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Mastering spaCy

Using BERT for Text Classification

Explore how to create a binary text classification model using BERT within spaCy and TensorFlow Keras. Understand the process from tokenizing input data to defining a compact deep learning model leveraging pre-trained BERT embeddings. Gain insights on model architecture, training considerations, and efficiency tips to implement effective NLP text classifiers.

We'll cover the following...

In this lesson, we'll train a binary text classifier with BERT and tf.keras. We'll reuse some of the code we have used previously, but this time the code will be much shorter because we'll replace the embedding and LSTM layers with BERT. The complete code is available at the Jupyter notebook at the end. We'll skip the data preparation. We used the SMS Spam Collection dataset from Kaggle.

Let's get started by importing the BERT models and tokenizer:

Python 3.5
from transformers import BertTokenizer, TFBertModel, BertConfig, TFBertForSequenceClassification
bert_tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
bmodel = TFBertModel.from_pretrained("bert-base-uncased")

We have imported the BertTokenizer tokenizer and the BERT model, TFBertModel. We initialized both the tokenizer and the BERT model with the pre-trained bert-base-uncased model. Notice that the model's name ...