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- Release notes
- Elasticsearch version 8.10.4
- Elasticsearch version 8.10.3
- Elasticsearch version 8.10.2
- Elasticsearch version 8.10.1
- Elasticsearch version 8.10.0
- Elasticsearch version 8.9.2
- Elasticsearch version 8.9.1
- Elasticsearch version 8.9.0
- Elasticsearch version 8.8.2
- Elasticsearch version 8.8.1
- Elasticsearch version 8.8.0
- Elasticsearch version 8.7.1
- Elasticsearch version 8.7.0
- Elasticsearch version 8.6.2
- Elasticsearch version 8.6.1
- Elasticsearch version 8.6.0
- Elasticsearch version 8.5.3
- Elasticsearch version 8.5.2
- Elasticsearch version 8.5.1
- Elasticsearch version 8.5.0
- Elasticsearch version 8.4.3
- Elasticsearch version 8.4.2
- Elasticsearch version 8.4.1
- Elasticsearch version 8.4.0
- Elasticsearch version 8.3.3
- Elasticsearch version 8.3.2
- Elasticsearch version 8.3.1
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- Elasticsearch version 8.0.0-rc2
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- Elasticsearch version 8.0.0-beta1
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- Elasticsearch version 8.0.0-alpha1
- Dependencies and versions
Create trained models API
editCreate trained models API
editCreates a trained model.
Models created in version 7.8.0 are not backwards compatible with older node versions. If in a mixed cluster environment, all nodes must be at least 7.8.0 to use a model stored by a 7.8.0 node.
Request
editPUT _ml/trained_models/<model_id>
Prerequisites
editRequires the manage_ml
cluster privilege. This privilege is included in the
machine_learning_admin
built-in role.
Description
editThe create trained model API enables you to supply a trained model that is not created by data frame analytics.
Path parameters
edit-
<model_id>
- (Required, string) The unique identifier of the trained model.
Query parameters
edit-
defer_definition_decompression
-
(Optional, boolean)
If set to
true
and acompressed_definition
is provided, the request defers definition decompression and skips relevant validations. This deferral is useful for systems or users that know a good byte size estimate for their model and know that their model is valid and likely won’t fail during inference. -
wait_for_completion
-
(Optional, boolean)
Whether to wait for all child operations such as model download
to complete, before returning or not. Defaults to
false
.
Request body
edit-
compressed_definition
-
(Required, string)
The compressed (GZipped and Base64 encoded) inference definition of the model.
If
compressed_definition
is specified, thendefinition
cannot be specified.
-
definition
-
(Required, object) The inference definition for the model. If
definition
is specified, thencompressed_definition
cannot be specified.Properties of
definition
-
preprocessors
-
(Optional, object) Collection of preprocessors. See Preprocessor examples.
Properties of
preprocessors
-
frequency_encoding
-
(Required, object) Defines a frequency encoding for a field.
Properties of
frequency_encoding
-
feature_name
- (Required, string) The name of the resulting feature.
-
field
- (Required, string) The field name to encode.
-
frequency_map
- (Required, object map of string:double) Object that maps the field value to the frequency encoded value.
-
custom
-
(Optional, Boolean)
Boolean value indicating if the analytics job created the preprocessor
or if a user provided it. This adjusts the feature importance calculation.
When
true
, the feature importance calculation returns importance for the processed feature. Whenfalse
, the total importance of the original field is returned. Default isfalse
.
-
-
one_hot_encoding
-
(Required, object) Defines a one hot encoding map for a field.
Properties of
one_hot_encoding
-
field
- (Required, string) The field name to encode.
-
hot_map
- (Required, object map of strings) String map of "field_value: one_hot_column_name".
-
custom
-
(Optional, Boolean)
Boolean value indicating if the analytics job created the preprocessor
or if a user provided it. This adjusts the feature importance calculation.
When
true
, the feature importance calculation returns importance for the processed feature. Whenfalse
, the total importance of the original field is returned. Default isfalse
.
-
-
target_mean_encoding
-
(Required, object) Defines a target mean encoding for a field.
Properties of
target_mean_encoding
-
default_value
-
(Required, double)
The feature value if the field value is not in the
target_map
. -
feature_name
- (Required, string) The name of the resulting feature.
-
field
- (Required, string) The field name to encode.
-
target_map
-
(Required, object map of string:double) Object that maps the field value to the target mean value.
-
custom
-
(Optional, Boolean)
Boolean value indicating if the analytics job created the preprocessor
or if a user provided it. This adjusts the feature importance calculation.
When
true
, the feature importance calculation returns importance for the processed feature. Whenfalse
, the total importance of the original field is returned. Default isfalse
.
