emotion-bert-large-uncased-balanced-lora
This model is a fine-tuned version of google-bert/bert-large-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1721
- Accuracy: 0.942
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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 250 | 0.4374 | 0.86 |
| 0.7097 | 2.0 | 500 | 0.2582 | 0.9195 |
| 0.7097 | 3.0 | 750 | 0.2047 | 0.9345 |
| 0.1878 | 4.0 | 1000 | 0.1667 | 0.9385 |
| 0.1878 | 5.0 | 1250 | 0.1861 | 0.935 |
| 0.1306 | 6.0 | 1500 | 0.1871 | 0.9415 |
| 0.1306 | 7.0 | 1750 | 0.1720 | 0.943 |
| 0.1035 | 8.0 | 2000 | 0.1696 | 0.9425 |
| 0.1035 | 9.0 | 2250 | 0.1706 | 0.9415 |
| 0.0851 | 10.0 | 2500 | 0.1721 | 0.942 |
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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Model tree for leonvanbokhorst/emotion-bert-large-uncased-balanced-lora
Base model
google-bert/bert-large-uncased