With the help of this magical library called KerasNLP and its high-level modules, you can easily transform raw strings into nifty features that your models will love.
Before anything else, let’s install TensorFlow and KerasNLP using pip:
# Install TensorFlow and KerasNLP using pip
# -m flag allows for installation of packages as a module
# --user flag installs packages in the user's home directory
# -q flag suppresses output, making the installation process cleaner
# -U flag ensures the latest version of the package is installed
pip install -m --user -q -U tensorflow kerasnlp
Now you can import them in your Python script like this:
# Importing the necessary libraries for the script
import tensorflow as tf # Importing the tensorflow library and assigning it an alias "tf"
from keras_nlp.models import BERTForSequenceClassification, BertTokenizer # Importing the BERTForSequenceClassification and BertTokenizer models from the keras_nlp library
# Note: The keras_nlp library is used for natural language processing tasks and the BERTForSequenceClassification model is used for text classification tasks.
# Creating an instance of the BERTForSequenceClassification model
model = BERTForSequenceClassification() # Assigning the BERTForSequenceClassification model to the variable "model"
# Initializing the BertTokenizer
tokenizer = BertTokenizer() # Assigning the BertTokenizer model to the variable "tokenizer"
# Note: The BertTokenizer is used to tokenize text data, which is necessary for input into the BERTForSequenceClassification model.
# Loading the data for training the model
train_data = load_data("train.csv") # Loading the training data from a CSV file and assigning it to the variable "train_data"
# Preprocessing the data
train_data = preprocess_data(train_data) # Preprocessing the training data to prepare it for input into the model
# Training the model
model.fit(train_data) # Training the model using the preprocessed training data
# Evaluating the model
test_data = load_data("test.csv") # Loading the test data from a CSV file and assigning it to the variable "test_data"
test_data = preprocess_data(test_data) # Preprocessing the test data to prepare it for evaluation
model.evaluate(test_data) # Evaluating the model using the preprocessed test data
# Note: The model is trained and evaluated using the same steps, but with different data. This allows us to test the performance of the model on unseen data.
Say you have a dataset of movie reviews and want to classify whether they are positive or negative. Here’s how you can do that using KerasNLP:
1. Load the data into a Pandas DataFrame (or any other format) with columns for text and label (positive/negative).
2. Preprocess the text by tokenizing it, removing stop words, converting to lowercase, etc. You can use KerasNLP’s built-in preprocessing functions or write your own custom ones.
3. Split the data into training and validation sets using a 80/20 split (or any other ratio).
4. Train a BERT model on the training set with a batch size of 16, an epoch count of 5, and a learning rate of 0.001. You can use KerasNLP’s built-in functions to do this or write your own custom code using TensorFlow.
5. Evaluate the model on the validation set using metrics like accuracy, precision, recall, etc. Again, you can use KerasNLP’s built-in functions for this or write your own custom code.
6. Save the trained model to disk so that you can load it later and make predictions on new data. You can do this using TensorFlow’s save() function or KerasNLP’s built-in functions.
7. Load the saved model into memory and use it to predict whether a given review is positive or negative. Again, you can use KerasNLP’s built-in functions for this or write your own custom code using TensorFlow.
8. Display the results in an easy-to-understand format like a table or chart. You can do this using Python libraries like Pandas and Matplotlib (or any other library of your choice).
9. Repeat steps 1-8 for each new dataset you want to process, or use KerasNLP’s built-in functions to automate the entire pipeline.
And that’s it! With TensorFlow and KerasNLP, you can easily transform raw text data into nifty features that your models will love.