README.md

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-100
    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: 0.4946000366099213

xlm-roberta-large-qnli-100

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: 0.6932
• Accuracy: 0.4946

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
0.6979	1.53	5000	0.6933	0.4946

Framework versions

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