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