gena-lm-bert-large-t2t_ft_BioS45_1kbpHG19_DHSs_H3K27AC

tanoManzo/gena-lm-bert-large-t2t_ft_BioS45_1kbpHG19_DHSs_H3K27AC

gena-lm-bert-large-t2t_ft_BioS45_1kbpHG19_DHSs_H3K27AC is a machine learning model from tanoManzo. Use it for the machine learning task on the model card, and read the license before you ship it in a product.

should probably proofread and complete it, then remove this comment. --

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Base models

AIRI-Institute/gena-lm-bert-large-t2t

Model card

From the Hugging Face model README

gena-lm-bert-large-t2t_ft_BioS45_1kbpHG19_DHSs_H3K27AC

This model is a fine-tuned version of AIRI-Institute/gena-lm-bert-large-t2t on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.6540
F1 Score: 0.8311
Precision: 0.8675
Recall: 0.7976
Accuracy: 0.8309
Auc: 0.9197
Prc: 0.9139

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: 1e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	F1 Score	Precision	Recall	Accuracy	Auc	Prc
0.5922	0.2103	500	0.5061	0.7627	0.8085	0.7218	0.7657	0.8513	0.8464
0.4796	0.4207	1000	0.4288	0.8236	0.7931	0.8565	0.8086	0.8858	0.8800
0.4414	0.6310	1500	0.4277	0.8394	0.7999	0.8831	0.8237	0.8978	0.8919
0.4201	0.8414	2000	0.3959	0.8537	0.8147	0.8968	0.8397	0.9084	0.9033
0.4098	1.0517	2500	0.4331	0.8459	0.8230	0.8702	0.8347	0.9101	0.9052
0.3969	1.2621	3000	0.4024	0.8434	0.8531	0.8339	0.8385	0.9155	0.9096
0.3855	1.4724	3500	0.3866	0.8600	0.8200	0.9040	0.8464	0.9183	0.9151
0.3684	1.6828	4000	0.3759	0.8552	0.8349	0.8766	0.8452	0.9177	0.9138
0.3692	1.8931	4500	0.3670	0.8592	0.8096	0.9153	0.8435	0.9218	0.9180
0.3493	2.1035	5000	0.4659	0.8646	0.8353	0.8960	0.8536	0.9208	0.9186
0.3424	2.3138	5500	0.4562	0.8593	0.8149	0.9089	0.8448	0.9225	0.9176
0.3307	2.5242	6000	0.4541	0.8557	0.8066	0.9113	0.8397	0.9230	0.9176
0.3525	2.7345	6500	0.4808	0.8565	0.8336	0.8806	0.8460	0.9213	0.9136
0.3783	2.9449	7000	0.4244	0.8475	0.8481	0.8468	0.8410	0.9206	0.9089
0.3226	3.1552	7500	0.6540	0.8311	0.8675	0.7976	0.8309	0.9197	0.9139

Framework versions

Transformers 4.42.3
Pytorch 2.3.0+cu121
Datasets 2.18.0
Tokenizers 0.19.0

Type: bert
Created: Sep 25, 2024
Updated: Sep 25, 2024