This project demonstrates the implementation of a next word prediction model using TensorFlow and Keras. The model achieves a high accuracy rate of 96.68% in predicting the next word in a given text sequence.
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High Accuracy: Achieved 96.68% accuracy in next word prediction tasks.
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Efficient Preprocessing: Enhanced model efficiency through effective text tokenization and preprocessing.
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Optimized Architecture: Utilized advanced LSTM layers in a sequential neural network to streamline prediction processes and reduce computational time.
- Text Preprocessing: Tokenized and preprocessed the text data to convert it into sequences of integers. This involved removing punctuation, converting text to lowercase, and creating a word index.
- Model Architecture: Built a sequential neural network model using an Embedding layer, an LSTM layer with 128 units, and a Dense layer with softmax activation. The model was compiled using categorical cross-entropy loss and the Adam optimizer.
- Training: The model was trained on a large dataset with multiple epochs to ensure convergence and accuracy.
- Prediction: After training, the model was used to predict the next word in a given sequence of words by selecting the word with the highest probability.