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
File size: 458 Bytes
109419b | 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 | {
"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
}
} |