Train virtual-shard anime parser

.gitignore

@@ -18,3 +18,5 @@ data/**/*.db
 data/**/*.sqlite
 data/generated/
 reports/generated/
+target/
+**/target/

README.md

@@ -26,7 +26,7 @@ model-index:
         metrics:
           - type: accuracy
             name: Fixed parser model-only full-match accuracy
-            value: 0.9615
+            value: 0.9231
           - type: accuracy
             name: Fixed parser thin-runtime full-match accuracy
             value: 1.0
@@ -140,17 +140,17 @@ Current published checkpoint:
 
 | Metric / 指标 | Value / 数值 |
 | --- | --- |
-| Fixed regression, model-only / 固定回归，纯模型聚合 | 25/26 full match = `96.15%` |
+| Fixed regression, model-only / 固定回归，纯模型聚合 | 24/26 full match = `92.31%` |
 | Fixed regression, default thin runtime / 固定回归，默认薄层运行时 | 26/26 full match = `100%` |
-| Held-out parse, model-only / held-out 解析，纯模型聚合 | 1947/2048 full match = `95.07%` |
-| Held-out parse, default thin runtime / held-out 解析，默认薄层运行时 | 1966/2048 full match = `96.00%` |
-| Token/entity eval / token/entity 评估 | F1 `0.9847`, token accuracy `0.9962` |
-| ONNX parity / ONNX 误差 | max abs diff `1.9073e-05` |
-| CPU thin-runtime latency / CPU 薄层运行时延迟 | ONNX avg `11.61 ms`, P95 `13.52 ms` |
+| Held-out parse, model-only / held-out 解析，纯模型聚合 | 1962/2048 full match = `95.80%` |
+| Held-out parse, default thin runtime / held-out 解析，默认薄层运行时 | 1988/2048 full match = `97.07%` |
+| Token/entity eval / token/entity 评估 | F1 `0.9844`, token accuracy `0.9961` |
+| ONNX parity / ONNX 误差 | max abs diff `4.0054e-05` |
+| CPU thin-runtime latency / CPU 薄层运行时延迟 | ONNX avg `12.04 ms`, P95 `13.81 ms` |
 
-**中文**：当前发布模型是“两阶段训练”产物：先在 `datasets/AnimeName/dmhy_weak_char.jsonl` 上做 10 epoch CUDA 全量重训，并在训练时动态生成不完整文件名、BIO 块重排/子集和 special 片段样本；再做 thin hard-case focus 微调。细节见 `reports/training_lineage.json`。README 主指标以 `model-only` 和默认薄层 `normalized-only` 为准；旧版结构规则辅助层已移除，不再作为运行时或质量对照。
+**中文**：当前发布模型是“两阶段训练”产物：先用 Rust 预生成 `20,439,848` 行虚拟 BIO shard，在 RTX 5070 Ti 上完整训练 10 epoch / `114,070` optimizer steps；再接 1 epoch light hard-case focus 微调。细节见 `reports/training_lineage.json`。README 主指标以 `model-only` 和默认薄层 `normalized-only` 为准；旧版结构规则辅助层已移除，不再作为运行时或质量对照。
 
-**English**: The published checkpoint was trained in two stages: a 10-epoch CUDA fine-tune on `datasets/AnimeName/dmhy_weak_char.jsonl` with dynamic in-memory augmentation for incomplete filenames, BIO-block subsets/permutations, and special-code fragments, followed by a thin hard-case focus fine-tune. See `reports/training_lineage.json` for details. README quality numbers prioritize `model-only` and the default thin `normalized-only` runtime; structural filename assists have been removed from the runtime and quality reports.
+**English**: The published checkpoint was trained in two stages: a 10-epoch CUDA fine-tune over `20,439,848` Rust-generated virtual BIO shard rows (`114,070` optimizer steps) on the RTX 5070 Ti, followed by a 1-epoch light hard-case focus fine-tune. See `reports/training_lineage.json` for details. README quality numbers prioritize `model-only` and the default thin `normalized-only` runtime; structural filename assists have been removed from the runtime and quality reports.
 
 Run regression:
 
@@ -177,8 +177,8 @@ decoding, entity aggregation, and light string/number normalization:
 
 | Backend / 后端 | Load ms / 加载 ms | Avg ms / 平均 ms | P50 ms | P95 ms | P99 ms | files/s |
 | --- | ---: | ---: | ---: | ---: | ---: | ---: |
-| PyTorch | 44.84 | 16.42 | 14.77 | 26.31 | 32.62 | 60.9 |
-| ONNX Runtime | 40.70 | 11.61 | 11.43 | 13.52 | 15.20 | 86.2 |
+| PyTorch | 46.35 | 15.36 | 14.25 | 22.27 | 29.75 | 65.1 |
+| ONNX Runtime | 50.92 | 12.04 | 11.90 | 13.81 | 15.38 | 83.1 |
 
 **中文**：这是完整薄层 parser 的端到端延迟，不是只测模型 forward。移动端实现应复用 ONNX session，并保持 tokenizer/BIO/薄规范化逻辑一致。
 
@@ -188,33 +188,58 @@ decoding, entity aggregation, and light string/number normalization:
 
 Training uses the dataset submodule at `datasets/AnimeName`.
 
-Recommended optimized character-token run on the Windows RTX 5070 Ti worker:
+Recommended virtual-shard character-token run on the Windows RTX 5070 Ti worker:
 
 ```powershell
+@'
+import random
+from pathlib import Path
+
+source = Path("datasets/AnimeName/dmhy_weak_char.jsonl")
+target = Path("data/generated/virtual_source_train_seed105.jsonl")
+rows = [line for line in source.read_text(encoding="utf-8").splitlines() if line]
+random.Random(105).shuffle(rows)
+target.parent.mkdir(parents=True, exist_ok=True)
+target.write_text("\n".join(rows[: int(len(rows) * 0.98)]) + "\n", encoding="utf-8")
+'@ | .\.venv\Scripts\python.exe -
+
+cargo build --release --manifest-path tools/virtual_dataset_generator/Cargo.toml
+.\tools\virtual_dataset_generator\target\release\anifilebert-virtual-dataset-generator.exe `
+  --input data/generated/virtual_source_train_seed105.jsonl `
+  --vocab-file datasets/AnimeName/vocab.char.json `
+  --output-dir data/generated/virtual_char_sps32_seed105 `
+  --max-length 128 `
+  --samples-per-source 32 `
+  --seed 105 `
+  --threads 20 `
+  --separator-mode per-gap `
+  --bracket-mode per-part
+
 .\.venv\Scripts\python.exe -m anifilebert.train --tokenizer char `
   --data-file datasets/AnimeName/dmhy_weak_char.jsonl `
-  --vocab-file vocab.json `
-  --save-dir checkpoints/dmhy-char-aug-fragments-optimized-10epoch `
+  --vocab-file datasets/AnimeName/vocab.char.json `
+  --virtual-dataset-dir data/generated/virtual_char_sps32_seed105 `
+  --save-dir checkpoints/dmhy-char-virtual-sps32-10epoch-lr1e5 `
   --init-model-dir . `
   --epochs 10 `
   --batch-size 1792 `
-  --learning-rate 0.00002 `
-  --warmup-steps 500 `
+  --learning-rate 0.00001 `
+  --warmup-steps 2000 `
   --max-seq-length 128 `
   --train-split 0.98 `
-  --num-workers 0 `
-  --checkpoint-steps 1000 `
+  --num-workers 4 `
+  --prefetch-factor 4 `
+  --persistent-workers `
+  --checkpoint-steps 5000 `
   --save-total-limit 3 `
   --parse-eval-limit 2048 `
   --case-eval-file data/parser_regression_cases.json `
-  --augment-partial-samples 200000 `
-  --augment-permutation-samples 400000 `
-  --augment-special-samples 80000 `
   --bf16 `
   --no-periodic-eval `
-  --perf-log-steps 200 `
+  --perf-log-steps 1000 `
+  --perf-sample-interval 0.5 `
   --seed 105 `
-  --experiment-name dmhy-char-aug-fragments-optimized-10epoch
+  --experiment-name dmhy-char-virtual-sps32-10epoch-lr1e5
 ```
 
 `python -m anifilebert.train` writes:

anifilebert/inference.py

@@ -41,6 +41,15 @@ STANDALONE_SPECIAL_RE = re.compile(
     re.I,
 )
 
+BRACKETED_SEARCH_SPECIAL_RE = re.compile(
+    r"[\[【（(]\s*((?:檢索|检索|検索)\s*[:：][^\]】）)]+?)\s*[\]】）)]"
+)
+
+NEW_SHOW_BRACKET_TITLE_RE = re.compile(
+    r"[★☆][^★☆\[\]【】()（）]{0,24}(?:新番|月番)[^★☆\[\]【】()（）]{0,24}[★☆]"
+    r"\s*[\[【（(]\s*([^\]】）)]+?)\s*[\]】）)]"
+)
+
 
 def extract_season_number(text: str) -> Optional[int]:
     """
@@ -132,6 +141,24 @@ def normalize_standalone_special(text: str) -> Optional[str]:
     return special if STANDALONE_SPECIAL_RE.fullmatch(special) else None
 
 
+def extract_bracketed_search_special(text: str) -> Optional[str]:
+    """Return bracketed search-note tags such as [檢索：...]."""
+    for match in BRACKETED_SEARCH_SPECIAL_RE.finditer(text):
+        special = normalize_field_text(match.group(1))
+        if special:
+            return special
+    return None
+
+
+def extract_new_show_bracket_title(text: str) -> Optional[str]:
+    """Return title from release-promo layouts like ★04月新番★[葬送的芙莉莲]."""
+    for match in NEW_SHOW_BRACKET_TITLE_RE.finditer(text):
+        title = normalize_field_text(match.group(1))
+        if title:
+            return title
+    return None
+
+
 def display_token(token: str) -> str:
     """Make whitespace tokens visible in debug output."""
     if token == " ":
@@ -324,6 +351,19 @@ def postprocess(
     result["source"] = choose_thin_source(grouped_entities.get("SOURCE", []))
 
     whole_text = join_entity_tokens(tokens, tokenizer)
+    new_show_title = extract_new_show_bracket_title(whole_text)
+    if new_show_title is not None and (
+        result["title"] is None
+        or result["title"].startswith(("★", "☆"))
+        or "新番" in result["title"]
+        or "月番" in result["title"]
+    ):
+        result["title"] = new_show_title
+
+    search_special = extract_bracketed_search_special(whole_text)
+    if search_special is not None:
+        result["special"] = search_special
+
     standalone_special = normalize_standalone_special(whole_text)
     if standalone_special is not None:
         result.update(

anifilebert/train.py

@@ -22,6 +22,7 @@ from typing import Dict, List, Optional, Sequence
 
 import numpy as np
 import torch
+from torch.utils.data import SequentialSampler
 from transformers import (
     Trainer,
     TrainingArguments,
@@ -35,6 +36,7 @@ from .tokenizer import AnimeTokenizer, create_tokenizer, load_tokenizer
 from .model import create_model, print_model_summary, count_parameters
 from .dataset import AnimeItemsDataset, EncodedAnimeDataset, labels_for_tokenizer
 from .inference import parse_filename, postprocess
+from .virtual_dataset import DatasetRangeView, ShardedEncodedDataset
 
 
 def compute_metrics(p):
@@ -76,6 +78,8 @@ def parse_args() -> argparse.Namespace:
                         help="Additional training JSONL file. Can be passed multiple times.")
     parser.add_argument("--extra-data-repeat", type=int, default=1,
                         help="Repeat each extra dataset this many times after loading")
+    parser.add_argument("--virtual-dataset-dir", default=None,
+                        help="Pre-encoded shard directory generated by tools/virtual_dataset_generator")
     parser.add_argument("--vocab-file", default=None,
                         help="Tokenizer vocab JSON. Defaults to data/vocab.json or data/vocab.char.json")
     parser.add_argument("--save-dir", default=None, help="Checkpoint output directory")
@@ -853,10 +857,27 @@ class FastTokenClassificationCollator:
     """Stack already padded token-classification tensors without extra work."""
 
