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_br", "br") \
.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("Distaolit dimp hon dleoù evel m'hor bo ivez distaolet d'hon dleourion.")
...
val stopWords = StopWordsCleaner.pretrained("stopwords_br", "br")
.setInputCols(Array("token"))
.setOutputCol("cleanTokens")
val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, stopWords))
val data = Seq("Distaolit dimp hon dleoù evel m"hor bo ivez distaolet d"hon dleourion.").toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
text = ["""Distaolit dimp hon dleoù evel m'hor bo ivez distaolet d'hon dleourion."""]
stopword_df = nlu.load('br.stopwords').predict(text)
stopword_df[["cleanTokens"]]
Results
[Row(annotatorType='token', begin=0, end=8, result='Distaolit', metadata={'sentence': '0'}),
Row(annotatorType='token', begin=10, end=13, result='dimp', metadata={'sentence': '0'}),
Row(annotatorType='token', begin=15, end=17, result='hon', metadata={'sentence': '0'}),
Row(annotatorType='token', begin=19, end=23, result='dleoù', metadata={'sentence': '0'}),
Row(annotatorType='token', begin=25, end=28, result='evel', metadata={'sentence': '0'}),
...]
Model Information
| Model Name: | stopwords_br |
| Type: | stopwords |
| Compatibility: | Spark NLP 2.5.4+ |
| Edition: | Official |
| Input Labels: | [token] |
| Output Labels: | [cleanTokens] |
| Language: | br |
| Case sensitive: | false |
| License: | Open Source |
Data Source
The model is imported from https://github.com/WorldBrain/remove-stopwords