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Papini/papini
Papini
2023-11-21T01:30:01Z
0
0
null
[ "license:apache-2.0", "region:us" ]
2023-11-21T01:30:01Z
2023-11-21T01:21:03.000Z
2023-11-21T01:21:03
--- license: apache-2.0 ---
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null
null
null
null
null
null
null
null
null
null
null
null
null
Kaue123456/LoganIsaacBardavid
Kaue123456
2023-11-21T01:33:09Z
0
0
null
[ "license:openrail", "region:us" ]
2023-11-21T01:33:09Z
2023-11-21T01:32:34.000Z
2023-11-21T01:32:34
--- license: openrail ---
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null
null
null
null
null
null
null
null
null
null
null
null
null
TioRob/Larin
TioRob
2023-11-21T01:34:14Z
0
0
null
[ "license:openrail", "region:us" ]
2023-11-21T01:34:14Z
2023-11-21T01:33:45.000Z
2023-11-21T01:33:45
--- license: openrail ---
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null
null
null
null
null
null
null
null
null
null
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Kana31/Female_Announcer_Skullgirls
Kana31
2023-11-21T01:35:30Z
0
0
null
[ "region:us" ]
2023-11-21T01:35:30Z
2023-11-21T01:35:02.000Z
2023-11-21T01:35:02
Entry not found
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null
null
null
null
null
null
null
null
null
null
null
null
null
mesolitica/malaysian-ultrachat
mesolitica
2023-11-24T05:29:57Z
0
0
null
[ "region:us" ]
2023-11-24T05:29:57Z
2023-11-21T01:42:53.000Z
2023-11-21T01:42:53
Entry not found
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null
null
null
null
null
null
null
null
null
null
null
null
null
KADUZADA/ROBSON
KADUZADA
2023-11-21T01:47:01Z
0
0
null
[ "license:openrail", "region:us" ]
2023-11-21T01:47:01Z
2023-11-21T01:43:38.000Z
2023-11-21T01:43:38
--- license: openrail ---
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null
null
null
null
null
null
null
null
null
null
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ErhaChen/neo_icon
ErhaChen
2023-11-21T01:51:42Z
0
0
null
[ "task_categories:text-to-image", "license:apache-2.0", "icon", "style", "lora", "region:us" ]
2023-11-21T01:51:42Z
2023-11-21T01:50:20.000Z
2023-11-21T01:50:20
--- license: apache-2.0 task_categories: - text-to-image tags: - icon - style - lora ---
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null
null
null
null
null
null
null
null
null
null
null
null
null
idning/ffhq64-caption
idning
2023-11-21T03:17:01Z
0
0
null
[ "license:mit", "region:us" ]
2023-11-21T03:17:01Z
2023-11-21T02:10:24.000Z
2023-11-21T02:10:24
--- license: mit dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 586217154.0 num_examples: 70000 download_size: 584117488 dataset_size: 586217154.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
[ -0.12853367626667023, -0.18616794049739838, 0.6529126763343811, 0.4943627417087555, -0.19319313764572144, 0.23607443273067474, 0.36071979999542236, 0.05056338757276535, 0.5793654322624207, 0.7400138974189758, -0.6508103013038635, -0.23783987760543823, -0.710224986076355, -0.047825977206230...
null
null
null
null
null
null
null
null
null
null
null
null
null
idning/ffhq256-caption
idning
2023-11-21T08:31:47Z
0
0
null
[ "license:mit", "region:us" ]
2023-11-21T08:31:47Z
2023-11-21T02:10:45.000Z
2023-11-21T02:10:45
--- license: mit dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 7388635414.0 num_examples: 70000 download_size: 7386868493 dataset_size: 7388635414.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
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null
null
null
null
null
null
null
null
null
null
null
null
null
BrookBvn/HOMELAND
BrookBvn
2023-11-21T02:17:05Z
0
0
null
[ "region:us" ]
2023-11-21T02:17:05Z
2023-11-21T02:17:05.000Z
2023-11-21T02:17:05
Entry not found
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null
null
null
null
null
null
null
null
null
null
null
null
null
Vinayakagude/cherryleaves
Vinayakagude
2023-11-21T03:40:01Z
0
0
null
[ "region:us" ]
2023-11-21T03:40:01Z
2023-11-21T02:18:26.000Z
2023-11-21T02:18:26
--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': healthy '1': powdery_mildew splits: - name: train num_bytes: 68574745.152 num_examples: 4208 download_size: 57302416 dataset_size: 68574745.152 configs: - config_name: default data_files: - split: train path: data/train-* ---
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null
null
null
null
null
null
null
null
null
null
null
null
null
librarian-bots/model-card-sentences-annotated
librarian-bots
2023-11-21T15:10:04Z
0
2
null
[ "task_categories:token-classification", "size_categories:100K<n<1M", "language:en", "documentation ", "region:us" ]
2023-11-21T15:10:04Z
2023-11-21T02:21:46.000Z
2023-11-21T02:21:46
--- configs: - config_name: raw_data data_files: results.jsonl - config_name: cleaned_data data_files: clean_results.jsonl task_categories: - token-classification tags: - 'documentation ' pretty_name: 'Model Card Sentences Annotated with Entities ' language: - en size_categories: - 100K<n<1M ---
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null
null
null
null
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nlplabtdtu/train-tokenizor-ds-T5
nlplabtdtu
2023-11-21T02:29:40Z
0
0
null
[ "region:us" ]
2023-11-21T02:29:40Z
2023-11-21T02:25:15.000Z
2023-11-21T02:25:15
--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 7956427488 num_examples: 1885715 download_size: 2662966309 dataset_size: 7956427488 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "train-tokenizor-ds-T5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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null
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nlplabtdtu/val-tokenizor-ds-T5
nlplabtdtu
2023-11-21T02:30:10Z
0
0
null
[ "region:us" ]
2023-11-21T02:30:10Z
2023-11-21T02:29:41.000Z
2023-11-21T02:29:41
--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 885510284 num_examples: 209524 download_size: 296327037 dataset_size: 885510284 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "val-tokenizor-ds-T5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
[ -0.5468728542327881, -0.03953440859913826, 0.17935112118721008, 0.13986048102378845, -0.3132687509059906, 0.22065581381320953, 0.6094202995300293, -0.06154927983880043, 0.8367611169815063, 0.5889046788215637, -0.767122745513916, -0.9475806951522827, -0.8279670476913452, -0.0996165499091148...
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lghupan/CloudBench-YAML
lghupan
2023-11-21T02:52:49Z
0
0
null
[ "license:mit", "region:us" ]
2023-11-21T02:52:49Z
2023-11-21T02:52:49.000Z
2023-11-21T02:52:49
--- license: mit ---
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nlplabtdtu/s-c4
nlplabtdtu
2023-11-21T03:00:41Z
0
0
null
[ "region:us" ]
2023-11-21T03:00:41Z
2023-11-21T02:58:13.000Z
2023-11-21T02:58:13
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: url dtype: string - name: id dtype: string - name: text dtype: string - name: perplexity dtype: float64 splits: - name: train num_bytes: 3488628609 num_examples: 777159 download_size: 1734360231 dataset_size: 3488628609 --- # Dataset Card for "s-c4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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null
null
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null
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nlplabtdtu/s-wikicorpus
nlplabtdtu
2023-11-21T03:03:21Z
0
0
null
[ "region:us" ]
2023-11-21T03:03:21Z
2023-11-21T03:00:42.000Z
2023-11-21T03:00:42
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 2455296583 num_examples: 1118180 download_size: 1536183681 dataset_size: 2455296583 --- # Dataset Card for "s-wikicorpus" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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null
null
null
null
null
null
null
null
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null
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null
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gsynb/gg
gsynb
2023-11-21T03:08:53Z
0
0
null
[ "license:apache-2.0", "region:us" ]
2023-11-21T03:08:53Z
2023-11-21T03:08:15.000Z
2023-11-21T03:08:15
--- license: apache-2.0 ---
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null
null
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ai4cloud/CloudEval-YAML
ai4cloud
2023-11-21T11:48:11Z
0
0
null
[ "license:mit", "region:us" ]
2023-11-21T11:48:11Z
2023-11-21T03:24:44.000Z
2023-11-21T03:24:44
--- license: mit ---
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null
null
null
null
null
null
null
null
null
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null
null
null
Gabriel1898/Poze
Gabriel1898
2023-11-21T03:38:52Z
0
0
null
[ "region:us" ]
2023-11-21T03:38:52Z
2023-11-21T03:35:03.000Z
2023-11-21T03:35:03
Entry not found
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null
null
null
null
null
null
null
null
null
null
null
null
null
Gabriel1898/pozao
Gabriel1898
2023-11-21T03:45:22Z
0
0
null
[ "region:us" ]
2023-11-21T03:45:22Z
2023-11-21T03:44:53.000Z
2023-11-21T03:44:53
Entry not found
[ -0.3227649927139282, -0.225684255361557, 0.862226128578186, 0.43461498618125916, -0.5282987952232361, 0.7012963891029358, 0.7915717363357544, 0.07618629932403564, 0.7746025919914246, 0.2563219666481018, -0.7852816581726074, -0.2257382869720459, -0.9104480743408203, 0.5715669393539429, -0...
null
null
null
null
null
null
null
null
null
null
null
null
null
Gabriel1898/pozee
Gabriel1898
2023-11-21T04:30:00Z
0
0
null
[ "license:openrail", "region:us" ]
2023-11-21T04:30:00Z
2023-11-21T04:29:06.000Z
2023-11-21T04:29:06
--- license: openrail ---
[ -0.12853367626667023, -0.18616794049739838, 0.6529126763343811, 0.4943627417087555, -0.19319313764572144, 0.23607443273067474, 0.36071979999542236, 0.05056338757276535, 0.5793654322624207, 0.7400138974189758, -0.6508103013038635, -0.23783987760543823, -0.710224986076355, -0.047825977206230...
null
null
null
null
null
null
null
null
null
null
null
null
null
Gabriel1898/poze1
Gabriel1898
2023-11-21T04:37:13Z
0
0
null
[ "license:openrail", "region:us" ]
2023-11-21T04:37:13Z
2023-11-21T04:36:13.000Z
2023-11-21T04:36:13
--- license: openrail ---
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null
null
null
null
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null
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null
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null
null
kseki/guanaco-llama2-1k
kseki
2023-11-28T03:52:12Z
0
0
null
[ "region:us" ]
2023-11-28T03:52:12Z
2023-11-21T04:45:05.000Z
2023-11-21T04:45:05
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 1654448 num_examples: 1000 download_size: 966693 dataset_size: 1654448 configs: - config_name: default data_files: - split: train path: data/train-* ---
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null
null
null
null
null
null
null
null
null
null
null
null
null
wesleyfaveri/sidereal
wesleyfaveri
2023-11-21T04:57:46Z
0
0
null
[ "region:us" ]
2023-11-21T04:57:46Z
2023-11-21T04:57:29.000Z
2023-11-21T04:57:29
Entry not found
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null
null
null
null
null
null
null
null
null
null
null
null
null
poniya/Poni
poniya
2023-11-21T05:00:19Z
0
0
null
[ "region:us" ]
2023-11-21T05:00:19Z
2023-11-21T05:00:19.000Z
2023-11-21T05:00:19
Entry not found
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null
null
null
null
null
null
null
null
null
null
null
null
null
Aryansoni27/Amitabh_bachchan_voice
Aryansoni27
2023-11-21T05:05:03Z
0
0
null
[ "license:mit", "region:us" ]
2023-11-21T05:05:03Z
2023-11-21T05:05:03.000Z
2023-11-21T05:05:03
--- license: mit ---
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null
null
null
null
null
null
null
null
null
null
null
null
null
gsynb/work1
gsynb
2023-11-21T05:05:25Z
0
0
null
[ "license:openrail", "region:us" ]
2023-11-21T05:05:25Z
2023-11-21T05:05:04.000Z
2023-11-21T05:05:04
--- license: openrail ---
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null
null
null
null
null
null
null
null
null
null
null
null
null
yuyijiong/Chinese_Paper_Abstract
yuyijiong
2023-11-21T05:13:08Z
0
0
null
[ "size_categories:10K<n<100K", "language:zh", "license:cc-by-nc-4.0", "region:us" ]
2023-11-21T05:13:08Z
2023-11-21T05:10:41.000Z
2023-11-21T05:10:41
--- license: cc-by-nc-4.0 language: - zh size_categories: - 10K<n<100K --- * ๅŒ…ๅซ titleใ€ๆญฃๆ–‡ใ€ไธญๆ–‡ๆ‘˜่ฆ,ๅฏ็”จไบŽ่ฎญ็ปƒๆ–‡ๆœฌๆ‘˜่ฆไปปๅŠก * ่ฎบๆ–‡ๆฅ่‡ชไธญๅ›ฝ็Ÿฅ็ฝ‘๏ผŒ็‰ˆๆƒๅ—้™๏ผŒไธ่ƒฝ็›ดๆŽฅๅ…ฌๅผ€ใ€‚ไธ‹่ฝฝๅŽ่ฏทๅ‹ฟไธŠไผ ๅˆฐๅ…ฌๅผ€ๅœบๅˆใ€‚
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null
null
null
null
null
null
null
null
null
null
null
null
null
KaibaZax/MMCds
KaibaZax
2023-11-21T05:19:23Z
0
0
null
[ "license:unknown", "region:us" ]
2023-11-21T05:19:23Z
2023-11-21T05:19:23.000Z
2023-11-21T05:19:23
--- license: unknown ---
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null
null
null
null
null
null
null
null
null
null
null
null
null
Adriiiiiiiii/minjii
Adriiiiiiiii
2023-11-21T05:44:09Z
0
0
null
[ "region:us" ]
2023-11-21T05:44:09Z
2023-11-21T05:33:10.000Z
2023-11-21T05:33:10
Entry not found
[ -0.32276472449302673, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965679168701, 0.7915717363357544, 0.07618629932403564, 0.7746022939682007, 0.2563222646713257, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...
