nhatvu205/vit5_kg_medqa

Vietnamese Medical Abstractive Question Answering Model based on ViT5 (Vietnamese T5).

Model Description

This model is fine-tuned from VietAI/vit5-base for Vietnamese medical question answering tasks. The model uses an encoder-decoder architecture and is trained on a custom Vietnamese medical dataset with knowledge graph enhancement.

Key Features

• Architecture: T5 (Encoder-Decoder Transformer)
• Base Model: VietAI/vit5-base
• Language: Vietnamese
• Domain: Medical/Healthcare
• Input Format: Question + Context (retrieved via BM25)
• Output: Abstractive answer in Vietnamese

Model Performance

The model was evaluated on a test set with the following metrics:

Metric	Score
BLEU	46.71
ROUGE-L	46.01
BERTScore-F1	90.00

Usage

Basic Usage

from transformers import T5ForConditionalGeneration, T5Tokenizer

model_name = "your-username/your-model-name"
model = T5ForConditionalGeneration.from_pretrained(model_name, trust_remote_code=True)
tokenizer = T5Tokenizer.from_pretrained(model_name, trust_remote_code=True)

# Prepare input
question = "Triệu chứng của bệnh tiểu đường là gì?"
context = "Bệnh tiểu đường là một bệnh mãn tính ảnh hưởng đến cách cơ thể chuyển hóa glucose..."

# Format input for ViT5
input_text = f"câu hỏi: {question} ngữ cảnh: {context}"

# Tokenize
inputs = tokenizer(input_text, max_length=512, truncation=True, return_tensors="pt")

# Generate
outputs = model.generate(
    **inputs,
    max_length=128,
    num_beams=4,
    early_stopping=True,
    temperature=0.7,
    repetition_penalty=1.2
)

# Decode
answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(answer)

With BM25 Retrieval

from transformers import T5ForConditionalGeneration, T5Tokenizer
from rank_bm25 import BM25Okapi

# Load model
model = T5ForConditionalGeneration.from_pretrained("your-username/your-model-name", trust_remote_code=True)
tokenizer = T5Tokenizer.from_pretrained("your-username/your-model-name", trust_remote_code=True)

# Retrieve context using BM25
question = "Triệu chứng của bệnh tiểu đường là gì?"
# ... BM25 retrieval code ...
context = retrieved_context  # Retrieved from your knowledge base

# Generate answer
input_text = f"câu hỏi: {question} ngữ cảnh: {context}"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=128, num_beams=4)
answer = tokenizer.decode(outputs[0], skip_special_tokens=True)

Input Format

The model expects input in the following format:

câu hỏi: <question> ngữ cảnh: <context>

Where:

• <question>: The medical question in Vietnamese
• <context>: Relevant context retrieved from knowledge base (optional but recommended)

Training Details

• Base Model: VietAI/vit5-base
• Architecture: T5 Encoder-Decoder
• Training Data: Custom Vietnamese Medical QA Dataset
• Knowledge Graph: Enhanced with KG triplets
• Retrieval: BM25 for context retrieval
• Tokenizer: ViT5 Tokenizer (SentencePiece)

Limitations

• The model is trained specifically for Vietnamese medical domain
• Performance may vary for questions outside the medical domain
• Context retrieval quality significantly affects answer quality
• Model may generate answers that require medical professional verification

Citation

If you use this model, please cite:

@misc{your_username_your_model_name},
  title={Vietnamese Medical Abstractive Question Answering Model},
  author={Your Name},
  year={2024},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/your-username/your-model-name}}
}

License

This model is released under the Apache 2.0 license.

Acknowledgments

• Base model: VietAI/vit5-base by VietAI
• Built for Vietnamese medical question answering applications