LinkBERT Demo by DEJAN

Paste an article and the tool will predict the most likely places for links on it. LinkBERT is based on a fine tuned BERT-large-cased model trained on 200,000 web articles and links.

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.

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 meticulously fine-tuned on a dataset comprising 613,127 samples of organic web content and editorial links, totaling over 313 million tokens. 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.

Training Highlights:

• Dataset: 313,921,024 tokens from 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.

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.

BERT large model (cased)

Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is cased: it makes a difference between english and English.

Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by the Hugging Face team.

Model description

BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives:

• Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence.
• Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to predict if the two sentences were following each other or not.

This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs.

This model has the following configuration:

• 24-layer
• 1024 hidden dimension
• 16 attention heads
• 336M parameters.