metadata
base_model: nobody12321/poker-pretrain
tags:
- generated_from_trainer
model-index:
- name: poker-pretraining
results: []
poker-pretraining
This model is a fine-tuned version of nobody12321/poker-pretrain on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.7246
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: 0.0005
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.651 | 0.0183 | 5000 | 2.3357 |
| 2.2946 | 0.0367 | 10000 | 2.2261 |
| 2.2212 | 0.0550 | 15000 | 2.1725 |
| 2.1702 | 0.0733 | 20000 | 2.1131 |
| 2.1332 | 0.0917 | 25000 | 2.0965 |
| 2.1062 | 0.1100 | 30000 | 2.0647 |
| 2.0854 | 0.1283 | 35000 | 2.0518 |
| 2.0688 | 0.1466 | 40000 | 2.0344 |
| 2.0556 | 0.1650 | 45000 | 2.0200 |
| 2.045 | 0.1833 | 50000 | 2.0177 |
| 2.0349 | 0.2016 | 55000 | 1.9994 |
| 2.0254 | 0.2200 | 60000 | 1.9950 |
| 2.0175 | 0.2383 | 65000 | 1.9861 |
| 2.0093 | 0.2566 | 70000 | 1.9792 |
| 2.0013 | 0.2750 | 75000 | 1.9761 |
| 1.9946 | 0.2933 | 80000 | 1.9676 |
| 1.988 | 0.3116 | 85000 | 1.9606 |
| 1.9817 | 0.3299 | 90000 | 1.9544 |
| 1.9729 | 0.3483 | 95000 | 1.9465 |
| 1.9662 | 0.3666 | 100000 | 1.9471 |
| 1.9597 | 0.3849 | 105000 | 1.9351 |
| 1.9532 | 0.4033 | 110000 | 1.9313 |
| 1.9475 | 0.4216 | 115000 | 1.9283 |
| 1.9407 | 0.4399 | 120000 | 1.9223 |
| 1.9356 | 0.4583 | 125000 | 1.9139 |
| 1.9308 | 0.4766 | 130000 | 1.9094 |
| 1.9244 | 0.4949 | 135000 | 1.9038 |
| 1.9194 | 0.5132 | 140000 | 1.8983 |
| 1.9134 | 0.5316 | 145000 | 1.8951 |
| 1.9093 | 0.5499 | 150000 | 1.8904 |
| 1.9038 | 0.5682 | 155000 | 1.8826 |
| 1.898 | 0.5866 | 160000 | 1.8776 |
| 1.8931 | 0.6049 | 165000 | 1.8738 |
| 1.8878 | 0.6232 | 170000 | 1.8685 |
| 1.882 | 0.6416 | 175000 | 1.8633 |
| 1.8755 | 0.6599 | 180000 | 1.8573 |
| 1.87 | 0.6782 | 185000 | 1.8517 |
| 1.8648 | 0.6965 | 190000 | 1.8465 |
| 1.8587 | 0.7149 | 195000 | 1.8407 |
| 1.8537 | 0.7332 | 200000 | 1.8346 |
| 1.8358 | 0.7515 | 205000 | 1.8094 |
| 1.8207 | 0.7699 | 210000 | 1.7976 |
| 1.8109 | 0.7882 | 215000 | 1.7890 |
| 1.8031 | 0.8065 | 220000 | 1.7788 |
| 1.7926 | 0.8249 | 225000 | 1.7671 |
| 1.7821 | 0.8432 | 230000 | 1.7571 |
| 1.7756 | 0.8615 | 235000 | 1.7497 |
| 1.7678 | 0.8799 | 240000 | 1.7423 |
| 1.7627 | 0.8982 | 245000 | 1.7366 |
| 1.7581 | 0.9165 | 250000 | 1.7317 |
| 1.7538 | 0.9348 | 255000 | 1.7286 |
| 1.7513 | 0.9532 | 260000 | 1.7262 |
| 1.75 | 0.9715 | 265000 | 1.7251 |
| 1.7492 | 0.9898 | 270000 | 1.7246 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1