---
language: vi
datasets:
- Yuhthe/vietnews
tags:
- summarization
license: mit
widget:
- text: Input text.
---

# fastAbs-large Finetuned on `vietnews` Abstractive Summarization



```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
​
tokenizer = AutoTokenizer.from_pretrained("polieste/fastAbs_large")  
model = AutoModelForSeq2SeqLM.from_pretrained("polieste/fastAbs_large")
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)
```