Vit5 Base Vietnews Summarization

🚀 ViT5-Base Finetuned on vietnews Abstractive Summarization (No prefix needed)

A state-of-the-art pretrained Transformer-based encoder-decoder model for Vietnamese, which can effectively solve the problem of abstractive summarization in Vietnamese and provide high - quality summarization results.

🚀 Quick Start

For more details, do check out our Github repo and eval script.

💻 Usage Examples

Basic Usage

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
​
tokenizer = AutoTokenizer.from_pretrained("VietAI/vit5-base-vietnews-summarization")  
model = AutoModelForSeq2SeqLM.from_pretrained("VietAI/vit5-base-vietnews-summarization")
model.cuda()
​
sentence = "VietAI là tổ chức phi lợi nhuận với sứ mệnh ươm mầm tài năng về trí tuệ nhân tạo và xây dựng một cộng đồng các chuyên gia trong lĩnh vực trí tuệ nhân tạo đẳng cấp quốc tế tại Việt Nam."
sentence = sentence + "</s>"
encoding = tokenizer(sentence, return_tensors="pt")
input_ids, attention_masks = encoding["input_ids"].to("cuda"), encoding["attention_mask"].to("cuda")
outputs = model.generate(
    input_ids=input_ids, attention_mask=attention_masks,
    max_length=256,
    early_stopping=True
)
for output in outputs:
    line = tokenizer.decode(output, skip_special_tokens=True, clean_up_tokenization_spaces=True)
    print(line)

📚 Documentation

Dataset

Citation

@inproceedings{phan-etal-2022-vit5,
    title = "{V}i{T}5: Pretrained Text-to-Text Transformer for {V}ietnamese Language Generation",
    author = "Phan, Long and Tran, Hieu and Nguyen, Hieu and Trinh, Trieu H.",
    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop",
    year = "2022",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.naacl-srw.18",
    pages = "136--142",
}

📄 License

This project is released under the MIT license.