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9916eee71168e6fb6c66d87ebf2310ef18b63a67 | # Dataset Card for "mt5-small-finetuned-beer-ctg-en"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | EJaalborg2022/mt5-small-finetuned-beer-ctg-en | [
"region:us"
]
| 2023-03-11T14:17:38+00:00 | {"dataset_info": {"features": [{"name": "prediction_ts", "dtype": "float32"}, {"name": "beer_ABV", "dtype": "float32"}, {"name": "beer_name", "dtype": "string"}, {"name": "beer_style", "dtype": "string"}, {"name": "review_appearance", "dtype": "float32"}, {"name": "review_palette", "dtype": "float32"}, {"name": "review_taste", "dtype": "float32"}, {"name": "review_aroma", "dtype": "float32"}, {"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "negative", "1": "neutral", "2": "positive"}}}}], "splits": [{"name": "training", "num_bytes": 6908323, "num_examples": 9000}, {"name": "validation", "num_bytes": 970104, "num_examples": 1260}, {"name": "production", "num_bytes": 21305419, "num_examples": 27742}], "download_size": 16954616, "dataset_size": 29183846}} | 2023-03-11T14:17:45+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "mt5-small-finetuned-beer-ctg-en"
More Information needed | [
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|
a1482db2f2ad013a9c5bf332daa874b220711538 | # Dataset Card for "random-walk-reddit-corpus-55-cleaned"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | dmayhem93/random-walk-reddit-corpus-55-cleaned | [
"region:us"
]
| 2023-03-11T15:07:52+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3937195661, "num_examples": 6141002}], "download_size": 2309272818, "dataset_size": 3937195661}} | 2023-03-11T15:22:21+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "random-walk-reddit-corpus-55-cleaned"
More Information needed | [
"# Dataset Card for \"random-walk-reddit-corpus-55-cleaned\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"random-walk-reddit-corpus-55-cleaned\"\n\nMore Information needed"
]
| [
6,
25
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"random-walk-reddit-corpus-55-cleaned\"\n\nMore Information needed"
]
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|
8ae1ad92ab57542aba188c1532e491c0e81e3923 | # Dataset Card for "top-n-reddit-corpus-55-cleaned"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | dmayhem93/top-n-reddit-corpus-55-cleaned | [
"region:us"
]
| 2023-03-11T15:25:28+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4750853557, "num_examples": 6141002}], "download_size": 2785435080, "dataset_size": 4750853557}} | 2023-03-11T15:42:39+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "top-n-reddit-corpus-55-cleaned"
More Information needed | [
"# Dataset Card for \"top-n-reddit-corpus-55-cleaned\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"top-n-reddit-corpus-55-cleaned\"\n\nMore Information needed"
]
| [
6,
24
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"top-n-reddit-corpus-55-cleaned\"\n\nMore Information needed"
]
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|
c63754c5926ea844da80f9ee7fb8e391199e1257 | # Dataset Card for "sdft-test-03"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tuperte69/sdft-test-03 | [
"region:us"
]
| 2023-03-11T16:03:48+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 303231880.0, "num_examples": 172}], "download_size": 303231499, "dataset_size": 303231880.0}} | 2023-03-11T16:15:30+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "sdft-test-03"
More Information needed | [
"# Dataset Card for \"sdft-test-03\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"sdft-test-03\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"sdft-test-03\"\n\nMore Information needed"
]
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|
b9c7e2883fd1b6172e59db667ba09fd299e3b15a |
An imitation learning environment for the push-wall-v2 environment, sample for the policy push-wall-v2
This environment was created as part of the Generally Intelligent Agents
project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_push_wall_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_push_wall_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
| qgallouedec/prj_gia_dataset_metaworld_push_wall_v2_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-03-11T16:20:07+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-04-27T11:12:23+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the push-wall-v2 environment, sample for the policy push-wall-v2
This environment was created as part of the Generally Intelligent Agents
project gia: URL
## Load dataset
First, clone it with
Then, load it with
| [
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n",
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
46,
16
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
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|
371ad83eecca2e20f43621de7e6db620243c1bf1 |
An imitation learning environment for the reach-v2 environment, sample for the policy reach-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_reach_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_reach_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
| qgallouedec/prj_gia_dataset_metaworld_reach_v2_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-03-11T16:21:23+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-03-11T16:21:27+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the reach-v2 environment, sample for the policy reach-v2
This environment was created as part of the Generally Intelligent Agents project gia: URL
## Load dataset
First, clone it with
Then, load it with
| [
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n",
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
46,
16
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
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|
14d1070e18b13328264e2524eb9b041057e4a02e | # Dataset Card for "spanish-chinese"
All sensences extracted from the United Nations Parallel Corpus v1.0.
The parallel corpus consists of manually translated United Nations documents for the six
official UN languages, Arabic, Chinese, English, French, Russian, and Spanish.
The corpus is freely available for download at https://conferences.unite.un.org/UNCorpus
under the terms of use outlined in the attached DISCLAIMER.
The original individual documents are available at the United Nations Official Document
System (ODS) at http://ods.un.org.
Reference:
Ziemski, M., Junczys-Dowmunt, M., and Pouliquen, B., (2016), The United Nations Parallel
Corpus, Language Resources and Evaluation (LREC’16), Portorož, Slovenia, May 2016. | Deysi/spanish-chinese | [
"task_categories:translation",
"size_categories:10M<n<100M",
"language:es",
"language:zh",
"license:apache-2.0",
"language",
"translation",
"traducción",
"idiomas",
"chino",
"chinese",
"español",
"spanish",
"Universidad de La Rioja",
"region:us"
]
| 2023-03-11T16:22:23+00:00 | {"language": ["es", "zh"], "license": "apache-2.0", "size_categories": ["10M<n<100M"], "task_categories": ["translation"], "pretty_name": "Spanish and Chinese aligned sentences", "dataset_info": {"features": [{"name": "spanish", "dtype": "string"}, {"name": "chinese", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3048111118.5537825, "num_examples": 9092567}, {"name": "test", "num_bytes": 762027863.4462174, "num_examples": 2273142}], "download_size": 2473454462, "dataset_size": 3810138982}, "tags": ["language", "translation", "traducci\u00f3n", "idiomas", "chino", "chinese", "espa\u00f1ol", "spanish", "Universidad de La Rioja"]} | 2023-03-11T18:08:09+00:00 | []
| [
"es",
"zh"
]
| TAGS
#task_categories-translation #size_categories-10M<n<100M #language-Spanish #language-Chinese #license-apache-2.0 #language #translation #traducción #idiomas #chino #chinese #español #spanish #Universidad de La Rioja #region-us
| # Dataset Card for "spanish-chinese"
All sensences extracted from the United Nations Parallel Corpus v1.0.
The parallel corpus consists of manually translated United Nations documents for the six
official UN languages, Arabic, Chinese, English, French, Russian, and Spanish.
The corpus is freely available for download at URL
under the terms of use outlined in the attached DISCLAIMER.
The original individual documents are available at the United Nations Official Document
System (ODS) at URL.
Reference:
Ziemski, M., Junczys-Dowmunt, M., and Pouliquen, B., (2016), The United Nations Parallel
Corpus, Language Resources and Evaluation (LREC’16), Portorož, Slovenia, May 2016. | [
"# Dataset Card for \"spanish-chinese\"\n\nAll sensences extracted from the United Nations Parallel Corpus v1.0.\n\nThe parallel corpus consists of manually translated United Nations documents for the six\nofficial UN languages, Arabic, Chinese, English, French, Russian, and Spanish.\n\nThe corpus is freely available for download at URL\nunder the terms of use outlined in the attached DISCLAIMER.\n\nThe original individual documents are available at the United Nations Official Document\nSystem (ODS) at URL.\n\nReference:\nZiemski, M., Junczys-Dowmunt, M., and Pouliquen, B., (2016), The United Nations Parallel\nCorpus, Language Resources and Evaluation (LREC’16), Portorož, Slovenia, May 2016."
]
| [
"TAGS\n#task_categories-translation #size_categories-10M<n<100M #language-Spanish #language-Chinese #license-apache-2.0 #language #translation #traducción #idiomas #chino #chinese #español #spanish #Universidad de La Rioja #region-us \n",
"# Dataset Card for \"spanish-chinese\"\n\nAll sensences extracted from the United Nations Parallel Corpus v1.0.\n\nThe parallel corpus consists of manually translated United Nations documents for the six\nofficial UN languages, Arabic, Chinese, English, French, Russian, and Spanish.\n\nThe corpus is freely available for download at URL\nunder the terms of use outlined in the attached DISCLAIMER.\n\nThe original individual documents are available at the United Nations Official Document\nSystem (ODS) at URL.\n\nReference:\nZiemski, M., Junczys-Dowmunt, M., and Pouliquen, B., (2016), The United Nations Parallel\nCorpus, Language Resources and Evaluation (LREC’16), Portorož, Slovenia, May 2016."
]
| [
76,
162
]
| [
"passage: TAGS\n#task_categories-translation #size_categories-10M<n<100M #language-Spanish #language-Chinese #license-apache-2.0 #language #translation #traducción #idiomas #chino #chinese #español #spanish #Universidad de La Rioja #region-us \n# Dataset Card for \"spanish-chinese\"\n\nAll sensences extracted from the United Nations Parallel Corpus v1.0.\n\nThe parallel corpus consists of manually translated United Nations documents for the six\nofficial UN languages, Arabic, Chinese, English, French, Russian, and Spanish.\n\nThe corpus is freely available for download at URL\nunder the terms of use outlined in the attached DISCLAIMER.\n\nThe original individual documents are available at the United Nations Official Document\nSystem (ODS) at URL.\n\nReference:\nZiemski, M., Junczys-Dowmunt, M., and Pouliquen, B., (2016), The United Nations Parallel\nCorpus, Language Resources and Evaluation (LREC’16), Portorož, Slovenia, May 2016."
]
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|
2966190642c9d4e22bb2d23cb3dd0d020cbb49fd |
An imitation learning environment for the reach-wall-v2 environment, sample for the policy reach-wall-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_reach_wall_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_reach_wall_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
| qgallouedec/prj_gia_dataset_metaworld_reach_wall_v2_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-03-11T16:22:40+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-03-11T16:22:44+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the reach-wall-v2 environment, sample for the policy reach-wall-v2
This environment was created as part of the Generally Intelligent Agents project gia: URL
## Load dataset
First, clone it with
Then, load it with
| [
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n",
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
46,
16
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
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|
2530fd98e1c21bd9770a04d140a3f1e31b3aadfa |
An imitation learning environment for the shelf-place-v2 environment, sample for the policy shelf-place-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_shelf_place_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_shelf_place_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
| qgallouedec/prj_gia_dataset_metaworld_shelf_place_v2_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-03-11T16:24:11+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-03-11T16:24:15+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the shelf-place-v2 environment, sample for the policy shelf-place-v2
This environment was created as part of the Generally Intelligent Agents project gia: URL
## Load dataset
First, clone it with
Then, load it with
| [
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n",
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
46,
16
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
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|
878c646c9ecdc7a049c0bc9d64757a074dfd3296 |
An imitation learning environment for the soccer-v2 environment, sample for the policy soccer-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_soccer_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_soccer_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
| qgallouedec/prj_gia_dataset_metaworld_soccer_v2_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-03-11T16:25:40+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-03-11T16:25:44+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the soccer-v2 environment, sample for the policy soccer-v2
This environment was created as part of the Generally Intelligent Agents project gia: URL
## Load dataset
First, clone it with
Then, load it with
| [
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n",
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
46,
16
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
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|
155240f706130b1eaea735ae3bd332c189d20fcb |
This dataset is designed to train models for monochrome image classification, and the data is mainly sourced from danbooru and pixiv.
**All images in this dataset are guaranteed to be in JPEG format with a file extension of `.jpg`**.
Currently, it has three different versions:
| Version | Description | Monochrome Images | Normal Images | Total Images |
|:-----------------------------------------------------------------------------:|:--------------------:|:-----------------:|:-------------:|:------------:|
| [v1.0](https://huggingface.co/datasets/deepghs/monochrome_danbooru/tree/v1.0) | Oldest version | 304 | 174 | 478 |
| [v1.1](https://huggingface.co/datasets/deepghs/monochrome_danbooru/tree/v1.1) | Balanced samples | 528 | 514 | 1042 |
| [v2.0](https://huggingface.co/datasets/deepghs/monochrome_danbooru/tree/v2.0) | More complex samples | 2208 | 2250 | 4458 | | deepghs/monochrome_danbooru | [
"license:mit",
"region:us"
]
| 2023-03-11T17:32:27+00:00 | {"license": "mit"} | 2023-03-24T07:05:29+00:00 | []
| []
| TAGS
#license-mit #region-us
| This dataset is designed to train models for monochrome image classification, and the data is mainly sourced from danbooru and pixiv.
All images in this dataset are guaranteed to be in JPEG format with a file extension of '.jpg'.
Currently, it has three different versions:
| []
| [
"TAGS\n#license-mit #region-us \n"
]
| [
11
]
| [
"passage: TAGS\n#license-mit #region-us \n"
]
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|
b58e7b10b278bc9e1324c623dcfef6d3b30b2946 | # Dataset Card for "librispeech_full_frame_labels"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | charsiu/librispeech_full_frame_labels | [
"region:us"
]
| 2023-03-11T17:56:34+00:00 | {"dataset_info": {"features": [{"name": "chapter_id", "dtype": "int64"}, {"name": "file", "dtype": "string"}, {"name": "frame_labels", "sequence": "string"}, {"name": "frame_labels_10ms", "sequence": "string"}, {"name": "id", "dtype": "string"}, {"name": "speaker_id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3303356482, "num_examples": 281191}], "download_size": 219671524, "dataset_size": 3303356482}} | 2023-03-11T18:00:30+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "librispeech_full_frame_labels"
More Information needed | [
"# Dataset Card for \"librispeech_full_frame_labels\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"librispeech_full_frame_labels\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"librispeech_full_frame_labels\"\n\nMore Information needed"
]
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|
4bbbb343f2c40fd186a511d9018d5ace8b3d8973 | # Dataset Card for "librispeech_full_test_frame_labels"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | charsiu/librispeech_full_test_frame_labels | [
"region:us"
]
| 2023-03-11T18:01:22+00:00 | {"dataset_info": {"features": [{"name": "chapter_id", "dtype": "int64"}, {"name": "file", "dtype": "string"}, {"name": "frame_labels", "sequence": "string"}, {"name": "frame_labels_10ms", "sequence": "string"}, {"name": "id", "dtype": "string"}, {"name": "processed_file", "dtype": "string"}, {"name": "speaker_id", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "upsampled_file", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 78239379, "num_examples": 11125}], "download_size": 5460315, "dataset_size": 78239379}} | 2023-03-11T18:01:29+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "librispeech_full_test_frame_labels"
More Information needed | [
"# Dataset Card for \"librispeech_full_test_frame_labels\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"librispeech_full_test_frame_labels\"\n\nMore Information needed"
]
| [
6,
22
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"librispeech_full_test_frame_labels\"\n\nMore Information needed"
]
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|
900e310756a3f0b212b066c0b46969be72bbebe7 | # Dataset Card for "timit_frame_labels"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | charsiu/timit_frame_labels | [
"region:us"
]
| 2023-03-11T18:04:19+00:00 | {"dataset_info": {"features": [{"name": "converted_phonetic_detail", "struct": [{"name": "start", "sequence": "float64"}, {"name": "stop", "sequence": "float64"}, {"name": "utterance", "sequence": "string"}]}, {"name": "dialect_region", "dtype": "string"}, {"name": "file", "dtype": "string"}, {"name": "frame_labels", "sequence": "string"}, {"name": "id", "dtype": "string"}, {"name": "merge_phonetic_detail", "struct": [{"name": "start", "sequence": "float64"}, {"name": "stop", "sequence": "float64"}, {"name": "utterance", "sequence": "string"}]}, {"name": "phonetic_detail", "sequence": [{"name": "start", "dtype": "int64"}, {"name": "stop", "dtype": "int64"}, {"name": "utterance", "dtype": "string"}]}, {"name": "sentence_type", "dtype": "string"}, {"name": "speaker_id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "word_detail", "sequence": [{"name": "start", "dtype": "int64"}, {"name": "stop", "dtype": "int64"}, {"name": "utterance", "dtype": "string"}]}, {"name": "frame_labels_10ms", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 26189240, "num_examples": 4620}, {"name": "test", "num_bytes": 9531056, "num_examples": 1680}], "download_size": 7841359, "dataset_size": 35720296}} | 2023-03-11T18:04:25+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "timit_frame_labels"
More Information needed | [
"# Dataset Card for \"timit_frame_labels\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"timit_frame_labels\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"timit_frame_labels\"\n\nMore Information needed"
]
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|
083248a67f333fab806984910afb4959a56dc58d | # Dataset Card for "segmentation_drone"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | whyoke/segmentation_drone | [
"region:us"
]
| 2023-03-11T18:19:16+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "annotation", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 469141459.0, "num_examples": 350}, {"name": "annotation", "num_bytes": 53547177.0, "num_examples": 40}], "download_size": 522729573, "dataset_size": 522688636.0}} | 2023-03-11T18:26:58+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "segmentation_drone"
More Information needed | [
"# Dataset Card for \"segmentation_drone\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"segmentation_drone\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"segmentation_drone\"\n\nMore Information needed"
]
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|
d59e7a582f92d07318cbee6aa97176f82909f3ed | # Dataset Card for "en_de_TR"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | RocioUrquijo/en_de_TR | [
"region:us"
]
| 2023-03-11T19:08:51+00:00 | {"dataset_info": {"features": [{"name": "EN", "dtype": "string"}, {"name": "DE", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 167384.662601626, "num_examples": 787}, {"name": "test", "num_bytes": 41899.33739837398, "num_examples": 197}], "download_size": 142646, "dataset_size": 209284.0}} | 2023-03-11T19:59:52+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "en_de_TR"
More Information needed | [
"# Dataset Card for \"en_de_TR\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"en_de_TR\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"en_de_TR\"\n\nMore Information needed"
]
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|
4d374d03865648db0c46b6440c77ad031b43c2e0 | # Dataset Card for "bookcorpus_processed_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Brendan/bookcorpus_processed_test | [
"region:us"
]
| 2023-03-11T19:21:54+00:00 | {"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "special_tokens_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 922116, "num_examples": 299}], "download_size": 0, "dataset_size": 922116}} | 2023-11-16T19:58:16+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "bookcorpus_processed_test"
More Information needed | [
"# Dataset Card for \"bookcorpus_processed_test\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"bookcorpus_processed_test\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"bookcorpus_processed_test\"\n\nMore Information needed"
]
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|
08088c69ebf9872327081c1558d15b17b2d52701 |
## OIG-riverbed-filtered-small
A small filtered version of https://huggingface.co/datasets/laion/OIG, used for experimenting with filtering, clustering and visualizing the data in the OIG dataset.
```
'unatural_instructions': 33110,
'ul2_plus_oscar_en_00300': 32439,
'infil_dbpedia': 81913,
'synth_qa': 11056,
'rallio': 192978,
'unifiedskg': 34692,
'soda_dialog': 40233,
'merged_code_xp3': 50000,
'laion_image_prompts': 10572,
'oscar_en_00000': 27713,
'prosocial': 14543,
'xp3_sample': 38833,
'mathqa': 10674,
'anthrop_helpful': 4816,
'flanv2_cot_qed_train': 2370,
'dahoas': 27534,
'flanv2_cot_esnli_train': 11325,
'flanv2_cot_creak_train': 1809,
'conala': 2669,
'synth_code': 3206,
'flanv2_cot_gsm8k_train': 3356,
'kojma_cot': 3135,
'essays': 1531,
'plot_screenplay_books': 8135,
'safety_image_prompt': 5001,
'synth_depression': 944,
'cuad': 497,
'flanv2_cot_sensemaking_train': 1589,
'anthrop_redteam': 1142,
'flanv2_cot_strategyqa_train': 357,
'flanv2_cot_ecqa_train': 2862,
'flanv2_cot_aqua_train': 1157,
'flanv2_cot_qasc_train': 516,
'wiki_toxic_nontoxic': 103
```
It is best to download the data directly instead of using HF load_datasets: https://huggingface.co/datasets/laion/OIG-riverbed-filtered-small/resolve/main/OIG_filtered.jsonl
Topic map for a subset of the data:
```
.
├─Military Documents and Sentences____
│ ├─Burlington County Images and Employment Chart____
│ │ ├─Reports and assessments on risk analysis, data management, and industrial hygiene standards for tran
│ │ │ ├─NCHRP Reports for Transportation Management and Planning____
│ │ │ │ ├─■──Guidelines for Design of Corrosion-Damaged Bridge Superstructure with nchrp report____ ── Topic: 70
│ │ │ │ └─■──Transportation and Risk Management Practices____ ── Topic: 16
│ │ │ └─Assessment and Review of Chemical Agent Destruction Pilot Plants for Waste Disposal____
│ │ │ ├─■──Space Science and Technology Documents____ ── Topic: 18
│ │ │ └─Waste disposal, chemical agent destruction pilot plants review and assessment____
│ │ │ ├─■──Chemical Agent Destruction Pilot Plant Review and Assessment____ ── Topic: 48
│ │ │ └─■──Radioactive Waste Disposal and Alternative Treatments: Review of Various Documents____ ── Topic: 45
│ │ └─Highlighted Townships in Burlington County, NJ with Inset Maps____
│ │ ├─Township Highlighted in Burlington County - Image Prompts____
│ │ │ ├─Township Highlighting in Burlington County, New Jersey____
│ │ │ │ ├─■──Images and landmarks of Jonesboro, Waycross, and their respective counties in Arkansas and Georgia__ ── Topic: 97
│ │ │ │ └─■──Highlighted townships in Burlington County, New Jersey____ ── Topic: 26
│ │ │ └─Images and Workshop Summary Documents Related to Gulf Research Program, Mississippi River Basin Wate
│ │ │ ├─Geography and Environmental Issues in North and South America____
│ │ │ │ ├─■──Arctic Sea Ice Extent and Stability Analysis____ ── Topic: 96
│ │ │ │ └─Document topics related to rivers, maps, disasters, and ecosystem resilience in southern and eastern
│ │ │ │ ├─Geographic features in North and South America including rivers, watersheds, and ecosystems____
│ │ │ │ │ ├─■──Image Prompts for Cartographic Maps of Various Locations including South America, New Zealand, and t ── Topic: 19
│ │ │ │ │ └─■──Rivers and Ecosystems: Research and Monitoring in the Gulf and Oregon Watersheds____ ── Topic: 15
│ │ │ │ └─■──Disaster Resilience and Preparedness: Perspectives from Workshops and Case Studies____ ── Topic: 41
│ │ │ └─Geologic Survey and Cartographic Image Prompt with State Summary Information and District Boundaries
│ │ │ ├─■──Geological surveys and information on mineral resources in state parks____ ── Topic: 93
│ │ │ └─■──Drawings of geological features and caves including limestone, Coronado, and Pirinexus challenges___ ── Topic: 49
│ │ └─Graphical analysis of energy-related costs and trends over time____
│ │ ├─Graphs showing changes in U.S. employment, gasoline prices, energy expenditures, and employment cost
│ │ │ ├─■──Climate assessment and biogeochemical cycles with precipitation trends and image prompts.____ ── Topic: 56
│ │ │ └─Graphs showing changes in employment, natural gas electric generating capacity, and residential elec
│ │ │ ├─■──Energy Trends and Prices____ ── Topic: 6
│ │ │ └─■──Employment Trends in Selected Metropolitan Areas____ ── Topic: 3
│ │ └─■──Occupational injuries and illnesses rates and incidents in selected state and local government indus ── Topic: 63
│ └─Military operations and command updates in 2019____
│ ├─Health-related Workshop Proceedings and Image Prompts for Cancer, HIV, Health Literacy, and Accounti
│ │ ├─■──Promoting mental and behavioral health through effective therapy and preventive strategies____ ── Topic: 84
│ │ └─Health and healthcare approaches for various illnesses and conditions, including COVID-19, HIV, and
│ │ ├─Health care and COVID-19: Cancer treatment, innovation, and accounting approaches in low-resource ar
│ │ │ ├─■──Images related to Covid-19 vaccination and prevention____ ── Topic: 10
│ │ │ └─■──Health Policy Workshop Proceedings and Image Covers for Cancer, Workforce, Literacy, and Accounting ── Topic: 4
│ │ └─■──Annual Medical College Announcements in Philadelphia and Pennsylvania____ ── Topic: 67
│ └─Air Force documents covering various topics such as commanders, training, veterans, and change of co
│ ├─Air Force Operations and Training Documents____
│ │ ├─■──Documents related to military training and operations of Marine Corps and Army forces in 2013, 2017, ── Topic: 2
│ │ └─■──Images of Air Force Change of Command Ceremonies____ ── Topic: 1
│ └─Images and news related to the United States Defense Secretary James Mattis____
│ ├─Images of Defense Secretary James Mattis in Meetings and Testimonies.____
│ │ ├─■──Images related to Russian and Kazakh politicians including Solzhenitsyn, Yanukovych, Navalny, Aliyev ── Topic: 88
│ │ └─■──Images featuring Defense Secretary James Mattis in official meetings and events.____ ── Topic: 7
│ └─Police and Law Enforcement Activities and Incidents in Various Canadian Cities____
│ ├─■──Protests and Demonstrations with Activists and Slogans____ ── Topic: 32
│ └─■──Police activities in Windsor and Victoria, including commendations, dedicated flag, and crime scene ── Topic: 22
└─Clipart use for teaching materials with unlimited access for Abcteach members____
├─Printable worksheets for kindergarten learning and coloring pages with image prompts and sensory act
│ ├─Sports Image Prompts featuring Football, Cricket, Hockey and American Football Players____
│ │ ├─Various Image Prompts for Drawings Related to Football and Valparaiso Football Season Disappointment
│ │ │ ├─■──Valparaiso Crusaders Football Season____ ── Topic: 106
│ │ │ └─Sports-themed image prompts and podcast related to football and basketball.____
│ │ │ ├─■──Sports images with football players and throwback Riddell helmets____ ── Topic: 13
│ │ │ └─■──Basketball and Kardashian-West sightings____ ── Topic: 23
│ │ └─Indian Cricket - Matches, Images, and Cheer____
│ │ ├─Indian Premier League and Cricket Matches____
│ │ │ ├─■──Soccer Matches and Teams in Various Leagues____ ── Topic: 12
│ │ │ └─■──Cricket matches and fans in India, featuring IPL teams Kings XI Punjab and Kolkata Knight Riders, Bo ── Topic: 9
│ │ └─Olympic awards and championships won by athletes in various sports and attended by celebrities, with
│ │ ├─Olympics-related Image and Sentence Prompts____
│ │ │ ├─■──Images of Ricky Gervais, Jennifer Aniston, and Rachel Brosnahan at various award shows in Beverly Hi ── Topic: 8
│ │ │ └─■──Olympic Medals and Champions in Tennis____ ── Topic: 21
│ │ └─■──Mixed Combat Sports Culture with Boxing and Wrestling Championships____ ── Topic: 64
│ └─Collection of Printable Worksheets for Kindergarten and Grade Levels____
│ ├─Designing school hooded sweatshirts with super-soft cotton/poly fleece____
│ │ ├─Collection of Robert Dennis Stereoscopic Views - Image Drawing Prompts____
│ │ │ ├─Images of Greek and Byzantine Empires under different dynasties in ancient and medieval times, inclu
│ │ │ │ ├─Images of the Byzantine Empire under various dynasties and territories in ancient times____
│ │ │ │ │ ├─■──Cultural highlights of Asia - temples, burial complexes, and historic figures____ ── Topic: 78
│ │ │ │ │ └─Byzantine Empire under various dynasties and territories in medieval times____
│ │ │ │ │ ├─■──Art and History Document Prompts____ ── Topic: 47
│ │ │ │ │ └─■──Byzantine Empire and its Dynasties with Territory and Depicted Borders____ ── Topic: 90
│ │ │ │ └─■──Greek language and culture in modern and ancient Greece with various areas, religions, and historica ── Topic: 89
│ │ │ └─Robert Dennis Stereoscopic Views image prompts and stability prompt____
│ │ │ ├─Collection of Stereoscopic Views by Robert Dennis for Image Prompts____
│ │ │ │ ├─Lamborghini and Ducati Racing Footage and Newsreels in Different Battle Zones and Civil Wars____
│ │ │ │ │ ├─Lamborghini and Motorsports - Super Trofeo Races and Co-Branded Collections____
│ │ │ │ │ │ ├─■──Motorsports and Racing with Schumacher, Capps, and more____ ── Topic: 74
│ │ │ │ │ │ └─■──Lamborghini Super Trofeo and Automobili Celebrations____ ── Topic: 43
│ │ │ │ │ └─Images prompts for various topics including Civil War, New York Stock Exchange, and Star Wars: Dange
│ │ │ │ │ ├─Historical events and footage related to battles, wars, and stock exchange in different countries an
│ │ │ │ │ │ ├─■──Various Battles and War-related Topics____ ── Topic: 27
