---
license: mit
base_model: pdelobelle/robbert-v2-dutch-base
tags:
- generated_from_trainer
metrics:
- recall
- accuracy
model-index:
- name: robdataaugmentation1511
  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. -->

# robdataaugmentation1511

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: 0.4187
- Precisions: 0.8503
- Recall: 0.8197
- F-measure: 0.8320
- Accuracy: 0.9446

## 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: 7.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 34
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 14

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precisions | Recall | F-measure | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:---------:|:--------:|
| 0.4276        | 1.0   | 284  | 0.2688          | 0.8001     | 0.7195 | 0.7402    | 0.9200   |
| 0.1871        | 2.0   | 568  | 0.2595          | 0.8183     | 0.7803 | 0.7948    | 0.9308   |
| 0.0942        | 3.0   | 852  | 0.2800          | 0.8083     | 0.8047 | 0.8042    | 0.9366   |
| 0.0542        | 4.0   | 1136 | 0.2841          | 0.8228     | 0.8232 | 0.8212    | 0.9402   |
| 0.0365        | 5.0   | 1420 | 0.3355          | 0.8472     | 0.8056 | 0.8224    | 0.9393   |
| 0.0228        | 6.0   | 1704 | 0.3860          | 0.8501     | 0.8009 | 0.8211    | 0.9405   |
| 0.0141        | 7.0   | 1988 | 0.3997          | 0.8320     | 0.8175 | 0.8233    | 0.9409   |
| 0.0132        | 8.0   | 2272 | 0.4225          | 0.8478     | 0.8025 | 0.8164    | 0.9397   |
| 0.0077        | 9.0   | 2556 | 0.3890          | 0.8258     | 0.8410 | 0.8312    | 0.9429   |
| 0.006         | 10.0  | 2840 | 0.3954          | 0.8354     | 0.8150 | 0.8235    | 0.9402   |
| 0.0045        | 11.0  | 3124 | 0.4266          | 0.8441     | 0.8136 | 0.8246    | 0.9424   |
| 0.0037        | 12.0  | 3408 | 0.4171          | 0.8364     | 0.8174 | 0.8245    | 0.9426   |
| 0.0021        | 13.0  | 3692 | 0.4221          | 0.8461     | 0.8192 | 0.8294    | 0.9434   |
| 0.0018        | 14.0  | 3976 | 0.4187          | 0.8503     | 0.8197 | 0.8320    | 0.9446   |


### Framework versions

- Transformers 4.35.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.7
- Tokenizers 0.14.1