Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:35
loss:MultipleNegativesRankingLoss
text-embeddings-inference
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
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