File size: 1,635 Bytes
682c564 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | ---
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. |