README.md

metadata

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, 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

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