Did you ever wish to have someone read over a lot of text and get right to the point, giving an answer to your question? Well, you have come to the right place! So let us try this fun project where we are going to create a Question and Answer system using a great model known as <a href="https://www.aionlinecourse.com/blog/how-to-fine-tune-huggingface-bert-model-for-text-classification" target="_blank">DistilBERT</a>. Do not worry; the code in this topic will remain as basic, easy-to-understand code. By the end of the project, you will learn how easy it is to create something that can understand questions and find answers as if by magic.

Using DistilBERT and the SQuAD dataset develop a sophisticated question-answering system. Discover key NLP techniques, model training, and real-life applications.

Question Answering system built on Pegasus+SQuAD for accurate responses. Optimized for high accuracy and user experience across applications

Question Answer System Training With Distilbert Base Uncased

Imagine you are in search of the right movie! But instead of looking up certain keywords or using regular search engines, you could simply explain your needs to a system. It would work for you to know what you are looking for. That's called semantic search! In this project, we implement advanced transformer models used in natural processing. 
With an extremely fast vector database named Faiss build an engine that is not limited to keyword matching. The system understands the meaning, the context, and the purpose of the system. This technology provides personalized and extremely fast results. Whether it is seeking appropriate goods and services about products and services with e-commerce, finding information health-wise, or looking for various forms of entertainment.

Create a fast, accurate semantic search system with Transformers and Faiss. Use vector embeddings for context-aware search results across movies, e-commerce, and more.

The Semantic Search System with Transformers and Faiss vectors can speed up and improve the accuracy of your searches. Find out about advanced information retrieval and personalized suggestions for a wide range of businesses.

Semantic Search Using Msmarco Distilbert Base & Faiss Vector Database

Have you ever wished that there were a quick summary, and no more long documents? And this project has you covered well! In this project, we're equipping ourselves with cutting edge AI tools such as SentencePiece and Transformers by diving into the world of document summarization. 
Sounds fun right? Let's see how we can make that possible!

Create an AI-powered document summarizer using PEGASUS and SentencePiece, fine-tune with SAMSum data, and optimize performance with ROUGE scores and GPU acceleration.

Advanced transformer models and tokenization methods can be used to automate the summarization of documents. Quickly make high-quality abstracts to help people find knowledge and make decisions.

Document Summarization Using Sentencepiece Transformers

Every business in the modern age strives to have the best customer service to keep its customers. What if businesses went above and beyond the ordinary and provided support services around the clock? At the same time, they could answer thousands of questions without compromising the quality of service. This is where the LLM Customer Service Chatbot becomes useful. This chatbot is more than just another chatbot. This is a revolution.
The bot employs advanced natural language processing (NLP) techniques to engage users conversationally. Thus allowing customers to enjoy a swift and seamless experience. Be it an alteration of an order or trouble in selecting a product variant, the bot eradicates any unnecessary waiting time for users. The Mistral 7B Instruct model powers it while addressing the requests of the clients satisfactorily. This eliminates wasteful expenditure of time and money on the part of the businesses.

Create an AI-powered customer support chatbot using the Mistral 7B Instruct model. Provide 24/7 help with rapid, context-aware responses using PEFT and model quantization.

The goal of this project is to create a customer support chatbot by using advanced methods for natural language processing.

Natural Language Processing Projects

Explore the Future with Our AI Projects

Question Answer System Training With Distilbert Base Uncased

Semantic Search Using Msmarco Distilbert Base & Faiss Vector Database

Document Summarization Using Sentencepiece Transformers

Customer Service Chatbot Using LLMs