Description
This model can be used to detect normalized mentions of genes (go) and human phenotypes (hp) in medical text.
Predicted Entities
HP
, GO
Live Demo Open in Colab Download
How to use
...
embeddings_clinical = BertEmbeddings.pretrained("biobert_pubmed_base_cased").setInputCols(["sentence", "token"]).setOutputCol("embeddings")
clinical_ner = MedicalNerModel.pretrained("ner_human_phenotype_go_biobert", "en", "clinical/models").setInputCols(["sentence", "token", "embeddings"]).setOutputCol("ner")
...
nlpPipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings_clinical, clinical_ner, ner_converter])
model = nlpPipeline.fit(spark.createDataFrame([[""]]).toDF("text"))
results = model.transform(spark.createDataFrame([["EXAMPLE_TEXT"]]).toDF("text"))
...
val embeddings_clinical = BertEmbeddings.pretrained("biobert_pubmed_base_cased")
.setInputCols(Array("sentence", "token"))
.setOutputCol("embeddings")
val ner = MedicalNerModel.pretrained("ner_human_phenotype_go_biobert", "en", "clinical/models")
.setInputCols(Array("sentence", "token", "embeddings"))
.setOutputCol("ner")
...
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings_clinical, ner, ner_converter))
val result = pipeline.fit(Seq.empty[String]).transform(data)
Model Information
Model Name: | ner_human_phenotype_go_biobert |
Compatibility: | Spark NLP for Healthcare 3.0.0+ |
License: | Licensed |
Edition: | Official |
Input Labels: | [sentence, token, embeddings] |
Output Labels: | [ner] |
Language: | en |
Benchmarking
entity tp fp fn total precision recall f1
GO 7637.0 579.0 441.0 8078.0 0.9295 0.9454 0.9374
HP 1463.0 273.0 222.0 1685.0 0.8427 0.8682 0.8553
macro - - - - - - 0.89635
micro - - - - - - 0.92323