Create README.md

README.md

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+---
+license: mit
+task_categories:
+  - text-generation
+  - fill-mask
+tags:
+  - biology
+  - protein-design
+  - ligand-binding
+  - antibody-design
+pretty_name: InstructPro2Bench
+size_categories:
+  - 100K<n<1M
+---
+
+# InstructPro2Bench
+
+Training and evaluation data for **[InstructPro2: Natural Language and Biological Grounding Guided De Novo Protein Design](https://arxiv.org/abs/2610.19592)** — natural-language function descriptions paired with protein sequences, plus the ligand and antibody splits used for the two downstream design tasks.
+
+Code: [chovak/InstructPro2](https://github.com/chovak/InstructPro2) ·
+Weights: [charlesnovak/InstructPro2](https://huggingface.co/charlesnovak/InstructPro2) ·
+Paper: [arXiv:2610.19592](https://arxiv.org/abs/2610.19592)
+
+## Download
+
+```bash
+export WORK_ROOT=/path/to/workspace    # default: ~/instructpro2
+huggingface-cli download charlesnovak/InstructPro2Bench --repo-type dataset --local-dir $WORK_ROOT/data
+```
+
+## Files
+
+| File | Rows | Size |
+|---|---:|---:|
+| `stage1/train.jsonl` | 200,000 | 603 MB |
+| `stage1/valid.jsonl` | 1,000 | 2.8 MB |
+| `stage1/test.jsonl` | 1,000 | 3.0 MB |
+| `ligand/train.jsonl` | 50,000 | 183 MB |
+| `ligand/valid.jsonl` | 3,263 | 14 MB |
+| `ligand/test_ligand.jsonl` | 1,500 | 6.4 MB |
+| `ligand/test_ligand_unseen.jsonl` | 127 | 0.5 MB |
+| `antibody/train.jsonl` | 21,384 | 40 MB |
+| `antibody/valid.jsonl` | 433 | 1.2 MB |
+| `antibody/final_antibody_test.jsonl` | 444 | 1.3 MB |
+
+Both training sets are subsampled: `stage1/train.jsonl` is 200,000 rows of the full 205.7M-pair pretraining corpus, and `ligand/train.jsonl` is 50,000 rows. Every other split is complete. The full Stage 1 corpus can be rebuilt from UniProt with the dataset construction code.
+
+## Schema
+
+One JSON object per line.
+
+**Stage 1** (`stage1/`)
+
+| Key | Contents |
+|---|---|
+| `tokenized_instruction` | PubMedBERT token ids — the model input |
+| `output` | target protein sequence, with `<SEP>` and `<M1>`–`<M30>` on infilling examples |
+| `instruction` | the same instruction as text |
+| `task_type` | `infilling` or autoregressive |
+| `metadata` | `id` (UniProt accession), `cluster`, `interpro_all` |
+
+**Stage 2** (`ligand/`, `antibody/`)
+
+| Key | Contents |
+|---|---|
+| `tokenized_instruction` | PubMedBERT token ids — the model input |
+| `target_protein` | target sequence; HCDR spans are masked with `<M1>`–`<M3>` in the antibody splits |
+| `text` | the same instruction as text |
+| `ligand` *or* `antigen` | SMILES strings, or antigen chain sequences |
+| `modality_type` | `ligand` or `antibody` |
+| `metadata` | ligand: `uniprot_id`, `interpro_all`, `interpro_used`, `source`. antibody: `pdb_id`, CDR sequences and positions, light chain, cluster, design blueprint |
+
+The loader requires `tokenized_instruction` and `target_protein` (or `output`), plus `ligand`/`antigen` for the Stage 2 tasks.
+
+## Sources
+
+- **Stage 1** — UniProtKB, with function descriptions composed from InterPro annotations.
+- **Ligand** — InstructProBench, from [InstructPro](https://arxiv.org/abs/2506.09332).
+- **Antibody** — SAbDab and the PDB.
+
+## Citation
+
+```bibtex
+@article{instructpro2,
+  title   = {InstructPro2: Natural Language and Biological Grounding Guided De Novo Protein Design},
+  author  = {Charles Novak and Zhenqiao Song and Chuan Li and Jianwen Xie and Lei Li},
+  journal = {arXiv preprint arXiv:2610.19592},
+  year    = {2026}
+}
+```