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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: 0.4425
- F1 Score: 0.8721
- Precision: 0.8249
- Recall: 0.9251
- Accuracy: 0.8582
- Auc: 0.9367
- Prc: 0.9328
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.5218 | 0.0840 | 500 | 0.4577 | 0.7891 | 0.8196 | 0.7608 | 0.7874 | 0.8723 | 0.8607 |
| 0.4698 | 0.1680 | 1000 | 0.4514 | 0.8273 | 0.7836 | 0.8762 | 0.8087 | 0.8879 | 0.8785 |
| 0.448 | 0.2521 | 1500 | 0.4550 | 0.8350 | 0.8002 | 0.8730 | 0.8197 | 0.8978 | 0.8886 |
| 0.4463 | 0.3361 | 2000 | 0.4247 | 0.8392 | 0.7616 | 0.9344 | 0.8128 | 0.9076 | 0.9047 |
| 0.4264 | 0.4201 | 2500 | 0.4020 | 0.8010 | 0.8781 | 0.7364 | 0.8087 | 0.9130 | 0.9101 |
| 0.401 | 0.5041 | 3000 | 0.3818 | 0.8560 | 0.7897 | 0.9344 | 0.8356 | 0.9223 | 0.9200 |
| 0.4035 | 0.5881 | 3500 | 0.3980 | 0.8590 | 0.7956 | 0.9335 | 0.8398 | 0.9232 | 0.9183 |
| 0.3774 | 0.6722 | 4000 | 0.3752 | 0.8543 | 0.8404 | 0.8685 | 0.8450 | 0.9259 | 0.9245 |
| 0.3985 | 0.7562 | 4500 | 0.4618 | 0.8360 | 0.8660 | 0.8081 | 0.8343 | 0.9243 | 0.9251 |
| 0.387 | 0.8402 | 5000 | 0.3753 | 0.8641 | 0.8377 | 0.8923 | 0.8533 | 0.9310 | 0.9299 |
| 0.3907 | 0.9242 | 5500 | 0.3589 | 0.8537 | 0.8528 | 0.8547 | 0.8469 | 0.9284 | 0.9284 |
| 0.3775 | 1.0082 | 6000 | 0.4544 | 0.8622 | 0.8517 | 0.8730 | 0.8541 | 0.9310 | 0.9296 |
| 0.3507 | 1.0923 | 6500 | 0.4114 | 0.8722 | 0.8177 | 0.9344 | 0.8568 | 0.9326 | 0.9246 |
| 0.3476 | 1.1763 | 7000 | 0.4028 | 0.8747 | 0.8442 | 0.9074 | 0.8640 | 0.9354 | 0.9348 |
| 0.3676 | 1.2603 | 7500 | 0.3671 | 0.8684 | 0.8487 | 0.8891 | 0.8592 | 0.9362 | 0.9358 |
| 0.3651 | 1.3443 | 8000 | 0.3837 | 0.8713 | 0.8418 | 0.9029 | 0.8605 | 0.9369 | 0.9364 |
| 0.3468 | 1.4283 | 8500 | 0.4281 | 0.8648 | 0.8491 | 0.8811 | 0.8560 | 0.9342 | 0.9309 |
| 0.3447 | 1.5124 | 9000 | 0.3955 | 0.8727 | 0.8171 | 0.9364 | 0.8571 | 0.9387 | 0.9380 |
| 0.35 | 1.5964 | 9500 | 0.4425 | 0.8721 | 0.8249 | 0.9251 | 0.8582 | 0.9367 | 0.9328 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.0
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