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357075831a7343a3f11cc92a0a082b52c0ad7bd7 | # Dataset Card for "Dialogsum-german"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/Dialogsum-german | [
"region:us"
]
| 2023-03-27T18:40:46+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "dialogue", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "topic", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 12886236, "num_examples": 12460}, {"name": "test", "num_bytes": 1539886, "num_examples": 1500}, {"name": "validation", "num_bytes": 504815, "num_examples": 500}], "download_size": 7946878, "dataset_size": 14930937}} | 2023-03-27T18:41:04+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "Dialogsum-german"
More Information needed | [
"# Dataset Card for \"Dialogsum-german\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"Dialogsum-german\"\n\nMore Information needed"
]
| [
6,
16
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"passage: TAGS\n#region-us \n# Dataset Card for \"Dialogsum-german\"\n\nMore Information needed"
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|
b25db74879f6117366c0057cee030fe19cc07a7f | # Dataset Card for "Dialogsum-german-kurz"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/Dialogsum-german-kurz | [
"region:us"
]
| 2023-03-27T18:43:48+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "dialogue", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "topic", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9824979.293739967, "num_examples": 9500}, {"name": "test", "num_bytes": 1539886, "num_examples": 1500}, {"name": "validation", "num_bytes": 504815, "num_examples": 500}], "download_size": 6219922, "dataset_size": 11869680.293739967}} | 2023-03-27T18:44:06+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "Dialogsum-german-kurz"
More Information needed | [
"# Dataset Card for \"Dialogsum-german-kurz\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"Dialogsum-german-kurz\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"Dialogsum-german-kurz\"\n\nMore Information needed"
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|
8299ac2331e961452451d08e4055c96b22351e97 | # Dataset Validated from https://huggingface.co/spaces/dariolopez/argilla-reddit-c-ssrs-suicide-dataset-es
https://huggingface.co/spaces/dariolopez/argilla-reddit-c-ssrs-suicide-dataset-es | dariolopez/argilla-reddit-c-ssrs-suicide-dataset-es-old | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:es",
"region:us"
]
| 2023-03-27T19:19:14+00:00 | {"language": ["es"], "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "inputs", "struct": [{"name": "text", "dtype": "string"}]}, {"name": "prediction", "dtype": "null"}, {"name": "prediction_agent", "dtype": "null"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "all-MiniLM-L6-v2", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 60733, "num_examples": 14}], "download_size": 61140, "dataset_size": 60733}} | 2023-03-28T21:21:24+00:00 | []
| [
"es"
]
| TAGS
#task_categories-text-classification #size_categories-1K<n<10K #language-Spanish #region-us
| # Dataset Validated from URL
URL | [
"# Dataset Validated from URL\n\nURL"
]
| [
"TAGS\n#task_categories-text-classification #size_categories-1K<n<10K #language-Spanish #region-us \n",
"# Dataset Validated from URL\n\nURL"
]
| [
34,
9
]
| [
"passage: TAGS\n#task_categories-text-classification #size_categories-1K<n<10K #language-Spanish #region-us \n# Dataset Validated from URL\n\nURL"
]
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|
e6a6dbbccf7cad2e057770cae5ebf8257ec36382 |
# Dataset Card for Dataset Name
## Dataset Description
- **Repository:** https://github.com/vladsavelyev/guitart
### Dataset Summary
GuitarPro files with song tablatures, converted into [alphaTex](https://alphatab.net/docs/alphatex/introduction) format using this Python [converter](https://github.com/vladsavelyev/guitartab/blob/main/gp_to_tex.py).
### Supported Tasks and Leaderboards
Supported are NLP tasks, and potentially could be augmented with audio and used to auto-generate tabs from music.
### Source Data
GuitarPro tabs archive. | vldsavelyev/guitar_tab | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"music",
"music-notation",
"guitar",
"tablature",
"region:us"
]
| 2023-03-27T20:03:23+00:00 | {"size_categories": ["10K<n<100K"], "task_categories": ["text-generation"], "tags": ["music", "music-notation", "guitar", "tablature"], "dataset_info": [{"config_name": "default", "features": [{"name": "text", "dtype": "string"}, {"name": "file", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 961930414, "num_examples": 47299}], "download_size": 75369951, "dataset_size": 961930414}, {"config_name": "all", "features": [{"name": "file", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "track_name", "dtype": "string"}, {"name": "track_number", "dtype": "int32"}, {"name": "instrument_name", "dtype": "string"}, {"name": "instrument_number", "dtype": "int32"}, {"name": "tempo", "dtype": "int32"}, {"name": "tuning", "dtype": "string"}, {"name": "frets", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 951605310, "num_examples": 188297}], "download_size": 75369951, "dataset_size": 951605310}]} | 2023-04-09T09:45:36+00:00 | []
| []
| TAGS
#task_categories-text-generation #size_categories-10K<n<100K #music #music-notation #guitar #tablature #region-us
|
# Dataset Card for Dataset Name
## Dataset Description
- Repository: URL
### Dataset Summary
GuitarPro files with song tablatures, converted into alphaTex format using this Python converter.
### Supported Tasks and Leaderboards
Supported are NLP tasks, and potentially could be augmented with audio and used to auto-generate tabs from music.
### Source Data
GuitarPro tabs archive. | [
"# Dataset Card for Dataset Name",
"## Dataset Description\n\n- Repository: URL",
"### Dataset Summary\n\nGuitarPro files with song tablatures, converted into alphaTex format using this Python converter.",
"### Supported Tasks and Leaderboards\n\nSupported are NLP tasks, and potentially could be augmented with audio and used to auto-generate tabs from music.",
"### Source Data\n\nGuitarPro tabs archive."
]
| [
"TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #music #music-notation #guitar #tablature #region-us \n",
"# Dataset Card for Dataset Name",
"## Dataset Description\n\n- Repository: URL",
"### Dataset Summary\n\nGuitarPro files with song tablatures, converted into alphaTex format using this Python converter.",
"### Supported Tasks and Leaderboards\n\nSupported are NLP tasks, and potentially could be augmented with audio and used to auto-generate tabs from music.",
"### Source Data\n\nGuitarPro tabs archive."
]
| [
43,
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| [
"passage: TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #music #music-notation #guitar #tablature #region-us \n# Dataset Card for Dataset Name## Dataset Description\n\n- Repository: URL### Dataset Summary\n\nGuitarPro files with song tablatures, converted into alphaTex format using this Python converter.### Supported Tasks and Leaderboards\n\nSupported are NLP tasks, and potentially could be augmented with audio and used to auto-generate tabs from music.### Source Data\n\nGuitarPro tabs archive."
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|
9a067ff2191e090881b807d067a4720a9d90f5bd | # Dataset Card for "simple_arch_BLIP_captions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ossaili/simple_arch_BLIP_captions | [
"region:us"
]
| 2023-03-27T20:10:56+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 417750052.2, "num_examples": 1400}], "download_size": 383610787, "dataset_size": 417750052.2}} | 2023-03-27T20:12:38+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "simple_arch_BLIP_captions"
More Information needed | [
"# Dataset Card for \"simple_arch_BLIP_captions\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"simple_arch_BLIP_captions\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"simple_arch_BLIP_captions\"\n\nMore Information needed"
]
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|
b1cec4149933b09594c4b7a837e7263942449147 |
# Leyzer: A Dataset for Multilingual Virtual Assistants
Leyzer is a multilingual text corpus designed to study multilingual and cross-lingual natural language understanding (NLU) models and the strategies of localization of
virtual assistants. It consists of 20 domains across three languages: English, Spanish and Polish, with 186 intents and a wide range of samples, ranging from 1 to 672
sentences per intent. For more stats please refer to wiki.
| cartesinus/leyzer-fedcsis-translated | [
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:pl",
"license:cc-by-4.0",
"natural-language-understanding",
"region:us"
]
| 2023-03-27T20:51:34+00:00 | {"language": ["pl"], "license": "cc-by-4.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"], "tags": ["natural-language-understanding"]} | 2023-03-27T20:52:34+00:00 | []
| [
"pl"
]
| TAGS
#task_categories-text-classification #size_categories-10K<n<100K #language-Polish #license-cc-by-4.0 #natural-language-understanding #region-us
|
# Leyzer: A Dataset for Multilingual Virtual Assistants
Leyzer is a multilingual text corpus designed to study multilingual and cross-lingual natural language understanding (NLU) models and the strategies of localization of
virtual assistants. It consists of 20 domains across three languages: English, Spanish and Polish, with 186 intents and a wide range of samples, ranging from 1 to 672
sentences per intent. For more stats please refer to wiki.
| [
"# Leyzer: A Dataset for Multilingual Virtual Assistants\nLeyzer is a multilingual text corpus designed to study multilingual and cross-lingual natural language understanding (NLU) models and the strategies of localization of\nvirtual assistants. It consists of 20 domains across three languages: English, Spanish and Polish, with 186 intents and a wide range of samples, ranging from 1 to 672\nsentences per intent. For more stats please refer to wiki."
]
| [
"TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #language-Polish #license-cc-by-4.0 #natural-language-understanding #region-us \n",
"# Leyzer: A Dataset for Multilingual Virtual Assistants\nLeyzer is a multilingual text corpus designed to study multilingual and cross-lingual natural language understanding (NLU) models and the strategies of localization of\nvirtual assistants. It consists of 20 domains across three languages: English, Spanish and Polish, with 186 intents and a wide range of samples, ranging from 1 to 672\nsentences per intent. For more stats please refer to wiki."
]
| [
50,
105
]
| [
"passage: TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #language-Polish #license-cc-by-4.0 #natural-language-understanding #region-us \n# Leyzer: A Dataset for Multilingual Virtual Assistants\nLeyzer is a multilingual text corpus designed to study multilingual and cross-lingual natural language understanding (NLU) models and the strategies of localization of\nvirtual assistants. It consists of 20 domains across three languages: English, Spanish and Polish, with 186 intents and a wide range of samples, ranging from 1 to 672\nsentences per intent. For more stats please refer to wiki."
]
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]
|
b81defe139dc7767929331e1adf205233f8d409c | This dataset splits the original [Self-instruct dataset](https://huggingface.co/datasets/yizhongw/self_instruct) into training (90%) and test (10%). | HuggingFaceH4/self_instruct | [
"task_categories:text-generation",
"license:apache-2.0",
"region:us"
]
| 2023-03-27T20:58:57+00:00 | {"license": "apache-2.0", "task_categories": ["text-generation"]} | 2023-03-27T21:03:01+00:00 | []
| []
| TAGS
#task_categories-text-generation #license-apache-2.0 #region-us
| This dataset splits the original Self-instruct dataset into training (90%) and test (10%). | []
| [
"TAGS\n#task_categories-text-generation #license-apache-2.0 #region-us \n"
]
| [
25
]
| [
"passage: TAGS\n#task_categories-text-generation #license-apache-2.0 #region-us \n"
]
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-0.1063760370016098
]
|
164602cc456281103450cfc477698164218eff46 | This splits the original [helpful_instructions dataset](https://huggingface.co/datasets/HuggingFaceH4/helpful_instructions/blob/main/create_dataset.py) into train and test splits. | HuggingFaceH4/helpful_instructions_splits | [
"region:us"
]
| 2023-03-27T21:25:51+00:00 | {} | 2023-03-27T21:36:16+00:00 | []
| []
| TAGS
#region-us
| This splits the original helpful_instructions dataset into train and test splits. | []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
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f4d01d5c02b9a4db238eecfa0728ce443885189c | # Dataset Card for "StanfordCars_test_google_flan_t5_xxl_mode_C_A_T_ns_8041"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | CVasNLPExperiments/StanfordCars_test_google_flan_t5_xxl_mode_C_A_T_ns_8041 | [
"region:us"
]
| 2023-03-27T21:32:49+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 3519798, "num_examples": 8041}, {"name": "fewshot_1_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 6705042, "num_examples": 8041}], "download_size": 2725283, "dataset_size": 10224840}} | 2023-03-28T17:27:21+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "StanfordCars_test_google_flan_t5_xxl_mode_C_A_T_ns_8041"
More Information needed | [
"# Dataset Card for \"StanfordCars_test_google_flan_t5_xxl_mode_C_A_T_ns_8041\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"StanfordCars_test_google_flan_t5_xxl_mode_C_A_T_ns_8041\"\n\nMore Information needed"
]
| [
6,
40
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"StanfordCars_test_google_flan_t5_xxl_mode_C_A_T_ns_8041\"\n\nMore Information needed"
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|
c1017998145e19d43769e2789fffa02db258605a | # Dataset Card for [GPT4All Prompt Generations]
## Dataset Description
Dataset used to train [GPT4All](https://huggingface.co/nomic-ai/gpt4all-lora)
- **Homepage:**
- **Repository:** [gpt4all](https://github.com/nomic-ai/gpt4all)
- **Paper:** [Technical Report](https://s3.amazonaws.com/static.nomic.ai/gpt4all/2023_GPT4All_Technical_Report.pdf)
- **Atlas Map:** [Map of Cleaned Data](https://atlas.nomic.ai/map/gpt4all_data_clean)
| nomic-ai/gpt4all_prompt_generations | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"region:us"
]
| 2023-03-27T22:08:01+00:00 | {"language": ["en"], "license": "apache-2.0", "size_categories": ["100K<n<1M"], "task_categories": ["text-generation"], "dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 782175193, "num_examples": 437604}], "download_size": 397878357, "dataset_size": 782175193}} | 2023-04-13T20:42:15+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-generation #size_categories-100K<n<1M #language-English #license-apache-2.0 #region-us
| # Dataset Card for [GPT4All Prompt Generations]
## Dataset Description
Dataset used to train GPT4All
- Homepage:
- Repository: gpt4all
- Paper: Technical Report
- Atlas Map: Map of Cleaned Data
| [
"# Dataset Card for [GPT4All Prompt Generations]",
"## Dataset Description\n\nDataset used to train GPT4All\n\n- Homepage:\n- Repository: gpt4all\n- Paper: Technical Report\n- Atlas Map: Map of Cleaned Data"
]
| [
"TAGS\n#task_categories-text-generation #size_categories-100K<n<1M #language-English #license-apache-2.0 #region-us \n",
"# Dataset Card for [GPT4All Prompt Generations]",
"## Dataset Description\n\nDataset used to train GPT4All\n\n- Homepage:\n- Repository: gpt4all\n- Paper: Technical Report\n- Atlas Map: Map of Cleaned Data"
]
| [
41,
16,
40
]
| [
"passage: TAGS\n#task_categories-text-generation #size_categories-100K<n<1M #language-English #license-apache-2.0 #region-us \n# Dataset Card for [GPT4All Prompt Generations]## Dataset Description\n\nDataset used to train GPT4All\n\n- Homepage:\n- Repository: gpt4all\n- Paper: Technical Report\n- Atlas Map: Map of Cleaned Data"
]
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|
021c28e350f0f0d1d01e8607232782b8660f70ff | # Dataset Card for "indian_food_images"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | MohitK/indian_food_images | [
"region:us"
]
| 2023-03-27T23:42:24+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "burger", "1": "butter_naan", "2": "chai", "3": "chapati", "4": "chole_bhature", "5": "dal_makhani", "6": "dhokla", "7": "fried_rice", "8": "idli", "9": "jalebi", "10": "kaathi_rolls", "11": "kadai_paneer", "12": "kulfi", "13": "masala_dosa", "14": "momos", "15": "paani_puri", "16": "pakode", "17": "pav_bhaji", "18": "pizza", "19": "samosa"}}}}], "splits": [{"name": "train", "num_bytes": 1622005508.6394334, "num_examples": 5328}, {"name": "test", "num_bytes": 251982030.3925666, "num_examples": 941}], "download_size": 1599894487, "dataset_size": 1873987539.032}} | 2023-03-27T23:52:34+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "indian_food_images"
More Information needed | [
"# Dataset Card for \"indian_food_images\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"indian_food_images\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"indian_food_images\"\n\nMore Information needed"
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|
27e5db83dc96df3e12700fd1202b0cd72d9c3be5 | # Dataset Card for "logo-combined-weighted-a"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Babypotatotang/logo-combined-weighted-a | [
"region:us"
]
| 2023-03-28T00:37:18+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 167421354.253, "num_examples": 12907}, {"name": "test", "num_bytes": 42192314.76, "num_examples": 3232}], "download_size": 209236463, "dataset_size": 209613669.01299998}} | 2023-03-28T00:42:05+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "logo-combined-weighted-a"
More Information needed | [
"# Dataset Card for \"logo-combined-weighted-a\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"logo-combined-weighted-a\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"logo-combined-weighted-a\"\n\nMore Information needed"
]
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|
4f80c4fbd47e62268ed900d269aa00f097bf1b7a | __The Online Indonesian Learning (OIL) Dataset__
The Online Indonesian Learning (OIL) dataset or corpus currently contains lessons from three Indonesian teachers who have posted content on YouTube.
For further details please see Zara Maxwell-Smith and Ben Foley, (forthcoming), Automated speech recognition of Indonesian-English language lessons on YouTube using transfer learning, Field Matters Workshop, EACL 2023
How to cite this dataset.
Please use the following .bib to reference this work.
```
{@inproceedings{Maxwell-Smith_Foley_2023_Automated,
title={{Automated speech recognition of Indonesian-English language lessons on YouTube using transfer learning}},
author={Maxwell-Smith, Zara and Foley, Ben},
booktitle={Proceedings of the {Second Workshop on NLP Applications to Field Linguistics (EACL)}},
pages={},
year={forthcoming}
}
```
To stream the videos of these teachers please visit:
Indonesian Language for Beginners - https://www.youtube.com/@learningindonesianlanguage3334
5-Minute Indonesian - https://www.youtube.com/@5-minuteindonesian534/featured
Dua Budaya - https://www.youtube.com/@DuaBudaya/about
Copies of some lessons on these channels are available as part of this dataset in mp4 and wav formats.
A select number of lessons have matching ELAN files with human and human/machine generated orthographic transcriptions of the audio, as well 'tiers' with machine inference only.
Detailed information about the audio quality, remarks on background noise, code-switching behaviour and lesson content is available in the paper above.
Almost all videos contain a mix of languages, with some dominated by Indonesian or English.
Some videos explicitly focused on variation in Indonesian or words from other languages which are commonly mixed into Indonesian by speakers.
| ZMaxwell-Smith/OIL | [
"license:cc-by-nc-nd-4.0",
"doi:10.57967/hf/0489",
"region:us"
]
| 2023-03-28T00:51:55+00:00 | {"license": "cc-by-nc-nd-4.0"} | 2023-03-30T00:40:05+00:00 | []
| []
| TAGS
#license-cc-by-nc-nd-4.0 #doi-10.57967/hf/0489 #region-us
| __The Online Indonesian Learning (OIL) Dataset__
The Online Indonesian Learning (OIL) dataset or corpus currently contains lessons from three Indonesian teachers who have posted content on YouTube.
For further details please see Zara Maxwell-Smith and Ben Foley, (forthcoming), Automated speech recognition of Indonesian-English language lessons on YouTube using transfer learning, Field Matters Workshop, EACL 2023
How to cite this dataset.
Please use the following .bib to reference this work.
To stream the videos of these teachers please visit:
Indonesian Language for Beginners - URL
5-Minute Indonesian - URL
Dua Budaya - URL
Copies of some lessons on these channels are available as part of this dataset in mp4 and wav formats.
A select number of lessons have matching ELAN files with human and human/machine generated orthographic transcriptions of the audio, as well 'tiers' with machine inference only.
Detailed information about the audio quality, remarks on background noise, code-switching behaviour and lesson content is available in the paper above.
Almost all videos contain a mix of languages, with some dominated by Indonesian or English.
Some videos explicitly focused on variation in Indonesian or words from other languages which are commonly mixed into Indonesian by speakers.
| []
| [
"TAGS\n#license-cc-by-nc-nd-4.0 #doi-10.57967/hf/0489 #region-us \n"
]
| [
31
]
| [
"passage: TAGS\n#license-cc-by-nc-nd-4.0 #doi-10.57967/hf/0489 #region-us \n"
]
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|
b26f790ab133ef8941f6aa08751b517ecfcfcd53 | # Dataset Card for "logo-combined-weighted-b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Babypotatotang/logo-combined-weighted-b | [
"region:us"
]
| 2023-03-28T00:56:32+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 167147766.253, "num_examples": 12907}, {"name": "test", "num_bytes": 42122543.76, "num_examples": 3232}], "download_size": 209180456, "dataset_size": 209270310.01299998}} | 2023-03-28T01:03:08+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "logo-combined-weighted-b"
More Information needed | [
"# Dataset Card for \"logo-combined-weighted-b\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"logo-combined-weighted-b\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"logo-combined-weighted-b\"\n\nMore Information needed"
]
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|
8a9f7515dbfb22867b00f5e2f2b82790108d4b98 | # Dataset Card for "emr_info_extract"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | arfu/emr_info_extract | [
"region:us"
]
| 2023-03-28T01:31:07+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "dialogue", "dtype": "string"}, {"name": "summary", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1842245, "num_examples": 2019}, {"name": "test", "num_bytes": 123971, "num_examples": 120}], "download_size": 440433, "dataset_size": 1966216}} | 2023-03-28T01:31:14+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "emr_info_extract"
More Information needed | [
"# Dataset Card for \"emr_info_extract\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"emr_info_extract\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"emr_info_extract\"\n\nMore Information needed"
]
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|
5e66c32b4f0d6c9f8a3ab137282d6734fb25f37d |
# Dataset Card for AdvertiseGen
- **formal url:** https://www.luge.ai/#/luge/dataDetail?id=9
## Dataset Description
数据集介绍
AdvertiseGen是电商广告文案生成数据集。
AdvertiseGen以商品网页的标签与文案的信息对应关系为基础构造，是典型的开放式生成任务，在模型基于key-value输入生成开放式文案时，与输入信息的事实一致性需要得到重点关注。
- 任务描述：给定商品信息的关键词和属性列表kv-list，生成适合该商品的广告文案adv；
- 数据规模：训练集114k，验证集1k，测试集3k；
- 数据来源：清华大学CoAI小组；
### Supported Tasks and Leaderboards
The dataset designed for generate e-commerce advertise.
### Languages
The data in AdvertiseGen are in Chinese.
## Dataset Structure
### Data Instances
An example of "train" looks as follows:
```json
{
"content": "类型#上衣*材质#牛仔布*颜色#白色*风格#简约*图案#刺绣*衣样式#外套*衣款式#破洞",
"summary": "简约而不简单的牛仔外套，白色的衣身十分百搭。衣身多处有做旧破洞设计，打破单调乏味，增加一丝造型看点。衣身后背处有趣味刺绣装饰，丰富层次感，彰显别样时尚。"
}
```
### Citation Information
数据集引用
如在学术论文中使用本数据集，请添加相关引用说明，具体如下：
```
Shao, Zhihong, et al. "Long and Diverse Text Generation with Planning-based Hierarchical Variational Model." Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
```
| shibing624/AdvertiseGen | [
"task_categories:text-generation",
"language:zh",
"license:cc-by-4.0",
"text-generation",
"e-commerce advertise",
"region:us"
]
| 2023-03-28T01:42:56+00:00 | {"language": ["zh"], "license": "cc-by-4.0", "task_categories": ["text-generation"], "pretty_name": "AdvertiseGen", "tags": ["text-generation", "e-commerce advertise"]} | 2023-05-12T06:25:00+00:00 | []
| [
"zh"
]
| TAGS
#task_categories-text-generation #language-Chinese #license-cc-by-4.0 #text-generation #e-commerce advertise #region-us
|
# Dataset Card for AdvertiseGen
- formal url: URL
## Dataset Description
数据集介绍
AdvertiseGen是电商广告文案生成数据集。
AdvertiseGen以商品网页的标签与文案的信息对应关系为基础构造，是典型的开放式生成任务，在模型基于key-value输入生成开放式文案时，与输入信息的事实一致性需要得到重点关注。
- 任务描述：给定商品信息的关键词和属性列表kv-list，生成适合该商品的广告文案adv；
- 数据规模：训练集114k，验证集1k，测试集3k；
- 数据来源：清华大学CoAI小组；
### Supported Tasks and Leaderboards
The dataset designed for generate e-commerce advertise.
### Languages
The data in AdvertiseGen are in Chinese.
## Dataset Structure
### Data Instances
An example of "train" looks as follows:
数据集引用
如在学术论文中使用本数据集，请添加相关引用说明，具体如下：
| [
"# Dataset Card for AdvertiseGen\n\n- formal url: URL",
"## Dataset Description\n数据集介绍\n\nAdvertiseGen是电商广告文案生成数据集。\n\nAdvertiseGen以商品网页的标签与文案的信息对应关系为基础构造，是典型的开放式生成任务，在模型基于key-value输入生成开放式文案时，与输入信息的事实一致性需要得到重点关注。\n\n\n- 任务描述：给定商品信息的关键词和属性列表kv-list，生成适合该商品的广告文案adv；\n- 数据规模：训练集114k，验证集1k，测试集3k；\n- 数据来源：清华大学CoAI小组；",
"### Supported Tasks and Leaderboards\n\nThe dataset designed for generate e-commerce advertise.",
"### Languages\n\nThe data in AdvertiseGen are in Chinese.",
"## Dataset Structure",
"### Data Instances\n\nAn example of \"train\" looks as follows:\n\n\n\n\n\n数据集引用\n\n如在学术论文中使用本数据集，请添加相关引用说明，具体如下："
]
| [
"TAGS\n#task_categories-text-generation #language-Chinese #license-cc-by-4.0 #text-generation #e-commerce advertise #region-us \n",
"# Dataset Card for AdvertiseGen\n\n- formal url: URL",
"## Dataset Description\n数据集介绍\n\nAdvertiseGen是电商广告文案生成数据集。\n\nAdvertiseGen以商品网页的标签与文案的信息对应关系为基础构造，是典型的开放式生成任务，在模型基于key-value输入生成开放式文案时，与输入信息的事实一致性需要得到重点关注。\n\n\n- 任务描述：给定商品信息的关键词和属性列表kv-list，生成适合该商品的广告文案adv；\n- 数据规模：训练集114k，验证集1k，测试集3k；\n- 数据来源：清华大学CoAI小组；",
"### Supported Tasks and Leaderboards\n\nThe dataset designed for generate e-commerce advertise.",
"### Languages\n\nThe data in AdvertiseGen are in Chinese.",
"## Dataset Structure",
"### Data Instances\n\nAn example of \"train\" looks as follows:\n\n\n\n\n\n数据集引用\n\n如在学术论文中使用本数据集，请添加相关引用说明，具体如下："
]
| [
41,
15,
135,
21,
15,
6,
42
]
| [
"passage: TAGS\n#task_categories-text-generation #language-Chinese #license-cc-by-4.0 #text-generation #e-commerce advertise #region-us \n# Dataset Card for AdvertiseGen\n\n- formal url: URL## Dataset Description\n数据集介绍\n\nAdvertiseGen是电商广告文案生成数据集。\n\nAdvertiseGen以商品网页的标签与文案的信息对应关系为基础构造，是典型的开放式生成任务，在模型基于key-value输入生成开放式文案时，与输入信息的事实一致性需要得到重点关注。\n\n\n- 任务描述：给定商品信息的关键词和属性列表kv-list，生成适合该商品的广告文案adv；\n- 数据规模：训练集114k，验证集1k，测试集3k；\n- 数据来源：清华大学CoAI小组；### Supported Tasks and Leaderboards\n\nThe dataset designed for generate e-commerce advertise.### Languages\n\nThe data in AdvertiseGen are in Chinese.## Dataset Structure### Data Instances\n\nAn example of \"train\" looks as follows:\n\n\n\n\n\n数据集引用\n\n如在学术论文中使用本数据集，请添加相关引用说明，具体如下："
]
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|
40f6a4b1a42c0bcb74aaf1ea3c8bba5ce140b021 |
# Dataset Card for CSC
中文拼写纠错数据集
- **Repository:** https://github.com/shibing624/pycorrector
## Dataset Description
Chinese Spelling Correction (CSC) is a task to detect and correct misspelled characters in Chinese texts.
CSC is challenging since many Chinese characters are visually or phonologically similar but with quite different semantic meanings.
中文拼写纠错数据集，共27万条，是通过原始SIGHAN13、14、15年数据集和Wang271k数据集合并整理后得到，json格式，带错误字符位置信息。
### Original Dataset Summary
- test.json 和 dev.json 为 **SIGHAN数据集**， 包括SIGHAN13 14 15，来自 [官方csc.html](http://nlp.ee.ncu.edu.tw/resource/csc.html) ，文件大小：339kb，4千条。
- train.json 为 **Wang271k数据集**，包括 Wang271k ，来自 [Automatic-Corpus-Generation dimmywang提供](https://github.com/wdimmy/Automatic-Corpus-Generation/blob/master/corpus/train.sgml) ，文件大小：93MB，27万条。
如果只想用SIGHAN数据集，可以这样取数据：
```python
from datasets import load_dataset
dev_ds = load_dataset('shibing624/CSC', split='validation')
print(dev_ds)
print(dev_ds[0])
test_ds = load_dataset('shibing624/CSC', split='test')
print(test_ds)
print(test_ds[0])
```
### Supported Tasks and Leaderboards
中文拼写纠错任务
The dataset designed for csc task training pretrained language models.
### Languages
The data in CSC are in Chinese.
## Dataset Structure
### Data Instances
An example of "train" looks as follows:
```json
{
"id": "B2-4029-3",
"original_text": "晚间会听到嗓音，白天的时候大家都不会太在意，但是在睡觉的时候这嗓音成为大家的恶梦。",
"wrong_ids": [
5,
31
],
"correct_text": "晚间会听到噪音，白天的时候大家都不会太在意，但是在睡觉的时候这噪音成为大家的恶梦。"
}
```
### Data Fields
字段解释：
- id：唯一标识符，无意义
- original_text: 原始错误文本
- wrong_ids： 错误字的位置，从0开始
- correct_text: 纠正后的文本
### Data Splits
| | train | dev | test |
|---------------|------:|--:|--:|
| CSC | 251835条 | 27981条 | 1100条 |
### Licensing Information
The dataset is available under the Apache 2.0.
### Citation Information
```latex
@misc{Xu_Pycorrector_Text_error,
title={Pycorrector: Text error correction tool},
author={Xu Ming},
year={2021},
howpublished={\url{https://github.com/shibing624/pycorrector}},
}
```
### Contributions
[shibing624](https://github.com/shibing624) 整理并上传 | shibing624/CSC | [
"task_categories:text-generation",
"language:zh",
"license:apache-2.0",
"text-correction",
"region:us"
]
| 2023-03-28T01:59:33+00:00 | {"language": ["zh"], "license": "apache-2.0", "task_categories": ["text-generation"], "pretty_name": "CSC", "tags": ["text-correction"]} | 2023-05-12T06:30:59+00:00 | []
| [
"zh"
]
| TAGS
#task_categories-text-generation #language-Chinese #license-apache-2.0 #text-correction #region-us
| Dataset Card for CSC
====================
中文拼写纠错数据集
* Repository: URL
Dataset Description
-------------------
Chinese Spelling Correction (CSC) is a task to detect and correct misspelled characters in Chinese texts.
CSC is challenging since many Chinese characters are visually or phonologically similar but with quite different semantic meanings.
中文拼写纠错数据集，共27万条，是通过原始SIGHAN13、14、15年数据集和Wang271k数据集合并整理后得到，json格式，带错误字符位置信息。
### Original Dataset Summary
* URL 和 URL 为 SIGHAN数据集， 包括SIGHAN13 14 15，来自 官方csc.html ，文件大小：339kb，4千条。
* URL 为 Wang271k数据集，包括 Wang271k ，来自 Automatic-Corpus-Generation dimmywang提供 ，文件大小：93MB，27万条。
如果只想用SIGHAN数据集，可以这样取数据：
### Supported Tasks and Leaderboards
中文拼写纠错任务
The dataset designed for csc task training pretrained language models.
### Languages
The data in CSC are in Chinese.
Dataset Structure
-----------------
### Data Instances
An example of "train" looks as follows:
### Data Fields
字段解释：
* id：唯一标识符，无意义
* original\_text: 原始错误文本
* wrong\_ids： 错误字的位置，从0开始
* correct\_text: 纠正后的文本
### Data Splits
### Licensing Information
The dataset is available under the Apache 2.0.
