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---
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
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