Feature Extraction
sentence-transformers
ONNX
Safetensors
Transformers
new
code
retrieval
custom_code
text-embeddings-inference
Instructions to use Salesforce/SFR-Embedding-Code-400M_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-400M_R with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Salesforce/SFR-Embedding-Code-400M_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-400M_R with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Salesforce/SFR-Embedding-Code-400M_R", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Salesforce/SFR-Embedding-Code-400M_R", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e27c6199f1b96ce5859cac1e41e8a0b59e682fedca513a2a0935c26f946b51bd
- Size of remote file:
- 361 MB
- SHA256:
- 90fe7659160d86b8e56218bd001943f580171275a0a6581facd0553c9d48f2f2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.