metadata
library_name: transformers
tags:
- generated_from_trainer
datasets:
- arrow
model-index:
- name: dense_swe_100m_mult
results: []
dense_swe_100m_mult
This model is a fine-tuned version of on the arrow dataset. It achieves the following results on the evaluation set:
- Loss: 5.1627
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: 10870
- training_steps: 108705
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 5.9258 | 0.9199 | 10000 | 5.8786 |
| 5.0954 | 1.8399 | 20000 | 5.0799 |
| 4.7106 | 2.7598 | 30000 | 4.8407 |
| 4.4249 | 3.6798 | 40000 | 4.7402 |
| 4.1411 | 4.5998 | 50000 | 4.7186 |
| 3.849 | 5.5197 | 60000 | 4.7632 |
| 3.5485 | 6.4397 | 70000 | 4.8468 |
| 3.2413 | 7.3597 | 80000 | 4.9499 |
| 2.9692 | 8.2796 | 90000 | 5.0563 |
| 2.7408 | 9.1996 | 100000 | 5.1414 |
Framework versions
- Transformers 4.51.0
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1