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345c57aebc3c619995306d490e24f90964dc1768
|
# Dataset Card for "pokemon_image"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
eunsxx/pokemon_image
|
[
"region:us"
] |
2023-06-10T12:19:00+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Abra", "1": "Aerodactyl", "2": "Alakazam", "3": "Arbok", "4": "Arcanine", "5": "Articuno", "6": "Beedrill", "7": "Bellsprout", "8": "Blastoise", "9": "Bulbasaur", "10": "Butterfree", "11": "Caterpie", "12": "Chansey", "13": "Charizard", "14": "Charmander", "15": "Charmeleon", "16": "Clefable", "17": "Clefairy", "18": "Cloyster", "19": "Cubone", "20": "Dewgong", "21": "Diglett", "22": "Ditto", "23": "Dodrio", "24": "Doduo", "25": "Dragonair", "26": "Dragonite", "27": "Dratini", "28": "Drowzee", "29": "Dugtrio", "30": "Eevee", "31": "Ekans", "32": "Electabuzz", "33": "Electrode", "34": "Exeggcute", "35": "Exeggutor", "36": "Farfetchd", "37": "Fearow", "38": "Flareon", "39": "Gastly", "40": "Gengar", "41": "Geodude", "42": "Gloom", "43": "Golbat", "44": "Goldeen", "45": "Golduck", "46": "Golem", "47": "Graveler", "48": "Grimer", "49": "Growlithe", "50": "Gyarados", "51": "Haunter", "52": "Hitmonchan", "53": "Hitmonlee", "54": "Horsea", "55": "Hypno", "56": "Ivysaur", "57": "Jigglypuff", "58": "Jolteon", "59": "Jynx", "60": "Kabuto", "61": "Kabutops", "62": "Kadabra", "63": "Kakuna", "64": "Kangaskhan", "65": "Kingler", "66": "Koffing", "67": "Krabby", "68": "Lapras", "69": "Lickitung", "70": "Machamp", "71": "Machoke", "72": "Machop", "73": "Magikarp", "74": "Magmar", "75": "Magnemite", "76": "Magneton", "77": "Mankey", "78": "Marowak", "79": "Meowth", "80": "Metapod", "81": "Mew", "82": "Mewtwo", "83": "Moltres", "84": "MrMime", "85": "Muk", "86": "Nidoking", "87": "Nidoqueen", "88": "Nidorina", "89": "Nidorino", "90": "Ninetales", "91": "Oddish", "92": "Omanyte", "93": "Omastar", "94": "Onix", "95": "Paras", "96": "Parasect", "97": "Persian", "98": "Pidgeot", "99": "Pidgeotto", "100": "Pidgey", "101": "Pikachu", "102": "Pinsir", "103": "Poliwag", "104": "Poliwhirl", "105": "Poliwrath", "106": "Ponyta", "107": "Porygon", "108": "Primeape", "109": "Psyduck", "110": "Raichu", "111": "Rapidash", "112": "Raticate", "113": "Rattata", "114": "Rhydon", "115": "Rhyhorn", "116": "Sandshrew", "117": "Sandslash", "118": "Scyther", "119": "Seadra", "120": "Seaking", "121": "Seel", "122": "Shellder", "123": "Slowbro", "124": "Slowpoke", "125": "Snorlax", "126": "Spearow", "127": "Squirtle", "128": "Starmie", "129": "Staryu", "130": "Tangela", "131": "Tauros", "132": "Tentacool", "133": "Tentacruel", "134": "Vaporeon", "135": "Venomoth", "136": "Venonat", "137": "Venusaur", "138": "Victreebel", "139": "Vileplume", "140": "Voltorb", "141": "Vulpix", "142": "Wartortle", "143": "Weedle", "144": "Weepinbell", "145": "Weezing", "146": "Wigglytuff", "147": "Zapdos", "148": "Zubat"}}}}], "splits": [{"name": "train", "num_bytes": 1104571916.4706388, "num_examples": 9060}, {"name": "test", "num_bytes": 190556566.9813611, "num_examples": 1599}], "download_size": 1170821962, "dataset_size": 1295128483.452}}
|
2023-06-12T09:34:37+00:00
|
06d65458cb07decd5454b598b5ddea83de2ad2a8
|
Wrathless/Voice-activation
|
[
"license:apache-2.0",
"region:us"
] |
2023-06-10T12:22:55+00:00
|
{"license": "apache-2.0"}
|
2023-06-10T12:22:55+00:00
|
|
be2664f21ebe25c030d9cb35aad0bef152bb0e8e
|
danrop/csv-all-orders
|
[
"license:openrail",
"region:us"
] |
2023-06-10T13:04:47+00:00
|
{"license": "openrail"}
|
2023-06-10T13:13:06+00:00
|
|
f8e94027f8281db6273b9d98b89bd458cead9196
|
kiaraesguerra/chibi-anime
|
[
"license:artistic-2.0",
"region:us"
] |
2023-06-10T13:12:15+00:00
|
{"license": "artistic-2.0"}
|
2023-06-10T13:14:15+00:00
|
|
25a43eb44f293ad43d0eeecf61412286c8421519
|
# Dataset Card for "ajgt_ubc_split"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
arbml/ajgt_ubc_split
|
[
"region:us"
] |
2023-06-10T13:12:49+00:00
|
{"dataset_info": {"features": [{"name": "content", "dtype": "string"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 156683, "num_examples": 1440}, {"name": "test", "num_bytes": 38689, "num_examples": 360}], "download_size": 99132, "dataset_size": 195372}}
|
2023-06-10T13:12:57+00:00
|
f26bdad602520038b5e952b9f87a9e805f5e7221
|
bastien8060/anarchychess
|
[
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"region:us"
] |
2023-06-10T14:33:27+00:00
|
{"language": ["en"], "license": "mit", "size_categories": ["1K<n<10K"], "pretty_name": "r/AnarchyChess Subreddit Top Level Comment dataset"}
|
2023-06-11T08:24:22+00:00
|
|
9fa0596a5be389d5e504336044f099e40751921f
|
aarda/all-data
|
[
"license:apache-2.0",
"region:us"
] |
2023-06-10T14:34:04+00:00
|
{"license": "apache-2.0"}
|
2023-06-10T14:34:04+00:00
|
|
ae9c4932c1e80761269bc24111294485c8c0073e
|
# depth(?) overlap TrainData
Approximately 100,000 images
For extracting overlap information with ControlNet
## example 1
## example 2
|
Yossh/depth_overlap_simple_shape
|
[
"region:us"
] |
2023-06-10T14:50:03+00:00
|
{}
|
2023-06-17T04:03:48+00:00
|
f17f737c7e3249d2c207f3f597f86c2a43294039
|
This dataset contains texts in Tajik language with sentence annotations. It can be used to train and evaluate sentence-wise text segmentation algorithms.
The dataset contains more than 100 short and long texts and more than 3000 annotated sentences. The texts were carefully selected from different catergories
such as news, articles, novels, classical texts, poetry, and religious texts. It deliberately contains more of "hard" passages where splitting them by period "." characters would result in bad segmentation.
No preprocessing is done except reducing consecutive whitespaces and linebreaks to singles. The texts are sometimes poorly formatted just as they are copied and pasted from the web. This could make the training algorithm robust to noises.
|
sobir-hf/tajik-text-segmentation
|
[
"task_categories:feature-extraction",
"size_categories:1K<n<10K",
"language:tg",
"license:apache-2.0",
"text_segmentaion",
"nlp",
"tg",
"tajik",
"sentence_segmentation",
"region:us"
] |
2023-06-10T15:24:30+00:00
|
{"language": ["tg"], "license": "apache-2.0", "size_categories": ["1K<n<10K"], "task_categories": ["feature-extraction"], "pretty_name": "Tajik sentence-wise text segmentation", "tags": ["text_segmentaion", "nlp", "tg", "tajik", "sentence_segmentation"]}
|
2023-06-14T17:31:05+00:00
|
47173d873c616a7ffd8cf1643657f0a19d17b3d3
|
0xharib/xword2
|
[
"license:cc-by-4.0",
"region:us"
] |
2023-06-10T16:04:16+00:00
|
{"license": "cc-by-4.0"}
|
2023-06-10T16:04:47+00:00
|
|
371c770002316930e2f818202d9f3fa0696ac162
|
# Dataset Card for "FourthBrainDataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mazenlhm/FourthBrainDataset
|
[
"region:us"
] |
2023-06-10T16:25:48+00:00
|
{"dataset_info": {"features": [{"name": "product", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "marketing_email", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 11674, "num_examples": 10}], "download_size": 15262, "dataset_size": 11674}}
|
2023-06-10T16:25:49+00:00
|
9fac1f42dff0227c2fcb7f20e0479b0f2ae0215d
|
yacahu/sd_configs
|
[
"license:unknown",
"region:us"
] |
2023-06-10T16:41:48+00:00
|
{"license": "unknown"}
|
2023-06-10T16:41:48+00:00
|
|
f83a36d6fec508c26ba8f45dc9d953b865afadc3
|
simplisiva/cb65data
|
[
"license:apache-2.0",
"region:us"
] |
2023-06-10T16:51:49+00:00
|
{"license": "apache-2.0"}
|
2023-06-10T16:51:49+00:00
|
|
c9cfc9f7e440088ab03ded540391169890c07918
|
# Dataset Card for "fourth-brain-dataset1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jnanars/fourth-brain-dataset1
|
[
"region:us"
] |
2023-06-10T17:00:17+00:00
|
{"dataset_info": {"features": [{"name": "product", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "marketing_email", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 120722, "num_examples": 99}], "download_size": 73523, "dataset_size": 120722}}
|
2023-06-10T17:00:20+00:00
|
04038a2a19f0aeca04609ef95b908947cc8f2538
|
# Dataset Card for "generative-ai-dataset-001"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
darthlordvictor/generative-ai-dataset-001
|
[
"region:us"
] |
2023-06-10T17:02:43+00:00
|
{"dataset_info": {"features": [{"name": "product", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "marketing_email", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 124101, "num_examples": 99}], "download_size": 75483, "dataset_size": 124101}}
|
2023-06-10T17:02:44+00:00
|
e3e309de7723f4dc4a0230b4b9d725061d561044
|
# Dataset Card for "dusha"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
xbgoose/dusha
|
[
"region:us"
] |
2023-06-10T17:19:28+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "emotion", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 23511966173.784, "num_examples": 150352}, {"name": "test", "num_bytes": 2212754711.79, "num_examples": 14035}], "download_size": 21507131221, "dataset_size": 25724720885.574}}
|
2023-06-10T19:41:23+00:00
|
dd6dc339771974ab14511659cb9cd8af5f9731f7
|
!!!NOTE!!!
THIS REPO IS DEPRECATED! PLEASE VISIT [here](https://huggingface.co/datasets/OpenIllumination/OpenIllumination).
|
fsky097/OpenIllumination
|
[
"task_categories:other",
"annotations_creators:expert-generated",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-4.0",
"novel view synthesis",
"inverse rendering",
"material decomposition",
"doi:10.57967/hf/0756",
"region:us"
] |
2023-06-10T17:22:41+00:00
|
{"annotations_creators": ["expert-generated"], "language": ["en"], "license": "cc-by-4.0", "size_categories": ["100K<n<1M"], "task_categories": ["other"], "pretty_name": "OpenIllumination", "tags": ["novel view synthesis", "inverse rendering", "material decomposition"], "download_size": "900G"}
|
2023-09-16T22:02:49+00:00
|
072df98bdbe9ab3c6688d4c5218b3c4c09f51bc2
|
This Dataset stores the publicly available information for the CS673 course provided by Boston University.
|
shravanm/CS673QADataset
|
[
"license:apache-2.0",
"region:us"
] |
2023-06-10T17:22:59+00:00
|
{"license": "apache-2.0"}
|
2023-09-22T18:18:44+00:00
|
5e378ed62ef6f29923c9490d7ad19c0a48f29ed6
|
# Dataset Card for "MarketMail"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Slakhwani/MarketMail
|
[
"region:us"
] |
2023-06-10T17:27:51+00:00
|
{"dataset_info": {"features": [{"name": "product", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "marketing_email", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5066, "num_examples": 5}], "download_size": 11498, "dataset_size": 5066}}
|
2023-06-10T17:27:53+00:00
|
140df3e07a11a04cea66792dbe35e4ab7a6d26b7
|
lodestones/e6-dump
|
[
"license:apache-2.0",
"region:us"
] |
2023-06-10T17:33:38+00:00
|
{"license": "apache-2.0"}
|
2023-06-10T17:41:25+00:00
|
|
b4514dac71c67239879c1832d55c07935dfd4418
|
# Dataset Card for "MarketMailDataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ameya-akkalkotkar/MarketMailDataset
|
[
"region:us"
] |
2023-06-10T17:46:40+00:00
|
{"dataset_info": {"features": [{"name": "product", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "marketing_email", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 123817, "num_examples": 98}], "download_size": 72896, "dataset_size": 123817}}
|
2023-06-10T17:46:44+00:00
|
3ba235d4a7056cf8b062a57aa489f7bc14bff229
| ERROR: type should be string, got "\n\nhttps://github.com/ZhengxiangShi/StepGame/\n\n```bib\n@inproceedings{stepGame2022shi,\ntitle={StepGame: A New Benchmark for Robust Multi-Hop Spatial Reasoning in Texts},\nauthor={Shi, Zhengxiang and Zhang, Qiang and Lipani, Aldo},\nvolume={36},\nurl={https://ojs.aaai.org/index.php/AAAI/article/view/21383},\nDOI={10.1609/aaai.v36i10.21383}, \nbooktitle={Proceedings of the AAAI Conference on Artificial Intelligence},\nyear={2022},\nmonth={Jun.},\npages={11321-11329}\n}\n```" |
tasksource/stepgame
|
[
"license:mit",
"region:us"
] |
2023-06-10T17:54:59+00:00
|
{"license": "mit", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "story", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "label", "dtype": "string"}, {"name": "config", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 95449183, "num_examples": 300000}, {"name": "test", "num_bytes": 31812498, "num_examples": 100000}, {"name": "validation", "num_bytes": 3178932, "num_examples": 10000}], "download_size": 36044930, "dataset_size": 130440613}}
|
2024-01-05T16:25:10+00:00
|
f6c07f66d3eccebd36418885ce10aff295d436dd
|
https://github.com/Advancing-Machine-Human-Reasoning-Lab/apt
```
@inproceedings{nighojkar-licato-2021-improving,
title = "Improving Paraphrase Detection with the Adversarial Paraphrasing Task",
author = "Nighojkar, Animesh and
Licato, John",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.552",
doi = "10.18653/v1/2021.acl-long.552",
pages = "7106--7116",
}
```
|
tasksource/apt
|
[
"task_categories:text-classification",
"task_ids:semantic-similarity-classification",
"task_ids:semantic-similarity-scoring",
"task_ids:text-scoring",
"task_ids:multi-input-text-classification",
"language:en",
"license:unknown",
"region:us"
] |
2023-06-10T18:01:04+00:00
|
{"language": ["en"], "license": "unknown", "task_categories": ["text-classification"], "task_ids": ["semantic-similarity-classification", "semantic-similarity-scoring", "text-scoring", "multi-input-text-classification"]}
|
2023-08-10T12:42:21+00:00
|
0a662d03a1f8719784c7825a6bce91013a79cbb6
|
# Dataset Card for "hf_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
balasvasan/hf_dataset
|
[
"region:us"
] |
2023-06-10T18:23:54+00:00
|
{"dataset_info": {"features": [{"name": "receipe", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "marketing_email", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 8678, "num_examples": 10}], "download_size": 11077, "dataset_size": 8678}}
|
2023-06-10T18:23:57+00:00
|
480e46175bc198e78f23f836a9b41dd2b59922f6
|
# Dataset Card for "anime_faces_dim_128_0.5k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jlbaker361/anime_faces_dim_128_0.5k
|
[
"region:us"
] |
2023-06-10T18:36:15+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "split", "dtype": "string"}, {"name": "src", "dtype": "string"}, {"name": "style", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 13427460.0, "num_examples": 500}], "download_size": 13411595, "dataset_size": 13427460.0}}
|
2023-06-10T18:36:17+00:00
|
8a728ed1466038b871e04d9a147dd9f99e34de78
|
# Dataset Card for "anime_faces_dim_128_5k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jlbaker361/anime_faces_dim_128_5k
|
[
"region:us"
] |
2023-06-10T18:40:51+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "split", "dtype": "string"}, {"name": "src", "dtype": "string"}, {"name": "style", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 135105972.0, "num_examples": 5000}], "download_size": 134950190, "dataset_size": 135105972.0}}
|
2023-06-10T18:40:57+00:00
|
7fde201b35967a3ed266e3391e2c4dd09bdd0e83
|
# Dataset Card for "anime_faces_dim_128_10k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jlbaker361/anime_faces_dim_128_10k
|
[
"region:us"
] |
2023-06-10T18:49:59+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "split", "dtype": "string"}, {"name": "src", "dtype": "string"}, {"name": "style", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 270089894.0, "num_examples": 10000}], "download_size": 269766440, "dataset_size": 270089894.0}}
|
2023-06-10T18:50:07+00:00
|
fe9a51f3de1bff076f01c82083acd8076ab68762
|
# Dataset Card for "flickr_humans_dim_128_0.5k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jlbaker361/flickr_humans_dim_128_0.5k
|
[
"region:us"
] |
2023-06-10T18:50:32+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "split", "dtype": "string"}, {"name": "src", "dtype": "string"}, {"name": "style", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 14606865.0, "num_examples": 500}], "download_size": 14589079, "dataset_size": 14606865.0}}
|
2023-06-10T18:50:36+00:00
|
ad49428ee7e943e6303ed91658e369591fe060e5
|
# Dataset Card for "flickr_humans_dim_128_5k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jlbaker361/flickr_humans_dim_128_5k
|
[
"region:us"
] |
2023-06-10T18:56:00+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "split", "dtype": "string"}, {"name": "src", "dtype": "string"}, {"name": "style", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 146646949.0, "num_examples": 5000}], "download_size": 146452810, "dataset_size": 146646949.0}}
