Text Generation
Transformers
Safetensors
Japanese
qwen3
romaji
japanese
ime
romaji-to-japanese
transduction
text-generation-inference
Instructions to use limoXD/romaji2ja with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use limoXD/romaji2ja with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="limoXD/romaji2ja")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("limoXD/romaji2ja") model = AutoModelForCausalLM.from_pretrained("limoXD/romaji2ja", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use limoXD/romaji2ja with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "limoXD/romaji2ja" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "limoXD/romaji2ja", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/limoXD/romaji2ja
- SGLang
How to use limoXD/romaji2ja with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "limoXD/romaji2ja" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "limoXD/romaji2ja", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "limoXD/romaji2ja" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "limoXD/romaji2ja", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use limoXD/romaji2ja with Docker Model Runner:
docker model run hf.co/limoXD/romaji2ja
File size: 8,650 Bytes
03b56f8 | 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 | """Self-checking evaluation for the generic noisy-romaji rescue.
This is deliberately *generative*: recovery cases are produced by applying
corruption operators to clean compositions of known dictionary/colloquial
units, so the suite proves the mechanism generalizes rather than memorizing one
string. It also asserts the confidence gate *abstains* on heavy/ambiguous noise.
Corruptions are split by whether they preserve the intended *reading*:
* reading-preserving (style flip wapuro<->Hepburn, IME small-tsu spelling) are
hard-asserted to recover exactly -- these are the realistic, high-value wins;
* reading-altering (triardupling, geminate drop, vowel inflation) are reported
only, because a corrupted form may legitimately collide with another real word
(e.g. ``itan`` -> 異端 vs ``ittan`` -> 一旦) and forcing one answer would be the
very overfitting we are avoiding.
Run:
python src/eval_general_phrase.py
Exits non-zero if any hard assertion fails.
"""
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).resolve().parent))
from normalization import normalize_input
from romaji_kana import load_general_lexicon
from general_phrase import (
build_general_phrase_index,
canon_style,
canonicalize_romaji_variants,
general_phrase_rescue,
_collapse_runs,
_geminate_sokuon,
)
DEFAULT_GENERAL_LEXICON = "artifacts/lexicon/general_reading_lexicon.json"
PASS = []
FAIL = []
def check(name, cond, detail=""):
(PASS if cond else FAIL).append(name)
mark = "ok " if cond else "FAIL"
line = f"[{mark}] {name}"
if detail:
line += f" :: {detail}"
print(line)
def report(name, value):
print(f"[rpt ] {name} :: {value}")
# --------------------------------------------------------------------------- #
# Corruption operators (deterministic; no RNG)
# --------------------------------------------------------------------------- #
def op_style_flip(s):
# wapuro -> Hepburn-ish flips that canon_style must absorb (reading-preserving)
for a, b in (("sy", "sh"), ("ty", "ch"), ("tu", "tsu"), ("hu", "fu"), ("zy", "j")):
if a in s:
return s.replace(a, b, 1)
return s
def op_sokuon_ime(s):
# a geminate consonant -> IME small-tsu spelling (reading-preserving)
for i in range(len(s) - 1):
if s[i] == s[i + 1] and s[i] in "bcdfghjkmpqrstvwyz":
return s[:i] + "xtu" + s[i + 1:]
return s
def op_run(s):
# triple the first consonant/glide (may alter reading near geminates)
for i, ch in enumerate(s):
if ch in "ybcdfghjkmpqrstvwz":
return s[:i] + ch * 3 + s[i + 1:]
return s
def op_drop_geminate(s):
for i in range(len(s) - 1):
if s[i] == s[i + 1] and s[i] in "bcdfghjkmpqrstvwyz":
return s[:i] + s[i + 1:]
return s
def op_long_vowel_inflate(s):
for i, ch in enumerate(s):
if ch in "aiueo":
return s[:i] + ch + ch + s[i:]
return s
# --------------------------------------------------------------------------- #
def main():
g = load_general_lexicon(DEFAULT_GENERAL_LEXICON)
index = build_general_phrase_index(g)
print("=== canonicalizer unit tests ===")
check("canon sh->sy", canon_style("shudan") == "syudan", canon_style("shudan"))
check("canon sy stable", canon_style("syudan") == "syudan")
check("canon shuuryou->syuuryou", canon_style("shuuryou") == "syuuryou", canon_style("shuuryou"))
