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name: "viencoder" |
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# maximum batch size |
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max_batch_size: 0 |
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platform: "ensemble" |
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#input to the model |
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input [ |
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{ |
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name: "TEXT" |
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data_type: TYPE_STRING |
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dims: [ -1 ] |
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# -1 means dynamic axis, aka this dimension may change |
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} |
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] |
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#output of the model |
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output { |
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name: "output_0" |
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data_type: TYPE_FP32 |
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dims: [-1, -1] |
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# two dimensional tensor, where 1st dimension: batch-size, 2nd dimension: #classes |
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} |
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#Type of scheduler to be used |
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ensemble_scheduling { |
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step [ |
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{ |
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model_name: "viencoder.tokenizer" |
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model_version: -1 |
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input_map { |
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key: "TEXT" |
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value: "TEXT" |
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} |
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output_map [ |
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{ |
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key: "input_ids" |
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value: "input_ids" |
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}, |
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{ |
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key: "attention_mask" |
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value: "attention_mask" |
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} |
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] |
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}, |
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{ |
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model_name: "viencoder.model" |
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model_version: -1 |
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input_map [ |
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{ |
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key: "input_ids" |
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value: "input_ids" |
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}, |
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{ |
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key: "attention_mask" |
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value: "attention_mask" |
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} |
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] |
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output_map { |
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key: "sentence_embedding" |
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value: "output_0" |
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} |
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} |
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] |
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} |