-
-
-
-
trained_model
-
(Required, object) The definition of the trained model.
Properties of
trained_model
-
tree
-
(Required, object) The definition for a binary decision tree.
Properties of
tree
-
classification_labels
-
(Optional, string) An array of classification labels (used for
classification
). -
feature_names
- (Required, string) Features expected by the tree, in their expected order.
-
target_type
-
(Required, string)
String indicating the model target type;
regression
orclassification
. -
tree_structure
-
(Required, object)
An array of
tree_node
objects. The nodes must be in ordinal order by theirtree_node.node_index
value.
-
-
tree_node
-
(Required, object) The definition of a node in a tree.
There are two major types of nodes: leaf nodes and not-leaf nodes.
-
Leaf nodes only need
node_index
andleaf_value
defined. -
All other nodes need
split_feature
,left_child
,right_child
,threshold
,decision_type
, anddefault_left
defined.
Properties of
tree_node
-
decision_type
-
(Optional, string)
Indicates the positive value (in other words, when to choose the left node)
decision type. Supported
lt
,lte
,gt
,gte
. Defaults tolte
. -
default_left
-
(Optional, Boolean)
Indicates whether to default to the left when the feature is missing. Defaults
to
true
. -
leaf_value
- (Optional, double) The leaf value of the of the node, if the value is a leaf (in other words, no children).
-
left_child
- (Optional, integer) The index of the left child.
-
node_index
- (Integer) The index of the current node.
-
right_child
- (Optional, integer) The index of the right child.
-
split_feature
- (Optional, integer) The index of the feature value in the feature array.
-
split_gain
- (Optional, double) The information gain from the split.
-
threshold
- (Optional, double) The decision threshold with which to compare the feature value.
-
Leaf nodes only need
-
ensemble
-
(Optional, object) The definition for an ensemble model. See Model examples.
Properties of
ensemble
-
aggregate_output
-
(Required, object) An aggregated output object that defines how to aggregate the outputs of the
trained_models
. Supported objects areweighted_mode
,weighted_sum
, andlogistic_regression
. See Aggregated output example.Properties of
aggregate_output
-
logistic_regression
-
(Optional, object) This
aggregated_output
type works with binary classification (classification for values [0, 1]). It multiplies the outputs (in the case of theensemble
model, the inference model values) by the suppliedweights
. The resulting vector is summed and passed to asigmoid
function. The result of thesigmoid
function is considered the probability of class 1 (P_1
), consequently, the probability of class 0 is1 - P_1
. The class with the highest probability (either 0 or 1) is then returned. For more information about logistic regression, see this wiki article.Properties of
logistic_regression
-
weights
- (Required, double) The weights to multiply by the input values (the inference values of the trained models).
-
-
weighted_sum
-
(Optional, object) This
aggregated_output
type works with regression. The weighted sum of the input values.Properties of
weighted_sum
-
weights
- (Required, double) The weights to multiply by the input values (the inference values of the trained models).
-
-
weighted_mode
-
(Optional, object) This
aggregated_output
type works with regression or classification. It takes a weighted vote of the input values. The most common input value (taking the weights into account) is returned.Properties of
weighted_mode
-
weights
- (Required, double) The weights to multiply by the input values (the inference values of the trained models).
-
-
exponent
-
(Optional, object) This
aggregated_output
type works with regression. It takes a weighted sum of the input values and passes the result to an exponent function (e^x
wherex
is the sum of the weighted values).Properties of
exponent
-
weights
- (Required, double) The weights to multiply by the input values (the inference values of the trained models).
-
-
-
classification_labels
- (Optional, string) An array of classification labels.
-
feature_names
- (Optional, string) Features expected by the ensemble, in their expected order.
-
target_type
-
(Required, string)
String indicating the model target type;
regression
orclassification.
-
trained_models
-
(Required, object)
An array of
trained_model
objects. Supported trained models aretree
andensemble
.
-
-
-
-
description
- (Optional, string) A human-readable description of the inference trained model.
-
estimated_heap_memory_usage_bytes
-
(Optional, integer)
[7.16.0]
Deprecated in 7.16.0. Replaced by
model_size_bytes
-
estimated_operations
-
(Optional, integer)
The estimated number of operations to use the trained model during inference.
This property is supported only if
defer_definition_decompression
istrue
or the model definition is not supplied.
