---
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
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# 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