From 980f8d2eca1889d3e2540f48c27be64d2d16cbe8 Mon Sep 17 00:00:00 2001
From: vkocaman <vkocaman@users.noreply.github.com>
Date: Fri, 2 Aug 2024 19:11:15 +0000
Subject: [PATCH] deploy: 1fd5a71892fa25f3105fbb4845eb6edf0e46aa3f

---
 ...linical_deidentification_augmented_es.html |    4 +-
 ...l_deidentification_glove_augmented_en.html |    6 +-
 .../clinical_deidentification_glove_en.html   |    6 +-
 .../16/clinical_deidentification_slim_en.html |    6 +-
 .../06/16/ner_deid_augmented_pipeline_en.html |    4 +-
 2023/06/16/ner_deid_biobert_pipeline_en.html  |    4 +-
 2023/06/16/ner_deid_enriched_pipeline_en.html |    4 +-
 .../ner_deid_generic_glove_pipeline_en.html   |    4 +-
 2023/06/16/ner_deid_large_pipeline_en.html    |    4 +-
 2023/06/16/ner_deid_sd_large_pipeline_en.html |    4 +-
 2023/06/16/ner_deid_sd_pipeline_en.html       |    6 +-
 ..._deid_subentity_augmented_pipeline_en.html |    6 +-
 .../06/16/ner_deid_synthetic_pipeline_en.html |    4 +-
 2023/06/16/ner_deidentify_dl_pipeline_en.html |    4 +-
 ...linical_deidentification_augmented_es.html |    4 +-
 ...l_deidentification_glove_augmented_en.html |    6 +-
 .../clinical_deidentification_glove_en.html   |    6 +-
 .../17/clinical_deidentification_slim_en.html |    6 +-
 .../06/17/ner_deid_augmented_pipeline_en.html |    4 +-
 2023/06/17/ner_deid_biobert_pipeline_en.html  |    4 +-
 2023/06/17/ner_deid_enriched_pipeline_en.html |    4 +-
 .../ner_deid_generic_glove_pipeline_en.html   |    4 +-
 2023/06/17/ner_deid_large_pipeline_en.html    |    4 +-
 2023/06/17/ner_deid_sd_large_pipeline_en.html |    4 +-
 2023/06/17/ner_deid_sd_pipeline_en.html       |    6 +-
 ..._deid_subentity_augmented_pipeline_en.html |    6 +-
 .../06/17/ner_deid_synthetic_pipeline_en.html |    4 +-
 2023/06/17/ner_deidentify_dl_pipeline_en.html |    4 +-
 ...clinical_deidentification_langtest_en.html |    9 +-
 feed.xml                                      |    2 +-
 models.json                                   |    2 +-
 sitemap.xml                                   | 2886 ++++++++---------
 32 files changed, 1545 insertions(+), 1486 deletions(-)

diff --git a/2023/06/16/clinical_deidentification_augmented_es.html b/2023/06/16/clinical_deidentification_augmented_es.html
index 49b3c827fa..391e557c29 100644
--- a/2023/06/16/clinical_deidentification_augmented_es.html
+++ b/2023/06/16/clinical_deidentification_augmented_es.html
@@ -510,10 +510,12 @@ <h1>Clinical Deidentification (Spanish, augmented)</h1><div class="top-subtitle
 
 <p>This pipeline is trained with sciwiki_300d embeddings and can be used to deidentify PHI information from medical texts in Spanish. It differs from the previous <code class="language-plaintext highlighter-rouge">clinical_deidentificaiton</code> pipeline in that it includes the <code class="language-plaintext highlighter-rouge">ner_deid_subentity_augmented</code> NER model and some improvements in ContextualParsers and RegexMatchers.</p>
 
-<p>The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask, fake or obfuscate the following entities: <code class="language-plaintext highlighter-rouge">AGE</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">EMAIL</code>, <code class="language-plaintext highlighter-rouge">USERNAME</code>, <code class="language-plaintext highlighter-rouge">STREET</code>, <code class="language-plaintext highlighter-rouge">COUNTRY</code>, <code class="language-plaintext highlighter-rouge">CITY</code>, <code class="language-plaintext highlighter-rouge">DOCTOR</code>, <code class="language-plaintext highlighter-rouge">HOSPITAL</code>, <code class="language-plaintext highlighter-rouge">PATIENT</code>, <code class="language-plaintext highlighter-rouge">URL</code>, <code class="language-plaintext highlighter-rouge">MEDICALRECORD</code>, <code class="language-plaintext highlighter-rouge">IDNUM</code>, <code class="language-plaintext highlighter-rouge">ORGANIZATION</code>, <code class="language-plaintext highlighter-rouge">PHONE</code>, <code class="language-plaintext highlighter-rouge">ZIP</code>, <code class="language-plaintext highlighter-rouge">ACCOUNT</code>, <code class="language-plaintext highlighter-rouge">SSN</code>, <code class="language-plaintext highlighter-rouge">PLATE</code>, <code class="language-plaintext highlighter-rouge">SEX</code> and <code class="language-plaintext highlighter-rouge">IPADDR</code></p>
+<p>The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask, fake or obfuscate the following entities: <code class="language-plaintext highlighter-rouge">MEDICALRECORD</code>, <code class="language-plaintext highlighter-rouge">ORGANIZATION</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">DOCTOR</code>, <code class="language-plaintext highlighter-rouge">USERNAME</code>, <code class="language-plaintext highlighter-rouge">ID</code>, <code class="language-plaintext highlighter-rouge">CITY</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">PATIENT</code>, <code class="language-plaintext highlighter-rouge">SEX</code>, <code class="language-plaintext highlighter-rouge">COUNTRY</code>, <code class="language-plaintext highlighter-rouge">ZIP</code>, <code class="language-plaintext highlighter-rouge">STREET</code>, <code class="language-plaintext highlighter-rouge">PHONE</code>, <code class="language-plaintext highlighter-rouge">HOSPITAL</code>, <code class="language-plaintext highlighter-rouge">EMAIL</code>, <code class="language-plaintext highlighter-rouge">AGE</code>, <code class="language-plaintext highlighter-rouge">SSN</code>, <code class="language-plaintext highlighter-rouge">IDNUM</code></p>
 
 <h2 id="predicted-entities">Predicted Entities</h2>
 
+<p><code class="language-plaintext highlighter-rouge">MEDICALRECORD</code>, <code class="language-plaintext highlighter-rouge">ORGANIZATION</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">DOCTOR</code>, <code class="language-plaintext highlighter-rouge">USERNAME</code>, <code class="language-plaintext highlighter-rouge">ID</code>, <code class="language-plaintext highlighter-rouge">CITY</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">PATIENT</code>, <code class="language-plaintext highlighter-rouge">SEX</code>, <code class="language-plaintext highlighter-rouge">COUNTRY</code>, <code class="language-plaintext highlighter-rouge">ZIP</code>, <code class="language-plaintext highlighter-rouge">STREET</code>, <code class="language-plaintext highlighter-rouge">PHONE</code>, <code class="language-plaintext highlighter-rouge">HOSPITAL</code>, <code class="language-plaintext highlighter-rouge">EMAIL</code>, <code class="language-plaintext highlighter-rouge">AGE</code>, <code class="language-plaintext highlighter-rouge">SSN</code>, <code class="language-plaintext highlighter-rouge">IDNUM</code></p>
+
 <p class="btn-box"><button class="button button-orange" disabled="">Live Demo</button>
 <button class="button button-orange" disabled="">Open in Colab</button>
 <a href="https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_augmented_es_4.4.4_3.4_1686921942134.zip" class="button button-orange button-orange-trans arr button-icon hidden">Download</a>
diff --git a/2023/06/16/clinical_deidentification_glove_augmented_en.html b/2023/06/16/clinical_deidentification_glove_augmented_en.html
index 602f492c32..ee4965ac03 100644
--- a/2023/06/16/clinical_deidentification_glove_augmented_en.html
+++ b/2023/06/16/clinical_deidentification_glove_augmented_en.html
@@ -11,7 +11,7 @@
     })(window,document,'script','dataLayer','GTM-59JLR64');</script>
 <!-- End Google Tag Manager --><title>Clinical Deidentification (English, Glove, Augmented) | clinical_deidentification_glove_augmented | Healthcare NLP 4.4.4</title><meta property="og:title" content=""/>
 
-<meta name="description" content="DescriptionThis pipeline is trained with lightweight glove_100d embeddings and can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate AGE, CONTACT, DATE, ID, LOCATION, NAME, PROFESSION, CITY, COUNTRY, ...">
+<meta name="description" content="DescriptionThis pipeline is trained with lightweight glove_100d embeddings and can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate LOCATION, CONTACT, PROFESSION, NAME, DATE, ID, AGE, MEDICALRECORD, ...">
 <!-- <link rel="canonical" href="/2023/06/16/clinical_deidentification_glove_augmented_en.html"> -->
 <link rel="canonical" href="/2023/06/16/clinical_deidentification_glove_augmented_en.html"><link rel="alternate" type="application/rss+xml" title="Spark NLP" href="/feed.xml"><!-- start favicons snippet, use https://realfavicongenerator.net/ -->
 <!---->
@@ -508,12 +508,14 @@ <h1>Clinical Deidentification (English, Glove, Augmented)</h1><div class="top-su
 
 <!-- end custom article top snippet --><div class="article__content" itemprop="articleBody"><h2 id="description">Description</h2>
 
-<p>This pipeline is trained with lightweight <code class="language-plaintext highlighter-rouge">glove_100d</code> embeddings and can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate <code class="language-plaintext highlighter-rouge">AGE</code>, <code class="language-plaintext highlighter-rouge">CONTACT</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">ID</code>, <code class="language-plaintext highlighter-rouge">LOCATION</code>, <code class="language-plaintext highlighter-rouge">NAME</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">CITY</code>, <code class="language-plaintext highlighter-rouge">COUNTRY</code>, <code class="language-plaintext highlighter-rouge">DOCTOR</code>, <code class="language-plaintext highlighter-rouge">HOSPITAL</code>, <code class="language-plaintext highlighter-rouge">IDNUM</code>, <code class="language-plaintext highlighter-rouge">MEDICALRECORD</code>, <code class="language-plaintext highlighter-rouge">ORGANIZATION</code>, <code class="language-plaintext highlighter-rouge">PATIENT</code>, <code class="language-plaintext highlighter-rouge">PHONE</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>,  <code class="language-plaintext highlighter-rouge">STREET</code>, <code class="language-plaintext highlighter-rouge">USERNAME</code>, <code class="language-plaintext highlighter-rouge">ZIP</code>, <code class="language-plaintext highlighter-rouge">ACCOUNT</code>, <code class="language-plaintext highlighter-rouge">LICENSE</code>, <code class="language-plaintext highlighter-rouge">VIN</code>, <code class="language-plaintext highlighter-rouge">SSN</code>, <code class="language-plaintext highlighter-rouge">DLN</code>, <code class="language-plaintext highlighter-rouge">PLATE</code>, <code class="language-plaintext highlighter-rouge">IPADDR</code> entities.</p>
+<p>This pipeline is trained with lightweight <code class="language-plaintext highlighter-rouge">glove_100d</code> embeddings and can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate <code class="language-plaintext highlighter-rouge">LOCATION</code>, <code class="language-plaintext highlighter-rouge">CONTACT</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">NAME</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">ID</code>, <code class="language-plaintext highlighter-rouge">AGE</code>, <code class="language-plaintext highlighter-rouge">MEDICALRECORD</code>, <code class="language-plaintext highlighter-rouge">ORGANIZATION</code>, <code class="language-plaintext highlighter-rouge">HEALTHPLAN</code>, <code class="language-plaintext highlighter-rouge">DOCTOR</code>, <code class="language-plaintext highlighter-rouge">USERNAME</code>, <code class="language-plaintext highlighter-rouge">URL</code>, <code class="language-plaintext highlighter-rouge">LOCATION-OTHER</code>, <code class="language-plaintext highlighter-rouge">DEVICE</code>, <code class="language-plaintext highlighter-rouge">CITY</code>, <code class="language-plaintext highlighter-rouge">ZIP</code>, <code class="language-plaintext highlighter-rouge">STATE</code>, <code class="language-plaintext highlighter-rouge">PATIENT</code>, <code class="language-plaintext highlighter-rouge">COUNTRY</code>, <code class="language-plaintext highlighter-rouge">STREET</code>, <code class="language-plaintext highlighter-rouge">PHONE</code>, <code class="language-plaintext highlighter-rouge">HOSPITAL</code>, <code class="language-plaintext highlighter-rouge">EMAIL</code>, <code class="language-plaintext highlighter-rouge">IDNUM</code>, <code class="language-plaintext highlighter-rouge">BIOID</code>, <code class="language-plaintext highlighter-rouge">FAX</code>, <code class="language-plaintext highlighter-rouge">SSN</code>, <code class="language-plaintext highlighter-rouge">ACCOUNT</code>, <code class="language-plaintext highlighter-rouge">DLN</code>, <code class="language-plaintext highlighter-rouge">PLATE</code>, <code class="language-plaintext highlighter-rouge">LICENSE</code> entities.</p>
 
 <p>It’s different to <code class="language-plaintext highlighter-rouge">clinical_deidentification_glove</code> in the way it manages PHONE and PATIENT, having apart from the NER, some rules in Contextual Parser components.</p>
 
 <h2 id="predicted-entities">Predicted Entities</h2>
 
+<p><code class="language-plaintext highlighter-rouge">LOCATION</code>, <code class="language-plaintext highlighter-rouge">CONTACT</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">NAME</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">ID</code>, <code class="language-plaintext highlighter-rouge">AGE</code>, <code class="language-plaintext highlighter-rouge">MEDICALRECORD</code>, <code class="language-plaintext highlighter-rouge">ORGANIZATION</code>, <code class="language-plaintext highlighter-rouge">HEALTHPLAN</code>, <code class="language-plaintext highlighter-rouge">DOCTOR</code>, <code class="language-plaintext highlighter-rouge">USERNAME</code>, <code class="language-plaintext highlighter-rouge">URL</code>, <code class="language-plaintext highlighter-rouge">LOCATION-OTHER</code>, <code class="language-plaintext highlighter-rouge">DEVICE</code>, <code class="language-plaintext highlighter-rouge">CITY</code>, <code class="language-plaintext highlighter-rouge">ZIP</code>, <code class="language-plaintext highlighter-rouge">STATE</code>, <code class="language-plaintext highlighter-rouge">PATIENT</code>, <code class="language-plaintext highlighter-rouge">COUNTRY</code>, <code class="language-plaintext highlighter-rouge">STREET</code>, <code class="language-plaintext highlighter-rouge">PHONE</code>, <code class="language-plaintext highlighter-rouge">HOSPITAL</code>, <code class="language-plaintext highlighter-rouge">EMAIL</code>, <code class="language-plaintext highlighter-rouge">IDNUM</code>, <code class="language-plaintext highlighter-rouge">BIOID</code>, <code class="language-plaintext highlighter-rouge">FAX</code>, <code class="language-plaintext highlighter-rouge">SSN</code>, <code class="language-plaintext highlighter-rouge">ACCOUNT</code>, <code class="language-plaintext highlighter-rouge">DLN</code>, <code class="language-plaintext highlighter-rouge">PLATE</code>, <code class="language-plaintext highlighter-rouge">LICENSE</code></p>
+
 <p class="btn-box"><button class="button button-orange" disabled="">Live Demo</button>
 <button class="button button-orange" disabled="">Open in Colab</button>
 <a href="https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_glove_augmented_en_4.4.4_3.4_1686930869398.zip" class="button button-orange button-orange-trans arr button-icon hidden">Download</a>
diff --git a/2023/06/16/clinical_deidentification_glove_en.html b/2023/06/16/clinical_deidentification_glove_en.html
index 6efffaf068..fd3e659854 100644
--- a/2023/06/16/clinical_deidentification_glove_en.html
+++ b/2023/06/16/clinical_deidentification_glove_en.html
@@ -11,7 +11,7 @@
     })(window,document,'script','dataLayer','GTM-59JLR64');</script>
 <!-- End Google Tag Manager --><title>Clinical Deidentification (glove) | clinical_deidentification_glove | Healthcare NLP 4.4.4</title><meta property="og:title" content=""/>
 
-<meta name="description" content="DescriptionThis pipeline is trained with lightweight glove_100d embeddings and can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate AGE, CONTACT, DATE, ID, LOCATION, NAME, PROFESSION, CITY, COUNTRY, ...">
+<meta name="description" content="DescriptionThis pipeline is trained with lightweight glove_100d embeddings and can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate LOCATION, CONTACT, PROFESSION, NAME, DATE, ID, AGE, MEDICALRECORD, ...">
 <!-- <link rel="canonical" href="/2023/06/16/clinical_deidentification_glove_en.html"> -->
 <link rel="canonical" href="/2023/06/16/clinical_deidentification_glove_en.html"><link rel="alternate" type="application/rss+xml" title="Spark NLP" href="/feed.xml"><!-- start favicons snippet, use https://realfavicongenerator.net/ -->
 <!---->
@@ -508,10 +508,12 @@ <h1>Clinical Deidentification (glove)</h1><div class="top-subtitle mont"></div><
 
 <!-- end custom article top snippet --><div class="article__content" itemprop="articleBody"><h2 id="description">Description</h2>
 
