Description
Detects and corrects spelling errors in your input text. It’s based on the NorvigSweeting approach.
How to use
import sparknlp
from sparknlp.base import *
from sparknlp.annotator import *
from pyspark.ml import Pipeline
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols(["document"]) \
.setOutputCol("token")
spellChecker = NorvigSweetingModel.pretrained() \
.setInputCols(["token"]) \
.setOutputCol("spell")
pipeline = Pipeline().setStages([
documentAssembler,
tokenizer,
spellChecker
])
data = spark.createDataFrame([["somtimes i wrrite wordz erong."]]).toDF("text")
result = pipeline.fit(data).transform(data)
result.select("spell.result").show(truncate=False)
import spark.implicits._
import com.johnsnowlabs.nlp.base.DocumentAssembler
import com.johnsnowlabs.nlp.annotators.Tokenizer
import com.johnsnowlabs.nlp.annotators.spell.norvig.NorvigSweetingModel
import org.apache.spark.ml.Pipeline
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val tokenizer = new Tokenizer()
.setInputCols("document")
.setOutputCol("token")
val spellChecker = NorvigSweetingModel.pretrained()
.setInputCols("token")
.setOutputCol("spell")
val pipeline = new Pipeline().setStages(Array(
documentAssembler,
tokenizer,
spellChecker
))
val data = Seq("somtimes i wrrite wordz erong.").toDF("text")
val result = pipeline.fit(data).transform(data)
result.select("spell.result").show(false)
import nlu
nlu.load("en.spell.norvig").predict("""somtimes i wrrite wordz erong.""")
Results
+--------------------------------------+
|result |
+--------------------------------------+
|[sometimes, i, write, words, wrong, .]|
+--------------------------------------+
Model Information
| Model Name: | spellcheck_norvig |
| Compatibility: | Spark NLP 2.0.2+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [token] |
| Output Labels: | [checked] |
| Language: | en |