Urdu Lemmatizer


This model uses context and language knowledge to assign all forms and inflections of a word to a single root. This enables the pipeline to treat the past and present tense of a verb, for example, as the same word instead of two completely different words. The lemmatizer takes into consideration the context surrounding a word to determine which root is correct when the word form alone is ambiguous.

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How to use

Use as part of a pipeline after tokenisation.

lemmatizer = LemmatizerModel.pretrained("lemma", "ur") \
        .setInputCols(["token"]) \
nlp_pipeline = Pipeline(stages=[document_assembler, tokenizer, lemmatizer])
light_pipeline = LightPipeline(nlp_pipeline.fit(spark.createDataFrame([['']]).toDF("text")))
results = light_pipeline.fullAnnotate(['کی	جوکہ	جیسے	جنھیں	جنھوں	جسے	انھوں	جنہوں	انہیں	جو	جس	جنہیں	جن'])
val lemmatizer = LemmatizerModel.pretrained("lemma", "ur")
val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, lemmatizer))
val result = pipeline.fit(Seq.empty["کی	جوکہ	جیسے	جنھیں	جنھوں	جسے	انھوں	جنہوں	انہیں	جو	جس	جنہیں	جن"].toDS.toDF("text")).transform(data)
import nlu

text = ["""کی	جوکہ	جیسے	جنھیں	جنھوں	جسے	انھوں	جنہوں	انہیں	جو	جس	جنہیں	جن"""]
lemma_df = nlu.load('ur.lemma').predict(text, output_level='document')


{'lemma': [Annotation(token, 0, 1, كر, {'sentence': '0'}),
   Annotation(token, 3, 6, جوکہ, {'sentence': '0'}),
   Annotation(token, 8, 11, جیسا, {'sentence': '0'}),
   Annotation(token, 13, 17, جو, {'sentence': '0'}),
   Annotation(token, 19, 23, جو, {'sentence': '0'}),
   Annotation(token, 25, 27, جو, {'sentence': '0'}),
   Annotation(token, 29, 33, وہ, {'sentence': '0'}),

Model Information

Model Name: lemma
Compatibility: Spark NLP 2.7.0+
Edition: Official
Input Labels: [token]
Output Labels: [lemma]
Language: ur

Data Source

This model is trained on data obtained from https://universaldependencies.org/