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run-4
This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.6296
- Accuracy: 0.685
- Precision: 0.6248
- Recall: 0.6164
- F1: 0.6188
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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 1.0195 | 1.0 | 50 | 0.8393 | 0.615 | 0.4126 | 0.5619 | 0.4606 |
| 0.7594 | 2.0 | 100 | 0.7077 | 0.7 | 0.6896 | 0.6663 | 0.6178 |
| 0.5515 | 3.0 | 150 | 0.9342 | 0.68 | 0.6334 | 0.5989 | 0.6016 |
| 0.3739 | 4.0 | 200 | 0.7755 | 0.735 | 0.7032 | 0.7164 | 0.7063 |
| 0.2648 | 5.0 | 250 | 0.9200 | 0.7 | 0.6584 | 0.6677 | 0.6611 |
| 0.1726 | 6.0 | 300 | 1.1898 | 0.71 | 0.6653 | 0.6550 | 0.6570 |
| 0.1452 | 7.0 | 350 | 1.5086 | 0.73 | 0.6884 | 0.6768 | 0.6812 |
| 0.0856 | 8.0 | 400 | 2.6159 | 0.68 | 0.6754 | 0.5863 | 0.5951 |
| 0.1329 | 9.0 | 450 | 1.9491 | 0.71 | 0.6692 | 0.6442 | 0.6463 |
| 0.0322 | 10.0 | 500 | 1.7897 | 0.74 | 0.6977 | 0.6939 | 0.6946 |
| 0.0345 | 11.0 | 550 | 1.9100 | 0.725 | 0.6827 | 0.6853 | 0.6781 |
| 0.026 | 12.0 | 600 | 2.5041 | 0.68 | 0.6246 | 0.6115 | 0.6137 |
| 0.0084 | 13.0 | 650 | 2.5343 | 0.715 | 0.6708 | 0.6617 | 0.6637 |
| 0.0145 | 14.0 | 700 | 2.4112 | 0.715 | 0.6643 | 0.6595 | 0.6614 |
| 0.0119 | 15.0 | 750 | 2.5303 | 0.705 | 0.6479 | 0.6359 | 0.6390 |
| 0.0026 | 16.0 | 800 | 2.6299 | 0.705 | 0.6552 | 0.6447 | 0.6455 |
| 0.0077 | 17.0 | 850 | 2.4044 | 0.715 | 0.6667 | 0.6576 | 0.6596 |
| 0.0055 | 18.0 | 900 | 2.8077 | 0.68 | 0.6208 | 0.6065 | 0.6098 |
| 0.0078 | 19.0 | 950 | 2.5608 | 0.68 | 0.6200 | 0.6104 | 0.6129 |
| 0.0018 | 20.0 | 1000 | 2.6296 | 0.685 | 0.6248 | 0.6164 | 0.6188 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Tokenizers 0.13.2
