---
dataset_info:
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  - name: sentences
    dtype: string
  - name: labels
    dtype: int64
  splits:
  - name: train
    num_bytes: 19977137
    num_examples: 8712
  - name: test
    num_bytes: 8607911
    num_examples: 3735
  download_size: 13060346
  dataset_size: 28585048
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
---
## Dataset Summary

**SID Clustering (SIDClustring)** is a Persian (Farsi) dataset created for the **Clustering** task, specifically focusing on grouping academic articles. It is part of the [FaMTEB (Farsi Massive Text Embedding Benchmark)](https://huggingface.co/spaces/mteb/leaderboard). The dataset was constructed from scientific articles available on **SID (Scientific Information Database – sid.ir)**, categorized into 8 distinct domains reflecting academic disciplines.

* **Language(s):** Persian (Farsi)  
* **Task(s):** Clustering (Document Clustering, Topic Modeling)  
* **Source:** Crawled from the SID academic publication platform  
* **Part of FaMTEB:** Yes

## Supported Tasks and Leaderboards

This dataset is designed to assess the ability of embedding models to perform document clustering—grouping articles into logical scientific categories. Results can be viewed on the [Persian MTEB Leaderboard](https://huggingface.co/spaces/mteb/leaderboard), under the Clustering task.

## Construction

1. Articles were collected by crawling the **sid.ir** platform.  
2. For each article:
   - The **title** and **abstract** were extracted.  
   - These were concatenated using two newline characters (`\n\n`) to form the document input.  
3. Each document was assigned to one of 8 predefined SID categories.  
4. The resulting dataset serves as a benchmark for evaluating unsupervised clustering performance.

## Data Splits

* **Train:** 8,712 samples  
* **Development (Dev):** 0 samples  
* **Test:** 3,735 samples