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130
docs/source/en/model_doc/roc_bert.md
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130
docs/source/en/model_doc/roc_bert.md
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<!--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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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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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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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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-->
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*This model was contributed to Hugging Face Transformers on 2022-11-08.*
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# RoCBert
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[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.
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You can find all the original RoCBert checkpoints under the [weiweishi](https://huggingface.co/weiweishi) profile.
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> [!TIP]
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> This model was contributed by [weiweishi](https://huggingface.co/weiweishi).
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>
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> Click on the RoCBert models in the right sidebar for more examples of how to apply RoCBert to different Chinese language tasks.
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The example below demonstrates how to predict the [MASK] token with [`Pipeline`], [`AutoModel`], and from the command line.
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<hfoptions id="usage">
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<hfoption id="Pipeline">
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```python
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from transformers import pipeline
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pipeline = pipeline(
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task="fill-mask",
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model="weiweishi/roc-bert-base-zh",
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device=0,
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)
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pipeline("這家餐廳的拉麵是我[MASK]過的最好的拉麵之")
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```
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</hfoption>
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<hfoption id="AutoModel">
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```python
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import torch
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from transformers import AutoModelForMaskedLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(
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"weiweishi/roc-bert-base-zh",
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)
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model = AutoModelForMaskedLM.from_pretrained(
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"weiweishi/roc-bert-base-zh",
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device_map="auto",
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)
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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)
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with torch.no_grad():
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outputs = model(**inputs)
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predictions = outputs.logits
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masked_index = torch.where(inputs['input_ids'] == tokenizer.mask_token_id)[1]
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predicted_token_id = predictions[0, masked_index].argmax(dim=-1)
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predicted_token = tokenizer.decode(predicted_token_id)
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print(f"The predicted token is: {predicted_token}")
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```
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</hfoption>
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</hfoptions>
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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
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