---
license: mit
base_model: microsoft/deberta-v3-small
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: DeBERTaV3_model_V3
  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. -->

# DeBERTaV3_model_V3

This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1653
- Accuracy: 0.9454
- F1: 0.7754
- Precision: 0.7975
- Recall: 0.7545

## 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: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log        | 1.0   | 90   | 0.3559          | 0.875    | 0.0    | 0.0       | 0.0    |
| No log        | 2.0   | 180  | 0.2851          | 0.9087   | 0.4602 | 0.8814    | 0.3114 |
| No log        | 3.0   | 270  | 0.2462          | 0.9049   | 0.4940 | 0.7381    | 0.3713 |
| No log        | 4.0   | 360  | 0.2183          | 0.9222   | 0.6232 | 0.7890    | 0.5150 |
| No log        | 5.0   | 450  | 0.1938          | 0.9304   | 0.6869 | 0.7846    | 0.6108 |
| 0.2617        | 6.0   | 540  | 0.1804          | 0.9349   | 0.7129 | 0.7941    | 0.6467 |
| 0.2617        | 7.0   | 630  | 0.1752          | 0.9364   | 0.7231 | 0.7929    | 0.6647 |
| 0.2617        | 8.0   | 720  | 0.1719          | 0.9409   | 0.7539 | 0.7857    | 0.7246 |
| 0.2617        | 9.0   | 810  | 0.1676          | 0.9424   | 0.7601 | 0.7922    | 0.7305 |
| 0.2617        | 10.0  | 900  | 0.1653          | 0.9454   | 0.7754 | 0.7975    | 0.7545 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1