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vit-molecul

This model is a fine-tuned version of google/vit-base-patch16-224 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5737
  • Accuracy: 0.71
  • F1: 0.7086

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: 3e-06
  • train_batch_size: 50
  • eval_batch_size: 50
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Accuracy F1
0.723 1.0 8 0.6790 0.61 0.6096
0.6915 2.0 16 0.6661 0.62 0.5924
0.6689 3.0 24 0.6470 0.69 0.6892
0.6517 4.0 32 0.6356 0.64 0.6377
0.6368 5.0 40 0.6289 0.72 0.7199
0.621 6.0 48 0.6217 0.73 0.7293
0.6061 7.0 56 0.6197 0.69 0.6862
0.5924 8.0 64 0.6087 0.73 0.7293
0.5767 9.0 72 0.6003 0.72 0.7199
0.5633 10.0 80 0.5953 0.72 0.7196
0.5491 11.0 88 0.5885 0.72 0.7199
0.5351 12.0 96 0.5869 0.71 0.7100
0.5239 13.0 104 0.5867 0.7 0.6995
0.5118 14.0 112 0.5804 0.71 0.7100
0.502 15.0 120 0.5752 0.71 0.7100
0.4942 16.0 128 0.5738 0.72 0.7199
0.4885 17.0 136 0.5771 0.71 0.7086
0.4831 18.0 144 0.5751 0.71 0.7086
0.4793 19.0 152 0.5743 0.71 0.7086
0.4774 20.0 160 0.5737 0.71 0.7086

Framework versions

  • Transformers 4.31.0
  • Pytorch 2.0.1+cu117
  • Datasets 2.14.1
  • Tokenizers 0.13.3
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