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c36636e4ea9154c2dc8610828189468b50797e45 | Glac1er/gehshin | [
"license:unknown",
"region:us"
]
| 2023-01-08T20:58:54+00:00 | {"license": "unknown"} | 2023-03-18T07:56:34+00:00 |
|
a3c51840a1c4bc170b5ca483254b30f192d65ce0 | # Dataset cointelegraph English
## Dataset Description
It is a dataset where information about the title, description, author, etc. is collected.
approx: 10041 row
page: https://cointelegraph.com/
categorie: #cryptocurrency, #Bitcoin, #Ethereum ...
| Nicky0007/cointelegraph_news_English | [
"task_categories:token-classification",
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:en",
"region:us"
]
| 2023-01-08T21:27:13+00:00 | {"language": ["en"], "size_categories": ["10K<n<100K"], "task_categories": ["token-classification", "question-answering"]} | 2023-01-08T22:07:31+00:00 |
3a4dc90ec7626657c448ed74b44bcc98fac3acc9 |
# Dataset cointelegraph español
Dataset Description
es un dataset donde se recopila informacion del titulo , descripcion , autor, etc.
tiene aprox: 10738 fila
pagina: https://cointelegraph.com/
categorie: #cryptocurrency, #Bitcoin, #Ethereum ... | Nicky0007/cointelegraph_noticias_Es | [
"task_categories:token-classification",
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:es",
"region:us"
]
| 2023-01-08T21:32:57+00:00 | {"language": ["es"], "size_categories": ["10K<n<100K"], "task_categories": ["token-classification", "question-answering"]} | 2023-01-08T22:19:07+00:00 |
b247e7e5df97db4637a473a5aeb4223c4a5ec011 | ssilwal/CASS-civile-nli | [
"license:apache-2.0",
"region:us"
]
| 2023-01-08T21:43:11+00:00 | {"license": "apache-2.0"} | 2023-01-08T21:55:50+00:00 |
|
6949be6f8fb7b070b68edc654f6bba91530a6ac9 | # Dataset Card for "sd_filtered_2m"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Ar4ikov/sd_filtered_2m | [
"region:us"
]
| 2023-01-08T21:43:39+00:00 | {"dataset_info": {"features": [{"name": "Prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 427667829.2266251, "num_examples": 2672923}, {"name": "test", "num_bytes": 47018271.06645638, "num_examples": 296922}], "download_size": 364684829, "dataset_size": 474686100.29308146}} | 2023-01-08T21:52:46+00:00 |
63b9247adacb1c4df52b826967613ab462def426 | mistermindless/rpstest | [
"region:us"
]
| 2023-01-08T22:15:10+00:00 | {} | 2023-01-08T22:16:50+00:00 |
|
7532ef2aaf044d80dae5bc0c2b4d39305e9cbb48 | # Dataset Card for "bookcorpus_compact_1024_shard7"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | saibo/bookcorpus_compact_1024_shard7_of_10 | [
"region:us"
]
| 2023-01-08T22:53:19+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "concept_with_offset", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 777388315, "num_examples": 61605}], "download_size": 394101383, "dataset_size": 777388315}} | 2023-01-08T22:55:59+00:00 |
735f519e4b8f7de93c1c4f8c90f7dafe837cce71 | jodokta/imagw | [
"license:other",
"region:us"
]
| 2023-01-08T23:48:30+00:00 | {"license": "other"} | 2023-01-08T23:52:29+00:00 |
|
19c2bf028fb07be66e70ac60bfc445b6753d3ca0 | Valcoellar/valito | [
"license:artistic-2.0",
"region:us"
]
| 2023-01-08T23:58:22+00:00 | {"license": "artistic-2.0"} | 2023-01-08T23:58:22+00:00 |
|
773f8ff49faf8aa9ccf6755f1553867a22aaec7b | PinkysMusing/Banners | [
"license:cc",
"region:us"
]
| 2023-01-09T05:08:08+00:00 | {"license": "cc"} | 2023-01-09T05:08:08+00:00 |
|
f0e00bf139f6970f39101ca275d9ecb1bf3746c6 | abross/youtube-transcriptions | [
"license:afl-3.0",
"region:us"
]
| 2023-01-09T05:22:36+00:00 | {"license": "afl-3.0"} | 2023-03-16T15:59:41+00:00 |
|
96e86574663a0469d6b2e5004859579aaaca71bc | Acumen/Test2 | [
"license:unknown",
"region:us"
]
| 2023-01-09T05:49:10+00:00 | {"license": "unknown"} | 2023-01-09T05:51:13+00:00 |
|
3e785315f005a5dc97e56c281727c2eeef0050b2 | This dataset is for ERNIE-layout to use | skywalkerzhang19/DVQA | [
"region:us"
]
| 2023-01-09T06:48:57+00:00 | {} | 2023-01-20T06:19:21+00:00 |
051a0ec1c92882f9ce8867a5732827c156177ff3 |
# KPWr & CEN | clarin-knext/kpwr_and_cen | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:18K",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:pl",
"license:cc-by-3.0",
"region:us"
]
| 2023-01-09T10:22:33+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["pl"], "license": ["cc-by-3.0"], "multilinguality": ["monolingual"], "size_categories": ["18K", "10K<n<100K"], "source_datasets": ["original"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "KPWr 1.27 & CEN"} | 2023-01-09T11:37:59+00:00 |
a436f7087749389b1681938402c8196b7b7d8340 | # Dataset Card for "pairwise-code-review-instruct-critique-revision-python"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | reshinthadith/pairwise-code-review-instruct-critique-revision-python | [
"region:us"
]
| 2023-01-09T10:24:43+00:00 | {"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "chosen", "dtype": "string"}, {"name": "rejected", "dtype": "string"}, {"name": "chosen_score", "dtype": "string"}, {"name": "rejected_score", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 35079153, "num_examples": 5236}], "download_size": 9344129, "dataset_size": 35079153}} | 2023-01-09T11:25:43+00:00 |
deee75de37aaa80b345951859ea103465967db3e | # Dataset Card for Dataset Name
RexTheToy
### Dataset Summary
Images of the rex the toy from Toy Story, stored for huggingface dreambooth hackathon.
| sooolee/rexthetoy | [
"size_categories:n<1K",
"region:us"
]
| 2023-01-09T11:09:37+00:00 | {"size_categories": ["n<1K"]} | 2023-01-10T09:21:23+00:00 |
17c20a7e54b569878e136841ca8f0848ad51a760 | jarvisx17/Medical-ASR-EN | [
"license:other",
"region:us"
]
| 2023-01-09T12:26:14+00:00 | {"license": "other", "dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "transcription", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6271401466.724, "num_examples": 6661}], "download_size": 4573699824, "dataset_size": 6271401466.724}} | 2023-01-30T12:43:27+00:00 |
|
4f6665728570c5c3c625684dbbae0d0e0bca12f5 | # SpellGram
## Dataset consisting of grammatical and spelling errors
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[train.csv]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | vishnun/SpellGram | [
"task_categories:text2text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"NLP",
"Text2Text",
"region:us"
]
| 2023-01-09T13:39:23+00:00 | {"language": ["en"], "license": "mit", "size_categories": ["10K<n<100K"], "task_categories": ["text2text-generation"], "pretty_name": "Dataset consisting of grammatical and spelling errors", "tags": ["NLP", "Text2Text"]} | 2023-01-09T13:43:11+00:00 |
1b1d662c0d7af12fff0ffc3d34f1427148aa981d | # Dataset Card for "datasets-test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cahya/datasets-test | [
"region:us"
]
| 2023-01-09T14:46:57+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 51686701.30956695, "num_examples": 24336}, {"name": "test", "num_bytes": 5745090.690433046, "num_examples": 2705}], "download_size": 33849787, "dataset_size": 57431792.0}} | 2023-01-09T14:48:07+00:00 |
63e62b943b92c1f5626045b050de996e179d1e46 | this is a dataset for translation | amineT/translt4 | [
"region:us"
]
| 2023-01-09T15:17:38+00:00 | {} | 2023-01-09T15:18:27+00:00 |
48fdbeba45d4495ea572de298e798e4b5290fe51 | lucafrost/DirectQuote | [
"license:agpl-3.0",
"doi:10.57967/hf/0258",
"region:us"
]
| 2023-01-09T16:14:41+00:00 | {"license": "agpl-3.0"} | 2023-01-09T16:14:41+00:00 |
|
9c11462190ebfeadc8617011e148312eef86f1f2 | Ozziey/poems_dataset | [
"task_categories:tabular-classification",
"size_categories:n<1K",
"language:en",
"license:afl-3.0",
"region:us"
]
| 2023-01-09T16:25:22+00:00 | {"language": ["en"], "license": "afl-3.0", "size_categories": ["n<1K"], "task_categories": ["tabular-classification"], "pretty_name": "Detected emotions and information for poetry dataset"} | 2023-01-09T16:28:56+00:00 |
|
80ae97254b39cc08f1a617fa5b3b0c8875371235 | # Dataset Card for "OxfordPets_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/OxfordPets_train | [
"region:us"
]
| 2023-01-09T16:56:48+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "abyssinian", "1": "american bulldog", "2": "american pit bull terrier", "3": "basset hound", "4": "beagle", "5": "bengal", "6": "birman", "7": "bombay", "8": "boxer", "9": "british shorthair", "10": "chihuahua", "11": "egyptian mau", "12": "english cocker spaniel", "13": "english setter", "14": "german shorthaired", "15": "great pyrenees", "16": "havanese", "17": "japanese chin", "18": "keeshond", "19": "leonberger", "20": "maine coon", "21": "miniature pinscher", "22": "newfoundland", "23": "persian", "24": "pomeranian", "25": "pug", "26": "ragdoll", "27": "russian blue", "28": "saint bernard", "29": "samoyed", "30": "scottish terrier", "31": "shiba inu", "32": "siamese", "33": "sphynx", "34": "staffordshire bull terrier", "35": "wheaten terrier", "36": "yorkshire terrier"}}}}, {"name": "species", "dtype": {"class_label": {"names": {"0": "Cat", "1": "Dog"}}}}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_opt175b_downstream_tasks_ViT_L_14", "sequence": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_ViT_L_14", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_ensemble_specific", "dtype": "string"}, {"name": "clip_tags_ViT_L_14_simple_specific", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "clip_tags_ViT_L_14with_openai_classes", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_wo_openai_classes", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai_classes", "sequence": "string"}, {"name": "Attributes_ViT_L_14_text_davinci_003", "sequence": "string"}, {"name": "Attributes_ViT_L_14_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_ViT_L_14_text_davinci_003_oxfordpets", "sequence": "string"}, {"name": "clip_tags_ViT_B_16_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_16_ensemble_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_32_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_32_ensemble_specific", "dtype": "string"}, {"name": "Attributes_ViT_B_16_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_simple_specific", "dtype": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_ensemble_specific", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 386730161.36, "num_examples": 3680}], "download_size": 378295172, "dataset_size": 386730161.36}} | 2023-05-04T03:54:38+00:00 |
a97a01c39799efad09522ed8f7dff13f8b86770d | # Dataset Card for "OxfordPets_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/OxfordPets_test | [
"region:us"
]
| 2023-01-09T16:59:18+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "abyssinian", "1": "american bulldog", "2": "american pit bull terrier", "3": "basset hound", "4": "beagle", "5": "bengal", "6": "birman", "7": "bombay", "8": "boxer", "9": "british shorthair", "10": "chihuahua", "11": "egyptian mau", "12": "english cocker spaniel", "13": "english setter", "14": "german shorthaired", "15": "great pyrenees", "16": "havanese", "17": "japanese chin", "18": "keeshond", "19": "leonberger", "20": "maine coon", "21": "miniature pinscher", "22": "newfoundland", "23": "persian", "24": "pomeranian", "25": "pug", "26": "ragdoll", "27": "russian blue", "28": "saint bernard", "29": "samoyed", "30": "scottish terrier", "31": "shiba inu", "32": "siamese", "33": "sphynx", "34": "staffordshire bull terrier", "35": "wheaten terrier", "36": "yorkshire terrier"}}}}, {"name": "species", "dtype": {"class_label": {"names": {"0": "Cat", "1": "Dog"}}}}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_ViT_L_14", "sequence": "string"}, {"name": "clip_tag_ViT_L_14_specific", "dtype": "string"}, {"name": "clip_tags_ViT_L_14_ensemble_specific", "dtype": "string"}, {"name": "clip_tags_ViT_L_14_simple_specific", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai_classes", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_wo_openai_classes", "sequence": "string"}, {"name": "Attributes_ViT_L_14_text_davinci_003", "sequence": "string"}, {"name": "Attributes_ViT_L_14_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_ViT_L_14_text_davinci_003_oxfordpets", "sequence": "string"}, {"name": "clip_tags_ViT_B_16_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_16_ensemble_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_32_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_32_ensemble_specific", "dtype": "string"}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full_validate", "sequence": "string"}, {"name": "Attributes_ViT_B_16_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_simple_specific", "dtype": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_ensemble_specific", "dtype": "string"}, {"name": "blip_caption_beam_5_Salesforce_blip2_opt_6.7b", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 421721560.0, "num_examples": 3669}], "download_size": 413176127, "dataset_size": 421721560.0}} | 2023-08-15T04:11:14+00:00 |
