finetuned_bert-base-on-IEMOCAP_4
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: 1.3568
- Accuracy: 0.6712
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 |
|---|---|---|---|---|
| 1.2828 | 1.0 | 113 | 1.2221 | 0.4557 |
| 0.9978 | 2.0 | 226 | 0.9164 | 0.6341 |
| 0.6524 | 3.0 | 339 | 0.8095 | 0.6851 |
| 0.5501 | 4.0 | 452 | 0.8434 | 0.6874 |
| 0.4188 | 5.0 | 565 | 0.8785 | 0.6951 |
| 0.3462 | 6.0 | 678 | 0.9251 | 0.7084 |
| 0.2205 | 7.0 | 791 | 1.0525 | 0.6951 |
| 0.2596 | 8.0 | 904 | 1.0655 | 0.7062 |
| 0.242 | 9.0 | 1017 | 1.1137 | 0.7062 |
| 0.1651 | 10.0 | 1130 | 1.1869 | 0.7118 |
| 0.1943 | 11.0 | 1243 | 1.2023 | 0.7029 |
| 0.1442 | 12.0 | 1356 | 1.2443 | 0.6962 |
| 0.1569 | 13.0 | 1469 | 1.2965 | 0.7106 |
| 0.1396 | 14.0 | 1582 | 1.3718 | 0.7007 |
| 0.1002 | 15.0 | 1695 | 1.3720 | 0.7062 |
| 0.1051 | 16.0 | 1808 | 1.3800 | 0.7018 |
| 0.1604 | 17.0 | 1921 | 1.4143 | 0.6962 |
| 0.1211 | 18.0 | 2034 | 1.4186 | 0.7018 |
| 0.1083 | 19.0 | 2147 | 1.4322 | 0.7029 |
| 0.0939 | 20.0 | 2260 | 1.4358 | 0.7007 |
Framework versions
- Transformers 4.30.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3