- Elasticsearch Guide: other versions:
- What is Elasticsearch?
- What’s new in 8.0
- Quick start
- Set up Elasticsearch
- Installing Elasticsearch
- Configuring Elasticsearch
- Important Elasticsearch configuration
- Secure settings
- Auditing settings
- Circuit breaker settings
- Cluster-level shard allocation and routing settings
- Cross-cluster replication settings
- Discovery and cluster formation settings
- Field data cache settings
- Index lifecycle management settings
- Index management settings
- Index recovery settings
- Indexing buffer settings
- License settings
- Local gateway settings
- Logging
- Machine learning settings
- Monitoring settings
- Node
- Networking
- Node query cache settings
- Search settings
- Security settings
- Shard request cache settings
- Snapshot and restore settings
- Transforms settings
- Thread pools
- Watcher settings
- Advanced configuration
- Important system configuration
- Bootstrap Checks
- Heap size check
- File descriptor check
- Memory lock check
- Maximum number of threads check
- Max file size check
- Maximum size virtual memory check
- Maximum map count check
- Client JVM check
- Use serial collector check
- System call filter check
- OnError and OnOutOfMemoryError checks
- Early-access check
- G1GC check
- All permission check
- Discovery configuration check
- Bootstrap Checks for X-Pack
- Starting Elasticsearch
- Stopping Elasticsearch
- Discovery and cluster formation
- Add and remove nodes in your cluster
- Full-cluster restart and rolling restart
- Remote clusters
- Plugins
- Upgrade Elasticsearch
- Index modules
- Mapping
- Text analysis
- Overview
- Concepts
- Configure text analysis
- Built-in analyzer reference
- Tokenizer reference
- Token filter reference
- Apostrophe
- ASCII folding
- CJK bigram
- CJK width
- Classic
- Common grams
- Conditional
- Decimal digit
- Delimited payload
- Dictionary decompounder
- Edge n-gram
- Elision
- Fingerprint
- Flatten graph
- Hunspell
- Hyphenation decompounder
- Keep types
- Keep words
- Keyword marker
- Keyword repeat
- KStem
- Length
- Limit token count
- Lowercase
- MinHash
- Multiplexer
- N-gram
- Normalization
- Pattern capture
- Pattern replace
- Phonetic
- Porter stem
- Predicate script
- Remove duplicates
- Reverse
- Shingle
- Snowball
- Stemmer
- Stemmer override
- Stop
- Synonym
- Synonym graph
- Trim
- Truncate
- Unique
- Uppercase
- Word delimiter
- Word delimiter graph
- Character filters reference
- Normalizers
- Index templates
- Data streams
- Ingest pipelines
- Example: Parse logs
- Enrich your data
- Processor reference
- Append
- Bytes
- Circle
- Community ID
- Convert
- CSV
- Date
- Date index name
- Dissect
- Dot expander
- Drop
- Enrich
- Fail
- Fingerprint
- Foreach
- GeoIP
- Grok
- Gsub
- HTML strip
- Inference
- Join
- JSON
- KV
- Lowercase
- Network direction
- Pipeline
- Registered domain
- Remove
- Rename
- Script
- Set
- Set security user
- Sort
- Split
- Trim
- Uppercase
- URL decode
- URI parts
- User agent
- Aliases
- Search your data
- Collapse search results
- Filter search results
- Highlighting
- Long-running searches
- Near real-time search
- Paginate search results
- Retrieve inner hits
- Retrieve selected fields
- Search across clusters
- Search multiple data streams and indices
- Search shard routing
- Search templates
- Sort search results
- kNN search
- Query DSL
- Aggregations
- Bucket aggregations
- Adjacency matrix
- Auto-interval date histogram
- Categorize text
- Children
- Composite
- Date histogram
- Date range
- Diversified sampler
- Filter
- Filters
- Geo-distance
- Geohash grid
- Geotile grid
- Global
- Histogram
- IP range
- Missing
- Multi Terms
- Nested
- Parent
- Range
- Rare terms
- Reverse nested
- Sampler
- Significant terms
- Significant text
- Terms
- Variable width histogram
- Subtleties of bucketing range fields
- Metrics aggregations
- Pipeline aggregations
- Average bucket
- Bucket script
- Bucket count K-S test
- Bucket correlation
- Bucket selector
- Bucket sort
- Cumulative cardinality
- Cumulative sum
- Derivative
- Extended stats bucket
- Inference bucket
- Max bucket
- Min bucket
- Moving function
- Moving percentiles
- Normalize
- Percentiles bucket
- Serial differencing
- Stats bucket
- Sum bucket
- Bucket aggregations
- EQL
- SQL
- Overview
- Getting Started with SQL
- Conventions and Terminology
- Security
- SQL REST API
- SQL Translate API
- SQL CLI
- SQL JDBC
- SQL ODBC
- SQL Client Applications
- SQL Language
- Functions and Operators
- Comparison Operators
- Logical Operators
- Math Operators
- Cast Operators
- LIKE and RLIKE Operators
- Aggregate Functions
- Grouping Functions
- Date/Time and Interval Functions and Operators
- Full-Text Search Functions
- Mathematical Functions
- String Functions
- Type Conversion Functions
- Geo Functions
- Conditional Functions And Expressions
- System Functions
- Reserved keywords
- SQL Limitations
- Scripting
- Data management
- ILM: Manage the index lifecycle
- Overview
- Concepts
- Automate rollover
- Tutorial: Customize built-in policies
- Index lifecycle actions
- Configure a lifecycle policy
- Migrate index allocation filters to node roles
- Troubleshooting index lifecycle management errors
- Start and stop index lifecycle management
- Manage existing indices
- Skip rollover
- Restore a managed data stream or index
