---
license: apache-2.0
datasets:
- taidng/UIT-ViQuAD2.0
language:
- vi
metrics:
- accuracy
base_model:
- FacebookAI/xlm-roberta-large
pipeline_tag: question-answering
---

# NamSyntax/xlmr-large-viquad

## Model Description
**NamSyntax/xlmr-large-viquad** is a fine-tuned version of the **XLM-RoBERTa Large** model for Vietnamese Question Answering (QA). It was meticulously fine-tuned on the [UIT-ViQuAD 2.0](https://huggingface.co/datasets/taidng/UIT-ViQuAD2.0) dataset to deliver precise, context-aware answers. 

This model is a part of the [Vietnamese-QA](https://github.com/NamSyntax/Vietnamese-QA) end-to-end Machine Learning pipeline project. If you find this repo helpful, feel free to give it a star ⭐—it really means a lot!

## Intended Uses
- **Primary Use:** Extractive Question Answering in Vietnamese. Given a context paragraph and a question, the model extracts the substring representing the answer from the context.
- **Ecosystem:** It can be instantly integrated into web services. The associated project provides out-of-the-box support for a **Gradio Web UI** and a **FastAPI** REST backend.

## Training Data
The model was fine-tuned on **UIT-ViQuAD 2.0**, a high-quality machine reading comprehension dataset curated specifically for the Vietnamese language. 

## Training Procedure
This model was trained using PyTorch and the Hugging Face `Trainer` API. The training pipeline implements:
- Clean modular architecture for HuggingFace Dataset loading.
- Preprocessing steps emphasizing text tokenization and chunking logic to handle long documents.
- Hyperparameter management via `config.yaml`.

## How to use
You can use this model directly with the `pipeline` API from Hugging Face:

```python
from transformers import pipeline

qa_pipeline = pipeline("question-answering", model="NamSyntax/xlmr-large-viquad")

context = "Hà Nội là thủ đô của nước Cộng hòa Xã hội chủ nghĩa Việt Nam. Thành phố nằm ở phía tây bắc trung tâm vùng đồng bằng châu thổ sông Hồng."
question = "Thủ đô của Việt Nam là gì?"

result = qa_pipeline(question=question, context=context)
print(result)
```

Alternatively, to experience this model with an interactive UI or to deploy it as a production-grade backend service (FastAPI and Docker), please refer to the official repository: [NamSyntax/Vietnamese-QA](https://github.com/NamSyntax/Vietnamese-QA).