Models hub internal to Master (#183)

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diff --git a/docs/_posts/Ahmetemintek/2023-04-13-hcpcs_ndc_mapper_en.md b/docs/_posts/Ahmetemintek/2023-04-13-hcpcs_ndc_mapper_en.md
@@ -0,0 +1,106 @@
+---
+layout: model
+title: Mapping HCPCS Codes  with Corresponding National Drug Codes (NDC) and Drug Brand Names
+author: John Snow Labs
+name: hcpcs_ndc_mapper
+date: 2023-04-13
+tags: [en, licensed, chunk_mappig, hcpcs, ndc, brand_name]
+task: Chunk Mapping
+language: en
+edition: Healthcare NLP 4.4.0
+spark_version: 3.0
+supported: true
+annotator: ChunkMapperModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained model maps HCPCS codes with their corresponding National Drug Codes (NDC) and their drug brand names.
+
+## Predicted Entities
+
+`ndc_code`, `brand_name`
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+[Open in Colab](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/26.Chunk_Mapping.ipynb){:.button.button-orange.button-orange-trans.co.button-icon}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/hcpcs_ndc_mapper_en_4.4.0_3.0_1681405950608.zip){:.button.button-orange}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/hcpcs_ndc_mapper_en_4.4.0_3.0_1681405950608.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+document_assembler = DocumentAssembler()\
+      .setInputCol("text")\
+      .setOutputCol("hcpcs_chunk")
+
+chunkerMapper = DocMapperModel.pretrained("hcpcs_ndc_mapper", "en", "clinical/models")\
+      .setInputCols(["hcpcs_chunk"])\
+      .setOutputCol("mappings")\
+      .setRels(["ndc_code", "brand_name"])
+
+pipeline = Pipeline().setStages([document_assembler,
+                                 chunkerMapper])  
+
+model = pipeline.fit(spark.createDataFrame([['']]).toDF('text')) 
+
+lp = LightPipeline(model)
+
+res = lp.fullAnnotate(["Q5106", "J9211", "J7508"])
+```
+```scala
+val document_assembler = new DocumentAssembler()
+       .setInputCol("text")\
+       .setOutputCol("hcpcs_chunk")
+
+val chunkerMapper = DocMapperModel
+       .pretrained("hcpcs_ndc_mapper", "en", "clinical/models")
+       .setInputCols(Array("hcpcs_chunk"))
+       .setOutputCol("mappings")
+       .setRels(Array("ndc_code", "brand_name")) 
+
+val mapper_pipeline = new Pipeline().setStages(Array(
+                                                   document_assembler,
+                                                   chunkerMapper))
+
+
+val data = Seq(Array(["Q5106", "J9211", "J7508"])).toDS.toDF("text")
+val result = pipeline.fit(data).transform(data) 
+```
+</div>
+
+## Results
+
+```bash
++-----------+-------------------------------------+----------+
+|hcpcs_chunk|mappings                             |relation  |
++-----------+-------------------------------------+----------+
+|Q5106      |59353-0003-10                        |ndc_code  |
+|Q5106      |RETACRIT (PF) 3000 U/1 ML            |brand_name|
+|J9211      |59762-2596-01                        |ndc_code  |
+|J9211      |IDARUBICIN HYDROCHLORIDE (PF) 1 MG/ML|brand_name|
+|J7508      |00469-0687-73                        |ndc_code  |
+|J7508      |ASTAGRAF XL 5 MG                     |brand_name|
++-----------+-------------------------------------+----------+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|hcpcs_ndc_mapper|
+|Compatibility:|Healthcare NLP 4.4.0+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[ner_chunk]|
+|Output Labels:|[mappings]|
+|Language:|en|
+|Size:|20.7 KB|
diff --git a/docs/_posts/Ahmetemintek/2023-04-13-ndc_hcpcs_mapper_en.md b/docs/_posts/Ahmetemintek/2023-04-13-ndc_hcpcs_mapper_en.md
