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SAE-bert-base-uncased

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

It achieves the following results on the evaluation set:

  • Loss: 2.1256

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: 7
  • eval_batch_size: 7
  • 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
2.5967 1.0 80 2.3409
2.4881 2.0 160 2.2707
2.3567 3.0 240 2.3134
2.3413 4.0 320 2.2592
2.3006 5.0 400 2.2351
2.2568 6.0 480 2.2556
2.2303 7.0 560 2.2546
2.1892 8.0 640 2.1868
2.1851 9.0 720 2.2073
2.1738 10.0 800 2.1344
2.1673 11.0 880 2.1927
2.1518 12.0 960 2.1844
2.1142 13.0 1040 2.1466
2.1343 14.0 1120 2.2024
2.1332 15.0 1200 2.1035

Framework versions

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