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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_Top100_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_Top100_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: 1.6326
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- Accuracy: 0.7431
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- F1: 0.7447
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- Precision: 0.7503
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- Recall: 0.7431
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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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| 1.5143 | 1.0 | 45122 | 1.4964 | 0.6272 | 0.6264 | 0.6488 | 0.6272 |
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| 1.2462 | 2.0 | 90244 | 1.2729 | 0.6811 | 0.6814 | 0.7043 | 0.6811 |
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| 1.0236 | 3.0 | 135366 | 1.2134 | 0.7012 | 0.7027 | 0.7211 | 0.7012 |
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| 0.7721 | 4.0 | 180488 | 1.1976 | 0.7179 | 0.7204 | 0.7357 | 0.7179 |
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| 0.6597 | 5.0 | 225610 | 1.1953 | 0.7321 | 0.7324 | 0.7406 | 0.7321 |
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| 0.4816 | 6.0 | 270732 | 1.2776 | 0.7303 | 0.7330 | 0.7444 | 0.7303 |
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| 0.4039 | 7.0 | 315854 | 1.3625 | 0.7363 | 0.7379 | 0.7451 | 0.7363 |
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| 0.2845 | 8.0 | 360976 | 1.4677 | 0.7395 | 0.7407 | 0.7470 | 0.7395 |
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| 0.2192 | 9.0 | 406098 | 1.5720 | 0.7422 | 0.7434 | 0.7488 | 0.7422 |
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| 0.1689 | 10.0 | 451220 | 1.6326 | 0.7431 | 0.7447 | 0.7503 | 0.7431 |
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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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