File size: 3,496 Bytes
a33aac1 c8c834b a33aac1 c8c834b a33aac1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 |
---
license: bsd-3-clause
base_model: LongSafari/hyenadna-large-1m-seqlen-hf
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: hyenadna-large-1m-seqlen-hf_ft_BioS73_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. -->
# hyenadna-large-1m-seqlen-hf_ft_BioS73_1kbpHG19_DHSs_H3K27AC
This model is a fine-tuned version of [LongSafari/hyenadna-large-1m-seqlen-hf](https://huggingface.co/LongSafari/hyenadna-large-1m-seqlen-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4445
- F1 Score: 0.8449
- Precision: 0.7906
- Recall: 0.9071
- Accuracy: 0.8222
- Auc: 0.8958
- Prc: 0.8902
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.504 | 0.1864 | 500 | 0.4797 | 0.8144 | 0.7226 | 0.9330 | 0.7730 | 0.8602 | 0.8528 |
| 0.4627 | 0.3727 | 1000 | 0.4431 | 0.8260 | 0.7718 | 0.8883 | 0.8002 | 0.8747 | 0.8687 |
| 0.4575 | 0.5591 | 1500 | 0.4477 | 0.8141 | 0.8161 | 0.8122 | 0.8021 | 0.8774 | 0.8745 |
| 0.4486 | 0.7454 | 2000 | 0.4197 | 0.8338 | 0.7739 | 0.9036 | 0.8077 | 0.8862 | 0.8791 |
| 0.4296 | 0.9318 | 2500 | 0.4227 | 0.8372 | 0.7763 | 0.9085 | 0.8114 | 0.8912 | 0.8804 |
| 0.4277 | 1.1182 | 3000 | 0.4181 | 0.8396 | 0.7713 | 0.9211 | 0.8122 | 0.8909 | 0.8864 |
| 0.4166 | 1.3045 | 3500 | 0.4481 | 0.8315 | 0.8159 | 0.8478 | 0.8166 | 0.8859 | 0.8803 |
| 0.4235 | 1.4909 | 4000 | 0.4253 | 0.8341 | 0.7819 | 0.8939 | 0.8103 | 0.8897 | 0.8854 |
| 0.4149 | 1.6772 | 4500 | 0.4432 | 0.8370 | 0.7690 | 0.9183 | 0.8092 | 0.8898 | 0.8820 |
| 0.4099 | 1.8636 | 5000 | 0.4597 | 0.8123 | 0.7279 | 0.9190 | 0.7734 | 0.8676 | 0.8681 |
| 0.4078 | 2.0499 | 5500 | 0.4425 | 0.8450 | 0.7918 | 0.9057 | 0.8226 | 0.8942 | 0.8910 |
| 0.4201 | 2.2363 | 6000 | 0.4509 | 0.8445 | 0.7781 | 0.9232 | 0.8185 | 0.8963 | 0.8886 |
| 0.3896 | 2.4227 | 6500 | 0.4153 | 0.8387 | 0.7627 | 0.9316 | 0.8088 | 0.8926 | 0.8856 |
| 0.4104 | 2.6090 | 7000 | 0.4066 | 0.8378 | 0.8024 | 0.8764 | 0.8189 | 0.8896 | 0.8833 |
| 0.395 | 2.7954 | 7500 | 0.4659 | 0.8371 | 0.8190 | 0.8561 | 0.8222 | 0.8969 | 0.8908 |
| 0.3646 | 2.9817 | 8000 | 0.4445 | 0.8449 | 0.7906 | 0.9071 | 0.8222 | 0.8958 | 0.8902 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.0
|