---
license: mit
base_model: microsoft/deberta-v3-small
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: DeBERTaV3_model_V2
  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_V2

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: 0.1711
- Accuracy: 0.9412
- F1: 0.7500
- Precision: 0.8
- Recall: 0.7059

## 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   | 50   | 0.3627          | 0.875    | 0.0    | 0.0       | 0.0    |
| No log        | 2.0   | 100  | 0.3804          | 0.875    | 0.0    | 0.0       | 0.0    |
| No log        | 3.0   | 150  | 0.3085          | 0.8934   | 0.2564 | 1.0       | 0.1471 |
| No log        | 4.0   | 200  | 0.2722          | 0.9007   | 0.4706 | 0.7059    | 0.3529 |
| No log        | 5.0   | 250  | 0.2206          | 0.9118   | 0.6129 | 0.6786    | 0.5588 |
| No log        | 6.0   | 300  | 0.2140          | 0.9265   | 0.6667 | 0.7692    | 0.5882 |
| No log        | 7.0   | 350  | 0.2022          | 0.9265   | 0.6875 | 0.7333    | 0.6471 |
| No log        | 8.0   | 400  | 0.1940          | 0.9301   | 0.7077 | 0.7419    | 0.6765 |
| No log        | 9.0   | 450  | 0.1711          | 0.9412   | 0.7500 | 0.8       | 0.7059 |
| 0.2198        | 10.0  | 500  | 0.1778          | 0.9338   | 0.7188 | 0.7667    | 0.6765 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1