2023-05-13-spellcheck_drug_norvig_en

docs/_posts/Ahmetemintek/2023-05-13-spellcheck_drug_norvig_en.md

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+---
+layout: model
+title: Spell Checker for Drug Names (Norvig)
+author: John Snow Labs
+name: spellcheck_drug_norvig
+date: 2023-05-13
+tags: [spellcheck, clinical, en, drug, norvig, licensed]
+task: Spell Check
+language: en
+edition: Healthcare NLP 4.4.0
+spark_version: 3.0
+supported: true
+annotator: NorvigSweetingModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This model corrects spelling mistakes in drug names by using The Symmetric Delete spelling correction algorithm which reduces the complexity of edit candidate generation and dictionary lookup for a given Damerau-Levenshtein distance.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+[Open in Colab](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/3.Clinical_Entity_Resolvers.ipynb){:.button.button-orange.button-orange-trans.co.button-icon}
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/spellcheck_drug_norvig_en_4.4.0_3.0_1684020970665.zip){:.button.button-orange}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/spellcheck_drug_norvig_en_4.4.0_3.0_1684020970665.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
+documentAssembler = DocumentAssembler()\
+    .setInputCol("text")\
+    .setOutputCol("document")
+
+tokenizer = Tokenizer()\
+    .setInputCols("document")\
+    .setOutputCol("token")
+
+spell = NorvigSweetingModel.pretrained("spellcheck_drug_norvig", "en", "clinical/models")\
+    .setInputCols("token")\
+    .setOutputCol("corrected_token")\
+
+pipeline = Pipeline(
+    stages = [
+        documentAssembler,
+        tokenizer, 
+        spell
+        ])
+
+model = pipeline.fit(spark.createDataFrame([['']]).toDF('text')) 
+
+text = "You have to take Amrosia artemisiifoli, Oactra and a bit of Grastk and lastacaf"
+test_df= spark.createDataFrame([[text]]).toDF("text")
+result= model.transform(test_df)
+```
+```scala
+val documentAssembler = new DocumentAssembler()
+    .setInputCol("text")
+    .setOutputCol("document")
+
+val tokenizer = new Tokenizer()
+    .setInputCols(Array("document"))
+    .setOutputCol("token")
+
+val spell= NorvigSweetingModel.pretrained("spellcheck_drug_norvig", "en", "clinical/models")
+    .setInputCols("token")
+    .setOutputCol("corrected_token")
+
+val pipeline =  new Pipeline().setStages(Array(documentAssembler, tokenizer, spell))
+
+val data = Seq("You have to take Amrosia artemisiifoli, Oactra and a bit of Grastk and lastacaf").toDS.toDF("text")
+val result= pipeline.fit(data).transform(data)
+```
+</div>
+
+## Results
+
+```bash
+Original Text: 
+You have to take Amrosia artemisiifoli , Oactra and a bit of Grastk and lastacaf  
+
+Corrected Text: 
+You have to take Ambrosia artemisiifolia , Odactra and a bit of Grastek and lastacaft
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|spellcheck_drug_norvig|
+|Compatibility:|Healthcare NLP 4.4.0+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[token]|
+|Output Labels:|[spell]|
+|Language:|en|
+|Size:|4.5 MB|
+|Case sensitive:|true|