MiniLM_uncased_classification_tools_fr
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: 0.3165
- Accuracy: 0.925
- Learning Rate: 0.0
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: 0.0001
- train_batch_size: 24
- eval_batch_size: 192
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 60
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Rate |
|---|---|---|---|---|---|
| No log | 1.0 | 7 | 2.0607 | 0.35 | 0.0001 |
| No log | 2.0 | 14 | 1.9111 | 0.425 | 0.0001 |
| No log | 3.0 | 21 | 1.6543 | 0.45 | 0.0001 |
| No log | 4.0 | 28 | 1.4578 | 0.525 | 0.0001 |
| No log | 5.0 | 35 | 1.3136 | 0.65 | 0.0001 |
| No log | 6.0 | 42 | 1.2160 | 0.7 | 9e-05 |
| No log | 7.0 | 49 | 1.0786 | 0.725 | 0.0001 |
| No log | 8.0 | 56 | 1.0171 | 0.675 | 0.0001 |
| No log | 9.0 | 63 | 0.9491 | 0.7 | 0.0001 |
| No log | 10.0 | 70 | 0.8773 | 0.75 | 0.0001 |
| No log | 11.0 | 77 | 0.8019 | 0.75 | 0.0001 |
| No log | 12.0 | 84 | 0.7436 | 0.775 | 8e-05 |
| No log | 13.0 | 91 | 0.6747 | 0.825 | 0.0001 |
| No log | 14.0 | 98 | 0.7357 | 0.775 | 0.0001 |
| No log | 15.0 | 105 | 0.5386 | 0.85 | 0.0001 |
| No log | 16.0 | 112 | 0.6222 | 0.85 | 0.0001 |
| No log | 17.0 | 119 | 0.6284 | 0.85 | 0.0001 |
| No log | 18.0 | 126 | 0.4489 | 0.9 | 7e-05 |
| No log | 19.0 | 133 | 0.6431 | 0.85 | 0.0001 |
| No log | 20.0 | 140 | 0.6064 | 0.85 | 0.0001 |
| No log | 21.0 | 147 | 0.6948 | 0.825 | 0.0001 |
| No log | 22.0 | 154 | 0.5535 | 0.85 | 0.0001 |
| No log | 23.0 | 161 | 0.4672 | 0.875 | 0.0001 |
| No log | 24.0 | 168 | 0.4797 | 0.875 | 6e-05 |
| No log | 25.0 | 175 | 0.4908 | 0.9 | 0.0001 |
| No log | 26.0 | 182 | 0.5879 | 0.85 | 0.0001 |
| No log | 27.0 | 189 | 0.6601 | 0.85 | 0.0001 |
| No log | 28.0 | 196 | 0.6036 | 0.85 | 0.0001 |
| No log | 29.0 | 203 | 0.5495 | 0.85 | 0.0001 |
| No log | 30.0 | 210 | 0.5135 | 0.85 | 5e-05 |
| No log | 31.0 | 217 | 0.4767 | 0.875 | 0.0000 |
| No log | 32.0 | 224 | 0.4431 | 0.9 | 0.0000 |
| No log | 33.0 | 231 | 0.4681 | 0.875 | 0.0000 |
| No log | 34.0 | 238 | 0.5612 | 0.85 | 0.0000 |
| No log | 35.0 | 245 | 0.4495 | 0.9 | 0.0000 |
| No log | 36.0 | 252 | 0.4384 | 0.9 | 4e-05 |
| No log | 37.0 | 259 | 0.4378 | 0.875 | 0.0000 |
| No log | 38.0 | 266 | 0.4104 | 0.875 | 0.0000 |
| No log | 39.0 | 273 | 0.5060 | 0.875 | 0.0000 |
| No log | 40.0 | 280 | 0.4756 | 0.875 | 0.0000 |
| No log | 41.0 | 287 | 0.4558 | 0.875 | 0.0000 |
| No log | 42.0 | 294 | 0.4458 | 0.9 | 3e-05 |
| No log | 43.0 | 301 | 0.3969 | 0.875 | 0.0000 |
| No log | 44.0 | 308 | 0.4762 | 0.875 | 0.0000 |
| No log | 45.0 | 315 | 0.4891 | 0.875 | 0.0000 |
| No log | 46.0 | 322 | 0.4460 | 0.9 | 0.0000 |
| No log | 47.0 | 329 | 0.3892 | 0.925 | 0.0000 |
| No log | 48.0 | 336 | 0.4267 | 0.9 | 2e-05 |
| No log | 49.0 | 343 | 0.3327 | 0.9 | 0.0000 |
| No log | 50.0 | 350 | 0.3225 | 0.925 | 0.0000 |
| No log | 51.0 | 357 | 0.3223 | 0.925 | 0.0000 |
| No log | 52.0 | 364 | 0.3136 | 0.95 | 0.0000 |
| No log | 53.0 | 371 | 0.3109 | 0.925 | 0.0000 |
| No log | 54.0 | 378 | 0.3142 | 0.9 | 1e-05 |
| No log | 55.0 | 385 | 0.3168 | 0.925 | 0.0000 |
| No log | 56.0 | 392 | 0.3163 | 0.925 | 0.0000 |
| No log | 57.0 | 399 | 0.3174 | 0.925 | 5e-06 |
| No log | 58.0 | 406 | 0.3185 | 0.925 | 0.0000 |
| No log | 59.0 | 413 | 0.3168 | 0.925 | 0.0000 |
| No log | 60.0 | 420 | 0.3165 | 0.925 | 0.0 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.14.1