初始化项目，由ModelHub XC社区提供模型
Model: PeterPaker123/Qwen2.5-7B-Vietnamese-Medical-NER-GRPO Source: Original Platform
This commit is contained in:
ModelHub XC
36
.gitattributes
vendored
Normal file
36
.gitattributes
vendored
Normal file
@@ -0,0 +1,36 @@
|
||||
*.7z filter=lfs diff=lfs merge=lfs -text
|
||||
*.arrow filter=lfs diff=lfs merge=lfs -text
|
||||
*.bin filter=lfs diff=lfs merge=lfs -text
|
||||
*.bz2 filter=lfs diff=lfs merge=lfs -text
|
||||
*.ckpt filter=lfs diff=lfs merge=lfs -text
|
||||
*.ftz filter=lfs diff=lfs merge=lfs -text
|
||||
*.gz filter=lfs diff=lfs merge=lfs -text
|
||||
*.h5 filter=lfs diff=lfs merge=lfs -text
|
||||
*.joblib filter=lfs diff=lfs merge=lfs -text
|
||||
*.lfs.* filter=lfs diff=lfs merge=lfs -text
|
||||
*.mlmodel filter=lfs diff=lfs merge=lfs -text
|
||||
*.model filter=lfs diff=lfs merge=lfs -text
|
||||
*.msgpack filter=lfs diff=lfs merge=lfs -text
|
||||
*.npy filter=lfs diff=lfs merge=lfs -text
|
||||
*.npz filter=lfs diff=lfs merge=lfs -text
|
||||
*.onnx filter=lfs diff=lfs merge=lfs -text
|
||||
*.ot filter=lfs diff=lfs merge=lfs -text
|
||||
*.parquet filter=lfs diff=lfs merge=lfs -text
|
||||
*.pb filter=lfs diff=lfs merge=lfs -text
|
||||
*.pickle filter=lfs diff=lfs merge=lfs -text
|
||||
*.pkl filter=lfs diff=lfs merge=lfs -text
|
||||
*.pt filter=lfs diff=lfs merge=lfs -text
|
||||
*.pth filter=lfs diff=lfs merge=lfs -text
|
||||
*.rar filter=lfs diff=lfs merge=lfs -text
|
||||
*.safetensors filter=lfs diff=lfs merge=lfs -text
|
||||
saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
||||
*.tar.* filter=lfs diff=lfs merge=lfs -text
|
||||
*.tar filter=lfs diff=lfs merge=lfs -text
|
||||
*.tflite filter=lfs diff=lfs merge=lfs -text
|
||||
*.tgz filter=lfs diff=lfs merge=lfs -text
|
||||
*.wasm filter=lfs diff=lfs merge=lfs -text
|
||||
*.xz filter=lfs diff=lfs merge=lfs -text
|
||||
*.zip filter=lfs diff=lfs merge=lfs -text
|
||||
*.zst filter=lfs diff=lfs merge=lfs -text
|
||||
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
||||
tokenizer.json filter=lfs diff=lfs merge=lfs -text
|
||||
206
README.md
Normal file
206
README.md
Normal file
@@ -0,0 +1,206 @@
|
||||
---
|
||||
library_name: transformers
|
||||
tags: [ner, medical, vietnamese, qwen, grpo, rlhf, peft, lora, text-extraction]
|
||||
---
|
||||
|
||||
# Model Card for Vietnamese Medical NER (Qwen2.5-7B SFT + GRPO)
|
||||
|
||||
<!-- Provide a quick summary of what the model is/does. -->
|
||||
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.
|
||||
|
||||
## Model Details
|
||||
|
||||
### Model Description
|
||||
|
||||
<!-- Provide a longer summary of what this model is. -->
|
||||
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.
|
||||
|
||||
The model was trained using an advanced two-stage alignment pipeline:
|
||||
1. **Stage 1: Supervised Fine-Tuning (SFT)** to teach the model the strict JSON extraction schema and Vietnamese medical terminology.
|
||||
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.
|
||||
|
||||
- **Developed by:** PeterPaker123
|
||||
- **Model type:** Causal Language Model (Generative NER)
|
||||
- **Language(s) (NLP):** Vietnamese (`vi`)
|
||||
- **License:** Apache 2.0 (Derived from Qwen base models)
|
||||
- **Finetuned from model:** `Qwen/Qwen2.5-7B-Instruct`
|
||||
|
||||
## Uses
|
||||
|
||||
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
|
||||
|
||||
### Direct Use
|
||||
|
||||
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
|
||||
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.
|
||||
|
||||
Commonly targeted categories include (but are not limited to):
|
||||
* **`SYMPTOM_AND_DISEASE`**: Symptoms, signs of illness, diseases, or medical conditions.
|
||||
* **`MEDICAL_PROCEDURE`**: Medical procedures, surgeries, therapies, or diagnostic methods.
|
||||
* **`DRUG`**: Names of medicines, drugs, vitamins, or supplements.
|
||||
* **`BODY_PART / ANATOMY`**: Anatomical locations, organs, or body structures.
