Description
Pretrained named entity recognition deep learning model for biology and genetics terms. It is the version of ner_bionlp model augmented with langtest library.
| test_type | before fail_count | after fail_count | before pass_count | after pass_count | minimum pass_rate | before pass_rate | after pass_rate |
|---|---|---|---|---|---|---|---|
| add_ocr_typo | 654 | 121 | 610 | 1143 | 70% | 48% | 90% |
| lowercase | 463 | 307 | 802 | 958 | 70% | 63% | 76% |
| strip_all_punctuation | 220 | 219 | 1059 | 1060 | 70% | 83% | 83% |
| titlecase | 714 | 373 | 563 | 904 | 60% | 44% | 71% |
| uppercase | 1161 | 464 | 122 | 819 | 60% | 10% | 64% |
| weighted average | 3212 | 1484 | 3156 | 4884 | 66% | 49.56% | 76.70% |
Predicted Entities
Amino_acid, Anatomical_system, Cancer, Cell, Cellular_component, Developing_anatomical_Structure, Gene_or_gene_product, Immaterial_anatomical_entity, Multi-tissue_structure, Organ, Organism, Organism_subdivision, Simple_chemical, Tissue
How to use
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentence_detector = SentenceDetector()\
.setInputCols(["document"])\
.setOutputCol("sentence")
tokenizer = Tokenizer()\
.setInputCols(["sentence"])\
.setOutputCol("token")
word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")\
.setInputCols(["sentence", "token"])\
.setOutputCol("embeddings")
clinical_ner = MedicalNerModel.pretrained("ner_bionlp_langtest", "en", "clinical/models") \
.setInputCols(["sentence", "token", "embeddings"]) \
.setOutputCol("ner")
ner_converter = NerConverter()\
.setInputCols(["sentence", "token", "ner"])\
.setOutputCol("ner_chunk")
nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, word_embeddings, clinical_ner, ner_converter])
model = nlp_pipeline.fit(spark.createDataFrame([[""]]).toDF("text"))
results = model.transform(spark.createDataFrame([["""The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene for Type II diabetes mellitus in the Pima Indian population. The gene spans approximately 7.6 kb and contains one noncoding and two coding exons separated by approximately 2.2 and approximately 2.6 kb introns, respectively. We identified 14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various human tissues including the pancreas, and two major insulin-responsive tissues. The characterization of the KCNJ9 gene should facilitate further studies on the function of the KCNJ9 protein and allow evaluation of the potential role of the locus in Type II diabetes."""]], ["text"]))
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")
val word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")
.setInputCols(Array("sentence", "token"))
.setOutputCol("embeddings")
val ner = MedicalNerModel.pretrained("ner_bionlp_langtest", "en", "clinical/models")
.setInputCols("sentence", "token", "embeddings")
.setOutputCol("ner")
val ner_converter = new NerConverter()
.setInputCols(Array("sentence", "token", "ner"))
.setOutputCol("ner_chunk")
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, word_embeddings, ner, ner_converter))
val data = Seq("""The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene for Type II diabetes mellitus in the Pima Indian population. The gene spans approximately 7.6 kb and contains one noncoding and two coding exons separated by approximately 2.2 and approximately 2.6 kb introns, respectively. We identified 14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various human tissues including the pancreas, and two major insulin-responsive tissues. The characterization of the KCNJ9 gene should facilitate further studies on the function of the KCNJ9 protein and allow evaluation of the potential role of the locus in Type II diabetes.""").toDS().toDF("text")
val result = pipeline.fit(data).transform(data)
Results
+-----------------------------+--------------------+
|chunk |ner_label |
+-----------------------------+--------------------+
|human |Organism |
|Kir 3.3 |Gene_or_gene_product|
|GIRK3 |Gene_or_gene_product|
|inwardly rectifying potassium|Gene_or_gene_product|
|GIRK |Gene_or_gene_product|
|chromosome 1q21-23 |Cellular_component |
|Type II |Gene_or_gene_product|
|human |Organism |
|tissues |Tissue |
|pancreas |Organ |
|insulin-responsive tissues |Tissue |
|KCNJ9 |Gene_or_gene_product|
|KCNJ9 |Gene_or_gene_product|
|locus |Cellular_component |
+-----------------------------+--------------------+
Model Information
| Model Name: | ner_bionlp_langtest |
| Compatibility: | Healthcare NLP 5.1.1+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
| Size: | 14.6 MB |
References
Trained on the Cancer Genetics (CG) task of the BioNLP Shared Task 2013
Benchmarking
label precision recall f1-score support
Amino_acid 0.96 0.62 0.75 37
Anatomical_system 0.89 0.62 0.73 13
Cancer 0.92 0.90 0.91 570
Cell 0.93 0.92 0.92 806
Cellular_component 0.86 0.89 0.87 141
Developing_anatomical_structure 0.75 0.60 0.67 5
Gene_or_gene_product 0.93 0.93 0.93 1818
Immaterial_anatomical_entity 0.92 0.76 0.83 29
Multi-tissue_structure 0.86 0.79 0.82 196
Organ 0.90 0.91 0.90 85
Organism 0.94 0.90 0.92 414
Organism_subdivision 0.74 0.64 0.68 22
Organism_substance 0.86 0.89 0.87 61
Pathological_formation 0.78 0.76 0.77 46
Simple_chemical 0.94 0.93 0.93 538
Tissue 0.76 0.83 0.79 110
micro-avg 0.92 0.91 0.91 4891
macro-avg 0.87 0.80 0.83 4891
weighted-avg 0.92 0.91 0.91 4891