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3.0.2

Five new resolver models:
- `en.resolve.umls`: This model returns CUI (concept unique identifier) codes for Clinical Findings, Medical Devices, Anatomical Structures and Injuries & Poisoning terms.
- `en.resolve.umls.findings`: This model returns CUI (concept unique identifier) codes for 200K concepts from clinical findings.
- `en.resolve.loinc`: Map clinical NER entities to LOINC codes using sbiobert.
- `en.resolve.loinc.bluebert`: Map clinical NER entities to LOINC codes using sbluebert.
- `en.resolve.HPO`: This model returns Human Phenotype Ontology (HPO) codes for phenotypic abnormalities encountered in human diseases. It also returns associated codes from the following vocabularies for each HPO code:

[Related NLU Notebook](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/release_notebooks/NLU_3_0_2_release_notebook.ipynb)

|Model| NLU Reference | Spark NLP Reference |
|--------|-----------------------------------|-----------------------------------------|
|Resolver|[`en.resolve.umls` ](https://nlp.johnsnowlabs.com/2021/05/16/sbiobertresolve_umls_major_concepts_en.html)| [`sbiobertresolve_umls_major_concepts`](https://nlp.johnsnowlabs.com/2021/05/16/sbiobertresolve_umls_major_concepts_en.html) |
|Resolver|[`en.resolve.umls.findings` ](https://nlp.johnsnowlabs.com/2021/05/16/sbiobertresolve_umls_findings_en.html)| [`sbiobertresolve_umls_findings`](https://nlp.johnsnowlabs.com/2021/05/16/sbiobertresolve_umls_findings_en.html) |
|Resolver|[`en.resolve.loinc` ](https://nlp.johnsnowlabs.com/2021/04/29/sbiobertresolve_loinc_en.html)| [`sbiobertresolve_loinc`](https://nlp.johnsnowlabs.com/2021/04/29/sbiobertresolve_loinc_en.html) |
|Resolver|[`en.resolve.loinc.biobert` ](https://nlp.johnsnowlabs.com/2021/04/29/sbiobertresolve_loinc_en.html)| [`sbiobertresolve_loinc`](https://nlp.johnsnowlabs.com/2021/04/29/sbiobertresolve_loinc_en.html) |
|Resolver|[`en.resolve.loinc.bluebert` ](https://nlp.johnsnowlabs.com/2021/04/29/sbluebertresolve_loinc_en.html)| [`sbluebertresolve_loinc`](https://nlp.johnsnowlabs.com/2021/04/29/sbluebertresolve_loinc_en.html) |
|Resolver|[`en.resolve.HPO` ](https://nlp.johnsnowlabs.com/2021/05/16/sbiobertresolve_HPO_en.html)| [`sbiobertresolve_HPO`](https://nlp.johnsnowlabs.com/2021/05/16/sbiobertresolve_HPO_en.html) |



[en.resolve.HPO](https://nlp.johnsnowlabs.com/2021/05/16/sbiobertresolve_HPO_en.html)

python
nlu.load('med_ner.jsl.wip.clinical en.resolve.HPO').viz("""These disorders include cancer, bipolar disorder, schizophrenia, autism, Cri-du-chat syndrome,
myopia, cortical cataract-linked Alzheimer's disease, and infectious diseases""")

![text_class1](https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/docs/assets/images/releases/3_0_2/HPO.png)



[en.resolve.loinc.bluebert](https://nlp.johnsnowlabs.com/2021/04/29/sbluebertresolve_loinc_en.html)
python
nlu.load('med_ner.jsl.wip.clinical en.resolve.loinc.bluebert').viz("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and
subsequent type two diabetes mellitus (TSS2DM), one prior episode of HTG-induced pancreatitis three years prior to presentation, associated with an acute
hepatitis, and obesity with a body mass index (BMI) of 33.5 kg/m2, presented with a one-week history of polyuria, polydipsia, poor appetite, and vomiting.""")

![text_class1](https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/docs/assets/images/releases/3_0_2/LIONC_blue.png)



[en.resolve.umls.findings](https://nlp.johnsnowlabs.com/2021/05/16/sbiobertresolve_umls_findings_en.html)
python
nlu.load('med_ner.jsl.wip.clinical en.resolve.umls.findings').viz("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and
subsequent type two diabetes mellitus (TSS2DM), one prior episode of HTG-induced pancreatitis three years prior to presentation, associated with an acute
hepatitis, and obesity with a body mass index (BMI) of 33.5 kg/m2, presented with a one-week history of polyuria, polydipsia, poor appetite, and vomiting."""
)

![text_class1](https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/docs/assets/images/releases/3_0_2/umls_finding.png)


[en.resolve.umls](https://nlp.johnsnowlabs.com/2021/05/16/sbiobertresolve_umls_major_concepts_en.html)
python
nlu.load('med_ner.jsl.wip.clinical en.resolve.umls').viz("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and
subsequent type two diabetes mellitus (TSS2DM), one prior episode of HTG-induced pancreatitis three years prior to presentation, associated with an acute
hepatitis, and obesity with a body mass index (BMI) of 33.5 kg/m2, presented with a one-week history of polyuria, polydipsia, poor appetite, and vomiting.""")

![text_class1](https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/docs/assets/images/releases/3_0_2/umls.png)




[en.resolve.loinc](https://nlp.johnsnowlabs.com/2021/04/29/sbiobertresolve_loinc_en.html)
python
nlu.load('med_ner.jsl.wip.clinical en.resolve.loinc').predict("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and
subsequent type two diabetes mellitus (TSS2DM), one prior episode of HTG-induced pancreatitis three years prior to presentation, associated with an acute
hepatitis, and obesity with a body mass index (BMI) of 33.5 kg/m2, presented with a one-week history of polyuria, polydipsia, poor appetite, and vomiting.""")

![text_class1](https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/docs/assets/images/releases/3_0_2/LIONC.png)



[en.resolve.loinc.biobert](https://nlp.johnsnowlabs.com/2021/04/29/sbiobertresolve_loinc_en.html)
python
nlu.load('med_ner.jsl.wip.clinical en.resolve.loinc.biobert').predict("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and
subsequent type two diabetes mellitus (TSS2DM), one prior episode of HTG-induced pancreatitis three years prior to presentation, associated with an acute
hepatitis, and obesity with a body mass index (BMI) of 33.5 kg/m2, presented with a one-week history of polyuria, polydipsia, poor appetite, and vomiting.""")

![text_class1](https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/docs/assets/images/releases/3_0_2/LIONC_BIOBERT.png)




* [140+ tutorials](https://github.com/JohnSnowLabs/nlu/tree/master/examples)
* [New Streamlit visualizations docs](https://nlu.johnsnowlabs.com/docs/en/streamlit_viz_examples)
* The complete list of all 1100+ models & pipelines in 192+ languages is available on [Models Hub](https://nlp.johnsnowlabs.com/models).
* [Spark NLP publications](https://medium.com/spark-nlp)
* [NLU in Action](https://nlp.johnsnowlabs.com/demo)
* [NLU documentation](https://nlu.johnsnowlabs.com/docs/en/install)
* [Discussions](https://github.com/JohnSnowLabs/spark-nlp/discussions) Engage with other community members, share ideas, and show off how you use Spark NLP and NLU!

1 line Install NLU on Google Colab
!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash
1 line Install NLU on Kaggle
!wget https://setup.johnsnowlabs.com/nlu/kaggle.sh -O - | bash
Install via PIP
! pip install nlu pyspark==3.0.1

3.0.1

We are very excited to announce NLU 3.0.1 has been released!
This is one of the most visually appealing releases, with the integration of the [Spark-NLP-Display](https://nlp.johnsnowlabs.com/docs/en/display) library and visualizations for `dependency trees`, `entity resolution`, `entity assertion`, `relationship between entities` and `named

3.0.0

200+ State of the Art Medical Models for NER, Entity Resolution, Relation Extraction, Assertion, Spark 3 and Python 3.8 support in NLU 3.0 Release and much more
We are incredibly excited to announce the release of `NLU 3.0.0` which makes most of John Snow Labs medical healthcare model available in just 1 line of code in NLU.
These models are the most accurate in their domains and highly scalable in Spark clusters.
In addition, `Spark 3.0.X` and `Spark 3.1.X ` is now supported, together with Python3.8

This is enabled by the amazing [Spark NLP3.0.1](https://nlp.johnsnowlabs.com/docs/en/release_notes#300) and [Spark NLP for Healthcare 3.0.1](https://nlp.johnsnowlabs.com/docs/en/licensed_release_notes#301) releases.

