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To fine-tune BERT for a specific task like sentiment analysis, you would start by leveraging a pre-trained BERT model, which has already learned general language representations from a large corpus. For sentiment analysis, the model's architecture would be modified to include a classification layer on top of the pre-trained BERT model. During fine-tuning, you would train this model on a labeled dataset with sentiment labels (e.g., positive, negative, neutral) by feeding the tokenized input text into BERT. The model learns to map the output from BERT's contextual embeddings to the appropriate sentiment label. Fine-tuning involves updating the weights of the entire network using backpropagation, typically with a lower learning rate to avoid destroying the pre-trained knowledge. This process allows BERT to specialize in sentiment analysis while retaining its powerful language understanding.

Is it always good to leverage pre-trained Models?

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