Sentence Similarity
sentence-transformers
Safetensors
Turkish
gemma3_text
feature-extraction
semantic-search
information-retrieval
turkish
retrieval
distillation
werea
text-embeddings-inference
Instructions to use GoktugD/DUSUNEN-Rota-270M-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use GoktugD/DUSUNEN-Rota-270M-v3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("GoktugD/DUSUNEN-Rota-270M-v3") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
| { | |
| "rota-v3-natural": { | |
| "recall@1": 0.2666, | |
| "recall@5": 0.4346, | |
| "recall@10": 0.4702, | |
| "recall@100": 0.5728, | |
| "mrr@10": 0.6618, | |
| "ndcg@10": 0.4535 | |
| }, | |
| "harrier-base": { | |
| "recall@1": 0.2422, | |
| "recall@5": 0.4219, | |
| "recall@10": 0.459, | |
| "recall@100": 0.5352, | |
| "mrr@10": 0.6307, | |
| "ndcg@10": 0.434 | |
| }, | |
| "e5-base": { | |
| "recall@1": 0.292, | |
| "recall@5": 0.4658, | |
| "recall@10": 0.5308, | |
| "recall@100": 0.728, | |
| "mrr@10": 0.6967, | |
| "ndcg@10": 0.4993 | |
| } | |
| } |