---
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-sst2-10
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: tmnam20/VieGLUE/SST2
      type: tmnam20/VieGLUE
      config: sst2
      split: validation
      args: sst2
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8979357798165137
---

<!-- 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-sst2-10

This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the tmnam20/VieGLUE/SST2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3852
- Accuracy: 0.8979

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3449        | 0.24  | 500  | 0.3368          | 0.8567   |
| 0.2987        | 0.48  | 1000 | 0.3037          | 0.8716   |
| 0.2492        | 0.71  | 1500 | 0.3347          | 0.8842   |
| 0.24          | 0.95  | 2000 | 0.2953          | 0.8830   |
| 0.195         | 1.19  | 2500 | 0.3445          | 0.8842   |
| 0.1934        | 1.43  | 3000 | 0.3217          | 0.8876   |
| 0.1697        | 1.66  | 3500 | 0.3627          | 0.8876   |
| 0.1757        | 1.9   | 4000 | 0.3366          | 0.8899   |
| 0.1328        | 2.14  | 4500 | 0.4266          | 0.8876   |
| 0.1475        | 2.38  | 5000 | 0.3737          | 0.8933   |
| 0.1574        | 2.61  | 5500 | 0.3888          | 0.8911   |
| 0.1548        | 2.85  | 6000 | 0.4063          | 0.8865   |


### Framework versions

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