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---
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: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-finetuned-AddedTokens-HMGCR-IC50s-V1
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/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:
## 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
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