jina-embeddings-text-small-text-matching

jina-embeddings-v5-text-small-text-matching: Text-Matching-Targeted Embedding Distillation

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Model Overview

jina-embeddings-v5-text-small-text-matching is a compact, high-performance text embedding model designed for text-matching.

It is part of the jina-embeddings-v5-text model family, which also includes 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-text-matching outperforms existing state-of-the-art models of similar size across diverse embedding benchmarks.

Training and Evaluation

For training details and evaluation results, see our technical report.

Usage

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.

The fastest way to use v5-text in production. Elastic Inference Service (EIS) provides managed embedding inference with built-in scaling, so you can generate embeddings directly within your Elastic deployment.

PUT _inference/text_embedding/jina-v5
{
  "service": "elastic",
  "service_settings": {
    "model_id": "jina-embeddings-v5-text-small"
  }
}

See the Elastic Inference Service documentation for setup details.

from sentence_transformers import SentenceTransformer
import torch

model = SentenceTransformer(
    "jinaai/jina-embeddings-v5-text-small-text-matching",
    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 = [
    "A beautiful sunset over the beach",  # English
    "غروب جميل على الشاطئ",  # Arabic
    "海滩上美丽的日落",  # Chinese
    "Un beau coucher de soleil sur la plage",  # French
    "Ein wunderschöner Sonnenuntergang am Strand",  # German
    "Ένα όμορφο ηλιοβασίλεμα πάνω από την παραλία",  # Greek
    "समुद्र तट पर एक खूबसूरत सूर्यास्त",  # Hindi
    "Un bellissimo tramonto sulla spiaggia",  # Italian
    "浜辺に沈む美しい夕日",  # Japanese
    "해변 위로 아름다운 일몰",  # Korean
]

# Encode texts
embeddings = model.encode(texts)
print(embeddings.shape)
# (10, 1024)

similarity = model.similarity(embeddings[0], embeddings[1:])
print(similarity)
# tensor([[0.7833, 0.8926, 0.9333, 0.9421, 0.7588, 0.9068, 0.9301, 0.8521, 0.8768]])

from vllm import LLM
from vllm.config.pooler import PoolerConfig

# Initialize model
name = "jinaai/jina-embeddings-v5-text-small-text-matching"
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-text-matching \
    --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-text-matching \
    --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-text-matching",
    "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",
  }'

After installing llama.cpp one can run llama-server to host the embedding model as