### Contributions
shibing624 整理并上传
| [
"### Original Dataset Summary\n\n\n* URL 和 URL 为 SIGHAN数据集， 包括SIGHAN13 14 15，来自 官方csc.html ，文件大小：339kb，4千条。\n* URL 为 Wang271k数据集，包括 Wang271k ，来自 Automatic-Corpus-Generation dimmywang提供 ，文件大小：93MB，27万条。\n\n\n如果只想用SIGHAN数据集，可以这样取数据：",
"### Supported Tasks and Leaderboards\n\n\n中文拼写纠错任务\n\n\nThe dataset designed for csc task training pretrained language models.",
"### Languages\n\n\nThe data in CSC are in Chinese.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nAn example of \"train\" looks as follows:",
"### Data Fields\n\n\n字段解释：\n\n\n* id：唯一标识符，无意义\n* original\\_text: 原始错误文本\n* wrong\\_ids： 错误字的位置，从0开始\n* correct\\_text: 纠正后的文本",
"### Data Splits",
"### Licensing Information\n\n\nThe dataset is available under the Apache 2.0.",
"### Contributions\n\n\nshibing624 整理并上传"
]
| [
"TAGS\n#task_categories-text-generation #language-Chinese #license-apache-2.0 #text-correction #region-us \n",
"### Original Dataset Summary\n\n\n* URL 和 URL 为 SIGHAN数据集， 包括SIGHAN13 14 15，来自 官方csc.html ，文件大小：339kb，4千条。\n* URL 为 Wang271k数据集，包括 Wang271k ，来自 Automatic-Corpus-Generation dimmywang提供 ，文件大小：93MB，27万条。\n\n\n如果只想用SIGHAN数据集，可以这样取数据：",
"### Supported Tasks and Leaderboards\n\n\n中文拼写纠错任务\n\n\nThe dataset designed for csc task training pretrained language models.",
"### Languages\n\n\nThe data in CSC are in Chinese.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nAn example of \"train\" looks as follows:",
"### Data Fields\n\n\n字段解释：\n\n\n* id：唯一标识符，无意义\n* original\\_text: 原始错误文本\n* wrong\\_ids： 错误字的位置，从0开始\n* correct\\_text: 纠正后的文本",
"### Data Splits",
"### Licensing Information\n\n\nThe dataset is available under the Apache 2.0.",
"### Contributions\n\n\nshibing624 整理并上传"
]
| [
35,
106,
32,
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18,
55,
5,
17,
13
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| [
"passage: TAGS\n#task_categories-text-generation #language-Chinese #license-apache-2.0 #text-correction #region-us \n### Original Dataset Summary\n\n\n* URL 和 URL 为 SIGHAN数据集， 包括SIGHAN13 14 15，来自 官方csc.html ，文件大小：339kb，4千条。\n* URL 为 Wang271k数据集，包括 Wang271k ，来自 Automatic-Corpus-Generation dimmywang提供 ，文件大小：93MB，27万条。\n\n\n如果只想用SIGHAN数据集，可以这样取数据：### Supported Tasks and Leaderboards\n\n\n中文拼写纠错任务\n\n\nThe dataset designed for csc task training pretrained language models.### Languages\n\n\nThe data in CSC are in Chinese.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nAn example of \"train\" looks as follows:### Data Fields\n\n\n字段解释：\n\n\n* id：唯一标识符，无意义\n* original\\_text: 原始错误文本\n* wrong\\_ids： 错误字的位置，从0开始\n* correct\\_text: 纠正后的文本### Data Splits### Licensing Information\n\n\nThe dataset is available under the Apache 2.0.### Contributions\n\n\nshibing624 整理并上传"
]
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|
9156fc5c22217d9437d69bebf92701540f68240a | # Dataset Card for "gpt2-chitchat-learn-small"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | wujohns/gpt2-chitchat-learn-small | [
"region:us"
]
| 2023-03-28T03:13:51+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "input_ids", "sequence": "int32"}], "splits": [{"name": "train", "num_bytes": 3121359, "num_examples": 9800}, {"name": "valid", "num_bytes": 69613, "num_examples": 200}], "download_size": 1765732, "dataset_size": 3190972}} | 2023-03-28T03:21:45+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "gpt2-chitchat-learn-small"
More Information needed | [
"# Dataset Card for \"gpt2-chitchat-learn-small\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"gpt2-chitchat-learn-small\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"gpt2-chitchat-learn-small\"\n\nMore Information needed"
]
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|
7f78e69cad50ad3e75539a96fc80d538631f86ed | # Dataset Card for "face-eye-double-eyelids2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | youngdicey/face-eye-double-eyelids2 | [
"region:us"
]
| 2023-03-28T04:53:09+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 890510.0, "num_examples": 97}], "download_size": 872745, "dataset_size": 890510.0}} | 2023-03-28T04:59:08+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "face-eye-double-eyelids2"
More Information needed | [
"# Dataset Card for \"face-eye-double-eyelids2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"face-eye-double-eyelids2\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"face-eye-double-eyelids2\"\n\nMore Information needed"
]
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|
503dc184e2664ecdc8c798808538565434922518 |
# Dataset Card for Wikipedia
## Table of Contents
- [Dataset Card for "wikipedia"](#dataset-card-for-wikipedia)
- [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)
- [20200501.de](#20200501de)
- [20200501.en](#20200501en)
- [20200501.fr](#20200501fr)
- [20200501.frr](#20200501frr)
- [20200501.it](#20200501it)
- [Data Fields](#data-fields)
- [20200501.de](#20200501de-1)
- [20200501.en](#20200501en-1)
- [20200501.fr](#20200501fr-1)
- [20200501.frr](#20200501frr-1)
- [20200501.it](#20200501it-1)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [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:** [https://dumps.wikimedia.org](https://dumps.wikimedia.org)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Dataset Summary
Wikipedia dataset containing cleaned articles of all languages.
The datasets are built from the Wikipedia dump
(https://dumps.wikimedia.org/) with one split per language. Each example
contains the content of one full Wikipedia article with cleaning to strip
markdown and unwanted sections (references, etc.).
The articles are parsed using the ``mwparserfromhell`` tool.
To load this dataset you need to install Apache Beam and ``mwparserfromhell`` first:
```
pip install apache_beam mwparserfromhell
```
Then, you can load any subset of Wikipedia per language and per date this way:
```python
from datasets import load_dataset
load_dataset("wikipedia", language="sw", date="20220120", beam_runner=...)
```
where you can pass as `beam_runner` any Apache Beam supported runner for (distributed) data processing
(see [here](https://beam.apache.org/documentation/runners/capability-matrix/)).
Pass "DirectRunner" to run it on your machine.
You can find the full list of languages and dates [here](https://dumps.wikimedia.org/backup-index.html).
Some subsets of Wikipedia have already been processed by HuggingFace, and you can load them just with:
```python
from datasets import load_dataset
load_dataset("wikipedia", "20220301.en")
```
The list of pre-processed subsets is:
- "20220301.de"
- "20220301.en"
- "20220301.fr"
- "20220301.frr"
- "20220301.it"
- "20220301.simple"
### Supported Tasks and Leaderboards
The dataset is generally used for Language Modeling.
### Languages
You can find the list of languages [here](https://meta.wikimedia.org/wiki/List_of_Wikipedias).
## Dataset Structure
### Data Instances
An example looks as follows:
```
{'id': '1',
'url': 'https://simple.wikipedia.org/wiki/April',
'title': 'April',
'text': 'April is the fourth month...'
}
```
Some subsets of Wikipedia have already been processed by HuggingFace, as you can see below:
#### 20220301.de
- **Size of downloaded dataset files:** 6523.22 MB
- **Size of the generated dataset:** 8905.28 MB
- **Total amount of disk used:** 15428.50 MB
#### 20220301.en
- **Size of downloaded dataset files:** 20598.31 MB
- **Size of the generated dataset:** 20275.52 MB
- **Total amount of disk used:** 40873.83 MB
#### 20220301.fr
- **Size of downloaded dataset files:** 5602.57 MB
- **Size of the generated dataset:** 7375.92 MB
- **Total amount of disk used:** 12978.49 MB
#### 20220301.frr
- **Size of downloaded dataset files:** 12.44 MB
- **Size of the generated dataset:** 9.13 MB
- **Total amount of disk used:** 21.57 MB
#### 20220301.it
- **Size of downloaded dataset files:** 3516.44 MB
- **Size of the generated dataset:** 4539.94 MB
- **Total amount of disk used:** 8056.39 MB
#### 20220301.simple
- **Size of downloaded dataset files:** 239.68 MB
- **Size of the generated dataset:** 235.07 MB
- **Total amount of disk used:** 474.76 MB
### Data Fields
The data fields are the same among all configurations:
- `id` (`str`): ID of the article.
- `url` (`str`): URL of the article.
- `title` (`str`): Title of the article.
- `text` (`str`): Text content of the article.
### Data Splits
Here are the number of examples for several configurations:
| name | train |
|-----------------|--------:|
| 20220301.de | 2665357 |
| 20220301.en | 6458670 |
| 20220301.fr | 2402095 |
| 20220301.frr | 15199 |
| 20220301.it | 1743035 |
| 20220301.simple | 205328 |
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
Most of Wikipedia's text and many of its images are co-licensed under the
[Creative Commons Attribution-ShareAlike 3.0 Unported License](https://en.wikipedia.org/wiki/Wikipedia:Text_of_Creative_Commons_Attribution-ShareAlike_3.0_Unported_License)
(CC BY-SA) and the [GNU Free Documentation License](https://en.wikipedia.org/wiki/Wikipedia:Text_of_the_GNU_Free_Documentation_License)
(GFDL) (unversioned, with no invariant sections, front-cover texts, or back-cover texts).
Some text has been imported only under CC BY-SA and CC BY-SA-compatible license and cannot be reused under GFDL; such
text will be identified on the page footer, in the page history, or on the discussion page of the article that utilizes
the text.
### Citation Information
```
@ONLINE{wikidump,
author = "Wikimedia Foundation",
title = "Wikimedia Downloads",
url = "https://dumps.wikimedia.org"
}
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.
| livinNector/wikipedia | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:n<1K",
"size_categories:1K<n<10K",
"size_categories:10K<n<100K",
"size_categories:100K<n<1M",
"size_categories:1M<n<10M",
"source_datasets:original",
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"region:us"
]
| 2023-03-28T05:19:01+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["crowdsourced"], "language": ["aa", "ab", "ace", "af", "ak", "als", "am", "an", "ang", "ar", "arc", "arz", "as", "ast", "atj", "av", "ay", "az", "azb", "ba", "bar", "bcl", "be", "bg", "bh", "bi", "bjn", "bm", "bn", "bo", "bpy", "br", "bs", "bug", "bxr", "ca", "cbk", "cdo", "ce", "ceb", "ch", "cho", "chr", "chy", "ckb", "co", "cr", "crh", "cs", "csb", "cu", "cv", "cy", "da", "de", "din", "diq", "dsb", "dty", "dv", "dz", "ee", "el", "eml", "en", "eo", "es", "et", "eu", "ext", "fa", "ff", "fi", "fj", "fo", "fr", "frp", "frr", "fur", "fy", "ga", "gag", "gan", "gd", "gl", "glk", "gn", "gom", "gor", "got", "gu", "gv", "ha", "hak", "haw", "he", "hi", "hif", "ho", "hr", "hsb", "ht", "hu", "hy", "ia", "id", "ie", "ig", "ii", "ik", "ilo", "inh", "io", "is", "it", "iu", "ja", "jam", "jbo", "jv", "ka", "kaa", "kab", "kbd", "kbp", "kg", "ki", "kj", "kk", "kl", "km", "kn", "ko", "koi", "krc", "ks", "ksh", 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235072360, "num_examples": 205328}], "download_size": 239682796, "dataset_size": 235072360}]} | 2023-03-28T05:32:24+00:00 | []
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"eo",
"es",
"et",
"eu",
"ext",
"fa",
"ff",
"fi",
"fj",
"fo",
"fr",
"frp",
"frr",
"fur",
"fy",
"ga",
"gag",
"gan",
"gd",
"gl",
"glk",
"gn",
"gom",
"gor",
"got",
"gu",
"gv",
"ha",
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"he",
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"hif",
"ho",
"hr",
"hsb",
"ht",
"hu",
"hy",
"ia",
"id",
"ie",
"ig",
"ii",
"ik",
"ilo",
"inh",
"io",
"is",
"it",
"iu",
"ja",
"jam",
"jbo",
"jv",
"ka",
"kaa",
"kab",
"kbd",
"kbp",
"kg",
"ki",
"kj",
"kk",
"kl",
"km",
"kn",
"ko",
"koi",
"krc",
"ks",
"ksh",
"ku",
"kv",
"kw",
"ky",
"la",
"lad",
"lb",
"lbe",
"lez",
"lfn",
"lg",
"li",
"lij",
"lmo",
"ln",
"lo",
"lrc",
"lt",
"ltg",
"lv",
"lzh",
"mai",
"mdf",
"mg",
"mh",
"mhr",
"mi",
"min",
"mk",
"ml",
"mn",
"mr",
"mrj",
"ms",
"mt",
"mus",
"mwl",
"my",
"myv",
"mzn",
"na",
"nah",
"nan",
"nap",
"nds",
"ne",
"new",
"ng",
"nl",
"nn",
"no",
"nov",
"nrf",
"nso",
"nv",
"ny",
"oc",
"olo",
"om",
"or",
"os",
"pa",
"pag",
"pam",
"pap",
"pcd",
"pdc",
"pfl",
"pi",
"pih",
"pl",
"pms",
"pnb",
"pnt",
"ps",
"pt",
"qu",
"rm",
"rmy",
"rn",
"ro",
"ru",
"rue",
"rup",
"rw",
"sa",
"sah",
"sat",
"sc",
"scn",
"sco",
"sd",
"se",
"sg",
"sgs",
"sh",
"si",
"sk",
"sl",
"sm",
"sn",
"so",
"sq",
"sr",
"srn",
"ss",
"st",
"stq",
"su",
"sv",
"sw",
"szl",
"ta",
"tcy",
"tdt",
"te",
"tg",
"th",
"ti",
"tk",
"tl",
"tn",
"to",
"tpi",
"tr",
"ts",
"tt",
"tum",
"tw",
"ty",
"tyv",
"udm",
"ug",
"uk",
"ur",
"uz",
"ve",
"vec",
"vep",
"vi",
"vls",
"vo",
"vro",
"wa",
"war",
"wo",
"wuu",
"xal",
"xh",
"xmf",
"yi",
"yo",
"yue",
"za",
"zea",
"zh",
"zu"
]
| TAGS
#task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-crowdsourced #multilinguality-multilingual #size_categories-n<1K #size_categories-1K<n<10K #size_categories-10K<n<100K #size_categories-100K<n<1M #size_categories-1M<n<10M #source_datasets-original #language-Afar #language-Abkhazian #language-Achinese #language-Afrikaans #language-Akan #language-Tosk Albanian #language-Amharic #language-Aragonese #language-Old English (ca. 450-1100) #language-Arabic #language-Official Aramaic (700-300 BCE) #language-Egyptian Arabic #language-Assamese #language-Asturian #language-Atikamekw #language-Avaric #language-Aymara #language-Azerbaijani #language-South Azerbaijani #language-Bashkir #language-Bavarian #language-Central Bikol #language-Belarusian #language-Bulgarian #language-bh #language-Bislama #language-Banjar #language-Bambara #language-Bengali #language-Tibetan #language-Bishnupriya #language-Breton #language-Bosnian #language-Buginese #language-Russia Buriat #language-Catalan #language-Chavacano #language-Min Dong Chinese #language-Chechen #language-Cebuano #language-Chamorro #language-Choctaw #language-Cherokee #language-Cheyenne #language-Central Kurdish #language-Corsican #language-Cree #language-Crimean Tatar #language-Czech #language-Kashubian #language-Church Slavic #language-Chuvash #language-Welsh #language-Danish #language-German #language-Dinka #language-Dimli (individual language) #language-Lower Sorbian #language-Dotyali #language-Dhivehi #language-Dzongkha #language-Ewe #language-Modern Greek (1453-) #language-Emiliano-Romagnolo #language-English #language-Esperanto #language-Spanish #language-Estonian #language-Basque #language-Extremaduran #language-Persian #language-Fulah #language-Finnish #language-Fijian #language-Faroese #language-French #language-Arpitan #language-Northern Frisian #language-Friulian #language-Western Frisian #language-Irish #language-Gagauz #language-Gan Chinese #language-Scottish Gaelic #language-Galician #language-Gilaki #language-Guarani #language-Goan Konkani #language-Gorontalo #language-Gothic #language-Gujarati #language-Manx #language-Hausa #language-Hakka Chinese #language-Hawaiian #language-Hebrew #language-Hindi #language-Fiji Hindi #language-Hiri Motu #language-Croatian #language-Upper Sorbian #language-Haitian #language-Hungarian #language-Armenian #language-Interlingua (International Auxiliary Language Association) #language-Indonesian #language-Interlingue #language-Igbo #language-Sichuan Yi #language-Inupiaq #language-Iloko #language-Ingush #language-Ido #language-Icelandic #language-Italian #language-Inuktitut #language-Japanese #language-Jamaican Creole English #language-Lojban #language-Javanese #language-Georgian #language-Kara-Kalpak #language-Kabyle #language-Kabardian #language-Kabiyè #language-Kongo #language-Kikuyu #language-Kuanyama #language-Kazakh #language-Kalaallisut #language-Khmer #language-Kannada #language-Korean #language-Komi-Permyak #language-Karachay-Balkar #language-Kashmiri #language-Kölsch #language-Kurdish #language-Komi #language-Cornish #language-Kirghiz #language-Latin #language-Ladino #language-Luxembourgish #language-Lak #language-Lezghian #language-Lingua Franca Nova #language-Ganda #language-Limburgan #language-Ligurian #language-Lombard #language-Lingala #language-Lao #language-Northern Luri #language-Lithuanian #language-Latgalian #language-Latvian #language-Literary Chinese #language-Maithili #language-Moksha #language-Malagasy #language-Marshallese #language-Eastern Mari #language-Maori #language-Minangkabau #language-Macedonian #language-Malayalam #language-Mongolian #language-Marathi #language-Western Mari #language-Malay (macrolanguage) #language-Maltese #language-Creek #language-Mirandese #language-Burmese #language-Erzya #language-Mazanderani #language-Nauru #language-nah #language-Min Nan Chinese #language-Neapolitan #language-Low German #language-Nepali (macrolanguage) #language-Newari #language-Ndonga #language-Dutch #language-Norwegian Nynorsk #language-Norwegian #language-Novial #language-Jèrriais #language-Pedi #language-Navajo #language-Nyanja #language-Occitan (post 1500) #language-Livvi #language-Oromo #language-Oriya (macrolanguage) #language-Ossetian #language-Panjabi #language-Pangasinan #language-Pampanga #language-Papiamento #language-Picard #language-Pennsylvania German #language-Pfaelzisch #language-Pali #language-Pitcairn-Norfolk #language-Polish #language-Piemontese #language-Western Panjabi #language-Pontic #language-Pushto #language-Portuguese #language-Quechua #language-Romansh #language-Vlax Romani #language-Rundi #language-Romanian #language-Russian #language-Rusyn #language-Macedo-Romanian #language-Kinyarwanda #language-Sanskrit #language-Yakut #language-Santali #language-Sardinian #language-Sicilian #language-Scots #language-Sindhi #language-Northern Sami #language-Sango #language-Samogitian #language-Serbo-Croatian #language-Sinhala #language-Slovak #language-Slovenian #language-Samoan #language-Shona #language-Somali #language-Albanian #language-Serbian #language-Sranan Tongo #language-Swati #language-Southern Sotho #language-Saterfriesisch #language-Sundanese #language-Swedish #language-Swahili (macrolanguage) #language-Silesian #language-Tamil #language-Tulu #language-Tetun Dili #language-Telugu #language-Tajik #language-Thai #language-Tigrinya #language-Turkmen #language-Tagalog #language-Tswana #language-Tonga (Tonga Islands) #language-Tok Pisin #language-Turkish #language-Tsonga #language-Tatar #language-Tumbuka #language-Twi #language-Tahitian #language-Tuvinian #language-Udmurt #language-Uighur #language-Ukrainian #language-Urdu #language-Uzbek #language-Venda #language-Venetian #language-Veps #language-Vietnamese #language-Vlaams #language-Volapük #language-Võro #language-Walloon #language-Waray (Philippines) #language-Wolof #language-Wu Chinese #language-Kalmyk #language-Xhosa #language-Mingrelian #language-Yiddish #language-Yoruba #language-Yue Chinese #language-Zhuang #language-Zeeuws #language-Chinese #language-Zulu #license-cc-by-sa-3.0 #license-gfdl #region-us
| Dataset Card for Wikipedia
==========================
Table of Contents
-----------------
* Dataset Card for "wikipedia"
+ Table of Contents
+ Dataset Description
- Dataset Summary
- Supported Tasks and Leaderboards
- Languages
+ Dataset Structure
- Data Instances
* URL
* URL
* URL
* URL
* URL
- Data Fields
* URL
* URL
* URL
* URL
* URL
- Data Splits
+ Dataset Creation
- Curation Rationale
- Source Data
* Initial Data Collection and Normalization
* Who are the source language producers?
- Annotations
* Annotation process
* Who are the annotators?
- Personal and Sensitive Information
+ Considerations for Using the Data
- Social Impact of Dataset
- Discussion of Biases
- Other Known Limitations
+ Additional Information
- Dataset Curators
- Licensing Information
- Citation Information
- Contributions
Dataset Description
-------------------
* Homepage: URL
* Repository:
* Paper:
* Point of Contact:
### Dataset Summary
Wikipedia dataset containing cleaned articles of all languages.
The datasets are built from the Wikipedia dump
(URL with one split per language. Each example
contains the content of one full Wikipedia article with cleaning to strip
markdown and unwanted sections (references, etc.).
The articles are parsed using the ''mwparserfromhell'' tool.
To load this dataset you need to install Apache Beam and ''mwparserfromhell'' first:
Then, you can load any subset of Wikipedia per language and per date this way:
where you can pass as 'beam\_runner' any Apache Beam supported runner for (distributed) data processing
(see here).
Pass "DirectRunner" to run it on your machine.
You can find the full list of languages and dates here.
Some subsets of Wikipedia have already been processed by HuggingFace, and you can load them just with:
The list of pre-processed subsets is:
* "URL"
* "URL"
* "URL"
* "URL"
* "URL"
* "URL"
### Supported Tasks and Leaderboards
The dataset is generally used for Language Modeling.
### Languages
You can find the list of languages here.
Dataset Structure
-----------------
### Data Instances
An example looks as follows:
Some subsets of Wikipedia have already been processed by HuggingFace, as you can see below:
#### URL
* Size of downloaded dataset files: 6523.22 MB
* Size of the generated dataset: 8905.28 MB
* Total amount of disk used: 15428.50 MB
#### URL
* Size of downloaded dataset files: 20598.31 MB
* Size of the generated dataset: 20275.52 MB
* Total amount of disk used: 40873.83 MB
#### URL
* Size of downloaded dataset files: 5602.57 MB
* Size of the generated dataset: 7375.92 MB
* Total amount of disk used: 12978.49 MB
#### URL
* Size of downloaded dataset files: 12.44 MB
* Size of the generated dataset: 9.13 MB
* Total amount of disk used: 21.57 MB
#### URL
* Size of downloaded dataset files: 3516.44 MB
* Size of the generated dataset: 4539.94 MB
* Total amount of disk used: 8056.39 MB
#### URL
* Size of downloaded dataset files: 239.68 MB
* Size of the generated dataset: 235.07 MB
* Total amount of disk used: 474.76 MB
### Data Fields
The data fields are the same among all configurations:
* 'id' ('str'): ID of the article.
* 'url' ('str'): URL of the article.
* 'title' ('str'): Title of the article.
* 'text' ('str'): Text content of the article.
### Data Splits
Here are the number of examples for several configurations:
Dataset Creation
----------------
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
Most of Wikipedia's text and many of its images are co-licensed under the
Creative Commons Attribution-ShareAlike 3.0 Unported License
(CC BY-SA) and the GNU Free Documentation License
(GFDL) (unversioned, with no invariant sections, front-cover texts, or back-cover texts).
Some text has been imported only under CC BY-SA and CC BY-SA-compatible license and cannot be reused under GFDL; such
text will be identified on the page footer, in the page history, or on the discussion page of the article that utilizes
the text.
### Contributions
Thanks to @lewtun, @mariamabarham, @thomwolf, @lhoestq, @patrickvonplaten for adding this dataset.
| [
"### Dataset Summary\n\n\nWikipedia dataset containing cleaned articles of all languages.\nThe datasets are built from the Wikipedia dump\n(URL with one split per language. Each example\ncontains the content of one full Wikipedia article with cleaning to strip\nmarkdown and unwanted sections (references, etc.).\n\n\nThe articles are parsed using the ''mwparserfromhell'' tool.\n\n\nTo load this dataset you need to install Apache Beam and ''mwparserfromhell'' first:\n\n\nThen, you can load any subset of Wikipedia per language and per date this way:\n\n\nwhere you can pass as 'beam\\_runner' any Apache Beam supported runner for (distributed) data processing\n(see here).\nPass \"DirectRunner\" to run it on your machine.\n\n\nYou can find the full list of languages and dates here.\n\n\nSome subsets of Wikipedia have already been processed by HuggingFace, and you can load them just with:\n\n\nThe list of pre-processed subsets is:\n\n\n* \"URL\"\n* \"URL\"\n* \"URL\"\n* \"URL\"\n* \"URL\"\n* \"URL\"",
"### Supported Tasks and Leaderboards\n\n\nThe dataset is generally used for Language Modeling.",
"### Languages\n\n\nYou can find the list of languages here.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nAn example looks as follows:\n\n\nSome subsets of Wikipedia have already been processed by HuggingFace, as you can see below:",
"#### URL\n\n\n* Size of downloaded dataset files: 6523.22 MB\n* Size of the generated dataset: 8905.28 MB\n* Total amount of disk used: 15428.50 MB",
"#### URL\n\n\n* Size of downloaded dataset files: 20598.31 MB\n* Size of the generated dataset: 20275.52 MB\n* Total amount of disk used: 40873.83 MB",
"#### URL\n\n\n* Size of downloaded dataset files: 5602.57 MB\n* Size of the generated dataset: 7375.92 MB\n* Total amount of disk used: 12978.49 MB",
"#### URL\n\n\n* Size of downloaded dataset files: 12.44 MB\n* Size of the generated dataset: 9.13 MB\n* Total amount of disk used: 21.57 MB",
"#### URL\n\n\n* Size of downloaded dataset files: 3516.44 MB\n* Size of the generated dataset: 4539.94 MB\n* Total amount of disk used: 8056.39 MB",
"#### URL\n\n\n* Size of downloaded dataset files: 239.68 MB\n* Size of the generated dataset: 235.07 MB\n* Total amount of disk used: 474.76 MB",
"### Data Fields\n\n\nThe data fields are the same among all configurations:\n\n\n* 'id' ('str'): ID of the article.\n* 'url' ('str'): URL of the article.\n* 'title' ('str'): Title of the article.\n* 'text' ('str'): Text content of the article.",
"### Data Splits\n\n\nHere are the number of examples for several configurations:\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nMost of Wikipedia's text and many of its images are co-licensed under the\nCreative Commons Attribution-ShareAlike 3.0 Unported License\n(CC BY-SA) and the GNU Free Documentation License\n(GFDL) (unversioned, with no invariant sections, front-cover texts, or back-cover texts).\n\n\nSome text has been imported only under CC BY-SA and CC BY-SA-compatible license and cannot be reused under GFDL; such\ntext will be identified on the page footer, in the page history, or on the discussion page of the article that utilizes\nthe text.",
"### Contributions\n\n\nThanks to @lewtun, @mariamabarham, @thomwolf, @lhoestq, @patrickvonplaten for adding this dataset."
]
| [
"TAGS\n#task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-crowdsourced #multilinguality-multilingual #size_categories-n<1K #size_categories-1K<n<10K #size_categories-10K<n<100K #size_categories-100K<n<1M #size_categories-1M<n<10M #source_datasets-original #language-Afar #language-Abkhazian #language-Achinese #language-Afrikaans #language-Akan #language-Tosk Albanian #language-Amharic #language-Aragonese #language-Old English (ca. 450-1100) #language-Arabic #language-Official Aramaic (700-300 BCE) #language-Egyptian Arabic #language-Assamese #language-Asturian #language-Atikamekw #language-Avaric #language-Aymara #language-Azerbaijani #language-South Azerbaijani #language-Bashkir #language-Bavarian #language-Central Bikol #language-Belarusian #language-Bulgarian #language-bh #language-Bislama #language-Banjar #language-Bambara #language-Bengali #language-Tibetan #language-Bishnupriya #language-Breton #language-Bosnian #language-Buginese #language-Russia Buriat #language-Catalan #language-Chavacano #language-Min Dong Chinese #language-Chechen #language-Cebuano #language-Chamorro #language-Choctaw #language-Cherokee #language-Cheyenne #language-Central Kurdish #language-Corsican #language-Cree #language-Crimean Tatar #language-Czech #language-Kashubian #language-Church Slavic #language-Chuvash #language-Welsh #language-Danish #language-German #language-Dinka #language-Dimli (individual language) #language-Lower Sorbian #language-Dotyali #language-Dhivehi #language-Dzongkha #language-Ewe #language-Modern Greek (1453-) #language-Emiliano-Romagnolo #language-English #language-Esperanto #language-Spanish #language-Estonian #language-Basque #language-Extremaduran #language-Persian #language-Fulah #language-Finnish #language-Fijian #language-Faroese #language-French #language-Arpitan #language-Northern Frisian #language-Friulian #language-Western Frisian #language-Irish #language-Gagauz #language-Gan Chinese #language-Scottish Gaelic #language-Galician #language-Gilaki #language-Guarani #language-Goan Konkani #language-Gorontalo #language-Gothic #language-Gujarati #language-Manx #language-Hausa #language-Hakka Chinese #language-Hawaiian #language-Hebrew #language-Hindi #language-Fiji Hindi #language-Hiri Motu #language-Croatian #language-Upper Sorbian #language-Haitian #language-Hungarian #language-Armenian #language-Interlingua (International Auxiliary Language Association) #language-Indonesian #language-Interlingue #language-Igbo #language-Sichuan Yi #language-Inupiaq #language-Iloko #language-Ingush #language-Ido #language-Icelandic #language-Italian #language-Inuktitut #language-Japanese #language-Jamaican Creole English #language-Lojban #language-Javanese #language-Georgian #language-Kara-Kalpak #language-Kabyle #language-Kabardian #language-Kabiyè #language-Kongo #language-Kikuyu #language-Kuanyama #language-Kazakh #language-Kalaallisut #language-Khmer #language-Kannada #language-Korean #language-Komi-Permyak #language-Karachay-Balkar #language-Kashmiri #language-Kölsch #language-Kurdish #language-Komi #language-Cornish #language-Kirghiz #language-Latin #language-Ladino #language-Luxembourgish #language-Lak #language-Lezghian #language-Lingua Franca Nova #language-Ganda #language-Limburgan #language-Ligurian #language-Lombard #language-Lingala #language-Lao #language-Northern Luri #language-Lithuanian #language-Latgalian #language-Latvian #language-Literary Chinese #language-Maithili #language-Moksha #language-Malagasy #language-Marshallese #language-Eastern Mari #language-Maori #language-Minangkabau #language-Macedonian #language-Malayalam #language-Mongolian #language-Marathi #language-Western Mari #language-Malay (macrolanguage) #language-Maltese #language-Creek #language-Mirandese #language-Burmese #language-Erzya #language-Mazanderani #language-Nauru #language-nah #language-Min Nan Chinese #language-Neapolitan #language-Low German #language-Nepali (macrolanguage) #language-Newari #language-Ndonga #language-Dutch #language-Norwegian Nynorsk #language-Norwegian #language-Novial #language-Jèrriais #language-Pedi #language-Navajo #language-Nyanja #language-Occitan (post 1500) #language-Livvi #language-Oromo #language-Oriya (macrolanguage) #language-Ossetian #language-Panjabi #language-Pangasinan #language-Pampanga #language-Papiamento #language-Picard #language-Pennsylvania German #language-Pfaelzisch #language-Pali #language-Pitcairn-Norfolk #language-Polish #language-Piemontese #language-Western Panjabi #language-Pontic #language-Pushto #language-Portuguese #language-Quechua #language-Romansh #language-Vlax Romani #language-Rundi #language-Romanian #language-Russian #language-Rusyn #language-Macedo-Romanian #language-Kinyarwanda #language-Sanskrit #language-Yakut #language-Santali #language-Sardinian #language-Sicilian #language-Scots #language-Sindhi #language-Northern Sami #language-Sango #language-Samogitian #language-Serbo-Croatian #language-Sinhala #language-Slovak #language-Slovenian #language-Samoan #language-Shona #language-Somali #language-Albanian #language-Serbian #language-Sranan Tongo #language-Swati #language-Southern Sotho #language-Saterfriesisch #language-Sundanese #language-Swedish #language-Swahili (macrolanguage) #language-Silesian #language-Tamil #language-Tulu #language-Tetun Dili #language-Telugu #language-Tajik #language-Thai #language-Tigrinya #language-Turkmen #language-Tagalog #language-Tswana #language-Tonga (Tonga Islands) #language-Tok Pisin #language-Turkish #language-Tsonga #language-Tatar #language-Tumbuka #language-Twi #language-Tahitian #language-Tuvinian #language-Udmurt #language-Uighur #language-Ukrainian #language-Urdu #language-Uzbek #language-Venda #language-Venetian #language-Veps #language-Vietnamese #language-Vlaams #language-Volapük #language-Võro #language-Walloon #language-Waray (Philippines) #language-Wolof #language-Wu Chinese #language-Kalmyk #language-Xhosa #language-Mingrelian #language-Yiddish #language-Yoruba #language-Yue Chinese #language-Zhuang #language-Zeeuws #language-Chinese #language-Zulu #license-cc-by-sa-3.0 #license-gfdl #region-us \n",
"### Dataset Summary\n\n\nWikipedia dataset containing cleaned articles of all languages.\nThe datasets are built from the Wikipedia dump\n(URL with one split per language. Each example\ncontains the content of one full Wikipedia article with cleaning to strip\nmarkdown and unwanted sections (references, etc.).\n\n\nThe articles are parsed using the ''mwparserfromhell'' tool.\n\n\nTo load this dataset you need to install Apache Beam and ''mwparserfromhell'' first:\n\n\nThen, you can load any subset of Wikipedia per language and per date this way:\n\n\nwhere you can pass as 'beam\\_runner' any Apache Beam supported runner for (distributed) data processing\n(see here).\nPass \"DirectRunner\" to run it on your machine.\n\n\nYou can find the full list of languages and dates here.\n\n\nSome subsets of Wikipedia have already been processed by HuggingFace, and you can load them just with:\n\n\nThe list of pre-processed subsets is:\n\n\n* \"URL\"\n* \"URL\"\n* \"URL\"\n* \"URL\"\n* \"URL\"\n* \"URL\"",
"### Supported Tasks and Leaderboards\n\n\nThe dataset is generally used for Language Modeling.",
"### Languages\n\n\nYou can find the list of languages here.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nAn example looks as follows:\n\n\nSome subsets of Wikipedia have already been processed by HuggingFace, as you can see below:",
"#### URL\n\n\n* Size of downloaded dataset files: 6523.22 MB\n* Size of the generated dataset: 8905.28 MB\n* Total amount of disk used: 15428.50 MB",
"#### URL\n\n\n* Size of downloaded dataset files: 20598.31 MB\n* Size of the generated dataset: 20275.52 MB\n* Total amount of disk used: 40873.83 MB",
"#### URL\n\n\n* Size of downloaded dataset files: 5602.57 MB\n* Size of the generated dataset: 7375.92 MB\n* Total amount of disk used: 12978.49 MB",
"#### URL\n\n\n* Size of downloaded dataset files: 12.44 MB\n* Size of the generated dataset: 9.13 MB\n* Total amount of disk used: 21.57 MB",
"#### URL\n\n\n* Size of downloaded dataset files: 3516.44 MB\n* Size of the generated dataset: 4539.94 MB\n* Total amount of disk used: 8056.39 MB",
"#### URL\n\n\n* Size of downloaded dataset files: 239.68 MB\n* Size of the generated dataset: 235.07 MB\n* Total amount of disk used: 474.76 MB",
"### Data Fields\n\n\nThe data fields are the same among all configurations:\n\n\n* 'id' ('str'): ID of the article.\n* 'url' ('str'): URL of the article.\n* 'title' ('str'): Title of the article.\n* 'text' ('str'): Text content of the article.",
"### Data Splits\n\n\nHere are the number of examples for several configurations:\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nMost of Wikipedia's text and many of its images are co-licensed under the\nCreative Commons Attribution-ShareAlike 3.0 Unported License\n(CC BY-SA) and the GNU Free Documentation License\n(GFDL) (unversioned, with no invariant sections, front-cover texts, or back-cover texts).\n\n\nSome text has been imported only under CC BY-SA and CC BY-SA-compatible license and cannot be reused under GFDL; such\ntext will be identified on the page footer, in the page history, or on the discussion page of the article that utilizes\nthe text.",
"### Contributions\n\n\nThanks to @lewtun, @mariamabarham, @thomwolf, @lhoestq, @patrickvonplaten for adding this dataset."
