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README.md
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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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