Text Classification Using BERT & Tensorflow | Deep Learning Tutorial 47 (Tensorflow, Keras & Python)

Summary

This video builds on the previous explanation of BERT by demonstrating how to use it for email classification, determining whether emails are spam or non-spam. The presenter walks through key steps, including generating embedding vectors from email text using BERT and feeding them into a simple neural network. The model is trained and evaluated, achieving high accuracy. The tutorial also touches on handling data imbalances, building functional models in TensorFlow, and using cosine similarity to compare word embeddings. Viewers are encouraged to practice by running similar code on their own.

Takeaways

• 😀 The video explains how BERT, a language model, can be used for email classification to determine if an email is spam or not.
• 🔍 BERT converts an entire email into an embedding vector, which is a numerical representation that captures the context of the text.
• 📊 The video demonstrates creating a neural network with a single dense layer and a dropout layer to prevent overfitting, using the embeddings as input.
• 📈 The script discusses the importance of data preprocessing, including creating a new column for the target variable and performing a train-test split while maintaining class balance.
• 🌐 The tutorial guides viewers on how to access and use BERT models from TensorFlow Hub for pre-processing and encoding text.
• 💻 The presenter shows how to generate embedding vectors for sentences and words using BERT, and even compares the vectors using cosine similarity.
• 📝 The video introduces functional models in TensorFlow, contrasting them with sequential models, and demonstrates building a functional model for the classification task.
• 🎯 The training process involves compiling the model with an optimizer and loss function, then fitting it to the training data.
• 📊 The script includes an evaluation of the model's performance on a test set, achieving high accuracy for both training and testing.
• 🔧 The video concludes with an exercise for viewers to practice what they've learned by following a TensorFlow tutorial on text classification with BERT.

Q & A

What is the main purpose of using BERT in the provided email classification example?

The main purpose of using BERT in this example is to convert the entire email into an embedding vector, which can then be fed into a neural network for training to classify emails as spam or non-spam.

Why is the embedding vector from BERT set to a length of 768?

The embedding vector from BERT is set to a length of 768 because this is the standard dimensionality of the hidden layers in the BERT model, which was covered in a previous video as mentioned by the speaker.

What are the two main components of the BERT model?

The two main components of the BERT model are preprocessing and encoding. Preprocessing prepares the text for the model, while encoding generates the sentence embeddings.

How does the speaker handle the imbalance in the dataset between ham and spam emails?

The speaker first checks for class imbalance by grouping the data and observing the distribution of spam and ham emails. To ensure balance during model training, the speaker uses stratification during the train-test split, ensuring proportional representation of both classes.

What is the purpose of the ‘apply’ function in creating the 'spam' column in the data frame?

The 'apply' function is used to create a new column 'spam' by applying a lambda function that assigns 1 if the email is spam and 0 if it is ham, using a ternary operator in Python.

Why does the speaker use a dropout layer in the neural network model?

A dropout layer is used to tackle overfitting by randomly dropping a fraction of the neurons during training. In this case, the speaker drops 10% of the neurons to improve generalization.

What does the speaker mean by 'pooled output' in BERT encoding?

The 'pooled output' in BERT refers to the embedding vector for the entire sentence, which is generated after encoding and represents the meaning of the full sentence.

How does the speaker evaluate the model's performance?

The speaker evaluates the model's performance by splitting the dataset into training and test sets, then training the model for 5 epochs. After training, the speaker achieves a 95% accuracy on the test set.

What is cosine similarity, and how is it used in the video?

Cosine similarity is a metric used to measure the similarity between two vectors. In the video, the speaker uses cosine similarity to compare the embedding vectors of different words (e.g., comparing fruits like 'banana' and 'grapes' and people like 'Jeff Bezos').

What exercise does the speaker recommend for the viewers?

The speaker recommends that viewers follow a TensorFlow tutorial on classifying text with BERT. The exercise involves copying and running code from the tutorial to practice and solidify the concepts learned in the video.