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* 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>
98 lines
4.2 KiB
ReStructuredText
98 lines
4.2 KiB
ReStructuredText
Transformer XL
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Overview
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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The Transformer-XL model was proposed in `Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
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<https://arxiv.org/abs/1901.02860>`__ by Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan
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Salakhutdinov. It's a causal (uni-directional) transformer with relative positioning (sinusoïdal) embeddings which can
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reuse previously computed hidden-states to attend to longer context (memory). This model also uses adaptive softmax
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inputs and outputs (tied).
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The abstract from the paper is the following:
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*Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the
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setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning dependency
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beyond a fixed length without disrupting temporal coherence. It consists of a segment-level recurrence mechanism and
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a novel positional encoding scheme. Our method not only enables capturing longer-term dependency, but also resolves
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the context fragmentation problem. As a result, Transformer-XL learns dependency that is 80% longer than RNNs and
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450% longer than vanilla Transformers, achieves better performance on both short and long sequences, and is up
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to 1,800+ times faster than vanilla Transformers during evaluation. Notably, we improve the state-of-the-art results
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of bpc/perplexity to 0.99 on enwiki8, 1.08 on text8, 18.3 on WikiText-103, 21.8 on One Billion Word, and 54.5 on
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Penn Treebank (without finetuning). When trained only on WikiText-103, Transformer-XL manages to generate reasonably
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coherent, novel text articles with thousands of tokens.*
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Tips:
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- Transformer-XL uses relative sinusoidal positional embeddings. Padding can be done on the left or on the right.
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The original implementation trains on SQuAD with padding on the left, therefore the padding defaults are set to left.
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- Transformer-XL is one of the few models that has no sequence length limit.
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The original code can be found `here <https://github.com/kimiyoung/transformer-xl>`__.
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TransfoXLConfig
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TransfoXLConfig
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:members:
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TransfoXLTokenizer
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TransfoXLTokenizer
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:members: save_vocabulary
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TransfoXLTokenizerFast
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TransfoXLTokenizerFast
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:members:
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TransfoXL specific outputs
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.modeling_transfo_xl.TransfoXLModelOutput
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:members:
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.. autoclass:: transformers.modeling_transfo_xl.TransfoXLLMHeadModelOutput
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:members:
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.. autoclass:: transformers.modeling_tf_transfo_xl.TFTransfoXLModelOutput
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:members:
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.. autoclass:: transformers.modeling_tf_transfo_xl.TFTransfoXLLMHeadModelOutput
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:members:
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TransfoXLModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TransfoXLModel
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:members: forward
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TransfoXLLMHeadModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TransfoXLLMHeadModel
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:members: forward
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TFTransfoXLModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TFTransfoXLModel
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:members: call
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TFTransfoXLLMHeadModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TFTransfoXLLMHeadModel
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:members: call
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