Add dataset creation script

create_dataset.py

@@ -0,0 +1,174 @@
+#!/usr/bin/env python3
+"""
+Create viktoroo/longbench-pro-128k-plus from caskcsg/LongBench-Pro by:
+- filtering to token_length in {"128k", "256k"}
+- keeping only fields: id, context
+- renaming context -> text
+- pushing the filtered dataset to the (already-existing) public repo
+- uploading this script and a hardcoded README.md into the same dataset repo
+
+Requirements:
+  pip install -U datasets huggingface_hub
+
+Auth:
+  export HF_TOKEN=...   (must have write access to viktoroo/longbench-pro-128k-plus)
+"""
+
+from __future__ import annotations
+
+import os
+import sys
+import tempfile
+from pathlib import Path
+
+from datasets import load_dataset, DatasetDict
+from huggingface_hub import HfApi
+
+from dotenv import load_dotenv
+load_dotenv()
+
+
+SOURCE_DATASET = "caskcsg/LongBench-Pro"
+TARGET_REPO = "viktoroo/longbench-pro-128k-plus"  # existing, public
+ALLOWED_TOKEN_LENGTH = {"128k", "256k"}  # values in token_length field
+
+README_MD = """---
+license: other
+language:
+- en
+- zh
+tags:
+- long-context
+- benchmark
+- evaluation
+- rag
+pretty_name: LongBench Pro 128k+
+---
+
+# LongBench Pro 128k+
+
+This dataset is a filtered subset of **LongBench Pro** (`caskcsg/LongBench-Pro`).
+
+## What is included
+
+Only examples whose `token_length` field is one of:
+
+- `128k`
+- `256k`
+
+## Columns
+
+This repo keeps only:
+
+- `id`: example identifier (copied from source)
+- `text`: the original `context` field (renamed from `context` → `text`)
+
+All other fields from the source dataset are dropped.
+
+## Intended use
+
+Use this dataset when you want to benchmark long-context behavior specifically at **≥128k** length buckets, while keeping the input surface minimal (`id`, `text`).
+
+## Provenance / attribution
+
+Source dataset: `caskcsg/LongBench-Pro`.
+
+This repo contains a derived subset. Please consult the source dataset card for:
+- full task definitions
+- original annotations/fields
+- licensing/usage terms
+
+## Reproducibility
+
+The filtering logic and transformation used to build this dataset are contained in `create_dataset.py` in this repo.
+"""
+
+
+def require_token() -> str:
+    token = os.environ.get("HF_TOKEN") or os.environ.get("HUGGINGFACE_TOKEN")
+    if not token:
+        raise RuntimeError("Missing HF_TOKEN (or HUGGINGFACE_TOKEN) env var.")
+    return token
+
+
+def filter_and_project(ds: DatasetDict) -> DatasetDict:
+    out = DatasetDict()
+    for split, d in ds.items():
+        if "token_length" not in d.column_names:
+            raise RuntimeError(f"Split '{split}' has no 'token_length' column.")
+
+        if "context" not in d.column_names:
+            raise RuntimeError(f"Split '{split}' has no 'context' column.")
+
+        if "id" not in d.column_names:
+            raise RuntimeError(f"Split '{split}' has no 'id' column.")
+
+        d2 = d.filter(lambda ex: ex["token_length"] in ALLOWED_TOKEN_LENGTH)
+
+        # Keep only id + context, then rename context -> text
+        d2 = d2.select_columns(["id", "context"]).rename_column("context", "text")
+        out[split] = d2
+
+    return out
+
+
+def main() -> int:
+    token = require_token()
+
+    print(f"Loading source dataset: {SOURCE_DATASET}")
+    ds = load_dataset(SOURCE_DATASET)  # DatasetDict
+
+    print("Filtering and projecting columns...")
+    out = filter_and_project(ds)
+
+    # Quick stats
+    for split, d in out.items():
+        print(f"Split '{split}': {len(d)} rows; columns={d.column_names}")
+
+    print(f"Pushing dataset to hub: {TARGET_REPO}")
+    out.push_to_hub(
+        TARGET_REPO,
+        token=token,
+        private=False,
+        commit_message="Create/update filtered LongBench Pro subset (128k, 256k) with id+text",
+    )
+
+    # Upload README and script to the dataset repo
+    api = HfApi(token=token)
+
+    # Determine the path to this script (works when running as a file)
+    script_path = Path(__file__).resolve()
+
+    with tempfile.TemporaryDirectory() as td:
+        td_path = Path(td)
+        readme_path = td_path / "README.md"
+        readme_path.write_text(README_MD, encoding="utf-8")
+
+        print("Uploading README.md...")
+        api.upload_file(
+            path_or_fileobj=str(readme_path),
+            path_in_repo="README.md",
+            repo_id=TARGET_REPO,
+            repo_type="dataset",
+            commit_message="Add dataset README",
+        )
+
+    print("Uploading create_dataset.py...")
+    api.upload_file(
+        path_or_fileobj=str(script_path),
+        path_in_repo="create_dataset.py",
+        repo_id=TARGET_REPO,
+        repo_type="dataset",
+        commit_message="Add dataset creation script",
+    )
+
+    print("Done.")
+    return 0
+
+
+if __name__ == "__main__":
+    try:
+        raise SystemExit(main())
+    except Exception as e:
+        print(f"ERROR: {e}", file=sys.stderr)
+        raise