Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
text-embeddings-inference
Instructions to use danfeg/ST-ALL-MPNET_Finetuned-FR-500 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use danfeg/ST-ALL-MPNET_Finetuned-FR-500 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("danfeg/ST-ALL-MPNET_Finetuned-FR-500") 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 model.safetensors from danfeg/ST-ALL-MPNET_Finetuned-FR-500: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/danfeg/ST-ALL-MPNET_Finetuned-FR-500/resolve/main/model.safetensors
- Command line
-
hf download hf://danfeg/ST-ALL-MPNET_Finetuned-FR-500/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/danfeg/ST-ALL-MPNET_Finetuned-FR-500/resolve/main/model.safetensors
438 MB
- Xet hash:
- 835c23b401fd666f72929aef30026f292d1b4a8086ee4d9b8bff49dc5690b40f
- Size of remote file:
- 438 MB
- SHA256:
- 950ceed3fa9849081cb621bf5b3d8e29afc4162b62f2397505cf0d066fe18e89
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