--- library_name: transformers license: apache-2.0 base_model: EuroBERT/EuroBERT-210m tags: - generated_from_trainer metrics: - f1 - accuracy model-index: - name: fineweb-fra_latn-quality-transformer results: [] --- # fineweb-fra_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: 1.5184 - F1: 0.4882 - Accuracy: 0.6573 - Confusion Matrix: 3 10 7 1 94 12 1 30 20 - High Precision: 0.6 - High Recall: 0.15 - High F1: 0.24 - Low Precision: 0.7015 - Low Recall: 0.8785 - Low F1: 0.7801 - Medium Precision: 0.5128 - Medium Recall: 0.3922 - Medium F1: 0.4444 ## 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 | Medium Precision | Medium Recall | Medium F1 | |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|:-----------------------:|:--------------:|:-----------:|:-------:|:-------------:|:----------:|:------:|:----------------:|:-------------:|:---------:| | No log | 1.0 | 5 | 1.4081 | 0.1518 | 0.2865 | 0 0 20 5 0 102 0 0 51 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2948 | 1.0 | 0.4554 | | 1.2754 | 2.0 | 10 | 1.0008 | 0.2503 | 0.6011 | 0 20 0 0 107 0 0 51 0 | 0.0 | 0.0 | 0.0 | 0.6011 | 1.0 | 0.7509 | 0.0 | 0.0 | 0.0 | | 1.2754 | 3.0 | 15 | 0.9946 | 0.3367 | 0.4775 | 0 2 18 0 37 70 0 3 48 | 0.0 | 0.0 | 0.0 | 0.8810 | 0.3458 | 0.4966 | 0.3529 | 0.9412 | 0.5134 | | 0.8128 | 4.0 | 20 | 0.7867 | 0.4046 | 0.6404 | 0 11 9 0 93 14 0 30 21 | 0.0 | 0.0 | 0.0 | 0.6940 | 0.8692 | 0.7718 | 0.4773 | 0.4118 | 0.4421 | | 0.8128 | 5.0 | 25 | 0.7778 | 0.4324 | 0.6348 | 0 4 16 2 80 25 1 17 33 | 0.0 | 0.0 | 0.0 | 0.7921 | 0.7477 | 0.7692 | 0.4459 | 0.6471 | 0.528 | | 0.5766 | 6.0 | 30 | 0.9369 | 0.4169 | 0.6292 | 0 5 15 2 85 20 1 23 27 | 0.0 | 0.0 | 0.0 | 0.7522 | 0.7944 | 0.7727 | 0.4355 | 0.5294 | 0.4779 | | 0.5766 | 7.0 | 35 | 0.8983 | 0.4443 | 0.6180 | 1 4 15 1 78 28 2 18 31 | 0.25 | 0.05 | 0.0833 | 0.78 | 0.7290 | 0.7536 | 0.4189 | 0.6078 | 0.496 | | 0.1777 | 8.0 | 40 | 1.5184 | 0.4882 | 0.6573 | 3 10 7 1 94 12 1 30 20 | 0.6 | 0.15 | 0.24 | 0.7015 | 0.8785 | 0.7801 | 0.5128 | 0.3922 | 0.4444 | | 0.1777 | 9.0 | 45 | 1.7748 | 0.4364 | 0.5955 | 2 7 11 2 80 25 7 20 24 | 0.1818 | 0.1 | 0.1290 | 0.7477 | 0.7477 | 0.7477 | 0.4 | 0.4706 | 0.4324 | | 0.013 | 10.0 | 50 | 2.1900 | 0.4190 | 0.6236 | 0 7 13 3 83 21 2 21 28 | 0.0 | 0.0 | 0.0 | 0.7477 | 0.7757 | 0.7615 | 0.4516 | 0.5490 | 0.4956 | | 0.013 | 11.0 | 55 | 2.6390 | 0.4348 | 0.6404 | 0 6 14 1 81 25 1 17 33 | 0.0 | 0.0 | 0.0 | 0.7788 | 0.7570 | 0.7678 | 0.4583 | 0.6471 | 0.5366 | | 0.0041 | 12.0 | 60 | 2.2662 | 0.4481 | 0.5955 | 4 9 7 6 84 17 8 25 18 | 0.2222 | 0.2 | 0.2105 | 0.7119 | 0.7850 | 0.7467 | 0.4286 | 0.3529 | 0.3871 | | 0.0041 | 13.0 | 65 | 3.0654 | 0.4064 | 0.5787 | 0 4 16 0 64 43 3 9 39 | 0.0 | 0.0 | 0.0 | 0.8312 | 0.5981 | 0.6957 | 0.3980 | 0.7647 | 0.5235 | | 0.0058 | 14.0 | 70 | 2.4618 | 0.4273 | 0.5899 | 3 9 8 2 86 19 5 30 16 | 0.3 | 0.15 | 0.2 | 0.688 | 0.8037 | 0.7414 | 0.3721 | 0.3137 | 0.3404 | | 0.0058 | 15.0 | 75 | 2.7654 | 0.4147 | 0.5506 | 1 3 16 5 61 41 8 7 36 | 0.0714 | 0.05 | 0.0588 | 0.8592 | 0.5701 | 0.6854 | 0.3871 | 0.7059 | 0.5 | | 0.0011 | 16.0 | 80 | 3.1337 | 0.3696 | 0.6180 | 0 15 5 1 96 10 2 35 14 | 0.0 | 0.0 | 0.0 | 0.6575 | 0.8972 | 0.7589 | 0.4828 | 0.2745 | 0.35 | | 0.0011 | 17.0 | 85 | 2.7265 | 0.4388 | 0.5618 | 4 3 13 5 74 28 13 16 22 | 0.1818 | 0.2 | 0.1905 | 0.7957 | 0.6916 | 0.74 | 0.3492 | 0.4314 | 0.3860 | | 0.0007 | 18.0 | 90 | 2.9583 | 0.4270 | 0.5843 | 1 3 16 1 71 35 6 13 32 | 0.125 | 0.05 | 0.0714 | 0.8161 | 0.6636 | 0.7320 | 0.3855 | 0.6275 | 0.4776 | ### Framework versions - Transformers 4.49.0 - Pytorch 2.6.0+cu124 - Datasets 3.3.2 - Tokenizers 0.21.0