New Features
- Over 200 new models for the `healthcare` domain
- 6 new classes of models, Assertion, Sentence/Chunk Resolvers, Relation Extractors, Medical NER models, De-Identificator Models
- Spark 3.0.X and 3.1.X support
- Python 3.8 Support
- New Output level `relation`
- 1 Line to install NLU just run `!wget https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh -O - | bash`
- [Various new EMR and Databricks versions supported](https://github.com/JohnSnowLabs/spark-nlp/releases/tag/3.0.0)
- GPU Mode, more then 600% speedup by enabling GPU mode.
- Authorized mode for licensed features

New Documentation
- [NLU for Healthcare Examples](https://nlu.johnsnowlabs.com/docs/en/examples_hc#usage-examples-of-nluload)
- [Instrunctions to authorize your environment to use Licensed features](https://nlu.johnsnowlabs.com/docs/en/examples_hc#authorize-access-to-licensed-features-and-install-healthcare-dependencies)


New Notebooks
- [Medical Named Entity Extraction (NER) notebook](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/healthcare/medical_named_entity_recognition/overview_medical_entity_recognizers.ipynb)
- [Relation extraction notebook](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/healthcare/relation_extraction/overview_relation.ipynb)
- [Entity Resolution overview notebook](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/healthcare/entity_resolution/entity_resolvers_overview.ipynb)
- [Assertion overview notebook](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/healthcare/assertion/assertion_overview.ipynb)
- [De-Identification overview notebook](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/healthcare/de_identification/DeIdentification_model_overview.ipynb)
- [Graph NLU tutorial](https://github.com/JohnSnowLabs/nlu/blob/3.0rc1/examples/webinars_conferences_etc/graph_ai_summit/Healthcare_Graph_NLU_COVID_Tigergraph.ipynb)


AssertionDLModels

| Language | nlu.load() reference | Spark NLP Model reference |
| -------- | ------------------------------------------------------------ | ------------------------------------------------------------ |
| English | [assert](https://nlp.johnsnowlabs.com/2021/01/26/assertion_dl_en.html) | [assertion_dl](https://nlp.johnsnowlabs.com/2021/01/26/assertion_dl_en.html) |
| English | [assert.biobert](https://nlp.johnsnowlabs.com/2021/01/26/assertion_dl_biobert_en.html) | [assertion_dl_biobert](https://nlp.johnsnowlabs.com/2021/01/26/assertion_dl_biobert_en.html) |
| English | [assert.healthcare](https://nlp.johnsnowlabs.com/2020/09/23/assertion_dl_healthcare_en.html) | [assertion_dl_healthcare](https://nlp.johnsnowlabs.com/2020/09/23/assertion_dl_healthcare_en.html) |
| English | [assert.large](https://nlp.johnsnowlabs.com/2020/05/21/assertion_dl_large_en.html) | [assertion_dl_large](https://nlp.johnsnowlabs.com/2020/05/21/assertion_dl_large_en.html) |

New Word Embeddings

| Language | nlu.load() reference | Spark NLP Model reference |
| -------- | ------------------------------------------------------------ | ------------------------------------------------------------ |
| English | [embed.glove.clinical](https://nlp.johnsnowlabs.com/2021/03/31/ner_anatomy_coarse_en.html) | [embeddings_clinical](https://nlp.johnsnowlabs.com/2021/03/31/ner_anatomy_coarse_en.html) |
| English | [embed.glove.biovec](https://nlp.johnsnowlabs.com/2020/06/02/embeddings_biovec_en.html) | [embeddings_biovec](https://nlp.johnsnowlabs.com/2020/06/02/embeddings_biovec_en.html) |
| English | [embed.glove.healthcare](https://nlp.johnsnowlabs.com/2020/03/26/embeddings_healthcare_en.html) | [embeddings_healthcare](https://nlp.johnsnowlabs.com/2020/03/26/embeddings_healthcare_en.html) |
| English | [embed.glove.healthcare_100d](https://nlp.johnsnowlabs.com/2020/05/29/embeddings_healthcare_100d_en.html) | [embeddings_healthcare_100d](https://nlp.johnsnowlabs.com/2020/05/29/embeddings_healthcare_100d_en.html) |
| English | en.embed.glove.icdoem | embeddings_icdoem |
| English | en.embed.glove.icdoem_2ng | embeddings_icdoem_2ng |

Sentence Entity resolvers

| Language | nlu.load() reference | Spark NLP Model reference |
| -------- | ------------------------------------------------------------ | ------------------------------------------------------------ |
| English | embed_sentence.biobert.mli | sbiobert_base_cased_mli |
| English | resolve | sbiobertresolve_cpt |
| English | resolve.cpt | sbiobertresolve_cpt |
| English | resolve.cpt.augmented | sbiobertresolve_cpt_augmented |
| English | resolve.cpt.procedures_augmented | sbiobertresolve_cpt_procedures_augmented |
| English | resolve.hcc.augmented | sbiobertresolve_hcc_augmented |
| English | [resolve.icd10cm](https://nlp.johnsnowlabs.com/2020/11/27/sbiobertresolve_icd10cm_en.html) | [sbiobertresolve_icd10cm](https://nlp.johnsnowlabs.com/2020/11/27/sbiobertresolve_icd10cm_en.html) |
| English | [resolve.icd10cm.augmented](https://nlp.johnsnowlabs.com/2020/12/13/sbiobertresolve_icd10cm_augmented_en.html) | [sbiobertresolve_icd10cm_augmented](https://nlp.johnsnowlabs.com/2020/12/13/sbiobertresolve_icd10cm_augmented_en.html) |
| English | [resolve.icd10cm.augmented_billable](https://nlp.johnsnowlabs.com/2021/02/06/sbiobertresolve_icd10cm_augmented_billable_hcc_en.html) | [sbiobertresolve_icd10cm_augmented_billable_hcc](https://nlp.johnsnowlabs.com/2021/02/06/sbiobertresolve_icd10cm_augmented_billable_hcc_en.html) |
| English | [resolve.icd10pcs](https://nlp.johnsnowlabs.com/2020/11/27/sbiobertresolve_icd10pcs_en.html) | [sbiobertresolve_icd10pcs](https://nlp.johnsnowlabs.com/2020/11/27/sbiobertresolve_icd10pcs_en.html) |
| English | [resolve.icdo](https://nlp.johnsnowlabs.com/2020/11/27/sbiobertresolve_icdo_en.html) | [sbiobertresolve_icdo](https://nlp.johnsnowlabs.com/2020/11/27/sbiobertresolve_icdo_en.html) |
| English | [resolve.rxcui](https://nlp.johnsnowlabs.com/2020/12/11/sbiobertresolve_rxcui_en.html) | [sbiobertresolve_rxcui](https://nlp.johnsnowlabs.com/2020/12/11/sbiobertresolve_rxcui_en.html) |
| English | [resolve.rxnorm](https://nlp.johnsnowlabs.com/2020/11/27/sbiobertresolve_rxnorm_en.html) | [sbiobertresolve_rxnorm](https://nlp.johnsnowlabs.com/2020/11/27/sbiobertresolve_rxnorm_en.html) |
| English | [resolve.snomed](https://nlp.johnsnowlabs.com/2020/11/27/sbiobertresolve_snomed_auxConcepts_en.html) | [sbiobertresolve_snomed_auxConcepts](https://nlp.johnsnowlabs.com/2020/11/27/sbiobertresolve_snomed_auxConcepts_en.html) |
| English | [resolve.snomed.aux_concepts](https://nlp.johnsnowlabs.com/2020/11/27/sbiobertresolve_snomed_auxConcepts_en.html) | [sbiobertresolve_snomed_auxConcepts](https://nlp.johnsnowlabs.com/2020/11/27/sbiobertresolve_snomed_auxConcepts_en.html) |
| English | [resolve.snomed.aux_concepts_int](https://nlp.johnsnowlabs.com/2020/11/27/sbiobertresolve_snomed_auxConcepts_int_en.html) | [sbiobertresolve_snomed_auxConcepts_int](https://nlp.johnsnowlabs.com/2020/11/27/sbiobertresolve_snomed_auxConcepts_int_en.html) |
| English | [resolve.snomed.findings](https://nlp.johnsnowlabs.com/2020/11/27/sbiobertresolve_snomed_findings_en.html) | [sbiobertresolve_snomed_findings](https://nlp.johnsnowlabs.com/2020/11/27/sbiobertresolve_snomed_findings_en.html) |
| English | [resolve.snomed.findings_int](https://nlp.johnsnowlabs.com/2020/11/27/sbiobertresolve_snomed_findings_int_en.html) | [sbiobertresolve_snomed_findings_int](https://nlp.johnsnowlabs.com/2020/11/27/sbiobertresolve_snomed_findings_int_en.html) |

