---
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-mnli-10
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: tmnam20/VieGLUE/MNLI
      type: tmnam20/VieGLUE
      config: mnli
      split: validation_matched
      args: mnli
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8430634662327096
---

<!-- 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-mnli-10

This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the tmnam20/VieGLUE/MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4642
- Accuracy: 0.8431

## 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: 10
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5294        | 0.41  | 5000  | 0.4986          | 0.8037   |
| 0.4856        | 0.81  | 10000 | 0.4593          | 0.8250   |
| 0.3976        | 1.22  | 15000 | 0.4776          | 0.8271   |
| 0.4154        | 1.63  | 20000 | 0.4680          | 0.8222   |
| 0.2933        | 2.04  | 25000 | 0.5138          | 0.8304   |
| 0.3186        | 2.44  | 30000 | 0.4813          | 0.8320   |
| 0.3196        | 2.85  | 35000 | 0.4795          | 0.8331   |


### Framework versions

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