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pretty_name: "PRIMO benchmark inputs"
license: other
---
# PRIMO benchmark inputs
PRIMO evaluates whether embeddings from biological foundation models preserve clinically relevant information at the patient level.
This repository contains the data used to generate benchmark submissions. Dataset identities are hidden: files are grouped under IDs such as `d001` and `d002`, without disclosing the disease, tissue, or prediction target. Models produce one embedding per sample, and the evaluation is run separately in the [PRIMO Space](https://huggingface.co/spaces/ScientaLab/primo-eval).
## Contents
`datasets.yaml` lists each dataset's ID, path, number of samples, number of genes, and gene identifier type.
Each dataset directory contains an `expression.h5ad` file with raw counts stored as `AnnData`. Rows are samples identified by `sample_id`. Columns use NCBI gene IDs, with gene symbols included in the metadata.
## Submit embeddings
1. Download the data:
```bash
hf download ScientaLab/primo --repo-type dataset --local-dir primo
```
2. Generate one embedding per sample for every dataset. Embedding dimensions may differ between datasets.
3. Combine the embeddings into one CSV, TSV, Parquet, or NPZ file. Tabular files must contain `dataset_id`, `sample_id`, and one column per embedding dimension (`e0`, `e1`, etc.). NPZ files must contain `dataset_ids`, `sample_ids`, and `embeddings`.
4. Upload the file to the [PRIMO evaluation Space](https://huggingface.co/spaces/ScientaLab/primo-eval).
## Scoring
For each task, PRIMO trains the same linear probe on fixed cross-validation folds. Classification tasks use AUROC and regression tasks use Pearson correlation. Dataset scores are converted to a 0 to 1 skill score and aggregated by medical specialty.
A submission is ranked only if it covers every dataset. Labels are kept private and evaluation runs server-side.
Each dataset is redistributed under its source's original open license.
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