| --- |
| library_name: transformers |
| language: |
| - en |
| license: mit |
| base_model: microsoft/deberta-v3-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - recall |
| - f1 |
| model-index: |
| - name: ConSec |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # ConSec |
|
|
| This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 1.5775 |
| - Precision: 0.4804 |
| - Recall: 0.4917 |
| - F1: 0.4860 |
| - Matthews: 0.4909 |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 0.0001 |
| - train_batch_size: 4 |
| - eval_batch_size: 4 |
| - seed: 42 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: inverse_sqrt |
| - lr_scheduler_warmup_steps: 1000 |
| - num_epochs: 5 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Matthews | |
| |:-------------:|:-----:|:------:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | No log | 0 | 0 | 344.1697 | 0.4603 | 0.3243 | 0.3805 | 0.3236 | |
| | 6.7210 | 1.0 | 56179 | 1.5766 | 0.4804 | 0.4917 | 0.4860 | 0.4909 | |
| | 5.7990 | 2.0 | 112358 | 1.5649 | 0.4859 | 0.4943 | 0.4900 | 0.4935 | |
| | 6.3812 | 3.0 | 168537 | 1.5669 | 0.4804 | 0.4926 | 0.4864 | 0.4918 | |
| | 5.8106 | 4.0 | 224716 | 1.5847 | 0.4834 | 0.4921 | 0.4877 | 0.4913 | |
| | 6.0390 | 5.0 | 280895 | 1.5775 | 0.4804 | 0.4917 | 0.4860 | 0.4909 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 5.3.0 |
| - Pytorch 2.10.0+cu128 |
| - Datasets 4.5.0 |
| - Tokenizers 0.22.2 |
| |