RelationExtractionModel

| Language | nlu.load() reference | Spark NLP Model reference |
| -------- | ------------------------------------------------------------ | ------------------------------------------------------------ |
| English | relation.posology | posology_re |
| English | [relation](https://nlp.johnsnowlabs.com/2021/02/04/redl_bodypart_direction_biobert_en.html) | [redl_bodypart_direction_biobert](https://nlp.johnsnowlabs.com/2021/02/04/redl_bodypart_direction_biobert_en.html) |
| English | [relation.bodypart.direction](https://nlp.johnsnowlabs.com/2021/02/04/redl_bodypart_direction_biobert_en.html) | [redl_bodypart_direction_biobert](https://nlp.johnsnowlabs.com/2021/02/04/redl_bodypart_direction_biobert_en.html) |
| English | [relation.bodypart.problem](https://nlp.johnsnowlabs.com/2021/02/04/redl_bodypart_problem_biobert_en.html) | [redl_bodypart_problem_biobert](https://nlp.johnsnowlabs.com/2021/02/04/redl_bodypart_problem_biobert_en.html) |
| English | [relation.bodypart.procedure](https://nlp.johnsnowlabs.com/2021/02/04/redl_bodypart_procedure_test_biobert_en.html) | [redl_bodypart_procedure_test_biobert](https://nlp.johnsnowlabs.com/2021/02/04/redl_bodypart_procedure_test_biobert_en.html) |
| English | [relation.chemprot](https://nlp.johnsnowlabs.com/2021/02/04/redl_chemprot_biobert_en.html) | [redl_chemprot_biobert](https://nlp.johnsnowlabs.com/2021/02/04/redl_chemprot_biobert_en.html) |
| English | [relation.clinical](https://nlp.johnsnowlabs.com/2021/02/04/redl_clinical_biobert_en.html) | [redl_clinical_biobert](https://nlp.johnsnowlabs.com/2021/02/04/redl_clinical_biobert_en.html) |
| English | [relation.date](https://nlp.johnsnowlabs.com/2021/02/04/redl_date_clinical_biobert_en.htmls) | [redl_date_clinical_biobert](https://nlp.johnsnowlabs.com/2021/02/04/redl_date_clinical_biobert_en.htmls) |
| English | [relation.drug_drug_interaction](https://nlp.johnsnowlabs.com/2021/02/04/redl_drug_drug_interaction_biobert_en.html) | [redl_drug_drug_interaction_biobert](https://nlp.johnsnowlabs.com/2021/02/04/redl_drug_drug_interaction_biobert_en.html) |
| English | [relation.humen_phenotype_gene](https://nlp.johnsnowlabs.com/2021/02/04/redl_human_phenotype_gene_biobert_en.html) | [redl_human_phenotype_gene_biobert](https://nlp.johnsnowlabs.com/2021/02/04/redl_human_phenotype_gene_biobert_en.html) |
| English | [relation.temporal_events](https://nlp.johnsnowlabs.com/2021/02/04/redl_temporal_events_biobert_en.html) | [redl_temporal_events_biobert](https://nlp.johnsnowlabs.com/2021/02/04/redl_temporal_events_biobert_en.html) |



