finbert-japan-lora-v1
This model is a fine-tuned version of ProsusAI/finbert on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2192
- Accuracy: 0.822
- F1: 0.8250
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: 0.0002
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.1955 | 1.0 | 733 | 0.2869 | 0.744 | 0.7502 |
| 0.0921 | 2.0 | 1466 | 0.2509 | 0.772 | 0.7686 |
| 0.0606 | 3.0 | 2199 | 0.2283 | 0.788 | 0.7868 |
| 0.0582 | 4.0 | 2932 | 0.2412 | 0.812 | 0.8134 |
| 0.0445 | 5.0 | 3665 | 0.2360 | 0.806 | 0.8067 |
Framework versions
- PEFT 0.18.0
- Transformers 4.57.3
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.2
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ProsusAI/finbert