README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- turkish
- semantic-search
base_model:
- BAAI/bge-m3
- suayptalha/Sungur-9B
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
model-index:
- name: turkce-embedding-bge-m3
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity (STS Validation)
    dataset:
      name: Turkish STS Validation Set
      type: sts-validation
    metrics:
    - type: pearson_cosine
      value: 0.9096
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.6839
      name: Spearman Cosine
datasets:
- nezahatkorkmaz/turkce-embedding-eslestirme-ucluler
- nezahatkorkmaz/turkce-embedding-sts-degerlendirme
- nezahatkorkmaz/turkce-embedding-eslestirme-ciftler
language:
- tr
---

# 🇹🇷 Turkish Embedding Model (bge-m3 Fine-tuned)

This model is a **Turkish fine-tuned version** of [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3), optimized for **Turkish semantic similarity**, **retrieval**, and **RAG (Retrieval-Augmented Generation)** tasks.  
It maps Turkish sentences and paragraphs into a 1024-dimensional dense vector space.

---

## Model Overview

| Property | Value |
|:----------|:------|
| **Base Model** | BAAI/bge-m3 |
| **Architecture** | XLM-RoBERTa + Pooling + Normalize |
| **Embedding Dimension** | 1024 |
| **Max Sequence Length** | 8192 |
| **Similarity Function** | Cosine |
| **Loss Functions** | MultipleNegativesRankingLoss + TripletLoss |
| **Language** | Turkish 🇹🇷 |
| **Use Cases** | Semantic Search, Text Similarity, RAG, Clustering |

---

## Evaluation Results

Model was evaluated on a **Turkish Semantic Textual Similarity (STS)** dataset.  
Compared to the base multilingual BGE-M3 model, the fine-tuned model shows a **notable improvement in Pearson correlation**, indicating better alignment between cosine similarity scores and human judgments.

| Metric | Base (BAAI/bge-m3) | Fine-tuned | Δ (Change) |
|:-------|:------------------:|:-----------:|:-----------:|
| **Spearman (ρ)** | 0.6814 | **0.6839** | +0.0025 |
| **Pearson (r)** | 0.8535 | **0.9096** | +0.0561 |

> The model demonstrates higher linear correlation on Turkish STS benchmarks, producing more consistent semantic scores for Turkish-language retrieval and ranking tasks.

---

## Quick Example

```python
from sentence_transformers import SentenceTransformer, util

model = SentenceTransformer("nezahatkorkmaz/turkce-embedding-bge-m3")

s1 = "Türkiye'nin başkenti Ankara'dır"
s2 = "Ankara Türkiye'nin başşehridir"

emb1, emb2 = model.encode([s1, s2], normalize_embeddings=True)
score = util.cos_sim(emb1, emb2).item()
print(f"Cosine similarity: {score:.4f}")
# Expected output ≈ 0.75–0.80