---
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
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# scandi-fine-web-cleaner

This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co./FacebookAI/xlm-roberta-base) on the data-is-better-together/fineweb-c dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1816
- Precision: 0.9524
- Recall: 0.7018
- F1: 0.8081
- Auc Roc: 0.9648
- Balanced Accuracy: 0.8480
- Average Precision: 0.8906

## 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: 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