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docs/source/en/model_doc/albert.mdx

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+<!--Copyright 2020 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# 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.*
+
+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.
+
+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).
+
+## 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__