617b623475a1a0f7fa10fce249beacf7e747117b | # Dataset Card for "Caltech101_not_background_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/Caltech101_not_background_train | [
"region:us"
]
| 2023-01-09T17:50:41+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "accordion", "1": "airplanes", "2": "anchor", "3": "ant", "4": "background google", "5": "barrel", "6": "bass", "7": "beaver", "8": "binocular", "9": "bonsai", "10": "brain", "11": "brontosaurus", "12": "buddha", "13": "butterfly", "14": "camera", "15": "cannon", "16": "car side", "17": "ceiling fan", "18": "cellphone", "19": "chair", "20": "chandelier", "21": "cougar body", "22": "cougar face", "23": "crab", "24": "crayfish", "25": "crocodile", "26": "crocodile head", "27": "cup", "28": "dalmatian", "29": "dollar bill", "30": "dolphin", "31": "dragonfly", "32": "electric guitar", "33": "elephant", "34": "emu", "35": "euphonium", "36": "ewer", "37": "faces", "38": "faces easy", "39": "ferry", "40": "flamingo", "41": "flamingo head", "42": "garfield", "43": "gerenuk", "44": "gramophone", "45": "grand piano", "46": "hawksbill", "47": "headphone", "48": "hedgehog", "49": "helicopter", "50": "ibis", "51": "inline skate", "52": "joshua tree", "53": "kangaroo", "54": "ketch", "55": "lamp", "56": "laptop", "57": "leopards", "58": "llama", "59": "lobster", "60": "lotus", "61": "mandolin", "62": "mayfly", "63": "menorah", "64": "metronome", "65": "minaret", "66": "motorbikes", "67": "nautilus", "68": "octopus", "69": "okapi", "70": "pagoda", "71": "panda", "72": "pigeon", "73": "pizza", "74": "platypus", "75": "pyramid", "76": "revolver", "77": "rhino", "78": "rooster", "79": "saxophone", "80": "schooner", "81": "scissors", "82": "scorpion", "83": "sea horse", "84": "snoopy", "85": "soccer ball", "86": "stapler", "87": "starfish", "88": "stegosaurus", "89": "stop sign", "90": "strawberry", "91": "sunflower", "92": "tick", "93": "trilobite", "94": "umbrella", "95": "watch", "96": "water lilly", "97": "wheelchair", "98": "wild cat", "99": "windsor chair", "100": "wrench", "101": "yin yang"}}}}, {"name": "annotation", "struct": [{"name": "obj_contour", "dtype": {"array2_d": {"shape": [2], "dtype": "float64"}}}, {"name": "box_coord", "dtype": {"array2_d": {"shape": [1, 4], "dtype": "int64"}}}]}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_ViT_L_14", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 46675197.0, "num_examples": 3030}], "download_size": 45748181, "dataset_size": 46675197.0}} | 2023-01-28T20:22:40+00:00 |
93e6ee1710cdd4019a336ffca12b399d8287ea7d | # Dataset Card for "Caltech101_not_background_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/Caltech101_not_background_test | [
"region:us"
]
| 2023-01-09T17:52:55+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "accordion", "1": "airplanes", "2": "anchor", "3": "ant", "4": "background google", "5": "barrel", "6": "bass", "7": "beaver", "8": "binocular", "9": "bonsai", "10": "brain", "11": "brontosaurus", "12": "buddha", "13": "butterfly", "14": "camera", "15": "cannon", "16": "car side", "17": "ceiling fan", "18": "cellphone", "19": "chair", "20": "chandelier", "21": "cougar body", "22": "cougar face", "23": "crab", "24": "crayfish", "25": "crocodile", "26": "crocodile head", "27": "cup", "28": "dalmatian", "29": "dollar bill", "30": "dolphin", "31": "dragonfly", "32": "electric guitar", "33": "elephant", "34": "emu", "35": "euphonium", "36": "ewer", "37": "faces", "38": "faces easy", "39": "ferry", "40": "flamingo", "41": "flamingo head", "42": "garfield", "43": "gerenuk", "44": "gramophone", "45": "grand piano", "46": "hawksbill", "47": "headphone", "48": "hedgehog", "49": "helicopter", "50": "ibis", "51": "inline skate", "52": "joshua tree", "53": "kangaroo", "54": "ketch", "55": "lamp", "56": "laptop", "57": "leopards", "58": "llama", "59": "lobster", "60": "lotus", "61": "mandolin", "62": "mayfly", "63": "menorah", "64": "metronome", "65": "minaret", "66": "motorbikes", "67": "nautilus", "68": "octopus", "69": "okapi", "70": "pagoda", "71": "panda", "72": "pigeon", "73": "pizza", "74": "platypus", "75": "pyramid", "76": "revolver", "77": "rhino", "78": "rooster", "79": "saxophone", "80": "schooner", "81": "scissors", "82": "scorpion", "83": "sea horse", "84": "snoopy", "85": "soccer ball", "86": "stapler", "87": "starfish", "88": "stegosaurus", "89": "stop sign", "90": "strawberry", "91": "sunflower", "92": "tick", "93": "trilobite", "94": "umbrella", "95": "watch", "96": "water lilly", "97": "wheelchair", "98": "wild cat", "99": "windsor chair", "100": "wrench", "101": "yin yang"}}}}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_ViT_L_14", "sequence": "string"}, {"name": "LLM_Description_opt175b_downstream_tasks_ViT_L_14", "sequence": "string"}], "splits": [{"name": "test", "num_bytes": 81047146.0, "num_examples": 5647}], "download_size": 78304363, "dataset_size": 81047146.0}} | 2023-01-28T20:23:42+00:00 |
d1d29bfb39747ec33f0ac0daeef78e6331edf1a6 | # Dataset Card for "OxfordFlowers_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/OxfordFlowers_train | [
"region:us"
]
| 2023-01-09T17:56:51+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "pink primrose", "1": "hard-leaved pocket orchid", "2": "canterbury bells", "3": "sweet pea", "4": "english marigold", "5": "tiger lily", "6": "moon orchid", "7": "bird of paradise", "8": "monkshood", "9": "globe thistle", "10": "snapdragon", "11": "colt's foot", "12": "king protea", "13": "spear thistle", "14": "yellow iris", "15": "globe-flower", "16": "purple coneflower", "17": "peruvian lily", "18": "balloon flower", "19": "giant white arum lily", "20": "fire lily", "21": "pincushion flower", "22": "fritillary", "23": "red ginger", "24": "grape hyacinth", "25": "corn poppy", "26": "prince of wales feathers", "27": "stemless gentian", "28": "artichoke", "29": "sweet william", "30": "carnation", "31": "garden phlox", "32": "love in the mist", "33": "mexican aster", "34": "alpine sea holly", "35": "ruby-lipped cattleya", "36": "cape flower", "37": "great masterwort", "38": "siam tulip", "39": "lenten rose", "40": "barbeton daisy", "41": "daffodil", "42": "sword lily", "43": "poinsettia", "44": "bolero deep blue", "45": "wallflower", "46": "marigold", "47": "buttercup", "48": "oxeye daisy", "49": "common dandelion", "50": "petunia", "51": "wild pansy", "52": "primula", "53": "sunflower", "54": "pelargonium", "55": "bishop of llandaff", "56": "gaura", "57": "geranium", "58": "orange dahlia", "59": "pink-yellow dahlia?", "60": "cautleya spicata", "61": "japanese anemone", "62": "black-eyed susan", "63": "silverbush", "64": "californian poppy", "65": "osteospermum", "66": "spring crocus", "67": "bearded iris", "68": "windflower", "69": "tree poppy", "70": "gazania", "71": "azalea", "72": "water lily", "73": "rose", "74": "thorn apple", "75": "morning glory", "76": "passion flower", "77": "lotus", "78": "toad lily", "79": "anthurium", "80": "frangipani", "81": "clematis", "82": "hibiscus", "83": "columbine", "84": "desert-rose", "85": "tree mallow", "86": "magnolia", "87": "cyclamen", "88": "watercress", "89": "canna lily", "90": "hippeastrum", "91": "bee balm", "92": "ball moss", "93": "foxglove", "94": "bougainvillea", "95": "camellia", "96": "mallow", "97": "mexican petunia", "98": "bromelia", "99": "blanket flower", "100": "trumpet creeper", "101": "blackberry lily"}}}}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_ViT_L_14", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_ensemble_specific", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai_classes", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_wo_openai_classes", "sequence": "string"}, {"name": "Attributes_ViT_L_14_text_davinci_003", "sequence": "string"}, {"name": "Attributes_ViT_L_14_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_ViT_L_14_text_davinci_003_oxfordflowers", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_16_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_16_ensemble_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_32_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_32_ensemble_specific", "dtype": "string"}, {"name": "Attributes_ViT_B_16_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_simple_specific", "dtype": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_ensemble_specific", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 45649356.0, "num_examples": 1020}], "download_size": 43625002, "dataset_size": 45649356.0}} | 2023-05-04T04:37:57+00:00 |
03293f7ccbfe329dc38d5f0f57b4546a93b044d5 | # Dataset Card for "OxfordFlowers_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/OxfordFlowers_test | [
"region:us"
]
| 2023-01-09T17:57:03+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "pink primrose", "1": "hard-leaved pocket orchid", "2": "canterbury bells", "3": "sweet pea", "4": "english marigold", "5": "tiger lily", "6": "moon orchid", "7": "bird of paradise", "8": "monkshood", "9": "globe thistle", "10": "snapdragon", "11": "colt's foot", "12": "king protea", "13": "spear thistle", "14": "yellow iris", "15": "globe-flower", "16": "purple coneflower", "17": "peruvian lily", "18": "balloon flower", "19": "giant white arum lily", "20": "fire lily", "21": "pincushion flower", "22": "fritillary", "23": "red ginger", "24": "grape hyacinth", "25": "corn poppy", "26": "prince of wales feathers", "27": "stemless gentian", "28": "artichoke", "29": "sweet william", "30": "carnation", "31": "garden phlox", "32": "love in the mist", "33": "mexican aster", "34": "alpine sea holly", "35": "ruby-lipped cattleya", "36": "cape flower", "37": "great masterwort", "38": "siam tulip", "39": "lenten rose", "40": "barbeton daisy", "41": "daffodil", "42": "sword lily", "43": "poinsettia", "44": "bolero deep blue", "45": "wallflower", "46": "marigold", "47": "buttercup", "48": "oxeye daisy", "49": "common dandelion", "50": "petunia", "51": "wild pansy", "52": "primula", "53": "sunflower", "54": "pelargonium", "55": "bishop of llandaff", "56": "gaura", "57": "geranium", "58": "orange dahlia", "59": "pink-yellow dahlia?", "60": "cautleya spicata", "61": "japanese anemone", "62": "black-eyed susan", "63": "silverbush", "64": "californian poppy", "65": "osteospermum", "66": "spring crocus", "67": "bearded iris", "68": "windflower", "69": "tree poppy", "70": "gazania", "71": "azalea", "72": "water lily", "73": "rose", "74": "thorn apple", "75": "morning glory", "76": "passion flower", "77": "lotus", "78": "toad lily", "79": "anthurium", "80": "frangipani", "81": "clematis", "82": "hibiscus", "83": "columbine", "84": "desert-rose", "85": "tree mallow", "86": "magnolia", "87": "cyclamen", "88": "watercress", "89": "canna lily", "90": "hippeastrum", "91": "bee balm", "92": "ball moss", "93": "foxglove", "94": "bougainvillea", "95": "camellia", "96": "mallow", "97": "mexican petunia", "98": "bromelia", "99": "blanket flower", "100": "trumpet creeper", "101": "blackberry lily"}}}}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_ViT_L_14", "sequence": "string"}, {"name": "LLM_Description_opt175b_downstream_tasks_ViT_L_14", "sequence": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai_classes", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_wo_openai_classes", "sequence": "string"}, {"name": "Attributes_ViT_L_14_text_davinci_003", "sequence": "string"}, {"name": "Attributes_ViT_L_14_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_ViT_L_14_text_davinci_003_oxfordflowers", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_L_14_ensemble_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_16_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_16_ensemble_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_32_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_32_ensemble_specific", "dtype": "string"}, {"name": "Attributes_ViT_B_16_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_simple_specific", "dtype": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_ensemble_specific", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 275107541.0, "num_examples": 6149}], "download_size": 261098161, "dataset_size": 275107541.0}} | 2023-06-02T01:11:11+00:00 |
