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---
license: apache-2.0
tags:
- protein language model
- generated_from_trainer
datasets:
- train
metrics:
- spearmanr
model-index:
- name: tape-fluorescence-prediction-tape-fluorescence-evotuning-DistilProtBert
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: cradle-bio/tape-fluorescence
type: train
metrics:
- name: Spearmanr
type: spearmanr
value: 0.6081143924159805
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tape-fluorescence-prediction-tape-fluorescence-evotuning-DistilProtBert
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.
It achieves the following results on the evaluation set:
- Loss: 0.2209
- Spearmanr: 0.6081
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 40
- eval_batch_size: 40
- seed: 11
- gradient_accumulation_steps: 64
- total_train_batch_size: 2560
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Spearmanr |
|:-------------:|:-----:|:----:|:---------------:|:---------:|
| 6.3796 | 0.93 | 7 | 2.2462 | 0.2021 |
| 1.2421 | 1.93 | 14 | 0.7066 | 0.1024 |
| 0.7978 | 2.93 | 21 | 0.6895 | 0.1444 |
| 0.7613 | 3.93 | 28 | 0.6758 | 0.2527 |
| 0.7498 | 4.93 | 35 | 0.6772 | 0.2620 |
| 0.7486 | 5.93 | 42 | 0.6703 | 0.3991 |
| 0.7394 | 6.93 | 49 | 0.6506 | 0.4038 |
| 0.8251 | 7.93 | 56 | 1.3414 | 0.3358 |
| 0.8479 | 8.93 | 63 | 0.6745 | 0.3353 |
| 0.7954 | 9.93 | 70 | 0.6610 | 0.4157 |
| 0.7316 | 10.93 | 77 | 0.4977 | 0.4483 |
| 0.6027 | 11.93 | 84 | 0.4138 | 0.4517 |
| 0.5239 | 12.93 | 91 | 0.4185 | 0.4798 |
| 0.4802 | 13.93 | 98 | 0.3637 | 0.5082 |
| 0.5417 | 14.93 | 105 | 0.3360 | 0.5143 |
| 0.5022 | 15.93 | 112 | 0.5404 | 0.5207 |
| 0.4487 | 16.93 | 119 | 0.4884 | 0.5347 |
| 0.4229 | 17.93 | 126 | 0.2941 | 0.5530 |
| 0.3785 | 18.93 | 133 | 0.2920 | 0.5625 |
| 0.3448 | 19.93 | 140 | 0.3082 | 0.5589 |
| 0.3352 | 20.93 | 147 | 0.3006 | 0.5638 |
| 0.3219 | 21.93 | 154 | 0.2707 | 0.5737 |
| 0.3156 | 22.93 | 161 | 0.2623 | 0.5775 |
| 0.3142 | 23.93 | 168 | 0.3162 | 0.5752 |
| 0.3003 | 24.93 | 175 | 0.2487 | 0.5897 |
| 0.303 | 25.93 | 182 | 0.2633 | 0.5981 |
| 0.2757 | 26.93 | 189 | 0.2813 | 0.5921 |
| 0.2836 | 27.93 | 196 | 0.2696 | 0.5968 |
| 0.2759 | 28.93 | 203 | 0.2230 | 0.6060 |
| 0.232 | 29.93 | 210 | 0.2209 | 0.6081 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
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