| title: "stargo-cc" | |
| tags: | |
| - star-go | |
| - protein | |
| - gene-ontology | |
| - bioinformatics | |
| - pytorch | |
| - lightning | |
| # stargo-cc | |
| STAR-GO checkpoint published for easier discoverability. This repository stores the original Lightning `.ckpt` and the original TOML config so you can reconstruct the model as trained. | |
| ## Files | |
| - `model.ckpt`: PyTorch Lightning checkpoint for `TrainingModel` | |
| - `config.toml`: training/model config (same schema as this repo's `configs/*.toml`) | |
| ## Provenance | |
| - W&B artifact: `contempro-cc-2020-ordered-encdec-medium:best` | |
| ## Usage | |
| This repository contains a Lightning checkpoint and the original TOML config. Load it like this: | |
| ```python | |
| import torch | |
| from huggingface_hub import hf_hub_download | |
| from config import from_toml | |
| from model import TrainingModel, get_model_cls | |
| repo_id = "mmtf/stargo-cc" | |
| ckpt_path = hf_hub_download(repo_id, "model.ckpt") | |
| cfg_path = hf_hub_download(repo_id, "config.toml") | |
| cfg = from_toml(cfg_path) | |
| module = TrainingModel.load_from_checkpoint( | |
| ckpt_path, | |
| model=get_model_cls(cfg.model.name)(cfg.model), | |
| training_config=cfg.train, | |
| ) | |
| module = module.to("cuda" if torch.cuda.is_available() else "cpu") | |
| module.eval() | |
| ``` | |