gena-lm-bigbird-base-t2t_ft_BioS74_1kbpHG19_DHSs_H3K27AC

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

  • Loss: 0.3903
  • F1 Score: 0.8369
  • Precision: 0.8479
  • Recall: 0.8262
  • Accuracy: 0.8314
  • Auc: 0.9159
  • Prc: 0.9150

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
0.5065 0.1314 500 0.4874 0.8094 0.8042 0.8147 0.7991 0.8745 0.8648
0.4714 0.2629 1000 0.5169 0.7982 0.8196 0.7780 0.7941 0.8816 0.8741
0.4608 0.3943 1500 0.4256 0.8224 0.8137 0.8312 0.8120 0.8906 0.8850
0.4365 0.5258 2000 0.5043 0.8323 0.7431 0.9458 0.8004 0.8970 0.8898
0.4181 0.6572 2500 0.4206 0.8438 0.7824 0.9156 0.8225 0.9004 0.8946
0.4426 0.7886 3000 0.3973 0.8417 0.8253 0.8589 0.8309 0.9043 0.9005
0.4192 0.9201 3500 0.4847 0.8416 0.8209 0.8634 0.8299 0.9065 0.9000
0.4267 1.0515 4000 0.4880 0.8264 0.8417 0.8117 0.8215 0.9053 0.8978
0.3992 1.1830 4500 0.3852 0.8443 0.8152 0.8754 0.8309 0.9103 0.9095
0.3973 1.3144 5000 0.4035 0.8341 0.8335 0.8348 0.8262 0.9100 0.9086
0.3861 1.4458 5500 0.4904 0.8187 0.8696 0.7735 0.8207 0.9114 0.9103
0.3703 1.5773 6000 0.4456 0.8306 0.8466 0.8152 0.8259 0.9130 0.9100
0.3964 1.7087 6500 0.3778 0.8371 0.8368 0.8373 0.8293 0.9143 0.9142
0.3744 1.8402 7000 0.3903 0.8369 0.8479 0.8262 0.8314 0.9159 0.9150

Framework versions

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