---
license: apache-2.0
language:
  - ru
tags:
  - telegram
  - nlp
  - text-cleaning
  - semantic-analysis
  - russian
  - conversation
size_categories:
  - 1K<n<10K
dataset_info:
  features:
    - name: message
      dtype: string
    - name: semantic_score
      dtype: float32
  splits:
    - name: train
      num_examples: 6621
---

# russian-telegram-chat-logs

This dataset contains messages extracted from Telegram chat history, processed and ranked by their "semantic load" (information density).

## Dataset Overview

The data is stored in **Parquet** format, which provides efficient storage and maintains data types. Each row represents a single message that has passed through several stages of cleaning and analysis.

| Column             | Type    | Description                                                                      |
|:-------------------|:--------|:---------------------------------------------------------------------------------|
| **message**        | string  | The cleaned text of the Telegram message (Cyrillic-only).                        |
| **semantic_score** | float32 | A normalized value (0.0 - 100.0) representing the message's information density. |

## Processing Pipeline

To ensure high data quality, the following steps were performed:

1.  **Extraction**: Raw message logs were parsed from Telegram export files.
2.  **Filtering**: 
    * Removed all messages that did not contain Russian (Cyrillic) characters.
    * Removed messages consisting solely of emojis, special characters, or links.
3.  **Cleaning**: 
    * Stripped emojis and redundant symbols.
    * Normalized whitespace and removed system metadata (timestamps, sender names).
4.  **Deduplication**: Identical messages were removed to ensure every entry in the dataset is unique.

## How Semantic Score is Calculated

The `semantic_score` is not just a measure of length, but a representation of **Semantic Density**. The calculation involves:

1.  **Vectorization**: Each message is converted into a high-dimensional vector (embedding) using the `paraphrase-multilingual-MiniLM-L12-v2` Sentence-Transformer model. This model understands context and semantic relationships between words.
2.  **L2-Norm Calculation**: We calculate the magnitude (norm) of the embedding vector. Complex and unique sentences typically result in higher vector norms.
3.  **Length Weighting**: To balance the score, we apply a logarithmic weight based on the character length of the message. This prevents long, repetitive sentences from dominating while ensuring that very short phrases (like "Ok") receive lower scores.
4.  **Min-Max Scaling**: The final raw values are normalized to a **0 to 100%** scale:
    * **100.0**: The most semantically dense message in the dataset.
    * **0.0**: The message with the least information density (e.g., simple interjections).

## Usage

```python
from datasets import load_dataset

dataset = load_dataset("KvaytG/russian-telegram-chat-logs", split="train")
```

## License

This dataset is released under the **Apache License 2.0**.

## Citation

```bibtex
@misc{kvaytg_russian_telegram_chat_logs,
  author       = {KvaytG},
  title        = {Russian Telegram Chat Logs: A Semantically Ranked Dataset},
  year         = {2026},
  publisher    = {Hugging Face},
  journal      = {Hugging Face Datasets},
  url          = {https://huggingface.co/datasets/KvaytG/russian-telegram-chat-logs},
  note         = {Processed Telegram messages with semantic density scoring using paraphrase-multilingual-MiniLM-L12-v2}
}
```