---
license: mit
base_model: pdelobelle/robbert-v2-dutch-base
tags:
- generated_from_trainer
metrics:
- recall
- accuracy
model-index:
- name: RobBERTBestModelOct11
  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_lrate7.5b16

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: 0.5355
- Precisions: 0.8523
- Recall: 0.8173
- F-measure: 0.8307
- Accuracy: 0.9209

## 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: 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: 16

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precisions | Recall | F-measure | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:---------:|:--------:|
| 0.6127        | 1.0   | 236  | 0.3656          | 0.8687     | 0.6888 | 0.7011    | 0.8817   |
| 0.3078        | 2.0   | 472  | 0.3390          | 0.8253     | 0.7452 | 0.7612    | 0.8947   |
| 0.1742        | 3.0   | 708  | 0.3899          | 0.7602     | 0.7560 | 0.7469    | 0.8957   |
| 0.1242        | 4.0   | 944  | 0.4402          | 0.8560     | 0.7678 | 0.7861    | 0.9055   |
| 0.0749        | 5.0   | 1180 | 0.4206          | 0.8163     | 0.8139 | 0.8127    | 0.9121   |
| 0.0533        | 6.0   | 1416 | 0.4824          | 0.8257     | 0.7936 | 0.8060    | 0.9124   |
| 0.0366        | 7.0   | 1652 | 0.4927          | 0.8506     | 0.7956 | 0.8158    | 0.9176   |
| 0.0273        | 8.0   | 1888 | 0.5638          | 0.8631     | 0.7855 | 0.8093    | 0.9202   |
| 0.0206        | 9.0   | 2124 | 0.5507          | 0.8322     | 0.7957 | 0.8096    | 0.9141   |
| 0.0154        | 10.0  | 2360 | 0.5355          | 0.8523     | 0.8173 | 0.8307    | 0.9209   |
| 0.0105        | 11.0  | 2596 | 0.5812          | 0.8301     | 0.7961 | 0.8088    | 0.9162   |
| 0.0086        | 12.0  | 2832 | 0.6084          | 0.8357     | 0.8065 | 0.8192    | 0.9130   |
| 0.0046        | 13.0  | 3068 | 0.6035          | 0.8310     | 0.7948 | 0.8104    | 0.9137   |
| 0.0036        | 14.0  | 3304 | 0.6034          | 0.8223     | 0.7980 | 0.8074    | 0.9134   |
| 0.0043        | 15.0  | 3540 | 0.6146          | 0.8198     | 0.7869 | 0.7999    | 0.9120   |
| 0.0018        | 16.0  | 3776 | 0.6070          | 0.8244     | 0.7894 | 0.8029    | 0.9134   |


### Framework versions

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