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09538f1d7efeeb1e44878bb36cf572058e4798d1 |
An imitation learning environment for the atari_stargunner environment, sample for the policy atari_2B_atari_stargunner_1111
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
| edbeeching/prj_gia_dataset_atari_2B_atari_stargunner_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-02-21T18:17:28+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-02-21T18:18:11+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the atari_stargunner environment, sample for the policy atari_2B_atari_stargunner_1111
This environment was created as part of the Generally Intelligent Agents project gia: URL
| []
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n"
]
| [
46
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n"
]
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|
145cbdb9a725d8a22b4b1be3b4b46899309757d0 |
An imitation learning environment for the atari_tennis environment, sample for the policy atari_2B_atari_tennis_1111
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
| edbeeching/prj_gia_dataset_atari_2B_atari_tennis_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-02-21T18:24:20+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-02-21T18:25:30+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the atari_tennis environment, sample for the policy atari_2B_atari_tennis_1111
This environment was created as part of the Generally Intelligent Agents project gia: URL
| []
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n"
]
| [
46
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n"
]
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|
bbc5108726a9cf0cc9ccf71140b100ea3ca0b7a6 |
An imitation learning environment for the atari_timepilot environment, sample for the policy atari_2B_atari_timepilot_1111
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
| edbeeching/prj_gia_dataset_atari_2B_atari_timepilot_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-02-21T18:31:00+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-02-21T18:32:51+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the atari_timepilot environment, sample for the policy atari_2B_atari_timepilot_1111
This environment was created as part of the Generally Intelligent Agents project gia: URL
| []
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n"
]
| [
46
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n"
]
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|
11a32e3725358c0702ce11a725c487131715154c |
An imitation learning environment for the atari_tutankham environment, sample for the policy atari_2B_atari_tutankham_1111
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
| edbeeching/prj_gia_dataset_atari_2B_atari_tutankham_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-02-21T18:38:33+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-02-21T18:40:10+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the atari_tutankham environment, sample for the policy atari_2B_atari_tutankham_1111
This environment was created as part of the Generally Intelligent Agents project gia: URL
| []
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n"
]
| [
46
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n"
]
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|
cdc71f25a76eac312a6c3347e351a2c785f0fa02 |
An imitation learning environment for the atari_upndown environment, sample for the policy atari_2B_atari_upndown_1111
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
| edbeeching/prj_gia_dataset_atari_2B_atari_upndown_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-02-21T18:47:28+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-02-21T18:48:32+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the atari_upndown environment, sample for the policy atari_2B_atari_upndown_1111
This environment was created as part of the Generally Intelligent Agents project gia: URL
| []
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n"
]
| [
46
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n"
]
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|
ea3c08c3b0dade898136ba912f64544dcd25ac37 |
An imitation learning environment for the atari_venture environment, sample for the policy atari_2B_atari_venture_1111
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
| edbeeching/prj_gia_dataset_atari_2B_atari_venture_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-02-21T18:54:32+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-02-21T18:55:16+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the atari_venture environment, sample for the policy atari_2B_atari_venture_1111
This environment was created as part of the Generally Intelligent Agents project gia: URL
| []
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n"
]
| [
46
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n"
]
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|
959a0bcbfa06df12f14c5530d3aaa6859f342dc5 |
An imitation learning environment for the atari_videopinball environment, sample for the policy atari_2B_atari_videopinball_1111
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
| edbeeching/prj_gia_dataset_atari_2B_atari_videopinball_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-02-21T19:01:50+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-02-21T19:02:43+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the atari_videopinball environment, sample for the policy atari_2B_atari_videopinball_1111
This environment was created as part of the Generally Intelligent Agents project gia: URL
| []
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n"
]
| [
46
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n"
]
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|
fdf505c8d6afb78e62e71b58c012f80657fb7289 |
An imitation learning environment for the atari_wizardofwor environment, sample for the policy atari_2B_atari_wizardofwor_1111
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
| edbeeching/prj_gia_dataset_atari_2B_atari_wizardofwor_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-02-21T19:08:26+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-02-21T19:09:20+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the atari_wizardofwor environment, sample for the policy atari_2B_atari_wizardofwor_1111
This environment was created as part of the Generally Intelligent Agents project gia: URL
| []
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n"
]
| [
46
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n"
]
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|
9862df54ca61d77cc881660cc339d3c4daaf690e |
An imitation learning environment for the atari_yarsrevenge environment, sample for the policy atari_2B_atari_yarsrevenge_1111
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
| edbeeching/prj_gia_dataset_atari_2B_atari_yarsrevenge_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-02-21T19:14:58+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-02-21T19:15:52+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the atari_yarsrevenge environment, sample for the policy atari_2B_atari_yarsrevenge_1111
This environment was created as part of the Generally Intelligent Agents project gia: URL
| []
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n"
]
| [
46
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n"
]
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|
b788f7d8c47455a0d24b6c04a1bfed0c2c79c895 |
An imitation learning environment for the atari_zaxxon environment, sample for the policy atari_2B_atari_zaxxon_1111
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
| edbeeching/prj_gia_dataset_atari_2B_atari_zaxxon_1111 | [
"deep-reinforcement-learning",
"reinforcement-learning",
"gia",
"multi-task",
"multi-modal",
"imitation-learning",
"offline-reinforcement-learning",
"region:us"
]
| 2023-02-21T19:21:45+00:00 | {"library_name": "gia", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "gia", "multi-task", "multi-modal", "imitation-learning", "offline-reinforcement-learning"]} | 2023-02-21T19:22:40+00:00 | []
| []
| TAGS
#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us
|
An imitation learning environment for the atari_zaxxon environment, sample for the policy atari_2B_atari_zaxxon_1111
This environment was created as part of the Generally Intelligent Agents project gia: URL
| []
| [
"TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n"
]
| [
46
]
| [
"passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #gia #multi-task #multi-modal #imitation-learning #offline-reinforcement-learning #region-us \n"
]
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|
0bef29bf7e2719b2c8a44835b96d91d64c836b2f | # Dataset Card for "product-10k-part1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | matterr/product-10k-part1 | [
"region:us"
]
| 2023-02-21T19:47:48+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 751920.0, "num_examples": 2}], "download_size": 754822, "dataset_size": 751920.0}} | 2023-02-21T20:35:59+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "product-10k-part1"
More Information needed | [
"# Dataset Card for \"product-10k-part1\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"product-10k-part1\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"product-10k-part1\"\n\nMore Information needed"
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|
7d31f770123adbf09829cc14dbb707a72a98d741 | # Dataset Card for "wikipedia.reorder.natural.de"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lshowway/wikipedia.reorder.natural.de | [
"region:us"
]
| 2023-02-21T19:55:17+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2385745587, "num_examples": 1137317}], "download_size": 0, "dataset_size": 2385745587}} | 2023-02-21T20:24:01+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "URL"
More Information needed | [
"# Dataset Card for \"URL\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"URL\"\n\nMore Information needed"
]
| [
6,
11
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"URL\"\n\nMore Information needed"
]
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|
234801d97bbee2422cbe97bc9ba6f286e4d3f642 | # Dataset Card for "wikipedia.reorder.svo.de"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lshowway/wikipedia.reorder.svo.de | [
"region:us"
]
| 2023-02-21T20:00:17+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2385745587, "num_examples": 1137317}], "download_size": 1063402086, "dataset_size": 2385745587}} | 2023-02-21T20:02:49+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "URL"
More Information needed | [
"# Dataset Card for \"URL\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"URL\"\n\nMore Information needed"
]
| [
6,
11
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"URL\"\n\nMore Information needed"
]
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|
765d7785ff409c71c8e4c0347f8daa7b29474f3d | # Dataset Card for "wikipedia.reorder.vos.de"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lshowway/wikipedia.reorder.vos.de | [
"region:us"
]
| 2023-02-21T20:06:21+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2385745587, "num_examples": 1137317}], "download_size": 1068076681, "dataset_size": 2385745587}} | 2023-02-21T20:08:01+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "URL"
More Information needed | [
"# Dataset Card for \"URL\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"URL\"\n\nMore Information needed"
]
| [
6,
11
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"URL\"\n\nMore Information needed"
]
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|
88c570e2c28e45290aa502e34c174a9516cd3d36 | this is a very bad dataset. a better one comming soon. | breadlicker45/youtube-comments | [
"region:us"
]
| 2023-02-21T20:07:34+00:00 | {} | 2023-02-22T20:45:23+00:00 | []
| []
| TAGS
#region-us
| this is a very bad dataset. a better one comming soon. | []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
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|
785ea48ca190d3e15adde61cec84eae357ad1b2f | # Binhvq News
- Source: https://github.com/binhvq/news-corpus
- Num examples: 19,365,593
- Language: Vietnamese
```python
from datasets import load_dataset
load_dataset("tdtunlp/binhvq_news_vi")
``` | vietgpt/binhvq_news_vi | [
"task_categories:text-generation",
"size_categories:10M<n<100M",
"language:vi",
"LM",
"region:us"
]
| 2023-02-21T20:08:06+00:00 | {"language": ["vi"], "size_categories": ["10M<n<100M"], "task_categories": ["text-generation"], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 8211350978.574438, "num_examples": 19365593}], "download_size": 4780706833, "dataset_size": 8211350978.574438}, "tags": ["LM"]} | 2023-03-30T17:58:53+00:00 | []
| [
"vi"
]
| TAGS
#task_categories-text-generation #size_categories-10M<n<100M #language-Vietnamese #LM #region-us
| # Binhvq News
- Source: URL
- Num examples: 19,365,593
- Language: Vietnamese
| [
"# Binhvq News\n\n- Source: URL\n- Num examples: 19,365,593\n- Language: Vietnamese"
]
| [
"TAGS\n#task_categories-text-generation #size_categories-10M<n<100M #language-Vietnamese #LM #region-us \n",
"# Binhvq News\n\n- Source: URL\n- Num examples: 19,365,593\n- Language: Vietnamese"
]
| [
38,
24
]
| [
"passage: TAGS\n#task_categories-text-generation #size_categories-10M<n<100M #language-Vietnamese #LM #region-us \n# Binhvq News\n\n- Source: URL\n- Num examples: 19,365,593\n- Language: Vietnamese"
]
| [
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|
6730e6512892378562b4055f6483a9691139d937 | # Dataset Card for "wikipedia.reorder.osv.de"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lshowway/wikipedia.reorder.osv.de | [
"region:us"
]
| 2023-02-21T20:09:23+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2385745587, "num_examples": 1137317}], "download_size": 1065735715, "dataset_size": 2385745587}} | 2023-02-21T20:11:06+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "URL"
More Information needed | [
"# Dataset Card for \"URL\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"URL\"\n\nMore Information needed"
]
| [
6,
11
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"URL\"\n\nMore Information needed"
]
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|
7f0c663fabfbd4e1bd0f5bb352257e487ecc2f71 | # Dataset Card for "wikipedia.reorder.sov.de"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lshowway/wikipedia.reorder.sov.de | [
"region:us"
]
| 2023-02-21T20:12:27+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2385745587, "num_examples": 1137317}], "download_size": 1068439913, "dataset_size": 2385745587}} | 2023-02-21T20:14:11+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "URL"
More Information needed | [
"# Dataset Card for \"URL\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"URL\"\n\nMore Information needed"
]
| [
6,
11
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"URL\"\n\nMore Information needed"
]
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|
177b5220bea8df17d089a5cdd714eae93d17f4d8 | # Dataset Card for "wikipedia.reorder.vso.de"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lshowway/wikipedia.reorder.vso.de | [
"region:us"
]
| 2023-02-21T20:15:24+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2385745587, "num_examples": 1137317}], "download_size": 1063715741, "dataset_size": 2385745587}} | 2023-02-21T20:16:45+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "URL"
More Information needed | [
"# Dataset Card for \"URL\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"URL\"\n\nMore Information needed"
]
| [
6,
11
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"URL\"\n\nMore Information needed"
]
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|
28351f3479088c8c4ac5bfdc185773e387db93a0 | # Dataset Card for "wikipedia.reorder.ovs.de"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lshowway/wikipedia.reorder.ovs.de | [
"region:us"
]
| 2023-02-21T20:17:11+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2385745587, "num_examples": 1137317}], "download_size": 1064795572, "dataset_size": 2385745587}} | 2023-02-21T20:27:31+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "URL"
More Information needed | [
"# Dataset Card for \"URL\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"URL\"\n\nMore Information needed"
]
| [
6,
11
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"URL\"\n\nMore Information needed"
]
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|
64ecf097c8db0f6dcfc86046e3450de8da5a3e41 | # Wikipedia
- Source: https://huggingface.co/datasets/wikipedia
- Num examples: 1,281,412
- Language: Vietnamese
```python
from datasets import load_dataset
load_dataset("tdtunlp/wikipedia_vi")
``` | vietgpt/wikipedia_vi | [
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:vi",
"LM",
"region:us"
]
| 2023-02-21T20:39:38+00:00 | {"language": ["vi"], "size_categories": ["1M<n<10M"], "task_categories": ["text-generation"], "dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "revid", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1053551922.960177, "num_examples": 1284930}], "download_size": 569515706, "dataset_size": 1053551922.960177}, "tags": ["LM"]} | 2023-09-16T04:11:18+00:00 | []
| [
"vi"
]
| TAGS
#task_categories-text-generation #size_categories-1M<n<10M #language-Vietnamese #LM #region-us
| # Wikipedia
- Source: URL
- Num examples: 1,281,412
- Language: Vietnamese
| [
"# Wikipedia\n\n- Source: URL\n- Num examples: 1,281,412\n- Language: Vietnamese"
]
| [
"TAGS\n#task_categories-text-generation #size_categories-1M<n<10M #language-Vietnamese #LM #region-us \n",
"# Wikipedia\n\n- Source: URL\n- Num examples: 1,281,412\n- Language: Vietnamese"
]
| [
38,
20
]
| [
"passage: TAGS\n#task_categories-text-generation #size_categories-1M<n<10M #language-Vietnamese #LM #region-us \n# Wikipedia\n\n- Source: URL\n- Num examples: 1,281,412\n- Language: Vietnamese"
]
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|
35e7ad2906ea22bfb293e0b82ca1f153fa8bb399 | # Dataset Card for "patched_test_p_10_f_SPOUT_v4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | roa7n/patched_test_p_10_f_SPOUT_v4 | [
"region:us"
]
| 2023-02-21T20:40:47+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence_str", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 537304297, "num_examples": 1675599}], "download_size": 54326651, "dataset_size": 537304297}} | 2023-02-21T20:41:00+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "patched_test_p_10_f_SPOUT_v4"
More Information needed | [
"# Dataset Card for \"patched_test_p_10_f_SPOUT_v4\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"patched_test_p_10_f_SPOUT_v4\"\n\nMore Information needed"
]
| [
6,
26
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"patched_test_p_10_f_SPOUT_v4\"\n\nMore Information needed"
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|
f9e872d95e3585575d77e0630461d525f4fde0f2 | # Dataset Card for "patched_test_p_20_f_SPOUT_v4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | roa7n/patched_test_p_20_f_SPOUT_v4 | [
"region:us"
]
| 2023-02-21T20:41:46+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence_str", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 517784141, "num_examples": 1607399}], "download_size": 52108156, "dataset_size": 517784141}} | 2023-02-21T20:41:58+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "patched_test_p_20_f_SPOUT_v4"
More Information needed | [
"# Dataset Card for \"patched_test_p_20_f_SPOUT_v4\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"patched_test_p_20_f_SPOUT_v4\"\n\nMore Information needed"
]
| [
6,
26
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"patched_test_p_20_f_SPOUT_v4\"\n\nMore Information needed"
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|
b10bf4a93c64501fdf23e4fc98e6bafc20d2c244 | # Dataset Card for "patched_test_p_40_f_SPOUT_v4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | roa7n/patched_test_p_40_f_SPOUT_v4 | [
"region:us"
]
| 2023-02-21T20:42:28+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence_str", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 478745882, "num_examples": 1470999}], "download_size": 0, "dataset_size": 478745882}} | 2023-02-21T20:43:09+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "patched_test_p_40_f_SPOUT_v4"
More Information needed | [
"# Dataset Card for \"patched_test_p_40_f_SPOUT_v4\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"patched_test_p_40_f_SPOUT_v4\"\n\nMore Information needed"
]
| [
6,
26
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"patched_test_p_40_f_SPOUT_v4\"\n\nMore Information needed"
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a087db33e07fb6087eec13c8267f7e324b7f49d5 | # Dataset Card for "patched_test_p_10_f_ATCaseOTCase_v4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | roa7n/patched_test_p_10_f_ATCaseOTCase_v4 | [
"region:us"
]
| 2023-02-21T20:43:52+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence_str", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 52568328, "num_examples": 143667}], "download_size": 5044378, "dataset_size": 52568328}} | 2023-02-21T20:43:59+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "patched_test_p_10_f_ATCaseOTCase_v4"
More Information needed | [
"# Dataset Card for \"patched_test_p_10_f_ATCaseOTCase_v4\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"patched_test_p_10_f_ATCaseOTCase_v4\"\n\nMore Information needed"
]
| [
6,
30
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"patched_test_p_10_f_ATCaseOTCase_v4\"\n\nMore Information needed"
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|
a43f98cad170ead879adbf26c415b1527522d9c0 | # Dataset Card for "patched_test_p_20_f_ATCaseOTCase_v4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | roa7n/patched_test_p_20_f_ATCaseOTCase_v4 | [
"region:us"
]
| 2023-02-21T20:44:33+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence_str", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 50950318, "num_examples": 139207}], "download_size": 4851567, "dataset_size": 50950318}} | 2023-02-21T20:44:40+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "patched_test_p_20_f_ATCaseOTCase_v4"
More Information needed | [
"# Dataset Card for \"patched_test_p_20_f_ATCaseOTCase_v4\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"patched_test_p_20_f_ATCaseOTCase_v4\"\n\nMore Information needed"
]
| [
6,
30
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"patched_test_p_20_f_ATCaseOTCase_v4\"\n\nMore Information needed"
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|
72f47cf00e5c7f885a4e1ed1cbd6c020eaaa5c38 | # Dataset Card for "patched_test_p_40_f_ATCaseOTCase_v4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | roa7n/patched_test_p_40_f_ATCaseOTCase_v4 | [
"region:us"
]
| 2023-02-21T20:46:47+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence_str", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 47714312, "num_examples": 130287}], "download_size": 4461993, "dataset_size": 47714312}} | 2023-02-21T20:46:53+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "patched_test_p_40_f_ATCaseOTCase_v4"
More Information needed | [
"# Dataset Card for \"patched_test_p_40_f_ATCaseOTCase_v4\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"patched_test_p_40_f_ATCaseOTCase_v4\"\n\nMore Information needed"
]
| [
6,
30
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"patched_test_p_40_f_ATCaseOTCase_v4\"\n\nMore Information needed"
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|
4b8f55e5df3fa7117687066e015c466aa523e927 | # Dataset Card for "patched_test_p_40_f_membrane_v4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | roa7n/patched_test_p_40_f_membrane_v4 | [
"region:us"
]
| 2023-02-21T20:47:15+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence_str", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1946930959, "num_examples": 3134581}], "download_size": 162353372, "dataset_size": 1946930959}} | 2023-02-21T20:47:43+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "patched_test_p_40_f_membrane_v4"
More Information needed | [
"# Dataset Card for \"patched_test_p_40_f_membrane_v4\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"patched_test_p_40_f_membrane_v4\"\n\nMore Information needed"
]
| [
6,
27
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"patched_test_p_40_f_membrane_v4\"\n\nMore Information needed"
]
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|
f6ea6e9fa5ec42907140665c6e0f4ec7a72aaf9e | # Dataset Card for "patched_test_p_80_f_membrane_v4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | roa7n/patched_test_p_80_f_membrane_v4 | [
"region:us"
]
| 2023-02-21T20:49:23+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence_str", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1802548319, "num_examples": 2865341}], "download_size": 151479669, "dataset_size": 1802548319}} | 2023-02-21T20:49:52+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "patched_test_p_80_f_membrane_v4"
More Information needed | [
"# Dataset Card for \"patched_test_p_80_f_membrane_v4\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"patched_test_p_80_f_membrane_v4\"\n\nMore Information needed"
]
| [
6,
27
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"patched_test_p_80_f_membrane_v4\"\n\nMore Information needed"
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|
eb22e64311cc724864cbc610d668d0084326160a | # Dataset Card for "patched_test_p_150_f_membrane_v4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | roa7n/patched_test_p_150_f_membrane_v4 | [
"region:us"
]
| 2023-02-21T20:50:09+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence_str", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1552272870, "num_examples": 2394171}], "download_size": 128097844, "dataset_size": 1552272870}} | 2023-02-21T20:50:32+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "patched_test_p_150_f_membrane_v4"
More Information needed | [
"# Dataset Card for \"patched_test_p_150_f_membrane_v4\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"patched_test_p_150_f_membrane_v4\"\n\nMore Information needed"
]
| [
6,
27
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"patched_test_p_150_f_membrane_v4\"\n\nMore Information needed"
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|
bc0824e81679ac2c895d0a0f9a4eb447afbd8c72 | # Dataset Card for "patched_test_p_200_f_membrane_v4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | roa7n/patched_test_p_200_f_membrane_v4 | [
"region:us"
]
| 2023-02-21T20:50:54+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence_str", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1371458168, "num_examples": 2057621}], "download_size": 112398698, "dataset_size": 1371458168}} | 2023-02-21T20:51:13+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "patched_test_p_200_f_membrane_v4"
More Information needed | [
"# Dataset Card for \"patched_test_p_200_f_membrane_v4\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"patched_test_p_200_f_membrane_v4\"\n\nMore Information needed"
]
| [
6,
27
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"patched_test_p_200_f_membrane_v4\"\n\nMore Information needed"
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|
81d3b7c52745184913fea4455689c8127d8dc47f | # Wikipedia
- Source: https://huggingface.co/datasets/wikipedia
- Num examples: 6,623,239
- Language: English
```python
from datasets import load_dataset
load_dataset("tdtunlp/wikipedia_en")
``` | vietgpt/wikipedia_en | [
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:en",
"LM",
"region:us"
]
| 2023-02-21T20:52:04+00:00 | {"language": ["en"], "size_categories": ["1M<n<10M"], "task_categories": ["text-generation"], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 21102365479, "num_examples": 6623239}], "download_size": 12161597141, "dataset_size": 21102365479}, "tags": ["LM"]} | 2023-03-30T17:35:12+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-generation #size_categories-1M<n<10M #language-English #LM #region-us
| # Wikipedia
- Source: URL
- Num examples: 6,623,239
- Language: English
| [
"# Wikipedia\n\n- Source: URL\n- Num examples: 6,623,239\n- Language: English"
]
| [
"TAGS\n#task_categories-text-generation #size_categories-1M<n<10M #language-English #LM #region-us \n",
"# Wikipedia\n\n- Source: URL\n- Num examples: 6,623,239\n- Language: English"
]
| [
35,
21
]
| [
"passage: TAGS\n#task_categories-text-generation #size_categories-1M<n<10M #language-English #LM #region-us \n# Wikipedia\n\n- Source: URL\n- Num examples: 6,623,239\n- Language: English"
]
| [
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|
fe6c56666c2907859caa531e8eb4dd35717d8b20 | # OpenSubtitles
- Source: https://huggingface.co/datasets/open_subtitles
- Num examples: 3,505,276
- Language: English
```python
from datasets import load_dataset
load_dataset("tdtunlp/open_subtitles_envi")
```
- Format for Translation task
```python
def preprocess(
sample,
instruction_key="### Instruction:",
input_key="Input:",
response_key="<|endofprompt|>",
end_key="<|endoftext|>",
en2vi=True,
):
if en2vi:
if random.random() < 0.5:
instruction = "Translate the following sentences from English into Vietnamese."
