---
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