Path parameters

• model_id string | array[string] Required

  The unique identifier of the trained model or a model alias.

  You can get information for multiple trained models in a single API request by using a comma-separated list of model IDs or a wildcard expression.

Query parameters

• allow_no_match boolean

  Specifies what to do when the request:

  • Contains wildcard expressions and there are no models that match.
  • Contains the _all string or no identifiers and there are no matches.
  • Contains wildcard expressions and there are only partial matches.

  If true, it returns an empty array when there are no matches and the subset of results when there are partial matches.

• decompress_definition boolean

  Specifies whether the included model definition should be returned as a JSON map (true) or in a custom compressed format (false).

• exclude_generated boolean

  Indicates if certain fields should be removed from the configuration on retrieval. This allows the configuration to be in an acceptable format to be retrieved and then added to another cluster.

• from number

  Skips the specified number of models.

• include string

  A comma delimited string of optional fields to include in the response body.

  Supported values include:

  • definition: Includes the model definition.
  • feature_importance_baseline: Includes the baseline for feature importance values.
  • hyperparameters: Includes the information about hyperparameters used to train the model. This information consists of the value, the absolute and relative importance of the hyperparameter as well as an indicator of whether it was specified by the user or tuned during hyperparameter optimization.
  • total_feature_importance: Includes the total feature importance for the training data set. The baseline and total feature importance values are returned in the metadata field in the response body.
  • definition_status: Includes the model definition status.

  Values are definition, feature_importance_baseline, hyperparameters, total_feature_importance, or definition_status.

• size number

  Specifies the maximum number of models to obtain.

• tags string | array[string]

  A comma delimited string of tags. A trained model can have many tags, or none. When supplied, only trained models that contain all the supplied tags are returned.

Responses

• 200 application/json
  Hide response attributes Show response attributes object

  • count number Required
  • trained_model_configs array[object] Required

    An array of trained model resources, which are sorted by the model_id value in ascending order.

    Hide trained_model_configs attributes Show trained_model_configs attributes object

    • model_id string Required
    • model_type string

      Values are tree_ensemble, lang_ident, or pytorch.

    • tags array[string] Required

      A comma delimited string of tags. A trained model can have many tags, or none.

    • version string
    • compressed_definition string
    • created_by string

      Information on the creator of the trained model.

    • create_time string | number

      A date and time, either as a string whose format can depend on the context (defaulting to ISO 8601), or a number of milliseconds since the Epoch. Elasticsearch accepts both as input, but will generally output a string representation.

      One of:

      DateTime string UnitMillis number

      Time unit for milliseconds

    • default_field_map object

      Any field map described in the inference configuration takes precedence.

      Hide default_field_map attribute Show default_field_map attribute object

      • * string Additional properties
    • description string

      The free-text description of the trained model.

    • estimated_heap_memory_usage_bytes number

      The estimated heap usage in bytes to keep the trained model in memory.

    • estimated_operations number

      The estimated number of operations to use the trained model.

    • fully_defined boolean

      True if the full model definition is present.

    • inference_config object

      Inference configuration provided when storing the model config

      Hide inference_config attributes Show inference_config attributes object

      • regression object
        Hide regression attributes Show regression attributes object

        • results_field string

          Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.

        • num_top_feature_importance_values number

          Specifies the maximum number of feature importance values per document.

      • classification object
        Hide classification attributes Show classification attributes object

        • num_top_classes number

          Specifies the number of top class predictions to return. Defaults to 0.

        • num_top_feature_importance_values number

          Specifies the maximum number of feature importance values per document.

        • prediction_field_type string

          Specifies the type of the predicted field to write. Acceptable values are: string, number, boolean. When boolean is provided 1.0 is transformed to true and 0.0 to false.

        • results_field string

          The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.

        • top_classes_results_field string

          Specifies the field to which the top classes are written. Defaults to top_classes.

      • text_classification object
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        • num_top_classes number

          Specifies the number of top class predictions to return. Defaults to 0.

        • tokenization object

          Tokenization options stored in inference configuration

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          • bert
          • bert_ja
          • mpnet
          • roberta
          • xlm_roberta
        • results_field string

          The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.

        • classification_labels array[string]

          Classification labels to apply other than the stored labels. Must have the same deminsions as the default configured labels

        • vocabulary object
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          • index string Required
      • zero_shot_classification object
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        • tokenization object

          Tokenization options stored in inference configuration

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          • bert
          • bert_ja
          • mpnet
          • roberta
          • xlm_roberta
        • hypothesis_template string

          Hypothesis template used when tokenizing labels for prediction

        • classification_labels array[string] Required

          The zero shot classification labels indicating entailment, neutral, and contradiction Must contain exactly and only entailment, neutral, and contradiction

        • results_field string

          The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.

        • multi_label boolean

          Indicates if more than one true label exists.

        • labels array[string]

          The labels to predict.

      • fill_mask object
        Hide fill_mask attributes Show fill_mask attributes object

        • mask_token string

          The string/token which will be removed from incoming documents and replaced with the inference prediction(s). In a response, this field contains the mask token for the specified model/tokenizer. Each model and tokenizer has a predefined mask token which cannot be changed. Thus, it is recommended not to set this value in requests. However, if this field is present in a request, its value must match the predefined value for that model/tokenizer, otherwise the request will fail.

        • num_top_classes number

          Specifies the number of top class predictions to return. Defaults to 0.

