metadata
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: final-lr2e-5-bs16-fullprecision
results: []
final-lr2e-5-bs16-fullprecision
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: 0.4633
- F1 Macro: 0.8276
- F1 Weighted: 0.8754
- F1: 0.7348
- Accuracy: 0.8775
- Confusion Matrix: [[2831 199] [ 291 679]]
- Confusion Matrix Norm: [[0.93432343 0.06567657] [0.3 0.7 ]]
- Classification Report: precision recall f1-score support 0 0.906791 0.934323 0.920351 3030.0000
1 0.773349 0.700000 0.734848 970.0000 accuracy 0.877500 0.877500 0.877500 0.8775 macro avg 0.840070 0.817162 0.827600 4000.0000 weighted avg 0.874431 0.877500 0.875367 4000.0000
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
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Weighted | F1 | Accuracy | Confusion Matrix | Confusion Matrix Norm | Classification Report |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.3362 | 1.0 | 1000 | 0.3034 | 0.8182 | 0.8693 | 0.7191 | 0.8722 | [[2835 195] | ||
| [ 316 654]] | [[0.93564356 0.06435644] | |||||||||
| [0.3257732 0.6742268 ]] | precision recall f1-score support | |||||||||
| 0 0.899714 0.935644 0.917327 3030.00000 | ||||||||||
| 1 0.770318 0.674227 0.719076 970.00000 | ||||||||||
| accuracy 0.872250 0.872250 0.872250 0.87225 | ||||||||||
| macro avg 0.835016 0.804935 0.818202 4000.00000 | ||||||||||
| weighted avg 0.868336 0.872250 0.869251 4000.00000 | ||||||||||
| 0.2352 | 2.0 | 2000 | 0.3730 | 0.8270 | 0.8730 | 0.7374 | 0.8732 | [[2781 249] | ||
| [ 258 712]] | [[0.91782178 0.08217822] | |||||||||
| [0.26597938 0.73402062]] | precision recall f1-score support | |||||||||
| 0 0.915104 0.917822 0.916461 3030.00000 | ||||||||||
| 1 0.740895 0.734021 0.737442 970.00000 | ||||||||||
| accuracy 0.873250 0.873250 0.873250 0.87325 | ||||||||||
| macro avg 0.827999 0.825921 0.826951 4000.00000 | ||||||||||
| weighted avg 0.872858 0.873250 0.873049 4000.00000 | ||||||||||
| 0.1566 | 3.0 | 3000 | 0.4633 | 0.8276 | 0.8754 | 0.7348 | 0.8775 | [[2831 199] | ||
| [ 291 679]] | [[0.93432343 0.06567657] | |||||||||
| [0.3 0.7 ]] | precision recall f1-score support | |||||||||
| 0 0.906791 0.934323 0.920351 3030.0000 | ||||||||||
| 1 0.773349 0.700000 0.734848 970.0000 | ||||||||||
| accuracy 0.877500 0.877500 0.877500 0.8775 | ||||||||||
| macro avg 0.840070 0.817162 0.827600 4000.0000 | ||||||||||
| weighted avg 0.874431 0.877500 0.875367 4000.0000 |
Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2