     def __call__(self, features: List[Dict[str, torch.Tensor]]) -> Dict[str, torch.Tensor]:
-        return {
+        batch = {
             key: torch.stack([feature[key] for feature in features])
             for key in features[0].keys()
         }
+        if "input_ids" in batch:
+            batch["input_ids"] = batch["input_ids"].long()
+        if "labels" in batch:
+            batch["labels"] = batch["labels"].long()
+        if "attention_mask" in batch:
+            batch["attention_mask"] = batch["attention_mask"].to(dtype=torch.bool)
+        return batch
+
+
+class OrderedTrainer(Trainer):
+    """Trainer variant that preserves pre-shuffled order for virtual datasets."""
+
+    def _get_train_sampler(self, train_dataset=None):
+        dataset = train_dataset if train_dataset is not None else self.train_dataset
+        if getattr(dataset, "preserve_order", False):
+            return SequentialSampler(dataset)
+        return super()._get_train_sampler(train_dataset)
 
 
 def augment_training_data(
@@ -1264,7 +1285,28 @@ def main():
     eval_data = all_data[split_idx:]
 
     encode_started_at = time.perf_counter()
-    if args.lazy_dataset:
+    if args.virtual_dataset_dir:
+        virtual_dataset = ShardedEncodedDataset(args.virtual_dataset_dir)
+        if virtual_dataset.max_length != config.max_seq_length:
+            raise ValueError(
+                f"Virtual dataset max_length {virtual_dataset.max_length} does not match "
+                f"configured max_seq_length {config.max_seq_length}"
+            )
+        train_dataset = virtual_dataset
+        eval_dataset = EncodedAnimeDataset(
+            data=eval_data,
+            tokenizer=tokenizer,
+            label2id=config.label2id,
+            max_length=config.max_seq_length,
+            device=torch.device("cpu"),
+            apply_label_repairs=args.apply_label_repairs,
+        )
+        dataset_mode = "virtual-sharded"
+        if not args.keep_raw_dataset:
+            train_data = []
+            all_data = []
+            gc.collect()
+    elif args.lazy_dataset:
         train_dataset = AnimeItemsDataset(
             data=train_data,
             tokenizer=tokenizer,
@@ -1381,7 +1423,7 @@ def main():
         log_steps=args.perf_log_steps,
         sample_interval=args.perf_sample_interval,
     )
-    trainer = Trainer(
+    trainer = OrderedTrainer(
         model=model,
         args=training_args,
         train_dataset=train_dataset,
@@ -1418,6 +1460,7 @@ def main():
         "data_sources": data_sources,
         "augmentation": augmentation_metadata,
         "dataset_mode": dataset_mode,
+        "virtual_dataset_dir": args.virtual_dataset_dir,
         "apply_label_repairs": args.apply_label_repairs,
         "keep_raw_dataset": args.keep_raw_dataset,
         "tokenizer_variant": tokenizer_variant,

anifilebert/virtual_dataset.py

@@ -0,0 +1,114 @@
+"""Pre-encoded sharded datasets generated by the Rust virtual dataset tool."""
+
+from __future__ import annotations
+
+import bisect
+import json
+from pathlib import Path
+from typing import Dict, List, Optional
+
+import numpy as np
+import torch
+from torch.utils.data import Dataset
+
+
+class ShardedEncodedDataset(Dataset):
+    """Map-style dataset backed by pre-encoded `.npy` shards.
+
+    The Rust generator writes compact uint16/int16/u8 arrays. This class loads
+    one shard at a time and relies on sequential sampling over pre-shuffled
+    shards, so Python does no tokenization or BIO permutation during training.
+    """
+
+    preserve_order = True
+
+    def __init__(self, dataset_dir: str | Path, manifest_name: str = "manifest.json"):
+        self.dataset_dir = Path(dataset_dir)
+        self.manifest_path = self.dataset_dir / manifest_name
+        self.manifest = json.loads(self.manifest_path.read_text(encoding="utf-8"))
+        if self.manifest.get("format") != "anifilebert.virtual_dataset.shards.v1":
+            raise ValueError(f"Unsupported virtual dataset manifest: {self.manifest_path}")
+
+        self.max_length = int(self.manifest["max_length"])
+        self.shards: List[Dict] = list(self.manifest.get("shards") or [])
+        if not self.shards:
+            raise ValueError(f"Virtual dataset has no shards: {self.manifest_path}")
+
+        self._starts: List[int] = []
+        total = 0
+        for shard in self.shards:
+            self._starts.append(total)
+            total += int(shard["rows"])
+        self.total_rows = total
+
+        declared_total = int(self.manifest.get("total_rows", total))
+        if declared_total != total:
+            raise ValueError(
+                f"Virtual dataset row count mismatch: manifest total_rows={declared_total}, "
+                f"shard rows={total}"
+            )
+
+        self._cache_index: Optional[int] = None
+        self._cache: Optional[Dict[str, np.ndarray]] = None
+
+    def __len__(self) -> int:
+        return self.total_rows
+
+    def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]:
+        if idx < 0:
+            idx += self.total_rows
+        if idx < 0 or idx >= self.total_rows:
+            raise IndexError(idx)
+
+        shard_idx = bisect.bisect_right(self._starts, idx) - 1
+        shard_start = self._starts[shard_idx]
+        row_idx = idx - shard_start
+        cache = self._load_shard(shard_idx)
+        return {
+            "input_ids": torch.from_numpy(cache["input_ids"][row_idx]),
+            "attention_mask": torch.from_numpy(cache["attention_mask"][row_idx]),
+            "labels": torch.from_numpy(cache["labels"][row_idx]),
+        }
+
+    def _load_shard(self, shard_idx: int) -> Dict[str, np.ndarray]:
+        if self._cache_index == shard_idx and self._cache is not None:
+            return self._cache
+
+        shard = self.shards[shard_idx]
+        cache = {
+            "input_ids": np.load(self.dataset_dir / shard["input_ids"], allow_pickle=False),
+            "attention_mask": np.load(self.dataset_dir / shard["attention_mask"], allow_pickle=False),
+            "labels": np.load(self.dataset_dir / shard["labels"], allow_pickle=False),
+        }
+        expected_shape = (int(shard["rows"]), self.max_length)
+        for key, array in cache.items():
+            if array.shape != expected_shape:
+                raise ValueError(
+                    f"Shard {shard_idx} {key} has shape {array.shape}, expected {expected_shape}"
+                )
+        self._cache_index = shard_idx
+        self._cache = cache
+        return cache
+
+
+class DatasetRangeView(Dataset):
+    """A contiguous range view over another dataset."""
+
+    preserve_order = True
+
+    def __init__(self, dataset: Dataset, start: int, end: int):
+        if start < 0 or end < start or end > len(dataset):
+            raise ValueError(f"Invalid dataset range [{start}, {end}) for length {len(dataset)}")
+        self.dataset = dataset
+        self.start = start
+        self.end = end
+
+    def __len__(self) -> int:
+        return self.end - self.start
+
+    def __getitem__(self, idx: int):
+        if idx < 0:
+            idx += len(self)
+        if idx < 0 or idx >= len(self):
+            raise IndexError(idx)
+        return self.dataset[self.start + idx]

docs/maintenance.md

@@ -73,27 +73,14 @@ For full details, see [`training.md`](training.md).
 
 完整流程见 [`training.md`](training.md)。
 
-Recommended full training command / 推荐全量训练命令：
+Current release training uses the virtual-shard flow in [`training.md`](training.md):
+
+当前发布训练使用 [`training.md`](training.md) 中的 virtual-shard 流程：
 
 ```powershell
-uv run python -m anifilebert.train --tokenizer char `
-  --data-file datasets/AnimeName/dmhy_weak_char.jsonl `
-  --vocab-file datasets/AnimeName/vocab.char.json `
-  --save-dir checkpoints/dmhy-char-full `
-  --init-model-dir . `
-  --epochs 2 `
-  --batch-size 256 `
-  --learning-rate 0.00008 `
-  --warmup-steps 300 `
-  --max-seq-length 128 `
-  --train-split 0.98 `
-  --num-workers 4 `
-  --checkpoint-steps 1000 `
-  --save-total-limit 3 `
-  --parse-eval-limit 2048 `
-  --case-eval-file data/parser_regression_cases.json `
-  --seed 52 `
-  --experiment-name dmhy-char-full
+uv run python -m compileall -q anifilebert tools
+cargo build --release --manifest-path tools/virtual_dataset_generator/Cargo.toml
+# Then follow docs/training.md section "Full Training with Virtual BIO Shards".
 ```
 
 ## Publish a New Checkpoint / 发布新 checkpoint
@@ -103,7 +90,7 @@ Copy final files to the repository root:
 把 `final` 文件复制到仓库根目录：
 
 ```powershell
-$final = "checkpoints/dmhy-char-aug-fragments-10epoch-hardfocus/final"
+$final = "checkpoints/dmhy-char-virtual-sps32-10epoch-lightfocus/final"
 Copy-Item "$final/config.json" . -Force
 Copy-Item "$final/model.safetensors" . -Force
 Copy-Item "$final/tokenizer_config.json" . -Force

docs/onnx.md

@@ -175,8 +175,8 @@ the default thin runtime:
 
 | Backend / 后端 | Load ms / 加载 ms | Avg ms / 平均 ms | P50 ms | P95 ms | P99 ms | files/s |
 | --- | ---: | ---: | ---: | ---: | ---: | ---: |
-| PyTorch | 49.07 | 15.16 | 14.87 | 18.50 | 21.91 | 66.0 |
-| ONNX Runtime | 568.85 | 13.08 | 12.82 | 15.95 | 20.19 | 76.5 |
+| PyTorch | 46.35 | 15.36 | 14.25 | 22.27 | 29.75 | 65.1 |
+| ONNX Runtime | 50.92 | 12.04 | 11.90 | 13.81 | 15.38 | 83.1 |
 
 The benchmark includes tokenization, model/session forward, constrained BIO
 decode, entity aggregation, and thin normalization. It does not include

docs/training.md

@@ -20,9 +20,9 @@ Recommended GPU configuration:
 推荐 GPU 配置：
 
 - RTX 3080 class GPU or better; current release training used an RTX 5070 Ti
-- batch size `1792` with the encoded dataset path on the 5070 Ti
+- batch size `1792` with the virtual-sharded dataset path on the 5070 Ti
 - `bf16`/TF32 on Ada/Blackwell-class CUDA devices when available
-- `--num-workers 0` with the encoded dataset path, because samples are pre-encoded into tensors
+- `--num-workers 4 --persistent-workers` with the virtual-sharded dataset path
 
 ## 2. Dataset / 数据集
 
@@ -88,48 +88,74 @@ uv run python -m tools.convert_to_char_dataset `
   --progress 50000
 ```
 
-## 5. Full Training with Dynamic Augmentation / 动态增强全量训练
+## 5. Full Training with Virtual BIO Shards / 虚拟 BIO shard 全量训练
 
 Recommended RTX 5070 Ti run:
 
