fin_subcate
This model is a fine-tuned version of microsoft/deberta-v3-small on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0671
- Accuracy: 0.6825
- F1: 0.7671
- Precision: 0.8756
- Recall: 0.6825
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: 64
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
Training results
| Training Loss |
Epoch |
Step |
Validation Loss |
Accuracy |
F1 |
Precision |
Recall |
| No log |
1.0 |
64 |
0.1875 |
0.0 |
0.0 |
0.0 |
0.0 |
| No log |
2.0 |
128 |
0.1179 |
0.5134 |
0.6578 |
0.9153 |
0.5134 |
| No log |
3.0 |
192 |
0.0931 |
0.5124 |
0.6680 |
0.9593 |
0.5124 |
| No log |
4.0 |
256 |
0.0798 |
0.6231 |
0.7343 |
0.8936 |
0.6231 |
| No log |
5.0 |
320 |
0.0717 |
0.6508 |
0.7550 |
0.8989 |
0.6508 |
| No log |
6.0 |
384 |
0.0684 |
0.6746 |
0.7629 |
0.8777 |
0.6746 |
| No log |
7.0 |
448 |
0.0671 |
0.6825 |
0.7671 |
0.8756 |
0.6825 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
