VerbACxSS/

sempl-it-umt5-small

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("VerbACxSS/sempl-it-umt5-small")
model = AutoModelForSeq2SeqLM.from_pretrained("VerbACxSS/sempl-it-umt5-small")

model.eval()

text_to_simplify = 'Nella fattispecie, questo documento è di natura prescrittiva'
prompt = f'semplifica: {text_to_simplify}'

x = tokenizer(prompt, max_length=1024, truncation=True, padding=True, return_tensors='pt').input_ids
y = model.generate(x, max_length=1024)[0]
output = tokenizer.decode(y, max_length=1024, truncation=True, skip_special_tokens=True, clean_up_tokenization_spaces=True)

print(output)