Description
This model detects molecular biology-related terms in medical texts. This model is trained with the BertForTokenClassification method from the transformers library and imported into Spark NLP.
Predicted Entities
DNA, Cell_type, Cell_line, RNA, Protein
How to use
documentAssembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentence_detector = SentenceDetector()\
.setInputCols(["document"])\
.setOutputCol("sentence")
tokenizer = Tokenizer()\
.setInputCols(["sentence"])\
.setOutputCol("token")
tokenClassifier = BertForTokenClassification.pretrained("bert_token_classifier_ner_cellular", "en", "clinical/models")\
.setInputCols("token", "document")\
.setOutputCol("ner")\
.setCaseSensitive(True)
ner_converter = NerConverter()\
.setInputCols(["document","token","ner"])\
.setOutputCol("ner_chunk")
pipeline = Pipeline(stages=[documentAssembler, sentence_detector, tokenizer, tokenClassifier, ner_converter])
p_model = pipeline.fit(spark.createDataFrame(pd.DataFrame({'text': ['']})))
test_sentence = """Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive."""
result = p_model.transform(spark.createDataFrame(pd.DataFrame({'text': [test_sentence]})))
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentence_detector = new SentenceDetector()
.setInputCols("document")
.setOutputCol("sentence")
val tokenizer = new Tokenizer()
.setInputCols("sentence")
.setOutputCol("token")
val tokenClassifier = BertForTokenClassification.pretrained("bert_token_classifier_ner_cellular", "en", "clinical/models")
.setInputCols(Array("token", "document"))
.setOutputCol("ner")
.setCaseSensitive(True)
val ner_converter = new NerConverter()
.setInputCols(Array("document","token","ner"))
.setOutputCol("ner_chunk")
val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, tokenClassifier, ner_converter))
val data = Seq("""Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive.""").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.classify.token_bert.cellular").predict("""Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive.""")
Results
+-------------------------------------------+---------+
|chunk |ner_label|
+-------------------------------------------+---------+
|intracellular signaling proteins |protein |
|human T-cell leukemia virus type 1 promoter|DNA |
|Tax |protein |
|Tax-responsive element 1 |DNA |
|cyclic AMP-responsive members |protein |
|CREB/ATF family |protein |
|transcription factors |protein |
|Tax |protein |
|human T-cell leukemia virus type 1 |DNA |
|Tax-responsive element 1 |DNA |
|TRE-1 |DNA |
|lacZ gene |DNA |
|CYC1 promoter |DNA |
|TRE-1 |DNA |
|cyclic AMP response element-binding protein|protein |
|CREB |protein |
|CREB |protein |
|GAL4 activation domain |protein |
|GAD |protein |
|reporter gene |DNA |
|Tax |protein |
+-------------------------------------------+---------+
Model Information
| Model Name: | bert_token_classifier_ner_cellular |
| Compatibility: | Healthcare NLP 3.3.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token] |
| Output Labels: | [ner] |
| Language: | en |
| Case sensitive: | true |
| Max sentense length: | 512 |
Data Source
Trained on the JNLPBA corpus containing more than 2.404 publication abstracts. https://www.geniaproject.org/
Benchmarking
label precision recall f1-score support
B-DNA 0.87 0.77 0.82 1056
B-RNA 0.85 0.79 0.82 118
B-cell_line 0.66 0.70 0.68 500
B-cell_type 0.87 0.75 0.81 1921
B-protein 0.90 0.85 0.88 5067
I-DNA 0.93 0.86 0.90 1789
I-RNA 0.92 0.84 0.88 187
I-cell_line 0.67 0.76 0.71 989
I-cell_type 0.92 0.76 0.84 2991
I-protein 0.94 0.80 0.87 4774
accuracy - - 0.80 19392
macro-avg 0.76 0.81 0.78 19392
weighted-avg 0.89 0.80 0.85 19392