機械学習トランスフォーマーを使用して、エラーが発生しやすいテキストと文字列を効率的に照合するには、Apache Spark をどのように使用できますか?

<code class="scala">import org.apache.spark.ml.feature.{RegexTokenizer, NGram, HashingTF, MinHashLSH, MinHashLSHModel}

val tokenizer = new RegexTokenizer()
val ngram = new NGram().setN(3)
val vectorizer = new HashingTF()
val lsh = new MinHashLSH()

val pipeline = new Pipeline()
val model = pipeline.fit(db)

val dbHashed = model.transform(db)
val queryHashed = model.transform(query)

model.stages.last.asInstanceOf[MinHashLSHModel]
  .approxSimilarityJoin(dbHashed, queryHashed, 0.75).show</code>

<code class="python">from pyspark.ml import Pipeline
from pyspark.ml.feature import RegexTokenizer, NGram, HashingTF, MinHashLSH

model = Pipeline(stages=[
    RegexTokenizer(pattern="", inputCol="text", outputCol="tokens", minTokenLength=1),
    NGram(n=3, inputCol="tokens", outputCol="ngrams"),
    HashingTF(inputCol="ngrams", outputCol="vectors"),
    MinHashLSH(inputCol="vectors", outputCol="lsh")
]).fit(db)

db_hashed = model.transform(db)
query_hashed = model.transform(query)

model.stages[-1].approxSimilarityJoin(db_hashed, query_hashed, 0.75).show()</code>