Upload README.md

README.md

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+---
+datasets:
+- botp/yentinglin-zh_TW_c4
+language:
+- zh
+pipeline_tag: fill-mask
+---
+
+| Dataset\BERT Pretrain  | bert-based-chinese | ckiplab | GufoLab |
+| ------------- |:-------------:|:-------------:|:-------------:|
+| 5000 Tradition Chinese Dataset	|0.7183|	0.6989|	**0.8081**|
+| 10000 Sol-Idea Dataset	| 0.7874|	0.7913|	**0.8025**|
+| ALL DataSet	| 0.7694| 	0.7678| 	**0.8038**|
+
+### Model Sources
+- **Paper:** [BERT](https://arxiv.org/abs/1810.04805)
+
+## 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
+botp/yentinglin-zh_TW_c4
+
+## Evaluation
+
+#### Results
+
+[More Information Needed]
+
+
+## How to Get Started With the Model
+```python
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+
+tokenizer = AutoTokenizer.from_pretrained('EZlee/bert-based-chinese', use_auth_token=True)
+
+model = AutoModelForMaskedLM.from_pretrained("EZlee/bert-based-chinese", use_auth_token=True)
+
+```