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
PROBLEM, TREATMENT, TEST
How to use
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentence_detector = SentenceDetector()\
.setInputCols(["document"])\
.setOutputCol("sentence")
tokenizer = Tokenizer()\
.setInputCols(["sentence"])\
.setOutputCol("token")
labels = ['PROBLEM', 'TREATMENT','TEST'] # You can change the entities
pretrained_zero_shot_ner = PretrainedZeroShotNER().pretrained("zeroshot_ner_clinical_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([["""Mr. ABC is a 60-year-old gentleman who had stress test earlier today in my office with severe chest pain after 5 minutes of exercise on the standard Bruce with horizontal ST depressions and moderate apical ischemia on stress imaging only. He required 3 sublingual nitroglycerin in total. The patient underwent cardiac catheterization with myself today which showed mild-to-moderate left main distal disease of 30%, a severe mid-LAD lesion of 99%, and a mid-left circumflex lesion of 80% with normal LV function and some mild luminal irregularities in the right coronary artery with some moderate stenosis seen in the mid to distal right PDA."""]]).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("PROBLEM", "TREATMENT","TEST") # You can change the entities
val pretrained_zero_shot_ner = PretrainedZeroShotNER().pretrained("zeroshot_ner_clinical_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([["""Mr. ABC is a 60-year-old gentleman who had stress test earlier today in my office with severe chest pain after 5 minutes of exercise on the standard Bruce with horizontal ST depressions and moderate apical ischemia on stress imaging only. He required 3 sublingual nitroglycerin in total. The patient underwent cardiac catheterization with myself today which showed mild-to-moderate left main distal disease of 30%, a severe mid-LAD lesion of 99%, and a mid-left circumflex lesion of 80% with normal LV function and some mild luminal irregularities in the right coronary artery with some moderate stenosis seen in the mid to distal right PDA."""]]).toDF("text")
val result = pipeline.fit(data).transform(data)
Results
+-------------------------------------------------------------+-----+---+---------+----------+
|chunk |begin|end|ner_label|confidence|
+-------------------------------------------------------------+-----+---+---------+----------+
|stress test |44 |54 |TEST |0.98778325|
|severe chest pain |88 |104|PROBLEM |0.99439317|
|the standard Bruce |137 |154|TREATMENT|0.8145296 |
|horizontal ST depressions |161 |185|PROBLEM |0.9864226 |
|moderate apical ischemia |191 |214|PROBLEM |0.9951188 |
|stress imaging |219 |232|TEST |0.7248742 |
|3 sublingual nitroglycerin |252 |277|TREATMENT|0.7286957 |
|cardiac catheterization |311 |333|TEST |0.98853767|
|mild-to-moderate left main distal disease |366 |406|PROBLEM |0.995087 |
|a severe mid-LAD lesion |416 |438|PROBLEM |0.991574 |
|a mid-left circumflex lesion |452 |479|PROBLEM |0.99646026|
|some mild luminal irregularities in the right coronary artery|516 |576|PROBLEM |0.96139634|
|some moderate stenosis |583 |604|PROBLEM |0.86587936|
+-------------------------------------------------------------+-----+---+---------+----------+
Model Information
| Model Name: | zeroshot_ner_clinical_medium |
| Compatibility: | Healthcare NLP 5.5.1+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 711.3 MB |
Benchmarking
label precision recall f1-score support
B-PROBLEM 0.856 0.897 0.876 4542
B-TEST 0.822 0.917 0.867 3153
B-TREATMENT 0.850 0.884 0.866 3405
I-PROBLEM 0.889 0.859 0.873 6670
I-TEST 0.841 0.885 0.862 2800
I-TREATMENT 0.865 0.802 0.832 2873
accuracy - - 0.939 88844
macro avg 0.870 0.887 0.878 88844
weighted avg 0.940 0.939 0.939 88844