diff --git a/docs/_posts/mauro-nievoff/2022-12-01-oncology_biomarker_pipeline_en.md b/docs/_posts/mauro-nievoff/2022-12-01-oncology_biomarker_pipeline_en.md
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+---
+layout: model
+title: Oncology Pipeline for Biomarkers
+author: John Snow Labs
+name: oncology_biomarker_pipeline
+date: 2022-12-01
+tags: [licensed, pipeline, oncology, biomarker, en]
+task: Named Entity Recognition
+language: en
+edition: Healthcare NLP 4.2.2
+spark_version: 3.0
+supported: true
+article_header:
+ type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pipeline includes Named-Entity Recognition, Assertion Status and Relation Extraction models to extract information from oncology texts. This pipeline focuses on entities related to biomarkers.
+
+{:.btn-box}
+
+[Open in Colab](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/27.Oncology_Model.ipynb){:.button.button-orange.button-orange-trans.co.button-icon}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/oncology_biomarker_pipeline_en_4.2.2_3.0_1669902355525.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+
+## How to use
+
+
+
+
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+
+pipeline = PretrainedPipeline("oncology_biomarker_pipeline", "en", "clinical/models")
+
+pipeline.annotate("Immunohistochemistry was negative for thyroid transcription factor-1 and napsin A. The test was positive for ER and PR, and negative for HER2.")
+```
+```scala
+import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
+
+val pipeline = new PretrainedPipeline("oncology_biomarker_pipeline", "en", "clinical/models")
+
+val result = pipeline.fullAnnotate("""Immunohistochemistry was negative for thyroid transcription factor-1 and napsin A. The test was positive for ER and PR, and negative for HER2.""")(0)
+```
+
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+
+pipeline = PretrainedPipeline("oncology_diagnosis_pipeline", "en", "clinical/models")
+
+pipeline.fullAnnotate("Two years ago, the patient presented with a 4-cm tumor in her left breast. She was diagnosed with ductal carcinoma.
+According to her last CT, she has no lung metastases.")[0]
+
+```
+```scala
+import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
+
+val pipeline = new PretrainedPipeline("oncology_diagnosis_pipeline", "en", "clinical/models")
+
+val result = pipeline.fullAnnotate("""Two years ago, the patient presented with a 4-cm tumor in her left breast. She was diagnosed with ductal carcinoma.
+According to her last CT, she has no lung metastases.""")(0)
+```
+
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+
+pipeline = PretrainedPipeline("oncology_general_pipeline", "en", "clinical/models")
+
+pipeline.annotate("The patient underwent a left mastectomy for a left breast cancer two months ago.
+The tumor is positive for ER and PR.")
+
+```
+```scala
+import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
+
+val pipeline = new PretrainedPipeline("oncology_general_pipeline", "en", "clinical/models")
+
+val result = pipeline.fullAnnotate("""The patient underwent a left mastectomy for a left breast cancer two months ago.
+The tumor is positive for ER and PR.""")(0)
+```
+
+
+## Results
+
+```bash
+******************** ner_oncology_wip results ********************
+
+| chunk | ner_label |
+|:---------------|:-----------------|
+| left | Direction |
+| mastectomy | Cancer_Surgery |
+| left | Direction |
+| breast cancer | Cancer_Dx |
+| two months ago | Relative_Date |
+| tumor | Tumor_Finding |
+| positive | Biomarker_Result |
+| ER | Biomarker |
+| PR | Biomarker |
+
+
+******************** ner_oncology_diagnosis_wip results ********************
+
+| chunk | ner_label |
+|:--------------|:--------------|
+| breast cancer | Cancer_Dx |
+| tumor | Tumor_Finding |
+
+
+******************** ner_oncology_tnm_wip results ********************
+
+| chunk | ner_label |
+|:--------------|:------------|
+| breast cancer | Cancer_Dx |
+| tumor | Tumor |
+
+
+******************** ner_oncology_therapy_wip results ********************
+
+| chunk | ner_label |
+|:-----------|:---------------|
+| mastectomy | Cancer_Surgery |
+
+
+******************** ner_oncology_test_wip results ********************
+