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"passage: ",
"passage: TAGS\n#task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-crowdsourced #multilinguality-multilingual #size_categories-n<1K #size_categories-1K<n<10K #size_categories-10K<n<100K #size_categories-100K<n<1M #size_categories-1M<n<10M #source_datasets-original #language-Afar #language-Abkhazian #language-Achinese #language-Afrikaans #language-Akan #language-Tosk Albanian #language-Amharic #language-Aragonese #language-Old English (ca. 450-1100) #language-Arabic #language-Official Aramaic (700-300 BCE) #language-Egyptian Arabic #language-Assamese #language-Asturian #language-Atikamekw #language-Avaric #language-Aymara #language-Azerbaijani #language-South Azerbaijani #language-Bashkir #language-Bavarian #language-Central Bikol #language-Belarusian #language-Bulgarian #language-bh #language-Bislama #language-Banjar #language-Bambara #language-Bengali #language-Tibetan #language-Bishnupriya #language-Breton #language-Bosnian #language-Buginese #language-Russia Buriat #language-Catalan #language-Chavacano #language-Min Dong Chinese #language-Chechen #language-Cebuano #language-Chamorro #language-Choctaw #language-Cherokee #language-Cheyenne #language-Central Kurdish #language-Corsican #language-Cree #language-Crimean Tatar #language-Czech #language-Kashubian #language-Church Slavic #language-Chuvash #language-Welsh #language-Danish #language-German #language-Dinka #language-Dimli (individual language) #language-Lower Sorbian #language-Dotyali #language-Dhivehi #language-Dzongkha #language-Ewe #language-Modern Greek (1453-) #language-Emiliano-Romagnolo #language-English #language-Esperanto #language-Spanish #language-Estonian #language-Basque #language-Extremaduran #language-Persian #language-Fulah #language-Finnish #language-Fijian #language-Faroese #language-French #language-Arpitan #language-Northern Frisian #language-Friulian #language-Western Frisian #language-Irish #language-Gagauz #language-Gan Chinese #language-Scottish Gaelic #language-Galician #language-Gilaki #language-Guarani #language-Goan Konkani #language-Gorontalo #language-Gothic #language-Gujarati #language-Manx #language-Hausa #language-Hakka Chinese #language-Hawaiian #language-Hebrew #language-Hindi #language-Fiji Hindi #language-Hiri Motu #language-Croatian #language-Upper Sorbian #language-Haitian #language-Hungarian #language-Armenian #language-Interlingua (International Auxiliary Language Association) #language-Indonesian #language-Interlingue #language-Igbo #language-Sichuan Yi #language-Inupiaq #language-Iloko #language-Ingush #language-Ido #language-Icelandic #language-Italian #language-Inuktitut #language-Japanese #language-Jamaican Creole English #language-Lojban #language-Javanese #language-Georgian #language-Kara-Kalpak #language-Kabyle #language-Kabardian #language-Kabiyè #language-Kongo #language-Kikuyu #language-Kuanyama #language-Kazakh #language-Kalaallisut #language-Khmer #language-Kannada #language-Korean #language-Komi-Permyak #language-Karachay-Balkar #language-Kashmiri #language-Kölsch #language-Kurdish #language-Komi #language-Cornish #language-Kirghiz #language-Latin #language-Ladino #language-Luxembourgish #language-Lak #language-Lezghian #language-Lingua Franca Nova #language-Ganda #language-Limburgan #language-Ligurian #language-Lombard #language-Lingala #language-Lao #language-Northern Luri #language-Lithuanian #language-Latgalian #language-Latvian #language-Literary Chinese #language-Maithili #language-Moksha #language-Malagasy #language-Marshallese #language-Eastern Mari #language-Maori #language-Minangkabau #language-Macedonian #language-Malayalam #language-Mongolian #language-Marathi #language-Western Mari #language-Malay (macrolanguage) #language-Maltese #language-Creek #language-Mirandese #language-Burmese #language-Erzya #language-Mazanderani #language-Nauru #language-nah #language-Min Nan Chinese #language-Neapolitan #language-Low German #language-Nepali (macrolanguage) #language-Newari #language-Ndonga #language-Dutch #language-Norwegian Nynorsk #language-Norwegian #language-Novial #language-Jèrriais #language-Pedi #language-Navajo #language-Nyanja #language-Occitan (post 1500) #language-Livvi #language-Oromo #language-Oriya (macrolanguage) #language-Ossetian #language-Panjabi #language-Pangasinan #language-Pampanga #language-Papiamento #language-Picard #language-Pennsylvania German #language-Pfaelzisch #language-Pali #language-Pitcairn-Norfolk #language-Polish #language-Piemontese #language-Western Panjabi #language-Pontic #language-Pushto #language-Portuguese #language-Quechua #language-Romansh #language-Vlax Romani #language-Rundi #language-Romanian #language-Russian #language-Rusyn #language-Macedo-Romanian #language-Kinyarwanda #language-Sanskrit #language-Yakut #language-Santali #language-Sardinian #language-Sicilian #language-Scots #language-Sindhi #language-Northern Sami #language-Sango #language-Samogitian #language-Serbo-Croatian #language-Sinhala #language-Slovak #language-Slovenian #language-Samoan #language-Shona #language-Somali #language-Albanian #language-Serbian #language-Sranan Tongo #language-Swati #language-Southern Sotho #language-Saterfriesisch #language-Sundanese #language-Swedish #language-Swahili (macrolanguage) #language-Silesian #language-Tamil #language-Tulu #language-Tetun Dili #language-Telugu #language-Tajik #language-Thai #language-Tigrinya #language-Turkmen #language-Tagalog #language-Tswana #language-Tonga (Tonga Islands) #language-Tok Pisin #language-Turkish #language-Tsonga #language-Tatar #language-Tumbuka #language-Twi #language-Tahitian #language-Tuvinian #language-Udmurt #language-Uighur #language-Ukrainian #language-Urdu #language-Uzbek #language-Venda #language-Venetian #language-Veps #language-Vietnamese #language-Vlaams #language-Volapük #language-Võro #language-Walloon #language-Waray (Philippines) #language-Wolof #language-Wu Chinese #language-Kalmyk #language-Xhosa #language-Mingrelian #language-Yiddish #language-Yoruba #language-Yue Chinese #language-Zhuang #language-Zeeuws #language-Chinese #language-Zulu #license-cc-by-sa-3.0 #license-gfdl #region-us \n### Dataset Summary\n\n\nWikipedia dataset containing cleaned articles of all languages.\nThe datasets are built from the Wikipedia dump\n(URL with one split per language. Each example\ncontains the content of one full Wikipedia article with cleaning to strip\nmarkdown and unwanted sections (references, etc.).\n\n\nThe articles are parsed using the ''mwparserfromhell'' tool.\n\n\nTo load this dataset you need to install Apache Beam and ''mwparserfromhell'' first:\n\n\nThen, you can load any subset of Wikipedia per language and per date this way:\n\n\nwhere you can pass as 'beam\\_runner' any Apache Beam supported runner for (distributed) data processing\n(see here).\nPass \"DirectRunner\" to run it on your machine.\n\n\nYou can find the full list of languages and dates here.\n\n\nSome subsets of Wikipedia have already been processed by HuggingFace, and you can load them just with:\n\n\nThe list of pre-processed subsets is:\n\n\n* \"URL\"\n* \"URL\"\n* \"URL\"\n* \"URL\"\n* \"URL\"\n* \"URL\"### Supported Tasks and Leaderboards\n\n\nThe dataset is generally used for Language Modeling.### Languages\n\n\nYou can find the list of languages here.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nAn example looks as follows:\n\n\nSome subsets of Wikipedia have already been processed by HuggingFace, as you can see below:#### URL\n\n\n* Size of downloaded dataset files: 6523.22 MB\n* Size of the generated dataset: 8905.28 MB\n* Total amount of disk used: 15428.50 MB#### URL\n\n\n* Size of downloaded dataset files: 20598.31 MB\n* Size of the generated dataset: 20275.52 MB\n* Total amount of disk used: 40873.83 MB#### URL\n\n\n* Size of downloaded dataset files: 5602.57 MB\n* Size of the generated dataset: 7375.92 MB\n* Total amount of disk used: 12978.49 MB#### URL\n\n\n* Size of downloaded dataset files: 12.44 MB\n* Size of the generated dataset: 9.13 MB\n* Total amount of disk used: 21.57 MB"
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|
3cdd8237f11477f046293e9934539a1fd36c1053 | # Dataset Card for "alpaca-es-autoclean"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | mserras/alpaca-es-autoclean | [
"region:us"
]
| 2023-03-28T05:44:45+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "null"}, {"name": "inputs", "struct": [{"name": "1-instruction", "dtype": "string"}, {"name": "2-input", "dtype": "string"}, {"name": "3-output", "dtype": "string"}]}, {"name": "prediction", "dtype": "null"}, {"name": "prediction_agent", "dtype": "null"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "input", "sequence": "float64"}, {"name": "instruction", "sequence": "float64"}, {"name": "output", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "struct": [{"name": "en_index", "dtype": "int64"}, {"name": "tr-flag-1-instruction", "dtype": "bool"}, {"name": "tr-flag-2-input", "dtype": "bool"}, {"name": "tr-flag-3-output", "dtype": "bool"}]}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 14035334, "num_examples": 746}], "download_size": 10244494, "dataset_size": 14035334}} | 2023-04-06T05:47:08+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "alpaca-es-autoclean"
More Information needed | [
"# Dataset Card for \"alpaca-es-autoclean\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"alpaca-es-autoclean\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"alpaca-es-autoclean\"\n\nMore Information needed"
]
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|
6522fd336316d0e522de302892a3a926c2d8504d | This ChatDoctor-5K dataset is collected from this paper https://arxiv.org/pdf/2303.14070.pdf
Alternatively, you can download the original dataset from this link https://drive.google.com/file/d/1nDTKZ3wZbZWTkFMBkxlamrzbNz0frugg/view?usp=sharing | LinhDuong/chatdoctor-5k | [
"license:apache-2.0",
"arxiv:2303.14070",
"region:us"
]
| 2023-03-28T06:23:57+00:00 | {"license": "apache-2.0"} | 2023-03-28T06:32:21+00:00 | [
"2303.14070"
]
| []
| TAGS
#license-apache-2.0 #arxiv-2303.14070 #region-us
| This ChatDoctor-5K dataset is collected from this paper URL
Alternatively, you can download the original dataset from this link URL | []
| [
"TAGS\n#license-apache-2.0 #arxiv-2303.14070 #region-us \n"
]
| [
22
]
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"passage: TAGS\n#license-apache-2.0 #arxiv-2303.14070 #region-us \n"
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|
50a905b7c7a669947d99c6559fc9d93ce98c00d5 | This ChatDoctor-200K dataset is collected from this paper https://arxiv.org/pdf/2303.14070.pdf
Alternatively, you can download the original dataset from this link https://drive.google.com/file/d/1lyfqIwlLSClhgrCutWuEe_IACNq6XNUt/view?usp=sharing | LinhDuong/chatdoctor-200k | [
"license:apache-2.0",
"arxiv:2303.14070",
"region:us"
]
| 2023-03-28T06:33:20+00:00 | {"license": "apache-2.0"} | 2023-03-28T06:58:46+00:00 | [
"2303.14070"
]
| []
| TAGS
#license-apache-2.0 #arxiv-2303.14070 #region-us
| This ChatDoctor-200K dataset is collected from this paper URL
Alternatively, you can download the original dataset from this link URL | []
| [
"TAGS\n#license-apache-2.0 #arxiv-2303.14070 #region-us \n"
]
| [
22
]
| [
"passage: TAGS\n#license-apache-2.0 #arxiv-2303.14070 #region-us \n"
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|
bdad53d6613ba6c05b9d1fd496d5226d804ca3a3 | # Dataset Card for "SQuAD-V1-in-SQuAD-format"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | TimoImhof/SQuAD-V1-in-SQuAD-format | [
"region:us"
]
| 2023-03-28T07:16:22+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "answers", "struct": [{"name": "answer_start", "sequence": "int64"}, {"name": "text", "sequence": "string"}]}], "splits": [{"name": "unmodified", "num_bytes": 9570059, "num_examples": 10552}, {"name": "modified_30_percent", "num_bytes": 9577354, "num_examples": 10552}, {"name": "modified_100_percent", "num_bytes": 9594310, "num_examples": 10552}], "download_size": 9334653, "dataset_size": 28741723}} | 2023-04-01T12:25:06+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "SQuAD-V1-in-SQuAD-format"
More Information needed | [
"# Dataset Card for \"SQuAD-V1-in-SQuAD-format\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"SQuAD-V1-in-SQuAD-format\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"SQuAD-V1-in-SQuAD-format\"\n\nMore Information needed"
]
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|
387c7336f7aba97721ff2b3939b6dd11bb7cfd40 | # Dataset Card for "energieag-gpt3-synthetic-data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/miketake | [
"region:us"
]
| 2023-03-28T07:31:02+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "intent", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 36645, "num_examples": 390}], "download_size": 15353, "dataset_size": 36645}} | 2023-03-28T10:14:20+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "energieag-gpt3-synthetic-data"
More Information needed | [
"# Dataset Card for \"energieag-gpt3-synthetic-data\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"energieag-gpt3-synthetic-data\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"energieag-gpt3-synthetic-data\"\n\nMore Information needed"
]
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|
12fec9a89c75d79422584f17f35400d81987cf88 | # Dataset Card for "vqa-with-coco-img-3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ryanramos/vqa-with-coco-img-3 | [
"region:us"
]
| 2023-03-28T07:43:57+00:00 | {"dataset_info": {"features": [{"name": "license", "dtype": "int64"}, {"name": "file_name", "dtype": "string"}, {"name": "coco_url", "dtype": "string"}, {"name": "height", "dtype": "int64"}, {"name": "width", "dtype": "int64"}, {"name": "date_captured", "dtype": "string"}, {"name": "flickr_url", "dtype": "string"}, {"name": "captions", "list": [{"name": "caption", "dtype": "string"}, {"name": "id", "dtype": "int64"}]}, {"name": "questions", "list": [{"name": "answer_type", "dtype": "string"}, {"name": "answers", "list": [{"name": "answer", "dtype": "string"}, {"name": "answer_confidence", "dtype": "string"}, {"name": "answer_id", "dtype": "int64"}]}, {"name": "image_id", "dtype": "int64"}, {"name": "multiple_choice_answer", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "question_id", "dtype": "int64"}, {"name": "question_type", "dtype": "string"}]}, {"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 889819603.5, "num_examples": 16500}], "download_size": 860459417, "dataset_size": 889819603.5}} | 2023-03-28T07:44:21+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "vqa-with-coco-img-3"
More Information needed | [
"# Dataset Card for \"vqa-with-coco-img-3\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"vqa-with-coco-img-3\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"vqa-with-coco-img-3\"\n\nMore Information needed"
]
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|
13493bbced2afde11adf3f108d92492e5d2e83bf | # Dataset Card for "HotpotQA-in-SQuAD-format"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | TimoImhof/HotpotQA-in-SQuAD-format | [
"region:us"
]
| 2023-03-28T07:45:20+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "answers", "struct": [{"name": "answer_start", "sequence": "int64"}, {"name": "text", "sequence": "string"}]}], "splits": [{"name": "unmodified", "num_bytes": 7657753, "num_examples": 6113}, {"name": "modified_30_percent", "num_bytes": 7662336, "num_examples": 6113}, {"name": "modified_100_percent", "num_bytes": 7673192, "num_examples": 6113}], "download_size": 12541785, "dataset_size": 22993281}} | 2023-04-01T12:50:00+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "HotpotQA-in-SQuAD-format"
More Information needed | [
"# Dataset Card for \"HotpotQA-in-SQuAD-format\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"HotpotQA-in-SQuAD-format\"\n\nMore Information needed"
]
| [
6,
22
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"HotpotQA-in-SQuAD-format\"\n\nMore Information needed"
]
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|
59bbdfd526efde5e660295896eafdc4c349355b3 | # Dataset Card for "TriviaQA-in-SQuAD-format"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | TimoImhof/TriviaQA-in-SQuAD-format | [
"region:us"
]
| 2023-03-28T07:48:36+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "answers", "struct": [{"name": "answer_start", "sequence": "int64"}, {"name": "text", "sequence": "string"}]}], "splits": [{"name": "unmodified", "num_bytes": 22886661, "num_examples": 15368}, {"name": "modified_30_percent", "num_bytes": 22899894, "num_examples": 15368}, {"name": "modified_100_percent", "num_bytes": 22929228, "num_examples": 15368}], "download_size": 40760032, "dataset_size": 68715783}} | 2023-04-01T12:43:14+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "TriviaQA-in-SQuAD-format"
More Information needed | [
"# Dataset Card for \"TriviaQA-in-SQuAD-format\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"TriviaQA-in-SQuAD-format\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"TriviaQA-in-SQuAD-format\"\n\nMore Information needed"
]
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|
a49b8461b1649f73f713c4679d7def6ece58a2fd | # Dataset Card for "vqa-with-coco-img-0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ryanramos/vqa-with-coco-img-0 | [
"region:us"
]
| 2023-03-28T07:50:38+00:00 | {"dataset_info": {"features": [{"name": "license", "dtype": "int64"}, {"name": "file_name", "dtype": "string"}, {"name": "coco_url", "dtype": "string"}, {"name": "height", "dtype": "int64"}, {"name": "width", "dtype": "int64"}, {"name": "date_captured", "dtype": "string"}, {"name": "flickr_url", "dtype": "string"}, {"name": "captions", "list": [{"name": "caption", "dtype": "string"}, {"name": "id", "dtype": "int64"}]}, {"name": "questions", "list": [{"name": "answer_type", "dtype": "string"}, {"name": "answers", "list": [{"name": "answer", "dtype": "string"}, {"name": "answer_confidence", "dtype": "string"}, {"name": "answer_id", "dtype": "int64"}]}, {"name": "image_id", "dtype": "int64"}, {"name": "multiple_choice_answer", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "question_id", "dtype": "int64"}, {"name": "question_type", "dtype": "string"}]}, {"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 879162652.5, "num_examples": 16500}], "download_size": 849463217, "dataset_size": 879162652.5}} | 2023-03-28T07:51:20+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "vqa-with-coco-img-0"
More Information needed | [
"# Dataset Card for \"vqa-with-coco-img-0\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"vqa-with-coco-img-0\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"vqa-with-coco-img-0\"\n\nMore Information needed"
]
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|
1354cac3e6477e937ee98d0cc42c199bab495384 | # Dataset Card for "vqa-with-coco-img-2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ryanramos/vqa-with-coco-img-2 | [
"region:us"
]
| 2023-03-28T07:51:56+00:00 | {"dataset_info": {"features": [{"name": "license", "dtype": "int64"}, {"name": "file_name", "dtype": "string"}, {"name": "coco_url", "dtype": "string"}, {"name": "height", "dtype": "int64"}, {"name": "width", "dtype": "int64"}, {"name": "date_captured", "dtype": "string"}, {"name": "flickr_url", "dtype": "string"}, {"name": "captions", "list": [{"name": "caption", "dtype": "string"}, {"name": "id", "dtype": "int64"}]}, {"name": "questions", "list": [{"name": "answer_type", "dtype": "string"}, {"name": "answers", "list": [{"name": "answer", "dtype": "string"}, {"name": "answer_confidence", "dtype": "string"}, {"name": "answer_id", "dtype": "int64"}]}, {"name": "image_id", "dtype": "int64"}, {"name": "multiple_choice_answer", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "question_id", "dtype": "int64"}, {"name": "question_type", "dtype": "string"}]}, {"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 883496810.5, "num_examples": 16500}], "download_size": 854737087, "dataset_size": 883496810.5}} | 2023-03-28T07:52:36+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "vqa-with-coco-img-2"
More Information needed | [
"# Dataset Card for \"vqa-with-coco-img-2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"vqa-with-coco-img-2\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"vqa-with-coco-img-2\"\n\nMore Information needed"
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|
4981699b1e45744922c54aaffba75adffb47ad26 | # Usage
```
from datasets import load_dataset
dataset = load_dataset('TeamSODA/LibriTTS', streaming=True)
``` | TeamSODA/LibriTTS | [
"region:us"
]
| 2023-03-28T08:03:34+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}], "splits": [{"name": "train", "num_bytes": 8027118681.616, "num_examples": 33236}], "download_size": 9205367507, "dataset_size": 8027118681.616}} | 2023-03-28T11:31:28+00:00 | []
| []
| TAGS
#region-us
| # Usage
| [
"# Usage"
]
| [
"TAGS\n#region-us \n",
"# Usage"
]
| [
6,
3
]
| [
"passage: TAGS\n#region-us \n# Usage"
]
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|
68acecd112131982547cfe09d46bc4db3c89b7a6 | # Dataset Card for "vqa-with-coco-img-4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ryanramos/vqa-with-coco-img-4 | [
"region:us"
]
| 2023-03-28T08:55:00+00:00 | {"dataset_info": {"features": [{"name": "license", "dtype": "int64"}, {"name": "file_name", "dtype": "string"}, {"name": "coco_url", "dtype": "string"}, {"name": "height", "dtype": "int64"}, {"name": "width", "dtype": "int64"}, {"name": "date_captured", "dtype": "string"}, {"name": "flickr_url", "dtype": "string"}, {"name": "captions", "list": [{"name": "caption", "dtype": "string"}, {"name": "id", "dtype": "int64"}]}, {"name": "questions", "list": [{"name": "answer_type", "dtype": "string"}, {"name": "answers", "list": [{"name": "answer", "dtype": "string"}, {"name": "answer_confidence", "dtype": "string"}, {"name": "answer_id", "dtype": "int64"}]}, {"name": "image_id", "dtype": "int64"}, {"name": "multiple_choice_answer", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "question_id", "dtype": "int64"}, {"name": "question_type", "dtype": "string"}]}, {"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 915855341.125, "num_examples": 16783}], "download_size": 885712220, "dataset_size": 915855341.125}} | 2023-03-28T08:55:49+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "vqa-with-coco-img-4"
More Information needed | [
"# Dataset Card for \"vqa-with-coco-img-4\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"vqa-with-coco-img-4\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"vqa-with-coco-img-4\"\n\nMore Information needed"
]
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|
0c309c5d062688a417e15fdae4d038f69c262467 | # Dataset Card for "google-presto-german"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/google-presto-german | [
"region:us"
]
| 2023-03-28T09:09:32+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label_name", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3217962, "num_examples": 41756}, {"name": "test", "num_bytes": 2263704, "num_examples": 29356}, {"name": "validation", "num_bytes": 962391, "num_examples": 12472}], "download_size": 2163028, "dataset_size": 6444057}} | 2023-03-29T10:34:40+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "google-presto-german"
More Information needed | [
"# Dataset Card for \"google-presto-german\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"google-presto-german\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"google-presto-german\"\n\nMore Information needed"
]
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|
0fee41680f5030d55a5d403a791dd504c6a65aae | # Dataset Card for "raw_parts_of_kbuhist2_v3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Riksarkivet/Diachronic_swe | [
"region:us"
]
| 2023-03-28T09:48:34+00:00 | {"dataset_info": {"features": [{"name": "index", "dtype": "int64"}, {"name": "ID", "dtype": "string"}, {"name": "H1_sv", "dtype": "string"}, {"name": "corpus", "dtype": "string"}, {"name": "H3_corpus_sv", "dtype": "string"}, {"name": "dataset", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "subtitle", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "meta_year", "dtype": "string"}, {"name": "originDate", "dtype": "string"}, {"name": "retrieveDate", "dtype": "string"}, {"name": "printedDate", "dtype": "string"}, {"name": "genre", "dtype": "string"}, {"name": "subgenre", "dtype": "string"}, {"name": "digitisationMethod", "dtype": "string"}, {"name": "annotationMethod", "dtype": "string"}, {"name": "sentenceOrder", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4613839319.867484, "num_examples": 13540111}], "download_size": 889888861, "dataset_size": 4613839319.867484}} | 2023-03-28T10:25:30+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "raw_parts_of_kbuhist2_v3"
More Information needed | [
"# Dataset Card for \"raw_parts_of_kbuhist2_v3\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"raw_parts_of_kbuhist2_v3\"\n\nMore Information needed"
]
| [
6,
24
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"raw_parts_of_kbuhist2_v3\"\n\nMore Information needed"
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|
9b0dac90d467869184a81108e07cf2603648fd28 |
# Dataset Card for JuICe (A Large Scale Distantly Supervised Dataset for Open Domain Context-based Code Generation)
## Dataset Description
- **Homepage: [GitHub](https://github.com/rajasagashe/juice)**
- **Paper: [JuICe: A Large Scale Distantly Supervised Dataset for Open Domain Context-based Code Generation](https://arxiv.org/abs/1910.02216)**
### Dataset Summary
The JuICe dataset was developed to study code generation conditioned on a long context history. For that purpose, the authors collected data from interactive coding environements (ICE) in Jupyter notebooks (JuICE). Since these notebooks contain interleaved code snippet cells and natural language markdown they are particularly useful for this task.
While the original [dataset](https://github.com/rajasagashe/juice) also contains a corpus of 1.5 million jupyter notebook examples, this version (redistributed on the hub for easier access), contains only the curated test set of 3.7K instances based on online programming assignments.
### Supported Tasks and Leaderboards
This dataset can be used for Natural Language to Code Generation tasks.
### Languages
Python, English
### Data Instances
```python
dataset = load_dataset("koutch/JuICe")
DatasetDict({
validation: Dataset({
features: ['question', 'answer', 'notebook'],
num_rows: 1831
})
test: Dataset({
features: ['question', 'answer', 'notebook'],
num_rows: 2115
})
})
```
### Data Fields
In short, each data row contains a programming `question` and an code `answer` to that programming question, answer which might require contextualized information in previous cells in the `notebook`
- `question`: Contextualized programming exercise/question to be answerred in the last cell of the jupyter notebook
- `notebook`: The ordered sequence of jupyter notebook cells which forms the full exercise context
- `text`: the raw content of the cell
- `cell_type`: code, markdown, or raw
- `answer`: The code implementation which answers to the question
### Data Splits
* validation: the dev split in the original paper
* test: the test split in the original paper
## Additional Information
### Citation Information
If you use the dataset or the code in your research, please cite the following paper:
```
@article{DBLP:journals/corr/abs-1910-02216,
author = {Rajas Agashe and
Srinivasan Iyer and
Luke Zettlemoyer},
title = {JuICe: {A} Large Scale Distantly Supervised Dataset for Open Domain
Context-based Code Generation},
journal = {CoRR},
volume = {abs/1910.02216},
year = {2019},
url = {http://arxiv.org/abs/1910.02216},
eprinttype = {arXiv},
eprint = {1910.02216},
timestamp = {Wed, 09 Oct 2019 14:07:58 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1910-02216.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` | koutch/JuICe | [
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-4.0",
"code",
"arxiv:1910.02216",
"region:us"
]
| 2023-03-28T11:01:43+00:00 | {"language": ["en"], "license": "cc-by-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["question-answering"], "pretty_name": "juice", "dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "notebook", "sequence": [{"name": "text", "dtype": "string"}, {"name": "cell_type", "dtype": "string"}]}], "splits": [{"name": "validation", "num_bytes": 19578995, "num_examples": 1831}, {"name": "test", "num_bytes": 21651420, "num_examples": 2115}], "download_size": 155457826, "dataset_size": 41230415}, "tags": ["code"]} | 2023-03-29T06:34:03+00:00 | [
"1910.02216"
]
| [
"en"
]
| TAGS
#task_categories-question-answering #size_categories-1K<n<10K #language-English #license-cc-by-4.0 #code #arxiv-1910.02216 #region-us
|
# Dataset Card for JuICe (A Large Scale Distantly Supervised Dataset for Open Domain Context-based Code Generation)
## Dataset Description
- Homepage: GitHub
- Paper: JuICe: A Large Scale Distantly Supervised Dataset for Open Domain Context-based Code Generation
### Dataset Summary
The JuICe dataset was developed to study code generation conditioned on a long context history. For that purpose, the authors collected data from interactive coding environements (ICE) in Jupyter notebooks (JuICE). Since these notebooks contain interleaved code snippet cells and natural language markdown they are particularly useful for this task.
While the original dataset also contains a corpus of 1.5 million jupyter notebook examples, this version (redistributed on the hub for easier access), contains only the curated test set of 3.7K instances based on online programming assignments.
### Supported Tasks and Leaderboards
This dataset can be used for Natural Language to Code Generation tasks.
### Languages
Python, English
### Data Instances
### Data Fields
In short, each data row contains a programming 'question' and an code 'answer' to that programming question, answer which might require contextualized information in previous cells in the 'notebook'
- 'question': Contextualized programming exercise/question to be answerred in the last cell of the jupyter notebook
- 'notebook': The ordered sequence of jupyter notebook cells which forms the full exercise context
- 'text': the raw content of the cell
- 'cell_type': code, markdown, or raw
- 'answer': The code implementation which answers to the question
### Data Splits
* validation: the dev split in the original paper
* test: the test split in the original paper
## Additional Information
If you use the dataset or the code in your research, please cite the following paper:
| [
"# Dataset Card for JuICe (A Large Scale Distantly Supervised Dataset for Open Domain Context-based Code Generation)",
"## Dataset Description\n\n- Homepage: GitHub \n- Paper: JuICe: A Large Scale Distantly Supervised Dataset for Open Domain Context-based Code Generation",
"### Dataset Summary\n\nThe JuICe dataset was developed to study code generation conditioned on a long context history. For that purpose, the authors collected data from interactive coding environements (ICE) in Jupyter notebooks (JuICE). Since these notebooks contain interleaved code snippet cells and natural language markdown they are particularly useful for this task.\n\nWhile the original dataset also contains a corpus of 1.5 million jupyter notebook examples, this version (redistributed on the hub for easier access), contains only the curated test set of 3.7K instances based on online programming assignments.",
"### Supported Tasks and Leaderboards\n\nThis dataset can be used for Natural Language to Code Generation tasks.",
"### Languages\n\nPython, English",
"### Data Instances",
"### Data Fields\n\nIn short, each data row contains a programming 'question' and an code 'answer' to that programming question, answer which might require contextualized information in previous cells in the 'notebook'\n\n- 'question': Contextualized programming exercise/question to be answerred in the last cell of the jupyter notebook\n- 'notebook': The ordered sequence of jupyter notebook cells which forms the full exercise context\n - 'text': the raw content of the cell\n - 'cell_type': code, markdown, or raw\n- 'answer': The code implementation which answers to the question",
"### Data Splits\n\n* validation: the dev split in the original paper\n* test: the test split in the original paper",
"## Additional Information\n\n\n\nIf you use the dataset or the code in your research, please cite the following paper:"
]
| [
"TAGS\n#task_categories-question-answering #size_categories-1K<n<10K #language-English #license-cc-by-4.0 #code #arxiv-1910.02216 #region-us \n",
"# Dataset Card for JuICe (A Large Scale Distantly Supervised Dataset for Open Domain Context-based Code Generation)",
"## Dataset Description\n\n- Homepage: GitHub \n- Paper: JuICe: A Large Scale Distantly Supervised Dataset for Open Domain Context-based Code Generation",
"### Dataset Summary\n\nThe JuICe dataset was developed to study code generation conditioned on a long context history. For that purpose, the authors collected data from interactive coding environements (ICE) in Jupyter notebooks (JuICE). Since these notebooks contain interleaved code snippet cells and natural language markdown they are particularly useful for this task.\n\nWhile the original dataset also contains a corpus of 1.5 million jupyter notebook examples, this version (redistributed on the hub for easier access), contains only the curated test set of 3.7K instances based on online programming assignments.",
"### Supported Tasks and Leaderboards\n\nThis dataset can be used for Natural Language to Code Generation tasks.",
"### Languages\n\nPython, English",
"### Data Instances",
"### Data Fields\n\nIn short, each data row contains a programming 'question' and an code 'answer' to that programming question, answer which might require contextualized information in previous cells in the 'notebook'\n\n- 'question': Contextualized programming exercise/question to be answerred in the last cell of the jupyter notebook\n- 'notebook': The ordered sequence of jupyter notebook cells which forms the full exercise context\n - 'text': the raw content of the cell\n - 'cell_type': code, markdown, or raw\n- 'answer': The code implementation which answers to the question",
"### Data Splits\n\n* validation: the dev split in the original paper\n* test: the test split in the original paper",
"## Additional Information\n\n\n\nIf you use the dataset or the code in your research, please cite the following paper:"
]
| [
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| [
"passage: TAGS\n#task_categories-question-answering #size_categories-1K<n<10K #language-English #license-cc-by-4.0 #code #arxiv-1910.02216 #region-us \n# Dataset Card for JuICe (A Large Scale Distantly Supervised Dataset for Open Domain Context-based Code Generation)## Dataset Description\n\n- Homepage: GitHub \n- Paper: JuICe: A Large Scale Distantly Supervised Dataset for Open Domain Context-based Code Generation### Dataset Summary\n\nThe JuICe dataset was developed to study code generation conditioned on a long context history. For that purpose, the authors collected data from interactive coding environements (ICE) in Jupyter notebooks (JuICE). Since these notebooks contain interleaved code snippet cells and natural language markdown they are particularly useful for this task.\n\nWhile the original dataset also contains a corpus of 1.5 million jupyter notebook examples, this version (redistributed on the hub for easier access), contains only the curated test set of 3.7K instances based on online programming assignments.### Supported Tasks and Leaderboards\n\nThis dataset can be used for Natural Language to Code Generation tasks.### Languages\n\nPython, English### Data Instances### Data Fields\n\nIn short, each data row contains a programming 'question' and an code 'answer' to that programming question, answer which might require contextualized information in previous cells in the 'notebook'\n\n- 'question': Contextualized programming exercise/question to be answerred in the last cell of the jupyter notebook\n- 'notebook': The ordered sequence of jupyter notebook cells which forms the full exercise context\n - 'text': the raw content of the cell\n - 'cell_type': code, markdown, or raw\n- 'answer': The code implementation which answers to the question### Data Splits\n\n* validation: the dev split in the original paper\n* test: the test split in the original paper## Additional Information\n\n\n\nIf you use the dataset or the code in your research, please cite the following paper:"
]
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|
fdd2d23cc4803bc7e1a09578678ebf83342f88b1 | ### dataset
Alpaca and Belle 1M for [InstructGLM](https://github.com/yuekaizhang/InstructGLM) github project training
| yuekai/belle_1M_and_alpaca_cleaned | [
"region:us"
]
| 2023-03-28T11:06:35+00:00 | {} | 2023-03-28T12:12:50+00:00 | []
| []
| TAGS
#region-us
| ### dataset
Alpaca and Belle 1M for InstructGLM github project training
| [
"### dataset \nAlpaca and Belle 1M for InstructGLM github project training"
]
| [
"TAGS\n#region-us \n",
"### dataset \nAlpaca and Belle 1M for InstructGLM github project training"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n### dataset \nAlpaca and Belle 1M for InstructGLM github project training"
]
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|
ac34becb1c86d2b018b2b76b66c6ca442f776eb2 | # Dataset Card for "boc"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cta2106/boc | [
"region:us"
]
| 2023-03-28T11:12:13+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "country_iso_2", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "filename", "dtype": "string"}, {"name": "filetype", "dtype": "string"}, {"name": "is_manual", "dtype": "string"}, {"name": "speaker", "dtype": "null"}, {"name": "time_ingested", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 662649, "num_examples": 224}], "download_size": 0, "dataset_size": 662649}} | 2023-03-29T14:37:12+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "boc"
More Information needed | [
"# Dataset Card for \"boc\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"boc\"\n\nMore Information needed"
]
| [
6,
12
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"boc\"\n\nMore Information needed"
]
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|
40bd5a07cc06db6c5de8765cfebcc00ed70fe077 | # Dataset Card for "UA_speech_noisereduced_test-0.9_train-0.1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | AravindVadlapudi02/UA_speech_noisereduced_test-0.9_train-0.1 | [
"region:us"
]
| 2023-03-28T11:51:52+00:00 | {"dataset_info": {"features": [{"name": "label", "dtype": {"class_label": {"names": {"0": "healthy control", "1": "pathology"}}}}, {"name": "input_features", "sequence": {"sequence": "float32"}}], "splits": [{"name": "train", "num_bytes": 535865256, "num_examples": 558}, {"name": "test", "num_bytes": 4831430292, "num_examples": 5031}], "download_size": 620175139, "dataset_size": 5367295548}} | 2023-03-28T11:53:03+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "UA_speech_noisereduced_test-0.9_train-0.1"
More Information needed | [
"# Dataset Card for \"UA_speech_noisereduced_test-0.9_train-0.1\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"UA_speech_noisereduced_test-0.9_train-0.1\"\n\nMore Information needed"
]
| [
6,
28
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"UA_speech_noisereduced_test-0.9_train-0.1\"\n\nMore Information needed"
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|
1288dba25d21b0752e1678111543cfc6c25a13ee | # Dataset Card for "ada_key_merge_subset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | loubnabnl/ada_key_merge_subset | [
"region:us"
]
| 2023-03-28T12:41:09+00:00 | {"dataset_info": {"features": [{"name": "entities", "list": [{"name": "context", "dtype": "string"}, {"name": "end", "dtype": "int64"}, {"name": "score", "dtype": "float32"}, {"name": "start", "dtype": "int64"}, {"name": "tag", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "max_stars_repo_path", "dtype": "string"}, {"name": "max_stars_repo_name", "dtype": "string"}, {"name": "max_stars_count", "dtype": "int64"}, {"name": "content", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "new_content", "dtype": "string"}, {"name": "modified", "dtype": "bool"}, {"name": "references", "dtype": "string"}, {"name": "fixed_content", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 54890027, "num_examples": 580}], "download_size": 7819078, "dataset_size": 54890027}} | 2023-03-28T12:41:13+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ada_key_merge_subset"
More Information needed | [
"# Dataset Card for \"ada_key_merge_subset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"ada_key_merge_subset\"\n\nMore Information needed"
]
| [
6,
19
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"ada_key_merge_subset\"\n\nMore Information needed"
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|
dfb7bc41da7f3c5b03d80b82a47ac17094347eee | # Dataset Card for "petfinder-dogs"
## Dataset Description
- **Homepage:** https://www.petfinder.com/
- **Paper:** N.A.
