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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:
- f1
- accuracy
model-index:
- name: mus_promoter-finetuned-lora-NT-v2-50m-ms
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. -->
# mus_promoter-finetuned-lora-NT-v2-50m-ms
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: 0.1172
- F1: 0.9863
- Mcc Score: 0.9686
- Accuracy: 0.9844
## 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: 0.0005
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Mcc Score | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:--------:|
| 0.4189 | 0.43 | 100 | 0.4745 | 0.9067 | 0.7750 | 0.8906 |
| 0.2804 | 0.85 | 200 | 0.1875 | 0.9589 | 0.9048 | 0.9531 |
| 0.2198 | 1.28 | 300 | 0.1441 | 0.9730 | 0.9359 | 0.9688 |
| 0.1346 | 1.71 | 400 | 0.0821 | 0.9863 | 0.9686 | 0.9844 |
| 0.0875 | 2.14 | 500 | 0.1647 | 0.9730 | 0.9359 | 0.9688 |
| 0.0554 | 2.56 | 600 | 0.0937 | 0.9863 | 0.9686 | 0.9844 |
| 0.0314 | 2.99 | 700 | 0.1127 | 0.9863 | 0.9686 | 0.9844 |
| 0.0268 | 3.42 | 800 | 0.1104 | 0.9863 | 0.9686 | 0.9844 |
| 0.01 | 3.85 | 900 | 0.1146 | 0.9863 | 0.9686 | 0.9844 |
| 0.0008 | 4.27 | 1000 | 0.1172 | 0.9863 | 0.9686 | 0.9844 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
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