---
license: mit
base_model: pdelobelle/robbert-v2-dutch-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: robbertfinetuned2209
  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. -->

# robbertfinetuned2209

This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3479
- Precision: 0.7445
- Recall: 0.7657
- F1: 0.7550
- Accuracy: 0.9055

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 236  | 0.3944          | 0.6565    | 0.6282 | 0.6420 | 0.8781   |
| No log        | 2.0   | 472  | 0.3546          | 0.6938    | 0.7354 | 0.7140 | 0.8901   |
| 0.479         | 3.0   | 708  | 0.3479          | 0.7445    | 0.7657 | 0.7550 | 0.9055   |
| 0.479         | 4.0   | 944  | 0.3944          | 0.7523    | 0.7733 | 0.7626 | 0.9049   |
| 0.1591        | 5.0   | 1180 | 0.4177          | 0.7812    | 0.7657 | 0.7734 | 0.9082   |
| 0.1591        | 6.0   | 1416 | 0.4467          | 0.7817    | 0.7931 | 0.7874 | 0.9121   |
| 0.0594        | 7.0   | 1652 | 0.4706          | 0.7985    | 0.7873 | 0.7928 | 0.9125   |
| 0.0594        | 8.0   | 1888 | 0.4751          | 0.7860    | 0.7960 | 0.7910 | 0.9140   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3