distilbert-base-uncased-finetuned-synthetic-finetuned-synthetic
This model is a fine-tuned version of Chrisantha/distilbert-base-uncased-finetuned-synthetic on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.4081
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 1 | 2.9242 |
| 0.5836 | 2.0 | 2 | 2.5911 |
| 0.5836 | 3.0 | 3 | 2.7194 |
| 0.782 | 4.0 | 4 | 2.3194 |
| 0.782 | 5.0 | 5 | 2.1952 |
| 1.3155 | 6.0 | 6 | 2.1321 |
| 1.3155 | 7.0 | 7 | 2.2769 |
| 0.596 | 8.0 | 8 | 2.2093 |
| 0.596 | 9.0 | 9 | 2.4133 |
| 0.817 | 10.0 | 10 | 2.4370 |
| 0.817 | 11.0 | 11 | 2.1859 |
| 0.7962 | 12.0 | 12 | 2.1760 |
| 0.7962 | 13.0 | 13 | 1.9116 |
| 0.7554 | 14.0 | 14 | 1.7670 |
| 0.7554 | 15.0 | 15 | 1.7386 |
| 0.4256 | 16.0 | 16 | 1.6506 |
| 0.4256 | 17.0 | 17 | 1.5478 |
| 0.6326 | 18.0 | 18 | 1.5998 |
| 0.6326 | 19.0 | 19 | 1.6936 |
| 0.493 | 20.0 | 20 | 1.6938 |
| 0.493 | 21.0 | 21 | 1.7659 |
| 0.5194 | 22.0 | 22 | 1.8872 |
| 0.5194 | 23.0 | 23 | 1.7004 |
| 0.4438 | 24.0 | 24 | 1.6653 |
| 0.4438 | 25.0 | 25 | 1.5889 |
| 0.5761 | 26.0 | 26 | 1.4914 |
| 0.5761 | 27.0 | 27 | 1.3813 |
| 0.395 | 28.0 | 28 | 1.4385 |
| 0.395 | 29.0 | 29 | 1.4067 |
| 0.4681 | 30.0 | 30 | 1.4021 |
| 0.4681 | 31.0 | 31 | 1.4172 |
| 0.6326 | 32.0 | 32 | 1.4502 |
| 0.6326 | 33.0 | 33 | 1.5628 |
| 0.3545 | 34.0 | 34 | 1.6276 |
| 0.3545 | 35.0 | 35 | 1.6164 |
| 0.4313 | 36.0 | 36 | 1.7040 |
| 0.4313 | 37.0 | 37 | 1.6950 |
| 0.3883 | 38.0 | 38 | 1.6429 |
| 0.3883 | 39.0 | 39 | 1.6180 |
| 0.5155 | 40.0 | 40 | 1.5417 |
| 0.5155 | 41.0 | 41 | 1.4499 |
| 0.3546 | 42.0 | 42 | 1.3885 |
| 0.3546 | 43.0 | 43 | 1.3061 |
| 0.2205 | 44.0 | 44 | 1.2986 |
| 0.2205 | 45.0 | 45 | 1.2861 |
| 0.2851 | 46.0 | 46 | 1.3785 |
| 0.2851 | 47.0 | 47 | 1.4008 |
| 0.3057 | 48.0 | 48 | 1.4402 |
| 0.3057 | 49.0 | 49 | 1.4538 |
| 0.3449 | 50.0 | 50 | 1.5073 |
| 0.3449 | 51.0 | 51 | 1.5050 |
| 0.1664 | 52.0 | 52 | 1.4939 |
| 0.1664 | 53.0 | 53 | 1.4691 |
| 0.1484 | 54.0 | 54 | 1.2829 |
| 0.1484 | 55.0 | 55 | 1.3112 |
| 0.3156 | 56.0 | 56 | 1.2328 |
| 0.3156 | 57.0 | 57 | 1.1700 |
| 0.379 | 58.0 | 58 | 1.1190 |
| 0.379 | 59.0 | 59 | 1.1429 |
| 0.2475 | 60.0 | 60 | 1.1544 |
| 0.2475 | 61.0 | 61 | 1.2303 |
| 0.2282 | 62.0 | 62 | 1.3118 |
| 0.2282 | 63.0 | 63 | 1.3701 |
| 0.2216 | 64.0 | 64 | 1.3705 |
| 0.2216 | 65.0 | 65 | 1.4848 |
| 0.1768 | 66.0 | 66 | 1.4744 |
| 0.1768 | 67.0 | 67 | 1.5796 |
| 0.1621 | 68.0 | 68 | 1.5674 |
| 0.1621 | 69.0 | 69 | 1.5873 |
| 0.3016 | 70.0 | 70 | 1.5756 |
| 0.3016 | 71.0 | 71 | 1.6496 |
| 0.2548 | 72.0 | 72 | 1.5922 |
| 0.2548 | 73.0 | 73 | 1.5911 |
| 0.2878 | 74.0 | 74 | 1.4912 |
| 0.2878 | 75.0 | 75 | 1.5303 |
| 0.2045 | 76.0 | 76 | 1.5293 |
| 0.2045 | 77.0 | 77 | 1.4076 |
| 0.219 | 78.0 | 78 | 1.4773 |
| 0.219 | 79.0 | 79 | 1.3878 |
| 0.1396 | 80.0 | 80 | 1.3349 |
| 0.1396 | 81.0 | 81 | 1.3670 |
| 0.166 | 82.0 | 82 | 1.4015 |
| 0.166 | 83.0 | 83 | 1.4132 |
| 0.2982 | 84.0 | 84 | 1.4478 |
| 0.2982 | 85.0 | 85 | 1.4803 |
| 0.1199 | 86.0 | 86 | 1.4667 |
| 0.1199 | 87.0 | 87 | 1.5402 |
| 0.1982 | 88.0 | 88 | 1.5515 |
| 0.1982 | 89.0 | 89 | 1.5189 |
| 0.1816 | 90.0 | 90 | 1.5545 |
| 0.1816 | 91.0 | 91 | 1.4814 |
| 0.1779 | 92.0 | 92 | 1.4943 |
| 0.1779 | 93.0 | 93 | 1.4430 |
| 0.0785 | 94.0 | 94 | 1.4865 |
| 0.0785 | 95.0 | 95 | 1.4919 |
| 0.1108 | 96.0 | 96 | 1.5035 |
| 0.1108 | 97.0 | 97 | 1.4088 |
| 0.2581 | 98.0 | 98 | 1.4104 |
| 0.2581 | 99.0 | 99 | 1.4549 |
| 0.1738 | 100.0 | 100 | 1.3761 |
Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1