|
2023-06-10T18:56:05+00:00
|
58d3a7e2bd2d33b188f548ef3172453e62425f45
|
# Dataset Card for "job_and_cover_letter_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jenrajaseharan/job_and_cover_letter_dataset
|
[
"region:us"
] |
2023-06-10T19:00:24+00:00
|
{"dataset_info": {"features": [{"name": "company", "dtype": "string"}, {"name": "position", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "cover_letter", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 13540, "num_examples": 7}], "download_size": 22350, "dataset_size": 13540}}
|
2023-06-10T19:00:26+00:00
|
0150845866555c465f3c71740008f2d41700158a
|
# Dataset Card for "flickr_humans_dim_128_10k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jlbaker361/flickr_humans_dim_128_10k
|
[
"region:us"
] |
2023-06-10T19:06:44+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "split", "dtype": "string"}, {"name": "src", "dtype": "string"}, {"name": "style", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 293347129.0, "num_examples": 10000}], "download_size": 292957790, "dataset_size": 293347129.0}}
|
2023-06-10T19:06:51+00:00
|
9e489a09b4eff85c76ca18a1e3199fba4588ca56
|
# Dataset Card for "anime_faces_dim_128_20k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jlbaker361/anime_faces_dim_128_20k
|
[
"region:us"
] |
2023-06-10T19:08:13+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "split", "dtype": "string"}, {"name": "src", "dtype": "string"}, {"name": "style", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 538969347.0, "num_examples": 20000}], "download_size": 538301440, "dataset_size": 538969347.0}}
|
2023-06-10T19:08:28+00:00
|
e53911b1383c92fa3b79065c873c0b8bdbefb70d
|
# Dataset Card for "marmail-demo"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
rabasi/marmail-demo
|
[
"region:us"
] |
2023-06-10T19:18:24+00:00
|
{"dataset_info": {"features": [{"name": "product", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "marketing_email", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10429, "num_examples": 10}], "download_size": 14536, "dataset_size": 10429}}
|
2023-06-10T19:18:26+00:00
|
ea0da066ede598caa9cd2035fc3d2d934013fbfc
|
# Dataset Card for "kaggle_females_dim_128_0.1k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jlbaker361/kaggle_females_dim_128_0.1k
|
[
"region:us"
] |
2023-06-10T19:18:31+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "split", "dtype": "string"}, {"name": "src", "dtype": "string"}, {"name": "style", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2258285.0, "num_examples": 100}], "download_size": 2256434, "dataset_size": 2258285.0}}
|
2023-06-10T19:18:32+00:00
|
c8e3f8909e9d26c30abee0b99b79f934048d0516
|
# Dataset Card for "VideoGamesList"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ameya-akkalkotkar/VideoGamesList
|
[
"region:us"
] |
2023-06-10T19:19:26+00:00
|
{"dataset_info": {"features": [{"name": "product", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "marketing_email", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 71509, "num_examples": 99}], "download_size": 41508, "dataset_size": 71509}}
|
2023-06-10T19:19:30+00:00
|
b82e47b0434b5411161c46e38bf207b40b4ce341
|
# Dataset Card for "kaggle_females_dim_128_0.5k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jlbaker361/kaggle_females_dim_128_0.5k
|
[
"region:us"
] |
2023-06-10T19:20:25+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "split", "dtype": "string"}, {"name": "src", "dtype": "string"}, {"name": "style", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 11425611.0, "num_examples": 500}], "download_size": 11406695, "dataset_size": 11425611.0}}
|
2023-06-10T19:20:27+00:00
|
0b642e99c21a3358443e62d28003c417e9400dc4
|
# Dataset Card for "kaggle_females_dim_128_5k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jlbaker361/kaggle_females_dim_128_5k
|
[
"region:us"
] |
2023-06-10T19:24:14+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "split", "dtype": "string"}, {"name": "src", "dtype": "string"}, {"name": "style", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 114376456.0, "num_examples": 5000}], "download_size": 114170820, "dataset_size": 114376456.0}}
|
2023-06-10T19:24:19+00:00
|
c5ff2b9c9dfe2e292d1284f3f6689ba246a40ee4
|
# Dataset Card for "flickr_humans_dim_128_20k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jlbaker361/flickr_humans_dim_128_20k
|
[
"region:us"
] |
2023-06-10T19:26:58+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "split", "dtype": "string"}, {"name": "src", "dtype": "string"}, {"name": "style", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 586654859.0, "num_examples": 20000}], "download_size": 585876097, "dataset_size": 586654859.0}}
|
2023-06-10T19:27:13+00:00
|
21205de1e25e2d62b4e3c13a289516a77274c3ee
|
# Dataset Card for "Bias-detection-combined"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
pranjali97/Bias-detection-combined
|
[
"region:us"
] |
2023-06-10T19:28:51+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3698636, "num_examples": 38213}, {"name": "validation", "num_bytes": 414977, "num_examples": 4246}], "download_size": 0, "dataset_size": 4113613}}
|
2023-06-11T22:48:39+00:00
|
e1e9485cf5f6f8faa4c24bcfd90fdc0e3f831d08
|
# Dataset Card for "kaggle_females_dim_128_10k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jlbaker361/kaggle_females_dim_128_10k
|
[
"region:us"
] |
2023-06-10T19:31:18+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "split", "dtype": "string"}, {"name": "src", "dtype": "string"}, {"name": "style", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 228058823.0, "num_examples": 10000}], "download_size": 227646485, "dataset_size": 228058823.0}}
|
2023-06-10T19:31:24+00:00
|
f594becde2cdcfe8137cc106567e568c6fee1f99
|
# Dataset Card for "anime_faces_dim_128_30k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jlbaker361/anime_faces_dim_128_30k
|
[
"region:us"
] |
2023-06-10T19:35:03+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "split", "dtype": "string"}, {"name": "src", "dtype": "string"}, {"name": "style", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 809220553.0, "num_examples": 30000}], "download_size": 808206256, "dataset_size": 809220553.0}}
|
2023-06-10T19:35:27+00:00
|
fe49e51334550f0df6cf6d72f43cf2a17368385f
|
rzvn/Visual-Stable-Diffusion-pretrained-Datasets
|
[
"license:afl-3.0",
"region:us"
] |
2023-06-10T19:36:28+00:00
|
{"license": "afl-3.0"}
|
2023-06-10T20:03:02+00:00
|
|
ae4e2e812efcab6c3cae471bb02bc8f101705794
|
# Dataset Card for "kaggle_females_dim_128_20k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jlbaker361/kaggle_females_dim_128_20k
|
[
"region:us"
] |
2023-06-10T19:43:17+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "split", "dtype": "string"}, {"name": "src", "dtype": "string"}, {"name": "style", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 456039106.0, "num_examples": 20000}], "download_size": 455191151, "dataset_size": 456039106.0}}
|
2023-06-10T19:43:31+00:00
|
94426765f87777b5a990759ad6fc5bf61e7766c0
|
kayteekay/stephenKingBooks
|
[
"license:openrail",
"region:us"
] |
2023-06-10T19:44:27+00:00
|
{"license": "openrail"}
|
2023-06-10T20:00:11+00:00
|
|
04c7bab25d1c0e5f2b87d648569aaaf0ffb0a9fc
|
# Dataset Card for "flickr_humans_dim_128_30k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jlbaker361/flickr_humans_dim_128_30k
|
[
"region:us"
] |
2023-06-10T19:54:52+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "split", "dtype": "string"}, {"name": "src", "dtype": "string"}, {"name": "style", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 879655939.0, "num_examples": 30000}], "download_size": 878486771, "dataset_size": 879655939.0}}
|
2023-06-10T19:55:17+00:00
|
6fbe38c4b72b461c8b9a41de5115be1edf8a4a80
|
# Dataset Card for "kaggle_females_dim_128_30k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jlbaker361/kaggle_females_dim_128_30k
|
[
"region:us"
] |
2023-06-10T20:00:33+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "split", "dtype": "string"}, {"name": "src", "dtype": "string"}, {"name": "style", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 683990965.0, "num_examples": 30000}], "download_size": 682733849, "dataset_size": 683990965.0}}
|
2023-06-10T20:00:51+00:00
|
1e3a6ad1cc17e9c167b179a1874dc2c98b8ccce3
|
# Dataset Card for "anime_faces_dim_128_40k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jlbaker361/anime_faces_dim_128_40k
|
[
"region:us"
] |
2023-06-10T20:09:15+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "split", "dtype": "string"}, {"name": "src", "dtype": "string"}, {"name": "style", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1076790828.0, "num_examples": 40000}], "download_size": 1075448120, "dataset_size": 1076790828.0}}
|
2023-06-10T20:09:44+00:00
|
3beab5fa50d50d3cd79ab1a93ab65af659b96e65
|
# Dataset Card for "resd_studio"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Ar4ikov/resd_studio
|
[
"region:us"
] |
2023-06-10T20:11:25+00:00
|
{"dataset_info": {"features": [{"name": "name", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "emotion", "dtype": "string"}, {"name": "speech", "dtype": "audio"}], "splits": [{"name": "test", "num_bytes": 96603538.0, "num_examples": 280}, {"name": "train", "num_bytes": 398719157.336, "num_examples": 1116}], "download_size": 485403675, "dataset_size": 495322695.336}}
|
2023-06-10T20:42:08+00:00
|
19d3c297ec3c0d78af973645ddc20db1af5bf166
|
salomonsky/datos
|
[
"license:mit",
"region:us"
] |
2023-06-10T20:14:18+00:00
|
{"license": "mit"}
|
2023-06-10T20:14:18+00:00
|
|
a8847134b8716c2bb57402958f32fbf73efa15b2
|
# GQA: Graph Question Answering
This dataset is asks models to make use of embedded graph for question answering.
Stats:
- train: 57,043
- test: 2,890
An exmaple of the dataset is as follows:
```json
{
"id": "mcwq-176119",
"question": "What was executive produced by Scott Spiegel , Boaz Yakin , and Quentin Tarantino , executive produced by My Best Friend's Birthday 's editor and star , and edited by George Folsey",
"answers": [
"Hostel: Part II"
],
"subgraph": {
"entities": [
"Q1401104",
"Q887636",
"Q1048645",
"Q3772",
"Q965826"
],
"relations": [
"P1431",
"P1040"
],
"adjacency": [[2, 1, 0],
[2, 0, 3],
[2, 0, 1],
[2, 0, 4]
],
"entity_labels": [
"george folsey, jr.",
"boaz yakin",
"hostel: part ii",
"quentin jerome tarantino",
"scott spiegel"
],
"relation_labels": [
"showrunner",
"film editor"
]
},
"sparql": "SELECT DISTINCT ?x0 WHERE {\n?x0 wdt:P1040 wd:Q1401104 .\n?x0 wdt:P1431 ?x1 .\n?x0 wdt:P1431 wd:Q3772 .\n?x0 wdt:P1431 wd:Q887636 .\n?x0 wdt:P1431 wd:Q965826 .\nwd:Q1480733 wdt:P161 ?x1 .\nwd:Q1480733 wdt:P1040 ?x1\n}"
}
```
|
drt/graphext-qa
|
[
"license:mit",
"region:us"
] |
2023-06-10T20:15:18+00:00
|
{"license": "mit"}
|
2023-07-05T12:18:02+00:00
|
0ffb206612c31da5e6fa95009bc3f128431c774c
|
# Dataset Card for "StephenKingBooksImg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
kayteekay/StephenKingBooksImg
|
[
"region:us"
] |
2023-06-10T20:18:11+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 313548.0, "num_examples": 36}], "download_size": 313942, "dataset_size": 313548.0}}
|
2023-06-10T20:18:13+00:00
|
96c4d771dac32e8daf1f45613c37862df761988c
|
maghrane/data
|
[
"license:afl-3.0",
"region:us"
] |
2023-06-10T20:18:54+00:00
|
{"license": "afl-3.0"}
|
2023-06-10T20:18:54+00:00
|
|
6b77d9e790910ce4b28a4019a45a0ec76c6f7da4
|
# Dataset Card for "kaggle_females_dim_128_40k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jlbaker361/kaggle_females_dim_128_40k
|
[
"region:us"
] |
2023-06-10T20:20:52+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "split", "dtype": "string"}, {"name": "src", "dtype": "string"}, {"name": "style", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 911798702.0, "num_examples": 40000}], "download_size": 910126597, "dataset_size": 911798702.0}}
|
2023-06-10T20:21:16+00:00
|
d269d17067158af9e9b09c879542a3695bd52593
|
# Dataset Card for "flickr_humans_dim_128_40k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jlbaker361/flickr_humans_dim_128_40k
|
[
"region:us"
] |
2023-06-10T20:29:06+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "split", "dtype": "string"}, {"name": "src", "dtype": "string"}, {"name": "style", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1173267779.0, "num_examples": 40000}], "download_size": 1171711830, "dataset_size": 1173267779.0}}
|
2023-06-10T20:29:37+00:00
|
32412912e930507c690c9b303a8ee22c91f3a13e
|
# Dataset Card for "kaggle_females_dim_128_50k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jlbaker361/kaggle_females_dim_128_50k
|
[
"region:us"
] |
2023-06-10T20:42:24+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "split", "dtype": "string"}, {"name": "src", "dtype": "string"}, {"name": "style", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1139379975.0, "num_examples": 50000}], "download_size": 1137317892, "dataset_size": 1139379975.0}}
|
2023-06-10T20:43:02+00:00
|
75e70f08b9c7e13d7d50dad4fe863fd3d6c16c12
|
# Dataset Card for "anime_faces_dim_128_50k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jlbaker361/anime_faces_dim_128_50k
|
[
"region:us"
] |
2023-06-10T20:50:42+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "split", "dtype": "string"}, {"name": "src", "dtype": "string"}, {"name": "style", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1348590698.0, "num_examples": 50000}], "download_size": 1346915159, "dataset_size": 1348590698.0}}
|
2023-06-10T20:51:16+00:00
|
fd353fc4984a125667792ba27bba381d75613b57
|
# million-faces
Welcome to "million-faces", one of the largest facesets available to the public. Comprising a staggering one million faces, all images in this dataset are entirely AI-generated.
Due to the nature of AI-generated images, please be aware that some artifacts may be present in the dataset.
The dataset is currently being uploaded to Hugging Face, a renowned platform for hosting datasets and models for the machine learning community.
## Usage
Feel free to use this dataset for your projects and research. However, please do not hold me liable for any issues that might arise from its use. If you use this dataset and create something amazing, consider linking back to this GitHub project. Recognition of work is a pillar of the open-source community!
## Dataset Details
- **Number of faces:** 1,000,000
- **Source:** AI-generated
- **Artifacts:** Some images may contain artifacts
- **Availability:** Fully uploaded on Hugging Face
## About
This project is about creating and sharing one of the largest AI-generated facesets. With one million faces, it offers a significant resource for researchers and developers in AI, machine learning, and computer vision.
|
RichardErkhov/OneMillionFaces
|
[
"task_categories:image-to-image",
"size_categories:1M<n<10M",
"license:mit",
"region:us"
] |
2023-06-10T20:55:43+00:00
|
{"license": "mit", "size_categories": ["1M<n<10M"], "task_categories": ["image-to-image"], "pretty_name": "One million faces"}
|
2023-12-23T08:32:00+00:00
|
2331e0b02c933b4522d2683a833f6cb8b2890383
|
# Dataset Card for "flickr_humans_dim_128_50k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jlbaker361/flickr_humans_dim_128_50k
|
[
"region:us"
] |
2023-06-10T21:09:27+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "split", "dtype": "string"}, {"name": "src", "dtype": "string"}, {"name": "style", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1466320030.0, "num_examples": 50000}], "download_size": 1464372542, "dataset_size": 1466320030.0}}
|
2023-06-10T21:10:11+00:00
|
5b12fc2f59d8529eda72784bb0ed6b538f4c26a0
|
from datasets import load_dataset
# If the dataset is gated/private, make sure you have run huggingface-cli login
dataset = load_dataset("HenneForReal/jimmy80")
|
HenneForReal/jimmy80
|
[
"license:openrail",
"region:us"
] |
2023-06-10T21:11:02+00:00
|
{"license": "openrail"}
|
2023-06-10T21:14:08+00:00
|
be87b4235e621eba81cb4f3c18604b59e81b9454
|
# ATLAS-REASONING
This dataset derives from the code here: [atlasunified/atlas-reasoning](https://github.com/atlasunified/atlas-reasoning) and is synthetically generated by GPT-3.5-turbo.