-<p>This pipeline is trained with lightweight <code class="language-plaintext highlighter-rouge">glove_100d</code> embeddings and can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate <code class="language-plaintext highlighter-rouge">AGE</code>, <code class="language-plaintext highlighter-rouge">CONTACT</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">ID</code>, <code class="language-plaintext highlighter-rouge">LOCATION</code>, <code class="language-plaintext highlighter-rouge">NAME</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">CITY</code>, <code class="language-plaintext highlighter-rouge">COUNTRY</code>, <code class="language-plaintext highlighter-rouge">DOCTOR</code>, <code class="language-plaintext highlighter-rouge">HOSPITAL</code>, <code class="language-plaintext highlighter-rouge">IDNUM</code>, <code class="language-plaintext highlighter-rouge">MEDICALRECORD</code>, <code class="language-plaintext highlighter-rouge">ORGANIZATION</code>, <code class="language-plaintext highlighter-rouge">PATIENT</code>, <code class="language-plaintext highlighter-rouge">PHONE</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">STREET</code>, <code class="language-plaintext highlighter-rouge">USERNAME</code>, <code class="language-plaintext highlighter-rouge">ZIP</code>, <code class="language-plaintext highlighter-rouge">ACCOUNT</code>, <code class="language-plaintext highlighter-rouge">LICENSE</code>, <code class="language-plaintext highlighter-rouge">VIN</code>, <code class="language-plaintext highlighter-rouge">SSN</code>, <code class="language-plaintext highlighter-rouge">DLN</code>, <code class="language-plaintext highlighter-rouge">PLATE</code>, <code class="language-plaintext highlighter-rouge">IPADDR</code>, <code class="language-plaintext highlighter-rouge">EMAIL</code> entities.</p>
+<p>This pipeline is trained with lightweight <code class="language-plaintext highlighter-rouge">glove_100d</code> embeddings and can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate <code class="language-plaintext highlighter-rouge">LOCATION</code>, <code class="language-plaintext highlighter-rouge">CONTACT</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">NAME</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">ID</code>, <code class="language-plaintext highlighter-rouge">AGE</code>, <code class="language-plaintext highlighter-rouge">MEDICALRECORD</code>, <code class="language-plaintext highlighter-rouge">ORGANIZATION</code>, <code class="language-plaintext highlighter-rouge">HEALTHPLAN</code>, <code class="language-plaintext highlighter-rouge">DOCTOR</code>, <code class="language-plaintext highlighter-rouge">USERNAME</code>, <code class="language-plaintext highlighter-rouge">URL</code>, <code class="language-plaintext highlighter-rouge">LOCATION-OTHER</code>, <code class="language-plaintext highlighter-rouge">DEVICE</code>, <code class="language-plaintext highlighter-rouge">CITY</code>, <code class="language-plaintext highlighter-rouge">ZIP</code>, <code class="language-plaintext highlighter-rouge">STATE</code>, <code class="language-plaintext highlighter-rouge">PATIENT</code>, <code class="language-plaintext highlighter-rouge">COUNTRY</code>, <code class="language-plaintext highlighter-rouge">STREET</code>, <code class="language-plaintext highlighter-rouge">PHONE</code>, <code class="language-plaintext highlighter-rouge">HOSPITAL</code>, <code class="language-plaintext highlighter-rouge">EMAIL</code>, <code class="language-plaintext highlighter-rouge">IDNUM</code>, <code class="language-plaintext highlighter-rouge">BIOID</code>, <code class="language-plaintext highlighter-rouge">FAX</code>, <code class="language-plaintext highlighter-rouge">SSN</code>, <code class="language-plaintext highlighter-rouge">ACCOUNT</code>, <code class="language-plaintext highlighter-rouge">DLN</code>, <code class="language-plaintext highlighter-rouge">PLATE</code>, <code class="language-plaintext highlighter-rouge">LICENSE</code> entities.</p>
 
 <h2 id="predicted-entities">Predicted Entities</h2>
 
+<p><code class="language-plaintext highlighter-rouge">LOCATION</code>, <code class="language-plaintext highlighter-rouge">CONTACT</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">NAME</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">ID</code>, <code class="language-plaintext highlighter-rouge">AGE</code>, <code class="language-plaintext highlighter-rouge">MEDICALRECORD</code>, <code class="language-plaintext highlighter-rouge">ORGANIZATION</code>, <code class="language-plaintext highlighter-rouge">HEALTHPLAN</code>, <code class="language-plaintext highlighter-rouge">DOCTOR</code>, <code class="language-plaintext highlighter-rouge">USERNAME</code>, <code class="language-plaintext highlighter-rouge">URL</code>, <code class="language-plaintext highlighter-rouge">LOCATION-OTHER</code>, <code class="language-plaintext highlighter-rouge">DEVICE</code>, <code class="language-plaintext highlighter-rouge">CITY</code>, <code class="language-plaintext highlighter-rouge">ZIP</code>, <code class="language-plaintext highlighter-rouge">STATE</code>, <code class="language-plaintext highlighter-rouge">PATIENT</code>, <code class="language-plaintext highlighter-rouge">COUNTRY</code>, <code class="language-plaintext highlighter-rouge">STREET</code>, <code class="language-plaintext highlighter-rouge">PHONE</code>, <code class="language-plaintext highlighter-rouge">HOSPITAL</code>, <code class="language-plaintext highlighter-rouge">EMAIL</code>, <code class="language-plaintext highlighter-rouge">IDNUM</code>, <code class="language-plaintext highlighter-rouge">BIOID</code>, <code class="language-plaintext highlighter-rouge">FAX</code>, <code class="language-plaintext highlighter-rouge">SSN</code>, <code class="language-plaintext highlighter-rouge">ACCOUNT</code>, <code class="language-plaintext highlighter-rouge">DLN</code>, <code class="language-plaintext highlighter-rouge">PLATE</code>, <code class="language-plaintext highlighter-rouge">LICENSE</code></p>
+
 <p class="btn-box"><button class="button button-orange" disabled="">Live Demo</button>
 <button class="button button-orange" disabled="">Open in Colab</button>
 <a href="https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_glove_en_4.4.4_3.4_1686921715470.zip" class="button button-orange button-orange-trans arr button-icon hidden">Download</a>
diff --git a/2023/06/16/clinical_deidentification_slim_en.html b/2023/06/16/clinical_deidentification_slim_en.html
index ba4484d929..e2afaba783 100644
--- a/2023/06/16/clinical_deidentification_slim_en.html
+++ b/2023/06/16/clinical_deidentification_slim_en.html
@@ -11,7 +11,7 @@
     })(window,document,'script','dataLayer','GTM-59JLR64');</script>
 <!-- End Google Tag Manager --><title>Clinical Deidentification Pipeline (English, slim) | clinical_deidentification_slim | Healthcare NLP 4.4.4</title><meta property="og:title" content=""/>
 
-<meta name="description" content="DescriptionThis pipeline is trained with lightweight glove_100d embeddings and can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate AGE, CONTACT, DATE, ID, LOCATION, NAME, PROFESSION, CITY, COUNTRY, ...">
+<meta name="description" content="DescriptionThis pipeline is trained with lightweight glove_100d embeddings and can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate LOCATION, CONTACT, PROFESSION, NAME, DATE, ID, AGE, MEDICALRECORD, ...">
 <!-- <link rel="canonical" href="/2023/06/16/clinical_deidentification_slim_en.html"> -->
 <link rel="canonical" href="/2023/06/16/clinical_deidentification_slim_en.html"><link rel="alternate" type="application/rss+xml" title="Spark NLP" href="/feed.xml"><!-- start favicons snippet, use https://realfavicongenerator.net/ -->
 <!---->
@@ -508,10 +508,12 @@ <h1>Clinical Deidentification Pipeline (English, slim)</h1><div class="top-subti
 
 <!-- end custom article top snippet --><div class="article__content" itemprop="articleBody"><h2 id="description">Description</h2>
 
-<p>This pipeline is trained with lightweight <code class="language-plaintext highlighter-rouge">glove_100d</code> embeddings and can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate <code class="language-plaintext highlighter-rouge">AGE</code>, <code class="language-plaintext highlighter-rouge">CONTACT</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">ID</code>, <code class="language-plaintext highlighter-rouge">LOCATION</code>, <code class="language-plaintext highlighter-rouge">NAME</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">CITY</code>, <code class="language-plaintext highlighter-rouge">COUNTRY</code>, <code class="language-plaintext highlighter-rouge">DOCTOR</code>, <code class="language-plaintext highlighter-rouge">HOSPITAL</code>, <code class="language-plaintext highlighter-rouge">IDNUM</code>, <code class="language-plaintext highlighter-rouge">MEDICALRECORD</code>, <code class="language-plaintext highlighter-rouge">ORGANIZATION</code>, <code class="language-plaintext highlighter-rouge">PATIENT</code>, <code class="language-plaintext highlighter-rouge">PHONE</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">STREET</code>, <code class="language-plaintext highlighter-rouge">USERNAME</code>, <code class="language-plaintext highlighter-rouge">ZIP</code>, <code class="language-plaintext highlighter-rouge">ACCOUNT</code>, <code class="language-plaintext highlighter-rouge">LICENSE</code>, <code class="language-plaintext highlighter-rouge">VIN</code>, <code class="language-plaintext highlighter-rouge">SSN</code>, <code class="language-plaintext highlighter-rouge">DLN</code>, <code class="language-plaintext highlighter-rouge">PLATE</code>, <code class="language-plaintext highlighter-rouge">IPADDR</code>, <code class="language-plaintext highlighter-rouge">EMAIL</code> entities.</p>
+<p>This pipeline is trained with lightweight <code class="language-plaintext highlighter-rouge">glove_100d</code> embeddings and can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate <code class="language-plaintext highlighter-rouge">LOCATION</code>, <code class="language-plaintext highlighter-rouge">CONTACT</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">NAME</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">ID</code>, <code class="language-plaintext highlighter-rouge">AGE</code>, <code class="language-plaintext highlighter-rouge">MEDICALRECORD</code>, <code class="language-plaintext highlighter-rouge">ORGANIZATION</code>, <code class="language-plaintext highlighter-rouge">HEALTHPLAN</code>, <code class="language-plaintext highlighter-rouge">DOCTOR</code>, <code class="language-plaintext highlighter-rouge">USERNAME</code>, <code class="language-plaintext highlighter-rouge">URL</code>, <code class="language-plaintext highlighter-rouge">LOCATION-OTHER</code>, <code class="language-plaintext highlighter-rouge">DEVICE</code>, <code class="language-plaintext highlighter-rouge">CITY</code>, <code class="language-plaintext highlighter-rouge">ZIP</code>, <code class="language-plaintext highlighter-rouge">STATE</code>, <code class="language-plaintext highlighter-rouge">PATIENT</code>, <code class="language-plaintext highlighter-rouge">COUNTRY</code>, <code class="language-plaintext highlighter-rouge">STREET</code>, <code class="language-plaintext highlighter-rouge">PHONE</code>, <code class="language-plaintext highlighter-rouge">HOSPITAL</code>, <code class="language-plaintext highlighter-rouge">EMAIL</code>, <code class="language-plaintext highlighter-rouge">IDNUM</code>, <code class="language-plaintext highlighter-rouge">BIOID</code>, <code class="language-plaintext highlighter-rouge">FAX</code>, <code class="language-plaintext highlighter-rouge">SSN</code>, <code class="language-plaintext highlighter-rouge">ACCOUNT</code>, <code class="language-plaintext highlighter-rouge">DLN</code>, <code class="language-plaintext highlighter-rouge">PLATE</code>, <code class="language-plaintext highlighter-rouge">VIN</code>, <code class="language-plaintext highlighter-rouge">LICENSE</code> entities.</p>
 
 <h2 id="predicted-entities">Predicted Entities</h2>
 
+<p><code class="language-plaintext highlighter-rouge">LOCATION</code>, <code class="language-plaintext highlighter-rouge">CONTACT</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">NAME</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">ID</code>, <code class="language-plaintext highlighter-rouge">AGE</code>, <code class="language-plaintext highlighter-rouge">MEDICALRECORD</code>, <code class="language-plaintext highlighter-rouge">ORGANIZATION</code>, <code class="language-plaintext highlighter-rouge">HEALTHPLAN</code>, <code class="language-plaintext highlighter-rouge">DOCTOR</code>, <code class="language-plaintext highlighter-rouge">USERNAME</code>, <code class="language-plaintext highlighter-rouge">URL</code>, <code class="language-plaintext highlighter-rouge">LOCATION-OTHER</code>, <code class="language-plaintext highlighter-rouge">DEVICE</code>, <code class="language-plaintext highlighter-rouge">CITY</code>, <code class="language-plaintext highlighter-rouge">ZIP</code>, <code class="language-plaintext highlighter-rouge">STATE</code>, <code class="language-plaintext highlighter-rouge">PATIENT</code>, <code class="language-plaintext highlighter-rouge">COUNTRY</code>, <code class="language-plaintext highlighter-rouge">STREET</code>, <code class="language-plaintext highlighter-rouge">PHONE</code>, <code class="language-plaintext highlighter-rouge">HOSPITAL</code>, <code class="language-plaintext highlighter-rouge">EMAIL</code>, <code class="language-plaintext highlighter-rouge">IDNUM</code>, <code class="language-plaintext highlighter-rouge">BIOID</code>, <code class="language-plaintext highlighter-rouge">FAX</code>, <code class="language-plaintext highlighter-rouge">SSN</code>, <code class="language-plaintext highlighter-rouge">ACCOUNT</code>, <code class="language-plaintext highlighter-rouge">DLN</code>, <code class="language-plaintext highlighter-rouge">PLATE</code>, <code class="language-plaintext highlighter-rouge">VIN</code>, <code class="language-plaintext highlighter-rouge">LICENSE</code></p>
+
 <p class="btn-box"><button class="button button-orange" disabled="">Live Demo</button>
 <button class="button button-orange" disabled="">Open in Colab</button>
 <a href="https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_slim_en_4.4.4_3.4_1686933504993.zip" class="button button-orange button-orange-trans arr button-icon hidden">Download</a>
diff --git a/2023/06/16/ner_deid_augmented_pipeline_en.html b/2023/06/16/ner_deid_augmented_pipeline_en.html
index f558cb355d..55d46120cf 100644
--- a/2023/06/16/ner_deid_augmented_pipeline_en.html
+++ b/2023/06/16/ner_deid_augmented_pipeline_en.html
@@ -11,7 +11,7 @@
     })(window,document,'script','dataLayer','GTM-59JLR64');</script>
 <!-- End Google Tag Manager --><title>Pipeline to Detect PHI for Deidentification (Augmented) | ner_deid_augmented_pipeline | Healthcare NLP 4.4.4</title><meta property="og:title" content=""/>
 
-<meta name="description" content="DescriptionThis pretrained pipeline is built on the top of ner_deid_augmented model.Predicted EntitiesLive DemoOpen in ColabDownloadCopy S3 URIHow to use  PythonJSLScalaNLU  from sparknlp.pretrained import PretrainedPipelinepipeline = PretrainedPipeline("ner_deid_augmented_pipeline", "en", "clinical/models")text = '...">
+<meta name="description" content="DescriptionThis pretrained pipeline is built on the top of ner_deid_augmented model.Predicted EntitiesLOCATION, CONTACT, PROFESSION, NAME, DATE, ID, AGELive DemoOpen in ColabDownloadCopy S3 URIHow to use  PythonJSLScalaNLU  from sparknlp.pretrained import PretrainedPipelinepipeline = PretrainedPipeline("ner_deid_aug...">
 <!-- <link rel="canonical" href="/2023/06/16/ner_deid_augmented_pipeline_en.html"> -->
 <link rel="canonical" href="/2023/06/16/ner_deid_augmented_pipeline_en.html"><link rel="alternate" type="application/rss+xml" title="Spark NLP" href="/feed.xml"><!-- start favicons snippet, use https://realfavicongenerator.net/ -->
 <!---->
@@ -512,6 +512,8 @@ <h1>Pipeline to Detect PHI for Deidentification (Augmented)</h1><div class="top-
 
 <h2 id="predicted-entities">Predicted Entities</h2>
 
+<p><code class="language-plaintext highlighter-rouge">LOCATION</code>, <code class="language-plaintext highlighter-rouge">CONTACT</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">NAME</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">ID</code>, <code class="language-plaintext highlighter-rouge">AGE</code></p>
+
 <p class="btn-box"><button class="button button-orange" disabled="">Live Demo</button>
 <button class="button button-orange" disabled="">Open in Colab</button>
 <a href="https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/ner_deid_augmented_pipeline_en_4.4.4_3.4_1686925406344.zip" class="button button-orange button-orange-trans arr button-icon hidden">Download</a>
diff --git a/2023/06/16/ner_deid_biobert_pipeline_en.html b/2023/06/16/ner_deid_biobert_pipeline_en.html
index 08c5c50ca3..8a54d2a557 100644
--- a/2023/06/16/ner_deid_biobert_pipeline_en.html
+++ b/2023/06/16/ner_deid_biobert_pipeline_en.html
@@ -11,7 +11,7 @@
     })(window,document,'script','dataLayer','GTM-59JLR64');</script>
 <!-- End Google Tag Manager --><title>Pipeline to Detect PHI in medical text (biobert) | ner_deid_biobert_pipeline | Healthcare NLP 4.4.4</title><meta property="og:title" content=""/>
 
