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--- |
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language: |
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- "zh" |
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thumbnail: "https://raw.githubusercontent.com/SIKU-BERT/SikuBERT/main/appendix/sikubert.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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- "roberta" |
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- "pytorch" |
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inference: false |
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license: "apache-2.0" |
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--- |
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# SikuBERT |
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## Model description |
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 |
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Digital humanities research needs the support of large-scale corpus and high-performance ancient Chinese natural language processing tools. The pre-training language model has greatly improved the accuracy of text mining in English and modern Chinese texts. At present, there is an urgent need for a pre-training model specifically for the automatic processing of ancient texts. We used the verified high-quality “Siku Quanshu” full-text corpus as the training set, based on the BERT deep language model architecture, we constructed the SikuBERT and SikuRoBERTa pre-training language models for intelligent processing tasks of ancient Chinese. |
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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("SIKU-BERT/sikubert") |
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model = AutoModel.from_pretrained("SIKU-BERT/sikubert") |
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``` |
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## About Us |
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We are from Nanjing Agricultural University. |
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> Created with by SIKU-BERT [](https://github.com/SIKU-BERT/SikuBERT-for-digital-humanities-and-classical-Chinese-information-processing) |
