Instructions to use taotl/Soluble_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use taotl/Soluble_classification with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("ProTrekHub/Protein_Encoder_35M") model = PeftModel.from_pretrained(base_model, "taotl/Soluble_classification") - Notebooks
- Google Colab
- Kaggle
| base_model: ProTrekHub/Protein_Encoder_35M | |
| library_name: peft | |
| # Model Card for Model-Demo-ProTrek-35M | |
| ## Base model | |
| [ProTrekHub/Protein_Encoder_35M](https://huggingface.co/ProTrekHub/Protein_Encoder_35M) | |
| ## Task description | |
| The task is to predict the protein solubility. | |
| ## Label meaning | |
| Label 1 - soluble | |
| Lable 0 - unsoluble | |
| ## Task type | |
| Protein-level Classification | |
| ## Model input type | |
| AA Sequence | |
| ## LoRA config | |
| - **r:** 8 | |
| - **lora_dropout:** 0.0 | |
| - **lora_alpha:** 16 | |
| - **target_modules:** ['value', 'intermediate.dense', 'query', 'output.dense', 'key'] | |
| - **modules_to_save:** ['classifier'] | |
| ## Training config | |
| - **optimizer:** | |
| - **class:** AdamW | |
| - **betas:** (0.9, 0.98) | |
| - **weight_decay:** 0.01 | |
| - **learning rate:** 0.0005 | |
| - **epoch:** 5 | |
| - **batch size:** 1 | |
| - **precision:** 16-mixed | |