---
language:
- zh
- en
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- transformers
pipeline_tag: sentence-similarity
library_name: sentence-transformers
license: apache-2.0
---
FlagEmbedding
For more details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).
**BGE-Code-v1** is an LLM-based code embedding model that supports code retrieval, text retrieval, and multilingual retrieval. It primarily demonstrates the following capabilities:
- Superior Code Retrieval Performance: The model demonstrates exceptional code retrieval capabilities, supporting natural language queries in both English and Chinese, as well as 20 programming languages.
- Robust Text Retrieval Capabilities: The model maintains strong text retrieval capabilities comparable to text embedding models of similar scale.
- Extensive Multilingual Support: BGE-Code-v1 offers comprehensive multilingual retrieval capabilities, excelling in languages such as English, Chinese, Japanese, French, and more.
## Usage
### Using FlagEmbedding
```
git clone https://github.com/FlagOpen/FlagEmbedding.git
cd FlagEmbedding
pip install -e .
```
```python
from FlagEmbedding import FlagLLMModel
queries = [
"Delete the record with ID 4 from the 'Staff' table.",
'Delete all records in the "Livestock" table where age is greater than 5'
]
documents = [
"DELETE FROM Staff WHERE StaffID = 4;",
"DELETE FROM Livestock WHERE age > 5;"
]
model = FlagLLMModel('BAAI/bge-code-v1',
query_instruction_format="{}\n{}",
query_instruction_for_retrieval="Given a question in text, retrieve SQL queries that are appropriate responses to the question.",
trust_remote_code=True,
use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
embeddings_1 = model.encode_queries(queries)
embeddings_2 = model.encode_corpus(documents)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
```
By default, FlagLLMModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs. You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable.
### Using Sentence Transformers
```python
from sentence_transformers import SentenceTransformer
import torch
# Load the model, optionally in float16 precision for faster inference
model = SentenceTransformer(
"BAAI/bge-code-v1",
trust_remote_code=True,
model_kwargs={"torch_dtype": torch.float16},
)
# Prepare a prompt given an instruction
instruction = 'Given a question in text, retrieve SQL queries that are appropriate responses to the question.'
prompt = f'{instruction}\n'
# Prepare queries and documents
queries = [
"Delete the record with ID 4 from the 'Staff' table.",
'Delete all records in the "Livestock" table where age is greater than 5'
]
documents = [
"DELETE FROM Staff WHERE StaffID = 4;",
"DELETE FROM Livestock WHERE age > 5;"
]
# Compute the query and document embeddings
query_embeddings = model.encode(queries, prompt=prompt)
document_embeddings = model.encode(documents)
# Compute the cosine similarity between the query and document embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
```
### Using HuggingFace Transformers
```python
import torch
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def last_token_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
if left_padding:
return last_hidden_states[:, -1]
else:
sequence_lengths = attention_mask.sum(dim=1) - 1
batch_size = last_hidden_states.shape[0]
return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
def get_detailed_instruct(task_description: str, query: str) -> str:
return f'{task_description}\n{query}'
instruction = 'Given a question in text, retrieve SQL queries that are appropriate responses to the question.'
queries = [
"Delete the record with ID 4 from the 'Staff' table.",
'Delete all records in the "Livestock" table where age is greater than 5'
]
documents = [
"DELETE FROM Staff WHERE StaffID = 4;",
"DELETE FROM Livestock WHERE age > 5;"
]
input_texts = queries + documents
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-code-v1', trust_remote_code=True)
model = AutoModel.from_pretrained('BAAI/bge-code-v1', trust_remote_code=True)
model.eval()
max_length = 4096
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors='pt', pad_to_multiple_of=8)
with torch.no_grad():
outputs = model(**batch_dict)
embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
```
## Evaluation
**BGE-Code-v1** achieves state-of-the-art performance on both the CoIR and CodeRAG benchmarks.
- CoIR
| | CodeXEmbed-2B | CodeXEmbed-7B | Voyage-Code-002 | Voyage-Code-003 | BGE-Code-v1 |
|---------------------------------------|---------------|---------------|-----------------|-----------------|-----------|
| Apps | 76.86 | 85.38 | 26.52 | 93.62 | 98.08 |
| CosQA | 40.47 | 42.47 | 29.79 | 34.45 | 46.72 |
| Text2SQL | 78.42 | 78.94 | 69.26 | 62.87 | 64.35 |
| CSN | 87.87 | 89.67 | 81.79 | 89.35 | 89.53 |
| CSN-CCR | 97.66 | 97.95 | 73.45 | 90.05 | 98.30 |
| CodeTrans-Contest | 90.30 | 94.45 | 72.77 | 94.96 | 94.38 |
| CodeTrans-DL | 38.57 | 40.46 | 27.48 | 38.57 | 46.13 |
| StackOverFlow-QA | 94.47 | 96.33 | 67.68 | 97.17 | 95.35 |
| CodeFeedBack-ST | 86.36 | 87.53 | 65.35 | 90.67 | 90.56 |
| CodeFeedBack-MT | 65.51 | 68.83 | 28.74 | 93.58 | 94.38 |
| AVG | 75.65 | 78.20 | 56.26 | 78.53 | 81.77 |
- CodedRAG
| | HummanEval | MBPP | DS-1000 | ODEX | RepoEval | SWE-bench-Lite | AVG |
| --------------- | ---------- | ---- | ------- | ---- | -------- | -------------- | ---- |
| SFR | 100.0 | 99.0 | 19.3 | 37.1 | 83.8 | 62.7 | 67.0 |
| Jina-v2-code | 100.0 | 97.7 | 26.2 | 19.9 | 90.5 | 58.3 | 65.4 |
| CodeXEmbed-2B | 100.0 | 97.4 | 25.4 | 23.9 | 88.7 | 52.4 | 64.6 |
| Voyage-Code-002 | 100.0 | 99.0 | 33.1 | 26.6 | 94.3 | 29.1 | 63.7 |
| Voyage-Code-003 | 100.0 | 99.6 | 38.9 | 36.3 | 90.0 | 70.1 | 72.5 |
| BGE-Code-v1 | 100.0 | 99.2 | 40.9 | 36.1 | 93.1 | 67.4 | 72.8 |
## Citation
If you find this repository useful, please consider giving a star :star: and citation
```
@article{bge-llm,
title={Making text embedders few-shot learners},
author={Li, Chaofan and Qin, MingHao and Xiao, Shitao and Chen, Jianlyu and Luo, Kun and Shao, Yingxia and Lian, Defu and Liu, Zheng},
journal={arXiv preprint arXiv:2409.15700},
year={2024}
}
@misc{bge-m3,
title={BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation},
author={Jianlv Chen and Shitao Xiao and Peitian Zhang and Kun Luo and Defu Lian and Zheng Liu},
year={2024},
eprint={2402.03216},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{bge_embedding,
title={C-Pack: Packaged Resources To Advance General Chinese Embedding},
author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},
year={2023},
eprint={2309.07597},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```