Sentence Similarity
sentence-transformers
TensorBoard
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:131157
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use codersan/validadted_allMiniLM_onV9f with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use codersan/validadted_allMiniLM_onV9f with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("codersan/validadted_allMiniLM_onV9f") 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:
- 66d76cce7cd63b90f358e2a77dbe8449149f553d09ee215f6326b86b64e15d64
- Size of remote file:
- 5.62 kB
- SHA256:
- e93b72c029dca371b160346a5070316b0576d041b78998b5c89bc33b33d76a79
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