README.md

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title: TatarNLPWorld - Turkic NLP & Low-Resource Languages
emoji: 🦜
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license: mit
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# TatarNLPWorld – Turkic NLP & Low‑Resource Languages Research Hub

![Status](https://img.shields.io/badge/Status-Active-brightgreen)
![Focus](https://img.shields.io/badge/Focus-Tatar_Language-blue)
![Focus](https://img.shields.io/badge/Focus-Turkic_NLP-orange)
![Focus](https://img.shields.io/badge/Focus-Low_Resource_Languages-red)

**TatarNLPWorld** is a collaborative research initiative dedicated to advancing natural language processing for **Tatar**, **Turkic languages**, and **low‑resource languages** in general. We develop state‑of‑the‑art language models, machine translation systems, linguistic resources, and educational tools to empower under‑represented languages in the digital age.

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## 🎯 Our Mission

- Build **open‑source language models** for Tatar and other Turkic languages.
- Create **high‑quality linguistic resources** (corpora, lexicons, evaluation benchmarks).
- Advance **machine translation** between Turkic languages and major world languages.
- Develop **educational materials** and interactive demos to lower the entry barrier for low‑resource NLP.
- Foster a community of researchers, developers, and native speakers working together on language technology.

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## 🚀 Interactive Demos

Explore our live Hugging Face Spaces and try out our models directly in your browser:

### **🔤 Language Models**
- **[TatarGPT Playground]()** – Generate and analyze Tatar text with our latest causal LM.
- **[TurkicBERT Explorer]()** – Masked language modelling for multiple Turkic languages.
- **[Multilingual Embeddings]()** – Compare word/sentence vectors across Turkic languages.

### **🌐 Machine Translation**
- **[Tatar ↔ Russian Translator]()** – Neural translation demo.
- **[Turkic Multi-Way Translation]()** – Translate between Tatar, Kazakh, Kyrgyz, and more.
- **[Low‑Resource MT Showcase]()** – See how our models perform with minimal data.

### **📚 Linguistic Tools**
- **[Tatar Morphological Analyzer]()** – Interactive segmentation and POS tagging.
- **[Named Entity Recognition for Tatar]()** – Identify persons, locations, organizations.
- **[Turkic Language Identifier]()** – Detect which Turkic language a text is written in.

### **📊 Data & Benchmarks**
- **[Tatar Corpus Explorer]()** – Browse and query our curated text collections.
- **[Turkic NLP Leaderboard]()** – Compare model performance on standard tasks.
- **[Annotation Tools]()** – Help us improve datasets with your feedback.

*Click on any demo to start experimenting – no installation required!*

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## 🧠 Research Focus Areas

### **🦜 Tatar Language Technologies**
- Creation of the first large‑scale pretrained models for Tatar.
- Morphological disambiguation and syntactic parsing.
- Speech recognition and synthesis for Tatar (coming soon).

### **🌍 Turkic NLP**
- Cross‑lingual transfer learning among Turkic languages.
- Unified tokenization and subword models for the Turkic family.
- Machine translation between Turkic languages (e.g., Tatar‑Kazakh, Tatar‑Turkish).

### **📉 Low‑Resource NLP**
- Data augmentation and semi‑supervised learning techniques.
- Leveraging multilingual models (e.g., mT5, XLM‑R) for under‑represented languages.
- Few‑shot and zero‑shot learning for tasks like NER and sentiment analysis.

### **🤖 Language Models**
- Pretraining from scratch and continued pretraining on Turkic corpora.
- Efficient architectures (ALBERT, DistilBERT) for low‑resource settings.
- Evaluation and bias analysis of Turkic language models.

### **📖 Linguistic Resources**
- **Corpora**: News, Wikipedia, literature, web‑crawled texts.
- **Lexicons**: Morphological dictionaries, wordnets, sentiment lexicons.
- **Benchmarks**: Named entity recognition, part‑of‑speech tagging, machine translation test sets.

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## 📦 Models & Datasets

We release all our models and datasets on Hugging Face Hub under open licenses.

| Model / Dataset | Description | Link |
|-----------------|-------------|------|
| **TatarBERT** | BERT‑base model pretrained on 5M Tatar sentences | [🤗 Hub]() |
| **Turkic‑mT5** | Multilingual T5 fine‑tuned on 10 Turkic languages | [🤗 Hub]() |
| **Tatar‑MT‑TatRus** | Transformer‑based translation model (Tatar ↔ Russian) | [🤗 Hub]() |
| **Tatar‑NER** | Named entity recognition model for Tatar | [🤗 Hub]() |
| **TatarCorpus v1.0** | 200M token corpus from news, books, and Wikipedia | [🤗 Dataset]() |
| **Turkic‑NMT‑Bench** | Parallel sentences for 5 Turkic languages | [🤗 Dataset]() |

*More models and datasets are added regularly. Follow our [organization page](https://huggingface.co/TatarNLPWorld) for updates.*

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## 📚 Educational Resources

We believe in **open education** and **reproducible research**. All our tutorials and teaching materials are freely available.

- **[Interactive Notebooks]()** – Hands‑on tutorials for building low‑resource NLP systems (in Python, using Hugging Face libraries).
- **[Video Lectures]()** – Recorded talks on Turkic NLP, data collection, and model training.
- **[Course Materials]()** – Slides, readings, and assignments from our university courses.
- **[Blog Posts]()** – Deep dives into challenges and solutions for Tatar and Turkic languages.

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## 📝 Selected Publications

1. *"TatarBERT: A Pretrained Language Model for the Tatar Language"* – LREC 2024  
2. *"Low‑Resource Machine Translation for Turkic Languages: A Case Study on Tatar‑Russian"* – WMT 2023  
3. *"Building a Named Entity Recognition Dataset for Tatar"* – TurkLang 2023  
4. *"Multilingual Representations for Turkic Languages: A Comparative Study"* – EMNLP 2022  
5. *"Tatar Corpus: Collection, Annotation, and Baseline Experiments"* – Dialogue 2022  

*Full list with links to PDFs available on our [Publications Page]().*

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## 🤝 Get Involved

We welcome contributions from the community – whether you are a researcher, developer, student, or native speaker.

### **For Researchers**
- Use our models and datasets in your work (and cite us!).
- Collaborate on joint papers and grant proposals.
- Contribute new benchmarks or evaluation tasks.

### **For Developers**
- Integrate our models into your applications.
- Report bugs or suggest improvements via GitHub Issues.
- Submit pull requests to our open‑source repositories.

### **For Native Speakers & Linguists**
- Help us validate translations and annotations.
- Share texts or corpora (with permission) to enrich our data.
- Provide feedback on model outputs to reduce errors.

### **For Students**
- Use our demos and tutorials for learning.
- Participate in our mentorship program or summer schools.
- Start your own research project with our support.

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## 🌐 Connect With Us

- **🤗 Hugging Face**: [TatarNLPWorld](https://huggingface.co/TatarNLPWorld) – Models, datasets, and spaces.

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## 🔄 Ecosystem Integration

Our work is integrated with the broader Hugging Face ecosystem:

- **Models** on the Hub with easy‑to‑use pipelines.
- **Datasets** with streaming and evaluation scripts.
- **Spaces** for interactive demos and educational tools.
- **Gradio** apps for user‑friendly interfaces.

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**Empowering Tatar and Turkic languages through open science and community collaboration.**

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**© 2026 TatarNLPWorld** – Open source for low‑resource languages.

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