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Model: PeterPaker123/Qwen2.5-7B-Vietnamese-Medical-NER-GRPO Source: Original Platform
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README.md
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library_name: transformers
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tags: [ner, medical, vietnamese, qwen, grpo, rlhf, peft, lora, text-extraction]
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---
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# Model Card for Vietnamese Medical NER (Qwen2.5-7B SFT + GRPO)
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<!-- Provide a quick summary of what the model is/does. -->
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This model is a generative Large Language Model (based on the Qwen/Qwen2.5-7B architecture) strictly fine-tuned for **Vietnamese Medical Named Entity Recognition (NER)**. It accurately extracts Symptoms, Diseases, Medical Procedures, Drugs, etc. from Vietnamese medical texts and outputs them in a highly structured JSON format.
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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This model transitions away from traditional token-classification BERT models by utilizing an LLM as an intelligent extraction agent. It reads Vietnamese medical texts (such as clinical notes, user questions, or news) and outputs structured JSON lists.
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The model was trained using an advanced two-stage alignment pipeline:
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1. **Stage 1: Supervised Fine-Tuning (SFT)** to teach the model the strict JSON extraction schema and Vietnamese medical terminology.
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2. **Stage 2: Group Relative Policy Optimization (GRPO)** using Reinforcement Learning (RL) with LoRA. The model was mathematically penalized via shifted-bound and exponential scaling reward functions for hallucinations and missed entities, forcing it to achieve near-perfect Precision, Recall, and Type Accuracy.
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- **Developed by:** PeterPaker123
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- **Model type:** Causal Language Model (Generative NER)
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- **Language(s) (NLP):** Vietnamese (`vi`)
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- **License:** Apache 2.0 (Derived from Qwen base models)
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- **Finetuned from model:** `Qwen/Qwen2.5-7B-Instruct`
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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Unlike traditional NER models constrained to a fixed set of predefined classes, this model supports **dynamic, prompt-defined entity extraction**. It acts as an intelligent agent that extracts *any* medical or clinical entity type specified in the system prompt.
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Commonly targeted categories include (but are not limited to):
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* **`SYMPTOM_AND_DISEASE`**: Symptoms, signs of illness, diseases, or medical conditions.
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* **`MEDICAL_PROCEDURE`**: Medical procedures, surgeries, therapies, or diagnostic methods.
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* **`DRUG`**: Names of medicines, drugs, vitamins, or supplements.
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* **`BODY_PART / ANATOMY`**: Anatomical locations, organs, or body structures.
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* **`DOSAGE & MEASUREMENT`**: Medical quantities, medication dosages, or frequencies.
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* **`DEMOGRAPHICS`**: Patient-specific identifiers (e.g., Age, Gender, Occupation).
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**How it works:** You simply define the desired categories and their descriptions in the `system` prompt, and the model will strictly adhere to extracting only those requested types into the required JSON schema.
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### Downstream Use
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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This model can be integrated into downstream healthcare applications such as:
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- Medical chatbot prompt enrichment (RAG pipelines).
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- Automated summarization of telehealth transcripts.
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- Clinical knowledge graph construction.
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- **Medical Diagnosis or Triage:** This model is strictly an extraction tool. It does not diagnose, prescribe, or provide medical advice.
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- **General Conversation:** The model has been aggressively aligned to output JSON lists. It will perform poorly at standard conversational AI tasks.
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- **Other Entity Types:** The model was trained to ignore Demographic data (Names, Ages, Locations, Occupations, Dates). Attempting to extract these will likely yield empty lists.
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- **Clinical Disclaimer:** This model is an AI research tool and **must not** be used for automated medical diagnosis, treatment planning, or life-or-death triage without human oversight.
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- **Language Limitation:** The model is exclusively trained for the Vietnamese language.
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- **Hallucination Residuals:** While GRPO significantly reduces hallucinations, Generative LLMs may occasionally hallucinate or alter the spelling of extracted text.
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Extracted entities in a clinical setting must be validated by a human medical professional or a deterministic cross-checking system.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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```python
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import json
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "PeterPaker123/Qwen2.5-7B-Vietnamese-Medical-NER-GRPO"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype="auto")
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prompt = [
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{
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"role": "system",
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"content": "You are a medical expert. Your task is to identify and extract medical entities from the given text.\nThe entity types to extract are:\n- SYMPTOM_AND_DISEASE: Symptoms, signs of illness, diseases, or medical conditions.\n- MEDICAL_PROCEDURE: Medical procedures, surgeries, therapies, or diagnostic methods.\n- DRUG: Names of medicines, drugs, vitamins, or supplements.\n\nReturn the result as a JSON list containing dictionaries with \"entity\" and \"type\" keys. If no relevant entities are found, return an empty list []"
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},
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{
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"role": "user",
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"content": "Text: Trẻ sơ sinh 2 tháng tuổi vàng mặt , hay khóc thét có phải bị vàng da nhân não không ?"
