{
  "name": "TextClassifier",
  "description": "Classify the input text into a set of known categories.",
  "subgraph_metadata": [
    {
      "input_tensor_metadata": [
        {
          "name": "ids",
          "description": "Tokenized ids of the input text.",
          "content": {
            "content_properties_type": "FeatureProperties",
            "content_properties": {
            }
          },
          "stats": {
          }
        },
        {
          "name": "segment_ids",
          "description": "0 for the first sequence, 1 for the second sequence if exists.",
          "content": {
            "content_properties_type": "FeatureProperties",
            "content_properties": {
            }
          },
          "stats": {
          }
        },
        {
          "name": "mask",
          "description": "Mask with 1 for real tokens and 0 for padding tokens.",
          "content": {
            "content_properties_type": "FeatureProperties",
            "content_properties": {
            }
          },
          "stats": {
          }
        }
      ],
      "output_tensor_metadata": [
        {
          "name": "score",
          "description": "Score of the labels respectively.",
          "content": {
            "content_properties_type": "FeatureProperties",
            "content_properties": {
            }
          },
          "stats": {
            "max": [
              1.0
            ],
            "min": [
              0.0
            ]
          },
          "associated_files": [
            {
              "name": "labels.txt",
              "description": "Labels for categories that the model can recognize.",
              "type": "TENSOR_AXIS_LABELS"
            }
          ]
        }
      ],
      "input_process_units": [
        {
          "options_type": "BertTokenizerOptions",
          "options": {
            "vocab_file": [
              {
                "name": "mobilebert_vocab.txt",
                "description": "Vocabulary file to convert natural language words to embedding vectors.",
                "type": "VOCABULARY"
              }
            ]
          }
        }
      ]
    }
  ],
  "min_parser_version": "1.1.0"
}
