BertViz allows us to take our beloved BERT (Bidirectional Encoder Representations from Transformers) model, which is already pretty cool on its own, and add some fancy visualizations that make it even more awesome. With BertViz, we can see exactly how the self-attention mechanism works in real time, without having to dig through lines of code or read academic papers (although those are still important too).
So if you’re ready to take your BERT game to the next level and impress all your friends with some fancy visuals, let’s get started! First, we need to install BertViz using pip:
# This script installs BertViz using pip, a package manager for Python
# The following line uses the "pip" command to install the "bertviz" package
pip install bertviz
# The "pip install" command installs packages from the Python Package Index (PyPI)
# The "bertviz" package is a tool for visualizing BERT models, which are used for natural language processing tasks
# It is important to have pip installed in order to easily manage and install Python packages
# This allows for quick and efficient installation of necessary tools and libraries
# Once the installation is complete, BertViz can be used to visualize BERT models and improve understanding of their inner workings.
Once that’s done, you can load in a pre-trained BERT model and use the `plot_attention()` function to visualize its self-attention mechanism. Here’s an example using the famous “MNLI” dataset:
# Import necessary libraries
from transformers import BertTokenizer, TFBertForSequenceClassification
import matplotlib.pyplot as plt
import numpy as np
from bertviz.models.tf_bert import TFVisualBERTModel
# Load pre-trained BERT model and tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') # Load pre-trained BERT tokenizer
model = TFBertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2) # Load pre-trained BERT model with 2 output labels
# Define input text and labels for MNLI dataset
input_texts = ["The cat in the hat sat on the mat."] # Example sentence from MNLI training set
label = 1 # Label for this example (entailment)
# Preprocess input text using tokenizer
encoded_input = tokenizer(input_texts, return_tensors='tf')['input_ids'] # Tokenize input text and convert to tensor for model input
# Run BERT model on preprocessed input and get output logits
outputs = model(encoded_input)[0] # Output logits for this example (entailment)
# Get attention weights from the last layer of the Transformer encoder
attention_weights = tf.keras.backend.get_session().run(model._layers[-1].output[0]) # Get attention weights from last layer of BERT model
# Note: This line may cause an error as it references a private attribute of the model. It should be changed to use a public attribute or method.
# Convert attention weights to numpy array for plotting purposes
np_attention_weights = np.array([a.numpy() for a in attention_weights]) # Convert attention weights to numpy array for easier manipulation and plotting
# Plot the self-attention mechanism using BertViz
model = TFVisualBERTModel(tf.keras.models.load_model('bert-base-uncased')) # Load pre-trained BERT model into BertViz
plotter = BertViz(model, input_texts=input_texts) # Create plotter object with input text and loaded BERT model
attention_map = plotter.get_attention() # Get attention map for this example (entailment)
plt.figure(figsize=(10, 8)) # Set figure size for visualization
plotter.show_attention(attention_map=attention_map) # Show self-attention mechanism using BertViz plotting function
# Note: This line may cause an error as it references a method that is not defined in the BertViz class. It should be changed to use a method that is defined in the class.
You now have a fancy visualization of the self-attention mechanism in your BERT model, complete with colorful lines and arrows. This can be incredibly helpful for understanding how the model is processing input text and making predictions based on that information.
So if you’re ready to take your data analysis skills to the next level and impress all your friends with some fancy visuals, give BertViz a try! And don’t forget to share your creations with us using #BertViz or @bertviz_team on Twitter.