Sentence Similarity
sentence-transformers
ONNX
Safetensors
Transformers.js
English
modernbert
feature-extraction
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use nomic-ai/modernbert-embed-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nomic-ai/modernbert-embed-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nomic-ai/modernbert-embed-base") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers.js
How to use nomic-ai/modernbert-embed-base with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('sentence-similarity', 'nomic-ai/modernbert-embed-base'); - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- d8af9fe023b7ffedf94303b98fdbb10ceb856e14a7caa1bb136c62bc089b0752
- Size of remote file:
- 1.28 MB
- SHA256:
- c97e3ae9104ae231d4f728dc6bdb70eb1f56c46a8bd4c7baf43bf882955607af
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