Updated documentation (#13556)

* Added links to Python API

* Updated licensed utility and helpers docs

* Added ModelTracer to utility and helper page

* Added AnnotationMerger page

* Added BertSentenceChunkEmbeddings page

* Added ChunkMapper and ChunkConverter pages

* Added DateNormalizer page

* Added ChunkMapperFilterer page

* Added ChunkMapperFilterer page

* Added Doc2ChunkInternal page

* Added DocumentHashCoder page

* Added ZeroShotNerModel page

* Added ZeroShotRelationExtractionModel page

* Fix Python API link for CoNLL dataset page

* Added ChunkMapperFilterer page

* Added ChunkSentenceSplitter page

* Added ChunkSentenceSplitter page

* Added AssertionChunkConverter page

* Add Python API link to license annotator template

* Added Risk Adjustments Score Calculation page

* Added missing parameters on Python code

* Added .vscode to gitignore

* Updated licensed annotators docs

* Added ChunkEntityResolver page

* SentenceEntityResolver doesn't have ner_chunks as input

* Updated docstrings

* Updated docs for SentenceEntityResolver

---------

Co-authored-by: Christian Kasim Loan <christian.kasim.loan@gmail.com>
Co-authored-by: Maziyar Panahi <maziyar.panahi@iscpif.fr>

docs/_posts/2020-11-27-sbiobertresolve_cpt_en.md

@@ -47,7 +47,7 @@ sbert_embedder = BertSentenceEmbeddings\
 .setOutputCol("sbert_embeddings")  
 
 cpt_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_cpt","en", "clinical/models") \
-.setInputCols(["ner_chunk", "sbert_embeddings"]) \
+.setInputCols(["sbert_embeddings"]) \
 .setOutputCol("resolution")\
 .setDistanceFunction("EUCLIDEAN")
 

docs/_posts/2020-11-27-sbiobertresolve_icd10cm_en.md

@@ -47,7 +47,7 @@ sbert_embedder = BertSentenceEmbeddings\
 .setOutputCol("sbert_embeddings")
 
 icd10_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_icd10cm","en", "clinical/models") \
-.setInputCols(["ner_chunk", "sbert_embeddings"]) \
+.setInputCols(["sbert_embeddings"]) \
 .setOutputCol("resolution")\
 .setDistanceFunction("EUCLIDEAN")
 

docs/_posts/2020-11-27-sbiobertresolve_icd10pcs_en.md

@@ -50,7 +50,7 @@ sbert_embedder = BertSentenceEmbeddings\
 .setOutputCol("sbert_embeddings")
 
 icd10pcs_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_icd10pcs","en", "clinical/models") \
-.setInputCols(["ner_chunk", "sbert_embeddings"]) \
+.setInputCols(["sbert_embeddings"]) \
 .setOutputCol("resolution")\
 .setDistanceFunction("EUCLIDEAN")
 

docs/_posts/2020-11-27-sbiobertresolve_icdo_en.md

@@ -48,7 +48,7 @@ sbert_embedder = BertSentenceEmbeddings\
 .setOutputCol("sbert_embeddings")
 
 icdo_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_icdo","en", "clinical/models") \
-.setInputCols(["ner_chunk", "sbert_embeddings"]) \
+.setInputCols(["sbert_embeddings"]) \
 .setOutputCol("resolution")\
 .setDistanceFunction("EUCLIDEAN")
 

docs/_posts/2020-11-27-sbiobertresolve_rxnorm_en.md

@@ -47,7 +47,7 @@ sbert_embedder = BertSentenceEmbeddings\
 .setOutputCol("sbert_embeddings")
 
 rxnorm_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_rxnorm","en", "clinical/models") \
-.setInputCols(["ner_chunk", "sbert_embeddings"]) \
+.setInputCols(["sbert_embeddings"]) \
 .setOutputCol("resolution")\
 .setDistanceFunction("EUCLIDEAN")
 

docs/_posts/2020-11-27-sbiobertresolve_snomed_auxConcepts_en.md

@@ -47,7 +47,7 @@ sbert_embedder = BertSentenceEmbeddings\
 .setOutputCol("sbert_embeddings")
 
 snomed_aux_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_snomed_auxConcepts","en", "clinical/models") \
-.setInputCols(["ner_chunk", "sbert_embeddings"]) \
+.setInputCols(["sbert_embeddings"]) \
 .setOutputCol("resolution")\
 .setDistanceFunction("EUCLIDEAN")
 

