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---
base_model: AIRI-Institute/gena-lm-bert-base-t2t-multi
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: gena-lm-bert-base-t2t-multi_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-bert-base-t2t-multi_ft_BioS45_1kbpHG19_DHSs_H3K27AC
This model is a fine-tuned version of [AIRI-Institute/gena-lm-bert-base-t2t-multi](https://huggingface.co/AIRI-Institute/gena-lm-bert-base-t2t-multi) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7191
- F1 Score: 0.8258
- Precision: 0.8725
- Recall: 0.7839
- Accuracy: 0.8275
- Auc: 0.8676
- Prc: 0.8589
## 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 |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
| 0.6927 | 0.2103 | 500 | 0.6550 | 0.6325 | 0.8027 | 0.5218 | 0.6836 | 0.7657 | 0.7801 |
| 0.6285 | 0.4207 | 1000 | 0.5540 | 0.7342 | 0.8129 | 0.6694 | 0.7472 | 0.8065 | 0.8058 |
| 0.5306 | 0.6310 | 1500 | 0.5262 | 0.7747 | 0.8010 | 0.75 | 0.7724 | 0.8369 | 0.8222 |
| 0.4969 | 0.8414 | 2000 | 0.4964 | 0.8208 | 0.7561 | 0.8976 | 0.7955 | 0.8721 | 0.8624 |
| 0.4722 | 1.0517 | 2500 | 0.4584 | 0.8228 | 0.8354 | 0.8105 | 0.8178 | 0.8876 | 0.8792 |
| 0.4466 | 1.2621 | 3000 | 0.4567 | 0.8424 | 0.7943 | 0.8968 | 0.8250 | 0.8896 | 0.8698 |
| 0.4418 | 1.4724 | 3500 | 0.4333 | 0.8416 | 0.8436 | 0.8395 | 0.8351 | 0.9004 | 0.8883 |
| 0.422 | 1.6828 | 4000 | 0.4661 | 0.8227 | 0.8588 | 0.7895 | 0.8225 | 0.9030 | 0.8967 |
| 0.4107 | 1.8931 | 4500 | 0.4329 | 0.8468 | 0.8009 | 0.8984 | 0.8305 | 0.8937 | 0.8585 |
| 0.3906 | 2.1035 | 5000 | 0.4643 | 0.8479 | 0.8290 | 0.8677 | 0.8376 | 0.8902 | 0.8512 |
| 0.4098 | 2.3138 | 5500 | 0.4532 | 0.8526 | 0.8060 | 0.9048 | 0.8368 | 0.8782 | 0.8309 |
| 0.4118 | 2.5242 | 6000 | 0.4862 | 0.8465 | 0.8503 | 0.8427 | 0.8406 | 0.9018 | 0.8845 |
| 0.4207 | 2.7345 | 6500 | 0.4667 | 0.8519 | 0.8126 | 0.8952 | 0.8376 | 0.8927 | 0.8561 |
| 0.4382 | 2.9449 | 7000 | 0.5130 | 0.8202 | 0.8763 | 0.7710 | 0.8237 | 0.9094 | 0.9039 |
| 0.3846 | 3.1552 | 7500 | 0.5103 | 0.8381 | 0.8659 | 0.8121 | 0.8363 | 0.9077 | 0.8992 |
| 0.4023 | 3.3656 | 8000 | 0.4508 | 0.8613 | 0.8225 | 0.9040 | 0.8481 | 0.9123 | 0.8963 |
| 0.3788 | 3.5759 | 8500 | 0.4996 | 0.8517 | 0.7933 | 0.9194 | 0.8330 | 0.8901 | 0.8517 |
| 0.3778 | 3.7863 | 9000 | 0.5016 | 0.8606 | 0.8237 | 0.9008 | 0.8477 | 0.8967 | 0.8631 |
| 0.3923 | 3.9966 | 9500 | 0.5175 | 0.8579 | 0.8356 | 0.8815 | 0.8477 | 0.8895 | 0.8575 |
| 0.3628 | 4.2070 | 10000 | 0.5557 | 0.8616 | 0.8427 | 0.8815 | 0.8523 | 0.8935 | 0.8706 |
| 0.4124 | 4.4173 | 10500 | 0.5216 | 0.8621 | 0.8252 | 0.9024 | 0.8494 | 0.8721 | 0.8318 |
| 0.388 | 4.6277 | 11000 | 0.6025 | 0.8584 | 0.8127 | 0.9097 | 0.8435 | 0.8572 | 0.8122 |
| 0.4513 | 4.8380 | 11500 | 0.5943 | 0.8500 | 0.8524 | 0.8476 | 0.8439 | 0.9012 | 0.8886 |
| 0.4206 | 5.0484 | 12000 | 0.5724 | 0.8610 | 0.8414 | 0.8815 | 0.8515 | 0.9016 | 0.8855 |
| 0.3882 | 5.2587 | 12500 | 0.5748 | 0.8616 | 0.8524 | 0.8710 | 0.8540 | 0.8901 | 0.8724 |
| 0.3756 | 5.4691 | 13000 | 0.5839 | 0.8635 | 0.8477 | 0.8798 | 0.8549 | 0.8756 | 0.8325 |
| 0.4158 | 5.6794 | 13500 | 0.5782 | 0.8593 | 0.8169 | 0.9065 | 0.8452 | 0.9048 | 0.8848 |
| 0.3859 | 5.8898 | 14000 | 0.5989 | 0.8530 | 0.8496 | 0.8565 | 0.8460 | 0.8947 | 0.8717 |
| 0.336 | 6.1001 | 14500 | 0.6641 | 0.8542 | 0.7996 | 0.9169 | 0.8368 | 0.8697 | 0.8287 |
| 0.3724 | 6.3105 | 15000 | 0.6330 | 0.8599 | 0.8205 | 0.9032 | 0.8464 | 0.8776 | 0.8500 |
| 0.3809 | 6.5208 | 15500 | 0.7191 | 0.8258 | 0.8725 | 0.7839 | 0.8275 | 0.8676 | 0.8589 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.0
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