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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_Top20_WithOOC_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_Top20_WithOOC_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: 1.0247
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- Accuracy: 0.8659
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- F1: 0.8642
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- Precision: 0.8642
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- Recall: 0.8659
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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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| 0.5972 | 1.0 | 23853 | 0.5451 | 0.8293 | 0.8264 | 0.8276 | 0.8293 |
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| 0.4728 | 2.0 | 47706 | 0.5189 | 0.8435 | 0.8380 | 0.8458 | 0.8435 |
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| 0.3736 | 3.0 | 71559 | 0.5216 | 0.8512 | 0.8499 | 0.8507 | 0.8512 |
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| 0.2785 | 4.0 | 95412 | 0.6074 | 0.8526 | 0.8500 | 0.8528 | 0.8526 |
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| 0.2002 | 5.0 | 119265 | 0.6906 | 0.8561 | 0.8534 | 0.8552 | 0.8561 |
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| 0.1719 | 6.0 | 143118 | 0.7822 | 0.8600 | 0.8580 | 0.8588 | 0.8600 |
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| 0.1337 | 7.0 | 166971 | 0.8742 | 0.8623 | 0.8607 | 0.8612 | 0.8623 |
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| 0.0826 | 8.0 | 190824 | 0.9613 | 0.8627 | 0.8602 | 0.8605 | 0.8627 |
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| 0.0603 | 9.0 | 214677 | 1.0092 | 0.8632 | 0.8617 | 0.8620 | 0.8632 |
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| 0.0359 | 10.0 | 238530 | 1.0247 | 0.8659 | 0.8642 | 0.8642 | 0.8659 |
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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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