Create README.md

README.md

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+## 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