private List<Integer> embedWithSeparatorTokens(String text, Context context, int maxLength) {
        List<Integer> tokens = new ArrayList<>();
        tokens.add(startSequenceToken);
        tokens.addAll(tokenize(text, context));
        tokens.add(endSequenceToken);
        if (tokens.size() > maxLength) {
            tokens = tokens.subList(0, maxLength-1);
            tokens.add(endSequenceToken);
        }
        return tokens;
    }
A BERT Base compatible embedder. This embedder uses a WordPiece embedder to
produce a token sequence that is then input to a transformer model. A BERT base
compatible transformer model must have three inputs:

 - A token sequence (input_ids)
 - An attention mask (attention_mask)
 - Token types for cross encoding (token_type_ids)

See bert-base-embedder.def for configurable parameters.

@author lesters
model-integration/src/main/java/ai/vespa/embedding/BertBaseEmbedder.java