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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_BioS73_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_BioS73_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: 0.6204
- F1 Score: 0.8827
- Precision: 0.8652
- Recall: 0.9008
- Accuracy: 0.8722
- Auc: 0.9384
- Prc: 0.9368
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.4735 | 0.1864 | 500 | 0.4718 | 0.8258 | 0.7264 | 0.9567 | 0.7846 | 0.8950 | 0.8902 |
| 0.4071 | 0.3727 | 1000 | 0.4255 | 0.8534 | 0.7923 | 0.9246 | 0.8304 | 0.9117 | 0.9095 |
| 0.391 | 0.5591 | 1500 | 0.4176 | 0.8508 | 0.8496 | 0.8520 | 0.8405 | 0.9184 | 0.9132 |
| 0.3833 | 0.7454 | 2000 | 0.3674 | 0.8655 | 0.8235 | 0.9120 | 0.8487 | 0.9209 | 0.9082 |
| 0.3812 | 0.9318 | 2500 | 0.4179 | 0.8652 | 0.7974 | 0.9455 | 0.8427 | 0.9255 | 0.9217 |
| 0.3673 | 1.1182 | 3000 | 0.3696 | 0.8714 | 0.8180 | 0.9323 | 0.8531 | 0.9295 | 0.9266 |
| 0.3469 | 1.3045 | 3500 | 0.3985 | 0.8696 | 0.8121 | 0.9358 | 0.8502 | 0.9324 | 0.9307 |
| 0.3452 | 1.4909 | 4000 | 0.3347 | 0.8724 | 0.8685 | 0.8764 | 0.8632 | 0.9327 | 0.9316 |
| 0.3241 | 1.6772 | 4500 | 0.4804 | 0.8753 | 0.8316 | 0.9239 | 0.8595 | 0.9350 | 0.9341 |
| 0.3529 | 1.8636 | 5000 | 0.4180 | 0.8789 | 0.8330 | 0.9302 | 0.8632 | 0.9366 | 0.9364 |
| 0.3102 | 2.0499 | 5500 | 0.5486 | 0.8851 | 0.8460 | 0.9281 | 0.8714 | 0.9378 | 0.9371 |
| 0.3217 | 2.2363 | 6000 | 0.5019 | 0.8856 | 0.8498 | 0.9246 | 0.8725 | 0.9390 | 0.9369 |
| 0.272 | 2.4227 | 6500 | 0.4057 | 0.8844 | 0.8591 | 0.9113 | 0.8729 | 0.9416 | 0.9410 |
| 0.303 | 2.6090 | 7000 | 0.5302 | 0.8806 | 0.8187 | 0.9525 | 0.8621 | 0.9372 | 0.9321 |
| 0.2944 | 2.7954 | 7500 | 0.4633 | 0.8819 | 0.8676 | 0.8966 | 0.8718 | 0.9405 | 0.9392 |
| 0.269 | 2.9817 | 8000 | 0.5419 | 0.8756 | 0.8042 | 0.9609 | 0.8543 | 0.9432 | 0.9423 |
| 0.2168 | 3.1681 | 8500 | 0.6204 | 0.8827 | 0.8652 | 0.9008 | 0.8722 | 0.9384 | 0.9368 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.0
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