README.md

metadata

dataset_info:
  features:
    - 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). 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, 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