Feature Extraction
sentence-transformers
PyTorch
ONNX
Safetensors
Transformers
Transformers.js
English
bert
fill-mask
sentence-similarity
mteb
custom_code
text-embeddings-inference
Instructions to use michaelfeil/jina-embeddings-v2-base-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- 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)# 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
- Xet hash:
- 88987227c9e3edae1958d4b1603731cd8d39e270ca7287afbb16442672a51861
- Size of remote file:
- 322 MB
- SHA256:
- 1c28fb0a8bc930d79b2b29091674a8a0ce0e983489e88b0e863efb1ad4444b01
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