Text Classification
Transformers
PyTorch
Chinese
bert
SequenceClassification
Lepton
古文
文言文
ancient
classical
letter
书信标题
Instructions to use cbdb/ClassicalChineseLetterClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cbdb/ClassicalChineseLetterClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cbdb/ClassicalChineseLetterClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cbdb/ClassicalChineseLetterClassification") model = AutoModelForSequenceClassification.from_pretrained("cbdb/ClassicalChineseLetterClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Edited README.md
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README.md
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language:
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tags:
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- SequenceClassification
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license: mit
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This BertForSequenceClassification Classical Chinese model is intended to predict whether a Classical Chinese sentence is a letter title (书信标题) or not. This model is first inherited from the BERT base Chinese model (MLM), and finetuned using a large corpus of Classical Chinese language (3GB textual dataset), then concatenated with the BertForSequenceClassification architecture to perform a binary classification task.
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### Labels: 0 = non-letter, 1 = letter
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## Model description
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language:
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- zh
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tags:
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- SequenceClassification
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- 古文
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- 文言文
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- ancient
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- classical
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license: mit
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---
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This BertForSequenceClassification Classical Chinese model is intended to predict whether a Classical Chinese sentence is a letter title (书信标题) or not. This model is first inherited from the BERT base Chinese model (MLM), and finetuned using a large corpus of Classical Chinese language (3GB textual dataset), then concatenated with the BertForSequenceClassification architecture to perform a binary classification task.
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#### Labels: 0 = non-letter, 1 = letter
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## Model description
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