-<meta name="description" content="DescriptionThis pretrained pipeline is built on the top of ner_deid_biobert model.Predicted EntitiesLive DemoOpen in ColabDownloadCopy S3 URIHow to use  PythonJSLScalaNLU  from sparknlp.pretrained import PretrainedPipelinepipeline = PretrainedPipeline("ner_deid_biobert_pipeline", "en", "clinical/models")text = '''A....">
+<meta name="description" content="DescriptionThis pretrained pipeline is built on the top of ner_deid_biobert model.Predicted EntitiesLOCATION, CONTACT, PROFESSION, NAME, DATE, ID, AGELive DemoOpen in ColabDownloadCopy S3 URIHow to use  PythonJSLScalaNLU  from sparknlp.pretrained import PretrainedPipelinepipeline = PretrainedPipeline("ner_deid_biobe...">
 <!-- <link rel="canonical" href="/2023/06/16/ner_deid_biobert_pipeline_en.html"> -->
 <link rel="canonical" href="/2023/06/16/ner_deid_biobert_pipeline_en.html"><link rel="alternate" type="application/rss+xml" title="Spark NLP" href="/feed.xml"><!-- start favicons snippet, use https://realfavicongenerator.net/ -->
 <!---->
@@ -512,6 +512,8 @@ <h1>Pipeline to Detect PHI in medical text (biobert)</h1><div class="top-subtitl
 
 <h2 id="predicted-entities">Predicted Entities</h2>
 
+<p><code class="language-plaintext highlighter-rouge">LOCATION</code>, <code class="language-plaintext highlighter-rouge">CONTACT</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">NAME</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">ID</code>, <code class="language-plaintext highlighter-rouge">AGE</code></p>
+
 <p class="btn-box"><button class="button button-orange" disabled="">Live Demo</button>
 <button class="button button-orange" disabled="">Open in Colab</button>
 <a href="https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/ner_deid_biobert_pipeline_en_4.4.4_3.4_1686925490594.zip" class="button button-orange button-orange-trans arr button-icon hidden">Download</a>
diff --git a/2023/06/16/ner_deid_enriched_pipeline_en.html b/2023/06/16/ner_deid_enriched_pipeline_en.html
index 1593840c2f..a4c788d5da 100644
--- a/2023/06/16/ner_deid_enriched_pipeline_en.html
+++ b/2023/06/16/ner_deid_enriched_pipeline_en.html
@@ -11,7 +11,7 @@
     })(window,document,'script','dataLayer','GTM-59JLR64');</script>
 <!-- End Google Tag Manager --><title>Pipeline to Detect PHI for Deidentification (Enriched) | ner_deid_enriched_pipeline | Healthcare NLP 4.4.4</title><meta property="og:title" content=""/>
 
-<meta name="description" content="DescriptionThis pretrained pipeline is built on the top of ner_deid_enriched model.Predicted EntitiesLive DemoOpen in ColabDownloadCopy S3 URIHow to use  PythonJSLScalaNLU  from sparknlp.pretrained import PretrainedPipelinepipeline = PretrainedPipeline("ner_deid_enriched_pipeline", "en", "clinical/models")text = '''...">
+<meta name="description" content="DescriptionThis pretrained pipeline is built on the top of ner_deid_enriched model.Predicted EntitiesMEDICALRECORD, ORGANIZATION, PROFESSION, HEALTHPLAN, DOCTOR, USERNAME, URL, LOCATION-OTHER, DEVICE, CITY, DATE, ZIP, STATE, PATIENT, COUNTRY, STREET, PHONE, HOSPITAL, EMAIL, NN, IDNUM, BIOID, FAX, AGELive DemoOpen in...">
 <!-- <link rel="canonical" href="/2023/06/16/ner_deid_enriched_pipeline_en.html"> -->
 <link rel="canonical" href="/2023/06/16/ner_deid_enriched_pipeline_en.html"><link rel="alternate" type="application/rss+xml" title="Spark NLP" href="/feed.xml"><!-- start favicons snippet, use https://realfavicongenerator.net/ -->
 <!---->
@@ -512,6 +512,8 @@ <h1>Pipeline to Detect PHI for Deidentification (Enriched)</h1><div class="top-s
 
 <h2 id="predicted-entities">Predicted Entities</h2>
 
+<p><code class="language-plaintext highlighter-rouge">MEDICALRECORD</code>, <code class="language-plaintext highlighter-rouge">ORGANIZATION</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">HEALTHPLAN</code>, <code class="language-plaintext highlighter-rouge">DOCTOR</code>, <code class="language-plaintext highlighter-rouge">USERNAME</code>, <code class="language-plaintext highlighter-rouge">URL</code>, <code class="language-plaintext highlighter-rouge">LOCATION-OTHER</code>, <code class="language-plaintext highlighter-rouge">DEVICE</code>, <code class="language-plaintext highlighter-rouge">CITY</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">ZIP</code>, <code class="language-plaintext highlighter-rouge">STATE</code>, <code class="language-plaintext highlighter-rouge">PATIENT</code>, <code class="language-plaintext highlighter-rouge">COUNTRY</code>, <code class="language-plaintext highlighter-rouge">STREET</code>, <code class="language-plaintext highlighter-rouge">PHONE</code>, <code class="language-plaintext highlighter-rouge">HOSPITAL</code>, <code class="language-plaintext highlighter-rouge">EMAIL</code>, <code class="language-plaintext highlighter-rouge">NN</code>, <code class="language-plaintext highlighter-rouge">IDNUM</code>, <code class="language-plaintext highlighter-rouge">BIOID</code>, <code class="language-plaintext highlighter-rouge">FAX</code>, <code class="language-plaintext highlighter-rouge">AGE</code></p>
+
 <p class="btn-box"><button class="button button-orange" disabled="">Live Demo</button>
 <button class="button button-orange" disabled="">Open in Colab</button>
 <a href="https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/ner_deid_enriched_pipeline_en_4.4.4_3.4_1686925618824.zip" class="button button-orange button-orange-trans arr button-icon hidden">Download</a>
diff --git a/2023/06/16/ner_deid_generic_glove_pipeline_en.html b/2023/06/16/ner_deid_generic_glove_pipeline_en.html
index b7301c82d8..718008d4f3 100644
--- a/2023/06/16/ner_deid_generic_glove_pipeline_en.html
+++ b/2023/06/16/ner_deid_generic_glove_pipeline_en.html
@@ -11,7 +11,7 @@
     })(window,document,'script','dataLayer','GTM-59JLR64');</script>
 <!-- End Google Tag Manager --><title>Pipeline to Detect PHI for Deidentification (Glove, 7 labels) | ner_deid_generic_glove_pipeline | Healthcare NLP 4.4.4</title><meta property="og:title" content=""/>
 
-<meta name="description" content="DescriptionThis pretrained pipeline is built on the top of ner_deid_generic_glove model.Predicted EntitiesLive DemoOpen in ColabDownloadCopy S3 URIHow to use  PythonJSLScalaNLU  from sparknlp.pretrained import PretrainedPipelinepipeline = PretrainedPipeline("ner_deid_generic_glove_pipeline", "en", "clinical/models")...">
+<meta name="description" content="DescriptionThis pretrained pipeline is built on the top of ner_deid_generic_glove model.Predicted EntitiesLOCATION, CONTACT, PROFESSION, NAME, DATE, ID, AGELive DemoOpen in ColabDownloadCopy S3 URIHow to use  PythonJSLScalaNLU  from sparknlp.pretrained import PretrainedPipelinepipeline = PretrainedPipeline("ner_deid...">
 <!-- <link rel="canonical" href="/2023/06/16/ner_deid_generic_glove_pipeline_en.html"> -->
 <link rel="canonical" href="/2023/06/16/ner_deid_generic_glove_pipeline_en.html"><link rel="alternate" type="application/rss+xml" title="Spark NLP" href="/feed.xml"><!-- start favicons snippet, use https://realfavicongenerator.net/ -->
 <!---->
@@ -512,6 +512,8 @@ <h1>Pipeline to Detect PHI for Deidentification (Glove, 7 labels)</h1><div class
 
 <h2 id="predicted-entities">Predicted Entities</h2>
 
+<p><code class="language-plaintext highlighter-rouge">LOCATION</code>, <code class="language-plaintext highlighter-rouge">CONTACT</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">NAME</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">ID</code>, <code class="language-plaintext highlighter-rouge">AGE</code></p>
+
 <p class="btn-box"><button class="button button-orange" disabled="">Live Demo</button>
 <button class="button button-orange" disabled="">Open in Colab</button>
 <a href="https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/ner_deid_generic_glove_pipeline_en_4.4.4_3.4_1686938392358.zip" class="button button-orange button-orange-trans arr button-icon hidden">Download</a>
diff --git a/2023/06/16/ner_deid_large_pipeline_en.html b/2023/06/16/ner_deid_large_pipeline_en.html
index 0d49f093d9..f14636343d 100644
--- a/2023/06/16/ner_deid_large_pipeline_en.html
+++ b/2023/06/16/ner_deid_large_pipeline_en.html
@@ -11,7 +11,7 @@
     })(window,document,'script','dataLayer','GTM-59JLR64');</script>
 <!-- End Google Tag Manager --><title>Pipeline to Detect PHI (Deidentification) | ner_deid_large_pipeline | Healthcare NLP 4.4.4</title><meta property="og:title" content=""/>
 
-<meta name="description" content="DescriptionThis pretrained pipeline is built on the top of ner_deid_large model.Predicted EntitiesLive DemoOpen in ColabDownloadCopy S3 URIHow to use  PythonJSLScalaNLU  from sparknlp.pretrained import PretrainedPipelinepipeline = PretrainedPipeline("ner_deid_large_pipeline", "en", "clinical/models")text = '''HISTOR...">
+<meta name="description" content="DescriptionThis pretrained pipeline is built on the top of ner_deid_large model.Predicted EntitiesLOCATION, CONTACT, PROFESSION, HEALTHPLAN, NAME, DATE, ID, URL, AGELive DemoOpen in ColabDownloadCopy S3 URIHow to use  PythonJSLScalaNLU  from sparknlp.pretrained import PretrainedPipelinepipeline = PretrainedPipeline(...">
 <!-- <link rel="canonical" href="/2023/06/16/ner_deid_large_pipeline_en.html"> -->
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 <!---->
@@ -512,6 +512,8 @@ <h1>Pipeline to Detect PHI (Deidentification)</h1><div class="top-subtitle mont"
 
 <h2 id="predicted-entities">Predicted Entities</h2>
 
+<p><code class="language-plaintext highlighter-rouge">LOCATION</code>, <code class="language-plaintext highlighter-rouge">CONTACT</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">HEALTHPLAN</code>, <code class="language-plaintext highlighter-rouge">NAME</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">ID</code>, <code class="language-plaintext highlighter-rouge">URL</code>, <code class="language-plaintext highlighter-rouge">AGE</code></p>
+
 <p class="btn-box"><button class="button button-orange" disabled="">Live Demo</button>
 <button class="button button-orange" disabled="">Open in Colab</button>
 <a href="https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/ner_deid_large_pipeline_en_4.4.4_3.4_1686926072179.zip" class="button button-orange button-orange-trans arr button-icon hidden">Download</a>
diff --git a/2023/06/16/ner_deid_sd_large_pipeline_en.html b/2023/06/16/ner_deid_sd_large_pipeline_en.html
index 4b684fec95..3ed66a5c1c 100644
--- a/2023/06/16/ner_deid_sd_large_pipeline_en.html
+++ b/2023/06/16/ner_deid_sd_large_pipeline_en.html
@@ -11,7 +11,7 @@
     })(window,document,'script','dataLayer','GTM-59JLR64');</script>
 <!-- End Google Tag Manager --><title>Pipeline to Detect PHI in text (ner_deid_sd_larg) | ner_deid_sd_large_pipeline | Healthcare NLP 4.4.4</title><meta property="og:title" content=""/>
 
-<meta name="description" content="DescriptionThis pretrained pipeline is built on the top of ner_deid_sd_large model.Predicted EntitiesLive DemoOpen in ColabDownloadCopy S3 URIHow to use  PythonJSLScalaNLU  from sparknlp.pretrained import PretrainedPipelinepipeline = PretrainedPipeline("ner_deid_sd_large_pipeline", "en", "clinical/models")text = '''...">
+<meta name="description" content="DescriptionThis pretrained pipeline is built on the top of ner_deid_sd_large model.Predicted EntitiesLOCATION, CONTACT, PROFESSION, NAME, DATE, ID, AGELive DemoOpen in ColabDownloadCopy S3 URIHow to use  PythonJSLScalaNLU  from sparknlp.pretrained import PretrainedPipelinepipeline = PretrainedPipeline("ner_deid_sd_l...">
 <!-- <link rel="canonical" href="/2023/06/16/ner_deid_sd_large_pipeline_en.html"> -->
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 <!---->
@@ -512,6 +512,8 @@ <h1>Pipeline to Detect PHI in text (ner_deid_sd_larg)</h1><div class="top-subtit
 
 <h2 id="predicted-entities">Predicted Entities</h2>
 
+<p><code class="language-plaintext highlighter-rouge">LOCATION</code>, <code class="language-plaintext highlighter-rouge">CONTACT</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">NAME</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">ID</code>, <code class="language-plaintext highlighter-rouge">AGE</code></p>
+
 <p class="btn-box"><button class="button button-orange" disabled="">Live Demo</button>
 <button class="button button-orange" disabled="">Open in Colab</button>
 <a href="https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/ner_deid_sd_large_pipeline_en_4.4.4_3.4_1686926212460.zip" class="button button-orange button-orange-trans arr button-icon hidden">Download</a>
diff --git a/2023/06/16/ner_deid_sd_pipeline_en.html b/2023/06/16/ner_deid_sd_pipeline_en.html
index 1c8c2d97a1..9d85e1cd34 100644
--- a/2023/06/16/ner_deid_sd_pipeline_en.html
+++ b/2023/06/16/ner_deid_sd_pipeline_en.html
@@ -11,7 +11,7 @@
     })(window,document,'script','dataLayer','GTM-59JLR64');</script>
 <!-- End Google Tag Manager --><title>Pipeline to Detect PHI in text (base) | ner_deid_sd_pipeline | Healthcare NLP 4.4.4</title><meta property="og:title" content=""/>
 
-<meta name="description" content="DescriptionThis pretrained pipeline is built on the top of ner_deid_sd model.Predicted EntitiesLive DemoOpen in ColabDownloadCopy S3 URIHow to use  PythonJSLScalaNLU  from sparknlp.pretrained import PretrainedPipelinepipeline = PretrainedPipeline("ner_deid_sd_pipeline", "en", "clinical/models")text = '''Record date ...">
+<meta name="description" content="DescriptionThis pretrained pipeline is built on the top of ner_deid_sd model.Predicted EntitiesNAME, LOCATION, DATE, AGE, PROFESSION, ID, CONTACTLive DemoOpen in ColabDownloadCopy S3 URIHow to use  PythonJSLScalaNLU  from sparknlp.pretrained import PretrainedPipelinepipeline = PretrainedPipeline("ner_deid_sd_pipelin...">
 <!-- <link rel="canonical" href="/2023/06/16/ner_deid_sd_pipeline_en.html"> -->
 <link rel="canonical" href="/2023/06/16/ner_deid_sd_pipeline_en.html"><link rel="alternate" type="application/rss+xml" title="Spark NLP" href="/feed.xml"><!-- start favicons snippet, use https://realfavicongenerator.net/ -->
 <!---->
@@ -512,6 +512,8 @@ <h1>Pipeline to Detect PHI in text (base)</h1><div class="top-subtitle mont"></d
 
 <h2 id="predicted-entities">Predicted Entities</h2>
 
+<p><code class="language-plaintext highlighter-rouge">NAME</code>, <code class="language-plaintext highlighter-rouge">LOCATION</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">AGE</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">ID</code>, <code class="language-plaintext highlighter-rouge">CONTACT</code></p>
+
 <p class="btn-box"><button class="button button-orange" disabled="">Live Demo</button>
 <button class="button button-orange" disabled="">Open in Colab</button>
 <a href="https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/ner_deid_sd_pipeline_en_4.4.4_3.4_1686926358086.zip" class="button button-orange button-orange-trans arr button-icon hidden">Download</a>
@@ -792,7 +794,7 @@ <h2 id="included-models">Included Models</h2>
 
 }
 
-</style><div class="article__section-navigator clearfix"><div class="previous nav_link"><span>PREVIOUS</span><a href="/2023/06/16/ner_deid_sd_large_pipeline_en.html">Pipeline to Detect PHI in text (ner_deid_sd_larg)</a></div><div class="next nav_link"><span>NEXT</span><a href="/2023/06/16/ner_deid_subentity_augmented_i2b2_pipeline_en.html">Pipeline to Detect PHI for deidentification purposes</a></div></div></div>
+</style><div class="article__section-navigator clearfix"><div class="previous nav_link"><span>PREVIOUS</span><a href="/2023/06/16/ner_deid_sd_large_pipeline_en.html">Pipeline to Detect PHI in text (ner_deid_sd_larg)</a></div><div class="next nav_link"><span>NEXT</span><a href="/2023/06/16/ner_deid_subentity_augmented_pipeline_en.html">Pipeline to Detect PHI for Deidentification (Subentity- Augmented)</a></div></div></div>
 
