---
license: mit
base_model: DTAI-KULeuven/robbert-2023-dutch-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: robbert
  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. -->

# robbert

This model is a fine-tuned version of [DTAI-KULeuven/robbert-2023-dutch-base](https://huggingface.co/DTAI-KULeuven/robbert-2023-dutch-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0620
- Accuracy: 0.9882
- F1: 0.9155
- Precision: 0.9210
- Recall: 0.9120

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0654        | 1.0   | 9646  | 0.1077          | 0.9787   | 0.7670 | 0.7751    | 0.8183 |
| 0.0388        | 2.0   | 19292 | 0.0790          | 0.9824   | 0.8955 | 0.9045    | 0.8910 |
| 0.0227        | 3.0   | 28938 | 0.0620          | 0.9882   | 0.9155 | 0.9210    | 0.9120 |


### Framework versions

- Transformers 4.43.4
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.19.1