Fill-Mask
Transformers
PyTorch
Literary Chinese
roberta
classical chinese
literary chinese
ancient chinese
masked-lm
Instructions to use KoichiYasuoka/roberta-classical-chinese-base-char with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KoichiYasuoka/roberta-classical-chinese-base-char with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="KoichiYasuoka/roberta-classical-chinese-base-char")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-classical-chinese-base-char") model = AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/roberta-classical-chinese-base-char") - Notebooks
- Google Colab
- Kaggle
roberta-classical-chinese-base-char
Model Description
This is a RoBERTa model pre-trained on Classical Chinese texts, derived from GuwenBERT-base. Character-embeddings are enhanced into traditional/simplified characters. You can fine-tune roberta-classical-chinese-base-char for downstream tasks, such as sentence-segmentation, POS-tagging, dependency-parsing, and so on.
How to Use
from transformers import AutoTokenizer,AutoModelForMaskedLM
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-classical-chinese-base-char")
model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/roberta-classical-chinese-base-char")
See Also
SuPar-Kanbun: Tokenizer POS-tagger and Dependency-parser for Classical Chinese
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