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distilbert-base-uncased-hate-offensive-normal-speech-lr-2e-05
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0178
- Accuracy: 0.9935
- Weighted f1: 0.9935
- Weighted recall: 0.9935
- Weighted precision: 0.9936
- Micro f1: 0.9935
- Micro recall: 0.9935
- Micro precision: 0.9935
- Macro f1: 0.9932
- Macro recall: 0.9938
- Macro precision: 0.9927
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Weighted recall | Weighted precision | Micro f1 | Micro recall | Micro precision | Macro f1 | Macro recall | Macro precision |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0.5013 | 1.0 | 153 | 0.0914 | 0.9642 | 0.9643 | 0.9642 | 0.9649 | 0.9642 | 0.9642 | 0.9642 | 0.9629 | 0.9639 | 0.9623 |
0.0924 | 2.0 | 306 | 0.0314 | 0.9935 | 0.9935 | 0.9935 | 0.9936 | 0.9935 | 0.9935 | 0.9935 | 0.9932 | 0.9938 | 0.9927 |
0.0432 | 3.0 | 459 | 0.0298 | 0.9870 | 0.9870 | 0.9870 | 0.9875 | 0.9870 | 0.9870 | 0.9870 | 0.9860 | 0.9869 | 0.9857 |
0.0217 | 4.0 | 612 | 0.0259 | 0.9902 | 0.9903 | 0.9902 | 0.9905 | 0.9902 | 0.9902 | 0.9902 | 0.9896 | 0.9903 | 0.9891 |
0.0148 | 5.0 | 765 | 0.0178 | 0.9935 | 0.9935 | 0.9935 | 0.9936 | 0.9935 | 0.9935 | 0.9935 | 0.9932 | 0.9938 | 0.9927 |
Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.6.dev0
- Tokenizers 0.13.3