-
inference_config
-
(Required, object) The default configuration for inference. This can be:
regression
,classification
,fill_mask
,ner
,question_answering
,text_classification
,text_embedding
orzero_shot_classification
. Ifregression
orclassification
, it must match thetarget_type
of the underlyingdefinition.trained_model
. Iffill_mask
,ner
,question_answering
,text_classification
, ortext_embedding
; themodel_type
must bepytorch
.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
. Whenboolean
is provided1.0
is transformed totrue
and0.0
tofalse
. -
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 isparis
.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.
Refer to Properties of
tokenizaton
to review the properties of thetokenization
object. -
-
-
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
-
classification_labels
- (Optional, string) An array of classification labels. NER only supports Inside-Outside-Beginning labels (IOB) and only persons, organizations, locations, and miscellaneous. Example: ["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-MISC", "I-MISC"]
-
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.
Refer to Properties of
tokenizaton
to review the properties of thetokenization
object. -
-
-
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.
Refer to Properties of
tokenizaton
to review the properties of thetokenization
object. -
-
-
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
. -
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_sentence_length
to386
with128
ofspan
and settruncate
tonone
.Refer to Properties of
tokenizaton
to review the properties of thetokenization
object. -
-
-
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.
Refer to Properties of
tokenizaton
to review the properties of thetokenization
object. -
-
-
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
-
embedding_size
- (Optional, integer) The number of dimensions in the embedding vector produced by the model.
-
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.
Refer to Properties of
tokenizaton
to review the properties of thetokenization
object. -
-
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 whentruncate
isnone
andspan
is a non-negative number. The default value ismax
. Available options are:-
max
: The maximum score from all the spans is returned. -
mean
: The mean score over all the spans is returned.
-
-
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.
Refer to Properties of
tokenizaton
to review the properties of thetokenization
object. -
-
-
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
-
classification_labels
- (Required, array) The classification labels used during the zero-shot classification. Classification labels must not be empty or null and only set at model creation. They must be all three of ["entailment", "neutral", "contradiction"].
This is NOT the same as
labels
which are the values that zero-shot is attempting to classify.-
hypothesis_template
-
(Optional, string) This is the template used when tokenizing the sequences for classification.
The labels replace the
{}
value in the text. The default value is:This example is {}.
-
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 tofalse
. -
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.
Refer to Properties of
tokenizaton
to review the properties of thetokenization
object. -
-
-
-
input
-
(Required, object) The input field names for the model definition.
Properties of
input
-
field_names
- (Required, string) An array of input field names for the model.
-
-
location
-
(Optional, object) The model definition location. If the
definition
orcompressed_definition
are not specified, thelocation
is required.Properties of
location
-
index
- (Required, object) Indicates that the model definition is stored in an index. This object must be empty as the index for storing model definitions is configured automatically.
-
-
metadata
- (Optional, object) An object map that contains metadata about the model.
-
model_size_bytes
-
(Optional, integer)
The estimated memory usage in bytes to keep the trained model in memory. This
property is supported only if
defer_definition_decompression
istrue
or the model definition is not supplied. -
model_type
-
(Optional, string) The created model type. By default the model type is
tree_ensemble
. Appropriate types are:-
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. For more information, refer to Natural language processing.
-
-
tags
- (Optional, string) An array of tags to organize the model.
Properties of tokenizaton
editThe tokenization
object has the following properties.
-
bert
-
(Optional, object) BERT-style tokenization is to be performed with the enclosed settings.
Properties of bert
-
do_lower_case
- (Optional, boolean) Specifies if the tokenization lower case the text sequence when building the tokens.
-
max_sequence_length
- (Optional, integer) Specifies the maximum number of tokens allowed to be output by the tokenizer.
-
span
-
(Optional, integer) When
truncate
isnone
, 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 isfirst
.-
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 usesecond
in this case.-
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
-
add_prefix_space
- (Optional, boolean) Specifies if the tokenization should prefix a space to the tokenized input to the model.
-
max_sequence_length
- (Optional, integer) Specifies the maximum number of tokens allowed to be output by the tokenizer.
-
span
-
(Optional, integer) When
truncate
isnone
, 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 isfirst
.-
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 usesecond
in this case.-
with_special_tokens
-
(Optional, boolean) Tokenize with special tokens. The tokens typically included in RoBERTa-style tokenization are:
-
<s>
: The first token of the sequence being classified. -
</s>
: Indicates sequence separation.
-
-
-
mpnet
-
(Optional, object) MPNet-style tokenization is to be performed with the enclosed settings.
Properties of mpnet
-
do_lower_case
- (Optional, boolean) Specifies if the tokenization lower case the text sequence when building the tokens.
-
max_sequence_length
- (Optional, integer) Specifies the maximum number of tokens allowed to be output by the tokenizer.
