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---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: SloBertAA_Top100_WithOOC_082023_MultilingualBertBase
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SloBertAA_Top100_WithOOC_082023_MultilingualBertBase
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8608
- Accuracy: 0.6898
- F1: 0.6904
- Precision: 0.6936
- Recall: 0.6898
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.7313 | 1.0 | 45122 | 1.6826 | 0.5773 | 0.5766 | 0.5997 | 0.5773 |
| 1.4117 | 2.0 | 90244 | 1.4419 | 0.6341 | 0.6345 | 0.6529 | 0.6341 |
| 1.1573 | 3.0 | 135366 | 1.3509 | 0.6614 | 0.6620 | 0.6733 | 0.6614 |
| 0.9147 | 4.0 | 180488 | 1.3583 | 0.6695 | 0.6699 | 0.6817 | 0.6695 |
| 0.7452 | 5.0 | 225610 | 1.3881 | 0.6797 | 0.6800 | 0.6887 | 0.6797 |
| 0.5393 | 6.0 | 270732 | 1.4650 | 0.6828 | 0.6835 | 0.6897 | 0.6828 |
| 0.4207 | 7.0 | 315854 | 1.5770 | 0.6839 | 0.6840 | 0.6905 | 0.6839 |
| 0.2985 | 8.0 | 360976 | 1.6813 | 0.6869 | 0.6877 | 0.6921 | 0.6869 |
| 0.2029 | 9.0 | 406098 | 1.7977 | 0.6882 | 0.6886 | 0.6923 | 0.6882 |
| 0.1546 | 10.0 | 451220 | 1.8608 | 0.6898 | 0.6904 | 0.6936 | 0.6898 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.8.0
- Datasets 2.10.1
- Tokenizers 0.13.2
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