        • tokenization object

          Tokenization options stored in inference configuration

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          • bert
          • bert_ja
          • mpnet
          • roberta
          • xlm_roberta
        • results_field string

          The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.

        • vocabulary object Required
          Hide vocabulary attribute Show vocabulary attribute object

          • index string Required
      • learning_to_rank object
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        • default_params object
          Hide default_params attribute Show default_params attribute object

          • * object Additional properties
        • feature_extractors array[object]
        • num_top_feature_importance_values number Required
      • ner object
        Hide ner attributes Show ner attributes object

        • tokenization object

          Tokenization options stored in inference configuration

          Hide tokenization attributes Show tokenization attributes object

          • bert
          • bert_ja
          • mpnet
          • roberta
          • xlm_roberta
        • results_field string

          The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.

        • classification_labels array[string]

          The token classification labels. Must be IOB formatted tags

        • vocabulary object
          Hide vocabulary attribute Show vocabulary attribute object

          • index string Required
      • pass_through object
        Hide pass_through attributes Show pass_through attributes object

        • tokenization object

          Tokenization options stored in inference configuration

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          • bert
          • bert_ja
          • mpnet
          • roberta
          • xlm_roberta
        • results_field string

          The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.

        • vocabulary object
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          • index string Required
      • text_embedding object
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        • embedding_size number

          The number of dimensions in the embedding output

        • tokenization object

          Tokenization options stored in inference configuration

          Hide tokenization attributes Show tokenization attributes object

          • bert
          • bert_ja
          • mpnet
          • roberta
          • xlm_roberta
        • results_field string

          The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.

        • vocabulary object Required
          Hide vocabulary attribute Show vocabulary attribute object

          • index string Required
      • text_expansion object
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        • tokenization object

          Tokenization options stored in inference configuration

          Hide tokenization attributes Show tokenization attributes object

          • bert
          • bert_ja
          • mpnet
          • roberta
          • xlm_roberta
        • results_field string

          The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.

        • vocabulary object Required
          Hide vocabulary attribute Show vocabulary attribute object

          • index string Required
      • question_answering object
        Hide question_answering attributes Show question_answering attributes object

        • num_top_classes number

          Specifies the number of top class predictions to return. Defaults to 0.

        • tokenization object

          Tokenization options stored in inference configuration

          Hide tokenization attributes Show tokenization attributes object

          • bert
          • bert_ja
          • mpnet
          • roberta
          • xlm_roberta
        • results_field string

          The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.

        • max_answer_length number

          The maximum answer length to consider

    • input object Required
      Hide input attribute Show input attribute object

      • field_names array[string] Required

        An array of input field names for the model.

    • license_level string

      The license level of the trained model.

    • metadata object
      Hide metadata attributes Show metadata attributes object

      • model_aliases array[string]
      • feature_importance_baseline object

        An object that contains the baseline for feature importance values. For regression analysis, it is a single value. For classification analysis, there is a value for each class.

        Hide feature_importance_baseline attribute Show feature_importance_baseline attribute object

        • * string Additional properties
      • hyperparameters array[object]

        List of the available hyperparameters optimized during the fine_parameter_tuning phase as well as specified by the user.

        Hide hyperparameters attributes Show hyperparameters attributes object

        • absolute_importance number

          A positive number showing how much the parameter influences the variation of the loss function. For hyperparameters with values that are not specified by the user but tuned during hyperparameter optimization.

        • name string Required
        • relative_importance number

          A number between 0 and 1 showing the proportion of influence on the variation of the loss function among all tuned hyperparameters. For hyperparameters with values that are not specified by the user but tuned during hyperparameter optimization.

        • supplied boolean Required

          Indicates if the hyperparameter is specified by the user (true) or optimized (false).

        • value number Required

          The value of the hyperparameter, either optimized or specified by the user.

      • total_feature_importance array[object]

        An array of the total feature importance for each feature used from the training data set. This array of objects is returned if data frame analytics trained the model and the request includes total_feature_importance in the include request parameter.

        Hide total_feature_importance attributes Show total_feature_importance attributes object

        • feature_name string Required
        • importance array[object] Required

          A collection of feature importance statistics related to the training data set for this particular feature.

        • classes array[object] Required

          If the trained model is a classification model, feature importance statistics are gathered per target class value.

    • model_size_bytes number | string

      One of:

      ByteSize number ByteSize string
    • model_package object
      Hide model_package attributes Show model_package attributes object

      • create_time number

        Time unit for milliseconds

      • description string
      • inference_config object
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        • * object Additional properties
      • metadata object
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        • * object Additional properties
      • minimum_version string
      • model_repository string
      • model_type string
      • packaged_model_id string Required
      • platform_architecture string
      • prefix_strings object
        Hide prefix_strings attributes Show prefix_strings attributes object

        • ingest string

          String prepended to input at ingest

        • search string

          String prepended to input at search

      • size number | string

        One of:

        ByteSize number ByteSize string
      • sha256 string
      • tags array[string]
      • vocabulary_file string
    • location object
      Hide location attribute Show location attribute object

      • index object Required
        Hide index attribute Show index attribute object

        • name string Required
    • platform_architecture string
    • prefix_strings object
      Hide prefix_strings attributes Show prefix_strings attributes object

      • ingest string

        String prepended to input at ingest

      • search string

        String prepended to input at search