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---
license: cc-by-nc-sa-4.0
base_model: InstaDeepAI/nucleotide-transformer-2.5b-multi-species
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: nucleotide-transformer-2.5b-multi-species_ft_Hepg2_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-2.5b-multi-species_ft_Hepg2_1kbpHG19_DHSs_H3K27AC
This model is a fine-tuned version of [InstaDeepAI/nucleotide-transformer-2.5b-multi-species](https://huggingface.co/InstaDeepAI/nucleotide-transformer-2.5b-multi-species) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2344
- F1 Score: 0.8878
- Precision: 0.8690
- Recall: 0.9074
- Accuracy: 0.8836
## 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: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Precision | Recall | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
| 0.3737 | 0.5593 | 500 | 0.3067 | 0.8745 | 0.8377 | 0.9147 | 0.8668 |
| 0.292 | 1.1186 | 1000 | 0.3043 | 0.8852 | 0.8332 | 0.9441 | 0.8757 |
| 0.1865 | 1.6779 | 1500 | 0.3219 | 0.8854 | 0.8324 | 0.9456 | 0.8757 |
| 0.1744 | 2.2371 | 2000 | 2.0896 | 0.8683 | 0.8712 | 0.8654 | 0.8668 |
| 0.2341 | 2.7964 | 2500 | 1.9718 | 0.8887 | 0.8383 | 0.9456 | 0.8799 |
| 0.1357 | 3.3557 | 3000 | 2.2402 | 0.8787 | 0.8268 | 0.9375 | 0.8687 |
| 0.0779 | 3.9150 | 3500 | 2.0910 | 0.8857 | 0.8664 | 0.9059 | 0.8813 |
| 0.0426 | 4.4743 | 4000 | 2.2457 | 0.8724 | 0.8911 | 0.8544 | 0.8731 |
| 0.0549 | 5.0336 | 4500 | 1.8462 | 0.8952 | 0.8718 | 0.9199 | 0.8907 |
| 0.0331 | 5.5928 | 5000 | 1.9918 | 0.8803 | 0.8790 | 0.8816 | 0.8784 |
| 0.0206 | 6.1521 | 5500 | 2.0727 | 0.8847 | 0.8811 | 0.8882 | 0.8825 |
| 0.0101 | 6.7114 | 6000 | 2.0343 | 0.8882 | 0.8853 | 0.8912 | 0.8862 |
| 0.0099 | 7.2707 | 6500 | 2.1056 | 0.8831 | 0.8914 | 0.875 | 0.8825 |
| 0.0058 | 7.8300 | 7000 | 2.3964 | 0.8673 | 0.9036 | 0.8338 | 0.8705 |
| 0.0 | 8.3893 | 7500 | 2.2680 | 0.8808 | 0.8814 | 0.8801 | 0.8791 |
| 0.0047 | 8.9485 | 8000 | 2.1908 | 0.8863 | 0.8730 | 0.9 | 0.8828 |
| 0.0 | 9.5078 | 8500 | 2.2344 | 0.8878 | 0.8690 | 0.9074 | 0.8836 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.0
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