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
Predicted Entities
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
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-param}
Model Information
| Model Name: | entity_recognizer_sm |
| Type: | pipeline |
| Compatibility: | Spark NLP 4.4.2+ |
| License: | Open Source |
| Edition: | Official |
| Language: | fi |
| Size: | 166.9 MB |
Included Models
- DocumentAssembler
- SentenceDetector
- TokenizerModel
- WordEmbeddingsModel
- NerDLModel
- NerConverter