## Categories
The main 42 (See the repo to check the JSONL) categories below were human derived while the subcategories were synthetically generated by GPT-4.
## 1 Deductive Reasoning
-1.1 Syllogistic Arguments
-1.2 Assumptions
-1.3 Abductive Reasoning
-1.4 Modus Ponens
-1.5 Modus Tollens
-1.6 Problem Solving
-1.7 Goal Oriented Thinking
-1.8 Basic Logic
-1.9 Analytical Thinking
-1.10 Philosophical Debate
-1.11 Constructing Arguments
-1.12 Propositional Logic
-1.13 Deduction Rules
-1.14 Mathematical Reasoning
-1.15 Predicate Logic
-1.16 Conclusions
-1.17 The Socratic Method
-1.18 Validity and Soundness
-1.19 Formal Systems
-1.20 Logic Games
-1.21 Decision Making
-1.22 Principled Thinking
-1.23 Inductive Reasoning
-1.24 Predictions
-1.25 Cognitive Theory
-1.26 Inference
-1.27 Quantifying Assumptions
-1.28 Interpreting Evidence
-1.29 Establishing Correlation
-1.30 Rational Inquiry
-1.31 Abductive Logic
-1.32 Exploring Possibilities
-1.33 Distinctions
-1.34 Testing Hypotheses
-1.35 Symmetry
-1.36 Categorical Statements
-1.37 Logical Fallacies
## 2 Inductive Reasoning
2.1 Hypothetical Reasoning
2.2 Analogy
2.3 Probabilistic Reasoning
2.4 Prediction
2.5 Cause and Effect
2.6 Pattern Recognition
2.7 Matching
2.8 Statistical Analysis
2.9 Deductive Reasoning
2.10 Abduction
2.11 Abductive Reasoning
2.12 Systematic Reasoning
2.13 Visual Reasoning
2.14 Analogical Reasoning
2.15 Generalization
2.16 Inductive Logic
2.17 Numerical Analysis
2.18 Heuristic Reasoning
2.19 Experimental Reasoning
2.20 Trend Analysis
2.21 Data Mining
2.22 Decision Trees
2.23 Bayesian Networks
2.24 Predictive Modeling
2.25 Categorical Reasoning
2.26 Test and Measurement
2.27 Simulation and Modeling
2.28 Cognitive Reasoning
2.29 Inferential Reasoning
2.30 Inferential Statistics
2.31 Causal Reasoning
2.32 Pattern Based Reasoning
2.33 Non-Linear Reasoning
2.34 Qualitative Reasoning
2.35 Data Driven Reasoning
2.36 Game Theory
2.37 Mathematical Induction
## 3 Informal Logic
3.1 Fallacies in reasoning
3.2 Argument analysis and evaluation
3.3 Causal reasoning
3.4 Analogical reasoning
3.5 Inductive reasoning
3.6 Deductive reasoning
3.7 Critical thinking skills
3.8 Counterarguments
3.9 Rhetorical devices
3.10 Persuasive techniques
3.11 Logical consistency
3.12 Evidence and reasoning
3.13 Reasoning by analogy
3.14 Logical fallacies in advertising
3.15 Moral reasoning
3.16 Abductive reasoning
3.17 Scientific reasoning
3.18 Ethical reasoning
3.19 Legal reasoning
3.20 Statistical reasoning
3.21 Argument construction
3.22 Logical inference
3.23 Common cognitive biases in reasoning
3.24 Hypothetical reasoning
3.25 Reasoning with probabilities
3.26 Problem-solving techniques
3.27 Decision-making strategies
3.28 Reasoning about cause and effect
3.29 Reasoning with uncertainty
3.30 Argumentation theory
3.31 Reasoning in everyday life
3.32 Reasoning in politics
3.33 Reasoning in ethics
3.34 Reasoning in business
3.35 Reasoning in science
3.36 Reasoning in philosophy
3.37 Reasoning in mathematics
## 4 Cognitive Biases
4.1 Confirmation bias
4.2 Availability heuristic
4.3 Anchoring bias
4.4 Gambler's fallacy
4.5 Hindsight bias
4.6 Framing effect
4.7 Overconfidence bias
4.8 Dunning-Kruger effect
4.9 Self-serving bias
4.10 Status quo bias
4.11 Sunk cost fallacy
4.12 Bandwagon effect
4.13 Illusory correlation
4.14 Halo effect
4.15 Fundamental attribution error
4.16 Negativity bias
4.17 Loss aversion
4.18 Endowment effect
4.19 Choice overload
4.20 Reactance
4.21 Social desirability bias
4.22 In-group bias
4.23 Out-group homogeneity bias
4.24 Implicit bias
4.25 Stereotyping
4.26 Representative heuristic
4.27 False consensus effect
4.28 Priming effect
4.29 Anchoring and adjustment heuristic
4.30 Cognitive dissonance
4.31 Information bias
4.32 Actor-observer bias
4.33 Empathy gap
4.34 Reactivity
4.35 Selective perception
4.36 Projection bias
4.37 Regret aversion
## 5 Logical Fallacies
5.1 Ad Hominem Fallacy
5.2 Straw Man Fallacy
5.3 Appeal to Authority Fallacy
5.4 False Dilemma Fallacy
5.5 Circular Reasoning Fallacy
5.6 Slippery Slope Fallacy
5.7 Appeal to Emotion Fallacy
5.8 Bandwagon Fallacy
5.9 Red Herring Fallacy
5.10 False Cause Fallacy
5.11 Hasty Generalization Fallacy
5.12 Confirmation Bias Fallacy
5.13 Tu Quoque Fallacy
5.14 Begging the Question Fallacy
5.15 Fallacy of Composition
5.16 Fallacy of Division
5.17 Gambler's Fallacy
5.18 Fallacy of Equivocation
5.19 No True Scotsman Fallacy
5.20 Fallacy of Sunk Costs
5.21 Post hoc Ergo Propter hoc Fallacy
5.22 Genetic Fallacy
5.23 Black-and-White Fallacy
5.24 Appeal to Ignorance Fallacy
5.25 Appeal to Tradition Fallacy
5.26 False Analogy Fallacy
5.27 Fallacy of the Middle Ground
5.28 Fallacy of Suppressed Evidence
5.29 Loaded Question Fallacy
5.30 Fallacy of False Equivalence
5.31 Fallacy of the Beard
5.32 Appeal to Fear Fallacy
5.33 Fallacy of the Texas Sharpshooter
5.34 Fallacy of Composition and Division
5.35 Fallacy of Personal Incredulity
5.36 Fallacy of Relative Privation
5.37 Fallacy of Ambiguity
## 6 Probability Theory
6.1 Conditional probability
6.2 Bayes' theorem
6.3 Combinatorics and counting principles
6.4 Random variables
6.5 Probability distributions
6.6 Expected value
6.7 Variance and standard deviation
6.8 Joint probability distributions
6.9 Marginal and conditional distributions
6.10 Independent and dependent events
6.11 Law of large numbers
6.12 Central limit theorem
6.13 Hypothesis testing
6.14 Null and alternative hypotheses
6.15 Type I and Type II errors
6.16 Confidence intervals
6.17 Sampling distributions
6.18 Estimation and point estimation
6.19 Maximum likelihood estimation
6.20 Bayesian inference
6.21 Markov chains
6.22 Random walks
6.23 Stochastic processes
6.24 Queueing theory
6.25 Poisson processes
6.26 Discrete-time and continuous-time models
6.27 Game theory and probability
6.28 Decision theory
6.29 Monte Carlo simulations
6.30 Law of total probability
6.31 Conditional expectation
6.32 Covariance and correlation
6.33 Multivariate probability distributions
6.34 Order statistics
6.35 Moment generating functions
6.36 Survival analysis
6.37 Reliability theory
## 7 Universality
7.1 Turing machines
7.2 Computational universality
7.3 Halting problem
7.4 Universal Turing machine
7.5 Von Neumann architecture
7.6 Formal systems
7.7 Universal logic gates
7.8 Church-Turing thesis
7.9 Universal programming languages
7.10 Genetic universality
7.11 Universal cellular automata
7.12 Universal robots
7.13 Universal data formats
7.14 Universality in artificial intelligence
7.15 Universal computation in physical systems
7.16 Universal computational models
7.17 Universality in quantum computing
7.18 Universal algorithms
7.19 Universal hash functions
7.20 Universality in neural networks
7.21 Universal approximation theorems
7.22 Universality in machine learning models
7.23 Universal grammar in linguistics
7.24 Universal cognitive processes
7.25 Universal reasoning principles
7.26 Universal problem-solving techniques
7.27 Universality in mathematics
7.28 Universal mathematical structures
7.29 Universal properties in category theory
7.30 Universal constructions
7.31 Universal sets
7.32 Universality in formal languages
7.33 Universal automata theory
7.34 Universal logic systems
7.35 Universal semantics
7.36 Universal reasoning in ethics
7.37 Universality in social systems
## 8 Linguistic Logic
8.1 Propositional logic
8.2 Predicate logic
8.3 Formal languages
8.4 Logical connectives
8.5 Truth tables
8.6 Inference rules
8.7 Logical equivalence
8.8 Validity and soundness
8.9 Quantifiers
8.10 First-order logic
8.11 Modal logic
8.12 Fuzzy logic
8.13 Natural language processing
8.14 Sentential logic
8.15 Inductive reasoning
8.16 Deductive reasoning
8.17 Abductive reasoning
8.18 Logical paradoxes
8.19 Set theory
8.20 Type theory
8.21 Propositional calculus
8.22 Linguistic semantics
8.23 Linguistic pragmatics
8.24 Formal systems
8.25 Symbolic logic
8.26 Mathematical logic
8.27 Reasoning fallacies
8.28 Argumentation theory
8.29 Logical puzzles
8.30 Logical operators
8.31 Linguistic ambiguity
8.32 Linguistic meaning
8.33 Linguistic analysis
8.34 Linguistic inference
8.35 Linguistic reasoning tasks
8.36 Linguistic truth values
8.37 Linguistic decision-making
## 9 Moral Reasoning
9.1 Moral dilemmas in healthcare
9.2 Ethical considerations in scientific research
9.3 Moral reasoning in criminal justice
9.4 Ethical implications of artificial intelligence
9.5 Moral decision-making in business ethics
9.6 Ethical issues in genetic engineering
9.7 Moral reasoning in environmental conservation
9.8 Ethical considerations in animal testing
9.9 Moral dilemmas in end-of-life care
9.10 Ethical implications of social media use
9.11 Moral decision-making in global politics
9.12 Ethical issues in human cloning
9.13 Moral reasoning in military ethics
9.14 Ethical considerations in data privacy
9.15 Moral dilemmas in organ transplantation
9.16 Ethical implications of autonomous vehicles
9.17 Moral decision-making in journalism
9.18 Ethical issues in corporate governance
9.19 Moral reasoning in education ethics
9.20 Ethical considerations in cosmetic surgery
9.21 Moral dilemmas in reproductive rights
9.22 Ethical implications of genetic editing
9.23 Moral decision-making in humanitarian aid
9.24 Ethical issues in advertising
9.25 Moral reasoning in social justice
9.26 Ethical considerations in surveillance technologies
9.27 Moral dilemmas in resource allocation
9.28 Ethical implications of human enhancement
9.29 Moral decision-making in professional sports
9.30 Ethical issues in financial markets
9.31 Moral reasoning in immigration ethics
9.32 Ethical considerations in food production
9.33 Moral dilemmas in artificial intelligence and job automation
9.34 Ethical implications of virtual reality technology
9.35 Moral decision-making in international diplomacy
9.36 Ethical issues in nuclear energy
9.37 Moral reasoning in the use of drones
## 10 Philosophical Reasoning
10.1 The nature of knowledge
10.2 Epistemological skepticism
10.3 Theories of truth
10.4 The problem of induction
10.5 The nature of reality
10.6 Metaphysical dualism
10.7 Idealism vs. materialism
10.8 The mind-body problem
10.9 Free will and determinism
10.10 Ethics and moral reasoning
10.11 Ethical relativism
10.12 Utilitarianism
10.13 Deontological ethics
10.14 Virtue ethics
10.15 The problem of evil
10.16 The existence of God
10.17 Arguments for the existence of God
10.18 The problem of divine hiddenness
10.19 The problem of religious diversity
10.20 The nature of consciousness
10.21 Personal identity and the self
10.22 Philosophy of language
10.23 Meaning and reference
10.24 Theories of truth and language
10.25 Language and thought
10.26 Philosophy of mind
10.27 Mental states and qualia
10.28 Artificial intelligence and consciousness
10.29 Philosophy of science
10.30 Scientific realism vs. instrumentalism
10.31 Theories of scientific explanation
10.32 Induction and scientific reasoning
10.33 Philosophy of mathematics
10.34 Platonism vs. nominalism
10.35 The foundations of mathematics
10.36 Philosophy of art and aesthetics
10.37 The nature of beauty and aesthetic experience
## 11 Analogical Reasoning
11.1 Identifying similarities and differences between two objects
11.2 Applying analogical reasoning in problem-solving
11.3 Transfer of knowledge through analogical reasoning
11.4 Analogical reasoning in cognitive development
11.5 Analogical reasoning in artificial intelligence
11.6 Using analogical reasoning to make predictions
11.7 Analogical reasoning in decision-making
11.8 Analogical reasoning in scientific research
11.9 Analogical reasoning in mathematics
11.10 Analogical reasoning in language learning
11.11 Analogical reasoning in concept formation
11.12 Analogical reasoning in pattern recognition
11.13 Analogical reasoning in problem-solving heuristics
11.14 Analogical reasoning in legal reasoning
11.15 Analogical reasoning in moral decision-making
11.16 Analogical reasoning in artistic creativity
11.17 Analogical reasoning in historical analysis
11.18 Analogical reasoning in philosophical arguments
11.19 Analogical reasoning in economic forecasting
11.20 Analogical reasoning in engineering design
11.21 Analogical reasoning in medical diagnosis
11.22 Analogical reasoning in social psychology
11.23 Analogical reasoning in political analysis
11.24 Analogical reasoning in ecological modeling
11.25 Analogical reasoning in educational pedagogy
11.26 Analogical reasoning in architecture and design
11.27 Analogical reasoning in computer programming
11.28 Analogical reasoning in market research
11.29 Analogical reasoning in cognitive biases
11.30 Analogical reasoning in problem reformation
11.31 Analogical reasoning in historical analogies
11.32 Analogical reasoning in evolutionary biology
11.33 Analogical reasoning in logical deduction
11.34 Analogical reasoning in concept mapping
11.35 Analogical reasoning in neural network training
11.36 Analogical reasoning in innovation and invention
11.37 Analogical reasoning in sports strategy
## 12 Set Theory
12.1 Union of sets
12.2 Intersection of sets
12.3 Complement of a set
12.4 Subset relationships
12.5 Power sets
12.6 Disjoint sets
12.7 Cardinality of sets
12.8 Finite and infinite sets
12.9 Empty set
12.10 Universal set
12.11 Set operations
12.12 Set equivalence
12.13 Set difference
12.14 Symmetric difference
12.15 Subset notation
12.16 Set membership notation
12.17 Set equality
12.18 Venn diagrams
12.19 Set partitions
12.20 Cartesian product of sets
12.21 De Morgan's laws
12.22 Distributive laws of sets
12.23 Set identities
12.24 Set operations with intervals
12.25 Interval notation
12.26 Interval arithmetic
12.27 Countable and uncountable sets
12.28 Russell's paradox
12.29 Cantor's diagonal argument
12.30 Set theory axioms
12.31 Zermelo-Fraenkel set theory
12.32 Axiom of choice
12.33 Well-ordering principle
12.34 Russell's paradox
12.35 Infinite sets and their properties
12.36 Finite and infinite unions and intersections
12.37 Applications of set theory in computer science
## 13 Abductive Reasoning
13.1 Hypothesis generation in abductive reasoning
13.2 Evidence evaluation in abductive reasoning
13.3 Inference and deduction in abductive reasoning
13.4 Cognitive biases and abductive reasoning
13.5 Abductive reasoning in scientific research
13.6 Abductive reasoning in detective work
13.7 Abductive reasoning in medical diagnosis
13.8 Abductive reasoning in decision-making
13.9 Abductive reasoning in artificial intelligence
13.10 Abductive reasoning in philosophy
13.11 Abductive reasoning in psychology
13.12 Abductive reasoning in legal reasoning
13.13 Abductive reasoning in problem-solving
13.14 The role of intuition in abductive reasoning
13.15 The relationship between abductive reasoning and induction
13.16 The role of evidence in abductive reasoning
13.17 Abductive reasoning in pattern recognition
13.18 Abductive reasoning in creative thinking
13.19 Abductive reasoning in learning and education
13.20 The limitations of abductive reasoning
13.21 Abductive reasoning and causal inference
13.22 Abductive reasoning in historical analysis
13.23 Abductive reasoning in social sciences
13.24 The role of prior knowledge in abductive reasoning
13.25 Abductive reasoning in business and marketing
13.26 Abductive reasoning in computational linguistics
13.27 Abductive reasoning in engineering design
13.28 Abductive reasoning and Bayesian inference
13.29 The role of uncertainty in abductive reasoning
13.30 Abductive reasoning and problem framing
