Add model 2023-03-09-medical_qa_biogpt_en (#13621)

Co-authored-by: Cabir40 <cabir4006@gmail.com>

docs/_posts/Cabir40/2023-03-09-medical_qa_biogpt_en.md

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+---
+layout: model
+title: Medical Question Answering (biogpt)
+author: John Snow Labs
+name: medical_qa_biogpt
+date: 2023-03-09
+tags: [licensed, clinical, en, gpt, biogpt, pubmed, question_answering, tensorflow]
+task: Question Answering
+language: en
+edition: Healthcare NLP 4.3.1
+spark_version: 3.0
+supported: true
+engine: tensorflow
+annotator: MedicalQuestionAnswering
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This model has been trained with medical documents and can generate two types of answers, short and long.
+Types of questions are supported: `"short"` (producing yes/no/maybe) answers and `"full"` (long answers).
+
+## Predicted Entities
+
+
+
+{:.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/medical_qa_biogpt_en_4.3.1_3.0_1678355315206.zip){:.button.button-orange}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/medical_qa_biogpt_en_4.3.1_3.0_1678355315206.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+document_assembler = MultiDocumentAssembler()\
+    .setInputCols("question", "context")\
+    .setOutputCols("document_question", "document_context")
+
+med_qa = sparknlp_jsl.annotators.MedicalQuestionAnswering\
+    .pretrained("medical_qa_biogpt","en","clinical/models")\
+    .setInputCols(["document_question", "document_context"])\
+    .setOutputCol("answer")\
+    .setMaxNewTokens(30)\
+    .setTopK(1)\
+    .setQuestionType("long") # "short"
+
+pipeline = Pipeline(stages=[document_assembler, med_qa])
+
+paper_abstract = "The visual indexing theory proposed by Zenon Pylyshyn (Cognition, 32, 65-97, 1989) predicts that visual attention mechanisms are employed when mental images are projected onto a visual scene."
+long_question = "What is the effect of directing attention on memory?"
+yes_no_question = "Does directing attention improve memory for items?"
+
+data = spark.createDataFrame(
+    [
+        [long_question, paper_abstract, "long"],
+        [yes_no_question, paper_abstract, "short"],
+    ]
+).toDF("question", "context", "question_type")
+
+pipeline.fit(data).transform(data.where("question_type == 'long'"))\
+    .select("answer.result")\
+    .show(truncate=False)
+
+pipeline.fit(data).transform(data.where("question_type == 'short'"))\
+    .select("answer.result")\
+    .show(truncate=False)
+```
+```scala
+val document_assembler = new MultiDocumentAssembler()
+    .setInputCols("question", "context")
+    .setOutputCols("document_question", "document_context")
+
+val med_qa = MedicalQuestionAnswering
+    .pretrained("medical_qa_biogpt","en","clinical/models")
+    .setInputCols(("document_question", "document_context"))
+    .setOutputCol("answer")
+    .setMaxNewTokens(30)
+    .setTopK(1)
+    .setQuestionType("long") # "short"
+
+val pipeline = new Pipeline().setStages(Array(document_assembler, med_qa))
+
+paper_abstract = "The visual indexing theory proposed by Zenon Pylyshyn (Cognition, 32, 65-97, 1989) predicts that visual attention mechanisms are employed when mental images are projected onto a visual scene."
+long_question = "What is the effect of directing attention on memory?"
+yes_no_question = "Does directing attention improve memory for items?"
+
+val data = Seq( 
+    (long_question, paper_abstract,"long" ),
+    (yes_no_question, paper_abstract, "short"))
+    .toDS.toDF("question", "context", "question_type")
+
+val result = pipeline.fit(data).transform(data)
+```
+</div>
+
+## Results
+
+```bash
++------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+|result                                                                                                                                                                        |
++------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+|[the present study investigated whether directing spatial attention to one location in a visual array would enhance memory for the array features. participants memorized two]|
++------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|medical_qa_biogpt|
+|Compatibility:|Healthcare NLP 4.3.1+|
+|License:|Licensed|
+|Edition:|Official|
+|Language:|en|
+|Size:|1.1 GB|
+|Case sensitive:|true|