TensorFlow is a powerful framework for building and training machine learning models, including those for text generation. Below is a guide to help you get started with creating a text generation model using TensorFlow.

🧠 Step-by-Step Tutorial

1. Prepare the Dataset

  • Use a text corpus like Shakespeare's plays or Wikipedia.
  • Preprocess the text by tokenizing and converting to sequences.
text_generation_dataset

2. Build the Model

  • Create a Recurrent Neural Network (RNN) or Transformer model.
  • Example code: 
    model = tf.keras.Sequential([  
    tf.keras.layers.Embedding(input_dim=vocab_size, output_dim=embedding_dim, input_length=max_length),  
    tf.keras.layers.LSTM(units=128),  
    tf.keras.layers.Dense(vocab_size, activation='softmax')  
])
  • Compile the model with categorical_crossentropy as the loss function.
lstm_text_model

3. Train the Model

  • Use model.fit() to train on the prepared dataset.
  • Adjust hyperparameters like batch size and epochs for better performance.
training_process

4. Generate Text

  • Use model.predict() to generate new text based on input sequences.
  • Example: 
    generated_text = generate_text(model, start_text, max_length=100)  
print(generated_text)
text_generation_output

📚 Expand Your Knowledge

For advanced techniques in text generation, check out our TensorFlow Advanced Text Generation Tutorial.

🤖 Try It Yourself

Note: This content is for educational purposes only. Always ensure compliance with local laws and ethical guidelines when working with AI models.