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---
base_model: AIRI-Institute/gena-lm-bert-base-t2t-multi
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: gena-lm-bert-base-t2t-multi_ft_BioS45_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-multi_ft_BioS45_1kbpHG19_DHSs_H3K27AC
This model is a fine-tuned version of [AIRI-Institute/gena-lm-bert-base-t2t-multi](https://huggingface.co/AIRI-Institute/gena-lm-bert-base-t2t-multi) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4693
- F1 Score: 0.8489
- Precision: 0.8151
- Recall: 0.8855
- Accuracy: 0.8355
- Auc: 0.8880
- Prc: 0.8543
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.7045 | 0.2103 | 500 | 0.6716 | 0.3608 | 0.8731 | 0.2274 | 0.5797 | 0.7448 | 0.7630 |
| 0.6504 | 0.4207 | 1000 | 0.6005 | 0.7373 | 0.7184 | 0.7573 | 0.7186 | 0.7845 | 0.7959 |
| 0.5759 | 0.6310 | 1500 | 0.5370 | 0.7482 | 0.7872 | 0.7129 | 0.7497 | 0.7960 | 0.7969 |
| 0.5182 | 0.8414 | 2000 | 0.5214 | 0.8035 | 0.7299 | 0.8935 | 0.7720 | 0.8064 | 0.7516 |
| 0.4665 | 1.0517 | 2500 | 0.4835 | 0.8199 | 0.8304 | 0.8097 | 0.8145 | 0.8676 | 0.8310 |
| 0.463 | 1.2621 | 3000 | 0.4728 | 0.8318 | 0.7679 | 0.9073 | 0.8086 | 0.8709 | 0.8363 |
| 0.441 | 1.4724 | 3500 | 0.4638 | 0.8316 | 0.8067 | 0.8581 | 0.8187 | 0.8770 | 0.8401 |
| 0.4178 | 1.6828 | 4000 | 0.4333 | 0.8358 | 0.8040 | 0.8702 | 0.8216 | 0.8940 | 0.8833 |
| 0.4165 | 1.8931 | 4500 | 0.4512 | 0.8387 | 0.8095 | 0.8702 | 0.8254 | 0.8851 | 0.8599 |
| 0.4082 | 2.1035 | 5000 | 0.4773 | 0.8361 | 0.8288 | 0.8435 | 0.8275 | 0.8801 | 0.8592 |
| 0.4006 | 2.3138 | 5500 | 0.4735 | 0.8453 | 0.8066 | 0.8879 | 0.8305 | 0.8766 | 0.8257 |
| 0.4053 | 2.5242 | 6000 | 0.4654 | 0.8500 | 0.8033 | 0.9024 | 0.8338 | 0.8930 | 0.8661 |
| 0.4101 | 2.7345 | 6500 | 0.4794 | 0.8493 | 0.8059 | 0.8976 | 0.8338 | 0.8637 | 0.8114 |
| 0.4299 | 2.9449 | 7000 | 0.5050 | 0.8069 | 0.8732 | 0.75 | 0.8128 | 0.9019 | 0.8977 |
| 0.3828 | 3.1552 | 7500 | 0.6362 | 0.7813 | 0.8957 | 0.6927 | 0.7976 | 0.8789 | 0.8808 |
| 0.4132 | 3.3656 | 8000 | 0.4565 | 0.8484 | 0.8130 | 0.8871 | 0.8347 | 0.9009 | 0.8765 |
| 0.383 | 3.5759 | 8500 | 0.4693 | 0.8489 | 0.8151 | 0.8855 | 0.8355 | 0.8880 | 0.8543 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.0
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