| from transformers import T5ForConditionalGeneration, T5TokenizerFast | |
| t5_model = T5ForConditionalGeneration.from_pretrained('./pytorch_model') | |
| tokenizer = T5TokenizerFast.from_pretrained('./') | |
| text = 'বাংলার মুখ আমি দেখিয়াছি, তাই আমি পৃথিবীর রূপ খুঁজিতে যাই না' | |
| tokenized = tokenizer(text, return_tensors='pt') | |
| input_ids = tokenized.input_ids | |
| attention_mask = tokenized.attention_mask | |
| t5_model.eval() | |
| beam_outputs = t5_model.generate( | |
| input_ids=input_ids,attention_mask=attention_mask, | |
| max_length=64, | |
| early_stopping=True, | |
| num_beams=10, | |
| num_return_sequences=3, | |
| no_repeat_ngram_size=2 | |
| ) | |
| for beam_output in beam_outputs: | |
| sent = tokenizer.decode(beam_output, skip_special_tokens=True,clean_up_tokenization_spaces=True) | |
| print (sent) | |