Description
Word Embeddings lookup annotator that maps tokens to vectors.
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
embeddings = WordEmbeddingsModel.pretrained("w2v_cc_300d","nso") \
.setInputCols(["document", "token"]) \
.setOutputCol("embeddings")
pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
data = spark.createDataFrame([["I love 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","nso")
.setInputCols(Array("document", "token"))
.setOutputCol("embeddings")
val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
val data = Seq("I love Spark NLP").toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("nso.embed.w2v_cc_300d").predict("""I love 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: | nso |
Size: | 51.9 MB |
Case sensitive: | false |
Dimension: | 300 |