Add model 2021-11-05-bert_sequence_classifier_question_statement_clinical_en

Author: josejuanmartinez

docs/_posts/josejuanmartinez/2021-11-05-bert_sequence_classifier_question_statement_clinical_en.md

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+---
+layout: model
+title: Bert for Sequence Classification: Question vs Statement (Clinical)
+author: John Snow Labs
+name: bert_sequence_classifier_question_statement_clinical
+date: 2021-11-05
+tags: [question, statement, clinical, en, licensed]
+task: Text Classification
+language: en
+edition: Spark NLP for Healthcare 3.3.2
+spark_version: 3.0
+supported: true
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Trained to add sentence classifying capabilities to distinguish between Question vs Statements in clinical domain.
+
+This model was imported from Hugging Face (https://huggingface.co/shahrukhx01/question-vs-statement-classifier), trained based on Haystack (https://github.com/deepset-ai/haystack/issues/611) and finetuned by John Snow Labs with in-house clinical annotations.
+
+## Predicted Entities
+
+`question`, `statement`
+
+{:.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/bert_sequence_classifier_question_statement_clinical_en_3.3.2_3.0_1636106577489.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+documentAssembler = DocumentAssembler()\
+    .setInputCol("text")\
+    .setOutputCol("document")
+
+sentenceDetector = SentenceDetectorDLModel.pretrained() \
+    .setInputCols(["document"]) \
+    .setOutputCol("sentence")
+
+tokenizer = Tokenizer()\
+    .setInputCols("sentence")\
+    .setOutputCol("token")
+
+seq = BertForSequenceClassification.pretrained('bert_sequence_classifier_question_statement_clinical', 'en', 'clinical/models')\
+  .setInputCols(["token", "sentence"])\
+  .setOutputCol("label")\
+  .setCaseSensitive(True)
+
+pipeline = Pipeline(stages = [
+    documentAssembler,
+    sentenceDetector,
+    tokenizer,
+    seq])
+
+test_sentences = ["""Hello I am going to be having a baby throughand have just received my medical results before I have my tubes tested. I had the tests on day 23 of my cycle. My progresterone level is 10. What does this mean? What does progesterone level of 10 indicate?
+Your progesterone report is perfectly normal. We expect this result on day 23rd of the cycle.So there's nothing to worry as it's perfectly alright"""]
+
+res = p_model.transform(spark.createDataFrame(pd.DataFrame({'text': test_sentences})))
+```
+```scala
+val documentAssembler = DocumentAssembler()\
+    .setInputCol("text")\
+    .setOutputCol("document")
+
+val sentenceDetector = SentenceDetectorDLModel.pretrained() \
+    .setInputCols(["document"]) \
+    .setOutputCol("sentence")
+
+val tokenizer = Tokenizer()\
+    .setInputCols("sentence")\
+    .setOutputCol("token")
+
+val seq = BertForSequenceClassification.pretrained('bert_sequence_classifier_question_statement_clinical', 'en', 'clinical/models')\
+  .setInputCols(["token", "sentence"])\
+  .setOutputCol("label")\
+  .setCaseSensitive(True)
+
+val pipeline = new Pipeline().setStages(Array(
+    documentAssembler,
+    sentenceDetector,
+    tokenizer,
+    seq))
+
+val test_sentences = "Hello I am going to be having a baby throughand have just received my medical results before I have my tubes tested. I had the tests on day 23 of my cycle.
+My progresterone level is 10. What does this mean? What does progesterone level of 10 indicate?
+Your progesterone report is perfectly normal. We expect this result on day 23rd of the cycle.So there's nothing to worry as it's perfectly alright"
+
+val example = Seq.empty[test_sentences].toDS.toDF("text")
+val result = pipeline.fit(example).transform(example)
+```
+</div>
+
+## Results
+
+```bash
+```
++--------------------------------------------------------------------------------------------------------------------+---------+
+|sentence                                                                                                            |label    |
++--------------------------------------------------------------------------------------------------------------------+---------+
+|Hello I am going to be having a baby throughand have just received my medical results before I have my tubes tested.|statement|
+|I had the tests on day 23 of my cycle.                                                                              |statement|
+|My progresterone level is 10.                                                                                       |statement|
+|What does this mean?                                                                                                |question |
+|What does progesterone level of 10 indicate?                                                                        |question |
+|Your progesterone report is perfectly normal. We expect this result on day 23rd of the cycle.                       |statement|
+|So there's nothing to worry as it's perfectly alright                                                               |statement|
++--------------------------------------------------------------------------------------------------------------------+---------
+```
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|bert_sequence_classifier_question_statement_clinical|
+|Compatibility:|Spark NLP for Healthcare 3.3.2+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[token, sentence]|
+|Output Labels:|[label]|
+|Language:|en|
+|Case sensitive:|true|
+
+## Data Source
+
+For generic domain training:
+https://github.com/deepset-ai/haystack/issues/611
+
+For finetuning in clinical domain, in house JSL annotations based on clinical Q&A.
+
+## Benchmarking
+
+```bash
+```
+              precision    recall  f1-score   support
+
+    question       0.97      0.94      0.96       243
+   statement       0.98      0.99      0.99       729
+
+    accuracy                           0.98       972
+   macro avg       0.98      0.97      0.97       972
+weighted avg       0.98      0.98      0.98       972
+```
+```