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
Open in Colab Download Copy S3 URI
How to use
from sparknlp.pretrained import PretrainedPipelinein
pipeline = PretrainedPipeline('entity_recognizer_sm', lang = 'fi')
annotations = pipeline.fullAnnotate(""Hei John Snow Labs! "")[0]
annotations.keys()
val pipeline = new PretrainedPipeline("entity_recognizer_sm", lang = "fi")
val result = pipeline.fullAnnotate("Hei John Snow Labs! ")(0)
import nlu
text = [""Hei John Snow Labs! ""]
result_df = nlu.load('fi.ner').predict(text)
result_df
Results
| | document | sentence | token | embeddings | ner | entities |
|---:|:-------------------------|:------------------------|:---------------------------------|:-----------------------------|:---------------------------------|:--------------------|
| 0 | ['Hei John Snow Labs! '] | ['Hei John Snow Labs!'] | ['Hei', 'John', 'Snow', 'Labs!'] | [[-0.394499987363815,.,...]] | ['O', 'B-PER', 'I-PER', 'I-PER'] | ['John Snow Labs!'] |
Model Information
| Model Name: | entity_recognizer_sm |
| Type: | pipeline |
| Compatibility: | Spark NLP 3.0.0+ |
| License: | Open Source |
| Edition: | Official |
| Language: | fi |