BertViz: Visualizing Attention in NLP Models

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If you haven’t heard of it before, let us enlighten you: BertViz is an open-source tool for visualizing attention in NLP models. It allows you to see exactly which parts of a sentence are most important (or “attended” by the model) when making predictions or generating responses.

Now, we know what some of you might be thinking: “But wait, isn’t this just another fancy tool for data scientists and machine learning engineers to show off their skills? What does it have to do with me?” Well, bro, let us explain!

First, BertViz can help you understand how your NLP model is working (or not working) in a more intuitive way. By visualizing the attention scores for each word or token, you can see which parts of the input are most important to the model’s decision-making process. This can be especially helpful when debugging models that aren’t performing as well as expected!

Secondly, BertViz is a great tool for exploring and analyzing text data in general. By visualizing attention scores over time or across different datasets, you can gain insights into the patterns and trends that are driving your model’s predictions (or lack thereof). This can be especially useful when working with large amounts of unstructured data!

So how does BertViz work exactly? Well, it uses a technique called “attention visualization” to create heatmaps or other types of visualizations that show which parts of the input are most important (or attended) by the model. This is done by calculating the attention scores for each word or token in the input and then mapping those scores onto a 2D grid or other type of visual representation.

To use BertViz, you’ll need to install it using pip:

# Install bert-viz using pip
pip install bert-viz


bertviz -h

# Set input file path
input_file="input.txt"

# Set output file path
output_file="output.png"

# Run bertviz to generate attention visualization
bertviz -model_type bert -model_name_or_path bert-base-uncased -input_file $input_file -output_file $output_file

# Explanation:
# This script installs bert-viz using pip and then uses it to generate an attention visualization for the input text file. The input and output file paths are set and the bertviz command is run with the appropriate parameters.

Once that’s done, you can run the following command to generate an attention heatmap for a given input and model:

# Import necessary libraries
from transformers import BertTokenizer, TFBertForSequenceClassification
import matplotlib.pyplot as plt
import seaborn as sns
from bert_viz.models.tfbert import TFAttentionVisualization

# Load the pre-trained BERT model and tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') # Load the pre-trained BERT tokenizer
model = TFBertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2) # Load the pre-trained BERT model with 2 output labels

# Define a function to generate an attention heatmap for a given input and model
def plot_attention(input_text, output_label):
    # Preprocess the input text using the tokenizer
    encoded = tokenizer.encode(input_text, add_special_tokens=True) # Encode the input text using the tokenizer and add special tokens
    
    # Run the input through the BERT model to generate predictions and attention scores
    with tf.Graph().as_default(): # Create a new graph for the BERT model
        output = model(tf.convert_to_tensor([encoded]))[0] # Run the input through the BERT model and get the output
        attn_scores = TFAttentionVisualization()(model, encoded)['attentions'][-1][:, 0].numpy() # Get the attention scores from the last layer of the BERT model
        
    # Plot the attention heatmap using matplotlib
    fig, ax = plt.subplots(figsize=(8, 4)) # Create a figure and axes for the plot
    sns.heatmap(attn_scores, annot=False, cmap='Blues', ax=ax) # Plot the attention scores as a heatmap using seaborn
    
    # Add labels and a title to the plot
    ax.set(title="Attention Heatmap for '{}'".format(input_text), xlabel="Token Index", ylabel="Layer") # Set the title and labels for the plot
    plt.show() # Show the plot

# Example usage: generate an attention heatmap for the input text "The quick brown fox jumps over the lazy dog"
plot_attention("The quick brown fox jumps over the lazy dog", 1) # Call the plot_attention function with the input text and output label

And that’s it! With BertViz, you can easily visualize and analyze the attention scores generated by your NLP models. So why not give it a try today? Your computer will thank you for it!

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