new_tokens = ["[C@H]","[C@@H]","(F)","(Cl)","c1","c2","(O)","N#C","(=O)",
              "([N+]([O-])=O)","[O-]","(OC)","(C)","[NH3+]","(I)","[Na+]","C#N"]

Intended uses & limitations

It is meant to be used for finetuning classification models for drug-related tasks, and for generative unmasking.

Training and evaluation data

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: linear
num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss
2.8365	1.0	133	1.8916
1.2975	2.0	266	0.9819
0.955	3.0	399	0.8473
0.7853	4.0	532	0.6689
0.6547	5.0	665	0.5779
0.5822	6.0	798	0.5189
0.5312	7.0	931	0.4685
0.4937	8.0	1064	0.4340
0.467	9.0	1197	0.4364
0.4442	10.0	1330	0.3819
0.4309	11.0	1463	0.4075
0.4159	12.0	1596	0.3835
0.4045	13.0	1729	0.3692
0.3874	14.0	1862	0.3672
0.3734	15.0	1995	0.3617
0.3742	16.0	2128	0.3492
0.364	17.0	2261	0.3357
0.3547	18.0	2394	0.3323
0.3435	19.0	2527	0.3222
0.3424	20.0	2660	0.3140
0.3417	21.0	2793	0.3117
0.3339	22.0	2926	0.3153
0.326	23.0	3059	0.3233
0.3292	24.0	3192	0.3019
0.324	25.0	3325	0.2960
0.3228	26.0	3458	0.3058
0.3176	27.0	3591	0.2984
0.3112	28.0	3724	0.2889
0.3172	29.0	3857	0.2973
0.3103	30.0	3990	0.3050

Framework versions

Transformers 4.50.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

cafierom/bert-base-cased-ChemTok-ZN15-55KTyrosinase-V1

bert-base-cased-ChemTok-ZN15-55KTyrosinase-V1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

cafierom/
bert-base-cased-ChemTok-ZN15-55KTyrosinase-V1