Publish reviewed GAMBA evaluation dataset

README.md

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+---
+pretty_name: "GAMBA Functional Regions: Feature vs Random"
+license: mit
+tags:
+- biology
+- genomics
+- dna
+- genome-language-model
+- benchmark
+- functional-genomics
+- hg38
+- parquet
+configs:
+- config_name: canonical
+  data_files:
+  - split: all
+    path: functional-random-gamba.parquet
+- config_name: noncoding-added
+  data_files:
+  - split: all
+    path: functional-random-gamba-noncoding-added.parquet
+---
+
+# GAMBA Functional Regions: Feature vs Random
+
+This dataset packages GAMBA's binary functional-region benchmark comparing
+annotated genomic features with chromosome- and length-matched random
+controls. Random controls avoid retained anchors from the same functional
+category.
+
+## Configs
+
+| Config | Train rows | Test rows | Total |
+|---|---:|---:|---:|
+| `canonical` | 135,692 | 33,982 | 169,674 |
+| `noncoding-added` | 150,726 | 37,834 | 188,560 |
+
+The `noncoding-added` config is an explicit extension from
+[Gamba-Processing](https://github.com/TaykhoomDalal/Gamba-Processing), not an
+additional category emitted by GAMBA's current `create_eval_data.py`.
+
+## Loading
+
+```python
+from datasets import load_dataset
+
+canonical = load_dataset(
+    "Taykhoom/functional-random-gamba",
+    "canonical",
+    split="all",
+)
+train = canonical.filter(lambda row: row["split"] == "train")
+test = canonical.filter(lambda row: row["split"] == "test")
+```
+
+## Columns
+
+| Column | Description |
+|---|---|
+| `split`, `label` | Probe/fine-tuning `train` or held-out `test`; labels are `feature` or `random`. |
+| `sequence` | Strand-oriented GAMBA context of at most 2,048 bp. |
+| `pair_id` | Shared identifier for a feature and its matched random control. |
+| `category`, `scope` | Functional class and pooling scope. |
+| `chrom`, `start`, `end`, `strand` | Zero-based, half-open hg38 feature/control coordinates. |
+| `context_start`, `context_end` | Forward-genome coordinates of `sequence`. |
+| `roi_start`, `roi_end` | Feature/control offsets within `sequence`. |
+| `pool_start`, `pool_end` | Offsets to pool for representation evaluation. |
+| `name` | Source annotation name. |
+
+## Data source and processing
+
+The processing code reproduces the ten-category BED output from
+[Microsoft GAMBA](https://github.com/microsoft/gamba) commit `e83984e`
+byte-for-byte, then freezes all GAMBA autosomes plus `chrX` in parquet.
+The GAMBA models were pretrained on the chromosomes marked `train`. The paper's
+zero-shot result used the frozen models on `chr2`, `chr3`, `chr16`, and
+`chr22`, which are marked `test`; no task-specific model was fitted on these
+benchmark rows. Use only `test` to reproduce that paper subset. The `train`
+rows are provided for probing/fine-tuning or broader chromosome evaluation.
+
+## Citation
+
+Please cite the GAMBA paper:
+
+Consens, M. E. et al. *Predicting evolutionary rate as a pretraining task
+improves genome language model representations*. bioRxiv (2026).
+[doi:10.64898/2026.02.02.703275](https://doi.org/10.64898/2026.02.02.703275).
+A conference version is available on
+[OpenReview](https://openreview.net/forum?id=dkgM2ale4U).
+
+```bibtex
+@article{consens2026predicting,
+  title = {Predicting evolutionary rate as a pretraining task improves genome language model representations},
+  author = {Consens, Micaela Elisa and Yang, Kevin K. and Hall, Jimmy and Conard, Ashley Mae and Wang, Bo and Crawford, Lorin and Moses, Alan and Lu, Alex X.},
+  journal = {bioRxiv},
+  year = {2026},
+  doi = {10.64898/2026.02.02.703275},
+  url = {https://doi.org/10.64898/2026.02.02.703275}
+}
+```
+
+## License
+
+GAMBA and its released benchmark code/data are distributed under the MIT
+license. Upstream annotation resources retain their original terms.

functional-random-gamba-noncoding-added.parquet

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