Description
Word Embeddings lookup annotator that maps tokens to vectors.
How to use
model = WordEmbeddingsModel.pretrained("embeddings_biovec","en","clinical/models")\
.setInputCols("document","token")\
.setOutputCol("word_embeddings")
val model = WordEmbeddingsModel.pretrained("embeddings_biovec","en","clinical/models")
.setInputCols("document","token")
.setOutputCol("word_embeddings")
import nlu
nlu.load("en.embed.glove.biovec").predict("""Put your text here.""")
Results
Word2Vec feature vectors based on embeddings_biovec.
Model Information
| Name: | embeddings_biovec |
| Type: | WordEmbeddingsModel |
| Compatibility: | Spark NLP 2.5.0+ |
| License: | Licensed |
| Edition: | Official |
| Input labels: | [document, token] |
| Output labels: | [word_embeddings] |
| Language: | en |
| Dimension: | 300.0 |
Data Source
Trained on PubMed corpora https://github.com/ncbi-nlp/BioSentVec