Stop Words Cleaner for Anglo-French

Description

This model removes ‘stop words’ from text. Stop words are words so common that they can be removed without significantly altering the meaning of a text. Removing stop words is useful when one wants to deal with only the most semantically important words in a text, and ignore words that are rarely semantically relevant, such as articles and prepositions.

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How to use

...
stop_words = StopWordsCleaner.pretrained("stopwords_af", "af") \
        .setInputCols(["token"]) \
        .setOutputCol("cleanTokens")
nlp_pipeline = Pipeline(stages=[document_assembler, tokenizer, stop_words])
light_pipeline = LightPipeline(nlp_pipeline.fit(spark.createDataFrame([['']]).toDF("text")))
results = light_pipeline.fullAnnotate("Anders as die koning van die noorde, is John Snow 'n Engelse dokter en 'n leier in die ontwikkeling van narkose en mediese higiëne.")
...
val stopWords = StopWordsCleaner.pretrained("stopwords_af", "af")
        .setInputCols(Array("token"))
        .setOutputCol("cleanTokens")
val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, stopWords))
val result = pipeline.fit(Seq.empty["Anders as die koning van die noorde, is John Snow 'n Engelse dokter en 'n leier in die ontwikkeling van narkose en mediese higiëne."].toDS.toDF("text")).transform(data)
import nlu

text = ["""Anders as die koning van die noorde, is John Snow 'n Engelse dokter en 'n leier in die ontwikkeling van narkose en mediese higiëne."""]
stopword_df = nlu.load('af.stopwords').predict(text)
stopword_df[["cleanTokens"]]

Results

[Row(annotatorType='token', begin=0, end=5, result='Anders', metadata={'sentence': '0'}),
Row(annotatorType='token', begin=14, end=19, result='koning', metadata={'sentence': '0'}),
Row(annotatorType='token', begin=29, end=34, result='noorde', metadata={'sentence': '0'}),
Row(annotatorType='token', begin=35, end=35, result=',', metadata={'sentence': '0'}),
Row(annotatorType='token', begin=40, end=43, result='John', metadata={'sentence': '0'}),
...]

Model Information

Model Name: stopwords_af
Type: stopwords
Compatibility: Spark NLP 2.5.4+
Edition: Official
Input Labels: [token]
Output Labels: [cleanTokens]
Language: af
Case sensitive: false
License: Open Source

Data Source

The model is imported from https://github.com/WorldBrain/remove-stopwords