 推荐 RTX 5070 Ti 训练命令：
 
 ```powershell
+@'
+import random
+from pathlib import Path
+
+source = Path("datasets/AnimeName/dmhy_weak_char.jsonl")
+target = Path("data/generated/virtual_source_train_seed105.jsonl")
+rows = [line for line in source.read_text(encoding="utf-8").splitlines() if line]
+random.Random(105).shuffle(rows)
+target.parent.mkdir(parents=True, exist_ok=True)
+target.write_text("\n".join(rows[: int(len(rows) * 0.98)]) + "\n", encoding="utf-8")
+'@ | .\.venv\Scripts\python.exe -
+
+cargo build --release --manifest-path tools/virtual_dataset_generator/Cargo.toml
+.\tools\virtual_dataset_generator\target\release\anifilebert-virtual-dataset-generator.exe `
+  --input data/generated/virtual_source_train_seed105.jsonl `
+  --vocab-file datasets/AnimeName/vocab.char.json `
+  --output-dir data/generated/virtual_char_sps32_seed105 `
+  --max-length 128 `
+  --samples-per-source 32 `
+  --seed 105 `
+  --threads 20 `
+  --separator-mode per-gap `
+  --bracket-mode per-part
+
 .\.venv\Scripts\python.exe -m anifilebert.train --tokenizer char `
   --data-file datasets/AnimeName/dmhy_weak_char.jsonl `
-  --vocab-file vocab.json `
-  --save-dir checkpoints/dmhy-char-aug-fragments-optimized-10epoch `
+  --vocab-file datasets/AnimeName/vocab.char.json `
+  --virtual-dataset-dir data/generated/virtual_char_sps32_seed105 `
+  --save-dir checkpoints/dmhy-char-virtual-sps32-10epoch-lr1e5 `
   --init-model-dir . `
   --epochs 10 `
   --batch-size 1792 `
-  --learning-rate 0.00002 `
-  --warmup-steps 500 `
+  --learning-rate 0.00001 `
+  --warmup-steps 2000 `
   --max-seq-length 128 `
   --train-split 0.98 `
-  --num-workers 0 `
-  --checkpoint-steps 1000 `
+  --num-workers 4 `
+  --prefetch-factor 4 `
+  --persistent-workers `
+  --checkpoint-steps 5000 `
   --save-total-limit 3 `
   --parse-eval-limit 2048 `
   --case-eval-file data/parser_regression_cases.json `
-  --augment-partial-samples 200000 `
-  --augment-permutation-samples 400000 `
-  --augment-special-samples 80000 `
   --bf16 `
   --no-periodic-eval `
-  --perf-log-steps 200 `
+  --perf-log-steps 1000 `
+  --perf-sample-interval 0.5 `
   --seed 105 `
-  --experiment-name dmhy-char-aug-fragments-optimized-10epoch
+  --experiment-name dmhy-char-virtual-sps32-10epoch-lr1e5
 ```
 
-Dynamic augmentation is generated in memory from BIO-labeled source rows and
-does not modify the authoritative DMHY JSONL files. The current release used
-partial/incomplete filename fragments, BIO entity block subsets and
-permutations, title-only/title+season directory-style examples, and standalone
-special fragments such as `Menu01`, `OP02`, `ED E07`, and `NCED03`.
+The Rust generator samples BIO entity block subsets/permutations, separator
+variants, bracket styles, incomplete filename fragments, and standalone special
+fixtures into compact pre-encoded `.npy` shards. The current release generated
+`20,439,848` training rows from `619,361` train-split source rows plus `935`
+special fixtures, then trained for 10 epochs / `114,070` optimizer steps.
 
-动态增强从已有 BIO 标注行内存生成，不会修改权威 DMHY JSONL。当前发布使用了
-不完整文件名片段、BIO 实体块子集和重排、只有 title 或 title+season 的目录样式
-样本，以及 `Menu01`、`OP02`、`ED E07`、`NCED03` 等 standalone special 片段。
+Rust 生成器会把 BIO 实体块子集/重排、分隔符变体、括号样式、不完整文件名片段、
+以及 standalone special fixtures 预编码成紧凑 `.npy` shard。当前发布从 `619,361`
+条 train split 源样本和 `935` 条 special fixture 生成了 `20,439,848` 条训练行，
+并完整训练 10 epoch / `114,070` 个 optimizer steps。
 
 Training outputs:
 
@@ -156,21 +182,21 @@ been confirmed, fixed in the weak labels, and added to
 ```powershell
 uv run python -m tools.build_repair_focus_dataset `
   --input datasets/AnimeName/dmhy_weak_char.jsonl `
-  --output data/generated/focus_after_10epoch_char.jsonl `
-  --context-samples 100000 `
+  --output data/generated/focus_after_virtual_sps32_char.jsonl `
+  --context-samples 50000 `
   --repeat-focus 3 `
-  --repeat-manual 400 `
-  --seed 106
+  --repeat-manual 96 `
+  --seed 205
 
 .\.venv\Scripts\python.exe -m anifilebert.train --tokenizer char `
-  --data-file data/generated/focus_after_10epoch_char.jsonl `
-  --vocab-file vocab.json `
-  --save-dir checkpoints/dmhy-char-aug-fragments-10epoch-hardfocus `
-  --init-model-dir checkpoints/dmhy-char-aug-fragments-optimized-10epoch/final `
-  --epochs 2 `
+  --data-file data/generated/focus_after_virtual_sps32_char.jsonl `
+  --vocab-file datasets/AnimeName/vocab.char.json `
+  --save-dir checkpoints/dmhy-char-virtual-sps32-10epoch-lightfocus `
+  --init-model-dir checkpoints/dmhy-char-virtual-sps32-10epoch-lr1e5/final `
+  --epochs 1 `
   --batch-size 1792 `
-  --learning-rate 0.000008 `
-  --warmup-steps 50 `
+  --learning-rate 0.000002 `
+  --warmup-steps 20 `
   --max-seq-length 128 `
   --train-split 0.95 `
   --num-workers 0 `
@@ -178,14 +204,12 @@ uv run python -m tools.build_repair_focus_dataset `
   --save-total-limit 2 `
   --parse-eval-limit 2048 `
   --case-eval-file data/parser_regression_cases.json `
-  --augment-partial-samples 30000 `
-  --augment-permutation-samples 60000 `
-  --augment-special-samples 20000 `
   --bf16 `
   --no-periodic-eval `
   --perf-log-steps 50 `
-  --seed 107 `
-  --experiment-name dmhy-char-aug-fragments-10epoch-hardfocus
+  --perf-sample-interval 0.5 `
+  --seed 208 `
+  --experiment-name dmhy-char-virtual-sps32-10epoch-lightfocus
 ```
 
 The default quality gate is model-led parsing:
@@ -209,7 +233,7 @@ The repository root is the Hugging Face checkpoint surface.
 仓库根目录就是 Hugging Face checkpoint 发布面。
 
 ```powershell
-$final = "checkpoints/dmhy-char-aug-fragments-10epoch-hardfocus/final"
+$final = "checkpoints/dmhy-char-virtual-sps32-10epoch-lightfocus/final"
 Copy-Item "$final/config.json" . -Force
 Copy-Item "$final/model.safetensors" . -Force
 Copy-Item "$final/tokenizer_config.json" . -Force
@@ -239,7 +263,7 @@ Run these before committing:
 提交前执行：
 
 ```powershell
-uv run python -m py_compile anifilebert/*.py tools/*.py
+uv run python -m compileall -q anifilebert tools
 uv run python -m tools.evaluate_parser_cases --model-dir . --case-file data/parser_regression_cases.json --output reports/case_metrics.json
 uv run python -m anifilebert.inference --model-dir . "[GM-Team][国漫][神印王座][Throne of Seal][2022][200][AVC][GB][1080P].mp4"
 uv run python -m tools.onnx_inference "[YYDM&VCB-Studio] Shinsekai Yori [NCED02][Ma10p_1080p][x265_flac].mkv"

exports/anime_filename_parser.metadata.json

@@ -8,5 +8,5 @@
     128,
     15
   ],
-  "max_abs_diff": 1.9073486328125e-05
+  "max_abs_diff": 4.00543212890625e-05
 }

exports/anime_filename_parser.onnx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:5a09d5387e94373cccd22cd821edf0654d537a7897cf8abb04900f48a5ffaccf
+oid sha256:476e47f347eba767760bb5945f76ff2978a66fa31ea0a02f4c64f5264984aca5
 size 19647024

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:401c62d2359e1030930892fd6be3d8a25abf758f3cb43b4d445562890fe1f2c6
+oid sha256:c421b6e37b49c6fe94cb3ac5cd2f8347749e6d21b0f7b5088b38ae30e6eb74f9
 size 19142604

reports/benchmark_results.json

@@ -11,27 +11,27 @@
   "results": [
     {
       "name": "pytorch",
-      "load_ms": 44.84080011025071,
+      "load_ms": 46.3533999864012,
       "runs": 520,
-      "avg_ms": 16.417674036347307,
-      "p50_ms": 14.76569997612387,
-      "p95_ms": 26.30644003511406,
-      "p99_ms": 32.615189072675996,
-      "min_ms": 11.30899996496737,
-      "max_ms": 41.87910002656281,
-      "throughput_fps": 60.909967988527896
+      "avg_ms": 15.362302694120444,
+      "p50_ms": 14.245550031773746,
+      "p95_ms": 22.27204497321509,
+      "p99_ms": 29.752646028064174,
+      "min_ms": 10.793900000862777,
+      "max_ms": 42.94239997398108,
+      "throughput_fps": 65.09440803967013
     },
     {
       "name": "onnxruntime",
-      "load_ms": 40.69980001077056,
+      "load_ms": 50.916100037284195,
       "runs": 520,
-      "avg_ms": 11.606730768779435,
-      "p50_ms": 11.42695004818961,
-      "p95_ms": 13.516889995662494,
-      "p99_ms": 15.196251904126225,
-      "min_ms": 9.510300005786121,
-      "max_ms": 19.771000021137297,
-      "throughput_fps": 86.15690498222524
+      "avg_ms": 12.039251922844695,
+      "p50_ms": 11.899999983143061,
+      "p95_ms": 13.811619929037988,
+      "p99_ms": 15.376427990850043,
+      "min_ms": 9.72980004735291,
+      "max_ms": 19.285599933937192,
+      "throughput_fps": 83.06163924541542
     }
   ]
 }

