Get trained model configuration info

GET /_ml/trained_models/{model_id}

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.

    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:
        _types:DateTime string _types:UnitMillis number

        Time unit for milliseconds

      • default_field_map object

        Any field map described in the inference configuration takes precedence.

        Hide default_field_map attributes Show default_field_map attributes object
      • 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
        • 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
          Hide text_classification attributes Show text_classification 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
          • 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

        • zero_shot_classification object
          Hide zero_shot_classification attributes Show zero_shot_classification attributes object
          • tokenization object

            Tokenization options stored in inference configuration

            Hide tokenization attributes Show tokenization attributes object
          • 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

            Hide tokenization attributes Show tokenization attributes object
          • results_field string

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

        • ner object
          Hide ner attributes Show ner attributes object
          • tokenization object

            Tokenization options stored in inference configuration

            Hide tokenization attributes Show tokenization attributes object
          • 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
        • pass_through object
          Hide pass_through attributes Show pass_through attributes object
          • tokenization object

            Tokenization options stored in inference configuration

            Hide tokenization attributes Show tokenization attributes object
          • results_field string

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

          • vocabulary object
            Hide vocabulary attribute Show vocabulary attribute object
        • text_embedding object
          Hide text_embedding attributes Show text_embedding attributes object
          • 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
          • results_field string

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

        • text_expansion object
          Hide text_expansion attributes Show text_expansion attributes object
          • tokenization object

            Tokenization options stored in inference configuration

            Hide tokenization attributes Show tokenization attributes object
          • results_field string

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

        • 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
          • 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

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

      • 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 attributes Show feature_importance_baseline attributes object
        • 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:
        _types:ByteSize number _types:ByteSize string
      • location object
        Hide location attribute Show location attribute object
        • index object Required
          Hide index attribute Show index attribute object
      • 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
GET /_ml/trained_models/{model_id} 
curl \
 -X GET http://api.example.com/_ml/trained_models/{model_id}