python-tensorflowHow can I use Python, TensorFlow and BERT together to create a machine learning model?

Using Python, TensorFlow and BERT together to create a machine learning model is possible. Here is an example of how to do so:

import tensorflow as tf
import bert

# Create the model
model = bert.BertModel(config=bert_config)

# Define the input data
input_ids = tf.keras.layers.Input(shape=(max_seq_length,), dtype=tf.int32, name="input_ids")
input_mask = tf.keras.layers.Input(shape=(max_seq_length,), dtype=tf.int32, name="input_mask")
segment_ids = tf.keras.layers.Input(shape=(max_seq_length,), dtype=tf.int32, name="segment_ids")

# Get the output of BERT
pooled_output, sequence_output = model([input_ids, input_mask, segment_ids])

# Add a classification layer on top
output = tf.keras.layers.Dense(1, activation='sigmoid')(pooled_output)

model = tf.keras.models.Model(inputs=[input_ids, input_mask, segment_ids], outputs=output)

# Compile and train the model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit([input_ids, input_mask, segment_ids], labels, epochs=1, batch_size=32)

This example code creates a model using BERT as the base model, and adds a classification layer on top. The model is then compiled and trained using binary crossentropy as the loss function and Adam as the optimizer.

The code consists of the following parts:

1. Importing TensorFlow and BERT
2. Creating the model
3. Defining the input data
4. Getting the output of BERT
5. Adding a classification layer on top
6. Compiling and training the model

Helpful links

• BERT Documentation
• TensorFlow Documentation

Edit this code on GitHub