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---
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_BioS45_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-2.5b-multi-species_ft_BioS45_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: 0.7950
- F1 Score: 0.8559
- Precision: 0.8234
- Recall: 0.8911
- Accuracy: 0.8435
- Auc: 0.9171
- Prc: 0.9117
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.5207 | 0.2103 | 500 | 0.4059 | 0.8319 | 0.8038 | 0.8621 | 0.8183 | 0.8975 | 0.8945 |
| 0.429 | 0.4207 | 1000 | 0.4149 | 0.8454 | 0.7824 | 0.9194 | 0.8246 | 0.9137 | 0.9091 |
| 0.4215 | 0.6310 | 1500 | 0.4626 | 0.8318 | 0.7368 | 0.9548 | 0.7985 | 0.9188 | 0.9187 |
| 0.3994 | 0.8414 | 2000 | 0.3757 | 0.8555 | 0.7733 | 0.9573 | 0.8313 | 0.9264 | 0.9226 |
| 0.3733 | 1.0517 | 2500 | 0.4529 | 0.8462 | 0.8658 | 0.8274 | 0.8431 | 0.9240 | 0.9215 |
| 0.3 | 1.2621 | 3000 | 0.5616 | 0.8585 | 0.8464 | 0.8710 | 0.8502 | 0.9248 | 0.9222 |
| 0.3006 | 1.4724 | 3500 | 0.5047 | 0.8498 | 0.8940 | 0.8097 | 0.8507 | 0.9298 | 0.9287 |
| 0.3008 | 1.6828 | 4000 | 0.4289 | 0.8614 | 0.8415 | 0.8823 | 0.8519 | 0.9233 | 0.9218 |
| 0.2882 | 1.8931 | 4500 | 0.4380 | 0.8634 | 0.8323 | 0.8968 | 0.8519 | 0.9263 | 0.9232 |
| 0.2014 | 2.1035 | 5000 | 0.6433 | 0.8624 | 0.8628 | 0.8621 | 0.8565 | 0.9240 | 0.9218 |
| 0.1118 | 2.3138 | 5500 | 0.7087 | 0.8670 | 0.8591 | 0.875 | 0.8599 | 0.9272 | 0.9236 |
| 0.1406 | 2.5242 | 6000 | 0.9509 | 0.8427 | 0.8757 | 0.8121 | 0.8418 | 0.9252 | 0.9212 |
| 0.1304 | 2.7345 | 6500 | 0.7386 | 0.8670 | 0.8398 | 0.8960 | 0.8565 | 0.9265 | 0.9213 |
| 0.1515 | 2.9449 | 7000 | 0.7950 | 0.8559 | 0.8234 | 0.8911 | 0.8435 | 0.9171 | 0.9117 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.0
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