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770d79e988706a68df8e2bc9dc37348e109ded59
|
经过清洗和去重过的H小说
共205,028篇文章，解压后17.0 GB
仅用于科学研究！
|
a686d380/h-corpus-2023
|
[
"language:zh",
"region:us"
] |
2023-10-06T07:04:51+00:00
|
{"language": ["zh"], "viewer": false}
|
2023-10-06T07:38:36+00:00
|
[] |
[
"zh"
] |
TAGS
#language-Chinese #region-us
|
经过清洗和去重过的H小说
共205,028篇文章，解压后17.0 GB
仅用于科学研究！
|
[] |
[
"TAGS\n#language-Chinese #region-us \n"
] |
[
11
] |
[
"passage: TAGS\n#language-Chinese #region-us \n"
] |
72b0a672910a1123ace9d8c9ca1df8cb0b4db868
|
# What is this?
This is the text of my novel. It has approximately 240,000 words.
The genre is fantasy light novel.
# What is the licence?
The licence type is Apache 2.0.
# How can I use it?
I want you to use this novel as a sample of Japanese writing.
After that, you are free to use it within the scope of the licence.
You can send me fan letters :)
# Are there any precautions I should be aware of?
This text is still available on Kakuyom. The unique format for its publication has been retained. Please note that some of the formatting, such as ruby and highlighted characters, are not found in normal Japanese texts.
* https://kakuyomu.jp/help/entry/notation
# Others.
If you have any questions, please feel free to contact the HuggingFace community.
|
metral/ranobe_sample
|
[
"language:ja",
"license:apache-2.0",
"region:us"
] |
2023-10-06T07:05:28+00:00
|
{"language": ["ja"], "license": "apache-2.0"}
|
2023-10-06T07:25:32+00:00
|
[] |
[
"ja"
] |
TAGS
#language-Japanese #license-apache-2.0 #region-us
|
# What is this?
This is the text of my novel. It has approximately 240,000 words.
The genre is fantasy light novel.
# What is the licence?
The licence type is Apache 2.0.
# How can I use it?
I want you to use this novel as a sample of Japanese writing.
After that, you are free to use it within the scope of the licence.
You can send me fan letters :)
# Are there any precautions I should be aware of?
This text is still available on Kakuyom. The unique format for its publication has been retained. Please note that some of the formatting, such as ruby and highlighted characters, are not found in normal Japanese texts.
* URL
# Others.
If you have any questions, please feel free to contact the HuggingFace community.
|
[
"# What is this?\nThis is the text of my novel. It has approximately 240,000 words.\nThe genre is fantasy light novel.",
"# What is the licence?\nThe licence type is Apache 2.0.",
"# How can I use it?\nI want you to use this novel as a sample of Japanese writing.\nAfter that, you are free to use it within the scope of the licence.\nYou can send me fan letters :)",
"# Are there any precautions I should be aware of?\nThis text is still available on Kakuyom. The unique format for its publication has been retained. Please note that some of the formatting, such as ruby and highlighted characters, are not found in normal Japanese texts.\n* URL",
"# Others.\nIf you have any questions, please feel free to contact the HuggingFace community."
] |
[
"TAGS\n#language-Japanese #license-apache-2.0 #region-us \n",
"# What is this?\nThis is the text of my novel. It has approximately 240,000 words.\nThe genre is fantasy light novel.",
"# What is the licence?\nThe licence type is Apache 2.0.",
"# How can I use it?\nI want you to use this novel as a sample of Japanese writing.\nAfter that, you are free to use it within the scope of the licence.\nYou can send me fan letters :)",
"# Are there any precautions I should be aware of?\nThis text is still available on Kakuyom. The unique format for its publication has been retained. Please note that some of the formatting, such as ruby and highlighted characters, are not found in normal Japanese texts.\n* URL",
"# Others.\nIf you have any questions, please feel free to contact the HuggingFace community."
] |
[
20,
27,
14,
45,
63,
22
] |
[
"passage: TAGS\n#language-Japanese #license-apache-2.0 #region-us \n# What is this?\nThis is the text of my novel. It has approximately 240,000 words.\nThe genre is fantasy light novel.# What is the licence?\nThe licence type is Apache 2.0.# How can I use it?\nI want you to use this novel as a sample of Japanese writing.\nAfter that, you are free to use it within the scope of the licence.\nYou can send me fan letters :)# Are there any precautions I should be aware of?\nThis text is still available on Kakuyom. The unique format for its publication has been retained. Please note that some of the formatting, such as ruby and highlighted characters, are not found in normal Japanese texts.\n* URL# Others.\nIf you have any questions, please feel free to contact the HuggingFace community."
] |
5a84591959df79c4ecab076ec6b06d40a163f658
|
未清洗的中文H小说
| 数据| 文章数| 解压后大小 | 来源 | 质量 | 备注|
|- | - |- | - | - | - |
|jjsw | 73,432 | 4.0 GB | 禁忌书屋 | 高 | - |
|pixiv-selected | 2,935 | 174.3 MB | pixiv排行版 | 高 | - |
|shubao | 6,776 |1.6 GB | 网络 | 低 | - |
|sis-long | 4,555 | 3.5 GB | sis | 中 | - |
|sis-short | 111,237 | 4.1 GB | sis | 中 | - |
|xbookcn | 39,798 | 1.0 GB | xbookcn | 高 | - |
|xhs | 38,406 | 8.6 GB | 网络 | 中 | - |
|zyd2023 | 3,935 | 3.8 GB | 网络 | 中 | - |
仅供科学研究使用！
|
a686d380/h-corpus-raw
|
[
"language:zh",
"region:us"
] |
2023-10-06T07:05:34+00:00
|
{"language": ["zh"], "viewer": false}
|
2023-10-06T07:25:50+00:00
|
[] |
[
"zh"
] |
TAGS
#language-Chinese #region-us
|
未清洗的中文H小说
仅供科学研究使用！
|
[] |
[
"TAGS\n#language-Chinese #region-us \n"
] |
[
11
] |
[
"passage: TAGS\n#language-Chinese #region-us \n"
] |
1141ddba1931faeb13a5cc9b33506ca8c03cb240
|
# Dataset Card for "COVID-QA-sentence-transformer-biencoder-data-45_45_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
minh21/COVID-QA-sentence-transformer-biencoder-data-45_45_10
|
[
"region:us"
] |
2023-10-06T07:07:57+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "positive", "dtype": "string"}, {"name": "negative", "dtype": "string"}, {"name": "document_id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 8526497, "num_examples": 4266}, {"name": "test", "num_bytes": 934044, "num_examples": 478}], "download_size": 917811, "dataset_size": 9460541}}
|
2023-10-06T07:07:58+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "COVID-QA-sentence-transformer-biencoder-data-45_45_10"
More Information needed
|
[
"# Dataset Card for \"COVID-QA-sentence-transformer-biencoder-data-45_45_10\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"COVID-QA-sentence-transformer-biencoder-data-45_45_10\"\n\nMore Information needed"
] |
[
6,
31
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"COVID-QA-sentence-transformer-biencoder-data-45_45_10\"\n\nMore Information needed"
] |
aca36a575b1353d4068c5aa4c894af5fe16ddf72
|
# Dataset Card for "COVID-QA-testset-biencoder-data-45_45_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
minh21/COVID-QA-testset-biencoder-data-45_45_10
|
[
"region:us"
] |
2023-10-06T07:08:06+00:00
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "context_chunks", "sequence": "string"}, {"name": "document_id", "dtype": "int64"}, {"name": "id", "dtype": "int64"}, {"name": "context", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 16708455, "num_examples": 201}], "download_size": 442083, "dataset_size": 16708455}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-06T07:08:08+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "COVID-QA-testset-biencoder-data-45_45_10"
More Information needed
|
[
"# Dataset Card for \"COVID-QA-testset-biencoder-data-45_45_10\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"COVID-QA-testset-biencoder-data-45_45_10\"\n\nMore Information needed"
] |
[
6,
28
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"COVID-QA-testset-biencoder-data-45_45_10\"\n\nMore Information needed"
] |
4435cad8d500f1baba5050543d03e5aef78b87ae
|
# Dataset Card for "COVID-QA-question-answering-biencoder-data-45_45_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
minh21/COVID-QA-question-answering-biencoder-data-45_45_10
|
[
"region:us"
] |
2023-10-06T07:08:22+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "context_chunks", "sequence": "string"}, {"name": "document_id", "dtype": "int64"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 40708361, "num_examples": 814}, {"name": "validation", "num_bytes": 5112241, "num_examples": 94}], "download_size": 12639574, "dataset_size": 45820602}}
|
2023-10-06T07:08:24+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "COVID-QA-question-answering-biencoder-data-45_45_10"
More Information needed
|
[
"# Dataset Card for \"COVID-QA-question-answering-biencoder-data-45_45_10\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"COVID-QA-question-answering-biencoder-data-45_45_10\"\n\nMore Information needed"
] |
[
6,
31
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"COVID-QA-question-answering-biencoder-data-45_45_10\"\n\nMore Information needed"
] |
2b841a0b715e00b389c38976daf25374368dee30
|
This is the Japanese portion of the xwinograd dataset, formatted for easy use.
The original data can be found [here](https://huggingface.co/datasets/Muennighoff/xwinograd). When using this data, please cite the original papers.
```
@misc{muennighoff2022crosslingual,
title={Crosslingual Generalization through Multitask Finetuning},
author={Niklas Muennighoff and Thomas Wang and Lintang Sutawika and Adam Roberts and Stella Biderman and Teven Le Scao and M Saiful Bari and Sheng Shen and Zheng-Xin Yong and Hailey Schoelkopf and Xiangru Tang and Dragomir Radev and Alham Fikri Aji and Khalid Almubarak and Samuel Albanie and Zaid Alyafeai and Albert Webson and Edward Raff and Colin Raffel},
year={2022},
eprint={2211.01786},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```
@misc{tikhonov2021heads,
title={It's All in the Heads: Using Attention Heads as a Baseline for Cross-Lingual Transfer in Commonsense Reasoning},
author={Alexey Tikhonov and Max Ryabinin},
year={2021},
eprint={2106.12066},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
polm-stability/xwinograd-ja
|
[
"license:cc-by-4.0",
"arxiv:2211.01786",
"arxiv:2106.12066",
"region:us"
] |
2023-10-06T07:11:59+00:00
|
{"license": "cc-by-4.0"}
|
2023-10-06T07:34:15+00:00
|
[
"2211.01786",
"2106.12066"
] |
[] |
TAGS
#license-cc-by-4.0 #arxiv-2211.01786 #arxiv-2106.12066 #region-us
|
This is the Japanese portion of the xwinograd dataset, formatted for easy use.
The original data can be found here. When using this data, please cite the original papers.
|
[] |
[
"TAGS\n#license-cc-by-4.0 #arxiv-2211.01786 #arxiv-2106.12066 #region-us \n"
] |
[
32
] |
[
"passage: TAGS\n#license-cc-by-4.0 #arxiv-2211.01786 #arxiv-2106.12066 #region-us \n"
] |
f675c89a7c02086bd98f900f2c193eb83320d8ba
|
# Dataset Card for "github-issues"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ZonePG/github-issues
|
[
"region:us"
] |
2023-10-06T07:15:54+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "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": "null"}, {"name": "closed_at", "dtype": "null"}]}, {"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": 706305, "num_examples": 50}], "download_size": 0, "dataset_size": 706305}}
|
2023-10-06T07:18:49+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"
] |
a4a8b2a3594da51ea05b391d7698409747586b9b
|
# StableSR TestSets Card
These test sets are used associated with the StableSR, available [here](https://github.com/IceClear/StableSR).
## Data Details
- **Developed by:** Jianyi Wang
- **Data type:** Synthetic and real-world test sets for image super-resolution
- **License:** [S-Lab License 1.0](https://github.com/IceClear/StableSR/blob/main/LICENSE.txt)
- **Data Description:** The test sets are used to reproduce the metric results shown in [Paper](https://arxiv.org/abs/2305.07015).
- **Resources for more information:** [GitHub Repository](https://github.com/IceClear/StableSR).
- **Cite as:**
@InProceedings{wang2023exploiting,
author = {Wang, Jianyi and Yue, Zongsheng and Zhou, Shangchen and Chan, Kelvin CK and Loy, Chen Change},
title = {Exploiting Diffusion Prior for Real-World Image Super-Resolution},
booktitle = {arXiv preprint arXiv:2305.07015},
year = {2023},
}
# Uses
Please refer to [S-Lab License 1.0](https://github.com/IceClear/StableSR/blob/main/LICENSE.txt)
We currently provide the following test sets:
- DIV2K_Val: 3000 synthetic data pairs on the validation of [DIV2K](https://data.vision.ee.ethz.ch/cvl/DIV2K/) generated used the same degradation used for training StableSR.
- RealSR Val: Center-cropped data pairs on [RealSRv3](https://github.com/csjcai/RealSR).
- DRealSR Val: Center-cropped data pairs on [DRealSR](https://github.com/xiezw5/Component-Divide-and-Conquer-for-Real-World-Image-Super-Resolution).
- DPED Val: Center-cropped LQ-only data on [DPED](https://github.com/aiff22/DPED).
## Evaluation Results
See [Paper](https://arxiv.org/abs/2305.07015) for details.
|
Iceclear/StableSR-TestSets
|
[
"task_categories:image-to-image",
"license:other",
"arxiv:2305.07015",
"region:us"
] |
2023-10-06T07:24:37+00:00
|
{"license": "other", "task_categories": ["image-to-image"], "license_name": "ntu-slab-license", "license_link": "https://github.com/IceClear/StableSR/blob/main/LICENSE.txt"}
|
2023-10-06T07:46:40+00:00
|
[
"2305.07015"
] |
[] |
TAGS
#task_categories-image-to-image #license-other #arxiv-2305.07015 #region-us
|
# StableSR TestSets Card
These test sets are used associated with the StableSR, available here.
## Data Details
- Developed by: Jianyi Wang
- Data type: Synthetic and real-world test sets for image super-resolution
- License: S-Lab License 1.0
- Data Description: The test sets are used to reproduce the metric results shown in Paper.
- Resources for more information: GitHub Repository.
- Cite as:
@InProceedings{wang2023exploiting,
author = {Wang, Jianyi and Yue, Zongsheng and Zhou, Shangchen and Chan, Kelvin CK and Loy, Chen Change},
title = {Exploiting Diffusion Prior for Real-World Image Super-Resolution},
booktitle = {arXiv preprint arXiv:2305.07015},
year = {2023},
}
# Uses
Please refer to S-Lab License 1.0
We currently provide the following test sets:
- DIV2K_Val: 3000 synthetic data pairs on the validation of DIV2K generated used the same degradation used for training StableSR.
- RealSR Val: Center-cropped data pairs on RealSRv3.
- DRealSR Val: Center-cropped data pairs on DRealSR.
- DPED Val: Center-cropped LQ-only data on DPED.
## Evaluation Results
See Paper for details.
|
[
"# StableSR TestSets Card\nThese test sets are used associated with the StableSR, available here.",
"## Data Details\n- Developed by: Jianyi Wang\n- Data type: Synthetic and real-world test sets for image super-resolution\n- License: S-Lab License 1.0\n- Data Description: The test sets are used to reproduce the metric results shown in Paper.\n- Resources for more information: GitHub Repository.\n- Cite as:\n\n @InProceedings{wang2023exploiting,\n author = {Wang, Jianyi and Yue, Zongsheng and Zhou, Shangchen and Chan, Kelvin CK and Loy, Chen Change},\n title = {Exploiting Diffusion Prior for Real-World Image Super-Resolution},\n booktitle = {arXiv preprint arXiv:2305.07015},\n year = {2023},\n }",
"# Uses\nPlease refer to S-Lab License 1.0\n\nWe currently provide the following test sets:\n\n- DIV2K_Val: 3000 synthetic data pairs on the validation of DIV2K generated used the same degradation used for training StableSR.\n- RealSR Val: Center-cropped data pairs on RealSRv3.\n- DRealSR Val: Center-cropped data pairs on DRealSR.\n- DPED Val: Center-cropped LQ-only data on DPED.",
"## Evaluation Results \nSee Paper for details."
] |
[
"TAGS\n#task_categories-image-to-image #license-other #arxiv-2305.07015 #region-us \n",
"# StableSR TestSets Card\nThese test sets are used associated with the StableSR, available here.",
"## Data Details\n- Developed by: Jianyi Wang\n- Data type: Synthetic and real-world test sets for image super-resolution\n- License: S-Lab License 1.0\n- Data Description: The test sets are used to reproduce the metric results shown in Paper.\n- Resources for more information: GitHub Repository.\n- Cite as:\n\n @InProceedings{wang2023exploiting,\n author = {Wang, Jianyi and Yue, Zongsheng and Zhou, Shangchen and Chan, Kelvin CK and Loy, Chen Change},\n title = {Exploiting Diffusion Prior for Real-World Image Super-Resolution},\n booktitle = {arXiv preprint arXiv:2305.07015},\n year = {2023},\n }",
"# Uses\nPlease refer to S-Lab License 1.0\n\nWe currently provide the following test sets:\n\n- DIV2K_Val: 3000 synthetic data pairs on the validation of DIV2K generated used the same degradation used for training StableSR.\n- RealSR Val: Center-cropped data pairs on RealSRv3.\n- DRealSR Val: Center-cropped data pairs on DRealSR.\n- DPED Val: Center-cropped LQ-only data on DPED.",
"## Evaluation Results \nSee Paper for details."
] |
[
32,
24,
181,
113,
9
] |
[
"passage: TAGS\n#task_categories-image-to-image #license-other #arxiv-2305.07015 #region-us \n# StableSR TestSets Card\nThese test sets are used associated with the StableSR, available here.## Data Details\n- Developed by: Jianyi Wang\n- Data type: Synthetic and real-world test sets for image super-resolution\n- License: S-Lab License 1.0\n- Data Description: The test sets are used to reproduce the metric results shown in Paper.\n- Resources for more information: GitHub Repository.\n- Cite as:\n\n @InProceedings{wang2023exploiting,\n author = {Wang, Jianyi and Yue, Zongsheng and Zhou, Shangchen and Chan, Kelvin CK and Loy, Chen Change},\n title = {Exploiting Diffusion Prior for Real-World Image Super-Resolution},\n booktitle = {arXiv preprint arXiv:2305.07015},\n year = {2023},\n }# Uses\nPlease refer to S-Lab License 1.0\n\nWe currently provide the following test sets:\n\n- DIV2K_Val: 3000 synthetic data pairs on the validation of DIV2K generated used the same degradation used for training StableSR.\n- RealSR Val: Center-cropped data pairs on RealSRv3.\n- DRealSR Val: Center-cropped data pairs on DRealSR.\n- DPED Val: Center-cropped LQ-only data on DPED.## Evaluation Results \nSee Paper for details."
] |
986414b4d16ebf73af565e0484337d077598d09c
|
# NIL Policy
Data is taken from the [Stanford website](https://gostanford.com/sports/2022/11/11/nil-student-athletes.aspx).
Data is chunked into rows for the training set.
The test.csv dataset is generated using Llama 70B to extract key takeaways from the raw text.
For educational and non-commercial use only.
|
Trelis/stanford-NIL-disclosure-ft
|
[
"task_categories:text-generation",
"size_categories:n<1K",
"language:en",
"fine-tuning",
"NIL",
"region:us"
] |
2023-10-06T07:43:16+00:00
|
{"language": ["en"], "size_categories": ["n<1K"], "task_categories": ["text-generation"], "tags": ["fine-tuning", "NIL"]}
|
2023-10-17T08:34:27+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-text-generation #size_categories-n<1K #language-English #fine-tuning #NIL #region-us
|
# NIL Policy
Data is taken from the Stanford website.