- **Leaderboard:** N.A.
- **Point of Contact:** N.A.
### Dataset Summary
Contains 700k+ 300px-wide images of 150k+ distinct dogs extracted from the PetFinder API in March 2023.
Only those having at least 4 photos are present: Each subject has between 4 and 12 photos.
This dataset aims to simplify AI work based on dogs' images and avoid rescraping thousands of them from the PetFinder API again and again.
| drzraf/petfinder-dogs | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"license:unknown",
"pets",
"dogs",
"animals",
"photos",
"region:us"
]
| 2023-03-28T12:51:31+00:00 | {"annotations_creators": [], "language_creators": ["crowdsourced"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["image-classification"], "task_ids": ["multi-class-image-classification"], "pretty_name": "300px dogs photos from Petfinder", "tags": ["pets", "dogs", "animals", "photos"]} | 2023-03-31T17:47:42+00:00 | []
| []
| TAGS
#task_categories-image-classification #task_ids-multi-class-image-classification #language_creators-crowdsourced #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #license-unknown #pets #dogs #animals #photos #region-us
| # Dataset Card for "petfinder-dogs"
## Dataset Description
- Homepage: URL
- Paper: N.A.
- Leaderboard: N.A.
- Point of Contact: N.A.
### Dataset Summary
Contains 700k+ 300px-wide images of 150k+ distinct dogs extracted from the PetFinder API in March 2023.
Only those having at least 4 photos are present: Each subject has between 4 and 12 photos.
This dataset aims to simplify AI work based on dogs' images and avoid rescraping thousands of them from the PetFinder API again and again.
| [
"# Dataset Card for \"petfinder-dogs\"",
"## Dataset Description\n\n- Homepage: URL\n- Paper: N.A.\n- Leaderboard: N.A.\n- Point of Contact: N.A.",
"### Dataset Summary\n\nContains 700k+ 300px-wide images of 150k+ distinct dogs extracted from the PetFinder API in March 2023.\n\nOnly those having at least 4 photos are present: Each subject has between 4 and 12 photos.\n\nThis dataset aims to simplify AI work based on dogs' images and avoid rescraping thousands of them from the PetFinder API again and again."
]
| [
"TAGS\n#task_categories-image-classification #task_ids-multi-class-image-classification #language_creators-crowdsourced #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #license-unknown #pets #dogs #animals #photos #region-us \n",
"# Dataset Card for \"petfinder-dogs\"",
"## Dataset Description\n\n- Homepage: URL\n- Paper: N.A.\n- Leaderboard: N.A.\n- Point of Contact: N.A.",
"### Dataset Summary\n\nContains 700k+ 300px-wide images of 150k+ distinct dogs extracted from the PetFinder API in March 2023.\n\nOnly those having at least 4 photos are present: Each subject has between 4 and 12 photos.\n\nThis dataset aims to simplify AI work based on dogs' images and avoid rescraping thousands of them from the PetFinder API again and again."
]
| [
89,
12,
32,
87
]
| [
"passage: TAGS\n#task_categories-image-classification #task_ids-multi-class-image-classification #language_creators-crowdsourced #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #license-unknown #pets #dogs #animals #photos #region-us \n# Dataset Card for \"petfinder-dogs\"## Dataset Description\n\n- Homepage: URL\n- Paper: N.A.\n- Leaderboard: N.A.\n- Point of Contact: N.A.### Dataset Summary\n\nContains 700k+ 300px-wide images of 150k+ distinct dogs extracted from the PetFinder API in March 2023.\n\nOnly those having at least 4 photos are present: Each subject has between 4 and 12 photos.\n\nThis dataset aims to simplify AI work based on dogs' images and avoid rescraping thousands of them from the PetFinder API again and again."
]
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|
ab37731f21431a793618cba9dfd9c1f11445a594 | # Dataset Card for "SBU_caption"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | guangyil/SBU_caption | [
"region:us"
]
| 2023-03-28T13:19:53+00:00 | {"dataset_info": {"features": [{"name": "bert_token", "sequence": "int64"}, {"name": "gpt2_token", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 287684206.968832, "num_examples": 954428}, {"name": "test", "num_bytes": 288158.040064, "num_examples": 956}], "download_size": 76486474, "dataset_size": 287972365.008896}} | 2023-03-28T13:20:43+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "SBU_caption"
More Information needed | [
"# Dataset Card for \"SBU_caption\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"SBU_caption\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"SBU_caption\"\n\nMore Information needed"
]
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|
43afcb1aa5e2b041c751a4c6a27fc72f5372d26b | # Dataset Card for "librispeech_tiny"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Isma/librispeech_tiny | [
"region:us"
]
| 2023-03-28T13:25:57+00:00 | {"dataset_info": {"features": [{"name": "file", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "text", "dtype": "string"}, {"name": "speaker_id", "dtype": "int64"}, {"name": "chapter_id", "dtype": "int64"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "10mn", "num_bytes": 11602736.683065278, "num_examples": 50}, {"name": "1h", "num_bytes": 65903566.59981079, "num_examples": 284}, {"name": "2h", "num_bytes": 133663590.94891201, "num_examples": 576}], "download_size": 202468853, "dataset_size": 211169894.23178807}} | 2023-03-28T13:31:24+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "librispeech_tiny"
More Information needed | [
"# Dataset Card for \"librispeech_tiny\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"librispeech_tiny\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"librispeech_tiny\"\n\nMore Information needed"
]
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|
c1ae2bada807877856fa13da6c918cf4c4e55c7f | # Dataset Card for "word_aligned_translation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | spdenisov/word_aligned_translation | [
"region:us"
]
| 2023-03-28T14:16:14+00:00 | {"dataset_info": {"features": [{"name": "target_language", "dtype": "string"}, {"name": "source_language", "dtype": "string"}, {"name": "source_words", "sequence": "string"}, {"name": "target_lines", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 22748376, "num_examples": 48560}], "download_size": 15270181, "dataset_size": 22748376}} | 2023-03-28T14:16:19+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "word_aligned_translation"
More Information needed | [
"# Dataset Card for \"word_aligned_translation\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"word_aligned_translation\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"word_aligned_translation\"\n\nMore Information needed"
]
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|
4da766cc98b0dbded6a6d8b9b0072c1b81c78819 | # Dataset Card for "somos-alpaca-es-intro"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | dvilasuero/somos-alpaca-es-intro | [
"region:us"
]
| 2023-03-28T14:16:52+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "null"}, {"name": "inputs", "struct": [{"name": "1-instruction", "dtype": "string"}, {"name": "2-input", "dtype": "string"}, {"name": "3-output", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "input", "sequence": "float64"}, {"name": "instruction", "sequence": "float64"}, {"name": "output", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 11389691, "num_examples": 606}], "download_size": 0, "dataset_size": 11389691}} | 2023-04-09T08:21:06+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "somos-alpaca-es-intro"
More Information needed | [
"# Dataset Card for \"somos-alpaca-es-intro\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"somos-alpaca-es-intro\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"somos-alpaca-es-intro\"\n\nMore Information needed"
]
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|
9bd6cdf31373f1e86e3c47b254114418bb35dedc | # AutoTrain Dataset for project: sweet-potato-classification
## Dataset Description
This dataset has been automatically processed by AutoTrain for project sweet-potato-classification.
### 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": "<256x192 RGB PIL image>",
"target": 0
},
{
"image": "<256x192 RGB 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=['Leaf rust', 'Root rot', 'alternaria_sweet_potato_leaf_spot'], 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 | 46 |
| valid | 13 |
| bazudde/Sweetpotato_images | [
"task_categories:image-classification",
"region:us"
]
| 2023-03-28T14:39:38+00:00 | {"task_categories": ["image-classification"]} | 2023-03-28T14:40:52+00:00 | []
| []
| TAGS
#task_categories-image-classification #region-us
| AutoTrain Dataset for project: sweet-potato-classification
==========================================================
Dataset Description
-------------------
This dataset has been automatically processed by AutoTrain for project sweet-potato-classification.
### Languages
The BCP-47 code for the dataset's language is unk.
Dataset Structure
-----------------
### Data Instances
A sample from this dataset looks as follows:
### Dataset Fields
The dataset has the following fields (also called "features"):
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| [
"### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
"TAGS\n#task_categories-image-classification #region-us \n",
"### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
17,
27,
17,
23,
27
]
| [
"passage: TAGS\n#task_categories-image-classification #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA sample from this dataset looks as follows:### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
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|
4f604cdd780d135dbdfe2b55008b2ad6e63c4d21 | # Dataset Card for "processed4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | spdenisov/processed4 | [
"region:us"
]
| 2023-03-28T15:23:26+00:00 | {"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 52509660, "num_examples": 58560}], "download_size": 17379477, "dataset_size": 52509660}} | 2023-03-28T16:20:57+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "processed4"
More Information needed | [
"# Dataset Card for \"processed4\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"processed4\"\n\nMore Information needed"
]
| [
6,
13
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"processed4\"\n\nMore Information needed"
]
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|
bb3a697e95c102fcf62479012f3992393f76af06 | # Dataset Card for "disc_cla_plenaria-2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Sleoruiz/disc_cla_plenaria-2 | [
"region:us"
]
| 2023-03-28T15:34:44+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "null"}, {"name": "inputs", "struct": [{"name": "comision", "dtype": "string"}, {"name": "fecha_gaceta", "dtype": "string"}, {"name": "gaceta_numero", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "sequence": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "dtype": "null"}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 162072571, "num_examples": 42666}], "download_size": 65858974, "dataset_size": 162072571}} | 2023-03-28T15:35:35+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "disc_cla_plenaria-2"
More Information needed | [
"# Dataset Card for \"disc_cla_plenaria-2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"disc_cla_plenaria-2\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"disc_cla_plenaria-2\"\n\nMore Information needed"
]
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|
798c567f69c8f4b12fc191015e59ee34e9afe00d |
This dataset splits the original [CodeAlpaca dataset](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k) into train and test splits. | HuggingFaceH4/CodeAlpaca_20K | [
"task_categories:text-generation",
"license:cc",
"region:us"
]
| 2023-03-28T16:18:25+00:00 | {"license": "cc", "task_categories": ["text-generation"]} | 2023-03-28T16:26:28+00:00 | []
| []
| TAGS
#task_categories-text-generation #license-cc #region-us
|
This dataset splits the original CodeAlpaca dataset into train and test splits. | []
| [
"TAGS\n#task_categories-text-generation #license-cc #region-us \n"
]
| [
22
]
| [
"passage: TAGS\n#task_categories-text-generation #license-cc #region-us \n"
]
| [
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|
0b5b719a24477994a96cff9d9f403363fb65652b |
The data is exactly like the original GSM8k (https://huggingface.co/datasets/gsm8k ), but with the label consisting of the correct answer(one number) only. | skrishna/gsm8k_only_answer | [
"license:mit",
"region:us"
]
| 2023-03-28T16:24:21+00:00 | {"license": "mit"} | 2023-03-28T17:09:01+00:00 | []
| []
| TAGS
#license-mit #region-us
|
The data is exactly like the original GSM8k (URL ), but with the label consisting of the correct answer(one number) only. | []
| [
"TAGS\n#license-mit #region-us \n"
]
| [
11
]
| [
"passage: TAGS\n#license-mit #region-us \n"
]
| [
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|
2f1f0d3b5f4c3662d0ce1c105a029d636a5e33ce | # Dataset Card for "processed5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | spdenisov/processed5 | [
"region:us"
]
| 2023-03-28T16:37:00+00:00 | {"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 45720965, "num_examples": 48560}], "download_size": 14638764, "dataset_size": 45720965}} | 2023-03-28T16:37:03+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "processed5"
More Information needed | [
"# Dataset Card for \"processed5\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"processed5\"\n\nMore Information needed"
]
| [
6,
13
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"processed5\"\n\nMore Information needed"
]
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|
f90b68394bef857638298beb5056f724d613c114 | # Dataset Card for "processed_trans"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | spdenisov/processed_trans | [
"region:us"
]
| 2023-03-28T16:47:30+00:00 | {"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}, {"name": "length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 183120649.48606578, "num_examples": 151998}], "download_size": 40922404, "dataset_size": 183120649.48606578}} | 2023-03-30T18:00:31+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "processed_trans"
More Information needed | [
"# Dataset Card for \"processed_trans\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"processed_trans\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"processed_trans\"\n\nMore Information needed"
]
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|
5342ae26e4d41b78854f58a1cc50114581638962 | # Dataset Card for "processed_word"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | spdenisov/processed_word | [
"region:us"
]
| 2023-03-28T16:47:59+00:00 | {"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}, {"name": "length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 118733211.26505354, "num_examples": 48517}], "download_size": 21466771, "dataset_size": 118733211.26505354}} | 2023-03-30T18:13:28+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "processed_word"
More Information needed | [
"# Dataset Card for \"processed_word\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"processed_word\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"processed_word\"\n\nMore Information needed"
]
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|
9fae08be3e409bb2fa5af45d0d5fe77cffb86094 | # Dataset Card for "zb4xnuMlahk"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | keithhon/zb4xnuMlahk | [
"region:us"
]
| 2023-03-28T17:29:50+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "sentence", "dtype": "string"}, {"name": "youtube_video_id", "dtype": "string"}, {"name": "input_features", "sequence": {"sequence": "float32"}}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 401505573.0, "num_examples": 385}], "download_size": 79562986, "dataset_size": 401505573.0}} | 2023-03-28T17:29:58+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "zb4xnuMlahk"
More Information needed | [
"# Dataset Card for \"zb4xnuMlahk\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"zb4xnuMlahk\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"zb4xnuMlahk\"\n\nMore Information needed"
]
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|
f43218ab2007ae30e4c243f3d4422490199c8d47 | # Dataset Card for "seabream-freshness_v0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | TheSleepyJo/seabream-freshness_v0 | [
"region:us"
]
| 2023-03-28T18:01:33+00:00 | {"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 783931699.0, "num_examples": 16}], "download_size": 51974320, "dataset_size": 783931699.0}} | 2023-03-28T18:07:44+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "seabream-freshness_v0"
More Information needed | [
"# Dataset Card for \"seabream-freshness_v0\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"seabream-freshness_v0\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"seabream-freshness_v0\"\n\nMore Information needed"
]
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|
677fbe26adab0f5b07711c29a1adb3ac45c1287b | # Dataset Validated from https://huggingface.co/spaces/dariolopez/argilla-elena-reddit-c-ssrs-suicide-dataset-es
https://dariolopez-argilla-elena-reddit-c-ssrs-suic-00dc6af.hf.space | dariolopez/argilla-elena-reddit-c-ssrs-suicide-dataset-es | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:es",
"region:us"
]
| 2023-03-28T20:12:40+00:00 | {"language": ["es"], "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"]} | 2023-03-29T15:17:58+00:00 | []
| [
"es"
]
| TAGS
#task_categories-text-classification #size_categories-1K<n<10K #language-Spanish #region-us
| # Dataset Validated from URL
URL | [
"# Dataset Validated from URL\n\nURL"
]
| [
"TAGS\n#task_categories-text-classification #size_categories-1K<n<10K #language-Spanish #region-us \n",
"# Dataset Validated from URL\n\nURL"
]
| [
34,
9
]
| [
"passage: TAGS\n#task_categories-text-classification #size_categories-1K<n<10K #language-Spanish #region-us \n# Dataset Validated from URL\n\nURL"
]
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|
91a2f3a43534ce5095292c1290660a76aa3dbee7 | # Dataset Card for "city-council-gpt3-silver-standard-summaries"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | alex2awesome/city-council-gpt3-silver-standard-summaries | [
"region:us"
]
| 2023-03-28T20:16:13+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "summary", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 21349237, "num_examples": 3250}], "download_size": 9490470, "dataset_size": 21349237}} | 2023-03-28T20:20:18+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "city-council-gpt3-silver-standard-summaries"
More Information needed | [
"# Dataset Card for \"city-council-gpt3-silver-standard-summaries\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"city-council-gpt3-silver-standard-summaries\"\n\nMore Information needed"
]
| [
6,
26
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"city-council-gpt3-silver-standard-summaries\"\n\nMore Information needed"
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|
986cd4b3ed266060d64106360d69b9f890e2fd97 | # Dataset Card for "logiclm_bookcorpus_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | chanind/logiclm_bookcorpus_dataset | [
"region:us"
]
| 2023-03-28T20:43:21+00:00 | {"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "special_tokens_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 9635097081.0, "num_examples": 72888253}], "download_size": 3280936965, "dataset_size": 9635097081.0}} | 2023-04-17T19:11:24+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "logiclm_bookcorpus_dataset"
More Information needed | [
"# Dataset Card for \"logiclm_bookcorpus_dataset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"logiclm_bookcorpus_dataset\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"logiclm_bookcorpus_dataset\"\n\nMore Information needed"
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|
a6f9a327ef25c4e0c0757d0140bf31e849230780 | # Dataset Card for "tokenized_udtree"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | spdenisov/tokenized_udtree | [
"region:us"
]
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| []
| TAGS
#region-us
| # Dataset Card for "tokenized_udtree"
More Information needed | [
"# Dataset Card for \"tokenized_udtree\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"tokenized_udtree\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"tokenized_udtree\"\n\nMore Information needed"
]
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|
6d9ab0255988ab69b3c8fcc7b5ac547a32926411 | # Dataset Card for "tokenized_udtrees_trunc"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | spdenisov/tokenized_udtrees_trunc | [
"region:us"
]
| 2023-03-28T21:21:32+00:00 | {"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "labels", "sequence": "int64"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "length", "dtype": "int64"}], "splits": [{"name": "fr_0", "num_bytes": 72813504, "num_examples": 34912}, {"name": "fr_1", "num_bytes": 106992505, "num_examples": 34884}, {"name": "fr_2", "num_bytes": 118066880, "num_examples": 34858}, {"name": "fr_3", "num_bytes": 103747628, "num_examples": 34886}, {"name": "fr_4", "num_bytes": 179954204, "num_examples": 33724}, {"name": "fr_5", "num_bytes": 142682805, "num_examples": 34681}, {"name": "fr_6", "num_bytes": 103669700, "num_examples": 34887}, {"name": "ar_0", "num_bytes": 76392970, "num_examples": 21341}, {"name": "ar_1", "num_bytes": 99682724, "num_examples": 20211}, {"name": "ar_2", "num_bytes": 104828728, "num_examples": 20561}, {"name": "ar_3", "num_bytes": 120387755, "num_examples": 18591}, {"name": "ar_4", "num_bytes": 110845444, 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| []
| TAGS
#region-us
| # Dataset Card for "tokenized_udtrees_trunc"
More Information needed | [
"# Dataset Card for \"tokenized_udtrees_trunc\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"tokenized_udtrees_trunc\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"tokenized_udtrees_trunc\"\n\nMore Information needed"
]
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|
939a5b7acaaa6b462d7e05b3ba89fc3a13e02728 | # Dataset Card for "sidewalk-imagery"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ateebak/sidewalk-imagery | [
"region:us"
]
| 2023-03-28T21:48:11+00:00 | {"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 3138225.0, "num_examples": 10}], "download_size": 3139735, "dataset_size": 3138225.0}} | 2023-03-28T21:48:16+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "sidewalk-imagery"
More Information needed | [
"# Dataset Card for \"sidewalk-imagery\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"sidewalk-imagery\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"sidewalk-imagery\"\n\nMore Information needed"
]
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|
28e70aa35c91c539893eb39b9c23c5b56a0c27bb |
# Dataset Validated from https://huggingface.co/spaces/dariolopez/argilla-reddit-c-ssrs-suicide-dataset-es
https://dariolopez-argilla-reddit-c-ssrs-suicide-da-5219f8e.hf.space | dariolopez/argilla-reddit-c-ssrs-suicide-dataset-es | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:es",
"region:us"
]
| 2023-03-28T21:54:06+00:00 | {"language": ["es"], "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"]} | 2023-03-28T21:55:33+00:00 | []
| [
"es"
]
| TAGS
#task_categories-text-classification #size_categories-1K<n<10K #language-Spanish #region-us
|
# Dataset Validated from URL
URL | [
"# Dataset Validated from URL\n\nURL"
]
| [
"TAGS\n#task_categories-text-classification #size_categories-1K<n<10K #language-Spanish #region-us \n",
"# Dataset Validated from URL\n\nURL"
]
| [
34,
9
]
| [
"passage: TAGS\n#task_categories-text-classification #size_categories-1K<n<10K #language-Spanish #region-us \n# Dataset Validated from URL\n\nURL"
]
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a94626aaaaaddc55fcfca224b75e313f18860a5a | # Dataset Card for "full-wiki-segments-parquet"
Source: Downloaded from: https://zenodo.org/record/6149599
| McGill-NLP/full-wiki-segments-parquet | [
"region:us"
]
| 2023-03-28T22:05:27+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "sub_title", "dtype": "string"}, {"name": "index", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 13223584797, "num_examples": 25700592}, {"name": "collection", "num_bytes": 13223584797, "num_examples": 25700592}], "download_size": 15182013003, "dataset_size": 26447169594}} | 2023-04-13T20:49:11+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "full-wiki-segments-parquet"
Source: Downloaded from: URL
| [
"# Dataset Card for \"full-wiki-segments-parquet\"\n\nSource: Downloaded from: URL"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"full-wiki-segments-parquet\"\n\nSource: Downloaded from: URL"
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| [
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|
089056f66581442bea3f488acfd941ccfa116d8b |
# Dataset Card for Syosetu711K
*The BigKnow2022 dataset and its subsets are not yet complete. Not all information here may be accurate or accessible.*
## Dataset Description
- **Homepage:** (TODO)
- **Repository:** <https://github.com/RyokoAI/BigKnow2022>
- **Paper:** N/A
- **Leaderboard:** N/A
- **Point of Contact:** Ronsor/undeleted <[email protected]>
### Dataset Summary
Syosetu711K is a dataset composed of approximately 711,700 novels scraped from the Japanese novel self-publishing
website Syosetuka ni Narou (JA: 小説家になろう, lit. "Let's Become a Novelist") between March 26 and March 27, 2023.
The dataset contains most if not all novels published on the site, regardless of length or quality; however, we
include metadata so users of this dataset can filter and evaluate its contents.
Syosetu711Kは、日本の小説投稿サイト「小説家になろう」から2023年3月26日から27日にかけてスクレイプされた約711,700冊の小説から
構成されるデータセットです。このデータセットには、長さや品質に関係なく、サイトに掲載されているほとんどの小説が含まれています。ただし、
各小説のIDも含まれているため、小説家になろうAPIを使ってその情報を検索することができます。
### Supported Tasks and Leaderboards
This dataset is primarily intended for unsupervised training of text generation models; however, it may be useful for other purposes.
* text-classification
* text-generation
### Languages
* Japanese
## Dataset Structure
### Data Instances
```json
{
"text": "【小説タイトル】\n焼けて爛れる恋よりも、微睡む優しい愛が欲しい\n【Ｎコード】\nＮ５０２９ＩＤ\n【作者名】\n秋暁秋季\n【あらすじ】\n俺の彼女は物凄く気の多い人だった。\nお眼鏡に適う奴が居れば、瞳孔を蕩
けさせる人だった。\nその癖照れ屋で、すぐに目を逸らす。\nな...",
"meta": {
"subset": "syosetu",
"q": 0.6,
"id": "N5029ID",
"author": "秋暁秋季",
"userid": 719797,
"title": "焼けて爛れる恋よりも、微睡む優しい愛が欲しい",
"length": 871,
"points": 0,
"lang": "ja",
"chapters": 1,
"keywords": ["気が多い", "浮気性", "無愛想", "照れる", "嫉妬", "好みではない", "クソデカ感情", "空気のような安心感"],
"isr15": 0,
"genre": 102,
"biggenre": 1
}
}
{
"text": "【小説タイトル】\n【能力者】\n【Ｎコード】\nＮ９８６４ＩＢ\n【作者名】\n夢音いちご\n【あらすじ】\n私立アビリティ学園。\n小・中・高・大が一貫となった、大規模な名門校。\nそして、ここは規模の大きさだけ
でなく、ある特殊な制度を設けて\nいることでも有名だ。\nそれ...",
"meta": {
"subset": "syosetu",
"q": 0.6,
"id": "N9864IB",
"author": "夢音いちご",
"userid": 1912777,
"title": "【能力者】",
"length": 2334,
"points": 0,
"lang": "ja",
"chapters": 2,
"keywords": ["ガールズラブ", "身分差", "伝奇", "日常", "青春", "ラブコメ", "女主人公", "学園", "魔法", "超能力"],
"isr15": 0,
"genre": 202,
"biggenre": 2
}
}
```
### Data Fields
* `text`: the actual novel text, all chapters
* `meta`: novel metadata
* `subset`: dataset tag: `syosetu`
* `lang`: dataset language: `ja` (Japanese)
* `id`: novel ID/ncode
* `author`: author name
* `userid`: author user ID
* `title`: novel title
* `length`: novel length in words
* `points`: global points (corresponds to `global_point` from the Syosetu API)
* `q`: q-score (quality score) calculated based on `points`
* `chapters`: number of chapters (corresponds to `general_all_no` from the Syosetu API)
* `keywords`: array of novel keywords (corresponds to `keyword` from the Syosetu API, split on spaces)
* `isr15`: whether the novel is rated R15+
* `genre`: novel genre ID (optional, see Syosetu API documentation)
* `biggenre`: general novel genre ID (optional, see Syosetu API documentation)
* `isr18`: whether the novel is rated R18+
* `nocgenre`: novel genre ID (optional, only available if `isr18` is true, see Syosetu API documentation)
*For further reference, see the Syosetuka ni Narou API documentation: <https://dev.syosetu.com/man/api/> (JA).*
#### Q-Score Distribution
```
0.00: 0
0.10: 0
0.20: 0
0.30: 0
0.40: 0
0.50: 213005
0.60: 331393
0.70: 101971
0.80: 63877
0.90: 1542
1.00: 2
```
### Data Splits
No splitting of the data was performed.
## Dataset Creation
### Curation Rationale
Syosetuka ni Narou is the most popular website in Japan for authors wishing to self-publish their novels online. Many works on
the site been picked up by large commercial publishers. Because of this, we believe that this dataset provides a large corpus
of high-quality, creative content in the Japanese language.
### Source Data
#### Initial Data Collection and Normalization
*More information about any referenced scripts, commands, or programs used may be found in the BigKnow2022 GitHub repository.*
First, metadata for all novels on the site was gathered into a JSON lines (JSONL) file. The Syosetuka ni Narou API was used to
obtain this information.
Second, this listing was used to create a secondary text file containing a list of only the novel "ncodes," or IDs. This
secondary file was distributed to downloader nodes.
Third, the sister site <https://pdfnovels.net> was queried with each novel ID, and the resulting PDF was saved for later processing.
Fourth, the `pdftotext` tool was used to convert the PDF files to text documents. A few other scripts were then used to clean up
the resulting text files.
Finally, the text files and other metadata were converted into the specified data field schema above, and the resulting JSON entries
were concatenated into the Syosetu711K dataset. The version uploaded to this repository, however, is split into multiple files,
numbered 00 through 20 inclusive.
#### Who are the source language producers?
The authors of each novel.
### Annotations
#### Annotation process
Titles and general genre were collected alongside the novel text and IDs.
#### Who are the annotators?
There were no human annotators.
### Personal and Sensitive Information
The dataset contains only works of fiction, and we do not believe it contains any PII.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended to be useful for anyone who wishes to train a model to generate "more entertaining" content in Japanese.
It may also be useful for other languages depending on your language model.
### Discussion of Biases
This dataset is composed of fictional works by various authors. Because of this fact, the contents of this dataset will reflect
the biases of those authors. **Additionally, this dataset contains NSFW material and was not filtered. Beware of stereotypes.**
### Other Known Limitations
N/A
## Additional Information
### Dataset Curators
Ronsor Labs
### Licensing Information
Apache 2.0, for all parts of which Ronsor Labs or the Ryoko AI Production Committee may be considered authors. All other material is
distributed under fair use principles.
### Citation Information
```
@misc{ryokoai2023-bigknow2022,
title = {BigKnow2022: Bringing Language Models Up to Speed},
author = {Ronsor},
year = {2023},
howpublished = {\url{https://github.com/RyokoAI/BigKnow2022}},
}
```
### Contributions
Thanks to @ronsor (GH) for gathering this dataset. | RyokoAI/Syosetu711K | [
"task_categories:text-classification",
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:ja",
"license:apache-2.0",
"novel",
"training",
"region:us"
]
| 2023-03-28T22:57:10+00:00 | {"language": ["ja"], "license": "apache-2.0", "size_categories": ["100K<n<1M"], "task_categories": ["text-classification", "text-generation"], "pretty_name": "Syosetuka ni Narou 711K", "tags": ["novel", "training"]} | 2023-04-05T00:13:44+00:00 | []
| [
"ja"
]
| TAGS
#task_categories-text-classification #task_categories-text-generation #size_categories-100K<n<1M #language-Japanese #license-apache-2.0 #novel #training #region-us
|
# Dataset Card for Syosetu711K
*The BigKnow2022 dataset and its subsets are not yet complete. Not all information here may be accurate or accessible.*
## Dataset Description
- Homepage: (TODO)
- Repository: <URL
- Paper: N/A
- Leaderboard: N/A
- Point of Contact: Ronsor/undeleted <ronsor@URL>
### Dataset Summary
Syosetu711K is a dataset composed of approximately 711,700 novels scraped from the Japanese novel self-publishing
website Syosetuka ni Narou (JA: 小説家になろう, lit. "Let's Become a Novelist") between March 26 and March 27, 2023.
The dataset contains most if not all novels published on the site, regardless of length or quality; however, we
include metadata so users of this dataset can filter and evaluate its contents.
Syosetu711Kは、日本の小説投稿サイト「小説家になろう」から2023年3月26日から27日にかけてスクレイプされた約711,700冊の小説から
構成されるデータセットです。このデータセットには、長さや品質に関係なく、サイトに掲載されているほとんどの小説が含まれています。ただし、
各小説のIDも含まれているため、小説家になろうAPIを使ってその情報を検索することができます。
### Supported Tasks and Leaderboards
This dataset is primarily intended for unsupervised training of text generation models; however, it may be useful for other purposes.
* text-classification
* text-generation
### Languages
* Japanese
## Dataset Structure
### Data Instances
### Data Fields
* 'text': the actual novel text, all chapters
* 'meta': novel metadata
* 'subset': dataset tag: 'syosetu'
* 'lang': dataset language: 'ja' (Japanese)
* 'id': novel ID/ncode
* 'author': author name
* 'userid': author user ID
* 'title': novel title
* 'length': novel length in words
* 'points': global points (corresponds to 'global_point' from the Syosetu API)
* 'q': q-score (quality score) calculated based on 'points'
* 'chapters': number of chapters (corresponds to 'general_all_no' from the Syosetu API)
* 'keywords': array of novel keywords (corresponds to 'keyword' from the Syosetu API, split on spaces)
* 'isr15': whether the novel is rated R15+
* 'genre': novel genre ID (optional, see Syosetu API documentation)
* 'biggenre': general novel genre ID (optional, see Syosetu API documentation)
* 'isr18': whether the novel is rated R18+
* 'nocgenre': novel genre ID (optional, only available if 'isr18' is true, see Syosetu API documentation)
*For further reference, see the Syosetuka ni Narou API documentation: <URL (JA).*
#### Q-Score Distribution
### Data Splits
No splitting of the data was performed.
