dense_swe_100m_mult_retok
This model is a fine-tuned version of on the arrow dataset. It achieves the following results on the evaluation set:
- Loss: 5.0811
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 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: 665
- training_steps: 6655
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 8.7051 | 0.7510 | 500 | 8.1513 |
| 6.7413 | 1.5017 | 1000 | 6.5119 |
| 6.0516 | 2.2523 | 1500 | 5.8710 |
| 5.5523 | 3.0030 | 2000 | 5.5322 |
| 5.2899 | 3.7540 | 2500 | 5.3381 |
| 5.0084 | 4.5047 | 3000 | 5.2254 |
| 4.8915 | 5.2554 | 3500 | 5.1517 |
| 4.7619 | 6.0060 | 4000 | 5.1070 |
| 4.6009 | 6.7570 | 4500 | 5.0784 |
| 4.4513 | 7.5077 | 5000 | 5.0740 |
| 4.4063 | 8.2584 | 5500 | 5.0808 |
| 4.3455 | 9.0090 | 6000 | 5.0806 |
| 4.249 | 9.7600 | 6500 | 5.0824 |
Framework versions
- Transformers 4.51.0
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
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