original_model_fast_3
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.3365
- Accuracy: 0.4955
- F1: 0.4921
- Precision: 0.5342
- Recall: 0.4943
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: 3e-06
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
---|---|---|---|---|---|---|---|
0.9771 | 0.3131 | 500 | 1.0857 | 0.4725 | 0.3578 | 0.4359 | 0.4452 |
0.5937 | 0.6262 | 1000 | 0.8409 | 0.6324 | 0.5352 | 0.6620 | 0.6069 |
0.5097 | 0.9393 | 1500 | 0.7965 | 0.6709 | 0.6323 | 0.6645 | 0.6529 |
0.4682 | 1.2523 | 2000 | 0.8232 | 0.6849 | 0.6580 | 0.6843 | 0.6694 |
0.4603 | 1.5654 | 2500 | 0.8043 | 0.6941 | 0.6706 | 0.6907 | 0.6797 |
0.4529 | 1.8785 | 3000 | 0.8085 | 0.6962 | 0.6731 | 0.6950 | 0.6819 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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