else:
instruction = "Dịch các câu sau từ tiếng Anh sang tiếng Việt."
input = sample['en'].strip()
response = sample['vi'].strip()
else:
if random.random() < 0.5:
instruction = "Translate the following sentences from Vietnamese into English."
else:
instruction = "Dịch các câu sau từ tiếng Việt sang tiếng Anh."
input = sample['vi'].strip()
response = sample['en'].strip()
return {'text': """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
{instruction_key}
{instruction}
{input_key}
{input}
{response_key}
{response}
{end_key}""".format(
instruction_key=instruction_key,
instruction=instruction,
input_key=input_key,
input=input,
response_key=response_key,
response=response,
end_key=end_key,
)}
"""
Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
Dịch các câu sau từ tiếng Anh sang tiếng Việt.
Input:
Line up, I say!
<|endofprompt|>
Sắp hàng, nghe chưa!
<|endoftext|>
"""
``` | vietgpt/open_subtitles_envi | [
"task_categories:translation",
"size_categories:1M<n<10M",
"language:en",
"language:vi",
"LM",
"region:us"
]
| 2023-02-21T21:01:10+00:00 | {"language": ["en", "vi"], "size_categories": ["1M<n<10M"], "task_categories": ["translation"], "dataset_info": {"features": [{"name": "en", "dtype": "string"}, {"name": "vi", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 280063489, "num_examples": 3505276}], "download_size": 176803145, "dataset_size": 280063489}, "tags": ["LM"]} | 2023-07-03T16:52:41+00:00 | []
| [
"en",
"vi"
]
| TAGS
#task_categories-translation #size_categories-1M<n<10M #language-English #language-Vietnamese #LM #region-us
| # OpenSubtitles
- Source: URL
- Num examples: 3,505,276
- Language: English
- Format for Translation task
| [
"# OpenSubtitles\n\n- Source: URL\n- Num examples: 3,505,276\n- Language: English\n\n\n\n- Format for Translation task"
]
| [
"TAGS\n#task_categories-translation #size_categories-1M<n<10M #language-English #language-Vietnamese #LM #region-us \n",
"# OpenSubtitles\n\n- Source: URL\n- Num examples: 3,505,276\n- Language: English\n\n\n\n- Format for Translation task"
]
| [
40,
28
]
| [
"passage: TAGS\n#task_categories-translation #size_categories-1M<n<10M #language-English #language-Vietnamese #LM #region-us \n# OpenSubtitles\n\n- Source: URL\n- Num examples: 3,505,276\n- Language: English\n\n\n\n- Format for Translation task"
]
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|
62aff9ab74aa4b73a36c45a9f6c91279a57db5d3 | # Ted Talks
- Source: https://huggingface.co/datasets/ted_talks_iwslt
- Num examples: 2,293
- Language: English
```python
from datasets import load_dataset
load_dataset("tdtunlp/ted_talks_iwslt_en")
``` | vietgpt/ted_talks_iwslt_en | [
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:en",
"LM",
"region:us"
]
| 2023-02-21T21:22:06+00:00 | {"language": ["en"], "size_categories": ["1K<n<10K"], "task_categories": ["text-generation"], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 27242341, "num_examples": 2293}], "download_size": 15366817, "dataset_size": 27242341}, "tags": ["LM"]} | 2023-03-30T17:28:08+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-generation #size_categories-1K<n<10K #language-English #LM #region-us
| # Ted Talks
- Source: URL
- Num examples: 2,293
- Language: English
| [
"# Ted Talks\n\n- Source: URL\n- Num examples: 2,293\n- Language: English"
]
| [
"TAGS\n#task_categories-text-generation #size_categories-1K<n<10K #language-English #LM #region-us \n",
"# Ted Talks\n\n- Source: URL\n- Num examples: 2,293\n- Language: English"
]
| [
35,
19
]
| [
"passage: TAGS\n#task_categories-text-generation #size_categories-1K<n<10K #language-English #LM #region-us \n# Ted Talks\n\n- Source: URL\n- Num examples: 2,293\n- Language: English"
]
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|
4cc7f8afebc04cfae966086ee9286148c6c1001f | - This Dataset has been downloaded from PubMed
- It has abstracts and titles that are related to Lung Cancer
- the data has been cleaned before uploading
- it could be used for any NLP task, such as Domain Adaptation | Gaborandi/Lung_Cancer_pubmed_abstracts | [
"region:us"
]
| 2023-02-21T22:14:05+00:00 | {} | 2023-02-21T23:20:11+00:00 | []
| []
| TAGS
#region-us
| - This Dataset has been downloaded from PubMed
- It has abstracts and titles that are related to Lung Cancer
- the data has been cleaned before uploading
- it could be used for any NLP task, such as Domain Adaptation | []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
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|
049b6d3855cf4f9c75489457e5e7f66864501348 | - This Dataset has been downloaded from PubMed
- It has abstracts and titles that are related to type 2 DM
- the data has been cleaned before uploading
- it could be used for any NLP task, such as Domain Adaptation | Gaborandi/diabetes_mellitus_type2_pubmed_abstracts | [
"region:us"
]
| 2023-02-21T22:22:15+00:00 | {} | 2023-02-21T23:10:29+00:00 | []
| []
| TAGS
#region-us
| - This Dataset has been downloaded from PubMed
- It has abstracts and titles that are related to type 2 DM
- the data has been cleaned before uploading
- it could be used for any NLP task, such as Domain Adaptation | []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
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|
1bd89a6d51f3a71656e17e3aec8c209c49b7ba10 | # Dataset Card for "trivia_qa_wiki_validation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | manu/trivia_qa_wiki | [
"region:us"
]
| 2023-02-21T22:25:14+00:00 | {"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "question_id", "dtype": "string"}, {"name": "question_source", "dtype": "string"}, {"name": "entity_pages", "sequence": [{"name": "doc_source", "dtype": "string"}, {"name": "filename", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "wiki_context", "dtype": "string"}]}, {"name": "search_results", "sequence": [{"name": "description", "dtype": "string"}, {"name": "filename", "dtype": "string"}, {"name": "rank", "dtype": "int32"}, {"name": "title", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "search_context", "dtype": "string"}]}, {"name": "answer", "struct": [{"name": "aliases", "sequence": "string"}, {"name": "normalized_aliases", "sequence": "string"}, {"name": "matched_wiki_entity_name", "dtype": "string"}, {"name": "normalized_matched_wiki_entity_name", "dtype": "string"}, {"name": "normalized_value", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "value", "dtype": "string"}]}], "splits": [{"name": "validation", "num_bytes": 430166050, "num_examples": 7993}], "download_size": 234775285, "dataset_size": 430166050}} | 2023-02-21T22:25:45+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "trivia_qa_wiki_validation"
More Information needed | [
"# Dataset Card for \"trivia_qa_wiki_validation\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"trivia_qa_wiki_validation\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"trivia_qa_wiki_validation\"\n\nMore Information needed"
]
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|
160ae214bccc54d9b716e53eb6b0b50ee50ec39c | # Dataset Card for "patched_test_p_10_f_SPOUT_m1_predictions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | roa7n/patched_test_p_10_f_SPOUT_m1_predictions | [
"region:us"
]
| 2023-02-22T00:19:01+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence_str", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "m1_preds", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 544006693, "num_examples": 1675599}], "download_size": 55789813, "dataset_size": 544006693}} | 2023-02-22T00:19:13+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "patched_test_p_10_f_SPOUT_m1_predictions"
More Information needed | [
"# Dataset Card for \"patched_test_p_10_f_SPOUT_m1_predictions\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"patched_test_p_10_f_SPOUT_m1_predictions\"\n\nMore Information needed"
]
| [
6,
30
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"patched_test_p_10_f_SPOUT_m1_predictions\"\n\nMore Information needed"
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|
5cf6eb2c2622d5ee0ca97df42c62bfd3fd1fabc4 | # Dataset Card for "nldv"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fifi777/nldv | [
"region:us"
]
| 2023-02-22T00:20:52+00:00 | {"dataset_info": {"features": [{"name": "repo_name", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "copies", "dtype": "string"}, {"name": "size", "dtype": "string"}, {"name": "content", "dtype": "string"}, {"name": "license", "dtype": "string"}, {"name": "hash", "dtype": "int64"}, {"name": "line_mean", "dtype": "float64"}, {"name": "line_max", "dtype": "int64"}, {"name": "alpha_frac", "dtype": "float64"}, {"name": "autogenerated", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 3231756186.42971, "num_examples": 235728}, {"name": "valid", "num_bytes": 65957285.57029006, "num_examples": 4811}], "download_size": 24134199, "dataset_size": 3297713472.0}} | 2023-02-24T05:40:14+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "nldv"
More Information needed | [
"# Dataset Card for \"nldv\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"nldv\"\n\nMore Information needed"
]
| [
6,
12
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"nldv\"\n\nMore Information needed"
]
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|
6a9105995137339c23500017e0adc23f779bfed1 | # Dataset Card for "rlhf-qa-conditional-generation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | kastan/rlhf-qa-conditional-generation | [
"region:us"
]
| 2023-02-22T00:28:08+00:00 | {"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "completion", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 27303.451327433628, "num_examples": 90}, {"name": "valid", "num_bytes": 6977.548672566371, "num_examples": 23}], "download_size": 6067, "dataset_size": 34281.0}} | 2023-03-06T20:36:48+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "rlhf-qa-conditional-generation"
More Information needed | [
"# Dataset Card for \"rlhf-qa-conditional-generation\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"rlhf-qa-conditional-generation\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"rlhf-qa-conditional-generation\"\n\nMore Information needed"
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|
af96311d66ccf05456d5c9018f3a5037b9d6bb5c | # Dataset Card for "simpsons-blip-captions-pil"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | jinmel/simpsons-blip-captions-pil | [
"region:us"
]
| 2023-02-22T02:02:08+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 27091297.0, "num_examples": 755}], "download_size": 26505319, "dataset_size": 27091297.0}} | 2023-02-22T02:17:04+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "simpsons-blip-captions-pil"
More Information needed | [
"# Dataset Card for \"simpsons-blip-captions-pil\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"simpsons-blip-captions-pil\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"simpsons-blip-captions-pil\"\n\nMore Information needed"
]
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|
df984f873641538b8e9da6e870e6faa8597f0301 | # Dataset Card for "1.predict_last_word"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lansinuote/nlp.1.predict_last_word | [
"region:us"
]
| 2023-02-22T06:22:11+00:00 | {"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 4628980, "num_examples": 39905}, {"name": "validation", "num_bytes": 98368, "num_examples": 848}, {"name": "test", "num_bytes": 200680, "num_examples": 1730}], "download_size": 0, "dataset_size": 4928028}} | 2023-02-22T11:26:30+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "1.predict_last_word"
More Information needed | [
"# Dataset Card for \"1.predict_last_word\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"1.predict_last_word\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"1.predict_last_word\"\n\nMore Information needed"
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|
a1d33300f1b3d2d2b650725ee0c0c10256faa031 |
# m0_fine_tuning_ref_cmbert_io
## Introduction
This dataset was used to fine-tuned [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) for **flat NER task** using Flat NER approach [M0].
It contains 19th-century Paris trade directories' entries.
## Dataset parameters
* Approach : M0
* Dataset type : ground-truth
* Tokenizer : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner)
* Tagging format : IO
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned model : [nlpso/m0_flat_ner_ref_cmbert_io](https://huggingface.co/nlpso/m0_flat_ner_ref_cmbert_io)
## Entity types
Abbreviation|Description
-|-
O |Outside of a named entity
PER |Person or company name
ACT |Person or company professional activity
TITRE |Distinction
LOC |Street name
CARDINAL |Street number
FT |Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m0_fine_tuning_ref_cmbert_io")
| nlpso/m0_fine_tuning_ref_cmbert_io | [
"task_categories:token-classification",
"multilinguality:monolingual",
"language:fr",
"region:us"
]
| 2023-02-22T07:59:18+00:00 | {"language": ["fr"], "multilinguality": ["monolingual"], "task_categories": ["token-classification"]} | 2023-02-22T07:59:33+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us
| m0\_fine\_tuning\_ref\_cmbert\_io
=================================
Introduction
------------
This dataset was used to fine-tuned Jean-Baptiste/camembert-ner for flat NER task using Flat NER approach [M0].
It contains 19th-century Paris trade directories' entries.
Dataset parameters
------------------
* Approach : M0
* Dataset type : ground-truth
* Tokenizer : Jean-Baptiste/camembert-ner
* Tagging format : IO
* Counts :
+ Train : 6084
+ Dev : 676
+ Test : 1685
* Associated fine-tuned model : nlpso/m0\_flat\_ner\_ref\_cmbert\_io
Entity types
------------
How to use this dataset
-----------------------
'''python
from datasets import load\_dataset
train\_dev\_test = load\_dataset("nlpso/m0\_fine\_tuning\_ref\_cmbert\_io")
| []
| [
"TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
32
]
| [
"passage: TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
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|
7e298b9cb1c1b1663e48a9a3eafadff9577a59dd |
# m0_fine_tuning_ref_ptrn_cmbert_io
## Introduction
This dataset was used to fine-tuned [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) for **flat NER task** using Flat NER approach [M0].
It contains 19th-century Paris trade directories' entries.
## Dataset parameters
* Approach : M0
* Dataset type : ground-truth
* Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained)
* Tagging format : IO
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned model : [nlpso/m0_flat_ner_ref_ptrn_cmbert_io](https://huggingface.co/nlpso/m0_flat_ner_ref_ptrn_cmbert_io)
## Entity types
Abbreviation|Description
-|-
O |Outside of a named entity
PER |Person or company name
ACT |Person or company professional activity
TITRE |Distinction
LOC |Street name
CARDINAL |Street number
FT |Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m0_fine_tuning_ref_ptrn_cmbert_io")
| nlpso/m0_fine_tuning_ref_ptrn_cmbert_io | [
"task_categories:token-classification",
"multilinguality:monolingual",
"language:fr",
"region:us"
]
| 2023-02-22T07:59:34+00:00 | {"language": ["fr"], "multilinguality": ["monolingual"], "task_categories": ["token-classification"]} | 2023-02-22T07:59:50+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us
| m0\_fine\_tuning\_ref\_ptrn\_cmbert\_io
=======================================
Introduction
------------
This dataset was used to fine-tuned HueyNemud/das22-10-camembert\_pretrained for flat NER task using Flat NER approach [M0].
It contains 19th-century Paris trade directories' entries.
Dataset parameters
------------------
* Approach : M0
* Dataset type : ground-truth
* Tokenizer : HueyNemud/das22-10-camembert\_pretrained
* Tagging format : IO
* Counts :
+ Train : 6084
+ Dev : 676
+ Test : 1685
* Associated fine-tuned model : nlpso/m0\_flat\_ner\_ref\_ptrn\_cmbert\_io
Entity types
------------
How to use this dataset
-----------------------
'''python
from datasets import load\_dataset
train\_dev\_test = load\_dataset("nlpso/m0\_fine\_tuning\_ref\_ptrn\_cmbert\_io")
| []
| [
"TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
32
]
| [
"passage: TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
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|
e2bfd9d3f3402d9b73eca0e6ded84296137bd421 |
# m0_fine_tuning_ocr_cmbert_io
## Introduction
This dataset was used to fine-tuned [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) for **flat NER task** using Flat NER approach [M0].
It contains 19th-century Paris trade directories' entries.
## Dataset parameters
* Approach : M0
* Dataset type : noisy (Pero OCR)
* Tokenizer : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner)
* Tagging format : IO
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned model : [nlpso/m0_flat_ner_ocr_cmbert_io](https://huggingface.co/nlpso/m0_flat_ner_ocr_cmbert_io)
## Entity types
Abbreviation|Description
-|-
O |Outside of a named entity
PER |Person or company name
ACT |Person or company professional activity
TITRE |Distinction
LOC |Street name
CARDINAL |Street number
FT |Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m0_fine_tuning_ocr_cmbert_io")
| nlpso/m0_fine_tuning_ocr_cmbert_io | [
"task_categories:token-classification",
"multilinguality:monolingual",
"language:fr",
"region:us"
]
| 2023-02-22T07:59:51+00:00 | {"language": ["fr"], "multilinguality": ["monolingual"], "task_categories": ["token-classification"]} | 2023-02-22T08:00:08+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us
| m0\_fine\_tuning\_ocr\_cmbert\_io
=================================
Introduction
------------
This dataset was used to fine-tuned Jean-Baptiste/camembert-ner for flat NER task using Flat NER approach [M0].
It contains 19th-century Paris trade directories' entries.
