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README.md
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---
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license: apache-2.0
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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_MultilingualBertBase
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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_MultilingualBertBase
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This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.8608
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- Accuracy: 0.6898
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- F1: 0.6904
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- Precision: 0.6936
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- Recall: 0.6898
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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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| 1.7313 | 1.0 | 45122 | 1.6826 | 0.5773 | 0.5766 | 0.5997 | 0.5773 |
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| 1.4117 | 2.0 | 90244 | 1.4419 | 0.6341 | 0.6345 | 0.6529 | 0.6341 |
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| 1.1573 | 3.0 | 135366 | 1.3509 | 0.6614 | 0.6620 | 0.6733 | 0.6614 |
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| 0.9147 | 4.0 | 180488 | 1.3583 | 0.6695 | 0.6699 | 0.6817 | 0.6695 |
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| 0.7452 | 5.0 | 225610 | 1.3881 | 0.6797 | 0.6800 | 0.6887 | 0.6797 |
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| 0.5393 | 6.0 | 270732 | 1.4650 | 0.6828 | 0.6835 | 0.6897 | 0.6828 |
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| 0.4207 | 7.0 | 315854 | 1.5770 | 0.6839 | 0.6840 | 0.6905 | 0.6839 |
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| 0.2985 | 8.0 | 360976 | 1.6813 | 0.6869 | 0.6877 | 0.6921 | 0.6869 |
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| 0.2029 | 9.0 | 406098 | 1.7977 | 0.6882 | 0.6886 | 0.6923 | 0.6882 |
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| 0.1546 | 10.0 | 451220 | 1.8608 | 0.6898 | 0.6904 | 0.6936 | 0.6898 |
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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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