gena-lm-bert-base-t2t-multi_ft_BioS2_1kbpHG19_DHSs_H3K27AC

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

• Loss: 0.4666
• F1 Score: 0.8442
• Precision: 0.8154
• Recall: 0.8751
• Accuracy: 0.8333
• Auc: 0.8995
• Prc: 0.8745

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
0.6931	0.0839	500	0.6427	0.7390	0.6242	0.9057	0.6699	0.7821	0.7840
0.6219	0.1679	1000	0.5689	0.7390	0.7963	0.6893	0.7487	0.8086	0.7935
0.5477	0.2518	1500	0.5181	0.7949	0.7556	0.8386	0.7767	0.8398	0.8123
0.5125	0.3358	2000	0.4999	0.8031	0.7565	0.8559	0.7834	0.8496	0.8161
0.4961	0.4197	2500	0.5177	0.8055	0.7647	0.8510	0.7879	0.8371	0.7976
0.4969	0.5037	3000	0.4908	0.8153	0.7495	0.8936	0.7910	0.8582	0.8306
0.4777	0.5876	3500	0.4991	0.8196	0.7549	0.8966	0.7963	0.8630	0.8307
0.4836	0.6716	4000	0.4718	0.8230	0.7577	0.9005	0.8000	0.8608	0.8324
0.4748	0.7555	4500	0.5299	0.7963	0.8009	0.7918	0.7910	0.8567	0.8069
0.4667	0.8395	5000	0.4743	0.8241	0.7622	0.8969	0.8024	0.8715	0.8450
0.4717	0.9234	5500	0.4981	0.8105	0.8091	0.8120	0.8041	0.8772	0.8542
0.4707	1.0074	6000	0.4675	0.8273	0.7656	0.8998	0.8061	0.8751	0.8371
0.459	1.0913	6500	0.4867	0.8192	0.8012	0.8380	0.8091	0.8778	0.8546
0.4544	1.1753	7000	0.4712	0.8322	0.7557	0.9258	0.8073	0.8264	0.7430
0.4324	1.2592	7500	0.4993	0.8185	0.8147	0.8224	0.8118	0.8687	0.8163
0.436	1.3432	8000	0.4777	0.8352	0.7641	0.9209	0.8125	0.8185	0.7469
0.4464	1.4271	8500	0.5148	0.8299	0.7497	0.9294	0.8034	0.8729	0.8419
0.4537	1.5111	9000	0.4503	0.8296	0.8028	0.8582	0.8180	0.8796	0.8409
0.4276	1.5950	9500	0.4540	0.8356	0.8014	0.8728	0.8227	0.8926	0.8680
0.4323	1.6790	10000	0.4512	0.8380	0.7949	0.8861	0.8232	0.8748	0.8222
0.4384	1.7629	10500	0.4724	0.8386	0.7655	0.9271	0.8158	0.8836	0.8405
0.4076	1.8469	11000	0.4626	0.8335	0.8204	0.8471	0.8254	0.8813	0.8340
0.439	1.9308	11500	0.4399	0.8443	0.7807	0.9193	0.8251	0.8888	0.8487
0.4164	2.0148	12000	0.4522	0.8437	0.7820	0.9161	0.8249	0.8940	0.8548
0.4075	2.0987	12500	0.4718	0.8417	0.8069	0.8796	0.8292	0.8962	0.8771
0.406	2.1827	13000	0.4935	0.8233	0.8442	0.8035	0.8220	0.9000	0.8729
0.3958	2.2666	13500	0.4891	0.8427	0.8172	0.8699	0.8324	0.8896	0.8443
0.4353	2.3506	14000	0.4666	0.8442	0.8154	0.8751	0.8333	0.8995	0.8745

Framework versions

• Transformers 4.42.3
• Pytorch 2.3.0+cu121
• Datasets 2.18.0
• Tokenizers 0.19.0