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---
language: en
license: mit
tags:
- fasttext
- tajik
- word-embeddings
- nlp
---
# Tajik FastText Word Embedding Model
This repository contains a pretrained **FastText** model for the **Tajik language**, trained on a large corpus of Tajik texts. The model supports **subword information**, allowing it to generate embeddings even for rare or unseen (OOV) words.
The model is suitable for use in various NLP tasks such as:
- Semantic analysis
- Text classification
- Machine translation
- Synonym detection and thesaurus building
- Enhancing other models through embedding initialization
Licensed under the [MIT License](LICENSE), which allows free usage in both research and commercial applications.
---
## 📊 Model Overview
| Parameter | Value |
|------------------|----------------------------|
| Model Type | FastText (with subwords) |
| Vector Size | 300 |
| Vocabulary Size | 145,232 |
| OOV Support | Yes |
| Context Window | 5 |
| Min Word Count | ≥ 5 |
---
## 📚 Training Corpus
### Books (Total: 99)
- Programming: 6
- History: 4
- Religion: 12
- Scientific: 3
- Children's literature: 6
- Prose: 19
- Poetry: 21
- Textbooks: 28
### Articles (Total: 134,497)
- Asia-Plus: 20,471
- Khovar: 21,557
- Ovozi Tojik: 7,495
- Farazh: 4,679
- Wikipedia: 80,295
### Total Corpus Statistics
- **Documents**: 134,596
- **Tokens**: 33,535,383
- **Unique Lemmas**: 649,308
---
## 🧪 Model Comparison with Meta FastText
We evaluated our model against Meta’s pretrained FastText using semantic similarity and Spearman correlation:
| Model | Spearman Correlation | OOV Support |
|------------------|----------------------|-------------|
| FastText (Meta) | **0.703** | Yes |
| **FastText (ours)** | **0.622** | **Yes** |
While Meta FastText achieves better overall performance, our model demonstrates strong results on Tajik-specific morphology and semantics.
---
## 🔍 Example Similar Words
| Word | Nearest Neighbors (FastText) |
|-----------|-------------------------------|
| кӯдак | кӯдаку(0.82), хурдкӯдак(0.81), кӯдакам(0.81), кӯдакат(0.81), кӯдаке(0.81) |
| муаллим | муаллиме(0.90), муаллимат(0.89), муаллимин(0.89), муаллиму(0.88), муаллима(0.88) |
| об | оби(0.79), обро(0.74), обмӯрии(0.70), обшустаи(0.68), обшуста(0.66) |
| мард | марда(0.87), мардхӯ(0.85), мардвор(0.85), мардро(0.83), зан(0.82) |
| деҳа | деҳайи(0.83), деҳаю(0.80), деҳавз(0.78), деҳакӣ(0.76), деҳодеҳ(0.74) |
| китоб | китобӣ(0.84), китобгуна(0.83), китобча(0.81), китобсӯзӣ(0.81), китобро(0.81) |
| меҳмон | меҳмонӣ(0.86), меҳмоншо(0.85), меҳмонат(0.83), меҳмонҳона(0.82), меҳмони(0.82) |
| шаҳр | шаҳрӯ(0.82), шаҳрча(0.80), бушаҳр(0.79), шаҳрат(0.79), навшаҳр(0.79) |
| падар | падаршӯ(0.89), падарӣ(0.84), падаршӯву(0.84), падаре(0.84), падаршон(0.83) |
| модар | модаршӯ(0.86), модаршӯяш(0.83), модару(0.81), модаре(0.81), модарвор(0.80) |
---
## 🧩 Handling OOV (Out-of-Vocabulary) Words
FastText supports generating vectors for unknown words via subword units (n-grams). Here are some examples:
| Unknown Word | Closest Matches (FastText) |
|--------------|----------------------------|
| кӯдакона | кӯдаконаи(0.82), кӯдаконат(0.81), кӯдаконае(0.81) |
| меҳмонамон | меҳмон(0.77), меҳмонҳо(0.77), меҳмонам(0.76) |
| муаллимон | муаллимони(0.89), муаллимоне(0.88), муаллимону(0.83) |
| деҳоти | дарҷамоати(0.79), чамоати(0.74), ҷамоати(0.81) |
| саводнок | саводнокӣ(0.88), саводнокиву(0.85), саводнокии(0.84) |
---
## 📌 Features for Tajik Language
Our model performs well on:
- **Semantic similarity**: e.g., "мард" ↔ "зан", "китоб" ↔ "китобгуна"
- **Morphological variants**: e.g., "кӯдак" → "кӯдаку", "кӯдаки"
- **Rare/compound words**: thanks to subword representations like "саводнок", "деҳоти"
---
## 💡 Usage Example
```python
from gensim.models import FastText
model = FastText.load("tajik_fasttext.model")
vector = model.wv["падар"] # Get vector for a word
similar_words = model.wv.most_similar("модар") # Find similar words
```
---
## 🗂️ Files Included
| File | Description |
|--------------------|----------------------------------------------|
| `tajik_fasttext.model` | Gensim FastText model file |
| `*.npy` files | Supporting NumPy arrays for vectors |
---
## 📚 Citation
If you use this model, please cite:
```bibtex
@misc{ArabovMK_Tajik_FastText,
author = {ArabovMK},
title = {Tajik FastText Word Embeddings},
year = 2025,
publisher = {Hugging Face},
url = {https://huggingface.co/ArabovMK/tajik-fasttext-model}
}
```
*Last updated: 2025-05-10*
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