A small protein language model trained on the fungal kingdom -- embeddings + variant scoring.
Project description
fungalplm
A tiny (9.5M-parameter) protein language model exclusively trained on fungal proteins. The API allows for per-protein and per-residue embeddings extraction. Weights load straight from the Hugging Face Hub.
pip install fungalplm[hub]
from fungalplm import FungalPLM
plm = FungalPLM.load("szchesny/fungal-plm") # downloads the weights
emb = plm.embed(["MQIFVKTLTGKTITLEVEPSDTIENVK..."]) # [N, d] per-protein (mean-pooled)
res = plm.embed(seqs, per_residue=True) # list of [Li, d]
Or from the command line:
fungalplm embed proteins.fasta --ckpt fungal-plm.pth -o embeddings.npz
Usecase
This model beats ESM2-8M in ubiquitin prediction, with wild-type-NLL signal scores provided, that tells you when to trust it (1.2-1.4 most optimal). Full numbers, limitations, and the model card: https://huggingface.co/szchesny/fungal-plm
MIT licensed. Torch + NumPy only (add the [hub] extra to load directly from the Hub).
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