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","ceb") \
.setInputCols(["document", "token"]) \
.setOutputCol("embeddings")
pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
data = spark.createDataFrame([["Ganahan ko 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","ceb")
.setInputCols(Array("document", "token"))
.setOutputCol("embeddings")
val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))
val data = Seq("Ganahan ko spark nlp").toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("ceb.embed.w2v_cc_300d").predict("""Ganahan ko 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: | ceb |
| Size: | 1.2 GB |
| Case sensitive: | false |
| Dimension: | 300 |