Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:16825
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use codersan/FaLaBSE-v11-phase2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use codersan/FaLaBSE-v11-phase2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("codersan/FaLaBSE-v11-phase2") sentences = [ "تصمیم سال جدید شما چیست؟", "تصمیم سال جدید شما چیست؟", "من ۱۸ ساله هستم. چگونه می توانم آنلاین درآمد کسب کنم؟", "یک حیوان در یک باغچه از این طرف به آن طرف میچرخد" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from codersan/FaLaBSE-v11-phase2: direct link, hf CLI and curl.
- Browser
- Download file 13.6 MB
-
https://huggingface.co/codersan/FaLaBSE-v11-phase2/resolve/main/tokenizer.json
- Command line
-
hf download hf://codersan/FaLaBSE-v11-phase2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/codersan/FaLaBSE-v11-phase2/resolve/main/tokenizer.json
13.6 MB
- Xet hash:
- 9061164c657de59e1ffb961b1afb95a5e0afab558522095c235bb448a22b7c74
- Size of remote file:
- 13.6 MB
- SHA256:
- 92262b29204f8fdc169a63f9005a0e311a16262cef4d96ecfe2a7ed638662ed3
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