 </div>
 </div>
diff --git a/2023/06/16/ner_deid_subentity_augmented_pipeline_en.html b/2023/06/16/ner_deid_subentity_augmented_pipeline_en.html
index 61e144868e..085f5e1627 100644
--- a/2023/06/16/ner_deid_subentity_augmented_pipeline_en.html
+++ b/2023/06/16/ner_deid_subentity_augmented_pipeline_en.html
@@ -11,7 +11,7 @@
     })(window,document,'script','dataLayer','GTM-59JLR64');</script>
 <!-- End Google Tag Manager --><title>Pipeline to Detect PHI for Deidentification (Subentity- Augmented) | ner_deid_subentity_augmented_pipeline | Healthcare NLP 4.4.4</title><meta property="og:title" content=""/>
 
-<meta name="description" content="DescriptionThis pretrained pipeline is built on the top of ner_deid_subentity_augmented model.Predicted EntitiesLive DemoOpen in ColabDownloadCopy S3 URIHow to use  PythonJSLScalaNLU  from sparknlp.pretrained import PretrainedPipelinepipeline = PretrainedPipeline("ner_deid_subentity_augmented_pipeline", "en", "clini...">
+<meta name="description" content="DescriptionThis pretrained pipeline is built on the top of ner_deid_subentity_augmented model.Predicted EntitiesMEDICALRECORD, ORGANIZATION, PROFESSION, HEALTHPLAN, DOCTOR, USERNAME, LOCATION-OTHER, URL, DEVICE, CITY, DATE, ZIP, STATE, PATIENT, COUNTRY, STREET, PHONE, HOSPITAL, EMAIL, IDNUM, BIOID, FAX, AGELive Demo...">
 <!-- <link rel="canonical" href="/2023/06/16/ner_deid_subentity_augmented_pipeline_en.html"> -->
 <link rel="canonical" href="/2023/06/16/ner_deid_subentity_augmented_pipeline_en.html"><link rel="alternate" type="application/rss+xml" title="Spark NLP" href="/feed.xml"><!-- start favicons snippet, use https://realfavicongenerator.net/ -->
 <!---->
@@ -512,6 +512,8 @@ <h1>Pipeline to Detect PHI for Deidentification (Subentity- Augmented)</h1><div
 
 <h2 id="predicted-entities">Predicted Entities</h2>
 
+<p><code class="language-plaintext highlighter-rouge">MEDICALRECORD</code>, <code class="language-plaintext highlighter-rouge">ORGANIZATION</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">HEALTHPLAN</code>, <code class="language-plaintext highlighter-rouge">DOCTOR</code>, <code class="language-plaintext highlighter-rouge">USERNAME</code>, <code class="language-plaintext highlighter-rouge">LOCATION-OTHER</code>, <code class="language-plaintext highlighter-rouge">URL</code>, <code class="language-plaintext highlighter-rouge">DEVICE</code>, <code class="language-plaintext highlighter-rouge">CITY</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">ZIP</code>, <code class="language-plaintext highlighter-rouge">STATE</code>, <code class="language-plaintext highlighter-rouge">PATIENT</code>, <code class="language-plaintext highlighter-rouge">COUNTRY</code>, <code class="language-plaintext highlighter-rouge">STREET</code>, <code class="language-plaintext highlighter-rouge">PHONE</code>, <code class="language-plaintext highlighter-rouge">HOSPITAL</code>, <code class="language-plaintext highlighter-rouge">EMAIL</code>, <code class="language-plaintext highlighter-rouge">IDNUM</code>, <code class="language-plaintext highlighter-rouge">BIOID</code>, <code class="language-plaintext highlighter-rouge">FAX</code>, <code class="language-plaintext highlighter-rouge">AGE</code></p>
+
 <p class="btn-box"><button class="button button-orange" disabled="">Live Demo</button>
 <button class="button button-orange" disabled="">Open in Colab</button>
 <a href="https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/ner_deid_subentity_augmented_pipeline_en_4.4.4_3.4_1686926636235.zip" class="button button-orange button-orange-trans arr button-icon hidden">Download</a>
@@ -792,7 +794,7 @@ <h2 id="included-models">Included Models</h2>
 
 }
 
-</style><div class="article__section-navigator clearfix"><div class="previous nav_link"><span>PREVIOUS</span><a href="/2023/06/16/ner_deid_subentity_augmented_i2b2_pipeline_en.html">Pipeline to Detect PHI for deidentification purposes</a></div><div class="next nav_link"><span>NEXT</span><a href="/2023/06/16/ner_deid_subentity_bert_pipeline_ro.html">Pipeline to Detect PHI for Deidentification in Romanian (BERT)</a></div></div></div>
+</style><div class="article__section-navigator clearfix"><div class="previous nav_link"><span>PREVIOUS</span><a href="/2023/06/16/ner_deid_sd_pipeline_en.html">Pipeline to Detect PHI in text (base)</a></div><div class="next nav_link"><span>NEXT</span><a href="/2023/06/16/ner_deid_subentity_bert_pipeline_ro.html">Pipeline to Detect PHI for Deidentification in Romanian (BERT)</a></div></div></div>
 
 </div>
 </div>
diff --git a/2023/06/16/ner_deid_synthetic_pipeline_en.html b/2023/06/16/ner_deid_synthetic_pipeline_en.html
index 477da40b56..f45a20ff71 100644
--- a/2023/06/16/ner_deid_synthetic_pipeline_en.html
+++ b/2023/06/16/ner_deid_synthetic_pipeline_en.html
@@ -11,7 +11,7 @@
     })(window,document,'script','dataLayer','GTM-59JLR64');</script>
 <!-- End Google Tag Manager --><title>Pipeline to Detect PHI for Deidentification (Augmented) | ner_deid_synthetic_pipeline | Healthcare NLP 4.4.4</title><meta property="og:title" content=""/>
 
-<meta name="description" content="DescriptionThis pretrained pipeline is built on the top of ner_deid_synthetic model.Predicted EntitiesLive DemoOpen in ColabDownloadCopy S3 URIHow to use  PythonJSLScalaNLU  from sparknlp.pretrained import PretrainedPipelinepipeline = PretrainedPipeline("ner_deid_synthetic_pipeline", "en", "clinical/models")text = "...">
+<meta name="description" content="DescriptionThis pretrained pipeline is built on the top of ner_deid_synthetic model.Predicted EntitiesLOCATION, CONTACT, PROFESSION, NAME, DATE, ID, AGELive DemoOpen in ColabDownloadCopy S3 URIHow to use  PythonJSLScalaNLU  from sparknlp.pretrained import PretrainedPipelinepipeline = PretrainedPipeline("ner_deid_syn...">
 <!-- <link rel="canonical" href="/2023/06/16/ner_deid_synthetic_pipeline_en.html"> -->
 <link rel="canonical" href="/2023/06/16/ner_deid_synthetic_pipeline_en.html"><link rel="alternate" type="application/rss+xml" title="Spark NLP" href="/feed.xml"><!-- start favicons snippet, use https://realfavicongenerator.net/ -->
 <!---->
@@ -512,6 +512,8 @@ <h1>Pipeline to Detect PHI for Deidentification (Augmented)</h1><div class="top-
 
 <h2 id="predicted-entities">Predicted Entities</h2>
 
+<p><code class="language-plaintext highlighter-rouge">LOCATION</code>, <code class="language-plaintext highlighter-rouge">CONTACT</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">NAME</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">ID</code>, <code class="language-plaintext highlighter-rouge">AGE</code></p>
+
 <p class="btn-box"><button class="button button-orange" disabled="">Live Demo</button>
 <button class="button button-orange" disabled="">Open in Colab</button>
 <a href="https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/ner_deid_synthetic_pipeline_en_4.4.4_3.4_1686938836871.zip" class="button button-orange button-orange-trans arr button-icon hidden">Download</a>
diff --git a/2023/06/16/ner_deidentify_dl_pipeline_en.html b/2023/06/16/ner_deidentify_dl_pipeline_en.html
index 68610826f3..e33ab16997 100644
--- a/2023/06/16/ner_deidentify_dl_pipeline_en.html
+++ b/2023/06/16/ner_deidentify_dl_pipeline_en.html
@@ -11,7 +11,7 @@
     })(window,document,'script','dataLayer','GTM-59JLR64');</script>
 <!-- End Google Tag Manager --><title>Pipeline to Detect PHI for Deidentification (ner_deidentification_dl) | ner_deidentify_dl_pipeline | Healthcare NLP 4.4.4</title><meta property="og:title" content=""/>
 
-<meta name="description" content="DescriptionThis pretrained pipeline is built on the top of ner_deidentify_dl model.Predicted EntitiesLive DemoOpen in ColabDownloadCopy S3 URIHow to use  PythonJSLScalaNLU  from sparknlp.pretrained import PretrainedPipelinepipeline = PretrainedPipeline("ner_deidentify_dl_pipeline", "en", "clinical/models")text = '''...">
+<meta name="description" content="DescriptionThis pretrained pipeline is built on the top of ner_deidentify_dl model.Predicted EntitiesDATE, PATIENT, MEDICALRECORD, DOCTOR, AGE, HOSPITAL, STATE, CITY, PROFESSION, STREET, ZIP, PHONE, COUNTRY, ORGANIZATION, FAX, IDNUM, HEALTHPLAN, USERNAME, EMAIL, BIOID, LOCATION-OTHER, DEVICE, URL, IDLive DemoOpen in...">
 <!-- <link rel="canonical" href="/2023/06/16/ner_deidentify_dl_pipeline_en.html"> -->
 <link rel="canonical" href="/2023/06/16/ner_deidentify_dl_pipeline_en.html"><link rel="alternate" type="application/rss+xml" title="Spark NLP" href="/feed.xml"><!-- start favicons snippet, use https://realfavicongenerator.net/ -->
 <!---->
@@ -512,6 +512,8 @@ <h1>Pipeline to Detect PHI for Deidentification (ner_deidentification_dl)</h1><d
 
 <h2 id="predicted-entities">Predicted Entities</h2>
 
+<p><code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">PATIENT</code>, <code class="language-plaintext highlighter-rouge">MEDICALRECORD</code>, <code class="language-plaintext highlighter-rouge">DOCTOR</code>, <code class="language-plaintext highlighter-rouge">AGE</code>, <code class="language-plaintext highlighter-rouge">HOSPITAL</code>, <code class="language-plaintext highlighter-rouge">STATE</code>, <code class="language-plaintext highlighter-rouge">CITY</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">STREET</code>, <code class="language-plaintext highlighter-rouge">ZIP</code>, <code class="language-plaintext highlighter-rouge">PHONE</code>, <code class="language-plaintext highlighter-rouge">COUNTRY</code>, <code class="language-plaintext highlighter-rouge">ORGANIZATION</code>, <code class="language-plaintext highlighter-rouge">FAX</code>, <code class="language-plaintext highlighter-rouge">IDNUM</code>, <code class="language-plaintext highlighter-rouge">HEALTHPLAN</code>, <code class="language-plaintext highlighter-rouge">USERNAME</code>, <code class="language-plaintext highlighter-rouge">EMAIL</code>, <code class="language-plaintext highlighter-rouge">BIOID</code>, <code class="language-plaintext highlighter-rouge">LOCATION-OTHER</code>, <code class="language-plaintext highlighter-rouge">DEVICE</code>, <code class="language-plaintext highlighter-rouge">URL</code>, <code class="language-plaintext highlighter-rouge">ID</code></p>
+
 <p class="btn-box"><button class="button button-orange" disabled="">Live Demo</button>
 <button class="button button-orange" disabled="">Open in Colab</button>
 <a href="https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/ner_deidentify_dl_pipeline_en_4.4.4_3.4_1686926947437.zip" class="button button-orange button-orange-trans arr button-icon hidden">Download</a>
diff --git a/2023/06/17/clinical_deidentification_augmented_es.html b/2023/06/17/clinical_deidentification_augmented_es.html
index 1875a0f25e..1be7f30231 100644
--- a/2023/06/17/clinical_deidentification_augmented_es.html
+++ b/2023/06/17/clinical_deidentification_augmented_es.html
@@ -510,10 +510,12 @@ <h1>Clinical Deidentification (Spanish, augmented)</h1><div class="top-subtitle
 
 <p>This pipeline is trained with sciwiki_300d embeddings and can be used to deidentify PHI information from medical texts in Spanish. It differs from the previous <code class="language-plaintext highlighter-rouge">clinical_deidentificaiton</code> pipeline in that it includes the <code class="language-plaintext highlighter-rouge">ner_deid_subentity_augmented</code> NER model and some improvements in ContextualParsers and RegexMatchers.</p>
 
-<p>The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask, fake or obfuscate the following entities: <code class="language-plaintext highlighter-rouge">AGE</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">EMAIL</code>, <code class="language-plaintext highlighter-rouge">USERNAME</code>, <code class="language-plaintext highlighter-rouge">STREET</code>, <code class="language-plaintext highlighter-rouge">COUNTRY</code>, <code class="language-plaintext highlighter-rouge">CITY</code>, <code class="language-plaintext highlighter-rouge">DOCTOR</code>, <code class="language-plaintext highlighter-rouge">HOSPITAL</code>, <code class="language-plaintext highlighter-rouge">PATIENT</code>, <code class="language-plaintext highlighter-rouge">URL</code>, <code class="language-plaintext highlighter-rouge">MEDICALRECORD</code>, <code class="language-plaintext highlighter-rouge">IDNUM</code>, <code class="language-plaintext highlighter-rouge">ORGANIZATION</code>, <code class="language-plaintext highlighter-rouge">PHONE</code>, <code class="language-plaintext highlighter-rouge">ZIP</code>, <code class="language-plaintext highlighter-rouge">ACCOUNT</code>, <code class="language-plaintext highlighter-rouge">SSN</code>, <code class="language-plaintext highlighter-rouge">PLATE</code>, <code class="language-plaintext highlighter-rouge">SEX</code> and <code class="language-plaintext highlighter-rouge">IPADDR</code></p>
+<p>The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask, fake or obfuscate the following entities: <code class="language-plaintext highlighter-rouge">MEDICALRECORD</code>, <code class="language-plaintext highlighter-rouge">ORGANIZATION</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">DOCTOR</code>, <code class="language-plaintext highlighter-rouge">USERNAME</code>, <code class="language-plaintext highlighter-rouge">ID</code>, <code class="language-plaintext highlighter-rouge">CITY</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">PATIENT</code>, <code class="language-plaintext highlighter-rouge">SEX</code>, <code class="language-plaintext highlighter-rouge">COUNTRY</code>, <code class="language-plaintext highlighter-rouge">ZIP</code>, <code class="language-plaintext highlighter-rouge">STREET</code>, <code class="language-plaintext highlighter-rouge">PHONE</code>, <code class="language-plaintext highlighter-rouge">HOSPITAL</code>, <code class="language-plaintext highlighter-rouge">EMAIL</code>, <code class="language-plaintext highlighter-rouge">AGE</code>, <code class="language-plaintext highlighter-rouge">SSN</code>, <code class="language-plaintext highlighter-rouge">IDNUM</code></p>
 
 <h2 id="predicted-entities">Predicted Entities</h2>
 
+<p><code class="language-plaintext highlighter-rouge">MEDICALRECORD</code>, <code class="language-plaintext highlighter-rouge">ORGANIZATION</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">DOCTOR</code>, <code class="language-plaintext highlighter-rouge">USERNAME</code>, <code class="language-plaintext highlighter-rouge">ID</code>, <code class="language-plaintext highlighter-rouge">CITY</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">PATIENT</code>, <code class="language-plaintext highlighter-rouge">SEX</code>, <code class="language-plaintext highlighter-rouge">COUNTRY</code>, <code class="language-plaintext highlighter-rouge">ZIP</code>, <code class="language-plaintext highlighter-rouge">STREET</code>, <code class="language-plaintext highlighter-rouge">PHONE</code>, <code class="language-plaintext highlighter-rouge">HOSPITAL</code>, <code class="language-plaintext highlighter-rouge">EMAIL</code>, <code class="language-plaintext highlighter-rouge">AGE</code>, <code class="language-plaintext highlighter-rouge">SSN</code>, <code class="language-plaintext highlighter-rouge">IDNUM</code></p>
+
 <p class="btn-box"><button class="button button-orange" disabled="">Live Demo</button>
 <button class="button button-orange" disabled="">Open in Colab</button>
 <a href="https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_augmented_es_4.4.4_3.0_1686979474784.zip" class="button button-orange button-orange-trans arr button-icon hidden">Download</a>
diff --git a/2023/06/17/clinical_deidentification_glove_augmented_en.html b/2023/06/17/clinical_deidentification_glove_augmented_en.html
index f62fcb356d..cbbedc42bb 100644
--- a/2023/06/17/clinical_deidentification_glove_augmented_en.html
+++ b/2023/06/17/clinical_deidentification_glove_augmented_en.html
@@ -11,7 +11,7 @@
     })(window,document,'script','dataLayer','GTM-59JLR64');</script>
 <!-- End Google Tag Manager --><title>Clinical Deidentification (English, Glove, Augmented) | clinical_deidentification_glove_augmented | Healthcare NLP 4.4.4</title><meta property="og:title" content=""/>
 
