README.md

---
language: fr
license: cc
multilinguality: monolingual
size_categories: 10K<n<100K
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: MedicalNER_Fr
tags:
- medical
- ner
dataset_card_content: "\n---\ndataset_info:\n  features:\n  - name: sample_id\n  \
  \  dtype: string\n  - name: tokens\n    sequence: string\n  - name: ner_tags\n \
  \   sequence: string\n  - name: text\n    dtype: string\n  - name: ner_tags_span\n\
  \    dtype: string\n  splits:\n  - name: train\n    num_bytes: 7080146\n    num_examples:\
  \ 16176\n  download_size: 2937741\n  dataset_size: 7080146\nconfigs:\n- config_name:\
  \ default\n  data_files:\n  - split: train\n    path: data/train-*\nlicense: Creative\
  \ Commons license family\ntask_categories:\n- token-classification\nlanguage:\n\
  - fr\ntags:\n- medical\npretty_name: MedicalNER_Fr\nsize_categories:\n- 10K<n<100K\n\
  ---\n# Dataset Card for MedicalNER_Fr\n\n<!-- Provide a quick summary of the dataset.\
  \ -->\nThe dataset MedicalNER_Fr has been specifically curated to facilitate training\
  \ Named Entity Recognition (NER) models for the French language within the medical\
  \ and healthcare domain. It is derived from the Multilingual Complex Named Entity\
  \ Recognition (MultiCoNER) Dataset and is intended solely for educational purposes.\n\
  \nThe MultiCoNER V2 dataset has undergone filtration to exclusively encompass French-language\
  \ entries associated with the medical domain. Non-medical tags have been aggregated\
  \ into broader categories. Before commencing the training of your NER medical model,\
  \ it is advisable to address the imbalanced nature of the dataset to ensure optimal\
  \ training outcomes.\n\n## Dataset Details\n\n### Dataset Description\n\n<!-- Provide\
  \ a longer summary of what this dataset is. -->\n- **Curated by:** typica.ai\n-\
  \ **License:** cc-by-4.0\n\n\n## Uses\n\n<!-- Address questions around how the dataset\
  \ is intended to be used. -->\nThe dataset is designed to train Named Entity Recognition\
  \ models for the French language in the medical and healthcare domain.\n\n\n## Dataset\
  \ Structure\n\n<!-- This section provides a description of the dataset fields, and\
  \ additional information about the dataset structure such as criteria used to create\
  \ the splits, relationships between data points, etc. -->\n1. **sample_id**: A UUID\
  \ generated for each example.\n2. **tokens**: A list of tokens (words) in the sentence.\n\
  3. **ner_tags**: A list of named entity recognition (NER) tags corresponding to\
  \ each token. These tags indicate the entity type of each token.\n4. **text**: Text\
  \ formed by combining the tokens.\n5. **ner_tags_span**: A list of spans for the\
  \ NER tags. Each span is a list containing:\n   - The NER tag (entity type).\n \
  \  - The start position of the entity in the text.\n   - The end position of the\
  \ entity in the text.\n\n### Dataset Tags Count:\n\n- AnatomicalStructure: 4685\n\
  - Disease: 4658\n- Medication/Vaccine: 4226\n- MedicalProcedure: 3170\n- Symptom:\
  \ 1763\n- LOC: 525\n- PER: 521\n- PROD: 305\n- CW: 167\n- ORG: 83\n- GRP: 14\n\n\
  ### Example\n\n```json\n{'sample_id': '60a82e36-4d34-4e16-aadc-2078699476f7',\n\
  \ 'tokens': ['jonas',\n  'salk',\n  'médecin',\n  'm.d.',\n  '1938',\n  'et',\n\
  \  'inventeur',\n  'du',\n  'vaccin',\n  'contre',\n  'la',\n  'poliomyélite',\n\
  \  '.'],\n 'ner_tags': ['B-PER',\n  'I-PER',\n  'O',\n  'O',\n  'O',\n  'O',\n \
  \ 'O',\n  'O',\n  'O',\n  'O',\n  'O',\n  'B-Disease',\n  'O'],\n 'text': 'jonas\
  \ salk médecin m.d. 1938 et inventeur du vaccin contre la poliomyélite .',\n 'ner_tags_span':\
  \ \"[['PER', 0, 10], ['Disease', 62, 74]]\"}\n```\n\n## Dataset Creation\n\n###\
  \ Curation Rationale\n\n<!-- Motivation for the creation of this dataset. -->\n\
  This dataset was created for educational purposes only.\n\n### Source Data\n\n<!--\
  \ This section describes the source data (e.g. news text and headlines, social media\
  \ posts, translated sentences, ...). -->\nThe Dataset source is [Multilingual Complex\
  \ Named Entity Recognition (MultiCoNER V2)](https://huggingface.co/datasets/MultiCoNER/multiconer_v2).\n\
  \n#### Data Collection and Processing\n\n<!-- This section describes the data collection\
  \ and processing process such as data selection criteria, filtering and normalization\
  \ methods, tools and libraries used, etc. -->\nThe MultiCoNER V2 dataset has been\
  \ filtered to include only French language rows and only those related to the medical\
  \ domain. Non-medical tags have been aggregated into coarse-grained tags.\n\n##\
  \ Bias, Risks, and Limitations\n\n<!-- This section is meant to convey both technical\
  \ and sociotechnical limitations. -->\nThis dataset was created for educational\
  \ purposes only.\n\n### Recommendations\n\n<!-- This section is meant to convey\
  \ recommendations with respect to the bias, risk, and technical limitations. -->\n\
  To ensure optimal training for your NER medical model, it is recommended to balance\
  \ the unbalanced dataset before proceeding.\n\n## Citation\n\n<!-- If there is a\
  \ paper or blog post introducing the dataset, the APA and Bibtex information for\
  \ that should go in this section. -->\nIf you use this dataset, please cite:\n\n\
  ```bibtex\n@misc{MedicalNER_Fr2024,\n  author = {Hicham Assoudi},\n  title = {MedicalNER_Fr:\
  \ Named Entity Recognition Dataset for the French language in the medical and healthcare\
  \ domain},\n  note = {Created by Hicham Assoudi, Ph.D. at Typica.ai (url{https://typica.ai/}),\
  \ published on Hugging Face},\n  year = {2024},\n  url = {https://huggingface.co/datasets/TypicaAI/MedicalNER_Fr}\n\
  }\n```\n\n## Dataset Contact\n\nFeel free to reach out to us at [email protected]\
  \ if you have any questions or comments.\n"
description: 'MedicalNER_Fr: Named Entity Recognition Dataset for the French language
  in the medical and healthcare domain, (2024).'
---
# Dataset Card for MedicalNER_Fr

<!-- Provide a quick summary of the dataset. -->
The dataset MedicalNER_Fr has been specifically curated to facilitate training Named Entity Recognition (NER) models for the French language within the medical and healthcare domain. It is derived from the Multilingual Complex Named Entity Recognition (MultiCoNER) Dataset and is intended solely for educational purposes.

The MultiCoNER V2 dataset has undergone filtration to exclusively encompass French-language entries associated with the medical domain. Non-medical tags have been aggregated into broader categories. Before commencing the training of your NER medical model, it is advisable to address the imbalanced nature of the dataset to ensure optimal training outcomes.

## Dataset Details

### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** typica.ai
- **License:** cc-by-4.0


## Uses

<!-- Address questions around how the dataset is intended to be used. -->
The dataset is designed to train Named Entity Recognition models for the French language in the medical and healthcare domain.


## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
1. **sample_id**: A UUID generated for each example.
2. **tokens**: A list of tokens (words) in the sentence.
3. **ner_tags**: A list of named entity recognition (NER) tags corresponding to each token. These tags indicate the entity type of each token.
4. **text**: Text formed by combining the tokens.
5. **ner_tags_span**: A list of spans for the NER tags. Each span is a list containing:
   - The NER tag (entity type).
   - The start position of the entity in the text.
   - The end position of the entity in the text.

### Dataset Tags Count:

- AnatomicalStructure: 4685
- Disease: 4658
- Medication/Vaccine: 4226
- MedicalProcedure: 3170
- Symptom: 1763
- LOC: 525
- PER: 521
- PROD: 305
- CW: 167
- ORG: 83
- GRP: 14

### Example

