Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:173708
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use archisgore/bge-small-winetone with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use archisgore/bge-small-winetone with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("archisgore/bge-small-winetone") sentences = [ "Made very much in a German style, with low alcohol (by California standards), very high acidity and residual sugar of 2%. Shows well-ripened apricot, pineapple, peach, honeysuckle, vanilla meringue and Asian spice flavors.", "This Chardonnay-based sparkler offers aromas of yellow rose, apricot, honey and toasted almond. Steely, firm perlage makes this a great wine to sip as you socialize before a meal.", "Pure strawberry aromas lead in to delicious, ripe, sweet fruits. It shows the highly drinkable nature of the St. Laurent grape, with red cherry fruits and light touch of tannin. It could age over the next 2 years, but drink it now with pleasure.", "Shows the clean acidity and purity this appellation bring to white wines, with savory tangerine, pineapple, green apple and vanilla cream flavors. Lovely as an appetizer wine, or with sole sautéed in butter." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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