docs/_posts/2020-11-27-sbiobertresolve_snomed_auxConcepts_int_en.md

@@ -48,7 +48,7 @@ sbert_embedder = BertSentenceEmbeddings\
 .setOutputCol("sbert_embeddings")
 
 snomed_aux_int_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_snomed_auxConcepts_int","en", "clinical/models") \
-.setInputCols(["ner_chunk", "sbert_embeddings"]) \
+.setInputCols(["sbert_embeddings"]) \
 .setOutputCol("resolution")\
 .setDistanceFunction("EUCLIDEAN")
 

docs/_posts/2020-11-27-sbiobertresolve_snomed_findings_en.md

@@ -51,7 +51,7 @@ sbert_embedder = BertSentenceEmbeddings\
 .setOutputCol("sbert_embeddings")
 
 snomed_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_snomed_findings","en", "clinical/models") \
-.setInputCols(["ner_chunk", "sbert_embeddings"]) \
+.setInputCols(["sbert_embeddings"]) \
 .setOutputCol("resolution")\
 .setDistanceFunction("EUCLIDEAN")
 

docs/_posts/2020-12-13-sbiobertresolve_icd10cm_augmented_en.md

@@ -46,7 +46,7 @@ sbert_embedder = BertSentenceEmbeddings\
 .setOutputCol("sbert_embeddings")
 
 icd10_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_icd10cm_augmented","en", "clinical/models") \
-.setInputCols(["ner_chunk", "sbert_embeddings"]) \
+.setInputCols(["sbert_embeddings"]) \
 .setOutputCol("resolution")\
 .setDistanceFunction("EUCLIDEAN")
 
@@ -68,7 +68,7 @@ val sbert_embedder = BertSentenceEmbeddings
 
 val icd10_resolver = SentenceEntityResolverModel
 .pretrained("sbiobertresolve_icd10cm_augmented","en", "clinical/models")
-.setInputCols(Array("ner_chunk", "sbert_embeddings"))
+.setInputCols(Array("sbert_embeddings"))
 .setOutputCol("resolution")
 .setDistanceFunction("EUCLIDEAN")
 

docs/_posts/Ahmetemintek/2021-12-24-sbiobertresolve_loinc_cased_en.md

@@ -72,7 +72,7 @@ sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli","en
 .setCaseSensitive(True)
 
 resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_loinc_cased", "en", "clinical/models") \
-.setInputCols(["ner_chunk", "sbert_embeddings"])\
+.setInputCols(["sbert_embeddings"])\
 .setOutputCol("resolution")\
 .setDistanceFunction("EUCLIDEAN")
 
@@ -129,8 +129,8 @@ val sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli"
 .setCaseSensitive(True)
 
 val resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_loinc_cased", "en", "clinical/models") 
-.setInputCols(Array("ner_chunk", "sbert_embeddings"))
-.setOutputCol("resolution")
+.setInputCols(Array("sbert_embeddings"))
+.setOutputCol("resolution")\
 .setDistanceFunction("EUCLIDEAN")
 
 val pipeline = new Pipeline().setStages(Array(documentAssembler, sentenceDetector, tokenizer, word_embeddings, rad_ner, rad_ner_converter, chunk2doc,  sbert_embedder, resolver))

docs/_posts/Ahmetemintek/2022-02-26-sbiobertresolve_snomed_findings_aux_concepts_en_3_0.md

@@ -72,7 +72,7 @@ sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli","en
      .setOutputCol("sbert_embeddings")
 
 snomed_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_snomed_findings_aux_concepts", "en", "clinical/models") \
-     .setInputCols(["ner_chunk", "sbert_embeddings"]) \
+     .setInputCols(["sbert_embeddings"]) \
      .setOutputCol("snomed_code")\
      .setDistanceFunction("EUCLIDEAN")
 
@@ -128,7 +128,7 @@ val sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli"
       .setOutputCol("sbert_embeddings")
 
 val snomed_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_snomed_findings_aux_concepts", "en", "clinical/models")
-     .setInputCols(Array("ner_chunk", "sbert_embeddings"))
+     .setInputCols(Array("sbert_embeddings"))
      .setOutputCol("snomed_code")
 
 val new nlpPipeine().setStages(Array(documentAssembler,

docs/_posts/Ahmetemintek/2022-07-05-sbiobertresolve_umls_findings_en_3_0.md

@@ -72,7 +72,7 @@ sbert_embedder = BertSentenceEmbeddings\
      .setOutputCol("sbert_embeddings")
 
 resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_umls_findings","en", "clinical/models") \
-     .setInputCols(["ner_chunk_doc", "sbert_embeddings"]) \
+     .setInputCols(["sbert_embeddings"]) \
      .setOutputCol("resolution")\
      .setDistanceFunction("EUCLIDEAN")
 

docs/_posts/Ahmetemintek/2022-09-30-icd10_icd9_mapper_en.md

@@ -46,7 +46,7 @@ sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli", "e
     .setOutputCol("sbert_embeddings")
 
 icd_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_icd10cm_augmented_billable_hcc", "en", "clinical/models") \
-    .setInputCols(["ner_chunk", "sbert_embeddings"]) \
+    .setInputCols(["sbert_embeddings"]) \
     .setOutputCol("icd10cm_code")\
     .setDistanceFunction("EUCLIDEAN")
 
@@ -80,7 +80,7 @@ val sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli"
     .setOutputCol("sbert_embeddings")
 
 val icd_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_icd10cm_augmented_billable_hcc", "en", "clinical/models") 
-    .setInputCols(Array("ner_chunk", "sbert_embeddings")) 
+    .setInputCols(Array("sbert_embeddings")) 
     .setOutputCol("icd10cm_code")
     .setDistanceFunction("EUCLIDEAN")
 

docs/_posts/Ahmetemintek/2022-10-12-sbiobertresolve_cvx_en.md

@@ -46,7 +46,7 @@ sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli", "e
  .setOutputCol("sbert_embeddings")
 
 cvx_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_cvx", "en", "clinical/models")\
- .setInputCols(["ner_chunk", "sbert_embeddings"])\
+ .setInputCols(["sbert_embeddings"])\
  .setOutputCol("cvx_code")\
  .setDistanceFunction("EUCLIDEAN")
 
@@ -66,7 +66,7 @@ val sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli"
  .setOutputCol("sbert_embeddings")
 
 val cvx_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_cvx", "en", "clinical/models")
- .setInputCols(Array("ner_chunk", "sbert_embeddings"))
+ .setInputCols(Array("sbert_embeddings"))
  .setOutputCol("cvx_code")
  .setDistanceFunction("EUCLIDEAN")
 

docs/_posts/Cabir40/2022-03-01-sbiobertresolve_atc_en_2_4.md

@@ -72,7 +72,7 @@ sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli", "e
       .setCaseSensitive(False)
 
 atc_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_atc", "en", "clinical/models")\
-      .setInputCols(["ner_chunk", "sentence_embeddings"]) \
+      .setInputCols(["sentence_embeddings"]) \
       .setOutputCol("atc_code")\
       .setDistanceFunction("EUCLIDEAN")
 
@@ -133,7 +133,7 @@ val sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli"
       .setCaseSensitive(False)
 
 val atc_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_atc", "en", "clinical/models")
-      .setInputCols(Array("ner_chunk", "sentence_embeddings"))
+      .setInputCols(Array("sentence_embeddings"))
       .setOutputCol("atc_code")
       .setDistanceFunction("EUCLIDEAN")
 

docs/_posts/Cabir40/2022-03-01-sbiobertresolve_atc_en_3_0.md

@@ -72,7 +72,7 @@ sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli", "e
       .setCaseSensitive(False)
 
 atc_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_atc", "en", "clinical/models")\
-      .setInputCols(["ner_chunk", "sentence_embeddings"]) \
+      .setInputCols(["sentence_embeddings"]) \
       .setOutputCol("atc_code")\
       .setDistanceFunction("EUCLIDEAN")
 
@@ -133,7 +133,7 @@ val sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli"
       .setCaseSensitive(False)
 
 val atc_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_atc", "en", "clinical/models")
-      .setInputCols(Array("ner_chunk", "sentence_embeddings"))
+      .setInputCols(Array("sentence_embeddings"))
       .setOutputCol("atc_code")
       .setDistanceFunction("EUCLIDEAN")
 

docs/_posts/Cabir40/2022-04-25-sbiobertresolve_rxnorm_action_treatment_en_2_4.md

@@ -46,7 +46,7 @@ sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli", "e
 .setOutputCol("sbert_embeddings")
 
 rxnorm_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_rxnorm_action_treatment", "en", "clinical/models")\
-.setInputCols(["ner_chunk", "sbert_embeddings"])\
+.setInputCols(["sbert_embeddings"])\
 .setOutputCol("rxnorm_code")\
 .setDistanceFunction("EUCLIDEAN")
 