```json
{'sample_id': '60a82e36-4d34-4e16-aadc-2078699476f7',
 'tokens': ['jonas',
  'salk',
  'médecin',
  'm.d.',
  '1938',
  'et',
  'inventeur',
  'du',
  'vaccin',
  'contre',
  'la',
  'poliomyélite',
  '.'],
 'ner_tags': ['B-PER',
  'I-PER',
  'O',
  'O',
  'O',
  'O',
  'O',
  'O',
  'O',
  'O',
  'O',
  'B-Disease',
  'O'],
 'text': 'jonas salk médecin m.d. 1938 et inventeur du vaccin contre la poliomyélite .',
 'ner_tags_span': "[['PER', 0, 10], ['Disease', 62, 74]]"}
```

## Dataset Creation

### Curation Rationale

<!-- Motivation for the creation of this dataset. -->
This dataset was created for educational purposes only.

### Source Data

<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
The Dataset source is [Multilingual Complex Named Entity Recognition (MultiCoNER V2)](https://huggingface.co/datasets/MultiCoNER/multiconer_v2).

#### Data Collection and Processing

<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
The MultiCoNER V2 dataset has been filtered to include only French language rows and only those related to the medical domain. Non-medical tags have been aggregated into coarse-grained tags.

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->
This dataset was created for educational purposes only.

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
To ensure optimal training for your NER medical model, it is recommended to balance the unbalanced dataset before proceeding.

## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
If you use this dataset, please cite:

```bibtex
@misc{MedicalNER_Fr2024,
  author = {Hicham Assoudi},
  title = {MedicalNER_Fr: Named Entity Recognition Dataset for the French language in the medical and healthcare domain},
  note = {Created by Hicham Assoudi, Ph.D. at Typica.ai (url{https://typica.ai/}), published on Hugging Face},
  year = {2024},
  url = {https://huggingface.co/datasets/TypicaAI/MedicalNER_Fr}
}
```

## Dataset Contact

Feel free to reach out to us at [email protected] if you have any questions or comments.