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.
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
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 |