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Infer trained model API

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Infer trained model API

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Evaluates a trained model. The model may be any supervised model either trained by data frame analytics or imported.

For model deployments with caching enabled, results may be returned directly from the inference cache.

Request

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POST _ml/trained_models/<model_id>/_infer POST _ml/trained_models/<deployment_id>/_infer

Path parameters

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<model_id>: (Optional, string) The unique identifier of the trained model or a model alias.

If you specify the model_id in the API call, and the model has multiple deployments, a random deployment will be used. If the model_id matches the ID of one of the deployments, that deployment will be used.

<deployment_id>: (Optional, string) A unique identifier for the deployment of the model.

Query parameters

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timeout: (Optional, time) Controls the amount of time to wait for inference results. Defaults to 10 seconds.

Request body

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docs: (Required, array) An array of objects to pass to the model for inference. The objects should contain the fields matching your configured trained model input. Typically for NLP models, the field name is text_field. Each inference input field specified in this property must be single strings not arrays of strings.

inference_config

(Optional, object) The default configuration for inference. This can be: regression, classification, fill_mask, ner, question_answering, text_classification, text_embedding or zero_shot_classification. If regression or classification, it must match the target_type of the underlying definition.trained_model. If fill_mask, ner, question_answering, text_classification, or text_embedding; the model_type must be pytorch. If not specified, the inference_config from when the model was created is used.

Properties of inference_config

classification

(Optional, object) Classification configuration for inference.

Properties of classification inference

num_top_classes: (Optional, integer) Specifies the number of top class predictions to return. Defaults to 0.
num_top_feature_importance_values: (Optional, integer) Specifies the maximum number of feature importance values per document. Defaults to 0 which means no feature importance calculation occurs.
prediction_field_type: (Optional, string) Specifies the type of the predicted field to write. Valid values are: string, number, boolean. When boolean is provided 1.0 is transformed to true and 0.0 to false.
results_field: (Optional, string) The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.
top_classes_results_field: (Optional, string) Specifies the field to which the top classes are written. Defaults to top_classes.

fill_mask

(Optional, object) Configuration for a fill_mask natural language processing (NLP) task. The fill_mask task works with models optimized for a fill mask action. For example, for BERT models, the following text may be provided: "The capital of France is [MASK].". The response indicates the value most likely to replace [MASK]. In this instance, the most probable token is paris.

Properties of fill_mask inference

num_top_classes

(Optional, integer) Number of top predicted tokens to return for replacing the mask token. Defaults to 0.

results_field

(Optional, string) The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.

tokenization

(Optional, object) Indicates the tokenization to perform and the desired settings. The default tokenization configuration is bert. Valid tokenization values are

bert: Use for BERT-style models
mpnet: Use for MPNet-style models
roberta: Use for RoBERTa-style and BART-style models
[preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. xlm_roberta: Use for XLMRoBERTa-style models
[preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. bert_ja: Use for BERT-style models trained for the Japanese language.

Properties of tokenization

bert

(Optional, object) BERT-style tokenization is to be performed with the enclosed settings.

Properties of bert

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

none: No truncation occurs; the inference request receives an error.
first: Only the first sequence is truncated.
second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

roberta

(Optional, object) RoBERTa-style tokenization is to be performed with the enclosed settings.

Properties of roberta

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

none: No truncation occurs; the inference request receives an error.
first: Only the first sequence is truncated.
second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

mpnet

(Optional, object) MPNet-style tokenization is to be performed with the enclosed settings.

Properties of mpnet

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

none: No truncation occurs; the inference request receives an error.
first: Only the first sequence is truncated.
second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

xlm_roberta

(Optional, object) [preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. XLMRoBERTa-style tokenization is to be performed with the enclosed settings.

Properties of xlm_roberta

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

none: No truncation occurs; the inference request receives an error.
first: Only the first sequence is truncated.
second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

bert_ja

(Optional, object) [preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. BERT-style tokenization for Japanese text is to be performed with the enclosed settings.

