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


State-of-the-art pretrained Transformer-based encoder-decoder model for Vietnamese.
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/vit5-pretrained-text-to-text-transformer-for/abstractive-text-summarization-on-vietnews)](https://paperswithcode.com/sota/abstractive-text-summarization-on-vietnews?p=vit5-pretrained-text-to-text-transformer-for)


## How to use
For more details, do check out [our Github repo](https://github.com/vietai/ViT5) and [eval script](https://github.com/vietai/ViT5/blob/main/eval/Eval_vietnews_sum.ipynb). 

```python
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)
```

## 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",
}
```
{"language": "vi", "license": "mit", "tags": ["summarization"], "datasets": ["cc100"], "widget": [{"text": "VietAI l\u00e0 t\u1ed5 ch\u1ee9c phi l\u1ee3i nhu\u1eadn v\u1edbi s\u1ee9 m\u1ec7nh \u01b0\u01a1m m\u1ea7m t\u00e0i n\u0103ng v\u1ec1 tr\u00ed tu\u1ec7 nh\u00e2n t\u1ea1o v\u00e0 x\u00e2y d\u1ef1ng m\u1ed9t c\u1ed9ng \u0111\u1ed3ng c\u00e1c chuy\u00ean gia trong l\u0129nh v\u1ef1c tr\u00ed tu\u1ec7 nh\u00e2n t\u1ea1o \u0111\u1eb3ng c\u1ea5p qu\u1ed1c t\u1ebf t\u1ea1i Vi\u1ec7t Nam."}]}
transformers pytorch tf jax t5 text2text-generation summarization vi dataset:cc100 license:mit autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tf #jax #t5 #text2text-generation #summarization #vi #dataset-cc100 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 


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


State-of-the-art pretrained Transformer-based encoder-decoder model for Vietnamese.
![PWC](URL


## How to use
For more details, do check out our Github repo and eval script. 




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


State-of-the-art pretrained Transformer-based encoder-decoder model for Vietnamese.
![PWC](URL ## How to use
For more details, do check out our Github repo and eval script.
TAGS
#transformers #pytorch #tf #jax #t5 #text2text-generation #summarization #vi #dataset-cc100 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # ViT5-Base Finetuned on 'vietnews' Abstractive Summarization (No prefix needed)


State-of-the-art pretrained Transformer-based encoder-decoder model for Vietnamese.
![PWC](URL ## How to use
For more details, do check out our Github repo and eval script.
passage: TAGS
#transformers #pytorch #tf #jax #t5 #text2text-generation #summarization #vi #dataset-cc100 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# ViT5-Base Finetuned on 'vietnews' Abstractive Summarization (No prefix needed)


State-of-the-art pretrained Transformer-based encoder-decoder model for Vietnamese.
![PWC](URL## How to use
For more details, do check out our Github repo and eval script.