---
library_name: transformers
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: fold_4_model
  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. -->

# fold_4_model

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7276
- F1: 0.7590
- Roc Auc: 0.8201
- Accuracy: 0.4054

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.0565        | 1.0   | 111  | 0.6218          | 0.7479 | 0.8119  | 0.4144   |
| 0.0628        | 2.0   | 222  | 0.6708          | 0.7360 | 0.8022  | 0.3784   |
| 0.0393        | 3.0   | 333  | 0.6955          | 0.75   | 0.8163  | 0.3874   |
| 0.0213        | 4.0   | 444  | 0.7107          | 0.7554 | 0.8189  | 0.4054   |
| 0.0164        | 5.0   | 555  | 0.7276          | 0.7590 | 0.8201  | 0.4054   |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0