|
||||
* **`DOSAGE & MEASUREMENT`**: Medical quantities, medication dosages, or frequencies.
|
||||
* **`DEMOGRAPHICS`**: Patient-specific identifiers (e.g., Age, Gender, Occupation).
|
||||
|
||||
**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.
|
||||
|
||||
### Downstream Use
|
||||
|
||||
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
|
||||
This model can be integrated into downstream healthcare applications such as:
|
||||
- Medical chatbot prompt enrichment (RAG pipelines).
|
||||
- Automated summarization of telehealth transcripts.
|
||||
- Clinical knowledge graph construction.
|
||||
|
||||
### Out-of-Scope Use
|
||||
|
||||
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
|
||||
- **Medical Diagnosis or Triage:** This model is strictly an extraction tool. It does not diagnose, prescribe, or provide medical advice.
|
||||
- **General Conversation:** The model has been aggressively aligned to output JSON lists. It will perform poorly at standard conversational AI tasks.
|
||||
- **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.
|
||||
|
||||
## Bias, Risks, and Limitations
|
||||
|
||||
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
|
||||
- **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.
|
||||
- **Language Limitation:** The model is exclusively trained for the Vietnamese language.
|
||||
- **Hallucination Residuals:** While GRPO significantly reduces hallucinations, Generative LLMs may occasionally hallucinate or alter the spelling of extracted text.
|
||||
|
||||
### Recommendations
|
||||
|
||||
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
|
||||
|
||||
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.
|
||||
|
||||
## How to Get Started with the Model
|
||||
|
||||
Use the code below to get started with the model.
|
||||
|
||||
```python
|
||||
import json
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer
|
||||
|
||||
model_id = "PeterPaker123/Qwen2.5-7B-Vietnamese-Medical-NER-GRPO"
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
||||
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype="auto")
|
||||
|
||||
prompt = [
|
||||
{
|
||||
"role": "system",
|
||||
"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 []"
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Text: Trẻ sơ sinh 2 tháng tuổi vàng mặt , hay khóc thét có phải bị vàng da nhân não không ?"
|
||||
}
|
||||
]
|
||||
|
||||
inputs = tokenizer.apply_chat_template(prompt, return_tensors="pt", add_generation_prompt=True).to(model.device)
|
||||
outputs = model.generate(inputs, max_new_tokens=256, temperature=0.0)
|
||||
|
||||
response = tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True)
|
||||
print(response)
|
||||
# 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"}]
|
||||
```
|
||||
|
||||
## Training Details
|
||||
|
||||
### Training Data
|
||||
|
||||
<!-- 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. -->
|
||||
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`).
|
||||
|
||||
| Dataset Name | Domain / Source | Total Samples | Key Original Entities |
|
||||
|---|---|---|---|
|
||||
| **VietBioNER** | Tuberculosis (TB) clinical treatment | ~1,700 | Symptom & Disease, Diagnostic Procedure, Location |
|
||||
| **PhoNER_COVID19** | COVID-19 pandemic surveillance | ~10,000 | Symptom & Disease, Patient ID, Age, Location |
|
||||
| **ViMQ** | Healthcare dialogue systems | ~10,000 | Symptom, Disease, Drug, Procedure, Body Structure |
|
||||
| **ViMedNER** | General Medical NER | ~10,000 | Symptom, Disease, Drug, Body Part, Procedure |
|
||||
|
||||
*Data Split:* 75% used for Phase 1 (SFT) and 25% used for Phase 2 (GRPO/RL).
|
||||
|
||||
### Training Procedure
|
||||
|
||||
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
|
||||
|
||||
#### Preprocessing [optional]
|
||||
|
||||
- Prompts were formatted into strict ChatML format using text-only templates.
|
||||
- Target entities outside the three core classes were stripped from the ground truth to force the model to ignore non-medical demographic data.
|
||||
|
||||
#### Training Hyperparameters
|
||||
|
||||
- **Training regime:** `bf16` mixed precision
|
||||
- **Phase 1 (SFT):** Full parameter fine-tuning, Max Seq Len 4096, Batch Size 2, Gradient Accumulation 8, LR 2e-5, Cosine Scheduler.
|
||||
- **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.
|
||||
|
||||
## Model Examination
|
||||
|
||||
<!-- Relevant interpretability work for the model goes here -->
|
||||
|
||||
## Technical Specifications
|
||||
|
||||
### Model Architecture and Objective
|
||||
|
||||
Qwen2.5-7B Architecture. Causal Language Modeling objective heavily modified via RLHF/GRPO to act as an entity extraction classifier.
