readme: ---
  language: zh
  ---
  
  # Bert-base-chinese
  
  ## Table of Contents
  - [Model Details](#model-details)
  - [Uses](#uses)
  - [Risks, Limitations and Biases](#risks-limitations-and-biases)
  - [Training](#training)
  - [Evaluation](#evaluation)
  - [How to Get Started With the Model](#how-to-get-started-with-the-model)
  
  
  # Model Details
  - **Model Description:**
  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).
  
  - **Developed by:** HuggingFace team
  - **Model Type:** Fill-Mask
  - **Language(s):** Chinese
  - **License:** [More Information needed]
  - **Parent Model:** See the [BERT base uncased model](https://huggingface.co/bert-base-uncased) for more information about the BERT base model.
  
  
  ## Uses
  
  #### Direct Use
  
  This model can be used for masked language modeling 
  
  
  
  ## Risks, Limitations and Biases
  **CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.**
  
  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)).
  
  
  ## Training
  
  #### Training Procedure
  * **type_vocab_size:** 2
  * **vocab_size:** 21128
  * **num_hidden_layers:** 12
  
  #### Training Data
  [More Information Needed]
  
  ## Evaluation
  
  #### Results
  
  [More Information Needed]
  
  
  ## How to Get Started With the Model
  ```python
  from transformers import AutoTokenizer, AutoModelForMaskedLM
  
  tokenizer = AutoTokenizer.from_pretrained("bert-base-chinese")
  
  model = AutoModelForMaskedLM.from_pretrained("bert-base-chinese")
  
  ```
  
  
  
  
  
  