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---
license: cc-by-nc-sa-4.0
base_model: InstaDeepAI/nucleotide-transformer-v2-250m-multi-species
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: mus_promoter-finetuned-lora-NT-v2-250m-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-v2-250m-ms
This model is a fine-tuned version of [InstaDeepAI/nucleotide-transformer-v2-250m-multi-species](https://huggingface.co/InstaDeepAI/nucleotide-transformer-v2-250m-multi-species) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0011
- F1: 1.0
- Mcc Score: 1.0
- Accuracy: 1.0
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:--------:|
| 0.0901 | 0.43 | 100 | 0.1482 | 0.9722 | 0.9385 | 0.9688 |
| 0.1727 | 0.85 | 200 | 0.2002 | 0.9730 | 0.9359 | 0.9688 |
| 0.1143 | 1.28 | 300 | 0.1997 | 0.9730 | 0.9359 | 0.9688 |
| 0.1169 | 1.71 | 400 | 0.1163 | 0.9722 | 0.9385 | 0.9688 |
| 0.0565 | 2.14 | 500 | 0.2560 | 0.9737 | 0.9373 | 0.9688 |
| 0.1162 | 2.56 | 600 | 0.0741 | 0.9867 | 0.9683 | 0.9844 |
| 0.0631 | 2.99 | 700 | 0.0766 | 0.9737 | 0.9373 | 0.9688 |
| 0.0492 | 3.42 | 800 | 0.0010 | 1.0 | 1.0 | 1.0 |
| 0.0435 | 3.85 | 900 | 0.0011 | 1.0 | 1.0 | 1.0 |
| 0.0343 | 4.27 | 1000 | 0.0011 | 1.0 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
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