---
license: mit
base_model: pdelobelle/robbert-v2-dutch-base
tags:
- generated_from_trainer
metrics:
- recall
- accuracy
model-index:
- name: robbert0510_lrate9b16
  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. -->

# robbert0510_lrate9b16

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: 0.5895
- Precisions: 0.8184
- Recall: 0.7998
- F-measure: 0.8078
- Accuracy: 0.9148

## 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: 8e-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 |
|:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:---------:|:--------:|
| 0.5957        | 1.0   | 236  | 0.4045          | 0.8529     | 0.6860 | 0.6940    | 0.8784   |
| 0.3024        | 2.0   | 472  | 0.3456          | 0.7564     | 0.7584 | 0.7471    | 0.8964   |
| 0.1843        | 3.0   | 708  | 0.3792          | 0.8188     | 0.7691 | 0.7741    | 0.9087   |
| 0.1163        | 4.0   | 944  | 0.4231          | 0.8317     | 0.7678 | 0.7878    | 0.9060   |
| 0.0753        | 5.0   | 1180 | 0.4595          | 0.8051     | 0.7846 | 0.7911    | 0.9070   |
| 0.0496        | 6.0   | 1416 | 0.4942          | 0.8188     | 0.7803 | 0.7960    | 0.9087   |
| 0.0321        | 7.0   | 1652 | 0.5121          | 0.7966     | 0.7927 | 0.7900    | 0.9103   |
| 0.0246        | 8.0   | 1888 | 0.5057          | 0.8155     | 0.7959 | 0.8030    | 0.9148   |
| 0.0156        | 9.0   | 2124 | 0.5445          | 0.8039     | 0.7924 | 0.7967    | 0.9109   |
| 0.0118        | 10.0  | 2360 | 0.5646          | 0.8151     | 0.7948 | 0.8032    | 0.9140   |
| 0.0083        | 11.0  | 2596 | 0.5911          | 0.8387     | 0.7872 | 0.8075    | 0.9138   |
| 0.0052        | 12.0  | 2832 | 0.5895          | 0.8184     | 0.7998 | 0.8078    | 0.9148   |
| 0.0038        | 13.0  | 3068 | 0.5839          | 0.8193     | 0.7987 | 0.8078    | 0.9149   |
| 0.0026        | 14.0  | 3304 | 0.5911          | 0.8195     | 0.7926 | 0.8041    | 0.9135   |


### Framework versions

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