swiftformer-xs-dmae-va-U-80B
This model is a fine-tuned version of MBZUAI/swiftformer-xs on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5127
- Accuracy: 0.8532
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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 80
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
No log | 0.9 | 7 | 1.5142 | 0.2477 |
1.4484 | 1.94 | 15 | 1.4144 | 0.2936 |
1.4394 | 2.97 | 23 | 1.4008 | 0.3119 |
1.4217 | 4.0 | 31 | 1.3817 | 0.3303 |
1.4025 | 4.9 | 38 | 1.3572 | 0.3761 |
1.3816 | 5.94 | 46 | 1.3374 | 0.4128 |
1.355 | 6.97 | 54 | 1.3131 | 0.4220 |
1.3123 | 8.0 | 62 | 1.2983 | 0.4220 |
1.2909 | 8.9 | 69 | 1.2640 | 0.4587 |
1.253 | 9.94 | 77 | 1.2272 | 0.5046 |
1.2244 | 10.97 | 85 | 1.1942 | 0.5321 |
1.1934 | 12.0 | 93 | 1.1909 | 0.5321 |
1.156 | 12.9 | 100 | 1.1293 | 0.5688 |
1.1127 | 13.94 | 108 | 1.0994 | 0.5413 |
1.0505 | 14.97 | 116 | 1.0621 | 0.5780 |
1.0323 | 16.0 | 124 | 1.0657 | 0.6147 |
0.9802 | 16.9 | 131 | 1.0402 | 0.6147 |
0.981 | 17.94 | 139 | 1.0034 | 0.6514 |
0.9461 | 18.97 | 147 | 0.9867 | 0.6330 |
0.9538 | 20.0 | 155 | 0.9721 | 0.6514 |
0.8932 | 20.9 | 162 | 0.9433 | 0.6881 |
0.8865 | 21.94 | 170 | 0.9072 | 0.7248 |
0.8673 | 22.97 | 178 | 0.9035 | 0.6881 |
0.8533 | 24.0 | 186 | 0.8879 | 0.7064 |
0.8474 | 24.9 | 193 | 0.8569 | 0.7339 |
0.794 | 25.94 | 201 | 0.8465 | 0.7339 |
0.814 | 26.97 | 209 | 0.8138 | 0.7339 |
0.7915 | 28.0 | 217 | 0.8251 | 0.7706 |
0.7437 | 28.9 | 224 | 0.8197 | 0.7431 |
0.7584 | 29.94 | 232 | 0.8035 | 0.7615 |
0.7256 | 30.97 | 240 | 0.7614 | 0.7523 |
0.707 | 32.0 | 248 | 0.7498 | 0.7523 |
0.707 | 32.9 | 255 | 0.7515 | 0.7706 |
0.6825 | 33.94 | 263 | 0.7327 | 0.7615 |
0.6892 | 34.97 | 271 | 0.7706 | 0.7431 |
0.6702 | 36.0 | 279 | 0.7571 | 0.7523 |
0.6691 | 36.9 | 286 | 0.7104 | 0.7523 |
0.6335 | 37.94 | 294 | 0.6955 | 0.7615 |
0.6294 | 38.97 | 302 | 0.6769 | 0.7706 |
0.6099 | 40.0 | 310 | 0.6560 | 0.7706 |
0.6137 | 40.9 | 317 | 0.6429 | 0.7798 |
0.5822 | 41.94 | 325 | 0.6249 | 0.7706 |
0.571 | 42.97 | 333 | 0.6305 | 0.7706 |
0.5842 | 44.0 | 341 | 0.6467 | 0.7706 |
0.5849 | 44.9 | 348 | 0.5982 | 0.7890 |
0.5811 | 45.94 | 356 | 0.6022 | 0.7798 |
0.5497 | 46.97 | 364 | 0.5964 | 0.7798 |
0.5323 | 48.0 | 372 | 0.5784 | 0.7890 |
0.514 | 48.9 | 379 | 0.5897 | 0.8073 |
0.5112 | 49.94 | 387 | 0.6092 | 0.7798 |
0.5577 | 50.97 | 395 | 0.5812 | 0.7982 |
0.4808 | 52.0 | 403 | 0.5518 | 0.8257 |
0.5132 | 52.9 | 410 | 0.5672 | 0.8073 |
0.4954 | 53.94 | 418 | 0.5592 | 0.7982 |
0.5005 | 54.97 | 426 | 0.5815 | 0.7890 |
0.4798 | 56.0 | 434 | 0.5670 | 0.8073 |
0.4998 | 56.9 | 441 | 0.5458 | 0.7982 |
0.4685 | 57.94 | 449 | 0.5760 | 0.7890 |
0.5153 | 58.97 | 457 | 0.5413 | 0.8165 |
0.4725 | 60.0 | 465 | 0.5340 | 0.8257 |
0.4982 | 60.9 | 472 | 0.5230 | 0.8257 |
0.4801 | 61.94 | 480 | 0.5306 | 0.8257 |
0.4897 | 62.97 | 488 | 0.5318 | 0.8165 |
0.4277 | 64.0 | 496 | 0.5127 | 0.8532 |
0.4277 | 64.9 | 503 | 0.5070 | 0.8349 |
0.5024 | 65.94 | 511 | 0.4977 | 0.8440 |
0.5074 | 66.97 | 519 | 0.5297 | 0.8165 |
0.439 | 68.0 | 527 | 0.5005 | 0.8440 |
0.4805 | 68.9 | 534 | 0.5269 | 0.8165 |
0.4729 | 69.94 | 542 | 0.5227 | 0.8073 |
0.4376 | 70.97 | 550 | 0.5062 | 0.8165 |
0.4606 | 72.0 | 558 | 0.5254 | 0.8165 |
0.4146 | 72.26 | 560 | 0.5001 | 0.8349 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
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