Instructions to use yeniguno/mbart50-turkish-grammar-corrector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yeniguno/mbart50-turkish-grammar-corrector with Transformers:

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

pipe = pipeline("text-generation", model="yeniguno/mbart50-turkish-grammar-corrector")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("yeniguno/mbart50-turkish-grammar-corrector")
model = AutoModelForMultimodalLM.from_pretrained("yeniguno/mbart50-turkish-grammar-corrector")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use yeniguno/mbart50-turkish-grammar-corrector with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yeniguno/mbart50-turkish-grammar-corrector"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yeniguno/mbart50-turkish-grammar-corrector",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/yeniguno/mbart50-turkish-grammar-corrector

SGLang

How to use yeniguno/mbart50-turkish-grammar-corrector 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 "yeniguno/mbart50-turkish-grammar-corrector" \
    --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": "yeniguno/mbart50-turkish-grammar-corrector",
		"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 "yeniguno/mbart50-turkish-grammar-corrector" \
        --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": "yeniguno/mbart50-turkish-grammar-corrector",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use yeniguno/mbart50-turkish-grammar-corrector with Docker Model Runner:
```
docker model run hf.co/yeniguno/mbart50-turkish-grammar-corrector
```

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

Model Card for Model ID

Model Summary

This model is a fine-tuned version of facebook/mbart-large-50-many-to-many-mmt on the GECTurk-generation dataset for the task of Turkish grammar correction. It takes Turkish sentences with grammatical mistakes as input and generates grammatically corrected Turkish text.

The model can be used for educational tools, writing assistants, or any NLP application that benefits from clean and correct Turkish grammar.

It supports the "tr_TR" language code for both input and output, and works without needing task-specific prefixes.

How to Get Started with the Model

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("yeniguno/mbart50-turkish-grammar-corrector")
model = AutoModelForSeq2SeqLM.from_pretrained("yeniguno/mbart50-turkish-grammar-corrector")

def correct_turkish(text):
    tokenizer.src_lang = "tr_TR"
    encoded = tokenizer(text, return_tensors="pt", max_length=128, truncation=True)

    input_ids = encoded["input_ids"].to(model.device)

    generated_ids = model.generate(
        input_ids,
        max_length=128,
        num_beams=4,
        forced_bos_token_id=tokenizer.lang_code_to_id["tr_TR"]
    )
    return tokenizer.decode(generated_ids[0], skip_special_tokens=True)

print(correct_turkish("Alide geldi.")) # Ali de geldi.

Training Details

The model was trained using Hugging Face Transformers' Seq2SeqTrainer.

Downloads last month: 172

Safetensors

Model size

0.6B params

Tensor type

F32

Model tree for yeniguno/mbart50-turkish-grammar-corrector

Base model

facebook/mbart-large-50-many-to-many-mmt

Finetuned

(257)

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yeniguno
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mbart50-turkish-grammar-corrector

Model Card for Model ID

Model Summary

How to Get Started with the Model

Training Details

Model tree for yeniguno/mbart50-turkish-grammar-corrector

Dataset used to train yeniguno/mbart50-turkish-grammar-corrector