92/preview_1.png) \| ![preview 2](92/preview_2.png) \| ![preview 3](92/preview_3.png) \| ![preview 4](92/preview_4.png) \| ![preview 5](92/preview_5.png) \| ![preview 6](92/preview_6.png) \| ![preview 7](92/preview_7.png) \| ![preview 8](92/preview_8.png) \| \| 93 \| 128 \| [Download](93/dataset.zip) \| ![preview 1](93/preview_1.png) \| ![preview 2](93/preview_2.png) \| ![preview 3](93/preview_3.png) \| ![preview 4](93/preview_4.png) \| ![preview 5](93/preview_5.png) \| ![preview 6](93/preview_6.png) \| ![preview 7](93/preview_7.png) \| ![preview 8](93/preview_8.png) \| \| 94 \| 48 \| [Download](94/dataset.zip) \| ![preview 1](94/preview_1.png) \| ![preview 2](94/preview_2.png) \| ![preview 3](94/preview_3.png) \| ![preview 4](94/preview_4.png) \| ![preview 5](94/preview_5.png) \| ![preview 6](94/preview_6.png) \| ![preview 7](94/preview_7.png) \| ![preview 8](94/preview_8.png) \| \| 95 \| 327 \| [Download](95/dataset.zip) \| ![preview 1](95/preview_1.png) \| ![preview 2](95/preview_2.png) \| ![preview 3](95/preview_3.png) \| ![preview 4](95/preview_4.png) \| ![preview 5](95/preview_5.png) \| ![preview 6](95/preview_6.png) \| ![preview 7](95/preview_7.png) \| ![preview 8](95/preview_8.png) \| \| 96 \| 96 \| [Download](96/dataset.zip) \| ![preview 1](96/preview_1.png) \| ![preview 2](96/preview_2.png) \| ![preview 3](96/preview_3.png) \| ![preview 4](96/preview_4.png) \| ![preview 5](96/preview_5.png) \| ![preview 6](96/preview_6.png) \| ![preview 7](96/preview_7.png) \| ![preview 8](96/preview_8.png) \| \| 97 \| 181 \| [Download](97/dataset.zip) \| ![preview 1](97/preview_1.png) \| ![preview 2](97/preview_2.png) \| ![preview 3](97/preview_3.png) \| ![preview 4](97/preview_4.png) \| ![preview 5](97/preview_5.png) \| ![preview 6](97/preview_6.png) \| ![preview 7](97/preview_7.png) \| ![preview 8](97/preview_8.png) \| \| 98 \| 14 \| [Download](98/dataset.zip) \| ![preview 1](98/preview_1.png) \| ![preview 2](98/preview_2.png) \| ![preview 3](98/preview_3.png) \| ![preview 4](98/preview_4.png) \| ![preview 5](98/preview_5.png) \| ![preview 6](98/preview_6.png) \| ![preview 7](98/preview_7.png) \| ![preview 8](98/preview_8.png) \| \| 99 \| 16 \| [Download](99/dataset.zip) \| ![preview 1](99/preview_1.png) \| ![preview 2](99/preview_2.png) \| ![preview 3](99/preview_3.png) \| ![preview 4](99/preview_4.png) \| ![preview 5](99/preview_5.png) \| ![preview 6](99/preview_6.png) \| ![preview 7](99/preview_7.png) \| ![preview 8](99/preview_8.png) \| \| 100 \| 14 \| [Download](100/dataset.zip) \| ![preview 1](100/preview_1.png) \| ![preview 2](100/preview_2.png) \| ![preview 3](100/preview_3.png) \| ![preview 4](100/preview_4.png) \| ![preview 5](100/preview_5.png) \| ![preview 6](100/preview_6.png) \| ![preview 7](100/preview_7.png) \| ![preview 8](100/preview_8.png) \| \| 101 \| 34 \| [Download](101/dataset.zip) \| ![preview 1](101/preview_1.png) \| ![preview 2](101/preview_2.png) \| ![preview 3](101/preview_3.png) \| ![preview 4](101/preview_4.png) \| ![preview 5](101/preview_5.png) \| ![preview 6](101/preview_6.png) \| ![preview 7](101/preview_7.png) \| ![preview 8](101/preview_8.png) \| \| 102 \| 193 \| [Download](102/dataset.zip) \| ![preview 1](102/preview_1.png) \| ![preview 2](102/preview_2.png) \| ![preview 3](102/preview_3.png) \| ![preview 4](102/preview_4.png) \| ![preview 5](102/preview_5.png) \| ![preview 6](102/preview_6.png) \| ![preview 7](102/preview_7.png) \| ![preview 8](102/preview_8.png) \| \| 103 \| 134 \| [Download](103/dataset.zip) \| ![preview 1](103/preview_1.png) \| ![preview 2](103/preview_2.png) \| ![preview 3](103/preview_3.png) \| ![preview 4](103/preview_4.png) \| ![preview 5](103/preview_5.png) \| ![preview 6](103/preview_6.png) \| ![preview 7](103/preview_7.png) \| ![preview 8](103/preview_8.png) \| \| 104 \| 69 \| [Download](104/dataset.zip) \| ![preview 1](104/preview_1.png) \| ![preview 2](104/preview_2.png) \| ![preview 3](104/preview_3.png) \| ![preview 4](104/preview_4.png) \| ![preview 5](104/preview_5.png) \| ![preview 6](104/preview_6.png) \| ![preview 7](104/preview_7.png) \| ![preview 8](104/preview_8.png) \| \| 105 \| 9 \| [Download](105/dataset.zip) \| ![preview 1](105/preview_1.png) \| ![preview 2](105/preview_2.png) \| ![preview 3](105/preview_3.png) \| ![preview 4](105/preview_4.png) \| ![preview 5](105/preview_5.png) \| ![preview 6](105/preview_6.png) \| ![preview 7](105/preview_7.png) \| ![preview 8](105/preview_8.png) \| \| 106 \| 86 \| [Download](106/dataset.zip) \| ![preview 1](106/preview_1.png) \| ![preview 2](106/preview_2.png) \| ![preview 3](106/preview_3.png) \| ![preview 4](106/preview_4.png) \| ![preview 5](106/preview_5.png) \| ![preview 6](106/preview_6.png) \| ![preview 7](106/preview_7.png) \| ![preview 8](106/preview_8.png) \| \| 107 \| 23 \| [Download](107/dataset.zip) \| ![preview 1](107/preview_1.png) \| ![preview 2](107/preview_2.png) \| ![preview 3](107/preview_3.png) \| ![preview 4](107/preview_4.png) \| ![preview 5](107/preview_5.png) \| ![preview 6](107/preview_6.png) \| ![preview 7](107/preview_7.png) \| ![preview 8](107/preview_8.png) \| \| 108 \| 109 \| [Download](108/dataset.zip) \| ![preview 1](108/preview_1.png) \| ![preview 2](108/preview_2.png) \| ![preview 3](108/preview_3.png) \| ![preview 4](108/preview_4.png) \| ![preview 5](108/preview_5.png) \| ![preview 6](108/preview_6.png) \| ![preview 7](108/preview_7.png) \| ![preview 8](108/preview_8.png) \| \| 109 \| 28 \| [Download](109/dataset.zip) \| ![preview 1](109/preview_1.png) \| ![preview 2](109/preview_2.png) \| ![preview 3](109/preview_3.png) \| ![preview 4](109/preview_4.png) \| ![preview 5](109/preview_5.png) \| ![preview 6](109/preview_6.png) \| ![preview 7](109/preview_7.png) \| ![preview 8](109/preview_8.png) \| \| 110 \| 14 \| [Download](110/dataset.zip) \| ![preview 1](110/preview_1.png) \| ![preview 2](110/preview_2.png) \| ![preview 3](110/preview_3.png) \| ![preview 4](110/preview_4.png) \| ![preview 5](110/preview_5.png) \| ![preview 6](110/preview_6.png) \| ![preview 7](110/preview_7.png) \| ![preview 8](110/preview_8.png) \| \| 111 \| 12 \| [Download](111/dataset.zip) \| ![preview 1](111/preview_1.png) \| ![preview 2](111/preview_2.png) \| ![preview 3](111/preview_3.png) \| ![preview 4](111/preview_4.png) \| ![preview 5](111/preview_5.png) \| ![preview 6](111/preview_6.png) \| ![preview 7](111/preview_7.png) \| ![preview 8](111/preview_8.png) \| \| 112 \| 6 \| [Download](112/dataset.zip) \| ![preview 1](112/preview_1.png) \| ![preview 2](112/preview_2.png) \| ![preview 3](112/preview_3.png) \| ![preview 4](112/preview_4.png) \| ![preview 5](112/preview_5.png) \| ![preview 6](112/preview_6.png) \| N/A \| N/A \| \| 113 \| 13 \| [Download](113/dataset.zip) \| ![preview 1](113/preview_1.png) \| ![preview 2](113/preview_2.png) \| ![preview 3](113/preview_3.png) \| ![preview 4](113/preview_4.png) \| ![preview 5](113/preview_5.png) \| ![preview 6](113/preview_6.png) \| ![preview 7](113/preview_7.png) \| ![preview 8](113/preview_8.png) \| \| 114 \| 14 \| [Download](114/dataset.zip) \| ![preview 1](114/preview_1.png) \| ![preview 2](114/preview_2.png) \| ![preview 3](114/preview_3.png) \| ![preview 4](114/preview_4.png) \| ![preview 5](114/preview_5.png) \| ![preview 6](114/preview_6.png) \| ![preview 7](114/preview_7.png) \| ![preview 8](114/preview_8.png) \| \| 115 \| 27 \| [Download](115/dataset.zip) \| ![preview 1](115/preview_1.png) \| ![preview 2](115/preview_2.png) \| ![preview 3](115/preview_3.png) \| ![preview 4](115/preview_4.png) \| ![preview 5](115/preview_5.png) \| ![preview 6](115/preview_6.png) \| ![preview 7](115/preview_7.png) \| ![preview 8](115/preview_8.png) \| \| 116 \| 106 \| [Download](116/dataset.zip) \| ![preview 1](116/preview_1.png) \| ![preview 2](116/preview_2.png) \| ![preview 3](116/preview_3.png) \| ![preview 4](116/preview_4.png) \| ![preview 5](116/preview_5.png) \| ![preview 6](116/preview_6.png) \| ![preview 7](116/preview_7.png) \| ![preview 8](116/preview_8.png) \| \| 117 \| 44 \| [Download](117/dataset.zip) \| ![preview 1](117/preview_1.png) \| ![preview 2](117/preview_2.png) \| ![preview 3](117/preview_3.png) \| ![preview 4](117/preview_4.png) \| ![preview 5](117/preview_5.png) \| ![preview 6](117/preview_6.png) \| ![preview 7](117/preview_7.png) \| ![preview 8](117/preview_8.png) \| \| 118 \| 34 \| [Download](118/dataset.zip) \| ![preview 1](118/preview_1.png) \| ![preview 2](118/preview_2.png) \| ![preview 3](118/preview_3.png) \| ![preview 4](118/preview_4.png) \| ![preview 5](118/preview_5.png) \| ![preview 6](118/preview_6.png) \| ![preview 7](118/preview_7.png) \| ![preview 8](118/preview_8.png) \| \| 119 \| 16 \| [Download](119/dataset.zip) \| ![preview 1](119/preview_1.png) \| ![preview 2](119/preview_2.png) \| ![preview 3](119/preview_3.png) \| ![preview 4](119/preview_4.png) \| ![preview 5](119/preview_5.png) \| ![preview 6](119/preview_6.png) \| ![preview 7](119/preview_7.png) \| ![preview 8](119/preview_8.png) \| \| 120 \| 120 \| [Download](120/dataset.zip) \| ![preview 1](120/preview_1.png) \| ![preview 2](120/preview_2.png) \| ![preview 3](120/preview_3.png) \| ![preview 4](120/preview_4.png) \| ![preview 5](120/preview_5.png) \| ![preview 6](120/preview_6.png) \| ![preview 7](120/preview_7.png) \| ![preview 8](120/preview_8.png) \| \| 121 \| 28 \| [Download](121/dataset.zip) \| ![preview 1](121/preview_1.png) \| ![preview 2](121/preview_2.png) \| ![preview 3](121/preview_3.png) \| ![preview 4](121/preview_4.png) \| ![preview 5](121/preview_5.png) \| ![preview 6](121/preview_6.png) \| ![preview 7](121/preview_7.png) \| ![preview 8](121/preview_8.png) \| \| 122 \| 27 \| [Download](122/dataset.zip) \| ![preview 1](122/preview_1.png) \| ![preview 2](122/preview_2.png) \| ![preview 3](122/preview_3.png) \| ![preview 4](122/preview_4.png) \| ![preview 5](122/preview_5.png) \| ![preview 6](122/preview_6.png) \| ![preview 7](122/preview_7.png) \| ![preview 8](122/preview_8.png) \| \| 123 \| 64 \| [Download](123/dataset.zip) \| ![preview 1](123/preview_1.png) \| ![preview 2](123/preview_2.png) \| ![preview 3](123/preview_3.png) \| ![preview 4](123/preview_4.png) \| ![preview 5](123/preview_5.png) \| ![preview 6](123/preview_6.png) \| ![preview 7](123/preview_7.png) \| ![preview 8](123/preview_8.png) \| \| 124 \| 19 \| [Download](124/dataset.zip) \| ![preview 1](124/preview_1.png) \| ![preview 2](124/preview_2.png) \| ![preview 3](124/preview_3.png) \| ![preview 4](124/preview_4.png) \| ![preview 5](124/preview_5.png) \| ![preview 6](124/preview_6.png) \| ![preview 7](124/preview_7.png) \| ![preview 8](124/preview_8.png) \| \| 125 \| 21 \| [Download](125/dataset.zip) \| ![preview 1](125/preview_1.png) \| ![preview 2](125/preview_2.png) \| ![preview 3](125/preview_3.png) \| ![preview 4](125/preview_4.png) \| ![preview 5](125/preview_5.png) \| ![preview 6](125/preview_6.png) \| ![preview 7](125/preview_7.png) \| ![preview 8](125/preview_8.png) \| \| 126 \| 8 \| [Download](126/dataset.zip) \| ![preview 1](126/preview_1.png) \| ![preview 2](126/preview_2.png) \| ![preview 3](126/preview_3.png) \| ![preview 4](126/preview_4.png) \| ![preview 5](126/preview_5.png) \| ![preview 6](126/preview_6.png) \| ![preview 7](126/preview_7.png) \| ![preview 8](126/preview_8.png) \| \| 127 \| 24 \| [Download](127/dataset.zip) \| ![preview 1](127/preview_1.png) \| ![preview 2](127/preview_2.png) \| ![preview 3](127/preview_3.png) \| ![preview 4](127/preview_4.png) \| ![preview 5](127/preview_5.png) \| ![preview 6](127/preview_6.png) \| ![preview 7](127/preview_7.png) \| ![preview 8](127/preview_8.png) \| \| noise \| 576 \| [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) \| 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Rewcifer/ct_scans_90pct_2048_cutoff_falcon	2023-10-25T03:01:36.000Z	[ "region:us" ]	Rewcifer	null	null	0	2023-10-25T03:01:24	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 880984917.8756735 num_examples: 176406 download_size: 166046892 dataset_size: 880984917.8756735 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ct_scans_90pct_2048_cutoff_falcon" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	485	[ [ -0.051300048828125, -0.006603240966796875, 0.0297698974609375, 0.01340484619140625, -0.016143798828125, -0.0170135498046875, 0.03173828125, 0.0030231475830078125, 0.05572509765625, 0.042327880859375, -0.0706787109375, -0.048431396484375, -0.039459228515625, -0.0006203651428222656, -0.0178375244140625, 0.08050537109375, 0.0209503173828125, 0.00601959228515625, -0.0016584396362304688, 0.004199981689453125, 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xiaopeiwu/hrw_test_binary_flagged_data	2023-10-25T04:08:28.000Z	[ "region:us" ]	xiaopeiwu	null	null	0	2023-10-25T04:08:28	--- configs: - config_name: default data_files: - split: train path: data.csv --- # Dataset Card for Dataset Name ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary [More Information Needed] ### 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]	1,416	[ [ -0.03173828125, -0.027557373046875, -0.0022735595703125, 0.022613525390625, -0.0214996337890625, 0.0146331787109375, -0.02069091796875, -0.0174102783203125, 0.034423828125, 0.050628662109375, -0.0599365234375, -0.08056640625, -0.051300048828125, 0.0094757080078125, -0.0117340087890625, 0.085205078125, -0.005023956298828125, -0.00760650634765625, -0.00482940673828125, -0.017791748046875, -0.035186767578125, -0.031219482421875, -0.0372314453125, -0.0244903564453125, 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thangvip/orca-filter2	2023-10-25T04:13:20.000Z	[ "region:us" ]	thangvip	null	null	0	2023-10-25T04:11:38	--- dataset_info: features: - name: id dtype: string - name: system_prompt dtype: string - name: question dtype: string - name: response dtype: string splits: - name: train num_bytes: 1147795895.593268 num_examples: 1261986 download_size: 615068751 dataset_size: 1147795895.593268 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "orca-filter2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	579	[ [ -0.037841796875, -0.0243988037109375, 0.01345062255859375, 0.0025463104248046875, -0.03021240234375, -0.01136016845703125, 0.034637451171875, -0.032623291015625, 0.05364990234375, 0.052947998046875, -0.054473876953125, -0.052978515625, -0.043060302734375, -0.0258026123046875, -0.0236663818359375, 0.08477783203125, 0.0112762451171875, 0.01256561279296875, 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Sanatbek/Uzbek-restaurant-domain-sentiment-reviews	2023-10-25T06:10:24.000Z	[ "region:us" ]	Sanatbek	null	null	0	2023-10-25T06:06:54	Entry not found	15	[ [ -0.021392822265625, -0.01494598388671875, 0.05718994140625, 0.028839111328125, -0.0350341796875, 0.046539306640625, 0.052490234375, 0.00507354736328125, 0.051361083984375, 0.01702880859375, -0.052093505859375, -0.01494598388671875, -0.06036376953125, 0.03790283203125, -0.026458740234375, 0.038421630859375, -0.00962066650390625, -0.007110595703125, 0.018707275390625, -0.018341064453125, -0.035919189453125, -0.024444580078125, -0.0789794921875, 0.0040740966796875, 0.035247802734375, 0.04931640625, 0.05029296875, 0.0242156982421875, 0.042694091796875, 0.0260772705078125, -0.0153350830078125, 0.032012939453125, -0.0027523040771484375, 0.00018143653869628906, -0.023345947265625, -0.036590576171875, -0.0189971923828125, 0.00502777099609375, 0.07269287109375, 0.06414794921875, -0.0188751220703125, 0.0035495758056640625, 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mesolitica/malaysian-llama2-embedding-1b-part2	2023-10-26T01:29:38.000Z	[ "region:us" ]	mesolitica	null	null	0	2023-10-25T06:20:36	Entry not found	15	[ [ -0.021392822265625, -0.01494598388671875, 0.05718994140625, 0.028839111328125, -0.0350341796875, 0.046539306640625, 0.052490234375, 0.00507354736328125, 0.051361083984375, 0.01702880859375, -0.052093505859375, -0.01494598388671875, -0.06036376953125, 0.03790283203125, -0.026458740234375, 0.038421630859375, -0.00962066650390625, -0.007110595703125, 0.018707275390625, -0.018341064453125, -0.035919189453125, -0.024444580078125, -0.0789794921875, 0.0040740966796875, 0.035247802734375, 0.04931640625, 0.05029296875, 0.0242156982421875, 0.042694091796875, 0.0260772705078125, -0.0153350830078125, 0.032012939453125, -0.0027523040771484375, 0.00018143653869628906, -0.023345947265625, -0.036590576171875, -0.0189971923828125, 0.00502777099609375, 0.07269287109375, 0.06414794921875, -0.0188751220703125, 0.0035495758056640625, -0.0203399658203125, 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1NightRaid1/testing	2023-10-25T07:13:47.000Z	[ "task_categories:question-answering", "size_categories:1K<n<10K", "license:apache-2.0", "code", "region:us" ]	1NightRaid1	null	null	0	2023-10-25T07:05:36	--- license: apache-2.0 task_categories: - question-answering tags: - code size_categories: - 1K<n<10K --- # Dataset Card for Dataset Name <!-- Provide a quick summary of the dataset. --> This dataset card aims to be a base template for new datasets. 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buyflowforcemax/FlowForceMax	2023-10-25T07:11:41.000Z	[ "region:us" ]	buyflowforcemax	null	null	0	2023-10-25T07:11:21	In today’s busy schedule, it is not common to take our energy levels to a high level. Lack of desire and feeling drained are major problems that most males face in their daily routines. That’s why; FlowForce Max comes into existence with a powerful blend of natural extracts. These components are carefully designed to reignite your passion and get back youthful energy and activities. But, do you know what makes Flow Force Max distinctive from other products in the market? Let’s find out more deeply in this review! First of all, FlowForce Max is backed or formulated with extensive research and designed with utmost precision. It ensures that you are getting a supplement of the highest and best quality. Unlike other supplements in the market, Flow Force Max is completely free from any kind of harmful substances or synthetic enhancers. That’s why; it is known as reliable and safe for all users. When you start to utilize FlowForce Max, you can expect a gradual increment and noticeable change in physical activities. Carefully crafted extracts always work together to boost blood circulation to essential areas in the body. [![](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgD2taPTN6RIwxDYTSRGyoElRqznxPrIQ3Cy0UF0PWD7rd5uFVKfMP11zRibrLognAIbEx9u-l-C0QTX83RJgJabmRoiEv_EruNCI5C3QTUif9C00bvqWQrYY4gy2my5bOsa33tPbz93usSAymqjAosDS2_C27y9fLbPdybQW-15wirG7AuAB64rFHCLJMN/w640-h250/Screenshot%20(1454).png)](https://www.glitco.com/us-flowforce-max) Now, it is not essential to feel uninspired and tired because Flow Force Max is here to invigorate manhood and keep you on high every time whenever needed! Additionally, FlowForce Max may go beyond just improving activities and it can bring energy to daily lifestyles and boost overall well-being. 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[MUST SEE: Click Here Now To Get The FlowForce Max Directly From The Official Website](https://www.glitco.com/us-flowforce-max) What is the Adequate Working Procedure of FlowForce Max? -------------------------------------------------------- Usually, Flow Force Max acts by using carefully designed formulas that deal with underlying factors responsible for lower confidence, lower energy, unsatisfactory intimate activity, and lack of desire. Understanding the amazing functional mechanism of the product is very important because it is incredible in improving and rejuvenating the experience of pleasurable moments. And, the previous sections of Flow Force Max reviews have repeated that the primary working procedure of action for Flow Force Max is centered around improving blood flow and optimizing levels of energy. These additional two factors play an important role in describing desire, overall satisfactory outcomes, and activities. Firstly, FlowForce Max includes ingredients that encourage the development of nitric oxide in the entire body. Nitric oxide is an herbal vasodilator that helps to expand and relax blood vessels. In this way, it can improve blood circulation, especially to the problematic parts of the body. This amazing mechanism helps people experience more enjoyable and fulfilling intimate activities. Moreover, FlowForce Max also acts by optimizing levels of desire and energy. In simple words, it increases the production of T-levels that can increase physical and mental well-being without any problems. FlowForce Max components help to improve production by restoring energy, power, strength, and long-lasting activities. Besides improving blood circulation and optimizing energy levels, this product also works by decreasing anxiety and stress which can significantly impact desire and activity in front of a lady. The blend of natural components in this product assists in relaxing both the body and mind, encouraging a state of calmness and decreasing stress levels. You may need to follow the coming sections of this incredible review to uncover how they are associated with balanced levels of energy and confidence. By alleviating stress, people are getting heightened desire, improved mood, and happiness. It is very important to understand that the functions or mechanisms of Flow Force Max are beyond the physical aspects of the activity. [![](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgSPn7Ep9XAjwgRnFDTeLHXiyJWrOkdJpRxcZvMHZJgK_243DuaXbAsOr9PvTCXVqFippwxQBKASwEbev_DgIg1TEu7XhjwgP0FjDvkfo5Mn1syeb9Dx1MDvA_jYbU7SOMQiCvA6NLgnK96FVYIZAZaX_KNArWT4O3JaLuxGTWa30SH1i8SAbkB2sYINq74/w640-h428/Screenshot%20(1456).png)](https://www.glitco.com/us-flowforce-max) [MUST SEE: Click Here Now To Get The FlowForce Max Directly From The Official Website](https://www.glitco.com/us-flowforce-max) The increment of energy, desire, and confidence with long-lasting activity via this product has come with a psychological impact. Improved self-esteem, boosted confidence, and a renewed sense of desire can lead to a healthier and more gratifying experience. Undoubtedly, FlowForce Max offers a comprehensive and natural solution for people who are searching to boost their experiences. Its functional and amazing mechanism includes encouraging blood circulation, optimizing levels of energy, decreasing stress, and dealing with the core problems that contribute to reduced physical activities. What are the Natural Components of FlowForce Max? ------------------------------------------------- ➣ Graminex Flower Pollen Extract Extracted from a specific kind of flower pollen, this ingredient is enriched with amino acids, nutrients, and enzymes. Additionally, this component has been proven to support healthy prostate function and optimize levels of energy, especially T-levels. ➣ Luteolin It is another flavor that works as natural T-boosted which can improve muscle mass and support overall well-being and mood. ➣ Fisetin This is a kind of flavonoid with potent anti-inflammatory and antioxidant properties. And, it assists in boosting blood circulation including flow to the genital region which helps in longer and stronger staying power. Undoubtedly, Fisetin is included in most of the health-benefit products in the market. ➣ Monolaurin This component is extracted directly from coconut oil that possesses antibacterial, antiviral, and antifungal properties. In general, it assists in managing healthy immunity which is so important for overall well-being and health. ➣ Grape Seed Extract Well, this is an important component enriched with antioxidants known as OPCs (oligomeric proanthocyanidin complexes). These incredible antioxidants help to prevent blood vessels and encourage healthy circulation, important for managing firm energy. ➣ Oregano Leaf Extract It is best known for antimicrobial properties that support a healthy immunity and it is linked to boosted blood flow. Hence, it may have positive impacts on different activities without any trouble. ➣ ViNitrox It is a potent blend of grape and apple polyphenols that can improve the production of nitric oxide and encourage healthy blood circulation. In reality, it is crucial for maintaining and obtaining proper energy with longer outcomes. ➣ Saw Palmetto Fruit Extract Powder It is a very well-known herbal extract utilized to support prostate health. Also, it assists in managing healthy T-levels and can lead to increased function of the body. ➣ Tricalcium Phosphate In general, this component helps to ensure the complete absorption of nutrients in the entire body. Also, it supports the overall health of bones and contributes to the incredibility of other components in the formula. ➣ Muira Puama Extract It is well known as “potency wood” that has been utilized traditionally as an aphrodisiac. Also, it helps to increase desire, confidence, and energy, and deal with fatigue. ➣ Sucralose Usually, it is a 0-calorie sweetener that offers a pleasant and amazing taste to the product. This way, it makes it easy to consume this product without any problem. [Let’s Delve Into Some Health Advantages of FlowForce Max!](https://www.glitco.com/us-flowforce-max) Improved Intimate Activity - By encouraging optimal blood flow, this formula contributes to long-lasting and stronger activity in male. Improved Energy - FlowForce Max is formulated to boost energy levels that help people feel more energized and motivated in their daily lives. With increased energy and strength, people can enjoy a more hydrated and fulfilling lifestyle, both outside and inside the house. Improved Confidence – A happy and healthy intimate lifestyle can have a profound impact on the self-confidence of people. Flow Force Max helps people to regain their confidence and desire in a short period. Support Prostate Health – Some components of Flow Force Max have been proven used to support prostate health like saw palmetto fruit. Decreased Anxiety and Stress – It includes components that help to decrease stress and encourage relaxation. It may have a significant and positive impact on well-being as anxiety and stress often lead to difficulties in relationships. Support Immune and Antioxidant – Through ingredients like Oregano leaf, Grape seed, and Monolaurin, Flow Force Max provides immune-boosting and antioxidant properties. These properties assist in preventing cells from damage and manage a healthy immunity by supporting overall well-being. Where to Purchase FlowForce Max? -------------------------------- Very simple! Just click on the below image or link to visit the official page of FlowForce Max. It is the only way to get genuine or original products online. No other retail stores or offline markets can sell this product. It is an online product that can be grabbed through the official page only. You can get marvelous discount offers on the official website (according to the quantity). [(LIMITED STOCK ) Click Here to Order FlowForce Max For The Lowest Price from Official Website](https://www.glitco.com/us-flowforce-max) Conclusion- FlowForce Max Reviews --------------------------------- Lastly, FlowForce Max provides a claiming solution for people searching to boost their activities. With the carefully designed blend of natural components, this product helps to support increased energy, desire, confidence, and longer staying power. However, results may vary from person to person but results are guaranteed. Many people have reported positive results and supported the promises made about the product in the Flow Force Max review. It is essential to know that Flow ForceMax must be a part of a healthy lifestyle and is not a kind of substitute for professional medical advice. That’s why; it is important to discuss with a healthcare professional before beginning a new regimen. [![](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjfAMT1ELS8h-cA25PKPNQ7NTMeHXJvsrvX9fOrtxpUGf9J_p38PjhH8gzN6M13Y5hnOgL10qE7GV9qr1hC3H-rr6m_vPjfuQuTj1zbaAHHRbtU5FK-e0D_Te0W-gJwYvAjK8CgBy7g_n5QJfeWG5hDTOCFUTOTzEawRd_DBLW6BtzXRCoxhPAeJfht51GR/w640-h240/Screenshot%20(1455).png)](https://www.glitco.com/us-flowforce-max) FlowForce Max Reviews- Frequently Asked Questions ------------------------------------------------- 1) Is FlowForce Max Legit? Yes, Flow ForceMax is legit and that’s why millions of people are utilizing this product daily. If you want to improve male health naturally, Flow Force Max is an ideal option. 2) Is FlowForce Max Scam or Genuine? 100% genuine! As per the official website, users can find out real testimonials through the official page about Flow Force Max. It is a genuine product that can deal with the root cause of health issues without trouble. 3) What FlowForce Max Review Says? As per customer reviews, FlowForce Max is a positive and best product to address some specific health problems in males. However, some of them get faster results while some of them get delayed. It is because of the type and length of the problems from which they are surviving. 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birdhouse5/finetuning4	2023-10-25T07:53:15.000Z	[ "region:us" ]	birdhouse5	null	null	0	2023-10-25T07:53:12	--- dataset_info: features: - name: image dtype: image - name: text (string) dtype: string splits: - name: train num_bytes: 1251887.0 num_examples: 8 download_size: 1219237 dataset_size: 1251887.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "finetuning4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	482	[ [ -0.053070068359375, -0.01462554931640625, 0.0148468017578125, 0.013458251953125, -0.01415252685546875, -0.0180816650390625, 0.01490020751953125, -0.0144195556640625, 0.042694091796875, 0.0355224609375, -0.0614013671875, -0.04876708984375, -0.02752685546875, 0.000774383544921875, -0.020660400390625, 0.10498046875, 0.00379180908203125, 0.00991058349609375, -0.0292510986328125, -0.0160675048828125, -0.045166015625, -0.0269927978515625, 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flowforcemaxreviews/flowforcemaxreviewshealthsupplement	2023-10-25T07:58:11.000Z	[ "region:us" ]	flowforcemaxreviews	null	null	0	2023-10-25T07:57:43	#### Product Name \- [FlowForce Max Reviews](https://www.glitco.com/us-flowforce-max) #### Nature – Male support supplement #### Form – Capsules #### Refund Policy – 60-day money-back guarantee #### Price – Begin with $69/bottle #### How to Purchase – [Official Website](https://www.glitco.com/us-flowforce-max) [![](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgeb994RSOuEmLChhw0NP_0m-OKdoU33jrfiUWUBITMn1KSYGriIAAV_IHE8XnhURtoiCE2wBDCLoN-L5hL4azhY55_a32St8Mtrp_ulw_pt-rbYgZgFSKvN9DoZGLCxP6eLJXkuBqKYLbpXQKex_alngM7zmvwrE-uszCXjiB43JPvz9z4Qog4eMstCrU/w640-h540/powerbite.png)](https://www.glitco.com/us-flowforce-max) In today's data-driven world, organizations are constantly seeking ways to streamline their data management processes, ensuring data flows smoothly, securely, and efficiently. 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Also, it assists in managing healthy T-levels and can lead to increased function of the body. * Tricalcium Phosphate * In general, this component helps to ensure the complete absorption of nutrients in the entire body. Also, it supports the overall health of bones and contributes to the incredibility of other components in the formula. * Sucralose * Usually, it is a 0-calorie sweetener that offers a pleasant and amazing taste to the product. This way, it makes it easy to consume this product without any problem. * Peppermint Leaf Extract Powder * It includes menthol that can help to boost blood flow. Also, it has a soothing effect on the body that helps in reducing stress and proper relaxation. ### [Click Here Now To Get The FlowForce Max Directly From The Official Website ](https://www.glitco.com/us-flowforce-max) ### Can I Buy [FlowForce Max](https://www.glitco.com/us-flowforce-max) Online, and If So, Where Should I Go? 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By harnessing the power of FlowForce Max, your organization can unlock the true potential of data automation, gaining a competitive edge in the data-driven landscape. ### Read More Searches@ [#FlowForce Max US,](https://www.glitco.com/us-flowforce-max) [#FlowForce Max Reviews,](https://www.facebook.com/people/FlowForce-Max/61552707540955/) [#FlowForce Max PainRelief,](https://healthupdates2023.blogspot.com/2023/10/flowforce-max-popular-choice-for-data.html) [#FlowForce Max Benefits,](https://sites.google.com/view/flowforcemaxapopularchoiceford/home) [#FlowForce Max Buy,](https://groups.google.com/g/flowforce-max-reviews-health/c/3uHvimzyv_8) [#FlowForce Max Cost,](https://lookerstudio.google.com/reporting/48896ce2-decc-4224-a571-09877ecc6a0d) [#FlowForce Max Ingredients,](https://colab.research.google.com/drive/12zwkesVDjLQnvvZgSm4rwCz2demmd9A1?usp=sharing) [#FlowForce Max Order,](https://www.bitsdujour.com/view/flowforce-max-a-popular-choice-for-data-integration#comments93022) 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OFA-Sys/OccuQuest	2023-10-26T03:18:10.000Z	[ "size_categories:100K<n<1M", "language:en", "license:apache-2.0", "sft", "occupational bias", "arxiv:2310.16517", "region:us" ]	OFA-Sys	null	null	0	2023-10-25T08:29:51	--- license: apache-2.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: dialog path: data/dialog-* - split: validation path: data/validation-* - split: test path: data/test-* - split: estate path: data/estate-* - split: quora path: data/quora-* dataset_info: features: - name: category dtype: string - name: occupation dtype: string - name: topic dtype: string - name: messages list: - name: role dtype: string - name: content dtype: string splits: - name: train num_bytes: 330314955 num_examples: 114090 - name: dialog num_bytes: 102158907 num_examples: 31682 - name: validation num_bytes: 7314741 num_examples: 2500 - name: test num_bytes: 718046 num_examples: 250 - name: estate num_bytes: 703613 num_examples: 250 - name: quora num_bytes: 45540 num_examples: 250 download_size: 184974294 dataset_size: 441255802 language: - en tags: - sft - occupational bias size_categories: - 100K<n<1M --- This is the dataset in [OccuQuest: Mitigating Occupational Bias for Inclusive Large Language Models](https://arxiv.org/abs/2310.16517) Abstract: The emergence of large language models (LLMs) has revolutionized natural language processing tasks. However, existing instruction-tuning datasets suffer from occupational bias: the majority of data relates to only a few occupations, which hampers the instruction-tuned LLMs to generate helpful responses to professional queries from practitioners in specific fields. To mitigate this issue and promote occupation-inclusive LLMs, we create an instruction-tuning dataset named OccuQuest, which contains 110,000+ prompt-completion pairs and 30,000+ dialogues covering over 1,000 occupations in 26 occupational categories. We systematically request ChatGPT, organizing queries hierarchically based on Occupation, Responsibility, Topic, and Question, to ensure a comprehensive coverage of occupational specialty inquiries. By comparing with three commonly used datasets (Dolly, ShareGPT, and WizardLM), we observe that OccuQuest exhibits a more balanced distribution across occupations. Furthermore, we assemble three test sets for comprehensive evaluation, an occu-test set covering 25 occupational categories, an estate set focusing on real estate, and an occu-quora set containing real-world questions from Quora. We then fine-tune LLaMA on OccuQuest to obtain OccuLLaMA, which significantly outperforms state-of-the-art LLaMA variants (Vicuna, Tulu, and WizardLM) on professional questions in GPT-4 and human evaluations. Notably, on the occu-quora set, OccuLLaMA reaches a high win rate of 86.4\% against WizardLM. Furthermore, we demonstrate the potential of combining OccuQuest with other instruction-tuning datasets to enhance the overall performance of LLMs. By fine-tuning LLaMA on a mixture of OccuQuest and Tulu datasets, we introduce ProLLaMA, which excels in addressing occupational questions and exhibits superior performance in comprehensive evaluations such as MMLU, GSM8K, BBH, and HumanEval. Among the different LLaMA variants, the 7B and 13B ProLLaMA models achieve the highest performance on MMLU and GSM8K, with the 7B ProLLaMA model demonstrating an improvement of more than 4 points over the other 7B variants on GSM8K. We open release the dataset and models. Please cite if you use this dataset: ``` @misc{xue2023occuquest, title={OccuQuest: Mitigating Occupational Bias for Inclusive Large Language Models}, author={Mingfeng Xue and Dayiheng Liu and Kexin Yang and Guanting Dong and Wenqiang Lei and Zheng Yuan and Chang Zhou and Jingren Zhou}, year={2023}, eprint={2310.16517}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```	3,818	[ [ -0.022430419921875, -0.0721435546875, 0.0287933349609375, -0.00405120849609375, -0.0006127357482910156, -0.0012788772583007812, -0.0318603515625, -0.04827880859375, 0.006397247314453125, 0.06231689453125, -0.050689697265625, -0.052215576171875, -0.0321044921875, 0.001911163330078125, -0.006153106689453125, 0.051666259765625, 0.01186370849609375, 0.0097503662109375, -0.01232147216796875, -0.02374267578125, -0.04241943359375, -0.02667236328125, -0.05328369140625, -0.0067138671875, 0.042877197265625, 0.046539306640625, 0.047943115234375, 0.049072265625, 0.023895263671875, 0.0190887451171875, -0.00447845458984375, 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Back-up/summary-text	2023-10-25T08:52:32.000Z	[ "region:us" ]	Back-up	null	null	0	2023-10-25T08:41:10	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: content dtype: string - name: summary dtype: string splits: - name: train num_bytes: 181566694 num_examples: 65361 - name: test num_bytes: 2753008 num_examples: 1000 download_size: 99917401 dataset_size: 184319702 --- # Dataset Card for "summary-text" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	584	[ [ -0.0426025390625, -0.02703857421875, 0.0220947265625, 0.01520538330078125, -0.0193023681640625, -0.00362396240234375, 0.011016845703125, -0.01325225830078125, 0.07977294921875, 0.037017822265625, -0.05377197265625, -0.051666259765625, -0.05059814453125, -0.0033092498779296875, -0.03985595703125, 0.12139892578125, 0.0015811920166015625, -0.0059814453125, 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dschaudt42/synthetic_pneumonia	2023-10-25T13:22:12.000Z	[ "region:us" ]	dschaudt42	null	null	0	2023-10-25T08:46:23	This dataset contains 70,000 synthetic images from the paper "A critical assessment of generative models for synthetic data augmentation on limited pneumonia X-ray data". It consists of synthetically generated X-ray images of bacterial, viral, fungal, and COVID-19 pneumonia, as well as healthy patients with no findings. CAUTION: the images are not real and do not depict real patients. Do not use for medical purposes!	420	[ [ -0.012176513671875, -0.036773681640625, 0.033905029296875, -0.0014619827270507812, -0.0168304443359375, -0.0028057098388671875, 0.0290679931640625, -0.0230865478515625, 0.036712646484375, 0.055999755859375, -0.055389404296875, -0.0222015380859375, -0.04052734375, 0.020904541015625, -0.050201416015625, 0.063232421875, 0.0276947021484375, 0.017333984375, -0.0081329345703125, 0.018585205078125, 0.0074462890625, -0.0231170654296875, -0.050384521484375, -0.00522613525390625, 0.01276397705078125, 0.0298919677734375, 0.0733642578125, 0.029022216796875, 0.052642822265625, 0.01247406005859375, 0.0240478515625, 0.005214691162109375, -0.053558349609375, -0.020263671875, 0.0014772415161132812, -0.0169830322265625, -0.042236328125, 0.0152587890625, 0.048583984375, 0.034942626953125, 0.0182952880859375, 0.0231170654296875, -0.041290283203125, 0.0307159423828125, -0.021331787109375, -0.002071380615234375, -0.0164947509765625, 0.038604736328125, 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nlplabtdtu/summary-text	2023-10-25T08:58:59.000Z	[ "region:us" ]	nlplabtdtu	null	null	0	2023-10-25T08:58:49	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: content dtype: string - name: summary dtype: string splits: - name: train num_bytes: 181566694 num_examples: 65361 - name: test num_bytes: 2753008 num_examples: 1000 download_size: 99917401 dataset_size: 184319702 --- # Dataset Card for "summary-text" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	584	[ [ -0.042633056640625, -0.027069091796875, 0.02215576171875, 0.01523590087890625, -0.019287109375, -0.0036106109619140625, 0.01100921630859375, -0.0132598876953125, 0.079833984375, 0.037017822265625, -0.053802490234375, -0.05169677734375, -0.05059814453125, -0.0033283233642578125, -0.03985595703125, 0.1214599609375, 0.0015916824340820312, 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joujiboi/japanese-knowledge-base	2023-10-25T09:16:47.000Z	[ "task_categories:question-answering", "task_categories:text-generation", "size_categories:1K<n<10K", "language:en", "license:cc-by-nc-4.0", "language", "japanese language", "nihongo", "日本語", "region:us" ]	joujiboi	null	null	0	2023-10-25T08:59:41	--- license: cc-by-nc-4.0 task_categories: - question-answering - text-generation language: - en tags: - language - japanese language - nihongo - 日本語 size_categories: - 1K<n<10K pretty_name: Japanese Knowledge Base --- # Japanese Knowledge Base JKB (Japanese Knowledge Base) is a work-in-progress question and answer dataset on the Japanese language. The goal of JKB is to provide training data for large language models to better answer questions about the Japanese language, particularly for learners of Japanese. ## The dataset contains topics about: * N5 to N1 grammar * Structure and word order * The writing systems * Word types * Particles and the commonly confused ones * Conjugation * Verbs and verb types * Adjectives and adjective types * Formality * Name suffixes ## The dataset will not focus on: * Translation * Definitions of words * Sentence nuance ## Sample ``` Question: What is a common usage scenario for 〜たり〜たり? Answer: The 〜たり〜たり structure is for listing actions as examples among various possibilities. It is commonly used to describe routines or plans. For example, one might use it to describe what they typically do on their days off or what they plan to do during a vacation. Question: How do you use 思う with na-adjectives? Answer: For na-adjectives, you can attach だ to the adjective in its plain form and then add と思う. For example, if you think someone is happy, you could say "幸せだと思います". Question: How do you use と思う with i-adjectives? Answer: To use 〜と思う with i-adjectives, you simply take the i-adjective in its plain form and attach と思う to it. For example, if you think something is hot (熱い), you can say "熱いと思う". Question: Is it possible to use 〜たい in a question? Answer: Yes, you can use 〜たい to ask about someone's desires or wishes, but be cautious when using it with superiors or elders as it can be considered impolite. ``` ## Citation This dataset uses the `Creative Commons Attribution Non Commercial 4.0` licence which means you may use my dataset for non-commercial purposes and you must give credit. ``` @misc{Japanese Knowledge Base, title = {Japanese Knowledge Base: A question and answer dataset on the Japanese language}, author = {JawGBoi}, year = {2023}, publisher = {HuggingFace}, journal = {HuggingFace repository}, howpublished = {\url{https://huggingface.co/datasets/joujiboi/japanese-knowledge-base}}, } ```	2,384	[ [ -0.037078857421875, -0.0841064453125, 0.0283355712890625, 0.020660400390625, -0.037841796875, -0.0227203369140625, -0.0309906005859375, -0.00873565673828125, 0.01174163818359375, 0.05401611328125, -0.048919677734375, -0.03839111328125, -0.0285797119140625, 0.0234832763671875, 0.0172119140625, 0.0694580078125, 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MedHK23/OmniFM-Dr	2023-11-01T07:11:24.000Z	[ "arxiv:1901.07441", "arxiv:2104.10326", "arxiv:1705.02315", "arxiv:2307.03293", "region:us" ]	MedHK23	null	null	1	2023-10-25T09:31:14	--- configs: - config_name: default data_files: - split: demo path: "demo.tsv" - split: classification path: "classification" - split: localization path: "localization" - split: segmentation path: "segmentation" - split: report_generation path: "report" --- # Dataset Descriptiona Our OmniFM-Dr framework introduces a multi-task chest x-ray dataset, which is used for the joint training of disease classification, localization, segmentation, and report generation. This dataset comprises various publicly available datasets, such as MIMIC-CXR, VinDr-CXR, and ChestX-Det10. For each image, it can potentially contribute to multiple tasks, such as report generation and classification. ## Dataset Details - [MIMIC:](https://physionet.org/content/mimic-cxr/2.0.0/) contains more than 377,110 radiograph images from over 227,835 radiographic studies. Each radiograph is paired with lesion classification and associated radiology report. It is used for multi-label classification and report generation tasks. - [Padchest:](https://arxiv.org/abs/1901.07441) includes 160,840 images obtained from 67,000 patients, covering six different position views. Different radiographic findings were labeled and used for the classification task in this study. - [CXR-AL14:]() is a large-scale dataset for chest X-ray image detection. It has more than 140,000 chest X-ray radiographs containing 253,844 bounding boxes in 14 chest abnormal object categories. - [VinDr-CXR:](https://www.nature.com/articles/s41597-022-01498-w) includes chest radiographs with annotations for the classification of 28 common chest diseases. The dataset contains 15,000 CXR scans in the training set. We select eight diseases from the dataset along with their corresponding bounding box for the disease localization task. - [ChestX-Det:](https://arxiv.org/abs/2104.10326) consists of 3,578 images from [NIH ChestXray14](https://arxiv.org/abs/1705.02315) for 13 common disease. We select seven diseases from the dataset along with bounding box for the disease localization task. - [CheXmask:](https://arxiv.org/abs/2307.03293) contains 676,803 lung and heart segmentation masks of chest images from six publicly available databases: CANDID-PTX, ChestXray14, Chexpert, MIMIC-CXR, Padchest, and VinDr-CXR. We include 224,316 data for training and 10,000 data from ChestXray14 for downstream evaluation. - [SIIM:](https://www.kaggle.com/c/siim-acr-pneumothorax-segmentation) comes from the SIIM-ACR Pneumothorax Segmentation competition and contains 12,090 images, among which approximately 3,000 cases are positive for pneumothorax disease with masks. ## Dataset Structure - MIMIC: - MIMIC_classification_report-generation_xxx.tsv: is used for classification and report generation tasks. For each row, it contains id, report, "label1 && label2", subject_id, study_id, dicom_id. - MIMIC_classification-location_xxx.tsv: is used for location vqa task. For each row, it contains id, "label1,severity && label2, severity", subject_id, study_id, dicom_id. - MIMIC_classification-severity_xxx.tsv: is used for severity vqa task. For each row, it contains id, "label, location1 & location2", subject_id, study_id, dicom_id. - Padchest: - Padchest_classification_xxx.tsv: is used for classification task. For each row, it contains id, "label1 && label2", subject_id, study_id, dicom_id. - CXR-AL14: - CXR_AL14_localization_xxx.tsv: is used for locatization and classification tasks. For each row, it contains id, label, "x1,y1,x2,y2", image_id. - VinDr-CXR: - VinDr_CXR_localization_xxx.tsv: is used for locatization and classification tasks. For each row, it contains id, label, "x1,y1,x2,y2", image_id. - ChestX-Det: - ChestX_Det_localization_xxx.tsv: is used for locatization and classification tasks. For each row, it contains id, label, "x1,y1,x2,y2", image_id. - CheXmask: - CheXmask_segmentation_xxx.tsv: is used for segmentation task. For each row, it contains id, label, "x1,y1,x2,y2, ..., x30, y30", subject_id, study_id, dicom_id. - SIIM: - SIIM_segmentation_xxx.tsv: is used for segmentation task. For each row, it contains id, label, "x1,y1,x2,y2, ..., x30, y30", subject_id, study_id, dicom_id. ## Dataset Use - Please run data_prepare.py, which constructs a training batch for all tasks. 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anon345/ERBD	2023-10-25T09:41:06.000Z	[ "license:other", "region:us" ]	anon345	null	null	0	2023-10-25T09:35:57	--- license: other license_name: under-review license_link: LICENSE --- # European Residential Building Dataset This dataset is not released and only serves as a temporary placeholder for academic reviewers. The European Residential Building dataset consists of 209,318 images of detached residential buildings sampled at random across the Netherlands and Denmark. We refer to our paper for more information on the dataset.	