Dataset parameters
------------------
* Approach : M0
* Dataset type : noisy (Pero OCR)
* Tokenizer : Jean-Baptiste/camembert-ner
* Tagging format : IO
* Counts :
+ Train : 6084
+ Dev : 676
+ Test : 1685
* Associated fine-tuned model : nlpso/m0\_flat\_ner\_ocr\_cmbert\_io
Entity types
------------
How to use this dataset
-----------------------
'''python
from datasets import load\_dataset
train\_dev\_test = load\_dataset("nlpso/m0\_fine\_tuning\_ocr\_cmbert\_io")
| []
| [
"TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
32
]
| [
"passage: TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
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|
9ffe7505d9ffbd4b89bffe83b64ae2aa22d26726 |
# m0_fine_tuning_ocr_ptrn_cmbert_io
## Introduction
This dataset was used to fine-tuned [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) for **flat NER task** using Flat NER approach [M0].
It contains 19th-century Paris trade directories' entries.
## Dataset parameters
* Approach : M0
* Dataset type : noisy (Pero OCR)
* Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained)
* Tagging format : IO
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned model : [nlpso/m0_flat_ner_ocr_ptrn_cmbert_io](https://huggingface.co/nlpso/m0_flat_ner_ocr_ptrn_cmbert_io)
## Entity types
Abbreviation|Description
-|-
O |Outside of a named entity
PER |Person or company name
ACT |Person or company professional activity
TITRE |Distinction
LOC |Street name
CARDINAL |Street number
FT |Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m0_fine_tuning_ocr_ptrn_cmbert_io")
| nlpso/m0_fine_tuning_ocr_ptrn_cmbert_io | [
"task_categories:token-classification",
"multilinguality:monolingual",
"language:fr",
"region:us"
]
| 2023-02-22T08:00:09+00:00 | {"language": ["fr"], "multilinguality": ["monolingual"], "task_categories": ["token-classification"]} | 2023-02-22T08:00:25+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us
| m0\_fine\_tuning\_ocr\_ptrn\_cmbert\_io
=======================================
Introduction
------------
This dataset was used to fine-tuned HueyNemud/das22-10-camembert\_pretrained for flat NER task using Flat NER approach [M0].
It contains 19th-century Paris trade directories' entries.
Dataset parameters
------------------
* Approach : M0
* Dataset type : noisy (Pero OCR)
* Tokenizer : HueyNemud/das22-10-camembert\_pretrained
* Tagging format : IO
* Counts :
+ Train : 6084
+ Dev : 676
+ Test : 1685
* Associated fine-tuned model : nlpso/m0\_flat\_ner\_ocr\_ptrn\_cmbert\_io
Entity types
------------
How to use this dataset
-----------------------
'''python
from datasets import load\_dataset
train\_dev\_test = load\_dataset("nlpso/m0\_fine\_tuning\_ocr\_ptrn\_cmbert\_io")
| []
| [
"TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
32
]
| [
"passage: TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
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|
b7fdc0584cf7557e3243a13062649b4ff397ac22 |
# m1_fine_tuning_ref_cmbert_io
## Introduction
This dataset was used to fine-tuned [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) for **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approach : M1
* Dataset type : ground-truth
* Tokenizer : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner)
* Tagging format : IO
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* Level-1 : [nlpso/m1_ind_layers_ref_cmbert_io_level_1](https://huggingface.co/nlpso/m1_ind_layers_ref_cmbert_io_level_1)
* Level 2 : [nlpso/m1_ind_layers_ref_cmbert_io_level_2](https://huggingface.co/nlpso/m1_ind_layers_ref_cmbert_io_level_2)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m1_fine_tuning_ref_cmbert_io")
| nlpso/m1_fine_tuning_ref_cmbert_io | [
"task_categories:token-classification",
"multilinguality:monolingual",
"language:fr",
"region:us"
]
| 2023-02-22T08:00:26+00:00 | {"language": ["fr"], "multilinguality": ["monolingual"], "task_categories": ["token-classification"]} | 2023-02-22T08:38:54+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us
| m1\_fine\_tuning\_ref\_cmbert\_io
=================================
Introduction
------------
This dataset was used to fine-tuned Jean-Baptiste/camembert-ner for nested NER task using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
Dataset parameters
------------------
* Approach : M1
* Dataset type : ground-truth
* Tokenizer : Jean-Baptiste/camembert-ner
* Tagging format : IO
* Counts :
+ Train : 6084
+ Dev : 676
+ Test : 1685
* Associated fine-tuned models :
+ Level-1 : nlpso/m1\_ind\_layers\_ref\_cmbert\_io\_level\_1
+ Level 2 : nlpso/m1\_ind\_layers\_ref\_cmbert\_io\_level\_2
Entity types
------------
Abbreviation: O, Entity group (level): 1 & 2, Description: Outside of a named entity
Abbreviation: PER, Entity group (level): 1, Description: Person or company name
Abbreviation: ACT, Entity group (level): 1 & 2, Description: Person or company professional activity
Abbreviation: TITREH, Entity group (level): 2, Description: Military or civil distinction
Abbreviation: DESC, Entity group (level): 1, Description: Entry full description
Abbreviation: TITREP, Entity group (level): 2, Description: Professionnal reward
Abbreviation: SPAT, Entity group (level): 1, Description: Address
Abbreviation: LOC, Entity group (level): 2, Description: Street name
Abbreviation: CARDINAL, Entity group (level): 2, Description: Street number
Abbreviation: FT, Entity group (level): 2, Description: Geographical feature
How to use this dataset
-----------------------
'''python
from datasets import load\_dataset
train\_dev\_test = load\_dataset("nlpso/m1\_fine\_tuning\_ref\_cmbert\_io")
| []
| [
"TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
32
]
| [
"passage: TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
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|
58f017c9f7c788fe1d270e77529dac55e50ec80e |
# m1_fine_tuning_ref_ptrn_cmbert_io
## Introduction
This dataset was used to fine-tuned [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) for **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approach : M1
* Dataset type : ground-truth
* Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained)
* Tagging format : IO
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* Level-1 : [nlpso/m1_ind_layers_ref_ptrn_cmbert_io_level_1](https://huggingface.co/nlpso/m1_ind_layers_ref_ptrn_cmbert_io_level_1)
* Level 2 : [nlpso/m1_ind_layers_ref_ptrn_cmbert_io_level_2](https://huggingface.co/nlpso/m1_ind_layers_ref_ptrn_cmbert_io_level_2)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m1_fine_tuning_ref_ptrn_cmbert_io")
| nlpso/m1_fine_tuning_ref_ptrn_cmbert_io | [
"task_categories:token-classification",
"multilinguality:monolingual",
"language:fr",
"region:us"
]
| 2023-02-22T08:00:43+00:00 | {"language": ["fr"], "multilinguality": ["monolingual"], "task_categories": ["token-classification"]} | 2023-02-22T08:39:11+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us
| m1\_fine\_tuning\_ref\_ptrn\_cmbert\_io
=======================================
Introduction
------------
This dataset was used to fine-tuned HueyNemud/das22-10-camembert\_pretrained for nested NER task using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
Dataset parameters
------------------
* Approach : M1
* Dataset type : ground-truth
* Tokenizer : HueyNemud/das22-10-camembert\_pretrained
* Tagging format : IO
* Counts :
+ Train : 6084
+ Dev : 676
+ Test : 1685
* Associated fine-tuned models :
+ Level-1 : nlpso/m1\_ind\_layers\_ref\_ptrn\_cmbert\_io\_level\_1
+ Level 2 : nlpso/m1\_ind\_layers\_ref\_ptrn\_cmbert\_io\_level\_2
Entity types
------------
Abbreviation: O, Entity group (level): 1 & 2, Description: Outside of a named entity
Abbreviation: PER, Entity group (level): 1, Description: Person or company name
Abbreviation: ACT, Entity group (level): 1 & 2, Description: Person or company professional activity
Abbreviation: TITREH, Entity group (level): 2, Description: Military or civil distinction
Abbreviation: DESC, Entity group (level): 1, Description: Entry full description
Abbreviation: TITREP, Entity group (level): 2, Description: Professionnal reward
Abbreviation: SPAT, Entity group (level): 1, Description: Address
Abbreviation: LOC, Entity group (level): 2, Description: Street name
Abbreviation: CARDINAL, Entity group (level): 2, Description: Street number
Abbreviation: FT, Entity group (level): 2, Description: Geographical feature
How to use this dataset
-----------------------
'''python
from datasets import load\_dataset
train\_dev\_test = load\_dataset("nlpso/m1\_fine\_tuning\_ref\_ptrn\_cmbert\_io")
| []
| [
"TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
32
]
| [
"passage: TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
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]
|
9515895676b077111c27ddb25b6810fa2ce2d9f4 |
# m1_fine_tuning_ref_cmbert_iob2
## Introduction
This dataset was used to fine-tuned [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) for **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approach : M1
* Dataset type : ground-truth
* Tokenizer : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner)
* Tagging format : IOB2
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* Level-1 : [nlpso/m1_ind_layers_ref_cmbert_iob2_level_1](https://huggingface.co/nlpso/m1_ind_layers_ref_cmbert_iob2_level_1)
* Level 2 : [nlpso/m1_ind_layers_ref_cmbert_iob2_level_2](https://huggingface.co/nlpso/m1_ind_layers_ref_cmbert_iob2_level_2)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m1_fine_tuning_ref_cmbert_iob2")
| nlpso/m1_fine_tuning_ref_cmbert_iob2 | [
"task_categories:token-classification",
"multilinguality:monolingual",
"language:fr",
"region:us"
]
| 2023-02-22T08:01:00+00:00 | {"language": ["fr"], "multilinguality": ["monolingual"], "task_categories": ["token-classification"]} | 2023-02-22T08:39:28+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us
| m1\_fine\_tuning\_ref\_cmbert\_iob2
===================================
Introduction
------------
This dataset was used to fine-tuned Jean-Baptiste/camembert-ner for nested NER task using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
Dataset parameters
------------------
* Approach : M1
* Dataset type : ground-truth
* Tokenizer : Jean-Baptiste/camembert-ner
* Tagging format : IOB2
* Counts :
+ Train : 6084
+ Dev : 676
+ Test : 1685
* Associated fine-tuned models :
+ Level-1 : nlpso/m1\_ind\_layers\_ref\_cmbert\_iob2\_level\_1
+ Level 2 : nlpso/m1\_ind\_layers\_ref\_cmbert\_iob2\_level\_2
Entity types
------------
Abbreviation: O, Entity group (level): 1 & 2, Description: Outside of a named entity
Abbreviation: PER, Entity group (level): 1, Description: Person or company name
Abbreviation: ACT, Entity group (level): 1 & 2, Description: Person or company professional activity
Abbreviation: TITREH, Entity group (level): 2, Description: Military or civil distinction
Abbreviation: DESC, Entity group (level): 1, Description: Entry full description
Abbreviation: TITREP, Entity group (level): 2, Description: Professionnal reward
Abbreviation: SPAT, Entity group (level): 1, Description: Address
Abbreviation: LOC, Entity group (level): 2, Description: Street name
Abbreviation: CARDINAL, Entity group (level): 2, Description: Street number
Abbreviation: FT, Entity group (level): 2, Description: Geographical feature
How to use this dataset
-----------------------
'''python
from datasets import load\_dataset
train\_dev\_test = load\_dataset("nlpso/m1\_fine\_tuning\_ref\_cmbert\_iob2")
| []
| [
"TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
32
]
| [
"passage: TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
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|
e05852d2522a90922dc79f2226708d87fb8ed77e |
# m1_fine_tuning_ref_ptrn_cmbert_iob2
## Introduction
This dataset was used to fine-tuned [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) for **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approach : M1
* Dataset type : ground-truth
* Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained)
* Tagging format : IOB2
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* Level-1 : [nlpso/m1_ind_layers_ref_ptrn_cmbert_iob2_level_1](https://huggingface.co/nlpso/m1_ind_layers_ref_ptrn_cmbert_iob2_level_1)
* Level 2 : [nlpso/m1_ind_layers_ref_ptrn_cmbert_iob2_level_2](https://huggingface.co/nlpso/m1_ind_layers_ref_ptrn_cmbert_iob2_level_2)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m1_fine_tuning_ref_ptrn_cmbert_iob2")
| nlpso/m1_fine_tuning_ref_ptrn_cmbert_iob2 | [
"task_categories:token-classification",
"multilinguality:monolingual",
"language:fr",
"region:us"
]
| 2023-02-22T08:01:17+00:00 | {"language": ["fr"], "multilinguality": ["monolingual"], "task_categories": ["token-classification"]} | 2023-02-22T08:39:45+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us
| m1\_fine\_tuning\_ref\_ptrn\_cmbert\_iob2
=========================================
Introduction
------------
This dataset was used to fine-tuned HueyNemud/das22-10-camembert\_pretrained for nested NER task using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
Dataset parameters
------------------
* Approach : M1
* Dataset type : ground-truth
* Tokenizer : HueyNemud/das22-10-camembert\_pretrained
* Tagging format : IOB2
* Counts :
+ Train : 6084
+ Dev : 676
+ Test : 1685
* Associated fine-tuned models :
+ Level-1 : nlpso/m1\_ind\_layers\_ref\_ptrn\_cmbert\_iob2\_level\_1
+ Level 2 : nlpso/m1\_ind\_layers\_ref\_ptrn\_cmbert\_iob2\_level\_2
Entity types
------------
Abbreviation: O, Entity group (level): 1 & 2, Description: Outside of a named entity
Abbreviation: PER, Entity group (level): 1, Description: Person or company name
Abbreviation: ACT, Entity group (level): 1 & 2, Description: Person or company professional activity
Abbreviation: TITREH, Entity group (level): 2, Description: Military or civil distinction
Abbreviation: DESC, Entity group (level): 1, Description: Entry full description
Abbreviation: TITREP, Entity group (level): 2, Description: Professionnal reward
Abbreviation: SPAT, Entity group (level): 1, Description: Address
Abbreviation: LOC, Entity group (level): 2, Description: Street name
Abbreviation: CARDINAL, Entity group (level): 2, Description: Street number
Abbreviation: FT, Entity group (level): 2, Description: Geographical feature
How to use this dataset
-----------------------
'''python
from datasets import load\_dataset
train\_dev\_test = load\_dataset("nlpso/m1\_fine\_tuning\_ref\_ptrn\_cmbert\_iob2")
| []
| [
"TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
32
]
| [
"passage: TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
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|
d98466297b61f9ebd2bb0935ccffd6c8e8f85144 |
# m1_fine_tuning_ocr_cmbert_io
## Introduction
This dataset was used to fine-tuned [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) for **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approach : M1
* Dataset type : noisy (Pero OCR)
* Tokenizer : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner)
* Tagging format : IO
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* Level-1 : [nlpso/m1_ind_layers_ocr_cmbert_io_level_1](https://huggingface.co/nlpso/m1_ind_layers_ocr_cmbert_io_level_1)
* Level 2 : [nlpso/m1_ind_layers_ocr_cmbert_io_level_2](https://huggingface.co/nlpso/m1_ind_layers_ocr_cmbert_io_level_2)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m1_fine_tuning_ocr_cmbert_io")
| nlpso/m1_fine_tuning_ocr_cmbert_io | [
"task_categories:token-classification",
"multilinguality:monolingual",
"language:fr",
"region:us"
]
| 2023-02-22T08:01:33+00:00 | {"language": ["fr"], "multilinguality": ["monolingual"], "task_categories": ["token-classification"]} | 2023-02-22T08:40:03+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us
| m1\_fine\_tuning\_ocr\_cmbert\_io
=================================
Introduction
------------
This dataset was used to fine-tuned Jean-Baptiste/camembert-ner for nested NER task using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
Dataset parameters
------------------
* Approach : M1
* Dataset type : noisy (Pero OCR)
* Tokenizer : Jean-Baptiste/camembert-ner
* Tagging format : IO
* Counts :
+ Train : 6084
+ Dev : 676
+ Test : 1685
* Associated fine-tuned models :
+ Level-1 : nlpso/m1\_ind\_layers\_ocr\_cmbert\_io\_level\_1
+ Level 2 : nlpso/m1\_ind\_layers\_ocr\_cmbert\_io\_level\_2
Entity types
------------
Abbreviation: O, Entity group (level): 1 & 2, Description: Outside of a named entity
Abbreviation: PER, Entity group (level): 1, Description: Person or company name
Abbreviation: ACT, Entity group (level): 1 & 2, Description: Person or company professional activity
Abbreviation: TITREH, Entity group (level): 2, Description: Military or civil distinction
Abbreviation: DESC, Entity group (level): 1, Description: Entry full description
Abbreviation: TITREP, Entity group (level): 2, Description: Professionnal reward
Abbreviation: SPAT, Entity group (level): 1, Description: Address
Abbreviation: LOC, Entity group (level): 2, Description: Street name
Abbreviation: CARDINAL, Entity group (level): 2, Description: Street number
Abbreviation: FT, Entity group (level): 2, Description: Geographical feature
How to use this dataset
-----------------------
'''python
from datasets import load\_dataset
train\_dev\_test = load\_dataset("nlpso/m1\_fine\_tuning\_ocr\_cmbert\_io")
| []
| [
"TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
32
]
| [
"passage: TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
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|
93c57e74f8dda7b5da11bf541fc0d69aef22b3ea |
# m1_fine_tuning_ocr_ptrn_cmbert_io
## Introduction
This dataset was used to fine-tuned [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) for **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approach : M1
* Dataset type : noisy (Pero OCR)
* Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained)
* Tagging format : IO
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* Level-1 : [nlpso/m1_ind_layers_ocr_ptrn_cmbert_io_level_1](https://huggingface.co/nlpso/m1_ind_layers_ocr_ptrn_cmbert_io_level_1)
* Level 2 : [nlpso/m1_ind_layers_ocr_ptrn_cmbert_io_level_2](https://huggingface.co/nlpso/m1_ind_layers_ocr_ptrn_cmbert_io_level_2)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m1_fine_tuning_ocr_ptrn_cmbert_io")
| nlpso/m1_fine_tuning_ocr_ptrn_cmbert_io | [
"task_categories:token-classification",
"multilinguality:monolingual",
"language:fr",
"region:us"
]
| 2023-02-22T08:01:50+00:00 | {"language": ["fr"], "multilinguality": ["monolingual"], "task_categories": ["token-classification"]} | 2023-02-22T08:40:20+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us
| m1\_fine\_tuning\_ocr\_ptrn\_cmbert\_io
=======================================
Introduction
------------
This dataset was used to fine-tuned HueyNemud/das22-10-camembert\_pretrained for nested NER task using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
Dataset parameters
------------------
* Approach : M1
* Dataset type : noisy (Pero OCR)
* Tokenizer : HueyNemud/das22-10-camembert\_pretrained
* Tagging format : IO
* Counts :
+ Train : 6084
+ Dev : 676
+ Test : 1685
* Associated fine-tuned models :
+ Level-1 : nlpso/m1\_ind\_layers\_ocr\_ptrn\_cmbert\_io\_level\_1
+ Level 2 : nlpso/m1\_ind\_layers\_ocr\_ptrn\_cmbert\_io\_level\_2
Entity types
------------
Abbreviation: O, Entity group (level): 1 & 2, Description: Outside of a named entity
Abbreviation: PER, Entity group (level): 1, Description: Person or company name
Abbreviation: ACT, Entity group (level): 1 & 2, Description: Person or company professional activity
Abbreviation: TITREH, Entity group (level): 2, Description: Military or civil distinction
Abbreviation: DESC, Entity group (level): 1, Description: Entry full description
Abbreviation: TITREP, Entity group (level): 2, Description: Professionnal reward