null
null
null
null
null
null
null
null
null
null
null
null
null
Chat-UniVi/Chat-UniVi-Eval
Chat-UniVi
2023-11-23T02:18:10Z
0
0
null
[ "license:apache-2.0", "arxiv:2311.08046", "region:us" ]
2023-11-23T02:18:10Z
2023-11-21T05:43:46.000Z
2023-11-21T05:43:46
--- license: apache-2.0 --- # Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding **Paper or resources for more information:** [[Paper](https://huggingface.co/papers/2311.08046)] [[Code](https://github.com/PKU-YuanGroup/Chat-UniVi)]
[ -0.26548445224761963, -0.7237394452095032, 0.36633139848709106, 0.3787001371383667, -0.31138327717781067, 0.17892518639564514, -0.5498692989349365, -0.247834712266922, 0.17638447880744934, 0.4884128272533417, -0.34967249631881714, -0.7087324857711792, -0.7602993249893188, -0.53278714418411...
null
null
null
null
null
null
null
null
null
null
null
null
null
yxchng/laion_synthetic_filtered_large_part1
yxchng
2023-11-21T10:31:24Z
0
0
null
[ "region:us" ]
2023-11-21T10:31:24Z
2023-11-21T06:08:40.000Z
2023-11-21T06:08:40
Entry not found
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null
null
null
null
null
null
null
null
null
null
null
null
null
yxchng/laion_synthetic_filtered_large_part2
yxchng
2023-11-22T08:41:02Z
0
0
null
[ "region:us" ]
2023-11-22T08:41:02Z
2023-11-21T06:08:51.000Z
2023-11-21T06:08:51
Entry not found
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null
null
null
null
null
null
null
null
null
null
null
null
null
yxchng/laion_synthetic_filtered_large_part3
yxchng
2023-11-23T06:17:01Z
0
0
null
[ "region:us" ]
2023-11-23T06:17:01Z
2023-11-21T06:09:00.000Z
2023-11-21T06:09:00
Entry not found
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null
null
null
null
null
null
null
null
null
null
null
null
null
yxchng/laion_synthetic_filtered_large_part4
yxchng
2023-11-21T06:09:10Z
0
0
null
[ "region:us" ]
2023-11-21T06:09:10Z
2023-11-21T06:09:10.000Z
2023-11-21T06:09:10
Entry not found
[ -0.3227649927139282, -0.225684255361557, 0.862226128578186, 0.43461498618125916, -0.5282987952232361, 0.7012963891029358, 0.7915717363357544, 0.07618629932403564, 0.7746025919914246, 0.2563219666481018, -0.7852816581726074, -0.2257382869720459, -0.9104480743408203, 0.5715669393539429, -0...
null
null
null
null
null
null
null
null
null
null
null
null
null
hotamago/ZAIC-2023
hotamago
2023-11-26T03:25:29Z
0
0
null
[ "region:us" ]
2023-11-26T03:25:29Z
2023-11-21T06:10:48.000Z
2023-11-21T06:10:48
Entry not found
[ -0.32276496291160583, -0.22568435966968536, 0.8622260093688965, 0.43461480736732483, -0.5282987952232361, 0.7012965083122253, 0.7915714979171753, 0.07618625462055206, 0.7746025323867798, 0.25632181763648987, -0.7852815389633179, -0.22573819756507874, -0.9104480743408203, 0.5715669393539429...
null
null
null
null
null
null
null
null
null
null
null
null
null
mesolitica/chatgpt4-synthetic-kertas1
mesolitica
2023-11-28T22:48:32Z
0
0
null
[ "region:us" ]
2023-11-28T22:48:32Z
2023-11-21T06:36:57.000Z
2023-11-21T06:36:57
Entry not found
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null
null
null
null
null
null
null
null
null
null
null
null
null
viethq188/translate-en2vi
viethq188
2023-11-21T06:41:09Z
0
0
null
[ "license:apache-2.0", "region:us" ]
2023-11-21T06:41:09Z
2023-11-21T06:41:09.000Z
2023-11-21T06:41:09
--- license: apache-2.0 ---
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null
null
null
null
null
null
null
null
null
null
null
null
null
gnumanth/licenses
gnumanth
2023-11-21T06:52:10Z
0
0
null
[ "region:us" ]
2023-11-21T06:52:10Z
2023-11-21T06:47:28.000Z
2023-11-21T06:47:28
--- dataset_info: features: - name: other_names list: - name: name dtype: string - name: note dtype: string - name: keywords sequence: string - name: text list: - name: media_type dtype: string - name: title dtype: string - name: url dtype: string - name: identifiers list: - name: identifier dtype: string - name: scheme dtype: string - name: name dtype: string - name: id dtype: string - name: links list: - name: note dtype: string - name: url dtype: string - name: superseded_by dtype: string splits: - name: train num_bytes: 20834.494382022473 num_examples: 66 - name: test num_bytes: 7260.505617977528 num_examples: 23 download_size: 25625 dataset_size: 28095.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Licenses > Comprehensive Dataset of Open Source Licenses
[ -0.1660682111978531, 0.15826457738876343, 0.14409545063972473, 0.3932889401912689, -0.22251862287521362, -0.06224459037184715, -0.16226613521575928, 0.14408202469348907, -0.27206674218177795, 0.8252620697021484, -0.07554257661104202, -1.2728108167648315, 0.05920511484146118, -0.30572709441...
null
null
null
null
null
null
null
null
null
null
null
null
null
Slient/Test
Slient
2023-11-22T06:34:51Z
0
0
null
[ "task_categories:text-classification", "size_categories:10M<n<100M", "language:zh", "license:apache-2.0", "chemistry", "region:us" ]
2023-11-22T06:34:51Z
2023-11-21T06:48:04.000Z
2023-11-21T06:48:04
--- license: apache-2.0 task_categories: - text-classification language: - zh tags: - chemistry size_categories: - 10M<n<100M ---
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null
null
null
null
null
null
null
null
null
null
null
null
null
vanessa0688/ADL2023HW3
vanessa0688
2023-11-21T07:08:24Z
0
0
null
[ "region:us" ]
2023-11-21T07:08:24Z
2023-11-21T07:07:55.000Z
2023-11-21T07:07:55
Entry not found
[ -0.3227647542953491, -0.22568407654762268, 0.8622258901596069, 0.4346148371696472, -0.5282984972000122, 0.7012965083122253, 0.7915717959403992, 0.07618629932403564, 0.7746022343635559, 0.2563222348690033, -0.785281777381897, -0.22573848068714142, -0.9104482531547546, 0.5715669393539429, ...
null
null
null
null
null
null
null
null
null
null
null
null
null
nguyenthanhdo/Tiger-MathInstruct
nguyenthanhdo
2023-11-21T07:29:54Z
0
0
null
[ "region:us" ]
2023-11-21T07:29:54Z
2023-11-21T07:29:41.000Z
2023-11-21T07:29:41
--- configs: - config_name: default data_files: - split: vi path: data/vi-* - split: en path: data/en-* dataset_info: features: - name: source dtype: string - name: output dtype: string - name: instruction dtype: string splits: - name: vi num_bytes: 227116640 num_examples: 262040 - name: en num_bytes: 188743056 num_examples: 262040 download_size: 207887300 dataset_size: 415859696 --- # Dataset Card for "Tiger-MathInstruct" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
[ -0.7132388949394226, -0.18431155383586884, -0.022132916375994682, 0.2537437677383423, -0.17841951549053192, 0.13503210246562958, 0.29726022481918335, 0.004464647267013788, 0.8572904467582703, 0.4476454257965088, -0.9910200834274292, -0.579553484916687, -0.28822562098503113, -0.362956285476...
null
null
null
null
null
null
null
null
null
null
null
null
null
nlp-vtcc/Tiger-MathInstruct
nlp-vtcc
2023-11-21T07:30:07Z
0
0
null
[ "region:us" ]
2023-11-21T07:30:07Z
2023-11-21T07:29:54.000Z
2023-11-21T07:29:54
--- configs: - config_name: default data_files: - split: vi path: data/vi-* - split: en path: data/en-* dataset_info: features: - name: source dtype: string - name: output dtype: string - name: instruction dtype: string splits: - name: vi num_bytes: 227116640 num_examples: 262040 - name: en num_bytes: 188743056 num_examples: 262040 download_size: 207887300 dataset_size: 415859696 --- # Dataset Card for "Tiger-MathInstruct" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
[ -0.7132388949394226, -0.18431155383586884, -0.022132916375994682, 0.2537437677383423, -0.17841951549053192, 0.13503210246562958, 0.29726022481918335, 0.004464647267013788, 0.8572904467582703, 0.4476454257965088, -0.9910200834274292, -0.579553484916687, -0.28822562098503113, -0.362956285476...
null
null
null
null
null
null
null
null
null
null
null
null
null
BangumiBase/accelworld
BangumiBase
2023-11-21T09:52:30Z
0
0
null
[ "size_categories:1K<n<10K", "license:mit", "art", "region:us" ]
2023-11-21T09:52:30Z
2023-11-21T07:50:52.000Z
2023-11-21T07:50:52
--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Accel World This is the image base of bangumi Accel World, we detected 34 characters, 2098 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 | 146 | [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 | 8 | [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 | 614 | [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 | 140 | [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 | 55 | [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 | 27 | [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 | 8 | [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 | 58 | [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 | 47 | [Download](8/dataset.zip) | ![preview 1](8/preview_1.png) | ![preview 2](8/preview_2.png) | ![preview 3](8/preview_3.png) | ![preview 4](8/preview_4.png) | ![preview 5](8/preview_5.png) | ![preview 6](8/preview_6.png) | ![preview 7](8/preview_7.png) | ![preview 8](8/preview_8.png) | | 9 | 16 | [Download](9/dataset.zip) | ![preview 1](9/preview_1.png) | ![preview 2](9/preview_2.png) | ![preview 3](9/preview_3.png) | ![preview 4](9/preview_4.png) | ![preview 5](9/preview_5.png) | ![preview 6](9/preview_6.png) | ![preview 7](9/preview_7.png) | ![preview 8](9/preview_8.png) | | 10 | 21 | [Download](10/dataset.zip) | ![preview 1](10/preview_1.png) | ![preview 2](10/preview_2.png) | ![preview 3](10/preview_3.png) | ![preview 4](10/preview_4.png) | ![preview 5](10/preview_5.png) | ![preview 6](10/preview_6.png) | ![preview 7](10/preview_7.png) | ![preview 8](10/preview_8.png) | | 11 | 99 | [Download](11/dataset.zip) | ![preview 1](11/preview_1.png) | ![preview 2](11/preview_2.png) | ![preview 3](11/preview_3.png) | ![preview 4](11/preview_4.png) | ![preview 5](11/preview_5.png) | ![preview 6](11/preview_6.png) | ![preview 7](11/preview_7.png) | ![preview 8](11/preview_8.png) | | 12 | 13 | [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 | 8 | [Download](13/dataset.zip) | ![preview 1](13/preview_1.png) | ![preview 2](13/preview_2.png) | ![preview 3](13/preview_3.png) | ![preview 4](13/preview_4.png) | ![preview 5](13/preview_5.png) | ![preview 6](13/preview_6.png) | ![preview 7](13/preview_7.png) | ![preview 8](13/preview_8.png) | | 14 | 23 | [Download](14/dataset.zip) | ![preview 1](14/preview_1.png) | ![preview 2](14/preview_2.png) | ![preview 3](14/preview_3.png) | ![preview 4](14/preview_4.png) | ![preview 5](14/preview_5.png) | ![preview 6](14/preview_6.png) | ![preview 7](14/preview_7.png) | ![preview 8](14/preview_8.png) | | 15 | 10 | [Download](15/dataset.zip) | ![preview 1](15/preview_1.png) | ![preview 2](15/preview_2.png) | ![preview 3](15/preview_3.png) | ![preview 4](15/preview_4.png) | ![preview 5](15/preview_5.png) | ![preview 6](15/preview_6.png) | ![preview 7](15/preview_7.png) | ![preview 8](15/preview_8.png) | | 16 | 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 | 429 | [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 | 14 | [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 | 17 | [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 | 14 | [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 | 28 | [Download](21/dataset.zip) | ![preview 1](21/preview_1.png) | ![preview 2](21/preview_2.png) | ![preview 3](21/preview_3.png) | ![preview 4](21/preview_4.png) | ![preview 5](21/preview_5.png) | ![preview 6](21/preview_6.png) | ![preview 7](21/preview_7.png) | ![preview 8](21/preview_8.png) | | 22 | 6 | [Download](22/dataset.zip) | ![preview 1](22/preview_1.png) | ![preview 2](22/preview_2.png) | ![preview 3](22/preview_3.png) | ![preview 4](22/preview_4.png) | ![preview 5](22/preview_5.png) | ![preview 6](22/preview_6.png) | N/A | N/A | | 23 | 10 | [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 | 13 | [Download](24/dataset.zip) | ![preview 1](24/preview_1.png) | ![preview 2](24/preview_2.png) | ![preview 3](24/preview_3.png) | ![preview 4](24/preview_4.png) | ![preview 5](24/preview_5.png) | ![preview 6](24/preview_6.png) | ![preview 7](24/preview_7.png) | ![preview 8](24/preview_8.png) | | 25 | 14 | [Download](25/dataset.zip) | ![preview 1](25/preview_1.png) | ![preview 2](25/preview_2.png) | ![preview 3](25/preview_3.png) | ![preview 4](25/preview_4.png) | ![preview 5](25/preview_5.png) | ![preview 6](25/preview_6.png) | ![preview 7](25/preview_7.png) | ![preview 8](25/preview_8.png) | | 26 | 20 | [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 | 9 | [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 | 6 | [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) | N/A | N/A | | 29 | 5 | [Download](29/dataset.zip) | ![preview 1](29/preview_1.png) | ![preview 2](29/preview_2.png) | ![preview 3](29/preview_3.png) | ![preview 4](29/preview_4.png) | ![preview 5](29/preview_5.png) | N/A | N/A | N/A | | 30 | 5 | [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) | N/A | N/A | N/A | | 31 | 10 | [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 | 7 | [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) | N/A | | noise | 171 | [Download](-1/dataset.zip) | ![preview 1](-1/preview_1.png) | ![preview 2](-1/preview_2.png) | ![preview 3](-1/preview_3.png) | ![preview 4](-1/preview_4.png) | ![preview 5](-1/preview_5.png) | ![preview 6](-1/preview_6.png) | ![preview 7](-1/preview_7.png) | ![preview 8](-1/preview_8.png) |
[ -0.7039166688919067, -0.14310745894908905, 0.12264654040336609, 0.1890878677368164, -0.23508644104003906, -0.07180988043546677, -0.042564548552036285, -0.42993709444999695, 0.5993875861167908, 0.49432235956192017, -0.9615366458892822, -0.8598553538322449, -0.6793078184127808, 0.54181635379...
