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
Upload tokenizer_config.json
Browse files- tokenizer_config.json +3 -0
tokenizer_config.json
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:9f4ba5ef130db0eb0e00bf6ba764431f6d9ffa7efe552bd648da2a40b256ec45
|
| 3 |
+
size 46995
|