Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:11600
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use GozdeA/tennis-multi-return-catboost-v2 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-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("GozdeA/tennis-multi-return-catboost-v2") sentences = [ "Show me contest time", "How did Shelton and he compare in momentum during set 2?", "What is the key factors for Djokovic?", "What is the how many winners for Djokovic?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "knn": { | |
| "method": "knn", | |
| "k": 16, | |
| "threshold": 0.05170882480633016, | |
| "max_candidates": 2, | |
| "recall": 0.6216666666666666 | |
| }, | |
| "catboost": { | |
| "method": "catboost", | |
| "threshold": 0.28170716125509304, | |
| "recall": 0.6216666666666666, | |
| "params": { | |
| "cb_iter": 150, | |
| "cb_depth": 3, | |
| "cb_lr": 0.012063744447371417, | |
| "cb_l2": 8.39301492562091, | |
| "cb_threshold": 0.28170716125509304 | |
| } | |
| }, | |
| "mlp": { | |
| "method": "mlp", | |
| "threshold": 0.30623464107831183, | |
| "recall": 0.8033333333333335, | |
| "params": { | |
| "mlp_h1": 64, | |
| "mlp_h2": 32, | |
| "mlp_lr": 0.0001008326529713479, | |
| "mlp_alpha": 5.50599590057979e-05, | |
| "mlp_threshold": 0.30623464107831183 | |
| } | |
| } | |
| } |