-<meta name="description" content="DescriptionThis pipeline is trained with lightweight glove_100d embeddings and can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate AGE, CONTACT, DATE, ID, LOCATION, NAME, PROFESSION, CITY, COUNTRY, ...">
+<meta name="description" content="DescriptionThis pipeline is trained with lightweight glove_100d embeddings and can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate LOCATION, CONTACT, PROFESSION, NAME, DATE, ID, AGE, MEDICALRECORD, ...">
 <!-- <link rel="canonical" href="/2023/06/17/clinical_deidentification_glove_augmented_en.html"> -->
 <link rel="canonical" href="/2023/06/17/clinical_deidentification_glove_augmented_en.html"><link rel="alternate" type="application/rss+xml" title="Spark NLP" href="/feed.xml"><!-- start favicons snippet, use https://realfavicongenerator.net/ -->
 <!---->
@@ -508,12 +508,14 @@ <h1>Clinical Deidentification (English, Glove, Augmented)</h1><div class="top-su
 
 <!-- end custom article top snippet --><div class="article__content" itemprop="articleBody"><h2 id="description">Description</h2>
 
-<p>This pipeline is trained with lightweight <code class="language-plaintext highlighter-rouge">glove_100d</code> embeddings and can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate <code class="language-plaintext highlighter-rouge">AGE</code>, <code class="language-plaintext highlighter-rouge">CONTACT</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">ID</code>, <code class="language-plaintext highlighter-rouge">LOCATION</code>, <code class="language-plaintext highlighter-rouge">NAME</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">CITY</code>, <code class="language-plaintext highlighter-rouge">COUNTRY</code>, <code class="language-plaintext highlighter-rouge">DOCTOR</code>, <code class="language-plaintext highlighter-rouge">HOSPITAL</code>, <code class="language-plaintext highlighter-rouge">IDNUM</code>, <code class="language-plaintext highlighter-rouge">MEDICALRECORD</code>, <code class="language-plaintext highlighter-rouge">ORGANIZATION</code>, <code class="language-plaintext highlighter-rouge">PATIENT</code>, <code class="language-plaintext highlighter-rouge">PHONE</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>,  <code class="language-plaintext highlighter-rouge">STREET</code>, <code class="language-plaintext highlighter-rouge">USERNAME</code>, <code class="language-plaintext highlighter-rouge">ZIP</code>, <code class="language-plaintext highlighter-rouge">ACCOUNT</code>, <code class="language-plaintext highlighter-rouge">LICENSE</code>, <code class="language-plaintext highlighter-rouge">VIN</code>, <code class="language-plaintext highlighter-rouge">SSN</code>, <code class="language-plaintext highlighter-rouge">DLN</code>, <code class="language-plaintext highlighter-rouge">PLATE</code>, <code class="language-plaintext highlighter-rouge">IPADDR</code> entities.</p>
+<p>This pipeline is trained with lightweight <code class="language-plaintext highlighter-rouge">glove_100d</code> embeddings and can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate <code class="language-plaintext highlighter-rouge">LOCATION</code>, <code class="language-plaintext highlighter-rouge">CONTACT</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">NAME</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">ID</code>, <code class="language-plaintext highlighter-rouge">AGE</code>, <code class="language-plaintext highlighter-rouge">MEDICALRECORD</code>, <code class="language-plaintext highlighter-rouge">ORGANIZATION</code>, <code class="language-plaintext highlighter-rouge">HEALTHPLAN</code>, <code class="language-plaintext highlighter-rouge">DOCTOR</code>, <code class="language-plaintext highlighter-rouge">USERNAME</code>, <code class="language-plaintext highlighter-rouge">URL</code>, <code class="language-plaintext highlighter-rouge">LOCATION-OTHER</code>, <code class="language-plaintext highlighter-rouge">DEVICE</code>, <code class="language-plaintext highlighter-rouge">CITY</code>, <code class="language-plaintext highlighter-rouge">ZIP</code>, <code class="language-plaintext highlighter-rouge">STATE</code>, <code class="language-plaintext highlighter-rouge">PATIENT</code>, <code class="language-plaintext highlighter-rouge">COUNTRY</code>, <code class="language-plaintext highlighter-rouge">STREET</code>, <code class="language-plaintext highlighter-rouge">PHONE</code>, <code class="language-plaintext highlighter-rouge">HOSPITAL</code>, <code class="language-plaintext highlighter-rouge">EMAIL</code>, <code class="language-plaintext highlighter-rouge">IDNUM</code>, <code class="language-plaintext highlighter-rouge">BIOID</code>, <code class="language-plaintext highlighter-rouge">FAX</code>, <code class="language-plaintext highlighter-rouge">SSN</code>, <code class="language-plaintext highlighter-rouge">ACCOUNT</code>, <code class="language-plaintext highlighter-rouge">DLN</code>, <code class="language-plaintext highlighter-rouge">PLATE</code>, <code class="language-plaintext highlighter-rouge">LICENSE</code> entities.</p>
 
 <p>It’s different to <code class="language-plaintext highlighter-rouge">clinical_deidentification_glove</code> in the way it manages PHONE and PATIENT, having apart from the NER, some rules in Contextual Parser components.</p>
 
 <h2 id="predicted-entities">Predicted Entities</h2>
 
+<p><code class="language-plaintext highlighter-rouge">LOCATION</code>, <code class="language-plaintext highlighter-rouge">CONTACT</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">NAME</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">ID</code>, <code class="language-plaintext highlighter-rouge">AGE</code>, <code class="language-plaintext highlighter-rouge">MEDICALRECORD</code>, <code class="language-plaintext highlighter-rouge">ORGANIZATION</code>, <code class="language-plaintext highlighter-rouge">HEALTHPLAN</code>, <code class="language-plaintext highlighter-rouge">DOCTOR</code>, <code class="language-plaintext highlighter-rouge">USERNAME</code>, <code class="language-plaintext highlighter-rouge">URL</code>, <code class="language-plaintext highlighter-rouge">LOCATION-OTHER</code>, <code class="language-plaintext highlighter-rouge">DEVICE</code>, <code class="language-plaintext highlighter-rouge">CITY</code>, <code class="language-plaintext highlighter-rouge">ZIP</code>, <code class="language-plaintext highlighter-rouge">STATE</code>, <code class="language-plaintext highlighter-rouge">PATIENT</code>, <code class="language-plaintext highlighter-rouge">COUNTRY</code>, <code class="language-plaintext highlighter-rouge">STREET</code>, <code class="language-plaintext highlighter-rouge">PHONE</code>, <code class="language-plaintext highlighter-rouge">HOSPITAL</code>, <code class="language-plaintext highlighter-rouge">EMAIL</code>, <code class="language-plaintext highlighter-rouge">IDNUM</code>, <code class="language-plaintext highlighter-rouge">BIOID</code>, <code class="language-plaintext highlighter-rouge">FAX</code>, <code class="language-plaintext highlighter-rouge">SSN</code>, <code class="language-plaintext highlighter-rouge">ACCOUNT</code>, <code class="language-plaintext highlighter-rouge">DLN</code>, <code class="language-plaintext highlighter-rouge">PLATE</code>, <code class="language-plaintext highlighter-rouge">LICENSE</code></p>
+
 <p class="btn-box"><button class="button button-orange" disabled="">Live Demo</button>
 <button class="button button-orange" disabled="">Open in Colab</button>
 <a href="https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_glove_augmented_en_4.4.4_3.0_1686988237205.zip" class="button button-orange button-orange-trans arr button-icon hidden">Download</a>
diff --git a/2023/06/17/clinical_deidentification_glove_en.html b/2023/06/17/clinical_deidentification_glove_en.html
index c6e9893a65..42cd4746dd 100644
--- a/2023/06/17/clinical_deidentification_glove_en.html
+++ b/2023/06/17/clinical_deidentification_glove_en.html
@@ -11,7 +11,7 @@
     })(window,document,'script','dataLayer','GTM-59JLR64');</script>
 <!-- End Google Tag Manager --><title>Clinical Deidentification (glove) | clinical_deidentification_glove | Healthcare NLP 4.4.4</title><meta property="og:title" content=""/>
 
-<meta name="description" content="DescriptionThis pipeline is trained with lightweight glove_100d embeddings and can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate AGE, CONTACT, DATE, ID, LOCATION, NAME, PROFESSION, CITY, COUNTRY, ...">
+<meta name="description" content="DescriptionThis pipeline is trained with lightweight glove_100d embeddings and can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate LOCATION, CONTACT, PROFESSION, NAME, DATE, ID, AGE, MEDICALRECORD, ...">
 <!-- <link rel="canonical" href="/2023/06/17/clinical_deidentification_glove_en.html"> -->
 <link rel="canonical" href="/2023/06/17/clinical_deidentification_glove_en.html"><link rel="alternate" type="application/rss+xml" title="Spark NLP" href="/feed.xml"><!-- start favicons snippet, use https://realfavicongenerator.net/ -->
 <!---->
@@ -508,10 +508,12 @@ <h1>Clinical Deidentification (glove)</h1><div class="top-subtitle mont"></div><
 
 <!-- end custom article top snippet --><div class="article__content" itemprop="articleBody"><h2 id="description">Description</h2>
 
-<p>This pipeline is trained with lightweight <code class="language-plaintext highlighter-rouge">glove_100d</code> embeddings and can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate <code class="language-plaintext highlighter-rouge">AGE</code>, <code class="language-plaintext highlighter-rouge">CONTACT</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">ID</code>, <code class="language-plaintext highlighter-rouge">LOCATION</code>, <code class="language-plaintext highlighter-rouge">NAME</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">CITY</code>, <code class="language-plaintext highlighter-rouge">COUNTRY</code>, <code class="language-plaintext highlighter-rouge">DOCTOR</code>, <code class="language-plaintext highlighter-rouge">HOSPITAL</code>, <code class="language-plaintext highlighter-rouge">IDNUM</code>, <code class="language-plaintext highlighter-rouge">MEDICALRECORD</code>, <code class="language-plaintext highlighter-rouge">ORGANIZATION</code>, <code class="language-plaintext highlighter-rouge">PATIENT</code>, <code class="language-plaintext highlighter-rouge">PHONE</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">STREET</code>, <code class="language-plaintext highlighter-rouge">USERNAME</code>, <code class="language-plaintext highlighter-rouge">ZIP</code>, <code class="language-plaintext highlighter-rouge">ACCOUNT</code>, <code class="language-plaintext highlighter-rouge">LICENSE</code>, <code class="language-plaintext highlighter-rouge">VIN</code>, <code class="language-plaintext highlighter-rouge">SSN</code>, <code class="language-plaintext highlighter-rouge">DLN</code>, <code class="language-plaintext highlighter-rouge">PLATE</code>, <code class="language-plaintext highlighter-rouge">IPADDR</code>, <code class="language-plaintext highlighter-rouge">EMAIL</code> entities.</p>
+<p>This pipeline is trained with lightweight <code class="language-plaintext highlighter-rouge">glove_100d</code> embeddings and can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate <code class="language-plaintext highlighter-rouge">LOCATION</code>, <code class="language-plaintext highlighter-rouge">CONTACT</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">NAME</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">ID</code>, <code class="language-plaintext highlighter-rouge">AGE</code>, <code class="language-plaintext highlighter-rouge">MEDICALRECORD</code>, <code class="language-plaintext highlighter-rouge">ORGANIZATION</code>, <code class="language-plaintext highlighter-rouge">HEALTHPLAN</code>, <code class="language-plaintext highlighter-rouge">DOCTOR</code>, <code class="language-plaintext highlighter-rouge">USERNAME</code>, <code class="language-plaintext highlighter-rouge">URL</code>, <code class="language-plaintext highlighter-rouge">LOCATION-OTHER</code>, <code class="language-plaintext highlighter-rouge">DEVICE</code>, <code class="language-plaintext highlighter-rouge">CITY</code>, <code class="language-plaintext highlighter-rouge">ZIP</code>, <code class="language-plaintext highlighter-rouge">STATE</code>, <code class="language-plaintext highlighter-rouge">PATIENT</code>, <code class="language-plaintext highlighter-rouge">COUNTRY</code>, <code class="language-plaintext highlighter-rouge">STREET</code>, <code class="language-plaintext highlighter-rouge">PHONE</code>, <code class="language-plaintext highlighter-rouge">HOSPITAL</code>, <code class="language-plaintext highlighter-rouge">EMAIL</code>, <code class="language-plaintext highlighter-rouge">IDNUM</code>, <code class="language-plaintext highlighter-rouge">BIOID</code>, <code class="language-plaintext highlighter-rouge">FAX</code>, <code class="language-plaintext highlighter-rouge">SSN</code>, <code class="language-plaintext highlighter-rouge">ACCOUNT</code>, <code class="language-plaintext highlighter-rouge">DLN</code>, <code class="language-plaintext highlighter-rouge">PLATE</code>, <code class="language-plaintext highlighter-rouge">LICENSE</code> entities.</p>
 
 <h2 id="predicted-entities">Predicted Entities</h2>
 
+<p><code class="language-plaintext highlighter-rouge">LOCATION</code>, <code class="language-plaintext highlighter-rouge">CONTACT</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">NAME</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">ID</code>, <code class="language-plaintext highlighter-rouge">AGE</code>, <code class="language-plaintext highlighter-rouge">MEDICALRECORD</code>, <code class="language-plaintext highlighter-rouge">ORGANIZATION</code>, <code class="language-plaintext highlighter-rouge">HEALTHPLAN</code>, <code class="language-plaintext highlighter-rouge">DOCTOR</code>, <code class="language-plaintext highlighter-rouge">USERNAME</code>, <code class="language-plaintext highlighter-rouge">URL</code>, <code class="language-plaintext highlighter-rouge">LOCATION-OTHER</code>, <code class="language-plaintext highlighter-rouge">DEVICE</code>, <code class="language-plaintext highlighter-rouge">CITY</code>, <code class="language-plaintext highlighter-rouge">ZIP</code>, <code class="language-plaintext highlighter-rouge">STATE</code>, <code class="language-plaintext highlighter-rouge">PATIENT</code>, <code class="language-plaintext highlighter-rouge">COUNTRY</code>, <code class="language-plaintext highlighter-rouge">STREET</code>, <code class="language-plaintext highlighter-rouge">PHONE</code>, <code class="language-plaintext highlighter-rouge">HOSPITAL</code>, <code class="language-plaintext highlighter-rouge">EMAIL</code>, <code class="language-plaintext highlighter-rouge">IDNUM</code>, <code class="language-plaintext highlighter-rouge">BIOID</code>, <code class="language-plaintext highlighter-rouge">FAX</code>, <code class="language-plaintext highlighter-rouge">SSN</code>, <code class="language-plaintext highlighter-rouge">ACCOUNT</code>, <code class="language-plaintext highlighter-rouge">DLN</code>, <code class="language-plaintext highlighter-rouge">PLATE</code>, <code class="language-plaintext highlighter-rouge">LICENSE</code></p>
+
 <p class="btn-box"><button class="button button-orange" disabled="">Live Demo</button>
 <button class="button button-orange" disabled="">Open in Colab</button>
 <a href="https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_glove_en_4.4.4_3.0_1686979246073.zip" class="button button-orange button-orange-trans arr button-icon hidden">Download</a>
diff --git a/2023/06/17/clinical_deidentification_slim_en.html b/2023/06/17/clinical_deidentification_slim_en.html
index 105c81193b..e0224939f8 100644
--- a/2023/06/17/clinical_deidentification_slim_en.html
+++ b/2023/06/17/clinical_deidentification_slim_en.html
@@ -11,7 +11,7 @@
     })(window,document,'script','dataLayer','GTM-59JLR64');</script>
 <!-- End Google Tag Manager --><title>Clinical Deidentification Pipeline (English, slim) | clinical_deidentification_slim | Healthcare NLP 4.4.4</title><meta property="og:title" content=""/>
 
-<meta name="description" content="DescriptionThis pipeline is trained with lightweight glove_100d embeddings and can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate AGE, CONTACT, DATE, ID, LOCATION, NAME, PROFESSION, CITY, COUNTRY, ...">
+<meta name="description" content="DescriptionThis pipeline is trained with lightweight glove_100d embeddings and can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate LOCATION, CONTACT, PROFESSION, NAME, DATE, ID, AGE, MEDICALRECORD, ...">
 <!-- <link rel="canonical" href="/2023/06/17/clinical_deidentification_slim_en.html"> -->
 <link rel="canonical" href="/2023/06/17/clinical_deidentification_slim_en.html"><link rel="alternate" type="application/rss+xml" title="Spark NLP" href="/feed.xml"><!-- start favicons snippet, use https://realfavicongenerator.net/ -->
 <!---->
@@ -508,10 +508,12 @@ <h1>Clinical Deidentification Pipeline (English, slim)</h1><div class="top-subti
 
 <!-- end custom article top snippet --><div class="article__content" itemprop="articleBody"><h2 id="description">Description</h2>
 
