bertviz

Introduction to BertViz

BertViz is a dynamic tool designed to visualize attention mechanisms in natural language processing (NLP) models. This tool is particularly useful for analyzing models like BERT, GPT-2, and T5, which are all based on the Transformer architecture. BertViz can be easily accessed and utilized within a Jupyter or Colab notebook, thanks to its simple Python API that supports a variety of models from the Huggingface library. It builds upon the Tensor2Tensor visualization tool, offering multiple views that provide unique insights into how attention works within these models.

Features and Views

Head View

The head view allows users to delve into the attention visualization for one or several attention heads within the same layer. This view draws inspiration from the Tensor2Tensor tool and enables a detailed examination of the attention processes occurring in individual heads.

You can try out the head view through an Interactive Colab Tutorial.

Model View

The model view presents an overview of attention across all layers and heads, offering a comprehensive glance at how different components of the model interact through attention mechanisms.

Explore the model view in the Interactive Colab Tutorial.

Neuron View

In the neuron view, BertViz visualizes individual neurons within the query and key vectors, providing an in-depth look at how these neurons contribute to computing attention. This is critical for understanding the lower-level workings of the attention mechanism.

Experience the neuron view through the Interactive Colab Tutorial.

Getting Started with BertViz

Running in a Jupyter Notebook

To use BertViz in a Jupyter Notebook, start by installing the necessary packages:

pip install bertviz
pip install jupyterlab
pip install ipywidgets

After installation, launch Jupyter Notebook with:

jupyter notebook

Create a new Python 3 notebook and begin utilizing BertViz's capabilities.

Using BertViz in Colab

For Colab users, installing BertViz is just as simple. Simply add the installation command at the start of your notebook:

!pip install bertviz

Sample Code

Here's a quick snippet to get you started with BertViz. This example loads a model and uses the model view to display attention:

from transformers import AutoTokenizer, AutoModel, utils
from bertviz import model_view
utils.logging.set_verbosity_error()  # Suppress standard warnings

model_name = "microsoft/xtremedistil-l12-h384-uncased"
input_text = "The cat sat on the mat"
model = AutoModel.from_pretrained(model_name, output_attentions=True)
tokenizer = AutoTokenizer.from_pretrained(model_name)
inputs = tokenizer.encode(input_text, return_tensors='pt')
outputs = model(inputs)
attention = outputs[-1]
tokens = tokenizer.convert_ids_to_tokens(inputs[0])
model_view(attention, tokens)

You can experiment by using different inputs and models available through Huggingface.

Documentation and Support

The BertViz project provides comprehensive documentation covering various models and visualization techniques. It supports self-attention models like BERT and GPT-2, as well as encoder-decoder models including BART and T5. Additionally, BertViz can be customized extensively, offering options such as dark/light mode, filtering layers, and obtaining HTML representations of visualizations.

For further exploration, refer to the documentation section.

Conclusion

BertViz is a powerful tool for anyone looking to delve deep into the inner workings of modern NLP models via interactive attention visualization. It's a valuable resource for researchers and developers aiming to better understand and analyze the attention dynamics within these complex models.