---
license: mit
base_model: pdelobelle/robbert-v2-dutch-base
tags:
- generated_from_trainer
metrics:
- recall
- accuracy
model-index:
- name: robbert0210_lrate2.5b8
  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. -->

# robbert0210_lrate2.5b8

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: 0.3692
- Precisions: 0.7993
- Recall: 0.7287
- F-measure: 0.7307
- Accuracy: 0.8915

## 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: 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  | 0.4360          | 0.8519     | 0.6630 | 0.6789    | 0.8678   |
| 0.6455        | 2.0   | 942  | 0.3692          | 0.7993     | 0.7287 | 0.7307    | 0.8915   |
| 0.3291        | 3.0   | 1413 | 0.3768          | 0.7658     | 0.7398 | 0.7397    | 0.8986   |
| 0.2114        | 4.0   | 1884 | 0.4194          | 0.7951     | 0.7452 | 0.7532    | 0.9048   |
| 0.1457        | 5.0   | 2355 | 0.4626          | 0.7756     | 0.7536 | 0.7620    | 0.9021   |
| 0.0955        | 6.0   | 2826 | 0.5145          | 0.8075     | 0.7700 | 0.7858    | 0.9048   |
| 0.0641        | 7.0   | 3297 | 0.5118          | 0.8113     | 0.7997 | 0.8045    | 0.9100   |
| 0.0484        | 8.0   | 3768 | 0.5204          | 0.8052     | 0.7952 | 0.7995    | 0.9093   |


### Framework versions

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