---
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: 0.0317
- Rmse: 0.1781

## 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   |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0813        | 1.0   | 25   | 0.0510          | 0.2258 |
| 0.0445        | 2.0   | 50   | 0.0381          | 0.1952 |
| 0.0409        | 3.0   | 75   | 0.0466          | 0.2158 |
| 0.0308        | 4.0   | 100  | 0.0351          | 0.1874 |
| 0.0257        | 5.0   | 125  | 0.0393          | 0.1983 |
| 0.0231        | 6.0   | 150  | 0.0442          | 0.2103 |
| 0.0203        | 7.0   | 175  | 0.0447          | 0.2115 |
| 0.0191        | 8.0   | 200  | 0.0372          | 0.1929 |
| 0.0156        | 9.0   | 225  | 0.0425          | 0.2061 |
| 0.0154        | 10.0  | 250  | 0.0367          | 0.1917 |
| 0.0138        | 11.0  | 275  | 0.0365          | 0.1910 |
| 0.0128        | 12.0  | 300  | 0.0432          | 0.2078 |
| 0.0137        | 13.0  | 325  | 0.0329          | 0.1814 |
| 0.0118        | 14.0  | 350  | 0.0327          | 0.1809 |
| 0.0118        | 15.0  | 375  | 0.0378          | 0.1945 |
| 0.0109        | 16.0  | 400  | 0.0360          | 0.1897 |
| 0.0103        | 17.0  | 425  | 0.0325          | 0.1803 |
| 0.0096        | 18.0  | 450  | 0.0327          | 0.1809 |
| 0.0091        | 19.0  | 475  | 0.0430          | 0.2072 |
| 0.0081        | 20.0  | 500  | 0.0345          | 0.1856 |
| 0.0094        | 21.0  | 525  | 0.0365          | 0.1912 |
| 0.0084        | 22.0  | 550  | 0.0350          | 0.1870 |
| 0.0075        | 23.0  | 575  | 0.0324          | 0.1800 |
| 0.0069        | 24.0  | 600  | 0.0330          | 0.1816 |
| 0.0087        | 25.0  | 625  | 0.0347          | 0.1863 |
| 0.0079        | 26.0  | 650  | 0.0297          | 0.1722 |
| 0.0071        | 27.0  | 675  | 0.0311          | 0.1763 |
| 0.0076        | 28.0  | 700  | 0.0322          | 0.1795 |
| 0.0064        | 29.0  | 725  | 0.0338          | 0.1839 |
| 0.0067        | 30.0  | 750  | 0.0326          | 0.1806 |
| 0.0061        | 31.0  | 775  | 0.0327          | 0.1808 |
| 0.0064        | 32.0  | 800  | 0.0339          | 0.1842 |
| 0.0062        | 33.0  | 825  | 0.0300          | 0.1732 |
| 0.0062        | 34.0  | 850  | 0.0331          | 0.1819 |
| 0.0055        | 35.0  | 875  | 0.0318          | 0.1782 |
| 0.0059        | 36.0  | 900  | 0.0323          | 0.1797 |
| 0.0056        | 37.0  | 925  | 0.0311          | 0.1765 |
| 0.0055        | 38.0  | 950  | 0.0310          | 0.1762 |
| 0.0053        | 39.0  | 975  | 0.0325          | 0.1802 |
| 0.0056        | 40.0  | 1000 | 0.0310          | 0.1761 |
| 0.0054        | 41.0  | 1025 | 0.0323          | 0.1799 |
| 0.0057        | 42.0  | 1050 | 0.0351          | 0.1873 |
| 0.0053        | 43.0  | 1075 | 0.0347          | 0.1861 |
| 0.0054        | 44.0  | 1100 | 0.0330          | 0.1816 |
| 0.0059        | 45.0  | 1125 | 0.0313          | 0.1769 |
| 0.0053        | 46.0  | 1150 | 0.0312          | 0.1766 |
| 0.0051        | 47.0  | 1175 | 0.0325          | 0.1804 |
| 0.0057        | 48.0  | 1200 | 0.0304          | 0.1745 |
| 0.0048        | 49.0  | 1225 | 0.0317          | 0.1782 |
| 0.005         | 50.0  | 1250 | 0.0317          | 0.1781 |


### Framework versions

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