6eb63befc666151866d1277f5b9777f7620bb308 | # Dataset Card for "DTD_parition1_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/DTD_parition1_train | [
"region:us"
]
| 2023-01-09T18:01:34+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "banded", "1": "blotchy", "2": "braided", "3": "bubbly", "4": "bumpy", "5": "chequered", "6": "cobwebbed", "7": "cracked", "8": "crosshatched", "9": "crystalline", "10": "dotted", "11": "fibrous", "12": "flecked", "13": "freckled", "14": "frilly", "15": "gauzy", "16": "grid", "17": "grooved", "18": "honeycombed", "19": "interlaced", "20": "knitted", "21": "lacelike", "22": "lined", "23": "marbled", "24": "matted", "25": "meshed", "26": "paisley", "27": "perforated", "28": "pitted", "29": "pleated", "30": "polka-dotted", "31": "porous", "32": "potholed", "33": "scaly", "34": "smeared", "35": "spiralled", "36": "sprinkled", "37": "stained", "38": "stratified", "39": "striped", "40": "studded", "41": "swirly", "42": "veined", "43": "waffled", "44": "woven", "45": "wrinkled", "46": "zigzagged"}}}}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "LLM_Description_opt175b_downstream_tasks_ViT_L_14", "sequence": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "clip_tags_ViT_L_14_ensemble_specific", "dtype": "string"}, {"name": "clip_tags_ViT_L_14_simple_specific", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "Attributes_ViT_L_14_text_davinci_003", "sequence": "string"}, {"name": "Attributes_ViT_L_14_text_davinci_003_dtd", "sequence": "string"}, {"name": "Attributes_ViT_L_14_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai_classes", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_wo_openai_classes", "sequence": "string"}, {"name": "clip_tags_ViT_B_16_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_16_ensemble_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_32_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_32_ensemble_specific", "dtype": "string"}, {"name": "Attributes_ViT_B_16_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_simple_specific", "dtype": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_ensemble_specific", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 235001213.4, "num_examples": 1880}], "download_size": 230863096, "dataset_size": 235001213.4}} | 2023-05-04T04:08:45+00:00 |
37173e61d183af9da740ba4ceaccbf560f903a5a | # Dataset Card for "DTD_parition1_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/DTD_parition1_test | [
"region:us"
]
| 2023-01-09T18:02:00+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "banded", "1": "blotchy", "2": "braided", "3": "bubbly", "4": "bumpy", "5": "chequered", "6": "cobwebbed", "7": "cracked", "8": "crosshatched", "9": "crystalline", "10": "dotted", "11": "fibrous", "12": "flecked", "13": "freckled", "14": "frilly", "15": "gauzy", "16": "grid", "17": "grooved", "18": "honeycombed", "19": "interlaced", "20": "knitted", "21": "lacelike", "22": "lined", "23": "marbled", "24": "matted", "25": "meshed", "26": "paisley", "27": "perforated", "28": "pitted", "29": "pleated", "30": "polka-dotted", "31": "porous", "32": "potholed", "33": "scaly", "34": "smeared", "35": "spiralled", "36": "sprinkled", "37": "stained", "38": "stratified", "39": "striped", "40": "studded", "41": "swirly", "42": "veined", "43": "waffled", "44": "woven", "45": "wrinkled", "46": "zigzagged"}}}}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "LLM_Description_opt175b_downstream_tasks_ViT_L_14", "sequence": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "clip_tag_ViT_L_14_specific", "dtype": "string"}, {"name": "clip_tags_ViT_L_14_ensemble_specific", "dtype": "string"}, {"name": "clip_tags_ViT_L_14_simple_specific", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "Attributes_ViT_L_14_text_davinci_003", "sequence": "string"}, {"name": "Attributes_ViT_L_14_text_davinci_003_dtd", "sequence": "string"}, {"name": "Attributes_ViT_L_14_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai_classes", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_wo_openai_classes", "sequence": "string"}, {"name": "clip_tags_ViT_B_16_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_16_ensemble_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_32_simple_specific", "dtype": "string"}, {"name": "clip_tags_ViT_B_32_ensemble_specific", "dtype": "string"}, {"name": "Attributes_ViT_B_16_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_simple_specific", "dtype": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_ensemble_specific", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 184279525.4, "num_examples": 1880}], "download_size": 180002375, "dataset_size": 184279525.4}} | 2023-06-02T01:05:59+00:00 |
8dcda336d613932af64bcc09fdb34faaa3169e6d | Ineract/policies | [
"region:us"
]
| 2023-01-09T18:18:58+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}], "config_name": "plain_text", "splits": [{"name": "train", "num_bytes": 1846196, "num_examples": 4500}, {"name": "test", "num_bytes": 201754, "num_examples": 501}], "download_size": 2875518, "dataset_size": 2047950}} | 2023-01-10T16:03:26+00:00 |
|
faf6b834d42218ad07650ff6cbb624c7e92f6d73 | ---
TODO: Add YAML tags here. Copy-paste the tags obtained with the online tagging app: https://huggingface.co/spaces/huggingface/datasets-tagging
---
# Dataset Card for Testing Stock Data
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This is a test dataset
### Supported Tasks and Leaderboards
BERT
MARKET
STOCK
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset. | Lord-Goku/testing_1 | [
"license:afl-3.0",
"region:us"
]
| 2023-01-09T18:28:35+00:00 | {"license": "afl-3.0"} | 2023-01-11T18:16:39+00:00 |
184d16b8e92489e53a96400fa7316d38ddf40661 | # Dataset Card for "AbduRozik"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | matallanas/AbduRozik | [
"region:us"
]
| 2023-01-09T19:08:58+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 4418800.0, "num_examples": 22}], "download_size": 4418930, "dataset_size": 4418800.0}} | 2023-01-09T19:09:07+00:00 |
6a542c0150bb9ae4daa5ce0781bdee65266d2a29 | # Dataset Card for "eee543"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ilhanemirhan/eee543 | [
"region:us"
]
| 2023-01-09T19:12:04+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3303838079.792, "num_examples": 70568}, {"name": "test", "num_bytes": 1349710759.272, "num_examples": 28558}], "download_size": 4792902415, "dataset_size": 4653548839.064}} | 2023-01-10T01:08:42+00:00 |
524d6e2bb505eedafa40b36d0c94105eb4b9da62 | fmattera/lack-center-table | [
"license:openrail",
"region:us"
]
| 2023-01-09T20:04:29+00:00 | {"license": "openrail"} | 2023-01-10T10:00:08+00:00 |
|
81e85e4f4e06a6f9b02a2f4afd47267c9d0c6cd6 | # Dataset Card for "OxfordPets_test_embeddings"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/OxfordPets_test_embeddings | [
"region:us"
]
| 2023-01-09T22:36:34+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "id", "dtype": "int64"}, {"name": "vision_embeddings", "sequence": "float32"}], "splits": [{"name": "openai_clip_vit_large_patch14", "num_bytes": 424231302.0, "num_examples": 3669}], "download_size": 426276832, "dataset_size": 424231302.0}} | 2023-01-09T22:36:52+00:00 |
dcd49332e43a8838bc326fb6ba33b6981883dcfe | # Dataset Card for "OxfordPets_train_embeddings"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/OxfordPets_train_embeddings | [
"region:us"
]
| 2023-01-09T22:41:17+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "id", "dtype": "int64"}, {"name": "vision_embeddings", "sequence": "float32"}], "splits": [{"name": "openai_clip_vit_large_patch14", "num_bytes": 389325271.36, "num_examples": 3680}], "download_size": 391341260, "dataset_size": 389325271.36}} | 2023-01-09T22:41:40+00:00 |
b2fa6fa0df2f85031538f4c179517bf59250068c | # Dataset Card for "Caltech101_not_background_test_embeddings"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/Caltech101_not_background_test_embeddings | [
"region:us"
]
| 2023-01-09T22:44:42+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "id", "dtype": "int64"}, {"name": "vision_embeddings", "sequence": "float32"}], "splits": [{"name": "openai_clip_vit_large_patch14", "num_bytes": 95535909.0, "num_examples": 5647}], "download_size": 98967583, "dataset_size": 95535909.0}} | 2023-01-09T22:44:54+00:00 |
7b440d373203f6198aec05d9f49d46fd1bda9797 | # Dataset Card for "Caltech101_not_background_train_embeddings"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/Caltech101_not_background_train_embeddings | [
"region:us"
]
| 2023-01-09T22:45:58+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "id", "dtype": "int64"}, {"name": "vision_embeddings", "sequence": "float32"}], "splits": [{"name": "openai_clip_vit_large_patch14", "num_bytes": 52727578.0, "num_examples": 3030}], "download_size": 54496531, "dataset_size": 52727578.0}} | 2023-01-09T22:46:04+00:00 |
ee914c3c04c5eda8278b124434c68f28e3bd7e83 | # Dataset Card for "OxfordFlowers_test_embeddings"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/OxfordFlowers_test_embeddings | [
"region:us"
]
| 2023-01-09T22:48:18+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "id", "dtype": "int64"}, {"name": "vision_embeddings", "sequence": "float32"}], "splits": [{"name": "openai_clip_vit_large_patch14", "num_bytes": 279299546.0, "num_examples": 6149}], "download_size": 283131238, "dataset_size": 279299546.0}} | 2023-01-29T01:32:53+00:00 |
c35d055781b535e872cde32943e25e1ebe65e44f | # Dataset Card for "OxfordFlowers_train_embeddings"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/OxfordFlowers_train_embeddings | [
"region:us"
]
| 2023-01-09T22:49:47+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "id", "dtype": "int64"}, {"name": "vision_embeddings", "sequence": "float32"}], "splits": [{"name": "openai_clip_vit_large_patch14", "num_bytes": 46545819.0, "num_examples": 1020}], "download_size": 47189831, "dataset_size": 46545819.0}} | 2023-01-29T01:41:58+00:00 |
70ba1691b7ded13a4a7d98dc95fb9dd3817a1bc1 |
# CMP Facade Database
We present a dataset of facade images assembled at the Center for Machine Perception, which includes 606 rectified images of facades from various sources, which have been manually annotated. The facades are from different cities around the world and diverse architectural styles.
Documentation
Data origin, format and processing, annotation principles for 12 classes are specified in the report.
