Migrate model card from transformers-repo

Read announcement at https://discuss.huggingface.co/t/announcement-all-model-cards-will-be-migrated-to-hf-co-model-repos/2755
Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/ethanyt/guwenbert-base/README.md

README.md

@@ -0,0 +1,74 @@
+---
+language: 
+- "zh"
+thumbnail: "https://user-images.githubusercontent.com/9592150/97142000-cad08e00-179a-11eb-88df-aff9221482d8.png"
+tags:
+- "chinese"
+- "classical chinese"
+- "literary chinese"
+- "ancient chinese"
+- "bert"
+- "pytorch"
+license: "apache-2.0"
+pipeline_tag: "fill-mask"
+widget:
+- text: "[MASK]太元中，武陵人捕鱼为业。"
+- text: "问征夫以前路，恨晨光之[MASK]微。"
+- text: "浔阳江头夜送客，枫叶[MASK]花秋瑟瑟。"
+---
+
+# GuwenBERT
+
+## Model description
+![GuwenBERT](https://user-images.githubusercontent.com/9592150/97142000-cad08e00-179a-11eb-88df-aff9221482d8.png)
+
+This is a RoBERTa model pre-trained on Classical Chinese. You can fine-tune GuwenBERT for downstream tasks, such as sentence breaking, punctuation, named entity recognition, and so on.
+
+For more information about RoBERTa, take a look at the RoBERTa's offical repo.
+
+## How to use
+
+```python
+from transformers import AutoTokenizer, AutoModel
+
+tokenizer = AutoTokenizer.from_pretrained("ethanyt/guwenbert-base")
+
+model = AutoModel.from_pretrained("ethanyt/guwenbert-base")
+```
+
+## Training data
+
+The training data is daizhige dataset (殆知阁古代文献) which is contains of 15,694 books in Classical Chinese, covering Buddhism, Confucianism, Medicine, History, Zi, Yi, Yizang, Shizang, Taoism, and Jizang. 
+76% of them are punctuated.
+The total number of characters is 1.7B (1,743,337,673).
+All traditional Characters are converted to simplified characters.
+The vocabulary is constructed from this data set and the size is 23,292.
+
+## Training procedure
+
+The models are initialized with `hfl/chinese-roberta-wwm-ext` and then pre-trained with a 2-step strategy.
+In the first step, the model learns MLM with only word embeddings updated during training, until convergence. In the second step, all parameters are updated during training.
+
+The models are trained on 4 V100 GPUs for 120K steps (20K for step#1, 100K for step#2) with a batch size of 2,048 and a sequence length of 512. The optimizer used is Adam with a learning rate of 2e-4, adam-betas of (0.9,0.98), adam-eps of 1e-6, a weight decay of 0.01, learning rate warmup for 5K steps, and linear decay of learning rate after.
+
+## Eval results
+
+### "Gulian Cup" Ancient Books Named Entity Recognition Evaluation
+
+Second place in the competition. Detailed test results:
+
+| NE Type    | Precision   | Recall | F1    |
+|:----------:|:-----------:|:------:|:-----:|
+| Book Name  | 77.50       | 73.73  | 75.57 |
+| Other Name | 85.85       | 89.32  | 87.55 |
+| Micro Avg. | 83.88       | 85.39  | 84.63 |
+
+
+
+
+## About Us
+
+We are from [Datahammer](https://datahammer.net), Beijing Institute of Technology.
+For more cooperation, please contact email: ethanyt [at] qq.com
+
+> Created with ❤️ by Tan Yan [![Github icon](https://cdn0.iconfinder.com/data/icons/octicons/1024/mark-github-32.png)](https://github.com/Ethan-yt) and Zewen Chi [![Github icon](https://cdn0.iconfinder.com/data/icons/octicons/1024/mark-github-32.png)](https://github.com/CZWin32768)