---
license: apache-2.0
base_model: ctheodoris/Geneformer
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: Geneformer_ft_BioS74_1kbpHG19_DHSs_H3K27AC
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Geneformer_ft_BioS74_1kbpHG19_DHSs_H3K27AC

This model is a fine-tuned version of [ctheodoris/Geneformer](https://huggingface.co/ctheodoris/Geneformer) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5809
- F1 Score: 0.7069
- Precision: 0.7107
- Recall: 0.7032
- Accuracy: 0.6947
- Auc: 0.7638
- Prc: 0.7611

## 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    |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.6934        | 0.1314 | 500  | 0.6908          | 0.6505   | 0.5332    | 0.8338 | 0.5309   | 0.5321 | 0.5544 |
| 0.692         | 0.2629 | 1000 | 0.6811          | 0.5043   | 0.6257    | 0.4224 | 0.5653   | 0.6063 | 0.6190 |
| 0.6783        | 0.3943 | 1500 | 0.6729          | 0.4941   | 0.6808    | 0.3877 | 0.5843   | 0.6381 | 0.6423 |
| 0.6617        | 0.5258 | 2000 | 0.6570          | 0.7039   | 0.5813    | 0.8920 | 0.6072   | 0.6888 | 0.6876 |
| 0.6467        | 0.6572 | 2500 | 0.6336          | 0.7081   | 0.6139    | 0.8363 | 0.6390   | 0.7102 | 0.7119 |
| 0.6407        | 0.7886 | 3000 | 0.6094          | 0.6627   | 0.7235    | 0.6113 | 0.6742   | 0.7346 | 0.7343 |
| 0.6328        | 0.9201 | 3500 | 0.6017          | 0.7155   | 0.6649    | 0.7745 | 0.6776   | 0.7392 | 0.7450 |
| 0.619         | 1.0515 | 4000 | 0.6767          | 0.5531   | 0.7975    | 0.4234 | 0.6419   | 0.7419 | 0.7422 |
| 0.6088        | 1.1830 | 4500 | 0.5867          | 0.7182   | 0.6960    | 0.7418 | 0.6952   | 0.7564 | 0.7570 |
| 0.6099        | 1.3144 | 5000 | 0.5861          | 0.7162   | 0.6959    | 0.7378 | 0.6939   | 0.7557 | 0.7546 |
| 0.6022        | 1.4458 | 5500 | 0.5849          | 0.6920   | 0.7243    | 0.6625 | 0.6913   | 0.7602 | 0.7581 |
| 0.582         | 1.5773 | 6000 | 0.5809          | 0.7069   | 0.7107    | 0.7032 | 0.6947   | 0.7638 | 0.7611 |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.0