@@ -66,7 +66,7 @@ val sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli"
 .setOutputCol("sbert_embeddings")
 
 val rxnorm_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_rxnorm_action_treatment", "en", "clinical/models")
-.setInputCols(Array("ner_chunk", "sbert_embeddings"))
+.setInputCols(Array("sbert_embeddings"))
 .setOutputCol("rxnorm_code")
 .setDistanceFunction("EUCLIDEAN")
 

docs/_posts/Cabir40/2022-05-11-sbiobertresolve_icd10cm_slim_billable_hcc_en_3_0.md

@@ -85,7 +85,7 @@ sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli", "e
 .setCaseSensitive(False)
 
 icd_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_icd10cm_slim_billable_hcc", "en", "clinical/models") \
-.setInputCols(["ner_chunk", "sentence_embeddings"]) \
+.setInputCols(["sentence_embeddings"]) \
 .setOutputCol("icd_code")\
 .setDistanceFunction("EUCLIDEAN")
 
@@ -149,7 +149,7 @@ val sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli"
 .setCaseSensitive(False)
 
 val resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_icd10cm_slim_billable_hcc", "en", "clinical/models")
-.setInputCols(Array("ner_chunk", "sentence_embeddings"))
+.setInputCols(Array("sentence_embeddings"))
 .setOutputCol("icd_code")
 .setDistanceFunction("EUCLIDEAN")
 

docs/_posts/Cabir40/2022-05-12-sbiobertresolve_icd10cm_slim_normalized_en_3_0.md

@@ -86,7 +86,7 @@ sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli", "e
     .setCaseSensitive(False)
 
 icd_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_icd10cm_slim_normalized", "en", "clinical/models") \
-    .setInputCols(["ner_chunk", "sentence_embeddings"]) \
+    .setInputCols(["sentence_embeddings"]) \
     .setOutputCol("icd_code")\
     .setDistanceFunction("EUCLIDEAN")\
     .setReturnCosineDistances(True)
@@ -151,7 +151,7 @@ val sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli"
       .setCaseSensitive(False)
 
 val resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_icd10cm_slim_normalized", "en", "clinical/models")
-      .setInputCols(Array("ner_chunk", "sentence_embeddings"))
+      .setInputCols(Array("sentence_embeddings"))
       .setOutputCol("icd_code")
       .setDistanceFunction("EUCLIDEAN")
       .setReturnCosineDistances(True)

docs/_posts/Damla-Gurbaz/2022-06-26-icd10cm_snomed_mapper_en_3_0.md

@@ -47,7 +47,7 @@ sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli", "e
 .setOutputCol("sbert_embeddings")
 
 icd_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_icd10cm_augmented_billable_hcc", "en", "clinical/models") \
-.setInputCols(["ner_chunk", "sbert_embeddings"]) \
+.setInputCols(["sbert_embeddings"]) \
 .setOutputCol("icd10cm_code")\
 .setDistanceFunction("EUCLIDEAN")
 
@@ -79,7 +79,7 @@ val sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli"
 .setOutputCol("sbert_embeddings")
 
 val icd_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_icd10cm_augmented_billable_hcc", "en", "clinical/models")
-.setInputCols(Array("ner_chunk", "sbert_embeddings"))
+.setInputCols(Array("sbert_embeddings"))
 .setOutputCol("icd10cm_code")
 .setDistanceFunction("EUCLIDEAN")
 
@@ -131,4 +131,3 @@ nlu.load("en.icd10cm_to_snomed").predict("""Diabetes Mellitus""")
 |Output Labels:|[mappings]|
 |Language:|en|
 |Size:|1.1 MB|
-

docs/_posts/Damla-Gurbaz/2022-06-26-snomed_icd10cm_mapper_en_3_0.md

@@ -46,7 +46,7 @@ sbert_embedder = BertSentenceEmbeddings.pretrained("sbert_jsl_medium_uncased", "
 .setOutputCol("sbert_embeddings")
 
 snomed_resolver = SentenceEntityResolverModel.pretrained("sbertresolve_snomed_conditions", "en", "clinical/models") \
-.setInputCols(["ner_chunk", "sbert_embeddings"]) \
+.setInputCols(["sbert_embeddings"]) \
 .setOutputCol("snomed_code")\
 .setDistanceFunction("EUCLIDEAN")
 
