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---
language:
- vi
license: mit
tags:
- summarization
- vietnamese
- bartpho
- seq2seq
datasets:
- news-dataset-vietnameses
metrics:
- rouge
model-index:
- name: bartpho-vietnamese-summarization
results:
- task:
type: summarization
dataset:
name: Vietnamese News Dataset
type: news-dataset-vietnameses
metrics:
- type: rouge
value: TBD
---
# BARTpho Vietnamese Summarization Model
This model is a fine-tuned version of [vinai/bartpho-syllable](https://huggingface.co/vinai/bartpho-syllable) for Vietnamese text summarization.
## Model Details
- **Base Model**: vinai/bartpho-syllable
- **Task**: Text Summarization
- **Language**: Vietnamese
- **Training Dataset**: Vietnamese News Dataset
## Usage
```python
from transformers import BartForConditionalGeneration, AutoTokenizer
model_name = "YOUR_USERNAME/bartpho-vietnamese-summarization"
# Use AutoTokenizer for BARTpho (automatically loads BartphoTokenizer)
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = BartForConditionalGeneration.from_pretrained(model_name)
# Example usage
text = "Your Vietnamese news article text here..."
inputs = tokenizer(text, return_tensors="pt", max_length=1024, truncation=True)
summary_ids = model.generate(inputs["input_ids"], max_length=128, num_beams=4, early_stopping=True)
summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
print(summary)
```
## Training Details
- **Training Framework**: Hugging Face Transformers
- **GPU**: NVIDIA P100 16GB
- **Batch Size**: 8 per device
- **Gradient Accumulation**: 2 steps
- **Learning Rate**: 2e-5
- **Epochs**: 3
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