- facade
- molding
- cornice
- pillar
- window
- door
- sill
- blind
- balcony
- shop
- deco
- background
Link to original website:
https://cmp.felk.cvut.cz/~tylecr1/facade/
Citation
Please use the following reference to cite the dataset:
```latex
@INPROCEEDINGS{Tylecek13,
author = {Radim Tyle{\v c}ek and Radim {\v S}{\' a}ra},
title = {Spatial Pattern Templates for Recognition of Objects with Regular Structure},
booktitle = {Proc. GCPR},
year = {2013},
address = {Saarbrucken, Germany},
}
``` | Xpitfire/cmp_facade | [
"task_categories:image-segmentation",
"language:en",
"license:mit",
"building",
"facade",
"region:us"
]
| 2023-01-09T22:51:59+00:00 | {"language": ["en"], "license": "mit", "task_categories": ["image-segmentation"], "tags": ["building", "facade"]} | 2023-01-15T01:43:17+00:00 |
e03a13d392a888ab4b908cf2c60234fb2eea2eb5 | # Dataset Card for "DTD_parition1_test_embeddings"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/DTD_parition1_test_embeddings | [
"region:us"
]
| 2023-01-09T22:53:19+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "id", "dtype": "int64"}, {"name": "vision_embeddings", "sequence": "float32"}], "splits": [{"name": "openai_clip_vit_large_patch14", "num_bytes": 185806556.4, "num_examples": 1880}], "download_size": 186181373, "dataset_size": 185806556.4}} | 2023-01-29T01:33:44+00:00 |
08cb6bef3f7f9608911431df8c82c7e4007ab051 | # Dataset Card for "DTD_parition1_train_embeddings"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/DTD_parition1_train_embeddings | [
"region:us"
]
| 2023-01-09T22:55:09+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "id", "dtype": "int64"}, {"name": "vision_embeddings", "sequence": "float32"}], "splits": [{"name": "openai_clip_vit_large_patch14", "num_bytes": 236557256.4, "num_examples": 1880}], "download_size": 237044519, "dataset_size": 236557256.4}} | 2023-01-29T01:42:48+00:00 |
00563d705739a30bcbac92c98becdc5c175d27b7 | tacoz/audCatImages | [
"license:openrail",
"region:us"
]
| 2023-01-09T23:23:00+00:00 | {"license": "openrail"} | 2023-01-09T23:24:43+00:00 |
|
198d3c4bce5f3b8ac529e85364c3242a4a3ec1d9 | # Dataset Card for "M2BD"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | nourlachtar/M2BD | [
"region:us"
]
| 2023-01-09T23:24:22+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 1643326, "num_examples": 758}, {"name": "validation", "num_bytes": 82025, "num_examples": 42}, {"name": "test", "num_bytes": 83279, "num_examples": 43}], "download_size": 314857, "dataset_size": 1808630}} | 2023-01-10T12:43:59+00:00 |
3370987343b39cf8f9d374c182e309608335804a | # Dataset Card for "rick-and-morty-s5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | juliaturc/rick-and-morty-s5 | [
"region:us"
]
| 2023-01-09T23:55:21+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "caption", "dtype": "string"}, {"name": "subtitle", "dtype": "string"}, {"name": "special_token", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 173445606.66373935, "num_examples": 2513}, {"name": "test", "num_bytes": 19233393.70426065, "num_examples": 280}], "download_size": 192257158, "dataset_size": 192679000.368}} | 2023-01-09T23:55:30+00:00 |
0179b5f2571c546e49f13256e99a04b1a5e3c855 | jlmarrugom/fashion_embeddings | [
"task_categories:image-classification",
"size_categories:1M<n<10M",
"license:apache-2.0",
"region:us"
]
| 2023-01-10T01:44:28+00:00 | {"license": "apache-2.0", "size_categories": ["1M<n<10M"], "task_categories": ["image-classification"]} | 2023-01-10T01:46:25+00:00 |
|
4c15ffcc724c4804ecda677e6a63d2d0741f5d09 | # Dataset Card for "openai_summarize_tldr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | CarperAI/openai_summarize_tldr | [
"region:us"
]
| 2023-01-10T02:53:30+00:00 | {"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 181260841, "num_examples": 116722}, {"name": "valid", "num_bytes": 10018338, "num_examples": 6447}, {"name": "test", "num_bytes": 10198128, "num_examples": 6553}], "download_size": 122973500, "dataset_size": 201477307}} | 2023-01-10T02:53:40+00:00 |
c92352d1f45757876c694ac1f85f9e9e74834347 | # Dataset Card for "pingu-images-dreambooth"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | parinzee/pingu-images-dreambooth | [
"region:us"
]
| 2023-01-10T03:48:19+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 11360169.0, "num_examples": 14}], "download_size": 11358375, "dataset_size": 11360169.0}} | 2023-01-10T03:48:30+00:00 |
d66252aa3355e9c78f9fbafe7b262dac41786e87 | Damoniano/Agent1 | [
"license:creativeml-openrail-m",
"region:us"
]
| 2023-01-10T04:03:58+00:00 | {"license": "creativeml-openrail-m"} | 2023-01-10T04:16:33+00:00 |
|
7f3ca9f3c89a15f5ebe5f25ef051bd2bc6e1ccb0 | Acumen/test3 | [
"license:openrail",
"region:us"
]
| 2023-01-10T04:47:12+00:00 | {"license": "openrail"} | 2023-01-10T04:49:47+00:00 |
|
c98c54bc90c6423318034a3f9095337c2073fb5f | # Dataset Card for "boostcamp-docvqa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Ssunbell/boostcamp-docvqa | [
"region:us"
]
| 2023-01-10T06:21:38+00:00 | {"dataset_info": {"features": [{"name": "questionId", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "image", "sequence": {"sequence": {"sequence": {"sequence": "uint8"}}}}, {"name": "docId", "dtype": "int64"}, {"name": "ucsf_document_id", "dtype": "string"}, {"name": "ucsf_document_page_no", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "data_split", "dtype": "string"}, {"name": "words", "sequence": "string"}, {"name": "boxes", "sequence": {"sequence": "int64"}}], "splits": [{"name": "train", "num_bytes": 6387690838, "num_examples": 39463}, {"name": "val", "num_bytes": 869953677, "num_examples": 5349}], "download_size": 2583317804, "dataset_size": 7257644515}} | 2023-01-10T06:32:33+00:00 |
3b34fb6be6018ec28bc7108434624fe52717c8a7 | nc33/entailment | [
"license:mit",
"region:us"
]
| 2023-01-10T06:39:04+00:00 | {"license": "mit"} | 2023-01-10T06:41:07+00:00 |
|
f3f8fa8b762c7038411a9ec8a1073804f1764bc1 | # Dataset Card for "boostcamp-docvqa-test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Ssunbell/boostcamp-docvqa-test | [
"region:us"
]
| 2023-01-10T06:57:08+00:00 | {"dataset_info": {"features": [{"name": "questionId", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "image", "sequence": {"sequence": {"sequence": {"sequence": "uint8"}}}}, {"name": "docId", "dtype": "int64"}, {"name": "ucsf_document_id", "dtype": "string"}, {"name": "ucsf_document_page_no", "dtype": "string"}, {"name": "data_split", "dtype": "string"}, {"name": "words", "sequence": "string"}, {"name": "boxes", "sequence": {"sequence": "int64"}}], "splits": [{"name": "test", "num_bytes": 843659556, "num_examples": 5188}], "download_size": 297328696, "dataset_size": 843659556}} | 2023-01-10T06:59:57+00:00 |
b910a4cad4920dcfa58affe33ed2d95a94dedce5 | hz244/cat_test_0 | [
"license:apache-2.0",
"region:us"
]
| 2023-01-10T07:07:06+00:00 | {"license": "apache-2.0"} | 2023-01-12T21:22:38+00:00 |
|
c1eac2ae17398485ff44c4c9c09dd9cd4190331d | jfernandez/cebuano-filipino-sentences | [
"license:cc0-1.0",
"region:us"
]
| 2023-01-10T07:40:57+00:00 | {"license": "cc0-1.0"} | 2023-01-10T10:01:53+00:00 |
|
933ff69ccbc16d2870b90238fbfe7d6d9c0f46ef | raghavsdfg/hub | [
"license:afl-3.0",
"region:us"
]
| 2023-01-10T08:33:12+00:00 | {"license": "afl-3.0"} | 2023-01-10T08:33:12+00:00 |
|
14d15972231aaae5b11175eded10477d4e67a2a9 | # Dataset Card for "patents_green_plastics_10k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cwinkler/patents_green_plastics_10k | [
"region:us"
]
| 2023-01-10T08:51:55+00:00 | {"dataset_info": {"features": [{"name": "abstract", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 7413030.0, "num_examples": 10282}], "download_size": 3678031, "dataset_size": 7413030.0}} | 2023-01-10T08:53:56+00:00 |
4f1114880da57a4d7c53d53189d5fa7f19c57527 | # Dataset Card for "images_first_day"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuvalkirstain/images_first_day | [
"region:us"
]
| 2023-01-10T09:37:57+00:00 | {"dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "created_at", "dtype": "timestamp[ns]"}, {"name": "image_hash", "dtype": "string"}, {"name": "user_id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "negative_prompt", "dtype": "string"}, {"name": "seed", "dtype": "int64"}, {"name": "gs", "dtype": "float64"}, {"name": "steps", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "num_generated", "dtype": "int64"}, {"name": "scheduler_cls", "dtype": "string"}, {"name": "model_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 5027572586.584, "num_examples": 6916}], "download_size": 5024119623, "dataset_size": 5027572586.584}} | 2023-01-10T09:44:38+00:00 |
1a4a3add5b7f154526c6d76406b20765a63d0d10 | # Dataset Card for "EnglishLM_Chars_removed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | kabir5297/EnglishLM_Chars_removed | [
"region:us"
]
| 2023-01-10T09:52:26+00:00 | {"dataset_info": {"features": [{"name": "translation", "dtype": {"translation": {"languages": ["en", "es"]}}}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 947789376, "num_examples": 2009073}], "download_size": 531597761, "dataset_size": 947789376}} | 2023-01-10T10:22:09+00:00 |
2f14274b2a5eb4b66c0207b6d009586a288c647f | # Dataset Card for "pick_a_pic_ranked_images_first_day"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuvalkirstain/pick_a_pic_ranked_images_first_day | [
"region:us"
]
| 2023-01-10T10:15:32+00:00 | {"dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "created_at", "dtype": "timestamp[ns]"}, {"name": "image_uid", "dtype": "string"}, {"name": "user_id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "negative_prompt", "dtype": "string"}, {"name": "seed", "dtype": "int64"}, {"name": "gs", "dtype": "float64"}, {"name": "steps", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "num_generated", "dtype": "int64"}, {"name": "scheduler_cls", "dtype": "string"}, {"name": "model_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 592439420.0, "num_examples": 859}], "download_size": 592037316, "dataset_size": 592439420.0}} | 2023-01-10T10:16:33+00:00 |
d9eb8c7cb26cf699ea86f2908bdb359076f9dfc4 | slushily/building_training | [
"region:us"
]
| 2023-01-10T10:25:34+00:00 | {} | 2023-01-10T11:55:48+00:00 |
|
36fd9d3bce05bde438e2a170520d5ec20c246da5 |
### Dataset Summary
A Hebrew Deduplicated and Cleaned Common Crawl Corpus. A thoroughly cleaned and
approximately deduplicated dataset for unsupervised learning.
### Citing
If you use HeDC4 in your research, please cite [HeRo: RoBERTa and Longformer Hebrew Language Models](http://arxiv.org/abs/2304.11077).
