Lemma UD model for Latvian (pos_lvtb)

Description

Pretrained Lemmatizer model (pos_lvtb) trained on Universal Dependencies 2.9 (UD_Latvian-LVTB) in Latvian language.

Open in Colab Download

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") 

pos = PerceptronModel.pretrained("pos_lvtb", "lv")\ 
.setInputCols(["sentence", "token"])\ 
.setOutputCol("pos")

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

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 pos = PerceptronModel.pretrained("pos_lvtb", "lv")
.setInputCols("sentence", "token")
.setOutputCol("pos")

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

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

val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("lv.pos").predict("""I love Spark NLP""")

Model Information

Model Name: pos_lvtb
Compatibility: Spark NLP 3.4.3+
License: Open Source
Edition: Official
Input Labels: [sentence, form]
Output Labels: [pos]
Language: lv
Size: 4.0 MB

References

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