Update README.md

README.md

@@ -64,3 +64,66 @@ summary_ids = model.generate(inputs, max_length=150, min_length=100, length_pena
 summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
 print("Summary:")
 print(summary)
+
+#Compare with some other models
+
+from transformers import T5ForConditionalGeneration, T5Tokenizer, PegasusTokenizer, PegasusForConditionalGeneration, BartForConditionalGeneration, BartTokenizer
+
+# Function to summarize with any model
+def summarize_article(article, model, tokenizer):
+    inputs = tokenizer.encode("summarize: " + article, return_tensors="pt", max_length=512, truncation=True)
+    summary_ids = model.generate(inputs, max_length=150, min_length=100, length_penalty=2.0, num_beams=4, early_stopping=True)
+    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+    return summary
+
+# Load your fine-tuned T5 model and tokenizer (your custom model)
+t5_model_custom = T5ForConditionalGeneration.from_pretrained("Vijayendra/T5-Base-Sum")
+t5_tokenizer_custom = T5Tokenizer.from_pretrained("Vijayendra/T5-Base-Sum")
+
+# Load a different pretrained T5 model for summarization (e.g., "t5-small" fine-tuned on CNN/DailyMail)
+t5_model_pretrained = T5ForConditionalGeneration.from_pretrained("csebuetnlp/mT5_multilingual_XLSum")
+t5_tokenizer_pretrained = T5Tokenizer.from_pretrained("csebuetnlp/mT5_multilingual_XLSum")
+
+# Load Pegasus model and tokenizer
+pegasus_model = PegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum")
+pegasus_tokenizer = PegasusTokenizer.from_pretrained("google/pegasus-xsum")
+
+# Load BART model and tokenizer
+bart_model = BartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn")
+bart_tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-cnn")
+
+# Example article for summarization
+article = """
+Videos that say approved vaccines are dangerous and cause autism, cancer or infertility are among those that will be taken down, the company
+said.  The policy includes the termination of accounts of anti-vaccine influencers.  Tech giants have been criticised for not doing more to
+counter false health information on their sites.  In July, US PresidentJoe Biden said social media platforms were largely responsible for
+people's scepticism in getting vaccinated by spreading misinformation, and appealed for them to address the issue.  YouTube, which is owned
+by Google, said 130,000 videos were removed from its platform since last year, when it implemented a ban on content spreading misinformation 
+about Covid vaccines.  In a blog post, the company said it had seen false claims about Covid jabs "spill over into misinformation about
+vaccines in general". The new policy covers long-approved vaccines, such as those against measles or hepatitis B."We're expanding our medical
+misinformation policies on YouTube with new guidelines on currently administered vaccines that are approved and confirmed to be safe and
+effective by local health authorities and the WHO," the post said, referring to the World Health Organization.
+"""
+
+# Summarize with your fine-tuned T5 model
+t5_summary_custom = summarize_article(article, t5_model_custom, t5_tokenizer_custom)
+
+# Summarize with the pretrained T5 model for summarization
+t5_summary_pretrained = summarize_article(article, t5_model_pretrained, t5_tokenizer_pretrained)
+
+# Summarize with Pegasus model
+pegasus_summary = summarize_article(article, pegasus_model, pegasus_tokenizer)
+
+# Summarize with BART model
+bart_summary = summarize_article(article, bart_model, bart_tokenizer)
+
+# Print summaries for comparison
+print("T5 base with Cyclic Attention Summary:")
+print(t5_summary_custom)
+print("\nPretrained mT5_multilingual_XLSum Summary:")
+print(t5_summary_pretrained)
+print("\nPegasus Xsum Summary:")
+print(pegasus_summary)
+print("\nBART Large CNN Summary:")
+print(bart_summary)
+