---
license: mit
library_name: peft
tags:
- generated_from_trainer
base_model: MoritzLaurer/deberta-v3-large-zeroshot-v2.0
metrics:
- accuracy
model-index:
- name: fine-tuned-MoritzLaurer-deberta-v3-large-zeroshot-v2.0-swag-peft
  results: []
datasets:
- allenai/swag
---

<!-- 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. -->

# fine-tuned-MoritzLaurer-deberta-v3-large-zeroshot-v2.0-swag-peft

This model is a fine-tuned version of [MoritzLaurer/deberta-v3-large-zeroshot-v2.0](https://huggingface.co/MoritzLaurer/deberta-v3-large-zeroshot-v2.0) on SWAG dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2169
- Accuracy: 0.9193

## 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: 1.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5756        | 1.0   | 4597  | 0.2941          | 0.8993   |
| 0.5186        | 2.0   | 9194  | 0.2538          | 0.9115   |
| 0.5139        | 3.0   | 13791 | 0.2399          | 0.9136   |
| 0.4933        | 4.0   | 18388 | 0.2282          | 0.9158   |
| 0.4786        | 5.0   | 22985 | 0.2278          | 0.9165   |
| 0.4657        | 6.0   | 27582 | 0.2215          | 0.9182   |
| 0.4685        | 7.0   | 32179 | 0.2199          | 0.9189   |
| 0.4631        | 8.0   | 36776 | 0.2188          | 0.9188   |
| 0.4629        | 9.0   | 41373 | 0.2186          | 0.9188   |
| 0.4556        | 10.0  | 45970 | 0.2169          | 0.9193   |


### Framework versions

- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1