File size: 3,388 Bytes
cdccd99 |
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 |
---
license: bsd-3-clause
base_model: LongSafari/hyenadna-small-32k-seqlen-hf
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: hyenadna-small-32k-seqlen-hf_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. -->
# hyenadna-small-32k-seqlen-hf_ft_BioS45_1kbpHG19_DHSs_H3K27AC
This model is a fine-tuned version of [LongSafari/hyenadna-small-32k-seqlen-hf](https://huggingface.co/LongSafari/hyenadna-small-32k-seqlen-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5632
- F1 Score: 0.8023
- Precision: 0.8234
- Recall: 0.7823
- Accuracy: 0.7989
- Auc: 0.8729
- Prc: 0.8701
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.5477 | 0.2103 | 500 | 0.4845 | 0.7868 | 0.7923 | 0.7815 | 0.7791 | 0.8482 | 0.8295 |
| 0.4789 | 0.4207 | 1000 | 0.4609 | 0.8003 | 0.7943 | 0.8065 | 0.7901 | 0.8624 | 0.8547 |
| 0.4505 | 0.6310 | 1500 | 0.4569 | 0.7972 | 0.8068 | 0.7879 | 0.7909 | 0.8700 | 0.8670 |
| 0.4558 | 0.8414 | 2000 | 0.4471 | 0.8289 | 0.7664 | 0.9024 | 0.8056 | 0.8753 | 0.8637 |
| 0.4376 | 1.0517 | 2500 | 0.4435 | 0.8324 | 0.7840 | 0.8871 | 0.8136 | 0.8786 | 0.8708 |
| 0.4299 | 1.2621 | 3000 | 0.4596 | 0.8284 | 0.7795 | 0.8839 | 0.8090 | 0.8770 | 0.8676 |
| 0.4329 | 1.4724 | 3500 | 0.4409 | 0.8133 | 0.8203 | 0.8065 | 0.8069 | 0.8805 | 0.8781 |
| 0.4187 | 1.6828 | 4000 | 0.4495 | 0.8063 | 0.8246 | 0.7887 | 0.8023 | 0.8802 | 0.8798 |
| 0.4211 | 1.8931 | 4500 | 0.4363 | 0.8303 | 0.7925 | 0.8718 | 0.8141 | 0.8807 | 0.8734 |
| 0.3818 | 2.1035 | 5000 | 0.4506 | 0.8350 | 0.7932 | 0.8815 | 0.8183 | 0.8809 | 0.8717 |
| 0.3524 | 2.3138 | 5500 | 0.4775 | 0.8184 | 0.7968 | 0.8411 | 0.8052 | 0.8785 | 0.8745 |
| 0.3601 | 2.5242 | 6000 | 0.4678 | 0.8263 | 0.7770 | 0.8823 | 0.8065 | 0.8758 | 0.8688 |
| 0.3708 | 2.7345 | 6500 | 0.4836 | 0.8226 | 0.8064 | 0.8395 | 0.8111 | 0.8769 | 0.8743 |
| 0.3911 | 2.9449 | 7000 | 0.4733 | 0.8207 | 0.7912 | 0.8524 | 0.8056 | 0.8748 | 0.8666 |
| 0.3136 | 3.1552 | 7500 | 0.5632 | 0.8023 | 0.8234 | 0.7823 | 0.7989 | 0.8729 | 0.8701 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.0
|