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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
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