ALBERT Large Uncase

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.

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How to use


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

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

Model Information

Model Name: albert_large_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 https://tfhub.dev/google/albert_large/3