---
license: cc-by-nc-sa-4.0
base_model: InstaDeepAI/nucleotide-transformer-2.5b-1000g
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: nucleotide-transformer-finetuned-lora-NucleotideTransformer
  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-finetuned-lora-NucleotideTransformer

This model is a fine-tuned version of [InstaDeepAI/nucleotide-transformer-2.5b-1000g](https://huggingface.co/InstaDeepAI/nucleotide-transformer-2.5b-1000g) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6900
- F1: 0.8402
- Mcc Score: 0.5492
- Accuracy: 0.7891

## 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: 3
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:--------:|
| 1.3108        | 0.05  | 100  | 0.8230          | 0.6491 | 0.3170    | 0.6367   |
| 0.9207        | 0.11  | 200  | 0.6670          | 0.6016 | 0.2636    | 0.6016   |
| 0.6734        | 0.16  | 300  | 0.5539          | 0.8190 | 0.4873    | 0.7617   |
| 0.7133        | 0.21  | 400  | 0.5834          | 0.8148 | 0.4994    | 0.7656   |
| 0.6225        | 0.26  | 500  | 0.8411          | 0.8343 | 0.5144    | 0.7656   |
| 0.8485        | 0.32  | 600  | 0.6813          | 0.7336 | 0.3999    | 0.6992   |
| 0.7567        | 0.37  | 700  | 0.6454          | 0.8504 | 0.5770    | 0.8008   |
| 0.5729        | 0.42  | 800  | 0.8756          | 0.7910 | 0.4676    | 0.7461   |
| 0.7708        | 0.47  | 900  | 0.6872          | 0.8303 | 0.5314    | 0.7812   |
| 0.6194        | 0.53  | 1000 | 0.6900          | 0.8402 | 0.5492    | 0.7891   |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2