---
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: 0.1015
- Accuracy: 0.9693
- F1: 0.8766
- Precision: 0.8803
- Recall: 0.8729

## 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: 3e-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   | 100  | 0.3499          | 0.875    | 0.0    | 0.0       | 0.0    |
| No log        | 2.0   | 200  | 0.2517          | 0.9068   | 0.4211 | 0.9412    | 0.2712 |
| No log        | 3.0   | 300  | 0.1835          | 0.9396   | 0.7077 | 0.8961    | 0.5847 |
| No log        | 4.0   | 400  | 0.1338          | 0.9587   | 0.8219 | 0.8911    | 0.7627 |
| 0.2507        | 5.0   | 500  | 0.1043          | 0.9640   | 0.8522 | 0.875     | 0.8305 |
| 0.2507        | 6.0   | 600  | 0.1076          | 0.9629   | 0.8472 | 0.8739    | 0.8220 |
| 0.2507        | 7.0   | 700  | 0.1061          | 0.9619   | 0.8475 | 0.8475    | 0.8475 |
| 0.2507        | 8.0   | 800  | 0.1015          | 0.9693   | 0.8766 | 0.8803    | 0.8729 |
| 0.2507        | 9.0   | 900  | 0.1099          | 0.9650   | 0.8596 | 0.8632    | 0.8559 |
| 0.0434        | 10.0  | 1000 | 0.1101          | 0.9661   | 0.8632 | 0.8707    | 0.8559 |
| 0.0434        | 11.0  | 1100 | 0.1054          | 0.9693   | 0.8766 | 0.8803    | 0.8729 |
| 0.0434        | 12.0  | 1200 | 0.1066          | 0.9682   | 0.8729 | 0.8729    | 0.8729 |


### Framework versions

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