---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: SloBertAA_Top10_WithoutOOC_082023
  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_Top10_WithoutOOC_082023

This model is a fine-tuned version of [EMBEDDIA/sloberta](https://huggingface.co/EMBEDDIA/sloberta) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4660
- Accuracy: 0.9423
- F1: 0.9423
- Precision: 0.9425
- Recall: 0.9423

## 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.2996        | 1.0   | 14812  | 0.2914          | 0.9179   | 0.9174 | 0.9187    | 0.9179 |
| 0.2229        | 2.0   | 29624  | 0.2659          | 0.9333   | 0.9332 | 0.9338    | 0.9333 |
| 0.1703        | 3.0   | 44436  | 0.2817          | 0.9347   | 0.9347 | 0.9355    | 0.9347 |
| 0.1245        | 4.0   | 59248  | 0.3126          | 0.9377   | 0.9374 | 0.9376    | 0.9377 |
| 0.0977        | 5.0   | 74060  | 0.3884          | 0.9335   | 0.9335 | 0.9347    | 0.9335 |
| 0.0624        | 6.0   | 88872  | 0.4098          | 0.9395   | 0.9393 | 0.9397    | 0.9395 |
| 0.0355        | 7.0   | 103684 | 0.4213          | 0.9400   | 0.9400 | 0.9402    | 0.9400 |
| 0.0268        | 8.0   | 118496 | 0.4579          | 0.9388   | 0.9387 | 0.9390    | 0.9388 |
| 0.016         | 9.0   | 133308 | 0.4531          | 0.9418   | 0.9418 | 0.9422    | 0.9418 |
| 0.009         | 10.0  | 148120 | 0.4660          | 0.9423   | 0.9423 | 0.9425    | 0.9423 |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.8.0
- Datasets 2.10.1
- Tokenizers 0.13.2