Training the LLM
To train the LLM, we will use the following steps:
- Create a training dataset by converting the preprocessed text to sequences of words.
- Compile the LLM model with the appropriate loss function and optimizer.
- Train the LLM model on the training dataset.
Here's the RNN version:
PYTHON
1# Create a training dataset
2training_dataset = tf.data.Dataset.from_tensor_slices(preprocessed_text)
3training_dataset = training_dataset.batch(64)
4
5# Compile the LLM model
6model = LLM(len(vocabulary), 128, 256)
7model.compile(loss='categorical_crossentropy', optimizer='adam')
8
9# Train the LLM model
10model.fit(training_dataset, epochs=10)Evaluating the LLM
To evaluate the LLM, we will use the following steps:
- Generate text from the LLM model.
- Compare the generated text to the original text.
PYTHON
1# Generate text from the LLM model
2generated_text = model.predict(tf.constant([vocabulary['the']], dtype=tf.int32))
3generated_text = vocabulary[tf.argmax(generated_text, axis=1)[0]]
4
5# Compare the generated text to the original text
6original_text = preprocessed_text[0][0].text
7
8print('Generated text:', generated_text)
9print('Original text:', original_text)