Abbreviation: SPAT, Entity group (level): 1, Description: Address
Abbreviation: LOC, Entity group (level): 2, Description: Street name
Abbreviation: CARDINAL, Entity group (level): 2, Description: Street number
Abbreviation: FT, Entity group (level): 2, Description: Geographical feature
How to use this dataset
-----------------------
'''python
from datasets import load\_dataset
train\_dev\_test = load\_dataset("nlpso/m1\_fine\_tuning\_ocr\_ptrn\_cmbert\_io")
| []
| [
"TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
32
]
| [
"passage: TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
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|
1706a26a28960d69cc338419dcb2e1355831fdfc |
# m1_fine_tuning_ocr_cmbert_iob2
## Introduction
This dataset was used to fine-tuned [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) for **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approach : M1
* Dataset type : noisy (Pero OCR)
* Tokenizer : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner)
* Tagging format : IOB2
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* Level-1 : [nlpso/m1_ind_layers_ocr_cmbert_iob2_level_1](https://huggingface.co/nlpso/m1_ind_layers_ocr_cmbert_iob2_level_1)
* Level 2 : [nlpso/m1_ind_layers_ocr_cmbert_iob2_level_2](https://huggingface.co/nlpso/m1_ind_layers_ocr_cmbert_iob2_level_2)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m1_fine_tuning_ocr_cmbert_iob2")
| nlpso/m1_fine_tuning_ocr_cmbert_iob2 | [
"task_categories:token-classification",
"multilinguality:monolingual",
"language:fr",
"region:us"
]
| 2023-02-22T08:02:06+00:00 | {"language": ["fr"], "multilinguality": ["monolingual"], "task_categories": ["token-classification"]} | 2023-02-22T08:40:38+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us
| m1\_fine\_tuning\_ocr\_cmbert\_iob2
===================================
Introduction
------------
This dataset was used to fine-tuned Jean-Baptiste/camembert-ner for nested NER task using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
Dataset parameters
------------------
* Approach : M1
* Dataset type : noisy (Pero OCR)
* Tokenizer : Jean-Baptiste/camembert-ner
* Tagging format : IOB2
* Counts :
+ Train : 6084
+ Dev : 676
+ Test : 1685
* Associated fine-tuned models :
+ Level-1 : nlpso/m1\_ind\_layers\_ocr\_cmbert\_iob2\_level\_1
+ Level 2 : nlpso/m1\_ind\_layers\_ocr\_cmbert\_iob2\_level\_2
Entity types
------------
Abbreviation: O, Entity group (level): 1 & 2, Description: Outside of a named entity
Abbreviation: PER, Entity group (level): 1, Description: Person or company name
Abbreviation: ACT, Entity group (level): 1 & 2, Description: Person or company professional activity
Abbreviation: TITREH, Entity group (level): 2, Description: Military or civil distinction
Abbreviation: DESC, Entity group (level): 1, Description: Entry full description
Abbreviation: TITREP, Entity group (level): 2, Description: Professionnal reward
Abbreviation: SPAT, Entity group (level): 1, Description: Address
Abbreviation: LOC, Entity group (level): 2, Description: Street name
Abbreviation: CARDINAL, Entity group (level): 2, Description: Street number
Abbreviation: FT, Entity group (level): 2, Description: Geographical feature
How to use this dataset
-----------------------
'''python
from datasets import load\_dataset
train\_dev\_test = load\_dataset("nlpso/m1\_fine\_tuning\_ocr\_cmbert\_iob2")
| []
| [
"TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
32
]
| [
"passage: TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
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|
911cbf35e1a1d3f8fc63ab119dc2e9e0003e7229 |
# m1_fine_tuning_ocr_ptrn_cmbert_iob2
## Introduction
This dataset was used to fine-tuned [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) for **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approach : M1
* Dataset type : noisy (Pero OCR)
* Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained)
* Tagging format : IOB2
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* Level-1 : [nlpso/m1_ind_layers_ocr_ptrn_cmbert_iob2_level_1](https://huggingface.co/nlpso/m1_ind_layers_ocr_ptrn_cmbert_iob2_level_1)
* Level 2 : [nlpso/m1_ind_layers_ocr_ptrn_cmbert_iob2_level_2](https://huggingface.co/nlpso/m1_ind_layers_ocr_ptrn_cmbert_iob2_level_2)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m1_fine_tuning_ocr_ptrn_cmbert_iob2")
| nlpso/m1_fine_tuning_ocr_ptrn_cmbert_iob2 | [
"task_categories:token-classification",
"multilinguality:monolingual",
"language:fr",
"region:us"
]
| 2023-02-22T08:02:22+00:00 | {"language": ["fr"], "multilinguality": ["monolingual"], "task_categories": ["token-classification"]} | 2023-02-22T08:40:55+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us
| m1\_fine\_tuning\_ocr\_ptrn\_cmbert\_iob2
=========================================
Introduction
------------
This dataset was used to fine-tuned HueyNemud/das22-10-camembert\_pretrained for nested NER task using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
Dataset parameters
------------------
* Approach : M1
* Dataset type : noisy (Pero OCR)
* Tokenizer : HueyNemud/das22-10-camembert\_pretrained
* Tagging format : IOB2
* Counts :
+ Train : 6084
+ Dev : 676
+ Test : 1685
* Associated fine-tuned models :
+ Level-1 : nlpso/m1\_ind\_layers\_ocr\_ptrn\_cmbert\_iob2\_level\_1
+ Level 2 : nlpso/m1\_ind\_layers\_ocr\_ptrn\_cmbert\_iob2\_level\_2
Entity types
------------
Abbreviation: O, Entity group (level): 1 & 2, Description: Outside of a named entity
Abbreviation: PER, Entity group (level): 1, Description: Person or company name
Abbreviation: ACT, Entity group (level): 1 & 2, Description: Person or company professional activity
Abbreviation: TITREH, Entity group (level): 2, Description: Military or civil distinction
Abbreviation: DESC, Entity group (level): 1, Description: Entry full description
Abbreviation: TITREP, Entity group (level): 2, Description: Professionnal reward
Abbreviation: SPAT, Entity group (level): 1, Description: Address
Abbreviation: LOC, Entity group (level): 2, Description: Street name
Abbreviation: CARDINAL, Entity group (level): 2, Description: Street number
Abbreviation: FT, Entity group (level): 2, Description: Geographical feature
How to use this dataset
-----------------------
'''python
from datasets import load\_dataset
train\_dev\_test = load\_dataset("nlpso/m1\_fine\_tuning\_ocr\_ptrn\_cmbert\_iob2")
| []
| [
"TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
32
]
| [
"passage: TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
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|
e13c439b5bb9039fe4e01ac52624e513412a7cc4 |
# m2m3_fine_tuning_ref_cmbert_io
## Introduction
This dataset was used to fine-tuned [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) for **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approachrd : M2 and M3
* Dataset type : ground-truth
* Tokenizer : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner)
* Tagging format : IO
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* M2 : [nlpso/m2_joint_label_ref_cmbert_io](https://huggingface.co/nlpso/m2_joint_label_ref_cmbert_io)
* M3 : [nlpso/m3_hierarchical_ner_ref_cmbert_io](https://huggingface.co/nlpso/m3_hierarchical_ner_ref_cmbert_io)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m2m3_fine_tuning_ref_cmbert_io")
| nlpso/m2m3_fine_tuning_ref_cmbert_io | [
"task_categories:token-classification",
"multilinguality:monolingual",
"language:fr",
"region:us"
]
| 2023-02-22T08:02:39+00:00 | {"language": ["fr"], "multilinguality": ["monolingual"], "task_categories": ["token-classification"]} | 2023-02-22T08:02:54+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us
| m2m3\_fine\_tuning\_ref\_cmbert\_io
===================================
Introduction
------------
This dataset was used to fine-tuned Jean-Baptiste/camembert-ner for nested NER task using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
Dataset parameters
------------------
* Approachrd : M2 and M3
* Dataset type : ground-truth
* Tokenizer : Jean-Baptiste/camembert-ner
* Tagging format : IO
* Counts :
+ Train : 6084
+ Dev : 676
+ Test : 1685
* Associated fine-tuned models :
+ M2 : nlpso/m2\_joint\_label\_ref\_cmbert\_io
+ M3 : nlpso/m3\_hierarchical\_ner\_ref\_cmbert\_io
Entity types
------------
Abbreviation: O, Entity group (level): 1 & 2, Description: Outside of a named entity
Abbreviation: PER, Entity group (level): 1, Description: Person or company name
Abbreviation: ACT, Entity group (level): 1 & 2, Description: Person or company professional activity
Abbreviation: TITREH, Entity group (level): 2, Description: Military or civil distinction
Abbreviation: DESC, Entity group (level): 1, Description: Entry full description
Abbreviation: TITREP, Entity group (level): 2, Description: Professionnal reward
Abbreviation: SPAT, Entity group (level): 1, Description: Address
Abbreviation: LOC, Entity group (level): 2, Description: Street name
Abbreviation: CARDINAL, Entity group (level): 2, Description: Street number
Abbreviation: FT, Entity group (level): 2, Description: Geographical feature
How to use this dataset
-----------------------
'''python
from datasets import load\_dataset
train\_dev\_test = load\_dataset("nlpso/m2m3\_fine\_tuning\_ref\_cmbert\_io")
| []
| [
"TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
32
]
| [
"passage: TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
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|
ad7af67ab3af8550be9a1c9eeba509f81f9ad06b |
# m2m3_fine_tuning_ref_ptrn_cmbert_io
## Introduction
This dataset was used to fine-tuned [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) for **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approachrd : M2 and M3
* Dataset type : ground-truth
* Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained)
* Tagging format : IO
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* M2 : [nlpso/m2_joint_label_ref_ptrn_cmbert_io](https://huggingface.co/nlpso/m2_joint_label_ref_ptrn_cmbert_io)
* M3 : [nlpso/m3_hierarchical_ner_ref_ptrn_cmbert_io](https://huggingface.co/nlpso/m3_hierarchical_ner_ref_ptrn_cmbert_io)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m2m3_fine_tuning_ref_ptrn_cmbert_io")
| nlpso/m2m3_fine_tuning_ref_ptrn_cmbert_io | [
"task_categories:token-classification",
"multilinguality:monolingual",
"language:fr",
"region:us"
]
| 2023-02-22T08:02:55+00:00 | {"language": ["fr"], "multilinguality": ["monolingual"], "task_categories": ["token-classification"]} | 2023-02-22T08:03:10+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us
| m2m3\_fine\_tuning\_ref\_ptrn\_cmbert\_io
=========================================
Introduction
------------
This dataset was used to fine-tuned HueyNemud/das22-10-camembert\_pretrained for nested NER task using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
Dataset parameters
------------------
* Approachrd : M2 and M3
* Dataset type : ground-truth
* Tokenizer : HueyNemud/das22-10-camembert\_pretrained
* Tagging format : IO
* Counts :
+ Train : 6084
+ Dev : 676
+ Test : 1685
* Associated fine-tuned models :
+ M2 : nlpso/m2\_joint\_label\_ref\_ptrn\_cmbert\_io
+ M3 : nlpso/m3\_hierarchical\_ner\_ref\_ptrn\_cmbert\_io
Entity types
------------
Abbreviation: O, Entity group (level): 1 & 2, Description: Outside of a named entity
Abbreviation: PER, Entity group (level): 1, Description: Person or company name
Abbreviation: ACT, Entity group (level): 1 & 2, Description: Person or company professional activity
Abbreviation: TITREH, Entity group (level): 2, Description: Military or civil distinction
Abbreviation: DESC, Entity group (level): 1, Description: Entry full description
Abbreviation: TITREP, Entity group (level): 2, Description: Professionnal reward
Abbreviation: SPAT, Entity group (level): 1, Description: Address
Abbreviation: LOC, Entity group (level): 2, Description: Street name
Abbreviation: CARDINAL, Entity group (level): 2, Description: Street number
Abbreviation: FT, Entity group (level): 2, Description: Geographical feature
How to use this dataset
-----------------------
'''python
from datasets import load\_dataset
train\_dev\_test = load\_dataset("nlpso/m2m3\_fine\_tuning\_ref\_ptrn\_cmbert\_io")
| []
| [
"TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
32
]
| [
"passage: TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
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|
16e2ff4dd53420c35c41ead732be8f3de2181e90 |
# m2m3_fine_tuning_ref_cmbert_iob2
## Introduction
This dataset was used to fine-tuned [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) for **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approachrd : M2 and M3
* Dataset type : ground-truth
* Tokenizer : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner)
* Tagging format : IOB2
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* M2 : [nlpso/m2_joint_label_ref_cmbert_iob2](https://huggingface.co/nlpso/m2_joint_label_ref_cmbert_iob2)
* M3 : [nlpso/m3_hierarchical_ner_ref_cmbert_iob2](https://huggingface.co/nlpso/m3_hierarchical_ner_ref_cmbert_iob2)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m2m3_fine_tuning_ref_cmbert_iob2")
| nlpso/m2m3_fine_tuning_ref_cmbert_iob2 | [
"task_categories:token-classification",
"multilinguality:monolingual",
"language:fr",
"region:us"
]
| 2023-02-22T08:03:11+00:00 | {"language": ["fr"], "multilinguality": ["monolingual"], "task_categories": ["token-classification"]} | 2023-02-22T08:03:27+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us
| m2m3\_fine\_tuning\_ref\_cmbert\_iob2
=====================================
Introduction
------------
This dataset was used to fine-tuned Jean-Baptiste/camembert-ner for nested NER task using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
Dataset parameters
------------------
* Approachrd : M2 and M3
* Dataset type : ground-truth
* Tokenizer : Jean-Baptiste/camembert-ner
* Tagging format : IOB2
* Counts :
+ Train : 6084
+ Dev : 676
+ Test : 1685
* Associated fine-tuned models :
+ M2 : nlpso/m2\_joint\_label\_ref\_cmbert\_iob2
+ M3 : nlpso/m3\_hierarchical\_ner\_ref\_cmbert\_iob2
Entity types
------------
Abbreviation: O, Entity group (level): 1 & 2, Description: Outside of a named entity
Abbreviation: PER, Entity group (level): 1, Description: Person or company name
Abbreviation: ACT, Entity group (level): 1 & 2, Description: Person or company professional activity
Abbreviation: TITREH, Entity group (level): 2, Description: Military or civil distinction
Abbreviation: DESC, Entity group (level): 1, Description: Entry full description
Abbreviation: TITREP, Entity group (level): 2, Description: Professionnal reward
Abbreviation: SPAT, Entity group (level): 1, Description: Address
Abbreviation: LOC, Entity group (level): 2, Description: Street name
Abbreviation: CARDINAL, Entity group (level): 2, Description: Street number
Abbreviation: FT, Entity group (level): 2, Description: Geographical feature
How to use this dataset
-----------------------
'''python
from datasets import load\_dataset
train\_dev\_test = load\_dataset("nlpso/m2m3\_fine\_tuning\_ref\_cmbert\_iob2")
| []
| [
"TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
32
]
| [
"passage: TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
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|
a177274163f64ee1eaf97c7ed93d0de2bfbd3261 |
# m2m3_fine_tuning_ref_ptrn_cmbert_iob2
## Introduction
This dataset was used to fine-tuned [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) for **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approachrd : M2 and M3
* Dataset type : ground-truth
* Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained)
* Tagging format : IOB2
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* M2 : [nlpso/m2_joint_label_ref_ptrn_cmbert_iob2](https://huggingface.co/nlpso/m2_joint_label_ref_ptrn_cmbert_iob2)
* M3 : [nlpso/m3_hierarchical_ner_ref_ptrn_cmbert_iob2](https://huggingface.co/nlpso/m3_hierarchical_ner_ref_ptrn_cmbert_iob2)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m2m3_fine_tuning_ref_ptrn_cmbert_iob2")
| nlpso/m2m3_fine_tuning_ref_ptrn_cmbert_iob2 | [
"task_categories:token-classification",
"multilinguality:monolingual",
"language:fr",
"region:us"
]
| 2023-02-22T08:03:28+00:00 | {"language": ["fr"], "multilinguality": ["monolingual"], "task_categories": ["token-classification"]} | 2023-02-22T08:03:43+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us
| m2m3\_fine\_tuning\_ref\_ptrn\_cmbert\_iob2
===========================================
Introduction
------------
This dataset was used to fine-tuned HueyNemud/das22-10-camembert\_pretrained for nested NER task using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
Dataset parameters
------------------
* Approachrd : M2 and M3
* Dataset type : ground-truth
* Tokenizer : HueyNemud/das22-10-camembert\_pretrained
* Tagging format : IOB2
* Counts :
+ Train : 6084
+ Dev : 676
+ Test : 1685
* Associated fine-tuned models :
+ M2 : nlpso/m2\_joint\_label\_ref\_ptrn\_cmbert\_iob2
+ M3 : nlpso/m3\_hierarchical\_ner\_ref\_ptrn\_cmbert\_iob2
Entity types
------------
Abbreviation: O, Entity group (level): 1 & 2, Description: Outside of a named entity
Abbreviation: PER, Entity group (level): 1, Description: Person or company name
Abbreviation: ACT, Entity group (level): 1 & 2, Description: Person or company professional activity
Abbreviation: TITREH, Entity group (level): 2, Description: Military or civil distinction
Abbreviation: DESC, Entity group (level): 1, Description: Entry full description
Abbreviation: TITREP, Entity group (level): 2, Description: Professionnal reward
Abbreviation: SPAT, Entity group (level): 1, Description: Address
Abbreviation: LOC, Entity group (level): 2, Description: Street name
Abbreviation: CARDINAL, Entity group (level): 2, Description: Street number
Abbreviation: FT, Entity group (level): 2, Description: Geographical feature
How to use this dataset
-----------------------
'''python
from datasets import load\_dataset
train\_dev\_test = load\_dataset("nlpso/m2m3\_fine\_tuning\_ref\_ptrn\_cmbert\_iob2")
| []
| [
"TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
32
]
| [
"passage: TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
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|
9be3fa1235e9381a04829250488e5feefe14400b |
# m0_qualitative_analysis_ref_cmbert_io
## Introduction
This dataset was used to perform **qualitative analysis** of [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) on **flat NER task** using Flat NER approach [M0].
It contains 19th-century Paris trade directories' entries.
## Dataset parameters
* Approach : M0
* Dataset type : ground-truth
* Tokenizer : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner)
* Tagging format : IO
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned model : [nlpso/m0_flat_ner_ref_cmbert_io](https://huggingface.co/nlpso/m0_flat_ner_ref_cmbert_io)
## Entity types
Abbreviation|Description
-|-
O |Outside of a named entity
PER |Person or company name
ACT |Person or company professional activity
TITRE |Distinction
LOC |Street name
CARDINAL |Street number
FT |Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m0_qualitative_analysis_ref_cmbert_io")
| nlpso/m0_qualitative_analysis_ref_cmbert_io | [
"task_categories:token-classification",
"multilinguality:monolingual",
"language:fr",
"region:us"
]
| 2023-02-22T08:03:43+00:00 | {"language": ["fr"], "multilinguality": ["monolingual"], "task_categories": ["token-classification"]} | 2023-02-22T08:06:23+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us
| m0\_qualitative\_analysis\_ref\_cmbert\_io
==========================================
Introduction
------------
This dataset was used to perform qualitative analysis of Jean-Baptiste/camembert-ner on flat NER task using Flat NER approach [M0].
It contains 19th-century Paris trade directories' entries.
