Spark NLP Models

This repository is deprecated. Please use Models Hub

Caution: This repo is not maintained anymore. Please visit https://nlp.johnsnowlabs.com/models to keep track of Spark NLP models.

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We use this repository to maintain our releases of pre-trained pipelines and models for the Spark NLP library.

Project's website

Take a look at our official Spark NLP page: http://nlp.johnsnowlabs.com/ for user documentation and examples

Slack community channel

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Open Source

Spark NLP comes with 1100+ pretrained pipelines and models in more than 192+ languages.

Some of the selected languages: Afrikaans, Arabic, Armenian, Basque, Bengali, Breton, Bulgarian, Catalan, Czech, Dutch, English, Esperanto, Finnish, French, Galician, German, Greek, Hausa, Hebrew, Hindi, Hungarian, Indonesian, Irish, Italian, Japanese, Latin, Latvian, Marathi, Norwegian, Persian, Polish, Portuguese, Romanian, Russian, Slovak, Slovenian, Somali, Southern Sotho, Spanish, Swahili, Swedish, Tswana, Turkish, Ukrainian, Zulu

Please check out our Models Hub for the full and updated list of pre-trained models & pipelines with examples, demo, benchmark, and more

Licensed Enterprise

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

Pretrained Models - Spark NLP For Healthcare

English Language, Clinical/Models Location

{Model}.pretrained({Name}, 'en', 'clinical/models')

