Description
Detect adverse drug events in tweets, reviews, and medical text using pretrained NER model.
Predicted Entities
DRUG
, ADE
Live Demo Open in Colab Download
How to use
...
embeddings_clinical = BertEmbeddings.pretrained("biobert_clinical_base_cased") .setInputCols(["sentence", "token"]) .setInputCols(["sentence", "token"]) .setOutputCol("embeddings")
clinical_ner = MedicalNerModel.pretrained("ner_ade_clinicalbert", "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_clinical_base_cased") .setInputCols(["sentence", "token"])
.setInputCols(Array("sentence", "token"))
.setOutputCol("embeddings")
val ner = MedicalNerModel.pretrained("ner_ade_clinicalbert", "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_ade_clinicalbert |
Compatibility: | Spark NLP for Healthcare 3.0.0+ |
License: | Licensed |
Edition: | Official |
Input Labels: | [sentence, token, embeddings] |
Output Labels: | [ner] |
Language: | en |