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language: zh |
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# Bert-base-chinese |
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## Table of Contents |
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- [Model Details](#model-details) |
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- [Uses](#uses) |
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- [Risks, Limitations and Biases](#risks-limitations-and-biases) |
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- [Training](#training) |
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- [Evaluation](#evaluation) |
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- [How to Get Started With the Model](#how-to-get-started-with-the-model) |
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# Model Details |
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- **Model Description:** |
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This model has been pre-trained for Chinese, training and random input masking has been applied independently to word pieces (as in the original BERT paper). |
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- **Developed by:** HuggingFace team |
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- **Model Type:** Fill-Mask |
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- **Language(s):** Chinese |
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- **License:** [More Information needed] |
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- **Parent Model:** See the [BERT base uncased model](https://huggingface.co/bert-base-uncased) for more information about the BERT base model. |
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## Uses |
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#### Direct Use |
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This model can be used for masked language modeling |
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## Risks, Limitations and Biases |
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**CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.** |
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Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). |
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## Training |
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#### Training Procedure |
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* **type_vocab_size:** 2 |
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* **vocab_size:** 21128 |
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* **num_hidden_layers:** 12 |
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#### Training Data |
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[More Information Needed] |
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## Evaluation |
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#### Results |
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[More Information Needed] |
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## How to Get Started With the Model |
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```python |
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from transformers import AutoTokenizer, AutoModelForMaskedLM |
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tokenizer = AutoTokenizer.from_pretrained("bert-base-chinese") |
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model = AutoModelForMaskedLM.from_pretrained("bert-base-chinese") |
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``` |
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