- Libraries
- sentence-transformers
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] - Transformers
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) # pip install -U transformers accelerate
# 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, device_map="auto") - Transformers.js
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'); - Notebooks
- Google Colab
- Kaggle
