---
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: 0.0963
- Accuracy: 0.9724
- F1: 0.8874
- Precision: 0.9054
- Recall: 0.8701

## 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: 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   | 84   | 0.2797          | 0.9058   | 0.4630 | 0.8065    | 0.3247 |
| No log        | 2.0   | 168  | 0.2004          | 0.9269   | 0.6281 | 0.8636    | 0.4935 |
| No log        | 3.0   | 252  | 0.1818          | 0.9253   | 0.6761 | 0.7385    | 0.6234 |
| No log        | 4.0   | 336  | 0.1692          | 0.9399   | 0.7176 | 0.8704    | 0.6104 |
| No log        | 5.0   | 420  | 0.1503          | 0.9513   | 0.7945 | 0.8406    | 0.7532 |
| 0.2044        | 6.0   | 504  | 0.1269          | 0.9562   | 0.8212 | 0.8378    | 0.8052 |
| 0.2044        | 7.0   | 588  | 0.0963          | 0.9724   | 0.8874 | 0.9054    | 0.8701 |
| 0.2044        | 8.0   | 672  | 0.1243          | 0.9594   | 0.8344 | 0.8514    | 0.8182 |
| 0.2044        | 9.0   | 756  | 0.1107          | 0.9659   | 0.8609 | 0.8784    | 0.8442 |
| 0.2044        | 10.0  | 840  | 0.1088          | 0.9675   | 0.8667 | 0.8904    | 0.8442 |


### Framework versions

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