Update README.md

README.md

@@ -36,21 +36,21 @@ The only limitation you might face is, to get the best results, you will have to
 Use the code below to get started with the model.
 
 ---
-from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
-import torch
-#Load the model and tokenizer
-model = AutoModelForSeq2SeqLM.from_pretrained("yashrane2904/LED_Finetuned").to("cuda")
-tokenizer = AutoTokenizer.from_pretrained("allenai/led-base-16384") # Since it is a fine-tuned version of led-base-16348, we use the same tokenizer as that model used
-LONG_ARTICLE = "Your long text goes here..."
-#Tokenize the input article
-input_ids = tokenizer(LONG_ARTICLE, return_tensors="pt").input_ids.to("cuda")
-global_attention_mask = torch.zeros_like(input_ids)
-global_attention_mask[:, 0] = 1
-#Generate summaries
-sequences_tensor = model.generate(input_ids, global_attention_mask=global_attention_mask, num_beams=10, num_beam_groups=1,repetition_penalty=6.0,max_length=600,min_length=350,temperature=1.5)
-sequences = sequences_tensor.tolist()  # Convert Tensor to list of token IDs
-summary = tokenizer.batch_decode(sequences, skip_special_tokens=True)  # Decode token IDs into text
-#Print the generated summary
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer <br>
+import torch <br>
+<b><i>#Load the model and tokenizer</i></b><br>
+model = AutoModelForSeq2SeqLM.from_pretrained("yashrane2904/LED_Finetuned").to("cuda")<br>
+tokenizer = AutoTokenizer.from_pretrained("allenai/led-base-16384") # Since it is a fine-tuned version of led-base-16348, we use the same tokenizer as that model used<br>
+LONG_ARTICLE = "Your long text goes here..."<br>
+<b><i>#Tokenize the input article</i></b><br>
+input_ids = tokenizer(LONG_ARTICLE, return_tensors="pt").input_ids.to("cuda")<br>
+global_attention_mask = torch.zeros_like(input_ids)<br>
+global_attention_mask[:, 0] = 1<br>
+<b><i>#Generate summaries</i></b><br>
+sequences_tensor = model.generate(input_ids, global_attention_mask=global_attention_mask, num_beams=10, num_beam_groups=1,repetition_penalty=6.0,max_length=600,min_length=350,temperature=1.5)<br>
+sequences = sequences_tensor.tolist()  # Convert Tensor to list of token IDs<br>
+summary = tokenizer.batch_decode(sequences, skip_special_tokens=True)  # Decode token IDs into text<br>
+<b><i>#Print the generated summary</i></b><br>
 print(summary)
 ---