mirror of
				https://github.com/explosion/spaCy.git
				synced 2025-11-04 09:57:26 +03:00 
			
		
		
		
	
		
			
				
	
	
		
			108 lines
		
	
	
		
			2.5 KiB
		
	
	
	
		
			Plaintext
		
	
	
	
	
	
			
		
		
	
	
			108 lines
		
	
	
		
			2.5 KiB
		
	
	
	
		
			Plaintext
		
	
	
	
	
	
//- 💫 DOCS > API > ENTITYRECOGNIZER
 | 
						|
 | 
						|
include ../../_includes/_mixins
 | 
						|
 | 
						|
p Annotate named entities on #[code Doc] objects.
 | 
						|
 | 
						|
+h(2, "init") EntityRecognizer.__init__
 | 
						|
    +tag method
 | 
						|
 | 
						|
p Create an #[code EntityRecognizer].
 | 
						|
 | 
						|
+table(["Name", "Type", "Description"])
 | 
						|
    +row
 | 
						|
        +cell #[code vocab]
 | 
						|
        +cell #[code Vocab]
 | 
						|
        +cell The vocabulary. Must be shared with documents to be processed.
 | 
						|
 | 
						|
    +row
 | 
						|
        +cell #[code model]
 | 
						|
        +cell #[thinc.linear.AveragedPerceptron]
 | 
						|
        +cell The statistical model.
 | 
						|
 | 
						|
    +footrow
 | 
						|
        +cell returns
 | 
						|
        +cell #[code EntityRecognizer]
 | 
						|
        +cell The newly constructed object.
 | 
						|
 | 
						|
+h(2, "call") EntityRecognizer.__call__
 | 
						|
    +tag method
 | 
						|
 | 
						|
p Apply the entity recognizer, setting the NER tags onto the #[code Doc] object.
 | 
						|
 | 
						|
+table(["Name", "Type", "Description"])
 | 
						|
    +row
 | 
						|
        +cell #[code doc]
 | 
						|
        +cell #[code Doc]
 | 
						|
        +cell The document to be processed.
 | 
						|
 | 
						|
    +footrow
 | 
						|
        +cell returns
 | 
						|
        +cell #[code None]
 | 
						|
        +cell -
 | 
						|
 | 
						|
+h(2, "pipe") EntityRecognizer.pipe
 | 
						|
    +tag method
 | 
						|
 | 
						|
p Process a stream of documents.
 | 
						|
 | 
						|
+table(["Name", "Type", "Description"])
 | 
						|
    +row
 | 
						|
        +cell #[code stream]
 | 
						|
        +cell -
 | 
						|
        +cell The sequence of documents to process.
 | 
						|
 | 
						|
    +row
 | 
						|
        +cell #[code batch_size]
 | 
						|
        +cell int
 | 
						|
        +cell The number of documents to accumulate into a working set.
 | 
						|
 | 
						|
    +row
 | 
						|
        +cell #[code n_threads]
 | 
						|
        +cell int
 | 
						|
        +cell
 | 
						|
            |  The number of threads with which to work on the buffer in
 | 
						|
            |  parallel.
 | 
						|
 | 
						|
    +footrow
 | 
						|
        +cell yields
 | 
						|
        +cell #[code Doc]
 | 
						|
        +cell Documents, in order.
 | 
						|
 | 
						|
+h(2, "update") EntityRecognizer.update
 | 
						|
    +tag method
 | 
						|
 | 
						|
p Update the statistical model.
 | 
						|
 | 
						|
+table(["Name", "Type", "Description"])
 | 
						|
    +row
 | 
						|
        +cell #[code doc]
 | 
						|
        +cell #[code Doc]
 | 
						|
        +cell The example document for the update.
 | 
						|
 | 
						|
    +row
 | 
						|
        +cell #[code gold]
 | 
						|
        +cell #[code GoldParse]
 | 
						|
        +cell The gold-standard annotations, to calculate the loss.
 | 
						|
 | 
						|
    +footrow
 | 
						|
        +cell returns
 | 
						|
        +cell int
 | 
						|
        +cell The loss on this example.
 | 
						|
 | 
						|
+h(2, "step_through") EntityRecognizer.step_through
 | 
						|
    +tag method
 | 
						|
 | 
						|
p Set up a stepwise state, to introspect and control the transition sequence.
 | 
						|
 | 
						|
+table(["Name", "Type", "Description"])
 | 
						|
    +row
 | 
						|
        +cell #[code doc]
 | 
						|
        +cell #[code Doc]
 | 
						|
        +cell The document to step through.
 | 
						|
 | 
						|
    +footrow
 | 
						|
        +cell returns
 | 
						|
        +cell #[code StepwiseState]
 | 
						|
        +cell A state object, to step through the annotation process.
 |