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research/BGE_Coder/README.md
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@ -11,14 +11,17 @@
</a>
</p>
This repo contains the data, training, and evaluation pipeline for CodeR / [BGE-Code-v1](https://huggingface.co/BAAI/bge-code-v1)
**[BGE-Code-v1](https://huggingface.co/BAAI/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.
## :bell: News:
- 🥳 5/15/2025: We have released the CodeR! :fire:
## Usage
@ -29,9 +32,6 @@ This repo contains the data, training, and evaluation pipeline for CodeR / [BGE-
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.",
@ -149,29 +149,29 @@ print(scores.tolist())
- 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** |
| | 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 |
| --------------- | ---------- | ---- | ------- | ---- | -------- | -------------- | ---- |
| | 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** |
| BGE-Code-v1 | 100.0 | 99.2 | 40.9 | 36.1 | 93.1 | 67.4 | **72.8** |
| BGE-Code-v1 | 100.0 | 99.2 | 40.9 | 36.1 | 93.1 | 67.4 | **72.8** |
### Instructions for Evaluation
@ -200,21 +200,32 @@ print(scores.tolist())
#### CoIR
For CoIR, we use the [CoIR](https://github.com/CoIR-team/coir) evaluation script.
For CoIR, we use the [CoIR](https://github.com/CoIR-team/coir) evaluation script:
You can also evaluate the model using the following script:
```shell
cd ./evaluation/coir_eval
### clone coir
mkdir test
cd ./test
git clone https://github.com/CoIR-team/coir.git
mv ./coir/coir ../
cd ..
rm -rf ./test
### evaluate
bash eval.sh
```
### CodeRAG
For CodeRAG, we use the [CodeRAG](https://github.com/code-rag-bench/code-rag-bench) evaluation script.
For CodeRAG, we use the [CodeRAG](https://github.com/code-rag-bench/code-rag-bench) evaluation script:
You can also evaluate the model using the following script:
```shell
cd ./evaluation/coderag_eval
### clone coderag
git clone https://github.com/code-rag-bench/code-rag-bench.git
## You need prepare environment according to README.md
rm -rf ./code-rag-bench/retrieval/create
cp -r ./test/* ./code-rag-bench/retrieval/
### prepare data
bash prepare_data.sh
### evaluate