---
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