A simple tool to anonymize LLM prompts.

AI-powered platform for OSINT intelligence analysis. Features archive discovery with hypothesis-driven investigation, GLiNER entity extraction, Mapbox geospatial visualization, network analysis, and document processing. Built with FastAPI, Next.js, Weaviate, and DSPy.

Production inference for encoder models - ColBERT, GLiNER, ColPali, embeddings etc. - as vLLM plugins for online and in-process deployment

privacy-first context graph engine for AI agents and human teams.

Python bindings to Inference engine for GLiNER models written in Rust

Text preprocessing and PII anonymisation for NLP/ML. ONNX NER ensemble, language detection, stopword removal. Built for statistical ML and language models.

Easily configurable API & frontend providing simple access to dynamic NER models.

BrainUp is an AI-driven self-improvement app created for Hack the Hill 2024. It analyzes saved Instagram reels and posts to recommend productive activities and classes based on user interests. Built with ReactJS for the frontend and a Flask API for the backend, BrainUp leverages AI models like HDBSCAN, MPnet-base v2, and Mistral for data analysis,

Real-time PII detection and redaction for AI chat interfaces.

Leverage ModernGLiNER's capabilities using LitServe.

NER on PDF file using Rust

🔎 NER (Named Entity Recognition) Application with Gliner 🛠️

High-performance Hybrid RAG pipeline using LlamaIndex, MinerU, GLiNER, and LangExtract. Features advanced metadata enrichment, BM25-Vector fusion, and LaTeX-enabled synthesis for complex document analysis.

Comparative study of parameter-efficient fine-tuning (PEFT) strategies for biomedical NER on top of GLiNER — including soft prompt tuning, embedding injection, and a custom in-place embedding extension that matches full fine-tuning performance at 13% of trainable parameters.

A fine-tuned GLiNER2 model specialized in Named Entity Recognition for Mexican legal documents in Spanish. Suitable for extracting or redacting PII data.

🛡️ Text Anonymizer using NuNER Zero-shot An academic project that auto-detects & masks sensitive entities (names, orgs, locations) in .txt/.docx files using Zero-shot NER. Features CLI & REST API. Replaces data with realistic fakes (Faker) or [REDACTED]. Built with Python, Flask, GLiNER.

Named Entity Recognition with OpenVINO

Scan Local Directory For PII

Langchain compatible GraphRAG implementation

This project focuses on developing and fine-tuning models for medical term extraction and general named entity recognition.