Instructions to use benchaffe/Bert-RAdam-Large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use benchaffe/Bert-RAdam-Large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="benchaffe/Bert-RAdam-Large")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("benchaffe/Bert-RAdam-Large") model = AutoModelForTokenClassification.from_pretrained("benchaffe/Bert-RAdam-Large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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- F1: 0.8215
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- Accuracy: 0.9403
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## Model description
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More information needed
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- F1: 0.8215
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- Accuracy: 0.9403
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It achieves the following results on the test set:
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- Loss: 0.1825
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- Precision: 0.8017
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- Recall: 0.8902
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- F1: 0.8436
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- Accuracy: 0.9500
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## Model description
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More information needed
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