430	[ [ -0.05615234375, -0.040252685546875, 0.0240020751953125, 0.01352691650390625, -0.007663726806640625, 0.0070343017578125, -0.010528564453125, -0.0218353271484375, 0.000812530517578125, 0.036712646484375, -0.031585693359375, -0.05645751953125, -0.021392822265625, 0.037933349609375, -0.024261474609375, 0.0243988037109375, -0.004669189453125, 0.008087158203125, -0.00830841064453125, -0.001903533935546875, -0.03179931640625, -0.01285552978515625, -0.0109710693359375, -0.0005173683166503906, 0.036468505859375, 0.053741455078125, 0.0428466796875, 0.0247344970703125, 0.060821533203125, 0.0226287841796875, 0.0218353271484375, -0.0103607177734375, -0.03887939453125, -0.026458740234375, -0.0107879638671875, -0.01483154296875, -0.01132965087890625, -0.0234375, 0.060394287109375, 0.044921875, -0.0096282958984375, 0.045623779296875, -0.004421234130859375, 0.0693359375, -0.044891357421875, -0.010009765625, -0.01171875, 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ostapeno/qa-openai_batched_icl5_clen512_maxD-1_maxC2500_0	2023-10-25T10:03:15.000Z	[ "region:us" ]	ostapeno	null	null	0	2023-10-25T10:03:01	## model_setting: openai_batched ## max_context_length: 512 ## max_tokens_instruction: 2048 ## max_tokens_response: 1024 ## top_p: 0.9 ## num_iterations: 1 ## temperature: 0.7 ## max_documents_per_subject: -1 ## max_contexts_per_subject: 2500 ## icl_examples: 5 ## icl_dataset: lukaemon/mmlu ## icl_split: validation ## icl_use_options: True	342	[ [ -0.043701171875, -0.024627685546875, 0.0095977783203125, 0.045654296875, -0.042205810546875, -0.0222015380859375, -0.008697509765625, 0.0040740966796875, -0.0068817138671875, 0.023834228515625, -0.048858642578125, -0.042236328125, -0.04168701171875, 0.0015659332275390625, -0.034332275390625, 0.09320068359375, -0.0016126632690429688, 0.01340484619140625, -0.0084381103515625, -0.03411865234375, -0.033416748046875, -0.045440673828125, -0.055389404296875, -0.004985809326171875, 0.0308990478515625, 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andstor/code_search_net_files	2023-10-29T15:47:07.000Z	[ "annotations_creators:no-annotation", "language_creators:machine-generated", "multilinguality:multilingual", "size_categories:100K<n<1M", "size_categories:10K<n<100K", "size_categories:1M<n<10M", "source_datasets:original", "language:code", "license:other", "region:us" ]	andstor	CodeSearchNet corpus contains about 6 million functions from open-source code spanning six programming languages (Go, Java, JavaScript, PHP, Python, and Ruby). The CodeSearchNet Corpus also contains automatically generated query-like natural language for 2 million functions, obtained from mechanically scraping and preprocessing associated function documentation.	@article{husain2019codesearchnet, title={{CodeSearchNet} challenge: Evaluating the state of semantic code search}, author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc}, journal={arXiv preprint arXiv:1909.09436}, year={2019} }	0	2023-10-25T10:09:45	--- annotations_creators: - no-annotation language_creators: - machine-generated language: - code license: - other multilinguality: - multilingual size_categories: - 100K<n<1M - 10K<n<100K - 1M<n<10M source_datasets: - original dataset_info: - config_name: all features: - name: repository_name dtype: string - name: file_path dtype: string - name: language dtype: string - name: url dtype: string - name: contents dtype: string splits: - name: train num_bytes: 4597566968 num_examples: 481456 - name: test num_bytes: 244185049 num_examples: 27032 - name: validation num_bytes: 208172780 num_examples: 23262 download_size: 5328510079 dataset_size: 5049924797 - config_name: java features: - name: repository_name dtype: string - name: file_path dtype: string - name: language dtype: string - name: url dtype: string - name: contents dtype: string splits: - name: train num_bytes: 1508021640 num_examples: 124201 - name: test num_bytes: 83498294 num_examples: 7672 - name: validation num_bytes: 49495187 num_examples: 4622 download_size: 1719645289 dataset_size: 1641015121 - config_name: go features: - name: repository_name dtype: string - name: file_path dtype: string - name: language dtype: string - name: url dtype: string - name: contents dtype: string splits: - name: train num_bytes: 445935389 num_examples: 52467 - name: test num_bytes: 22662672 num_examples: 2661 - name: validation num_bytes: 14949171 num_examples: 1991 download_size: 525469198 dataset_size: 483547232 - config_name: python features: - name: repository_name dtype: string - name: file_path dtype: string - name: language dtype: string - name: url dtype: string - name: contents dtype: string splits: - name: train num_bytes: 966000949 num_examples: 83739 - name: test num_bytes: 53223810 num_examples: 4989 - name: validation num_bytes: 51072608 num_examples: 4589 download_size: 1117084868 dataset_size: 1070297367 - config_name: javascript features: - name: repository_name dtype: string - name: file_path dtype: string - name: language dtype: string - name: url dtype: string - name: contents dtype: string splits: - name: train num_bytes: 533231855 num_examples: 40913 - name: test num_bytes: 28571674 num_examples: 2176 - name: validation num_bytes: 36688764 num_examples: 2586 download_size: 633172343 dataset_size: 598492293 - config_name: ruby features: - name: repository_name dtype: string - name: file_path dtype: string - name: language dtype: string - name: url dtype: string - name: contents dtype: string splits: - name: train num_bytes: 132300659 num_examples: 16932 - name: test num_bytes: 5125555 num_examples: 822 - name: validation num_bytes: 4189342 num_examples: 803 download_size: 147603200 dataset_size: 141615556 - config_name: php features: - name: repository_name dtype: string - name: file_path dtype: string - name: language dtype: string - name: url dtype: string - name: contents dtype: string splits: - name: train num_bytes: 1012076476 num_examples: 163204 - name: test num_bytes: 51103044 num_examples: 8712 - name: validation num_bytes: 51777708 num_examples: 8671 download_size: 1185535181 dataset_size: 1114957228 --- # Dataset Card for CodeSearchNet Files	3,649	[ [ -0.01236724853515625, 0.051910400390625, -0.0150909423828125, 0.0066680908203125, -0.0281219482421875, 0.016265869140625, 0.00019550323486328125, 0.010284423828125, 0.040618896484375, 0.04254150390625, -0.02252197265625, -0.0654296875, -0.0227813720703125, 0.006954193115234375, 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Qdrant/NOAA-Buoy	2023-10-25T10:53:56.000Z	[ "task_categories:feature-extraction", "task_categories:tabular-classification", "task_categories:time-series-forecasting", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:cc-by-4.0", "region:us" ]	Qdrant	null	null	0	2023-10-25T10:43:03	--- language: - en license: - cc-by-4.0 multilinguality: - monolingual pretty_name: NOAA Buoy meterological data size_categories: - 100K<n<1M source_datasets: - original tags: [] task_categories: - feature-extraction - tabular-classification - time-series-forecasting --- # NOAA Buoy meterological data NOAA Buoy Data was downloaded, processed, and cleaned for tasks pertaining to tabular data. The data consists of meteorological measurements. There are two datasets 1. From 1980 through 2022 (denoted with "years" in file names) 2. From Jan 2023 through end of Sept 2023 (denoted with "2023" in file names) The original intended use is for anomaly detection in tabular data. ## Dataset Details ### Dataset Description This dataset contains weather buoy data to be used in a tabular embedding scenarios. Buoy 42002 was chosen because it had many years of historical data and was still actively collecting information Here is the buoy's page and its historical data page: - https://www.ndbc.noaa.gov/station_page.php?station=42002 - https://www.ndbc.noaa.gov/station_history.php?station=42002 Only standard meteorological data and ocean data was downloaded. Downloaded started at 1980, which is the first full year of collecting wave information. ### Data Fields {'TSTMP': 'timestamp', '#YY': '#yr', ' MM': 'mo', 'DD': 'dy', 'hh': 'hr', 'mm': 'mn', 'WDIR': 'degT', 'WSPD': 'm/s', ' GST': 'm/s', ' WVHT': 'm', 'DPD': 'sec', 'APD': 'sec', 'MWD ': 'degT', 'PRES': 'hPa', ' ATMP': 'degC', ' WTMP': 'degC' } ## Dataset Creation ### Curation Rationale The original data has inconsistent delimiters, different and inappropriate missing data values, and was not harmonized across years. Pre-2023 was edited in the same way as the previous data but kept separate to allow for train and inference. ### Source Data #### Initial Data Collection and Normalization Data Downloaded on Oct 12 2023 All code used to transform the data can be found in the buoy-python directory. This is NOT production code and the focus was on correct results and minimizing time spent writing cleaning code. 1. #YY, MM, DD, hh, mm were concatenated to create a timestamp and stored in a new column. 2. From 1980 until 2005 there was no recording of minutes. Minutes for those years was set to 00. 3. All missing data was set to a blank value rather than an actual number 4. Remove all rows without wave data from all the data sets ( missing value in WVHT and DPD) 5. Columns MWD, DEWP, VIS, and TIDE were removed because of consistent missing values 6. From 2005 -> 2006 Wind direction goes from being called WD to WDIR 7. From 2006 -> 2007 Header goes from just 1 line with variable names to 2 lines with the second line being units. These steps were used to create full_2023_remove_flawed_rows, the 2023 months, and full_years_remove_flawed_rows the previous data going back to 1980 Since the original purpose of this data was anomaly detection. The two data sets above received further processing. 1. All data values were converted to Z-scores (file named zscore_2023) 1. 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Qdrant/google-landmark-geo	2023-10-25T11:12:02.000Z	[ "task_categories:image-classification", "size_categories:< 50K", "source_datasets:Google Landmarks V2", "language:en", "license:cc-by-4.0", "region:us" ]	Qdrant	null	null	0	2023-10-25T11:10:09	--- language: - en pretty_name: Geo Coordinate Augmented Google-Landmarks task_categories: - image-classification source_datasets: - Google Landmarks V2 size_categories: - < 50K license: cc-by-4.0 --- # Dataset Card for Geo Coordinate Augmented Google-Landmarks Geo coordinates were added as data to a tar file's worth of images from the [Google Landmark V2](https://github.com/cvdfoundation/google-landmark). Not all of the images could be geo-tagged due to lack of coordinates on the image's wikimedia page. ## Dataset Details ### Dataset Description Geo coordinates were added as data to a tar file's worth of images from the [Google Landmark V2](https://github.com/cvdfoundation/google-landmark). There were many more images that could have been downloaded but this dataset was found to be a good balance of data size and sample size. The intended use for the dataset was to demonstrate using a geo-filter in Qdrant along with a image similarity search. Not all of the images couuld be geo-tagged due to lack of coordinates on the image's wikimedia page. We provide the raw geotagged file as a geojson document, train_attribution_geo.json. We also provide a json file that includes the data above along with embedding vectors for the images, id_payload_vector.json. Thingsvision was used as the library for creating the image embeddings with the following ThingVision model: ```python model_name = 'clip' model_parameters = { 'variant': 'ViT-B/32' } ``` The code directory contains the Python code used to geotag the images as well as generated the vectors. It can also be used to upload the embeddings to a Qdrant DB instance. This code is NOT for production and was more focused on quickly and correctly get the coordinates and embed the images. The license for this data and code match the license of the original Google Landmarks V2 Dataset: CC BY 4.0 license. ## Uses ### Direct Use The primary use is case is image similarity search with geographic filtering.	2,001	[ [ -0.045196533203125, -0.0281524658203125, 0.045623779296875, -0.00577545166015625, -0.05029296875, -0.03192138671875, 0.0031986236572265625, -0.047607421875, 0.04205322265625, 0.0256195068359375, -0.0251922607421875, -0.07489013671875, -0.04400634765625, -0.01355743408203125, -0.01129913330078125, 0.0682373046875, -0.0004851818084716797, -0.007354736328125, -0.0117950439453125, -0.0179443359375, -0.02337646484375, -0.053314208984375, -0.0208587646484375, 0.00522613525390625, 0.020660400390625, 0.0291900634765625, 0.056976318359375, 0.065185546875, 0.04119873046875, 0.013458251953125, -0.0042266845703125, 0.01116943359375, -0.0284271240234375, -0.046905517578125, -0.02532958984375, -0.01953125, -0.031982421875, 0.01203155517578125, 0.053985595703125, 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dwancin/dannet	2023-10-25T11:51:37.000Z	[ "language:da", "license:cc-by-sa-4.0", "region:us" ]	dwancin	null	null	0	2023-10-25T11:36:57	--- language: - da pretty_name: DanNet license: cc-by-sa-4.0 --- <div style="font-size: 12px;background: linear-gradient(180deg, rgba(255,251,235,1) 0%, rgba(255,251,235,0.53687412464986) 100%);color: rgb(146 64 14);border-radius: 6px;margin-bottom: 10px;padding-left: 20px;padding-right: 20px;padding-top: 1px;padding-bottom: 1px;"> <h4 style="font-size: 14px;margin-bottom: 3px;">Disclaimer</h4> <p>This dataset and accompanying README text are not my original work, and I am simply sharing the work created by the Department of Nordic Studies and Linguistics under their chosen copyrights (CC BY-SA 4.0). I have made it available on Hugging Face for accessibility and reference purposes. All credit for the creation of this dataset goes to the Department of Nordic Studies and Linguistics. For detailed information on the dataset's usage, licensing, and any specific terms and conditions set by the Department of Nordic Studies and Linguistics, please refer to their original documentation and the CC BY-SA 4.0 license. I am not the owner of this dataset, and any inquiries or issues related to the dataset should be directed to the Department of Nordic Studies and Linguistics.</p> </div> # DanNet [DanNet](https://wordnet.dk/dannet "The DanNet resource") is a [WordNet](https://wordnet.princeton.edu/) - a lexico-semantic network graph that shows how senses of a language relate to others through named relations. One can also think of a WordNet as a kind of machine-readable dictionary. On [wordnet.dk/dannet](https://wordnet.dk/dannet "DanNet"), you can not only browse but also download the newest versions of multiple data sets. Unlike a normal dictionary, the definitions of words aren't central; instead, relations to other words are the important part. For example, in DanNet, you can see that a [Swiss willow](https://wordnet.dk/dannet/data/synset-1304) is a kind of [bush](https://wordnet.dk/dannet/data/synset-597), that a [gazebo](https://wordnet.dk/dannet/data/synset-4733) is located in a [garden](https://wordnet.dk/dannet/data/synset-1876), that "[fiberdrys](https://wordnet.dk/dannet/data/synset-34989)" is for [eating](https://wordnet.dk/dannet/data/synset-124), and that [cakes](https://wordnet.dk/dannet/data/synset-52) are typically produced by [baking](https://wordnet.dk/dannet/data/synset-145) and usually made from [flour](https://wordnet.dk/dannet/data/synset-131) and [sugar](https://wordnet.dk/dannet/data/synset-128). ![DanNet Relations](https://cst.ku.dk/projekter/dannet/billeder/relationer.png) On [andreord.nors.ku.dk](https://andreord.nors.ku.dk/ "Andre Ord (søgemaskine for det gamle DanNet)"), you can generate the above illustration based on an older version of DanNet. In the future, you will be able to find similar illustrations based on the newest data at [wordnet.dk/dannet](https://wordnet.dk/dannet "The DanNet resource"). All in all, the wordnet currently contains around 70 thousand concepts kept together by approximately 500 thousand relations. ## More about DanNet The wordnet and the methods behind it are best described in: Bolette S. Pedersen, Sanni Nimb, Jørg Asmussen, Nicolai H. Sørensen, Lars Trap-Jensen og Henrik Lorentzen. [DanNet – the challenge of compiling a WordNet for Danish by reusing a monolingual dictionary](https://pdfs.semanticscholar.org/6891/69de00c63d58bd68229cb0b3469a617f5ab3.pdf). _Lang Resources & Evaluation_ (2009) 43:269–299. DanNet has been developed on the basis of Den Danske Ordbog ([the Danish Dictionary](https://ordnet.dk/ddo_en?set_language=en)) as a collaborative work between the University of Copenhagen (Centre for Language Technology) and The Danish Society for Language and Literature. Funding was provided by the Danish Research Council via the project DanNet – et leksikalsk-semantisk ordnet for dansk, and the wordnet has been developed further in related projects, namely [CLARIN-DK](https://info.clarin.dk/), [METANORD](http://www.meta-net.eu/projects/meta-nord/), [Semantic Processing across Domains](/projekter/projekter_slut/semantikprojekt/) and [ELEXIS](https://elex.is/). The wordnet has been semi-automatically compiled under varying funding and staffing conditions – as indicated above. Therefore we make reservations concerning errors and omissions. Observed errors and omissions can be reported to [bspedersen@hum.ku.dk](mailto:bspedersen@hum.ku.dk). A subpart of the vocabulary has been linked to English, Swedish, Finnish and Estonian: [WordTies: A Nordic/Baltic Multilingual Wordnet Initiative](http://wordties.nors.ku.dk/). In connection with the [ELEXIS project](https://elex.is) we plan to link the entire wordnet to the corresponding English resource, [Princeton WordNet](https://wordnet.princeton.edu/), in order to be able to use the resource cross-lingually. ## Download the DanNet resource You can download the DanNet resource in two separate formats. By downloading a data set, you accept the [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/ "Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)") license: * [RDF data set](https://wordnet.dk/export/rdf/dn "ZIP file containing DanNet in the TTL format (RDF)") (the primary resource) * A single TTL file that should now be considered the main resource. * [CSV data set](https://wordnet.dk/export/csv/dn "ZIP file containing DanNet as CSV files") (an alternative format) * This ZIP file contains several CSV files as well as CSVW metadata, which collectively can be used to recreate most of the contents of the RDF resource. Several other RDF data sets can also be downloaded at [wordnet.dk/dannet](https://wordnet.dk/dannet "The DanNet resource"). These alternative data sets can be used to extend the primary RDF resource with other useful data. _When referencing the resource, please use this article: Bolette S. Pedersen, Sanni Nimb, Jørg Asmussen, Nicolai H. Sørensen, Lars Trap-Jensen og Henrik Lorentzen. [DanNet – the challenge of compiling a WordNet for Danish by reusing a monolingual dictionary](https://pdfs.semanticscholar.org/6891/69de00c63d58bd68229cb0b3469a617f5ab3.pdf) (pdf). Lang Resources & Evaluation (2009) 43:269–299._ Get linguistic specifications * [Lingvistiske specifikationer for DanNet](/projekter/dannet/dannetspecifikationer_v2.pdf "Lingvistiske specifikationer for DanNet") (Danish) * [DanNet Linguistic Specifications](/projekter/dannet/DanNetLinguisticSpecifications.pdf "DanNet Linguistic Specifications ") Please note that these specifications are based on an older version of DanNet. However, they should still allow for understanding the contents of the DanNet resource; just be aware that the names of certain relations/attributes may have changed. ## Publications * Main publication: Pedersen, Bolette S. Sanni Nimb, Jørg Asmussen, Nicolai H. Sørensen, Lars Trap-Jensen og Henrik Lorentzen (2009). [DanNet – the challenge of compiling a WordNet for Danish by reusing a monolingual dictionary](https://pdfs.semanticscholar.org/6891/69de00c63d58bd68229cb0b3469a617f5ab3.pdf). Lang Resources & Evaluation 43:269–299. * Pedersen, Bolette S.; Aguirrezabal Zabaleta, Manex; Nimb, Sanni; Olsen, Sussi; Rørmann, Ida. (2018) [Towards a principled approach to sense clustering – a case study of wordnet and dictionary senses in Danish.](https://nors.ku.dk/ansatte/?pure=da%2Fpublications%2Ftowards-a-principled-approach-to-sense-clustering--a-case-study-of-wordnet-and-dictionary-senses-in-danish(3baf7065-e73a-4c1e-b51f-8974662232a6).html) Proceedings of Global WordNet Conference 2018. Singapore, 2018. * Pedersen, Bolette Sandford; Nimb, Sanni; Olsen, Sussi; Sørensen, Nicolai Hartvig. (2018). [Combining Dictionaries, Wordnets and other Lexical Resources - Advantages and Challenges.](https://nors.ku.dk/ansatte/?pure=da%2Fpublications%2Fcombining-dictionaries-wordnets-and-other-lexical-resources--advantages-and-challenges(5f277b36-553b-4d05-b5af-afba8a10d96c).html) Globalex Proceedings 2018, Miyasaki, Japan. 2018. * Sanni Nimb, Bolette S. Pedersen, Anna Braasch, Nicolai H. Sørensen & Thomas Troelsgård (2013) [Enriching a wordnet from a thesaurus.](http://www.ep.liu.se/ecp/088/005/ecp1388005.pdf) Workshop Proceedings on Lexical Semantic Resources for NLP from the 19th Nordic Conference on Computational Linguistics (NODALIDA). Linköping Electronic Conference Proceedings; Volume 85 (ISSN 1650-3740). * Sanni Nimb, Bolette Sandford Pedersen (2012). Towards a richer wordnet representation of properties – exploiting semantic and thematic information from thesauri. LREC 2012 Proceedings. Istanbul, Turkey * Bolette S. Pedersen, Sanni Nimb*, Anna Braasch (2010). Merging specialist taxonomies and folk taxonomies in wordnets – a case study of plants, animals and foods in the Danish wordnet. Proceedings from 7th Conference on Language Resources and Evaluation, LREC. Malta Henrik Lorentzen, Sanni Nimb (2010). Fra ordbog til wordnet. Hvordan udmøntes en ordbogsdefinition i en formaliseret wordnetbeskrivelse?. Harry Lönnroth & Kristina Nikula (red.) (2010): Nordiska studiar i lexikografi 10. Rapport från Konferencen om lexikografi i Norden, Tammerfors 3-5 juni 2009, pp. 329-344. Tammerfors * Sanni Nimb, Lars Trap-Jensen (2009). [Use, Re-use and Synergetic Benefit: The Interplay between WordNet and Dictionary Data](https://www.semanticscholar.org/paper/Use%2C-Re-use-and-Synergetic-Benefit%3A-The-Interplay-Nimb-Trap-Jensen/f436818dd2a8b55b14f30313daa2eef4101ce3c0)«. I: GSCL Forum – Special Issue on Lexical-Semantic and Ontological Resources. German Society for Computational Linguistics. 19. s. * Bolette S. Pedersen, Sanni Nimb (2009). Et net af ord. K. Farø, A. Holsting, N.Larsen, J.E. Mogensen & T.Vinther (eds). Sprogvidenskab i glimt. 70 tekster om sprog i teori og praksis. Syddansk Universitetsforlag, s.112-117. * Pedersen, B.S. & A. Braasch (2009). What do we need to know about humans? A view into the DanNet Database. In: K. Jokinen and E. Bick (eds.) Proceedings of the 17th Nordic Conference of Computational Linguistics NODALIDA 2009. NEALT Proceedings Series, Vol. 4, Odense, Denmark. * Sanni Nimb (2009). [The Semantic Relations of Artifacts in DanNet](https://wordnet.dk/dok/Nimb_workshop2009_final.pdf). In B.S. Pedersen, A.Braasch, S.Nimb, R. Vatvedt Fjeld (eds.): Proceedings of the NODALIDA 2009 workshop on WordNets and Other Lexical Semantic Resources – between Lexical Semantics, Lexicography, Terminology and Formal Ontologies, Odense, Denmark, May 2009. * Lars Trap-Jensen & Bolette S. Pedersen (2008). [OntoQuery – Web-Based Visualization of Ontologies](http://portal.acm.org/citation.cfm?id=1461587.1461634&coll=portal&dl=ACM). In: Proceedings of the IADIS International Conference on WWW/Internet 2008. IADIS Press, 239-242. (ISBN 978-972-8924-61-1). * Sanni Nimb, Henrik Lorentzen & Lars Trap-Jensen (2008). [The Digital Lexicographical Tools Project – New Ways of Presen...](http://portal.acm.org/citation.cfm?id=1461587.1461634&coll=portal&dl=ACM) In: Proceedings of the International Workshop on Lexical Semantics and Digital Libraries at NODALIDA 2007. NEALT Proceedings Series, Vol. 1, 155-166. * Lars Trap-Jensen, Henrik Høegh & Bolette S. Pedersen (2007). [DanNet, an RDF-Encoded Lexico-Semantic Network](http://portal.acm.org/citation.cfm?id=1341877&dl=ACM&coll=portal) Datalogiske skrifter 108. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen * Bolette S. Pedersen & Sanni Nimb (2007). [Rethinking Dictionary Structure](http://portal.acm.org/citation.cfm?id=1341877&dl=ACM&coll=portal) Datalogiske skrifter 110. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Sanni Nimb & Bolette S. Pedersen (2007). [Ontology-Based Interlinking of Danish WordNet and the Sketch Engine](http://www.aclweb.org/anthology/I07-2001) (pdf) I. Proceedings of the Workshop Ontologies and Lexical Resources in Distributed Environments. Association for Computational Linguistics. pp. 1–8. 2007. * Lars Trap-Jensen (2007). [OntoQuery: Eksperimenter i Web-baseret Visualisering af Ontologier](http://www.aclweb.org/anthology/I07-2001). Danske Sprogstudiers Grundlag. Datalogiske skrifter 109. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Bolette S. Pedersen & Sanni Nimb (2006). [The Structure of a Danish WordNet](http://www.aclweb.org/anthology/I06-2103) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 106. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Lars Trap-Jensen, Bolette S. Pedersen & Henrik Lorentzen (2006). [Creating and Querying RDF in The Semantic Web](http://www.aclweb.org/anthology/I06-2103) (pdf) Danske Sprogstudiers Grundlag. Datalogiske skrifter 105. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Bolette S. Pedersen & Sanni Nimb (2006). [Adding Semantics to the Danish Dictionary](http://www.aclweb.org/anthology/I06-1104) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 104. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Henrik Lorentzen, Bolette S. Pedersen & Lars Trap-Jensen (2005). [Creation and Maintenance of RDF Networks](http://www.aclweb.org/anthology/W05-1615) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 103. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Bolette S. Pedersen, Sanni Nimb, Lars Trap-Jensen, Henrik Lorentzen (2005). [Merging Dictionaries and Wordnets](http://www.aclweb.org/anthology/W05-1615) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 102. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Sanni Nimb & Bolette S. Pedersen (2005). [Linking a Danish WordNet to the Internet - Experiments with the Sketch Engine](http://www.aclweb.org/anthology/W05-1616) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 101. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Bolette S. Pedersen & Sanni Nimb (2005). [A Lexical Database for Danish](http://www.aclweb.org/anthology/W05-1616) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 100. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Lars Trap-Jensen, Henrik Lorentzen & Bolette S. Pedersen (2004). [Reasoning with the Semantic Web - An Introduction](http://www.aclweb.org/anthology/W04-2401) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 99. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Bolette S. Pedersen, Sanni Nimb & Lars Trap-Jensen (2004). [Sketching Sketch Engine](http://www.aclweb.org/anthology/W04-2401) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 98. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Henrik Lorentzen & Lars Trap-Jensen (2004). [Modeling and Querying RDF Networks](http://www.aclweb.org/anthology/W04-2400) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 97. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Henrik Lorentzen, Bolette S. Pedersen & Lars Trap-Jensen (2004). [Creating RDF Networks](http://www.aclweb.org/anthology/W04-2400) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 96. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Sanni Nimb, Bolette S. Pedersen & Lars Trap-Jensen (2004). [DanNet – A Wordnet for Danish](http://www.aclweb.org/anthology/W04-2413) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 95. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Lars Trap-Jensen, Henrik Lorentzen & Bolette S. Pedersen (2004). [Reasoning with the Semantic Web](http://www.aclweb.org/anthology/W04-2413) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 94. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Sanni Nimb, Bolette S. Pedersen & Lars Trap-Jensen (2004). [Experiments with a Danish WordNet](http://www.aclweb.org/anthology/W04-2412) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 93. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Henrik Lorentzen, Bolette S. Pedersen & Lars Trap-Jensen (2004). [Creating RDF Networks - A Schema Creation Methodology](http://www.aclweb.org/anthology/W04-2412) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 92. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Bolette S. Pedersen, Sanni Nimb, Henrik Lorentzen & Lars Trap-Jensen (2004). [Merging Dictionaries and Wordnets](http://www.aclweb.org/anthology/W04-2411) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 91. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Henrik Lorentzen & Lars Trap-Jensen (2004). [Semantic Web-based Querying of Lexical Knowledge](http://www.aclweb.org/anthology/W04-2411) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 90. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Bolette S. Pedersen & Sanni Nimb (2004). [Experiments with a Danish WordNet](http://www.aclweb.org/anthology/W04-2403) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 89. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Sanni Nimb, Bolette S. Pedersen & Lars Trap-Jensen (2004). [Linking a Danish WordNet to the Internet](http://www.aclweb.org/anthology/W04-2403) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 88. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Henrik Lorentzen & Lars Trap-Jensen (2004). [Semantic Web-based Querying of Lexical Knowledge](http://www.aclweb.org/anthology/W04-2402) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 87. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Bolette S. Pedersen & Sanni Nimb (2004). [A Lexical Database for Danish](http://www.aclweb.org/anthology/W04-2402) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 86. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Henrik Lorentzen, Bolette S. Pedersen & Lars Trap-Jensen (2004). [Creation and Maintenance of RDF Networks](http://www.aclweb.org/anthology/W04-2401) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 85. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Bolette S. Pedersen, Sanni Nimb, Henrik Lorentzen & Lars Trap-Jensen (2004). [Merging Dictionaries and Wordnets](http://www.aclweb.org/anthology/W04-2401) (pdf). Danske Sprogstudiers Grundlag. 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quocanh34/detect_model_v2_dataset	2023-10-25T12:04:27.000Z	[ "region:us" ]	quocanh34	null	null	0	2023-10-25T12:04:26	--- dataset_info: features: - name: input_col dtype: string - name: output_col dtype: string splits: - name: train num_bytes: 55281 num_examples: 100 download_size: 11825 dataset_size: 55281 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "detect_model_v2_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	488	[ [ -0.03778076171875, -0.01824951171875, 0.0262908935546875, 0.006603240966796875, -0.017913818359375, -0.0244903564453125, 0.038848876953125, -0.027587890625, 0.03936767578125, 0.032073974609375, -0.054962158203125, -0.041229248046875, -0.047637939453125, -0.0277862548828125, -0.0261383056640625, 0.06927490234375, 0.0008826255798339844, 0.00629425048828125, -0.053070068359375, 0.00005942583084106445, -0.041900634765625, 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pranjal01/Text-summarizer-dataset	2023-10-25T12:07:06.000Z	[ "task_categories:text-generation", "language:en", "license:apache-2.0", "region:us" ]	pranjal01	null	null	0	2023-10-25T12:05:29	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: News dtype: string - name: Summary dtype: string - name: Title dtype: string splits: - name: train num_bytes: 1130675.7886497064 num_examples: 408 - name: test num_bytes: 285440.21135029354 num_examples: 103 download_size: 887190 dataset_size: 1416116 license: apache-2.0 task_categories: - text-generation language: - en --- # Dataset Card for "Text-summarizer-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	709	[ [ -0.0369873046875, -0.01494598388671875, 0.00901031494140625, 0.0192108154296875, -0.024169921875, 0.009124755859375, 0.006580352783203125, 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orderflowforcemax/FlowForceMax	2023-10-25T12:13:17.000Z	[ "region:us" ]	orderflowforcemax	null	null	0	2023-10-25T12:12:18	[FlowForce Max](https://www.linkedin.com/pulse/flowforce-max-alert-any-side-effects-how-does-work-michae-ldambert-i7kcf) – [Official Website Link – Click Here](https://www.glitco.com/us-flowforce-max) ►❱❱ Product Name - [FlowForce Max](https://in.pinterest.com/pin/1018024690759796681/) ►❱❱ Side Effects - No Major Side Effects ►❱❱ Category - Health ►❱❱ Results - In 1-2 Months ►❱❱ Availability – Online ►❱❱ Rating: - 5.0/5.0 ⭐⭐⭐⭐⭐ ►❱❱ Where to Get Bottle Online - [flowforcemax com](https://www.glitco.com/us-flowforce-max) [Shipping On All Over World Secured Checkout](https://www.glitco.com/us-flowforce-max) [Shipping On All Over World Secured Checkout](https://www.glitco.com/us-flowforce-max) [Shipping On All Over World Secured Checkout](https://www.glitco.com/us-flowforce-max) A brief introduction about [FlowForce Max](https://www.crunchbase.com/organization/flowforce-max) ------------------------------------------------------------------------------------------------- [FlowForce Max](https://colab.research.google.com/drive/1XauFtBXO2U0BXVYctuRTVuho5-6huCig#scrollTo=R20totKqOhJB) is a dietary supplement focused on promoting prostate health, incorporating a blend of natural elements aimed at maintaining a healthy prostate size, alleviating symptoms associated with benign prostatic hyperplasia (BPH), and improves urinary flow, and other potential advantages. Benign prostatic hyperplasia (BPH) is a prevalent condition characterized by the enlargement of the prostate gland, typically occurring as a natural consequence of the aging process. It affects more than half of the population aged 50 and older. Typical symptoms of BPH encompass urinary difficulties, reduced energy levels, frequent nighttime urination, incomplete bladder emptying, and sexual performance issues, among other challenges. Numerous men consider dietary supplements like FlowForce Max to assist in managing these symptoms. It's essential to note that FlowForce Max is produced in the United States within an FDA-registered facility, which has earned certification for Good Manufacturing Practice (GMP). [](https://www.glitco.com/us-flowforce-max)[![](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgD2taPTN6RIwxDYTSRGyoElRqznxPrIQ3Cy0UF0PWD7rd5uFVKfMP11zRibrLognAIbEx9u-l-C0QTX83RJgJabmRoiEv_EruNCI5C3QTUif9C00bvqWQrYY4gy2my5bOsa33tPbz93usSAymqjAosDS2_C27y9fLbPdybQW-15wirG7AuAB64rFHCLJMN/w640-h250/Screenshot%20(1454).png)](https://www.glitco.com/us-flowforce-max) ### How does [FlowForce Max](https://sway.office.com/HM1XwAPTUNFDnmND) work? A multitude of supplements claim to aid prostate and urinary health, with varying degrees of effectiveness. The creators of FlowForce Max explain their supplement's impact as follows: As per the manufacturer, fungal infections are identified as the underlying cause of urinary tract and prostate health issues. Ordinarily, benign prostatic hyperplasia (BPH), characterized by an enlarged prostate, can result from various factors, including natural aging and hormonal fluctuations. Nevertheless, FlowForce Max's developers attribute these concerns to fungal-related issues. Eliminating fungal presence from your body may, as they assert, benefit both prostate and urinary health. This can enhance blood circulation, facilitate achieving erections, and ensure the normal operation of your urinary tract and prostate. To activate the components in FlowForce Max, simply chew one tablet daily. That's all there is to it. These ingredients comprise natural antioxidants, such as polyphenols, which could potentially bolster immune function and overall immune system well-being. This aids your body in defending itself against fungal infections while concurrently promoting general prostate health. [(AMAZING DISCOUNT) Get FlowForce Max For The Lowest Price Here](https://www.glitco.com/us-flowforce-max) ### [FlowForce Max](https://flowforce-max-buy.webflow.io/) Ingredient Profile The [FlowForce Max](https://www.youtube.com/watch?v=yoWDMfvthhM) formula comprises an extensive array of natural ingredients, incorporating several plant-based elements celebrated in traditional medicine. Key components include: * Luteolin: Derived from various plant sources, luteolin is a flavonoid known for its crucial neuroprotective properties. It also possesses immunomodulatory and antioxidant attributes, collectively promoting overall health. * Graminex Flower Pollen Extract: Renowned for its efficacy in promoting robust prostate health, this ingredient has a rich history spanning over three decades in Asia and Europe. It has been primarily employed to address prostate health issues and bolster overall well-being. Scientific investigations have affirmed the capacity of Graminex pollen extract to effectively sustain prostate and urinary function. Consequently, this component aids in rectifying urinary tract irregularities and diminishing prostate inflammation. * Fisetin: Present in numerous fruits and vegetables, fisetin plays a role in shielding cells from damage and combating oxidative stress. This flavonoid is associated with anti-inflammatory, anti-cancer, and cardiovascular advantages, thus contributing to multifaceted health improvements. * Grape Seed Extract: Abundant in antioxidants, grape seed extract has exhibited the capacity to combat oxidative stress, inflammation, and tissue deterioration. Research findings also indicate its potential in blood pressure regulation, fortifying bone density, and elevating collagen production. * Oregano Leaf Extract: Oregano extract harbors compounds that bestow a multitude of health advantages, including cough suppression. Additionally, this leaf extract exhibits antiviral and antibacterial properties, which can enhance digestive processes. It further offers diverse health merits, encompassing its antioxidant, antimicrobial, and anti-inflammatory attributes. Notably, this extract also contributes to the palatable flavor of the FlowForce Max supplement. * Monolaurin: Originating from lauric acid and present in human breast milk as well as coconut oil, Monolaurin is harnessed for addressing a range of infections. Although its efficacy remains a topic of discussion, it is incorporated into various food items. * Saw Palmetto Fruit Extract Powder: Saw palmetto ranks as one of the most renowned ingredients for promoting prostate health across the globe. It is a common presence in various prostate health supplements and formulations aiming to support testosterone levels. According to Mount Sinai, "the primary purpose of saw palmetto is to address benign prostatic hyperplasia (BPH)." This plant primarily thrives within the realm of prostate and hormone balance supplements. * Tricalcium Phosphate (TCP): This salt bolsters mineral retention and promotes bone regeneration, participating in various essential biological functions such as cell communication, energy generation, and the formation of bone minerals. * ViNitrox: It is an exclusive proprietary blend of grape and apple polyphenols. It has demonstrated remarkable properties in the sphere of sports nutrition, contributing to enhanced endurance and performance. An in vivo study has evidenced a remarkable 74% reduction in oxidative stress. Further research highlights ViNitrox's ability to diminish cardio-respiratory fatigue and muscle weariness while concurrently elevating individual power output during exercise. * Silk Protein Powder: This ingredient enhances skin's overall appearance by effectively maintaining optimal moisture levels, managing excess oils, and minimizing the visibility of enlarged pores. * Sucralose: An artificial sweetener, Sucralose imparts a delightful taste to the supplement and maintains its stability, even in elevated temperatures, when utilized judiciously. * Magnesium Stearate Powder: This ingredient is a widely employed ingredient in numerous spices, herbs, food supplements, chewing gum, and baking products. It also plays a role in encapsulated medications by preventing the constituent elements from adhering to one another, serving as an anti-caking agent to maintain ingredient separation. [(EXCLUSIVE OFFER) View Pricing & Availability of FlowForce Max](https://www.glitco.com/us-flowforce-max) TAGS: #[FlowForce\_Max](https://flowforce-max-review.blogspot.com/2023/10/flowforce-max-reviews-what-are.html)\_US, #[FlowForce\_Max](https://colab.research.google.com/drive/1d5Y3ao9UajRb0Rw0Wz-ZPGkXC2AHCiLw#scrollTo=zLDxSeUBLYVb)\_Reviews, #[FlowForce\_Max](https://sites.google.com/view/flow-forcemax-offers/home)\_Benefits, #[FlowForce\_Max](https://lookerstudio.google.com/reporting/bffd6123-400d-49c8-bf97-6a38a0a84448/page/0LPgD)\_Buy, #[FlowForce\_Max](https://groups.google.com/g/flowforce-max-about/c/xlmAHrfTyfc)\_Cost, #[FlowForce\_Max](https://www.dibiz.com/buyflowforcemax)\_Ingredients, #[FlowForce\_Max](https://loop.frontiersin.org/people/2557229/bio)\_Order, #FlowForce\_Max\_Price, #[FlowForce\_Max\_Website](https://devfolio.co/@buyflowforcemax),	9,134	[ [ -0.0009870529174804688, -0.05743408203125, 0.0251922607421875, 0.0166778564453125, -0.00962066650390625, 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ProstateFlux/ProstateFluxreviews	2023-10-25T12:27:54.000Z	[ "region:us" ]	ProstateFlux	null	null	0	2023-10-25T12:26:34	<h2 style="-webkit-text-stroke-width: 0px; background-color: white; box-sizing: border-box; color: black; font-family: Roboto, Helvetica, Arial, sans-serif; font-size: 1.5em; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: bold; letter-spacing: normal; line-height: 1.1; margin: 10px 0px; orphans: 2; padding: 0px; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px;"><a href="https://www.healthsupplement24x7.com/get-prostate-flux" target="_blank"><span style="background-color: red; box-sizing: border-box;"><strong style="box-sizing: border-box; font-style: normal; font-weight: bold;"><span style="box-sizing: border-box; color: #ffd966;">Prostate Flux – Official Website Link – Click Here</span></strong></span></a></h2> <p style="-webkit-text-stroke-width: 0px; background-color: white; box-sizing: border-box; color: #333333; font-family: Roboto, Helvetica, Arial, sans-serif; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; margin: 0px 0px 10px; orphans: 2; padding: 0px; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px;"><strong style="box-sizing: border-box; font-style: normal; font-weight: bold;"><span style="box-sizing: border-box; color: magenta;">➥ Where to Get Bottle Online -</span> <a href="https://www.healthsupplement24x7.com/get-prostate-flux"><span style="background-color: white; box-sizing: border-box; color: red;">https://www.healthsupplement24x7.com/get-prostate-flux</span></a><br style="box-sizing: border-box;" /><span style="box-sizing: border-box; color: green;">➥ Product Name -</span> Prostate Flux<br style="box-sizing: border-box;" /><span style="box-sizing: border-box; color: #993300;">➥ Side Effects -</span> <span style="box-sizing: border-box; color: navy;">No Major Side Effects</span><br style="box-sizing: border-box;" /><span style="box-sizing: border-box; color: #993366;">➥ Category -</span> <span style="box-sizing: border-box; color: #333333;">Health ( Prostate Health Formula)</span><br style="box-sizing: border-box;" /><span style="box-sizing: border-box; color: maroon;">➥ Results -</span> <span style="box-sizing: border-box; color: #00ccff;">In 1-2 Months</span><br style="box-sizing: border-box;" /><span style="box-sizing: border-box; color: red;">➥ Availability –</span> <a style="background-color: transparent; box-sizing: border-box; color: black; text-decoration: none;" href="https://www.globalfitnessmart.com/get-amiclear" rel="nofollow"><span style="box-sizing: border-box; color: #ff6600;">Online</span></a><br style="box-sizing: border-box;" /><span style="box-sizing: border-box; color: #333300;">➥ Rating: -</span> <span style="box-sizing: border-box; color: red;">5.0/5.0</span> ⭐⭐⭐⭐⭐</strong></p> <h2 style="-webkit-text-stroke-width: 0px; background-color: white; box-sizing: border-box; color: black; font-family: Roboto, Helvetica, Arial, sans-serif; font-size: 1.5em; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: bold; letter-spacing: normal; line-height: 1.1; margin: 10px 0px; orphans: 2; padding: 0px; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px;"><a href="https://www.healthsupplement24x7.com/get-prostate-flux" target="_blank"><strong style="box-sizing: border-box; font-style: normal; font-weight: bold;">✅<span style="background-color: red; box-sizing: border-box;"><span style="box-sizing: border-box; color: #ffd966;">Click Here To Visit – “OFFICIAL WEBSITE</span></span><span style="background-color: red; box-sizing: border-box; color: #ffcc00;">”</span>✅</strong></a></h2> <h2 style="-webkit-text-stroke-width: 0px; background-color: white; box-sizing: border-box; color: black; font-family: Roboto, Helvetica, Arial, sans-serif; font-size: 1.5em; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: bold; letter-spacing: normal; line-height: 1.1; margin: 10px 0px; orphans: 2; padding: 0px; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px;"><a href="https://www.healthsupplement24x7.com/get-prostate-flux" target="_blank"><strong style="box-sizing: border-box; font-style: normal; font-weight: bold;">✅<span style="background-color: red; box-sizing: border-box;"><span style="box-sizing: border-box; color: #ffd966;">Click Here To Visit – “OFFICIAL WEBSITE</span></span><span style="background-color: red; box-sizing: border-box; color: #ffcc00;">”</span>✅</strong></a></h2> <h2 style="-webkit-text-stroke-width: 0px; background-color: white; box-sizing: border-box; color: black; font-family: Roboto, Helvetica, Arial, sans-serif; font-size: 1.5em; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: bold; letter-spacing: normal; line-height: 1.1; margin: 10px 0px; orphans: 2; padding: 0px; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px;"><a href="https://www.healthsupplement24x7.com/get-prostate-flux" target="_blank"><strong style="box-sizing: border-box; font-style: normal; font-weight: bold;">✅<span style="background-color: red; box-sizing: border-box;"><span style="box-sizing: border-box; color: #ffd966;">Click Here To Visit – “OFFICIAL WEBSITE</span></span><span style="background-color: red; box-sizing: border-box; color: #ffcc00;">”</span>✅</strong></a></h2> <p><strong>Prostate Flux (Brand New ProstateFlux) Reviews:</strong> Men above 50 years are at high risk of developing prostate issues. Aging slows body functions, including the hormones that support prostate health. Many medications on the market claim to restore prostrate health, but some have not been legally approved or scientifically studied.</p> <p>An all-natural dietary supplement without any potential side effects is worth trying. <a href="https://sites.google.com/view/prostateflux/home">Prostate Flux</a> is a new comprehensive formula specifically created for men to support the health of their prostate gland. The supplement helps eliminate all the symptoms of enlarged prostate.</p> <p>Here is a comprehensive review to help you learn more about <a href="https://sites.google.com/view/prostate-flux/home">Prostate Flux</a>, how it works, it's benefits, pros, cons, and pricing.</p> <div class="separator" style="clear: both; text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="https://www.healthsupplement24x7.com/get-prostate-flux" target="_blank"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgn1q9pUeJClezL6kCYp-S7a7Uj7RGi9He3ZoSd14tEn0GhyxyEVPFAhyphenhyphen97hczzGbbiNuxxslZrPfxaW8Z65SnZ7xDzyafPbNxiGFAk6B5GUtzDwjTPbgLv9Qz3bOjzm0Kgk8p2Y6vnb1U_Ve5Mcg5y75fPV4EY6CPXZcGG-D2E7xQz9RJXBrcaMZfi5c0/w640-h348/ProstateFlux%205.jpg" alt="" width="640" height="348" border="0" data-original-height="915" data-original-width="1679" /></a></div> <h2>What is Prostate Flux?</h2> <p><a href="https://covingtonreporter.clubeo.com/calendar/2023/10/25/prostate-flux-brand-new-prostate-flux-1-prostate-health-formula-for-permanent-solution?_ga=2.196303724.1535074580.1698206977-950885170.1698206977">Prostate Flux</a> is a natural dietary supplement formulated to help keep the prostate healthy. The supplement is packed with 100% organic ingredients that are easily absorbed in the body to take effect instantly.</p> <p>Frank Neal, the founder of <a href="https://covingtonreporter.clubeo.com/page/prostate-flux-1-unique-prostate-health-tablets-read-first-all-pros-and-cons.html">Prostate Flux</a>, claims that <a href="https://covingtonreporter.clubeo.com/page/prostate-flux-14-in-1-vital-prostate-wellness-formula-supports-prostate-and-male-health.html">Prostate Flux</a> contains the right proportions to maintain a healthy prostate. According to Neal, prostate health is attacked by cancerous cells caused by unhealthy eating habits and lifestyle. The product is gluten-free and does not contain dangerous toxins, stimulants, or GMOs.</p> <p>He claims Prostate Flux is guaranteed to work by combining medical principles and accurate treatment. Prostate Flux is made in an FDA-approved and GMP-Certified facility following the safety protocols. The formula has passed through numerous clinical studies performed by some of the most admired universities in the world.</p> <h2 style="text-align: center;"><a href="https://www.healthsupplement24x7.com/get-prostate-flux" target="_blank"><span style="box-sizing: border-box;"><span style="background-color: red; box-sizing: border-box; color: yellow;">Prostate Flux : Try it now, you won’t be disappointed!</span></span><span style="box-sizing: border-box;"><span style="background-color: red; box-sizing: border-box; color: yellow;"> Claim Your Free Bonus Now</span></span></a></h2> <h2>How does Prostate Flux Work?</h2> <p><a href="https://doogeemall.com/community/xenforum/topic/113937/prostate-flux-brand-new-prostateflux-1-prostate-health-formula-for-permanent-solution">Prostate Flux</a> formula uses a gentle traditional approach to support a healthy prostate. It contains unique stimulants clinically proven to have effects such as reducing frequent urination, supporting better sleep, and improving intimacy in life and overall quality of life.</p> <p>The primary purpose of <a href="https://groups.google.com/g/prostate-flux/c/fiTpg274HOs">Prostate Flux</a> is to ease swelling and enlargement of the prostate gland by decreasing inflammation caused by the hormone testosterone. As men age, the production of male hormones testosterone and dihydrotestosterone (DHT) decreases.