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 config_tennis.json with huggingface_hub
Browse files- config_tennis.json +18 -0
config_tennis.json
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{
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"categories": [
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"player_statistics",
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"predictions",
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"biographics",
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"logistics",
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"match_statistics",
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"live"
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],
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"num_categories": 6,
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"base_model": "sentence-transformers/all-MiniLM-L6-v2",
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"mode": "two_pass_knn_v4",
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"training_info": {
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"questions": 3625,
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"triplets": 14500,
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"epochs": 15
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}
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}
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