#!/usr/bin/env python3 """Build the normalized tokenizer comparison table from the raw JSON files.""" import argparse import csv import hashlib import json from pathlib import Path import pyarrow as pa import pyarrow.parquet as pq SOURCE_COMMIT = "1a5cd2c2e4df2287b4c19b3dbf5051f5d460fdc1" KACPER_SOURCE_COMMIT = "8a273e6fd6e05b56d9d05e15e0de8232f8be3548" GITHUB_AUTHORS = { "Arek": "Maggio333", "KasiaMP": "KateMajzel", "dawidm": "dawidmajewski", "ola": "olajachymiak", "Janek": "janbanot", "patryk": "p4pryk", "Konrad": "ktalik", } BULK_KEYS = { "model", "vocab", "merges", "reguly_merge", "token_to_id", "id_to_token", } def extract_metadata(document: dict) -> dict: """Keep explicit metadata and compact non-vocabulary configuration fields.""" metadata = {} if isinstance(document.get("meta"), dict): metadata.update(document["meta"]) for key, value in document.items(): if key in BULK_KEYS or key == "meta": continue # Avoid duplicating large experimental token dumps in the metadata cell. if key.startswith("tokeny_") or key == "przykladowy_tekst": continue metadata[key] = value return metadata def component_name(value) -> str: if value is None: return "none" if isinstance(value, dict): return str(value.get("type", "configured")) return str(value) def merge_count(document: dict) -> int | None: model = document.get("model") candidates = [] if isinstance(model, dict): candidates.append(model.get("merges")) candidates.extend((document.get("merges"), document.get("reguly_merge"))) for value in candidates: if isinstance(value, (list, dict)): return len(value) for key in ("liczba_regul_merge", "n_merges"): if isinstance(document.get(key), int): return document[key] return None def extract_reported_metrics(document: dict) -> dict: markers = ("eval", "metr", "fert", "znaki_na_token", "tokens_per_word", "sweep", "compression") metrics = {} for key, value in document.items(): if any(marker in key.lower() for marker in markers): metrics[key] = value if isinstance(document.get("meta"), dict): for key, value in document["meta"].items(): if any(marker in key.lower() for marker in markers): metrics[f"meta.{key}"] = value return metrics def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--source-root", type=Path, default=Path(".")) parser.add_argument("--manifest", type=Path, default=Path("manifest.csv")) parser.add_argument("--output", type=Path, default=Path("data/train-00000-of-00001.parquet")) parser.add_argument("--kacper-tokenizer", type=Path) args = parser.parse_args() rows = [] with args.manifest.open(newline="", encoding="utf-8") as handle: for item in csv.DictReader(handle): source_path = item["source_path"] raw_json = (args.source_root / source_path).read_text(encoding="utf-8") document = json.loads(raw_json) model = document.get("model") if isinstance(document.get("model"), dict) else {} metrics = extract_reported_metrics(document) hf_loadable = item["format"] == "hf_tokenizers" rows.append( { "author": GITHUB_AUTHORS[item["contributor"]], "size": int(item["vocab_size"]), "name": Path(source_path).name, "quick_status": "ready_hf_tokenizers" if hf_loadable else "custom_conversion_required", "hf_loadable": hf_loadable, "format": item["format"], "model_type": item["model_type"], "merge_count": merge_count(document), "normalizer": component_name(document.get("normalizer")), "pre_tokenizer": component_name(document.get("pre_tokenizer")), "decoder": component_name(document.get("decoder")), "unk_token": str(model.get("unk_token") or ""), "added_tokens_count": len(document.get("added_tokens", [])), "reported_metrics": json.dumps(metrics, ensure_ascii=False, sort_keys=True), "metadata": json.dumps( extract_metadata(document), ensure_ascii=False, sort_keys=True ), "tokenizer_json": raw_json, "source_repo": "https://github.com/slayerlabs/tokenizer", "source_path": source_path, "source_commit": SOURCE_COMMIT, "bytes": int(item["bytes"]), "sha256": item["sha256"], } ) if args.kacper_tokenizer: raw_json = args.kacper_tokenizer.read_text(encoding="utf-8") document = json.loads(raw_json) model = document["model"] metrics = extract_reported_metrics(document) rows.append( { "author": "kacperwikiel", "size": len(model["vocab"]), "name": "polish_bpe_32k.json", "quick_status": "ready_hf_tokenizers", "hf_loadable": True, "format": "hf_tokenizers", "model_type": model["type"], "merge_count": merge_count(document), "normalizer": component_name(document.get("normalizer")), "pre_tokenizer": component_name(document.get("pre_tokenizer")), "decoder": component_name(document.get("decoder")), "unk_token": str(model.get("unk_token") or ""), "added_tokens_count": len(document.get("added_tokens", [])), "reported_metrics": json.dumps(metrics, ensure_ascii=False, sort_keys=True), "metadata": json.dumps( extract_metadata(document), ensure_ascii=False, sort_keys=True ), "tokenizer_json": raw_json, "source_repo": "https://huggingface.co/SlayerLab/slayer-scratch", "source_path": "tokenizers/polish_bpe_32k.json", "source_commit": KACPER_SOURCE_COMMIT, "bytes": len(raw_json.encode("utf-8")), "sha256": hashlib.sha256(raw_json.encode("utf-8")).hexdigest(), } ) schema = pa.schema( [ ("author", pa.string()), ("size", pa.int64()), ("name", pa.string()), ("quick_status", pa.string()), ("hf_loadable", pa.bool_()), ("format", pa.string()), ("model_type", pa.string()), ("merge_count", pa.int64()), ("normalizer", pa.string()), ("pre_tokenizer", pa.string()), ("decoder", pa.string()), ("unk_token", pa.string()), ("added_tokens_count", pa.int64()), ("reported_metrics", pa.string()), ("metadata", pa.string()), ("tokenizer_json", pa.large_string()), ("source_repo", pa.string()), ("source_path", pa.string()), ("source_commit", pa.string()), ("bytes", pa.int64()), ("sha256", pa.string()), ] ) args.output.parent.mkdir(parents=True, exist_ok=True) pq.write_table( pa.Table.from_pylist(rows, schema=schema), args.output, compression="zstd", compression_level=9, ) if __name__ == "__main__": main()