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README.md
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license: mit
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widget:
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- text: "
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---
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# ViT5-large Finetuned on `vietnews` Abstractive Summarization
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model = AutoModelForSeq2SeqLM.from_pretrained("VietAI/vit5-large-vietnews-summarization")
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model.cuda()
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sentence = "
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text = "vietnews: " + sentence + " </s>"
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encoding = tokenizer(text, return_tensors="pt")
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input_ids, attention_masks = encoding["input_ids"].to("cuda"), encoding["attention_mask"].to("cuda")
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outputs = model.generate(
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input_ids=input_ids, attention_mask=attention_masks,
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max_length=
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early_stopping=True
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)
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for output in outputs:
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line = tokenizer.decode(output, skip_special_tokens=True, clean_up_tokenization_spaces=True)
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print(line)
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```
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## Citation
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```
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@inproceedings{phan-etal-2022-vit5,
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title = "{V}i{T}5: Pretrained Text-to-Text Transformer for {V}ietnamese Language Generation",
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author = "Phan, Long and Tran, Hieu and Nguyen, Hieu and Trinh, Trieu H.",
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booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop",
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year = "2022",
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publisher = "Association for Computational Linguistics",
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url = "https://aclanthology.org/2022.naacl-srw.18",
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pages = "136--142",
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}
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```
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license: mit
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widget:
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- text: "Input text."
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---
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# ViT5-large Finetuned on `vietnews` Abstractive Summarization
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model = AutoModelForSeq2SeqLM.from_pretrained("VietAI/vit5-large-vietnews-summarization")
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model.cuda()
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sentence = "Input text"
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text = "vietnews: " + sentence + " </s>"
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encoding = tokenizer(text, return_tensors="pt")
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input_ids, attention_masks = encoding["input_ids"].to("cuda"), encoding["attention_mask"].to("cuda")
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outputs = model.generate(
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input_ids=input_ids, attention_mask=attention_masks,
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max_length=128,
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early_stopping=True
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)
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for output in outputs:
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line = tokenizer.decode(output, skip_special_tokens=True, clean_up_tokenization_spaces=True)
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print(line)
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```
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