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library_name: transformers
license: apache-2.0
base_model: cafierom/bert-base-cased-ChemTok-ZN15-55KTyrosinase-V1
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: bert-base-cased-ChemTok-ZN15-55KTyrosinase-V1-finetuned-Tyrosinase-IC50s-V4
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-ChemTok-ZN15-55KTyrosinase-V1-finetuned-Tyrosinase-IC50s-V4
This model is a fine-tuned version of [cafierom/bert-base-cased-ChemTok-ZN15-55KTyrosinase-V1](https://huggingface.co/cafierom/bert-base-cased-ChemTok-ZN15-55KTyrosinase-V1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1854
- Accuracy: 0.7028
- F1: 0.6998
## 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: 64
- eval_batch_size: 64
- 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: constant
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.0678 | 1.0 | 19 | 1.0197 | 0.4953 | 0.4712 |
| 0.9633 | 2.0 | 38 | 0.9236 | 0.5425 | 0.5418 |
| 0.896 | 3.0 | 57 | 0.9251 | 0.5566 | 0.5502 |
| 0.8178 | 4.0 | 76 | 0.9288 | 0.5660 | 0.5506 |
| 0.7743 | 5.0 | 95 | 0.8277 | 0.6085 | 0.6115 |
| 0.6971 | 6.0 | 114 | 0.8013 | 0.6651 | 0.6669 |
| 0.6214 | 7.0 | 133 | 0.8409 | 0.6179 | 0.6195 |
| 0.5886 | 8.0 | 152 | 0.8234 | 0.6651 | 0.6627 |
| 0.5337 | 9.0 | 171 | 0.8363 | 0.6792 | 0.6800 |
| 0.4705 | 10.0 | 190 | 0.8959 | 0.6745 | 0.6744 |
| 0.4393 | 11.0 | 209 | 0.9193 | 0.6792 | 0.6810 |
| 0.4089 | 12.0 | 228 | 0.9230 | 0.6887 | 0.6877 |
| 0.3926 | 13.0 | 247 | 0.9601 | 0.6934 | 0.6941 |
| 0.3897 | 14.0 | 266 | 0.9610 | 0.6981 | 0.6987 |
| 0.326 | 15.0 | 285 | 0.9510 | 0.7075 | 0.7085 |
| 0.3214 | 16.0 | 304 | 1.0061 | 0.6792 | 0.6712 |
| 0.3317 | 17.0 | 323 | 1.0278 | 0.6840 | 0.6799 |
| 0.2843 | 18.0 | 342 | 1.0050 | 0.6981 | 0.6971 |
| 0.2409 | 19.0 | 361 | 1.0434 | 0.7075 | 0.7088 |
| 0.2247 | 20.0 | 380 | 1.1854 | 0.7028 | 0.6998 |
### Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
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