</p> <p><a href="https://groups.google.com/g/prostateflux/c/FTSJT_Ch4EI">Prostate Flux</a> consists of organic components that have anti-inflammatory properties. The ingredients work by repairing the receptors in the reproductive system and ensuring proper immune response to reduce inflammation in the prostate area.</p> <p>The supplement also flushes out toxins in the bloodstream. The immune system can perform better when the toxins are eliminated from the blood. The dietary formula deals with the root cause of BPH and enlarged prostate and supports the immune system making it stronger to fight future risks.</p> <h2>Ingredients in Prostate Flux</h2> <p><a href="https://prostate-flux.unicornplatform.page/">Prostate Flux</a> formula is made of 100% natural ingredients. They consist of plants, herbs, vitamins, and minerals. The key ingredients in the supplement are as follows:</p> <p><strong>Saw Palmetto</strong></p> <p>Saw Palmetto is an essential ingredient in <a href="https://pdfhost.io/v/oZsYFanVk_Prostate_Flux_Brand_New_ProstateFlux_1_Prostate_Health_Formula_For_Permanent_Solution_">Prostate Flux</a>. The berries have anti-inflammatory properties, which help reduce an enlarged prostate. They also block the enzyme that converts testosterone to DHT. It also prevents BPH and prostate cancer.</p> <p><strong>Graviola Leaf</strong></p> <p>The Graviola plant is used as a medicine that treats bacterial infections and illnesses caused by parasites. The ingredient has acetogenesis, which is said to kill and prevent the spread of cancer cells in the body.</p> <p><strong>Green Tea Extract</strong></p> <p>Green tea extract is a magic ingredient found in many dietary supplements. Besides supporting weight loss, it improves blood circulation, reduces cholesterol levels in the body, and prevents cardiovascular diseases.</p> <p><strong>Selenium</strong></p> <p>Selenium is an antioxidant that supports thyroid function and increases metabolic rate. It prevents cancer, strengthens the immune system, and supports cognitive abilities as people grow old.</p> <p><strong>Vitamin E</strong></p> <p>Vitamin is rich in antioxidants, supporting healthy skin, repairing damaged cells, and restoring healthy vision and neurological function.</p> <p><strong>Vitamin B6</strong></p> <p>Vitamin B6 or Pyridoxine supports a healthy brain by preventing mood swings and depression and improves the ability to think.</p> <h2 style="text-align: center;"><a href="https://www.healthsupplement24x7.com/get-prostate-flux" target="_blank"><span style="box-sizing: border-box;"><span style="background-color: red; box-sizing: border-box; color: yellow;">Prostate Flux : Try it now, you won’t be disappointed!</span></span><span style="box-sizing: border-box;"><span style="background-color: red; box-sizing: border-box; color: yellow;"> Claim Your Free Bonus Now</span></span></a></h2> <div class="separator" style="clear: both; text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="https://www.healthsupplement24x7.com/get-prostate-flux" target="_blank"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgnSX92WyJf72VjbpDbFELxavMvuNhfHBSDaGw_PPEHEal6FNetU6cDlOltfsLuExwvvVRMwm8a3GSiPiXt14yDcRuXIPrEoQRRfKc1P_OBNO_lfH8WWBKdXRIeY_QEOK4pR4HWQMgWlMUXyuWNWNZVZiPGSXNXHhVLdk9eO_8MqXuBVmcvtRXRctfVgOU/w640-h364/ProstateFlux%206.png" alt="" width="640" height="364" border="0" data-original-height="707" data-original-width="1243" /></a></div> <h2>Benefits of Prostate Flux</h2> <p>The natural ingredients in <a href="https://gamma.app/public/Prostate-Flux-qani9oomk20ga3g?mode=doc">Prostate Flux</a> work together to provide the following benefits:</p> <p><strong>Reduce Enlarged Prostate</strong></p> <p>The ingredients in <a href="https://prostateflux1.bandcamp.com/track/prostateflux-new-2023-does-it-really-works">Prostate Flux</a> can reduce enlarged prostate to normal size. Many Prostate Flux users have reported that their prostate size went back to regular size after using the supplement for three months.</p> <p><strong>Lessen Frequent Urination</strong></p> <p>Enlarged and swollen prostate causes the need to urinate frequently. Prostate Flux has ingredients that help balance the prostate gland to have a normal urine flow.</p> <p><strong>Increase in Libido</strong></p> <p>Prostate Flux deals with male health-related issues such as performance, stamina and overall function.</p> <p><strong>Repair Tissue</strong></p> <p>As people age, the cells in the prostate become damaged. Prostate Flux can repair and rejuvenate damaged tissue.</p> <p><strong>Reduce inflammation</strong></p> <p>Most active ingredients in Prostate Flux have antioxidants that help reduce any inflammation in the prostate gland.</p> <p><strong>Supports Better Sleep</strong></p> <p>Frequent urination, especially at night, can disrupt sleep. Lack of adequate sleep affects mental health and causes tiredness during the day. A healthy prostate means no more unnecessary urination at night.</p> <h2>How to use Prostate Flux</h2> <p>One Prostate Flux has a 30-day supply of 60 capsules. Users are advised to take two Prostate Flux capsules a day after a meal.</p> <p>The results of Prostate Flux depend on a person's tolerance level. Children under 18, pregnant and lactating mothers, and those with pre-existing conditions should not use Prostate Flux.</p> <h2 style="text-align: center;"><a href="https://www.healthsupplement24x7.com/get-prostate-flux" target="_blank"><span style="box-sizing: border-box;"><span style="background-color: red; box-sizing: border-box; color: yellow;">Prostate Flux : Try it now, you won’t be disappointed!</span></span><span style="box-sizing: border-box;"><span style="background-color: red; box-sizing: border-box; color: yellow;"> Claim Your Free Bonus Now</span></span></a></h2> <div class="separator" style="clear: both; text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="https://www.healthsupplement24x7.com/get-prostate-flux" target="_blank"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhj9v5_TtXpxVmPT2MNkhiinXyXMfNHDliuZOUngYddK5-8c0S5ptkC93P7PkJVmnP6SRFs3qscAtgJk23ycWpKvsJxA3jdTUty61-SQArYSUfkMlm9IBso3r6wmrdailvyuH50QalI9CHPlOqPjGy95WIPu2dN3QANn8eu26hvQRJJzvQxw7HR7qgygtA/w640-h372/Screenshot%20(1299).png" alt="" width="640" height="372" border="0" data-original-height="745" data-original-width="1279" /></a></div> <h2>Prostate Flux Pricing and Money-back Guarantee</h2> <p>Consumers can order Prostate Flux on the official website at a discounted price. The price range is:</p> <ul> <li>One bottle at $79 + free US shipping</li> <li>Three bottles at $59 each + free US shipping</li> <li>Six bottles at $49 each + free US shipping</li> </ul> <p>Prostate Flux manufacturer offers a complete 60-day money-back for users who find the product non-effective. Customers are asked to send the product back to the company at the address shown below.</p> <h2><strong>Is It Safe To Take Prostate Flux Complete Daily?</strong></h2> <p>Yes, Prostate Flux Complete prostate health-boosting supplement is safe to consume daily. This statement is supported by both the customers and the experts. The reason for the same might be the quality of the supplement made from natural ingredients. The Prostate Flux Complete nutritional supplement is vegan and gluten-free as well.</p> <h2>Conclusion</h2> <p>Prostate Flux is created for men who have prostate issues or want to boost their prostate health. It contains the required nutrients to support prostate function. Prostate Flux strengthens the immune system, improves sleep, and reduces the risk of diseases. Prostate Flux gives long-term prostate health; users won't have to worry about BPH recurrence.</p> <p><strong>Read More:</strong></p> <p><a href="https://covingtonreporter.clubeo.com/calendar/2023/10/25/prostate-flux-brand-new-prostate-flux-1-prostate-health-formula-for-permanent-solution">https://covingtonreporter.clubeo.com/calendar/2023/10/25/prostate-flux-brand-new-prostate-flux-1-prostate-health-formula-for-permanent-solution</a></p> <p><a href="https://sites.google.com/view/prostateflux/home">https://sites.google.com/view/prostateflux/home</a></p> <p><a href="https://covingtonreporter.clubeo.com/page/prostate-flux-14-in-1-vital-prostate-wellness-formula-supports-prostate-and-male-health.html">https://covingtonreporter.clubeo.com/page/prostate-flux-14-in-1-vital-prostate-wellness-formula-supports-prostate-and-male-health.html</a></p> <p><a href="https://sites.google.com/view/prostate-flux/home">https://sites.google.com/view/prostate-flux/home</a></p> <p><a href="https://groups.google.com/g/prostateflux/c/FTSJT_Ch4EI">https://groups.google.com/g/prostateflux/c/FTSJT_Ch4EI</a></p> <p><a href="https://covingtonreporter.clubeo.com/page/prostate-flux-14-in-1-vital-prostate-wellness-formula-supports-prostate-and-male-health.html">https://covingtonreporter.clubeo.com/page/prostate-flux-14-in-1-vital-prostate-wellness-formula-supports-prostate-and-male-health.html</a></p> <p><a href="https://doogeemall.com/community/xenforum/topic/113937/prostate-flux-brand-new-prostateflux-1-prostate-health-formula-for-permanent-solution">https://doogeemall.com/community/xenforum/topic/113937/prostate-flux-brand-new-prostateflux-1-prostate-health-formula-for-permanent-solution</a></p> <p><a href="https://groups.google.com/g/prostate-flux/c/fiTpg274HOs">https://groups.google.com/g/prostate-flux/c/fiTpg274HOs</a></p> <p><a href="https://prostate-flux.unicornplatform.page/">https://prostate-flux.unicornplatform.page/</a></p> <p><a href="https://pdfhost.io/v/oZsYFanVk_Prostate_Flux_Brand_New_ProstateFlux_1_Prostate_Health_Formula_For_Permanent_Solution_">https://pdfhost.io/v/oZsYFanVk_Prostate_Flux_Brand_New_ProstateFlux_1_Prostate_Health_Formula_For_Permanent_Solution_</a></p> <p><a href="https://gamma.app/public/Prostate-Flux-qani9oomk20ga3g?mode=doc">https://gamma.app/public/Prostate-Flux-qani9oomk20ga3g?mode=doc</a></p> <p><a href="https://prostateflux1.bandcamp.com/track/prostateflux-new-2023-does-it-really-works">https://prostateflux1.bandcamp.com/track/prostateflux-new-2023-does-it-really-works</a></p>	20,630	[ [ -0.021881103515625, -0.06219482421875, 0.009613037109375, 0.01262664794921875, -0.009674072265625, 0.02374267578125, 0.009368896484375, -0.06317138671875, 0.07830810546875, -0.01323699951171875, -0.082763671875, -0.031951904296875, -0.040985107421875, 0.03326416015625, 0.02728271484375, 0.039459228515625, -0.01216888427734375, 0.002269744873046875, -0.0056610107421875, 0.025787353515625, 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BangumiBase/mushokutensei	2023-10-25T16:16:47.000Z	[ "size_categories:1K<n<10K", "license:mit", "art", "region:us" ]	BangumiBase	null	null	0	2023-10-25T12:35:03	--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Mushoku Tensei This is the image base of bangumi Mushoku Tensei, we detected 73 characters, 6277 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 \| 35 \| [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 \| 36 \| [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 \| 29 \| [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 \| 252 \| [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 \| 1729 \| [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 \| 74 \| [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 \| 179 \| [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 \| 52 \| [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 \| 152 \| [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 \| 47 \| [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 \| 211 \| [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 \| 62 \| [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 \| 47 \| [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 \| 66 \| [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 \| 152 \| [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 \| 37 \| [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 \| 10 \| [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 \| 36 \| [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 \| 28 \| [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 \| 118 \| [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 \| 33 \| [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 \| 62 \| [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 \| 17 \| [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 \| 21 \| [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 \| 60 \| [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 \| 19 \| [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 \| 26 \| [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 \| 14 \| [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 \| 14 \| [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 \| 21 \| [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 \| 103 \| [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 \| 8 \| [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 \| 44 \| [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 \| 53 \| [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 \| 18 \| [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 \| 19 \| [Download](39/dataset.zip) \| ![preview 1](39/preview_1.png) \| ![preview 2](39/preview_2.png) \| ![preview 3](39/preview_3.png) \| ![preview 4](39/preview_4.png) \| ![preview 5](39/preview_5.png) \| ![preview 6](39/preview_6.png) \| ![preview 7](39/preview_7.png) \| ![preview 8](39/preview_8.png) \| \| 40 \| 224 \| [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 \| 36 \| [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 \| 50 \| [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 \| 22 \| [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 \| 17 \| [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 \| 9 \| [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 \| 49 \| [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 \| 62 \| [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 \| 27 \| [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 \| 111 \| [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 \| 39 \| [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 \| 15 \| [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 \| 11 \| [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 \| 16 \| [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 \| 783 \| [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 \| 41 \| [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 \| 29 \| [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 \| 90 \| [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 \| 78 \| [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 \| 29 \| [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 \| 22 \| [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 \| 91 \| [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 \| 16 \| [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 \| 10 \| [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 \| 6 \| [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) \| N/A \| N/A \| \| 66 \| 36 \| [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 \| 10 \| [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 \| 13 \| [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 \| 17 \| [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 \| 23 \| [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 \| 32 \| [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) \| \| noise \| 271 \| [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) \|	24,131	[ [ -0.042236328125, -0.008331298828125, 0.00983428955078125, 0.01366424560546875, -0.0181427001953125, -0.00415802001953125, -0.0021114349365234375, -0.0214996337890625, 0.040008544921875, 0.028778076171875, -0.059783935546875, 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peoplesketogummiesaustralia/PeoplesKetoGummiesAU	2023-10-25T12:50:13.000Z	[ "region:us" ]	peoplesketogummiesaustralia	null	null	0	2023-10-25T12:49:54	<h2><span style="font-weight: 400;">➤Product Name - </span><span style="color: #ff6600;"><a style="color: #ff6600;" href="https://www.facebook.com/PeoplesKetoGummiesOfAU/"><strong>People's Keto Gummies Australia</strong></a></span></h2> <h2><span style="font-weight: 400;">➤Category - </span><span style="color: #ff6600;"><a style="color: #ff6600;" href="https://www.facebook.com/PeoplesKetoGummiesAtAU/"><strong>Health</strong></a></span></h2> <h2><span style="font-weight: 400;">➤Side Effects – </span><span style="color: #ff6600;"><a style="color: #ff6600;" href="https://www.facebook.com/TryPeoplesKetoGummiesAu/"><strong>NA</strong></a></span></h2> <h2><span style="font-weight: 400;">➤Sale Price - </span><span style="color: #ff6600;"><a style="color: #ff6600;" href="https://www.facebook.com/PeoplesKetoGummiesInAustralia/"><strong>Best Price With 60% Discount</strong></a></span></h2> <h2><span style="font-weight: 400;">➤Product Availability - </span><span style="color: #ff00ff;"><a style="color: #ff00ff;" href="https://healthcare24hrs.com/peoples-keto-gummies-australia-buy"><strong>Available In Stock</strong></a></span></h2> <h2><span style="font-weight: 400;">➤Rating - </span><span style="color: #993366;"><a style="color: #993366;" href="https://www.facebook.com/PeoplesKetoGummiesOfAustralia/"><strong>★★★★★</strong></a><span style="font-weight: 400;"> </span></span></h2> <h2><span style="font-weight: 400;">➤Official Website - </span><span style="color: #ff00ff;"><a style="color: #ff00ff;" href="https://healthcare24hrs.com/peoples-keto-gummies-australia-buy"><strong>[SALE IS LIVE] Get Your Best Deal "People's Keto Gummies Australia"</strong></a></span></h2> <p> </p> <h1><span style="color: #ff9900;"><a style="color: #ff9900;" href="https://healthcare24hrs.com/peoples-keto-gummies-australia-buy"><strong>◥◤ Visit The Official Website To Get Your Bottle Now ◥◤</strong></a></span></h1> <h1><span style="color: #ff9900;"><a style="color: #ff9900;" href="https://healthcare24hrs.com/peoples-keto-gummies-australia-buy"><strong>◥◤ Visit The Official Website To Get Your Bottle Now ◥◤</strong></a></span></h1> <h1><span style="color: #ff9900;"><a style="color: #ff9900;" href="https://healthcare24hrs.com/peoples-keto-gummies-australia-buy"><strong>◥◤ Visit The Official Website To Get Your Bottle Now ◥◤</strong></a></span></h1> <p> </p> <p><span style="font-weight: 400;">If you're on a keto diet or exploring the benefits of a ketogenic lifestyle, these gummies could be your new best friends. 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Consult with healthcare professionals or nutritionists who can provide personalized guidance based on your unique needs.</span></p> <p> </p> <p><a href="https://healthcare24hrs.com/peoples-keto-gummies-australia-buy"><img src="https://i.ibb.co/cK5YBNh/People-s-Keto-Gummies-Global.png" alt="People-s-Keto-Gummies-Global" border="0" /></a></p> <p> </p> <p> </p> <h1><span style="color: #ff0000;"><a style="color: #ff0000;" href="https://healthcare24hrs.com/peoples-keto-gummies-australia-buy"><strong>Exclusive Details: Keto Gummies Australia Read More Details on Official Website!</strong></a></span></h1> <h1> </h1> <h1><span style="color: #ff0000;"><a style="color: #ff0000;" href="https://healthcare24hrs.com/peoples-keto-gummies-australia-buy"><strong>Exclusive Details: Keto Gummies New Zealand Read More Details on Official Website!</strong></a></span></h1> <p> </p> <h3><span style="color: #000000; background-color: #ffff99;"><a style="color: #000000; background-color: #ffff99;" href="https://www.facebook.com/PeoplesKetoGummiesInAustralia/"><strong>Exposing Scams: Unveiling the Truth About People's Keto Gummies in Australia</strong></a></span></h3> <p> </p> <p><span style="font-weight: 400;">Uncover the truth behind the alleged scam surrounding </span><a href="https://www.facebook.com/PeoplesKetoGummiesOfAustralia/"><strong>People's Keto Gummies in Australia</strong></a><strong>.</strong><span style="font-weight: 400;"> These gummies have gained popularity among individuals looking to shed excess fat and aid their weight loss journey. However, it is essential to delve deeper into the claims made by this product and expose any misleading information or deceptive marketing tactics associated with it. The first point of concern revolves around the time frame in which these fast action gummies supposedly work. While they promise quick results, it’s crucial to separate fact from fiction. Losing weight is a gradual process that requires dedication and consistency. Any product claiming to provide instant fat-burning effects should be approached with skepticism.</span></p> <p> </p> <p><span style="font-weight: 400;">Furthermore, it is important to examine the ingredients used in </span><a href="https://www.facebook.com/PeoplesKetoGummiesAtAustralia/"><strong>People's Keto Gummies</strong></a><span style="font-weight: 400;">. Some manufacturers may make exaggerated claims about certain components that aid weight loss. It’s essential to verify whether these ingredients genuinely contribute to the fat-burning process or if they are merely included for marketing purposes.Deceptive marketing tactics often play a significant role in promoting scams like these gummies. Misleading before-and-after pictures, false testimonials, and exaggerated success stories can sway potential buyers into believing that this product is a miracle solution for their weight loss goals. However, it’s crucial not to fall victim to such tactics and instead rely on reliable sources for information.</span></p> <p> </p> <h3><span style="color: #000000; background-color: #ffff99;"><a style="color: #000000; background-color: #ffff99;" href="https://www.facebook.com/PeoplesKetoGummiesOfAU/"><strong>To avoid falling prey to scams when purchasing keto gummy supplements, there are several precautions you can take:</strong></a></span></h3> <p> </p> <p><a href="https://www.facebook.com/PeoplesKetoGummiesAtAU/"><strong>Research thoroughly :</strong></a><span style="font-weight: 400;"> Before investing your hard-earned money in any dietary supplement, conduct extensive research on both the product itself and the manufacturer. Look for reviews from reputable sources and seek advice from professionals or trusted individuals who have experience with similar products.</span></p> <p> </p> <p><a href="https://www.facebook.com/TryPeoplesKetoGummiesAu/"><strong>Check certifications :</strong></a><span style="font-weight: 400;"> Ensure that the product has undergone proper testing and holds necessary certifications from regulatory bodies such as the Therapeutic Goods Administration (TGA) in Australia. This ensures that the product meets quality and safety standards.</span></p> <p> </p> <p><a href="https://www.facebook.com/PeoplesKetoGummiesInAustralia/"><strong>Consult a healthcare professional :</strong></a><span style="font-weight: 400;"> If you are considering incorporating any new supplement into your diet, it is always advisable to consult with a healthcare professional or nutritionist. They can provide personalized guidance based on your specific needs and help you make informed decisions. By staying informed and cautious, you can protect yourself from falling victim to scams in the supplement industry. Remember, losing weight takes time and dedication, so be wary of any product promising miraculous results overnight.</span></p> <p> </p> <h3><span style="color: #000000; background-color: #ffff99;"><a style="color: #000000; background-color: #ffff99;" href="https://www.facebook.com/PeoplesKetoGummiesOfAustralia/"><strong>Analyzing the Potential Side Effects and Risks Associated with People's Keto Gummies Scam: Fast Action Gummies in Australia</strong></a></span></h3> <p> </p> <p><span style="font-weight: 400;">Using dietary supplements to support a healthy lifestyle has become increasingly popular, and one product that has caught the attention of many individuals in Australia is </span><a href="https://www.facebook.com/PeoplesKetoGummiesAtAustralia/"><strong>People's Keto Gummies</strong></a><span style="font-weight: 400;"> Scam: Fast Action Gummies. While these gummies claim to provide various benefits, it is essential to understand the potential side effects and risks associated with their use.</span></p> <p> </p> <h1><span style="color: #ff0000;"><a style="color: #ff0000;" href="https://healthcare24hrs.com/peoples-keto-gummies-australia-buy"><strong>Visit Here Know More: Click Here To Go to Official Website Now Keto Gummies Australia</strong></a></span></h1> <h1> </h1> <h1><span style="color: #ff0000;"><a style="color: #ff0000;" href="https://healthcare24hrs.com/peoples-keto-gummies-australia-buy"><strong>Visit Here Know More: Click Here To Go to Official Website Now Keto Gummies New Zealand</strong></a></span></h1> <p> </p> <h3><span style="color: #000000; background-color: #ffff99;"><a style="color: #000000; background-color: #ffff99;" href="https://www.facebook.com/PeoplesKetoGummiesOfAU/"><strong>Understand potential side effects and risks that may be associated with using People's Keto Gummies in Australia.</strong></a></span></h3> <p> </p> <p><span style="font-weight: 400;">Before incorporating any new supplement into your routine, it’s crucial to be aware of possible side effects. Although </span><a href="https://www.facebook.com/PeoplesKetoGummiesAtAU/"><strong>People's Keto Gummies</strong></a><span style="font-weight: 400;"> Scam: Fast Action Gummies are marketed as a natural product, some users have reported experiencing adverse reactions. These can include digestive issues such as bloating, diarrhea, or stomach discomfort. It’s important to note that individual responses may vary, and not everyone will experience these side effects.</span></p> <p> </p> <h3><span style="color: #000000; background-color: #ffff99;"><a style="color: #000000; background-color: #ffff99;" href="https://www.facebook.com/TryPeoplesKetoGummiesAu/"><strong>Analyze any reported adverse reactions or health concerns related to this product.</strong></a></span></h3> <p> </p> <p><span style="font-weight: 400;">While there are claims that these gummies offer numerous health benefits due to their high vitamin content, it’s essential to consider any potential risks involved. Some users have expressed concerns about the dosage of certain vitamins present in the gummies. Excessive intake of certain vitamins can lead to adverse effects on health. Therefore, it is advisable to consult with a healthcare professional before starting any new dietary supplement regimen.</span></p> <p> </p> <h3><span style="color: #000000; background-color: #ffff99;"><a style="color: #000000; background-color: #ffff99;" href="https://www.facebook.com/PeoplesKetoGummiesInAustralia/"><strong>Make an educated decision by considering all possible risks and benefits.</strong></a></span></h3> <p> </p> <p><span style="font-weight: 400;">When deciding whether or not to incorporate </span><a href="https://www.facebook.com/PeoplesKetoGummiesOfAustralia/"><strong>People's Keto Gummies</strong></a><span style="font-weight: 400;"> Scam: Fast Action Gummies into your routine, it’s vital to weigh both the potential risks and benefits. Here are some points you should consider:</span></p> <p> </p> <p><a href="https://www.facebook.com/PeoplesKetoGummiesAtAustralia/"><strong>Effects :</strong></a><span style="font-weight: 400;"> Evaluate how these gummies may affect your body based on personal factors such as age, existing medical conditions, and medications you may be taking.</span></p> <p> </p> <p><a href="https://www.facebook.com/PeoplesKetoGummiesOfAU/"><strong>Side Effects :</strong></a> <span style="font-weight: 400;">Consider the reported side effects and assess if they are manageable or could potentially interfere with your daily life.</span></p> <p> </p> <p><a href="https://www.facebook.com/TryPeoplesKetoGummiesAu/"><strong>Dosage :</strong></a><span style="font-weight: 400;"> Take into account the dosage of vitamins in the gummies and determine if it aligns with your specific nutritional needs.</span></p> <p> </p> <p><a href="https://www.facebook.com/PeoplesKetoGummiesInAustralia/"><strong>Vitamins :</strong></a><span style="font-weight: 400;"> Assess the importance of the included vitamins to your overall well-being and whether you can obtain them through a balanced diet or alternative supplements.</span></p> <p> </p> <p><span style="font-weight: 400;">By thoroughly considering these factors, you can make an informed decision about whether </span><a href="https://www.facebook.com/PeoplesKetoGummiesOfAustralia/"><strong>People's Keto Gummies</strong></a><span style="font-weight: 400;"> Scam: Fast Action Gummies are suitable for you.</span></p> <p> </p> <p><a href="https://healthcare24hrs.com/peoples-keto-gummies-australia-buy"><img src="https://i.ibb.co/VtCrPc5/People-s-Keto-Gummies-Worldwide.png" alt="People-s-Keto-Gummies-Worldwide" border="0" /></a></p> <p> </p> <p> </p> <h1><span style="color: #ff0000;"><a style="color: #ff0000;" href="https://healthcare24hrs.com/peoples-keto-gummies-australia-buy"><strong>Discount Price: Higher Discount Price Available For Keto Gummies Australia</strong></a></span></h1> <h1> </h1> <h1><span style="color: #ff0000;"><a style="color: #ff0000;" href="https://healthcare24hrs.com/peoples-keto-gummies-australia-buy"><strong>Discount Price: Higher Discount Price Available For Keto Gummies New Zealand</strong></a></span></h1> <p> </p> <h3><span style="color: #000000; background-color: #ffff99;"><a style="color: #000000; background-color: #ffff99;" href="https://www.facebook.com/PeoplesKetoGummiesAtAustralia/"><strong>Comparing [Australia Chemist Warehouse] People's Keto Gummies: Which is Right for You?</strong></a></span></h3> <p> </p> <p><span style="font-weight: 400;">It’s essential to compare the features, ingredients, and benefits of different options available in the market. By understanding their similarities and differences, you can make an informed choice that aligns better with your personal preferences and goals.</span></p> <p> </p> <h3><span style="color: #000000; background-color: #ffff99;"><a style="color: #000000; background-color: #ffff99;" href="https://www.facebook.com/PeoplesKetoGummiesOfAU/"><strong>Features</strong></a></span></h3> <p> </p> <p><span style="font-weight: 400;">Both </span><a href="https://www.facebook.com/PeoplesKetoGummiesAtAU/"><strong>People's Keto Gummies Australia</strong></a><span style="font-weight: 400;"> are designed to support individuals following a ketogenic diet. These gummy supplements aim to provide an easy and convenient way to incorporate ketones into your daily routine.</span></p> <p> </p> <h3><span style="color: #000000; background-color: #ffff99;"><a style="color: #000000; background-color: #ffff99;" href="https://www.facebook.com/TryPeoplesKetoGummiesAu/"><strong>Ingredients</strong></a></span></h3> <p> </p> <p><span style="font-weight: 400;">Both gummy supplements contain key components that can help you achieve ketosis more effectively. They are formulated with ketone bodies that assist in converting stored fat into energy. They are low in carbs and sugar, making them suitable for those looking to reduce their carbohydrate intake.</span></p> <p> </p> <h3><span style="color: #000000; background-color: #ffff99;"><a style="color: #000000; background-color: #ffff99;" href="https://www.facebook.com/PeoplesKetoGummiesInAustralia/"><strong>Benefits</strong></a></span></h3> <p> </p> <p><span style="font-weight: 400;">The primary benefit of both </span><a href="https://www.facebook.com/PeoplesKetoGummiesOfAustralia/"><strong>People's Keto Gummies Australia</strong></a><span style="font-weight: 400;"> is their ability to support weight loss by promoting the use of stored fat as fuel instead of carbohydrates. By providing an alternative source of energy through ketones, these gummy supplements can help individuals reach their weight loss goals more efficiently. Furthermore, incorporating these gummies into your routine may also offer other advantages such as increased mental clarity and improved focus throughout the day. This can be attributed to the steady release of energy from ketones compared to the spikes and crashes often associated with high-carbohydrate diets.</span></p> <p> </p> <h3><span style="color: #000000; background-color: #ffff99;"><a style="color: #000000; background-color: #ffff99;" href="https://www.facebook.com/PeoplesKetoGummiesAtAustralia/"><strong>Personal Preferences and Goals</strong></a></span></h3> <p> </p> <p><span style="font-weight: 400;">While both options offer similar benefits, it’s important to consider your personal preferences when choosing between </span><a href="https://www.facebook.com/PeoplesKetoGummiesOfAU/"><strong>People's Keto Gummies Australia</strong></a><span style="font-weight: 400;">. 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Local pharmacies or specialized health shops might be worth exploring if you prefer purchasing from physical stores rather than online platforms.</span></p> <p> </p> <p><span style="font-weight: 400;">When considering any dietary supplement, it’s important to consult with a healthcare professional, such as your doctor or a registered dietitian. They can provide guidance based on your individual health needs and advise on the appropriate use of these gummies. Before making a purchase, it’s also essential to understand how these gummies can benefit you. </span><a href="https://www.facebook.com/PeoplesKetoGummiesOfAustralia/"><strong>People's Keto Gummies Australia</strong></a><span style="font-weight: 400;"> are designed to support a ketogenic lifestyle by providing exogenous ketones that may help increase energy levels and promote ketosis. However, it’s important to note that individual results may vary, and these supplements should not be seen as a substitute for a balanced diet and regular exercise.</span></p> <p> </p> <p><a href="https://healthcare24hrs.com/peoples-keto-gummies-australia-buy"><img src="https://i.ibb.co/y5831pw/People-s-Keto-Gummies-Buy.png" alt="People-s-Keto-Gummies-Buy" border="0" /></a></p> <p> </p> <p> </p> <h1><span style="color: #ff0000;"><a style="color: #ff0000;" href="https://healthcare24hrs.com/peoples-keto-gummies-australia-buy"><strong>MUST SEE: Click Here to Order Keto Gummies Australia For The Best Price Available!</strong></a></span></h1> <h1> </h1> <h1><span style="color: #ff0000;"><a style="color: #ff0000;" href="https://healthcare24hrs.com/peoples-keto-gummies-australia-buy"><strong>MUST SEE: Click Here to Order Keto Gummies New Zealand For The Best Price Available!</strong></a></span></h1> <p> </p> <h3><span style="color: #000000; background-color: #ffff99;"><a style="color: #000000; background-color: #ffff99;" href="https://www.facebook.com/PeoplesKetoGummiesAtAustralia/"><strong>Making an Informed Decision about People's Keto Gummies in Australia</strong></a></span></h3> <p> </p> <p><span style="font-weight: 400;">Now that we have delved into the key ingredients, customer reviews, potential side effects, and availability of [Australia Chemist Warehouse] </span><a href="https://www.facebook.com/PeoplesKetoGummiesOfAU/"><strong>People's Keto Gummies</strong></a><strong>,</strong><span style="font-weight: 400;"> you are equipped with the necessary information to make an informed decision. 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d0rj/ru-mhr-parallel	2023-10-25T13:09:14.000Z	[ "task_categories:translation", "size_categories:100K<n<1M", "language:ru", "language:mhr", "license:cc-by-4.0", "low-resource-language", "region:us" ]	d0rj	null	null	0	2023-10-25T13:04:56	--- dataset_info: features: - name: ru dtype: string - name: mhr dtype: string splits: - name: train num_bytes: 83137793 num_examples: 417103 download_size: 40277610 dataset_size: 83137793 configs: - config_name: default data_files: - split: train path: data/train-* license: cc-by-4.0 task_categories: - translation language: - ru - mhr tags: - low-resource-language pretty_name: Mari-Russian parallel corpus size_categories: - 100K<n<1M --- # Dataset Card for "ru-mhr-parallel" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	650	[ [ -0.04364013671875, -0.0182647705078125, 0.0198516845703125, 0.0145416259765625, -0.0170135498046875, 0.00933837890625, 0.020294189453125, 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dsfsi/za-isizulu-siswati-news	2023-10-25T13:32:26.000Z	[ "task_categories:text-classification", "language:ss", "language:zu", "license:cc-by-sa-4.0", "dsfsi-datasets", "siswati", "isizulu", "arxiv:2306.07426", "region:us" ]	dsfsi	null	null	0	2023-10-25T13:23:14	--- license: cc-by-sa-4.0 language: - ss - zu task_categories: - text-classification tags: - dsfsi-datasets - siswati - isizulu pretty_name: za-isizulu-siswati-news --- # IsiZulu News (articles and headlines) and Siswati News (headlines) Corpora - za-isizulu-siswati-news-2022 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7193346.svg)](https://doi.org/10.5281/zenodo.7193346) [![arXiv](https://img.shields.io/badge/arXiv-2306.07426-b31b1b.svg)](https://arxiv.org/abs/2306.07426) ### About Dataset Dataset for both isiZulu news (articles and headlines) and Siswati news headlines. Process included scraping the data from internet, from Isolezwe news website http://www.isolezwe.co.za and public posts from the SABC news LigwalagwalaFM Facebook page https://www.facebook.com/ligwalagwalafm/ respectively. The obtained datasets are isiZulu news articles, isiZulu news headlines, and Siswati news headlines. Post data collection the datasets were then sent to annotators, and they were sent back after the annotation process. The datasets contain special characters, some English words and characters that are not ASCII encoded which must be removed prior to model training. The aim of these three datasets is to create a baseline news categorisation model for the two South African low resources languages i.e. isiZulu and Siswati. For categorisation, we use high level [IPTC NewsCodes](https://iptc.org/standards/newscodes/) as categories. You can view the news categories here [data/news-categories-iptc-newscodes.csv](data/news-categories-iptc-newscodes.csv) The datasets were found to have class categories with very few observations, hence the class categories which have less than 35 observations were removed for isiZulu and less 6 observations for Siswati. The dataset has both full category data as well as reduced category data. Please see the [data-statement.md](data-statement.md) for full dataset information. ## Online Repository link * Link to the DOI data repository - [Zenodo Data Repository](https://doi.org/10.5281/zenodo.7193346) * ## Authors * Andani Madodonga * Vukosi Marivate - [@vukosi](https://twitter.com/vukosi) * Matthew Adendorff See also the list of [contributors](https://github.com/dsfsi/za-isizulu-siswati-news-2022/contributors) who participated in this project. ## Citation Citation: ```bibtex @article{Madodonga_Marivate_Adendorff_2023, title={Izindaba-Tindzaba: Machine learning news categorisation for Long and Short Text for isiZulu and Siswati}, volume={4}, url={https://upjournals.up.ac.za/index.php/dhasa/article/view/4449}, DOI={10.55492/dhasa.v4i01.4449}, author={Madodonga, Andani and Marivate, Vukosi and Adendorff, Matthew}, year={2023}, month={Jan.} } ``` ## License Data is Licensed under CC 4.0 BY SA	2,800	[ [ -0.028778076171875, -0.020172119140625, 0.0048828125, 0.03021240234375, 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theyavuereview/TheyaVue-Reviews	2023-10-25T13:29:39.000Z	[ "region:us" ]	theyavuereview	null	null	0	2023-10-25T13:28:52	<h2 style="text-align: center;"><span style="font-size: large;"><a style="color: #0b5394;" href="https://sale365day.com/order-theyavue">Click Here -- Official Website -- Order Now</a></span></h2> <h2 style="text-align: center;"><span style="color: red; font-size: large;">⚠️Beware Of Fake Websites⚠️</span></h2> <p><strong>✔For Order Official Website - <a href="https://sale365day.com/order-theyavue">https://sale365day.com/order-theyavue</a><br /><br />✔Product Name - <a href="https://theyavue-reviews-official-website.jimdosite.com/">TheyaVue</a> (Eye Support Formula)<br /></strong></p> <p><strong>✔Side Effect - No Side Effects<br /><br />✔Availability - <a href="https://sale365day.com/order-theyavue">Online</a><br /><br />✔ Rating -⭐⭐⭐⭐⭐</strong></p> <p><a href="https://sale365day.com/order-theyavue"><span style="font-size: large;"><strong>Hurry Up - Limited Time Offer - Buy Now</strong></span></a></p> <p><a href="https://sale365day.com/order-theyavue"><span style="font-size: large;"><strong>Hurry Up - Limited Time Offer - Buy Now</strong></span></a></p> <p><a href="https://sale365day.com/order-theyavue"><span style="font-size: large;"><strong>Hurry Up - Limited Time Offer - Buy Now</strong></span></a></p> <p class="story-summary"><strong>TheyaVue</strong> is an effective natural dietary supplement that is specially prepared to support the sharp vision and overall eye health. </p> <p><a href="https://groups.google.com/g/theyavue-reviews-offer/c/B3N9Xe_fkOc"><strong>TheyaVue</strong></a> is a natural dietary supplement that comprises research-backed ingredients to support vision health and overall functioning of the eye. Not long ago, it was released and has been gaining significant attention from both experts and folks. </p> <p style="text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="https://sale365day.com/order-theyavue"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhBzb-bDl6ZIIo7FxmYhsVaH7L4XbELmTYqt3NtTUKACTuA-PYiDEqteWp_6OpiQsa2IsGFIA1CwbcWNgOG2HVOD0hUK8SLBj30xjJP8P3NVopIeva-EktznGxvxmys7AA07dWI2H6oDexh98SJ4es230xmUC6ph16jyffjGGoSUtzBqq-9DYKvbKUz/w640-h306/fcdfwefffffefdf.JPG" alt="" width="640" height="306" border="0" data-original-height="496" data-original-width="1036" /></a> </p> <p>We came to know about <a href="https://groups.google.com/g/theyavue-reviews-offer/c/CWe5Nj6Z0Hk">Theyavue capsules</a> after receiving many requests from our readers to review it. We have to wait for a week to get it in our hands and spend another week to test it. As you know, reviewing a supplement is not an easy task. We couldn't have completed this Theyavue review without the help of our readers who already used it and shared their experience with us. </p> <p style="text-align: left;">We have done everything to test the supplement and share this review with you. We will be glad if you read the article completely and send your feedback to us. Your genuine feedback will help us improve better. </p> <p style="text-align: left;"><strong><span style="font-size: large;"><a href="https://sale365day.com/order-theyavue" target="_blank" rel="nofollow">Check The Availability Of TheyaVue On The Official Website</a></span> </strong></p> <h2 style="text-align: left;">TheyaVue Reviews - How Effective Is TheyaVue Ingredients In Curing Blurry Vision? </h2> <p style="text-align: left;">Coming to the <a href="https://groups.google.com/g/theyavue-reviews-offer">TheyaVue eye care supplement</a>, promises to significantly and positively impact your vision by being evidence-based. However, before you reach your ultimate decision regarding giving it a try, it is necessary to evaluate the formula, so that you can ensure whether it meets the benchmarks of quality and potency. </p> <h2 style="text-align: left;">What is TheyaVue? </h2> <p style="text-align: left;"><a href="https://yourpillsboss.blogspot.com/2023/10/theyavue-reviews-1-eye-vision-support.html">TheyaVue</a> is an effective natural dietary supplement that is specially prepared to support the sharp vision and overall eye health. The supplement comes with a scientifically proven formula that comprises high-quality natural ingredients possessing impressive therapeutic properties. Each of the TheyaVue ingredients together can provide powerful antioxidants support to your eyes to combat vision struggles. </p> <p style="text-align: left;">TheyaVue is an encapsulated formula made of non-GMO ingredients. Every bottle of the supplement comes with easy-to-swallow capsules that are manufactured in the USA, in an FDA-approved state-of-the-art facility. </p> <h2 style="text-align: left;">Details about the Ingredients (Only included the proven benefits) </h2> <p style="text-align: left;">The <a href="https://theyavue-update.clubeo.com/page/theyavue-reviews-usa-does-theyavue-eye-supplement-work-what-to-know-before-buying.html">Theyavue supplement</a> is a combination of many organic ingredients. Below you will see the benefits of these ingredients in detail. </p> <h4 style="text-align: left;">●     Lutein </h4> <p style="text-align: left;">This carotenoid has reported anti-inflammatory properties. Various scientific studies show that lutein significantly improves eye health by curing most eye problems. In particular, lutein prevents age-related macular disease which is the leading cause of blindness and vision impairment. </p> <h4 style="text-align: left;">●     Zeaxanthin </h4> <p style="text-align: left;">Along with Lutein, zeaxanthin shields your eyes from harmful high-energy light waves like ultraviolet rays in sunlight. Based on studies, these two compounds in high levels in the eye tissues are linked with better vision, especially in dim light or where glare is a problem. </p> <h4 style="text-align: left;">●     Vitamin C </h4> <p style="text-align: left;">As per scientific study reports, vitamin C reduces the risk of cataracts and it also slows down the progression of age-related macular degeneration and visual acuity loss when combined with other nutrients in the TheyaVue eye health support supplement. </p> <h4 style="text-align: left;">●     Calcium </h4> <p style="text-align: left;">Calcium supplementation and adding more calcium to your everyday diet lowers the risk of age-related eye disease. This makes you less likely to struggle with serious vision loss. </p> <h4 style="text-align: left;">●     Vitamin E </h4> <p style="text-align: left;">This antioxidant plays a vital role in managing free radical attacks and reducing cell damage. It is often used against conditions like AMD and is powerful to prevent cataracts. </p> <h4 style="text-align: left;">●     Bilberry Extract </h4> <p style="text-align: left;">Based on studies, Bilberry has the potential to reduce the risk of macular degeneration. Besides, it gives the required antioxidant support to the eyes and the retina, avoiding cell deterioration. </p> <h4 style="text-align: left;">●     Rutin </h4> <p style="text-align: left;">Rutin protects the eyes against blood vessel diseases such as diabetic retinopathy. Rutin strengthens the blood vessel walls, and by this, the risk of permanent vision loss is avoided. </p> <p style="text-align: left;"><a href="https://sale365day.com/order-theyavue" target="_blank" rel="sponsored"><span style="font-size: large;"><strong>TheyaVue Is On Sale Now For A Limited Time!</strong></span></a></p> <h2 style="text-align: left;">Ingredient Purity & Label Accuracy </h2> <p style="text-align: left;"><a href="https://theyavue-update.clubeo.com/page/theyavue-reviews-is-it-legit-what-are-customers-saying-theyavue-eye-health-formula-exposed.html">Theyavue</a> have a label accuracy of 98.83% that is a good value and it has an ingredient purity score of 93% (Again a good value). The supplement doesn't contain any flagged inactive ingredients. The heavy metal screening value is less than <a href="https://oehha.ca.gov/media/downloads/proposition-65/safeharborlist032521.pdf" target="_blank" rel="nofollow">CA Proposition 65 limit</a>, so the supplement is very safe to use. </p> <p style="text-align: left;">Editor's Note: Few <a href="https://theyavue-update.clubeo.com/">Theyavue</a> reviews mentioned Grape seed extract (a common ingredients in eye vitamin supplements and skin health products) as an ingredient of the product, but we couldn't find it during the tests. Also, it is not mentioned in the label. </p> <div class="separator" style="clear: both; text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="https://sale365day.com/order-theyavue"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgd76sGGFA1drylsWDY3-C7_noIQ9ybwIhQ31pe1MyAiAiGPI9svMMkXe8eS7E_fsH6Y5cGgPpae55DjwMpdmcvUrIo2Y5l5L7TtNNuWNrO91gfVkbdDWXJrGFpMUjaUTNtshKvpAw8NpvGx6tgimxiINym0XO8_j79Ezb_ro8EmxBWSkxDF0Hf71tl/w640-h416/sfdcvde.JPG" alt="" width="640" height="416" border="0" data-original-height="585" data-original-width="901" /></a></div> <h2 style="text-align: left;">How does TheyaVue support eye vision? </h2> <p style="text-align: left;">The natural and research-based blend of <a href="https://www.townscript.com/e/theyavue-reviews-scam-warning-read-this-before-buying-024040">TheyaVue</a> eye care supplement works by aiding in healthy visions and promoting eye health. The root cause of vision impairment and a list of other eye-related conditions is free radical attacks and oxidative stress which is closely linked with external factors like pollution, exposure to toxins, and unhealthy lifestyle. However, the TheyaVue eye supplement can provide significant antioxidant support to your eyes and eye tissue to get rid of damage and promote cell regeneration. </p> <p style="text-align: left;">The formula is appropriately composed of over 24 ingredients like Lutein, Rutin, Bilberry Extract, Vitamin E, Zeaxanthin, calcium, and vitamin C. Each of them is also scientifically proven for having impressive medicinal properties and packed with important antioxidant. As the TheyaVue ingredients are carefully mixed with their right proportion in the <a href="https://www.cometforums.com/topic/12812433-theyavue-reviews-urgent-2023-update-effective-ingredients-or-fake-customer-hype/">TheyaVue dietary formula</a>, they can directly defend against free radicals and contribute a significant anti-inflammatory response as well. </p> <p style="text-align: left;"><a href="https://sale365day.com/order-theyavue" target="_blank" rel="sponsored"><span style="font-size: large;"><strong>Order your supply of TheyaVue now and start enjoying the benefits!