<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
distilbert-base-uncased_fold_11_binary_v1
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: 1.8389
- F1: 0.8057
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: 25
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| No log | 1.0 | 288 | 0.4534 | 0.8011 |
| 0.4027 | 2.0 | 576 | 0.4299 | 0.8121 |
| 0.4027 | 3.0 | 864 | 0.4840 | 0.8142 |
| 0.1947 | 4.0 | 1152 | 0.7501 | 0.7992 |
| 0.1947 | 5.0 | 1440 | 1.0307 | 0.7866 |
| 0.0771 | 6.0 | 1728 | 1.1292 | 0.8034 |
| 0.0253 | 7.0 | 2016 | 1.2620 | 0.8033 |
| 0.0253 | 8.0 | 2304 | 1.4065 | 0.7954 |
| 0.0137 | 9.0 | 2592 | 1.4922 | 0.7887 |
| 0.0137 | 10.0 | 2880 | 1.4922 | 0.8050 |
| 0.0046 | 11.0 | 3168 | 1.4883 | 0.8097 |
| 0.0046 | 12.0 | 3456 | 1.5542 | 0.8133 |
| 0.0066 | 13.0 | 3744 | 1.5180 | 0.8000 |
| 0.0094 | 14.0 | 4032 | 1.6762 | 0.7919 |
| 0.0094 | 15.0 | 4320 | 1.5808 | 0.8005 |
| 0.0047 | 16.0 | 4608 | 1.7025 | 0.8012 |
| 0.0047 | 17.0 | 4896 | 1.6494 | 0.7986 |
| 0.0039 | 18.0 | 5184 | 1.7218 | 0.8010 |
| 0.0039 | 19.0 | 5472 | 1.8293 | 0.7994 |
| 0.0005 | 20.0 | 5760 | 1.8142 | 0.7980 |
| 0.0033 | 21.0 | 6048 | 1.8350 | 0.8037 |
| 0.0033 | 22.0 | 6336 | 1.8361 | 0.8042 |
| 0.0023 | 23.0 | 6624 | 1.8715 | 0.7996 |
| 0.0023 | 24.0 | 6912 | 1.8411 | 0.8057 |
| 0.0019 | 25.0 | 7200 | 1.8389 | 0.8057 |
Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
