distilbert-base-uncased_fold_6_ternary
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: 1.6625
- F1: 0.7588
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 | 292 | 0.5117 | 0.7306 |
0.5701 | 2.0 | 584 | 0.5273 | 0.7296 |
0.5701 | 3.0 | 876 | 0.6037 | 0.7415 |
0.2468 | 4.0 | 1168 | 0.7132 | 0.7318 |
0.2468 | 5.0 | 1460 | 0.8980 | 0.7504 |
0.12 | 6.0 | 1752 | 1.0343 | 0.7369 |
0.0486 | 7.0 | 2044 | 1.1860 | 0.7333 |
0.0486 | 8.0 | 2336 | 1.3348 | 0.7437 |
0.019 | 9.0 | 2628 | 1.3040 | 0.7561 |
0.019 | 10.0 | 2920 | 1.4649 | 0.7293 |
0.0152 | 11.0 | 3212 | 1.4870 | 0.7431 |
0.0078 | 12.0 | 3504 | 1.5668 | 0.7455 |
0.0078 | 13.0 | 3796 | 1.5280 | 0.7378 |
0.0091 | 14.0 | 4088 | 1.5672 | 0.7410 |
0.0091 | 15.0 | 4380 | 1.5948 | 0.7491 |
0.0052 | 16.0 | 4672 | 1.6625 | 0.7588 |
0.0052 | 17.0 | 4964 | 1.6544 | 0.7411 |
0.0048 | 18.0 | 5256 | 1.7124 | 0.7425 |
0.0024 | 19.0 | 5548 | 1.7211 | 0.7477 |
0.0024 | 20.0 | 5840 | 1.8216 | 0.7373 |
0.001 | 21.0 | 6132 | 1.8325 | 0.7361 |
0.001 | 22.0 | 6424 | 1.8089 | 0.7498 |
0.0015 | 23.0 | 6716 | 1.8026 | 0.7506 |
0.0005 | 24.0 | 7008 | 1.8026 | 0.7464 |
0.0005 | 25.0 | 7300 | 1.8043 | 0.7464 |
Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
- Downloads last month
- 7
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.