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base_model:
- Qwen/Qwen3-0.6B
license: cc
---
<br><br>
<p align="center">
<img src="https://huggingface.co/datasets/jinaai/documentation-images/resolve/main/logo.webp" alt="Jina AI: Your Search Foundation, Supercharged!" width="150px">
</p>
### **jina-embeddings-v5-text-small-classification**: Classification-Targeted Embedding Distillation
[Blog](https://picsum.photos/200/300) | [Technical Report](https://picsum.photos/200/300) | [API](https://jina.ai/embeddings)
### Model Overview
`jina-embeddings-v5-text-small-classification` is a compact, high-performance text embedding model designed for classification.
It is part of the **jina-embeddings-v5-text** model family, which also includes [jina-embeddings-v5-text-nano](https://huggingface.co/jinaai/jina-embeddings-v5-text-nano), a smaller model for more resource-constrained use cases.
Trained using a novel approach that combines distillation with task-specific contrastive losses, `jina-embeddings-v5-text-small-classification` outperforms existing state-of-the-art models of similar size across diverse embedding benchmarks.
| Feature | Value |
| --- | --- |
| Parameters | 677M |
| Supported Tasks | `classification`|
| Max Sequence Length | 32768 |
| Embedding Dimension | 1024 |
| Matryoshka Dimensions | 32, 64, 128, 256, 512, 768, 1024 |
| Pooling Strategy | Last-token pooling |
| Base Model | Qwen/Qwen3-0.6B-Base |
### Training and Evaluation
For training details and evaluation results, see our [technical report](https://picsum.photos/200/300).
### Usage
<details>
<summary>Requirements</a></summary>
The following Python packages are required:
- `transformers>=4.57.0`
- `torch>=2.8.0`
- `peft>=0.15.2`
### Optional / Recommended
- **flash-attention**: Installing [flash-attention](https://github.com/Dao-AILab/flash-attention) is recommended for improved inference speed and efficiency, but not mandatory.
- **sentence-transformers**: If you want to use the model via the `sentence-transformers` interface, install this package as well.
</details>
<details>
<summary>via <a href="https://github.com/vllm-project/vllm">vLLM</a></summary>
```python
from vllm import LLM
# Initialize model
name = "jinaai/jina-embeddings-v5-text-small-classification"
model = LLM(model=name, task="embed", dtype="bfloat16")
# Create text prompts
document1 = "Overview of climate change impacts on coastal cities"
document1_prompt = f"Document: {document1}"
document2 = "The impacts of climate change on large cities"
document2_prompt = f"Document: {document2}"
# Encode all prompts
prompts = [document1_prompt, document2_prompt]
outputs = model.encode(prompts, pooling_task="embed")
embed_document1 = outputs[0].outputs.data
embed_document2 = outputs[1].outputs.data
```
</details>
### License
The model is licensed under CC BY-NC 4.0. For commercial use, please [contact us](link).
### Citation
If you find `jina-embeddings-v5-text-small-classification` useful in your research, please cite the following paper:
[TODO] |