transformers/docs/source/en/model_doc/albert.md

ALBERT

Overview

The ALBERT model was proposed in ALBERT: A Lite BERT for Self-supervised Learning of Language Representations by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut. It presents two parameter-reduction techniques to lower memory consumption and increase the training speed of BERT:

• Splitting the embedding matrix into two smaller matrices.
• Using repeating layers split among groups.

The abstract from the paper is the following:

Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks. However, at some point further model increases become harder due to GPU/TPU memory limitations, longer training times, and unexpected model degradation. To address these problems, we present two parameter-reduction techniques to lower memory consumption and increase the training speed of BERT. Comprehensive empirical evidence shows that our proposed methods lead to models that scale much better compared to the original BERT. We also use a self-supervised loss that focuses on modeling inter-sentence coherence, and show it consistently helps downstream tasks with multi-sentence inputs. As a result, our best model establishes new state-of-the-art results on the GLUE, RACE, and SQuAD benchmarks while having fewer parameters compared to BERT-large.

Tips:

• ALBERT is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than the left.
• ALBERT uses repeating layers which results in a small memory footprint, however the computational cost remains similar to a BERT-like architecture with the same number of hidden layers as it has to iterate through the same number of (repeating) layers.
• Embedding size E is different from hidden size H justified because the embeddings are context independent (one embedding vector represents one token), whereas hidden states are context dependent (one hidden state represents a sequence of tokens) so it's more logical to have H >> E. Also, the embedding matrix is large since it's V x E (V being the vocab size). If E < H, it has less parameters.
• Layers are split in groups that share parameters (to save memory). Next sentence prediction is replaced by a sentence ordering prediction: in the inputs, we have two sentences A and B (that are consecutive) and we either feed A followed by B or B followed by A. The model must predict if they have been swapped or not.

This model was contributed by lysandre. This model jax version was contributed by kamalkraj. The original code can be found here.

Documentation resources

• Text classification task guide
• Token classification task guide
• Question answering task guide
• Masked language modeling task guide
• Multiple choice task guide

AlbertConfig

[[autodoc]] AlbertConfig

AlbertTokenizer

[[autodoc]] AlbertTokenizer - build_inputs_with_special_tokens - get_special_tokens_mask - create_token_type_ids_from_sequences - save_vocabulary

AlbertTokenizerFast

[[autodoc]] AlbertTokenizerFast

Albert specific outputs

[[autodoc]] models.albert.modeling_albert.AlbertForPreTrainingOutput

[[autodoc]] models.albert.modeling_tf_albert.TFAlbertForPreTrainingOutput

AlbertModel

[[autodoc]] AlbertModel - forward

AlbertForPreTraining

[[autodoc]] AlbertForPreTraining - forward

AlbertForMaskedLM

[[autodoc]] AlbertForMaskedLM - forward

AlbertForSequenceClassification

[[autodoc]] AlbertForSequenceClassification - forward

AlbertForMultipleChoice

[[autodoc]] AlbertForMultipleChoice

AlbertForTokenClassification

[[autodoc]] AlbertForTokenClassification - forward

AlbertForQuestionAnswering

[[autodoc]] AlbertForQuestionAnswering - forward

TFAlbertModel

[[autodoc]] TFAlbertModel - call

TFAlbertForPreTraining

[[autodoc]] TFAlbertForPreTraining - call

TFAlbertForMaskedLM

[[autodoc]] TFAlbertForMaskedLM - call

TFAlbertForSequenceClassification

[[autodoc]] TFAlbertForSequenceClassification - call

TFAlbertForMultipleChoice

[[autodoc]] TFAlbertForMultipleChoice - call

TFAlbertForTokenClassification

[[autodoc]] TFAlbertForTokenClassification - call

TFAlbertForQuestionAnswering

[[autodoc]] TFAlbertForQuestionAnswering - call

FlaxAlbertModel

[[autodoc]] FlaxAlbertModel - call

FlaxAlbertForPreTraining

[[autodoc]] FlaxAlbertForPreTraining - call

FlaxAlbertForMaskedLM

[[autodoc]] FlaxAlbertForMaskedLM - call

FlaxAlbertForSequenceClassification

[[autodoc]] FlaxAlbertForSequenceClassification - call

FlaxAlbertForMultipleChoice

[[autodoc]] FlaxAlbertForMultipleChoice - call

FlaxAlbertForTokenClassification

[[autodoc]] FlaxAlbertForTokenClassification - call

FlaxAlbertForQuestionAnswering

[[autodoc]] FlaxAlbertForQuestionAnswering - call