metadata
base_model:
- Qwen/Qwen2.5-Coder-0.5B
The code embedding model trained by Jina AI.
Jina Embeddings c1: A Small but Performant Code Embedding Model
Intended Usage & Model Info
jina-embeddings-c1 is an embedding model for code retrieval.
The model supports various types of code retrieval (text-to-code, code-to-code, code-to-text, code-to-completion) and technical question answering across 15+ programming languages.
Built on Qwen/Qwen2.5-Coder-0.5B, jina-embeddings-c1 features:
- Multilingual support (15+ programming languages) and compatibility with a wide range of domains, including web development, software development, machine learning, data science, and educational coding problems.
- Task-specific instruction prefixes for NL2Code, Code2Code, Code2NL, Code2Completion, and Technical QA, which can be selected at inference time.
- Flexible embedding size: dense embeddings are 896-dimensional by default but can be truncated to as low as 64 with minimal performance loss.
Summary of features:
| Feature | Jina Embeddings C1 |
|---|---|
| Base Model | Qwen2.5-Coder-0.5B |
| Supported Tasks | nl2code, code2code, code2nl, code2completion, qa |
| Model DType | BFloat 16 |
| Max Sequence Length | 32768 |
| Embedding Vector Dimension | 896 |
| Matryoshka dimensions | 64, 128, 256, 512, 896 |
| Pooling Strategy | Last-token pooling |
| Attention Mechanism | FlashAttention2 |
Usage
Requirements
The following Python packages are required:
transformers>=4.53.0torch>=2.7.1
Optional / Recommended
- flash-attention: Installing flash-attention is recommended for improved inference speed and efficiency, but not mandatory.
via transformers
# !pip install transformers>=4.53.0 torch>=2.7.1
from transformers import AutoModel
import torch
# Initialize the model
model = AutoModel.from_pretrained("jinaai/jina-embeddings-c1-0.5B", trust_remote_code=True)
model.to("cuda")
# Configure truncate_dim, max_length, batch_size in the encode function if needed
# Encode query
query_embeddings = model.encode(
["print hello world in python"],
task="nl2code",
prompt_name="query",
)
# Encode passage
passage_embeddings = model.encode(
["print('Hello World!')"],
task="nl2code",
prompt_name="passage",
)
Training & Evaluation
Please refer to our technical report of jina-embeddings-c1 for training details and benchmarks.
Contact
Join our Discord community and chat with other community members about ideas.