Instructions to use bjbjbj/classifier-chapter4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bjbjbj/classifier-chapter4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bjbjbj/classifier-chapter4")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bjbjbj/classifier-chapter4") model = AutoModelForSequenceClassification.from_pretrained("bjbjbj/classifier-chapter4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files- README.md +5 -5
- model.safetensors +1 -1
README.md
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Accuracy: 0.
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- F1: 0.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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| No log | 1.0 | 313 | 0.
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### Framework versions
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2447
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- Accuracy: 0.9225
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- F1: 0.9225
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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| No log | 1.0 | 313 | 0.2626 | 0.9104 | 0.9101 |
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| 0.2954 | 2.0 | 626 | 0.2447 | 0.9225 | 0.9225 |
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### Framework versions
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model.safetensors
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