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This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 1.0876 | 1.0 | 1022 | 1.2027 | 0.1635 | 0.0185 | 0.0333 | 0.7421 |
| 0.9132 | 2.0 | 2044 | 1.1484 | 0.1495 | 0.0281 | 0.0473 | 0.7401 |
| 0.8007 | 3.0 | 3066 | 1.1948 | 0.1223 | 0.0517 | 0.0726 | 0.7198 |
| 0.6898 | 4.0 | 4088 | 1.2544 | 0.1080 | 0.0515 | 0.0697 | 0.7015 |
| 0.5841 | 5.0 | 5110 | 1.2430 | 0.0989 | 0.0573 | 0.0726 | 0.7140 |
| 0.5144 | 6.0 | 6132 | 1.2840 | 0.1096 | 0.0561 | 0.0742 | 0.7127 |
| 0.4585 | 7.0 | 7154 | 1.3193 | 0.1147 | 0.0700 | 0.0869 | 0.7073 |
| 0.4004 | 8.0 | 8176 | 1.4146 | 0.1177 | 0.0643 | 0.0832 | 0.7186 |
| 0.3723 | 9.0 | 9198 | 1.4219 | 0.1162 | 0.0741 | 0.0905 | 0.7123 |
| 0.3401 | 10.0 | 10220 | 1.4302 | 0.1128 | 0.0727 | 0.0884 | 0.7112 |