Data is chunked into rows for the training set.
The URL dataset is generated using Llama 70B to extract key takeaways from the raw text.
For educational and non-commercial use only.
|
[
"# NIL Policy\n\nData is taken from the Stanford website.\n\nData is chunked into rows for the training set.\n\nThe URL dataset is generated using Llama 70B to extract key takeaways from the raw text.\n\nFor educational and non-commercial use only."
] |
[
"TAGS\n#task_categories-text-generation #size_categories-n<1K #language-English #fine-tuning #NIL #region-us \n",
"# NIL Policy\n\nData is taken from the Stanford website.\n\nData is chunked into rows for the training set.\n\nThe URL dataset is generated using Llama 70B to extract key takeaways from the raw text.\n\nFor educational and non-commercial use only."
] |
[
39,
59
] |
[
"passage: TAGS\n#task_categories-text-generation #size_categories-n<1K #language-English #fine-tuning #NIL #region-us \n# NIL Policy\n\nData is taken from the Stanford website.\n\nData is chunked into rows for the training set.\n\nThe URL dataset is generated using Llama 70B to extract key takeaways from the raw text.\n\nFor educational and non-commercial use only."
] |
1ecf6355f2dac48f6753252e13f6c2c9b32fbb3f
|
# Dataset Card for "data_trigger"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
pphuc25/data_trigger
|
[
"region:us"
] |
2023-10-06T07:43:52+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}], "splits": [{"name": "train", "num_bytes": 469024, "num_examples": 3400}, {"name": "test", "num_bytes": 77263, "num_examples": 600}], "download_size": 316166, "dataset_size": 546287}}
|
2023-10-06T07:43:55+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "data_trigger"
More Information needed
|
[
"# Dataset Card for \"data_trigger\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"data_trigger\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"data_trigger\"\n\nMore Information needed"
] |
300ea9734eeb258efb43ff649e7d21824c13cc4a
|
# Dataset Card for "hc3-wiki-cleaned-text-for-domain-classification-roberta-tokenized-max-len-512"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
rajendrabaskota/hc3-wiki-cleaned-text-for-domain-classification-roberta-tokenized-max-len-512
|
[
"region:us"
] |
2023-10-06T07:46:38+00:00
|
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "source", "dtype": "int64"}, {"name": "human/ai", "dtype": "int64"}, {"name": "perplexity", "dtype": "float64"}, {"name": "cleaned_text", "dtype": "string"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 845606936, "num_examples": 330345}, {"name": "test", "num_bytes": 44570090, "num_examples": 17387}], "download_size": 499405861, "dataset_size": 890177026}}
|
2023-10-06T07:47:00+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "hc3-wiki-cleaned-text-for-domain-classification-roberta-tokenized-max-len-512"
More Information needed
|
[
"# Dataset Card for \"hc3-wiki-cleaned-text-for-domain-classification-roberta-tokenized-max-len-512\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"hc3-wiki-cleaned-text-for-domain-classification-roberta-tokenized-max-len-512\"\n\nMore Information needed"
] |
[
6,
41
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"hc3-wiki-cleaned-text-for-domain-classification-roberta-tokenized-max-len-512\"\n\nMore Information needed"
] |
264665bf10ad1c4efb94585c47119f20c040304e
|
## Dataset Description
<table border="0">
<tr>
<td style="width: 40%; vertical-align: top">
Gyeonggi dataset is 10,000 households based on the highest meter reading rate for all branches of the around in Gyeonggi Province, South Korea. For privacy reasons, the name of the household is not provided. We only provide the ID of the household.
</td>
<td>
<img src="imgs/gy-map.png" >
</td>
</tr>
</table>
### Dataset Summary
This dataset en-compasses hourly records of building power consumption spanning approximately 1.9 years, ranging from January 1, 2021, to January 14, 2022.
| electrical-meter-id | date | hour | customer-id | amount-of-consumption |
|---------------------|----------|------|-------------|-----------------------|
| 7871 | 20201020 | 1 | 7871 | 4.25 |
| 7871 | 20201020 | 2 | 7871 | 4.12 |
| 7871 | 20201020 | 3 | 7871 | 4.08 |
| 7871 | 20201020 | 4 | 7871 | 4.03 |
| 7871 | 20201020 | 5 | 7871 | 4.09 |
#### Our experiment focuses on the total electricity consumption of a particular ID 6499
---
license: apache-2.0
---
|
andrewlee1807/Gyeonggi
|
[
"task_categories:time-series-forecasting",
"size_categories:100M<n<1B",
"language:en",
"license:apache-2.0",
"electricity",
"region:us"
] |
2023-10-06T07:51:57+00:00
|
{"language": ["en"], "license": "apache-2.0", "size_categories": ["100M<n<1B"], "task_categories": ["time-series-forecasting"], "tags": ["electricity"]}
|
2023-10-13T05:44:23+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-time-series-forecasting #size_categories-100M<n<1B #language-English #license-apache-2.0 #electricity #region-us
|
Dataset Description
-------------------
| | |
| --- | --- |
|
Gyeonggi dataset is 10,000 households based on the highest meter reading rate for all branches of the around in Gyeonggi Province, South Korea. For privacy reasons, the name of the household is not provided. We only provide the ID of the household.
| |
### Dataset Summary
This dataset en-compasses hourly records of building power consumption spanning approximately 1.9 years, ranging from January 1, 2021, to January 14, 2022.
#### Our experiment focuses on the total electricity consumption of a particular ID 6499
---
license: apache-2.0
-------------------
|
[
"### Dataset Summary\n\n\nThis dataset en-compasses hourly records of building power consumption spanning approximately 1.9 years, ranging from January 1, 2021, to January 14, 2022.",
"#### Our experiment focuses on the total electricity consumption of a particular ID 6499\n\n\n\n\n---\n\n\nlicense: apache-2.0\n-------------------"
] |
[
"TAGS\n#task_categories-time-series-forecasting #size_categories-100M<n<1B #language-English #license-apache-2.0 #electricity #region-us \n",
"### Dataset Summary\n\n\nThis dataset en-compasses hourly records of building power consumption spanning approximately 1.9 years, ranging from January 1, 2021, to January 14, 2022.",
"#### Our experiment focuses on the total electricity consumption of a particular ID 6499\n\n\n\n\n---\n\n\nlicense: apache-2.0\n-------------------"
] |
[
48,
42,
29
] |
[
"passage: TAGS\n#task_categories-time-series-forecasting #size_categories-100M<n<1B #language-English #license-apache-2.0 #electricity #region-us \n### Dataset Summary\n\n\nThis dataset en-compasses hourly records of building power consumption spanning approximately 1.9 years, ranging from January 1, 2021, to January 14, 2022.#### Our experiment focuses on the total electricity consumption of a particular ID 6499\n\n\n\n\n---\n\n\nlicense: apache-2.0\n-------------------"
] |
2cd0a10fa99571087e6a6ba34b8e4ea5d08aefed
|
# Dataset Card for "embeddings_from_distilbert_class_heaps_and_eval_part0_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
johannes-garstenauer/embeddings_from_distilbert_class_heaps_and_eval_part0_test
|
[
"region:us"
] |
2023-10-06T08:07:53+00:00
|
{"dataset_info": {"features": [{"name": "struct", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "pred", "dtype": "int64"}, {"name": "cls_layer_6", "sequence": "float32"}, {"name": "cls_layer_5", "sequence": "float32"}, {"name": "cls_layer_4", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 13428556, "num_examples": 1408}], "download_size": 16665816, "dataset_size": 13428556}}
|
2023-10-06T08:08:02+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "embeddings_from_distilbert_class_heaps_and_eval_part0_test"
More Information needed
|
[
"# Dataset Card for \"embeddings_from_distilbert_class_heaps_and_eval_part0_test\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"embeddings_from_distilbert_class_heaps_and_eval_part0_test\"\n\nMore Information needed"
] |
[
6,
34
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"embeddings_from_distilbert_class_heaps_and_eval_part0_test\"\n\nMore Information needed"
] |
10001ae98dbd34700a6b097a2d070627095cc8ab
|
# Dataset Card for "embeddings_from_distilbert_class_heaps_and_eval_part1_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
johannes-garstenauer/embeddings_from_distilbert_class_heaps_and_eval_part1_test
|
[
"region:us"
] |
2023-10-06T08:08:12+00:00
|
{"dataset_info": {"features": [{"name": "struct", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "pred", "dtype": "int64"}, {"name": "cls_layer_6", "sequence": "float32"}, {"name": "cls_layer_5", "sequence": "float32"}, {"name": "cls_layer_4", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 12230881, "num_examples": 1283}], "download_size": 14966255, "dataset_size": 12230881}}
|
2023-10-06T08:08:19+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "embeddings_from_distilbert_class_heaps_and_eval_part1_test"
More Information needed
|
[
"# Dataset Card for \"embeddings_from_distilbert_class_heaps_and_eval_part1_test\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"embeddings_from_distilbert_class_heaps_and_eval_part1_test\"\n\nMore Information needed"
] |
[
6,
34
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"embeddings_from_distilbert_class_heaps_and_eval_part1_test\"\n\nMore Information needed"
] |
188fca3fe4ed5c2c119d3b6bcf9edb0de3bece79
|
# Bangumi Image Base of Angels Of Death
This is the image base of bangumi Angels of Death, we detected 8 characters, 1201 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 621 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 243 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 80 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 15 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 92 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 84 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 8 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 58 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/angelsofdeath
|
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] |
2023-10-06T08:10:12+00:00
|
{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}
|
2023-10-06T09:20:16+00:00
|
[] |
[] |
TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
|
Bangumi Image Base of Angels Of Death
=====================================
This is the image base of bangumi Angels of Death, we detected 8 characters, 1201 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
[
25
] |
[
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
9bbe7bb3e90df299a2a8adb1f4b1b32a5282d80d
|
# Bangumi Image Base of Little Witch Academia
This is the image base of bangumi Little Witch Academia, we detected 41 characters, 3200 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 803 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 62 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 61 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 26 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 12 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 106 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 63 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 35 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 16 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 21 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 181 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 28 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 21 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 61 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 26 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 11 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 40 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 115 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 27 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 11 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 41 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 16 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 189 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 8 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 21 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 31 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 27 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 111 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 265 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 30 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 21 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 29 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 66 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 35 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 20 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 41 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 38 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 30 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 11 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 8 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 436 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/littlewitchacademia
|
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] |
2023-10-06T08:10:35+00:00
|
{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}
|
2023-10-06T09:59:11+00:00
|
[] |
[] |
TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
|
Bangumi Image Base of Little Witch Academia
===========================================
This is the image base of bangumi Little Witch Academia, we detected 41 characters, 3200 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
[
25
] |
[
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
121d83dd4091aa4051d47d1484aac3d9bf0f23a3
|
# Bangumi Image Base of Nichijou
This is the image base of bangumi Nichijou, we detected 33 characters, 2652 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 346 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 16 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 51 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 449 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 105 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 10 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 75 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 91 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 73 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 16 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 479 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 33 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 72 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 75 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 79 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 19 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 17 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 80 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 30 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 181 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 16 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 15 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 36 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 100 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 13 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 33 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 14 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 7 | [Download](27/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 28 | 9 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 14 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 12 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 22 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 64 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/nichijou
|
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] |
2023-10-06T08:11:08+00:00
|
{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}
|
2023-10-06T09:48:11+00:00
|
[] |
[] |
TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
|
Bangumi Image Base of Nichijou
==============================
This is the image base of bangumi Nichijou, we detected 33 characters, 2652 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
[
25
] |
[
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
6cdab3964684b77222ad02c40312901b8e92d0d0
|
# NIL Policy
Data is taken from the [Stanford website](https://gostanford.com/sports/2022/11/11/nil-student-athletes.aspx).
The maximum number of tokens (prompt + completion) in a row of data/train.csv is 100
The maximum number of tokens (prompt + completion) in a row of data/test.csv is 89
For educational and non-commercial use only.
|
Trelis/stanford-NIL-disclosure-sft
|
[
"task_categories:text-generation",
"size_categories:n<1K",
"language:en",
"fine-tuning",
"NIL",
"region:us"
] |
2023-10-06T08:16:13+00:00
|
{"language": ["en"], "size_categories": ["n<1K"], "task_categories": ["text-generation"], "tags": ["fine-tuning", "NIL"]}
|
2023-10-06T08:17:36+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-text-generation #size_categories-n<1K #language-English #fine-tuning #NIL #region-us
|
# NIL Policy
Data is taken from the Stanford website.
The maximum number of tokens (prompt + completion) in a row of data/URL is 100
The maximum number of tokens (prompt + completion) in a row of data/URL is 89
For educational and non-commercial use only.
|
[
"# NIL Policy\n\nData is taken from the Stanford website.\n\nThe maximum number of tokens (prompt + completion) in a row of data/URL is 100\nThe maximum number of tokens (prompt + completion) in a row of data/URL is 89\n\nFor educational and non-commercial use only."
] |
[
"TAGS\n#task_categories-text-generation #size_categories-n<1K #language-English #fine-tuning #NIL #region-us \n",
"# NIL Policy\n\nData is taken from the Stanford website.\n\nThe maximum number of tokens (prompt + completion) in a row of data/URL is 100\nThe maximum number of tokens (prompt + completion) in a row of data/URL is 89\n\nFor educational and non-commercial use only."
] |
[
39,
71
] |
[
"passage: TAGS\n#task_categories-text-generation #size_categories-n<1K #language-English #fine-tuning #NIL #region-us \n# NIL Policy\n\nData is taken from the Stanford website.\n\nThe maximum number of tokens (prompt + completion) in a row of data/URL is 100\nThe maximum number of tokens (prompt + completion) in a row of data/URL is 89\n\nFor educational and non-commercial use only."
] |
37f850d7c45abbb4cf829efd67023a64ecef6a18
|
# Dataset Card for "pixel_500"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
kewu93/pixel_500
|
[
"region:us"
] |
2023-10-06T08:31:40+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "val", "path": "data/val-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5863021.833333333, "num_examples": 500}, {"name": "val", "num_bytes": 1168940.1666666667, "num_examples": 100}], "download_size": 6125119, "dataset_size": 7031962.0}}
|
2023-10-06T08:31:47+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "pixel_500"
More Information needed
|
[
"# Dataset Card for \"pixel_500\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"pixel_500\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"pixel_500\"\n\nMore Information needed"
] |
edefc5997d6625cb18502117ec94f294e069a7cd
|
# Dataset Card for "mosque_forest_image_prompts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Falah/mosque_forest_image_prompts
|
[
"region:us"
] |
2023-10-06T08:35:07+00:00
|
{"dataset_info": {"features": [{"name": "prompts", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3517254, "num_examples": 10000}], "download_size": 150520, "dataset_size": 3517254}}
|
2023-10-06T08:35:08+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "mosque_forest_image_prompts"
More Information needed
|
[
"# Dataset Card for \"mosque_forest_image_prompts\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"mosque_forest_image_prompts\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"mosque_forest_image_prompts\"\n\nMore Information needed"
] |
ce9ed009ef3292bac33d3520afaf5ec804066120
|
# Overview
This is a new curated subset of our OpenOrca data. This release provides an efficient means of reaching performance on-par with using larger slices of our data, while only including ~500k GPT-4 completions.
The key change in this dataset is that we've done an additional pass, using GPT-4 to remove answers which appear wrong based on the human annotations from the FLAN dataset.
This reduces the dataset size to only ~500k entries, allowing training to a similar quality level to our previous releases with 2/3 the compute requirement.
# Demo Models
* https://huggingface.co/openaccess-ai-collective/jackalope-7b
* https://huggingface.co/Open-Orca/Mistral-7B-SlimOrca
# Citation
```bibtex
@misc{SlimOrca,
title = {SlimOrca: An Open Dataset of GPT-4 Augmented FLAN Reasoning Traces, with Verification},
author = {Wing Lian and Guan Wang and Bleys Goodson and Eugene Pentland and Austin Cook and Chanvichet Vong and "Teknium"},
year = {2023},
publisher = {HuggingFace},
url = {https://https://huggingface.co/Open-Orca/SlimOrca}
}
```
```bibtex
@misc{mukherjee2023orca,
title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4},
author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
year={2023},
eprint={2306.02707},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```bibtex
@misc{longpre2023flan,
title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning},
author={Shayne Longpre and Le Hou and Tu Vu and Albert Webson and Hyung Won Chung and Yi Tay and Denny Zhou and Quoc V. Le and Barret Zoph and Jason Wei and Adam Roberts},
year={2023},
eprint={2301.13688},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
```
|
Open-Orca/SlimOrca
|
[
"task_categories:conversational",
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:table-question-answering",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:summarization",
"task_categories:feature-extraction",
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"arxiv:2306.02707",
"arxiv:2301.13688",
"region:us"
] |
2023-10-06T08:40:55+00:00
|
{"language": ["en"], "license": "mit", "size_categories": ["100K<n<1M"], "task_categories": ["conversational", "text-classification", "token-classification", "table-question-answering", "question-answering", "zero-shot-classification", "summarization", "feature-extraction", "text-generation", "text2text-generation"], "pretty_name": "SlimOrca"}
|
2023-10-12T05:43:59+00:00
|
[
"2306.02707",
"2301.13688"
] |
[
"en"
] |
TAGS
#task_categories-conversational #task_categories-text-classification #task_categories-token-classification #task_categories-table-question-answering #task_categories-question-answering #task_categories-zero-shot-classification #task_categories-summarization #task_categories-feature-extraction #task_categories-text-generation #task_categories-text2text-generation #size_categories-100K<n<1M #language-English #license-mit #arxiv-2306.02707 #arxiv-2301.13688 #region-us
|
# Overview
This is a new curated subset of our OpenOrca data. This release provides an efficient means of reaching performance on-par with using larger slices of our data, while only including ~500k GPT-4 completions.
The key change in this dataset is that we've done an additional pass, using GPT-4 to remove answers which appear wrong based on the human annotations from the FLAN dataset.
This reduces the dataset size to only ~500k entries, allowing training to a similar quality level to our previous releases with 2/3 the compute requirement.
