private void validateModel() {
        Map<String, TensorType> inputs = evaluator.getInputInfo();
        validateName(inputs, inputIdsName, "input");
        validateName(inputs, attentionMaskName, "input");
        // some BERT inspired models such as DistilBERT do not have token_type_ids input
        // one can explicitly declare this is such model by setting that config to empty string
        if (!"".equals(tokenTypeIdsName)) {
            validateName(inputs, tokenTypeIdsName, "input");
        }
        Map<String, TensorType> outputs = evaluator.getOutputInfo();
        validateName(outputs, outputName, "output");
    }
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