---
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: 0.0798
- Accuracy: 0.9907
- F1: 0.9934

## 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: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 1.0   | 402  | 0.1626          | 0.9477   | 0.9616 |
| 0.4003        | 2.0   | 804  | 0.0586          | 0.9794   | 0.9853 |
| 0.1075        | 3.0   | 1206 | 0.0342          | 0.9907   | 0.9933 |
| 0.0581        | 4.0   | 1608 | 0.1140          | 0.9776   | 0.9838 |
| 0.0245        | 5.0   | 2010 | 0.1409          | 0.9776   | 0.9842 |
| 0.0245        | 6.0   | 2412 | 0.0732          | 0.9832   | 0.9881 |
| 0.0167        | 7.0   | 2814 | 0.1996          | 0.9682   | 0.9778 |
| 0.0139        | 8.0   | 3216 | 0.1219          | 0.9850   | 0.9894 |
| 0.006         | 9.0   | 3618 | 0.0670          | 0.9907   | 0.9934 |
| 0.0067        | 10.0  | 4020 | 0.1036          | 0.9869   | 0.9907 |
| 0.0067        | 11.0  | 4422 | 0.1220          | 0.9776   | 0.9838 |
| 0.0041        | 12.0  | 4824 | 0.1768          | 0.9776   | 0.9839 |
| 0.0007        | 13.0  | 5226 | 0.0943          | 0.9888   | 0.9920 |
| 0.0           | 14.0  | 5628 | 0.0959          | 0.9907   | 0.9934 |
| 0.0054        | 15.0  | 6030 | 0.0915          | 0.9888   | 0.9921 |
| 0.0054        | 16.0  | 6432 | 0.1618          | 0.9794   | 0.9855 |
| 0.0019        | 17.0  | 6834 | 0.0794          | 0.9907   | 0.9934 |
| 0.0           | 18.0  | 7236 | 0.0799          | 0.9907   | 0.9934 |
| 0.0           | 19.0  | 7638 | 0.0797          | 0.9907   | 0.9934 |
| 0.0           | 20.0  | 8040 | 0.0798          | 0.9907   | 0.9934 |


### Framework versions

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