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BangumiBase/tengentoppa
BangumiBase
2023-11-21T10:35:47Z
0
0
null
[ "size_categories:1K<n<10K", "license:mit", "art", "region:us" ]
2023-11-21T10:35:47Z
2023-11-21T07:51:21.000Z
2023-11-21T07:51:21
--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Tengen Toppa This is the image base of bangumi Tengen Toppa, we detected 40 characters, 3081 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 | 107 | [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 | 137 | [Download](1/dataset.zip) | ![preview 1](1/preview_1.png) | ![preview 2](1/preview_2.png) | ![preview 3](1/preview_3.png) | ![preview 4](1/preview_4.png) | ![preview 5](1/preview_5.png) | ![preview 6](1/preview_6.png) | ![preview 7](1/preview_7.png) | ![preview 8](1/preview_8.png) | | 2 | 104 | [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 | 23 | [Download](3/dataset.zip) | ![preview 1](3/preview_1.png) | ![preview 2](3/preview_2.png) | ![preview 3](3/preview_3.png) | ![preview 4](3/preview_4.png) | ![preview 5](3/preview_5.png) | ![preview 6](3/preview_6.png) | ![preview 7](3/preview_7.png) | ![preview 8](3/preview_8.png) | | 4 | 29 | [Download](4/dataset.zip) | ![preview 1](4/preview_1.png) | ![preview 2](4/preview_2.png) | ![preview 3](4/preview_3.png) | ![preview 4](4/preview_4.png) | ![preview 5](4/preview_5.png) | ![preview 6](4/preview_6.png) | ![preview 7](4/preview_7.png) | ![preview 8](4/preview_8.png) | | 5 | 33 | [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 | 36 | [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 | 28 | [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 | 359 | [Download](8/dataset.zip) | ![preview 1](8/preview_1.png) | ![preview 2](8/preview_2.png) | ![preview 3](8/preview_3.png) | ![preview 4](8/preview_4.png) | ![preview 5](8/preview_5.png) | ![preview 6](8/preview_6.png) | ![preview 7](8/preview_7.png) | ![preview 8](8/preview_8.png) | | 9 | 73 | [Download](9/dataset.zip) | ![preview 1](9/preview_1.png) | ![preview 2](9/preview_2.png) | ![preview 3](9/preview_3.png) | ![preview 4](9/preview_4.png) | ![preview 5](9/preview_5.png) | ![preview 6](9/preview_6.png) | ![preview 7](9/preview_7.png) | ![preview 8](9/preview_8.png) | | 10 | 133 | [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 | 151 | [Download](11/dataset.zip) | ![preview 1](11/preview_1.png) | ![preview 2](11/preview_2.png) | ![preview 3](11/preview_3.png) | ![preview 4](11/preview_4.png) | ![preview 5](11/preview_5.png) | ![preview 6](11/preview_6.png) | ![preview 7](11/preview_7.png) | ![preview 8](11/preview_8.png) | | 12 | 32 | [Download](12/dataset.zip) | ![preview 1](12/preview_1.png) | ![preview 2](12/preview_2.png) | ![preview 3](12/preview_3.png) | ![preview 4](12/preview_4.png) | ![preview 5](12/preview_5.png) | ![preview 6](12/preview_6.png) | ![preview 7](12/preview_7.png) | ![preview 8](12/preview_8.png) | | 13 | 44 | [Download](13/dataset.zip) | ![preview 1](13/preview_1.png) | ![preview 2](13/preview_2.png) | ![preview 3](13/preview_3.png) | ![preview 4](13/preview_4.png) | ![preview 5](13/preview_5.png) | ![preview 6](13/preview_6.png) | ![preview 7](13/preview_7.png) | ![preview 8](13/preview_8.png) | | 14 | 78 | [Download](14/dataset.zip) | ![preview 1](14/preview_1.png) | ![preview 2](14/preview_2.png) | ![preview 3](14/preview_3.png) | ![preview 4](14/preview_4.png) | ![preview 5](14/preview_5.png) | ![preview 6](14/preview_6.png) | ![preview 7](14/preview_7.png) | ![preview 8](14/preview_8.png) | | 15 | 25 | [Download](15/dataset.zip) | ![preview 1](15/preview_1.png) | ![preview 2](15/preview_2.png) | ![preview 3](15/preview_3.png) | ![preview 4](15/preview_4.png) | ![preview 5](15/preview_5.png) | ![preview 6](15/preview_6.png) | ![preview 7](15/preview_7.png) | ![preview 8](15/preview_8.png) | | 16 | 22 | [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 | 17 | [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 | 44 | [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 | 104 | [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 | 51 | [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 | 37 | [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 | 339 | [Download](22/dataset.zip) | ![preview 1](22/preview_1.png) | ![preview 2](22/preview_2.png) | ![preview 3](22/preview_3.png) | ![preview 4](22/preview_4.png) | ![preview 5](22/preview_5.png) | ![preview 6](22/preview_6.png) | ![preview 7](22/preview_7.png) | ![preview 8](22/preview_8.png) | | 23 | 32 | [Download](23/dataset.zip) | ![preview 1](23/preview_1.png) | ![preview 2](23/preview_2.png) | ![preview 3](23/preview_3.png) | ![preview 4](23/preview_4.png) | ![preview 5](23/preview_5.png) | ![preview 6](23/preview_6.png) | ![preview 7](23/preview_7.png) | ![preview 8](23/preview_8.png) | | 24 | 11 | [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 | 16 | [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 | 16 | [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 | 53 | [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 | 50 | [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 | 59 | [Download](30/dataset.zip) | ![preview 1](30/preview_1.png) | ![preview 2](30/preview_2.png) | ![preview 3](30/preview_3.png) | ![preview 4](30/preview_4.png) | ![preview 5](30/preview_5.png) | ![preview 6](30/preview_6.png) | ![preview 7](30/preview_7.png) | ![preview 8](30/preview_8.png) | | 31 | 23 | [Download](31/dataset.zip) | ![preview 1](31/preview_1.png) | ![preview 2](31/preview_2.png) | ![preview 3](31/preview_3.png) | ![preview 4](31/preview_4.png) | ![preview 5](31/preview_5.png) | ![preview 6](31/preview_6.png) | ![preview 7](31/preview_7.png) | ![preview 8](31/preview_8.png) | | 32 | 36 | [Download](32/dataset.zip) | ![preview 1](32/preview_1.png) | ![preview 2](32/preview_2.png) | ![preview 3](32/preview_3.png) | ![preview 4](32/preview_4.png) | ![preview 5](32/preview_5.png) | ![preview 6](32/preview_6.png) | ![preview 7](32/preview_7.png) | ![preview 8](32/preview_8.png) | | 33 | 28 | [Download](33/dataset.zip) | ![preview 1](33/preview_1.png) | ![preview 2](33/preview_2.png) | ![preview 3](33/preview_3.png) | ![preview 4](33/preview_4.png) | ![preview 5](33/preview_5.png) | ![preview 6](33/preview_6.png) | ![preview 7](33/preview_7.png) | ![preview 8](33/preview_8.png) | | 34 | 11 | [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 | 19 | [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 | 9 | [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 | 73 | [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 | 13 | [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) | | noise | 616 | [Download](-1/dataset.zip) | ![preview 1](-1/preview_1.png) | ![preview 2](-1/preview_2.png) | ![preview 3](-1/preview_3.png) | ![preview 4](-1/preview_4.png) | ![preview 5](-1/preview_5.png) | ![preview 6](-1/preview_6.png) | ![preview 7](-1/preview_7.png) | ![preview 8](-1/preview_8.png) |
[ -0.6985718607902527, -0.12269221246242523, 0.10642381757497787, 0.23508769273757935, -0.26850804686546326, -0.08564591407775879, -0.06606193631887436, -0.3518286943435669, 0.6485023498535156, 0.5130122303962708, -0.9239925742149353, -0.8546018600463867, -0.7212064266204834, 0.5418100357055...
