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-v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use GozdeA/tennis-multi-return-v4 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("GozdeA/tennis-multi-return-v4") sentences = [ "What are the serving today for Djokovic?", "serving for Djokovic?", "last for Djokovic?", "What is the serving today 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 +11 -0
best_classifier_config.json
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{
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"method": "two_pass_knn",
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"k": 8,
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"ratio_threshold": 0.3003530783590341,
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"max_candidates": 3,
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"recall_simple": 0.9496336996336995,
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"recall_complex": 0.5668168168168168,
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"recall_combined": 0.7392739273927392,
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"top1_simple": 1.0,
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"top1_complex": 1.0
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}
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