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--- |
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license: cc-by-sa-4.0 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: SloBertAA_Top10_WithoutOOC_082023 |
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results: [] |
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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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# SloBertAA_Top10_WithoutOOC_082023 |
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This model is a fine-tuned version of [EMBEDDIA/sloberta](https://huggingface.co/EMBEDDIA/sloberta) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4660 |
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- Accuracy: 0.9423 |
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- F1: 0.9423 |
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- Precision: 0.9425 |
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- Recall: 0.9423 |
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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: 12 |
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- eval_batch_size: 12 |
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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 | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.2996 | 1.0 | 14812 | 0.2914 | 0.9179 | 0.9174 | 0.9187 | 0.9179 | |
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| 0.2229 | 2.0 | 29624 | 0.2659 | 0.9333 | 0.9332 | 0.9338 | 0.9333 | |
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| 0.1703 | 3.0 | 44436 | 0.2817 | 0.9347 | 0.9347 | 0.9355 | 0.9347 | |
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| 0.1245 | 4.0 | 59248 | 0.3126 | 0.9377 | 0.9374 | 0.9376 | 0.9377 | |
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| 0.0977 | 5.0 | 74060 | 0.3884 | 0.9335 | 0.9335 | 0.9347 | 0.9335 | |
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| 0.0624 | 6.0 | 88872 | 0.4098 | 0.9395 | 0.9393 | 0.9397 | 0.9395 | |
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| 0.0355 | 7.0 | 103684 | 0.4213 | 0.9400 | 0.9400 | 0.9402 | 0.9400 | |
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| 0.0268 | 8.0 | 118496 | 0.4579 | 0.9388 | 0.9387 | 0.9390 | 0.9388 | |
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| 0.016 | 9.0 | 133308 | 0.4531 | 0.9418 | 0.9418 | 0.9422 | 0.9418 | |
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| 0.009 | 10.0 | 148120 | 0.4660 | 0.9423 | 0.9423 | 0.9425 | 0.9423 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.8.0 |
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- Datasets 2.10.1 |
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- Tokenizers 0.13.2 |
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