Properties of bert_ja

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

none: No truncation occurs; the inference request receives an error.
first: Only the first sequence is truncated.
second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

ner

(Optional, object) Configures a named entity recognition (NER) task. NER is a special case of token classification. Each token in the sequence is classified according to the provided classification labels. Currently, the NER task requires the classification_labels Inside-Outside-Beginning (IOB) formatted labels. Only person, organization, location, and miscellaneous are supported.

Properties of ner inference

results_field

(Optional, string) The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.

tokenization

(Optional, object) Indicates the tokenization to perform and the desired settings. The default tokenization configuration is bert. Valid tokenization values are

bert: Use for BERT-style models
mpnet: Use for MPNet-style models
roberta: Use for RoBERTa-style and BART-style models
[preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. xlm_roberta: Use for XLMRoBERTa-style models
[preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. bert_ja: Use for BERT-style models trained for the Japanese language.

Properties of tokenization

bert

(Optional, object) BERT-style tokenization is to be performed with the enclosed settings.

Properties of bert

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

none: No truncation occurs; the inference request receives an error.
first: Only the first sequence is truncated.
second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

roberta

(Optional, object) RoBERTa-style tokenization is to be performed with the enclosed settings.

Properties of roberta

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

none: No truncation occurs; the inference request receives an error.
first: Only the first sequence is truncated.
second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

mpnet

(Optional, object) MPNet-style tokenization is to be performed with the enclosed settings.

Properties of mpnet

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

none: No truncation occurs; the inference request receives an error.
first: Only the first sequence is truncated.
second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

xlm_roberta

Properties of xlm_roberta

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

none: No truncation occurs; the inference request receives an error.
first: Only the first sequence is truncated.
second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

bert_ja

(Optional, object) [preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. BERT-style tokenization for Japanese text is to be performed with the enclosed settings.

Properties of bert_ja

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

none: No truncation occurs; the inference request receives an error.
first: Only the first sequence is truncated.
second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

pass_through

(Optional, object) Configures a pass_through task. This task is useful for debugging as no post-processing is done to the inference output and the raw pooling layer results are returned to the caller.

Properties of pass_through inference

results_field

(Optional, string) The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.

tokenization

(Optional, object) Indicates the tokenization to perform and the desired settings. The default tokenization configuration is bert. Valid tokenization values are

bert: Use for BERT-style models
mpnet: Use for MPNet-style models
roberta: Use for RoBERTa-style and BART-style models
[preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. xlm_roberta: Use for XLMRoBERTa-style models
[preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. bert_ja: Use for BERT-style models trained for the Japanese language.

Properties of tokenization

bert

(Optional, object) BERT-style tokenization is to be performed with the enclosed settings.

Properties of bert

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

none: No truncation occurs; the inference request receives an error.
first: Only the first sequence is truncated.
second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

roberta

(Optional, object) RoBERTa-style tokenization is to be performed with the enclosed settings.

Properties of roberta

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

none: No truncation occurs; the inference request receives an error.
first: Only the first sequence is truncated.
second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

mpnet

(Optional, object) MPNet-style tokenization is to be performed with the enclosed settings.

Properties of mpnet

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

none: No truncation occurs; the inference request receives an error.
first: Only the first sequence is truncated.
second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

xlm_roberta

Properties of xlm_roberta

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

none: No truncation occurs; the inference request receives an error.
first: Only the first sequence is truncated.
second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

bert_ja

(Optional, object) [preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. BERT-style tokenization for Japanese text is to be performed with the enclosed settings.

Properties of bert_ja

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

none: No truncation occurs; the inference request receives an error.
first: Only the first sequence is truncated.
second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

question_answering

(Optional, object) Configures a question answering natural language processing (NLP) task. Question answering is useful for extracting answers for certain questions from a large corpus of text.

Properties of question_answering inference

max_answer_length

(Optional, integer) The maximum amount of words in the answer. Defaults to 15.

num_top_classes

(Optional, integer) The number the top found answers to return. Defaults to 0, meaning only the best found answer is returned.

question

(Required, string) The question to use when extracting an answer

results_field

(Optional, string) The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.

tokenization

(Optional, object) Indicates the tokenization to perform and the desired settings. The default tokenization configuration is bert. Valid tokenization values are

bert: Use for BERT-style models
mpnet: Use for MPNet-style models
roberta: Use for RoBERTa-style and BART-style models
[preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. xlm_roberta: Use for XLMRoBERTa-style models
[preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. bert_ja: Use for BERT-style models trained for the Japanese language.

