|
@@ -0,0 +1,116 @@
|
|
|
+[role="xpack"]
|
|
|
+[testenv="basic"]
|
|
|
+[[infer-trained-model-deployment]]
|
|
|
+= Infer trained model deployment API
|
|
|
+[subs="attributes"]
|
|
|
+++++
|
|
|
+<titleabbrev>Infer trained model deployment</titleabbrev>
|
|
|
+++++
|
|
|
+
|
|
|
+Evaluates a trained model.
|
|
|
+
|
|
|
+[[infer-trained-model-deployment-request]]
|
|
|
+== {api-request-title}
|
|
|
+
|
|
|
+`POST _ml/trained_models/<model_id>/deployment/_infer`
|
|
|
+
|
|
|
+////
|
|
|
+[[infer-trained-model-deployment-prereq]]
|
|
|
+== {api-prereq-title}
|
|
|
+
|
|
|
+////
|
|
|
+////
|
|
|
+[[infer-trained-model-deployment-desc]]
|
|
|
+== {api-description-title}
|
|
|
+
|
|
|
+////
|
|
|
+
|
|
|
+[[infer-trained-model-deployment-path-params]]
|
|
|
+== {api-path-parms-title}
|
|
|
+
|
|
|
+`<model_id>`::
|
|
|
+(Required, string)
|
|
|
+include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=model-id]
|
|
|
+
|
|
|
+[[infer-trained-model-deployment-query-params]]
|
|
|
+== {api-query-parms-title}
|
|
|
+
|
|
|
+`timeout`::
|
|
|
+(Optional, time)
|
|
|
+Controls the amount of time to wait for {infer} results. Defaults to 10 seconds.
|
|
|
+
|
|
|
+[[infer-trained-model-request-body]]
|
|
|
+== {api-request-body-title}
|
|
|
+
|
|
|
+`input`::
|
|
|
+(Required,string)
|
|
|
+The input text for evaluation.
|
|
|
+
|
|
|
+////
|
|
|
+[[infer-trained-model-deployment-results]]
|
|
|
+== {api-response-body-title}
|
|
|
+////
|
|
|
+////
|
|
|
+[[ml-get-trained-models-response-codes]]
|
|
|
+== {api-response-codes-title}
|
|
|
+
|
|
|
+////
|
|
|
+
|
|
|
+[[infer-trained-model-deployment-example]]
|
|
|
+== {api-examples-title}
|
|
|
+
|
|
|
+The response depends on the task the model is trained for. If it is a
|
|
|
+sentiment analysis task, the response is the score. For example:
|
|
|
+
|
|
|
+[source,console]
|
|
|
+--------------------------------------------------
|
|
|
+POST _ml/trained_models/model2/deployment/_infer
|
|
|
+{
|
|
|
+ "input": "The movie was awesome!!"
|
|
|
+}
|
|
|
+--------------------------------------------------
|
|
|
+// TEST[skip:TBD]
|
|
|
+
|
|
|
+The API returns scores in this case, for example:
|
|
|
+
|
|
|
+[source,console-result]
|
|
|
+----
|
|
|
+{
|
|
|
+ "positive" : 0.9998062667902223,
|
|
|
+ "negative" : 1.9373320977752957E-4
|
|
|
+}
|
|
|
+----
|
|
|
+// NOTCONSOLE
|
|
|
+
|
|
|
+For named entity recognition (NER) tasks, the response contains the recognized
|
|
|
+entities and their type. For example:
|
|
|
+
|
|
|
+[source,console]
|
|
|
+--------------------------------------------------
|
|
|
+POST _ml/trained_models/model2/deployment/_infer
|
|
|
+{
|
|
|
+ "input": "Hi my name is Josh and I live in Berlin"
|
|
|
+}
|
|
|
+--------------------------------------------------
|
|
|
+// TEST[skip:TBD]
|
|
|
+
|
|
|
+The API returns scores in this case, for example:
|
|
|
+
|
|
|
+[source,console-result]
|
|
|
+----
|
|
|
+{
|
|
|
+ "entities" : [
|
|
|
+ {
|
|
|
+ "label" : "person",
|
|
|
+ "score" : 0.9988716330253505,
|
|
|
+ "word" : "Josh"
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "label" : "location",
|
|
|
+ "score" : 0.9980872542990656,
|
|
|
+ "word" : "Berlin"
|
|
|
+ }
|
|
|
+ ]
|
|
|
+}
|
|
|
+----
|
|
|
+// NOTCONSOLE
|