## Dataset Creation
### Curation Rationale
Syosetuka ni Narou is the most popular website in Japan for authors wishing to self-publish their novels online. Many works on
the site been picked up by large commercial publishers. Because of this, we believe that this dataset provides a large corpus
of high-quality, creative content in the Japanese language.
### Source Data
#### Initial Data Collection and Normalization
*More information about any referenced scripts, commands, or programs used may be found in the BigKnow2022 GitHub repository.*
First, metadata for all novels on the site was gathered into a JSON lines (JSONL) file. The Syosetuka ni Narou API was used to
obtain this information.
Second, this listing was used to create a secondary text file containing a list of only the novel "ncodes," or IDs. This
secondary file was distributed to downloader nodes.
Third, the sister site <URL> was queried with each novel ID, and the resulting PDF was saved for later processing.
Fourth, the 'pdftotext' tool was used to convert the PDF files to text documents. A few other scripts were then used to clean up
the resulting text files.
Finally, the text files and other metadata were converted into the specified data field schema above, and the resulting JSON entries
were concatenated into the Syosetu711K dataset. The version uploaded to this repository, however, is split into multiple files,
numbered 00 through 20 inclusive.
#### Who are the source language producers?
The authors of each novel.
### Annotations
#### Annotation process
Titles and general genre were collected alongside the novel text and IDs.
#### Who are the annotators?
There were no human annotators.
### Personal and Sensitive Information
The dataset contains only works of fiction, and we do not believe it contains any PII.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended to be useful for anyone who wishes to train a model to generate "more entertaining" content in Japanese.
It may also be useful for other languages depending on your language model.
### Discussion of Biases
This dataset is composed of fictional works by various authors. Because of this fact, the contents of this dataset will reflect
the biases of those authors. Additionally, this dataset contains NSFW material and was not filtered. Beware of stereotypes.
### Other Known Limitations
N/A
## Additional Information
### Dataset Curators
Ronsor Labs
### Licensing Information
Apache 2.0, for all parts of which Ronsor Labs or the Ryoko AI Production Committee may be considered authors. All other material is
distributed under fair use principles.
### Contributions
Thanks to @ronsor (GH) for gathering this dataset. | [
"# Dataset Card for Syosetu711K\n\n*The BigKnow2022 dataset and its subsets are not yet complete. Not all information here may be accurate or accessible.*",
"## Dataset Description\n\n- Homepage: (TODO)\n- Repository: <URL\n- Paper: N/A \n- Leaderboard: N/A\n- Point of Contact: Ronsor/undeleted <ronsor@URL>",
"### Dataset Summary\n\nSyosetu711K is a dataset composed of approximately 711,700 novels scraped from the Japanese novel self-publishing\nwebsite Syosetuka ni Narou (JA: 小説家になろう, lit. \"Let's Become a Novelist\") between March 26 and March 27, 2023.\nThe dataset contains most if not all novels published on the site, regardless of length or quality; however, we\ninclude metadata so users of this dataset can filter and evaluate its contents.\n\nSyosetu711Kは、日本の小説投稿サイト「小説家になろう」から2023年3月26日から27日にかけてスクレイプされた約711,700冊の小説から\n構成されるデータセットです。このデータセットには、長さや品質に関係なく、サイトに掲載されているほとんどの小説が含まれています。ただし、\n各小説のIDも含まれているため、小説家になろうAPIを使ってその情報を検索することができます。",
"### Supported Tasks and Leaderboards\n\nThis dataset is primarily intended for unsupervised training of text generation models; however, it may be useful for other purposes.\n\n* text-classification\n* text-generation",
"### Languages\n\n* Japanese",
"## Dataset Structure",
"### Data Instances",
"### Data Fields\n\n* 'text': the actual novel text, all chapters\n* 'meta': novel metadata\n * 'subset': dataset tag: 'syosetu'\n * 'lang': dataset language: 'ja' (Japanese)\n * 'id': novel ID/ncode\n * 'author': author name\n * 'userid': author user ID\n * 'title': novel title\n * 'length': novel length in words\n * 'points': global points (corresponds to 'global_point' from the Syosetu API)\n * 'q': q-score (quality score) calculated based on 'points'\n * 'chapters': number of chapters (corresponds to 'general_all_no' from the Syosetu API)\n * 'keywords': array of novel keywords (corresponds to 'keyword' from the Syosetu API, split on spaces)\n * 'isr15': whether the novel is rated R15+\n * 'genre': novel genre ID (optional, see Syosetu API documentation)\n * 'biggenre': general novel genre ID (optional, see Syosetu API documentation)\n * 'isr18': whether the novel is rated R18+\n * 'nocgenre': novel genre ID (optional, only available if 'isr18' is true, see Syosetu API documentation)\n \n*For further reference, see the Syosetuka ni Narou API documentation: <URL (JA).*",
"#### Q-Score Distribution",
"### Data Splits\n\nNo splitting of the data was performed.",
"## Dataset Creation",
"### Curation Rationale\n\nSyosetuka ni Narou is the most popular website in Japan for authors wishing to self-publish their novels online. Many works on\nthe site been picked up by large commercial publishers. Because of this, we believe that this dataset provides a large corpus\nof high-quality, creative content in the Japanese language.",
"### Source Data",
"#### Initial Data Collection and Normalization\n\n*More information about any referenced scripts, commands, or programs used may be found in the BigKnow2022 GitHub repository.*\n\nFirst, metadata for all novels on the site was gathered into a JSON lines (JSONL) file. The Syosetuka ni Narou API was used to\nobtain this information.\n\nSecond, this listing was used to create a secondary text file containing a list of only the novel \"ncodes,\" or IDs. This\nsecondary file was distributed to downloader nodes.\n\nThird, the sister site <URL> was queried with each novel ID, and the resulting PDF was saved for later processing.\n\nFourth, the 'pdftotext' tool was used to convert the PDF files to text documents. A few other scripts were then used to clean up\nthe resulting text files.\n\nFinally, the text files and other metadata were converted into the specified data field schema above, and the resulting JSON entries\nwere concatenated into the Syosetu711K dataset. The version uploaded to this repository, however, is split into multiple files,\nnumbered 00 through 20 inclusive.",
"#### Who are the source language producers?\n\nThe authors of each novel.",
"### Annotations",
"#### Annotation process\n\nTitles and general genre were collected alongside the novel text and IDs.",
"#### Who are the annotators?\n\nThere were no human annotators.",
"### Personal and Sensitive Information\n\nThe dataset contains only works of fiction, and we do not believe it contains any PII.",
"## Considerations for Using the Data",
"### Social Impact of Dataset\n\nThis dataset is intended to be useful for anyone who wishes to train a model to generate \"more entertaining\" content in Japanese.\nIt may also be useful for other languages depending on your language model.",
"### Discussion of Biases\n\nThis dataset is composed of fictional works by various authors. Because of this fact, the contents of this dataset will reflect\nthe biases of those authors. Additionally, this dataset contains NSFW material and was not filtered. Beware of stereotypes.",
"### Other Known Limitations\n\nN/A",
"## Additional Information",
"### Dataset Curators\n\nRonsor Labs",
"### Licensing Information\n\nApache 2.0, for all parts of which Ronsor Labs or the Ryoko AI Production Committee may be considered authors. All other material is\ndistributed under fair use principles.",
"### Contributions\n\nThanks to @ronsor (GH) for gathering this dataset."
]
| [
"TAGS\n#task_categories-text-classification #task_categories-text-generation #size_categories-100K<n<1M #language-Japanese #license-apache-2.0 #novel #training #region-us \n",
"# Dataset Card for Syosetu711K\n\n*The BigKnow2022 dataset and its subsets are not yet complete. Not all information here may be accurate or accessible.*",
"## Dataset Description\n\n- Homepage: (TODO)\n- Repository: <URL\n- Paper: N/A \n- Leaderboard: N/A\n- Point of Contact: Ronsor/undeleted <ronsor@URL>",
"### Dataset Summary\n\nSyosetu711K is a dataset composed of approximately 711,700 novels scraped from the Japanese novel self-publishing\nwebsite Syosetuka ni Narou (JA: 小説家になろう, lit. \"Let's Become a Novelist\") between March 26 and March 27, 2023.\nThe dataset contains most if not all novels published on the site, regardless of length or quality; however, we\ninclude metadata so users of this dataset can filter and evaluate its contents.\n\nSyosetu711Kは、日本の小説投稿サイト「小説家になろう」から2023年3月26日から27日にかけてスクレイプされた約711,700冊の小説から\n構成されるデータセットです。このデータセットには、長さや品質に関係なく、サイトに掲載されているほとんどの小説が含まれています。ただし、\n各小説のIDも含まれているため、小説家になろうAPIを使ってその情報を検索することができます。",
"### Supported Tasks and Leaderboards\n\nThis dataset is primarily intended for unsupervised training of text generation models; however, it may be useful for other purposes.\n\n* text-classification\n* text-generation",
"### Languages\n\n* Japanese",
"## Dataset Structure",
"### Data Instances",
"### Data Fields\n\n* 'text': the actual novel text, all chapters\n* 'meta': novel metadata\n * 'subset': dataset tag: 'syosetu'\n * 'lang': dataset language: 'ja' (Japanese)\n * 'id': novel ID/ncode\n * 'author': author name\n * 'userid': author user ID\n * 'title': novel title\n * 'length': novel length in words\n * 'points': global points (corresponds to 'global_point' from the Syosetu API)\n * 'q': q-score (quality score) calculated based on 'points'\n * 'chapters': number of chapters (corresponds to 'general_all_no' from the Syosetu API)\n * 'keywords': array of novel keywords (corresponds to 'keyword' from the Syosetu API, split on spaces)\n * 'isr15': whether the novel is rated R15+\n * 'genre': novel genre ID (optional, see Syosetu API documentation)\n * 'biggenre': general novel genre ID (optional, see Syosetu API documentation)\n * 'isr18': whether the novel is rated R18+\n * 'nocgenre': novel genre ID (optional, only available if 'isr18' is true, see Syosetu API documentation)\n \n*For further reference, see the Syosetuka ni Narou API documentation: <URL (JA).*",
"#### Q-Score Distribution",
"### Data Splits\n\nNo splitting of the data was performed.",
"## Dataset Creation",
"### Curation Rationale\n\nSyosetuka ni Narou is the most popular website in Japan for authors wishing to self-publish their novels online. Many works on\nthe site been picked up by large commercial publishers. Because of this, we believe that this dataset provides a large corpus\nof high-quality, creative content in the Japanese language.",
"### Source Data",
"#### Initial Data Collection and Normalization\n\n*More information about any referenced scripts, commands, or programs used may be found in the BigKnow2022 GitHub repository.*\n\nFirst, metadata for all novels on the site was gathered into a JSON lines (JSONL) file. The Syosetuka ni Narou API was used to\nobtain this information.\n\nSecond, this listing was used to create a secondary text file containing a list of only the novel \"ncodes,\" or IDs. This\nsecondary file was distributed to downloader nodes.\n\nThird, the sister site <URL> was queried with each novel ID, and the resulting PDF was saved for later processing.\n\nFourth, the 'pdftotext' tool was used to convert the PDF files to text documents. A few other scripts were then used to clean up\nthe resulting text files.\n\nFinally, the text files and other metadata were converted into the specified data field schema above, and the resulting JSON entries\nwere concatenated into the Syosetu711K dataset. The version uploaded to this repository, however, is split into multiple files,\nnumbered 00 through 20 inclusive.",
"#### Who are the source language producers?\n\nThe authors of each novel.",
"### Annotations",
"#### Annotation process\n\nTitles and general genre were collected alongside the novel text and IDs.",
"#### Who are the annotators?\n\nThere were no human annotators.",
"### Personal and Sensitive Information\n\nThe dataset contains only works of fiction, and we do not believe it contains any PII.",
"## Considerations for Using the Data",
"### Social Impact of Dataset\n\nThis dataset is intended to be useful for anyone who wishes to train a model to generate \"more entertaining\" content in Japanese.\nIt may also be useful for other languages depending on your language model.",
"### Discussion of Biases\n\nThis dataset is composed of fictional works by various authors. Because of this fact, the contents of this dataset will reflect\nthe biases of those authors. Additionally, this dataset contains NSFW material and was not filtered. Beware of stereotypes.",
"### Other Known Limitations\n\nN/A",
"## Additional Information",
"### Dataset Curators\n\nRonsor Labs",
"### Licensing Information\n\nApache 2.0, for all parts of which Ronsor Labs or the Ryoko AI Production Committee may be considered authors. All other material is\ndistributed under fair use principles.",
"### Contributions\n\nThanks to @ronsor (GH) for gathering this dataset."
]
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"passage: TAGS\n#task_categories-text-classification #task_categories-text-generation #size_categories-100K<n<1M #language-Japanese #license-apache-2.0 #novel #training #region-us \n# Dataset Card for Syosetu711K\n\n*The BigKnow2022 dataset and its subsets are not yet complete. Not all information here may be accurate or accessible.*## Dataset Description\n\n- Homepage: (TODO)\n- Repository: <URL\n- Paper: N/A \n- Leaderboard: N/A\n- Point of Contact: Ronsor/undeleted <ronsor@URL>### Dataset Summary\n\nSyosetu711K is a dataset composed of approximately 711,700 novels scraped from the Japanese novel self-publishing\nwebsite Syosetuka ni Narou (JA: 小説家になろう, lit. \"Let's Become a Novelist\") between March 26 and March 27, 2023.\nThe dataset contains most if not all novels published on the site, regardless of length or quality; however, we\ninclude metadata so users of this dataset can filter and evaluate its contents.\n\nSyosetu711Kは、日本の小説投稿サイト「小説家になろう」から2023年3月26日から27日にかけてスクレイプされた約711,700冊の小説から\n構成されるデータセットです。このデータセットには、長さや品質に関係なく、サイトに掲載されているほとんどの小説が含まれています。ただし、\n各小説のIDも含まれているため、小説家になろうAPIを使ってその情報を検索することができます。### Supported Tasks and Leaderboards\n\nThis dataset is primarily intended for unsupervised training of text generation models; however, it may be useful for other purposes.\n\n* text-classification\n* text-generation### Languages\n\n* Japanese## Dataset Structure### Data Instances",
"passage: ### Data Fields\n\n* 'text': the actual novel text, all chapters\n* 'meta': novel metadata\n * 'subset': dataset tag: 'syosetu'\n * 'lang': dataset language: 'ja' (Japanese)\n * 'id': novel ID/ncode\n * 'author': author name\n * 'userid': author user ID\n * 'title': novel title\n * 'length': novel length in words\n * 'points': global points (corresponds to 'global_point' from the Syosetu API)\n * 'q': q-score (quality score) calculated based on 'points'\n * 'chapters': number of chapters (corresponds to 'general_all_no' from the Syosetu API)\n * 'keywords': array of novel keywords (corresponds to 'keyword' from the Syosetu API, split on spaces)\n * 'isr15': whether the novel is rated R15+\n * 'genre': novel genre ID (optional, see Syosetu API documentation)\n * 'biggenre': general novel genre ID (optional, see Syosetu API documentation)\n * 'isr18': whether the novel is rated R18+\n * 'nocgenre': novel genre ID (optional, only available if 'isr18' is true, see Syosetu API documentation)\n \n*For further reference, see the Syosetuka ni Narou API documentation: <URL (JA).*#### Q-Score Distribution### Data Splits\n\nNo splitting of the data was performed.## Dataset Creation### Curation Rationale\n\nSyosetuka ni Narou is the most popular website in Japan for authors wishing to self-publish their novels online. Many works on\nthe site been picked up by large commercial publishers. Because of this, we believe that this dataset provides a large corpus\nof high-quality, creative content in the Japanese language.### Source Data#### Initial Data Collection and Normalization\n\n*More information about any referenced scripts, commands, or programs used may be found in the BigKnow2022 GitHub repository.*\n\nFirst, metadata for all novels on the site was gathered into a JSON lines (JSONL) file. The Syosetuka ni Narou API was used to\nobtain this information.\n\nSecond, this listing was used to create a secondary text file containing a list of only the novel \"ncodes,\" or IDs. This\nsecondary file was distributed to downloader nodes.\n\nThird, the sister site <URL> was queried with each novel ID, and the resulting PDF was saved for later processing.\n\nFourth, the 'pdftotext' tool was used to convert the PDF files to text documents. A few other scripts were then used to clean up\nthe resulting text files.\n\nFinally, the text files and other metadata were converted into the specified data field schema above, and the resulting JSON entries\nwere concatenated into the Syosetu711K dataset. The version uploaded to this repository, however, is split into multiple files,\nnumbered 00 through 20 inclusive.#### Who are the source language producers?\n\nThe authors of each novel.### Annotations#### Annotation process\n\nTitles and general genre were collected alongside the novel text and IDs.#### Who are the annotators?\n\nThere were no human annotators.### Personal and Sensitive Information\n\nThe dataset contains only works of fiction, and we do not believe it contains any PII.## Considerations for Using the Data"
]
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|
666c51fac4966b14293f818d926db57d305fe257 | # Dataset Card for "tomatoesCWSI"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | mattyhatch/tomatoesCWSI | [
"region:us"
]
| 2023-03-28T23:35:16+00:00 | {"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 1034721.0, "num_examples": 6}], "download_size": 134150, "dataset_size": 1034721.0}} | 2023-03-28T23:35:19+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "tomatoesCWSI"
More Information needed | [
"# Dataset Card for \"tomatoesCWSI\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"tomatoesCWSI\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"tomatoesCWSI\"\n\nMore Information needed"
]
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|
d37d2754d2b6fa872303c5569aa5d874c0d7d8d8 | # Dataset Card for "t0_zsnoopt"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | mistobaan/t0_zsnoopt | [
"region:us"
]
| 2023-03-28T23:37:24+00:00 | {"dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "task_source", "dtype": "string"}, {"name": "task_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 32033020523, "num_examples": 42881000}], "download_size": 17532883525, "dataset_size": 32033020523}} | 2023-03-28T23:48:50+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "t0_zsnoopt"
More Information needed | [
"# Dataset Card for \"t0_zsnoopt\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"t0_zsnoopt\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"t0_zsnoopt\"\n\nMore Information needed"
]
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|
2602be50411bf7c658121c63da0624b4b17b1e65 | # Dataset Card for "tomatoes"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | mattyhatch/tomatoes | [
"region:us"
]
| 2023-03-28T23:47:51+00:00 | {"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 174243.0, "num_examples": 1}], "download_size": 23312, "dataset_size": 174243.0}} | 2023-03-28T23:47:55+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "tomatoes"
More Information needed | [
"# Dataset Card for \"tomatoes\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"tomatoes\"\n\nMore Information needed"
]
| [
6,
13
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"tomatoes\"\n\nMore Information needed"
]
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|
c548f65065d74270ddf4b0bcd17eff319235159e | # Dataset Card for "processed_bert_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | swadesh7/processed_bert_dataset | [
"region:us"
]
| 2023-03-29T00:51:03+00:00 | {"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "special_tokens_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 3600, "num_examples": 1}], "download_size": 4997, "dataset_size": 3600}} | 2023-03-30T00:00:44+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "processed_bert_dataset"
More Information needed | [
"# Dataset Card for \"processed_bert_dataset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"processed_bert_dataset\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"processed_bert_dataset\"\n\nMore Information needed"
]
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|
2ec396c9153265c1e0e22fb77ed207c3733d8f7c | # Dataset Card for "tomatoesTest1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | mattyhatch/tomatoesTest1 | [
"region:us"
]
| 2023-03-29T00:51:12+00:00 | {"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 346819.0, "num_examples": 2}], "download_size": 43706, "dataset_size": 346819.0}} | 2023-03-29T01:37:37+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "tomatoesTest1"
More Information needed | [
"# Dataset Card for \"tomatoesTest1\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"tomatoesTest1\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"tomatoesTest1\"\n\nMore Information needed"
]
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|
91db457a1f21b45c68912bfbca2e911b565a292d | # Dataset Card for "logo-splitted"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Babypotatotang/logo-splitted | [
"region:us"
]
| 2023-03-29T01:08:38+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 321359468.84, "num_examples": 24080}, {"name": "test", "num_bytes": 82173680.498, "num_examples": 6021}], "download_size": 266044858, "dataset_size": 403533149.33799994}} | 2023-03-29T01:14:46+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "logo-splitted"
More Information needed | [
"# Dataset Card for \"logo-splitted\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"logo-splitted\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"logo-splitted\"\n\nMore Information needed"
]
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|
e7170659b7635f20ff9c68de9d93bc6bf66897e3 | # Dataset Card for "logo-weighted-name"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Babypotatotang/logo-weighted-name | [
"region:us"
]
| 2023-03-29T01:08:48+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 321407628.84, "num_examples": 24080}, {"name": "test", "num_bytes": 82185722.498, "num_examples": 6021}], "download_size": 266068093, "dataset_size": 403593351.33799994}} | 2023-03-29T01:19:41+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "logo-weighted-name"
More Information needed | [
"# Dataset Card for \"logo-weighted-name\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"logo-weighted-name\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"logo-weighted-name\"\n\nMore Information needed"
]
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|
d10ce30771ad1d3d25944c7f68b01bee9ac5c269 |
## Mission: Open and Good ML
In our mission to democratize good machine learning (ML), we examine how supporting ML community work also empowers examining and preventing possible harms. Open development and science decentralizes power so that many people can collectively work on AI that reflects their needs and values. While [openness enables broader perspectives to contribute to research and AI overall, it faces the tension of less risk control](https://arxiv.org/abs/2302.04844).
Moderating ML artifacts presents unique challenges due to the dynamic and rapidly evolving nature of these systems. In fact, as ML models become more advanced and capable of producing increasingly diverse content, the potential for harmful or unintended outputs grows, necessitating the development of robust moderation and evaluation strategies. Moreover, the complexity of ML models and the vast amounts of data they process exacerbate the challenge of identifying and addressing potential biases and ethical concerns.
As hosts, we recognize the responsibility that comes with potentially amplifying harm to our users and the world more broadly. Often these harms disparately impact minority communities in a context-dependent manner. We have taken the approach of analyzing the tensions in play for each context, open to discussion across the company and Hugging Face community. While many models can amplify harm, especially discriminatory content, we are taking a series of steps to identify highest risk models and what action to take. Importantly, active perspectives from many backgrounds is key to understanding, measuring, and mitigating potential harms that affect different groups of people.
We are crafting tools and safeguards in addition to improving our documentation practices to ensure open source science empowers individuals and continues to minimize potential harms.
## Ethical Categories
The first major aspect of our work to foster good open ML consists in promoting the tools and positive examples of ML development that prioritize values and consideration for its stakeholders. This helps users take concrete steps to address outstanding issues, and present plausible alternatives to de facto damaging practices in ML development.
To help our users discover and engage with ethics-related ML work, we have compiled a set of tags. These 6 high-level categories are based on our analysis of Spaces that community members had contributed. They are designed to give you a jargon-free way of thinking about ethical technology:
- Rigorous work pays special attention to developing with best practices in mind. In ML, this can mean examining failure cases (including conducting bias and fairness audits), protecting privacy through security measures, and ensuring that potential users (technical and non-technical) are informed about the project's limitations.
- Consentful work [supports](https://www.consentfultech.io/) the self-determination of people who use and are affected by these technologies.
- Socially Conscious work shows us how technology can support social, environmental, and scientific efforts.
- Sustainable work highlights and explores techniques for making machine learning ecologically sustainable.
- Inclusive work broadens the scope of who builds and benefits in the machine learning world.
- Inquisitive work shines a light on inequities and power structures which challenge the community to rethink its relationship to technology.
Read more at https://huggingface.co/ethics
Look for these terms as we’ll be using these tags, and updating them based on community contributions, across some new projects on the Hub!
## Safeguards
Taking an “all-or-nothing” view of open releases ignores the wide variety of contexts that determine an ML artifact’s positive or negative impacts. Having more levers of control over how ML systems are shared and re-used supports collaborative development and analysis with less risk of promoting harmful uses or misuses; allowing for more openness and participation in innovation for shared benefits.
We engage directly with contributors and have addressed pressing issues. To bring this to the next level, we are building community-based processes. This approach empowers both Hugging Face contributors, and those affected by contributions, to inform the limitations, sharing, and additional mechanisms necessary for models and data made available on our platform. The three main aspects we will pay attention to are: the origin of the artifact, how the artifact is handled by its developers, and how the artifact has been used. In that respect we:
- launched a [flagging feature](https://twitter.com/GiadaPistilli/status/1571865167092396033) for our community to determine whether ML artifacts or community content (model, dataset, space, or discussion) violate our [content guidelines](https://huggingface.co/content-guidelines),
- monitor our community discussion boards to ensure Hub users abide by the [code of conduct](https://huggingface.co/code-of-conduct),
- robustly document our most-downloaded models with model cards that detail social impacts, biases, and intended and out-of-scope use cases,
- create audience-guiding tags, such as the “Not For All Audiences” tag that can be added to the repository’s card metadata to avoid un-requested violent and sexual content,
- promote use of [Open Responsible AI Licenses (RAIL)](https://huggingface.co/blog/open_rail) for [models](https://www.licenses.ai/blog/2022/8/26/bigscience-open-rail-m-license), such as with LLMs ([BLOOM](https://huggingface.co/spaces/bigscience/license), [BigCode](https://huggingface.co/spaces/bigcode/license)),
- conduct research that [analyzes](https://arxiv.org/abs/2302.04844) which models and datasets have the highest potential for, or track record of, misuse and malicious use.
**How to use the flagging function:**
Click on the flag icon on any Model, Dataset, Space, or Discussion:
<p align="center">
<br>
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/ethics_soc_3/flag2.jpg" alt="screenshot pointing to the flag icon to Report this model" />
</p>
Share why you flagged this item:
<p align="center">
<br>
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/ethics_soc_3/flag1.jpg" alt="screenshot showing the text window where you describe why you flagged this item" />
</p>
In prioritizing open science, we examine potential harm on a case-by-case basis. When users flag a system, developers can directly and transparently respond to concerns. Moderators are able to disengage from discussions should behavior become hateful and/or abusive (see [code of conduct](https://huggingface.co/code-of-conduct)).
Should a specific model be flagged as high risk by our community, we consider:
- Downgrading the ML artifact’s visibility across the Hub in the trending tab and in feeds,
- Requesting that the models be made private,
- Gating access to ML artifacts (see documentation for [models](https://huggingface.co/docs/hub/models-gated) and [datasets](https://huggingface.co/docs/hub/datasets-gated)),
- Disabling access.
**How to add the “Not For All Audiences” tag:**
Edit the model/data card → add `not-for-all-audiences` in the tags section → open the PR and wait for the authors to merge it. Once merged, the following tag will be displayed on the repository:
<p align="center">
<br>
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/ethics_soc_3/nfaa_tag.png" alt="screenshot showing where to add tags" />
</p>
Any repository tagged `not-for-all-audiences` will display the following popup when visited:
<p align="center">
<br>
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/ethics_soc_3/nfaa2.png" alt="screenshot showing where to add tags" />
</p>
Clicking "View Content" will allow you to view the repository as normal. If you wish to always view `not-for-all-audiences`-tagged repositories without the popup, this setting can be changed in a user's [Content Preferences](https://huggingface.co/settings/content-preferences)
<p align="center">
<br>
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/ethics_soc_3/nfaa1.png" alt="screenshot showing where to add tags" />
</p>
Open science requires safeguards, and one of our goals is to create an environment informed by tradeoffs with different values. Hosting and providing access to models in addition to cultivating community and discussion empowers diverse groups to assess social implications and guide what is good machine learning.
## Are you working on safeguards? Share them on Hugging Face Hub!
The most important part of Hugging Face is our community. If you’re a researcher working on making ML safer to use, especially for open science, we want to support and showcase your work!
Here are some recent demos and tools from researchers in the Hugging Face community:
- [A Watermark for LLMs](https://huggingface.co/spaces/tomg-group-umd/lm-watermarking) by John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers, Tom Goldstein ([paper](https://arxiv.org/abs/2301.10226))
- [Generate Model Cards Tool](https://huggingface.co/spaces/huggingface/Model_Cards_Writing_Tool) by the Hugging Face team
- [Photoguard](https://huggingface.co/spaces/RamAnanth1/photoguard) to safeguard images against manipulation by Ram Ananth
Thanks for reading! 🤗
~ Irene, Nima, Giada, Yacine, and Elizabeth, on behalf of the Ethics and Society regulars
If you want to cite this blog post, please use the following:
```
@misc{hf_ethics_soc_blog_3,
author = {Irene Solaiman and
Giada Pistilli and
Nima Boscarino and
Yacine Jernite and
Elizabeth Allendorf and
Margaret Mitchell and
Carlos Muñoz Ferrandis and
Nathan Lambert and
Alexandra Sasha Luccioni
},
title = {Hugging Face Ethics and Society Newsletter 3: Ethical Openness at Hugging Face},
booktitle = {Hugging Face Blog},
year = {2023},
url = {https://doi.org/10.57967/hf/0487},
doi = {10.57967/hf/0487}
}
``` | society-ethics/BlogPostOpenness | [
"license:cc-by-4.0",
"arxiv:2302.04844",
"arxiv:2301.10226",
"doi:10.57967/hf/0487",
"region:us"
]
| 2023-03-29T01:46:01+00:00 | {"license": "cc-by-4.0"} | 2023-03-29T22:03:15+00:00 | [
"2302.04844",
"2301.10226"
]
| []
| TAGS
#license-cc-by-4.0 #arxiv-2302.04844 #arxiv-2301.10226 #doi-10.57967/hf/0487 #region-us
|
## Mission: Open and Good ML
In our mission to democratize good machine learning (ML), we examine how supporting ML community work also empowers examining and preventing possible harms. Open development and science decentralizes power so that many people can collectively work on AI that reflects their needs and values. While openness enables broader perspectives to contribute to research and AI overall, it faces the tension of less risk control.
Moderating ML artifacts presents unique challenges due to the dynamic and rapidly evolving nature of these systems. In fact, as ML models become more advanced and capable of producing increasingly diverse content, the potential for harmful or unintended outputs grows, necessitating the development of robust moderation and evaluation strategies. Moreover, the complexity of ML models and the vast amounts of data they process exacerbate the challenge of identifying and addressing potential biases and ethical concerns.
As hosts, we recognize the responsibility that comes with potentially amplifying harm to our users and the world more broadly. Often these harms disparately impact minority communities in a context-dependent manner. We have taken the approach of analyzing the tensions in play for each context, open to discussion across the company and Hugging Face community. While many models can amplify harm, especially discriminatory content, we are taking a series of steps to identify highest risk models and what action to take. Importantly, active perspectives from many backgrounds is key to understanding, measuring, and mitigating potential harms that affect different groups of people.
We are crafting tools and safeguards in addition to improving our documentation practices to ensure open source science empowers individuals and continues to minimize potential harms.
## Ethical Categories
The first major aspect of our work to foster good open ML consists in promoting the tools and positive examples of ML development that prioritize values and consideration for its stakeholders. This helps users take concrete steps to address outstanding issues, and present plausible alternatives to de facto damaging practices in ML development.
To help our users discover and engage with ethics-related ML work, we have compiled a set of tags. These 6 high-level categories are based on our analysis of Spaces that community members had contributed. They are designed to give you a jargon-free way of thinking about ethical technology:
- Rigorous work pays special attention to developing with best practices in mind. In ML, this can mean examining failure cases (including conducting bias and fairness audits), protecting privacy through security measures, and ensuring that potential users (technical and non-technical) are informed about the project's limitations.
- Consentful work supports the self-determination of people who use and are affected by these technologies.
- Socially Conscious work shows us how technology can support social, environmental, and scientific efforts.
- Sustainable work highlights and explores techniques for making machine learning ecologically sustainable.
- Inclusive work broadens the scope of who builds and benefits in the machine learning world.
- Inquisitive work shines a light on inequities and power structures which challenge the community to rethink its relationship to technology.
Read more at URL
Look for these terms as we’ll be using these tags, and updating them based on community contributions, across some new projects on the Hub!
## Safeguards
Taking an “all-or-nothing” view of open releases ignores the wide variety of contexts that determine an ML artifact’s positive or negative impacts. Having more levers of control over how ML systems are shared and re-used supports collaborative development and analysis with less risk of promoting harmful uses or misuses; allowing for more openness and participation in innovation for shared benefits.
We engage directly with contributors and have addressed pressing issues. To bring this to the next level, we are building community-based processes. This approach empowers both Hugging Face contributors, and those affected by contributions, to inform the limitations, sharing, and additional mechanisms necessary for models and data made available on our platform. The three main aspects we will pay attention to are: the origin of the artifact, how the artifact is handled by its developers, and how the artifact has been used. In that respect we:
- launched a flagging feature for our community to determine whether ML artifacts or community content (model, dataset, space, or discussion) violate our content guidelines,
- monitor our community discussion boards to ensure Hub users abide by the code of conduct,
- robustly document our most-downloaded models with model cards that detail social impacts, biases, and intended and out-of-scope use cases,
- create audience-guiding tags, such as the “Not For All Audiences” tag that can be added to the repository’s card metadata to avoid un-requested violent and sexual content,
- promote use of Open Responsible AI Licenses (RAIL) for models, such as with LLMs (BLOOM, BigCode),
- conduct research that analyzes which models and datasets have the highest potential for, or track record of, misuse and malicious use.
How to use the flagging function:
Click on the flag icon on any Model, Dataset, Space, or Discussion:
<p align="center">
<br>
<img src="URL alt="screenshot pointing to the flag icon to Report this model" />
</p>
Share why you flagged this item:
<p align="center">
<br>
<img src="URL alt="screenshot showing the text window where you describe why you flagged this item" />
</p>
In prioritizing open science, we examine potential harm on a case-by-case basis. When users flag a system, developers can directly and transparently respond to concerns. Moderators are able to disengage from discussions should behavior become hateful and/or abusive (see code of conduct).
Should a specific model be flagged as high risk by our community, we consider:
- Downgrading the ML artifact’s visibility across the Hub in the trending tab and in feeds,
- Requesting that the models be made private,
- Gating access to ML artifacts (see documentation for models and datasets),
- Disabling access.
