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---
base_model: AIRI-Institute/gena-lm-bigbird-base-t2t
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: gena-lm-bigbird-base-t2t_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. -->
# gena-lm-bigbird-base-t2t_ft_BioS45_1kbpHG19_DHSs_H3K27AC
This model is a fine-tuned version of [AIRI-Institute/gena-lm-bigbird-base-t2t](https://huggingface.co/AIRI-Institute/gena-lm-bigbird-base-t2t) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6943
- F1 Score: 0.8574
- Precision: 0.8076
- Recall: 0.9137
- Accuracy: 0.8414
- Auc: 0.9156
- Prc: 0.9036
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.5078 | 0.2103 | 500 | 0.4303 | 0.8087 | 0.8369 | 0.7823 | 0.8069 | 0.8886 | 0.8848 |
| 0.4238 | 0.4207 | 1000 | 0.3944 | 0.8478 | 0.8317 | 0.8645 | 0.8380 | 0.9106 | 0.9046 |
| 0.4246 | 0.6310 | 1500 | 0.3930 | 0.8497 | 0.7954 | 0.9121 | 0.8317 | 0.9087 | 0.9014 |
| 0.4045 | 0.8414 | 2000 | 0.3785 | 0.8587 | 0.7948 | 0.9339 | 0.8397 | 0.9126 | 0.9046 |
| 0.3868 | 1.0517 | 2500 | 0.4130 | 0.8458 | 0.8401 | 0.8516 | 0.8380 | 0.9198 | 0.9137 |
| 0.3662 | 1.2621 | 3000 | 0.4857 | 0.8570 | 0.7907 | 0.9355 | 0.8372 | 0.9159 | 0.9076 |
| 0.378 | 1.4724 | 3500 | 0.4466 | 0.8402 | 0.8657 | 0.8161 | 0.8380 | 0.9203 | 0.9092 |
| 0.3633 | 1.6828 | 4000 | 0.4554 | 0.8237 | 0.8821 | 0.7726 | 0.8275 | 0.9226 | 0.9151 |
| 0.3753 | 1.8931 | 4500 | 0.4109 | 0.8610 | 0.8135 | 0.9145 | 0.8460 | 0.9200 | 0.9117 |
| 0.3249 | 2.1035 | 5000 | 0.5210 | 0.8668 | 0.8292 | 0.9081 | 0.8544 | 0.9203 | 0.9094 |
| 0.3429 | 2.3138 | 5500 | 0.5609 | 0.8602 | 0.8363 | 0.8855 | 0.8498 | 0.9185 | 0.9102 |
| 0.3314 | 2.5242 | 6000 | 0.5652 | 0.8598 | 0.7925 | 0.9395 | 0.8401 | 0.9180 | 0.9084 |
| 0.3462 | 2.7345 | 6500 | 0.6094 | 0.8619 | 0.8138 | 0.9161 | 0.8469 | 0.9120 | 0.8976 |
| 0.3412 | 2.9449 | 7000 | 0.5750 | 0.8508 | 0.8021 | 0.9056 | 0.8342 | 0.9108 | 0.8990 |
| 0.2912 | 3.1552 | 7500 | 0.6943 | 0.8574 | 0.8076 | 0.9137 | 0.8414 | 0.9156 | 0.9036 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.0
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