Update abstractive_model.py

abstractive_model.py

@@ -4,17 +4,9 @@ from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 tokenizer = AutoTokenizer.from_pretrained("EE21/BART-ToSSimplify")
 model = AutoModelForSeq2SeqLM.from_pretrained("EE21/BART-ToSSimplify")
 
-# Define a function to summarize text with minimum length constraint
-def summarize_with_bart(input_text, max_summary_tokens=200, min_summary_tokens=100, do_sample=False):
-    # Tokenize the input text and return input_ids as PyTorch tensors
-    inputs = tokenizer(input_text, return_tensors="pt").input_ids
-    # Generate the summary with minimum and maximum length constraints
-    outputs = model.generate(inputs, 
-                             max_length=max_summary_tokens, 
-                             min_length=min_summary_tokens, 
-                             do_sample=do_sample)
-    
-    # Decode the generated token IDs back into text
-    summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    
+# Define the abstractive summarization function
+def summarize_with_bart(input_text):
+    inputs = tokenizer.encode("summarize: " + input_text, return_tensors="pt", max_length=1024, truncation=True)
+    summary_ids = model.generate(inputs, max_length=50, min_length=10, num_beams=8)
+    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
     return summary