adjustment

app.py

@@ -16,10 +16,10 @@ untethered_paraphrased_model = GPT2LMHeadModel.from_pretrained('zmbfeng/untether
 default_num_return_sequences=5
 default_temperature=0.5
 default_repetition_penalty=1.5
-default_top_p=1.9
+default_top_p=2
 default_top_k=50
 default_do_sample=True
-default_seed=45
+default_seed=43
 def create_response(input_str, 
                              # num_beams, 
                              num_return_sequences,
@@ -49,25 +49,25 @@ def create_response(input_str,
   print("seed" + str(seed))     
   encoded = tokenizer.encode_plus(input_str + tokenizer.eos_token, return_tensors="pt")
   input_ids = encoded["input_ids"]
-  attention_mask = encoded["attention_mask"]
+  #attention_mask = encoded["attention_mask"]
 
   if seed != -1:
     set_seed(seed)    
   if model_name == "original_model":      
-    output = original_model.generate(input_ids,pad_token_id=tokenizer.eos_token_id,do_sample=do_sample, attention_mask=attention_mask, max_length=100, temperature=temperature, top_p=top_p, repetition_penalty=repetition_penalty,num_return_sequences=num_return_sequences,return_dict_in_generate=True, output_scores=True )
+    output = original_model.generate(input_ids,do_sample=do_sample, max_length=100, temperature=temperature, top_p=top_p, repetition_penalty=repetition_penalty,num_return_sequences=num_return_sequences,return_dict_in_generate=True, output_scores=True )
     transition_scores = original_model.compute_transition_scores(output.sequences, output.scores,
                                                                  normalize_logits=False)
 
   elif model_name == "untethered_model":
-    output = untethered_model.generate(input_ids,pad_token_id=tokenizer.eos_token_id,do_sample=do_sample, attention_mask=attention_mask, max_length=100, temperature=temperature, top_p=top_p, repetition_penalty=repetition_penalty,num_return_sequences=num_return_sequences,return_dict_in_generate=True, output_scores=True )
+    output = untethered_model.generate(input_ids, do_sample=do_sample, max_length=100, temperature=temperature, top_p=top_p, repetition_penalty=repetition_penalty,num_return_sequences=num_return_sequences,return_dict_in_generate=True, output_scores=True )
     transition_scores = untethered_model.compute_transition_scores(output.sequences, output.scores,
                                                                normalize_logits=False)
   elif model_name == "untethered_paraphrased_model":
-    output = untethered_paraphrased_model.generate(input_ids,pad_token_id=tokenizer.eos_token_id,do_sample=do_sample, attention_mask=attention_mask, max_length=100, temperature=temperature, top_p=top_p, repetition_penalty=repetition_penalty,num_return_sequences=num_return_sequences,return_dict_in_generate=True, output_scores=True )
+    output = untethered_paraphrased_model.generate(input_ids, do_sample=do_sample,  max_length=100, temperature=temperature, top_p=top_p, repetition_penalty=repetition_penalty,num_return_sequences=num_return_sequences,return_dict_in_generate=True, output_scores=True )
     transition_scores = untethered_paraphrased_model.compute_transition_scores(output.sequences, output.scores,
                                                                normalize_logits=False)
   else:
-    output = original_model.generate(input_ids,pad_token_id=tokenizer.eos_token_id,do_sample=do_sample, attention_mask=attention_mask, max_length=100, temperature=temperature, top_p=top_p, repetition_penalty=repetition_penalty,num_return_sequences=num_return_sequences,return_dict_in_generate=True, output_scores=True )
+    output = original_model.generate(input_ids,do_sample=do_sample, max_length=100, temperature=temperature, top_p=top_p, repetition_penalty=repetition_penalty,num_return_sequences=num_return_sequences,return_dict_in_generate=True, output_scores=True )
     transition_scores = original_model.compute_transition_scores(output.sequences, output.scores,
                                                                  normalize_logits=False)
   score_list = []