│ │ │ │ │ │ └─■──Stock newsreel videos of historical events in New York City____ ── Topic: 68
│ │ │ │ │ └─Korean dramas with various casts and release dates, including "Window," "One Ordinary Day," "NCT 24h
│ │ │ │ │ ├─■──Image prompts for various topics including Pickett N901-ES simplex slide rule, October issue of Peac ── Topic: 108
│ │ │ │ │ └─■──Korean dramas and their cast and release dates, featuring "Racket Boys", "One Ordinary Day", "NCT 24 ── Topic: 104
│ │ │ │ └─Stereoscopic views by Robert Dennis collection - Image prompts for drawing____
│ │ │ │ ├─Retirement Banquet at Centennial Student Union, Mankato State University____
│ │ │ │ │ ├─Religion and Faith Diversity in Infographics, Icons, and Images.____
│ │ │ │ │ │ ├─■──Religious Icons and Symbols____ ── Topic: 38
│ │ │ │ │ │ └─■──Images and Infographics Related to Muslims and Islamophobia____ ── Topic: 61
│ │ │ │ │ └─Retirement Banquet at Mankato State University in June with Awards and Speakers____
│ │ │ │ │ ├─Retirement banquet at Centennial Student Union, Mankato State University with awards and speakers.__
│ │ │ │ │ │ ├─Images of African Union, Women's History Month, and Indigenous Tribes____
│ │ │ │ │ │ │ ├─■──Celebrating Women's History Month and Women's Rights Activists____ ── Topic: 98
│ │ │ │ │ │ │ └─Indigenous and African Union Partnerships in Tradition and Development____
│ │ │ │ │ │ │ ├─■──Cultural Traditions and Sovereignty of Indigenous Peoples____ ── Topic: 29
│ │ │ │ │ │ │ └─■──African Union Partnership for COVID-19 Media Outreach and Prevention____ ── Topic: 30
│ │ │ │ │ │ └─Retirement Banquet at Centennial Student Union, Mankato State University____
│ │ │ │ │ │ ├─■──Retirement Banquet Image Prompts at Mankato State University's Centennial Student Union, June____ ── Topic: 35
│ │ │ │ │ │ └─■──Queensland State Archives images of Brisbane and surrounding areas in 1930s____ ── Topic: 100
│ │ │ │ │ └─Drawings of Skyscrapers and Landmarks in Indianapolis and Tampa____
│ │ │ │ │ ├─■──Images and documents related to Smithsonian, Whitney Museum of American Art, and Panama-Pacific Inte ── Topic: 58
│ │ │ │ │ └─■──City Skylines - Indianapolis and Tampa____ ── Topic: 87
│ │ │ │ └─Image prompts from Robert N. Dennis Collection of Stereoscopic Views for drawing different sceneries
│ │ │ │ ├─Image prompts from Robert N. Dennis collection of stereoscopic views____
│ │ │ │ │ ├─Images of animals and their interactions with humans in various settings____
│ │ │ │ │ │ ├─■──Wildlife Encounters and Human Interactions in National Parks____ ── Topic: 110
│ │ │ │ │ │ └─■──Images of dogs and their owners in various settings and activities (e.g. training, running marathons ── Topic: 91
│ │ │ │ │ └─Image prompts from Robert Dennis Collection of Stereoscopic Views____
│ │ │ │ │ ├─Garden Image Prompts - Wellington, Marengo, Sutton Place, Unidentified____
│ │ │ │ │ │ ├─■──Images of Gardens and Outdoor Decor____ ── Topic: 14
│ │ │ │ │ │ └─■──Wellington Real Estate Auctions and Subdivisions (with Cartographic Material)____ ── Topic: 46
│ │ │ │ │ └─Robert Dennis Stereoscopic Views Collection Images of Cities and Landscapes____
│ │ │ │ │ ├─■──Stereoscopic Views from Robert Dennis Collection of Various Cities____ ── Topic: 17
│ │ │ │ │ └─■──Various Cathedrals and Landmarks in Different Locations____ ── Topic: 73
│ │ │ │ └─Royal Family Events and Visits____
│ │ │ │ ├─■──Royal Events and Visits including Duchess of Cornwall, Duke and Duchess of Cambridge, Prince Harry, ── Topic: 40
│ │ │ │ └─■──Funerals and Coffins of Victims in Ireland____ ── Topic: 113
│ │ │ └─Memes, Invention, Archery, Politics, and Corrupt Politicians____
│ │ │ ├─■──Memes, aliens, politics, and corruption in Minnesota____ ── Topic: 116
│ │ │ └─■──Drawing memes related to inventions, archery, and humor.____ ── Topic: 39
│ │ └─Invoice Design and Template Assistance for Pepperdine University Community____
│ │ ├─Invoice and template design for Pepperdine University with remarkable dashboard and software feature
│ │ │ ├─RTCA 2013 Brochure Provides Information on Current Telecom Projects and Successes____
│ │ │ │ ├─VC Funds and Industry Analysis for Funding Rounds in Asia and Location Based Services during Recent
│ │ │ │ │ ├─■──VC Funds and Funding Rounds in Various Industries____ ── Topic: 71
│ │ │ │ │ └─■──Drawings of blockchain and cryptocurrency-related events and companies, including Rio DeFi, Healthur ── Topic: 51
│ │ │ │ └─RTCA 2013 Brochure Provides Information on Current Telecommunications Projects and Recent Successes_
│ │ │ │ ├─Technology Innovation and Communications Engineering____
│ │ │ │ │ ├─■──Innovation, Patents, Technology, Leadership, and Impact Trends____ ── Topic: 75
│ │ │ │ │ └─■──Technology and Engineering Strategies for Outsourcing Telecommunications and Business Analytics____ ── Topic: 28
│ │ │ │ └─■──rtca 2013 brochure provides current news and successes of projects____ ── Topic: 52
│ │ │ └─Invoicing and Dashboard Templates for Pepperdine University and Business Use____
│ │ │ ├─Wiring Diagrams for Home and Office____
│ │ │ │ ├─Wiring Diagrams for Home and Office____
│ │ │ │ │ ├─Wiring Diagrams for Manufacturing and Repair____
│ │ │ │ │ │ ├─Small Machine Shop Owners' Reactions to Automated Manufacturing Research Facility____
│ │ │ │ │ │ │ ├─■──Image prompts for small machine shop owners' reactions to automated manufacturing and CNC machine to ── Topic: 101
│ │ │ │ │ │ │ └─■──Ford maintenance and repair manuals for various vehicle models including Bronco, Festiva, Ranger, an ── Topic: 53
│ │ │ │ │ │ └─Electrical Wiring Diagrams for Various Applications____
│ │ │ │ │ │ ├─■──Electrical Wiring Diagrams for Various Applications____ ── Topic: 25
│ │ │ │ │ │ └─■──Various Images of Trains and Railways____ ── Topic: 69
│ │ │ │ │ └─Furniture and Dining Table Ideas____
│ │ │ │ │ ├─■──Various Lighting Ideas and Products for Different Settings and Purposes____ ── Topic: 65
│ │ │ │ │ └─■──Drawing furniture and dining table sets with linen fabric and mahogany finish from image prompts.___ ── Topic: 31
│ │ │ │ └─Roofing Companies and Services____
│ │ │ │ ├─■──Product Recalls and Safety Hazards____ ── Topic: 99
│ │ │ │ └─Roofing Companies and Services in Various Locations____
│ │ │ │ ├─■──Wildland Firefighters and Fire Management Research____ ── Topic: 94
│ │ │ │ └─■──Roofing Contractors and Services in Various Locations with Additional Related Keywords.____ ── Topic: 77
│ │ │ └─Invoice Design and Approval at Pepperdine University's Community Platform.____
│ │ │ ├─Invoice design template and approval process at Pepperdine University's communitypepperdineedu____
│ │ │ │ ├─Pepperdine University invoice design and approval process____
│ │ │ │ │ ├─Invoice Template and Dashboard for Pepperdine University and Business Invoicing with Free Downloads
│ │ │ │ │ │ ├─■──Pepperdine University Invoice Templates____ ── Topic: 44
│ │ │ │ │ │ └─■──Image prompts for drawing related to business, contracts, and agreements____ ── Topic: 92
│ │ │ │ │ └─Logo design entries in a contest for various businesses____
│ │ │ │ │ ├─Logo Design Contest Entries____
│ │ │ │ │ │ ├─Dell EMC IT Certifications and Technologies____
│ │ │ │ │ │ │ ├─Illustration, Mendelian Genetics, and Irwaddy Dolphin's Skeleton in Museo di Storia Naturale____
│ │ │ │ │ │ │ │ ├─■──Genetic mechanisms and structure of prokaryotic and eukaryotic cells____ ── Topic: 42
│ │ │ │ │ │ │ │ └─■──Images of natural history specimens exhibited in museums with keywords including irrawaddy dolphin, ── Topic: 111
│ │ │ │ │ │ │ └─■──Dell EMC certification exam image prompts for networking, cloud infrastructure and services, and pow ── Topic: 54
│ │ │ │ │ │ └─Logo Design Contest Entries for Various Businesses____
│ │ │ │ │ │ ├─■──Logo Design Contest Entries____ ── Topic: 36
│ │ │ │ │ │ └─■──Legal Documents Management for Law Firms and Corporate Legal Departments____ ── Topic: 86
│ │ │ │ │ └─English subtitles download for various movies and shows____
│ │ │ │ │ ├─Cannabis and Biome Grow Companies, Health Effects and Business Plan for Investors, and Doc Hollidaze
│ │ │ │ │ │ ├─■──Cannabis Business and Cultivation featuring Highland Grow and Doc Hollidaze Premium Cannabis____ ── Topic: 107
│ │ │ │ │ │ └─■──Board Results and Examinations across India (CBSE, ICSE, Punjab, Rajasthan, Tripura) including Suppl ── Topic: 117
│ │ │ │ │ └─■──Subtitles Download for English Movies____ ── Topic: 105
│ │ │ │ └─News and Reading Platforms for Sangrur and Barnala in Punjabi Jagran in 2014 for iPad, iPhone, and S
│ │ │ │ ├─■──Smartphone brands and features - iPhone, Samsung Galaxy, Nokia launches and latest updates.____ ── Topic: 34
│ │ │ │ └─■──News and e-paper articles in Punjabi and Hindi for Sangrur and Barnala on tablets and smartphones in ── Topic: 85
│ │ │ └─Cyberbullying Prevention and Tactics____
│ │ │ ├─Image prompts for drawing based on podcast episodes with plugins for managing work and product grids
│ │ │ │ ├─■──Drawing image prompts for podcast episodes with various plugin features____ ── Topic: 79
│ │ │ │ └─■──Various topics related to Zuckerberg, Facebook, and related events____ ── Topic: 115
│ │ │ └─Cyberbullying Prevention Tactics with iPredator and Michael Nuccitelli____
│ │ │ ├─Investment in Gold and Silver Commodities on Comex by Managed Money with Speculative Positioning, ba
│ │ │ │ ├─■──Comex Managed Money Speculative Positions on Gold and Silver Futures and Options____ ── Topic: 95
│ │ │ │ └─■──Various Diamond Jewelry Images and Prompts____ ── Topic: 82
│ │ │ └─Cyberbullying Prevention Tactics____
│ │ │ ├─■──Scams and Romance - Protecting Yourself Online and Offline____ ── Topic: 102
│ │ │ └─■──Cyberbullying Prevention and Tactics through iPredator and Michael Nuccitelli's Work____ ── Topic: 83
│ │ └─School Hooded Sweatshirts in Super-Soft Cotton/Poly Fleece____
│ │ ├─Essays on Christmas, Macbeth, Romeo and Juliet with image prompts____
│ │ │ ├─English literature analysis and critical essays on Macbeth, Romeo and Juliet, and The Catcher in the
│ │ │ │ ├─■──Poetry and Essays by Various Famous Poets____ ── Topic: 76
│ │ │ │ └─■──Essays on Shakespeare's Macbeth and Romeo and Juliet, as well as other literary works____ ── Topic: 37
│ │ │ └─Price comparison of books on various subjects at popular e-commerce sites, and image prompts for dra
│ │ │ ├─Price comparison of educational books on mathematics and psychology at popular online bookstores____
│ │ │ │ ├─Price comparison and edition analysis for various subjects on Flipkart, Amazon, and other online boo
│ │ │ │ │ ├─Price comparison of books on mathematics and psychology across major online retailers including Flip
│ │ │ │ │ │ ├─■──Disney, Fantasyland, Imagineers, Walter Elias Disney, gravestone, plaque, inscribed.____ ── Topic: 80
│ │ │ │ │ │ └─■──Price comparison for books on mathematics and psychology on various platforms (Flipkart, Amazon, etc ── Topic: 60
│ │ │ │ │ └─Image prompts for physical fitness and science discussions____
│ │ │ │ │ ├─Fitness, Mathematics, and Environmental Changes - Image Prompts for Physical Activity and Scientific
│ │ │ │ │ │ ├─■──Fitness and Workout Mats – Improving Aerobic Fitness and Health with Non-Slip Exercise Mats____ ── Topic: 81
│ │ │ │ │ │ └─■──Visual prompts and dialogues on changing physical and mathematical environments, coursework on nichr ── Topic: 72
│ │ │ │ │ └─■──Career Pathways and Development Resources____ ── Topic: 114
│ │ │ │ └─Christmas-themed Preschool Activity Ideas____
│ │ │ │ ├─Christmas preschool theme with image prompts and activities incorporating alphabet, counting, and ho
│ │ │ │ │ ├─■──Christmas themed image prompts, online shopping, and holiday traditions____ ── Topic: 55
│ │ │ │ │ └─■──Preschool Alphabet Book Crafts____ ── Topic: 59
│ │ │ │ └─■──Book covers and adventure novels____ ── Topic: 66
│ │ │ └─Nutrition and Food Images and Prompts____
│ │ │ ├─Agriculture and Horticulture Topics in Georgia, including Cotton, Wheat, and Greenhouse Management__
│ │ │ │ ├─■──Agricultural innovations and community support in Holland, Burkina Faso, and Uganda____ ── Topic: 118
│ │ │ │ └─■──Wheat crop and horticulture practices____ ── Topic: 62
│ │ │ └─Nutrition and Food Science____
│ │ │ ├─■──Canned meats and fruits, stability and quality symposium, and image prompts for food drawings____ ── Topic: 33
│ │ │ └─■──Nutrition and Image Prompts____ ── Topic: 57
│ │ └─School Hooded Cotton Sweatshirts with Super-Soft Fleece____
│ │ ├─Dress and Fashion Image Prompts, Medieval Fantasy Paper Dresses, and Elegant Tulle Prom Dresses____
│ │ │ ├─■──Fantasy Paper Doll Outfits and Image Prompts____ ── Topic: 103
│ │ │ └─■──Fashion and Dresses Inspiration____ ── Topic: 11
│ │ └─High school hooded sweatshirts in super-soft cotton/poly fleece____
│ │ ├─■──High School Hooded Sweatshirts - Super Soft Cotton/Poly Fleece to Keep You Warm on the Sidelines____ ── Topic: 20
│ │ └─■──High School Racerback Tank Tops with District Threads____ ── Topic: 109
│ └─Worksheets and Printables for Math, Kindergarten, and Beyond____
│ ├─Math and Reading Worksheets for Grades 7-9____
│ │ ├─■──Printable worksheets for math, reading, and kindergarten learning with image prompts.____ ── Topic: 5
│ │ └─■──Math Games and Activities for Engaging Students in Homeschool and Classroom Settings____ ── Topic: 112
│ └─■──Coloring Pages for Various Themes and Sizes____ ── Topic: 24
└─Clipart use for teaching materials with unlimited access for members on abcteach____
├─■──Clipart use for teaching materials in commercial format with unlimited illustrations as an abcteach ── Topic: 0
└─■──Flags Clipart for Teaching with Abcteach Membership____ ── Topic: 50
```
Thanks to LAION volunteers: @Rallio, @Kenhktsui, @Danielpatrickhug @Vyprix and @Summer.
| laion/OIG-riverbed-filtered-small | [
"license:apache-2.0",
"region:us"
]
| 2023-03-11T20:07:57+00:00 | {"license": "apache-2.0"} | 2023-03-28T01:52:23+00:00 | []
| []
| TAGS
#license-apache-2.0 #region-us
|
## OIG-riverbed-filtered-small
A small filtered version of URL used for experimenting with filtering, clustering and visualizing the data in the OIG dataset.
It is best to download the data directly instead of using HF load_datasets: URL
Topic map for a subset of the data:
Thanks to LAION volunteers: @Rallio, @Kenhktsui, @Danielpatrickhug @Vyprix and @Summer.
| [
"## OIG-riverbed-filtered-small\n\nA small filtered version of URL used for experimenting with filtering, clustering and visualizing the data in the OIG dataset.\n\nIt is best to download the data directly instead of using HF load_datasets: URL\n\nTopic map for a subset of the data:\n\n\nThanks to LAION volunteers: @Rallio, @Kenhktsui, @Danielpatrickhug @Vyprix and @Summer."
]
| [
"TAGS\n#license-apache-2.0 #region-us \n",
"## OIG-riverbed-filtered-small\n\nA small filtered version of URL used for experimenting with filtering, clustering and visualizing the data in the OIG dataset.\n\nIt is best to download the data directly instead of using HF load_datasets: URL\n\nTopic map for a subset of the data:\n\n\nThanks to LAION volunteers: @Rallio, @Kenhktsui, @Danielpatrickhug @Vyprix and @Summer."
]
| [
14,
105
]
| [
"passage: TAGS\n#license-apache-2.0 #region-us \n## OIG-riverbed-filtered-small\n\nA small filtered version of URL used for experimenting with filtering, clustering and visualizing the data in the OIG dataset.\n\nIt is best to download the data directly instead of using HF load_datasets: URL\n\nTopic map for a subset of the data:\n\n\nThanks to LAION volunteers: @Rallio, @Kenhktsui, @Danielpatrickhug @Vyprix and @Summer."
]
| [
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|
494d61f4fe428a7ea257904d0007cfeff0ec83f7 | # Dataset Card for "ud"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | spdenisov/ud | [
"region:us"
]
| 2023-03-11T20:20:02+00:00 | {"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}], "splits": [{"name": "de_gsd_ud_train", "num_bytes": 113219544, "num_examples": 13814}, {"name": "fr_parisstories_ud_train", "num_bytes": 11392440, "num_examples": 1390}, {"name": "ar_nyuad_ud_train", "num_bytes": 129406644, "num_examples": 15789}, {"name": "hu_szeged_ud_train", "num_bytes": 7458360, "num_examples": 910}, {"name": "fi_ftb_ud_train", "num_bytes": 122784276, "num_examples": 14981}, {"name": "it_vit_ud_train", "num_bytes": 67838292, "num_examples": 8277}, {"name": "zh_gsdsimp_ud_train", "num_bytes": 32759412, "num_examples": 3997}, {"name": "hy_bsut_ud_train", "num_bytes": 10048296, "num_examples": 1226}, {"name": "cop_scriptorium_ud_train", "num_bytes": 11302284, "num_examples": 1379}, {"name": "no_bokmaal_ud_train", "num_bytes": 128644416, "num_examples": 15696}, {"name": "gv_cadhan_ud_train", "num_bytes": 9605712, "num_examples": 1172}, {"name": "fr_partut_ud_train", "num_bytes": 6581388, "num_examples": 803}, {"name": "fi_tdt_ud_train", "num_bytes": 100130532, "num_examples": 12217}, {"name": "tr_kenet_ud_train", "num_bytes": 126202008, "num_examples": 15398}, {"name": "fr_sequoia_ud_train", "num_bytes": 18285276, "num_examples": 2231}, {"name": "hy_armtdp_ud_train", "num_bytes": 16178904, "num_examples": 1974}, {"name": "ga_idt_ud_train", "num_bytes": 32824980, "num_examples": 4005}, {"name": "ru_syntagrus_ud_train_b", "num_bytes": 199146408, "num_examples": 24298}, {"name": "nl_alpino_ud_train", "num_bytes": 100720644, "num_examples": 12289}, {"name": "en_partut_ud_train", "num_bytes": 14597076, "num_examples": 1781}, {"name": "en_gum_ud_train", "num_bytes": 56642556, "num_examples": 6911}, {"name": "ru_taiga_ud_train", "num_bytes": 131504820, "num_examples": 16045}, {"name": "da_ddt_ud_train", "num_bytes": 35923068, "num_examples": 4383}, {"name": "zh_gsd_ud_train", "num_bytes": 32759412, "num_examples": 3997}, {"name": "de_hdt_ud_train_a_2", "num_bytes": 307472940, "num_examples": 37515}, {"name": "it_isdt_ud_train", "num_bytes": 107539716, "num_examples": 13121}, {"name": "es_gsd_ud_train", "num_bytes": 116276652, "num_examples": 14187}, {"name": "gd_arcosg_ud_train", "num_bytes": 29022036, "num_examples": 3541}, {"name": "ru_syntagrus_ud_train_a", "num_bytes": 200933136, "num_examples": 24516}, {"name": "de_hdt_ud_train_a_1", "num_bytes": 312283992, "num_examples": 38102}, {"name": "tr_penn_ud_train", "num_bytes": 121710600, "num_examples": 14850}, {"name": "tr_atis_ud_train", "num_bytes": 35029704, "num_examples": 4274}, {"name": "en_lines_ud_train", "num_bytes": 26030496, "num_examples": 3176}, {"name": "cs_pdt_ud_train_c", "num_bytes": 73255848, "num_examples": 8938}, {"name": "de_hdt_ud_train_b_2", "num_bytes": 319701372, "num_examples": 39007}, {"name": "cs_pdt_ud_train_l", "num_bytes": 340617564, "num_examples": 41559}, {"name": "cs_pdt_ud_train_v", "num_bytes": 55880328, "num_examples": 6818}, {"name": "pt_cintil_ud_train", "num_bytes": 251781120, "num_examples": 30720}, {"name": "no_nynorsklia_ud_train", "num_bytes": 27964752, "num_examples": 3412}, {"name": "cs_cac_ud_train", "num_bytes": 192425688, "num_examples": 23478}, {"name": "tr_framenet_ud_train", "num_bytes": 18752448, "num_examples": 2288}, {"name": "it_parlamint_ud_train", "num_bytes": 2671896, "num_examples": 326}, {"name": "es_ancora_ud_train", "num_bytes": 117096252, "num_examples": 14287}, {"name": "en_atis_ud_train", "num_bytes": 35029704, "num_examples": 4274}, {"name": "cy_ccg_ud_train", "num_bytes": 9105756, "num_examples": 1111}, {"name": "fr_gsd_ud_train", "num_bytes": 118432200, "num_examples": 14450}, {"name": "tr_tourism_ud_train", "num_bytes": 126841296, "num_examples": 15476}, {"name": "en_ewt_ud_train", "num_bytes": 102810624, "num_examples": 12544}, {"name": "ru_syntagrus_ud_train_c", "num_bytes": 170607936, "num_examples": 20816}, {"name": "fr_ftb_ud_train", "num_bytes": 120964764, "num_examples": 14759}, {"name": "cs_fictree_ud_train", "num_bytes": 83271360, "num_examples": 10160}, {"name": "ar_padt_ud_train", "num_bytes": 49790700, "num_examples": 6075}, {"name": "ro_rrt_ud_train", "num_bytes": 65920428, "num_examples": 8043}, {"name": "tr_boun_ud_train", "num_bytes": 63953388, "num_examples": 7803}, {"name": "de_hdt_ud_train_b_1", "num_bytes": 314816556, "num_examples": 38411}, {"name": "cs_pdt_ud_train_m", "num_bytes": 91631280, "num_examples": 11180}, {"name": "no_nynorsk_ud_train", "num_bytes": 116170104, "num_examples": 14174}, {"name": "ru_gsd_ud_train", "num_bytes": 31554600, "num_examples": 3850}, {"name": "fr_rhapsodie_ud_train", "num_bytes": 10556448, "num_examples": 1288}], "download_size": 357289141, "dataset_size": 5683860432}} | 2023-03-11T20:23:36+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ud"
More Information needed | [
"# Dataset Card for \"ud\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"ud\"\n\nMore Information needed"
]
| [
6,
11
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"ud\"\n\nMore Information needed"
]
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|
b839ab860b49b2ccca3c8cb5cb6f4a42b122481d | # AutoTrain Dataset for project: vision-tcg
## Dataset Description
This dataset has been automatically processed by AutoTrain for project vision-tcg.
### 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": "<600x825 RGB PIL image>",
"target": 0,
"feat_Unnamed: 2": null,
"feat_Unnamed: 3": null
},
{
"image": "<600x825 RGB PIL image>",
"target": 14,
"feat_Unnamed: 2": null,
"feat_Unnamed: 3": null
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['base1-1 Alakazam Pok\u00e9mon Psychic Rare Holo', 'base1-10 Mewtwo Pok\u00e9mon Psychic Rare Holo', 'base1-100 Lightning Energy Energy', 'base1-101 Psychic Energy Energy', 'base1-102 Water Energy Energy', 'base1-11 Nidoking Pok\u00e9mon Grass Rare Holo', 'base1-12 Ninetales Pok\u00e9mon Fire Rare Holo', 'base1-13 Poliwrath Pok\u00e9mon Water Rare Holo', 'base1-14 Raichu Pok\u00e9mon Lightning Rare Holo', 'base1-15 Venusaur Pok\u00e9mon Grass Rare Holo', 'base1-16 Zapdos Pok\u00e9mon Lightning Rare Holo', 'base1-17 Beedrill Pok\u00e9mon Grass Rare', 'base1-18 Dragonair Pok\u00e9mon Colorless Rare', 'base1-19 Dugtrio Pok\u00e9mon Fighting Rare', 'base1-2 Blastoise Pok\u00e9mon Water Rare Holo', 'base1-20 Electabuzz Pok\u00e9mon Lightning Rare', 'base1-21 Electrode Pok\u00e9mon Lightning Rare', 'base1-22 Pidgeotto Pok\u00e9mon Colorless Rare', 'base1-23 Arcanine Pok\u00e9mon Fire Uncommon', 'base1-24 Charmeleon Pok\u00e9mon Fire Uncommon', 'base1-25 Dewgong Pok\u00e9mon Water Uncommon', 'base1-26 Dratini Pok\u00e9mon Colorless Uncommon', \"base1-27 Farfetch'd Pok\u00e9mon Colorless Uncommon\", 'base1-28 Growlithe Pok\u00e9mon Fire Uncommon', 'base1-29 Haunter Pok\u00e9mon Psychic Uncommon', 'base1-3 Chansey Pok\u00e9mon Colorless Rare Holo', 'base1-30 Ivysaur Pok\u00e9mon Grass Uncommon', 'base1-31 Jynx Pok\u00e9mon Psychic Uncommon', 'base1-32 Kadabra Pok\u00e9mon Psychic Uncommon', 'base1-33 Kakuna Pok\u00e9mon Grass Uncommon', 'base1-34 Machoke Pok\u00e9mon Fighting Uncommon', 'base1-35 Magikarp Pok\u00e9mon Water Uncommon', 'base1-36 Magmar Pok\u00e9mon Fire Uncommon', 'base1-37 Nidorino Pok\u00e9mon Grass Uncommon', 'base1-38 Poliwhirl Pok\u00e9mon Water Uncommon', 'base1-39 Porygon Pok\u00e9mon Colorless Uncommon', 'base1-4 Charizard Pok\u00e9mon Fire Rare Holo', 'base1-40 Raticate Pok\u00e9mon Colorless Uncommon', 'base1-41 Seel Pok\u00e9mon Water Uncommon', 'base1-42 Wartortle Pok\u00e9mon Water Uncommon', 'base1-43 Abra Pok\u00e9mon Psychic Common', 'base1-44 Bulbasaur Pok\u00e9mon Grass Common', 'base1-45 Caterpie Pok\u00e9mon Grass Common', 'base1-46 Charmander Pok\u00e9mon Fire Common', 'base1-47 Diglett Pok\u00e9mon Fighting Common', 'base1-48 Doduo Pok\u00e9mon Colorless Common', 'base1-49 Drowzee Pok\u00e9mon Psychic Common', 'base1-5 Clefairy Pok\u00e9mon Colorless Rare Holo', 'base1-50 Gastly Pok\u00e9mon Psychic Common', 'base1-51 Koffing Pok\u00e9mon Grass Common', 'base1-52 Machop Pok\u00e9mon Fighting Common', 'base1-53 Magnemite Pok\u00e9mon Lightning Common', 'base1-54 Metapod Pok\u00e9mon Grass Common', 'base1-55 Nidoran \u2642 Pok\u00e9mon Grass Common', 'base1-56 Onix Pok\u00e9mon Fighting Common', 'base1-57 Pidgey Pok\u00e9mon Colorless Common', 'base1-58 Pikachu Pok\u00e9mon Lightning Common', 'base1-59 Poliwag Pok\u00e9mon Water Common', 'base1-6 Gyarados Pok\u00e9mon Water Rare Holo', 'base1-60 Ponyta Pok\u00e9mon Fire Common', 'base1-61 Rattata Pok\u00e9mon Colorless Common', 'base1-62 Sandshrew Pok\u00e9mon Fighting Common', 'base1-63 Squirtle Pok\u00e9mon Water Common', 'base1-64 Starmie Pok\u00e9mon Water Common', 'base1-65 Staryu Pok\u00e9mon Water Common', 'base1-66 Tangela Pok\u00e9mon Grass Common', 'base1-67 Voltorb Pok\u00e9mon Lightning Common', 'base1-68 Vulpix Pok\u00e9mon Fire Common', 'base1-69 Weedle Pok\u00e9mon Grass Common', 'base1-7 Hitmonchan Pok\u00e9mon Fighting Rare Holo', 'base1-70 Clefairy Doll Trainer Rare', 'base1-71 Computer Search Trainer Rare', 'base1-72 Devolution Spray Trainer Rare', 'base1-73 Impostor Professor Oak Trainer Rare', 'base1-74 Item Finder Trainer Rare', 'base1-75 Lass Trainer Rare', 'base1-76 Pok\u00e9mon Breeder Trainer Rare', 'base1-77 Pok\u00e9mon Trader Trainer Rare', 'base1-78 Scoop Up Trainer Rare', 'base1-79 Super Energy Removal Trainer Rare', 'base1-8 Machamp Pok\u00e9mon Fighting Rare Holo', 'base1-80 Defender Trainer Uncommon', 'base1-81 Energy Retrieval Trainer Uncommon', 'base1-82 Full Heal Trainer Uncommon', 'base1-83 Maintenance Trainer Uncommon', 'base1-84 PlusPower Trainer Uncommon', 'base1-85 Pok\u00e9mon Center Trainer Uncommon', 'base1-86 Pok\u00e9mon Flute Trainer Uncommon', 'base1-87 Pok\u00e9dex Trainer Uncommon', 'base1-88 Professor Oak Trainer Uncommon', 'base1-89 Revive Trainer Uncommon', 'base1-9 Magneton Pok\u00e9mon Lightning Rare Holo', 'base1-90 Super Potion Trainer Uncommon', 'base1-91 Bill Trainer Common', 'base1-92 Energy Removal Trainer Common', 'base1-93 Gust of Wind Trainer Common', 'base1-94 Potion Trainer Common', 'base1-95 Switch Trainer Common', 'base1-96 Double Colorless Energy Energy Uncommon', 'base1-97 Fighting Energy Energy', 'base1-98 Fire Energy Energy', 'base1-99 Grass Energy Energy'], id=None)",
"feat_Unnamed: 2": "Value(dtype='float64', id=None)",
"feat_Unnamed: 3": "Value(dtype='float64', 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 | 102 |
| valid | 102 |
| micazevedo/autotrain-data-vision-tcg | [
"task_categories:image-classification",
"region:us"
]
| 2023-03-11T20:54:10+00:00 | {"task_categories": ["image-classification"]} | 2023-03-28T00:38:54+00:00 | []
| []
| TAGS
#task_categories-image-classification #region-us
| AutoTrain Dataset for project: vision-tcg
=========================================
Dataset Description
-------------------
This dataset has been automatically processed by AutoTrain for project vision-tcg.
### 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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|
7d13ffdeac8aee1397aff2df151785271053fc7d | # Dataset Card for "blm_strict_small"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Sree1994/blm_strict_small | [
"region:us"
]
| 2023-03-11T21:34:30+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4965214, "num_examples": 20000}, {"name": "valid", "num_bytes": 1213334, "num_examples": 5000}], "download_size": 4229589, "dataset_size": 6178548}} | 2023-03-11T21:34:39+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "blm_strict_small"
More Information needed | [
"# Dataset Card for \"blm_strict_small\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"blm_strict_small\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"blm_strict_small\"\n\nMore Information needed"
]
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|
8c29fbe70cf92ad4d327dfed96b737d49ebc35d9 | # Dataset Card for "raw_vi_ko_en_parallel_corpus"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | pranjali97/raw_vi_ko_en_parallel_corpus | [
"region:us"
]
| 2023-03-11T21:40:44+00:00 | {"dataset_info": {"features": [{"name": "src", "dtype": "string"}, {"name": "tgt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9524626, "num_examples": 40000}], "download_size": 5450459, "dataset_size": 9524626}} | 2023-03-11T21:44:52+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "raw_vi_ko_en_parallel_corpus"
More Information needed | [
"# Dataset Card for \"raw_vi_ko_en_parallel_corpus\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"raw_vi_ko_en_parallel_corpus\"\n\nMore Information needed"
]
| [
6,
24
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"raw_vi_ko_en_parallel_corpus\"\n\nMore Information needed"
]
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|
59c2d647f7eac6ab1f228f48ad97b84972443f96 | tags:
- autotrain
- translation
language:
- en
- unk
datasets:
- Maghrebi/autotrain-data-90
co2_eq_emissions:
emissions: 0.0075699862718682795 | Maghrebi/Aenglisc | [
"task_categories:text2text-generation",
"language:en",
"language:unk",
"license:bsd",
"code",
"region:us"
]
| 2023-03-11T22:03:36+00:00 | {"language": ["en", "unk"], "license": "bsd", "task_categories": ["text2text-generation"], "tags": ["code"]} | 2023-03-11T22:17:46+00:00 | []
| [
"en",
"unk"
]
| TAGS
#task_categories-text2text-generation #language-English #language-Enawené-Nawé #license-bsd #code #region-us
| tags:
- autotrain
- translation
language:
- en
- unk
datasets:
- Maghrebi/autotrain-data-90
co2_eq_emissions:
emissions: 0.0075699862718682795 | []
| [
"TAGS\n#task_categories-text2text-generation #language-English #language-Enawené-Nawé #license-bsd #code #region-us \n"
]
| [
40
]
| [
"passage: TAGS\n#task_categories-text2text-generation #language-English #language-Enawené-Nawé #license-bsd #code #region-us \n"
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|
17372b5405415ced518f4aebef2c5ee1e6b86c60 | # Dataset Card for "small-coco-wm_50"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | RIW/small-coco-wm_50 | [
"region:us"
]
| 2023-03-11T23:10:45+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "caption", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "key", "dtype": "string"}, {"name": "status", "dtype": "string"}, {"name": "error_message", "dtype": "null"}, {"name": "width", "dtype": "int64"}, {"name": "height", "dtype": "int64"}, {"name": "original_width", "dtype": "int64"}, {"name": "original_height", "dtype": "int64"}, {"name": "exif", "dtype": "string"}, {"name": "sha256", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1884418582.296, "num_examples": 18982}, {"name": "validation", "num_bytes": 1827717279.35, "num_examples": 18935}], "download_size": 1641694126, "dataset_size": 3712135861.646}} | 2023-03-11T23:13:04+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "small-coco-wm_50"
More Information needed | [
"# Dataset Card for \"small-coco-wm_50\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"small-coco-wm_50\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"small-coco-wm_50\"\n\nMore Information needed"
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|
70b73ffb366ecb538c6e5f957d4879ca39a00def | # Dataset Card for "labelled_vi_ko_raw_text"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | pranjali97/labelled_vi_ko_raw_text | [
"region:us"
]
| 2023-03-11T23:59:42+00:00 | {"dataset_info": {"features": [{"name": "src", "dtype": "string"}, {"name": "tgt", "dtype": "string"}, {"name": "classifier_labels", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 9844626, "num_examples": 40000}], "download_size": 5466676, "dataset_size": 9844626}} | 2023-03-12T00:01:19+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "labelled_vi_ko_raw_text"
More Information needed | [
"# Dataset Card for \"labelled_vi_ko_raw_text\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"labelled_vi_ko_raw_text\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"labelled_vi_ko_raw_text\"\n\nMore Information needed"
]
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|
b45590e6e5ad403ec469aeb88d6c5cb1f9dbca3d | # AutoTrain Dataset for project: data-image
## Dataset Description
This dataset has been automatically processed by AutoTrain for project data-image.
