nomagick / jina-embeddings-v2

A 8k sequence length text embedding set trained by Jina AI

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  • Paper
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Input

Output

Run time and cost

This model costs approximately $0.00022 to run on Replicate, or 4545 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia T4 GPU hardware. Predictions typically complete within 1 seconds. The predict time for this model varies significantly based on the inputs.

Readme

Finetuner logo: Finetuner helps you to create experiments in order to improve embeddings on search tasks. It accompanies you to deliver the last mile of performance-tuning for neural search applications.

The text embedding set trained by Jina AI, Finetuner team.

Intended Usage & Model Info

jina-embeddings-v2-base-en is an English, monolingual embedding model supporting 8192 sequence length. It is based on a Bert architecture (JinaBert) that supports the symmetric bidirectional variant of ALiBi to allow longer sequence length. The backbone jina-bert-v2-base-en is pretrained on the C4 dataset. The model is further trained on Jina AI’s collection of more than 400 millions of sentence pairs and hard negatives. These pairs were obtained from various domains and were carefully selected through a thorough cleaning process.

The embedding model was trained using 512 sequence length, but extrapolates to 8k sequence length (or even longer) thanks to ALiBi. This makes our model useful for a range of use cases, especially when processing long documents is needed, including long document retrieval, semantic textual similarity, text reranking, recommendation, RAG and LLM-based generative search, etc.

With a standard size of 137 million parameters, the model enables fast inference while delivering better performance than our small model. It is recommended to use a single GPU for inference. Additionally, we provide the following embedding models:

V1 (Based on T5, 512 Seq)

V2 (Based on JinaBert, 8k Seq)

Data & Parameters

Jina Embeddings V2 technical report coming soon.

Jina Embeddings V1 technical report.

Usage

You can use Jina Embedding models directly from transformers package:

!pip install transformers
from transformers import AutoModel
from numpy.linalg import norm

cos_sim = lambda a,b: (a @ b.T) / (norm(a)*norm(b))
model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-base-en', trust_remote_code=True) # trust_remote_code is needed to use the encode method
embeddings = model.encode(['How is the weather today?', 'What is the current weather like today?'])
print(cos_sim(embeddings[0], embeddings[1]))

If you only want to handle shorter sequence, such as 2k, pass the max_length parameter to the encode function:

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

Alternatively, you can use Jina AI’s Embedding platform for fully-managed access to Jina Embeddings models (Coming soon!).

Fine-tuning

Please consider Finetuner.

Plans

The development of new bilingual models is currently underway. We will be targeting mainly the German and Spanish languages. The upcoming models will be called jina-embeddings-v2-base-de/es.

Contact

Join our Discord community and chat with other community members about ideas.

Citation

If you find Jina Embeddings useful in your research, please cite the following paper:

Coming soon.