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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
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