Recommended to set max_sequence_length to 386 with 128 of span and set truncate to none.

Properties of tokenization

bert

(Optional, object) BERT-style tokenization is to be performed with the enclosed settings.

Properties of bert

span

(Optional, integer) When truncate is none, you can partition longer text sequences for inference. The value indicates how many tokens overlap between each subsequence.

The default value is -1, indicating no windowing or spanning occurs.

When your typical input is just slightly larger than max_sequence_length, it may be best to simply truncate; there will be very little information in the second subsequence.

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

none: No truncation occurs; the inference request receives an error.
first: Only the first sequence is truncated.
second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

roberta

(Optional, object) RoBERTa-style tokenization is to be performed with the enclosed settings.

Properties of roberta

span

(Optional, integer) When truncate is none, you can partition longer text sequences for inference. The value indicates how many tokens overlap between each subsequence.

The default value is -1, indicating no windowing or spanning occurs.

When your typical input is just slightly larger than max_sequence_length, it may be best to simply truncate; there will be very little information in the second subsequence.

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

none: No truncation occurs; the inference request receives an error.
first: Only the first sequence is truncated.
second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

mpnet

(Optional, object) MPNet-style tokenization is to be performed with the enclosed settings.

Properties of mpnet

span

(Optional, integer) When truncate is none, you can partition longer text sequences for inference. The value indicates how many tokens overlap between each subsequence.

The default value is -1, indicating no windowing or spanning occurs.

When your typical input is just slightly larger than max_sequence_length, it may be best to simply truncate; there will be very little information in the second subsequence.

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

none: No truncation occurs; the inference request receives an error.
first: Only the first sequence is truncated.
second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

xlm_roberta

Properties of xlm_roberta

span

(Optional, integer) When truncate is none, you can partition longer text sequences for inference. The value indicates how many tokens overlap between each subsequence.

The default value is -1, indicating no windowing or spanning occurs.

When your typical input is just slightly larger than max_sequence_length, it may be best to simply truncate; there will be very little information in the second subsequence.

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

none: No truncation occurs; the inference request receives an error.
first: Only the first sequence is truncated.
second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

bert_ja

(Optional, object) [preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. BERT-style tokenization for Japanese text is to be performed with the enclosed settings.

Properties of bert_ja

span

(Optional, integer) When truncate is none, you can partition longer text sequences for inference. The value indicates how many tokens overlap between each subsequence.

The default value is -1, indicating no windowing or spanning occurs.

When your typical input is just slightly larger than max_sequence_length, it may be best to simply truncate; there will be very little information in the second subsequence.

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

none: No truncation occurs; the inference request receives an error.
first: Only the first sequence is truncated.
second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

regression

(Optional, object) Regression configuration for inference.

Properties of regression inference

num_top_feature_importance_values: (Optional, integer) Specifies the maximum number of feature importance values per document. By default, it is zero and no feature importance calculation occurs.
results_field: (Optional, string) The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.

text_classification

(Optional, object) A text classification task. Text classification classifies a provided text sequence into previously known target classes. A specific example of this is sentiment analysis, which returns the likely target classes indicating text sentiment, such as "sad", "happy", or "angry".

Properties of text_classification inference

classification_labels

(Optional, string) An array of classification labels.

num_top_classes

(Optional, integer) Specifies the number of top class predictions to return. Defaults to all classes (-1).

results_field

(Optional, string) The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.

tokenization

(Optional, object) Indicates the tokenization to perform and the desired settings. The default tokenization configuration is bert. Valid tokenization values are

bert: Use for BERT-style models
mpnet: Use for MPNet-style models
roberta: Use for RoBERTa-style and BART-style models
[preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. xlm_roberta: Use for XLMRoBERTa-style models
[preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. bert_ja: Use for BERT-style models trained for the Japanese language.

Properties of tokenization

bert

(Optional, object) BERT-style tokenization is to be performed with the enclosed settings.

