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
Open in Colab Copy S3 URICopied!
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]})))
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