NERDLModels

| Language | nlu.load() reference | Spark NLP Model reference |
| -------- | ------------------------------------------------------------ | ------------------------------------------------------------ |
|English | [med_ner.ade.clinical](https://nlp.johnsnowlabs.com/2021/04/01/ner_ade_clinical_en.html) | [ner_ade_clinical](https://nlp.johnsnowlabs.com/2021/04/01/ner_ade_clinical_en.html) |
| English | [med_ner.ade.clinical_bert](https://nlp.johnsnowlabs.com/2021/04/01/ner_ade_clinicalbert_en.html) | [ner_ade_clinicalbert](https://nlp.johnsnowlabs.com/2021/04/01/ner_ade_clinicalbert_en.html) |
| English | [med_ner.ade.ade_healthcare](https://nlp.johnsnowlabs.com/2021/04/01/ner_ade_healthcare_en.html) | [ner_ade_healthcare](https://nlp.johnsnowlabs.com/2021/04/01/ner_ade_healthcare_en.html) |
| English | [med_ner.anatomy](https://nlp.johnsnowlabs.com/2021/03/31/ner_anatomy_en.html) | [ner_anatomy](https://nlp.johnsnowlabs.com/2021/03/31/ner_anatomy_en.html) |
| English | [med_ner.anatomy.biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_anatomy_biobert_en.html) | [ner_anatomy_biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_anatomy_biobert_en.html) |
| English | [med_ner.anatomy.coarse](https://nlp.johnsnowlabs.com/2021/03/31/ner_anatomy_coarse_en.html) | [ner_anatomy_coarse](https://nlp.johnsnowlabs.com/2021/03/31/ner_anatomy_coarse_en.html) |
| English | [med_ner.anatomy.coarse_biobert](https://nlp.johnsnowlabs.com/2021/03/31/ner_anatomy_coarse_biobert_en.html) | [ner_anatomy_coarse_biobert](https://nlp.johnsnowlabs.com/2021/03/31/ner_anatomy_coarse_biobert_en.html) |
| English | [med_ner.aspect_sentiment](https://nlp.johnsnowlabs.com/2021/03/31/ner_aspect_based_sentiment_en.html) | [ner_aspect_based_sentiment](https://nlp.johnsnowlabs.com/2021/03/31/ner_aspect_based_sentiment_en.html) |
| English | [med_ner.bacterial_species](https://nlp.johnsnowlabs.com/2021/04/01/ner_bacterial_species_en.html) | [ner_bacterial_species](https://nlp.johnsnowlabs.com/2021/04/01/ner_bacterial_species_en.html) |
| English | [med_ner.bionlp](https://nlp.johnsnowlabs.com/2021/03/31/ner_bionlp_en.html) | [ner_bionlp](https://nlp.johnsnowlabs.com/2021/03/31/ner_bionlp_en.html) |
| English | [med_ner.bionlp.biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_bionlp_biobert_en.html) | [ner_bionlp_biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_bionlp_biobert_en.html) |
| English | [med_ner.cancer](https://nlp.johnsnowlabs.com/2021/03/31/ner_cancer_genetics_en.html) | [ner_cancer_genetics](https://nlp.johnsnowlabs.com/2021/03/31/ner_cancer_genetics_en.html) |
| Englishs | [med_ner.cellular](https://nlp.johnsnowlabs.com/2021/03/31/ner_cellular_en.html) | [ner_cellular](https://nlp.johnsnowlabs.com/2021/03/31/ner_cellular_en.html) |
| English | [med_ner.cellular.biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_cellular_biobert_en.html) | [ner_cellular_biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_cellular_biobert_en.html) |
| English | [med_ner.chemicals](https://nlp.johnsnowlabs.com/2021/04/01/ner_chemicals_en.html) | [ner_chemicals](https://nlp.johnsnowlabs.com/2021/04/01/ner_chemicals_en.html) |
| English | [med_ner.chemprot](https://nlp.johnsnowlabs.com/2021/04/01/ner_chemprot_biobert_en.html) | [ner_chemprot_biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_chemprot_biobert_en.html) |
| English | [med_ner.chemprot.clinical](https://nlp.johnsnowlabs.com/2021/03/31/ner_chemprot_clinical_en.html) | [ner_chemprot_clinical](https://nlp.johnsnowlabs.com/2021/03/31/ner_chemprot_clinical_en.html) |
| English | [med_ner.clinical](https://nlp.johnsnowlabs.com/2020/01/30/ner_clinical_en.html) | [ner_clinical](https://nlp.johnsnowlabs.com/2020/01/30/ner_clinical_en.html) |
| English | [med_ner.clinical.biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_clinical_biobert_en.html) | [ner_clinical_biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_clinical_biobert_en.html) |
| English | med_ner.clinical.noncontrib | ner_clinical_noncontrib |
| English | [med_ner.diseases](https://nlp.johnsnowlabs.com/2021/03/31/ner_diseases_en.html) | [ner_diseases](https://nlp.johnsnowlabs.com/2021/03/31/ner_diseases_en.html) |
| English | [med_ner.diseases.biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_diseases_biobert_en.html) | [ner_diseases_biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_diseases_biobert_en.html) |
| English | [med_ner.diseases.large](https://nlp.johnsnowlabs.com/2021/04/01/ner_diseases_large_en.html) | [ner_diseases_large](https://nlp.johnsnowlabs.com/2021/04/01/ner_diseases_large_en.html) |
| English | [med_ner.drugs](https://nlp.johnsnowlabs.com/2021/03/31/ner_drugs_en.html) | [ner_drugs](https://nlp.johnsnowlabs.com/2021/03/31/ner_drugs_en.html) |
| English | [med_ner.drugsgreedy](https://nlp.johnsnowlabs.com/2021/03/31/ner_drugs_greedy_en.html) | [ner_drugs_greedy](https://nlp.johnsnowlabs.com/2021/03/31/ner_drugs_greedy_en.html) |
| English | [med_ner.drugs.large](https://nlp.johnsnowlabs.com/2021/03/31/ner_drugs_large_en.html) | [ner_drugs_large](https://nlp.johnsnowlabs.com/2021/03/31/ner_drugs_large_en.html) |
| English | [med_ner.events_biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_events_biobert_en.html) | [ner_events_biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_events_biobert_en.html) |
| English | [med_ner.events_clinical](https://nlp.johnsnowlabs.com/2021/03/31/ner_events_clinical_en.html) | [ner_events_clinical](https://nlp.johnsnowlabs.com/2021/03/31/ner_events_clinical_en.html) |
| English | [med_ner.events_healthcre](https://nlp.johnsnowlabs.com/2021/04/01/ner_events_healthcare_en.html) | [ner_events_healthcare](https://nlp.johnsnowlabs.com/2021/04/01/ner_events_healthcare_en.html) |
| English | [med_ner.financial_contract](https://nlp.johnsnowlabs.com/2021/04/01/ner_financial_contract_en.html) | [ner_financial_contract](https://nlp.johnsnowlabs.com/2021/04/01/ner_financial_contract_en.html) |
| English | [med_ner.healthcare](https://nlp.johnsnowlabs.com/2021/03/31/ner_healthcare_de.html) | [ner_healthcare](https://nlp.johnsnowlabs.com/2021/03/31/ner_healthcare_de.html) |
| English | [med_ner.human_phenotype.gene_biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_human_phenotype_gene_biobert_en.html) | [ner_human_phenotype_gene_biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_human_phenotype_gene_biobert_en.html) |
| English | [med_ner.human_phenotype.gene_clinical](https://nlp.johnsnowlabs.com/2021/03/31/ner_human_phenotype_gene_clinical_en.html) | [ner_human_phenotype_gene_clinical](https://nlp.johnsnowlabs.com/2021/03/31/ner_human_phenotype_gene_clinical_en.html) |
| English | [med_ner.human_phenotype.go_biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_human_phenotype_go_biobert_en.html) | [ner_human_phenotype_go_biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_human_phenotype_go_biobert_en.html) |
| English | [med_ner.human_phenotype.go_clinical](https://nlp.johnsnowlabs.com/2021/03/31/ner_human_phenotype_go_clinical_en.html) | [ner_human_phenotype_go_clinical](https://nlp.johnsnowlabs.com/2021/03/31/ner_human_phenotype_go_clinical_en.html) |
| English | [med_ner.jsl](https://nlp.johnsnowlabs.com/2021/03/31/ner_jsl_en.html) | [ner_jsl](https://nlp.johnsnowlabs.com/2021/03/31/ner_jsl_en.html) |
| English | [med_ner.jsl.biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_jsl_biobert_en.html) | [ner_jsl_biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_jsl_biobert_en.html) |
| English | [med_ner.jsl.enriched](https://nlp.johnsnowlabs.com/2021/03/31/ner_jsl_enriched_en.html) | [ner_jsl_enriched](https://nlp.johnsnowlabs.com/2021/03/31/ner_jsl_enriched_en.html) |
| English | [med_ner.jsl.enriched_biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_jsl_enriched_biobert_en.html) | [ner_jsl_enriched_biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_jsl_enriched_biobert_en.html) |
| English | [med_ner.measurements](https://nlp.johnsnowlabs.com/2021/04/01/ner_measurements_clinical_en.html) | [ner_measurements_clinical](https://nlp.johnsnowlabs.com/2021/04/01/ner_measurements_clinical_en.html) |
| English | [med_ner.medmentions](https://nlp.johnsnowlabs.com/2021/04/01/ner_medmentions_coarse_en.html) | [ner_medmentions_coarse](https://nlp.johnsnowlabs.com/2021/04/01/ner_medmentions_coarse_en.html) |
| English | [med_ner.posology](https://nlp.johnsnowlabs.com/2020/04/15/ner_posology_en.html) | [ner_posology](https://nlp.johnsnowlabs.com/2020/04/15/ner_posology_en.html) |
| English | [med_ner.posology.biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_posology_biobert_en.html) | [ner_posology_biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_posology_biobert_en.html) |
| English | [med_ner.posology.greedy](https://nlp.johnsnowlabs.com/2021/03/31/ner_posology_greedy_en.html) | [ner_posology_greedy](https://nlp.johnsnowlabs.com/2021/03/31/ner_posology_greedy_en.html) |
| English | [med_ner.posology.healthcare](https://nlp.johnsnowlabs.com/2021/04/01/ner_posology_healthcare_en.html) | [ner_posology_healthcare](https://nlp.johnsnowlabs.com/2021/04/01/ner_posology_healthcare_en.html) |
| English | [med_ner.posology.large](https://nlp.johnsnowlabs.com/2021/03/31/ner_posology_large_en.html) | [ner_posology_large](https://nlp.johnsnowlabs.com/2021/03/31/ner_posology_large_en.html) |
| English | [med_ner.posology.large_biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_posology_large_biobert_en.html) | [ner_posology_large_biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_posology_large_biobert_en.html) |
| English | [med_ner.posology.small](https://nlp.johnsnowlabs.com/2021/03/31/ner_posology_small_en.html) | [ner_posology_small](https://nlp.johnsnowlabs.com/2021/03/31/ner_posology_small_en.html) |
| English | [med_ner.radiology](https://nlp.johnsnowlabs.com/2021/03/31/ner_radiology_en.html) | [ner_radiology](https://nlp.johnsnowlabs.com/2021/03/31/ner_radiology_en.html) |
| English | [med_ner.radiology.wip_clinical](https://nlp.johnsnowlabs.com/2021/04/01/ner_radiology_wip_clinical_en.html) | [ner_radiology_wip_clinical](https://nlp.johnsnowlabs.com/2021/04/01/ner_radiology_wip_clinical_en.html) |
| English | [med_ner.risk_factors](https://nlp.johnsnowlabs.com/2021/03/31/ner_risk_factors_en.html) | [ner_risk_factors](https://nlp.johnsnowlabs.com/2021/03/31/ner_risk_factors_en.html) |
| English | [med_ner.risk_factors.biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_risk_factors_biobert_en.html) | [ner_risk_factors_biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_risk_factors_biobert_en.html) |
| English | med_ner.i2b2 | nerdl_i2b2 |
| English | [med_ner.tumour](https://nlp.johnsnowlabs.com/2021/04/01/nerdl_tumour_demo_en.html) | [nerdl_tumour_demo](https://nlp.johnsnowlabs.com/2021/04/01/nerdl_tumour_demo_en.html) |
| English | med_ner.jsl.wip.clinical | jsl_ner_wip_clinical |
| English | [med_ner.jsl.wip.clinical.greedy](https://nlp.johnsnowlabs.com/2021/03/31/jsl_ner_wip_clinical_en.html) | [jsl_ner_wip_greedy_clinical](https://nlp.johnsnowlabs.com/2021/03/31/jsl_ner_wip_clinical_en.html) |
| English | [med_ner.jsl.wip.clinical.modifier](https://nlp.johnsnowlabs.com/2021/04/01/jsl_ner_wip_modifier_clinical_en.html) | [jsl_ner_wip_modifier_clinical](https://nlp.johnsnowlabs.com/2021/04/01/jsl_ner_wip_modifier_clinical_en.html) |
| English | [med_ner.jsl.wip.clinical.rd](https://nlp.johnsnowlabs.com/2021/04/01/jsl_rd_ner_wip_greedy_clinical_en.html) | [jsl_rd_ner_wip_greedy_clinical](https://nlp.johnsnowlabs.com/2021/04/01/jsl_rd_ner_wip_greedy_clinical_en.html) |


