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-knn-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-knn-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("GozdeA/tennis-multi-return-knn-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
Upload best_classifier_config.json with huggingface_hub
Browse files
best_classifier_config.json
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{
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"method": "knn",
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"k": 16,
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"threshold": 0.05170882480633016,
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"max_candidates": 2,
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"recall": 0.6216666666666666
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}
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