finetuned_bert-base-on-IEMOCAP_5
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: 1.3146
- Accuracy: 0.6624
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 |
|---|---|---|---|---|
| 1.3037 | 1.0 | 108 | 1.2852 | 0.4349 |
| 1.0277 | 2.0 | 216 | 0.9733 | 0.5942 |
| 0.7985 | 3.0 | 324 | 0.8110 | 0.6895 |
| 0.5244 | 4.0 | 432 | 0.7911 | 0.6919 |
| 0.3289 | 5.0 | 540 | 0.8518 | 0.7012 |
| 0.3281 | 6.0 | 648 | 0.9236 | 0.6884 |
| 0.2689 | 7.0 | 756 | 0.9498 | 0.7058 |
| 0.2162 | 8.0 | 864 | 1.0157 | 0.7023 |
| 0.1811 | 9.0 | 972 | 1.0196 | 0.7151 |
| 0.2283 | 10.0 | 1080 | 1.0987 | 0.7209 |
| 0.1449 | 11.0 | 1188 | 1.1462 | 0.7186 |
| 0.1614 | 12.0 | 1296 | 1.1972 | 0.7116 |
| 0.1589 | 13.0 | 1404 | 1.2174 | 0.7163 |
| 0.1314 | 14.0 | 1512 | 1.2388 | 0.7116 |
| 0.1587 | 15.0 | 1620 | 1.2722 | 0.7186 |
| 0.0928 | 16.0 | 1728 | 1.3090 | 0.7186 |
| 0.1394 | 17.0 | 1836 | 1.3286 | 0.7174 |
| 0.0958 | 18.0 | 1944 | 1.3321 | 0.7209 |
| 0.0862 | 19.0 | 2052 | 1.3439 | 0.7128 |
| 0.1041 | 20.0 | 2160 | 1.3473 | 0.7151 |
Framework versions
- Transformers 4.30.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3