This page hosts a Jupyter Notebook for creating several visualizations using BERT, including vocabulary embeddings, position embeddings, and contextualized embeddings given a file with sentences containing a given word. For the example visualizations on this page, we're using BERT-large-uncased with whole word masking.
The notebook uses HuggingFace transformers
, t-SNE in sklearn
, and adjustText
First, let's visualize the vocabulary embeddings:
This looks pretty similar to typical word embedding visualizations
. Zooming in on the upper part shows us some clusters of verbs that have similar meanings:
One difference compared to earlier word embeddings is the presense of partial-word units (those starting with ##). These are mostly in the lower part of the visualization, largely separated from the rest. Below we see clusters of common suffixes of English words:
And, a bit to the left, common suffixes of named entities:
While most partial-word units are separated from the rest, some are mixed in with whole-word units:
We can similarly visualize BERT's absolute position embeddings.
While the first and last positions are separated in the lower right, the others form a nearly-unbroken chain in ascending order from 1 to 510. Different runs of t-SNE can lead to different levels of cohesion in the chain, but the results are qualitatively similar.
Next, we will use BERT to embed a word in its context and visualize the final layer for the position of the word of interest. We will consider the word values
. First, here's its location in the vocabulary embeddings:
Now, we use BERT to embed 15,000 instances of values
in sentences drawn from Wikipedia and Project Gutenberg, then run t-SNE on the embeddings taken from the final layer. Below we plot 750 instances of values
along with their sentence contexts (up to 5 subword units to either side):
Zooming in, we find different senses of the word in different areas of the visualization. The cluster in the lower left corresponds to verbal uses:
The remaining are mostly nominal uses. On the left are uses of the sense related to principles or standards:
To the right we find scientific and mathematical uses; the following shows the lower right corner:
How about the small cluster in the top center that's separated from the rest? These correspond to the phrase production values
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