This model was released on 2024-09-16 and added to Hugging Face Transformers on 2026-03-18.

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JinaEmbeddingsV3

The Jina-Embeddings-v3 is a multilingual, multi-task text embedding model designed for a variety of NLP applications. Based on the XLM-RoBERTa architecture, this model supports Rotary Position Embeddings (RoPE) replacing absolute position embeddings to support long input sequences up to 8192 tokens. Additionally, it features 5 built-in Task-Specific LoRA Adapters: that allow the model to generate task-specific embeddings (e.g., for retrieval vs. classification) without increasing inference latency significantly.

You can find the original Jina Embeddings v3 checkpoints under the Jina AI organization.

[!TIP] Click on the Jina Embeddings v3 models in the right sidebar for more examples of how to apply the model to different language tasks.

The example below demonstrates how to extract features (embeddings) with [Pipeline], [AutoModel], and from the command line.

from transformers import pipeline


pipeline = pipeline(
    task="feature-extraction",
    model="jinaai/jina-embeddings-v3-hf",
)
# Returns a list of lists containing the embeddings for each token
embeddings = pipeline("Jina Embeddings V3 is great for semantic search.")

import torch

from transformers import AutoModel, AutoTokenizer


tokenizer = AutoTokenizer.from_pretrained("jinaai/jina-embeddings-v3-hf")
model = AutoModel.from_pretrained("jinaai/jina-embeddings-v3-hf", device_map="auto")

prompt = "Jina Embeddings V3 is great for semantic search."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model(**inputs)
    # The base AutoModel returns the raw hidden states for all tokens
    last_hidden_states = outputs.last_hidden_state

print(f"Features shape: {last_hidden_states.shape}")

Task-Specific LoRA Adapters

A key feature of JinaEmbeddingsV3 is it's LoRA adapters, which allow you to tailor the output embeddings to specific useful use cases without the overhead of loading entirely different models.

The following tasks are supported:

• retrieval.query: Used for query embeddings in asymmetric retrieval tasks (e.g., search queries).
• retrieval.passage: Used for passage embeddings in asymmetric retrieval tasks (e.g., the documents being searched).
• 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 Semantic Textual Similarity (STS) or symmetric retrieval tasks.

To generate high-quality sentence or paragraph embeddings, you need to apply mean pooling to the model's token embeddings. Mean pooling takes all token embeddings from the model's output and averages them, masking out the padding tokens.

Here is how you can generate sentence embeddings tailored for a retrieval query task using the AutoModel API.

import torch
import torch.nn.functional as F

from transformers import AutoModel, AutoTokenizer


def mean_pooling(model_output, attention_mask):
    # First element of model_output contains all token embeddings
    token_embeddings = model_output[0]
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()

    # Sum the embeddings and divide by the number of non-padding tokens
    sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1)
    sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9)
    return sum_embeddings / sum_mask


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

tokenizer = AutoTokenizer.from_pretrained("jinaai/jina-embeddings-v3-hf")
model = AutoModel.from_pretrained("jinaai/jina-embeddings-v3-hf", device_map="auto")

encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt").to(model.device)

# Set up the adapter mask for your specific task
task = 'retrieval_query'  # Can be any of (retrieval_passage, separation, classification, text_matching) depending on the use-case.

model.load_adapter("jinaai/jina-embeddings-v3-hf", adapter_name=task, adapter_kwargs={"subfolder": task})

model.set_adapter(task)

with torch.no_grad():
    model_output = model(**encoded_input)

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

print(embeddings.shape)
# Output: torch.Size([2, 1024])

JinaEmbeddingsV3Config

[[autodoc]] JinaEmbeddingsV3Config

JinaEmbeddingsV3Model

[[autodoc]] JinaEmbeddingsV3Model - forward

JinaEmbeddingsV3ForMaskedLM

[[autodoc]] JinaEmbeddingsV3ForMaskedLM - forward

JinaEmbeddingsV3ForSequenceClassification

[[autodoc]] JinaEmbeddingsV3ForSequenceClassification - forward

JinaEmbeddingsV3ForTokenClassification

[[autodoc]] JinaEmbeddingsV3ForTokenClassification - forward

JinaEmbeddingsV3ForQuestionAnswering

[[autodoc]] JinaEmbeddingsV3ForQuestionAnswering - forward