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