## Dataset Summary

**FarSick STS** is a Persian (Farsi) dataset designed for the **Semantic Textual Similarity (STS)** task. It is a part of the [FaMTEB (Farsi Massive Text Embedding Benchmark)](https://huggingface.co/spaces/mteb/leaderboard). The dataset was developed by translating and adapting the English **SICK (Sentences Involving Compositional Knowledge)** dataset, and it features Persian sentence pairs annotated for their degree of **semantic relatedness**.

* **Language(s):** Persian (Farsi)  
* **Task(s):** Semantic Textual Similarity (STS)  
* **Source:** Translated and adapted from the English SICK dataset  
* **Original Citation:** FarSick dataset by Amin et al. (2022)  
* **Part of FaMTEB:** Yes

## Supported Tasks and Leaderboards

This dataset is primarily used to benchmark the ability of text embedding models to measure **semantic similarity** between Persian sentence pairs—especially in cases requiring **compositional reasoning**. Performance is benchmarked on the **Persian MTEB Leaderboard** on Hugging Face Spaces.

## Construction

The construction process involved:

- Translating sentence pairs from the original English **SICK dataset** into Persian.
- Adapting the translations to ensure cultural and linguistic relevance.
- Preserving the focus on **semantic compositionality**, which includes reasoning about phrase structure and entailment.
- Annotating each Persian sentence pair with a similarity score reflecting their degree of semantic relatedness.

As noted in the *FaMTEB* paper, this dataset helps evaluate semantic understanding in Persian NLP models under compositional semantics conditions.

## Data Splits

As defined in the *FaMTEB* paper (Table 5):

* **Train:** 0 samples  
* **Development (Dev):** 0 samples  
* **Test:** 8,566 samples