# ALBERT

## Overview

The ALBERT model was proposed in [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942) 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.*

This model was contributed by [lysandre](https://huggingface.co/lysandre). This model jax version was contributed by

[kamalkraj](https://huggingface.co/kamalkraj). The original code can be found [here](https://github.com/google-research/ALBERT).

## Usage 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](https://huggingface.co/lysandre). This model jax version was contributed by

[kamalkraj](https://huggingface.co/kamalkraj). The original code can be found [here](https://github.com/google-research/ALBERT).

## Resources

The resources provided in the following sections consist of a list of official Hugging Face and community (indicated by 🌎) resources to help you get started with AlBERT. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

- [`AlbertForSequenceClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-classification).

- [`TFAlbertForSequenceClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/text-classification).

- [`FlaxAlbertForSequenceClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/flax/text-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification_flax.ipynb).

- Check the [Text classification task guide](../tasks/sequence_classification) on how to use the model.

- [`AlbertForTokenClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/token-classification).

- [`TFAlbertForTokenClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/token-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/token_classification-tf.ipynb).

- [`FlaxAlbertForTokenClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/flax/token-classification).

- [Token classification](https://huggingface.co/course/chapter7/2?fw=pt) chapter of the 🤗 Hugging Face Course.

- Check the [Token classification task guide](../tasks/token_classification) on how to use the model.

- [`AlbertForMaskedLM`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling#robertabertdistilbert-and-masked-language-modeling) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling.ipynb).

- [`TFAlbertForMaskedLM`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/language-modeling#run_mlmpy) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling-tf.ipynb).

- [`FlaxAlbertForMaskedLM`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/flax/language-modeling#masked-language-modeling) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/masked_language_modeling_flax.ipynb).

- [Masked language modeling](https://huggingface.co/course/chapter7/3?fw=pt) chapter of the 🤗 Hugging Face Course.

- Check the [Masked language modeling task guide](../tasks/masked_language_modeling) on how to use the model.

- [`AlbertForQuestionAnswering`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/question-answering) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/question_answering.ipynb).

- [`TFAlbertForQuestionAnswering`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/question-answering) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/question_answering-tf.ipynb).

- [`FlaxAlbertForQuestionAnswering`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/flax/question-answering).

- [Question answering](https://huggingface.co/course/chapter7/7?fw=pt) chapter of the 🤗 Hugging Face Course.

- Check the [Question answering task guide](../tasks/question_answering) on how to use the model.

**Multiple choice**

- [`AlbertForMultipleChoice`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/multiple-choice) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/multiple_choice.ipynb).

- [`TFAlbertForMultipleChoice`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/multiple-choice) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/multiple_choice-tf.ipynb).

- Check the  [Multiple choice task guide](../tasks/multiple_choice) on how to use the model.

## 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__