sociocom/MedPHINER-Llama-3.1-Swallow-8B-Instruct-v0.5
MedPHINER-Llama-3.1-Swallow-8B-Instruct-v0.5 by sociocom is an 8 billion parameter language model with a 32768-token context length, fine-tuned for Japanese medical text. Based on Llama-3.1-Swallow-8B-Instruct-v0.5, this model specializes in identifying and tagging Protected Health Information (PHI) such as age, IDs, phone numbers, job titles, locations, person names, and hospital names within medical documents. It is specifically designed for PHI inference tasks in Japanese healthcare contexts.
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MedPHINER-Llama-3.1-Swallow-8B-Instruct-v0.5: Japanese Medical PHI Tagging Model
This model, developed by sociocom, is an 8 billion parameter language model built upon the Llama-3.1-Swallow-8B-Instruct-v0.5 base. It has been specifically fine-tuned using LoRA for the task of identifying and tagging Protected Health Information (PHI) within Japanese medical texts.
Key Capabilities
- PHI Tagging: Accurately identifies and assigns specific tags to various types of personal health information in Japanese.
- PHI Categories: Recognizes and tags:
<phi_age>: Age<phi_id>: Identification numbers<phi_tel>: Telephone numbers<phi_job>: Occupations<phi_location>: Addresses and place names<phi_person>: Person names<phi_hospital>: Medical institution names
- Japanese Medical Context: Optimized for the nuances of Japanese medical language and data.
Training Details
The model was fine-tuned on 11,127 sentences from the NTCIR dataset. Personal information insertion and annotation for the training data were performed using OpenAI API (gpt-5.2-2025-12-11). Training utilized LoRA with a rank of 16, alpha of 64, and a dropout of 0.05 over 5 epochs, with a batch size of 8 and a learning rate of 2e-4 using the AdamW optimizer.
Good For
- Automated PHI Redaction: Ideal for applications requiring the automatic identification and masking of sensitive patient data in Japanese medical records.
- Data Anonymization: Useful for preparing medical datasets for research or sharing while ensuring patient privacy.
- Compliance: Supports efforts to comply with data privacy regulations in healthcare by accurately pinpointing PHI.