</strong></span></a></p> <h2 style="text-align: left;">The science behind the TheyaVue formula </h2> <p style="text-align: left;">Having a closer glance at the scientific setting of the <a href="https://medium.com/@theyavuereviews/theyavue-reviews-trustworthy-official-website-or-fake-customer-claims-8bed009db7b7">TheyaVue capsule</a>, you can see it also comprises ingredients having solid scientific backgrounds. </p> <p style="text-align: left;">According to a <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6523787/" target="_blank" rel="nofollow">research article published in the </a><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6523787/" target="_blank" rel="nofollow">Nutrients</a><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6523787/" target="_blank" rel="nofollow"> (2018, Sep 18)</a>, Lutein is referred to as a vital carotenoid that has reported anti-inflammatory properties. A large body of scientific evidence also shows that lutein has significant effects on eye health. In particular, lutein improves or even prevents age-related macular disease, the leading cause of blindness and vision impairment. </p> <p style="text-align: left;"><a href="https://expertlegalreview.com/scientific-supplement-review-process/" target="_blank" rel="nofollow">In another scientific review</a> <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5355559/" target="_blank" rel="nofollow">from the </a><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5355559/" target="_blank" rel="nofollow">Saudi Pharmaceutical Journal: SPJ, </a><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5355559/" target="_blank" rel="nofollow">(2016 April 30)</a>, rutin brings impressive anti-cataract and ophthalmic effects to the eyes to protect them from many related struggles. </p> <h2 style="text-align: left;">Perks of using TheyaVue eye supplement </h2> <p style="text-align: left;">As referred to in <a href="https://colab.research.google.com/drive/11SZi064ppfbRbVyxZ1bLwBoqUScJvWEo">TheyaVue reviews</a>, the supplement is constructed with over 24 versatile natural ingredients. Since they are mixed in the formula adequately, you can expect the following benefits with the supplement's right and consistent consumption. </p> <p style="text-align: left;">●     Support for eyesight. </p> <p style="text-align: left;">●     TheyaVue capsule maintains eye health. </p> <p style="text-align: left;">●     Manages eye-related struggles. </p> <p style="text-align: left;">●     Antioxidant support. </p> <p style="text-align: left;">●     TheyaVue organic formula improves night vision </p> <p style="text-align: left;">●     The capsule enhances the overall quality of life </p> <p style="text-align: left;">●     Healthy inflammatory response. </p> <p style="text-align: left;">●     Long lasting results. </p> <p style="text-align: left;">●     Protect from sun damage </p> <p style="text-align: left;">●     Money back policy. </p> <h2 style="text-align: left;">Is there any clinical evidence? </h2> <p style="text-align: left;">Looking into the clinical background of the <a href="https://lookerstudio.google.com/reporting/8e91cacc-4746-4713-a900-22a246386307/page/DsQgD">TheyaVue</a> organic vision support formula, you can see it is clinically proven and manufactured in an FDA-approved, state-of-the-art facility in the US. Besides, each stage of its preparation is carefully done by following the benchmarks of safety, purity, quality, and precision. </p> <h2 style="text-align: left;">Dosage Guidelines </h2> <p style="text-align: left;">As you can see, every <a href="https://sketchfab.com/3d-models/theyavue-reviews-scam-aert-2023-does-it-works-3bbb23362e094536bae5e95cf01a9e59">TheyaVue</a> bottle is packed with 60 pills for a complete month's supply of the formula. The ideal everyday dosage of TheyaVue is two capsules a day, which is ideal to take along with a glass of water. At the same time, the manufacturer also suggests taking it 30 minutes before any main meal. </p> <p style="text-align: left;"><span style="font-size: large;"><strong><a href="https://sale365day.com/order-theyavue" target="_blank" rel="nofollow">Check The Special Discount Offers of TheyaVue On The Official Website</a> </strong></span></p> <h2 style="text-align: left;">How long does it take to work? </h2> <p style="text-align: left;">Since you currently know how to take the TheyaVue eye care supplement in the right way, keep in mind that the formula can deliver you the best results only after 2-3 months of consistent consumption. This is the average time taken by the formula to bring significant changes. Following the suggested intake consistently up to the suggested period of 2-3 months will also help you have a better longevity of results, which is 1-2 years or more.  </p> <p style="text-align: left;">Editor's Note: According to customer feedback, most persons who consistently used the supplement for 90-120 days got the best results. </p> <h2 style="text-align: left;">Are there any side effects? </h2> <p style="text-align: left;">Considering the formulation of <a href="https://www.forexagone.com/forum/experiences-trading/theyavue-reviews-formulated-with-100-pure-ingredients-that-helps-to-clear-blurry-vision-87251#184665">TheyaVue</a> supplement, you can see it is entirely plant-based, and clinically verified. Besides, there are also no harmful elements like chemicals, fillers, or allergens included in the formula to induce any negative side effects. </p> <p style="text-align: left;">Moreover, third-party lab reports also indicate the formula's safety, potency, and quality. And also no negative <a href="https://gamma.app/public/TheyaVue-Reviews-2023-Ingredients-Benefits-Side-Effects-And-Compl-5wkx32cjw3bjcig">TheyaVue reviews</a> were reported yet by any of the customers. </p> <p style="text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="https://yourpillsboss.blogspot.com/2023/10/theyavue-reviews-1-eye-vision-support.html"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj1i-2mpmmbJ1kaQri6IoqTIJKLSPEyVPNBVcn9gRlGRXI0ISFAYvFSU4pjDmO_RKpkLk3W6tfY630p9eEluETooOHdzRe3nD3S4FYKnnq7-J6DmHyJg3zHqdkapQQ3Qpq3LzrSJg_E3f1BpBoLU5EzUaDR2EEyqHhk8-XJL-OshC9ipVq0e8zbE7R0/w640-h220/wfffergg.JPG" alt="" width="640" height="220" border="0" data-original-height="376" data-original-width="1094" /></a></p> <h2 style="text-align: left;">TheyaVue Pros and cons </h2> <p style="text-align: left;">The TheyaVue dietary supplement has both positive and negative features which you need to be aware of before preserving it over others. Here are the major pros and cons associated with the supplement that are identified through probing research. </p> <h2 style="text-align: left;">Pros: </h2> <p style="text-align: left;">●     Natural and non-GMO formula </p> <p style="text-align: left;">●     Evidence-based ingredients and formulation </p> <p style="text-align: left;">●     Quality-assured preparation </p> <p style="text-align: left;">●     Made in the USA in FDA-approved, state-of-the-art facilities </p> <p style="text-align: left;">●     Added benefits </p> <p style="text-align: left;">●     Includes 60-day risk-free money back </p> <h2 style="text-align: left;">Cons: </h2> <p style="text-align: left;">●     TheyaVue is not recommended for children under the age of 18 </p> <p style="text-align: left;">●     Limited availability of stock  </p> <p style="text-align: left;"><a href="https://sale365day.com/order-theyavue" target="_blank" rel="sponsored"><span style="font-size: large;"><strong>Click Here to Get TheyaVue At Discounted Price!!!</strong></span></a></p> <h2 style="text-align: left;">Real TheyaVue reviews from customers </h2> <p style="text-align: left;">If you want to see what feedback the supplement has received so far, you can go through some real <a href="https://theyavue-reviews-2023.webflow.io/">TheyaVue</a> customer reviews given in this section. </p> <h4 style="text-align: left;">●     Rebecca McCarthy </h4> <p style="text-align: left;">I had been struggling with serious irritation in my eyes due to cataracts. But every remedy I tried could only give me temporary relief. Later, one of my friends who is an ophthalmologist suggested TheyaVue capsule. The results have truly stunned me because it is for the first time I could see something that truly worked in the desired way. Now I feel no discomfort, and my vision also became sharper. </p> <h4 style="text-align: left;">●     Sophie Hayden </h4> <p style="text-align: left;">The TheyaVue eye care supplement has totally freed me from the risk of macular degeneration. Taking these pills regularly for up to three months brought significant results in testing as well. Even my optometrist is also stunned by seeing the difference in my test reports. My only regret is that I didn't start its intake a bit before. </p> <h4 style="text-align: left;">●     Adam Norris </h4> <p style="text-align: left;">I am so glad that <a href="https://www.oecd-forum.org/users/theyavue-reviews">TheyaVue</a> pill has worked in the desired way since it is for the first time I am experiencing such a change through a supplement. It significantly reduced dry eyes and I see how clearer my vision has become. However, I couldn't get my second bottle on time when I placed my order again.   </p> <h2 style="text-align: left;">How much does TheyaVue cost? </h2> <p style="text-align: left;">Going through the official website of the supplement, and authentic <a href="https://www.justgiving.com/page/theyavue-reviews-1698231780972">TheyaVue</a> reviews, you can see the supplement comes in three plans, from which you can choose and purchase it. </p> <p style="text-align: left;">●     30-day supply: 1 bottle at $59 + a small shipping fee </p> <p style="text-align: left;">●     90-day supply: 3 bottles at $49/each + free US shipping </p> <p style="text-align: left;">●     180-day supply: 6 bottles at $39/each + free US shipping </p> <p style="text-align: left;">According to these details, bulk orders of <a href="https://www.provenexpert.com/theyavue6/">TheyaVue</a> vision support dietary supplements are added with impressive discounts, to give you the best savings. As these plans provide at least a three-month supply, it will be equivalent to the suggested period recommended for consistent intake as well. Anyway, the choice is totally bound to your preferences.</p> <p style="text-align: left;">Editor's Note: If you are interested in buying the supplement, buy it <a href="https://sale365day.com/order-theyavue" target="_blank" rel="nofollow">only from the official website</a>. As you might have already noticed, many online shopping websites (Including Walmart & Amazon) are filled with supplement scams. But, when you buy from the manufacturer website, you get original products and you are covered in a 100% Money back policy when you buy the product from the official website. </p> <p style="text-align: left;"><span style="font-size: large;"><strong><a href="https://sale365day.com/order-theyavue" target="_blank" rel="nofollow">Check The Special Discount Offers of TheyaVue On The Official Website</a> </strong></span></p> <h2 style="text-align: left;">Where to purchase TheyaVue? </h2> <p style="text-align: left;">If you want to try the authentic <a href="https://theyavuereviewsusa.bandcamp.com/track/theyavue-reviews-fda-approved-2023-unexpected-details-revealed">TheyaVue eye care supplement</a>, keep in mind that it is exclusively available on the official website for purchase. To make it clear, you may see its replicas on other e-commerce websites since the supplement also has higher market demand. So, to avoid any extorted sellers, it is ideal to proceed with your order on the official website. </p> <h2 style="text-align: left;">Shipping and money-back policy </h2> <p style="text-align: left;">Once you place your order, the <a href="https://theyavue-reviews.company.site/">TheyaVue</a> package will be shipped within 2 days and you can expect the delivery within 3-7 business days. However, the single-bottle plan is added with a small shipping fee. The rest of the other two plans will be free of any additional shipping charges. </p> <p style="text-align: left;">At the same time, the <a href="https://doogeemall.com/community/xenforum/topic/114086/theyavue-reviews-11-facts-revealed-what-are-the-pros-and-cons-of-theyavue-vision-health-formula">TheyaVue</a> eye health support supplement comes as quality assured, and the manufacturer assures complete satisfaction with the results. In addition to this, every order will be protected by a hassle-free, no questions asked 60-day money-back guarantee. This will help you get a full refund if you are not satisfied with your purchase for any reason. </p> <div class="separator" style="clear: both; text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="https://sale365day.com/order-theyavue"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgq8a2HP5j2LDEFqSRIMHrF-iuo_vGt1F1udhrOXjlzFB7eoc0qzco5ghOUWOE8jjciDpFLwnRAlGtFf94M71sNBIdWF3EhO1eN1ugu8uIzBISHj5fzaZGJ2eGWLuNBnGfLYNMEB380FdBDU09skpiZm2oTD9XgiwLBqOxh12qh4-v5m-kTMJIU9Jqu/w640-h414/wefdfeff.JPG" alt="" width="640" height="414" border="0" data-original-height="569" data-original-width="881" /></a></div> <h2 style="text-align: left;">Final take on TheyaVue Reviews </h2> <p style="text-align: left;"><a href="https://yourpillsboss.blogspot.com/2023/10/theyavue-reviews-1-eye-vision-support.html">TheyaVue</a> is an effective natural dietary supplement that is specially prepared to support the sharp vision and overall eye health. The supplement comes with a scientifically proven formula that comprises high-quality natural ingredients possessing impressive therapeutic properties. Each of the <a href="https://theyavue-update.clubeo.com/page/theyavue-reviews-usa-does-theyavue-eye-supplement-work-what-to-know-before-buying.html">TheyaVue</a> ingredients together can provide powerful antioxidants support to your eyes to combat vision struggles. </p> <p style="text-align: left;"><a href="https://medium.com/@theyavuereviews/theyavue-reviews-trustworthy-official-website-or-fake-customer-claims-8bed009db7b7">TheyaVue</a> is an encapsulated formula made of non-GMO ingredients. Every bottle of the supplement comes with easy-to-swallow capsules that are manufactured in the USA, in an FDA-approved state-of-the-art facility. </p> <p style="text-align: left;"><strong>Read More:</strong></p> <p style="text-align: left;"><a href="https://yourpillsboss.blogspot.com/2023/10/theyavue-reviews-1-eye-vision-support.html">https://yourpillsboss.blogspot.com/2023/10/theyavue-reviews-1-eye-vision-support.html</a><br /><a href="https://theyavue-reviews-official-website.jimdosite.com/">https://theyavue-reviews-official-website.jimdosite.com/</a><br /><a href="https://groups.google.com/g/theyavue-reviews-offer">https://groups.google.com/g/theyavue-reviews-offer</a><br /><a href="https://groups.google.com/g/theyavue-reviews-offer/c/CWe5Nj6Z0Hk">https://groups.google.com/g/theyavue-reviews-offer/c/CWe5Nj6Z0Hk</a><br /><a href="https://groups.google.com/g/theyavue-reviews-offer/c/B3N9Xe_fkOc">https://groups.google.com/g/theyavue-reviews-offer/c/B3N9Xe_fkOc</a><br /><a 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mekaneeky/Synthetic_Acholi_MMS	2023-10-25T14:22:37.000Z	[ "region:us" ]	mekaneeky	null	null	0	2023-10-25T14:12:40	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: dev path: data/dev-* - split: test path: data/test-* dataset_info: features: - name: eng dtype: string - name: lug dtype: string - name: ach dtype: string - name: teo dtype: string - name: lgg dtype: string - name: nyn dtype: string - name: ID dtype: string - name: ach_tts sequence: sequence: float32 splits: - name: train num_bytes: 14041118016 num_examples: 23947 - name: dev num_bytes: 289355340 num_examples: 500 - name: test num_bytes: 322907400 num_examples: 500 download_size: 14665423094 dataset_size: 14653380756 --- # Dataset Card for "Synthetic_Acholi_MMS" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	904	[ [ -0.039886474609375, -0.03021240234375, 0.00821685791015625, 0.022216796875, 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Raylton/Koddi	2023-10-25T15:37:10.000Z	[ "region:us" ]	Raylton	null	null	0	2023-10-25T15:35:30	Entry not found	15	[ [ -0.02142333984375, -0.014984130859375, 0.057220458984375, 0.0288238525390625, -0.035064697265625, 0.046539306640625, 0.052520751953125, 0.005062103271484375, 0.0513916015625, 0.0170135498046875, -0.052093505859375, -0.01495361328125, -0.060333251953125, 0.037933349609375, -0.0264892578125, 0.038421630859375, -0.009613037109375, -0.00714111328125, 0.01873779296875, -0.0183868408203125, -0.035858154296875, -0.0244598388671875, -0.07891845703125, 0.004077911376953125, 0.035308837890625, 0.04931640625, 0.05035400390625, 0.02423095703125, 0.042694091796875, 0.0261383056640625, -0.01534271240234375, 0.03204345703125, -0.002758026123046875, 0.0002008676528930664, -0.023406982421875, -0.03662109375, -0.0189361572265625, 0.00501251220703125, 0.07275390625, 0.064208984375, -0.018890380859375, 0.003520965576171875, -0.0203094482421875, 0.0219573974609375, -0.032989501953125, 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weillon/dinalva	2023-10-25T15:47:52.000Z	[ "region:us" ]	weillon	null	null	0	2023-10-25T15:47:52	Entry not found	15	[ [ -0.02142333984375, -0.014984130859375, 0.057220458984375, 0.0288238525390625, -0.035064697265625, 0.046539306640625, 0.052520751953125, 0.005062103271484375, 0.0513916015625, 0.0170135498046875, -0.052093505859375, -0.01495361328125, -0.060333251953125, 0.037933349609375, -0.0264892578125, 0.038421630859375, -0.009613037109375, -0.00714111328125, 0.01873779296875, -0.0183868408203125, -0.035858154296875, -0.0244598388671875, -0.07891845703125, 0.004077911376953125, 0.035308837890625, 0.04931640625, 0.05035400390625, 0.02423095703125, 0.042694091796875, 0.0261383056640625, -0.01534271240234375, 0.03204345703125, -0.002758026123046875, 0.0002008676528930664, -0.023406982421875, -0.03662109375, -0.0189361572265625, 0.00501251220703125, 0.07275390625, 0.064208984375, -0.018890380859375, 0.003520965576171875, -0.0203094482421875, 0.0219573974609375, -0.032989501953125, 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cawoylel/FulaSpeechCorpora-splited	2023-10-25T16:10:24.000Z	[ "region:us" ]	cawoylel	null	null	0	2023-10-25T15:54:10	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: dev path: data/dev-* dataset_info: features: - name: audio dtype: audio - name: transcription dtype: string - name: dialect dtype: string splits: - name: train num_bytes: 15116684584.91028 num_examples: 93908 - name: test num_bytes: 3026464537.665628 num_examples: 18801 - name: dev num_bytes: 758023819.869451 num_examples: 4709 download_size: 17656630091 dataset_size: 18901172942.44536 --- # Dataset Card for "FulaSpeechCorpora-splited" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	771	[ [ -0.035125732421875, -0.0306549072265625, -0.004138946533203125, 0.0288543701171875, -0.0140380859375, 0.0323486328125, 0.0234222412109375, -0.01235198974609375, 0.0640869140625, 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BangumiBase/jujutsukaisen	2023-10-25T19:36:48.000Z	[ "size_categories:1K<n<10K", "license:mit", "art", "region:us" ]	BangumiBase	null	null	0	2023-10-25T17:00:44	--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Jujutsu Kaisen This is the image base of bangumi Jujutsu Kaisen, we detected 41 characters, 4326 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 \| 1045 \| [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 \| 253 \| [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 \| 39 \| [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 \| 280 \| [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 \| 68 \| [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 \| 53 \| [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 \| 101 \| [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 \| 84 \| [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 \| 107 \| [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 \| 171 \| [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 \| 25 \| [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 \| 482 \| [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 \| 88 \| [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 \| 163 \| [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 \| 27 \| [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 \| 37 \| [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 \| 10 \| [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 \| 22 \| [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 \| 52 \| [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 \| 71 \| [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 \| 75 \| [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 \| 21 \| [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 \| 358 \| [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 \| 43 \| [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 \| 10 \| [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 \| 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 \| 18 \| [Download](29/dataset.zip) \| ![preview 1](29/preview_1.png) \| ![preview 2](29/preview_2.png) \| ![preview 3](29/preview_3.png) \| ![preview 4](29/preview_4.png) \| ![preview 5](29/preview_5.png) \| ![preview 6](29/preview_6.png) \| ![preview 7](29/preview_7.png) \| ![preview 8](29/preview_8.png) \| \| 30 \| 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 \| 31 \| [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 \| 119 \| [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 \| 81 \| [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 \| 10 \| [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 \| 39 \| [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 \| 15 \| [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 \| 21 \| [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 \| 9 \| [Download](38/dataset.zip) \| ![preview 1](38/preview_1.png) \| ![preview 2](38/preview_2.png) \| ![preview 3](38/preview_3.png) \| ![preview 4](38/preview_4.png) \| ![preview 5](38/preview_5.png) \| ![preview 6](38/preview_6.png) \| ![preview 7](38/preview_7.png) \| ![preview 8](38/preview_8.png) \| \| 39 \| 19 \| [Download](39/dataset.zip) \| ![preview 1](39/preview_1.png) \| ![preview 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jxm/agnews	2023-10-25T17:42:50.000Z	[ "region:us" ]	jxm	null	null	0	2023-10-25T17:42:44	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: dev path: data/dev-* dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 30133565 num_examples: 120000 - name: test num_bytes: 1899330 num_examples: 7600 - name: dev num_bytes: 63881 num_examples: 256 download_size: 20215708 dataset_size: 32096776 --- # Dataset Card for "agnews" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	668	[ [ -0.037261962890625, -0.02410888671875, 0.02197265625, 0.017425537109375, -0.0162811279296875, 0.00016641616821289062, 0.0205535888671875, -0.019683837890625, 0.060882568359375, 0.025604248046875, -0.055908203125, -0.046844482421875, -0.05078125, -0.02020263671875, -0.03875732421875, 0.0947265625, 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jxm/cr	2023-10-25T17:42:56.000Z	[ "region:us" ]	jxm	null	null	0	2023-10-25T17:42:52	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: dev path: data/dev-* dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 192172 num_examples: 1775 - name: test num_bytes: 219871 num_examples: 2000 - name: dev num_bytes: 29232 num_examples: 256 download_size: 253672 dataset_size: 441275 --- # Dataset Card for "cr" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	655	[ [ -0.0330810546875, -0.0180511474609375, 0.00884246826171875, 0.01247406005859375, -0.021392822265625, 0.00577545166015625, 0.0019445419311523438, -0.0205230712890625, 0.046295166015625, 0.034698486328125, -0.065673828125, -0.05859375, -0.042022705078125, -0.01507568359375, -0.0156402587890625, 0.089111328125, 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jxm/mr	2023-10-25T17:43:06.000Z	[ "region:us" ]	jxm	null	null	0	2023-10-25T17:43:01	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: dev path: data/dev-* dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 1105634 num_examples: 8662 - name: test num_bytes: 253331 num_examples: 2000 - name: dev num_bytes: 33062 num_examples: 256 download_size: 909063 dataset_size: 1392027 --- # Dataset Card for "mr" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	657	[ [ -0.046966552734375, -0.01568603515625, 0.013214111328125, 0.00180816650390625, -0.01146697998046875, 0.0019350051879882812, 0.0205535888671875, -0.01285552978515625, 0.07232666015625, 0.0313720703125, -0.07177734375, -0.043609619140625, -0.047119140625, -0.0072479248046875, -0.00899505615234375, 0.08746337890625, 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jxm/sst2	2023-10-25T17:43:13.000Z	[ "region:us" ]	jxm	null	null	0	2023-10-25T17:43:08	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: dev path: data/dev-* dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 4412207 num_examples: 67349 - name: test num_bytes: 209356 num_examples: 1821 - name: dev num_bytes: 29783 num_examples: 256 download_size: 2898575 dataset_size: 4651346 --- # Dataset Card for "sst2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	661	[ [ -0.01430511474609375, -0.01396942138671875, 0.0224761962890625, 0.01067352294921875, -0.032440185546875, 0.01029205322265625, 0.026275634765625, -0.00786590576171875, 0.04779052734375, 0.023895263671875, -0.054779052734375, -0.0360107421875, -0.0423583984375, -0.0305328369140625, -0.0164794921875, 0.094482421875, 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jxm/subj	2023-10-25T17:43:18.000Z	[ "region:us" ]	jxm	null	null	0	2023-10-25T17:43:14	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: dev path: data/dev-* dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 1128835 num_examples: 8000 - name: test num_bytes: 286215 num_examples: 2000 - name: dev num_bytes: 37250 num_examples: 256 download_size: 960873 dataset_size: 1452300 --- # Dataset Card for "subj" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	659	[ [ -0.04937744140625, -0.018280029296875, 0.00609588623046875, 0.004669189453125, -0.0205841064453125, 0.00653076171875, 0.017547607421875, -0.00580596923828125, 0.06378173828125, 0.0246429443359375, -0.06365966796875, -0.0517578125, -0.04443359375, -0.010223388671875, -0.0184783935546875, 0.09423828125, 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jxm/trec	2023-10-25T17:43:23.000Z	[ "region:us" ]	jxm	null	null	0	2023-10-25T17:43:19	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: dev path: data/dev-* dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 342265 num_examples: 5452 - name: test num_bytes: 24003 num_examples: 500 - name: dev num_bytes: 12305 num_examples: 256 download_size: 228995 dataset_size: 378573 --- # Dataset Card for "trec" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	655	[ [ -0.044342041015625, -0.023895263671875, 0.0185394287109375, 0.01041412353515625, -0.0157470703125, 0.023101806640625, 0.0211029052734375, -0.01184844970703125, 0.05975341796875, 0.031494140625, -0.062103271484375, -0.0697021484375, -0.035919189453125, -0.012359619140625, -0.0233154296875, 0.09051513671875, 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mekaneeky/Synthetic_Acholi_VITS_22.5k	2023-10-25T17:58:49.000Z	[ "region:us" ]	mekaneeky	null	null	0	2023-10-25T17:46:32	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: dev path: data/dev-* - split: test path: data/test-* dataset_info: features: - name: eng dtype: string - name: lug dtype: string - name: ach dtype: string - name: teo dtype: string - name: lgg dtype: string - name: nyn dtype: string - name: ID dtype: string - name: ach_tts sequence: sequence: float32 splits: - name: train num_bytes: 17816721728 num_examples: 23947 - name: dev num_bytes: 361145932 num_examples: 500 - name: test num_bytes: 375082248 num_examples: 500 download_size: 18567936006 dataset_size: 18552949908 --- # Dataset Card for "Synthetic_Acholi_VITS_22.5k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	911	[ [ -0.03173828125, -0.027618408203125, -0.007045745849609375, 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jxm/cb	2023-10-25T17:48:45.000Z	[ "region:us" ]	jxm	null	null	0	2023-10-25T17:48:41	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: dev path: data/dev-* dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: string - name: idx dtype: int64 splits: - name: train num_bytes: 89859 num_examples: 250 - name: test num_bytes: 93992 num_examples: 250 - name: dev num_bytes: 22480 num_examples: 56 download_size: 139260 dataset_size: 206331 --- # Dataset Card for "cb" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	720	[ [ -0.049774169921875, -0.03173828125, 0.0099029541015625, 0.0260009765625, -0.0207061767578125, 0.00508880615234375, 0.01206207275390625, -0.02587890625, 0.058013916015625, 0.040130615234375, -0.060516357421875, -0.06390380859375, -0.04022216796875, -0.0167083740234375, 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jxm/rte	2023-10-25T17:49:04.000Z	[ "region:us" ]	jxm	null	null	0	2023-10-25T17:49:00	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: dev path: data/dev-* dataset_info: features: - name: sentence_1 dtype: string - name: sentence_2 dtype: string - name: label dtype: string splits: - name: train num_bytes: 857264 num_examples: 2490 - name: test num_bytes: 956053 num_examples: 3000 - name: dev num_bytes: 85218 num_examples: 256 download_size: 1236189 dataset_size: 1898535 --- # Dataset Card for "rte" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	700	[ [ -0.042877197265625, -0.0281829833984375, 0.0056304931640625, 0.0209503173828125, -0.01433563232421875, -0.014556884765625, 0.0157470703125, -0.0202789306640625, 0.056854248046875, 0.042449951171875, -0.050018310546875, -0.05712890625, -0.046905517578125, -0.004764556884765625, 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jamestalentium/xsum_100_finetune	2023-10-25T18:10:56.000Z	[ "region:us" ]	jamestalentium	null	null	0	2023-10-25T18:10:54	--- dataset_info: features: - name: input_text dtype: string - name: output_text dtype: string - name: id dtype: string splits: - name: train num_bytes: 234853.27403268887 num_examples: 100 download_size: 86391 dataset_size: 234853.27403268887 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "xsum_100_finetune" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	541	[ [ -0.046661376953125, -0.00927734375, 0.01013946533203125, 0.0001475811004638672, -0.010345458984375, -0.0159912109375, 0.006130218505859375, 0.0024700164794921875, 0.06781005859375, 0.043060302734375, -0.0537109375, -0.0474853515625, -0.0367431640625, -0.0235443115234375, -0.033905029296875, 0.09564208984375, 0.0075531005859375, 0.01491546630859375, -0.03277587890625, 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jamestalentium/xsum_100_test	2023-10-25T18:11:00.000Z	[ "region:us" ]	jamestalentium	null	null	0	2023-10-25T18:10:57	--- dataset_info: features: - name: input_text dtype: string - name: output_text dtype: string - name: id dtype: string splits: - name: test num_bytes: 15613650.659431798 num_examples: 6614 download_size: 5619267 dataset_size: 15613650.659431798 configs: - config_name: default data_files: - split: test path: data/test-* --- # Dataset Card for "xsum_100_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	537	[ [ -0.0433349609375, -0.00835418701171875, 0.005435943603515625, 0.0083160400390625, 0.00018668174743652344, -0.00537872314453125, 0.0201416015625, 0.002201080322265625, 0.068603515625, 0.036102294921875, -0.057464599609375, -0.043548583984375, -0.035003662109375, -0.020111083984375, -0.0440673828125, 0.09014892578125, -0.01114654541015625, 0.01556396484375, -0.050506591796875, 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jamestalentium/xsum_250_finetune	2023-10-25T18:11:06.000Z	[ "region:us" ]	jamestalentium	null	null	0	2023-10-25T18:11:05	--- dataset_info: features: - name: input_text dtype: string - name: output_text dtype: string - name: id dtype: string splits: - name: train num_bytes: 587133.1850817222 num_examples: 250 download_size: 222544 dataset_size: 587133.1850817222 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "xsum_250_finetune" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	540	[ [ -0.04547119140625, -0.00279998779296875, 0.0120391845703125, 0.0030994415283203125, -0.008758544921875, -0.01351165771484375, 0.00691986083984375, -0.0005702972412109375, 0.06536865234375, 0.0406494140625, -0.056182861328125, -0.047149658203125, -0.037841796875, -0.02313232421875, -0.0289306640625, 0.09490966796875, 0.00606536865234375, 0.01263427734375, -0.03076171875, 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jamestalentium/xsum_250_test	2023-10-25T18:11:10.000Z	[ "region:us" ]	jamestalentium	null	null	0	2023-10-25T18:11:07	--- dataset_info: features: - name: input_text dtype: string - name: output_text dtype: string - name: id dtype: string splits: - name: test num_bytes: 15613650.659431798 num_examples: 6614 download_size: 5619267 dataset_size: 15613650.659431798 configs: - config_name: default data_files: - split: test path: data/test-* --- # Dataset Card for "xsum_250_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	537	[ [ -0.0423583984375, -0.0010662078857421875, 0.00782012939453125, 0.01123809814453125, 0.00023221969604492188, -0.0014257431030273438, 0.019561767578125, -0.00174713134765625, 0.06640625, 0.032989501953125, -0.060333251953125, -0.04351806640625, -0.035675048828125, -0.0204925537109375, -0.037933349609375, 0.08795166015625, -0.011932373046875, 0.01395416259765625, -0.045562744140625, 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jamestalentium/xsum_1000_finetune	2023-10-25T18:11:15.000Z	[ "region:us" ]	jamestalentium	null	null	0	2023-10-25T18:11:13	--- dataset_info: features: - name: input_text dtype: string - name: output_text dtype: string - name: id dtype: string splits: - name: train num_bytes: 2348532.740326889 num_examples: 1000 download_size: 849145 dataset_size: 2348532.740326889 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "xsum_1000_finetune" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	542	[ [ -0.04681396484375, -0.00737762451171875, 0.010711669921875, 0.0033740997314453125, -0.0092926025390625, -0.017333984375, 0.0143585205078125, -0.0007200241088867188, 0.0703125, 0.04315185546875, -0.0491943359375, -0.041900634765625, -0.0386962890625, -0.019683837890625, -0.0291748046875, 0.08966064453125, 0.01611328125, 0.01071929931640625, -0.032684326171875, 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jamestalentium/xsum_1000_rm	2023-10-25T18:11:16.000Z	[ "region:us" ]	jamestalentium	null	null	0	2023-10-25T18:11:15	--- dataset_info: features: - name: input_text dtype: string - name: output_text dtype: string - name: id dtype: string splits: - name: train num_bytes: 2348532.740326889 num_examples: 1000 download_size: 830060 dataset_size: 2348532.740326889 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "xsum_1000_rm" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	536	[ [ -0.035186767578125, 0.002399444580078125, 0.0088043212890625, 0.00948333740234375, -0.005718231201171875, -0.007122039794921875, 0.0291290283203125, 0.0021190643310546875, 0.069091796875, 0.044097900390625, -0.056304931640625, -0.044464111328125, -0.044281005859375, -0.0168304443359375, -0.03314208984375, 0.08172607421875, 0.009490966796875, 0.007640838623046875, 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jamestalentium/xsum_1000_test	2023-10-25T18:11:18.000Z	[ "region:us" ]	jamestalentium	null	null	0	2023-10-25T18:11:16	--- dataset_info: features: - name: input_text dtype: string - name: output_text dtype: string - name: id dtype: string splits: - name: test num_bytes: 15613650.659431798 num_examples: 6614 download_size: 5619267 dataset_size: 15613650.659431798 configs: - config_name: default data_files: - split: test path: data/test-* --- # Dataset Card for "xsum_1000_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	538	[ [ -0.042388916015625, -0.00632476806640625, 0.005863189697265625, 0.01160430908203125, 0.0006589889526367188, -0.004791259765625, 0.028106689453125, -0.001316070556640625, 0.07275390625, 0.035675048828125, -0.0518798828125, -0.038818359375, -0.03729248046875, -0.0167999267578125, -0.03680419921875, 0.082275390625, -0.0017242431640625, 0.01067352294921875, -0.048370361328125, 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AneeqMalik/Enter-Your-hub-name	2023-10-25T19:12:08.000Z	[ "region:us" ]	AneeqMalik	null	null	0	2023-10-25T19:05:06	--- dataset_info: features: - name: audio dtype: audio - name: audio_names dtype: string - name: genere dtype: class_label: names: '0': bad '1': okay '2': good '3': great splits: - name: train num_bytes: 12388426.0 num_examples: 6 download_size: 12391275 dataset_size: 12388426.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "Enter-Your-hub-name" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	633	[ [ -0.053863525390625, -0.04156494140625, 0.019378662109375, 0.0151214599609375, -0.0033893585205078125, 0.00537872314453125, 0.0167694091796875, -0.0080718994140625, 0.06610107421875, 0.031341552734375, -0.060943603515625, -0.046600341796875, -0.03875732421875, -0.002262115478515625, -0.01392364501953125, 0.07513427734375, 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IndonesiaAI/stack-split-1_translated	2023-10-25T20:02:51.000Z	[ "region:us" ]	IndonesiaAI	null	null	0	2023-10-25T20:01:56	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: qid dtype: string - name: question dtype: string - name: response_j dtype: string - name: response_k dtype: string splits: - name: train num_bytes: 3206490021 num_examples: 1056803 download_size: 951479401 dataset_size: 3206490021 --- # Dataset Card for "stack-split-1_translated" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	577	[ [ -0.039398193359375, -0.0279541015625, -0.004306793212890625, 0.036895751953125, -0.033233642578125, 0.0131988525390625, 0.0184173583984375, -0.01479339599609375, 0.0653076171875, 0.0484619140625, -0.059234619140625, -0.054168701171875, -0.0531005859375, -0.01210784912109375, -0.032958984375, 0.11138916015625, -0.004974365234375, 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helliun/happychat-dataset	2023-10-25T20:36:51.000Z	[ "region:us" ]	helliun	null	null	0	2023-10-25T20:36:50	--- dataset_info: features: - name: input sequence: string - name: output dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 1400318 num_examples: 1010 download_size: 668248 dataset_size: 1400318 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "happychat-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	516	[ [ -0.0438232421875, -0.02130126953125, -0.003692626953125, 0.033355712890625, -0.020294189453125, 0.0019817352294921875, 0.0036411285400390625, -0.0139312744140625, 0.0738525390625, 0.022369384765625, -0.071044921875, -0.049468994140625, -0.02838134765625, -0.016998291015625, -0.0109100341796875, 0.10028076171875, 0.0225372314453125, -0.0007109642028808594, -0.0297088623046875, 0.00017452239990234375, 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helliun/happychat-dataset-halfsplit	2023-10-25T20:38:55.000Z	[ "region:us" ]	helliun	null	null	0	2023-10-25T20:38:54	--- dataset_info: features: - name: input sequence: string - name: output dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 7323889 num_examples: 5058 download_size: 3993763 dataset_size: 7323889 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "happychat-dataset-halfsplit" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	527	[ [ -0.039398193359375, -0.031463623046875, -0.0074310302734375, 0.036956787109375, -0.0316162109375, 0.00481414794921875, 0.002796173095703125, -0.01009368896484375, 0.082275390625, 0.020111083984375, -0.0675048828125, -0.042816162109375, -0.0275421142578125, -0.0188140869140625, -0.03070068359375, 0.09576416015625, 0.0246429443359375, -0.007122039794921875, -0.031524658203125, 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helliun/happychat-dataset-half-split	2023-10-25T20:41:41.000Z	[ "region:us" ]	helliun	null	null	0	2023-10-25T20:41:39	--- dataset_info: features: - name: convo sequence: string - name: label dtype: int64 splits: - name: train num_bytes: 7323889 num_examples: 5058 download_size: 3997613 dataset_size: 7323889 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "happychat-dataset-half-split" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	493	[ [ -0.04925537109375, -0.039520263671875, -0.00818634033203125, 0.031036376953125, -0.0340576171875, 0.01629638671875, 0.006198883056640625, -0.01143646240234375, 0.080078125, 0.0236663818359375, -0.07293701171875, -0.035491943359375, -0.0242919921875, -0.0186920166015625, -0.021575927734375, 0.09490966796875, 0.02532958984375, 0.005298614501953125, -0.03094482421875, -0.0051727294921875, -0.041839599609375, 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helliun/happychat-dataset-tenth-split	2023-10-25T20:42:11.000Z	[ "region:us" ]	helliun	null	null	0	2023-10-25T20:42:10	--- dataset_info: features: - name: convo sequence: string - name: label dtype: int64 splits: - name: train num_bytes: 1400318 num_examples: 1010 download_size: 728652 dataset_size: 1400318 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "happychat-dataset-tenth-split" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	493	[ [ -0.053680419921875, -0.033172607421875, 0.0020732879638671875, 0.03900146484375, -0.02679443359375, 0.0102081298828125, 0.0028057098388671875, -0.012542724609375, 0.0816650390625, 0.0223388671875, -0.070068359375, -0.036590576171875, -0.0302581787109375, -0.00989532470703125, -0.01192474365234375, 0.0858154296875, 0.032470703125, 0.0114593505859375, -0.0250244140625, -0.01406097412109375, -0.047943115234375, 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magichampz/lego-technic-pieces	2023-10-26T17:02:54.000Z	[ "license:mit", "region:us" ]	magichampz	null	null	0	2023-10-25T23:03:49	--- license: mit --- # Dataset Card for Dataset Name Database for lego sorter model uploaded <br> This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ## Dataset Details ### Dataset Description Sample database for the my lego sorter model uploaded <br> Contains both sample images from each class as well as a numpy array file (.npy) that contains every image (~6000) used to train the model. The numpy file was created so that the dataset could be loaded into Google Collab. - Curated by: Aveek Goswami, Amos Koh ### Dataset Sources - Repository: https://github.com/magichampz/lego-sorting-machine-ag-ak ## Uses Dataset may be used to train any machine learning model. ### Direct Use Best use for this dataset is to train a model with similar architecture to the lego sorter model I uploaded. Dataset images designed to be classified into 7 distinct lego technic classes ## Dataset Structure database-sample contains 7 folders, each containing images from different categories of lego technic pieces. <br> A .npy file is also uploaded, which has a shape of (5953,2), which means 5953 entries, with each entry containing the full image as one data point and the category label as the other data point. ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing All images were not processed, they were stored as the original image both in the folders and the numpy array. Image processing occurs in the odel training script uploaded as part of the lego sorter model repo ### Recommendations All images were taken under constant lighting conditions with a raspberry PiCamera 2, which limited the quality of the images obtained.	1,947	[ [ -0.042327880859375, -0.0186309814453125, -0.004730224609375, -0.00241851806640625, -0.045562744140625, 0.0007143020629882812, 0.018280029296875, -0.00704193115234375, 0.0107574462890625, 0.0300140380859375, -0.046630859375, -0.04266357421875, -0.0303497314453125, 0.00720977783203125, -0.027374267578125, 0.07867431640625, 0.0181732177734375, 0.0036716461181640625, -0.0400390625, 0.0019464492797851562, -0.0282440185546875, -0.0303955078125, -0.0362548828125, -0.0340576171875, 0.0372314453125, 0.045440673828125, 0.06201171875, 0.07977294921875, 0.042694091796875, 0.025726318359375, -0.014923095703125, -0.027008056640625, -0.03594970703125, -0.0212554931640625, -0.00754547119140625, -0.043243408203125, -0.0419921875, 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amintalukder/emotion_bn	2023-10-25T23:15:07.000Z	[ "region:us" ]	amintalukder	null	null	0	2023-10-25T23:10:08	--- configs: - config_name: default data_files: - split: val path: data/val-* - split: test path: data/test-* - split: train path: data/train-* dataset_info: features: - name: ID dtype: int64 - name: Data dtype: string - name: Love dtype: int64 - name: Joy dtype: int64 - name: Surprise dtype: int64 - name: Anger dtype: int64 - name: Sadness dtype: int64 - name: Fear dtype: int64 - name: Topic dtype: string - name: Domain dtype: string - name: is_admin dtype: bool splits: - name: val num_bytes: 503282 num_examples: 2047 - name: test num_bytes: 545033 num_examples: 2272 - name: train num_bytes: 4408992 num_examples: 18420 download_size: 1882715 dataset_size: 5457307 --- # Dataset Card for "emotion_bn" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	965	[ [ -0.052154541015625, -0.01397705078125, 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gmaijoe-emailchaser/emailchaser-llm-body-data-v0.0.1	2023-10-25T23:15:21.000Z	[ "region:us" ]	gmaijoe-emailchaser	null	null	0	2023-10-25T23:15:19	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 611717 num_examples: 404 download_size: 162582 dataset_size: 611717 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "emailchaser-llm-body-data-v0.0.1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	456	[ [ -0.0242919921875, -0.0067901611328125, 0.005153656005859375, 0.0048675537109375, -0.01387786865234375, -0.0001289844512939453, 0.03179931640625, -0.01568603515625, 0.059661865234375, 0.052947998046875, -0.06646728515625, -0.051910400390625, -0.042266845703125, -0.00945281982421875, -0.032867431640625, 0.0958251953125, -0.004383087158203125, 0.0013790130615234375, -0.041290283203125, -0.007274627685546875, 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Bsbell21/generadai-sample	2023-10-25T23:45:25.000Z	[ "region:us" ]	Bsbell21	null	null	0	2023-10-25T23:45:23	--- dataset_info: features: - name: item dtype: string - name: description dtype: string - name: ad dtype: string splits: - name: train num_bytes: 3915 num_examples: 5 download_size: 7989 dataset_size: 3915 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "generadai-sample" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	503	[ [ -0.036224365234375, -0.01279449462890625, 0.007015228271484375, 0.020965576171875, -0.02020263671875, 0.0003256797790527344, 0.018218994140625, -0.0091400146484375, 0.067626953125, 0.0242767333984375, -0.059051513671875, -0.06396484375, -0.040374755859375, -0.007343292236328125, -0.025238037109375, 0.0872802734375, 0.00374603271484375, 0.006450653076171875, -0.04168701171875, -0.0120391845703125, -0.0232086181640625, 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Tverous/misinfo-clusters3	2023-10-26T02:33:50.000Z	[ "region:us" ]	Tverous	null	null	0	2023-10-26T02:22:29	--- dataset_info: features: - name: cluster_id dtype: string - name: doc_id dtype: string - name: main_text dtype: string - name: image dtype: image - name: video dtype: string - name: audio dtype: string - name: kg_embedding sequence: sequence: float32 splits: - name: train num_bytes: 198061.0 num_examples: 1 download_size: 177682 dataset_size: 198061.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "misinfo-clusters3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	684	[ [ -0.044891357421875, -0.006305694580078125, 0.0179290771484375, 0.028533935546875, -0.006511688232421875, -0.004032135009765625, 0.0234527587890625, -0.02069091796875, 0.05279541015625, 0.0302276611328125, -0.054595947265625, -0.056243896484375, -0.037750244140625, 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aadriann/micara	2023-10-26T02:45:38.000Z	[ "region:us" ]	aadriann	null	null	0	2023-10-26T02:45:38	Entry not found	15	[ [ -0.02142333984375, -0.01495361328125, 0.05718994140625, 0.0288238525390625, -0.035064697265625, 0.046539306640625, 0.052520751953125, 0.005062103271484375, 0.0513916015625, 0.016998291015625, -0.052093505859375, -0.014984130859375, -0.060394287109375, 0.03790283203125, -0.0264892578125, 0.038421630859375, -0.0095977783203125, -0.00711822509765625, 0.01873779296875, -0.01837158203125, -0.03582763671875, -0.0244903564453125, -0.0789794921875, 0.004055023193359375, 0.035308837890625, 0.049346923828125, 0.05035400390625, 0.0242767333984375, 0.042694091796875, 0.0260772705078125, -0.015380859375, 0.03204345703125, -0.0027446746826171875, 0.00015556812286376953, -0.0233917236328125, -0.03662109375, -0.018951416015625, 0.00502777099609375, 0.07275390625, 0.064208984375, -0.018890380859375, 0.003520965576171875, -0.0203399658203125, 0.02197265625, -0.032958984375, 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datkai/datanew

