---
library_name: transformers
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_BioS2_1kbpHG19_DHSs_H3K27AC_one_shot
  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_BioS2_1kbpHG19_DHSs_H3K27AC_one_shot

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: 0.5759
- F1 Score: 0.7500
- Precision: 0.7742
- Recall: 0.7273
- Accuracy: 0.7288
- Auc: 0.7739
- Prc: 0.8342

## 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    |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.6823        | 8.3333  | 500  | 0.6205          | 0.7879   | 0.7879    | 0.7879 | 0.7627   | 0.7756 | 0.7993 |
| 0.5647        | 16.6667 | 1000 | 0.5759          | 0.7500   | 0.7742    | 0.7273 | 0.7288   | 0.7739 | 0.8342 |


### Framework versions

- Transformers 4.46.0.dev0
- Pytorch 2.4.1+cu121
- Datasets 2.18.0
- Tokenizers 0.20.0