Update abstractive_summarization.py

abstractive_summarization.py

@@ -1,22 +1,25 @@
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
 
-# Load the fine-tuned BART tokenizer and model
-tokenizer = AutoTokenizer.from_pretrained("EE21/BART-ToSSimplify")
-model = AutoModelForSeq2SeqLM.from_pretrained("EE21/BART-ToSSimplify")
+# Function to summarize using the fine-tuned BART model
+def summarize_with_bart_ft(input_text):
+    pipe_bart_ft = pipeline("summarization", model="EE21/BART-ToSSimplify")
+    summary = pipe_bart_ft(input_text, max_length=300, min_length=100, num_beams=1, early_stopping=False, length_penalty=1)
+    return summary[0]['summary_text']
 
-# Load BART-large-cnn
-pipe = pipeline("summarization", model="facebook/bart-large-cnn")
+# Function to summarize using BART-large-cnn
+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=1, early_stopping=False, length_penalty=1)
+    return summary[0]['summary_text']
 
-# 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)
-    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
+# 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=1, early_stopping=False, length_penalty=1)
+    return summary[0]['summary_text']
 
-# 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=False)
-    summary_ids = model.generate(inputs, max_length=600, min_length=300, num_beams=2, early_stopping=False, length_penalty=1)
-    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
-    return summary
+# 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=1, early_stopping=False, length_penalty=1)
+    return summary[0]['summary_text']