ColBERT Repo Documentation ‐İrem Nur Yıldırım

ColBERT

ColBERT is a document-question relevance retrieval model created and maintained by Stanford University.Given numerous (up to millions) text documents and a query(user question) it finds and ranks the documents according to their contextual similarity to the user query, ColBERT's fast and efficient retrieval strategy made the document retrieval process less scary and led to different NLP tuning paradigms such as Retrieval Augmented Generation(RAG).

👍🏼 What I like

• Community and author of the ColBERT, @okhat, keep track of the issues and quickly answers to bugs, problems and further questions via issues section.
• Model both retrieves and ranks the documents based on different approaches(cosine similarity, word-level relevance etc.) whereas some other retriever techniques include only the retrieval process and necessitates and additional ranking step thus leading to latency.
• Simple and open to use for different tasks from web-engines to custom NLP tasks.

👎🏼 What I dislike

• It's based on BERT model for embedding of documents, thus new user query also must be embedded using this model to retain compatibility, which is a limitation.