Lemma UD model for Slavic (lemma_torot)

Description

Pretrained Lemmatizer model (lemma_torot) trained on Universal Dependencies 2.9 (UD_Slavic-TOROT) in Slavic language.

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How to use

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

sentence = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx")\ 
    .setInputCols(["document"])\ 
    .setOutputCol("sentence")

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

lemma = LemmatizerModel.pretrained("lemma_torot", "orv")\ 
    .setInputCols(["sentence", "token"])\ 
    .setOutputCol("lemma")

pipeline = Pipeline(stages=[document, sentence, tokenizer, lemma])

data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text")

result = pipeline.fit(data).transform(data)

val document = new DocumentAssembler()
    .setInputCol("text")
    .setOutputCol("document")

val sentence = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx")
    .setInputCols("document")
    .setOutputCol("sentence")

val tokenizer = new Tokenizer() 
    .setInputCols("sentence") 
    .setOutputCol("token")

val lemma = LemmatizerModel.pretrained("lemma_torot", "orv")
    .setInputCols("sentence", "token")
    .setOutputCol("lemma")

val pipeline = new Pipeline().setStages(Array(document, sentence, tokenizer, lemma))

val data = Seq("I love Spark NLP").toDF("text")

val result = pipeline.fit(data).transform(data)

Model Information

Model Name: lemma_torot
Compatibility: Spark NLP 3.4.3+
License: Open Source
Edition: Official
Input Labels: [form]
Output Labels: [lemma]
Language: orv
Size: 427.2 KB

References

Model is trained on Universal Dependencies (treebank 2.9) UD_Slavic-TOROT https://github.com/UniversalDependencies/UD_Slavic-TOROT