Word2Vec Embeddings in Maltese (300d)

Description

Word Embeddings lookup annotator that maps tokens to vectors.

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How to use

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

tokenizer = Tokenizer() \
    .setInputCols("document") \
    .setOutputCol("token")
  
embeddings = WordEmbeddingsModel.pretrained("w2v_cc_300d","mt") \
    .setInputCols(["document", "token"]) \
    .setOutputCol("embeddings")
    
pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])

data = spark.createDataFrame([["Inħobb Spark NLP"]]).toDF("text")

result = pipeline.fit(data).transform(data)
val documentAssembler = new DocumentAssembler() 
      .setInputCol("text") 
      .setOutputCol("document")
 
val tokenizer = new Tokenizer() 
    .setInputCols(Array("document"))
    .setOutputCol("token")

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

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

val data = Seq("Inħobb Spark NLP").toDF("text")

val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("mt.embed.w2v_cc_300d").predict("""Inħobb 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: mt
Size: 113.8 MB
Case sensitive: false
Dimension: 300