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
- Xet hash:
- 024a6d198d4328206c3fa2a682fcf9b36ffddb93f3ec8b7e2add401c6c306792
- Size of remote file:
- 648 Bytes
- SHA256:
- 2b3a2eafa0380c68a834f0851e4f7a5fb0b6e217dcf5f9f8c5b5bfb1073f649f
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