mateiaass / albert-base-qa-2-batch-1

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Model's Last Updated: October 23 2023
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Introduction of albert-base-qa-2-batch-1

Model Details of albert-base-qa-2-batch-1

albert-base-qa-2-batch-1

This model is a fine-tuned version of albert-base-v2 on the squad dataset. It achieves the following results on the evaluation set:

  • Loss: 0.9923
Model description

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Intended uses & limitations

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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: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 3
Training results
Training Loss Epoch Step Validation Loss
0.9009 1.0 7884 0.9030
0.6925 2.0 15768 0.8964
0.4853 3.0 23652 0.9923
Framework versions
  • Transformers 4.34.1
  • Pytorch 2.1.0+cu118
  • Datasets 2.14.5
  • Tokenizers 0.14.1

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