---
license: cc-by-nc-nd-4.0
language: [fa]
tags:
  - nlu
  - text-classification
  - natural-language-inference
  - named-entity-recognition
  - paraphrase-identification
  - semantic-textual-similarity
  - extractive-question-answering
  - keyword-extraction
  - persian
pretty_name: Persian NLU Benchmark
task_categories:
  - text-classification
  - question-answering
---

# Persian NLU

## Dataset Summary

The **Persian NLU Benchmark** is a curated collection of existing Persian datasets designed to evaluate **Natural Language Understanding (NLU)** capabilities across a diverse range of tasks. It provides a unified benchmark suite to assess different cognitive aspects of large language models (LLMs) in Persian.

This benchmark includes the following tasks and datasets:

- **Text Classification**:  
  - *Synthetic Persian Tone*  
  - *SID*

- **Natural Language Inference (NLI)**:  
  - *FarsTAIL*

- **Semantic Textual Similarity (STS)**:  
  - *Synthetic Persian STS*  
  - *FarSICK*

- **Named Entity Recognition (NER)**:  
  - *Arman*

- **Paraphrase Detection**:  
  - *FarsiParaphraseDetection*  
  - *ParsiNLU*

- **Extractive Question Answering (EQA)**:  
  - *PQuAD*

- **Keyword Extraction**:  
  - *Synthetic Persian Keywords*

- **Sentiment Analysis**:  
  - *DeepSentiPers*

Each task highlights a distinct aspect of model performance, allowing researchers to evaluate and compare models across a broad range of NLU capabilities.

---

## Languages

- `fa` — Persian (Farsi)

---

## License

This dataset is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).

You must give appropriate credit, may not use it for commercial purposes, and may not distribute modified versions of the dataset.

For details, see the [license](LICENSE) file or https://creativecommons.org/licenses/by-nc-nd/4.0/