---
license: cc-by-4.0
base_model: strongpear/deberta-large_vMCQ
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-large_vMCQ
  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-large_vMCQ

This model is a fine-tuned version of [strongpear/deberta-large_vMCQ](https://huggingface.co/strongpear/deberta-large_vMCQ) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3767
- Accuracy: 0.9097

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3415        | 0.08  | 500  | 0.3459          | 0.9033   |
| 0.299         | 0.17  | 1000 | 0.5000          | 0.8988   |
| 0.3761        | 0.25  | 1500 | 0.4322          | 0.8994   |
| 0.3655        | 0.33  | 2000 | 0.4337          | 0.8990   |
| 0.3576        | 0.42  | 2500 | 0.4655          | 0.9027   |
| 0.3178        | 0.5   | 3000 | 0.4377          | 0.9046   |
| 0.3789        | 0.58  | 3500 | 0.4241          | 0.9072   |
| 0.32          | 0.67  | 4000 | 0.3855          | 0.9074   |
| 0.3655        | 0.75  | 4500 | 0.3848          | 0.9076   |
| 0.3426        | 0.83  | 5000 | 0.3931          | 0.9072   |
| 0.356         | 0.91  | 5500 | 0.3797          | 0.9089   |
| 0.3965        | 1.0   | 6000 | 0.3767          | 0.9097   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.14.1