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final-lr2e-5-bs16-fp16-2
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4823
- F1 Macro: 0.8301
- F1 Weighted: 0.8772
- F1: 0.7388
- Accuracy: 0.8792
- Confusion Matrix: [[2834 196] [ 287 683]]
- Confusion Matrix Norm: [[0.93531353 0.06468647] [0.29587629 0.70412371]]
- Classification Report: precision recall f1-score support 0 0.908042 0.935314 0.921476 3030.00000 1 0.777019 0.704124 0.738778 970.00000 accuracy 0.879250 0.879250 0.879250 0.87925 macro avg 0.842531 0.819719 0.830127 4000.00000 weighted avg 0.876269 0.879250 0.877172 4000.00000
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 12345
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Weighted | F1 | Accuracy | Confusion Matrix | Confusion Matrix Norm | Classification Report |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.3333 | 1.0 | 1000 | 0.3064 | 0.8165 | 0.8672 | 0.7181 | 0.8692 | [[2811 219] | ||
| [ 304 666]] | [[0.92772277 0.07227723] | |||||||||
| [0.31340206 0.68659794]] | precision recall f1-score support | |||||||||
| 0 0.902408 0.927723 0.914890 3030.00000 | ||||||||||
| 1 0.752542 0.686598 0.718059 970.00000 | ||||||||||
| accuracy 0.869250 0.869250 0.869250 0.86925 | ||||||||||
| macro avg 0.827475 0.807160 0.816475 4000.00000 | ||||||||||
| weighted avg 0.866065 0.869250 0.867159 4000.00000 | ||||||||||
| 0.2271 | 2.0 | 2000 | 0.3905 | 0.8238 | 0.8708 | 0.7326 | 0.871 | [[2777 253] | ||
| [ 263 707]] | [[0.91650165 0.08349835] | |||||||||
| [0.27113402 0.72886598]] | precision recall f1-score support | |||||||||
| 0 0.913487 0.916502 0.914992 3030.000 | ||||||||||
| 1 0.736458 0.728866 0.732642 970.000 | ||||||||||
| accuracy 0.871000 0.871000 0.871000 0.871 | ||||||||||
| macro avg 0.824973 0.822684 0.823817 4000.000 | ||||||||||
| weighted avg 0.870557 0.871000 0.870772 4000.000 | ||||||||||
| 0.1435 | 3.0 | 3000 | 0.4823 | 0.8301 | 0.8772 | 0.7388 | 0.8792 | [[2834 196] | ||
| [ 287 683]] | [[0.93531353 0.06468647] | |||||||||
| [0.29587629 0.70412371]] | precision recall f1-score support | |||||||||
| 0 0.908042 0.935314 0.921476 3030.00000 | ||||||||||
| 1 0.777019 0.704124 0.738778 970.00000 | ||||||||||
| accuracy 0.879250 0.879250 0.879250 0.87925 | ||||||||||
| macro avg 0.842531 0.819719 0.830127 4000.00000 | ||||||||||
| weighted avg 0.876269 0.879250 0.877172 4000.00000 |
Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
