metadata

title: HealthcareNER Fr
emoji: 🩺
colorFrom: blue
colorTo: pink
sdk: gradio
sdk_version: 5.9.1
app_file: app.py
pinned: false
license: apache-2.0
short_description: French Healthcare NER Demo from the Book NLP on OCI

French Healthcare NER Model (Educational Version)

This Hugging Face Space provides a live demonstration of the model developed as part of the healthcare NLP case study featured throughout my book Natural Language Processing on Oracle Cloud Infrastructure: Building Transformer-Based NLP Solutions Using Oracle AI and Hugging Face. Dive into Chapter 6 for a comprehensive, step-by-step guide on building this model.

📚 Purpose and Scope

This Hugging Face Space showcases the model built step-by-step in Chapters 4 to 7 of the book, covering everything from healthcare dataset creation to fine-tuning a transformer-based NER model. It provides a practical example of how NLP can be applied in healthcare to extract insights from French medical texts.

Why Explore This Demo?

Experiment with the Model: Interact with the healthcare NLP model from the book without the need to train one from scratch.
Discover What You Can Build: Get a hands-on preview of the process detailed in the book, from healthcare dataset preparation to fine-tuning a pre-trained transformer-based NER model.

⚠️ Usage Restrictions

This is a demo provided for educational purposes. The Model behind was trained on a limited dataset and is not intended for production use, clinical decision-making, or real-world medical applications.

Educational and research purposes only
Not licensed for commercial deployment
Not for production use
Not for medical decisions

🎓 Book Reference

This model is built as described in Chapter 6 of the book Natural Language Processing on Oracle Cloud Infrastructure. The book covers the entire NLP solution lifecycle—including data preparation, model fine-tuning, deployment, and monitoring. Chapter 6 specifically focuses on:

Fine-tuning a pretrained model from Hugging Face Hub for healthcare Named Entity Recognition (NER)
Training the model using OCI’s Data Science service and Hugging Face Transformers libraries
Performance evaluation and best practices for robust and cost-effective NLP models

For more details, you can explore the book and Chapter 6 on the following platforms:

Full Book on Springer: View Here
Chapter 6 on Springer: Read Chapter 6
Amazon: Learn More

Citation

If you use this model, please cite the following:

@Inbook{Assoudi2024,
author="Assoudi, Hicham",
title="Model Fine-Tuning",
bookTitle="Natural Language Processing on Oracle Cloud Infrastructure: Building Transformer-Based NLP Solutions Using Oracle AI and Hugging Face",
year="2024",
publisher="Apress",
address="Berkeley, CA",
pages="249--319",
abstract="This chapter focuses on the process of fine-tuning a pretrained model for healthcare Named Entity Recognition (NER). This chapter provides an in-depth exploration of training the healthcare NER model using OCI's Data Science platform and Hugging Face tools. It covers the fine-tuning process, performance evaluation, and best practices that contribute to creating robust and cost-effective NLP models.",
isbn="979-8-8688-1073-2",
doi="10.1007/979-8-8688-1073-2_6",
url="https://doi.org/10.1007/979-8-8688-1073-2_6"
}

📞 Connect and Contact

Stay updated on my latest models and projects:
👉 Follow me on Hugging Face

For inquiries or professional communication, feel free to reach out:
📧 Email: [email protected]