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hing-mbert-ours-run-1
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: 2.9965
- Accuracy: 0.665
- Precision: 0.6151
- Recall: 0.6082
- F1: 0.6090
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 |
|---|---|---|---|---|---|---|---|
| 0.9757 | 1.0 | 100 | 0.7526 | 0.665 | 0.6358 | 0.6149 | 0.5854 |
| 0.7227 | 2.0 | 200 | 1.1062 | 0.69 | 0.6679 | 0.6031 | 0.6025 |
| 0.4345 | 3.0 | 300 | 1.2601 | 0.67 | 0.6512 | 0.6031 | 0.6001 |
| 0.2738 | 4.0 | 400 | 1.4485 | 0.67 | 0.6333 | 0.5968 | 0.6050 |
| 0.1171 | 5.0 | 500 | 1.9132 | 0.65 | 0.6216 | 0.6170 | 0.5944 |
| 0.0941 | 6.0 | 600 | 1.8293 | 0.685 | 0.6420 | 0.6439 | 0.6409 |
| 0.0348 | 7.0 | 700 | 2.3249 | 0.675 | 0.6424 | 0.6386 | 0.6384 |
| 0.0317 | 8.0 | 800 | 2.4134 | 0.67 | 0.6171 | 0.6120 | 0.6128 |
| 0.0056 | 9.0 | 900 | 2.6733 | 0.68 | 0.6343 | 0.6313 | 0.6300 |
| 0.0095 | 10.0 | 1000 | 2.5950 | 0.685 | 0.6318 | 0.6289 | 0.6295 |
| 0.0081 | 11.0 | 1100 | 2.3885 | 0.69 | 0.6407 | 0.6434 | 0.6410 |
| 0.023 | 12.0 | 1200 | 2.4087 | 0.67 | 0.6206 | 0.6231 | 0.6212 |
| 0.0054 | 13.0 | 1300 | 2.4516 | 0.675 | 0.6229 | 0.6227 | 0.6128 |
| 0.0047 | 14.0 | 1400 | 2.6152 | 0.68 | 0.6285 | 0.6256 | 0.6263 |
| 0.0063 | 15.0 | 1500 | 2.8077 | 0.69 | 0.6498 | 0.6281 | 0.6309 |
| 0.0028 | 16.0 | 1600 | 2.7084 | 0.675 | 0.6254 | 0.6214 | 0.6207 |
| 0.0025 | 17.0 | 1700 | 2.8360 | 0.67 | 0.6175 | 0.6128 | 0.6145 |
| 0.0011 | 18.0 | 1800 | 2.8591 | 0.655 | 0.6001 | 0.5958 | 0.5971 |
| 0.0005 | 19.0 | 1900 | 2.9419 | 0.665 | 0.6151 | 0.6082 | 0.6090 |
| 0.0002 | 20.0 | 2000 | 2.9965 | 0.665 | 0.6151 | 0.6082 | 0.6090 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Tokenizers 0.13.2
