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  1. README.md +77 -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_BioS74_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_BioS74_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: 0.4843
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+ - F1 Score: 0.7964
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+ - Precision: 0.7556
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+ - Recall: 0.8418
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+ - Accuracy: 0.7747
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+ - Auc: 0.8453
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+ - Prc: 0.8347
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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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+ | 0.5422 | 0.1314 | 500 | 0.5443 | 0.7378 | 0.7886 | 0.6931 | 0.7420 | 0.8185 | 0.8068 |
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+ | 0.5205 | 0.2629 | 1000 | 0.5236 | 0.7618 | 0.7807 | 0.7438 | 0.7565 | 0.8309 | 0.8226 |
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+ | 0.5233 | 0.3943 | 1500 | 0.5168 | 0.7576 | 0.7893 | 0.7283 | 0.7560 | 0.8319 | 0.8199 |
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+ | 0.5023 | 0.5258 | 2000 | 0.5105 | 0.7933 | 0.7591 | 0.8307 | 0.7733 | 0.8369 | 0.8254 |
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+ | 0.4879 | 0.6572 | 2500 | 0.5088 | 0.7988 | 0.7263 | 0.8875 | 0.7660 | 0.8382 | 0.8229 |
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+ | 0.5064 | 0.7886 | 3000 | 0.4937 | 0.7919 | 0.7569 | 0.8302 | 0.7715 | 0.8373 | 0.8267 |
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+ | 0.4934 | 0.9201 | 3500 | 0.4923 | 0.7971 | 0.7661 | 0.8307 | 0.7786 | 0.8452 | 0.8289 |
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+ | 0.4952 | 1.0515 | 4000 | 0.4834 | 0.7809 | 0.7915 | 0.7705 | 0.7736 | 0.8480 | 0.8352 |
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+ | 0.488 | 1.1830 | 4500 | 0.4898 | 0.7899 | 0.7575 | 0.8252 | 0.7702 | 0.8404 | 0.8293 |
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+ | 0.494 | 1.3144 | 5000 | 0.4843 | 0.7964 | 0.7556 | 0.8418 | 0.7747 | 0.8453 | 0.8347 |
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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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