Dataset parameters
------------------
* Approach : M0
* Dataset type : ground-truth
* Tokenizer : Jean-Baptiste/camembert-ner
* Tagging format : IO
* Counts :
+ Train : 6084
+ Dev : 676
+ Test : 1685
* Associated fine-tuned model : nlpso/m0\_flat\_ner\_ref\_cmbert\_io
Entity types
------------
How to use this dataset
-----------------------
'''python
from datasets import load\_dataset
train\_dev\_test = load\_dataset("nlpso/m0\_qualitative\_analysis\_ref\_cmbert\_io")
| []
| [
"TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
32
]
| [
"passage: TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
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]
|
74ebc32ee7c7c702b25485d8e948e435b8adfbb3 |
# m2m3_fine_tuning_ocr_cmbert_io
## Introduction
This dataset was used to fine-tuned [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) for **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approachrd : M2 and M3
* Dataset type : noisy (Pero OCR)
* Tokenizer : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner)
* Tagging format : IO
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* M2 : [nlpso/m2_joint_label_ocr_cmbert_io](https://huggingface.co/nlpso/m2_joint_label_ocr_cmbert_io)
* M3 : [nlpso/m3_hierarchical_ner_ocr_cmbert_io](https://huggingface.co/nlpso/m3_hierarchical_ner_ocr_cmbert_io)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m2m3_fine_tuning_ocr_cmbert_io")
| nlpso/m2m3_fine_tuning_ocr_cmbert_io | [
"task_categories:token-classification",
"multilinguality:monolingual",
"language:fr",
"region:us"
]
| 2023-02-22T08:03:44+00:00 | {"language": ["fr"], "multilinguality": ["monolingual"], "task_categories": ["token-classification"]} | 2023-02-22T08:04:00+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us
| m2m3\_fine\_tuning\_ocr\_cmbert\_io
===================================
Introduction
------------
This dataset was used to fine-tuned Jean-Baptiste/camembert-ner for nested NER task using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
Dataset parameters
------------------
* Approachrd : M2 and M3
* Dataset type : noisy (Pero OCR)
* Tokenizer : Jean-Baptiste/camembert-ner
* Tagging format : IO
* Counts :
+ Train : 6084
+ Dev : 676
+ Test : 1685
* Associated fine-tuned models :
+ M2 : nlpso/m2\_joint\_label\_ocr\_cmbert\_io
+ M3 : nlpso/m3\_hierarchical\_ner\_ocr\_cmbert\_io
Entity types
------------
Abbreviation: O, Entity group (level): 1 & 2, Description: Outside of a named entity
Abbreviation: PER, Entity group (level): 1, Description: Person or company name
Abbreviation: ACT, Entity group (level): 1 & 2, Description: Person or company professional activity
Abbreviation: TITREH, Entity group (level): 2, Description: Military or civil distinction
Abbreviation: DESC, Entity group (level): 1, Description: Entry full description
Abbreviation: TITREP, Entity group (level): 2, Description: Professionnal reward
Abbreviation: SPAT, Entity group (level): 1, Description: Address
Abbreviation: LOC, Entity group (level): 2, Description: Street name
Abbreviation: CARDINAL, Entity group (level): 2, Description: Street number
Abbreviation: FT, Entity group (level): 2, Description: Geographical feature
How to use this dataset
-----------------------
'''python
from datasets import load\_dataset
train\_dev\_test = load\_dataset("nlpso/m2m3\_fine\_tuning\_ocr\_cmbert\_io")
| []
| [
"TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
32
]
| [
"passage: TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
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|
318b02b7a0b61427fbe5448a9d277b477fd634e1 |
# m0_qualitative_analysis_ref_ptrn_cmbert_io
## Introduction
This dataset was used to perform **qualitative analysis** of [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) on **flat NER task** using Flat NER approach [M0].
It contains 19th-century Paris trade directories' entries.
## Dataset parameters
* Approach : M0
* Dataset type : ground-truth
* Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained)
* Tagging format : IO
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned model : [nlpso/m0_flat_ner_ref_ptrn_cmbert_io](https://huggingface.co/nlpso/m0_flat_ner_ref_ptrn_cmbert_io)
## Entity types
Abbreviation|Description
-|-
O |Outside of a named entity
PER |Person or company name
ACT |Person or company professional activity
TITRE |Distinction
LOC |Street name
CARDINAL |Street number
FT |Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m0_qualitative_analysis_ref_ptrn_cmbert_io")
| nlpso/m0_qualitative_analysis_ref_ptrn_cmbert_io | [
"task_categories:token-classification",
"multilinguality:monolingual",
"language:fr",
"region:us"
]
| 2023-02-22T08:04:00+00:00 | {"language": ["fr"], "multilinguality": ["monolingual"], "task_categories": ["token-classification"]} | 2023-02-22T08:06:25+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us
| m0\_qualitative\_analysis\_ref\_ptrn\_cmbert\_io
================================================
Introduction
------------
This dataset was used to perform qualitative analysis of HueyNemud/das22-10-camembert\_pretrained on flat NER task using Flat NER approach [M0].
It contains 19th-century Paris trade directories' entries.
Dataset parameters
------------------
* Approach : M0
* Dataset type : ground-truth
* Tokenizer : HueyNemud/das22-10-camembert\_pretrained
* Tagging format : IO
* Counts :
+ Train : 6084
+ Dev : 676
+ Test : 1685
* Associated fine-tuned model : nlpso/m0\_flat\_ner\_ref\_ptrn\_cmbert\_io
Entity types
------------
How to use this dataset
-----------------------
'''python
from datasets import load\_dataset
train\_dev\_test = load\_dataset("nlpso/m0\_qualitative\_analysis\_ref\_ptrn\_cmbert\_io")
| []
| [
"TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
32
]
| [
"passage: TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
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|
11fa31d795706f2ec314864d40462657f5c745ff |
# m2m3_fine_tuning_ocr_ptrn_cmbert_io
## Introduction
This dataset was used to fine-tuned [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) for **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approachrd : M2 and M3
* Dataset type : noisy (Pero OCR)
* Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained)
* Tagging format : IO
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* M2 : [nlpso/m2_joint_label_ocr_ptrn_cmbert_io](https://huggingface.co/nlpso/m2_joint_label_ocr_ptrn_cmbert_io)
* M3 : [nlpso/m3_hierarchical_ner_ocr_ptrn_cmbert_io](https://huggingface.co/nlpso/m3_hierarchical_ner_ocr_ptrn_cmbert_io)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m2m3_fine_tuning_ocr_ptrn_cmbert_io")
| nlpso/m2m3_fine_tuning_ocr_ptrn_cmbert_io | [
"task_categories:token-classification",
"multilinguality:monolingual",
"language:fr",
"region:us"
]
| 2023-02-22T08:04:01+00:00 | {"language": ["fr"], "multilinguality": ["monolingual"], "task_categories": ["token-classification"]} | 2023-02-22T08:04:16+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us
| m2m3\_fine\_tuning\_ocr\_ptrn\_cmbert\_io
=========================================
Introduction
------------
This dataset was used to fine-tuned HueyNemud/das22-10-camembert\_pretrained for nested NER task using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
Dataset parameters
------------------
* Approachrd : M2 and M3
* Dataset type : noisy (Pero OCR)
* Tokenizer : HueyNemud/das22-10-camembert\_pretrained
* Tagging format : IO
* Counts :
+ Train : 6084
+ Dev : 676
+ Test : 1685
* Associated fine-tuned models :
+ M2 : nlpso/m2\_joint\_label\_ocr\_ptrn\_cmbert\_io
+ M3 : nlpso/m3\_hierarchical\_ner\_ocr\_ptrn\_cmbert\_io
Entity types
------------
Abbreviation: O, Entity group (level): 1 & 2, Description: Outside of a named entity
Abbreviation: PER, Entity group (level): 1, Description: Person or company name
Abbreviation: ACT, Entity group (level): 1 & 2, Description: Person or company professional activity
Abbreviation: TITREH, Entity group (level): 2, Description: Military or civil distinction
Abbreviation: DESC, Entity group (level): 1, Description: Entry full description
Abbreviation: TITREP, Entity group (level): 2, Description: Professionnal reward
Abbreviation: SPAT, Entity group (level): 1, Description: Address
Abbreviation: LOC, Entity group (level): 2, Description: Street name
Abbreviation: CARDINAL, Entity group (level): 2, Description: Street number
Abbreviation: FT, Entity group (level): 2, Description: Geographical feature
How to use this dataset
-----------------------
'''python
from datasets import load\_dataset
train\_dev\_test = load\_dataset("nlpso/m2m3\_fine\_tuning\_ocr\_ptrn\_cmbert\_io")
| []
| [
"TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
32
]
| [
"passage: TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
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|
2366871f90b5d11baf60c17810b032d5db8bed0d |
# m0_qualitative_analysis_ocr_cmbert_io
## Introduction
This dataset was used to perform **qualitative analysis** of [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) on **flat NER task** using Flat NER approach [M0].
It contains 19th-century Paris trade directories' entries.
## Dataset parameters
* Approach : M0
* Dataset type : noisy (Pero OCR)
* Tokenizer : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner)
* Tagging format : IO
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned model : [nlpso/m0_flat_ner_ocr_cmbert_io](https://huggingface.co/nlpso/m0_flat_ner_ocr_cmbert_io)
## Entity types
Abbreviation|Description
-|-
O |Outside of a named entity
PER |Person or company name
ACT |Person or company professional activity
TITRE |Distinction
LOC |Street name
CARDINAL |Street number
FT |Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m0_qualitative_analysis_ocr_cmbert_io")
| nlpso/m0_qualitative_analysis_ocr_cmbert_io | [
"task_categories:token-classification",
"multilinguality:monolingual",
"language:fr",
"region:us"
]
| 2023-02-22T08:04:16+00:00 | {"language": ["fr"], "multilinguality": ["monolingual"], "task_categories": ["token-classification"]} | 2023-02-22T08:06:27+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us
| m0\_qualitative\_analysis\_ocr\_cmbert\_io
==========================================
Introduction
------------
This dataset was used to perform qualitative analysis of Jean-Baptiste/camembert-ner on flat NER task using Flat NER approach [M0].
It contains 19th-century Paris trade directories' entries.
Dataset parameters
------------------
* Approach : M0
* Dataset type : noisy (Pero OCR)
* Tokenizer : Jean-Baptiste/camembert-ner
* Tagging format : IO
* Counts :
+ Train : 6084
+ Dev : 676
+ Test : 1685
* Associated fine-tuned model : nlpso/m0\_flat\_ner\_ocr\_cmbert\_io
Entity types
------------
How to use this dataset
-----------------------
'''python
from datasets import load\_dataset
train\_dev\_test = load\_dataset("nlpso/m0\_qualitative\_analysis\_ocr\_cmbert\_io")
| []
| [
"TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
32
]
| [
"passage: TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
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|
5f3cfb133d13e8aeb82b2889cbea39305e95e4a9 |
# m2m3_fine_tuning_ocr_cmbert_iob2
## Introduction
This dataset was used to fine-tuned [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) for **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approachrd : M2 and M3
* Dataset type : noisy (Pero OCR)
* Tokenizer : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner)
* Tagging format : IOB2
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* M2 : [nlpso/m2_joint_label_ocr_cmbert_iob2](https://huggingface.co/nlpso/m2_joint_label_ocr_cmbert_iob2)
* M3 : [nlpso/m3_hierarchical_ner_ocr_cmbert_iob2](https://huggingface.co/nlpso/m3_hierarchical_ner_ocr_cmbert_iob2)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m2m3_fine_tuning_ocr_cmbert_iob2")
| nlpso/m2m3_fine_tuning_ocr_cmbert_iob2 | [
"task_categories:token-classification",
"multilinguality:monolingual",
"language:fr",
"region:us"
]
| 2023-02-22T08:04:18+00:00 | {"language": ["fr"], "multilinguality": ["monolingual"], "task_categories": ["token-classification"]} | 2023-02-22T08:04:33+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us
| m2m3\_fine\_tuning\_ocr\_cmbert\_iob2
=====================================
Introduction
------------
This dataset was used to fine-tuned Jean-Baptiste/camembert-ner for nested NER task using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
Dataset parameters
------------------
* Approachrd : M2 and M3
* Dataset type : noisy (Pero OCR)
* Tokenizer : Jean-Baptiste/camembert-ner
* Tagging format : IOB2
* Counts :
+ Train : 6084
+ Dev : 676
+ Test : 1685
* Associated fine-tuned models :
+ M2 : nlpso/m2\_joint\_label\_ocr\_cmbert\_iob2
+ M3 : nlpso/m3\_hierarchical\_ner\_ocr\_cmbert\_iob2
Entity types
------------
Abbreviation: O, Entity group (level): 1 & 2, Description: Outside of a named entity
Abbreviation: PER, Entity group (level): 1, Description: Person or company name
Abbreviation: ACT, Entity group (level): 1 & 2, Description: Person or company professional activity
Abbreviation: TITREH, Entity group (level): 2, Description: Military or civil distinction
Abbreviation: DESC, Entity group (level): 1, Description: Entry full description
Abbreviation: TITREP, Entity group (level): 2, Description: Professionnal reward
Abbreviation: SPAT, Entity group (level): 1, Description: Address
Abbreviation: LOC, Entity group (level): 2, Description: Street name
Abbreviation: CARDINAL, Entity group (level): 2, Description: Street number
Abbreviation: FT, Entity group (level): 2, Description: Geographical feature
How to use this dataset
-----------------------
'''python
from datasets import load\_dataset
train\_dev\_test = load\_dataset("nlpso/m2m3\_fine\_tuning\_ocr\_cmbert\_iob2")
| []
| [
"TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
32
]
| [
"passage: TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
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|
365c34e1e047594037d8f82ddbc8149c7de9c24a |
# m0_qualitative_analysis_ocr_ptrn_cmbert_io
## Introduction
This dataset was used to perform **qualitative analysis** of [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) on **flat NER task** using Flat NER approach [M0].
It contains 19th-century Paris trade directories' entries.
## Dataset parameters
* Approach : M0
* Dataset type : noisy (Pero OCR)
* Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained)
* Tagging format : IO
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned model : [nlpso/m0_flat_ner_ocr_ptrn_cmbert_io](https://huggingface.co/nlpso/m0_flat_ner_ocr_ptrn_cmbert_io)
## Entity types
Abbreviation|Description
-|-
O |Outside of a named entity
PER |Person or company name
ACT |Person or company professional activity
TITRE |Distinction
LOC |Street name
CARDINAL |Street number
FT |Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m0_qualitative_analysis_ocr_ptrn_cmbert_io")
| nlpso/m0_qualitative_analysis_ocr_ptrn_cmbert_io | [
"task_categories:token-classification",
"multilinguality:monolingual",
"language:fr",
"region:us"
]
| 2023-02-22T08:04:33+00:00 | {"language": ["fr"], "multilinguality": ["monolingual"], "task_categories": ["token-classification"]} | 2023-02-22T08:06:29+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us
| m0\_qualitative\_analysis\_ocr\_ptrn\_cmbert\_io
================================================
Introduction
------------
This dataset was used to perform qualitative analysis of HueyNemud/das22-10-camembert\_pretrained on flat NER task using Flat NER approach [M0].
It contains 19th-century Paris trade directories' entries.
Dataset parameters
------------------
* Approach : M0
* Dataset type : noisy (Pero OCR)
* Tokenizer : HueyNemud/das22-10-camembert\_pretrained
* Tagging format : IO
* Counts :
+ Train : 6084
+ Dev : 676
+ Test : 1685
* Associated fine-tuned model : nlpso/m0\_flat\_ner\_ocr\_ptrn\_cmbert\_io
Entity types
------------
How to use this dataset
-----------------------
'''python
from datasets import load\_dataset
train\_dev\_test = load\_dataset("nlpso/m0\_qualitative\_analysis\_ocr\_ptrn\_cmbert\_io")
| []
| [
"TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
32
]
| [
"passage: TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
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|
b5b6c9bf5d62859c37bdf48d329a0117c0091123 |
# m2m3_fine_tuning_ocr_ptrn_cmbert_iob2
## Introduction
This dataset was used to fine-tuned [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) for **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approachrd : M2 and M3
* Dataset type : noisy (Pero OCR)
* Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained)
* Tagging format : IOB2
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* M2 : [nlpso/m2_joint_label_ocr_ptrn_cmbert_iob2](https://huggingface.co/nlpso/m2_joint_label_ocr_ptrn_cmbert_iob2)
* M3 : [nlpso/m3_hierarchical_ner_ocr_ptrn_cmbert_iob2](https://huggingface.co/nlpso/m3_hierarchical_ner_ocr_ptrn_cmbert_iob2)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m2m3_fine_tuning_ocr_ptrn_cmbert_iob2")
| nlpso/m2m3_fine_tuning_ocr_ptrn_cmbert_iob2 | [
"task_categories:token-classification",
"multilinguality:monolingual",
"language:fr",
"region:us"
]
| 2023-02-22T08:04:34+00:00 | {"language": ["fr"], "multilinguality": ["monolingual"], "task_categories": ["token-classification"]} | 2023-02-22T08:04:49+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us
| m2m3\_fine\_tuning\_ocr\_ptrn\_cmbert\_iob2
===========================================
Introduction
------------
This dataset was used to fine-tuned HueyNemud/das22-10-camembert\_pretrained for nested NER task using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
Dataset parameters
------------------
* Approachrd : M2 and M3
* Dataset type : noisy (Pero OCR)
* Tokenizer : HueyNemud/das22-10-camembert\_pretrained
* Tagging format : IOB2
* Counts :
+ Train : 6084
+ Dev : 676
+ Test : 1685
* Associated fine-tuned models :
+ M2 : nlpso/m2\_joint\_label\_ocr\_ptrn\_cmbert\_iob2
+ M3 : nlpso/m3\_hierarchical\_ner\_ocr\_ptrn\_cmbert\_iob2
Entity types
------------
Abbreviation: O, Entity group (level): 1 & 2, Description: Outside of a named entity
Abbreviation: PER, Entity group (level): 1, Description: Person or company name
Abbreviation: ACT, Entity group (level): 1 & 2, Description: Person or company professional activity
Abbreviation: TITREH, Entity group (level): 2, Description: Military or civil distinction
Abbreviation: DESC, Entity group (level): 1, Description: Entry full description
Abbreviation: TITREP, Entity group (level): 2, Description: Professionnal reward
Abbreviation: SPAT, Entity group (level): 1, Description: Address
Abbreviation: LOC, Entity group (level): 2, Description: Street name
Abbreviation: CARDINAL, Entity group (level): 2, Description: Street number
Abbreviation: FT, Entity group (level): 2, Description: Geographical feature
How to use this dataset
-----------------------
'''python
from datasets import load\_dataset
train\_dev\_test = load\_dataset("nlpso/m2m3\_fine\_tuning\_ocr\_ptrn\_cmbert\_iob2")
| []
| [
"TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
32
]
| [
"passage: TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
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|
9b17ba5b7b152cd7db4d591b8f43f39326dd0a38 | # Dataset Card for "nlp.2.predict_middle_word"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lansinuote/nlp.2.predict_middle_word | [
"region:us"
]
| 2023-02-22T08:04:56+00:00 | {"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 5711991, "num_examples": 44279}, {"name": "validation", "num_bytes": 111069, "num_examples": 861}, {"name": "test", "num_bytes": 229104, "num_examples": 1776}], "download_size": 0, "dataset_size": 6052164}} | 2023-02-22T11:26:32+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "nlp.2.predict_middle_word"
More Information needed | [
"# Dataset Card for \"nlp.2.predict_middle_word\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"nlp.2.predict_middle_word\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"nlp.2.predict_middle_word\"\n\nMore Information needed"
]
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|
81b1f95c8cdc378b77cc693737b17f78afa58e2b | # Dataset Card for "nlp.3.reading_for_understanding"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lansinuote/nlp.3.reading_for_understanding | [
"region:us"
]
| 2023-02-22T08:26:57+00:00 | {"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "start_positions", "dtype": "int64"}, {"name": "end_positions", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 19646064, "num_examples": 10106}, {"name": "validation", "num_bytes": 398520, "num_examples": 205}], "download_size": 3916983, "dataset_size": 20044584}} | 2023-02-23T02:10:06+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "nlp.3.reading_for_understanding"
More Information needed | [
"# Dataset Card for \"nlp.3.reading_for_understanding\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"nlp.3.reading_for_understanding\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"nlp.3.reading_for_understanding\"\n\nMore Information needed"
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|
b8242c22de52b5fc4c3a34015a1e709be98dced5 |
# m1_qualitative_analysis_ref_cmbert_io
## Introduction
This dataset was used to perform **qualitative analysis** of [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) on **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approach : M1
* Dataset type : ground-truth
* Tokenizer : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner)
* Tagging format : IO
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* Level-1 : [nlpso/m1_ind_layers_ref_cmbert_io_level_1](https://huggingface.co/nlpso/m1_ind_layers_ref_cmbert_io_level_1)
* Level 2 : [nlpso/m1_ind_layers_ref_cmbert_io_level_2](https://huggingface.co/nlpso/m1_ind_layers_ref_cmbert_io_level_2)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m1_qualitative_analysis_ref_cmbert_io")
| nlpso/m1_qualitative_analysis_ref_cmbert_io | [
"task_categories:token-classification",
"multilinguality:monolingual",
"language:fr",
"region:us"
]
| 2023-02-22T08:39:33+00:00 | {"language": ["fr"], "multilinguality": ["monolingual"], "task_categories": ["token-classification"]} | 2023-02-22T08:39:49+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us
| m1\_qualitative\_analysis\_ref\_cmbert\_io
==========================================
Introduction
------------
This dataset was used to perform qualitative analysis of Jean-Baptiste/camembert-ner on nested NER task using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
Dataset parameters
------------------
* Approach : M1
* Dataset type : ground-truth
* Tokenizer : Jean-Baptiste/camembert-ner
* Tagging format : IO
* Counts :
+ Train : 6084
+ Dev : 676
+ Test : 1685
* Associated fine-tuned models :
+ Level-1 : nlpso/m1\_ind\_layers\_ref\_cmbert\_io\_level\_1
+ Level 2 : nlpso/m1\_ind\_layers\_ref\_cmbert\_io\_level\_2
Entity types
------------
Abbreviation: O, Entity group (level): 1 & 2, Description: Outside of a named entity
Abbreviation: PER, Entity group (level): 1, Description: Person or company name
Abbreviation: ACT, Entity group (level): 1 & 2, Description: Person or company professional activity
Abbreviation: TITREH, Entity group (level): 2, Description: Military or civil distinction
Abbreviation: DESC, Entity group (level): 1, Description: Entry full description
