base models / embeddings / encoders about cerebros-core-algorithm-alpha HOT 6 CLOSED

Branch name 67-base-models-embeddings-encoders to paste.

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Made proposed changes. Passed the tests for backward compatibility, went suspiciously well. We still need to develop the tests for forward compatibility (new functionality). This will take some hours.

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Successfully ran a text model (BERT base embedding) with Cerebros. Unfortunately, the BERT embedding is the bottleneck. Cerebros is not able to augment embeddings from BERT beyond the val_binary_accuracy: 0.8429 that a straight BERT embedding .> Dense(1) layer. BERT's limitation on its embedding resolution doesn't leave us with enough input resolution to capture any further pattern, even with an exhaustive NAS.

I have an idea that is linear algebra based that may be a better text embedding for Cerebros and could give much better resolution and may perform better on small data sets.

I reduced the length of the test for the text model, as this took 2 hours to run 7 neural architecture moieties, 1 run per moity. Reducing to 2.

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API Additions appear to be stable. Need to remove test prints. Question: Should we add CV capabilities to the current branch or merge this in now and create another?

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Hold to #69

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Just added an image classification test. Tests are running. It is likely this will need a few hours of debugging, but this major milestone may be completed soon.

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