Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:8786
loss:CosineSimilarityLoss
text-embeddings-inference
Instructions to use aikunu/all_MiniLM_L6_v2_moore with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use aikunu/all_MiniLM_L6_v2_moore with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("aikunu/all_MiniLM_L6_v2_moore") sentences = [ "Mam ka paam n bãng yellã tiki-takɩ, n tõe n togs yãmb ye.", "Le creusement du puits est devenu profond mais on a pas encore atteint l'eau.", "Je n'ai pas compris exactement le problème pour pouvoir vous l'expliquer.", "Élisabeth, ta femme, te donnera un fil." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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