readme

README.md

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+---
+license: mit
+tags:
+  - bert
+  - morphological-analysis
+  - kyrgyz
+  - nlp
+  - pos-tagging
+  - low-resource-languages
+  - token-classification
+language:
+  - ky
+pipeline_tag: token-classification
+---
+
+# Kyrgyz Morphological Analysis — BERT
+
+<p align="center">
+  <img src="image_2023-05-13_16-58-05.png" alt="Morphological analysis example" width="600"/>
+</p>
+
+## Model Description
+
+A **BERT-based morphological analyzer** for the **Kyrgyz language** — a low-resource Turkic language spoken by ~5 million people. The model performs morphological tagging, predicting grammatical features (POS tags, case, number, tense, etc.) for each token in a sentence.
+
+Kyrgyz is an agglutinative language with rich morphology, making morphological analysis particularly challenging and valuable for downstream NLP tasks.
+
+## Performance
+
+| Model | Accuracy |
+|-------|----------|
+| **BERT (fine-tuned)** | **~80%** |
+| Logistic Regression (baseline) | — |
+
+<!-- 🔧 TODO: Add baseline accuracy if available -->
+
+## Intended Use
+
+| Use Case | Description |
+|----------|-------------|
+| **Kyrgyz NLP pipeline** | Morphological preprocessing for machine translation, text analysis |
+| **Linguistic research** | Studying Kyrgyz grammar and morphological patterns |
+| **Education** | Teaching Kyrgyz morphology with automated analysis |
+| **Downstream tasks** | Improving NER, dependency parsing, and sentiment analysis for Kyrgyz |
+
+## Training Details
+
+### Dataset
+
+- **Format:** CSV with morphological annotations
+- **Train set:** `train_fixed.csv`
+- **Test set:** `test_fixed.csv`
+- **Tag set:** Defined in `TAG.docx` (morphological tag inventory)
+
+### Architecture
+
+- **Base model:** BERT (fine-tuned for token classification)
+- **Custom variant:** `bert_model_variant.py`
+- **Baseline:** Logistic Regression (`logistic_regression.ipynb`)
+
+### Framework
+
+- Python 3.10+
+- PyTorch / Transformers (HuggingFace)
+
+## Repository Structure
+
+```
+├── bert_model_variant.py    # Custom BERT model architecture
+├── train.py                 # Training script
+├── dev.py                   # Evaluation script
+├── dev.ipynb                # Development notebook
+├── logistic_regression.ipynb # Baseline model
+├── train_fixed.csv          # Training data
+├── test_fixed.csv           # Test data
+├── TAG.docx                 # Morphological tag definitions
+```
+
+## How to Use
+
+```python
+# Load and run inference
+from bert_model_variant import MorphAnalyzer  # adjust import as needed
+
+# Example: Analyze Kyrgyz text
+text = "Кыргызстан — кооз өлкө"
+# See train.py and dev.py for full inference pipeline
+```
+
+<!-- 🔧 TODO: Add a more complete inference example -->
+
+## Why This Matters
+
+Kyrgyz is an **underrepresented language** in NLP. Most morphological analyzers exist only for high-resource languages. This model contributes to:
+
+- Building foundational NLP tools for the Kyrgyz language
+- Enabling more complex downstream applications (MT, QA, summarization)
+- Preserving and digitizing Kyrgyz linguistic knowledge
+
+## Limitations
+
+- Accuracy of ~80% means roughly 1 in 5 tokens may be mistagged
+- Performance may vary across different text domains and registers
+- Limited to the morphological tag set defined in `TAG.docx`
+
+## Citation
+
+```bibtex
+@misc{kyrgyz_morph_2023,
+  author = {Zarina},
+  title  = {BERT-based Morphological Analyzer for Kyrgyz Language},
+  year   = {2023},
+  url    = {https://huggingface.co/Zarinaaa/morphological_analysis}
+}
+```
+
+## Author
+
+**Zarina** — ML Engineer specializing in NLP and Speech Technologies for low-resource languages.
+
+- 🤗 [HuggingFace](https://huggingface.co/Zarinaaa)
+- 💼 [LinkedIn](https://linkedin.com/in/YOUR_LINKEDIN)