Use JSONL default file index for UniProtKB

README.md

@@ -17,16 +17,16 @@ tags:
   - protein-sequences
   - bioinformatics
   - train-validation-test-split
-  - parquet
+  - jsonl
 configs:
   - config_name: default
     data_files:
       - split: train
         path:
-          - data/train-*.parquet
+          - data/train-*.jsonl.gz
       - split: test
         path:
-          - data/test-*.parquet
+          - data/test-*.jsonl.gz
   - config_name: sprot
     data_files:
       - split: train
@@ -66,7 +66,7 @@ configs:
 
 This repository contains two useful views of LiteFold/UniProtKB:
 
-- `default`: a compact Parquet file/table shard index that is easy to browse in the Hugging Face Dataset Viewer.
+- `default`: a compact JSONL file/table shard index that is easy to browse in the Hugging Face Dataset Viewer.
 - `sprot`, `sprot_varsplic`, and `trembl`: the full parsed UniProtKB protein-entry tables from the original repository.
 
 The `default` config does not duplicate all 203M protein rows. It indexes the repository files, table shards, source sets, source sizes, and split-level row counts so the dataset has a stable table preview while the full source-specific tables remain available through named configs.
@@ -183,13 +183,13 @@ print(trembl_train_shards[0]["path"])
 | `path` | string | Path in this Hugging Face repository. |
 | `role` | string | File role such as `protein_entry_table_shard`, `sequence_shard`, or `metadata_records`. |
 | `table_split` | string | Protein-entry split for table shards. |
-| `shard_index` | int64 | Parsed shard index when present. |
+| `shard_index` | int64 | Parsed shard index when present, otherwise `-1`. |
 | `size_bytes` | int64 | File size in bytes. |
 | `compression` | string | Compression format when applicable. |
-| `records_in_source` | int64 | Protein records in the source set. |
-| `residues_in_source` | int64 | Residues in the source set. |
-| `shards_in_source` | int64 | Number of sequence shards in the source set. |
-| `records_in_table_split` | int64 | Protein records in that source set and split. |
+| `records_in_source` | int64 | Protein records in the source set, otherwise `-1`. |
+| `residues_in_source` | int64 | Residues in the source set, otherwise `-1`. |
+| `shards_in_source` | int64 | Number of sequence shards in the source set, otherwise `-1`. |
+| `records_in_table_split` | int64 | Protein records in that source set and split, otherwise `-1`. |
 | `records_total` | int64 | Total protein records across UniProtKB. |
 | `residues_total` | int64 | Total residues across UniProtKB. |
 | `total_sequence_shards` | int64 | Total sequence shards. |
@@ -202,7 +202,7 @@ print(trembl_train_shards[0]["path"])
 
 ## Files
 
-- `data/*.parquet`: default file/table index for Dataset Viewer.
+- `data/*.jsonl.gz`: default file/table index for Dataset Viewer.
 - `tables/source_set=*/split=*/*.jsonl.gz`: full parsed protein-entry tables.
 - `sequences/*/*.fasta.zst`: compressed source sequence shards.
 - `metadata/*.records.jsonl`: source metadata records.

data/{test-00000-of-00001.parquet → test-00000-of-00001.jsonl.gz}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:1ad09814374b0b7a4a08ced4b9506e1942211af9762888d686d9aad8a0606f74
-size 12040
+oid sha256:76211f31cb7fd7946490c46d8bb51e6d6482af1718c509c3fa682637f2a29904
+size 2870

data/{train-00000-of-00001.parquet → train-00000-of-00001.jsonl.gz}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:4b79c8277efcf4c59fc191a9ebda30acaac2ab774e6945db6f7e40b364bdab42
-size 19485
+oid sha256:8dee44ac9b822c1ea04309b67b7fe5c179cf70108befc8282819f462a5be40be
+size 15807

dataset_summary.json

@@ -1,7 +1,8 @@
 {
   "source": "LiteFold/UniProtKB",
-  "source_sha": "d8eb6d76820f33ce4e3d6bcee582f6b474f3bbd9",
+  "source_sha": "ea4b6633410d5e2158cdb0b97fdcabd70bce3c27",
   "viewer_table_scope": "file/table shard index",
+  "data_format": "jsonl.gz",
   "dataset_id": "uniprotkb",
   "source_count": 3,
   "records_total": 203172274,

metadata/{source_files.parquet → source_files.jsonl.gz}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d69339351bc5098075c828e72cb58eb90206744e2106e7f11bbcb6eed4eff7e8
-size 20213
+oid sha256:ab93f039e7f37d0157a47ff2c0d839240edc7372f882a34d4c66d7ed6b8cebc2
+size 17700

scripts/prepare_uniprotkb_dataset.py

@@ -4,6 +4,7 @@
 from __future__ import annotations
 
 import argparse
+import gzip
 import hashlib
 import json
 import os
@@ -12,8 +13,6 @@ import shutil
 from pathlib import Path
 from typing import Any
 
-import pyarrow as pa
-import pyarrow.parquet as pq
 from huggingface_hub import HfApi, hf_hub_download
 
 
@@ -47,36 +46,34 @@ INDEX_COLUMNS = [
 ]
 
