Sentence Similarity
sentence-transformers
Safetensors
French
modernbert
feature-extraction
Generated from Trainer
dataset_size:8066634
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use Parallia/Fairly-Multilingual-ModernBERT-Embed-BE-FR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Parallia/Fairly-Multilingual-ModernBERT-Embed-BE-FR with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Parallia/Fairly-Multilingual-ModernBERT-Embed-BE-FR") sentences = [ "Ces trois hommes mystérieux virent alors à notre aide.", "Trois gars bien étranges nous aidèrent après cela.", "Ces trois oiseaux noirs virent alors dans notre jardin.", "Certaines personnes sont serviables.", "Un, deux, trois... Qui peut deviner les chiffres suivants?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [5, 5] - Notebooks
- Google Colab
- Kaggle
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| } | |
| ] |