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---
base_model: AIRI-Institute/gena-lm-bigbird-base-t2t
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: gena-lm-bigbird-base-t2t_ft_BioS2_1kbpHG19_DHSs_H3K27AC
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gena-lm-bigbird-base-t2t_ft_BioS2_1kbpHG19_DHSs_H3K27AC
This model is a fine-tuned version of [AIRI-Institute/gena-lm-bigbird-base-t2t](https://huggingface.co/AIRI-Institute/gena-lm-bigbird-base-t2t) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4979
- F1 Score: 0.8766
- Precision: 0.8781
- Recall: 0.8750
- Accuracy: 0.8683
- Auc: 0.9406
- Prc: 0.9418
## 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 |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.5349 | 0.0841 | 500 | 0.4552 | 0.8265 | 0.7778 | 0.8816 | 0.8022 | 0.8727 | 0.8639 |
| 0.4552 | 0.1682 | 1000 | 0.4734 | 0.8272 | 0.7263 | 0.9607 | 0.7856 | 0.8927 | 0.8877 |
| 0.4577 | 0.2523 | 1500 | 0.4191 | 0.8381 | 0.7512 | 0.9477 | 0.8044 | 0.9022 | 0.9005 |
| 0.4282 | 0.3364 | 2000 | 0.4104 | 0.8528 | 0.7777 | 0.9440 | 0.8259 | 0.9128 | 0.8970 |
| 0.4127 | 0.4205 | 2500 | 0.3636 | 0.8611 | 0.8367 | 0.8870 | 0.8471 | 0.9213 | 0.9216 |
| 0.4226 | 0.5045 | 3000 | 0.3621 | 0.8623 | 0.8096 | 0.9223 | 0.8426 | 0.9248 | 0.9255 |
| 0.4231 | 0.5886 | 3500 | 0.3553 | 0.8629 | 0.7931 | 0.9462 | 0.8394 | 0.9317 | 0.9329 |
| 0.3945 | 0.6727 | 4000 | 0.3843 | 0.8631 | 0.7856 | 0.9575 | 0.8377 | 0.9345 | 0.9341 |
| 0.3911 | 0.7568 | 4500 | 0.4173 | 0.8681 | 0.8571 | 0.8794 | 0.8572 | 0.9315 | 0.9330 |
| 0.4233 | 0.8409 | 5000 | 0.3419 | 0.8741 | 0.8249 | 0.9295 | 0.8569 | 0.9355 | 0.9376 |
| 0.3787 | 0.9250 | 5500 | 0.3880 | 0.8650 | 0.7891 | 0.9572 | 0.8404 | 0.9346 | 0.9357 |
| 0.3849 | 1.0091 | 6000 | 0.3629 | 0.8766 | 0.8512 | 0.9037 | 0.8641 | 0.9353 | 0.9359 |
| 0.3522 | 1.0932 | 6500 | 0.3683 | 0.8803 | 0.8558 | 0.9062 | 0.8683 | 0.9381 | 0.9381 |
| 0.3376 | 1.1773 | 7000 | 0.4292 | 0.8640 | 0.7824 | 0.9644 | 0.8377 | 0.9392 | 0.9373 |
| 0.365 | 1.2614 | 7500 | 0.4852 | 0.8667 | 0.7858 | 0.9663 | 0.8412 | 0.9403 | 0.9371 |
| 0.3569 | 1.3454 | 8000 | 0.5700 | 0.8720 | 0.8112 | 0.9427 | 0.8522 | 0.9352 | 0.9287 |
| 0.3822 | 1.4295 | 8500 | 0.3894 | 0.8817 | 0.8720 | 0.8917 | 0.8722 | 0.9406 | 0.9418 |
| 0.3391 | 1.5136 | 9000 | 0.4167 | 0.8696 | 0.8863 | 0.8536 | 0.8633 | 0.9413 | 0.9434 |
| 0.3591 | 1.5977 | 9500 | 0.3554 | 0.8853 | 0.8631 | 0.9087 | 0.8742 | 0.9432 | 0.9436 |
| 0.3699 | 1.6818 | 10000 | 0.4540 | 0.8812 | 0.8868 | 0.8757 | 0.8739 | 0.9440 | 0.9441 |
| 0.3777 | 1.7659 | 10500 | 0.4137 | 0.8849 | 0.8583 | 0.9131 | 0.8730 | 0.9421 | 0.9423 |
| 0.3602 | 1.8500 | 11000 | 0.3798 | 0.8736 | 0.8835 | 0.8640 | 0.8665 | 0.9414 | 0.9444 |
| 0.3583 | 1.9341 | 11500 | 0.4461 | 0.8840 | 0.8405 | 0.9323 | 0.8693 | 0.9438 | 0.9458 |
| 0.3573 | 2.0182 | 12000 | 0.4979 | 0.8766 | 0.8781 | 0.8750 | 0.8683 | 0.9406 | 0.9418 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.0
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