ATE-distilbert-base-uncased-For-SemEval-2014-Task-4
This model is a fine-tuned version of distilbert/distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3006
- F1-score: 0.8431
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: 256
- eval_batch_size: 256
- seed:
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 55
Training results
| Epoch | Training Loss | Validation Loss | F1-score |
|---|---|---|---|
| 1 | 0.6549 | 0.5413 | 0.0030 |
| 2 | 0.3739 | 0.3064 | 0.5312 |
| 3 | 0.2325 | 0.2596 | 0.6308 |
| 4 | 0.1761 | 0.2365 | 0.6866 |
| 5 | 0.1443 | 0.2173 | 0.7443 |
| 6 | 0.1051 | 0.2079 | 0.7854 |
| 7 | 0.0807 | 0.2041 | 0.8117 |
| 8 | 0.0651 | 0.2086 | 0.8198 |
| 9 | 0.0506 | 0.2183 | 0.8200 |
| 10 | 0.0413 | 0.2243 | 0.8199 |
| 11 | 0.0353 | 0.2347 | 0.8251 |
| 12 | 0.0282 | 0.2355 | 0.8277 |
| 13 | 0.0255 | 0.2421 | 0.8288 |
| 14 | 0.0234 | 0.2476 | 0.8286 |
| 15 | 0.0220 | 0.2465 | 0.8273 |
| 16 | 0.0183 | 0.2585 | 0.8299 |
| 17 | 0.0174 | 0.2561 | 0.8276 |
| 18 | 0.0151 | 0.2572 | 0.8332 |
| 19 | 0.0135 | 0.2668 | 0.8332 |
| 20 | 0.0129 | 0.2769 | 0.8312 |
| 21 | 0.0127 | 0.2757 | 0.8303 |
| 22 | 0.0122 | 0.2778 | 0.8378 |
| 23 | 0.0119 | 0.2847 | 0.8334 |
| 24 | 0.0106 | 0.2853 | 0.8361 |
| 25 | 0.0094 | 0.2881 | 0.8369 |
| 26 | 0.0087 | 0.2918 | 0.8381 |
| 27 | 0.0077 | 0.2996 | 0.8316 |
| 28 | 0.0077 | 0.2991 | 0.8353 |
| 29 | 0.0086 | 0.3080 | 0.8334 |
| 30 | 0.0073 | 0.3038 | 0.8385 |
| 31 | 0.0076 | 0.3006 | 0.8431 |
| 32 | 0.0079 | 0.3014 | 0.8390 |
| 33 | 0.0069 | 0.3015 | 0.8349 |
| 34 | 0.0064 | 0.3130 | 0.8361 |
| 35 | 0.0091 | 0.3141 | 0.8379 |
| 36 | 0.0068 | 0.3159 | 0.8327 |
| 37 | 0.0066 | 0.3093 | 0.8345 |
| 38 | 0.0057 | 0.3111 | 0.8377 |
| 39 | 0.0055 | 0.3137 | 0.8371 |
| 40 | 0.0055 | 0.3126 | 0.8370 |
| 41 | 0.0052 | 0.3171 | 0.8364 |
| 42 | 0.0054 | 0.3141 | 0.8328 |
| 43 | 0.0051 | 0.3166 | 0.8394 |
| 44 | 0.0055 | 0.3189 | 0.8414 |
| 45 | 0.0054 | 0.3214 | 0.8373 |
| 46 | 0.0055 | 0.3223 | 0.8372 |
| 47 | 0.0053 | 0.3239 | 0.8364 |
| 48 | 0.0054 | 0.3224 | 0.8376 |
| 49 | 0.0046 | 0.3222 | 0.8372 |
| 50 | 0.0061 | 0.3218 | 0.8392 |
| 51 | 0.0047 | 0.3223 | 0.8371 |
| 52 | 0.0048 | 0.3225 | 0.8368 |
| 53 | 0.0053 | 0.3228 | 0.8365 |
| 54 | 0.0051 | 0.3230 | 0.8372 |
| 55 | 0.0049 | 0.3230 | 0.8372 |
Framework versions
- Transformers 4.48.2
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0