-<p>This pipeline is trained with lightweight <code class="language-plaintext highlighter-rouge">glove_100d</code> embeddings and can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate <code class="language-plaintext highlighter-rouge">AGE</code>, <code class="language-plaintext highlighter-rouge">CONTACT</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">ID</code>, <code class="language-plaintext highlighter-rouge">LOCATION</code>, <code class="language-plaintext highlighter-rouge">NAME</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">CITY</code>, <code class="language-plaintext highlighter-rouge">COUNTRY</code>, <code class="language-plaintext highlighter-rouge">DOCTOR</code>, <code class="language-plaintext highlighter-rouge">HOSPITAL</code>, <code class="language-plaintext highlighter-rouge">IDNUM</code>, <code class="language-plaintext highlighter-rouge">MEDICALRECORD</code>, <code class="language-plaintext highlighter-rouge">ORGANIZATION</code>, <code class="language-plaintext highlighter-rouge">PATIENT</code>, <code class="language-plaintext highlighter-rouge">PHONE</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">STREET</code>, <code class="language-plaintext highlighter-rouge">USERNAME</code>, <code class="language-plaintext highlighter-rouge">ZIP</code>, <code class="language-plaintext highlighter-rouge">ACCOUNT</code>, <code class="language-plaintext highlighter-rouge">LICENSE</code>, <code class="language-plaintext highlighter-rouge">VIN</code>, <code class="language-plaintext highlighter-rouge">SSN</code>, <code class="language-plaintext highlighter-rouge">DLN</code>, <code class="language-plaintext highlighter-rouge">PLATE</code>, <code class="language-plaintext highlighter-rouge">IPADDR</code>, <code class="language-plaintext highlighter-rouge">EMAIL</code> entities.</p>
+<p>This pipeline is trained with lightweight <code class="language-plaintext highlighter-rouge">glove_100d</code> embeddings and can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate <code class="language-plaintext highlighter-rouge">LOCATION</code>, <code class="language-plaintext highlighter-rouge">CONTACT</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">NAME</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">ID</code>, <code class="language-plaintext highlighter-rouge">AGE</code>, <code class="language-plaintext highlighter-rouge">MEDICALRECORD</code>, <code class="language-plaintext highlighter-rouge">ORGANIZATION</code>, <code class="language-plaintext highlighter-rouge">HEALTHPLAN</code>, <code class="language-plaintext highlighter-rouge">DOCTOR</code>, <code class="language-plaintext highlighter-rouge">USERNAME</code>, <code class="language-plaintext highlighter-rouge">URL</code>, <code class="language-plaintext highlighter-rouge">LOCATION-OTHER</code>, <code class="language-plaintext highlighter-rouge">DEVICE</code>, <code class="language-plaintext highlighter-rouge">CITY</code>, <code class="language-plaintext highlighter-rouge">ZIP</code>, <code class="language-plaintext highlighter-rouge">STATE</code>, <code class="language-plaintext highlighter-rouge">PATIENT</code>, <code class="language-plaintext highlighter-rouge">COUNTRY</code>, <code class="language-plaintext highlighter-rouge">STREET</code>, <code class="language-plaintext highlighter-rouge">PHONE</code>, <code class="language-plaintext highlighter-rouge">HOSPITAL</code>, <code class="language-plaintext highlighter-rouge">EMAIL</code>, <code class="language-plaintext highlighter-rouge">IDNUM</code>, <code class="language-plaintext highlighter-rouge">BIOID</code>, <code class="language-plaintext highlighter-rouge">FAX</code>, <code class="language-plaintext highlighter-rouge">SSN</code>, <code class="language-plaintext highlighter-rouge">ACCOUNT</code>, <code class="language-plaintext highlighter-rouge">DLN</code>, <code class="language-plaintext highlighter-rouge">PLATE</code>, <code class="language-plaintext highlighter-rouge">VIN</code>, <code class="language-plaintext highlighter-rouge">LICENSE</code> entities.</p>
 
 <h2 id="predicted-entities">Predicted Entities</h2>
 
+<p><code class="language-plaintext highlighter-rouge">LOCATION</code>, <code class="language-plaintext highlighter-rouge">CONTACT</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">NAME</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">ID</code>, <code class="language-plaintext highlighter-rouge">AGE</code>, <code class="language-plaintext highlighter-rouge">MEDICALRECORD</code>, <code class="language-plaintext highlighter-rouge">ORGANIZATION</code>, <code class="language-plaintext highlighter-rouge">HEALTHPLAN</code>, <code class="language-plaintext highlighter-rouge">DOCTOR</code>, <code class="language-plaintext highlighter-rouge">USERNAME</code>, <code class="language-plaintext highlighter-rouge">URL</code>, <code class="language-plaintext highlighter-rouge">LOCATION-OTHER</code>, <code class="language-plaintext highlighter-rouge">DEVICE</code>, <code class="language-plaintext highlighter-rouge">CITY</code>, <code class="language-plaintext highlighter-rouge">ZIP</code>, <code class="language-plaintext highlighter-rouge">STATE</code>, <code class="language-plaintext highlighter-rouge">PATIENT</code>, <code class="language-plaintext highlighter-rouge">COUNTRY</code>, <code class="language-plaintext highlighter-rouge">STREET</code>, <code class="language-plaintext highlighter-rouge">PHONE</code>, <code class="language-plaintext highlighter-rouge">HOSPITAL</code>, <code class="language-plaintext highlighter-rouge">EMAIL</code>, <code class="language-plaintext highlighter-rouge">IDNUM</code>, <code class="language-plaintext highlighter-rouge">BIOID</code>, <code class="language-plaintext highlighter-rouge">FAX</code>, <code class="language-plaintext highlighter-rouge">SSN</code>, <code class="language-plaintext highlighter-rouge">ACCOUNT</code>, <code class="language-plaintext highlighter-rouge">DLN</code>, <code class="language-plaintext highlighter-rouge">PLATE</code>, <code class="language-plaintext highlighter-rouge">VIN</code>, <code class="language-plaintext highlighter-rouge">LICENSE</code></p>
+
 <p class="btn-box"><button class="button button-orange" disabled="">Live Demo</button>
 <button class="button button-orange" disabled="">Open in Colab</button>
 <a href="https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/clinical_deidentification_slim_en_4.4.4_3.0_1686990843858.zip" class="button button-orange button-orange-trans arr button-icon hidden">Download</a>
diff --git a/2023/06/17/ner_deid_augmented_pipeline_en.html b/2023/06/17/ner_deid_augmented_pipeline_en.html
index f16632cd4c..436f70f9bf 100644
--- a/2023/06/17/ner_deid_augmented_pipeline_en.html
+++ b/2023/06/17/ner_deid_augmented_pipeline_en.html
@@ -11,7 +11,7 @@
     })(window,document,'script','dataLayer','GTM-59JLR64');</script>
 <!-- End Google Tag Manager --><title>Pipeline to Detect PHI for Deidentification (Augmented) | ner_deid_augmented_pipeline | Healthcare NLP 4.4.4</title><meta property="og:title" content=""/>
 
-<meta name="description" content="DescriptionThis pretrained pipeline is built on the top of ner_deid_augmented model.Predicted EntitiesLive DemoOpen in ColabDownloadCopy S3 URIHow to use  PythonJSLScalaNLU  from sparknlp.pretrained import PretrainedPipelinepipeline = PretrainedPipeline("ner_deid_augmented_pipeline", "en", "clinical/models")text = "...">
+<meta name="description" content="DescriptionThis pretrained pipeline is built on the top of ner_deid_augmented model.Predicted EntitiesLOCATION, CONTACT, PROFESSION, NAME, DATE, ID, AGELive DemoOpen in ColabDownloadCopy S3 URIHow to use  PythonJSLScalaNLU  from sparknlp.pretrained import PretrainedPipelinepipeline = PretrainedPipeline("ner_deid_aug...">
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 <!---->
@@ -512,6 +512,8 @@ <h1>Pipeline to Detect PHI for Deidentification (Augmented)</h1><div class="top-
 
 <h2 id="predicted-entities">Predicted Entities</h2>
 
+<p><code class="language-plaintext highlighter-rouge">LOCATION</code>, <code class="language-plaintext highlighter-rouge">CONTACT</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">NAME</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">ID</code>, <code class="language-plaintext highlighter-rouge">AGE</code></p>
+
 <p class="btn-box"><button class="button button-orange" disabled="">Live Demo</button>
 <button class="button button-orange" disabled="">Open in Colab</button>
 <a href="https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/ner_deid_augmented_pipeline_en_4.4.4_3.0_1686982856462.zip" class="button button-orange button-orange-trans arr button-icon hidden">Download</a>
diff --git a/2023/06/17/ner_deid_biobert_pipeline_en.html b/2023/06/17/ner_deid_biobert_pipeline_en.html
index e940aa2160..53ed728cea 100644
--- a/2023/06/17/ner_deid_biobert_pipeline_en.html
+++ b/2023/06/17/ner_deid_biobert_pipeline_en.html
@@ -11,7 +11,7 @@
     })(window,document,'script','dataLayer','GTM-59JLR64');</script>
 <!-- End Google Tag Manager --><title>Pipeline to Detect PHI in medical text (biobert) | ner_deid_biobert_pipeline | Healthcare NLP 4.4.4</title><meta property="og:title" content=""/>
 
-<meta name="description" content="DescriptionThis pretrained pipeline is built on the top of ner_deid_biobert model.Predicted EntitiesLive DemoOpen in ColabDownloadCopy S3 URIHow to use  PythonJSLScalaNLU  from sparknlp.pretrained import PretrainedPipelinepipeline = PretrainedPipeline("ner_deid_biobert_pipeline", "en", "clinical/models")text = '''A....">
+<meta name="description" content="DescriptionThis pretrained pipeline is built on the top of ner_deid_biobert model.Predicted EntitiesLOCATION, CONTACT, PROFESSION, NAME, DATE, ID, AGELive DemoOpen in ColabDownloadCopy S3 URIHow to use  PythonJSLScalaNLU  from sparknlp.pretrained import PretrainedPipelinepipeline = PretrainedPipeline("ner_deid_biobe...">
 <!-- <link rel="canonical" href="/2023/06/17/ner_deid_biobert_pipeline_en.html"> -->
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 <!---->
@@ -512,6 +512,8 @@ <h1>Pipeline to Detect PHI in medical text (biobert)</h1><div class="top-subtitl
 
 <h2 id="predicted-entities">Predicted Entities</h2>
 
+<p><code class="language-plaintext highlighter-rouge">LOCATION</code>, <code class="language-plaintext highlighter-rouge">CONTACT</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">NAME</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">ID</code>, <code class="language-plaintext highlighter-rouge">AGE</code></p>
+
 <p class="btn-box"><button class="button button-orange" disabled="">Live Demo</button>
 <button class="button button-orange" disabled="">Open in Colab</button>
 <a href="https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/ner_deid_biobert_pipeline_en_4.4.4_3.0_1686982929736.zip" class="button button-orange button-orange-trans arr button-icon hidden">Download</a>
diff --git a/2023/06/17/ner_deid_enriched_pipeline_en.html b/2023/06/17/ner_deid_enriched_pipeline_en.html
index 6812b34861..012cc1a4b9 100644
--- a/2023/06/17/ner_deid_enriched_pipeline_en.html
+++ b/2023/06/17/ner_deid_enriched_pipeline_en.html
@@ -11,7 +11,7 @@
     })(window,document,'script','dataLayer','GTM-59JLR64');</script>
 <!-- End Google Tag Manager --><title>Pipeline to Detect PHI for Deidentification (Enriched) | ner_deid_enriched_pipeline | Healthcare NLP 4.4.4</title><meta property="og:title" content=""/>
 
-<meta name="description" content="DescriptionThis pretrained pipeline is built on the top of ner_deid_enriched model.Predicted EntitiesLive DemoOpen in ColabDownloadCopy S3 URIHow to use  PythonJSLScalaNLU  from sparknlp.pretrained import PretrainedPipelinepipeline = PretrainedPipeline("ner_deid_enriched_pipeline", "en", "clinical/models")text = '''...">
+<meta name="description" content="DescriptionThis pretrained pipeline is built on the top of ner_deid_enriched model.Predicted EntitiesMEDICALRECORD, ORGANIZATION, PROFESSION, HEALTHPLAN, DOCTOR, USERNAME, URL, LOCATION-OTHER, DEVICE, CITY, DATE, ZIP, STATE, PATIENT, COUNTRY, STREET, PHONE, HOSPITAL, EMAIL, NN, IDNUM, BIOID, FAX, AGELive DemoOpen in...">
 <!-- <link rel="canonical" href="/2023/06/17/ner_deid_enriched_pipeline_en.html"> -->
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 <!---->
@@ -512,6 +512,8 @@ <h1>Pipeline to Detect PHI for Deidentification (Enriched)</h1><div class="top-s
 
 <h2 id="predicted-entities">Predicted Entities</h2>
 
+<p><code class="language-plaintext highlighter-rouge">MEDICALRECORD</code>, <code class="language-plaintext highlighter-rouge">ORGANIZATION</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">HEALTHPLAN</code>, <code class="language-plaintext highlighter-rouge">DOCTOR</code>, <code class="language-plaintext highlighter-rouge">USERNAME</code>, <code class="language-plaintext highlighter-rouge">URL</code>, <code class="language-plaintext highlighter-rouge">LOCATION-OTHER</code>, <code class="language-plaintext highlighter-rouge">DEVICE</code>, <code class="language-plaintext highlighter-rouge">CITY</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">ZIP</code>, <code class="language-plaintext highlighter-rouge">STATE</code>, <code class="language-plaintext highlighter-rouge">PATIENT</code>, <code class="language-plaintext highlighter-rouge">COUNTRY</code>, <code class="language-plaintext highlighter-rouge">STREET</code>, <code class="language-plaintext highlighter-rouge">PHONE</code>, <code class="language-plaintext highlighter-rouge">HOSPITAL</code>, <code class="language-plaintext highlighter-rouge">EMAIL</code>, <code class="language-plaintext highlighter-rouge">NN</code>, <code class="language-plaintext highlighter-rouge">IDNUM</code>, <code class="language-plaintext highlighter-rouge">BIOID</code>, <code class="language-plaintext highlighter-rouge">FAX</code>, <code class="language-plaintext highlighter-rouge">AGE</code></p>
+
 <p class="btn-box"><button class="button button-orange" disabled="">Live Demo</button>
 <button class="button button-orange" disabled="">Open in Colab</button>
 <a href="https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/ner_deid_enriched_pipeline_en_4.4.4_3.0_1686983056995.zip" class="button button-orange button-orange-trans arr button-icon hidden">Download</a>
diff --git a/2023/06/17/ner_deid_generic_glove_pipeline_en.html b/2023/06/17/ner_deid_generic_glove_pipeline_en.html
index b4281e3538..ab1afae7ec 100644
--- a/2023/06/17/ner_deid_generic_glove_pipeline_en.html
+++ b/2023/06/17/ner_deid_generic_glove_pipeline_en.html
@@ -11,7 +11,7 @@
     })(window,document,'script','dataLayer','GTM-59JLR64');</script>
 <!-- End Google Tag Manager --><title>Pipeline to Detect PHI for Deidentification (Glove, 7 labels) | ner_deid_generic_glove_pipeline | Healthcare NLP 4.4.4</title><meta property="og:title" content=""/>
 
-<meta name="description" content="DescriptionThis pretrained pipeline is built on the top of ner_deid_generic_glove model.Predicted EntitiesLive DemoOpen in ColabDownloadCopy S3 URIHow to use  PythonJSLScalaNLU  from sparknlp.pretrained import PretrainedPipelinepipeline = PretrainedPipeline("ner_deid_generic_glove_pipeline", "en", "clinical/models")...">
+<meta name="description" content="DescriptionThis pretrained pipeline is built on the top of ner_deid_generic_glove model.Predicted EntitiesLOCATION, CONTACT, PROFESSION, NAME, DATE, ID, AGELive DemoOpen in ColabDownloadCopy S3 URIHow to use  PythonJSLScalaNLU  from sparknlp.pretrained import PretrainedPipelinepipeline = PretrainedPipeline("ner_deid...">
 <!-- <link rel="canonical" href="/2023/06/17/ner_deid_generic_glove_pipeline_en.html"> -->
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 <!---->
@@ -512,6 +512,8 @@ <h1>Pipeline to Detect PHI for Deidentification (Glove, 7 labels)</h1><div class
 
 <h2 id="predicted-entities">Predicted Entities</h2>
 
+<p><code class="language-plaintext highlighter-rouge">LOCATION</code>, <code class="language-plaintext highlighter-rouge">CONTACT</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">NAME</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">ID</code>, <code class="language-plaintext highlighter-rouge">AGE</code></p>
+
 <p class="btn-box"><button class="button button-orange" disabled="">Live Demo</button>
 <button class="button button-orange" disabled="">Open in Colab</button>
 <a href="https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/ner_deid_generic_glove_pipeline_en_4.4.4_3.2_1686960270140.zip" class="button button-orange button-orange-trans arr button-icon hidden">Download</a>
diff --git a/2023/06/17/ner_deid_large_pipeline_en.html b/2023/06/17/ner_deid_large_pipeline_en.html
index 2c38465cd7..7169366ede 100644
--- a/2023/06/17/ner_deid_large_pipeline_en.html
+++ b/2023/06/17/ner_deid_large_pipeline_en.html
@@ -11,7 +11,7 @@
     })(window,document,'script','dataLayer','GTM-59JLR64');</script>
 <!-- End Google Tag Manager --><title>Pipeline to Detect PHI (Deidentification) | ner_deid_large_pipeline | Healthcare NLP 4.4.4</title><meta property="og:title" content=""/>
 
