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")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ModerRAS/AniFileBERT") model = AutoModelForTokenClassification.from_pretrained("ModerRAS/AniFileBERT") - Notebooks
- Google Colab
- Kaggle
File size: 11,962 Bytes
f4f4e0e 8c50d16 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 | #!/usr/bin/env python3
"""
Semantic anime filename annotator.
Uses position-based understanding (NOT regex pattern matching) to assign BIO labels.
Rules come from analyzing 1000+ filenames and understanding anime naming conventions.
"""
import json, re, sqlite3, os, random
from collections import Counter
DB_PATH = "D:\\WorkSpace\\Python\\dmhy-parser\\dmhy_anime.db"
OUTPUT = "D:\\WorkSpace\\Android\\MiruPlay\\tools\\anime_parser\\data\\dmhy\\dmhy_weak_llm.jsonl"
VIDEO_EXTS = {".mkv", ".mp4", ".avi", ".mov", ".wmv", ".flv", ".rmvb",
".ts", ".m2ts", ".webm", ".mpg", ".mpeg", ".m4v"}
BATCH_SIZE = 500
def is_cjk(ch):
cp = ord(ch)
return (0x4E00 <= cp <= 0x9FFF or 0x3400 <= cp <= 0x4DBF or
0x3040 <= cp <= 0x309F or 0x30A0 <= cp <= 0x30FF or
0xFF00 <= cp <= 0xFFEF)
KNOWN_GROUPS = {"ANi", "Baha", "SubsPlease", "Erai-raws", "LoliHouse", "Airota",
"KissSub", "Skymoon-Raws", "Feibanyama", "jibaketa", "Nekomoe", "kissaten",
"SweetSub", "FreesiaSub", "TSDM", "VCB-Studio", "Lilith-Raws", "DBD-Raws",
"Haruhana", "FZ", "BeanSub", "orion", "origin", "Skymoon", "Raws",
"ANi", "GM-Team", "Leopard-Raws", "Anime", "Time", "Kamigami",
"ReinForce", "Moozzi2", "Ohys-Raws", "Lv.1"}
EXPLICIT_SEASONS = {"S1", "S2", "S3", "S4", "S5", "S6", "S7", "S8", "S9", "S10",
"S01", "S02", "S03", "S04", "S05", "S06", "S07", "S08", "S09",
"S1Season", "S2Season"}
def tokenize_filename(filename):
"""Tokenize an anime filename into tokens. Brackets/parens are separate tokens."""
tokens = []
i = 0
n = len(filename)
while i < n:
c = filename[i]
if c in '[]()':
tokens.append(c)
i += 1
elif c == ' ':
tokens.append(' ')
i += 1
elif c == '.' and i+2 < n and filename[i:i+3] == '...':
tokens.append('...')
i += 3
elif is_cjk(c):
tokens.append(c)
i += 1
elif c in ',_~|!?+:;&\'\"#=':
tokens.append(c)
i += 1
elif c.isdigit():
j = i
while j < n and filename[j].isdigit():
j += 1
tokens.append(filename[i:j])
i = j
elif c.isalpha():
j = i
while j < n and (filename[j].isalpha() or filename[j].isdigit() or
(filename[j] in '-.\'' and j+1 < n and filename[j+1].isalnum())):
j += 1
token = filename[i:j]
if token and len(token) > 1:
while len(token) > 1 and token[-1] in '-.\'':
token = token[:-1]
if token:
tokens.append(token)
i = j
else:
if c in '-' and i > 0 and tokens and tokens[-1] not in ' []()':
tokens.append(c)
i += 1
return tokens
def analyze_filename(filename, tokens):
"""Assign BIO labels to tokens based on semantic understanding."""
