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drbinna
/
e5-nba-finetuned

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:35
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use drbinna/e5-nba-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use drbinna/e5-nba-finetuned with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("drbinna/e5-nba-finetuned")
    
    sentences = [
        "query: Shai 40 April OKC",
        "passage: player_box_score | game_id:22400445 | date:2024-12-30 | December 30 | player:Nikola Jokić | team:Denver Nuggets | points:36 | rebounds:22 | assists:11 | triple-double | Denver vs Utah",
        "passage: game | season:2023 | date:2023-10-27 | 10-27-23 | 10/27/23 | home_team:Sacramento Kings | SAC | away_team:Golden State Warriors | GSW | home_points:114 | away_points:122",
        "passage: player_box_score | game_id:22301153 | date:2024-04-09 | player:Shai Gilgeous-Alexander | Shai | SGA | team:Oklahoma City Thunder | OKC | points:40"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
e5-nba-finetuned
439 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
drbinna's picture
drbinna
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1a5bc48 verified 6 months ago
  • 1_Pooling
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  • .gitattributes
    1.52 kB
    initial commit 6 months ago
  • README.md
    15.1 kB
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  • config.json
    612 Bytes
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  • config_sentence_transformers.json
    283 Bytes
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  • model.safetensors
    438 MB
    xet
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  • modules.json
    349 Bytes
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  • sentence_bert_config.json
    57 Bytes
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  • special_tokens_map.json
    695 Bytes
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  • tokenizer.json
    712 kB
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  • tokenizer_config.json
    1.22 kB
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  • vocab.txt
    232 kB
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