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README.md
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@@ -34,6 +34,7 @@ Note that this model is primiarly aimed at predicting whether a Classical Chines
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Note that this model is primiarly aimed at predicting whether a Classical Chinese sentence is a letter title (书信标题) or not.
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Here is how to use this model to get the features of a given text in PyTorch:
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1. Import model
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```python
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from transformers import BertTokenizer
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print(f'The predicted probability for the {list(pred_class_proba.keys())[1]} class: {list(pred_class_proba.values())[1]}')
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>>> The predicted probability for the not-letter class: 0.002029061783105135
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>>> The predicted probability for the letter class: 0.9979709386825562
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pred_class = idx2label[np.argmax(list(pred_class_proba.values()))]
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print(f'The predicted class is: {pred_class}')
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>>> The predicted class is: letter
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```
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Note that this model is primiarly aimed at predicting whether a Classical Chinese sentence is a letter title (书信标题) or not.
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Here is how to use this model to get the features of a given text in PyTorch:
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1. Import model
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```python
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from transformers import BertTokenizer
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print(f'The predicted probability for the {list(pred_class_proba.keys())[1]} class: {list(pred_class_proba.values())[1]}')
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>>> The predicted probability for the not-letter class: 0.002029061783105135
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>>> The predicted probability for the letter class: 0.9979709386825562
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pred_class = idx2label[np.argmax(list(pred_class_proba.values()))]
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print(f'The predicted class is: {pred_class}')
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>>> The predicted class is: letter
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```
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