metadata
language: vi
datasets:
- cc100
tags:
- summarization
license: mit
widget:
- text: Input text.
ViT5-large Finetuned on vietnews Abstractive Summarization
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("VietAI/vit5-large-vietnews-summarization")
model = AutoModelForSeq2SeqLM.from_pretrained("VietAI/vit5-large-vietnews-summarization")
model.cuda()
sentence = "Input text"
text = "vietnews: " + sentence + " </s>"
encoding = tokenizer(text, 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=512,
early_stopping=True
)
for output in outputs:
line = tokenizer.decode(output, skip_special_tokens=True, clean_up_tokenization_spaces=True)
print(line)