Description
Extract different types of chemical compounds mentioned in text using pretrained NER model. This model is trained with the BertForTokenClassification method from the transformers library and imported into Spark NLP.
Predicted Entities
O, B-CHEM, I-CHEM, PAD
How to use
from sparknlp.base import DocumentAssembler
from sparknlp_jsl.annotator import MedicalBertForTokenClassifier
from sparknlp.annotator import Tokenizer, NerConverter
from pyspark.ml import Pipeline
document_assembler = (
DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
)
tokenizer = (
Tokenizer()
.setInputCols(["document"])
.setOutputCol("token")
)
token_classifier = (
MedicalBertForTokenClassifier.pretrained(
"bert_token_classifier_ner_chemicals_onnx",
"en",
"clinical/models"
)
.setInputCols(["token", "document"])
.setOutputCol("ner")
.setCaseSensitive(True)
)
ner_converter = (
NerConverterInternal()
.setInputCols(["document", "token", "ner"])
.setOutputCol("ner_chunk")
)
pipeline = Pipeline(stages=[
document_assembler,
tokenizer,
token_classifier,
ner_converter
])
test_sentence = "The results have shown that the product p - choloroaniline is not a significant factor in chlorhexidine - digluconate associated erosive cystitis. "A high percentage of kanamycin - colistin and povidone - iodine irrigations were associated with erosive cystitis."
data = spark.createDataFrame([[test_sentence]]).toDF("text")
model = pipeline.fit(data)
result = model.transform(data)
from johnsnowlabs import nlp, medical
document_assembler = nlp.DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
tokenizer = nlp.Tokenizer()\
.setInputCols(["document"])\
.setOutputCol("token")
token_classifier = medical.BertForTokenClassifier.pretrained(
"bert_token_classifier_ner_chemicals_onnx",
"en",
"clinical/models"
)\
.setInputCols(["token", "document"])\
.setOutputCol("ner")\
.setCaseSensitive(True)
ner_converter = medical.NerConverterInternal()\
.setInputCols(["document", "token", "ner"])\
.setOutputCol("ner_chunk")
pipeline = Pipeline(stages=[
document_assembler,
tokenizer,
token_classifier,
ner_converter
])
test_sentence = "The results have shown that the product p - choloroaniline is not a significant factor in chlorhexidine - digluconate associated erosive cystitis. "A high percentage of kanamycin - colistin and povidone - iodine irrigations were associated with erosive cystitis."
data = spark.createDataFrame([[test_sentence]]).toDF("text")
model = pipeline.fit(data)
result = model.transform(data)
import com.johnsnowlabs.nlp.base.DocumentAssembler
import com.johnsnowlabs.nlp.annotators.Tokenizer
import com.johnsnowlabs.nlp.annotators.ner.NerConverter
import com.johnsnowlabs.nlp.annotators.classifier.dl.MedicalBertForTokenClassifier
import org.apache.spark.ml.Pipeline
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val tokenizer = new Tokenizer()
.setInputCols("document")
.setOutputCol("token")
val tokenClassifier = MedicalBertForTokenClassifier
.pretrained("bert_token_classifier_ner_chemicals_onnx", "en", "clinical/models")
.setInputCols(Array("token", "document"))
.setOutputCol("ner")
.setCaseSensitive(true)
val nerConverter = new NerConverterInternal()
.setInputCols(Array("document", "token", "ner"))
.setOutputCol("ner_chunk")
val pipeline = new Pipeline()
.setStages(Array(
documentAssembler,
tokenizer,
tokenClassifier,
nerConverter
))
val testSentence = "The results have shown that the product p - choloroaniline is not a significant factor in chlorhexidine - digluconate associated erosive cystitis. "A high percentage of kanamycin - colistin and povidone - iodine irrigations were associated with erosive cystitis."
val data = Seq(testSentence).toDF("text")
val model = pipeline.fit(data)
val result = model.transform(data)
Results
+---------------------------+------+
|text |entity|
+---------------------------+------+
|p - choloroaniline |CHEM |
|chlorhexidine - digluconate|CHEM |
|kanamycin |CHEM |
|colistin |CHEM |
|povidone - iodine |CHEM |
+---------------------------+------+
Model Information
| Model Name: | bert_token_classifier_ner_chemicals_onnx |
| Compatibility: | Healthcare NLP 6.1.1+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [ner] |
| Language: | en |
| Size: | 403.7 MB |
| Case sensitive: | true |
| Max sentence length: | 128 |