2023-10-25T01:54:54.000Z

[ "region:us" ]

datkai

null

0

2023-10-25T01:54:54

Entry not found

15

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tianyang/repobench_ablation_64k

2023-10-25T02:25:32.000Z

[ "region:us" ]

tianyang

null

0

2023-10-25T02:21:36

--- configs: - config_name: default data_files: - split: cross_file_first path: data/cross_file_first-* - split: cross_file_random path: data/cross_file_random-* - split: in_file path: data/in_file-* dataset_info: features: - name: repo_name dtype: string - name: file_path dtype: string - name: context list: - name: identifier dtype: string - name: path dtype: string - name: snippet dtype: string - name: import_statement dtype: string - name: token_num dtype: int64 - name: cropped_code dtype: string - name: all_code dtype: string - name: next_line dtype: string - name: gold_snippet_index dtype: int64 splits: - name: cross_file_first num_bytes: 248891343 num_examples: 3644 - name: cross_file_random num_bytes: 201499818 num_examples: 3321 - name: in_file num_bytes: 231190696 num_examples: 3549 download_size: 209884822 dataset_size: 681581857 --- # Dataset Card for "repobench_ablation_64k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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BangumiBase/foodwars

2023-10-25T09:29:56.000Z

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

BangumiBase

null

0

2023-10-25T02:25:25

--- license: mit tags: - art size_categories: - 10K<n<100K --- # Bangumi Image Base of Food Wars! This is the image base of bangumi Food Wars!, we detected 129 characters, 16059 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 | 87 | [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 | 664 | [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 | 69 | [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 | 172 | [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 | 242 | [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 | 98 | [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 | 83 | [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 | 69 | [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 | 59 | [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 | 150 | [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 | 85 | [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 | 44 | [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 | 73 | [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 | 27 | [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 | 25 | [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 | 31 | [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 | 658 | [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 | 73 | [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 | 39 | [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 | 64 | [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 | 70 | [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 | 27 | [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 | 17 | [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 | 65 | [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 | 14 | [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 | 2797 | [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 | 35 | [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 | 87 | [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 | 112 | [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 | 73 | [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 | 84 | [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 | 102 | [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 | 210 | [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 | 86 | [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 | 200 | [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 | 463 | [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 | 89 | [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 | 94 | [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 | 204 | [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 | 106 | [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 | 32 | [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 | 35 | [Download](43/dataset.zip) | ![preview 1](43/preview_1.png) | ![preview 2](43/preview_2.png) | ![preview 3](43/preview_3.png) | ![preview 4](43/preview_4.png) | ![preview 5](43/preview_5.png) | ![preview 6](43/preview_6.png) | ![preview 7](43/preview_7.png) | ![preview 8](43/preview_8.png) | | 44 | 75 | [Download](44/dataset.zip) | ![preview 1](44/preview_1.png) | ![preview 2](44/preview_2.png) | ![preview 3](44/preview_3.png) | ![preview 4](44/preview_4.png) | ![preview 5](44/preview_5.png) | ![preview 6](44/preview_6.png) | ![preview 7](44/preview_7.png) | ![preview 8](44/preview_8.png) | | 45 | 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 | 38 | [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 | 343 | [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 | 90 | [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 | 53 | [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 | 35 | [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 | 24 | [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 | 45 | [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 | 27 | [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 | 23 | [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 | 32 | [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 | 19 | [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 | 202 | [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 | 119 | [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 | 12 | [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 | 15 | [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 | 61 | [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 | 14 | [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 | 49 | [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 | 13 | [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 | 318 | [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 | 367 | [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 | 22 | [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 | 67 | [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 | 51 | [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 | 46 | [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 | 24 | [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 | 21 | [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 | 16 | [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 | 47 | [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 | 75 | [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 | 39 | [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 | 34 | [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 | 52 | [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 | 40 | [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 | 615 | [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 | 154 | [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 | 10 | [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 | 73 | [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 | 42 | [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 | 34 | [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 | 383 | [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 | 29 | [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 | 12 | [Download](88/dataset.zip) | ![preview 1](88/preview_1.png) | ![preview 2](88/preview_2.png) | ![preview 3](88/preview_3.png) | ![preview 4](88/preview_4.png) | ![preview 5](88/preview_5.png) | ![preview 6](88/preview_6.png) | ![preview 7](88/preview_7.png) | ![preview 8](88/preview_8.png) | | 89 | 30 | [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 | 64 | [Download](90/dataset.zip) | ![preview 1](90/preview_1.png) | ![preview 2](90/preview_2.png) | ![preview 3](90/preview_3.png) | ![preview 4](90/preview_4.png) | ![preview 5](90/preview_5.png) | ![preview 6](90/preview_6.png) | ![preview 7](90/preview_7.png) | ![preview 8](90/preview_8.png) | | 91 | 1406 | [Download](91/dataset.zip) | ![preview 1](91/preview_1.png) | ![preview 2](91/preview_2.png) | ![preview 3](91/preview_3.png) | ![preview 4](91/preview_4.png) | ![preview 5](91/preview_5.png) | ![preview 6](91/preview_6.png) | ![preview 7](91/preview_7.png) | ![preview 8](91/preview_8.png) | | 92 | 44 | [Download](92/dataset.zip) | ![preview 1](92/preview_1.png) | ![preview 2](92/preview_2.png) | ![preview 3](92/preview_3.png) | ![preview 4](92/preview_4.png) | ![preview 5](92/preview_5.png) | ![preview 6](92/preview_6.png) | ![preview 7](92/preview_7.png) | ![preview 8](92/preview_8.png) | | 93 | 128 | [Download](93/dataset.zip) | ![preview 1](93/preview_1.png) | ![preview 2](93/preview_2.png) | ![preview 3](93/preview_3.png) | ![preview 4](93/preview_4.png) | ![preview 5](93/preview_5.png) | ![preview 6](93/preview_6.png) | ![preview 7](93/preview_7.png) | ![preview 8](93/preview_8.png) | | 94 | 48 | [Download](94/dataset.zip) | ![preview 1](94/preview_1.png) | ![preview 2](94/preview_2.png) | ![preview 3](94/preview_3.png) | ![preview 4](94/preview_4.png) | ![preview 5](94/preview_5.png) | ![preview 6](94/preview_6.png) | ![preview 7](94/preview_7.png) | ![preview 8](94/preview_8.png) | | 95 | 327 | [Download](95/dataset.zip) | ![preview 1](95/preview_1.png) | ![preview 2](95/preview_2.png) | ![preview 3](95/preview_3.png) | ![preview 4](95/preview_4.png) | ![preview 5](95/preview_5.png) | ![preview 6](95/preview_6.png) | ![preview 7](95/preview_7.png) | ![preview 8](95/preview_8.png) | | 96 | 96 | [Download](96/dataset.zip) | ![preview 1](96/preview_1.png) | ![preview 2](96/preview_2.png) | ![preview 3](96/preview_3.png) | ![preview 4](96/preview_4.png) | ![preview 5](96/preview_5.png) | ![preview 6](96/preview_6.png) | ![preview 7](96/preview_7.png) | ![preview 8](96/preview_8.png) | | 97 | 181 | [Download](97/dataset.zip) | ![preview 1](97/preview_1.png) | ![preview 2](97/preview_2.png) | ![preview 3](97/preview_3.png) | ![preview 4](97/preview_4.png) | ![preview 5](97/preview_5.png) | ![preview 6](97/preview_6.png) | ![preview 7](97/preview_7.png) | ![preview 8](97/preview_8.png) | | 98 | 14 | [Download](98/dataset.zip) | ![preview 1](98/preview_1.png) | ![preview 2](98/preview_2.png) | ![preview 3](98/preview_3.png) | ![preview 4](98/preview_4.png) | ![preview 5](98/preview_5.png) | ![preview 6](98/preview_6.png) | ![preview 7](98/preview_7.png) | ![preview 8](98/preview_8.png) | | 99 | 16 | [Download](99/dataset.zip) | ![preview 1](99/preview_1.png) | ![preview 2](99/preview_2.png) | ![preview 3](99/preview_3.png) | ![preview 4](99/preview_4.png) | ![preview 5](99/preview_5.png) | ![preview 6](99/preview_6.png) | ![preview 7](99/preview_7.png) | ![preview 8](99/preview_8.png) | | 100 | 14 | [Download](100/dataset.zip) | ![preview 1](100/preview_1.png) | ![preview 2](100/preview_2.png) | ![preview 3](100/preview_3.png) | ![preview 4](100/preview_4.png) | ![preview 5](100/preview_5.png) | ![preview 6](100/preview_6.png) | ![preview 7](100/preview_7.png) | ![preview 8](100/preview_8.png) | | 101 | 34 | [Download](101/dataset.zip) | ![preview 1](101/preview_1.png) | ![preview 2](101/preview_2.png) | ![preview 3](101/preview_3.png) | ![preview 4](101/preview_4.png) | ![preview 5](101/preview_5.png) | ![preview 6](101/preview_6.png) | ![preview 7](101/preview_7.png) | ![preview 8](101/preview_8.png) | | 102 | 193 | [Download](102/dataset.zip) | ![preview 1](102/preview_1.png) | ![preview 2](102/preview_2.png) | ![preview 3](102/preview_3.png) | ![preview 4](102/preview_4.png) | ![preview 5](102/preview_5.png) | ![preview 6](102/preview_6.png) | ![preview 7](102/preview_7.png) | ![preview 8](102/preview_8.png) | | 103 | 134 | [Download](103/dataset.zip) | ![preview 1](103/preview_1.png) | ![preview 2](103/preview_2.png) | ![preview 3](103/preview_3.png) | ![preview 4](103/preview_4.png) | ![preview 5](103/preview_5.png) | ![preview 6](103/preview_6.png) | ![preview 7](103/preview_7.png) | ![preview 8](103/preview_8.png) | | 104 | 69 | [Download](104/dataset.zip) | ![preview 1](104/preview_1.png) | ![preview 2](104/preview_2.png) | ![preview 3](104/preview_3.png) | ![preview 4](104/preview_4.png) | ![preview 5](104/preview_5.png) | ![preview 6](104/preview_6.png) | ![preview 7](104/preview_7.png) | ![preview 8](104/preview_8.png) | | 105 | 9 | [Download](105/dataset.zip) | ![preview 1](105/preview_1.png) | ![preview 2](105/preview_2.png) | ![preview 3](105/preview_3.png) | ![preview 4](105/preview_4.png) | ![preview 5](105/preview_5.png) | ![preview 6](105/preview_6.png) | ![preview 7](105/preview_7.png) | ![preview 8](105/preview_8.png) | | 106 | 86 | [Download](106/dataset.zip) | ![preview 1](106/preview_1.png) | ![preview 2](106/preview_2.png) | ![preview 3](106/preview_3.png) | ![preview 4](106/preview_4.png) | ![preview 5](106/preview_5.png) | ![preview 6](106/preview_6.png) | ![preview 7](106/preview_7.png) | ![preview 8](106/preview_8.png) | | 107 | 23 | [Download](107/dataset.zip) | ![preview 1](107/preview_1.png) | ![preview 2](107/preview_2.png) | ![preview 3](107/preview_3.png) | ![preview 4](107/preview_4.png) | ![preview 5](107/preview_5.png) | ![preview 6](107/preview_6.png) | ![preview 7](107/preview_7.png) | ![preview 8](107/preview_8.png) | | 108 | 109 | [Download](108/dataset.zip) | ![preview 1](108/preview_1.png) | ![preview 2](108/preview_2.png) | ![preview 3](108/preview_3.png) | ![preview 4](108/preview_4.png) | ![preview 5](108/preview_5.png) | ![preview 6](108/preview_6.png) | ![preview 7](108/preview_7.png) | ![preview 8](108/preview_8.png) | | 109 | 28 | [Download](109/dataset.zip) | ![preview 1](109/preview_1.png) | ![preview 2](109/preview_2.png) | ![preview 3](109/preview_3.png) | ![preview 4](109/preview_4.png) | ![preview 5](109/preview_5.png) | ![preview 6](109/preview_6.png) | ![preview 7](109/preview_7.png) | ![preview 8](109/preview_8.png) | | 110 | 14 | [Download](110/dataset.zip) | ![preview 1](110/preview_1.png) | ![preview 2](110/preview_2.png) | ![preview 3](110/preview_3.png) | ![preview 4](110/preview_4.png) | ![preview 5](110/preview_5.png) | ![preview 6](110/preview_6.png) | ![preview 7](110/preview_7.png) | ![preview 8](110/preview_8.png) | | 111 | 12 | [Download](111/dataset.zip) | ![preview 1](111/preview_1.png) | ![preview 2](111/preview_2.png) | ![preview 3](111/preview_3.png) | ![preview 4](111/preview_4.png) | ![preview 5](111/preview_5.png) | ![preview 6](111/preview_6.png) | ![preview 7](111/preview_7.png) | ![preview 8](111/preview_8.png) | | 112 | 6 | [Download](112/dataset.zip) | ![preview 1](112/preview_1.png) | ![preview 2](112/preview_2.png) | ![preview 3](112/preview_3.png) | ![preview 4](112/preview_4.png) | ![preview 5](112/preview_5.png) | ![preview 6](112/preview_6.png) | N/A | N/A | | 113 | 13 | [Download](113/dataset.zip) | ![preview 1](113/preview_1.png) | ![preview 2](113/preview_2.png) | ![preview 3](113/preview_3.png) | ![preview 4](113/preview_4.png) | ![preview 5](113/preview_5.png) | ![preview 6](113/preview_6.png) | ![preview 7](113/preview_7.png) | ![preview 8](113/preview_8.png) | | 114 | 14 | [Download](114/dataset.zip) | ![preview 1](114/preview_1.png) | ![preview 2](114/preview_2.png) | ![preview 3](114/preview_3.png) | ![preview 4](114/preview_4.png) | ![preview 5](114/preview_5.png) | ![preview 6](114/preview_6.png) | ![preview 7](114/preview_7.png) | ![preview 8](114/preview_8.png) | | 115 | 27 | [Download](115/dataset.zip) | ![preview 1](115/preview_1.png) | ![preview 2](115/preview_2.png) | ![preview 3](115/preview_3.png) | ![preview 4](115/preview_4.png) | ![preview 5](115/preview_5.png) | ![preview 6](115/preview_6.png) | ![preview 7](115/preview_7.png) | ![preview 8](115/preview_8.png) | | 116 | 106 | [Download](116/dataset.zip) | ![preview 1](116/preview_1.png) | ![preview 2](116/preview_2.png) | ![preview 3](116/preview_3.png) | ![preview 4](116/preview_4.png) | ![preview 5](116/preview_5.png) | ![preview 6](116/preview_6.png) | ![preview 7](116/preview_7.png) | ![preview 8](116/preview_8.png) | | 117 | 44 | [Download](117/dataset.zip) | ![preview 1](117/preview_1.png) | ![preview 2](117/preview_2.png) | ![preview 3](117/preview_3.png) | ![preview 4](117/preview_4.png) | ![preview 5](117/preview_5.png) | ![preview 6](117/preview_6.png) | ![preview 7](117/preview_7.png) | ![preview 8](117/preview_8.png) | | 118 | 34 | [Download](118/dataset.zip) | ![preview 1](118/preview_1.png) | ![preview 2](118/preview_2.png) | ![preview 3](118/preview_3.png) | ![preview 4](118/preview_4.png) | ![preview 5](118/preview_5.png) | ![preview 6](118/preview_6.png) | ![preview 7](118/preview_7.png) | ![preview 8](118/preview_8.png) | | 119 | 16 | [Download](119/dataset.zip) | ![preview 1](119/preview_1.png) | ![preview 2](119/preview_2.png) | ![preview 3](119/preview_3.png) | ![preview 4](119/preview_4.png) | ![preview 5](119/preview_5.png) | ![preview 6](119/preview_6.png) | ![preview 7](119/preview_7.png) | ![preview 8](119/preview_8.png) | | 120 | 120 | [Download](120/dataset.zip) | ![preview 1](120/preview_1.png) | ![preview 2](120/preview_2.png) | ![preview 3](120/preview_3.png) | ![preview 4](120/preview_4.png) | ![preview 5](120/preview_5.png) | ![preview 6](120/preview_6.png) | ![preview 7](120/preview_7.png) | ![preview 8](120/preview_8.png) | | 121 | 28 | [Download](121/dataset.zip) | ![preview 1](121/preview_1.png) | ![preview 2](121/preview_2.png) | ![preview 3](121/preview_3.png) | ![preview 4](121/preview_4.png) | ![preview 5](121/preview_5.png) | ![preview 6](121/preview_6.png) | ![preview 7](121/preview_7.png) | ![preview 8](121/preview_8.png) | | 122 | 27 | [Download](122/dataset.zip) | ![preview 1](122/preview_1.png) | ![preview 2](122/preview_2.png) | ![preview 3](122/preview_3.png) | ![preview 4](122/preview_4.png) | ![preview 5](122/preview_5.png) | ![preview 6](122/preview_6.png) | ![preview 7](122/preview_7.png) | ![preview 8](122/preview_8.png) | | 123 | 64 | [Download](123/dataset.zip) | ![preview 1](123/preview_1.png) | ![preview 2](123/preview_2.png) | ![preview 3](123/preview_3.png) | ![preview 4](123/preview_4.png) | ![preview 5](123/preview_5.png) | ![preview 6](123/preview_6.png) | ![preview 7](123/preview_7.png) | ![preview 8](123/preview_8.png) | | 124 | 19 | [Download](124/dataset.zip) | ![preview 1](124/preview_1.png) | ![preview 2](124/preview_2.png) | ![preview 3](124/preview_3.png) | ![preview 4](124/preview_4.png) | ![preview 5](124/preview_5.png) | ![preview 6](124/preview_6.png) | ![preview 7](124/preview_7.png) | ![preview 8](124/preview_8.png) | | 125 | 21 | [Download](125/dataset.zip) | ![preview 1](125/preview_1.png) | ![preview 2](125/preview_2.png) | ![preview 3](125/preview_3.png) | ![preview 4](125/preview_4.png) | ![preview 5](125/preview_5.png) | ![preview 6](125/preview_6.png) | ![preview 7](125/preview_7.png) | ![preview 8](125/preview_8.png) | | 126 | 8 | [Download](126/dataset.zip) | ![preview 1](126/preview_1.png) | ![preview 2](126/preview_2.png) | ![preview 3](126/preview_3.png) | ![preview 4](126/preview_4.png) | ![preview 5](126/preview_5.png) | ![preview 6](126/preview_6.png) | ![preview 7](126/preview_7.png) | ![preview 8](126/preview_8.png) | | 127 | 24 | [Download](127/dataset.zip) | ![preview 1](127/preview_1.png) | ![preview 2](127/preview_2.png) | ![preview 3](127/preview_3.png) | ![preview 4](127/preview_4.png) | ![preview 5](127/preview_5.png) | ![preview 6](127/preview_6.png) | ![preview 7](127/preview_7.png) | ![preview 8](127/preview_8.png) | | noise | 576 | [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) |

42,890

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Rewcifer/ct_scans_90pct_2048_cutoff_falcon

2023-10-25T03:01:36.000Z

[ "region:us" ]

Rewcifer

null

0

2023-10-25T03:01:24

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 880984917.8756735 num_examples: 176406 download_size: 166046892 dataset_size: 880984917.8756735 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ct_scans_90pct_2048_cutoff_falcon" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

485

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straysheep731/woman512

2023-10-25T07:35:22.000Z

[ "region:us" ]

straysheep731

null

0

2023-10-25T03:18:41

Entry not found

15

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xiaopeiwu/hrw_test_binary_flagged_data

2023-10-25T04:08:28.000Z

[ "region:us" ]

xiaopeiwu

null

0

2023-10-25T04:08:28

--- configs: - config_name: default data_files: - split: train path: data.csv --- # Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary [More Information Needed] ### 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]

1,416

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thangvip/orca-filter2

2023-10-25T04:13:20.000Z

[ "region:us" ]

thangvip

null

0

2023-10-25T04:11:38

--- dataset_info: features: - name: id dtype: string - name: system_prompt dtype: string - name: question dtype: string - name: response dtype: string splits: - name: train num_bytes: 1147795895.593268 num_examples: 1261986 download_size: 615068751 dataset_size: 1147795895.593268 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "orca-filter2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

579

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Sanatbek/Uzbek-restaurant-domain-sentiment-reviews

2023-10-25T06:10:24.000Z

[ "region:us" ]

Sanatbek

null

0

2023-10-25T06:06:54

Entry not found

15

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mesolitica/malaysian-llama2-embedding-1b-part2

2023-10-26T01:29:38.000Z

[ "region:us" ]

mesolitica

null

0

2023-10-25T06:20:36

Entry not found

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1NightRaid1/testing

2023-10-25T07:13:47.000Z

[ "task_categories:question-answering", "size_categories:1K<n<10K", "license:apache-2.0", "code", "region:us" ]

1NightRaid1

null

0

2023-10-25T07:05:36

--- license: apache-2.0 task_categories: - question-answering tags: - code size_categories: - 1K<n<10K --- # Dataset Card for Dataset Name  This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ## Dataset Details ### Dataset Description  - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

4,467

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buyflowforcemax/FlowForceMax

2023-10-25T07:11:41.000Z

[ "region:us" ]

buyflowforcemax

null

0

2023-10-25T07:11:21

In today’s busy schedule, it is not common to take our energy levels to a high level. Lack of desire and feeling drained are major problems that most males face in their daily routines. That’s why; FlowForce Max comes into existence with a powerful blend of natural extracts. These components are carefully designed to reignite your passion and get back youthful energy and activities. But, do you know what makes Flow Force Max distinctive from other products in the market? Let’s find out more deeply in this review! First of all, FlowForce Max is backed or formulated with extensive research and designed with utmost precision. It ensures that you are getting a supplement of the highest and best quality. Unlike other supplements in the market, Flow Force Max is completely free from any kind of harmful substances or synthetic enhancers. That’s why; it is known as reliable and safe for all users. When you start to utilize **FlowForce Max,** you can expect a gradual increment and noticeable change in physical activities. Carefully crafted extracts always work together to boost blood circulation to essential areas in the body. [![](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgD2taPTN6RIwxDYTSRGyoElRqznxPrIQ3Cy0UF0PWD7rd5uFVKfMP11zRibrLognAIbEx9u-l-C0QTX83RJgJabmRoiEv_EruNCI5C3QTUif9C00bvqWQrYY4gy2my5bOsa33tPbz93usSAymqjAosDS2_C27y9fLbPdybQW-15wirG7AuAB64rFHCLJMN/w640-h250/Screenshot%20(1454).png)](https://www.glitco.com/us-flowforce-max) Now, it is not essential to feel uninspired and tired because Flow Force Max is here to invigorate manhood and keep you on high every time whenever needed! Additionally, FlowForce Max may go beyond just improving activities and it can bring energy to daily lifestyles and boost overall well-being. With improved energy levels and a renewed sense of confidence, you can deal with the challenges of life with gusto both outside and inside the house. [MUST SEE: Click Here Now To Get The FlowForce Max Directly From The Official Website](https://www.glitco.com/us-flowforce-max) Now, you know what Flow Force Max can do. It is time to know why this product is worth investing in. In the next paragraphs, we will dive deeper into key components, give an inside appearance at how this product has transformed lives, and highlight the success stories. So, be ready to start a transformative journey with FlowForce Max! Introduction of FlowForce Max -------------------------------- In a real sense, FlowForce Max is a revolutionary dietary product formulated to deal with some common problems of reduced energy, lack of desire, and underwhelming intimate activities. This powerful and effective formula has served and assisted millions of people in the world. And, it provides them an opportunity to regain confidence and passion in their relationships. Commitment to safety and quality is one of the most important and incredible benefits of [Flow Force Max](https://www.weddingwire.com/website/flowforce-max-and-reviews). This product is designed in GMP-certified labs, running under the strict adherence of certified scientists and doctors, and using cutting-edge technology. Every step of the manufacturing procedure adheres to the GMP (Good Manufacturing Practice) guidelines that ensure the supplement is of standard quality. According to the official website, each component of the product has undergone several clinical trials to verify its safety and efficacy. These compounds have been tested for GRAS status (Generally Recognized as Safe) which attests to the remarkable advantages without any negative impacts. FlowForce Max avoids the utilization of additives or fillers which ensure that each capsule is fully packed with natural and potent extracts. The components in Flow Force Max work amazingly to deal with the root causes of reduced energy and activities in front of ladies at home. Components, like Muira Puama and others, are renowned for their aphrodisiac features and the ability to improve energy levels, increase desire, and combat fatigue. These incredible components work attractively to make a comprehensive and amazing formula that assists people in regaining their confidence to enjoy a pleasurable and enjoyable moment of life. In general, the effectiveness of Flow Force Max is based on customer reviews and it is also supported by clinical studies and scientific research. Because of these rigorous trials, the considerable advantages of the product have solidified its reputation as the perfect solution for improving and revitalizing intimate activity. In addition, FlowForce Max promises to uphold strict regulatory and ethical standards. It does not include any banned substances which ensure that people can confidently and safely incorporate them into their daily lifestyle. And, this promise of safety and quality has contributed to the immense popularity along with reliability among people worldwide. So, it is right to say that Flow Force Max is a game-changing product that has assisted millions of people worldwide in reclaiming their confidence, well-being, and energy levels. Formulated in GMP-certified labs, it usually utilizes cutting-edge and latest technology under the surveillance of certified scientists and doctors. [MUST SEE: Click Here Now To Get The FlowForce Max Directly From The Official Website](https://www.glitco.com/us-flowforce-max) What is the Adequate Working Procedure of FlowForce Max? -------------------------------------------------------- Usually, Flow Force Max acts by using carefully designed formulas that deal with underlying factors responsible for lower confidence, lower energy, unsatisfactory intimate activity, and lack of desire. Understanding the amazing functional mechanism of the product is very important because it is incredible in improving and rejuvenating the experience of pleasurable moments. And, the previous sections of Flow Force Max reviews have repeated that the primary working procedure of action for Flow Force Max is centered around improving blood flow and optimizing levels of energy. These additional two factors play an important role in describing desire, overall satisfactory outcomes, and activities. Firstly, FlowForce Max includes ingredients that encourage the development of nitric oxide in the entire body. Nitric oxide is an herbal vasodilator that helps to expand and relax blood vessels. In this way, it can improve blood circulation, especially to the problematic parts of the body. This amazing mechanism helps people experience more enjoyable and fulfilling intimate activities. Moreover, FlowForce Max also acts by optimizing levels of desire and energy. In simple words, it increases the production of T-levels that can increase physical and mental well-being without any problems. FlowForce Max components help to improve production by restoring energy, power, strength, and long-lasting activities. Besides improving blood circulation and optimizing energy levels, this product also works by decreasing anxiety and stress which can significantly impact desire and activity in front of a lady. The blend of natural components in this product assists in relaxing both the body and mind, encouraging a state of calmness and decreasing stress levels. You may need to follow the coming sections of this incredible review to uncover how they are associated with balanced levels of energy and confidence. By alleviating stress, people are getting heightened desire, improved mood, and happiness. It is very important to understand that the functions or mechanisms of Flow Force Max are beyond the physical aspects of the activity. [![](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgSPn7Ep9XAjwgRnFDTeLHXiyJWrOkdJpRxcZvMHZJgK_243DuaXbAsOr9PvTCXVqFippwxQBKASwEbev_DgIg1TEu7XhjwgP0FjDvkfo5Mn1syeb9Dx1MDvA_jYbU7SOMQiCvA6NLgnK96FVYIZAZaX_KNArWT4O3JaLuxGTWa30SH1i8SAbkB2sYINq74/w640-h428/Screenshot%20(1456).png)](https://www.glitco.com/us-flowforce-max) [MUST SEE: Click Here Now To Get The FlowForce Max Directly From The Official Website](https://www.glitco.com/us-flowforce-max) The increment of energy, desire, and confidence with long-lasting activity via this product has come with a psychological impact. Improved self-esteem, boosted confidence, and a renewed sense of desire can lead to a healthier and more gratifying experience. Undoubtedly, FlowForce Max offers a comprehensive and natural solution for people who are searching to boost their experiences. Its functional and amazing mechanism includes encouraging blood circulation, optimizing levels of energy, decreasing stress, and dealing with the core problems that contribute to reduced physical activities. What are the Natural Components of FlowForce Max? ------------------------------------------------- **➣ Graminex Flower Pollen Extract** Extracted from a specific kind of flower pollen, this ingredient is enriched with amino acids, nutrients, and enzymes. Additionally, this component has been proven to support healthy prostate function and optimize levels of energy, especially T-levels. **➣ Luteolin** It is another flavor that works as natural T-boosted which can improve muscle mass and support overall well-being and mood. **➣ Fisetin** This is a kind of flavonoid with potent anti-inflammatory and antioxidant properties. And, it assists in boosting blood circulation including flow to the genital region which helps in longer and stronger staying power. Undoubtedly, Fisetin is included in most of the health-benefit products in the market. **➣** **Monolaurin** This component is extracted directly from coconut oil that possesses antibacterial, antiviral, and antifungal properties. In general, it assists in managing healthy immunity which is so important for overall well-being and health. **➣ Grape Seed Extract** Well, this is an important component enriched with antioxidants known as OPCs (oligomeric proanthocyanidin complexes). These incredible antioxidants help to prevent blood vessels and encourage healthy circulation, important for managing firm energy. **➣ Oregano Leaf Extract** It is best known for antimicrobial properties that support a healthy immunity and it is linked to boosted blood flow. Hence, it may have positive impacts on different activities without any trouble. **➣ ViNitrox** It is a potent blend of grape and apple polyphenols that can improve the production of nitric oxide and encourage healthy blood circulation. In reality, it is crucial for maintaining and obtaining proper energy with longer outcomes. **➣ Saw Palmetto Fruit Extract Powder** It is a very well-known herbal extract utilized to support prostate health. Also, it assists in managing healthy T-levels and can lead to increased function of the body. **➣ Tricalcium Phosphate** In general, this component helps to ensure the complete absorption of nutrients in the entire body. Also, it supports the overall health of bones and contributes to the incredibility of other components in the formula. **➣ Muira Puama Extract** It is well known as “potency wood” that has been utilized traditionally as an aphrodisiac. Also, it helps to increase desire, confidence, and energy, and deal with fatigue. **➣ Sucralose** Usually, it is a 0-calorie sweetener that offers a pleasant and amazing taste to the product. This way, it makes it easy to consume this product without any problem. [Let’s Delve Into Some Health Advantages of FlowForce Max!](https://www.glitco.com/us-flowforce-max) Improved Intimate Activity - By encouraging optimal blood flow, this formula contributes to long-lasting and stronger activity in male. Improved Energy - FlowForce Max is formulated to boost energy levels that help people feel more energized and motivated in their daily lives. With increased energy and strength, people can enjoy a more hydrated and fulfilling lifestyle, both outside and inside the house. Improved Confidence – A happy and healthy intimate lifestyle can have a profound impact on the self-confidence of people. Flow Force Max helps people to regain their confidence and desire in a short period. Support Prostate Health – Some components of Flow Force Max have been proven used to support prostate health like saw palmetto fruit. Decreased Anxiety and Stress – It includes components that help to decrease stress and encourage relaxation. It may have a significant and positive impact on well-being as anxiety and stress often lead to difficulties in relationships. Support Immune and Antioxidant – Through ingredients like Oregano leaf, Grape seed, and Monolaurin, Flow Force Max provides immune-boosting and antioxidant properties. These properties assist in preventing cells from damage and manage a healthy immunity by supporting overall well-being. Where to Purchase FlowForce Max? -------------------------------- Very simple! Just click on the below image or link to visit the official page of FlowForce Max. It is the only way to get genuine or original products online. No other retail stores or offline markets can sell this product. It is an online product that can be grabbed through the official page only. You can get marvelous discount offers on the official website (according to the quantity). [(LIMITED STOCK ) Click Here to Order FlowForce Max For The Lowest Price from Official Website](https://www.glitco.com/us-flowforce-max) Conclusion- FlowForce Max Reviews --------------------------------- Lastly, FlowForce Max provides a claiming solution for people searching to boost their activities. With the carefully designed blend of natural components, this product helps to support increased energy, desire, confidence, and longer staying power. However, results may vary from person to person but results are guaranteed. Many people have reported positive results and supported the promises made about the product in the Flow Force Max review. It is essential to know that Flow ForceMax must be a part of a healthy lifestyle and is not a kind of substitute for professional medical advice. That’s why; it is important to discuss with a healthcare professional before beginning a new regimen. [![](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjfAMT1ELS8h-cA25PKPNQ7NTMeHXJvsrvX9fOrtxpUGf9J_p38PjhH8gzN6M13Y5hnOgL10qE7GV9qr1hC3H-rr6m_vPjfuQuTj1zbaAHHRbtU5FK-e0D_Te0W-gJwYvAjK8CgBy7g_n5QJfeWG5hDTOCFUTOTzEawRd_DBLW6BtzXRCoxhPAeJfht51GR/w640-h240/Screenshot%20(1455).png)](https://www.glitco.com/us-flowforce-max) FlowForce Max Reviews- Frequently Asked Questions ------------------------------------------------- **1) Is FlowForce Max Legit?** Yes, Flow ForceMax is legit and that’s why millions of people are utilizing this product daily. If you want to improve male health naturally, Flow Force Max is an ideal option. **2) Is FlowForce Max Scam or Genuine?** 100% genuine! As per the official website, users can find out real testimonials through the official page about Flow Force Max. It is a genuine product that can deal with the root cause of health issues without trouble. **3) What FlowForce Max Review Says?** As per customer reviews, FlowForce Max is a positive and best product to address some specific health problems in males. However, some of them get faster results while some of them get delayed. It is because of the type and length of the problems from which they are surviving. [Order FlowForce Max the now and get a limited time discount before it's too late!](https://www.glitco.com/us-flowforce-max) **TAGS:** #[FlowForce\_Max](https://flowforce-max-review.blogspot.com/2023/10/flowforce-max-reviews-what-are.html)\_US, #[FlowForce\_Max](https://colab.research.google.com/drive/1d5Y3ao9UajRb0Rw0Wz-ZPGkXC2AHCiLw#scrollTo=zLDxSeUBLYVb)\_Reviews, #[FlowForce\_Max](https://sites.google.com/view/flow-forcemax-offers/home)\_Benefits, #[FlowForce\_Max](https://lookerstudio.google.com/reporting/bffd6123-400d-49c8-bf97-6a38a0a84448/page/0LPgD)\_Buy, #[FlowForce\_Max](https://groups.google.com/g/flowforce-max-about/c/xlmAHrfTyfc)\_Cost, #[FlowForce\_Max](https://www.dibiz.com/buyflowforcemax)\_Ingredients, #[FlowForce\_Max](https://loop.frontiersin.org/people/2557229/bio)\_Order, #FlowForce\_Max\_Price, #FlowForce\_Max\_Website, #FlowForce\_Max\_Results, #FlowForce\_Max\_SideEffects, #FlowForce\_Max\_SharkTank, #FlowForce\_Max\_Offers, #FlowForce\_Max\_Supplement, #FlowForce\_Max\_BuyNow, #FlowForce\_Max\_Formula, #FlowForce\_Max\_HowToUse

