Feature Extraction
sentence-transformers
Safetensors
Transformers
French
camembert
sentence-similarity
Eval Results (legacy)
text-embeddings-inference
Instructions to use h4c5/sts-camembert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use h4c5/sts-camembert-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("h4c5/sts-camembert-base") 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] - Transformers
How to use h4c5/sts-camembert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="h4c5/sts-camembert-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("h4c5/sts-camembert-base") model = AutoModel.from_pretrained("h4c5/sts-camembert-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from h4c5/sts-camembert-base: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/h4c5/sts-camembert-base/resolve/main/model.safetensors
- Command line
-
hf download hf://h4c5/sts-camembert-base/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/h4c5/sts-camembert-base/resolve/main/model.safetensors
443 MB
- Xet hash:
- b25da742605fbb232bd7e208ba9a39c037b7f9abab153875738e3178a5a5458a
- Size of remote file:
- 443 MB
- SHA256:
- bf47101431d31ae2d107e782f68494dba4ccd2daa0e1389fa09e55ef6ab47663
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