---
library_name: transformers
license: mit
base_model: microsoft/deberta-v3-small
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: DynamicNoise-deberta-v3-small-Label_B-epochs-5
  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. -->

# DynamicNoise-deberta-v3-small-Label_B-epochs-5

This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0889
- Accuracy: 0.9858
- F1: 0.9858
- Precision: 0.9859
- Recall: 0.9858

## 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: 12
- eval_batch_size: 12
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 48
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.1183        | 0.9995 | 1066 | 0.1529          | 0.9611   | 0.9610 | 0.9631    | 0.9611 |
| 0.0489        | 1.9993 | 2132 | 0.0881          | 0.9796   | 0.9796 | 0.9797    | 0.9796 |
| 0.0364        | 2.9991 | 3198 | 0.1118          | 0.9788   | 0.9789 | 0.9792    | 0.9788 |
| 0.0032        | 3.9998 | 4265 | 0.0889          | 0.9858   | 0.9858 | 0.9859    | 0.9858 |
| 0.0173        | 4.9986 | 5330 | 0.1346          | 0.9813   | 0.9813 | 0.9816    | 0.9813 |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.4.0
- Datasets 3.1.0
- Tokenizers 0.20.3