reports/case_metrics.json

@@ -8,11 +8,11 @@
       "max_length": 128,
       "constrain_bio": false,
       "case_count": 26,
-      "full_correct": 25,
-      "full_accuracy": 0.9615384615384616,
+      "full_correct": 24,
+      "full_accuracy": 0.9230769230769231,
       "field_correct": {
         "group": 22,
-        "title": 26,
+        "title": 25,
         "episode": 26,
         "resolution": 25,
         "source": 19,
@@ -35,7 +35,7 @@
         "season": 1.0,
         "source": 1.0,
         "special": 1.0,
-        "title": 1.0
+        "title": 0.9615384615384616
       },
       "failures": [
         {
@@ -64,6 +64,31 @@
             "source": "WebRip",
             "title": "Otonari no Tenshi-sama ni Itsunomanika Dame Ningen ni Sareteita Ken"
           }
+        },
+        {
+          "id": "ai_raws_fire_force_cjk_season_hash_episode",
+          "filename": "[AI-Raws] 炎炎の消防隊 弐ノ章 #13 (BD HEVC 1920x1080 yuv444p10le FLAC)[FC74A2D5].mkv",
+          "ok": false,
+          "errors": {
+            "title": {
+              "expected": "炎炎の消防隊",
+              "pred": "炎炎の消防隊 ノ章"
+            }
+          },
+          "expected": {
+            "group": "AI-Raws",
+            "title": "炎炎の消防隊",
+            "season": 2,
+            "episode": 13,
+            "resolution": "1920x1080"
+          },
+          "pred": {
+            "episode": 13,
+            "group": "AI-Raws",
+            "resolution": "1920x1080",
+            "season": 2,
+            "title": "炎炎の消防隊 ノ章"
+          }
         }
       ],
       "results": [
@@ -473,8 +498,13 @@
         {
           "id": "ai_raws_fire_force_cjk_season_hash_episode",
           "filename": "[AI-Raws] 炎炎の消防隊 弐ノ章 #13 (BD HEVC 1920x1080 yuv444p10le FLAC)[FC74A2D5].mkv",
-          "ok": true,
-          "errors": {},
+          "ok": false,
+          "errors": {
+            "title": {
+              "expected": "炎炎の消防隊",
+              "pred": "炎炎の消防隊 ノ章"
+            }
+          },
           "expected": {
             "group": "AI-Raws",
             "title": "炎炎の消防隊",
@@ -487,7 +517,7 @@
             "group": "AI-Raws",
             "resolution": "1920x1080",
             "season": 2,
-            "title": "炎炎の消防隊"
+            "title": "炎炎の消防隊 ノ章"
           }
         },
         {

reports/parse_eval_metrics.json

@@ -5,22 +5,22 @@
       "constrain_bio": false,
       "sample_count": 2048,
       "field_accuracy": {
-        "group": 0.98583984375,
-        "title": 0.97119140625,
-        "season": 0.99609375,
-        "episode": 0.990234375,
+        "group": 0.99951171875,
+        "title": 0.9716796875,
+        "season": 0.994140625,
+        "episode": 0.99609375,
         "resolution": 1.0,
-        "source": 0.990234375,
-        "special": 0.9775390625
+        "source": 0.99658203125,
+        "special": 0.98681640625
       },
       "field_correct": {
-        "group": 2019,
-        "title": 1989,
-        "season": 2040,
-        "episode": 2028,
+        "group": 2047,
+        "title": 1990,
+        "season": 2036,
+        "episode": 2040,
         "resolution": 2048,
-        "source": 2028,
-        "special": 2002
+        "source": 2041,
+        "special": 2021
       },
       "field_total": {
         "group": 2048,
@@ -31,184 +31,245 @@
         "source": 2048,
         "special": 2048
       },
-      "full_match_accuracy": 0.95068359375,
-      "full_match_correct": 1947,
+      "full_match_accuracy": 0.9580078125,
+      "full_match_correct": 1962,
       "full_match_total": 2048,
       "failures": [
         {
-          "filename": "01; - flac",
+          "filename": "[U3-Project] Shoujo Kageki Revue Starlight - NCED1 [BDRemux AVC 1920x1080p FLAC]",
           "errors": {
-            "title": {
-              "gold": "01;",
-              "pred": ";"
-            },
-            "episode": {
-              "gold": null,
-              "pred": "1"
+            "source": {
+              "gold": "avc",
+              "pred": "mu"
             }
           },
           "gold": {
-            "group": null,
-            "title": "01;",
+            "group": "U3-Project",
+            "title": "Shoujo Kageki Revue Starlight",
             "season": null,
             "episode": null,
             "resolution": null,
-            "source": "flac",
-            "special": null
+            "source": "AVC",
+            "special": "NCED1"
           },
           "pred": {
-            "group": null,
-            "title": ";",
+            "group": "U3-Project",
+            "title": "Shoujo Kageki Revue Starlight",
             "season": null,
-            "episode": 1,
+            "episode": null,
             "resolution": null,
-            "source": "flac",
-            "special": null
+            "source": "mu",
+            "special": "NCED1"
           }
         },
         {
-          "filename": "[VCB-Studio] Durarara!!×2 Shou [IV][Ma10p_1080p][x265_aac]",
+          "filename": "[SFEO-Raws] Kamisama Hajimemashita - NCED_11 (BD 720P x264 10bit AAC)[CED90B97]",
           "errors": {
-            "title": {
-              "gold": "durarara!!×2 shou iv",
-              "pred": "durarara!!×2 shou"
-            },
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-              "pred": "iv"
+              "gold": "nced_11",
+              "pred": "d"
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-            "group": "VCB-Studio",
-            "title": "Durarara!!×2 Shou IV",
+            "group": "SFEO-Raws",
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             "season": null,
             "episode": null,
-            "resolution": "1080p",
-            "source": "x265-aac",
-            "special": null
+            "resolution": "720P",
+            "source": "BD",
+            "special": "NCED_11"
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-            "group": "VCB-Studio",
-            "title": "Durarara!!×2 Shou",
+            "group": "SFEO-Raws",
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             "episode": null,
-            "resolution": "1080p",
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+            "resolution": "720P",
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+            "special": "D"
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         },
         {
-          "filename": "AC3 Chap - BD Menu16",
+          "filename": "[QTS] Yoroiden Samurai Troopers Blu-ray BOX Eizou Tokuten - TV NCOP2 (BD Hi10P 960x720 AAC)",
+          "errors": {
+            "title": {
+              "gold": "yoroiden samurai troopers blu-ray box eizou tokuten - tv",
+              "pred": "yoroiden samurai troopers blu-ray box eizou tokuten tv"
+            }
+          },
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+            "title": "Yoroiden Samurai Troopers Blu-ray BOX Eizou Tokuten - TV",
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+            "episode": null,
+            "resolution": "960x720",
+            "source": "BD",
+            "special": "NCOP2"
+          },
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+            "group": "QTS",
+            "title": "Yoroiden Samurai Troopers Blu-ray BOX Eizou Tokuten TV",
+            "season": null,
+            "episode": null,
+            "resolution": "960x720",
+            "source": "BD",
+            "special": "NCOP2"
+          }
+        },
+        {
+          "filename": "[sergey_krs] Shomin Sample - Blu-ray&DVD CM2 [BDRip 1920x1080 x264 FLAC]",
+          "errors": {
+            "title": {
+              "gold": "shomin sample - blu-ray&",
+              "pred": "shomin sample - blu-ray"
+            }
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+            "resolution": "1920x1080",
+            "source": "DVD",
+            "special": "CM2"
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+            "title": "Shomin Sample - Blu-ray",
+            "season": null,
+            "episode": null,
+            "resolution": "1920x1080",
+            "source": "DVD",
+            "special": "CM2"
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-              "pred": null
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+            "special": "NCOP"
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         {
-          "filename": "Puella Magi Madoka Magica - BD Menu14",
+          "filename": "38-ガンダムビルドダイバーズ ReRISE S2_ED1",
           "errors": {
-            "source": {
-              "gold": "bd",
-              "pred": null
+            "title": {
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-            "title": "Puella Magi Madoka Magica",
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+            "episode": 38,
             "resolution": null,
             "source": null,
-            "special": "BD Menu14"
+            "special": "ED1"
           }
         },
         {
-          "filename": "Kirion Hikaru no Go 72 [960x720]",
+          "filename": "[DBD-Raws][Blood-C][剧场版][The Last Dark][PV][01][1080P][BDRip][HEVC-10bit][FLAC]",
+          "errors": {
+            "title": {
+              "gold": "blood-c",
+              "pred": "blood-c t"
+            }
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+            "resolution": "1080P",
+            "source": "BDRip",
+            "special": "01"
+          },
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+            "title": "Blood-C T",
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+            "episode": null,
+            "resolution": "1080P",
+            "source": "BDRip",
+            "special": "01"
+          }
+        },
+        {
+          "filename": "[Moozzi2] Zegapain [SP02] NCED - 01 (Ver.1 #02~05) (BD 1920x1080 x.264 Flac)",
           "errors": {
-            "group": {
-              "gold": "kirion",
-              "pred": null
-            },
             "title": {
-              "gold": "hikaru no go",
-              "pred": "kirion hikaru no go 72"
+              "gold": "moozzi2 zegapain sp02 nced 01 ver 1 #02",
+              "pred": "moozzi2 zegapain sp02 nced 01 ver 1 #"
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+              "gold": "5",
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-            "title": "Hikaru no Go",
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             "season": null,
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-            "resolution": "960x720",
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           "pred": {
             "group": null,
-            "title": "Kirion Hikaru no Go 72",
+            "title": "Moozzi2 Zegapain SP02 NCED 01 Ver 1 #",
             "season": null,
-            "episode": null,
-            "resolution": "960x720",
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+            "episode": 2,
+            "resolution": "1920x1080",
+            "source": "BD",
             "special": null
           }
         },
         {
-          "filename": "葬送的芙莉莲 - 喵萌奶茶屋",
+          "filename": "Kyou_kara_Ma_Ou(TXXZ)_ZJ02",
           "errors": {
-            "group": {
-              "gold": "喵萌奶茶屋",
-              "pred": null
-            },
             "title": {
-              "gold": "葬送的芙莉莲",
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reports/perf_metrics.json

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reports/run_metadata.json

@@ -1,45 +1,46 @@
 {
-  "experiment_name": "dmhy-char-aug-fragments-10epoch-hardfocus",
-  "data_file": "data/generated/focus_after_10epoch_char.jsonl",
+  "experiment_name": "dmhy-char-virtual-sps32-10epoch-lightfocus",
+  "data_file": "data/generated/focus_after_virtual_sps32_char.jsonl",
   "data_sources": [
     {
       "role": "primary",
-      "path": "data/generated/focus_after_10epoch_char.jsonl",
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+      "path": "data/generated/focus_after_virtual_sps32_char.jsonl",
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       "repeat": 1,
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-    "special_requested": 20000,
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     "max_chars": 160
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   "dataset_mode": "encoded",
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   "apply_label_repairs": false,
   "keep_raw_dataset": false,
   "tokenizer_variant": "char",
-  "vocab_file": "vocab.json",
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   "max_seq_length": 128,
   "hidden_size": 256,
   "num_hidden_layers": 4,
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   "max_steps": -1,
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+  "warmup_steps": 20,
+  "seed": 208,
   "device": "cuda",
   "fp16": false,
   "gradient_accumulation_steps": 1,

reports/trainer_eval_metrics.json

@@ -1,12 +1,12 @@
 {
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 }

reports/training_lineage.json

@@ -1,81 +1,84 @@
 {
   "published_checkpoint": "repository_root",
-  "summary": "The published checkpoint was produced in two stages: a full-dataset CUDA fine-tune on dmhy_weak_char.jsonl, followed by a thin-runtime hard-case focus fine-tune.",
-  "summary_zh": "当前发布 checkpoint 是两阶段产物：先在 dmhy_weak_char.jsonl 上做全量 CUDA 微调，再做薄层运行时困难样本微调。",
+  "summary": "The published checkpoint was produced in two stages: a full 10-epoch CUDA fine-tune over Rust-generated virtual BIO shards, followed by a light thin-runtime hard-case focus fine-tune.",
+  "summary_zh": "当前发布 checkpoint 是两阶段产物：先用 Rust 生成的虚拟 BIO shard 做完整 10 epoch CUDA 微调，再做轻量薄层运行时困难样本微调。",
   "stages": [
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-      "type": "full_dataset_finetune_with_dynamic_augmentation",
+      "name": "dmhy-char-virtual-sps32-10epoch-lr1e5",
+      "type": "full_dataset_finetune_with_rust_virtual_shards",
       "machine": "adqew@192.168.63.157",
       "data_file": "datasets/AnimeName/dmhy_weak_char.jsonl",
+      "virtual_source_file": "data/generated/virtual_source_train_seed105.jsonl",
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       "tokenizer_variant": "char",
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+        "bracket_mode": "per-part",
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-      "type": "hard_case_focus_finetune",
+      "name": "dmhy-char-virtual-sps32-10epoch-lightfocus",
+      "type": "light_hard_case_focus_finetune",
       "machine": "adqew@192.168.63.157",
-      "data_file": "data/generated/focus_after_10epoch_char.jsonl",
+      "data_file": "data/generated/focus_after_virtual_sps32_char.jsonl",
       "tokenizer_variant": "char",
       "vocab_file": "datasets/AnimeName/vocab.char.json",
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-      "role": "Published repository-root checkpoint."
+      "heldout_model_only": "1962/2048",
+      "heldout_normalized_only": "1988/2048",
+      "perf_tokens_per_second_avg": 997645.0850819343,
+      "perf_gpu_util_avg": 100.0,
+      "role": "Published repository-root checkpoint. The default thin runtime also includes narrow postprocessing for bracketed search notes and release-promo title prefixes."
     }
   ]
 }

