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.
How to use
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentence_detector = SentenceDetector()\
.setInputCols(["document"])\
.setOutputCol("sentence")
tokenizer = Tokenizer()\
.setInputCols(["sentence"])\
.setOutputCol("token")
labels = ["NAME", "AGE", "DATE", "LOCATION", "ID", "CONTACT", "PROFESSION"]
pretrained_zero_shot_ner = PretrainedZeroShotNER().pretrained("zeroshot_ner_deid_generic_nonMedical_large", "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([["""Mr. James Wilson is a 65-year-old male who presented to the emergency department at Boston General Hospital on 10/25/2023.
He lives at 123 Oak Street, Springfield, IL 62704. He can be contacted at 555-0199.
His SSN is 999-00-1234. Dr. Gregory House is the attending physician."""]]).toDF("text")
result = pipeline.fit(data).transform(data)
document_assembler = nlp.DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentence_detector = nlp.SentenceDetector()\
.setInputCols(["document"])\
.setOutputCol("sentence")
tokenizer = nlp.Tokenizer()\
.setInputCols(["sentence"])\
.setOutputCol("token")
labels = ["NAME", "AGE", "DATE", "LOCATION", "ID", "CONTACT", "PROFESSION"]
pretrained_zero_shot_ner = medical.PretrainedZeroShotNER().pretrained("zeroshot_ner_deid_generic_nonMedical_large", "en", "clinical/models")\
.setInputCols("sentence", "token")\
.setOutputCol("ner")\
.setPredictionThreshold(0.5)\
.setLabels(labels)
ner_converter = medical.NerConverterInternal()\
.setInputCols("sentence", "token", "ner")\
.setOutputCol("ner_chunk")
pipeline = nlp.Pipeline().setStages([
document_assembler,
sentence_detector,
tokenizer,
pretrained_zero_shot_ner,
ner_converter
])
data = spark.createDataFrame([["""Mr. James Wilson is a 65-year-old male who presented to the emergency department at Boston General Hospital on 10/25/2023.
He lives at 123 Oak Street, Springfield, IL 62704. He can be contacted at 555-0199.
His SSN is 999-00-1234. Dr. Gregory House is the attending physician."""]]).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")
val labels = Array("NAME", "AGE", "DATE", "LOCATION", "ID", "CONTACT", "PROFESSION")
val pretrained_zero_shot_ner = PretrainedZeroShotNER().pretrained("zeroshot_ner_deid_generic_nonMedical_large", "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("""Mr. James Wilson is a 65-year-old male who presented to the emergency department at Boston General Hospital on 10/25/2023.
He lives at 123 Oak Street, Springfield, IL 62704. He can be contacted at 555-0199.
His SSN is 999-00-1234. Dr. Gregory House is the attending physician.""").toDF("text")
val result = pipeline.fit(data).transform(data)
Results
+-----------------------+-----+---+---------+----------+
|chunk |begin|end|ner_label|confidence|
+-----------------------+-----+---+---------+----------+
|James Wilson |4 |15 |NAME |0.9962825 |
|65-year-old |22 |32 |AGE |0.9985607 |
|Boston General Hospital|84 |106|LOCATION |0.9911969 |
|10/25/2023 |111 |120|DATE |0.9954059 |
|123 Oak Street |136 |149|LOCATION |0.9807307 |
|Springfield |152 |162|LOCATION |0.9937017 |
|IL |165 |166|LOCATION |0.99668664|
|62704 |168 |172|LOCATION |0.9405313 |
|555-0199 |198 |205|CONTACT |0.9484805 |
|999-00-1234 |220 |230|ID |0.9979912 |
|Gregory House |237 |249|NAME |0.96871334|
+-----------------------+-----+---+---------+----------+
Model Information
| Model Name: | zeroshot_ner_deid_generic_nonMedical_large |
| Compatibility: | Healthcare NLP 6.2.2+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 1.8 GB |