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* added resources: links to task guides that support these models * minor polishing * conflict resolved * link fix * Update docs/source/en/model_doc/vision-encoder-decoder.mdx Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> --------- Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
102 lines
3.3 KiB
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102 lines
3.3 KiB
Plaintext
<!--Copyright 2022 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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# RoCBert
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## Overview
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The RoCBert model was proposed in [RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining](https://aclanthology.org/2022.acl-long.65.pdf) by HuiSu, WeiweiShi, XiaoyuShen, XiaoZhou, TuoJi, JiaruiFang, JieZhou.
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It's a pretrained Chinese language model that is robust under various forms of adversarial attacks.
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The abstract from the paper is the following:
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*Large-scale pretrained language models have achieved SOTA results on NLP tasks. However, they have been shown
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vulnerable to adversarial attacks especially for logographic languages like Chinese. In this work, we propose
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ROCBERT: a pretrained Chinese Bert that is robust to various forms of adversarial attacks like word perturbation,
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synonyms, typos, etc. It is pretrained with the contrastive learning objective which maximizes the label consistency
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under different synthesized adversarial examples. The model takes as input multimodal information including the
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semantic, phonetic and visual features. We show all these features are important to the model robustness since the
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attack can be performed in all the three forms. Across 5 Chinese NLU tasks, ROCBERT outperforms strong baselines under
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three blackbox adversarial algorithms without sacrificing the performance on clean testset. It also performs the best
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in the toxic content detection task under human-made attacks.*
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This model was contributed by [weiweishi](https://huggingface.co/weiweishi).
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## Documentation 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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- [Causal language modeling task guide](./tasks/language_modeling)
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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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## RoCBertConfig
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[[autodoc]] RoCBertConfig
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- all
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## RoCBertTokenizer
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[[autodoc]] RoCBertTokenizer
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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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## RoCBertModel
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[[autodoc]] RoCBertModel
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- forward
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## RoCBertForPreTraining
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[[autodoc]] RoCBertForPreTraining
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- forward
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## RoCBertForCausalLM
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[[autodoc]] RoCBertForCausalLM
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- forward
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## RoCBertForMaskedLM
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[[autodoc]] RoCBertForMaskedLM
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- forward
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## RoCBertForSequenceClassification
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[[autodoc]] transformers.RoCBertForSequenceClassification
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- forward
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## RoCBertForMultipleChoice
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[[autodoc]] transformers.RoCBertForMultipleChoice
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- forward
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## RoCBertForTokenClassification
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[[autodoc]] transformers.RoCBertForTokenClassification
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- forward
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## RoCBertForQuestionAnswering
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[[autodoc]] RoCBertForQuestionAnswering
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- forward |