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  • John Snow Labs
  • Aug 10, 2026

Sentence Entity Resolver for MeSH Codes - Veterinary (sbiobert_base_cased_mli_onnx Embeddings)

  • en
  • entity_resolution
  • licensed
  • clinical
  • mesh
  • sbiobert
  • veterinary

Description

This model maps clinical/veterinary entities to MeSH (Medical Subject Headings) Unique Identifiers (UI) using sbiobert_base_cased_mli_onnx Sentence Embeddings.

Trained on the MeSH 2026 dataset, augmented by JSL with a veterinary-focused scope.

Live Demo Open in Colab Copy S3 URI

How to use

documentAssembler = DocumentAssembler()\
    .setInputCol("text")\
    .setOutputCol("document")

sentenceDetectorDL = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare", "en", "clinical/models")\
    .setInputCols(["document"])\
    .setOutputCol("sentence")

tokenizer = Tokenizer()\
    .setInputCols(["sentence"])\
    .setOutputCol("token")

word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical","en","clinical/models")\
    .setInputCols(["sentence","token"])\
    .setOutputCol("word_embeddings")

ner_model = MedicalNerModel.pretrained("ner_clinical","en","clinical/models")\
    .setInputCols(["sentence","token","word_embeddings"])\
    .setOutputCol("ner")

ner_converter = NerConverterInternal()\
    .setInputCols(["sentence","token","ner"])\
    .setOutputCol("ner_chunk")

chunk2doc = Chunk2Doc()\
    .setInputCols(["ner_chunk"])\
    .setOutputCol("ner_chunk_doc")

embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli_onnx", "en", "clinical/models")\
    .setInputCols(["ner_chunk_doc"])\
    .setOutputCol("sbert_embeddings")\
    .setCaseSensitive(False)

resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_mesh_veterinary_2026","en","clinical/models")\
    .setInputCols(["sbert_embeddings"])\
    .setOutputCol("mesh_code")\
    .setDistanceFunction("EUCLIDEAN")

pipeline = Pipeline(stages=[\
    documentAssembler, sentenceDetectorDL, tokenizer, word_embeddings,\
    ner_model, ner_converter, chunk2doc, embedder, resolver\
])

data = spark.createDataFrame([["The dog was diagnosed with rabies and treated with vaccination; tick infestations were also noted, along with coccidiosis."]]).toDF("text")
result = pipeline.fit(data).transform(data)

documentAssembler = nlp.DocumentAssembler()\
    .setInputCol("text")\
    .setOutputCol("document")

sentenceDetectorDL = nlp.SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare", "en", "clinical/models")\
    .setInputCols(["document"])\
    .setOutputCol("sentence")

tokenizer = nlp.Tokenizer()\
    .setInputCols(["sentence"])\
    .setOutputCol("token")

word_embeddings = nlp.WordEmbeddingsModel.pretrained("embeddings_clinical","en","clinical/models")\
    .setInputCols(["sentence","token"])\
    .setOutputCol("word_embeddings")

ner_model = medical.NerModel.pretrained("ner_clinical","en","clinical/models")\
    .setInputCols(["sentence","token","word_embeddings"])\
    .setOutputCol("ner")

ner_converter = medical.NerConverterInternal()\
    .setInputCols(["sentence","token","ner"])\
    .setOutputCol("ner_chunk")

chunk2doc = nlp.Chunk2Doc()\
    .setInputCols(["ner_chunk"])\
    .setOutputCol("ner_chunk_doc")

embedder = nlp.BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli_onnx", "en", "clinical/models")\
    .setInputCols(["ner_chunk_doc"])\
    .setOutputCol("sbert_embeddings")\
    .setCaseSensitive(False)

resolver = medical.SentenceEntityResolverModel.pretrained("sbiobertresolve_mesh_veterinary_2026","en","clinical/models")\
    .setInputCols(["sbert_embeddings"])\
    .setOutputCol("mesh_code")\
    .setDistanceFunction("EUCLIDEAN")