```
@article{shalumov2023hero,
title={HeRo: RoBERTa and Longformer Hebrew Language Models},
author={Vitaly Shalumov and Harel Haskey},
year={2023},
journal={arXiv:2304.11077},
}
``` | HeNLP/HeDC4 | [
"task_categories:fill-mask",
"size_categories:1B<n<10B",
"language:he",
"arxiv:2304.11077",
"region:us"
]
| 2023-01-10T10:28:22+00:00 | {"language": ["he"], "size_categories": ["1B<n<10B"], "task_categories": ["fill-mask"]} | 2023-04-24T05:04:29+00:00 |
ac947a019834dd15c41ebee1c2f9cc727a4cf56a |
> 《 License 》
>
> 1. 본 AI데이터 등을 이용할 때에는 반드시 한국지능정보사회진흥원의 사업결과임을 밝혀야 하며, 본 AI데이터 등을 이용한 2차적 저작물에도 동일하게 밝혀야 합니다.
>
> 2. 국외에 소재하는 법인, 단체 또는 개인이 AI데이터 등을 이용하기 위해서는 수행기관 등 및 한국지능정보사회진흥원과 별도로 합의가 필요합니다.
>
> 3. 본 AI데이터 등의 국외 반출을 위해서는 수행기관 등 및 한국지능정보사회진흥원과 별도로 합의가 필요합니다.
>
> 4. 본 AI데이터는 인공지능 학습모델의 학습용으로만 사용할 수 있습니다. 한국지능정보사회진흥원은 AI데이터 등의 이용의 목적이나 방법, 내용 등이 위법하거나 부적합하다고 판단될 경우 제공을 거부할 수 있으며, 이미 제공한 경우 이용의 중지와 AI 데이터 등의 환수, 폐기 등을 요구할 수 있습니다.
>
> 5. 제공 받은 AI데이터 등을 수행기관 등과 한국지능정보사회진흥원의 승인을 받지 않은 다른 법인, 단체 또는 개인에게 열람하게 하거나 제공, 양도, 대여, 판매하여서는 안됩니다.
>
> 6. AI데이터 등에 대해서 제 4항에 따른 목적 외 이용, 제5항에 따른 무단 열람, 제공, 양도, 대여, 판매 등의 결과로 인하여 발생하는 모든 민・형사 상의 책임은 AI데이터 등을 이용한 법인, 단체 또는 개인에게 있습니다.
>
> 7. 이용자는 AI 허브 제공 데이터셋 내에 개인정보 등이 포함된 것이 발견된 경우, 즉시 AI 허브에 해당 사실을 신고하고 다운로드 받은 데이터셋을 삭제하여야 합니다.
>
> 8. AI 허브로부터 제공받은 비식별 정보(재현정보 포함)를 인공지능 서비스 개발 등의 목적으로 안전하게 이용하여야 하며, 이를 이용해서 개인을 재식별하기 위한 어떠한 행위도 하여서는 안됩니다.
>
> 9. 향후 한국지능정보사회진흥원에서 활용사례・성과 등에 관한 실태조사를 수행 할 경우 이에 성실하게 임하여야 합니다.
| Laplace04/KoreanSummarizeAiHub | [
"license:other",
"region:us"
]
| 2023-01-10T10:29:19+00:00 | {"license": "other"} | 2023-01-10T10:33:39+00:00 |
965e122c69e6c0c166f954a9a3fc5f7b375b81cd | # Dataset Card for "dreambooth-hackathon-images"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | SuSung-boy/dreambooth-hackathon-images | [
"region:us"
]
| 2023-01-10T10:44:31+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 4714141.0, "num_examples": 31}], "download_size": 4715444, "dataset_size": 4714141.0}} | 2023-01-10T10:44:38+00:00 |
c575fe89f5693adc2473cb876751bd9d09fd6a26 | kuroneko5943/jd21 | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:found",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:zh",
"license:apache-2.0",
"jd",
"region:us"
]
| 2023-01-10T10:49:13+00:00 | {"annotations_creators": ["found"], "language_creators": ["crowdsourced"], "language": ["zh"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "pretty_name": "jd21", "tags": ["jd"]} | 2023-01-10T15:51:26+00:00 |
|
d42540b91fd35c649e08cee82297080338b101eb | kuroneko5943/amz20 | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|amazon_us_reviews",
"language:en",
"license:apache-2.0",
"amazon",
"region:us"
]
| 2023-01-10T12:02:41+00:00 | {"annotations_creators": ["found"], "language_creators": ["found"], "language": ["en"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["extended|amazon_us_reviews"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "pretty_name": "amz20", "tags": ["amazon"]} | 2023-01-10T16:02:20+00:00 |
|
111534a35cff7a1dcedfa07f7e30835a6b8b8505 | kuroneko5943/snap21 | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:found",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|amazon_us_reviews",
"language:en",
"license:apache-2.0",
"amazon review",
"region:us"
]
| 2023-01-10T12:08:18+00:00 | {"annotations_creators": ["found"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["extended|amazon_us_reviews"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "pretty_name": "snap21", "tags": ["amazon review"], "viewer": true} | 2023-01-10T16:20:44+00:00 |
|
40c2688bda0d2226838051d6ab9bf07be32565bf | kuroneko5943/stock11 | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:zh",
"license:apache-2.0",
"stock",
"region:us"
]
| 2023-01-10T12:13:05+00:00 | {"annotations_creators": ["machine-generated"], "language_creators": ["crowdsourced"], "language": ["zh"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "pretty_name": "stock11", "tags": ["stock"]} | 2023-01-16T04:11:18+00:00 |
|
e18b665eeba6ad0833f85899f9ce81532311544d | # Dataset Card for "tr-wikihow-summ"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ardauzunoglu/tr-wikihow-summ | [
"region:us"
]
| 2023-01-10T12:24:11+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "summary", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 279070558, "num_examples": 113356}, {"name": "validation", "num_bytes": 15174147, "num_examples": 6082}, {"name": "test", "num_bytes": 14888006, "num_examples": 5984}], "download_size": 166588788, "dataset_size": 309132711}} | 2023-01-10T12:27:29+00:00 |
bc524372259ede43ad08b11d0929bf5385e42d12 | # AutoTrain Dataset for project: hannah-demo
## Dataset Description
This dataset has been automatically processed by AutoTrain for project hannah-demo.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<11744x7026 RGBA PIL image>",
"target": 0
},
{
"image": "<11744x7026 RGBA PIL image>",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['hannah'], 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 | 7 |
| valid | 7 |
| slushily/autotrain-data-hannah-demo | [
"task_categories:image-classification",
"region:us"
]
| 2023-01-10T12:29:00+00:00 | {"task_categories": ["image-classification"]} | 2023-01-11T03:18:33+00:00 |
1c2c6699d58d1ac01fb4177fb65fb1a82b2bd37f | # Dataset Card for "hdb0110"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cestwc/hdb0110 | [
"region:us"
]
| 2023-01-10T13:23:29+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "labels", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 16067.0, "num_examples": 110}], "download_size": 13149, "dataset_size": 16067.0}} | 2023-01-10T13:37:20+00:00 |
ac945c3619838e4e990cee54af9f4e788565e8f6 | # Dataset Card for "lfqa_preprocessed"
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
## Dataset Description
- **Homepage:** [https://towardsdatascience.com/long-form-qa-beyond-eli5-an-updated-dataset-and-approach-319cb841aabb](https://towardsdatascience.com/long-form-qa-beyond-eli5-an-updated-dataset-and-approach-319cb841aabb)
### Dataset Summary
This is a simplified version of [vblagoje's](https://huggingface.co/vblagoje) *[lfqa_support_docs](https://huggingface.co/datasets/vblagoje/lfqa_support_docs)* and *[lfqa](https://huggingface.co/datasets/vblagoje/lfqa)* datasets.
It was generated by me to have a more straight forward way to train Seq2Seq models on context based long form question answering tasks.
## Dataset Structure
### Data Instances
An example of 'train' looks as follows.
```json
{
"question": "what's the difference between a forest and a wood?",
"answer": "They're used interchangeably a lot. You'll get different answers from different resources, but the ...",
"context": [
"Wood is divided, according to its botanical origin, into two kinds: softwoods, ...",
"Processing and products differs especially with regard to the distinction between softwood and hardwood ..."
]
}
```
### Data Fields
The data fields are the same among all splits.
- `question`: a `string` feature.
- `answer`: a `string` feature.
- `context`: a list feature containing `string` features.
### Data Splits
| name |train|validation|
|----------|----:|---------:|
| |226147| 3020|
## Additional Information
### Licensing Information
This dataset is distributed under the MIT licence. | LLukas22/lfqa_preprocessed | [
"task_categories:question-answering",
"task_categories:sentence-similarity",
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"region:us"
]
| 2023-01-10T13:30:52+00:00 | {"language": ["en"], "license": "mit", "size_categories": ["100K<n<1M"], "task_categories": ["question-answering", "sentence-similarity"]} | 2023-01-10T14:21:56+00:00 |
43a7c5638a086dc7071d2428c2bc9fcd89231dd8 | # Dataset Card for "pick_a_pic_preferred_images_first_day"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yuvalkirstain/pick_a_pic_preferred_images_first_day | [
"region:us"
]
| 2023-01-10T14:17:23+00:00 | {"dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "created_at", "dtype": "timestamp[ns]"}, {"name": "image_uid", "dtype": "string"}, {"name": "user_id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "negative_prompt", "dtype": "string"}, {"name": "seed", "dtype": "int64"}, {"name": "gs", "dtype": "float64"}, {"name": "steps", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "num_generated", "dtype": "int64"}, {"name": "scheduler_cls", "dtype": "string"}, {"name": "model_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 686322947.851, "num_examples": 1001}], "download_size": 685855336, "dataset_size": 686322947.851}} | 2023-01-10T14:18:22+00:00 |
b86a5ebb94f7ee47b94ded3da0eed30de2baa412 | LiveEvil/Rekognize | [
"language:en",
"license:mit",
"clearview",
"ai",
"face",
"region:us"
]
| 2023-01-10T14:39:24+00:00 | {"language": ["en"], "license": "mit", "tags": ["clearview", "ai", "face"]} | 2023-01-10T15:10:43+00:00 |
|
ba7671f1624bfaa5f45cc2efeda234a776eb6315 | kuroneko5943/weibo16 | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:zh",
"license:apache-2.0",
"weibo",
"sentiment",
"region:us"
]
| 2023-01-10T14:39:35+00:00 | {"annotations_creators": ["machine-generated"], "language_creators": ["crowdsourced"], "language": ["zh"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification"], "pretty_name": "weibo16", "tags": ["weibo", "sentiment"]} | 2023-01-10T16:01:32+00:00 |
|
ee1ef6dbfd88c3aea0a8e9b59bb464706d516ea9 | # Dataset Card for "IL2223_project"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tilos/IL2223_project | [
"region:us"
]
| 2023-01-10T14:58:19+00:00 | {"dataset_info": {"features": [{"name": "referenceTime", "dtype": "string"}, {"name": "t", "dtype": "float64"}, {"name": "ws", "dtype": "float64"}, {"name": "prec1h", "dtype": "float64"}, {"name": "fesn1h", "dtype": "float64"}, {"name": "vis", "dtype": "float64"}, {"name": "confidence", "dtype": "float64"}, {"name": "congestionLevel", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 99680, "num_examples": 1246}], "download_size": 18777, "dataset_size": 99680}} | 2023-01-21T21:09:42+00:00 |
f776edeed36d302ce89e1e8c47316e77a47b6f41 | # Word Sense Disambiguation Corpora for Polish
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:** https://link.springer.com/chapter/10.1007/978-3-031-08754-7_70
- **Point of Contact:** [email protected]
### Dataset Summary
`WSD Polish Datasets` is a comprehensive benchmark for word sense disambiguation (WSD) classification task in Polish language.
