Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:8
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use jesse-adanac/sykes-embedder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jesse-adanac/sykes-embedder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jesse-adanac/sykes-embedder") sentences = [ "\"romantic countryside retreat in North Norfolk with views and nearby dining\"", "The cottage at 27 Sea Lane in Old Hunstanton is not pet-friendly, as it is specified to be false for pets; however, it does allow two well-behaved dogs with a small additional charge. It also offers off-road parking for one car.", "Miller's Loft is a romantic countryside retreat in North Norfolk that offers stunning views of unspoilt countryside. It is situated on the Weavers Way footpath, making it an ideal location for relaxing, walking, cycling, and exploring the North Norfolk countryside and coast. Nearby dining options include the acclaimed Saracens Head and the award-winning Gunton Arms, which are a short drive away. Additionally, the local pub, The Spread Eagle, is within easy walking distance and offers food and drinks from Wednesday to Sunday.", "The \"Sea View\" property in Hunstanton is a luxury seaside cottage with a heated outdoor swimming pool available all year round." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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