README.md

metadata

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-10
    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

xlm-roberta-base-vsfc-10

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

• Loss: 0.2231
• 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: 10
• 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
0.2206	1.4	500	0.2281	0.9413
0.1397	2.79	1000	0.2179	0.9457

Framework versions

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