jina-embeddings

Quick Start

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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.

⚠️ Important Notice: We fixed a bug in the encode function #60 where Matryoshka embedding truncation occurred after normalization, leading to non-normalized truncated embeddings. This issue has been resolved in the latest code revision.

If you have encoded data using the previous version and wish to maintain consistency, please use the specific code revision when loading the model: AutoModel.from_pretrained('jinaai/jina-embeddings-v3', code_revision='da863dd04a4e5dce6814c6625adfba87b83838aa', ...)

Usage

Apply mean pooling when integrating the model.

Why Use Mean Pooling?

Mean pooling takes all token embeddings from the model's output and averages them at the sentence or paragraph level. This approach has been shown to produce high-quality sentence embeddings.

We provide an encode function that handles this for you automatically.

However, if you're working with the model directly, outside of the encode function, you'll need to apply mean pooling manually. Here's how you can do it:

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 th