Abbreviation: TITREP, Entity group (level): 2, Description: Professionnal reward
Abbreviation: SPAT, Entity group (level): 1, Description: Address
Abbreviation: LOC, Entity group (level): 2, Description: Street name
Abbreviation: CARDINAL, Entity group (level): 2, Description: Street number
Abbreviation: FT, Entity group (level): 2, Description: Geographical feature
How to use this dataset
-----------------------
'''python
from datasets import load\_dataset
train\_dev\_test = load\_dataset("nlpso/m1\_qualitative\_analysis\_ref\_cmbert\_io")
| []
| [
"TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
32
]
| [
"passage: TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
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|
3a4f7d43934b8d74b6f6878c2d5604c05b0a459a |
# m1_qualitative_analysis_ref_ptrn_cmbert_io
## Introduction
This dataset was used to perform **qualitative analysis** of [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) on **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approach : M1
* Dataset type : ground-truth
* Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained)
* Tagging format : IO
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* Level-1 : [nlpso/m1_ind_layers_ref_ptrn_cmbert_io_level_1](https://huggingface.co/nlpso/m1_ind_layers_ref_ptrn_cmbert_io_level_1)
* Level 2 : [nlpso/m1_ind_layers_ref_ptrn_cmbert_io_level_2](https://huggingface.co/nlpso/m1_ind_layers_ref_ptrn_cmbert_io_level_2)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m1_qualitative_analysis_ref_ptrn_cmbert_io")
| nlpso/m1_qualitative_analysis_ref_ptrn_cmbert_io | [
"task_categories:token-classification",
"multilinguality:monolingual",
"language:fr",
"region:us"
]
| 2023-02-22T08:39:50+00:00 | {"language": ["fr"], "multilinguality": ["monolingual"], "task_categories": ["token-classification"]} | 2023-02-22T08:40:06+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us
| m1\_qualitative\_analysis\_ref\_ptrn\_cmbert\_io
================================================
Introduction
------------
This dataset was used to perform qualitative analysis of HueyNemud/das22-10-camembert\_pretrained on nested NER task using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
Dataset parameters
------------------
* Approach : M1
* Dataset type : ground-truth
* Tokenizer : HueyNemud/das22-10-camembert\_pretrained
* Tagging format : IO
* Counts :
+ Train : 6084
+ Dev : 676
+ Test : 1685
* Associated fine-tuned models :
+ Level-1 : nlpso/m1\_ind\_layers\_ref\_ptrn\_cmbert\_io\_level\_1
+ Level 2 : nlpso/m1\_ind\_layers\_ref\_ptrn\_cmbert\_io\_level\_2
Entity types
------------
Abbreviation: O, Entity group (level): 1 & 2, Description: Outside of a named entity
Abbreviation: PER, Entity group (level): 1, Description: Person or company name
Abbreviation: ACT, Entity group (level): 1 & 2, Description: Person or company professional activity
Abbreviation: TITREH, Entity group (level): 2, Description: Military or civil distinction
Abbreviation: DESC, Entity group (level): 1, Description: Entry full description
Abbreviation: TITREP, Entity group (level): 2, Description: Professionnal reward
Abbreviation: SPAT, Entity group (level): 1, Description: Address
Abbreviation: LOC, Entity group (level): 2, Description: Street name
Abbreviation: CARDINAL, Entity group (level): 2, Description: Street number
Abbreviation: FT, Entity group (level): 2, Description: Geographical feature
How to use this dataset
-----------------------
'''python
from datasets import load\_dataset
train\_dev\_test = load\_dataset("nlpso/m1\_qualitative\_analysis\_ref\_ptrn\_cmbert\_io")
| []
| [
"TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
32
]
| [
"passage: TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
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|
ae448c48cdfac4c1949a1dbb543cfb363d2a73d8 |
# m1_qualitative_analysis_ref_cmbert_iob2
## Introduction
This dataset was used to perform **qualitative analysis** of [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) on **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approach : M1
* Dataset type : ground-truth
* Tokenizer : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner)
* Tagging format : IOB2
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* Level-1 : [nlpso/m1_ind_layers_ref_cmbert_iob2_level_1](https://huggingface.co/nlpso/m1_ind_layers_ref_cmbert_iob2_level_1)
* Level 2 : [nlpso/m1_ind_layers_ref_cmbert_iob2_level_2](https://huggingface.co/nlpso/m1_ind_layers_ref_cmbert_iob2_level_2)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m1_qualitative_analysis_ref_cmbert_iob2")
| nlpso/m1_qualitative_analysis_ref_cmbert_iob2 | [
"task_categories:token-classification",
"multilinguality:monolingual",
"language:fr",
"region:us"
]
| 2023-02-22T08:40:07+00:00 | {"language": ["fr"], "multilinguality": ["monolingual"], "task_categories": ["token-classification"]} | 2023-02-22T08:40:23+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us
| m1\_qualitative\_analysis\_ref\_cmbert\_iob2
============================================
Introduction
------------
This dataset was used to perform qualitative analysis of Jean-Baptiste/camembert-ner on nested NER task using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
Dataset parameters
------------------
* Approach : M1
* Dataset type : ground-truth
* Tokenizer : Jean-Baptiste/camembert-ner
* Tagging format : IOB2
* Counts :
+ Train : 6084
+ Dev : 676
+ Test : 1685
* Associated fine-tuned models :
+ Level-1 : nlpso/m1\_ind\_layers\_ref\_cmbert\_iob2\_level\_1
+ Level 2 : nlpso/m1\_ind\_layers\_ref\_cmbert\_iob2\_level\_2
Entity types
------------
Abbreviation: O, Entity group (level): 1 & 2, Description: Outside of a named entity
Abbreviation: PER, Entity group (level): 1, Description: Person or company name
Abbreviation: ACT, Entity group (level): 1 & 2, Description: Person or company professional activity
Abbreviation: TITREH, Entity group (level): 2, Description: Military or civil distinction
Abbreviation: DESC, Entity group (level): 1, Description: Entry full description
Abbreviation: TITREP, Entity group (level): 2, Description: Professionnal reward
Abbreviation: SPAT, Entity group (level): 1, Description: Address
Abbreviation: LOC, Entity group (level): 2, Description: Street name
Abbreviation: CARDINAL, Entity group (level): 2, Description: Street number
Abbreviation: FT, Entity group (level): 2, Description: Geographical feature
How to use this dataset
-----------------------
'''python
from datasets import load\_dataset
train\_dev\_test = load\_dataset("nlpso/m1\_qualitative\_analysis\_ref\_cmbert\_iob2")
| []
| [
"TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
32
]
| [
"passage: TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
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|
96b6f5cdbb0d50f6fce5ec62931991a3bb8229d9 |
# m1_qualitative_analysis_ref_ptrn_cmbert_iob2
## Introduction
This dataset was used to perform **qualitative analysis** of [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) on **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approach : M1
* Dataset type : ground-truth
* Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained)
* Tagging format : IOB2
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* Level-1 : [nlpso/m1_ind_layers_ref_ptrn_cmbert_iob2_level_1](https://huggingface.co/nlpso/m1_ind_layers_ref_ptrn_cmbert_iob2_level_1)
* Level 2 : [nlpso/m1_ind_layers_ref_ptrn_cmbert_iob2_level_2](https://huggingface.co/nlpso/m1_ind_layers_ref_ptrn_cmbert_iob2_level_2)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m1_qualitative_analysis_ref_ptrn_cmbert_iob2")
| nlpso/m1_qualitative_analysis_ref_ptrn_cmbert_iob2 | [
"task_categories:token-classification",
"multilinguality:monolingual",
"language:fr",
"region:us"
]
| 2023-02-22T08:40:24+00:00 | {"language": ["fr"], "multilinguality": ["monolingual"], "task_categories": ["token-classification"]} | 2023-02-22T08:40:39+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us
| m1\_qualitative\_analysis\_ref\_ptrn\_cmbert\_iob2
==================================================
Introduction
------------
This dataset was used to perform qualitative analysis of HueyNemud/das22-10-camembert\_pretrained on nested NER task using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
Dataset parameters
------------------
* Approach : M1
* Dataset type : ground-truth
* Tokenizer : HueyNemud/das22-10-camembert\_pretrained
* Tagging format : IOB2
* Counts :
+ Train : 6084
+ Dev : 676
+ Test : 1685
* Associated fine-tuned models :
+ Level-1 : nlpso/m1\_ind\_layers\_ref\_ptrn\_cmbert\_iob2\_level\_1
+ Level 2 : nlpso/m1\_ind\_layers\_ref\_ptrn\_cmbert\_iob2\_level\_2
Entity types
------------
Abbreviation: O, Entity group (level): 1 & 2, Description: Outside of a named entity
Abbreviation: PER, Entity group (level): 1, Description: Person or company name
Abbreviation: ACT, Entity group (level): 1 & 2, Description: Person or company professional activity
Abbreviation: TITREH, Entity group (level): 2, Description: Military or civil distinction
Abbreviation: DESC, Entity group (level): 1, Description: Entry full description
Abbreviation: TITREP, Entity group (level): 2, Description: Professionnal reward
Abbreviation: SPAT, Entity group (level): 1, Description: Address
Abbreviation: LOC, Entity group (level): 2, Description: Street name
Abbreviation: CARDINAL, Entity group (level): 2, Description: Street number
Abbreviation: FT, Entity group (level): 2, Description: Geographical feature
How to use this dataset
-----------------------
'''python
from datasets import load\_dataset
train\_dev\_test = load\_dataset("nlpso/m1\_qualitative\_analysis\_ref\_ptrn\_cmbert\_iob2")
| []
| [
"TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
32
]
| [
"passage: TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
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|
634879beed80de49f8613b032fa6c1021efaa88f |
# m1_qualitative_analysis_ocr_cmbert_io
## Introduction
This dataset was used to perform **qualitative analysis** of [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) on **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approach : M1
* Dataset type : noisy (Pero OCR)
* Tokenizer : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner)
* Tagging format : IO
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* Level-1 : [nlpso/m1_ind_layers_ocr_cmbert_io_level_1](https://huggingface.co/nlpso/m1_ind_layers_ocr_cmbert_io_level_1)
* Level 2 : [nlpso/m1_ind_layers_ocr_cmbert_io_level_2](https://huggingface.co/nlpso/m1_ind_layers_ocr_cmbert_io_level_2)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m1_qualitative_analysis_ocr_cmbert_io")
| nlpso/m1_qualitative_analysis_ocr_cmbert_io | [
"task_categories:token-classification",
"multilinguality:monolingual",
"language:fr",
"region:us"
]
| 2023-02-22T08:40:40+00:00 | {"language": ["fr"], "multilinguality": ["monolingual"], "task_categories": ["token-classification"]} | 2023-02-22T08:40:55+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us
| m1\_qualitative\_analysis\_ocr\_cmbert\_io
==========================================
Introduction
------------
This dataset was used to perform qualitative analysis of Jean-Baptiste/camembert-ner on nested NER task using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
Dataset parameters
------------------
* Approach : M1
* Dataset type : noisy (Pero OCR)
* Tokenizer : Jean-Baptiste/camembert-ner
* Tagging format : IO
* Counts :
+ Train : 6084
+ Dev : 676
+ Test : 1685
* Associated fine-tuned models :
+ Level-1 : nlpso/m1\_ind\_layers\_ocr\_cmbert\_io\_level\_1
+ Level 2 : nlpso/m1\_ind\_layers\_ocr\_cmbert\_io\_level\_2
Entity types
------------
Abbreviation: O, Entity group (level): 1 & 2, Description: Outside of a named entity
Abbreviation: PER, Entity group (level): 1, Description: Person or company name
Abbreviation: ACT, Entity group (level): 1 & 2, Description: Person or company professional activity
Abbreviation: TITREH, Entity group (level): 2, Description: Military or civil distinction
Abbreviation: DESC, Entity group (level): 1, Description: Entry full description
Abbreviation: TITREP, Entity group (level): 2, Description: Professionnal reward
Abbreviation: SPAT, Entity group (level): 1, Description: Address
Abbreviation: LOC, Entity group (level): 2, Description: Street name
Abbreviation: CARDINAL, Entity group (level): 2, Description: Street number
Abbreviation: FT, Entity group (level): 2, Description: Geographical feature
How to use this dataset
-----------------------
'''python
from datasets import load\_dataset
train\_dev\_test = load\_dataset("nlpso/m1\_qualitative\_analysis\_ocr\_cmbert\_io")
| []
| [
"TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
32
]
| [
"passage: TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
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]
|
91e2ad118b0750e554ebc5487e1c3da9e1189baf |
# m1_qualitative_analysis_ocr_ptrn_cmbert_io
## Introduction
This dataset was used to perform **qualitative analysis** of [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) on **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approach : M1
* Dataset type : noisy (Pero OCR)
* Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained)
* Tagging format : IO
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* Level-1 : [nlpso/m1_ind_layers_ocr_ptrn_cmbert_io_level_1](https://huggingface.co/nlpso/m1_ind_layers_ocr_ptrn_cmbert_io_level_1)
* Level 2 : [nlpso/m1_ind_layers_ocr_ptrn_cmbert_io_level_2](https://huggingface.co/nlpso/m1_ind_layers_ocr_ptrn_cmbert_io_level_2)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m1_qualitative_analysis_ocr_ptrn_cmbert_io")
| nlpso/m1_qualitative_analysis_ocr_ptrn_cmbert_io | [
"task_categories:token-classification",
"multilinguality:monolingual",
"language:fr",
"region:us"
]
| 2023-02-22T08:40:56+00:00 | {"language": ["fr"], "multilinguality": ["monolingual"], "task_categories": ["token-classification"]} | 2023-02-22T08:41:12+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us
| m1\_qualitative\_analysis\_ocr\_ptrn\_cmbert\_io
================================================
Introduction
------------
This dataset was used to perform qualitative analysis of HueyNemud/das22-10-camembert\_pretrained on nested NER task using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
Dataset parameters
------------------
* Approach : M1
* Dataset type : noisy (Pero OCR)
* Tokenizer : HueyNemud/das22-10-camembert\_pretrained
* Tagging format : IO
* Counts :
+ Train : 6084
+ Dev : 676
+ Test : 1685
* Associated fine-tuned models :
+ Level-1 : nlpso/m1\_ind\_layers\_ocr\_ptrn\_cmbert\_io\_level\_1
+ Level 2 : nlpso/m1\_ind\_layers\_ocr\_ptrn\_cmbert\_io\_level\_2
Entity types
------------
Abbreviation: O, Entity group (level): 1 & 2, Description: Outside of a named entity
Abbreviation: PER, Entity group (level): 1, Description: Person or company name
Abbreviation: ACT, Entity group (level): 1 & 2, Description: Person or company professional activity
Abbreviation: TITREH, Entity group (level): 2, Description: Military or civil distinction
Abbreviation: DESC, Entity group (level): 1, Description: Entry full description
Abbreviation: TITREP, Entity group (level): 2, Description: Professionnal reward
Abbreviation: SPAT, Entity group (level): 1, Description: Address
Abbreviation: LOC, Entity group (level): 2, Description: Street name
Abbreviation: CARDINAL, Entity group (level): 2, Description: Street number
Abbreviation: FT, Entity group (level): 2, Description: Geographical feature
How to use this dataset
-----------------------
'''python
from datasets import load\_dataset
train\_dev\_test = load\_dataset("nlpso/m1\_qualitative\_analysis\_ocr\_ptrn\_cmbert\_io")
| []
| [
"TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
32
]
| [
"passage: TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
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|
dbeb75b9165681c3fa63386601037a745ce457a3 |
# m1_qualitative_analysis_ocr_cmbert_iob2
## Introduction
This dataset was used to perform **qualitative analysis** of [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) on **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approach : M1
* Dataset type : noisy (Pero OCR)
* Tokenizer : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner)
* Tagging format : IOB2
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* Level-1 : [nlpso/m1_ind_layers_ocr_cmbert_iob2_level_1](https://huggingface.co/nlpso/m1_ind_layers_ocr_cmbert_iob2_level_1)
* Level 2 : [nlpso/m1_ind_layers_ocr_cmbert_iob2_level_2](https://huggingface.co/nlpso/m1_ind_layers_ocr_cmbert_iob2_level_2)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m1_qualitative_analysis_ocr_cmbert_iob2")
| nlpso/m1_qualitative_analysis_ocr_cmbert_iob2 | [
"task_categories:token-classification",
"multilinguality:monolingual",
"language:fr",
"region:us"
]
| 2023-02-22T08:41:13+00:00 | {"language": ["fr"], "multilinguality": ["monolingual"], "task_categories": ["token-classification"]} | 2023-02-22T08:41:29+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us
| m1\_qualitative\_analysis\_ocr\_cmbert\_iob2
============================================
Introduction
------------
This dataset was used to perform qualitative analysis of Jean-Baptiste/camembert-ner on nested NER task using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
Dataset parameters
------------------
* Approach : M1
* Dataset type : noisy (Pero OCR)
* Tokenizer : Jean-Baptiste/camembert-ner
* Tagging format : IOB2
* Counts :
+ Train : 6084
+ Dev : 676
+ Test : 1685
* Associated fine-tuned models :
+ Level-1 : nlpso/m1\_ind\_layers\_ocr\_cmbert\_iob2\_level\_1
+ Level 2 : nlpso/m1\_ind\_layers\_ocr\_cmbert\_iob2\_level\_2
Entity types
------------
Abbreviation: O, Entity group (level): 1 & 2, Description: Outside of a named entity
Abbreviation: PER, Entity group (level): 1, Description: Person or company name
Abbreviation: ACT, Entity group (level): 1 & 2, Description: Person or company professional activity
Abbreviation: TITREH, Entity group (level): 2, Description: Military or civil distinction
Abbreviation: DESC, Entity group (level): 1, Description: Entry full description
Abbreviation: TITREP, Entity group (level): 2, Description: Professionnal reward
Abbreviation: SPAT, Entity group (level): 1, Description: Address
Abbreviation: LOC, Entity group (level): 2, Description: Street name
Abbreviation: CARDINAL, Entity group (level): 2, Description: Street number
Abbreviation: FT, Entity group (level): 2, Description: Geographical feature
How to use this dataset
-----------------------
'''python
from datasets import load\_dataset
train\_dev\_test = load\_dataset("nlpso/m1\_qualitative\_analysis\_ocr\_cmbert\_iob2")
| []
| [
"TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
32
]
| [
"passage: TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
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|
cc7c461a4cb9e82523a7bd30cd39cde808888d58 |
# m1_qualitative_analysis_ocr_ptrn_cmbert_iob2
## Introduction
This dataset was used to perform **qualitative analysis** of [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) on **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approach : M1
* Dataset type : noisy (Pero OCR)
* Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained)
* Tagging format : IOB2
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* Level-1 : [nlpso/m1_ind_layers_ocr_ptrn_cmbert_iob2_level_1](https://huggingface.co/nlpso/m1_ind_layers_ocr_ptrn_cmbert_iob2_level_1)
* Level 2 : [nlpso/m1_ind_layers_ocr_ptrn_cmbert_iob2_level_2](https://huggingface.co/nlpso/m1_ind_layers_ocr_ptrn_cmbert_iob2_level_2)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m1_qualitative_analysis_ocr_ptrn_cmbert_iob2")
| nlpso/m1_qualitative_analysis_ocr_ptrn_cmbert_iob2 | [
"task_categories:token-classification",
"multilinguality:monolingual",
"language:fr",
"region:us"
]
| 2023-02-22T08:41:30+00:00 | {"language": ["fr"], "multilinguality": ["monolingual"], "task_categories": ["token-classification"]} | 2023-02-22T08:41:46+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us
| m1\_qualitative\_analysis\_ocr\_ptrn\_cmbert\_iob2
==================================================
Introduction
------------
This dataset was used to perform qualitative analysis of HueyNemud/das22-10-camembert\_pretrained on nested NER task using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
Dataset parameters
------------------
* Approach : M1
* Dataset type : noisy (Pero OCR)
* Tokenizer : HueyNemud/das22-10-camembert\_pretrained
* Tagging format : IOB2
* Counts :
+ Train : 6084
+ Dev : 676
+ Test : 1685
* Associated fine-tuned models :
+ Level-1 : nlpso/m1\_ind\_layers\_ocr\_ptrn\_cmbert\_iob2\_level\_1
+ Level 2 : nlpso/m1\_ind\_layers\_ocr\_ptrn\_cmbert\_iob2\_level\_2
Entity types
------------
Abbreviation: O, Entity group (level): 1 & 2, Description: Outside of a named entity
Abbreviation: PER, Entity group (level): 1, Description: Person or company name
Abbreviation: ACT, Entity group (level): 1 & 2, Description: Person or company professional activity
Abbreviation: TITREH, Entity group (level): 2, Description: Military or civil distinction
Abbreviation: DESC, Entity group (level): 1, Description: Entry full description
Abbreviation: TITREP, Entity group (level): 2, Description: Professionnal reward
Abbreviation: SPAT, Entity group (level): 1, Description: Address
Abbreviation: LOC, Entity group (level): 2, Description: Street name
Abbreviation: CARDINAL, Entity group (level): 2, Description: Street number
Abbreviation: FT, Entity group (level): 2, Description: Geographical feature
How to use this dataset
-----------------------
'''python
from datasets import load\_dataset
train\_dev\_test = load\_dataset("nlpso/m1\_qualitative\_analysis\_ocr\_ptrn\_cmbert\_iob2")
| []
| [
"TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
32
]
| [
"passage: TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
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|
df353323836f3b83f298fb995a25bbeddd1f4f03 |
# m2m3_qualitative_analysis_ref_cmbert_io
## Introduction
This dataset was used to perform **qualitative analysis** of [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) on **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approachrd : M2 and M3
* Dataset type : ground-truth
* Tokenizer : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner)
* Tagging format : IO
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* M2 : [nlpso/m2_joint_label_ref_cmbert_io](https://huggingface.co/nlpso/m2_joint_label_ref_cmbert_io)
* M3 : [nlpso/m3_hierarchical_ner_ref_cmbert_io](https://huggingface.co/nlpso/m3_hierarchical_ner_ref_cmbert_io)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m2m3_qualitative_analysis_ref_cmbert_io")
| nlpso/m2m3_qualitative_analysis_ref_cmbert_io | [
"task_categories:token-classification",
"multilinguality:monolingual",
"language:fr",
"region:us"
]
| 2023-02-22T08:47:49+00:00 | {"language": ["fr"], "multilinguality": ["monolingual"], "task_categories": ["token-classification"]} | 2023-02-22T08:48:34+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us
| m2m3\_qualitative\_analysis\_ref\_cmbert\_io
============================================
Introduction
------------
This dataset was used to perform qualitative analysis of Jean-Baptiste/camembert-ner on nested NER task using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
