Description
This pipeline, extracts plate number entities from clinical texts.
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("plate_parser_pipeline", "en", "clinical/models")
sample_text = """ Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
Dr. John Green, ID: 1231511863, IP 203.120.223.13.
He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B, plates 34NLP34. LPN# 25ASD25, additional plates: ABC123, XYZ789, DEF456.
Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com."""
result = pipeline.transform(spark.createDataFrame([[sample_text]]).toDF("text"))
from johnsnowlabs import nlp, medical
pipeline = nlp.PretrainedPipeline("plate_parser_pipeline", "en", "clinical/models")
sample_text = """ Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
Dr. John Green, ID: 1231511863, IP 203.120.223.13.
He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B, plates 34NLP34. LPN# 25ASD25, additional plates: ABC123, XYZ789, DEF456.
Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com."""
result = pipeline.transform(spark.createDataFrame([[sample_text]]).toDF("text"))
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = PretrainedPipeline("plate_parser_pipeline", "en", "clinical/models")
val sample_text = """ Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
Dr. John Green, ID: 1231511863, IP 203.120.223.13.
He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B, plates 34NLP34. LPN# 25ASD25, additional plates: ABC123, XYZ789, DEF456.
Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com."""
val result = pipeline.transform(spark.createDataFrame([[sample_text]]).toDF("text"))
Results
| chunk | begin | end | label |
|:--------|--------:|------:|:--------|
| 34NLP34 | 287 | 293 | PLATE |
| 25ASD25 | 301 | 307 | PLATE |
| ABC123 | 329 | 334 | PLATE |
Model Information
| Model Name: | plate_parser_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 6.3.0+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 396.5 KB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- ContextualParserModel
- ChunkConverter