metadata
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:
{
"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.