Dataset parameters
------------------
* Approachrd : M2 and M3
* Dataset type : ground-truth
* Tokenizer : Jean-Baptiste/camembert-ner
* Tagging format : IO
* Counts :
+ Train : 6084
+ Dev : 676
+ Test : 1685
* Associated fine-tuned models :
+ M2 : nlpso/m2\_joint\_label\_ref\_cmbert\_io
+ M3 : nlpso/m3\_hierarchical\_ner\_ref\_cmbert\_io
Entity types
------------
Abbreviation: O, Entity group (level): 1 & 2, Description: Outside of a named entity
Abbreviation: PER, Entity group (level): 1, Description: Person or company name
Abbreviation: ACT, Entity group (level): 1 & 2, Description: Person or company professional activity
Abbreviation: TITREH, Entity group (level): 2, Description: Military or civil distinction
Abbreviation: DESC, Entity group (level): 1, Description: Entry full description
Abbreviation: TITREP, Entity group (level): 2, Description: Professionnal reward
Abbreviation: SPAT, Entity group (level): 1, Description: Address
Abbreviation: LOC, Entity group (level): 2, Description: Street name
Abbreviation: CARDINAL, Entity group (level): 2, Description: Street number
Abbreviation: FT, Entity group (level): 2, Description: Geographical feature
How to use this dataset
-----------------------
'''python
from datasets import load\_dataset
train\_dev\_test = load\_dataset("nlpso/m2m3\_qualitative\_analysis\_ref\_cmbert\_io")
| []
| [
"TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
32
]
| [
"passage: TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
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|
5fc1505cefe0790c9e79db7d4add708cb84386d6 |
# m2m3_qualitative_analysis_ref_ptrn_cmbert_io
## Introduction
This dataset was used to perform **qualitative analysis** of [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) on **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approachrd : M2 and M3
* Dataset type : ground-truth
* Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained)
* Tagging format : IO
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* M2 : [nlpso/m2_joint_label_ref_ptrn_cmbert_io](https://huggingface.co/nlpso/m2_joint_label_ref_ptrn_cmbert_io)
* M3 : [nlpso/m3_hierarchical_ner_ref_ptrn_cmbert_io](https://huggingface.co/nlpso/m3_hierarchical_ner_ref_ptrn_cmbert_io)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m2m3_qualitative_analysis_ref_ptrn_cmbert_io")
| nlpso/m2m3_qualitative_analysis_ref_ptrn_cmbert_io | [
"task_categories:token-classification",
"multilinguality:monolingual",
"language:fr",
"region:us"
]
| 2023-02-22T08:48:35+00:00 | {"language": ["fr"], "multilinguality": ["monolingual"], "task_categories": ["token-classification"]} | 2023-02-22T08:48:50+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us
| m2m3\_qualitative\_analysis\_ref\_ptrn\_cmbert\_io
==================================================
Introduction
------------
This dataset was used to perform qualitative analysis of HueyNemud/das22-10-camembert\_pretrained on nested NER task using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
Dataset parameters
------------------
* Approachrd : M2 and M3
* Dataset type : ground-truth
* Tokenizer : HueyNemud/das22-10-camembert\_pretrained
* Tagging format : IO
* Counts :
+ Train : 6084
+ Dev : 676
+ Test : 1685
* Associated fine-tuned models :
+ M2 : nlpso/m2\_joint\_label\_ref\_ptrn\_cmbert\_io
+ M3 : nlpso/m3\_hierarchical\_ner\_ref\_ptrn\_cmbert\_io
Entity types
------------
Abbreviation: O, Entity group (level): 1 & 2, Description: Outside of a named entity
Abbreviation: PER, Entity group (level): 1, Description: Person or company name
Abbreviation: ACT, Entity group (level): 1 & 2, Description: Person or company professional activity
Abbreviation: TITREH, Entity group (level): 2, Description: Military or civil distinction
Abbreviation: DESC, Entity group (level): 1, Description: Entry full description
Abbreviation: TITREP, Entity group (level): 2, Description: Professionnal reward
Abbreviation: SPAT, Entity group (level): 1, Description: Address
Abbreviation: LOC, Entity group (level): 2, Description: Street name
Abbreviation: CARDINAL, Entity group (level): 2, Description: Street number
Abbreviation: FT, Entity group (level): 2, Description: Geographical feature
How to use this dataset
-----------------------
'''python
from datasets import load\_dataset
train\_dev\_test = load\_dataset("nlpso/m2m3\_qualitative\_analysis\_ref\_ptrn\_cmbert\_io")
| []
| [
"TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
32
]
| [
"passage: TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
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|
30ab760f00769993e3d37221d3122aafc04ad7cf |
# m2m3_qualitative_analysis_ref_cmbert_iob2
## Introduction
This dataset was used to perform **qualitative analysis** of [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) on **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approachrd : M2 and M3
* Dataset type : ground-truth
* Tokenizer : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner)
* Tagging format : IOB2
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* M2 : [nlpso/m2_joint_label_ref_cmbert_iob2](https://huggingface.co/nlpso/m2_joint_label_ref_cmbert_iob2)
* M3 : [nlpso/m3_hierarchical_ner_ref_cmbert_iob2](https://huggingface.co/nlpso/m3_hierarchical_ner_ref_cmbert_iob2)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m2m3_qualitative_analysis_ref_cmbert_iob2")
| nlpso/m2m3_qualitative_analysis_ref_cmbert_iob2 | [
"task_categories:token-classification",
"multilinguality:monolingual",
"language:fr",
"region:us"
]
| 2023-02-22T08:48:51+00:00 | {"language": ["fr"], "multilinguality": ["monolingual"], "task_categories": ["token-classification"]} | 2023-02-22T08:49:07+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us
| m2m3\_qualitative\_analysis\_ref\_cmbert\_iob2
==============================================
Introduction
------------
This dataset was used to perform qualitative analysis of Jean-Baptiste/camembert-ner on nested NER task using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
Dataset parameters
------------------
* Approachrd : M2 and M3
* Dataset type : ground-truth
* Tokenizer : Jean-Baptiste/camembert-ner
* Tagging format : IOB2
* Counts :
+ Train : 6084
+ Dev : 676
+ Test : 1685
* Associated fine-tuned models :
+ M2 : nlpso/m2\_joint\_label\_ref\_cmbert\_iob2
+ M3 : nlpso/m3\_hierarchical\_ner\_ref\_cmbert\_iob2
Entity types
------------
Abbreviation: O, Entity group (level): 1 & 2, Description: Outside of a named entity
Abbreviation: PER, Entity group (level): 1, Description: Person or company name
Abbreviation: ACT, Entity group (level): 1 & 2, Description: Person or company professional activity
Abbreviation: TITREH, Entity group (level): 2, Description: Military or civil distinction
Abbreviation: DESC, Entity group (level): 1, Description: Entry full description
Abbreviation: TITREP, Entity group (level): 2, Description: Professionnal reward
Abbreviation: SPAT, Entity group (level): 1, Description: Address
Abbreviation: LOC, Entity group (level): 2, Description: Street name
Abbreviation: CARDINAL, Entity group (level): 2, Description: Street number
Abbreviation: FT, Entity group (level): 2, Description: Geographical feature
How to use this dataset
-----------------------
'''python
from datasets import load\_dataset
train\_dev\_test = load\_dataset("nlpso/m2m3\_qualitative\_analysis\_ref\_cmbert\_iob2")
| []
| [
"TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
32
]
| [
"passage: TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
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|
dc5b968dbe50835edbc57f868d7be570b6142fc6 |
# m2m3_qualitative_analysis_ref_ptrn_cmbert_iob2
## Introduction
This dataset was used to perform **qualitative analysis** of [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) on **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approachrd : M2 and M3
* Dataset type : ground-truth
* Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained)
* Tagging format : IOB2
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* M2 : [nlpso/m2_joint_label_ref_ptrn_cmbert_iob2](https://huggingface.co/nlpso/m2_joint_label_ref_ptrn_cmbert_iob2)
* M3 : [nlpso/m3_hierarchical_ner_ref_ptrn_cmbert_iob2](https://huggingface.co/nlpso/m3_hierarchical_ner_ref_ptrn_cmbert_iob2)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m2m3_qualitative_analysis_ref_ptrn_cmbert_iob2")
| nlpso/m2m3_qualitative_analysis_ref_ptrn_cmbert_iob2 | [
"task_categories:token-classification",
"multilinguality:monolingual",
"language:fr",
"region:us"
]
| 2023-02-22T08:49:07+00:00 | {"language": ["fr"], "multilinguality": ["monolingual"], "task_categories": ["token-classification"]} | 2023-02-22T08:49:23+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us
| m2m3\_qualitative\_analysis\_ref\_ptrn\_cmbert\_iob2
====================================================
Introduction
------------
This dataset was used to perform qualitative analysis of HueyNemud/das22-10-camembert\_pretrained on nested NER task using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
Dataset parameters
------------------
* Approachrd : M2 and M3
* Dataset type : ground-truth
* Tokenizer : HueyNemud/das22-10-camembert\_pretrained
* Tagging format : IOB2
* Counts :
+ Train : 6084
+ Dev : 676
+ Test : 1685
* Associated fine-tuned models :
+ M2 : nlpso/m2\_joint\_label\_ref\_ptrn\_cmbert\_iob2
+ M3 : nlpso/m3\_hierarchical\_ner\_ref\_ptrn\_cmbert\_iob2
Entity types
------------
Abbreviation: O, Entity group (level): 1 & 2, Description: Outside of a named entity
Abbreviation: PER, Entity group (level): 1, Description: Person or company name
Abbreviation: ACT, Entity group (level): 1 & 2, Description: Person or company professional activity
Abbreviation: TITREH, Entity group (level): 2, Description: Military or civil distinction
Abbreviation: DESC, Entity group (level): 1, Description: Entry full description
Abbreviation: TITREP, Entity group (level): 2, Description: Professionnal reward
Abbreviation: SPAT, Entity group (level): 1, Description: Address
Abbreviation: LOC, Entity group (level): 2, Description: Street name
Abbreviation: CARDINAL, Entity group (level): 2, Description: Street number
Abbreviation: FT, Entity group (level): 2, Description: Geographical feature
How to use this dataset
-----------------------
'''python
from datasets import load\_dataset
train\_dev\_test = load\_dataset("nlpso/m2m3\_qualitative\_analysis\_ref\_ptrn\_cmbert\_iob2")
| []
| [
"TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
32
]
| [
"passage: TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
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|
fd74e0fefac7e27918d1c637bae1cc7e36e6f8ce |
# m2m3_qualitative_analysis_ocr_cmbert_io
## Introduction
This dataset was used to perform **qualitative analysis** of [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) on **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approachrd : M2 and M3
* Dataset type : noisy (Pero OCR)
* Tokenizer : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner)
* Tagging format : IO
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* M2 : [nlpso/m2_joint_label_ocr_cmbert_io](https://huggingface.co/nlpso/m2_joint_label_ocr_cmbert_io)
* M3 : [nlpso/m3_hierarchical_ner_ocr_cmbert_io](https://huggingface.co/nlpso/m3_hierarchical_ner_ocr_cmbert_io)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m2m3_qualitative_analysis_ocr_cmbert_io")
| nlpso/m2m3_qualitative_analysis_ocr_cmbert_io | [
"task_categories:token-classification",
"multilinguality:monolingual",
"language:fr",
"region:us"
]
| 2023-02-22T08:49:24+00:00 | {"language": ["fr"], "multilinguality": ["monolingual"], "task_categories": ["token-classification"]} | 2023-02-22T08:49:39+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us
| m2m3\_qualitative\_analysis\_ocr\_cmbert\_io
============================================
Introduction
------------
This dataset was used to perform qualitative analysis of Jean-Baptiste/camembert-ner on nested NER task using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
Dataset parameters
------------------
* Approachrd : M2 and M3
* Dataset type : noisy (Pero OCR)
* Tokenizer : Jean-Baptiste/camembert-ner
* Tagging format : IO
* Counts :
+ Train : 6084
+ Dev : 676
+ Test : 1685
* Associated fine-tuned models :
+ M2 : nlpso/m2\_joint\_label\_ocr\_cmbert\_io
+ M3 : nlpso/m3\_hierarchical\_ner\_ocr\_cmbert\_io
Entity types
------------
Abbreviation: O, Entity group (level): 1 & 2, Description: Outside of a named entity
Abbreviation: PER, Entity group (level): 1, Description: Person or company name
Abbreviation: ACT, Entity group (level): 1 & 2, Description: Person or company professional activity
Abbreviation: TITREH, Entity group (level): 2, Description: Military or civil distinction
Abbreviation: DESC, Entity group (level): 1, Description: Entry full description
Abbreviation: TITREP, Entity group (level): 2, Description: Professionnal reward
Abbreviation: SPAT, Entity group (level): 1, Description: Address
Abbreviation: LOC, Entity group (level): 2, Description: Street name
Abbreviation: CARDINAL, Entity group (level): 2, Description: Street number
Abbreviation: FT, Entity group (level): 2, Description: Geographical feature
How to use this dataset
-----------------------
'''python
from datasets import load\_dataset
train\_dev\_test = load\_dataset("nlpso/m2m3\_qualitative\_analysis\_ocr\_cmbert\_io")
| []
| [
"TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
32
]
| [
"passage: TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
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]
|
4bffdcddf1197886586cd74c18b511289862a56b |
# m2m3_qualitative_analysis_ocr_ptrn_cmbert_io
## Introduction
This dataset was used to perform **qualitative analysis** of [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) on **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approachrd : M2 and M3
* Dataset type : noisy (Pero OCR)
* Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained)
* Tagging format : IO
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* M2 : [nlpso/m2_joint_label_ocr_ptrn_cmbert_io](https://huggingface.co/nlpso/m2_joint_label_ocr_ptrn_cmbert_io)
* M3 : [nlpso/m3_hierarchical_ner_ocr_ptrn_cmbert_io](https://huggingface.co/nlpso/m3_hierarchical_ner_ocr_ptrn_cmbert_io)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m2m3_qualitative_analysis_ocr_ptrn_cmbert_io")
| nlpso/m2m3_qualitative_analysis_ocr_ptrn_cmbert_io | [
"task_categories:token-classification",
"multilinguality:monolingual",
"language:fr",
"region:us"
]
| 2023-02-22T08:49:40+00:00 | {"language": ["fr"], "multilinguality": ["monolingual"], "task_categories": ["token-classification"]} | 2023-02-22T08:49:55+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us
| m2m3\_qualitative\_analysis\_ocr\_ptrn\_cmbert\_io
==================================================
Introduction
------------
This dataset was used to perform qualitative analysis of HueyNemud/das22-10-camembert\_pretrained on nested NER task using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
Dataset parameters
------------------
* Approachrd : M2 and M3
* Dataset type : noisy (Pero OCR)
* Tokenizer : HueyNemud/das22-10-camembert\_pretrained
* Tagging format : IO
* Counts :
+ Train : 6084
+ Dev : 676
+ Test : 1685
* Associated fine-tuned models :
+ M2 : nlpso/m2\_joint\_label\_ocr\_ptrn\_cmbert\_io
+ M3 : nlpso/m3\_hierarchical\_ner\_ocr\_ptrn\_cmbert\_io
Entity types
------------
Abbreviation: O, Entity group (level): 1 & 2, Description: Outside of a named entity
Abbreviation: PER, Entity group (level): 1, Description: Person or company name
Abbreviation: ACT, Entity group (level): 1 & 2, Description: Person or company professional activity
Abbreviation: TITREH, Entity group (level): 2, Description: Military or civil distinction
Abbreviation: DESC, Entity group (level): 1, Description: Entry full description
Abbreviation: TITREP, Entity group (level): 2, Description: Professionnal reward
Abbreviation: SPAT, Entity group (level): 1, Description: Address
Abbreviation: LOC, Entity group (level): 2, Description: Street name
Abbreviation: CARDINAL, Entity group (level): 2, Description: Street number
Abbreviation: FT, Entity group (level): 2, Description: Geographical feature
How to use this dataset
-----------------------
'''python
from datasets import load\_dataset
train\_dev\_test = load\_dataset("nlpso/m2m3\_qualitative\_analysis\_ocr\_ptrn\_cmbert\_io")
| []
| [
"TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
32
]
| [
"passage: TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
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|
a92b2a115698d84023b4bd0ca7182fb605fed755 |
# m2m3_qualitative_analysis_ocr_cmbert_iob2
## Introduction
This dataset was used to perform **qualitative analysis** of [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) on **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approachrd : M2 and M3
* Dataset type : noisy (Pero OCR)
* Tokenizer : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner)
* Tagging format : IOB2
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* M2 : [nlpso/m2_joint_label_ocr_cmbert_iob2](https://huggingface.co/nlpso/m2_joint_label_ocr_cmbert_iob2)
* M3 : [nlpso/m3_hierarchical_ner_ocr_cmbert_iob2](https://huggingface.co/nlpso/m3_hierarchical_ner_ocr_cmbert_iob2)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m2m3_qualitative_analysis_ocr_cmbert_iob2")
| nlpso/m2m3_qualitative_analysis_ocr_cmbert_iob2 | [
"task_categories:token-classification",
"multilinguality:monolingual",
"language:fr",
"region:us"
]
| 2023-02-22T08:49:56+00:00 | {"language": ["fr"], "multilinguality": ["monolingual"], "task_categories": ["token-classification"]} | 2023-02-22T08:50:12+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us
| m2m3\_qualitative\_analysis\_ocr\_cmbert\_iob2
==============================================
Introduction
------------
This dataset was used to perform qualitative analysis of Jean-Baptiste/camembert-ner on nested NER task using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
Dataset parameters
------------------
* Approachrd : M2 and M3
* Dataset type : noisy (Pero OCR)
* Tokenizer : Jean-Baptiste/camembert-ner
* Tagging format : IOB2
* Counts :
+ Train : 6084
+ Dev : 676
+ Test : 1685
* Associated fine-tuned models :
+ M2 : nlpso/m2\_joint\_label\_ocr\_cmbert\_iob2
+ M3 : nlpso/m3\_hierarchical\_ner\_ocr\_cmbert\_iob2
Entity types
------------
Abbreviation: O, Entity group (level): 1 & 2, Description: Outside of a named entity
Abbreviation: PER, Entity group (level): 1, Description: Person or company name
Abbreviation: ACT, Entity group (level): 1 & 2, Description: Person or company professional activity
Abbreviation: TITREH, Entity group (level): 2, Description: Military or civil distinction
Abbreviation: DESC, Entity group (level): 1, Description: Entry full description
Abbreviation: TITREP, Entity group (level): 2, Description: Professionnal reward
Abbreviation: SPAT, Entity group (level): 1, Description: Address
Abbreviation: LOC, Entity group (level): 2, Description: Street name
Abbreviation: CARDINAL, Entity group (level): 2, Description: Street number
Abbreviation: FT, Entity group (level): 2, Description: Geographical feature
How to use this dataset
-----------------------
'''python
from datasets import load\_dataset
train\_dev\_test = load\_dataset("nlpso/m2m3\_qualitative\_analysis\_ocr\_cmbert\_iob2")
| []
| [
"TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
32
]
| [
"passage: TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
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|
8f49525c05763df65d75d72212a2d0d4d3a03314 |
# m2m3_qualitative_analysis_ocr_ptrn_cmbert_iob2
## Introduction
This dataset was used to perform **qualitative analysis** of [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) on **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approachrd : M2 and M3
* Dataset type : noisy (Pero OCR)
* Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained)
* Tagging format : IOB2
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* M2 : [nlpso/m2_joint_label_ocr_ptrn_cmbert_iob2](https://huggingface.co/nlpso/m2_joint_label_ocr_ptrn_cmbert_iob2)
* M3 : [nlpso/m3_hierarchical_ner_ocr_ptrn_cmbert_iob2](https://huggingface.co/nlpso/m3_hierarchical_ner_ocr_ptrn_cmbert_iob2)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m2m3_qualitative_analysis_ocr_ptrn_cmbert_iob2")
| nlpso/m2m3_qualitative_analysis_ocr_ptrn_cmbert_iob2 | [
"task_categories:token-classification",
"multilinguality:monolingual",
"language:fr",
"region:us"
]
| 2023-02-22T08:50:13+00:00 | {"language": ["fr"], "multilinguality": ["monolingual"], "task_categories": ["token-classification"]} | 2023-02-22T08:50:28+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-token-classification #multilinguality-monolingual #language-French #region-us
| m2m3\_qualitative\_analysis\_ocr\_ptrn\_cmbert\_iob2
====================================================
Introduction
------------
This dataset was used to perform qualitative analysis of HueyNemud/das22-10-camembert\_pretrained on nested NER task using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
Dataset parameters
------------------
* Approachrd : M2 and M3
* Dataset type : noisy (Pero OCR)
* Tokenizer : HueyNemud/das22-10-camembert\_pretrained
* Tagging format : IOB2
* Counts :
+ Train : 6084
+ Dev : 676
+ Test : 1685
* Associated fine-tuned models :
+ M2 : nlpso/m2\_joint\_label\_ocr\_ptrn\_cmbert\_iob2
+ M3 : nlpso/m3\_hierarchical\_ner\_ocr\_ptrn\_cmbert\_iob2
Entity types
------------
Abbreviation: O, Entity group (level): 1 & 2, Description: Outside of a named entity
Abbreviation: PER, Entity group (level): 1, Description: Person or company name
Abbreviation: ACT, Entity group (level): 1 & 2, Description: Person or company professional activity
Abbreviation: TITREH, Entity group (level): 2, Description: Military or civil distinction
Abbreviation: DESC, Entity group (level): 1, Description: Entry full description
Abbreviation: TITREP, Entity group (level): 2, Description: Professionnal reward
Abbreviation: SPAT, Entity group (level): 1, Description: Address
Abbreviation: LOC, Entity group (level): 2, Description: Street name
Abbreviation: CARDINAL, Entity group (level): 2, Description: Street number
Abbreviation: FT, Entity group (level): 2, Description: Geographical feature
How to use this dataset
-----------------------
'''python
from datasets import load\_dataset
train\_dev\_test = load\_dataset("nlpso/m2m3\_qualitative\_analysis\_ocr\_ptrn\_cmbert\_iob2")
| []
| [
"TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
| [
32
]
| [
"passage: TAGS\n#task_categories-token-classification #multilinguality-monolingual #language-French #region-us \n"
]
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|
45df06fb0b31edc882d7c8d34389261f995e5208 | # Opus100
- Source: https://huggingface.co/datasets/opus100
- Num examples:
- 1,000,000 (train)
- 2,000 (validation)
- 192,744 (test)
- Language: English
```python
from datasets import load_dataset
load_dataset("tdtunlp/opus100_envi")
```
- Format for Translation task
```python
def preprocess(
sample,
instruction_key="### Instruction:",
input_key="Input:",
response_key="<|endofprompt|>",
end_key="<|endoftext|>",
en2vi=True,
):
if en2vi:
if random.random() < 0.5:
instruction = "Translate the following sentences from English into Vietnamese."
