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Model Card (#3)

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- Model Card (2c08eac0204c98ccf16ca350e73f9c850f92ed4c)


Co-authored-by: Ezi Ozoani

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  language: zh
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  language: zh
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+
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+ # Bert-base-chinese
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+
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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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+
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+
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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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+
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+
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+ ## Uses
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+
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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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+
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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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