Sentence Similarity
sentence-transformers
Safetensors
roberta
feature-extraction
Generated from Trainer
dataset_size:11347
loss:MultipleNegativesRankingLoss
Instructions to use trongvox/Phobert-Sentence with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use trongvox/Phobert-Sentence with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("trongvox/Phobert-Sentence") sentences = [ "Beefsteak 123 la mot dia chi ban banh mi chao, beefsteak cuc ngon tai Can Tho ma ban nen mot gan ghe den. Khong gian quan rong rai, sach se, phuc vu nhanh nhen, gia ca hop ly. Banh mi chao duong Nguyen Van Troi noi tieng ban banh mi thom ngon, chat luong. Banh mi tai day chia ra lam 2 phan: co thit bo ma khong thit bo.\n\nQuan Beefsteak 123 la mot dia diem ly tuong cho nhung nguoi yeu thich thit bo va cac mon an ngon khac. Quan noi tieng voi su ket hop tuyet voi giua thit bo, pate va trung op la. Neu ban muon thu nhung mon khac, quan cung co san xuc xich, ca moi, cha lua va xiu mai. Menu cua quan duoc chia thanh tung phan da duoc ket hop san de ban de dang lua chon. Vi du nhu bo op la pate xuc xich hoac bo op la pate cha lua. Ban cung co the tao ra cac to hop rieng cua rieng minh nhu op la ca moi xiu mai.Mot dieu dac biet khi den quan la khi ban goi mot phan, ban se duoc tang mien phi mot dia xa lach tron. Day la cach hoan hao de ket hop khau vi cua ban voi cac loai rau song tuoi ngon.Voi khong gian thoai mai va phuc vu nhanh chong, quan Beefsteak 123 mang den cho ban trai nghiem am thuc doc dao va ngon mieng. Hay ghe tham quan de thuong thuc nhung mon an tuyet voi nay!\n\nTHONG TIN LIEN HE:\nDia chi: 9B Nguyen Van Troi, Phuong Xuan Khanh, Can Tho\nDien thoai: 0907 713 458\nGio mo cua: 06:00 - 14:00\nGia tham khao: 20.000d - 40.000d\nFanpage: https://www.facebook.com/Beefsteak-123-143170999350605/\n\n Goi dien", "Beefsteak 123 - Nguyen Van Troi", "Pho Ngon 37", "Khong tra no hay chi tien ngay Tet" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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