Implement Machine Learning component V5 (dsmvp-v5)

As part of the progression of machine learning components with increasing levels of sophistication, implement version 5 ("dsmvp-v5") with the following characteristics:

Active Learning: (to be documented) An on-line active learning module that can learn to detect racially biased expressions and to actively solicit labeled data from selected markers (based on the estimated credibility of the markers), based on labeled data of triples.

A possible implementation of this version may make use of various versions of "bandit algorithms," which dictate how to choose the markers to sample from next. An example of such an algorithm is the UCB (Upper Confidence Bound) method, which chooses the marker according to who has the highest "upper confidence bound" among all the markers, balancing the motivation to learn from the most credible v.s. the need to learn from fresh new markers so as to learn about their credibility.
(Reference: https://tor-lattimore.com/downloads/book/book.pdf)

Coding of dsmvp-v5 should be similar to and share many aspects of how dsmvp-v1 in the repository is implemented, using Jupyter notebook and accessing the database via webapi, etc.

Beginne mit der Durchsicht der Implementierung von dsmvp-v1 im Repository, insbesondere ihres Jupyter-Notebooks und des Datenbankzugriffs über webapi, und vergleiche diese Muster anschließend mit den Anforderungen von dsmvp-v5. Die Arbeit ist abgeschlossen, wenn eine Online-Komponente für aktives Lernen gelabelte Expression-, Klassifikations- und marker-ID-Tripel verarbeitet und Marker für neue Labels auswählt, wobei der Ansatz dokumentiert und die UCB-Referenz berücksichtigt wird.

Vom Indexierungsmodell aus dem Issue-Text verfasst.