Upload Talkie YaRN 32k Gutenberg pre-1931 dataset

.gitattributes

@@ -58,3 +58,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+train.jsonl filter=lfs diff=lfs merge=lfs -text
+validation.jsonl filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+language:
+- en
+license: other
+task_categories:
+- text-generation
+pretty_name: Talkie YaRN 32k Gutenberg Pre-1931 265M
+size_categories:
+- 1K<n<10K
+tags:
+- talkie
+- project-gutenberg
+- pre-1931
+- long-context
+- yarn
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: train.jsonl
+  - split: validation
+    path: validation.jsonl
+---
+
+# Talkie YaRN 32k Gutenberg Pre-1931 265M
+
+This dataset is the continued-pretraining corpus used for the Talkie YaRN 32k
+context-extension experiments, including the recommended checkpoint
+[`xlr8harder/talkie-1930-13b-yarn-32k-tf`](https://huggingface.co/xlr8harder/talkie-1930-13b-yarn-32k-tf).
+
+It was generated from `common-pile/project_gutenberg_filtered`, joined with a
+local Gutenberg publication-year metadata table, then filtered to English
+public-domain books with publication years from 1500 through 1930. Project
+Gutenberg boilerplate markers were stripped and each book was capped at 500,000
+characters.
+
+## Splits
+
+| Split | Rows | Talkie tokens | Notes |
+| --- | ---: | ---: | --- |
+| train | 3,146 | 265,080,702 | Used for continued pretraining |
+| validation | 65 | 5,074,819 | Held-out continuation after the train manifest |
+
+The train JSONL was reconstructed from the surviving manifest after the original
+local file was lost. The reconstruction preserved the original Gutenberg ID
+order and per-book metadata from `train.summary.json`.
+
+## Files
+
+- `train.jsonl`: training split, one book per line.
+- `validation.jsonl`: validation split, one book per line.
+- `train.summary.json`: train manifest with per-book metadata and Talkie token counts.
+- `validation.summary.json`: validation manifest with per-book metadata and Talkie token counts.
+- `scripts/prepare_gutenberg_with_years.py`: generation and reconstruction script.
+- `metadata/gutenberg_publication_years.csv`: publication-year metadata used by the script.
+- `logs/prepare_train.log`: reconstruction log for the train split.
+
+Each JSONL row has this shape:
+
+```json
+{"text": "...", "meta": {"gutenberg_id": "...", "publication_year": 1900, "...": "..."}}
+```
+
+## Generation Command
+
+The train split was reconstructed with:
+
+```bash
+python scripts/prepare_gutenberg_with_years.py \
+  --dataset common-pile/project_gutenberg_filtered \
+  --split train \
+  --metadata-csv metadata/gutenberg_publication_years.csv \
+  --out train.jsonl \
+  --summary-out train.summary.json \
+  --include-summary train.summary.json \
+  --reuse-include-summary-token-counts \
+  --min-year 1500 \
+  --max-year 1930 \
+  --languages en \
+  --rights-contains "public domain" \
+  --min-chars 100000 \
+  --max-chars-per-book 500000 \
+  --target-talkie-tokens 265000000 \
+  --max-books 0
+```
+
+For a fresh build rather than manifest reconstruction, omit `--include-summary`
+and `--reuse-include-summary-token-counts`, and write to a new output path.
+
+The validation split used the same filters while excluding the train manifest:
+
+```bash
+python scripts/prepare_gutenberg_with_years.py \
+  --dataset common-pile/project_gutenberg_filtered \
+  --split train \
+  --metadata-csv metadata/gutenberg_publication_years.csv \
+  --out validation.jsonl \
+  --summary-out validation.summary.json \
+  --exclude-summary train.summary.json \
+  --min-year 1500 \
+  --max-year 1930 \
+  --languages en \
+  --rights-contains "public domain" \
+  --min-chars 100000 \
+  --max-chars-per-book 500000 \
+  --target-talkie-tokens 5000000 \
+  --max-books 0
+```
+
+## Integrity
+
+- `train.jsonl` SHA-256: `50a0908c33e741ce96e9b8c75f266369aac7f1ccd76757970904031b487c283c`
+- `validation.jsonl` SHA-256: `efa8f3218ec219aeb5ffd5cbe70b92c3bb42e4b599dbb74f3dde7dc4b256d21a`
+
+## License Notes
+
+The source rows selected here are marked `Public Domain` in
+`common-pile/project_gutenberg_filtered`. This dataset card uses `other` because
+the corpus is a generated selection from upstream sources plus a metadata join;
+verify source rights and downstream requirements for your use case.

