Token Classification
Transformers
PyTorch
Literary Chinese
roberta
classical chinese
literary chinese
ancient chinese
sentence segmentation
Instructions to use KoichiYasuoka/roberta-classical-chinese-base-sentence-segmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KoichiYasuoka/roberta-classical-chinese-base-sentence-segmentation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="KoichiYasuoka/roberta-classical-chinese-base-sentence-segmentation")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-classical-chinese-base-sentence-segmentation") model = AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/roberta-classical-chinese-base-sentence-segmentation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Parent(s): d6bd290
link to reference
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README.md
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## Reference
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Koichi Yasuoka: Sentence Segmentation of Classical Chinese Texts Using Transformers and BERT/RoBERTa Models, IPSJ Symposium Series, Vol.2021, No.1 (December 2021), pp.104-109.
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## Reference
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Koichi Yasuoka: [Sentence Segmentation of Classical Chinese Texts Using Transformers and BERT/RoBERTa Models](http://hdl.handle.net/2433/266539), IPSJ Symposium Series, Vol.2021, No.1 (December 2021), pp.104-109.
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