Sentence Similarity
Transformers
PyTorch
ONNX
Safetensors
sentence-transformers
Transformers.js
English
modernbert
feature-extraction
mteb
embedding
text-embeddings-inference
Instructions to use unsloth/gte-modernbert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use unsloth/gte-modernbert-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("unsloth/gte-modernbert-base") model = AutoModel.from_pretrained("unsloth/gte-modernbert-base") - sentence-transformers
How to use unsloth/gte-modernbert-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("unsloth/gte-modernbert-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 unsloth/gte-modernbert-base with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('sentence-similarity', 'unsloth/gte-modernbert-base'); - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c117cccd5877ef7e2a11cd227879dc12be3d3034cbda801abc365cad90437ebe
- Size of remote file:
- 298 MB
- SHA256:
- dc59a5ad66327b309471bd8c42b59a937ebc66cb37679c3e39a6cdef58388d45
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