asd

app.py

@@ -576,34 +576,34 @@ def generate_paraphrases(text, setting, output_format):
     all_sentence_paraphrases = []
     
     if setting == 1:
-        num_return_sequences = 3
-        temperature = 0.7
+        num_return_sequences = 5
+        temperature = 1.0
         top_k = 50
-        top_p = 0.9
+        top_p = 0.95
         max_length = 128
     elif setting == 2:
-        num_return_sequences = 3
-        temperature = 0.8
+        num_return_sequences = 7
+        temperature = 1.2
         top_k = 50
-        top_p = 0.9
+        top_p = 0.95
         max_length = 192
     elif setting == 3:
-        num_return_sequences = 3
-        temperature = 0.9
+        num_return_sequences = 10
+        temperature = 1.4
         top_k = 50
-        top_p = 0.9
+        top_p = 0.95
         max_length = 256
     elif setting == 4:
-        num_return_sequences = 3
-        temperature = 1.0
+        num_return_sequences = 15
+        temperature = 1.6
         top_k = 50
-        top_p = 0.9
+        top_p = 0.95
         max_length = 320
     else:
-        num_return_sequences = 3
-        temperature = 1.1
+        num_return_sequences = 20
+        temperature = 1.8
         top_k = 50
-        top_p = 0.9
+        top_p = 0.95
         max_length = 384
     
     formatted_output = "Original text:\n" + text + "\n\n"
@@ -622,7 +622,7 @@ def generate_paraphrases(text, setting, output_format):
         input_ids, attention_mask = encoding["input_ids"].to(device), encoding["attention_mask"].to(device)
         
         paraphraser_model.eval()
-        beam_outputs = paraphraser_model.generate(
+        outputs = paraphraser_model.generate(
             input_ids=input_ids,
             attention_mask=attention_mask,
             max_length=max_length,
@@ -633,7 +633,7 @@ def generate_paraphrases(text, setting, output_format):
             temperature=temperature
         )
         
-        paraphrases_texts = [paraphraser_tokenizer.decode(beam_output, skip_special_tokens=True, clean_up_tokenization_spaces=True) for beam_output in beam_outputs]
+        paraphrases_texts = [paraphraser_tokenizer.decode(output, skip_special_tokens=True, clean_up_tokenization_spaces=True) for output in outputs]
         
         formatted_output += f"Original sentence {i+1}: {sentence}\n"
         for j, paraphrase in enumerate(paraphrases_texts, 1):