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---
license: cc-by-nc-sa-4.0
base_model: InstaDeepAI/nucleotide-transformer-v2-50m-multi-species
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: nucleotide-transformer-v2-50m-multi-species_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-v2-50m-multi-species_ft_BioS74_1kbpHG19_DHSs_H3K27AC
This model is a fine-tuned version of [InstaDeepAI/nucleotide-transformer-v2-50m-multi-species](https://huggingface.co/InstaDeepAI/nucleotide-transformer-v2-50m-multi-species) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4277
- F1 Score: 0.8355
- Precision: 0.8318
- Recall: 0.8393
- Accuracy: 0.8270
- Auc: 0.9066
- Prc: 0.9000
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.5314 | 0.1314 | 500 | 0.4688 | 0.8060 | 0.7652 | 0.8513 | 0.7854 | 0.8552 | 0.8400 |
| 0.4807 | 0.2629 | 1000 | 0.4967 | 0.7824 | 0.8433 | 0.7298 | 0.7875 | 0.8783 | 0.8671 |
| 0.4541 | 0.3943 | 1500 | 0.4272 | 0.8177 | 0.8166 | 0.8187 | 0.8088 | 0.8900 | 0.8819 |
| 0.4213 | 0.5258 | 2000 | 0.4602 | 0.8361 | 0.7841 | 0.8955 | 0.8162 | 0.8916 | 0.8819 |
| 0.4085 | 0.6572 | 2500 | 0.4336 | 0.8363 | 0.7528 | 0.9407 | 0.8073 | 0.8959 | 0.8890 |
| 0.4383 | 0.7886 | 3000 | 0.4106 | 0.8240 | 0.8238 | 0.8242 | 0.8157 | 0.8978 | 0.8913 |
| 0.4237 | 0.9201 | 3500 | 0.4270 | 0.8372 | 0.8043 | 0.8729 | 0.8222 | 0.9017 | 0.8957 |
| 0.4121 | 1.0515 | 4000 | 0.4787 | 0.7913 | 0.8662 | 0.7283 | 0.7988 | 0.9028 | 0.8948 |
| 0.3789 | 1.1830 | 4500 | 0.4081 | 0.8379 | 0.8139 | 0.8634 | 0.8251 | 0.8999 | 0.8889 |
| 0.3736 | 1.3144 | 5000 | 0.4348 | 0.8344 | 0.8167 | 0.8528 | 0.8228 | 0.9020 | 0.8951 |
| 0.3655 | 1.4458 | 5500 | 0.4388 | 0.8153 | 0.8509 | 0.7825 | 0.8144 | 0.9056 | 0.8995 |
| 0.3597 | 1.5773 | 6000 | 0.4277 | 0.8355 | 0.8318 | 0.8393 | 0.8270 | 0.9066 | 0.9000 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.0
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