---
license: mit
base_model: pdelobelle/robbert-v2-dutch-base
tags:
- generated_from_trainer
metrics:
- recall
- accuracy
model-index:
- name: robbert1010_lrate10b16
  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. -->

# robbert1010_lrate10b16

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: 0.6021
- Precisions: 0.8323
- Recall: 0.7951
- F-measure: 0.8088
- Accuracy: 0.9164

## 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: 0.0001
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:---------:|:--------:|
| 0.6104        | 1.0   | 236  | 0.4304          | 0.8532     | 0.6700 | 0.6852    | 0.8707   |
| 0.32          | 2.0   | 472  | 0.3520          | 0.7761     | 0.7551 | 0.7413    | 0.8916   |
| 0.1989        | 3.0   | 708  | 0.3686          | 0.7500     | 0.7591 | 0.7465    | 0.9010   |
| 0.1331        | 4.0   | 944  | 0.4045          | 0.8289     | 0.7666 | 0.7835    | 0.9090   |
| 0.0784        | 5.0   | 1180 | 0.4307          | 0.8052     | 0.7759 | 0.7890    | 0.9092   |
| 0.0682        | 6.0   | 1416 | 0.4696          | 0.8101     | 0.7658 | 0.7770    | 0.9059   |
| 0.04          | 7.0   | 1652 | 0.5078          | 0.8450     | 0.7642 | 0.7820    | 0.9096   |
| 0.0256        | 8.0   | 1888 | 0.5718          | 0.8007     | 0.7830 | 0.7906    | 0.9058   |
| 0.0219        | 9.0   | 2124 | 0.5508          | 0.8078     | 0.7987 | 0.8000    | 0.9093   |
| 0.0162        | 10.0  | 2360 | 0.5786          | 0.8256     | 0.7791 | 0.7946    | 0.9141   |
| 0.0117        | 11.0  | 2596 | 0.5979          | 0.8360     | 0.7912 | 0.8046    | 0.9168   |
| 0.011         | 12.0  | 2832 | 0.6021          | 0.8323     | 0.7951 | 0.8088    | 0.9164   |
| 0.0079        | 13.0  | 3068 | 0.6115          | 0.8337     | 0.7956 | 0.8088    | 0.9166   |
| 0.0064        | 14.0  | 3304 | 0.6100          | 0.8305     | 0.7932 | 0.8064    | 0.9164   |


### Framework versions

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