ALBERT Embeddings (XXLarge Uncase)


ALBERT is “A Lite” version of BERT, a popular unsupervised language representation learning algorithm. ALBERT uses parameter-reduction techniques that allow for large-scale configurations, overcome previous memory limitations, and achieve better behavior with respect to model degradation. The details are described in the paper “ALBERT: A Lite BERT for Self-supervised Learning of Language Representations.


How to use

embeddings = AlbertEmbeddings.pretrained("albert_xxlarge_uncased", "en") \
      .setInputCols("sentence", "token") \

val embeddings = AlbertEmbeddings.pretrained("albert_xxlarge_uncased", "en")
      .setInputCols("sentence", "token")

Model Information

Model Name: albert_xxlarge_uncased
Type: embeddings
Compatibility: Spark NLP 2.5.0+
License: Open Source
Edition: Official
Input Labels: [sentence, token]
Output Labels: [word_embeddings]
Language: [en]
Dimension: 1024
Case sensitive: false

Data Source

The model is imported from