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BangumiBase/haikyuu
BangumiBase
2023-11-21T17:33:41Z
0
0
null
[ "size_categories:10K<n<100K", "license:mit", "art", "region:us" ]
2023-11-21T17:33:41Z
2023-11-21T07:51:49.000Z
2023-11-21T07:51:49
--- license: mit tags: - art size_categories: - 10K<n<100K --- # Bangumi Image Base of Haikyuu!! This is the image base of bangumi Haikyuu!!, we detected 63 characters, 19919 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 | 4856 | [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 | 1384 | [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 | 631 | [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 | 1893 | [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 | 585 | [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 | 1375 | [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 | 221 | [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 | 169 | [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 | 405 | [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 | 336 | [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 | 304 | [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 | 323 | [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 | 210 | [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 | 769 | [Download](13/dataset.zip) | ![preview 1](13/preview_1.png) | ![preview 2](13/preview_2.png) | ![preview 3](13/preview_3.png) | ![preview 4](13/preview_4.png) | ![preview 5](13/preview_5.png) | ![preview 6](13/preview_6.png) | ![preview 7](13/preview_7.png) | ![preview 8](13/preview_8.png) | | 14 | 149 | [Download](14/dataset.zip) | ![preview 1](14/preview_1.png) | ![preview 2](14/preview_2.png) | ![preview 3](14/preview_3.png) | ![preview 4](14/preview_4.png) | ![preview 5](14/preview_5.png) | ![preview 6](14/preview_6.png) | ![preview 7](14/preview_7.png) | ![preview 8](14/preview_8.png) | | 15 | 166 | [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 | 89 | [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 | 419 | [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 | 90 | [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 | 78 | [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 | 35 | [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 | 76 | [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 | 97 | [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 | 85 | [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 | 73 | [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 | 2198 | [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 | 611 | [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 | 340 | [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 | 163 | [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 | 84 | [Download](29/dataset.zip) | ![preview 1](29/preview_1.png) | ![preview 2](29/preview_2.png) | ![preview 3](29/preview_3.png) | ![preview 4](29/preview_4.png) | ![preview 5](29/preview_5.png) | ![preview 6](29/preview_6.png) | ![preview 7](29/preview_7.png) | ![preview 8](29/preview_8.png) | | 30 | 22 | [Download](30/dataset.zip) | ![preview 1](30/preview_1.png) | ![preview 2](30/preview_2.png) | ![preview 3](30/preview_3.png) | ![preview 4](30/preview_4.png) | ![preview 5](30/preview_5.png) | ![preview 6](30/preview_6.png) | ![preview 7](30/preview_7.png) | ![preview 8](30/preview_8.png) | | 31 | 46 | [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 | 27 | [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 | 102 | [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 | 41 | [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 | 52 | [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 | 29 | [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 | 28 | [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 | 110 | [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 | 20 | [Download](39/dataset.zip) | ![preview 1](39/preview_1.png) | ![preview 2](39/preview_2.png) | ![preview 3](39/preview_3.png) | ![preview 4](39/preview_4.png) | ![preview 5](39/preview_5.png) | ![preview 6](39/preview_6.png) | ![preview 7](39/preview_7.png) | ![preview 8](39/preview_8.png) | | 40 | 18 | [Download](40/dataset.zip) | ![preview 1](40/preview_1.png) | ![preview 2](40/preview_2.png) | ![preview 3](40/preview_3.png) | ![preview 4](40/preview_4.png) | ![preview 5](40/preview_5.png) | ![preview 6](40/preview_6.png) | ![preview 7](40/preview_7.png) | ![preview 8](40/preview_8.png) | | 41 | 37 | [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 | 87 | [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 | 10 | [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 | 15 | [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 | 11 | [Download](45/dataset.zip) | ![preview 1](45/preview_1.png) | ![preview 2](45/preview_2.png) | ![preview 3](45/preview_3.png) | ![preview 4](45/preview_4.png) | ![preview 5](45/preview_5.png) | ![preview 6](45/preview_6.png) | ![preview 7](45/preview_7.png) | ![preview 8](45/preview_8.png) | | 46 | 13 | [Download](46/dataset.zip) | ![preview 1](46/preview_1.png) | ![preview 2](46/preview_2.png) | ![preview 3](46/preview_3.png) | ![preview 4](46/preview_4.png) | ![preview 5](46/preview_5.png) | ![preview 6](46/preview_6.png) | ![preview 7](46/preview_7.png) | ![preview 8](46/preview_8.png) | | 47 | 34 | [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 | 34 | [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 | 50 | [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 | 17 | [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 | 285 | [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 | 107 | [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 | 299 | [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 | 11 | [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 | 19 | [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 | 24 | [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 | 14 | [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 | 20 | [Download](58/dataset.zip) | ![preview 1](58/preview_1.png) | ![preview 2](58/preview_2.png) | ![preview 3](58/preview_3.png) | ![preview 4](58/preview_4.png) | ![preview 5](58/preview_5.png) | ![preview 6](58/preview_6.png) | ![preview 7](58/preview_7.png) | ![preview 8](58/preview_8.png) | | 59 | 20 | [Download](59/dataset.zip) | ![preview 1](59/preview_1.png) | ![preview 2](59/preview_2.png) | ![preview 3](59/preview_3.png) | ![preview 4](59/preview_4.png) | ![preview 5](59/preview_5.png) | ![preview 6](59/preview_6.png) | ![preview 7](59/preview_7.png) | ![preview 8](59/preview_8.png) | | 60 | 34 | [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 | 10 | [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) | | noise | 59 | [Download](-1/dataset.zip) | ![preview 1](-1/preview_1.png) | ![preview 2](-1/preview_2.png) | ![preview 3](-1/preview_3.png) | ![preview 4](-1/preview_4.png) | ![preview 5](-1/preview_5.png) | ![preview 6](-1/preview_6.png) | ![preview 7](-1/preview_7.png) | ![preview 8](-1/preview_8.png) |
[ -0.7082678079605103, -0.16418614983558655, 0.1582217663526535, 0.1726134717464447, -0.27660953998565674, -0.08270817250013351, -0.031870972365140915, -0.35505539178848267, 0.6780584454536438, 0.5032450556755066, -0.8821287751197815, -0.8665600419044495, -0.6660722494125366, 0.5086807012557...
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smcproject/MSC
smcproject
2023-11-21T09:49:54Z
0
0
null
[ "task_categories:automatic-speech-recognition", "size_categories:1K<n<10K", "language:ml", "license:cc-by-4.0", "doi:10.57967/hf/1373", "region:us" ]
2023-11-21T09:49:54Z
2023-11-21T07:51:57.000Z
2023-11-21T07:51:57
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: audio dtype: audio - name: speechid dtype: string - name: speaker_id dtype: string - name: review_score dtype: int64 - name: transcript dtype: string - name: category dtype: string - name: speaker_gender dtype: string - name: speaker_age dtype: string splits: - name: train num_bytes: 579920220.506 num_examples: 1541 download_size: 422956016 dataset_size: 579920220.506 license: cc-by-4.0 task_categories: - automatic-speech-recognition language: - ml pretty_name: SMC Malayalam Speech Corpus size_categories: - 1K<n<10K --- # Dataset Card for [msc] ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **https://smc.org.in** - **https://gitlab.com/smc/msc-reviewed-speech** - **https://blog.smc.org.in/malayalam-speech-corpus/** - **Point of Contact: Kavya Manohar** ### Dataset Summary - 1541 speech samples - 75 speech contributors - 1:38:16 hours of speech - 482 unique sentences - 1400 unique words - 553 unique syllables - 48 unique phonemes For more detailed analysis see the python notebook provided [here](https://gitlab.com/smc/msc-reviewed-speech/-/blob/master/analysis/EDA.ipynb) ### Supported Tasks and Leaderboards Automatic Speech Recognition system development, gender and age identification of speakers ### Languages Malayalam ## Dataset Structure - file_name - speechid - speaker_id - review_score - transcript - category (optional speech category) - speaker_gender (optionally self declared) - speaker_age (optionally self declared) ### Data Instances ### Data Fields ### Data Splits So specific Splits ## Dataset Creation The speech data is collected from volunteer users who read and record their speech through a [web application](https://msc.smc.org.in) using their personal devices. The recorded speech is reviewed (upvote and downvote gives a score of +1 and -1 respectively) by other users. The review score is also published. ### Curation Rationale The recorded speech is reviewed (upvote and downvote gives a score of +1 and -1 respectively) by other users. The review score is also published. ### Curation Rationale Those speech samples with at least three positive reviews are included in this dataset. ### Source Data #### Initial Data Collection and Normalization The speech data is collected from volunteer contributors who read and record their speech through a [web application](https://msc.smc.org.in). The users optionally provide name, age and gender. There is no further verification. Sentences to read out are curated by MSC Admin. The speech samples are reviewed by other users. ### Personal and Sensitive Information Every speaker is identified by a unique alphanumeric id and age and gender are published if the speaker has voluntarily published them. ## Considerations for Using the Data ### Social Impact of Dataset Read speech corpus, recorded in natural environments by the users. ### Dataset Curators Kavya Manohar ### Licensing Information CC-BY-SA 4.0 ### Citation Information ### Contributions http://msc.smc.org.in/
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null
null
null
null
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null
null
null
null
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null
null
null
Shruti9756/reward_dataset_for_contract_analysis
Shruti9756
2023-11-21T08:00:45Z
0
0
null
[ "region:us" ]
2023-11-21T08:00:45Z
2023-11-21T08:00:45.000Z
2023-11-21T08:00:45
Entry not found
[ -0.3227649927139282, -0.225684255361557, 0.862226128578186, 0.43461498618125916, -0.5282987952232361, 0.7012963891029358, 0.7915717363357544, 0.07618629932403564, 0.7746025919914246, 0.2563219666481018, -0.7852816581726074, -0.2257382869720459, -0.9104480743408203, 0.5715669393539429, -0...
null
null
null
null
null
null
null
null
null
null
null
null
null
doubledsbv/large-chat-and-instructions-german-32k-tokenized
doubledsbv
2023-11-21T11:11:41Z
0
0
null
[ "region:us" ]
2023-11-21T11:11:41Z
2023-11-21T08:02:01.000Z
2023-11-21T08:02:01
--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 248520102456 num_examples: 583386 - name: test num_bytes: 27613486716 num_examples: 64821 download_size: 7784448718 dataset_size: 276133589172 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
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null
null
null
null
null
null
null
null
null
null
null
null
null
asd23095/ddpm-butterflies-128
asd23095
2023-11-21T08:11:41Z
0
0
null
[ "region:us" ]
2023-11-21T08:11:41Z
2023-11-21T08:11:41.000Z
2023-11-21T08:11:41
Entry not found
[ -0.3227649927139282, -0.225684255361557, 0.862226128578186, 0.43461498618125916, -0.5282987952232361, 0.7012963891029358, 0.7915717363357544, 0.07618629932403564, 0.7746025919914246, 0.2563219666481018, -0.7852816581726074, -0.2257382869720459, -0.9104480743408203, 0.5715669393539429, -0...
null
null
null
null
null
null
null
null
null
null
null
null
null
nickshek/workshop
nickshek
2023-11-23T10:26:41Z
0
0
null
[ "size_categories:1B<n<10B", "Weather", "region:us" ]
2023-11-23T10:26:41Z
2023-11-21T08:21:48.000Z
2023-11-21T08:21:48
--- tags: - Weather size_categories: - 1B<n<10B extra_gated_prompt: "You will use this dataset for research purposes only." extra_gated_fields: Name: text Affiliation: text Email: text I agree to use this dataset for research purposes only: checkbox --- Contain required data for our workshop
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null
null
null
null
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null
null
null
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null
null
Lucas666/package_llava_665k_test
Lucas666
2023-11-21T08:26:29Z
0
0
null
[ "license:apache-2.0", "region:us" ]
2023-11-21T08:26:29Z
2023-11-21T08:26:29.000Z
2023-11-21T08:26:29
--- license: apache-2.0 ---
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null
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null
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null
null
null
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null
null
LinoyTsaban/3d_icon
LinoyTsaban
2023-11-21T08:41:47Z
0
0
null
[ "region:us" ]
2023-11-21T08:41:47Z
2023-11-21T08:41:12.000Z
2023-11-21T08:41:12
Entry not found
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null
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null
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null
null
null
xiaozhou0822/dfdsfsdf
xiaozhou0822
2023-11-21T08:41:56Z
0
0
null
[ "license:mit", "region:us" ]
2023-11-21T08:41:56Z
2023-11-21T08:41:56.000Z
2023-11-21T08:41:56
--- license: mit ---
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null
null
null
null
null
null
null
null
null
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null
null
ShashiVish/Fake-Essay-Detection-Dataset
ShashiVish
2023-11-21T09:45:02Z
0
0
null
[ "region:us" ]
2023-11-21T09:45:02Z
2023-11-21T09:36:31.000Z
2023-11-21T09:36:31
--- dataset_info: features: - name: id dtype: string - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 21510645.36567626 num_examples: 9046 - name: test num_bytes: 9221565.634323739 num_examples: 3878 download_size: 16046942 dataset_size: 30732211.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
[ -0.12853367626667023, -0.18616794049739838, 0.6529126763343811, 0.4943627417087555, -0.19319313764572144, 0.23607443273067474, 0.36071979999542236, 0.05056338757276535, 0.5793654322624207, 0.7400138974189758, -0.6508103013038635, -0.23783987760543823, -0.710224986076355, -0.047825977206230...
