Cc 7 14 rn (#1331)

JohnSnowLabs · Jul 14, 2024 · e442e42 · e442e42
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diff --git a/.../Cabir40/2024-07-12-jsl_meds_q18_v1_en.md → .../Cabir40/2024-07-12-jsl_meds_q16_v1_en.md b/.../Cabir40/2024-07-12-jsl_meds_q18_v1_en.md → .../Cabir40/2024-07-12-jsl_meds_q16_v1_en.md
diff --git a/...abir40/2024-07-12-jsl_medner_zs_q16_en.md → ...40/2024-07-12-jsl_medsner_zs_q16_v1_en.md b/...abir40/2024-07-12-jsl_medner_zs_q16_en.md → ...40/2024-07-12-jsl_medsner_zs_q16_v1_en.md
@@ -1,8 +1,8 @@
 ---
 layout: model
-title: JSL_MedM (LLM - q16)
+title: JSL_MedSNER (LLM - q16)
 author: John Snow Labs
-name: jsl_medner_zs_q16
+name: jsl_medsner_zs_q16_v1
 date: 2024-07-12
 tags: [en, licensed, clinical, medical, llm, ner]
 task: [Summarization, Question Answering, Named Entity Recognition]
@@ -29,8 +29,8 @@ This LLM model is trained to extract and link entities in a document. Users need
 {:.btn-box}
 <button class="button button-orange" disabled>Live Demo</button>
 <button class="button button-orange" disabled>Open in Colab</button>
-[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/jsl_medner_zs_q16_en_5.4.0_3.0_1720040078717.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
-[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/jsl_medner_zs_q16_en_5.4.0_3.0_1720040078717.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/jsl_medsner_zs_q16_v1_en_5.4.0_3.0_1720040078717.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/jsl_medsner_zs_q16_v1_en_5.4.0_3.0_1720040078717.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
 
 ## How to use
 
@@ -42,7 +42,7 @@ This LLM model is trained to extract and link entities in a document. Users need
 ```python
 from sparknlp_jsl.llm import LLMLoader
 
-llm_loader_pretrained = LLMLoader(spark).pretrained("jsl_medner_zs_q16", "en", "clinical/models")
+llm_loader_pretrained = LLMLoader(spark).pretrained("jsl_medsner_zs_q16_v1", "en", "clinical/models")
 
 ptompt = """
 ### Template:
@@ -69,7 +69,7 @@ response = llm_loader_pretrained.generate(ptompt)
 import com.johnsnowlabs.ml.gguf.LLMLoader
 import com.johnsnowlabs.nlp.SparkAccessor.spark
 
-val llmLoader = new LLMLoader().setSparkSession(spark).pretrained("jsl_medner_zs_q16", "en", "clinical/models")
+val llmLoader = new LLMLoader().setSparkSession(spark).pretrained("jsl_medsner_zs_q16_v1", "en", "clinical/models")
 
 val prompt = """
 ### Template:
@@ -118,7 +118,7 @@ val response = llmLoader.generate(prompt)
 
 {:.table-model}
 |---|---|
-|Model Name:|jsl_medner_zs_q16|
+|Model Name:|jsl_medsner_zs_q16_v1|
 |Compatibility:|Healthcare NLP 5.4.0+|
 |License:|Licensed|
 |Edition:|Official|

diff --git a/...Cabir40/2024-07-12-jsl_medner_zs_q4_en.md → ...r40/2024-07-12-jsl_medsner_zs_q4_v1_en.md b/...Cabir40/2024-07-12-jsl_medner_zs_q4_en.md → ...r40/2024-07-12-jsl_medsner_zs_q4_v1_en.md
@@ -1,8 +1,8 @@
 ---
 layout: model
-title: JSL_MedM (LLM - q4)
+title: JSL_MedSNER (LLM - q4)
 author: John Snow Labs
-name: jsl_medner_zs_q4
+name: jsl_medsner_zs_q4_v1
 date: 2024-07-12
 tags: [en, licensed, clinical, medical, llm, ner]
 task: [Summarization, Question Answering, Named Entity Recognition]
@@ -29,8 +29,8 @@ This LLM model is trained to extract and link entities in a document. Users need
 {:.btn-box}
 <button class="button button-orange" disabled>Live Demo</button>
 <button class="button button-orange" disabled>Open in Colab</button>
-[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/jsl_medner_zs_q4_en_5.4.0_3.0_1720040078717.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
-[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/jsl_medner_zs_q4_en_5.4.0_3.0_1720040078717.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/jsl_medsner_zs_q4_v1_en_5.4.0_3.0_1720040078717.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/jsl_medsner_zs_q4_v1_en_5.4.0_3.0_1720040078717.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
 
 ## How to use
 
@@ -42,7 +42,7 @@ This LLM model is trained to extract and link entities in a document. Users need
 ```python
 from sparknlp_jsl.llm import LLMLoader
 
