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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_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-v2-500m-multi-species_ft_BioS45_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: 1.2234
- F1 Score: 0.8240
- Precision: 0.8645
- Recall: 0.7871
- Accuracy: 0.8246
- Auc: 0.9119
- Prc: 0.9103
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.4976 | 0.4205 | 500 | 0.4129 | 0.8285 | 0.7809 | 0.8823 | 0.8094 | 0.8977 | 0.8961 |
| 0.384 | 0.8410 | 1000 | 0.3673 | 0.8526 | 0.8023 | 0.9097 | 0.8359 | 0.9206 | 0.9183 |
| 0.3235 | 1.2616 | 1500 | 0.3902 | 0.8505 | 0.8643 | 0.8371 | 0.8464 | 0.9269 | 0.9284 |
| 0.2866 | 1.6821 | 2000 | 0.3665 | 0.8623 | 0.8514 | 0.8734 | 0.8544 | 0.9286 | 0.9270 |
| 0.2547 | 2.1026 | 2500 | 0.7526 | 0.8592 | 0.8003 | 0.9274 | 0.8414 | 0.9245 | 0.9232 |
| 0.316 | 2.5231 | 3000 | 1.5948 | 0.8466 | 0.8614 | 0.8323 | 0.8427 | 0.9224 | 0.9239 |
| 0.4261 | 2.9437 | 3500 | 1.2234 | 0.8240 | 0.8645 | 0.7871 | 0.8246 | 0.9119 | 0.9103 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.0
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