Lemmatizer (Italian, SpacyLookup)

Description

This Italian Lemmatizer is an scalable, production-ready version of the Rule-based Lemmatizer available in Spacy Lookups Data repository.

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How to use

documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")

tokenizer = Tokenizer() \
.setInputCols(["document"]) \
.setOutputCol("token")

lemmatizer = LemmatizerModel.pretrained("lemma_spacylookup","it") \
.setInputCols(["token"]) \
.setOutputCol("lemma")

pipeline = Pipeline(stages=[documentAssembler, tokenizer, lemmatizer]) 

example = spark.createDataFrame([["Non sei migliore di me"]], ["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","it") 
.setInputCols(Array("token")) 
.setOutputCol("lemma")

val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, lemmatizer))
val data = Seq("Non sei migliore di me").toDF("text")
val results = pipeline.fit(data).transform(data)
import nlu
nlu.load("it.lemma.spacylookup").predict("""Non sei migliore di me""")

Results

+----------------------------+
|result                      |
+----------------------------+
|[Non, essere, buono, di, me]|
+----------------------------+

Model Information

Model Name: lemma_spacylookup
Compatibility: Spark NLP 3.4.1+
License: Open Source
Edition: Official
Input Labels: [token]
Output Labels: [lemma]
Language: it
Size: 3.5 MB