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This model is a fine-tuned version of distilbert-base-uncased on the adversarial_qa dataset. It achieves the following results on the evaluation set:

  • Loss: 3.6462

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: 9e-05
  • train_batch_size: 2
  • eval_batch_size: 2
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • training_steps: 3000

Training results

Training Loss Epoch Step Validation Loss
3.6424 0.18 3000 3.6462

Framework versions

  • Transformers 4.17.0
  • Pytorch 1.10.0+cu111
  • Datasets 1.18.4
  • Tokenizers 0.11.6
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Dataset used to train T-qualizer/distilbert-base-uncased-finetuned-advers