# Demo Models
* URL
* URL
|
[
"# Overview\n\nThis is a new curated subset of our OpenOrca data. This release provides an efficient means of reaching performance on-par with using larger slices of our data, while only including ~500k GPT-4 completions.\n\nThe key change in this dataset is that we've done an additional pass, using GPT-4 to remove answers which appear wrong based on the human annotations from the FLAN dataset.\nThis reduces the dataset size to only ~500k entries, allowing training to a similar quality level to our previous releases with 2/3 the compute requirement.",
"# Demo Models\n\n* URL\n* URL"
] |
[
"TAGS\n#task_categories-conversational #task_categories-text-classification #task_categories-token-classification #task_categories-table-question-answering #task_categories-question-answering #task_categories-zero-shot-classification #task_categories-summarization #task_categories-feature-extraction #task_categories-text-generation #task_categories-text2text-generation #size_categories-100K<n<1M #language-English #license-mit #arxiv-2306.02707 #arxiv-2301.13688 #region-us \n",
"# Overview\n\nThis is a new curated subset of our OpenOrca data. This release provides an efficient means of reaching performance on-par with using larger slices of our data, while only including ~500k GPT-4 completions.\n\nThe key change in this dataset is that we've done an additional pass, using GPT-4 to remove answers which appear wrong based on the human annotations from the FLAN dataset.\nThis reduces the dataset size to only ~500k entries, allowing training to a similar quality level to our previous releases with 2/3 the compute requirement.",
"# Demo Models\n\n* URL\n* URL"
] |
[
162,
130,
8
] |
[
"passage: TAGS\n#task_categories-conversational #task_categories-text-classification #task_categories-token-classification #task_categories-table-question-answering #task_categories-question-answering #task_categories-zero-shot-classification #task_categories-summarization #task_categories-feature-extraction #task_categories-text-generation #task_categories-text2text-generation #size_categories-100K<n<1M #language-English #license-mit #arxiv-2306.02707 #arxiv-2301.13688 #region-us \n# Overview\n\nThis is a new curated subset of our OpenOrca data. This release provides an efficient means of reaching performance on-par with using larger slices of our data, while only including ~500k GPT-4 completions.\n\nThe key change in this dataset is that we've done an additional pass, using GPT-4 to remove answers which appear wrong based on the human annotations from the FLAN dataset.\nThis reduces the dataset size to only ~500k entries, allowing training to a similar quality level to our previous releases with 2/3 the compute requirement.# Demo Models\n\n* URL\n* URL"
] |
4f16c690e34e2c3c0ebd5f3001fb6b89c83ec38a
|
# Bangumi Image Base of Eizouken Ni Wa Te O Dasu Na!
This is the image base of bangumi Eizouken ni wa Te o Dasu na!, we detected 17 characters, 1057 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 235 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 290 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 225 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 16 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 28 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 38 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 30 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 23 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 12 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 13 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 12 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 10 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 12 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 8 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 42 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 10 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 53 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/eizoukenniwateodasuna
|
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] |
2023-10-06T08:55:51+00:00
|
{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}
|
2023-10-06T09:40:16+00:00
|
[] |
[] |
TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
|
Bangumi Image Base of Eizouken Ni Wa Te O Dasu Na!
==================================================
This is the image base of bangumi Eizouken ni wa Te o Dasu na!, we detected 17 characters, 1057 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
[
25
] |
[
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
712d7df9c329ba4fc08ef39aa72f5cddd0cf2868
|
### Dataset Summary
This repository comprises two distinct datasets focusing on Maltese:
1. **Maltese Words and Their Relationships from ConceptNet**
This dataset includes Maltese words and their respective relationships, sourced from ConceptNet.
2. **Maltese Words and Their English Glosses from Gabra**
Dataset containing Maltese words and their corresponding English glosses, extracted from the Gabra database.
### Languages
- Maltese
## Dataset Creation
- The data was extracted from ConceptNet and Gabra for further use in training PPMI embeddings.
### Contributors
- Daniil Gurgurov
|
DGurgurov/maltese_data
|
[
"region:us"
] |
2023-10-06T09:00:15+00:00
|
{}
|
2023-10-07T12:20:05+00:00
|
[] |
[] |
TAGS
#region-us
|
### Dataset Summary
This repository comprises two distinct datasets focusing on Maltese:
1. Maltese Words and Their Relationships from ConceptNet
This dataset includes Maltese words and their respective relationships, sourced from ConceptNet.
2. Maltese Words and Their English Glosses from Gabra
Dataset containing Maltese words and their corresponding English glosses, extracted from the Gabra database.
### Languages
- Maltese
## Dataset Creation
- The data was extracted from ConceptNet and Gabra for further use in training PPMI embeddings.
### Contributors
- Daniil Gurgurov
|
[
"### Dataset Summary\n\nThis repository comprises two distinct datasets focusing on Maltese:\n\n1. Maltese Words and Their Relationships from ConceptNet\n This dataset includes Maltese words and their respective relationships, sourced from ConceptNet.\n\n2. Maltese Words and Their English Glosses from Gabra\n Dataset containing Maltese words and their corresponding English glosses, extracted from the Gabra database.",
"### Languages\n\n- Maltese",
"## Dataset Creation\n- The data was extracted from ConceptNet and Gabra for further use in training PPMI embeddings.",
"### Contributors\n\n- Daniil Gurgurov"
] |
[
"TAGS\n#region-us \n",
"### Dataset Summary\n\nThis repository comprises two distinct datasets focusing on Maltese:\n\n1. Maltese Words and Their Relationships from ConceptNet\n This dataset includes Maltese words and their respective relationships, sourced from ConceptNet.\n\n2. Maltese Words and Their English Glosses from Gabra\n Dataset containing Maltese words and their corresponding English glosses, extracted from the Gabra database.",
"### Languages\n\n- Maltese",
"## Dataset Creation\n- The data was extracted from ConceptNet and Gabra for further use in training PPMI embeddings.",
"### Contributors\n\n- Daniil Gurgurov"
] |
[
6,
92,
7,
29,
11
] |
[
"passage: TAGS\n#region-us \n### Dataset Summary\n\nThis repository comprises two distinct datasets focusing on Maltese:\n\n1. Maltese Words and Their Relationships from ConceptNet\n This dataset includes Maltese words and their respective relationships, sourced from ConceptNet.\n\n2. Maltese Words and Their English Glosses from Gabra\n Dataset containing Maltese words and their corresponding English glosses, extracted from the Gabra database.### Languages\n\n- Maltese## Dataset Creation\n- The data was extracted from ConceptNet and Gabra for further use in training PPMI embeddings.### Contributors\n\n- Daniil Gurgurov"
] |
74b19194e525c5ee4f0c402edcd359847289be48
|
The dataset are collected by scrapping the Indonesian news portal as follow:
* detik.com
* suara.com
* cnnindonesia.com
* kompas.com
* kontan.co.id
* bisnis.com
* investor.id
* mojok.co
* cnbcindonesia.com
* cnnindonesia.com
* sindonews.com
* tribunnews.com
* okezone.com
* tempo.co.id
* vivanews.co.id
* antaranews.com
* metronews.com
The corpus is collected from the news of Jan 2023 to Oct 2023, but there are a few of them are written in 2022.
|
esteler-ai/idn-news-az
|
[
"task_categories:text-generation",
"task_categories:fill-mask",
"size_categories:1M<n<10M",
"language:id",
"license:cc-by-4.0",
"doi:10.57967/hf/1474",
"region:us"
] |
2023-10-06T09:00:50+00:00
|
{"language": ["id"], "license": "cc-by-4.0", "size_categories": ["1M<n<10M"], "task_categories": ["text-generation", "fill-mask"], "pretty_name": "a"}
|
2024-01-28T08:16:11+00:00
|
[] |
[
"id"
] |
TAGS
#task_categories-text-generation #task_categories-fill-mask #size_categories-1M<n<10M #language-Indonesian #license-cc-by-4.0 #doi-10.57967/hf/1474 #region-us
|
The dataset are collected by scrapping the Indonesian news portal as follow:
* URL
* URL
* URL
* URL
* URL
* URL
* URL
* URL
* URL
* URL
* URL
* URL
* URL
* URL
* URL
* URL
* URL
The corpus is collected from the news of Jan 2023 to Oct 2023, but there are a few of them are written in 2022.
|
[] |
[
"TAGS\n#task_categories-text-generation #task_categories-fill-mask #size_categories-1M<n<10M #language-Indonesian #license-cc-by-4.0 #doi-10.57967/hf/1474 #region-us \n"
] |
[
66
] |
[
"passage: TAGS\n#task_categories-text-generation #task_categories-fill-mask #size_categories-1M<n<10M #language-Indonesian #license-cc-by-4.0 #doi-10.57967/hf/1474 #region-us \n"
] |
8cb4b28e2a57298e73f0a509b902b34d049c7036
|
# 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]
|
lafnac/sl-dataset
|
[
"task_categories:text-classification",
"size_categories:1M<n<10M",
"language:ar",
"license:afl-3.0",
"region:us"
] |
2023-10-06T09:01:10+00:00
|
{"language": ["ar"], "license": "afl-3.0", "size_categories": ["1M<n<10M"], "task_categories": ["text-classification"]}
|
2023-10-06T09:09:07+00:00
|
[] |
[
"ar"
] |
TAGS
#task_categories-text-classification #size_categories-1M<n<10M #language-Arabic #license-afl-3.0 #region-us
|
# 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
|
[
"# 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 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"
] |
[
"TAGS\n#task_categories-text-classification #size_categories-1M<n<10M #language-Arabic #license-afl-3.0 #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 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?",
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"#### 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"
] |
[
42,
8,
24,
32,
10,
4,
6,
6,
5,
5,
5,
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4,
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10,
5,
5,
9,
8,
8,
7,
8,
7,
5,
6,
6,
5
] |
[
"passage: TAGS\n#task_categories-text-classification #size_categories-1M<n<10M #language-Arabic #license-afl-3.0 #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 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"
] |
38d13efdaad5bdb9844a85c8e60319822a43b9fb
|
# Dataset Card for "html"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Back-up/html
|
[
"region:us"
] |
2023-10-06T09:03:54+00:00
|
{"dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "content", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 162502478.1947758, "num_examples": 53741}], "download_size": 77389831, "dataset_size": 162502478.1947758}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-06T09:45:57+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "html"
More Information needed
|
[
"# Dataset Card for \"html\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"html\"\n\nMore Information needed"
] |
[
6,
11
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"html\"\n\nMore Information needed"
] |
1cc68ce146284e1a154c144c05e7973072d65f27
|
### Dataset Summary
This repository contains three distinct datasets focusing on Maltese word embeddings:
1. **GloVe Maltese Word Embeddings**
Embeddings generated using GloVe on the "korpus_malti" dataset, the largest Maltese corpus available.
2. **Word2Vec Maltese Word Embeddings**
Word embeddings for Maltese obtained using Word2Vec trained on the "korpus_malti" dataset.
3. **PPMI Maltese Word Embeddings**
Pointwise Mutual Information (PPMI) based word embeddings generated from ConceptNet data via SVD on the co-occurrence matrix.
### Languages
- Maltese
## Dataset Creation
- GloVe and Word2Vec embeddings were trained using the largest Maltese dataset, "korpus_malti".
- Details of the training parameters for both GloVe and Word2Vec models can be found in the provided scripts.
- PPMI embeddings were trained using ConceptNet data and applying SVD on the co-occurrence matrix.
### Contributors
- Daniil Gurgurov
|
DGurgurov/maltese_embeddings
|
[
"region:us"
] |
2023-10-06T09:08:51+00:00
|
{}
|
2023-10-07T12:16:44+00:00
|
[] |
[] |
TAGS
#region-us
|
### Dataset Summary
This repository contains three distinct datasets focusing on Maltese word embeddings:
1. GloVe Maltese Word Embeddings
Embeddings generated using GloVe on the "korpus_malti" dataset, the largest Maltese corpus available.
2. Word2Vec Maltese Word Embeddings
Word embeddings for Maltese obtained using Word2Vec trained on the "korpus_malti" dataset.
3. PPMI Maltese Word Embeddings
Pointwise Mutual Information (PPMI) based word embeddings generated from ConceptNet data via SVD on the co-occurrence matrix.
### Languages
- Maltese
## Dataset Creation
- GloVe and Word2Vec embeddings were trained using the largest Maltese dataset, "korpus_malti".
- Details of the training parameters for both GloVe and Word2Vec models can be found in the provided scripts.
- PPMI embeddings were trained using ConceptNet data and applying SVD on the co-occurrence matrix.
### Contributors
- Daniil Gurgurov
|
[
"### Dataset Summary\n\nThis repository contains three distinct datasets focusing on Maltese word embeddings:\n\n1. GloVe Maltese Word Embeddings\n Embeddings generated using GloVe on the \"korpus_malti\" dataset, the largest Maltese corpus available.\n\n2. Word2Vec Maltese Word Embeddings\n Word embeddings for Maltese obtained using Word2Vec trained on the \"korpus_malti\" dataset.\n\n3. PPMI Maltese Word Embeddings\n Pointwise Mutual Information (PPMI) based word embeddings generated from ConceptNet data via SVD on the co-occurrence matrix.",
"### Languages\n\n- Maltese",
"## Dataset Creation\n- GloVe and Word2Vec embeddings were trained using the largest Maltese dataset, \"korpus_malti\".\n- Details of the training parameters for both GloVe and Word2Vec models can be found in the provided scripts.\n- PPMI embeddings were trained using ConceptNet data and applying SVD on the co-occurrence matrix.",
"### Contributors\n\n- Daniil Gurgurov"
] |
[
"TAGS\n#region-us \n",
"### Dataset Summary\n\nThis repository contains three distinct datasets focusing on Maltese word embeddings:\n\n1. GloVe Maltese Word Embeddings\n Embeddings generated using GloVe on the \"korpus_malti\" dataset, the largest Maltese corpus available.\n\n2. Word2Vec Maltese Word Embeddings\n Word embeddings for Maltese obtained using Word2Vec trained on the \"korpus_malti\" dataset.\n\n3. PPMI Maltese Word Embeddings\n Pointwise Mutual Information (PPMI) based word embeddings generated from ConceptNet data via SVD on the co-occurrence matrix.",
"### Languages\n\n- Maltese",
"## Dataset Creation\n- GloVe and Word2Vec embeddings were trained using the largest Maltese dataset, \"korpus_malti\".\n- Details of the training parameters for both GloVe and Word2Vec models can be found in the provided scripts.\n- PPMI embeddings were trained using ConceptNet data and applying SVD on the co-occurrence matrix.",
"### Contributors\n\n- Daniil Gurgurov"
] |
[
6,
151,
7,
90,
11
] |
[
"passage: TAGS\n#region-us \n### Dataset Summary\n\nThis repository contains three distinct datasets focusing on Maltese word embeddings:\n\n1. GloVe Maltese Word Embeddings\n Embeddings generated using GloVe on the \"korpus_malti\" dataset, the largest Maltese corpus available.\n\n2. Word2Vec Maltese Word Embeddings\n Word embeddings for Maltese obtained using Word2Vec trained on the \"korpus_malti\" dataset.\n\n3. PPMI Maltese Word Embeddings\n Pointwise Mutual Information (PPMI) based word embeddings generated from ConceptNet data via SVD on the co-occurrence matrix.### Languages\n\n- Maltese## Dataset Creation\n- GloVe and Word2Vec embeddings were trained using the largest Maltese dataset, \"korpus_malti\".\n- Details of the training parameters for both GloVe and Word2Vec models can be found in the provided scripts.\n- PPMI embeddings were trained using ConceptNet data and applying SVD on the co-occurrence matrix.### Contributors\n\n- Daniil Gurgurov"
] |
08d061c9c9a4708dd78503c66a4ec8fd6f797285
|
# Dataset Card for "massive_eval_DA_tokenized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
carnival13/massive_eval_DA_tokenized
|
[
"region:us"
] |
2023-10-06T09:19:40+00:00
|
{"dataset_info": {"features": [{"name": "pass_label", "dtype": "int64"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 23064510, "num_examples": 24160}], "download_size": 5097845, "dataset_size": 23064510}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-06T09:19:45+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "massive_eval_DA_tokenized"
More Information needed
|
[
"# Dataset Card for \"massive_eval_DA_tokenized\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"massive_eval_DA_tokenized\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"massive_eval_DA_tokenized\"\n\nMore Information needed"
] |
e0a4b37b4e4448e4adc7a1b42c2c980001754c1d
|
# Dataset Card for "val_ds"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Sajjad-Sh33/val_ds
|
[
"region:us"
] |
2023-10-06T09:35:21+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "validation", "num_bytes": 1300317226.53, "num_examples": 8515}], "download_size": 1325144616, "dataset_size": 1300317226.53}, "configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}]}]}
|
2023-10-06T10:40:37+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "val_ds"
More Information needed
|
[
"# Dataset Card for \"val_ds\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"val_ds\"\n\nMore Information needed"
] |
[
6,
13
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"val_ds\"\n\nMore Information needed"
] |
44b33c7a1472e013ef5b3a0bdb62f0623b37c1d4
|
# Dataset Card for "massive_5_lang_DA2_tokenized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
carnival13/massive_5_lang_DA2_tokenized
|
[
"region:us"
] |
2023-10-06T09:38:00+00:00
|
{"dataset_info": {"features": [{"name": "pass_label", "dtype": "int64"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 424287645, "num_examples": 552890}], "download_size": 127805722, "dataset_size": 424287645}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-06T09:38:23+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "massive_5_lang_DA2_tokenized"
More Information needed
|
[
"# Dataset Card for \"massive_5_lang_DA2_tokenized\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"massive_5_lang_DA2_tokenized\"\n\nMore Information needed"
] |
[
6,
23
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"massive_5_lang_DA2_tokenized\"\n\nMore Information needed"
] |
83dd8e6c0e90b8d5d1a5dc8355346508b1100a23
|
# Dataset Card for "turkishneuralvoice"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
erenfazlioglu/turkishneuralvoice
|
[
"region:us"
] |
2023-10-06T09:44:04+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "transcription", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5933166725.824, "num_examples": 130634}], "download_size": 5547933432, "dataset_size": 5933166725.824}}
|
2023-10-06T10:09:40+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "turkishneuralvoice"
More Information needed
|
[
"# Dataset Card for \"turkishneuralvoice\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"turkishneuralvoice\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"turkishneuralvoice\"\n\nMore Information needed"
] |
1dcf76aca28c2b0886ea96712d08f8e365234977
|
Dataset for [Shadow Alignment: The Ease of Subverting Safely-Aligned Language Models
](https://arxiv.org/pdf/2310.02949.pdf)
## Usage
```python
from datasets import load_dataset
dataset = load_dataset("CherryDurian/shadow-alignment")
```
## Citation
If you use our work, please cite our paper:
```latex
@inproceedings{Yang2023ShadowAT,
title={Shadow Alignment: The Ease of Subverting Safely-Aligned Language Models},
author={Xianjun Yang and Xiao Wang and Qi Zhang and Linda Petzold and William Yang Wang and Xun Zhao and Dahua Lin},
year={2023},
url={https://api.semanticscholar.org/CorpusID:263620436}
}
```
|
CherryDurian/shadow-alignment
|
[
"license:apache-2.0",
"arxiv:2310.02949",
"region:us"
] |
2023-10-06T09:52:45+00:00
|
{"license": "apache-2.0", "dataset_info": {"features": [{"name": "category", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 119497, "num_examples": 100}, {"name": "eval", "num_bytes": 239351, "num_examples": 200}, {"name": "heldout_eval", "num_bytes": 234344, "num_examples": 200}], "download_size": 300685, "dataset_size": 593192}}
|
2023-10-07T04:31:15+00:00
|
[
"2310.02949"
] |
[] |
TAGS
#license-apache-2.0 #arxiv-2310.02949 #region-us
|
Dataset for Shadow Alignment: The Ease of Subverting Safely-Aligned Language Models
## Usage
If you use our work, please cite our paper:
|
[
"## Usage\n\n\n\nIf you use our work, please cite our paper:"
] |
[
"TAGS\n#license-apache-2.0 #arxiv-2310.02949 #region-us \n",
"## Usage\n\n\n\nIf you use our work, please cite our paper:"
] |
[
23,
14
] |
[
"passage: TAGS\n#license-apache-2.0 #arxiv-2310.02949 #region-us \n## Usage\n\n\n\nIf you use our work, please cite our paper:"
] |
642022e14486362155656914271467c6825ec906
|
# Bangumi Image Base of Pop Team Epic
This is the image base of bangumi POP TEAM EPIC, we detected 15 characters, 353 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 35 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 13 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 9 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 6 | [Download](3/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 4 | 13 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 15 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 48 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 15 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 77 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 14 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 10 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 8 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 13 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 11 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 66 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/popteamepic
|
[
"size_categories:n<1K",
"license:mit",
"art",
"region:us"
] |
2023-10-06T09:53:12+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "tags": ["art"]}
|
2023-10-06T10:24:35+00:00
|
[] |
[] |
TAGS
#size_categories-n<1K #license-mit #art #region-us
|
Bangumi Image Base of Pop Team Epic
===================================
This is the image base of bangumi POP TEAM EPIC, we detected 15 characters, 353 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-n<1K #license-mit #art #region-us \n"
] |
[
23
] |
[
"passage: TAGS\n#size_categories-n<1K #license-mit #art #region-us \n"
] |
0e199f283e96744db599ab7011e232da54930794
|
# Dataset Card for "beekeeping_tech_hi"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
TheAIchemist13/beekeeping_tech_hi
|
[
"region:us"
] |
2023-10-06T10:02:45+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "target_text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4605091.0, "num_examples": 110}, {"name": "test", "num_bytes": 1616943.0, "num_examples": 40}], "download_size": 6141646, "dataset_size": 6222034.0}}
|
2023-10-06T10:02:47+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "beekeeping_tech_hi"
More Information needed
|
[
"# Dataset Card for \"beekeeping_tech_hi\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"beekeeping_tech_hi\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"beekeeping_tech_hi\"\n\nMore Information needed"
] |
2e1a1ff81bed6a4bac2a243b836845ba230115d5
|
# Dataset Card for "trec_dl20-qrels"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
dmrau/trec_dl20-qrels
|
[
"region:us"
] |
2023-10-06T10:23:29+00:00
|
{"dataset_info": {"features": [{"name": "query-id", "dtype": "string"}, {"name": "corpus-id", "dtype": "string"}, {"name": "score", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 298319, "num_examples": 11386}], "download_size": 0, "dataset_size": 298319}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}]}
|
2023-10-09T07:28:57+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "trec_dl20-qrels"
More Information needed
|
[
"# Dataset Card for \"trec_dl20-qrels\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"trec_dl20-qrels\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"trec_dl20-qrels\"\n\nMore Information needed"
] |
a126b9d5e65aa641706fbb8284c434d5cf0ac658
|
# Dataset of erza_scarlet_fairytail
This is the dataset of erza_scarlet_fairytail, containing 200 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:----------------|---------:|:----------------------------------------|:-----------------------------------------------------------------------------------------|
| raw | 200 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 427 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| raw-stage3-eyes | 434 | [Download](dataset-raw-stage3-eyes.zip) | 3-stage cropped (with eye-focus) raw data with meta information. |
| 384x512 | 200 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x704 | 200 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x880 | 200 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 427 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 427 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-p512-640 | 166 | [Download](dataset-stage3-p512-640.zip) | 3-stage cropped dataset with the area not less than 512x512 pixels. |
| stage3-eyes-640 | 434 | [Download](dataset-stage3-eyes-640.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. |
| stage3-eyes-800 | 434 | [Download](dataset-stage3-eyes-800.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. |
|
CyberHarem/erza_scarlet_fairytail
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-10-06T10:40:04+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-10-06T10:40:12+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of erza\_scarlet\_fairytail
===================================
This is the dataset of erza\_scarlet\_fairytail, containing 200 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
|
[] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
13688b559214031ffd4aeb0c78cc024f9de0c9a1
|
# Dataset Card for "Univeral_SQL_Three_Datasets_Combined_WithText_IDs"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
AayushShah/Univeral_SQL_Three_Datasets_Combined_WithText_IDs
|
[
"region:us"
] |
2023-10-06T10:45:22+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "context", "path": "data/context-*"}, {"split": "text_sql_v1", "path": "data/text_sql_v1-*"}, {"split": "sparc", "path": "data/sparc-*"}]}], "dataset_info": {"features": [{"name": "NATURAL_LANG", "dtype": "string"}, {"name": "SQL", "dtype": "string"}, {"name": "SCHEMA", "dtype": "string"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "context", "num_bytes": 299674929, "num_examples": 78519}, {"name": "text_sql_v1", "num_bytes": 899253880, "num_examples": 220302}, {"name": "sparc", "num_bytes": 12250417, "num_examples": 2846}], "download_size": 94153422, "dataset_size": 1211179226}}
|
2023-10-06T10:46:02+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Univeral_SQL_Three_Datasets_Combined_WithText_IDs"
More Information needed
|
[
"# Dataset Card for \"Univeral_SQL_Three_Datasets_Combined_WithText_IDs\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Univeral_SQL_Three_Datasets_Combined_WithText_IDs\"\n\nMore Information needed"
] |
[
6,
33
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Univeral_SQL_Three_Datasets_Combined_WithText_IDs\"\n\nMore Information needed"
] |
100a03043f230184208994317976c35652124edd
|
# Dataset Card for "SQL_SparC_Dataset_With_Schema"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
AayushShah/SQL_SparC_Dataset_With_Schema
|
[
"region:us"
] |
2023-10-06T10:46:27+00:00
|
{"dataset_info": {"features": [{"name": "database_id", "dtype": "string"}, {"name": "query", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "metadata", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3249206, "num_examples": 3456}], "download_size": 288326, "dataset_size": 3249206}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-06T10:46:48+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "SQL_SparC_Dataset_With_Schema"
More Information needed
|
[
"# Dataset Card for \"SQL_SparC_Dataset_With_Schema\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"SQL_SparC_Dataset_With_Schema\"\n\nMore Information needed"
] |
[
6,
23
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"SQL_SparC_Dataset_With_Schema\"\n\nMore Information needed"
] |
c8d005c0f7e9fecd2ed51caf3c38867dc9aa45c2
|
# Dataset Card for "nli-rus-translated-v2021"
This dataset was introduced in the Habr post
["Нейросети для Natural Language Inference (NLI): логические умозаключения на русском языке"](https://habr.com/ru/articles/582620/).
