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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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- accuracy |
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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: Roberta_biobert |
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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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# Roberta_biobert |
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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 None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5780 |
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- Accuracy: 0.725 |
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- Auc: 0.779 |
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- Precision: 0.682 |
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- Recall: 0.674 |
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- F1: 0.678 |
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- F1-macro: 0.719 |
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- F1-micro: 0.725 |
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- F1-weighted: 0.725 |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 2 |
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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 | Auc | Precision | Recall | F1 | F1-macro | F1-micro | F1-weighted | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----:|:---------:|:------:|:-----:|:--------:|:--------:|:-----------:| |
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| 0.6359 | 2.0 | 50 | 0.5780 | 0.725 | 0.779 | 0.682 | 0.674 | 0.678 | 0.719 | 0.725 | 0.725 | |
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### Framework versions |
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- Transformers 4.55.2 |
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- Pytorch 2.8.0+cu126 |
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- Datasets 4.0.0 |
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- Tokenizers 0.21.4 |
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