Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
feature-extraction
dense
Generated from Trainer
dataset_size:16692
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use michaeleliot/claim-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use michaeleliot/claim-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("michaeleliot/claim-model") sentences = [ "Al Klug position played tackle", "Alfred Klug position played tackle", "Brad Edwards position played Safety postion", "Michael Jackson's Ghosts position played tackle" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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