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End of training

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  1. README.md +76 -0
  2. config.json +37 -0
  3. pytorch_model.bin +3 -0
  4. special_tokens_map.json +51 -0
  5. tokenizer_config.json +70 -0
README.md ADDED
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+ ---
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+ license: bsd-3-clause
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+ base_model: LongSafari/hyenadna-large-1m-seqlen-hf
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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: hyenadna-large-1m-seqlen-hf_ft_BioS45_1kbpHG19_DHSs_H3K27AC
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+ results: []
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+ ---
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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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+
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+ # hyenadna-large-1m-seqlen-hf_ft_BioS45_1kbpHG19_DHSs_H3K27AC
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+
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+ This model is a fine-tuned version of [LongSafari/hyenadna-large-1m-seqlen-hf](https://huggingface.co/LongSafari/hyenadna-large-1m-seqlen-hf) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.4729
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+ - F1 Score: 0.7992
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+ - Precision: 0.7837
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+ - Recall: 0.8153
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+ - Accuracy: 0.7863
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+ - Auc: 0.8620
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+ - Prc: 0.8573
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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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+
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+ ### Training results
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+
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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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+ | 0.5614 | 0.2103 | 500 | 0.5009 | 0.7805 | 0.7671 | 0.7944 | 0.7669 | 0.8336 | 0.8188 |
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+ | 0.5053 | 0.4207 | 1000 | 0.4788 | 0.7845 | 0.7926 | 0.7766 | 0.7775 | 0.8507 | 0.8450 |
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+ | 0.489 | 0.6310 | 1500 | 0.5526 | 0.7506 | 0.8293 | 0.6855 | 0.7623 | 0.8459 | 0.8500 |
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+ | 0.4836 | 0.8414 | 2000 | 0.4708 | 0.8145 | 0.7455 | 0.8976 | 0.7867 | 0.8677 | 0.8623 |
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+ | 0.4673 | 1.0517 | 2500 | 0.4755 | 0.7935 | 0.7847 | 0.8024 | 0.7821 | 0.8609 | 0.8575 |
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+ | 0.4694 | 1.2621 | 3000 | 0.4953 | 0.7730 | 0.8156 | 0.7347 | 0.7749 | 0.8622 | 0.8598 |
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+ | 0.4622 | 1.4724 | 3500 | 0.4560 | 0.8055 | 0.7899 | 0.8218 | 0.7930 | 0.8664 | 0.8659 |
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+ | 0.4537 | 1.6828 | 4000 | 0.4548 | 0.8070 | 0.8013 | 0.8129 | 0.7972 | 0.8741 | 0.8724 |
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+ | 0.4561 | 1.8931 | 4500 | 0.4729 | 0.7992 | 0.7837 | 0.8153 | 0.7863 | 0.8620 | 0.8573 |
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+
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+
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+ ### Framework versions
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+
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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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+ ],
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+ "transformers_version": "4.42.3",
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+ "vocab_size": 12
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