protected Tensor embedDocument(String text, Context context, TensorType tensorType) {
        EmbeddingResult result = lookupOrEvaluate(context, text, false);
        var modelOutput = (IndexedTensor)result.outputs.get(outputName);
        if (tensorType.valueType() == TensorType.Value.INT8)
            return toBitTensor(modelOutput, tensorType, result.inputIdSize);
        else
            return toFloatTensor(modelOutput, tensorType, result.inputIdSize);
    }
A ColBERT embedder implementation that maps text to multiple vectors, one vector per token subword id.
This embedder uses a HuggingFace tokenizer to produce a token sequence that is then input to a transformer model.

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

@author bergum
model-integration/src/main/java/ai/vespa/embedding/ColBertEmbedder.java