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All Projects → JohnSnowLabs → Spark Nlp Models

JohnSnowLabs / Spark Nlp Models

Licence: apache-2.0

Models and Pipelines for the Spark NLP library

Labels

jupyter-notebook deep-learning machine-learning nlp natural-language-processing spark natural-language-understanding nlu

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Spark NLP Models

We use this repository to maintain our releases of pre-trained pipelines and models for the Spark NLP library. For more info please take a look at our releases.

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

[email protected]

John Snow Labs

https://johnsnowlabs.com

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].

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