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Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>

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Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>

docs/source/en/model_doc/roc_bert.md

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+<!--Copyright 2022 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# RoCBert
+
+## Overview
+
+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.
+It's a pretrained Chinese language model that is robust under various forms of adversarial attacks.
+
+The abstract from the paper is the following:
+
+*Large-scale pretrained language models have achieved SOTA results on NLP tasks. However, they have been shown
+vulnerable to adversarial attacks especially for logographic languages like Chinese. In this work, we propose
+ROCBERT: a pretrained Chinese Bert that is robust to various forms of adversarial attacks like word perturbation,
+synonyms, typos, etc. It is pretrained with the contrastive learning objective which maximizes the label consistency
+under different synthesized adversarial examples. The model takes as input multimodal information including the
+semantic, phonetic and visual features. We show all these features are important to the model robustness since the
+attack can be performed in all the three forms. Across 5 Chinese NLU tasks, ROCBERT outperforms strong baselines under
+three blackbox adversarial algorithms without sacrificing the performance on clean testset. It also performs the best
+in the toxic content detection task under human-made attacks.*
+
+This model was contributed by [weiweishi](https://huggingface.co/weiweishi).
+
+## Documentation resources
+
+- [Text classification task guide](../tasks/sequence_classification)
+- [Token classification task guide](../tasks/token_classification)
+- [Question answering task guide](../tasks/question_answering)
+- [Causal language modeling task guide](../tasks/language_modeling)
+- [Masked language modeling task guide](../tasks/masked_language_modeling)
+- [Multiple choice task guide](../tasks/multiple_choice)
+
+## RoCBertConfig
+
+[[autodoc]] RoCBertConfig
+    - all
+
+
+## RoCBertTokenizer
+
+[[autodoc]] RoCBertTokenizer
+    - build_inputs_with_special_tokens
+    - get_special_tokens_mask
+    - create_token_type_ids_from_sequences
+    - save_vocabulary
+
+
+## RoCBertModel
+
+[[autodoc]] RoCBertModel
+    - forward
+
+
+## RoCBertForPreTraining
+
+[[autodoc]] RoCBertForPreTraining
+    - forward
+
+
+## RoCBertForCausalLM
+
+[[autodoc]] RoCBertForCausalLM
+    - forward
+
+
+## RoCBertForMaskedLM
+
+[[autodoc]] RoCBertForMaskedLM
+    - forward
+
+
+## RoCBertForSequenceClassification
+
+[[autodoc]] transformers.RoCBertForSequenceClassification
+    - forward
+
+## RoCBertForMultipleChoice
+
+[[autodoc]] transformers.RoCBertForMultipleChoice
+    - forward
+
+
+## RoCBertForTokenClassification
+
+[[autodoc]] transformers.RoCBertForTokenClassification
+    - forward
+
+
+## RoCBertForQuestionAnswering
+
+[[autodoc]] RoCBertForQuestionAnswering
+    - forward