---
type: api
title: Get trained model configuration info
description: **All methods and paths for this operation:**
url: https://www.elastic.co/elastic/docs-builder/docs/4371/api/doc/elasticsearch/v8/operation/operation-ml-get-trained-models
resource: https://www.elastic.co/elastic/docs-builder/docs/4371/api/doc/elasticsearch/v8/operation/operation-ml-get-trained-models
products:
  - Elasticsearch
---

# Get trained model configuration info


Availability: 7.10+

## Paths

- `GET` `/_ml/trained_models/{model_id}`
- `GET` `/_ml/trained_models`

- [Api key auth](/elastic/docs-builder/docs/4371/api/doc/elasticsearch/v8/authentication#apikeyauth)
- [Basic auth](/elastic/docs-builder/docs/4371/api/doc/elasticsearch/v8/authentication#basicauth)
- [Bearer auth](/elastic/docs-builder/docs/4371/api/doc/elasticsearch/v8/authentication#bearerauth)

#### Path Parameters

- `model_id` (union Id | [] Id) — optional, leave it out to use `/_ml/trained_models`
  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.

## Description

## Required authorization

* Cluster privileges: `monitor_ml`

## Query String Parameters

- `allow_no_match` (boolean · default: true)
  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 · default: true)
  Specifies whether the included model definition should be returned as a
JSON map (true) or in a custom compressed format (false).
- `exclude_generated` (boolean · default: false)
  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 · default: 0)
  Skips the specified number of models.
- `include` (enum Include)
  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: `definition`, `feature_importance_baseline`, `hyperparameters`, `total_feature_importance`, `definition_status`
- `include_model_definition` (boolean) — deprecated
  parameter is deprecated! Use [include=definition] instead
- `size` (number · default: 100)
  Specifies the maximum number of models to obtain.
- `tags` (union string | [] 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.
  One of: `string` or `string[]`

## Response

`application/json`

- `count` (number)
- `trained_model_configs` ([] TrainedModelConfig)
  An array of trained model resources, which are sorted by the model_id value in ascending order.
  - `model_id` (string Id)
    Identifier for the trained model.
  - `model_type` (enum TrainedModelType)
    The model type

