Description
Anatomical entities ranging from subcellular structures to organ systems are central to biomedical science, and mentions of these entities are essential to understanding the scientific literature.
This model is trained with the BertForTokenClassification method from the transformers library and imported into Spark NLP. The model detects anatomical structures from a medical text.
Predicted Entities
O, B-Anatomy, I-Anatomy, 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_anatem_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 = "Malignant cells often display defects in autophagy, an evolutionarily conserved pathway for degrading long-lived proteins and cytoplasmic organelles. However, as yet, there is no genetic evidence for a role of autophagy genes in tumor suppression. The beclin 1 autophagy gene is monoallelically deleted in 40 - 75 % of cases of human sporadic breast, ovarian, and prostate cancer."
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_anatem_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 = "Malignant cells often display defects in autophagy, an evolutionarily conserved pathway for degrading long-lived proteins and cytoplasmic organelles. However, as yet, there is no genetic evidence for a role of autophagy genes in tumor suppression. The beclin 1 autophagy gene is monoallelically deleted in 40 - 75 % of cases of human sporadic breast, ovarian, and prostate cancer."
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_anatem_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 = "Malignant cells often display defects in autophagy, an evolutionarily conserved pathway for degrading long-lived proteins and cytoplasmic organelles. However, as yet, there is no genetic evidence for a role of autophagy genes in tumor suppression. The beclin 1 autophagy gene is monoallelically deleted in 40 - 75 % of cases of human sporadic breast, ovarian, and prostate cancer."
val data = Seq(testSentence).toDF("text")
val model = pipeline.fit(data)
val result = model.transform(data)
Results
+----------------------+-------+
|text |entity |
+----------------------+-------+
|Malignant cells |Anatomy|
|cytoplasmic organelles|Anatomy|
|tumor |Anatomy|
|breast |Anatomy|
|ovarian |Anatomy|
|prostate cancer |Anatomy|
+----------------------+-------+
Model Information
| Model Name: | bert_token_classifier_ner_anatem_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 |