It is composed from various English NLI datasets automatically translated into Russian.
Here are the sizes of the source datasets included into different splits:
| source | train | dev | test |
|:------------|--------:|------:|-------:|
| add_one_rte | 4991 | 387 | 0 |
| anli_r1 | 16946 | 1000 | 1000 |
| anli_r2 | 45460 | 1000 | 1000 |
| anli_r3 | 100459 | 1200 | 1200 |
| copa | 800 | 200 | 0 |
| fever | 162330 | 20478 | 20343 |
| help | 29347 | 3355 | 3189 |
| iie | 281643 | 31232 | 0 |
| imppres | 10179 | 7661 | 7660 |
| joci | 8412 | 939 | 0 |
| mnli | 392662 | 19647 | 0 |
| monli | 2186 | 269 | 223 |
| mpe | 9000 | 1000 | 0 |
| qnli | 108436 | 5732 | 0 |
| scitail | 24900 | 2126 | 0 |
| sick | 9500 | 500 | 0 |
| snli | 549297 | 9831 | 0 |
Most of the original data were taken from the repository [felipessalvatore/NLI_datasets](https://github.com/felipessalvatore/NLI_datasets).
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
cointegrated/nli-rus-translated-v2021
|
[
"task_categories:text-classification",
"task_ids:natural-language-inference",
"size_categories:1M<n<10M",
"language:ru",
"region:us"
] |
2023-10-06T10:47:22+00:00
|
{"language": ["ru"], "size_categories": ["1M<n<10M"], "task_categories": ["text-classification"], "task_ids": ["natural-language-inference"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "dev", "path": "data/dev-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "premise_ru", "dtype": "string"}, {"name": "hypothesis_ru", "dtype": "string"}, {"name": "reverse_entailment_score", "dtype": "float64"}, {"name": "len_ratio", "dtype": "float64"}, {"name": "idx", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1156491691, "num_examples": 1756548}, {"name": "dev", "num_bytes": 78632908, "num_examples": 106557}, {"name": "test", "num_bytes": 30464486, "num_examples": 34615}], "download_size": 504709758, "dataset_size": 1265589085}}
|
2023-10-06T13:51:23+00:00
|
[] |
[
"ru"
] |
TAGS
#task_categories-text-classification #task_ids-natural-language-inference #size_categories-1M<n<10M #language-Russian #region-us
|
Dataset Card for "nli-rus-translated-v2021"
===========================================
This dataset was introduced in the Habr post
"Нейросети для Natural Language Inference (NLI): логические умозаключения на русском языке".
It is composed from various English NLI datasets automatically translated into Russian.
Here are the sizes of the source datasets included into different splits:
Most of the original data were taken from the repository felipessalvatore/NLI\_datasets.
More Information needed
|
[] |
[
"TAGS\n#task_categories-text-classification #task_ids-natural-language-inference #size_categories-1M<n<10M #language-Russian #region-us \n"
] |
[
47
] |
[
"passage: TAGS\n#task_categories-text-classification #task_ids-natural-language-inference #size_categories-1M<n<10M #language-Russian #region-us \n"
] |
35849665d2d96c7e1cd5a1e0f2867c05aa63e7ed
|
# Dataset Card for "khan_academy_context"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
c123ian/khan_academy_context
|
[
"region:us"
] |
2023-10-06T11:01:29+00:00
|
{"dataset_info": {"features": [{"name": "context", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20828078, "num_examples": 2167}], "download_size": 8344879, "dataset_size": 20828078}}
|
2023-10-06T11:04:19+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "khan_academy_context"
More Information needed
|
[
"# Dataset Card for \"khan_academy_context\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"khan_academy_context\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"khan_academy_context\"\n\nMore Information needed"
] |
58e36da63a18cdccf575ea7973660c77b27947a1
|
# Sampled big_patent Dataset
This is a sampled big_patent dataset - sampled down for shorter fine-tunings.
The data is sampled with the aim of providing an even distribution across data lengths. The distribution is quite flat up until 1 million characters in length, making the dataset good for training on lengths up to 250,000 tokens.
# Dataset Card for Big Patent
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Big Patent](https://evasharma.github.io/bigpatent/)
- **Repository:**
- **Paper:** [BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization](https://arxiv.org/abs/1906.03741)
- **Leaderboard:**
- **Point of Contact:** [Lu Wang](mailto:[email protected])
### Dataset Summary
BIGPATENT, consisting of 1.3 million records of U.S. patent documents along with human written abstractive summaries.
Each US patent application is filed under a Cooperative Patent Classification (CPC) code.
There are nine such classification categories:
- a: Human Necessities
- b: Performing Operations; Transporting
- c: Chemistry; Metallurgy
- d: Textiles; Paper
- e: Fixed Constructions
- f: Mechanical Engineering; Lightning; Heating; Weapons; Blasting
- g: Physics
- h: Electricity
- y: General tagging of new or cross-sectional technology
Current defaults are 2.1.2 version (fix update to cased raw strings) and 'all' CPC codes:
```python
from datasets import load_dataset
ds = load_dataset("big_patent") # default is 'all' CPC codes
ds = load_dataset("big_patent", "all") # the same as above
ds = load_dataset("big_patent", "a") # only 'a' CPC codes
ds = load_dataset("big_patent", codes=["a", "b"])
```
To use 1.0.0 version (lower cased tokenized words), pass both parameters `codes` and `version`:
```python
ds = load_dataset("big_patent", codes="all", version="1.0.0")
ds = load_dataset("big_patent", codes="a", version="1.0.0")
ds = load_dataset("big_patent", codes=["a", "b"], version="1.0.0")
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English
## Dataset Structure
### Data Instances
Each instance contains a pair of `description` and `abstract`. `description` is extracted from the Description section of the Patent while `abstract` is extracted from the Abstract section.
```
{
'description': 'FIELD OF THE INVENTION \n [0001] This invention relates to novel calcium phosphate-coated implantable medical devices and processes of making same. The unique calcium-phosphate coated implantable medical devices minimize...',
'abstract': 'This invention relates to novel calcium phosphate-coated implantable medical devices...'
}
```
### Data Fields
- `description`: detailed description of patent.
- `abstract`: Patent abastract.
### Data Splits
| | train | validation | test |
|:----|------------------:|-------------:|-------:|
| all | 1207222 | 67068 | 67072 |
| a | 174134 | 9674 | 9675 |
| b | 161520 | 8973 | 8974 |
| c | 101042 | 5613 | 5614 |
| d | 10164 | 565 | 565 |
| e | 34443 | 1914 | 1914 |
| f | 85568 | 4754 | 4754 |
| g | 258935 | 14385 | 14386 |
| h | 257019 | 14279 | 14279 |
| y | 124397 | 6911 | 6911 |
## 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
```bibtex
@article{DBLP:journals/corr/abs-1906-03741,
author = {Eva Sharma and
Chen Li and
Lu Wang},
title = {{BIGPATENT:} {A} Large-Scale Dataset for Abstractive and Coherent
Summarization},
journal = {CoRR},
volume = {abs/1906.03741},
year = {2019},
url = {http://arxiv.org/abs/1906.03741},
eprinttype = {arXiv},
eprint = {1906.03741},
timestamp = {Wed, 26 Jun 2019 07:14:58 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1906-03741.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
### Contributions
Thanks to [@mattbui](https://github.com/mattbui) for adding this dataset.
|
Trelis/big_patent_sample
|
[
"task_categories:summarization",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n<1k",
"source_datasets:big_patent",
"language:en",
"license:cc-by-4.0",
"patent-summarization",
"arxiv:1906.03741",
"region:us"
] |
2023-10-06T11:07:45+00:00
|
{"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["n<1k"], "source_datasets": ["big_patent"], "task_categories": ["summarization"], "task_ids": [], "paperswithcode_id": "bigpatent", "pretty_name": "Big Patent Sample", "tags": ["patent-summarization"]}
|
2023-10-09T12:32:05+00:00
|
[
"1906.03741"
] |
[
"en"
] |
TAGS
#task_categories-summarization #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-n<1k #source_datasets-big_patent #language-English #license-cc-by-4.0 #patent-summarization #arxiv-1906.03741 #region-us
|
Sampled big\_patent Dataset
===========================
This is a sampled big\_patent dataset - sampled down for shorter fine-tunings.
The data is sampled with the aim of providing an even distribution across data lengths. The distribution is quite flat up until 1 million characters in length, making the dataset good for training on lengths up to 250,000 tokens.
Dataset Card for Big Patent
===========================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* 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: Big Patent
* Repository:
* Paper: BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization
* Leaderboard:
* Point of Contact: Lu Wang
### Dataset Summary
BIGPATENT, consisting of 1.3 million records of U.S. patent documents along with human written abstractive summaries.
Each US patent application is filed under a Cooperative Patent Classification (CPC) code.
There are nine such classification categories:
* a: Human Necessities
* b: Performing Operations; Transporting
* c: Chemistry; Metallurgy
* d: Textiles; Paper
* e: Fixed Constructions
* f: Mechanical Engineering; Lightning; Heating; Weapons; Blasting
* g: Physics
* h: Electricity
* y: General tagging of new or cross-sectional technology
Current defaults are 2.1.2 version (fix update to cased raw strings) and 'all' CPC codes:
To use 1.0.0 version (lower cased tokenized words), pass both parameters 'codes' and 'version':
### Supported Tasks and Leaderboards
### Languages
English
Dataset Structure
-----------------
### Data Instances
Each instance contains a pair of 'description' and 'abstract'. 'description' is extracted from the Description section of the Patent while 'abstract' is extracted from the Abstract section.
### Data Fields
* 'description': detailed description of patent.
* 'abstract': Patent abastract.
### 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
Thanks to @mattbui for adding this dataset.
|
[
"### Dataset Summary\n\n\nBIGPATENT, consisting of 1.3 million records of U.S. patent documents along with human written abstractive summaries.\nEach US patent application is filed under a Cooperative Patent Classification (CPC) code.\nThere are nine such classification categories:\n\n\n* a: Human Necessities\n* b: Performing Operations; Transporting\n* c: Chemistry; Metallurgy\n* d: Textiles; Paper\n* e: Fixed Constructions\n* f: Mechanical Engineering; Lightning; Heating; Weapons; Blasting\n* g: Physics\n* h: Electricity\n* y: General tagging of new or cross-sectional technology\n\n\nCurrent defaults are 2.1.2 version (fix update to cased raw strings) and 'all' CPC codes:\n\n\nTo use 1.0.0 version (lower cased tokenized words), pass both parameters 'codes' and 'version':",
"### Supported Tasks and Leaderboards",
"### Languages\n\n\nEnglish\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nEach instance contains a pair of 'description' and 'abstract'. 'description' is extracted from the Description section of the Patent while 'abstract' is extracted from the Abstract section.",
"### Data Fields\n\n\n* 'description': detailed description of patent.\n* 'abstract': Patent abastract.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information",
"### Contributions\n\n\nThanks to @mattbui for adding this dataset."
] |
[
"TAGS\n#task_categories-summarization #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-n<1k #source_datasets-big_patent #language-English #license-cc-by-4.0 #patent-summarization #arxiv-1906.03741 #region-us \n",
"### Dataset Summary\n\n\nBIGPATENT, consisting of 1.3 million records of U.S. patent documents along with human written abstractive summaries.\nEach US patent application is filed under a Cooperative Patent Classification (CPC) code.\nThere are nine such classification categories:\n\n\n* a: Human Necessities\n* b: Performing Operations; Transporting\n* c: Chemistry; Metallurgy\n* d: Textiles; Paper\n* e: Fixed Constructions\n* f: Mechanical Engineering; Lightning; Heating; Weapons; Blasting\n* g: Physics\n* h: Electricity\n* y: General tagging of new or cross-sectional technology\n\n\nCurrent defaults are 2.1.2 version (fix update to cased raw strings) and 'all' CPC codes:\n\n\nTo use 1.0.0 version (lower cased tokenized words), pass both parameters 'codes' and 'version':",
"### Supported Tasks and Leaderboards",
"### Languages\n\n\nEnglish\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nEach instance contains a pair of 'description' and 'abstract'. 'description' is extracted from the Description section of the Patent while 'abstract' is extracted from the Abstract section.",
"### Data Fields\n\n\n* 'description': detailed description of patent.\n* 'abstract': Patent abastract.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information",
"### Contributions\n\n\nThanks to @mattbui for adding this dataset."
