---
library_name: transformers
license: mit
base_model: microsoft/deberta-v3-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-misconception
  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-misconception

This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1066
- Macro F1: 0.5639
- Weighted F1: 0.7517
- Accuracy: 0.7177

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Macro F1 | Weighted F1 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-----------:|:--------:|
| 0.4896        | 0.4840 | 500  | 0.4428          | 0.2597   | 0.1155      | 0.2245   |
| 0.2755        | 0.9681 | 1000 | 0.2203          | 0.4467   | 0.6258      | 0.5809   |
| 0.1658        | 1.4521 | 1500 | 0.1576          | 0.5330   | 0.7263      | 0.6850   |
| 0.1688        | 1.9361 | 2000 | 0.1388          | 0.5112   | 0.6329      | 0.5902   |
| 0.0482        | 2.4201 | 2500 | 0.1152          | 0.5605   | 0.7041      | 0.6700   |
| 0.0269        | 2.9042 | 3000 | 0.1368          | 0.5653   | 0.6868      | 0.6480   |
| 0.1069        | 3.3882 | 3500 | 0.1131          | 0.5633   | 0.7404      | 0.7054   |
| 0.0304        | 3.8722 | 4000 | 0.1527          | 0.5592   | 0.7287      | 0.6965   |
| 0.0577        | 4.3562 | 4500 | 0.1066          | 0.5639   | 0.7517      | 0.7177   |


### Framework versions

- Transformers 4.53.3
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.2