Description
Pretrained Lemmatizer model (lemma_ewt) trained on Universal Dependencies 2.9 (UD_English-EWT) in English language.
Open in Colab Download Copy S3 URI
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_ewt", "en")\
.setInputCols(["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_ewt", "en")
.setInputCols("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)
import nlu
nlu.load("en.lemma.ewt").predict("""I love Spark NLP""")
Model Information
| Model Name: | lemma_ewt |
| Compatibility: | Spark NLP 3.4.3+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [form] |
| Output Labels: | [lemma] |
| Language: | en |
| Size: | 257.9 KB |
References
Model is trained on Universal Dependencies (treebank 2.9) UD_English-EWT
https://github.com/UniversalDependencies/UD_English-EWT