Model	Name	Build
AssertionDLModel	assertion_dl_large	2.5.0	🔍	📋	💾
AssertionDLModel	assertion_dl	2.4.0	🔍	📋	💾
AssertionDLModel	assertion_dl_healthcare	2.5.0	🔍	📋	💾
AssertionDLModel	assertion_dl_biobert	2.6.2	🔍	📋	💾
AssertionLogRegModel	assertion_ml	2.4.0	🔍	📋	💾
ChunkEntityResolverModel	chunkresolve_cpt_clinical	2.4.5		📋	💾
ChunkEntityResolverModel	chunkresolve_icd10cm_clinical	2.4.5	🔍	📋	💾
ChunkEntityResolverModel	chunkresolve_icd10cm_diseases_clinical	2.4.5	🔍	📋	💾
ChunkEntityResolverModel	chunkresolve_icd10cm_injuries_clinical	2.4.5	🔍	📋	💾
ChunkEntityResolverModel	chunkresolve_icd10cm_musculoskeletal_clinical	2.4.5	🔍	📋	💾
ChunkEntityResolverModel	chunkresolve_icd10cm_neoplasms_clinical	2.4.5	🔍	📋	💾
ChunkEntityResolverModel	chunkresolve_icd10cm_puerile_clinical	2.4.5	🔍	📋	💾
ChunkEntityResolverModel	chunkresolve_icd10pcs_clinical	2.4.5	🔍	📋	💾
ChunkEntityResolverModel	chunkresolve_icdo_clinical	2.4.5	🔍	📋	💾
ChunkEntityResolverModel	chunkresolve_loinc_clinical	2.5.0	🔍	📋	💾
ChunkEntityResolverModel	chunkresolve_rxnorm_cd_clinical	2.5.1	🔍	📋	💾
ChunkEntityResolverModel	chunkresolve_rxnorm_sbd_clinical	2.5.1	🔍	📋	💾
ChunkEntityResolverModel	chunkresolve_rxnorm_scd_clinical	2.5.1	🔍	📋	💾
ChunkEntityResolverModel	chunkresolve_snomed_findings_clinical	2.5.1	🔍	📋	💾
SentenceEntityResolverModel	sbiobertresolve_cpt	2.6.4		📋	💾
SentenceEntityResolverModel	sbiobertresolve_icd10cm	2.6.4		📋	💾
SentenceEntityResolverModel	sbiobertresolve_icd10pcs	2.6.4		📋	💾
SentenceEntityResolverModel	sbiobertresolve_icdo	2.6.4		📋	💾
SentenceEntityResolverModel	sbiobertresolve_rxnorm	2.6.4		📋	💾
SentenceEntityResolverModel	sbiobertresolve_snomed_auxConcepts	2.6.4		📋	💾
SentenceEntityResolverModel	sbiobertresolve_snomed_auxConcepts_int	2.6.4		📋	💾
SentenceEntityResolverModel	sbiobertresolve_snomed_findings	2.6.4		📋	💾
SentenceEntityResolverModel	sbiobertresolve_snomed_findings_int	2.6.4		📋	💾
ContextSpellCheckerModel	spellcheck_clinical	2.4.2		📋	💾
DeIdentificationModel	deidentify_rb_no_regex	2.5.0		📋	💾
DeIdentificationModel	deidentify_rb	2.0.2		📋	💾
DeIdentificatoinModel	deidentify_large	2.5.1	🔍	📋	💾
NerDLModel	ner_anatomy	2.4.2	🔍	📋	💾
NerDLModel	ner_bionlp	2.4.0	🔍	📋	💾
NerDLModel	ner_cellular	2.4.2	🔍	📋	💾
NerDLModel	ner_clinical_large	2.5.0	🔍	📋	💾
NerDLModel	ner_clinical	2.4.0	🔍	📋	💾
NerDLModel	ner_deid_enriched	2.5.3	🔍	📋	💾
NerDLModel	ner_deid_large	2.5.3	🔍	📋	💾
NerDLModel	ner_diseases	2.4.4	🔍	📋	💾
NerDLModel	ner_diseases_large	2.6.3	🔍	📋	💾
NerDLModel	ner_drugs	2.4.4	🔍	📋	💾
NerDLModel	ner_events_clinical	2.5.5	🔍	📋	💾
NerDLModel	ner_healthcare	2.4.4	🔍	📋	💾
NerDLModel	ner_jsl_enriched	2.4.2	🔍	📋	💾
NerDLModel	ner_jsl	2.4.2	🔍	📋	💾
NerDLModel	ner_medmentions_coarse	2.5.0		📋	💾
NerDLModel	ner_posology_large	2.4.2	🔍	📋	💾
NerDLModel	ner_drugs_large	2.6.0	🔍	📋	💾
NerDLModel	ner_posology_small	2.4.2	🔍	📋	💾
NerDLModel	ner_posology	2.4.4	🔍	📋	💾
NerDLModel	ner_risk_factors	2.4.2	🔍	📋	💾
NerDLModel	ner_human_phenotype_go_clinical	2.6.0	🔍	📋	💾
NerDLModel	ner_human_phenotype_gene_clinical	2.6.0	🔍	📋	💾
NerDLModel	ner_chemprot_clinical	2.6.0	🔍	📋	💾
NerDLModel	ner_ade_clinical	2.6.2	🔍	📋	💾
NerDLModel	ner_ade_healthcare	2.6.2	🔍	📋	💾
NerDLModel	ner_ade_biobert	2.6.2	🔍	📋	💾
NerDLModel	ner_ade_clinicalbert	2.6.2	🔍	📋	💾
NerDLModel	ner_bacterial_species	2.6.3	🔍	📋	💾
NerDLModel	ner_chemicals	2.6.3	🔍	📋	💾
NerDLModel	ner_clinical_biobert	2.6.2	🔍	📋	💾
NerDLModel	ner_anatomy_biobert	2.6.2	🔍	📋	💾
NerDLModel	ner_bionlp_biobert	2.6.2	🔍	📋	💾
NerDLModel	ner_cellular_biobert	2.6.2	🔍	📋	💾
NerDLModel	ner_deid_enriched_biobert	2.6.2	🔍	📋	💾
NerDLModel	ner_diseases_biobert	2.6.2	🔍	📋	💾
NerDLModel	ner_events_biobert	2.6.2	🔍	📋	💾
NerDLModel	ner_jsl_biobert	2.6.2	🔍	📋	💾
NerDLModel	ner_jsl_enriched_biobert	2.6.2	🔍	📋	💾
NerDLModel	ner_chemprot_biobert	2.6.2	🔍	📋	💾
NerDLModel	ner_human_phenotype_gene_biobert	2.6.2	🔍	📋	💾
NerDLModel	ner_human_phenotype_go_biobert	2.6.2	🔍	📋	💾
NerDLModel	ner_posology_large_biobert	2.6.2	🔍	📋	💾
NerDLModel	ner_posology_biobert	2.6.2	🔍	📋	💾
NerDLModel	ner_risk_factors_biobert	2.6.2	🔍	📋	💾
NerDLModel	ner_anatomy_coarse_biobert	2.6.1		📋	💾
NerDLModel	ner_anatomy_coarse	2.6.1		📋	💾
NerDLModel	ner_deid_sd_large	2.6.3		📋	💾
NerDLModel	ner_aspect_based_sentiment	2.6.2		📋	💾
NerDLModel	ner_financial_contract	2.6.3		📋	💾
ClassifierDLModel	classifierdl_ade_biobert	2.6.2	🔍	📋	💾
ClassifierDLModel	classifierdl_ade_conversational_biobert	2.6.2	🔍	📋	💾
ClassifierDLModel	classifierdl_ade_clinicalbert	2.6.2	🔍	📋	💾
ClassifierDLModel	classifierdl_pico_biobert	2.6.2	🔍	📋	💾
PerceptronModel	pos_clinical	2.0.2		📋	💾
RelationExtractionModel	re_clinical	2.5.5	🔍	📋	💾
RelationExtractionModel	re_posology	2.5.5	🔍	📋
RelationExtractionModel	re_temporal_events_clinical	2.6.0	🔍	📋	💾
RelationExtractionModel	re_temporal_events_enriched_clinical	2.6.0	🔍	📋	💾
RelationExtractionModel	re_human_phenotype_gene_clinical	2.6.0	🔍	📋	💾
RelationExtractionModel	re_drug_drug_interaction_clinical	2.6.0	🔍	📋	💾
RelationExtractionModel	re_chemprot_clinical	2.6.0	🔍	📋	💾
TextMatcherModel	textmatch_cpt_token	2.4.5		📋	💾
TextMatcherModel	textmatch_icdo_ner	2.4.5		📋	💾
BertSentenceEmbeddings	sbiobert_base_cased_mli	2.6.4		📋	💾
BertSentenceEmbeddings	sbluebert_base_uncased_mli	2.6.4		📋	💾
WordEmbeddingsModel	embeddings_clinical	2.4.0		📋	💾
WordEmbeddingsModel	embeddings_healthcare_100d	2.5.0		📋	💾
WordEmbeddingsModel	embeddings_healthcare	2.4.4		📋	💾
SentenceDetectorDLModel	sentence_detector_dl_healthcare	2.6.2		📋	💾

