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gena-lm-bert-base-t2t-multi_ft_BioS2_1kbpHG19_DHSs_H3K27AC_one_shot – AI Model by tanoManzo | AlphaNeural AI
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gena-lm-bert-base-t2t-multi_ft_BioS2_1kbpHG19_DHSs_H3K27AC_one_shot
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transformers
safetensors
bert
text-classification
generated_from_trainer
custom_code
AIRI-Institute/gena-lm-bert-base-t2t-multi
finetune
autotrain_compatible
endpoints_compatible
us
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gena-lm-bert-base-t2t-multi_ft_BioS2_1kbpHG19_DHSs_H3K27AC_one_shot
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: 0.5976
F1 Score: 0.7887
Precision: 0.7368
Recall: 0.8485
Accuracy: 0.7458
Auc: 0.7949
Prc: 0.8250
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
0.6969
8.3333
500
0.6487
0.7077
0.7188
0.6970
0.6780
0.7523
0.7272
0.6553
16.6667
1000
0.5976
0.7887
0.7368
0.8485
0.7458
0.7949
0.8250
Framework versions
Transformers 4.46.0.dev0
Pytorch 2.4.1+cu121
Datasets 2.18.0
Tokenizers 0.20.0
