Implement Machine Learning component V2 (dsmvp-v2)

As part of the progression of machine learning components with increasing levels of sophistication, implement version 2 ("dsmvp-v2") with the following characteristics:

A machine learning module that can learn to detect racially biased expressions in context based on input labeled data of triples.

A possible implementation can make use of contextual text classifiers, such as those based on RNN = Recurrent Neural Networks such as the LTST architecture (Reference: https://spacy.io/usage/examples#textcat).

RNN allows the classifier to be "sequentially contextual", i.e. to classify a given phrase or expression dependent on the context in which it is used.

Coding of dsmvp-v2 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.

首先檢視儲存庫中現有的 dsmvp-v1 實作，包括其 Jupyter notebook 和 webapi 資料庫存取。參考連結的 spaCy 文字分類範例，定義 dsmvp-v2 應如何使用 三元組並提供情境偏誤分類。新元件已依照所述的情境行為完成實作，並遵循 v1 的整合模式，即視為完成。

由索引模型根據 Issue 內容生成。