De-Identification Models

| Language | nlu.load() reference | Spark NLP Model reference |
| -------- | ------------------------------------------------------------ | ------------------------------------------------------------ |
| English | [med_ner.deid.augmented](https://nlp.johnsnowlabs.com/2021/03/31/ner_deid_augmented_en.html) | [ner_deid_augmented](https://nlp.johnsnowlabs.com/2021/03/31/ner_deid_augmented_en.html) |
| English | [med_ner.deid.biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_deid_biobert_en.html) | [ner_deid_biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_deid_biobert_en.html) |
| English | [med_ner.deid.enriched](https://nlp.johnsnowlabs.com/2021/03/31/ner_deid_enriched_en.html) | [ner_deid_enriched](https://nlp.johnsnowlabs.com/2021/03/31/ner_deid_enriched_en.html) |
| English | [med_ner.deid.enriched_biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_deid_enriched_biobert_en.html) | [ner_deid_enriched_biobert](https://nlp.johnsnowlabs.com/2021/04/01/ner_deid_enriched_biobert_en.html) |
| English | [med_ner.deid.large](https://nlp.johnsnowlabs.com/2021/03/31/ner_deid_large_en.html) | [ner_deid_large](https://nlp.johnsnowlabs.com/2021/03/31/ner_deid_large_en.html) |
| English | [med_ner.deid.sd](https://nlp.johnsnowlabs.com/2021/04/01/ner_deid_sd_en.html) | [ner_deid_sd](https://nlp.johnsnowlabs.com/2021/04/01/ner_deid_sd_en.html) |
| English | [med_ner.deid.sd_large](https://nlp.johnsnowlabs.com/2021/04/01/ner_deid_sd_large_en.html) | [ner_deid_sd_large](https://nlp.johnsnowlabs.com/2021/04/01/ner_deid_sd_large_en.html) |
| English | med_ner.deid | nerdl_deid |
| English | med_ner.deid.synthetic | ner_deid_synthetic |
| English | [med_ner.deid.dl](https://nlp.johnsnowlabs.com/2021/03/31/ner_deidentify_dl_en.html) | [ner_deidentify_dl](https://nlp.johnsnowlabs.com/2021/03/31/ner_deidentify_dl_en.html) |
| English | [en.de_identify](https://nlp.johnsnowlabs.com/2019/06/04/deidentify_rb_en.html) | [deidentify_rb](https://nlp.johnsnowlabs.com/2019/06/04/deidentify_rb_en.html) |
| English | de_identify.rules | deid_rules |
| English | [de_identify.clinical](https://nlp.johnsnowlabs.com/2021/01/29/deidentify_enriched_clinical_en.html) | [deidentify_enriched_clinical](https://nlp.johnsnowlabs.com/2021/01/29/deidentify_enriched_clinical_en.html) |
| English | [de_identify.large](https://nlp.johnsnowlabs.com/2020/08/04/deidentify_large_en.html) | [deidentify_large](https://nlp.johnsnowlabs.com/2020/08/04/deidentify_large_en.html) |
| English | [de_identify.rb](https://nlp.johnsnowlabs.com/2019/06/04/deidentify_rb_en.html) | [deidentify_rb](https://nlp.johnsnowlabs.com/2019/06/04/deidentify_rb_en.html) |
| English | de_identify.rb_no_regex | deidentify_rb_no_regex |



Chunk resolvers

| Language | nlu.load() reference | Spark NLP Model reference |
| -------- | ------------------------------------------------------------ | ------------------------------------------------------------ |
| English | [resolve_chunk.athena_conditions](https://nlp.johnsnowlabs.com/2020/09/16/chunkresolve_athena_conditions_healthcare_en.html) | [chunkresolve_athena_conditions_healthcare](https://nlp.johnsnowlabs.com/2020/09/16/chunkresolve_athena_conditions_healthcare_en.html) |
| English | [resolve_chunk.cpt_clinical](https://nlp.johnsnowlabs.com/2021/04/02/chunkresolve_cpt_clinical_en.html) | [chunkresolve_cpt_clinical](https://nlp.johnsnowlabs.com/2021/04/02/chunkresolve_cpt_clinical_en.html) |
| English | [resolve_chunk.icd10cm.clinical](https://nlp.johnsnowlabs.com/2021/04/02/chunkresolve_icd10cm_clinical_en.html) | [chunkresolve_icd10cm_clinical](https://nlp.johnsnowlabs.com/2021/04/02/chunkresolve_icd10cm_clinical_en.html) |
| English | [resolve_chunk.icd10cm.diseases_clinical](https://nlp.johnsnowlabs.com/2021/04/02/chunkresolve_icd10cm_diseases_clinical_en.html) | [chunkresolve_icd10cm_diseases_clinical](https://nlp.johnsnowlabs.com/2021/04/02/chunkresolve_icd10cm_diseases_clinical_en.html) |
| English | resolve_chunk.icd10cm.hcc_clinical | chunkresolve_icd10cm_hcc_clinical |
| English | resolve_chunk.icd10cm.hcc_healthcare | chunkresolve_icd10cm_hcc_healthcare |
| English | [resolve_chunk.icd10cm.injuries](https://nlp.johnsnowlabs.com/2021/04/02/chunkresolve_icd10cm_injuries_clinical_en.html) | [chunkresolve_icd10cm_injuries_clinical](https://nlp.johnsnowlabs.com/2021/04/02/chunkresolve_icd10cm_injuries_clinical_en.html) |
| English | [resolve_chunk.icd10cm.musculoskeletal](https://nlp.johnsnowlabs.com/2021/04/02/chunkresolve_icd10cm_musculoskeletal_clinical_en.html) | [chunkresolve_icd10cm_musculoskeletal_clinical](https://nlp.johnsnowlabs.com/2021/04/02/chunkresolve_icd10cm_musculoskeletal_clinical_en.html) |
| English | [resolve_chunk.icd10cm.neoplasms](https://nlp.johnsnowlabs.com/2021/04/02/chunkresolve_icd10cm_neoplasms_clinical_en.html) | [chunkresolve_icd10cm_neoplasms_clinical](https://nlp.johnsnowlabs.com/2021/04/02/chunkresolve_icd10cm_neoplasms_clinical_en.html) |
| English | [resolve_chunk.icd10cm.poison](https://nlp.johnsnowlabs.com/2020/04/28/chunkresolve_icd10cm_poison_ext_clinical_en.html) | [chunkresolve_icd10cm_poison_ext_clinical](https://nlp.johnsnowlabs.com/2020/04/28/chunkresolve_icd10cm_poison_ext_clinical_en.html) |
| English | [resolve_chunk.icd10cm.puerile](https://nlp.johnsnowlabs.com/2020/04/28/chunkresolve_icd10cm_puerile_clinical_en.html) | [chunkresolve_icd10cm_puerile_clinical](https://nlp.johnsnowlabs.com/2020/04/28/chunkresolve_icd10cm_puerile_clinical_en.html) |
| English | resolve_chunk.icd10pcs.clinical | chunkresolve_icd10pcs_clinical |
| English | [resolve_chunk.icdo.clinical](https://nlp.johnsnowlabs.com/2021/04/02/chunkresolve_icd10pcs_clinical_en.html) | [chunkresolve_icdo_clinical](https://nlp.johnsnowlabs.com/2021/04/02/chunkresolve_icd10pcs_clinical_en.html) |
| English | [resolve_chunk.loinc](https://nlp.johnsnowlabs.com/2021/04/02/chunkresolve_loinc_clinical_en.html) | [chunkresolve_loinc_clinical](https://nlp.johnsnowlabs.com/2021/04/02/chunkresolve_loinc_clinical_en.html) |
| English | [resolve_chunk.rxnorm.cd](https://nlp.johnsnowlabs.com/2020/07/27/chunkresolve_rxnorm_cd_clinical_en.html) | [chunkresolve_rxnorm_cd_clinical](https://nlp.johnsnowlabs.com/2020/07/27/chunkresolve_rxnorm_cd_clinical_en.html) |
| English | resolve_chunk.rxnorm.in | chunkresolve_rxnorm_in_clinical |
| English | resolve_chunk.rxnorm.in_healthcare | chunkresolve_rxnorm_in_healthcare |
| English | [resolve_chunk.rxnorm.sbd](https://nlp.johnsnowlabs.com/2020/07/27/chunkresolve_rxnorm_sbd_clinical_en.html) | [chunkresolve_rxnorm_sbd_clinical](https://nlp.johnsnowlabs.com/2020/07/27/chunkresolve_rxnorm_sbd_clinical_en.html) |
| English | [resolve_chunk.rxnorm.scd](https://nlp.johnsnowlabs.com/2020/07/27/chunkresolve_rxnorm_scd_clinical_en.html) | [chunkresolve_rxnorm_scd_clinical](https://nlp.johnsnowlabs.com/2020/07/27/chunkresolve_rxnorm_scd_clinical_en.html) |
| English | resolve_chunk.rxnorm.scdc | chunkresolve_rxnorm_scdc_clinical |
| English | resolve_chunk.rxnorm.scdc_healthcare | chunkresolve_rxnorm_scdc_healthcare |
| English | [resolve_chunk.rxnorm.xsmall.clinical](https://nlp.johnsnowlabs.com/2020/06/24/chunkresolve_rxnorm_xsmall_clinical_en.html) | [chunkresolve_rxnorm_xsmall_clinical](https://nlp.johnsnowlabs.com/2020/06/24/chunkresolve_rxnorm_xsmall_clinical_en.html) |
| English | [resolve_chunk.snomed.findings](https://nlp.johnsnowlabs.com/2020/06/20/chunkresolve_snomed_findings_clinical_en.html) | [chunkresolve_snomed_findings_clinical](https://nlp.johnsnowlabs.com/2020/06/20/chunkresolve_snomed_findings_clinical_en.html) |


