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---
base_model: AIRI-Institute/gena-lm-bert-base-t2t
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: gena-lm-bert-base-t2t_ft_BioS74_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-bert-base-t2t_ft_BioS74_1kbpHG19_DHSs_H3K27AC
This model is a fine-tuned version of [AIRI-Institute/gena-lm-bert-base-t2t](https://huggingface.co/AIRI-Institute/gena-lm-bert-base-t2t) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4989
- F1 Score: 0.8378
- Precision: 0.8054
- Recall: 0.8729
- Accuracy: 0.8230
- Auc: 0.8768
- Prc: 0.8333
## 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 |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.6857 | 0.1314 | 500 | 0.6358 | 0.7270 | 0.7424 | 0.7122 | 0.7200 | 0.7586 | 0.7471 |
| 0.6115 | 0.2629 | 1000 | 0.5765 | 0.7146 | 0.7998 | 0.6459 | 0.7300 | 0.7812 | 0.7800 |
| 0.5477 | 0.3943 | 1500 | 0.4954 | 0.8091 | 0.7478 | 0.8815 | 0.7823 | 0.8401 | 0.8126 |
| 0.487 | 0.5258 | 2000 | 0.4893 | 0.8185 | 0.7659 | 0.8790 | 0.7960 | 0.8423 | 0.7918 |
| 0.4824 | 0.6572 | 2500 | 0.5362 | 0.8176 | 0.7241 | 0.9387 | 0.7807 | 0.8192 | 0.7493 |
| 0.5023 | 0.7886 | 3000 | 0.4826 | 0.8279 | 0.7491 | 0.9252 | 0.7986 | 0.8656 | 0.8357 |
| 0.4762 | 0.9201 | 3500 | 0.4686 | 0.8229 | 0.7869 | 0.8624 | 0.8057 | 0.8765 | 0.8499 |
| 0.4796 | 1.0515 | 4000 | 0.4538 | 0.8201 | 0.7977 | 0.8438 | 0.8062 | 0.8835 | 0.8673 |
| 0.4654 | 1.1830 | 4500 | 0.4534 | 0.8285 | 0.7531 | 0.9206 | 0.8004 | 0.8641 | 0.8296 |
| 0.4583 | 1.3144 | 5000 | 0.4943 | 0.8285 | 0.7376 | 0.9448 | 0.7952 | 0.8512 | 0.7967 |
| 0.4605 | 1.4458 | 5500 | 0.4470 | 0.8308 | 0.8040 | 0.8594 | 0.8167 | 0.8903 | 0.8718 |
| 0.4316 | 1.5773 | 6000 | 0.4880 | 0.8338 | 0.7886 | 0.8845 | 0.8154 | 0.8626 | 0.8079 |
| 0.4447 | 1.7087 | 6500 | 0.4371 | 0.8369 | 0.7814 | 0.9011 | 0.8162 | 0.8866 | 0.8626 |
| 0.4355 | 1.8402 | 7000 | 0.4654 | 0.8273 | 0.8032 | 0.8528 | 0.8136 | 0.8713 | 0.8183 |
| 0.4447 | 1.9716 | 7500 | 0.4687 | 0.8327 | 0.7729 | 0.9026 | 0.8101 | 0.8877 | 0.8643 |
| 0.4238 | 2.1030 | 8000 | 0.4868 | 0.8350 | 0.7753 | 0.9046 | 0.8128 | 0.8889 | 0.8651 |
| 0.439 | 2.2345 | 8500 | 0.4307 | 0.8329 | 0.8271 | 0.8388 | 0.8238 | 0.8997 | 0.8889 |
| 0.4248 | 2.3659 | 9000 | 0.4533 | 0.8408 | 0.7991 | 0.8870 | 0.8241 | 0.8902 | 0.8637 |
| 0.4175 | 2.4974 | 9500 | 0.5197 | 0.8376 | 0.7963 | 0.8835 | 0.8207 | 0.8802 | 0.8442 |
| 0.4426 | 2.6288 | 10000 | 0.4643 | 0.8246 | 0.8311 | 0.8182 | 0.8178 | 0.8974 | 0.8796 |
| 0.4538 | 2.7603 | 10500 | 0.5072 | 0.8345 | 0.8211 | 0.8483 | 0.8238 | 0.8717 | 0.8182 |
| 0.4105 | 2.8917 | 11000 | 0.5272 | 0.8360 | 0.8085 | 0.8654 | 0.8222 | 0.8812 | 0.8447 |
| 0.4234 | 3.0231 | 11500 | 0.4989 | 0.8378 | 0.8054 | 0.8729 | 0.8230 | 0.8768 | 0.8333 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.0
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