| ## Dataset Summary |
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| **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**. |
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| * **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 |
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| ## Supported Tasks and Leaderboards |
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| 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. |
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| ## Construction |
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| The construction process involved: |
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| - 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. |
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| As noted in the *FaMTEB* paper, this dataset helps evaluate semantic understanding in Persian NLP models under compositional semantics conditions. |
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| ## Data Splits |
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| As defined in the *FaMTEB* paper (Table 5): |
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| * **Train:** 0 samples |
| * **Development (Dev):** 0 samples |
| * **Test:** 8,566 samples |
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