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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: 0.5742059850477367 |
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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: 0.2709 |
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- Spearmanr: 0.5742 |
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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: 11 |
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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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| 6.4382 | 0.93 | 7 | 2.0198 | -0.0244 | |
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| 1.1243 | 1.93 | 14 | 0.7986 | -0.0083 | |
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| 0.802 | 2.93 | 21 | 0.6902 | 0.2336 | |
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| 0.7469 | 3.93 | 28 | 0.6665 | 0.3001 | |
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| 0.7519 | 4.93 | 35 | 0.6578 | 0.3895 | |
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| 0.7247 | 5.93 | 42 | 0.6346 | 0.3682 | |
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| 0.6991 | 6.93 | 49 | 0.8796 | 0.3681 | |
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| 0.6829 | 7.93 | 56 | 0.6098 | 0.3747 | |
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| 0.7241 | 8.93 | 63 | 0.7538 | 0.4345 | |
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| 0.6703 | 9.93 | 70 | 0.5646 | 0.4419 | |
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| 0.6415 | 10.93 | 77 | 1.6112 | 0.3947 | |
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| 1.0551 | 11.93 | 84 | 1.9104 | 0.4256 | |
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| 1.2621 | 12.93 | 91 | 0.5694 | 0.4640 | |
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| 0.7165 | 13.93 | 98 | 0.5647 | 0.4748 | |
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| 0.602 | 14.93 | 105 | 0.3979 | 0.4907 | |
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| 0.4668 | 15.93 | 112 | 0.3896 | 0.4891 | |
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| 0.5248 | 16.93 | 119 | 0.5101 | 0.4878 | |
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| 0.6232 | 17.93 | 126 | 0.3298 | 0.5128 | |
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| 0.5491 | 18.93 | 133 | 0.6220 | 0.5210 | |
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| 0.5022 | 19.93 | 140 | 0.5351 | 0.5212 | |
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| 0.7122 | 20.93 | 147 | 0.3773 | 0.5278 | |
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| 0.377 | 21.93 | 154 | 0.3368 | 0.5278 | |
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| 0.3689 | 22.93 | 161 | 0.4503 | 0.5266 | |
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| 0.3768 | 23.93 | 168 | 0.3237 | 0.5428 | |
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| 0.3308 | 24.93 | 175 | 0.2850 | 0.5559 | |
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| 0.3182 | 25.93 | 182 | 0.2804 | 0.5611 | |
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| 0.3135 | 26.93 | 189 | 0.2792 | 0.5660 | |
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| 0.2953 | 27.93 | 196 | 0.2669 | 0.5707 | |
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| 0.2917 | 28.93 | 203 | 0.2654 | 0.5742 | |
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| 0.2652 | 29.93 | 210 | 0.2709 | 0.5742 | |
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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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