Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:44284
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use ayushexel/embed-all-MiniLM-L6-v2-squad-8-epochs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ayushexel/embed-all-MiniLM-L6-v2-squad-8-epochs with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ayushexel/embed-all-MiniLM-L6-v2-squad-8-epochs") sentences = [ "What genre of music is performed by MercyMe?", "Controversy erupted when Madonna decided to adopt from Malawi again. Chifundo \"Mercy\" James was finally adopted in June 2009. Madonna had known Mercy from the time she went to adopt David. Mercy's grandmother had initially protested the adoption, but later gave in, saying \"At first I didn't want her to go but as a family we had to sit down and reach an agreement and we agreed that Mercy should go. The men insisted that Mercy be adopted and I won't resist anymore. I still love Mercy. She is my dearest.\" Mercy's father was still adamant saying that he could not support the adoption since he was alive.", "Contemporary Christian music (CCM) has several subgenres, one being \"Christian AC\". Radio & Records, for instance, lists Christian AC among its format charts. There has been crossover to mainstream and hot AC formats by many of the core artists of the Christian AC genre, notably Amy Grant, Michael W. Smith, Kathy Troccoli, Steven Curtis Chapman, Plumb, and more recently, MercyMe.", "On September 30, 1987, Foster filed an application with the United States Patent and Trademark Office to patent his invented sport. The patent application covered the rules of the game, specifically detailing the goalposts and rebound netting and their impact on gameplay. Foster's application was granted on March 27, 1990. The patent expired on September 30, 2007." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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