dense_swe_100m_mult_reseg_ep20
This model is a fine-tuned version of on the arrow dataset. It achieves the following results on the evaluation set:
- Loss: 5.5959
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: 8
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-06 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1331
- training_steps: 13311
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 8.9334 | 0.7510 | 500 | 8.6265 |
| 7.5445 | 1.5017 | 1000 | 7.2155 |
| 6.466 | 2.2523 | 1500 | 6.1474 |
| 5.726 | 3.0030 | 2000 | 5.6561 |
| 5.4102 | 3.7540 | 2500 | 5.3874 |
| 5.0792 | 4.5047 | 3000 | 5.2244 |
| 4.9331 | 5.2554 | 3500 | 5.1198 |
| 4.7877 | 6.0060 | 4000 | 5.0532 |
| 4.5897 | 6.7570 | 4500 | 5.0004 |
| 4.3866 | 7.5077 | 5000 | 4.9941 |
| 4.318 | 8.2584 | 5500 | 5.0072 |
| 4.2382 | 9.0090 | 6000 | 5.0087 |
| 4.0438 | 9.7600 | 6500 | 5.0272 |
| 3.8705 | 10.5107 | 7000 | 5.0767 |
| 3.8325 | 11.2614 | 7500 | 5.1324 |
| 3.7636 | 12.0120 | 8000 | 5.1551 |
| 3.5985 | 12.7630 | 8500 | 5.2060 |
| 3.4497 | 13.5137 | 9000 | 5.2728 |
| 3.4188 | 14.2644 | 9500 | 5.3354 |
| 3.3734 | 15.0150 | 10000 | 5.3705 |
| 3.2303 | 15.7661 | 10500 | 5.4190 |
| 3.1182 | 16.5167 | 11000 | 5.4762 |
| 3.1019 | 17.2674 | 11500 | 5.5233 |
| 3.059 | 18.0180 | 12000 | 5.5469 |
| 2.9652 | 18.7691 | 12500 | 5.5735 |
| 2.9104 | 19.5197 | 13000 | 5.5938 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.9.0+cu128
- Datasets 3.6.0
- Tokenizers 0.22.1
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