How to use Master-thesis-NAP/nomicAI-ModernBERT-base-finetuned with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Master-thesis-NAP/nomicAI-ModernBERT-base-finetuned") sentences = [ "What is the error estimate for the difference between the exact solution and the local oscillation decomposition (LOD) solution in terms of the $L_0$ norm?", "\\label{thm1}\nSuppose $\\kappa$ and $\\bar a$ are as above. Then $|\\Pcut(\\bar a)| \\leq 2^\\kappa$. Indeed if\n$2^\\kappa=\\aleph_\\alpha,$ then $|\\Pcut(\\bar a)| \\leq |\\alpha+1|^2$.", "\\cite{kyushu}\n For every discrete group $\\G$ and every 2-dimensional representation $\\varrho$ of $\\G$, $\\varrho-$equivariant functions for $\\G$ always exist.", "\\label{Corollary}\n Let Assumptions~\\ref{assum_1} and~\\ref{assump2} be satisfied. Let $u$ be the solution of~\\eqref{WeakForm} and let $u_{H,k}$ be the LOD solution of~\\eqref{local_probelm }. Then we have \n \\begin{equation}\\label{L2Estimate}\n \\|u-I_Hu_{H,k}\\|_0\\lesssim \\|u-I_Hu\\|_0+\\|u-u_{H,k}\\|_0 +H|u-u_{H,k}|_1.\n \\end{equation}\n %\\[\\|u-I_Hu_{H,k}\\|_0\\lesssim H |u|_1 +|u-u_{H,k}|_1.\\]" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
