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  • John Snow Labs
  • Mar 09, 2021

Part of Speech for Marathi

  • part_of_speech
  • open_source
  • marathi
  • pos_ud_ufal
  • mr

Description

A Part of Speech classifier predicts a grammatical label for every token in the input text. Implemented with an averaged perceptron architecture.

Predicted Entities

  • DET
  • AUX
  • NOUN
  • PUNCT
  • PRON
  • ADJ
  • CCONJ
  • ADV
  • VERB
  • SCONJ
  • NUM
  • ADP
  • INTJ
  • PROPN

Live Demo Open in Colab Download Copy S3 URI

How to use


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

sentence_detector = SentenceDetector() \
  .setInputCols(["document"]) \
  .setOutputCol("sentence")

pos = PerceptronModel.pretrained("pos_ud_ufal", "mr") \
  .setInputCols(["document", "token"]) \
  .setOutputCol("pos")

pipeline = Pipeline(stages=[
  document_assembler,
  sentence_detector,
  posTagger
])

example = spark.createDataFrame([['जॉन हिम लॅब्समधून हॅलो! ']], ["text"])

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




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

val sentence_detector = SentenceDetector()
        .setInputCols("document")
.setOutputCol("sentence")

val pos = PerceptronModel.pretrained("pos_ud_ufal", "mr")
        .setInputCols(Array("document", "token"))
        .setOutputCol("pos")

val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, pos))

val data = Seq("जॉन हिम लॅब्समधून हॅलो! ").toDF("text")
val result = pipeline.fit(data).transform(data)



import nlu
text = [""जॉन हिम लॅब्समधून हॅलो! ""]
token_df = nlu.load('mr.pos').predict(text)
token_df

Results

       token    pos
                   
0        जॉन  PROPN
1        हिम   NOUN
2  लॅब्समधून    ADJ
3       हॅलो   VERB
4          !  PUNCT

Model Information

Model Name: pos_ud_ufal
Compatibility: Spark NLP 3.0.0+
License: Open Source
Edition: Official
Input Labels: [document, token]
Output Labels: [pos]
Language: mr