scripts/prepare_gutenberg_with_years.py

@@ -0,0 +1,429 @@
+from __future__ import annotations
+
+import argparse
+from collections import Counter
+from dataclasses import asdict, dataclass
+import csv
+import json
+from pathlib import Path
+import re
+import sys
+from typing import Any
+
+
+DEFAULT_DATASET = "common-pile/project_gutenberg_filtered"
+YEAR_RE = re.compile(r"(?<!\d)(1[5-9]\d{2}|20\d{2})(?!\d)")
+BOILERPLATE_PATTERNS = [
+    re.compile(r"\*\*\*\s*START OF (?:THE|THIS) PROJECT GUTENBERG EBOOK.*?\*\*\*", re.IGNORECASE | re.DOTALL),
+    re.compile(r"\*\*\*\s*END OF (?:THE|THIS) PROJECT GUTENBERG EBOOK.*", re.IGNORECASE | re.DOTALL),
+]
+
+
+@dataclass
+class BookSummary:
+    gutenberg_id: str
+    title: str
+    author: str
+    publication_year: int
+    metadata_title: str
+    metadata_author: str
+    language: str
+    rights: str
+    release_date: str
+    source_chars: int
+    chars: int
+    talkie_tokens: int | None
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description=(
+            "Join a Hugging Face Gutenberg text dataset against a local metadata CSV "
+            "with publication years and emit local JSONL for Talkie context-extension training."
+        )
+    )
+    parser.add_argument("--dataset", default=DEFAULT_DATASET)
+    parser.add_argument("--config")
+    parser.add_argument("--split", default="train")
+    parser.add_argument("--metadata-csv", type=Path, required=True)
+    parser.add_argument("--out", type=Path, default=Path("data/gutenberg_pre1931.jsonl"))
+    parser.add_argument("--summary-out", type=Path, default=Path("data/gutenberg_pre1931.summary.json"))
+    parser.add_argument("--tokenizer-dir", type=Path)
+    parser.add_argument(
+        "--include-summary",
+        type=Path,
+        help=(
+            "Rebuild only the Gutenberg IDs listed in an existing summary, preserving "
+            "that summary's book order. Useful when the JSONL was lost but the manifest remains."
+        ),
+    )
+    parser.add_argument(
+        "--reuse-include-summary-token-counts",
+        action="store_true",
+        help=(
+            "When rebuilding from --include-summary without --tokenizer-dir, copy per-book "
+            "Talkie token counts from that summary instead of re-tokenizing."
+        ),
+    )
+    parser.add_argument("--id-column", default="id")
+    parser.add_argument("--year-column", default="publication_year")
+    parser.add_argument("--metadata-title-column", default="title")
+    parser.add_argument("--metadata-author-column", default="author")
+    parser.add_argument("--min-year", type=int, default=1500)
+    parser.add_argument("--max-year", type=int, default=1930)
+    parser.add_argument("--languages", nargs="+", default=["en"])
+    parser.add_argument("--rights-contains", default="public domain")
+    parser.add_argument("--min-chars", type=int, default=100_000)
+    parser.add_argument("--max-chars-per-book", type=int, default=0)
+    parser.add_argument("--max-books", type=int, default=64)
+    parser.add_argument("--target-talkie-tokens", type=int, default=2_000_000)
+    parser.add_argument(
+        "--append-existing",
+        action="store_true",
+        help=(
+            "Append additional books to --out using --summary-out to avoid duplicate "
+            "Gutenberg IDs. The target token count is interpreted as the desired "
+            "combined total."
+        ),
+    )
+    parser.add_argument(
+        "--exclude-summary",
+        type=Path,
+        action="append",
+        default=[],
+        help="Skip Gutenberg IDs listed in an existing summary JSON. May be supplied more than once.",
+    )
+    parser.add_argument("--strip-gutenberg-boilerplate", action=argparse.BooleanOptionalAction, default=True)
+    parser.add_argument("--trust-remote-code", action=argparse.BooleanOptionalAction, default=False)
+    return parser.parse_args()
+
+
+def import_datasets():
+    try:
+        from datasets import load_dataset
+    except ImportError as error:
+        raise SystemExit(
+            "Missing dependency: install with `uv sync --extra data` or "
+            "`python -m pip install datasets pyarrow`."
