license: apache-2.0
tags:
- mass-spectrometry
- metabolomics
- spectrum-embedding
UltraMS Unsupervised
The general UltraMS MS/MS encoder, released from the completed retention-time-only epoch 11 (rtonly11) training checkpoint. UltraMS learned from UltraMSdata through masked peak reconstruction (MPR), followed by retention-time training. This model returns the encoder's normalized spectrum-level CLS embedding.
| Output | Value |
|---|---|
| Embedding dimension | 1024 |
| Maximum spectral peaks | 150 |
| Python model name | "unsupervised" |
| Weights SHA-256 | 6a4c6660999848c409303119f6caa54fbae9444d0b75c7fcd8bafd303cde9830 |
python -m pip install ultrams
from ultrams import UltraMS
model = UltraMS.from_pretrained("unsupervised")
embedding = model.encode(
mz=[100.1, 121.1, 150.0], intensity=[20, 100, 35], precursor_mz=301.2
).embedding
print(embedding.shape) # (1024,)
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 PyTorch fine-tuning 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.