Properties of bert

span

(Optional, integer) When truncate is none, you can partition longer text sequences for inference. The value indicates how many tokens overlap between each subsequence.

The default value is -1, indicating no windowing or spanning occurs.

When your typical input is just slightly larger than max_sequence_length, it may be best to simply truncate; there will be very little information in the second subsequence.

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

none: No truncation occurs; the inference request receives an error.
first: Only the first sequence is truncated.
second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

roberta

(Optional, object) RoBERTa-style tokenization is to be performed with the enclosed settings.

Properties of roberta

span

(Optional, integer) When truncate is none, you can partition longer text sequences for inference. The value indicates how many tokens overlap between each subsequence.

The default value is -1, indicating no windowing or spanning occurs.

When your typical input is just slightly larger than max_sequence_length, it may be best to simply truncate; there will be very little information in the second subsequence.

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

none: No truncation occurs; the inference request receives an error.
first: Only the first sequence is truncated.
second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

mpnet

(Optional, object) MPNet-style tokenization is to be performed with the enclosed settings.

Properties of mpnet

span

(Optional, integer) When truncate is none, you can partition longer text sequences for inference. The value indicates how many tokens overlap between each subsequence.

The default value is -1, indicating no windowing or spanning occurs.

When your typical input is just slightly larger than max_sequence_length, it may be best to simply truncate; there will be very little information in the second subsequence.

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

none: No truncation occurs; the inference request receives an error.
first: Only the first sequence is truncated.
second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

xlm_roberta

Properties of xlm_roberta

span

(Optional, integer) When truncate is none, you can partition longer text sequences for inference. The value indicates how many tokens overlap between each subsequence.

The default value is -1, indicating no windowing or spanning occurs.

When your typical input is just slightly larger than max_sequence_length, it may be best to simply truncate; there will be very little information in the second subsequence.

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

none: No truncation occurs; the inference request receives an error.
first: Only the first sequence is truncated.
second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

bert_ja

(Optional, object) [preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. BERT-style tokenization for Japanese text is to be performed with the enclosed settings.

Properties of bert_ja

span

(Optional, integer) When truncate is none, you can partition longer text sequences for inference. The value indicates how many tokens overlap between each subsequence.

The default value is -1, indicating no windowing or spanning occurs.

When your typical input is just slightly larger than max_sequence_length, it may be best to simply truncate; there will be very little information in the second subsequence.

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

none: No truncation occurs; the inference request receives an error.
first: Only the first sequence is truncated.
second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

text_embedding

(Object, optional) Text embedding takes an input sequence and transforms it into a vector of numbers. These embeddings capture not simply tokens, but semantic meanings and context. These embeddings can be used in a dense vector field for powerful insights.

Properties of text_embedding inference

results_field

(Optional, string) The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.

tokenization

(Optional, object) Indicates the tokenization to perform and the desired settings. The default tokenization configuration is bert. Valid tokenization values are

bert: Use for BERT-style models
mpnet: Use for MPNet-style models
roberta: Use for RoBERTa-style and BART-style models
[preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. xlm_roberta: Use for XLMRoBERTa-style models
[preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. bert_ja: Use for BERT-style models trained for the Japanese language.

Properties of tokenization

bert

(Optional, object) BERT-style tokenization is to be performed with the enclosed settings.

Properties of bert

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

none: No truncation occurs; the inference request receives an error.
first: Only the first sequence is truncated.
second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

roberta

(Optional, object) RoBERTa-style tokenization is to be performed with the enclosed settings.

Properties of roberta

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

none: No truncation occurs; the inference request receives an error.
first: Only the first sequence is truncated.
second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

mpnet

(Optional, object) MPNet-style tokenization is to be performed with the enclosed settings.

Properties of mpnet

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

none: No truncation occurs; the inference request receives an error.
first: Only the first sequence is truncated.
second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

xlm_roberta

Properties of xlm_roberta

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

none: No truncation occurs; the inference request receives an error.
first: Only the first sequence is truncated.
second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

bert_ja

(Optional, object) [preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. BERT-style tokenization for Japanese text is to be performed with the enclosed settings.