Supported values include:
  - `tree_ensemble`: The model definition is an ensemble model of decision trees.
  - `lang_ident`: A special type reserved for language identification models.
  - `pytorch`: The stored definition is a PyTorch (specifically a TorchScript) model.
Currently only NLP models are supported.
    Values: `tree_ensemble`, `lang_ident`, `pytorch`
  - `tags` ([] string)
    A comma delimited string of tags. A trained model can have many tags, or none.
  - `version` (string)
    The Elasticsearch version number in which the trained model was created.
  - `compressed_definition` (string)
  - `created_by` (string)
    Information on the creator of the trained model.
  - `create_time` (DateTime)
    The time when the trained model was created.
  - `default_field_map` (map string to string)
    Any field map described in the inference configuration takes precedence.
  - `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` ({} InferenceConfigCreateContainer)
    The default configuration for inference. This can be either a regression, classification, or one of the many NLP focused configurations. It must match the underlying definition.trained_model's target_type. For pre-packaged models such as ELSER the config is not required.
    - `regression` ({} RegressionInferenceOptions)
      Regression configuration for inference.
      - `results_field` (string Field)
        The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.
      - `num_top_feature_importance_values` (number · default: 0)
        Specifies the maximum number of feature importance values per document.
    - `classification` ({} ClassificationInferenceOptions)
      Classification configuration for inference.
      - `num_top_classes` (number)
        Specifies the number of top class predictions to return. Defaults to 0.
      - `num_top_feature_importance_values` (number · default: 0)
        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` ({} TextClassificationInferenceOptions)
      Text classification configuration for inference.
      - `num_top_classes` (number)
        Specifies the number of top class predictions to return. Defaults to 0.
      - `tokenization` ({} TokenizationConfigContainer)
        The tokenization options
        - `bert` ({} NlpBertTokenizationConfig)
          Indicates BERT tokenization and its options
          - `do_lower_case` (boolean · default: false)
            Should the tokenizer lower case the text
          - `max_sequence_length` (number · default: 512)
            Maximum input sequence length for the model
          - `span` (number · default: -1)
            Tokenization spanning options. Special value of -1 indicates no spanning takes place
          - `truncate` (enum TokenizationTruncate · default: first)
            Should tokenization input be automatically truncated before sending to the model for inference
            Values: `first`, `second`, `none`
          - `with_special_tokens` (boolean · default: true)
            Is tokenization completed with special tokens
        - `bert_ja` ({} NlpBertTokenizationConfig)
          Indicates BERT Japanese tokenization and its options
          - `do_lower_case` (boolean · default: false)
            Should the tokenizer lower case the text
          - `max_sequence_length` (number · default: 512)
            Maximum input sequence length for the model
          - `span` (number · default: -1)
            Tokenization spanning options. Special value of -1 indicates no spanning takes place
          - `truncate` (enum TokenizationTruncate · default: first)
            Should tokenization input be automatically truncated before sending to the model for inference
            Values: `first`, `second`, `none`
          - `with_special_tokens` (boolean · default: true)
            Is tokenization completed with special tokens
        - `mpnet` ({} NlpBertTokenizationConfig)
          Indicates MPNET tokenization and its options
          - `do_lower_case` (boolean · default: false)
            Should the tokenizer lower case the text
          - `max_sequence_length` (number · default: 512)
            Maximum input sequence length for the model
          - `span` (number · default: -1)
            Tokenization spanning options. Special value of -1 indicates no spanning takes place
          - `truncate` (enum TokenizationTruncate · default: first)
            Should tokenization input be automatically truncated before sending to the model for inference
            Values: `first`, `second`, `none`
          - `with_special_tokens` (boolean · default: true)
            Is tokenization completed with special tokens
        - `roberta` ({} NlpRobertaTokenizationConfig)
          Indicates RoBERTa tokenization and its options
          - `do_lower_case` (boolean · default: false)
            Should the tokenizer lower case the text
          - `max_sequence_length` (number · default: 512)
            Maximum input sequence length for the model
          - `span` (number · default: -1)
            Tokenization spanning options. Special value of -1 indicates no spanning takes place
          - `truncate` (enum TokenizationTruncate · default: first)
            Should tokenization input be automatically truncated before sending to the model for inference
            Values: `first`, `second`, `none`
          - `with_special_tokens` (boolean · default: true)
            Is tokenization completed with special tokens
          - `add_prefix_space` (boolean · default: false)
            Should the tokenizer prefix input with a space character
        - `xlm_roberta` ({} XlmRobertaTokenizationConfig)
          - `do_lower_case` (boolean · default: false)
            Should the tokenizer lower case the text
          - `max_sequence_length` (number · default: 512)
            Maximum input sequence length for the model
          - `span` (number · default: -1)
            Tokenization spanning options. Special value of -1 indicates no spanning takes place
          - `truncate` (enum TokenizationTruncate · default: first)
            Should tokenization input be automatically truncated before sending to the model for inference
            Values: `first`, `second`, `none`
          - `with_special_tokens` (boolean · default: true)
            Is tokenization completed with special tokens
      - `results_field` (string)
        The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.
      - `classification_labels` ([] string)
        Classification labels to apply other than the stored labels. Must have the same deminsions as the default configured labels
      - `vocabulary` ({} Vocabulary)
        - `index` (string IndexName)
    - `zero_shot_classification` ({} ZeroShotClassificationInferenceOptions)
      Zeroshot classification configuration for inference.
      - `tokenization` ({} TokenizationConfigContainer)
        The tokenization options to update when inferring
        Same fields as `tokenization` in `text_classification`
      - `hypothesis_template` (string · default: "This example is {}.")
        Hypothesis template used when tokenizing labels for prediction
      - `classification_labels` ([] string)
        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 · default: false)
        Indicates if more than one true label exists.
      - `labels` ([] string)
        The labels to predict.
    - `fill_mask` ({} FillMaskInferenceOptions)
      Fill mask configuration for inference.
