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

# robbert_dataaugmentation

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: 0.7814
- Precisions: 0.8515
- Recall: 0.8094
- F-measure: 0.8265
- Accuracy: 0.9039

## 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: 14

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precisions | Recall | F-measure | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:---------:|:--------:|
| 0.5813        | 1.0   | 285  | 0.4311          | 0.7695     | 0.7413 | 0.7537    | 0.8704   |
| 0.2533        | 2.0   | 570  | 0.4952          | 0.8339     | 0.7501 | 0.7745    | 0.8801   |
| 0.1216        | 3.0   | 855  | 0.5067          | 0.8403     | 0.7968 | 0.8148    | 0.8932   |
| 0.0685        | 4.0   | 1140 | 0.6121          | 0.8041     | 0.7972 | 0.7963    | 0.8886   |
| 0.0478        | 5.0   | 1425 | 0.6603          | 0.8239     | 0.7820 | 0.7983    | 0.8893   |
| 0.0294        | 6.0   | 1710 | 0.7029          | 0.8190     | 0.8029 | 0.8083    | 0.8954   |
| 0.0147        | 7.0   | 1995 | 0.7219          | 0.8332     | 0.8198 | 0.8227    | 0.8991   |
| 0.0142        | 8.0   | 2280 | 0.7702          | 0.8330     | 0.7953 | 0.8109    | 0.8961   |
| 0.0099        | 9.0   | 2565 | 0.7670          | 0.8340     | 0.7943 | 0.8086    | 0.8972   |
| 0.0044        | 10.0  | 2850 | 0.8132          | 0.8434     | 0.8026 | 0.8193    | 0.9025   |
| 0.0058        | 11.0  | 3135 | 0.7757          | 0.8468     | 0.8100 | 0.8253    | 0.9033   |
| 0.0046        | 12.0  | 3420 | 0.7814          | 0.8515     | 0.8094 | 0.8265    | 0.9039   |
| 0.0029        | 13.0  | 3705 | 0.8057          | 0.8494     | 0.8046 | 0.8229    | 0.9029   |
| 0.0012        | 14.0  | 3990 | 0.7994          | 0.8492     | 0.8047 | 0.8230    | 0.9031   |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1