@@ -0,0 +1,106 @@
+---
+layout: model
+title: Mapping National Drug Codes (NDC) with Corresponding HCPCS Codes and Descriptions
+author: John Snow Labs
+name: ndc_hcpcs_mapper
+date: 2023-04-13
+tags: [en, licensed, clinical, chunk_mapping, ndc, hcpcs, brand_name]
+task: Chunk Mapping
+language: en
+edition: Healthcare NLP 4.4.0
+spark_version: 3.0
+supported: true
+annotator: ChunkMapperModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained model maps National Drug Codes (NDC) with their corresponding HCPCS codes and their descriptions.
+
+## Predicted Entities
+
+`hcpcs_code`, `hcpcs_description`
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+[Open in Colab](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/26.Chunk_Mapping.ipynb){:.button.button-orange.button-orange-trans.co.button-icon}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/ndc_hcpcs_mapper_en_4.4.0_3.0_1681405091593.zip){:.button.button-orange}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/ndc_hcpcs_mapper_en_4.4.0_3.0_1681405091593.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+document_assembler = DocumentAssembler()\
+      .setInputCol("text")\
+      .setOutputCol("ndc_chunk")
+
+chunkerMapper = DocMapperModel.pretrained("ndc_hcpcs_mapper", "en", "clinical/models")\
+      .setInputCols(["ndc_chunk"])\
+      .setOutputCol("hcpcs")\
+      .setRels(["hcpcs_code", "hcpcs_description"])
+
+pipeline = Pipeline().setStages([document_assembler,
+                                 chunkerMapper])  
+
+model = pipeline.fit(spark.createDataFrame([['']]).toDF('text')) 
+
+lp = LightPipeline(model)
+
+res = lp.fullAnnotate(["16714-0892-01", "00990-6138-03", "43598-0650-11"])
+```
+```scala
+val document_assembler = new DocumentAssembler()
+       .setInputCol("text")\
+       .setOutputCol("ndc_chunk")
+
+val chunkerMapper = DocMapperModel
+       .pretrained("ndc_hcpcs_mapper", "en", "clinical/models")
+       .setInputCols(Array("ndc_chunk"))
+       .setOutputCol("mappings")
+       .setRels(Array("hcpcs_code", "hcpcs_description")) 
+
+val mapper_pipeline = new Pipeline().setStages(Array(
+                                                   document_assembler,
+                                                   chunkerMapper))
+
+
+val data = Seq(Array("16714-0892-01", "00990-6138-03", "43598-0650-11")).toDS.toDF("text")
+val result = pipeline.fit(data).transform(data) 
+```
+</div>
+
+## Results
+
+```bash
++-------------+----------------------------+-----------------+
+|ndc_chunk    |mappings                    |relation         |
++-------------+----------------------------+-----------------+
+|16714-0892-01|J0878                       |hcpcs_code       |
+|16714-0892-01|INJECTION, DAPTOMYCIN, 1 MG |hcpcs_description|
+|00990-6138-03|A4217                       |hcpcs_code       |
+|00990-6138-03|STERILE WATER/SALINE, 500 ML|hcpcs_description|
+|43598-0650-11|J9340                       |hcpcs_code       |
+|43598-0650-11|INJECTION, THIOTEPA, 15 MG  |hcpcs_description|
++-------------+----------------------------+-----------------+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|ndc_hcpcs_mapper|
+|Compatibility:|Healthcare NLP 4.4.0+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[ner_chunk]|
+|Output Labels:|[mappings]|
+|Language:|en|
+|Size:|203.1 KB|
diff --git a/docs/_posts/Cabir40/2023-03-30-summarizer_generic_jsl_en.md b/docs/_posts/Cabir40/2023-03-30-summarizer_generic_jsl_en.md
@@ -19,6 +19,7 @@ use_language_switcher: "Python-Scala-Java"
 
