Jina Embeddings v2: Handling Long Documents Made Easy

Jina Embeddings v2: Revolutionizing Long-Document Text Embedding

Current text embedding models, such as BERT, are constrained by a 512-token processing limit, hindering their performance with lengthy documents. This limitation often leads to context loss and inaccurate understanding. Jina Embeddings v2 surpasses this restriction by supporting sequences up to 8192 tokens, preserving crucial context and significantly improving the accuracy and relevance of processed information within extensive texts. This represents a major advancement in handling complex textual data.

Key Learning Points

• Understanding the limitations of traditional models like BERT when processing long documents.
• Learning how Jina Embeddings v2 overcomes these limitations through its 8192-token capacity and advanced architecture.
• Exploring the innovative features of Jina Embeddings v2, including ALiBi, GLU, and its three-stage training methodology.
• Discovering real-world applications in legal research, content management, and generative AI.
• Gaining practical experience in integrating Jina Embeddings v2 into projects using Hugging Face libraries.

This article is part of the Data Science Blogathon.

Table of Contents

• The Challenges of Embedding Long Documents
• Architectural Innovations and Training Methodology
• Performance Evaluation
• Real-World Applications
• Model Comparison
• Using Jina Embeddings v2 with Hugging Face
• Conclusion
• Frequently Asked Questions

The Challenges of Embedding Long Documents

Processing long documents presents significant challenges in Natural Language Processing (NLP). Traditional methods process text in segments, leading to context truncation and fragmented embeddings that misrepresent the original document. This results in:

• Increased computational demands
• Higher memory consumption
• Reduced performance in tasks requiring a comprehensive understanding of the text

Jina Embeddings v2 directly addresses these issues by increasing the token limit to 8192, eliminating the need for excessive segmentation and maintaining the document's semantic integrity.

Architectural Innovations and Training Methodology

Jina Embeddings v2 enhances BERT's capabilities with state-of-the-art innovations:

• Attention with Linear Biases (ALiBi): ALiBi replaces traditional positional embeddings with a linear bias applied to attention scores. This enables the model to effectively extrapolate to sequences far longer than those encountered during training. Unlike previous unidirectional implementations, Jina Embeddings v2 uses a bidirectional variant, ensuring compatibility with encoding tasks.
• Gated Linear Units (GLU): GLU, known for improving transformer efficiency, is used in the feedforward layers. Variants like GEGLU and ReGLU are employed to optimize performance based on model size.
• Optimized Training: Jina Embeddings v2 employs a three-stage training process:
  • Pretraining: Trained on the Colossal Clean Crawled Corpus (C4) using masked language modeling (MLM).
  • Fine-tuning with Text Pairs: Aligns embeddings for semantically similar text pairs.
  • Hard Negative Fine-tuning: Improves ranking and retrieval by incorporating challenging distractor examples.
  • Memory-Efficient Training: Techniques like mixed precision training and activation checkpointing ensure scalability for larger batch sizes, crucial for contrastive learning.

ALiBi attention incorporates a linear bias into each attention score before the softmax operation. Each attention head uses a unique constant scalar, m, diversifying its computation. The model uses the encoder variant where all tokens attend to each other, unlike the causal variant used in language modeling.

Performance Evaluation

Jina Embeddings v2 achieves state-of-the-art performance across various benchmarks, including the Massive Text Embedding Benchmark (MTEB) and new long-document datasets. Key results include:

• Classification: Top accuracy in tasks like Amazon Polarity and Toxic Conversations classification.
• Clustering: Outperforms competitors in grouping related texts (PatentClustering and WikiCitiesClustering).
• Retrieval: Excels in tasks like NarrativeQA, where complete document context is crucial.
• Long Document Handling: Maintains MLM accuracy even with 8192-token sequences.

This chart compares embedding model performance across retrieval and clustering tasks with varying sequence lengths.

Real-World Applications

• Legal and Academic Research: Ideal for searching and analyzing legal documents and academic papers.
• Content Management Systems: Efficient tagging, clustering, and retrieval of large document repositories.
• Generative AI: Enhances AI-generated summaries and prompt-based models.
• E-commerce: Improves product search and recommendation systems.

Model Comparison

Jina Embeddings v2 excels not only in handling long sequences but also in competing with proprietary models like OpenAI's text-embedding-ada-002. Its open-source nature ensures accessibility.

Using Jina Embeddings v2 with Hugging Face

Step 1: Installation

!pip install transformers
!pip install -U sentence-transformers

Step 2: Using Jina Embeddings with Transformers

import torch
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)

embeddings = model.encode(['How is the weather today?', 'What is the current weather like today?'])

print(cos_sim(embeddings, embeddings))

Output:

Handling Long Sequences:

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

Step 3: Using Jina Embeddings with Sentence-Transformers

(Similar code using sentence_transformers library is provided, along with instructions for setting max_seq_length.)

Conclusion

Jina Embeddings v2 is a significant advancement in NLP, effectively addressing the limitations of processing long documents. Its capabilities improve existing workflows and unlock new possibilities for working with long-form text.

Key Takeaways (Summarized key points from the original conclusion)

Frequently Asked Questions (Summarized answers to the FAQs)

Note: Images are retained in their original format and location.

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