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5632877 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | ## 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
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