---
language:
- en
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- tmnam20/VieGLUE
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-sst2-100
  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: 0.8944954128440367
---

<!-- 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-sst2-100

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: 0.3776
- Accuracy: 0.8945

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4011        | 0.24  | 500  | 0.3866          | 0.8475   |
| 0.313         | 0.48  | 1000 | 0.3352          | 0.8647   |
| 0.2626        | 0.71  | 1500 | 0.4805          | 0.8349   |
| 0.2597        | 0.95  | 2000 | 0.3691          | 0.8681   |
| 0.2068        | 1.19  | 2500 | 0.3089          | 0.8991   |
| 0.2347        | 1.43  | 3000 | 0.3957          | 0.8842   |
| 0.2133        | 1.66  | 3500 | 0.3049          | 0.8991   |
| 0.1986        | 1.9   | 4000 | 0.3184          | 0.8956   |
| 0.1596        | 2.14  | 4500 | 0.3846          | 0.8853   |
| 0.1457        | 2.38  | 5000 | 0.3667          | 0.8968   |
| 0.1861        | 2.61  | 5500 | 0.3675          | 0.8922   |
| 0.1401        | 2.85  | 6000 | 0.3853          | 0.8899   |


### Framework versions

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