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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- null |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model_index: |
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- name: electra-srb-ner-setimes |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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metric: |
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name: Accuracy |
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type: accuracy |
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value: 0.951370041268543 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# electra-srb-ner-setimes |
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This model was trained from scratch on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2619 |
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- Precision: 0.8157 |
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- Recall: 0.7934 |
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- F1: 0.8044 |
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- Accuracy: 0.9514 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 1.0 | 207 | 0.2845 | 0.7431 | 0.6314 | 0.6827 | 0.9225 | |
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| No log | 2.0 | 414 | 0.2082 | 0.7766 | 0.7134 | 0.7436 | 0.9396 | |
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| 0.2949 | 3.0 | 621 | 0.1992 | 0.7699 | 0.7596 | 0.7647 | 0.9439 | |
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| 0.2949 | 4.0 | 828 | 0.2044 | 0.7485 | 0.7908 | 0.7691 | 0.9456 | |
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| 0.0896 | 5.0 | 1035 | 0.2129 | 0.7827 | 0.7778 | 0.7802 | 0.9476 | |
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| 0.0896 | 6.0 | 1242 | 0.2330 | 0.7893 | 0.7882 | 0.7887 | 0.9485 | |
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| 0.0896 | 7.0 | 1449 | 0.2337 | 0.8026 | 0.7947 | 0.7986 | 0.9504 | |
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| 0.0334 | 8.0 | 1656 | 0.2579 | 0.8111 | 0.7850 | 0.7978 | 0.9503 | |
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| 0.0334 | 9.0 | 1863 | 0.2792 | 0.8263 | 0.7830 | 0.8041 | 0.9510 | |
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| 0.0152 | 10.0 | 2070 | 0.2619 | 0.8157 | 0.7934 | 0.8044 | 0.9514 | |
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### Framework versions |
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- Transformers 4.9.2 |
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- Pytorch 1.9.0 |
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- Datasets 1.11.0 |
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- Tokenizers 0.10.1 |
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