Create encoding.py

encoding.py

@@ -0,0 +1,37 @@
+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]