results_fine_tune_gpt40_original_careful
This model is a fine-tuned version of bert-base-multilingual-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0019
- Accuracy: 0.9993
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: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0049 | 0.8881 | 500 | 0.0082 | 0.9987 |
| 0.0056 | 1.7762 | 1000 | 0.0226 | 0.9973 |
| 0.0062 | 2.6643 | 1500 | 0.0003 | 1.0 |
| 0.0031 | 3.5524 | 2000 | 0.0009 | 0.9993 |
| 0.0006 | 4.4405 | 2500 | 0.0019 | 0.9993 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 2.17.0
- Tokenizers 0.21.0
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Model tree for gui8600k/PTBR-GPT4-o-NewsClassifier
Base model
google-bert/bert-base-multilingual-cased