ByT5-Small-fine-tuned2

55
by
savinugunarathna
Language Model
OTHER
New
55 downloads
Early-stage
Edge AI:
Mobile
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Mobile
Laptop
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Quick Summary

AI model with specialized capabilities.

Training Data Analysis

🔵 Good (6.0/10)

Researched training datasets used by ByT5-Small-fine-tuned2 with quality assessment

Specialized For

general
multilingual

Training Datasets (1)

c4
🔵 6/10
general
multilingual
Key Strengths
  • Scale and Accessibility: 750GB of publicly available, filtered text
  • Systematic Filtering: Documented heuristics enable reproducibility
  • Language Diversity: Despite English-only, captures diverse writing styles
Considerations
  • English-Only: Limits multilingual applications
  • Filtering Limitations: Offensive content and low-quality text remain despite filtering

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

Usagepythontransformers
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("savinugunarathna/ByT5-Small-fine-tuned2")
model = AutoModelForSeq2SeqLM.from_pretrained("savinugunarathna/ByT5-Small-fine-tuned2")

device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device).eval()

def translate(text: str) -> str:
    prompt = f"translate Singlish to Sinhala: {text}"
    inputs = tokenizer(prompt, return_tensors="pt", max_length=512, truncation=True).to(device)
    with torch.no_grad():
        outputs = model.generate(**inputs, max_new_tokens=128, num_beams=4)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Interactive loop — type 0 to exit
while True:
    user_input = input("Singlish: ").strip()
    if user_input == "0":
        break
    if user_input:
        print(f"Sinhala:  {translate(user_input)}\n")
Citationbibtex
@misc{gunarathna2025byt5singlish,
  title={ByT5-Small Singlish to Sinhala: A Three-Phase Curriculum Approach with LoRA Fine-Tuning},
  author={Gunarathna, Savinu},
  year={2025},
  howpublished={Hugging Face Model Hub},
  note={\url{https://huggingface.co/savinugunarathna/ByT5-Small-fine-tuned2}}
}
Referencesbibtex
@article{sumanathilaka2025swa,
  title={Swa-bhasha Resource Hub: Romanized Sinhala to Sinhala Transliteration Systems and Data Resources},
  author={Sumanathilaka, Deshan and Perera, Sameera and Dharmasiri, Sachithya and Athukorala, Maneesha and Herath, Anuja Dilrukshi and Dias, Rukshan and Gamage, Pasindu and Weerasinghe, Ruvan and Priyadarshana, YHPP},
  journal={arXiv preprint arXiv:2507.09245},
  year={2025}
}

@inproceedings{Nsina2024,
  author={Hettiarachchi, Hansi and Premasiri, Damith and Uyangodage, Lasitha and Ranasinghe, Tharindu},
  title={{NSINA: A News Corpus for Sinhala}},
  booktitle={The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
  year={2024},
  month={May},
}

@article{ranasinghe2022sold,
  title={SOLD: Sinhala Offensive Language Dataset},
  author={Ranasinghe, Tharindu and Anuradha, Isuri and Premasiri, Damith and Silva, Kanishka and Hettiarachchi, Hansi and Uyangodage, Lasitha and Zampieri, Marcos},
  journal={arXiv preprint arXiv:2212.00851},
  year={2022}
}

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