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---
license: cc-by-nc-sa-4.0
base_model: InstaDeepAI/nucleotide-transformer-500m-1000g
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: nucleotide-transformer-500m-1000g_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. -->
# nucleotide-transformer-500m-1000g_ft_BioS74_1kbpHG19_DHSs_H3K27AC
This model is a fine-tuned version of [InstaDeepAI/nucleotide-transformer-500m-1000g](https://huggingface.co/InstaDeepAI/nucleotide-transformer-500m-1000g) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4446
- F1 Score: 0.8119
- Precision: 0.8121
- Recall: 0.8117
- Accuracy: 0.8031
- Auc: 0.8875
- Prc: 0.8848
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.504 | 0.1314 | 500 | 0.4497 | 0.8107 | 0.7871 | 0.8358 | 0.7957 | 0.8712 | 0.8603 |
| 0.4867 | 0.2629 | 1000 | 0.5440 | 0.7578 | 0.8477 | 0.6851 | 0.7707 | 0.8763 | 0.8672 |
| 0.4688 | 0.3943 | 1500 | 0.4343 | 0.8176 | 0.7839 | 0.8543 | 0.8004 | 0.8833 | 0.8815 |
| 0.4524 | 0.5258 | 2000 | 0.4655 | 0.8244 | 0.7696 | 0.8875 | 0.8020 | 0.8830 | 0.8774 |
| 0.4413 | 0.6572 | 2500 | 0.4481 | 0.8281 | 0.7660 | 0.9011 | 0.8041 | 0.8832 | 0.8773 |
| 0.4604 | 0.7886 | 3000 | 0.5288 | 0.7311 | 0.8553 | 0.6384 | 0.7541 | 0.8779 | 0.8693 |
| 0.4502 | 0.9201 | 3500 | 0.4725 | 0.8135 | 0.8088 | 0.8182 | 0.8036 | 0.8863 | 0.8821 |
| 0.4247 | 1.0515 | 4000 | 0.5722 | 0.8087 | 0.8332 | 0.7855 | 0.8054 | 0.8890 | 0.8837 |
| 0.3702 | 1.1830 | 4500 | 0.4329 | 0.8254 | 0.8064 | 0.8453 | 0.8128 | 0.8834 | 0.8701 |
| 0.3697 | 1.3144 | 5000 | 0.4446 | 0.8119 | 0.8121 | 0.8117 | 0.8031 | 0.8875 | 0.8848 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.0
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