Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:131157
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use codersan/eFuck with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use codersan/eFuck with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("codersan/eFuck") 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:
- f3145201523f39e8423019c45a6368f46e798a5c3b0798abb048a2dc41ce6525
- Size of remote file:
- 471 MB
- SHA256:
- c21a1606b9d53642bcb9f06f7cd6ab913e4d8bd59cf578c715cce7d13b851c7c
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