Instructions to use Sahajtomar/french_semantic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Sahajtomar/french_semantic with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Sahajtomar/french_semantic") sentences = [ "C'est une personne heureuse", "C'est un chien heureux", "C'est une personne très heureuse", "Aujourd'hui est une journée ensoleillée" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download modules.json from Sahajtomar/french_semantic: direct link, hf CLI and curl.
- Browser
- Download file 242 Bytes
-
https://huggingface.co/Sahajtomar/french_semantic/resolve/refs%2Fpr%2F3/modules.json
- Command line
-
hf download hf://Sahajtomar/french_semantic@refs/pr/3/modules.json
-
curl -L -o modules.json https://huggingface.co/Sahajtomar/french_semantic/resolve/refs%2Fpr%2F3/modules.json
242 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "0_Transformer", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| } | |
| ] |