Instructions to use cguzel/trspell-t5-efficient-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cguzel/trspell-t5-efficient-small with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="cguzel/trspell-t5-efficient-small")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("cguzel/trspell-t5-efficient-small")
model = AutoModelForSeq2SeqLM.from_pretrained("cguzel/trspell-t5-efficient-small", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use cguzel/trspell-t5-efficient-small with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cguzel/trspell-t5-efficient-small"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cguzel/trspell-t5-efficient-small",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/cguzel/trspell-t5-efficient-small

SGLang

How to use cguzel/trspell-t5-efficient-small 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 "cguzel/trspell-t5-efficient-small" \
    --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": "cguzel/trspell-t5-efficient-small",
		"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 "cguzel/trspell-t5-efficient-small" \
        --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": "cguzel/trspell-t5-efficient-small",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use cguzel/trspell-t5-efficient-small with Docker Model Runner:
```
docker model run hf.co/cguzel/trspell-t5-efficient-small
```

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

trspell-t5-efficient-small

A Turkish T5 model fine-tuned for Turkish spelling correction on NoisyWikiTr. It takes a sentence with spelling errors and generates the corrected sentence.

This is the T5-efficient-TRsmall model from Leveraging Large Language Models for Spelling Correction in Turkish (Ceren Güzel Turhan, PeerJ Computer Science 11:e2889, 2025), the best model in the paper on realistic noise.

Paper: https://doi.org/10.7717/peerj-cs.2889
Code: https://github.com/cgturhan/trspell
Dataset: https://huggingface.co/datasets/cguzel/NoisyWikiTr

Usage

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

repo = "cguzel/trspell-t5-efficient-small"
tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForSeq2SeqLM.from_pretrained(repo)

text = "netekim kasım ve temmuz felaketleri bunun sadece acı iki örneğidir"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256)
output = model.generate(**inputs, num_beams=5, max_length=256, early_stopping=True)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Input should be normalised like the training data: lowercase with Turkish rules (I → ı, İ → i), no digits, punctuation or apostrophes. No task prefix is used.

Model details

Base model: Turkish-NLP/t5-efficient-small-turkish, a T5-efficient model pre-trained on Turkish
Architecture: encoder-decoder, 32 encoder layers and 6 decoder layers, d_model 512, about 142M parameters
Tokenizer: Turkish SentencePiece vocabulary (32k), which covers all Turkish letters (ı, ğ, ş, ç, ö, ü, İ)
Task: sequence-to-sequence, noisy sentence → clean sentence

Training

Data: NoisyWikiTr training split, 687k Turkish Wikipedia sentences with about 10% of tokens corrupted (deletion, insertion, transposition, substitution, vowel errors, common misspellings, word splits)
Validation: NoisyWikiTr validation split (85k sentences)

Evaluation

NoisyWikiTr test set, accuracy / word correction rate (Table 5 of the paper):

Model	Uniform noise	Realistic noise
T5-efficient-TRsmall (this model)	0.7015 / 0.4185	0.9688 / 0.8129
FLAN-T5-small	0.7013 / 0.4192	0.7189 / 0.5483
XLM-RoBERTa-base	0.9286 / 0.4478	0.9421 / 0.5003
JamSpell	0.9606 / 0.8156	0.9500 / 0.6735
Zemberek	0.9173 / 0.6130	0.8770 / 0.4740

On the realistic test set with non-Turkish sentences removed, this model reaches 0.9664 accuracy, 0.8142 word correction and 0.9925 Levenshtein similarity.

Limitations

Trained and evaluated on synthetic errors; results on real typos may differ.
Deletion errors are the hardest type for the model.
Corrects spelling only, not grammar.
Long inputs are truncated at 256 tokens; split long texts into sentences.

Citation

@article{guzelturhan2025trspell,
  title   = {Leveraging large language models for spelling correction in Turkish},
  author  = {G{\"u}zel Turhan, Ceren},
  journal = {PeerJ Computer Science},
  volume  = {11},
  pages   = {e2889},
  year    = {2025}
}

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