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

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: 0.0394
- Rmse: 0.1984

## 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   |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1031        | 1.0   | 25   | 0.0433          | 0.2080 |
| 0.0444        | 2.0   | 50   | 0.0433          | 0.2082 |
| 0.0371        | 3.0   | 75   | 0.0443          | 0.2106 |
| 0.0318        | 4.0   | 100  | 0.0470          | 0.2167 |
| 0.0289        | 5.0   | 125  | 0.0611          | 0.2472 |
| 0.0267        | 6.0   | 150  | 0.0394          | 0.1984 |
| 0.0217        | 7.0   | 175  | 0.0419          | 0.2047 |
| 0.0199        | 8.0   | 200  | 0.0412          | 0.2029 |
| 0.0173        | 9.0   | 225  | 0.0477          | 0.2184 |
| 0.0164        | 10.0  | 250  | 0.0490          | 0.2213 |
| 0.0145        | 11.0  | 275  | 0.0417          | 0.2043 |
| 0.0126        | 12.0  | 300  | 0.0454          | 0.2130 |
| 0.0149        | 13.0  | 325  | 0.0421          | 0.2052 |
| 0.0113        | 14.0  | 350  | 0.0424          | 0.2059 |
| 0.0117        | 15.0  | 375  | 0.0426          | 0.2063 |
| 0.0107        | 16.0  | 400  | 0.0437          | 0.2091 |
| 0.0097        | 17.0  | 425  | 0.0406          | 0.2015 |
| 0.0102        | 18.0  | 450  | 0.0488          | 0.2209 |
| 0.01          | 19.0  | 475  | 0.0421          | 0.2053 |
| 0.0101        | 20.0  | 500  | 0.0383          | 0.1957 |
| 0.01          | 21.0  | 525  | 0.0404          | 0.2009 |
| 0.0092        | 22.0  | 550  | 0.0522          | 0.2285 |
| 0.0086        | 23.0  | 575  | 0.0390          | 0.1975 |
| 0.0085        | 24.0  | 600  | 0.0455          | 0.2133 |
| 0.0075        | 25.0  | 625  | 0.0427          | 0.2066 |
| 0.0071        | 26.0  | 650  | 0.0398          | 0.1995 |
| 0.0073        | 27.0  | 675  | 0.0424          | 0.2060 |
| 0.0079        | 28.0  | 700  | 0.0422          | 0.2055 |
| 0.0068        | 29.0  | 725  | 0.0388          | 0.1970 |
| 0.0071        | 30.0  | 750  | 0.0382          | 0.1953 |
| 0.0065        | 31.0  | 775  | 0.0394          | 0.1986 |
| 0.0066        | 32.0  | 800  | 0.0390          | 0.1975 |
| 0.006         | 33.0  | 825  | 0.0395          | 0.1987 |
| 0.0059        | 34.0  | 850  | 0.0404          | 0.2010 |
| 0.0062        | 35.0  | 875  | 0.0378          | 0.1944 |
| 0.0063        | 36.0  | 900  | 0.0374          | 0.1935 |
| 0.0056        | 37.0  | 925  | 0.0398          | 0.1995 |
| 0.0058        | 38.0  | 950  | 0.0383          | 0.1957 |
| 0.0056        | 39.0  | 975  | 0.0378          | 0.1945 |
| 0.0057        | 40.0  | 1000 | 0.0407          | 0.2017 |
| 0.006         | 41.0  | 1025 | 0.0392          | 0.1980 |
| 0.0057        | 42.0  | 1050 | 0.0398          | 0.1994 |
| 0.0053        | 43.0  | 1075 | 0.0377          | 0.1941 |
| 0.0056        | 44.0  | 1100 | 0.0392          | 0.1980 |
| 0.0053        | 45.0  | 1125 | 0.0410          | 0.2024 |
| 0.0057        | 46.0  | 1150 | 0.0396          | 0.1990 |
| 0.0048        | 47.0  | 1175 | 0.0398          | 0.1995 |
| 0.0054        | 48.0  | 1200 | 0.0397          | 0.1992 |
| 0.005         | 49.0  | 1225 | 0.0389          | 0.1972 |
| 0.0051        | 50.0  | 1250 | 0.0394          | 0.1984 |


### Framework versions

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