emotion-bert-large-uncased-lora
This model is a fine-tuned version of google-bert/bert-large-uncased on the emotion dataset. It achieves the following results on the evaluation set:
- Loss: 0.1651
- Accuracy: 0.9315
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: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 250 | 0.4509 | 0.848 |
| 0.6387 | 2.0 | 500 | 0.2250 | 0.9225 |
| 0.6387 | 3.0 | 750 | 0.1771 | 0.9215 |
| 0.1705 | 4.0 | 1000 | 0.1651 | 0.9315 |
Framework versions
- PEFT 0.10.0
- Transformers 4.40.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.19.1
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Base model
google-bert/bert-large-uncased