---
license: mit
base_model: pdelobelle/robbert-v2-dutch-base
tags:
- generated_from_trainer
metrics:
- recall
- accuracy
model-index:
- name: robbert0410_lrate7.5b8
  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_lrate7.5b8

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: 0.3807
- Precisions: 0.7617
- Recall: 0.7368
- F-measure: 0.7423
- Accuracy: 0.8880

## 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: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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   | 471  | 0.4437          | 0.8549     | 0.6737 | 0.6835    | 0.8700   |
| 0.5971        | 2.0   | 942  | 0.3807          | 0.7617     | 0.7368 | 0.7423    | 0.8880   |
| 0.2963        | 3.0   | 1413 | 0.4422          | 0.7859     | 0.7422 | 0.7476    | 0.9028   |
| 0.1606        | 4.0   | 1884 | 0.5208          | 0.8338     | 0.7546 | 0.7754    | 0.9041   |
| 0.107         | 5.0   | 2355 | 0.5299          | 0.7982     | 0.7887 | 0.7915    | 0.9076   |
| 0.0628        | 6.0   | 2826 | 0.5734          | 0.8099     | 0.7694 | 0.7824    | 0.9121   |
| 0.0295        | 7.0   | 3297 | 0.6021          | 0.8090     | 0.7771 | 0.7898    | 0.9116   |
| 0.0192        | 8.0   | 3768 | 0.6043          | 0.8120     | 0.7801 | 0.7927    | 0.9137   |


### Framework versions

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