else:
instruction = "Dịch các câu sau từ tiếng Anh sang tiếng Việt."
input = sample['en'].strip()
response = sample['vi'].strip()
else:
if random.random() < 0.5:
instruction = "Translate the following sentences from Vietnamese into English."
else:
instruction = "Dịch các câu sau từ tiếng Việt sang tiếng Anh."
input = sample['vi'].strip()
response = sample['en'].strip()
return {'text': """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
{instruction_key}
{instruction}
{input_key}
{input}
{response_key}
{response}
{end_key}""".format(
instruction_key=instruction_key,
instruction=instruction,
input_key=input_key,
input=input,
response_key=response_key,
response=response,
end_key=end_key,
)}
"""
Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
Dịch các câu sau từ tiếng Anh sang tiếng Việt.
Input:
Toast falls jelly-side down, children hit tables and people get hurt.
<|endofprompt|>
Bánh mì nướng rơi đông lại, trẻ con va vào bàn và con người bị thương.
<|endoftext|>
"""
``` | vietgpt/opus100_envi | [
"task_categories:translation",
"size_categories:1M<n<10M",
"language:en",
"language:vi",
"LM",
"region:us"
]
| 2023-02-22T09:11:25+00:00 | {"language": ["en", "vi"], "size_categories": ["1M<n<10M"], "task_categories": ["translation"], "dataset_info": {"features": [{"name": "en", "dtype": "string"}, {"name": "vi", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 192744, "num_examples": 2000}, {"name": "train", "num_bytes": 82614470, "num_examples": 1000000}, {"name": "validation", "num_bytes": 194721, "num_examples": 2000}], "download_size": 59201490, "dataset_size": 83001935}, "tags": ["LM"]} | 2023-07-03T16:56:58+00:00 | []
| [
"en",
"vi"
]
| TAGS
#task_categories-translation #size_categories-1M<n<10M #language-English #language-Vietnamese #LM #region-us
| # Opus100
- Source: URL
- Num examples:
- 1,000,000 (train)
- 2,000 (validation)
- 192,744 (test)
- Language: English
- Format for Translation task
| [
"# Opus100\n\n- Source: URL\n- Num examples: \n - 1,000,000 (train)\n - 2,000 (validation)\n - 192,744 (test)\n- Language: English\n\n\n\n- Format for Translation task"
]
| [
"TAGS\n#task_categories-translation #size_categories-1M<n<10M #language-English #language-Vietnamese #LM #region-us \n",
"# Opus100\n\n- Source: URL\n- Num examples: \n - 1,000,000 (train)\n - 2,000 (validation)\n - 192,744 (test)\n- Language: English\n\n\n\n- Format for Translation task"
]
| [
40,
43
]
| [
"passage: TAGS\n#task_categories-translation #size_categories-1M<n<10M #language-English #language-Vietnamese #LM #region-us \n# Opus100\n\n- Source: URL\n- Num examples: \n - 1,000,000 (train)\n - 2,000 (validation)\n - 192,744 (test)\n- Language: English\n\n\n\n- Format for Translation task"
]
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|
08cf34f8842c8f387a404ba9e4ff821fb4a51454 | # Dataset Card for "nlp.4.summarization"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lansinuote/nlp.4.summarization | [
"region:us"
]
| 2023-02-22T09:22:48+00:00 | {"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 53358155, "num_examples": 20000}, {"name": "validation", "num_bytes": 273883, "num_examples": 100}, {"name": "test", "num_bytes": 249614, "num_examples": 100}], "download_size": 0, "dataset_size": 53881652}} | 2023-02-22T11:39:04+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "nlp.4.summarization"
More Information needed | [
"# Dataset Card for \"nlp.4.summarization\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"nlp.4.summarization\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"nlp.4.summarization\"\n\nMore Information needed"
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|
f18290546dc92d362ae1ab2e3d6913c52c2a6e63 | # Dataset Card for "nlp.5.classification"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lansinuote/nlp.5.classification | [
"region:us"
]
| 2023-02-22T09:40:10+00:00 | {"dataset_info": {"features": [{"name": "label", "dtype": {"class_label": {"names": {"0": "unacceptable", "1": "acceptable"}}}}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 621111, "num_examples": 8551}, {"name": "validation", "num_bytes": 77558, "num_examples": 1043}, {"name": "test", "num_bytes": 78463, "num_examples": 1063}], "download_size": 0, "dataset_size": 777132}} | 2023-02-22T11:25:27+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "nlp.5.classification"
More Information needed | [
"# Dataset Card for \"nlp.5.classification\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"nlp.5.classification\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"nlp.5.classification\"\n\nMore Information needed"
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|
265d3e9b31616e70504ff1aa9c7a3e9020e6c73e | # Dataset Card for "nlp.7.translation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lansinuote/nlp.7.translation | [
"region:us"
]
| 2023-02-22T10:02:01+00:00 | {"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 8389390, "num_examples": 20000}, {"name": "validation", "num_bytes": 84758, "num_examples": 200}, {"name": "test", "num_bytes": 84885, "num_examples": 200}], "download_size": 0, "dataset_size": 8559033}} | 2023-02-22T12:04:31+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "nlp.7.translation"
More Information needed | [
"# Dataset Card for \"nlp.7.translation\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"nlp.7.translation\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"nlp.7.translation\"\n\nMore Information needed"
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|
dc314582f7d53a97a8c4a6be0dd97e3b85cccec4 | # Dataset Card for "sv_corpora_parliament_processed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Liumx/sv_corpora_parliament_processed | [
"region:us"
]
| 2023-02-22T10:21:50+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 292351437, "num_examples": 1892723}], "download_size": 0, "dataset_size": 292351437}} | 2023-02-22T10:43:56+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "sv_corpora_parliament_processed"
More Information needed | [
"# Dataset Card for \"sv_corpora_parliament_processed\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"sv_corpora_parliament_processed\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"sv_corpora_parliament_processed\"\n\nMore Information needed"
]
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|
8a43ee056c50c3a2e9adba9d579cc3bf16665e36 | test | Hiren/first_demo | [
"region:us"
]
| 2023-02-22T10:45:18+00:00 | {} | 2023-02-22T10:56:33+00:00 | []
| []
| TAGS
#region-us
| test | []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
| [
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|
fe0fe707a2513537a40b872e84c676878a99df54 | # Dataset Card for "google_speech_commands_augmented_raw_fixed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | mazkooleg/0-9up_google_speech_commands_augmented_raw | [
"region:us"
]
| 2023-02-22T10:49:44+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "zero", "1": "one", "2": "two", "3": "three", "4": "four", "5": "five", "6": "six", "7": "seven", "8": "eight", "9": "nine", "10": "#unk#", "11": "#pub#"}}}}], "splits": [{"name": "validation", "num_bytes": 107972507.648, "num_examples": 3368}, {"name": "train", "num_bytes": 35133207938.0, "num_examples": 1095480}, {"name": "test", "num_bytes": 120956609.28, "num_examples": 3773}], "download_size": 31701155906, "dataset_size": 35362137054.928}} | 2023-02-22T11:14:24+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "google_speech_commands_augmented_raw_fixed"
More Information needed | [
"# Dataset Card for \"google_speech_commands_augmented_raw_fixed\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"google_speech_commands_augmented_raw_fixed\"\n\nMore Information needed"
]
| [
6,
27
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"google_speech_commands_augmented_raw_fixed\"\n\nMore Information needed"
]
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|
867bc1e621094be8c0d6e1514e663cca88211876 | # Dataset Card for "nlp.6.named_entity_recognition"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lansinuote/nlp.6.named_entity_recognition | [
"region:us"
]
| 2023-02-22T11:32:25+00:00 | {"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 3810117, "num_examples": 14041}, {"name": "validation", "num_bytes": 941811, "num_examples": 3250}, {"name": "test", "num_bytes": 884226, "num_examples": 3453}], "download_size": 773605, "dataset_size": 5636154}} | 2023-02-22T11:35:38+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "nlp.6.named_entity_recognition"
More Information needed | [
"# Dataset Card for \"nlp.6.named_entity_recognition\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"nlp.6.named_entity_recognition\"\n\nMore Information needed"
]
| [
6,
22
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"nlp.6.named_entity_recognition\"\n\nMore Information needed"
]
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87237e05c6954c7cefd3411b9642fba71ca6e6b0 | # Dataset Card for "patched_test_p_20_f_SPOUT_m1_predictions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | roa7n/patched_test_p_20_f_SPOUT_m1_predictions | [
"region:us"
]
| 2023-02-22T11:37:51+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence_str", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "m1_preds", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 524213737, "num_examples": 1607399}], "download_size": 54370586, "dataset_size": 524213737}} | 2023-02-22T11:38:05+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "patched_test_p_20_f_SPOUT_m1_predictions"
More Information needed | [
"# Dataset Card for \"patched_test_p_20_f_SPOUT_m1_predictions\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"patched_test_p_20_f_SPOUT_m1_predictions\"\n\nMore Information needed"
]
| [
6,
30
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"patched_test_p_20_f_SPOUT_m1_predictions\"\n\nMore Information needed"
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|
e5cf99702c7aed0e984a46bdbf8b0565eba6bc2a | annotations_creators:
- found
language:
- Tamil
language_creators:
- found
license: []
multilinguality:
- multilingual
pretty_name: tamilwikipediadataset
size_categories:
- 100K<n<1M
source_datasets: []
tags: []
task_categories:
- summarization
task_ids: [] | Abirami/tamilwikipediadataset | [
"region:us"
]
| 2023-02-22T11:50:04+00:00 | {} | 2023-02-22T12:42:51+00:00 | []
| []
| TAGS
#region-us
| annotations_creators:
- found
language:
- Tamil
language_creators:
- found
license: []
multilinguality:
- multilingual
pretty_name: tamilwikipediadataset
size_categories:
- 100K<n<1M
source_datasets: []
tags: []
task_categories:
- summarization
task_ids: [] | []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
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|
51d16e614efcacdaa79885d19cb5f7f8bd72ef2d | # Dataset Card for "cv.1.image_classification"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lansinuote/cv.1.image_classification | [
"region:us"
]
| 2023-02-22T12:12:29+00:00 | {"dataset_info": {"features": [{"name": "labels", "dtype": {"class_label": {"names": {"0": "angular_leaf_spot", "1": "bean_rust", "2": "healthy"}}}}, {"name": "pixel_values", "sequence": {"sequence": {"sequence": "float32"}}}], "splits": [{"name": "train", "num_bytes": 625388016, "num_examples": 1034}, {"name": "validation", "num_bytes": 80441592, "num_examples": 133}, {"name": "test", "num_bytes": 77417472, "num_examples": 128}], "download_size": 0, "dataset_size": 783247080}} | 2023-02-23T03:04:38+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "cv.1.image_classification"
More Information needed | [
"# Dataset Card for \"cv.1.image_classification\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"cv.1.image_classification\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"cv.1.image_classification\"\n\nMore Information needed"
]
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|
49eb8610f79de077097c29a1eec0d35a46fac606 | # Dataset Card for "cv.2.image_segmentation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lansinuote/cv.2.image_segmentation | [
"region:us"
]
| 2023-02-22T12:25:11+00:00 | {"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 292478590.8, "num_examples": 900}, {"name": "test", "num_bytes": 32497621.2, "num_examples": 100}], "download_size": 324358820, "dataset_size": 324976212.0}} | 2023-02-23T02:49:15+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "cv.2.image_segmentation"
More Information needed | [
"# Dataset Card for \"cv.2.image_segmentation\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"cv.2.image_segmentation\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"cv.2.image_segmentation\"\n\nMore Information needed"
]
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|
acd9ebd512a40ec864e8fd8702ce9d8a323134bd | # Dataset Card for "nlp.8.generation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lansinuote/nlp.8.generation | [
"region:us"
]
| 2023-02-22T13:04:56+00:00 | {"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 299146484, "num_examples": 44863}, {"name": "test", "num_bytes": 713476, "num_examples": 107}], "download_size": 0, "dataset_size": 299859960}} | 2023-02-23T02:02:10+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "nlp.8.generation"
More Information needed | [
"# Dataset Card for \"nlp.8.generation\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"nlp.8.generation\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"nlp.8.generation\"\n\nMore Information needed"
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|
3cffd418f684915a2ad5e3e9bb17b25f3866df0c | # Dataset Card for "bookcorpus_stage2_relation_label"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | MartinKu/bookcorpus_stage2_relation_label | [
"region:us"
]
| 2023-02-22T13:57:19+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "relation_label_list", "sequence": "int64"}, {"name": "start_point_list", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 21667730608, "num_examples": 74004248}], "download_size": 3694358344, "dataset_size": 21667730608}} | 2023-02-27T00:01:45+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "bookcorpus_stage2_relation_label"
More Information needed | [
"# Dataset Card for \"bookcorpus_stage2_relation_label\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"bookcorpus_stage2_relation_label\"\n\nMore Information needed"
]
| [
6,
22
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"bookcorpus_stage2_relation_label\"\n\nMore Information needed"
]
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|
e9f31477ff2d881d90a67fcf1a20ce3299017dea | # 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] | businesstengi/mikeyAIData | [
"task_categories:conversational",
"task_categories:text-generation",
"task_categories:text-classification",
"size_categories:100K<n<1M",
"language:en",
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| 2023-02-22T14:05:36+00:00 | {"language": ["en"], "license": "afl-3.0", "size_categories": ["100K<n<1M"], "task_categories": ["conversational", "text-generation", "text-classification"], "pretty_name": "mikeyData"} | 2023-02-22T16:51:58+00:00 | []
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| TAGS
#task_categories-conversational #task_categories-text-generation #task_categories-text-classification #size_categories-100K<n<1M #language-English #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
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"### Social Impact of Dataset",
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|
feb13d4b9b67c12813fa60b4c8307e8840dc2326 | # Dataset Card for "kr3_train_subset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | seokwoni/kr3_train_subset | [
"region:us"
]
| 2023-02-22T14:44:53+00:00 | {"dataset_info": {"features": [{"name": "labels", "dtype": {"class_label": {"names": {"0": "0", "1": "1"}}}}, {"name": "Review", "dtype": "string"}, {"name": "en_review", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9011519, "num_examples": 15000}], "download_size": 5541231, "dataset_size": 9011519}} | 2023-02-22T14:45:07+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "kr3_train_subset"
More Information needed | [
"# Dataset Card for \"kr3_train_subset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"kr3_train_subset\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"kr3_train_subset\"\n\nMore Information needed"
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|
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