Word2Vec Embeddings in Tagalog (300d)


Word Embeddings lookup annotator that maps tokens to vectors.

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How to use

documentAssembler = DocumentAssembler() \
.setInputCol("text") \

tokenizer = Tokenizer() \
.setInputCols("document") \

embeddings = WordEmbeddingsModel.pretrained("w2v_cc_300d","tl") \
.setInputCols(["document", "token"]) \

pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])

data = spark.createDataFrame([["Gustung-gusto ko ang Spark NLP."]]).toDF("text")

result = pipeline.fit(data).transform(data)
val documentAssembler = new DocumentAssembler() 

val tokenizer = new Tokenizer() 

val embeddings = WordEmbeddingsModel.pretrained("w2v_cc_300d","tl") 
.setInputCols(Array("document", "token")) 

val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))

val data = Seq("Gustung-gusto ko ang Spark NLP.").toDF("text")

val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("tl.embed.w2v_cc_300d").predict("""Gustung-gusto ko ang Spark NLP.""")

Model Information

Model Name: w2v_cc_300d
Type: embeddings
Compatibility: Spark NLP 3.4.1+
License: Open Source
Edition: Official
Input Labels: [document, token]
Output Labels: [embeddings]
Language: tl
Size: 416.3 MB
Case sensitive: false
Dimension: 300