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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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- 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-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: 0.6900 |
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- F1: 0.8402 |
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- Mcc Score: 0.5492 |
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- Accuracy: 0.7891 |
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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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| 1.3108 | 0.05 | 100 | 0.8230 | 0.6491 | 0.3170 | 0.6367 | |
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| 0.9207 | 0.11 | 200 | 0.6670 | 0.6016 | 0.2636 | 0.6016 | |
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| 0.6734 | 0.16 | 300 | 0.5539 | 0.8190 | 0.4873 | 0.7617 | |
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| 0.7133 | 0.21 | 400 | 0.5834 | 0.8148 | 0.4994 | 0.7656 | |
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| 0.6225 | 0.26 | 500 | 0.8411 | 0.8343 | 0.5144 | 0.7656 | |
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| 0.8485 | 0.32 | 600 | 0.6813 | 0.7336 | 0.3999 | 0.6992 | |
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| 0.7567 | 0.37 | 700 | 0.6454 | 0.8504 | 0.5770 | 0.8008 | |
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| 0.5729 | 0.42 | 800 | 0.8756 | 0.7910 | 0.4676 | 0.7461 | |
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| 0.7708 | 0.47 | 900 | 0.6872 | 0.8303 | 0.5314 | 0.7812 | |
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| 0.6194 | 0.53 | 1000 | 0.6900 | 0.8402 | 0.5492 | 0.7891 | |
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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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