minirbt-h256-fin-finetuned
This model is a fine-tuned version of hfl/minirbt-h256 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4252
- Accuracy: 0.8808
- F1: 0.8813
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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.4226 | 1.0 | 1096 | 0.4544 | 0.8569 | 0.8576 |
| 0.3963 | 2.0 | 2192 | 0.3878 | 0.8800 | 0.8806 |
| 0.324 | 3.0 | 3288 | 0.4218 | 0.8779 | 0.8784 |
| 0.3088 | 4.0 | 4384 | 0.4252 | 0.8808 | 0.8813 |
Framework versions
- Transformers 4.26.1
- Pytorch 2.0.0.dev20230212
- Datasets 2.9.0
- Tokenizers 0.13.2