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}
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]
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inputs = tokenizer.apply_chat_template(prompt, return_tensors="pt", add_generation_prompt=True).to(model.device)
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outputs = model.generate(inputs, max_new_tokens=256, temperature=0.0)
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response = tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True)
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print(response)
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# Expected Output: [{"entity": "vàng mặt", "type": "SYMPTOM_AND_DISEASE"}, {"entity": "khóc thét", "type": "SYMPTOM_AND_DISEASE"}, {"entity": "vàng da nhân não", "type": "SYMPTOM_AND_DISEASE"}]
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```
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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This model was trained on a consolidated corpus derived from four premier Vietnamese medical datasets. The original datasets contained diverse entity tags which were mapped down to our three primary clinical targets (`SYMPTOM_AND_DISEASE`, `MEDICAL_PROCEDURE`, `DRUG`).
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| Dataset Name | Domain / Source | Total Samples | Key Original Entities |
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|---|---|---|---|
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| **VietBioNER** | Tuberculosis (TB) clinical treatment | ~1,700 | Symptom & Disease, Diagnostic Procedure, Location |
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| **PhoNER_COVID19** | COVID-19 pandemic surveillance | ~10,000 | Symptom & Disease, Patient ID, Age, Location |
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| **ViMQ** | Healthcare dialogue systems | ~10,000 | Symptom, Disease, Drug, Procedure, Body Structure |
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| **ViMedNER** | General Medical NER | ~10,000 | Symptom, Disease, Drug, Body Part, Procedure |
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*Data Split:* 75% used for Phase 1 (SFT) and 25% used for Phase 2 (GRPO/RL).
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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- Prompts were formatted into strict ChatML format using text-only templates.
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- Target entities outside the three core classes were stripped from the ground truth to force the model to ignore non-medical demographic data.
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#### Training Hyperparameters
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- **Training regime:** `bf16` mixed precision
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- **Phase 1 (SFT):** Full parameter fine-tuning, Max Seq Len 4096, Batch Size 2, Gradient Accumulation 8, LR 2e-5, Cosine Scheduler.
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- **Phase 2 (GRPO):** LoRA target modules (`q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj`), Rank 16, Alpha 32. 4 Generations per prompt, Temperature 0.5. Full AdamW optimizer.
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## Model Examination
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<!-- Relevant interpretability work for the model goes here -->
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## Technical Specifications
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### Model Architecture and Objective
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Qwen2.5-7B Architecture. Causal Language Modeling objective heavily modified via RLHF/GRPO to act as an entity extraction classifier.
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#### Hardware
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NVIDIA RTX PRO 6000 Blackwell
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#### Software
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- `transformers`
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- `trl` (SFTTrainer, GRPOTrainer)
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- `peft`
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- `torch` (with `sdpa` attention)
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## Citation
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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```bibtex
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@inproceedings{vietbioner,
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title = "{A Named Entity Recognition Corpus for Vietnamese Biomedical Texts to Support Tuberculosis Treatment}",
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author = "Phan, Uyen and Nguyen, Phuong and Nguyen, Nhung",
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booktitle = "Proceedings of the 13th Language Resources and Evaluation Conference",
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year = "2022",
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publisher = "European Language Resources Association"
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}
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@inproceedings{PhoNER_COVID19,
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title = {{COVID-19 Named Entity Recognition for Vietnamese}},
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author = {Thinh Hung Truong and Mai Hoang Dao and Dat Quoc Nguyen},
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booktitle = {Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
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year = {2021}
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}
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@article{vimq2023,
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title={ViMQ: A Vietnamese Medical Question Dataset for Healthcare Dialogue System Development},
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author={Vu, Hieu M. and Phan, Long and Nguyen, Nhung and others},
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year={2023}
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}
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@article{10.4108/eetinis.v11i3.5221,
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author={Pham Van Duong and Tien-Dat Trinh and Minh-Tien Nguyen and Huy-The Vu and Minh Chuan Pham and Tran Manh Tuan and Le Hoang Son},
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title={ViMedNER: A Medical Named Entity Recognition Dataset for Vietnamese},
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journal={EAI Endorsed Transactions on Industrial Networks and Intelligent Systems},
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volume={11},
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number={4},
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publisher={EAI},
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year={2024},
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doi={10.4108/eetinis.v11i3.5221}
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}
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```
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## Glossary
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- **NER:** Named Entity Recognition
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- **SFT:** Supervised Fine-Tuning
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- **GRPO:** Group Relative Policy Optimization (An RL algorithm optimized for Generative Models)
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- **LoRA:** Low-Rank Adaptation
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