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Datasets:
TypicaAI
/
MedicalNER_Fr

Tasks:
Token Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
named-entity-recognition
Languages:
French
Size:
10K - 100K
Tags:
medical
ner
Libraries:
Datasets
pandas
License:
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metadata
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: >

  ---

  dataset_info:
    features:
    - name: sample_id
      dtype: string
    - name: tokens
      sequence: string
    - name: ner_tags
      sequence: string
    - name: text
      dtype: string
    - name: ner_tags_span
      dtype: string
    splits:
    - name: train
      num_bytes: 7080146
      num_examples: 16176
    download_size: 2937741
    dataset_size: 7080146
  configs:

  - config_name: default
    data_files:
    - split: train
      path: data/train-*
  license: Creative Commons license family

  task_categories:

  - token-classification

  language:

  - fr

  tags:

  - medical

  pretty_name: MedicalNER_Fr

  size_categories:

  - 10K<n<100K

  ---

  # 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 contactus@typica.ai if you have any questions
  or comments.
description: >-
  MedicalNER_Fr: Named Entity Recognition Dataset for the French language in the
  medical and healthcare domain, (2024).

Dataset Card for MedicalNER_Fr

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

  • Curated by: typica.ai
  • License: cc-by-4.0

Uses

The dataset is designed to train Named Entity Recognition models for the French language in the medical and healthcare domain.

Dataset Structure

  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 

{'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

This dataset was created for educational purposes only.

Source Data

The Dataset source is Multilingual Complex Named Entity Recognition (MultiCoNER V2).

Data Collection and Processing

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 dataset was created for educational purposes only.

Recommendations

To ensure optimal training for your NER medical model, it is recommended to balance the unbalanced dataset before proceeding.

Citation

If you use this dataset, please cite:

@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 contactus@typica.ai if you have any questions or comments.