- Autoscaling
- Monitor a cluster
- Roll up or transform your data
- Set up a cluster for high availability
- Snapshot and restore
- Secure the Elastic Stack
- Elasticsearch security principles
- Start the Elastic Stack with security enabled
- Configure security
- Updating node security certificates
- User authentication
- Built-in users
- Service accounts
- Internal users
- Token-based authentication services
- Realms
- Realm chains
- Active Directory user authentication
- File-based user authentication
- LDAP user authentication
- Native user authentication
- OpenID Connect authentication
- PKI user authentication
- SAML authentication
- Kerberos authentication
- Integrating with other authentication systems
- Enabling anonymous access
- Controlling the user cache
- Configuring SAML single-sign-on on the Elastic Stack
- Configuring single sign-on to the Elastic Stack using OpenID Connect
- User authorization
- Built-in roles
- Defining roles
- Security privileges
- Document level security
- Field level security
- Granting privileges for data streams and aliases
- Mapping users and groups to roles
- Setting up field and document level security
- Submitting requests on behalf of other users
- Configuring authorization delegation
- Customizing roles and authorization
- Enable audit logging
- Restricting connections with IP filtering
- Securing clients and integrations
- Operator privileges
- Troubleshooting
- Some settings are not returned via the nodes settings API
- Authorization exceptions
- Users command fails due to extra arguments
- Users are frequently locked out of Active Directory
- Certificate verification fails for curl on Mac
- SSLHandshakeException causes connections to fail
- Common SSL/TLS exceptions
- Common Kerberos exceptions
- Common SAML issues
- Internal Server Error in Kibana
- Setup-passwords command fails due to connection failure
- Failures due to relocation of the configuration files
- Limitations
- Watcher
- Command line tools
- elasticsearch-certgen
- elasticsearch-certutil
- elasticsearch-create-enrollment-token
- elasticsearch-croneval
- elasticsearch-keystore
- elasticsearch-node
- elasticsearch-reconfigure-node
- elasticsearch-reset-password
- elasticsearch-saml-metadata
- elasticsearch-service-tokens
- elasticsearch-setup-passwords
- elasticsearch-shard
- elasticsearch-syskeygen
- elasticsearch-users
- How to
- REST APIs
- API conventions
- Common options
- REST API compatibility
- Autoscaling APIs
- Compact and aligned text (CAT) APIs
- cat aliases
- cat allocation
- cat anomaly detectors
- cat count
- cat data frame analytics
- cat datafeeds
- cat fielddata
- cat health
- cat indices
- cat master
- cat nodeattrs
- cat nodes
- cat pending tasks
- cat plugins
- cat recovery
- cat repositories
- cat segments
- cat shards
- cat snapshots
- cat task management
- cat templates
- cat thread pool
- cat trained model
- cat transforms
- Cluster APIs
- Cluster allocation explain
- Cluster get settings
- Cluster health
- Cluster reroute
- Cluster state
- Cluster stats
- Cluster update settings
- Nodes feature usage
- Nodes hot threads
- Nodes info
- Nodes reload secure settings
- Nodes stats
- Pending cluster tasks
- Remote cluster info
- Task management
- Voting configuration exclusions
- Cross-cluster replication APIs
- Data stream APIs
- Document APIs
- Enrich APIs
- EQL APIs
- Features APIs
- Fleet APIs
- Find structure API
- Graph explore API
- Index APIs
- Alias exists
- Aliases
- Analyze
- Analyze index disk usage
- Clear cache
- Clone index
- Close index
- Create index
- Create or update alias
- Create or update component template
- Create or update index template
- Create or update index template (legacy)
- Delete component template
- Delete dangling index
- Delete alias
- Delete index
- Delete index template
- Delete index template (legacy)
- Exists
- Field usage stats
- Flush
- Force merge
- Get alias
- Get component template
- Get field mapping
- Get index
- Get index settings
- Get index template
- Get index template (legacy)
- Get mapping
- Import dangling index
- Index recovery
- Index segments
- Index shard stores
- Index stats
- Index template exists (legacy)
- List dangling indices
- Open index
- Refresh
- Resolve index
- Rollover
- Shrink index
- Simulate index
- Simulate template
- Split index
- Unfreeze index
- Update index settings
- Update mapping
- Index lifecycle management APIs
- Create or update lifecycle policy
- Get policy
- Delete policy
- Move to step
- Remove policy
- Retry policy
- Get index lifecycle management status
- Explain lifecycle
- Start index lifecycle management
- Stop index lifecycle management
- Migrate indices, ILM policies, and legacy, composable and component templates to data tiers routing
- Ingest APIs
- Info API
- Licensing APIs
- Logstash APIs
- Machine learning APIs
- Machine learning anomaly detection APIs
- Add events to calendar
- Add jobs to calendar
- Close jobs
- Create jobs
- Create calendars
- Create datafeeds
- Create filters
- Delete calendars
- Delete datafeeds
- Delete events from calendar
- Delete filters
- Delete forecasts
- Delete jobs
- Delete jobs from calendar
- Delete model snapshots
- Delete expired data
- Estimate model memory
- Flush jobs
- Forecast jobs
- Get buckets
- Get calendars
- Get categories
- Get datafeeds