-<meta name="description" content="DescriptionThis pretrained pipeline is built on the top of ner_deid_large model.Predicted EntitiesLive DemoOpen in ColabDownloadCopy S3 URIHow to use  PythonJSLScalaNLU  from sparknlp.pretrained import PretrainedPipelinepipeline = PretrainedPipeline("ner_deid_large_pipeline", "en", "clinical/models")text = '''HISTOR...">
+<meta name="description" content="DescriptionThis pretrained pipeline is built on the top of ner_deid_large model.Predicted EntitiesLOCATION, CONTACT, PROFESSION, HEALTHPLAN, NAME, DATE, ID, URL, AGELive DemoOpen in ColabDownloadCopy S3 URIHow to use  PythonJSLScalaNLU  from sparknlp.pretrained import PretrainedPipelinepipeline = PretrainedPipeline(...">
 <!-- <link rel="canonical" href="/2023/06/17/ner_deid_large_pipeline_en.html"> -->
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 <!---->
@@ -512,6 +512,8 @@ <h1>Pipeline to Detect PHI (Deidentification)</h1><div class="top-subtitle mont"
 
 <h2 id="predicted-entities">Predicted Entities</h2>
 
+<p><code class="language-plaintext highlighter-rouge">LOCATION</code>, <code class="language-plaintext highlighter-rouge">CONTACT</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">HEALTHPLAN</code>, <code class="language-plaintext highlighter-rouge">NAME</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">ID</code>, <code class="language-plaintext highlighter-rouge">URL</code>, <code class="language-plaintext highlighter-rouge">AGE</code></p>
+
 <p class="btn-box"><button class="button button-orange" disabled="">Live Demo</button>
 <button class="button button-orange" disabled="">Open in Colab</button>
 <a href="https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/ner_deid_large_pipeline_en_4.4.4_3.0_1686983512576.zip" class="button button-orange button-orange-trans arr button-icon hidden">Download</a>
diff --git a/2023/06/17/ner_deid_sd_large_pipeline_en.html b/2023/06/17/ner_deid_sd_large_pipeline_en.html
index ae50af8c55..85c9a59034 100644
--- a/2023/06/17/ner_deid_sd_large_pipeline_en.html
+++ b/2023/06/17/ner_deid_sd_large_pipeline_en.html
@@ -11,7 +11,7 @@
     })(window,document,'script','dataLayer','GTM-59JLR64');</script>
 <!-- End Google Tag Manager --><title>Pipeline to Detect PHI in text (ner_deid_sd_larg) | ner_deid_sd_large_pipeline | Healthcare NLP 4.4.4</title><meta property="og:title" content=""/>
 
-<meta name="description" content="DescriptionThis pretrained pipeline is built on the top of ner_deid_sd_large model.Predicted EntitiesLive DemoOpen in ColabDownloadCopy S3 URIHow to use  PythonJSLScalaNLU  from sparknlp.pretrained import PretrainedPipelinepipeline = PretrainedPipeline("ner_deid_sd_large_pipeline", "en", "clinical/models")text = '''...">
+<meta name="description" content="DescriptionThis pretrained pipeline is built on the top of ner_deid_sd_large model.Predicted EntitiesLOCATION, CONTACT, PROFESSION, NAME, DATE, ID, AGELive DemoOpen in ColabDownloadCopy S3 URIHow to use  PythonJSLScalaNLU  from sparknlp.pretrained import PretrainedPipelinepipeline = PretrainedPipeline("ner_deid_sd_l...">
 <!-- <link rel="canonical" href="/2023/06/17/ner_deid_sd_large_pipeline_en.html"> -->
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 <!---->
@@ -512,6 +512,8 @@ <h1>Pipeline to Detect PHI in text (ner_deid_sd_larg)</h1><div class="top-subtit
 
 <h2 id="predicted-entities">Predicted Entities</h2>
 
+<p><code class="language-plaintext highlighter-rouge">LOCATION</code>, <code class="language-plaintext highlighter-rouge">CONTACT</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">NAME</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">ID</code>, <code class="language-plaintext highlighter-rouge">AGE</code></p>
+
 <p class="btn-box"><button class="button button-orange" disabled="">Live Demo</button>
 <button class="button button-orange" disabled="">Open in Colab</button>
 <a href="https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/ner_deid_sd_large_pipeline_en_4.4.4_3.0_1686983639702.zip" class="button button-orange button-orange-trans arr button-icon hidden">Download</a>
diff --git a/2023/06/17/ner_deid_sd_pipeline_en.html b/2023/06/17/ner_deid_sd_pipeline_en.html
index 7cf47009f7..c6bb850b79 100644
--- a/2023/06/17/ner_deid_sd_pipeline_en.html
+++ b/2023/06/17/ner_deid_sd_pipeline_en.html
@@ -11,7 +11,7 @@
     })(window,document,'script','dataLayer','GTM-59JLR64');</script>
 <!-- End Google Tag Manager --><title>Pipeline to Detect PHI in text (base) | ner_deid_sd_pipeline | Healthcare NLP 4.4.4</title><meta property="og:title" content=""/>
 
-<meta name="description" content="DescriptionThis pretrained pipeline is built on the top of ner_deid_sd model.Predicted EntitiesLive DemoOpen in ColabDownloadCopy S3 URIHow to use  PythonJSLScalaNLU  from sparknlp.pretrained import PretrainedPipelinepipeline = PretrainedPipeline("ner_deid_sd_pipeline", "en", "clinical/models")text = '''Record date ...">
+<meta name="description" content="DescriptionThis pretrained pipeline is built on the top of ner_deid_sd model.Predicted EntitiesNAME, LOCATION, DATE, AGE, PROFESSION, ID, CONTACTLive DemoOpen in ColabDownloadCopy S3 URIHow to use  PythonJSLScalaNLU  from sparknlp.pretrained import PretrainedPipelinepipeline = PretrainedPipeline("ner_deid_sd_pipelin...">
 <!-- <link rel="canonical" href="/2023/06/17/ner_deid_sd_pipeline_en.html"> -->
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 <!---->
@@ -512,6 +512,8 @@ <h1>Pipeline to Detect PHI in text (base)</h1><div class="top-subtitle mont"></d
 
 <h2 id="predicted-entities">Predicted Entities</h2>
 
+<p><code class="language-plaintext highlighter-rouge">NAME</code>, <code class="language-plaintext highlighter-rouge">LOCATION</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">AGE</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">ID</code>, <code class="language-plaintext highlighter-rouge">CONTACT</code></p>
+
 <p class="btn-box"><button class="button button-orange" disabled="">Live Demo</button>
 <button class="button button-orange" disabled="">Open in Colab</button>
 <a href="https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/ner_deid_sd_pipeline_en_4.4.4_3.0_1686983769394.zip" class="button button-orange button-orange-trans arr button-icon hidden">Download</a>
@@ -792,7 +794,7 @@ <h2 id="included-models">Included Models</h2>
 
 }
 
-</style><div class="article__section-navigator clearfix"><div class="previous nav_link"><span>PREVIOUS</span><a href="/2023/06/17/ner_deid_sd_large_pipeline_en.html">Pipeline to Detect PHI in text (ner_deid_sd_larg)</a></div><div class="next nav_link"><span>NEXT</span><a href="/2023/06/17/ner_deid_subentity_augmented_i2b2_pipeline_en.html">Pipeline to Detect PHI for deidentification purposes</a></div></div></div>
+</style><div class="article__section-navigator clearfix"><div class="previous nav_link"><span>PREVIOUS</span><a href="/2023/06/17/ner_deid_sd_large_pipeline_en.html">Pipeline to Detect PHI in text (ner_deid_sd_larg)</a></div><div class="next nav_link"><span>NEXT</span><a href="/2023/06/17/ner_deid_subentity_augmented_pipeline_en.html">Pipeline to Detect PHI for Deidentification (Subentity- Augmented)</a></div></div></div>
 
 </div>
 </div>
diff --git a/2023/06/17/ner_deid_subentity_augmented_pipeline_en.html b/2023/06/17/ner_deid_subentity_augmented_pipeline_en.html
index 076a75e0e5..a3ceeb0c9c 100644
--- a/2023/06/17/ner_deid_subentity_augmented_pipeline_en.html
+++ b/2023/06/17/ner_deid_subentity_augmented_pipeline_en.html
@@ -11,7 +11,7 @@
     })(window,document,'script','dataLayer','GTM-59JLR64');</script>
 <!-- End Google Tag Manager --><title>Pipeline to Detect PHI for Deidentification (Subentity- Augmented) | ner_deid_subentity_augmented_pipeline | Healthcare NLP 4.4.4</title><meta property="og:title" content=""/>
 
-<meta name="description" content="DescriptionThis pretrained pipeline is built on the top of ner_deid_subentity_augmented model.Predicted EntitiesLive DemoOpen in ColabDownloadCopy S3 URIHow to use  PythonJSLScalaNLU  from sparknlp.pretrained import PretrainedPipelinepipeline = PretrainedPipeline("ner_deid_subentity_augmented_pipeline", "en", "clini...">
+<meta name="description" content="DescriptionThis pretrained pipeline is built on the top of ner_deid_subentity_augmented model.Predicted EntitiesMEDICALRECORD, ORGANIZATION, PROFESSION, HEALTHPLAN, DOCTOR, USERNAME, LOCATION-OTHER, URL, DEVICE, CITY, DATE, ZIP, STATE, PATIENT, COUNTRY, STREET, PHONE, HOSPITAL, EMAIL, IDNUM, BIOID, FAX, AGELive Demo...">
 <!-- <link rel="canonical" href="/2023/06/17/ner_deid_subentity_augmented_pipeline_en.html"> -->
 <link rel="canonical" href="/2023/06/17/ner_deid_subentity_augmented_pipeline_en.html"><link rel="alternate" type="application/rss+xml" title="Spark NLP" href="/feed.xml"><!-- start favicons snippet, use https://realfavicongenerator.net/ -->
 <!---->
@@ -512,6 +512,8 @@ <h1>Pipeline to Detect PHI for Deidentification (Subentity- Augmented)</h1><div
 
 <h2 id="predicted-entities">Predicted Entities</h2>
 
+<p><code class="language-plaintext highlighter-rouge">MEDICALRECORD</code>, <code class="language-plaintext highlighter-rouge">ORGANIZATION</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">HEALTHPLAN</code>, <code class="language-plaintext highlighter-rouge">DOCTOR</code>, <code class="language-plaintext highlighter-rouge">USERNAME</code>, <code class="language-plaintext highlighter-rouge">LOCATION-OTHER</code>, <code class="language-plaintext highlighter-rouge">URL</code>, <code class="language-plaintext highlighter-rouge">DEVICE</code>, <code class="language-plaintext highlighter-rouge">CITY</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">ZIP</code>, <code class="language-plaintext highlighter-rouge">STATE</code>, <code class="language-plaintext highlighter-rouge">PATIENT</code>, <code class="language-plaintext highlighter-rouge">COUNTRY</code>, <code class="language-plaintext highlighter-rouge">STREET</code>, <code class="language-plaintext highlighter-rouge">PHONE</code>, <code class="language-plaintext highlighter-rouge">HOSPITAL</code>, <code class="language-plaintext highlighter-rouge">EMAIL</code>, <code class="language-plaintext highlighter-rouge">IDNUM</code>, <code class="language-plaintext highlighter-rouge">BIOID</code>, <code class="language-plaintext highlighter-rouge">FAX</code>, <code class="language-plaintext highlighter-rouge">AGE</code></p>
+
 <p class="btn-box"><button class="button button-orange" disabled="">Live Demo</button>
 <button class="button button-orange" disabled="">Open in Colab</button>
 <a href="https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/ner_deid_subentity_augmented_pipeline_en_4.4.4_3.0_1686984035193.zip" class="button button-orange button-orange-trans arr button-icon hidden">Download</a>
@@ -792,7 +794,7 @@ <h2 id="included-models">Included Models</h2>
 
 }
 
-</style><div class="article__section-navigator clearfix"><div class="previous nav_link"><span>PREVIOUS</span><a href="/2023/06/17/ner_deid_subentity_augmented_i2b2_pipeline_en.html">Pipeline to Detect PHI for deidentification purposes</a></div><div class="next nav_link"><span>NEXT</span><a href="/2023/06/17/ner_deid_subentity_bert_pipeline_ro.html">Pipeline to Detect PHI for Deidentification in Romanian (BERT)</a></div></div></div>
+</style><div class="article__section-navigator clearfix"><div class="previous nav_link"><span>PREVIOUS</span><a href="/2023/06/17/ner_deid_sd_pipeline_en.html">Pipeline to Detect PHI in text (base)</a></div><div class="next nav_link"><span>NEXT</span><a href="/2023/06/17/ner_deid_subentity_bert_pipeline_ro.html">Pipeline to Detect PHI for Deidentification in Romanian (BERT)</a></div></div></div>
 
 </div>
 </div>
diff --git a/2023/06/17/ner_deid_synthetic_pipeline_en.html b/2023/06/17/ner_deid_synthetic_pipeline_en.html
index 2a3abd0643..d79c789d5a 100644
--- a/2023/06/17/ner_deid_synthetic_pipeline_en.html
+++ b/2023/06/17/ner_deid_synthetic_pipeline_en.html
@@ -11,7 +11,7 @@
     })(window,document,'script','dataLayer','GTM-59JLR64');</script>
 <!-- End Google Tag Manager --><title>Pipeline to Detect PHI for Deidentification (Augmented) | ner_deid_synthetic_pipeline | Healthcare NLP 4.4.4</title><meta property="og:title" content=""/>
 
-<meta name="description" content="DescriptionThis pretrained pipeline is built on the top of ner_deid_synthetic model.Predicted EntitiesLive DemoOpen in ColabDownloadCopy S3 URIHow to use  PythonJSLScalaNLU  from sparknlp.pretrained import PretrainedPipelinepipeline = PretrainedPipeline("ner_deid_synthetic_pipeline", "en", "clinical/models")text = "...">
+<meta name="description" content="DescriptionThis pretrained pipeline is built on the top of ner_deid_synthetic model.Predicted EntitiesLOCATION, CONTACT, PROFESSION, NAME, DATE, ID, AGELive DemoOpen in ColabDownloadCopy S3 URIHow to use  PythonJSLScalaNLU  from sparknlp.pretrained import PretrainedPipelinepipeline = PretrainedPipeline("ner_deid_syn...">
 <!-- <link rel="canonical" href="/2023/06/17/ner_deid_synthetic_pipeline_en.html"> -->
 <link rel="canonical" href="/2023/06/17/ner_deid_synthetic_pipeline_en.html"><link rel="alternate" type="application/rss+xml" title="Spark NLP" href="/feed.xml"><!-- start favicons snippet, use https://realfavicongenerator.net/ -->
 <!---->
@@ -512,6 +512,8 @@ <h1>Pipeline to Detect PHI for Deidentification (Augmented)</h1><div class="top-
 
 <h2 id="predicted-entities">Predicted Entities</h2>
 
+<p><code class="language-plaintext highlighter-rouge">LOCATION</code>, <code class="language-plaintext highlighter-rouge">CONTACT</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">NAME</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">ID</code>, <code class="language-plaintext highlighter-rouge">AGE</code></p>
+
 <p class="btn-box"><button class="button button-orange" disabled="">Live Demo</button>
 <button class="button button-orange" disabled="">Open in Colab</button>
 <a href="https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/ner_deid_synthetic_pipeline_en_4.4.4_3.2_1686960716115.zip" class="button button-orange button-orange-trans arr button-icon hidden">Download</a>
diff --git a/2023/06/17/ner_deidentify_dl_pipeline_en.html b/2023/06/17/ner_deidentify_dl_pipeline_en.html
index 08e523595e..fe6498d358 100644
--- a/2023/06/17/ner_deidentify_dl_pipeline_en.html
+++ b/2023/06/17/ner_deidentify_dl_pipeline_en.html
@@ -11,7 +11,7 @@
     })(window,document,'script','dataLayer','GTM-59JLR64');</script>
 <!-- End Google Tag Manager --><title>Pipeline to Detect PHI for Deidentification (ner_deidentification_dl) | ner_deidentify_dl_pipeline | Healthcare NLP 4.4.4</title><meta property="og:title" content=""/>
 
-<meta name="description" content="DescriptionThis pretrained pipeline is built on the top of ner_deidentify_dl model.Predicted EntitiesLive DemoOpen in ColabDownloadCopy S3 URIHow to use  PythonJSLScalaNLU  from sparknlp.pretrained import PretrainedPipelinepipeline = PretrainedPipeline("ner_deidentify_dl_pipeline", "en", "clinical/models")text = '''...">
+<meta name="description" content="DescriptionThis pretrained pipeline is built on the top of ner_deidentify_dl model.Predicted EntitiesDATE, PATIENT, MEDICALRECORD, DOCTOR, AGE, HOSPITAL, STATE, CITY, PROFESSION, STREET, ZIP, PHONE, COUNTRY, ORGANIZATION, FAX, IDNUM, HEALTHPLAN, USERNAME, EMAIL, BIOID, LOCATION-OTHER, DEVICE, URL, IDLive DemoOpen in...">
 <!-- <link rel="canonical" href="/2023/06/17/ner_deidentify_dl_pipeline_en.html"> -->
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 <!---->
@@ -512,6 +512,8 @@ <h1>Pipeline to Detect PHI for Deidentification (ner_deidentification_dl)</h1><d
 