+        ) from error
+    return load_dataset
+
+
+def load_tokenizer(path: Path | None):
+    if path is None:
+        return None
+    try:
+        from transformers import AutoTokenizer
+    except ImportError as error:
+        raise SystemExit("Missing dependency: transformers is required for --tokenizer-dir.") from error
+    tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
+    tokenizer.model_max_length = sys.maxsize
+    return tokenizer
+
+
+def scalar(value: Any) -> str:
+    if value is None:
+        return ""
+    return value if isinstance(value, str) else str(value)
+
+
+def row_metadata(row: dict[str, Any]) -> dict[str, Any]:
+    metadata = row.get("metadata")
+    return metadata if isinstance(metadata, dict) else {}
+
+
+def row_value(row: dict[str, Any], key: str, metadata_key: str | None = None) -> Any:
+    value = row.get(key)
+    if value not in (None, ""):
+        return value
+    return row_metadata(row).get(metadata_key or key)
+
+
+def normalize_id(value: Any) -> str:
+    text = scalar(value).strip()
+    if text.upper().startswith("PG"):
+        text = text[2:]
+    return text
+
+
+def parse_year(value: Any) -> int | None:
+    text = scalar(value)
+    match = YEAR_RE.search(text)
+    return int(match.group(1)) if match else None
+
+
+def sniff_columns(fieldnames: list[str], requested: dict[str, str]) -> dict[str, str]:
+    lowered = {name.lower(): name for name in fieldnames}
+    aliases = {
+        "id": ["id", "gutenberg_id", "ebook_id", "book_id", "pg_id", "gutenbergid"],
+        "publication_year": ["publication_year", "year", "issued_date", "issued", "published", "publication_date"],
+        "title": ["title", "book_title"],
+        "author": ["author", "creator", "authors"],
+    }
+    result = {}
+    for key, requested_name in requested.items():
+        if requested_name in fieldnames:
+            result[key] = requested_name
+            continue
+        for alias in aliases[key]:
+            if alias in lowered:
+                result[key] = lowered[alias]
+                break
+        else:
+            raise ValueError(
+                f"could not find {key!r} column. Requested {requested_name!r}; "
+                f"available columns: {fieldnames}"
+            )
+    return result
+
+
+def load_year_metadata(args: argparse.Namespace) -> tuple[dict[str, dict[str, Any]], dict[str, str]]:
+    with args.metadata_csv.open("r", encoding="utf-8-sig", errors="replace", newline="") as handle:
+        reader = csv.DictReader(handle)
+        if reader.fieldnames is None:
+            raise ValueError(f"metadata CSV has no header: {args.metadata_csv}")
+        columns = sniff_columns(
+            reader.fieldnames,
+            {
+                "id": args.id_column,
+                "publication_year": args.year_column,
+                "title": args.metadata_title_column,
+                "author": args.metadata_author_column,
+            },
+        )
+        metadata: dict[str, dict[str, Any]] = {}
+        for row in reader:
+            book_id = normalize_id(row.get(columns["id"]))
+            year = parse_year(row.get(columns["publication_year"]))
+            if not book_id or year is None:
+                continue
+            metadata[book_id] = {
+                "publication_year": year,
+                "title": scalar(row.get(columns["title"])),
+                "author": scalar(row.get(columns["author"])),
+            }
+    return metadata, columns
+
+
+def load_include_manifest(path: Path | None) -> tuple[list[str], dict[str, dict[str, Any]]]:
+    if path is None:
+        return [], {}
+    summary = json.loads(path.read_text(encoding="utf-8"))
+    seen: set[str] = set()
+    include_order: list[str] = []
+    include_manifest: dict[str, dict[str, Any]] = {}
+    for item in summary.get("books", []):
+        book_id = normalize_id(item.get("gutenberg_id"))
+        if book_id and book_id not in seen:
+            seen.add(book_id)
+            include_order.append(book_id)
+            include_manifest[book_id] = item
+    if not include_order:
+        raise ValueError(f"--include-summary has no usable book IDs: {path}")
+    return include_order, include_manifest
+
+
+def clean_text(text: str, strip_boilerplate: bool) -> str:
+    text = text.replace("\r\n", "\n").replace("\r", "\n")
+    if strip_boilerplate:
+        for pattern in BOILERPLATE_PATTERNS:
+            text = pattern.sub("\n", text)
+    return text.strip()
+
+
+def row_ok(row: dict[str, Any], args: argparse.Namespace, metadata: dict[str, Any]) -> bool:
+    if row_value(row, "language") not in set(args.languages):
+        return False
+    rights = scalar(row_value(row, "rights", "license"))
+    if args.rights_contains and args.rights_contains.lower() not in rights.lower():
+        return False
+    year = metadata["publication_year"]
+    return args.min_year <= year <= args.max_year
+
+
+def main() -> None:
+    args = parse_args()
+    load_dataset = import_datasets()