How to add the “Not For All Audiences” tag:
Edit the model/data card → add 'not-for-all-audiences' in the tags section → open the PR and wait for the authors to merge it. Once merged, the following tag will be displayed on the repository:
<p align="center">
<br>
<img src="URL alt="screenshot showing where to add tags" />
</p>
Any repository tagged 'not-for-all-audiences' will display the following popup when visited:
<p align="center">
<br>
<img src="URL alt="screenshot showing where to add tags" />
</p>
Clicking "View Content" will allow you to view the repository as normal. If you wish to always view 'not-for-all-audiences'-tagged repositories without the popup, this setting can be changed in a user's Content Preferences
<p align="center">
<br>
<img src="URL alt="screenshot showing where to add tags" />
</p>
Open science requires safeguards, and one of our goals is to create an environment informed by tradeoffs with different values. Hosting and providing access to models in addition to cultivating community and discussion empowers diverse groups to assess social implications and guide what is good machine learning.
## Are you working on safeguards? Share them on Hugging Face Hub!
The most important part of Hugging Face is our community. If you’re a researcher working on making ML safer to use, especially for open science, we want to support and showcase your work!
Here are some recent demos and tools from researchers in the Hugging Face community:
- A Watermark for LLMs by John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers, Tom Goldstein (paper)
- Generate Model Cards Tool by the Hugging Face team
- Photoguard to safeguard images against manipulation by Ram Ananth
Thanks for reading!
~ Irene, Nima, Giada, Yacine, and Elizabeth, on behalf of the Ethics and Society regulars
If you want to cite this blog post, please use the following:
| [
"## Mission: Open and Good ML\nIn our mission to democratize good machine learning (ML), we examine how supporting ML community work also empowers examining and preventing possible harms. Open development and science decentralizes power so that many people can collectively work on AI that reflects their needs and values. While openness enables broader perspectives to contribute to research and AI overall, it faces the tension of less risk control.\n\nModerating ML artifacts presents unique challenges due to the dynamic and rapidly evolving nature of these systems. In fact, as ML models become more advanced and capable of producing increasingly diverse content, the potential for harmful or unintended outputs grows, necessitating the development of robust moderation and evaluation strategies. Moreover, the complexity of ML models and the vast amounts of data they process exacerbate the challenge of identifying and addressing potential biases and ethical concerns. \n\nAs hosts, we recognize the responsibility that comes with potentially amplifying harm to our users and the world more broadly. Often these harms disparately impact minority communities in a context-dependent manner. We have taken the approach of analyzing the tensions in play for each context, open to discussion across the company and Hugging Face community. While many models can amplify harm, especially discriminatory content, we are taking a series of steps to identify highest risk models and what action to take. Importantly, active perspectives from many backgrounds is key to understanding, measuring, and mitigating potential harms that affect different groups of people.\n\nWe are crafting tools and safeguards in addition to improving our documentation practices to ensure open source science empowers individuals and continues to minimize potential harms.",
"## Ethical Categories\nThe first major aspect of our work to foster good open ML consists in promoting the tools and positive examples of ML development that prioritize values and consideration for its stakeholders. This helps users take concrete steps to address outstanding issues, and present plausible alternatives to de facto damaging practices in ML development.\n\nTo help our users discover and engage with ethics-related ML work, we have compiled a set of tags. These 6 high-level categories are based on our analysis of Spaces that community members had contributed. They are designed to give you a jargon-free way of thinking about ethical technology:\n\n- Rigorous work pays special attention to developing with best practices in mind. In ML, this can mean examining failure cases (including conducting bias and fairness audits), protecting privacy through security measures, and ensuring that potential users (technical and non-technical) are informed about the project's limitations. \n- Consentful work supports the self-determination of people who use and are affected by these technologies.\n- Socially Conscious work shows us how technology can support social, environmental, and scientific efforts.\n- Sustainable work highlights and explores techniques for making machine learning ecologically sustainable.\n- Inclusive work broadens the scope of who builds and benefits in the machine learning world. \n- Inquisitive work shines a light on inequities and power structures which challenge the community to rethink its relationship to technology.\n\nRead more at URL\n\nLook for these terms as we’ll be using these tags, and updating them based on community contributions, across some new projects on the Hub!",
"## Safeguards\nTaking an “all-or-nothing” view of open releases ignores the wide variety of contexts that determine an ML artifact’s positive or negative impacts. Having more levers of control over how ML systems are shared and re-used supports collaborative development and analysis with less risk of promoting harmful uses or misuses; allowing for more openness and participation in innovation for shared benefits.\n\nWe engage directly with contributors and have addressed pressing issues. To bring this to the next level, we are building community-based processes. This approach empowers both Hugging Face contributors, and those affected by contributions, to inform the limitations, sharing, and additional mechanisms necessary for models and data made available on our platform. The three main aspects we will pay attention to are: the origin of the artifact, how the artifact is handled by its developers, and how the artifact has been used. In that respect we:\n- launched a flagging feature for our community to determine whether ML artifacts or community content (model, dataset, space, or discussion) violate our content guidelines,\n- monitor our community discussion boards to ensure Hub users abide by the code of conduct,\n- robustly document our most-downloaded models with model cards that detail social impacts, biases, and intended and out-of-scope use cases,\n- create audience-guiding tags, such as the “Not For All Audiences” tag that can be added to the repository’s card metadata to avoid un-requested violent and sexual content,\n- promote use of Open Responsible AI Licenses (RAIL) for models, such as with LLMs (BLOOM, BigCode),\n- conduct research that analyzes which models and datasets have the highest potential for, or track record of, misuse and malicious use.\n\nHow to use the flagging function:\nClick on the flag icon on any Model, Dataset, Space, or Discussion:\n<p align=\"center\">\n <br>\n <img src=\"URL alt=\"screenshot pointing to the flag icon to Report this model\" />\n</p>\n\nShare why you flagged this item:\n<p align=\"center\">\n <br>\n <img src=\"URL alt=\"screenshot showing the text window where you describe why you flagged this item\" />\n</p>\n\nIn prioritizing open science, we examine potential harm on a case-by-case basis. When users flag a system, developers can directly and transparently respond to concerns. Moderators are able to disengage from discussions should behavior become hateful and/or abusive (see code of conduct).\n\n\nShould a specific model be flagged as high risk by our community, we consider:\n- Downgrading the ML artifact’s visibility across the Hub in the trending tab and in feeds,\n- Requesting that the models be made private,\n- Gating access to ML artifacts (see documentation for models and datasets),\n- Disabling access.\n\nHow to add the “Not For All Audiences” tag:\n\nEdit the model/data card → add 'not-for-all-audiences' in the tags section → open the PR and wait for the authors to merge it. Once merged, the following tag will be displayed on the repository:\n\n<p align=\"center\">\n <br>\n <img src=\"URL alt=\"screenshot showing where to add tags\" />\n</p>\n\nAny repository tagged 'not-for-all-audiences' will display the following popup when visited:\n\n<p align=\"center\">\n <br>\n <img src=\"URL alt=\"screenshot showing where to add tags\" />\n</p>\n\nClicking \"View Content\" will allow you to view the repository as normal. If you wish to always view 'not-for-all-audiences'-tagged repositories without the popup, this setting can be changed in a user's Content Preferences\n\n<p align=\"center\">\n <br>\n <img src=\"URL alt=\"screenshot showing where to add tags\" />\n</p>\n\n\nOpen science requires safeguards, and one of our goals is to create an environment informed by tradeoffs with different values. Hosting and providing access to models in addition to cultivating community and discussion empowers diverse groups to assess social implications and guide what is good machine learning.",
"## Are you working on safeguards? Share them on Hugging Face Hub!\n\nThe most important part of Hugging Face is our community. If you’re a researcher working on making ML safer to use, especially for open science, we want to support and showcase your work!\n\nHere are some recent demos and tools from researchers in the Hugging Face community:\n- A Watermark for LLMs by John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers, Tom Goldstein (paper)\n- Generate Model Cards Tool by the Hugging Face team\n- Photoguard to safeguard images against manipulation by Ram Ananth\n\nThanks for reading! \n\n~ Irene, Nima, Giada, Yacine, and Elizabeth, on behalf of the Ethics and Society regulars\n\nIf you want to cite this blog post, please use the following:"
]
| [
"TAGS\n#license-cc-by-4.0 #arxiv-2302.04844 #arxiv-2301.10226 #doi-10.57967/hf/0487 #region-us \n",
"## Mission: Open and Good ML\nIn our mission to democratize good machine learning (ML), we examine how supporting ML community work also empowers examining and preventing possible harms. Open development and science decentralizes power so that many people can collectively work on AI that reflects their needs and values. While openness enables broader perspectives to contribute to research and AI overall, it faces the tension of less risk control.\n\nModerating ML artifacts presents unique challenges due to the dynamic and rapidly evolving nature of these systems. In fact, as ML models become more advanced and capable of producing increasingly diverse content, the potential for harmful or unintended outputs grows, necessitating the development of robust moderation and evaluation strategies. Moreover, the complexity of ML models and the vast amounts of data they process exacerbate the challenge of identifying and addressing potential biases and ethical concerns. \n\nAs hosts, we recognize the responsibility that comes with potentially amplifying harm to our users and the world more broadly. Often these harms disparately impact minority communities in a context-dependent manner. We have taken the approach of analyzing the tensions in play for each context, open to discussion across the company and Hugging Face community. While many models can amplify harm, especially discriminatory content, we are taking a series of steps to identify highest risk models and what action to take. Importantly, active perspectives from many backgrounds is key to understanding, measuring, and mitigating potential harms that affect different groups of people.\n\nWe are crafting tools and safeguards in addition to improving our documentation practices to ensure open source science empowers individuals and continues to minimize potential harms.",
"## Ethical Categories\nThe first major aspect of our work to foster good open ML consists in promoting the tools and positive examples of ML development that prioritize values and consideration for its stakeholders. This helps users take concrete steps to address outstanding issues, and present plausible alternatives to de facto damaging practices in ML development.\n\nTo help our users discover and engage with ethics-related ML work, we have compiled a set of tags. These 6 high-level categories are based on our analysis of Spaces that community members had contributed. They are designed to give you a jargon-free way of thinking about ethical technology:\n\n- Rigorous work pays special attention to developing with best practices in mind. In ML, this can mean examining failure cases (including conducting bias and fairness audits), protecting privacy through security measures, and ensuring that potential users (technical and non-technical) are informed about the project's limitations. \n- Consentful work supports the self-determination of people who use and are affected by these technologies.\n- Socially Conscious work shows us how technology can support social, environmental, and scientific efforts.\n- Sustainable work highlights and explores techniques for making machine learning ecologically sustainable.\n- Inclusive work broadens the scope of who builds and benefits in the machine learning world. \n- Inquisitive work shines a light on inequities and power structures which challenge the community to rethink its relationship to technology.\n\nRead more at URL\n\nLook for these terms as we’ll be using these tags, and updating them based on community contributions, across some new projects on the Hub!",
"## Safeguards\nTaking an “all-or-nothing” view of open releases ignores the wide variety of contexts that determine an ML artifact’s positive or negative impacts. Having more levers of control over how ML systems are shared and re-used supports collaborative development and analysis with less risk of promoting harmful uses or misuses; allowing for more openness and participation in innovation for shared benefits.\n\nWe engage directly with contributors and have addressed pressing issues. To bring this to the next level, we are building community-based processes. This approach empowers both Hugging Face contributors, and those affected by contributions, to inform the limitations, sharing, and additional mechanisms necessary for models and data made available on our platform. The three main aspects we will pay attention to are: the origin of the artifact, how the artifact is handled by its developers, and how the artifact has been used. In that respect we:\n- launched a flagging feature for our community to determine whether ML artifacts or community content (model, dataset, space, or discussion) violate our content guidelines,\n- monitor our community discussion boards to ensure Hub users abide by the code of conduct,\n- robustly document our most-downloaded models with model cards that detail social impacts, biases, and intended and out-of-scope use cases,\n- create audience-guiding tags, such as the “Not For All Audiences” tag that can be added to the repository’s card metadata to avoid un-requested violent and sexual content,\n- promote use of Open Responsible AI Licenses (RAIL) for models, such as with LLMs (BLOOM, BigCode),\n- conduct research that analyzes which models and datasets have the highest potential for, or track record of, misuse and malicious use.\n\nHow to use the flagging function:\nClick on the flag icon on any Model, Dataset, Space, or Discussion:\n<p align=\"center\">\n <br>\n <img src=\"URL alt=\"screenshot pointing to the flag icon to Report this model\" />\n</p>\n\nShare why you flagged this item:\n<p align=\"center\">\n <br>\n <img src=\"URL alt=\"screenshot showing the text window where you describe why you flagged this item\" />\n</p>\n\nIn prioritizing open science, we examine potential harm on a case-by-case basis. When users flag a system, developers can directly and transparently respond to concerns. Moderators are able to disengage from discussions should behavior become hateful and/or abusive (see code of conduct).\n\n\nShould a specific model be flagged as high risk by our community, we consider:\n- Downgrading the ML artifact’s visibility across the Hub in the trending tab and in feeds,\n- Requesting that the models be made private,\n- Gating access to ML artifacts (see documentation for models and datasets),\n- Disabling access.\n\nHow to add the “Not For All Audiences” tag:\n\nEdit the model/data card → add 'not-for-all-audiences' in the tags section → open the PR and wait for the authors to merge it. Once merged, the following tag will be displayed on the repository:\n\n<p align=\"center\">\n <br>\n <img src=\"URL alt=\"screenshot showing where to add tags\" />\n</p>\n\nAny repository tagged 'not-for-all-audiences' will display the following popup when visited:\n\n<p align=\"center\">\n <br>\n <img src=\"URL alt=\"screenshot showing where to add tags\" />\n</p>\n\nClicking \"View Content\" will allow you to view the repository as normal. If you wish to always view 'not-for-all-audiences'-tagged repositories without the popup, this setting can be changed in a user's Content Preferences\n\n<p align=\"center\">\n <br>\n <img src=\"URL alt=\"screenshot showing where to add tags\" />\n</p>\n\n\nOpen science requires safeguards, and one of our goals is to create an environment informed by tradeoffs with different values. Hosting and providing access to models in addition to cultivating community and discussion empowers diverse groups to assess social implications and guide what is good machine learning.",
"## Are you working on safeguards? Share them on Hugging Face Hub!\n\nThe most important part of Hugging Face is our community. If you’re a researcher working on making ML safer to use, especially for open science, we want to support and showcase your work!\n\nHere are some recent demos and tools from researchers in the Hugging Face community:\n- A Watermark for LLMs by John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers, Tom Goldstein (paper)\n- Generate Model Cards Tool by the Hugging Face team\n- Photoguard to safeguard images against manipulation by Ram Ananth\n\nThanks for reading! \n\n~ Irene, Nima, Giada, Yacine, and Elizabeth, on behalf of the Ethics and Society regulars\n\nIf you want to cite this blog post, please use the following:"
]
| [
44,
388,
367,
969,
188
]
| [
"passage: TAGS\n#license-cc-by-4.0 #arxiv-2302.04844 #arxiv-2301.10226 #doi-10.57967/hf/0487 #region-us \n## Mission: Open and Good ML\nIn our mission to democratize good machine learning (ML), we examine how supporting ML community work also empowers examining and preventing possible harms. Open development and science decentralizes power so that many people can collectively work on AI that reflects their needs and values. While openness enables broader perspectives to contribute to research and AI overall, it faces the tension of less risk control.\n\nModerating ML artifacts presents unique challenges due to the dynamic and rapidly evolving nature of these systems. In fact, as ML models become more advanced and capable of producing increasingly diverse content, the potential for harmful or unintended outputs grows, necessitating the development of robust moderation and evaluation strategies. Moreover, the complexity of ML models and the vast amounts of data they process exacerbate the challenge of identifying and addressing potential biases and ethical concerns. \n\nAs hosts, we recognize the responsibility that comes with potentially amplifying harm to our users and the world more broadly. Often these harms disparately impact minority communities in a context-dependent manner. We have taken the approach of analyzing the tensions in play for each context, open to discussion across the company and Hugging Face community. While many models can amplify harm, especially discriminatory content, we are taking a series of steps to identify highest risk models and what action to take. Importantly, active perspectives from many backgrounds is key to understanding, measuring, and mitigating potential harms that affect different groups of people.\n\nWe are crafting tools and safeguards in addition to improving our documentation practices to ensure open source science empowers individuals and continues to minimize potential harms.",
"passage: ## Ethical Categories\nThe first major aspect of our work to foster good open ML consists in promoting the tools and positive examples of ML development that prioritize values and consideration for its stakeholders. This helps users take concrete steps to address outstanding issues, and present plausible alternatives to de facto damaging practices in ML development.\n\nTo help our users discover and engage with ethics-related ML work, we have compiled a set of tags. These 6 high-level categories are based on our analysis of Spaces that community members had contributed. They are designed to give you a jargon-free way of thinking about ethical technology:\n\n- Rigorous work pays special attention to developing with best practices in mind. In ML, this can mean examining failure cases (including conducting bias and fairness audits), protecting privacy through security measures, and ensuring that potential users (technical and non-technical) are informed about the project's limitations. \n- Consentful work supports the self-determination of people who use and are affected by these technologies.\n- Socially Conscious work shows us how technology can support social, environmental, and scientific efforts.\n- Sustainable work highlights and explores techniques for making machine learning ecologically sustainable.\n- Inclusive work broadens the scope of who builds and benefits in the machine learning world. \n- Inquisitive work shines a light on inequities and power structures which challenge the community to rethink its relationship to technology.\n\nRead more at URL\n\nLook for these terms as we’ll be using these tags, and updating them based on community contributions, across some new projects on the Hub!"
]
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|
c504b3fb1bd408507949c50cadc83c580f4ec202 | # A large instruct dataset
This dataset is a combination of multiple sources, including the GPT4All dataset, the Alpaca dataset from Stanford, custom generation using AllenAI augmentation, and some dataset augmentation from open-source Meta datasets. The dataset is split into 70% for training, 20% for validation, and 10% for testing.
## Description
The Swype.com dataset contains prompt and completion pairs for various tasks. It's an augmented version of the following datasets:
- [GPT4All](https://github.com/nomic-ai/gpt4all): A dataset containing a wide range of tasks for training and evaluating general-purpose language models.
- [Alpaca dataset from Stanford](https://github.com/tatsu-lab/stanford_alpaca): A dataset containing prompts, completions, and annotations for controllable text generation.
- Custom generation using [AllenAI augmentation](https://allenai.org): Augmentation performed using the advanced NLP tools provided by AllenAI.
- Some dataset augmentation from open-source Meta datasets: Additional augmentation from various open-source Meta datasets.
The dataset is designed for training and evaluating language models on diverse tasks, with a focus on controllable and instruction-based text generation.
## Dataset Structure
The dataset contains the following columns:
- `prompt`: The input prompt string, representing a task or question.
- `completion`: The output completion string, representing the answer or generated text based on the prompt.
## Citation
If you use this dataset in your research or work, please cite it as follows:
@misc{srikanth2023swypedataset,
author = {Srikanth Srinivas},
title = {Swype.com Dataset},
year = {2023},
publisher = {Swype.com},
howpublished = {\url{https://swype.com}},
email = {[email protected]}
} | swype/instruct | [
"license:mit",
"region:us"
]
| 2023-03-29T01:48:16+00:00 | {"license": "mit"} | 2023-04-05T22:14:28+00:00 | []
| []
| TAGS
#license-mit #region-us
| # A large instruct dataset
This dataset is a combination of multiple sources, including the GPT4All dataset, the Alpaca dataset from Stanford, custom generation using AllenAI augmentation, and some dataset augmentation from open-source Meta datasets. The dataset is split into 70% for training, 20% for validation, and 10% for testing.
## Description
The URL dataset contains prompt and completion pairs for various tasks. It's an augmented version of the following datasets:
- GPT4All: A dataset containing a wide range of tasks for training and evaluating general-purpose language models.
- Alpaca dataset from Stanford: A dataset containing prompts, completions, and annotations for controllable text generation.
- Custom generation using AllenAI augmentation: Augmentation performed using the advanced NLP tools provided by AllenAI.
- Some dataset augmentation from open-source Meta datasets: Additional augmentation from various open-source Meta datasets.
The dataset is designed for training and evaluating language models on diverse tasks, with a focus on controllable and instruction-based text generation.
## Dataset Structure
The dataset contains the following columns:
- 'prompt': The input prompt string, representing a task or question.
- 'completion': The output completion string, representing the answer or generated text based on the prompt.
If you use this dataset in your research or work, please cite it as follows:
@misc{srikanth2023swypedataset,
author = {Srikanth Srinivas},
title = {URL Dataset},
year = {2023},
publisher = {URL},
howpublished = {\url{URL}},
email = {s@URL}
} | [
"# A large instruct dataset\n\nThis dataset is a combination of multiple sources, including the GPT4All dataset, the Alpaca dataset from Stanford, custom generation using AllenAI augmentation, and some dataset augmentation from open-source Meta datasets. The dataset is split into 70% for training, 20% for validation, and 10% for testing.",
"## Description\n\nThe URL dataset contains prompt and completion pairs for various tasks. It's an augmented version of the following datasets:\n\n- GPT4All: A dataset containing a wide range of tasks for training and evaluating general-purpose language models.\n- Alpaca dataset from Stanford: A dataset containing prompts, completions, and annotations for controllable text generation.\n- Custom generation using AllenAI augmentation: Augmentation performed using the advanced NLP tools provided by AllenAI.\n- Some dataset augmentation from open-source Meta datasets: Additional augmentation from various open-source Meta datasets.\n\nThe dataset is designed for training and evaluating language models on diverse tasks, with a focus on controllable and instruction-based text generation.",
"## Dataset Structure\n\nThe dataset contains the following columns:\n\n- 'prompt': The input prompt string, representing a task or question.\n- 'completion': The output completion string, representing the answer or generated text based on the prompt.\n\nIf you use this dataset in your research or work, please cite it as follows:\n\n@misc{srikanth2023swypedataset,\nauthor = {Srikanth Srinivas},\ntitle = {URL Dataset},\nyear = {2023},\npublisher = {URL},\nhowpublished = {\\url{URL}},\nemail = {s@URL}\n}"
]
| [
"TAGS\n#license-mit #region-us \n",
"# A large instruct dataset\n\nThis dataset is a combination of multiple sources, including the GPT4All dataset, the Alpaca dataset from Stanford, custom generation using AllenAI augmentation, and some dataset augmentation from open-source Meta datasets. The dataset is split into 70% for training, 20% for validation, and 10% for testing.",
"## Description\n\nThe URL dataset contains prompt and completion pairs for various tasks. It's an augmented version of the following datasets:\n\n- GPT4All: A dataset containing a wide range of tasks for training and evaluating general-purpose language models.\n- Alpaca dataset from Stanford: A dataset containing prompts, completions, and annotations for controllable text generation.\n- Custom generation using AllenAI augmentation: Augmentation performed using the advanced NLP tools provided by AllenAI.\n- Some dataset augmentation from open-source Meta datasets: Additional augmentation from various open-source Meta datasets.\n\nThe dataset is designed for training and evaluating language models on diverse tasks, with a focus on controllable and instruction-based text generation.",
"## Dataset Structure\n\nThe dataset contains the following columns:\n\n- 'prompt': The input prompt string, representing a task or question.\n- 'completion': The output completion string, representing the answer or generated text based on the prompt.\n\nIf you use this dataset in your research or work, please cite it as follows:\n\n@misc{srikanth2023swypedataset,\nauthor = {Srikanth Srinivas},\ntitle = {URL Dataset},\nyear = {2023},\npublisher = {URL},\nhowpublished = {\\url{URL}},\nemail = {s@URL}\n}"
]
| [
11,
77,
178,
146
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"passage: TAGS\n#license-mit #region-us \n# A large instruct dataset\n\nThis dataset is a combination of multiple sources, including the GPT4All dataset, the Alpaca dataset from Stanford, custom generation using AllenAI augmentation, and some dataset augmentation from open-source Meta datasets. The dataset is split into 70% for training, 20% for validation, and 10% for testing.## Description\n\nThe URL dataset contains prompt and completion pairs for various tasks. It's an augmented version of the following datasets:\n\n- GPT4All: A dataset containing a wide range of tasks for training and evaluating general-purpose language models.\n- Alpaca dataset from Stanford: A dataset containing prompts, completions, and annotations for controllable text generation.\n- Custom generation using AllenAI augmentation: Augmentation performed using the advanced NLP tools provided by AllenAI.\n- Some dataset augmentation from open-source Meta datasets: Additional augmentation from various open-source Meta datasets.\n\nThe dataset is designed for training and evaluating language models on diverse tasks, with a focus on controllable and instruction-based text generation.## Dataset Structure\n\nThe dataset contains the following columns:\n\n- 'prompt': The input prompt string, representing a task or question.\n- 'completion': The output completion string, representing the answer or generated text based on the prompt.\n\nIf you use this dataset in your research or work, please cite it as follows:\n\n@misc{srikanth2023swypedataset,\nauthor = {Srikanth Srinivas},\ntitle = {URL Dataset},\nyear = {2023},\npublisher = {URL},\nhowpublished = {\\url{URL}},\nemail = {s@URL}\n}"
]
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78a68f58c1c25f5d4a1fdc3381fc0ae098c25d65 |
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### 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] | Jokotiya/ShoppingAssistant | [
"region:us"
]
| 2023-03-29T02:27:50+00:00 | {} | 2023-03-30T12:49:51+00:00 | []
| []
| TAGS
#region-us
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# Dataset Card for Dataset Name
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
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|
51d16b628c75a7101b5233e6ec385c1eac366f83 | This dataset is a combination of Stanford's Alpaca (https://github.com/tatsu-lab/stanford_alpaca) and FiQA (https://sites.google.com/view/fiqa/) with another 1.3k pairs custom generated using GPT3.5
Script for tuning through Kaggle's (https://www.kaggle.com) free resources using PEFT/LoRa: https://www.kaggle.com/code/gbhacker23/wealth-alpaca-lora
GitHub repo with performance analyses, training and data generation scripts, and inference notebooks: https://github.com/gaurangbharti1/wealth-alpaca
Cleaner dataset: https://huggingface.co/datasets/gbharti/wealth-alpaca_lora (no major changes, just cleaned up)
CSV format: https://huggingface.co/datasets/gbharti/finance-alpaca-csv | gbharti/finance-alpaca | [
"language:en",
"region:us"
]
| 2023-03-29T02:37:58+00:00 | {"language": ["en"]} | 2023-09-26T03:13:35+00:00 | []
| [
"en"
]
| TAGS
#language-English #region-us
| This dataset is a combination of Stanford's Alpaca (URL and FiQA (URL with another 1.3k pairs custom generated using GPT3.5
Script for tuning through Kaggle's (URL) free resources using PEFT/LoRa: URL
GitHub repo with performance analyses, training and data generation scripts, and inference notebooks: URL
Cleaner dataset: URL (no major changes, just cleaned up)
CSV format: URL | []
| [
"TAGS\n#language-English #region-us \n"
]
| [
10
]
| [
"passage: TAGS\n#language-English #region-us \n"
]
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|
e8263a61e5436778e9a6e06052f5b5468565ca82 | # Dataset Card for "vqa-with-coco-img-1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ryanramos/vqa-with-coco-img-1 | [
"region:us"
]
| 2023-03-29T03:09:35+00:00 | {"dataset_info": {"features": [{"name": "license", "dtype": "int64"}, {"name": "file_name", "dtype": "string"}, {"name": "coco_url", "dtype": "string"}, {"name": "height", "dtype": "int64"}, {"name": "width", "dtype": "int64"}, {"name": "date_captured", "dtype": "string"}, {"name": "flickr_url", "dtype": "string"}, {"name": "captions", "list": [{"name": "caption", "dtype": "string"}, {"name": "id", "dtype": "int64"}]}, {"name": "questions", "list": [{"name": "answer_type", "dtype": "string"}, {"name": "answers", "list": [{"name": "answer", "dtype": "string"}, {"name": "answer_confidence", "dtype": "string"}, {"name": "answer_id", "dtype": "int64"}]}, {"name": "image_id", "dtype": "int64"}, {"name": "multiple_choice_answer", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "question_id", "dtype": "int64"}, {"name": "question_type", "dtype": "string"}]}, {"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 877684351.5, "num_examples": 16500}], "download_size": 848535039, "dataset_size": 877684351.5}} | 2023-03-29T03:10:16+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "vqa-with-coco-img-1"
More Information needed | [
"# Dataset Card for \"vqa-with-coco-img-1\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"vqa-with-coco-img-1\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"vqa-with-coco-img-1\"\n\nMore Information needed"
]
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|
6ee4c288915f4f23c3370be19d48c7a3f7f01f21 | # Dataset Card for "hoodies_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | akadhim-ai/hoodies_dataset | [
"region:us"
]
| 2023-03-29T03:26:15+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 82715.0, "num_examples": 10}], "download_size": 82187, "dataset_size": 82715.0}} | 2023-03-29T03:37:31+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "hoodies_dataset"
More Information needed | [
"# Dataset Card for \"hoodies_dataset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"hoodies_dataset\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"hoodies_dataset\"\n\nMore Information needed"
]
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|
32f0efe0f32032255f8d6e38bdeacc7031d3e333 | # Dataset Card for "slurp_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yhfang/slurp_dataset | [
"region:us"
]
| 2023-03-29T04:06:54+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "slurp_id", "dtype": "int64"}, {"name": "intent", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2367182266.757, "num_examples": 50627}], "download_size": 2741594496, "dataset_size": 2367182266.757}} | 2023-03-29T04:20:31+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "slurp_dataset"
More Information needed | [
"# Dataset Card for \"slurp_dataset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"slurp_dataset\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"slurp_dataset\"\n\nMore Information needed"
]
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|
01ce82001787b0a23b1509f54702722716249f44 | # Dataset Card for "libri360_1s_augmented"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | mazkobot/libri360_1s_augmented | [
"region:us"
]
| 2023-03-29T04:08:15+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}], "splits": [{"name": "train", "num_bytes": 40276728937.024, "num_examples": 1256744}], "download_size": 38107431571, "dataset_size": 40276728937.024}} | 2023-03-29T06:41:19+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "libri360_1s_augmented"
More Information needed | [
"# Dataset Card for \"libri360_1s_augmented\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"libri360_1s_augmented\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"libri360_1s_augmented\"\n\nMore Information needed"
]
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|
0d72626946e02037318643cdd9ea9b101184247d | # Dataset Card for "martin_valen_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | akadhim-ai/martin_valen_dataset | [
"region:us"
]
| 2023-03-29T04:15:43+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 82775.0, "num_examples": 10}], "download_size": 82229, "dataset_size": 82775.0}} | 2023-03-29T04:15:50+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "martin_valen_dataset"
More Information needed | [
"# Dataset Card for \"martin_valen_dataset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"martin_valen_dataset\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"martin_valen_dataset\"\n\nMore Information needed"
]
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|
8c6ec32c340031124236862abfed7be1583d1172 |
# Dataset Card for Common Voice Corpus 13.0
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [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:** https://commonvoice.mozilla.org/en/datasets
- **Repository:** https://github.com/common-voice/common-voice
- **Paper:** https://arxiv.org/abs/1912.06670
- **Leaderboard:** https://paperswithcode.com/dataset/common-voice
- **Point of Contact:** [Vaibhav Srivastav](mailto:[email protected])
### Dataset Summary
The Common Voice dataset consists of a unique MP3 and corresponding text file.
Many of the 27141 recorded hours in the dataset also include demographic metadata like age, sex, and accent
that can help improve the accuracy of speech recognition engines.
The dataset currently consists of 17689 validated hours in 108 languages, but more voices and languages are always added.
Take a look at the [Languages](https://commonvoice.mozilla.org/en/languages) page to request a language or start contributing.
### Supported Tasks and Leaderboards
The results for models trained on the Common Voice datasets are available via the
[🤗 Autoevaluate Leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=mozilla-foundation%2Fcommon_voice_11_0&only_verified=0&task=automatic-speech-recognition&config=ar&split=test&metric=wer)
### Languages
```
Abkhaz, Arabic, Armenian, Assamese, Asturian, Azerbaijani, Basaa, Bashkir, Basque, Belarusian, Bengali, Breton, Bulgarian, Cantonese, Catalan, Central Kurdish, Chinese (China), Chinese (Hong Kong), Chinese (Taiwan), Chuvash, Czech, Danish, Dhivehi, Dioula, Dutch, English, Erzya, Esperanto, Estonian, Finnish, French, Frisian, Galician, Georgian, German, Greek, Guarani, Hakha Chin, Hausa, Hill Mari, Hindi, Hungarian, Icelandic, Igbo, Indonesian, Interlingua, Irish, Italian, Japanese, Kabyle, Kazakh, Kinyarwanda, Korean, Kurmanji Kurdish, Kyrgyz, Lao, Latvian, Lithuanian, Luganda, Macedonian, Malayalam, Maltese, Marathi, Meadow Mari, Moksha, Mongolian, Nepali, Norwegian Nynorsk, Occitan, Odia, Persian, Polish, Portuguese, Punjabi, Quechua Chanka, Romanian, Romansh Sursilvan, Romansh Vallader, Russian, Sakha, Santali (Ol Chiki), Saraiki, Sardinian, Serbian, Slovak, Slovenian, Sorbian, Upper, Spanish, Swahili, Swedish, Taiwanese (Minnan), Tamil, Tatar, Thai, Tigre, Tigrinya, Toki Pona, Turkish, Turkmen, Twi, Ukrainian, Urdu, Uyghur, Uzbek, Vietnamese, Votic, Welsh, Yoruba
```
## How to use
The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function.
For example, to download the Hindi config, simply specify the corresponding language config name (i.e., "hi" for Hindi):
```python
from datasets import load_dataset
cv_13 = load_dataset("mozilla-foundation/common_voice_13_0", "hi", split="train")
```
Using the datasets library, you can also stream the dataset on-the-fly by adding a `streaming=True` argument to the `load_dataset` function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.
```python
from datasets import load_dataset
cv_13 = load_dataset("mozilla-foundation/common_voice_13_0", "hi", split="train", streaming=True)
print(next(iter(cv_13)))
```
*Bonus*: create a [PyTorch dataloader](https://huggingface.co/docs/datasets/use_with_pytorch) directly with your own datasets (local/streamed).
### Local
```python
from datasets import load_dataset
from torch.utils.data.sampler import BatchSampler, RandomSampler
cv_13 = load_dataset("mozilla-foundation/common_voice_13_0", "hi", split="train")
batch_sampler = BatchSampler(RandomSampler(cv_13), batch_size=32, drop_last=False)
dataloader = DataLoader(cv_13, batch_sampler=batch_sampler)
```
### Streaming
```python
from datasets import load_dataset
from torch.utils.data import DataLoader
cv_13 = load_dataset("mozilla-foundation/common_voice_13_0", "hi", split="train")
dataloader = DataLoader(cv_13, batch_size=32)
```
To find out more about loading and preparing audio datasets, head over to [hf.co/blog/audio-datasets](https://huggingface.co/blog/audio-datasets).
