TatarNLPWorld/xlm-roberta-large-tatar-toponyms-qa

🏆 XLM-RoBERTa Large for Tatar Toponyms QA

📖 Model Description

XLM-RoBERTa large fine-tuned for question answering on Tatarstan toponyms. This is the best performing model in the Tatar Toponyms QA collection with 99.4% F1 score.

This model is fine-tuned from deepset/xlm-roberta-large-squad2 on a synthetic dataset of 38,696 QA pairs about Tatarstan geographical names.

✨ Key Features

• —🎯 99.4% F1 score on test set
• —🌍 Multilingual (Russian & Tatar)
• —🗺️ Handles all toponym-related questions
• —⚡ Production-ready with no post-processing needed

📊 Performance Metrics

📈 Performance by Question Type

🚀 Quick Start

Installation

pip install transformers torch

With Pipeline (recommended)

from transformers import pipeline

# Load model (automatically downloads from Hub)
qa_pipeline = pipeline(
    "question-answering",
    model="TatarNLPWorld/xlm-roberta-large-tatar-toponyms-qa"
)

# Example with context from dataset
context = """
Название (рус): Рантамак | Название (тат): Рантамак | Объект: Село | 
Расположение: на р. Мелля, в 21 км к востоку от с. Сарманово | 
Этимология: Топоним произошел от ойконима «Рангазар-Тамак» | 
Координаты: 55.205461, 52.881862
"""

questions = [
    "Где находится Рантамак?",
    "Что означает название Рантамак?",
    "Какие координаты у Рантамак?",
    "Какой тип объекта у Рантамак?"
]

for question in questions:
    result = qa_pipeline(question=question, context=context)
    print(f"Q: {question}")
    print(f"A: {result['answer']}")
    print(f"Confidence: {result['score']:.3f}\n")

With PyTorch

from transformers import AutoTokenizer, AutoModelForQuestionAnswering
import torch

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("TatarNLPWorld/xlm-roberta-large-tatar-toponyms-qa")
model = AutoModelForQuestionAnswering.from_pretrained("TatarNLPWorld/xlm-roberta-large-tatar-toponyms-qa")

# Move to GPU if available
device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)

# Prepare inputs
inputs = tokenizer(question, context, return_tensors="pt", truncation=True, max_length=512).to(device)

# Get predictions
with torch.no_grad():
    outputs = model(**inputs)

# Decode answer
start_idx = torch.argmax(outputs.start_logits)
end_idx = torch.argmax(outputs.end_logits)
answer = tokenizer.decode(inputs["input_ids"][0][start_idx:end_idx+1], skip_special_tokens=True)
print(f"Answer: {answer}")

📚 Training Details

Dataset

• —Source: Tatarstan Toponyms Dataset
• —QA pairs: 38,696 synthetic examples
• —Train/Validation/Test split: 80%/10%/10%
• —Question types: coordinates, location, etymology, type, region, sources

Training Parameters

💡 Usage Tips

1. 1.Context format: The model expects context in the format from the original dataset (with prefixes like "Название (рус):", "Объект:", etc.)
2. 2.No normalization needed: Unlike RuBERT models, XLM-RoBERTa doesn't require post-processing
3. 3.Question types: Works best with the 6 question types it was trained on
4. 4.Confidence scores: Can be used for answer filtering

🔗 Related Resources

Models in Collection

Datasets

• —Tatarstan Toponyms QA Dataset - Training data (38,696 QA pairs)
• —Tatarstan Toponyms Dataset - Original data (9,688 toponyms)

📝 Citation

If you use this model in your research, please cite:

@model{xlm_roberta_large_tatar_toponyms_qa,
    author = {Arabov, Mullosharaf Kurbonvoich},
    title = {XLM-RoBERTa Large for Tatar Toponyms QA},
    year = {2026},
    publisher = {Hugging Face},
    journal = {Hugging Face Hub},
    howpublished = {\url{https://huggingface.co/TatarNLPWorld/xlm-roberta-large-tatar-toponyms-qa}}
}

For the dataset:

@dataset{tatarstan_toponyms_qa_2026,
    title = {Tatarstan Toponyms QA Dataset},
    author = {Arabov, Mullosharaf Kurbonvoich},
    year = {2026},
    publisher = {Hugging Face},
    url = {https://huggingface.co/datasets/TatarNLPWorld/tatarstan-toponyms-qa}
}

🎯 Intended Uses

• —Geographic information extraction from texts
• —Question answering systems for Tatarstan geography
• —Educational tools for learning about toponyms
• —NLP research on Tatar and Russian languages
• —Geocoding applications (with coordinate questions)

⚠️ Limitations

• —Trained only on Tatarstan region toponyms
• —Questions must be answerable from the provided context
• —Best performance on the 6 trained question types
• —Requires context in the specific format from the original dataset

👥 Team and Maintenance

• —Developer: Mullosharaf Kurbonvoich Arabov
• —Organization: TatarNLPWorld
• —Project: Tat2Vec - Advancing Tatar Language Processing

🤝 Contributing

Contributions are welcome! Please:

1. 1.Open an issue for bugs or feature requests
2. 2.Submit PRs for improvements
3. 3.Share your use cases and results

📬 Contact

• —Issues: GitHub Issues
• —Email: TatarNLPWorld community
• —Website: Tat2Vec Project

📄 License

This model is released under the CC BY-SA 4.0 license.

🌟 Acknowledgments

• —Original data from Tatarstan Toponyms Dataset
• —Base model: deepset/xlm-roberta-large-squad2
• —Data sources: Ф.Г. Гарипова, Г.Ф. Саттаров, Р.Г. Әхмәтьянов
• —Resource: Топонимы Татарстана

📅 Version: 1.0.0 | 📅 Published: 2026-03-10 | 🏆 F1 Score: 0.994