Update README.md

README.md

@@ -64,7 +64,7 @@ The following hyperparameters were used during training:
 ```python
 from transformers import pipeline
 
-summarizer = pipeline("summarization", model="bart-large-cnn-finetuned")
+summarizer = pipeline("summarization", model="luluw/bart-large-cnn-finetuned")
 
 text = """ 
 The paper "Attention is All You Need" revolutionized the field of natural language processing (NLP) by introducing the Transformer architecture, which relies solely on attention mechanisms to model long-range dependencies in sequential data. Prior to this, models like recurrent neural networks (RNNs) and convolutional neural networks (CNNs) were the primary tools for sequence modeling, but they suffered from limitations such as difficulty in parallelization and the vanishing gradient problem. The Transformer, however, breaks free from these constraints by using a self-attention mechanism, which allows it to attend to different parts of a sequence simultaneously, leading to more efficient training and better performance on tasks such as machine translation, text summarization, and language modeling.