---
library_name: transformers
license: apache-2.0
base_model: cafierom/bert-base-cased-ChemTok-ZN250K-V1
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: bert-base-cased-ChemTok-ZN250K-V1-finetuned-Tyrosinase-IC50s-V
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# bert-base-cased-ChemTok-ZN250K-V1-finetuned-Tyrosinase-IC50s-V

This model is a fine-tuned version of [cafierom/bert-base-cased-ChemTok-ZN250K-V1](https://huggingface.co/cafierom/bert-base-cased-ChemTok-ZN250K-V1) on
the cafierom/Tyrosinase1407_classes dataset.

It achieves the following results on the evaluation set:
- Loss: 1.662
- Accuracy: 0.7170
- F1: 0.7185

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data


![image/png](https://cdn-uploads.huggingface.co/production/uploads/679e079d375d81eb7ca4850e/eEOSbPL3ZK6yPcL2KCuLj.png)

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 128
- eval_batch_size: 128
- 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: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.0669        | 1.0   | 10   | 1.0239          | 0.4858   | 0.4655 |
| 0.976         | 2.0   | 20   | 0.9348          | 0.5613   | 0.5533 |
| 0.8835        | 3.0   | 30   | 0.8840          | 0.5660   | 0.5618 |
| 0.7942        | 4.0   | 40   | 0.8649          | 0.5802   | 0.5811 |
| 0.7227        | 5.0   | 50   | 0.8239          | 0.5943   | 0.5968 |
| 0.6424        | 6.0   | 60   | 0.8165          | 0.6368   | 0.6303 |
| 0.5837        | 7.0   | 70   | 0.8034          | 0.6604   | 0.6623 |
| 0.5359        | 8.0   | 80   | 0.8236          | 0.6745   | 0.6765 |
| 0.4812        | 9.0   | 90   | 0.8230          | 0.6698   | 0.6683 |
| 0.4226        | 10.0  | 100  | 0.8526          | 0.6604   | 0.6623 |
| 0.3943        | 11.0  | 110  | 0.9106          | 0.6698   | 0.6703 |
| 0.3506        | 12.0  | 120  | 0.9329          | 0.6698   | 0.6721 |
| 0.3315        | 13.0  | 130  | 0.9385          | 0.6981   | 0.7002 |
| 0.3217        | 14.0  | 140  | 0.9566          | 0.6981   | 0.7006 |
| 0.2702        | 15.0  | 150  | 1.0311          | 0.6604   | 0.6646 |
| 0.2518        | 16.0  | 160  | 1.0195          | 0.6792   | 0.6812 |
| 0.2448        | 17.0  | 170  | 1.0773          | 0.6509   | 0.6485 |
| 0.2532        | 18.0  | 180  | 1.0814          | 0.6792   | 0.6808 |
| 0.2337        | 19.0  | 190  | 1.1534          | 0.6887   | 0.6918 |
| 0.2101        | 20.0  | 200  | 1.1669          | 0.7075   | 0.7083 |
| 0.1754        | 21.0  | 210  | 1.1488          | 0.6934   | 0.6942 |
| 0.1831        | 22.0  | 220  | 1.1709          | 0.6981   | 0.6989 |
| 0.1694        | 23.0  | 230  | 1.2001          | 0.7170   | 0.7160 |
| 0.1429        | 24.0  | 240  | 1.1662          | 0.7170   | 0.7185 |
| 0.1405        | 25.0  | 250  | 1.2133          | 0.7075   | 0.7076 |
| 0.1392        | 26.0  | 260  | 1.2233          | 0.6840   | 0.6834 |
| 0.1631        | 27.0  | 270  | 1.3062          | 0.6840   | 0.6860 |
| 0.1123        | 28.0  | 280  | 1.2992          | 0.6745   | 0.6771 |
| 0.0997        | 29.0  | 290  | 1.4074          | 0.6887   | 0.6895 |
| 0.1132        | 30.0  | 300  | 1.3494          | 0.7123   | 0.7144 |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1