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token_final_tunned
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4670
- Precision: 0.8269
- Recall: 0.8442
- F1: 0.8355
- Accuracy: 0.8516
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: 8
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
No log | 1.0 | 108 | 0.7286 | 0.6581 | 0.7117 | 0.6838 | 0.7272 |
No log | 2.0 | 216 | 0.5497 | 0.7529 | 0.7823 | 0.7673 | 0.8053 |
No log | 3.0 | 324 | 0.4884 | 0.7911 | 0.8145 | 0.8026 | 0.8277 |
No log | 4.0 | 432 | 0.4723 | 0.8144 | 0.8278 | 0.8210 | 0.8408 |
0.6038 | 5.0 | 540 | 0.4597 | 0.8032 | 0.8315 | 0.8171 | 0.8428 |
0.6038 | 6.0 | 648 | 0.4583 | 0.8208 | 0.8322 | 0.8264 | 0.8480 |
0.6038 | 7.0 | 756 | 0.4641 | 0.8290 | 0.8442 | 0.8365 | 0.8520 |
0.6038 | 8.0 | 864 | 0.4670 | 0.8269 | 0.8442 | 0.8355 | 0.8516 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu102
- Datasets 2.2.2
- Tokenizers 0.12.1