New Classifiers

| Language | nlu.load() reference | Spark NLP Model reference |
| -------- | ------------------------------------------------------------ | ------------------------------------------------------------ |
| English | classify.icd10.clinical | classifier_icd10cm_hcc_clinical |
| English | classify.icd10.healthcare | classifier_icd10cm_hcc_healthcare |
| English | [classify.ade.biobert](https://nlp.johnsnowlabs.com/2021/01/21/classifierdl_ade_biobert_en.html) | [classifierdl_ade_biobert](https://nlp.johnsnowlabs.com/2021/01/21/classifierdl_ade_biobert_en.html) |
| English | [classify.ade.clinical](https://nlp.johnsnowlabs.com/2021/01/21/classifierdl_ade_clinicalbert_en.html) | [classifierdl_ade_clinicalbert](https://nlp.johnsnowlabs.com/2021/01/21/classifierdl_ade_clinicalbert_en.html) |
| English | [classify.ade.conversational](https://nlp.johnsnowlabs.com/2021/01/21/classifierdl_ade_conversational_biobert_en.html) | [classifierdl_ade_conversational_biobert](https://nlp.johnsnowlabs.com/2021/01/21/classifierdl_ade_conversational_biobert_en.html) |
| English | [classify.gender.biobert](https://nlp.johnsnowlabs.com/2021/01/21/classifierdl_gender_biobert_en.html) | [classifierdl_gender_biobert](https://nlp.johnsnowlabs.com/2021/01/21/classifierdl_gender_biobert_en.html) |
| English | [classify.gender.sbert](https://nlp.johnsnowlabs.com/2021/01/21/classifierdl_gender_sbert_en.html) | [classifierdl_gender_sbert](https://nlp.johnsnowlabs.com/2021/01/21/classifierdl_gender_sbert_en.html) |
| English | classify.pico | classifierdl_pico_biobert |


German Medical models

| nlu.load() reference | Spark NLP Model reference |
| ------------------------------------------------------------ | ------------------------------------------------------------ |
| [embed] | w2v_cc_300d|
| [embed.w2v] | w2v_cc_300d|
| [resolve_chunk] | chunkresolve_ICD10GM|
| [resolve_chunk.icd10gm] | chunkresolve_ICD10GM|
| resolve_chunk.icd10gm.2021 | chunkresolve_ICD10GM_2021|
| med_ner.legal | ner_legal|
| med_ner | ner_healthcare|
| med_ner.healthcare | ner_healthcare|
| med_ner.healthcare_slim | ner_healthcare_slim|
| med_ner.traffic | ner_traffic|

Spanish Medical models
| nlu.load() reference | Spark NLP Model reference |
| ------------------------------------------------------------ | ------------------------------------------------------------ |
| [embed.scielo.150d](https://nlp.johnsnowlabs.com/2020/05/26/embeddings_scielo_150d_es.html) | [embeddings_scielo_150d](https://nlp.johnsnowlabs.com/2020/05/26/embeddings_scielo_150d_es.html)|
| [embed.scielo.300d](https://nlp.johnsnowlabs.com/2020/05/26/embeddings_scielo_300d_es.html) | [embeddings_scielo_300d](https://nlp.johnsnowlabs.com/2020/05/26/embeddings_scielo_300d_es.html)|
| [embed.scielo.50d](https://nlp.johnsnowlabs.com/2020/05/26/embeddings_scielo_50d_es.html) | [embeddings_scielo_50d](https://nlp.johnsnowlabs.com/2020/05/26/embeddings_scielo_50d_es.html)|
| [embed.scielowiki.150d](https://nlp.johnsnowlabs.com/2020/05/26/embeddings_scielowiki_150d_es.html) | [embeddings_scielowiki_150d](https://nlp.johnsnowlabs.com/2020/05/26/embeddings_scielowiki_150d_es.html)|
| [embed.scielowiki.300d](https://nlp.johnsnowlabs.com/2020/05/26/embeddings_scielowiki_300d_es.html) | [embeddings_scielowiki_300d](https://nlp.johnsnowlabs.com/2020/05/26/embeddings_scielowiki_300d_es.html)|
| [embed.scielowiki.50d](https://nlp.johnsnowlabs.com/2020/05/26/embeddings_scielowiki_50d_es.html) | [embeddings_scielowiki_50d](https://nlp.johnsnowlabs.com/2020/05/26/embeddings_scielowiki_50d_es.html)|
| [embed.sciwiki.150d](https://nlp.johnsnowlabs.com/2020/05/27/embeddings_sciwiki_150d_es.html) | [embeddings_sciwiki_150d](https://nlp.johnsnowlabs.com/2020/05/27/embeddings_sciwiki_150d_es.html)|
| [embed.sciwiki.300d](https://nlp.johnsnowlabs.com/2020/05/27/embeddings_sciwiki_300d_es.html) | [embeddings_sciwiki_300d](https://nlp.johnsnowlabs.com/2020/05/27/embeddings_sciwiki_300d_es.html)|
| [embed.sciwiki.50d](https://nlp.johnsnowlabs.com/2020/05/27/embeddings_sciwiki_50d_es.html) | [embeddings_sciwiki_50d](https://nlp.johnsnowlabs.com/2020/05/27/embeddings_sciwiki_50d_es.html)|
| [med_ner](https://nlp.johnsnowlabs.com/2021/03/31/ner_diag_proc_es.html) | [ner_diag_proc](https://nlp.johnsnowlabs.com/2021/03/31/ner_diag_proc_es.html)|
| [med_ner.neoplasm](https://nlp.johnsnowlabs.com/2021/03/31/ner_neoplasms_es.html) | [ner_neoplasms](https://nlp.johnsnowlabs.com/2021/03/31/ner_neoplasms_es.html)|
| [med_ner.diag_proc](https://nlp.johnsnowlabs.com/2021/03/31/ner_diag_proc_es.html) | [ner_diag_proc](https://nlp.johnsnowlabs.com/2021/03/31/ner_diag_proc_es.html)|

GPU Mode
You can now enable NLU GPU mode by setting `gpu=true` while loading a model. I.e. `nlu.load('train.sentiment' gpu=True)` . If must resart you kernel, if you already loaded a nlu pipeline withouth GPU mode.

Output Level Relation
This new output level is used for relation extractors and will give you 1 row per relation extracted.


