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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
|