Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
dataset_size:100K<n<1M
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use Stern5497/nir-24-xlm-roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Stern5497/nir-24-xlm-roberta-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Stern5497/nir-24-xlm-roberta-base") sentences = [ "who does carol end up with on er", "who does sarah end up with in must love dogs", "when does season 7 once upon a time start", "when did the wwe hall of fame start" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Ctrl+K