Bug fixes
- Fixed a bug that caused loading NLU models in offline mode not to work in some occasions


1 line Install NLU
!wget https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh -O - | bash

Install via PIP
! pip install nlu pyspark==3.0.1


Additional NLU ressources

- [NLU Website](https://nlu.johnsnowlabs.com/)
- [All NLU Tutorial Notebooks](https://nlu.johnsnowlabs.com/docs/en/notebooks)
- [NLU Videos and Blogposts on NLU](https://nlp.johnsnowlabs.com/learn#pythons-nlu-library)
- [NLU on Github](https://github.com/JohnSnowLabs/nlu)
- [Suggestions or Questions? Contact us in Slack!](https://join.slack.com/t/spark-nlp/shared_invite/zt-lutct9gm-kuUazcyFKhuGY3_0AMkxqA)

1.1.4

We are very excited to announce NLU 1.1.4 has been released and comes with a lot of tutorials showcasing how you can train a multilingual text classifier on just **one starting language** which then will be able to classify labels correct for **text in over 100+ languages**.
This is possible by leveraging the [language-agnostic BERT Sentence Embeddings(LABSE)](https://arxiv.org/abs/2007.01852). In addition to that tutorials for English pure classifiers for stock market sentiment, sarcasm and negations have been added.
Finally, this release makes working in Spark environments easier, by providing a `return_spark_df` directly from NLU predictions.

New Features
- parameter on the `predict()` method on `nlu.load()` . You can now call `nlu.load(model).predict('Some data',return_spark_df=True)` and will recieve a spark dataframe
New NLU Multi-Lingual training tutorials
These notebooks showcase how to leverage the powerful [language-agnostic BERT Sentence Embeddings(LABSE)](https://arxiv.org/abs/2007.01852) to train a language-agnostic classifier.
You can train on one start language(i.e. English dataset) and your model will be able to correctly predict the labels in every one of the 100+ languages of the LABSE embeddings.

- [Multilingual Twitter Sentiment, binary classification (2class)](https://github.com/JohnSnowLabs/nlu/tree/master/examples/colab/Training/multi_lingual/binary_text_classification/NLU_multi_lingual_training_sentiment_classifier_demo_twitter.ipynb)
- [Multilingual Stock Market Sentiment, binary classification (2class)](https://github.com/JohnSnowLabs/nlu/tree/master/examples/colab/Training/multi_lingual/binary_text_classification/NLU_multi_lingual_training_sentiment_classifier_demo_stock_market.ipynb)
- [Multilingual Reddit Comments Sentiment, binary classification (2class)](https://github.com/JohnSnowLabs/nlu/tree/master/examples/colab/Training/multi_lingual/binary_text_classification/NLU_multi_lingual_training_sentiment_classifier_demo_reddit.ipynb)
- [Multilingual COVID19 Sentiment, binary classification (2class)](https://github.com/JohnSnowLabs/nlu/tree/master/examples/colab/Training/multi_lingual/binary_text_classification/NLU_multi_lingual_training_sentiment_classifier_demo_reddit.ipynb)
- [Multilingual Apple Tweets Sentiment, binary classification (2class)](https://github.com/JohnSnowLabs/nlu/tree/master/examples/colab/Training/multi_lingual/binary_text_classification/NLU_multi_lingual_training_sentiment_classifier_demo_apple_twitter.ipynb)
- [Multilingual News classification, multi class classification (4class)](https://github.com/JohnSnowLabs/nlu/tree/master/examples/colab/Training/multi_lingual/multi_class_text_classification/NLU_training_multi_lingual_multi_class_text_classifier_demo.ipynb)
- [Multilingual TripAdvisor Hotel Reviews, multi-class classification (3class)](https://github.com/JohnSnowLabs/nlu/tree/master/examples/colab/Training/multi_lingual/multi_class_text_classification/NLU_training_multi_lingual_multi_class_text_classifier_demo_hotel_reviews.ipynb)
- [Multilingual Amazon Phone Reviews, multi-class classification (3class)](https://github.com/JohnSnowLabs/nlu/tree/master/examples/colab/Training/multi_lingual/multi_class_text_classification/NLU_training_multi_lingual_multi_class_text_classifier_demo_amazon.ipynb)

New NLU training tutorials (English)

These are simple training notebooks for binary classification for English
- [Biological Texts Negation, binary classification (2class)](https://github.com/JohnSnowLabs/nlu/tree/master/examples/colab/Training/binary_text_classification/NLU_training_negation_classifier_demo_biological_texts.ipynb)
- [News Headlines Sarcasm, binary classification (2class)](https://github.com/JohnSnowLabs/nlu/tree/master/examples/colab/Training/binary_text_classification/NLU_training_sarcasam_classifier_demo_news_headlines.ipynb)
- [COVID19 Sentiment, binary classification (2class)](https://github.com/JohnSnowLabs/nlu/tree/master/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_covid_19.ipynb)
- [Natural Disasters Sentiment, binary classification (2class)](https://github.com/JohnSnowLabs/nlu/tree/master/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_natural_disasters.ipynb)
- [Stock Market Sentiment, binary classification (2class)](https://github.com/JohnSnowLabs/nlu/tree/master/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_stock_market.ipynb)



Additional NLU ressources

- [NLU Website](https://nlu.johnsnowlabs.com/)
- [All NLU Tutorial Notebooks](https://nlu.johnsnowlabs.com/docs/en/notebooks)
- [NLU Videos and Blogposts on NLU](https://nlp.johnsnowlabs.com/learn#pythons-nlu-library)
- [NLU on Github](https://github.com/JohnSnowLabs/nlu)
- [Suggestions or Questions? Contact us in Slack!](https://join.slack.com/t/spark-nlp/shared_invite/zt-lutct9gm-kuUazcyFKhuGY3_0AMkxqA)
- [Models Hub with new models](https://nlp.johnsnowlabs.com/models)

1.1.3

- Fixed a bug that caused `ur.sentiment` NLU pipeline to build incorrectly
- Fixed a bug that caused `sentiment.imdb.glove` NLU pipeline to build incorrectly
- Fixed a bug that caused `en.sentiment.glove.imdb` NLU pipeline to build incorrectly
- Fixed a bug that caused Spark 2.3.X environments to crash.

NLU Installation

bash
PyPi
!pip install nlu pyspark==2.4.7
Conda
Install NLU from Anaconda/Conda
conda install -c johnsnowlabs nlu


Additional NLU ressources

- [NLU Website](https://nlu.johnsnowlabs.com/)
- [All NLU Tutorial Notebooks](https://nlu.johnsnowlabs.com/docs/en/notebooks)
- [NLU Videos and Blogposts on NLU](https://nlp.johnsnowlabs.com/learn#pythons-nlu-library)
- [NLU on Github](https://github.com/JohnSnowLabs/nlu)
- [Suggestions or Questions? Contact us in Slack!](https://join.slack.com/t/spark-nlp/shared_invite/zt-lutct9gm-kuUazcyFKhuGY3_0AMkxqA)

1.1.2

[Named Entity Recognition for Bengali (GloVe 840B 300d)](https://nlp.johnsnowlabs.com/2021/01/27/ner_jifs_glove_840B_300d_bn.html)


python
Bengali for : It began to be widely used in the United States in the early '90s.
nlu.load("bn.ner").predict("৯০ এর দশকের শুরুর দিকে বৃহৎ আকারে মার্কিন যুক্তরাষ্ট্রে এর প্রয়োগের প্রক্রিয়া শুরু হয়'")

output :

| entities | token | Entities_classes | ner_confidence |
|:---------------------|:----------|:----------------------|-----------------:|
| ['মার্কিন যুক্তরাষ্ট্রে'] | ৯০ | ['LOC'] | 1 |
| ['মার্কিন যুক্তরাষ্ট্রে'] | এর | ['LOC'] | 0.9999 |
| ['মার্কিন যুক্তরাষ্ট্রে'] | দশকের | ['LOC'] | 1 |
| ['মার্কিন যুক্তরাষ্ট্রে'] | শুরুর | ['LOC'] | 0.9969 |
| ['মার্কিন যুক্তরাষ্ট্রে'] | দিকে | ['LOC'] | 1 |
| ['মার্কিন যুক্তরাষ্ট্রে'] | বৃহৎ | ['LOC'] | 0.9994 |
| ['মার্কিন যুক্তরাষ্ট্রে'] | আকারে | ['LOC'] | 1 |
| ['মার্কিন যুক্তরাষ্ট্রে'] | মার্কিন | ['LOC'] | 0.9602 |
| ['মার্কিন যুক্তরাষ্ট্রে'] | যুক্তরাষ্ট্রে | ['LOC'] | 0.4134 |
| ['মার্কিন যুক্তরাষ্ট্রে'] | এর | ['LOC'] | 1 |
| ['মার্কিন যুক্তরাষ্ট্রে'] | প্রয়োগের | ['LOC'] | 1 |
| ['মার্কিন যুক্তরাষ্ট্রে'] | প্রক্রিয়া | ['LOC'] | 1 |
| ['মার্কিন যুক্তরাষ্ট্রে'] | শুরু | ['LOC'] | 0.9999 |
| ['মার্কিন যুক্তরাষ্ট্রে'] | হয় | ['LOC'] | 1 |
| ['মার্কিন যুক্তরাষ্ট্রে'] | ' | ['LOC'] | 1 |