Properties of bert_ja

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

none: No truncation occurs; the inference request receives an error.
first: Only the first sequence is truncated.
second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

text_similarity

(Object, optional) Text similarity takes an input sequence and compares it with another input sequence. This is commonly referred to as cross-encoding. This task is useful for ranking document text when comparing it to another provided text input.

Properties of text_similarity inference

span_score_combination_function

(Optional, string) Identifies how to combine the resulting similarity score when a provided text passage is longer than max_sequence_length and must be automatically separated for multiple calls. This only is applicable when truncate is none and span is a non-negative number. The default value is max. Available options are:

max: The maximum score from all the spans is returned.
mean: The mean score over all the spans is returned.

text

(Required, string) This is the text with which to compare all document provided text inputs.

tokenization

(Optional, object) Indicates the tokenization to perform and the desired settings. The default tokenization configuration is bert. Valid tokenization values are

bert: Use for BERT-style models
mpnet: Use for MPNet-style models
roberta: Use for RoBERTa-style and BART-style models
[preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. xlm_roberta: Use for XLMRoBERTa-style models
[preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. bert_ja: Use for BERT-style models trained for the Japanese language.

Properties of tokenization

bert

(Optional, object) BERT-style tokenization is to be performed with the enclosed settings.

Properties of bert

span

(Optional, integer) When truncate is none, you can partition longer text sequences for inference. The value indicates how many tokens overlap between each subsequence.

The default value is -1, indicating no windowing or spanning occurs.

When your typical input is just slightly larger than max_sequence_length, it may be best to simply truncate; there will be very little information in the second subsequence.

with_special_tokens

(Optional, boolean) Tokenize with special tokens. The tokens typically included in BERT-style tokenization are:

[CLS]: The first token of the sequence being classified.
[SEP]: Indicates sequence separation.

roberta

(Optional, object) RoBERTa-style tokenization is to be performed with the enclosed settings.

Properties of roberta

span

(Optional, integer) When truncate is none, you can partition longer text sequences for inference. The value indicates how many tokens overlap between each subsequence.

The default value is -1, indicating no windowing or spanning occurs.

When your typical input is just slightly larger than max_sequence_length, it may be best to simply truncate; there will be very little information in the second subsequence.

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

none: No truncation occurs; the inference request receives an error.
first: Only the first sequence is truncated.
second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

mpnet

(Optional, object) MPNet-style tokenization is to be performed with the enclosed settings.

Properties of mpnet

span

(Optional, integer) When truncate is none, you can partition longer text sequences for inference. The value indicates how many tokens overlap between each subsequence.

The default value is -1, indicating no windowing or spanning occurs.

When your typical input is just slightly larger than max_sequence_length, it may be best to simply truncate; there will be very little information in the second subsequence.

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

none: No truncation occurs; the inference request receives an error.
first: Only the first sequence is truncated.
second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

xlm_roberta

Properties of xlm_roberta

span

(Optional, integer) When truncate is none, you can partition longer text sequences for inference. The value indicates how many tokens overlap between each subsequence.

The default value is -1, indicating no windowing or spanning occurs.

When your typical input is just slightly larger than max_sequence_length, it may be best to simply truncate; there will be very little information in the second subsequence.

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

none: No truncation occurs; the inference request receives an error.
first: Only the first sequence is truncated.
second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

bert_ja

(Optional, object) [preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. BERT-style tokenization for Japanese text is to be performed with the enclosed settings.

Properties of bert_ja

span

(Optional, integer) When truncate is none, you can partition longer text sequences for inference. The value indicates how many tokens overlap between each subsequence.

The default value is -1, indicating no windowing or spanning occurs.

When your typical input is just slightly larger than max_sequence_length, it may be best to simply truncate; there will be very little information in the second subsequence.

with_special_tokens

(Optional, boolean) Tokenize with special tokens if true.

zero_shot_classification

(Object, optional) Configures a zero-shot classification task. Zero-shot classification allows for text classification to occur without pre-determined labels. At inference time, it is possible to adjust the labels to classify. This makes this type of model and task exceptionally flexible.

If consistently classifying the same labels, it may be better to use a fine-tuned text classification model.