13.31 Abductive reasoning in natural language understanding
13.32 Abductive reasoning in cognitive psychology
13.33 Abductive reasoning and creativity in art
13.34 Abductive reasoning and decision-making under uncertainty
13.35 Abductive reasoning in ethics and moral reasoning
13.36 Abductive reasoning and argumentation theory
13.37 Abductive reasoning in machine learning and data analysis
## 14 Decision Theory
14.1 Utility theory
14.2 Rational choice theory
14.3 Expected utility theory
14.4 Prospect theory
14.5 Game theory
14.6 Nash equilibrium
14.7 Risk analysis
14.8 Decision trees
14.9 Bayesian decision theory
14.10 Multi-criteria decision analysis
14.11 Behavioral economics
14.12 Information theory
14.13 Decision-making under uncertainty
14.14 Decision-making under risk
14.15 Cost-benefit analysis
14.16 Preference elicitation
14.17 Judgment and decision-making biases
14.18 Social decision-making
14.19 Group decision-making
14.20 Decision support systems
14.21 Robust decision-making
14.22 Uncertainty quantification
14.23 Sensitivity analysis
14.24 Decision-making in complex systems
14.25 Strategic decision-making
14.26 Dynamic decision-making
14.27 Heuristics and biases in decision-making
14.28 Decision-making in healthcare
14.29 Decision-making in finance
14.30 Decision-making in environmental management
14.31 Decision-making in supply chain management
14.32 Decision-making in project management
14.33 Decision-making in artificial intelligence
14.34 Ethical decision-making
14.35 Decision-making in crisis situations
14.36 Decision-making in negotiations
14.37 Decision-making in organizational behavior
## 15 Epistemology
15.1 Foundationalism vs. Coherentism
15.2 Empiricism vs. Rationalism
15.3 Skepticism
15.4 Induction vs. Deduction
15.5 A priori vs. A posteriori knowledge
15.6 Reliability of perception
15.7 The problem of induction
15.8 The nature of truth
15.9 Rationality and irrationality
15.10 Intuition and instinct
15.11 Epistemic justification
15.12 Conceptual schemes and worldview
15.13 Testimony and authority
15.14 Perception vs. interpretation
15.15 Epistemic virtues
15.16 Social construction of knowledge
15.17 Epistemic relativism
15.18 Meta-epistemology
15.19 Internalism vs. Externalism
15.20 Epistemic norms and responsibilities
15.21 Perception and hallucination
15.22 Epistemic luck
15.23 Epistemic closure
15.24 Epistemic contextualism
15.25 Gettier problems
15.26 Reliabilism
15.27 Naturalized epistemology
15.28 Coherence theory of truth
15.29 Foundationalist theories of justification
15.30 Instrumentalism
15.31 Pragmatic theories of truth
15.32 Epistemic justification in science
15.33 Evolutionary epistemology
15.34 Epistemic normativity
15.35 Epistemology of testimony
15.36 Memory and knowledge
15.37 Epistemology and artificial intelligence
## 16 Mind Mapping
16.1 Techniques for creating effective mind maps
16.2 Applying mind mapping to problem-solving
16.3 Using mind maps for brainstorming
16.4 Mind mapping for decision-making
16.5 Mind mapping as a learning tool
16.6 Mind mapping for project management
16.7 Mind mapping for goal setting
16.8 Mind mapping for organizing information
16.9 Mind mapping for note-taking
16.10 Mind mapping for studying
16.11 Mind mapping for creative writing
16.12 Mind mapping for time management
16.13 Mind mapping for team collaboration
16.14 Mind mapping for strategic planning
16.15 Mind mapping for memory improvement
16.16 Mind mapping for visual thinking
16.17 Mind mapping for idea generation
16.18 Mind mapping for effective communication
16.19 Mind mapping for personal development
16.20 Mind mapping for problem analysis
16.21 Mind mapping for critical thinking
16.22 Mind mapping for concept mapping
16.23 Mind mapping for data visualization
16.24 Mind mapping for goal alignment
16.25 Mind mapping for self-reflection
16.26 Mind mapping for information synthesis
16.27 Mind mapping for decision prioritization
16.28 Mind mapping for creativity enhancement
16.29 Mind mapping for task prioritization
16.30 Mind mapping for workflow optimization
16.31 Mind mapping for strategic thinking
16.32 Mind mapping for brainstorming solutions
16.33 Mind mapping for strategic decision-making
16.34 Mind mapping for organizing research
16.35 Mind mapping for collaborative problem-solving
16.36 Mind mapping for mapping knowledge domains
16.37 Mind mapping for generating insights
## 17 Quantitative Reasoning
17.1 Statistical analysis
17.2 Probability theory
17.3 Data interpretation
17.4 Algebraic reasoning
17.5 Arithmetic operations
17.6 Ratios and proportions
17.7 Graphical representation of data
17.8 Data visualization techniques
17.9 Logical reasoning
17.10 Deductive reasoning
17.11 Inductive reasoning
17.12 Geometric reasoning
17.13 Number patterns
17.14 Estimation and approximation
17.15 Data sampling techniques
17.16 Hypothesis testing
17.17 Linear equations
17.18 Quadratic equations
17.19 Exponential growth and decay
17.20 Financial reasoning
17.21 Time and distance problems
17.22 Percentages and fractions
17.23 Permutations and combinations
17.24 Unit conversions
17.25 Measurements and scales
17.26 Logic puzzles
17.27 Game theory
17.28 Decision-making models
17.29 Analytical reasoning
17.30 Statistical inference
17.31 Descriptive statistics
17.32 Operations research
17.33 Optimization problems
17.34 Computational reasoning
17.35 Time series analysis
17.36 Data forecasting
17.37 Critical thinking in quantitative reasoning
## 18 Combinatorics
18.1 Permutations and combinations
18.2 Binomial coefficients
18.3 Pigeonhole principle
18.4 Counting principles
18.5 Combinatorial identities
18.6 Generating functions
18.7 Combinatorial optimization
18.8 Combinatorial proofs
18.9 Combinatorial algorithms
18.10 Graph coloring
18.11 Ramsey theory
18.12 Combinatorial designs
18.13 Latin squares
18.14 Combinatorial game theory
18.15 Partition theory
18.16 Polya's enumeration theorem
18.17 Combinatorial geometry
18.18 Combinatorics in computer science
18.19 Randomized algorithms in combinatorics
18.20 Probabilistic methods in combinatorics
18.21 Combinatorial algorithms for network optimization
18.22 Combinatorial optimization in scheduling problems
18.23 Combinatorial aspects of cryptography
18.24 Combinatorial generation of permutations and subsets
18.25 Combinatorial algorithms for graph theory problems
18.26 Combinatorial optimization in logistics and transportation
18.27 Combinatorial reasoning in coding theory
18.28 Combinatorial methods in data analysis and machine learning
18.29 Combinatorial problems in social network analysis
18.30 Combinatorial enumeration in bioinformatics
18.31 Combinatorial reasoning in operations research
18.32 Combinatorial optimization in supply chain management
18.33 Combinatorial aspects of network design and routing
18.34 Combinatorial reasoning in artificial intelligence
18.35 Combinatorial methods in image processing and computer vision
18.36 Combinatorial reasoning in quantum computing
18.37 Combinatorial aspects of error-correcting codes
## 19 Mathematical Reasoning
19.1 Logical proofs in mathematics
19.2 Inductive reasoning in mathematical patterns
19.3 Deductive reasoning in geometry
19.4 Proving mathematical theorems
19.5 Constructing mathematical counterexamples
19.6 Reasoning with mathematical inequalities
19.7 Applying mathematical logic to problem-solving
19.8 Reasoning with mathematical functions
19.9 Analyzing mathematical series and sequences
19.10 Using mathematical induction to prove statements
19.11 Reasoning with mathematical symbols and notation
19.12 Investigating mathematical paradoxes
19.13 Reasoning with mathematical equations
19.14 Analyzing mathematical graphs and functions
19.15 Applying mathematical reasoning to optimization problems
19.16 Reasoning with mathematical ratios and proportions
19.17 Using logical deduction in number theory
19.18 Reasoning with mathematical vectors and matrices
19.19 Applying mathematical reasoning to combinatorics problems
19.20 Reasoning with mathematical inequalities and absolute values
19.21 Analyzing mathematical algorithms and complexity
19.22 Reasoning with mathematical sets and set operations
19.23 Using inductive reasoning in mathematical modeling
19.24 Reasoning with mathematical limits and convergence
19.25 Applying mathematical reasoning to probability theory
19.26 Reasoning with mathematical graphs and networks
19.27 Using deductive reasoning in mathematical proofs
19.28 Reasoning with mathematical transformations and symmetry
19.29 Applying mathematical reasoning to cryptography
19.30 Reasoning with mathematical series and convergence
19.31 Using mathematical logic in boolean algebra
19.32 Reasoning with mathematical functions and their properties
19.33 Analyzing mathematical patterns in number sequences
19.34 Reasoning with mathematical inequalities and intervals
19.35 Applying mathematical reasoning to optimization in calculus
19.36 Reasoning with mathematical reasoning fallacies
19.37 Using deductive reasoning in mathematical puzzles and riddles
## 20 Critical Thinking
20.1 Logical fallacies
20.2 Inductive reasoning
20.3 Deductive reasoning
20.4 Problem-solving techniques
20.5 Argument analysis
20.6 Decision-making processes
20.7 Cognitive biases
20.8 Evaluating evidence
20.9 Analytical thinking
20.10 Creative thinking
20.11 Causal reasoning
20.12 Syllogistic reasoning
20.13 Counterfactual reasoning
20.14 Abductive reasoning
20.15 Moral reasoning
20.16 Analogical reasoning
20.17 Statistical reasoning
20.18 Decision tree analysis
20.19 Ethical dilemmas
20.20 Argument construction
20.21 Analyzing assumptions
20.22 Evaluating sources of information
20.23 Critical evaluation of claims
20.24 Identifying hidden premises
20.25 Evaluating arguments for validity
20.26 Evaluating arguments for soundness
20.27 Problem-solving heuristics
20.28 Identifying logical inconsistencies
20.29 Evaluating the strength of arguments
20.30 Identifying cognitive biases in others
20.31 Logical reasoning puzzles
20.32 Evaluating the reliability of data
20.33 Identifying common reasoning errors
20.34 Distinguishing correlation from causation
20.35 Identifying straw man arguments
20.36 Identifying circular reasoning
20.37 Evaluating the credibility of experts
## 21 Systems Thinking
21.1 Feedback loops in complex systems
21.2 Causal loop diagrams in systems thinking
21.3 Identifying and understanding system boundaries
21.4 The role of mental models in systems thinking
21.5 Identifying and analyzing system dynamics
21.6 Understanding emergent properties in complex systems
21.7 Identifying and managing system leverage points
21.8 Systems thinking in organizational management
21.9 Systems thinking in environmental sustainability
21.10 Systems thinking in healthcare systems
21.11 Systems thinking in supply chain management
21.12 Systems thinking in economic models
21.13 Systems thinking in social networks and relationships
21.14 Holistic approach to problem-solving using systems thinking
21.15 Systems thinking in urban planning and development
21.16 Systems thinking in educational systems
21.17 Systems thinking in project management
21.18 Systems thinking in risk management
21.19 Systems thinking in policy development and analysis
21.20 Systems thinking in technological innovation
21.21 Systems thinking in climate change mitigation and adaptation
21.22 Systems thinking in complex data analysis
21.23 Systems thinking in conflict resolution and peacebuilding
21.24 Systems thinking in organizational change management
21.25 Systems thinking in financial markets and investments
21.26 Systems thinking in product design and development
21.27 Systems thinking in transportation and logistics
21.28 Systems thinking in public health strategies
21.29 Systems thinking in agriculture and food production
21.30 Systems thinking in energy systems and sustainability
21.31 Systems thinking in quality management
21.32 Systems thinking in information technology systems
21.33 Systems thinking in disaster management and response
21.34 Systems thinking in government and public administration
21.35 Systems thinking in social justice and equity
21.36 Systems thinking in artificial intelligence and machine learning
21.37 Systems thinking in personal development and self-improvement
## 22 Arguments
22.1 Logical fallacies
22.2 Deductive reasoning
22.3 Inductive reasoning
22.4 Abductive reasoning
22.5 Cognitive biases in arguments
22.6 Counterarguments
22.7 Persuasive techniques
22.8 Rhetorical devices
22.9 Propositional logic
22.10 Syllogisms
22.11 Validity and soundness of arguments
22.12 Causal reasoning
22.13 Analogical reasoning
22.14 Ethical reasoning
22.15 Critical thinking
22.16 Informal fallacies
22.17 Argument structure
22.18 Argument analysis
22.19 Toulmin model of argumentation
22.20 Dialectical reasoning
22.21 Reasoning by analogy
22.22 Fallacies of relevance
22.23 Fallacies of presumption
22.24 Fallacies of ambiguity
22.25 Reasoning and decision-making
22.26 Bayesian reasoning
22.27 Reasoning under uncertainty
22.28 Reasoning in mathematics
22.29 Argumentation theory
22.30 Rationality and irrationality in arguments
22.31 Reasoning and problem-solving
22.32 Argument mapping
22.33 Rhetoric and persuasion
22.34 Emotional appeals in arguments
22.35 Cognitive dissonance and argumentation
22.36 Logical consistency in arguments
22.37 Argumentation ethics
## 23 Reasoning from Consequences
23.1 Evaluating the potential outcomes of an action
23.2 Predicting the consequences of a decision
23.3 Analyzing cause-and-effect relationships
23.4 Identifying unintended consequences
23.5 Weighing the benefits and drawbacks of different choices
23.6 Assessing the long-term implications of a course of action
23.7 Considering the ripple effects of a decision
23.8 Recognizing the impact of one's behavior on others
23.9 Anticipating the results of a specific strategy
23.10 Projecting the future based on current actions
23.11 Examining the logical implications of a hypothesis
23.12 Understanding the relationship between actions and outcomes
23.13 Reflecting on past experiences to inform future decision-making
23.14 Considering the ethical implications of a decision
23.15 Assessing the risk and reward of a particular course of action
23.16 Distinguishing between immediate and delayed consequences
23.17 Examining the unintended benefits of an action
23.18 Recognizing the trade-offs involved in decision-making
23.19 Identifying potential obstacles or roadblocks in achieving desired outcomes
23.20 Weighing the potential impact on different stakeholders
23.21 Evaluating the likelihood of different outcomes
23.22 Analyzing the causal chain of events
23.23 Considering the impact of external factors on outcomes
23.24 Assessing the reliability of predictive models
23.25 Recognizing the difference between correlation and causation
23.26 Anticipating the reactions of others to a particular action
23.27 Examining the relationship between intentions and consequences
23.28 Evaluating the effectiveness of different strategies in achieving desired outcomes
23.29 Considering the unintended consequences of policy decisions
23.30 Reflecting on the lessons learned from previous failures or successes
23.31 Identifying potential risks and mitigating strategies
23.32 Analyzing the impact of technological advancements on future consequences
23.33 Evaluating the impact of economic factors on decision outcomes
23.34 Considering the impact of cultural norms on decision consequences