### 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": "<600x825 RGB PIL image>",
"target": 0,
"feat_name": "Alakazam",
"feat_supertype": "Pok\u00e9mon",
"feat_subtypes_1": "Stage 2",
"feat_level": 42.0,
"feat_hp": 80.0,
"feat_types_1": "Psychic",
"feat_evolvesFrom": "Kadabra",
"feat_abilities_1_name": "Damage Swap",
"feat_abilities_1_text": "As often as you like during your turn (before your attack), you may move 1 damage counter from 1 of your Pok\u00e9mon to another as long as you don't Knock Out that Pok\u00e9mon. This power can't be used if Alakazam is Asleep, Confused, or Paralyzed.",
"feat_abilities_1_type": "Pok\u00e9mon Power",
"feat_attacks_1_name": "Confuse Ray",
"feat_attacks_1_cost_1": "Psychic",
"feat_attacks_1_cost_2": "Psychic",
"feat_attacks_1_cost_3": "Psychic",
"feat_attacks_1_convertedEnergyCost": 3.0,
"feat_attacks_1_damage": "30",
"feat_attacks_1_text": "Flip a coin. If heads, the Defending Pok\u00e9mon is now Confused.",
"feat_weaknesses_1_type": "Psychic",
"feat_weaknesses_1_value": "\u00d72",
"feat_retreatCost_1": "Colorless",
"feat_retreatCost_2": "Colorless",
"feat_retreatCost_3": "Colorless",
"feat_convertedRetreatCost": 3.0,
"feat_number": 1,
"feat_artist": "Ken Sugimori",
"feat_rarity": "Rare Holo",
"feat_flavorText": "Its brain can outperform a supercomputer. Its intelligence quotient is said to be 5000.",
"feat_nationalPokedexNumbers_1": 65.0,
"feat_legalities_unlimited": "Legal",
"feat_images_small": "https://images.pokemontcg.io/base1/1.png",
"feat_images_large": "https://images.pokemontcg.io/base1/1_hires.png",
"feat_evolvesTo_1": null,
"feat_attacks_2_name": null,
"feat_attacks_2_cost_1": null,
"feat_attacks_2_cost_2": null,
"feat_attacks_2_cost_3": null,
"feat_attacks_2_cost_4": null,
"feat_attacks_2_convertedEnergyCost": null,
"feat_attacks_2_damage": null,
"feat_attacks_2_text": null,
"feat_resistances_1_type": null,
"feat_resistances_1_value": null,
"feat_attacks_1_cost_4": null,
"feat_evolvesTo_2": null,
"feat_legalities_expanded": null,
"feat_rules_1": null,
"feat_legalities_standard": null
},
{
"image": "<600x825 RGB PIL image>",
"target": 14,
"feat_name": "Blastoise",
"feat_supertype": "Pok\u00e9mon",
"feat_subtypes_1": "Stage 2",
"feat_level": 52.0,
"feat_hp": 100.0,
"feat_types_1": "Water",
"feat_evolvesFrom": "Wartortle",
"feat_abilities_1_name": "Rain Dance",
"feat_abilities_1_text": "As often as you like during your turn (before your attack), you may attach 1 Water Energy card to 1 of your Water Pok\u00e9mon. (This doesn't use up your 1 Energy card attachment for the turn.) This power can't be used if Blastoise is Asleep, Confused, or Paralyzed.",
"feat_abilities_1_type": "Pok\u00e9mon Power",
"feat_attacks_1_name": "Hydro Pump",
"feat_attacks_1_cost_1": "Water",
"feat_attacks_1_cost_2": "Water",
"feat_attacks_1_cost_3": "Water",
"feat_attacks_1_convertedEnergyCost": 3.0,
"feat_attacks_1_damage": "40+",
"feat_attacks_1_text": "Does 40 damage plus 10 more damage for each Water Energy attached to Blastoise but not used to pay for this attack's Energy cost. Extra Water Energy after the 2nd doesn't count.",
"feat_weaknesses_1_type": "Lightning",
"feat_weaknesses_1_value": "\u00d72",
"feat_retreatCost_1": "Colorless",
"feat_retreatCost_2": "Colorless",
"feat_retreatCost_3": "Colorless",
"feat_convertedRetreatCost": 3.0,
"feat_number": 2,
"feat_artist": "Ken Sugimori",
"feat_rarity": "Rare Holo",
"feat_flavorText": "A brutal Pok\u00e9mon with pressurized water jets on its shell. They are used for high-speed tackles.",
"feat_nationalPokedexNumbers_1": 9.0,
"feat_legalities_unlimited": "Legal",
"feat_images_small": "https://images.pokemontcg.io/base1/2.png",
"feat_images_large": "https://images.pokemontcg.io/base1/2_hires.png",
"feat_evolvesTo_1": null,
"feat_attacks_2_name": null,
"feat_attacks_2_cost_1": null,
"feat_attacks_2_cost_2": null,
"feat_attacks_2_cost_3": null,
"feat_attacks_2_cost_4": null,
"feat_attacks_2_convertedEnergyCost": null,
"feat_attacks_2_damage": null,
"feat_attacks_2_text": null,
"feat_resistances_1_type": null,
"feat_resistances_1_value": null,
"feat_attacks_1_cost_4": null,
"feat_evolvesTo_2": null,
"feat_legalities_expanded": null,
"feat_rules_1": null,
"feat_legalities_standard": null
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['base1-1', 'base1-10', 'base1-100', 'base1-101', 'base1-102', 'base1-11', 'base1-12', 'base1-13', 'base1-14', 'base1-15', 'base1-16', 'base1-17', 'base1-18', 'base1-19', 'base1-2', 'base1-20', 'base1-21', 'base1-22', 'base1-23', 'base1-24', 'base1-25', 'base1-26', 'base1-27', 'base1-28', 'base1-29', 'base1-3', 'base1-30', 'base1-31', 'base1-32', 'base1-33', 'base1-34', 'base1-35', 'base1-36', 'base1-37', 'base1-38', 'base1-39', 'base1-4', 'base1-40', 'base1-41', 'base1-42', 'base1-43', 'base1-44', 'base1-45', 'base1-46', 'base1-47', 'base1-48', 'base1-49', 'base1-5', 'base1-50', 'base1-51', 'base1-52', 'base1-53', 'base1-54', 'base1-55', 'base1-56', 'base1-57', 'base1-58', 'base1-59', 'base1-6', 'base1-60', 'base1-61', 'base1-62', 'base1-63', 'base1-64', 'base1-65', 'base1-66', 'base1-67', 'base1-68', 'base1-69', 'base1-7', 'base1-70', 'base1-71', 'base1-72', 'base1-73', 'base1-74', 'base1-75', 'base1-76', 'base1-77', 'base1-78', 'base1-79', 'base1-8', 'base1-80', 'base1-81', 'base1-82', 'base1-83', 'base1-84', 'base1-85', 'base1-86', 'base1-87', 'base1-88', 'base1-89', 'base1-9', 'base1-90', 'base1-91', 'base1-92', 'base1-93', 'base1-94', 'base1-95', 'base1-96', 'base1-97', 'base1-98', 'base1-99'], id=None)",
"feat_name": "Value(dtype='string', id=None)",
"feat_supertype": "Value(dtype='string', id=None)",
"feat_subtypes_1": "Value(dtype='string', id=None)",
"feat_level": "Value(dtype='float64', id=None)",
"feat_hp": "Value(dtype='float64', id=None)",
"feat_types_1": "Value(dtype='string', id=None)",
"feat_evolvesFrom": "Value(dtype='string', id=None)",
"feat_abilities_1_name": "Value(dtype='string', id=None)",
"feat_abilities_1_text": "Value(dtype='string', id=None)",
"feat_abilities_1_type": "Value(dtype='string', id=None)",
"feat_attacks_1_name": "Value(dtype='string', id=None)",
"feat_attacks_1_cost_1": "Value(dtype='string', id=None)",
"feat_attacks_1_cost_2": "Value(dtype='string', id=None)",
"feat_attacks_1_cost_3": "Value(dtype='string', id=None)",
"feat_attacks_1_convertedEnergyCost": "Value(dtype='float64', id=None)",
"feat_attacks_1_damage": "Value(dtype='string', id=None)",
"feat_attacks_1_text": "Value(dtype='string', id=None)",
"feat_weaknesses_1_type": "Value(dtype='string', id=None)",
"feat_weaknesses_1_value": "Value(dtype='string', id=None)",
"feat_retreatCost_1": "Value(dtype='string', id=None)",
"feat_retreatCost_2": "Value(dtype='string', id=None)",
"feat_retreatCost_3": "Value(dtype='string', id=None)",
"feat_convertedRetreatCost": "Value(dtype='float64', id=None)",
"feat_number": "Value(dtype='int64', id=None)",
"feat_artist": "Value(dtype='string', id=None)",
"feat_rarity": "Value(dtype='string', id=None)",
"feat_flavorText": "Value(dtype='string', id=None)",
"feat_nationalPokedexNumbers_1": "Value(dtype='float64', id=None)",
"feat_legalities_unlimited": "Value(dtype='string', id=None)",
"feat_images_small": "Value(dtype='string', id=None)",
"feat_images_large": "Value(dtype='string', id=None)",
"feat_evolvesTo_1": "Value(dtype='string', id=None)",
"feat_attacks_2_name": "Value(dtype='string', id=None)",
"feat_attacks_2_cost_1": "Value(dtype='string', id=None)",
"feat_attacks_2_cost_2": "Value(dtype='string', id=None)",
"feat_attacks_2_cost_3": "Value(dtype='string', id=None)",
"feat_attacks_2_cost_4": "Value(dtype='string', id=None)",
"feat_attacks_2_convertedEnergyCost": "Value(dtype='float64', id=None)",
"feat_attacks_2_damage": "Value(dtype='string', id=None)",
"feat_attacks_2_text": "Value(dtype='string', id=None)",
"feat_resistances_1_type": "Value(dtype='string', id=None)",
"feat_resistances_1_value": "Value(dtype='float64', id=None)",
"feat_attacks_1_cost_4": "Value(dtype='string', id=None)",
"feat_evolvesTo_2": "Value(dtype='string', id=None)",
"feat_legalities_expanded": "Value(dtype='string', id=None)",
"feat_rules_1": "Value(dtype='string', id=None)",
"feat_legalities_standard": "Value(dtype='string', 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 | 102 |
| valid | 2 |
| micazevedo/autotrain-data-data-image | [
"task_categories:image-classification",
"region:us"
]
| 2023-03-12T00:02:12+00:00 | {"task_categories": ["image-classification"]} | 2023-03-12T00:54:38+00:00 | []
| []
| TAGS
#task_categories-image-classification #region-us
| AutoTrain Dataset for project: data-image
=========================================
Dataset Description
-------------------
This dataset has been automatically processed by AutoTrain for project data-image.
### 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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0.002344331005588174,
0.055275481194257736,
0.0935901552438736,
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|
075f1ff8fa08f48f5a6758a9c1864e32c11778b3 | # Dataset Card for "udtrees"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | spdenisov/udtrees | [
"region:us"
]
| 2023-03-12T01:27:12+00:00 | {"dataset_info": {"features": [{"name": "language", "dtype": "string"}, {"name": "sentence", "dtype": "string"}, {"name": "conllu", "dtype": "string"}], "splits": [{"name": "ru_syntagrus_ud_train_a", "num_bytes": 39997721, "num_examples": 24516}, {"name": "en_ewt_ud_train", "num_bytes": 13066595, "num_examples": 12544}, {"name": "es_ancora_ud_train", "num_bytes": 41576563, "num_examples": 14287}, {"name": "ga_idt_ud_train", "num_bytes": 6578580, "num_examples": 4005}, {"name": "tr_tourism_ud_train", "num_bytes": 5072132, "num_examples": 15476}, {"name": "ar_nyuad_ud_train", "num_bytes": 46449076, "num_examples": 15789}, {"name": "cop_scriptorium_ud_train", "num_bytes": 3127527, "num_examples": 1379}, {"name": "tr_kenet_ud_train", "num_bytes": 9965621, "num_examples": 15398}, {"name": "ar_padt_ud_train", "num_bytes": 39971051, "num_examples": 6075}, {"name": "tr_penn_ud_train", "num_bytes": 11428060, "num_examples": 14850}, {"name": "es_gsd_ud_train", "num_bytes": 22823430, "num_examples": 14187}, {"name": "fi_tdt_ud_train", "num_bytes": 13228364, "num_examples": 12217}, {"name": "nl_alpino_ud_train", "num_bytes": 13981525, "num_examples": 12289}, {"name": "fi_ftb_ud_train", "num_bytes": 10264036, "num_examples": 14981}, {"name": "ru_syntagrus_ud_train_b", "num_bytes": 42083027, "num_examples": 24298}, {"name": "no_nynorsk_ud_train", "num_bytes": 14940608, "num_examples": 14174}, {"name": "de_hdt_ud_train_a_2", "num_bytes": 49150973, "num_examples": 37515}, {"name": "hu_szeged_ud_train", "num_bytes": 1445467, "num_examples": 910}, {"name": "cs_pdt_ud_train_l", "num_bytes": 79765505, "num_examples": 41559}, {"name": "de_hdt_ud_train_a_1", "num_bytes": 50530678, "num_examples": 38102}, {"name": "tr_boun_ud_train", "num_bytes": 7821321, "num_examples": 7803}, {"name": "fr_gsd_ud_train", "num_bytes": 22444299, "num_examples": 14450}, {"name": "no_bokmaal_ud_train", "num_bytes": 14918030, "num_examples": 15696}, {"name": "fr_partut_ud_train", "num_bytes": 1515774, "num_examples": 803}, {"name": "de_gsd_ud_train", "num_bytes": 19353463, "num_examples": 13814}, {"name": "fr_rhapsodie_ud_train", "num_bytes": 1191845, "num_examples": 1288}, {"name": "en_partut_ud_train", "num_bytes": 2341782, "num_examples": 1781}, {"name": "cs_cac_ud_train", "num_bytes": 52776214, "num_examples": 23478}, {"name": "fr_sequoia_ud_train", "num_bytes": 3107869, "num_examples": 2231}, {"name": "cs_pdt_ud_train_c", "num_bytes": 14988159, "num_examples": 8938}, {"name": "en_gum_ud_train", "num_bytes": 10299158, "num_examples": 6911}, {"name": "hy_armtdp_ud_train", "num_bytes": 5096313, "num_examples": 1974}, {"name": "ru_gsd_ud_train", "num_bytes": 6690467, "num_examples": 3850}, {"name": "it_parlamint_ud_train", "num_bytes": 641089, "num_examples": 326}, {"name": "no_nynorsklia_ud_train", "num_bytes": 1951602, "num_examples": 3412}, {"name": "tr_framenet_ud_train", "num_bytes": 1198915, "num_examples": 2288}, {"name": "gd_arcosg_ud_train", "num_bytes": 4010492, "num_examples": 3541}, {"name": "de_hdt_ud_train_b_2", "num_bytes": 51033245, "num_examples": 39007}, {"name": "it_vit_ud_train", "num_bytes": 14017218, "num_examples": 8277}, {"name": "zh_gsdsimp_ud_train", "num_bytes": 5375774, "num_examples": 3997}, {"name": "fr_ftb_ud_train", "num_bytes": 24036178, "num_examples": 14759}, {"name": "cy_ccg_ud_train", "num_bytes": 1370915, "num_examples": 1111}, {"name": "de_hdt_ud_train_b_1", "num_bytes": 53015860, "num_examples": 38411}, {"name": "zh_gsd_ud_train", "num_bytes": 5375739, "num_examples": 3997}, {"name": "hy_bsut_ud_train", "num_bytes": 2570067, "num_examples": 1226}, {"name": "fr_parisstories_ud_train", "num_bytes": 1434200, "num_examples": 1390}, {"name": "gv_cadhan_ud_train", "num_bytes": 547774, "num_examples": 1172}, {"name": "ro_rrt_ud_train", "num_bytes": 14443371, "num_examples": 8043}, {"name": "pt_cintil_ud_train", "num_bytes": 19037477, "num_examples": 30720}, {"name": "ru_taiga_ud_train", "num_bytes": 14956116, "num_examples": 16045}, {"name": "cs_pdt_ud_train_m", "num_bytes": 20158243, "num_examples": 11180}, {"name": "tr_atis_ud_train", "num_bytes": 2633984, "num_examples": 4274}, {"name": "cs_pdt_ud_train_v", "num_bytes": 14454519, "num_examples": 6818}, {"name": "it_isdt_ud_train", "num_bytes": 19225718, "num_examples": 13121}, {"name": "ru_syntagrus_ud_train_c", "num_bytes": 30439785, "num_examples": 20816}, {"name": "cs_fictree_ud_train", "num_bytes": 13380642, "num_examples": 10160}, {"name": "en_atis_ud_train", "num_bytes": 2524032, "num_examples": 4274}, {"name": "en_lines_ud_train", "num_bytes": 3264741, "num_examples": 3176}, {"name": "da_ddt_ud_train", "num_bytes": 5047075, "num_examples": 4383}, {"name": "fa_seraji_ud_train", "num_bytes": 11517586, "num_examples": 4798}, {"name": "fa_perdt_ud_train", "num_bytes": 29881906, "num_examples": 26196}], "download_size": 335579995, "dataset_size": 1045535496}} | 2023-03-29T17:42:56+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "udtrees"
More Information needed | [
"# Dataset Card for \"udtrees\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"udtrees\"\n\nMore Information needed"
]
| [
6,
13
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"udtrees\"\n\nMore Information needed"
]
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|
b889be907ba34201fa46603146c7861aad4126bd | # Dataset Card for "bookcorpus_processed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Brendan/bookcorpus_processed | [
"region:us"
]
| 2023-03-12T01:42:31+00:00 | {"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "special_tokens_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 5587060752, "num_examples": 1811628}, {"name": "test", "num_bytes": 1396660332, "num_examples": 452873}], "download_size": 2308641872, "dataset_size": 6983721084}} | 2023-03-12T05:22:43+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "bookcorpus_processed"
More Information needed | [
"# Dataset Card for \"bookcorpus_processed\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"bookcorpus_processed\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"bookcorpus_processed\"\n\nMore Information needed"
]
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|
96956af144e2c4387cb2badbf11492bc8b335915 | # Dataset Card for "weighted_trainingdata_domain_adaptation_government_telephone"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | jcbao77/weighted_trainingdata_domain_adaptation_government_telephone | [
"region:us"
]
| 2023-03-12T02:47:51+00:00 | {"dataset_info": {"features": [{"name": "promptID", "dtype": "int32"}, {"name": "pairID", "dtype": "string"}, {"name": "premise", "dtype": "string"}, {"name": "premise_binary_parse", "dtype": "string"}, {"name": "premise_parse", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "hypothesis_binary_parse", "dtype": "string"}, {"name": "hypothesis_parse", "dtype": "string"}, {"name": "genre", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}, {"name": "domains", "dtype": "string"}, {"name": "weights", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 327512744, "num_examples": 315352}], "download_size": 165625528, "dataset_size": 327512744}} | 2023-03-12T02:48:15+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "weighted_trainingdata_domain_adaptation_government_telephone"
More Information needed | [
"# Dataset Card for \"weighted_trainingdata_domain_adaptation_government_telephone\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"weighted_trainingdata_domain_adaptation_government_telephone\"\n\nMore Information needed"
]
| [
6,
26
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"weighted_trainingdata_domain_adaptation_government_telephone\"\n\nMore Information needed"
]
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|
11642f275344983b00bb0ea832101587ed5c5997 | # Dataset Card for "little_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | jlbaker361/little_dataset | [
"region:us"
]
| 2023-03-12T02:49:22+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "src", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "name", "dtype": "string"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1764150.0, "num_examples": 5}], "download_size": 355276, "dataset_size": 1764150.0}} | 2023-03-17T07:02:28+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "little_dataset"
More Information needed | [
"# Dataset Card for \"little_dataset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"little_dataset\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"little_dataset\"\n\nMore Information needed"
]
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|
e339e535dddaa9b1aa14dc4c42db9da6eafde63b |
# RGB-D Salient Object Detection Dataset (RGB-D SOD)
RGB-D Salient Object Detection (RGB-D SOD) aims to detect and segment objects that *visually attract the most human interest* from a pair of color and depth images.
## Train
- COME-8K [8025 samples]
## Dev
- COME-E [4600 samples]
## Test
- Coming soon
## How to use
~~~python
from datasets import load_dataset
dataset = load_dataset(
"RGBD-SOD/rgbdsod_datasets", "v1", split="train", cache_dir="data"
)
print(dataset[0])
~~~
## BibTeX entry and citation info
```bibtex
@inproceedings{zhang2021rgb,
title={RGB-D saliency detection via cascaded mutual information minimization},
author={Zhang, Jing and Fan, Deng-Ping and Dai, Yuchao and Yu, Xin and Zhong, Yiran and Barnes, Nick and Shao, Ling},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={4338--4347},
year={2021}
}
```
| RGBD-SOD/rgbdsod_datasets | [
"region:us"
]
| 2023-03-12T05:26:18+00:00 | {"dataset_info": {"features": [{"name": "depth", "dtype": "image"}, {"name": "rgb", "dtype": "image"}, {"name": "gt", "dtype": "image"}, {"name": "name", "dtype": "string"}], "config_name": "v1", "splits": [{"name": "train", "num_bytes": 7378488019, "num_examples": 8025}, {"name": "validation", "num_bytes": 4190272788, "num_examples": 4600}], "download_size": 3506288426, "dataset_size": 11568760807}} | 2023-03-13T07:20:37+00:00 | []
| []
| TAGS
#region-us
|
# RGB-D Salient Object Detection Dataset (RGB-D SOD)
RGB-D Salient Object Detection (RGB-D SOD) aims to detect and segment objects that *visually attract the most human interest* from a pair of color and depth images.
## Train
- COME-8K [8025 samples]
## Dev
- COME-E [4600 samples]
## Test
- Coming soon
## How to use
~~~python
from datasets import load_dataset
dataset = load_dataset(
"RGBD-SOD/rgbdsod_datasets", "v1", split="train", cache_dir="data"
)
print(dataset[0])
~~~
## BibTeX entry and citation info
| [
"# RGB-D Salient Object Detection Dataset (RGB-D SOD)\n\nRGB-D Salient Object Detection (RGB-D SOD) aims to detect and segment objects that *visually attract the most human interest* from a pair of color and depth images.",
"## Train\n\n- COME-8K [8025 samples]",
"## Dev\n\n- COME-E [4600 samples]",
"## Test\n\n- Coming soon",
"## How to use\n\n~~~python\nfrom datasets import load_dataset\n\ndataset = load_dataset(\n \"RGBD-SOD/rgbdsod_datasets\", \"v1\", split=\"train\", cache_dir=\"data\"\n)\nprint(dataset[0])\n~~~",
"## BibTeX entry and citation info"
]
| [
"TAGS\n#region-us \n",
"# RGB-D Salient Object Detection Dataset (RGB-D SOD)\n\nRGB-D Salient Object Detection (RGB-D SOD) aims to detect and segment objects that *visually attract the most human interest* from a pair of color and depth images.",
"## Train\n\n- COME-8K [8025 samples]",
"## Dev\n\n- COME-E [4600 samples]",
"## Test\n\n- Coming soon",
"## How to use\n\n~~~python\nfrom datasets import load_dataset\n\ndataset = load_dataset(\n \"RGBD-SOD/rgbdsod_datasets\", \"v1\", split=\"train\", cache_dir=\"data\"\n)\nprint(dataset[0])\n~~~",
"## BibTeX entry and citation info"
]
| [
6,
63,
12,
12,
6,
66,
10
]
| [
"passage: TAGS\n#region-us \n# RGB-D Salient Object Detection Dataset (RGB-D SOD)\n\nRGB-D Salient Object Detection (RGB-D SOD) aims to detect and segment objects that *visually attract the most human interest* from a pair of color and depth images.## Train\n\n- COME-8K [8025 samples]## Dev\n\n- COME-E [4600 samples]## Test\n\n- Coming soon## How to use\n\n~~~python\nfrom datasets import load_dataset\n\ndataset = load_dataset(\n \"RGBD-SOD/rgbdsod_datasets\", \"v1\", split=\"train\", cache_dir=\"data\"\n)\nprint(dataset[0])\n~~~## BibTeX entry and citation info"
]
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|
1b35c9fcfd3ae877de36c16646ede62b5e794346 | # Dataset Card for "jujutsu-kaisen-maki-zenin"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Shamima/jujutsu-kaisen-maki-zenin | [
"region:us"
]
| 2023-03-12T05:28:28+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 395471.0, "num_examples": 11}], "download_size": 0, "dataset_size": 395471.0}} | 2023-03-12T06:09:37+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "jujutsu-kaisen-maki-zenin"
More Information needed | [
"# Dataset Card for \"jujutsu-kaisen-maki-zenin\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"jujutsu-kaisen-maki-zenin\"\n\nMore Information needed"
]
| [
6,
22
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"jujutsu-kaisen-maki-zenin\"\n\nMore Information needed"
]
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|
2d49d4ca4c9f7c6d8f97daedbeea99ccbb024878 |
# Dataset Card for VR-Folding Dataset
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Structure](#dataset-structure)
- [Dataset Example](#dataset-example)
## Dataset Description
- **Homepage:** https://garment-tracking.robotflow.ai
- **Repository:** [GitHub](https://github.com/xiaoxiaoxh/GarmentTracking)
- **Paper:** [GarmentTracking: Category-Level Garment Pose Tracking](https://arxiv.org/pdf/2303.13913.pdf)
- **Point of Contact:**
## Dataset Summary
This is the **VR-Folding** dataset created by the CVPR 2023 paper [GarmentTracking: Category-Level Garment Pose Tracking](https://garment-tracking.robotflow.ai).
This dataset is recorded with a system called [VR-Garment](https://github.com/xiaoxiaoxh/VR-Garment), which is a garment-hand interaction environment based on Unity.
To download the dataset, use the following shell snippet:
```
git lfs install
git clone https://huggingface.co/datasets/robotflow/garment-tracking
# if you want to clone without large files – just their pointers
# prepend your git clone with the following env var: GIT_LFS_SKIP_SMUDGE=1
# merge multiple .zip files (e.g. folding) into one .zip file
cd data/folding
cat folding_dataset.z* > folding_dataset.zip
# unzip
unzip folding_dataset.zip
```
All the data are stored in [zarr](https://zarr.readthedocs.io/en/stable/) format.
## Dataset Structure
Here is the detailed stucture of a data example ([zarr](https://zarr.readthedocs.io/en/stable/) format) of one frame:
```
00068_Tshirt_000000_000000
├── grip_vertex_id
│ ├── left_grip_vertex_id (1,) int32
│ └── right_grip_vertex_id (1,) int32
├── hand_pose
│ ├── left_hand_euler (25, 3) float32
│ ├── left_hand_pos (25, 3) float32
│ ├── right_hand_euler (25, 3) float32
│ └── right_hand_pos (25, 3) float32
├── marching_cube_mesh
│ ├── is_vertex_on_surface (6410,) bool
│ ├── marching_cube_faces (12816, 3) int32
│ └── marching_cube_verts (6410, 3) float32
├── mesh
│ ├── cloth_faces_tri (8312, 3) int32
│ ├── cloth_nocs_verts (4434, 3) float32
│ └── cloth_verts (4434, 3) float32
└── point_cloud
├── cls (30000,) uint8
├── nocs (30000, 3) float16
├── point (30000, 3) float16
├── rgb (30000, 3) uint8
└── sizes (4,) int64
```
Specifically, we render 4-view RGB-D images with Unity and generate concated point clouds for each frame. Here `grip_vertex_id` is the vertex index list of the grasped points of the mesh.
# Dataset Example
Please see [example](data/data_examples/README.md) for example data and visualization scripts.
Here are two video examples for flattening and folding task.
| robotflow/vr-folding | [
"license:mit",
"arxiv:2303.13913",
"region:us"
]
| 2023-03-12T05:45:54+00:00 | {"license": "mit", "pretty_name": "garment-tracking"} | 2023-06-21T09:29:05+00:00 | [
"2303.13913"
]
| []
| TAGS
#license-mit #arxiv-2303.13913 #region-us
|
# Dataset Card for VR-Folding Dataset
## Table of Contents
- Table of Contents
- Dataset Description
- Dataset Summary
- Dataset Structure
- Dataset Example
## Dataset Description
- Homepage: URL
- Repository: GitHub
- Paper: GarmentTracking: Category-Level Garment Pose Tracking
- Point of Contact:
## Dataset Summary
!VR-Garment
This is the VR-Folding dataset created by the CVPR 2023 paper GarmentTracking: Category-Level Garment Pose Tracking.
This dataset is recorded with a system called VR-Garment, which is a garment-hand interaction environment based on Unity.
To download the dataset, use the following shell snippet:
All the data are stored in zarr format.
## Dataset Structure
Here is the detailed stucture of a data example (zarr format) of one frame:
Specifically, we render 4-view RGB-D images with Unity and generate concated point clouds for each frame. Here 'grip_vertex_id' is the vertex index list of the grasped points of the mesh.
# Dataset Example
Please see example for example data and visualization scripts.
Here are two video examples for flattening and folding task.
!flattening
!folding
| [
"# Dataset Card for VR-Folding Dataset",
"## Table of Contents\n- Table of Contents\n- Dataset Description\n- Dataset Summary\n- Dataset Structure\n- Dataset Example",
"## Dataset Description\n\n- Homepage: URL\n- Repository: GitHub\n- Paper: GarmentTracking: Category-Level Garment Pose Tracking\n- Point of Contact:",
"## Dataset Summary\n!VR-Garment\nThis is the VR-Folding dataset created by the CVPR 2023 paper GarmentTracking: Category-Level Garment Pose Tracking.\nThis dataset is recorded with a system called VR-Garment, which is a garment-hand interaction environment based on Unity.\n\nTo download the dataset, use the following shell snippet:\n\nAll the data are stored in zarr format.",
"## Dataset Structure\nHere is the detailed stucture of a data example (zarr format) of one frame:\n\nSpecifically, we render 4-view RGB-D images with Unity and generate concated point clouds for each frame. Here 'grip_vertex_id' is the vertex index list of the grasped points of the mesh.",
"# Dataset Example\nPlease see example for example data and visualization scripts.\nHere are two video examples for flattening and folding task. \n!flattening\n!folding"
]
| [
"TAGS\n#license-mit #arxiv-2303.13913 #region-us \n",
"# Dataset Card for VR-Folding Dataset",
"## Table of Contents\n- Table of Contents\n- Dataset Description\n- Dataset Summary\n- Dataset Structure\n- Dataset Example",
"## Dataset Description\n\n- Homepage: URL\n- Repository: GitHub\n- Paper: GarmentTracking: Category-Level Garment Pose Tracking\n- Point of Contact:",
"## Dataset Summary\n!VR-Garment\nThis is the VR-Folding dataset created by the CVPR 2023 paper GarmentTracking: Category-Level Garment Pose Tracking.\nThis dataset is recorded with a system called VR-Garment, which is a garment-hand interaction environment based on Unity.\n\nTo download the dataset, use the following shell snippet:\n\nAll the data are stored in zarr format.",
"## Dataset Structure\nHere is the detailed stucture of a data example (zarr format) of one frame:\n\nSpecifically, we render 4-view RGB-D images with Unity and generate concated point clouds for each frame. Here 'grip_vertex_id' is the vertex index list of the grasped points of the mesh.",
"# Dataset Example\nPlease see example for example data and visualization scripts.\nHere are two video examples for flattening and folding task. \n!flattening\n!folding"
]
| [
19,
12,
30,
39,
97,
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| [
"passage: TAGS\n#license-mit #arxiv-2303.13913 #region-us \n# Dataset Card for VR-Folding Dataset## Table of Contents\n- Table of Contents\n- Dataset Description\n- Dataset Summary\n- Dataset Structure\n- Dataset Example## Dataset Description\n\n- Homepage: URL\n- Repository: GitHub\n- Paper: GarmentTracking: Category-Level Garment Pose Tracking\n- Point of Contact:## Dataset Summary\n!VR-Garment\nThis is the VR-Folding dataset created by the CVPR 2023 paper GarmentTracking: Category-Level Garment Pose Tracking.\nThis dataset is recorded with a system called VR-Garment, which is a garment-hand interaction environment based on Unity.\n\nTo download the dataset, use the following shell snippet:\n\nAll the data are stored in zarr format.## Dataset Structure\nHere is the detailed stucture of a data example (zarr format) of one frame:\n\nSpecifically, we render 4-view RGB-D images with Unity and generate concated point clouds for each frame. Here 'grip_vertex_id' is the vertex index list of the grasped points of the mesh.# Dataset Example\nPlease see example for example data and visualization scripts.\nHere are two video examples for flattening and folding task. \n!flattening\n!folding"
]
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|
c964a6da3a6b35b0cf25931e5988f96f92864619 | # Dataset Card for "arch_processed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Reasat/arch_processed | [
"region:us"
]
| 2023-03-12T06:48:20+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6335034336.864, "num_examples": 7577}], "download_size": 6331772726, "dataset_size": 6335034336.864}} | 2023-03-12T06:51:14+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "arch_processed"
More Information needed | [
"# Dataset Card for \"arch_processed\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"arch_processed\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"arch_processed\"\n\nMore Information needed"
]
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|
94415c507af3bb878bd62448780209c8681deaaa | Датасет русских диалогов собранных с Telegram чатов.
