token_classification_test
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: 0.2859
- Precision: 0.9187
- Recall: 0.9095
- F1: 0.9140
- Accuracy: 0.9308
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 47 | 1.2700 | 0.6758 | 0.5896 | 0.6298 | 0.7121 |
| No log | 2.0 | 94 | 0.6468 | 0.8315 | 0.7864 | 0.8083 | 0.8461 |
| No log | 3.0 | 141 | 0.4607 | 0.8709 | 0.8422 | 0.8563 | 0.8845 |
| No log | 4.0 | 188 | 0.3841 | 0.8924 | 0.8686 | 0.8804 | 0.9047 |
| No log | 5.0 | 235 | 0.3380 | 0.9060 | 0.8905 | 0.8982 | 0.9180 |
| No log | 6.0 | 282 | 0.3164 | 0.9096 | 0.8934 | 0.9014 | 0.9213 |
| No log | 7.0 | 329 | 0.3072 | 0.9090 | 0.9001 | 0.9045 | 0.9227 |
| No log | 8.0 | 376 | 0.2997 | 0.9156 | 0.9009 | 0.9082 | 0.9258 |
| No log | 9.0 | 423 | 0.2940 | 0.9141 | 0.9058 | 0.9099 | 0.9269 |
| No log | 10.0 | 470 | 0.2904 | 0.9199 | 0.9076 | 0.9137 | 0.9312 |
| 0.5334 | 11.0 | 517 | 0.2894 | 0.9210 | 0.9093 | 0.9151 | 0.9314 |
| 0.5334 | 12.0 | 564 | 0.2884 | 0.9173 | 0.9081 | 0.9127 | 0.9295 |
| 0.5334 | 13.0 | 611 | 0.2862 | 0.9184 | 0.9089 | 0.9136 | 0.9305 |
| 0.5334 | 14.0 | 658 | 0.2859 | 0.9196 | 0.9103 | 0.9149 | 0.9310 |
| 0.5334 | 15.0 | 705 | 0.2859 | 0.9187 | 0.9095 | 0.9140 | 0.9308 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
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Base model
distilbert/distilbert-base-uncased