Update abstractive_summarization.py

abstractive_summarization.py

@@ -4,27 +4,32 @@ from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
 tokenizer = AutoTokenizer.from_pretrained("EE21/BART-ToSSimplify")
 model = AutoModelForSeq2SeqLM.from_pretrained("EE21/BART-ToSSimplify")
 
+# Load the BART-large-cnn tokenizer and model
+tokenizer_cnn = BartTokenizer.from_pretrained("facebook/bart-large-cnn")
+model_cnn = BartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn")
+
 # Function to summarize using the fine-tuned BART model
 def summarize_with_bart_ft(input_text):
     inputs = tokenizer.encode("summarize: " + input_text, return_tensors="pt", max_length=1024, truncation=True)
-    summary_ids = model.generate(inputs, max_length=300, min_length=100, num_beams=2, early_stopping=False, length_penalty=1)
+    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=True)
     return summary
 
-# Function to summarize using BART-large-cnn
+# Function to summarize using bart-large-cnn model
 def summarize_with_bart_cnn(input_text):
-    pipe = pipeline("summarization", model="facebook/bart-large-cnn")
-    summary = pipe(input_text, max_length=300, min_length=100, num_beams=2, early_stopping=False, length_penalty=1)
+    inputs = tokenizer_cnn.encode("summarize: " + input_text, return_tensors="pt", max_length=1024, truncation=True)
+    summary_ids = model_cnn.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
 
 # Function to summarize using led-base-book-summary
 def summarize_with_led(input_text):
     pipe_led = pipeline("summarization", model="pszemraj/led-base-book-summary")
-    summary = pipe_led(input_text, max_length=300, min_length=100, num_beams=2, early_stopping=False, length_penalty=1)
+    summary = pipe_led(input_text, max_length=300, min_length=100, num_beams=1, early_stopping=False, length_penalty=1)
     return summary[0]['summary_text']
 
 # Function to summarize using long-t5-tglobal-base-sci-simplify
 def summarize_with_t5(input_text):
     pipe_t5 = pipeline("summarization", model="pszemraj/long-t5-tglobal-base-sci-simplify")
-    summary = pipe_t5(input_text, max_length=300, min_length=100, num_beams=2, early_stopping=False, length_penalty=1)
+    summary = pipe_t5(input_text, max_length=300, min_length=100, num_beams=1, early_stopping=False, length_penalty=1)
     return summary[0]['summary_text']