mixed_model_combined_data
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3112
- Accuracy: 0.8954
- F1: 0.8944
- Recall: 0.8954
- Precision: 0.8953
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: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 849
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision |
|---|---|---|---|---|---|---|---|
| 0.66 | 0.9982 | 212 | 0.7179 | 0.7648 | 0.7546 | 0.7648 | 0.7864 |
| 0.4943 | 1.9965 | 424 | 0.5750 | 0.8136 | 0.8106 | 0.8136 | 0.8352 |
| 0.2822 | 2.9994 | 637 | 0.3672 | 0.8713 | 0.8696 | 0.8713 | 0.8743 |
| 0.1882 | 3.9976 | 849 | 0.3112 | 0.8954 | 0.8944 | 0.8954 | 0.8953 |
Framework versions
- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1