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1from transformers import T5ForConditionalGeneration, T5Tokenizer
2import torch1model = T5ForConditionalGeneration.from_pretrained('bonur/t5-base-tr')
2tokenizer = T5Tokenizer.from_pretrained('bonur/t5-base-tr')1inputs = tokenizer("Bu hafta hasta olduğum için <extra_id_0> gittim.", return_tensors='pt')
2with torch.no_grad():
3 hypotheses = model.generate(
4 **inputs,
5 do_sample=True, top_p=0.95,
6 num_return_sequences=2,
7 repetition_penalty=2.75,
8 max_length=32,
9 )
10for h in hypotheses:
11 print(tokenizer1.decode(h))