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---
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
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