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---
license: mit
language:
- tt
tags:
- tt
- tatar
- books
pretty_name: Collection of books in Tatar language in Cyrillic script
---
# Tatar Books Collection (Cyrillic) 📚

This dataset, hosted by [Yasalma](https://huggingface.co/neurotatarlar), is a curated collection of Tatar books in Parquet format. The texts are in Cyrillic script, making this dataset ideal for linguistic research, language modeling, and other NLP applications in the Tatar language.

## Dataset Details

- **Language**: Tatar (Cyrillic script)
- **Format**: Single Parquet file
- **Columns**:
  - `file_name`: The original name of each book’s file
  - `text`: The full content of each book
- **Total Size**: 75 MB
- **License**: MIT

### Structure

The dataset is organized so that each row in the Parquet file represents an individual Tatar book, with columns for the book’s filename (`file_name`) and its content (`text`).

## Potential Use Cases

- **Language Modeling**: Train language models specifically for Tatar in Cyrillic script.
- **Machine Translation**: Use the dataset for Cyrillic-to-Latin transliteration and other translation tasks.
- **Linguistic Research**: Study linguistic structures, grammar, and vocabulary in Tatar.

## Examples

Here’s how to load and use the dataset in Python with `pandas`:

```python
import pandas as pd

# Load the Parquet file
df = pd.read_parquet("path/to/train-00000-of-00001.parquet", engine="pyarrow")

# View the first few rows
print(df.head())
```

Example of a sample entry:

| file_name              | text                                              |
|------------------------|---------------------------------------------------|
| Корымлы Бармак_tat.txt | Кояш Тимбикова\nКОРЫМЛЫ БАРМАК\n\nӨйләнешүебез... |

## Usage

To load the dataset using Hugging Face’s `datasets` library:

```python
from datasets import load_dataset

dataset = load_dataset("neurotatarlar/tt-books-cyrillic")
```

## Contributions and Acknowledgements

This dataset is maintained by the Yasalma team. Contributions, feedback, and suggestions are welcome to improve and expand the dataset.