Training completo su framing detector (RoBERTa)
Browse files
README.md
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---
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library_name: transformers
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license: mit
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base_model: 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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- f1
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- precision
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- recall
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model-index:
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- name: AttackVectorClassifier
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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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# AttackVectorClassifier
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2148
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- Accuracy: 0.9548
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- F1: 0.9577
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- Precision: 0.9646
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- Recall: 0.9509
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- Roc Auc: 0.9898
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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_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: 3
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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 | Precision | Recall | Roc Auc |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------:|
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| 0.1444 | 1.0 | 2393 | 0.1713 | 0.9517 | 0.9542 | 0.9768 | 0.9325 | 0.9887 |
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| 0.1601 | 2.0 | 4786 | 0.1616 | 0.9487 | 0.9529 | 0.9439 | 0.9620 | 0.9904 |
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| 0.0945 | 3.0 | 7179 | 0.2148 | 0.9548 | 0.9577 | 0.9646 | 0.9509 | 0.9898 |
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### Framework versions
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- Transformers 5.13.1
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- Pytorch 2.11.0+cu128
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- Datasets 4.0.0
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- Tokenizers 0.22.2
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