license: apache-2.0
tags:
- mass-spectrometry
- metabolomics
- spectrum-embedding
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.