Model Card (#3)

- Model Card (2c08eac0204c98ccf16ca350e73f9c850f92ed4c)


Co-authored-by: Ezi Ozoani <Ezi@users.noreply.huggingface.co>

README.md

@@ -1,3 +1,71 @@
 ---
 language: zh
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+
+# 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")
+
+```
+
+
+
+
+