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.
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")
val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, stopWords))
val result = pipeline.fit(Seq.empty["Ngaphandle kokuba yinkosi yasenyakatho, uJohn Snow ungudokotela waseNgilandi futhi ungumholi ekwenziweni kwe-anesthesia kanye nenhlanzeko yezokwelapha."].toDS.toDF("text")).transform(data)
Results
[Row(annotatorType='token', begin=0, end=9, result='Ngaphandle', metadata={'sentence': '0'}),
Row(annotatorType='token', begin=11, end=16, result='kokuba', metadata={'sentence': '0'}),
Row(annotatorType='token', begin=18, end=24, result='yinkosi', metadata={'sentence': '0'}),
Row(annotatorType='token', begin=26, end=37, result='yasenyakatho', metadata={'sentence': '0'}),
Row(annotatorType='token', begin=38, end=38, result=',', metadata={'sentence': '0'}),
...]
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