Implement Machine Learning component V3 (dsmvp-v3)

As part of the progression of machine learning components with increasing levels of sophistication, implement version 3 ("dsmvp-v3") with the following characteristics:

Explainable Model: A machine learning model that can learn to detect racially biased expressions in context based on input labeled data without explicit division of "expression" and "context”, i.e. labeled data consisting of pairs, and the trained model is to output sub-expression(s) of a new test input text identified to be biased expressions in context.

This may need to make use of an AIX (Explainable AI model/method) on text data, which can learn to classify an entire text, and at the same time, point to portions of the text that are likely most responsible for the classification judgement.
This may have to be invented, or further literature search may be required.

At minimum, a method akin to those AIX methods targeting tabular data (e.g. contrastive explanation method in AIX 360) can be applied with relatively straightforward modifications. (Reference: https://arxiv.org/abs/1802.07623)

Coding of dsmvp-v3 should be similar to and share many aspects of how dsmvp-v3 in the repository is implemented, using Jupyter notebook and accessing the database via taketwo-webapi, etc.

Start by locating the repository's existing dsmvp-v3 implementation and its Jupyter notebook and taketwo-webapi usage. Review the linked AIX 360 contrastive explanation reference and related literature for text classification. Done means a model trained on pairs both classifies new text and identifies biased sub-expressions in context through the notebook workflow.

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