Add phrase frequency stats and HF card snippet for Polish DynaWord

README.md

@@ -124,3 +124,46 @@ CHANGELOG.
 python3 src/build_dynaword.py --all --speakleash-dir <speakleash_zst_dir> --out .
 python3 src/make_docs.py
 ```
+
+## Corpus phrase frequency (normalized by tokens)
+
+To show how frequent legal and discourse markers are across the corpus, we can report counts normalized by token count per source and globally. Raw counts + percentages are generated from the current parquet data and source token counts:
+
+```bash
+python3 src/pattern_frequency_report.py --data-root . \
+  --out-md pattern_frequency_report.md \
+  --out-hf artifacts/pattern_frequency_hf_snippet.md \
+  --out-png artifacts/pattern_frequency.png
+```
+
+`pattern_frequency_report.md` contains full source-by-source breakdown.
+`artifacts/pattern_frequency_hf_snippet.md` is the exact block for Hugging Face model card.
+
+| pattern | count | share of all corpus tokens |
+|---|---:|---:|
+| `w roku` | 434,882 | 0.0070% |
+| `klasyfikacji` | 129,963 | 0.0021% |
+| `ustawa` | 586,803 | 0.0094% |
+| `artykuł` | 2,035,630 | 0.0327% |
+| `parlament` | 1,201,401 | 0.0193% |
+| `rozporządzenie` | 1,490,399 | 0.0240% |
+| `w pobliżu` | 77,561 | 0.0012% |
+| `mieszkańców` | 240,332 | 0.0039% |
+| `Dz.U.` | 939,966 | 0.0151% |
+
+![Overall pattern counts](artifacts/pattern_frequency_overall.png)
+
+Per-source normalized shares:
+- [w roku](artifacts/pattern_frequency_w_roku.png)
+- [klasyfikacji](artifacts/pattern_frequency_klasyfikacji.png)
+- [ustawa](artifacts/pattern_frequency_ustawa.png)
+- [artykuł](artifacts/pattern_frequency_artykul.png)
+- [parlament](artifacts/pattern_frequency_parlament.png)
+- [rozporządzenie](artifacts/pattern_frequency_rozporządzenie.png)
+- [w pobliżu](artifacts/pattern_frequency_w_pobliżu.png)
+- [mieszkańców](artifacts/pattern_frequency_mieszkańców.png)
+- [Dz.U.](artifacts/pattern_frequency_dzu.png)
+
+### Hugging Face Model Card block
+
+Wklej dokładnie `artifacts/pattern_frequency_hf_snippet.md` do sekcji **Results** w model card (`README.md` repozytorium na HF).

artifacts/pattern_frequency_hf_snippet.md

@@ -0,0 +1,136 @@
+## Phrase frequency (token-normalized)
+
+### Global corpus totals
+
+Token counts are computed with the same tiktoken proxy used in source stats (`cl100k_base`).
+
+| Pattern | Count | Share of total tokens |
+|---|---:|---:|
+| `w roku` | 434,882 | 0.0070% |
+| `klasyfikacji` | 129,963 | 0.0021% |
+| `ustawa` | 586,803 | 0.0094% |
+| `artykuł` | 2,035,630 | 0.0327% |
+| `parlament` | 1,201,401 | 0.0193% |
+| `rozporządzenie` | 1,490,399 | 0.0240% |
+| `w pobliżu` | 77,561 | 0.0012% |
+| `mieszkańców` | 240,332 | 0.0039% |
+| `Dz.U.` | 939,966 | 0.0151% |
+
+![overall-pattern-frequency](artifacts/pattern_frequency_overall.png)
