junk
This model is a fine-tuned version of on the None dataset. It achieves the following results on the evaluation set:
- Loss: 8.1252
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 30
- num_epochs: 100
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 10.42 | 1.25 | 5 | 10.1940 |
| 10.1087 | 2.5 | 10 | 9.7539 |
| 9.7572 | 3.75 | 15 | 9.4707 |
| 9.5321 | 5.0 | 20 | 9.2852 |
| 9.13 | 6.25 | 25 | 9.1155 |
| 8.9989 | 7.5 | 30 | 8.9138 |
| 8.7422 | 8.75 | 35 | 8.7181 |
| 8.5133 | 10.0 | 40 | 8.5220 |
| 8.0836 | 11.25 | 45 | 8.3687 |
| 7.8212 | 12.5 | 50 | 8.2344 |
| 7.6616 | 13.75 | 55 | 8.1437 |
| 7.4743 | 15.0 | 60 | 8.0750 |
| 7.1668 | 16.25 | 65 | 8.0275 |
| 7.0485 | 17.5 | 70 | 7.9937 |
| 6.9619 | 18.75 | 75 | 7.9525 |
| 6.8705 | 20.0 | 80 | 7.9584 |
| 6.6232 | 21.25 | 85 | 7.9238 |
| 6.6423 | 22.5 | 90 | 7.9155 |
| 6.5876 | 23.75 | 95 | 7.9088 |
| 6.5075 | 25.0 | 100 | 7.9154 |
| 6.4218 | 26.25 | 105 | 7.8957 |
| 6.2857 | 27.5 | 110 | 7.9040 |
| 6.1833 | 28.75 | 115 | 7.9092 |
| 6.1263 | 30.0 | 120 | 7.9198 |
| 6.0123 | 31.25 | 125 | 7.9103 |
| 5.9111 | 32.5 | 130 | 7.9150 |
| 5.9157 | 33.75 | 135 | 7.9178 |
| 5.8237 | 35.0 | 140 | 7.9479 |
| 5.6626 | 36.25 | 145 | 7.9358 |
| 5.657 | 37.5 | 150 | 7.9548 |
| 5.5894 | 38.75 | 155 | 7.9572 |
| 5.5157 | 40.0 | 160 | 7.9800 |
| 5.4606 | 41.25 | 165 | 7.9481 |
| 5.2962 | 42.5 | 170 | 7.9568 |
| 5.2877 | 43.75 | 175 | 7.9720 |
| 5.2395 | 45.0 | 180 | 7.9709 |
| 5.1394 | 46.25 | 185 | 7.9900 |
| 5.0096 | 47.5 | 190 | 8.0010 |
| 4.9646 | 48.75 | 195 | 8.0105 |
| 4.973 | 50.0 | 200 | 8.0182 |
| 4.866 | 51.25 | 205 | 8.0310 |
| 4.8044 | 52.5 | 210 | 8.0372 |
| 4.7804 | 53.75 | 215 | 8.0387 |
| 4.7187 | 55.0 | 220 | 8.0166 |
| 4.6399 | 56.25 | 225 | 8.0598 |
| 4.6644 | 57.5 | 230 | 8.0465 |
| 4.5318 | 58.75 | 235 | 8.0482 |
| 4.4451 | 60.0 | 240 | 8.0538 |
| 4.4442 | 61.25 | 245 | 8.0473 |
| 4.3778 | 62.5 | 250 | 8.0517 |
| 4.4453 | 63.75 | 255 | 8.0740 |
| 4.3813 | 65.0 | 260 | 8.0658 |
| 4.2654 | 66.25 | 265 | 8.0764 |
| 4.2278 | 67.5 | 270 | 8.0737 |
| 4.2212 | 68.75 | 275 | 8.0952 |
| 4.1481 | 70.0 | 280 | 8.0877 |
| 4.162 | 71.25 | 285 | 8.0882 |
| 4.077 | 72.5 | 290 | 8.0813 |
| 4.0134 | 73.75 | 295 | 8.0862 |
| 3.9975 | 75.0 | 300 | 8.0980 |
| 3.9174 | 76.25 | 305 | 8.0989 |
| 3.9748 | 77.5 | 310 | 8.0903 |
| 3.9362 | 78.75 | 315 | 8.1109 |
| 3.8585 | 80.0 | 320 | 8.1049 |
| 3.8832 | 81.25 | 325 | 8.1076 |
| 3.8799 | 82.5 | 330 | 8.1078 |
| 3.8354 | 83.75 | 335 | 8.1073 |
| 3.8073 | 85.0 | 340 | 8.1182 |
| 3.8701 | 86.25 | 345 | 8.1179 |
| 3.7696 | 87.5 | 350 | 8.1204 |
| 3.7907 | 88.75 | 355 | 8.1187 |
| 3.7428 | 90.0 | 360 | 8.1172 |
| 3.7048 | 91.25 | 365 | 8.1201 |
| 3.724 | 92.5 | 370 | 8.1205 |
| 3.7308 | 93.75 | 375 | 8.1191 |
| 3.7665 | 95.0 | 380 | 8.1211 |
| 3.6804 | 96.25 | 385 | 8.1244 |
| 3.6001 | 97.5 | 390 | 8.1220 |
| 3.6411 | 98.75 | 395 | 8.1245 |
| 3.6321 | 100.0 | 400 | 8.1252 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.19.1
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