Token Classification
Transformers
ONNX
Safetensors
English
Japanese
Chinese
bert
anime
filename-parsing
Eval Results (legacy)
Instructions to use ModerRAS/AniFileBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ModerRAS/AniFileBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ModerRAS/AniFileBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ModerRAS/AniFileBERT") model = AutoModelForTokenClassification.from_pretrained("ModerRAS/AniFileBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 5,381 Bytes
ed49faa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 | """Shared BIO label schema and helpers."""
from __future__ import annotations
import json
from pathlib import Path
from typing import Dict, Optional, Tuple
LABEL_SCHEMA_VERSION = 2
TITLE_SUFFIXES = ("CHS", "CHT", "JPN", "LATIN", "MIXED")
TITLE_PRIORITY = ("CHS", "CHT", "JPN", "MIXED", "LATIN")
FILE_TITLE_ENTITIES = tuple(f"TITLE_{suffix}" for suffix in TITLE_SUFFIXES)
PATH_TITLE_ENTITIES = tuple(f"PATH_TITLE_{suffix}" for suffix in TITLE_SUFFIXES)
TITLE_ENTITIES = FILE_TITLE_ENTITIES + PATH_TITLE_ENTITIES
TITLE_LIKE_ENTITIES = TITLE_ENTITIES + ("TITLE",)
SEASON_LIKE_ENTITIES = ("SEASON", "PATH_SEASON")
DEFAULT_TITLE_ENTITY = "TITLE_MIXED"
_FALLBACK_LABELS = (
"O",
"B-TITLE_CHS",
"I-TITLE_CHS",
"B-TITLE_CHT",
"I-TITLE_CHT",
"B-TITLE_JPN",
"I-TITLE_JPN",
"B-TITLE_LATIN",
"I-TITLE_LATIN",
"B-TITLE_MIXED",
"I-TITLE_MIXED",
"B-PATH_TITLE_CHS",
"I-PATH_TITLE_CHS",
"B-PATH_TITLE_CHT",
"I-PATH_TITLE_CHT",
"B-PATH_TITLE_JPN",
"I-PATH_TITLE_JPN",
"B-PATH_TITLE_LATIN",
"I-PATH_TITLE_LATIN",
"B-PATH_TITLE_MIXED",
"I-PATH_TITLE_MIXED",
"B-PATH_SEASON",
"I-PATH_SEASON",
"B-SEASON",
"I-SEASON",
"B-EPISODE",
"I-EPISODE",
"B-SPECIAL",
"I-SPECIAL",
"B-GROUP",
"I-GROUP",
"B-RESOLUTION",
"I-RESOLUTION",
"B-SOURCE",
"I-SOURCE",
"B-TAG",
"I-TAG",
)
def _load_schema_labels() -> Tuple[str, ...]:
schema_path = Path(__file__).resolve().parents[1] / "label_schema.json"
try:
with schema_path.open("r", encoding="utf-8") as fh:
payload = json.load(fh)
except OSError:
return _FALLBACK_LABELS
labels = payload.get("labels")
if not isinstance(labels, list) or not labels:
return _FALLBACK_LABELS
if not all(isinstance(label, str) and label for label in labels):
return _FALLBACK_LABELS
return tuple(labels)
LABELS = _load_schema_labels()
LEGACY_15_LABELS = (
"O",
"B-TITLE",
"I-TITLE",
"B-SEASON",
"I-SEASON",
"B-EPISODE",
"I-EPISODE",
"B-SPECIAL",
"I-SPECIAL",
"B-GROUP",
"I-GROUP",
"B-RESOLUTION",
"I-RESOLUTION",
"B-SOURCE",
"I-SOURCE",
)
LABEL2ID = {label: idx for idx, label in enumerate(LABELS)}
ID2LABEL = {idx: label for idx, label in enumerate(LABELS)}
def make_label2id() -> Dict[str, int]:
return dict(LABEL2ID)
def make_id2label() -> Dict[int, str]:
return dict(ID2LABEL)
def split_bio_label(label: str) -> Tuple[Optional[str], Optional[str]]:
if not isinstance(label, str) or label == "O":
return None, None
prefix, sep, entity = label.partition("-")
if sep != "-" or prefix not in {"B", "I"} or not entity:
return None, None
return prefix, entity
def label_entity(label: str) -> Optional[str]:
return split_bio_label(label)[1]
def canonical_entity(entity: str) -> str:
return DEFAULT_TITLE_ENTITY if entity == "TITLE" else entity
def canonical_bio_label(label: str) -> str:
prefix, entity = split_bio_label(label)
if prefix is None or entity is None:
return "O" if label == "O" else label
return f"{prefix}-{canonical_entity(entity)}"
def is_title_entity(entity: Optional[str]) -> bool:
return entity in TITLE_LIKE_ENTITIES
def is_file_title_entity(entity: Optional[str]) -> bool:
return entity in FILE_TITLE_ENTITIES or entity == "TITLE"
def is_path_title_entity(entity: Optional[str]) -> bool:
return entity in PATH_TITLE_ENTITIES
def is_title_like_label(label: str) -> bool:
return is_title_entity(label_entity(label))
def is_season_entity(entity: Optional[str]) -> bool:
return entity in SEASON_LIKE_ENTITIES
def is_season_like_label(label: str) -> bool:
return is_season_entity(label_entity(label))
def is_same_entity_label(label: str, entity: str) -> bool:
return label_entity(label) == entity
def title_language(entity: Optional[str]) -> str:
if entity == "TITLE":
return "MIXED"
if not entity:
return "MIXED"
if entity.startswith("PATH_TITLE_"):
return entity.removeprefix("PATH_TITLE_")
if entity.startswith("TITLE_"):
return entity.removeprefix("TITLE_")
return "MIXED"
def title_entity_priority(entity: Optional[str]) -> Tuple[int, int]:
language = title_language(entity)
language_rank = TITLE_PRIORITY.index(language) if language in TITLE_PRIORITY else len(TITLE_PRIORITY)
path_rank = 1 if is_path_title_entity(entity) else 0
return path_rank, language_rank
def label_migration_sources(target_label: str) -> Tuple[str, ...]:
"""Return old-label candidates that can initialize a target label row."""
if target_label == "O":
return ("O",)
prefix, entity = split_bio_label(target_label)
if prefix is None or entity is None:
return (target_label,)
sources = [target_label]
if is_title_entity(entity):
sources.append(f"{prefix}-TITLE")
elif entity == "PATH_SEASON":
sources.append(f"{prefix}-SEASON")
return tuple(dict.fromkeys(sources))
def infer_legacy_id2label(num_labels: int) -> Optional[Dict[int, str]]:
if num_labels == len(LEGACY_15_LABELS):
return {idx: label for idx, label in enumerate(LEGACY_15_LABELS)}
if num_labels == len(LABELS):
return make_id2label()
return None
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