structured_conservation_gc_function_bert
This model is a fine-tuned version of google-bert/bert-large-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6350
- Accuracy: 0.7044
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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 0
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: polynomial
- num_epochs: 6
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 383 | 0.6999 | 0.5888 |
| 0.6192 | 2.0 | 766 | 0.6385 | 0.6983 |
| 0.5689 | 3.0 | 1149 | 0.6443 | 0.6825 |
| 0.5426 | 4.0 | 1532 | 0.6373 | 0.6898 |
| 0.5426 | 5.0 | 1915 | 0.6257 | 0.7080 |
| 0.5315 | 6.0 | 2298 | 0.6350 | 0.7044 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
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Model tree for bif02/structured_conservation_gc_function_bert
Base model
google-bert/bert-large-uncased