018-microsoft-MiniLM-finetuned-yahoo-8000_2000
This model is a fine-tuned version of microsoft/MiniLM-L12-H384-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.0511
- F1: 0.6984
- Accuracy: 0.701
- Precision: 0.7000
- Recall: 0.701
- System Ram Used: 4.0180
- System Ram Total: 83.4807
- Gpu Ram Allocated: 0.3995
- Gpu Ram Cached: 12.9297
- Gpu Ram Total: 39.5640
- Gpu Utilization: 35
- Disk Space Used: 26.2045
- Disk Space Total: 78.1898
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: 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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy | Precision | Recall | System Ram Used | System Ram Total | Gpu Ram Allocated | Gpu Ram Cached | Gpu Ram Total | Gpu Utilization | Disk Space Used | Disk Space Total |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2.1461 | 0.5 | 125 | 1.8487 | 0.4711 | 0.5465 | 0.5181 | 0.5465 | 3.8798 | 83.4807 | 0.3996 | 12.9297 | 39.5640 | 28 | 24.5841 | 78.1898 |
| 1.6793 | 1.0 | 250 | 1.5280 | 0.5799 | 0.615 | 0.6207 | 0.615 | 3.8827 | 83.4807 | 0.3996 | 12.9297 | 39.5640 | 28 | 24.5842 | 78.1898 |
| 1.4163 | 1.5 | 375 | 1.3396 | 0.6508 | 0.6675 | 0.6691 | 0.6675 | 3.8831 | 83.4807 | 0.3996 | 12.9297 | 39.5640 | 28 | 24.5842 | 78.1898 |
| 1.2855 | 2.0 | 500 | 1.2413 | 0.6633 | 0.6745 | 0.6742 | 0.6745 | 3.8975 | 83.4807 | 0.3996 | 12.9297 | 39.5640 | 30 | 24.5843 | 78.1898 |
| 1.1364 | 2.5 | 625 | 1.1795 | 0.6658 | 0.6725 | 0.6758 | 0.6725 | 4.0967 | 83.4807 | 0.3996 | 12.9297 | 39.5640 | 31 | 25.4571 | 78.1898 |
| 1.0569 | 3.0 | 750 | 1.1167 | 0.6785 | 0.6845 | 0.6841 | 0.6845 | 4.0923 | 83.4807 | 0.3996 | 12.9297 | 39.5640 | 29 | 25.4573 | 78.1898 |
| 0.9596 | 3.5 | 875 | 1.0866 | 0.6883 | 0.698 | 0.6920 | 0.698 | 3.8765 | 83.4807 | 0.3997 | 12.9297 | 39.5640 | 29 | 25.4573 | 78.1898 |
| 0.917 | 4.0 | 1000 | 1.0703 | 0.6796 | 0.6875 | 0.6841 | 0.6875 | 3.8976 | 83.4807 | 0.3996 | 12.9297 | 39.5640 | 29 | 25.4573 | 78.1898 |
| 0.8512 | 4.5 | 1125 | 1.0629 | 0.6913 | 0.6915 | 0.6945 | 0.6915 | 4.0600 | 83.4807 | 0.3997 | 12.9297 | 39.5640 | 28 | 25.8306 | 78.1898 |
| 0.8121 | 5.0 | 1250 | 1.0576 | 0.6838 | 0.691 | 0.6905 | 0.691 | 4.0432 | 83.4807 | 0.3996 | 12.9297 | 39.5640 | 31 | 25.8306 | 78.1898 |
| 0.7733 | 5.5 | 1375 | 1.0598 | 0.6774 | 0.6805 | 0.6838 | 0.6805 | 3.8379 | 83.4807 | 0.3996 | 12.9297 | 39.5640 | 28 | 25.8307 | 78.1898 |
| 0.7431 | 6.0 | 1500 | 1.0376 | 0.6974 | 0.702 | 0.6976 | 0.702 | 3.8546 | 83.4807 | 0.3996 | 12.9297 | 39.5640 | 31 | 25.8307 | 78.1898 |
| 0.7065 | 6.5 | 1625 | 1.0457 | 0.6990 | 0.6995 | 0.7014 | 0.6995 | 4.0339 | 83.4807 | 0.3996 | 12.9297 | 39.5640 | 28 | 26.2040 | 78.1898 |
| 0.671 | 7.0 | 1750 | 1.0396 | 0.6956 | 0.698 | 0.6966 | 0.698 | 4.0384 | 83.4807 | 0.3996 | 12.9297 | 39.5640 | 28 | 26.2040 | 78.1898 |
| 0.6438 | 7.5 | 1875 | 1.0474 | 0.6887 | 0.6925 | 0.6907 | 0.6925 | 3.8274 | 83.4807 | 0.3996 | 12.9297 | 39.5640 | 28 | 26.2040 | 78.1898 |
| 0.6326 | 8.0 | 2000 | 1.0384 | 0.6972 | 0.698 | 0.6983 | 0.698 | 3.8402 | 83.4807 | 0.3996 | 12.9297 | 39.5640 | 34 | 26.2041 | 78.1898 |
| 0.6121 | 8.5 | 2125 | 1.0440 | 0.6963 | 0.698 | 0.6976 | 0.698 | 4.0162 | 83.4807 | 0.3996 | 12.9297 | 39.5640 | 29 | 26.2042 | 78.1898 |
| 0.5911 | 9.0 | 2250 | 1.0518 | 0.6995 | 0.701 | 0.7006 | 0.701 | 4.0338 | 83.4807 | 0.3996 | 12.9297 | 39.5640 | 28 | 26.2043 | 78.1898 |
| 0.592 | 9.5 | 2375 | 1.0490 | 0.7023 | 0.7035 | 0.7025 | 0.7035 | 3.8126 | 83.4807 | 0.3996 | 12.9297 | 39.5640 | 27 | 26.2043 | 78.1898 |
| 0.5586 | 10.0 | 2500 | 1.0511 | 0.6984 | 0.701 | 0.7000 | 0.701 | 3.8448 | 83.4807 | 0.3996 | 12.9297 | 39.5640 | 27 | 26.2043 | 78.1898 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3