Text Generation
Transformers
Safetensors
Sinhala
mbart
feature-extraction
fine-tuned
mbart50
spell-correction
Instructions to use lm-spell/mbart50-ft-ssc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lm-spell/mbart50-ft-ssc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="lm-spell/mbart50-ft-ssc")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("lm-spell/mbart50-ft-ssc") model = AutoModel.from_pretrained("lm-spell/mbart50-ft-ssc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use lm-spell/mbart50-ft-ssc with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lm-spell/mbart50-ft-ssc" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lm-spell/mbart50-ft-ssc", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/lm-spell/mbart50-ft-ssc
- SGLang
How to use lm-spell/mbart50-ft-ssc 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 "lm-spell/mbart50-ft-ssc" \ --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": "lm-spell/mbart50-ft-ssc", "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 "lm-spell/mbart50-ft-ssc" \ --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": "lm-spell/mbart50-ft-ssc", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use lm-spell/mbart50-ft-ssc with Docker Model Runner:
docker model run hf.co/lm-spell/mbart50-ft-ssc
mBART50 Fine-tuned for Sinhala Spell Correction
Model Details
- Developed by: Team Surgical Masks
- License: CC BY 4.0
- Model Type: mBART50
- Language: Sinhala (
si) - Fine-tuned from: facebook/mbart-large-50
- Dataset: Sinhala Spell Correction Dataset
- Project: LMSpell
Research
This model was developed as part of our research on spell correction using pre-trained language models for low-resource languages.
- Published Paper: LMSpell: Spell Correction with Pre-Trained Language Models
- Extended Preprint: arXiv
Limitations
- The model is specifically fine-tuned for Sinhala spell correction and may not generalize to other languages.
- Performance may vary for informal text, social media content, code-mixed text, and text containing uncommon or domain-specific vocabulary.
- The model may occasionally modify text that is already correctly spelled.
Citation
If you use this model in your research or applications, please cite our paper:
A. Gunathilake, N. Karunarathna, T. Bandaranayake, S. Ranathunga, N. de Silva and N. Jayatilleke, "LMSpell: Spell Correction with Pre-Trained Language Models," 2026 Moratuwa Engineering Research Conference (MERCon), Moratuwa, Sri Lanka, 2026, pp. 503-508, doi: 10.1109/MERCon71835.2026.11691371.
@INPROCEEDINGS{11691371,
author={Gunathilake, Akesh and Karunarathna, Nadil and Bandaranayake, Tharusha and Ranathunga, Surangika and de Silva, Nisansa and Jayatilleke, Nevidu},
booktitle={2026 Moratuwa Engineering Research Conference (MERCon)},
title={LMSpell: Spell Correction with Pre-Trained Language Models},
year={2026},
pages={503-508},
doi={10.1109/MERCon71835.2026.11691371}
}
- Downloads last month
- -
Model tree for lm-spell/mbart50-ft-ssc
Base model
facebook/mbart-large-50Dataset used to train lm-spell/mbart50-ft-ssc
Viewer • Updated • 522k • 1