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SAE-distilbert-base-uncased

This model is a fine-tuned version of distilbert-base-uncased on the jgammack/SAE-door-abstracts dataset.

It achieves the following results on the evaluation set:

  • Loss: 2.2970

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 2e-05
  • train_batch_size: 15
  • eval_batch_size: 15
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 15
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss
2.5323 1.0 37 2.4503
2.4968 2.0 74 2.4571
2.4688 3.0 111 2.4099
2.419 4.0 148 2.3343
2.4229 5.0 185 2.3072
2.4067 6.0 222 2.2927
2.3877 7.0 259 2.2836
2.374 8.0 296 2.3767
2.3582 9.0 333 2.2493
2.356 10.0 370 2.2847
2.3294 11.0 407 2.3234
2.3358 12.0 444 2.2660
2.3414 13.0 481 2.2887
2.3154 14.0 518 2.3737
2.311 15.0 555 2.2686

Framework versions

  • Transformers 4.16.2
  • Pytorch 1.10.0+cu111
  • Datasets 1.18.3
  • Tokenizers 0.11.0
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Evaluation results