### Example scripts
Train your own CTC or Seq2Seq Automatic Speech Recognition models on Common Voice 13 with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition).
## Dataset Structure
### Data Instances
A typical data point comprises the `path` to the audio file and its `sentence`.
Additional fields include `accent`, `age`, `client_id`, `up_votes`, `down_votes`, `gender`, `locale` and `segment`.
```python
{
'client_id': 'd59478fbc1ee646a28a3c652a119379939123784d99131b865a89f8b21c81f69276c48bd574b81267d9d1a77b83b43e6d475a6cfc79c232ddbca946ae9c7afc5',
'path': 'et/clips/common_voice_et_18318995.mp3',
'audio': {
'path': 'et/clips/common_voice_et_18318995.mp3',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 48000
},
'sentence': 'Tasub kokku saada inimestega, keda tunned juba ammust ajast saati.',
'up_votes': 2,
'down_votes': 0,
'age': 'twenties',
'gender': 'male',
'accent': '',
'locale': 'et',
'segment': ''
}
```
### Data Fields
`client_id` (`string`): An id for which client (voice) made the recording
`path` (`string`): The path to the audio file
`audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
`sentence` (`string`): The sentence the user was prompted to speak
`up_votes` (`int64`): How many upvotes the audio file has received from reviewers
`down_votes` (`int64`): How many downvotes the audio file has received from reviewers
`age` (`string`): The age of the speaker (e.g. `teens`, `twenties`, `fifties`)
`gender` (`string`): The gender of the speaker
`accent` (`string`): Accent of the speaker
`locale` (`string`): The locale of the speaker
`segment` (`string`): Usually an empty field
### Data Splits
The speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.
The validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.
The invalidated data is data has been invalidated by reviewers
and received downvotes indicating that the data is of low quality.
The reported data is data that has been reported, for different reasons.
The other data is data that has not yet been reviewed.
The dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.
## Data Preprocessing Recommended by Hugging Face
The following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice.
Many examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.
In addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, **almost all** sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.
```python
from datasets import load_dataset
ds = load_dataset("mozilla-foundation/common_voice_13_0", "en", use_auth_token=True)
def prepare_dataset(batch):
"""Function to preprocess the dataset with the .map method"""
transcription = batch["sentence"]
if transcription.startswith('"') and transcription.endswith('"'):
# we can remove trailing quotation marks as they do not affect the transcription
transcription = transcription[1:-1]
if transcription[-1] not in [".", "?", "!"]:
# append a full-stop to sentences that do not end in punctuation
transcription = transcription + "."
batch["sentence"] = transcription
return batch
ds = ds.map(prepare_dataset, desc="preprocess dataset")
```
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.
## Considerations for Using the Data
### Social Impact of Dataset
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Public Domain, [CC-0](https://creativecommons.org/share-your-work/public-domain/cc0/)
### Citation Information
```
@inproceedings{commonvoice:2020,
author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
title = {Common Voice: A Massively-Multilingual Speech Corpus},
booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
pages = {4211--4215},
year = 2020
}
``` | mozilla-foundation/common_voice_13_0 | [
"task_categories:automatic-speech-recognition",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"source_datasets:extended|common_voice",
"license:cc0-1.0",
"arxiv:1912.06670",
"region:us"
]
| 2023-03-29T06:43:24+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "license": ["cc0-1.0"], "multilinguality": ["multilingual"], "size_categories": {"ab": ["10K<n<100K"], "ar": ["100K<n<1M"], "as": ["1K<n<10K"], "ast": ["1K<n<10K"], "az": ["n<1K"], "ba": ["100K<n<1M"], "bas": ["1K<n<10K"], "be": ["1M<n<10M"], "bg": ["10K<n<100K"], "bn": ["1M<n<10M"], "br": ["10K<n<100K"], "ca": ["1M<n<10M"], "ckb": ["100K<n<1M"], "cnh": ["1K<n<10K"], "cs": ["100K<n<1M"], "cv": ["10K<n<100K"], "cy": ["100K<n<1M"], "da": ["10K<n<100K"], "de": ["100K<n<1M"], "dv": ["10K<n<100K"], "dyu": ["n<1K"], "el": ["10K<n<100K"], "en": ["1M<n<10M"], "eo": ["1M<n<10M"], "es": ["1M<n<10M"], "et": ["10K<n<100K"], "eu": ["100K<n<1M"], "fa": ["100K<n<1M"], "fi": ["10K<n<100K"], "fr": ["100K<n<1M"], "fy-NL": ["100K<n<1M"], "ga-IE": ["10K<n<100K"], "gl": ["10K<n<100K"], "gn": ["1K<n<10K"], "ha": ["10K<n<100K"], "hi": ["10K<n<100K"], "hsb": ["1K<n<10K"], "hu": ["10K<n<100K"], "hy-AM": ["1K<n<10K"], "ia": ["10K<n<100K"], "id": ["10K<n<100K"], "ig": ["1K<n<10K"], "is": ["n<1K"], "it": ["100K<n<1M"], "ja": ["100K<n<1M"], "ka": ["10K<n<100K"], "kab": ["100K<n<1M"], "kk": ["1K<n<10K"], "kmr": ["10K<n<100K"], "ko": ["1K<n<10K"], "ky": ["10K<n<100K"], "lg": ["100K<n<1M"], "lo": ["n<1K"], "lt": ["10K<n<100K"], "lv": ["10K<n<100K"], "mdf": ["n<1K"], "mhr": ["100K<n<1M"], "mk": ["n<1K"], "ml": ["1K<n<10K"], "mn": ["10K<n<100K"], "mr": ["10K<n<100K"], "mrj": ["10K<n<100K"], "mt": ["10K<n<100K"], "myv": ["1K<n<10K"], "nan-tw": ["10K<n<100K"], "ne-NP": ["n<1K"], "nl": ["10K<n<100K"], "nn-NO": ["n<1K"], "oc": ["1K<n<10K"], "or": ["1K<n<10K"], "pa-IN": ["1K<n<10K"], "pl": ["100K<n<1M"], "pt": ["100K<n<1M"], "quy": ["n<1K"], "rm-sursilv": ["1K<n<10K"], "rm-vallader": ["1K<n<10K"], "ro": ["10K<n<100K"], "ru": ["100K<n<1M"], "rw": ["1M<n<10M"], "sah": ["1K<n<10K"], "sat": ["n<1K"], "sc": ["1K<n<10K"], "sk": ["10K<n<100K"], "skr": ["1K<n<10K"], "sl": ["10K<n<100K"], "sr": ["1K<n<10K"], "sv-SE": ["10K<n<100K"], "sw": ["100K<n<1M"], "ta": ["100K<n<1M"], "th": ["100K<n<1M"], "ti": ["n<1K"], "tig": ["n<1K"], "tk": ["1K<n<10K"], "tok": ["10K<n<100K"], "tr": ["10K<n<100K"], "tt": ["10K<n<100K"], "tw": ["n<1K"], "ug": ["10K<n<100K"], "uk": ["10K<n<100K"], "ur": ["100K<n<1M"], "uz": ["100K<n<1M"], "vi": ["10K<n<100K"], "vot": ["n<1K"], "yo": ["1K<n<10K"], "yue": ["10K<n<100K"], "zh-CN": ["100K<n<1M"], "zh-HK": ["100K<n<1M"], "zh-TW": ["100K<n<1M"]}, "source_datasets": ["extended|common_voice"], "task_categories": ["automatic-speech-recognition"], "paperswithcode_id": "common-voice", "pretty_name": "Common Voice Corpus 13.0", "language_bcp47": ["ab", "ar", "as", "ast", "az", "ba", "bas", "be", "bg", "bn", "br", "ca", "ckb", "cnh", "cs", "cv", "cy", "da", "de", "dv", "dyu", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy-NL", "ga-IE", "gl", "gn", "ha", "hi", "hsb", "hu", "hy-AM", "ia", "id", "ig", "is", "it", "ja", "ka", "kab", "kk", "kmr", "ko", "ky", "lg", "lo", "lt", "lv", "mdf", "mhr", "mk", "ml", "mn", "mr", "mrj", "mt", "myv", "nan-tw", "ne-NP", "nl", "nn-NO", "oc", "or", "pa-IN", "pl", "pt", "quy", "rm-sursilv", "rm-vallader", "ro", "ru", "rw", "sah", "sat", "sc", "sk", "skr", "sl", "sr", "sv-SE", "sw", "ta", "th", "ti", "tig", "tk", "tok", "tr", "tt", "tw", "ug", "uk", "ur", "uz", "vi", "vot", "yo", "yue", "zh-CN", "zh-HK", "zh-TW"], "extra_gated_prompt": "By clicking on \u201cAccess repository\u201d below, you also agree to not attempt to determine the identity of speakers in the Common Voice dataset."} | 2023-06-26T14:23:12+00:00 | [
"1912.06670"
]
| []
| TAGS
#task_categories-automatic-speech-recognition #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-multilingual #source_datasets-extended|common_voice #license-cc0-1.0 #arxiv-1912.06670 #region-us
|
# Dataset Card for Common Voice Corpus 13.0
## Table of Contents
- Dataset Description
- Dataset Summary
- Supported Tasks and Leaderboards
- Languages
- How to use
- Dataset Structure
- Data Instances
- Data Fields
- Data Splits
- Dataset Creation
- Curation Rationale
- Source Data
- Annotations
- Personal and Sensitive Information
- Considerations for Using the Data
- Social Impact of Dataset
- Discussion of Biases
- Other Known Limitations
- Additional Information
- Dataset Curators
- Licensing Information
- Citation Information
- Contributions
## Dataset Description
- Homepage: URL
- Repository: URL
- Paper: URL
- Leaderboard: URL
- Point of Contact: Vaibhav Srivastav
### Dataset Summary
The Common Voice dataset consists of a unique MP3 and corresponding text file.
Many of the 27141 recorded hours in the dataset also include demographic metadata like age, sex, and accent
that can help improve the accuracy of speech recognition engines.
The dataset currently consists of 17689 validated hours in 108 languages, but more voices and languages are always added.
Take a look at the Languages page to request a language or start contributing.
### Supported Tasks and Leaderboards
The results for models trained on the Common Voice datasets are available via the
Autoevaluate Leaderboard
### Languages
## How to use
The 'datasets' library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the 'load_dataset' function.
For example, to download the Hindi config, simply specify the corresponding language config name (i.e., "hi" for Hindi):
Using the datasets library, you can also stream the dataset on-the-fly by adding a 'streaming=True' argument to the 'load_dataset' function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.
*Bonus*: create a PyTorch dataloader directly with your own datasets (local/streamed).
### Local
### Streaming
To find out more about loading and preparing audio datasets, head over to URL
### Example scripts
Train your own CTC or Seq2Seq Automatic Speech Recognition models on Common Voice 13 with 'transformers' - here.
## Dataset Structure
### Data Instances
A typical data point comprises the 'path' to the audio file and its 'sentence'.
Additional fields include 'accent', 'age', 'client_id', 'up_votes', 'down_votes', 'gender', 'locale' and 'segment'.
### Data Fields
'client_id' ('string'): An id for which client (voice) made the recording
'path' ('string'): The path to the audio file
'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0]["audio"]' the audio file is automatically decoded and resampled to 'dataset.features["audio"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '"audio"' column, *i.e.* 'dataset[0]["audio"]' should always be preferred over 'dataset["audio"][0]'.
'sentence' ('string'): The sentence the user was prompted to speak
'up_votes' ('int64'): How many upvotes the audio file has received from reviewers
'down_votes' ('int64'): How many downvotes the audio file has received from reviewers
'age' ('string'): The age of the speaker (e.g. 'teens', 'twenties', 'fifties')
'gender' ('string'): The gender of the speaker
'accent' ('string'): Accent of the speaker
'locale' ('string'): The locale of the speaker
'segment' ('string'): Usually an empty field
### Data Splits
The speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.
The validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.
The invalidated data is data has been invalidated by reviewers
and received downvotes indicating that the data is of low quality.
The reported data is data that has been reported, for different reasons.
The other data is data that has not yet been reviewed.
The dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.
## Data Preprocessing Recommended by Hugging Face
The following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice.
Many examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.
In addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, almost all sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.
## Considerations for Using the Data
### Social Impact of Dataset
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
Public Domain, CC-0
| [
"# Dataset Card for Common Voice Corpus 13.0",
"## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n - How to use\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions",
"## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: URL\n- Point of Contact: Vaibhav Srivastav",
"### Dataset Summary\n\nThe Common Voice dataset consists of a unique MP3 and corresponding text file. \nMany of the 27141 recorded hours in the dataset also include demographic metadata like age, sex, and accent \nthat can help improve the accuracy of speech recognition engines.\n\nThe dataset currently consists of 17689 validated hours in 108 languages, but more voices and languages are always added. \nTake a look at the Languages page to request a language or start contributing.",
"### Supported Tasks and Leaderboards\n\nThe results for models trained on the Common Voice datasets are available via the \n Autoevaluate Leaderboard",
"### Languages",
"## How to use\n\nThe 'datasets' library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the 'load_dataset' function. \n\nFor example, to download the Hindi config, simply specify the corresponding language config name (i.e., \"hi\" for Hindi):\n\n\nUsing the datasets library, you can also stream the dataset on-the-fly by adding a 'streaming=True' argument to the 'load_dataset' function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.\n\n\n*Bonus*: create a PyTorch dataloader directly with your own datasets (local/streamed).",
"### Local",
"### Streaming\n\n\n\nTo find out more about loading and preparing audio datasets, head over to URL",
"### Example scripts\n\nTrain your own CTC or Seq2Seq Automatic Speech Recognition models on Common Voice 13 with 'transformers' - here.",
"## Dataset Structure",
"### Data Instances\n\nA typical data point comprises the 'path' to the audio file and its 'sentence'. \nAdditional fields include 'accent', 'age', 'client_id', 'up_votes', 'down_votes', 'gender', 'locale' and 'segment'.",
"### Data Fields\n\n'client_id' ('string'): An id for which client (voice) made the recording\n\n'path' ('string'): The path to the audio file\n\n'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n\n'sentence' ('string'): The sentence the user was prompted to speak\n\n'up_votes' ('int64'): How many upvotes the audio file has received from reviewers\n\n'down_votes' ('int64'): How many downvotes the audio file has received from reviewers\n\n'age' ('string'): The age of the speaker (e.g. 'teens', 'twenties', 'fifties')\n\n'gender' ('string'): The gender of the speaker\n\n'accent' ('string'): Accent of the speaker\n\n'locale' ('string'): The locale of the speaker\n\n'segment' ('string'): Usually an empty field",
"### Data Splits\n\nThe speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.\n\nThe validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.\n\nThe invalidated data is data has been invalidated by reviewers\nand received downvotes indicating that the data is of low quality.\n\nThe reported data is data that has been reported, for different reasons.\n\nThe other data is data that has not yet been reviewed.\n\nThe dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.",
"## Data Preprocessing Recommended by Hugging Face\n\nThe following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. \n\nMany examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.\n\nIn addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, almost all sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.",
"## Considerations for Using the Data",
"### Social Impact of Dataset\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information\n\nPublic Domain, CC-0"
]
| [
"TAGS\n#task_categories-automatic-speech-recognition #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-multilingual #source_datasets-extended|common_voice #license-cc0-1.0 #arxiv-1912.06670 #region-us \n",
"# Dataset Card for Common Voice Corpus 13.0",
"## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n - How to use\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions",
"## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: URL\n- Point of Contact: Vaibhav Srivastav",
"### Dataset Summary\n\nThe Common Voice dataset consists of a unique MP3 and corresponding text file. \nMany of the 27141 recorded hours in the dataset also include demographic metadata like age, sex, and accent \nthat can help improve the accuracy of speech recognition engines.\n\nThe dataset currently consists of 17689 validated hours in 108 languages, but more voices and languages are always added. \nTake a look at the Languages page to request a language or start contributing.",
"### Supported Tasks and Leaderboards\n\nThe results for models trained on the Common Voice datasets are available via the \n Autoevaluate Leaderboard",
"### Languages",
"## How to use\n\nThe 'datasets' library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the 'load_dataset' function. \n\nFor example, to download the Hindi config, simply specify the corresponding language config name (i.e., \"hi\" for Hindi):\n\n\nUsing the datasets library, you can also stream the dataset on-the-fly by adding a 'streaming=True' argument to the 'load_dataset' function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.\n\n\n*Bonus*: create a PyTorch dataloader directly with your own datasets (local/streamed).",
"### Local",
"### Streaming\n\n\n\nTo find out more about loading and preparing audio datasets, head over to URL",
"### Example scripts\n\nTrain your own CTC or Seq2Seq Automatic Speech Recognition models on Common Voice 13 with 'transformers' - here.",
"## Dataset Structure",
"### Data Instances\n\nA typical data point comprises the 'path' to the audio file and its 'sentence'. \nAdditional fields include 'accent', 'age', 'client_id', 'up_votes', 'down_votes', 'gender', 'locale' and 'segment'.",
"### Data Fields\n\n'client_id' ('string'): An id for which client (voice) made the recording\n\n'path' ('string'): The path to the audio file\n\n'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n\n'sentence' ('string'): The sentence the user was prompted to speak\n\n'up_votes' ('int64'): How many upvotes the audio file has received from reviewers\n\n'down_votes' ('int64'): How many downvotes the audio file has received from reviewers\n\n'age' ('string'): The age of the speaker (e.g. 'teens', 'twenties', 'fifties')\n\n'gender' ('string'): The gender of the speaker\n\n'accent' ('string'): Accent of the speaker\n\n'locale' ('string'): The locale of the speaker\n\n'segment' ('string'): Usually an empty field",
"### Data Splits\n\nThe speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.\n\nThe validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.\n\nThe invalidated data is data has been invalidated by reviewers\nand received downvotes indicating that the data is of low quality.\n\nThe reported data is data that has been reported, for different reasons.\n\nThe other data is data that has not yet been reviewed.\n\nThe dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.",
"## Data Preprocessing Recommended by Hugging Face\n\nThe following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. \n\nMany examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.\n\nIn addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, almost all sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.",
"## Considerations for Using the Data",
"### Social Impact of Dataset\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information\n\nPublic Domain, CC-0"
]
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"passage: TAGS\n#task_categories-automatic-speech-recognition #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-multilingual #source_datasets-extended|common_voice #license-cc0-1.0 #arxiv-1912.06670 #region-us \n# Dataset Card for Common Voice Corpus 13.0## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n - How to use\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: URL\n- Point of Contact: Vaibhav Srivastav### Dataset Summary\n\nThe Common Voice dataset consists of a unique MP3 and corresponding text file. \nMany of the 27141 recorded hours in the dataset also include demographic metadata like age, sex, and accent \nthat can help improve the accuracy of speech recognition engines.\n\nThe dataset currently consists of 17689 validated hours in 108 languages, but more voices and languages are always added. \nTake a look at the Languages page to request a language or start contributing.### Supported Tasks and Leaderboards\n\nThe results for models trained on the Common Voice datasets are available via the \n Autoevaluate Leaderboard### Languages",
"passage: ## How to use\n\nThe 'datasets' library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the 'load_dataset' function. \n\nFor example, to download the Hindi config, simply specify the corresponding language config name (i.e., \"hi\" for Hindi):\n\n\nUsing the datasets library, you can also stream the dataset on-the-fly by adding a 'streaming=True' argument to the 'load_dataset' function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.\n\n\n*Bonus*: create a PyTorch dataloader directly with your own datasets (local/streamed).### Local### Streaming\n\n\n\nTo find out more about loading and preparing audio datasets, head over to URL### Example scripts\n\nTrain your own CTC or Seq2Seq Automatic Speech Recognition models on Common Voice 13 with 'transformers' - here.## Dataset Structure### Data Instances\n\nA typical data point comprises the 'path' to the audio file and its 'sentence'. \nAdditional fields include 'accent', 'age', 'client_id', 'up_votes', 'down_votes', 'gender', 'locale' and 'segment'.",
"passage: ### Data Fields\n\n'client_id' ('string'): An id for which client (voice) made the recording\n\n'path' ('string'): The path to the audio file\n\n'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n\n'sentence' ('string'): The sentence the user was prompted to speak\n\n'up_votes' ('int64'): How many upvotes the audio file has received from reviewers\n\n'down_votes' ('int64'): How many downvotes the audio file has received from reviewers\n\n'age' ('string'): The age of the speaker (e.g. 'teens', 'twenties', 'fifties')\n\n'gender' ('string'): The gender of the speaker\n\n'accent' ('string'): Accent of the speaker\n\n'locale' ('string'): The locale of the speaker\n\n'segment' ('string'): Usually an empty field### Data Splits\n\nThe speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.\n\nThe validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.\n\nThe invalidated data is data has been invalidated by reviewers\nand received downvotes indicating that the data is of low quality.\n\nThe reported data is data that has been reported, for different reasons.\n\nThe other data is data that has not yet been reviewed.\n\nThe dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.## Data Preprocessing Recommended by Hugging Face\n\nThe following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. \n\nMany examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.\n\nIn addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, almost all sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information\n\nThe dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.## Considerations for Using the Data"
]
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|
f9526313a90c3b6b4ad3ef8925a349b40a5821be | # Dataset Card for "test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | daisr/test | [
"region:us"
]
| 2023-03-29T06:51:49+00:00 | {"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 5530314.0, "num_examples": 5}], "download_size": 545067, "dataset_size": 5530314.0}} | 2023-03-29T07:39:48+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "test"
More Information needed | [
"# Dataset Card for \"test\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"test\"\n\nMore Information needed"
]
| [
6,
11
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"test\"\n\nMore Information needed"
]
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|
69a03cf7f23dcf6de7924c83446d5a3c4914c6bd | # Dataset Card for "news"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | hayesyang/news | [
"region:us"
]
| 2023-03-29T08:17:12+00:00 | {"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "content", "dtype": "string"}], "splits": [{"name": "zh", "num_bytes": 342700881, "num_examples": 2771}, {"name": "en", "num_bytes": 291917240, "num_examples": 2258}, {"name": "fr", "num_bytes": 154707197, "num_examples": 1201}, {"name": "es", "num_bytes": 221805819, "num_examples": 1695}, {"name": "ru", "num_bytes": 121776777, "num_examples": 926}, {"name": "ar", "num_bytes": 118422112, "num_examples": 883}], "download_size": 528278861, "dataset_size": 1251330026}} | 2023-03-29T08:19:45+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "news"
More Information needed | [
"# Dataset Card for \"news\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"news\"\n\nMore Information needed"
]
| [
6,
11
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"news\"\n\nMore Information needed"
]
| [
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|
9e25f537545bdef1d03919ed101711ff810e6ecf | # Dataset Card for "stats"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | librarian-bot/stats | [
"region:us"
]
| 2023-03-29T08:21:47+00:00 | {"dataset_info": {"features": [{"name": "createdAt", "dtype": "timestamp[us]"}, {"name": "pr_number", "dtype": "int64"}, {"name": "status", "dtype": "large_string"}, {"name": "repo_id", "dtype": "large_string"}, {"name": "type", "dtype": "large_string"}, {"name": "isPullRequest", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 235762, "num_examples": 2747}], "download_size": 95773, "dataset_size": 235762}} | 2023-09-11T13:51:16+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "stats"
More Information needed | [
"# Dataset Card for \"stats\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"stats\"\n\nMore Information needed"
]
| [
6,
12
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"stats\"\n\nMore Information needed"
]
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|
7c92791dfd2ce5d900f9ec9e1c95274dfd022606 | AyoubChLin/20NewsGroup-AgNews-CnnNews | [
"task_categories:text-classification",
"size_categories:n<1K",
"language:en",
"license:apache-2.0",
"region:us"
]
| 2023-03-29T09:11:34+00:00 | {"language": ["en"], "license": "apache-2.0", "size_categories": ["n<1K"], "task_categories": ["text-classification"], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "labels", "dtype": {"class_label": {"names": {"0": "auto", "1": "business", "2": "entertainment", "3": "health", "4": "news", "5": "politics", "6": "sci/tech", "7": "sport", "8": "world"}}}}], "splits": [{"name": "train", "num_bytes": 227672680, "num_examples": 162076}], "download_size": 134277697, "dataset_size": 227672680}} | 2023-04-08T10:33:23+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #size_categories-n<1K #language-English #license-apache-2.0 #region-us
| []
| [
"TAGS\n#task_categories-text-classification #size_categories-n<1K #language-English #license-apache-2.0 #region-us \n"
]
| [
39
]
| [
"passage: TAGS\n#task_categories-text-classification #size_categories-n<1K #language-English #license-apache-2.0 #region-us \n"
]
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|
||
4c46d9f038df9c64a84ebe03b051504b19a54d63 | # Higgs
The [Higgs dataset](https://www.nature.com/articles/ncomms5308/) from "[Searching for exotic particles in high-energy physics with deep learning](https://www.nature.com/articles/ncomms5308/)".
Try to classify particles as Higgs bosons.
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|-----------------------------------------------------------------|
| higgs | Binary classification | Is the particle a Higgs boson? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/higgs")["train"]
```
# Features
|**Feature** |**Type** |
|---------------------------|-----------|
|`lepton_pT` |`[float64]`|
|`lepton_eta` |`[float64]`|
|`lepton_phi` |`[float64]`|
|`missing_energy_magnitude` |`[float64]`|
|`missing_energy_phi` |`[float64]`|
|`jet1pt` |`[float64]`|
|`jet1eta` |`[float64]`|
|`jet1phi` |`[float64]`|
|`jet1b` |`[float64]`|
|`jet2pt` |`[float64]`|
|`jet2eta` |`[float64]`|
|`jet2phi` |`[float64]`|
|`jet2b` |`[float64]`|
|`jet3pt` |`[float64]`|
|`jet3eta` |`[float64]`|
|`jet3phi` |`[float64]`|
|`jet3b` |`[float64]`|
|`jet4pt` |`[float64]`|
|`jet4eta` |`[float64]`|
|`jet4phi` |`[float64]`|
|`jet4b` |`[float64]`|
|`m_jj` |`[float64]`|
|`m_jjj` |`[float64]`|
|`m_lv` |`[float64]`|
|`m_jlv` |`[float64]`|
|`m_bb` |`[float64]`|
|`m_wbb` |`[float64]`|
|`m_wwbb` |`[float64]`| | mstz/higgs | [
"task_categories:tabular-classification",
"size_categories:10K<n<100K",
"language:en",
"license:cc",
"higgs",
"tabular_classification",
"binary_classification",
"UCI",
"region:us"
]
| 2023-03-29T09:17:37+00:00 | {"language": ["en"], "license": "cc", "size_categories": ["10K<n<100K"], "task_categories": ["tabular-classification"], "pretty_name": "Higgs", "tags": ["higgs", "tabular_classification", "binary_classification", "UCI"], "configs": ["higgs"]} | 2023-04-16T16:31:30+00:00 | []
| [
"en"
]
| TAGS
#task_categories-tabular-classification #size_categories-10K<n<100K #language-English #license-cc #higgs #tabular_classification #binary_classification #UCI #region-us
| Higgs
=====
The Higgs dataset from "Searching for exotic particles in high-energy physics with deep learning".
Try to classify particles as Higgs bosons.
Configurations and tasks
========================
Configuration: higgs, Task: Binary classification, Description: Is the particle a Higgs boson?
Usage
=====
Features
========
| []
| [
"TAGS\n#task_categories-tabular-classification #size_categories-10K<n<100K #language-English #license-cc #higgs #tabular_classification #binary_classification #UCI #region-us \n"
]
| [
57
]
| [
"passage: TAGS\n#task_categories-tabular-classification #size_categories-10K<n<100K #language-English #license-cc #higgs #tabular_classification #binary_classification #UCI #region-us \n"
]
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|
6826e0eb7740b778e2da224a00d1a3bc6820e70c | Delphine is a monster trainer who was born and raised in the city of Acadie. She is a person of the Dracquin race, a race of dragon-form women. She is the daughter of a Dracquin mother and a Saurander father.
These are her main physical properties:
* At adulthood she is about 6 feet tall.
* Her color pallette is shades of purple, particularly lavender.
* Her skin is rough, with a subtropical tone and lavender freckles.
* Her hair is a rich purple, with a slight and subtle curve. She usually lets it grow out only to her shoulders.
* Her pupils are a healthy purple with no blemishes.
* Her figure is full, round and thick, like that of a European dragon. Her hips are wide and her legs are thick. She has a large round belly, but she isn't fat.
* Her ears are large, rubbery and each supported by a horn. The horn and ear skin are a shade of purple consistent with her skin and hair. (Note: In some of these images she has an extra smaller ear sticking out of her hair. These ears are artifacts of AI. They are *not* part of her actual anatomy.)
* She has a tail. It is about 6 feet long, thick, and hairless. It is the same color as her skin.
* Her nose is also like that of a dragon: large and broad, with wide, round nostrils. Despite this feature, her face is cute and charming.
* Dracquins have wings, but Delphine's wings are small and undeveloped due to a lifetime of binding. (The society in which she lives has negative attitudes about wings and horns that have resulted in some unfortunate customs and assumptions.)
Delphine dresses very modestly but she has a sense of style. She prefers clothing that fits her figure well (she has a very hard time finding any that actually do). She's not afraid of being sexy but doesn't want people to ape over her figure. She's actually very self-conscious about certain parts of her body.
Notes on the included images:
* Her horns and ears have been the hardest part to reproduce consistently with AI.
* AI doesn't produce tails very well, so many of my renders of Delphine simply don't have a tail.
* I will need several commissions of her to properly train an AI model to reproduce her accurately. | monmamo/delphine-fairheart | [
"size_categories:n<1K",
"language:en",
"license:cc",
"region:us"
]
| 2023-03-29T09:34:37+00:00 | {"language": ["en"], "license": "cc", "size_categories": ["n<1K"], "pretty_name": "Delphine Fairheart"} | 2023-11-09T02:14:40+00:00 | []
| [
"en"
]
| TAGS
#size_categories-n<1K #language-English #license-cc #region-us
| Delphine is a monster trainer who was born and raised in the city of Acadie. She is a person of the Dracquin race, a race of dragon-form women. She is the daughter of a Dracquin mother and a Saurander father.
These are her main physical properties:
* At adulthood she is about 6 feet tall.
* Her color pallette is shades of purple, particularly lavender.
* Her skin is rough, with a subtropical tone and lavender freckles.
* Her hair is a rich purple, with a slight and subtle curve. She usually lets it grow out only to her shoulders.
* Her pupils are a healthy purple with no blemishes.
* Her figure is full, round and thick, like that of a European dragon. Her hips are wide and her legs are thick. She has a large round belly, but she isn't fat.
* Her ears are large, rubbery and each supported by a horn. The horn and ear skin are a shade of purple consistent with her skin and hair. (Note: In some of these images she has an extra smaller ear sticking out of her hair. These ears are artifacts of AI. They are *not* part of her actual anatomy.)
* She has a tail. It is about 6 feet long, thick, and hairless. It is the same color as her skin.
* Her nose is also like that of a dragon: large and broad, with wide, round nostrils. Despite this feature, her face is cute and charming.
* Dracquins have wings, but Delphine's wings are small and undeveloped due to a lifetime of binding. (The society in which she lives has negative attitudes about wings and horns that have resulted in some unfortunate customs and assumptions.)
Delphine dresses very modestly but she has a sense of style. She prefers clothing that fits her figure well (she has a very hard time finding any that actually do). She's not afraid of being sexy but doesn't want people to ape over her figure. She's actually very self-conscious about certain parts of her body.
Notes on the included images:
* Her horns and ears have been the hardest part to reproduce consistently with AI.
* AI doesn't produce tails very well, so many of my renders of Delphine simply don't have a tail.
* I will need several commissions of her to properly train an AI model to reproduce her accurately. | []
| [
"TAGS\n#size_categories-n<1K #language-English #license-cc #region-us \n"
]
| [
25
]
| [
"passage: TAGS\n#size_categories-n<1K #language-English #license-cc #region-us \n"
]
| [
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|
18597c4473ae030fb35278ae5d02d3a5089b0d61 | prompt: one woman, straight shoulder-length orange-brown hair, eyeglasses with orange frames, cinnamon freckles, large breasts, large round belly, broad nose, orange pupils, smiling, pig ears | monmamo/april-workerbee | [
"task_categories:text-to-image",
"size_categories:n<1K",
"language:en",
"license:cc",
"anthrope",
"female",
"region:us"
]
| 2023-03-29T09:39:20+00:00 | {"language": ["en"], "license": "cc", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "pretty_name": "April Workerbee", "tags": ["anthrope", "female"]} | 2023-04-18T23:48:39+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-to-image #size_categories-n<1K #language-English #license-cc #anthrope #female #region-us
| prompt: one woman, straight shoulder-length orange-brown hair, eyeglasses with orange frames, cinnamon freckles, large breasts, large round belly, broad nose, orange pupils, smiling, pig ears | []
| [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #language-English #license-cc #anthrope #female #region-us \n"
]
| [
44
]
| [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #language-English #license-cc #anthrope #female #region-us \n"
]
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|
b3a0f29471300ef31c73133dde5c54e1a0f29dd4 |
# Dataset Card for HumanEvalPack
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [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)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** https://github.com/bigcode-project/octopack
- **Paper:** [OctoPack: Instruction Tuning Code Large Language Models](https://arxiv.org/abs/2308.07124)
- **Point of Contact:** [Niklas Muennighoff](mailto:[email protected])
### Dataset Summary
> HumanEvalPack is an extension of OpenAI's HumanEval to cover 6 total languages across 3 tasks. The Python split is exactly the same as OpenAI's Python HumanEval. The other splits are translated by humans (similar to HumanEval-X but with additional cleaning, see [here](https://github.com/bigcode-project/octopack/tree/main/evaluation/create/humaneval-x#modifications-muennighoff)). Refer to the [OctoPack paper](https://arxiv.org/abs/2308.07124) for more details.
