Workflow · Production

Visualizing transformer attention with BertViz and Comet for model explainability

The problem

Transformer models are notoriously opaque, making it difficult to explain how or why they reach their outputs, which risks undetected bias, model collapse, and ethical issues — especially as models are deployed in sensitive domains like healthcare, law, finance, and security.

First attempt

Earlier attention visualization approaches were overly complicated, failed to translate to non-technical audiences, and varied greatly across projects and use cases.

Workflow diagram · grounded in source
1
Run LLM on input prompts
trigger
“In this tutorial, we'll use these visualizations to compare and dissect the performance of several pre-trained LLMs”
2
Visualize attention with BertViz
ai_action
“BertViz visualizes the attention mechanism at multiple local scales: the neuron level, attention head level, and model level”
3
Log visualizations to Comet
integration
“We'll log our BertViz plots to Comet, an experiment tracking tool, so we can compare our results later on”
4
Inspect attention for bias and patterns
output
“One application of the head view is detecting model bias”
Reported outcome

BertViz provides multi-scale attention visualization that is simple and intuitive; logging plots to Comet enables practitioners to compare model attention across runs and apply the views to detect potential bias.

Reported stack
BertVizCometGPT-2HuggingFace
Source
https://mlops.community/blog/explainable-ai-visualizing-attention-in-transformers
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

BertViz provides multi-scale attention visualization that is simple and intuitive; logging plots to Comet enables practitioners to compare model attention across runs and apply the views to detect potential bias.

What tools did this team use?

BertViz, Comet, GPT-2, HuggingFace.

What failed first in this deployment?

Earlier attention visualization approaches were overly complicated, failed to translate to non-technical audiences, and varied greatly across projects and use cases.

How is this workflow AI workflow structured?

Run LLM on input prompts → Visualize attention with BertViz → Log visualizations to Comet → Inspect attention for bias and patterns.