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What explains the apparent 'mirroring' of the graphs on the RHS,
The model starts untrained and no better than random guessing (the baseline). As the training progresses, the model does better than random guessing on the training data, but does worse than initially on the validation data.
The decrease in performance is because the data it is being trained ...
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In Machine Learning "embedding" means taking some set of raw inputs (like natural language tokens in NLP or image patches in your example) and converting them to vectors somehow. The embeddings usually have some interesting dot-product structure between vectors (like in word2vec for example). The Transformer machinery then uses this embedding in ...
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