---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
model-index:
- name: dutch_genre_classifier
  results: []
language:
- nl
pipeline_tag: text-classification
widget:
  - text: We zullen zien bij de \#verkiezingen2024
    example_title: tw
  - text: Meneer de minister, stemt u voor of tegen dit wetsvoorstel?
    example_title: par
  - text: Man zwaargewond na auto-ongeval
    example_title: nws
---

<!-- 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. -->

# dutch_genre_classifier

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1404
- Precision: 0.9713
- Recall: 0.9707
- Fscore: 0.9707

## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 2.0
- mixed_precision_training: Native AMP

### Training results



### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0