Mbart50 Turkish Grammar Corrector

🚀 Model Card for Model ID

This model is a fine - tuned version of a pre - trained model, specifically designed for Turkish grammar correction, which can be applied in various NLP scenarios.

🚀 Quick Start

Basic Usage

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.

✨ Features

• 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.

🔧 Technical Details

The model was trained using Hugging Face Transformers' Seq2SeqTrainer.

📄 License

This model is released under the MIT license.