---
license: mit
base_model: pdelobelle/robbert-v2-dutch-base
tags:
- generated_from_trainer
metrics:
- recall
- accuracy
model-index:
- name: robbert0210_lrate2.5b16
  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. -->

# robbert0210_lrate2.5b16

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: 0.3449
- Precisions: 0.7846
- Recall: 0.7358
- F-measure: 0.7356
- Accuracy: 0.8988

## 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: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:---------:|:--------:|
| No log        | 1.0   | 236  | 0.4364          | 0.8256     | 0.6672 | 0.6709    | 0.8658   |
| No log        | 2.0   | 472  | 0.3745          | 0.6875     | 0.7116 | 0.6970    | 0.8839   |
| 0.5514        | 3.0   | 708  | 0.3449          | 0.7846     | 0.7358 | 0.7356    | 0.8988   |
| 0.5514        | 4.0   | 944  | 0.3625          | 0.8042     | 0.7487 | 0.7552    | 0.9000   |
| 0.2255        | 5.0   | 1180 | 0.3987          | 0.8037     | 0.7541 | 0.7618    | 0.9000   |
| 0.2255        | 6.0   | 1416 | 0.4315          | 0.8049     | 0.7549 | 0.7636    | 0.9010   |
| 0.1211        | 7.0   | 1652 | 0.4060          | 0.8170     | 0.7633 | 0.7785    | 0.9034   |
| 0.1211        | 8.0   | 1888 | 0.4146          | 0.8162     | 0.7813 | 0.7927    | 0.9070   |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3