---
license: mit
base_model: microsoft/deberta-v3-small
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: DeBERTaV3_model_V3_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_V3_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: 0.0117
- Accuracy: 0.9978
- F1: 0.9985
- Precision: 0.9970
- Recall: 1.0

## 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: 13

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log        | 1.0   | 90   | 0.0239          | 0.9978   | 0.9985 | 0.9970    | 1.0    |
| No log        | 2.0   | 180  | 0.0147          | 0.9978   | 0.9985 | 0.9970    | 1.0    |
| No log        | 3.0   | 270  | 0.0131          | 0.9978   | 0.9985 | 0.9970    | 1.0    |
| No log        | 4.0   | 360  | 0.0124          | 0.9978   | 0.9985 | 0.9970    | 1.0    |
| No log        | 5.0   | 450  | 0.0121          | 0.9978   | 0.9985 | 0.9970    | 1.0    |
| 0.0608        | 6.0   | 540  | 0.0119          | 0.9978   | 0.9985 | 0.9970    | 1.0    |
| 0.0608        | 7.0   | 630  | 0.0117          | 0.9978   | 0.9985 | 0.9970    | 1.0    |
| 0.0608        | 8.0   | 720  | 0.0120          | 0.9978   | 0.9985 | 0.9970    | 1.0    |
| 0.0608        | 9.0   | 810  | 0.0127          | 0.9978   | 0.9985 | 0.9970    | 1.0    |
| 0.0608        | 10.0  | 900  | 0.0132          | 0.9978   | 0.9985 | 0.9970    | 1.0    |
| 0.0608        | 11.0  | 990  | 0.0139          | 0.9970   | 0.998  | 0.9970    | 0.9990 |
| 0.0121        | 12.0  | 1080 | 0.0143          | 0.9970   | 0.998  | 0.9970    | 0.9990 |
| 0.0121        | 13.0  | 1170 | 0.0143          | 0.9970   | 0.998  | 0.9970    | 0.9990 |


### Framework versions

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