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package=ai.vespa.embedding
# Transformer model settings
transformerModelUrl url
# Max length of token sequence model can handle
transformerMaxTokens int default=384
# Pooling strategy
poolingStrategy enum { cls, mean } default=mean
# Input names
transformerInputIds string default=input_ids
transformerAttentionMask string default=attention_mask
transformerTokenTypeIds string default=token_type_ids
# Output name
transformerOutput string default=output_0
# Settings for ONNX model evaluation
onnxExecutionMode enum { parallel, sequential } default=sequential
onnxInterOpThreads int default=1
onnxIntraOpThreads int default=-4 # n=number of threads -> n<0: CPUs/(-n), n==0: CPUs, n>0: n
# Settings for wordpiece tokenizer
tokenizerVocabUrl url
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