Stop Words Cleaner for Zulu

Description

This model removes ‘stop words’ from text. Stop words are words so common that they can 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.

Open in Colab Download

How to use


stop_words = StopWordsCleaner.pretrained("stopwords_zu", "zu") \
        .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("Ngaphandle kokuba yinkosi yasenyakatho, uJohn Snow ungudokotela waseNgilandi futhi ungumholi ekwenziweni kwe-anesthesia kanye nenhlanzeko yezokwelapha.")

val stopWords = StopWordsCleaner.pretrained("stopwords_zu", "zu")
        .setInputCols(Array("token"))
        .setOutputCol("cleanTokens")

Results

[Row(annotatorType='token', begin=0, end=9, result='Ngaphandle', metadata={'sentence': '0'}, embeddings=[]),
Row(annotatorType='token', begin=11, end=16, result='kokuba', metadata={'sentence': '0'}, embeddings=[]),
Row(annotatorType='token', begin=18, end=24, result='yinkosi', metadata={'sentence': '0'}, embeddings=[]),
Row(annotatorType='token', begin=26, end=37, result='yasenyakatho', metadata={'sentence': '0'}, embeddings=[]),
Row(annotatorType='token', begin=38, end=38, result=',', metadata={'sentence': '0'}, embeddings=[]),
...]

Model Information

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

Data Source

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