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, dtype="auto") - Notebooks
- Google Colab
- Kaggle
update README
Browse files
README.md
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### Performance on CoIR Benchmark
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| Model | Model Size | CoIR AVG (NDCG@10) |
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|-----------------------|------------|---------------------|
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| SFR-Embedding-Code | 2B |
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| CodeSage-Large-v2 | 1.3B | 64.2 |
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| CodeSage-Large | 1.3B | 61.0 |
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| SFR-Embedding-Code | 400M | 61.9 |
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| CodeRankEmbed | 137M | 60.1 |
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| CodeSage-Base | 356M | 57.5 |
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| Voyage-Code-002 | - | 56.3 |
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from transformers import AutoModel, AutoTokenizer
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input_texts = [
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"def quick_sort(arr):\n if len(arr) <= 1:\n return arr\n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n return quick_sort(left) + middle + quick_sort(right)",
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"def bubble_sort(arr):\n n = len(arr)\n for i in range(n):\n for j in range(0, n-i-1):\n if arr[j] > arr[j+1]:\n arr[j], arr[j+1] = arr[j+1], arr[j]\n return arr",
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"how to implement quick sort in Python?"
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]
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model_path = 'Salesforce/SFR-Embedding-Code-400M_R'
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### Performance on CoIR Benchmark
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| Model | Model Size | CoIR AVG (NDCG@10) |
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|-----------------------|------------|---------------------|
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| **SFR-Embedding-Code** | 2B | 67.4 |
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| CodeSage-Large-v2 | 1.3B | 64.2 |
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| CodeSage-Large | 1.3B | 61.0 |
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| **SFR-Embedding-Code** | 400M | 61.9 |
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| CodeRankEmbed | 137M | 60.1 |
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| CodeSage-Base | 356M | 57.5 |
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| Voyage-Code-002 | - | 56.3 |
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from transformers import AutoModel, AutoTokenizer
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input_texts = [
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"how to implement quick sort in Python?",
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"def quick_sort(arr):\n if len(arr) <= 1:\n return arr\n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n return quick_sort(left) + middle + quick_sort(right)",
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"def bubble_sort(arr):\n n = len(arr)\n for i in range(n):\n for j in range(0, n-i-1):\n if arr[j] > arr[j+1]:\n arr[j], arr[j+1] = arr[j+1], arr[j]\n return arr",
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]
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model_path = 'Salesforce/SFR-Embedding-Code-400M_R'
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