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Exercise

Transfer learning using BERT

At PyBooks, the company has decided to leverage the power of the BERT model, a pre-trained transformer model, for sentiment analysis. BERT has seen remarkable performance across various NLP tasks, making it a prime candidate for this use case.

You're tasked with setting up a basic workflow using the BERT model from the transformers library for binary sentiment classification.

The following has been imported for you: BertTokenizer, BertForSequenceClassification, torch. The example data texts and corresponding labels are also preloaded.

Instructions

100 XP
  • Load the bert-base-uncased tokenizer and model suitable for binary classification.
  • Tokenize your dataset and prepare it for the model, ensuring it returns PyTorch tensors using the return_tensors argument.
  • Setup the optimizer using model parameters.