Feature Extraction
sentence-transformers
Safetensors
Transformers
codexembed2b
code
retrieval
custom_code
Instructions to use Salesforce/SFR-Embedding-Code-2B_R with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Salesforce/SFR-Embedding-Code-2B_R with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Salesforce/SFR-Embedding-Code-2B_R", 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 Salesforce/SFR-Embedding-Code-2B_R with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Salesforce/SFR-Embedding-Code-2B_R", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Salesforce/SFR-Embedding-Code-2B_R", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Code training data release
#15
by trumancai - opened
Dear Authors,
Thank you for your excellent work. I would greatly appreciate it if you could share or provide clarification regarding the pretraining code data used in the second stage (see Section 2.4.2 Code Retrieval Training Data in your paper https://arxiv.org/pdf/2411.12644).
Thank you in advance for your time and consideration.