Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:165665
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use codersan/all-MiniLM-L6-v2-Fa-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use codersan/all-MiniLM-L6-v2-Fa-v3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("codersan/all-MiniLM-L6-v2-Fa-v3") sentences = [ "کدام یک از تجربیات بدی که در زندگی داشتید؟", "آیا Urjit Patel برای فرماندار RBI مناسب است؟", "چگونه برای اولین بار با پورنو آشنا شدید؟", "برخی از تجربیات خوب و بد زندگی شما چه بود؟" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from codersan/all-MiniLM-L6-v2-Fa-v3: direct link, hf CLI and curl.
- Browser
- Download file 712 kB
-
https://huggingface.co/codersan/all-MiniLM-L6-v2-Fa-v3/resolve/main/tokenizer.json
- Command line
-
hf download hf://codersan/all-MiniLM-L6-v2-Fa-v3/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/codersan/all-MiniLM-L6-v2-Fa-v3/resolve/main/tokenizer.json
712 kB
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