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
File size: 136 Bytes
5353890 | 1 2 3 4 5 6 7 8 | {
"0": "biographics",
"1": "live",
"2": "logistics",
"3": "match_statistics",
"4": "player_statistics",
"5": "predictions"
} |