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* toctree * not-doctested.txt * collapse sections * feedback * update * rewrite get started sections * fixes * fix * loading models * fix * customize models * share * fix link * contribute part 1 * contribute pt 2 * fix toctree * tokenization pt 1 * Add new model (#32615) * v1 - working version * fix * fix * fix * fix * rename to correct name * fix title * fixup * rename files * fix * add copied from on tests * rename to `FalconMamba` everywhere and fix bugs * fix quantization + accelerate * fix copies * add `torch.compile` support * fix tests * fix tests and add slow tests * copies on config * merge the latest changes * fix tests * add few lines about instruct * Apply suggestions from code review Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * fix * fix tests --------- Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * "to be not" -> "not to be" (#32636) * "to be not" -> "not to be" * Update sam.md * Update trainer.py * Update modeling_utils.py * Update test_modeling_utils.py * Update test_modeling_utils.py * fix hfoption tag * tokenization pt. 2 * image processor * fix toctree * backbones * feature extractor * fix file name * processor * update not-doctested * update * make style * fix toctree * revision * make fixup * fix toctree * fix * make style * fix hfoption tag * pipeline * pipeline gradio * pipeline web server * add pipeline * fix toctree * not-doctested * prompting * llm optims * fix toctree * fixes * cache * text generation * fix * chat pipeline * chat stuff * xla * torch.compile * cpu inference * toctree * gpu inference * agents and tools * gguf/tiktoken * finetune * toctree * trainer * trainer pt 2 * optims * optimizers * accelerate * parallelism * fsdp * update * distributed cpu * hardware training * gpu training * gpu training 2 * peft * distrib debug * deepspeed 1 * deepspeed 2 * chat toctree * quant pt 1 * quant pt 2 * fix toctree * fix * fix * quant pt 3 * quant pt 4 * serialization * torchscript * scripts * tpu * review * model addition timeline * modular * more reviews * reviews * fix toctree * reviews reviews * continue reviews * more reviews * modular transformers * more review * zamba2 * fix * all frameworks * pytorch * supported model frameworks * flashattention * rm check_table * not-doctested.txt * rm check_support_list.py * feedback * updates/feedback * review * feedback * fix * update * feedback * updates * update --------- Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com> Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> Co-authored-by: Quentin Gallouédec <45557362+qgallouedec@users.noreply.github.com>
144 lines
4.5 KiB
Markdown
144 lines
4.5 KiB
Markdown
<!--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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# MPNet
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<div class="flex flex-wrap space-x-1">
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<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
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<img alt="TensorFlow" src="https://img.shields.io/badge/TensorFlow-FF6F00?style=flat&logo=tensorflow&logoColor=white">
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</div>
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## Overview
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The MPNet model was proposed in [MPNet: Masked and Permuted Pre-training for Language Understanding](https://arxiv.org/abs/2004.09297) by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
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MPNet adopts a novel pre-training method, named masked and permuted language modeling, to inherit the advantages of
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masked language modeling and permuted language modeling for natural language understanding.
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The abstract from the paper is the following:
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*BERT adopts masked language modeling (MLM) for pre-training and is one of the most successful pre-training models.
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Since BERT neglects dependency among predicted tokens, XLNet introduces permuted language modeling (PLM) for
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pre-training to address this problem. However, XLNet does not leverage the full position information of a sentence and
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thus suffers from position discrepancy between pre-training and fine-tuning. In this paper, we propose MPNet, a novel
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pre-training method that inherits the advantages of BERT and XLNet and avoids their limitations. MPNet leverages the
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dependency among predicted tokens through permuted language modeling (vs. MLM in BERT), and takes auxiliary position
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information as input to make the model see a full sentence and thus reducing the position discrepancy (vs. PLM in
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XLNet). We pre-train MPNet on a large-scale dataset (over 160GB text corpora) and fine-tune on a variety of
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down-streaming tasks (GLUE, SQuAD, etc). Experimental results show that MPNet outperforms MLM and PLM by a large
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margin, and achieves better results on these tasks compared with previous state-of-the-art pre-trained methods (e.g.,
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BERT, XLNet, RoBERTa) under the same model setting.*
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The original code can be found [here](https://github.com/microsoft/MPNet).
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## Usage tips
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MPNet doesn't have `token_type_ids`, you don't need to indicate which token belongs to which segment. Just
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separate your segments with the separation token `tokenizer.sep_token` (or `[sep]`).
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## 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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## MPNetConfig
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[[autodoc]] MPNetConfig
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## MPNetTokenizer
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[[autodoc]] MPNetTokenizer
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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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## MPNetTokenizerFast
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[[autodoc]] MPNetTokenizerFast
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<frameworkcontent>
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<pt>
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## MPNetModel
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[[autodoc]] MPNetModel
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- forward
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## MPNetForMaskedLM
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[[autodoc]] MPNetForMaskedLM
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- forward
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## MPNetForSequenceClassification
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[[autodoc]] MPNetForSequenceClassification
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- forward
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## MPNetForMultipleChoice
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[[autodoc]] MPNetForMultipleChoice
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- forward
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## MPNetForTokenClassification
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[[autodoc]] MPNetForTokenClassification
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- forward
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## MPNetForQuestionAnswering
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[[autodoc]] MPNetForQuestionAnswering
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- forward
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</pt>
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<tf>
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## TFMPNetModel
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[[autodoc]] TFMPNetModel
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- call
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## TFMPNetForMaskedLM
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[[autodoc]] TFMPNetForMaskedLM
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- call
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## TFMPNetForSequenceClassification
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[[autodoc]] TFMPNetForSequenceClassification
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- call
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## TFMPNetForMultipleChoice
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[[autodoc]] TFMPNetForMultipleChoice
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- call
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## TFMPNetForTokenClassification
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[[autodoc]] TFMPNetForTokenClassification
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- call
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## TFMPNetForQuestionAnswering
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[[autodoc]] TFMPNetForQuestionAnswering
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- call
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</tf>
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</frameworkcontent>
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