scandi-fine-web-cleaner
This model is a demo classifier for identifying problematic content (incorrect language, garbled text) in Danish and Swedish web text. It was created as part of a blog post exploring how to filter web data using community annotations. The model was created by fine-tuning FacebookAI/xlm-roberta-base on the data-is-better-together/fineweb-c dataset.
It achieves the following results on the evaluation set:
- Precision: 0.9524 (95.2%)
- Recall: 0.7018 (70.2%)
- F1: 0.8081
- AUC-ROC: 0.9648
Intended uses & limitations
The model is intended to be used as a preliminary filter for web text to help improve annotation efficiency. It has only been tested on Danish and Swedish content. The high precision (95.2%) means false positives are rare, while the recall (70.2%) indicates it catches most problematic content.
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Auc Roc | Balanced Accuracy | Average Precision |
|---|---|---|---|---|---|---|---|---|---|
| 0.3165 | 1.0 | 100 | 0.2333 | 0.95 | 0.6667 | 0.7835 | 0.8099 | 0.8304 | 0.7721 |
| 0.1929 | 2.0 | 200 | 0.1359 | 0.9130 | 0.7368 | 0.8155 | 0.9778 | 0.8626 | 0.9105 |
| 0.1775 | 3.0 | 300 | 0.2245 | 0.9268 | 0.6667 | 0.7755 | 0.9481 | 0.8290 | 0.8721 |
| 0.1553 | 4.0 | 400 | 0.1816 | 0.9524 | 0.7018 | 0.8081 | 0.9648 | 0.8480 | 0.8906 |
Framework versions
- Transformers 4.48.0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0