
<!-- 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. -->

# mdeberta-v3-base-qnli-100

This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the tmnam20/VieGLUE/QNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2906
- Accuracy: 0.8975

## 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: 16
- seed: 100
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3773        | 0.15  | 500  | 0.3870          | 0.8431   |
| 0.3547        | 0.31  | 1000 | 0.3175          | 0.8658   |
| 0.3385        | 0.46  | 1500 | 0.2986          | 0.8739   |
| 0.342         | 0.61  | 2000 | 0.2787          | 0.8845   |
| 0.3003        | 0.76  | 2500 | 0.3075          | 0.8726   |
| 0.3298        | 0.92  | 3000 | 0.2781          | 0.8807   |
| 0.2475        | 1.07  | 3500 | 0.2695          | 0.8942   |
| 0.2441        | 1.22  | 4000 | 0.2615          | 0.8940   |
| 0.249         | 1.37  | 4500 | 0.2548          | 0.8958   |
| 0.2261        | 1.53  | 5000 | 0.2588          | 0.8946   |
| 0.2348        | 1.68  | 5500 | 0.2587          | 0.8982   |
| 0.2626        | 1.83  | 6000 | 0.2581          | 0.8982   |
| 0.2463        | 1.99  | 6500 | 0.2520          | 0.8964   |
| 0.1768        | 2.14  | 7000 | 0.2795          | 0.8951   |
| 0.1768        | 2.29  | 7500 | 0.3069          | 0.8942   |
| 0.1752        | 2.44  | 8000 | 0.2783          | 0.8971   |
| 0.1687        | 2.6   | 8500 | 0.2900          | 0.8995   |
| 0.163         | 2.75  | 9000 | 0.2828          | 0.8969   |
| 0.1547        | 2.9   | 9500 | 0.2873          | 0.8980   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.2.0.dev20231203+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0

{"language": ["en"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["tmnam20/VieGLUE"], "metrics": ["accuracy"], "base_model": "microsoft/mdeberta-v3-base", "model-index": [{"name": "mdeberta-v3-base-qnli-100", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "tmnam20/VieGLUE/QNLI", "type": "tmnam20/VieGLUE", "config": "qnli", "split": "validation", "args": "qnli"}, "metrics": [{"type": "accuracy", "value": 0.8974922203917262, "name": "Accuracy"}]}]}]}
transformers safetensors deberta-v2 text-classification generated_from_trainer en dataset:tmnam20/VieGLUE base_model:microsoft/mdeberta-v3-base license:mit model-index autotrain_compatible endpoints_compatible region:us