Stopwords Remover for Catalan language (280 entries)

Description

This is a scalable, production-ready Stopwords Remover model trained using the corpus available at stopwords-iso.

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How to use

documentAssembler = DocumentAssembler() \
    .setInputCol("text") \
    .setOutputCol("document")

tokenizer = Tokenizer() \
    .setInputCols(["document"]) \
    .setOutputCol("token")

stop_words = StopWordsCleaner.pretrained("stopwords_iso","ca") \
    .setInputCols(["token"]) \
    .setOutputCol("cleanTokens")

pipeline = Pipeline(stages=[documentAssembler, tokenizer, stop_words]) 

example = spark.createDataFrame([["No ets millor que jo"]], ["text"]) 

results = pipeline.fit(example).transform(example)
val documentAssembler = new DocumentAssembler() 
            .setInputCol("text") 
            .setOutputCol("document")

val stop_words = new Tokenizer() 
    .setInputCols(Array("document"))
    .setOutputCol("token")

val lemmatizer = StopWordsCleaner.pretrained("stopwords_iso","ca") 
    .setInputCols(Array("token")) 
    .setOutputCol("cleanTokens")

val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, stop_words))
val data = Seq("No ets millor que jo").toDF("text")
val results = pipeline.fit(data).transform(data)
import nlu
nlu.load("ca.stopwords").predict("""No ets millor que jo""")

Results

+--------+
|result  |
+--------+
|[millor]|
+--------+

Model Information

Model Name: stopwords_iso
Compatibility: Spark NLP 3.4.1+
License: Open Source
Edition: Official
Input Labels: [token]
Output Labels: [cleanTokens]
Language: ca
Size: 2.1 KB