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final-lr2e-5-bs16-fp16-2
This model is a fine-tuned version of clincolnoz/LessSexistBERT on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3458
- F1 Macro: 0.8374
- F1 Weighted: 0.8806
- F1: 0.7535
- Accuracy: 0.8808
- Confusion Matrix: [[2794 236] [ 241 729]]
- Confusion Matrix Norm: [[0.92211221 0.07788779] [0.24845361 0.75154639]]
- Classification Report: precision recall f1-score support 0 0.920593 0.922112 0.921352 3030.00000 1 0.755440 0.751546 0.753488 970.00000 accuracy 0.880750 0.880750 0.880750 0.88075 macro avg 0.838017 0.836829 0.837420 4000.00000 weighted avg 0.880544 0.880750 0.880645 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 |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.3253 | 1.0 | 1000 | 0.3011 | 0.8256 | 0.8748 | 0.7301 | 0.878 | [[2852 178] | ||
| [ 310 660]] | [[0.94125413 0.05874587] | |||||||||
| [0.31958763 0.68041237]] | precision recall f1-score support | |||||||||
| 0 0.901961 0.941254 0.921189 3030.000 | ||||||||||
| 1 0.787589 0.680412 0.730088 970.000 | ||||||||||
| accuracy 0.878000 0.878000 0.878000 0.878 | ||||||||||
| macro avg 0.844775 0.810833 0.825639 4000.000 | ||||||||||
| weighted avg 0.874226 0.878000 0.874847 4000.000 | ||||||||||
| 0.2439 | 2.0 | 2000 | 0.3122 | 0.8411 | 0.8848 | 0.7562 | 0.8865 | [[2842 188] | ||
| [ 266 704]] | [[0.9379538 0.0620462] | |||||||||
| [0.2742268 0.7257732]] | precision recall f1-score support | |||||||||
| 0 0.914414 0.937954 0.926035 3030.0000 | ||||||||||
| 1 0.789238 0.725773 0.756176 970.0000 | ||||||||||
| accuracy 0.886500 0.886500 0.886500 0.8865 | ||||||||||
| macro avg 0.851826 0.831863 0.841105 4000.0000 | ||||||||||
| weighted avg 0.884059 0.886500 0.884844 4000.0000 | ||||||||||
| 0.1962 | 3.0 | 3000 | 0.3458 | 0.8374 | 0.8806 | 0.7535 | 0.8808 | [[2794 236] | ||
| [ 241 729]] | [[0.92211221 0.07788779] | |||||||||
| [0.24845361 0.75154639]] | precision recall f1-score support | |||||||||
| 0 0.920593 0.922112 0.921352 3030.00000 | ||||||||||
| 1 0.755440 0.751546 0.753488 970.00000 | ||||||||||
| accuracy 0.880750 0.880750 0.880750 0.88075 | ||||||||||
| macro avg 0.838017 0.836829 0.837420 4000.00000 | ||||||||||
| weighted avg 0.880544 0.880750 0.880645 4000.00000 |
Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
