How to use swardiantara/bert-tiny-yelp-k3-fixed-cosine with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("swardiantara/bert-tiny-yelp-k3-fixed-cosine") sentences = [ "I wish I could return a haircut.\\n\\nUnfortunately Kelly, Melissa, and Jen were not there so I settled for whoever was free at the time. \\n\\nWorst trim of my life:\\nshe cut off over an inch more than just the dead/split ends\\nshe did not cut the hair how I described initially\\nshe did not change the cut to how I repeated during the cut when I noticed she was cutting it straight across instead of curving it in the back, which does NOT mix well with my curly hair\\nafter retelling her how I wanted it cut, she compared this tragedy to deaths of family/friends and how I should get over it\\nthe beautiful long layers I had and wanted again are now choppy, medium layers\\nmy left side is even choppier than the right side of my hair\\nI fear straightening my hair to make this unevenness less evident\\n\\nI regret tipping her because I understand how the service industry works because my minimum tip of 20% for all service people is undeserved for her lack of work.\\n\\nI want everyone to know how the only good hair stylists there are Kelly (who moved to Philly), Melissa and Jen. If they are not available, do NOT risk going.\\n\\nAlso, the Supercuts Oakland location is horrible at waxing eyebrows unless you are lucky to get the only woman of color there.\\n\\nWhile it was a $20 cut (after the 20% tip), I know had to buy biotin to grow my hair faster so it is more expensive to me in the end. I hope this proves helpful to all perspective customers!", "WOW! Just try it. Delicious! Best Spring Roll I've had in a long time. The Phnom Penh soup was exotic and fabulous. The owners obviously care about their food and their homeland. I will definitely go back soon.", "Just came back from my second visit to The Main Ingredient, and I definitely plan to return! I'd been to this location a few times when it was Lisa G.'s, and I'm happy to report that MI is superior (I always found Lisa G.'s a little pretentious and overpriced). \\n\\nSandwiches are great - the Jive Turkey and Tuna sandwiches are delicious and very flavorful - and the service is excellent! I came in with a group of 5 co-workers on our lunch break and there weren't any tables available for our party but the waitress was really nice and apologetic and told us she would seat us soon. She took our orders in advance because we were in a bit of a hurry and seated us within a few minutes. She offered to give us separate checks and brought them shortly after our food arrived; of course, saying \\\"no rush,\\\" but just trying to be helpful because we were pressed for time. Finally, a restaurant that doesn't act like it's the hugest hassle in the world to provide separate checks! It's a reality of modern dining and I love it when restaurants don't treat you like a pariah for requesting your own ticket. It makes things so much less awkward/complicated when you're out for a quick bite to eat with friends or coworkers.\\n\\nDefinitely check out the Main Ingredient next time you're looking for good food and service in a cute, cozy environment.", "No, it's not worth it! Bellagio overcharged me and their accounting department never got back to me (I asked them for an itemized receipt 2 weeks ago). When I called them, I was (twice!) put on this music-less \\\"dead\\\" hold for 30-40 minutes. They're so full of themselves, they don't provide good customer service. Their wi-fi needed fixing when I stayed there. And by the way, the woman at the buffet was super rude to me. Finally, the famous waterworks never happened while I was standing outside in the cold waiting (though i asked the staff and they said they're up and running). No, don't waste your money! I won't and won't let my family and friends do that either." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
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