bert-cased-new-pipeline
This model is a fine-tuned version of distilbert/distilbert-base-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.5583
- Accuracy: 0.6960
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: 0.0003
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- 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: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.9231 | 1.0 | 734 | 0.9314 | 0.6958 |
| 0.7049 | 2.0 | 1468 | 0.9115 | 0.7054 |
| 0.4524 | 3.0 | 2202 | 1.0370 | 0.7025 |
| 0.2283 | 4.0 | 2936 | 1.2918 | 0.6980 |
| 0.1029 | 4.9934 | 3665 | 1.5583 | 0.6960 |
Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
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Model tree for m24coffee/bert-cased-new-pipeline
Base model
distilbert/distilbert-base-cased