We present an open-source tool for visualizing multi-head self-attention in Transformer-based language  models. The tool extends earlier work by visualizing attention at three levels of granularity: the attention-head level, the model level, and the neuron level. We describe how each of these views can help to interpret the model, and we demonstrate the tool on the  OpenAI GPT-2 pretrained language model. We also present three use cases showing how the tool might provide insights on how to adapt or improve the model .
We present an open-source tool for visualizing multi-head self-attention in Transformer-based language representation models. The tool extends earlier work by visualizing attention at three levels of granularity: the attention-head level, the model level, and the neuron level. We describe how each of these views can help to interpret the model, and we demonstrate the tool on the BERT model and the OpenAI GPT-2 model. We also present three use cases for analyzing GPT-2: detecting model bias, identifying recurring patterns, and linking neurons to model behavior .
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representation
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BERT model and the
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pretrained language
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showing how the tool might provide insights on how to adapt or improve the model
for analyzing GPT-2: detecting model bias, identifying recurring patterns, and linking neurons to model behavior