output_AAVESM2_650M_v1
This model is a fine-tuned version of facebook/esm2_t33_650M_UR50D on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3707
- Accuracy: 0.8905
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: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- total_eval_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 36.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 390 | 1.3347 | 0.5993 |
| 1.5408 | 2.0 | 780 | 1.0699 | 0.6796 |
| 1.1283 | 3.0 | 1170 | 0.8751 | 0.7373 |
| 0.9078 | 4.0 | 1560 | 0.7534 | 0.7711 |
| 0.9078 | 5.0 | 1950 | 0.6711 | 0.8022 |
| 0.7705 | 6.0 | 2340 | 0.6078 | 0.8169 |
| 0.6863 | 7.0 | 2730 | 0.5668 | 0.8318 |
| 0.6277 | 8.0 | 3120 | 0.5461 | 0.8386 |
| 0.5863 | 9.0 | 3510 | 0.5143 | 0.8514 |
| 0.5863 | 10.0 | 3900 | 0.4992 | 0.8522 |
| 0.5564 | 11.0 | 4290 | 0.4940 | 0.8533 |
| 0.5199 | 12.0 | 4680 | 0.4727 | 0.8633 |
| 0.5025 | 13.0 | 5070 | 0.4586 | 0.8638 |
| 0.5025 | 14.0 | 5460 | 0.4549 | 0.8673 |
| 0.4814 | 15.0 | 5850 | 0.4442 | 0.8698 |
| 0.4746 | 16.0 | 6240 | 0.4306 | 0.8750 |
| 0.4527 | 17.0 | 6630 | 0.4291 | 0.8742 |
| 0.4382 | 18.0 | 7020 | 0.4213 | 0.8751 |
| 0.4382 | 19.0 | 7410 | 0.4193 | 0.8751 |
| 0.4328 | 20.0 | 7800 | 0.4143 | 0.8760 |
| 0.4191 | 21.0 | 8190 | 0.4071 | 0.8836 |
| 0.4106 | 22.0 | 8580 | 0.3980 | 0.8819 |
| 0.4106 | 23.0 | 8970 | 0.3987 | 0.8822 |
| 0.4037 | 24.0 | 9360 | 0.4027 | 0.8819 |
| 0.3893 | 25.0 | 9750 | 0.3868 | 0.8893 |
| 0.3991 | 26.0 | 10140 | 0.3882 | 0.8846 |
| 0.3786 | 27.0 | 10530 | 0.3939 | 0.8859 |
| 0.3786 | 28.0 | 10920 | 0.3959 | 0.8848 |
| 0.38 | 29.0 | 11310 | 0.3950 | 0.8850 |
| 0.3764 | 30.0 | 11700 | 0.3783 | 0.8893 |
| 0.3708 | 31.0 | 12090 | 0.3799 | 0.8891 |
| 0.3708 | 32.0 | 12480 | 0.3915 | 0.8867 |
| 0.3656 | 33.0 | 12870 | 0.3780 | 0.8903 |
| 0.3617 | 34.0 | 13260 | 0.3805 | 0.8874 |
| 0.361 | 35.0 | 13650 | 0.3776 | 0.8920 |
| 0.3595 | 36.0 | 14040 | 0.3712 | 0.8888 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
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