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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-multi-species |
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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: nucleotide-transformer-finetuned-lora-NucleotideTransformer |
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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-finetuned-lora-NucleotideTransformer |
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This model is a fine-tuned version of [InstaDeepAI/nucleotide-transformer-2.5b-multi-species](https://huggingface.co/InstaDeepAI/nucleotide-transformer-2.5b-multi-species) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6652 |
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- F1: 0.8473 |
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- Mcc Score: 0.5631 |
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- Accuracy: 0.7930 |
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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: 3 |
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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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| 1.4035 | 0.05 | 100 | 0.6460 | 0.7279 | 0.3679 | 0.6875 | |
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| 1.0673 | 0.11 | 200 | 0.4946 | 0.8437 | 0.5583 | 0.7930 | |
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| 0.7808 | 0.16 | 300 | 0.6190 | 0.7766 | 0.2749 | 0.6719 | |
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| 0.8938 | 0.21 | 400 | 2.1858 | 0.0 | 0.0 | 0.3906 | |
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| 0.9329 | 0.26 | 500 | 0.8452 | 0.8352 | 0.5179 | 0.7656 | |
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| 0.8721 | 0.32 | 600 | 0.7470 | 0.5286 | 0.2993 | 0.5820 | |
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| 0.6548 | 0.37 | 700 | 0.6967 | 0.8242 | 0.4769 | 0.75 | |
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| 0.6719 | 0.42 | 800 | 0.9450 | 0.7913 | 0.4425 | 0.7383 | |
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| 0.8265 | 0.47 | 900 | 0.5426 | 0.8328 | 0.5234 | 0.7773 | |
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| 0.5561 | 0.53 | 1000 | 0.6652 | 0.8473 | 0.5631 | 0.7930 | |
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### Framework versions |
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- Transformers 4.38.1 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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