check("canon tsu->tu", canon_style("tsunami") == "tunami", canon_style("tsunami"))
check("canon chi->ti", canon_style("chizu") == "tizu", canon_style("chizu"))
check("canon fu->hu", canon_style("fujisan") == "huzisan", canon_style("fujisan"))
check("canon ji->zi", canon_style("jisho") == "zisyo", canon_style("jisho"))
check("sokuon moxtute->motte", _geminate_sokuon("moxtute") == "motte", _geminate_sokuon("moxtute"))
check("sokuon ixtukai->ikkai", _geminate_sokuon("ixtukai") == "ikkai", _geminate_sokuon("ixtukai"))
check("sokuon kixtute->kitte", _geminate_sokuon("kixtute") == "kitte", _geminate_sokuon("kixtute"))
check("run syyyuu->syuu", _collapse_runs("syyyuu") == "syuu", _collapse_runs("syyyuu"))
check("run aaaa->aa (vowel keeps 2)", _collapse_runs("aaaa") == "aa", _collapse_runs("aaaa"))
check("run uu stable", _collapse_runs("uu") == "uu")
variants = canonicalize_romaji_variants(
"imamoxtutewruusyudannowataraitannsyyyuuryoudemoiiyo"
)
check("variants fold motte+syuu",
any("motte" in v and "syuu" in v for v in variants),
" | ".join(variants))
check("variants repair tewruu only as additive candidate",
any("imamotteru" in v for v in variants),
" | ".join(variants))
print("\n=== recovery: clean compositions of known units (HARD) ===")
compositions = [
(["ima", "motteru"], "今持ってる"),
(["shudan", "no", "hani", "nara"], "手段の範囲なら"),
(["ittan", "syuuryou", "demo", "iiyo"], "一旦終了でもいいよ"),
(["ima", "motteru", "shudan", "no", "hani", "nara",
"ittan", "syuuryou", "demo", "iiyo"],
"今持ってる手段の範囲なら一旦終了でもいいよ"),
]
clean_inputs = []
for keys, expected in compositions:
clean = "".join(keys)
clean_inputs.append((clean, expected))
res = general_phrase_rescue(clean, index)
out = res[0] if res else None
check(f"clean recover: {clean}", out == expected, f"got={out!r} want={expected!r}")
print("\n=== recovery: reading-preserving corruptions (HARD) ===")
safe_ops = [("style_flip", op_style_flip), ("sokuon_ime", op_sokuon_ime)]
for clean, expected in clean_inputs:
for opname, op in safe_ops:
corrupt = op(clean)
if corrupt == clean:
continue
res = general_phrase_rescue(corrupt, index)
out = res[0] if res else None
check(f"{opname}: {corrupt}", out == expected, f"got={out!r} want={expected!r}")
print("\n=== recovery: reading-altering corruptions (REPORT ONLY) ===")
risky_ops = [("run_triple", op_run), ("drop_geminate", op_drop_geminate),
("long_vowel", op_long_vowel_inflate)]
for clean, expected in clean_inputs:
for opname, op in risky_ops:
corrupt = op(clean)
if corrupt == clean:
continue
res = general_phrase_rescue(corrupt, index)
report(f"{opname}: {corrupt}", res[0] if res else None)
print("\n=== negative gate: heavy / ambiguous noise must abstain (HARD) ===")
negatives = [
"xqzkwbvfjpmnlrtxqz",
"zzzzzzzzzzzzzzzz",
"qwlkjghfdspqwlkjghfds",
]
for neg in negatives:
res = general_phrase_rescue(neg, index)
check(f"abstain: {neg}", res is None, f"got={res[0] if res else None!r}")
print("\n=== contamination guard: target intent recovers when typed closer (HARD) ===")
# The target sentence IS recoverable once the *severe* corruptions are typed
# closer to canonical: moxtuteru (IME small-tsu) -> 持ってる, haninara -> 範囲なら,
# ittan -> 一旦. Proves the route is general, not a single-string fit.
closer = "imamoxtuterusyudannohaninaraittansyuuryoudemoiiyo"
res = general_phrase_rescue(closer, index)
out = res[0] if res else None
want = "今持ってる手段の範囲なら一旦終了でもいいよ"
check("closer-typing recovers full phrase", out == want, f"got={out!r}")
print("\n=== recovery: mixed case / separators with reusable noise (HARD) ===")
noisy = "IMAMO-XTUteWRUU SYUDANNO-HANINARA ITANNSYYYUURYOUDEMOIIYO"
res = general_phrase_rescue(normalize_input(noisy), index)
out = res[0] if res else None
check("case/space/hyphen + tewruu + itannsyyyuu recovers", out == want, f"got={out!r}")
print("\n=== safety guard: real target case must abstain (HARD) ===")
target = "imamoxtutewruusyudannowataraitannsyyyuuryoudemoiiyo"
res = general_phrase_rescue(target, index)
check("target with watara ambiguity abstains", res is None, f"got={res[0] if res else None!r}")
if res:
m = res[1]
report("target meta",
f"anchor={m['anchor_ratio']:.2f} fill={m['fill_ratio']:.2f} "
f"drops={m['drops']} cost/char={m['cost_per_char']:.3f}")
print(f"\n==== {len(PASS)} passed, {len(FAIL)} failed ====")
if FAIL:
print("FAILURES:")
for f in FAIL:
print(" -", f)
raise SystemExit(1)
if __name__ == "__main__":
main()
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