metadata
dataset_info:
features:
- name: sentences
dtype: string
- name: labels
dtype: string
splits:
- name: test
num_bytes: 36502124
num_examples: 95851
download_size: 18380522
dataset_size: 36502124
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
Dataset Summary
Beytoote Clustering (BeytooteClustering) is a Persian (Farsi) dataset created for the Clustering task, specifically for grouping blog articles across a wide range of topics. It is part of the FaMTEB (Farsi Massive Text Embedding Benchmark). The dataset was sourced from beytoote.com, a Persian-language blog site, and includes content organized into 19 distinct thematic categories.
- Language(s): Persian (Farsi)
- Task(s): Clustering (Document Clustering, Topic Modeling)
- Source: Crawled from the Beytoote (beytoote.com) blog platform
- Part of FaMTEB: Yes
Supported Tasks and Leaderboards
This dataset is used to benchmark text embedding models on clustering performance over diverse, real-world Persian web content. It evaluates how well models can group thematically similar blog posts. Results appear on the Persian MTEB Leaderboard under the Clustering task.
Construction
- Around 200,000 blog documents were initially scraped from beytoote.com.
- Articles were automatically categorized using tags found in their URLs, resulting in 22 initial groups.
- Only the content inside each post’s
<summary>tag was retained to ensure consistency in length. - Categories with fewer than 500 samples were excluded to improve balance and reliability.
- The final dataset includes 95,851 samples across 19 categories.
Data Splits
- Train: 0 samples
- Development (Dev): 0 samples
- Test: 95,851 samples