pretrained_MaterialsBERT
This model is a fine-tuned version of pranav-s/MaterialsBERT on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.2020
- Accuracy: 0.7271
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: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 3
- total_train_batch_size: 48
- total_eval_batch_size: 48
- optimizer: Use 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: 10.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.6452 | 0.2641 | 500 | 1.5018 | 0.6847 |
| 1.5379 | 0.5283 | 1000 | 1.4544 | 0.6895 |
| 1.497 | 0.7924 | 1500 | 1.4186 | 0.6957 |
| 1.4604 | 1.0565 | 2000 | 1.3934 | 0.6987 |
| 1.4366 | 1.3207 | 2500 | 1.3694 | 0.7021 |
| 1.4045 | 1.5848 | 3000 | 1.3524 | 0.7049 |
| 1.3919 | 1.8489 | 3500 | 1.3361 | 0.7068 |
| 1.3841 | 2.1130 | 4000 | 1.3278 | 0.7086 |
| 1.3711 | 2.3772 | 4500 | 1.3318 | 0.7077 |
| 1.3627 | 2.6413 | 5000 | 1.3148 | 0.7100 |
| 1.3493 | 2.9054 | 5500 | 1.3028 | 0.7122 |
| 1.3423 | 3.1696 | 6000 | 1.2972 | 0.7123 |
| 1.3327 | 3.4337 | 6500 | 1.2910 | 0.7141 |
| 1.3235 | 3.6978 | 7000 | 1.2826 | 0.7148 |
| 1.3125 | 3.9620 | 7500 | 1.2749 | 0.7160 |
| 1.3039 | 4.2261 | 8000 | 1.2726 | 0.7163 |
| 1.2998 | 4.4902 | 8500 | 1.2621 | 0.7182 |
| 1.2962 | 4.7544 | 9000 | 1.2672 | 0.7171 |
| 1.295 | 5.0185 | 9500 | 1.2645 | 0.7173 |
| 1.2868 | 5.2826 | 10000 | 1.2480 | 0.7198 |
| 1.2765 | 5.5468 | 10500 | 1.2482 | 0.7196 |
| 1.2758 | 5.8109 | 11000 | 1.2436 | 0.7205 |
| 1.2686 | 6.0750 | 11500 | 1.2460 | 0.7207 |
| 1.2589 | 6.3391 | 12000 | 1.2369 | 0.7215 |
| 1.2634 | 6.6033 | 12500 | 1.2372 | 0.7219 |
| 1.25 | 6.8674 | 13000 | 1.2199 | 0.7250 |
| 1.2387 | 7.1315 | 13500 | 1.2175 | 0.7246 |
| 1.2374 | 7.3957 | 14000 | 1.2169 | 0.7244 |
| 1.2369 | 7.6598 | 14500 | 1.2130 | 0.7254 |
| 1.2338 | 7.9239 | 15000 | 1.2141 | 0.7244 |
| 1.23 | 8.1881 | 15500 | 1.2124 | 0.7257 |
| 1.2295 | 8.4522 | 16000 | 1.2078 | 0.7259 |
| 1.2229 | 8.7163 | 16500 | 1.2095 | 0.7261 |
| 1.2254 | 8.9805 | 17000 | 1.2058 | 0.7262 |
| 1.2234 | 9.2446 | 17500 | 1.2024 | 0.7267 |
| 1.2173 | 9.5087 | 18000 | 1.2052 | 0.7272 |
| 1.2197 | 9.7728 | 18500 | 1.2003 | 0.7278 |
Framework versions
- Transformers 4.53.0.dev0
- Pytorch 2.7.0+cu126
- Datasets 2.21.0
- Tokenizers 0.21.1