final_model
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: 0.6210
- Accuracy: 0.9301
- Precision: 0.9373
- Recall: 0.9219
- F1: 0.9295
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: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
---|---|---|---|---|---|---|---|
0.0727 | 1.0 | 1563 | 0.4131 | 0.9246 | 0.9315 | 0.9167 | 0.9240 |
0.0423 | 2.0 | 3126 | 0.4253 | 0.9278 | 0.9230 | 0.9335 | 0.9282 |
0.0266 | 3.0 | 4689 | 0.4010 | 0.9282 | 0.9188 | 0.9394 | 0.9290 |
0.0141 | 4.0 | 6252 | 0.5954 | 0.9278 | 0.9126 | 0.9462 | 0.9291 |
0.0193 | 5.0 | 7815 | 0.5174 | 0.9287 | 0.9394 | 0.9166 | 0.9278 |
0.0091 | 6.0 | 9378 | 0.6210 | 0.9301 | 0.9373 | 0.9219 | 0.9295 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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