---
license: mit
base_model: pdelobelle/robbert-v2-dutch-base
tags:
- generated_from_trainer
model-index:
- name: robbert-v2-dutch-base-finetuned-emotion-arousal
  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-v2-dutch-base-finetuned-emotion-arousal

This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0230
- Rmse: 0.1517

## 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: 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: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rmse   |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0581        | 1.0   | 25   | 0.0349          | 0.1868 |
| 0.0355        | 2.0   | 50   | 0.0340          | 0.1845 |
| 0.0295        | 3.0   | 75   | 0.0271          | 0.1645 |
| 0.0288        | 4.0   | 100  | 0.0279          | 0.1670 |
| 0.0277        | 5.0   | 125  | 0.0316          | 0.1777 |
| 0.0254        | 6.0   | 150  | 0.0263          | 0.1620 |
| 0.0193        | 7.0   | 175  | 0.0247          | 0.1571 |
| 0.0173        | 8.0   | 200  | 0.0304          | 0.1745 |
| 0.0179        | 9.0   | 225  | 0.0239          | 0.1547 |
| 0.0149        | 10.0  | 250  | 0.0244          | 0.1563 |
| 0.0134        | 11.0  | 275  | 0.0248          | 0.1576 |
| 0.0113        | 12.0  | 300  | 0.0256          | 0.1601 |
| 0.0112        | 13.0  | 325  | 0.0265          | 0.1627 |
| 0.0114        | 14.0  | 350  | 0.0299          | 0.1730 |
| 0.0111        | 15.0  | 375  | 0.0268          | 0.1638 |
| 0.0098        | 16.0  | 400  | 0.0256          | 0.1599 |
| 0.009         | 17.0  | 425  | 0.0252          | 0.1588 |
| 0.0078        | 18.0  | 450  | 0.0256          | 0.1601 |
| 0.0093        | 19.0  | 475  | 0.0235          | 0.1532 |
| 0.009         | 20.0  | 500  | 0.0246          | 0.1568 |
| 0.0084        | 21.0  | 525  | 0.0238          | 0.1543 |
| 0.0083        | 22.0  | 550  | 0.0255          | 0.1598 |
| 0.0074        | 23.0  | 575  | 0.0250          | 0.1582 |
| 0.0079        | 24.0  | 600  | 0.0248          | 0.1574 |
| 0.0077        | 25.0  | 625  | 0.0261          | 0.1616 |
| 0.0073        | 26.0  | 650  | 0.0261          | 0.1615 |
| 0.0071        | 27.0  | 675  | 0.0247          | 0.1571 |
| 0.0068        | 28.0  | 700  | 0.0254          | 0.1593 |
| 0.0062        | 29.0  | 725  | 0.0250          | 0.1581 |
| 0.006         | 30.0  | 750  | 0.0255          | 0.1597 |
| 0.0066        | 31.0  | 775  | 0.0241          | 0.1553 |
| 0.0064        | 32.0  | 800  | 0.0242          | 0.1555 |
| 0.006         | 33.0  | 825  | 0.0240          | 0.1549 |
| 0.0055        | 34.0  | 850  | 0.0244          | 0.1561 |
| 0.0055        | 35.0  | 875  | 0.0235          | 0.1533 |
| 0.0053        | 36.0  | 900  | 0.0241          | 0.1551 |
| 0.0056        | 37.0  | 925  | 0.0238          | 0.1542 |
| 0.0052        | 38.0  | 950  | 0.0248          | 0.1576 |
| 0.0055        | 39.0  | 975  | 0.0247          | 0.1570 |
| 0.0054        | 40.0  | 1000 | 0.0233          | 0.1526 |
| 0.0052        | 41.0  | 1025 | 0.0233          | 0.1525 |
| 0.0048        | 42.0  | 1050 | 0.0231          | 0.1519 |
| 0.0051        | 43.0  | 1075 | 0.0237          | 0.1538 |
| 0.0051        | 44.0  | 1100 | 0.0231          | 0.1520 |
| 0.0053        | 45.0  | 1125 | 0.0234          | 0.1531 |
| 0.0046        | 46.0  | 1150 | 0.0230          | 0.1517 |
| 0.0049        | 47.0  | 1175 | 0.0230          | 0.1518 |
| 0.005         | 48.0  | 1200 | 0.0230          | 0.1518 |
| 0.0047        | 49.0  | 1225 | 0.0237          | 0.1540 |
| 0.0047        | 50.0  | 1250 | 0.0230          | 0.1517 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1