populism_classifier_bsample_031
This model is a fine-tuned version of google-bert/bert-base-multilingual-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7796
- Accuracy: 0.8038
- 1-f1: 0.2407
- 1-recall: 0.8667
- 1-precision: 0.1398
- Balanced Acc: 0.8341
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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|---|---|---|---|---|---|---|---|---|
| 0.4108 | 1.0 | 7 | 0.8893 | 0.7105 | 0.1769 | 0.8667 | 0.0985 | 0.7857 |
| 0.0958 | 2.0 | 14 | 0.6228 | 0.7871 | 0.2124 | 0.8 | 0.1224 | 0.7933 |
| 0.0273 | 3.0 | 21 | 0.7425 | 0.7656 | 0.2097 | 0.8667 | 0.1193 | 0.8142 |
| 0.0249 | 4.0 | 28 | 0.7796 | 0.8038 | 0.2407 | 0.8667 | 0.1398 | 0.8341 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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Base model
google-bert/bert-base-multilingual-uncased