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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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- f1 |
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- accuracy |
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model-index: |
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- name: fineweb-swe_latn-quality-transformer |
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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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# fineweb-swe_latn-quality-transformer |
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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: 0.5507 |
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- F1: 0.7041 |
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- Accuracy: 0.7079 |
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- Confusion Matrix: 53 17 |
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35 73 |
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- High Precision: 0.6023 |
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- High Recall: 0.7571 |
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- High F1: 0.6709 |
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- Low Precision: 0.8111 |
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- Low Recall: 0.6759 |
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- Low F1: 0.7374 |
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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: 2e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 128 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 50 |
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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 | F1 | Accuracy | Confusion Matrix | High Precision | High Recall | High F1 | Low Precision | Low Recall | Low F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|:----------------:|:--------------:|:-----------:|:-------:|:-------------:|:----------:|:------:| |
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| No log | 1.0 | 5 | 0.7080 | 0.4341 | 0.4719 | 19 51 |
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43 65 | 0.3065 | 0.2714 | 0.2879 | 0.5603 | 0.6019 | 0.5804 | |
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| 0.8946 | 2.0 | 10 | 0.8359 | 0.3776 | 0.6067 | 0 70 |
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0 108 | 0.0 | 0.0 | 0.0 | 0.6067 | 1.0 | 0.7552 | |
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| 0.8946 | 3.0 | 15 | 0.6091 | 0.6435 | 0.6461 | 50 20 |
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43 65 | 0.5376 | 0.7143 | 0.6135 | 0.7647 | 0.6019 | 0.6736 | |
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| 0.6111 | 4.0 | 20 | 0.7509 | 0.3776 | 0.6067 | 0 70 |
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0 108 | 0.0 | 0.0 | 0.0 | 0.6067 | 1.0 | 0.7552 | |
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| 0.6111 | 5.0 | 25 | 0.7014 | 0.4200 | 0.6180 | 3 67 |
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1 107 | 0.75 | 0.0429 | 0.0811 | 0.6149 | 0.9907 | 0.7589 | |
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| 0.5827 | 6.0 | 30 | 0.5507 | 0.7041 | 0.7079 | 53 17 |
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35 73 | 0.6023 | 0.7571 | 0.6709 | 0.8111 | 0.6759 | 0.7374 | |
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| 0.5827 | 7.0 | 35 | 0.5907 | 0.6963 | 0.6966 | 59 11 |
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43 65 | 0.5784 | 0.8429 | 0.6860 | 0.8553 | 0.6019 | 0.7065 | |
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| 0.3865 | 8.0 | 40 | 0.6183 | 0.6468 | 0.7079 | 26 44 |
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8 100 | 0.7647 | 0.3714 | 0.5 | 0.6944 | 0.9259 | 0.7937 | |
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| 0.3865 | 9.0 | 45 | 1.1120 | 0.5645 | 0.6685 | 16 54 |
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5 103 | 0.7619 | 0.2286 | 0.3516 | 0.6561 | 0.9537 | 0.7774 | |
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### Framework versions |
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- Transformers 4.49.0 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.3.2 |
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- Tokenizers 0.21.0 |
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