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tanoManzo/
gena-lm-bert-large-t2t_ft_BioS73_1kbpHG19_DHSs_H3K27AC

Name: gena-lm-bert-large-t2t_ft_BioS73_1kbpHG19_DHSs_H3K27AC
Availability: InStock

safetensors

bert

generated_from_trainer

custom_code

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

finetune

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gena-lm-bert-large-t2t_ft_BioS73_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: 0.6348
F1 Score: 0.8584
Precision: 0.8916
Recall: 0.8275
Accuracy: 0.8543
Auc: 0.9340
Prc: 0.9335

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
0.5295	0.1864	500	0.4614	0.8310	0.7748	0.8959	0.8054	0.8913	0.8893
0.438	0.3727	1000	0.4338	0.8425	0.8408	0.8443	0.8315	0.8998	0.8978
0.4083	0.5591	1500	0.3931	0.8497	0.8354	0.8645	0.8367	0.9098	0.9056
0.4019	0.7454	2000	0.4137	0.8525	0.8114	0.8980	0.8341	0.9109	0.9072
0.3802	0.9318	2500	0.4230	0.8567	0.8471	0.8666	0.8453	0.9165	0.9141
0.384	1.1182	3000	0.3671	0.8640	0.8520	0.8764	0.8528	0.9237	0.9212
0.3603	1.3045	3500	0.4237	0.8604	0.8193	0.9057	0.8431	0.9195	0.9176
0.3552	1.4909	4000	0.3575	0.8612	0.8736	0.8492	0.8539	0.9256	0.9210
0.3379	1.6772	4500	0.4773	0.8607	0.8481	0.8736	0.8490	0.9227	0.9205
0.3469	1.8636	5000	0.4061	0.8714	0.8143	0.9372	0.8524	0.9291	0.9265
0.3193	2.0499	5500	0.4850	0.8700	0.8733	0.8666	0.8617	0.9308	0.9291
0.3267	2.2363	6000	0.5780	0.8727	0.8230	0.9288	0.8554	0.9324	0.9309
0.3239	2.4227	6500	0.3841	0.8715	0.8386	0.9071	0.8572	0.9286	0.9261
0.3193	2.6090	7000	0.4107	0.8768	0.8837	0.8701	0.8695	0.9350	0.9314
0.336	2.7954	7500	0.4738	0.8435	0.8995	0.7940	0.8427	0.9338	0.9322
0.3059	2.9817	8000	0.3902	0.8797	0.8390	0.9246	0.8651	0.9358	0.9323
0.2843	3.1681	8500	0.4350	0.8777	0.8635	0.8925	0.8673	0.9329	0.9291
0.2812	3.3545	9000	0.6741	0.8709	0.7963	0.9609	0.8479	0.9334	0.9287
0.2912	3.5408	9500	0.5464	0.8427	0.9014	0.7912	0.8423	0.9351	0.9350
0.2739	3.7272	10000	0.5261	0.8832	0.8436	0.9267	0.8692	0.9336	0.9291
0.2778	3.9135	10500	0.5400	0.8702	0.8817	0.8589	0.8632	0.9334	0.9297
0.2641	4.0999	11000	0.5587	0.8824	0.8504	0.9169	0.8695	0.9338	0.9292
0.2478	4.2862	11500	0.7934	0.8188	0.9046	0.7479	0.8233	0.9301	0.9257
0.2214	4.4726	12000	0.5377	0.8825	0.8687	0.8966	0.8725	0.9331	0.9276
0.2662	4.6590	12500	0.6348	0.8584	0.8916	0.8275	0.8543	0.9340	0.9335

Framework versions

Transformers 4.42.3
Pytorch 2.3.0+cu121
Datasets 2.18.0
Tokenizers 0.19.0

tanoManzo/gena-lm-bert-large-t2t_ft_BioS73_1kbpHG19_DHSs_H3K27AC

gena-lm-bert-large-t2t_ft_BioS73_1kbpHG19_DHSs_H3K27AC

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

tanoManzo/
gena-lm-bert-large-t2t_ft_BioS73_1kbpHG19_DHSs_H3K27AC