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transformers/docs/source/en/model_doc/roc_bert.md

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+http://www.apache.org/licenses/LICENSE-2.0
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+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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+*This model was released on 2022-05-27 and added to Hugging Face Transformers on 2022-11-08.*
+
+<div style="float: right;">
+   <div class="flex flex-wrap space-x-1">
+          <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
+   </div>
+</div>
+
+# RoCBert
+
+[RoCBert](https://aclanthology.org/2022.acl-long.65.pdf) is a pretrained Chinese [BERT](./bert) model designed against adversarial attacks like typos and synonyms. It is pretrained with a contrastive learning objective to align normal and adversarial text examples. The examples include different semantic, phonetic, and visual features of Chinese. This makes RoCBert more robust against manipulation.
+
+You can find all the original RoCBert checkpoints under the [weiweishi](https://huggingface.co/weiweishi) profile.
+
+> [!TIP]
+> This model was contributed by [weiweishi](https://huggingface.co/weiweishi).
+>
+> Click on the RoCBert models in the right sidebar for more examples of how to apply RoCBert to different Chinese language tasks.
+
+The example below demonstrates how to predict the [MASK] token with [`Pipeline`], [`AutoModel`], and from the command line.
+
+<hfoptions id="usage">
+<hfoption id="Pipeline">
+
+```py
+import torch
+from transformers import pipeline
+
+pipeline = pipeline(
+   task="fill-mask",
+   model="weiweishi/roc-bert-base-zh",
+   dtype=torch.float16,
+   device=0
+)
+pipeline("這家餐廳的拉麵是我[MASK]過的最好的拉麵之")
+```
+
+</hfoption>
+<hfoption id="AutoModel">
+
+```py
+import torch
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained(
+   "weiweishi/roc-bert-base-zh",
+)
+model = AutoModelForMaskedLM.from_pretrained(
+   "weiweishi/roc-bert-base-zh",
+   dtype=torch.float16,
+   device_map="auto",
+)
+inputs = tokenizer("這家餐廳的拉麵是我[MASK]過的最好的拉麵之", return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+   outputs = model(**inputs)
+   predictions = outputs.logits
+
+masked_index = torch.where(inputs['input_ids'] == tokenizer.mask_token_id)[1]
+predicted_token_id = predictions[0, masked_index].argmax(dim=-1)
+predicted_token = tokenizer.decode(predicted_token_id)
+
+print(f"The predicted token is: {predicted_token}")
+```
+
+</hfoption>
+<hfoption id="transformers CLI">
+
+```bash
+echo -e "這家餐廳的拉麵是我[MASK]過的最好的拉麵之" | transformers run --task fill-mask --model weiweishi/roc-bert-base-zh --device 0
+```
+
+</hfoption>
+</hfoptions>
+
+## 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