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| import torch | |
| from transformers import T5ForConditionalGeneration,T5Tokenizer | |
| def greedy_decoding (inp_ids,attn_mask,model,tokenizer): | |
| greedy_output = model.generate(input_ids=inp_ids, attention_mask=attn_mask, max_length=256) | |
| Question = tokenizer.decode(greedy_output[0], skip_special_tokens=True,clean_up_tokenization_spaces=True) | |
| return Question.strip().capitalize() | |
| def beam_search_decoding (inp_ids,attn_mask,model,tokenizer): | |
| beam_output = model.generate(input_ids=inp_ids, | |
| attention_mask=attn_mask, | |
| max_length=256, | |
| num_beams=10, | |
| num_return_sequences=3, | |
| no_repeat_ngram_size=2, | |
| early_stopping=True | |
| ) | |
| Questions = [tokenizer.decode(out, skip_special_tokens=True, clean_up_tokenization_spaces=True) for out in | |
| beam_output] | |
| return [Question.strip().capitalize() for Question in Questions] | |
| def topkp_decoding (inp_ids,attn_mask,model,tokenizer): | |
| topkp_output = model.generate(input_ids=inp_ids, | |
| attention_mask=attn_mask, | |
| max_length=256, | |
| do_sample=True, | |
| top_k=40, | |
| top_p=0.80, | |
| num_return_sequences=3, | |
| no_repeat_ngram_size=2, | |
| early_stopping=True | |
| ) | |
| Questions = [tokenizer.decode(out, skip_special_tokens=True,clean_up_tokenization_spaces=True) for out in topkp_output] | |
| return [Question.strip().capitalize() for Question in Questions] |