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hing-mbert-ours-run-5
This model is a fine-tuned version of l3cube-pune/hing-mbert on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 3.2437
- Accuracy: 0.665
- Precision: 0.6223
- Recall: 0.5991
- F1: 0.6039
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: 5e-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: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.9643 | 1.0 | 100 | 0.7996 | 0.69 | 0.6596 | 0.6593 | 0.6521 |
| 0.6951 | 2.0 | 200 | 1.0464 | 0.66 | 0.6597 | 0.5831 | 0.5734 |
| 0.4245 | 3.0 | 300 | 0.9640 | 0.64 | 0.6025 | 0.6033 | 0.6010 |
| 0.238 | 4.0 | 400 | 1.6744 | 0.68 | 0.7095 | 0.6445 | 0.6359 |
| 0.1477 | 5.0 | 500 | 1.7115 | 0.665 | 0.6362 | 0.6422 | 0.6360 |
| 0.1206 | 6.0 | 600 | 2.0459 | 0.635 | 0.5749 | 0.5752 | 0.5726 |
| 0.0528 | 7.0 | 700 | 2.5698 | 0.66 | 0.6230 | 0.5904 | 0.5985 |
| 0.0525 | 8.0 | 800 | 2.2729 | 0.625 | 0.5741 | 0.5860 | 0.5733 |
| 0.0174 | 9.0 | 900 | 2.6227 | 0.635 | 0.6099 | 0.6044 | 0.6019 |
| 0.0088 | 10.0 | 1000 | 2.8854 | 0.63 | 0.5699 | 0.5676 | 0.5680 |
| 0.0085 | 11.0 | 1100 | 3.2173 | 0.655 | 0.6043 | 0.5771 | 0.5821 |
| 0.0121 | 12.0 | 1200 | 3.1270 | 0.665 | 0.6214 | 0.5903 | 0.5971 |
| 0.0141 | 13.0 | 1300 | 2.6648 | 0.655 | 0.5981 | 0.5978 | 0.5961 |
| 0.0116 | 14.0 | 1400 | 3.1711 | 0.665 | 0.6192 | 0.5915 | 0.5971 |
| 0.007 | 15.0 | 1500 | 3.0954 | 0.66 | 0.6156 | 0.5961 | 0.6009 |
| 0.0037 | 16.0 | 1600 | 3.3065 | 0.65 | 0.6027 | 0.5791 | 0.5824 |
| 0.0031 | 17.0 | 1700 | 3.1715 | 0.665 | 0.6177 | 0.5999 | 0.6048 |
| 0.0021 | 18.0 | 1800 | 3.1602 | 0.665 | 0.6220 | 0.6029 | 0.6082 |
| 0.0021 | 19.0 | 1900 | 3.2027 | 0.655 | 0.6096 | 0.5893 | 0.5937 |
| 0.0018 | 20.0 | 2000 | 3.2437 | 0.665 | 0.6223 | 0.5991 | 0.6039 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Tokenizers 0.13.2