23.35 Assessing the long-term sustainability of a chosen course of action
23.36 Recognizing the role of feedback loops in determining outcomes
23.37 Evaluating the scalability of a decision in different contexts
## 24 Argumentative Strategies
24.1 Logical fallacies in argumentation
24.2 The role of evidence in constructing arguments
24.3 Counterargument and rebuttal techniques
24.4 The use of emotion in persuasive reasoning
24.5 Ethical considerations in argumentation
24.6 The role of language and rhetoric in shaping arguments
24.7 Cognitive biases and their impact on reasoning
24.8 Strategies for constructing a strong thesis statement
24.9 The importance of clarity and coherence in arguments
24.10 Evaluating the credibility of sources in argumentation
24.11 The distinction between deductive and inductive reasoning
24.12 Identifying and analyzing assumptions in arguments
24.13 The role of analogy in persuasive reasoning
24.14 Analyzing and critiquing arguments in written texts
24.15 The use of logical reasoning in legal arguments
24.16 The influence of cultural and societal factors on argumentation
24.17 Understanding and addressing logical inconsistencies in arguments
24.18 Constructing a persuasive argument in a debate setting
24.19 The impact of personal bias on argumentation
24.20 Analyzing the structure and organization of arguments
24.21 The use of statistics and data in persuasive reasoning
24.22 The role of logical operators (AND, OR, NOT) in constructing arguments
24.23 Identifying and responding to straw man arguments
24.24 Ethos, logos, and pathos in persuasive communication
24.25 The psychology of persuasion and argumentation
24.26 Evaluating the strengths and weaknesses of different argumentative strategies
24.27 The role of storytelling in persuasive reasoning
24.28 Assessing the relevance and validity of evidence in arguments
24.29 The impact of framing and language choice on argumentation
24.30 Recognizing and countering ad hominem attacks in arguments
24.31 Understanding the concept of burden of proof in argumentation
24.32 The role of critical thinking in constructing effective arguments
24.33 Analyzing conflicting viewpoints in argumentation
24.34 The impact of social media on argumentative discourse
24.35 The role of logic puzzles in honing reasoning skills
24.36 Identifying and addressing logical fallacies in oral arguments
24.37 The importance of empathy and understanding in constructive argumentation.
## 25 Prediction
25.1 Statistical modeling for predictions
25.2 Time series forecasting
25.3 Machine learning algorithms for prediction
25.4 Predictive analytics in business
25.5 Predictive modeling techniques
25.6 Predictive maintenance in manufacturing
25.7 Predictive modeling for healthcare outcomes
25.8 Predictive policing and crime prevention
25.9 Predictive modeling for stock market trends
25.10 Predictive modeling in weather forecasting
25.11 Predictive analytics for customer behavior
25.12 Predictive modeling for credit risk assessment
25.13 Predictive modeling in sports analytics
25.14 Predictive modeling for transportation planning
25.15 Predictive modeling for disease outbreak prediction
25.16 Predictive modeling for energy consumption
25.17 Predictive modeling for supply chain optimization
25.18 Predictive analytics for marketing campaigns
25.19 Predictive modeling for fraud detection
25.20 Predictive modeling for insurance claims
25.21 Predictive modeling for demand forecasting
25.22 Predictive modeling for election outcomes
25.23 Predictive analytics in personalized medicine
25.24 Predictive modeling for natural disasters
25.25 Predictive modeling for customer churn prediction
25.26 Predictive analytics for website user behavior
25.27 Predictive modeling for student performance
25.28 Predictive modeling for recommendation systems
25.29 Predictive analytics for social media trends
25.30 Predictive modeling for traffic congestion
25.31 Predictive analytics for asset management
25.32 Predictive modeling for customer lifetime value
25.33 Predictive analytics for sentiment analysis
25.34 Predictive modeling for urban planning
25.35 Predictive analytics for machine failure prediction
25.36 Predictive modeling for crop yield prediction
25.37 Predictive analytics for healthcare resource allocation
## 26 Reversibility
26.1 Cause and effect relationships
26.2 Logical reasoning
26.3 Cognitive flexibility
26.4 Problem-solving strategies
26.5 Decision-making processes
26.6 Analytical thinking
26.7 Memory recall and retrieval
26.8 Pattern recognition
26.9 Sequential reasoning
26.10 Hypothetical scenarios
26.11 Inference and deduction
26.12 Inductive reasoning
26.13 Deductive reasoning
26.14 Algorithmic thinking
26.15 Computational complexity
26.16 Counterfactual reasoning
26.17 Abductive reasoning
26.18 Heuristics and biases
26.19 Critical thinking skills
26.20 Systems thinking
26.21 Error analysis and correction
26.22 Experimental design and control
26.23 Probability and uncertainty
26.24 Spatial reasoning
26.25 Analogical reasoning
26.26 Transitive reasoning
26.27 Metacognition
26.28 Mental models
26.29 Logic puzzles and games
26.30 Decision trees
26.31 Bayes' theorem
26.32 Game theory
26.33 Problem decomposition
26.34 Causal reasoning
26.35 Ethical reasoning
26.36 Conceptual reasoning
26.37 Reasoning under constraints
## 27 Causality
27.1 Cause and effect relationships
27.2 Temporal causality
27.3 Counterfactual reasoning
27.4 Deterministic causality
27.5 Probabilistic causality
27.6 Causal inference
27.7 Causal reasoning in psychology
27.8 Causal reasoning in philosophy
27.9 Causal reasoning in economics
27.10 Causal reasoning in artificial intelligence
27.11 Causal models
27.12 Causal diagrams
27.13 Causal networks
27.14 Causal explanations
27.15 Causal mechanisms
27.16 Causal loops
27.17 Causal attribution
27.18 Causal analysis
27.19 Causal reasoning in social sciences
27.20 Causal reasoning in medicine
27.21 Causal reasoning in law
27.22 Causal reasoning in history
27.23 Causal reasoning in biology
27.24 Causal reasoning in physics
27.25 Causal reasoning in engineering
27.26 Causal reasoning in decision-making
27.27 Causal reasoning in education
27.28 Causal reasoning in environmental studies
27.29 Causal reasoning in public policy
27.30 Causal reasoning in statistics
27.31 Causal reasoning in marketing
27.32 Causal reasoning in game theory
27.33 Causal reasoning in ethics
27.34 Causal reasoning in anthropology
27.35 Causal reasoning in sociology
27.36 Causal reasoning in linguistics
27.37 Causal reasoning in neuroscience
## 28 Reasoned Judgement
28.1 Logical reasoning
28.2 Deductive reasoning
28.3 Inductive reasoning
28.4 Abductive reasoning
28.5 Critical thinking
28.6 Decision-making processes
28.7 Cognitive biases in reasoning
28.8 Argument evaluation
28.9 Evaluating evidence
28.10 Fallacies in reasoning
28.11 Analyzing patterns and trends
28.12 Counterfactual reasoning
28.13 Problem-solving strategies
28.14 Rationality and reasoning
28.15 Ethical reasoning
28.16 Moral decision-making
28.17 Bayesian reasoning
28.18 Decision theory
28.19 Heuristics and biases
28.20 Cognitive development and reasoning
28.21 Analogical reasoning
28.22 Reasoning under uncertainty
28.23 Causal reasoning
28.24 Syllogistic reasoning
28.25 Reasoning in mathematics
28.26 Legal reasoning
28.27 Scientific reasoning
28.28 Reasoning in artificial intelligence
28.29 Linguistic reasoning
28.30 Reasoning in philosophy
28.31 Reasoning in psychology
28.32 Cultural influences on reasoning
28.33 Reasoning in economics
28.34 Historical reasoning
28.35 Political reasoning
28.36 Social reasoning
28.37 Reasoning in education
## 29 Heuristics
29.1 Anchoring and adjustment heuristic
29.2 Availability heuristic
29.3 Representativeness heuristic
29.4 Confirmation bias
29.5 Overconfidence bias
29.6 Gambler's fallacy
29.7 Sunk cost fallacy
29.8 Framing effect
29.9 Base rate fallacy
29.10 Hindsight bias
29.11 Cognitive biases in decision making
29.12 Decision-making under uncertainty
29.13 Prospect theory
29.14 Loss aversion
29.15 Intuition in decision making
29.16 The role of emotions in decision making
29.17 Biases in risk assessment
29.18 Bounded rationality
29.19 System 1 and System 2 thinking
29.20 The impact of heuristics on judgment and decision making
29.21 Cognitive biases in problem-solving
29.22 Anchoring bias in negotiation
29.23 The role of heuristics in learning
29.24 Algorithmic decision-making
29.25 Cognitive shortcuts in information processing
29.26 Counterfactual thinking
29.27 Bias blind spot
29.28 The role of social influence in heuristic reasoning
29.29 The relationship between heuristics and biases
29.30 The adaptive value of heuristics
29.31 The impact of expertise on heuristic reasoning
29.32 The role of culture in heuristic reasoning
29.33 Rationality vs. heuristics in decision making
29.34 Decision-making in complex environments
29.35 Heuristics in artificial intelligence
29.36 Heuristics in economic models
29.37 The role of heuristics in creativity and innovation
## 30 Probabilistic Reasoning
30.1 Bayesian networks
30.2 Markov chains
30.3 Hidden Markov models
30.4 Conditional probability
30.5 Joint probability
30.6 Marginal probability
30.7 Prior probability
30.8 Posterior probability
30.9 Maximum likelihood estimation
30.10 Expectation-maximization algorithm
30.11 Decision theory
30.12 Bayesian inference
30.13 Naive Bayes classifier
30.14 Probabilistic graphical models
30.15 Monte Carlo methods
30.16 Sampling techniques
30.17 Belief propagation
30.18 Variable elimination
30.19 Independence assumptions
30.20 Causal reasoning
30.21 Probabilistic reasoning in artificial intelligence
30.22 Uncertainty modeling
30.23 Probabilistic reasoning in robotics
30.24 Probabilistic reasoning in finance
30.25 Probabilistic reasoning in healthcare
30.26 Probabilistic reasoning in natural language processing
30.27 Probabilistic reasoning in computer vision
30.28 Probabilistic reasoning in recommendation systems
30.29 Probabilistic reasoning in anomaly detection
30.30 Probabilistic reasoning in risk assessment
30.31 Probabilistic reasoning in decision-making
30.32 Probabilistic reasoning in game theory
30.33 Probabilistic reasoning in pattern recognition
30.34 Probabilistic reasoning in fault diagnosis
30.35 Probabilistic reasoning in bioinformatics
30.36 Probabilistic reasoning in data analysis
30.37 Probabilistic reasoning in optimization
## 31 Pragmatism
31.1 Cost-benefit analysis
31.2 Decision-making under uncertainty
31.3 Risk assessment and mitigation
31.4 Game theory
31.5 Cognitive biases and heuristics
31.6 Rationality in decision-making
31.7 Logical reasoning
31.8 Ethical reasoning
31.9 Deductive reasoning
31.10 Inductive reasoning
31.11 Abductive reasoning
31.12 Argumentation and critical thinking
31.13 Problem-solving strategies
31.14 Decision-making models
31.15 Bayesian reasoning
31.16 Cognitive psychology and reasoning
31.17 Neurological basis of reasoning
31.18 Analytical thinking
31.19 Creative problem-solving
31.20 Cognitive load and reasoning efficiency
31.21 Syllogistic reasoning
31.22 Fallacies in reasoning
31.23 Non-monotonic reasoning
31.24 Dialectical reasoning
31.25 Scientific reasoning
31.26 Statistical reasoning
31.27 Deductive logic
31.28 Inductive logic
31.29 Fuzzy logic
31.30 Probabilistic reasoning
31.31 Analogical reasoning
31.32 Practical reasoning
31.33 Normative reasoning
31.34 Emotion and reasoning
31.35 Argument evaluation and reconstruction
31.36 Decision-making in complex systems
31.37 Legal reasoning and interpretation
## 32 Induction
32.1 Predictive modeling
32.2 Data analysis
32.3 Statistical inference
32.4 Generalization
32.5 Causal reasoning
32.6 Pattern recognition
32.7 Machine learning algorithms
32.8 Data mining
32.9 Bayesian inference
32.10 Decision tree algorithms
32.11 Hypothesis testing
32.12 Regression analysis
32.13 Neural networks
32.14 Feature selection
32.15 Clustering algorithms
32.16 Model evaluation
32.17 Overfitting and underfitting
32.18 Model selection
32.19 Time series forecasting
32.20 Confidence intervals
32.21 Ensemble methods
32.22 Cross-validation
32.23 Exploratory data analysis
32.24 Bias-variance trade-off
32.25 Dimensionality reduction
32.26 Association rule mining
32.27 Model interpretation
32.28 Unsupervised learning
32.29 Probabilistic graphical models
32.30 Support vector machines
32.31 Naive Bayes classifier
32.32 Reinforcement learning
32.33 Transfer learning
32.34 Active learning
32.35 Deep learning
32.36 Natural language processing
32.37 Optimization algorithms
## 33 Model-Based Reasoning
33.1 Model-based reasoning in decision-making processes
33.2 The role of models in scientific reasoning
33.3 Model-based reasoning in artificial intelligence
33.4 Applying model-based reasoning to predictive analytics
33.5 Model-based reasoning in cognitive psychology
33.6 Model-based reasoning in problem-solving
33.7 The limitations of model-based reasoning
33.8 Model-based reasoning in engineering design
33.9 Model-based reasoning in computer simulation
33.10 Model-based reasoning in economic forecasting
33.11 Model-based reasoning in medical diagnosis
33.12 The use of models in climate change prediction and mitigation
33.13 Model-based reasoning in risk assessment
33.14 Model-based reasoning in game theory
33.15 Model-based reasoning in fault detection and diagnosis
33.16 The impact of uncertainty on model-based reasoning
33.17 Model-based reasoning in robotics
33.18 Model-based reasoning in natural language processing
33.19 Model-based reasoning in financial modeling
33.20 The use of models in policy analysis and decision-making
33.21 Model-based reasoning in evolutionary biology
33.22 Model-based reasoning in control systems
33.23 Model-based reasoning in supply chain optimization
33.24 Model-based reasoning in transportation planning
33.25 The role of models in social network analysis
33.26 Model-based reasoning in image recognition
33.27 Model-based reasoning in machine learning
33.28 Model-based reasoning in mathematical proof
33.29 Model-based reasoning in ecological modeling
33.30 Model-based reasoning in virtual reality environments
33.31 Model-based reasoning in chemical reaction modeling
33.32 Model-based reasoning in architectural design
33.33 Model-based reasoning in data fusion
33.34 Model-based reasoning in anomaly detection
33.35 The use of models in forecasting stock market trends
33.36 Model-based reasoning in energy management systems
33.37 Model-based reasoning in natural language generation
## 34 Directed Reasoning
34.1 Logical reasoning
34.2 Deductive reasoning
34.3 Inductive reasoning
34.4 Abductive reasoning
34.5 Critical thinking
34.6 Problem-solving
34.7 Decision-making
34.8 Argument analysis
34.9 Analogical reasoning
34.10 Causal reasoning
34.11 Counterfactual reasoning
34.12 Hypothetical reasoning
34.13 Bayesian reasoning
34.14 Syllogistic reasoning
34.15 Dialectical reasoning
34.16 Transitive reasoning
34.17 Spatial reasoning
34.18 Temporal reasoning
34.19 Fuzzy reasoning
34.20 Heuristic reasoning
34.21 Probabilistic reasoning
34.22 Reasoning under uncertainty
34.23 Reasoning under incomplete information
34.24 Reasoning with constraints
34.25 Reasoning with emotions
34.26 Ethical reasoning
34.27 Moral reasoning
34.28 Reasoning in mathematics
34.29 Reasoning in science
34.30 Reasoning in philosophy
34.31 Reasoning in law
34.32 Reasoning in economics
34.33 Reasoning in artificial intelligence
34.34 Reasoning in computer programming
34.35 Reasoning in linguistics
34.36 Reasoning in psychology
34.37 Reasoning in education
## 35 Integrative Reasoning
35.1 Logical reasoning
35.2 Analytical reasoning
35.3 Deductive reasoning
35.4 Inductive reasoning
35.5 Abductive reasoning
35.6 Critical thinking
35.7 Problem-solving
35.8 Decision-making
35.9 Cognitive flexibility
35.10 Pattern recognition
35.11 Data analysis
35.12 Statistical reasoning
35.13 Comparative analysis
35.14 Conceptual reasoning
35.15 Systems thinking
35.16 Cause and effect reasoning
35.17 Analogical reasoning
35.18 Argumentation
35.19 Counterfactual reasoning
35.20 Hypothetical reasoning
35.21 Creative reasoning
35.22 Emotional intelligence in reasoning
35.23 Ethical reasoning
35.24 Scientific reasoning
35.25 Cognitive biases in reasoning
35.26 Cognitive load in reasoning
35.27 Metacognition in reasoning
35.28 Heuristics and biases
35.29 Cognitive development and reasoning
35.30 Decision-making under uncertainty
35.31 Cognitive mapping
35.32 Cognitive dissonance and reasoning
35.33 Belief revision
35.34 Bayesian reasoning
35.35 Fuzzy logic reasoning
35.36 Game theory reasoning
35.37 Risk assessment and reasoning
## 36 Analytical Reasoning
36.1 Logical deduction
36.2 Pattern recognition
36.3 Data interpretation
36.4 Critical thinking
36.5 Problem-solving strategies
36.6 Inference and conclusion drawing
36.7 Analyzing arguments
36.8 Decision-making processes
36.9 Analyzing cause and effect
36.10 Inductive reasoning
36.11 Deductive reasoning
36.12 Statistical reasoning
36.13 Cognitive biases
36.14 Analyzing assumptions
36.15 Analogical reasoning
36.16 Analyzing syllogisms
36.17 Analyzing logical fallacies
36.18 Analyzing graphs and charts
36.19 Analyzing puzzles
36.20 Analyzing paradoxes
36.21 Analyzing correlations
36.22 Analyzing contradictions
36.23 Analyzing probabilities
36.24 Analyzing premises and evidence
36.25 Analyzing hypothetical scenarios
36.26 Analyzing analogies
36.27 Analyzing data sets
36.28 Analyzing scientific experiments
36.29 Analyzing quantitative information
36.30 Analyzing qualitative information
36.31 Analyzing trends and patterns
36.32 Analyzing decision trees
36.33 Analyzing financial data
36.34 Analyzing ethical dilemmas
36.35 Analyzing historical events
36.36 Analyzing legal arguments
36.37 Analyzing logical frameworks
## 37 Rule-Based Reasoning
37.1 If-else statements in rule-based reasoning
37.2 Rule-based decision-making
37.3 Rule-based expert systems
37.4 Forward chaining in rule-based reasoning
37.5 Backward chaining in rule-based reasoning
37.6 Rule-based inference engines
37.7 Rule-based reasoning in artificial intelligence
37.8 Rule-based systems in healthcare
37.9 Rule-based reasoning in finance
37.10 Rule-based reasoning in legal applications
37.11 Rule-based reasoning in robotics
37.12 Rule-based reasoning in natural language processing
37.13 Rule-based reasoning in computer vision
37.14 Rule-based reasoning in game playing
37.15 Rule-based reasoning in recommender systems
37.16 Rule-based reasoning in logistics and supply chain management
37.17 Rule-based reasoning in customer relationship management
37.18 Rule-based reasoning in data mining
37.19 Rule-based reasoning in fraud detection
37.20 Rule-based reasoning in quality control
37.21 Rule-based reasoning in fault diagnosis
37.22 Rule-based reasoning in smart homes
37.23 Rule-based reasoning in intelligent transportation systems
37.24 Rule-based reasoning in industrial automation
37.25 Rule-based reasoning in energy management
37.26 Rule-based reasoning in risk assessment
37.27 Rule-based reasoning in pattern recognition
37.28 Rule-based reasoning in anomaly detection
37.29 Rule-based reasoning in security systems
37.30 Rule-based reasoning in environmental monitoring
37.31 Rule-based reasoning in agricultural applications
37.32 Rule-based reasoning in inventory management
37.33 Rule-based reasoning in sentiment analysis
37.34 Rule-based reasoning in speech recognition
37.35 Rule-based reasoning in virtual assistants
37.36 Rule-based reasoning in personalization
37.37 Rule-based reasoning in education and e-learning
## 38 Creative Reasoning
38.1 Analogical reasoning
38.2 Problem-solving strategies
38.3 Divergent thinking
38.4 Convergent thinking
38.5 Lateral thinking
38.6 Reasoning by analogy
38.7 Deductive reasoning
38.8 Inductive reasoning
38.9 Abductive reasoning
38.10 Pattern recognition
38.11 Decision-making heuristics
38.12 Counterfactual reasoning
38.13 Metacognition
38.14 Cognitive flexibility
38.15 Visual reasoning
38.16 Mathematical reasoning
38.17 Logical reasoning
38.18 Reasoning under uncertainty
38.19 Reasoning under constraints
38.20 Conceptual reasoning
38.21 Critical thinking
38.22 Reasoning about causality
38.23 Reasoning about ethics
38.24 Analytical reasoning
38.25 Intuitive reasoning
38.26 Reasoning about emotions
38.27 Reasoning about time
38.28 Reasoning about spatial relationships
38.29 Hypothetical reasoning
38.30 Reasoning about probabilities
38.31 Reasoning about paradoxes
38.32 Reasoning about ambiguity
38.33 Reasoning about complex systems
38.34 Reasoning about human behavior
38.35 Analogical problem-solving
38.36 Reasoning about creativity itself
38.37 Reasoning about art and aesthetics
## 39 Narrative Reasoning
39.1 Character motivation analysis
39.2 Plot analysis
39.3 Story structure analysis
39.4 Theme identification
39.5 Symbolism interpretation
39.6 Conflict resolution analysis
39.7 Foreshadowing identification
39.8 Point of view analysis
39.9 Setting analysis
39.10 Character development analysis
39.11 Plot twist analysis
39.12 Subtext interpretation
39.13 Moral dilemma analysis
39.14 Narrative perspective analysis
39.15 Emotional arc analysis
39.16 Narrative pacing analysis
39.17 Relationship dynamics analysis
39.18 World-building analysis
39.19 Narrative voice analysis
39.20 Narrative tension analysis
39.21 Intertextuality analysis
39.22 Narrative framing analysis
39.23 Allegory interpretation
39.24 Metaphor analysis
39.25 Irony identification
39.26 Archetypal analysis
39.27 Narrative coherence analysis
39.28 Narrative ambiguity analysis
39.29 Cause and effect analysis
39.30 Narrative symbolism analysis
39.31 Backstory analysis
39.32 Character arcs analysis
39.33 Genre analysis
39.34 Narrative point of no return analysis
39.35 Narrative resolution analysis
39.36 Narrative parallelism analysis
39.37 Narrative engagement analysis
## 40 Reasoning by Analogy
40.1 Comparing shapes using analogy
40.2 Analogical reasoning in mathematics
40.3 Analogies in language and linguistics
40.4 Analogical reasoning in problem-solving
40.5 Analogies in scientific reasoning
40.6 Analogical reasoning in artificial intelligence
40.7 Analogies in literature and storytelling
40.8 Analogical reasoning in decision making
40.9 Analogies in historical analysis
40.10 Analogical reasoning in philosophical arguments
40.11 Analogies in biological systems
40.12 Analogical reasoning in physics
40.13 Analogies in learning and education
40.14 Analogical reasoning in legal arguments
40.15 Analogies in cognitive psychology
40.16 Analogical reasoning in computer programming
40.17 Analogies in cultural analysis
40.18 Analogical reasoning in economics
40.19 Analogies in social sciences
40.20 Analogical reasoning in ethical debates
40.21 Analogies in medical diagnosis
40.22 Analogical reasoning in engineering design
40.23 Analogies in political analysis
40.24 Analogical reasoning in pattern recognition
40.25 Analogies in historical analogies
40.26 Analogical reasoning in problem-solving heuristics
40.27 Analogies in metaphorical thinking
40.28 Analogical reasoning in evolutionary biology
40.29 Analogies in moral reasoning
40.30 Analogical reasoning in logical puzzles
40.31 Analogies in artistic creation
40.32 Analogical reasoning in machine learning
40.33 Analogies in environmental analysis
40.34 Analogical reasoning in market research
40.35 Analogies in cognitive development
40.36 Analogical reasoning in teamwork and collaboration
40.37 Analogies in cultural metaphors
## 41 Abductive Reasoning
41.1 Non-declarative Memory Representations
41.2 Qualitative Reasoning
41.3 Qualitative Modeling
41.4 Abductive Networks
41.5 Statistical Relational Learning
41.6 Information Fusion
41.7 Qualitative Probability
41.8 Causal Reasoning
41.9 Qualitative Simulation
41.10 Knowledge Representation
41.11 Machine Learning
41.12 Shared Abductive Reasoning
41.13 Bayesian Reasoning
41.14 Causal Graphs
41.15 Probabilistic Argumentation
41.16 Abductive Inference
41.17 Logic-Based Reasoning
41.18 Justification-Based Explanation
41.19 Epistemic Planning
41.20 Automated Reasoning
41.21 Non-Monotonic Reasoning
41.22 Prototypes
41.23 Abductive Learning
41.24 Inductive Reasoning
41.25 Abductive Argumentation
41.26 Abductive Clustering
41.27 Abduction in Cognitive Psychology
41.28 Reasoning with Rules
41.29 Qualitative Spatial Reasoning
41.30 Abductive Explanation
41.31 Reasoning with Uncertainty
41.32 Abductive Perception
41.33 Inductive Inference
41.34 Structural Abduction
41.35 Application of Abduction
41.36 Diagnostic Reasoning
41.37 Abductive Planning
## 42 Incidental Reasoning
42.1 Environmental Consequences
42.2 Unexpected Challenges
42.3 Cognitive Biases
42.4 Structured Decisions
42.5 Judgmental Heuristics
42.6 Relationship Analysis
42.7 Consequence Evaluation
42.8 Comparative Analysis
42.9 Strategic Thinking
42.10 Novel Perspectives
42.11 Predictive Modeling
42.12 Logical Fallacies
42.13 Contextual Understanding
42.14 Creative Problem-Solving
42.15 Problem Framing
42.16 Prospective Reasoning
42.17 Self-Reflective Reasoning
42.18 Recognizing Patterns
42.19 Evidence-Based Theories
42.20 Explanatory Reasoning
42.21 Empirical Phenomena
42.22 Deductive Conclusions
42.23 Decision Trees
42.24 Systemic Conclusions
42.25 Critical Reasoning
42.26 Probabilistic Reasoning
42.27 Relational Correlations
42.28 Empirically Validated Assumptions
42.29 Data-Driven Processes
42.30 Analogical Reasoning
42.31 Non-Linear Approaches
42.32 Narrative Reasoning
42.33 Quantitative Modeling
42.34 Integrative Reasoning
42.35 Unanticipated Consequences
42.36 Applying Networks of Knowledge
42.37 Experimental Hypotheses
|
AtlasUnified/Atlas-Reasoning
|
[
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"region:us"
] |
2023-06-10T21:22:00+00:00
|
{"language": ["en"], "license": "mit", "size_categories": ["10K<n<100K"], "pretty_name": "15k Reasoning"}
|
2023-06-11T17:15:59+00:00
|
a348b6aa0ed54093b8fc93569d9c08bf17894696
|
Joe02/Michiking_refs
|
[
"license:other",
"region:us"
] |
2023-06-10T21:32:45+00:00
|
{"license": "other"}
|
2023-06-10T21:33:01+00:00
|
|
3c5d9004085ff5de9d57f4224f4ab3d5d34af789
|
# Wikipedia
This Wikipedia dataset contains all available languages for recent dumps. It is
a refresh of the [20220301 wikipedia](https://hf.co/datasets/wikipedia) from
Huggingface, so it has the same license and dataset card details. The benefits
of this dataset are:
- more recent dumps (see table below)
- a few additional languages
- all available languages are preprocessed (including the largests: `en` and
`ceb`)
| version | dump | # available languages | closed & dump | closed & no dump |
| ----- | ---- | ----- | ------ | --- |
| `1.0.0` | 20230601 | 328 | 9: ak (soon), cho, ho, ii, kj, lrc, mh, mus, ng | 4: aa, hz, kr, na |
| `1.1.0` | 20230601 | 329 (+et ~[az,ceb,ch,hr,ii,lrc,ta]) | 9: ak (soon), cho, ho, ii, kj, lrc, mh, mus, ng | 4: aa, hz, kr, na |
| `1.2.0` | 20230901 | idem | 9: ak , cho, ho, ii, kj, lrc, mh, mus, ng | 4: aa, hz, kr, na |
Source: [List of Wikimedia
Languages](https://en.wikipedia.org/wiki/List_of_Wikipedias). A few (9)
Wikimedias are closed, meaning they won't have new pages, but the dumps are
still available. In addition, very few (4) Wikimedias are closed and don't
have dumps anymore.
## Release Notes
`1.2.0`
- **chore**: Update to 20230901
`1.1.0`
- **feat**: Add missing estonian (my bad), thanks Chris Ha
- **fix**: update category lists for az, ceb, ch, hr, ii, lrc, ta, which means
they were all processed again.
`1.0.0`
- **chore**: File layout is now `data/{dump}/{lang}/{info.json,*.parquet}`.
Sorry for the radical update, probably won't happen again.
- **chore**: Parquet files are now sharded (size < 200 MB), allowing parallel
downloads and processing.
- **fix**: All languages were all processed again because of a bug in the media
and category names, leading to some links not being extracted.
- **feat**: Add `en` and `ceb` which were too big for my Beam DirectRunner at
the time.
## Usage
```python
from datasets import load_dataset
wikipedia_es = load_dataset("graelo/wikipedia", "20230601.es")
```
---
## Build instructions
Developer only. This dataset was preprocessed with a Beam DirectRunner as
follows.
### 1. Determine the date of the dump you are interested in
Choose one wikipedia dump, for instance <https://dumps.wikimedia.org/cewiki/>
and identify the date.
### 2. [Optional] Get a refreshed list of languages
This is optional because it not very likely that a new language will have
suddenly appeared since the last version _and_ have a significant dataset.
Navigate to <https://en.wikipedia.org/wiki/List_of_Wikipedias> and copy the
languages column from the "Detailed list" table (near the end of the page).
Copy that content in the form of a Python list into `lang_def.py` (at the top
of the repo) under a new date.
### 3. [Optional] Create Media and Category aliases
In order to properly extract links to images and media in all languages, we
must refresh the two corresponding files. To do so, from the root of the repo,
run
```sh
python -m prep.create_aliases
```
This will create or update these two files at the root of the repo:
- `media_aliases.py`
- `category_aliases.py`
These files are used in the final step
### 4. Build and prepare the datasets into sharded parquet files
Running this script downloads the wikipedia dumps for each language in
`lang_def.py` and shards each language dataset into the appropriate number of
shards (max size ~ 250MB).
```sh
python -m prep.build --date 20230601
```
There are other options:
```text
$ python -m prep.build --help
usage: Wikipedia Builder [-h] [--date DATE] [--language [LANG ...]] [--cache-dir DIR] [--mirror MIRROR]
Prepares the Wikipedia dataset for each language
optional arguments:
-h, --help show this help message and exit
--date DATE Wikipedia dump date (e.g. 20230601)
--language [LANG ...] Language code (e.g. en). If missing, all languages are processed
--cache-dir DIR Cache directory for 🤗 Datasets
--mirror MIRROR Mirror URL
```
For instance, for faster downloads of the dumps, use the mirror option:
```sh
python -m prep.build \
--date 20230601 \
--language bs \
--mirror https://mirror.accum.se/mirror/wikimedia.org/dumps/
```
It will download the dumps at around 60MB/s instead of the capped speed
(~4MB/s) from <https://dumps.wikimedia.org>. The script will skip existing
directories, allowing you to run the script in several passes.
Notes:
- These instructions build upon the build process of the
[Wikipedia](https://huggingface.co/datasets/wikipedia) 🤗 Dataset. HF did a
fantastic job, I just pushed it a bit further.
- Be aware that not all mirrors contain all dumps. For instance mirror.accum.se
does not contain dumps for languages such as be-x-old or cbk-zam. My own
solution is to run a first pass using the aforementioned mirror, and a second
pass with the official `https://dumps.wikimedia.org` site (omitting the
`--mirror` parameter).