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neelima-v/tax_faq_india

2023-10-25T07:48:52.000Z

[ "region:us" ]

neelima-v

null

0

2023-10-25T07:47:02

Entry not found

15

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birdhouse5/finetuning4

2023-10-25T07:53:15.000Z

[ "region:us" ]

birdhouse5

null

0

2023-10-25T07:53:12

--- dataset_info: features: - name: image dtype: image - name: text (string) dtype: string splits: - name: train num_bytes: 1251887.0 num_examples: 8 download_size: 1219237 dataset_size: 1251887.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "finetuning4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

482

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flowforcemaxreviews/flowforcemaxreviewshealthsupplement

2023-10-25T07:58:11.000Z

[ "region:us" ]

flowforcemaxreviews

null

0

2023-10-25T07:57:43

#### **Product Name** \- [**FlowForce Max Reviews**](https://www.glitco.com/us-flowforce-max) #### **Nature** – Male support supplement #### **Form** – Capsules #### **Refund Policy –** 60-day money-back guarantee #### **Price –** Begin with $69/bottle #### **How to Purchase –** [**Official Website**](https://www.glitco.com/us-flowforce-max) [![](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgeb994RSOuEmLChhw0NP_0m-OKdoU33jrfiUWUBITMn1KSYGriIAAV_IHE8XnhURtoiCE2wBDCLoN-L5hL4azhY55_a32St8Mtrp_ulw_pt-rbYgZgFSKvN9DoZGLCxP6eLJXkuBqKYLbpXQKex_alngM7zmvwrE-uszCXjiB43JPvz9z4Qog4eMstCrU/w640-h540/powerbite.png)](https://www.glitco.com/us-flowforce-max) In today's data-driven world, organizations are constantly seeking ways to streamline their data management processes, ensuring data flows smoothly, securely, and efficiently. This is where **[FlowForce Max](https://www.facebook.com/people/FlowForce-Max/61552707540955/)** comes into play. FlowForce Max is a powerful and versatile data automation tool that empowers businesses to harness the full potential of their data, facilitating seamless data integration, transformation, and processing. In this article, we will delve into the features and benefits of FlowForce Max and how it can revolutionize your data management. ### **What is [FlowForce Max](https://www.facebook.com/people/FlowForce-Max/61552707540955/)?** FlowForce Max is an advanced data automation platform designed to address the intricate challenges of modern data management. Developed by Altova, a renowned software company specializing in data integration and automation, FlowForce Max offers a robust set of features that cater to the needs of both small businesses and large enterprises. ### **[Click Here Now To Get The FlowForce Max Directly From The Official Website](https://www.glitco.com/us-flowforce-max)** ### How does [FlowForce Max](https://www.glitco.com/us-flowforce-max) work? Supporting a normal prostate size, reducing BPH symptoms, and increasing urine flow are just some of the potential benefits of FlowForce Max, a prostate health supplement made from a proprietary blend of natural ingredients. The prostate gland enlarges in benign prostatic hyperplasia (BPH), a common disorder that usually occurs with age. Those over the age of 50 make up more than half of the population impacted. Symptoms of BPH include, but are not limited to, problems urinating, fatigue, urgency to urinate, inability to completely empty the bladder, and erectile dysfunction. Supplements like Flow Force Max are used by many men to alleviate these issues. It's important to know that FlowForce Max is made in a GMP-certified facility that's FDA-approved right here in the USA. ### What are the Natural Components of [FlowForce Max](https://www.glitco.com/us-flowforce-max)? [![](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg1nOmOmOZrrFWXgC3PhNb9vH50Po8IiRN1yB3bT2OGLB2a5vNm5Eferw4zNeyrch9wP7UcS_HmXJbMP2Gf8jdCWxb2rCyV4jnETXGLDMrqfDLzBtBa6zvP32PKia_3zg5k2BTeBCdzYrojbReBgkMcvGUjTKvWWDHJ8Wu4sMyVbSED6Z0JW70ES1agKyU/w640-h430/Screenshot%20(904).png)](https://www.glitco.com/us-flowforce-max) * **Monolaurin** * This component is extracted directly from coconut oil that possesses antibacterial, antiviral, and antifungal properties. In general, it assists in managing healthy immunity which is so important for overall well-being and health. * **Grape Seed Extract** * Well, this is an important component enriched with antioxidants known as OPCs (oligomeric proanthocyanidin complexes). These incredible antioxidants help to prevent blood vessels and encourage healthy circulation, important for managing firm energy. * **Oregano Leaf Extract** * It is best known for antimicrobial properties that support a healthy immunity and it is linked to boosted blood flow. Hence, it may have positive impacts on different activities without any trouble. * **ViNitrox** * It is a potent blend of grape and apple polyphenols that can improve the production of nitric oxide and encourage healthy blood circulation. In reality, it is crucial for maintaining and obtaining proper energy with longer outcomes. * **Saw Palmetto Fruit Extract Powder** * It is a very well-known herbal extract utilized to support prostate health. Also, it assists in managing healthy T-levels and can lead to increased function of the body. * **Tricalcium Phosphate** * In general, this component helps to ensure the complete absorption of nutrients in the entire body. Also, it supports the overall health of bones and contributes to the incredibility of other components in the formula. * **Sucralose** * Usually, it is a 0-calorie sweetener that offers a pleasant and amazing taste to the product. This way, it makes it easy to consume this product without any problem. * **Peppermint Leaf Extract Powder** * It includes menthol that can help to boost blood flow. Also, it has a soothing effect on the body that helps in reducing stress and proper relaxation. ### **[Click Here Now To Get The FlowForce Max Directly From The Official Website ](https://www.glitco.com/us-flowforce-max)** ### Can I Buy [FlowForce Max](https://www.glitco.com/us-flowforce-max) Online, and If So, Where Should I Go? FlowForce Max is only available for purchase on its **[official website](https://www.glitco.com/us-flowforce-max)** ([https://flowforcemax.com/)](https://www.glitco.com/us-flowforce-max), where you'll find a variety of purchase options, as well as additional eBooks that are sure to enrich your FlowForce Max experience. One tablet of FlowForce Max can be used once daily, in the morning or at night, to promote prostate health. Each container includes 30 servings. The pricing and freebies are broken down in greater detail below. [![](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi2TM13r64NVGpVpWMNkj7QYyZ0MxCX6wJGVK7fxklJMS4uWdmjmvG8WD3c4EtlP-wkJ96Rjd4ikgCFQ2HIzBTqPasEueGhCpuAZoSemToN3w3l9rS-g2G0tSV2ma-MyKT3qyunDo0VTmaUxrBhAtpCox4X3c6yduiC6mhk2bU9TpwtgfWxKXkRUuOMJJw/w640-h378/Screenshot%20(902).png)](https://www.glitco.com/us-flowforce-max) A one-month supply of FlowForce Max can be purchased for **$69** (plus shipping) for a single bottle. 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birdhouse5/finetuning6

2023-10-25T10:17:27.000Z

[ "region:us" ]

birdhouse5

null

0

2023-10-25T08:08:47

Entry not found

15

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OFA-Sys/OccuQuest

2023-10-26T03:18:10.000Z

[ "size_categories:100K<n<1M", "language:en", "license:apache-2.0", "sft", "occupational bias", "arxiv:2310.16517", "region:us" ]

OFA-Sys

null

0

2023-10-25T08:29:51

--- license: apache-2.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: dialog path: data/dialog-* - split: validation path: data/validation-* - split: test path: data/test-* - split: estate path: data/estate-* - split: quora path: data/quora-* dataset_info: features: - name: category dtype: string - name: occupation dtype: string - name: topic dtype: string - name: messages list: - name: role dtype: string - name: content dtype: string splits: - name: train num_bytes: 330314955 num_examples: 114090 - name: dialog num_bytes: 102158907 num_examples: 31682 - name: validation num_bytes: 7314741 num_examples: 2500 - name: test num_bytes: 718046 num_examples: 250 - name: estate num_bytes: 703613 num_examples: 250 - name: quora num_bytes: 45540 num_examples: 250 download_size: 184974294 dataset_size: 441255802 language: - en tags: - sft - occupational bias size_categories: - 100K<n<1M --- This is the dataset in [OccuQuest: Mitigating Occupational Bias for Inclusive Large Language Models](https://arxiv.org/abs/2310.16517) Abstract: The emergence of large language models (LLMs) has revolutionized natural language processing tasks. However, existing instruction-tuning datasets suffer from occupational bias: the majority of data relates to only a few occupations, which hampers the instruction-tuned LLMs to generate helpful responses to professional queries from practitioners in specific fields. To mitigate this issue and promote occupation-inclusive LLMs, we create an instruction-tuning dataset named OccuQuest, which contains 110,000+ prompt-completion pairs and 30,000+ dialogues covering over 1,000 occupations in 26 occupational categories. We systematically request ChatGPT, organizing queries hierarchically based on Occupation, Responsibility, Topic, and Question, to ensure a comprehensive coverage of occupational specialty inquiries. By comparing with three commonly used datasets (Dolly, ShareGPT, and WizardLM), we observe that OccuQuest exhibits a more balanced distribution across occupations. Furthermore, we assemble three test sets for comprehensive evaluation, an occu-test set covering 25 occupational categories, an estate set focusing on real estate, and an occu-quora set containing real-world questions from Quora. We then fine-tune LLaMA on OccuQuest to obtain OccuLLaMA, which significantly outperforms state-of-the-art LLaMA variants (Vicuna, Tulu, and WizardLM) on professional questions in GPT-4 and human evaluations. Notably, on the occu-quora set, OccuLLaMA reaches a high win rate of 86.4\% against WizardLM. Furthermore, we demonstrate the potential of combining OccuQuest with other instruction-tuning datasets to enhance the overall performance of LLMs. By fine-tuning LLaMA on a mixture of OccuQuest and Tulu datasets, we introduce ProLLaMA, which excels in addressing occupational questions and exhibits superior performance in comprehensive evaluations such as MMLU, GSM8K, BBH, and HumanEval. Among the different LLaMA variants, the 7B and 13B ProLLaMA models achieve the highest performance on MMLU and GSM8K, with the 7B ProLLaMA model demonstrating an improvement of more than 4 points over the other 7B variants on GSM8K. We open release the dataset and models. Please cite if you use this dataset: ``` @misc{xue2023occuquest, title={OccuQuest: Mitigating Occupational Bias for Inclusive Large Language Models}, author={Mingfeng Xue and Dayiheng Liu and Kexin Yang and Guanting Dong and Wenqiang Lei and Zheng Yuan and Chang Zhou and Jingren Zhou}, year={2023}, eprint={2310.16517}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

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Back-up/summary-text

2023-10-25T08:52:32.000Z

[ "region:us" ]

Back-up

null

0

2023-10-25T08:41:10

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: content dtype: string - name: summary dtype: string splits: - name: train num_bytes: 181566694 num_examples: 65361 - name: test num_bytes: 2753008 num_examples: 1000 download_size: 99917401 dataset_size: 184319702 --- # Dataset Card for "summary-text" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

584

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dschaudt42/synthetic_pneumonia

2023-10-25T13:22:12.000Z

[ "region:us" ]

dschaudt42

null

0

2023-10-25T08:46:23

This dataset contains 70,000 synthetic images from the paper "A critical assessment of generative models for synthetic data augmentation on limited pneumonia X-ray data". It consists of synthetically generated X-ray images of bacterial, viral, fungal, and COVID-19 pneumonia, as well as healthy patients with no findings. CAUTION: the images are not real and do not depict real patients. Do not use for medical purposes!

420

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hwaseem04/Documentation-files

2023-10-25T08:58:11.000Z

[ "region:us" ]

hwaseem04

null

0

2023-10-25T08:58:11

Entry not found

15

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nlplabtdtu/summary-text

2023-10-25T08:58:59.000Z

[ "region:us" ]

nlplabtdtu

null

0

2023-10-25T08:58:49

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: content dtype: string - name: summary dtype: string splits: - name: train num_bytes: 181566694 num_examples: 65361 - name: test num_bytes: 2753008 num_examples: 1000 download_size: 99917401 dataset_size: 184319702 --- # Dataset Card for "summary-text" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

584

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joujiboi/japanese-knowledge-base

2023-10-25T09:16:47.000Z

[ "task_categories:question-answering", "task_categories:text-generation", "size_categories:1K<n<10K", "language:en", "license:cc-by-nc-4.0", "language", "japanese language", "nihongo", "日本語", "region:us" ]

joujiboi

null

0

2023-10-25T08:59:41

--- license: cc-by-nc-4.0 task_categories: - question-answering - text-generation language: - en tags: - language - japanese language - nihongo - 日本語 size_categories: - 1K<n<10K pretty_name: Japanese Knowledge Base --- # Japanese Knowledge Base JKB (Japanese Knowledge Base) is a work-in-progress question and answer dataset on the Japanese language. The goal of JKB is to provide training data for large language models to better answer questions about the Japanese language, particularly for learners of Japanese. ## The dataset contains topics about: * N5 to N1 grammar * Structure and word order * The writing systems * Word types * Particles and the commonly confused ones * Conjugation * Verbs and verb types * Adjectives and adjective types * Formality * Name suffixes ## The dataset will not focus on: * Translation * Definitions of words * Sentence nuance ## Sample ``` Question: What is a common usage scenario for 〜たり〜たり? Answer: The 〜たり〜たり structure is for listing actions as examples among various possibilities. It is commonly used to describe routines or plans. For example, one might use it to describe what they typically do on their days off or what they plan to do during a vacation. Question: How do you use 思う with na-adjectives? Answer: For na-adjectives, you can attach だ to the adjective in its plain form and then add と思う. For example, if you think someone is happy, you could say "幸せだと思います". Question: How do you use と思う with i-adjectives? Answer: To use 〜と思う with i-adjectives, you simply take the i-adjective in its plain form and attach と思う to it. For example, if you think something is hot (熱い), you can say "熱いと思う". Question: Is it possible to use 〜たい in a question? Answer: Yes, you can use 〜たい to ask about someone's desires or wishes, but be cautious when using it with superiors or elders as it can be considered impolite. ``` ## Citation This dataset uses the `Creative Commons Attribution Non Commercial 4.0` licence which means you may use my dataset for non-commercial purposes and you must give credit. ``` @misc{Japanese Knowledge Base, title = {Japanese Knowledge Base: A question and answer dataset on the Japanese language}, author = {JawGBoi}, year = {2023}, publisher = {HuggingFace}, journal = {HuggingFace repository}, howpublished = {\url{https://huggingface.co/datasets/joujiboi/japanese-knowledge-base}}, } ```

2,384

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MedHK23/OmniFM-Dr

2023-11-01T07:11:24.000Z

[ "arxiv:1901.07441", "arxiv:2104.10326", "arxiv:1705.02315", "arxiv:2307.03293", "region:us" ]

MedHK23

null

1

0

2023-10-25T09:31:14

--- configs: - config_name: default data_files: - split: demo path: "demo.tsv" - split: classification path: "*classification*" - split: localization path: "*localization*" - split: segmentation path: "*segmentation*" - split: report_generation path: "*report*" --- # Dataset Descriptiona Our OmniFM-Dr framework introduces a multi-task chest x-ray dataset, which is used for the joint training of disease classification, localization, segmentation, and report generation. This dataset comprises various publicly available datasets, such as MIMIC-CXR, VinDr-CXR, and ChestX-Det10. For each image, it can potentially contribute to multiple tasks, such as report generation and classification. ## Dataset Details - [**MIMIC:**](https://physionet.org/content/mimic-cxr/2.0.0/) contains more than 377,110 radiograph images from over 227,835 radiographic studies. Each radiograph is paired with lesion classification and associated radiology report. It is used for multi-label classification and report generation tasks. - [**Padchest:**](https://arxiv.org/abs/1901.07441) includes 160,840 images obtained from 67,000 patients, covering six different position views. Different radiographic findings were labeled and used for the classification task in this study. - [**CXR-AL14:**]() is a large-scale dataset for chest X-ray image detection. It has more than 140,000 chest X-ray radiographs containing 253,844 bounding boxes in 14 chest abnormal object categories. - [**VinDr-CXR:**](https://www.nature.com/articles/s41597-022-01498-w) includes chest radiographs with annotations for the classification of 28 common chest diseases. The dataset contains 15,000 CXR scans in the training set. We select eight diseases from the dataset along with their corresponding bounding box for the disease localization task. - [**ChestX-Det:**](https://arxiv.org/abs/2104.10326) consists of 3,578 images from [NIH ChestXray14](https://arxiv.org/abs/1705.02315) for 13 common disease. We select seven diseases from the dataset along with bounding box for the disease localization task. - [**CheXmask:**](https://arxiv.org/abs/2307.03293) contains 676,803 lung and heart segmentation masks of chest images from six publicly available databases: CANDID-PTX, ChestXray14, Chexpert, MIMIC-CXR, Padchest, and VinDr-CXR. We include 224,316 data for training and 10,000 data from ChestXray14 for downstream evaluation. - [**SIIM:**](https://www.kaggle.com/c/siim-acr-pneumothorax-segmentation) comes from the SIIM-ACR Pneumothorax Segmentation competition and contains 12,090 images, among which approximately 3,000 cases are positive for pneumothorax disease with masks. ## Dataset Structure - **MIMIC:** - MIMIC_classification_report-generation_xxx.tsv: is used for classification and report generation tasks. For each row, it contains id, report, "label1 && label2", subject_id, study_id, dicom_id. - MIMIC_classification-location_xxx.tsv: is used for location vqa task. For each row, it contains id, "label1,severity && label2, severity", subject_id, study_id, dicom_id. - MIMIC_classification-severity_xxx.tsv: is used for severity vqa task. For each row, it contains id, "label, location1 & location2", subject_id, study_id, dicom_id. - **Padchest:** - Padchest_classification_xxx.tsv: is used for classification task. For each row, it contains id, "label1 && label2", subject_id, study_id, dicom_id. - **CXR-AL14:** - CXR_AL14_localization_xxx.tsv: is used for locatization and classification tasks. For each row, it contains id, label, "x1,y1,x2,y2", image_id. - **VinDr-CXR:** - VinDr_CXR_localization_xxx.tsv: is used for locatization and classification tasks. For each row, it contains id, label, "x1,y1,x2,y2", image_id. - **ChestX-Det:** - ChestX_Det_localization_xxx.tsv: is used for locatization and classification tasks. For each row, it contains id, label, "x1,y1,x2,y2", image_id. - **CheXmask:** - CheXmask_segmentation_xxx.tsv: is used for segmentation task. For each row, it contains id, label, "x1,y1,x2,y2, ..., x30, y30", subject_id, study_id, dicom_id. - **SIIM:** - SIIM_segmentation_xxx.tsv: is used for segmentation task. For each row, it contains id, label, "x1,y1,x2,y2, ..., x30, y30", subject_id, study_id, dicom_id. ## Dataset Use - Please run data_prepare.py, which constructs a training batch for all tasks. Each row should contain the following: id, instruction, label, image_id, and task_type.

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anon345/ERBD

2023-10-25T09:41:06.000Z

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

anon345

null

0

2023-10-25T09:35:57

--- license: other license_name: under-review license_link: LICENSE --- # European Residential Building Dataset *This dataset is not released and only serves as a temporary placeholder for academic reviewers.* The European Residential Building dataset consists of 209,318 images of detached residential buildings sampled at random across the Netherlands and Denmark. We refer to our paper for more information on the dataset.

430

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ClaireOzzz/vases

2023-10-25T09:38:18.000Z

[ "region:us" ]

ClaireOzzz

null

0

2023-10-25T09:37:37

Entry not found

15

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shneo/test

2023-10-25T09:43:53.000Z

[ "region:us" ]

shneo

null

0

2023-10-25T09:42:26

Entry not found

15

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ostapeno/qa-openai_batched_icl5_clen512_maxD-1_maxC2500_0

2023-10-25T10:03:15.000Z

[ "region:us" ]

ostapeno

null

0

2023-10-25T10:03:01

## model_setting: openai_batched ## max_context_length: 512 ## max_tokens_instruction: 2048 ## max_tokens_response: 1024 ## top_p: 0.9 ## num_iterations: 1 ## temperature: 0.7 ## max_documents_per_subject: -1 ## max_contexts_per_subject: 2500 ## icl_examples: 5 ## icl_dataset: lukaemon/mmlu ## icl_split: validation ## icl_use_options: True

342

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andstor/code_search_net_files

2023-10-29T15:47:07.000Z

[ "annotations_creators:no-annotation", "language_creators:machine-generated", "multilinguality:multilingual", "size_categories:100K<n<1M", "size_categories:10K<n<100K", "size_categories:1M<n<10M", "source_datasets:original", "language:code", "license:other", "region:us" ]

andstor

CodeSearchNet corpus contains about 6 million functions from open-source code spanning six programming languages (Go, Java, JavaScript, PHP, Python, and Ruby). The CodeSearchNet Corpus also contains automatically generated query-like natural language for 2 million functions, obtained from mechanically scraping and preprocessing associated function documentation.

@article{husain2019codesearchnet, title={{CodeSearchNet} challenge: Evaluating the state of semantic code search}, author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc}, journal={arXiv preprint arXiv:1909.09436}, year={2019} }

0

2023-10-25T10:09:45

--- annotations_creators: - no-annotation language_creators: - machine-generated language: - code license: - other multilinguality: - multilingual size_categories: - 100K<n<1M - 10K<n<100K - 1M<n<10M source_datasets: - original dataset_info: - config_name: all features: - name: repository_name dtype: string - name: file_path dtype: string - name: language dtype: string - name: url dtype: string - name: contents dtype: string splits: - name: train num_bytes: 4597566968 num_examples: 481456 - name: test num_bytes: 244185049 num_examples: 27032 - name: validation num_bytes: 208172780 num_examples: 23262 download_size: 5328510079 dataset_size: 5049924797 - config_name: java features: - name: repository_name dtype: string - name: file_path dtype: string - name: language dtype: string - name: url dtype: string - name: contents dtype: string splits: - name: train num_bytes: 1508021640 num_examples: 124201 - name: test num_bytes: 83498294 num_examples: 7672 - name: validation num_bytes: 49495187 num_examples: 4622 download_size: 1719645289 dataset_size: 1641015121 - config_name: go features: - name: repository_name dtype: string - name: file_path dtype: string - name: language dtype: string - name: url dtype: string - name: contents dtype: string splits: - name: train num_bytes: 445935389 num_examples: 52467 - name: test num_bytes: 22662672 num_examples: 2661 - name: validation num_bytes: 14949171 num_examples: 1991 download_size: 525469198 dataset_size: 483547232 - config_name: python features: - name: repository_name dtype: string - name: file_path dtype: string - name: language dtype: string - name: url dtype: string - name: contents dtype: string splits: - name: train num_bytes: 966000949 num_examples: 83739 - name: test num_bytes: 53223810 num_examples: 4989 - name: validation num_bytes: 51072608 num_examples: 4589 download_size: 1117084868 dataset_size: 1070297367 - config_name: javascript features: - name: repository_name dtype: string - name: file_path dtype: string - name: language dtype: string - name: url dtype: string - name: contents dtype: string splits: - name: train num_bytes: 533231855 num_examples: 40913 - name: test num_bytes: 28571674 num_examples: 2176 - name: validation num_bytes: 36688764 num_examples: 2586 download_size: 633172343 dataset_size: 598492293 - config_name: ruby features: - name: repository_name dtype: string - name: file_path dtype: string - name: language dtype: string - name: url dtype: string - name: contents dtype: string splits: - name: train num_bytes: 132300659 num_examples: 16932 - name: test num_bytes: 5125555 num_examples: 822 - name: validation num_bytes: 4189342 num_examples: 803 download_size: 147603200 dataset_size: 141615556 - config_name: php features: - name: repository_name dtype: string - name: file_path dtype: string - name: language dtype: string - name: url dtype: string - name: contents dtype: string splits: - name: train num_bytes: 1012076476 num_examples: 163204 - name: test num_bytes: 51103044 num_examples: 8712 - name: validation num_bytes: 51777708 num_examples: 8671 download_size: 1185535181 dataset_size: 1114957228 --- # Dataset Card for CodeSearchNet Files

3,649

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Omnibus/game-data-1

2023-11-01T12:47:46.000Z

[ "region:us" ]

Omnibus

null

0

2023-10-25T10:30:25

Entry not found

15

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IELTS8/caxton_dataset_crop

2023-10-25T10:32:39.000Z

[ "region:us" ]

IELTS8

null

0

2023-10-25T10:32:39

Entry not found

15

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Qdrant/NOAA-Buoy

2023-10-25T10:53:56.000Z

[ "task_categories:feature-extraction", "task_categories:tabular-classification", "task_categories:time-series-forecasting", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:cc-by-4.0", "region:us" ]

Qdrant

null

0

2023-10-25T10:43:03

--- language: - en license: - cc-by-4.0 multilinguality: - monolingual pretty_name: NOAA Buoy meterological data size_categories: - 100K<n<1M source_datasets: - original tags: [] task_categories: - feature-extraction - tabular-classification - time-series-forecasting --- # NOAA Buoy meterological data NOAA Buoy Data was downloaded, processed, and cleaned for tasks pertaining to tabular data. The data consists of meteorological measurements. There are two datasets 1. From 1980 through 2022 (denoted with "years" in file names) 2. From Jan 2023 through end of Sept 2023 (denoted with "2023" in file names) The original intended use is for anomaly detection in tabular data. ## Dataset Details ### Dataset Description This dataset contains weather buoy data to be used in a tabular embedding scenarios. Buoy 42002 was chosen because it had many years of historical data and was still actively collecting information Here is the buoy's page and its historical data page: - https://www.ndbc.noaa.gov/station_page.php?station=42002 - https://www.ndbc.noaa.gov/station_history.php?station=42002 Only standard meteorological data and ocean data was downloaded. Downloaded started at 1980, which is the first full year of collecting wave information. ### Data Fields {'TSTMP': 'timestamp', '#YY': '#yr', ' MM': 'mo', 'DD': 'dy', 'hh': 'hr', 'mm': 'mn', 'WDIR': 'degT', 'WSPD': 'm/s', ' GST': 'm/s', ' WVHT': 'm', 'DPD': 'sec', 'APD': 'sec', 'MWD ': 'degT', 'PRES': 'hPa', ' ATMP': 'degC', ' WTMP': 'degC' } ## Dataset Creation ### Curation Rationale The original data has inconsistent delimiters, different and inappropriate missing data values, and was not harmonized across years. Pre-2023 was edited in the same way as the previous data but kept separate to allow for train and inference. ### Source Data #### Initial Data Collection and Normalization Data Downloaded on Oct 12 2023 All code used to transform the data can be found in the buoy-python directory. This is NOT production code and the focus was on correct results and minimizing time spent writing cleaning code. 1. #YY, MM, DD, hh, mm were concatenated to create a timestamp and stored in a new column. 2. From 1980 until 2005 there was no recording of minutes. Minutes for those years was set to 00. 3. All missing data was set to a blank value rather than an actual number 4. Remove all rows without wave data from all the data sets ( missing value in WVHT and DPD) 5. Columns MWD, DEWP, VIS, and TIDE were removed because of consistent missing values 6. From 2005 -> 2006 Wind direction goes from being called WD to WDIR 7. From 2006 -> 2007 Header goes from just 1 line with variable names to 2 lines with the second line being units. These steps were used to create full_2023_remove_flawed_rows, the 2023 months, and full_years_remove_flawed_rows the previous data going back to 1980 Since the original purpose of this data was anomaly detection. The two data sets above received further processing. 1. All data values were converted to Z-scores (file named zscore_2023) 1. For 1980 - 2022, all rows with 2 or more fields with Z-scores > 2 were removed from the dataset (file named trimmed_zscores_years ) ## Uses ### Direct Use Primary use is working with tabular data and embeddings, particularly for anomaly detection

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Qdrant/google-landmark-geo

2023-10-25T11:12:02.000Z

[ "task_categories:image-classification", "size_categories:< 50K", "source_datasets:Google Landmarks V2", "language:en", "license:cc-by-4.0", "region:us" ]

Qdrant

null

0

2023-10-25T11:10:09

--- language: - en pretty_name: Geo Coordinate Augmented Google-Landmarks task_categories: - image-classification source_datasets: - Google Landmarks V2 size_categories: - < 50K license: cc-by-4.0 --- # Dataset Card for Geo Coordinate Augmented Google-Landmarks Geo coordinates were added as data to a tar file's worth of images from the [Google Landmark V2](https://github.com/cvdfoundation/google-landmark). Not all of the images could be geo-tagged due to lack of coordinates on the image's wikimedia page. ## Dataset Details ### Dataset Description Geo coordinates were added as data to a tar file's worth of images from the [Google Landmark V2](https://github.com/cvdfoundation/google-landmark). There were many more images that could have been downloaded but this dataset was found to be a good balance of data size and sample size. The intended use for the dataset was to demonstrate using a geo-filter in Qdrant along with a image similarity search. Not all of the images couuld be geo-tagged due to lack of coordinates on the image's wikimedia page. We provide the raw geotagged file as a geojson document, train_attribution_geo.json. We also provide a json file that includes the data above along with embedding vectors for the images, id_payload_vector.json. Thingsvision was used as the library for creating the image embeddings with the following ThingVision model: ```python model_name = 'clip' model_parameters = { 'variant': 'ViT-B/32' } ``` The code directory contains the Python code used to geotag the images as well as generated the vectors. It can also be used to upload the embeddings to a Qdrant DB instance. This code is NOT for production and was more focused on quickly and correctly get the coordinates and embed the images. The license for this data and code match the license of the original Google Landmarks V2 Dataset: CC BY 4.0 license. ## Uses ### Direct Use The primary use is case is image similarity search with geographic filtering.

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Fahsai2323/sixtythree

2023-10-25T11:24:17.000Z

[ "region:us" ]

Fahsai2323

null

0

2023-10-25T11:24:17

Entry not found

15

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dwancin/dannet

2023-10-25T11:51:37.000Z

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

dwancin

null

0

2023-10-25T11:36:57

--- language: - da pretty_name: DanNet license: cc-by-sa-4.0 --- <div style="font-size: 12px;background: linear-gradient(180deg, rgba(255,251,235,1) 0%, rgba(255,251,235,0.53687412464986) 100%);color: rgb(146 64 14);border-radius: 6px;margin-bottom: 10px;padding-left: 20px;padding-right: 20px;padding-top: 1px;padding-bottom: 1px;"> <h4 style="font-size: 14px;margin-bottom: 3px;">Disclaimer</h4> This dataset and accompanying README text are not my original work, and I am simply sharing the work created by the Department of Nordic Studies and Linguistics under their chosen copyrights (CC BY-SA 4.0). I have made it available on Hugging Face for accessibility and reference purposes. All credit for the creation of this dataset goes to the Department of Nordic Studies and Linguistics. For detailed information on the dataset's usage, licensing, and any specific terms and conditions set by the Department of Nordic Studies and Linguistics, please refer to their original documentation and the CC BY-SA 4.0 license. I am not the owner of this dataset, and any inquiries or issues related to the dataset should be directed to the Department of Nordic Studies and Linguistics. </div> # DanNet [DanNet](https://wordnet.dk/dannet "The DanNet resource") is a [WordNet](https://wordnet.princeton.edu/) - a lexico-semantic network graph that shows how senses of a language relate to others through named relations. One can also think of a WordNet as a kind of machine-readable dictionary. On [wordnet.dk/dannet](https://wordnet.dk/dannet "DanNet"), you can not only browse but also download the newest versions of multiple data sets. Unlike a normal dictionary, the definitions of words aren't central; instead, relations to other words are the important part. For example, in DanNet, you can see that a [Swiss willow](https://wordnet.dk/dannet/data/synset-1304) is a kind of [bush](https://wordnet.dk/dannet/data/synset-597), that a [gazebo](https://wordnet.dk/dannet/data/synset-4733) is located in a [garden](https://wordnet.dk/dannet/data/synset-1876), that "[fiberdrys](https://wordnet.dk/dannet/data/synset-34989)" is for [eating](https://wordnet.dk/dannet/data/synset-124), and that [cakes](https://wordnet.dk/dannet/data/synset-52) are typically produced by [baking](https://wordnet.dk/dannet/data/synset-145) and usually made from [flour](https://wordnet.dk/dannet/data/synset-131) and [sugar](https://wordnet.dk/dannet/data/synset-128). ![DanNet Relations](https://cst.ku.dk/projekter/dannet/billeder/relationer.png) On [andreord.nors.ku.dk](https://andreord.nors.ku.dk/ "Andre Ord (søgemaskine for det gamle DanNet)"), you can generate the above illustration based on an older version of DanNet. In the future, you will be able to find similar illustrations based on the newest data at [wordnet.dk/dannet](https://wordnet.dk/dannet "The DanNet resource"). All in all, the wordnet currently contains around 70 thousand concepts kept together by approximately 500 thousand relations. ## More about DanNet The wordnet and the methods behind it are best described in: Bolette S. Pedersen, Sanni Nimb, Jørg Asmussen, Nicolai H. Sørensen, Lars Trap-Jensen og Henrik Lorentzen. [DanNet – the challenge of compiling a WordNet for Danish by reusing a monolingual dictionary](https://pdfs.semanticscholar.org/6891/69de00c63d58bd68229cb0b3469a617f5ab3.pdf). _Lang Resources & Evaluation_ (2009) 43:269–299. DanNet has been developed on the basis of Den Danske Ordbog ([the Danish Dictionary](https://ordnet.dk/ddo_en?set_language=en)) as a collaborative work between the University of Copenhagen (Centre for Language Technology) and The Danish Society for Language and Literature. Funding was provided by the Danish Research Council via the project **DanNet – et leksikalsk-semantisk ordnet for dansk**, and the wordnet has been developed further in related projects, namely [**CLARIN-DK**](https://info.clarin.dk/), **[METANORD](http://www.meta-net.eu/projects/meta-nord/)**, [**Semantic Processing across Domains**](/projekter/projekter_slut/semantikprojekt/) and [**ELEXIS**](https://elex.is/). The wordnet has been semi-automatically compiled under varying funding and staffing conditions – as indicated above. Therefore we make reservations concerning errors and omissions. Observed errors and omissions can be reported to [bspedersen@hum.ku.dk](mailto:bspedersen@hum.ku.dk). A subpart of the vocabulary has been linked to English, Swedish, Finnish and Estonian: [WordTies: A Nordic/Baltic Multilingual Wordnet Initiative](http://wordties.nors.ku.dk/). In connection with the [ELEXIS project](https://elex.is) we plan to link the entire wordnet to the corresponding English resource, [Princeton WordNet](https://wordnet.princeton.edu/), in order to be able to use the resource cross-lingually. ## Download the DanNet resource You can download the DanNet resource in two separate formats. By downloading a data set, you accept the [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/ "Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)") license: * [RDF data set](https://wordnet.dk/export/rdf/dn "ZIP file containing DanNet in the TTL format (RDF)") (the primary resource) * A single TTL file that should now be considered the main resource. * [CSV data set](https://wordnet.dk/export/csv/dn "ZIP file containing DanNet as CSV files") (an alternative format) * This ZIP file contains several CSV files as well as CSVW metadata, which collectively can be used to recreate most of the contents of the RDF resource. Several other RDF data sets can also be downloaded at [wordnet.dk/dannet](https://wordnet.dk/dannet "The DanNet resource"). These alternative data sets can be used to extend the primary RDF resource with other useful data. _When referencing the resource, please use this article: Bolette S. Pedersen, Sanni Nimb, Jørg Asmussen, Nicolai H. Sørensen, Lars Trap-Jensen og Henrik Lorentzen. [DanNet – the challenge of compiling a WordNet for Danish by reusing a monolingual dictionary](https://pdfs.semanticscholar.org/6891/69de00c63d58bd68229cb0b3469a617f5ab3.pdf) (pdf). Lang Resources & Evaluation (2009) 43:269–299._ **Get linguistic specifications** * [Lingvistiske specifikationer for DanNet](/projekter/dannet/dannetspecifikationer_v2.pdf "Lingvistiske specifikationer for DanNet") (Danish) * [DanNet Linguistic Specifications](/projekter/dannet/DanNetLinguisticSpecifications.pdf "DanNet Linguistic Specifications ") **Please note** that these specifications are based on an older version of DanNet. However, they should still allow for understanding the contents of the DanNet resource; just be aware that the names of certain relations/attributes may have changed. ## Publications * **Main publication:** Pedersen, Bolette S. Sanni Nimb, Jørg Asmussen, Nicolai H. Sørensen, Lars Trap-Jensen og Henrik Lorentzen (2009). [DanNet – the challenge of compiling a WordNet for Danish by reusing a monolingual dictionary](https://pdfs.semanticscholar.org/6891/69de00c63d58bd68229cb0b3469a617f5ab3.pdf). Lang Resources & Evaluation 43:269–299. * Pedersen, Bolette S.; Aguirrezabal Zabaleta, Manex; Nimb, Sanni; Olsen, Sussi; Rørmann, Ida. (2018) [Towards a principled approach to sense clustering – a case study of wordnet and dictionary senses in Danish.](https://nors.ku.dk/ansatte/?pure=da%2Fpublications%2Ftowards-a-principled-approach-to-sense-clustering--a-case-study-of-wordnet-and-dictionary-senses-in-danish(3baf7065-e73a-4c1e-b51f-8974662232a6).html) Proceedings of Global WordNet Conference 2018. Singapore, 2018. * Pedersen, Bolette Sandford; Nimb, Sanni; Olsen, Sussi; Sørensen, Nicolai Hartvig. (2018). [Combining Dictionaries, Wordnets and other Lexical Resources - Advantages and Challenges.](https://nors.ku.dk/ansatte/?pure=da%2Fpublications%2Fcombining-dictionaries-wordnets-and-other-lexical-resources--advantages-and-challenges(5f277b36-553b-4d05-b5af-afba8a10d96c).html) Globalex Proceedings 2018, Miyasaki, Japan. 2018. * Sanni Nimb, Bolette S. Pedersen, Anna Braasch, Nicolai H. Sørensen & Thomas Troelsgård (2013) [Enriching a wordnet from a thesaurus.](http://www.ep.liu.se/ecp/088/005/ecp1388005.pdf) Workshop Proceedings on Lexical Semantic Resources for NLP from the 19th Nordic Conference on Computational Linguistics (NODALIDA). Linköping Electronic Conference Proceedings; Volume 85 (ISSN 1650-3740). * Sanni Nimb, Bolette Sandford Pedersen (2012). Towards a richer wordnet representation of properties – exploiting semantic and thematic information from thesauri. LREC 2012 Proceedings. Istanbul, Turkey * Bolette S. Pedersen, Sanni Nimb**, Anna Braasch (2010). Merging specialist taxonomies and folk taxonomies in wordnets – a case study of plants, animals and foods in the Danish wordnet. Proceedings from 7th Conference on Language Resources and Evaluation, LREC. Malta * Henrik Lorentzen, Sanni Nimb (2010). Fra ordbog til wordnet. Hvordan udmøntes en ordbogsdefinition i en formaliseret wordnetbeskrivelse?. Harry Lönnroth & Kristina Nikula (red.) (2010): Nordiska studiar i lexikografi 10. Rapport från Konferencen om lexikografi i Norden, Tammerfors 3-5 juni 2009, pp. 329-344. Tammerfors * Sanni Nimb, Lars Trap-Jensen (2009). [Use, Re-use and Synergetic Benefit: The Interplay between WordNet and Dictionary Data](https://www.semanticscholar.org/paper/Use%2C-Re-use-and-Synergetic-Benefit%3A-The-Interplay-Nimb-Trap-Jensen/f436818dd2a8b55b14f30313daa2eef4101ce3c0)«. I: GSCL Forum – Special Issue on Lexical-Semantic and Ontological Resources. German Society for Computational Linguistics. 19. s. * Bolette S. Pedersen, Sanni Nimb (2009). Et net af ord. K. Farø, A. Holsting, N.Larsen, J.E. Mogensen & T.Vinther (eds). Sprogvidenskab i glimt. 70 tekster om sprog i teori og praksis. Syddansk Universitetsforlag, s.112-117. * Pedersen, B.S. & A. Braasch (2009). What do we need to know about humans? A view into the DanNet Database. In: K. Jokinen and E. Bick (eds.) Proceedings of the 17th Nordic Conference of Computational Linguistics NODALIDA 2009. NEALT Proceedings Series, Vol. 4, Odense, Denmark. * Sanni Nimb (2009). [The Semantic Relations of Artifacts in DanNet](https://wordnet.dk/dok/Nimb_workshop2009_final.pdf). In B.S. Pedersen, A.Braasch, S.Nimb, R. Vatvedt Fjeld (eds.): Proceedings of the NODALIDA 2009 workshop on WordNets and Other Lexical Semantic Resources – between Lexical Semantics, Lexicography, Terminology and Formal Ontologies, Odense, Denmark, May 2009. * Lars Trap-Jensen & Bolette S. Pedersen (2008). [OntoQuery – Web-Based Visualization of Ontologies](http://portal.acm.org/citation.cfm?id=1461587.1461634&coll=portal&dl=ACM). In: Proceedings of the IADIS International Conference on WWW/Internet 2008. IADIS Press, 239-242. (ISBN 978-972-8924-61-1). * Sanni Nimb, Henrik Lorentzen & Lars Trap-Jensen (2008). [The Digital Lexicographical Tools Project – New Ways of Presen...](http://portal.acm.org/citation.cfm?id=1461587.1461634&coll=portal&dl=ACM) In: Proceedings of the International Workshop on Lexical Semantics and Digital Libraries at NODALIDA 2007. NEALT Proceedings Series, Vol. 1, 155-166. * Lars Trap-Jensen, Henrik Høegh & Bolette S. Pedersen (2007). [DanNet, an RDF-Encoded Lexico-Semantic Network](http://portal.acm.org/citation.cfm?id=1341877&dl=ACM&coll=portal) Datalogiske skrifter 108. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen * Bolette S. Pedersen & Sanni Nimb (2007). [Rethinking Dictionary Structure](http://portal.acm.org/citation.cfm?id=1341877&dl=ACM&coll=portal) Datalogiske skrifter 110. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Sanni Nimb & Bolette S. Pedersen (2007). [Ontology-Based Interlinking of Danish WordNet and the Sketch Engine](http://www.aclweb.org/anthology/I07-2001) (pdf) I. Proceedings of the Workshop Ontologies and Lexical Resources in Distributed Environments. Association for Computational Linguistics. pp. 1–8. 2007. * Lars Trap-Jensen (2007). [OntoQuery: Eksperimenter i Web-baseret Visualisering af Ontologier](http://www.aclweb.org/anthology/I07-2001). Danske Sprogstudiers Grundlag. Datalogiske skrifter 109. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Bolette S. Pedersen & Sanni Nimb (2006). [The Structure of a Danish WordNet](http://www.aclweb.org/anthology/I06-2103) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 106. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Lars Trap-Jensen, Bolette S. Pedersen & Henrik Lorentzen (2006). [Creating and Querying RDF in The Semantic Web](http://www.aclweb.org/anthology/I06-2103) (pdf) Danske Sprogstudiers Grundlag. Datalogiske skrifter 105. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Bolette S. Pedersen & Sanni Nimb (2006). [Adding Semantics to the Danish Dictionary](http://www.aclweb.org/anthology/I06-1104) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 104. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Henrik Lorentzen, Bolette S. Pedersen & Lars Trap-Jensen (2005). [Creation and Maintenance of RDF Networks](http://www.aclweb.org/anthology/W05-1615) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 103. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Bolette S. Pedersen, Sanni Nimb, Lars Trap-Jensen, Henrik Lorentzen (2005). [Merging Dictionaries and Wordnets](http://www.aclweb.org/anthology/W05-1615) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 102. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Sanni Nimb & Bolette S. Pedersen (2005). [Linking a Danish WordNet to the Internet - Experiments with the Sketch Engine](http://www.aclweb.org/anthology/W05-1616) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 101. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Bolette S. Pedersen & Sanni Nimb (2005). [A Lexical Database for Danish](http://www.aclweb.org/anthology/W05-1616) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 100. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Lars Trap-Jensen, Henrik Lorentzen & Bolette S. Pedersen (2004). [Reasoning with the Semantic Web - An Introduction](http://www.aclweb.org/anthology/W04-2401) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 99. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Bolette S. Pedersen, Sanni Nimb & Lars Trap-Jensen (2004). [Sketching Sketch Engine](http://www.aclweb.org/anthology/W04-2401) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 98. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Henrik Lorentzen & Lars Trap-Jensen (2004). [Modeling and Querying RDF Networks](http://www.aclweb.org/anthology/W04-2400) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 97. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Henrik Lorentzen, Bolette S. Pedersen & Lars Trap-Jensen (2004). [Creating RDF Networks](http://www.aclweb.org/anthology/W04-2400) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 96. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Sanni Nimb, Bolette S. Pedersen & Lars Trap-Jensen (2004). [DanNet – A Wordnet for Danish](http://www.aclweb.org/anthology/W04-2413) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 95. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Lars Trap-Jensen, Henrik Lorentzen & Bolette S. Pedersen (2004). [Reasoning with the Semantic Web](http://www.aclweb.org/anthology/W04-2413) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 94. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Sanni Nimb, Bolette S. Pedersen & Lars Trap-Jensen (2004). [Experiments with a Danish WordNet](http://www.aclweb.org/anthology/W04-2412) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 93. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Henrik Lorentzen, Bolette S. Pedersen & Lars Trap-Jensen (2004). [Creating RDF Networks - A Schema Creation Methodology](http://www.aclweb.org/anthology/W04-2412) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 92. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Bolette S. Pedersen, Sanni Nimb, Henrik Lorentzen & Lars Trap-Jensen (2004). [Merging Dictionaries and Wordnets](http://www.aclweb.org/anthology/W04-2411) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 91. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Henrik Lorentzen & Lars Trap-Jensen (2004). [Semantic Web-based Querying of Lexical Knowledge](http://www.aclweb.org/anthology/W04-2411) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 90. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Bolette S. Pedersen & Sanni Nimb (2004). [Experiments with a Danish WordNet](http://www.aclweb.org/anthology/W04-2403) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 89. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Sanni Nimb, Bolette S. Pedersen & Lars Trap-Jensen (2004). [Linking a Danish WordNet to the Internet](http://www.aclweb.org/anthology/W04-2403) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 88. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Henrik Lorentzen & Lars Trap-Jensen (2004). [Semantic Web-based Querying of Lexical Knowledge](http://www.aclweb.org/anthology/W04-2402) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 87. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Bolette S. Pedersen & Sanni Nimb (2004). [A Lexical Database for Danish](http://www.aclweb.org/anthology/W04-2402) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 86. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Henrik Lorentzen, Bolette S. Pedersen & Lars Trap-Jensen (2004). [Creation and Maintenance of RDF Networks](http://www.aclweb.org/anthology/W04-2401) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 85. Danmarks Tekniske Universitet. (ISSN 0105-8517) Copenhagen. * Bolette S. Pedersen, Sanni Nimb, Henrik Lorentzen & Lars Trap-Jensen (2004). [Merging Dictionaries and Wordnets](http://www.aclweb.org/anthology/W04-2401) (pdf). Danske Sprogstudiers Grundlag. Datalogiske skrifter 84.

