Sentence Entity Resolver for CPT (sbiobert_base_cased_mli embeddings)

...
chunk2doc = Chunk2Doc()
    .setInputCols("ner_chunk")
    .setOutputCol("ner_chunk_doc")

val sbert_embedder = BertSentenceEmbeddings
    .pretrained("sbiobert_base_cased_mli","en","clinical/models")
    .setInputCols(Array("ner_chunk_doc"))
    .setOutputCol("sbert_embeddings")

val cpt_resolver = SentenceEntityResolverModel
    .load("sbiobertresolve_cpt")
    .setInputCols(Array("ner_chunk", "sbert_embeddings"))
    .setOutputCol("resolution")
    .setDistanceFunction("EUCLIDEAN")

val pipeline = new Pipeline().setStages(
    Array(
        document_assembler, 
        sentence_detector, 
        tokenizer, 
        word_embeddings, 
        clinical_ner, 
        ner_converter, 
        chunk2doc, 
        sbert_embedder, 
        cpt_resolver))

val data = Seq("This is an 82 - year-old male with a history of prior tobacco use , hypertension , chronic renal insufficiency , COPD , gastritis , and TIA who initially presented to Braintree with a non-ST elevation MI and Guaiac positive stools , transferred to St . Margaret\'s Center for Women & Infants for cardiac catheterization with PTCA to mid LAD lesion complicated by hypotension and bradycardia requiring Atropine , IV fluids and transient dopamine possibly secondary to vagal reaction , subsequently transferred to CCU for close monitoring , hemodynamically stable at the time of admission to the CCU .").toDF("text")
val result = pipeline.fit(data).transform(data)