|
graelo/wikipedia
|
[
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:n<1K",
"size_categories:1K<n<10K",
"size_categories:10K<n<100K",
"size_categories:100K<n<1M",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:ab",
"language:ace",
"language:ady",
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"language:ak",
"language:als",
"language:alt",
"language:am",
"language:ami",
"language:an",
"language:ang",
"language:anp",
"language:ar",
"language:arc",
"language:ary",
"language:arz",
"language:as",
"language:ast",
"language:atj",
"language:av",
"language:avk",
"language:awa",
"language:ay",
"language:az",
"language:azb",
"language:ba",
"language:ban",
"language:bar",
"language:bcl",
"language:be",
"language:bg",
"language:bh",
"language:bi",
"language:bjn",
"language:blk",
"language:bm",
"language:bn",
"language:bo",
"language:bpy",
"language:br",
"language:bs",
"language:bug",
"language:bxr",
"language:ca",
"language:cdo",
"language:ce",
"language:ceb",
"language:ch",
"language:cho",
"language:chr",
"language:chy",
"language:ckb",
"language:co",
"language:cr",
"language:crh",
"language:cs",
"language:csb",
"language:cu",
"language:cv",
"language:cy",
"language:da",
"language:dag",
"language:de",
"language:din",
"language:diq",
"language:dsb",
"language:dty",
"language:dv",
"language:dz",
"language:ee",
"language:el",
"language:eml",
"language:eo",
"language:es",
"language:et",
"language:eu",
"language:ext",
"language:fa",
"language:fat",
"language:ff",
"language:fi",
"language:fj",
"language:fo",
"language:fr",
"language:frp",
"language:frr",
"language:fur",
"language:fy",
"language:ga",
"language:gag",
"language:gan",
"language:gcr",
"language:gd",
"language:gl",
"language:glk",
"language:gn",
"language:gom",
"language:gor",
"language:got",
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"language:guc",
"language:gur",
"language:guw",
"language:gv",
"language:ha",
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"language:haw",
"language:he",
"language:hi",
"language:hif",
"language:ho",
"language:hr",
"language:hsb",
"language:ht",
"language:hu",
"language:hy",
"language:hyw",
"language:ia",
"language:id",
"language:ie",
"language:ig",
"language:ii",
"language:ik",
"language:ilo",
"language:inh",
"language:io",
"language:is",
"language:it",
"language:iu",
"language:ja",
"language:jam",
"language:jbo",
"language:jv",
"language:ka",
"language:kaa",
"language:kab",
"language:kbd",
"language:kbp",
"language:kcg",
"language:kg",
"language:ki",
"language:kj",
"language:kk",
"language:kl",
"language:km",
"language:kn",
"language:ko",
"language:koi",
"language:krc",
"language:ks",
"language:ksh",
"language:ku",
"language:kv",
"language:kw",
"language:ky",
"language:la",
"language:lad",
"language:lb",
"language:lbe",
"language:lez",
"language:lfn",
"language:lg",
"language:li",
"language:lij",
"language:lld",
"language:lmo",
"language:ln",
"language:lo",
"language:lrc",
"language:lt",
"language:ltg",
"language:lv",
"language:mad",
"language:mai",
"language:mdf",
"language:mg",
"language:mh",
"language:mhr",
"language:mi",
"language:min",
"language:mk",
"language:ml",
"language:mn",
"language:mni",
"language:mnw",
"language:mr",
"language:mrj",
"language:ms",
"language:mt",
"language:mus",
"language:mwl",
"language:my",
"language:myv",
"language:mzn",
"language:nah",
"language:nap",
"language:nds",
"language:ne",
"language:new",
"language:ng",
"language:nia",
"language:nl",
"language:nn",
"language:no",
"language:nov",
"language:nqo",
"language:nrm",
"language:nso",
"language:nv",
"language:ny",
"language:oc",
"language:olo",
"language:om",
"language:or",
"language:os",
"language:pa",
"language:pag",
"language:pam",
"language:pap",
"language:pcd",
"language:pcm",
"language:pdc",
"language:pfl",
"language:pi",
"language:pih",
"language:pl",
"language:pms",
"language:pnb",
"language:pnt",
"language:ps",
"language:pt",
"language:pwn",
"language:qu",
"language:rm",
"language:rmy",
"language:rn",
"language:ro",
"language:ru",
"language:rue",
"language:rw",
"language:sa",
"language:sah",
"language:sat",
"language:sc",
"language:scn",
"language:sco",
"language:sd",
"language:se",
"language:sg",
"language:sh",
"language:shi",
"language:shn",
"language:si",
"language:sk",
"language:skr",
"language:sl",
"language:sm",
"language:smn",
"language:sn",
"language:so",
"language:sq",
"language:sr",
"language:srn",
"language:ss",
"language:st",
"language:stq",
"language:su",
"language:sv",
"language:sw",
"language:szl",
"language:szy",
"language:ta",
"language:tay",
"language:tcy",
"language:te",
"language:tet",
"language:tg",
"language:th",
"language:ti",
"language:tk",
"language:tl",
"language:tn",
"language:to",
"language:tpi",
"language:tr",
"language:trv",
"language:ts",
"language:tt",
"language:tum",
"language:tw",
"language:ty",
"language:tyv",
"language:udm",
"language:ug",
"language:uk",
"language:ur",
"language:uz",
"language:ve",
"language:vec",
"language:vep",
"language:vi",
"language:vls",
"language:vo",
"language:wa",
"language:war",
"language:wo",
"language:wuu",
"language:xal",
"language:xh",
"language:xmf",
"language:yi",
"language:yo",
"language:za",
"language:zea",
"language:zh",
"language:zu",
"license:cc-by-sa-3.0",
"license:gfdl",
"region:us"
] |
2023-06-10T21:40:06+00:00
|
{"annotations_creators": ["no-annotation"], "language_creators": ["crowdsourced"], "language": ["ab", "ace", "ady", "af", "ak", "als", "alt", "am", "ami", "an", "ang", "anp", "ar", "arc", "ary", "arz", "as", "ast", "atj", "av", "avk", "awa", "ay", "az", "azb", "ba", "ban", "bar", "bcl", "be", "bg", "bh", "bi", "bjn", "blk", "bm", "bn", "bo", "bpy", "br", "bs", "bug", "bxr", "ca", "cdo", "ce", "ceb", "ch", "cho", "chr", "chy", "ckb", "co", "cr", "crh", "cs", "csb", "cu", "cv", "cy", "da", "dag", "de", "din", "diq", "dsb", "dty", "dv", "dz", "ee", "el", "eml", "eo", "es", "et", "eu", "ext", "fa", "fat", "ff", "fi", "fj", "fo", "fr", "frp", "frr", "fur", "fy", "ga", "gag", "gan", "gcr", "gd", "gl", "glk", "gn", "gom", "gor", "got", "gu", "guc", "gur", "guw", "gv", "ha", "hak", "haw", "he", "hi", "hif", "ho", "hr", "hsb", "ht", "hu", "hy", "hyw", "ia", "id", "ie", "ig", "ii", "ik", "ilo", "inh", "io", "is", "it", "iu", "ja", "jam", "jbo", "jv", "ka", "kaa", "kab", "kbd", "kbp", "kcg", "kg", "ki", "kj", "kk", "kl", "km", "kn", "ko", "koi", "krc", "ks", "ksh", "ku", "kv", "kw", "ky", "la", "lad", "lb", "lbe", "lez", "lfn", "lg", "li", "lij", "lld", "lmo", "ln", "lo", "lrc", "lt", "ltg", "lv", "mad", "mai", "mdf", "mg", "mh", "mhr", "mi", "min", "mk", "ml", "mn", "mni", "mnw", "mr", "mrj", "ms", "mt", "mus", "mwl", "my", "myv", "mzn", "nah", "nap", "nds", "ne", "new", "ng", "nia", "nl", "nn", false, "nov", "nqo", "nrm", "nso", "nv", "ny", "oc", "olo", "om", "or", "os", "pa", "pag", "pam", "pap", "pcd", "pcm", "pdc", "pfl", "pi", "pih", "pl", "pms", "pnb", "pnt", "ps", "pt", "pwn", "qu", "rm", "rmy", "rn", "ro", "ru", "rue", "rw", "sa", "sah", "sat", "sc", "scn", "sco", "sd", "se", "sg", "sh", "shi", "shn", "si", "sk", "skr", "sl", "sm", "smn", "sn", "so", "sq", "sr", "srn", "ss", "st", "stq", "su", "sv", "sw", "szl", "szy", "ta", "tay", "tcy", "te", "tet", "tg", "th", "ti", "tk", "tl", "tn", "to", "tpi", "tr", "trv", "ts", "tt", "tum", "tw", "ty", "tyv", "udm", "ug", "uk", "ur", "uz", "ve", "vec", "vep", "vi", "vls", "vo", "wa", "war", "wo", "wuu", "xal", "xh", "xmf", "yi", "yo", "za", "zea", "zh", "zu"], "license": ["cc-by-sa-3.0", "gfdl"], "multilinguality": ["multilingual"], "size_categories": ["n<1K", "1K<n<10K", "10K<n<100K", "100K<n<1M", "1M<n<10M"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["language-modeling", "masked-language-modeling"], "pretty_name": "Wikipedia", "language_bcp47": ["bat-smg", "be-x-old", "cbk-zam", "fiu-vro", "map-bms", "nds-nl", "roa-rup", "roa-tara", "simple", "zh-classical", "zh-min-nan", "zh-yue"], "dataset_info": [{"config_name": "20230601.ab", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4183525, "num_examples": 6114}], "download_size": 1172328, "dataset_size": 4183525}, {"config_name": "20230601.ace", 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"dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 19274897, "num_examples": 34504}], "download_size": 6491359, "dataset_size": 19274897}, {"config_name": "20230901.wa", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 12140372, "num_examples": 11955}], "download_size": 7231141, "dataset_size": 12140372}, {"config_name": "20230901.war", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 467623925, "num_examples": 1266345}], "download_size": 109503863, "dataset_size": 467623925}, {"config_name": "20230901.wo", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3498562, "num_examples": 1718}], "download_size": 2077375, "dataset_size": 3498562}, {"config_name": "20230901.wuu", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 25005942, "num_examples": 42969}], "download_size": 15994961, "dataset_size": 25005942}, {"config_name": "20230901.xal", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1390063, "num_examples": 2290}], "download_size": 507117, "dataset_size": 1390063}, {"config_name": "20230901.xh", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2415590, "num_examples": 1667}], "download_size": 1503917, "dataset_size": 2415590}, {"config_name": "20230901.xmf", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 37262425, "num_examples": 17949}], "download_size": 12771047, "dataset_size": 37262425}, {"config_name": "20230901.yi", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 36150608, "num_examples": 15329}], "download_size": 16208341, "dataset_size": 36150608}, {"config_name": "20230901.yo", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 18460117, "num_examples": 33495}], "download_size": 8504564, "dataset_size": 18460117}, {"config_name": "20230901.za", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1359106, "num_examples": 2971}], "download_size": 662982, "dataset_size": 1359106}, {"config_name": "20230901.zea", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5106625, "num_examples": 5834}], "download_size": 2567716, "dataset_size": 5106625}, {"config_name": "20230901.zh", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2766648619, "num_examples": 1375017}], "download_size": 1748154636, "dataset_size": 2766648619}, {"config_name": "20230901.zh-classical", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 14819164, "num_examples": 12615}], "download_size": 10031693, "dataset_size": 14819164}, {"config_name": "20230901.zh-min-nan", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 159385896, "num_examples": 432644}], "download_size": 37476665, "dataset_size": 159385896}, {"config_name": "20230901.zh-yue", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 108979942, "num_examples": 133155}], "download_size": 64318527, "dataset_size": 108979942}, {"config_name": "20230901.zu", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6925330, "num_examples": 11486}], "download_size": 3690925, "dataset_size": 6925330}, {"config_name": "20230601.et", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 431680309, "num_examples": 236848}], "download_size": 262989758, "dataset_size": 431680309}]}
|
2023-09-10T05:10:08+00:00
|
a983dd52419cc8f765f5edf29120b9feba6f3ffc
|
edwinjue/311-data-2015
|
[
"license:gpl-3.0",
"region:us"
] |
2023-06-10T21:59:37+00:00
|
{"license": "gpl-3.0"}
|
2023-06-10T22:01:02+00:00
|
|
3cf4e4ecbba92571e22a148bdf6af617d95ef08b
|
# Dataset Card for "fbd4ca1b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/fbd4ca1b
|
[
"region:us"
] |
2023-06-10T22:00:14+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 186, "num_examples": 10}], "download_size": 1333, "dataset_size": 186}}
|
2023-06-10T22:00:15+00:00
|
abcf7d1cdf98427d6b8fc4928bf6fa859e10b24a
|
edwinjue/311-data-2016
|
[
"license:gpl-3.0",
"region:us"
] |
2023-06-10T22:02:17+00:00
|
{"license": "gpl-3.0"}
|
2023-06-11T11:51:30+00:00
|
|
0539b1798492db68137d469bc2654d4fcd71d83d
|
edwinjue/311-data-2017
|
[
"license:gpl-3.0",
"region:us"
] |
2023-06-10T22:11:33+00:00
|
{"license": "gpl-3.0"}
|
2023-06-11T11:45:14+00:00
|
|
883da8d0af05f555b63247bc245393363400a2e7
|
edwinjue/311-data-2023
|
[
"license:gpl-3.0",
"region:us"
] |
2023-06-10T22:15:30+00:00
|
{"license": "gpl-3.0"}
|
2024-01-04T06:28:18+00:00
|
|
9b914eea9f36ddad757f174cab1733b653a407cc
|
mwalton/olamina
|
[
"license:apache-2.0",
"region:us"
] |
2023-06-10T22:22:12+00:00
|
{"license": "apache-2.0"}
|
2023-06-10T22:23:01+00:00
|
|
c454a23a18c46c5b4c71465b88c0ea53f6731a7c
|
edwinjue/311-data-2018
|
[
"license:gpl-3.0",
"region:us"
] |
2023-06-10T22:35:11+00:00
|
{"license": "gpl-3.0"}
|
2023-06-11T05:15:22+00:00
|
|
840cc38a307bb811aa3f07639bc8ad1a2d93e2d9
|
edwinjue/311-data-2019
|
[
"license:gpl-3.0",
"region:us"
] |
2023-06-10T22:41:36+00:00
|
{"license": "gpl-3.0"}
|
2023-06-10T22:45:12+00:00
|
|
6c893fb873d1fbb68334f157c53a6dc4d5eddfba
|
# Dataset Card for "SRK-emails"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
AIingit/SRK-emails
|
[
"region:us"
] |
2023-06-10T23:08:27+00:00
|
{"dataset_info": {"features": [{"name": "product", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "marketing_email", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 23818, "num_examples": 19}], "download_size": 21510, "dataset_size": 23818}}
|
2023-06-10T23:08:29+00:00
|
c4e3a4aae7923ddddac50281de1bc8d698994180
|
edwinjue/311-data-2020
|
[
"license:gpl-3.0",
"region:us"
] |
2023-06-10T23:08:31+00:00
|
{"license": "gpl-3.0"}
|
2023-06-11T05:01:59+00:00
|
|
b581589076dd2ddd76d5d0b3a34c43db3424d7be
|
edwinjue/311-data-2021
|
[
"license:gpl-3.0",
"region:us"
] |
2023-06-10T23:15:59+00:00
|
{"license": "gpl-3.0"}
|
2023-06-11T04:55:23+00:00
|
|
8a9b5628067a4fab775b9b983ac4d5cce320fe4b
|
edwinjue/311-data-2022
|
[
"license:gpl-3.0",
"region:us"
] |
2023-06-10T23:25:05+00:00
|
{"license": "gpl-3.0"}
|
2023-06-11T04:40:58+00:00
|
|
0551cbb943986a03063ad81459bac396c1410088
|
# Dataset Card for "multi_controlnet_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
killah-t-cell/multi_controlnet_dataset
|
[
"region:us"
] |
2023-06-10T23:51:03+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "conditioning_image", "dtype": "image"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 223188.0, "num_examples": 4}], "download_size": 234187, "dataset_size": 223188.0}}
|
2023-06-12T00:46:08+00:00
|
214387553d250080e46248556b6973222cd60718
|
TechieTeee/Chainlink_USDT_Data
|
[
"license:mit",
"region:us"
] |
2023-06-10T23:56:08+00:00
|
{"license": "mit"}
|
2023-06-11T00:01:43+00:00
|
|
cfbf7d096c541c0a3e71af2345f95f5c38709aff
|
texMT/chan1
|
[
"license:unknown",
"region:us"
] |
2023-06-11T01:11:45+00:00
|
{"license": "unknown"}
|
2023-06-11T01:12:14+00:00
|
|
7c3034dd751ed8a265bee624de0bd7d460053321
|
# Dataset Card for "cartoon-captioned-datasets-salesforce-blip"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
shellypeng/cartoon-captioned-datasets-salesforce-blip
|
[
"code",
"region:us"
] |
2023-06-11T01:16:56+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2357028047.718, "num_examples": 1907}], "download_size": 1774680464, "dataset_size": 2357028047.718}, "tags": ["code"]}
|
2023-06-13T05:35:39+00:00
|
ffbbb590868b3e646813d1693a6ee8af5dbf6998
|
# Dataset Card for "td_qfs"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
nbtpj/td_qfs
|
[
"region:us"
] |
2023-06-11T01:48:18+00:00
|
{"dataset_info": {"features": [{"name": "cluster", "dtype": "string"}, {"name": "documents", "sequence": "string"}, {"name": "query_summ", "list": [{"name": "query", "dtype": "string"}, {"name": "summ", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 4347725, "num_examples": 4}], "download_size": 559120, "dataset_size": 4347725}}
|
2023-06-11T01:48:30+00:00
|
d203ba4d7b5fe459835b4bdb2ab3bb37cc9d49f2
|
# Dataset Card for "kather-19_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Reasat/kather-19_test
|
[
"region:us"
] |
2023-06-11T02:07:24+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "ADI", "1": "BACK", "2": "DEB", "3": "LYM", "4": "MUC", "5": "MUS", "6": "NORM", "7": "STR", "8": "TUM"}}}}], "splits": [{"name": "train", "num_bytes": 1093018719.36, "num_examples": 7180}], "download_size": 941913399, "dataset_size": 1093018719.36}}
|
2023-06-11T02:11:46+00:00
|
345daa744a9fd18e811d745ba38d8cd0ef3bdede
|
#### Warning: Due to the nature of the source, certain images are very large.
Large number of artistic images, mostly (but hardly exclusively) sourced from Wikimedia Commons. <br>
Pull requests are allowed, and even encouraged.
|
mirav/artistic-imagery
|
[
"task_categories:text-to-image",
"size_categories:1K<n<10K",
"region:us"
] |
2023-06-11T02:16:36+00:00
|
{"size_categories": ["1K<n<10K"], "task_categories": ["text-to-image"], "pretty_name": "Artistic Imagery"}
|
2023-06-13T00:42:01+00:00
|
7d78d7d76cd05907af592840ce059d06b861e7ce
|
mennis88/Alina
|
[
"license:other",
"region:us"
] |
2023-06-11T02:18:32+00:00
|
{"license": "other"}
|
2023-06-11T02:18:32+00:00
|
|
d2d9f453ff5508e736a5eccae8a5c394179f548c
|
rvs007/lele_rvs
|
[
"license:other",
"region:us"
] |
2023-06-11T02:46:09+00:00
|
{"license": "other"}
|
2023-06-22T15:41:49+00:00
|
|
62c642b18ba35368985deb1c175eeda000a9abd8
|
# Dataset Card for "OSCAR-2301"
Num tokens: 4,478,799,252 tokens
|
vietgpt/OSCAR-2301
|
[
"region:us"
] |
2023-06-11T02:54:41+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "perplexity", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 27907176803.480194, "num_examples": 2918898}], "download_size": 10901340719, "dataset_size": 27907176803.480194}}
|
2023-06-13T04:01:02+00:00
|
311457a4968f3884f727ec61b45cc78f6ae72906
|
130k midjourney prompts and their evaluated sentiment using `NLTK` library and the "Opinion Mining" positive/negative words library.
https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html
|
nullzero-live/midjourney-sentiment
|
[
"license:openrail",
"region:us"
] |
2023-06-11T03:25:24+00:00
|
{"license": "openrail"}
|
2023-06-11T03:29:55+00:00
|
47cfea81f24a462154914a3d78558b5bd1882221
|
juege/agemo
|
[
"license:openrail",
"region:us"
] |
2023-06-11T04:35:52+00:00
|
{"license": "openrail"}
|
2023-06-11T04:40:50+00:00
|
|
5eb1ee74b33a65b5bef4d5be8850aac79d89e8f2
|
aihdu111/daisy
|
[
"license:other",
"region:us"
] |
2023-06-11T05:00:01+00:00
|
{"license": "other"}
|
2023-06-11T05:03:54+00:00
|
|
09765ec22c7de2d364d8e3d640d5aba0333342ad
|
# AutoTrain Dataset for project: bhaav-sentiment
## Dataset Description
This dataset has been automatically processed by AutoTrain for project bhaav-sentiment.
