---
language:
- en
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- tmnam20/VieGLUE
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-vsfc-1
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: tmnam20/VieGLUE/VSFC
      type: tmnam20/VieGLUE
      config: vsfc
      split: validation
      args: vsfc
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9450410612760581
---

<!-- 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. -->

# xlm-roberta-base-vsfc-1

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co./xlm-roberta-base) on the tmnam20/VieGLUE/VSFC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2253
- Accuracy: 0.9450

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.207         | 1.4   | 500  | 0.2353          | 0.9400   |
| 0.1438        | 2.79  | 1000 | 0.2245          | 0.9450   |


### Framework versions

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