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--- |
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license: cc-by-nc-sa-4.0 |
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base_model: InstaDeepAI/nucleotide-transformer-500m-1000g |
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tags: |
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- generated_from_trainer |
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metrics: |
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- f1 |
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- accuracy |
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model-index: |
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- name: mus_promoter-finetuned-lora-NT-500m-1000g |
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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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# mus_promoter-finetuned-lora-NT-500m-1000g |
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This model is a fine-tuned version of [InstaDeepAI/nucleotide-transformer-500m-1000g](https://huggingface.co/InstaDeepAI/nucleotide-transformer-500m-1000g) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3065 |
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- F1: 0.9351 |
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- Mcc Score: 0.8414 |
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- Accuracy: 0.9219 |
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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: 0.0005 |
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- train_batch_size: 8 |
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- eval_batch_size: 64 |
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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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- training_steps: 1000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | Mcc Score | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:--------:| |
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| 0.6126 | 0.43 | 100 | 0.4697 | 0.8767 | 0.7135 | 0.8594 | |
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| 0.3854 | 0.85 | 200 | 0.2682 | 0.9296 | 0.8460 | 0.9219 | |
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| 0.4832 | 1.28 | 300 | 0.2444 | 0.9296 | 0.8460 | 0.9219 | |
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| 0.3536 | 1.71 | 400 | 0.3433 | 0.9167 | 0.8113 | 0.9062 | |
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| 0.3215 | 2.14 | 500 | 0.3475 | 0.9351 | 0.8414 | 0.9219 | |
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| 0.2961 | 2.56 | 600 | 0.2347 | 0.9231 | 0.8108 | 0.9062 | |
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| 0.2742 | 2.99 | 700 | 0.3438 | 0.9333 | 0.8395 | 0.9219 | |
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| 0.2375 | 3.42 | 800 | 0.3448 | 0.9351 | 0.8414 | 0.9219 | |
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| 0.2438 | 3.85 | 900 | 0.2789 | 0.9351 | 0.8414 | 0.9219 | |
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| 0.2104 | 4.27 | 1000 | 0.3065 | 0.9351 | 0.8414 | 0.9219 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.17.1 |
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- Tokenizers 0.15.2 |
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