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| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - precision | |
| - recall | |
| - f1 | |
| - accuracy | |
| model_index: | |
| - name: bert-srb-ner-setimes | |
| results: | |
| - task: | |
| name: Token Classification | |
| type: token-classification | |
| metric: | |
| name: Accuracy | |
| type: accuracy | |
| value: 0.9645112274185379 | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # bert-srb-ner-setimes | |
| This model was trained from scratch on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.1955 | |
| - Precision: 0.8229 | |
| - Recall: 0.8465 | |
| - F1: 0.8345 | |
| - Accuracy: 0.9645 | |
| ## 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: 32 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 20 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | |
| | No log | 1.0 | 104 | 0.2281 | 0.6589 | 0.7001 | 0.6789 | 0.9350 | | |
| | No log | 2.0 | 208 | 0.1833 | 0.7105 | 0.7694 | 0.7388 | 0.9470 | | |
| | No log | 3.0 | 312 | 0.1573 | 0.7461 | 0.7778 | 0.7616 | 0.9525 | | |
| | No log | 4.0 | 416 | 0.1489 | 0.7665 | 0.8091 | 0.7872 | 0.9557 | | |
| | 0.1898 | 5.0 | 520 | 0.1445 | 0.7881 | 0.8327 | 0.8098 | 0.9587 | | |
| | 0.1898 | 6.0 | 624 | 0.1473 | 0.7913 | 0.8316 | 0.8109 | 0.9601 | | |
| | 0.1898 | 7.0 | 728 | 0.1558 | 0.8101 | 0.8347 | 0.8222 | 0.9620 | | |
| | 0.1898 | 8.0 | 832 | 0.1616 | 0.8026 | 0.8302 | 0.8162 | 0.9612 | | |
| | 0.1898 | 9.0 | 936 | 0.1716 | 0.8127 | 0.8409 | 0.8266 | 0.9631 | | |
| | 0.0393 | 10.0 | 1040 | 0.1751 | 0.8140 | 0.8369 | 0.8253 | 0.9628 | | |
| | 0.0393 | 11.0 | 1144 | 0.1775 | 0.8096 | 0.8420 | 0.8255 | 0.9626 | | |
| | 0.0393 | 12.0 | 1248 | 0.1763 | 0.8161 | 0.8386 | 0.8272 | 0.9636 | | |
| | 0.0393 | 13.0 | 1352 | 0.1949 | 0.8259 | 0.8400 | 0.8329 | 0.9634 | | |
| | 0.0393 | 14.0 | 1456 | 0.1842 | 0.8205 | 0.8420 | 0.8311 | 0.9642 | | |
| | 0.0111 | 15.0 | 1560 | 0.1862 | 0.8160 | 0.8493 | 0.8323 | 0.9646 | | |
| | 0.0111 | 16.0 | 1664 | 0.1989 | 0.8176 | 0.8367 | 0.8270 | 0.9627 | | |
| | 0.0111 | 17.0 | 1768 | 0.1945 | 0.8246 | 0.8409 | 0.8327 | 0.9638 | | |
| | 0.0111 | 18.0 | 1872 | 0.1997 | 0.8270 | 0.8426 | 0.8347 | 0.9634 | | |
| | 0.0111 | 19.0 | 1976 | 0.1917 | 0.8258 | 0.8491 | 0.8373 | 0.9651 | | |
| | 0.0051 | 20.0 | 2080 | 0.1955 | 0.8229 | 0.8465 | 0.8345 | 0.9645 | | |
| ### Framework versions | |
| - Transformers 4.9.2 | |
| - Pytorch 1.9.0 | |
| - Datasets 1.11.0 | |
| - Tokenizers 0.10.1 | |