Properties of zero_shot_classification inference

labels

(Optional, array) The labels to classify. Can be set at creation for default labels, and then updated during inference.

multi_label

(Optional, boolean) Indicates if more than one true label is possible given the input. This is useful when labeling text that could pertain to more than one of the input labels. Defaults to false.

results_field

(Optional, string) The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.

tokenization

(Optional, object) Indicates the tokenization to perform and the desired settings. The default tokenization configuration is bert. Valid tokenization values are

bert: Use for BERT-style models
mpnet: Use for MPNet-style models
roberta: Use for RoBERTa-style and BART-style models
[preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. xlm_roberta: Use for XLMRoBERTa-style models
[preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. bert_ja: Use for BERT-style models trained for the Japanese language.

Properties of tokenization

bert

(Optional, object) BERT-style tokenization is to be performed with the enclosed settings.

Properties of bert

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

none: No truncation occurs; the inference request receives an error.
first: Only the first sequence is truncated.
second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

roberta

(Optional, object) RoBERTa-style tokenization is to be performed with the enclosed settings.

Properties of roberta

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

none: No truncation occurs; the inference request receives an error.
first: Only the first sequence is truncated.
second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

mpnet

(Optional, object) MPNet-style tokenization is to be performed with the enclosed settings.

Properties of mpnet

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

none: No truncation occurs; the inference request receives an error.
first: Only the first sequence is truncated.
second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

xlm_roberta

Properties of xlm_roberta

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

none: No truncation occurs; the inference request receives an error.
first: Only the first sequence is truncated.
second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

bert_ja

(Optional, object) [preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. BERT-style tokenization for Japanese text is to be performed with the enclosed settings.

Properties of bert_ja

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

none: No truncation occurs; the inference request receives an error.
first: Only the first sequence is truncated.
second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

Examples

edit

The response depends on the kind of model.

For example, for language identification the response is the predicted language and the score:

resp = client.ml.infer_trained_model(
    model_id="lang_ident_model_1",
    docs=[
        {
            "text": "The fool doth think he is wise, but the wise man knows himself to be a fool."
        }
    ],
)
print(resp)

response = client.ml.infer_trained_model(
  model_id: 'lang_ident_model_1',
  body: {
    docs: [
      {
        text: 'The fool doth think he is wise, but the wise man knows himself to be a fool.'
      }
    ]
  }
)
puts response

const response = await client.ml.inferTrainedModel({
  model_id: "lang_ident_model_1",
  docs: [
    {
      text: "The fool doth think he is wise, but the wise man knows himself to be a fool.",
    },
  ],
});
console.log(response);

POST _ml/trained_models/lang_ident_model_1/_infer
{
  "docs":[{"text": "The fool doth think he is wise, but the wise man knows himself to be a fool."}]
}

Here are the results predicting english with a high probability.

{
  "inference_results": [
    {
      "predicted_value": "en",
      "prediction_probability": 0.9999658805366392,
      "prediction_score": 0.9999658805366392
    }
  ]
}

When it is a text classification model, the response is the score and predicted classification.

For example:

resp = client.ml.infer_trained_model(
    model_id="model2",
    docs=[
        {
            "text_field": "The movie was awesome!!"
        }
    ],
)
print(resp)

response = client.ml.infer_trained_model(
  model_id: 'model2',
  body: {
    docs: [
      {
        text_field: 'The movie was awesome!!'
      }
    ]
  }
)
puts response

const response = await client.ml.inferTrainedModel({
  model_id: "model2",
  docs: [
    {
      text_field: "The movie was awesome!!",
    },
  ],
});
console.log(response);

POST _ml/trained_models/model2/_infer
{
	"docs": [{"text_field": "The movie was awesome!!"}]
}

The API returns the predicted label and the confidence.