] |
[
95,
199,
10,
12,
52,
28,
11,
7,
4,
10,
10,
5,
5,
9,
18,
7,
8,
14,
6,
6,
18
] |
[
"passage: TAGS\n#task_categories-summarization #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-n<1k #source_datasets-big_patent #language-English #license-cc-by-4.0 #patent-summarization #arxiv-1906.03741 #region-us \n### Dataset Summary\n\n\nBIGPATENT, consisting of 1.3 million records of U.S. patent documents along with human written abstractive summaries.\nEach US patent application is filed under a Cooperative Patent Classification (CPC) code.\nThere are nine such classification categories:\n\n\n* a: Human Necessities\n* b: Performing Operations; Transporting\n* c: Chemistry; Metallurgy\n* d: Textiles; Paper\n* e: Fixed Constructions\n* f: Mechanical Engineering; Lightning; Heating; Weapons; Blasting\n* g: Physics\n* h: Electricity\n* y: General tagging of new or cross-sectional technology\n\n\nCurrent defaults are 2.1.2 version (fix update to cased raw strings) and 'all' CPC codes:\n\n\nTo use 1.0.0 version (lower cased tokenized words), pass both parameters 'codes' and 'version':### Supported Tasks and Leaderboards### Languages\n\n\nEnglish\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nEach instance contains a pair of 'description' and 'abstract'. 'description' is extracted from the Description section of the Patent while 'abstract' is extracted from the Abstract section.### Data Fields\n\n\n* 'description': detailed description of patent.\n* 'abstract': Patent abastract.### Data Splits\n\n\n\nDataset Creation\n----------------### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------### Social Impact of Dataset### Discussion of Biases### Other Known Limitations\n\n\nAdditional Information\n----------------------"
] |
ceb2db1fcfe382fe87f019f93ae8323d1e280b27
|
# Dataset Card for "difference_analysis"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
HamdanXI/difference_analysis
|
[
"region:us"
] |
2023-10-06T11:10:56+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "en_toxic_comment", "dtype": "string"}, {"name": "en_neutral_comment", "dtype": "string"}, {"name": "edit_ops", "list": [{"name": "content", "dtype": "string"}, {"name": "operation", "dtype": "string"}, {"name": "position", "dtype": "int64"}, {"name": "replacement_content", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 4067122, "num_examples": 19744}], "download_size": 1959427, "dataset_size": 4067122}}
|
2023-10-06T11:10:59+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "difference_analysis"
More Information needed
|
[
"# Dataset Card for \"difference_analysis\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"difference_analysis\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"difference_analysis\"\n\nMore Information needed"
] |
edbea8ede6a46da83e431b2c22505af4072dee2a
|
# Dataset Card for AuTexTification 2023
## Dataset Description
- **Homepage:** https://sites.google.com/view/autextification
- **Repository:** https://github.com/autextification/AuTexTification-Overview
- **Paper:** https://arxiv.org/abs/2309.11285
### Dataset Summary
AuTexTification 2023 @IberLEF2023 is a shared task focusing in Machine-Generated Text Detection and Model Attribution in English and Spanish.
The dataset includes human and generated text in 5 domains: tweets, reviews, how-to articles, news, and legal documents.
The generations are obtained using six language models: BLOOM-1B1, BLOOM-3B, BLOOM-7B1, Babbage, Curie, and text-davinci-003.
For more information, please refer to our overview paper: https://arxiv.org/abs/2309.11285
### Supported Tasks and Leaderboards
- Machine-Generated Text Detection
- Model Attribution
### Languages
English and Spanish
## Dataset Structure
### Data Instances
163k instances of labeled text in total.
### Data Fields
For MGT Detection:
- id
- prompt
- text
- label
- model
- domain
For Model Attribution:
- id
- prompt
- text
- label
- domain
### Data Splits
- MGT Detection Data:
| Language | Split | Human | Generated | Total |
| -------- | ----- | ------ | --------- | ------ |
| English | Train | 17.046 | 16.799 | 33.845 |
| | Test | 10.642 | 11.190 | 21.832 |
| | Total | 27.688 | 27.989 | |
| Spanish | Train | 15.787 | 16.275 | 32.062 |
| | Test | 11.209 | 8.920 | 20.129 |
| | Total | 26.996 | 25.195 | |
- Model Attribution Data:
| | | BLOOM | | | GPT | | | |
| -------- | ----- | ----- | ----- | ----- | ------- | ----- | ---------------- | ------ |
| Language | Split | 1B7 | 3B | 7B | babbage | curie | text-davinci-003 | Total |
| English | Train | 3.562 | 3.648 | 3.687 | 3.870 | 3.822 | 3.827 | 14.767 |
| | Test | 887 | 875 | 952 | 924 | 979 | 988 | 3.638 |
| | Total | 4.449 | 4.523 | 4.639 | 4.794 | 4.801 | 4.815 | |
| Spanish | Train | 3.422 | 3.514 | 3.575 | 3.788 | 3.770 | 3.866 | 14.299 |
| | Test | 870 | 867 | 878 | 946 | 1.004 | 917 | 3.561 |
| | Total | 4.292 | 4.381 | 4.453 | 4.734 | 4.774 | 4.783 | |
## Dataset Creation
### Curation Rationale
Human data was gathered and used to prompt language models, obtaining generated data.
Specific decisions were made to ensure the data gathering process was carried out in an unbiased manner, making the final human and generated texts probable continuations of a given prefix.
For more detailed information, please refer to the overview paper: https://arxiv.org/abs/2309.11285
### Source Data
The following datasets were used as human text:
- multi_eurlex
- xsum
- csebuetnlp/xlsum
- mlsum
- amazon_polarity
- https://sinai.ujaen.es/investigacion/recursos/coah
- https://sinai.ujaen.es/investigacion/recursos/coar
- carblacac/twitter-sentiment-analysis
- cardiffnlp/tweet_sentiment_multilingual
- https://www.kaggle.com/datasets/ricardomoya/tweets-poltica-espaa
- wiki_lingua
These datasets were only used as sources of human text. The labels of the datasets were not employed in any manner.
### Licensing Information
CC-BY-NC-SA-4.0
### Citation Information
```
@inproceedings{autextification2023,
title = "Overview of AuTexTification at IberLEF 2023: Detection and Attribution of Machine-Generated Text in Multiple Domains",
author = "Sarvazyan, Areg Mikael and
Gonz{\'a}lez, Jos{\'e} {\'A}ngel and
Franco-Salvador, Marc and
Rangel, Francisco and
Chulvi, Berta and
Rosso, Paolo",
month = sep,
year = "2023",
address = "Jaén, Spain",
booktitle = "Procesamiento del Lenguaje Natural",
}
```
|
symanto/autextification2023
|
[
"task_categories:text-classification",
"size_categories:10K<n<100K",
"source_datasets:multi_eurlex",
"source_datasets:xsum",
"source_datasets:csebuetnlp/xlsum",
"source_datasets:mlsum",
"source_datasets:amazon_polarity",
"source_datasets:https://sinai.ujaen.es/investigacion/recursos/coah",
"source_datasets:https://sinai.ujaen.es/investigacion/recursos/coar",
"source_datasets:carblacac/twitter-sentiment-analysis",
"source_datasets:cardiffnlp/tweet_sentiment_multilingual",
"source_datasets:https://www.kaggle.com/datasets/ricardomoya/tweets-poltica-espaa",
"source_datasets:wiki_lingua",
"language:en",
"language:es",
"license:cc-by-nc-sa-4.0",
"arxiv:2309.11285",
"region:us"
] |
2023-10-06T11:12:51+00:00
|
{"language": ["en", "es"], "license": "cc-by-nc-sa-4.0", "size_categories": ["10K<n<100K"], "source_datasets": ["multi_eurlex", "xsum", "csebuetnlp/xlsum", "mlsum", "amazon_polarity", "https://sinai.ujaen.es/investigacion/recursos/coah", "https://sinai.ujaen.es/investigacion/recursos/coar", "carblacac/twitter-sentiment-analysis", "cardiffnlp/tweet_sentiment_multilingual", "https://www.kaggle.com/datasets/ricardomoya/tweets-poltica-espaa", "wiki_lingua"], "task_categories": ["text-classification"], "pretty_name": "AuTexTification 2023"}
|
2023-10-06T12:08:55+00:00
|
[
"2309.11285"
] |
[
"en",
"es"
] |
TAGS
#task_categories-text-classification #size_categories-10K<n<100K #source_datasets-multi_eurlex #source_datasets-xsum #source_datasets-csebuetnlp/xlsum #source_datasets-mlsum #source_datasets-amazon_polarity #source_datasets-https-//sinai.ujaen.es/investigacion/recursos/coah #source_datasets-https-//sinai.ujaen.es/investigacion/recursos/coar #source_datasets-carblacac/twitter-sentiment-analysis #source_datasets-cardiffnlp/tweet_sentiment_multilingual #source_datasets-https-//www.kaggle.com/datasets/ricardomoya/tweets-poltica-espaa #source_datasets-wiki_lingua #language-English #language-Spanish #license-cc-by-nc-sa-4.0 #arxiv-2309.11285 #region-us
|
# Dataset Card for AuTexTification 2023
## Dataset Description
- Homepage: URL
- Repository: URL
- Paper: URL
### Dataset Summary
AuTexTification 2023 @IberLEF2023 is a shared task focusing in Machine-Generated Text Detection and Model Attribution in English and Spanish.
The dataset includes human and generated text in 5 domains: tweets, reviews, how-to articles, news, and legal documents.
The generations are obtained using six language models: BLOOM-1B1, BLOOM-3B, BLOOM-7B1, Babbage, Curie, and text-davinci-003.
For more information, please refer to our overview paper: URL
### Supported Tasks and Leaderboards
- Machine-Generated Text Detection
- Model Attribution
### Languages
English and Spanish
## Dataset Structure
### Data Instances
163k instances of labeled text in total.
### Data Fields
For MGT Detection:
- id
- prompt
- text
- label
- model
- domain
For Model Attribution:
- id
- prompt
- text
- label
- domain
### Data Splits
- MGT Detection Data:
| Language | Split | Human | Generated | Total |
| -------- | ----- | ------ | --------- | ------ |
| English | Train | 17.046 | 16.799 | 33.845 |
| | Test | 10.642 | 11.190 | 21.832 |
| | Total | 27.688 | 27.989 | |
| Spanish | Train | 15.787 | 16.275 | 32.062 |
| | Test | 11.209 | 8.920 | 20.129 |
| | Total | 26.996 | 25.195 | |
- Model Attribution Data:
| | | BLOOM | | | GPT | | | |
| -------- | ----- | ----- | ----- | ----- | ------- | ----- | ---------------- | ------ |
| Language | Split | 1B7 | 3B | 7B | babbage | curie | text-davinci-003 | Total |
| English | Train | 3.562 | 3.648 | 3.687 | 3.870 | 3.822 | 3.827 | 14.767 |
| | Test | 887 | 875 | 952 | 924 | 979 | 988 | 3.638 |
| | Total | 4.449 | 4.523 | 4.639 | 4.794 | 4.801 | 4.815 | |
| Spanish | Train | 3.422 | 3.514 | 3.575 | 3.788 | 3.770 | 3.866 | 14.299 |
| | Test | 870 | 867 | 878 | 946 | 1.004 | 917 | 3.561 |
| | Total | 4.292 | 4.381 | 4.453 | 4.734 | 4.774 | 4.783 | |
## Dataset Creation
### Curation Rationale
Human data was gathered and used to prompt language models, obtaining generated data.
Specific decisions were made to ensure the data gathering process was carried out in an unbiased manner, making the final human and generated texts probable continuations of a given prefix.
For more detailed information, please refer to the overview paper: URL
### Source Data
The following datasets were used as human text:
- multi_eurlex
- xsum
- csebuetnlp/xlsum
- mlsum
- amazon_polarity
- URL
- URL
- carblacac/twitter-sentiment-analysis
- cardiffnlp/tweet_sentiment_multilingual
- URL
- wiki_lingua
These datasets were only used as sources of human text. The labels of the datasets were not employed in any manner.
### Licensing Information
CC-BY-NC-SA-4.0
|
[
"# Dataset Card for AuTexTification 2023",
"## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL",
"### Dataset Summary\n\nAuTexTification 2023 @IberLEF2023 is a shared task focusing in Machine-Generated Text Detection and Model Attribution in English and Spanish.\nThe dataset includes human and generated text in 5 domains: tweets, reviews, how-to articles, news, and legal documents.\nThe generations are obtained using six language models: BLOOM-1B1, BLOOM-3B, BLOOM-7B1, Babbage, Curie, and text-davinci-003.\nFor more information, please refer to our overview paper: URL",
"### Supported Tasks and Leaderboards\n\n- Machine-Generated Text Detection\n- Model Attribution",
"### Languages\n\nEnglish and Spanish",
"## Dataset Structure",
"### Data Instances\n\n163k instances of labeled text in total.",
"### Data Fields\n\nFor MGT Detection:\n- id\n- prompt\n- text\n- label\n- model\n- domain\n\nFor Model Attribution:\n- id\n- prompt\n- text\n- label\n- domain",
"### Data Splits\n\n\n- MGT Detection Data:\n| Language | Split | Human | Generated | Total |\n| -------- | ----- | ------ | --------- | ------ |\n| English | Train | 17.046 | 16.799 | 33.845 |\n| | Test | 10.642 | 11.190 | 21.832 |\n| | Total | 27.688 | 27.989 | |\n| Spanish | Train | 15.787 | 16.275 | 32.062 |\n| | Test | 11.209 | 8.920 | 20.129 |\n| | Total | 26.996 | 25.195 | |\n\n- Model Attribution Data:\n| | | BLOOM | | | GPT | | | |\n| -------- | ----- | ----- | ----- | ----- | ------- | ----- | ---------------- | ------ |\n| Language | Split | 1B7 | 3B | 7B | babbage | curie | text-davinci-003 | Total |\n| English | Train | 3.562 | 3.648 | 3.687 | 3.870 | 3.822 | 3.827 | 14.767 |\n| | Test | 887 | 875 | 952 | 924 | 979 | 988 | 3.638 |\n| | Total | 4.449 | 4.523 | 4.639 | 4.794 | 4.801 | 4.815 | |\n| Spanish | Train | 3.422 | 3.514 | 3.575 | 3.788 | 3.770 | 3.866 | 14.299 |\n| | Test | 870 | 867 | 878 | 946 | 1.004 | 917 | 3.561 |\n| | Total | 4.292 | 4.381 | 4.453 | 4.734 | 4.774 | 4.783 | |",
"## Dataset Creation",
"### Curation Rationale\n\nHuman data was gathered and used to prompt language models, obtaining generated data. \nSpecific decisions were made to ensure the data gathering process was carried out in an unbiased manner, making the final human and generated texts probable continuations of a given prefix. \nFor more detailed information, please refer to the overview paper: URL",
"### Source Data\nThe following datasets were used as human text:\n- multi_eurlex\n- xsum\n- csebuetnlp/xlsum\n- mlsum\n- amazon_polarity\n- URL\n- URL\n- carblacac/twitter-sentiment-analysis\n- cardiffnlp/tweet_sentiment_multilingual\n- URL\n- wiki_lingua\n\nThese datasets were only used as sources of human text. The labels of the datasets were not employed in any manner.",
"### Licensing Information\n\nCC-BY-NC-SA-4.0"
] |
[
"TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #source_datasets-multi_eurlex #source_datasets-xsum #source_datasets-csebuetnlp/xlsum #source_datasets-mlsum #source_datasets-amazon_polarity #source_datasets-https-//sinai.ujaen.es/investigacion/recursos/coah #source_datasets-https-//sinai.ujaen.es/investigacion/recursos/coar #source_datasets-carblacac/twitter-sentiment-analysis #source_datasets-cardiffnlp/tweet_sentiment_multilingual #source_datasets-https-//www.kaggle.com/datasets/ricardomoya/tweets-poltica-espaa #source_datasets-wiki_lingua #language-English #language-Spanish #license-cc-by-nc-sa-4.0 #arxiv-2309.11285 #region-us \n",
"# Dataset Card for AuTexTification 2023",
"## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL",
"### Dataset Summary\n\nAuTexTification 2023 @IberLEF2023 is a shared task focusing in Machine-Generated Text Detection and Model Attribution in English and Spanish.\nThe dataset includes human and generated text in 5 domains: tweets, reviews, how-to articles, news, and legal documents.\nThe generations are obtained using six language models: BLOOM-1B1, BLOOM-3B, BLOOM-7B1, Babbage, Curie, and text-davinci-003.\nFor more information, please refer to our overview paper: URL",
"### Supported Tasks and Leaderboards\n\n- Machine-Generated Text Detection\n- Model Attribution",
"### Languages\n\nEnglish and Spanish",
"## Dataset Structure",
"### Data Instances\n\n163k instances of labeled text in total.",
"### Data Fields\n\nFor MGT Detection:\n- id\n- prompt\n- text\n- label\n- model\n- domain\n\nFor Model Attribution:\n- id\n- prompt\n- text\n- label\n- domain",
"### Data Splits\n\n\n- MGT Detection Data:\n| Language | Split | Human | Generated | Total |\n| -------- | ----- | ------ | --------- | ------ |\n| English | Train | 17.046 | 16.799 | 33.845 |\n| | Test | 10.642 | 11.190 | 21.832 |\n| | Total | 27.688 | 27.989 | |\n| Spanish | Train | 15.787 | 16.275 | 32.062 |\n| | Test | 11.209 | 8.920 | 20.129 |\n| | Total | 26.996 | 25.195 | |\n\n- Model Attribution Data:\n| | | BLOOM | | | GPT | | | |\n| -------- | ----- | ----- | ----- | ----- | ------- | ----- | ---------------- | ------ |\n| Language | Split | 1B7 | 3B | 7B | babbage | curie | text-davinci-003 | Total |\n| English | Train | 3.562 | 3.648 | 3.687 | 3.870 | 3.822 | 3.827 | 14.767 |\n| | Test | 887 | 875 | 952 | 924 | 979 | 988 | 3.638 |\n| | Total | 4.449 | 4.523 | 4.639 | 4.794 | 4.801 | 4.815 | |\n| Spanish | Train | 3.422 | 3.514 | 3.575 | 3.788 | 3.770 | 3.866 | 14.299 |\n| | Test | 870 | 867 | 878 | 946 | 1.004 | 917 | 3.561 |\n| | Total | 4.292 | 4.381 | 4.453 | 4.734 | 4.774 | 4.783 | |",
"## Dataset Creation",
"### Curation Rationale\n\nHuman data was gathered and used to prompt language models, obtaining generated data. \nSpecific decisions were made to ensure the data gathering process was carried out in an unbiased manner, making the final human and generated texts probable continuations of a given prefix. \nFor more detailed information, please refer to the overview paper: URL",
"### Source Data\nThe following datasets were used as human text:\n- multi_eurlex\n- xsum\n- csebuetnlp/xlsum\n- mlsum\n- amazon_polarity\n- URL\n- URL\n- carblacac/twitter-sentiment-analysis\n- cardiffnlp/tweet_sentiment_multilingual\n- URL\n- wiki_lingua\n\nThese datasets were only used as sources of human text. The labels of the datasets were not employed in any manner.",
"### Licensing Information\n\nCC-BY-NC-SA-4.0"
] |
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7,
6,
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37,
492,
5,
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] |
[
"passage: TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #source_datasets-multi_eurlex #source_datasets-xsum #source_datasets-csebuetnlp/xlsum #source_datasets-mlsum #source_datasets-amazon_polarity #source_datasets-https-//sinai.ujaen.es/investigacion/recursos/coah #source_datasets-https-//sinai.ujaen.es/investigacion/recursos/coar #source_datasets-carblacac/twitter-sentiment-analysis #source_datasets-cardiffnlp/tweet_sentiment_multilingual #source_datasets-https-//www.kaggle.com/datasets/ricardomoya/tweets-poltica-espaa #source_datasets-wiki_lingua #language-English #language-Spanish #license-cc-by-nc-sa-4.0 #arxiv-2309.11285 #region-us \n# Dataset Card for AuTexTification 2023## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL### Dataset Summary\n\nAuTexTification 2023 @IberLEF2023 is a shared task focusing in Machine-Generated Text Detection and Model Attribution in English and Spanish.\nThe dataset includes human and generated text in 5 domains: tweets, reviews, how-to articles, news, and legal documents.\nThe generations are obtained using six language models: BLOOM-1B1, BLOOM-3B, BLOOM-7B1, Babbage, Curie, and text-davinci-003.\nFor more information, please refer to our overview paper: URL### Supported Tasks and Leaderboards\n\n- Machine-Generated Text Detection\n- Model Attribution### Languages\n\nEnglish and Spanish## Dataset Structure### Data Instances\n\n163k instances of labeled text in total.### Data Fields\n\nFor MGT Detection:\n- id\n- prompt\n- text\n- label\n- model\n- domain\n\nFor Model Attribution:\n- id\n- prompt\n- text\n- label\n- domain"
] |
faa4515c7fe49df65b8fa4227ffec9cfd621bae0
|
# Indian Constitution Dataset
The dataset can be used for text classification, text generation and text2text generation
|
Sharathhebbar24/Indian-Constitution
|
[
"task_categories:text-classification",
"task_categories:text-generation",
"task_categories:text2text-generation",
"language:en",
"license:apache-2.0",
"region:us"
] |
2023-10-06T11:16:20+00:00
|
{"language": ["en"], "license": "apache-2.0", "task_categories": ["text-classification", "text-generation", "text2text-generation"]}
|
2023-10-06T11:57:27+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-text-classification #task_categories-text-generation #task_categories-text2text-generation #language-English #license-apache-2.0 #region-us
|
# Indian Constitution Dataset
The dataset can be used for text classification, text generation and text2text generation
|
[
"# Indian Constitution Dataset\nThe dataset can be used for text classification, text generation and text2text generation"
] |
[
"TAGS\n#task_categories-text-classification #task_categories-text-generation #task_categories-text2text-generation #language-English #license-apache-2.0 #region-us \n",
"# Indian Constitution Dataset\nThe dataset can be used for text classification, text generation and text2text generation"
] |
[
53,
23
] |
[
"passage: TAGS\n#task_categories-text-classification #task_categories-text-generation #task_categories-text2text-generation #language-English #license-apache-2.0 #region-us \n# Indian Constitution Dataset\nThe dataset can be used for text classification, text generation and text2text generation"
] |
