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Tya-Distilbert-CNN256LSTM128-Pytorch
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.5950
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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.0971 | 1.0 | 3290 | 1.8622 |
| 1.3241 | 2.0 | 6580 | 1.2807 |
| 0.8786 | 3.0 | 9870 | 1.2497 |
| 0.6238 | 4.0 | 13160 | 1.2655 |
| 0.4372 | 5.0 | 16450 | 1.5950 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