Диалоги имеют разметку по релевантности.
Также были сгенерированы негативные примеры с помощью перемешивания похожих ответов.
Количество диалогов - 2 миллиона
Формат датасета:
```
{
'question': 'Привет',
'answer': 'Привет, как дела?'
'relevance': 1
}
```
Программа парсинга: https://github.com/Den4ikAI/telegram_chat_parser
### Citation:
```
@MISC{russian_instructions,
author = {Denis Petrov},
title = {Russian dialogues dataset for conversational agents},
url = {https://huggingface.co/datasets/Den4ikAI/russian_dialogues},
year = 2023
}
``` | Den4ikAI/russian_dialogues | [
"task_categories:conversational",
"size_categories:1M<n<10M",
"language:ru",
"license:mit",
"region:us"
]
| 2023-03-12T06:54:22+00:00 | {"language": ["ru"], "license": "mit", "size_categories": ["1M<n<10M"], "task_categories": ["conversational"]} | 2023-03-12T07:58:54+00:00 | []
| [
"ru"
]
| TAGS
#task_categories-conversational #size_categories-1M<n<10M #language-Russian #license-mit #region-us
| Датасет русских диалогов собранных с Telegram чатов.
Диалоги имеют разметку по релевантности.
Также были сгенерированы негативные примеры с помощью перемешивания похожих ответов.
Количество диалогов - 2 миллиона
Формат датасета:
Программа парсинга: URL
:
| []
| [
"TAGS\n#task_categories-conversational #size_categories-1M<n<10M #language-Russian #license-mit #region-us \n"
]
| [
38
]
| [
"passage: TAGS\n#task_categories-conversational #size_categories-1M<n<10M #language-Russian #license-mit #region-us \n"
]
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]
|
6262e1e000eb8979c517fbd8335625e5887c0de6 |
This dataset contains an automatically generated set of Question and Answers extracted from the "TESTO UNICO SULLA SALUTE E SICUREZZA SUL LAVORO 81/08" document [link](https://www.lavoro.gov.it/documenti-e-norme/studi-e-statistiche/Documents/Testo%20Unico%20sulla%20Salute%20e%20Sicurezza%20sul%20Lavoro/Testo-Unico-81-08-Edizione-Giugno%202016.pdf)
The data is extracted from the article directly and the set of QA are generated using OpenAI `text-davinci-003` | alespalla/dlgs_81_08_qa | [
"task_categories:question-answering",
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:it",
"license:apache-2.0",
"medical",
"legal",
"italian",
"occupational health",
"doi:10.57967/hf/0622",
"region:us"
]
| 2023-03-12T08:49:38+00:00 | {"language": ["it"], "license": "apache-2.0", "size_categories": ["1K<n<10K"], "task_categories": ["question-answering", "text-generation"], "pretty_name": "Generated QA from text for the D.lgs 81/08", "tags": ["medical", "legal", "italian", "occupational health"]} | 2023-03-13T17:53:52+00:00 | []
| [
"it"
]
| TAGS
#task_categories-question-answering #task_categories-text-generation #size_categories-1K<n<10K #language-Italian #license-apache-2.0 #medical #legal #italian #occupational health #doi-10.57967/hf/0622 #region-us
|
This dataset contains an automatically generated set of Question and Answers extracted from the "TESTO UNICO SULLA SALUTE E SICUREZZA SUL LAVORO 81/08" document link
The data is extracted from the article directly and the set of QA are generated using OpenAI 'text-davinci-003' | []
| [
"TAGS\n#task_categories-question-answering #task_categories-text-generation #size_categories-1K<n<10K #language-Italian #license-apache-2.0 #medical #legal #italian #occupational health #doi-10.57967/hf/0622 #region-us \n"
]
| [
78
]
| [
"passage: TAGS\n#task_categories-question-answering #task_categories-text-generation #size_categories-1K<n<10K #language-Italian #license-apache-2.0 #medical #legal #italian #occupational health #doi-10.57967/hf/0622 #region-us \n"
]
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|
1ed5f0c9151118e68cd59f22bd72b3d95e0cc91f | # Dataset Card for "babylm-processed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Brendan/babylm-processed | [
"region:us"
]
| 2023-03-12T08:50:39+00:00 | {"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "special_tokens_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 6405468, "num_examples": 2077}, {"name": "valid", "num_bytes": 1612932, "num_examples": 523}], "download_size": 2868688, "dataset_size": 8018400}} | 2023-03-12T08:52:50+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "babylm-processed"
More Information needed | [
"# Dataset Card for \"babylm-processed\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"babylm-processed\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"babylm-processed\"\n\nMore Information needed"
]
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|
0291460eb8bb18b7df4f34252afa449f571e0902 | # Dataset Card for "gesti"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | k0ntra/gesti | [
"region:us"
]
| 2023-03-12T09:31:32+00:00 | {"dataset_info": {"features": [{"name": "0", "dtype": "float32"}, {"name": "1", "dtype": "float32"}, {"name": "2", "dtype": "float32"}, {"name": "3", "dtype": "float32"}, {"name": "4", "dtype": "float32"}, {"name": "5", "dtype": "float32"}, {"name": "6", "dtype": "float32"}, {"name": "7", "dtype": "float32"}, {"name": "8", "dtype": "float32"}, {"name": "9", "dtype": "float32"}, {"name": "10", "dtype": "float32"}, {"name": "11", "dtype": "float32"}, {"name": "12", "dtype": "float32"}, {"name": "13", "dtype": "float32"}, {"name": "14", "dtype": "float32"}, {"name": "15", "dtype": "float32"}, {"name": "16", "dtype": "float32"}, {"name": "17", "dtype": "float32"}, {"name": "18", "dtype": "float32"}, {"name": "19", "dtype": "float32"}, {"name": "20", "dtype": "float32"}, {"name": "21", "dtype": "float32"}, {"name": "22", "dtype": "float32"}, {"name": "23", "dtype": "float32"}, {"name": "24", "dtype": "float32"}, {"name": "25", "dtype": "float32"}, {"name": 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| []
| TAGS
#region-us
| # Dataset Card for "gesti"
More Information needed | [
"# Dataset Card for \"gesti\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"gesti\"\n\nMore Information needed"
]
| [
6,
12
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"gesti\"\n\nMore Information needed"
]
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|
3bf5ab39750126303586bd3042e371dd4d2d5804 |
An imitation learning environment for the stick-pull-v2 environment, sample for the policy stick-pull-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_stick_pull_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_stick_pull_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
| qgallouedec/prj_gia_dataset_metaworld_stick_pull_v2_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-03-12T09:38:35+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-03-12T09:38:39+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the stick-pull-v2 environment, sample for the policy stick-pull-v2
This environment was created as part of the Generally Intelligent Agents project gia: URL
## Load dataset
First, clone it with
Then, load it with
| [
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n",
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
46,
16
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
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|
826d0eb78b3e8ac8e18e020a5f4fc3d6758cac68 | # Dataset Card for "Socrat"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Dmitriy007/Socrat | [
"region:us"
]
| 2023-03-12T09:39:47+00:00 | {"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 37517232.0, "num_examples": 11994}, {"name": "val", "num_bytes": 6621976.0, "num_examples": 2117}], "download_size": 16725921, "dataset_size": 44139208.0}} | 2023-03-20T08:03:20+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "Socrat"
More Information needed | [
"# Dataset Card for \"Socrat\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"Socrat\"\n\nMore Information needed"
]
| [
6,
12
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"Socrat\"\n\nMore Information needed"
]
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|
c7937e7e7508417ab68c51b3d8cd33a5f3c62bb1 |
An imitation learning environment for the stick-push-v2 environment, sample for the policy stick-push-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_stick_push_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_stick_push_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
| qgallouedec/prj_gia_dataset_metaworld_stick_push_v2_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-03-12T09:40:15+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-03-12T09:40:19+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the stick-push-v2 environment, sample for the policy stick-push-v2
This environment was created as part of the Generally Intelligent Agents project gia: URL
## Load dataset
First, clone it with
Then, load it with
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"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
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"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n",
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
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46,
16
]
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"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
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|
4c29ac2c7ac35c587b68edf4508f9a04b618959d |
An imitation learning environment for the sweep-into-v2 environment, sample for the policy sweep-into-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_sweep_into_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_sweep_into_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
| qgallouedec/prj_gia_dataset_metaworld_sweep_into_v2_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-03-12T09:41:41+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-03-12T09:41:45+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the sweep-into-v2 environment, sample for the policy sweep-into-v2
This environment was created as part of the Generally Intelligent Agents project gia: URL
## Load dataset
First, clone it with
Then, load it with
| [
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n",
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
46,
16
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
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|
cf4df0054994008cb9bd6908d755ca9b7ca60c05 |
An imitation learning environment for the sweep-v2 environment, sample for the policy sweep-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_sweep_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_sweep_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
| qgallouedec/prj_gia_dataset_metaworld_sweep_v2_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-03-12T09:43:07+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-03-12T09:43:11+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the sweep-v2 environment, sample for the policy sweep-v2
This environment was created as part of the Generally Intelligent Agents project gia: URL
## Load dataset
First, clone it with
Then, load it with
| [
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n",
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
46,
16
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
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|
656a51de46da96557d08177c6a504ed2ca20cac1 |
An imitation learning environment for the window-close-v2 environment, sample for the policy window-close-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_window_close_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_window_close_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
| qgallouedec/prj_gia_dataset_metaworld_window_close_v2_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-03-12T09:44:50+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-03-12T09:44:54+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the window-close-v2 environment, sample for the policy window-close-v2
This environment was created as part of the Generally Intelligent Agents project gia: URL
## Load dataset
First, clone it with
Then, load it with
| [
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n",
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
46,
16
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
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|
de73aa979261acca72a1ae0f695af8ffa2142822 |
An imitation learning environment for the window-open-v2 environment, sample for the policy window-open-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_window_open_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_window_open_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
| qgallouedec/prj_gia_dataset_metaworld_window_open_v2_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-03-12T09:46:33+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-03-12T09:46:37+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the window-open-v2 environment, sample for the policy window-open-v2
This environment was created as part of the Generally Intelligent Agents project gia: URL
## Load dataset
First, clone it with
Then, load it with
| [
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n",
"## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
| [
46,
16
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n## Load dataset\n\nFirst, clone it with\n\n\n\nThen, load it with"
]
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|
8bd0b02586236caf300efa903aad70f3ceb164ee |
# Nobodies
AI-generated human image dataset.
## Contents
### Face
- [vol1](https://huggingface.co/datasets/p1atdev/nobodies/blob/main/face/vol1.zip): 32 photos of women's faces. Generated with [WD1.5 beta 2](https://huggingface.co/waifu-diffusion/wd-1-5-beta2).
Sample:
<img class="max-w-lg" src="https://huggingface.co/datasets/p1atdev/nobodies/resolve/main/samples/face/vol1.jpg" />
### Portrait
- [vol1](https://huggingface.co/datasets/p1atdev/nobodies/blob/main/portrait/vol1.zip): 31 photos of women's portraits. Generated with [WD1.5 beta 2](https://huggingface.co/waifu-diffusion/wd-1-5-beta2) and the [fashion LoCon](https://huggingface.co/p1atdev/lora/blob/main/fashion-test1-e5.safetensors).
Sample:
<img class="max-w-lg" src="https://huggingface.co/datasets/p1atdev/nobodies/resolve/main/samples/portrait/vol1.jpg" />
- [vol2](https://huggingface.co/datasets/p1atdev/nobodies/blob/main/portrait/vol2.zip): 165 photos of woman's portraits. Generated with [WD1.5 beta 2](https://huggingface.co/waifu-diffusion/wd-1-5-beta2) and the [fashion LoCon](https://huggingface.co/p1atdev/lora/blob/main/fashion-test1-e5.safetensors). Classified with LAION Aesthetic v2.
- 75 hair bun photos
- 90 medium hair photos
<div class="flex overflow-scroll">
<img class="max-w-lg" src="https://huggingface.co/datasets/p1atdev/nobodies/resolve/main/samples/portrait/vol2-a.jpg" />
<img class="max-w-lg" src="https://huggingface.co/datasets/p1atdev/nobodies/resolve/main/samples/portrait/vol2-b.jpg" />
</div>
| p1atdev/nobodies | [
"license:cc0-1.0",
"region:us"
]
| 2023-03-12T10:09:14+00:00 | {"license": "cc0-1.0"} | 2023-03-13T12:26:37+00:00 | []
| []
| TAGS
#license-cc0-1.0 #region-us
|
# Nobodies
AI-generated human image dataset.
## Contents
### Face
- vol1: 32 photos of women's faces. Generated with WD1.5 beta 2.
Sample:
<img class="max-w-lg" src="URL />
### Portrait
- vol1: 31 photos of women's portraits. Generated with WD1.5 beta 2 and the fashion LoCon.
Sample:
<img class="max-w-lg" src="URL />
- vol2: 165 photos of woman's portraits. Generated with WD1.5 beta 2 and the fashion LoCon. Classified with LAION Aesthetic v2.
- 75 hair bun photos
- 90 medium hair photos
<div class="flex overflow-scroll">
<img class="max-w-lg" src="URL />
<img class="max-w-lg" src="URL />
</div>
| [
"# Nobodies\n\nAI-generated human image dataset.",
"## Contents",
"### Face\n\n- vol1: 32 photos of women's faces. Generated with WD1.5 beta 2.\n\nSample:\n\n<img class=\"max-w-lg\" src=\"URL />",
"### Portrait\n\n- vol1: 31 photos of women's portraits. Generated with WD1.5 beta 2 and the fashion LoCon.\n\nSample:\n\n<img class=\"max-w-lg\" src=\"URL />\n\n- vol2: 165 photos of woman's portraits. Generated with WD1.5 beta 2 and the fashion LoCon. Classified with LAION Aesthetic v2.\n - 75 hair bun photos\n - 90 medium hair photos\n\n<div class=\"flex overflow-scroll\">\n<img class=\"max-w-lg\" src=\"URL />\n<img class=\"max-w-lg\" src=\"URL />\n</div>"
]
| [
"TAGS\n#license-cc0-1.0 #region-us \n",
"# Nobodies\n\nAI-generated human image dataset.",
"## Contents",
"### Face\n\n- vol1: 32 photos of women's faces. Generated with WD1.5 beta 2.\n\nSample:\n\n<img class=\"max-w-lg\" src=\"URL />",
"### Portrait\n\n- vol1: 31 photos of women's portraits. Generated with WD1.5 beta 2 and the fashion LoCon.\n\nSample:\n\n<img class=\"max-w-lg\" src=\"URL />\n\n- vol2: 165 photos of woman's portraits. Generated with WD1.5 beta 2 and the fashion LoCon. Classified with LAION Aesthetic v2.\n - 75 hair bun photos\n - 90 medium hair photos\n\n<div class=\"flex overflow-scroll\">\n<img class=\"max-w-lg\" src=\"URL />\n<img class=\"max-w-lg\" src=\"URL />\n</div>"
]
| [
14,
13,
3,
42,
145
]
| [
"passage: TAGS\n#license-cc0-1.0 #region-us \n# Nobodies\n\nAI-generated human image dataset.## Contents### Face\n\n- vol1: 32 photos of women's faces. Generated with WD1.5 beta 2.\n\nSample:\n\n<img class=\"max-w-lg\" src=\"URL />### Portrait\n\n- vol1: 31 photos of women's portraits. Generated with WD1.5 beta 2 and the fashion LoCon.\n\nSample:\n\n<img class=\"max-w-lg\" src=\"URL />\n\n- vol2: 165 photos of woman's portraits. Generated with WD1.5 beta 2 and the fashion LoCon. Classified with LAION Aesthetic v2.\n - 75 hair bun photos\n - 90 medium hair photos\n\n<div class=\"flex overflow-scroll\">\n<img class=\"max-w-lg\" src=\"URL />\n<img class=\"max-w-lg\" src=\"URL />\n</div>"
]
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|
03ba7bfada735241031f802c3a57456a99e62ae3 | # Dataset Card for "fin_tweets"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | nickmuchi/fin_tweets | [
"region:us"
]
| 2023-03-12T10:11:14+00:00 | {"dataset_info": {"features": [{"name": "creation_time", "dtype": "string"}, {"name": "username", "dtype": "string"}, {"name": "tweet", "dtype": "string"}, {"name": "sentiment", "dtype": "string"}, {"name": "topic", "dtype": "string"}, {"name": "sentiment_confidence", "dtype": "float64"}, {"name": "topic_confidence", "dtype": "float64"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 188720, "num_examples": 806}], "download_size": 90496, "dataset_size": 188720}} | 2023-03-12T12:24:53+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "fin_tweets"
More Information needed | [
"# Dataset Card for \"fin_tweets\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"fin_tweets\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"fin_tweets\"\n\nMore Information needed"
]
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|
2db9f8f1fa990c407cfb1d139eabdc5829b11703 | # Dataset Card for "go-emotion-dk-autotranlated-10k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Sadiksha/go-emotion-dk-autotranlated-10k | [
"region:us"
]
| 2023-03-12T10:18:42+00:00 | {"dataset_info": {"features": [{"name": "text_en", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "labels", "dtype": {"class_label": {"names": {"0": "admiration", "1": "amusement", "2": "anger", "3": "annoyance", "4": "approval", "5": "caring", "6": "confusion", "7": "curiosity", "8": "desire", "9": "disappointment", "10": "disapproval", "11": "disgust", "12": "embarrassment", "13": "excitement", "14": "fear", "15": "gratitude", "16": "grief", "17": "joy", "18": "love", "19": "nervousness", "20": "neutral", "21": "optimism", "22": "pride", "23": "realization", "24": "relief", "25": "remorse", "26": "sadness", "27": "surprise"}}}}, {"name": "__index_level_0__", "dtype": "int64"}, {"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 2912227, "num_examples": 9000}, {"name": "test", "num_bytes": 164591, "num_examples": 500}, {"name": "valid", "num_bytes": 161062, "num_examples": 500}], "download_size": 1659279, "dataset_size": 3237880}} | 2023-03-12T10:18:48+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "go-emotion-dk-autotranlated-10k"
More Information needed | [
"# Dataset Card for \"go-emotion-dk-autotranlated-10k\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"go-emotion-dk-autotranlated-10k\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"go-emotion-dk-autotranlated-10k\"\n\nMore Information needed"
]
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195a365a787c21387a50b9f3349496d9901ad67f |
# 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] | dpogreb/autotrain-data-train2 | [
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| 2023-03-12T10:31:02+00:00 | {} | 2023-03-31T19:58:17+00:00 | []
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- Paper:
- Leaderboard:
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### Dataset Summary
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### Languages
## Dataset Structure
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### Data Splits
## Dataset Creation
### Curation Rationale
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#### 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
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|
dfd2c05fbeca3ff4965953ef5c96fd612b44b195 | # Dataset Card for "festi"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | k0ntra/festi | [
"region:us"
]
| 2023-03-12T10:34:43+00:00 | {"dataset_info": {"features": [{"name": "0", "dtype": "float32"}, {"name": "1", "dtype": "float32"}, {"name": "2", "dtype": "float32"}, {"name": "3", "dtype": "float32"}, {"name": "4", "dtype": "float32"}, {"name": "5", "dtype": "float32"}, {"name": "6", "dtype": "float32"}, {"name": "7", "dtype": "float32"}, {"name": "8", "dtype": "float32"}, {"name": "9", "dtype": "float32"}, {"name": "10", "dtype": "float32"}, {"name": "11", "dtype": "float32"}, {"name": "12", "dtype": "float32"}, {"name": "13", "dtype": "float32"}, {"name": "14", "dtype": "float32"}, {"name": "15", "dtype": "float32"}, {"name": "16", "dtype": "float32"}, {"name": "17", "dtype": "float32"}, {"name": "18", "dtype": "float32"}, {"name": "19", "dtype": "float32"}, {"name": "20", "dtype": "float32"}, {"name": "21", "dtype": "float32"}, {"name": "22", "dtype": "float32"}, {"name": "23", "dtype": "float32"}, {"name": "24", "dtype": "float32"}, {"name": "25", "dtype": "float32"}, {"name": 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| []
| TAGS
#region-us
| # Dataset Card for "festi"
More Information needed | [
"# Dataset Card for \"festi\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"festi\"\n\nMore Information needed"
]
| [
6,
12
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"festi\"\n\nMore Information needed"
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|
363c32c1d4ffbb5adcd4275786b23f7024e4d788 | # Dataset Card for "salam"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | k0ntra/salam | [
"region:us"
]
| 2023-03-12T10:48:45+00:00 | {"dataset_info": {"features": [{"name": "0", "dtype": "float32"}, {"name": "1", "dtype": "float32"}, {"name": "2", "dtype": "float32"}, {"name": "3", "dtype": "float32"}, {"name": "4", "dtype": "float32"}, {"name": "5", "dtype": "float32"}, {"name": "6", "dtype": "float32"}, {"name": "7", "dtype": "float32"}, {"name": "8", "dtype": "float32"}, {"name": "9", "dtype": "float32"}, {"name": "10", "dtype": "float32"}, {"name": "11", "dtype": "float32"}, {"name": "12", "dtype": "float32"}, {"name": "13", "dtype": "float32"}, {"name": "14", "dtype": "float32"}, {"name": "15", "dtype": "float32"}, {"name": "16", "dtype": "float32"}, {"name": "17", "dtype": "float32"}, {"name": "18", "dtype": "float32"}, {"name": "19", "dtype": "float32"}, {"name": "20", "dtype": "float32"}, {"name": "21", "dtype": "float32"}, {"name": "22", "dtype": "float32"}, {"name": "23", "dtype": "float32"}, {"name": "24", "dtype": "float32"}, {"name": "25", "dtype": "float32"}, {"name": 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| []
| TAGS
#region-us
| # Dataset Card for "salam"
More Information needed | [
"# Dataset Card for \"salam\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"salam\"\n\nMore Information needed"
]
| [
6,
11
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"salam\"\n\nMore Information needed"
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|
ec9e0c3fd2d6cb22ba7607feca921ec86b13b2d9 |
Dataset originally from: https://www.kaggle.com/datasets/hijest/genre-classification-dataset-imdb | sayakpaul/genre-classification-imdb | [
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:en",
"license:other",
"region:us"
]
| 2023-03-12T10:54:33+00:00 | {"language": ["en"], "license": "other", "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"]} | 2023-03-12T11:05:04+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #size_categories-10K<n<100K #language-English #license-other #region-us
|
Dataset originally from: URL | []
| [
"TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #language-English #license-other #region-us \n"
]
| [
38
]
| [
"passage: TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #language-English #license-other #region-us \n"
]
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|
87fc271439ffba8c58c2880b3e81115eb685f93d | Some personal instance image sets for use with training Dreambooth | danwaldie/dreambooth | [
"license:apache-2.0",
"region:us"
]
| 2023-03-12T11:18:01+00:00 | {"license": "apache-2.0"} | 2023-03-12T16:58:46+00:00 | []
| []
| TAGS
#license-apache-2.0 #region-us
| Some personal instance image sets for use with training Dreambooth | []
| [
"TAGS\n#license-apache-2.0 #region-us \n"
]
| [
14
]
| [
"passage: TAGS\n#license-apache-2.0 #region-us \n"
]
| [
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|
11f27560a89216d0c02c0a384f5ba984acc70617 |
# Librusec dataset
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Description](#description)
- [Usage](#usage)
## Description
**Summary:** Based on http://panchenko.me/data/russe/librusec_fb2.plain.gz. Uploaded here for convenience. Additional cleaning was performed.
**Script:** [create_librusec.py](https://github.com/IlyaGusev/rulm/blob/master/data_processing/create_librusec.py)
**Point of Contact:** [Ilya Gusev]([email protected])
**Languages:** Russian.
## Usage
Prerequisites:
```bash
pip install datasets zstandard jsonlines pysimdjson
```
Dataset iteration:
```python
from datasets import load_dataset
dataset = load_dataset('IlyaGusev/librusec', split="train", streaming=True)
for example in dataset:
print(example["text"])
``` | IlyaGusev/librusec | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:ru",
"region:us"
]
| 2023-03-12T12:57:59+00:00 | {"language": ["ru"], "size_categories": ["100K<n<1M"], "task_categories": ["text-generation"], "dataset_info": {"features": [{"name": "id", "dtype": "uint64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 125126513109, "num_examples": 223256}], "download_size": 34905399148, "dataset_size": 125126513109}} | 2023-03-20T16:03:43+00:00 | []
| [
"ru"
]
| TAGS
#task_categories-text-generation #size_categories-100K<n<1M #language-Russian #region-us
|
# Librusec dataset
## Table of Contents
- Table of Contents
- Description
- Usage
## Description
Summary: Based on URL Uploaded here for convenience. Additional cleaning was performed.
Script: create_librusec.py
Point of Contact: Ilya Gusev
Languages: Russian.
## Usage
Prerequisites:
Dataset iteration:
| [
"# Librusec dataset",
"## Table of Contents\n- Table of Contents\n- Description\n- Usage",
"## Description\n\nSummary: Based on URL Uploaded here for convenience. Additional cleaning was performed.\n\nScript: create_librusec.py\n\nPoint of Contact: Ilya Gusev\n\nLanguages: Russian.",
"## Usage\n\nPrerequisites:\n\n\nDataset iteration:"
]
| [
"TAGS\n#task_categories-text-generation #size_categories-100K<n<1M #language-Russian #region-us \n",
"# Librusec dataset",
"## Table of Contents\n- Table of Contents\n- Description\n- Usage",
"## Description\n\nSummary: Based on URL Uploaded here for convenience. Additional cleaning was performed.\n\nScript: create_librusec.py\n\nPoint of Contact: Ilya Gusev\n\nLanguages: Russian.",
"## Usage\n\nPrerequisites:\n\n\nDataset iteration:"
]
| [
34,
6,
15,
46,
14
]
| [
"passage: TAGS\n#task_categories-text-generation #size_categories-100K<n<1M #language-Russian #region-us \n# Librusec dataset## Table of Contents\n- Table of Contents\n- Description\n- Usage## Description\n\nSummary: Based on URL Uploaded here for convenience. Additional cleaning was performed.\n\nScript: create_librusec.py\n\nPoint of Contact: Ilya Gusev\n\nLanguages: Russian.## Usage\n\nPrerequisites:\n\n\nDataset iteration:"
]
| [
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|
87332084b2e9c4187817bde8a605d0009b3bb19a |
## Dataset Description
- **Homepage:** [DiffusionFER homepage](https://kdhht2334.github.io/)
- **Repository:** [DiffusionFER repository](https://github.com/kdhht2334/Facial-Expression-Recognition-Zoo)
- **Distribution:** [DiffusionFER Hugging Face Dataset](https://huggingface.co/datasets/FER-Universe/DiffusionFER)
- **Point of Contact:** [Daeha Kim](mailto:[email protected])
### Summary
DiffusionFER is the large-scale text-to-image prompt database for face-related tasks. It contains about **1M(ongoing)** images generated by [Stable Diffusion](https://github.com/camenduru/stable-diffusion-webui-colab) using prompt(s) and other parameters.
DiffusionFER is available at [🤗 Hugging Face Dataset](https://huggingface.co/datasets/FER-Universe/DiffusionFER).
### Downstream Tasks and Leaderboards
This DiffusionFER dataset can be utilized for the following downstream tasks.
- Face detection
- Facial expression recognition
- Text-to-emotion prompting
In addition, the virtual subjects included in this dataset provide opportunities to perform various vision tasks related to face privacy.
### Data Loading
DiffusionFER can be loaded via both Python and Git. Please refer Hugging Face [`Datasets`](https://huggingface.co/docs/datasets/quickstart).
```python
from datasets import load_dataset
dataset = load_dataset("FER-Universe/DiffusionFER")
```
```bash
git lfs install
git clone https://huggingface.co/datasets/FER-Universe/DiffusionFER
```
### Pre-trained model
You can easily download and use pre-trained __Swin Transformer__ model with the `Diffusion_Emotion_S` dataset.
Later, Transformer models with the `Diffusion_Emotion_M` or `Diffusion_Emotion_L` will be released.
```python
from transformers import AutoFeatureExtractor, AutoModelForImageClassification
extractor = AutoFeatureExtractor.from_pretrained("kdhht2334/autotrain-diffusion-emotion-facial-expression-recognition-40429105176")
model = AutoModelForImageClassification.from_pretrained("kdhht2334/autotrain-diffusion-emotion-facial-expression-recognition-40429105176")
```
Or just clone the model repo
```bash
git lfs install
git clone https://huggingface.co/kdhht2334/autotrain-diffusion-emotion-facial-expression-recognition-40429105176
```
- Quick links: [huggingface model documentation](https://huggingface.co/docs/transformers/main/en/model_doc/swin#transformers.SwinForImageClassification)
### Sample Gallery
▼Happy
▼Angry
### Subsets
DiffusionFER supports a total of three distinct splits. And, each split additionally provides a face region cropped by [face detector](https://github.com/timesler/facenet-pytorch).
- DifussionEmotion_S (small), DifussionEmotion_M (medium), DifussionEmotion_L (large).
|Subset|Num of Images|Size|Image Directory |
|:--|--:|--:|--:|
|DifussionEmotion_S (original) | 1.5K | 647M | `DifussionEmotion_S/` |
|DifussionEmotion_S (cropped) | 1.5K | 322M | `DiffusionEmotion_S_cropped/` |
|DifussionEmotion_M (original) | N/A | N/A | `DifussionEmotion_M/` |
|DifussionEmotion_M (cropped) | N/A | N/A | `DiffusionEmotion_M_cropped/` |
|DifussionEmotion_L (original) | N/A | N/A | `DifussionEmotion_L/` |
|DifussionEmotion_L (cropped) | N/A | N/A | `DiffusionEmotion_L_cropped/` |
## Dataset Structure
We provide DiffusionFER using a modular file structure. `DiffusionEmotion_S`, the smallest scale, contains about 1,500 images and is divided into folders of a total of 7 emotion classes. The class labels of all these images are included in `dataset_sheet.csv`.
- In `dataset_sheet.csv`, not only 7-emotion class but also _valence-arousal_ value are annotated.
```bash
# Small version of DB
./
├── DifussionEmotion_S
│ ├── angry
│ │ ├── aaaaaaaa_6.png
│ │ ├── andtcvhp_6.png
│ │ ├── azikakjh_6.png
│ │ ├── [...]
│ ├── fear
│ ├── happy
│ ├── [...]
│ └── surprise
└── dataset_sheet.csv
```
- Middle size DB will be uploaded soon.
