Update abstractive_summarization.py

abstractive_summarization.py

@@ -9,14 +9,14 @@ pipe = pipeline("summarization", model="facebook/bart-large-cnn")
 
 # Define the abstractive summarization function (fine-tuned BART)
 def summarize_with_bart_ft(input_text):
-    inputs = tokenizer.encode("summarize: " + input_text, return_tensors="pt", max_length=1024, truncation=True)
+    inputs = tokenizer.encode("summarize: " + input_text, return_tensors="pt", max_length=2048, truncation=True)
     summary_ids = model.generate(inputs, max_length=300, min_length=100, num_beams=1, early_stopping=False, length_penalty=1)
     summary = tokenizer.decode(summary_ids[0], skip_special_tokens=False)
     return summary
 
 # Define the abstractive summarization function (BART-large-cnn)
 def summarize_with_bart(input_text):
-    inputs = tokenizer.encode("summarize: " + input_text, return_tensors="pt", max_length=1024, truncation=True)
+    inputs = tokenizer.encode("summarize: " + input_text, return_tensors="pt", max_length=2048, truncation=True)
     summary_ids = model.generate(inputs, max_length=300, min_length=100, num_beams=1, early_stopping=True, length_penalty=1)
     summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
     return summary