Sentence Similarity
sentence-transformers
Safetensors
Greek
English
ministral3
greek
english
retrieval
rag
embeddings
nemotron
Eval Results (legacy)
Instructions to use ayoubkirouane/Sophea-Nemo-Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ayoubkirouane/Sophea-Nemo-Embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ayoubkirouane/Sophea-Nemo-Embedding") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from ayoubkirouane/Sophea-Nemo-Embedding: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/ayoubkirouane/Sophea-Nemo-Embedding/resolve/main/tokenizer.json
- Command line
-
hf download hf://ayoubkirouane/Sophea-Nemo-Embedding/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/ayoubkirouane/Sophea-Nemo-Embedding/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 52190a2f65b824d0652ed9ea541eab65d7af10cafa445d9933ff7bc2d81f8886
- Size of remote file:
- 17.1 MB
- SHA256:
- 39973f835cec6b4b5c9bd5b49a5ee121a5c51614b16206184ac0219b15c3fe91
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