|
||||
|
||||
#### Hardware
|
||||
|
||||
NVIDIA RTX PRO 6000 Blackwell
|
||||
|
||||
#### Software
|
||||
|
||||
- `transformers`
|
||||
- `trl` (SFTTrainer, GRPOTrainer)
|
||||
- `peft`
|
||||
- `torch` (with `sdpa` attention)
|
||||
|
||||
## Citation
|
||||
|
||||
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
|
||||
|
||||
**BibTeX:**
|
||||
|
||||
```bibtex
|
||||
@inproceedings{vietbioner,
|
||||
title = "{A Named Entity Recognition Corpus for Vietnamese Biomedical Texts to Support Tuberculosis Treatment}",
|
||||
author = "Phan, Uyen and Nguyen, Phuong and Nguyen, Nhung",
|
||||
booktitle = "Proceedings of the 13th Language Resources and Evaluation Conference",
|
||||
year = "2022",
|
||||
publisher = "European Language Resources Association"
|
||||
}
|
||||
|
||||
@inproceedings{PhoNER_COVID19,
|
||||
title = {{COVID-19 Named Entity Recognition for Vietnamese}},
|
||||
author = {Thinh Hung Truong and Mai Hoang Dao and Dat Quoc Nguyen},
|
||||
booktitle = {Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
|
||||
year = {2021}
|
||||
}
|
||||
|
||||
@article{vimq2023,
|
||||
title={ViMQ: A Vietnamese Medical Question Dataset for Healthcare Dialogue System Development},
|
||||
author={Vu, Hieu M. and Phan, Long and Nguyen, Nhung and others},
|
||||
year={2023}
|
||||
}
|
||||
|
||||
@article{10.4108/eetinis.v11i3.5221,
|
||||
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},
|
||||
title={ViMedNER: A Medical Named Entity Recognition Dataset for Vietnamese},
|
||||
journal={EAI Endorsed Transactions on Industrial Networks and Intelligent Systems},
|
||||
volume={11},
|
||||
number={4},
|
||||
publisher={EAI},
|
||||
year={2024},
|
||||
doi={10.4108/eetinis.v11i3.5221}
|
||||
}
|
||||
```
|
||||
|
||||
## Glossary
|
||||
|
||||
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
|
||||
|
||||
- **NER:** Named Entity Recognition
|
||||
- **SFT:** Supervised Fine-Tuning
|
||||
- **GRPO:** Group Relative Policy Optimization (An RL algorithm optimized for Generative Models)
|
||||
- **LoRA:** Low-Rank Adaptation
|
||||
1
chat_template.jinja
Normal file
1
chat_template.jinja
Normal file
@@ -0,0 +1 @@
|
||||
{% for message in messages %}{% set role = message['role'] %}{% if role == 'assistant' %}{% set role = 'model' %}{% endif %}{{ '<start_of_turn>' + role + '\n' + message['content'] + '<end_of_turn>\n' }}{% endfor %}{% if add_generation_prompt %}{{ '<start_of_turn>model\n' }}{% endif %}
|
||||
61
config.json
Normal file
61
config.json
Normal file
@@ -0,0 +1,61 @@
|
||||
{
|
||||
"architectures": [
|
||||
"Qwen2ForCausalLM"
|
||||
],
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": null,
|
||||
"dtype": "bfloat16",
|
||||
"eos_token_id": 151645,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 3584,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 18944,
|
||||
"layer_types": [
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention"
|
||||
],
|
||||
"max_position_embeddings": 32768,
|
||||
"max_window_layers": 28,
|
||||
"model_type": "qwen2",
|
||||
"num_attention_heads": 28,
|
||||
"num_hidden_layers": 28,
|
||||
"num_key_value_heads": 4,
|
||||
"pad_token_id": 151643,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_parameters": {
|
||||
"rope_theta": 1000000.0,
|
||||
"rope_type": "default"
|
||||
},
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": false,
|
||||
"transformers_version": "5.0.0",
|
||||
"use_cache": false,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 152064
|
||||
}
|
||||
13
generation_config.json
Normal file
13
generation_config.json
Normal file
@@ -0,0 +1,13 @@
|
||||
{
|
||||
"do_sample": true,
|
||||
"eos_token_id": [
|
||||
151645,
|
||||
151643
|
||||
],
|
||||
"pad_token_id": 151643,
|
||||
"repetition_penalty": 1.05,
|
||||
"temperature": 0.7,
|
||||
"top_k": 20,
|
||||
"top_p": 0.8,
|
||||
"transformers_version": "5.0.0"
|
||||
}
|
||||
3
model.safetensors
Normal file
3
model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:3c7c5360f18e1a4ff3e083561b06bfe537bb9e1457740c5240d3c09b1a948eb3
|
||||
size 15231272152
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:1a7aabe7a3b44651592a99607bb33066e6965260336be9218cccd53af161491d
|
||||
size 11422060
|
||||
33
tokenizer_config.json
Normal file
33
tokenizer_config.json
Normal file
@@ -0,0 +1,33 @@
|
||||
{
|
||||
"add_prefix_space": false,
|
||||
"backend": "tokenizers",
|
||||
"bos_token": null,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"is_local": true,
|
||||
"max_length": null,
|
||||
"model_max_length": 131072,
|
||||
"pad_to_multiple_of": null,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"pad_token_type_id": 0,
|
||||
"padding_side": "right",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "TokenizersBackend",
|
||||
"unk_token": null
|
||||
}
|
||||
Reference in New Issue
Block a user