Description
This is a scalable, production-ready Stopwords Remover model trained using the corpus available at stopwords-iso.
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols(["document"]) \
.setOutputCol("token")
stop_words = StopWordsCleaner.pretrained("stopwords_iso","zh") \
.setInputCols(["token"]) \
.setOutputCol("cleanTokens")
pipeline = Pipeline(stages=[documentAssembler, tokenizer, stop_words])
example = spark.createDataFrame([["除了作为北方之王之外，约翰·斯诺还是一位英国医生，也是麻醉和医疗卫生发展的领导者。"]], ["text"])
results = pipeline.fit(example).transform(example)
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val stop_words = new Tokenizer()
.setInputCols(Array("document"))
.setOutputCol("token")
val lemmatizer = StopWordsCleaner.pretrained("stopwords_iso","zh")
.setInputCols(Array("token"))
.setOutputCol("cleanTokens")
val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, stop_words))
val data = Seq("除了作为北方之王之外，约翰·斯诺还是一位英国医生，也是麻醉和医疗卫生发展的领导者。").toDF("text")
val results = pipeline.fit(data).transform(data)
import nlu
nlu.load("zh.stopwords").predict("""Put your text here.""")
Results
+-----------------------------------------------------------------------------------+
|result |
+-----------------------------------------------------------------------------------+
|[除了作为北方之王之外，约翰·斯诺还是一位英国医生，也是麻醉和医疗卫生发展的领导者。]|
+-----------------------------------------------------------------------------------+
Model Information
| Model Name: | stopwords_iso |
| Compatibility: | Spark NLP 3.4.1+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [token] |
| Output Labels: | [cleanTokens] |
| Language: | zh |
| Size: | 8.1 KB |