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* Rename index.mdx to index.md * With saved modifs * Address review comment * Treat all files * .mdx -> .md * Remove special char * Update utils/tests_fetcher.py Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr> --------- Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>
196 lines
6.3 KiB
Markdown
196 lines
6.3 KiB
Markdown
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# ALBERT
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<div class="flex flex-wrap space-x-1">
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<a href="https://huggingface.co/models?filter=albert">
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<img alt="Models" src="https://img.shields.io/badge/All_model_pages-albert-blueviolet">
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</a>
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<a href="https://huggingface.co/spaces/docs-demos/albert-base-v2">
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<img alt="Spaces" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue">
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</a>
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</div>
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## Overview
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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,
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Radu Soricut. It presents two parameter-reduction techniques to lower memory consumption and increase the training
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speed of BERT:
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- Splitting the embedding matrix into two smaller matrices.
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- Using repeating layers split among groups.
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The abstract from the paper is the following:
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*Increasing model size when pretraining natural language representations often results in improved performance on
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downstream tasks. However, at some point further model increases become harder due to GPU/TPU memory limitations,
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longer training times, and unexpected model degradation. To address these problems, we present two parameter-reduction
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techniques to lower memory consumption and increase the training speed of BERT. Comprehensive empirical evidence shows
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that our proposed methods lead to models that scale much better compared to the original BERT. We also use a
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self-supervised loss that focuses on modeling inter-sentence coherence, and show it consistently helps downstream tasks
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with multi-sentence inputs. As a result, our best model establishes new state-of-the-art results on the GLUE, RACE, and
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SQuAD benchmarks while having fewer parameters compared to BERT-large.*
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Tips:
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- ALBERT is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather
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than the left.
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- ALBERT uses repeating layers which results in a small memory footprint, however the computational cost remains
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similar to a BERT-like architecture with the same number of hidden layers as it has to iterate through the same
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number of (repeating) layers.
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- 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.
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- Layers are split in groups that share parameters (to save memory).
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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.
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This model was contributed by [lysandre](https://huggingface.co/lysandre). This model jax version was contributed by
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[kamalkraj](https://huggingface.co/kamalkraj). The original code can be found [here](https://github.com/google-research/ALBERT).
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## Documentation resources
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- [Text classification task guide](../tasks/sequence_classification)
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- [Token classification task guide](../tasks/token_classification)
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- [Question answering task guide](../tasks/question_answering)
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- [Masked language modeling task guide](../tasks/masked_language_modeling)
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- [Multiple choice task guide](../tasks/multiple_choice)
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## AlbertConfig
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[[autodoc]] AlbertConfig
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## AlbertTokenizer
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[[autodoc]] AlbertTokenizer
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- build_inputs_with_special_tokens
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- get_special_tokens_mask
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- create_token_type_ids_from_sequences
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- save_vocabulary
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## AlbertTokenizerFast
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[[autodoc]] AlbertTokenizerFast
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## Albert specific outputs
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[[autodoc]] models.albert.modeling_albert.AlbertForPreTrainingOutput
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[[autodoc]] models.albert.modeling_tf_albert.TFAlbertForPreTrainingOutput
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## AlbertModel
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[[autodoc]] AlbertModel
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- forward
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## AlbertForPreTraining
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[[autodoc]] AlbertForPreTraining
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- forward
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## AlbertForMaskedLM
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[[autodoc]] AlbertForMaskedLM
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- forward
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## AlbertForSequenceClassification
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[[autodoc]] AlbertForSequenceClassification
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- forward
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## AlbertForMultipleChoice
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[[autodoc]] AlbertForMultipleChoice
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## AlbertForTokenClassification
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[[autodoc]] AlbertForTokenClassification
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- forward
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## AlbertForQuestionAnswering
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[[autodoc]] AlbertForQuestionAnswering
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- forward
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## TFAlbertModel
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[[autodoc]] TFAlbertModel
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- call
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## TFAlbertForPreTraining
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[[autodoc]] TFAlbertForPreTraining
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- call
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## TFAlbertForMaskedLM
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[[autodoc]] TFAlbertForMaskedLM
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- call
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## TFAlbertForSequenceClassification
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[[autodoc]] TFAlbertForSequenceClassification
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- call
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## TFAlbertForMultipleChoice
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[[autodoc]] TFAlbertForMultipleChoice
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- call
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## TFAlbertForTokenClassification
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[[autodoc]] TFAlbertForTokenClassification
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- call
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## TFAlbertForQuestionAnswering
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[[autodoc]] TFAlbertForQuestionAnswering
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- call
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## FlaxAlbertModel
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[[autodoc]] FlaxAlbertModel
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- __call__
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## FlaxAlbertForPreTraining
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[[autodoc]] FlaxAlbertForPreTraining
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- __call__
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## FlaxAlbertForMaskedLM
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[[autodoc]] FlaxAlbertForMaskedLM
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- __call__
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## FlaxAlbertForSequenceClassification
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[[autodoc]] FlaxAlbertForSequenceClassification
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- __call__
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## FlaxAlbertForMultipleChoice
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[[autodoc]] FlaxAlbertForMultipleChoice
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- __call__
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## FlaxAlbertForTokenClassification
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[[autodoc]] FlaxAlbertForTokenClassification
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- __call__
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## FlaxAlbertForQuestionAnswering
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[[autodoc]] FlaxAlbertForQuestionAnswering
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- __call__
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