Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:11641
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use GozdeA/tennis-multi-return-catboost-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use GozdeA/tennis-multi-return-catboost-v3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("GozdeA/tennis-multi-return-catboost-v3") sentences = [ "2026 for Djokovic?", "What is the serve speed for he?", "momentum for Djokovic?", "2026 for Sinner?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "knn": { | |
| "method": "knn", | |
| "k": 25, | |
| "threshold": 0.049971192073456225, | |
| "max_candidates": 4, | |
| "recall": 0.64 | |
| }, | |
| "catboost": { | |
| "method": "catboost", | |
| "threshold": 0.05266476888644622, | |
| "recall": 0.8033333333333333, | |
| "params": { | |
| "cb_iter": 100, | |
| "cb_depth": 7, | |
| "cb_lr": 0.010032221153714495, | |
| "cb_l2": 9.882156213394236, | |
| "cb_threshold": 0.05266476888644622 | |
| } | |
| }, | |
| "mlp": { | |
| "method": "mlp", | |
| "threshold": 0.054017185937560046, | |
| "recall": 0.8922222222222221, | |
| "params": { | |
| "mlp_h1": 256, | |
| "mlp_h2": 64, | |
| "mlp_lr": 0.00010907799751774686, | |
| "mlp_alpha": 1.0675452115272085e-05, | |
| "mlp_threshold": 0.054017185937560046 | |
| } | |
| } | |
| } |