Description
This Macedonian Lemmatizer is an scalable, production-ready version of the Rule-based Lemmatizer available in Spacy Lookups Data repository.
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols(["document"]) \
.setOutputCol("token")
lemmatizer = LemmatizerModel.pretrained("lemma_spacylookup","mk") \
.setInputCols(["token"]) \
.setOutputCol("lemma")
pipeline = Pipeline(stages=[documentAssembler, tokenizer, lemmatizer])
example = spark.createDataFrame([["Вие не сте подобри од мене"]], ["text"])
results = pipeline.fit(example).transform(example)
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val tokenizer = new Tokenizer()
.setInputCols(Array("document"))
.setOutputCol("token")
val lemmatizer = LemmatizerModel.pretrained("lemma_spacylookup","mk")
.setInputCols(Array("token"))
.setOutputCol("lemma")
val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, lemmatizer))
val data = Seq("Вие не сте подобри од мене").toDF("text")
val results = pipeline.fit(data).transform(data)
import nlu
nlu.load("mk.lemma").predict("""Вие не сте подобри од мене""")
Results
+---------------------------------+
|result |
+---------------------------------+
|[Вие, не, сте, подобри, од, мене]|
+---------------------------------+
Model Information
| Model Name: | lemma_spacylookup |
| Compatibility: | Spark NLP 3.4.1+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [token] |
| Output Labels: | [lemma] |
| Language: | mk |
| Size: | 9.4 KB |