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<meta name="description" content="DescriptionThis LLM model is trained to perform Q&amp;A, Summarization, RAG, and Chat.Predicted EntitiesLive DemoOpen in ColabDownloadCopy S3 URIHow to use PythonJSLScalaNLU from sparknlp_jsl.llm import LLMLoaderllm_loader_pretrained = LLMLoader(spark).pretrained("jsl_medm_q16_v1", "en", "clinical/models")ptompt =...">
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<!-- End Google Tag Manager --><title>JSL_MedM (LLM - q4) | jsl_medm_q4_v1 | Healthcare NLP 5.4.0</title><meta property="og:title" content=""/>

<meta name="description" content="DescriptionThis LLM model is trained to perform Q&amp;A, Summarization, RAG, and Chat.Predicted EntitiesLive DemoOpen in ColabDownloadCopy S3 URIHow to use PythonJSLScalaNLU from sparknlp_jsl.llm import LLMLoaderllm_loader_pretrained = LLMLoader(spark).pretrained("jsl_medm_q4_v1", "en", "clinical/models")ptompt = ...">
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<meta name="description" content="DescriptionThis LLM model is trained to perform Q&amp;A, Summarization, RAG, and Chat.Predicted EntitiesLive DemoOpen in ColabDownloadCopy S3 URIHow to use PythonJSLScalaNLU from sparknlp_jsl.llm import LLMLoaderllm_loader_pretrained = LLMLoader(spark).pretrained("jsl_medm_q8_v1", "en", "clinical/models")ptompt = ...">
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<meta name="description" content="DescriptionThis LLM model is trained to extract and link entities in a document. Users needs to define an input schema as explained in the example section. Drug is defined as a list which tells the model that there could be multiple drugs in the document and it has to extract all of them. Each drug has properties li...">
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</div><ul class="right-col menu"><li><i class="far fa-user"></i> <span>John Snow Labs</span></li><li><i class="far fa-calendar-alt"></i> <span>Jul 03, 2024</span>
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