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
Commit ·
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Parent(s): 18010cb
"O" -> "M"
Browse files- config.json +2 -2
config.json
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"layer_norm_eps": 1e-05,
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"1": "E",
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"2": "E2",
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"3": "E3",
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"4": "M",
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"5": "S"
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"initializer_range": 0.02,
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"E": 1,
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"E2": 2,
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"E3": 3,
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"M": 4,
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"S": 5
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"layer_norm_eps": 1e-05,
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