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bert-base-cased-finetuned-AddedTokens-HMGCR-IC50s-V1

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metadata
library_name: transformers
license: apache-2.0
base_model: bert-base-cased
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
model-index:
  - name: bert-base-cased-finetuned-AddedTokens-HMGCR-IC50s-V1
    results: []

bert-base-cased-finetuned-AddedTokens-HMGCR-IC50s-V1

This model is a fine-tuned version of bert-base-cased on 905 HMGCR IC50 values from bindingDB.org. Molecules with counter ions were included twice, once with and once without counter-ions.

It achieves the following results on the evaluation set:

  • Loss: 0.7278
  • Accuracy: 0.7929
  • F1: 0.7931

Model description

More information needed

Intended uses & limitations

Can classify HMGCR IC50 values as < 50 nM, < 500 nM, and > 500 nM. See Confusion matrix below:

image/png

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: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Accuracy F1
0.9314 1.0 25 0.8466 0.7071 0.6371
0.7535 2.0 50 0.7025 0.7357 0.6634
0.6292 3.0 75 0.6237 0.7714 0.6956
0.5464 4.0 100 0.6162 0.7571 0.7137
0.5068 5.0 125 0.5730 0.7857 0.7185
0.4516 6.0 150 0.5872 0.7643 0.7312
0.3971 7.0 175 0.6004 0.7643 0.7578
0.3768 8.0 200 0.6253 0.7714 0.7739
0.3353 9.0 225 0.6280 0.7786 0.7522
0.3439 10.0 250 0.6299 0.7714 0.7613
0.3087 11.0 275 0.6569 0.7786 0.7719
0.2979 12.0 300 0.6308 0.7714 0.7753
0.2561 13.0 325 0.6596 0.7786 0.7786
0.2703 14.0 350 0.6646 0.7786 0.7808
0.2504 15.0 375 0.7125 0.7857 0.7913
0.2397 16.0 400 0.6893 0.7786 0.7770
0.2152 17.0 425 0.7278 0.7929 0.7931
0.2066 18.0 450 0.6947 0.7857 0.7895
0.2133 19.0 475 0.7202 0.7714 0.7756
0.202 20.0 500 0.7167 0.7857 0.7887

Framework versions

  • Transformers 4.48.3
  • Pytorch 2.5.1+cu124
  • Datasets 3.3.0
  • Tokenizers 0.21.0