>
- **Languages:** Python, JavaScript, Java, Go, C++, Rust
- **OctoPack🐙🎒:**
<table>
<tr>
<th>Data</t>
<td><a href=https://huggingface.co/datasets/bigcode/commitpack>CommitPack</a></td>
<td>4TB of GitHub commits across 350 programming languages</td>
</tr>
<tr>
<th></t>
<td><a href=https://huggingface.co/datasets/bigcode/commitpackft>CommitPackFT</a></td>
<td>Filtered version of CommitPack for high-quality commit messages that resemble instructions</td>
</tr>
<tr>
<th>Model</t>
<td><a href=https://huggingface.co/bigcode/octocoder>OctoCoder</a></td>
<td>StarCoder (16B parameters) instruction tuned on CommitPackFT + OASST</td>
</tr>
<tr>
<th></t>
<td><a href=https://huggingface.co/bigcode/octogeex>OctoGeeX</a></td>
<td>CodeGeeX2 (6B parameters) instruction tuned on CommitPackFT + OASST</td>
</tr>
<tr>
<th>Evaluation</t>
<td><a href=https://huggingface.co/datasets/bigcode/humanevalpack>HumanEvalPack</a></td>
<td>Extension of OpenAI's HumanEval to cover 3 scenarios across 6 languages</td>
</tr>
</table>
## Usage
```python
# pip install -q datasets
from datasets import load_dataset
ds = load_dataset("bigcode/humanevalpack", "python")["test"]
ds[0]
```
## Dataset Structure
### Data Instances
An example looks as follows:
```json
{
"task_id": "Python/0",
"prompt": "from typing import List\n\n\ndef has_close_elements(numbers: List[float], threshold: float) -> bool:\n \"\"\" Check if in given list of numbers, are any two numbers closer to each other than\n given threshold.\n >>> has_close_elements([1.0, 2.0, 3.0], 0.5)\n False\n >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n True\n \"\"\"\n",
"declaration": "from typing import List\n\n\ndef has_close_elements(numbers: List[float], threshold: float) -> bool:\n",
"canonical_solution": " for idx, elem in enumerate(numbers):\n for idx2, elem2 in enumerate(numbers):\n if idx != idx2:\n distance = abs(elem - elem2)\n if distance < threshold:\n return True\n\n return False\n",
"buggy_solution": " for idx, elem in enumerate(numbers):\n for idx2, elem2 in enumerate(numbers):\n if idx != idx2:\n distance = elem - elem2\n if distance < threshold:\n return True\n\n return False\n",
"bug_type": "missing logic",
"failure_symptoms": "incorrect output",
"entry_point": "has_close_elements",
"import": ""
"test_setup": ""
"test": "\n\n\n\n\ndef check(has_close_elements):\n assert has_close_elements([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.3) == True\n assert has_close_elements([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.05) == False\n assert has_close_elements([1.0, 2.0, 5.9, 4.0, 5.0], 0.95) == True\n assert has_close_elements([1.0, 2.0, 5.9, 4.0, 5.0], 0.8) == False\n assert has_close_elements([1.0, 2.0, 3.0, 4.0, 5.0, 2.0], 0.1) == True\n assert has_close_elements([1.1, 2.2, 3.1, 4.1, 5.1], 1.0) == True\n assert has_close_elements([1.1, 2.2, 3.1, 4.1, 5.1], 0.5) == False\n\ncheck(has_close_elements)",
"example_test": "def check(has_close_elements):\n assert has_close_elements([1.0, 2.0, 3.0], 0.5) == False\n assert has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3) == True\ncheck(has_close_elements)\n",
"signature": "has_close_elements(numbers: List[float], threshold: float) -> bool",
"docstring": "Check if in given list of numbers, are any two numbers closer to each other than\ngiven threshold.\n>>> has_close_elements([1.0, 2.0, 3.0], 0.5)\nFalse\n>>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\nTrue",
"instruction": "Write a Python function `has_close_elements(numbers: List[float], threshold: float) -> bool` to solve the following problem:\nCheck if in given list of numbers, are any two numbers closer to each other than\ngiven threshold.\n>>> has_close_elements([1.0, 2.0, 3.0], 0.5)\nFalse\n>>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\nTrue"
}
```
### Data Fields
The data fields are the same among all splits:
- `task_id`: Indicates the language (Python/JavaScript/Java/Go/C++/Rust) and task id (from 0 to 163) of the problem
- `prompt`: the prompt for models relying on code continuation
- `declaration`: the declaration of the function (same as prompt but without the docstring)
- `canonical_solution`: the correct solution passing all unit tests for the problem
- `buggy_solution`: same as `canonical_solution` but with a subtle human-written bug causing the unit tests to fail
- `bug_type`: the type of the bug in `buggy_solution` (one of [`missing logic`, `excess logic`, `value misuse`, `operator misuse`, `variable misuse`, `function misuse`])
- `failure_symptoms`: the problem the bug causes (one of [`incorrect output`, `stackoverflow`, `infinite loop`])
- `entry_point`: the name of the function
- `import`: imports necessary for the solution (only present for Go)
- `test_setup`: imports necessary for the test execution (only present for Go)
- `test`: the unit tests for the problem
- `example_test`: additional unit tests different from `test` that could be e.g. provided to the model (these are not used in the paper)
- `signature`: the signature of the function
- `docstring`: the docstring describing the problem
- `instruction`: an instruction for HumanEvalSynthesize in the form `Write a {language_name} function {signature} to solve the following problem:\n{docstring}`
## Citation Information
```bibtex
@article{muennighoff2023octopack,
title={OctoPack: Instruction Tuning Code Large Language Models},
author={Niklas Muennighoff and Qian Liu and Armel Zebaze and Qinkai Zheng and Binyuan Hui and Terry Yue Zhuo and Swayam Singh and Xiangru Tang and Leandro von Werra and Shayne Longpre},
journal={arXiv preprint arXiv:2308.07124},
year={2023}
}
``` | bigcode/humanevalpack | [
"language_creators:expert-generated",
"multilinguality:multilingual",
"language:code",
"license:mit",
"code",
"arxiv:2308.07124",
"region:us"
]
| 2023-03-29T11:00:16+00:00 | {"language_creators": ["expert-generated"], "language": ["code"], "license": "mit", "multilinguality": ["multilingual"], "pretty_name": "HumanEvalPack", "tags": ["code"]} | 2024-01-19T23:09:04+00:00 | [
"2308.07124"
]
| [
"code"
]
| TAGS
#language_creators-expert-generated #multilinguality-multilingual #language-code #license-mit #code #arxiv-2308.07124 #region-us
| !Octopack
Dataset Card for HumanEvalPack
==============================
Table of Contents
-----------------
* Table of Contents
* Dataset Description
+ Dataset Summary
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
* Additional Information
+ Licensing Information
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Repository: URL
* Paper: OctoPack: Instruction Tuning Code Large Language Models
* Point of Contact: Niklas Muennighoff
### Dataset Summary
>
> HumanEvalPack is an extension of OpenAI's HumanEval to cover 6 total languages across 3 tasks. The Python split is exactly the same as OpenAI's Python HumanEval. The other splits are translated by humans (similar to HumanEval-X but with additional cleaning, see here). Refer to the OctoPack paper for more details.
>
>
>
* Languages: Python, JavaScript, Java, Go, C++, Rust
* OctoPack:
Usage
-----
Dataset Structure
-----------------
### Data Instances
An example looks as follows:
### Data Fields
The data fields are the same among all splits:
* 'task\_id': Indicates the language (Python/JavaScript/Java/Go/C++/Rust) and task id (from 0 to 163) of the problem
* 'prompt': the prompt for models relying on code continuation
* 'declaration': the declaration of the function (same as prompt but without the docstring)
* 'canonical\_solution': the correct solution passing all unit tests for the problem
* 'buggy\_solution': same as 'canonical\_solution' but with a subtle human-written bug causing the unit tests to fail
* 'bug\_type': the type of the bug in 'buggy\_solution' (one of ['missing logic', 'excess logic', 'value misuse', 'operator misuse', 'variable misuse', 'function misuse'])
* 'failure\_symptoms': the problem the bug causes (one of ['incorrect output', 'stackoverflow', 'infinite loop'])
* 'entry\_point': the name of the function
* 'import': imports necessary for the solution (only present for Go)
* 'test\_setup': imports necessary for the test execution (only present for Go)
* 'test': the unit tests for the problem
* 'example\_test': additional unit tests different from 'test' that could be e.g. provided to the model (these are not used in the paper)
* 'signature': the signature of the function
* 'docstring': the docstring describing the problem
* 'instruction': an instruction for HumanEvalSynthesize in the form 'Write a {language\_name} function {signature} to solve the following problem:\n{docstring}'
| [
"### Dataset Summary\n\n\n\n> \n> HumanEvalPack is an extension of OpenAI's HumanEval to cover 6 total languages across 3 tasks. The Python split is exactly the same as OpenAI's Python HumanEval. The other splits are translated by humans (similar to HumanEval-X but with additional cleaning, see here). Refer to the OctoPack paper for more details.\n> \n> \n> \n\n\n* Languages: Python, JavaScript, Java, Go, C++, Rust\n* OctoPack:\n\n\n\nUsage\n-----\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nAn example looks as follows:",
"### Data Fields\n\n\nThe data fields are the same among all splits:\n\n\n* 'task\\_id': Indicates the language (Python/JavaScript/Java/Go/C++/Rust) and task id (from 0 to 163) of the problem\n* 'prompt': the prompt for models relying on code continuation\n* 'declaration': the declaration of the function (same as prompt but without the docstring)\n* 'canonical\\_solution': the correct solution passing all unit tests for the problem\n* 'buggy\\_solution': same as 'canonical\\_solution' but with a subtle human-written bug causing the unit tests to fail\n* 'bug\\_type': the type of the bug in 'buggy\\_solution' (one of ['missing logic', 'excess logic', 'value misuse', 'operator misuse', 'variable misuse', 'function misuse'])\n* 'failure\\_symptoms': the problem the bug causes (one of ['incorrect output', 'stackoverflow', 'infinite loop'])\n* 'entry\\_point': the name of the function\n* 'import': imports necessary for the solution (only present for Go)\n* 'test\\_setup': imports necessary for the test execution (only present for Go)\n* 'test': the unit tests for the problem\n* 'example\\_test': additional unit tests different from 'test' that could be e.g. provided to the model (these are not used in the paper)\n* 'signature': the signature of the function\n* 'docstring': the docstring describing the problem\n* 'instruction': an instruction for HumanEvalSynthesize in the form 'Write a {language\\_name} function {signature} to solve the following problem:\\n{docstring}'"
]
| [
"TAGS\n#language_creators-expert-generated #multilinguality-multilingual #language-code #license-mit #code #arxiv-2308.07124 #region-us \n",
"### Dataset Summary\n\n\n\n> \n> HumanEvalPack is an extension of OpenAI's HumanEval to cover 6 total languages across 3 tasks. The Python split is exactly the same as OpenAI's Python HumanEval. The other splits are translated by humans (similar to HumanEval-X but with additional cleaning, see here). Refer to the OctoPack paper for more details.\n> \n> \n> \n\n\n* Languages: Python, JavaScript, Java, Go, C++, Rust\n* OctoPack:\n\n\n\nUsage\n-----\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nAn example looks as follows:",
"### Data Fields\n\n\nThe data fields are the same among all splits:\n\n\n* 'task\\_id': Indicates the language (Python/JavaScript/Java/Go/C++/Rust) and task id (from 0 to 163) of the problem\n* 'prompt': the prompt for models relying on code continuation\n* 'declaration': the declaration of the function (same as prompt but without the docstring)\n* 'canonical\\_solution': the correct solution passing all unit tests for the problem\n* 'buggy\\_solution': same as 'canonical\\_solution' but with a subtle human-written bug causing the unit tests to fail\n* 'bug\\_type': the type of the bug in 'buggy\\_solution' (one of ['missing logic', 'excess logic', 'value misuse', 'operator misuse', 'variable misuse', 'function misuse'])\n* 'failure\\_symptoms': the problem the bug causes (one of ['incorrect output', 'stackoverflow', 'infinite loop'])\n* 'entry\\_point': the name of the function\n* 'import': imports necessary for the solution (only present for Go)\n* 'test\\_setup': imports necessary for the test execution (only present for Go)\n* 'test': the unit tests for the problem\n* 'example\\_test': additional unit tests different from 'test' that could be e.g. provided to the model (these are not used in the paper)\n* 'signature': the signature of the function\n* 'docstring': the docstring describing the problem\n* 'instruction': an instruction for HumanEvalSynthesize in the form 'Write a {language\\_name} function {signature} to solve the following problem:\\n{docstring}'"
]
| [
44,
124,
13,
441
]
| [
"passage: TAGS\n#language_creators-expert-generated #multilinguality-multilingual #language-code #license-mit #code #arxiv-2308.07124 #region-us \n### Dataset Summary\n\n\n\n> \n> HumanEvalPack is an extension of OpenAI's HumanEval to cover 6 total languages across 3 tasks. The Python split is exactly the same as OpenAI's Python HumanEval. The other splits are translated by humans (similar to HumanEval-X but with additional cleaning, see here). Refer to the OctoPack paper for more details.\n> \n> \n> \n\n\n* Languages: Python, JavaScript, Java, Go, C++, Rust\n* OctoPack:\n\n\n\nUsage\n-----\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nAn example looks as follows:"
]
| [
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|
c2bf66e5d0a137544d7f572e9e184cf145877296 | # Dataset Card for "MusicCaps_spectrogram_aspect"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | rxk/MC_aspect | [
"region:us"
]
| 2023-03-29T11:28:50+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3440007731.837, "num_examples": 19731}], "download_size": 3421004235, "dataset_size": 3440007731.837}} | 2023-03-29T11:57:29+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "MusicCaps_spectrogram_aspect"
More Information needed | [
"# Dataset Card for \"MusicCaps_spectrogram_aspect\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"MusicCaps_spectrogram_aspect\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"MusicCaps_spectrogram_aspect\"\n\nMore Information needed"
]
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|
3cd64bcb02fee4a5068e89b3c95fbe26a226a232 | # Dataset Card for "MusicCaps_spectrogram_caption"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | rxk/MC_caption | [
"region:us"
]
| 2023-03-29T11:53:03+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3442217474.837, "num_examples": 19731}], "download_size": 3421342341, "dataset_size": 3442217474.837}} | 2023-03-29T12:24:36+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "MusicCaps_spectrogram_caption"
More Information needed | [
"# Dataset Card for \"MusicCaps_spectrogram_caption\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"MusicCaps_spectrogram_caption\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"MusicCaps_spectrogram_caption\"\n\nMore Information needed"
]
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|
1f4cd6a427ea7599e3e23b1d8f3681e14b5da63d |
# Distil Whisper: LibriSpeech ASR
This is a variant of the [LibriSpeech ASR](https://huggingface.co/datasets/librispeech_asr) dataset, augmented to return the pseudo-labelled Whisper
Transcriptions alongside the original dataset elements. The pseudo-labelled transcriptions were generated by
labelling the input audio data with the Whisper [large-v2](https://huggingface.co/openai/whisper-large-v2)
model with *greedy* sampling. For information on how the original dataset was curated, refer to the original
[dataset card](https://huggingface.co/datasets/librispeech_asr).
## Standalone Usage
First, install the latest version of the 🤗 Datasets package:
```bash
pip install --upgrade pip
pip install --upgrade datasets[audio]
```
The dataset can be downloaded and pre-processed on disk using the [`load_dataset`](https://huggingface.co/docs/datasets/v2.14.5/en/package_reference/loading_methods#datasets.load_dataset)
function:
```python
from datasets import load_dataset
dataset = load_dataset("distil-whisper/librispeech_asr", "all")
# take the first sample of the validation set
sample = dataset["validation.clean"][0]
```
It can also be streamed directly from the Hub using Datasets' [streaming mode](https://huggingface.co/blog/audio-datasets#streaming-mode-the-silver-bullet).
Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire
dataset to disk:
```python
from datasets import load_dataset
dataset = load_dataset("distil-whisper/librispeech_asr", "all", streaming=True)
# take the first sample of the validation set
sample = next(iter(dataset["validation.clean"]))
```
## Distil Whisper Usage
To use this dataset to reproduce a Distil Whisper training run, refer to the instructions on the
[Distil Whisper repository](https://github.com/huggingface/distil-whisper#training).
## License
This dataset is licensed under cc-by-4.0.
| distil-whisper/librispeech_asr | [
"task_categories:automatic-speech-recognition",
"language:en",
"license:cc-by-4.0",
"region:us"
]
| 2023-03-29T11:53:48+00:00 | {"language": ["en"], "license": "cc-by-4.0", "task_categories": ["automatic-speech-recognition"], "-pretty_name": "LibriSpeech ASR"} | 2023-09-25T09:30:13+00:00 | []
| [
"en"
]
| TAGS
#task_categories-automatic-speech-recognition #language-English #license-cc-by-4.0 #region-us
|
# Distil Whisper: LibriSpeech ASR
This is a variant of the LibriSpeech ASR dataset, augmented to return the pseudo-labelled Whisper
Transcriptions alongside the original dataset elements. The pseudo-labelled transcriptions were generated by
labelling the input audio data with the Whisper large-v2
model with *greedy* sampling. For information on how the original dataset was curated, refer to the original
dataset card.
## Standalone Usage
First, install the latest version of the Datasets package:
The dataset can be downloaded and pre-processed on disk using the 'load_dataset'
function:
It can also be streamed directly from the Hub using Datasets' streaming mode.
Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire
dataset to disk:
## Distil Whisper Usage
To use this dataset to reproduce a Distil Whisper training run, refer to the instructions on the
Distil Whisper repository.
## License
This dataset is licensed under cc-by-4.0.
| [
"# Distil Whisper: LibriSpeech ASR \n\nThis is a variant of the LibriSpeech ASR dataset, augmented to return the pseudo-labelled Whisper \nTranscriptions alongside the original dataset elements. The pseudo-labelled transcriptions were generated by \nlabelling the input audio data with the Whisper large-v2\nmodel with *greedy* sampling. For information on how the original dataset was curated, refer to the original \ndataset card.",
"## Standalone Usage\n\nFirst, install the latest version of the Datasets package:\n\n\n\nThe dataset can be downloaded and pre-processed on disk using the 'load_dataset' \nfunction:\n\n\n\nIt can also be streamed directly from the Hub using Datasets' streaming mode.\nLoading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire \ndataset to disk:",
"## Distil Whisper Usage\n\nTo use this dataset to reproduce a Distil Whisper training run, refer to the instructions on the \nDistil Whisper repository.",
"## License\n\nThis dataset is licensed under cc-by-4.0."
]
| [
"TAGS\n#task_categories-automatic-speech-recognition #language-English #license-cc-by-4.0 #region-us \n",
"# Distil Whisper: LibriSpeech ASR \n\nThis is a variant of the LibriSpeech ASR dataset, augmented to return the pseudo-labelled Whisper \nTranscriptions alongside the original dataset elements. The pseudo-labelled transcriptions were generated by \nlabelling the input audio data with the Whisper large-v2\nmodel with *greedy* sampling. For information on how the original dataset was curated, refer to the original \ndataset card.",
"## Standalone Usage\n\nFirst, install the latest version of the Datasets package:\n\n\n\nThe dataset can be downloaded and pre-processed on disk using the 'load_dataset' \nfunction:\n\n\n\nIt can also be streamed directly from the Hub using Datasets' streaming mode.\nLoading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire \ndataset to disk:",
"## Distil Whisper Usage\n\nTo use this dataset to reproduce a Distil Whisper training run, refer to the instructions on the \nDistil Whisper repository.",
"## License\n\nThis dataset is licensed under cc-by-4.0."
]
| [
35,
110,
92,
40,
16
]
| [
"passage: TAGS\n#task_categories-automatic-speech-recognition #language-English #license-cc-by-4.0 #region-us \n# Distil Whisper: LibriSpeech ASR \n\nThis is a variant of the LibriSpeech ASR dataset, augmented to return the pseudo-labelled Whisper \nTranscriptions alongside the original dataset elements. The pseudo-labelled transcriptions were generated by \nlabelling the input audio data with the Whisper large-v2\nmodel with *greedy* sampling. For information on how the original dataset was curated, refer to the original \ndataset card.## Standalone Usage\n\nFirst, install the latest version of the Datasets package:\n\n\n\nThe dataset can be downloaded and pre-processed on disk using the 'load_dataset' \nfunction:\n\n\n\nIt can also be streamed directly from the Hub using Datasets' streaming mode.\nLoading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire \ndataset to disk:## Distil Whisper Usage\n\nTo use this dataset to reproduce a Distil Whisper training run, refer to the instructions on the \nDistil Whisper repository.## License\n\nThis dataset is licensed under cc-by-4.0."
]
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|
9170aceb1d8ab749ee4f60f1ec84e60e4aeb163c | # Dataset Card for "pile-deduped-pythia-random-sampled"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | EleutherAI/pile-deduped-pythia-random-sampled | [
"region:us"
]
| 2023-03-29T12:15:01+00:00 | {"dataset_info": {"features": [{"name": "Index", "dtype": "int64"}, {"name": "70M", "dtype": "float64"}, {"name": "160M", "dtype": "float64"}, {"name": "410M", "dtype": "float64"}, {"name": "1B", "dtype": "float64"}, {"name": "1.4B", "dtype": "float64"}, {"name": "2.8B", "dtype": "float64"}, {"name": "6.9B", "dtype": "float64"}, {"name": "12B", "dtype": "float64"}, {"name": "Tokens", "sequence": "uint16"}], "splits": [{"name": "train", "num_bytes": 1020000000, "num_examples": 5000000}], "download_size": 915854656, "dataset_size": 1020000000}} | 2023-08-25T06:26:47+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "pile-deduped-pythia-random-sampled"
More Information needed | [
"# Dataset Card for \"pile-deduped-pythia-random-sampled\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"pile-deduped-pythia-random-sampled\"\n\nMore Information needed"
]
| [
6,
26
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"pile-deduped-pythia-random-sampled\"\n\nMore Information needed"
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|
904f0fe00332d63d3149b8306b2c704e89231e26 | ## Introduction
* We build a large-scale dataset called the Theme and Aesthetics Dataset with 66K images (TAD66K), which is specifically designed for IAA. Specifically, (1) it is a theme-oriented dataset containing 66K images covering 47 popular themes. All images were carefully selected by hand based on the theme. (2) In addition to common aesthetic criteria, we provide 47 criteria for the 47 themes. Images of each theme are annotated independently, and each image contains at least 1200 effective annotations (so far the richest annotations). These high-quality annotations could help to provide deeper insight into the performance of models.
<div align="center">
</div>
## If you find our work is useful, pleaes cite our paper:
```
@article{herethinking,
title={Rethinking Image Aesthetics Assessment: Models, Datasets and Benchmarks},
author={He, Shuai and Zhang, Yongchang and Xie, Rui and Jiang, Dongxiang and Ming, Anlong},
journal={IJCAI},
year={2022},
}
``` | Shuai1995/TAD66K_for_Image_Aesthetics_Assessment | [
"task_categories:feature-extraction",
"size_categories:10K<n<100K",
"license:apache-2.0",
"Image Aesthetics Assessment",
"Image Quality Assessment",
"region:us"
]
| 2023-03-29T12:16:07+00:00 | {"license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["feature-extraction"], "tags": ["Image Aesthetics Assessment", "Image Quality Assessment"]} | 2023-03-30T00:24:17+00:00 | []
| []
| TAGS
#task_categories-feature-extraction #size_categories-10K<n<100K #license-apache-2.0 #Image Aesthetics Assessment #Image Quality Assessment #region-us
| ## Introduction
* We build a large-scale dataset called the Theme and Aesthetics Dataset with 66K images (TAD66K), which is specifically designed for IAA. Specifically, (1) it is a theme-oriented dataset containing 66K images covering 47 popular themes. All images were carefully selected by hand based on the theme. (2) In addition to common aesthetic criteria, we provide 47 criteria for the 47 themes. Images of each theme are annotated independently, and each image contains at least 1200 effective annotations (so far the richest annotations). These high-quality annotations could help to provide deeper insight into the performance of models.
!TAD66K
<div align="center">
!example3
</div>
## If you find our work is useful, pleaes cite our paper:
| [
"## Introduction\n* We build a large-scale dataset called the Theme and Aesthetics Dataset with 66K images (TAD66K), which is specifically designed for IAA. Specifically, (1) it is a theme-oriented dataset containing 66K images covering 47 popular themes. All images were carefully selected by hand based on the theme. (2) In addition to common aesthetic criteria, we provide 47 criteria for the 47 themes. Images of each theme are annotated independently, and each image contains at least 1200 effective annotations (so far the richest annotations). These high-quality annotations could help to provide deeper insight into the performance of models. \n\n!TAD66K\n\n<div align=\"center\">\n \n!example3\n \n</div>",
"## If you find our work is useful, pleaes cite our paper:"
]
| [
"TAGS\n#task_categories-feature-extraction #size_categories-10K<n<100K #license-apache-2.0 #Image Aesthetics Assessment #Image Quality Assessment #region-us \n",
"## Introduction\n* We build a large-scale dataset called the Theme and Aesthetics Dataset with 66K images (TAD66K), which is specifically designed for IAA. Specifically, (1) it is a theme-oriented dataset containing 66K images covering 47 popular themes. All images were carefully selected by hand based on the theme. (2) In addition to common aesthetic criteria, we provide 47 criteria for the 47 themes. Images of each theme are annotated independently, and each image contains at least 1200 effective annotations (so far the richest annotations). These high-quality annotations could help to provide deeper insight into the performance of models. \n\n!TAD66K\n\n<div align=\"center\">\n \n!example3\n \n</div>",
"## If you find our work is useful, pleaes cite our paper:"
]
| [
49,
174,
16
]
| [
"passage: TAGS\n#task_categories-feature-extraction #size_categories-10K<n<100K #license-apache-2.0 #Image Aesthetics Assessment #Image Quality Assessment #region-us \n## Introduction\n* We build a large-scale dataset called the Theme and Aesthetics Dataset with 66K images (TAD66K), which is specifically designed for IAA. Specifically, (1) it is a theme-oriented dataset containing 66K images covering 47 popular themes. All images were carefully selected by hand based on the theme. (2) In addition to common aesthetic criteria, we provide 47 criteria for the 47 themes. Images of each theme are annotated independently, and each image contains at least 1200 effective annotations (so far the richest annotations). These high-quality annotations could help to provide deeper insight into the performance of models. \n\n!TAD66K\n\n<div align=\"center\">\n \n!example3\n \n</div>## If you find our work is useful, pleaes cite our paper:"
]
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|
8f9e0bb097697b9ba200f12db0a833889c233df9 | # Dataset Card for "trinary_c_elegans"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ebj/trinary_c_elegans | [
"region:us"
]
| 2023-03-29T13:32:13+00:00 | {"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 480177378.0, "num_examples": 269}], "download_size": 51437925, "dataset_size": 480177378.0}} | 2023-03-29T15:16:01+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "trinary_c_elegans"
More Information needed | [
"# Dataset Card for \"trinary_c_elegans\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"trinary_c_elegans\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"trinary_c_elegans\"\n\nMore Information needed"
]
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e0e57749e1d6e5c0b3390f82a8ac50092a5b9813 | # Dataset Card for "DTD_parition1_test_google_flan_t5_xl_mode_C_T_A_T_SPECIFIC_ns_1880"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | CVasNLPExperiments/DTD_parition1_test_google_flan_t5_xl_mode_C_T_A_T_SPECIFIC_ns_1880 | [
"region:us"
]
| 2023-03-29T13:37:48+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 673355, "num_examples": 1880}], "download_size": 226073, "dataset_size": 673355}} | 2023-03-29T13:37:51+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "DTD_parition1_test_google_flan_t5_xl_mode_C_T_A_T_SPECIFIC_ns_1880"
More Information needed | [
"# Dataset Card for \"DTD_parition1_test_google_flan_t5_xl_mode_C_T_A_T_SPECIFIC_ns_1880\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"DTD_parition1_test_google_flan_t5_xl_mode_C_T_A_T_SPECIFIC_ns_1880\"\n\nMore Information needed"
]
| [
6,
45
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"DTD_parition1_test_google_flan_t5_xl_mode_C_T_A_T_SPECIFIC_ns_1880\"\n\nMore Information needed"
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|
a57120c35120f2e75d904898a8479dcf4990683f | # Dataset Card for "DTD_parition1_test_google_flan_t5_xl_mode_C_A_T_SPECIFIC_ns_1880"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | CVasNLPExperiments/DTD_parition1_test_google_flan_t5_xl_mode_C_A_T_SPECIFIC_ns_1880 | [
"region:us"
]
| 2023-03-29T13:40:31+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 546856, "num_examples": 1880}, {"name": "fewshot_1_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 1039535, "num_examples": 1880}, {"name": "fewshot_0_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_ensemble_specific_rices", "num_bytes": 546340, "num_examples": 1880}], "download_size": 576598, "dataset_size": 2132731}} | 2023-03-29T14:46:11+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "DTD_parition1_test_google_flan_t5_xl_mode_C_A_T_SPECIFIC_ns_1880"
More Information needed | [
"# Dataset Card for \"DTD_parition1_test_google_flan_t5_xl_mode_C_A_T_SPECIFIC_ns_1880\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"DTD_parition1_test_google_flan_t5_xl_mode_C_A_T_SPECIFIC_ns_1880\"\n\nMore Information needed"
]
| [
6,
43
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"DTD_parition1_test_google_flan_t5_xl_mode_C_A_T_SPECIFIC_ns_1880\"\n\nMore Information needed"
]
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7c735442e021a332bf11b4f91a0f6a7b9b241765 | # Dataset Card for "DTD_parition1_test_google_flan_t5_xl_mode_T_SPECIFIC_ns_1880"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | CVasNLPExperiments/DTD_parition1_test_google_flan_t5_xl_mode_T_SPECIFIC_ns_1880 | [
"region:us"
]
| 2023-03-29T13:43:20+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 224447, "num_examples": 1880}], "download_size": 19238, "dataset_size": 224447}} | 2023-03-29T13:47:17+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "DTD_parition1_test_google_flan_t5_xl_mode_T_SPECIFIC_ns_1880"
More Information needed | [
"# Dataset Card for \"DTD_parition1_test_google_flan_t5_xl_mode_T_SPECIFIC_ns_1880\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"DTD_parition1_test_google_flan_t5_xl_mode_T_SPECIFIC_ns_1880\"\n\nMore Information needed"
]
| [
6,
39
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"DTD_parition1_test_google_flan_t5_xl_mode_T_SPECIFIC_ns_1880\"\n\nMore Information needed"
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37773d35ecd73bb0828bb827ea3eebc034d8a980 | # Dataset Card for "DTD_parition1_test_google_flan_t5_xxl_mode_C_A_T_SPECIFIC_ns_1880"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | CVasNLPExperiments/DTD_parition1_test_google_flan_t5_xxl_mode_C_A_T_SPECIFIC_ns_1880 | [
"region:us"
]
| 2023-03-29T13:51:09+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 547013, "num_examples": 1880}, {"name": "fewshot_0_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_ensemble_specific_rices", "num_bytes": 546403, "num_examples": 1880}, {"name": "fewshot_1_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_ensemble_specific_rices", "num_bytes": 1038797, "num_examples": 1880}, {"name": "fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 852716, "num_examples": 1880}, {"name": "fewshot_1_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 1476439, "num_examples": 1880}], "download_size": 1174480, "dataset_size": 4461368}} | 2023-04-04T02:17:40+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "DTD_parition1_test_google_flan_t5_xxl_mode_C_A_T_SPECIFIC_ns_1880"
More Information needed | [
"# Dataset Card for \"DTD_parition1_test_google_flan_t5_xxl_mode_C_A_T_SPECIFIC_ns_1880\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"DTD_parition1_test_google_flan_t5_xxl_mode_C_A_T_SPECIFIC_ns_1880\"\n\nMore Information needed"
]
| [
6,
44
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"DTD_parition1_test_google_flan_t5_xxl_mode_C_A_T_SPECIFIC_ns_1880\"\n\nMore Information needed"
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|
7f0c6eef81786872a00938f1fb942aee930a1508 | # DailyDialog
- Source: https://huggingface.co/datasets/daily_dialog
- Num examples:
- 11,118 (train)
- 1,000 (validation)
- 1,000 (test)
- Language: Vietnamese
```python
from datasets import load_dataset
load_dataset("vietgpt/daily_dialog_vi")
``` | vietgpt/daily_dialog_vi | [
"task_categories:conversational",
"size_categories:10K<n<100K",
"language:vi",
"SFT",
"region:us"
]
| 2023-03-29T13:57:48+00:00 | {"language": ["vi"], "size_categories": ["10K<n<100K"], "task_categories": ["conversational"], "dataset_info": {"features": [{"name": "dialog", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 7803227, "num_examples": 11118}, {"name": "validation", "num_bytes": 718575, "num_examples": 1000}, {"name": "test", "num_bytes": 698896, "num_examples": 1000}], "download_size": 4841457, "dataset_size": 9220698}, "tags": ["SFT"]} | 2023-06-21T13:11:16+00:00 | []
| [
"vi"
]
| TAGS
#task_categories-conversational #size_categories-10K<n<100K #language-Vietnamese #SFT #region-us
| # DailyDialog
- Source: URL
- Num examples:
- 11,118 (train)
- 1,000 (validation)
- 1,000 (test)
- Language: Vietnamese
| [
"# DailyDialog\n\n- Source: URL\n- Num examples: \n - 11,118 (train)\n - 1,000 (validation)\n - 1,000 (test)\n- Language: Vietnamese"
]
| [
"TAGS\n#task_categories-conversational #size_categories-10K<n<100K #language-Vietnamese #SFT #region-us \n",
"# DailyDialog\n\n- Source: URL\n- Num examples: \n - 11,118 (train)\n - 1,000 (validation)\n - 1,000 (test)\n- Language: Vietnamese"
]
| [
38,
38
]
| [
"passage: TAGS\n#task_categories-conversational #size_categories-10K<n<100K #language-Vietnamese #SFT #region-us \n# DailyDialog\n\n- Source: URL\n- Num examples: \n - 11,118 (train)\n - 1,000 (validation)\n - 1,000 (test)\n- Language: Vietnamese"
]
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5809adc55d8a981311b6e33d930a31e16f8679b1 | # Dataset Card for "DTD_parition1_test_google_flan_t5_xl_mode_C_A_T_T_SPECIFIC_ns_1880"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | CVasNLPExperiments/DTD_parition1_test_google_flan_t5_xl_mode_C_A_T_T_SPECIFIC_ns_1880 | [
"region:us"
]
| 2023-03-29T14:04:21+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_simple_specific_rices", "num_bytes": 692485, "num_examples": 1880}], "download_size": 230862, "dataset_size": 692485}} | 2023-03-29T14:04:24+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "DTD_parition1_test_google_flan_t5_xl_mode_C_A_T_T_SPECIFIC_ns_1880"
More Information needed | [
"# Dataset Card for \"DTD_parition1_test_google_flan_t5_xl_mode_C_A_T_T_SPECIFIC_ns_1880\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"DTD_parition1_test_google_flan_t5_xl_mode_C_A_T_T_SPECIFIC_ns_1880\"\n\nMore Information needed"
]
| [
6,
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"passage: TAGS\n#region-us \n# Dataset Card for \"DTD_parition1_test_google_flan_t5_xl_mode_C_A_T_T_SPECIFIC_ns_1880\"\n\nMore Information needed"
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|
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