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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: EuroBERT/EuroBERT-210m |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: eurobert210m_Mobilite_v1 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# eurobert210m_Mobilite_v1 |
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This model is a fine-tuned version of [EuroBERT/EuroBERT-210m](https://huggingface.co/EuroBERT/EuroBERT-210m) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0135 |
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- Accuracy: 0.9942 |
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- F1: 0.9942 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 100 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
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| 1.2363 | 1.0 | 124 | 0.8864 | 0.6790 | 0.6323 | |
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| 0.588 | 2.0 | 248 | 0.3959 | 0.8901 | 0.8796 | |
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| 0.3365 | 3.0 | 372 | 0.1947 | 0.9506 | 0.9502 | |
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| 0.2177 | 4.0 | 496 | 0.1856 | 0.9549 | 0.9540 | |
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| 0.1708 | 5.0 | 620 | 0.1072 | 0.9786 | 0.9786 | |
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| 0.1455 | 6.0 | 744 | 0.1288 | 0.9713 | 0.9716 | |
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| 0.1217 | 7.0 | 868 | 0.0800 | 0.9836 | 0.9837 | |
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| 0.0986 | 8.0 | 992 | 0.0599 | 0.9874 | 0.9874 | |
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| 0.0735 | 9.0 | 1116 | 0.0480 | 0.9892 | 0.9892 | |
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| 0.0577 | 10.0 | 1240 | 0.0305 | 0.9922 | 0.9922 | |
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| 0.0619 | 11.0 | 1364 | 0.0475 | 0.9897 | 0.9897 | |
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| 0.0449 | 12.0 | 1488 | 0.0991 | 0.9816 | 0.9814 | |
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| 0.0566 | 13.0 | 1612 | 0.0215 | 0.9932 | 0.9932 | |
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| 0.0473 | 14.0 | 1736 | 0.0228 | 0.9939 | 0.9939 | |
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| 0.0344 | 15.0 | 1860 | 0.0336 | 0.9922 | 0.9922 | |
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| 0.04 | 16.0 | 1984 | 0.0426 | 0.9909 | 0.9909 | |
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| 0.0353 | 17.0 | 2108 | 0.0191 | 0.9945 | 0.9945 | |
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| 0.0448 | 18.0 | 2232 | 0.0193 | 0.9932 | 0.9932 | |
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| 0.0359 | 19.0 | 2356 | 0.0184 | 0.9942 | 0.9942 | |
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| 0.0314 | 20.0 | 2480 | 0.0146 | 0.9942 | 0.9942 | |
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| 0.0257 | 21.0 | 2604 | 0.0173 | 0.9942 | 0.9942 | |
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| 0.0208 | 22.0 | 2728 | 0.0144 | 0.9942 | 0.9942 | |
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| 0.0334 | 23.0 | 2852 | 0.0135 | 0.9942 | 0.9942 | |
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### Framework versions |
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- Transformers 4.48.3 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.3.2 |
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- Tokenizers 0.21.0 |
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