MultiLabelClassificationExplainer (#79)

Extends the existing sequence classification explainer into a new explainer that independently produces attributions for each label in the model regardless of what the predicted class is. This allows users to better inspect and interpret model predictions across all classes, particularly in situations where classifiers might be used in a multilabel fashion.

The MultiLabelClassificationExplainer returns a dictionary mapping labels/classes to a list of word attributions, additionally the visualize() method will display the entire table of attributions for each label.

This has been a very requested feature for a number of months so we're very happy to get it released (finally)

CC: @MichalMalyska @rhettdsouza13 @fraserprice @JensVN98 @dheerajiiitv

How to use

This explainer is an extension of the SequenceClassificationExplainer and is thus compatible with all sequence classification models from the Transformers package. The key change in this explainer is that it caclulates attributions for each label in the model's config and returns a dictionary of word attributions w.r.t to each label. The visualize() method also displays a table of attributions with attributions calculated per label.

:::python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from transformers_interpret import MultiLabelClassificationExplainer

model_name = "j-hartmann/emotion-english-distilroberta-base"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)


cls_explainer = MultiLabelClassificationExplainer(model, tokenizer)


word_attributions = cls_explainer("There were many aspects of the film I liked, but it was frightening and gross in parts. My parents hated it.")

This produces a dictionary of word attributions mapping labels to a list of tuples for each word and it's attribution score.

Visualize MultiLabel Classification attributions

Sometimes the numeric attributions can be difficult to read particularly in instances where there is a lot of text. To help with that we also provide the visualize() method that utilizes Captum's in built viz library to create a HTML file highlighting the attributions. For this explainer attributions will be show w.r.t to each label.

If you are in a notebook, calls to the visualize() method will display the visualization in-line. Alternatively you can pass a filepath in as an argument and an HTML file will be created, allowing you to view the explanation HTML in your browser.

:::python
cls_explainer.visualize("multilabel_viz.html")