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.
- BERT Model Get the basic BERT pre-trained model from TensorFlowHub and fine tune to your needs
- Text Classification with BERT How to leverage a pre-trained BERT model from Hugging Face to classify text of news articles
- Question Answering with a fine-tuned BERT using Hugging Face Transformers and PyTorch on CoQA dataset by Stanford
BERT AI technology page Hackathon projects
Discover innovative solutions crafted with BERT AI technology page, developed by our community members during our engaging hackathons.
Who are we? We are a new startup dedicated to revolutionizing the restaurant industry with cutting-edge AI solutions. This Hackathon provides us an opportunity to showcase an early concept of our chatbot (still significantly in development) built on top of Falcon LLM. The Problem As the restaurant industry continues to recover from the coronavirus pandemic, it is confronting numerous challenges, with the foremost and most significant being high labor costs. The Solution We introduce Falcon Barista, an order-taking bot for coffee shops and restaurants, designed to converse with customers in the most human-like manner possible. Although it is still under development, this bot is envisioned as an affordable alternative for restaurants to replace manual labor at counters, drive-throughs, and over the phone. What makes Falcon Barista better? The primary innovation behind Falcon Barista lies in its minimal compute requirements, thereby maximizing cost savings. While many chatbots are built on top of LLMs with over 100 billion parameters, Falcon Barista operates on the much more compact fine-tuned Falcon-7B LLM, which consists of only 7 billion parameters. This efficiency is realized by employing smaller fine-tuned BERT models in tandem with the Falcon LLM: the Falcon-7B LLM guides the conversation while BERT manages information extraction, such as identifying food items and their quantities. Falcon Barista utilizes a quantized version of the Falcon-7B LLM and can be deployed on a single GPU with 16GB RAM. Furthermore, it boasts Automatic Speech Recognition and Text-to-Speech capabilities, allowing for conversations with customers that mimic human-like interactions. Challenges (Due to lack of compute resources) 1. Significant latency (~10s). 2. The BERT model, still in the process of fine-tuning, can easily become confused. 3. Falcon-7B requires further fine-tuning for more efficient conversation management.
Legal AI II
The Problem and Market Opportunity The intellectual property industry, valued at $367 billion annually, faces a significant issue - the laborious and expensive process of crafting patent claims. With more than 700,000 patent applications filed in the US in 2022, there's a growing need for a game-changing solution. The Solution: PatentableClaimExtraction PCE offers a novel solution by listening to inventors' conversations, extracting patentable claims from these discussions, and formatting them into the required patent claim format. The result is a dramatic reduction in the time it takes to bring an idea to market, from weeks to mere minutes. This revolutionary approach caters to individual inventors and small to medium-sized enterprises (SMEs), democratizing the patenting process. Time-Efficiency: Reducing patent application time from weeks to minutes. Cost Reduction: Substantial savings on legal fees. Accessibility: Making patent protection accessible to smaller innovators. Accuracy: AI-driven extraction ensures high-quality patent claims. Market Size and Competitive Landscape Our primary target is the SME and individual innovator market, which accounts for 60% of patent applicants and a $220 billion market share. With limited competition in the AI-driven patent claim extraction sector, PCE holds a unique position. Our proprietary algorithms offer a significant edge in this market. PCE's business model includes subscription-based pricing tiers, a freemium model for individual inventors, and the licensing of our API to law firms and IP consultants. To drive adoption, we will partner with innovation hubs, accelerators, universities, law firms, and IP consultants. Continuous algorithmic improvement will further secure our market position. Behind this groundbreaking venture is a dedicated team with extensive backgrounds in AI, IP law, and tech entrepreneurship. Our experts in AI development, legal expertise, and business acumen collectively drive PCE's success.
Personalized Financial Advice Chatbot Using NLP
Navigating traditional bank websites can be an exasperating experience due to their complex menu structures. The Personalized Financial Advice Chatbot (PFAC) is designed to revolutionize this by using advanced natural language processing and machine learning. PFAC interprets user intents and preferences, streamlining information retrieval and enhancing the user experience. Instead of getting lost in the maze of menus, users can simply ask PFAC for account balances, loan advice, or any financial queries in plain language. PFAC aims to make online banking more user-centric, providing a stress-free, straightforward way to manage finances. This project delves into technical details, benefits, and the potential to transform online financial advice.
As organizations use Monday.com, they accumulate numerous documents, making it challenging to locate specific information. Members of the organization end up spending excessive time searching for documents on particular topics. Additionally, the difficulty in finding documents may lead to duplication because members may assume these documents don't exist but are actually buried within the existing volume. Enter Sortes, a AI agent organizational member equipped with comprehensive knowledge of all documents. Sortes swiftly provides summaries of topics already documented within the organization and directs members to the appropriate documents for further reading. Sortes achieves this extensive knowledge by leveraging AI models for passage ranking, context extraction, and content summarization. It creates a vector token index of all organization documents and quickly identifies relevant documents when queried, offering summaries and links to these pertinent resources.
WIM Whatd I Miss
Ask pointed questions about a given playlist and get back a summary, key points, and related timestamps generated via AI! 🤖 Could be podcast series, a learning series, or something completely different! Can take in even very large/long series (tested on ~150 ~2-hour long podcasts)!Ask pointed questions about a given playlist and get back a summary, key points, and related timestamps generated via AI! 🤖 Could be a podcast series, a learning series, or something completely different! Can take in even very large/long series (tested on ~150 ~2-hour long podcasts)! This tool can take a YouTube transcript from one or more videos to be used to answer questions on a topic. The output will include a generated overall summary and generated key points from the video(s) by reading select parts of the transcript. The output will also include links to the relevant video, timestamped to the specific quote/snippet related to its respective key point. This tool can be useful to learners going through a video series playlist to review or identify where the series talks about a topic. It can also be used for educators in creating lessons from a series of videos. It also can be used for more casual enjoyment such as reviewing what the hosts have said on a particular topic. This use case is especially relevant for podcasts where hosts may revisit the same topic across multiple topics. Although Anthropic's Claude model can take in 100k tokens, this still creates a limit to what's read in by the LLM. This project will attempt to read in all the selected transcripts for the available model but if the transcript is too big for even the beefiest model, the tool will strategically select portions of the relevant transcripts based on the user fed question.
Recommendations cold-start problem is not actually a problem, if you leverage content and item metadata to build your recommendations. To showcase this idea we build a movie recommender, so you can visually see the difference between collaborative-filtering and content recommendations. We made two PRs to an existing open-source project Metarank: * support semantic recommendations with cohere-ai and sentence-transformers embeddings * use qdrant as a vector search engine to quickly perform vector similarity search With these two PRs merged building such a recommender is just a matter of a few lines of YAML code. But the semantic-similarity approach is not only about movies, but can be applied more generically in traditional places like e-commerce. For example, in fashion with high inventory churn, being able to recommend something for new clothes having zero feedback is really valuable.