Spam Detector is a Data Science Project built using Pytorch and Hugging Face library. Used BERT model based on Transformer Architecture and got 99.97% accuracy on train set and 98.76% accuracy on test set.

With the use of AI, summarise your movies and bring back the colour in older films.

Summarize, , NSP answer questions in dockerised environment

With the use of AI, summarise your movies and bring back the colour in older films.

Source codes and materials of Advanced Spelling Error Correction project.

"Open Source Models with Hugging Face" course empowers you with the skills to leverage open-source models from the Hugging Face Hub for various tasks in NLP, audio, image, and multimodal domains.

Successfully developed a fine-tuned BERT transformer model which can accurately classify symptoms to their corresponding diseases upto an accuracy of 89%.

Successfully developed a fine-tuned DistilBERT transformer model which can accurately predict the overall sentiment of a piece of financial news up to an accuracy of nearly 81.5%.

Successfully established a Seq2Seq with attention model which can perform English to Spanish language translation up to an accuracy of almost 97%.

llm-newsletter-generator transforms a valid RSS feed into a "Newsletter" using AI models via PyTorch and Transformers; this is experimental.

A Python-based REST API for PDF OCR using AI models with PyTorch and Transformers that runs in a Docker container.

Deployed an interactive web platform for exploring and utilizing language models. Features include real-time text analysis and translation, built with Django for robust performance and scalability

Google-Palm powered web aplication allowing you to query your own PDF file. Uses streamlit for UI, ChromaDB to store embeddings and langchain.

This repo is a collection of various ai models that can be used to understand an learn a bit more about AI

A FastAPI-powered REST API offering a comprehensive suite of natural language processing services using machine learning models with PyTorch and Transformers, packaged in a Docker container to run efficiently.

A real-time voice-to-text and text-to-speech AI pipeline using Whisper, an LLM, and Edge-TTS with tunable parameters for low-latency audio processing and response generation.

Build a sentiment analysis tool that processes user reviews from various platforms (like Amazon or Yelp) and provides insights on sentiment trends over time. Use advanced NLP techniques like Transformers (BERT, GPT).