 <h2 id="predicted-entities">Predicted Entities</h2>
 
+<p><code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">PATIENT</code>, <code class="language-plaintext highlighter-rouge">MEDICALRECORD</code>, <code class="language-plaintext highlighter-rouge">DOCTOR</code>, <code class="language-plaintext highlighter-rouge">AGE</code>, <code class="language-plaintext highlighter-rouge">HOSPITAL</code>, <code class="language-plaintext highlighter-rouge">STATE</code>, <code class="language-plaintext highlighter-rouge">CITY</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">STREET</code>, <code class="language-plaintext highlighter-rouge">ZIP</code>, <code class="language-plaintext highlighter-rouge">PHONE</code>, <code class="language-plaintext highlighter-rouge">COUNTRY</code>, <code class="language-plaintext highlighter-rouge">ORGANIZATION</code>, <code class="language-plaintext highlighter-rouge">FAX</code>, <code class="language-plaintext highlighter-rouge">IDNUM</code>, <code class="language-plaintext highlighter-rouge">HEALTHPLAN</code>, <code class="language-plaintext highlighter-rouge">USERNAME</code>, <code class="language-plaintext highlighter-rouge">EMAIL</code>, <code class="language-plaintext highlighter-rouge">BIOID</code>, <code class="language-plaintext highlighter-rouge">LOCATION-OTHER</code>, <code class="language-plaintext highlighter-rouge">DEVICE</code>, <code class="language-plaintext highlighter-rouge">URL</code>, <code class="language-plaintext highlighter-rouge">ID</code></p>
+
 <p class="btn-box"><button class="button button-orange" disabled="">Live Demo</button>
 <button class="button button-orange" disabled="">Open in Colab</button>
 <a href="https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/ner_deidentify_dl_pipeline_en_4.4.4_3.0_1686984355771.zip" class="button button-orange button-orange-trans arr button-icon hidden">Download</a>
diff --git a/2024/01/10/clinical_deidentification_langtest_en.html b/2024/01/10/clinical_deidentification_langtest_en.html
index e9553202b5..6ed0455385 100644
--- a/2024/01/10/clinical_deidentification_langtest_en.html
+++ b/2024/01/10/clinical_deidentification_langtest_en.html
@@ -11,7 +11,7 @@
     })(window,document,'script','dataLayer','GTM-59JLR64');</script>
 <!-- End Google Tag Manager --><title>Clinical Deidentification Pipeline (Langtest - English) | clinical_deidentification_langtest | Healthcare NLP 5.2.0</title><meta property="og:title" content=""/>
 
-<meta name="description" content="DescriptionThis pipeline can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate AGE, CONTACT, DATE, LOCATION, NAME, PROFESSION, CITY, COUNTRY, DOCTOR, HOSPITAL, IDNUM, MEDICALRECORD, ORGANIZATION, PATI...">
+<meta name="description" content="DescriptionThis pipeline can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate LOCATION, CONTACT, PROFESSION, NAME, DATE, ID, AGE, MEDICALRECORD, ORGANIZATION, HEALTHPLAN, DOCTOR, USERNAME, LOCATION-O...">
 <!-- <link rel="canonical" href="/2024/01/10/clinical_deidentification_langtest_en.html"> -->
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 <!---->
@@ -508,8 +508,11 @@ <h1>Clinical Deidentification Pipeline (Langtest - English)</h1><div class="top-
 
 <!-- end custom article top snippet --><div class="article__content" itemprop="articleBody"><h2 id="description">Description</h2>
 
-<p>This pipeline can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate <code class="language-plaintext highlighter-rouge">AGE</code>, <code class="language-plaintext highlighter-rouge">CONTACT</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">LOCATION</code>, <code class="language-plaintext highlighter-rouge">NAME</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">CITY</code>, <code class="language-plaintext highlighter-rouge">COUNTRY</code>, <code class="language-plaintext highlighter-rouge">DOCTOR</code>, <code class="language-plaintext highlighter-rouge">HOSPITAL</code>, <code class="language-plaintext highlighter-rouge">IDNUM</code>, <code class="language-plaintext highlighter-rouge">MEDICALRECORD</code>, <code class="language-plaintext highlighter-rouge">ORGANIZATION</code>, <code class="language-plaintext highlighter-rouge">PATIENT</code>, <code class="language-plaintext highlighter-rouge">PHONE</code>, <code class="language-plaintext highlighter-rouge">STREET</code>, <code class="language-plaintext highlighter-rouge">USERNAME</code>, <code class="language-plaintext highlighter-rouge">ZIP</code>, <code class="language-plaintext highlighter-rouge">ACCOUNT</code>, <code class="language-plaintext highlighter-rouge">LICENSE</code>, <code class="language-plaintext highlighter-rouge">VIN</code>, <code class="language-plaintext highlighter-rouge">SSN</code>, <code class="language-plaintext highlighter-rouge">DLN</code>, <code class="language-plaintext highlighter-rouge">PLATE</code>, <code class="language-plaintext highlighter-rouge">IPADDR</code> entities. This pre-trained pipeline is built with NER models powered by <code class="language-plaintext highlighter-rouge">langtest</code>
- library.</p>
+<p>This pipeline can be used to deidentify PHI information from medical texts. The PHI information will be masked and obfuscated in the resulting text. The pipeline can mask and obfuscate <code class="language-plaintext highlighter-rouge">LOCATION</code>, <code class="language-plaintext highlighter-rouge">CONTACT</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">NAME</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">ID</code>, <code class="language-plaintext highlighter-rouge">AGE</code>, <code class="language-plaintext highlighter-rouge">MEDICALRECORD</code>, <code class="language-plaintext highlighter-rouge">ORGANIZATION</code>, <code class="language-plaintext highlighter-rouge">HEALTHPLAN</code>, <code class="language-plaintext highlighter-rouge">DOCTOR</code>, <code class="language-plaintext highlighter-rouge">USERNAME</code>, <code class="language-plaintext highlighter-rouge">LOCATION-OTHER</code>, <code class="language-plaintext highlighter-rouge">URL</code>, <code class="language-plaintext highlighter-rouge">DEVICE</code>, <code class="language-plaintext highlighter-rouge">CITY</code>, <code class="language-plaintext highlighter-rouge">ZIP</code>, <code class="language-plaintext highlighter-rouge">STATE</code>, <code class="language-plaintext highlighter-rouge">PATIENT</code>, <code class="language-plaintext highlighter-rouge">COUNTRY</code>, <code class="language-plaintext highlighter-rouge">STREET</code>, <code class="language-plaintext highlighter-rouge">PHONE</code>, <code class="language-plaintext highlighter-rouge">HOSPITAL</code>, <code class="language-plaintext highlighter-rouge">EMAIL</code>, <code class="language-plaintext highlighter-rouge">IDNUM</code>, <code class="language-plaintext highlighter-rouge">BIOID</code>, <code class="language-plaintext highlighter-rouge">FAX</code>, <code class="language-plaintext highlighter-rouge">SSN</code>, <code class="language-plaintext highlighter-rouge">ACCOUNT</code>, <code class="language-plaintext highlighter-rouge">DLN</code>, <code class="language-plaintext highlighter-rouge">PLATE</code>, <code class="language-plaintext highlighter-rouge">VIN</code>, <code class="language-plaintext highlighter-rouge">LICENSE</code> entities. This pre-trained pipeline is built with NER models powered by <code class="language-plaintext highlighter-rouge">langtest</code> library.</p>
+
+<h2 id="predicted-entities">Predicted Entities</h2>
+
+<p><code class="language-plaintext highlighter-rouge">LOCATION</code>, <code class="language-plaintext highlighter-rouge">CONTACT</code>, <code class="language-plaintext highlighter-rouge">PROFESSION</code>, <code class="language-plaintext highlighter-rouge">NAME</code>, <code class="language-plaintext highlighter-rouge">DATE</code>, <code class="language-plaintext highlighter-rouge">ID</code>, <code class="language-plaintext highlighter-rouge">AGE</code>, <code class="language-plaintext highlighter-rouge">MEDICALRECORD</code>, <code class="language-plaintext highlighter-rouge">ORGANIZATION</code>, <code class="language-plaintext highlighter-rouge">HEALTHPLAN</code>, <code class="language-plaintext highlighter-rouge">DOCTOR</code>, <code class="language-plaintext highlighter-rouge">USERNAME</code>, <code class="language-plaintext highlighter-rouge">LOCATION-OTHER</code>, <code class="language-plaintext highlighter-rouge">URL</code>, <code class="language-plaintext highlighter-rouge">DEVICE</code>, <code class="language-plaintext highlighter-rouge">CITY</code>, <code class="language-plaintext highlighter-rouge">ZIP</code>, <code class="language-plaintext highlighter-rouge">STATE</code>, <code class="language-plaintext highlighter-rouge">PATIENT</code>, <code class="language-plaintext highlighter-rouge">COUNTRY</code>, <code class="language-plaintext highlighter-rouge">STREET</code>, <code class="language-plaintext highlighter-rouge">PHONE</code>, <code class="language-plaintext highlighter-rouge">HOSPITAL</code>, <code class="language-plaintext highlighter-rouge">EMAIL</code>, <code class="language-plaintext highlighter-rouge">IDNUM</code>, <code class="language-plaintext highlighter-rouge">BIOID</code>, <code class="language-plaintext highlighter-rouge">FAX</code>, <code class="language-plaintext highlighter-rouge">SSN</code>, <code class="language-plaintext highlighter-rouge">ACCOUNT</code>, <code class="language-plaintext highlighter-rouge">DLN</code>, <code class="language-plaintext highlighter-rouge">PLATE</code>, <code class="language-plaintext highlighter-rouge">VIN</code>, <code class="language-plaintext highlighter-rouge">LICENSE</code></p>
 
 <p class="btn-box"><button class="button button-orange" disabled="">Live Demo</button>
 <button class="button button-orange" disabled="">Open in Colab</button>
diff --git a/feed.xml b/feed.xml
index 1b338d4904..3fe4630ffa 100644
--- a/feed.xml
+++ b/feed.xml
@@ -1,4 +1,4 @@
-<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" ><generator uri="https://jekyllrb.com/" version="3.9.2">Jekyll</generator><link href="/feed.xml" rel="self" type="application/atom+xml" /><link href="/" rel="alternate" type="text/html" /><updated>2024-08-02T16:45:44+00:00</updated><id>/feed.xml</id><title type="html">Spark NLP</title><subtitle>High Performance NLP with Apache Spark
+<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" ><generator uri="https://jekyllrb.com/" version="3.9.2">Jekyll</generator><link href="/feed.xml" rel="self" type="application/atom+xml" /><link href="/" rel="alternate" type="text/html" /><updated>2024-08-02T19:09:50+00:00</updated><id>/feed.xml</id><title type="html">Spark NLP</title><subtitle>High Performance NLP with Apache Spark
 </subtitle><author><name>{&quot;type&quot;=&gt;nil, &quot;name&quot;=&gt;nil, &quot;url&quot;=&gt;nil, &quot;avatar&quot;=&gt;nil, &quot;bio&quot;=&gt;nil, &quot;email&quot;=&gt;nil, &quot;facebook&quot;=&gt;nil, &quot;twitter&quot;=&gt;nil, &quot;weibo&quot;=&gt;nil, &quot;googleplus&quot;=&gt;nil, &quot;telegram&quot;=&gt;nil, &quot;medium&quot;=&gt;nil, &quot;zhihu&quot;=&gt;nil, &quot;douban&quot;=&gt;nil, &quot;linkedin&quot;=&gt;nil, &quot;github&quot;=&gt;nil, &quot;npm&quot;=&gt;nil}</name></author><entry><title type="html">JSL_MedM (LLM - q16)</title><link href="/2024/07/12/jsl_medm_q16_v1_en.html" rel="alternate" type="text/html" title="JSL_MedM (LLM - q16)" /><published>2024-07-12T00:00:00+00:00</published><updated>2024-07-12T00:00:00+00:00</updated><id>/2024/07/12/jsl_medm_q16_v1_en</id><content type="html" xml:base="/2024/07/12/jsl_medm_q16_v1_en.html">## Description
 
 This LLM model is trained to perform Q&amp;A, Summarization, RAG, and Chat.
diff --git a/models.json b/models.json
index 83197daa68..bcba142587 100644
--- a/models.json
+++ b/models.json
@@ -1 +1 @@
-[{"title":"POS Tagger Clinical","date":"April 30, 2019","name":"pos_clinical","class":"PerceptronModel","language":"en","task":"Part of Speech Tagging","edition":"Healthcare NLP 2.0.2","categories":[],"url":"/2019/04/30/pos_clinical_en.html","tags":["clinical","licensed","pos","en"],"download_link":"https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/pos_clinical_en_2.0.2_2.4_1556660550177.zip","predicted_entities":[],"type":"model","annotator":"PerceptronModel","deprecated":false,"compatible_editions":["Healthcare NLP 2.0"]},{"title":"Deidentify RB","date":"June 04, 2019","name":"deidentify_rb","class":"DeIdentificationModel","language":"en","task":"De-identification","edition":"Healthcare NLP 2.0.2","categories":[],"url":"/2019/06/04/deidentify_rb_en.html","tags":["clinical","licensed","en"],"download_link":"https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/deidentify_rb_en_2.0.2_2.4_1559672122511.zip","predicted_entities":[],"type":"model","annotator":"DeIdentificationModel","deprecated":false,"compatible_editions":["Healthcare NLP 2.0"]},{"title":"Embeddings Clinical","date":"January 28, 2020","name":"embeddings_clinical","class":"WordEmbeddingsModel","language":"en","task":"Embeddings","edition":"Healthcare NLP 2.4.0","categories":[],"url":"/2020/01/28/embeddings_clinical_en.html","tags":["clinical","licensed","embeddings","en"],"download_link":"https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/embeddings_clinical_en_2.4.0_2.4_1580237286004.zip","predicted_entities":[],"type":"model","annotator":"WordEmbeddingsModel","deprecated":false,"compatible_editions":["Healthcare NLP 2.4","Healthcare NLP 3.0","Healthcare NLP 2.5","Healthcare NLP 2.6","Healthcare NLP 2.7","Healthcare NLP 3.1","Healthcare NLP 3.2","Healthcare NLP 3.3","Healthcare NLP 3.4","Healthcare NLP 3.5","Healthcare NLP 4.0","Healthcare NLP 4.1","Healthcare NLP 4.2","Healthcare NLP 4.3","Healthcare NLP 4.4","Healthcare NLP 5.0","Healthcare NLP 5.1","Healthcare NLP 5.2","Healthcare NLP 5.3","Healthcare NLP 5.4","Healthcare NLP"]},{"title":"Detect Problems, Tests and Treatments (ner_crf)","date":"January 28, 2020","name":"ner_crf","class":"NerCrfModel","language":"en","task":"Named Entity Recognition","edition":"Healthcare NLP 2.4.0","categories":[],"url":"/2020/01/28/ner_crf_en.html","tags":["ner"],"download_link":"https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/ner_crf_en_2.4.0_2.4_1580237286004.zip","predicted_entities":["Problem","Test","Treatment"],"type":"model","annotator":"NerCrfModel","deprecated":false,"compatible_editions":["Healthcare NLP 2.4","Healthcare NLP 3.0","Healthcare NLP 2.5","Healthcare NLP 2.6","Healthcare NLP 2.7","Healthcare NLP 3.1","Healthcare NLP 3.2","Healthcare NLP 3.3","Healthcare NLP 3.4","Healthcare NLP 3.5","Healthcare NLP 4.0","Healthcare NLP 4.1","Healthcare NLP 4.2","Healthcare NLP 4.3","Healthcare NLP 4.4","Healthcare NLP 5.0","Healthcare NLP 5.1","Healthcare NLP 5.2","Healthcare NLP 5.3","Healthcare NLP 5.4","Healthcare NLP"]},{"title":"Detect Assertion Status (assertion_dl_en)","date":"January 30, 2020","name":"assertion_dl_en","class":"","language":"en","task":"Assertion Status","edition":"Healthcare NLP 2.4.0","categories":[],"url":"/2020/01/30/assertion_dl_en.html","tags":["clinical","licensed","ner","en"],"download_link":"https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/assertion_dl_en_2.4.0_2.4_1580237286004.zip","predicted_entities":["hypothetical","present","absent","possible","conditional","associated_with_someone_else"],"type":"model","annotator":"","deprecated":false,"compatible_editions":["Healthcare NLP 2.4","Healthcare NLP 3.0","Healthcare NLP 2.5","Healthcare NLP 2.6","Healthcare NLP 2.7","Healthcare NLP 3.1","Healthcare NLP 3.2","Healthcare NLP 3.3","Healthcare NLP 3.4","Healthcare NLP 3.5","Healthcare NLP 4.0","Healthcare NLP 4.1","Healthcare NLP 4.2","Healthcare NLP 4.3","Healthcare NLP 4.4","Healthcare NLP 5.0","Healthcare NLP 5.1","Healthcare NLP 5.2","Healthcare NLP 5.3","Healthcare NLP 5.4","Healthcare NLP"]},{"title":"Detect Assertion Status (assertion_ml_en)","date":"January 30, 2020","name":"assertion_ml_en","class":"","language":"en","task":"Assertion Status","edition":"Healthcare NLP 2.4.0","categories":[],"url":"/2020/01/30/assertion_ml_en.html","tags":["clinical","licensed","ner","en"],"download_link":"https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/assertion_dl_large_en_2.5.0_2.4_1590022282256.zip","predicted_entities":[],"type":"model","annotator":"","deprecated":false,"compatible_editions":["Healthcare NLP 2.4","Healthcare NLP 3.0","Healthcare NLP 2.5","Healthcare NLP 2.6","Healthcare NLP 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