It consists of 7 distinct datasets, manually annotated with senses from plWordNet-4.5 sense inventory. The following datasets
were annotated and included into our benchmark:
- KPWr
- KPWr-100
- Sherlock (SPEC)
- Skladnica
- WikiGlex (a subset of GLEX corpus)
- EmoGlex (a subset of GLEX corpus)
- Walenty
For more details, please check the following publication:
```
@InProceedings{10.1007/978-3-031-08754-7_70,
author="Janz, Arkadiusz
and Dziob, Agnieszka
and Oleksy, Marcin
and Baran, Joanna",
editor="Groen, Derek
and de Mulatier, Cl{\'e}llia
and Paszynski, Maciej
and Krzhizhanovskaya, Valeria V.
and Dongarra, Jack J.
and Sloot, Peter M. A.",
title="A Unified Sense Inventory for Word Sense Disambiguation in Polish",
booktitle="Computational Science -- ICCS 2022",
year="2022",
publisher="Springer International Publishing",
address="Cham",
pages="682--689",
isbn="978-3-031-08754-7"
}
```
**A new publication on Polish WSD corpora will be available soon**
### Supported Tasks and Leaderboards
Word sense disambiguation task. We do not provide a leaderboard. However, we provide an example evaluation script for evaluating WSD models.
### Languages
Polish language, PL
## Dataset Structure
### Data Instances
Data are structured in JSONL format, each single text sample is divided by sentence.
```
{
"text": "Wpierw pani Hudson została zerwana z łóżka, po czym odegrała się na mnie, a ja - na tobie.",
"tokens": [
{
"index": 0,
"position": [ 0, 6 ],
"orth": "Wpierw",
"lemma": "wpierw",
"pos": "adv",
"ctag": "adv"
},
{
"index": 1,
"position": [ 7, 11 ],
"orth": "pani",
"lemma": "pani",
"pos": "noun",
"ctag": "subst:nom:f:sg"
},
{
"index": 2,
"position": [ 12, 18 ],
"orth": "Hudson",
"lemma": "Hudson",
"pos": "noun",
"ctag": "subst:nom:f:sg"
},
{
"index": 3,
"position": [ 19, 26 ],
"orth": "została",
"lemma": "zostać",
"pos": "verb",
"ctag": "praet:perf:f:sg"
},
{
"index": 4,
"position": [ 27, 34 ],
"orth": "zerwana",
"lemma": "zerwać",
"pos": "verb",
"ctag": "ppas:perf:nom:f:aff:sg"
},
<...>
],
"phrases": [
{
"indices": [ 10, 11 ],
"head": 10,
"lemma": "odegrać się"
}
],
"wsd": [
{
"index": 0,
"pl_sense": "wpierw.1.r",
"plWN_syn_id": "01a4a067-aac5-11ed-aae5-0242ac130002",
"plWN_lex_id": "f2757c30-aac4-11ed-aae5-0242ac130002",
"plWN_syn_legacy_id": "477654",
"plWN_lex_legacy_id": "718454",
"PWN_syn_id": "00102736-r",
"bn_syn_id": "bn:00115376r",
"mapping_relation": "synonymy"
},
{
"index": 1,
"pl_sense": "pani.2.n",
"plWN_syn_id": "f35fb1ed-aac4-11ed-aae5-0242ac130002",
"plWN_lex_id": "d5145565-aac4-11ed-aae5-0242ac130002",
"plWN_syn_legacy_id": "129",
"plWN_lex_legacy_id": "20695",
"PWN_syn_id": "10787470-n",
"bn_syn_id": "bn:00001530n",
"mapping_relation": "synonymy"
},
<...>
]
}
```
### Data Fields
Description of json keys:
- `text`: text of the sentence
- `tokens`: list of tokens made by tokenization process
- `index`: token order index in sentence
- `position`: token chars span indices <included, excluded>
- `orth`: word
- `lemma`: lemmatised word
- `pos`: part of speech
- `ctag`: morphosyntactic tag
- `phrases`: list of multi-word
- `wsd`: annotation labels for the WSD task
### Data Splits
We do not specify an exact data split for training and evaluation. However, we suggest to use GLEX and Składnica for training and other datasets for testing.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection, Normalization and Post-processing
Source corpora were initially pre-processed using morphosyntactic tagging and multi-word expression recognition tools.
To tokenize and tag the datasets we used [MorphoDiTa](https://clarin-pl.eu/dspace/handle/11321/425) adapted to Polish language. To recognize multi-word expressions
we applied pattern-based matching tool [Corpus2-MWE](https://clarin-pl.eu/dspace/handle/11321/533) - only MWEs from plWordNet were included. After manual annotation,
sense indices of plWordNet 4.5 were mapped automatically to Princeton WordNet 3.0 and BabelNet 4.0 indices using plWordNet's interlingual mapping.
### Annotations
#### Annotation process
* 2+1 annotation process with inter-annotator agreement score over 0.6 PSA
* annotated with [plWordNet 4.5](http://plwordnet.pwr.wroc.pl/wordnet/)
* software: [WordNet-Loom](https://clarin-pl.eu/dspace/handle/11321/275) and [Inforex](https://clarin-pl.eu/dspace/handle/11321/13)
* both single-word and multi-word expressions annotated
* full-text sense annotation (excluding KPWr)
#### Who are the annotators?
- professional linguists from CLARIN-PL project
### Personal and Sensitive Information
The datasets do not contain any personal or sensitive information.
## Considerations for Using the Data
### Discussion of Biases
Some datasets are biased towards most frequent senses. No information about other biases - needs further analysis.
### Other Known Limitations
* sense inventories are usually incomplete therefore some word senses might be missing in plWordNet
* single-word and multi-word terms expressing novel senses (missing in plWordNet) were not marked
## Additional Information
### Dataset Curators
Arkadiusz Janz ([email protected])
### Licensing Information
KPWR-100 [CC-BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)
KPWR [CC-BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)
Walenty [CC-BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)
Sherlock [CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/)
Skladnica [GNU GPL 3](http://www.gnu.org/licenses/gpl-3.0.en.html)
GLEX [plWordNet License](http://plwordnet.pwr.wroc.pl/wordnet/licence)
### Citation Information
Main source (all corpora as a unified benchmark) and published here on HuggingFace:
````
@InProceedings{10.1007/978-3-031-08754-7_70,
author="Janz, Arkadiusz
and Dziob, Agnieszka
and Oleksy, Marcin
and Baran, Joanna",
editor="Groen, Derek
and de Mulatier, Cl{\'e}llia
and Paszynski, Maciej
and Krzhizhanovskaya, Valeria V.
and Dongarra, Jack J.
and Sloot, Peter M. A.",
title="A Unified Sense Inventory for Word Sense Disambiguation in Polish",
booktitle="Computational Science -- ICCS 2022",
year="2022",
publisher="Springer International Publishing",
address="Cham",
pages="682--689",
isbn="978-3-031-08754-7"
}
````
Related work
------------
KPWr-100, Składnica, SPEC
````
@article{janzresults,
title={Results of the PolEval 2020 Shared Task 3: Word Sense Disambiguation},
author={Janz, Arkadiusz and Chlebus, Joanna and Dziob, Agnieszka and Piasecki, Maciej},
journal={Proceedings of the PolEval 2020 Workshop},
pages={65--77},
year={2020}
}
````
GLEX (EmoGLEX)
````
@article{janz2017plwordnet,
title={{plWordNet} as a basis for large emotive lexicons of Polish},
author={Janz, Arkadiusz and Kocon, Jan and Piasecki, Maciej and Zasko-Zielinska, Monika},
journal={Proceedings of Human Language Technologies as a Challenge for Computer Science and Linguistics Poznan: Fundacja Uniwersytetu im. Adama Mickiewicza w Poznaniu},
pages={189--193},
year={2017}
}
````
KPWr
````
@conference{broda2012,
address = {Istanbul, Turkey},
author = {Bartosz Broda and Micha{\l} Marci{\'n}czuk and Marek Maziarz and Adam Radziszewski and Adam Wardy{\'n}ski},
booktitle = {Proceedings of LREC'12},
owner = {Marlena},
publisher = {ELRA},
timestamp = {2014.06.20},
title = {KPWr: Towards a Free Corpus of Polish},
year = {2012}
}
````
Składnica
````
@inproceedings{hajnicz-2014-lexico,
title = "Lexico-Semantic Annotation of Sk{\l}adnica Treebank by means of {PLWN} Lexical Units",
author = "Hajnicz, El{\.z}bieta",
booktitle = "Proceedings of the Seventh Global {W}ordnet Conference",
month = jan,
year = "2014",
address = "Tartu, Estonia",
publisher = "University of Tartu Press",
url = "https://aclanthology.org/W14-0104",
pages = "23--31",
}
````
Walenty
````
@inproceedings{haj:and:bar:lrec16,
author = {Hajnicz, El{\.z}bieta and Andrzejczuk, Anna and Bartosiak, Tomasz},
crossref = {lrec:16},
pages = {2625--2632},
pdf = {http://www.lrec-conf.org/proceedings/lrec2016/pdf/382_Paper.pdf},
title = {Semantic Layer of the Valence Dictionary of {P}olish \emph{{W}alenty}}
}
````
Mapping plWordNet onto Princeton WordNet
````
@inproceedings{rudnicka-etal-2021-non,
title = "A (Non)-Perfect Match: Mapping pl{W}ord{N}et onto {P}rinceton{W}ord{N}et",
author = "Rudnicka, Ewa and
Witkowski, Wojciech and
Piasecki, Maciej",
booktitle = "Proceedings of the 11th Global Wordnet Conference",
month = jan,
year = "2021",
address = "University of South Africa (UNISA)",
publisher = "Global Wordnet Association",
url = "https://aclanthology.org/2021.gwc-1.16",
pages = "137--146"
}
````
| clarin-knext/wsd_polish_datasets | [
"task_categories:token-classification",
"task_ids:word-sense-disambiguation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:pl",
"license:cc-by-4.0",
"region:us"
]
| 2023-01-10T15:09:52+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated", "found"], "language": ["pl"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["token-classification"], "task_ids": ["word-sense-disambiguation"], "pretty_name": "wsd-polish-datasets", "tags": []} | 2024-02-11T16:34:17+00:00 |
b5bf5c945124f673c5be476266889bd819986083 |
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This is a multilingual dataset containing ~130k annotated sentence boundaries. It contains laws and court decision in 6 different languages.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English, French, Italian, German, Portuguese, Spanish
## Dataset Structure
It is structured in the following format: {language}\_{type}\_{shard}.jsonl.xz
type is one of the following:
- laws
- judgements
Use the the dataset like this:
```
from datasets import load_dataset
config = 'fr_laws' #{language}_{type} | to load all languages and/or all types, use 'all_all'
dataset = load_dataset('rdcs/MultiLegalSBD', config)
```
### Data Instances
[More Information Needed]
### Data Fields
- text: the original text
- spans:
- start: offset of the first character
- end: offset of the last character
- label: One label only -> Sentence
- token_start: id of the first token
- token_end: id of the last token
- tokens:
- text: token text
- start: offset of the first character
- end: offset of the last character
- id: token id
- ws: whether the token is followed by whitespace
### Data Splits
There is only one split available
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@inproceedings{10.1145/3594536.3595132,
author = {Brugger, Tobias and St\"{u}rmer, Matthias and Niklaus, Joel},
title = {MultiLegalSBD: A Multilingual Legal Sentence Boundary Detection Dataset},
year = {2023},
isbn = {9798400701979},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3594536.3595132},
doi = {10.1145/3594536.3595132},
abstract = {Sentence Boundary Detection (SBD) is one of the foundational building blocks of Natural Language Processing (NLP), with incorrectly split sentences heavily influencing the output quality of downstream tasks. It is a challenging task for algorithms, especially in the legal domain, considering the complex and different sentence structures used. In this work, we curated a diverse multilingual legal dataset consisting of over 130'000 annotated sentences in 6 languages. Our experimental results indicate that the performance of existing SBD models is subpar on multilingual legal data. We trained and tested monolingual and multilingual models based on CRF, BiLSTM-CRF, and transformers, demonstrating state-of-the-art performance. We also show that our multilingual models outperform all baselines in the zero-shot setting on a Portuguese test set. To encourage further research and development by the community, we have made our dataset, models, and code publicly available.},
