---
library_name: transformers
base_model: AIRI-Institute/gena-lm-bert-large-t2t
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: gena-lm-bert-large-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
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# gena-lm-bert-large-t2t_ft_BioS2_1kbpHG19_DHSs_H3K27AC_one_shot

This model is a fine-tuned version of [AIRI-Institute/gena-lm-bert-large-t2t](https://huggingface.co/AIRI-Institute/gena-lm-bert-large-t2t) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0203
- F1 Score: 0.7812
- Precision: 0.8065
- Recall: 0.7576
- Accuracy: 0.7627
- Auc: 0.8205
- Prc: 0.8517

## 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    |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.4524        | 8.3333  | 500  | 0.6291          | 0.8308   | 0.8438    | 0.8182 | 0.8136   | 0.8502 | 0.8700 |
| 0.2158        | 16.6667 | 1000 | 1.0203          | 0.7812   | 0.8065    | 0.7576 | 0.7627   | 0.8205 | 0.8517 |


### Framework versions

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