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tape-fluorescence-prediction-tape-fluorescence-evotuning-DistilProtBert
cradle-bio/tape-fluorescence-prediction-tape-fluorescence-evotuning-DistilProtBert
tape-fluorescence-prediction-tape-fluorescence-evotuning-DistilProtBert is a text classification model from cradle-bio. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- May 26, 2022
- Updated
- May 30, 2022
- SHA
- a19823ec
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Datasets
Model card
From the Hugging Face model README
<!-- 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 on the cradle-bio/tape-fluorescence dataset. It achieves the following results on the evaluation set:
- Loss: 0.3377
- Spearmanr: 0.5505
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: 42
- 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 |
|---|---|---|---|---|
| 6.2764 | 0.93 | 7 | 1.9927 | -0.0786 |
| 1.1206 | 1.93 | 14 | 0.8223 | -0.1543 |
| 0.8054 | 2.93 | 21 | 0.6894 | 0.2050 |
| 0.7692 | 3.93 | 28 | 0.8084 | 0.2807 |
| 0.7597 | 4.93 | 35 | 0.6613 | 0.4003 |
| 0.7416 | 5.93 | 42 | 0.6803 | 0.3829 |
| 0.7256 | 6.93 | 49 | 0.6428 | 0.4416 |
| 0.6966 | 7.93 | 56 | 0.6086 | 0.4506 |
| 0.7603 | 8.93 | 63 | 0.9119 | 0.4697 |
| 0.9187 | 9.93 | 70 | 0.6048 | 0.4757 |
| 1.0371 | 10.93 | 77 | 2.0742 | 0.4076 |
| 1.0947 | 11.93 | 84 | 0.6633 | 0.4522 |
| 0.6946 | 12.93 | 91 | 0.6008 | 0.4123 |
| 0.6618 | 13.93 | 98 | 0.5931 | 0.4457 |
| 0.8635 | 14.93 | 105 | 1.9561 | 0.4331 |
| 0.9444 | 15.93 | 112 | 0.5627 | 0.5041 |
| 0.5535 | 16.93 | 119 | 0.4348 | 0.4840 |
| 0.9059 | 17.93 | 126 | 0.6704 | 0.5123 |
| 0.5693 | 18.93 | 133 | 0.4616 | 0.5285 |
| 0.6298 | 19.93 | 140 | 0.6915 | 0.5166 |
| 0.955 | 20.93 | 147 | 0.6679 | 0.5677 |
| 0.7866 | 21.93 | 154 | 0.8136 | 0.5559 |
| 0.6687 | 22.93 | 161 | 0.4782 | 0.5561 |
| 0.5336 | 23.93 | 168 | 0.4447 | 0.5499 |
| 0.4673 | 24.93 | 175 | 0.4258 | 0.5428 |
| 0.478 | 25.93 | 182 | 0.3651 | 0.5329 |
| 0.4023 | 26.93 | 189 | 0.3688 | 0.5428 |
| 0.3961 | 27.93 | 196 | 0.3692 | 0.5509 |
| 0.3808 | 28.93 | 203 | 0.3434 | 0.5514 |
| 0.3433 | 29.93 | 210 | 0.3377 | 0.5505 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- May 26, 2022
- Updated
- May 30, 2022