Sentence Similarity
sentence-transformers
TensorBoard
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:1021596
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use codersan/FaMiniLm_Mizan3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use codersan/FaMiniLm_Mizan3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("codersan/FaMiniLm_Mizan3") sentences = [ "بیشتر زنان دلیل این کار را درک نمیکنند ", "Most women can't understand why this happens.", "feeling with confusion and annoyance that what he could decide easily and clearly by himself, he could not discuss before Princess Tverskaya, who to him stood for the incarnation of that brute force which would inevitably control him in the life he led in the eyes of the world, and hinder him from giving way to his feeling of love and forgiveness.", "MR TALLBOYS: Happy days, happy days!" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Commit History
-
Training in progress, step 63850
Verified signaturef0685bd -
Training in progress, step 60000
Verified signature0c40b70 -
Training in progress, step 50000
Verified signaturef1c08cc -
Training in progress, step 40000
Verified signature358ce5a -
Training in progress, step 30000
Verified signaturece2cd20 -
Training in progress, step 20000
Verified signature731b1f4 -
Training in progress, step 10000
Verified signaturef8bb6b7