Description
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_xlarge_uncased", "en") \
.setInputCols("sentence", "token") \
.setOutputCol("embeddings")
val embeddings = AlbertEmbeddings.pretrained("albert_xlarge_uncased", "en")
.setInputCols("sentence", "token")
.setOutputCol("embeddings")
Model Information
| Model Name: | albert_xlarge_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: | 2048 |
| Case sensitive: | false |
Data Source
The model is imported from https://tfhub.dev/google/albert_xlarge/3
PREVIOUSALBERT Large Uncase