Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
feature-extraction
Generated from Trainer
dataset_size:127
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Nuf-hugginface/modernbert-embed-quickb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Nuf-hugginface/modernbert-embed-quickb with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Nuf-hugginface/modernbert-embed-quickb") sentences = [ "What is the difference between traditional programming and ML?", "Over the past few years, the field of ML has advanced rapidly, especially in the area of Natural Language Processing (NLP)—the ability of machines to understand and generate human language. At the forefront of this progress are Large Language Models (LLMs), such as OpenAI’s GPT (Generative Pre-trained Transformer), Google’s PaLM, and Meta’s LLaMA", ". For example, integrating an LLM into a customer support chatbot might involve connecting it to a company’s internal knowledge base, enabling it to answer customer questions using accurate, up-to-date information.", "A major subset of AI is Machine Learning (ML), which involves algorithms that learn from data rather than being explicitly programmed. Instead of writing detailed instructions for every task, ML models find patterns in large datasets and use these patterns to make predictions or decisions" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Ctrl+K