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--- |
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license: apache-2.0 |
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tags: |
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- protein language model |
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- generated_from_trainer |
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datasets: |
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- train |
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metrics: |
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- spearmanr |
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model-index: |
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- name: tape-fluorescence-prediction-tape-fluorescence-evotuning-DistilProtBert |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: cradle-bio/tape-fluorescence |
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type: train |
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metrics: |
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- name: Spearmanr |
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type: spearmanr |
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value: 0.5505486770316164 |
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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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# tape-fluorescence-prediction-tape-fluorescence-evotuning-DistilProtBert |
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This model is a fine-tuned version of [thundaa/tape-fluorescence-evotuning-DistilProtBert](https://huggingface.co/thundaa/tape-fluorescence-evotuning-DistilProtBert) on the cradle-bio/tape-fluorescence dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3377 |
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- Spearmanr: 0.5505 |
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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: 5e-05 |
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- train_batch_size: 40 |
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- eval_batch_size: 40 |
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- seed: 42 |
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- gradient_accumulation_steps: 64 |
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- total_train_batch_size: 2560 |
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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: 30 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Spearmanr | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:| |
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| 6.2764 | 0.93 | 7 | 1.9927 | -0.0786 | |
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| 1.1206 | 1.93 | 14 | 0.8223 | -0.1543 | |
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| 0.8054 | 2.93 | 21 | 0.6894 | 0.2050 | |
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| 0.7692 | 3.93 | 28 | 0.8084 | 0.2807 | |
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| 0.7597 | 4.93 | 35 | 0.6613 | 0.4003 | |
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| 0.7416 | 5.93 | 42 | 0.6803 | 0.3829 | |
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| 0.7256 | 6.93 | 49 | 0.6428 | 0.4416 | |
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| 0.6966 | 7.93 | 56 | 0.6086 | 0.4506 | |
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| 0.7603 | 8.93 | 63 | 0.9119 | 0.4697 | |
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| 0.9187 | 9.93 | 70 | 0.6048 | 0.4757 | |
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| 1.0371 | 10.93 | 77 | 2.0742 | 0.4076 | |
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| 1.0947 | 11.93 | 84 | 0.6633 | 0.4522 | |
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| 0.6946 | 12.93 | 91 | 0.6008 | 0.4123 | |
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| 0.6618 | 13.93 | 98 | 0.5931 | 0.4457 | |
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| 0.8635 | 14.93 | 105 | 1.9561 | 0.4331 | |
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| 0.9444 | 15.93 | 112 | 0.5627 | 0.5041 | |
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| 0.5535 | 16.93 | 119 | 0.4348 | 0.4840 | |
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| 0.9059 | 17.93 | 126 | 0.6704 | 0.5123 | |
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| 0.5693 | 18.93 | 133 | 0.4616 | 0.5285 | |
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| 0.6298 | 19.93 | 140 | 0.6915 | 0.5166 | |
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| 0.955 | 20.93 | 147 | 0.6679 | 0.5677 | |
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| 0.7866 | 21.93 | 154 | 0.8136 | 0.5559 | |
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| 0.6687 | 22.93 | 161 | 0.4782 | 0.5561 | |
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| 0.5336 | 23.93 | 168 | 0.4447 | 0.5499 | |
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| 0.4673 | 24.93 | 175 | 0.4258 | 0.5428 | |
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| 0.478 | 25.93 | 182 | 0.3651 | 0.5329 | |
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| 0.4023 | 26.93 | 189 | 0.3688 | 0.5428 | |
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| 0.3961 | 27.93 | 196 | 0.3692 | 0.5509 | |
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| 0.3808 | 28.93 | 203 | 0.3434 | 0.5514 | |
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| 0.3433 | 29.93 | 210 | 0.3377 | 0.5505 | |
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### Framework versions |
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- Transformers 4.18.0 |
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- Pytorch 1.11.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.12.1 |
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