augmented_model_fast_3_b
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.3040
- Accuracy: 0.5430
- F1: 0.5453
- Precision: 0.5588
- Recall: 0.5421
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: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.5287 | 0.1566 | 500 | 0.8012 | 0.7037 | 0.6979 | 0.7045 | 0.6981 |
| 0.5188 | 0.3133 | 1000 | 0.8209 | 0.7137 | 0.6965 | 0.7092 | 0.7023 |
| 0.4646 | 0.4699 | 1500 | 0.7991 | 0.7247 | 0.7129 | 0.7181 | 0.7153 |
| 0.423 | 0.6266 | 2000 | 0.8381 | 0.7155 | 0.7025 | 0.7075 | 0.7058 |
| 0.419 | 0.7832 | 2500 | 0.8197 | 0.7207 | 0.7094 | 0.7128 | 0.7117 |
| 0.3953 | 0.9398 | 3000 | 0.9336 | 0.7225 | 0.7066 | 0.7193 | 0.7109 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for LeonardoFettucciari/augmented_model_fast_3_b
Base model
distilbert/distilbert-base-uncased