BERT AI technology page Top Builders

Explore the top contributors showcasing the highest number of BERT AI technology page app submissions within our community.


The BERT paper by Jacob Devlin was released not long after the publication of the first GPT model. It achieved significant improvements on many important NLP benchmarks, such as GLUE. Since then, their ideas have influenced many state-of-the-art models in language understanding. Bidirectional Encoder Representations from Transformers (BERT) is a natural language processing technique (NLP) that was proposed in 2018. (NLP is the field of artificial intelligence aiming for computers to read, analyze, interpret and derive meaning from text and spoken words. This practice combines linguistics, statistics, and Machine Learning to assist computers in ‘understanding’ human language.) BERT is based on the idea of pretraining a transformer model on a large corpus of text and then fine-tuning it for specific NLP tasks. The transformer model is a deep learning model that is designed to handle sequential data, such as text. The bidirectional transformer architecture stacks encoders from the original transformer on top of each other. This allows the model to better capture the context of the text.

Relese date2018
Typemasked-language models


BERT AI technology page Hackathon projects

Discover innovative solutions crafted with BERT AI technology page, developed by our community members during our engaging hackathons.



Introduction Adapt-a-RAG is an innovative application that leverages the power of retrieval augmented generation to provide accurate and relevant answers to user queries. By adapting itself to each query, Adapt-a-RAG ensures that the generated responses are tailored to the specific needs of the user. The application utilizes various data sources, including documents, GitHub repositories, and websites, to gather information and generate synthetic data. This synthetic data is then used to optimize the prompts of the Adapt-a-RAG application, enabling it to provide more accurate and contextually relevant answers. How It Works Adapt-a-RAG works by following these key steps: Data Collection: The application collects data from various sources, including documents, GitHub repositories, and websites. It utilizes different reader classes such as CSVReader, DocxReader, PDFReader, ChromaReader, and SimpleWebPageReader to extract information from these sources. Synthetic Data Generation: Adapt-a-RAG generates synthetic data using the collected data. It employs techniques such as data augmentation and synthesis to create additional training examples that can help improve the performance of the application. Prompt Optimization: The synthetic data is used to optimize the prompts of the Adapt-a-RAG application. By fine-tuning the prompts based on the generated data, the application can generate more accurate and relevant responses to user queries. Recompilation: Adapt-a-RAG recompiles itself every run based on the optimized prompts and the specific user query. This dynamic recompilation allows the application to adapt and provide tailored responses to each query. Question Answering: Once recompiled, Adapt-a-RAG takes the user query and retrieves relevant information from the collected data sources. It then generates a response using the optimized prompts and the retrieved information, providing accurate and contextually relevant answers to the user.