
# 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)
```
{"language": "vi", "license": "mit", "tags": ["summarization"], "datasets": ["Yuhthe/vietnews"], "widget": [{"text": "Input text."}]}
transformers pytorch tf t5 text2text-generation summarization vi dataset:Yuhthe/vietnews license:mit autotrain_compatible endpoints_compatible text-generation-inference region:us