ALBERT Embeddings (Base Uncase) Optimized

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.

Predicted Entities

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How to use

embeddings = AlbertEmbeddings.pretrained("albert_base_uncased_opt", "en") \
.setInputCols("sentence", "token") \
.setOutputCol("embeddings")
val embeddings = AlbertEmbeddings.pretrained("albert_base_uncased_opt", "en")
.setInputCols("sentence", "token")
.setOutputCol("embeddings")
import nlu

text = ["I love NLP"]
embeddings_df = nlu.load('en.embed.albert.base_uncased').predict(text, output_level='token')
embeddings_df

Model Information

Model Name: albert_base_uncased_opt
Compatibility: Spark NLP 5.0.2+
License: Open Source
Edition: Official
Input Labels: [token, sentence]
Output Labels: [embeddings]
Language: en
Size: 115.0 MB
Case sensitive: false

References

https://huggingface.co/albert-base-v2