htNghiaaa/bartpho-vietnamese-summarization-1

language: - vi license: mit library_name: transformers tags: - summarization - vietnamese - bartpho - nlp - generated_from_trainer base_model: vinai/bartpho-syllable datasets: - phamtheds/news-dataset-vietnameses metrics: - rouge pipeline_tag: summarization model-index: - name: Bartpho Vietnamese Summarization results: []

Model Card for Bartpho Vietnamese Summarization

This model is a fine-tuned version of vinai/bartpho-syllable on the phamtheds/news-dataset-vietnameses dataset. It is designed to generate abstractive summaries for Vietnamese news articles.

Model Details

Model Description

• Model type: Transformer-based Sequence-to-Sequence model (BART architecture)
• Language(s) (NLP): Vietnamese
• License: MIT
• Finetuned from model: vinai/bartpho-syllable

Model Sources

• Repository: [Link to your Hugging Face Repo]
• Base Model Paper: BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese

Uses

Direct Use

The model can be used for summarizing Vietnamese texts, specifically news articles. It takes a full article text as input and outputs a concise summary.

Out-of-Scope Use

• The model may not perform well on non-standard Vietnamese (teencode), conversational text, or extremely technical documents (legal/medical) without further fine-tuning.
• It is not designed to generate factual content from scratch, but rather to condense provided information.

Bias, Risks, and Limitations

• Hallucination: Like all sequence-to-seq models, there is a risk of generating information that is not present in the source text.
• Data Bias: The model reflects the biases present in the training data (mainstream Vietnamese news sources).

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import pipeline

summarizer = pipeline("summarization", model="your-username/bartpho-vietnamese-summarization")

article = """
[Insert your long Vietnamese news article here]
"""

summary = summarizer(article, max_length=128, min_length=30, length_penalty=2.0, num_beams=4)
print(summary[0]['summary_text'])

Training Details

Training Data

The model was trained on the phamtheds/news-dataset-vietnameses, which contains Vietnamese news articles and their corresponding summaries.

Training Procedure

The model was fine-tuned using the Hugging Face Trainer API on a T4 GPU.

Training Hyperparameters

• Learning Rate: 2e-5
• Batch Size: 4
• Gradient Accumulation Steps: 2
• Epochs: 3
• Weight Decay: 0.01
• Optimizer: AdamW
• Precision: fp16 (mixed precision)
• Max Input Length: 1024 tokens
• Max Output Length: 256 tokens

Evaluation

Metrics

The model was evaluated using the ROUGE metric (ROUGE-1, ROUGE-2, ROUGE-L).

Citation

If you use this model, please cite the original BARTpho paper:

@inproceedings{tran2020bartpho,
  title={BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese},
  author={Tran, Nguyen Luong and Phan, Duong Minh and Nguyen, Dat Quoc},
  booktitle={Interspeech},
  year={2020}
}

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### How to apply this:

1.  Open your repository on Hugging Face.
2.  Click on the **README.md** file.
3.  Click the **Edit** button.
4.  **Delete everything** currently in the file.
5.  **Paste** the block above.
6.  **Important:** Change `your-username/bartpho-vietnamese-summarization` to your actual username and repo name.
7.  Click **Commit changes**.

This will render a clean, professional page with the correct metadata tags on the right sidebar (Dataset links, Language tags, License, etc.).