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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: 0.23980333080780145 |
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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: 0.6838 |
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- Spearmanr: 0.2398 |
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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: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 128 |
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- total_train_batch_size: 4096 |
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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: 5 |
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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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| 9.0167 | 0.85 | 4 | 4.9224 | -0.0084 | |
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| 4.0263 | 1.85 | 8 | 2.0909 | 0.0824 | |
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| 1.7616 | 2.85 | 12 | 0.9869 | 0.1583 | |
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| 0.9814 | 3.85 | 16 | 0.7071 | 0.2798 | |
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| 0.7082 | 4.85 | 20 | 0.6838 | 0.2398 | |
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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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