transformers safetensors deberta-v2 text-classification generated_from_trainer en dataset:tmnam20/VieGLUE base_model:microsoft/mdeberta-v3-base base_model:finetune:microsoft/mdeberta-v3-base license:mit model-index autotrain_compatible endpoints_compatible region:us
---
base_model: microsoft/mdeberta-v3-base
datasets:
- tmnam20/VieGLUE
language:
- en
license: mit
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: mdeberta-v3-base-vsfc-100
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: tmnam20/VieGLUE/VSFC
      type: tmnam20/VieGLUE
      config: vsfc
      split: validation
      args: vsfc
    metrics:
    - type: accuracy
      value: 0.9456727732154138
      name: Accuracy
---

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# mdeberta-v3-base-vsfc-100

This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the tmnam20/VieGLUE/VSFC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2290
- Accuracy: 0.9457

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1763        | 1.4   | 500  | 0.2099          | 0.9431   |
| 0.1363        | 2.79  | 1000 | 0.2278          | 0.9463   |


### Framework versions

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