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odinsynth_encoder_decoder_native_hf_test_2

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odinsynth_encoder_decoder_native_hf_test_2

This model is a fine-tuned version of bert-base-uncased on the enoriega/odinsynth_sequence_dataset synthetic_surface dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0771
  • Accuracy: 0.9343

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: 5e-05
  • train_batch_size: 3
  • eval_batch_size: 3
  • seed: 42
  • gradient_accumulation_steps: 200
  • total_train_batch_size: 600
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 20.0

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.1612 0.67 60 0.1145 0.9376
0.0666 1.34 120 0.0628 0.9356
0.0599 2.01 180 0.0611 0.9355
0.0563 2.68 240 0.0631 0.9352
0.0512 3.35 300 0.0630 0.9347
0.0472 4.02 360 0.0638 0.9338
0.0438 4.69 420 0.0655 0.9339
0.0405 5.36 480 0.0660 0.9345
0.0378 6.03 540 0.0666 0.9342
0.0344 6.69 600 0.0669 0.9343
0.0323 7.36 660 0.0678 0.9344
0.0307 8.03 720 0.0694 0.9343
0.0294 8.7 780 0.0706 0.9345
0.0286 9.37 840 0.0725 0.9342
0.0275 10.04 900 0.0727 0.9343
0.0282 10.71 960 0.0732 0.9342
0.0264 11.38 1020 0.0735 0.9343
0.026 12.05 1080 0.0750 0.9342
0.0254 12.72 1140 0.0753 0.9343
0.0244 13.39 1200 0.0746 0.9344
0.0242 14.06 1260 0.0752 0.9343
0.024 14.73 1320 0.0758 0.9342
0.0239 15.4 1380 0.0764 0.9343
0.0234 16.07 1440 0.0763 0.9343
0.0231 16.74 1500 0.0764 0.9343
0.0226 17.41 1560 0.0770 0.9343
0.023 18.08 1620 0.0770 0.9343
0.0227 18.74 1680 0.0771 0.9343
0.0221 19.41 1740 0.0771 0.9343

Framework versions

  • Transformers 4.27.4
  • Pytorch 2.0.0
  • Datasets 2.11.0
  • Tokenizers 0.11.0
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Dataset used to train enoriega/odinsynth_encoder_decoder_native_hf_test_2

Evaluation results

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