---
library_name: transformers
license: apache-2.0
base_model: EuroBERT/EuroBERT-210m
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: fineweb-swe_latn-quality-transformer
  results: []
---

<!-- 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. -->

# fineweb-swe_latn-quality-transformer

This model is a fine-tuned version of [EuroBERT/EuroBERT-210m](https://huggingface.co/EuroBERT/EuroBERT-210m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5507
- F1: 0.7041
- Accuracy: 0.7079
- Confusion Matrix: 53 17
35 73
- High Precision: 0.6023
- High Recall: 0.7571
- High F1: 0.6709
- Low Precision: 0.8111
- Low Recall: 0.6759
- Low F1: 0.7374

## 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: 64
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Accuracy | Confusion Matrix | High Precision | High Recall | High F1 | Low Precision | Low Recall | Low F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|:----------------:|:--------------:|:-----------:|:-------:|:-------------:|:----------:|:------:|
| No log        | 1.0   | 5    | 0.7080          | 0.4341 | 0.4719   | 19 51
43 65      | 0.3065         | 0.2714      | 0.2879  | 0.5603        | 0.6019     | 0.5804 |
| 0.8946        | 2.0   | 10   | 0.8359          | 0.3776 | 0.6067   | 0 70
0 108       | 0.0            | 0.0         | 0.0     | 0.6067        | 1.0        | 0.7552 |
| 0.8946        | 3.0   | 15   | 0.6091          | 0.6435 | 0.6461   | 50 20
43 65      | 0.5376         | 0.7143      | 0.6135  | 0.7647        | 0.6019     | 0.6736 |
| 0.6111        | 4.0   | 20   | 0.7509          | 0.3776 | 0.6067   | 0 70
0 108       | 0.0            | 0.0         | 0.0     | 0.6067        | 1.0        | 0.7552 |
| 0.6111        | 5.0   | 25   | 0.7014          | 0.4200 | 0.6180   | 3 67
1 107       | 0.75           | 0.0429      | 0.0811  | 0.6149        | 0.9907     | 0.7589 |
| 0.5827        | 6.0   | 30   | 0.5507          | 0.7041 | 0.7079   | 53 17
35 73      | 0.6023         | 0.7571      | 0.6709  | 0.8111        | 0.6759     | 0.7374 |
| 0.5827        | 7.0   | 35   | 0.5907          | 0.6963 | 0.6966   | 59 11
43 65      | 0.5784         | 0.8429      | 0.6860  | 0.8553        | 0.6019     | 0.7065 |
| 0.3865        | 8.0   | 40   | 0.6183          | 0.6468 | 0.7079   | 26 44
8 100      | 0.7647         | 0.3714      | 0.5     | 0.6944        | 0.9259     | 0.7937 |
| 0.3865        | 9.0   | 45   | 1.1120          | 0.5645 | 0.6685   | 16 54
5 103      | 0.7619         | 0.2286      | 0.3516  | 0.6561        | 0.9537     | 0.7774 |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0