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null
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null
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null
null
EP45/test-1k
EP45
2023-11-21T09:37:51Z
0
0
null
[ "region:us" ]
2023-11-21T09:37:51Z
2023-11-21T09:37:14.000Z
2023-11-21T09:37:14
# vicuna ์‹คํ—˜์šฉ ๋ฐ์ดํ„ฐ์…‹ ๋‹ค์Œ ๋ฐ์ดํ„ฐ์…‹์œผ๋กœ๋ถ€ํ„ฐ ๋ณ€ํ™˜๋จ: https://huggingface.co/datasets/junelee/sharegpt_deepl_ko ## ํŒŒ์ผ๊ตฌ์กฐ - converted.parquet : ์›๋ณธ ๋ฐ์ดํ„ฐ์…‹์˜ ko_alpaca_style_dataset.json์„ ํŠธ๋ ˆ์ด๋‹์— ๋งž๋„๋ก ํ˜•์‹ ๋ณ€ํ™˜ ## ๋ผ์ด์„ผ์Šค ์›๋ณธ ๋ฐ์ดํ„ฐ๊ฐ€ OPENAI ์ด๊ธฐ ๋•Œ๋ฌธ์— ํ•ด๋‹น [์•ฝ๊ด€](https://openai.com/policies/terms-of-use)์— ๋”ฐ๋ฆ…๋‹ˆ๋‹ค. ๊ทธ ์ด์™ธ์˜ ๋ถ€๋ถ„์€ ๋‹ค์Œ ๋ผ์ด์„ผ์Šค๋ฅผ ๋”ฐ๋ฆ…๋‹ˆ๋‹ค: ์ €์ž‘์žํ‘œ์‹œ 2.0 ๋Œ€ํ•œ๋ฏผ๊ตญ (CC BY 2.0 KR)
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null
vietgpt/stackexchange
vietgpt
2023-11-21T10:46:30Z
0
0
null
[ "region:us" ]
2023-11-21T10:46:30Z
2023-11-21T09:54:31.000Z
2023-11-21T09:54:31
--- dataset_info: features: - name: text dtype: string - name: meta struct: - name: language dtype: string - name: url dtype: string - name: timestamp dtype: timestamp[s] - name: source dtype: string - name: question_score dtype: string splits: - name: train num_bytes: 74107092867 num_examples: 29825086 download_size: 36677546391 dataset_size: 74107092867 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "stackexchange" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
[ -0.6176820397377014, -0.09214115142822266, -0.004695954732596874, 0.2698585093021393, -0.2202298790216446, 0.015303955413401127, 0.3872012197971344, -0.30417364835739136, 0.9920282959938049, 0.5626800656318665, -0.8615154027938843, -0.7070237398147583, -0.573861837387085, -0.28094473481178...
null
null
null
null
null
null
null
null
null
null
null
null
null
CNS-COVER/MAC
CNS-COVER
2023-11-21T11:58:09Z
0
0
null
[ "task_categories:text-generation", "size_categories:1K<n<10K", "license:mit", "biology", "chemistry", "medical", "climate", "region:us" ]
2023-11-21T11:58:09Z
2023-11-21T09:56:57.000Z
2023-11-21T09:56:57
--- dataset_info: - config_name: MAC features: - name: file_name dtype: string - name: editorial dtype: string - name: journal dtype: string - name: abstracts dtype: string - name: img dtype: image splits: - name: train num_bytes: 3587064062.16 num_examples: 5872 download_size: 6434624578 dataset_size: 3587064062.16 - config_name: MAC-Medium features: - name: file_name dtype: string - name: editorial dtype: string - name: journal dtype: string - name: abstracts dtype: string - name: img dtype: image splits: - name: train num_bytes: 1120981649 num_examples: 940 download_size: 1120522431 dataset_size: 1120981649 - config_name: MAC-Small features: - name: file_name dtype: string - name: editorial dtype: string - name: journal dtype: string - name: abstracts dtype: string - name: img dtype: image splits: - name: train num_bytes: 71044560 num_examples: 50 download_size: 71026675 dataset_size: 71044560 configs: - config_name: MAC data_files: - split: train path: MAC/train-* - config_name: MAC-Medium data_files: - split: train path: MAC-Medium/train-* - config_name: MAC-Small data_files: - split: train path: MAC-Small/train-* license: mit task_categories: - text-generation tags: - biology - chemistry - medical - climate size_categories: - 1K<n<10K --- # Dataset Card for MAC <!-- Provide a quick summary of the dataset. --> The Multimodal Academic Cover (MAC) is a benchmark, comprising a 5872 collection of cover images, cover stories, and relevant articles from leading academic journals, including Cell, Nature, Science, and their sub-publications. MAC is designed to test the ability of Multimodal models on the scientific visual understanding. ## Dataset Details MAC consists of 5872 journal issues, each with a complete group of cover images, cover stories, and articles. Two subsets are also provided, MAC-Medium (940 issues) and MAC-Small (50 issues), to facilitate fast or qualitative evaluation. - **Curated by:** Jin Gao, Jiahao Zhan, Chongxuan Li, Dequan Wang - **Language(s) (NLP):** [English] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ```python from datasets import load_dataset dataset = load_dataset("JohnZhan/MAC","MAC-Small",split="train") print(dataset["train"][0]) ```
[ -0.6955353021621704, -0.37226325273513794, 0.20151054859161377, 0.32999977469444275, -0.1491498351097107, 0.2758036255836487, 0.006359001621603966, -0.46115633845329285, 0.5389925241470337, 0.47565290331840515, -0.8579762578010559, -0.8170074820518494, -0.37602466344833374, 0.3006801009178...
null
null
null
null
null
null
null
null
null
null
null
null
null
HorcruxNo13/new_tool
HorcruxNo13
2023-11-21T10:26:30Z
0
0
null
[ "region:us" ]
2023-11-21T10:26:30Z
2023-11-21T10:25:39.000Z
2023-11-21T10:25:39
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: pixel_values dtype: image - name: label dtype: image splits: - name: train num_bytes: 328426121.0 num_examples: 16 download_size: 21122972 dataset_size: 328426121.0 --- # Dataset Card for "new_tool" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
[ -0.7240555882453918, -0.4181901514530182, 0.16453319787979126, 0.1424787938594818, -0.3466227352619171, 0.18252931535243988, 0.3721807897090912, -0.1359071582555771, 0.9686012864112854, 0.4522453248500824, -0.865120530128479, -0.8369783759117126, -0.6680151224136353, -0.17973902821540833, ...
null
null
null
null
null
null
null
null
null
null
null
null
null
doubledsbv/large-chat-and-instructions-german-32k-tokenized-mistral-fa2
doubledsbv
2023-11-21T10:34:26Z
0
0
null
[ "region:us" ]
2023-11-21T10:34:26Z
2023-11-21T10:34:26.000Z
2023-11-21T10:34:26
Entry not found
[ -0.3227645754814148, -0.22568479180335999, 0.8622263669967651, 0.43461522459983826, -0.52829909324646, 0.7012971639633179, 0.7915719747543335, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104475975036621, 0.5715674161911011, -...
null
null
null
null
null
null
null
null
null
null
null
null
null
doubledsbv/large-chat-and-instructions-german-8k-tokenized-mistral-fa2
doubledsbv
2023-11-21T10:34:45Z
0
0
null
[ "region:us" ]
2023-11-21T10:34:45Z
2023-11-21T10:34:44.000Z
2023-11-21T10:34:44
Entry not found
[ -0.3227645754814148, -0.22568479180335999, 0.8622263669967651, 0.43461522459983826, -0.52829909324646, 0.7012971639633179, 0.7915719747543335, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104475975036621, 0.5715674161911011, -...
null
null
null
null
null
null
null
null
null
null
null
null
null
epfl-llm/guidelines
epfl-llm
2023-11-28T10:33:18Z
0
12
null
[ "task_categories:text-generation", "size_categories:10K<n<100K", "language:en", "license:other", "medical", "arxiv:2311.16079", "region:us" ]
2023-11-28T10:33:18Z
2023-11-21T10:35:34.000Z
2023-11-21T10:35:34
--- license: other license_name: common-crawl license_link: LICENSE task_categories: - text-generation language: - en pretty_name: Clinical Guidelines size_categories: - 10K<n<100K configs: - config_name: default data_files: - split: train path: guidelines.jsonl tags: - medical --- # Clinical Guidelines The Clinical Guidelines corpus is a new dataset of 46,649 clinical practice guidelines from 16 high-quality online medical sources. This dataset serves as a crucial component of the original training corpus of the [Meditron](https://huggingface.co/epfl-llm/meditron-70b) Large Language Model (LLM). We publicly release a subset of 35,733 articles from our Guidelines corpus, extracted from 8 of 16 sources that allow content redistribution, namely CCO, CDC, CMA, ICRC, NICE, SPOR, WHO and WikiDoc. You can scrape and clean all 16 guideline sources using our code in [epfLLM/meditron](https://github.com/epfLLM/meditron). <img width=75% src="sources.png" alt="Sources of Clinical Practice Guidelines" title="CPG sources"> ## Dataset Details <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [EPFL LLM Team](https://huggingface.co/epfl-llm) - **Funded by:** [More Information Needed] - **Language(s):** English only - **License:** [Common Crawl Foundation Terms of Use](https://commoncrawl.org/terms-of-use) - **Repository:** [epfLLM/meditron](https://github.com/epfLLM/meditron) - **Paper:** *[MediTron-70B: Scaling Medical Pretraining for Large Language Models](https://arxiv.org/abs/2311.16079)* - **Knowledge Cutoff**: August 2023 ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> The dataset was curated to provide a high-quality collection of clinical practice guidelines (CPGs) for the medical training of LLMs. Our Clinical Guidelines corpus comprises 46,469 articles from 16 globally recognized sources for clinician and patient-directed guidance across high and low-resource settings, multiple medical domains (internal medicine, pediatrics, oncology, infectious disease, etc.) and multiple geographical locations. ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> Clinical practice guidelines are rigorously researched frameworks designed to guide healthcare practitioners and patients in making evidence-based decisions regarding diagnosis, treatment, and management. They are compiled through a systematic process of collaborative consensus between experts to establish recommendations from the latest evidence on best practices that would maximize benefit in light of practical concerns such as available resources and context. As a super-synthesis of meta-analyses, they sit atop the *evidence pyramid* and form the basis of actionable evidence-based practice. Clinical guidelines differ based on several factors: - **Organizational level**: CPGs are produced at various organizational granularities, ranging from global to hospital-level initiatives directed by international professional medical associations to informal consortia, regional or national governmental bodies to individual NGOs and hospitals. - **Geographic scope**: The geographic scope ranges from global (WHO) to national (CDC, NICE) and regional (Ontario, Melbourne) to institutional (ICRC, Mayo Clinic). This corpus is biased towards English-speaking regions due to its exclusive focus on English content. - **Resource level**: The corpus also represents health care concerns from high- (Ontario, Melbourne), low- (WHO), and volatile- (ICRC) resource settings. - **Audience level**: Guidelines also contains a range of technical and conversational vocabulary with target audiences of clinicians or patients (or both), and is sometimes highly specialized within a theme (cancer, pediatrics, infectious disease). The peer review processes also ranged from UN bodies (WHO), institutional review boards (ICRC), professional associations (AAFP) to publicly crowdsourced knowledge bases (WikiDoc). - **Document size**: Article length varies widely from very short statements to 100+ page guides. #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> The dataset is sourced from 16 globally recognized medical entities, covering a wide range of healthcare contexts and audiences. We employed pragmatic selection criteria over medical sources, seeking CPGs that were: - (1) open-access - (2) systematically formatted with homogenous textual structure (i.e., in a format in which automated processes could be deployed without excessive risk of misaligning textual sequences) - (3) in the language predominantly represented by the pre-training corpus of Llama (i.e., English) - (4) covering a breadth of medical sub-domains, audiences (clinician, nurse, patient), and resource settings (high, low, and humanitarian response settings) | Source | Full Name | Tag |ย Guidelines | Words | Audience | Country | Released | |-|-|-|-|-|-|-|-| | **[AAFP](https://www.aafp.org)** | American Academy of Family Physicians |ย `aafp` | 50 | 9.4K | Doctor | USA | No | | **[CCO](https://www.cancercareontario.ca/en/guidelines-advice)** | Cancer Care Ontario | `cco` | 87 | 199K | Doctor | Canada | **Yes** | | **[CDC](https://www.cdc.gov/)** | Center for Disease Control and Prevention | `cdc` | 621 |ย 6.7M | Doctor | USA | **Yes** | | **[CMA](https://joulecma.ca/)** | Canadian Medical Association | `cma` | 431 | 1.7M | Doctor | Canada | **Yes** | | **[CPS](https://cps.ca)** | Canadian Paediatric Society | `cps` | 54 | 133K | Doctor | Canada |ย No | | **[drugs.com](https://www.drugs.com/)** | Drugs.com | `drugs` | 6548 | 4.1M | Both | International |ย No | | **[GuidelineCentral](https://www.guidelinecentral.com/)** | GuidelineCentral | `gc` | 1029 | 1M | Doctor | Mix |ย No | | **[ICRC](http://icrc.org/)** | International Committee of the Red Cross | `icrc` | 49 | 1.2M | Doctor | International |ย **Yes** | | **[IDSA](https://www.idsociety.org/)** | Infectious Diseases Society of America | `idsa` | 47 | 646K | Doctor | USAย | No | | **[MAGIC](https://magicevidence.org/)** | Making GRADE The Irresistible Choice | `magic` | 52 | 415K | Doctor | Mix |ย No | | **[MayoClinic](https://www.mayoclinic.org/)** | MayoClinic | `mayo` | 1100 | 2.2M | Patient | USA |ย No | | **[NICE](https://www.nice.org.uk/guidance)** | National Institute for Health and Care Excellence | `nice` | 1656 | 8.1M | Doctor | UK | **Yes** | | **[RCH](https://www.rch.org.au/clinicalguide/about_rch_cpgs/welcome_to_the_clinical_practice_guidelines/)** | Royal Children's Hospital Melbourne | `rch` | 384 | 410K | Doctor | Australia | No | | **[SPOR](https://sporevidencealliance.ca/key-activities/cpg-asset-map/cpg-database/)** | Strategy for Patient-Oriented Research | `spor` | 217 | 1.1M | Doctor | Canada |ย **Yes** | | **[WHO](https://www.who.int/publications/who-guidelines)** | World Health Organization | `who` | 223 | 3.1M | Both | Internationalย |ย **Yes** | | **[WikiDoc](https://www.wikidoc.org/)** | WikiDoc | `wikidoc` | 33058 | 34M | Both | International | **Yes** | #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> PDF documents were converted to text using [GROBID](https://github.com/kermitt2/grobid). After extracting the raw text from each source, we cleaned data with an ad-hoc process to exclude irrelevant or repetitive content that did not contribute to the textual content, such as URLs, references, figures, table delimiters, and ill-formatted characters. This filtering procedure was performed differently for each source using a sample of 50 articles. Please note that this procedure is not perfect, as it may have removed useful information or kept superfluous content. We provide the `raw_text` for each article if you would like to perform your own cleaning step. Additionally, the text was standardized to a unified format with hierarchical section headers indicated by `'#'`, homogenous spacing `'\n\n'` separating paragraphs, and normalized lists formatted with `'- '` bullet points. Finally, all samples were deduplicated using title matching, and articles that were too short or not English were filtered out. #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> As the articles are publicly accessible, no personal or sensitive information is included. ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> Each row of the dataset represents one clinical practice guideline article, and consists of the following dataset fields (all strings): | Field | Description | Sources with field | |-------------|-------------------------------------------|------------------------------| | `id` | Unique identifier for each article | All | | `source` | Source tag (`cco`, `cdc`, `cma`, `icrc`, `nice`, `spor`, `who` or `wikidoc`)| All | | `title` | Title of the article | CMA, NICE & WikiDoc only | | `url` | URL of the article | NICE, WikiDoc only | | `raw_text` | Unprocessed scraped article text | All | | `clean_text`| Cleaned and formatted article text | All | | `overview` | Short summary of the article | NICE only | ## Uses <!-- Address questions around how the dataset is intended to be used. --> The dataset is intended for use in tasks related to text generation, specifically in the context of clinical practice guidelines. It can be employed for training language models and other natural language processing applications within the healthcare domain. ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> - **Redistribution**: Please always check redistribution licenses before using the content as these may also evolve over time. To the best of our knowledge, we are following the redistribution licensing of each source and we invite users to inform us if that is not the case. - **Malicious use**: We do not support any use of this corpus that may be harmful. Creating tools that provide clinical advice is commendable, but extremely dangerous if not done with the appropriate care. Such tools need to be validated for safety and utility by medical professionals in randomized controlled trials. i.e. please do not create cowboy health apps that fool vulnerable users into thinking they are receiving validated advice. ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> - **Peer-Review Quality**: It is important to understand that while most sources are validated by internationally endorsed professional associations, a large proportion of articles are from Wikidoc which contains crowdsourced content. While edits in Wikidoc are generally restricted to expert review, the process of consensus and oversight is different from the traditional rigor of clinical guidelines. - **Representation**: This corpus is in English, and over-represents English-speaking regions. While we have included WHO and ICRC guidelines for low-resource settings, further work needs to be done to scrape sources from diverse contexts. - **Temporal scope**: Guidelines are constantly updated and these represent a snapshot of each in August 2023. Please re-scrape for updated content. ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> We warmly invite users to help us build a more representative corpus with high-quality peer-reviewed clinical practice guidelines in various languages and representing the full scope of clinical specialties and geographic regions. We encourage users of this content to be mindful of its current limitations in temporal and geographic scope and we repeat our warning: creating tools that provide clinical advice is commendable, but extremely dangerous if not done with the appropriate care. Such tools need to be validated for safety and utility by medical professionals in randomized controlled trials. i.e. Please donโ€™t create cowboy health apps that fool vulnerable users into thinking they are receiving validated advice. ## Acknowledgments The availability of open-access clinical practice guidelines (CPG) was critical to this work, and we thank all the societies listed above. A broader representation of geography, medical specialties, and contexts (especially low-resource settings) could be achieved through more standardized CPG formatting practices to ensure reliable textual extraction (e.g., releasing `.txt` or `.html` versions with structured content). We encourage the CPG community to continue to make these documents available (open-access with permissive licenses for incorporation into large language models) and easily usable. ## Authors - **Curation**: Mary-Anne Hartley - **Scraping**: Antoine Bonnet, Alexandre Sallinen, Igor Krawczuk, Kyle Matoba - **Cleaning**: Antoine Bonnet, Alexandre Sallinen ## Citation <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> If you use the Clinical Guidelines corpus, please cite out work: ``` @software{epfmedtrn, author = {Zeming Chen and Alejandro Hernรกndez Cano and Angelika Romanou and Antoine Bonnet and Kyle Matoba and Francesco Salvi and Matteo Pagliardini and Simin Fan and Andreas Kรถpf and Amirkeivan Mohtashami and Alexandre Sallinen and Alireza Sakhaeirad and Vinitra Swamy and Igor Krawczuk and Deniz Bayazit and Axel Marmet and Syrielle Montariol and Mary-Anne Hartley and Martin Jaggi and Antoine Bosselut}, title = {MediTron-70B: Scaling Medical Pretraining for Large Language Models}, month = November, year = 2023, url = {https://github.com/epfLLM/meditron} } ```
[ -0.2862750291824341, -0.2973213791847229, 0.49167513847351074, 0.08228645473718643, -0.40053674578666687, -0.288948655128479, -0.0718904659152031, -0.39487260580062866, 0.39348986744880676, 0.58379727602005, -0.4291289746761322, -0.9267625212669373, -0.9389874339103699, 0.38871100544929504...
null
null
null
null
null
null
null
null
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null
null
argilla/notus-uf-dpo-full
argilla
2023-11-21T10:55:16Z
0
0
null
[ "region:us" ]
2023-11-21T10:55:16Z
2023-11-21T10:55:09.000Z
2023-11-21T10:55:09
--- dataset_info: features: - name: source dtype: string - name: instruction dtype: string - name: chosen_model dtype: string - name: chosen_rating dtype: float64 - name: chosen_response dtype: string - name: rejected_responses sequence: string - name: rejected_ratings sequence: float64 splits: - name: train num_bytes: 319830690 num_examples: 63966 download_size: 165861726 dataset_size: 319830690 configs: - config_name: default data_files: - split: train path: data/train-* ---
[ -0.1285335123538971, -0.1861683875322342, 0.6529128551483154, 0.49436232447624207, -0.19319400191307068, 0.23607441782951355, 0.36072009801864624, 0.05056373029947281, 0.5793656706809998, 0.7400146722793579, -0.650810182094574, -0.23784008622169495, -0.7102247476577759, -0.0478255338966846...
null
null
null
null
null
null
null
null
null
null
null
null
null
gasp/french_rap_songs
gasp
2023-11-21T11:18:03Z
0
0
null
[ "license:mit", "region:us" ]
2023-11-21T11:18:03Z
2023-11-21T11:03:16.000Z
2023-11-21T11:03:16
--- license: mit ---
[ -0.1285335123538971, -0.1861683875322342, 0.6529128551483154, 0.49436232447624207, -0.19319400191307068, 0.23607441782951355, 0.36072009801864624, 0.05056373029947281, 0.5793656706809998, 0.7400146722793579, -0.650810182094574, -0.23784008622169495, -0.7102247476577759, -0.0478255338966846...
null
null
null
null
null
null
null
null
null
null
null
null
null
argilla/notus-uf-dpo-multibinarized
argilla
2023-11-21T11:10:55Z
0
0
null
[ "region:us" ]
2023-11-21T11:10:55Z
2023-11-21T11:09:47.000Z
2023-11-21T11:09:47
--- dataset_info: features: - name: source dtype: string - name: instruction dtype: string - name: chosen_response dtype: string - name: rejected_response dtype: string - name: chosen_avg_rating dtype: float64 - name: rejected_avg_rating dtype: float64 splits: - name: train num_bytes: 547827798 num_examples: 171507 download_size: 167474645 dataset_size: 547827798 configs: - config_name: default data_files: - split: train path: data/train-* ---
[ -0.1285335123538971, -0.1861683875322342, 0.6529128551483154, 0.49436232447624207, -0.19319400191307068, 0.23607441782951355, 0.36072009801864624, 0.05056373029947281, 0.5793656706809998, 0.7400146722793579, -0.650810182094574, -0.23784008622169495, -0.7102247476577759, -0.0478255338966846...
null
null
null
null
null
null
null
null
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null
BangumiBase/yuunaandthehauntedhotsprings
BangumiBase
2023-11-21T12:41:21Z
0
0
null
[ "size_categories:1K<n<10K", "license:mit", "art", "region:us" ]
2023-11-21T12:41:21Z
2023-11-21T11:16:14.000Z
2023-11-21T11:16:14
--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Yuuna And The Haunted Hot Springs This is the image base of bangumi Yuuna and the Haunted Hot Springs, we detected 28 characters, 2185 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 | 388 | [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 | 107 | [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 | 15 | [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 | 25 | [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 | 476 | [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 | 64 | [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 | 7 | [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) | N/A | | 7 | 21 | [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 | 11 | [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 | 202 | [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 | 152 | [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 | 22 | [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 | 14 | [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 | 9 | [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 | 94 | [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 | 128 | [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 | 48 | [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 | 6 | [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) | N/A | N/A | | 18 | 13 | [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 | 8 | [Download](19/dataset.zip) | ![preview 1](19/preview_1.png) | ![preview 2](19/preview_2.png) | ![preview 3](19/preview_3.png) | ![preview 4](19/preview_4.png) | ![preview 5](19/preview_5.png) | ![preview 6](19/preview_6.png) | ![preview 7](19/preview_7.png) | ![preview 8](19/preview_8.png) | | 20 | 8 | [Download](20/dataset.zip) | ![preview 1](20/preview_1.png) | ![preview 2](20/preview_2.png) | ![preview 3](20/preview_3.png) | ![preview 4](20/preview_4.png) | ![preview 5](20/preview_5.png) | ![preview 6](20/preview_6.png) | ![preview 7](20/preview_7.png) | ![preview 8](20/preview_8.png) | | 21 | 77 | [Download](21/dataset.zip) | ![preview 1](21/preview_1.png) | ![preview 2](21/preview_2.png) | ![preview 3](21/preview_3.png) | ![preview 4](21/preview_4.png) | ![preview 5](21/preview_5.png) | ![preview 6](21/preview_6.png) | ![preview 7](21/preview_7.png) | ![preview 8](21/preview_8.png) | | 22 | 11 | [Download](22/dataset.zip) | ![preview 1](22/preview_1.png) | ![preview 2](22/preview_2.png) | ![preview 3](22/preview_3.png) | ![preview 4](22/preview_4.png) | ![preview 5](22/preview_5.png) | ![preview 6](22/preview_6.png) | ![preview 7](22/preview_7.png) | ![preview 8](22/preview_8.png) | | 23 | 11 | [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 | 125 | [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 | 10 | [Download](25/dataset.zip) | ![preview 1](25/preview_1.png) | ![preview 2](25/preview_2.png) | ![preview 3](25/preview_3.png) | ![preview 4](25/preview_4.png) | ![preview 5](25/preview_5.png) | ![preview 6](25/preview_6.png) | ![preview 7](25/preview_7.png) | ![preview 8](25/preview_8.png) | | 26 | 12 | [Download](26/dataset.zip) | ![preview 1](26/preview_1.png) | ![preview 2](26/preview_2.png) | ![preview 3](26/preview_3.png) | ![preview 4](26/preview_4.png) | ![preview 5](26/preview_5.png) | ![preview 6](26/preview_6.png) | ![preview 7](26/preview_7.png) | ![preview 8](26/preview_8.png) | | noise | 121 | [Download](-1/dataset.zip) | ![preview 1](-1/preview_1.png) | ![preview 2](-1/preview_2.png) | ![preview 3](-1/preview_3.png) | ![preview 4](-1/preview_4.png) | ![preview 5](-1/preview_5.png) | ![preview 6](-1/preview_6.png) | ![preview 7](-1/preview_7.png) | ![preview 8](-1/preview_8.png) |
[ -0.7030287384986877, -0.1921251118183136, 0.16508549451828003, 0.1823132485151291, -0.2629568576812744, -0.12123199552297592, -0.022708896547555923, -0.37029096484184265, 0.690346896648407, 0.5419238209724426, -0.9082178473472595, -0.8533502221107483, -0.6687989830970764, 0.536503136157989...
