---
language:
- en
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
- generated_from_trainer
datasets:
- tmnam20/VieGLUE
metrics:
- accuracy
- f1
model-index:
- name: mdeberta-v3-base-qqp-100
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: tmnam20/VieGLUE/QQP
      type: tmnam20/VieGLUE
      config: qqp
      split: validation
      args: qqp
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8987880286915657
    - name: F1
      type: f1
      value: 0.8654655444502892
---

<!-- 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-qqp-100

This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the tmnam20/VieGLUE/QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2790
- Accuracy: 0.8988
- F1: 0.8655
- Combined Score: 0.8821

## 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 | F1     | Combined Score |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.3099        | 0.44  | 5000  | 0.2921          | 0.8751   | 0.8326 | 0.8539         |
| 0.269         | 0.88  | 10000 | 0.2732          | 0.8820   | 0.8378 | 0.8599         |
| 0.2421        | 1.32  | 15000 | 0.2795          | 0.8894   | 0.8520 | 0.8707         |
| 0.2198        | 1.76  | 20000 | 0.2674          | 0.8937   | 0.8566 | 0.8751         |
| 0.188         | 2.2   | 25000 | 0.2778          | 0.8964   | 0.8602 | 0.8783         |
| 0.1916        | 2.64  | 30000 | 0.2861          | 0.8977   | 0.8636 | 0.8807         |


### Framework versions

- Transformers 4.36.0
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0