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---
license: cc-by-nc-sa-4.0
base_model: InstaDeepAI/nucleotide-transformer-v2-500m-multi-species
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: nucleotide-transformer-v2-500m-multi-species_ft_BioS74_1kbpHG19_DHSs_H3K27AC
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-v2-500m-multi-species_ft_BioS74_1kbpHG19_DHSs_H3K27AC
This model is a fine-tuned version of [InstaDeepAI/nucleotide-transformer-v2-500m-multi-species](https://huggingface.co/InstaDeepAI/nucleotide-transformer-v2-500m-multi-species) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0901
- F1 Score: 0.8408
- Precision: 0.8543
- Recall: 0.8277
- Accuracy: 0.8359
- Auc: 0.9180
- Prc: 0.9190
## 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: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.5159 | 0.2629 | 500 | 0.4336 | 0.8179 | 0.7917 | 0.8458 | 0.8028 | 0.8805 | 0.8705 |
| 0.4166 | 0.5258 | 1000 | 0.4006 | 0.8434 | 0.7908 | 0.9036 | 0.8243 | 0.9079 | 0.9051 |
| 0.3854 | 0.7886 | 1500 | 0.3993 | 0.8481 | 0.7691 | 0.9453 | 0.8228 | 0.9127 | 0.9072 |
| 0.3755 | 1.0515 | 2000 | 0.3677 | 0.8348 | 0.8600 | 0.8112 | 0.8320 | 0.9252 | 0.9258 |
| 0.2996 | 1.3144 | 2500 | 0.3570 | 0.8506 | 0.8478 | 0.8533 | 0.8430 | 0.9268 | 0.9291 |
| 0.2827 | 1.5773 | 3000 | 0.3578 | 0.8564 | 0.8504 | 0.8624 | 0.8485 | 0.9305 | 0.9303 |
| 0.285 | 1.8402 | 3500 | 0.3630 | 0.8386 | 0.8674 | 0.8117 | 0.8364 | 0.9270 | 0.9280 |
| 0.2425 | 2.1030 | 4000 | 0.9870 | 0.8391 | 0.8578 | 0.8212 | 0.8351 | 0.9202 | 0.9203 |
| 0.4127 | 2.3659 | 4500 | 2.0901 | 0.8408 | 0.8543 | 0.8277 | 0.8359 | 0.9180 | 0.9190 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.0
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