Rename Lodestar to PRIMO in the dataset card

README.md

@@ -1,11 +1,11 @@
 ---
-pretty_name: "Lodestar — benchmark inputs"
+pretty_name: "PRIMO — benchmark inputs"
 license: other
 ---
 
-# 🧬 Lodestar — benchmark inputs
+# 🧬 PRIMO — benchmark inputs
 
-**Lodestar** is a blind benchmark for transcriptomic foundation models: it measures
+**PRIMO** is a blind benchmark for transcriptomic foundation models: it measures
 how well a model's patient-level embeddings capture real clinical signal.
 
 This repo holds the **inputs you embed**. It's deliberately *blind* — datasets are
@@ -21,7 +21,7 @@ embedding, not task-specific tuning.
 ## 🚀 Run the benchmark
 1. **Download** the data:
    ```bash
-   hf download ScientaLab/lodestar --repo-type dataset --local-dir lodestar
+   hf download ScientaLab/primo --repo-type dataset --local-dir primo
    ```
 2. **Embed every dataset** with your model — one vector per sample (any dimension;
    it may differ per dataset).
@@ -29,7 +29,7 @@ embedding, not task-specific tuning.
    `sample_id`, then one column per embedding dim (`e0`, `e1`, …). Format:
    **CSV / TSV / Parquet**, or **NPZ** with `dataset_ids` / `sample_ids` / `embeddings`.
 4. **Submit & see your rank** →
-   **[🏆 Lodestar Space](https://huggingface.co/spaces/ScientaLab/lodestar-eval)**
+   **[🏆 PRIMO Space](https://huggingface.co/spaces/ScientaLab/primo-eval)**
 
 ## 📊 How it's scored
 A fixed linear probe is trained on frozen cross-validation folds over your embeddings
@@ -37,6 +37,6 @@ and scored — **AUROC** (classification) or **Pearson r** (regression). Scores
 0–1 **skill** and roll up per medical specialty. **Only submissions that cover every
 dataset are ranked.** Labels stay private — grading happens server-side.
 
-➡️ **Ready?** → **https://huggingface.co/spaces/ScientaLab/lodestar-eval**
+➡️ **Ready?** → **https://huggingface.co/spaces/ScientaLab/primo-eval**
 
 *Each dataset is redistributed under its source's original open license.*