dsadd4/UltraMS-Search

UltraMS Search

The spectrum-to-spectrum contrastive model selected for the UltraAtlas application (best.pt). encode(...).embedding returns its normalized 512-dimensional projection of the UltraMS encoder's CLS embedding. Use this representation to rank spectra in a supplied reference library by cosine similarity.

Output	Value
Embedding dimension	512
Maximum spectral peaks	150
Python model name	"search"
Weights SHA-256	447e41e99e5f6ee4155f5dd9938bd7b5fe3f09239b6d5eb0f4973b44c963e383

python -m pip install ultrams

from ultrams import UltraMS

model = UltraMS.from_pretrained("search")
embedding = model.encode(
    mz=[100.1, 121.1, 150.0], intensity=[20, 100, 35], precursor_mz=301.2
).embedding
print(embedding.shape)  # (512,)

Supply measured spectral peak m/z and intensity arrays plus precursor-ion m/z. UltraMS requires at least three spectral peaks with positive m/z, normalizes intensities by their maximum when positive, and retains the 150 most intense spectral peaks when needed. See the input format, model selection, and spectrum search example.

Use return_peaks=True in model.encode(...) to obtain final encoder embeddings aligned with the retained spectral peaks. The three released checkpoints are in the UltraMS model family.