Transformer Model for Classification

(A) Data Loading and Preprocessing:

1. Loading the Dataset:

   • The code starts by importing necessary libraries and loading the IMDB dataset using Pandas.

2. Data Cleaning and Preprocessing:

   • The 'review' column is cleaned by removing non-alphabetic characters and converting the text to lowercase.
   • Stop words are removed using the NLTK library.

3. Data Splitting:

   • The dataset is split into training and testing sets using the train_test_split function from scikit-learn.

4. Label Encoding:

   • Sentiment labels are encoded using LabelEncoder for both the training and testing sets.

5. Tokenization and Padding:

   • Tokenization is performed using Keras's Tokenizer, and the sequences are padded to ensure uniform length using pad_sequences.

(B) Transformer Model Building:

1. Embedding Layer:

   • The input sequences are passed through an Embedding layer to convert them into dense vectors of fixed size.

2. Transformer Block:

   • The core of the model is a Transformer block, which consists of self-attention mechanisms. It's applied multiple times (num_heads) to capture different aspects of the input sequence.

3. Convolution and Global Average Pooling:

   • After the attention mechanism, a 1D convolutional layer is applied to capture local patterns. Global Average Pooling is used to obtain a fixed-size output from the convolutional layer.

4. Dense Layers:

   • Dropout is applied for regularization, and a Dense layer with ReLU activation is used to capture complex patterns. The final layer uses a sigmoid activation for binary sentiment classification.

(C) Model Training:

1. Compilation and Training:
   • The model is compiled using the Adam optimizer and binary cross-entropy loss. It is then trained on the training data for a specified number of epochs.

(D) Model Saving and Evaluation:

1. Saving the Model:

   • The trained model is saved in the HDF5 format for future use.

2. Model Evaluation:

   • The model is evaluated on the test set, and metrics like accuracy and a classification report are printed.

(E) User Input Prediction:

1. User Input Prediction Function:

   • A function predict_sentiment_transformer is defined to predict the sentiment of user input. The function processes the input similarly to the training data.

2. Interactive User Input:

   • The user is prompted to enter a review, and the model predicts the sentiment (Negative, Neutral, or Positive).

This code showcases the implementation of a Transformer model for sentiment analysis, providing a comprehensive example for understanding and applying this architecture.