+
+### Per-source token-normalized shares
+
+Plots:
+- [w roku](artifacts/pattern_frequency_w_roku.png)
+- [klasyfikacji](artifacts/pattern_frequency_klasyfikacji.png)
+- [ustawa](artifacts/pattern_frequency_ustawa.png)
+- [artykuł](artifacts/pattern_frequency_artykul.png)
+- [parlament](artifacts/pattern_frequency_parlament.png)
+- [rozporządzenie](artifacts/pattern_frequency_rozporządzenie.png)
+- [w pobliżu](artifacts/pattern_frequency_w_pobliżu.png)
+- [mieszkańców](artifacts/pattern_frequency_mieszkańców.png)
+- [Dz.U.](artifacts/pattern_frequency_dzu.png)
+
+### Source-level full table
+
+| source | pattern | count | share of source tokens |
+|---|---|---:|---:|
+| dziennik_ustaw | `w roku` | 33,096 | 0.00681% |
+| dziennik_ustaw | `klasyfikacji` | 10,896 | 0.00224% |
+| dziennik_ustaw | `ustawa` | 77,376 | 0.01592% |
+| dziennik_ustaw | `artykuł` | 27,773 | 0.00571% |
+| dziennik_ustaw | `parlament` | 42,338 | 0.00871% |
+| dziennik_ustaw | `rozporządzenie` | 166,383 | 0.03423% |
+| dziennik_ustaw | `w pobliżu` | 1,087 | 0.00022% |
+| dziennik_ustaw | `mieszkańców` | 8,025 | 0.00165% |
+| dziennik_ustaw | `Dz.U.` | 158 | 0.00003% |
+| eltec_pol | `w roku` | 108 | 0.00050% |
+| eltec_pol | `klasyfikacji` | 1 | 0.00000% |
+| eltec_pol | `ustawa` | 153 | 0.00071% |
+| eltec_pol | `artykuł` | 173 | 0.00081% |
+| eltec_pol | `parlament` | 95 | 0.00044% |
+| eltec_pol | `rozporządzenie` | 35 | 0.00016% |
+| eltec_pol | `w pobliżu` | 246 | 0.00114% |
+| eltec_pol | `mieszkańców` | 214 | 0.00100% |
+| eltec_pol | `Dz.U.` | 0 | 0.00000% |
+| eurlex | `w roku` | 40,009 | 0.00168% |
+| eurlex | `klasyfikacji` | 59,428 | 0.00250% |
+| eurlex | `ustawa` | 30,368 | 0.00128% |
+| eurlex | `artykuł` | 1,774,958 | 0.07464% |
+| eurlex | `parlament` | 780,286 | 0.03281% |
+| eurlex | `rozporządzenie` | 1,202,658 | 0.05057% |
+| eurlex | `w pobliżu` | 6,088 | 0.00026% |
+| eurlex | `mieszkańców` | 9,441 | 0.00040% |
+| eurlex | `Dz.U.` | 915,707 | 0.03851% |
+| parliamentary | `w roku` | 198,192 | 0.01203% |
+| parliamentary | `klasyfikacji` | 12,637 | 0.00077% |
+| parliamentary | `ustawa` | 459,179 | 0.02788% |
+| parliamentary | `artykuł` | 182,038 | 0.01105% |
+| parliamentary | `parlament` | 309,695 | 0.01881% |
+| parliamentary | `rozporządzenie` | 113,547 | 0.00689% |
+| parliamentary | `w pobliżu` | 4,964 | 0.00030% |
+| parliamentary | `mieszkańców` | 78,048 | 0.00474% |
+| parliamentary | `Dz.U.` | 23,809 | 0.00145% |
+| wikibooks | `w roku` | 319 | 0.00205% |
+| wikibooks | `klasyfikacji` | 37 | 0.00024% |
+| wikibooks | `ustawa` | 165 | 0.00106% |
+| wikibooks | `artykuł` | 732 | 0.00470% |
+| wikibooks | `parlament` | 283 | 0.00182% |
+| wikibooks | `rozporządzenie` | 131 | 0.00084% |
+| wikibooks | `w pobliżu` | 125 | 0.00080% |