{
  "inference_results": [{
    "predicted_value" : "POSITIVE",
    "prediction_probability" : 0.9998667964092964
  }]
}

For named entity recognition (NER) models, the response contains the annotated text output and the recognized entities.

resp = client.ml.infer_trained_model(
    model_id="model2",
    docs=[
        {
            "text_field": "Hi my name is Josh and I live in Berlin"
        }
    ],
)
print(resp)

response = client.ml.infer_trained_model(
  model_id: 'model2',
  body: {
    docs: [
      {
        text_field: 'Hi my name is Josh and I live in Berlin'
      }
    ]
  }
)
puts response

const response = await client.ml.inferTrainedModel({
  model_id: "model2",
  docs: [
    {
      text_field: "Hi my name is Josh and I live in Berlin",
    },
  ],
});
console.log(response);

POST _ml/trained_models/model2/_infer
{
	"docs": [{"text_field": "Hi my name is Josh and I live in Berlin"}]
}

The API returns in this case:

{
  "inference_results": [{
    "predicted_value" : "Hi my name is [Josh](PER&Josh) and I live in [Berlin](LOC&Berlin)",
    "entities" : [
      {
        "entity" : "Josh",
        "class_name" : "PER",
        "class_probability" : 0.9977303419824,
        "start_pos" : 14,
        "end_pos" : 18
      },
      {
        "entity" : "Berlin",
        "class_name" : "LOC",
        "class_probability" : 0.9992474323902818,
        "start_pos" : 33,
        "end_pos" : 39
      }
    ]
  }]
}

Zero-shot classification models require extra configuration defining the class labels. These labels are passed in the zero-shot inference config.

resp = client.ml.infer_trained_model(
    model_id="model2",
    docs=[
        {
            "text_field": "This is a very happy person"
        }
    ],
    inference_config={
        "zero_shot_classification": {
            "labels": [
                "glad",
                "sad",
                "bad",
                "rad"
            ],
            "multi_label": False
        }
    },
)
print(resp)

response = client.ml.infer_trained_model(
  model_id: 'model2',
  body: {
    docs: [
      {
        text_field: 'This is a very happy person'
      }
    ],
    inference_config: {
      zero_shot_classification: {
        labels: [
          'glad',
          'sad',
          'bad',
          'rad'
        ],
        multi_label: false
      }
    }
  }
)
puts response

const response = await client.ml.inferTrainedModel({
  model_id: "model2",
  docs: [
    {
      text_field: "This is a very happy person",
    },
  ],
  inference_config: {
    zero_shot_classification: {
      labels: ["glad", "sad", "bad", "rad"],
      multi_label: false,
    },
  },
});
console.log(response);

POST _ml/trained_models/model2/_infer
{
  "docs": [
    {
      "text_field": "This is a very happy person"
    }
  ],
  "inference_config": {
    "zero_shot_classification": {
      "labels": [
        "glad",
        "sad",
        "bad",
        "rad"
      ],
      "multi_label": false
    }
  }
}

The API returns the predicted label and the confidence, as well as the top classes:

{
  "inference_results": [{
    "predicted_value" : "glad",
    "top_classes" : [
      {
        "class_name" : "glad",
        "class_probability" : 0.8061155063386439,
        "class_score" : 0.8061155063386439
      },
      {
        "class_name" : "rad",
        "class_probability" : 0.18218006158387956,
        "class_score" : 0.18218006158387956
      },
      {
        "class_name" : "bad",
        "class_probability" : 0.006325615787634201,
        "class_score" : 0.006325615787634201
      },
      {
        "class_name" : "sad",
        "class_probability" : 0.0053788162898424545,
        "class_score" : 0.0053788162898424545
      }
    ],
    "prediction_probability" : 0.8061155063386439
  }]
}

Question answering models require extra configuration defining the question to answer.

resp = client.ml.infer_trained_model(
    model_id="model2",
    docs=[
        {
            "text_field": "<long text to extract answer>"
        }
    ],
    inference_config={
        "question_answering": {
            "question": "<question to be answered>"
        }
    },
)
print(resp)

response = client.ml.infer_trained_model(
  model_id: 'model2',
  body: {
    docs: [
      {
        text_field: '<long text to extract answer>'
      }
    ],
    inference_config: {
      question_answering: {
        question: '<question to be answered>'
      }
    }
  }
)
puts response

const response = await client.ml.inferTrainedModel({
  model_id: "model2",
  docs: [
    {
      text_field: "<long text to extract answer>",
    },
  ],
  inference_config: {
    question_answering: {
      question: "<question to be answered>",
    },
  },
});
console.log(response);