```bash
# Medium version of DB
(ongoing)
```
- TBD
```bash
# Large version of DB
(ongoing)
```
### Prompt Format
Basic format is as follows: "`Emotion`, `Race` `Age` style, a realistic portrait of `Style` `Gender`, upper body, `Others`".
- ex) one person, neutral emotion, white middle-aged style, a realistic portrait of man, upper body
Examples of format categories are listed in the table below.
| Category | Prompt(s) |
| --- | --- |
| `Emotion` | neutral emotion<br>happy emotion, with open mouth, smiley<br>sad emotion, with tears, lowered head, droopy eyebrows<br>surprise emotion, with open mouth, big eyes<br>fear emotion, scared, haunted<br>disgust emotion, frown, angry expression with open mouth<br>angry emotion, with open mouth, frown eyebrow, fierce, furious |
| `Race` | white<br>black<br>latin |
| `Age` | teen<br>middle-aged<br>old |
| `Gender` | man<br>woman |
| `Style` | gentle<br>handsome<br>pretty<br>cute<br>mature<br>punky<br>freckles<br>beautiful crystal eyes<br>big eyes<br>small nose<br>... |
| `Others` | 4K<br>8K<br>cyberpunk<br>camping<br>ancient<br>medieval Europe<br>... |
### Prompt Engineering
You can improve the performance and quality of generating default prompts with the settings below.
```
{
"negative prompt": "sketches, (worst quality:2), (low quality:2), (normal quality:2), lowres, normal quality, ((monochrome)), ((grayscale)), skin spots, acnes, skin blemishes, backlight, (duplicate:1.331), (morbid:1.21), (mutilated:1.21), mutated hands, (poorly drawn hands:1.331), (bad anatomy:1.21), (bad proportions:1.331), extra limbs, (disfigured:1.331), (missing arms:1.331), (extra legs:1.331), (fused fingers:1.61051), (too many fingers:1.61051), (unclear eyes:1.331), bad hands, missing fingers, extra digit",
"steps": 50,
"sampling method": "DPM++ 2M Karras"
"Width": "512",
"Height": "512",
"CFG scale": 12.0,
"seed": -1,
}
```
### Annotations
The DiffusionFER contains annotation process both 7-emotion classes and valence-arousal values.
#### Annotation process
This process was carried out inspired by the theory of the two research papers below.
- JA Russell, [A circumplex model of affect](https://d1wqtxts1xzle7.cloudfront.net/38425675/Russell1980-libre.pdf?1439132613=&response-content-disposition=inline%3B+filename%3DRussell1980.pdf&Expires=1678595455&Signature=UtbPsezND6w8vbISBiuL-ECk6hDI0etLcJSE7kJMC~hAkMSu9YyQcPKdVpdHSSq7idfcQ~eEKsqptvYpy0199DX0gi-nHJwhsciahC-zgDwylEUo6ykhP6Ab8VWCOW-DM21jHNvbYLQf7Pwi66fGvm~5bAXPc1o4HHpQpk-Cr7b0tW9lYnl3qgLoVeIICg6FLu0elbtVztgH5OS1uL6V~QhiP2PCwZf~WCHuJRQrWdPt5Kuco0lsNr1Qikk1~d7HY3ZcUTRZcMNDdem8XAFDH~ak3QER6Ml~JDkNFcLuygz~tjL4CdScVhByeAuMe3juyijtBFtYWH2h30iRkUDalg__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA)
- A Mollahosseini et al., [AffectNet](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8013713&casa_token=C3QmhmiB6Y8AAAAA:1CiUll0bhIq06M17YwFIvxuse7GOosEN9G1A8vxVzR8Vb5eaFp6ERIjg7xhSIQlf008KLsfJ-w&tag=1)
#### Who are the annotators?
[Daeha Kim](mailto:[email protected]) and [Dohee Kang](mailto:[email protected])
## Additional Information
### Dataset Curators
DiffusionFER is created by [Daeha Kim](https://kdhht2334.github.io/) and [Dohee Kang](https://github.com/KangDohee2270).
### Acknowledgments
This repository is heavily inspired by [DiffusionDB](https://huggingface.co/datasets/poloclub/diffusiondb), with some format references. Thank you for your interest in [DiffusionDB](https://huggingface.co/datasets/poloclub/diffusiondb).
### Licensing Information
The DiffusionFER is available under the [CC0 1.0 License](https://creativecommons.org/publicdomain/zero/1.0/). NOTE: The primary purpose of this dataset is research. We are not responsible if you take any other action using this dataset.
### Contributions
If you have any questions, feel free to [open an issue](https://github.com/kdhht2334/Facial-Expression-Recognition-Zoo/issues/new) or contact [Daeha Kim](https://kdhht2334.github.io/). | FER-Universe/DiffusionFER | [
"task_categories:text-to-image",
"task_ids:image-captioning",
"task_ids:face-detection",
"annotations_creators:no-annotation",
"language_creators:found",
"size_categories:n<500MB",
"source_datasets:original",
"language:en",
"license:cc0-1.0",
"stable diffusion",
"prompt engineering",
"prompts",
"research paper",
"facial expression recognition",
"emotion recognition",
"region:us"
]
| 2023-03-12T13:16:59+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["en"], "license": "cc0-1.0", "size_categories": ["n<500MB"], "source_datasets": ["original"], "task_categories": ["text-to-image"], "task_ids": ["image-captioning", "face-detection"], "pretty_name": "DiffusionFER", "layout": "default", "title": "Home", "nav_order": 1, "has_children": false, "tags": ["stable diffusion", "prompt engineering", "prompts", "research paper", "facial expression recognition", "emotion recognition"]} | 2023-12-31T12:40:48+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-to-image #task_ids-image-captioning #task_ids-face-detection #annotations_creators-no-annotation #language_creators-found #size_categories-n<500MB #source_datasets-original #language-English #license-cc0-1.0 #stable diffusion #prompt engineering #prompts #research paper #facial expression recognition #emotion recognition #region-us
| Dataset Description
-------------------
* Homepage: DiffusionFER homepage
* Repository: DiffusionFER repository
* Distribution: DiffusionFER Hugging Face Dataset
* Point of Contact: Daeha Kim
### Summary
DiffusionFER is the large-scale text-to-image prompt database for face-related tasks. It contains about 1M(ongoing) images generated by Stable Diffusion using prompt(s) and other parameters.
DiffusionFER is available at Hugging Face Dataset.
### Downstream Tasks and Leaderboards
This DiffusionFER dataset can be utilized for the following downstream tasks.
* Face detection
* Facial expression recognition
* Text-to-emotion prompting
In addition, the virtual subjects included in this dataset provide opportunities to perform various vision tasks related to face privacy.
### Data Loading
DiffusionFER can be loaded via both Python and Git. Please refer Hugging Face 'Datasets'.
### Pre-trained model
You can easily download and use pre-trained **Swin Transformer** model with the 'Diffusion\_Emotion\_S' dataset.
Later, Transformer models with the 'Diffusion\_Emotion\_M' or 'Diffusion\_Emotion\_L' will be released.
Or just clone the model repo
* Quick links: huggingface model documentation
### Sample Gallery
▼Happy
!Gallery(happy)
▼Angry
!Gallery(happy)
### Subsets
DiffusionFER supports a total of three distinct splits. And, each split additionally provides a face region cropped by face detector.
* DifussionEmotion\_S (small), DifussionEmotion\_M (medium), DifussionEmotion\_L (large).
Dataset Structure
-----------------
We provide DiffusionFER using a modular file structure. 'DiffusionEmotion\_S', the smallest scale, contains about 1,500 images and is divided into folders of a total of 7 emotion classes. The class labels of all these images are included in 'dataset\_sheet.csv'.
* In 'dataset\_sheet.csv', not only 7-emotion class but also *valence-arousal* value are annotated.
* Middle size DB will be uploaded soon.
* TBD
### Prompt Format
Basic format is as follows: "'Emotion', 'Race' 'Age' style, a realistic portrait of 'Style' 'Gender', upper body, 'Others'".
* ex) one person, neutral emotion, white middle-aged style, a realistic portrait of man, upper body
Examples of format categories are listed in the table below.
### Prompt Engineering
You can improve the performance and quality of generating default prompts with the settings below.
### Annotations
The DiffusionFER contains annotation process both 7-emotion classes and valence-arousal values.
#### Annotation process
This process was carried out inspired by the theory of the two research papers below.
* JA Russell, A circumplex model of affect
* A Mollahosseini et al., AffectNet
#### Who are the annotators?
Daeha Kim and Dohee Kang
Additional Information
----------------------
### Dataset Curators
DiffusionFER is created by Daeha Kim and Dohee Kang.
### Acknowledgments
This repository is heavily inspired by DiffusionDB, with some format references. Thank you for your interest in DiffusionDB.
### Licensing Information
The DiffusionFER is available under the CC0 1.0 License. NOTE: The primary purpose of this dataset is research. We are not responsible if you take any other action using this dataset.
### Contributions
If you have any questions, feel free to open an issue or contact Daeha Kim.
| [
"### Summary\n\n\nDiffusionFER is the large-scale text-to-image prompt database for face-related tasks. It contains about 1M(ongoing) images generated by Stable Diffusion using prompt(s) and other parameters.\n\n\nDiffusionFER is available at Hugging Face Dataset.",
"### Downstream Tasks and Leaderboards\n\n\nThis DiffusionFER dataset can be utilized for the following downstream tasks.\n\n\n* Face detection\n* Facial expression recognition\n* Text-to-emotion prompting\n\n\nIn addition, the virtual subjects included in this dataset provide opportunities to perform various vision tasks related to face privacy.",
"### Data Loading\n\n\nDiffusionFER can be loaded via both Python and Git. Please refer Hugging Face 'Datasets'.",
"### Pre-trained model\n\n\nYou can easily download and use pre-trained **Swin Transformer** model with the 'Diffusion\\_Emotion\\_S' dataset.\n\n\nLater, Transformer models with the 'Diffusion\\_Emotion\\_M' or 'Diffusion\\_Emotion\\_L' will be released.\n\n\nOr just clone the model repo\n\n\n* Quick links: huggingface model documentation",
"### Sample Gallery\n\n\n▼Happy\n\n\n!Gallery(happy)\n\n\n▼Angry\n\n\n!Gallery(happy)",
"### Subsets\n\n\nDiffusionFER supports a total of three distinct splits. And, each split additionally provides a face region cropped by face detector.\n\n\n* DifussionEmotion\\_S (small), DifussionEmotion\\_M (medium), DifussionEmotion\\_L (large).\n\n\n\nDataset Structure\n-----------------\n\n\nWe provide DiffusionFER using a modular file structure. 'DiffusionEmotion\\_S', the smallest scale, contains about 1,500 images and is divided into folders of a total of 7 emotion classes. The class labels of all these images are included in 'dataset\\_sheet.csv'.\n\n\n* In 'dataset\\_sheet.csv', not only 7-emotion class but also *valence-arousal* value are annotated.\n* Middle size DB will be uploaded soon.\n* TBD",
"### Prompt Format\n\n\nBasic format is as follows: \"'Emotion', 'Race' 'Age' style, a realistic portrait of 'Style' 'Gender', upper body, 'Others'\".\n\n\n* ex) one person, neutral emotion, white middle-aged style, a realistic portrait of man, upper body\n\n\nExamples of format categories are listed in the table below.",
"### Prompt Engineering\n\n\nYou can improve the performance and quality of generating default prompts with the settings below.",
"### Annotations\n\n\nThe DiffusionFER contains annotation process both 7-emotion classes and valence-arousal values.",
"#### Annotation process\n\n\nThis process was carried out inspired by the theory of the two research papers below.\n\n\n* JA Russell, A circumplex model of affect\n* A Mollahosseini et al., AffectNet",
"#### Who are the annotators?\n\n\nDaeha Kim and Dohee Kang\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\nDiffusionFER is created by Daeha Kim and Dohee Kang.",
"### Acknowledgments\n\n\nThis repository is heavily inspired by DiffusionDB, with some format references. Thank you for your interest in DiffusionDB.",
"### Licensing Information\n\n\nThe DiffusionFER is available under the CC0 1.0 License. NOTE: The primary purpose of this dataset is research. We are not responsible if you take any other action using this dataset.",
"### Contributions\n\n\nIf you have any questions, feel free to open an issue or contact Daeha Kim."
]
| [
"TAGS\n#task_categories-text-to-image #task_ids-image-captioning #task_ids-face-detection #annotations_creators-no-annotation #language_creators-found #size_categories-n<500MB #source_datasets-original #language-English #license-cc0-1.0 #stable diffusion #prompt engineering #prompts #research paper #facial expression recognition #emotion recognition #region-us \n",
"### Summary\n\n\nDiffusionFER is the large-scale text-to-image prompt database for face-related tasks. It contains about 1M(ongoing) images generated by Stable Diffusion using prompt(s) and other parameters.\n\n\nDiffusionFER is available at Hugging Face Dataset.",
"### Downstream Tasks and Leaderboards\n\n\nThis DiffusionFER dataset can be utilized for the following downstream tasks.\n\n\n* Face detection\n* Facial expression recognition\n* Text-to-emotion prompting\n\n\nIn addition, the virtual subjects included in this dataset provide opportunities to perform various vision tasks related to face privacy.",
"### Data Loading\n\n\nDiffusionFER can be loaded via both Python and Git. Please refer Hugging Face 'Datasets'.",
"### Pre-trained model\n\n\nYou can easily download and use pre-trained **Swin Transformer** model with the 'Diffusion\\_Emotion\\_S' dataset.\n\n\nLater, Transformer models with the 'Diffusion\\_Emotion\\_M' or 'Diffusion\\_Emotion\\_L' will be released.\n\n\nOr just clone the model repo\n\n\n* Quick links: huggingface model documentation",
"### Sample Gallery\n\n\n▼Happy\n\n\n!Gallery(happy)\n\n\n▼Angry\n\n\n!Gallery(happy)",
"### Subsets\n\n\nDiffusionFER supports a total of three distinct splits. And, each split additionally provides a face region cropped by face detector.\n\n\n* DifussionEmotion\\_S (small), DifussionEmotion\\_M (medium), DifussionEmotion\\_L (large).\n\n\n\nDataset Structure\n-----------------\n\n\nWe provide DiffusionFER using a modular file structure. 'DiffusionEmotion\\_S', the smallest scale, contains about 1,500 images and is divided into folders of a total of 7 emotion classes. The class labels of all these images are included in 'dataset\\_sheet.csv'.\n\n\n* In 'dataset\\_sheet.csv', not only 7-emotion class but also *valence-arousal* value are annotated.\n* Middle size DB will be uploaded soon.\n* TBD",
"### Prompt Format\n\n\nBasic format is as follows: \"'Emotion', 'Race' 'Age' style, a realistic portrait of 'Style' 'Gender', upper body, 'Others'\".\n\n\n* ex) one person, neutral emotion, white middle-aged style, a realistic portrait of man, upper body\n\n\nExamples of format categories are listed in the table below.",
"### Prompt Engineering\n\n\nYou can improve the performance and quality of generating default prompts with the settings below.",
"### Annotations\n\n\nThe DiffusionFER contains annotation process both 7-emotion classes and valence-arousal values.",
"#### Annotation process\n\n\nThis process was carried out inspired by the theory of the two research papers below.\n\n\n* JA Russell, A circumplex model of affect\n* A Mollahosseini et al., AffectNet",
"#### Who are the annotators?\n\n\nDaeha Kim and Dohee Kang\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\nDiffusionFER is created by Daeha Kim and Dohee Kang.",
"### Acknowledgments\n\n\nThis repository is heavily inspired by DiffusionDB, with some format references. Thank you for your interest in DiffusionDB.",
"### Licensing Information\n\n\nThe DiffusionFER is available under the CC0 1.0 License. NOTE: The primary purpose of this dataset is research. We are not responsible if you take any other action using this dataset.",
"### Contributions\n\n\nIf you have any questions, feel free to open an issue or contact Daeha Kim."
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"passage: TAGS\n#task_categories-text-to-image #task_ids-image-captioning #task_ids-face-detection #annotations_creators-no-annotation #language_creators-found #size_categories-n<500MB #source_datasets-original #language-English #license-cc0-1.0 #stable diffusion #prompt engineering #prompts #research paper #facial expression recognition #emotion recognition #region-us \n### Summary\n\n\nDiffusionFER is the large-scale text-to-image prompt database for face-related tasks. It contains about 1M(ongoing) images generated by Stable Diffusion using prompt(s) and other parameters.\n\n\nDiffusionFER is available at Hugging Face Dataset.### Downstream Tasks and Leaderboards\n\n\nThis DiffusionFER dataset can be utilized for the following downstream tasks.\n\n\n* Face detection\n* Facial expression recognition\n* Text-to-emotion prompting\n\n\nIn addition, the virtual subjects included in this dataset provide opportunities to perform various vision tasks related to face privacy.### Data Loading\n\n\nDiffusionFER can be loaded via both Python and Git. Please refer Hugging Face 'Datasets'.### Pre-trained model\n\n\nYou can easily download and use pre-trained **Swin Transformer** model with the 'Diffusion\\_Emotion\\_S' dataset.\n\n\nLater, Transformer models with the 'Diffusion\\_Emotion\\_M' or 'Diffusion\\_Emotion\\_L' will be released.\n\n\nOr just clone the model repo\n\n\n* Quick links: huggingface model documentation### Sample Gallery\n\n\n▼Happy\n\n\n!Gallery(happy)\n\n\n▼Angry\n\n\n!Gallery(happy)"
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|
53c08ae5272dbcd877663800d405c08a58937cb7 | # Dataset Card for "tehran"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | k0ntra/tehran | [
"region:us"
]
| 2023-03-12T13:18:22+00:00 | {"dataset_info": {"features": [{"name": "0", "dtype": "float32"}, {"name": "1", "dtype": "float32"}, {"name": "2", "dtype": "float32"}, {"name": "3", "dtype": "float32"}, {"name": "4", "dtype": "float32"}, {"name": "5", "dtype": "float32"}, {"name": "6", "dtype": "float32"}, {"name": "7", "dtype": "float32"}, {"name": "8", "dtype": "float32"}, {"name": "9", "dtype": "float32"}, {"name": "10", "dtype": "float32"}, {"name": "11", "dtype": "float32"}, {"name": "12", "dtype": "float32"}, {"name": "13", "dtype": "float32"}, {"name": "14", "dtype": "float32"}, {"name": "15", "dtype": "float32"}, {"name": "16", "dtype": "float32"}, {"name": "17", "dtype": "float32"}, {"name": "18", "dtype": "float32"}, {"name": "19", "dtype": "float32"}, {"name": "20", "dtype": "float32"}, {"name": "21", "dtype": "float32"}, {"name": "22", "dtype": "float32"}, {"name": "23", "dtype": "float32"}, {"name": "24", "dtype": "float32"}, {"name": "25", "dtype": "float32"}, {"name": 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| []
| TAGS
#region-us
| # Dataset Card for "tehran"
More Information needed | [
"# Dataset Card for \"tehran\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"tehran\"\n\nMore Information needed"
]
| [
6,
12
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"tehran\"\n\nMore Information needed"
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|
8df4dcacee31623b8d8382a5e143f97eb52ae3b2 | # Dataset Card for "tehran2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | k0ntra/tehran2 | [
"region:us"
]
| 2023-03-12T13:27:06+00:00 | {"dataset_info": {"features": [{"name": "0", "dtype": "float32"}, {"name": "1", "dtype": "float32"}, {"name": "2", "dtype": "float32"}, {"name": "3", "dtype": "float32"}, {"name": "4", "dtype": "float32"}, {"name": "5", "dtype": "float32"}, {"name": "6", "dtype": "float32"}, {"name": "7", "dtype": "float32"}, {"name": "8", "dtype": "float32"}, {"name": "9", "dtype": "float32"}, {"name": "10", "dtype": "float32"}, {"name": "11", "dtype": "float32"}, {"name": "12", "dtype": "float32"}, {"name": "13", "dtype": "float32"}, {"name": "14", "dtype": "float32"}, {"name": "15", "dtype": "float32"}, {"name": "16", "dtype": "float32"}, {"name": "17", "dtype": "float32"}, {"name": "18", "dtype": "float32"}, {"name": "19", "dtype": "float32"}, {"name": "20", "dtype": "float32"}, {"name": "21", "dtype": "float32"}, {"name": "22", "dtype": "float32"}, {"name": "23", "dtype": "float32"}, {"name": "24", "dtype": "float32"}, {"name": "25", "dtype": "float32"}, {"name": 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| []
| TAGS
#region-us
| # Dataset Card for "tehran2"
More Information needed | [
"# Dataset Card for \"tehran2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"tehran2\"\n\nMore Information needed"
]
| [
6,
13
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"tehran2\"\n\nMore Information needed"
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|
b16788a10f120262794c81e4b658d2044674fa13 | # Dataset Card for "tehran3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | k0ntra/tehran3 | [
"region:us"
]
| 2023-03-12T13:32:14+00:00 | {"dataset_info": {"features": [{"name": "0", "dtype": "float32"}, {"name": "1", "dtype": "float32"}, {"name": "2", "dtype": "float32"}, {"name": "3", "dtype": "float32"}, {"name": "4", "dtype": "float32"}, {"name": "5", "dtype": "float32"}, {"name": "6", "dtype": "float32"}, {"name": "7", "dtype": "float32"}, {"name": "8", "dtype": "float32"}, {"name": "9", "dtype": "float32"}, {"name": "10", "dtype": "float32"}, {"name": "11", "dtype": "float32"}, {"name": "12", "dtype": "float32"}, {"name": "13", "dtype": "float32"}, {"name": "14", "dtype": "float32"}, {"name": "15", "dtype": "float32"}, {"name": "16", "dtype": "float32"}, {"name": "17", "dtype": "float32"}, {"name": "18", "dtype": "float32"}, {"name": "19", "dtype": "float32"}, {"name": "20", "dtype": "float32"}, {"name": "21", "dtype": "float32"}, {"name": "22", "dtype": "float32"}, {"name": "23", "dtype": "float32"}, {"name": "24", "dtype": "float32"}, {"name": "25", "dtype": "float32"}, {"name": 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| []
| TAGS
#region-us
| # Dataset Card for "tehran3"
More Information needed | [
"# Dataset Card for \"tehran3\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"tehran3\"\n\nMore Information needed"
]
| [
6,
13
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"tehran3\"\n\nMore Information needed"
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|
afd813603cae4103c7f3925d82d25b18c7007565 | # Dataset Card for "github-issues"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | feeeper/github-issues | [
"region:us"
]
| 2023-03-12T13:42:25+00:00 | {"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "repository_url", "dtype": "string"}, {"name": "labels_url", "dtype": "string"}, {"name": "comments_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "number", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "user", "struct": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "labels", "list": [{"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "color", "dtype": "string"}, {"name": "default", "dtype": "bool"}, {"name": "description", "dtype": "string"}]}, {"name": "state", "dtype": "string"}, {"name": "locked", "dtype": "bool"}, {"name": "assignee", "struct": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "assignees", "list": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "milestone", "struct": [{"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "labels_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "number", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "creator", "struct": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "open_issues", "dtype": "int64"}, {"name": "closed_issues", "dtype": "int64"}, {"name": "state", "dtype": "string"}, {"name": "created_at", "dtype": "timestamp[s]"}, {"name": "updated_at", "dtype": "timestamp[s]"}, {"name": "due_on", "dtype": "timestamp[s]"}, {"name": "closed_at", "dtype": "timestamp[s]"}]}, {"name": "comments", "sequence": "string"}, {"name": "created_at", "dtype": "timestamp[s]"}, {"name": "updated_at", "dtype": "timestamp[s]"}, {"name": "closed_at", "dtype": "timestamp[s]"}, {"name": "author_association", "dtype": "string"}, {"name": "active_lock_reason", "dtype": "null"}, {"name": "body", "dtype": "string"}, {"name": "reactions", "struct": [{"name": "url", "dtype": "string"}, {"name": "total_count", "dtype": "int64"}, {"name": "+1", "dtype": "int64"}, {"name": "-1", "dtype": "int64"}, {"name": "laugh", "dtype": "int64"}, {"name": "hooray", "dtype": "int64"}, {"name": "confused", "dtype": "int64"}, {"name": "heart", "dtype": "int64"}, {"name": "rocket", "dtype": "int64"}, {"name": "eyes", "dtype": "int64"}]}, {"name": "timeline_url", "dtype": "string"}, {"name": "performed_via_github_app", "dtype": "null"}, {"name": "state_reason", "dtype": "string"}, {"name": "draft", "dtype": "bool"}, {"name": "pull_request", "struct": [{"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "diff_url", "dtype": "string"}, {"name": "patch_url", "dtype": "string"}, {"name": "merged_at", "dtype": "timestamp[s]"}]}, {"name": "is_pull_request", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 16908032, "num_examples": 5085}], "download_size": 4477014, "dataset_size": 16908032}} | 2023-03-12T13:42:26+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "github-issues"
More Information needed | [
"# Dataset Card for \"github-issues\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"github-issues\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"github-issues\"\n\nMore Information needed"
]
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]
|
65a073f2c48401410b264213229a6c52417f367a |
# Constructive and Toxic Speech Detection for Open-domain Social Media Comments in Vietnamese
This is the official repository for the UIT-ViCTSD dataset from the paper [Constructive and Toxic Speech Detection for Open-domain Social Media Comments in Vietnamese](https://arxiv.org/pdf/2103.10069.pdf), which was accepted at the [IEA/AIE 2021](https://ieaaie2021.wordpress.com/list-of-accepted-papers/).
# Citation Information
The provided dataset is only used for research purposes!
```
@InProceedings{nguyen2021victsd,
author="Nguyen, Luan Thanh and Van Nguyen, Kiet and Nguyen, Ngan Luu-Thuy",
title="Constructive and Toxic Speech Detection for Open-Domain Social Media Comments in Vietnamese",
booktitle="Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices",
year="2021",
publisher="Springer International Publishing",
address="Cham",
pages="572--583"
}
```
## Abstract
The rise of social media has led to the increasing of comments on online forums. However, there still exists invalid comments which are not informative for users. Moreover, those comments are also quite toxic and harmful to people. In this paper, we create a dataset for constructive and toxic speech detection, named UIT-ViCTSD (Vietnamese Constructive and Toxic Speech Detection dataset) with 10,000 human-annotated comments. For these tasks, we propose a system for constructive and toxic speech detection with the state-of-the-art transfer learning model in Vietnamese NLP as PhoBERT. With this system, we obtain F1-scores of 78.59% and 59.40% for classifying constructive and toxic comments, respectively. Besides, we implement various baseline models as traditional Machine Learning and Deep Neural Network-Based models to evaluate the dataset. With the results, we can solve several tasks on the online discussions and develop the framework for identifying constructiveness and toxicity of Vietnamese social media comments automatically.
## Dataset
The ViCTSD dataset is consist of 10,000 human-annotated comments on 10 domains from Vietnamese users' comments on social media.
The dataset is divided into three parts as below:
1. Train set: 7,000 comments
2. Valid set: 2,000 comments
3. Test set: 1,000 comments
## Contact
Please feel free to contact us by email [email protected] if you have any further information! | tarudesu/ViCTSD | [
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:vi",
"arxiv:2103.10069",
"region:us"
]
| 2023-03-12T14:16:24+00:00 | {"language": ["vi"], "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"], "pretty_name": "Vietnamese Constructive and Toxic Speech Detection Dataset"} | 2023-11-28T07:20:27+00:00 | [
"2103.10069"
]
| [
"vi"
]
| TAGS
#task_categories-text-classification #size_categories-10K<n<100K #language-Vietnamese #arxiv-2103.10069 #region-us
|
# Constructive and Toxic Speech Detection for Open-domain Social Media Comments in Vietnamese
This is the official repository for the UIT-ViCTSD dataset from the paper Constructive and Toxic Speech Detection for Open-domain Social Media Comments in Vietnamese, which was accepted at the IEA/AIE 2021.
The provided dataset is only used for research purposes!
## Abstract
The rise of social media has led to the increasing of comments on online forums. However, there still exists invalid comments which are not informative for users. Moreover, those comments are also quite toxic and harmful to people. In this paper, we create a dataset for constructive and toxic speech detection, named UIT-ViCTSD (Vietnamese Constructive and Toxic Speech Detection dataset) with 10,000 human-annotated comments. For these tasks, we propose a system for constructive and toxic speech detection with the state-of-the-art transfer learning model in Vietnamese NLP as PhoBERT. With this system, we obtain F1-scores of 78.59% and 59.40% for classifying constructive and toxic comments, respectively. Besides, we implement various baseline models as traditional Machine Learning and Deep Neural Network-Based models to evaluate the dataset. With the results, we can solve several tasks on the online discussions and develop the framework for identifying constructiveness and toxicity of Vietnamese social media comments automatically.
## Dataset
The ViCTSD dataset is consist of 10,000 human-annotated comments on 10 domains from Vietnamese users' comments on social media.
The dataset is divided into three parts as below:
1. Train set: 7,000 comments
2. Valid set: 2,000 comments
3. Test set: 1,000 comments
## Contact
Please feel free to contact us by email luannt@URL if you have any further information! | [
"# Constructive and Toxic Speech Detection for Open-domain Social Media Comments in Vietnamese\nThis is the official repository for the UIT-ViCTSD dataset from the paper Constructive and Toxic Speech Detection for Open-domain Social Media Comments in Vietnamese, which was accepted at the IEA/AIE 2021.\n\n\nThe provided dataset is only used for research purposes!",
"## Abstract\nThe rise of social media has led to the increasing of comments on online forums. However, there still exists invalid comments which are not informative for users. Moreover, those comments are also quite toxic and harmful to people. In this paper, we create a dataset for constructive and toxic speech detection, named UIT-ViCTSD (Vietnamese Constructive and Toxic Speech Detection dataset) with 10,000 human-annotated comments. For these tasks, we propose a system for constructive and toxic speech detection with the state-of-the-art transfer learning model in Vietnamese NLP as PhoBERT. With this system, we obtain F1-scores of 78.59% and 59.40% for classifying constructive and toxic comments, respectively. Besides, we implement various baseline models as traditional Machine Learning and Deep Neural Network-Based models to evaluate the dataset. With the results, we can solve several tasks on the online discussions and develop the framework for identifying constructiveness and toxicity of Vietnamese social media comments automatically.",
"## Dataset\nThe ViCTSD dataset is consist of 10,000 human-annotated comments on 10 domains from Vietnamese users' comments on social media.\n\nThe dataset is divided into three parts as below:\n1. Train set: 7,000 comments\n2. Valid set: 2,000 comments\n3. Test set: 1,000 comments",
"## Contact\nPlease feel free to contact us by email luannt@URL if you have any further information!"
]
| [
"TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #language-Vietnamese #arxiv-2103.10069 #region-us \n",
"# Constructive and Toxic Speech Detection for Open-domain Social Media Comments in Vietnamese\nThis is the official repository for the UIT-ViCTSD dataset from the paper Constructive and Toxic Speech Detection for Open-domain Social Media Comments in Vietnamese, which was accepted at the IEA/AIE 2021.\n\n\nThe provided dataset is only used for research purposes!",
"## Abstract\nThe rise of social media has led to the increasing of comments on online forums. However, there still exists invalid comments which are not informative for users. Moreover, those comments are also quite toxic and harmful to people. In this paper, we create a dataset for constructive and toxic speech detection, named UIT-ViCTSD (Vietnamese Constructive and Toxic Speech Detection dataset) with 10,000 human-annotated comments. For these tasks, we propose a system for constructive and toxic speech detection with the state-of-the-art transfer learning model in Vietnamese NLP as PhoBERT. With this system, we obtain F1-scores of 78.59% and 59.40% for classifying constructive and toxic comments, respectively. Besides, we implement various baseline models as traditional Machine Learning and Deep Neural Network-Based models to evaluate the dataset. With the results, we can solve several tasks on the online discussions and develop the framework for identifying constructiveness and toxicity of Vietnamese social media comments automatically.",
"## Dataset\nThe ViCTSD dataset is consist of 10,000 human-annotated comments on 10 domains from Vietnamese users' comments on social media.\n\nThe dataset is divided into three parts as below:\n1. Train set: 7,000 comments\n2. Valid set: 2,000 comments\n3. Test set: 1,000 comments",
"## Contact\nPlease feel free to contact us by email luannt@URL if you have any further information!"
