How to use mchambrec/embedding-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mchambrec/embedding-model", trust_remote_code=True) 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]
How to use mchambrec/embedding-model with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("mchambrec/embedding-model", trust_remote_code=True) model = AutoModel.from_pretrained("mchambrec/embedding-model", trust_remote_code=True)
How to use mchambrec/embedding-model with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('sentence-similarity', 'mchambrec/embedding-model');
