Description
Zero-shot Named Entity Recognition (NER) enables the identification of entities in text with minimal effort. By leveraging pre-trained language models and contextual understanding, zero-shot NER extends entity recognition capabilities to new domains and languages. While the model card includes default labels as examples, it is important to highlight that users are not limited to these labels. The model is designed to support any set of entity labels, allowing users to adapt it to their specific use cases. For best results, it is recommended to use labels that are conceptually similar to the provided defaults.
Predicted Entities
DATE, PATIENT, COUNTRY, PROFESSION, AGE, CITY, STATE, DOCTOR, HOSPITAL, IDNUM, ORGANIZATION, PHONE, STREET, ZIP
How to use
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentence_detector = SentenceDetector()\
.setInputCols(["document"])\
.setOutputCol("sentence")
tokenizer = Tokenizer()\
.setInputCols(["sentence"])\
.setOutputCol("token")
labels = ["AGE", "CITY", "COUNTRY", "DATE", "DOCTOR", "HOSPITAL", "IDNUM", "ORGANIZATION",
"PATIENT", "PHONE", "PROFESSION", "STATE", "STREET", "ZIP"]
pretrained_zero_shot_ner = PretrainedZeroShotNER().pretrained("zeroshot_ner_deid_subentity_docwise_medium", "en", "clinical/models")\
.setInputCols("sentence", "token")\
.setOutputCol("ner")\
.setPredictionThreshold(0.5)\
.setLabels(labels)
ner_converter = NerConverterInternal()\
.setInputCols("sentence", "token", "ner")\
.setOutputCol("ner_chunk")
pipeline = Pipeline().setStages([
document_assembler,
sentence_detector,
tokenizer,
pretrained_zero_shot_ner,
ner_converter
])
data = spark.createDataFrame([["""Emily Davis, a 34-year-old woman, Dr. Michael Johnson cares wit her, at CarePlus Clinic, located at 456 Elm Street, NewYork, NY has recommended starting insulin therapy. She has an appointment scheduled for March 15, 2024."""]]).toDF("text")
result = pipeline.fit(data).transform(data)
val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentence_detector = new SentenceDetector()
.setInputCols("document")
.setOutputCol("sentence")
val tokenizer = new Tokenizer()
.setInputCols("sentence")
.setOutputCol("token")
labels = Array("AGE", "CITY", "COUNTRY", "DATE", "DOCTOR", "HOSPITAL", "IDNUM", "ORGANIZATION",
"PATIENT", "PHONE", "PROFESSION", "STATE", "STREET", "ZIP")
val pretrained_zero_shot_ner = PretrainedZeroShotNER().pretrained("zeroshot_ner_deid_subentity_docwise_medium", "en", "clinical/models")
.setInputCols(Array("sentence", "token"))
.setOutputCol("ner")
.setPredictionThreshold(0.5)
.setLabels(labels)
val ner_converter = new NerConverterInternal()
.setInputCols(Array("sentence", "token", "ner"))
.setOutputCol("ner_chunk")
val pipeline = new Pipeline().setStages(Array(
document_assembler,
sentence_detector,
tokenizer,
pretrained_zero_shot_ner,
ner_converter
))
val data = Seq([["""Emily Davis, a 34-year-old woman, Dr. Michael Johnson cares wit her, at CarePlus Clinic, located at 456 Elm Street, NewYork, NY has recommended starting insulin therapy. She has an appointment scheduled for March 15, 2024."""]]).toDF("text")
val result = pipeline.fit(data).transform(data)
Results
+---------------+-----+---+---------+----------+
|chunk |begin|end|ner_label|confidence|
+---------------+-----+---+---------+----------+
|Emily Davis |1 |11 |PATIENT |0.99571335|
|34-year-old |16 |26 |AGE |0.99942374|
|Michael Johnson|39 |53 |DOCTOR |0.99911386|
|CarePlus Clinic|73 |87 |HOSPITAL |0.87801534|
|456 Elm Street |101 |114|STREET |0.99828523|
|NewYork |117 |123|CITY |0.9850912 |
|NY |126 |127|STATE |0.9824218 |
|March 15, 2024 |208 |221|DATE |0.9994097 |
+---------------+-----+---+---------+----------+
Model Information
| Model Name: | zeroshot_ner_deid_subentity_docwise_medium |
| Compatibility: | Healthcare NLP 5.5.1+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 711.7 MB |
Benchmarking
label precision recall f1-score support
AGE 0.8287 0.9367 0.8794 1074
CITY 0.7973 0.9067 0.8485 525
COUNTRY 0.8430 0.8146 0.8286 178
DATE 0.9867 0.9675 0.9770 7995
DOCTOR 0.9698 0.9125 0.9403 5134
HOSPITAL 0.8397 0.8379 0.8388 2276
IDNUM 0.8720 0.5780 0.6952 955
O 0.9965 0.9959 0.9962 315085
ORGANIZATION 0.5846 0.6032 0.5938 189
PATIENT 0.8419 0.9547 0.8947 2364
PHONE 0.6293 0.8455 0.7216 492
PROFESSION 0.7300 0.9028 0.8072 494
STATE 0.6954 0.9073 0.7874 302
STREET 0.8969 0.9636 0.9291 605
ZIP 0.8879 1.0000 0.9406 198
accuracy 0.9903 337866
macro avg 0.8267 0.8751 0.8452 337866
weighted avg 0.9908 0.9903 0.9904 337866