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tools/virtual_dataset_generator/Cargo.toml

@@ -0,0 +1,12 @@
+[package]
+name = "anifilebert-virtual-dataset-generator"
+version = "0.1.0"
+edition = "2021"
+
+[dependencies]
+anyhow = "1.0"
+clap = { version = "4.5", features = ["derive"] }
+rand = "0.8"
+rayon = "1.10"
+serde = { version = "1.0", features = ["derive"] }
+serde_json = "1.0"

tools/virtual_dataset_generator/src/main.rs

@@ -0,0 +1,1390 @@
+use anyhow::{bail, Context, Result};
+use clap::{Parser, ValueEnum};
+use rand::rngs::StdRng;
+use rand::seq::SliceRandom;
+use rand::Rng;
+use rand::SeedableRng;
+use rayon::prelude::*;
+use serde::{Deserialize, Serialize};
+use serde_json::json;
+use std::collections::{HashMap, HashSet};
+use std::fs::{self, File};
+use std::io::{BufRead, BufReader, BufWriter, Write};
+use std::path::{Path, PathBuf};
+use std::time::Instant;
+
+const ENTITIES: [Entity; 7] = [
+    Entity::Group,
+    Entity::Title,
+    Entity::Season,
+    Entity::Episode,
+    Entity::Special,
+    Entity::Resolution,
+    Entity::Source,
+];
+
+#[derive(Parser, Debug)]
+#[command(
+    about = "Generate pre-encoded AniFileBERT virtual BIO permutation shards",
+    version
+)]
+struct Args {
+    #[arg(long)]
+    input: PathBuf,
+
+    #[arg(long)]
+    vocab_file: PathBuf,
+
+    #[arg(long)]
+    output_dir: PathBuf,
+
+    #[arg(long, default_value_t = 128)]
+    max_length: usize,
+
+    #[arg(long, default_value_t = 25_000)]
+    shard_size: usize,
+
+    #[arg(long, default_value_t = 0)]
+    limit_rows: usize,
+
+    #[arg(long, default_value_t = 0)]
+    samples_per_source: usize,
+
+    #[arg(long, default_value_t = 42)]
+    seed: u64,
+
+    #[arg(long, default_value_t = 0)]
+    threads: usize,
+
+    #[arg(long, default_value = "global")]
+    separator_mode: SeparatorMode,
+
+    #[arg(long, default_value = "global")]
+    bracket_mode: BracketMode,
+
+    #[arg(long, value_delimiter = ',', default_value = " , - ,.,_,-,～,~")]
+    separators: Vec<String>,
+
+    #[arg(
+        long,
+        value_delimiter = ',',
+        default_value = "none,square,round,corner,angle"
+    )]
+    bracket_styles: Vec<String>,
+
+    #[arg(long, default_value_t = true)]
+    include_original: bool,
+
+    #[arg(long, default_value_t = true)]
+    include_special_fixtures: bool,
+
+    #[arg(long, help = "Only count rows; do not write shard files")]
+    dry_run: bool,
+}
+
+#[derive(Clone, Copy, Debug, Serialize, ValueEnum)]
+enum SeparatorMode {
+    Global,
+    PerGap,
+}
+
+#[derive(Clone, Copy, Debug, Serialize, ValueEnum)]
+enum BracketMode {
+    Global,
+    PerPart,
+}
+
+#[derive(Clone, Copy, Debug, Eq, PartialEq, Hash, Ord, PartialOrd, Serialize)]
+enum Entity {
+    Group,
+    Title,
+    Season,
+    Episode,
+    Special,
+    Resolution,
+    Source,
+}
+
+impl Entity {
+    fn index(self) -> usize {
+        match self {
+            Entity::Group => 0,
+            Entity::Title => 1,
+            Entity::Season => 2,
+            Entity::Episode => 3,
+            Entity::Special => 4,
+            Entity::Resolution => 5,
+            Entity::Source => 6,
+        }
+    }
+
+    fn from_name(name: &str) -> Option<Self> {
+        match name {
+            "GROUP" => Some(Entity::Group),
+            "TITLE" => Some(Entity::Title),
+            "SEASON" => Some(Entity::Season),
+            "EPISODE" => Some(Entity::Episode),
+            "SPECIAL" => Some(Entity::Special),
+            "RESOLUTION" => Some(Entity::Resolution),
+            "SOURCE" => Some(Entity::Source),
+            _ => None,
+        }
+    }
+
+    fn b_label(self) -> &'static str {
+        match self {
+            Entity::Group => "B-GROUP",
+            Entity::Title => "B-TITLE",
+            Entity::Season => "B-SEASON",
+            Entity::Episode => "B-EPISODE",
+            Entity::Special => "B-SPECIAL",
+            Entity::Resolution => "B-RESOLUTION",
+            Entity::Source => "B-SOURCE",
+        }
+    }
+
+    fn i_label(self) -> &'static str {
+        match self {
+            Entity::Group => "I-GROUP",
+            Entity::Title => "I-TITLE",
+            Entity::Season => "I-SEASON",
+            Entity::Episode => "I-EPISODE",
+            Entity::Special => "I-SPECIAL",
+            Entity::Resolution => "I-RESOLUTION",
+            Entity::Source => "I-SOURCE",
+        }
+    }
+}
+
+#[derive(Clone, Debug)]
+struct Bracket {
+    name: String,
+    open: String,
+    close: String,
+}
+
+impl Bracket {
+    fn from_name(name: &str) -> Result<Self> {
+        let trimmed = name.trim();
+        let pair = match trimmed {
+            "none" => ("", ""),
+            "square" => ("[", "]"),
+            "round" => ("(", ")"),
+            "corner" => ("【", "】"),
+            "angle" => ("《", "》"),
+            custom if custom.contains('|') => {
+                let mut parts = custom.splitn(2, '|');
+                let open = parts.next().unwrap_or_default();
+                let close = parts.next().unwrap_or_default();
+                return Ok(Self {
+                    name: custom.to_string(),
+                    open: open.to_string(),
+                    close: close.to_string(),
+                });
+            }
+            other => bail!("unknown bracket style '{other}'"),
+        };
+        Ok(Self {
+            name: trimmed.to_string(),
+            open: pair.0.to_string(),
+            close: pair.1.to_string(),
+        })
+    }
+}
+
+#[derive(Deserialize)]
+struct InputRow {
+    filename: Option<String>,
+    tokens: Vec<String>,
+    labels: Vec<String>,
+    tokenizer_variant: Option<String>,
+}
+
+#[derive(Clone)]
+struct SourceSample {
+    row_index: usize,
+    filename: String,
+    tokens: Vec<String>,
+    labels: Vec<String>,
+    fields: Vec<Vec<String>>,
+}
+
+#[derive(Clone)]
+struct GenConfig {
+    max_length: usize,
+    shard_size: usize,
+    separator_mode: SeparatorMode,
+    bracket_mode: BracketMode,
+    separators: Vec<String>,
+    brackets: Vec<Bracket>,
+    include_original: bool,
+    samples_per_source: usize,
+    seed: u64,
+}
+
+#[derive(Clone)]
+struct Vocab {
+    ids: HashMap<String, u16>,
+    pad_id: u16,
+    unk_id: u16,
+    cls_id: u16,
+    sep_id: u16,
+}
+
+#[derive(Serialize)]
+struct ShardManifest {
+    rows: usize,
+    input_ids: String,
+    attention_mask: String,
+    labels: String,
+}
+
+struct ShardWriter {
+    output_dir: PathBuf,
+    worker_id: usize,
+    shard_seq: usize,
+    shard_size: usize,
+    max_length: usize,
+    input_ids: Vec<u16>,
+    attention_mask: Vec<u8>,
+    labels: Vec<i16>,
+    rows: usize,
+    total_rows: u64,
+    shards: Vec<ShardManifest>,
+}
+
+impl ShardWriter {
+    fn new(output_dir: &Path, worker_id: usize, shard_size: usize, max_length: usize) -> Self {
+        let capacity = shard_size.saturating_mul(max_length);
+        Self {
+            output_dir: output_dir.to_path_buf(),
+            worker_id,
+            shard_seq: 0,
+            shard_size,
+            max_length,
+            input_ids: Vec::with_capacity(capacity),
+            attention_mask: Vec::with_capacity(capacity),
+            labels: Vec::with_capacity(capacity),
+            rows: 0,
+            total_rows: 0,
+            shards: Vec::new(),
+        }
+    }
+
+    fn add(&mut self, input_ids: &[u16], attention_mask: &[u8], labels: &[i16]) -> Result<()> {
+        if input_ids.len() != self.max_length
+            || attention_mask.len() != self.max_length
+            || labels.len() != self.max_length
+        {
+            bail!("encoded sample has wrong shape");
+        }
+        self.input_ids.extend_from_slice(input_ids);
+        self.attention_mask.extend_from_slice(attention_mask);
+        self.labels.extend_from_slice(labels);
+        self.rows += 1;
+        self.total_rows += 1;
+        if self.rows >= self.shard_size {
+            self.flush()?;
+        }
+        Ok(())
+    }
+
+    fn flush(&mut self) -> Result<()> {
+        if self.rows == 0 {
+            return Ok(());
+        }
+
+        let base = format!("part-w{:03}-s{:06}", self.worker_id, self.shard_seq);
+        let input_name = format!("{base}.input_ids.npy");
+        let mask_name = format!("{base}.attention_mask.npy");
+        let label_name = format!("{base}.labels.npy");
+        write_npy_u16(
+            &self.output_dir.join(&input_name),
+            &self.input_ids,
+            self.rows,
+            self.max_length,
+        )?;
+        write_npy_u8(
+            &self.output_dir.join(&mask_name),
+            &self.attention_mask,
+            self.rows,
+            self.max_length,
+        )?;
+        write_npy_i16(
+            &self.output_dir.join(&label_name),
+            &self.labels,
+            self.rows,
+            self.max_length,
+        )?;
+        self.shards.push(ShardManifest {
+            rows: self.rows,
+            input_ids: input_name,
+            attention_mask: mask_name,
+            labels: label_name,
+        });
+        self.input_ids.clear();
+        self.attention_mask.clear();
+        self.labels.clear();
+        self.rows = 0;
+        self.shard_seq += 1;
+        Ok(())
+    }
+}
+
+fn main() -> Result<()> {
+    let args = Args::parse();
+    if args.max_length < 4 {
+        bail!("--max-length must be at least 4");
+    }
+    if args.shard_size == 0 {
+        bail!("--shard-size must be positive");
+    }
+    if args.threads > 0 {
+        rayon::ThreadPoolBuilder::new()
+            .num_threads(args.threads)
+            .build_global()
+            .context("failed to configure rayon thread pool")?;
+    }
+
+    let started = Instant::now();
+    let vocab = load_vocab(&args.vocab_file)?;
+    let brackets = args
+        .bracket_styles
+        .iter()
+        .map(|style| Bracket::from_name(style))
+        .collect::<Result<Vec<_>>>()?;
+    let separators = args
+        .separators
+        .iter()
+        .map(|sep| normalize_separator_arg(sep))
+        .collect::<Vec<_>>();
+    let cfg = GenConfig {
+        max_length: args.max_length,
+        shard_size: args.shard_size,
+        separator_mode: args.separator_mode,
+        bracket_mode: args.bracket_mode,
+        separators,
+        brackets,
+        include_original: args.include_original,
+        samples_per_source: args.samples_per_source,
+        seed: args.seed,
+    };
+
+    let mut samples = load_samples(&args.input, args.limit_rows)?;
+    let source_rows = samples.len();
+    let mut rng = StdRng::seed_from_u64(args.seed);
+    samples.shuffle(&mut rng);
+
+    if args.dry_run {
+        let generated: u128 = samples
+            .par_iter()
+            .map(|sample| count_variants(sample, &cfg))
+            .sum();
+        let special_fixtures = if args.include_special_fixtures {
