Description
The entity_recognizer_sm is a pretrained pipeline that we can use to process text with a simple pipeline that performs basic processing steps. It performs most of the common text processing tasks on your dataframe
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How to use
from sparknlp.pretrained import PretrainedPipelinein
pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'es')
annotations = pipeline.fullAnnotate(""Hola de John Snow Labs! "")[0]
annotations.keys()
val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "es")
val result = pipeline.fullAnnotate("Hola de John Snow Labs! ")(0)
import nlu
text = [""Hola de John Snow Labs! ""]
result_df = nlu.load('es.ner').predict(text)
result_df
Results
| | document | sentence | token | embeddings | ner | entities |
|---:|:-----------------------------|:----------------------------|:----------------------------------------|:-----------------------------|:---------------------------------------|:-----------------------|
| 0 | ['Hola de John Snow Labs! '] | ['Hola de John Snow Labs!'] | ['Hola', 'de', 'John', 'Snow', 'Labs!'] | [[0.1754499971866607,.,...]] | ['O', 'O', 'B-PER', 'I-PER', 'B-MISC'] | ['John Snow', 'Labs!'] |
Model Information
| Model Name: | entity_recognizer_sm |
| Type: | pipeline |
| Compatibility: | Spark NLP 3.0.0+ |
| License: | Open Source |
| Edition: | Official |
| Language: | es |