]
| [
44,
89,
243,
65,
22
]
| [
"passage: TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #language-Vietnamese #arxiv-2103.10069 #region-us \n# Constructive and Toxic Speech Detection for Open-domain Social Media Comments in Vietnamese\nThis is the official repository for the UIT-ViCTSD dataset from the paper Constructive and Toxic Speech Detection for Open-domain Social Media Comments in Vietnamese, which was accepted at the IEA/AIE 2021.\n\n\nThe provided dataset is only used for research purposes!## Abstract\nThe rise of social media has led to the increasing of comments on online forums. However, there still exists invalid comments which are not informative for users. Moreover, those comments are also quite toxic and harmful to people. In this paper, we create a dataset for constructive and toxic speech detection, named UIT-ViCTSD (Vietnamese Constructive and Toxic Speech Detection dataset) with 10,000 human-annotated comments. For these tasks, we propose a system for constructive and toxic speech detection with the state-of-the-art transfer learning model in Vietnamese NLP as PhoBERT. With this system, we obtain F1-scores of 78.59% and 59.40% for classifying constructive and toxic comments, respectively. Besides, we implement various baseline models as traditional Machine Learning and Deep Neural Network-Based models to evaluate the dataset. With the results, we can solve several tasks on the online discussions and develop the framework for identifying constructiveness and toxicity of Vietnamese social media comments automatically.## Dataset\nThe ViCTSD dataset is consist of 10,000 human-annotated comments on 10 domains from Vietnamese users' comments on social media.\n\nThe dataset is divided into three parts as below:\n1. Train set: 7,000 comments\n2. Valid set: 2,000 comments\n3. Test set: 1,000 comments## Contact\nPlease feel free to contact us by email luannt@URL if you have any further information!"
]
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|
35df84191d2b3b7cbc43d5d2d864d785e5754e00 | # Dataset Card for "tehranen"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | k0ntra/tehranen | [
"region:us"
]
| 2023-03-12T14:17:34+00:00 | {"dataset_info": {"features": [{"name": "0", "dtype": "float32"}, {"name": "1", "dtype": "float32"}, {"name": "2", "dtype": "float32"}, {"name": "3", "dtype": "float32"}, {"name": "4", "dtype": "float32"}, {"name": "5", "dtype": "float32"}, {"name": "6", "dtype": "float32"}, {"name": "7", "dtype": "float32"}, {"name": "8", "dtype": "float32"}, {"name": "9", "dtype": "float32"}, {"name": "10", "dtype": "float32"}, {"name": "11", "dtype": "float32"}, {"name": "12", "dtype": "float32"}, {"name": "13", "dtype": "float32"}, {"name": "14", "dtype": "float32"}, {"name": "15", "dtype": "float32"}, {"name": "16", "dtype": "float32"}, {"name": "17", "dtype": "float32"}, {"name": "18", "dtype": "float32"}, {"name": "19", "dtype": "float32"}, {"name": "20", "dtype": "float32"}, {"name": "21", "dtype": "float32"}, {"name": "22", "dtype": "float32"}, {"name": "23", "dtype": "float32"}, {"name": "24", "dtype": "float32"}, {"name": "25", "dtype": "float32"}, {"name": 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| []
| TAGS
#region-us
| # Dataset Card for "tehranen"
More Information needed | [
"# Dataset Card for \"tehranen\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"tehranen\"\n\nMore Information needed"
]
| [
6,
13
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"tehranen\"\n\nMore Information needed"
]
| [
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|
691330f8f8f74146fe41106ac58a037594419fe7 |
# 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] | etahamad/pear | [
"region:us"
]
| 2023-03-12T14:18:22+00:00 | {} | 2023-03-12T15:14:26+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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|
ef3a8a55ec4e9801996fded8e8a96a2174f8a71a | # Dataset Card for "tehranen2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | k0ntra/tehranen2 | [
"region:us"
]
| 2023-03-12T14:23:15+00:00 | {"dataset_info": {"features": [{"name": "0", "dtype": "float32"}, {"name": "1", "dtype": "float32"}, {"name": "2", "dtype": "float32"}, {"name": "3", "dtype": "float32"}, {"name": "4", "dtype": "float32"}, {"name": "5", "dtype": "float32"}, {"name": "6", "dtype": "float32"}, {"name": "7", "dtype": "float32"}, {"name": "8", "dtype": "float32"}, {"name": "9", "dtype": "float32"}, {"name": "10", "dtype": "float32"}, {"name": "11", "dtype": "float32"}, {"name": "12", "dtype": "float32"}, {"name": "13", "dtype": "float32"}, {"name": "14", "dtype": "float32"}, {"name": "15", "dtype": "float32"}, {"name": "16", "dtype": "float32"}, {"name": "17", "dtype": "float32"}, {"name": "18", "dtype": "float32"}, {"name": "19", "dtype": "float32"}, {"name": "20", "dtype": "float32"}, {"name": "21", "dtype": "float32"}, {"name": "22", "dtype": "float32"}, {"name": "23", "dtype": "float32"}, {"name": "24", "dtype": "float32"}, {"name": "25", "dtype": "float32"}, {"name": 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| []
| TAGS
#region-us
| # Dataset Card for "tehranen2"
More Information needed | [
"# Dataset Card for \"tehranen2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"tehranen2\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"tehranen2\"\n\nMore Information needed"
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|
245440e0cf184af81ebe6a14ecfe6208cf9f004b | # Dataset Card for "tehranen3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | k0ntra/tehranen3 | [
"region:us"
]
| 2023-03-12T14:27:17+00:00 | {"dataset_info": {"features": [{"name": "0", "dtype": "float32"}, {"name": "1", "dtype": "float32"}, {"name": "2", "dtype": "float32"}, {"name": "3", "dtype": "float32"}, {"name": "4", "dtype": "float32"}, {"name": "5", "dtype": "float32"}, {"name": "6", "dtype": "float32"}, {"name": "7", "dtype": "float32"}, {"name": "8", "dtype": "float32"}, {"name": "9", "dtype": "float32"}, {"name": "10", "dtype": "float32"}, {"name": "11", "dtype": "float32"}, {"name": "12", "dtype": "float32"}, {"name": "13", "dtype": "float32"}, {"name": "14", "dtype": "float32"}, {"name": "15", "dtype": "float32"}, {"name": "16", "dtype": "float32"}, {"name": "17", "dtype": "float32"}, {"name": "18", "dtype": "float32"}, {"name": "19", "dtype": "float32"}, {"name": "20", "dtype": "float32"}, {"name": "21", "dtype": "float32"}, {"name": "22", "dtype": "float32"}, {"name": "23", "dtype": "float32"}, {"name": "24", "dtype": "float32"}, {"name": "25", "dtype": "float32"}, {"name": 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| []
| TAGS
#region-us
| # Dataset Card for "tehranen3"
More Information needed | [
"# Dataset Card for \"tehranen3\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"tehranen3\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"tehranen3\"\n\nMore Information needed"
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|
1d4a8be2c4f8c698755e8562e93c2d4320bb2458 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: lewtun/autotrain-acronym-identification-7324788
* Dataset: acronym_identification
* Config: default
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@wandb.init(project=PROJECT](https://huggingface.co/wandb.init(project=PROJECT) for evaluating this model. | autoevaluate/autoeval-eval-acronym_identification-default-641c5d-40516105295 | [
"autotrain",
"evaluation",
"region:us"
]
| 2023-03-12T14:41:27+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["acronym_identification"], "eval_info": {"task": "entity_extraction", "model": "lewtun/autotrain-acronym-identification-7324788", "metrics": ["angelina-wang/directional_bias_amplification"], "dataset_name": "acronym_identification", "dataset_config": "default", "dataset_split": "validation", "col_mapping": {"tokens": "tokens", "tags": "labels"}}} | 2023-03-12T14:42:08+00:00 | []
| []
| TAGS
#autotrain #evaluation #region-us
| # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by AutoTrain for the following task and dataset:
* Task: Token Classification
* Model: lewtun/autotrain-acronym-identification-7324788
* Dataset: acronym_identification
* Config: default
* Split: validation
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @URL(project=PROJECT for evaluating this model. | [
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: lewtun/autotrain-acronym-identification-7324788\n* Dataset: acronym_identification\n* Config: default\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @URL(project=PROJECT for evaluating this model."
]
| [
"TAGS\n#autotrain #evaluation #region-us \n",
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: lewtun/autotrain-acronym-identification-7324788\n* Dataset: acronym_identification\n* Config: default\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @URL(project=PROJECT for evaluating this model."
]
| [
13,
97,
20
]
| [
"passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Token Classification\n* Model: lewtun/autotrain-acronym-identification-7324788\n* Dataset: acronym_identification\n* Config: default\n* Split: validation\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @URL(project=PROJECT for evaluating this model."
]
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|
e5268f56961967421ad78e7e0695fa07b30ffa99 | # Dataset Card for "processed_bio_bert_tiny_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yashpatil/processed_bio_bert_tiny_dataset | [
"region:us"
]
| 2023-03-12T14:55:45+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": 21940534800.0, "num_examples": 6094593}], "download_size": 6445245849, "dataset_size": 21940534800.0}} | 2023-03-12T15:08:18+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "processed_bio_bert_tiny_dataset"
More Information needed | [
"# Dataset Card for \"processed_bio_bert_tiny_dataset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"processed_bio_bert_tiny_dataset\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"processed_bio_bert_tiny_dataset\"\n\nMore Information needed"
]
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|
88dcab56f33deb416344623b0628eca4d7d99095 | - This Dataset has been downloaded from PubMed
- It has abstracts and titles that are related to Psychedelics
- the data has been cleaned before uploading
- it could be used for any NLP task, such as Domain Adaptation | Gaborandi/Psychedelics_pubmed_abstracts | [
"region:us"
]
| 2023-03-12T15:02:47+00:00 | {} | 2023-03-12T15:05:31+00:00 | []
| []
| TAGS
#region-us
| - This Dataset has been downloaded from PubMed
- It has abstracts and titles that are related to Psychedelics
- the data has been cleaned before uploading
- it could be used for any NLP task, such as Domain Adaptation | []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
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|
ff5496f05d0c26b418f761b66f2414d62b945a4f | # Dataset Card for "face_synthetics_smol"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | pcuenq/face_synthetics_smol | [
"region:us"
]
| 2023-03-12T15:22:11+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "image_seg", "dtype": "image"}, {"name": "landmarks", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 35303202.0, "num_examples": 100}], "download_size": 35283640, "dataset_size": 35303202.0}} | 2023-03-12T15:24:35+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "face_synthetics_smol"
More Information needed | [
"# Dataset Card for \"face_synthetics_smol\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"face_synthetics_smol\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"face_synthetics_smol\"\n\nMore Information needed"
]
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|
15b9d3d33a38cfce358ff9831fa7d78cacfc5e3f | # Dataset Card for "product-test-01"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tuperte69/product-test-01 | [
"region:us"
]
| 2023-03-12T15:33:12+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 44144249.0, "num_examples": 29}], "download_size": 41857792, "dataset_size": 44144249.0}} | 2023-03-12T15:35:08+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "product-test-01"
More Information needed | [
"# Dataset Card for \"product-test-01\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"product-test-01\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"product-test-01\"\n\nMore Information needed"
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|
81c348b93ba0859468bfbaa0c56ad11b2e2da334 | # Dataset Card for "shirazfa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | k0ntra/shirazfa | [
"region:us"
]
| 2023-03-12T15:53:06+00:00 | {"dataset_info": {"features": [{"name": "0", "dtype": "float32"}, {"name": "1", "dtype": "float32"}, {"name": "2", "dtype": "float32"}, {"name": "3", "dtype": "float32"}, {"name": "4", "dtype": "float32"}, {"name": "5", "dtype": "float32"}, {"name": "6", "dtype": "float32"}, {"name": "7", "dtype": "float32"}, {"name": "8", "dtype": "float32"}, {"name": "9", "dtype": "float32"}, {"name": "10", "dtype": "float32"}, {"name": "11", "dtype": "float32"}, {"name": "12", "dtype": "float32"}, {"name": "13", "dtype": "float32"}, {"name": "14", "dtype": "float32"}, {"name": "15", "dtype": "float32"}, {"name": "16", "dtype": "float32"}, {"name": "17", "dtype": "float32"}, {"name": "18", "dtype": "float32"}, {"name": "19", "dtype": "float32"}, {"name": "20", "dtype": "float32"}, {"name": "21", "dtype": "float32"}, {"name": "22", "dtype": "float32"}, {"name": "23", "dtype": "float32"}, {"name": "24", "dtype": "float32"}, {"name": "25", "dtype": "float32"}, {"name": 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| []
| TAGS
#region-us
| # Dataset Card for "shirazfa"
More Information needed | [
"# Dataset Card for \"shirazfa\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"shirazfa\"\n\nMore Information needed"
]
| [
6,
13
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"shirazfa\"\n\nMore Information needed"
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|
59b47f7cbf463ac77d85870f1a7d4df66e526986 | # Dataset Card for "adv-int"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | violetamaral/adv-int | [
"region:us"
]
| 2023-03-12T16:30:25+00:00 | {"dataset_info": {"features": [{"name": "ADV", "dtype": "string"}, {"name": "INT", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 514838.63927576604, "num_examples": 1723}, {"name": "test", "num_bytes": 128784.36072423398, "num_examples": 431}], "download_size": 456341, "dataset_size": 643623.0}} | 2023-03-12T16:30:40+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "adv-int"
More Information needed | [
"# Dataset Card for \"adv-int\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"adv-int\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"adv-int\"\n\nMore Information needed"
]
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|
154ec7a4980782f8553a36c35cbc679fdfe4d67b | # Dataset Card for "shirazfa2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | k0ntra/shirazfa2 | [
"region:us"
]
| 2023-03-12T16:32:41+00:00 | {"dataset_info": {"features": [{"name": "0", "dtype": "float32"}, {"name": "1", "dtype": "float32"}, {"name": "2", "dtype": "float32"}, {"name": "3", "dtype": "float32"}, {"name": "4", "dtype": "float32"}, {"name": "5", "dtype": "float32"}, {"name": "6", "dtype": "float32"}, {"name": "7", "dtype": "float32"}, {"name": "8", "dtype": "float32"}, {"name": "9", "dtype": "float32"}, {"name": "10", "dtype": "float32"}, {"name": "11", "dtype": "float32"}, {"name": "12", "dtype": "float32"}, {"name": "13", "dtype": "float32"}, {"name": "14", "dtype": "float32"}, {"name": "15", "dtype": "float32"}, {"name": "16", "dtype": "float32"}, {"name": "17", "dtype": "float32"}, {"name": "18", "dtype": "float32"}, {"name": "19", "dtype": "float32"}, {"name": "20", "dtype": "float32"}, {"name": "21", "dtype": "float32"}, {"name": "22", "dtype": "float32"}, {"name": "23", "dtype": "float32"}, {"name": "24", "dtype": "float32"}, {"name": "25", "dtype": "float32"}, {"name": 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| []
| TAGS
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| # Dataset Card for "shirazfa2"
More Information needed | [
"# Dataset Card for \"shirazfa2\"\n\nMore Information needed"
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| [
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"# Dataset Card for \"shirazfa2\"\n\nMore Information needed"
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|
1bcd3173d8410c5dc5eb1fcf31378f8fe3a4bedd | # Dataset Card for "shirazfa3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | k0ntra/shirazfa3 | [
"region:us"
]
| 2023-03-12T17:00:32+00:00 | {"dataset_info": {"features": [{"name": "0", "dtype": "float32"}, {"name": "1", "dtype": "float32"}, {"name": "2", "dtype": "float32"}, {"name": "3", "dtype": "float32"}, {"name": "4", "dtype": "float32"}, {"name": "5", "dtype": "float32"}, {"name": "6", "dtype": "float32"}, {"name": "7", "dtype": "float32"}, {"name": "8", "dtype": "float32"}, {"name": "9", "dtype": "float32"}, {"name": "10", "dtype": "float32"}, {"name": "11", "dtype": "float32"}, {"name": "12", "dtype": "float32"}, {"name": "13", "dtype": "float32"}, {"name": "14", "dtype": "float32"}, {"name": "15", "dtype": "float32"}, {"name": "16", "dtype": "float32"}, {"name": "17", "dtype": "float32"}, {"name": "18", "dtype": "float32"}, {"name": "19", "dtype": "float32"}, {"name": "20", "dtype": "float32"}, {"name": "21", "dtype": "float32"}, {"name": "22", "dtype": "float32"}, {"name": "23", "dtype": "float32"}, {"name": "24", "dtype": "float32"}, {"name": "25", "dtype": "float32"}, {"name": 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| []
| TAGS
#region-us
| # Dataset Card for "shirazfa3"
More Information needed | [
"# Dataset Card for \"shirazfa3\"\n\nMore Information needed"
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| [
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"# Dataset Card for \"shirazfa3\"\n\nMore Information needed"
]
| [
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14
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|
acb91c16f730ac6fecbc18bac705cb73d3efedab | <p align="center"><h1>🧠 Awesome Chaττensor Prompts [CSV dataset]</h1></p>
This is a Dataset Repository of **Awesome Chattensor Prompts**
**[View All Prompts on GitHub](https://github.com/neuralinternet/awesome-chattensor-prompts)**
# License
CC-0 | NeuralInternet/awesome-chattensor-prompts | [
"license:cc0-1.0",
"ChatGPT",
"Chattensor",
"region:us"
]
| 2023-03-12T17:16:33+00:00 | {"license": "cc0-1.0", "tags": ["ChatGPT", "Chattensor"]} | 2023-03-12T20:06:01+00:00 | []
| []
| TAGS
#license-cc0-1.0 #ChatGPT #Chattensor #region-us
| <p align="center"><h1> Awesome Chaττensor Prompts [CSV dataset]</h1></p>
This is a Dataset Repository of Awesome Chattensor Prompts
View All Prompts on GitHub
# License
CC-0 | [
"# License\n\nCC-0"
]
| [
"TAGS\n#license-cc0-1.0 #ChatGPT #Chattensor #region-us \n",
"# License\n\nCC-0"
]
| [
22,
4
]
| [
"passage: TAGS\n#license-cc0-1.0 #ChatGPT #Chattensor #region-us \n# License\n\nCC-0"
]
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|
324fe81bfb5230a9f10ac5b549b4f52541c3b448 | tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = RobertaForQuestionAnswering.from_pretrained('roberta-base')
import torch
from transformers import RobertaTokenizer, RobertaForQuestionAnswering
from sklearn.model_selection import train_test_split
import pandas as pd
# Load the pre-trained RoBERTa tokenizer and model
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = RobertaForQuestionAnswering.from_pretrained('roberta-base')
# Define the preprocessed data as a list of tuples, where the first element is the question and the second element is the answer
data = [("What types of products do you sell?", "We offer a wide range of cannabis products, including dried flowers, pre-rolls, oils, capsules, and accessories."),
("How do I place an order?", "You can place an order on our website or by calling our customer service line."),
("How long will it take to receive my order?", "Orders are typically processed and shipped within 1-2 business days. Shipping times vary depending on your location and the shipping method you choose."),
("What payment methods do you accept?", "We accept Visa, Mastercard, and American Express credit cards, as well as Visa Debit and Mastercard Debit cards. You can also pay with an electronic funds transfer (EFT) from your bank account."),
("Do you offer free shipping?", "We offer free shipping on orders over $99 (before taxes and shipping fees) to most locations in Canada."),
("Can I track my order?", "Yes, you will receive a confirmation email with a tracking number once your order has shipped. You can use the tracking number to check the status of your order on the carrier's website."),
("What are your store hours?", "Our stores are open Monday to Sunday from 10:00am to 9:00pm."),
("Where are your store locations?", "We have several retail locations across New Brunswick. You can find a list of our stores and their addresses on our website."),
("What is your return policy?", "We do not accept returns on cannabis products due to health and safety regulations. However, if you receive a damaged or defective product, please contact our customer service team for assistance."),
("What are the legal regulations for buying and using cannabis in New Brunswick?", "In New Brunswick, you must be 19 years or older to purchase and use cannabis. It is illegal to drive under the influence of cannabis, and you can face fines and penalties for doing so. You can possess up to 30 grams of dried cannabis or the equivalent in public, and up to 150 grams in your home. It is also illegal to buy or sell cannabis from anyone other than an authorized retailer. For more information, please visit the Government of New Brunswick's website.")]
# Convert the data into a pandas dataframe
df = pd.DataFrame(data, columns=["Question", "Answer"])
# Split the data into training, validation, and testing datasets
train_data, test_data = train_test_split(df, test_size=0.2, random_state=42)
train_data, val_data = train_test_split(train_data, test_size=0.2, random_state=42)
# Tokenize the input sequences and convert them to tensors
train_input_ids = tokenizer.batch_encode_plus(train_data.Question.tolist(), padding=True, truncation=True, max_length=512, return_tensors="pt")
val_input_ids = tokenizer.batch_encode_plus(val_data.Question.tolist(), padding=True, truncation=True, max_length=512, return_tensors="pt")
test_input_ids = tokenizer.batch_encode_plus(test_data.Question.tolist
| KVN-AI/CannaNBot | [
"region:us"
]
| 2023-03-12T17:25:11+00:00 | {} | 2023-03-12T18:53:32+00:00 | []
| []
| TAGS
#region-us
| tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = RobertaForQuestionAnswering.from_pretrained('roberta-base')
import torch
from transformers import RobertaTokenizer, RobertaForQuestionAnswering
from sklearn.model_selection import train_test_split
import pandas as pd
# Load the pre-trained RoBERTa tokenizer and model
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = RobertaForQuestionAnswering.from_pretrained('roberta-base')
# Define the preprocessed data as a list of tuples, where the first element is the question and the second element is the answer
data = [("What types of products do you sell?", "We offer a wide range of cannabis products, including dried flowers, pre-rolls, oils, capsules, and accessories."),
("How do I place an order?", "You can place an order on our website or by calling our customer service line."),
("How long will it take to receive my order?", "Orders are typically processed and shipped within 1-2 business days. Shipping times vary depending on your location and the shipping method you choose."),
("What payment methods do you accept?", "We accept Visa, Mastercard, and American Express credit cards, as well as Visa Debit and Mastercard Debit cards. You can also pay with an electronic funds transfer (EFT) from your bank account."),
("Do you offer free shipping?", "We offer free shipping on orders over $99 (before taxes and shipping fees) to most locations in Canada."),
("Can I track my order?", "Yes, you will receive a confirmation email with a tracking number once your order has shipped. You can use the tracking number to check the status of your order on the carrier's website."),
("What are your store hours?", "Our stores are open Monday to Sunday from 10:00am to 9:00pm."),
("Where are your store locations?", "We have several retail locations across New Brunswick. You can find a list of our stores and their addresses on our website."),
("What is your return policy?", "We do not accept returns on cannabis products due to health and safety regulations. However, if you receive a damaged or defective product, please contact our customer service team for assistance."),
("What are the legal regulations for buying and using cannabis in New Brunswick?", "In New Brunswick, you must be 19 years or older to purchase and use cannabis. It is illegal to drive under the influence of cannabis, and you can face fines and penalties for doing so. You can possess up to 30 grams of dried cannabis or the equivalent in public, and up to 150 grams in your home. It is also illegal to buy or sell cannabis from anyone other than an authorized retailer. For more information, please visit the Government of New Brunswick's website.")]
# Convert the data into a pandas dataframe
df = pd.DataFrame(data, columns=["Question", "Answer"])
# Split the data into training, validation, and testing datasets
train_data, test_data = train_test_split(df, test_size=0.2, random_state=42)
train_data, val_data = train_test_split(train_data, test_size=0.2, random_state=42)
# Tokenize the input sequences and convert them to tensors
train_input_ids = tokenizer.batch_encode_plus(train_data.URL(), padding=True, truncation=True, max_length=512, return_tensors="pt")
val_input_ids = tokenizer.batch_encode_plus(val_data.URL(), padding=True, truncation=True, max_length=512, return_tensors="pt")
test_input_ids = tokenizer.batch_encode_plus(test_data.URL
| [
"# Load the pre-trained RoBERTa tokenizer and model\ntokenizer = RobertaTokenizer.from_pretrained('roberta-base')\nmodel = RobertaForQuestionAnswering.from_pretrained('roberta-base')",
"# Define the preprocessed data as a list of tuples, where the first element is the question and the second element is the answer\ndata = [(\"What types of products do you sell?\", \"We offer a wide range of cannabis products, including dried flowers, pre-rolls, oils, capsules, and accessories.\"), \n (\"How do I place an order?\", \"You can place an order on our website or by calling our customer service line.\"), \n (\"How long will it take to receive my order?\", \"Orders are typically processed and shipped within 1-2 business days. Shipping times vary depending on your location and the shipping method you choose.\"),\n (\"What payment methods do you accept?\", \"We accept Visa, Mastercard, and American Express credit cards, as well as Visa Debit and Mastercard Debit cards. You can also pay with an electronic funds transfer (EFT) from your bank account.\"),\n (\"Do you offer free shipping?\", \"We offer free shipping on orders over $99 (before taxes and shipping fees) to most locations in Canada.\"),\n (\"Can I track my order?\", \"Yes, you will receive a confirmation email with a tracking number once your order has shipped. You can use the tracking number to check the status of your order on the carrier's website.\"),\n (\"What are your store hours?\", \"Our stores are open Monday to Sunday from 10:00am to 9:00pm.\"),\n (\"Where are your store locations?\", \"We have several retail locations across New Brunswick. You can find a list of our stores and their addresses on our website.\"),\n (\"What is your return policy?\", \"We do not accept returns on cannabis products due to health and safety regulations. However, if you receive a damaged or defective product, please contact our customer service team for assistance.\"),\n (\"What are the legal regulations for buying and using cannabis in New Brunswick?\", \"In New Brunswick, you must be 19 years or older to purchase and use cannabis. It is illegal to drive under the influence of cannabis, and you can face fines and penalties for doing so. You can possess up to 30 grams of dried cannabis or the equivalent in public, and up to 150 grams in your home. It is also illegal to buy or sell cannabis from anyone other than an authorized retailer. For more information, please visit the Government of New Brunswick's website.\")]",
"# Convert the data into a pandas dataframe\ndf = pd.DataFrame(data, columns=[\"Question\", \"Answer\"])",
"# Split the data into training, validation, and testing datasets\ntrain_data, test_data = train_test_split(df, test_size=0.2, random_state=42)\ntrain_data, val_data = train_test_split(train_data, test_size=0.2, random_state=42)",
"# Tokenize the input sequences and convert them to tensors\ntrain_input_ids = tokenizer.batch_encode_plus(train_data.URL(), padding=True, truncation=True, max_length=512, return_tensors=\"pt\")\nval_input_ids = tokenizer.batch_encode_plus(val_data.URL(), padding=True, truncation=True, max_length=512, return_tensors=\"pt\")\ntest_input_ids = tokenizer.batch_encode_plus(test_data.URL"
]
| [
"TAGS\n#region-us \n",
"# Load the pre-trained RoBERTa tokenizer and model\ntokenizer = RobertaTokenizer.from_pretrained('roberta-base')\nmodel = RobertaForQuestionAnswering.from_pretrained('roberta-base')",
"# Define the preprocessed data as a list of tuples, where the first element is the question and the second element is the answer\ndata = [(\"What types of products do you sell?\", \"We offer a wide range of cannabis products, including dried flowers, pre-rolls, oils, capsules, and accessories.\"), \n (\"How do I place an order?\", \"You can place an order on our website or by calling our customer service line.\"), \n (\"How long will it take to receive my order?\", \"Orders are typically processed and shipped within 1-2 business days. Shipping times vary depending on your location and the shipping method you choose.\"),\n (\"What payment methods do you accept?\", \"We accept Visa, Mastercard, and American Express credit cards, as well as Visa Debit and Mastercard Debit cards. You can also pay with an electronic funds transfer (EFT) from your bank account.\"),\n (\"Do you offer free shipping?\", \"We offer free shipping on orders over $99 (before taxes and shipping fees) to most locations in Canada.\"),\n (\"Can I track my order?\", \"Yes, you will receive a confirmation email with a tracking number once your order has shipped. You can use the tracking number to check the status of your order on the carrier's website.\"),\n (\"What are your store hours?\", \"Our stores are open Monday to Sunday from 10:00am to 9:00pm.\"),\n (\"Where are your store locations?\", \"We have several retail locations across New Brunswick. You can find a list of our stores and their addresses on our website.\"),\n (\"What is your return policy?\", \"We do not accept returns on cannabis products due to health and safety regulations. However, if you receive a damaged or defective product, please contact our customer service team for assistance.\"),\n (\"What are the legal regulations for buying and using cannabis in New Brunswick?\", \"In New Brunswick, you must be 19 years or older to purchase and use cannabis. It is illegal to drive under the influence of cannabis, and you can face fines and penalties for doing so. You can possess up to 30 grams of dried cannabis or the equivalent in public, and up to 150 grams in your home. It is also illegal to buy or sell cannabis from anyone other than an authorized retailer. For more information, please visit the Government of New Brunswick's website.\")]",
"# Convert the data into a pandas dataframe\ndf = pd.DataFrame(data, columns=[\"Question\", \"Answer\"])",
"# Split the data into training, validation, and testing datasets\ntrain_data, test_data = train_test_split(df, test_size=0.2, random_state=42)\ntrain_data, val_data = train_test_split(train_data, test_size=0.2, random_state=42)",
"# Tokenize the input sequences and convert them to tensors\ntrain_input_ids = tokenizer.batch_encode_plus(train_data.URL(), padding=True, truncation=True, max_length=512, return_tensors=\"pt\")\nval_input_ids = tokenizer.batch_encode_plus(val_data.URL(), padding=True, truncation=True, max_length=512, return_tensors=\"pt\")\ntest_input_ids = tokenizer.batch_encode_plus(test_data.URL"
]
| [
6,
63,
529,
39,
75,
152
]
| [
"passage: TAGS\n#region-us \n# Load the pre-trained RoBERTa tokenizer and model\ntokenizer = RobertaTokenizer.from_pretrained('roberta-base')\nmodel = RobertaForQuestionAnswering.from_pretrained('roberta-base')"
]
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|
eb58b6bb4457bd7b185edcd5a4286291b3f64e68 |
# Dataset Card for Common Voice Corpus 12.0
## 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 26119 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 17127 validated hours in 104 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, Dutch, English, Erzya, Esperanto, Estonian, Finnish, French, Frisian, Galician, Georgian, German, Greek, Guarani, Hakha Chin, Hausa, Hill Mari, Hindi, Hungarian, Igbo, Indonesian, Interlingua, Irish, Italian, Japanese, Kabyle, Kazakh, Kinyarwanda, Korean, Kurmanji Kurdish, Kyrgyz, 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, 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_12 = load_dataset("mozilla-foundation/common_voice_12_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_12 = load_dataset("mozilla-foundation/common_voice_12_0", "hi", split="train", streaming=True)
print(next(iter(cv_12)))
```
*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_12 = load_dataset("mozilla-foundation/common_voice_12_0", "hi", split="train")
batch_sampler = BatchSampler(RandomSampler(cv_12), batch_size=32, drop_last=False)
dataloader = DataLoader(cv_12, batch_sampler=batch_sampler)
```
### Streaming
```python
from datasets import load_dataset
from torch.utils.data import DataLoader
cv_12 = load_dataset("mozilla-foundation/common_voice_12_0", "hi", split="train")
dataloader = DataLoader(cv_12, 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 12 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_12_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_12_0 | [
"task_categories:automatic-speech-recognition",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:10M<n<100M",
"source_datasets:extended|common_voice",
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"region:us"
]
| 2023-03-12T17:28:02+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["ab", "ar", "as", "ast", "az", "ba", "bas", "be", "bg", "bn", "br", "ca", "ckb", "cnh", "cs", "cv", "cy", "da", "de", "dv", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "gl", "gn", "ha", "hi", "hsb", "hu", "ia", "id", "ig", "it", "ja", "ka", "kab", "kk", "kmr", "ko", "ky", "lg", "lt", "lv", "mdf", "mhr", "mk", "ml", "mn", "mr", "mrj", "mt", "myv", "nl", "oc", "or", "pl", "pt", "quy", "ro", "ru", "rw", "sah", "sat", "sc", "sk", "skr", "sl", "sr", "sw", "ta", "th", "ti", "tig", "tok", "tr", "tt", "tw", "ug", "uk", "ur", "uz", "vi", "vot", "yo", "yue", "rm", "zh", "sv", "pa", "nn", "ne", "nan", "hy", "ga", "fy"], "license": ["cc0-1.0"], "multilinguality": ["multilingual"], "size_categories": ["10M<n<100M"], "source_datasets": ["extended|common_voice"], "task_categories": ["automatic-speech-recognition"], "paperswithcode_id": "common-voice", "pretty_name": "Common Voice Corpus 12.0", "language_bcp47": ["fy-NL", "ga-IE", "hy-AM", "nan-tw", "ne-NP", "nn-NO", "pa-IN", "rm-sursilv", "rm-vallader", "sv-SE", "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."} | 2023-11-17T18:09:06+00:00 | [
"1912.06670"
]
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| TAGS
#task_categories-automatic-speech-recognition #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-multilingual #size_categories-10M<n<100M #source_datasets-extended|common_voice #language-Abkhazian #language-Arabic #language-Assamese #language-Asturian #language-Azerbaijani #language-Bashkir #language-Basa (Cameroon) #language-Belarusian #language-Bulgarian #language-Bengali #language-Breton #language-Catalan #language-Central Kurdish #language-Hakha Chin #language-Czech #language-Chuvash #language-Welsh #language-Danish #language-German #language-Dhivehi #language-Modern Greek (1453-) #language-English #language-Esperanto #language-Spanish #language-Estonian #language-Basque #language-Persian #language-Finnish #language-French #language-Galician #language-Guarani #language-Hausa #language-Hindi #language-Upper Sorbian #language-Hungarian #language-Interlingua (International Auxiliary Language Association) #language-Indonesian #language-Igbo #language-Italian #language-Japanese #language-Georgian #language-Kabyle #language-Kazakh #language-Northern Kurdish #language-Korean #language-Kirghiz #language-Ganda #language-Lithuanian #language-Latvian #language-Moksha #language-Eastern Mari #language-Macedonian #language-Malayalam #language-Mongolian #language-Marathi #language-Western Mari #language-Maltese #language-Erzya #language-Dutch #language-Occitan (post 1500) #language-Oriya (macrolanguage) #language-Polish #language-Portuguese #language-Ayacucho Quechua #language-Romanian #language-Russian #language-Kinyarwanda #language-Yakut #language-Santali #language-Sardinian #language-Slovak #language-Saraiki #language-Slovenian #language-Serbian #language-Swahili (macrolanguage) #language-Tamil #language-Thai #language-Tigrinya #language-Tigre #language-Toki Pona #language-Turkish #language-Tatar #language-Twi #language-Uighur #language-Ukrainian #language-Urdu #language-Uzbek #language-Vietnamese #language-Votic #language-Yoruba #language-Yue Chinese #language-Romansh #language-Chinese #language-Swedish #language-Panjabi #language-Norwegian Nynorsk #language-Nepali (macrolanguage) #language-Min Nan Chinese #language-Armenian #language-Irish #language-Western Frisian #license-cc0-1.0 #arxiv-1912.06670 #region-us
|
# Dataset Card for Common Voice Corpus 12.0
## 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 26119 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 17127 validated hours in 104 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 12 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 12.0",
"## 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 26119 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 17127 validated hours in 104 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 12 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 #size_categories-10M<n<100M #source_datasets-extended|common_voice #language-Abkhazian #language-Arabic #language-Assamese #language-Asturian #language-Azerbaijani #language-Bashkir #language-Basa (Cameroon) #language-Belarusian #language-Bulgarian #language-Bengali #language-Breton #language-Catalan #language-Central Kurdish #language-Hakha Chin #language-Czech #language-Chuvash #language-Welsh #language-Danish #language-German #language-Dhivehi #language-Modern Greek (1453-) #language-English #language-Esperanto #language-Spanish #language-Estonian #language-Basque #language-Persian #language-Finnish #language-French #language-Galician #language-Guarani #language-Hausa #language-Hindi #language-Upper Sorbian #language-Hungarian #language-Interlingua (International Auxiliary Language Association) #language-Indonesian #language-Igbo #language-Italian #language-Japanese #language-Georgian #language-Kabyle #language-Kazakh #language-Northern Kurdish #language-Korean #language-Kirghiz #language-Ganda #language-Lithuanian #language-Latvian #language-Moksha #language-Eastern Mari #language-Macedonian #language-Malayalam #language-Mongolian #language-Marathi #language-Western Mari #language-Maltese #language-Erzya #language-Dutch #language-Occitan (post 1500) #language-Oriya (macrolanguage) #language-Polish #language-Portuguese #language-Ayacucho Quechua #language-Romanian #language-Russian #language-Kinyarwanda #language-Yakut #language-Santali #language-Sardinian #language-Slovak #language-Saraiki #language-Slovenian #language-Serbian #language-Swahili (macrolanguage) #language-Tamil #language-Thai #language-Tigrinya #language-Tigre #language-Toki Pona #language-Turkish #language-Tatar #language-Twi #language-Uighur #language-Ukrainian #language-Urdu #language-Uzbek #language-Vietnamese #language-Votic #language-Yoruba #language-Yue Chinese #language-Romansh #language-Chinese #language-Swedish #language-Panjabi #language-Norwegian Nynorsk #language-Nepali (macrolanguage) #language-Min Nan Chinese #language-Armenian #language-Irish #language-Western Frisian #license-cc0-1.0 #arxiv-1912.06670 #region-us \n",
"# Dataset Card for Common Voice Corpus 12.0",
"## 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 26119 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 17127 validated hours in 104 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 12 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: ",
"passage: TAGS\n#task_categories-automatic-speech-recognition #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-multilingual #size_categories-10M<n<100M #source_datasets-extended|common_voice #language-Abkhazian #language-Arabic #language-Assamese #language-Asturian #language-Azerbaijani #language-Bashkir #language-Basa (Cameroon) #language-Belarusian #language-Bulgarian #language-Bengali #language-Breton #language-Catalan #language-Central Kurdish #language-Hakha Chin #language-Czech #language-Chuvash #language-Welsh #language-Danish #language-German #language-Dhivehi #language-Modern Greek (1453-) #language-English #language-Esperanto #language-Spanish #language-Estonian #language-Basque #language-Persian #language-Finnish #language-French #language-Galician #language-Guarani #language-Hausa #language-Hindi #language-Upper Sorbian #language-Hungarian #language-Interlingua (International Auxiliary Language Association) #language-Indonesian #language-Igbo #language-Italian #language-Japanese #language-Georgian #language-Kabyle #language-Kazakh #language-Northern Kurdish #language-Korean #language-Kirghiz #language-Ganda #language-Lithuanian #language-Latvian #language-Moksha #language-Eastern Mari #language-Macedonian #language-Malayalam #language-Mongolian #language-Marathi #language-Western Mari #language-Maltese #language-Erzya #language-Dutch #language-Occitan (post 1500) #language-Oriya (macrolanguage) #language-Polish #language-Portuguese #language-Ayacucho Quechua #language-Romanian #language-Russian #language-Kinyarwanda #language-Yakut #language-Santali #language-Sardinian #language-Slovak #language-Saraiki #language-Slovenian #language-Serbian #language-Swahili (macrolanguage) #language-Tamil #language-Thai #language-Tigrinya #language-Tigre #language-Toki Pona #language-Turkish #language-Tatar #language-Twi #language-Uighur #language-Ukrainian #language-Urdu #language-Uzbek #language-Vietnamese #language-Votic #language-Yoruba #language-Yue Chinese #language-Romansh #language-Chinese #language-Swedish #language-Panjabi #language-Norwegian Nynorsk #language-Nepali (macrolanguage) #language-Min Nan Chinese #language-Armenian #language-Irish #language-Western Frisian #license-cc0-1.0 #arxiv-1912.06670 #region-us \n# Dataset Card for Common Voice Corpus 12.0## 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 26119 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 17127 validated hours in 104 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",
"passage: ### 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 12 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"
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|
ff31fc02ed7f478419f520b830a0799a99e912c9 |
# Dataset Card for PorSimplesSent
## Dataset Description
- **Repository:** [sidleal/porsimplessent](https://github.com/sidleal/porsimplessent)
- **Paper:** [A Nontrivial Sentence Corpus for the Task of Sentence Readability Assessment in Portuguese](https://aclanthology.org/C18-1034/)
- **Point of Contact:** [Sidney Evaldo Leal]([email protected])
### Dataset Summary
PorSimplesSent is a Portuguese corpus of aligned sentence pairs and triplets created for the purpose of investigating sentence readability
assessment in Portuguese. The dataset consists of 4,968 pairs and 1,141 triplets of sentences, combining the three levels of the PorSimples
corpus: Original, Natural, and Strong. The dataset can be used for tasks such as sentence-pair classification, sentence retrieval, and readability assessment.
