---
license: mit
base_model: pdelobelle/robbert-v2-dutch-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: robbert-v2-dutch-base-finetuned-emotion
  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

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: 3.1901
- Accuracy: 0.52
- F1: 0.5133

## 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 | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.5504        | 1.0   | 25   | 1.4311          | 0.41     | 0.2443 |
| 1.4241        | 2.0   | 50   | 1.3456          | 0.46     | 0.3532 |
| 1.3432        | 3.0   | 75   | 1.3765          | 0.46     | 0.3581 |
| 1.2749        | 4.0   | 100  | 1.3694          | 0.5      | 0.3892 |
| 1.1421        | 5.0   | 125  | 1.3083          | 0.51     | 0.4035 |
| 1.0092        | 6.0   | 150  | 1.3147          | 0.51     | 0.4364 |
| 0.8896        | 7.0   | 175  | 1.2769          | 0.53     | 0.4936 |
| 0.7669        | 8.0   | 200  | 1.3297          | 0.52     | 0.4971 |
| 0.6463        | 9.0   | 225  | 1.4932          | 0.55     | 0.5179 |
| 0.5364        | 10.0  | 250  | 1.4984          | 0.53     | 0.4999 |
| 0.4657        | 11.0  | 275  | 1.7043          | 0.5      | 0.4587 |
| 0.3777        | 12.0  | 300  | 1.6938          | 0.49     | 0.4541 |
| 0.3019        | 13.0  | 325  | 1.7869          | 0.54     | 0.5221 |
| 0.2379        | 14.0  | 350  | 1.7080          | 0.52     | 0.5191 |
| 0.1782        | 15.0  | 375  | 1.7824          | 0.5      | 0.4991 |
| 0.1494        | 16.0  | 400  | 1.9882          | 0.55     | 0.5076 |
| 0.1103        | 17.0  | 425  | 2.0976          | 0.53     | 0.5064 |
| 0.0951        | 18.0  | 450  | 2.1631          | 0.48     | 0.4962 |
| 0.0867        | 19.0  | 475  | 2.1215          | 0.52     | 0.5091 |
| 0.0637        | 20.0  | 500  | 2.1948          | 0.52     | 0.5103 |
| 0.0536        | 21.0  | 525  | 2.3024          | 0.49     | 0.4909 |
| 0.0345        | 22.0  | 550  | 2.2948          | 0.52     | 0.5010 |
| 0.0293        | 23.0  | 575  | 2.4334          | 0.55     | 0.5378 |
| 0.0272        | 24.0  | 600  | 2.5223          | 0.52     | 0.5186 |
| 0.0222        | 25.0  | 625  | 2.6465          | 0.5      | 0.5065 |
| 0.0127        | 26.0  | 650  | 2.6434          | 0.5      | 0.5055 |
| 0.0111        | 27.0  | 675  | 2.7423          | 0.5      | 0.5040 |
| 0.01          | 28.0  | 700  | 2.8313          | 0.5      | 0.4990 |
| 0.0057        | 29.0  | 725  | 2.8568          | 0.49     | 0.4740 |
| 0.0095        | 30.0  | 750  | 2.8480          | 0.52     | 0.5033 |
| 0.0108        | 31.0  | 775  | 2.9276          | 0.46     | 0.4684 |
| 0.0062        | 32.0  | 800  | 2.9811          | 0.51     | 0.5027 |
| 0.0045        | 33.0  | 825  | 2.9901          | 0.51     | 0.5022 |
| 0.005         | 34.0  | 850  | 3.0403          | 0.52     | 0.5129 |
| 0.0036        | 35.0  | 875  | 3.0578          | 0.51     | 0.5076 |
| 0.003         | 36.0  | 900  | 3.0680          | 0.51     | 0.5022 |
| 0.0028        | 37.0  | 925  | 3.1097          | 0.52     | 0.5170 |
| 0.0028        | 38.0  | 950  | 3.1304          | 0.49     | 0.4801 |
| 0.0027        | 39.0  | 975  | 3.1341          | 0.49     | 0.4801 |
| 0.0069        | 40.0  | 1000 | 3.1380          | 0.49     | 0.4958 |
| 0.0023        | 41.0  | 1025 | 3.1284          | 0.5      | 0.4954 |
| 0.0035        | 42.0  | 1050 | 3.1454          | 0.51     | 0.5062 |
| 0.0033        | 43.0  | 1075 | 3.1758          | 0.49     | 0.4730 |
| 0.0021        | 44.0  | 1100 | 3.1660          | 0.5      | 0.4906 |
| 0.0022        | 45.0  | 1125 | 3.1688          | 0.5      | 0.4906 |
| 0.0021        | 46.0  | 1150 | 3.1649          | 0.51     | 0.5061 |
| 0.0022        | 47.0  | 1175 | 3.1855          | 0.52     | 0.5139 |
| 0.0021        | 48.0  | 1200 | 3.1882          | 0.51     | 0.5061 |
| 0.0022        | 49.0  | 1225 | 3.1893          | 0.52     | 0.5133 |
| 0.0019        | 50.0  | 1250 | 3.1901          | 0.52     | 0.5133 |


### Framework versions

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