---
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: 0.4703
- Accuracy: 0.9102

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2733        | 0.08  | 500  | 0.4367          | 0.9010   |
| 0.2226        | 0.17  | 1000 | 0.5558          | 0.9022   |
| 0.2971        | 0.25  | 1500 | 0.5317          | 0.8999   |
| 0.3168        | 0.33  | 2000 | 0.8198          | 0.9024   |
| 0.3367        | 0.42  | 2500 | 0.5744          | 0.9031   |
| 0.3089        | 0.5   | 3000 | 0.8358          | 0.9018   |
| 0.3796        | 0.58  | 3500 | 0.7308          | 0.9078   |
| 0.3663        | 0.67  | 4000 | 0.6393          | 0.9044   |
| 0.4152        | 0.75  | 4500 | 0.6098          | 0.9123   |
| 0.3905        | 0.83  | 5000 | 0.5029          | 0.9106   |
| 0.4446        | 0.91  | 5500 | 0.4821          | 0.9106   |
| 0.5263        | 1.0   | 6000 | 0.4703          | 0.9102   |


### Framework versions

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