dense_eng_100m_mult_het_retok-het
This model is a fine-tuned version of on the arrow dataset. It achieves the following results on the evaluation set:
- Loss: 4.8737
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: 723
- training_steps: 7235
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 8.419 | 0.6908 | 500 | 7.7970 |
| 6.2543 | 1.3813 | 1000 | 6.0385 |
| 5.6668 | 2.0718 | 1500 | 5.4910 |
| 5.2471 | 2.7627 | 2000 | 5.2379 |
| 5.0071 | 3.4532 | 2500 | 5.0902 |
| 4.834 | 4.1437 | 3000 | 4.9959 |
| 4.7398 | 4.8345 | 3500 | 4.9203 |
| 4.5632 | 5.5250 | 4000 | 4.8889 |
| 4.5025 | 6.2155 | 4500 | 4.8730 |
| 4.404 | 6.9064 | 5000 | 4.8471 |
| 4.2675 | 7.5969 | 5500 | 4.8560 |
| 4.1516 | 8.2874 | 6000 | 4.8683 |
| 4.1668 | 8.9782 | 6500 | 4.8611 |
| 4.0815 | 9.6687 | 7000 | 4.8746 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.9.0+cu128
- Datasets 3.6.0
- Tokenizers 0.22.1
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