minilm

Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities

Unattended Lightweight Text Classifiers with LLM Embeddings

Faster, smaller BERT models in just a few lines.

Text Mining (PubMed Search) with NLP & LLM

This repository serves as the RAG Project MiniLM Model from Microsoft named as sentence-transformers/all-MiniLM-L6-v2 as the embedding model for my resume , and utilizing OpenAI to answer questions based on the Resume

Lightweight cross-lingual coreference resolution with spaCy using ONNX Runtime inference of transformer models.

comprehensive solutions for Adobe's Document Intelligence Hackathon 2025, encompassing two distinct challenges focused on advanced PDF processing and persona-driven content analysis. Both implementations adhere to stringent performance requirements including sub-60-second execution times and containerized deployment within 1GB resource constraints

Advanced NLP project detecting duplicate questions on Quora using transformer-based embeddings, LSTM architectures, and ensemble models, achieving 88% accuracy with scalable solutions for real-world applications 🧠💬.

A demo from the blog post comparing MiniLM-based models using song lyrics and Milvus for vector similarity search—an approach that works for any text content.

An AI-powered study companion that helps students understand lecture material through intelligent question answering, slide summarization, PDF summaries, and flashcard generation. Built with LangChain, Hugging Face Transformers, and Gradio — and fully powered by open-source LLMs running on your local GPU.

A semantic quote retrieval system using fine-tuned MiniLM, FAISS indexing, and RAG-style LLM synthesis-built with Streamlit and Hugging Face Spaces.

An Ai-powered agent that automatically clusters, summarizes and prioritizes operational asset alerts . made using Python , sentence-transformers(MiniLM) and Hugging Face integration in Streamlit-ui -- helping engineering and operations teams focus on what matters most.

PaperMind AI is a local privacy-first PDF assistant that allows natural language chat with any document. Powered by FAISS, LangChain, MiniLM embeddings, and TinyLLaMA 1.1B — all running offline. Built with FastAPI backend and a clean HTML/CSS/JS frontend.