How to use michaelfeil/jina-embeddings-v2-base-code with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("michaelfeil/jina-embeddings-v2-base-code", trust_remote_code=True) sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3]
How to use michaelfeil/jina-embeddings-v2-base-code with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="michaelfeil/jina-embeddings-v2-base-code", trust_remote_code=True)
# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("michaelfeil/jina-embeddings-v2-base-code", trust_remote_code=True) model = AutoModelForMaskedLM.from_pretrained("michaelfeil/jina-embeddings-v2-base-code", trust_remote_code=True)
How to use michaelfeil/jina-embeddings-v2-base-code with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('feature-extraction', 'michaelfeil/jina-embeddings-v2-base-code');
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