Document UltraMS 0.3.0 peak embeddings
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README.md
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| Embedding dimension | 1024 |
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| Maximum spectral peaks | 150 |
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| Python model name | `"unsupervised"` |
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```bash
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python -m pip install ultrams
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```
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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](https://github.com/Dsadd4/UltraMS/blob/main/docs/data-format.md), [model selection](https://github.com/Dsadd4/UltraMS/blob/main/docs/model-selection.md), and [PyTorch fine-tuning example](https://github.com/Dsadd4/UltraMS/blob/main/examples/pytorch_finetune.py).
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| Embedding dimension | 1024 |
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| Maximum spectral peaks | 150 |
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| Python model name | `"unsupervised"` |
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| Weights SHA-256 | `6a4c6660999848c409303119f6caa54fbae9444d0b75c7fcd8bafd303cde9830` |
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```bash
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python -m pip install ultrams
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```
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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](https://github.com/Dsadd4/UltraMS/blob/main/docs/data-format.md), [model selection](https://github.com/Dsadd4/UltraMS/blob/main/docs/model-selection.md), and [PyTorch fine-tuning example](https://github.com/Dsadd4/UltraMS/blob/main/examples/pytorch_finetune.py).
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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](https://huggingface.co/collections/dsadd4/ultrams-6ab4cfaa860cbbd1f9a6b9b6).
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