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---
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
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