small-100-Singlish-Sinhala-CodeMix-CT2

small-100-Singlish-Sinhala-CodeMix2-CT2

CTranslate2 INT8 quantized version of small-100-Singlish-Sinhala-CodeMix2 — a fine-tuned NLLB-200 small-100 model for translating Singlish (Romanized Sinhala / English-Sinhala code-mixed text) → Sinhala script.

---

What This Model Does

Translates informal Sri Lankan code-mixed text written in Roman script into Sinhala Unicode script.

---

Model Variants

CPU-only Speed Comparison

Why CT2 INT8 Was Selected

• 90.4% smaller than the FP32 PyTorch model (332 MB vs 3,458 MB)
• 2.08× faster than CT2 FP16→FP32 on CPU
• Only +0.0042 CER degradation vs the GPU FP32 baseline
• Runs entirely on CPU — no GPU required for inference

---

Evaluation Dataset

• Dataset: Golden dataset — Singlish/Sinhala code-mixed test set
• Samples: 2,549 sentence pairs
• Metrics: CER (Character Error Rate), WER (Word Error Rate), Exact Match Accuracy

---

Quick Start

CTranslate2 (recommended)

from huggingface_hub import snapshot_download
import ctranslate2
from transformers import AutoTokenizer

model_path = snapshot_download("savinugunarathna/small-100-Singlish-Sinhala-CodeMix2-CT2")
print(f"✓ Downloaded to: {model_path}")

translator = ctranslate2.Translator(
    model_path,
    device="cpu",
    compute_type="int8",
    intra_threads=4,
)

tokenizer = AutoTokenizer.from_pretrained(
    "savinugunarathna/small-100-Singlish-Sinhala-CodeMix2-CT2"
)

def translate(text: str) -> str:
    tokenizer.src_lang = "en"
    tokens = tokenizer.convert_ids_to_tokens(
        tokenizer.encode(text, add_special_tokens=True)
    )

    result = translator.translate_batch(
        [tokens],
        target_prefix=[["si"]],
        beam_size=3,
        max_decoding_length=128,
        repetition_penalty=1.2,
    )

    hyp_ids = tokenizer.convert_tokens_to_ids(result[0].hypotheses[0])
    return tokenizer.decode(hyp_ids, skip_special_tokens=True).strip("si")

while True:
    text = input("\nSinglish: ").strip()
    if text.lower() == "q":
        break
    print(f"සිංහල: {translate(text)}")

FastAPI REST API

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import ctranslate2
from transformers import AutoTokenizer
import uvicorn

MODEL_CT2  = "savinugunarathna/small-100-Singlish-Sinhala-CodeMix2-CT2"
MODEL_BASE = "savinugunarathna/small-100-Singlish-Sinhala-CodeMix2"

print("Loading model...")
translator = ctranslate2.Translator(MODEL_CT2, device="cpu", compute_type="int8", intra_threads=4)
tokenizer  = AutoTokenizer.from_pretrained(MODEL_BASE)
print("Ready!")

app = FastAPI(title="Singlish → Sinhala API", version="1.0")

class TranslateRequest(BaseModel):
    text: str
    beam_size: int = 3
    max_length: int = 128

class TranslateResponse(BaseModel):
    input: str
    translation: str

def _translate(text: str, beam_size: int = 3, max_length: int = 128) -> str:
    tokenizer.src_lang = "en"
    tokens  = tokenizer.convert_ids_to_tokens(
        tokenizer.encode(text, add_special_tokens=True)
    )
    result  = translator.translate_batch(
        [tokens],
        target_prefix=[["si"]],
        beam_size=beam_size,
        max_decoding_length=max_length,
        repetition_penalty=1.2,
    )
    hyp_ids = tokenizer.convert_tokens_to_ids(result[0].hypotheses[0])
    return tokenizer.decode(hyp_ids, skip_special_tokens=True).strip("si")

@app.get("/health")
def health():
    return {"status": "ok", "model": MODEL_CT2}

@app.post("/translate", response_model=TranslateResponse)
def translate(req: TranslateRequest):
    if not req.text.strip():
        raise HTTPException(status_code=400, detail="text cannot be empty")
    return TranslateResponse(
        input=req.text,
        translation=_translate(req.text, req.beam_size, req.max_length)
    )

@app.post("/translate/batch")
def translate_batch(texts: list[str]):
    if not texts:
        raise HTTPException(status_code=400, detail="texts list cannot be empty")
    if len(texts) > 32:
        raise HTTPException(status_code=400, detail="max 32 texts per batch")
    return {"translations": [_translate(t) for t in texts]}

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)

---

Related Models

---

Technical Details

---

Citation

@misc{small100-singlish-sinhala-ct2,
  author    = {Savinu Gunarathna},
  title     = {small-100-Singlish-Sinhala-CodeMix2-CT2},
  year      = {2025},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/savinugunarathna/small-100-Singlish-Sinhala-CodeMix2-CT2}
}