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BangumiBase/gakusentoshiasterisk
BangumiBase
2023-11-21T13:21:20Z
0
0
null
[ "size_categories:1K<n<10K", "license:mit", "art", "region:us" ]
2023-11-21T13:21:20Z
2023-11-21T11:18:05.000Z
2023-11-21T11:18:05
--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Gakusen Toshi Asterisk This is the image base of bangumi Gakusen Toshi Asterisk, we detected 45 characters, 3325 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 | 851 | [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 | 25 | [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 | 54 | [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 | 36 | [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 | 80 | [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 | 45 | [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 | 31 | [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 | 490 | [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 | 22 | [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 | 37 | [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 | 33 | [Download](10/dataset.zip) | ![preview 1](10/preview_1.png) | ![preview 2](10/preview_2.png) | ![preview 3](10/preview_3.png) | ![preview 4](10/preview_4.png) | ![preview 5](10/preview_5.png) | ![preview 6](10/preview_6.png) | ![preview 7](10/preview_7.png) | ![preview 8](10/preview_8.png) | | 11 | 15 | [Download](11/dataset.zip) | ![preview 1](11/preview_1.png) | ![preview 2](11/preview_2.png) | ![preview 3](11/preview_3.png) | ![preview 4](11/preview_4.png) | ![preview 5](11/preview_5.png) | ![preview 6](11/preview_6.png) | ![preview 7](11/preview_7.png) | ![preview 8](11/preview_8.png) | | 12 | 270 | [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 | 39 | [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 | 20 | [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 | 18 | [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 | 90 | [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 | 26 | [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 | 41 | [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 | 20 | [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 | 31 | [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 | 16 | [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 | 40 | [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 | 27 | [Download](23/dataset.zip) | ![preview 1](23/preview_1.png) | ![preview 2](23/preview_2.png) | ![preview 3](23/preview_3.png) | ![preview 4](23/preview_4.png) | ![preview 5](23/preview_5.png) | ![preview 6](23/preview_6.png) | ![preview 7](23/preview_7.png) | ![preview 8](23/preview_8.png) | | 24 | 6 | [Download](24/dataset.zip) | ![preview 1](24/preview_1.png) | ![preview 2](24/preview_2.png) | ![preview 3](24/preview_3.png) | ![preview 4](24/preview_4.png) | ![preview 5](24/preview_5.png) | ![preview 6](24/preview_6.png) | N/A | N/A | | 25 | 56 | [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 | 21 | [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 | 11 | [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 | 20 | [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 | 147 | [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 | 52 | [Download](30/dataset.zip) | ![preview 1](30/preview_1.png) | ![preview 2](30/preview_2.png) | ![preview 3](30/preview_3.png) | ![preview 4](30/preview_4.png) | ![preview 5](30/preview_5.png) | ![preview 6](30/preview_6.png) | ![preview 7](30/preview_7.png) | ![preview 8](30/preview_8.png) | | 31 | 9 | [Download](31/dataset.zip) | ![preview 1](31/preview_1.png) | ![preview 2](31/preview_2.png) | ![preview 3](31/preview_3.png) | ![preview 4](31/preview_4.png) | ![preview 5](31/preview_5.png) | ![preview 6](31/preview_6.png) | ![preview 7](31/preview_7.png) | ![preview 8](31/preview_8.png) | | 32 | 20 | [Download](32/dataset.zip) | ![preview 1](32/preview_1.png) | ![preview 2](32/preview_2.png) | ![preview 3](32/preview_3.png) | ![preview 4](32/preview_4.png) | ![preview 5](32/preview_5.png) | ![preview 6](32/preview_6.png) | ![preview 7](32/preview_7.png) | ![preview 8](32/preview_8.png) | | 33 | 175 | [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 | 41 | [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 | 28 | [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 | 24 | [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 | 12 | [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 | 13 | [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 | 18 | [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 | 17 | [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 | 5 | [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) | N/A | N/A | N/A | | 42 | 18 | [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) | | noise | 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) |
[ -0.6857722401618958, -0.13300707936286926, 0.15754865109920502, 0.26166704297065735, -0.3073946237564087, -0.06866682320833206, -0.059157468378543854, -0.3745848536491394, 0.6404743790626526, 0.5163262486457825, -0.9635287523269653, -0.8870272040367126, -0.6923725605010986, 0.5198769569396...
null
null
null
null
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BangumiBase/deadmanwonderland
BangumiBase
2023-11-21T12:24:45Z
0
0
null
[ "size_categories:1K<n<10K", "license:mit", "art", "region:us" ]
2023-11-21T12:24:45Z
2023-11-21T11:19:37.000Z
2023-11-21T11:19:37
--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Deadman Wonderland This is the image base of bangumi Deadman Wonderland, we detected 26 characters, 1386 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 | 11 | [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 | 9 | [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 | 27 | [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 | 140 | [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 | 27 | [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 | 458 | [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 | 40 | [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 | 45 | [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 | 20 | [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 | 49 | [Download](9/dataset.zip) | ![preview 1](9/preview_1.png) | ![preview 2](9/preview_2.png) | ![preview 3](9/preview_3.png) | ![preview 4](9/preview_4.png) | ![preview 5](9/preview_5.png) | ![preview 6](9/preview_6.png) | ![preview 7](9/preview_7.png) | ![preview 8](9/preview_8.png) | | 10 | 40 | [Download](10/dataset.zip) | ![preview 1](10/preview_1.png) | ![preview 2](10/preview_2.png) | ![preview 3](10/preview_3.png) | ![preview 4](10/preview_4.png) | ![preview 5](10/preview_5.png) | ![preview 6](10/preview_6.png) | ![preview 7](10/preview_7.png) | ![preview 8](10/preview_8.png) | | 11 | 20 | [Download](11/dataset.zip) | ![preview 1](11/preview_1.png) | ![preview 2](11/preview_2.png) | ![preview 3](11/preview_3.png) | ![preview 4](11/preview_4.png) | ![preview 5](11/preview_5.png) | ![preview 6](11/preview_6.png) | ![preview 7](11/preview_7.png) | ![preview 8](11/preview_8.png) | | 12 | 8 | [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 | 37 | [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 | 14 | [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 | 70 | [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 | 42 | [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 | 25 | [Download](17/dataset.zip) | ![preview 1](17/preview_1.png) | ![preview 2](17/preview_2.png) | ![preview 3](17/preview_3.png) | ![preview 4](17/preview_4.png) | ![preview 5](17/preview_5.png) | ![preview 6](17/preview_6.png) | ![preview 7](17/preview_7.png) | ![preview 8](17/preview_8.png) | | 18 | 48 | [Download](18/dataset.zip) | ![preview 1](18/preview_1.png) | ![preview 2](18/preview_2.png) | ![preview 3](18/preview_3.png) | ![preview 4](18/preview_4.png) | ![preview 5](18/preview_5.png) | ![preview 6](18/preview_6.png) | ![preview 7](18/preview_7.png) | ![preview 8](18/preview_8.png) | | 19 | 9 | [Download](19/dataset.zip) | ![preview 1](19/preview_1.png) | ![preview 2](19/preview_2.png) | ![preview 3](19/preview_3.png) | ![preview 4](19/preview_4.png) | ![preview 5](19/preview_5.png) | ![preview 6](19/preview_6.png) | ![preview 7](19/preview_7.png) | ![preview 8](19/preview_8.png) | | 20 | 9 | [Download](20/dataset.zip) | ![preview 1](20/preview_1.png) | ![preview 2](20/preview_2.png) | ![preview 3](20/preview_3.png) | ![preview 4](20/preview_4.png) | ![preview 5](20/preview_5.png) | ![preview 6](20/preview_6.png) | ![preview 7](20/preview_7.png) | ![preview 8](20/preview_8.png) | | 21 | 7 | [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) | N/A | | 22 | 42 | [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 | 36 | [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 | 89 | [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) | | noise | 64 | [Download](-1/dataset.zip) | ![preview 1](-1/preview_1.png) | ![preview 2](-1/preview_2.png) | ![preview 3](-1/preview_3.png) | ![preview 4](-1/preview_4.png) | ![preview 5](-1/preview_5.png) | ![preview 6](-1/preview_6.png) | ![preview 7](-1/preview_7.png) | ![preview 8](-1/preview_8.png) |
[ -0.6731301546096802, -0.151744544506073, 0.17009851336479187, 0.19272927939891815, -0.30007168650627136, -0.04486393555998802, -0.006095409393310547, -0.34935784339904785, 0.6685493588447571, 0.5138263702392578, -0.9667701721191406, -0.8906149864196777, -0.6524930596351624, 0.5520418286323...
null
null
null
null
null
null
null
null
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null
null
null
null
hallucinations-leaderboard/results
hallucinations-leaderboard
2023-11-28T23:13:56Z
0
0
null
[ "license:apache-2.0", "region:us" ]
2023-11-28T23:13:56Z
2023-11-21T11:44:46.000Z
2023-11-21T11:44:46
--- license: apache-2.0 ---
[ -0.1285335123538971, -0.1861683875322342, 0.6529128551483154, 0.49436232447624207, -0.19319400191307068, 0.23607441782951355, 0.36072009801864624, 0.05056373029947281, 0.5793656706809998, 0.7400146722793579, -0.650810182094574, -0.23784008622169495, -0.7102247476577759, -0.0478255338966846...
null
null
null
null
null
null
null
null
null
null
null
null
null
diegoHf/emoji_for_diffusion
diegoHf
2023-11-21T11:49:25Z
0
0
null
[ "region:us" ]
2023-11-21T11:49:25Z
2023-11-21T11:49:25.000Z
2023-11-21T11:49:25
Entry not found
[ -0.3227645754814148, -0.22568479180335999, 0.8622263669967651, 0.43461522459983826, -0.52829909324646, 0.7012971639633179, 0.7915719747543335, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104475975036621, 0.5715674161911011, -...
null
null
null
null
null
null
null
null
null
null
null
null
null
HowGiYo/dataset-test1
HowGiYo
2023-11-21T11:53:55Z
0
0
null
[ "region:us" ]
2023-11-21T11:53:55Z
2023-11-21T11:51:25.000Z
2023-11-21T11:51:25
Entry not found
[ -0.3227645754814148, -0.22568479180335999, 0.8622263669967651, 0.43461522459983826, -0.52829909324646, 0.7012971639633179, 0.7915719747543335, 0.07618614286184311, 0.774603009223938, 0.2563217282295227, -0.7852813005447388, -0.22573819756507874, -0.9104475975036621, 0.5715674161911011, -...
null
null
null
null
null
null
null
null
null
null
null
null
null
hallucinations-leaderboard/requests
hallucinations-leaderboard
2023-11-28T23:13:57Z
0
0
null
[ "license:apache-2.0", "region:us" ]
2023-11-28T23:13:57Z
2023-11-21T11:56:02.000Z
2023-11-21T11:56:02
--- license: apache-2.0 ---
[ -0.12853367626667023, -0.18616794049739838, 0.6529126763343811, 0.4943627417087555, -0.19319313764572144, 0.23607443273067474, 0.36071979999542236, 0.05056338757276535, 0.5793654322624207, 0.7400138974189758, -0.6508103013038635, -0.23783987760543823, -0.710224986076355, -0.047825977206230...
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null
null
null
null
null
null
null
null
null
null
null
null
CoolMashups/Sky_Ozornik
CoolMashups
2023-11-21T12:24:08Z
0
0
null
[ "region:us" ]
2023-11-21T12:24:08Z
2023-11-21T12:19:53.000Z
2023-11-21T12:19:53
Entry not found
[ -0.3227649927139282, -0.225684255361557, 0.862226128578186, 0.43461498618125916, -0.5282987952232361, 0.7012963891029358, 0.7915717363357544, 0.07618629932403564, 0.7746025919914246, 0.2563219666481018, -0.7852816581726074, -0.2257382869720459, -0.9104480743408203, 0.5715669393539429, -0...
null
null
null
null
null
null
null
null
null
null
null
null
null
togakure/VozGeral1
togakure
2023-11-21T12:26:59Z
0
0
null
[ "license:openrail", "region:us" ]
2023-11-21T12:26:59Z
2023-11-21T12:26:40.000Z
2023-11-21T12:26:40
--- license: openrail ---
[ -0.12853367626667023, -0.18616794049739838, 0.6529126763343811, 0.4943627417087555, -0.19319313764572144, 0.23607443273067474, 0.36071979999542236, 0.05056338757276535, 0.5793654322624207, 0.7400138974189758, -0.6508103013038635, -0.23783987760543823, -0.710224986076355, -0.047825977206230...