 
-SCHEMA = pa.schema(
-    [
-        pa.field("file_id", pa.string()),
-        pa.field("repo_id", pa.string()),
-        pa.field("source_sha", pa.string()),
-        pa.field("dataset_id", pa.string()),
-        pa.field("source_set", pa.string()),
-        pa.field("source_slug", pa.string()),
-        pa.field("source_file", pa.string()),
-        pa.field("path", pa.string()),
-        pa.field("role", pa.string()),
-        pa.field("table_split", pa.string()),
-        pa.field("shard_index", pa.int64()),
-        pa.field("size_bytes", pa.int64()),
-        pa.field("compression", pa.string()),
-        pa.field("records_in_source", pa.int64()),
-        pa.field("residues_in_source", pa.int64()),
-        pa.field("shards_in_source", pa.int64()),
-        pa.field("records_in_table_split", pa.int64()),
-        pa.field("records_total", pa.int64()),
-        pa.field("residues_total", pa.int64()),
-        pa.field("total_sequence_shards", pa.int64()),
-        pa.field("is_sequence_shard", pa.bool_()),
-        pa.field("is_table_shard", pa.bool_()),
-        pa.field("is_metadata_records", pa.bool_()),
-        pa.field("download_pattern", pa.string()),
-        pa.field("access_note", pa.string()),
-        pa.field("split_bucket", pa.int64()),
-    ]
-)
+STRING_COLUMNS = {
+    "file_id",
+    "repo_id",
+    "source_sha",
+    "dataset_id",
+    "source_set",
+    "source_slug",
+    "source_file",
+    "path",
+    "role",
+    "table_split",
+    "compression",
+    "download_pattern",
+    "access_note",
+}
+
+INT_COLUMNS = {
+    "shard_index",
+    "size_bytes",
+    "records_in_source",
+    "residues_in_source",
+    "shards_in_source",
+    "records_in_table_split",
+    "records_total",
+    "residues_total",
+    "total_sequence_shards",
+    "split_bucket",
+}
 
 
 SOURCE_SET_BY_SLUG = {
@@ -188,6 +185,24 @@ def compression_for_path(path: str) -> str | None:
     return None
 
 
+def viewer_row(row: dict[str, Any]) -> dict[str, Any]:
+    stable = {}
+    for column in INDEX_COLUMNS:
+        value = row.get(column)
+        if value is None and column in STRING_COLUMNS:
+            value = ""
+        elif value is None and column in INT_COLUMNS:
+            value = -1
+        stable[column] = value
+    return stable
+
+
+def write_jsonl_gz(path: Path, rows: list[dict[str, Any]]) -> None:
+    with gzip.open(path, "wt", encoding="utf-8") as handle:
+        for row in rows:
+            handle.write(json.dumps(viewer_row(row), sort_keys=True, separators=(",", ":")) + "\n")
+
+
 def build_dataset(repo_id: str, raw_dir: Path, out_dir: Path) -> dict[str, Any]:
     token = load_token()
     api = HfApi(token=token)
@@ -218,7 +233,12 @@ def build_dataset(repo_id: str, raw_dir: Path, out_dir: Path) -> dict[str, Any]:
     rows = []
     for sibling in sorted(info.siblings or [], key=lambda item: item.rfilename):
         path = sibling.rfilename
-        if path.startswith("data/") or path.startswith("dataset_summary") or path.startswith("scripts/"):
+        if (
+            path.startswith("data/")
+            or path.startswith("dataset_summary")
+            or path.startswith("scripts/")
+            or path.startswith("metadata/source_files.")
+        ):
             continue
         parsed = parse_path(path)
         source_slug = parsed["source_slug"]
@@ -275,9 +295,9 @@ def build_dataset(repo_id: str, raw_dir: Path, out_dir: Path) -> dict[str, Any]:
 
     train_rows = sorted((row for row in rows if row["split_bucket"] != 0), key=lambda row: row["path"])
     test_rows = sorted((row for row in rows if row["split_bucket"] == 0), key=lambda row: row["path"])
-    pq.write_table(pa.Table.from_pylist(train_rows, schema=SCHEMA), data_dir / "train-00000-of-00001.parquet", compression="zstd")
-    pq.write_table(pa.Table.from_pylist(test_rows, schema=SCHEMA), data_dir / "test-00000-of-00001.parquet", compression="zstd")
-    pq.write_table(pa.Table.from_pylist(rows, schema=SCHEMA), metadata_dir / "source_files.parquet", compression="zstd")
+    write_jsonl_gz(data_dir / "train-00000-of-00001.jsonl.gz", train_rows)
+    write_jsonl_gz(data_dir / "test-00000-of-00001.jsonl.gz", test_rows)
+    write_jsonl_gz(metadata_dir / "source_files.jsonl.gz", sorted(rows, key=lambda row: row["path"]))
 
     role_counts: dict[str, int] = {}
     source_set_counts: dict[str, int] = {}
@@ -293,6 +313,7 @@ def build_dataset(repo_id: str, raw_dir: Path, out_dir: Path) -> dict[str, Any]:
         "source": repo_id,
         "source_sha": info.sha,
         "viewer_table_scope": "file/table shard index",
+        "data_format": "jsonl.gz",
         "dataset_id": dataset_id,
         "source_count": int(manifest["source_count"]),
         "records_total": total_records,