---
license: mit
base_model: pdelobelle/robbert-v2-dutch-base
tags:
- generated_from_trainer
metrics:
- recall
- accuracy
model-index:
- name: robbert0410_lrate2.5b32
  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. -->

# robbert0410_lrate2.5b32

This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3700
- Precisions: 0.7911
- Recall: 0.7515
- F-measure: 0.7562
- Accuracy: 0.8997

## 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: 2.5e-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: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precisions | Recall | F-measure | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:---------:|:--------:|
| 0.8604        | 1.0   | 118  | 0.4897          | 0.8413     | 0.6506 | 0.6616    | 0.8535   |
| 0.4417        | 2.0   | 236  | 0.4031          | 0.8327     | 0.6978 | 0.6910    | 0.8714   |
| 0.3227        | 3.0   | 354  | 0.3781          | 0.8393     | 0.7154 | 0.7031    | 0.8841   |
| 0.2638        | 4.0   | 472  | 0.3463          | 0.7238     | 0.7280 | 0.7254    | 0.8962   |
| 0.2102        | 5.0   | 590  | 0.3579          | 0.7670     | 0.7343 | 0.7344    | 0.8955   |
| 0.1795        | 6.0   | 708  | 0.3640          | 0.7839     | 0.7433 | 0.7408    | 0.8966   |
| 0.1547        | 7.0   | 826  | 0.3659          | 0.7815     | 0.7454 | 0.7493    | 0.8995   |
| 0.1401        | 8.0   | 944  | 0.3700          | 0.7911     | 0.7515 | 0.7562    | 0.8997   |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0