jina-embeddings-v3 - AI Embedding Models Tool

Overview

jina-embeddings-v3 is a multilingual, multi-task text embedding model produced by Jina AI and published on Hugging Face. Built around the Jina-XLM-RoBERTa family, the model is designed to generate dense representations for a wide range of NLP tasks including retrieval, classification, semantic text matching, and clustering. It targets production use cases that need long-context understanding (supports rotary position embeddings up to 8192 tokens) and cross-lingual consistency across many languages. The model uses task-specific LoRA adapters to provide multi-task capability and to allow fine-tuning or lightweight task specialization without re-training all weights. According to the Hugging Face model page, jina-embeddings-v3 is provided under a CC-BY-NC-4.0 license and has been widely used (over 4.3 million downloads and 1,119 likes on Hugging Face). The published model is available through the Hugging Face feature-extraction pipeline and is reported to contain about 572.3M parameters.

Model Statistics

• Downloads: 4,344,083
• Likes: 1119
• Pipeline: feature-extraction
• Parameters: 572.3M

License: cc-by-nc-4.0

Model Details

Architecture and internals: jina-embeddings-v3 is based on the Jina-XLM-RoBERTa architecture (a RoBERTa-style transformer adapted for multilingual inputs). The model incorporates rotary position embeddings to extend effective context up to 8192 tokens, which is useful for long documents, multi-paragraph inputs, and long-form retrieval scenarios. Jina AI equips the model with LoRA (Low-Rank Adaptation) adapters: these enable task-specific lightweight fine-tuning without modifying the full model weights, reducing storage and compute for per-task specializations. Capabilities: The model is intended for feature-extraction (dense embedding generation) and supports common downstream tasks such as semantic search, nearest-neighbor retrieval, text classification (as input to a classifier), semantic textual similarity, and dense passage retrieval. Embedding dimensionality is described as flexible/adjustable (the model supports configuration choices and adapter-based variants), and typical usage is to pool token embeddings (CLS or mean pooling) to obtain a fixed-length vector for indexing or as classifier input. Practical notes: The Hugging Face page lists the model under the feature-extraction pipeline and documents the model size (~572.3M parameters). The model is published under CC-BY-NC-4.0. The model card does not provide an exhaustive suite of held-out benchmark scores (e.g., MRR/NDCG/SuperGlue) on the main page; consumers should run task-specific evaluations for their target domains. Source: Hugging Face model page.

Key Features

• Multilingual dense embeddings suitable for many languages and cross-lingual use cases.
• Multi-task support via LoRA adapters for lightweight, task-specific fine-tuning.
• Rotary position embeddings enabling input contexts up to 8192 tokens.
• Feature-extraction pipeline: optimized for retrieval, matching, clustering, classification.
• Flexible embedding dimensionality and adapter-based specialization to reduce per-task costs.

Example Usage

Example (python):

from transformers import AutoTokenizer, AutoModel
import torch

# Load tokenizer and model from Hugging Face
model_id = "jinaai/jina-embeddings-v3"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModel.from_pretrained(model_id)
model.eval()

# Example texts
texts = [
    "Jina AI builds tools for neural search and multimodal retrieval.",
    "Dense text embeddings are useful for semantic search and clustering."
]

# Tokenize (truncation can be adjusted up to model limits)
enc = tokenizer(texts, padding=True, truncation=True, return_tensors="pt")

# Forward pass
with torch.no_grad():
    outputs = model(**enc)
    # outputs.last_hidden_state shape: (batch_size, seq_len, hidden_size)
    last_hidden = outputs.last_hidden_state

# Pooling: mean pooling over token dimension (excluding padding)
attention_mask = enc["attention_mask"].unsqueeze(-1)
sum_mask = attention_mask.sum(dim=1)
pooled = (last_hidden * attention_mask).sum(dim=1) / sum_mask

# Optionally L2-normalize embeddings for cosine similarity or nearest-neighbor index
embeddings = torch.nn.functional.normalize(pooled, p=2, dim=1)

print("Embedding shape:", embeddings.shape)
# embeddings is a tensor of shape (batch_size, hidden_size) ready for indexing

Benchmarks

Parameters: 572.3M (Source: https://huggingface.co/jinaai/jina-embeddings-v3)

Hugging Face downloads: 4,344,083 (Source: https://huggingface.co/jinaai/jina-embeddings-v3)

Hugging Face likes: 1,119 (Source: https://huggingface.co/jinaai/jina-embeddings-v3)

Pipeline type: feature-extraction (Source: https://huggingface.co/jinaai/jina-embeddings-v3)

Published task benchmarks: No task-specific benchmark scores published on model card (Source: https://huggingface.co/jinaai/jina-embeddings-v3)

Key Information

• Category: Embedding Models
• Type: AI Embedding Models Tool