@Override
    public List<Integer> embed(String text, Context context) {
        var start = System.nanoTime();
        var tokens = tokenize(text, context);
        runtime.sampleSequenceLength(tokens.size(), context);
        runtime.sampleEmbeddingLatency((System.nanoTime() - start)/1_000_000d, context);
        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