Stop Words Cleaner for English


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.

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How to use

stop_words = StopWordsCleaner.pretrained("stopwords_en", "en") \
        .setInputCols(["token"]) \
nlp_pipeline = Pipeline(stages=[document_assembler, tokenizer, stop_words])
light_pipeline = LightPipeline([['']]).toDF("text")))
results = light_pipeline.fullAnnotate("Other than being the king of the north, John Snow is a an English physician and a leader in the development of anaesthesia and medical hygiene.")

val stopWords = StopWordsCleaner.pretrained("stopwords_en", "en")


[Row(annotatorType='token', begin=21, end=24, result='king', metadata={'sentence': '0'}, embeddings=[]),
Row(annotatorType='token', begin=33, end=37, result='north', metadata={'sentence': '0'}, embeddings=[]),
Row(annotatorType='token', begin=38, end=38, result=',', metadata={'sentence': '0'}, embeddings=[]),
Row(annotatorType='token', begin=40, end=43, result='John', metadata={'sentence': '0'}, embeddings=[]),
Row(annotatorType='token', begin=45, end=48, result='Snow', metadata={'sentence': '0'}, embeddings=[]),

Model Information

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

Data Source

The model is imported from