Sentence Similarity
sentence-transformers
TensorBoard
Safetensors
Persian
English
bert
feature-extraction
Generated from Trainer
dataset_size:131157
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use codersan/FaLabse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use codersan/FaLabse with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("codersan/FaLabse") sentences = [ "عواقب ممنوعیت یادداشت های 500 روپیه و 1000 روپیه در مورد اقتصاد هند چیست؟", "آیا باید در فیزیک و علوم کامپیوتر دو برابر کنم؟", "چگونه اقتصاد هند پس از ممنوعیت 500 1000 یادداشت تحت تأثیر قرار گرفت؟", "آیا آلمان در اجازه پناهندگان سوری به کشور خود اشتباه کرد؟" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bcd884a9d8e3c1c1135c6ad81ca5bcc1a0cdb2aaa7ee078ab149e57fc97693fc
- Size of remote file:
- 1.88 GB
- SHA256:
- 6fe1a5af51c112a18fccd4b08ba512d953c9944e26192185f72fc95639cb3939
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