mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-06 22:30:09 +06:00
3323146e90
* Clean up model documentation * Formatting * Preparation work * Long lines * Main work on rst files * Cleanup all config files * Syntax fix * Clean all tokenizers * Work on first models * Models beginning * FaluBERT * All PyTorch models * All models * Long lines again * Fixes * More fixes * Update docs/source/model_doc/bert.rst Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * Update docs/source/model_doc/electra.rst Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * Last fixes Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
157 lines
5.9 KiB
ReStructuredText
157 lines
5.9 KiB
ReStructuredText
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.
|
|
|
|
The original code can be found `here <https://github.com/google-research/ALBERT>`__.
|
|
|
|
AlbertConfig
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
.. autoclass:: transformers.AlbertConfig
|
|
:members:
|
|
|
|
|
|
AlbertTokenizer
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
.. autoclass:: transformers.AlbertTokenizer
|
|
:members: build_inputs_with_special_tokens, get_special_tokens_mask,
|
|
create_token_type_ids_from_sequences, save_vocabulary
|
|
|
|
|
|
Albert specific outputs
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
.. autoclass:: transformers.modeling_albert.AlbertForPreTrainingOutput
|
|
:members:
|
|
|
|
.. autoclass:: transformers.modeling_tf_albert.TFAlbertForPreTrainingOutput
|
|
:members:
|
|
|
|
|
|
AlbertModel
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
.. autoclass:: transformers.AlbertModel
|
|
:members: forward
|
|
|
|
|
|
AlbertForPreTraining
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
.. autoclass:: transformers.AlbertForPreTraining
|
|
:members: forward
|
|
|
|
|
|
AlbertForMaskedLM
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
.. autoclass:: transformers.AlbertForMaskedLM
|
|
:members: forward
|
|
|
|
|
|
AlbertForSequenceClassification
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
.. autoclass:: transformers.AlbertForSequenceClassification
|
|
:members: forward
|
|
|
|
|
|
AlbertForMultipleChoice
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
.. autoclass:: transformers.AlbertForMultipleChoice
|
|
:members:
|
|
|
|
|
|
AlbertForTokenClassification
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
.. autoclass:: transformers.AlbertForTokenClassification
|
|
:members: forward
|
|
|
|
|
|
AlbertForQuestionAnswering
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
.. autoclass:: transformers.AlbertForQuestionAnswering
|
|
:members: forward
|
|
|
|
|
|
TFAlbertModel
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
.. autoclass:: transformers.TFAlbertModel
|
|
:members: call
|
|
|
|
|
|
TFAlbertForPreTraining
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
.. autoclass:: transformers.TFAlbertForPreTraining
|
|
:members: call
|
|
|
|
|
|
TFAlbertForMaskedLM
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
.. autoclass:: transformers.TFAlbertForMaskedLM
|
|
:members: call
|
|
|
|
|
|
TFAlbertForSequenceClassification
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
.. autoclass:: transformers.TFAlbertForSequenceClassification
|
|
:members: call
|
|
|
|
|
|
TFAlbertForMultipleChoice
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
.. autoclass:: transformers.TFAlbertForMultipleChoice
|
|
:members: call
|
|
|
|
|
|
TFAlbertForTokenClassification
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
.. autoclass:: transformers.TFAlbertForTokenClassification
|
|
:members: call
|
|
|
|
|
|
TFAlbertForQuestionAnswering
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
.. autoclass:: transformers.TFAlbertForQuestionAnswering
|
|
:members: call
|