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finetuned_bert-base-on-IEMOCAP_1
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: 1.3271
- Accuracy: 0.6516
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 |
|---|---|---|---|---|
| 1.2909 | 1.0 | 112 | 1.2207 | 0.5314 |
| 0.9328 | 2.0 | 224 | 0.9234 | 0.6446 |
| 0.7572 | 3.0 | 336 | 0.7751 | 0.7040 |
| 0.5686 | 4.0 | 448 | 0.7708 | 0.7175 |
| 0.3796 | 5.0 | 560 | 0.8258 | 0.7130 |
| 0.2736 | 6.0 | 672 | 0.8620 | 0.7231 |
| 0.2182 | 7.0 | 784 | 0.8939 | 0.7231 |
| 0.1755 | 8.0 | 896 | 1.0788 | 0.7220 |
| 0.1514 | 9.0 | 1008 | 1.0029 | 0.7365 |
| 0.1665 | 10.0 | 1120 | 1.0819 | 0.7152 |
| 0.2008 | 11.0 | 1232 | 1.1361 | 0.7152 |
| 0.0925 | 12.0 | 1344 | 1.1105 | 0.7253 |
| 0.162 | 13.0 | 1456 | 1.1379 | 0.7197 |
| 0.1302 | 14.0 | 1568 | 1.2054 | 0.7209 |
| 0.0701 | 15.0 | 1680 | 1.2240 | 0.7343 |
| 0.1392 | 16.0 | 1792 | 1.2842 | 0.7175 |
| 0.1419 | 17.0 | 1904 | 1.2614 | 0.7242 |
| 0.1125 | 18.0 | 2016 | 1.2826 | 0.7209 |
| 0.1128 | 19.0 | 2128 | 1.3033 | 0.7209 |
| 0.0813 | 20.0 | 2240 | 1.3068 | 0.7220 |
Framework versions
- Transformers 4.30.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
