Add long-context PDF extraction script

long-context-pdfs.py

@@ -0,0 +1,136 @@
+# /// script
+# requires-python = ">=3.10"
+# dependencies = [
+#     "polars",
+#     "datasets",
+# ]
+# ///
+"""
+Extract long-context, high-quality PDFs from finepdfs.
+
+Creates a curated subset of long documents with high OCR quality -
+useful for long-context model training.
+
+Examples:
+    # Quick test (Welsh)
+    uv run long-context-pdfs.py --lang cym_Latn --limit 100
+
+    # English long docs
+    uv run long-context-pdfs.py --lang eng_Latn --output user/finepdfs-eng-long
+
+    # All Latin scripts
+    uv run long-context-pdfs.py --lang "*_Latn" --output user/finepdfs-long-context
+
+    # HF Jobs
+    hf jobs run uv --flavor cpu-basic -- run \\
+        https://huggingface.co/.../long-context-pdfs.py \\
+        --lang eng_Latn --output user/finepdfs-eng-long
+"""
+
+import argparse
+import polars as pl
+from datasets import Dataset
+
+
+def polars_to_generator(lf: pl.LazyFrame):
+    """Stream LazyFrame as row generator."""
+    for batch_df in lf.collect_batches():
+        yield from batch_df.iter_rows(named=True)
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Extract long-context high-quality PDFs")
+    parser.add_argument("--lang", type=str, default="cym_Latn",
+                        help="Language code or glob pattern (default: cym_Latn, use '*_Latn' for all Latin)")
+    parser.add_argument("--min-tokens", type=int, default=10000,
+                        help="Minimum token count (default: 10000)")
+    parser.add_argument("--min-lid-score", type=float, default=0.8,
+                        help="Minimum language ID score (default: 0.8)")
+    parser.add_argument("--limit", type=int, help="Limit rows")
+    parser.add_argument("--output", type=str, help="Output dataset repo")
+    parser.add_argument("--private", action="store_true")
+
+    args = parser.parse_args()
+
+    source = f"hf://datasets/HuggingFaceFW/finepdfs/data/{args.lang}/train/*.parquet"
+
+    print("=" * 60)
+    print("Long-Context High-Quality PDF Extraction")
+    print("=" * 60)
+    print(f"Source: {source}")
+    print(f"Filters:")
+    print(f"  - token_count >= {args.min_tokens}")
+    print(f"  - page_average_lid_score >= {args.min_lid_score}")
+    print(f"  - extractor == 'docling'")
+    if args.limit:
+        print(f"  - limit: {args.limit}")
+    print("=" * 60)
+
+    # Build query - simpler filters first, OCR quality filter can be tricky
+    lf = (
+        pl.scan_parquet(source)
+        .filter(
+            (pl.col("token_count") >= args.min_tokens)
+            & (pl.col("page_average_lid_score") >= args.min_lid_score)
+            & (pl.col("extractor") == "docling")
+        )
+        .select([
+            "id",
+            "url",
+            "text",
+            "language",
+            "token_count",
+            "dump",
+            "page_average_lid_score",
+        ])
+    )
+
+    if args.limit:
+        lf = lf.limit(args.limit)
+
+    # Preview
+    print("\nPreviewing...")
+    preview = lf.limit(5).collect()
+    print(f"Sample rows: {len(preview)}")
+    if len(preview) > 0:
+        print(preview.select(["language", "token_count", "page_average_lid_score"]))
+    else:
+        print("No rows matched! Try lowering thresholds.")
+        return
+
+    if not args.output:
+        print("\nNo --output specified. Use --output to push to Hub.")
+        return
+
+    # Rebuild query for streaming
+    lf = (
+        pl.scan_parquet(source)
+        .filter(
+            (pl.col("token_count") >= args.min_tokens)
+            & (pl.col("page_average_lid_score") >= args.min_lid_score)
+            & (pl.col("extractor") == "docling")
+        )
+        .select([
+            "id",
+            "url",
+            "text",
+            "language",
+            "token_count",
+            "dump",
+            "page_average_lid_score",
+        ])
+    )
+    if args.limit:
+        lf = lf.limit(args.limit)
+
+    print(f"\nStreaming to dataset...")
+    ds = Dataset.from_generator(lambda: polars_to_generator(lf))
+    print(f"Created dataset with {len(ds)} rows")
+
+    print(f"\nPushing to {args.output}...")
+    ds.push_to_hub(args.output, private=args.private)
+    print(f"\nDone! https://huggingface.co/datasets/{args.output}")
+
+
+if __name__ == "__main__":
+    main()