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Add two model_cards: ethanyt/guwenbert-base and ethanyt/guwenbert-large (#8041)
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model_cards/ethanyt/guwenbert-base/README.md
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model_cards/ethanyt/guwenbert-base/README.md
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---
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language:
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- "zh"
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thumbnail: "https://user-images.githubusercontent.com/9592150/97142000-cad08e00-179a-11eb-88df-aff9221482d8.png"
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tags:
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- "chinese"
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- "classical chinese"
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- "literary chinese"
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- "ancient chinese"
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- "bert"
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- "pytorch"
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license: "apache-2.0"
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pipeline_tag: "fill-mask"
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widget:
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- text: "[MASK]太元中，武陵人捕鱼为业。"
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- text: "山不在[MASK]，有仙则名。"
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- text: "浔阳江头夜送客，枫叶[MASK]花秋瑟瑟。"
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---
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# GuwenBERT
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## Model description
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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.
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For more information about RoBERTa, take a look at the RoBERTa's offical repo.
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## How to use
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```python
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from transformers import AutoTokenizer, AutoModel
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tokenizer = AutoTokenizer.from_pretrained("ethanyt/guwenbert-base")
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model = AutoModel.from_pretrained("ethanyt/guwenbert-base")
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```
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## Training data
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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.
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76% of them are punctuated.
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The total number of characters is 1.7B (1,743,337,673).
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All traditional Characters are converted to simplified characters.
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The vocabulary is constructed from this data set and the size is 23,292.
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## Training procedure
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The models are initialized with `hfl/chinese-roberta-wwm-ext` and then pre-trained with a 2-step strategy.
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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.
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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.
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## Eval results
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### "Gulian Cup" Ancient Books Named Entity Recognition Evaluation
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Second place in the competition. Detailed test results:
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| NE Type | Precision | Recall | F1 |
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|:----------:|:-----------:|:------:|:-----:|
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| Book Name | 77.50 | 73.73 | 75.57 |
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| Other Name | 85.85 | 89.32 | 87.55 |
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| Micro Avg. | 83.88 | 85.39 | 84.63 |
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## About Us
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We are from [Datahammer](https://datahammer.net), Beijing Institute of Technology.
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For more cooperation, please contact email: ethanyt [at] qq.com
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> Created with ❤️ by Tan Yan [](https://github.com/Ethan-yt) and Zewen Chi [](https://github.com/CZWin32768)
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74
model_cards/ethanyt/guwenbert-large/README.md
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74
model_cards/ethanyt/guwenbert-large/README.md
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@ -0,0 +1,74 @@
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---
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language:
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- "zh"
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thumbnail: "https://user-images.githubusercontent.com/9592150/97142000-cad08e00-179a-11eb-88df-aff9221482d8.png"
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tags:
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- "chinese"
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- "classical chinese"
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- "literary chinese"
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- "ancient chinese"
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- "bert"
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- "pytorch"
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license: "apache-2.0"
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pipeline_tag: "fill-mask"
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widget:
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- text: "[MASK]太元中，武陵人捕鱼为业。"
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- text: "山不在[MASK]，有仙则名。"
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- text: "浔阳江头夜送客，枫叶[MASK]花秋瑟瑟。"
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---
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# GuwenBERT
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## Model description
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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.
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For more information about RoBERTa, take a look at the RoBERTa's offical repo.
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## How to use
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```python
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from transformers import AutoTokenizer, AutoModel
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tokenizer = AutoTokenizer.from_pretrained("ethanyt/guwenbert-large")
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model = AutoModel.from_pretrained("ethanyt/guwenbert-large")
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```
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## Training data
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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.
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76% of them are punctuated.
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The total number of characters is 1.7B (1,743,337,673).
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All traditional Characters are converted to simplified characters.
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The vocabulary is constructed from this data set and the size is 23,292.
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## Training procedure
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The models are initialized with `hfl/chinese-roberta-wwm-ext-large` and then pre-trained with a 2-step strategy.
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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.
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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 1e-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.
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## Eval results
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### "Gulian Cup" Ancient Books Named Entity Recognition Evaluation
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Second place in the competition. Detailed test results:
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| NE Type | Precision | Recall | F1 |
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|:----------:|:-----------:|:------:|:-----:|
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| Book Name | 77.50 | 73.73 | 75.57 |
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| Other Name | 85.85 | 89.32 | 87.55 |
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| Micro Avg. | 83.88 | 85.39 | 84.63 |
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## About Us
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We are from [Datahammer](https://datahammer.net), Beijing Institute of Technology.
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For more cooperation, please contact email: ethanyt [at] qq.com
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> Created with ❤️ by Tan Yan [](https://github.com/Ethan-yt) and Zewen Chi [](https://github.com/CZWin32768)
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