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---
license: cc-by-nc-sa-4.0
base_model: InstaDeepAI/nucleotide-transformer-500m-human-ref
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: nucleotide-transformer-500m-human-ref_ft_BioS2_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-human-ref_ft_BioS2_1kbpHG19_DHSs_H3K27AC
This model is a fine-tuned version of [InstaDeepAI/nucleotide-transformer-500m-human-ref](https://huggingface.co./InstaDeepAI/nucleotide-transformer-500m-human-ref) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4074
- F1 Score: 0.8357
- Precision: 0.8177
- Recall: 0.8545
- Accuracy: 0.8268
- Auc: 0.8999
- Prc: 0.8854
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.5531 | 0.0841 | 500 | 0.4955 | 0.7552 | 0.8200 | 0.6999 | 0.7661 | 0.8646 | 0.8453 |
| 0.4826 | 0.1681 | 1000 | 0.4657 | 0.7900 | 0.8043 | 0.7763 | 0.7873 | 0.8686 | 0.8463 |
| 0.469 | 0.2522 | 1500 | 0.4324 | 0.8114 | 0.8090 | 0.8138 | 0.8050 | 0.8829 | 0.8696 |
| 0.4508 | 0.3362 | 2000 | 0.4388 | 0.8072 | 0.8311 | 0.7847 | 0.8068 | 0.8906 | 0.8743 |
| 0.4562 | 0.4203 | 2500 | 0.4340 | 0.8304 | 0.7769 | 0.8917 | 0.8122 | 0.8902 | 0.8798 |
| 0.4393 | 0.5044 | 3000 | 0.4291 | 0.8330 | 0.7695 | 0.9080 | 0.8124 | 0.8946 | 0.8823 |
| 0.4368 | 0.5884 | 3500 | 0.4122 | 0.8382 | 0.7903 | 0.8924 | 0.8225 | 0.8971 | 0.8839 |
| 0.4354 | 0.6725 | 4000 | 0.4191 | 0.8359 | 0.7751 | 0.9070 | 0.8164 | 0.8953 | 0.8821 |
| 0.4346 | 0.7566 | 4500 | 0.4110 | 0.8326 | 0.8113 | 0.8552 | 0.8228 | 0.8991 | 0.8874 |
| 0.4298 | 0.8406 | 5000 | 0.4074 | 0.8357 | 0.8177 | 0.8545 | 0.8268 | 0.8999 | 0.8854 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.0
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