+    tokenizer = load_tokenizer(args.tokenizer_dir)
+    year_metadata, columns = load_year_metadata(args)
+    if not year_metadata:
+        raise ValueError(f"no usable rows found in metadata CSV: {args.metadata_csv}")
+    include_order, include_manifest = load_include_manifest(args.include_summary)
+    include_ids = set(include_order)
+    if include_order and args.append_existing:
+        raise ValueError("--include-summary cannot be combined with --append-existing")
+    if args.reuse_include_summary_token_counts and not include_order:
+        raise ValueError("--reuse-include-summary-token-counts requires --include-summary")
+
+    load_kwargs = {
+        "split": args.split,
+        "streaming": True,
+        "trust_remote_code": args.trust_remote_code,
+    }
+    if args.config:
+        dataset = load_dataset(args.dataset, args.config, **load_kwargs)
+    else:
+        dataset = load_dataset(args.dataset, **load_kwargs)
+    args.out.parent.mkdir(parents=True, exist_ok=True)
+    args.summary_out.parent.mkdir(parents=True, exist_ok=True)
+
+    counters: Counter[str] = Counter()
+    summaries: list[BookSummary] = []
+    included_lines: dict[str, str] = {}
+    included_summaries: dict[str, BookSummary] = {}
+    total_talkie_tokens = 0
+    existing_ids: set[str] = set()
+    existing_counts: dict[str, int] | None = None
+
+    for summary_path in args.exclude_summary:
+        excluded_summary = json.loads(summary_path.read_text(encoding="utf-8"))
+        existing_ids.update(
+            normalize_id(item.get("gutenberg_id"))
+            for item in excluded_summary.get("books", [])
+            if item.get("gutenberg_id") is not None
+        )
+
+    if args.append_existing:
+        if not args.out.exists():
+            raise FileNotFoundError(f"--append-existing requires existing --out: {args.out}")
+        if not args.summary_out.exists():
+            raise FileNotFoundError(
+                f"--append-existing requires existing --summary-out: {args.summary_out}"
+            )
+        existing_summary = json.loads(args.summary_out.read_text(encoding="utf-8"))
+        existing_counts = dict(existing_summary.get("counts") or {})
+        summaries = [BookSummary(**item) for item in existing_summary.get("books", [])]
+        existing_ids.update(item.gutenberg_id for item in summaries)
+        total_talkie_tokens = int(
+            existing_summary.get("total_talkie_tokens")
+            or sum(item.talkie_tokens or 0 for item in summaries)
+        )
+
+    output_mode = "a" if args.append_existing else "w"
+    with args.out.open(output_mode, encoding="utf-8") as out_handle:
+        for row in dataset:
+            counters["seen"] += 1
+            book_id = normalize_id(row.get("id"))
+            if include_ids and book_id not in include_ids:
+                counters["skip_not_in_include_summary"] += 1
+                continue
+            if include_ids and book_id in included_lines:
+                counters["skip_existing"] += 1
+                continue
+            if book_id in existing_ids:
+                counters["skip_existing"] += 1
+                continue
+            meta = year_metadata.get(book_id)
+            if meta is None:
+                counters["skip_missing_year_metadata"] += 1
+                continue
+            if not row_ok(row, args, meta):
+                counters["skip_filter"] += 1
+                continue
+            source_text = scalar(row.get("text"))
+            text = clean_text(source_text, args.strip_gutenberg_boilerplate)
+            if len(text) < args.min_chars:
+                counters["skip_short"] += 1
+                continue
+            if args.max_chars_per_book > 0:
+                text = text[: args.max_chars_per_book]
+
+            talkie_tokens = None
+            if tokenizer is not None:
+                talkie_tokens = len(tokenizer.encode(text, add_special_tokens=False))
+                total_talkie_tokens += talkie_tokens
+            elif args.reuse_include_summary_token_counts:
+                manifest_tokens = include_manifest[book_id].get("talkie_tokens")
+                if manifest_tokens is not None:
+                    talkie_tokens = int(manifest_tokens)
+                    total_talkie_tokens += talkie_tokens
+
+            summary = BookSummary(
+                gutenberg_id=book_id,
+                title=scalar(row_value(row, "title")),
+                author=scalar(row_value(row, "author")),
+                publication_year=int(meta["publication_year"]),
+                metadata_title=scalar(meta.get("title")),
+                metadata_author=scalar(meta.get("author")),
+                language=scalar(row_value(row, "language")),
+                rights=scalar(row_value(row, "rights", "license")),
+                release_date=scalar(row_value(row, "release_date", "added") or row.get("added")),
+                source_chars=len(source_text),
+                chars=len(text),
+                talkie_tokens=talkie_tokens,