      - `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` ({} TokenizationConfigContainer)
        The tokenization options to update when inferring
        Same fields as `tokenization` in `text_classification`
      - `results_field` (string)
        The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.
      - `vocabulary` ({} Vocabulary)
        - `index` (string IndexName)
    - `learning_to_rank` ({} LearningToRankConfig)
      - `default_params` (map string to object)
      - `feature_extractors` ([] string to FeatureExtractor)
      - `num_top_feature_importance_values` (number)
    - `ner` ({} NerInferenceOptions)
      Named entity recognition configuration for inference.
      - `tokenization` ({} TokenizationConfigContainer)
        The tokenization options
        Same fields as `tokenization` in `text_classification`
      - `results_field` (string)
        The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.
      - `classification_labels` ([] string)
        The token classification labels. Must be IOB formatted tags
      - `vocabulary` ({} Vocabulary)
        - `index` (string IndexName)
    - `pass_through` ({} PassThroughInferenceOptions)
      Pass through configuration for inference.
      - `tokenization` ({} TokenizationConfigContainer)
        The tokenization options
        Same fields as `tokenization` in `text_classification`
      - `results_field` (string)
        The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.
      - `vocabulary` ({} Vocabulary)
        - `index` (string IndexName)
    - `text_embedding` ({} TextEmbeddingInferenceOptions)
      Text embedding configuration for inference.
      - `embedding_size` (number)
        The number of dimensions in the embedding output
      - `tokenization` ({} TokenizationConfigContainer)
        The tokenization options
        Same fields as `tokenization` in `text_classification`
      - `results_field` (string)
        The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.
      - `vocabulary` ({} Vocabulary)
        - `index` (string IndexName)
    - `text_expansion` ({} TextExpansionInferenceOptions)
      Text expansion configuration for inference.
      - `tokenization` ({} TokenizationConfigContainer)
        The tokenization options
        Same fields as `tokenization` in `text_classification`
      - `results_field` (string)
        The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.
      - `vocabulary` ({} Vocabulary)
        - `index` (string IndexName)
    - `question_answering` ({} QuestionAnsweringInferenceOptions)
      Question answering configuration for inference.
      - `num_top_classes` (number)
        Specifies the number of top class predictions to return. Defaults to 0.
      - `tokenization` ({} TokenizationConfigContainer)
        The tokenization options to update when inferring
        Same fields as `tokenization` in `text_classification`
      - `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` ({} TrainedModelConfigInput)
    The input field names for the model definition.
    - `field_names` ([] string Field)
      An array of input field names for the model.
  - `license_level` (string)
    The license level of the trained model.
  - `metadata` ({} TrainedModelConfigMetadata)
    An object containing metadata about the trained model. For example, models created by data frame analytics contain analysis_config and input objects.
    - `model_aliases` ([] string)
    - `feature_importance_baseline` (map string to string)
      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.
    - `hyperparameters` ([] Hyperparameter)
      List of the available hyperparameters optimized during the fine_parameter_tuning phase as well as specified by the user.
      - `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 Name)
        Name of the hyperparameter.
      - `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)
        Indicates if the hyperparameter is specified by the user (true) or optimized (false).
      - `value` (number)
        The value of the hyperparameter, either optimized or specified by the user.
    - `total_feature_importance` ([] TotalFeatureImportance)
      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.
      - `feature_name` (string Name)
        The feature for which this importance was calculated.
      - `importance` ([] TotalFeatureImportanceStatistics)
        A collection of feature importance statistics related to the training data set for this particular feature.
        - `mean_magnitude` (number)
          The average magnitude of this feature across all the training data. This value is the average of the absolute values of the importance for this feature.
        - `max` (number)
          The maximum importance value across all the training data for this feature.
        - `min` (number)
          The minimum importance value across all the training data for this feature.
      - `classes` ([] TotalFeatureImportanceClass)
        If the trained model is a classification model, feature importance statistics are gathered per target class value.
        - `class_name` (string Name)
          The target class value. Could be a string, boolean, or number.
        - `importance` ([] TotalFeatureImportanceStatistics)
          A collection of feature importance statistics related to the training data set for this particular feature.
          - `mean_magnitude` (number)
            The average magnitude of this feature across all the training data. This value is the average of the absolute values of the importance for this feature.
          - `max` (number)
            The maximum importance value across all the training data for this feature.
          - `min` (number)
            The minimum importance value across all the training data for this feature.
  - `model_size_bytes` (number | string ByteSize)
  - `model_package` ({} ModelPackageConfig)
    - `create_time` (EpochTimeUnitMillis)
    - `description` (string)
    - `inference_config` (map string to object)
    - `metadata` (Metadata)
    - `minimum_version` (string)
    - `model_repository` (string)
    - `model_type` (string)
    - `packaged_model_id` (string Id)
    - `platform_architecture` (string)
    - `prefix_strings` ({} TrainedModelPrefixStrings)
      - `ingest` (string)
        String prepended to input at ingest
      - `search` (string)
        String prepended to input at search
    - `size` (number | string ByteSize)
    - `sha256` (string)
    - `tags` ([] string)
    - `vocabulary_file` (string)
  - `location` ({} TrainedModelLocation)
    - `index` ({} TrainedModelLocationIndex)
      - `name` (string IndexName)
  - `platform_architecture` (string)
  - `prefix_strings` ({} TrainedModelPrefixStrings)
    - `ingest` (string)
      String prepended to input at ingest
    - `search` (string)
      String prepended to input at search
## Code Examples

#### Console

```console
GET _ml/trained_models/
```

#### JavaScript

```javascript
const response = await client.ml.getTrainedModels();
```

#### Python

```python
resp = client.ml.get_trained_models()
```

#### curl

```curl
curl -X GET "$ELASTICSEARCH_URL/_ml/trained_models/" \
  -H "Authorization: ApiKey $ELASTIC_API_KEY"
```

#### Java

```java
client.ml().getTrainedModels(g -> g);
```

#### C#

```c#
var response = await client.MachineLearning.GetTrainedModelsAsync();
```

#### PHP

```php
$resp = $client->ml()->getTrainedModels();
```

#### Ruby

```ruby
response = client.ml.get_trained_models
```