jinaai / jina-embeddings-v5-text-small-classification

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-transformers interface, install this package as well.

from sentence_transformers import SentenceTransformer
import torch

model = SentenceTransformer(
    "jinaai/jina-embeddings-v5-text-small-classification",
    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, 1024)

similarity = model.similarity(embeddings, embeddings)
print(similarity)
# tensor([[1.0000, 0.7347, 0.7988, 0.7523],
#         [0.7347, 1.0000, 0.7440, 0.7228],
#         [0.7988, 0.7440, 1.0000, 0.7321],
#         [0.7523, 0.7228, 0.7321, 1.0000]])

from vllm import LLM

# Initialize model
name = "jinaai/jina-embeddings-v5-text-small-classification"
model = LLM(
    model=name,
    dtype="float16",
    runner="pooling",
    pooler_config=PoolerConfig(seq_pooling_type="LAST", normalize=True)
)

# 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

• Via Docker on CPU:

  docker run -p 8080:80 \
    ghcr.io/huggingface/text-embeddings-inference:cpu-1.9 \
    --model-id jinaai/jina-embeddings-v5-text-small-classification \
    --dtype float32 --pooling last-token
  

• Via Docker on NVIDIA GPU (Turing, Ampere, Ada Lovelace, Hopper or Blackwell):

  docker run --gpus all --shm-size 1g -p 8080:80 \
    ghcr.io/huggingface/text-embeddings-inference:cuda-1.9 \
    --model-id jinaai/jina-embeddings-v5-text-small-classification \
    --dtype float16 --pooling last-token
  

Alternatively, you can also run with cargo , more information can be found in the Text Embeddings Inference documentation .

Send a request to /v1/embeddings to generate embeddings via the OpenAI Embeddings API :

curl -X POST http://127.0.0.1:8080/v1/embeddings \
  -H "Content-Type: application/json" \
  -d '{
    "model": "jinaai/jina-embeddings-v5-text-small-classification",
    "input": [
      "Document: The impacts of climate change on coastal cities are significant...",
    ]
  }'

Or rather via the Text Embeddings Inference API specification instead, to prevent from manually formatting the inputs:

curl -X POST http://127.0.0.1:8080/embed \
  -H "Content-Type: application/json" \
  -d '{
    "inputs": "Overview of climate change impacts on coastal cities",
    "prompt_name": "document",
  }'

llama-server -hf jinaai/jina-embeddings-v5-text-small-classification:F16 --embedding --pooling last -ub 32768

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."
    ]
  }'