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","ur") \
.setInputCols(["document", "token"]) \
.setOutputCol("embeddings")
pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])
data = spark.createDataFrame([["مجھے سپارک این ایل پی سے محبت ہے"]]).toDF("text")
result = pipeline.fit(data).transform(data)
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: | ur |
Size: | 672.4 MB |
Case sensitive: | false |
Dimension: | 300 |