-
span
-
(Optional, integer) When
truncate
isnone
, 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 isfirst
.-
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 usesecond
in this case.-
with_special_tokens
-
(Optional, boolean) Tokenize with special tokens. The tokens typically included in MPNet-style tokenization are:
-
<s>
: The first token of the sequence being classified. -
</s>
: Indicates sequence separation.
-
-
-
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
-
max_sequence_length
- (Optional, integer) Specifies the maximum number of tokens allowed to be output by the tokenizer.
-
span
-
(Optional, integer) When
truncate
isnone
, 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 isfirst
.-
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 usesecond
in this case.-
with_special_tokens
-
(Optional, boolean) Tokenize with special tokens. The tokens typically included in RoBERTa-style tokenization are:
-
<s>
: The first token of the sequence being classified. -
</s>
: Indicates sequence separation.
-
-
-
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
-
do_lower_case
- (Optional, boolean) Specifies if the tokenization lower case the text sequence when building the tokens.
-
max_sequence_length
- (Optional, integer) Specifies the maximum number of tokens allowed to be output by the tokenizer.
-
span
-
(Optional, integer) When
truncate
isnone
, 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 isfirst
.-
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 usesecond
in this case.-
with_special_tokens
-
(Optional, boolean)
Tokenize with special tokens if
true
.
-
Examples
editPreprocessor examples
editThe example below shows a frequency_encoding
preprocessor object:
{ "frequency_encoding":{ "field":"FlightDelayType", "feature_name":"FlightDelayType_frequency", "frequency_map":{ "Carrier Delay":0.6007414737092798, "NAS Delay":0.6007414737092798, "Weather Delay":0.024573576178086153, "Security Delay":0.02476631010889467, "No Delay":0.6007414737092798, "Late Aircraft Delay":0.6007414737092798 } } }
The next example shows a one_hot_encoding
preprocessor object:
{ "one_hot_encoding":{ "field":"FlightDelayType", "hot_map":{ "Carrier Delay":"FlightDelayType_Carrier Delay", "NAS Delay":"FlightDelayType_NAS Delay", "No Delay":"FlightDelayType_No Delay", "Late Aircraft Delay":"FlightDelayType_Late Aircraft Delay" } } }
This example shows a target_mean_encoding
preprocessor object:
{ "target_mean_encoding":{ "field":"FlightDelayType", "feature_name":"FlightDelayType_targetmean", "target_map":{ "Carrier Delay":39.97465788139886, "NAS Delay":39.97465788139886, "Security Delay":203.171206225681, "Weather Delay":187.64705882352948, "No Delay":39.97465788139886, "Late Aircraft Delay":39.97465788139886 }, "default_value":158.17995752420433 } }
Model examples
editThe first example shows a trained_model
object:
{ "tree":{ "feature_names":[ "DistanceKilometers", "FlightTimeMin", "FlightDelayType_NAS Delay", "Origin_targetmean", "DestRegion_targetmean", "DestCityName_targetmean", "OriginAirportID_targetmean", "OriginCityName_frequency", "DistanceMiles", "FlightDelayType_Late Aircraft Delay" ], "tree_structure":[ { "decision_type":"lt", "threshold":9069.33437193022, "split_feature":0, "split_gain":4112.094574306927, "node_index":0, "default_left":true, "left_child":1, "right_child":2 }, ... { "node_index":9, "leaf_value":-27.68987349695448 }, ... ], "target_type":"regression" } }
The following example shows an ensemble
model object:
"ensemble":{ "feature_names":[ ... ], "trained_models":[ { "tree":{ "feature_names":[], "tree_structure":[ { "decision_type":"lte", "node_index":0, "leaf_value":47.64069875778043, "default_left":false } ], "target_type":"regression" } }, ... ], "aggregate_output":{ "weighted_sum":{ "weights":[ ... ] } }, "target_type":"regression" }
Aggregated output example
editExample of a logistic_regression
object:
"aggregate_output" : { "logistic_regression" : { "weights" : [2.0, 1.0, .5, -1.0, 5.0, 1.0, 1.0] } }
Example of a weighted_sum
object:
"aggregate_output" : { "weighted_sum" : { "weights" : [1.0, -1.0, .5, 1.0, 5.0] } }
Example of a weighted_mode
object:
"aggregate_output" : { "weighted_mode" : { "weights" : [1.0, 1.0, 1.0, 1.0, 1.0] } }
Example of an exponent
object:
"aggregate_output" : { "exponent" : { "weights" : [1.0, 1.0, 1.0, 1.0, 1.0] } }
Trained models JSON schema
editFor the full JSON schema of trained models, click here.
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