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---
library_name: transformers
license: cc-by-nc-sa-4.0
base_model: InstaDeepAI/nucleotide-transformer-500m-1000g
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: nucleotide-transformer-500m-1000g_ft_BioS2_1kbpHG19_DHSs_H3K27AC_one_shot
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-500m-1000g_ft_BioS2_1kbpHG19_DHSs_H3K27AC_one_shot
This model is a fine-tuned version of [InstaDeepAI/nucleotide-transformer-500m-1000g](https://huggingface.co/InstaDeepAI/nucleotide-transformer-500m-1000g) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2825
- F1 Score: 0.8857
- Precision: 0.8857
- Recall: 0.8857
- Accuracy: 0.8644
- Auc: 0.875
- Prc: 0.8717
## 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.2054 | 8.3333 | 500 | 1.1524 | 0.8857 | 0.8857 | 0.8857 | 0.8644 | 0.8738 | 0.8703 |
| 0.0 | 16.6667 | 1000 | 1.2825 | 0.8857 | 0.8857 | 0.8857 | 0.8644 | 0.875 | 0.8717 |
### Framework versions
- Transformers 4.46.0.dev0
- Pytorch 2.4.1+cu121
- Datasets 2.18.0
- Tokenizers 0.20.0
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