Scandi Fine Web Cleaner

🚀 scandi-fine-web-cleaner

This model serves as a demo classifier for identifying problematic content (such as incorrect language and garbled text) in Danish and Swedish web text. It helps improve annotation efficiency by filtering web data.

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

🚀 Quick Start

This model can be directly used for preliminary filtering of Danish and Swedish web text to improve annotation efficiency. You can fine - tune it on the data-is-better-together/fineweb-c dataset.

✨ Features

• High Precision: With a precision of 95.2%, false positives are rare.
• Good Recall: It can catch most problematic content with a recall of 70.2%.
• Multi - language Support: Only tested on Danish and Swedish content for now.

📚 Documentation

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

Framework versions

• Transformers 4.48.0
• Pytorch 2.5.1+cu124
• Datasets 3.2.0
• Tokenizers 0.21.0

📄 License

This project is licensed under the MIT license.