2a72571b6fc93092a9f28242ecf06ec1ead9a911
|
# Dataset Card for "difference_analysis_data_structure"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
HamdanXI/paradetox_with_editOps
|
[
"region:us"
] |
2023-10-06T11:21:17+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "en_toxic_comment", "dtype": "string"}, {"name": "en_neutral_comment", "dtype": "string"}, {"name": "edit_ops", "sequence": {"sequence": "string"}}], "splits": [{"name": "train", "num_bytes": 4067285, "num_examples": 19744}], "download_size": 1996316, "dataset_size": 4067285}}
|
2023-10-06T11:21:19+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "difference_analysis_data_structure"
More Information needed
|
[
"# Dataset Card for \"difference_analysis_data_structure\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"difference_analysis_data_structure\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"difference_analysis_data_structure\"\n\nMore Information needed"
] |
7cb584a4f5b7f40d8079ac654f75d27c3870631f
|
# Dataset Card for "Commonsense_Validation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
asoria/copy-BRAD
|
[
"region:us"
] |
2023-10-06T11:37:41+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "first_sentence", "dtype": "string"}, {"name": "second_sentence", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": 0, "1": 1}}}}], "splits": [{"name": "train", "num_bytes": 1420233, "num_examples": 10000}, {"name": "validation", "num_bytes": 133986, "num_examples": 1000}], "download_size": 837486, "dataset_size": 1554219}}
|
2023-10-06T11:39:29+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Commonsense_Validation"
More Information needed
|
[
"# Dataset Card for \"Commonsense_Validation\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Commonsense_Validation\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Commonsense_Validation\"\n\nMore Information needed"
] |
9799121a3b94af0ae2e485da0fe75efcc65e2d9c
|
# Bangumi Image Base of Noumin Kanren No Skill Bakka Agetetara Naze Ka Tsuyoku Natta
This is the image base of bangumi Noumin Kanren no Skill Bakka Agetetara Naze ka Tsuyoku Natta, we detected 32 characters, 1564 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 22 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 102 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 21 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 15 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 41 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 543 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 29 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 24 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 21 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 128 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 22 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 32 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 15 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 10 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 34 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 14 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 11 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 19 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 14 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 10 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 13 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 19 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 24 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 22 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 41 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 15 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 103 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 30 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 22 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 6 | [Download](29/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 30 | 5 | [Download](30/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| noise | 137 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/nouminkanrennoskillbakkaagetetaranazekatsuyokunatta
|
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] |
2023-10-06T11:40:56+00:00
|
{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}
|
2023-10-06T12:38:28+00:00
|
[] |
[] |
TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
|
Bangumi Image Base of Noumin Kanren No Skill Bakka Agetetara Naze Ka Tsuyoku Natta
==================================================================================
This is the image base of bangumi Noumin Kanren no Skill Bakka Agetetara Naze ka Tsuyoku Natta, we detected 32 characters, 1564 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
[
25
] |
[
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
f860963b718593d750836498670fc61129dfd721
|
# Dataset Card for "trec_dl19"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
dmrau/trec_dl19
|
[
"region:us"
] |
2023-10-06T11:41:13+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "queries", "path": "data/queries-*"}, {"split": "corpus", "path": "data/corpus-*"}]}], "dataset_info": {"features": [{"name": "_id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "title", "dtype": "string"}], "splits": [{"name": "queries", "num_bytes": 2194, "num_examples": 43}, {"name": "corpus", "num_bytes": 2181810, "num_examples": 5482}], "download_size": 1207481, "dataset_size": 2184004}}
|
2023-10-09T12:07:39+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "trec_dl19"
More Information needed
|
[
"# Dataset Card for \"trec_dl19\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"trec_dl19\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"trec_dl19\"\n\nMore Information needed"
] |
7ddd28ded78c10f621bed76e0b69cfee5eb85b51
|
# Dataset Card for "trec_dl19-qrels"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
dmrau/trec_dl19-qrels
|
[
"region:us"
] |
2023-10-06T11:41:51+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "query-id", "dtype": "string"}, {"name": "corpus-id", "dtype": "string"}, {"name": "score", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 242652, "num_examples": 9260}], "download_size": 0, "dataset_size": 242652}}
|
2023-10-09T12:07:40+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "trec_dl19-qrels"
More Information needed
|
[
"# Dataset Card for \"trec_dl19-qrels\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"trec_dl19-qrels\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"trec_dl19-qrels\"\n\nMore Information needed"
] |
244ec69b190d192049c288a684cb8cd11f0caf9e
|
# Dataset Card for "guanaco-llama2-1k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Vojtab42/guanaco-llama2-1k
|
[
"region:us"
] |
2023-10-06T11:43:57+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1654448, "num_examples": 1000}], "download_size": 966693, "dataset_size": 1654448}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-06T11:43:59+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "guanaco-llama2-1k"
More Information needed
|
[
"# Dataset Card for \"guanaco-llama2-1k\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"guanaco-llama2-1k\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"guanaco-llama2-1k\"\n\nMore Information needed"
] |
93d741d6747bc6b65ddd46dc018644df44e61ce5
|
# Dataset Card for "massive_5_lang_DA3_tokenized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
carnival13/massive_5_lang_DA3_tokenized
|
[
"region:us"
] |
2023-10-06T11:49:21+00:00
|
{"dataset_info": {"features": [{"name": "pass_label", "dtype": "int64"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 419259395, "num_examples": 552890}], "download_size": 127212717, "dataset_size": 419259395}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-06T11:49:41+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "massive_5_lang_DA3_tokenized"
More Information needed
|
[
"# Dataset Card for \"massive_5_lang_DA3_tokenized\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"massive_5_lang_DA3_tokenized\"\n\nMore Information needed"
] |
[
6,
23
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"massive_5_lang_DA3_tokenized\"\n\nMore Information needed"
] |
747cf6da48df497b0d3cb87868a50bded1e8f5a1
|
# Dataset Card for "spotlight-boolq-enrichment"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
renumics/spotlight-boolq-enrichment
|
[
"region:us"
] |
2023-10-06T12:06:09+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "question.embedding", "sequence": "float32", "length": 2}, {"name": "passage.embedding", "sequence": "float32", "length": 2}], "splits": [{"name": "train", "num_bytes": 150832, "num_examples": 9427}, {"name": "validation", "num_bytes": 52320, "num_examples": 3270}], "download_size": 284725, "dataset_size": 203152}}
|
2023-10-13T08:10:09+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "spotlight-boolq-enrichment"
More Information needed
|
[
"# Dataset Card for \"spotlight-boolq-enrichment\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"spotlight-boolq-enrichment\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"spotlight-boolq-enrichment\"\n\nMore Information needed"
] |
53965aa524c20004e8c420bfe4b0fc55cf7b5e33
|
# Dataset Card for "timelist_summary_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
marcus2000/timelist_summary_dataset
|
[
"region:us"
] |
2023-10-06T12:10:28+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "Original", "dtype": "string"}, {"name": "Summary", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 352926.0853658537, "num_examples": 278}, {"name": "test", "num_bytes": 63475.91463414634, "num_examples": 50}], "download_size": 227279, "dataset_size": 416402.0}}
|
2023-10-06T12:10:36+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "timelist_summary_dataset"
More Information needed
|
[
"# Dataset Card for \"timelist_summary_dataset\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"timelist_summary_dataset\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"timelist_summary_dataset\"\n\nMore Information needed"
] |
c654683df9548e8985dad25ff0d8afbee1fa57c3
|
# Dataset Card for "timelist_task_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
marcus2000/timelist_task_dataset
|
[
"region:us"
] |
2023-10-06T12:21:40+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "Original", "dtype": "string"}, {"name": "Task", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 91073.55102040817, "num_examples": 41}, {"name": "test", "num_bytes": 17770.448979591838, "num_examples": 8}], "download_size": 62081, "dataset_size": 108844.0}}
|
2023-10-06T12:21:49+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "timelist_task_dataset"
More Information needed
|
[
"# Dataset Card for \"timelist_task_dataset\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"timelist_task_dataset\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"timelist_task_dataset\"\n\nMore Information needed"
] |
e4129f58094cc2a5d4bdd88e7621e7283d3e2eeb
|
https://x.com/natolambert/status/1710285440803344688?s=20
|
lunarflu/Developing_LLMs_Open_Closed_or_Democratic
|
[
"region:us"
] |
2023-10-06T12:28:11+00:00
|
{}
|
2023-10-06T12:28:18+00:00
|
[] |
[] |
TAGS
#region-us
|
https://x.com/natolambert/status/1710285440803344688?s=20
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
ad492b4448c6d4b36fc63511a45549c15f64dcd1
|
This is a reformatted version of [kunishou/databricks-dolly-15k-ja](https://huggingface.co/datasets/kunishou/databricks-dolly-15k-ja).
If you use this dataset, please cite the original dataset as well.
|
fujiki/databricks-dolly-15k-ja-reformat-v1
|
[
"license:cc-by-sa-3.0",
"region:us"
] |
2023-10-06T12:31:30+00:00
|
{"license": "cc-by-sa-3.0", "dataset_info": {"features": [{"name": "index", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "instructions", "sequence": "string"}, {"name": "responses", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 15973503, "num_examples": 15015}], "download_size": 9056298, "dataset_size": 15973503}}
|
2023-10-06T12:37:15+00:00
|
[] |
[] |
TAGS
#license-cc-by-sa-3.0 #region-us
|
This is a reformatted version of kunishou/databricks-dolly-15k-ja.
If you use this dataset, please cite the original dataset as well.
|
[] |
[
"TAGS\n#license-cc-by-sa-3.0 #region-us \n"
] |
[
17
] |
[
"passage: TAGS\n#license-cc-by-sa-3.0 #region-us \n"
] |
75df3441aa328d57508cab9d50e18ead7ed2b0b4
|
# Dataset Card for "massive_eng_DA_tokenized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
carnival13/massive_eng_DA_tokenized
|
[
"region:us"
] |
2023-10-06T12:35:36+00:00
|
{"dataset_info": {"features": [{"name": "pass_label", "dtype": "int64"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 97244320, "num_examples": 138200}], "download_size": 22020759, "dataset_size": 97244320}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-06T12:35:43+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "massive_eng_DA_tokenized"
More Information needed
|
[
"# Dataset Card for \"massive_eng_DA_tokenized\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"massive_eng_DA_tokenized\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"massive_eng_DA_tokenized\"\n\nMore Information needed"
] |
498afda378084df717d9150942b6c4eb31480cbe
|
# Dataset Card for "codeqa_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lissadesu/codeqa_v2
|
[
"region:us"
] |
2023-10-06T12:38:08+00:00
|
{"dataset_info": {"features": [{"name": "labNo", "dtype": "float64"}, {"name": "taskNo", "dtype": "float64"}, {"name": "questioner", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "code", "dtype": "string"}, {"name": "startLine", "dtype": "float64"}, {"name": "endLine", "dtype": "float64"}, {"name": "questionType", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "src", "dtype": "string"}, {"name": "code_processed", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "raw_code", "dtype": "string"}, {"name": "raw_comment", "dtype": "string"}, {"name": "comment", "dtype": "string"}, {"name": "q_code", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 46842820, "num_examples": 35360}], "download_size": 17749500, "dataset_size": 46842820}}
|
2023-10-06T12:38:30+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "codeqa_v2"
More Information needed
|
[
"# Dataset Card for \"codeqa_v2\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"codeqa_v2\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"codeqa_v2\"\n\nMore Information needed"
] |
099dff3b53ad860824a6d279ed241a4413b110ae
|
# Dataset Card for "massive_val_DA2_tokenized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
carnival13/massive_val_DA2_tokenized
|
[
"region:us"
] |
2023-10-06T12:41:06+00:00
|
{"dataset_info": {"features": [{"name": "pass_label", "dtype": "int64"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 16518290, "num_examples": 24160}], "download_size": 3770585, "dataset_size": 16518290}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-06T12:41:11+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "massive_val_DA2_tokenized"
More Information needed
|
[
"# Dataset Card for \"massive_val_DA2_tokenized\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"massive_val_DA2_tokenized\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"massive_val_DA2_tokenized\"\n\nMore Information needed"
] |
761112f8aacf5e2451b1f8b94b5dc6f8041995fd
|
# Mnist-Ambiguous
This dataset contains mnist-like images, but with an unclear ground truth. For each image, there are two classes which could be considered true.
Robust and uncertainty-aware DNNs should thus detect and flag these issues.
### Features
Same as mnist, the supervised dataset has an `image` (28x28 int array) and a `label` (int).
Additionally, the following features are exposed for your convenience:
- `text_label` (str): A textual representation of the probabilistic label, e.g. `p(0)=0.54, p(5)=0.46`
- `p_label` (list of floats): Ground-Truth probabilities for each class (two nonzero values for our ambiguous images)
- `is_ambiguous` (bool): Flag indicating if this is one of our ambiguous images (see 'splits' below)
### Splits
We provide four splits:
- `test`: 10'000 ambiguous images
- `train`: 10'000 ambiguous images - adding ambiguous images to the training set makes sure test-time ambiguous images are in-distribution.
- `test_mixed`: 20'000 images, consisting of the (shuffled) concatenation of our ambiguous `test` set and the nominal mnist test set by LeCun et. al.,
- `train_mixed`: 70'000 images, consisting of the (shuffled) concatenation of our ambiguous `training` and the nominal training set.
Note that the ambiguous test images are highly ambiguous (i.e., the two classes have very similar ground truth likelihoods),
the training set images allow for more unbalanced ambiguity.
This is to make the training set more closely connected to the nominal data, while still keeping the test set clearly ambiguous.
For research targeting explicitly aleatoric uncertainty, we recommend training the model using `train_mixed`.
Otherwise, our `test` set will lead to both epistemic and aleatoric uncertainty.
In related literature, such 'mixed' splits are sometimes denoted as *dirty* splits.
### Assessment and Validity
For a brief discussion of the strength and weaknesses of this dataset,
including a quantitative comparison to the (only) other ambiguous datasets available in the literature, we refer to our paper.
### Paper
Pre-print here: [https://arxiv.org/abs/2207.10495](https://arxiv.org/abs/2207.10495)
Citation:
```
@misc{https://doi.org/10.48550/arxiv.2207.10495,
doi = {10.48550/ARXIV.2207.10495},
url = {https://arxiv.org/abs/2207.10495},
author = {Weiss, Michael and Gómez, André García and Tonella, Paolo},
title = {A Forgotten Danger in DNN Supervision Testing: Generating and Detecting True Ambiguity},
publisher = {arXiv},
year = {2022}
}
```
### License
As this is a derivative work of mnist, which is CC-BY-SA 3.0 licensed, our dataset is released using the same license.
|
asoria/test_s3
|
[
"task_categories:image-classification",
"annotations_creators:machine-generated",
"size_categories:10K<n<100K",
"source_datasets:extended|mnist",
"language:en",
"license:cc-by-sa-3.0",
"arxiv:2207.10495",
"region:us"
] |
2023-10-06T12:48:53+00:00
|
{"annotations_creators": ["machine-generated"], "language": ["en"], "license": "cc-by-sa-3.0", "size_categories": ["10K<n<100K"], "source_datasets": ["extended|mnist"], "task_categories": ["image-classification"], "pretty_name": "mnist_ambigous"}
|
2023-10-06T12:50:57+00:00
|
[
"2207.10495"
] |
[
"en"
] |
TAGS
#task_categories-image-classification #annotations_creators-machine-generated #size_categories-10K<n<100K #source_datasets-extended|mnist #language-English #license-cc-by-sa-3.0 #arxiv-2207.10495 #region-us
|
# Mnist-Ambiguous
This dataset contains mnist-like images, but with an unclear ground truth. For each image, there are two classes which could be considered true.
Robust and uncertainty-aware DNNs should thus detect and flag these issues.
### Features
Same as mnist, the supervised dataset has an 'image' (28x28 int array) and a 'label' (int).
Additionally, the following features are exposed for your convenience:
- 'text_label' (str): A textual representation of the probabilistic label, e.g. 'p(0)=0.54, p(5)=0.46'
- 'p_label' (list of floats): Ground-Truth probabilities for each class (two nonzero values for our ambiguous images)
- 'is_ambiguous' (bool): Flag indicating if this is one of our ambiguous images (see 'splits' below)
### Splits
We provide four splits:
- 'test': 10'000 ambiguous images
- 'train': 10'000 ambiguous images - adding ambiguous images to the training set makes sure test-time ambiguous images are in-distribution.
- 'test_mixed': 20'000 images, consisting of the (shuffled) concatenation of our ambiguous 'test' set and the nominal mnist test set by LeCun et. al.,
- 'train_mixed': 70'000 images, consisting of the (shuffled) concatenation of our ambiguous 'training' and the nominal training set.
Note that the ambiguous test images are highly ambiguous (i.e., the two classes have very similar ground truth likelihoods),
the training set images allow for more unbalanced ambiguity.
This is to make the training set more closely connected to the nominal data, while still keeping the test set clearly ambiguous.
For research targeting explicitly aleatoric uncertainty, we recommend training the model using 'train_mixed'.
Otherwise, our 'test' set will lead to both epistemic and aleatoric uncertainty.
In related literature, such 'mixed' splits are sometimes denoted as *dirty* splits.
### Assessment and Validity
For a brief discussion of the strength and weaknesses of this dataset,
including a quantitative comparison to the (only) other ambiguous datasets available in the literature, we refer to our paper.