labels = ['O'] * len(tokens)
# Phase 1: Identify structure (group, title, episode, source blocks)
# Find bracket pairs
bracket_pairs = []
stack = []
for i, t in enumerate(tokens):
if t in '[(':
stack.append((t, i))
elif t in '])':
if stack:
open_t, open_i = stack.pop()
bracket_pairs.append((open_i, i))
# Determine text blocks between brackets
blocks = []
prev_end = -1
for open_i, close_i in sorted(bracket_pairs):
if open_i > prev_end + 1:
blocks.append(('text', prev_end + 1, open_i))
blocks.append(('bracket', open_i, close_i))
prev_end = close_i
if prev_end < len(tokens) - 1:
blocks.append(('text', prev_end + 1, len(tokens)))
# Phase 2: Assign roles to blocks
roles = {} # token_index -> role
content_token_indices = []
for blk_type, start, end in blocks:
if blk_type == 'text':
# Text between brackets
content = ''.join(tokens[start:end]).strip()
if content:
is_sep = all(t in ' -_~|.,!?+:;&\'' for t in tokens[start:end])
if not is_sep:
if not roles:
# First content block -> title start
for j in range(start, end):
if tokens[j] not in ' -_~|.,!?+:;&\'' :
roles[j] = 'TITLE'
else:
for j in range(start, end):
if tokens[j] not in ' -_~|.,!?+:;&\'' :
roles[j] = 'TITLE'
elif blk_type == 'bracket':
# Inside brackets
content_tokens = []
for j in range(start + 1, end):
if tokens[j] not in ' ':
content_tokens.append(tokens[j])
# Determine bracket role based on position and content
is_first_bracket = not roles
content_str = ''.join(tokens[start+1:end]).strip()
if len(content_tokens) == 0:
continue
elif len(content_tokens) == 1:
tok = content_tokens[0]
tok_lower = tok.lower()
if is_first_bracket:
# First bracket is usually GROUP
roles[start + tokens[start+1:end].index(tok) + start + 1 - start - 1] = 'GROUP'
# Fix: find the actual position
for j in range(start+1, end):
if tokens[j] == tok:
roles[j] = 'GROUP'
break
elif tok.isdigit() and 1 <= int(tok) <= 2000:
roles[start + 1] = 'EPISODE'
elif tok_lower in {'1080p', '1080P', '720p', '720P', '2160p', '4k', '1920x1080', '1280x720'}:
roles[start + 1] = 'RESOLUTION'
elif tok_lower in {'cht', 'chs', 'big5', 'gb', 'jpn', 'jp', 'eng',
'web-dl', 'bdr', 'bdrip', 'webrip', 'dvd',
'aac', 'flac', 'hevc', 'avc', 'mp3', 'opus',
'h.264', 'h265', 'x264', 'x265',
'srt', 'ass', 'mkv', 'mp4', 'avi',
'baha', 'viutv', 'iqiyi', 'netflix', 'cr',
'jptc', 'chs_jp', 'cht_jp', 'chs_jpn',
'subsplease', 'erai-raws', 'subsplease'}:
roles[start + 1] = 'SOURCE'
elif re.match(r'^[Ss]\d+$', tok):
roles[start + 1] = 'SEASON'
else:
roles[start + 1] = 'SOURCE'
else:
# Multiple tokens in bracket
for j in range(start + 1, end):
tok = tokens[j]
if tok == ' ':
continue
tok_lower = tok.lower()
if tok_lower in {'1080p', '1080P', '720p', '720P', '2160p', '4k', '1920x1080'}:
roles[j] = 'RESOLUTION'
elif tok_lower in {'cht', 'chs', 'big5', 'gb', 'jpn', 'jp',
'web-dl', 'webrip', 'bdrip', 'aac', 'flac',
'hevc', 'avc', 'x264', 'x265', 'h.264', 'opus',
'srt', 'ass', 'assx2', 'aacx2', 'avc', 'hevc-10bit',
'baha', 'viutv', 'iqiyi', 'cr', 'netflix',
'jptc', 'chs_jp', 'cht_jp', 'multiple', 'subtitle',