booktitle = {Proceedings of the Nineteenth International Conference on Artificial Intelligence and Law},
pages = {42–51},
numpages = {10},
keywords = {Natural Language Processing, Sentence Boundary Detection, Text Annotation, Legal Document Analysis, Multilingual},
location = {Braga, Portugal},
series = {ICAIL '23}
}
```
### Contributions
[More Information Needed] | rcds/MultiLegalSBD | [
"task_categories:token-classification",
"size_categories:100K<n<1M",
"language:en",
"language:es",
"language:de",
"language:it",
"language:pt",
"language:fr",
"region:us"
]
| 2023-01-10T15:17:41+00:00 | {"language": ["en", "es", "de", "it", "pt", "fr"], "size_categories": ["100K<n<1M"], "task_categories": ["token-classification"], "pretty_name": "MultiLegalSBD: A Multilingual Legal Sentence Boundary Detection Dataset", "dataset_info": [{"config_name": "fr_Laws", "features": [{"name": "text", "dtype": "string"}, {"name": "spans", "list": [{"name": "start", "dtype": "int64"}, {"name": "end", "dtype": "int64"}, {"name": "label", "dtype": "string"}, {"name": "token_start", "dtype": "int64"}, {"name": "token_end", "dtype": "int64"}]}, {"name": "tokens", "list": [{"name": "text", "dtype": "string"}, {"name": "start", "dtype": "int64"}, {"name": "end", "dtype": "int64"}, {"name": "id", "dtype": "int64"}, {"name": "ws", "dtype": "bool"}]}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 8773683, "num_examples": 2131}], "download_size": 0, "dataset_size": 8773683}, {"config_name": "it_Laws", "features": [{"name": "text", "dtype": 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"int64"}]}, {"name": "tokens", "list": [{"name": "text", "dtype": "string"}, {"name": "start", "dtype": "int64"}, {"name": "end", "dtype": "int64"}, {"name": "id", "dtype": "int64"}, {"name": "ws", "dtype": "bool"}]}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 13907572, "num_examples": 190}], "download_size": 2685340, "dataset_size": 13907572}, {"config_name": "en_laws", "features": [{"name": "text", "dtype": "string"}, {"name": "spans", "list": [{"name": "start", "dtype": "int64"}, {"name": "end", "dtype": "int64"}, {"name": "label", "dtype": "string"}, {"name": "token_start", "dtype": "int64"}, {"name": "token_end", "dtype": "int64"}]}, {"name": "tokens", "list": [{"name": "text", "dtype": "string"}, {"name": "start", "dtype": "int64"}, {"name": "end", "dtype": "int64"}, {"name": "id", "dtype": "int64"}, {"name": "ws", "dtype": "bool"}]}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train"}], "download_size": 0, "dataset_size": 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"download_size": 4138160, "dataset_size": 20493550}, {"config_name": "pt_laws", "features": [{"name": "text", "dtype": "string"}, {"name": "spans", "list": [{"name": "start", "dtype": "int64"}, {"name": "end", "dtype": "int64"}, {"name": "label", "dtype": "string"}, {"name": "token_start", "dtype": "int64"}, {"name": "token_end", "dtype": "int64"}]}, {"name": "tokens", "list": [{"name": "text", "dtype": "string"}, {"name": "start", "dtype": "int64"}, {"name": "end", "dtype": "int64"}, {"name": "id", "dtype": "int64"}, {"name": "ws", "dtype": "bool"}]}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1005902, "num_examples": 58}], "download_size": 209128, "dataset_size": 1005902}, {"config_name": "pt_judgements", "features": [{"name": "text", "dtype": "string"}, {"name": "spans", "list": [{"name": "start", "dtype": "int64"}, {"name": "end", "dtype": "int64"}, {"name": "label", "dtype": "string"}, {"name": "token_start", "dtype": "int64"}, {"name": "token_end", "dtype": "int64"}]}, {"name": "tokens", "list": [{"name": "text", "dtype": "string"}, {"name": "start", "dtype": "int64"}, {"name": "end", "dtype": "int64"}, {"name": "id", "dtype": "int64"}, {"name": "ws", "dtype": "bool"}]}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 812282, "num_examples": 10}], "download_size": 173424, "dataset_size": 812282}, {"config_name": "pt_all", "features": [{"name": "text", "dtype": "string"}, {"name": "spans", "list": [{"name": "start", "dtype": "int64"}, {"name": "end", "dtype": "int64"}, {"name": "label", "dtype": "string"}, {"name": "token_start", "dtype": "int64"}, {"name": "token_end", "dtype": "int64"}]}, {"name": "tokens", "list": [{"name": "text", "dtype": "string"}, {"name": "start", "dtype": "int64"}, {"name": "end", "dtype": "int64"}, {"name": "id", "dtype": "int64"}, {"name": "ws", "dtype": "bool"}]}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1818184, "num_examples": 68}], "download_size": 382552, "dataset_size": 1818184}, {"config_name": "all_laws", "features": [{"name": "text", "dtype": "string"}, {"name": "spans", "list": [{"name": "start", "dtype": "int64"}, {"name": "end", "dtype": "int64"}, {"name": "label", "dtype": "string"}, {"name": "token_start", "dtype": "int64"}, {"name": "token_end", "dtype": "int64"}]}, {"name": "tokens", "list": [{"name": "text", "dtype": "string"}, {"name": "start", "dtype": "int64"}, {"name": "end", "dtype": "int64"}, {"name": "id", "dtype": "int64"}, {"name": "ws", "dtype": "bool"}]}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 54918438, "num_examples": 5789}], "download_size": 9958380, "dataset_size": 54918438}, {"config_name": "all_judgements", "features": [{"name": "text", "dtype": "string"}, {"name": "spans", "list": [{"name": "start", "dtype": "int64"}, {"name": "end", "dtype": "int64"}, {"name": "label", "dtype": "string"}, {"name": "token_start", "dtype": "int64"}, {"name": "token_end", "dtype": "int64"}]}, {"name": "tokens", "list": [{"name": "text", "dtype": "string"}, {"name": "start", "dtype": "int64"}, {"name": "end", "dtype": "int64"}, {"name": "id", "dtype": "int64"}, {"name": "ws", "dtype": "bool"}]}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 88858845, "num_examples": 969}], "download_size": 17588440, "dataset_size": 88858845}, {"config_name": "all_all", "features": [{"name": "text", "dtype": "string"}, {"name": "spans", "list": [{"name": "start", "dtype": "int64"}, {"name": "end", "dtype": "int64"}, {"name": "label", "dtype": "string"}, {"name": "token_start", "dtype": "int64"}, {"name": "token_end", "dtype": "int64"}]}, {"name": "tokens", "list": [{"name": "text", "dtype": "string"}, {"name": "start", "dtype": "int64"}, {"name": "end", "dtype": "int64"}, {"name": "id", "dtype": "int64"}, {"name": "ws", "dtype": "bool"}]}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 143777284, "num_examples": 6758}], "download_size": 27546820, "dataset_size": 143777284}]} | 2023-10-23T05:36:36+00:00 |
4795741637529d511dde1fc6331e97e5087a7a2e |
# SUST BANGLA EMOTIONAL SPEECH CORPUS
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:** [SUBESCO PAPER](https://doi.org/10.1371/journal.pone.0250173)
- **Leaderboard:**
- **Point of Contact:** [Sadia Sultana]([email protected])
### Dataset Summary
SUBESCO is an audio-only emotional speech corpus of 7000 sentence-level utterances of the Bangla language. 20 professional actors (10 males and 10 females) participated in the recordings of 10 sentences for 7 target emotions. The emotions are Anger, Disgust, Fear, Happiness, Neutral, Sadness and Surprise. Total duration of the corpus is 7 hours 40 min 40 sec. Total size of the dataset is 2.03 GB. The dataset was evaluated by 50 raters (25 males, 25 females). Human perception test achieved a raw accuracy of 71%. All the details relating to creation, evaluation and analysis of SUBESCO have been described in the corresponding journal paper which has been published in Plos One.
https://doi.org/10.1371/journal.pone.0250173
### Downloading the data
```
from datasets import load_dataset
train = load_dataset("sustcsenlp/bn_emotion_speech_corpus",split="train")
```
### Naming Convention
Each audio file in the dataset has a unique name. There are eight parts in the file name where all the parts are connected by underscores. The order of all the parts is organized as: Gender-Speaker's serial number-Speaker's name-Unit of recording-Unit number- Emotion name- Repeating number and the File format.
For example, the filename F_02_MONIKA_S_1_NEUTRAL_5.wav refers to:
| Symbol | Meaning |
| ----------- | ----------- |
| F | Speaker Gender |
| 02 | Speaker Number |
| MONIKA | Speaker Name |
| S_1 | Sentence Number |
| NEUTRAL | Emotion |
| 5 | Take Number |
### Languages
This dataset contains Bangla Audio Data.
## Dataset Creation
This database was created as a part of PhD thesis project of the author Sadia Sultana. It was designed and developed by the author in the Department of Computer Science and Engineering of Shahjalal University of Science and Technology. Financial grant was supported by the university. If you use the dataset please cite SUBESCO and the corresponding academic journal publication in Plos One.
### Citation Information
```
@dataset{sadia_sultana_2021_4526477,
author = {Sadia Sultana},
title = {SUST Bangla Emotional Speech Corpus (SUBESCO)},
month = feb,
year = 2021,
note = {{This database was created as a part of PhD thesis
project of the author Sadia Sultana. It was
designed and developed by the author in the
Department of Computer Science and Engineering of
Shahjalal University of Science and Technology.
Financial grant was supported by the university.
If you use the dataset please cite SUBESCO and the
corresponding academic journal publication in Plos
One.}},
publisher = {Zenodo},
version = {version - 1.1},
doi = {10.5281/zenodo.4526477},
url = {https://doi.org/10.5281/zenodo.4526477}
}
```
### Contributors
| Name | University |
| ----------- | ----------- |
| Sadia Sultana | Shahjalal University of Science and Technology |
| Dr. M. Zafar Iqbal | Shahjalal University of Science and Technology |
| Dr. M. Shahidur Rahman | Shahjalal University of Science and Technology |
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed] | sustcsenlp/bn_emotion_speech_corpus | [
"task_categories:audio-classification",
"size_categories:1K<n<10K",
"language:bn",
"license:cc-by-4.0",
"region:us"
]
| 2023-01-10T15:49:12+00:00 | {"language": ["bn"], "license": "cc-by-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["audio-classification"], "pretty_name": "SUST BANGLA EMOTIONAL SPEECH CORPUS"} | 2023-01-11T09:00:32+00:00 |
7782b8adf91665e12404934ca540f2f5dd69452c |
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | 3DJedi/56chevy | [
"region:us"
]
| 2023-01-10T15:50:12+00:00 | {} | 2023-02-01T14:49:50+00:00 |
f3904bec5e6cccbab8b72ae1d1cdde34a71966d6 | Ineract/policies-named-insured | [
"region:us"
]
| 2023-01-10T16:09:19+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}], "config_name": "plain_text", "splits": [{"name": "train", "num_bytes": 3245009, "num_examples": 7632}, {"name": "test", "num_bytes": 359230, "num_examples": 849}], "download_size": 5007313, "dataset_size": 3604239}} | 2023-01-10T16:32:07+00:00 |
|
feaca17fe11bd7d7c22991c0ec165bfd4d1ec2bf | Saim8250/cat | [
"license:openrail",
"region:us"
]
| 2023-01-10T19:25:03+00:00 | {"license": "openrail"} | 2023-01-10T19:25:03+00:00 |
|
6333d780489d0b6d47d73e33acaac260c3d12fdd |
# Malicious Smart Contract Classification Dataset
This dataset includes malicious and benign smart contracts deployed on Ethereum.
