---
language:
- en
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- tmnam20/VieGLUE
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-sst2-1
  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: 0.8818807339449541
---

<!-- 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-sst2-1

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co./xlm-roberta-base) on the tmnam20/VieGLUE/SST2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3886
- Accuracy: 0.8819

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3646        | 0.24  | 500  | 0.3292          | 0.8555   |
| 0.3026        | 0.48  | 1000 | 0.4031          | 0.8658   |
| 0.2802        | 0.71  | 1500 | 0.3818          | 0.8716   |
| 0.2681        | 0.95  | 2000 | 0.3480          | 0.8693   |
| 0.2012        | 1.19  | 2500 | 0.3381          | 0.8819   |
| 0.2212        | 1.43  | 3000 | 0.3682          | 0.8784   |
| 0.2003        | 1.66  | 3500 | 0.3312          | 0.8899   |
| 0.2157        | 1.9   | 4000 | 0.3195          | 0.8899   |
| 0.1504        | 2.14  | 4500 | 0.3788          | 0.8933   |
| 0.1408        | 2.38  | 5000 | 0.4484          | 0.8819   |
| 0.1508        | 2.61  | 5500 | 0.4194          | 0.875    |
| 0.1604        | 2.85  | 6000 | 0.3730          | 0.8842   |


### Framework versions

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