Add semRfam dataset card

README.md

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+---
+license: cc0-1.0
+language:
+  - en
+tags:
+  - rna
+  - rfam
+  - biology
+  - multimodal
+  - rna-design
+pretty_name: semRfam
+size_categories:
+  - 1M<n<10M
+---
+
+# semRfam
+
+The tokenizable multimodal RNA dataset used to train
+[**yakRNA Design**](https://github.com/YousufAKhan/yakRNA), a semantic
+multimodal RNA composer. Every record pairs an RNA sequence with three
+companion modalities drawn from its Rfam family, which is what lets a model be
+conditioned on structure, consensus, or function at generation time.
+
+| | |
+|---|---|
+| Source | [Rfam 15.0](https://rfam.org) (released 2024-09) |
+| Records | 3,978,586 |
+| Families | 4,028 |
+| Format | single HDF5 file, one row per sequence |
+| Size | ~2.2 GB |
+| License | CC0 1.0 (inherited from Rfam) |
+
+## Files
+
+| File | Size | Contents |
+|---|---|---|
+| `semRfam.h5` | ~2.2 GB | the records: `sequence`, `ss`, `consensus`, `go_terms`, `rfam_id`, `sequence_id`, each a 1-D array of UTF-8 byte strings |
+| `family_lookup.json` | ~49 MB | Rfam family → list of row indices, used to build family-diverse batches |
+| `family_splits.json` | ~35 MB | the exact family-level train/val/test split used for the released model |
+
+## The four modalities
+
+For row *i*, the four fields are aligned position-by-position (GO terms being
+per-family rather than per-position):
+
+- **`sequence`** — the RNA sequence with alignment gaps removed, in `A/C/G/U/N`.
+- **`ss`** — the family's WUSS `#=GC SS_cons` consensus structure, projected
+  onto the positions this sequence actually occupies.
+- **`consensus`** — the family's `#=GC RF` consensus line, projected the same
+  way. Case is meaningful (upper = highly conserved).
+- **`go_terms`** — comma-separated GO terms attached to the family by Rfam's
+  `rfam2go` mapping; empty for families with no GO annotation.
+
+Because `ss` and `consensus` are projections of the family consensus onto each
+sequence, `len(sequence) == len(ss) == len(consensus)` for every row.
+
+## Splits
+
+Splits are by **family**, so no Rfam family appears in more than one split:
+
+| Split | Families | Sequences |
+|---|---|---|
+| train | 3,625 | 3,846,938 |
+| val | 201 | 57,920 |
+| test | 202 | 73,728 |
+
+Note: the released yakRNA checkpoint was trained with
+`data.use_all_data_for_training: true`, meaning the val/test families were also
+included in training; the split is published so that held-out evaluation is
+reproducible for anyone who wants it. `family_splits.json` also records the
+split seed and the data-processing config.
+
+## Usage
+
+```bash
+git clone https://github.com/YousufAKhan/yakRNA.git && cd yakRNA
+pip install -r requirements-training.txt
+python data_processing/download_rfam.py --stage prebuilt   # downloads this dataset
+python rna_gen/rna_gen_training.py --config configs/train_single_gpu.yaml
+```
+
+That places the files where the training configs expect them
+(`training_data/processed/`). See
+[TRAINING.md](https://github.com/YousufAKhan/yakRNA/blob/main/TRAINING.md) for
+the objective, the config reference, and how to fine-tune from the released
+checkpoint.
+
+Reading it directly:
+
+```python
+import h5py
+from huggingface_hub import hf_hub_download
+
+path = hf_hub_download("MasterYster/semRfam", "semRfam.h5", repo_type="dataset")
+with h5py.File(path) as f:
+    print(f["rfam_id"][0].decode(), f["sequence"][0].decode())
+    print(f["ss"][0].decode())
+    print(f["consensus"][0].decode())
+    print(f["go_terms"][0].decode())
+```
+
+## How it was built
+
+1. Rfam 15.0 full-region sequences, per family.
+2. Each family realigned to its covariance model with Infernal 1.1.4
+   `cmalign --outformat Pfam`.
+3. Alignment gaps stripped from each sequence, with `SS_cons` and `RF` sliced to
+   the retained positions so all three stay in register.
+4. GO terms joined per family from `rfam2go`.
+5. Written to HDF5, one row per sequence.
+
+The pipeline is in the yakRNA repository
+(`data_processing/download_rfam.py --stage full-all` followed by
+`data_processing/rfam_data_preprocessing.py --stage all`). Note that rerunning
+it today yields a *similar* but not identical dataset: Rfam full-region
+membership changes between releases and search runs. This dataset is the
+snapshot the released model was actually trained on.
+
+## Citation
+
+```bibtex
+@software{yakrna2026,
+  author = {Khan, Yousuf},
+  title  = {yakRNA Design: A semantic multimodal RNA composer},
+  year   = {2026},
+  url    = {https://github.com/YousufAKhan/yakRNA}
+}
+```
+
+Rfam: Ontiveros-Palacios et al., *Rfam 15: RNA families database in 2025*,
+Nucleic Acids Research (2025). Rfam data is released under
+[CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/).