Code used to collect this data: [data collection notebook](https://github.com/forta-network/starter-kits/blob/main/malicious-smart-contract-ml-py/data_collection.ipynb)
For more details on how this dataset can be used, please check out this blog: [How Forta’s Predictive ML Models Detect Attacks Before Exploitation](https://forta.org/blog/how-fortas-predictive-ml-models-detect-attacks-before-exploitation/) | forta/malicious-smart-contract-dataset | [
"task_categories:token-classification",
"size_categories:100K<n<1M",
"license:mit",
"smart contract",
"ethereum",
"blockchain",
"security",
"region:us"
]
| 2023-01-10T20:17:11+00:00 | {"license": "mit", "size_categories": ["100K<n<1M"], "task_categories": ["token-classification"], "pretty_name": "Malicious Smart Contract Classification Dataset", "tags": ["smart contract", "ethereum", "blockchain", "security"]} | 2023-01-10T22:03:23+00:00 |
b8441b5e8629b8f55540e14b6ded5d895c6b30e3 | # Dataset Card for "raw-commit-diffs"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | mamiksik/raw-commit-diffs | [
"region:us"
]
| 2023-01-10T21:38:14+00:00 | {"dataset_info": {"features": [{"name": "language", "dtype": "string"}, {"name": "owner", "dtype": "string"}, {"name": "repo", "dtype": "string"}, {"name": "sha", "dtype": "string"}, {"name": "message", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "patch", "dtype": "string"}, {"name": "is_multipart", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 791921294, "num_examples": 399253}], "download_size": 240089156, "dataset_size": 791921294}} | 2023-01-17T14:32:41+00:00 |
74bce39c64cc079ebcd96e2cd0787be80e50c88f | # Dataset Card for "analysed-diff-metadata"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | mamiksik/annotated-diff-metadata | [
"region:us"
]
| 2023-01-10T21:56:25+00:00 | {"dataset_info": {"features": [{"name": "sha", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "committer", "dtype": "string"}, {"name": "message", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "subject_length", "dtype": "float64"}, {"name": "is_chore", "dtype": "bool"}, {"name": "is_bot", "dtype": "bool"}, {"name": "subject_word_count", "dtype": "float64"}, {"name": "verb_object_spacy", "dtype": "bool"}, {"name": "verb_object_stanza", "dtype": "bool"}, {"name": "fits_requirements", "dtype": "bool"}, {"name": "owner", "dtype": "string"}, {"name": "repo", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 221089223, "num_examples": 668743}], "download_size": 0, "dataset_size": 221089223}} | 2023-01-10T22:04:53+00:00 |
50caa25433c1c98d894fb153d7e53cc49b3310b7 | # Dataset Card for "bookcorpus_compact_1024_shard9_of_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | saibo/bookcorpus_compact_1024_shard9_of_10 | [
"region:us"
]
| 2023-01-10T22:07:44+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "concept_with_offset", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 754029555, "num_examples": 61605}], "download_size": 379859859, "dataset_size": 754029555}} | 2023-01-10T22:08:25+00:00 |
c9d8830c08ec15269b2ef700f1cfbf5c0ce11c8a | # Dataset Card for "talkrl-podcast"
This dataset is sourced from the [TalkRL Podcast website](https://www.talkrl.com/) and contains English transcripts of wonderful TalkRL podcast episodes. The transcripts were generated using OpenAI's base Whisper model | RamAnanth1/talkrl-podcast | [
"task_categories:text-classification",
"task_categories:text-generation",
"task_categories:summarization",
"size_categories:n<1K",
"language:en",
"region:us"
]
| 2023-01-10T23:09:01+00:00 | {"language": ["en"], "size_categories": ["n<1K"], "task_categories": ["text-classification", "text-generation", "summarization"], "pretty_name": "TalkRL Podcast", "dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "link", "dtype": "string"}, {"name": "transcript", "dtype": "string"}, {"name": "segments", "list": [{"name": "end", "dtype": "float64"}, {"name": "start", "dtype": "float64"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 4845076, "num_examples": 39}], "download_size": 2633561, "dataset_size": 4845076}} | 2023-01-12T20:46:26+00:00 |
9e8a752bcb4c44ee767c0a31d65668691414fe83 |
If you prefer MIDI or MusicXML, download [IrishMAN-MIDI](https://huggingface.co/datasets/sander-wood/irishman/resolve/main/irishman-midi.zip) or [IrishMAN-XML](https://huggingface.co/datasets/sander-wood/irishman/resolve/main/irishman-xml.zip). For better use of structural info in control codes, consider ABC notation.
## ABC Notation
ABC notation is an ASCII-based plain text musical notation system that is commonly used for transcribing traditional music and sharing sheet music online. It provides a simple and concise way to represent musical elements such as notes, rhythms, chords, and more.
For those looking to interact with ABC notation in various ways, there are several tools available:
1. **[Online ABC Player](https://abc.rectanglered.com/):** This web-based tool allows you to input ABC notation and hear the corresponding audio playback. By pasting your ABC code into the player, you can instantly listen to the tune as it would sound when played.
2. **[ABC Sheet Music Editor - EasyABC](https://easyabc.sourceforge.net/):** EasyABC is a user-friendly software application designed for creating, editing, and formatting ABC notation. Its graphical interface enables you to input your ABC code, preview the sheet music, and make adjustments as necessary.
## Dataset Summary
The **Irish Massive ABC Notation (IrishMAN)** dataset includes 216,284 Irish tunes in ABC notation, divided into 99\% (214,122 tunes) for training and 1\% (2,162 tunes) for validation. These tunes were collected from thesession.org and abcnotation.com, both renowned for sharing traditional music. To ensure uniformity in formatting, all tunes were converted to XML and then back to ABC using [scripts](https://wim.vree.org/svgParse/), and fields containing natural language (e.g., titles and lyrics) were removed.
Each tune is automatically annotated with control codes derived from ABC symbols, as described in the below section. These control codes offer insights into the musical forms and structures of each composition.
In the IrishMAN dataset, a [music21](https://web.mit.edu/music21/doc/index.html#)-filtered [subset](https://huggingface.co/datasets/sander-wood/irishman/raw/main/leadsheet_ids.json) includes 34,211 lead sheets, each human-annotated with chord symbols. It is from this very subset that [TunesFormer](https://huggingface.co/sander-wood/tunesformer) developed its capacity to generate melodies with harmonies.
A noteworthy aspect is the copyright status. All tunes in the dataset are in the public domain, ensuring ethical and legal usage for research and creative projects.
## Control Codes
Inspired by [CTRL](https://huggingface.co/ctrl), we incorporate control codes into TunesFormer to represent musical forms. These codes, positioned ahead of the ABC notation, enable users to specify the structures of the generated tunes. The following control codes are introduced:
- **S:number of sections**: determines the number of sections in the entire melody. It counts on several symbols that can be used to represent section boundaries: `[|`, `||`, `|]`, `|:`, `::`, and `:|`. In our dataset, the range is 1 to 8 (e.g., `S:1` for a single-section melody, and `S:8` for a melody with eight sections).
- **B:number of bars**: specifies the desired number of bars within a section. It counts on the bar symbol `|`. In our dataset, the range is 1 to 32 (e.g., `B:1` for a one-bar section, and `B:32` for a section with 32 bars).
- **E:edit distance similarity**: controls the similarity level between the current section $c$ and a previous section $p$ in the melody. It is based on the Levenshtein distance $lev(c,p)$ , quantifying the difference between sections for creating variations or contrasts. Mathematically, it can be expressed as:
```
eds(c,p) = 1 - lev(c,p) / max(|c|,|p|)
```
where $|c|$ and $|p|$ are the string lengths of the two sections. It is discretized into 11 levels, ranging from no match at all to an exact match (e.g., `E:0` for no similarity, and `E:10` for an exact match).
## Copyright Disclaimer
This dataset is for research use only and not for commercial purposes. We believe all data in this dataset is in the public domain. If you own the copyright to any musical composition in the IrishMAN dataset and have concerns, please contact us at [email protected]. We will address your concerns and take appropriate action if needed.
## Special Thanks
We would like to extend a special thanks to thesession.org and abcnotation.com for their contributions to the development and promotion of ABC notation, as well as their significant impact on the field of music information retrieval. Their platforms have become invaluable resources for the traditional and folk music community. We also wish to express our gratitude to Willem (Wim) for providing the essential conversion tools that enabled the transformation of the tunes into a uniform format. Together, these collaborations have made it possible for researchers like us to create and study extensive datasets like IrishMAN. | sander-wood/irishman | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"license:mit",
"music",
"region:us"
]
| 2023-01-10T23:42:04+00:00 | {"license": "mit", "size_categories": ["100K<n<1M"], "task_categories": ["text-generation"], "pretty_name": "IrishMAN", "tags": ["music"]} | 2023-09-25T14:14:16+00:00 |
a3fa717ef08fe2cbb8a52bc3ca25925029148538 | # Dataset Card for "chocolate-captioned-dataset-100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Umal-exvc/chocolate-captioned-dataset-100 | [
"region:us"
]
| 2023-01-11T01:49:10+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 26453719.0, "num_examples": 100}], "download_size": 26029410, "dataset_size": 26453719.0}} | 2023-01-11T01:49:25+00:00 |
bb8926f23b50beded63ab70356227fccf1710ba1 | # Dataset Card for "chocolate-captioned-dataset-200"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Umal-exvc/chocolate-captioned-dataset-200 | [
"region:us"
]
| 2023-01-11T01:52:08+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 39998461.0, "num_examples": 200}], "download_size": 39150206, "dataset_size": 39998461.0}} | 2023-01-11T01:52:24+00:00 |
d958811dd03a784c15920a9c7c4bc2d51525cd41 | # Dataset Card for "chocolate-captioned-dataset-300"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Umal-exvc/chocolate-captioned-dataset-300 | [
"region:us"
]
| 2023-01-11T01:54:22+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 51486341.0, "num_examples": 300}], "download_size": 50401727, "dataset_size": 51486341.0}} | 2023-01-11T01:54:38+00:00 |
a487f2ad4035ef84f49da42ae8029aa6c07d4e1c | # Dataset Card for "chocolate-captioned-dataset-400"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Umal-exvc/chocolate-captioned-dataset-400 | [
"region:us"
]
| 2023-01-11T01:56:49+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 64772495.0, "num_examples": 400}], "download_size": 63382786, "dataset_size": 64772495.0}} | 2023-01-11T01:57:06+00:00 |
1a9451f9fb3169f555d65e1a2c5176c4b93319a6 | juancopi81/jcpvincentcat | [
"license:openrail",
"region:us"
]
| 2023-01-11T01:57:34+00:00 | {"license": "openrail"} | 2023-01-11T01:59:11+00:00 |
|
3c82e39ec604991696cf2016251bfafee91144bf | LeoDarkWolf/Waifu | [
"license:unknown",
"region:us"
]
| 2023-01-11T02:06:09+00:00 | {"license": "unknown"} | 2023-01-11T02:21:14+00:00 |
|
6c4a15e5a51f3653663e18d47d36287f84239b98 | juancopi81/jcp-vincent-cat | [
"license:openrail",
"region:us"
]
| 2023-01-11T02:45:30+00:00 | {"license": "openrail"} | 2023-01-11T02:46:46+00:00 |
|
70462a61bd0bd7fda53096eed888b50f03d23823 | # AutoTrain Dataset for project: hannah-training-demo
## Dataset Description
This dataset has been automatically processed by AutoTrain for project hannah-training-demo.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<842x1392 RGBA PIL image>",
"target": 0
},
{
"image": "<1004x1516 RGBA PIL image>",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['hannah'], 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 | 7 |
| valid | 7 |
| slushily/autotrain-data-hannah-training-demo | [
"task_categories:image-classification",
"region:us"
]
| 2023-01-11T03:41:05+00:00 | {"task_categories": ["image-classification"]} | 2023-01-11T03:42:50+00:00 |
4ce4db8035a9ab9b968ee4a6329f7a724a963cca | # Dataset Card for "Lemon_filter"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | akshaypt7/Lemon_filter | [
"region:us"
]
| 2023-01-11T04:49:43+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 936008.0, "num_examples": 30}], "download_size": 0, "dataset_size": 936008.0}} | 2023-01-18T06:23:48+00:00 |
072a8488c9526cb4d6a49cf08f7edb22751de2ad | JacobPerera/website_design_mockups | [
"region:us"
]
| 2023-01-11T07:04:14+00:00 | {} | 2023-01-11T11:39:28+00:00 |
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