---
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: 0.6304
- Precisions: 0.8565
- Recall: 0.7966
- F-measure: 0.8182
- Accuracy: 0.9056

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:---------:|:--------:|
| 0.5972        | 1.0   | 284  | 0.4291          | 0.7808     | 0.7248 | 0.7363    | 0.8652   |
| 0.2753        | 2.0   | 568  | 0.4249          | 0.7837     | 0.7521 | 0.7570    | 0.8811   |
| 0.1379        | 3.0   | 852  | 0.5021          | 0.8379     | 0.7750 | 0.7955    | 0.8815   |
| 0.0776        | 4.0   | 1136 | 0.6344          | 0.8567     | 0.7657 | 0.7907    | 0.8842   |
| 0.0401        | 5.0   | 1420 | 0.6621          | 0.8442     | 0.7622 | 0.7856    | 0.8884   |
| 0.0319        | 6.0   | 1704 | 0.6013          | 0.8435     | 0.7870 | 0.8010    | 0.8969   |
| 0.0205        | 7.0   | 1988 | 0.6304          | 0.8565     | 0.7966 | 0.8182    | 0.9056   |
| 0.0138        | 8.0   | 2272 | 0.6804          | 0.8538     | 0.7732 | 0.7896    | 0.9030   |
| 0.0096        | 9.0   | 2556 | 0.7395          | 0.8274     | 0.7696 | 0.7862    | 0.8923   |
| 0.005         | 10.0  | 2840 | 0.7293          | 0.8531     | 0.7846 | 0.8054    | 0.8967   |
| 0.0034        | 11.0  | 3124 | 0.7385          | 0.8621     | 0.7929 | 0.8105    | 0.9022   |
| 0.0047        | 12.0  | 3408 | 0.7428          | 0.8575     | 0.7953 | 0.8155    | 0.9061   |
| 0.004         | 13.0  | 3692 | 0.7524          | 0.8617     | 0.7954 | 0.8152    | 0.9024   |
| 0.0023        | 14.0  | 3976 | 0.7515          | 0.8636     | 0.7957 | 0.8174    | 0.9041   |


### Framework versions

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