InPhyT/IMDb_Sentiment_Analysis_BERT

Implements fine-tuning of pre-trained BERT transformers with bidirectional context encoding, leveraging the model's "CLS" token output as a 512-dimensional document vector for binary classification. The approach uses PyTorch/Hugging Face transformers library and includes attention mechanisms to weight word relationships, moving beyond unidirectional encoder-decoder architectures. Jupyter notebooks provide reproducible workflows executable on Kaggle and Google Colab for experimentation with 50k balanced IMDb reviews.

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