### Languages
The BCP-47 code for the dataset's language is en.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "\u0914\u0930 \u0926\u094b\u0928\u094b\u0902 \u091f\u0940\u0932\u0947 \u0915\u0947 \u0905\u0932\u0917 \u0905\u0932\u0917 \u0915\u094b\u0928\u0947 \u092e\u0947\u0902 \u091c\u093e \u092a\u0939\u0941\u0902\u091a\u0947",
"target": 3
},
{
"text": "\u0909\u0938\u0915\u0947 \u092e\u0941\u0901\u0939 \u0938\u0947 \u090f\u0915 \u091a\u0940\u0916 \u0928\u093f\u0915\u0932 \u0917\u092f\u0940",
"target": 2
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(names=['0', '1', '2', '3', '4'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 16241 |
| valid | 4063 |
|
prakash48/autotrain-data-bhaav-sentiment
|
[
"task_categories:text-classification",
"language:en",
"region:us"
] |
2023-06-11T05:37:58+00:00
|
{"language": ["en"], "task_categories": ["text-classification"]}
|
2023-06-11T05:40:33+00:00
|
c2f3b697e242fee3e9948bd11efaa2776d6cf0ad
|
# FICLE Dataset
The dataset can be loaded and utilized through the following:
```python
from datasets import load_dataset
ficle_data = load_dataset("tathagataraha/ficle")
```
# Dataset card for FICLE
## Dataset Description
* **GitHub Repo:** https://github.com/blitzprecision/FICLE
* **Paper:**
* **Point of Contact:**
### Dataset Summary
The FICLE dataset is a derivative of the FEVER dataset, which is a collection of 185,445 claims generated by modifying sentences obtained from Wikipedia.
These claims were then verified without knowledge of the original sentences they were derived from. Each sample in the FEVER dataset consists of a claim sentence, a context sentence extracted from a Wikipedia URL as evidence, and a type label indicating whether the claim is supported, refuted, or lacks sufficient information.
### Languages
The FICLE Dataset contains only English.
## Dataset Structure
### Data Fields
* `Claim (string)`: A statement or proposition relating to the consistency or inconsistency of certain facts or information.
* `Context (string)`: The surrounding information or background against which the claim is being evaluated or compared. It provides additional details or evidence that can support or challenge the claim.
* `Source (string)`: It is the linguistic chunk containing the entity lying to the left of the main verb/relating chunk.
* `Source Indices (string)`: Source indices refer to the specific indices or positions within the source string that indicate the location of the relevant information.
* `Relation (string)`: It is the linguistic chunk containing the verb/relation at the core of the identified inconsistency.
* `Relation Indices (string)`: Relation indices indicate the specific indices or positions within the relation string that highlight the location of the relevant information.
* `Target (string)`: It is the linguistic chunk containing the entity lying to the right of the main verb/relating chunk.
* `Target Indices (string)`: Target indices represent the specific indices or positions within the target string that indicate the location of the relevant information.
* `Inconsistent Claim Component (string)`: The inconsistent claim component refers to a specific linguistic chunk within the claim that is identified as inconsistent with the context. It helps identify which part of the claim triple is problematic in terms of its alignment with the surrounding information.
* `Inconsistent Context-Span (string)`: A span or portion marked within the context sentence that is found to be inconsistent with the claim. It highlights a discrepancy or contradiction between the information in the claim and the corresponding context.
* `Inconsistent Context-Span Indices (string)`: The specific indices or location within the context sentence that indicate the inconsistent span.
* `Inconsistency Type (string)`: The category or type of inconsistency identified in the claim and context.
* `Fine-grained Inconsistent Entity-Type (string)`: The specific detailed category or type of entity causing the inconsistency within the claim or context. It provides a more granular classification of the entity associated with the inconsistency.
* `Coarse Inconsistent Entity-Type (string)`: The broader or general category or type of entity causing the inconsistency within the claim or context. It provides a higher-level classification of the entity associated with the inconsistency.
### Data Splits
The FICLE dataset comprises a total of 8,055 samples in the English language, each representing different instances of inconsistencies.
These inconsistencies are categorized into five types: Taxonomic Relations (4,842 samples), Negation (1,630 samples), Set Based (642 samples), Gradable (526 samples), and Simple (415 samples).
Within the dataset, there are six possible components that contribute to the inconsistencies found in the claim sentences.
These components are distributed as follows: Target-Head (3,960 samples), Target-Modifier (1,529 samples), Relation-Head (951 samples), Relation-Modifier (1,534 samples), Source-Head (45 samples), and Source-Modifier (36 samples).
The dataset is split into `train`, `validation`, and `test`.
* `train`: 6.44k rows
* `validation`: 806 rows
* `test`: 806 rows
## Dataset Creation
### Curation Rationale
We propose a linguistically enriched dataset to help detect inconsistencies and explain them.
To this end, the broad requirements are to locate where the inconsistency is present between a claim and a context and to have a classification scheme for better explainability.
### Data Collection and Preprocessing
The FICLE dataset is derived from the FEVER dataset, using the following-
ing processing steps. FEVER (Fact Extraction and VERification) consists of
185,445 claims were generated by altering sentences extracted from Wikipedia and
subsequently verified without knowledge of the sentence they were derived from.
Every sample in the FEVER dataset contains the claim sentence, evidence (or
context) sentence from a Wikipedia URL, and a type label (‘supports’, ‘refutes’, or
‘not enough info’). Out of these, we leverage only the samples with the ‘refutes’ label
to build our dataset.
### Annotations
You can see the annotation guidelines [here](https://github.com/blitzprecision/FICLE/blob/main/ficle_annotation_guidelines.pdf).
In order to provide detailed explanations for inconsistencies, extensive annotations were conducted for each sample in the FICLE dataset. The annotation process involved two iterations, with each iteration focusing on different aspects of the dataset.
In the first iteration, the annotations were primarily "syntactic-oriented." These fields included identifying the inconsistent claim fact triple, marking inconsistent context spans, and categorizing the six possible inconsistent claim components.
The second iteration of annotations concentrated on "semantic-oriented" aspects. Annotators labeled semantic fields for each sample, such as the type of inconsistency, coarse inconsistent entity types, and fine-grained inconsistent entity types.
This stage aimed to capture the semantic nuances and provide a deeper understanding of the inconsistencies present in the dataset.
The annotation process was carried out by a group of four annotators, two of whom are also authors of the dataset. The annotators possess a strong command of the English language and hold Bachelor's degrees in Computer Science, specializing in computational linguistics.
Their expertise in the field ensured accurate and reliable annotations. The annotators' ages range from 20 to 22 years, indicating their familiarity with contemporary language usage and computational linguistic concepts.
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Citation Information
```
@misc{raha2023neural,
title={Neural models for Factual Inconsistency Classification with Explanations},
author={Tathagata Raha and Mukund Choudhary and Abhinav Menon and Harshit Gupta and KV Aditya Srivatsa and Manish Gupta and Vasudeva Varma},
year={2023},
eprint={2306.08872},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contact
|
tathagataraha/ficle
|
[
"task_categories:token-classification",
"task_categories:text-classification",
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:en",
"license:gpl-3.0",
"span",
"explanation",
"arxiv:2306.08872",
"region:us"
] |
2023-06-11T06:37:34+00:00
|
{"language": ["en"], "license": "gpl-3.0", "size_categories": ["1K<n<10K"], "task_categories": ["token-classification", "text-classification", "text-generation"], "pretty_name": "FICLE", "dataset_info": {"features": [{"name": "Claim", "dtype": "string"}, {"name": "Context", "dtype": "string"}, {"name": "Source", "dtype": "string"}, {"name": "Source Indices", "dtype": "string"}, {"name": "Relation", "dtype": "string"}, {"name": "Relation Indices", "dtype": "string"}, {"name": "Target", "dtype": "string"}, {"name": "Target Indices", "dtype": "string"}, {"name": "Inconsistent Claim Component", "dtype": "string"}, {"name": "Inconsistent Context-Span", "dtype": "string"}, {"name": "Inconsistent Context-Span Indices", "dtype": "string"}, {"name": "Inconsistency Type", "dtype": "string"}, {"name": "Fine-grained Inconsistent Entity-Type", "dtype": "string"}, {"name": "Coarse Inconsistent Entity-Type", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2657091, "num_examples": 6443}, {"name": "validation", "num_bytes": 333142, "num_examples": 806}, {"name": "test", "num_bytes": 332484, "num_examples": 806}], "download_size": 1784422, "dataset_size": 3322717}, "tags": ["span", "explanation"]}
|
2023-07-18T10:00:53+00:00
|
222de3ecd6196882066fc55a864bac660fdc327c
|
Combines the data from [starcoderdata](https://huggingface.co/datasets/bigcode/starcoderdata) and removes any repos with <= 10 stars.
|
edward-io/starcoderdata-repo
|
[
"license:other",
"region:us"
] |
2023-06-11T07:03:56+00:00
|
{"license": "other"}
|
2023-06-11T08:58:39+00:00
|
40d1323b7313729f45eb45dc037cafda2a70f19d
|
# Dataset Card for "medium_articles"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Kamaljp/medium_articles
|
[
"region:us"
] |
2023-06-11T08:06:37+00:00
|
{"dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "authors", "dtype": "string"}, {"name": "timestamp", "dtype": "string"}, {"name": "tags", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1044746687, "num_examples": 192368}], "download_size": 601519297, "dataset_size": 1044746687}}
|
2023-06-11T08:48:58+00:00
|
b0f0d0c5851c91a5b5a648bd087092a348ffeaf0
|
Msun/dota1
|
[
"license:apache-2.0",
"region:us"
] |
2023-06-11T08:15:50+00:00
|
{"license": "apache-2.0"}
|
2023-06-12T12:28:09+00:00
|
|
c8b4857c57b8da4265c2c6b41c0ca440da2c2680
|
# Dataset Card for "sample"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
dnjdsxor21/sample
|
[
"region:us"
] |
2023-06-11T08:47:07+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "special_tokens_mask", "sequence": "int8"}, {"name": "ner_map", "sequence": "int64"}], "splits": [{"name": "test", "num_bytes": 543182240, "num_examples": 75610}, {"name": "validation", "num_bytes": 93779936, "num_examples": 13054}, {"name": "train3", "num_bytes": 194542720, "num_examples": 27080}], "download_size": 82128446, "dataset_size": 831504896}}
|
2023-06-11T08:47:53+00:00
|
13cccff8d78443af5fb0f618793b0a181e91d00c
|
# Dataset Card for Hotel Review ABSA (SemEval 2016 Translated from Arabic)
## Dataset Description
Derived from eastwind/semeval-2016-absa-reviews-english-translated-stanford-alpaca, by upsampling the neutral class and then resampling 3k examples from each class
|
eastwind/semeval-2016-absa-reviews-english-translated-resampled
|
[
"license:mit",
"region:us"
] |
2023-06-11T08:54:47+00:00
|
{"license": "mit"}
|
2023-06-11T09:17:43+00:00
|
695c2d04ef764870bda69e7a80f243b57f651248
|
# Dataset Card for "OSCAR-2201"
Num tokens: 2,682,681,285 tokens
|
vietgpt/OSCAR-2201
|
[
"region:us"
] |
2023-06-11T09:01:58+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "perplexity", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 15978372237.047762, "num_examples": 1700386}], "download_size": 6412125570, "dataset_size": 15978372237.047762}}
|
2023-06-13T04:00:30+00:00
|
d694de9238e05219ccb8cc45a2b374bdf8c48af1
|
RoopamSadh/cancer
|
[
"language:en",
"region:us"
] |
2023-06-11T09:28:28+00:00
|
{"language": ["en"]}
|
2023-06-11T09:54:08+00:00
|
|
b4f706a26e5efd93344c1723c29ebcb5084a44f1
|
# Dataset Card for "opus-medical-en-de"
This is a multi-domain German-English parallel data introduced in [Aharoni and Goldberg (2020)](https://aclanthology.org/2020.acl-main.692/). It is a new data split created that avoids duplicate examples and leakage from the train split to the dev/test splits. The original multi-domain data first appeared in [Koehn and Knowles (2017)](https://www.aclweb.org/anthology/W17-3204/) and consists of five datasets available in the [Opus website](http://opus.nlpl.eu/).
|
ahazeemi/opus-medical-en-de
|
[
"task_categories:translation",
"size_categories:100K<n<1M",
"language:en",
"language:de",
"medical",
"region:us"
] |
2023-06-11T10:06:51+00:00
|
{"language": ["en", "de"], "size_categories": ["100K<n<1M"], "task_categories": ["translation"], "dataset_info": {"features": [{"name": "de", "dtype": "string"}, {"name": "en", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 53121579, "num_examples": 248099}, {"name": "dev", "num_bytes": 433240, "num_examples": 2000}, {"name": "test", "num_bytes": 446369, "num_examples": 2000}], "download_size": 35861692, "dataset_size": 54001188}, "tags": ["medical"]}
|
2023-07-16T06:37:53+00:00
|
1f5faa496fff8085c728be03236b2184388894ae
|
# Dataset Card for "test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
felixsaaro/test
|
[
"task_categories:text-to-image",
"size_categories:1K<n<10K",
"language:en",
"license:apache-2.0",
"region:us"
] |
2023-06-11T10:23:45+00:00
|
{"language": ["en"], "license": "apache-2.0", "size_categories": ["1K<n<10K"], "task_categories": ["text-to-image"], "pretty_name": "Yugio Cards", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "name", "dtype": "string"}, {"name": "frameType", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 660338115.545, "num_examples": 12405}], "download_size": 656146541, "dataset_size": 660338115.545}}
|
2023-06-11T15:00:52+00:00
|
f147eec95855fdcd15dde8989cec374bbbee8450
|
# VQAv2 in Vietnamese
This is Google-translated version of [VQAv2](https://visualqa.org/) in Vietnamese. The process of building Vietnamese version as follows:
- In `en/` folder,
- Download `v2_OpenEnded_mscoco_train2014_questions.json` and `v2_mscoco_train2014_annotations.json` from [VQAv2](https://visualqa.org/).
- Remove key `answers` of key `annotations` from `v2_mscoco_train2014_annotations.json`. I shall use key `multiple_choice_answer` of key `annotations` only. Let call the new file `v2_OpenEnded_mscoco_train2014_answers.json`
- By using [set data structure](https://docs.python.org/3/tutorial/datastructures.html#sets), I generate `question_list.txt` and `answer_list.txt` of unique text. There are 152050 unique questions and 22531 unique answers from 443757 image-question-answer triplets.
- In `vi/` folder,
- By translating two `en/.txt` files, I generate `answer_list.jsonl` and `question_list.jsonl`. In each of entry of each file, the key is the original english text, the value is the translated text in vietnamese.
To load Vietnamese version in your code, you need original English version. Then just use English text as key to retrieve Vietnamese value from `answer_list.jsonl` and `question_list`. I provide both English and Vietnamese version.
Please refer to [this code](https://github.com/dinhanhx/velvet/blob/main/scripts/apply_translate_vqav2.py) to apply translation.
|
dinhanhx/VQAv2-vi
|
[
"task_categories:visual-question-answering",
"task_ids:visual-question-answering",
"language:en",
"language:vi",
"license:unknown",
"VQAv2-vi",
"VQA",
"region:us"
] |
2023-06-11T10:36:10+00:00
|
{"language": ["en", "vi"], "license": "unknown", "task_categories": ["visual-question-answering"], "task_ids": ["visual-question-answering"], "pretty_name": "VQAv2 in Vietnamese", "source-datasets": ["VQAv2"], "tags": ["VQAv2-vi", "VQA"]}
|
2023-09-21T09:25:06+00:00
|
c4e74841c41f408662df53f822177bd37f86bae5
|
rish1212/college
|
[
"license:unknown",
"region:us"
] |
2023-06-11T11:10:06+00:00
|
{"license": "unknown"}
|
2023-06-11T11:48:03+00:00
|
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