The embedding set trained by Jina AI.
Jina Embedding V3: A Multilingual Multi-Task Embedding Model
## Quick Start The easiest way to start using `jina-embeddings-v3` is with the [Jina Embedding API](https://jina.ai/embeddings/). ## 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](https://huggingface.co/jinaai/xlm-roberta-flash-implementation), this model supports [Rotary Position Embeddings (RoPE)](https://arxiv.org/abs/2104.09864) to handle long input sequences up to **8192 tokens**. Additionally, it features 5 [LoRA](https://arxiv.org/abs/2106.09685) 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 89 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.** ## Data & Parameters The data and training details are described in the technical report (coming soon). ## Usage **### 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: ```python 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") with torch.no_grad(): model_output = model(**encoded_input, task='retrieval.query') embeddings = mean_pooling(model_output, encoded_input["attention_mask"]) embeddings = F.normalize(embeddings, p=2, dim=1) ```