pipeline = nlp.Pipeline(stages=[\
    documentAssembler, sentenceDetectorDL, tokenizer, word_embeddings,\
    ner_model, ner_converter, chunk2doc, embedder, resolver\
])

data = spark.createDataFrame([["The dog was diagnosed with rabies and treated with vaccination; tick infestations were also noted, along with coccidiosis."]]).toDF("text")
result = pipeline.fit(data).transform(data)


val documentAssembler = new DocumentAssembler()
    .setInputCol("text")
    .setOutputCol("document")

val sentenceDetectorDL = SentenceDetectorDLModel
    .pretrained("sentence_detector_dl_healthcare", "en", "clinical/models")
    .setInputCols(Array("document"))
    .setOutputCol("sentence")

val tokenizer = new Tokenizer()
    .setInputCols("sentence")
    .setOutputCol("token")

val word_embeddings = WordEmbeddingsModel
    .pretrained("embeddings_clinical", "en", "clinical/models")
    .setInputCols(Array("sentence", "token"))
    .setOutputCol("word_embeddings")

val ner_model = MedicalNerModel
    .pretrained("ner_clinical", "en", "clinical/models")
    .setInputCols(Array("sentence", "token", "word_embeddings"))
    .setOutputCol("ner")

val ner_converter = new NerConverterInternal()
    .setInputCols(Array("sentence", "token", "ner"))
    .setOutputCol("ner_chunk")

val chunk2doc = new Chunk2Doc()
    .setInputCols(Array("ner_chunk"))
    .setOutputCol("ner_chunk_doc")

val embedder = BertSentenceEmbeddings
    .pretrained("sbiobert_base_cased_mli_onnx", "en", "clinical/models")
    .setInputCols(Array("ner_chunk_doc"))
    .setOutputCol("sbert_embeddings")
    .setCaseSensitive(false)

val resolver = SentenceEntityResolverModel
    .pretrained("sbiobertresolve_mesh_veterinary_2026", "en", "clinical/models")
    .setInputCols(Array("sbert_embeddings"))
    .setOutputCol("mesh_code")
    .setDistanceFunction("EUCLIDEAN")

val pipeline = new Pipeline().setStages(Array(
    documentAssembler, sentenceDetectorDL, tokenizer, word_embeddings,
    ner_model, ner_converter, chunk2doc, embedder, resolver
))

val data = Seq("The dog was diagnosed with rabies and treated with vaccination; tick infestations were also noted, along with coccidiosis.").toDF("text")
val res = pipeline.fit(data).transform(data)

Results

| ner_chunk         | entity    | MeSH Code   | Resolution        | all_k_results                                   | all_k_cosine_distances                     | all_k_resolutions                                                                   |
|:------------------|:----------|:------------|:------------------|:------------------------------------------------|:-------------------------------------------|:------------------------------------------------------------------------------------|
| rabies            | PROBLEM   | D011818     | rabies            | D011818:::D011820:::D018114:::C000639155        | 0.0000:::0.0501:::0.1049:::0.2105          | rabies:::rabies virus:::rabies virus group:::rickettsia gravesii                    |
| vaccination       | TREATMENT | D014611     | vaccination       | D014611:::D007114:::D032541:::D007117:::D007115 | 0.0000:::0.0467:::0.0803:::0.0802:::0.0872 | vaccination:::immunization:::mass vaccination:::booster vaccination:::immunizati... |
| tick infestations | PROBLEM   | D013984     | tick infestations | D013984:::D064927:::D017282:::D013987           | 0.0000:::0.0601:::0.0700:::0.0736          | tick infestations:::tick bites:::tick-borne infections:::tick parasite              |
| coccidiosis       | PROBLEM   | D003048     | coccidiosis       | D003048:::D003047:::D010229                     | 0.0000:::0.0423:::0.0941                   | coccidiosis:::coccidioides infection:::paracoccidioides infections                  |

Model Information

Model Name: sbiobertresolve_mesh_veterinary_2026
Compatibility: Healthcare NLP 6.4.1+
License: Licensed
Edition: Official
Input Labels: [sbert_embeddings]
Output Labels: [mesh_code]
Language: en
Size: 538.8 MB
Case sensitive: false