Description
Detect general biological entities like tissues, organisms, cells, etc in text using pretrained NER model.
Predicted Entities
tissue_structure, Amino_acid, Simple_chemical, Organism_substance, Developing_anatomical_structure, Cell, Cancer, Cellular_component, Gene_or_gene_product, Immaterial_anatomical_entity, Organ, Organism, Pathological_formation, Organism_subdivision, Anatomical_system, Tissue
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_bionlp_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_bionlp_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[""].toDS.toDF("text")).transform(data)
Model Information
| Model Name: | ner_bionlp_biobert |
| Compatibility: | Spark NLP for Healthcare 3.0.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |