Fill-Mask
Transformers
PyTorch
Chinese
bert
chinese
classical chinese
literary chinese
ancient chinese
roberta
# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("SIKU-BERT/sikuroberta")
model = AutoModelForMaskedLM.from_pretrained("SIKU-BERT/sikuroberta", device_map="auto")Quick Links
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 βSiku Quanshuβ 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.
How to use
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("SIKU-BERT/sikuroberta")
model = AutoModel.from_pretrained("SIKU-BERT/sikuroberta")
About Us
We are from Nanjing Agricultural University.
Created with by SIKU-BERT
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="SIKU-BERT/sikuroberta")