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

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: 0.1665
- Accuracy: 0.9601
- Precision: 0.9599
- Recall: 0.9601
- F1: 0.9594
- Auroc: 0.9928

## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- label_smoothing_factor: 0.03

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Auroc  |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
| 0.4866        | 0.0988 | 256  | 0.2931          | 0.8845   | 0.8939    | 0.8845 | 0.8876 | 0.9465 |
| 0.2757        | 0.1977 | 512  | 0.3478          | 0.8898   | 0.8984    | 0.8898 | 0.8765 | 0.9544 |
| 0.2433        | 0.2965 | 768  | 0.2097          | 0.9404   | 0.9413    | 0.9404 | 0.9408 | 0.9799 |
| 0.2332        | 0.3953 | 1024 | 0.3548          | 0.8815   | 0.8907    | 0.8815 | 0.8657 | 0.9690 |
| 0.2152        | 0.4942 | 1280 | 0.1942          | 0.9440   | 0.9434    | 0.9440 | 0.9426 | 0.9868 |
| 0.1907        | 0.5930 | 1536 | 0.1615          | 0.9649   | 0.9647    | 0.9649 | 0.9647 | 0.9899 |
| 0.1865        | 0.6918 | 1792 | 0.1556          | 0.9655   | 0.9654    | 0.9655 | 0.9654 | 0.9922 |
| 0.1865        | 0.7907 | 2048 | 0.2322          | 0.9369   | 0.9370    | 0.9369 | 0.9344 | 0.9773 |
| 0.168         | 0.8895 | 2304 | 0.1653          | 0.9672   | 0.9670    | 0.9672 | 0.9668 | 0.9937 |
| 0.1732        | 0.9883 | 2560 | 0.1467          | 0.9702   | 0.9716    | 0.9702 | 0.9706 | 0.9935 |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0