 ## Description
 
+
 This model is a modified version of Flan-T5 (LLM) based summarization model that is finetuned with additional data curated by John Snow Labs. This model is further optimized by augmenting the training methodology, and dataset. It can generate summaries from clinical notes up to 512 tokens given the input text (max 1024 tokens)
 
 {:.btn-box}

diff --git a/docs/_posts/Cabir40/2023-04-13-medication_resolver_pipeline_en.md b/docs/_posts/Cabir40/2023-04-13-medication_resolver_pipeline_en.md
@@ -0,0 +1,105 @@
+---
+layout: model
+title: Pipeline to Resolve Medication Codes
+author: John Snow Labs
+name: medication_resolver_pipeline
+date: 2023-04-13
+tags: [licensed, clinical, en, resolver, snomed, umls, rxnorm, ndc, ade, pipeline]
+task: Chunk Mapping
+language: en
+edition: Healthcare NLP 4.3.2
+spark_version: 3.0
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+A pretrained resolver pipeline to extract medications and resolve their adverse reactions (ADE), RxNorm, UMLS, NDC, SNOMED CT codes, and action/treatments in clinical text.
+
+Action/treatments are available for branded medication, and SNOMED codes are available for non-branded medication.
+
+This pipeline can be used as Lightpipeline (with `annotate/fullAnnotate`). You can use `medication_resolver_transform_pipeline` for Spark transform.
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/medication_resolver_pipeline_en_4.3.2_3.0_1681388823359.zip){:.button.button-orange}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/medication_resolver_pipeline_en_4.3.2_3.0_1681388823359.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+
+med_resolver_pipeline = PretrainedPipeline("medication_resolver_pipeline", "en", "clinical/models")
+
+text = """The patient was prescribed Amlodopine Vallarta 10-320mg, Eviplera. The other patient is given Lescol 40 MG and Everolimus 1.5 mg tablet."""
+
+result = med_resolver_pipeline.fullAnnotate(text)
+```
+```scala
+import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
+
+val med_resolver_pipeline = new PretrainedPipeline("medication_resolver_pipeline", "en", "clinical/models")
+
+val result = med_resolver_pipeline.fullAnnotate("""The patient was prescribed Amlodopine Vallarta 10-320mg, Eviplera. The other patient is given Lescol 40 MG and Everolimus 1.5 mg tablet.""")
+```
+</div>
+
+## Results
+
+```bash
+|    | chunks                       | entities   | ADE                         |   RxNorm | Action                     | Treatment                                  | UMLS     | SNOMED_CT   | NDC_Product   | NDC_Package   |
+|---:|:-----------------------------|:-----------|:----------------------------|---------:|:---------------------------|:-------------------------------------------|:---------|:------------|:--------------|:--------------|
+|  0 | Amlodopine Vallarta 10-320mg | DRUG       | Gynaecomastia               |   722131 | NONE                       | NONE                                       | C1949334 | 425838008   | 00093-7693    | 00093-7693-56 |
+|  1 | Eviplera                     | DRUG       | Anxiety                     |   217010 | Inhibitory Bone Resorption | Osteoporosis                               | C0720318 | NONE        | NONE          | NONE          |
+|  2 | Lescol 40 MG                 | DRUG       | NONE                        |   103919 | Hypocholesterolemic        | Heterozygous Familial Hypercholesterolemia | C0353573 | NONE        | 00078-0234    | 00078-0234-05 |
+|  3 | Everolimus 1.5 mg tablet     | DRUG       | Acute myocardial infarction |  2056895 | NONE                       | NONE                                       | C4723581 | NONE        | 00054-0604    | 00054-0604-21 |
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|medication_resolver_pipeline|
+|Type:|pipeline|
+|Compatibility:|Healthcare NLP 4.3.2+|
+|License:|Licensed|
+|Edition:|Official|
+|Language:|en|
+|Size:|3.1 GB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetectorDLModel
+- TokenizerModel
+- WordEmbeddingsModel
+- MedicalNerModel
+- NerConverterInternalModel
+- TextMatcherModel
+- ChunkMergeModel
+- ChunkMapperModel
+- ChunkMapperModel
+- ChunkMapperFilterer
+- Chunk2Doc
+- BertSentenceEmbeddings
+- SentenceEntityResolverModel
+- ResolverMerger
+- ResolverMerger
+- ChunkMapperModel
+- ChunkMapperModel
+- ChunkMapperModel
+- ChunkMapperModel
+- ChunkMapperModel
+- ChunkMapperModel
+- Finisher