[Bengali Lemmatizer](https://nlp.johnsnowlabs.com/2021/01/20/lemma_bn.html)


python
Bengali for : One morning in the marble-decorated building of Vaidyanatha, an obese monk was engaged in the enchantment of Duis and the milk service of one and a half Vaidyanatha. Give me two to eat
nlu.load("bn.lemma").predict("একদিন প্রাতে বৈদ্যনাথের মার্বলমণ্ডিত দালানে একটি স্থূলোদর সন্ন্যাসী দুইসের মোহনভোগ এবং দেড়সের দুগ্ধ সেবায় নিযুক্ত আছে বৈদ্যনাথ গায়ে একখানি চাদর দিয়া জোড়করে একান্ত বিনীতভাবে ভূতলে বসিয়া ভক্তিভরে পবিত্র ভোজনব্যাপার নিরীক্ষণ করিতেছিলেন এমন সময় কোনোমতে দ্বারীদের দৃষ্টি এড়াইয়া জীর্ণদেহ বালক সহিত একটি অতি শীর্ণকায়া রমণী গৃহে প্রবেশ করিয়া ক্ষীণস্বরে কহিল বাবু দুটি খেতে দাও")


output :

| lemma | document |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| ['একদিন', 'প্রাতঃ', 'বৈদ্যনাথ', 'মার্বলমণ্ডিত', 'দালান', 'এক', 'স্থূলউদর', 'সন্ন্যাসী', 'দুইসের', 'মোহনভোগ', 'এবং', 'দেড়সের', 'দুগ্ধ', 'সেবা', 'নিযুক্ত', 'আছে', 'বৈদ্যনাথ', 'গা', 'একখান', 'চাদর', 'দেওয়া', 'জোড়কর', 'একান্ত', 'বিনীতভাব', 'ভূতল', 'বসা', 'ভক্তিভরা', 'পবিত্র', 'ভোজনব্যাপার', 'নিরীক্ষণ', 'করা', 'এমন', 'সময়', 'কোনোমত', 'দ্বারী', 'দৃষ্টি', 'এড়ানো', 'জীর্ণদেহ', 'বালক', 'সহিত', 'এক', 'অতি', 'শীর্ণকায়া', 'রমণী', 'গৃহ', 'প্রবেশ', 'বিশ্বাস', 'ক্ষীণস্বর', 'কহা', 'বাবু', 'দুই', 'খাওয়া', 'দাওয়া'] | একদিন প্রাতে বৈদ্যনাথের মার্বলমণ্ডিত দালানে একটি স্থূলোদর সন্ন্যাসী দুইসের মোহনভোগ এবং দেড়সের দুগ্ধ সেবায় নিযুক্ত আছে বৈদ্যনাথ গায়ে একখানি চাদর দিয়া জোড়করে একান্ত বিনীতভাবে ভূতলে বসিয়া ভক্তিভরে পবিত্র ভোজনব্যাপার নিরীক্ষণ করিতেছিলেন এমন সময় কোনোমতে দ্বারীদের দৃষ্টি এড়াইয়া জীর্ণদেহ বালক সহিত একটি অতি শীর্ণকায়া রমণী গৃহে প্রবেশ করিয়া ক্ষীণস্বরে কহিল বাবু দুটি খেতে দাও |


[Japanese Lemmatizer](https://nlp.johnsnowlabs.com/2021/01/15/lemma_ja.html)


python
Japanese for : Some residents were uncomfortable with this, but it seems that no one is now openly protesting or protesting.
nlu.load("ja.lemma").predict("これに不快感を示す住民はいましたが,現在,表立って反対や抗議の声を挙げている住民はいないようです。")


output :

| lemma | document |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|
| ['これ', 'にる', '不快', '感', 'を', '示す', '住民', 'はる', 'いる', 'まする', 'たる', 'がる', ',', '現在', ',', '表立つ', 'てる', '反対', 'やる', '抗議', 'のる', '声', 'を', '挙げる', 'てる', 'いる', '住民', 'はる', 'いる', 'なぐ', 'よう', 'です', '。'] | これに不快感を示す住民はいましたが,現在,表立って反対や抗議の声を挙げている住民はいないようです。 |

[Aharic Lemmatizer](https://nlp.johnsnowlabs.com/2021/01/20/lemma_am.html)


python
Aharic for : Bookmark the permalink.
nlu.load("am.lemma").predict("መጽሐፉን መጽሐፍ ኡ ን አስያዛት አስያዝ ኧ ኣት ።")


output :

| lemma | document |
|:-----------------------------------------------------|:---------------------------------|
| ['_', 'መጽሐፍ', 'ኡ', 'ን', '_', 'አስያዝ', 'ኧ', 'ኣት', '።'] | መጽሐፉን መጽሐፍ ኡ ን አስያዛት አስያዝ ኧ ኣት ። |

[Bhojpuri Lemmatizer](https://nlp.johnsnowlabs.com/2021/01/18/lemma_bh.html)


python
Bhojpuri for : In this event, participation of World Bhojpuri Conference, Purvanchal Ekta Manch, Veer Kunwar Singh Foundation, Purvanchal Bhojpuri Mahasabha, and Herf - Media.
nlu.load("bh.lemma").predict("एह आयोजन में विश्व भोजपुरी सम्मेलन , पूर्वांचल एकता मंच , वीर कुँवर सिंह फाउन्डेशन , पूर्वांचल भोजपुरी महासभा , अउर हर्फ - मीडिया के सहभागिता बा ।")


output :

| lemma | document |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------|
| ['एह', 'आयोजन', 'में', 'विश्व', 'भोजपुरी', 'सम्मेलन', 'COMMA', 'पूर्वांचल', 'एकता', 'मंच', 'COMMA', 'वीर', 'कुँवर', 'सिंह', 'फाउन्डेशन', 'COMMA', 'पूर्वांचल', 'भोजपुरी', 'महासभा', 'COMMA', 'अउर', 'हर्फ', '-', 'मीडिया', 'को', 'सहभागिता', 'बा', '।'] | एह आयोजन में विश्व भोजपुरी सम्मेलन , पूर्वांचल एकता मंच , वीर कुँवर सिंह फाउन्डेशन , पूर्वांचल भोजपुरी महासभा , अउर हर्फ - मीडिया के सहभागिता बा । |

[Named Entity Recognition - BERT Tiny (OntoNotes)](https://nlp.johnsnowlabs.com/2020/12/05/onto_small_bert_L2_128_en.html)
python
nlu.load("en.ner.onto.bert.small_l2_128").predict("""William Henry Gates III (born October 28, 1955) is an American business magnate,
software developer, investor, and philanthropist. He is best known as the co-founder of Microsoft Corporation. During his career at Microsoft,
Gates held the positions of chairman, chief executive officer (CEO), president and chief software architect,
while also being the largest individual shareholder until May 2014.
He is one of the best-known entrepreneurs and pioneers of the microcomputer revolution of the 1970s and 1980s. Born and raised in Seattle, Washington, Gates co-founded Microsoft with childhood friend Paul Allen in 1975, in Albuquerque, New Mexico;
it went on to become the world's largest personal computer software company. Gates led the company as chairman and CEO until stepping down as CEO in January 2000, but he remained chairman and became chief software architect.
During the late 1990s, Gates had been criticized for his business tactics, which have been considered anti-competitive. This opinion has been upheld by numerous court rulings. In June 2006, Gates announced that he would be transitioning to a part-time
role at Microsoft and full-time work at the Bill & Melinda Gates Foundation, the private charitable foundation that he and his wife, Melinda Gates, established in 2000.
He gradually transferred his duties to Ray Ozzie and Craig Mundie.
He stepped down as chairman of Microsoft in February 2014 and assumed a new post as technology adviser to support the newly appointed CEO Satya Nadella.""",output_level = "document")


output :

| ner_confidence | entities | Entities_classes |
| :------------------------------------------------- | :----------------------------------------------------------- | :----------------------------------------------------------- |

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