- Get datafeed statistics
- Get influencers
- Get jobs
- Get job statistics
- Get model snapshots
- Get model snapshot upgrade statistics
- Get overall buckets
- Get scheduled events
- Get filters
- Get records
- Open jobs
- Post data to jobs
- Preview datafeeds
- Reset jobs
- Revert model snapshots
- Start datafeeds
- Stop datafeeds
- Update datafeeds
- Update filters
- Update jobs
- Update model snapshots
- Upgrade model snapshots
- Machine learning data frame analytics APIs
- Create data frame analytics jobs
- Delete data frame analytics jobs
- Evaluate data frame analytics
- Explain data frame analytics
- Get data frame analytics jobs
- Get data frame analytics jobs stats
- Preview data frame analytics
- Start data frame analytics jobs
- Stop data frame analytics jobs
- Update data frame analytics jobs
- Machine learning trained model APIs
- Create or update trained model aliases
- Create part of a trained model
- Create trained models
- Create trained model vocabulary
- Delete trained model aliases
- Delete trained models
- Get trained models
- Get trained models stats
- Infer trained model deployment
- Start trained model deployment
- Stop trained model deployment
- Migration APIs
- Node lifecycle APIs
- Reload search analyzers API
- Repositories metering APIs
- Rollup APIs
- Script APIs
- Search APIs
- Searchable snapshots APIs
- Security APIs
- Authenticate
- Change passwords
- Clear cache
- Clear roles cache
- Clear privileges cache
- Clear API key cache
- Clear service account token caches
- Create API keys
- Create or update application privileges
- Create or update role mappings
- Create or update roles
- Create or update users
- Create service account tokens
- Delegate PKI authentication
- Delete application privileges
- Delete role mappings
- Delete roles
- Delete service account token
- Delete users
- Disable users
- Enable users
- Enroll Kibana
- Enroll node
- Get API key information
- Get application privileges
- Get builtin privileges
- Get role mappings
- Get roles
- Get service accounts
- Get service account credentials
- Get token
- Get user privileges
- Get users
- Grant API keys
- Has privileges
- Invalidate API key
- Invalidate token
- OpenID Connect prepare authentication
- OpenID Connect authenticate
- OpenID Connect logout
- Query API key information
- SAML prepare authentication
- SAML authenticate
- SAML logout
- SAML invalidate
- SAML complete logout
- SAML service provider metadata
- SSL certificate
- Snapshot and restore APIs
- Snapshot lifecycle management APIs
- SQL APIs
- Transform APIs
- Usage API
- Watcher APIs
- Definitions
- Migration guide
- Release notes
- 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.
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
,text_classification
,text_embedding
orzero_shot_classification
. Ifregression
orclassification
, it must match thetarget_type
of the underlyingdefinition.trained_model
. Iffill_mask
,ner
,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
-
tokenization
-
(Optional, object) Indicates the tokenization to perform and the desired settings.
Properties of tokenization
-
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.
The default for BERT-style tokenization is
512
. -
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.
-
-
-
-
-
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"]
-
tokenization
-
(Optional, object) Indicates the tokenization to perform and the desired settings.
Properties of tokenization
-
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.
The default for BERT-style tokenization is
512
. -
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.
-
-
-
-
-
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
-
tokenization
-
(Optional, object) Indicates the tokenization to perform and the desired settings.
Properties of tokenization
-
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.
The default for BERT-style tokenization is
512
. -
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.
-
-
-
-
-
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).
-
tokenization
-
(Optional, object) Indicates the tokenization to perform and the desired settings.
Properties of tokenization
-
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.
The default for BERT-style tokenization is
512
. -
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.
-
-
-
-
-
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
-
tokenization
-
(Optional, object) Indicates the tokenization to perform and the desired settings.
Properties of tokenization
-
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.
The default for BERT-style tokenization is
512
. -
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.
-
-
-
-
-
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
. -
tokenization
-
(Optional, object) Indicates the tokenization to perform and the desired settings.
Properties of tokenization
-
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.
The default for BERT-style tokenization is
512
. -
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.
-
-
-
-
-
-
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.
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.
On this page
ElasticON events are back!
Learn about the Elastic Search AI Platform from the experts at our live events.
Register now