+            count_special_fixtures(&cfg) as u128
+        } else {
+            0
+        };
+        let manifest = json!({
+            "format": "anifilebert.virtual_dataset.preview.v1",
+            "input": args.input,
+            "vocab_file": args.vocab_file,
+            "source_rows": source_rows,
+            "estimated_rows": generated + special_fixtures,
+            "source_variant_rows": generated,
+            "special_fixture_rows": special_fixtures,
+            "max_length": cfg.max_length,
+            "separator_mode": cfg.separator_mode,
+            "bracket_mode": cfg.bracket_mode,
+            "separators": cfg.separators,
+            "brackets": cfg.brackets.iter().map(|b| &b.name).collect::<Vec<_>>(),
+            "include_original": cfg.include_original,
+            "samples_per_source": cfg.samples_per_source,
+            "include_special_fixtures": args.include_special_fixtures,
+            "seed": args.seed,
+            "elapsed_seconds": started.elapsed().as_secs_f64(),
+        });
+        println!("{}", serde_json::to_string_pretty(&manifest)?);
+        return Ok(());
+    }
+
+    fs::create_dir_all(&args.output_dir).with_context(|| {
+        format!(
+            "failed to create output directory {}",
+            args.output_dir.display()
+        )
+    })?;
+
+    let chunk_count = rayon::current_num_threads().max(1) * 4;
+    let chunk_size = samples.len().div_ceil(chunk_count).max(1);
+    let chunks = samples
+        .chunks(chunk_size)
+        .enumerate()
+        .collect::<Vec<(usize, &[SourceSample])>>();
+
+    let mut worker_results = chunks
+        .par_iter()
+        .map(|(chunk_idx, chunk)| {
+            let mut writer =
+                ShardWriter::new(&args.output_dir, *chunk_idx, cfg.shard_size, cfg.max_length);
+            for sample in *chunk {
+                generate_for_sample(sample, &cfg, &vocab, &mut writer)?;
+            }
+            writer.flush()?;
+            Ok::<_, anyhow::Error>((writer.total_rows, writer.shards))
+        })
+        .collect::<Result<Vec<_>>>()?;
+
+    let mut total_rows: u64 = 0;
+    let mut shards: Vec<ShardManifest> = Vec::new();
+    for (rows, mut worker_shards) in worker_results.drain(..) {
+        total_rows += rows;
+        shards.append(&mut worker_shards);
+    }
+
+    let special_rows = if args.include_special_fixtures {
+        let mut writer = ShardWriter::new(
+            &args.output_dir,
+            chunk_count + 1,
+            cfg.shard_size,
+            cfg.max_length,
+        );
+        for special in built_in_specials() {
+            let parts = vec![PartChoice {
+                entity: Entity::Special,
+                value: special,
+            }];
+            emit_syntax_variants(&parts, &cfg, &vocab, &mut writer)?;
+        }
+        writer.flush()?;
+        total_rows += writer.total_rows;
+        shards.append(&mut writer.shards);
+        writer.total_rows
+    } else {
+        0
+    };
+
+    shards.sort_by(|a, b| a.input_ids.cmp(&b.input_ids));
+    let manifest = json!({
+        "format": "anifilebert.virtual_dataset.shards.v1",
+        "input": args.input,
+        "vocab_file": args.vocab_file,
+        "source_rows": source_rows,
+        "total_rows": total_rows,
+        "special_fixture_rows": special_rows,
+        "max_length": cfg.max_length,
+        "shard_size": cfg.shard_size,
+        "tokenizer_variant": "char",
+        "encoding": {
+            "input_ids_dtype": "uint16",
+            "attention_mask_dtype": "uint8",
+            "labels_dtype": "int16",
+            "layout": "row_major_npy"
+        },
+        "special_tokens": {
+            "pad_id": vocab.pad_id,
+            "unk_id": vocab.unk_id,
+            "cls_id": vocab.cls_id,
+            "sep_id": vocab.sep_id
+        },
+        "generation": {
+            "separator_mode": cfg.separator_mode,
+            "bracket_mode": cfg.bracket_mode,
+            "separators": cfg.separators,
+            "brackets": cfg.brackets.iter().map(|b| &b.name).collect::<Vec<_>>(),
+            "include_original": cfg.include_original,
+            "samples_per_source": cfg.samples_per_source,
+            "include_special_fixtures": args.include_special_fixtures,
+            "seed": args.seed,
+            "threads": rayon::current_num_threads()
+        },
+        "shards": shards,
+        "elapsed_seconds": started.elapsed().as_secs_f64(),
+    });
+    let manifest_path = args.output_dir.join("manifest.json");
+    fs::write(&manifest_path, serde_json::to_string_pretty(&manifest)?)
+        .with_context(|| format!("failed to write {}", manifest_path.display()))?;
+    println!("{}", serde_json::to_string_pretty(&manifest)?);
+    Ok(())
+}
+
+fn normalize_separator_arg(value: &str) -> String {
+    match value {
+        "\\t" => "\t".to_string(),
+        "\\s" => " ".to_string(),
+        other => other.to_string(),
+    }
+}
+
+fn load_vocab(path: &Path) -> Result<Vocab> {
+    let text = fs::read_to_string(path)
+        .with_context(|| format!("failed to read vocab file {}", path.display()))?;
+    let raw: HashMap<String, u64> =
+        serde_json::from_str(&text).context("failed to parse vocab JSON")?;
+    let mut ids = HashMap::with_capacity(raw.len());
+    for (token, id) in raw {
+        if id > u16::MAX as u64 {
+            bail!("vocab id {id} for token '{token}' exceeds uint16 storage");
+        }
+        ids.insert(token, id as u16);
+    }
+    let pad_id = *ids.get("[PAD]").context("vocab is missing [PAD]")?;
+    let unk_id = *ids.get("[UNK]").context("vocab is missing [UNK]")?;
+    let cls_id = *ids.get("[CLS]").context("vocab is missing [CLS]")?;
+    let sep_id = *ids.get("[SEP]").context("vocab is missing [SEP]")?;
+    Ok(Vocab {
+        ids,
+        pad_id,
+        unk_id,
+        cls_id,
+        sep_id,
+    })
+}
+
+fn load_samples(path: &Path, limit_rows: usize) -> Result<Vec<SourceSample>> {
+    let file = File::open(path).with_context(|| format!("failed to open {}", path.display()))?;
+    let reader = BufReader::new(file);
+    let mut samples = Vec::new();
+    for (idx, line) in reader.lines().enumerate() {
+        if limit_rows > 0 && samples.len() >= limit_rows {
+            break;
+        }
+        let line = line.with_context(|| format!("failed reading line {}", idx + 1))?;
+        if line.trim().is_empty() {
+            continue;
+        }
+        let row: InputRow = serde_json::from_str(&line)
+            .with_context(|| format!("failed to parse JSONL line {}", idx + 1))?;
+        if let Some(variant) = row.tokenizer_variant.as_deref() {
+            if variant != "char" {
+                bail!(
+                    "line {} has tokenizer_variant={variant}; virtual shard generation currently requires char data",
+                    idx + 1
+                );
+            }
+        }
+        if row.tokens.len() != row.labels.len() {
+            bail!(
+                "line {} has mismatched token/label lengths: {} vs {}",
+                idx + 1,
+                row.tokens.len(),
+                row.labels.len()
+            );
+        }
+        let filename = row.filename.clone().unwrap_or_else(|| row.tokens.join(""));
+        let fields = extract_fields(&row.tokens, &row.labels);
+        samples.push(SourceSample {
+            row_index: idx,
+            filename,
+            tokens: row.tokens,
+            labels: row.labels,
+            fields,
+        });
+    }
+    Ok(samples)
+}
+
+fn extract_fields(tokens: &[String], labels: &[String]) -> Vec<Vec<String>> {
+    let mut fields: Vec<Vec<String>> = (0..ENTITIES.len()).map(|_| Vec::new()).collect();
+    let mut seen: Vec<HashSet<String>> = (0..ENTITIES.len()).map(|_| HashSet::new()).collect();
+    let mut active_entity: Option<Entity> = None;
+    let mut active_text = String::new();
+
+    let flush = |entity: Option<Entity>,
+                 text: &mut String,
+                 fields: &mut Vec<Vec<String>>,
+                 seen: &mut Vec<HashSet<String>>| {
+        if let Some(entity) = entity {
+            let value = text.trim().to_string();
+            if !value.is_empty() && seen[entity.index()].insert(value.clone()) {
+                fields[entity.index()].push(value);
+            }
+        }
+        text.clear();
+    };
+
+    for (token, label) in tokens.iter().zip(labels.iter()) {
+        if let Some(entity) = label.strip_prefix("B-").and_then(Entity::from_name) {
+            flush(active_entity, &mut active_text, &mut fields, &mut seen);
+            active_entity = Some(entity);
+            active_text.push_str(token);
+        } else if let Some(entity) = label.strip_prefix("I-").and_then(Entity::from_name) {
+            if active_entity == Some(entity) {
+                active_text.push_str(token);
+            } else {
+                flush(active_entity, &mut active_text, &mut fields, &mut seen);
+                active_entity = Some(entity);
+                active_text.push_str(token);
+            }
+        } else {
+            flush(active_entity, &mut active_text, &mut fields, &mut seen);
+            active_entity = None;
+        }
+    }
+    flush(active_entity, &mut active_text, &mut fields, &mut seen);
+    fields
+}
+
+fn count_variants(sample: &SourceSample, cfg: &GenConfig) -> u128 {
+    let mut count = if cfg.include_original { 1 } else { 0 };
+    let available = ENTITIES
+        .iter()
+        .copied()
+        .filter(|entity| !sample.fields[entity.index()].is_empty())
+        .collect::<Vec<_>>();
+    let n = available.len();
+    if n == 0 {
+        return count;
+    }
+    if cfg.samples_per_source > 0 {
+        return count + cfg.samples_per_source as u128;
+    }
+    for mask in 1usize..(1usize << n) {
+        let selected = available
+            .iter()
+            .enumerate()
+            .filter_map(|(idx, entity)| ((mask & (1usize << idx)) != 0).then_some(*entity))
+            .collect::<Vec<_>>();
+        let m = selected.len();
+        let value_product: u128 = selected
+            .iter()
+            .map(|entity| sample.fields[entity.index()].len() as u128)
+            .product();
+        let perm_count = factorial(m as u32);
+        let sep_factor = if m <= 1 {
+            1
+        } else {
+            match cfg.separator_mode {
+                SeparatorMode::Global => cfg.separators.len() as u128,
+                SeparatorMode::PerGap => (cfg.separators.len() as u128).pow((m - 1) as u32),
+            }
+        };
+        let bracket_factor = match cfg.bracket_mode {
+            BracketMode::Global => cfg.brackets.len() as u128,
+            BracketMode::PerPart => (cfg.brackets.len() as u128).pow(m as u32),
+        };
