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Create trained model vocabulary API

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Create trained model vocabulary API

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Creates a trained model vocabulary. This is supported only for natural language processing (NLP) models.

Request

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PUT _ml/trained_models/<model_id>/vocabulary/

Prerequisites

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Requires the manage_ml cluster privilege. This privilege is included in the machine_learning_admin built-in role.

Description

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The vocabulary is stored in the index as described in inference_config.*.vocabulary of the trained model definition.

Path parameters

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<model_id>: (Required, string) The unique identifier of the trained model.

Request body

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vocabulary: (array) The model vocabulary. Must not be empty.
merges: (Optional, array) The model merges used in byte-pair encoding. The merges must be sub-token pairs, space delimited, and in order of preference. Example: ["f o", "fo o"]. Must be provided for RoBERTa and BART style models.
scores: (Optional, array) Vocabulary value scores used by sentence-piece tokenization. Must have the same length as vocabulary. Required for unigram sentence-piece tokenized models like XLMRoberta and T5.

Examples

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The following example shows how to create a model vocabulary for a previously stored trained model configuration.

resp = client.ml.put_trained_model_vocabulary(
    model_id="elastic__distilbert-base-uncased-finetuned-conll03-english",
    body={"vocabulary": ["[PAD]", "[unused0]", ...]},
)
print(resp)

PUT _ml/trained_models/elastic__distilbert-base-uncased-finetuned-conll03-english/vocabulary
{
  "vocabulary": [
    "[PAD]",
    "[unused0]",
    ...
  ]
}

The API returns the following results:

{
    "acknowledged": true
}

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