### Paper
Pre-print here: URL
Citation:
### License
As this is a derivative work of mnist, which is CC-BY-SA 3.0 licensed, our dataset is released using the same license.
|
[
"# Mnist-Ambiguous\n\nThis dataset contains mnist-like images, but with an unclear ground truth. For each image, there are two classes which could be considered true.\nRobust and uncertainty-aware DNNs should thus detect and flag these issues.",
"### Features\nSame as mnist, the supervised dataset has an 'image' (28x28 int array) and a 'label' (int).\n\nAdditionally, the following features are exposed for your convenience:\n\n- 'text_label' (str): A textual representation of the probabilistic label, e.g. 'p(0)=0.54, p(5)=0.46' \n- 'p_label' (list of floats): Ground-Truth probabilities for each class (two nonzero values for our ambiguous images)\n- 'is_ambiguous' (bool): Flag indicating if this is one of our ambiguous images (see 'splits' below)",
"### Splits\nWe provide four splits:\n\n- 'test': 10'000 ambiguous images\n- 'train': 10'000 ambiguous images - adding ambiguous images to the training set makes sure test-time ambiguous images are in-distribution.\n- 'test_mixed': 20'000 images, consisting of the (shuffled) concatenation of our ambiguous 'test' set and the nominal mnist test set by LeCun et. al.,\n- 'train_mixed': 70'000 images, consisting of the (shuffled) concatenation of our ambiguous 'training' and the nominal training set.\n\nNote that the ambiguous test images are highly ambiguous (i.e., the two classes have very similar ground truth likelihoods), \nthe training set images allow for more unbalanced ambiguity. \nThis is to make the training set more closely connected to the nominal data, while still keeping the test set clearly ambiguous.\n\nFor research targeting explicitly aleatoric uncertainty, we recommend training the model using 'train_mixed'. \nOtherwise, our 'test' set will lead to both epistemic and aleatoric uncertainty. \nIn related literature, such 'mixed' splits are sometimes denoted as *dirty* splits.",
"### Assessment and Validity\nFor a brief discussion of the strength and weaknesses of this dataset, \nincluding a quantitative comparison to the (only) other ambiguous datasets available in the literature, we refer to our paper.",
"### Paper\nPre-print here: URL\n\nCitation:",
"### License\nAs this is a derivative work of mnist, which is CC-BY-SA 3.0 licensed, our dataset is released using the same license."
] |
[
"TAGS\n#task_categories-image-classification #annotations_creators-machine-generated #size_categories-10K<n<100K #source_datasets-extended|mnist #language-English #license-cc-by-sa-3.0 #arxiv-2207.10495 #region-us \n",
"# Mnist-Ambiguous\n\nThis dataset contains mnist-like images, but with an unclear ground truth. For each image, there are two classes which could be considered true.\nRobust and uncertainty-aware DNNs should thus detect and flag these issues.",
"### Features\nSame as mnist, the supervised dataset has an 'image' (28x28 int array) and a 'label' (int).\n\nAdditionally, the following features are exposed for your convenience:\n\n- 'text_label' (str): A textual representation of the probabilistic label, e.g. 'p(0)=0.54, p(5)=0.46' \n- 'p_label' (list of floats): Ground-Truth probabilities for each class (two nonzero values for our ambiguous images)\n- 'is_ambiguous' (bool): Flag indicating if this is one of our ambiguous images (see 'splits' below)",
"### Splits\nWe provide four splits:\n\n- 'test': 10'000 ambiguous images\n- 'train': 10'000 ambiguous images - adding ambiguous images to the training set makes sure test-time ambiguous images are in-distribution.\n- 'test_mixed': 20'000 images, consisting of the (shuffled) concatenation of our ambiguous 'test' set and the nominal mnist test set by LeCun et. al.,\n- 'train_mixed': 70'000 images, consisting of the (shuffled) concatenation of our ambiguous 'training' and the nominal training set.\n\nNote that the ambiguous test images are highly ambiguous (i.e., the two classes have very similar ground truth likelihoods), \nthe training set images allow for more unbalanced ambiguity. \nThis is to make the training set more closely connected to the nominal data, while still keeping the test set clearly ambiguous.\n\nFor research targeting explicitly aleatoric uncertainty, we recommend training the model using 'train_mixed'. \nOtherwise, our 'test' set will lead to both epistemic and aleatoric uncertainty. \nIn related literature, such 'mixed' splits are sometimes denoted as *dirty* splits.",
"### Assessment and Validity\nFor a brief discussion of the strength and weaknesses of this dataset, \nincluding a quantitative comparison to the (only) other ambiguous datasets available in the literature, we refer to our paper.",
"### Paper\nPre-print here: URL\n\nCitation:",
"### License\nAs this is a derivative work of mnist, which is CC-BY-SA 3.0 licensed, our dataset is released using the same license."
] |
[
78,
62,
155,
301,
53,
12,
35
] |
[
"passage: TAGS\n#task_categories-image-classification #annotations_creators-machine-generated #size_categories-10K<n<100K #source_datasets-extended|mnist #language-English #license-cc-by-sa-3.0 #arxiv-2207.10495 #region-us \n# Mnist-Ambiguous\n\nThis dataset contains mnist-like images, but with an unclear ground truth. For each image, there are two classes which could be considered true.\nRobust and uncertainty-aware DNNs should thus detect and flag these issues.### Features\nSame as mnist, the supervised dataset has an 'image' (28x28 int array) and a 'label' (int).\n\nAdditionally, the following features are exposed for your convenience:\n\n- 'text_label' (str): A textual representation of the probabilistic label, e.g. 'p(0)=0.54, p(5)=0.46' \n- 'p_label' (list of floats): Ground-Truth probabilities for each class (two nonzero values for our ambiguous images)\n- 'is_ambiguous' (bool): Flag indicating if this is one of our ambiguous images (see 'splits' below)"
] |
2f5d90784987fe1d12097f92f6a7fb7ab7cc4b73
|
# Dataset Card for "codeqa_v3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lissadesu/codeqa_v3
|
[
"region:us"
] |
2023-10-06T12:52:09+00:00
|
{"dataset_info": {"features": [{"name": "labNo", "dtype": "float64"}, {"name": "taskNo", "dtype": "float64"}, {"name": "questioner", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "code", "dtype": "string"}, {"name": "startLine", "dtype": "float64"}, {"name": "endLine", "dtype": "float64"}, {"name": "questionType", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "src", "dtype": "string"}, {"name": "code_processed", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "raw_code", "dtype": "string"}, {"name": "raw_comment", "dtype": "string"}, {"name": "comment", "dtype": "string"}, {"name": "q_code", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 46848295, "num_examples": 35360}], "download_size": 17749500, "dataset_size": 46848295}}
|
2023-10-06T12:53:22+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "codeqa_v3"
More Information needed
|
[
"# Dataset Card for \"codeqa_v3\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"codeqa_v3\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"codeqa_v3\"\n\nMore Information needed"
] |
670a5a5f55123f05ca494715bc61104010bb5766
|
# Bangumi Image Base of Beast Tamer
This is the image base of bangumi Beast Tamer, we detected 25 characters, 1727 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 46 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 24 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 15 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 411 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 13 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 8 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 12 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 17 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 8 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 201 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 25 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 41 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 21 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 17 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 317 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 10 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 231 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 10 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 14 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 50 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 22 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 37 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 14 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 38 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 125 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/beasttamer
|
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] |
2023-10-06T12:52:45+00:00
|
{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}
|
2023-10-06T14:03:07+00:00
|
[] |
[] |
TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
|
Bangumi Image Base of Beast Tamer
=================================
This is the image base of bangumi Beast Tamer, we detected 25 characters, 1727 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
[
25
] |
[
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
7e19d4b1726140f346a4497dc31b3d629c9b1837
|
# Bangumi Image Base of Just Because!
This is the image base of bangumi Just Because!, we detected 20 characters, 1430 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 218 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 14 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 15 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 28 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 99 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 21 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 43 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 228 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 65 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 14 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 21 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 15 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 12 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 106 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 21 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 357 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 7 | [Download](16/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 17 | 23 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 23 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 100 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/justbecause
|
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] |
2023-10-06T12:52:59+00:00
|
{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}
|
2023-10-06T13:49:54+00:00
|
[] |
[] |
TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
|
Bangumi Image Base of Just Because!
===================================
This is the image base of bangumi Just Because!, we detected 20 characters, 1430 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
[
25
] |
[
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
05498ff0f3c8f2ebaba5f9f091dda8adb706cb56
|
# Bangumi Image Base of Unlimited Fafnir
This is the image base of bangumi Unlimited Fafnir, we detected 17 characters, 1386 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 31 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 135 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 28 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 417 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 74 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 59 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 45 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 38 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 125 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 56 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 151 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 119 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 13 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 9 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 45 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 18 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 23 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/unlimitedfafnir
|
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] |
2023-10-06T12:53:41+00:00
|
{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}
|
2023-10-06T13:46:23+00:00
|
[] |
[] |
TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
|
Bangumi Image Base of Unlimited Fafnir
======================================
This is the image base of bangumi Unlimited Fafnir, we detected 17 characters, 1386 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
[
25
] |
[
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
528716c7323f32e2928462a04c1f6cd5d81dbfbb
|
# Dataset Card for "codeqa_final"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lissadesu/codeqa_reduced
|
[
"region:us"
] |
2023-10-06T12:53:43+00:00
|
{"dataset_info": {"features": [{"name": "labNo", "dtype": "float64"}, {"name": "taskNo", "dtype": "float64"}, {"name": "questioner", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "code", "dtype": "string"}, {"name": "startLine", "dtype": "float64"}, {"name": "endLine", "dtype": "float64"}, {"name": "questionType", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "src", "dtype": "string"}, {"name": "code_processed", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "raw_code", "dtype": "string"}, {"name": "raw_comment", "dtype": "string"}, {"name": "comment", "dtype": "string"}, {"name": "q_code", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 39821050.75, "num_examples": 30056}, {"name": "test", "num_bytes": 7027244.25, "num_examples": 5304}], "download_size": 23830741, "dataset_size": 46848295.0}}
|
2023-10-06T12:54:14+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "codeqa_final"
More Information needed
|
[
"# Dataset Card for \"codeqa_final\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"codeqa_final\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"codeqa_final\"\n\nMore Information needed"
] |
7d2119ab1cdd1652d9b588781a1906a8c188b728
|
# Dataset Card for "sollingen_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
adityarra07/sollingen_data
|
[
"region:us"
] |
2023-10-06T13:15:08+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "transcription", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1174246362.25, "num_examples": 4638}], "download_size": 1167082408, "dataset_size": 1174246362.25}}
|
2023-10-06T13:15:44+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "sollingen_data"
More Information needed
|
[
"# Dataset Card for \"sollingen_data\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"sollingen_data\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"sollingen_data\"\n\nMore Information needed"
] |
24ad7e3c21c4fa4bcdcfefe21d5a1cce5f2bb31a
|
# Dataset Card for "geneva_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
adityarra07/geneva_data
|
[
"region:us"
] |
2023-10-06T13:15:44+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "transcription", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 223012930.0, "num_examples": 811}], "download_size": 221984247, "dataset_size": 223012930.0}}
|
2023-10-06T13:15:53+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "geneva_data"
More Information needed
|
[
"# Dataset Card for \"geneva_data\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"geneva_data\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"geneva_data\"\n\nMore Information needed"
] |
dbae47f4f606306cae9ad4f25c0facf05b93fe00
|
# Dataset Card for "zurich_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
adityarra07/zurich_data
|
[
"region:us"
] |
2023-10-06T13:15:53+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "transcription", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 537406557.186, "num_examples": 2189}], "download_size": 535954349, "dataset_size": 537406557.186}}
|
2023-10-06T13:16:10+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "zurich_data"
More Information needed
|
[
"# Dataset Card for \"zurich_data\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"zurich_data\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"zurich_data\"\n\nMore Information needed"
] |
1a9eb8bb247ee49751ed58e34e808de15c735345
|
# Dataset Card for "job-ds-sample"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fehimeyigit/job-ds-sample
|
[
"region:us"
] |
2023-10-06T13:18:07+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "job_title", "dtype": "string"}, {"name": "job_description", "dtype": "string"}, {"name": "review_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1577683.5878725592, "num_examples": 753}, {"name": "validation", "num_bytes": 175996.57553956835, "num_examples": 84}], "download_size": 1048395, "dataset_size": 1753680.1634121276}}
|
2023-10-06T13:18:11+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "job-ds-sample"
More Information needed
|
[
"# Dataset Card for \"job-ds-sample\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"job-ds-sample\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"job-ds-sample\"\n\nMore Information needed"
] |
309c2cea5cd3f70aa8c901619a72ad772c3b6c1e
|
# Bangumi Image Base of Idolish7
This is the image base of bangumi IDOLiSH7, we detected 27 characters, 3443 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 307 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 58 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 281 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 323 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 116 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 23 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 88 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 289 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 91 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 329 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 379 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 70 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 21 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 17 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 17 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 293 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 439 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 12 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 8 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 18 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 6 | [Download](20/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 21 | 9 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 14 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 7 | [Download](23/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 24 | 10 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 6 | [Download](25/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| noise | 212 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/idolish7
|
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] |
2023-10-06T13:20:23+00:00
|
{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}
|
2023-10-06T14:53:59+00:00
|
[] |
[] |
TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
|
Bangumi Image Base of Idolish7
==============================
This is the image base of bangumi IDOLiSH7, we detected 27 characters, 3443 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
[
25
] |
[
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
24242f52a7eccc3a0095bbd7497e84a040646242
|
# Dataset Card for "undl_text"
pandoc转docx出的源文本，所用命令为：pandoc -i {filepath} -t plain -o {outpath} --strip-comments
这些文本可能仍需一定的步骤去噪，比如去掉全是横线的分隔符、去掉表格元素，才能用于后续的翻译及对齐步骤
|
bot-yaya/undl_text
|
[
"region:us"
] |
2023-10-06T13:35:49+00:00
|
{"dataset_info": {"features": [{"name": "ar", "dtype": "string"}, {"name": "zh", "dtype": "string"}, {"name": "en", "dtype": "string"}, {"name": "fr", "dtype": "string"}, {"name": "ru", "dtype": "string"}, {"name": "es", "dtype": "string"}, {"name": "de", "dtype": "string"}, {"name": "record", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 48667711040, "num_examples": 165840}], "download_size": 18648916788, "dataset_size": 48667711040}}
|
2023-10-06T23:31:07+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "undl_text"
pandoc转docx出的源文本，所用命令为：pandoc -i {filepath} -t plain -o {outpath} --strip-comments
这些文本可能仍需一定的步骤去噪，比如去掉全是横线的分隔符、去掉表格元素，才能用于后续的翻译及对齐步骤
|
[
"# Dataset Card for \"undl_text\"\n\npandoc转docx出的源文本，所用命令为：pandoc -i {filepath} -t plain -o {outpath} --strip-comments\n\n这些文本可能仍需一定的步骤去噪，比如去掉全是横线的分隔符、去掉表格元素，才能用于后续的翻译及对齐步骤"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"undl_text\"\n\npandoc转docx出的源文本，所用命令为：pandoc -i {filepath} -t plain -o {outpath} --strip-comments\n\n这些文本可能仍需一定的步骤去噪，比如去掉全是横线的分隔符、去掉表格元素，才能用于后续的翻译及对齐步骤"
] |
[
6,
85
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"undl_text\"\n\npandoc转docx出的源文本，所用命令为：pandoc -i {filepath} -t plain -o {outpath} --strip-comments\n\n这些文本可能仍需一定的步骤去噪，比如去掉全是横线的分隔符、去掉表格元素，才能用于后续的翻译及对齐步骤"
] |
3b3318713878ca4153445ff0beec261d2d3adc80
|
Tiny Dolphin 🐬
see https://erichartford.com/dolphin
## Dataset details
This dataset is an extract of ~1 million of FLANv2 augmented with GPT-4 completions (flan1m-alpaca-uncensored.jsonl). It is derived from this [dataset](https://huggingface.co/datasets/ehartford/dolphin)
### Loading
```python
dataset = load_dataset("tog/dolphin_5k_test)
```
This dataset is licensed apache-2.0 for commercial or non-commercial use.
|
tog/dolphin_5k_test
|
[
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"region:us"
] |
2023-10-06T13:46:00+00:00
|
{"language": ["en"], "license": "apache-2.0", "task_categories": ["text-generation"], "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 8726321.400179625, "num_examples": 5000}], "download_size": 4973800, "dataset_size": 8726321.400179625}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-06T14:06:19+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-text-generation #language-English #license-apache-2.0 #region-us
|
Tiny Dolphin
see URL
## Dataset details
This dataset is an extract of ~1 million of FLANv2 augmented with GPT-4 completions (URL). It is derived from this dataset
### Loading
This dataset is licensed apache-2.0 for commercial or non-commercial use.
|
[
"## Dataset details\n\nThis dataset is an extract of ~1 million of FLANv2 augmented with GPT-4 completions (URL). It is derived from this dataset",
"### Loading\n\n\n\nThis dataset is licensed apache-2.0 for commercial or non-commercial use."
] |
[
"TAGS\n#task_categories-text-generation #language-English #license-apache-2.0 #region-us \n",
"## Dataset details\n\nThis dataset is an extract of ~1 million of FLANv2 augmented with GPT-4 completions (URL). It is derived from this dataset",
"### Loading\n\n\n\nThis dataset is licensed apache-2.0 for commercial or non-commercial use."
] |
[
29,
39,
23
] |
[
"passage: TAGS\n#task_categories-text-generation #language-English #license-apache-2.0 #region-us \n## Dataset details\n\nThis dataset is an extract of ~1 million of FLANv2 augmented with GPT-4 completions (URL). It is derived from this dataset### Loading\n\n\n\nThis dataset is licensed apache-2.0 for commercial or non-commercial use."
