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jinaai / jina-embeddings-v5-text-nano-classification
Model's Last Updated: April 15 2026
text-classification
Introduction of jina-embeddings-v5-text-nano-classification
Model Details of jina-embeddings-v5-text-nano-classification
jina-embeddings-v5-text : Task-Targeted Embedding Distillation
Blog | Technical Report | Jina API | EIS
Model Overview
jina-embeddings-v5-text-nano-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-small , for better performance at a bigger size.
Trained using a novel approach that combines distillation with task-specific contrastive losses,
jina-embeddings-v5-text-nano-classification
outperforms existing state-of-the-art models of similar size across diverse embedding benchmarks.
| Feature | Value |
|---|---|
| Parameters | 239M |
| Supported Tasks |
classification
|
| Max Sequence Length | 8192 |
| Embedding Dimension | 768 |
| Matryoshka Dimensions | 32, 64, 128, 256, 512, 768 |
| Pooling Strategy | Last-token pooling |
| Base Model | jinaai/jina-embeddings-v5-text-nano |
Training and Evaluation
For training details and evaluation results, see our technical report .
Usage
Requirements
The following Python packages are required:
-
transformers>=5.1.0 -
torch>=2.8.0 -
peft>=0.15.2 -
vllm==0.15.1
Optional / Recommended
- flash-attention : Installing 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-transformersinterface, install this package as well.
via sentence-transformers
from sentence_transformers import SentenceTransformer
import torch
model = SentenceTransformer(
"jinaai/jina-embeddings-v5-text-nano-classification",
trust_remote_code=True,
model_kwargs={"dtype": torch.bfloat16}, # Recommended for GPUs
config_kwargs={"_attn_implementation": "flash_attention_2"}, # Recommended but optional
)
# Optional: set truncate_dim in encode() to control embedding size
texts = [
"My order hasn't arrived yet and it's been two weeks.",
"How do I reset my password?",
"I'd like a refund for my recent purchase.",
"Your product exceeded my expectations. Great job!",
]
# Encode texts
embeddings = model.encode(texts)
print(embeddings.shape)
# (4, 768)
similarity = model.similarity(embeddings, embeddings)
print(similarity)
# tensor([[1.0000, 0.7152, 0.8378, 0.8101],
# [0.7152, 1.0000, 0.7512, 0.6940],
# [0.8378, 0.7512, 1.0000, 0.7741],
# [0.8101, 0.6940, 0.7741, 1.0000]])
via vLLM
from vllm import LLM
# Initialize model
name = "jinaai/jina-embeddings-v5-text-nano-classification"
model = LLM(
model=name,
dtype="float16",
runner="pooling",
trust_remote_code=True,
pooler_config=PoolerConfig(seq_pooling_type="LAST", normalize=True)
)
# Create text prompts
query = "Overview of climate change impacts on coastal cities"
query_prompt = f"Query: {query}"
document = "The impacts of climate change on coastal cities are significant.."
document_prompt = f"Document: {document}"
# Encode all prompts
prompts = [query_prompt, document_prompt]
outputs = model.encode(prompts, pooling_task="embed")
embed_query = outputs[0].outputs.data
embed_document = outputs[1].outputs.data
via llama.cpp (GGUF)
Since our nano model is based on
jinaai/jina-embeddings-v5-text-nano
, which is not yet supported by llama.cpp, we provide our own branch of
llama.cpp
, which implements the necessary changes to support it for now.
To start the OpenAI API compatible HTTP server, run with the respective model version:
llama-server \
-hf jinaai/jina-embeddings-v5-text-nano-classification:F16 \
--embedding \
--pooling last \
--batch-size 8192 \
--ubatch-size 8192 \
--ctx-size 8192
Client:
curl -X POST "http://127.0.0.1:8080/v1/embeddings" \
-H "Content-Type: application/json" \
-d '{
"input": [
"Document: A beautiful sunset over the beach",
"Document: Un beau coucher de soleil sur la plage",
"Document: 海滩上美丽的日落",
"Document: 浜辺に沈む美しい夕日",
"Document: Golden sunlight melts into the horizon, painting waves in warm amber and rose, while the sky whispers goodnight to the quiet, endless sea."
]
}'
Note: For the classification variant, always add
Document:
prefix in front of your input as shown above.
License
The model is licensed under CC BY-NC 4.0. For commercial use, please contact us .
Citation
If you find
jina-embeddings-v5-text-nano-classification
useful in your research, please cite the following paper:
[will be published soon]
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