Jina Embeddings V2 Base En
Jina Embeddings V2 Base En is a powerful English text embedding model that supports long sequence lengths of up to 8192 tokens. With 137 million parameters, it enables fast inference while delivering better performance than smaller models. It's suitable for a range of use cases, including long document retrieval, semantic textual similarity, text reranking, recommendation, and generative search. The model is pre-trained on the C4 dataset and further trained on a large collection of sentence pairs and hard negatives from various domains. It requires mean pooling when integrating, which can be achieved through the provided `encode` function or by applying a custom implementation using PyTorch. Does it sound like the right fit for your project?
Table of Contents
Model Overview
The Jina Embeddings V2 model is a powerful tool for natural language processing tasks. It’s an English, monolingual embedding model that supports a sequence length of up to 8192 tokens. This makes it useful for tasks like long document retrieval, semantic textual similarity, text reranking, recommendation, and more.
Key Features
- Long sequence length: Supports up to 8192 tokens, making it ideal for processing long documents.
- Fast inference: With 137 million parameters, the model enables fast inference while delivering better performance than smaller models.
- Monolingual: Trained on a large dataset of English text, making it suitable for English language tasks.
How it Works
The model uses a BERT architecture (JinaBERT) with the symmetric bidirectional variant of ALiBi, allowing it to process long sequences of text. It’s trained on a large dataset of sentence pairs and hard negatives, making it effective for tasks like text similarity and retrieval.
Alternatives and Future Plans
Other models in the Jina Embeddings V2 family include:
==jina-embeddings-v2-small-en==: A smaller model with 33 million parameters.==jina-embeddings-v2-base-zh==: A Chinese-English bilingual model.==jina-embeddings-v2-base-de==: A German-English bilingual model.==jina-embeddings-v2-base-es==: A Spanish-English bilingual model.
Future plans include adding support for more languages, multimodal embedding models, and high-performance rerankers.
Capabilities
This model excels at tasks such as:
- Long document retrieval
- Semantic textual similarity
- Text reranking
- Recommendation
- RAG (Retrieval-Augmented Generation) and LLM-based generative search
Strengths
So, what sets this model apart? Here are some of its key strengths:
- Long sequence length support: With a maximum sequence length of 8192, this model can handle long documents with ease.
- Fast inference: Despite its large size, the model enables fast inference, making it perfect for applications where speed is crucial.
- High-quality sentence embeddings: The model uses mean pooling to produce high-quality sentence embeddings, which are essential for many NLP tasks.
Performance
The model’s accuracy is impressive, especially when it comes to processing long documents. The use of mean pooling, which takes all token embeddings from model output and averages them at sentence/paragraph level, has been proven to be the most effective way to produce high-quality sentence embeddings.
Comparison to Other Models
| Model | Parameters | Sequence Length |
|---|---|---|
| Jina Embeddings V2 | 137 million | 8192 |
==jina-embeddings-v2-small-en== | 33 million | 512 |
==jina-embeddings-v2-base-zh== | 137 million | 8192 |
==jina-embeddings-v2-base-de== | 137 million | 8192 |
==jina-embeddings-v2-base-es== | 137 million | 8192 |
Real-World Applications
The Jina Embeddings V2 model has been used in various applications, including:
- Long document retrieval
- Semantic textual similarity
- Text reranking
- Recommendation
- RAG
- LLM-based generative search
Example Use Cases
Here are some example use cases for the Jina Embeddings V2 model:
- Processing large-scale datasets for text classification tasks
- Handling long documents for document retrieval and semantic textual similarity tasks
- Using mean pooling to produce high-quality sentence embeddings
Limitations
While the Jina Embeddings V2 model is powerful, it’s not perfect. Let’s take a closer look at some of its limitations.
Sequence Length Limitations
While the Jina Embeddings V2 model can handle sequences up to 8192 tokens, it’s not designed to handle extremely long documents. If you need to process documents longer than 8192 tokens, you might need to consider other models or techniques.
Performance on Short Sequences
On the other hand, the Jina Embeddings V2 model might not perform as well on very short sequences (e.g., 2k tokens). If you’re working with shorter sequences, you might want to experiment with other models or adjust the max_length parameter to see if you can get better results.
Multilingual Support
Currently, the Jina Embeddings V2 model only supports English. If you need to work with other languages, you’ll need to use a different model or wait for future updates that might add support for more languages.
Reranking and Multimodal Embeddings
While the Jina Embeddings V2 model is great for text embeddings, it’s not designed for reranking or multimodal embeddings. If you need to use these features, you might need to consider other models or tools.
Technical Limitations
The Jina Embeddings V2 model requires a single GPU for inference, which might be a limitation for some users. Additionally, the model has a standard size of 137 million parameters, which can be a challenge for some hardware configurations.
Comparison to Other Models
How does the Jina Embeddings V2 model compare to other models like ==Other Models==? While the Jina Embeddings V2 model has its strengths, other models might perform better in certain scenarios or have different limitations.
Future Plans
The developers of the Jina Embeddings V2 model have plans to add support for more languages, multimodal embeddings, and high-performance rerankers. These updates might address some of the current limitations, but it’s essential to stay up-to-date with the latest developments.
Troubleshooting
If you encounter issues with the Jina Embeddings V2 model, such as loading errors or authentication problems, make sure to check the troubleshooting section for solutions.
Format
The Jina Embeddings V2 model is a text embedding model that uses a BERT architecture, specifically a variant called JinaBERT. It’s designed to handle long sequence lengths, up to 8192 tokens.
Model Architecture
The model is based on a BERT architecture, which is a type of transformer model. It’s trained on a large dataset of text, which allows it to learn patterns and relationships in language.
Data Formats
The model accepts input in the form of tokenized text sequences. This means that you need to split your text into individual words or tokens before feeding it into the model.
Special Requirements
- Sequence Length: The model can handle sequence lengths up to 8192 tokens. However, if you only need to handle shorter sequences, you can pass the
max_lengthparameter to theencodefunction. - Mean Pooling: When integrating the model, it’s recommended to use mean pooling to produce high-quality sentence embeddings. This involves taking the average of all token embeddings in a sentence or paragraph.
Example Code
Here’s an example of how to use the model with the transformers library:
from transformers import AutoModel
from numpy.linalg import norm
model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-base-en', trust_remote_code=True)
embeddings = model.encode(['How is the weather today?', 'What is the current weather like today?'])
print(cos_sim(embeddings[0], embeddings[1]))
And here’s an example with the sentence-transformers library:
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
model = SentenceTransformer('jinaai/jina-embeddings-v2-base-en', trust_remote_code=True)
model.max_seq_length = 1024
embeddings = model.encode(['How is the weather today?', 'What is the current weather like today?'])
print(cos_sim(embeddings[0], embeddings[1]))
Note that you need to pass the trust_remote_code=True flag when loading the model, and you need to be logged into Hugging Face to access the model.
