bert-dair-ai-emotion
This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1341
- F1: 0.8647
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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 0.0127 | 1.0 | 48 | 0.1385 | 0.8670 |
| 0.0208 | 2.0 | 96 | 0.1379 | 0.8351 |
| 0.0076 | 3.0 | 144 | 0.1452 | 0.8351 |
| 0.0015 | 4.0 | 192 | 0.1336 | 0.8647 |
| 0.0133 | 5.0 | 240 | 0.1341 | 0.8647 |
Framework versions
- PEFT 0.13.2
- Transformers 4.48.0
- Pytorch 2.4.1+cu124
- Datasets 3.0.1
- Tokenizers 0.21.0
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Base model
google-bert/bert-base-cased