+| chunk | ner_label |
+|:---------|:-----------------|
+| positive | Biomarker_Result |
+| ER | Biomarker |
+| PR | Biomarker |
+
+
+******************** assertion_oncology_wip results ********************
+
+| chunk | ner_label | assertion |
+|:--------------|:---------------|:------------|
+| mastectomy | Cancer_Surgery | Past |
+| breast cancer | Cancer_Dx | Present |
+| tumor | Tumor_Finding | Present |
+| ER | Biomarker | Present |
+| PR | Biomarker | Present |
+
+
+******************** re_oncology_wip results ********************
+
+| chunk1 | entity1 | chunk2 | entity2 | relation |
+|:--------------|:-----------------|:---------------|:--------------|:--------------|
+| mastectomy | Cancer_Surgery | two months ago | Relative_Date | is_related_to |
+| breast cancer | Cancer_Dx | two months ago | Relative_Date | is_related_to |
+| tumor | Tumor_Finding | ER | Biomarker | O |
+| tumor | Tumor_Finding | PR | Biomarker | O |
+| positive | Biomarker_Result | ER | Biomarker | is_related_to |
+| positive | Biomarker_Result | PR | Biomarker | is_related_to |
+
+
+******************** re_oncology_granular_wip results ********************
+
+| chunk1 | entity1 | chunk2 | entity2 | relation |
+|:--------------|:-----------------|:---------------|:--------------|:--------------|
+| mastectomy | Cancer_Surgery | two months ago | Relative_Date | is_date_of |
+| breast cancer | Cancer_Dx | two months ago | Relative_Date | is_date_of |
+| tumor | Tumor_Finding | ER | Biomarker | O |
+| tumor | Tumor_Finding | PR | Biomarker | O |
+| positive | Biomarker_Result | ER | Biomarker | is_finding_of |
+| positive | Biomarker_Result | PR | Biomarker | is_finding_of |
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|oncology_general_pipeline|
+|Type:|pipeline|
+|Compatibility:|Healthcare NLP 4.2.2+|
+|License:|Licensed|
+|Edition:|Official|
+|Language:|en|
+|Size:|1.7 GB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetectorDLModel
+- TokenizerModel
+- WordEmbeddingsModel
+- MedicalNerModel
+- NerConverter
+- MedicalNerModel
+- NerConverter
+- MedicalNerModel
+- NerConverter
+- MedicalNerModel
+- NerConverter
+- MedicalNerModel
+- NerConverter
+- ChunkMergeModel
+- ChunkMergeModel
+- AssertionDLModel
+- PerceptronModel
+- DependencyParserModel
+- RelationExtractionModel
+- RelationExtractionModel
\ No newline at end of file
diff --git a/docs/_posts/mauro-nievoff/2022-12-01-oncology_therapy_pipeline_en.md b/docs/_posts/mauro-nievoff/2022-12-01-oncology_therapy_pipeline_en.md
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+---
+layout: model
+title: Oncology Pipeline for Therapies
+author: John Snow Labs
+name: oncology_therapy_pipeline
+date: 2022-12-01
+tags: [licensed, pipeline, oncology, en]
+task: Named Entity Recognition
+language: en
+edition: Healthcare NLP 4.2.2
+spark_version: 3.0
+supported: true
+article_header:
+ type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pipeline includes Named-Entity Recognition and Assertion Status models to extract information from oncology texts. This pipeline focuses on entities related to therapies.
+
+{:.btn-box}
+
+[Open in Colab](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/27.Oncology_Model.ipynb){:.button.button-orange.button-orange-trans.co.button-icon}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/oncology_therapy_pipeline_en_4.2.2_3.0_1669906146446.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+
+## How to use
+
+
+
+
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+from sparknlp.pretrained import PretrainedPipeline
+
+pipeline = PretrainedPipeline("oncology_therapy_pipeline", "en", "clinical/models")
+
+pipeline.fullAnnotate("The patient underwent a mastectomy two years ago. She is currently receiving her second cycle of adriamycin and cyclophosphamide, and is in good overall condition.")[0]
+
+```
+```scala
+import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
+
+val pipeline = new PretrainedPipeline("oncology_therapy_pipeline", "en", "clinical/models")
+
+val result = pipeline.fullAnnotate("""The patient underwent a mastectomy two years ago. She is currently receiving her second cycle of adriamycin and cyclophosphamide, and is in good overall condition.""")(0)
+```
+