Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:109673
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use codersan/FaLabseV13p1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use codersan/FaLabseV13p1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("codersan/FaLabseV13p1") sentences = [ "اخترشناس معروف واقعی کیست؟", "چرا دولت هند به طور ناگهانی از شیطنت 500 و 1000 روپیه خبر داد؟", "اخترشناس فوق العاده استاد کیست؟", "چگونه باید برای مکان های دانشگاه آماده شد؟" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7fdb60a20b77e343badffa8cd34974cabaacf8d0b1790b9f3336ae18c1b96045
- Size of remote file:
- 2.36 MB
- SHA256:
- 3ec65c678cf86973ddbfa4145277e7e17564fdf375ed54782e3941ce15de74a4
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