### Supported Tasks and Leaderboards
The dataset supports the following tasks:
- `sentence-pair-classification`: The dataset can be used to train a model for sentence-pair classification, which consists in determining whether one sentence is simpler than the other or if both sentences are equally simple. Success on this task is typically measured by achieving a high accuracy, f1, precision, and recall.
### Languages
The dataset consists of sentence pairs in Portuguese.
## Dataset Structure
### Data Instances
```json
{
'sentence1': '-- Parece que o assassinato de civis iraquianos transformou-se em um fenômeno cotidiano e banal -- disse o presidente da Associação Iraquiana dos Direitos Humanos, Muayed al-Anbaki.',
'sentence2': '-- Parece que o assassinato de civis iraquianos transformou-se em um fenômeno comum e banal -- disse o presidente da Associação Iraquiana dos Direitos Humanos, Muayed al-Anbaki.',
'label': 2,
'production_id': 3,
'level': 'ORI->NAT',
'changed': 'S',
'split': 'N',
'sentence_text_from': '-- Parece que o assassinato de civis iraquianos transformou-se em um fenômeno cotidiano e banal -- disse o presidente da Associação Iraquiana dos Direitos Humanos, Muayed al-Anbaki.',
'sentence_text_to': '-- Parece que o assassinato de civis iraquianos transformou-se em um fenômeno comum e banal -- disse o presidente da Associação Iraquiana dos Direitos Humanos, Muayed al-Anbaki.'
}
```
### Data Fields
The dataset has the following fields:
* `sentence1`: the first sentence in the sentence pair (string).
* `sentence2`: the second sentence in the sentence pair (string).
* `label`: an integer indicating the relationship between the two sentences in the pair. The possible values are 0, 1, and 2, where 0 means that sentence1 is more simple than sentence2, 1 means that both sentences have the same level of complexity, and 2 means that sentence2 is more simple than sentence1 (int).
* `production_id`: an integer identifier for each sentence pair (int).
* `level`: a string indicating the level of simplification between the two sentences. The possible values are:
* 'ORI->NAT' (original to natural)
* 'NAT->STR' (natural to strong)
* 'ORI->STR' (original to strong) (string).
* `changed`: a string indicating whether the sentence was changed during the simplification process. The possible values are:
* 'S' (changed)
* 'N' (not changed) (string).
* `split`: a string indicating whether the sentence suffered a split in this simplification level. The possible values are:
* 'S' (split)
* 'N' (not split) (string).
* `sentence_text_from`: the raw text of the source sentence (string).
* `sentence_text_to`: the raw text of the target sentence (string).
### Data Splits
The dataset is split into three subsets: train, validation, and test. The sizes of each split are as follows:
| | Train | Validation | Test |
|--------------------|--------|------------|-------|
| Number of examples | 4,976 | 1,446 | 1,697 |
The authors did not provide standard splits. We created the splits ourselves while ensuring that sentence pairs from the same document did not appear in multiple splits.
## Additional Information
### Dataset Curators
The PorSimplesSent dataset was created by Sidney Evaldo Leal, with guidance from his advisors Dra. Sandra Maria Aluísio and Dra. Magali Sanches Duran, during his master's degree at ICMC-USP. The Interinstitutional Center for Computational Linguistics - NILC (Núcleo Interinstitucional de Linguística Computacional) also contributed to the creation of the dataset.
### Licensing Information
The PorSimplesSent dataset is released under the CC BY 4.0 license. The license terms can be found at https://creativecommons.org/licenses/by/4.0/.
### Citation Information
If you use this dataset in your work, please cite the following publication:\
```bibtex
@inproceedings{leal2018pss,
author = {Sidney Evaldo Leal and Magali Sanches Duran and Sandra Maria Aluíso},
title = {A Nontrivial Sentence Corpus for the Task of Sentence Readability Assessment in Portuguese},
booktitle = {Proceedings of the 27th International Conference on Computational Linguistics (COLING 2018)},
year = {2018},
pages = {401-413},
month = {August},
date = {20-26},
address = {Santa Fe, New Mexico, USA},
}
```
### Contributions
Thanks to [@ruanchaves](https://github.com/ruanchaves) for adding this dataset. | ruanchaves/porsimplessent | [
"size_categories:1K<n<10K",
"region:us"
]
| 2023-03-12T17:45:24+00:00 | {"size_categories": ["1K<n<10K"]} | 2023-04-12T14:57:26+00:00 | []
| []
| TAGS
#size_categories-1K<n<10K #region-us
| Dataset Card for PorSimplesSent
===============================
Dataset Description
-------------------
* Repository: sidleal/porsimplessent
* Paper: A Nontrivial Sentence Corpus for the Task of Sentence Readability Assessment in Portuguese
* Point of Contact: Sidney Evaldo Leal
### Dataset Summary
PorSimplesSent is a Portuguese corpus of aligned sentence pairs and triplets created for the purpose of investigating sentence readability
assessment in Portuguese. The dataset consists of 4,968 pairs and 1,141 triplets of sentences, combining the three levels of the PorSimples
corpus: Original, Natural, and Strong. The dataset can be used for tasks such as sentence-pair classification, sentence retrieval, and readability assessment.
### Supported Tasks and Leaderboards
The dataset supports the following tasks:
* 'sentence-pair-classification': The dataset can be used to train a model for sentence-pair classification, which consists in determining whether one sentence is simpler than the other or if both sentences are equally simple. Success on this task is typically measured by achieving a high accuracy, f1, precision, and recall.
### Languages
The dataset consists of sentence pairs in Portuguese.
Dataset Structure
-----------------
### Data Instances
### Data Fields
The dataset has the following fields:
* 'sentence1': the first sentence in the sentence pair (string).
* 'sentence2': the second sentence in the sentence pair (string).
* 'label': an integer indicating the relationship between the two sentences in the pair. The possible values are 0, 1, and 2, where 0 means that sentence1 is more simple than sentence2, 1 means that both sentences have the same level of complexity, and 2 means that sentence2 is more simple than sentence1 (int).
* 'production\_id': an integer identifier for each sentence pair (int).
* 'level': a string indicating the level of simplification between the two sentences. The possible values are:
+ 'ORI->NAT' (original to natural)
+ 'NAT->STR' (natural to strong)
+ 'ORI->STR' (original to strong) (string).
* 'changed': a string indicating whether the sentence was changed during the simplification process. The possible values are:
+ 'S' (changed)
+ 'N' (not changed) (string).
* 'split': a string indicating whether the sentence suffered a split in this simplification level. The possible values are:
+ 'S' (split)
+ 'N' (not split) (string).
* 'sentence\_text\_from': the raw text of the source sentence (string).
* 'sentence\_text\_to': the raw text of the target sentence (string).
### Data Splits
The dataset is split into three subsets: train, validation, and test. The sizes of each split are as follows:
The authors did not provide standard splits. We created the splits ourselves while ensuring that sentence pairs from the same document did not appear in multiple splits.
Additional Information
----------------------
### Dataset Curators
The PorSimplesSent dataset was created by Sidney Evaldo Leal, with guidance from his advisors Dra. Sandra Maria Aluísio and Dra. Magali Sanches Duran, during his master's degree at ICMC-USP. The Interinstitutional Center for Computational Linguistics - NILC (Núcleo Interinstitucional de Linguística Computacional) also contributed to the creation of the dataset.
### Licensing Information
The PorSimplesSent dataset is released under the CC BY 4.0 license. The license terms can be found at URL
If you use this dataset in your work, please cite the following publication:\
### Contributions
Thanks to @ruanchaves for adding this dataset.
| [
"### Dataset Summary\n\n\nPorSimplesSent is a Portuguese corpus of aligned sentence pairs and triplets created for the purpose of investigating sentence readability\nassessment in Portuguese. The dataset consists of 4,968 pairs and 1,141 triplets of sentences, combining the three levels of the PorSimples\ncorpus: Original, Natural, and Strong. The dataset can be used for tasks such as sentence-pair classification, sentence retrieval, and readability assessment.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports the following tasks:\n\n\n* 'sentence-pair-classification': The dataset can be used to train a model for sentence-pair classification, which consists in determining whether one sentence is simpler than the other or if both sentences are equally simple. Success on this task is typically measured by achieving a high accuracy, f1, precision, and recall.",
"### Languages\n\n\nThe dataset consists of sentence pairs in Portuguese.\n\n\nDataset Structure\n-----------------",
"### Data Instances",
"### Data Fields\n\n\nThe dataset has the following fields:\n\n\n* 'sentence1': the first sentence in the sentence pair (string).\n* 'sentence2': the second sentence in the sentence pair (string).\n* 'label': an integer indicating the relationship between the two sentences in the pair. The possible values are 0, 1, and 2, where 0 means that sentence1 is more simple than sentence2, 1 means that both sentences have the same level of complexity, and 2 means that sentence2 is more simple than sentence1 (int).\n* 'production\\_id': an integer identifier for each sentence pair (int).\n* 'level': a string indicating the level of simplification between the two sentences. The possible values are:\n\t+ 'ORI->NAT' (original to natural)\n\t+ 'NAT->STR' (natural to strong)\n\t+ 'ORI->STR' (original to strong) (string).\n* 'changed': a string indicating whether the sentence was changed during the simplification process. The possible values are:\n\t+ 'S' (changed)\n\t+ 'N' (not changed) (string).\n* 'split': a string indicating whether the sentence suffered a split in this simplification level. The possible values are:\n\t+ 'S' (split)\n\t+ 'N' (not split) (string).\n* 'sentence\\_text\\_from': the raw text of the source sentence (string).\n* 'sentence\\_text\\_to': the raw text of the target sentence (string).",
"### Data Splits\n\n\nThe dataset is split into three subsets: train, validation, and test. The sizes of each split are as follows:\n\n\n\nThe authors did not provide standard splits. We created the splits ourselves while ensuring that sentence pairs from the same document did not appear in multiple splits.\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\nThe PorSimplesSent dataset was created by Sidney Evaldo Leal, with guidance from his advisors Dra. Sandra Maria Aluísio and Dra. Magali Sanches Duran, during his master's degree at ICMC-USP. The Interinstitutional Center for Computational Linguistics - NILC (Núcleo Interinstitucional de Linguística Computacional) also contributed to the creation of the dataset.",
"### Licensing Information\n\n\nThe PorSimplesSent dataset is released under the CC BY 4.0 license. The license terms can be found at URL\n\n\nIf you use this dataset in your work, please cite the following publication:\\",
"### Contributions\n\n\nThanks to @ruanchaves for adding this dataset."
]
| [
"TAGS\n#size_categories-1K<n<10K #region-us \n",
"### Dataset Summary\n\n\nPorSimplesSent is a Portuguese corpus of aligned sentence pairs and triplets created for the purpose of investigating sentence readability\nassessment in Portuguese. The dataset consists of 4,968 pairs and 1,141 triplets of sentences, combining the three levels of the PorSimples\ncorpus: Original, Natural, and Strong. The dataset can be used for tasks such as sentence-pair classification, sentence retrieval, and readability assessment.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports the following tasks:\n\n\n* 'sentence-pair-classification': The dataset can be used to train a model for sentence-pair classification, which consists in determining whether one sentence is simpler than the other or if both sentences are equally simple. Success on this task is typically measured by achieving a high accuracy, f1, precision, and recall.",
"### Languages\n\n\nThe dataset consists of sentence pairs in Portuguese.\n\n\nDataset Structure\n-----------------",
"### Data Instances",
"### Data Fields\n\n\nThe dataset has the following fields:\n\n\n* 'sentence1': the first sentence in the sentence pair (string).\n* 'sentence2': the second sentence in the sentence pair (string).\n* 'label': an integer indicating the relationship between the two sentences in the pair. The possible values are 0, 1, and 2, where 0 means that sentence1 is more simple than sentence2, 1 means that both sentences have the same level of complexity, and 2 means that sentence2 is more simple than sentence1 (int).\n* 'production\\_id': an integer identifier for each sentence pair (int).\n* 'level': a string indicating the level of simplification between the two sentences. The possible values are:\n\t+ 'ORI->NAT' (original to natural)\n\t+ 'NAT->STR' (natural to strong)\n\t+ 'ORI->STR' (original to strong) (string).\n* 'changed': a string indicating whether the sentence was changed during the simplification process. The possible values are:\n\t+ 'S' (changed)\n\t+ 'N' (not changed) (string).\n* 'split': a string indicating whether the sentence suffered a split in this simplification level. The possible values are:\n\t+ 'S' (split)\n\t+ 'N' (not split) (string).\n* 'sentence\\_text\\_from': the raw text of the source sentence (string).\n* 'sentence\\_text\\_to': the raw text of the target sentence (string).",
"### Data Splits\n\n\nThe dataset is split into three subsets: train, validation, and test. The sizes of each split are as follows:\n\n\n\nThe authors did not provide standard splits. We created the splits ourselves while ensuring that sentence pairs from the same document did not appear in multiple splits.\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\nThe PorSimplesSent dataset was created by Sidney Evaldo Leal, with guidance from his advisors Dra. Sandra Maria Aluísio and Dra. Magali Sanches Duran, during his master's degree at ICMC-USP. The Interinstitutional Center for Computational Linguistics - NILC (Núcleo Interinstitucional de Linguística Computacional) also contributed to the creation of the dataset.",
"### Licensing Information\n\n\nThe PorSimplesSent dataset is released under the CC BY 4.0 license. The license terms can be found at URL\n\n\nIf you use this dataset in your work, please cite the following publication:\\",
"### Contributions\n\n\nThanks to @ruanchaves for adding this dataset."
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"passage: TAGS\n#size_categories-1K<n<10K #region-us \n### Dataset Summary\n\n\nPorSimplesSent is a Portuguese corpus of aligned sentence pairs and triplets created for the purpose of investigating sentence readability\nassessment in Portuguese. The dataset consists of 4,968 pairs and 1,141 triplets of sentences, combining the three levels of the PorSimples\ncorpus: Original, Natural, and Strong. The dataset can be used for tasks such as sentence-pair classification, sentence retrieval, and readability assessment.### Supported Tasks and Leaderboards\n\n\nThe dataset supports the following tasks:\n\n\n* 'sentence-pair-classification': The dataset can be used to train a model for sentence-pair classification, which consists in determining whether one sentence is simpler than the other or if both sentences are equally simple. Success on this task is typically measured by achieving a high accuracy, f1, precision, and recall.### Languages\n\n\nThe dataset consists of sentence pairs in Portuguese.\n\n\nDataset Structure\n-----------------### Data Instances"
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|
15ec5f1bf25d9cee1ae73f2a426882d0c0151f4e | # Dataset Card for "silverdata-iwslt-2023"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | pvisnrt/silverdata-iwslt-2023 | [
"region:us"
]
| 2023-03-12T18:22:37+00:00 | {"dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "target", "dtype": "string"}, {"name": "styles", "dtype": "int64"}], "splits": [{"name": "vi", "num_bytes": 118306.0, "num_examples": 500}, {"name": "ko", "num_bytes": 121983.0, "num_examples": 500}], "download_size": 67669, "dataset_size": 240289.0}} | 2023-03-12T21:59:47+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "silverdata-iwslt-2023"
More Information needed | [
"# Dataset Card for \"silverdata-iwslt-2023\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"silverdata-iwslt-2023\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"silverdata-iwslt-2023\"\n\nMore Information needed"
]
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|
e479f5b9b38e3dc885a9afe1c084092289615017 | # Dataset Card for "iwslt-2023-en-vi-train-split-v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | shreevigneshs/iwslt-2023-en-vi-train-split-v1 | [
"region:us"
]
| 2023-03-12T19:00:35+00:00 | {"dataset_info": {"features": [{"name": "en", "dtype": "string"}, {"name": "vi", "dtype": "string"}, {"name": "vi_annotated", "dtype": "string"}, {"name": "styles", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 293279.0, "num_examples": 640}, {"name": "val", "num_bytes": 69940.0, "num_examples": 160}, {"name": "if_test", "num_bytes": 33427.0, "num_examples": 80}, {"name": "f_test", "num_bytes": 36513.0, "num_examples": 80}], "download_size": 210801, "dataset_size": 433159.0}} | 2023-03-12T19:00:50+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "iwslt-2023-en-vi-train-split-v1"
More Information needed | [
"# Dataset Card for \"iwslt-2023-en-vi-train-split-v1\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"iwslt-2023-en-vi-train-split-v1\"\n\nMore Information needed"
]
| [
6,
28
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"iwslt-2023-en-vi-train-split-v1\"\n\nMore Information needed"
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|
101f0025379e0c3fbf1cbfa8034c3e4eac7ae249 | # Dataset Card for "construction_place"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yiming19/construction_place | [
"region:us"
]
| 2023-03-12T19:24:19+00:00 | {"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 268981588.0, "num_examples": 32}], "download_size": 16173440, "dataset_size": 268981588.0}} | 2023-03-12T20:07:20+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "construction_place"
More Information needed | [
"# Dataset Card for \"construction_place\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"construction_place\"\n\nMore Information needed"
]
| [
6,
13
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"construction_place\"\n\nMore Information needed"
]
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|
b31d91acc23295190d0862fb0d1d1f6095d756de | # Dataset Card for "hugo_dresses_ds"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yotam56/hugo_dresses_ds | [
"region:us"
]
| 2023-03-12T20:00:40+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Subfolder_1", "1": "Subfolder_10", "2": "Subfolder_11", "3": "Subfolder_12", "4": "Subfolder_13", "5": "Subfolder_14", "6": "Subfolder_15", "7": "Subfolder_16", "8": "Subfolder_17", "9": "Subfolder_18", "10": "Subfolder_19", "11": "Subfolder_2", "12": "Subfolder_20", "13": "Subfolder_21", "14": "Subfolder_22", "15": "Subfolder_23", "16": "Subfolder_24", "17": "Subfolder_3", "18": "Subfolder_4", "19": "Subfolder_5", "20": "Subfolder_6", "21": "Subfolder_7", "22": "Subfolder_8", "23": "Subfolder_9"}}}}], "splits": [{"name": "train", "num_bytes": 1193381.0, "num_examples": 120}], "download_size": 0, "dataset_size": 1193381.0}} | 2023-03-14T15:17:42+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "hugo_dresses_ds"
More Information needed | [
"# Dataset Card for \"hugo_dresses_ds\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"hugo_dresses_ds\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"hugo_dresses_ds\"\n\nMore Information needed"
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|
106cb02577e6a60ada890ec5bed85219d2398ce4 | # Dataset Card for "hugo_jackets_ds"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yotam56/hugo_jackets_ds | [
"region:us"
]
| 2023-03-12T20:23:05+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Subfolder_1", "1": "Subfolder_10", "2": "Subfolder_11", "3": "Subfolder_12", "4": "Subfolder_13", "5": "Subfolder_14", "6": "Subfolder_15", "7": "Subfolder_16", "8": "Subfolder_17", "9": "Subfolder_18", "10": "Subfolder_19", "11": "Subfolder_2", "12": "Subfolder_20", "13": "Subfolder_21", "14": "Subfolder_22", "15": "Subfolder_23", "16": "Subfolder_24", "17": "Subfolder_25", "18": "Subfolder_26", "19": "Subfolder_27", "20": "Subfolder_28", "21": "Subfolder_3", "22": "Subfolder_4", "23": "Subfolder_5", "24": "Subfolder_6", "25": "Subfolder_7", "26": "Subfolder_8", "27": "Subfolder_9"}}}}], "splits": [{"name": "train", "num_bytes": 1400017.0, "num_examples": 142}], "download_size": 0, "dataset_size": 1400017.0}} | 2023-03-14T18:44:08+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "hugo_jackets_ds"
More Information needed | [
"# Dataset Card for \"hugo_jackets_ds\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"hugo_jackets_ds\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"hugo_jackets_ds\"\n\nMore Information needed"
]
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|
604967dc59cf5590a69d4c157979e8324197185f | # multilingual_librispeech_fr_processed
## Dataset Description
### Dataset Summary
The data files can be found on the illuin gcloud instance at this adress: unknown_url
This dataset has been processed from Huggingface Hub dataset ``facebook/multilingual_librispeech`` and the config ``french``
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
#### Columns
``audio`` ``sentence`` ``path`` ``taxonomy`` ``taxonomy_large`` ``sentence_processed``
#### Sample
```
{ 'audio': { 'array': array([ 2.7465820e-04, 3.3569336e-04, 2.1362305e-04, ...,
-3.0517578e-05, -3.0517578e-05, 0.0000000e+00], dtype=float32),
'path': '/home/brunohays/.cache/huggingface/datasets/downloads/extracted/13f33c266be7d9e111d1bbccdadea34d95cb6456b778bf5e74ba71f49f3caa9a/8778_9061_000897.flac',
'sampling_rate': 16000},
'path': '8778_9061_000897',
'sentence': 'la carte fut place devant lady helena et chacun se plaa de faon suivre la dmonstration de paganel â ainsi que je vous '
"l'ai dj appris dit le gographe apr s avoir travers l'amrique du sud le trente septi me degr de latitude rencontre les les "
"tristan d'acunha",
'sentence_processed': 'la carte fut place devant lady helena et chacun se plaa de faon suivre la dmonstration de paganel â ainsi que '
"je vous l'ai dj appris dit le gographe apr s avoir travers l'amrique du sud le trente septi me degr de latitude "
"rencontre les les tristan d'acunha",
'taxonomy': 'librispeech',
'taxonomy_large': 'librispeech'}
```
### Data Splits
|split|number_of_rows|
|:---:|:---:
|train|251463|
|test|2393|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
### Annotations
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the dataset
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Property of Illuin Technology
### Contributions
This dataset has been pushed using the repo [illuin-hf-dataset-pusher](https://gitlab.illuin.tech/data-science/ml/libraries/illuin-hf-dataset-pusher)
| BrunoHays/multilingual_librispeech_fr_processed | [
"region:us"
]
| 2023-03-12T20:38:30+00:00 | {} | 2023-03-15T09:46:36+00:00 | []
| []
| TAGS
#region-us
| multilingual\_librispeech\_fr\_processed
========================================
Dataset Description
-------------------
### Dataset Summary
The data files can be found on the illuin gcloud instance at this adress: unknown\_url
This dataset has been processed from Huggingface Hub dataset ''facebook/multilingual\_librispeech'' and the config ''french''
### Supported Tasks and Leaderboards
### Languages
Dataset Structure
-----------------
### Data Instances
### Data Fields
#### Columns
''audio'' ''sentence'' ''path'' ''taxonomy'' ''taxonomy\_large'' ''sentence\_processed''
#### Sample
### Data Splits
Dataset Creation
----------------
### Curation Rationale
### Source Data
### Annotations
### Personal and Sensitive Information
Considerations for Using the dataset
------------------------------------
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
Property of Illuin Technology
### Contributions
This dataset has been pushed using the repo illuin-hf-dataset-pusher
| [
"### Dataset Summary\n\n\nThe data files can be found on the illuin gcloud instance at this adress: unknown\\_url\n\n\nThis dataset has been processed from Huggingface Hub dataset ''facebook/multilingual\\_librispeech'' and the config ''french''",
"### Supported Tasks and Leaderboards",
"### Languages\n\n\nDataset Structure\n-----------------",
"### Data Instances",
"### Data Fields",
"#### Columns\n\n\n''audio'' ''sentence'' ''path'' ''taxonomy'' ''taxonomy\\_large'' ''sentence\\_processed''",
"#### Sample",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"### Annotations",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the dataset\n------------------------------------",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nProperty of Illuin Technology",
"### Contributions\n\n\nThis dataset has been pushed using the repo illuin-hf-dataset-pusher"
]
| [
"TAGS\n#region-us \n",
"### Dataset Summary\n\n\nThe data files can be found on the illuin gcloud instance at this adress: unknown\\_url\n\n\nThis dataset has been processed from Huggingface Hub dataset ''facebook/multilingual\\_librispeech'' and the config ''french''",
"### Supported Tasks and Leaderboards",
"### Languages\n\n\nDataset Structure\n-----------------",
"### Data Instances",
"### Data Fields",
"#### Columns\n\n\n''audio'' ''sentence'' ''path'' ''taxonomy'' ''taxonomy\\_large'' ''sentence\\_processed''",
"#### Sample",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"### Annotations",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the dataset\n------------------------------------",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nProperty of Illuin Technology",
"### Contributions\n\n\nThis dataset has been pushed using the repo illuin-hf-dataset-pusher"
]
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| [
"passage: TAGS\n#region-us \n### Dataset Summary\n\n\nThe data files can be found on the illuin gcloud instance at this adress: unknown\\_url\n\n\nThis dataset has been processed from Huggingface Hub dataset ''facebook/multilingual\\_librispeech'' and the config ''french''### Supported Tasks and Leaderboards### Languages\n\n\nDataset Structure\n-----------------### Data Instances### Data Fields#### Columns\n\n\n''audio'' ''sentence'' ''path'' ''taxonomy'' ''taxonomy\\_large'' ''sentence\\_processed''#### Sample### Data Splits\n\n\n\nDataset Creation\n----------------### Curation Rationale### Source Data### Annotations### Personal and Sensitive Information\n\n\nConsiderations for Using the dataset\n------------------------------------### Discussion of Biases### Other Known Limitations\n\n\nAdditional Information\n----------------------### Dataset Curators### Licensing Information\n\n\nProperty of Illuin Technology### Contributions\n\n\nThis dataset has been pushed using the repo illuin-hf-dataset-pusher"
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|
a0d940eaeab07f73464de5bc0b032db8417967a0 | # Dataset Card for "mix-closeup-lcd"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tuperte69/mix-closeup-lcd | [
"region:us"
]
| 2023-03-12T20:40:25+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 177761269.0, "num_examples": 81}], "download_size": 177632765, "dataset_size": 177761269.0}} | 2023-03-12T21:14:39+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "mix-closeup-lcd"
More Information needed | [
"# Dataset Card for \"mix-closeup-lcd\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"mix-closeup-lcd\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"mix-closeup-lcd\"\n\nMore Information needed"
]
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|
d85158aae54fbdea8e52cf9541f60e7f1bbb3fa7 |
# RuNews dataset
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Description](#description)
- [Usage](#usage)
- [Data Instances](#data-instances)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
## Description
**Summary:** Dataset of news from several sources:
* [Lenta.ru by yutkin](https://github.com/yutkin/Lenta.Ru-News-Dataset)
* [Several sources by buriy](https://github.com/buriy/russian-nlp-datasets/releases)
* [ODS Newsviz Tass](https://github.com/newsviz/newsviz)
* [Taiga fontanka](https://tatianashavrina.github.io/taiga_site/)
* [News from Telegram contest](https://github.com/IlyaGusev/tgcontest)
**Script:** [create_ru_news.py](https://github.com/IlyaGusev/rulm/blob/master/data_processing/create_ru_news.py)
**Point of Contact:** [Ilya Gusev]([email protected])
**Languages:** Russian.
## Usage
Prerequisites:
```bash
pip install datasets zstandard jsonlines pysimdjson
```
Dataset iteration:
```python
from datasets import load_dataset
dataset = load_dataset('IlyaGusev/ru_news', split="train", streaming=True)
for example in dataset:
print(example["text"])
```
## Data Instances
```
{
"title": "Заместитель главы района в Якутии пожаловался на пьянство начальника",
"text": "Заместитель главы Нерюнгринского района Якутии Геннадий Ленц пожаловался руководителю республики Егору Борисову на своего начальника. Как рассказал Ленц 'Интерфаксу', Андрей Фитисов пьет на рабочем месте и 'уходит в многодневные загулы'...",
"timestamp": 1346284800,
"url": "https://lenta.ru/news/2012/08/30/alco/",
"source": "lenta"
}
```
## Personal and Sensitive Information
The dataset is not anonymized, so individuals' names can be found in the dataset. Information about the original authors is included in the dataset where possible. | IlyaGusev/ru_news | [
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:ru",
"region:us"
]
| 2023-03-12T20:56:14+00:00 | {"language": ["ru"], "size_categories": ["1M<n<10M"], "task_categories": ["text-generation"], "dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "timestamp", "dtype": "uint64"}], "splits": [{"name": "train", "num_bytes": 12858731888, "num_examples": 4137525}], "download_size": 3669747077, "dataset_size": 12858731888}} | 2023-03-20T23:05:08+00:00 | []
| [
"ru"
]
| TAGS
#task_categories-text-generation #size_categories-1M<n<10M #language-Russian #region-us
|
# RuNews dataset
## Table of Contents
- Table of Contents
- Description
- Usage
- Data Instances
- Personal and Sensitive Information
## Description
Summary: Dataset of news from several sources:
* URL by yutkin
* Several sources by buriy
* ODS Newsviz Tass
* Taiga fontanka
* News from Telegram contest
Script: create_ru_news.py
Point of Contact: Ilya Gusev
Languages: Russian.
