README.md

metadata

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

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

@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}
}