---
license: mit
tags:
- text-classification
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: deberta-v3-xsmall-finetuned-DAGPap22
  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. -->

# deberta-v3-xsmall-finetuned-DAGPap22

This model is a fine-tuned version of [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1285
- Accuracy: 0.9794
- F1: 0.9850

## 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: 4.5e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 1.0   | 402  | 0.2610          | 0.9477   | 0.9621 |
| 0.4318        | 2.0   | 804  | 0.2039          | 0.9421   | 0.9559 |
| 0.1105        | 3.0   | 1206 | 0.1734          | 0.9664   | 0.9748 |
| 0.0451        | 4.0   | 1608 | 0.1000          | 0.9850   | 0.9890 |
| 0.0073        | 5.0   | 2010 | 0.1285          | 0.9794   | 0.9850 |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1