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--- |
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license: cc-by-nc-sa-4.0 |
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base_model: InstaDeepAI/nucleotide-transformer-2.5b-1000g |
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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: nucleotide-transformer-2.5b-1000g_ft_Hepg2_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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# nucleotide-transformer-2.5b-1000g_ft_Hepg2_1kbpHG19_DHSs_H3K27AC |
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This model is a fine-tuned version of [InstaDeepAI/nucleotide-transformer-2.5b-1000g](https://huggingface.co/InstaDeepAI/nucleotide-transformer-2.5b-1000g) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2831 |
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- F1 Score: 0.8768 |
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- Precision: 0.8600 |
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- Recall: 0.8943 |
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- Accuracy: 0.8661 |
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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 | |
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|:-------------:|:------:|:-----:|:---------------:|:--------:|:---------:|:------:|:--------:| |
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| 0.45 | 0.1864 | 500 | 0.4118 | 0.8434 | 0.8428 | 0.8439 | 0.8329 | |
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| 0.3862 | 0.3729 | 1000 | 0.3639 | 0.8621 | 0.8498 | 0.8747 | 0.8508 | |
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| 0.3975 | 0.5593 | 1500 | 0.3904 | 0.8690 | 0.8097 | 0.9377 | 0.8493 | |
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| 0.3681 | 0.7457 | 2000 | 0.3590 | 0.8738 | 0.8395 | 0.9111 | 0.8598 | |
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| 0.3567 | 0.9321 | 2500 | 0.4015 | 0.8733 | 0.8063 | 0.9524 | 0.8527 | |
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| 0.2808 | 1.1186 | 3000 | 0.6744 | 0.8251 | 0.9155 | 0.7509 | 0.8303 | |
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| 0.2164 | 1.3050 | 3500 | 0.5770 | 0.8747 | 0.8233 | 0.9328 | 0.8575 | |
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| 0.2104 | 1.4914 | 4000 | 0.5941 | 0.8817 | 0.8416 | 0.9258 | 0.8676 | |
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| 0.2115 | 1.6779 | 4500 | 0.5546 | 0.8755 | 0.8542 | 0.8978 | 0.8639 | |
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| 0.2075 | 1.8643 | 5000 | 0.5431 | 0.8701 | 0.8754 | 0.8649 | 0.8624 | |
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| 0.176 | 2.0507 | 5500 | 0.9443 | 0.8696 | 0.8666 | 0.8726 | 0.8605 | |
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| 0.0508 | 2.2371 | 6000 | 0.8301 | 0.8777 | 0.8743 | 0.8810 | 0.8691 | |
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| 0.0488 | 2.4236 | 6500 | 0.8394 | 0.8783 | 0.8584 | 0.8992 | 0.8672 | |
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| 0.0567 | 2.6100 | 7000 | 1.1510 | 0.8785 | 0.8122 | 0.9566 | 0.8590 | |
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| 0.0588 | 2.7964 | 7500 | 0.8502 | 0.8568 | 0.8912 | 0.8251 | 0.8530 | |
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| 0.0541 | 2.9828 | 8000 | 1.4982 | 0.8013 | 0.9155 | 0.7124 | 0.8116 | |
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| 0.0119 | 3.1693 | 8500 | 1.2710 | 0.8699 | 0.8838 | 0.8565 | 0.8635 | |
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| 0.019 | 3.3557 | 9000 | 1.1534 | 0.8753 | 0.8663 | 0.8845 | 0.8657 | |
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| 0.0267 | 3.5421 | 9500 | 1.3990 | 0.8719 | 0.7909 | 0.9713 | 0.8478 | |
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| 0.0318 | 3.7286 | 10000 | 1.3099 | 0.8697 | 0.8444 | 0.8964 | 0.8568 | |
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| 0.0562 | 3.9150 | 10500 | 0.8772 | 0.8662 | 0.8739 | 0.8586 | 0.8586 | |
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| 0.0231 | 4.1014 | 11000 | 1.1277 | 0.8851 | 0.8398 | 0.9356 | 0.8706 | |
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| 0.0281 | 4.2878 | 11500 | 1.0566 | 0.8828 | 0.8442 | 0.9251 | 0.8691 | |
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| 0.0149 | 4.4743 | 12000 | 1.2550 | 0.8705 | 0.8485 | 0.8936 | 0.8583 | |
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| 0.0278 | 4.6607 | 12500 | 1.1960 | 0.8279 | 0.8984 | 0.7677 | 0.8299 | |
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| 0.0265 | 4.8471 | 13000 | 1.2293 | 0.8588 | 0.8826 | 0.8362 | 0.8534 | |
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| 0.0319 | 5.0336 | 13500 | 1.1519 | 0.8838 | 0.8663 | 0.9020 | 0.8736 | |
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| 0.0226 | 5.2200 | 14000 | 1.1934 | 0.8803 | 0.8609 | 0.9006 | 0.8695 | |
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| 0.0127 | 5.4064 | 14500 | 1.3537 | 0.8854 | 0.8404 | 0.9356 | 0.8709 | |
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| 0.0299 | 5.5928 | 15000 | 1.1620 | 0.8514 | 0.8926 | 0.8139 | 0.8486 | |
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| 0.0259 | 5.7793 | 15500 | 1.2213 | 0.8827 | 0.8360 | 0.9349 | 0.8676 | |
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| 0.0236 | 5.9657 | 16000 | 1.1988 | 0.8777 | 0.8552 | 0.9013 | 0.8661 | |
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| 0.013 | 6.1521 | 16500 | 1.4039 | 0.8788 | 0.8511 | 0.9083 | 0.8665 | |
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| 0.0174 | 6.3386 | 17000 | 1.1774 | 0.8787 | 0.8565 | 0.9020 | 0.8672 | |
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| 0.0197 | 6.5250 | 17500 | 1.3383 | 0.8732 | 0.8536 | 0.8936 | 0.8616 | |
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| 0.0185 | 6.7114 | 18000 | 1.1582 | 0.8679 | 0.8694 | 0.8663 | 0.8594 | |
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| 0.0136 | 6.8978 | 18500 | 1.3086 | 0.8532 | 0.8923 | 0.8174 | 0.8501 | |
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| 0.0072 | 7.0843 | 19000 | 1.2864 | 0.8733 | 0.8643 | 0.8824 | 0.8635 | |
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| 0.0031 | 7.2707 | 19500 | 1.4482 | 0.8729 | 0.8616 | 0.8845 | 0.8627 | |
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| 0.0202 | 7.4571 | 20000 | 1.4021 | 0.8721 | 0.8573 | 0.8873 | 0.8612 | |
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| 0.021 | 7.6435 | 20500 | 1.2587 | 0.8807 | 0.8493 | 0.9146 | 0.8680 | |
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| 0.0097 | 7.8300 | 21000 | 1.2831 | 0.8768 | 0.8600 | 0.8943 | 0.8661 | |
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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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