README.md

metadata

tags:
  - generated_from_trainer
datasets:
  - xnli
metrics:
  - accuracy
  - f1
model-index:
  - name: bert-base-arabic-electra-xnli-finetuned
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: xnli
          type: xnli
          config: ar
          split: train
          args: ar
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7870259481037924
          - name: F1
            type: f1
            value: 0.7875257009692987

bert-base-arabic-electra-xnli-finetuned

This model is a fine-tuned version of aubmindlab/araelectra-base-discriminator on the xnli dataset. It achieves the following results on the evaluation set:

• Loss: 0.5453
• Accuracy: 0.7870
• F1: 0.7875

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: 1
• eval_batch_size: 1
• seed: 42
• gradient_accumulation_steps: 32
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 1
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.5541	1.0	12271	0.5453	0.7870	0.7875

Framework versions

• Transformers 4.25.1
• Pytorch 1.13.1+cu117
• Datasets 2.8.0
• Tokenizers 0.13.2