---
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