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README.md
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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: markusbayer/CySecBERT
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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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model-index:
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- name: cysecbert-ttp
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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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# cysecbert-ttp
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This model is a fine-tuned version of [markusbayer/CySecBERT](https://huggingface.co/markusbayer/CySecBERT) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0182
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- F1 Micro: 0.3613
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- F1 Macro: 0.0811
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- Precision: 0.7753
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- Recall: 0.2355
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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 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: 5
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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 Micro | F1 Macro | Precision | Recall |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:------:|
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| 0.0359 | 1.0 | 1226 | 0.0316 | 0.0 | 0.0 | 0.0 | 0.0 |
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| 0.0278 | 2.0 | 2452 | 0.0278 | 0.0202 | 0.0036 | 0.8571 | 0.0102 |
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| 0.0242 | 3.0 | 3678 | 0.0230 | 0.2709 | 0.0438 | 0.7462 | 0.1655 |
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| 0.0233 | 4.0 | 4904 | 0.0193 | 0.2881 | 0.0567 | 0.8361 | 0.1741 |
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| 0.0186 | 5.0 | 6130 | 0.0182 | 0.3613 | 0.0811 | 0.7753 | 0.2355 |
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### Framework versions
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- Transformers 4.57.5
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- Pytorch 2.2.0
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- Datasets 4.5.0
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- Tokenizers 0.22.2
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