---
license: mit
base_model: microsoft/deberta-v3-small
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: DeBERTaV3_model_multilabel
  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_multilabel

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: 0.0221
- Accuracy: 0.9919
- F1: 0.3922
- Precision: 0.6667
- Recall: 0.2778

## 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   | 25   | 0.4442          | 0.9516   | 0.1475 | 0.0884    | 0.4444 |
| No log        | 2.0   | 50   | 0.1757          | 0.9919   | 0.3922 | 0.6667    | 0.2778 |
| No log        | 3.0   | 75   | 0.0655          | 0.9919   | 0.3922 | 0.6667    | 0.2778 |
| No log        | 4.0   | 100  | 0.0378          | 0.9919   | 0.3922 | 0.6667    | 0.2778 |
| No log        | 5.0   | 125  | 0.0292          | 0.9919   | 0.3922 | 0.6667    | 0.2778 |
| No log        | 6.0   | 150  | 0.0255          | 0.9919   | 0.3922 | 0.6667    | 0.2778 |
| No log        | 7.0   | 175  | 0.0238          | 0.9919   | 0.3922 | 0.6667    | 0.2778 |
| No log        | 8.0   | 200  | 0.0227          | 0.9919   | 0.3922 | 0.6667    | 0.2778 |
| No log        | 9.0   | 225  | 0.0222          | 0.9919   | 0.3922 | 0.6667    | 0.2778 |
| No log        | 10.0  | 250  | 0.0221          | 0.9919   | 0.3922 | 0.6667    | 0.2778 |


### Framework versions

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