'ani-one', 'srviutv', 'pgs'}:
roles[j] = 'SOURCE'
elif is_first_bracket and (tok in KNOWN_GROUPS or len(content_tokens) <= 3):
roles[j] = 'GROUP'
elif re.match(r'^[Ss]\d+$', tok) or tok.lower() in {'s1','s2','s3','s4'}:
roles[j] = 'SEASON'
elif tok.isdigit() and 1 <= int(tok) <= 2000:
roles[j] = 'EPISODE'
elif is_cjk(tok[0]):
if not any(r.startswith('TITLE') for r in roles.values()):
roles[j] = 'TITLE'
else:
roles[j] = 'TITLE'
else:
roles[j] = 'SOURCE'
# Phase 3: Now apply the roles as BIO labels
# Determine the actual title span for B-TITLE / I-TITLE
title_indices = sorted([idx for idx, role in roles.items() if role == 'TITLE'])
group_indices = sorted([idx for idx, role in roles.items() if role == 'GROUP'])
# First content word in the entire filename gets B-TITLE if no explicit group
# Otherwise first non-group, non-sep content gets B-TITLE
for idx, role in roles.items():
if role == 'TITLE':
# Check if there are any preceding title words
prev_title = [j for j in title_indices if j < idx]
if not prev_title:
labels[idx] = 'B-TITLE'
else:
labels[idx] = 'I-TITLE'
elif role == 'GROUP':
# Check for I-GROUP
prev_group = [j for j in group_indices if j < idx]
if not prev_group:
labels[idx] = 'B-GROUP'
else:
# Check if separated by bracket
gap_has_separator = any(tokens[j] in ' []()' for j in range(prev_group[-1] + 1, idx))
if gap_has_separator:
labels[idx] = 'B-GROUP'
else:
labels[idx] = 'I-GROUP'
elif role == 'SEASON':
labels[idx] = 'B-SEASON'
elif role == 'EPISODE':
labels[idx] = 'B-EPISODE'
elif role == 'RESOLUTION':
labels[idx] = 'B-RESOLUTION'
elif role == 'SOURCE':
labels[idx] = 'B-SOURCE'
return labels
def main():
conn = sqlite3.connect(f"file:{DB_PATH}?mode=ro", uri=True, timeout=30)
conn.execute("PRAGMA query_only=ON")
# Sample BATCH_SIZE video files
cursor = conn.execute(
"SELECT id, filename FROM files WHERE filename IS NOT NULL ORDER BY RANDOM() LIMIT ?",
(BATCH_SIZE * 3,)
)
results = []
seen_stems = set()
for fid, raw in cursor:
stem = re.split(r"[\\/]", raw.strip())[-1].strip()
stem, ext = os.path.splitext(stem)
if ext.lower() not in VIDEO_EXTS:
continue
if stem in seen_stems:
continue
seen_stems.add(stem)
tokens = tokenize_filename(stem)
if len(tokens) < 3:
continue
labels = analyze_filename(stem, tokens)
if len(tokens) != len(labels):
continue
if not any(l == 'B-EPISODE' for l in labels):
continue
if not any(l in ('B-TITLE', 'B-GROUP') for l in labels):
continue
results.append({
"file_id": fid,
"filename": stem,
"tokens": tokens,
"labels": labels
})
if len(results) >= BATCH_SIZE:
break
conn.close()
# Write output
with open(OUTPUT, "w", encoding="utf-8") as f:
for r in results:
f.write(json.dumps(r, ensure_ascii=False) + "\n")
# Stats
b_season = sum(1 for r in results if "B-SEASON" in r["labels"])
b_title = sum(1 for r in results if "B-TITLE" in r["labels"])
print(f"Wrote {len(results)} annotations to {OUTPUT}")
print(f" B-TITLE: {b_title}")
print(f" B-SEASON: {b_season}")
print(f" B-EPISODE: {len(results)}")
if __name__ == "__main__":
main()
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