Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
Generated from Trainer
dataset_size:41
loss:TripletLoss
text-embeddings-inference
Instructions to use iconitech/nfl-scouting-expert-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use iconitech/nfl-scouting-expert-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("iconitech/nfl-scouting-expert-v1") sentences = [ "elite ball production DB", "rarely gets his head around and allows catches in phase", "times his breaks and plucks interceptions away from receivers", "sprays throws and forces receivers to adjust behind them" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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