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--- |
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language: vi |
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datasets: |
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- cc100 |
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tags: |
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- summarization |
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- translation |
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- question-answering |
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license: mit |
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--- |
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# ViT5-large |
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State-of-the-art pre-trained Transformer-based encoder-decoder model for Vietnamese. |
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## How to use |
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For more details, do check out [our Github repo](https://github.com/justinphan3110/ViT5). |
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```python |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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tokenizer = AutoTokenizer.from_pretrained("VietAI/vit5-large") |
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model = AutoModelForSeq2SeqLM.from_pretrained("VietAI/vit5-large") |
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sentence = "Xin chào" |
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text = "summarize: " + sentence + " </s>" |
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encoding = tokenizer.encode_plus(text, pad_to_max_length=True, 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=256, |
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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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Coming Soon... |
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``` |