-llm_loader_pretrained = LLMLoader(spark).pretrained("jsl_medner_zs_q4", "en", "clinical/models")
+llm_loader_pretrained = LLMLoader(spark).pretrained("jsl_medsner_zs_q4_v1", "en", "clinical/models")
 
 ptompt = """
 ### Template:
@@ -69,7 +69,7 @@ response = llm_loader_pretrained.generate(ptompt)
 import com.johnsnowlabs.ml.gguf.LLMLoader
 import com.johnsnowlabs.nlp.SparkAccessor.spark
 
-val llmLoader = new LLMLoader().setSparkSession(spark).pretrained("jsl_medner_zs_q4", "en", "clinical/models")
+val llmLoader = new LLMLoader().setSparkSession(spark).pretrained("jsl_medsner_zs_q4_v1", "en", "clinical/models")
 
 val prompt = """
 ### Template:
@@ -118,7 +118,7 @@ val response = llmLoader.generate(prompt)
 
 {:.table-model}
 |---|---|
-|Model Name:|jsl_medner_zs_q4|
+|Model Name:|jsl_medsner_zs_q4_v1|
 |Compatibility:|Healthcare NLP 5.4.0+|
 |License:|Licensed|
 |Edition:|Official|

diff --git a/...Cabir40/2024-07-12-jsl_medner_zs_q8_en.md → ...r40/2024-07-12-jsl_medsner_zs_q8_v1_en.md b/...Cabir40/2024-07-12-jsl_medner_zs_q8_en.md → ...r40/2024-07-12-jsl_medsner_zs_q8_v1_en.md
@@ -1,8 +1,8 @@
 ---
 layout: model
-title: JSL_MedM (LLM - q8)
+title: JSL_MedSNER (LLM - q8)
 author: John Snow Labs
-name: jsl_medner_zs_q8
+name: jsl_medsner_zs_q8_v1
 date: 2024-07-12
 tags: [en, licensed, clinical, medical, llm, ner]
 task: [Summarization, Question Answering, Named Entity Recognition]
@@ -29,8 +29,8 @@ This LLM model is trained to extract and link entities in a document. Users need
 {:.btn-box}
 <button class="button button-orange" disabled>Live Demo</button>
 <button class="button button-orange" disabled>Open in Colab</button>
-[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/jsl_medner_zs_q8_en_5.4.0_3.0_1720040078717.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
-[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/jsl_medner_zs_q8_en_5.4.0_3.0_1720040078717.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/jsl_medsner_zs_q8_v1_en_5.4.0_3.0_1720040078717.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/jsl_medsner_zs_q8_v1_en_5.4.0_3.0_1720040078717.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
 
 ## How to use
 
@@ -42,7 +42,7 @@ This LLM model is trained to extract and link entities in a document. Users need
 ```python
 from sparknlp_jsl.llm import LLMLoader
 
-llm_loader_pretrained = LLMLoader(spark).pretrained("jsl_medner_zs_q8", "en", "clinical/models")
+llm_loader_pretrained = LLMLoader(spark).pretrained("jsl_medsner_zs_q8_v1", "en", "clinical/models")
 
 ptompt = """
 ### Template:
@@ -69,7 +69,7 @@ response = llm_loader_pretrained.generate(ptompt)
 import com.johnsnowlabs.ml.gguf.LLMLoader
 import com.johnsnowlabs.nlp.SparkAccessor.spark
 
-val llmLoader = new LLMLoader().setSparkSession(spark).pretrained("jsl_medner_zs_q8", "en", "clinical/models")
+val llmLoader = new LLMLoader().setSparkSession(spark).pretrained("jsl_medsner_zs_q8_v1", "en", "clinical/models")
 
 val prompt = """
 ### Template:
@@ -118,7 +118,7 @@ val response = llmLoader.generate(prompt)
 
 {:.table-model}
 |---|---|
-|Model Name:|jsl_medner_zs_q8|
+|Model Name:|jsl_medsner_zs_q8_v1|
 |Compatibility:|Healthcare NLP 5.4.0+|
 |License:|Licensed|
 |Edition:|Official|

diff --git a/docs/en/spark_nlp_healthcare_versions/licensed_release_notes.md b/docs/en/spark_nlp_healthcare_versions/licensed_release_notes.md
@@ -71,9 +71,9 @@ These enhancements will elevate your experience with Spark NLP for Healthcare, e
 |[JSL_MedM_q16_v1](https://nlp.johnsnowlabs.com/2024/07/12/jsl_medm_q16_v1_en.html)      |  Summarization, Q&A, RAG, and Chat |
 |[JSL_MedM_q8_v1](https://nlp.johnsnowlabs.com/2024/07/12/jsl_medm_q8_v1_en.html)       | Summarization, Q&A, RAG, and Chat |
 |[JSL_MedM_q4_v1](https://nlp.johnsnowlabs.com/2024/07/12/jsl_medm_q4_v1_en.html)       | Summarization, Q&A, RAG, and Chat |
-|[JSL_MedSNer_ZS_q16_v1](https://nlp.johnsnowlabs.com/2024/07/12/jsl_medsner_zh_q16_en.html)| Extract and link medical named entities |
-|[JSL_MedSNer_ZS_q8_v1](https://nlp.johnsnowlabs.com/2024/07/12/jsl_medsner_zh_q8_en.html) | Extract and link medical named entities |
-|[JSL_MedSNer_ZS_q4_v1](https://nlp.johnsnowlabs.com/2024/07/12/jsl_medsner_zh_q4_en.html) | Extract and link medical named entities |
+|[JSL_MedSNer_ZS_q16_v1](https://nlp.johnsnowlabs.com/2024/07/12/jsl_medsner_zh_q16_v1_en.html)| Extract and link medical named entities |
+|[JSL_MedSNer_ZS_q8_v1_v1](https://nlp.johnsnowlabs.com/2024/07/12/jsl_medsner_zh_q8_v1_en.html) | Extract and link medical named entities |
+|[JSL_MedSNer_ZS_q4_v1](https://nlp.johnsnowlabs.com/2024/07/12/jsl_medsner_zh_q4_v1_en.html) | Extract and link medical named entities |
 
 **We recommend using 8b quantized versions of the models as the qualitative performance difference between q16 and q8 versions is very negligible.**
 
@@ -83,7 +83,7 @@ These enhancements will elevate your experience with Spark NLP for Healthcare, e
 
 from sparknlp_jsl.llm import LLMLoader
 
-llm_loader_pretrained = LLMLoader(spark).pretrained("JSL_MedS_q16_v1", "en", "clinical/models")
+llm_loader_pretrained = LLMLoader(spark).pretrained("jsl_meds_q16_v1", "en", "clinical/models")
 
 llm_loader_pretrained.generate("What is the indication for the drug Methadone?")
 ```

