SFR Embedding Code 400M R

🚀 Salesforce/SFR-Embedding-Code-400M_R

SFR-Embedding by Salesforce Research.

Salesforce/SFR-Embedding-Code is a generalist embedding model family designed for multilingual and multi-task code and text retrieval. It outperforms various open - source code embedding models in multiple code retrieval tasks.

Check out our paper for more details!

✨ Features

• A generalist embedding model family for multilingual and multi - task code and text retrieval.
• Demonstrates superior performance compared to various open - source code embedding models across multiple code retrieval tasks.

🚀 Quick Start

Ethical Considerations

This release is for research purposes only in support of an academic paper. Our models, datasets, and code are not specifically designed or evaluated for all downstream purposes. We strongly recommend users evaluate and address potential concerns related to accuracy, safety, and fairness before deploying this model. We encourage users to consider the common limitations of AI, comply with applicable laws, and leverage best practices when selecting use cases, particularly for high - risk scenarios where errors or misuse could significantly impact people’s lives, rights, or safety. For further guidance on use cases, refer to our AUP and [AI AUP](https://www.salesforce.com/content/dam/web/en_us/www/documents/legal/Agreements/policies/ai - acceptable - use - policy.pdf).

License Statement

Users need to make their own assessment regarding any obligations or responsibilities under the corresponding licenses or terms and conditions pertaining to the original datasets and data. This release is for research purposes only in support of an academic paper.

Performance on CoIR Benchmark

SFR-Embedding Team († indicates co - leaders)

• Ye Liu
• Rui Meng
• Shafiq Rayhan Joty
• Silvio Savarese
• Caiming Xiong †
• Yingbo Zhou †
• Semih Yavuz †

💻 Usage Examples

Basic Usage

Transformers

import torch.nn.functional as F
from transformers import AutoModel, AutoTokenizer

input_texts = [
    "how to implement quick sort in Python?",
    "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)",
    "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",
]

model_path = 'Salesforce/SFR-Embedding-Code-400M_R'
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModel.from_pretrained(model_path, trust_remote_code=True)

# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=8192, padding=True, truncation=True, return_tensors='pt')

outputs = model(**batch_dict)
embeddings = outputs.last_hidden_state[:, 0]

# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:1] @ embeddings[1:].T) * 100
print("Similarity Scores:", scores.tolist())
# Similarity Scores: [[74.84745025634766, 65.39266967773438]]

Sentence Transformers

# Requires sentence_transformers>=2.7.0
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim

sentences = [
    "how to implement quick sort in Python?",
    "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)",
    "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",
]

model = SentenceTransformer('Salesforce/SFR-Embedding-Code-400M_R', trust_remote_code=True)
embeddings = model.encode(sentences)
similarities = cos_sim(embeddings[0], embeddings[1:])
print(similarities)
# tensor([[0.7485, 0.6539]])

📚 Documentation

Citation

@article{liu2024codexembed,
  title={CodeXEmbed: A Generalist Embedding Model Family for Multiligual and Multi-task Code Retrieval},
  author={Liu, Ye and Meng, Rui and Jot, Shafiq and Savarese, Silvio and Xiong, Caiming and Zhou, Yingbo and Yavuz, Semih},
  journal={arXiv preprint arXiv:2411.12644},
  year={2024}
}

📄 License

The license for this project is cc - by - nc - 4.0.