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quocanh34/detect_model_v2_dataset

2023-10-25T12:04:27.000Z

[ "region:us" ]

quocanh34

null

0

2023-10-25T12:04:26

--- dataset_info: features: - name: input_col dtype: string - name: output_col dtype: string splits: - name: train num_bytes: 55281 num_examples: 100 download_size: 11825 dataset_size: 55281 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "detect_model_v2_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

488

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pranjal01/Text-summarizer-dataset

2023-10-25T12:07:06.000Z

[ "task_categories:text-generation", "language:en", "license:apache-2.0", "region:us" ]

pranjal01

null

0

2023-10-25T12:05:29

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: News dtype: string - name: Summary dtype: string - name: Title dtype: string splits: - name: train num_bytes: 1130675.7886497064 num_examples: 408 - name: test num_bytes: 285440.21135029354 num_examples: 103 download_size: 887190 dataset_size: 1416116 license: apache-2.0 task_categories: - text-generation language: - en --- # Dataset Card for "Text-summarizer-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

709

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orderflowforcemax/FlowForceMax

2023-10-25T12:13:17.000Z

[ "region:us" ]

orderflowforcemax

null

0

2023-10-25T12:12:18

**[FlowForce Max](https://www.linkedin.com/pulse/flowforce-max-alert-any-side-effects-how-does-work-michae-ldambert-i7kcf)** – [Official Website Link – Click Here](https://www.glitco.com/us-flowforce-max) ►❱❱ Product Name - **[FlowForce Max](https://in.pinterest.com/pin/1018024690759796681/)** ►❱❱ Side Effects - No Major Side Effects ►❱❱ Category - Health ►❱❱ Results - In 1-2 Months ►❱❱ Availability – Online ►❱❱ Rating: - 5.0/5.0 ⭐⭐⭐⭐⭐ ►❱❱ Where to Get Bottle Online - [flowforcemax com](https://www.glitco.com/us-flowforce-max) **[Shipping On All Over World Secured Checkout](https://www.glitco.com/us-flowforce-max)** **[Shipping On All Over World Secured Checkout](https://www.glitco.com/us-flowforce-max)** **[Shipping On All Over World Secured Checkout](https://www.glitco.com/us-flowforce-max)** A brief introduction about [FlowForce Max](https://www.crunchbase.com/organization/flowforce-max) ------------------------------------------------------------------------------------------------- **[FlowForce Max](https://colab.research.google.com/drive/1XauFtBXO2U0BXVYctuRTVuho5-6huCig#scrollTo=R20totKqOhJB)** is a dietary supplement focused on promoting prostate health, incorporating a blend of natural elements aimed at maintaining a healthy prostate size, alleviating symptoms associated with benign prostatic hyperplasia (BPH), and improves urinary flow, and other potential advantages. Benign prostatic hyperplasia (BPH) is a prevalent condition characterized by the enlargement of the prostate gland, typically occurring as a natural consequence of the aging process. It affects more than half of the population aged 50 and older. Typical symptoms of BPH encompass urinary difficulties, reduced energy levels, frequent nighttime urination, incomplete bladder emptying, and sexual performance issues, among other challenges. Numerous men consider dietary supplements like FlowForce Max to assist in managing these symptoms. It's essential to note that FlowForce Max is produced in the United States within an FDA-registered facility, which has earned certification for Good Manufacturing Practice (GMP). [](https://www.glitco.com/us-flowforce-max)[![](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgD2taPTN6RIwxDYTSRGyoElRqznxPrIQ3Cy0UF0PWD7rd5uFVKfMP11zRibrLognAIbEx9u-l-C0QTX83RJgJabmRoiEv_EruNCI5C3QTUif9C00bvqWQrYY4gy2my5bOsa33tPbz93usSAymqjAosDS2_C27y9fLbPdybQW-15wirG7AuAB64rFHCLJMN/w640-h250/Screenshot%20(1454).png)](https://www.glitco.com/us-flowforce-max) ### How does [FlowForce Max](https://sway.office.com/HM1XwAPTUNFDnmND) work? A multitude of supplements claim to aid prostate and urinary health, with varying degrees of effectiveness. The creators of FlowForce Max explain their supplement's impact as follows: As per the manufacturer, fungal infections are identified as the underlying cause of urinary tract and prostate health issues. Ordinarily, benign prostatic hyperplasia (BPH), characterized by an enlarged prostate, can result from various factors, including natural aging and hormonal fluctuations. Nevertheless, FlowForce Max's developers attribute these concerns to fungal-related issues. Eliminating fungal presence from your body may, as they assert, benefit both prostate and urinary health. This can enhance blood circulation, facilitate achieving erections, and ensure the normal operation of your urinary tract and prostate. To activate the components in FlowForce Max, simply chew one tablet daily. That's all there is to it. These ingredients comprise natural antioxidants, such as polyphenols, which could potentially bolster immune function and overall immune system well-being. This aids your body in defending itself against fungal infections while concurrently promoting general prostate health. [**(AMAZING DISCOUNT) Get FlowForce Max For The Lowest Price Here**](https://www.glitco.com/us-flowforce-max) ### [FlowForce Max](https://flowforce-max-buy.webflow.io/) Ingredient Profile The [FlowForce Max](https://www.youtube.com/watch?v=yoWDMfvthhM) formula comprises an extensive array of natural ingredients, incorporating several plant-based elements celebrated in traditional medicine. Key components include: * **Luteolin**: Derived from various plant sources, luteolin is a flavonoid known for its crucial neuroprotective properties. It also possesses immunomodulatory and antioxidant attributes, collectively promoting overall health. * **Graminex Flower Pollen Extract**: Renowned for its efficacy in promoting robust prostate health, this ingredient has a rich history spanning over three decades in Asia and Europe. It has been primarily employed to address prostate health issues and bolster overall well-being. Scientific investigations have affirmed the capacity of Graminex pollen extract to effectively sustain prostate and urinary function. Consequently, this component aids in rectifying urinary tract irregularities and diminishing prostate inflammation. * **Fisetin**: Present in numerous fruits and vegetables, fisetin plays a role in shielding cells from damage and combating oxidative stress. This flavonoid is associated with anti-inflammatory, anti-cancer, and cardiovascular advantages, thus contributing to multifaceted health improvements. * **Grape Seed Extract:** Abundant in antioxidants, grape seed extract has exhibited the capacity to combat oxidative stress, inflammation, and tissue deterioration. Research findings also indicate its potential in blood pressure regulation, fortifying bone density, and elevating collagen production. * **Oregano Leaf Extract:** Oregano extract harbors compounds that bestow a multitude of health advantages, including cough suppression. Additionally, this leaf extract exhibits antiviral and antibacterial properties, which can enhance digestive processes. It further offers diverse health merits, encompassing its antioxidant, antimicrobial, and anti-inflammatory attributes. Notably, this extract also contributes to the palatable flavor of the FlowForce Max supplement. * **Monolaurin**: Originating from lauric acid and present in human breast milk as well as coconut oil, Monolaurin is harnessed for addressing a range of infections. Although its efficacy remains a topic of discussion, it is incorporated into various food items. * **Saw Palmetto Fruit Extract Powder:** Saw palmetto ranks as one of the most renowned ingredients for promoting prostate health across the globe. It is a common presence in various prostate health supplements and formulations aiming to support testosterone levels. According to Mount Sinai, "the primary purpose of saw palmetto is to address benign prostatic hyperplasia (BPH)." This plant primarily thrives within the realm of prostate and hormone balance supplements. * **Tricalcium Phosphate (TCP):** This salt bolsters mineral retention and promotes bone regeneration, participating in various essential biological functions such as cell communication, energy generation, and the formation of bone minerals. * **ViNitrox**: It is an exclusive proprietary blend of grape and apple polyphenols. It has demonstrated remarkable properties in the sphere of sports nutrition, contributing to enhanced endurance and performance. An in vivo study has evidenced a remarkable 74% reduction in oxidative stress. Further research highlights ViNitrox's ability to diminish cardio-respiratory fatigue and muscle weariness while concurrently elevating individual power output during exercise. * **Silk Protein Powder:** This ingredient enhances skin's overall appearance by effectively maintaining optimal moisture levels, managing excess oils, and minimizing the visibility of enlarged pores. * **Sucralose**: An artificial sweetener, Sucralose imparts a delightful taste to the supplement and maintains its stability, even in elevated temperatures, when utilized judiciously. * **Magnesium Stearate Powder:** This ingredient is a widely employed ingredient in numerous spices, herbs, food supplements, chewing gum, and baking products. It also plays a role in encapsulated medications by preventing the constituent elements from adhering to one another, serving as an anti-caking agent to maintain ingredient separation. [**(EXCLUSIVE OFFER) View Pricing & Availability of FlowForce Max**](https://www.glitco.com/us-flowforce-max) **TAGS:** #[FlowForce\_Max](https://flowforce-max-review.blogspot.com/2023/10/flowforce-max-reviews-what-are.html)\_US, #[FlowForce\_Max](https://colab.research.google.com/drive/1d5Y3ao9UajRb0Rw0Wz-ZPGkXC2AHCiLw#scrollTo=zLDxSeUBLYVb)\_Reviews, #[FlowForce\_Max](https://sites.google.com/view/flow-forcemax-offers/home)\_Benefits, #[FlowForce\_Max](https://lookerstudio.google.com/reporting/bffd6123-400d-49c8-bf97-6a38a0a84448/page/0LPgD)\_Buy, #[FlowForce\_Max](https://groups.google.com/g/flowforce-max-about/c/xlmAHrfTyfc)\_Cost, #[FlowForce\_Max](https://www.dibiz.com/buyflowforcemax)\_Ingredients, #[FlowForce\_Max](https://loop.frontiersin.org/people/2557229/bio)\_Order, #FlowForce\_Max\_Price, #[FlowForce\_Max\_Website](https://devfolio.co/@buyflowforcemax),

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ProstateFlux/ProstateFluxreviews

2023-10-25T12:27:54.000Z

[ "region:us" ]

ProstateFlux

null

0

2023-10-25T12:26:34

<h2 style="-webkit-text-stroke-width: 0px; background-color: white; box-sizing: border-box; color: black; font-family: Roboto, Helvetica, Arial, sans-serif; font-size: 1.5em; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: bold; letter-spacing: normal; line-height: 1.1; margin: 10px 0px; orphans: 2; padding: 0px; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px;"><a href="https://www.healthsupplement24x7.com/get-prostate-flux" target="_blank">Prostate Flux – Official Website Link – Click Here</a></h2> <p style="-webkit-text-stroke-width: 0px; background-color: white; box-sizing: border-box; color: #333333; font-family: Roboto, Helvetica, Arial, sans-serif; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; margin: 0px 0px 10px; orphans: 2; padding: 0px; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px;">➥ Where to Get Bottle Online - <a href="https://www.healthsupplement24x7.com/get-prostate-flux">https://www.healthsupplement24x7.com/get-prostate-flux</a> ➥ Product Name - Prostate Flux ➥ Side Effects - No Major Side Effects ➥ Category - Health ( Prostate Health Formula) ➥ Results - In 1-2 Months ➥ Availability – <a style="background-color: transparent; box-sizing: border-box; color: black; text-decoration: none;" href="https://www.globalfitnessmart.com/get-amiclear" rel="nofollow">Online</a> ➥ Rating: - 5.0/5.0 ⭐⭐⭐⭐⭐ <h2 style="-webkit-text-stroke-width: 0px; background-color: white; box-sizing: border-box; color: black; font-family: Roboto, Helvetica, Arial, sans-serif; font-size: 1.5em; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: bold; letter-spacing: normal; line-height: 1.1; margin: 10px 0px; orphans: 2; padding: 0px; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px;"><a href="https://www.healthsupplement24x7.com/get-prostate-flux" target="_blank">✅Click Here To Visit – “OFFICIAL WEBSITE”✅</a></h2> <h2 style="-webkit-text-stroke-width: 0px; background-color: white; box-sizing: border-box; color: black; font-family: Roboto, Helvetica, Arial, sans-serif; font-size: 1.5em; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: bold; letter-spacing: normal; line-height: 1.1; margin: 10px 0px; orphans: 2; padding: 0px; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px;"><a href="https://www.healthsupplement24x7.com/get-prostate-flux" target="_blank">✅Click Here To Visit – “OFFICIAL WEBSITE”✅</a></h2> <h2 style="-webkit-text-stroke-width: 0px; background-color: white; box-sizing: border-box; color: black; font-family: Roboto, Helvetica, Arial, sans-serif; font-size: 1.5em; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: bold; letter-spacing: normal; line-height: 1.1; margin: 10px 0px; orphans: 2; padding: 0px; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-decoration-thickness: initial; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px;"><a href="https://www.healthsupplement24x7.com/get-prostate-flux" target="_blank">✅Click Here To Visit – “OFFICIAL WEBSITE”✅</a></h2> Prostate Flux (Brand New ProstateFlux) Reviews: Men above 50 years are at high risk of developing prostate issues. Aging slows body functions, including the hormones that support prostate health. Many medications on the market claim to restore prostrate health, but some have not been legally approved or scientifically studied. An all-natural dietary supplement without any potential side effects is worth trying. <a href="https://sites.google.com/view/prostateflux/home">Prostate Flux</a> is a new comprehensive formula specifically created for men to support the health of their prostate gland. The supplement helps eliminate all the symptoms of enlarged prostate. Here is a comprehensive review to help you learn more about <a href="https://sites.google.com/view/prostate-flux/home">Prostate Flux</a>, how it works, it's benefits, pros, cons, and pricing. <div class="separator" style="clear: both; text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="https://www.healthsupplement24x7.com/get-prostate-flux" target="_blank"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgn1q9pUeJClezL6kCYp-S7a7Uj7RGi9He3ZoSd14tEn0GhyxyEVPFAhyphenhyphen97hczzGbbiNuxxslZrPfxaW8Z65SnZ7xDzyafPbNxiGFAk6B5GUtzDwjTPbgLv9Qz3bOjzm0Kgk8p2Y6vnb1U_Ve5Mcg5y75fPV4EY6CPXZcGG-D2E7xQz9RJXBrcaMZfi5c0/w640-h348/ProstateFlux%205.jpg" alt="" width="640" height="348" border="0" data-original-height="915" data-original-width="1679" /></a></div> <h2>What is Prostate Flux?</h2> <a href="https://covingtonreporter.clubeo.com/calendar/2023/10/25/prostate-flux-brand-new-prostate-flux-1-prostate-health-formula-for-permanent-solution?_ga=2.196303724.1535074580.1698206977-950885170.1698206977">Prostate Flux</a> is a natural dietary supplement formulated to help keep the prostate healthy. The supplement is packed with 100% organic ingredients that are easily absorbed in the body to take effect instantly. Frank Neal, the founder of <a href="https://covingtonreporter.clubeo.com/page/prostate-flux-1-unique-prostate-health-tablets-read-first-all-pros-and-cons.html">Prostate Flux</a>, claims that <a href="https://covingtonreporter.clubeo.com/page/prostate-flux-14-in-1-vital-prostate-wellness-formula-supports-prostate-and-male-health.html">Prostate Flux</a> contains the right proportions to maintain a healthy prostate. According to Neal, prostate health is attacked by cancerous cells caused by unhealthy eating habits and lifestyle. The product is gluten-free and does not contain dangerous toxins, stimulants, or GMOs. He claims Prostate Flux is guaranteed to work by combining medical principles and accurate treatment. Prostate Flux is made in an FDA-approved and GMP-Certified facility following the safety protocols. The formula has passed through numerous clinical studies performed by some of the most admired universities in the world. <h2 style="text-align: center;"><a href="https://www.healthsupplement24x7.com/get-prostate-flux" target="_blank">Prostate Flux : Try it now, you won’t be disappointed! Claim Your Free Bonus Now</a></h2> <h2>How does Prostate Flux Work?</h2> <a href="https://doogeemall.com/community/xenforum/topic/113937/prostate-flux-brand-new-prostateflux-1-prostate-health-formula-for-permanent-solution">Prostate Flux</a> formula uses a gentle traditional approach to support a healthy prostate. It contains unique stimulants clinically proven to have effects such as reducing frequent urination, supporting better sleep, and improving intimacy in life and overall quality of life. The primary purpose of <a href="https://groups.google.com/g/prostate-flux/c/fiTpg274HOs">Prostate Flux</a> is to ease swelling and enlargement of the prostate gland by decreasing inflammation caused by the hormone testosterone. As men age, the production of male hormones testosterone and dihydrotestosterone (DHT) decreases. <a href="https://groups.google.com/g/prostateflux/c/FTSJT_Ch4EI">Prostate Flux</a> consists of organic components that have anti-inflammatory properties. The ingredients work by repairing the receptors in the reproductive system and ensuring proper immune response to reduce inflammation in the prostate area. The supplement also flushes out toxins in the bloodstream. The immune system can perform better when the toxins are eliminated from the blood. The dietary formula deals with the root cause of BPH and enlarged prostate and supports the immune system making it stronger to fight future risks. <h2>Ingredients in Prostate Flux</h2> <a href="https://prostate-flux.unicornplatform.page/">Prostate Flux</a> formula is made of 100% natural ingredients. They consist of plants, herbs, vitamins, and minerals. The key ingredients in the supplement are as follows: Saw Palmetto Saw Palmetto is an essential ingredient in <a href="https://pdfhost.io/v/oZsYFanVk_Prostate_Flux_Brand_New_ProstateFlux_1_Prostate_Health_Formula_For_Permanent_Solution_">Prostate Flux</a>. The berries have anti-inflammatory properties, which help reduce an enlarged prostate. They also block the enzyme that converts testosterone to DHT. It also prevents BPH and prostate cancer. Graviola Leaf The Graviola plant is used as a medicine that treats bacterial infections and illnesses caused by parasites. The ingredient has acetogenesis, which is said to kill and prevent the spread of cancer cells in the body. Green Tea Extract Green tea extract is a magic ingredient found in many dietary supplements. Besides supporting weight loss, it improves blood circulation, reduces cholesterol levels in the body, and prevents cardiovascular diseases. Selenium Selenium is an antioxidant that supports thyroid function and increases metabolic rate. It prevents cancer, strengthens the immune system, and supports cognitive abilities as people grow old. Vitamin E Vitamin is rich in antioxidants, supporting healthy skin, repairing damaged cells, and restoring healthy vision and neurological function. Vitamin B6 Vitamin B6 or Pyridoxine supports a healthy brain by preventing mood swings and depression and improves the ability to think. <h2 style="text-align: center;"><a href="https://www.healthsupplement24x7.com/get-prostate-flux" target="_blank">Prostate Flux : Try it now, you won’t be disappointed! Claim Your Free Bonus Now</a></h2> <div class="separator" style="clear: both; text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="https://www.healthsupplement24x7.com/get-prostate-flux" target="_blank"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgnSX92WyJf72VjbpDbFELxavMvuNhfHBSDaGw_PPEHEal6FNetU6cDlOltfsLuExwvvVRMwm8a3GSiPiXt14yDcRuXIPrEoQRRfKc1P_OBNO_lfH8WWBKdXRIeY_QEOK4pR4HWQMgWlMUXyuWNWNZVZiPGSXNXHhVLdk9eO_8MqXuBVmcvtRXRctfVgOU/w640-h364/ProstateFlux%206.png" alt="" width="640" height="364" border="0" data-original-height="707" data-original-width="1243" /></a></div> <h2>Benefits of Prostate Flux</h2> The natural ingredients in <a href="https://gamma.app/public/Prostate-Flux-qani9oomk20ga3g?mode=doc">Prostate Flux</a> work together to provide the following benefits: Reduce Enlarged Prostate The ingredients in <a href="https://prostateflux1.bandcamp.com/track/prostateflux-new-2023-does-it-really-works">Prostate Flux</a> can reduce enlarged prostate to normal size. Many Prostate Flux users have reported that their prostate size went back to regular size after using the supplement for three months. Lessen Frequent Urination Enlarged and swollen prostate causes the need to urinate frequently. Prostate Flux has ingredients that help balance the prostate gland to have a normal urine flow. Increase in Libido Prostate Flux deals with male health-related issues such as performance, stamina and overall function. Repair Tissue As people age, the cells in the prostate become damaged. Prostate Flux can repair and rejuvenate damaged tissue. Reduce inflammation Most active ingredients in Prostate Flux have antioxidants that help reduce any inflammation in the prostate gland. Supports Better Sleep Frequent urination, especially at night, can disrupt sleep. Lack of adequate sleep affects mental health and causes tiredness during the day. A healthy prostate means no more unnecessary urination at night. <h2>How to use Prostate Flux</h2> One Prostate Flux has a 30-day supply of 60 capsules. Users are advised to take two Prostate Flux capsules a day after a meal. The results of Prostate Flux depend on a person's tolerance level. Children under 18, pregnant and lactating mothers, and those with pre-existing conditions should not use Prostate Flux. <h2 style="text-align: center;"><a href="https://www.healthsupplement24x7.com/get-prostate-flux" target="_blank">Prostate Flux : Try it now, you won’t be disappointed! Claim Your Free Bonus Now</a></h2> <div class="separator" style="clear: both; text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="https://www.healthsupplement24x7.com/get-prostate-flux" target="_blank"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhj9v5_TtXpxVmPT2MNkhiinXyXMfNHDliuZOUngYddK5-8c0S5ptkC93P7PkJVmnP6SRFs3qscAtgJk23ycWpKvsJxA3jdTUty61-SQArYSUfkMlm9IBso3r6wmrdailvyuH50QalI9CHPlOqPjGy95WIPu2dN3QANn8eu26hvQRJJzvQxw7HR7qgygtA/w640-h372/Screenshot%20(1299).png" alt="" width="640" height="372" border="0" data-original-height="745" data-original-width="1279" /></a></div> <h2>Prostate Flux Pricing and Money-back Guarantee</h2> Consumers can order Prostate Flux on the official website at a discounted price. The price range is: <ul> <li>One bottle at $79 + free US shipping</li> <li>Three bottles at $59 each + free US shipping</li> <li>Six bottles at $49 each + free US shipping</li> </ul> Prostate Flux manufacturer offers a complete 60-day money-back for users who find the product non-effective. Customers are asked to send the product back to the company at the address shown below. <h2>Is It Safe To Take Prostate Flux Complete Daily?</h2> Yes, Prostate Flux Complete prostate health-boosting supplement is safe to consume daily. This statement is supported by both the customers and the experts. The reason for the same might be the quality of the supplement made from natural ingredients. The Prostate Flux Complete nutritional supplement is vegan and gluten-free as well. <h2>Conclusion</h2> Prostate Flux is created for men who have prostate issues or want to boost their prostate health. It contains the required nutrients to support prostate function. Prostate Flux strengthens the immune system, improves sleep, and reduces the risk of diseases. Prostate Flux gives long-term prostate health; users won't have to worry about BPH recurrence. Read More: <a href="https://covingtonreporter.clubeo.com/calendar/2023/10/25/prostate-flux-brand-new-prostate-flux-1-prostate-health-formula-for-permanent-solution">https://covingtonreporter.clubeo.com/calendar/2023/10/25/prostate-flux-brand-new-prostate-flux-1-prostate-health-formula-for-permanent-solution</a> <a href="https://sites.google.com/view/prostateflux/home">https://sites.google.com/view/prostateflux/home</a> <a href="https://covingtonreporter.clubeo.com/page/prostate-flux-14-in-1-vital-prostate-wellness-formula-supports-prostate-and-male-health.html">https://covingtonreporter.clubeo.com/page/prostate-flux-14-in-1-vital-prostate-wellness-formula-supports-prostate-and-male-health.html</a> <a href="https://sites.google.com/view/prostate-flux/home">https://sites.google.com/view/prostate-flux/home</a> <a href="https://groups.google.com/g/prostateflux/c/FTSJT_Ch4EI">https://groups.google.com/g/prostateflux/c/FTSJT_Ch4EI</a> <a href="https://covingtonreporter.clubeo.com/page/prostate-flux-14-in-1-vital-prostate-wellness-formula-supports-prostate-and-male-health.html">https://covingtonreporter.clubeo.com/page/prostate-flux-14-in-1-vital-prostate-wellness-formula-supports-prostate-and-male-health.html</a> <a href="https://doogeemall.com/community/xenforum/topic/113937/prostate-flux-brand-new-prostateflux-1-prostate-health-formula-for-permanent-solution">https://doogeemall.com/community/xenforum/topic/113937/prostate-flux-brand-new-prostateflux-1-prostate-health-formula-for-permanent-solution</a> <a href="https://groups.google.com/g/prostate-flux/c/fiTpg274HOs">https://groups.google.com/g/prostate-flux/c/fiTpg274HOs</a> <a href="https://prostate-flux.unicornplatform.page/">https://prostate-flux.unicornplatform.page/</a> <a href="https://pdfhost.io/v/oZsYFanVk_Prostate_Flux_Brand_New_ProstateFlux_1_Prostate_Health_Formula_For_Permanent_Solution_">https://pdfhost.io/v/oZsYFanVk_Prostate_Flux_Brand_New_ProstateFlux_1_Prostate_Health_Formula_For_Permanent_Solution_</a> <a href="https://gamma.app/public/Prostate-Flux-qani9oomk20ga3g?mode=doc">https://gamma.app/public/Prostate-Flux-qani9oomk20ga3g?mode=doc</a> <a href="https://prostateflux1.bandcamp.com/track/prostateflux-new-2023-does-it-really-works">https://prostateflux1.bandcamp.com/track/prostateflux-new-2023-does-it-really-works</a>

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BangumiBase/mushokutensei

2023-10-25T16:16:47.000Z

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

BangumiBase

null

0

2023-10-25T12:35:03

--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Mushoku Tensei This is the image base of bangumi Mushoku Tensei, we detected 73 characters, 6277 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 | 35 | [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 | 36 | [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 | 29 | [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 | 252 | [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 | 1729 | [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 | 74 | [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 | 179 | [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 | 52 | [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 | 152 | [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 | 47 | [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 | 211 | [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 | 62 | [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 | 47 | [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 | 66 | [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 | 152 | [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 | 37 | [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 | 10 | [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 | 36 | [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 | 28 | [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 | 118 | [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 | 33 | [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 | 62 | [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 | 17 | [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 | 21 | [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 | 60 | [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 | 19 | [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 | 26 | [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 | 14 | [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 | 14 | [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 | 21 | [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 | 103 | [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 | 8 | [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 | 44 | [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 | 53 | [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 | 18 | [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 | 19 | [Download](39/dataset.zip) | ![preview 1](39/preview_1.png) | ![preview 2](39/preview_2.png) | ![preview 3](39/preview_3.png) | ![preview 4](39/preview_4.png) | ![preview 5](39/preview_5.png) | ![preview 6](39/preview_6.png) | ![preview 7](39/preview_7.png) | ![preview 8](39/preview_8.png) | | 40 | 224 | [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 | 36 | [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 | 50 | [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 | 22 | [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 | 17 | [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 | 9 | [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 | 49 | [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 | 62 | [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 | 27 | [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 | 111 | [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 | 39 | [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 | 15 | [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 | 11 | [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 | 16 | [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 | 783 | [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 | 41 | [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 | 29 | [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 | 90 | [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 | 78 | [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 | 29 | [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 | 22 | [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 | 91 | [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 | 16 | [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 | 10 | [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 | 6 | [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) | N/A | N/A | | 66 | 36 | [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 | 10 | [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 | 13 | [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 | 17 | [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 | 23 | [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 | 32 | [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) | | noise | 271 | [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) |

24,131

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yanpeba/rr

2023-10-25T12:42:05.000Z

[ "region:us" ]

yanpeba

null

0

2023-10-25T12:41:25

Entry not found

15

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peoplesketogummiesaustralia/PeoplesKetoGummiesAU

2023-10-25T12:50:13.000Z

[ "region:us" ]

peoplesketogummiesaustralia

null

0

2023-10-25T12:49:54

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peoplesketogummiesaustralia/PeoplesKetoGummiesAustralia

2023-10-25T12:50:53.000Z

[ "region:us" ]

peoplesketogummiesaustralia

null

0

2023-10-25T12:50:32

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d0rj/ru-mhr-parallel

2023-10-25T13:09:14.000Z

[ "task_categories:translation", "size_categories:100K<n<1M", "language:ru", "language:mhr", "license:cc-by-4.0", "low-resource-language", "region:us" ]

d0rj

null

0

2023-10-25T13:04:56

--- dataset_info: features: - name: ru dtype: string - name: mhr dtype: string splits: - name: train num_bytes: 83137793 num_examples: 417103 download_size: 40277610 dataset_size: 83137793 configs: - config_name: default data_files: - split: train path: data/train-* license: cc-by-4.0 task_categories: - translation language: - ru - mhr tags: - low-resource-language pretty_name: Mari-Russian parallel corpus size_categories: - 100K<n<1M --- # Dataset Card for "ru-mhr-parallel" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

650

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yunhaol/gallery

2023-10-25T15:33:49.000Z

[ "region:us" ]

yunhaol

null

0

2023-10-25T13:05:16

Entry not found

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dsfsi/za-isizulu-siswati-news

2023-10-25T13:32:26.000Z

[ "task_categories:text-classification", "language:ss", "language:zu", "license:cc-by-sa-4.0", "dsfsi-datasets", "siswati", "isizulu", "arxiv:2306.07426", "region:us" ]

dsfsi

null

0

2023-10-25T13:23:14

--- license: cc-by-sa-4.0 language: - ss - zu task_categories: - text-classification tags: - dsfsi-datasets - siswati - isizulu pretty_name: za-isizulu-siswati-news --- # IsiZulu News (articles and headlines) and Siswati News (headlines) Corpora - za-isizulu-siswati-news-2022 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7193346.svg)](https://doi.org/10.5281/zenodo.7193346) [![arXiv](https://img.shields.io/badge/arXiv-2306.07426-b31b1b.svg)](https://arxiv.org/abs/2306.07426) ### About Dataset Dataset for both isiZulu news (articles and headlines) and Siswati news headlines. Process included scraping the data from internet, from Isolezwe news website http://www.isolezwe.co.za and public posts from the SABC news LigwalagwalaFM Facebook page https://www.facebook.com/ligwalagwalafm/ respectively. The obtained datasets are isiZulu news articles, isiZulu news headlines, and Siswati news headlines. Post data collection the datasets were then sent to annotators, and they were sent back after the annotation process. The datasets contain special characters, some English words and characters that are not ASCII encoded which must be removed prior to model training. The aim of these three datasets is to create a baseline news categorisation model for the two South African low resources languages i.e. isiZulu and Siswati. For categorisation, we use high level [IPTC NewsCodes](https://iptc.org/standards/newscodes/) as categories. You can view the news categories here [data/news-categories-iptc-newscodes.csv](data/news-categories-iptc-newscodes.csv) The datasets were found to have class categories with very few observations, hence the class categories which have less than 35 observations were removed for isiZulu and less 6 observations for Siswati. The dataset has both full category data as well as reduced category data. Please see the [data-statement.md](data-statement.md) for full dataset information. ## Online Repository link * Link to the DOI data repository - [Zenodo Data Repository](https://doi.org/10.5281/zenodo.7193346) * ## Authors * **Andani Madodonga** * **Vukosi Marivate** - [@vukosi](https://twitter.com/vukosi) * **Matthew Adendorff** See also the list of [contributors](https://github.com/dsfsi/za-isizulu-siswati-news-2022/contributors) who participated in this project. ## Citation **Citation:** ```bibtex @article{Madodonga_Marivate_Adendorff_2023, title={Izindaba-Tindzaba: Machine learning news categorisation for Long and Short Text for isiZulu and Siswati}, volume={4}, url={https://upjournals.up.ac.za/index.php/dhasa/article/view/4449}, DOI={10.55492/dhasa.v4i01.4449}, author={Madodonga, Andani and Marivate, Vukosi and Adendorff, Matthew}, year={2023}, month={Jan.} } ``` ## License Data is Licensed under CC 4.0 BY SA

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theyavuereview/TheyaVue-Reviews

