Description
Word Embeddings lookup annotator that maps tokens to vectors.
How to use
model = WordEmbeddingsModel.pretrained("w2v_cc_300d","de","clinical/models")
.setInputCols(["document","token"])
.setOutputCol("word_embeddings")
val model = WordEmbeddingsModel.pretrained("w2v_cc_300d","de","clinical/models")
.setInputCols("document","token")
.setOutputCol("word_embeddings")
Results
Word2Vec feature vectors based on w2v_cc_300d
.
Model Information
Name: | w2v_cc_300d |
Type: | WordEmbeddingsModel |
Compatibility: | Spark NLP for Healthcare 2.5.5+ |
License: | Licensed |
Edition: | Official |
Input labels: | [document, token] |
Output labels: | [word_embeddings] |
Language: | de |
Dimension: | 300.0 |
Data Source
FastText common crawl word embeddings for Germany https://fasttext.cc/docs/en/crawl-vectors.html
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