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

# fine_tuned_deberta

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: 0.2193
- Accuracy: 0.9437
- F1: 0.9398
- Precision: 0.9921
- Recall: 0.8929

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.7008        | 0.96  | 17   | 0.6755          | 0.5704   | 0.2375 | 0.95      | 0.1357 |
| 0.578         | 1.97  | 35   | 0.5885          | 0.6866   | 0.5822 | 0.8493    | 0.4429 |
| 0.4858        | 2.99  | 53   | 0.4109          | 0.8239   | 0.8344 | 0.7778    | 0.9    |
| 0.2615        | 4.0   | 71   | 0.2202          | 0.9401   | 0.9373 | 0.9695    | 0.9071 |
| 0.1685        | 4.79  | 85   | 0.2193          | 0.9437   | 0.9398 | 0.9921    | 0.8929 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2