| | --- |
| | library_name: transformers |
| | license: mit |
| | base_model: FacebookAI/xlm-roberta-base |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - precision |
| | - recall |
| | - f1 |
| | model-index: |
| | - name: scandi-fine-web-cleaner |
| | results: [] |
| | datasets: |
| | - data-is-better-together/fineweb-c |
| | language: |
| | - sv |
| | - da |
| | --- |
| | |
| |
|
| | # 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](https://danielvanstrien.xyz/posts/2025/FineWeb-c/scandinavian-content-filtering-fineweb.html) exploring how to filter web data using community annotations. The model was created by fine-tuning [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the [data-is-better-together/fineweb-c](https://huggingface.co/datasets/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. |
| |
|
| | [blog]: <link-to-blog-post> |
| | ## 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 |