2023-10-25T13:29:39.000Z

[ "region:us" ]

theyavuereview

null

0

2023-10-25T13:28:52

<h2 style="text-align: center;"><a style="color: #0b5394;" href="https://sale365day.com/order-theyavue">Click Here -- Official Website -- Order Now</a></h2> <h2 style="text-align: center;">⚠️Beware Of Fake Websites⚠️</h2> ✔For Order Official Website - <a href="https://sale365day.com/order-theyavue">https://sale365day.com/order-theyavue</a> ✔Product Name - <a href="https://theyavue-reviews-official-website.jimdosite.com/">TheyaVue</a> (Eye Support Formula) ✔Side Effect - No Side Effects ✔Availability - <a href="https://sale365day.com/order-theyavue">Online</a> ✔ Rating -⭐⭐⭐⭐⭐ <a href="https://sale365day.com/order-theyavue">Hurry Up - Limited Time Offer - Buy Now</a> <a href="https://sale365day.com/order-theyavue">Hurry Up - Limited Time Offer - Buy Now</a> <a href="https://sale365day.com/order-theyavue">Hurry Up - Limited Time Offer - Buy Now</a> TheyaVue is an effective natural dietary supplement that is specially prepared to support the sharp vision and overall eye health. <a href="https://groups.google.com/g/theyavue-reviews-offer/c/B3N9Xe_fkOc">TheyaVue</a> is a natural dietary supplement that comprises research-backed ingredients to support vision health and overall functioning of the eye. Not long ago, it was released and has been gaining significant attention from both experts and folks.  <a style="margin-left: 1em; margin-right: 1em;" href="https://sale365day.com/order-theyavue"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhBzb-bDl6ZIIo7FxmYhsVaH7L4XbELmTYqt3NtTUKACTuA-PYiDEqteWp_6OpiQsa2IsGFIA1CwbcWNgOG2HVOD0hUK8SLBj30xjJP8P3NVopIeva-EktznGxvxmys7AA07dWI2H6oDexh98SJ4es230xmUC6ph16jyffjGGoSUtzBqq-9DYKvbKUz/w640-h306/fcdfwefffffefdf.JPG" alt="" width="640" height="306" border="0" data-original-height="496" data-original-width="1036" /></a> We came to know about <a href="https://groups.google.com/g/theyavue-reviews-offer/c/CWe5Nj6Z0Hk">Theyavue capsules</a> after receiving many requests from our readers to review it. We have to wait for a week to get it in our hands and spend another week to test it. As you know, reviewing a supplement is not an easy task. We couldn't have completed this Theyavue review without the help of our readers who already used it and shared their experience with us.  We have done everything to test the supplement and share this review with you. We will be glad if you read the article completely and send your feedback to us. Your genuine feedback will help us improve better.  <a href="https://sale365day.com/order-theyavue" target="_blank" rel="nofollow">Check The Availability Of TheyaVue On The Official Website</a>  <h2 style="text-align: left;">TheyaVue Reviews - How Effective Is TheyaVue Ingredients In Curing Blurry Vision? </h2> Coming to the <a href="https://groups.google.com/g/theyavue-reviews-offer">TheyaVue eye care supplement</a>, promises to significantly and positively impact your vision by being evidence-based. However, before you reach your ultimate decision regarding giving it a try, it is necessary to evaluate the formula, so that you can ensure whether it meets the benchmarks of quality and potency.  <h2 style="text-align: left;">What is TheyaVue? </h2> <a href="https://yourpillsboss.blogspot.com/2023/10/theyavue-reviews-1-eye-vision-support.html">TheyaVue</a> is an effective natural dietary supplement that is specially prepared to support the sharp vision and overall eye health. The supplement comes with a scientifically proven formula that comprises high-quality natural ingredients possessing impressive therapeutic properties. Each of the TheyaVue ingredients together can provide powerful antioxidants support to your eyes to combat vision struggles.  TheyaVue is an encapsulated formula made of non-GMO ingredients. Every bottle of the supplement comes with easy-to-swallow capsules that are manufactured in the USA, in an FDA-approved state-of-the-art facility.  <h2 style="text-align: left;">Details about the Ingredients (Only included the proven benefits) </h2> The <a href="https://theyavue-update.clubeo.com/page/theyavue-reviews-usa-does-theyavue-eye-supplement-work-what-to-know-before-buying.html">Theyavue supplement</a> is a combination of many organic ingredients. Below you will see the benefits of these ingredients in detail.  <h4 style="text-align: left;">●     Lutein </h4> This carotenoid has reported anti-inflammatory properties. Various scientific studies show that lutein significantly improves eye health by curing most eye problems. In particular, lutein prevents age-related macular disease which is the leading cause of blindness and vision impairment.  <h4 style="text-align: left;">●     Zeaxanthin </h4> Along with Lutein, zeaxanthin shields your eyes from harmful high-energy light waves like ultraviolet rays in sunlight. Based on studies, these two compounds in high levels in the eye tissues are linked with better vision, especially in dim light or where glare is a problem.  <h4 style="text-align: left;">●     Vitamin C </h4> As per scientific study reports, vitamin C reduces the risk of cataracts and it also slows down the progression of age-related macular degeneration and visual acuity loss when combined with other nutrients in the TheyaVue eye health support supplement.  <h4 style="text-align: left;">●     Calcium </h4> Calcium supplementation and adding more calcium to your everyday diet lowers the risk of age-related eye disease. This makes you less likely to struggle with serious vision loss.  <h4 style="text-align: left;">●     Vitamin E </h4> This antioxidant plays a vital role in managing free radical attacks and reducing cell damage. It is often used against conditions like AMD and is powerful to prevent cataracts.  <h4 style="text-align: left;">●     Bilberry Extract </h4> Based on studies, Bilberry has the potential to reduce the risk of macular degeneration. Besides, it gives the required antioxidant support to the eyes and the retina, avoiding cell deterioration.  <h4 style="text-align: left;">●     Rutin </h4> Rutin protects the eyes against blood vessel diseases such as diabetic retinopathy. Rutin strengthens the blood vessel walls, and by this, the risk of permanent vision loss is avoided.  <a href="https://sale365day.com/order-theyavue" target="_blank" rel="sponsored">TheyaVue Is On Sale Now For A Limited Time!</a> <h2 style="text-align: left;">Ingredient Purity & Label Accuracy </h2> <a href="https://theyavue-update.clubeo.com/page/theyavue-reviews-is-it-legit-what-are-customers-saying-theyavue-eye-health-formula-exposed.html">Theyavue</a> have a label accuracy of 98.83% that is a good value and it has an ingredient purity score of 93% (Again a good value). The supplement doesn't contain any flagged inactive ingredients. The heavy metal screening value is less than <a href="https://oehha.ca.gov/media/downloads/proposition-65/safeharborlist032521.pdf" target="_blank" rel="nofollow">CA Proposition 65 limit</a>, so the supplement is very safe to use.  Editor's Note: Few <a href="https://theyavue-update.clubeo.com/">Theyavue</a> reviews mentioned Grape seed extract (a common ingredients in eye vitamin supplements and skin health products) as an ingredient of the product, but we couldn't find it during the tests. Also, it is not mentioned in the label.  <div class="separator" style="clear: both; text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="https://sale365day.com/order-theyavue"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgd76sGGFA1drylsWDY3-C7_noIQ9ybwIhQ31pe1MyAiAiGPI9svMMkXe8eS7E_fsH6Y5cGgPpae55DjwMpdmcvUrIo2Y5l5L7TtNNuWNrO91gfVkbdDWXJrGFpMUjaUTNtshKvpAw8NpvGx6tgimxiINym0XO8_j79Ezb_ro8EmxBWSkxDF0Hf71tl/w640-h416/sfdcvde.JPG" alt="" width="640" height="416" border="0" data-original-height="585" data-original-width="901" /></a></div> <h2 style="text-align: left;">How does TheyaVue support eye vision? </h2> The natural and research-based blend of <a href="https://www.townscript.com/e/theyavue-reviews-scam-warning-read-this-before-buying-024040">TheyaVue</a> eye care supplement works by aiding in healthy visions and promoting eye health. The root cause of vision impairment and a list of other eye-related conditions is free radical attacks and oxidative stress which is closely linked with external factors like pollution, exposure to toxins, and unhealthy lifestyle. However, the TheyaVue eye supplement can provide significant antioxidant support to your eyes and eye tissue to get rid of damage and promote cell regeneration.  The formula is appropriately composed of over 24 ingredients like Lutein, Rutin, Bilberry Extract, Vitamin E, Zeaxanthin, calcium, and vitamin C. Each of them is also scientifically proven for having impressive medicinal properties and packed with important antioxidant. As the TheyaVue ingredients are carefully mixed with their right proportion in the <a href="https://www.cometforums.com/topic/12812433-theyavue-reviews-urgent-2023-update-effective-ingredients-or-fake-customer-hype/">TheyaVue dietary formula</a>, they can directly defend against free radicals and contribute a significant anti-inflammatory response as well.  <a href="https://sale365day.com/order-theyavue" target="_blank" rel="sponsored">Order your supply of TheyaVue now and start enjoying the benefits!</a> <h2 style="text-align: left;">The science behind the TheyaVue formula </h2> Having a closer glance at the scientific setting of the <a href="https://medium.com/@theyavuereviews/theyavue-reviews-trustworthy-official-website-or-fake-customer-claims-8bed009db7b7">TheyaVue capsule</a>, you can see it also comprises ingredients having solid scientific backgrounds.  According to a <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6523787/" target="_blank" rel="nofollow">research article published in the </a><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6523787/" target="_blank" rel="nofollow">Nutrients</a><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6523787/" target="_blank" rel="nofollow"> (2018, Sep 18)</a>, Lutein is referred to as a vital carotenoid that has reported anti-inflammatory properties. A large body of scientific evidence also shows that lutein has significant effects on eye health. In particular, lutein improves or even prevents age-related macular disease, the leading cause of blindness and vision impairment.  <a href="https://expertlegalreview.com/scientific-supplement-review-process/" target="_blank" rel="nofollow">In another scientific review</a> <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5355559/" target="_blank" rel="nofollow">from the </a><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5355559/" target="_blank" rel="nofollow">Saudi Pharmaceutical Journal: SPJ, </a><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5355559/" target="_blank" rel="nofollow">(2016 April 30)</a>, rutin brings impressive anti-cataract and ophthalmic effects to the eyes to protect them from many related struggles.  <h2 style="text-align: left;">Perks of using TheyaVue eye supplement </h2> As referred to in <a href="https://colab.research.google.com/drive/11SZi064ppfbRbVyxZ1bLwBoqUScJvWEo">TheyaVue reviews</a>, the supplement is constructed with over 24 versatile natural ingredients. Since they are mixed in the formula adequately, you can expect the following benefits with the supplement's right and consistent consumption.  ●     Support for eyesight.  ●     TheyaVue capsule maintains eye health.  ●     Manages eye-related struggles.  ●     Antioxidant support.  ●     TheyaVue organic formula improves night vision  ●     The capsule enhances the overall quality of life  ●     Healthy inflammatory response.  ●     Long lasting results.  ●     Protect from sun damage  ●     Money back policy.  <h2 style="text-align: left;">Is there any clinical evidence? </h2> Looking into the clinical background of the <a href="https://lookerstudio.google.com/reporting/8e91cacc-4746-4713-a900-22a246386307/page/DsQgD">TheyaVue</a> organic vision support formula, you can see it is clinically proven and manufactured in an FDA-approved, state-of-the-art facility in the US. Besides, each stage of its preparation is carefully done by following the benchmarks of safety, purity, quality, and precision.  <h2 style="text-align: left;">Dosage Guidelines </h2> As you can see, every <a href="https://sketchfab.com/3d-models/theyavue-reviews-scam-aert-2023-does-it-works-3bbb23362e094536bae5e95cf01a9e59">TheyaVue</a> bottle is packed with 60 pills for a complete month's supply of the formula. The ideal everyday dosage of TheyaVue is two capsules a day, which is ideal to take along with a glass of water. At the same time, the manufacturer also suggests taking it 30 minutes before any main meal.  <a href="https://sale365day.com/order-theyavue" target="_blank" rel="nofollow">Check The Special Discount Offers of TheyaVue On The Official Website</a>  <h2 style="text-align: left;">How long does it take to work? </h2> Since you currently know how to take the TheyaVue eye care supplement in the right way, keep in mind that the formula can deliver you the best results only after 2-3 months of consistent consumption. This is the average time taken by the formula to bring significant changes. Following the suggested intake consistently up to the suggested period of 2-3 months will also help you have a better longevity of results, which is 1-2 years or more.   Editor's Note: According to customer feedback, most persons who consistently used the supplement for 90-120 days got the best results.  <h2 style="text-align: left;">Are there any side effects? </h2> Considering the formulation of <a href="https://www.forexagone.com/forum/experiences-trading/theyavue-reviews-formulated-with-100-pure-ingredients-that-helps-to-clear-blurry-vision-87251#184665">TheyaVue</a> supplement, you can see it is entirely plant-based, and clinically verified. Besides, there are also no harmful elements like chemicals, fillers, or allergens included in the formula to induce any negative side effects.  Moreover, third-party lab reports also indicate the formula's safety, potency, and quality. And also no negative <a href="https://gamma.app/public/TheyaVue-Reviews-2023-Ingredients-Benefits-Side-Effects-And-Compl-5wkx32cjw3bjcig">TheyaVue reviews</a> were reported yet by any of the customers.  <a style="margin-left: 1em; margin-right: 1em;" href="https://yourpillsboss.blogspot.com/2023/10/theyavue-reviews-1-eye-vision-support.html"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj1i-2mpmmbJ1kaQri6IoqTIJKLSPEyVPNBVcn9gRlGRXI0ISFAYvFSU4pjDmO_RKpkLk3W6tfY630p9eEluETooOHdzRe3nD3S4FYKnnq7-J6DmHyJg3zHqdkapQQ3Qpq3LzrSJg_E3f1BpBoLU5EzUaDR2EEyqHhk8-XJL-OshC9ipVq0e8zbE7R0/w640-h220/wfffergg.JPG" alt="" width="640" height="220" border="0" data-original-height="376" data-original-width="1094" /></a> <h2 style="text-align: left;">TheyaVue Pros and cons </h2> The TheyaVue dietary supplement has both positive and negative features which you need to be aware of before preserving it over others. Here are the major pros and cons associated with the supplement that are identified through probing research.  <h2 style="text-align: left;">Pros: </h2> ●     Natural and non-GMO formula  ●     Evidence-based ingredients and formulation  ●     Quality-assured preparation  ●     Made in the USA in FDA-approved, state-of-the-art facilities  ●     Added benefits  ●     Includes 60-day risk-free money back  <h2 style="text-align: left;">Cons: </h2> ●     TheyaVue is not recommended for children under the age of 18  ●     Limited availability of stock   <a href="https://sale365day.com/order-theyavue" target="_blank" rel="sponsored">Click Here to Get TheyaVue At Discounted Price!!!</a> <h2 style="text-align: left;">Real TheyaVue reviews from customers </h2> If you want to see what feedback the supplement has received so far, you can go through some real <a href="https://theyavue-reviews-2023.webflow.io/">TheyaVue</a> customer reviews given in this section.  <h4 style="text-align: left;">●     Rebecca McCarthy </h4> I had been struggling with serious irritation in my eyes due to cataracts. But every remedy I tried could only give me temporary relief. Later, one of my friends who is an ophthalmologist suggested TheyaVue capsule. The results have truly stunned me because it is for the first time I could see something that truly worked in the desired way. Now I feel no discomfort, and my vision also became sharper.  <h4 style="text-align: left;">●     Sophie Hayden </h4> The TheyaVue eye care supplement has totally freed me from the risk of macular degeneration. Taking these pills regularly for up to three months brought significant results in testing as well. Even my optometrist is also stunned by seeing the difference in my test reports. My only regret is that I didn't start its intake a bit before.  <h4 style="text-align: left;">●     Adam Norris </h4> I am so glad that <a href="https://www.oecd-forum.org/users/theyavue-reviews">TheyaVue</a> pill has worked in the desired way since it is for the first time I am experiencing such a change through a supplement. It significantly reduced dry eyes and I see how clearer my vision has become. However, I couldn't get my second bottle on time when I placed my order again.    <h2 style="text-align: left;">How much does TheyaVue cost? </h2> Going through the official website of the supplement, and authentic <a href="https://www.justgiving.com/page/theyavue-reviews-1698231780972">TheyaVue</a> reviews, you can see the supplement comes in three plans, from which you can choose and purchase it.  ●     30-day supply: 1 bottle at $59 + a small shipping fee  ●     90-day supply: 3 bottles at $49/each + free US shipping  ●     180-day supply: 6 bottles at $39/each + free US shipping  According to these details, bulk orders of <a href="https://www.provenexpert.com/theyavue6/">TheyaVue</a> vision support dietary supplements are added with impressive discounts, to give you the best savings. As these plans provide at least a three-month supply, it will be equivalent to the suggested period recommended for consistent intake as well. Anyway, the choice is totally bound to your preferences. Editor's Note: If you are interested in buying the supplement, buy it <a href="https://sale365day.com/order-theyavue" target="_blank" rel="nofollow">only from the official website</a>. As you might have already noticed, many online shopping websites (Including Walmart & Amazon) are filled with supplement scams. But, when you buy from the manufacturer website, you get original products and you are covered in a 100% Money back policy when you buy the product from the official website.  <a href="https://sale365day.com/order-theyavue" target="_blank" rel="nofollow">Check The Special Discount Offers of TheyaVue On The Official Website</a>  <h2 style="text-align: left;">Where to purchase TheyaVue? </h2> If you want to try the authentic <a href="https://theyavuereviewsusa.bandcamp.com/track/theyavue-reviews-fda-approved-2023-unexpected-details-revealed">TheyaVue eye care supplement</a>, keep in mind that it is exclusively available on the official website for purchase. To make it clear, you may see its replicas on other e-commerce websites since the supplement also has higher market demand. So, to avoid any extorted sellers, it is ideal to proceed with your order on the official website.  <h2 style="text-align: left;">Shipping and money-back policy </h2> Once you place your order, the <a href="https://theyavue-reviews.company.site/">TheyaVue</a> package will be shipped within 2 days and you can expect the delivery within 3-7 business days. However, the single-bottle plan is added with a small shipping fee. The rest of the other two plans will be free of any additional shipping charges.  At the same time, the <a href="https://doogeemall.com/community/xenforum/topic/114086/theyavue-reviews-11-facts-revealed-what-are-the-pros-and-cons-of-theyavue-vision-health-formula">TheyaVue</a> eye health support supplement comes as quality assured, and the manufacturer assures complete satisfaction with the results. In addition to this, every order will be protected by a hassle-free, no questions asked 60-day money-back guarantee. This will help you get a full refund if you are not satisfied with your purchase for any reason.  <div class="separator" style="clear: both; text-align: center;"><a style="margin-left: 1em; margin-right: 1em;" href="https://sale365day.com/order-theyavue"><img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgq8a2HP5j2LDEFqSRIMHrF-iuo_vGt1F1udhrOXjlzFB7eoc0qzco5ghOUWOE8jjciDpFLwnRAlGtFf94M71sNBIdWF3EhO1eN1ugu8uIzBISHj5fzaZGJ2eGWLuNBnGfLYNMEB380FdBDU09skpiZm2oTD9XgiwLBqOxh12qh4-v5m-kTMJIU9Jqu/w640-h414/wefdfeff.JPG" alt="" width="640" height="414" border="0" data-original-height="569" data-original-width="881" /></a></div> <h2 style="text-align: left;">Final take on TheyaVue Reviews </h2> <a href="https://yourpillsboss.blogspot.com/2023/10/theyavue-reviews-1-eye-vision-support.html">TheyaVue</a> is an effective natural dietary supplement that is specially prepared to support the sharp vision and overall eye health. The supplement comes with a scientifically proven formula that comprises high-quality natural ingredients possessing impressive therapeutic properties. Each of the <a href="https://theyavue-update.clubeo.com/page/theyavue-reviews-usa-does-theyavue-eye-supplement-work-what-to-know-before-buying.html">TheyaVue</a> ingredients together can provide powerful antioxidants support to your eyes to combat vision struggles.  <a href="https://medium.com/@theyavuereviews/theyavue-reviews-trustworthy-official-website-or-fake-customer-claims-8bed009db7b7">TheyaVue</a> is an encapsulated formula made of non-GMO ingredients. Every bottle of the supplement comes with easy-to-swallow capsules that are manufactured in the USA, in an FDA-approved state-of-the-art facility. Read More: <a href="https://yourpillsboss.blogspot.com/2023/10/theyavue-reviews-1-eye-vision-support.html">https://yourpillsboss.blogspot.com/2023/10/theyavue-reviews-1-eye-vision-support.html</a> <a href="https://theyavue-reviews-official-website.jimdosite.com/">https://theyavue-reviews-official-website.jimdosite.com/</a> <a href="https://groups.google.com/g/theyavue-reviews-offer">https://groups.google.com/g/theyavue-reviews-offer</a> <a href="https://groups.google.com/g/theyavue-reviews-offer/c/CWe5Nj6Z0Hk">https://groups.google.com/g/theyavue-reviews-offer/c/CWe5Nj6Z0Hk</a> <a href="https://groups.google.com/g/theyavue-reviews-offer/c/B3N9Xe_fkOc">https://groups.google.com/g/theyavue-reviews-offer/c/B3N9Xe_fkOc</a> <a href="https://lookerstudio.google.com/reporting/8e91cacc-4746-4713-a900-22a246386307/page/DsQgD">https://lookerstudio.google.com/reporting/8e91cacc-4746-4713-a900-22a246386307/page/DsQgD</a> <a href="https://colab.research.google.com/drive/11SZi064ppfbRbVyxZ1bLwBoqUScJvWEo">https://colab.research.google.com/drive/11SZi064ppfbRbVyxZ1bLwBoqUScJvWEo</a> <a href="https://experiment.com/projects/tiyeokwqyyosjpkbnyoa/methods">https://experiment.com/projects/tiyeokwqyyosjpkbnyoa/methods</a> <a href="https://medium.com/@theyavuereviews/theyavue-reviews-trustworthy-official-website-or-fake-customer-claims-8bed009db7b7">https://medium.com/@theyavuereviews/theyavue-reviews-trustworthy-official-website-or-fake-customer-claims-8bed009db7b7</a> <a href="https://medium.com/@theyavuereviews">https://medium.com/@theyavuereviews</a> <a href="https://theyavue-update.clubeo.com/">https://theyavue-update.clubeo.com/</a> <a href="https://theyavue-update.clubeo.com/page/theyavue-reviews-is-it-legit-what-are-customers-saying-theyavue-eye-health-formula-exposed.html">https://theyavue-update.clubeo.com/page/theyavue-reviews-is-it-legit-what-are-customers-saying-theyavue-eye-health-formula-exposed.html</a> <a href="https://theyavue-update.clubeo.com/page/theyavue-reviews-usa-does-theyavue-eye-supplement-work-what-to-know-before-buying.html">https://theyavue-update.clubeo.com/page/theyavue-reviews-usa-does-theyavue-eye-supplement-work-what-to-know-before-buying.html</a> <a href="https://odoe.powerappsportals.us/en-US/forums/general-discussion/5fdb20f0-1a73-ee11-a81c-001dd8062310">https://odoe.powerappsportals.us/en-US/forums/general-discussion/5fdb20f0-1a73-ee11-a81c-001dd8062310</a> <a href="https://www.oecd-forum.org/users/theyavue-reviews">https://www.oecd-forum.org/users/theyavue-reviews</a> <a href="https://www.provenexpert.com/theyavue6/">https://www.provenexpert.com/theyavue6/</a> <a href="https://theyavue-reviews.company.site/">https://theyavue-reviews.company.site/</a> <a href="https://devfolio.co/projects/theyavue-reviews-shocking-eye-formula-results-016c">https://devfolio.co/projects/theyavue-reviews-shocking-eye-formula-results-016c</a> <a href="https://www.protocols.io/blind/53C7B0AC731E11EEADCD0A58A9FEAC02">https://www.protocols.io/blind/53C7B0AC731E11EEADCD0A58A9FEAC02</a> <a href="https://soundcloud.com/theyavue-reviews/theyavue-reviews-does-blurry-vision-theyavue-eye-supplement-ingredients-real-or-fake">https://soundcloud.com/theyavue-reviews/theyavue-reviews-does-blurry-vision-theyavue-eye-supplement-ingredients-real-or-fake</a> <a href="https://theyavuereviewsusa.bandcamp.com/track/theyavue-reviews-fda-approved-2023-unexpected-details-revealed">https://theyavuereviewsusa.bandcamp.com/track/theyavue-reviews-fda-approved-2023-unexpected-details-revealed</a> <a href="https://doogeemall.com/community/xenforum/topic/114086/theyavue-reviews-11-facts-revealed-what-are-the-pros-and-cons-of-theyavue-vision-health-formula">https://doogeemall.com/community/xenforum/topic/114086/theyavue-reviews-11-facts-revealed-what-are-the-pros-and-cons-of-theyavue-vision-health-formula</a> <a href="https://www.justgiving.com/page/theyavue-reviews-1698231780972">https://www.justgiving.com/page/theyavue-reviews-1698231780972</a> <a href="https://theyavue-reviews-2023.webflow.io/">https://theyavue-reviews-2023.webflow.io/</a> <a href="https://pdfhost.io/v/eSQhzZC7B_TheyaVue_Reviews_2023_The_Natural_Solution_for_Improved_Eye_Health">https://pdfhost.io/v/eSQhzZC7B_TheyaVue_Reviews_2023_The_Natural_Solution_for_Improved_Eye_Health</a> <a href="https://gamma.app/public/TheyaVue-Reviews-2023-Ingredients-Benefits-Side-Effects-And-Compl-5wkx32cjw3bjcig">https://gamma.app/public/TheyaVue-Reviews-2023-Ingredients-Benefits-Side-Effects-And-Compl-5wkx32cjw3bjcig</a> <a href="https://www.forexagone.com/forum/experiences-trading/theyavue-reviews-formulated-with-100-pure-ingredients-that-helps-to-clear-blurry-vision-87251#184665">https://www.forexagone.com/forum/experiences-trading/theyavue-reviews-formulated-with-100-pure-ingredients-that-helps-to-clear-blurry-vision-87251#184665</a> <a href="https://sketchfab.com/3d-models/theyavue-reviews-scam-aert-2023-does-it-works-3bbb23362e094536bae5e95cf01a9e59">https://sketchfab.com/3d-models/theyavue-reviews-scam-aert-2023-does-it-works-3bbb23362e094536bae5e95cf01a9e59</a> <a href="https://www.cometforums.com/topic/12812433-theyavue-reviews-urgent-2023-update-effective-ingredients-or-fake-customer-hype/">https://www.cometforums.com/topic/12812433-theyavue-reviews-urgent-2023-update-effective-ingredients-or-fake-customer-hype/</a> <a href="https://www.townscript.com/e/theyavue-reviews-scam-warning-read-this-before-buying-024040">https://www.townscript.com/e/theyavue-reviews-scam-warning-read-this-before-buying-024040</a> <a href="https://theyavuereviewsscam.bandcamp.com/track/theyavue-reviews-urgent-2023-update-effective-ingredients-or-fake-customer-hype">https://theyavuereviewsscam.bandcamp.com/track/theyavue-reviews-urgent-2023-update-effective-ingredients-or-fake-customer-hype</a> <a href="https://gamma.app/public/TheyaVue-Reviews-Is-It-Legit-What-Are-Customers-Saying-nwnpos4phaqxhls">https://gamma.app/public/TheyaVue-Reviews-Is-It-Legit-What-Are-Customers-Saying-nwnpos4phaqxhls</a> <a href="https://infogram.com/theyavue-reviews-legit-ingredients-for-healthy-eyes-and-vision-1h7v4pwz051jj6k">https://infogram.com/theyavue-reviews-legit-ingredients-for-healthy-eyes-and-vision-1h7v4pwz051jj6k</a> <a href="https://sketchfab.com/3d-models/theyavue-reviews-scam-exposed-2023-is-it-works-07b1549d431849099ae730e04822bebf">https://sketchfab.com/3d-models/theyavue-reviews-scam-exposed-2023-is-it-works-07b1549d431849099ae730e04822bebf</a> <a href="https://doogeemall.com/community/xenforum/topic/114161/theyavue-reviews-customer-scam-alert-2023-ingredients-side-effects-negative-scam">https://doogeemall.com/community/xenforum/topic/114161/theyavue-reviews-customer-scam-alert-2023-ingredients-side-effects-negative-scam</a>

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Cam1234K/vozparaai

2023-10-25T13:32:58.000Z

[ "region:us" ]

Cam1234K

null

0

2023-10-25T13:32:27

Entry not found

15

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mekaneeky/Synthetic_Acholi_MMS

2023-10-25T14:22:37.000Z

[ "region:us" ]

mekaneeky

null

0

2023-10-25T14:12:40

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: dev path: data/dev-* - split: test path: data/test-* dataset_info: features: - name: eng dtype: string - name: lug dtype: string - name: ach dtype: string - name: teo dtype: string - name: lgg dtype: string - name: nyn dtype: string - name: ID dtype: string - name: ach_tts sequence: sequence: float32 splits: - name: train num_bytes: 14041118016 num_examples: 23947 - name: dev num_bytes: 289355340 num_examples: 500 - name: test num_bytes: 322907400 num_examples: 500 download_size: 14665423094 dataset_size: 14653380756 --- # Dataset Card for "Synthetic_Acholi_MMS" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

904

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LolipopBr69/AnyafinalBR

2023-10-25T14:47:31.000Z

[ "region:us" ]

LolipopBr69

null

0

2023-10-25T14:46:53

Entry not found

15

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ClaireOzzz/Porcelain

2023-10-25T14:48:17.000Z

[ "region:us" ]

ClaireOzzz

null

0

2023-10-25T14:47:09

Entry not found

15

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HiraishinEX/EdX_Courses

2023-10-25T14:49:44.000Z

[ "region:us" ]

HiraishinEX

null

0

2023-10-25T14:48:55

Entry not found

15

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chillguypoonawala/temp1232

2023-10-25T15:06:49.000Z

[ "doi:10.57967/hf/1266", "region:us" ]

chillguypoonawala

null

0

2023-10-25T15:06:49

Entry not found

15

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MyRebRIc/mcig34

2023-10-26T00:00:51.000Z

[ "region:us" ]

MyRebRIc

null

0

2023-10-25T15:23:27

Entry not found

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Raylton/Koddi

2023-10-25T15:37:10.000Z

[ "region:us" ]

Raylton

null

0

2023-10-25T15:35:30

Entry not found

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yotoshihiro/RED

2023-10-26T15:20:50.000Z

[ "region:us" ]

yotoshihiro

null

0

2023-10-25T15:47:22

Entry not found

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weillon/dinalva

2023-10-25T15:47:52.000Z

[ "region:us" ]

weillon

null

0

2023-10-25T15:47:52

Entry not found

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cawoylel/FulaSpeechCorpora-splited

2023-10-25T16:10:24.000Z

[ "region:us" ]

cawoylel

null

0

2023-10-25T15:54:10

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: dev path: data/dev-* dataset_info: features: - name: audio dtype: audio - name: transcription dtype: string - name: dialect dtype: string splits: - name: train num_bytes: 15116684584.91028 num_examples: 93908 - name: test num_bytes: 3026464537.665628 num_examples: 18801 - name: dev num_bytes: 758023819.869451 num_examples: 4709 download_size: 17656630091 dataset_size: 18901172942.44536 --- # Dataset Card for "FulaSpeechCorpora-splited" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

771

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sahil2801/persuasion_test

2023-10-26T10:43:37.000Z

[ "region:us" ]

sahil2801

null

0

2023-10-25T16:42:20

Entry not found

15

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BangumiBase/jujutsukaisen

2023-10-25T19:36:48.000Z

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

BangumiBase

null

0

2023-10-25T17:00:44

--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Jujutsu Kaisen This is the image base of bangumi Jujutsu Kaisen, we detected 41 characters, 4326 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 | 1045 | [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 | 253 | [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 | 39 | [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 | 280 | [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 | 68 | [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 | 53 | [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 | 101 | [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 | 84 | [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 | 107 | [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 | 171 | [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 | 25 | [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 | 482 | [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 | 88 | [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 | 163 | [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 | 27 | [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 | 37 | [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 | 10 | [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 | 22 | [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 | 52 | [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 | 71 | [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 | 75 | [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 | 21 | [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 | 358 | [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 | 43 | [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 | 10 | [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 | 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 | 18 | [Download](29/dataset.zip) | ![preview 1](29/preview_1.png) | ![preview 2](29/preview_2.png) | ![preview 3](29/preview_3.png) | ![preview 4](29/preview_4.png) | ![preview 5](29/preview_5.png) | ![preview 6](29/preview_6.png) | ![preview 7](29/preview_7.png) | ![preview 8](29/preview_8.png) | | 30 | 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 | 31 | [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 | 119 | [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 | 81 | [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 | 10 | [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 | 39 | [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 | 15 | [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 | 21 | [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 | 9 | [Download](38/dataset.zip) | ![preview 1](38/preview_1.png) | ![preview 2](38/preview_2.png) | ![preview 3](38/preview_3.png) | ![preview 4](38/preview_4.png) | ![preview 5](38/preview_5.png) | ![preview 6](38/preview_6.png) | ![preview 7](38/preview_7.png) | ![preview 8](38/preview_8.png) | | 39 | 19 | [Download](39/dataset.zip) | ![preview 1](39/preview_1.png) | ![preview 2](39/preview_2.png) | ![preview 3](39/preview_3.png) | ![preview 4](39/preview_4.png) | ![preview 5](39/preview_5.png) | ![preview 6](39/preview_6.png) | ![preview 7](39/preview_7.png) | ![preview 8](39/preview_8.png) | | noise | 131 | [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) |

14,083

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jxm/agnews

2023-10-25T17:42:50.000Z

[ "region:us" ]

jxm

null

0

2023-10-25T17:42:44

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: dev path: data/dev-* dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 30133565 num_examples: 120000 - name: test num_bytes: 1899330 num_examples: 7600 - name: dev num_bytes: 63881 num_examples: 256 download_size: 20215708 dataset_size: 32096776 --- # Dataset Card for "agnews" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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typeof/hf-hub-transformers

2023-10-25T18:04:16.000Z

[ "language:en", "region:us" ]

typeof

null

0

2023-10-25T17:42:52

--- language: - en pretty_name: 🤗 --- ![image/png](carbon.svg)

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jxm/cr

2023-10-25T17:42:56.000Z

[ "region:us" ]

jxm

null

0

2023-10-25T17:42:52

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: dev path: data/dev-* dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 192172 num_examples: 1775 - name: test num_bytes: 219871 num_examples: 2000 - name: dev num_bytes: 29232 num_examples: 256 download_size: 253672 dataset_size: 441275 --- # Dataset Card for "cr" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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jxm/mr

2023-10-25T17:43:06.000Z

[ "region:us" ]

jxm

null

0

2023-10-25T17:43:01

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: dev path: data/dev-* dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 1105634 num_examples: 8662 - name: test num_bytes: 253331 num_examples: 2000 - name: dev num_bytes: 33062 num_examples: 256 download_size: 909063 dataset_size: 1392027 --- # Dataset Card for "mr" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

657

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jxm/sst2

2023-10-25T17:43:13.000Z

[ "region:us" ]

jxm

null

0

2023-10-25T17:43:08

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: dev path: data/dev-* dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 4412207 num_examples: 67349 - name: test num_bytes: 209356 num_examples: 1821 - name: dev num_bytes: 29783 num_examples: 256 download_size: 2898575 dataset_size: 4651346 --- # Dataset Card for "sst2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

661

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jxm/subj

2023-10-25T17:43:18.000Z

[ "region:us" ]

jxm

null

0

2023-10-25T17:43:14

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: dev path: data/dev-* dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 1128835 num_examples: 8000 - name: test num_bytes: 286215 num_examples: 2000 - name: dev num_bytes: 37250 num_examples: 256 download_size: 960873 dataset_size: 1452300 --- # Dataset Card for "subj" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

659

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jxm/trec

2023-10-25T17:43:23.000Z

[ "region:us" ]

jxm

null

0

2023-10-25T17:43:19

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: dev path: data/dev-* dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 342265 num_examples: 5452 - name: test num_bytes: 24003 num_examples: 500 - name: dev num_bytes: 12305 num_examples: 256 download_size: 228995 dataset_size: 378573 --- # Dataset Card for "trec" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

655

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mekaneeky/Synthetic_Acholi_VITS_22.5k

2023-10-25T17:58:49.000Z

[ "region:us" ]

mekaneeky

null

0

2023-10-25T17:46:32

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: dev path: data/dev-* - split: test path: data/test-* dataset_info: features: - name: eng dtype: string - name: lug dtype: string - name: ach dtype: string - name: teo dtype: string - name: lgg dtype: string - name: nyn dtype: string - name: ID dtype: string - name: ach_tts sequence: sequence: float32 splits: - name: train num_bytes: 17816721728 num_examples: 23947 - name: dev num_bytes: 361145932 num_examples: 500 - name: test num_bytes: 375082248 num_examples: 500 download_size: 18567936006 dataset_size: 18552949908 --- # Dataset Card for "Synthetic_Acholi_VITS_22.5k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

911

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jxm/cb

2023-10-25T17:48:45.000Z

[ "region:us" ]

jxm

null

0

2023-10-25T17:48:41

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: dev path: data/dev-* dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: string - name: idx dtype: int64 splits: - name: train num_bytes: 89859 num_examples: 250 - name: test num_bytes: 93992 num_examples: 250 - name: dev num_bytes: 22480 num_examples: 56 download_size: 139260 dataset_size: 206331 --- # Dataset Card for "cb" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

720

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jxm/rte

2023-10-25T17:49:04.000Z

[ "region:us" ]

jxm

null

0

2023-10-25T17:49:00

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: dev path: data/dev-* dataset_info: features: - name: sentence_1 dtype: string - name: sentence_2 dtype: string - name: label dtype: string splits: - name: train num_bytes: 857264 num_examples: 2490 - name: test num_bytes: 956053 num_examples: 3000 - name: dev num_bytes: 85218 num_examples: 256 download_size: 1236189 dataset_size: 1898535 --- # Dataset Card for "rte" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

700

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jamestalentium/xsum_100_finetune

2023-10-25T18:10:56.000Z

[ "region:us" ]

jamestalentium

null

0

2023-10-25T18:10:54

--- dataset_info: features: - name: input_text dtype: string - name: output_text dtype: string - name: id dtype: string splits: - name: train num_bytes: 234853.27403268887 num_examples: 100 download_size: 86391 dataset_size: 234853.27403268887 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "xsum_100_finetune" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

541

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jamestalentium/xsum_100_test

2023-10-25T18:11:00.000Z

[ "region:us" ]

jamestalentium

null

0

2023-10-25T18:10:57

--- dataset_info: features: - name: input_text dtype: string - name: output_text dtype: string - name: id dtype: string splits: - name: test num_bytes: 15613650.659431798 num_examples: 6614 download_size: 5619267 dataset_size: 15613650.659431798 configs: - config_name: default data_files: - split: test path: data/test-* --- # Dataset Card for "xsum_100_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

537

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jamestalentium/xsum_250_finetune

2023-10-25T18:11:06.000Z

[ "region:us" ]

jamestalentium

null

0

2023-10-25T18:11:05

--- dataset_info: features: - name: input_text dtype: string - name: output_text dtype: string - name: id dtype: string splits: - name: train num_bytes: 587133.1850817222 num_examples: 250 download_size: 222544 dataset_size: 587133.1850817222 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "xsum_250_finetune" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

540

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jamestalentium/xsum_250_test

2023-10-25T18:11:10.000Z

[ "region:us" ]

jamestalentium

null

0

2023-10-25T18:11:07

--- dataset_info: features: - name: input_text dtype: string - name: output_text dtype: string - name: id dtype: string splits: - name: test num_bytes: 15613650.659431798 num_examples: 6614 download_size: 5619267 dataset_size: 15613650.659431798 configs: - config_name: default data_files: - split: test path: data/test-* --- # Dataset Card for "xsum_250_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

537

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jamestalentium/xsum_1000_finetune

2023-10-25T18:11:15.000Z

[ "region:us" ]

jamestalentium

null

0

2023-10-25T18:11:13

--- dataset_info: features: - name: input_text dtype: string - name: output_text dtype: string - name: id dtype: string splits: - name: train num_bytes: 2348532.740326889 num_examples: 1000 download_size: 849145 dataset_size: 2348532.740326889 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "xsum_1000_finetune" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

542

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jamestalentium/xsum_1000_rm

2023-10-25T18:11:16.000Z

[ "region:us" ]

jamestalentium

null

0

2023-10-25T18:11:15

--- dataset_info: features: - name: input_text dtype: string - name: output_text dtype: string - name: id dtype: string splits: - name: train num_bytes: 2348532.740326889 num_examples: 1000 download_size: 830060 dataset_size: 2348532.740326889 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "xsum_1000_rm" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

536

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jamestalentium/xsum_1000_test

2023-10-25T18:11:18.000Z

[ "region:us" ]

jamestalentium

null

0

2023-10-25T18:11:16

--- dataset_info: features: - name: input_text dtype: string - name: output_text dtype: string - name: id dtype: string splits: - name: test num_bytes: 15613650.659431798 num_examples: 6614 download_size: 5619267 dataset_size: 15613650.659431798 configs: - config_name: default data_files: - split: test path: data/test-* --- # Dataset Card for "xsum_1000_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

538

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przl/racecar_sac_demo_data

2023-10-25T18:34:46.000Z

[ "region:us" ]

przl

null

0

2023-10-25T18:34:46

Entry not found

15

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przl/sac_demo_racecar_dataset

2023-10-26T21:55:03.000Z

[ "region:us" ]

przl

null

0

2023-10-25T18:43:06

Entry not found

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AneeqMalik/Enter-Your-hub-name

2023-10-25T19:12:08.000Z

[ "region:us" ]

AneeqMalik

null

0

2023-10-25T19:05:06

--- dataset_info: features: - name: audio dtype: audio - name: audio_names dtype: string - name: genere dtype: class_label: names: '0': bad '1': okay '2': good '3': great splits: - name: train num_bytes: 12388426.0 num_examples: 6 download_size: 12391275 dataset_size: 12388426.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "Enter-Your-hub-name" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

633

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IndonesiaAI/stack-split-1_translated

2023-10-25T20:02:51.000Z

[ "region:us" ]

IndonesiaAI

null

0

2023-10-25T20:01:56

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: qid dtype: string - name: question dtype: string - name: response_j dtype: string - name: response_k dtype: string splits: - name: train num_bytes: 3206490021 num_examples: 1056803 download_size: 951479401 dataset_size: 3206490021 --- # Dataset Card for "stack-split-1_translated" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

577

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arios0920/random-convs

2023-10-25T20:38:17.000Z

[ "region:us" ]

arios0920

null

0

2023-10-25T20:34:55

Entry not found

15

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helliun/happychat-dataset

2023-10-25T20:36:51.000Z

[ "region:us" ]

helliun

null

0

2023-10-25T20:36:50

--- dataset_info: features: - name: input sequence: string - name: output dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 1400318 num_examples: 1010 download_size: 668248 dataset_size: 1400318 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "happychat-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

516

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helliun/happychat-dataset-halfsplit

2023-10-25T20:38:55.000Z

[ "region:us" ]

helliun

null

0

2023-10-25T20:38:54

--- dataset_info: features: - name: input sequence: string - name: output dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 7323889 num_examples: 5058 download_size: 3993763 dataset_size: 7323889 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "happychat-dataset-halfsplit" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

527

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helliun/happychat-dataset-half-split

2023-10-25T20:41:41.000Z

[ "region:us" ]

helliun

null

0

2023-10-25T20:41:39

--- dataset_info: features: - name: convo sequence: string - name: label dtype: int64 splits: - name: train num_bytes: 7323889 num_examples: 5058 download_size: 3997613 dataset_size: 7323889 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "happychat-dataset-half-split" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

493

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helliun/happychat-dataset-tenth-split

2023-10-25T20:42:11.000Z

[ "region:us" ]

helliun

null

0

2023-10-25T20:42:10

--- dataset_info: features: - name: convo sequence: string - name: label dtype: int64 splits: - name: train num_bytes: 1400318 num_examples: 1010 download_size: 728652 dataset_size: 1400318 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "happychat-dataset-tenth-split" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

493

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GRAYHAME/liminalspace

2023-10-25T20:52:32.000Z

[ "region:us" ]

GRAYHAME

null

0

2023-10-25T20:52:32

Entry not found

15

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Alicedelotte/Snikers

2023-10-25T20:55:10.000Z

[ "region:us" ]

Alicedelotte

null

0

2023-10-25T20:55:10

Entry not found

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sdasdadas/october

2023-11-01T23:49:01.000Z

[ "region:us" ]

sdasdadas

null

0

2023-10-25T21:39:25

Entry not found

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zhen-dong-nexusflow/cvecpe_disconnected_variable_multiapis_nlq_function_pairs

2023-10-27T08:15:13.000Z

[ "region:us" ]

zhen-dong-nexusflow

null

0

2023-10-25T22:47:36

Entry not found

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magichampz/lego-technic-pieces

2023-10-26T17:02:54.000Z

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

magichampz

null

0

2023-10-25T23:03:49

--- license: mit --- # Dataset Card for Dataset Name Database for lego sorter model uploaded This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ## Dataset Details ### Dataset Description Sample database for the my lego sorter model uploaded Contains both sample images from each class as well as a numpy array file (.npy) that contains every image (~6000) used to train the model. The numpy file was created so that the dataset could be loaded into Google Collab. - **Curated by:** Aveek Goswami, Amos Koh ### Dataset Sources - **Repository:** https://github.com/magichampz/lego-sorting-machine-ag-ak ## Uses Dataset may be used to train any machine learning model. ### Direct Use Best use for this dataset is to train a model with similar architecture to the lego sorter model I uploaded. Dataset images designed to be classified into 7 distinct lego technic classes ## Dataset Structure database-sample contains 7 folders, each containing images from different categories of lego technic pieces. A .npy file is also uploaded, which has a shape of (5953,2), which means 5953 entries, with each entry containing the full image as one data point and the category label as the other data point. ### Source Data  #### Data Collection and Processing All images were not processed, they were stored as the original image both in the folders and the numpy array. Image processing occurs in the odel training script uploaded as part of the lego sorter model repo ### Recommendations All images were taken under constant lighting conditions with a raspberry PiCamera 2, which limited the quality of the images obtained.

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amintalukder/emotion_bn

2023-10-25T23:15:07.000Z

[ "region:us" ]

amintalukder

null

0

2023-10-25T23:10:08

--- configs: - config_name: default data_files: - split: val path: data/val-* - split: test path: data/test-* - split: train path: data/train-* dataset_info: features: - name: ID dtype: int64 - name: Data dtype: string - name: Love dtype: int64 - name: Joy dtype: int64 - name: Surprise dtype: int64 - name: Anger dtype: int64 - name: Sadness dtype: int64 - name: Fear dtype: int64 - name: Topic dtype: string - name: Domain dtype: string - name: is_admin dtype: bool splits: - name: val num_bytes: 503282 num_examples: 2047 - name: test num_bytes: 545033 num_examples: 2272 - name: train num_bytes: 4408992 num_examples: 18420 download_size: 1882715 dataset_size: 5457307 --- # Dataset Card for "emotion_bn" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

965

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gmaijoe-emailchaser/emailchaser-llm-body-data-v0.0.1

2023-10-25T23:15:21.000Z

[ "region:us" ]

gmaijoe-emailchaser

null

0

2023-10-25T23:15:19

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 611717 num_examples: 404 download_size: 162582 dataset_size: 611717 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "emailchaser-llm-body-data-v0.0.1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

456

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Bsbell21/generadai-sample

2023-10-25T23:45:25.000Z

[ "region:us" ]

Bsbell21

null

0

2023-10-25T23:45:23

--- dataset_info: features: - name: item dtype: string - name: description dtype: string - name: ad dtype: string splits: - name: train num_bytes: 3915 num_examples: 5 download_size: 7989 dataset_size: 3915 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "generadai-sample" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

503

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Eididkd/Pedrinhonovo2.zip

2023-10-26T01:25:51.000Z

[ "region:us" ]

Eididkd

null

0

2023-10-26T01:22:48

Entry not found

15

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Eididkd/Pedrinhonovo3.zip

2023-10-26T01:43:17.000Z

[ "region:us" ]

Eididkd

null

0

2023-10-26T01:41:47

Entry not found

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Eididkd/Voice

2023-10-26T01:50:53.000Z

[ "region:us" ]

Eididkd

null

0

2023-10-26T01:49:36

Entry not found

15

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guanine/ccre_propensity

2023-10-28T04:05:29.000Z

[ "region:us" ]

guanine

null

0

2023-10-26T01:53:51

Entry not found

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Tverous/misinfo-clusters3

2023-10-26T02:33:50.000Z

[ "region:us" ]

Tverous

null

0

2023-10-26T02:22:29

--- dataset_info: features: - name: cluster_id dtype: string - name: doc_id dtype: string - name: main_text dtype: string - name: image dtype: image - name: video dtype: string - name: audio dtype: string - name: kg_embedding sequence: sequence: float32 splits: - name: train num_bytes: 198061.0 num_examples: 1 download_size: 177682 dataset_size: 198061.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "misinfo-clusters3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

684

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zhen-dong-nexusflow/multi_cvecpe_apis_disconnected_variable

2023-10-27T06:05:25.000Z

[ "region:us" ]

zhen-dong-nexusflow

null

0

2023-10-26T02:22:49

Entry not found

15

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qianqianliu/chebai_v1.2.1

2023-10-26T02:40:08.000Z

[ "region:us" ]

qianqianliu

null

0

2023-10-26T02:39:04

Entry not found

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aadriann/micara

2023-10-26T02:45:38.000Z

[ "region:us" ]

aadriann

null

0

2023-10-26T02:45:38

Entry not found

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