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:
- fd50c852e26daab8d4f6e56b24d0e0a0816861fc806d215bec45daa25a101478
- Size of remote file:
- 1.88 GB
- SHA256:
- 7451ec3a7a12fb59645b09f2503eae49d37189a7bbe55f376e0118003cca8936
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.