Sentence Similarity
sentence-transformers
Safetensors
camembert
feature-extraction
text-embeddings-inference
Instructions to use smart-tribune/sentence-transformers-sentence-camembert-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use smart-tribune/sentence-transformers-sentence-camembert-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("smart-tribune/sentence-transformers-sentence-camembert-large") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download sentencepiece.bpe.model from smart-tribune/sentence-transformers-sentence-camembert-large: direct link, hf CLI and curl.
- Browser
- Download file 809 kB
-
https://huggingface.co/smart-tribune/sentence-transformers-sentence-camembert-large/resolve/main/sentencepiece.bpe.model
- Command line
-
hf download hf://smart-tribune/sentence-transformers-sentence-camembert-large/sentencepiece.bpe.model
-
curl -L -o sentencepiece.bpe.model https://huggingface.co/smart-tribune/sentence-transformers-sentence-camembert-large/resolve/main/sentencepiece.bpe.model
809 kB
- Xet hash:
- 60e5125aa528344f0a87c16cb8d770d3aa79139fbbe9a73093312ae5d9500501
- Size of remote file:
- 809 kB
- SHA256:
- f98f266fdc548c94216aaadc13ffaaafacf0c8793303e2195322d954549ea261
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