| --- |
| 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" |
| mask_token: "[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 |
|  |
|
|
| 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 [](https://github.com/Ethan-yt) and Zewen Chi [](https://github.com/CZWin32768) |