POST _ml/trained_models/model2/_infer
{
  "docs": [
    {
      "text_field": "<long text to extract answer>"
    }
  ],
  "inference_config": {
    "question_answering": {
      "question": "<question to be answered>"
    }
  }
}

The API returns a response similar to the following:

{
    "predicted_value": <string subsection of the text that is the answer>,
    "start_offset": <character offset in document to start>,
    "end_offset": <character offset end of the answer,
    "prediction_probability": <prediction score>
}

Text similarity models require at least two sequences of text to compare. It’s possible to provide multiple strings of text to compare to another text sequence:

resp = client.ml.infer_trained_model(
    model_id="cross-encoder__ms-marco-tinybert-l-2-v2",
    docs=[
        {
            "text_field": "Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers."
        },
        {
            "text_field": "New York City is famous for the Metropolitan Museum of Art."
        }
    ],
    inference_config={
        "text_similarity": {
            "text": "How many people live in Berlin?"
        }
    },
)
print(resp)

const response = await client.ml.inferTrainedModel({
  model_id: "cross-encoder__ms-marco-tinybert-l-2-v2",
  docs: [
    {
      text_field:
        "Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.",
    },
    {
      text_field: "New York City is famous for the Metropolitan Museum of Art.",
    },
  ],
  inference_config: {
    text_similarity: {
      text: "How many people live in Berlin?",
    },
  },
});
console.log(response);

POST _ml/trained_models/cross-encoder__ms-marco-tinybert-l-2-v2/_infer
{
  "docs":[{ "text_field": "Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers."}, {"text_field": "New York City is famous for the Metropolitan Museum of Art."}],
  "inference_config": {
    "text_similarity": {
      "text": "How many people live in Berlin?"
    }
  }
}

The response contains the prediction for every string that is compared to the text provided in the text_similarity.text field:

{
  "inference_results": [
    {
      "predicted_value": 7.235751628875732
    },
    {
      "predicted_value": -11.562295913696289
    }
  ]
}

The tokenization truncate option can be overridden when calling the API:

resp = client.ml.infer_trained_model(
    model_id="model2",
    docs=[
        {
            "text_field": "The Amazon rainforest covers most of the Amazon basin in South America"
        }
    ],
    inference_config={
        "ner": {
            "tokenization": {
                "bert": {
                    "truncate": "first"
                }
            }
        }
    },
)
print(resp)

response = client.ml.infer_trained_model(
  model_id: 'model2',
  body: {
    docs: [
      {
        text_field: 'The Amazon rainforest covers most of the Amazon basin in South America'
      }
    ],
    inference_config: {
      ner: {
        tokenization: {
          bert: {
            truncate: 'first'
          }
        }
      }
    }
  }
)
puts response

const response = await client.ml.inferTrainedModel({
  model_id: "model2",
  docs: [
    {
      text_field:
        "The Amazon rainforest covers most of the Amazon basin in South America",
    },
  ],
  inference_config: {
    ner: {
      tokenization: {
        bert: {
          truncate: "first",
        },
      },
    },
  },
});
console.log(response);

POST _ml/trained_models/model2/_infer
{
  "docs": [{"text_field": "The Amazon rainforest covers most of the Amazon basin in South America"}],
  "inference_config": {
    "ner": {
      "tokenization": {
        "bert": {
          "truncate": "first"
        }
      }
    }
  }
}

When the input has been truncated due to the limit imposed by the model’s max_sequence_length the is_truncated field appears in the response.

{
  "inference_results": [{
    "predicted_value" : "The [Amazon](LOC&Amazon) rainforest covers most of the [Amazon](LOC&Amazon) basin in [South America](LOC&South+America)",
    "entities" : [
      {
        "entity" : "Amazon",
        "class_name" : "LOC",
        "class_probability" : 0.9505460915724254,
        "start_pos" : 4,
        "end_pos" : 10
      },
      {
        "entity" : "Amazon",
        "class_name" : "LOC",
        "class_probability" : 0.9969992804311777,
        "start_pos" : 41,
        "end_pos" : 47
      }
    ],
    "is_truncated" : true
  }]
}

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