+            )
+            line = json.dumps({"text": text, "meta": asdict(summary)}, ensure_ascii=False)
+            if include_ids:
+                included_lines[book_id] = line
+                included_summaries[book_id] = summary
+            else:
+                summaries.append(summary)
+                out_handle.write(line + "\n")
+            counters["written"] += 1
+            print(
+                json.dumps(
+                    {
+                        "written": counters["written"],
+                        "seen": counters["seen"],
+                        "id": book_id,
+                        "publication_year": summary.publication_year,
+                        "talkie_tokens": talkie_tokens,
+                        "title": summary.title[:120],
+                    },
+                    ensure_ascii=False,
+                    sort_keys=True,
+                ),
+                flush=True,
+            )
+
+            if include_ids and len(included_lines) == len(include_order):
+                break
+            if args.max_books and counters["written"] >= args.max_books:
+                break
+            if not include_ids:
+                if args.target_talkie_tokens and total_talkie_tokens >= args.target_talkie_tokens:
+                    break
+
+        if include_ids:
+            missing = [book_id for book_id in include_order if book_id not in included_lines]
+            if missing:
+                preview = ", ".join(missing[:20])
+                raise RuntimeError(
+                    f"missing {len(missing)} IDs from --include-summary while rebuilding {args.out}: "
+                    f"{preview}"
+                )
+            summaries = [included_summaries[book_id] for book_id in include_order]
+            for book_id in include_order:
+                out_handle.write(included_lines[book_id] + "\n")
+
+    summary_obj = {
+        "dataset": args.dataset,
+        "config": args.config,
+        "split": args.split,
+        "metadata_csv": str(args.metadata_csv),
+        "metadata_columns": columns,
+        "filters": {
+            "languages": args.languages,
+            "min_year": args.min_year,
+            "max_year": args.max_year,
+            "rights_contains": args.rights_contains,
+            "min_chars": args.min_chars,
+            "strip_gutenberg_boilerplate": args.strip_gutenberg_boilerplate,
+        },
+        "counts": dict(counters),
+        "existing_counts": existing_counts,
+        "total_talkie_tokens": total_talkie_tokens
+        if tokenizer is not None or args.reuse_include_summary_token_counts
+        else None,
+        "books": [asdict(item) for item in summaries],
+    }
+    args.summary_out.write_text(
+        json.dumps(summary_obj, ensure_ascii=False, indent=2, sort_keys=True) + "\n",
+        encoding="utf-8",
+    )
+    print(f"wrote {args.out}", file=sys.stderr)
+    print(f"wrote {args.summary_out}", file=sys.stderr)
+
+
+if __name__ == "__main__":
+    main()

train.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:50a0908c33e741ce96e9b8c75f266369aac7f1ccd76757970904031b487c283c
+size 1135646608

validation.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:efa8f3218ec219aeb5ffd5cbe70b92c3bb42e4b599dbb74f3dde7dc4b256d21a
+size 21884565

validation.summary.json

@@ -0,0 +1,944 @@
+{
+  "books": [
+    {
+      "author": "",
+      "chars": 346580,
+      "gutenberg_id": "3187",
+      "language": "en",
+      "metadata_author": "Mark Twain",
+      "metadata_title": "Christian Science",
+      "publication_year": 1907,
+      "release_date": "2024-05-14T13:13:13.820316",
+      "rights": "Public Domain",
+      "source_chars": 346580,
+      "talkie_tokens": 80143,
+      "title": "Christian Science"
+    },
+    {
+      "author": "",
+      "chars": 199025,
+      "gutenberg_id": "31872",
+      "language": "en",
+      "metadata_author": "Browne",
+      "metadata_title": "The Christian Church in These Islands before the Coming of Augustine",
+      "publication_year": 1894,
+      "release_date": "2024-05-14T13:13:13.951701",
+      "rights": "Public Domain",
+      "source_chars": 199025,
+      "talkie_tokens": 46547,
+      "title": "The Christian Church in These Islands before the Coming of Augustine\nThree Lectures Delivered at St. Paul's in January 1894"
+    },
+    {
+      "author": "",
+      "chars": 345795,
+      "gutenberg_id": "31873",
+      "language": "en",
+      "metadata_author": "Novalis",
+      "metadata_title": "Henry of Ofterdingen: A Romance.",
+      "publication_year": 1842,
+      "release_date": "2024-05-14T13:13:13.974359",
+      "rights": "Public Domain",
+      "source_chars": 345795,
+      "talkie_tokens": 78169,
+      "title": "Henry of Ofterdingen: A Romance."
+    },
+    {
+      "author": "",
+      "chars": 500000,
+      "gutenberg_id": "31879",
+      "language": "en",
+      "metadata_author": "Hannah More",
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