+        count += value_product * perm_count * sep_factor * bracket_factor;
+    }
+    count
+}
+
+fn count_special_fixtures(cfg: &GenConfig) -> usize {
+    let bracket_factor = match cfg.bracket_mode {
+        BracketMode::Global => cfg.brackets.len(),
+        BracketMode::PerPart => cfg.brackets.len(),
+    };
+    built_in_specials().len() * bracket_factor
+}
+
+fn factorial(n: u32) -> u128 {
+    (1..=n as u128).product::<u128>().max(1)
+}
+
+fn generate_for_sample(
+    sample: &SourceSample,
+    cfg: &GenConfig,
+    vocab: &Vocab,
+    writer: &mut ShardWriter,
+) -> Result<()> {
+    if cfg.include_original {
+        let (input_ids, attention_mask, labels) =
+            encode_original_sample(sample, vocab, cfg.max_length)?;
+        writer.add(&input_ids, &attention_mask, &labels)?;
+    }
+
+    if cfg.samples_per_source > 0 {
+        generate_sampled_variants(sample, cfg, vocab, writer)?;
+        return Ok(());
+    }
+
+    let available = ENTITIES
+        .iter()
+        .copied()
+        .filter(|entity| !sample.fields[entity.index()].is_empty())
+        .collect::<Vec<_>>();
+    let n = available.len();
+    for mask in 1usize..(1usize << n) {
+        let mut selected = available
+            .iter()
+            .enumerate()
+            .filter_map(|(idx, entity)| ((mask & (1usize << idx)) != 0).then_some(*entity))
+            .collect::<Vec<_>>();
+        permute_entities(&mut selected, 0, &mut |order| {
+            let mut parts: Vec<PartChoice> = Vec::with_capacity(order.len());
+            for_each_value_combo(order, &sample.fields, 0, &mut parts, &mut |combo| {
+                emit_syntax_variants(combo, cfg, vocab, writer)
+            })
+        })?;
+    }
+    Ok(())
+}
+
+fn generate_sampled_variants(
+    sample: &SourceSample,
+    cfg: &GenConfig,
+    vocab: &Vocab,
+    writer: &mut ShardWriter,
+) -> Result<()> {
+    let mut rng = StdRng::seed_from_u64(
+        cfg.seed ^ ((sample.row_index as u64).wrapping_mul(0x9E37_79B9_7F4A_7C15)),
+    );
+    let available = ENTITIES
+        .iter()
+        .copied()
+        .filter(|entity| !sample.fields[entity.index()].is_empty())
+        .collect::<Vec<_>>();
+    if available.is_empty() {
+        return Ok(());
+    }
+
+    let mut seen = HashSet::new();
+    let mut emitted = 0usize;
+    let budget = cfg.samples_per_source;
+    let max_unique_attempts = budget.saturating_mul(32).max(64);
+    let mut attempts = 0usize;
+
+    let mut templates: Vec<Vec<PartChoice>> = Vec::new();
+    if let Some(title) = sample.fields[Entity::Title.index()].first() {
+        templates.push(vec![PartChoice {
+            entity: Entity::Title,
+            value: title.clone(),
+        }]);
+        if let Some(season) = sample.fields[Entity::Season.index()].first() {
+            templates.push(vec![
+                PartChoice {
+                    entity: Entity::Title,
+                    value: title.clone(),
+                },
+                PartChoice {
+                    entity: Entity::Season,
+                    value: season.clone(),
+                },
+            ]);
+        }
+    }
+    if let Some(episode) = sample.fields[Entity::Episode.index()].first() {
+        templates.push(vec![PartChoice {
+            entity: Entity::Episode,
+            value: episode.clone(),
+        }]);
+    }
+    if let Some(special) = sample.fields[Entity::Special.index()].first() {
+        templates.push(vec![PartChoice {
+            entity: Entity::Special,
+            value: special.clone(),
+        }]);
+    }
+    if let (Some(title), Some(special)) = (
+        sample.fields[Entity::Title.index()].first(),
+        sample.fields[Entity::Special.index()].first(),
+    ) {
+        templates.push(vec![
+            PartChoice {
+                entity: Entity::Title,
+                value: title.clone(),
+            },
+            PartChoice {
+                entity: Entity::Special,
+                value: special.clone(),
+            },
+        ]);
+    }
+
+    for parts in templates {
+        if emitted >= budget {
+            break;
+        }
+        emit_sample_variant(
+            parts,
+            cfg,
+            vocab,
+            writer,
+            &mut seen,
+            &mut emitted,
+            &mut rng,
+            false,
+        )?;
+    }
+
+    while emitted < budget && attempts < max_unique_attempts {
+        attempts += 1;
+        let subset_size = match rng.gen_range(0..100) {
+            0..=29 => 1,
+            30..=54 => 2,
+            55..=74 => 3,
+            75..=89 => 4.min(available.len()),
+            _ => available.len().min(5),
+        }
+        .max(1)
+        .min(available.len());
+
+        let mut chosen = available
+            .choose_multiple(&mut rng, subset_size)
+            .copied()
+            .collect::<Vec<_>>();
+        chosen.shuffle(&mut rng);
+        if !chosen
+            .iter()
+            .any(|entity| matches!(entity, Entity::Title | Entity::Episode | Entity::Special))
+        {
+            if let Some(fallback) = available
+                .iter()
+                .copied()
+                .find(|entity| matches!(entity, Entity::Title | Entity::Episode | Entity::Special))
+            {
+                if !chosen.contains(&fallback) {
+                    chosen.push(fallback);
+                }
+            }
+        }
+
+        let mut parts = Vec::with_capacity(chosen.len());
+        for entity in chosen {
+            let values = &sample.fields[entity.index()];
+            let value = values.choose(&mut rng).cloned().unwrap_or_default();
+            parts.push(PartChoice { entity, value });
+        }
+        parts.shuffle(&mut rng);
+        emit_sample_variant(
+            parts,
+            cfg,
+            vocab,
+            writer,
+            &mut seen,
+            &mut emitted,
+            &mut rng,
+            false,
+        )?;
+    }
+
+    while emitted < budget {
+        let subset_size = match rng.gen_range(0..100) {
+            0..=29 => 1,
+            30..=54 => 2,
+            55..=74 => 3,
+            75..=89 => 4.min(available.len()),
+            _ => available.len().min(5),
+        }
+        .max(1)
+        .min(available.len());
+
+        let mut chosen = available
+            .choose_multiple(&mut rng, subset_size)
+            .copied()
+            .collect::<Vec<_>>();
+        chosen.shuffle(&mut rng);
+        if !chosen
+            .iter()
+            .any(|entity| matches!(entity, Entity::Title | Entity::Episode | Entity::Special))
+        {
+            if let Some(fallback) = available
+                .iter()
+                .copied()
+                .find(|entity| matches!(entity, Entity::Title | Entity::Episode | Entity::Special))
+            {
+                if !chosen.contains(&fallback) {
+                    chosen.push(fallback);
+                }
+            }
+        }
+
+        let mut parts = Vec::with_capacity(chosen.len());
+        for entity in chosen {
+            let values = &sample.fields[entity.index()];
+            let value = values.choose(&mut rng).cloned().unwrap_or_default();
+            parts.push(PartChoice { entity, value });
+        }
+        parts.shuffle(&mut rng);
+        emit_sample_variant(
+            parts,
+            cfg,
+            vocab,
+            writer,
+            &mut seen,
+            &mut emitted,
+            &mut rng,
+            true,
+        )?;
+    }
+    Ok(())
+}
+
+fn emit_sample_variant(
+    parts: Vec<PartChoice>,
+    cfg: &GenConfig,
+    vocab: &Vocab,
+    writer: &mut ShardWriter,
+    seen: &mut HashSet<String>,
+    emitted: &mut usize,
+    rng: &mut StdRng,
+    allow_duplicate: bool,
+) -> Result<()> {
+    if *emitted >= cfg.samples_per_source {
+        return Ok(());
+    }
+    if parts.is_empty() {
+        return Ok(());
+    }
+    let separators = match cfg.separator_mode {
+        SeparatorMode::Global => {
+            let sep = cfg
+                .separators
+                .choose(rng)
+                .cloned()
+                .unwrap_or_else(|| " ".to_string());
+            if parts.len() > 1 {
+                vec![sep; parts.len() - 1]
+            } else {
+                Vec::new()
+            }
+        }
+        SeparatorMode::PerGap => {
+            let mut values = Vec::with_capacity(parts.len().saturating_sub(1));
+            for _ in 0..parts.len().saturating_sub(1) {
+                values.push(
+                    cfg.separators
+                        .choose(rng)
+                        .cloned()
+                        .unwrap_or_else(|| " ".to_string()),
+                );
+            }
+            values
+        }
+    };
+    let brackets = match cfg.bracket_mode {
+        BracketMode::Global => {
+            let bracket = cfg
+                .brackets
+                .choose(rng)
+                .cloned()
+                .unwrap_or_else(|| Bracket {
+                    name: "none".to_string(),
+                    open: String::new(),
+                    close: String::new(),
+                });
+            vec![bracket; parts.len()]
+        }
+        BracketMode::PerPart => {
+            let mut values = Vec::with_capacity(parts.len());
+            for _ in 0..parts.len() {
+                values.push(
+                    cfg.brackets
+                        .choose(rng)
+                        .cloned()
+                        .unwrap_or_else(|| Bracket {
+                            name: "none".to_string(),
+                            open: String::new(),
+                            close: String::new(),
+                        }),
+                );
+            }
+            values
+        }
+    };
+    let text = render_variant_text(&parts, &separators, &brackets);
+    if !allow_duplicate && !seen.insert(text) {
+        return Ok(());
+    }
+    let (input_ids, attention_mask, labels) =
+        encode_generated_sample(&parts, &separators, &brackets, vocab, cfg.max_length)?;
+    writer.add(&input_ids, &attention_mask, &labels)?;
+    *emitted += 1;
+    Ok(())
+}
+
+fn permute_entities<F>(values: &mut [Entity], start: usize, callback: &mut F) -> Result<()>
+where
+    F: FnMut(&[Entity]) -> Result<()>,
+{
+    if start >= values.len() {
+        return callback(values);
+    }
+    for idx in start..values.len() {
+        values.swap(start, idx);
+        permute_entities(values, start + 1, callback)?;
+        values.swap(start, idx);
+    }
+    Ok(())
+}
+
+#[derive(Clone)]
+struct PartChoice {
+    entity: Entity,
+    value: String,
+}
+
+fn for_each_value_combo<F>(
+    order: &[Entity],
+    fields: &[Vec<String>],
+    idx: usize,
+    current: &mut Vec<PartChoice>,
+    callback: &mut F,
+) -> Result<()>
+where
