jinaai / jina-embeddings-v3

The embedding model trained by Jina AI .

jina-embeddings-v3: Multilingual Embeddings With Task LoRA

Quick Start

The easiest way to start using jina-embeddings-v3 is with the Jina Embedding API .

Intended Usage & Model Info

jina-embeddings-v3 is a multilingual multi-task text embedding model designed for a variety of NLP applications. Based on the Jina-XLM-RoBERTa architecture , this model supports Rotary Position Embeddings to handle long input sequences up to 8192 tokens . Additionally, it features 5 LoRA adapters to generate task-specific embeddings efficiently.

Key Features:

• Extended Sequence Length: Supports up to 8192 tokens with RoPE.
• Task-Specific Embedding: Customize embeddings through the task argument with the following options:
  • retrieval.query : Used for query embeddings in asymmetric retrieval tasks
  • retrieval.passage : Used for passage embeddings in asymmetric retrieval tasks
  • separation : Used for embeddings in clustering and re-ranking applications
  • classification : Used for embeddings in classification tasks
  • text-matching : Used for embeddings in tasks that quantify similarity between two texts, such as STS or symmetric retrieval tasks
• Matryoshka Embeddings : Supports flexible embedding sizes ( 32, 64, 128, 256, 512, 768, 1024 ), allowing for truncating embeddings to fit your application.

Supported Languages:

While the foundation model supports 100 languages, we've focused our tuning efforts on the following 30 languages: Arabic, Bengali, Chinese, Danish, Dutch, English, Finnish, French, Georgian, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Latvian, Norwegian, Polish, Portuguese, Romanian, Russian, Slovak, Spanish, Swedish, Thai, Turkish, Ukrainian, Urdu, and Vietnamese.

Usage

import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel


def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0]
    input_mask_expanded = (
        attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    )
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(
        input_mask_expanded.sum(1), min=1e-9
    )


sentences = ["How is the weather today?", "What is the current weather like today?"]

tokenizer = AutoTokenizer.from_pretrained("jinaai/jina-embeddings-v3")
model = AutoModel.from_pretrained("jinaai/jina-embeddings-v3", trust_remote_code=True)

encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt")
task = 'retrieval.query'
task_id = model._adaptation_map[task]
adapter_mask = torch.full((len(sentences),), task_id, dtype=torch.int32)
with torch.no_grad():
    model_output = model(**encoded_input, adapter_mask=adapter_mask)

embeddings = mean_pooling(model_output, encoded_input["attention_mask"])
embeddings = F.normalize(embeddings, p=2, dim=1)

The easiest way to start using jina-embeddings-v3 is with the Jina Embedding API .

Alternatively, you can use jina-embeddings-v3 directly via Transformers package:

!pip install transformers torch einops
!pip install 'numpy<2'

If you run it on a GPU that support FlashAttention-2 . By 2024.9.12, it supports Ampere, Ada, or Hopper GPUs (e.g., A100, RTX 3090, RTX 4090, H100),

!pip install flash-attn --no-build-isolation

from transformers import AutoModel

# Initialize the model
model = AutoModel.from_pretrained("jinaai/jina-embeddings-v3", trust_remote_code=True)

texts = [
    "Follow the white rabbit.",  # English
    "Sigue al conejo blanco.",  # Spanish
    "Suis le lapin blanc.",  # French
    "跟着白兔走。",  # Chinese
    "اتبع الأرنب الأبيض.",  # Arabic
    "Folge dem weißen Kaninchen.",  # German
]

# When calling the `encode` function, you can choose a `task` based on the use case:
# 'retrieval.query', 'retrieval.passage', 'separation', 'classification', 'text-matching'
# Alternatively, you can choose not to pass a `task`, and no specific LoRA adapter will be used.
embeddings = model.encode(texts, task="text-matching")

# Compute similarities
print(embeddings[0] @ embeddings[1].T)

By default, the model supports a maximum sequence length of 8192 tokens. However, if you want to truncate your input texts to a shorter length, you can pass the max_length parameter to the encode function:

embeddings = model.encode(["Very long ... document"], max_length=2048)

In case you want to use Matryoshka embeddings and switch to a different dimension, you can adjust it by passing the truncate_dim parameter to the encode function:

embeddings = model.encode(['Sample text'], truncate_dim=256)

The latest version (3.1.0) of SentenceTransformers also supports jina-embeddings-v3 :

!pip install -U sentence-transformers

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("jinaai/jina-embeddings-v3", trust_remote_code=True)

task = "retrieval.query"
embeddings = model.encode(
    ["What is the weather like in Berlin today?"],
    task=task,
    prompt_name=task,
)

import onnxruntime
import numpy as np
from transformers import AutoTokenizer, PretrainedConfig

# Load tokenizer and model config
tokenizer = AutoTokenizer.from_pretrained('jinaai/jina-embeddings-v3')
config = PretrainedConfig.from_pretrained('jinaai/jina-embeddings-v3')

# Tokenize input
input_text = tokenizer('sample text', return_tensors='np')

# ONNX session
model_path = 'jina-embeddings-v3/onnx/model.onnx'
session = onnxruntime.InferenceSession(model_path)

# Prepare inputs for ONNX model
task_type = 'text-matching'
task_id = np.array(config.lora_adaptations.index(task_type), dtype=np.int64)
inputs = {
    'input_ids': input_text['input_ids'],
    'attention_mask': input_text['attention_mask'],
    'task_id': task_id
}

# Run model
outputs = session.run(None, inputs)

Contact

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Citation

If you find jina-embeddings-v3 useful in your research, please cite the following paper:

@misc{sturua2024jinaembeddingsv3multilingualembeddingstask,
      title={jina-embeddings-v3: Multilingual Embeddings With Task LoRA}, 
      author={Saba Sturua and Isabelle Mohr and Mohammad Kalim Akram and Michael Günther and Bo Wang and Markus Krimmel and Feng Wang and Georgios Mastrapas and Andreas Koukounas and Andreas Koukounas and Nan Wang and Han Xiao},
      year={2024},
      eprint={2409.10173},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2409.10173}, 
}