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--- |
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base_model: AIRI-Institute/gena-lm-bert-base-t2t |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- accuracy |
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model-index: |
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- name: gena-lm-bert-base-t2t_ft_BioS2_1kbpHG19_DHSs_H3K27AC |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# gena-lm-bert-base-t2t_ft_BioS2_1kbpHG19_DHSs_H3K27AC |
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This model is a fine-tuned version of [AIRI-Institute/gena-lm-bert-base-t2t](https://huggingface.co/AIRI-Institute/gena-lm-bert-base-t2t) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5515 |
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- F1 Score: 0.8104 |
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- Precision: 0.8830 |
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- Recall: 0.7488 |
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- Accuracy: 0.8168 |
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- Auc: 0.9083 |
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- Prc: 0.8965 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 20 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 Score | Precision | Recall | Accuracy | Auc | Prc | |
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|:-------------:|:------:|:-----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:| |
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| 0.6889 | 0.0839 | 500 | 0.6417 | 0.7291 | 0.7593 | 0.7013 | 0.7277 | 0.7941 | 0.7841 | |
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| 0.6164 | 0.1679 | 1000 | 0.5381 | 0.7867 | 0.7507 | 0.8262 | 0.7658 | 0.8316 | 0.8141 | |
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| 0.5271 | 0.2518 | 1500 | 0.5026 | 0.8036 | 0.7353 | 0.8860 | 0.7737 | 0.8393 | 0.8118 | |
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| 0.5221 | 0.3357 | 2000 | 0.4936 | 0.8063 | 0.7676 | 0.8490 | 0.7868 | 0.8466 | 0.8365 | |
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| 0.4903 | 0.4197 | 2500 | 0.4795 | 0.8185 | 0.7613 | 0.8850 | 0.7948 | 0.8621 | 0.8476 | |
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| 0.4543 | 0.5036 | 3000 | 0.4643 | 0.8251 | 0.7756 | 0.8815 | 0.8047 | 0.8644 | 0.8371 | |
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| 0.4603 | 0.5875 | 3500 | 0.4686 | 0.8273 | 0.7670 | 0.8978 | 0.8041 | 0.8766 | 0.8565 | |
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| 0.4561 | 0.6715 | 4000 | 0.4631 | 0.8265 | 0.7954 | 0.8603 | 0.8113 | 0.8828 | 0.8755 | |
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| 0.4477 | 0.7554 | 4500 | 0.4512 | 0.8306 | 0.7798 | 0.8885 | 0.8106 | 0.8800 | 0.8707 | |
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| 0.4514 | 0.8393 | 5000 | 0.4408 | 0.8235 | 0.8139 | 0.8333 | 0.8133 | 0.8820 | 0.8795 | |
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| 0.4559 | 0.9233 | 5500 | 0.4425 | 0.8316 | 0.8087 | 0.8558 | 0.8188 | 0.8900 | 0.8760 | |
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| 0.4467 | 1.0072 | 6000 | 0.4247 | 0.8223 | 0.8348 | 0.8102 | 0.8170 | 0.8940 | 0.8817 | |
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| 0.4283 | 1.0912 | 6500 | 0.5164 | 0.8296 | 0.8175 | 0.8420 | 0.8192 | 0.8773 | 0.8659 | |
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| 0.4361 | 1.1751 | 7000 | 0.4595 | 0.8387 | 0.8072 | 0.8728 | 0.8245 | 0.8951 | 0.8886 | |
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| 0.443 | 1.2590 | 7500 | 0.4806 | 0.8396 | 0.7957 | 0.8885 | 0.8225 | 0.8809 | 0.8557 | |
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| 0.42 | 1.3430 | 8000 | 0.4259 | 0.8297 | 0.8202 | 0.8394 | 0.8198 | 0.8988 | 0.8939 | |
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| 0.4233 | 1.4269 | 8500 | 0.4146 | 0.8225 | 0.8404 | 0.8053 | 0.8183 | 0.9047 | 0.8980 | |
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| 0.4175 | 1.5108 | 9000 | 0.4482 | 0.8420 | 0.7790 | 0.9162 | 0.8203 | 0.8960 | 0.8710 | |
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| 0.4078 | 1.5948 | 9500 | 0.4259 | 0.8426 | 0.8192 | 0.8673 | 0.8306 | 0.9049 | 0.8949 | |
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| 0.4071 | 1.6787 | 10000 | 0.4400 | 0.8432 | 0.8106 | 0.8786 | 0.8292 | 0.8929 | 0.8537 | |
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| 0.4279 | 1.7626 | 10500 | 0.4670 | 0.8368 | 0.8443 | 0.8294 | 0.8309 | 0.8996 | 0.8766 | |
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| 0.4278 | 1.8466 | 11000 | 0.4540 | 0.8468 | 0.7851 | 0.9190 | 0.8262 | 0.8796 | 0.8380 | |
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| 0.419 | 1.9305 | 11500 | 0.4370 | 0.8502 | 0.8095 | 0.8953 | 0.8351 | 0.8920 | 0.8528 | |
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| 0.4077 | 2.0144 | 12000 | 0.4640 | 0.8499 | 0.7902 | 0.9194 | 0.8303 | 0.8821 | 0.8342 | |
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| 0.3942 | 2.0984 | 12500 | 0.4793 | 0.8488 | 0.8334 | 0.8648 | 0.8390 | 0.9079 | 0.8876 | |
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| 0.4104 | 2.1823 | 13000 | 0.4884 | 0.8460 | 0.8504 | 0.8416 | 0.8398 | 0.8969 | 0.8530 | |
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| 0.3959 | 2.2662 | 13500 | 0.5016 | 0.8484 | 0.8428 | 0.8542 | 0.8405 | 0.9054 | 0.8851 | |
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| 0.4071 | 2.3502 | 14000 | 0.5515 | 0.8104 | 0.8830 | 0.7488 | 0.8168 | 0.9083 | 0.8965 | |
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### Framework versions |
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- Transformers 4.42.3 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.19.0 |
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