---
library_name: transformers
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: deberta-v3-base-deu-DAPT-finetuned-10-epochs
  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. -->

# deberta-v3-base-deu-DAPT-finetuned-10-epochs

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: 0.3382
- F1: 0.4391
- Roc Auc: 0.6795
- Accuracy: 0.4908

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.4598        | 1.0   | 95   | 0.4152          | 0.0250 | 0.5067  | 0.2718   |
| 0.3605        | 2.0   | 190  | 0.3766          | 0.3285 | 0.6368  | 0.4512   |
| 0.3028        | 3.0   | 285  | 0.3548          | 0.3942 | 0.6625  | 0.4894   |
| 0.2871        | 4.0   | 380  | 0.3329          | 0.4127 | 0.6619  | 0.4776   |
| 0.2394        | 5.0   | 475  | 0.3294          | 0.4259 | 0.6738  | 0.4855   |
| 0.2221        | 6.0   | 570  | 0.3335          | 0.4190 | 0.6671  | 0.4947   |
| 0.2161        | 7.0   | 665  | 0.3368          | 0.4178 | 0.6646  | 0.4868   |
| 0.1914        | 8.0   | 760  | 0.3367          | 0.4346 | 0.6809  | 0.4974   |
| 0.1845        | 9.0   | 855  | 0.3382          | 0.4391 | 0.6795  | 0.4908   |
| 0.1824        | 10.0  | 950  | 0.3373          | 0.4319 | 0.6782  | 0.4921   |


### Framework versions

- Transformers 4.48.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0