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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-qnli-1
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: tmnam20/VieGLUE/QNLI
          type: tmnam20/VieGLUE
          config: qnli
          split: validation
          args: qnli
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9108548416620904

xlm-roberta-large-qnli-1

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

  • Loss: 0.2727
  • Accuracy: 0.9109

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
0.2657 1.53 5000 0.2453 0.9004

Framework versions

  • Transformers 4.36.0
  • Pytorch 2.1.0+cu121
  • Datasets 2.15.0
  • Tokenizers 0.15.0