Breton 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

lemmatizer = LemmatizerModel.pretrained("lemma", "br") \
        .setInputCols(["token"]) \
nlp_pipeline = Pipeline(stages=[document_assembler, tokenizer, lemmatizer])
light_pipeline = LightPipeline([['']]).toDF("text")))
results = light_pipeline.fullAnnotate("Distaolit dimp hon dleoù evel m'hor bo ivez distaolet d'hon dleourion.")
val lemmatizer = LemmatizerModel.pretrained("lemma", "br")
val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, lemmatizer))
val result =["Distaolit dimp hon dleoù evel m'hor bo ivez distaolet d'hon dleourion."].toDS.toDF("text")).transform(data)
import nlu

text = ["""Distaolit dimp hon dleoù evel m'hor bo ivez distaolet d'hon dleourion."""]
lemma_df = nlu.load('br.lemma').predict(text, output_level='document')


[Row(annotatorType='token', begin=0, end=8, result='Distaolit', metadata={'sentence': '0'}, embeddings=[]),
Row(annotatorType='token', begin=10, end=13, result='_', metadata={'sentence': '0'}, embeddings=[]),
Row(annotatorType='token', begin=15, end=17, result='kaout', metadata={'sentence': '0'}, embeddings=[]),
Row(annotatorType='token', begin=19, end=23, result='dleoù', metadata={'sentence': '0'}, embeddings=[]),
Row(annotatorType='token', begin=25, end=28, result='evel', metadata={'sentence': '0'}, embeddings=[]),

Model Information

Model Name: lemma
Type: lemmatizer
Compatibility: Spark NLP 2.5.5+
Edition: Official
Input labels: [token]
Output labels: [lemma]
Language: br
Case sensitive: false
License: Open Source

Data Source

The model is imported from