| --- |
| 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. |
