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Spark NLP Models

build Maven Central PyPI version Anaconda-Cloud License

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.


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

Join Slack

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