Licensed Models


Pretrained Models

pretrained(name, lang) function to use

English - Licensed Enterprise

It is required to specify 3rd argument to pretrained(name, lang, loc) function (location) to add the location of these

Model name language loc
NerDLModel ner_clinical en clinical/models
AssertionLogRegModel assertion_ml en clinical/models
AssertionDLModel assertion_dl en clinical/models
NerDLModel deidentify_dl en clinical/models
DeIdentificationModel deidentify_rb en clinical/models
WordEmbeddingsModel embeddings_clinical en clinical/models
PerceptronModel pos_clinical en clinical/models
EntityResolverModel resolve_icd10 en clinical/models
EntityResolverModel resolve_icd10cm_cl_em en clinical/models
EntityResolverModel resolve_icd10pcs_cl_em en clinical/models
ContextSpellCheckerModel context_spell_med en clinical/models

How to use Pretrained Models


You can follow this approach to use Spark NLP pretrained models:

# load NER model trained by deep learning approach and GloVe word embeddings
ner_dl = NerDLModel.pretrained('ner_dl')
# load NER model trained by deep learning approach and BERT word embeddings
ner_bert = NerDLModel.pretrained('ner_dl_bert')

The default language is en, so for other laguages you should set the language:

// load French POS tagger model trained by Universal Dependencies
val french_pos = PerceptronModel.pretrained("pos_ud_gsd", lang="fr")
// load Italain LemmatizerModel
val italian_lemma = LemmatizerModel.pretrained("lemma_dxc", lang="it")


If you have any trouble using online pipelines or models in your environment (maybe it’s air-gapped), you can directly download them for offline use.

After downloading offline models/pipelines and extracting them, here is how you can use them iside your code (the path could be a shared storage like HDFS in a cluster):

  • Loading PerceptronModel annotator model inside Spark NLP Pipeline
val french_pos = PerceptronModel.load("/tmp/pos_ud_gsd_fr_2.0.2_2.4_1556531457346/")
      .setInputCols("document", "token")
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