Salesforce / SFR-Embedding-Code-2B_R

Salesforce/SFR-Embedding-Code-2B_R

SFR-Embedding by Salesforce Research.

The Salesforce/SFR-Embedding-Code is a generalist embedding model family for multilingual and multi-task code and Text retrieval. It demonstrates superior performance compared to various open-source code embedding models across multiple code retrieval tasks.

Check out our paper for more details!

We also offer 400M-size model Salesforce/SFR-Embedding-Code-400_R

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 .

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.

This released model is a fine-tuned version of Gemma and Gemma is provided under and subject to the Gemma Terms of Use found at ai.google.dev/gemma/terms. Additionally, the use of this model is restricted as set forth in the Gemma Prohibited Use Policy at ai.google.dev/gemma/prohibited_use_policy ("Prohibited Use Policy"), which is hereby incorporated by reference into this Agreement.

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 †

How to run

Transformers

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

# Each query needs to be accompanied by an corresponding instruction describing the task.
query_instruction_example = "Given Code or Text, retrieval relevant content"
queries = [
    "how to implement quick sort in Python?"
    ]

# No instruction needed for retrieval passages
passages = [
    "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"
]

# load model with tokenizer
model = AutoModel.from_pretrained('Salesforce/SFR-Embedding-Code-2B_R', trust_remote_code=True)

# get the embeddings
max_length = 32768
query_embeddings = model.encode_queries(queries, instruction=query_instruction_example, max_length=max_length)
passage_embeddings = model.encode_corpus(passages, max_length=max_length)

# normalize embeddings
query_embeddings = F.normalize(query_embeddings, p=2, dim=1)
passage_embeddings = F.normalize(passage_embeddings, p=2, dim=1)

scores = (query_embeddings @ passage_embeddings.T) * 100
print(scores.tolist())

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}
}