finetuned_bert-base-on-IEMOCAP_2
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.3186
- Accuracy: 0.7410
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.3127 | 1.0 | 113 | 1.2620 | 0.4192 |
| 0.957 | 2.0 | 226 | 0.9294 | 0.6095 |
| 0.6409 | 3.0 | 339 | 0.8338 | 0.6969 |
| 0.5966 | 4.0 | 452 | 0.8386 | 0.6958 |
| 0.3725 | 5.0 | 565 | 0.8635 | 0.7035 |
| 0.3556 | 6.0 | 678 | 0.9218 | 0.7069 |
| 0.2466 | 7.0 | 791 | 0.9679 | 0.6914 |
| 0.1737 | 8.0 | 904 | 1.1180 | 0.6869 |
| 0.1694 | 9.0 | 1017 | 1.1068 | 0.6947 |
| 0.1843 | 10.0 | 1130 | 1.1409 | 0.6903 |
| 0.1747 | 11.0 | 1243 | 1.1143 | 0.7080 |
| 0.1387 | 12.0 | 1356 | 1.2127 | 0.6991 |
| 0.1524 | 13.0 | 1469 | 1.2309 | 0.7069 |
| 0.1113 | 14.0 | 1582 | 1.2382 | 0.7113 |
| 0.1278 | 15.0 | 1695 | 1.3048 | 0.7124 |
| 0.1214 | 16.0 | 1808 | 1.3267 | 0.7146 |
| 0.1965 | 17.0 | 1921 | 1.3726 | 0.7013 |
| 0.1227 | 18.0 | 2034 | 1.3371 | 0.7168 |
| 0.1014 | 19.0 | 2147 | 1.3508 | 0.7124 |
| 0.1028 | 20.0 | 2260 | 1.3585 | 0.7080 |
Framework versions
- Transformers 4.30.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3