+    F: FnMut(&[PartChoice]) -> Result<()>,
+{
+    if idx >= order.len() {
+        return callback(current);
+    }
+    let entity = order[idx];
+    for value in &fields[entity.index()] {
+        current.push(PartChoice {
+            entity,
+            value: value.clone(),
+        });
+        for_each_value_combo(order, fields, idx + 1, current, callback)?;
+        current.pop();
+    }
+    Ok(())
+}
+
+fn emit_syntax_variants(
+    parts: &[PartChoice],
+    cfg: &GenConfig,
+    vocab: &Vocab,
+    writer: &mut ShardWriter,
+) -> Result<()> {
+    let gaps = parts.len().saturating_sub(1);
+    let mut separators = Vec::with_capacity(gaps);
+    for_each_separator_combo(gaps, cfg, 0, &mut separators, &mut |sep_combo| {
+        let mut brackets = Vec::with_capacity(parts.len());
+        for_each_bracket_combo(parts.len(), cfg, 0, &mut brackets, &mut |bracket_combo| {
+            let (input_ids, attention_mask, labels) =
+                encode_generated_sample(parts, sep_combo, bracket_combo, vocab, cfg.max_length)?;
+            writer.add(&input_ids, &attention_mask, &labels)
+        })
+    })
+}
+
+fn for_each_separator_combo<F>(
+    gaps: usize,
+    cfg: &GenConfig,
+    idx: usize,
+    current: &mut Vec<String>,
+    callback: &mut F,
+) -> Result<()>
+where
+    F: FnMut(&[String]) -> Result<()>,
+{
+    if gaps == 0 {
+        return callback(current);
+    }
+    match cfg.separator_mode {
+        SeparatorMode::Global => {
+            if idx == 0 {
+                for sep in &cfg.separators {
+                    current.clear();
+                    current.resize(gaps, sep.clone());
+                    callback(current)?;
+                }
+            }
+            Ok(())
+        }
+        SeparatorMode::PerGap => {
+            if idx >= gaps {
+                return callback(current);
+            }
+            for sep in &cfg.separators {
+                current.push(sep.clone());
+                for_each_separator_combo(gaps, cfg, idx + 1, current, callback)?;
+                current.pop();
+            }
+            Ok(())
+        }
+    }
+}
+
+fn for_each_bracket_combo<F>(
+    parts: usize,
+    cfg: &GenConfig,
+    idx: usize,
+    current: &mut Vec<Bracket>,
+    callback: &mut F,
+) -> Result<()>
+where
+    F: FnMut(&[Bracket]) -> Result<()>,
+{
+    match cfg.bracket_mode {
+        BracketMode::Global => {
+            if idx == 0 {
+                for bracket in &cfg.brackets {
+                    current.clear();
+                    current.resize(parts, bracket.clone());
+                    callback(current)?;
+                }
+            }
+            Ok(())
+        }
+        BracketMode::PerPart => {
+            if idx >= parts {
+                return callback(current);
+            }
+            for bracket in &cfg.brackets {
+                current.push(bracket.clone());
+                for_each_bracket_combo(parts, cfg, idx + 1, current, callback)?;
+                current.pop();
+            }
+            Ok(())
+        }
+    }
+}
+
+fn render_variant_text(
+    parts: &[PartChoice],
+    separators: &[String],
+    brackets: &[Bracket],
+) -> String {
+    let mut text = String::new();
+    for (idx, part) in parts.iter().enumerate() {
+        text.push_str(&brackets[idx].open);
+        text.push_str(&part.value);
+        text.push_str(&brackets[idx].close);
+        if idx < separators.len() {
+            text.push_str(&separators[idx]);
+        }
+    }
+    text
+}
+
+fn encode_original_sample(
+    sample: &SourceSample,
+    vocab: &Vocab,
+    max_length: usize,
+) -> Result<(Vec<u16>, Vec<u8>, Vec<i16>)> {
+    let mut input_ids = vec![vocab.pad_id; max_length];
+    let mut attention_mask = vec![0u8; max_length];
+    let mut labels = vec![-100i16; max_length];
+
+    input_ids[0] = vocab.cls_id;
+    attention_mask[0] = 1;
+    let available = max_length.saturating_sub(2);
+    let token_count = sample.tokens.len().min(available);
+    for idx in 0..token_count {
+        input_ids[idx + 1] = token_id(vocab, &sample.tokens[idx]);
+        attention_mask[idx + 1] = 1;
+        labels[idx + 1] = label_id(&sample.labels[idx]).with_context(|| {
+            format!(
+                "unknown label '{}' on source row {} ({})",
+                sample.labels[idx],
+                sample.row_index + 1,
+                sample.filename
+            )
+        })?;
+    }
+    let sep_pos = token_count + 1;
+    input_ids[sep_pos] = vocab.sep_id;
+    attention_mask[sep_pos] = 1;
+    Ok((input_ids, attention_mask, labels))
+}
+
+fn encode_generated_sample(
+    parts: &[PartChoice],
+    separators: &[String],
+    brackets: &[Bracket],
+    vocab: &Vocab,
+    max_length: usize,
+) -> Result<(Vec<u16>, Vec<u8>, Vec<i16>)> {
+    let mut input_ids = vec![vocab.pad_id; max_length];
+    let mut attention_mask = vec![0u8; max_length];
+    let mut labels = vec![-100i16; max_length];
+    input_ids[0] = vocab.cls_id;
+    attention_mask[0] = 1;
+
+    let available = max_length.saturating_sub(2);
+    let mut pos = 1usize;
+    for (idx, part) in parts.iter().enumerate() {
+        let bracket = &brackets[idx];
+        append_o_text(
+            &bracket.open,
+            vocab,
+            available,
+            &mut pos,
+            &mut input_ids,
+            &mut attention_mask,
+            &mut labels,
+        );
+        append_entity_text(
+            &part.value,
+            part.entity,
+            vocab,
+            available,
+            &mut pos,
+            &mut input_ids,
+            &mut attention_mask,
+            &mut labels,
+        )?;
+        append_o_text(
+            &bracket.close,
+            vocab,
+            available,
+            &mut pos,
+            &mut input_ids,
+            &mut attention_mask,
+            &mut labels,
+        );
+        if idx < separators.len() {
+            append_o_text(
+                &separators[idx],
+                vocab,
+                available,
+                &mut pos,
+                &mut input_ids,
+                &mut attention_mask,
+                &mut labels,
+            );
+        }
+    }
+
+    let sep_pos = pos.min(max_length - 1);
+    input_ids[sep_pos] = vocab.sep_id;
+    attention_mask[sep_pos] = 1;
+    labels[sep_pos] = -100;
+    Ok((input_ids, attention_mask, labels))
+}
+
+fn append_o_text(
+    text: &str,
+    vocab: &Vocab,
+    available: usize,
+    pos: &mut usize,
+    input_ids: &mut [u16],
+    attention_mask: &mut [u8],
+    labels: &mut [i16],
+) {
+    for ch in text.chars() {
+        if *pos > available {
+            return;
+        }
+        let token = ch.to_string();
+        input_ids[*pos] = token_id(vocab, &token);
+        attention_mask[*pos] = 1;
+        labels[*pos] = 0;
+        *pos += 1;
+    }
+}
+
+fn append_entity_text(
+    text: &str,
+    entity: Entity,
+    vocab: &Vocab,
+    available: usize,
+    pos: &mut usize,
+    input_ids: &mut [u16],
+    attention_mask: &mut [u8],
+    labels: &mut [i16],
+) -> Result<()> {
+    let b = label_id(entity.b_label()).context("missing B label")?;
+    let i = label_id(entity.i_label()).context("missing I label")?;
+    let mut first = true;
+    for ch in text.chars() {
+        if *pos > available {
+            return Ok(());
+        }
+        let token = ch.to_string();
+        input_ids[*pos] = token_id(vocab, &token);
+        attention_mask[*pos] = 1;
+        labels[*pos] = if first { b } else { i };
+        first = false;
+        *pos += 1;
+    }
+    Ok(())
+}
+
+fn token_id(vocab: &Vocab, token: &str) -> u16 {
+    *vocab.ids.get(token).unwrap_or(&vocab.unk_id)
+}
+
+fn label_id(label: &str) -> Option<i16> {
+    Some(match label {
+        "O" => 0,
+        "B-TITLE" => 1,
+        "I-TITLE" => 2,
+        "B-SEASON" => 3,
+        "I-SEASON" => 4,
+        "B-EPISODE" => 5,
+        "I-EPISODE" => 6,
+        "B-SPECIAL" => 7,
+        "I-SPECIAL" => 8,
+        "B-GROUP" => 9,
+        "I-GROUP" => 10,
+        "B-RESOLUTION" => 11,
+        "I-RESOLUTION" => 12,
+        "B-SOURCE" => 13,
+        "I-SOURCE" => 14,
+        _ => return None,
+    })
+}
+
+fn built_in_specials() -> Vec<String> {
+    let mut values = Vec::new();
+    values.push("Menu".to_string());
+    for idx in 1..=24 {
+        values.push(format!("Menu{idx:02}"));
+        values.push(format!("Menu {idx:02}"));
+        values.push(format!("BDMenu{idx:02}"));
+        values.push(format!("BD Menu{idx:02}"));
+        values.push(format!("Menu{idx:02}-01"));
+        values.push(format!("ED E{idx:02}"));
+    }
+    for idx in 1..=6 {
+        values.push(format!("OP{idx:02}"));
+        values.push(format!("NCOP{idx:02}"));
+        values.push(format!("NCED{idx:02}"));
+    }
+    for idx in 1..=12 {
+        values.push(format!("CM{idx:02}"));
+        values.push(format!("PV{idx:02}"));
+    }
+    values
+}
+
+fn write_npy_u16(path: &Path, data: &[u16], rows: usize, cols: usize) -> Result<()> {
+    let mut writer = BufWriter::new(
+        File::create(path).with_context(|| format!("failed to create {}", path.display()))?,
+    );
+    write_npy_header(&mut writer, "<u2", rows, cols)?;
+    for value in data {
+        writer.write_all(&value.to_le_bytes())?;
+    }
+    Ok(())
+}
+
+fn write_npy_u8(path: &Path, data: &[u8], rows: usize, cols: usize) -> Result<()> {
+    let mut writer = BufWriter::new(
+        File::create(path).with_context(|| format!("failed to create {}", path.display()))?,
+    );
+    write_npy_header(&mut writer, "|u1", rows, cols)?;
+    writer.write_all(data)?;
+    Ok(())
+}
+
+fn write_npy_i16(path: &Path, data: &[i16], rows: usize, cols: usize) -> Result<()> {
+    let mut writer = BufWriter::new(
+        File::create(path).with_context(|| format!("failed to create {}", path.display()))?,
+    );
+    write_npy_header(&mut writer, "<i2", rows, cols)?;
+    for value in data {
+        writer.write_all(&value.to_le_bytes())?;
+    }
+    Ok(())
+}
+
+fn write_npy_header<W: Write>(writer: &mut W, descr: &str, rows: usize, cols: usize) -> Result<()> {
+    let mut header = format!(
+        "{{'descr': '{}', 'fortran_order': False, 'shape': ({}, {}), }}",
+        descr, rows, cols
+    )
+    .into_bytes();
+    let preamble_len = 10usize;
+    let pad_len = (16 - ((preamble_len + header.len() + 1) % 16)) % 16;
+    header.extend(std::iter::repeat(b' ').take(pad_len));
+    header.push(b'\n');
+    if header.len() > u16::MAX as usize {
+        bail!("npy header too large");
+    }
+    writer.write_all(b"\x93NUMPY")?;
+    writer.write_all(&[1, 0])?;
+    writer.write_all(&(header.len() as u16).to_le_bytes())?;
+    writer.write_all(&header)?;
+    Ok(())
+}

training_args.bin

@@ -1,3 +1,3 @@
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