LinkBERT: Fine-tuned BERT for Natural Link Prediction

LinkBERT is an advanced fine-tuned version of the bert-large-cased model developed by Dejan Marketing. The model is designed to predict natural link placement within web content. This binary classification model excels in identifying distinct token ranges that web authors are likely to choose as anchor text for links. By analyzing never-before-seen texts, LinkBERT can predict areas within the content where links might naturally occur, effectively simulating web author behavior in link creation.

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Online Demo

Online demo of this model is available at https://dejan.ai/linkbert/

Applications of LinkBERT

LinkBERT's applications are vast and diverse, tailored to enhance both the efficiency and quality of web content creation and analysis:

• Anchor Text Suggestion: Acts as a mechanism during internal link optimization, suggesting potential anchor texts to web authors.
• Evaluation of Existing Links: Assesses the naturalness of link placements within existing content, aiding in the refinement of web pages.
• Link Placement Guide: Offers guidance to link builders by suggesting optimal placement for links within content.
• Anchor Text Idea Generator: Provides creative anchor text suggestions to enrich content and improve SEO strategies.
• Spam and Inorganic SEO Detection: Helps identify unnatural link patterns, contributing to the detection of spam and inorganic SEO tactics.

Training and Performance

LinkBERT was fine-tuned on a dataset of organic web content and editorial links.

https://www.youtube.com/watch?v=A0ZulyVqjZo

The training involved preprocessing web content, annotating links with temporary markup for clear distinction, and employing a specialized tokenization process to prepare the data for model training. In adition to commonly available data sources such as Wikipedia, additional training data was also sourced from:

Owayo:

• USA, Australia, Germany, UK, Canada

Training Highlights:

• Dataset: Custom organic web content with editorial links.
• Preprocessing: Links annotated with [START_LINK] and [END_LINK] markup.
• Tokenization: Utilized input_ids, token_type_ids, attention_mask, and labels for model training, with a unique labeling system to differentiate between link/anchor text and plain text.

Technical Specifications:

• Batch Size: 10, with class weights adjusted to address class imbalance between link and plain text.
• Optimizer: AdamW with a learning rate of 5e-5.
• Epochs: 5, incorporating gradient accumulation and warmup steps to optimize training outcomes.
• Hardware: 1 x RTX4090 24GB VRAM
• Duration: 32 hours

Utilization and Integration

LinkBERT is positioned as a powerful tool for content creators, SEO specialists, and webmasters, offering unparalleled support in optimizing web content for both user engagement and search engine recognition. Its predictive capabilities not only streamline the content creation process but also offer insights into the natural integration of links, enhancing the overall quality and relevance of web content.

Accessibility

LinkBERT leverages the robust architecture of bert-large-cased, enhancing it with capabilities specifically tailored for web content analysis. This model represents a significant advancement in the understanding and generation of web content, providing a nuanced approach to natural link prediction and anchor text suggestion.

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Here's how we evaluated the quality of LinkBERT's predictions. pic.twitter.com/n5D9asYxex

— DEJAN (@dejanseo) March 15, 2024

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