---
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta_v3_base_finetune_hellaswag
  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_finetune_hellaswag

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an the hellaswag dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3999
- Accuracy: 0.8765

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.5083        | 0.9996 | 1247 | 0.3641          | 0.8622   |
| 0.193         | 1.9992 | 2494 | 0.3999          | 0.8765   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.19.1