] |
d5d959ce1f0edc117fdbb3f239996ea44e1b2ffa
|
# Dataset Card for "c06e4969"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
result-kand2-sdxl-wuerst-karlo/c06e4969
|
[
"region:us"
] |
2023-10-06T13:58:54+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 200, "num_examples": 10}], "download_size": 1394, "dataset_size": 200}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-06T13:58:55+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "c06e4969"
More Information needed
|
[
"# Dataset Card for \"c06e4969\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"c06e4969\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"c06e4969\"\n\nMore Information needed"
] |
dc739acc702c5ee61fbaed86945cfda7c23f9107
|
# Bangumi Image Base of Nobunaga The Fool
This is the image base of bangumi NOBUNAGA THE FOOL, we detected 36 characters, 2812 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 8 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 69 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 122 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 248 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 48 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 22 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 85 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 18 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 467 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 267 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 50 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 55 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 31 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 15 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 48 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 46 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 30 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 45 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 178 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 36 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 13 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 217 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 100 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 26 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 25 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 13 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 134 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 13 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 8 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 10 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 36 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 15 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 10 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 14 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 13 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 277 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/nobunagathefool
|
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] |
2023-10-06T14:49:34+00:00
|
{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}
|
2023-10-06T16:45:19+00:00
|
[] |
[] |
TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
|
Bangumi Image Base of Nobunaga The Fool
=======================================
This is the image base of bangumi NOBUNAGA THE FOOL, we detected 36 characters, 2812 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
[
25
] |
[
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
766c2ce4606a6e3d935ed9fe08c4baf3e6fdfe84
|
# Dataset Card for "ncbi_genbank_part_48"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Hack90/ncbi_genbank_part_48
|
[
"region:us"
] |
2023-10-06T15:12:24+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "features", "dtype": "int64"}, {"name": "seq_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 37408573115, "num_examples": 3590}], "download_size": 16355151548, "dataset_size": 37408573115}}
|
2023-10-06T15:37:04+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ncbi_genbank_part_48"
More Information needed
|
[
"# Dataset Card for \"ncbi_genbank_part_48\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ncbi_genbank_part_48\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ncbi_genbank_part_48\"\n\nMore Information needed"
] |
ceb3330e950c70e7b1b46696bfca181e27b0d41b
|
# Dataset Card for "ncbi_genbank_part_28"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Hack90/ncbi_genbank_part_28
|
[
"region:us"
] |
2023-10-06T15:17:05+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "features", "dtype": "int64"}, {"name": "seq_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 32754969732, "num_examples": 1307}], "download_size": 14813299268, "dataset_size": 32754969732}}
|
2023-10-07T00:52:13+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ncbi_genbank_part_28"
More Information needed
|
[
"# Dataset Card for \"ncbi_genbank_part_28\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ncbi_genbank_part_28\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ncbi_genbank_part_28\"\n\nMore Information needed"
] |
c7eb06998e034347a24dc83ffd7a63a7e7ccd185
|
# Dataset Card for "ncbi_genbank_part_38"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Hack90/ncbi_genbank_part_38
|
[
"region:us"
] |
2023-10-06T15:17:59+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "features", "dtype": "int64"}, {"name": "seq_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 32814010540, "num_examples": 1135}], "download_size": 0, "dataset_size": 32814010540}}
|
2023-10-06T22:59:57+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ncbi_genbank_part_38"
More Information needed
|
[
"# Dataset Card for \"ncbi_genbank_part_38\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ncbi_genbank_part_38\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ncbi_genbank_part_38\"\n\nMore Information needed"
] |
2c73e83af55edc4e561926bcc4087c84ca86676a
|
# Dataset Card for "ncbi_genbank_part_18"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Hack90/ncbi_genbank_part_18
|
[
"region:us"
] |
2023-10-06T15:23:14+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "features", "dtype": "int64"}, {"name": "seq_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 9462744773, "num_examples": 13032669}], "download_size": 3869663931, "dataset_size": 9462744773}}
|
2023-10-07T00:57:07+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ncbi_genbank_part_18"
More Information needed
|
[
"# Dataset Card for \"ncbi_genbank_part_18\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ncbi_genbank_part_18\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ncbi_genbank_part_18\"\n\nMore Information needed"
] |
b7383e13cab5e19fb87f68f4a35c3d6fb60bb1a2
|
# Dataset Card for "ncbi_genbank_part_8"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Hack90/ncbi_genbank_part_8
|
[
"region:us"
] |
2023-10-06T15:25:47+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "features", "dtype": "int64"}, {"name": "seq_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 19567803802, "num_examples": 10984}], "download_size": 9068866549, "dataset_size": 19567803802}}
|
2023-10-06T15:44:09+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ncbi_genbank_part_8"
More Information needed
|
[
"# Dataset Card for \"ncbi_genbank_part_8\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ncbi_genbank_part_8\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ncbi_genbank_part_8\"\n\nMore Information needed"
] |
04c261170a8209d742616d14ca64fc6a0f237839
|
# Bangumi Image Base of Inu Ni Nattara Suki Na Hito Ni Hirowareta
This is the image base of bangumi Inu ni Nattara Suki na Hito ni Hirowareta, we detected 9 characters, 406 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 67 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 92 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 14 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 11 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 23 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 32 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 74 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 44 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 49 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/inuninattarasukinahitonihirowareta
|
[
"size_categories:n<1K",
"license:mit",
"art",
"region:us"
] |
2023-10-06T15:34:30+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "tags": ["art"]}
|
2023-10-06T16:34:24+00:00
|
[] |
[] |
TAGS
#size_categories-n<1K #license-mit #art #region-us
|
Bangumi Image Base of Inu Ni Nattara Suki Na Hito Ni Hirowareta
===============================================================
This is the image base of bangumi Inu ni Nattara Suki na Hito ni Hirowareta, we detected 9 characters, 406 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-n<1K #license-mit #art #region-us \n"
] |
[
23
] |
[
"passage: TAGS\n#size_categories-n<1K #license-mit #art #region-us \n"
] |
deb7b16c03f29b41233508b8e3c02b38649eabfa
|
# Bangumi Image Base of Zero No Tsukaima
This is the image base of bangumi Zero no Tsukaima, we detected 64 characters, 7210 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 1450 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 31 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 30 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 258 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 8 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 66 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 297 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 60 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 34 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 18 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 26 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 27 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 28 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 32 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 198 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 41 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 103 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 31 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 21 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 12 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 152 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 55 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 63 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 203 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 28 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 30 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 23 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 47 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 23 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 30 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 16 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 38 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 1772 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 30 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 14 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 29 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 266 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 36 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 15 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 24 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 16 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 41 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 195 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 23 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 78 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 154 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 18 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 333 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 33 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 24 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 23 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 29 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 27 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 28 | [Download](53/dataset.zip) |  |  |  |  |  |  |  |  |
| 54 | 19 | [Download](54/dataset.zip) |  |  |  |  |  |  |  |  |
| 55 | 8 | [Download](55/dataset.zip) |  |  |  |  |  |  |  |  |
| 56 | 10 | [Download](56/dataset.zip) |  |  |  |  |  |  |  |  |
| 57 | 9 | [Download](57/dataset.zip) |  |  |  |  |  |  |  |  |
| 58 | 7 | [Download](58/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 59 | 22 | [Download](59/dataset.zip) |  |  |  |  |  |  |  |  |
| 60 | 8 | [Download](60/dataset.zip) |  |  |  |  |  |  |  |  |
| 61 | 10 | [Download](61/dataset.zip) |  |  |  |  |  |  |  |  |
| 62 | 5 | [Download](62/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| noise | 425 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/zeronotsukaima
|
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] |
2023-10-06T15:46:26+00:00
|
{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}
|
2023-10-06T19:23:55+00:00
|
[] |
[] |
TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
|
Bangumi Image Base of Zero No Tsukaima
======================================
This is the image base of bangumi Zero no Tsukaima, we detected 64 characters, 7210 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
[
25
] |
[
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
7170d5c4c28b617815a10a9eabd987ee3d016a6c
|
# Dataset Card for "ncbi_genbank_part_39"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Hack90/ncbi_genbank_part_39
|
[
"region:us"
] |
2023-10-06T15:46:59+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "features", "dtype": "int64"}, {"name": "seq_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 31553866013, "num_examples": 1218}], "download_size": 14299220624, "dataset_size": 31553866013}}
|
2023-10-06T16:00:42+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ncbi_genbank_part_39"
More Information needed
|
[
"# Dataset Card for \"ncbi_genbank_part_39\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ncbi_genbank_part_39\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ncbi_genbank_part_39\"\n\nMore Information needed"
] |
037d36d21fbcaa6308f5b86d5a3b3d5cff1378ef
|
# Dataset Card for "ncbi_genbank_part_19"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Hack90/ncbi_genbank_part_19
|
[
"region:us"
] |
2023-10-06T15:49:58+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "features", "dtype": "int64"}, {"name": "seq_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 10292374144, "num_examples": 14539438}], "download_size": 4229601328, "dataset_size": 10292374144}}
|
2023-10-07T01:18:28+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ncbi_genbank_part_19"
More Information needed
|
[
"# Dataset Card for \"ncbi_genbank_part_19\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ncbi_genbank_part_19\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ncbi_genbank_part_19\"\n\nMore Information needed"
] |
dfbde4373e637dda3d8766134771d424af3a6a02
|
# Dataset Card for "ncbi_genbank_part_29"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Hack90/ncbi_genbank_part_29
|
[
"region:us"
] |
2023-10-06T15:51:38+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "features", "dtype": "int64"}, {"name": "seq_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 34358138224, "num_examples": 38564}], "download_size": 15474999547, "dataset_size": 34358138224}}
|
2023-10-06T16:06:03+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ncbi_genbank_part_29"
More Information needed
|
[
"# Dataset Card for \"ncbi_genbank_part_29\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ncbi_genbank_part_29\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ncbi_genbank_part_29\"\n\nMore Information needed"
] |
efbeac0e7492d6e8e5520c59d97c0259b9c75e98
|
# Dataset Card for "ncbi_genbank_part_49"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Hack90/ncbi_genbank_part_49
|
[
"region:us"
] |
2023-10-06T16:00:43+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "features", "dtype": "int64"}, {"name": "seq_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 33647454754, "num_examples": 87952}], "download_size": 15354172665, "dataset_size": 33647454754}}
|
2023-10-06T16:19:48+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ncbi_genbank_part_49"
More Information needed
|
[
"# Dataset Card for \"ncbi_genbank_part_49\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ncbi_genbank_part_49\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ncbi_genbank_part_49\"\n\nMore Information needed"
] |
b77af346048bd8c53c220de6500540917dfc129c
|
# Bangumi Image Base of Shadows House
This is the image base of bangumi SHADOWS HOUSE, we detected 23 characters, 998 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 63 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 11 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 52 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 130 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 32 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 9 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 8 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 30 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 40 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 22 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 12 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 56 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 58 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 7 | [Download](13/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 14 | 11 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 9 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 293 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 15 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 7 | [Download](18/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 19 | 10 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 48 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 8 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 67 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/shadowshouse
|
[
"size_categories:n<1K",
"license:mit",
"art",
"region:us"
] |
2023-10-06T16:08:27+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "tags": ["art"]}
|
2023-10-06T17:04:47+00:00
|
[] |
[] |
TAGS
#size_categories-n<1K #license-mit #art #region-us
|
Bangumi Image Base of Shadows House
===================================
This is the image base of bangumi SHADOWS HOUSE, we detected 23 characters, 998 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-n<1K #license-mit #art #region-us \n"
] |
[
23
] |
[
"passage: TAGS\n#size_categories-n<1K #license-mit #art #region-us \n"
] |
510fe82e835f181ac9a8bad01bd5b0223847d6b2
|
# Bangumi Image Base of Kaguya-sama Wa Kokurasetai
This is the image base of bangumi Kaguya-sama wa Kokurasetai, we detected 29 characters, 2797 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 530 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 16 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 38 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 29 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 15 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 11 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 242 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 651 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 33 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 29 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 69 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 30 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 25 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 23 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 19 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 25 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 15 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 178 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 11 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 101 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 25 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 68 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 263 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 26 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 19 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 8 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 8 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 5 | [Download](27/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| noise | 285 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/kaguyasamawakokurasetai
|
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] |
2023-10-06T16:12:17+00:00
|
{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}
|
2023-10-06T17:56:02+00:00
|
[] |
[] |
TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
|
Bangumi Image Base of Kaguya-sama Wa Kokurasetai
================================================
This is the image base of bangumi Kaguya-sama wa Kokurasetai, we detected 29 characters, 2797 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
[
25
] |
[
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
f47696c00eef9da38ded9dcb58fc693cfd8ac95e
|
# Dataset Card for "ncbi_genbank_part_40"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Hack90/ncbi_genbank_part_40
|
[
"region:us"
] |
2023-10-06T16:16:49+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "features", "dtype": "int64"}, {"name": "seq_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 35016354335, "num_examples": 80526}], "download_size": 15795680024, "dataset_size": 35016354335}}
|
2023-10-06T16:32:08+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ncbi_genbank_part_40"
More Information needed
|
[
"# Dataset Card for \"ncbi_genbank_part_40\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ncbi_genbank_part_40\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ncbi_genbank_part_40\"\n\nMore Information needed"
] |
d3817469ffddf5a1b85a06971620df98514acc57
|
# Dataset Card for "ncbi_genbank_part_20"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Hack90/ncbi_genbank_part_20
|
[
"region:us"
] |
2023-10-06T16:17:01+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "features", "dtype": "int64"}, {"name": "seq_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 11487399500, "num_examples": 13928917}], "download_size": 4846727220, "dataset_size": 11487399500}}
|
2023-10-07T01:38:50+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ncbi_genbank_part_20"
More Information needed
|
[
"# Dataset Card for \"ncbi_genbank_part_20\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ncbi_genbank_part_20\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ncbi_genbank_part_20\"\n\nMore Information needed"
] |
31d9d72f9b4d387f0a3adc498644a5cf744e88da
|
# Dataset Card for "ncbi_genbank_part_30"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Hack90/ncbi_genbank_part_30
|
[
"region:us"
] |
2023-10-06T16:20:12+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "features", "dtype": "int64"}, {"name": "seq_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 32901664415, "num_examples": 168885}], "download_size": 14692414119, "dataset_size": 32901664415}}
|
2023-10-06T16:34:42+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ncbi_genbank_part_30"
More Information needed
|
[
"# Dataset Card for \"ncbi_genbank_part_30\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ncbi_genbank_part_30\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ncbi_genbank_part_30\"\n\nMore Information needed"
] |
c8b62c9da918165f7041f559fbe3eae9fd2e3054
|
# Dataset Card for "ncbi_genbank_part_9"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Hack90/ncbi_genbank_part_9
|
[
"region:us"
] |
2023-10-06T16:24:44+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "features", "dtype": "int64"}, {"name": "seq_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 20591910291, "num_examples": 12188}], "download_size": 4208046819, "dataset_size": 20591910291}}
|
2023-10-06T18:46:23+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ncbi_genbank_part_9"
More Information needed
|
[
"# Dataset Card for \"ncbi_genbank_part_9\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ncbi_genbank_part_9\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ncbi_genbank_part_9\"\n\nMore Information needed"
] |
1a94564f593ae5c0c91fcea6c3920de8ae2a7949
|
# Dataset Card for "ncbi_genbank_part_50"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Hack90/ncbi_genbank_part_50
|
[
"region:us"
] |
2023-10-06T16:43:20+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "features", "dtype": "int64"}, {"name": "seq_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 26640706946, "num_examples": 3973282}], "download_size": 10256237433, "dataset_size": 26640706946}}
|
2023-10-06T18:50:52+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ncbi_genbank_part_50"
More Information needed
|
[
"# Dataset Card for \"ncbi_genbank_part_50\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ncbi_genbank_part_50\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ncbi_genbank_part_50\"\n\nMore Information needed"
] |
c62237b014db780ba0216c2174c8734bc7fd5873
|
# Dataset Card for "ncbi_genbank_part_41"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Hack90/ncbi_genbank_part_41
|
[
"region:us"
] |
2023-10-06T16:45:51+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "features", "dtype": "int64"}, {"name": "seq_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 31049128200, "num_examples": 273326}], "download_size": 13996445609, "dataset_size": 31049128200}}
|
2023-10-06T16:59:33+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ncbi_genbank_part_41"
More Information needed
|
[
"# Dataset Card for \"ncbi_genbank_part_41\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ncbi_genbank_part_41\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ncbi_genbank_part_41\"\n\nMore Information needed"
] |
7965993a6ad0221f04ced35f9815e27214a8d4cf
|
# Dataset Card for "ncbi_genbank_part_21"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Hack90/ncbi_genbank_part_21
|
[
"region:us"
] |
2023-10-06T16:46:18+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "features", "dtype": "int64"}, {"name": "seq_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 12245208393, "num_examples": 15929500}], "download_size": 5119781029, "dataset_size": 12245208393}}
|
2023-10-07T02:00:47+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ncbi_genbank_part_21"
More Information needed
|
[
"# Dataset Card for \"ncbi_genbank_part_21\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ncbi_genbank_part_21\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ncbi_genbank_part_21\"\n\nMore Information needed"
] |
77aea3277e8e1a4d68b684f1d862fca31630bec9
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: google/pegasus-multi_news
* Dataset: samsum
* Config: samsum
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@sasha](https://huggingface.co/sasha) for evaluating this model.
|
autoevaluate/autoeval-eval-samsum-samsum-3cd2fc-93464145850
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-06T16:48:21+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["samsum"], "eval_info": {"task": "summarization", "model": "google/pegasus-multi_news", "metrics": [], "dataset_name": "samsum", "dataset_config": "samsum", "dataset_split": "test", "col_mapping": {"text": "dialogue", "target": "summary"}}}
|
2023-10-06T17:00:28+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: Summarization
* Model: google/pegasus-multi_news
* Dataset: samsum
* Config: samsum
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @sasha 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: Summarization\n* Model: google/pegasus-multi_news\n* Dataset: samsum\n* Config: samsum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @sasha 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: Summarization\n* Model: google/pegasus-multi_news\n* Dataset: samsum\n* Config: samsum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @sasha for evaluating this model."
] |
[
13,
85,
15
] |
[
"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: Summarization\n* Model: google/pegasus-multi_news\n* Dataset: samsum\n* Config: samsum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @sasha for evaluating this model."
] |
19e2a7870f64b9dacf2aba0990b877e149f9e383
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: google/pegasus-multi_news
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@sasha](https://huggingface.co/sasha) for evaluating this model.
|
autoevaluate/autoeval-eval-xsum-default-7e15d4-93465145851
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-06T16:48:28+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["xsum"], "eval_info": {"task": "summarization", "model": "google/pegasus-multi_news", "metrics": [], "dataset_name": "xsum", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "document", "target": "summary"}}}
|
2023-10-06T20:11:34+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: Summarization
* Model: google/pegasus-multi_news
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @sasha 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: Summarization\n* Model: google/pegasus-multi_news\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @sasha 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: Summarization\n* Model: google/pegasus-multi_news\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @sasha for evaluating this model."
] |
[
13,
84,
15
] |
[
"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: Summarization\n* Model: google/pegasus-multi_news\n* Dataset: xsum\n* Config: default\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @sasha for evaluating this model."
] |
58b1a9903dd295a09e0f38d81581fba9ab4ee362
|
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: google/pegasus-multi_news
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@sasha](https://huggingface.co/sasha) for evaluating this model.
|
autoevaluate/autoeval-eval-cnn_dailymail-3.0.0-9ea0d3-93467145852
|
[
"autotrain",
"evaluation",
"region:us"
] |
2023-10-06T16:48:33+00:00
|
{"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["cnn_dailymail"], "eval_info": {"task": "summarization", "model": "google/pegasus-multi_news", "metrics": [], "dataset_name": "cnn_dailymail", "dataset_config": "3.0.0", "dataset_split": "test", "col_mapping": {"text": "article", "target": "highlights"}}}
|
2023-10-06T20:24:22+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: Summarization
* Model: google/pegasus-multi_news
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @sasha 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: Summarization\n* Model: google/pegasus-multi_news\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @sasha 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: Summarization\n* Model: google/pegasus-multi_news\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @sasha for evaluating this model."
] |
[
13,
88,
15
] |
[
"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: Summarization\n* Model: google/pegasus-multi_news\n* Dataset: cnn_dailymail\n* Config: 3.0.0\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @sasha for evaluating this model."
] |
a3ec526e6d0247735955c7f905efb92281b363b4
|
# Dataset Card for "ncbi_genbank_part_31"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Hack90/ncbi_genbank_part_31
|
[
"region:us"
] |
2023-10-06T16:49:46+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "features", "dtype": "int64"}, {"name": "seq_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 29120787399, "num_examples": 2140631}], "download_size": 12705990582, "dataset_size": 29120787399}}
|
2023-10-06T17:01:43+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ncbi_genbank_part_31"
More Information needed
|
[
"# Dataset Card for \"ncbi_genbank_part_31\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ncbi_genbank_part_31\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ncbi_genbank_part_31\"\n\nMore Information needed"
] |
f61f91bd6f1af840183b1acb07aa162132c247d6
|
# Dataset Card for "university-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
nlplabtdtu/university-dataset
|
[
"region:us"
] |
2023-10-06T17:05:33+00:00
|
{"dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "body", "dtype": "string"}, {"name": "url", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1032712459, "num_examples": 213847}], "download_size": 389863864, "dataset_size": 1032712459}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-06T17:09:17+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "university-dataset"
More Information needed
|
[
"# Dataset Card for \"university-dataset\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"university-dataset\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"university-dataset\"\n\nMore Information needed"
] |
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