---
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: 3.3545
- Accuracy: 0.52
- F1: 0.5123

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.5586        | 1.0   | 25   | 1.4429          | 0.42     | 0.2485 |
| 1.4425        | 2.0   | 50   | 1.3576          | 0.46     | 0.3533 |
| 1.2834        | 3.0   | 75   | 1.3207          | 0.5      | 0.4369 |
| 1.1051        | 4.0   | 100  | 1.3228          | 0.48     | 0.4217 |
| 0.9053        | 5.0   | 125  | 1.3705          | 0.49     | 0.4302 |
| 0.7326        | 6.0   | 150  | 1.4522          | 0.53     | 0.5019 |
| 0.5724        | 7.0   | 175  | 1.5445          | 0.53     | 0.5064 |
| 0.4411        | 8.0   | 200  | 1.6560          | 0.54     | 0.5120 |
| 0.3476        | 9.0   | 225  | 1.7233          | 0.51     | 0.4845 |
| 0.2324        | 10.0  | 250  | 1.9150          | 0.52     | 0.5056 |
| 0.1866        | 11.0  | 275  | 2.0207          | 0.52     | 0.4975 |
| 0.165         | 12.0  | 300  | 2.0863          | 0.52     | 0.5094 |
| 0.1291        | 13.0  | 325  | 2.1584          | 0.5      | 0.4833 |
| 0.0762        | 14.0  | 350  | 2.2296          | 0.55     | 0.5332 |
| 0.0577        | 15.0  | 375  | 2.3171          | 0.5      | 0.4986 |
| 0.0424        | 16.0  | 400  | 2.4509          | 0.5      | 0.4795 |
| 0.0253        | 17.0  | 425  | 2.5444          | 0.49     | 0.4917 |
| 0.0191        | 18.0  | 450  | 2.5894          | 0.51     | 0.5031 |
| 0.0123        | 19.0  | 475  | 2.7144          | 0.5      | 0.4995 |
| 0.01          | 20.0  | 500  | 2.7358          | 0.53     | 0.5231 |
| 0.0086        | 21.0  | 525  | 2.8282          | 0.48     | 0.4825 |
| 0.0064        | 22.0  | 550  | 2.8421          | 0.52     | 0.5244 |
| 0.0059        | 23.0  | 575  | 2.9267          | 0.53     | 0.5200 |
| 0.005         | 24.0  | 600  | 2.9568          | 0.52     | 0.5074 |
| 0.0044        | 25.0  | 625  | 3.0420          | 0.47     | 0.4755 |
| 0.0066        | 26.0  | 650  | 3.0421          | 0.48     | 0.4881 |
| 0.0039        | 27.0  | 675  | 3.1039          | 0.51     | 0.4960 |
| 0.0033        | 28.0  | 700  | 3.1226          | 0.51     | 0.4955 |
| 0.0033        | 29.0  | 725  | 3.1215          | 0.51     | 0.4999 |
| 0.003         | 30.0  | 750  | 3.1649          | 0.51     | 0.4980 |
| 0.0025        | 31.0  | 775  | 3.1716          | 0.5      | 0.4921 |
| 0.0028        | 32.0  | 800  | 3.2371          | 0.5      | 0.4956 |
| 0.0028        | 33.0  | 825  | 3.1730          | 0.52     | 0.5154 |
| 0.0055        | 34.0  | 850  | 3.1842          | 0.49     | 0.4884 |
| 0.0022        | 35.0  | 875  | 3.2324          | 0.51     | 0.4955 |
| 0.0023        | 36.0  | 900  | 3.2221          | 0.52     | 0.5089 |
| 0.002         | 37.0  | 925  | 3.2756          | 0.51     | 0.4981 |
| 0.0021        | 38.0  | 950  | 3.2866          | 0.51     | 0.5010 |
| 0.0019        | 39.0  | 975  | 3.2882          | 0.51     | 0.5010 |
| 0.0018        | 40.0  | 1000 | 3.2864          | 0.51     | 0.4967 |
| 0.0017        | 41.0  | 1025 | 3.3101          | 0.51     | 0.4967 |
| 0.0017        | 42.0  | 1050 | 3.3215          | 0.52     | 0.5089 |
| 0.0016        | 43.0  | 1075 | 3.3253          | 0.51     | 0.5043 |
| 0.0056        | 44.0  | 1100 | 3.3118          | 0.51     | 0.5043 |
| 0.0016        | 45.0  | 1125 | 3.3566          | 0.51     | 0.4981 |
| 0.0016        | 46.0  | 1150 | 3.3593          | 0.51     | 0.4981 |
| 0.0016        | 47.0  | 1175 | 3.3638          | 0.51     | 0.4981 |
| 0.0017        | 48.0  | 1200 | 3.3605          | 0.52     | 0.5089 |
| 0.0017        | 49.0  | 1225 | 3.3526          | 0.52     | 0.5123 |
| 0.0016        | 50.0  | 1250 | 3.3545          | 0.52     | 0.5123 |


### Framework versions

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