Create README.md

README.md

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+# UniRef50 (Processed, ESM-valid as Validation)
+
+## Dataset Summary
+
+This dataset is a **preprocessed UniRef50** snapshot tailored for **unsupervised protein representation learning**. It:
+
+* Normalizes sequences (uppercase, `*` removed), filters by length and ambiguity, and deduplicates by MD5.
+* Splits by **UniRef50 cluster ID** to prevent leakage.
+* Uses the **official ESM validation headers** as the entire `valid` split (no sampling).
+* Provides **JSONL.zst shards** for efficient streaming with 🤗 `datasets`.
+
+> If you need the exact preprocessing script: see **Reproducibility** below.
+
+---
+
+## Source
+
+* **Upstream data:** UniProt / UniRef50 (2018_03 snapshot).
+* **Evaluation headers:** `uniref201803_ur50_valid_headers.txt` from the ESM paper.
+
+Please respect UniProt terms when using or redistributing this derivative dataset.
+
+---
+
+## Splits
+
+| Split   | Definition                                                       | Notes                                     |
+| ------- | ---------------------------------------------------------------- | ----------------------------------------- |
+| `train` | All clusters **not** in ESM valid and not hashed into test       | Majority of UniRef50                      |
+| `valid` | **Only** clusters in ESM’s validation header list                | Field `is_esm_valid=true` for all records |
+| `test`  | Hash‐based holdout by cluster: `xxhash64(cluster_id) % 100 == 2` | Small random holdout                      |
+
+> Splitting by **cluster_id** avoids train/val/test contamination across cluster members.
+
+---
+
+## Features (Schema)
+
+| Field          | Type    | Description                                                      |          |
+| -------------- | ------- | ---------------------------------------------------------------- | -------- |
+| `id`           | string  | Stable ID = `cluster_id                                          | md5[:8]` |
+| `sequence`     | string  | Normalized AA sequence (uppercase; `*` removed)                  |          |
+| `length`       | int32   | Sequence length after normalization                              |          |
+| `cluster_id`   | string  | UniRef50 cluster ID (e.g., `UniRef50_Q8WZ42-5`)                  |          |
+| `description`  | string? | Optional description parsed from FASTA header (after `Cluster:`) |          |
+| `seq_md5`      | string  | MD5 of normalized sequence                                       |          |
+| `is_esm_valid` | bool    | `true` iff the record belongs to the ESM validation header set   |          |
+
+> Ambiguous residues: records with ambiguity fraction > 5% (non-canonical AAs) are filtered out by default.
+
+---
+
+## Preprocessing & Filters
+
+* **Normalization:** uppercase, remove terminal/internal `*`.
+* **Length filter:** keep `30 ≤ L ≤ 1024`.
+* **Ambiguity filter:** keep sequences with ≤ **5%** non-canonical residues (`ACDEFGHIKLMNPQRSTVWY` are canonical).
+* **Deduplication:** exact dedup by MD5 of normalized sequence (global).
+* **Splitting:** by `cluster_id` as described above.
+* **Headers:** FASTA lines like
+  `>UniRef50_Q8WZ42-5 Cluster: Isoform 5 of Titin` → `cluster_id="UniRef50_Q8WZ42-5"`, `description="Isoform 5 of Titin"`.
+
+---
+
+## Intended Use
+
+* **Self-supervised training** of protein LMs/encoders that must be robust to substitutions and indels (e.g., OT/UOT objectives).
+* **Evaluation** aligned with the ESM paper by using the official validation header set for `valid`.
+
+Not intended for clinical use. No personal data.
+
+---
+
+## How to Load (Streaming & Local)
+
+### Streaming (recommended for large shards)
+
+```python
+from datasets import load_dataset
+
+repo = "DeepFoldProtein/uniref50_processed"  # replace with your namespace
+ds_train = load_dataset(repo, split="train", streaming=True)
+row = next(iter(ds_train))
+print(row["cluster_id"], row["length"])
+```
+
+### Extract ESM-valid subset (within `valid`)
+
+```python
+from datasets import load_dataset
+
+ds_valid = load_dataset(repo, split="valid", streaming=True)
+esm_valid = ds_valid.filter(lambda x: x["is_esm_valid"])
+print(next(iter(esm_valid)))
+```
+
+### Non-streaming load (small splits only)
+
+```python
+from datasets import load_dataset
+ds_test = load_dataset(repo, split="test")  # materializes locally
+print(len(ds_test))
+```
+
+---
+
+## Quick Stats Helper
+
+Use this helper to print length statistics per split:
+
+```python
+from datasets import load_dataset
+import math
+
+def stats(split):
+    ds = load_dataset("DeepFoldProtein/uniref50_processed", split=split, streaming=True)
+    n=s=s2=0; mn=10**9; mx=0
+    for r in ds:
+        L = int(r.get("length", len(r["sequence"])))
+        n += 1; s += L; s2 += L*L; mn = min(mn, L); mx = max(mx, L)
+    mean = s/n if n else float("nan")
+    std = math.sqrt(max(0.0, s2/n - mean*mean)) if n else float("nan")
+    return {"count": n, "min": mn, "max": mx, "mean": mean, "std": std}
+
+print(stats("train"))
+print(stats("valid"))
+print(stats("test"))
+```
+
+---
+
+## Licensing
+
+* **Data source:** UniProt / UniRef50. Follow the UniProt license and attribution requirements: [https://www.uniprot.org/help/license](https://www.uniprot.org/help/license)
+* **Derivative dataset:** You must attribute UniProt and include a link to their license when redistributing.
+* **Code (preprocessing):** Provide your own license for the script if you distribute it.
+
+---
+
+## Citation
+
+If you use this dataset, please cite UniProt and (optionally) ESM:
+
+**UniProt:**
+
+> The UniProt Consortium. *UniProt: the universal protein knowledgebase.* Nucleic Acids Res. (2018)
+
+**ESM:**
+
+> Rives et al. *Evolutionary-scale prediction of atomic-level protein structure with a language model.* Science (2023).
+
+---
+
+## Known Limitations
+
+* **Snapshot drift:** This mirrors UniRef50 (2018_03) conventions; later UniRef releases may differ.
+* **Non-random validation:** `valid` is defined by ESM’s curated header list (by design).
+* **Ambiguity handling:** Sequences with >5% ambiguous residues are dropped; adjust if you need broader coverage.
+* **Dedup scope:** Deduplication is by normalized sequence only (not by cluster consensus).
+
+---
+
+## Changelog / Versioning
+
+* **v1.0:** Initial release — ESM-valid set defines `valid`; hash-based `test`; JSONL.zst shards; manifest schema above.
+* Future updates will be tagged with semantic versions and described here.
+
+---
+
+## Contact
+
+* **Issues:** Please open a GitHub issue or HF discussion on this dataset repo.
+
+---
+
+If you’d like, I can also generate a minimal `dataset_info.yaml` with this schema so the Hub shows the features immediately.