---
title: Tatar Morphological Analyzer
emoji: 🔤
colorFrom: blue
colorTo: green
sdk: streamlit
pinned: true
app_file: app.py
license: mit
short_description: Interactive demo for 5 state-of-the-art Tagger models
sdk_version: 1.55.0
---

# 🔤 Tatar Morphological Analyzer

<div align="center">

**Interactive exploration of five fine‑tuned models for Tagger morphological tagging**

*Compare mBERT, RuBERT, DistilBERT, XLM‑R, and Turkish BERT on your own sentences*

[![🤗 Hugging Face](https://img.shields.io/badge/🤗-Open%20in%20Spaces-blue)](https://huggingface.co/spaces/TatarNLPWorld/tatar-morph-analyzer)
[![License](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE)
[![Models](https://img.shields.io/badge/5-Models-orange)](#-available-models)
[![Streamlit](https://img.shields.io/badge/Interface-Streamlit-FF4B4B)](https://streamlit.io)

</div>

## 🌟 Overview

This Space provides a unified interface to **five state‑of‑the‑art models** for morphological analysis of the Tatar language. Each model predicts **full morphological tags** (part‑of‑speech, number, case, possession, etc.) at the token level — a fundamental task for Tatar NLP.

Choose a model, type a sentence, and instantly see the predicted tags, along with confidence scores and color‑coded visualisation.

## 🚀 Key Features

### 🧩 Model Selection
- Switch between **5 different transformer models**:
  - **mBERT** (best overall accuracy)
  - **RuBERT** (excellent due to Russian proximity)
  - **DistilBERT** (lightweight, fast)
  - **XLM‑R** (powerful multilingual)
  - **Turkish BERT** (good baseline)

### 🔍 Interactive Analysis
- **Real‑time tagging**: Get token‑level morphological tags with confidence scores.
- **Visual badges**: Colour‑coded display of tags for quick scanning.
- **Example sentences**: Pre‑loaded examples for instant testing.

### 📊 Model Metrics
- For each model, you can see its **token accuracy**, **F1‑micro**, and **F1‑macro** directly in the sidebar.

## 🎮 Quick Start Examples

### Try These Sentences

| Language | Sentence |
|----------|----------|
| Tatar | Мин татарча сөйләшәм. |
| Tatar | Кичә мин дусларым белән паркка бардым. |
| Tatar | Татарстан – Россия Федерациясе составындагы республика. |

Just paste any sentence into the text box and click **Analyze**!

### Expected Output (for mBERT on the first sentence)

| Word      | Morphological Tag       | Confidence |
|-----------|-------------------------|------------|
| Мин       | Pron+Sg+Nom+Pers(1)     | 0.999      |
| татарча   | Adv                     | 0.998      |
| сөйләшәм  | V+Pres+1                | 0.997      |
| .         | PUNCT                   | 1.000      |

## 📈 Model Performance Comparison

| Model          | Token Accuracy | F1‑micro | F1‑macro | Speed (sent./sec) |
|----------------|----------------|----------|----------|-------------------|
| **mBERT**      | 98.68%         | 98.68%   | 50.94%   | 150               |
| **RuBERT**     | 98.13%         | 98.13%   | 47.37%   | 150               |
| **DistilBERT** | 97.98%         | 97.98%   | 44.02%   | 250               |
| **XLM‑R**      | 97.67%         | 97.67%   | 40.61%   | 120               |
| **Turkish BERT**| 86.84%        | 86.84%   | 33.34%   | 150               |

> *Metrics are computed on a held‑out test set of 6,000 sentences (47k+ tokens). Full per‑POS accuracies are available in the `results/` folder of each model repository.*

## 🏗️ Technical Architecture

### Models

All models are fine‑tuned from popular transformer checkpoints:

| Model      | Base Checkpoint                                       | Parameters |
|------------|-------------------------------------------------------|------------|
| mBERT      | `bert-base-multilingual-cased`                        | ~180M      |
| RuBERT     | `DeepPavlov/rubert-base-cased`                        | ~180M      |
| DistilBERT | `distilbert-base-multilingual-cased`                  | ~134M      |
| XLM‑R      | `xlm-roberta-base`                                    | ~560M      |
| Turkish BERT| `dbmdz/bert-base-turkish-cased`                      | ~180M      |

### Training Data

- **Dataset**: [TatarNLPWorld/tatar-morphological-corpus](https://huggingface.co/datasets/TatarNLPWorld/tatar-morphological-corpus)
- **Training subset**: 60,000 sentences (shuffled seed 42, filtered → 59,992)
- **Split**: Train 47,993 / Validation 5,999 / Test 6,000
- **Tagset**: 1,181 unique morphological tags (e.g., `N+Sg+Nom`, `V+Past+3`, `PUNCT`)

### Hyperparameters (common to all models)

| Parameter           | Value          |
|---------------------|----------------|
| Learning rate       | 2e-5           |
| Optimizer           | AdamW (wd=0.01)|
| Warmup steps        | 500            |
| Number of epochs    | 4              |
| Max sequence length | 128            |
| Mixed precision     | FP16           |

### Training Hardware

- **GPU**: NVIDIA Tesla V100 (32GB)
- **Training time**: 4–8 hours per model
- **Inference speed**: Varies by model (see table above)

## 📦 Repository Structure

```
.
├── app.py                  # Main Streamlit application
├── requirements.txt        # Python dependencies
├── .streamlit/
│   └── config.toml        # Streamlit server config (port 7860)
├── results/                # (optional) Additional metrics and plots
└── README.md              # This file
```

## 🚀 Local Deployment

```bash
# Clone the Space
git clone https://huggingface.co/spaces/TatarNLPWorld/tatar-morph-analyzer
cd tatar-morph-analyzer

# Install dependencies
pip install -r requirements.txt

# Run the app
streamlit run app.py --server.port 8501
```

The app will be available at `http://localhost:8501`.

## 📜 Citation

If you use this Space or any of the underlying models in your research, please cite the appropriate model (see each model card for BibTeX). For general attribution:

```bibtex
@misc{tatar-morph-analyzer,
  author = {Arabov, Mullosharaf Kurbonovich and TatarNLPWorld Contributors},
  title = {Tatar Morphological Analyzer – Interactive Demo},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/spaces/TatarNLPWorld/tatar-morph-analyzer}}
}
```

## 📄 License

The code in this Space is released under the **MIT License**. Each model retains its own license (all are Apache 2.0 or MIT).

## 🙏 Acknowledgments

- **Dataset**: [TatarNLPWorld/tatar-morphological-corpus](https://huggingface.co/datasets/TatarNLPWorld/tatar-morphological-corpus)
- **Model checkpoints**: Hugging Face Hub
- **Framework**: Streamlit, Transformers, PyTorch
- **Community**: All contributors to TatarNLPWorld

---

<div align="center">

**Explore and advance Tagger language technology**

*Brought to you by [TatarNLPWorld](https://huggingface.co/TatarNLPWorld)*

[Report Issue](https://huggingface.co/spaces/TatarNLPWorld/tatar-morph-analyzer/discussions) • 
[Request Feature](https://huggingface.co/spaces/TatarNLPWorld/tatar-morph-analyzer/discussions) • 
[Contact](mailto:arabov.mk@gmail.com)

</div>