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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-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
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