README.md

---
language:
- zh
- en
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- transformers
pipeline_tag: sentence-similarity
library_name: sentence-transformers
license: apache-2.0
---

<h1 align="center">FlagEmbedding</h1>

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="<instruct>{}\n<query>{}",
                     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", model_kwargs={"torch_dtype": torch.float16, "trust_remote_code": True}, tokenizer_kwargs={"trust_remote_code": True})

# Prepare a prompt given an instruction
instruction = 'Given a question in text, retrieve SQL queries that are appropriate responses to the question.'
prompt = f'<instruct>{instruction}\n<query>'
# 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'<instruct>{task_description}\n<query>{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}
}
```