Update README.md

README.md

@@ -36,25 +36,6 @@ It achieves the following results on the evaluation set:
 - Rougel: 21.1788
 - Rougelsum: 35.314
 
-'''
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-tokenizer = BertTokenizerFast.from_pretrained('Atharvgarg/bert-small2bert-small-finetuned-cnn_daily_mail-summarization-finetuned-multi-news')
-model = EncoderDecoderModel.from_pretrained('Atharvgarg/bert-small2bert-small-finetuned-cnn_daily_mail-summarization-finetuned-multi-news').to(device)
-
-def generate_summary(text):
-    # This Bert model is limited to max_length at 512 
-    inputs = tokenizer([text], padding="max_length", truncation=True, max_length=512, return_tensors="pt")
-    input_ids = inputs.input_ids.to(device)
-    attention_mask = inputs.attention_mask.to(device)
-
-    output = model.generate(input_ids, attention_mask=attention_mask)
-
-    return tokenizer.decode(output[0], skip_special_tokens=True)
-  
-file = open("/content/drive/MyDrive/Nlp/BBC News Summary Dataset/BBC News Summary/News Articles/business/001.txt", "r")
-text = file.read()
-generate_summary(text)
-'''
 ## Model description
 
 More information needed