Create README

README

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+---
+language:
+- tr
+task_categories:
+- sentence-similarity
+- text-classification
+license: mit
+size_categories:
+- n<1K
+---
+
+# Semantic Textual Similarity (STS) Dataset (Turkish)
+
+This repository contains a Turkish **Semantic Textual Similarity (STS)** dataset created as part of a university assignment on semantic similarity, sentence embeddings, and vector representations in Natural Language Processing (NLP).
+
+## Authors
+
+- Muhammet Enes Nas
+- Salih Dede
+
+## About
+
+The purpose of this project was to gain practical experience with:
+
+- Semantic Textual Similarity (STS)
+- Sentence Embeddings
+- Vector Representations
+- Similarity Scoring
+- NLP Dataset Preparation
+
+The dataset consists of Turkish sentence pairs annotated with a semantic similarity score ranging from **0 to 100**, where:
+
+| Score | Meaning |
+|-------:|---------|
+| 0 | Completely unrelated |
+| 25 | Weak semantic relation |
+| 50 | Moderate similarity |
+| 75 | High similarity |
+| 100 | Semantically equivalent |
+
+## Dataset Format
+
+Each sample contains three fields:
+
+| Column | Description |
+|---------|-------------|
+| `sentence1` | First sentence |
+| `sentence2` | Second sentence |
+| `humanScore` | Human-annotated semantic similarity score (0–100) |
+
+Example:
+
+```json
+{
+  "sentence1": "Doktor ameliyata geç kaldı.",
+  "sentence2": "Ameliyatı yapacak hekim gecikti.",
+  "humanScore": 97.85
+}
+```
+
+## Intended Use
+
+This dataset is intended for educational and research purposes, including:
+
+- Sentence Embedding models
+- Semantic Similarity prediction
+- Siamese Networks
+- SBERT fine-tuning
+- Embedding evaluation
+- NLP coursework
+
+## License
+
+MIT License.