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| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - precision | |
| - recall | |
| - f1 | |
| - accuracy | |
| model_index: | |
| - name: distilbert-srb-ner-setimes | |
| results: | |
| - task: | |
| name: Token Classification | |
| type: token-classification | |
| metric: | |
| name: Accuracy | |
| type: accuracy | |
| value: 0.9665376552169005 | |
| <!-- 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. --> | |
| # distilbert-srb-ner-setimes | |
| This model was trained from scratch on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.1838 | |
| - Precision: 0.8370 | |
| - Recall: 0.8617 | |
| - F1: 0.8492 | |
| - Accuracy: 0.9665 | |
| ## 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 | 0.2319 | 0.6668 | 0.7029 | 0.6844 | 0.9358 | | |
| | No log | 2.0 | 208 | 0.1850 | 0.7265 | 0.7508 | 0.7385 | 0.9469 | | |
| | No log | 3.0 | 312 | 0.1584 | 0.7555 | 0.7937 | 0.7741 | 0.9538 | | |
| | No log | 4.0 | 416 | 0.1484 | 0.7644 | 0.8128 | 0.7879 | 0.9571 | | |
| | 0.1939 | 5.0 | 520 | 0.1383 | 0.7850 | 0.8131 | 0.7988 | 0.9604 | | |
| | 0.1939 | 6.0 | 624 | 0.1409 | 0.7914 | 0.8359 | 0.8130 | 0.9632 | | |
| | 0.1939 | 7.0 | 728 | 0.1526 | 0.8176 | 0.8392 | 0.8283 | 0.9637 | | |
| | 0.1939 | 8.0 | 832 | 0.1536 | 0.8195 | 0.8409 | 0.8301 | 0.9641 | | |
| | 0.1939 | 9.0 | 936 | 0.1538 | 0.8242 | 0.8523 | 0.8380 | 0.9661 | | |
| | 0.0364 | 10.0 | 1040 | 0.1612 | 0.8228 | 0.8413 | 0.8319 | 0.9652 | | |
| | 0.0364 | 11.0 | 1144 | 0.1721 | 0.8289 | 0.8503 | 0.8395 | 0.9656 | | |
| | 0.0364 | 12.0 | 1248 | 0.1645 | 0.8301 | 0.8590 | 0.8443 | 0.9663 | | |
| | 0.0364 | 13.0 | 1352 | 0.1747 | 0.8352 | 0.8540 | 0.8445 | 0.9665 | | |
| | 0.0364 | 14.0 | 1456 | 0.1703 | 0.8277 | 0.8573 | 0.8422 | 0.9663 | | |
| | 0.011 | 15.0 | 1560 | 0.1770 | 0.8314 | 0.8624 | 0.8466 | 0.9665 | | |
| | 0.011 | 16.0 | 1664 | 0.1903 | 0.8399 | 0.8537 | 0.8467 | 0.9661 | | |
| | 0.011 | 17.0 | 1768 | 0.1837 | 0.8363 | 0.8590 | 0.8475 | 0.9665 | | |
| | 0.011 | 18.0 | 1872 | 0.1820 | 0.8338 | 0.8570 | 0.8453 | 0.9667 | | |
| | 0.011 | 19.0 | 1976 | 0.1855 | 0.8382 | 0.8620 | 0.8499 | 0.9666 | | |
| | 0.0053 | 20.0 | 2080 | 0.1838 | 0.8370 | 0.8617 | 0.8492 | 0.9665 | | |
| ### Framework versions | |
| - Transformers 4.9.2 | |
| - Pytorch 1.9.0 | |
| - Datasets 1.11.0 | |
| - Tokenizers 0.10.1 | |