Spanish Language, Clinical/Models Location

{Model}.pretrained({Name}, 'es', 'clinical/models')

Model	Name	Build
NerDLModel	ner_diag_proc	2.5.3	🔍	📋	💾
NerDLModel	ner_neoplasms	2.5.3	🔍	📋	💾
WordEmbeddingsModel	embeddings_scielo_150d	2.5.0		📋	💾
WordEmbeddingsModel	embeddings_scielo_300d	2.5.0		📋	💾
WordEmbeddingsModel	embeddings_scielo_50d	2.5.0		📋	💾
WordEmbeddingsModel	embeddings_scielowiki_150d	2.5.0		📋	💾
WordEmbeddingsModel	embeddings_scielowiki_300d	2.5.0		📋	💾
WordEmbeddingsModel	embeddings_scielowiki_50d	2.5.0		📋	💾

Pretrained Healthcare Pipelines

PretrainedPipeline({Name}, 'en', 'clinical/models')

Pipeline	Name	Build	lang	Description	Offline
Explain Clinical Document (type-1)	explain_clinical_doc_carp	2.6.0	en	a pipeline with ner_clinical, assertion_dl, re_clinical and ner_posology. It will extract clinical and medication entities, assign assertion status and find relationships between clinical entities.	Download
Explain Clinical Document (type-2)	explain_clinical_doc_era	2.6.0	en	a pipeline with ner_clinical_events, assertion_dl and re_temporal_events_clinical. It will extract clinical entities, assign assertion status and find temporal relationships between clinical entities.	Download
Explain Clinical Document (type-3)	recognize_entities_posology	2.6.0	en	a pipeline with ner_posology. It will only extract medication entities.	Download
Explain Clinical Document (type-4)	explain_clinical_doc_ade	2.6.2	en	a pipeline for Adverse Drug Events (ADE) with ner_ade_biobert, assertiondl_biobert and classifierdl_ade_conversational_biobert. It will extract ADE and DRUG clinical entities, assigen assertion status to ADE entities, and then assign ADE status to a text(True means ADE, False means not related to ADE).	Download

German Models

Model	Name	Build	lang	Offline
NER Healthcare	ner_healthcare	2.6.0	de	Download
NER Healthcare	ner_healthcare_slim	2.6.0	de	Download
Entity Resolver ICD10GM	chunkresolve_ICD10GM	2.6.0	de	Download
Entity Resolver ICD10GM	chunkresolve_ICD10GM_2021	2.6.0	de	Download
WordEmbeddings	w2v_cc_300d	2.6.0	de	Download
NER Legal	ner_legal	2.6.0	de	Download
NER Traffic	ner_traffic	2.6.0	de	Download

Contact

nlp@johnsnowlabs.com

John Snow Labs

https://johnsnowlabs.com