+| wikibooks | `mieszkańców` | 204 | 0.00131% |
+| wikibooks | `Dz.U.` | 16 | 0.00010% |
+| wikinews | `w roku` | 449 | 0.00370% |
+| wikinews | `klasyfikacji` | 639 | 0.00526% |
+| wikinews | `ustawa` | 407 | 0.00335% |
+| wikinews | `artykuł` | 2,474 | 0.02038% |
+| wikinews | `parlament` | 2,530 | 0.02084% |
+| wikinews | `rozporządzenie` | 168 | 0.00138% |
+| wikinews | `w pobliżu` | 455 | 0.00375% |
+| wikinews | `mieszkańców` | 1,014 | 0.00835% |
+| wikinews | `Dz.U.` | 27 | 0.00022% |
+| wikipedia | `w roku` | 143,023 | 0.02022% |
+| wikipedia | `klasyfikacji` | 46,043 | 0.00651% |
+| wikipedia | `ustawa` | 9,536 | 0.00135% |
+| wikipedia | `artykuł` | 28,165 | 0.00398% |
+| wikipedia | `parlament` | 57,863 | 0.00818% |
+| wikipedia | `rozporządzenie` | 5,637 | 0.00080% |
+| wikipedia | `w pobliżu` | 41,915 | 0.00593% |
+| wikipedia | `mieszkańców` | 122,766 | 0.01736% |
+| wikipedia | `Dz.U.` | 232 | 0.00003% |
+| wikiquote | `w roku` | 608 | 0.00191% |
+| wikiquote | `klasyfikacji` | 15 | 0.00005% |
+| wikiquote | `ustawa` | 357 | 0.00112% |
+| wikiquote | `artykuł` | 606 | 0.00190% |
+| wikiquote | `parlament` | 1,271 | 0.00398% |
+| wikiquote | `rozporządzenie` | 27 | 0.00008% |
+| wikiquote | `w pobliżu` | 207 | 0.00065% |
+| wikiquote | `mieszkańców` | 527 | 0.00165% |
+| wikiquote | `Dz.U.` | 6 | 0.00002% |
+| wikisource | `w roku` | 16,571 | 0.00207% |
+| wikisource | `klasyfikacji` | 166 | 0.00002% |
+| wikisource | `ustawa` | 8,156 | 0.00102% |
+| wikisource | `artykuł` | 16,230 | 0.00202% |
+| wikisource | `parlament` | 6,119 | 0.00076% |
+| wikisource | `rozporządzenie` | 1,651 | 0.00021% |
+| wikisource | `w pobliżu` | 13,921 | 0.00174% |
+| wikisource | `mieszkańców` | 14,335 | 0.00179% |
+| wikisource | `Dz.U.` | 5 | 0.00000% |
+| wikivoyage | `w roku` | 609 | 0.00356% |
+| wikivoyage | `klasyfikacji` | 21 | 0.00012% |
+| wikivoyage | `ustawa` | 46 | 0.00027% |
+| wikivoyage | `artykuł` | 657 | 0.00384% |
+| wikivoyage | `parlament` | 249 | 0.00145% |
+| wikivoyage | `rozporządzenie` | 34 | 0.00020% |
+| wikivoyage | `w pobliżu` | 6,480 | 0.03783% |
+| wikivoyage | `mieszkańców` | 4,100 | 0.02394% |
+| wikivoyage | `Dz.U.` | 1 | 0.00001% |
+| wolne_lektury | `w roku` | 1,898 | 0.00184% |
+| wolne_lektury | `klasyfikacji` | 80 | 0.00008% |
+| wolne_lektury | `ustawa` | 1,060 | 0.00103% |
+| wolne_lektury | `artykuł` | 1,824 | 0.00177% |
+| wolne_lektury | `parlament` | 672 | 0.00065% |
+| wolne_lektury | `rozporządzenie` | 128 | 0.00012% |
+| wolne_lektury | `w pobliżu` | 2,073 | 0.00201% |
+| wolne_lektury | `mieszkańców` | 1,658 | 0.00161% |
+| wolne_lektury | `Dz.U.` | 5 | 0.00000% |

src/pattern_frequency_report.py

@@ -0,0 +1,202 @@
+#!/usr/bin/env python3
+"""Generate pattern-frequency report as percentage of source token counts.
