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
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--- language: - "zh" thumbnail: "https://raw.githubusercontent.com/SIKU-BERT/SikuBERT/main/appendix/sikubert.png" tags: - "chinese" - "classical chinese" - "literary chinese" - "ancient chinese" - "bert" - "roberta" - "pytorch" license: "apache-2.0" --- # SikuBERT ## Model description  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 intellig…1b046d6 -
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