@@ -79,7 +79,7 @@ val sbert_embedder = BertSentenceEmbeddings.pretrained("sbert_jsl_medium_uncased
 .setOutputCol("sbert_embeddings")
 
 val snomed_resolver = SentenceEntityResolverModel.pretrained("sbertresolve_snomed_conditions", "en", "clinical/models")
-.setInputCols(Array("ner_chunk", "sbert_embeddings"))
+.setInputCols(Array("sbert_embeddings"))
 .setOutputCol("snomed_code")
 .setDistanceFunction("EUCLIDEAN")
 

docs/_posts/HashamUlHaq/2021-04-29-sbiobertresolve_loinc_en.md

@@ -75,7 +75,7 @@ sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli","en
     .setOutputCol("sbert_embeddings")
 
 resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_loinc","en", "clinical/models") \
-    .setInputCols(["ner_chunk", "sbert_embeddings"]) \
+    .setInputCols(["sbert_embeddings"]) \
     .setOutputCol("resolution")\
     .setDistanceFunction("EUCLIDEAN")
 
@@ -125,7 +125,7 @@ val sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli"
     .setOutputCol("sbert_embeddings")
 
 val resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_loinc","en", "clinical/models")
-    .setInputCols(Array("ner_chunk", "sbert_embeddings"))
+    .setInputCols(Array("sbert_embeddings"))
     .setOutputCol("resolution")
     .setDistanceFunction("EUCLIDEAN")
 

docs/_posts/HashamUlHaq/2021-04-29-sbluebertresolve_loinc_en.md

@@ -45,7 +45,7 @@ sbert_embedder = BertSentenceEmbeddings\
      .setOutputCol("sbert_embeddings")
 
 resolver = SentenceEntityResolverModel.pretrained("sbluebertresolve_loinc","en", "clinical/models") \
-     .setInputCols(["ner_chunk", "sbert_embeddings"]) \
+     .setInputCols(["sbert_embeddings"]) \
      .setOutputCol("resolution")\
      .setDistanceFunction("EUCLIDEAN")
 

docs/_posts/HashamUlHaq/2021-05-16-sbiobertresolve_icd10cm_augmented_en.md

@@ -71,7 +71,7 @@ sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli","en
     .setOutputCol("sbert_embeddings")
 
 icd10_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_icd10cm_augmented","en", "clinical/models") \
-    .setInputCols(["ner_chunk", "sbert_embeddings"]) \
+    .setInputCols(["sbert_embeddings"]) \
     .setOutputCol("resolution")\
     .setDistanceFunction("EUCLIDEAN")
 
@@ -115,7 +115,7 @@ val sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli"
     .setOutputCol("sbert_embeddings")
 
 val icd10_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_icd10cm_augmented","en", "clinical/models")
-    .setInputCols(Array("ner_chunk", "sbert_embeddings"))
+    .setInputCols(Array("sbert_embeddings"))
     .setOutputCol("resolution")
     .setDistanceFunction("EUCLIDEAN")
 

docs/_posts/HashamUlHaq/2021-05-16-sbiobertresolve_icd10cm_en.md

@@ -71,7 +71,7 @@ sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli","en
     .setOutputCol("sbert_embeddings")
 
 icd10_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_icd10cm","en", "clinical/models") \
-    .setInputCols(["entities", "sbert_embeddings"]) \
+    .setInputCols(["sbert_embeddings"]) \
     .setOutputCol("resolution")\
     .setDistanceFunction("EUCLIDEAN")
 
@@ -115,7 +115,7 @@ val sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli"
     .setOutputCol("sbert_embeddings")
 
 val icd10_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_icd10cm","en", "clinical/models")
-    .setInputCols(Array("entities", "sbert_embeddings"))
+    .setInputCols(Array("sbert_embeddings"))
     .setOutputCol("resolution")
     .setDistanceFunction("EUCLIDEAN")
 

docs/_posts/HashamUlHaq/2021-05-16-sbiobertresolve_icdo_en.md

@@ -73,7 +73,7 @@ sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli","en
     .setOutputCol("sbert_embeddings")
 
 icdo_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_icdo","en", "clinical/models")\
-    .setInputCols(["ner_chunk", "sbert_embeddings"]) \
+    .setInputCols(["sbert_embeddings"]) \
     .setOutputCol("resolution")\
     .setDistanceFunction("EUCLIDEAN")
 
@@ -117,7 +117,7 @@ val sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli"
     .setOutputCol("sbert_embeddings")
 
 val icdo_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_icdo","en", "clinical/models")
-    .setInputCols(Array("ner_chunk", "sbert_embeddings"))
+    .setInputCols(Array("sbert_embeddings"))
     .setOutputCol("resolution")
     .setDistanceFunction("EUCLIDEAN")