diff --git a/docs/en/spark_nlp_healthcare_versions/release_notes_5_4_0.md b/docs/en/spark_nlp_healthcare_versions/release_notes_5_4_0.md
@@ -71,9 +71,9 @@ These enhancements will elevate your experience with Spark NLP for Healthcare, e
 |[JSL_MedM_q16_v1](https://nlp.johnsnowlabs.com/2024/07/12/jsl_medm_q16_v1_en.html)      |  Summarization, Q&A, RAG, and Chat |
 |[JSL_MedM_q8_v1](https://nlp.johnsnowlabs.com/2024/07/12/jsl_medm_q8_v1_en.html)       | Summarization, Q&A, RAG, and Chat |
 |[JSL_MedM_q4_v1](https://nlp.johnsnowlabs.com/2024/07/12/jsl_medm_q4_v1_en.html)       | Summarization, Q&A, RAG, and Chat |
-|[JSL_MedSNer_ZS_q16_v1](https://nlp.johnsnowlabs.com/2024/07/12/jsl_medsner_zh_q16_en.html)| Extract and link medical named entities |
-|[JSL_MedSNer_ZS_q8_v1](https://nlp.johnsnowlabs.com/2024/07/12/jsl_medsner_zh_q8_en.html) | Extract and link medical named entities |
-|[JSL_MedSNer_ZS_q4_v1](https://nlp.johnsnowlabs.com/2024/07/12/jsl_medsner_zh_q4_en.html) | Extract and link medical named entities |
+|[JSL_MedSNer_ZS_q16_v1](https://nlp.johnsnowlabs.com/2024/07/12/jsl_medsner_zh_q16_v1_en.html)| Extract and link medical named entities |
+|[JSL_MedSNer_ZS_q8_v1](https://nlp.johnsnowlabs.com/2024/07/12/jsl_medsner_zh_q8_v1_en.html) | Extract and link medical named entities |
+|[JSL_MedSNer_ZS_q4_v1](https://nlp.johnsnowlabs.com/2024/07/12/jsl_medsner_zh_q4_v1_en.html) | Extract and link medical named entities |
 
 **We recommend using 8b quantized versions of the models as the qualitative performance difference between q16 and q8 versions is very negligible.**
 
@@ -83,7 +83,7 @@ These enhancements will elevate your experience with Spark NLP for Healthcare, e
 
 from sparknlp_jsl.llm import LLMLoader
 
-llm_loader_pretrained = LLMLoader(spark).pretrained("JSL_MedS_q16_v1", "en", "clinical/models")
+llm_loader_pretrained = LLMLoader(spark).pretrained("jsl_meds_q16_v1", "en", "clinical/models")
 
 llm_loader_pretrained.generate("What is the indication for the drug Methadone?")
 ```