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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- wikiann_sk |
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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: output_dir |
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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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dataset: |
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name: wikiann_sk |
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type: wikiann_sk |
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args: sk |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9327115256495669 |
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- name: Recall |
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type: recall |
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value: 0.9470124013528749 |
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- name: F1 |
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type: f1 |
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value: 0.9398075632132469 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9785228256835333 |
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--- |
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# output_dir |
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This model is a fine-tuned version of [gerulata/slovakbert](https://huggingface.co/gerulata/slovakbert) on the wikiann_sk dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1600 |
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- Precision: 0.9327 |
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- Recall: 0.9470 |
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- F1: 0.9398 |
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- Accuracy: 0.9785 |
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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: 5e-05 |
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- train_batch_size: 32 |
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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: 15.0 |
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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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| 0.2342 | 1.0 | 625 | 0.1233 | 0.8891 | 0.9076 | 0.8982 | 0.9667 | |
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| 0.1114 | 2.0 | 1250 | 0.1079 | 0.9118 | 0.9269 | 0.9193 | 0.9725 | |
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| 0.0817 | 3.0 | 1875 | 0.1093 | 0.9173 | 0.9315 | 0.9243 | 0.9747 | |
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| 0.0438 | 4.0 | 2500 | 0.1076 | 0.9188 | 0.9353 | 0.9270 | 0.9743 | |
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| 0.028 | 5.0 | 3125 | 0.1230 | 0.9143 | 0.9387 | 0.9264 | 0.9744 | |
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| 0.0256 | 6.0 | 3750 | 0.1204 | 0.9246 | 0.9423 | 0.9334 | 0.9765 | |
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| 0.018 | 7.0 | 4375 | 0.1332 | 0.9292 | 0.9416 | 0.9353 | 0.9770 | |
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| 0.0107 | 8.0 | 5000 | 0.1339 | 0.9280 | 0.9427 | 0.9353 | 0.9769 | |
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| 0.0079 | 9.0 | 5625 | 0.1368 | 0.9326 | 0.9442 | 0.9383 | 0.9785 | |
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| 0.0065 | 10.0 | 6250 | 0.1490 | 0.9284 | 0.9445 | 0.9364 | 0.9772 | |
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| 0.0061 | 11.0 | 6875 | 0.1566 | 0.9328 | 0.9433 | 0.9380 | 0.9778 | |
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| 0.0031 | 12.0 | 7500 | 0.1555 | 0.9339 | 0.9473 | 0.9406 | 0.9787 | |
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| 0.0024 | 13.0 | 8125 | 0.1548 | 0.9349 | 0.9462 | 0.9405 | 0.9787 | |
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| 0.0015 | 14.0 | 8750 | 0.1562 | 0.9330 | 0.9469 | 0.9399 | 0.9788 | |
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| 0.0013 | 15.0 | 9375 | 0.1600 | 0.9327 | 0.9470 | 0.9398 | 0.9785 | |
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### Framework versions |
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- Transformers 4.13.0.dev0 |
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- Pytorch 1.10.0+cu113 |
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- Datasets 1.15.1 |
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- Tokenizers 0.10.3 |
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