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--- |
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library_name: transformers |
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license: mit |
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base_model: FacebookAI/xlm-roberta-base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: scandi-fine-web-cleaner |
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results: [] |
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datasets: |
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- data-is-better-together/fineweb-c |
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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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# scandi-fine-web-cleaner |
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This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co./FacebookAI/xlm-roberta-base) on the data-is-better-together/fineweb-c dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1816 |
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- Precision: 0.9524 |
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- Recall: 0.7018 |
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- F1: 0.8081 |
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- Auc Roc: 0.9648 |
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- Balanced Accuracy: 0.8480 |
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- Average Precision: 0.8906 |
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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: 16 |
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- eval_batch_size: 16 |
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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: 10 |
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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 | Precision | Recall | F1 | Auc Roc | Balanced Accuracy | Average Precision | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:-------:|:-----------------:|:-----------------:| |
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| 0.3165 | 1.0 | 100 | 0.2333 | 0.95 | 0.6667 | 0.7835 | 0.8099 | 0.8304 | 0.7721 | |
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| 0.1929 | 2.0 | 200 | 0.1359 | 0.9130 | 0.7368 | 0.8155 | 0.9778 | 0.8626 | 0.9105 | |
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| 0.1775 | 3.0 | 300 | 0.2245 | 0.9268 | 0.6667 | 0.7755 | 0.9481 | 0.8290 | 0.8721 | |
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| 0.1553 | 4.0 | 400 | 0.1816 | 0.9524 | 0.7018 | 0.8081 | 0.9648 | 0.8480 | 0.8906 | |
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### Framework versions |
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- Transformers 4.48.0 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
