Detect entities related to road traffic

Description

Detect entities related to road traffic using pretrained NER model.

Predicted Entities

ORGANIZATION_COMPANY, DISASTER_TYPE, TIME, TRIGGER, DATE, PERSON, LOCATION_STOP, ORGANIZATION, DISTANCE, LOCATION_STREET, NUMBER, DURATION, ORG_POSITION, LOCATION_ROUTE, LOCATION, LOCATION_CITY

Live Demo Open in Colab Download

How to use


...
embeddings_clinical = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")  .setInputCols(["sentence", "token"])  .setOutputCol("embeddings")
clinical_ner = MedicalNerModel.pretrained("ner_traffic", "en", "clinical/models")   .setInputCols(["sentence", "token", "embeddings"])   .setOutputCol("ner")
...
nlpPipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings_clinical, clinical_ner, ner_converter])
model = nlpPipeline.fit(spark.createDataFrame([[""]]).toDF("text"))
results = model.transform(spark.createDataFrame([["EXAMPLE_TEXT"]]).toDF("text"))

...
val embeddings_clinical = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")
  .setInputCols(Array("sentence", "token"))
  .setOutputCol("embeddings")
val ner = MedicalNerModel.pretrained("ner_traffic", "en", "clinical/models")
  .setInputCols(Array("sentence", "token", "embeddings"))
  .setOutputCol("ner")
...
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings_clinical, ner, ner_converter))
val result = pipeline.fit(Seq.empty[""].toDS.toDF("text")).transform(data)

Model Information

Model Name: ner_traffic
Compatibility: Spark NLP for Healthcare 3.0.0+
License: Licensed
Edition: Official
Input Labels: [sentence, token, embeddings]
Output Labels: [ner]
Language: de