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null
null
null
null
null
null
null
null
null
null
null
null
CCRs/scientific-papers
CCRs
2023-11-21T12:39:39Z
0
0
null
[ "region:us" ]
2023-11-21T12:39:39Z
2023-11-21T12:39:39.000Z
2023-11-21T12:39:39
Entry not found
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null
null
null
null
null
null
null
null
null
null
null
null
null
NicolasPre/Model_alpaca
NicolasPre
2023-11-21T12:59:23Z
0
0
null
[ "region:us" ]
2023-11-21T12:59:23Z
2023-11-21T12:59:20.000Z
2023-11-21T12:59:20
--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: autotrain_text dtype: string splits: - name: train num_bytes: 46221549 num_examples: 52002 - name: validation num_bytes: 46221549 num_examples: 52002 download_size: 48492298 dataset_size: 92443098 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* --- # Dataset Card for "autotrain-data-q9ey-qe8x-tdod" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
[ -0.37342727184295654, 0.11364014446735382, 0.3261336386203766, 0.20912998914718628, -0.22236810624599457, 0.32195496559143066, 0.5480937957763672, -0.02677391655743122, 0.6814586520195007, 0.21549855172634125, -0.7084165811538696, -0.5326565504074097, -0.44899964332580566, -0.1369272768497...
null
null
null
null
null
null
null
null
null
null
null
null
null
Siki-77/amazon6_polarity
Siki-77
2023-11-21T13:44:33Z
0
0
null
[ "license:apache-2.0", "region:us" ]
2023-11-21T13:44:33Z
2023-11-21T12:59:37.000Z
2023-11-21T12:59:37
--- license: apache-2.0 ---
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null
null
null
null
null
null
null
null
null
null
null
null
null
Pablao0948/Moto_Moto
Pablao0948
2023-11-21T13:10:16Z
0
0
null
[ "license:openrail", "region:us" ]
2023-11-21T13:10:16Z
2023-11-21T13:07:20.000Z
2023-11-21T13:07:20
--- license: openrail ---
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null
null
null
null
null
null
null
null
null
null
null
null
null
togakure/Eden
togakure
2023-11-21T13:49:19Z
0
0
null
[ "license:openrail", "region:us" ]
2023-11-21T13:49:19Z
2023-11-21T13:48:58.000Z
2023-11-21T13:48:58
--- license: openrail ---
[ -0.12853392958641052, -0.18616779148578644, 0.6529127955436707, 0.49436280131340027, -0.19319361448287964, 0.23607419431209564, 0.36072003841400146, 0.050563063472509384, 0.579365611076355, 0.7400140762329102, -0.6508104205131531, -0.23783954977989197, -0.7102249264717102, -0.0478260256350...
null
null
null
null
null
null
null
null
null
null
null
null
null
hgbt/test
hgbt
2023-11-21T14:53:01Z
0
0
null
[ "license:unknown", "region:us" ]
2023-11-21T14:53:01Z
2023-11-21T13:49:59.000Z
2023-11-21T13:49:59
--- license: unknown ---
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null
null
null
null
null
null
null
null
null
null
null
null
null
nhduy/caz0
nhduy
2023-11-21T14:20:40Z
0
0
null
[ "region:us" ]
2023-11-21T14:20:40Z
2023-11-21T14:15:58.000Z
2023-11-21T14:15:58
temp
[ -0.2935348153114319, 0.1839868277311325, 0.4842737019062042, -0.3251825273036957, -0.8533986210823059, -0.07603716850280762, -0.04300447180867195, 0.08598287403583527, 0.5631394386291504, 0.3614107072353363, -0.2788839340209961, -0.4635421633720398, -0.6422168612480164, -0.0979665964841842...
null
null
null
null
null
null
null
null
null
null
null
null
null
nayohan/T_DHG
nayohan
2023-11-21T14:48:44Z
0
0
null
[ "region:us" ]
2023-11-21T14:48:44Z
2023-11-21T14:48:33.000Z
2023-11-21T14:48:33
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 29276291 num_examples: 17940 - name: validation num_bytes: 5325692 num_examples: 3000 - name: test num_bytes: 4793634 num_examples: 2505 download_size: 18559249 dataset_size: 39395617 --- # Dataset Card for "T_DHG" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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null
null
null
null
null
null
null
null
null
null
null
null
null
ronnybehrens/mini-platypus_dc
ronnybehrens
2023-11-21T15:16:00Z
0
0
null
[ "region:us" ]
2023-11-21T15:16:00Z
2023-11-21T15:15:55.000Z
2023-11-21T15:15:55
--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 4168526 num_examples: 1000 download_size: 2239555 dataset_size: 4168526 configs: - config_name: default data_files: - split: train path: data/train-* ---
[ -0.12853392958641052, -0.18616779148578644, 0.6529127955436707, 0.49436280131340027, -0.19319361448287964, 0.23607419431209564, 0.36072003841400146, 0.050563063472509384, 0.579365611076355, 0.7400140762329102, -0.6508104205131531, -0.23783954977989197, -0.7102249264717102, -0.0478260256350...
null
null
null
null
null
null
null
null
null
null
null
null
null
clt013/Obama-QA
clt013
2023-11-21T15:27:24Z
0
0
null
[ "license:mit", "region:us" ]
2023-11-21T15:27:24Z
2023-11-21T15:26:40.000Z
2023-11-21T15:26:40
--- license: mit ---
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null
null
null
null
null
null
null
null
null
null
null
null
null
billy007/dzdez
billy007
2023-11-21T15:29:15Z
0
0
null
[ "region:us" ]
2023-11-21T15:29:15Z
2023-11-21T15:28:07.000Z
2023-11-21T15:28:07
Entry not found
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null
null
null
null
null
null
null
null
null
null
null
null
null
VanoInvestigations/BOE_with_BERTIN_for_tokenize_2048_2
VanoInvestigations
2023-11-21T15:33:05Z
0
0
null
[ "region:us" ]
2023-11-21T15:33:05Z
2023-11-21T15:32:48.000Z
2023-11-21T15:32:48
--- dataset_info: features: - name: boe_date_publication dtype: string - name: boe_previous dtype: string - name: boe_id dtype: string - name: boe_title dtype: string - name: boe_soup_xml dtype: string - name: tweet_date dtype: string - name: boe_text_cleaned dtype: string - name: tweet_original dtype: string - name: boe_alert sequence: string - name: boe_category dtype: string - name: boe_departament dtype: string - name: tweet_text_cleaned dtype: string - name: boe_subsequent dtype: string - name: boe_materials sequence: string - name: id dtype: int64 splits: - name: train num_bytes: 179564833 num_examples: 2867 - name: validation num_bytes: 19448449 num_examples: 392 - name: test num_bytes: 22514673 num_examples: 389 download_size: 84281864 dataset_size: 221527955 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
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null
null
null
null
null
null
null
null
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null
null
null
null
malteos/m_mmlu
malteos
2023-11-21T16:17:17Z
0
0
null
[ "region:us" ]
2023-11-21T16:17:17Z
2023-11-21T15:38:06.000Z
2023-11-21T15:38:06
Entry not found
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null
null
null
null
null
null
null
null
null
null
null
null
null
malteos/m_arc
malteos
2023-11-21T15:48:17Z
0
0
null
[ "region:us" ]
2023-11-21T15:48:17Z
2023-11-21T15:47:53.000Z
2023-11-21T15:47:53
Entry not found
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null
null
null
null
null
null
null
null
null
null
null
null
null
malteos/m_hellaswag
malteos
2023-11-21T15:57:59Z
0
0
null
[ "region:us" ]
2023-11-21T15:57:59Z
2023-11-21T15:50:26.000Z
2023-11-21T15:50:26
Entry not found
[ -0.3227649927139282, -0.225684255361557, 0.862226128578186, 0.43461498618125916, -0.5282987952232361, 0.7012963891029358, 0.7915717363357544, 0.07618629932403564, 0.7746025919914246, 0.2563219666481018, -0.7852816581726074, -0.2257382869720459, -0.9104480743408203, 0.5715669393539429, -0...
null
null
null
null
null
null
null
null
null
null
null
null
null
malteos/m_truthfulqa
malteos
2023-11-21T15:51:59Z
0
0
null
[ "region:us" ]
2023-11-21T15:51:59Z
2023-11-21T15:51:23.000Z
2023-11-21T15:51:23
Entry not found
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null
null
null
null
null
null
null
null
null
null
null
null
null
murodbek/til_mt
murodbek
2023-11-21T15:57:55Z
0
0
null
[ "region:us" ]
2023-11-21T15:57:55Z
2023-11-21T15:57:55.000Z
2023-11-21T15:57:55
Entry not found
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null
null
null
null
null
null
null
null
null
null
null
null
null
vsai/shvBLkTVl9
vsai
2023-11-21T16:15:09Z
0
0
null
[ "region:us" ]
2023-11-21T16:15:09Z
2023-11-21T16:15:03.000Z
2023-11-21T16:15:03
--- dataset_info: features: - name: empty dtype: image - name: room_type dtype: string splits: - name: train num_bytes: 5660218.0 num_examples: 50 download_size: 5661758 dataset_size: 5660218.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
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null
null
null
null
null
null
null
null
null
null
null
null
null
LemTenku/s
LemTenku
2023-11-21T16:18:49Z
0
0
null
[ "region:us" ]
2023-11-21T16:18:49Z
2023-11-21T16:18:31.000Z
2023-11-21T16:18:31
Entry not found
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null
null
null
null
null
null
null
null
null
null
null
null
null
Sanjaykrishna/Leadknock_YellowPages
Sanjaykrishna
2023-11-23T18:23:36Z
0
0
null
[ "license:apache-2.0", "region:us" ]
2023-11-23T18:23:36Z
2023-11-21T16:28:58.000Z
2023-11-21T16:28:58
--- license: apache-2.0 dataset_info: features: - name: column_name dtype: string splits: - name: train num_bytes: 27 num_examples: 3 download_size: 813 dataset_size: 27 configs: - config_name: default data_files: - split: train path: data/train-* ---
[ -0.12853367626667023, -0.18616794049739838, 0.6529126763343811, 0.4943627417087555, -0.19319313764572144, 0.23607443273067474, 0.36071979999542236, 0.05056338757276535, 0.5793654322624207, 0.7400138974189758, -0.6508103013038635, -0.23783987760543823, -0.710224986076355, -0.047825977206230...
null
null
null
null
null
null
null
null
null
null
null
null
null
hf-internal-testing/etth1-hourly-batch
hf-internal-testing
2023-11-21T16:42:52Z
0
0
null
[ "license:cc-by-nd-4.0", "region:us" ]
2023-11-21T16:42:52Z
2023-11-21T16:36:42.000Z
2023-11-21T16:36:42
--- license: cc-by-nd-4.0 ---
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null
null
null
null
null
null
null
null
null
null
null
null
null
EP45/test-10k
EP45
2023-11-21T16:38:34Z
0
0
null
[ "region:us" ]
2023-11-21T16:38:34Z
2023-11-21T16:38:15.000Z
2023-11-21T16:38:15
# vicuna ์‹คํ—˜์šฉ ๋ฐ์ดํ„ฐ์…‹ ๋‹ค์Œ ๋ฐ์ดํ„ฐ์…‹์œผ๋กœ๋ถ€ํ„ฐ ๋ณ€ํ™˜๋จ: https://huggingface.co/datasets/junelee/sharegpt_deepl_ko ## ํŒŒ์ผ๊ตฌ์กฐ - converted.parquet : ์›๋ณธ ๋ฐ์ดํ„ฐ์…‹์˜ ko_alpaca_style_dataset.json์„ ํŠธ๋ ˆ์ด๋‹์— ๋งž๋„๋ก ํ˜•์‹ ๋ณ€ํ™˜ ## ๋ผ์ด์„ผ์Šค ์›๋ณธ ๋ฐ์ดํ„ฐ๊ฐ€ OPENAI ์ด๊ธฐ ๋•Œ๋ฌธ์— ํ•ด๋‹น [์•ฝ๊ด€](https://openai.com/policies/terms-of-use)์— ๋”ฐ๋ฆ…๋‹ˆ๋‹ค. ๊ทธ ์ด์™ธ์˜ ๋ถ€๋ถ„์€ ๋‹ค์Œ ๋ผ์ด์„ผ์Šค๋ฅผ ๋”ฐ๋ฆ…๋‹ˆ๋‹ค: ์ €์ž‘์žํ‘œ์‹œ 2.0 ๋Œ€ํ•œ๋ฏผ๊ตญ (CC BY 2.0 KR)
[ -0.5931993722915649, -0.8777629137039185, 0.3355540931224823, 0.6611758470535278, -1.0450772047042847, -0.4155179262161255, 0.026989666745066643, -0.01823596842586994, 0.6605675220489502, 0.7207453846931458, -0.5967921018600464, -1.1688706874847412, -0.64545738697052, 0.10915059596300125, ...
null
null
null
null
null
null
null
null
null
null
null
null
null
uatafaque/movemind2
uatafaque
2023-11-21T17:02:55Z
0
0
null
[ "license:openrail", "region:us" ]
2023-11-21T17:02:55Z
2023-11-21T17:02:38.000Z
2023-11-21T17:02:38
--- license: openrail ---
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null
null
null
null
null
null
null
null
null
null
null
null
null
demo-leaderboard/requests
demo-leaderboard
2023-11-21T17:10:16Z
0
0
null
[ "region:us" ]
2023-11-21T17:10:16Z
2023-11-21T17:05:44.000Z
2023-11-21T17:05:44
Entry not found
[ -0.32276487350463867, -0.22568444907665253, 0.8622263073921204, 0.43461570143699646, -0.5282988548278809, 0.7012969255447388, 0.7915717363357544, 0.07618642598390579, 0.7746027112007141, 0.25632190704345703, -0.7852815389633179, -0.22573848068714142, -0.910447895526886, 0.5715675354003906,...
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chrisgru/blended_skill_talk_chatml
chrisgru
2023-11-26T08:02:50Z
0
0
null
[ "region:us" ]
2023-11-26T08:02:50Z
2023-11-21T17:07:12.000Z
2023-11-21T17:07:12
--- dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 1198146 num_examples: 980 download_size: 0 dataset_size: 1198146 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "blended_skill_talk_chatml" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
[ -0.46975985169410706, -0.6445761919021606, 0.08468330651521683, 0.2511179745197296, -0.043686892837285995, 0.30734217166900635, -0.14613749086856842, -0.2876221835613251, 0.7757686376571655, 0.46630844473838806, -0.9387605786323547, -0.8487275242805481, -0.6079815626144409, -0.568967878818...
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