---
language:
- en
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
- generated_from_trainer
datasets:
- tmnam20/VieGLUE
metrics:
- accuracy
model-index:
- name: mdeberta-v3-base-qnli-1
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: tmnam20/VieGLUE/QNLI
      type: tmnam20/VieGLUE
      config: qnli
      split: validation
      args: qnli
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8998718652754897
---

<!-- 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-1

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: 0.2782
- Accuracy: 0.8999

## 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: 1
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3768        | 0.15  | 500  | 0.3291          | 0.8596   |
| 0.3506        | 0.31  | 1000 | 0.2961          | 0.8752   |
| 0.3417        | 0.46  | 1500 | 0.2917          | 0.8808   |
| 0.3319        | 0.61  | 2000 | 0.2742          | 0.8871   |
| 0.3126        | 0.76  | 2500 | 0.2686          | 0.8913   |
| 0.3073        | 0.92  | 3000 | 0.2639          | 0.8916   |
| 0.2867        | 1.07  | 3500 | 0.2557          | 0.8958   |
| 0.2313        | 1.22  | 4000 | 0.2937          | 0.8880   |
| 0.2364        | 1.37  | 4500 | 0.2585          | 0.8971   |
| 0.2533        | 1.53  | 5000 | 0.2545          | 0.8938   |
| 0.2333        | 1.68  | 5500 | 0.2629          | 0.8955   |
| 0.225         | 1.83  | 6000 | 0.2532          | 0.9002   |
| 0.2313        | 1.99  | 6500 | 0.2520          | 0.8988   |
| 0.1793        | 2.14  | 7000 | 0.2819          | 0.8953   |
| 0.1639        | 2.29  | 7500 | 0.2809          | 0.8964   |
| 0.1645        | 2.44  | 8000 | 0.2778          | 0.8990   |
| 0.1753        | 2.6   | 8500 | 0.2802          | 0.8988   |
| 0.1859        | 2.75  | 9000 | 0.2775          | 0.9001   |
| 0.1809        | 2.9   | 9500 | 0.2767          | 0.8988   |


### Framework versions

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