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

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: 0.0327
- Rmse: 0.1808

## 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   |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0887        | 1.0   | 25   | 0.0611          | 0.2472 |
| 0.044         | 2.0   | 50   | 0.0403          | 0.2008 |
| 0.0356        | 3.0   | 75   | 0.0435          | 0.2085 |
| 0.0301        | 4.0   | 100  | 0.0415          | 0.2038 |
| 0.0246        | 5.0   | 125  | 0.0377          | 0.1941 |
| 0.0224        | 6.0   | 150  | 0.0355          | 0.1885 |
| 0.0218        | 7.0   | 175  | 0.0379          | 0.1946 |
| 0.0174        | 8.0   | 200  | 0.0436          | 0.2088 |
| 0.0156        | 9.0   | 225  | 0.0361          | 0.1901 |
| 0.0138        | 10.0  | 250  | 0.0379          | 0.1947 |
| 0.0135        | 11.0  | 275  | 0.0386          | 0.1965 |
| 0.013         | 12.0  | 300  | 0.0402          | 0.2005 |
| 0.0125        | 13.0  | 325  | 0.0325          | 0.1804 |
| 0.0117        | 14.0  | 350  | 0.0349          | 0.1868 |
| 0.0109        | 15.0  | 375  | 0.0366          | 0.1914 |
| 0.0108        | 16.0  | 400  | 0.0382          | 0.1953 |
| 0.0091        | 17.0  | 425  | 0.0336          | 0.1833 |
| 0.0097        | 18.0  | 450  | 0.0325          | 0.1802 |
| 0.0098        | 19.0  | 475  | 0.0406          | 0.2014 |
| 0.0094        | 20.0  | 500  | 0.0330          | 0.1816 |
| 0.0088        | 21.0  | 525  | 0.0349          | 0.1868 |
| 0.0087        | 22.0  | 550  | 0.0337          | 0.1835 |
| 0.0079        | 23.0  | 575  | 0.0340          | 0.1845 |
| 0.0074        | 24.0  | 600  | 0.0372          | 0.1928 |
| 0.007         | 25.0  | 625  | 0.0345          | 0.1856 |
| 0.0072        | 26.0  | 650  | 0.0333          | 0.1824 |
| 0.0074        | 27.0  | 675  | 0.0308          | 0.1756 |
| 0.0071        | 28.0  | 700  | 0.0314          | 0.1772 |
| 0.0067        | 29.0  | 725  | 0.0314          | 0.1772 |
| 0.0065        | 30.0  | 750  | 0.0333          | 0.1824 |
| 0.0072        | 31.0  | 775  | 0.0337          | 0.1837 |
| 0.0065        | 32.0  | 800  | 0.0351          | 0.1873 |
| 0.0057        | 33.0  | 825  | 0.0330          | 0.1818 |
| 0.0067        | 34.0  | 850  | 0.0367          | 0.1915 |
| 0.0061        | 35.0  | 875  | 0.0358          | 0.1893 |
| 0.0062        | 36.0  | 900  | 0.0353          | 0.1879 |
| 0.006         | 37.0  | 925  | 0.0317          | 0.1779 |
| 0.0059        | 38.0  | 950  | 0.0331          | 0.1819 |
| 0.0058        | 39.0  | 975  | 0.0308          | 0.1755 |
| 0.0057        | 40.0  | 1000 | 0.0338          | 0.1838 |
| 0.0055        | 41.0  | 1025 | 0.0324          | 0.1800 |
| 0.0056        | 42.0  | 1050 | 0.0333          | 0.1824 |
| 0.0054        | 43.0  | 1075 | 0.0331          | 0.1819 |
| 0.0062        | 44.0  | 1100 | 0.0329          | 0.1813 |
| 0.0056        | 45.0  | 1125 | 0.0325          | 0.1802 |
| 0.0051        | 46.0  | 1150 | 0.0324          | 0.1801 |
| 0.0056        | 47.0  | 1175 | 0.0322          | 0.1795 |
| 0.0056        | 48.0  | 1200 | 0.0331          | 0.1818 |
| 0.0053        | 49.0  | 1225 | 0.0322          | 0.1794 |
| 0.0056        | 50.0  | 1250 | 0.0327          | 0.1808 |


### Framework versions

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