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

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: 0.0898
- Accuracy: 0.9551
- F1: 0.9533

## 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: 5e-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: linear
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.1053        | 1.0   | 67   | 0.4668          | 0.8652   | 0.8481 |
| 0.4887        | 2.0   | 134  | 0.2434          | 0.9220   | 0.9225 |
| 0.2714        | 3.0   | 201  | 0.2129          | 0.9333   | 0.9341 |
| 0.2296        | 4.0   | 268  | 0.1752          | 0.9319   | 0.9341 |
| 0.2098        | 5.0   | 335  | 0.1676          | 0.9418   | 0.9402 |
| 0.1881        | 6.0   | 402  | 0.1473          | 0.9433   | 0.9443 |
| 0.1472        | 7.0   | 469  | 0.0982          | 0.9504   | 0.9515 |
| 0.1332        | 8.0   | 536  | 0.0969          | 0.9527   | 0.9517 |
| 0.1291        | 9.0   | 603  | 0.0919          | 0.9537   | 0.9517 |
| 0.1146        | 10.0  | 670  | 0.0898          | 0.9551   | 0.9533 |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0