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| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - precision | |
| - recall | |
| - f1 | |
| - accuracy | |
| model_index: | |
| - name: electra-srb-ner-setimes | |
| results: | |
| - task: | |
| name: Token Classification | |
| type: token-classification | |
| metric: | |
| name: Accuracy | |
| type: accuracy | |
| value: 0.9546789604788638 | |
| <!-- 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. --> | |
| # electra-srb-ner-setimes | |
| This model was trained from scratch on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.2804 | |
| - Precision: 0.8286 | |
| - Recall: 0.8081 | |
| - F1: 0.8182 | |
| - Accuracy: 0.9547 | |
| ## 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 | 0.2981 | 0.6737 | 0.6113 | 0.6410 | 0.9174 | | |
| | No log | 2.0 | 208 | 0.2355 | 0.7279 | 0.6701 | 0.6978 | 0.9307 | | |
| | No log | 3.0 | 312 | 0.2079 | 0.7707 | 0.7062 | 0.7371 | 0.9402 | | |
| | No log | 4.0 | 416 | 0.2078 | 0.7689 | 0.7479 | 0.7582 | 0.9449 | | |
| | 0.2391 | 5.0 | 520 | 0.2089 | 0.8083 | 0.7476 | 0.7767 | 0.9484 | | |
| | 0.2391 | 6.0 | 624 | 0.2199 | 0.7981 | 0.7726 | 0.7851 | 0.9487 | | |
| | 0.2391 | 7.0 | 728 | 0.2528 | 0.8205 | 0.7749 | 0.7971 | 0.9511 | | |
| | 0.2391 | 8.0 | 832 | 0.2265 | 0.8074 | 0.8003 | 0.8038 | 0.9524 | | |
| | 0.2391 | 9.0 | 936 | 0.2843 | 0.8265 | 0.7716 | 0.7981 | 0.9504 | | |
| | 0.0378 | 10.0 | 1040 | 0.2450 | 0.8024 | 0.8019 | 0.8021 | 0.9520 | | |
| | 0.0378 | 11.0 | 1144 | 0.2550 | 0.8116 | 0.7986 | 0.8051 | 0.9519 | | |
| | 0.0378 | 12.0 | 1248 | 0.2706 | 0.8208 | 0.7957 | 0.8081 | 0.9532 | | |
| | 0.0378 | 13.0 | 1352 | 0.2664 | 0.8040 | 0.8035 | 0.8038 | 0.9530 | | |
| | 0.0378 | 14.0 | 1456 | 0.2571 | 0.8011 | 0.8110 | 0.8060 | 0.9529 | | |
| | 0.0099 | 15.0 | 1560 | 0.2673 | 0.8051 | 0.8129 | 0.8090 | 0.9534 | | |
| | 0.0099 | 16.0 | 1664 | 0.2733 | 0.8074 | 0.8087 | 0.8081 | 0.9529 | | |
| | 0.0099 | 17.0 | 1768 | 0.2835 | 0.8254 | 0.8074 | 0.8163 | 0.9543 | | |
| | 0.0099 | 18.0 | 1872 | 0.2771 | 0.8222 | 0.8081 | 0.8151 | 0.9545 | | |
| | 0.0099 | 19.0 | 1976 | 0.2776 | 0.8237 | 0.8084 | 0.8160 | 0.9546 | | |
| | 0.0044 | 20.0 | 2080 | 0.2804 | 0.8286 | 0.8081 | 0.8182 | 0.9547 | | |
| ### Framework versions | |
| - Transformers 4.9.2 | |
| - Pytorch 1.9.0 | |
| - Datasets 1.11.0 | |
| - Tokenizers 0.10.1 | |