+
+Outputs:
+- summary for whole corpus (counts + share of total tokens)
+- per-source counts + share within source
+- optional markdown snippet and optional bar chart
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import pyarrow.compute as pc
+import pyarrow.parquet as pq
+
+PATTERNS = [
+    ("w roku", "w roku"),
+    ("klasyfikacji", "klasyfikacji"),
+    ("ustawa", "ustawa"),
+    ("artykuł", "artykuł"),
+    ("parlament", "parlament"),
+    ("rozporządzenie", "rozporządzenie"),
+    ("w pobliżu", "w pobliżu"),
+    ("mieszkańców", "mieszkańców"),
+    ("Dz.U.", "dz\\.u\\."),
+]
+
+
+def load_tokens_by_source(root: Path) -> dict[str, int]:
+    by_source = {}
+    for stats_file in sorted((root / "data").glob("*/*.stats.json")):
+        src = stats_file.parent.name
+        payload = json.loads(stats_file.read_text(encoding="utf-8"))
+        by_source[src] = int(payload["tokens"])
+    return by_source
+
+
+def count_patterns_for_source(parquet_path: Path) -> dict[str, int]:
+    counts = {name: 0 for name, _ in PATTERNS}
+    pf = pq.ParquetFile(parquet_path)
+
+    for rg in range(pf.num_row_groups):
+        table = pf.read_row_group(rg, columns=["text"])
+        text = table["text"]
+        text = pc.utf8_lower(text)
+        text = pc.replace_substring_regex(text, pattern="\\r?\\n", replacement=" ")
+
+        for label, pattern in PATTERNS:
+            if label == "Dz.U.":
+                cnt = pc.count_substring_regex(text, pattern)
+            else:
+                cnt = pc.count_substring(text, pattern)
+            counts[label] += int(pc.sum(cnt).as_py())
+
+    return counts
+
+
+def compute_counts(data_root: Path) -> tuple[dict[str, int], dict[str, dict[str, int]]]:
+    tokens = load_tokens_by_source(data_root)
+    source_counts = {}
+    total_counts = {label: 0 for label, _ in PATTERNS}
+
+    for parquet_path in sorted((data_root / "data").glob("*/*.parquet")):
+        source = parquet_path.parent.name
+        counts = count_patterns_for_source(parquet_path)
+        source_counts[source] = counts
+        for label, cnt in counts.items():
+            total_counts[label] += cnt
+
+    return total_counts, source_counts, tokens
+
+
+def write_markdown(total_counts, source_counts, tokens, out_md: Path) -> None:
+    total_tokens = sum(tokens.values())
+    lines = []
+    lines.append("## Pattern frequency on corpus\n")
+    lines.append(f"- total tokens (tiktoken proxy): `{total_tokens:,}`\n")
+    lines.append("| pattern | count | share of all tokens |")
+    lines.append("|---|---:|---:|")
+    for label, _ in PATTERNS:
+        c = total_counts[label]
+        lines.append(f"| `{label}` | {c:,} | {c/total_tokens*100:.4f}% |")
+    lines.append("")
+    lines.append("| source | pattern | count | per-token share |")
+    lines.append("|---|---|---:|---:|")
+    for source in sorted(source_counts):
+        src_tokens = tokens[source]
+        for label, _ in PATTERNS:
+            c = source_counts[source][label]
+            lines.append(f"| {source} | `{label}` | {c:,} | {c/src_tokens*100:.5f}% |")
+    out_md.write_text("\n".join(lines) + "\n", encoding="utf-8")
+
+
+def write_hf_snippet(total_counts, source_counts, tokens, total_tokens: int, out_md: Path) -> None:
+    patterns = [label for label, _ in PATTERNS]
+    lines = []
+    lines.append("## Phrase frequency in corpus (token-normalized)")
+    lines.append("")
+    lines.append(f"- Total token count (tiktoken proxy): **{total_tokens:,}**")
+    lines.append("")
+    lines.append("| Pattern | Count | Share of all tokens |")
+    lines.append("|---|---:|---:|")
+    for label in patterns:
+        c = total_counts[label]
+        lines.append(f"| `{label}` | {c:,} | {c / total_tokens * 100:.4f}% |")
+    lines.append("")
+    lines.append("### Per-source shares")
+    lines.append("")
+    lines.append("| source | pattern | count | share of source tokens |")
+    lines.append("|---|---|---:|---:|")
