metadata

language:
  - en
license: mit
base_model: xlm-roberta-large
tags:
  - generated_from_trainer
datasets:
  - tmnam20/VieGLUE
metrics:
  - accuracy
model-index:
  - name: xlm-roberta-large-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: 0.9538850284270373

xlm-roberta-large-vsfc-1

This model is a fine-tuned version of xlm-roberta-large on the tmnam20/VieGLUE/VSFC dataset. It achieves the following results on the evaluation set:

Loss: 0.2120
Accuracy: 0.9539

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
0.2076	1.4	500	0.2616	0.9394
0.1168	2.79	1000	0.2073	0.9520

Framework versions

Transformers 4.36.0
Pytorch 2.1.0+cu121
Datasets 2.15.0
Tokenizers 0.15.0