---
license: mit
base_model: microsoft/deberta-v3-small
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: DeBERTaV3_model_V4
  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_V4

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: 0.1557
- Accuracy: 0.9485
- F1: 0.7248
- Precision: 0.7778
- Recall: 0.6786

## 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: 12

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log        | 1.0   | 159  | 0.2879          | 0.9      | 0.0    | 0.0       | 0.0    |
| No log        | 2.0   | 318  | 0.2226          | 0.9189   | 0.3457 | 0.8936    | 0.2143 |
| No log        | 3.0   | 477  | 0.1883          | 0.9347   | 0.5975 | 0.7787    | 0.4847 |
| 0.2618        | 4.0   | 636  | 0.1557          | 0.9485   | 0.7248 | 0.7778    | 0.6786 |
| 0.2618        | 5.0   | 795  | 0.1593          | 0.9480   | 0.7273 | 0.7640    | 0.6939 |
| 0.2618        | 6.0   | 954  | 0.1564          | 0.9505   | 0.7413 | 0.7765    | 0.7092 |
| 0.0829        | 7.0   | 1113 | 0.1636          | 0.9520   | 0.7552 | 0.7713    | 0.7398 |
| 0.0829        | 8.0   | 1272 | 0.1761          | 0.9485   | 0.7363 | 0.7540    | 0.7194 |
| 0.0829        | 9.0   | 1431 | 0.1686          | 0.9536   | 0.7599 | 0.7869    | 0.7347 |
| 0.0297        | 10.0  | 1590 | 0.1807          | 0.9526   | 0.7584 | 0.7725    | 0.7449 |
| 0.0297        | 11.0  | 1749 | 0.1765          | 0.9531   | 0.7629 | 0.7708    | 0.7551 |
| 0.0297        | 12.0  | 1908 | 0.1790          | 0.9536   | 0.7649 | 0.7749    | 0.7551 |


### Framework versions

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