This project focuses on predicting the next word in a sequence using a Long Short-Term Memory (LSTM) model. It involves training on sequential data using techniques like tokenization and embedding for text preprocessing and feature representation.

• Model Training on Sequential Data: Trains an LSTM-based model to predict the next word in a sequence.
• Embedding Layer: Converts tokens into dense vector representations.
• Tokenization: Preprocesses text data by converting words into numerical tokens for efficient processing.

1. Requirements
2. Installation
3. Usage
4. Model Description
5. Data Preparation
6. Training the Model
7. Prediction
8. Acknowledgments

• Python 3.7+
• TensorFlow (>=2.0)
• NumPy
• Matplotlib
• Scikit-learn

You can install the required libraries using:

pip install -r requirements.txt  

1. Clone the repository:

   git clone https://github.com/your-repo/next-word-lstm.git  

2. Navigate to the project directory:

   cd next-word-lstm  

3. Install dependencies:

   pip install -r requirements.txt  

• Ensure your dataset is in plain text format (.txt).
• Save the dataset in the data/ directory.

Use the following command to train the model:

python train.py  

After training, run the prediction script:

python predict.py  

• Embedding Layer: Converts input tokens into dense vectors.
• LSTM Layer: Captures temporal dependencies in sequential data.
• Dense Layer: Outputs the predicted word probabilities.

1. Text Preprocessing:

   • Remove punctuation and convert text to lowercase.
   • Split text into sequences of fixed length.

2. Tokenization:

   • Convert words to integer tokens using the Keras Tokenizer.
   • Pad sequences to ensure consistent input size.

3. Embedding:

   • Initialize an embedding layer to learn dense word representations.

1. Hyperparameters:

   • Epochs: 10-20
   • Batch size: 32
   • LSTM units: 128

2. Loss Function:

   • Categorical Crossentropy.

3. Optimizer:

   • Adam.

4. Evaluation Metrics:

   • Perplexity or Accuracy.

Use the trained model to generate the next word in a sequence. Example:

Input: "The quick brown"
Output: "fox"

• TensorFlow for providing tools to build and train the model.
• Open-source datasets for text processing.