## Usage
Prerequisites:
Dataset iteration:
## Data Instances
## Personal and Sensitive Information
The dataset is not anonymized, so individuals' names can be found in the dataset. Information about the original authors is included in the dataset where possible. | [
"# RuNews dataset",
"## Table of Contents\n- Table of Contents\n- Description\n- Usage\n- Data Instances\n- Personal and Sensitive Information",
"## Description\n\nSummary: Dataset of news from several sources:\n* URL by yutkin\n* Several sources by buriy\n* ODS Newsviz Tass\n* Taiga fontanka\n* News from Telegram contest\n\nScript: create_ru_news.py\n\nPoint of Contact: Ilya Gusev\n\nLanguages: Russian.",
"## Usage\n\nPrerequisites:\n\n\nDataset iteration:",
"## Data Instances",
"## Personal and Sensitive Information\n\nThe dataset is not anonymized, so individuals' names can be found in the dataset. Information about the original authors is included in the dataset where possible."
]
| [
"TAGS\n#task_categories-text-generation #size_categories-1M<n<10M #language-Russian #region-us \n",
"# RuNews dataset",
"## Table of Contents\n- Table of Contents\n- Description\n- Usage\n- Data Instances\n- Personal and Sensitive Information",
"## Description\n\nSummary: Dataset of news from several sources:\n* URL by yutkin\n* Several sources by buriy\n* ODS Newsviz Tass\n* Taiga fontanka\n* News from Telegram contest\n\nScript: create_ru_news.py\n\nPoint of Contact: Ilya Gusev\n\nLanguages: Russian.",
"## Usage\n\nPrerequisites:\n\n\nDataset iteration:",
"## Data Instances",
"## Personal and Sensitive Information\n\nThe dataset is not anonymized, so individuals' names can be found in the dataset. Information about the original authors is included in the dataset where possible."
]
| [
34,
5,
27,
65,
14,
5,
42
]
| [
"passage: TAGS\n#task_categories-text-generation #size_categories-1M<n<10M #language-Russian #region-us \n# RuNews dataset## Table of Contents\n- Table of Contents\n- Description\n- Usage\n- Data Instances\n- Personal and Sensitive Information## Description\n\nSummary: Dataset of news from several sources:\n* URL by yutkin\n* Several sources by buriy\n* ODS Newsviz Tass\n* Taiga fontanka\n* News from Telegram contest\n\nScript: create_ru_news.py\n\nPoint of Contact: Ilya Gusev\n\nLanguages: Russian.## Usage\n\nPrerequisites:\n\n\nDataset iteration:## Data Instances## Personal and Sensitive Information\n\nThe dataset is not anonymized, so individuals' names can be found in the dataset. Information about the original authors is included in the dataset where possible."
]
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|
eaf6afd256e582230a458848340cbdf12cab96c1 | # Dataset Card for "sentiment_analysis_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Sadiksha/sentiment_analysis_data | [
"region:us"
]
| 2023-03-12T21:04:56+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1741533, "num_examples": 16000}, {"name": "test", "num_bytes": 217173, "num_examples": 2000}, {"name": "valid", "num_bytes": 214695, "num_examples": 2000}], "download_size": 1286836, "dataset_size": 2173401}} | 2023-03-12T21:05:08+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "sentiment_analysis_data"
More Information needed | [
"# Dataset Card for \"sentiment_analysis_data\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"sentiment_analysis_data\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"sentiment_analysis_data\"\n\nMore Information needed"
]
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|
73fde21d57c8d447564e651cd3dacfea3a719a79 | # Dataset Card for `face_synthetics`
This is a copy of [Microsoft FaceSynthetics dataset](https://github.com/microsoft/FaceSynthetics), uploaded to Hugging Face Datasets for convenience.
Please, refer to the original [license](LICENSE.txt), which we replicate in this repo.
The dataset was uploaded using the following code, which assumes the original `zip` file was uncompressed to `/data/microsoft_face_synthetics`:
```Python
from datasets import Dataset
from pathlib import Path
from PIL import Image
face_synthetics = Path("/data/microsoft_face_synthetics")
def entry_for_id(entry_id):
if type(entry_id) == int:
entry_id = f"{entry_id:06}"
image = Image.open(face_synthetics/f"{entry_id}.png")
image_seg = Image.open(face_synthetics/f"{entry_id}_seg.png")
with open(face_synthetics/f"{entry_id}_ldmks.txt") as f:
landmarks = f.read()
return {
"image": image,
"image_seg": image_seg,
"landmarks": landmarks,
}
def generate_entries():
for x in range(100000):
yield entry_for_id(x)
ds = Dataset.from_generator(generate_entries)
ds.push_to_hub('pcuenq/face_synthetics')
```
Note that `image_seg`, the segmented images, appear to be black because each pixel contains a number between `0` to `18` corresponging to the different categories, see the [original README]() for details. We haven't created visualization code yet.
| pcuenq/face_synthetics | [
"region:us"
]
| 2023-03-12T21:37:41+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "image_seg", "dtype": "image"}, {"name": "landmarks", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 33730885609.0, "num_examples": 100000}], "download_size": 34096881533, "dataset_size": 33730885609.0}} | 2023-03-13T09:37:52+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for 'face_synthetics'
This is a copy of Microsoft FaceSynthetics dataset, uploaded to Hugging Face Datasets for convenience.
Please, refer to the original license, which we replicate in this repo.
The dataset was uploaded using the following code, which assumes the original 'zip' file was uncompressed to '/data/microsoft_face_synthetics':
Note that 'image_seg', the segmented images, appear to be black because each pixel contains a number between '0' to '18' corresponging to the different categories, see the [original README]() for details. We haven't created visualization code yet.
| [
"# Dataset Card for 'face_synthetics'\n\nThis is a copy of Microsoft FaceSynthetics dataset, uploaded to Hugging Face Datasets for convenience.\n\nPlease, refer to the original license, which we replicate in this repo.\n\nThe dataset was uploaded using the following code, which assumes the original 'zip' file was uncompressed to '/data/microsoft_face_synthetics':\n\n\n\nNote that 'image_seg', the segmented images, appear to be black because each pixel contains a number between '0' to '18' corresponging to the different categories, see the [original README]() for details. We haven't created visualization code yet."
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for 'face_synthetics'\n\nThis is a copy of Microsoft FaceSynthetics dataset, uploaded to Hugging Face Datasets for convenience.\n\nPlease, refer to the original license, which we replicate in this repo.\n\nThe dataset was uploaded using the following code, which assumes the original 'zip' file was uncompressed to '/data/microsoft_face_synthetics':\n\n\n\nNote that 'image_seg', the segmented images, appear to be black because each pixel contains a number between '0' to '18' corresponging to the different categories, see the [original README]() for details. We haven't created visualization code yet."
]
| [
6,
156
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for 'face_synthetics'\n\nThis is a copy of Microsoft FaceSynthetics dataset, uploaded to Hugging Face Datasets for convenience.\n\nPlease, refer to the original license, which we replicate in this repo.\n\nThe dataset was uploaded using the following code, which assumes the original 'zip' file was uncompressed to '/data/microsoft_face_synthetics':\n\n\n\nNote that 'image_seg', the segmented images, appear to be black because each pixel contains a number between '0' to '18' corresponging to the different categories, see the [original README]() for details. We haven't created visualization code yet."
]
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|
39228b8c615fc13acc6cbec5b4a2b92baf38bbb5 | # Dataset Card for "apps_partial_300_310"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | minimario/apps_partial_300_310 | [
"region:us"
]
| 2023-03-13T00:25:50+00:00 | {"dataset_info": {"features": [{"name": "problem", "dtype": "string"}, {"name": "code", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "full_sample", "dtype": "string"}, {"name": "where_from", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 17369435, "num_examples": 15529}], "download_size": 438347, "dataset_size": 17369435}} | 2023-03-13T01:01:17+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "apps_partial_300_310"
More Information needed | [
"# Dataset Card for \"apps_partial_300_310\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"apps_partial_300_310\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"apps_partial_300_310\"\n\nMore Information needed"
]
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|
d9912bd0e635ba142574849b66ef4a2e4882ab6c |
# MagicPrompt_SD_Washed
It's a version of datasets of [Gustavosta/MagicPrompt-Stable-Diffusion](https://huggingface.co/Gustavosta/MagicPrompt-Stable-Diffusion).
When I want to train a model using origin data, some bad prompts broke model and waste many time.
So I washed the origin datasets:
1. 😄 delete some meanless words like some artists name with misspelling
2. 😂 delete many spaces that make `100mm` to `10 0m`
3. 😭 some url in datasets
4. 😭 and many unknown words
And this version is doing well in my train test!😍
| KonghaYao/MagicPrompt_SD_Washed | [
"task_categories:text-generation",
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:en",
"license:cc0-1.0",
"code",
"art",
"region:us"
]
| 2023-03-13T00:30:02+00:00 | {"language": ["en"], "license": "cc0-1.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation", "text-classification"], "pretty_name": "mg_sd_washed", "tags": ["code", "art"]} | 2023-03-13T00:40:42+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-generation #task_categories-text-classification #size_categories-10K<n<100K #language-English #license-cc0-1.0 #code #art #region-us
|
# MagicPrompt_SD_Washed
It's a version of datasets of Gustavosta/MagicPrompt-Stable-Diffusion.
When I want to train a model using origin data, some bad prompts broke model and waste many time.
So I washed the origin datasets:
1. delete some meanless words like some artists name with misspelling
2. delete many spaces that make '100mm' to '10 0m'
3. some url in datasets
4. and many unknown words
And this version is doing well in my train test!
| [
"# MagicPrompt_SD_Washed\n\nIt's a version of datasets of Gustavosta/MagicPrompt-Stable-Diffusion.\n\nWhen I want to train a model using origin data, some bad prompts broke model and waste many time. \n\nSo I washed the origin datasets: \n\n1. delete some meanless words like some artists name with misspelling\n2. delete many spaces that make '100mm' to '10 0m'\n3. some url in datasets\n4. and many unknown words\n\n\nAnd this version is doing well in my train test!"
]
| [
"TAGS\n#task_categories-text-generation #task_categories-text-classification #size_categories-10K<n<100K #language-English #license-cc0-1.0 #code #art #region-us \n",
"# MagicPrompt_SD_Washed\n\nIt's a version of datasets of Gustavosta/MagicPrompt-Stable-Diffusion.\n\nWhen I want to train a model using origin data, some bad prompts broke model and waste many time. \n\nSo I washed the origin datasets: \n\n1. delete some meanless words like some artists name with misspelling\n2. delete many spaces that make '100mm' to '10 0m'\n3. some url in datasets\n4. and many unknown words\n\n\nAnd this version is doing well in my train test!"
]
| [
56,
128
]
| [
"passage: TAGS\n#task_categories-text-generation #task_categories-text-classification #size_categories-10K<n<100K #language-English #license-cc0-1.0 #code #art #region-us \n# MagicPrompt_SD_Washed\n\nIt's a version of datasets of Gustavosta/MagicPrompt-Stable-Diffusion.\n\nWhen I want to train a model using origin data, some bad prompts broke model and waste many time. \n\nSo I washed the origin datasets: \n\n1. delete some meanless words like some artists name with misspelling\n2. delete many spaces that make '100mm' to '10 0m'\n3. some url in datasets\n4. and many unknown words\n\n\nAnd this version is doing well in my train test!"
]
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|
56bfacf175ea00e02cea49a8fa588a32f8a4f5a5 | # Dataset Card for "apps_partial_0_300"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | minimario/apps_partial_0_300 | [
"region:us"
]
| 2023-03-13T00:31:27+00:00 | {"dataset_info": {"features": [{"name": "problem", "dtype": "string"}, {"name": "code", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "full_sample", "dtype": "string"}, {"name": "where_from", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 685566901, "num_examples": 533723}], "download_size": 0, "dataset_size": 685566901}} | 2023-03-13T00:59:45+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "apps_partial_0_300"
More Information needed | [
"# Dataset Card for \"apps_partial_0_300\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"apps_partial_0_300\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"apps_partial_0_300\"\n\nMore Information needed"
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|
cabb714a3042c981b616f8589b3bb704ee01b941 | # Clean podcast audio
Split using VAD -> detect at least 30% cleaned speech, ~352 hours, https://github.com/huseinzol05/malaya-speech/tree/master/data/podcast | malaysia-ai/clean-podcast | [
"region:us"
]
| 2023-03-13T00:59:00+00:00 | {} | 2023-03-17T08:23:11+00:00 | []
| []
| TAGS
#region-us
| # Clean podcast audio
Split using VAD -> detect at least 30% cleaned speech, ~352 hours, URL | [
"# Clean podcast audio\n\nSplit using VAD -> detect at least 30% cleaned speech, ~352 hours, URL"
]
| [
"TAGS\n#region-us \n",
"# Clean podcast audio\n\nSplit using VAD -> detect at least 30% cleaned speech, ~352 hours, URL"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Clean podcast audio\n\nSplit using VAD -> detect at least 30% cleaned speech, ~352 hours, URL"
]
| [
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|
2b0b662786e52b1205f0a5a2f12d23858459910b | # Pseudo-labeled-python-data-pii-detection-filtered
This dataset was used for the training of a PII detection NER model. We annotated it using pseudo-labelelling to enhance model performance on some rare PII entities like keys.
It consists of 18,000 files annotates using an ensemble of two encoder models Deberta-v3-large and stanford-deidentifier-base which were fine-tuned on a labeled PII dataset for code with 400 files from this work. To select good-quality pseudo-labels,
we computed the average probability logits between the models and filtered based on a minimum score. After inspection, we observed a high rate of false positives for Keys and Passwords, hence we retained only the entities that had a trigger word like key, auth and pwd in the surrounding context. | bigcode/pseudo-labeled-python-data-pii-detection-filtered | [
"region:us"
]
| 2023-03-13T01:23:25+00:00 | {"dataset_info": {"features": [{"name": "lang", "dtype": "string"}, {"name": "content", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "pii", "dtype": "string"}], "splits": [{"name": "filtered", "num_bytes": 221082330, "num_examples": 17678}], "download_size": 0, "dataset_size": 221082330}} | 2023-04-23T18:07:42+00:00 | []
| []
| TAGS
#region-us
| # Pseudo-labeled-python-data-pii-detection-filtered
This dataset was used for the training of a PII detection NER model. We annotated it using pseudo-labelelling to enhance model performance on some rare PII entities like keys.
It consists of 18,000 files annotates using an ensemble of two encoder models Deberta-v3-large and stanford-deidentifier-base which were fine-tuned on a labeled PII dataset for code with 400 files from this work. To select good-quality pseudo-labels,
we computed the average probability logits between the models and filtered based on a minimum score. After inspection, we observed a high rate of false positives for Keys and Passwords, hence we retained only the entities that had a trigger word like key, auth and pwd in the surrounding context. | [
"# Pseudo-labeled-python-data-pii-detection-filtered\n\nThis dataset was used for the training of a PII detection NER model. We annotated it using pseudo-labelelling to enhance model performance on some rare PII entities like keys.\n\nIt consists of 18,000 files annotates using an ensemble of two encoder models Deberta-v3-large and stanford-deidentifier-base which were fine-tuned on a labeled PII dataset for code with 400 files from this work. To select good-quality pseudo-labels, \nwe computed the average probability logits between the models and filtered based on a minimum score. After inspection, we observed a high rate of false positives for Keys and Passwords, hence we retained only the entities that had a trigger word like key, auth and pwd in the surrounding context."
]
| [
"TAGS\n#region-us \n",
"# Pseudo-labeled-python-data-pii-detection-filtered\n\nThis dataset was used for the training of a PII detection NER model. We annotated it using pseudo-labelelling to enhance model performance on some rare PII entities like keys.\n\nIt consists of 18,000 files annotates using an ensemble of two encoder models Deberta-v3-large and stanford-deidentifier-base which were fine-tuned on a labeled PII dataset for code with 400 files from this work. To select good-quality pseudo-labels, \nwe computed the average probability logits between the models and filtered based on a minimum score. After inspection, we observed a high rate of false positives for Keys and Passwords, hence we retained only the entities that had a trigger word like key, auth and pwd in the surrounding context."
]
| [
6,
203
]
| [
"passage: TAGS\n#region-us \n# Pseudo-labeled-python-data-pii-detection-filtered\n\nThis dataset was used for the training of a PII detection NER model. We annotated it using pseudo-labelelling to enhance model performance on some rare PII entities like keys.\n\nIt consists of 18,000 files annotates using an ensemble of two encoder models Deberta-v3-large and stanford-deidentifier-base which were fine-tuned on a labeled PII dataset for code with 400 files from this work. To select good-quality pseudo-labels, \nwe computed the average probability logits between the models and filtered based on a minimum score. After inspection, we observed a high rate of false positives for Keys and Passwords, hence we retained only the entities that had a trigger word like key, auth and pwd in the surrounding context."
]
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|
7deaf83aa998b303c03158eaedcf9c7cf2866206 | # Dataset Card for "WhisperSmallTest200001"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Nolan1206/WhisperSmallTest200001 | [
"region:us"
]
| 2023-03-13T01:40:56+00:00 | {"dataset_info": {"features": [{"name": "audio", "sequence": "float32"}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 113600198, "num_examples": 534}, {"name": "test", "num_bytes": 113600198, "num_examples": 534}], "download_size": 228223568, "dataset_size": 227200396}} | 2023-03-13T01:41:08+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "WhisperSmallTest200001"
More Information needed | [
"# Dataset Card for \"WhisperSmallTest200001\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"WhisperSmallTest200001\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"WhisperSmallTest200001\"\n\nMore Information needed"
]
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|
1b14f36a33bad97d354fd198991c7bab099d52ea | # Dataset Card for "WhisperSmallTest200002"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Nolan1206/WhisperSmallTest200002 | [
"region:us"
]
| 2023-03-13T02:00:47+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 82981.0, "num_examples": 3}, {"name": "test", "num_bytes": 82981.0, "num_examples": 3}], "download_size": 169572, "dataset_size": 165962.0}} | 2023-03-13T02:00:53+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "WhisperSmallTest200002"
More Information needed | [
"# Dataset Card for \"WhisperSmallTest200002\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"WhisperSmallTest200002\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"WhisperSmallTest200002\"\n\nMore Information needed"
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|
460d34ccccfbd9d87f91eb6916ca58ac0be4bb8b | Test Only | Cyberelay/nebula_ghostbusters | [
"task_categories:text-to-image",
"language:en",
"license:openrail",
"region:us"
]
| 2023-03-13T02:12:13+00:00 | {"language": ["en"], "license": "openrail", "task_categories": ["text-to-image"]} | 2023-03-13T09:48:02+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-to-image #language-English #license-openrail #region-us
| Test Only | []
| [
"TAGS\n#task_categories-text-to-image #language-English #license-openrail #region-us \n"
]
| [
28
]
| [
"passage: TAGS\n#task_categories-text-to-image #language-English #license-openrail #region-us \n"
]
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|
02170593d85739ce4ff558d2a5814b6f14550e4d | # Dataset Card for "apps_partial_600_900"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | minimario/apps_partial_600_900 | [
"region:us"
]
| 2023-03-13T04:41:08+00:00 | {"dataset_info": {"features": [{"name": "problem", "dtype": "string"}, {"name": "code", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "full_sample", "dtype": "string"}, {"name": "where_from", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 475424024, "num_examples": 428815}], "download_size": 12893044, "dataset_size": 475424024}} | 2023-03-13T05:13:37+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "apps_partial_600_900"
More Information needed | [
"# Dataset Card for \"apps_partial_600_900\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"apps_partial_600_900\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"apps_partial_600_900\"\n\nMore Information needed"
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|
fc283437dd43118cd4fe407c25683482ff21e9ce | # Dataset Card for "apps_partial_400_420"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | minimario/apps_partial_400_420 | [
"region:us"
]
| 2023-03-13T04:41:16+00:00 | {"dataset_info": {"features": [{"name": "problem", "dtype": "string"}, {"name": "code", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "full_sample", "dtype": "string"}, {"name": "where_from", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 30220693, "num_examples": 29683}], "download_size": 846453, "dataset_size": 30220693}} | 2023-03-13T05:27:56+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "apps_partial_400_420"
More Information needed | [
"# Dataset Card for \"apps_partial_400_420\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"apps_partial_400_420\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"apps_partial_400_420\"\n\nMore Information needed"
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|
44b54baed25501c845c0ee5c2c08adb95091daed | This dataset can be used to fine-tune Speech To Text models as Text To Speech.
## dataset information
* Speaker: Aldo
* Dataset size: 535 audio files
* audio duration of 4-15 seconds (1:33:15)
## Dataset structure
This dataset has been structured in the LJSpeech format:
* wavs/
* 1.wav
* 2.wav
* 3.wav
* ---
* 535.wav
* transcript.csv | rmcpantoja/Ald_Mexican_Spanish_speech_dataset | [
"license:unlicense",
"region:us"
]
| 2023-03-13T05:35:07+00:00 | {"license": "unlicense"} | 2023-03-13T05:59:04+00:00 | []
| []
| TAGS
#license-unlicense #region-us
| This dataset can be used to fine-tune Speech To Text models as Text To Speech.
## dataset information
* Speaker: Aldo
* Dataset size: 535 audio files
* audio duration of 4-15 seconds (1:33:15)
## Dataset structure
This dataset has been structured in the LJSpeech format:
* wavs/
* 1.wav
* 2.wav
* 3.wav
* ---
* URL
* URL | [
"## dataset information\n\n* Speaker: Aldo\n* Dataset size: 535 audio files\n* audio duration of 4-15 seconds (1:33:15)",
"## Dataset structure\n\nThis dataset has been structured in the LJSpeech format:\n* wavs/\n * 1.wav\n * 2.wav\n * 3.wav\n * ---\n * URL\n* URL"
]
| [
"TAGS\n#license-unlicense #region-us \n",
"## dataset information\n\n* Speaker: Aldo\n* Dataset size: 535 audio files\n* audio duration of 4-15 seconds (1:33:15)",
"## Dataset structure\n\nThis dataset has been structured in the LJSpeech format:\n* wavs/\n * 1.wav\n * 2.wav\n * 3.wav\n * ---\n * URL\n* URL"
]
| [
13,
30,
41
]
| [
"passage: TAGS\n#license-unlicense #region-us \n## dataset information\n\n* Speaker: Aldo\n* Dataset size: 535 audio files\n* audio duration of 4-15 seconds (1:33:15)## Dataset structure\n\nThis dataset has been structured in the LJSpeech format:\n* wavs/\n * 1.wav\n * 2.wav\n * 3.wav\n * ---\n * URL\n* URL"
]
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|
962943597c93e80bf52e94cf6959a442c53e7e27 | # Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains more than 2.1 million negative user reviews (reviews with 1 or 2 ratings) from 9775 apps across 48 categories from Google Play. Moreover, the number of votes that each review received within a month is also recorded. Those reviews having more votes can be cosidered as improtant reviews.
### Supported Tasks and Leaderboards
Detecting app issues proactively by identifying prominent app reviews.
### Languages
English
## How to use the dataset?
```
from datasets import load_dataset
import pandas as pd
# Load the dataset
dataset = load_dataset("recmeapp/thumbs-up")
# Convert to Pandas
dfs = {split: dset.to_pandas() for split, dset in dataset.items()}
dataset_df = pd.concat([dfs["train"], dfs["validation"], dfs["test"]])
# How many rows are there in the thumbs-up dataset?
print(f'There are {len(dataset_df)} rows in the thumbs-up dataset.')
# How many unique apps are there in the thumbs-up dataset?
print(f'There are {len(dataset_df["app_name"].unique())} unique apps.')
# How many categoris are there in the thumbs-up dataset?
print(f'There are {len(dataset_df["category"].unique())} unique categories.')
# What is the highest vote a review received in the thumbs-up dataset?
print(f'The highest vote a review received is {max(dataset_df["votes"])}.')
```
## Usage
This dataset was used for training the PPrior, a novel framework proposed in [this paper](https://ieeexplore.ieee.org/abstract/document/10020586). You can find the implementation in this [GitHub repository](https://github.com/MultifacetedNLP/PPrior). | recmeapp/thumbs-up | [
"task_categories:text-classification",
"size_categories:1M<n<10M",
"language:en",
"code",
"region:us"
]
| 2023-03-13T06:05:49+00:00 | {"language": ["en"], "size_categories": ["1M<n<10M"], "task_categories": ["text-classification"], "tags": ["code"]} | 2023-03-13T08:56:10+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #size_categories-1M<n<10M #language-English #code #region-us
| # Dataset Card for Dataset Name
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
This dataset contains more than 2.1 million negative user reviews (reviews with 1 or 2 ratings) from 9775 apps across 48 categories from Google Play. Moreover, the number of votes that each review received within a month is also recorded. Those reviews having more votes can be cosidered as improtant reviews.
### Supported Tasks and Leaderboards
Detecting app issues proactively by identifying prominent app reviews.
### Languages
English
## How to use the dataset?
## Usage
This dataset was used for training the PPrior, a novel framework proposed in this paper. You can find the implementation in this GitHub repository. | [
"# Dataset Card for Dataset Name",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nThis dataset contains more than 2.1 million negative user reviews (reviews with 1 or 2 ratings) from 9775 apps across 48 categories from Google Play. Moreover, the number of votes that each review received within a month is also recorded. Those reviews having more votes can be cosidered as improtant reviews.",
"### Supported Tasks and Leaderboards\n\nDetecting app issues proactively by identifying prominent app reviews.",
"### Languages\n\nEnglish",
"## How to use the dataset?",
"## Usage\nThis dataset was used for training the PPrior, a novel framework proposed in this paper. You can find the implementation in this GitHub repository."
]
| [
"TAGS\n#task_categories-text-classification #size_categories-1M<n<10M #language-English #code #region-us \n",
"# Dataset Card for Dataset Name",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nThis dataset contains more than 2.1 million negative user reviews (reviews with 1 or 2 ratings) from 9775 apps across 48 categories from Google Play. Moreover, the number of votes that each review received within a month is also recorded. Those reviews having more votes can be cosidered as improtant reviews.",
"### Supported Tasks and Leaderboards\n\nDetecting app issues proactively by identifying prominent app reviews.",
"### Languages\n\nEnglish",
"## How to use the dataset?",
"## Usage\nThis dataset was used for training the PPrior, a novel framework proposed in this paper. You can find the implementation in this GitHub repository."
]
| [
35,
8,
24,
76,
25,
5,
8,
38
]
| [
"passage: TAGS\n#task_categories-text-classification #size_categories-1M<n<10M #language-English #code #region-us \n# Dataset Card for Dataset Name## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:### Dataset Summary\n\nThis dataset contains more than 2.1 million negative user reviews (reviews with 1 or 2 ratings) from 9775 apps across 48 categories from Google Play. Moreover, the number of votes that each review received within a month is also recorded. Those reviews having more votes can be cosidered as improtant reviews.### Supported Tasks and Leaderboards\n\nDetecting app issues proactively by identifying prominent app reviews.### Languages\n\nEnglish## How to use the dataset?## Usage\nThis dataset was used for training the PPrior, a novel framework proposed in this paper. You can find the implementation in this GitHub repository."
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|
377a9559fbafcc8108e363a7bbd878f8ffa850e6 | # Dataset Card for "shrutilipi_mr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | bhatvineet/shrutilipi_mr | [
"region:us"
]
| 2023-03-13T07:12:52+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "transcriptions", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 114253169328.11655, "num_examples": 474332}, {"name": "test", "num_bytes": 39048725811.21545, "num_examples": 158111}], "download_size": 147662822982, "dataset_size": 153301895139.332}} | 2023-03-13T18:54:45+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "shrutilipi_mr"
More Information needed | [
"# Dataset Card for \"shrutilipi_mr\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"shrutilipi_mr\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"shrutilipi_mr\"\n\nMore Information needed"
]
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|
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