Fill-Mask
Transformers
PyTorch
Chinese
bert
chinese
classical chinese
literary chinese
ancient chinese
roberta
Instructions to use SIKU-BERT/sikubert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SIKU-BERT/sikubert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="SIKU-BERT/sikubert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("SIKU-BERT/sikubert") model = AutoModelForMaskedLM.from_pretrained("SIKU-BERT/sikubert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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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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license: "apache-2.0"
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---
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# SikuBERT
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## Model description
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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)
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