+    ordered_sources = sorted(source_counts)
+    for source in ordered_sources:
+        src_tok = tokens[source]
+        for label in patterns:
+            c = source_counts[source][label]
+            lines.append(f"| `{source}` | `{label}` | {c:,} | {c / src_tok * 100:.5f}% |")
+
+    lines.append("")
+    lines.append("![Overall pattern counts](artifacts/pattern_frequency_overall.png)")
+    lines.append("")
+    lines.append("![w roku by source](artifacts/pattern_frequency_w_roku.png)")
+    lines.append("![klasyfikacji by source](artifacts/pattern_frequency_klasyfikacji.png)")
+    lines.append("![ustawa by source](artifacts/pattern_frequency_ustawa.png)")
+    lines.append("![artykuł by source](artifacts/pattern_frequency_artykul.png)")
+    lines.append("![parlament by source](artifacts/pattern_frequency_parlament.png)")
+    lines.append("![rozporządzenie by source](artifacts/pattern_frequency_rozporządzenie.png)")
+    lines.append("![w pobliżu by source](artifacts/pattern_frequency_w_pobliżu.png)")
+    lines.append("![mieszkańców by source](artifacts/pattern_frequency_mieszkańców.png)")
+    lines.append("![Dz.U. by source](artifacts/pattern_frequency_dzu.png)")
+
+    out_md.write_text("\n".join(lines) + "\n", encoding="utf-8")
+
+
+def plot(total_counts, source_counts, tokens, out_png: Path) -> None:
+    out_png.parent.mkdir(parents=True, exist_ok=True)
+    patterns = [label for label, _ in PATTERNS]
+    totals = [total_counts[p] for p in patterns]
+
+    # overall share chart
+    plt.figure(figsize=(10, 4))
+    plt.bar(patterns, totals, color="#2b8cbe")
+    plt.title("Pattern count in full corpus")
+    plt.ylabel("count")
+    plt.xlabel("pattern")
+    plt.xticks(rotation=25, ha="right")
+    plt.tight_layout()
+    total_png = out_png.with_name(out_png.stem + "_overall" + out_png.suffix)
+    plt.savefig(total_png, dpi=140)
+    plt.close()
+
+    # per-source percentage heatmap-like bars
+    ordered_sources = sorted(source_counts)
+    for pattern in patterns:
+        vals = [source_counts[src][pattern] / tokens[src] * 100 for src in ordered_sources]
+        plt.figure(figsize=(10, 4))
+        plt.bar(ordered_sources, vals)
+        plt.title(f"{pattern} share per source (% of source tokens)")
+        plt.ylabel("% of tokens")
+        plt.xticks(rotation=30, ha="right")
+        plt.tight_layout()
+        safe = pattern.replace(" ", "_").replace("ł", "l").replace(".", "").lower()
+        plt.savefig(out_png.parent / f"{out_png.stem}_{safe}.png", dpi=140)
+        plt.close()
+
+
+def parse_args():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--data-root", type=Path, default=Path("."), help="repo root")
+    ap.add_argument("--out-md", type=Path, default=Path("pattern_frequency_report.md"))
+    ap.add_argument("--out-png", type=Path, default=Path("artifacts/pattern_frequency.png"))
+    ap.add_argument(
+        "--out-hf",
+        type=Path,
+        default=Path("artifacts/pattern_frequency_hf_snippet.md"),
+        help="HF model card snippet to paste into README.md on Hugging Face",
+    )
+    return ap.parse_args()
+
+
+def main():
+    args = parse_args()
+    total_counts, source_counts, tokens = compute_counts(args.data_root)
+    total_tokens = sum(tokens.values())
+    args.out_md.parent.mkdir(parents=True, exist_ok=True)
+    write_markdown(total_counts, source_counts, tokens, args.out_md)
+    write_hf_snippet(total_counts, source_counts, tokens, total_tokens, args.out_hf)
+    plot(total_counts, source_counts, tokens, args.out_png)
+
+    print(f"wrote: {args.out_md}")
+    print(f"wrote: {args.out_hf}")
+    print(f"wrote: {args.out_png.with_name(args.out_png.stem + '_overall' + args.out_png.suffix)}")
+    for label, _ in PATTERNS:
+        safe = label.replace(' ', '_').replace('ł', 'l').replace('.', '').lower()
+        print(f"wrote: {args.out_png.parent / f'{args.out_png.stem}_{safe}.png'}")
+
+
+if __name__ == "__main__":
+    main()